diff --git a/.gitignore b/.gitignore
index 660e106..df7c3fa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,7 @@
 data_very_raw/
 zzz_raman_spectroscopy-main/
+presentations/
+to_ignore/
 
 # ---> JupyterNotebooks
 # gitignore template for Jupyter Notebooks
diff --git a/classifiers/__init__.py b/classifiers/__init__.py
new file mode 100644
index 0000000..9365c12
--- /dev/null
+++ b/classifiers/__init__.py
@@ -0,0 +1 @@
+from classifiers.evaluation import *
\ No newline at end of file
diff --git a/classifiers/evaluation.py b/classifiers/evaluation.py
new file mode 100644
index 0000000..db10041
--- /dev/null
+++ b/classifiers/evaluation.py
@@ -0,0 +1,35 @@
+from data.data_processing import process_train_test
+from sklearn.model_selection import cross_validate, ParameterGrid
+
+
+def crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=5):
+    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
+    classifier_params = {key: params[key] for key in params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}
+    X_train, _ = process_train_test(process_params, experiments_train, metadata_train, scale=True)
+    clf = classifier(**classifier_params)
+    return cross_validate(clf, X_train, y_train.to_numpy().ravel(), cv=cv, return_estimator=True)
+
+
+def param_grid_search(classifier, param_grid, experiments_train, metadata_train, y_train, cv=5):
+    results = []
+    for params in ParameterGrid(param_grid):
+        try:
+            results.append([params, crossvalidate_params(classifier, params, experiments_train, metadata_train, y_train, cv=cv)])
+            print(results[-1])
+        except Exception as e:
+            pass  # print(params, e)
+    return results
+
+
+def evaluate_classifier_params(classifier, params, X_train, y_train, X_test, y_test, iters=10):
+    train_score_mean = 0
+    test_score_mean = 0
+    for i in range(iters):
+        clf = classifier(**params)
+        clf.fit(X_train, y_train.to_numpy().ravel())
+        train_score_mean += clf.score(X_train, y_train.to_numpy().ravel())
+        test_score_mean += clf.score(X_test, y_test.to_numpy().ravel())
+    return train_score_mean / iters, test_score_mean / iters
+
+
+
diff --git a/classifiers/random_forest.ipynb b/classifiers/random_forest.ipynb
index 89dc54e..4021b03 100644
--- a/classifiers/random_forest.ipynb
+++ b/classifiers/random_forest.ipynb
@@ -6,51 +6,69 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2024-04-16T17:40:39.605207Z",
-     "start_time": "2024-04-16T17:40:37.592172Z"
+     "end_time": "2024-04-29T12:42:26.431121700Z",
+     "start_time": "2024-04-29T12:42:25.507044600Z"
     }
    },
    "source": [
-    "from data import load_data, categorize_metadata, process_experiments\n",
+    "from data import *\n",
+    "from classifiers import *\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import os"
+    "import os\n",
+    "%matplotlib inline"
    ],
    "outputs": [],
    "execution_count": 1
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-04-16T17:40:40.100841Z",
-     "start_time": "2024-04-16T17:40:39.605207Z"
-    }
-   },
    "cell_type": "code",
+   "outputs": [],
    "source": [
     "metadata_train, experiments_train = load_data(os.path.join(\"..\", \"data\", \"train\"), \"\")\n",
     "truth_train, metadata_train = categorize_metadata(metadata_train)\n",
     "metadata_test, experiments_test = load_data(os.path.join(\"..\", \"data\", \"test\"), \"\")\n",
     "truth_test, metadata_test = categorize_metadata(metadata_test)"
    ],
-   "id": "f48d3dca7499a5f8",
-   "outputs": [],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T12:42:26.588454100Z",
+     "start_time": "2024-04-29T12:42:26.433343300Z"
+    }
+   },
+   "id": "572471b20c6dc13e",
    "execution_count": 2
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Look for optimal classifier parameters for arbitrary processing parameters"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "5e59ba986f3af3df"
+  },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T17:40:43.055129Z",
-     "start_time": "2024-04-16T17:40:40.100841Z"
+     "end_time": "2024-04-29T12:42:27.680898100Z",
+     "start_time": "2024-04-29T12:42:26.589519600Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "processed_train = process_experiments(experiments_train, baseline_lam=10, baseline_p=1e-2, smooth_window_length=7, smooth_polyorder=3)\n",
-    "X_train = pd.concat([metadata_train, processed_train], axis=1)\n",
-    "processed_test = process_experiments(experiments_test, baseline_lam=10, baseline_p=1e-2, smooth_window_length=7, smooth_polyorder=3)\n",
-    "X_test = pd.concat([metadata_test, processed_test], axis=1)"
+    "process_params = {\n",
+    "    'baseline_lam': 10,\n",
+    "    'baseline_p': 1e-2,\n",
+    "    'smooth_window_length': 7,\n",
+    "    'smooth_polyorder': 3\n",
+    "}\n",
+    "X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)"
    ],
    "id": "8fb458c0b78c9aa7",
    "outputs": [],
@@ -59,463 +77,50 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T17:40:43.127855Z",
-     "start_time": "2024-04-16T17:40:43.055129Z"
+     "end_time": "2024-04-29T12:43:46.201964400Z",
+     "start_time": "2024-04-29T12:42:27.682968Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "scaler.fit(X_train)\n",
-    "X_train = scaler.transform(X_train)\n",
-    "X_test = scaler.transform(X_test)"
+    "param_grid = {\n",
+    "    'n_estimators': range(1, 501, 100),\n",
+    "    'max_depth': range(1, 21, 5)\n",
+    "}\n",
+    "\n",
+    "clf = RandomForestClassifier()\n",
+    "\n",
+    "grid_clf = GridSearchCV(clf, param_grid, cv=5)\n",
+    "_ = grid_clf.fit(X_train, truth_train.to_numpy().ravel())"
    ],
-   "id": "2572f4508200d308",
+   "id": "80a355d2740ebf4a",
    "outputs": [],
    "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T17:42:11.247830Z",
-     "start_time": "2024-04-16T17:40:43.127855Z"
+     "end_time": "2024-04-29T12:43:46.213707800Z",
+     "start_time": "2024-04-29T12:43:46.203065Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "param_grid = {\n",
-    "    'n_estimators': range(1, 201, 50),\n",
-    "    'max_depth': range(10, 11, 10)\n",
-    "}\n",
-    "\n",
-    "clf = RandomForestClassifier()\n",
-    "\n",
-    "grid_clf = GridSearchCV(clf, param_grid, cv=20)\n",
-    "grid_clf.fit(X_train, truth_train.to_numpy().ravel())"
+    "print(grid_clf.best_params_)\n",
+    "grid_clf.cv_results_"
    ],
-   "id": "80a355d2740ebf4a",
+   "id": "790017144f8feaa6",
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'max_depth': 11, 'n_estimators': 301}\n"
+     ]
+    },
     {
      "data": {
-      "text/plain": [
-       "GridSearchCV(cv=20, estimator=RandomForestClassifier(),\n",
-       "             param_grid={'max_depth': range(10, 11, 10),\n",
-       "                         'n_estimators': range(1, 201, 50)})"
-      ],
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=20, estimator=RandomForestClassifier(),\n",
-       "             param_grid={&#x27;max_depth&#x27;: range(10, 11, 10),\n",
-       "                         &#x27;n_estimators&#x27;: range(1, 201, 50)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=20, estimator=RandomForestClassifier(),\n",
-       "             param_grid={&#x27;max_depth&#x27;: range(10, 11, 10),\n",
-       "                         &#x27;n_estimators&#x27;: range(1, 201, 50)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>"
-      ]
+      "text/plain": "{'mean_fit_time': array([0.00681539, 0.14610763, 0.27718945, 0.39620948, 0.52502027,\n        0.00736065, 0.43403726, 0.86569772, 1.2813745 , 1.70391393,\n        0.00718741, 0.48835073, 0.9537066 , 1.4265492 , 1.93588476,\n        0.00835772, 0.48221674, 0.94742947, 1.42890635, 1.93304458]),\n 'std_fit_time': array([0.00046738, 0.00867747, 0.01135155, 0.00112488, 0.00349504,\n        0.00088113, 0.00534944, 0.00746005, 0.00887032, 0.01338315,\n        0.00048124, 0.00671597, 0.01533272, 0.01902896, 0.03648975,\n        0.00067703, 0.00985057, 0.01882552, 0.0128699 , 0.01072727]),\n 'mean_score_time': array([0.00065007, 0.00185828, 0.00349832, 0.00409431, 0.00529351,\n        0.00024419, 0.00252962, 0.00395293, 0.00567269, 0.00726399,\n        0.0006793 , 0.00220752, 0.0038765 , 0.00551586, 0.00653486,\n        0.00045471, 0.00235896, 0.00373554, 0.00502024, 0.00725088]),\n 'std_score_time': array([0.00053118, 0.00048533, 0.00020484, 0.00057571, 0.00022786,\n        0.00048838, 0.00076014, 0.0004935 , 0.00059544, 0.00054311,\n        0.00055532, 0.0006391 , 0.00065416, 0.00067045, 0.00045184,\n        0.00055693, 0.00017962, 0.00043132, 0.00051096, 0.00048935]),\n 'param_max_depth': masked_array(data=[1, 1, 1, 1, 1, 6, 6, 6, 6, 6, 11, 11, 11, 11, 11, 16,\n                    16, 16, 16, 16],\n              mask=[False, False, False, False, False, False, False, False,\n                    False, False, False, False, False, False, False, False,\n                    False, False, False, False],\n        fill_value='?',\n             dtype=object),\n 'param_n_estimators': masked_array(data=[1, 101, 201, 301, 401, 1, 101, 201, 301, 401, 1, 101,\n                    201, 301, 401, 1, 101, 201, 301, 401],\n              mask=[False, False, False, False, False, False, False, False,\n                    False, False, False, False, False, False, False, False,\n                    False, False, False, False],\n        fill_value='?',\n             dtype=object),\n 'params': [{'max_depth': 1, 'n_estimators': 1},\n  {'max_depth': 1, 'n_estimators': 101},\n  {'max_depth': 1, 'n_estimators': 201},\n  {'max_depth': 1, 'n_estimators': 301},\n  {'max_depth': 1, 'n_estimators': 401},\n  {'max_depth': 6, 'n_estimators': 1},\n  {'max_depth': 6, 'n_estimators': 101},\n  {'max_depth': 6, 'n_estimators': 201},\n  {'max_depth': 6, 'n_estimators': 301},\n  {'max_depth': 6, 'n_estimators': 401},\n  {'max_depth': 11, 'n_estimators': 1},\n  {'max_depth': 11, 'n_estimators': 101},\n  {'max_depth': 11, 'n_estimators': 201},\n  {'max_depth': 11, 'n_estimators': 301},\n  {'max_depth': 11, 'n_estimators': 401},\n  {'max_depth': 16, 'n_estimators': 1},\n  {'max_depth': 16, 'n_estimators': 101},\n  {'max_depth': 16, 'n_estimators': 201},\n  {'max_depth': 16, 'n_estimators': 301},\n  {'max_depth': 16, 'n_estimators': 401}],\n 'split0_test_score': array([0.51886792, 0.59433962, 0.59433962, 0.61320755, 0.59433962,\n        0.68867925, 0.94339623, 0.95283019, 0.97169811, 0.96226415,\n        0.66037736, 0.91509434, 0.94339623, 0.95283019, 0.96226415,\n        0.67924528, 0.9245283 , 0.95283019, 0.95283019, 0.93396226]),\n 'split1_test_score': array([0.50943396, 0.58490566, 0.58490566, 0.58490566, 0.58490566,\n        0.77358491, 0.94339623, 0.9245283 , 0.93396226, 0.93396226,\n        0.69811321, 0.95283019, 0.93396226, 0.95283019, 0.94339623,\n        0.73584906, 0.91509434, 0.94339623, 0.96226415, 0.95283019]),\n 'split2_test_score': array([0.43396226, 0.66037736, 0.67924528, 0.66981132, 0.66037736,\n        0.52830189, 0.91509434, 0.9245283 , 0.90566038, 0.93396226,\n        0.71698113, 0.9245283 , 0.93396226, 0.94339623, 0.90566038,\n        0.67924528, 0.90566038, 0.9245283 , 0.93396226, 0.91509434]),\n 'split3_test_score': array([0.5047619 , 0.58095238, 0.57142857, 0.58095238, 0.58095238,\n        0.73333333, 0.98095238, 0.97142857, 0.99047619, 0.97142857,\n        0.77142857, 0.98095238, 0.98095238, 1.        , 0.99047619,\n        0.84761905, 0.97142857, 0.97142857, 0.99047619, 0.98095238]),\n 'split4_test_score': array([0.53333333, 0.65714286, 0.64761905, 0.65714286, 0.64761905,\n        0.75238095, 0.94285714, 0.92380952, 0.92380952, 0.94285714,\n        0.67619048, 0.94285714, 0.92380952, 0.91428571, 0.9047619 ,\n        0.78095238, 0.93333333, 0.93333333, 0.88571429, 0.93333333]),\n 'mean_test_score': array([0.50007188, 0.61554358, 0.61550764, 0.62120395, 0.61363881,\n        0.69525606, 0.94513926, 0.93942498, 0.94512129, 0.94889488,\n        0.70461815, 0.94325247, 0.94321653, 0.95266846, 0.94131177,\n        0.74458221, 0.93000898, 0.94510332, 0.94504942, 0.9432345 ]),\n 'std_test_score': array([0.03446663, 0.03556799, 0.0410394 , 0.03648556, 0.03348314,\n        0.08805168, 0.02095978, 0.01945042, 0.03130263, 0.01529003,\n        0.03853354, 0.02305058, 0.01985921, 0.02756642, 0.03306781,\n        0.0641147 , 0.02268027, 0.01623269, 0.03482546, 0.02231857]),\n 'rank_test_score': array([20, 17, 18, 16, 19, 15,  3, 11,  4,  2, 14,  7,  9,  1, 10, 13, 12,\n         5,  6,  8])}"
      },
      "execution_count": 5,
      "metadata": {},
@@ -525,132 +130,61 @@
    "execution_count": 5
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-04-16T17:42:11.260342Z",
-     "start_time": "2024-04-16T17:42:11.247830Z"
-    }
-   },
    "cell_type": "code",
-   "source": "grid_clf.cv_results_",
-   "id": "790017144f8feaa6",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'mean_fit_time': array([0.02515169, 0.81249317, 1.44318742, 2.0558908 ]),\n",
-       " 'std_fit_time': array([0.00768036, 0.233411  , 0.24691436, 0.13221224]),\n",
-       " 'mean_score_time': array([0.        , 0.00578324, 0.00520812, 0.00942556]),\n",
-       " 'std_score_time': array([0.        , 0.00711642, 0.00715995, 0.00759582]),\n",
-       " 'param_max_depth': masked_array(data=[10, 10, 10, 10],\n",
-       "              mask=[False, False, False, False],\n",
-       "        fill_value='?',\n",
-       "             dtype=object),\n",
-       " 'param_n_estimators': masked_array(data=[1, 51, 101, 151],\n",
-       "              mask=[False, False, False, False],\n",
-       "        fill_value='?',\n",
-       "             dtype=object),\n",
-       " 'params': [{'max_depth': 10, 'n_estimators': 1},\n",
-       "  {'max_depth': 10, 'n_estimators': 51},\n",
-       "  {'max_depth': 10, 'n_estimators': 101},\n",
-       "  {'max_depth': 10, 'n_estimators': 151}],\n",
-       " 'split0_test_score': array([0.77777778, 1.        , 1.        , 1.        ]),\n",
-       " 'split1_test_score': array([0.77777778, 1.        , 1.        , 1.        ]),\n",
-       " 'split2_test_score': array([0.74074074, 1.        , 1.        , 1.        ]),\n",
-       " 'split3_test_score': array([0.66666667, 0.92592593, 0.88888889, 0.88888889]),\n",
-       " 'split4_test_score': array([0.96296296, 0.96296296, 0.96296296, 0.96296296]),\n",
-       " 'split5_test_score': array([0.7037037 , 0.92592593, 0.92592593, 0.92592593]),\n",
-       " 'split6_test_score': array([0.88888889, 0.88888889, 0.96296296, 0.92592593]),\n",
-       " 'split7_test_score': array([0.74074074, 1.        , 1.        , 0.92592593]),\n",
-       " 'split8_test_score': array([0.76923077, 0.96153846, 0.96153846, 0.96153846]),\n",
-       " 'split9_test_score': array([0.80769231, 0.92307692, 0.92307692, 0.88461538]),\n",
-       " 'split10_test_score': array([0.53846154, 0.92307692, 0.84615385, 0.84615385]),\n",
-       " 'split11_test_score': array([0.80769231, 1.        , 1.        , 1.        ]),\n",
-       " 'split12_test_score': array([0.84615385, 1.        , 0.96153846, 0.92307692]),\n",
-       " 'split13_test_score': array([0.84615385, 0.96153846, 1.        , 1.        ]),\n",
-       " 'split14_test_score': array([0.92307692, 1.        , 0.96153846, 0.96153846]),\n",
-       " 'split15_test_score': array([0.88461538, 0.96153846, 1.        , 0.92307692]),\n",
-       " 'split16_test_score': array([0.38461538, 0.96153846, 0.88461538, 0.92307692]),\n",
-       " 'split17_test_score': array([0.57692308, 1.        , 1.        , 1.        ]),\n",
-       " 'split18_test_score': array([0.84615385, 0.96153846, 0.92307692, 0.96153846]),\n",
-       " 'split19_test_score': array([0.76923077, 0.88461538, 0.88461538, 0.92307692]),\n",
-       " 'mean_test_score': array([0.76296296, 0.96210826, 0.95434473, 0.9468661 ]),\n",
-       " 'std_test_score': array([0.13481565, 0.03776246, 0.04767395, 0.04404705]),\n",
-       " 'rank_test_score': array([4, 1, 2, 3])}"
-      ]
+      "text/plain": "(1.0, 0.9426553672316386)"
      },
      "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "evaluate_classifier_params(RandomForestClassifier, grid_clf.best_params_, X_train, truth_train, X_test, truth_test, iters=20)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T12:44:23.130616300Z",
+     "start_time": "2024-04-29T12:43:46.210540Z"
+    }
+   },
+   "id": "3c0217cdaea05b55",
    "execution_count": 6
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-04-16T17:42:12.818172Z",
-     "start_time": "2024-04-16T17:42:11.260342Z"
-    }
-   },
-   "cell_type": "code",
+   "cell_type": "markdown",
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "z = grid_clf.cv_results_['mean_test_score']\n",
-    "x = list(map(lambda x: x['max_depth'], grid_clf.cv_results_['params']))\n",
-    "y = list(map(lambda x: x['n_estimators'], grid_clf.cv_results_['params']))\n",
-    "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
-    "_ = ax.plot_trisurf(x, y, z)\n"
+    "# Look for optimal processing and classifier parameters"
    ],
-   "id": "7220b441cfbd90d7",
-   "outputs": [
-    {
-     "ename": "RuntimeError",
-     "evalue": "Error in qhull Delaunay triangulation calculation: input inconsistency (exitcode=1); use python verbose option (-v) to see original qhull error.",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mRuntimeError\u001B[0m                              Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[7], line 6\u001B[0m\n\u001B[0;32m      4\u001B[0m y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(\u001B[38;5;28mmap\u001B[39m(\u001B[38;5;28;01mlambda\u001B[39;00m x: x[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mn_estimators\u001B[39m\u001B[38;5;124m'\u001B[39m], grid_clf\u001B[38;5;241m.\u001B[39mcv_results_[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mparams\u001B[39m\u001B[38;5;124m'\u001B[39m]))\n\u001B[0;32m      5\u001B[0m fig, ax \u001B[38;5;241m=\u001B[39m plt\u001B[38;5;241m.\u001B[39msubplots(subplot_kw\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprojection\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m3d\u001B[39m\u001B[38;5;124m\"\u001B[39m})\n\u001B[1;32m----> 6\u001B[0m _ \u001B[38;5;241m=\u001B[39m \u001B[43max\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplot_trisurf\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mz\u001B[49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py:2003\u001B[0m, in \u001B[0;36mAxes3D.plot_trisurf\u001B[1;34m(self, color, norm, vmin, vmax, lightsource, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1999\u001B[0m cmap \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcmap\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m   2000\u001B[0m shade \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mshade\u001B[39m\u001B[38;5;124m'\u001B[39m, cmap \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[0;32m   2002\u001B[0m tri, args, kwargs \u001B[38;5;241m=\u001B[39m \\\n\u001B[1;32m-> 2003\u001B[0m     \u001B[43mTriangulation\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_from_args_and_kwargs\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2004\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   2005\u001B[0m     z \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mZ\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
-      "File \u001B[1;32m~\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\matplotlib\\tri\\_triangulation.py:167\u001B[0m, in \u001B[0;36mTriangulation.get_from_args_and_kwargs\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    164\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    165\u001B[0m     x, y, triangles, mask, args, kwargs \u001B[38;5;241m=\u001B[39m \\\n\u001B[0;32m    166\u001B[0m         Triangulation\u001B[38;5;241m.\u001B[39m_extract_triangulation_params(args, kwargs)\n\u001B[1;32m--> 167\u001B[0m     triangulation \u001B[38;5;241m=\u001B[39m \u001B[43mTriangulation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtriangles\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmask\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    168\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m triangulation, args, kwargs\n",
-      "File \u001B[1;32m~\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\matplotlib\\tri\\_triangulation.py:60\u001B[0m, in \u001B[0;36mTriangulation.__init__\u001B[1;34m(self, x, y, triangles, mask)\u001B[0m\n\u001B[0;32m     55\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mis_delaunay \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m     57\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m triangles \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m     58\u001B[0m     \u001B[38;5;66;03m# No triangulation specified, so use matplotlib._qhull to obtain\u001B[39;00m\n\u001B[0;32m     59\u001B[0m     \u001B[38;5;66;03m# Delaunay triangulation.\u001B[39;00m\n\u001B[1;32m---> 60\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtriangles, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_neighbors \u001B[38;5;241m=\u001B[39m \u001B[43m_qhull\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdelaunay\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msys\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mflags\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mverbose\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     61\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mis_delaunay \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[0;32m     62\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m     63\u001B[0m     \u001B[38;5;66;03m# Triangulation specified. Copy, since we may correct triangle\u001B[39;00m\n\u001B[0;32m     64\u001B[0m     \u001B[38;5;66;03m# orientation.\u001B[39;00m\n",
-      "\u001B[1;31mRuntimeError\u001B[0m: Error in qhull Delaunay triangulation calculation: input inconsistency (exitcode=1); use python verbose option (-v) to see original qhull error."
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 7
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "17ce0afe5a7a7b70"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T17:45:12.146424Z",
-     "start_time": "2024-04-16T17:42:15.736442Z"
+     "end_time": "2024-04-29T13:04:48.001960600Z",
+     "start_time": "2024-04-29T12:44:23.122083700Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "from sklearn.model_selection import cross_val_score\n",
-    "\n",
-    "for polyorder in range(3, 25, 2):\n",
-    "    processed_train = process_experiments(experiments_train, baseline_lam=10, baseline_p=1e-2, smooth_window_length=polyorder+2, smooth_polyorder=polyorder)\n",
-    "    X_train = pd.concat([metadata_train, processed_train], axis=1)\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train)\n",
-    "    X_train = scaler.transform(X_train)\n",
-    "    clf = RandomForestClassifier()\n",
-    "    print(polyorder, np.mean(cross_val_score(clf, X_train, truth_train.to_numpy().ravel(), cv=10)))\n",
-    "        "
+    "param_grid = {\n",
+    "    'baseline_lam': range(1,21,5),\n",
+    "    'baseline_p': [1e-1, 1e-2, 1e-3, 1e-4, 1e-5],\n",
+    "    'smooth_window_length': [3,5,9,15,21],\n",
+    "    'smooth_polyorder': [3,5,9,15,21],\n",
+    "    'n_estimators': [100], #range(1, 101, 100),\n",
+    "    'max_depth': [10] #range(5, 16, 5)\n",
+    "}\n",
+    "import warnings\n",
+    "with warnings.catch_warnings():\n",
+    "    warnings.filterwarnings('ignore')\n",
+    "    results = param_grid_search(RandomForestClassifier, param_grid, experiments_train, metadata_train, truth_train, cv=5)"
    ],
    "id": "e518d47d3a6aef5e",
    "outputs": [
@@ -658,6518 +192,768 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3 0.9468795355587808\n",
-      "5 0.9430696661828737\n",
-      "7 0.9431785195936138\n",
-      "9 0.9469158200290275\n",
-      "11 0.9526487663280117\n",
-      "13 0.9336357039187227\n",
-      "15 0.9488026124818576\n",
-      "17 0.9449927431059505\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "19 0.9166182873730044\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "21 0.928011611030479\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n",
-      "C:\\Users\\Ed\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:193: RankWarning: Polyfit may be poorly conditioned\n",
-      "  poly_coeffs = np.polyfit(np.arange(0, window_stop - window_start),\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "23 0.928011611030479\n"
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.5342443 , 0.5351305 , 0.51417208, 0.53326702, 0.52174711]), 'score_time': array([0.00225377, 0.00225544, 0.00217366, 0.0022366 , 0.00257421]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.8490566 , 0.83018868, 0.83018868, 0.91428571, 0.86666667])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.52323151, 0.51764464, 0.52236128, 0.52475786, 0.52849317]), 'score_time': array([0.00286651, 0.00227451, 0.00227356, 0.00338054, 0.00214696]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.85849057, 0.83018868, 0.8952381 , 0.87619048])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.52853799, 0.54870653, 0.50620103, 0.52176952, 0.51584792]), 'score_time': array([0.00261402, 0.00228   , 0.0026679 , 0.0026679 , 0.00225568]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.86792453, 0.83962264, 0.8490566 , 0.91428571, 0.88571429])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.52009225, 0.52740884, 0.51096416, 0.522264  , 0.52387953]), 'score_time': array([0.00329041, 0.00225878, 0.00232506, 0.00244737, 0.00227976]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.78301887, 0.8490566 , 0.8952381 , 0.87619048])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.53165364, 0.5209353 , 0.51807761, 0.53548908, 0.53231478]), 'score_time': array([0.00210094, 0.0034039 , 0.00237131, 0.00228   , 0.00382209]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.87735849, 0.87735849, 0.87735849, 0.91428571, 0.84761905])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.52925205, 0.52280021, 0.52203202, 0.52873111, 0.51950955]), 'score_time': array([0.00267816, 0.00243664, 0.00243306, 0.00225592, 0.00277925]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.87735849, 0.8490566 , 0.94285714, 0.87619048])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.52440596, 0.52496266, 0.50395179, 0.52605247, 0.52150369]), 'score_time': array([0.00211096, 0.00221109, 0.00227976, 0.0022912 , 0.00228596]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.86792453, 0.8490566 , 0.87735849, 0.93333333, 0.86666667])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.53493118, 0.51223302, 0.52570391, 0.5328269 , 0.53306437]), 'score_time': array([0.00259519, 0.00254083, 0.00259185, 0.00213337, 0.00336051]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.8490566 , 0.86792453, 0.88679245, 0.91428571, 0.85714286])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.53201365, 0.53170753, 0.51821184, 0.52443767, 0.50972915]), 'score_time': array([0.0022738 , 0.00289297, 0.00343561, 0.00260139, 0.0021162 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.86792453, 0.86792453, 0.86792453, 0.93333333, 0.88571429])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.1, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.54126811, 0.52926087, 0.52981567, 0.57177258, 0.53631258]), 'score_time': array([0.00342512, 0.00257134, 0.00252295, 0.00210452, 0.00229001]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.85849057, 0.83962264, 0.83018868, 0.9047619 , 0.84761905])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.50935388, 0.50514483, 0.48704553, 0.49426126, 0.50067449]), 'score_time': array([0.00212765, 0.00239921, 0.00226593, 0.00339222, 0.00233912]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.90566038, 0.92380952, 0.9047619 ])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.49256754, 0.50147533, 0.48508883, 0.50105143, 0.49664807]), 'score_time': array([0.0033226 , 0.00113726, 0.00223351, 0.00220418, 0.00339985]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.91509434, 0.94285714, 0.8952381 ])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.49202847, 0.48953962, 0.48896384, 0.50095057, 0.48813128]), 'score_time': array([0.00223804, 0.0025866 , 0.00243378, 0.00261283, 0.00129938]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.93396226, 0.91509434, 0.97142857, 0.87619048])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.49978018, 0.47929621, 0.53254366, 0.5612855 , 0.54925299]), 'score_time': array([0.00210524, 0.00219774, 0.00327563, 0.00214982, 0.00323415]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.88679245, 0.91509434, 0.90566038, 0.96190476, 0.8952381 ])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.50226331, 0.50715232, 0.48324561, 0.49798346, 0.49212313]), 'score_time': array([0.00268722, 0.00227356, 0.00216365, 0.00330615, 0.00343966]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.93396226, 0.91509434, 0.97142857, 0.9047619 ])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.4775362 , 0.50976062, 0.49008322, 0.50908399, 0.50268173]), 'score_time': array([0.00226688, 0.00210452, 0.00209761, 0.00212145, 0.00229573]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.95283019, 0.94339623, 0.89622642, 0.95238095, 0.8952381 ])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.49819136, 0.5010674 , 0.49012852, 0.50509   , 0.49800611]), 'score_time': array([0.00207639, 0.00224137, 0.00236702, 0.0022912 , 0.00206327]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.93396226, 0.94339623, 0.93396226, 0.95238095, 0.88571429])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.53041911, 0.52354741, 0.56365085, 0.54665589, 0.54504132]), 'score_time': array([0.00246453, 0.00311065, 0.00216365, 0.00319195, 0.0031395 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.97169811, 0.93396226, 0.93333333, 0.88571429])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.54345965, 0.5710752 , 0.53151894, 0.5547967 , 0.54563665]), 'score_time': array([0.00338101, 0.00257635, 0.00265837, 0.00339198, 0.00287819]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.91509434, 0.91509434, 0.94285714, 0.91428571])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.01, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.51513243, 0.49808645, 0.4871242 , 0.50093317, 0.49111056]), 'score_time': array([0.00211525, 0.00221825, 0.00228405, 0.00217891, 0.00215769]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.88679245, 0.91509434, 0.90566038, 0.95238095, 0.8952381 ])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.47366953, 0.48173428, 0.47270489, 0.4942553 , 0.47499871]), 'score_time': array([0.00249505, 0.00230169, 0.0022769 , 0.00340533, 0.00315237]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.9245283 , 0.95283019, 0.97142857, 0.91428571])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.47247076, 0.47326469, 0.45167184, 0.47404385, 0.47115397]), 'score_time': array([0.00217938, 0.00232434, 0.00205374, 0.0022459 , 0.00235701]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.9245283 , 0.99056604, 0.95283019, 0.98095238, 0.96190476])}]\n",
+      "[{'baseline_lam': 1, 'baseline_p': 0.001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.4731071 , 0.45436168, 0.45842218, 0.46580791, 0.47029614]), 'score_time': array([0.00247121, 0.00229073, 0.00232744, 0.00226855, 0.00149798]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.91509434, 0.97169811, 0.90566038, 0.99047619, 0.94285714])}]\n",
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+      "[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.51466799, 0.51861477, 0.51041293, 0.52379298, 0.5107007 ]), 'score_time': array([0.0022769 , 0.00362325, 0.00226569, 0.00228786, 0.00236082]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.76415094, 0.6509434 , 0.7047619 , 0.75238095])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.52684855, 0.52915597, 0.52124405, 0.52616858, 0.5323863 ]), 'score_time': array([0.0025053 , 0.00267386, 0.00367641, 0.00331354, 0.00222778]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.75471698, 0.77358491, 0.74528302, 0.73333333, 0.80952381])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.52670026, 0.51864815, 0.50877857, 0.51897407, 0.5189724 ]), 'score_time': array([0.00227356, 0.00391507, 0.00228024, 0.00316048, 0.0023191 ]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.75471698, 0.73584906, 0.71428571, 0.81904762])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.51453567, 0.51653242, 0.51602554, 0.51407623, 0.51877642]), 'score_time': array([0.00323844, 0.00330853, 0.00218058, 0.00206852, 0.00225616]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.79245283, 0.66981132, 0.75238095, 0.81904762])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 0.0001, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.53391051, 0.53223085, 0.52859545, 0.53306556, 0.53095341]), 'score_time': array([0.00332546, 0.00229716, 0.00217748, 0.00228   , 0.00209999]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.79245283, 0.76415094, 0.63207547, 0.77142857, 0.79047619])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 5}, {'fit_time': array([0.57309628, 0.58223128, 0.56289411, 0.57553363, 0.57948804]), 'score_time': array([0.00227642, 0.00229001, 0.00245333, 0.00212908, 0.00228977]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.70754717, 0.6509434 , 0.67619048, 0.72380952])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 9}, {'fit_time': array([0.56888056, 0.56287861, 0.55723619, 0.55990505, 0.57036185]), 'score_time': array([0.00340629, 0.00229549, 0.00268698, 0.00242639, 0.00226712]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.77358491, 0.73584906, 0.64150943, 0.67619048, 0.73333333])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 15}, {'fit_time': array([0.56206536, 0.56974959, 0.55887055, 0.5618372 , 0.57203674]), 'score_time': array([0.00230193, 0.00228477, 0.00207949, 0.00350881, 0.00257897]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.67924528, 0.67924528, 0.67619048, 0.68571429])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 3, 'smooth_window_length': 21}, {'fit_time': array([0.57169175, 0.56945419, 0.55793834, 0.56491661, 0.56198812]), 'score_time': array([0.00243926, 0.00257421, 0.00259924, 0.00227785, 0.00344634]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.72641509, 0.69811321, 0.6509434 , 0.65714286, 0.73333333])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 9}, {'fit_time': array([0.56784749, 0.56324482, 0.55494237, 0.57094359, 0.56542826]), 'score_time': array([0.0022614 , 0.00241065, 0.0035069 , 0.00226021, 0.00257826]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.70754717, 0.66981132, 0.68571429, 0.77142857])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 15}, {'fit_time': array([0.58082008, 0.57902908, 0.56728029, 0.58658981, 0.59594345]), 'score_time': array([0.00340176, 0.00230408, 0.00213933, 0.00227427, 0.00355196]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.72641509, 0.72641509, 0.66037736, 0.67619048, 0.68571429])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 5, 'smooth_window_length': 21}, {'fit_time': array([0.56925416, 0.57333541, 0.56887221, 0.57036018, 0.5799315 ]), 'score_time': array([0.00339007, 0.0022862 , 0.00243115, 0.00240493, 0.00230575]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.73584906, 0.68867925, 0.66037736, 0.66666667, 0.72380952])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 15}, {'fit_time': array([0.56541538, 0.56747699, 0.56590462, 0.57279587, 0.57426763]), 'score_time': array([0.00224066, 0.00222659, 0.00254583, 0.00339532, 0.00391769]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.74528302, 0.73584906, 0.67924528, 0.66666667, 0.71428571])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 9, 'smooth_window_length': 21}, {'fit_time': array([0.57916236, 0.57365298, 0.57374072, 0.57865381, 0.58455348]), 'score_time': array([0.00208807, 0.00359035, 0.00217104, 0.00320792, 0.00231767]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.70754717, 0.69811321, 0.64150943, 0.64761905, 0.73333333])}]\n",
+      "[{'baseline_lam': 16, 'baseline_p': 1e-05, 'max_depth': 10, 'n_estimators': 100, 'smooth_polyorder': 15, 'smooth_window_length': 21}, {'fit_time': array([0.57128263, 0.57569408, 0.55591512, 0.5729866 , 0.56995869]), 'score_time': array([0.00372767, 0.00213623, 0.00222516, 0.00275683, 0.00361252]), 'estimator': [RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10), RandomForestClassifier(max_depth=10)], 'test_score': array([0.71698113, 0.73584906, 0.66981132, 0.68571429, 0.71428571])}]\n"
      ]
     }
    ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(0.9697574123989219,\n {'baseline_lam': 11,\n  'baseline_p': 0.001,\n  'max_depth': 10,\n  'n_estimators': 100,\n  'smooth_polyorder': 5,\n  'smooth_window_length': 21})"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k = 0\n",
+    "best_params = dict()\n",
+    "for r in results:\n",
+    "    mean = np.mean(r[1]['test_score'])\n",
+    "    if mean > k:\n",
+    "        k = mean\n",
+    "        best_params = r[0]\n",
+    "k, best_params"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:04:48.007939600Z",
+     "start_time": "2024-04-29T13:04:48.002985100Z"
+    }
+   },
+   "id": "846605ecc9c07eb4",
    "execution_count": 8
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-04-16T17:46:29.122526Z",
-     "start_time": "2024-04-16T17:46:22.315641Z"
-    }
-   },
    "cell_type": "code",
-   "source": [
-    "temp = process_experiments(experiments_train, baseline_lam=10, baseline_p=1e-2, smooth_window_length=7, smooth_polyorder=3)\n",
-    "_ = temp.transpose().plot(legend=False)\n",
-    "temp = process_experiments(experiments_train, baseline_lam=10, baseline_p=1e-2, smooth_window_length=19, smooth_polyorder=17)\n",
-    "_ = temp.transpose().plot(legend=False)"
-   ],
-   "id": "181d38c70d5cd1a1",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "(1.0, 0.977966101694915)"
      },
+     "execution_count": 9,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
+   "source": [
+    "process_params = {key: best_params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "classifier_params = {key: best_params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
+    "evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=20)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:05:01.580495600Z",
+     "start_time": "2024-04-29T13:04:48.009042100Z"
+    }
+   },
+   "id": "f07d35308265f471",
    "execution_count": 9
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-04-16T18:00:26.278947Z",
-     "start_time": "2024-04-16T17:59:19.497419Z"
-    }
-   },
    "cell_type": "code",
-   "source": [
-    "for droprate in [1, 2, 3, 5, 10, 20, 50, 100, 200, 500, 1000]:\n",
-    "    dropped_train = experiments_train.iloc[:, ::droprate]\n",
-    "    processed_train = process_experiments(dropped_train, baseline_lam=10, baseline_p=1e-2, smooth_window_length=7, smooth_polyorder=3)\n",
-    "    X_train = pd.concat([metadata_train, processed_train], axis=1)\n",
-    "    scaler = StandardScaler()\n",
-    "    scaler.fit(X_train)\n",
-    "    X_train = scaler.transform(X_train)\n",
-    "    clf = RandomForestClassifier()\n",
-    "    print(droprate, dropped_train.shape, np.mean(cross_val_score(clf, X_train, truth_train.to_numpy().ravel(), cv=10)))"
-   ],
-   "id": "ebb00d3254a98b96",
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 (528, 1746) 0.9563134978229317\n",
-      "2 (528, 873) 0.9583454281567487\n",
-      "3 (528, 582) 0.9526124818577649\n",
-      "5 (528, 350) 0.9489477503628446\n",
-      "10 (528, 175) 0.9659288824383163\n",
-      "20 (528, 88) 0.9678156748911466\n",
-      "50 (528, 35) 0.9546081277213352\n",
-      "100 (528, 18) 0.9205370101596516\n",
-      "200 (528, 9) 0.9148403483309144\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "If mode is 'interp', window_length must be less than or equal to the size of x.",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
-      "Cell \u001B[1;32mIn[14], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m droprate \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m5\u001B[39m, \u001B[38;5;241m10\u001B[39m, \u001B[38;5;241m20\u001B[39m, \u001B[38;5;241m50\u001B[39m, \u001B[38;5;241m100\u001B[39m, \u001B[38;5;241m200\u001B[39m, \u001B[38;5;241m500\u001B[39m, \u001B[38;5;241m1000\u001B[39m]:\n\u001B[0;32m      2\u001B[0m     dropped_train \u001B[38;5;241m=\u001B[39m experiments_train\u001B[38;5;241m.\u001B[39miloc[:, ::droprate]\n\u001B[1;32m----> 3\u001B[0m     processed_train \u001B[38;5;241m=\u001B[39m \u001B[43mprocess_experiments\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdropped_train\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbaseline_lam\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m10\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbaseline_p\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1e-2\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msmooth_window_length\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m7\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msmooth_polyorder\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m3\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m      4\u001B[0m     X_train \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mconcat([metadata_train, processed_train], axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m      5\u001B[0m     scaler \u001B[38;5;241m=\u001B[39m StandardScaler()\n",
-      "File \u001B[1;32m~\\Desktop\\Programming\\RamanClassifier\\data\\data_processing.py:87\u001B[0m, in \u001B[0;36mprocess_experiments\u001B[1;34m(experiments, baseline_lam, baseline_p, smooth_window_length, smooth_polyorder)\u001B[0m\n\u001B[0;32m     85\u001B[0m experiments \u001B[38;5;241m=\u001B[39m adjust_all_baselines(experiments, lam\u001B[38;5;241m=\u001B[39mbaseline_lam, p\u001B[38;5;241m=\u001B[39mbaseline_p)\n\u001B[0;32m     86\u001B[0m experiments \u001B[38;5;241m=\u001B[39m scale_experiments(experiments)\n\u001B[1;32m---> 87\u001B[0m experiments \u001B[38;5;241m=\u001B[39m \u001B[43msmooth_experiments\u001B[49m\u001B[43m(\u001B[49m\u001B[43mexperiments\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwindow_length\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msmooth_window_length\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpolyorder\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msmooth_polyorder\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     88\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m experiments\n",
-      "File \u001B[1;32m~\\Desktop\\Programming\\RamanClassifier\\data\\data_processing.py:72\u001B[0m, in \u001B[0;36msmooth_experiments\u001B[1;34m(experiments, window_length, polyorder)\u001B[0m\n\u001B[0;32m     70\u001B[0m result \u001B[38;5;241m=\u001B[39m experiments\u001B[38;5;241m.\u001B[39mcopy(deep\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m     71\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m index, row \u001B[38;5;129;01min\u001B[39;00m result\u001B[38;5;241m.\u001B[39miterrows():\n\u001B[1;32m---> 72\u001B[0m     result\u001B[38;5;241m.\u001B[39miloc[index, :] \u001B[38;5;241m=\u001B[39m \u001B[43mapply_smoothing\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrow\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwindow_length\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mwindow_length\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpolyorder\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpolyorder\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     73\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
-      "File \u001B[1;32m~\\Desktop\\Programming\\RamanClassifier\\data\\data_processing.py:66\u001B[0m, in \u001B[0;36mapply_smoothing\u001B[1;34m(experiment, window_length, polyorder)\u001B[0m\n\u001B[0;32m     65\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mapply_smoothing\u001B[39m(experiment, window_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m7\u001B[39m, polyorder\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3\u001B[39m):\n\u001B[1;32m---> 66\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43msavgol_filter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mexperiment\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwindow_length\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpolyorder\u001B[49m\u001B[43m)\u001B[49m\n",
-      "File \u001B[1;32m~\\Desktop\\Programming\\RamanClassifier\\venv\\Lib\\site-packages\\scipy\\signal\\_savitzky_golay.py:345\u001B[0m, in \u001B[0;36msavgol_filter\u001B[1;34m(x, window_length, polyorder, deriv, delta, axis, mode, cval)\u001B[0m\n\u001B[0;32m    343\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m mode \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minterp\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m    344\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m window_length \u001B[38;5;241m>\u001B[39m x\u001B[38;5;241m.\u001B[39mshape[axis]:\n\u001B[1;32m--> 345\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIf mode is \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124minterp\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, window_length must be less \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    346\u001B[0m                          \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mthan or equal to the size of x.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    348\u001B[0m     \u001B[38;5;66;03m# Do not pad. Instead, for the elements within `window_length // 2`\u001B[39;00m\n\u001B[0;32m    349\u001B[0m     \u001B[38;5;66;03m# of the ends of the sequence, use the polynomial that is fitted to\u001B[39;00m\n\u001B[0;32m    350\u001B[0m     \u001B[38;5;66;03m# the last `window_length` elements.\u001B[39;00m\n\u001B[0;32m    351\u001B[0m     y \u001B[38;5;241m=\u001B[39m convolve1d(x, coeffs, axis\u001B[38;5;241m=\u001B[39maxis, mode\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mconstant\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
-      "\u001B[1;31mValueError\u001B[0m: If mode is 'interp', window_length must be less than or equal to the size of x."
-     ]
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
+   "source": [
+    "clf = RandomForestClassifier(**classifier_params)\n",
+    "clf.fit(X_train, truth_train.to_numpy().ravel())\n",
+    "_ = experiments_train.transpose().plot(legend=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:05:02.987612100Z",
+     "start_time": "2024-04-29T13:05:01.567576100Z"
+    }
+   },
+   "id": "fd7b893d195e56a2",
+   "execution_count": 10
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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ERCaESVTDLjVEvoVBDREREfkFBjVERCYEm5+IfBaDGiIiE4xpiHwXgxoiIiLyCwxqiIhMCLY/EfksBjVERCYY0hD5LqeCmqVLl6Jnz54ICwtDUlISdu7caTP9unXr0LdvX4SFhaF///7YuHGj5PUvvvgCt912G9q1awedToc9e/ZIXi8tLcVjjz2GPn36IDw8HN27d8ff//53lJeXO5N9IiIi8kMOBzVr167FzJkzMW/ePOzevRsDBw5EamoqioqKZNPv2LED48aNw5QpU5CdnY20tDSkpaUhJyfHmKaqqgrDhw/HP//5T9lj5OfnIz8/H6+//jpycnLwwQcfYNOmTZgyZYqj2ScisomtT0S+SyccbEBOSkrCddddhyVLlgAA9Ho9unXrhsceewzPPPOMRfqxY8eiqqoKGzZsMG67/vrrMWjQIKxYsUKS9uTJk4iPj0d2djYGDRpkMx/r1q3Dn//8Z1RVVSEoKMhuvisqKhAdHY3y8nJERUUpuFIiaomKKmsw9KV0AEDms7ciNirMyzkiatkcKb8dqqmpq6tDVlYWUlJSmg8QEICUlBRkZGTI7pORkSFJDwCpqalW0ytluDhrAU1tbS0qKiokP0REdrGmhshnORTUlJSUoLGxEbGxsZLtsbGxKCgokN2noKDAofRK8/HCCy9g2rRpVtMsXLgQ0dHRxp9u3bo5fT4iapnYFEXkW3xu9FNFRQVGjx6Nq6++GvPnz7eabtasWSgvLzf+nD592nOZJCKfxTiGyHfZ74xion379ggMDERhYaFke2FhIeLi4mT3iYuLcyi9LZWVlRg5ciRat26NL7/8EsHBwVbThoaGIjQ01OFzEFHLZlo7IxjiEPkUh2pqQkJCkJiYiPT0dOM2vV6P9PR0JCcny+6TnJwsSQ8AmzdvtpremoqKCtx2220ICQnB119/jbAwdt4jIiKiZg7V1ADAzJkzMXHiRAwZMgRDhw7FokWLUFVVhcmTJwMAJkyYgC5dumDhwoUAgBkzZmDEiBF44403MHr0aKxZswa7du3Ce++9ZzxmaWkp8vLykJ+fDwA4fPgwgKZanri4OGNAU11djY8//ljS8bdDhw4IDAx07V0gIrrMtHaGfWqIfIvDQc3YsWNRXFyMuXPnoqCgAIMGDcKmTZuMnYHz8vIQENBcATRs2DCsXr0as2fPxrPPPouEhASsX78e/fr1M6b5+uuvjUERANx///0AgHnz5mH+/PnYvXs3MjMzAQBXXHGFJD+5ubno2bOno5dBRCSLgQyR73J4nhpfxXlqiEiJs2WXcMMrWwEAvzxzC7rEhHs5R0Qtm9vmqSEiakle3XTI21kgIgcwqCEiMmFaef3Vnnwv5oSIHMWghojIRMtokCfyTwxqiIiIyC8wqCEiIiK/wKCGiMgEm5+IfBeDGiIiE1wagch3MaghIiIiv8CghojIBJufiHwXgxoiIhOMaYh8F4MaIiIi8gsMaoiITLSQ5fCI/BKDGiIiEwxpiHwXgxoiIhOsqCHyXQxqiIiIyC8wqCEikmBVDZGvYlBDRGSCzU9EvotBDREREfkFBjVERCZYUUPkuxjUEBGZYPMTke9iUENEZIKrdBP5LgY1RERE5BcY1BARmWDzE5HvYlBDRGSCQQ2R72JQQ0RERH6BQQ0RkQl2FCbyXQxqiIhMsPmJyHcxqCEiIiK/wKCGiIiI/AKDGiIiE2x+IvJdDGqIiEys3nnK21kgIicxqCEiMvHpztPezgIROYlBDREREfkFBjVERETkFxjUEBGp4PdzFSiqqPF2NohatCBvZ4CIyNedLKnCqLd/avr3K6O9nBuilos1NURELtp7pszbWSAiMKghIiIiP8GghoiIiPwCgxoiIiLyCwxqiIiIyC84FdQsXboUPXv2RFhYGJKSkrBz506b6detW4e+ffsiLCwM/fv3x8aNGyWvf/HFF7jtttvQrl076HQ67Nmzx+IYNTU1ePTRR9GuXTu0atUK9957LwoLC53JPhEREfkhh4OatWvXYubMmZg3bx52796NgQMHIjU1FUVFRbLpd+zYgXHjxmHKlCnIzs5GWloa0tLSkJOTY0xTVVWF4cOH45///KfV8/7v//4v/vvf/2LdunX48ccfkZ+fj3vuucfR7BMREZGf0gnh2Jq0SUlJuO6667BkyRIAgF6vR7du3fDYY4/hmWeesUg/duxYVFVVYcOGDcZt119/PQYNGoQVK1ZI0p48eRLx8fHIzs7GoEGDjNvLy8vRoUMHrF69Gn/6058AAIcOHcJVV12FjIwMXH/99Rbnra2tRW1trfH3iooKdOvWDeXl5YiKinLkkomoBen5zDeS35XMO/PVnrOYsWaP4vREpFxFRQWio6MVld8O1dTU1dUhKysLKSkpzQcICEBKSgoyMjJk98nIyJCkB4DU1FSr6eVkZWWhvr5ecpy+ffuie/fuVo+zcOFCREdHG3+6deum+HxERETkexwKakpKStDY2IjY2FjJ9tjYWBQUFMjuU1BQ4FB6a8cICQlBTEyM4uPMmjUL5eXlxp/Tp7nyLhERkT/z22USQkNDERoa6u1sEBERkYc4VFPTvn17BAYGWow6KiwsRFxcnOw+cXFxDqW3doy6ujqUlZW5dBwiIiLyXw4FNSEhIUhMTER6erpxm16vR3p6OpKTk2X3SU5OlqQHgM2bN1tNLycxMRHBwcGS4xw+fBh5eXkOHYeIiIj8l8PNTzNnzsTEiRMxZMgQDB06FIsWLUJVVRUmT54MAJgwYQK6dOmChQsXAgBmzJiBESNG4I033sDo0aOxZs0a7Nq1C++9957xmKWlpcjLy0N+fj6ApoAFaKqhiYuLQ3R0NKZMmYKZM2eibdu2iIqKwmOPPYbk5GTZkU9ERETU8jgc1IwdOxbFxcWYO3cuCgoKMGjQIGzatMnYGTgvLw8BAc0VQMOGDcPq1asxe/ZsPPvss0hISMD69evRr18/Y5qvv/7aGBQBwP333w8AmDdvHubPnw8AeOuttxAQEIB7770XtbW1SE1NxbJly5y6aCIiIvI/Ds9T46scGedORC0X56kh0ha3zVNDRESk1NyvcvDUf/Z6OxvUgjCoISIi1dXUN+LDjFP4bNcZnC275O3sUAvBoIaIiFSnN+nZ0NjYIno5kAYwqCEiIiK/wKCGiMhFLWO4BZH2MaghIiIiv8CghojIRTqdt3NARACDGiIil7H5iUgbGNQQERGRX2BQQ0TkIjY/EWkDgxoiIhex+YlIGxjUEBERkV9gUENERER+gUENERFJ1DY04lJdo0vHYJMceQODGiIiMtLrBYa+lI4bX/0BDY16b2eHyCEMaoiIyKiwsgbll+pRcrEWF6rrnT4OK2rIGxjUEBGRUX7ZJeO/gwI4Vp18C4MaIiIyqqxpUOU4gp1qyAsY1BARkSy1whLBxijyEAY1RESkOoYx5A0MaoiISBabkMjXMKghIiLVMR4ib2BQQ0RERsLKv4l8AYMaIiKS5VJtCyMi8gIGNURE1IzBCPkwBjVERCTLlaHYHMZN3sCghoiIjBiMkC9jUENERPJciG/k+uMs/eEYpn64iwtlktswqCEicpE/1W64cyj2a98dxuaDhdjye6H7TkItGoMaIiKS5a7BTzX1rKkh92BQQ0TkIh0cW836ZEkVFn77O4ora92UI+d5YtI8f6rZIm0J8nYGiIh8naOFdNqyX1BWXY8DZyvw8YNJbsqV61wJcLjEAnkDa2qIiDysrLoeALDrVKmXc2JJOqOwOoGJeXzDeIfchUENEZGLHG1+MvDnwt2PL400jEENEZGL/KmPiGmzkT8HXeSfGNQQEXmJP8cMtgIiBkvkLgxqiIjIyB2rdDOGIU9hUENE5C1+XNrbapLz48smL2NQQ0TkJVrsi2PaNKTWsGwO7yZPYVBDRESyXIpFGMeQFzgV1CxduhQ9e/ZEWFgYkpKSsHPnTpvp161bh759+yIsLAz9+/fHxo0bJa8LITB37lx06tQJ4eHhSElJwdGjRyVpjhw5grvuugvt27dHVFQUhg8fjh9++MGZ7BMRaYI2KzDUz5T5EVlzQ+7icFCzdu1azJw5E/PmzcPu3bsxcOBApKamoqioSDb9jh07MG7cOEyZMgXZ2dlIS0tDWloacnJyjGleffVVLF68GCtWrEBmZiYiIyORmpqKmpoaY5rbb78dDQ0N2Lp1K7KysjBw4EDcfvvtKCgocOKyiYi8T4tFu16lTEk6HGvxQskvORzUvPnmm5g6dSomT56Mq6++GitWrEBERARWrlwpm/7tt9/GyJEj8eSTT+Kqq67CCy+8gMGDB2PJkiUAmiL2RYsWYfbs2bjrrrswYMAAfPjhh8jPz8f69esBACUlJTh69CieeeYZDBgwAAkJCXjllVdQXV0tCY6IiMh5Z8su4W+f7PZ2Noic5lBQU1dXh6ysLKSkpDQfICAAKSkpyMjIkN0nIyNDkh4AUlNTjelzc3NRUFAgSRMdHY2kpCRjmnbt2qFPnz748MMPUVVVhYaGBrz77rvo2LEjEhMTZc9bW1uLiooKyQ8RkZZorRlmydZjkt9dW/tJ8hus/0akHoeCmpKSEjQ2NiI2NlayPTY21mozUEFBgc30hv/bSqPT6bBlyxZkZ2ejdevWCAsLw5tvvolNmzahTZs2sudduHAhoqOjjT/dunVz5FKJiIjIx/jE6CchBB599FF07NgRP/30E3bu3Im0tDTccccdOHfunOw+s2bNQnl5ufHn9OnTHs41EZFtWq+xcGXIuem+FjU+Wr9w8lkOBTXt27dHYGAgCgsLJdsLCwsRFxcnu09cXJzN9Ib/20qzdetWbNiwAWvWrMENN9yAwYMHY9myZQgPD8f//d//yZ43NDQUUVFRkh8iIi3RWOsTkc9zKKgJCQlBYmIi0tPTjdv0ej3S09ORnJwsu09ycrIkPQBs3rzZmD4+Ph5xcXGSNBUVFcjMzDSmqa6ubspsgDS7AQEB0Ov1jlwCEREppFafGsZu5ClBju4wc+ZMTJw4EUOGDMHQoUOxaNEiVFVVYfLkyQCACRMmoEuXLli4cCEAYMaMGRgxYgTeeOMNjB49GmvWrMGuXbvw3nvvAWjqL/P444/jxRdfREJCAuLj4zFnzhx07twZaWlpAJoCozZt2mDixImYO3cuwsPD8a9//Qu5ubkYPXq0Sm8FERGZUm3tJ0Y15CEOBzVjx45FcXEx5s6di4KCAgwaNAibNm0ydvTNy8uT1KgMGzYMq1evxuzZs/Hss88iISEB69evR79+/YxpnnrqKVRVVWHatGkoKyvD8OHDsWnTJoSFhQFoavbatGkTnnvuOdxyyy2or6/HNddcg6+++goDBw509T0gIvIrRwor8e3+Ajx4YzwiQx1+zKuCcQx5g1Of9unTp2P69Omyr23bts1i25gxYzBmzBirx9PpdFiwYAEWLFhgNc2QIUPw3XffOZxXIqKW5ra3tgMALlTXYf6d1zh9HNXWfrIY0s2Qh9zDJ0Y/ERGR4/acLvPaubU2Bw+1DAxqiIhcpNXy29VssU8N+RoGNURE/sqL0YStUzPIIXdhUENE5KdcrqlRa3FLBjHkIQxqiIhUpKW+JFrJimVHYSL3YFBDROSnXB9lxPCDfAuDGiIiP+VqTY1qMwozNiIPYVBDRGRFUnxbh/fRUgGupbyY0mq+yPcxqCEi8lPebHziBHvkDQxqiIj8lJY6LZtiwEPuwqCGiEhF/lRcs09NU2BYXl3v7WyQQgxqiIis8OGyGIBvBxNa8cKG3zFwwff4/kCBt7NCCjCoISLyU64287iyv5D822yeGh8Ktlb+kgsAeOXbQ17OCSnBoIaIyBonCl8t9WPRSla0kg9X+MEltAgMaoiI/JQ3l0mwFdz5YoCg94fIrAVgUENE5KdcrTVSbe0ndQ7jtF9PnMfi9KNo1LvQnObtiyBFgrydASIirXKmT4mWyj5v5kVL78P97/0KAOgcE44/JXZ16hgchu4bWFNDROQizX6Ld3WZBJUKciGEtNbIS2/YyZIqp/fV7D0mCQY1RERW+HpB5tWaGmH7d1/j6/lvKRjUEBG5SKdr/rc/FX7+0qdGDVoa1UbWMaghInKRu0YJucrxY6uZl+ZjCWE+b43v8cU8t0QMaoiIrPBEQebOCgAXBvuQGVbU+AYGNURELpI0PzkYCmmrrNTZT6KQNAgQPt98w9FPvoFBDRGRFUoLYs02P7m6TIKKWfP1kMDHY7IWg0ENEZEXubOs1Otd21+1tZ80EhC4cj1syvMNDGqIiKxwphxztABvKdPva2CaGhf5ZKZbHAY1RERepOUC3rVmNZN/u54Vr9PyfaJmDGqIiPyUVmqBmoZ0mw7x1ka+HKGV95JsY1BDRHSZeWHriXJMy2WlK1kz77+ihet0qeZJvWyQGzGoISLyIncOFdZCIAH4Zs2MOT+4hBaBQQ0R0WUW6xV54JzuHFXjapOJu4IRX4wP/CEwawkY1BAReZGWC0uXmp/YUZi8gEENEWlS3vlq7DhW4tFzqlFuOVr4ubOs1FI57OtDun0wyy0Sgxoi0qSbXvsBD/w7E3tOl3kvEx4ofd15CldrgVQb0i3c23dIKddqnryff7KPQQ0Radq+M2UeO5c3Ci4Wlr6Bd8k3MKghIrLCqRmFHdzrzIVLTpxFGdfjJVeWSRCSf/t6HxvGnr6BQQ0R0WXeKLemfrjLbcfWTDmsmYw4TwvNZ2Qfgxoi0jR//4Z8rrzGbcfWSp8awHyBS+/cVMNpF278HdNX73YoH1zQ0jcwqCEiusyiIHaiINNSEKaVglgj2TB6d/sJbNh3DvvPlivfSWsXQbIY1BCRpul03s6B73J58j2V8gFos0N0XYNecVo2P/kGBjVERJdZrFfk4wWZ3sWqGrXikKYh3drjSJ40GJORDKeCmqVLl6Jnz54ICwtDUlISdu7caTP9unXr0LdvX4SFhaF///7YuHGj5HUhBObOnYtOnTohPDwcKSkpOHr0qMVxvvnmGyQlJSE8PBxt2rRBWlqaM9knIpLlbMElrPzb2xq9WBL7QhDgSB65SrdvcDioWbt2LWbOnIl58+Zh9+7dGDhwIFJTU1FUVCSbfseOHRg3bhymTJmC7OxspKWlIS0tDTk5OcY0r776KhYvXowVK1YgMzMTkZGRSE1NRU1Ncwe6zz//HH/5y18wefJk7N27F7/88gseeOABJy6ZiKhlaHS5pkadgtxiSLdG4gNHrk8jWSY7HA5q3nzzTUydOhWTJ0/G1VdfjRUrViAiIgIrV66UTf/2229j5MiRePLJJ3HVVVfhhRdewODBg7FkyRIATR+qRYsWYfbs2bjrrrswYMAAfPjhh8jPz8f69esBAA0NDZgxYwZee+01PPzww7jyyitx9dVX47777nP+yomI7FBa5mm124+rQY0rJPPUCKC4stZrebHG3rtzovhic1pGNT7BoaCmrq4OWVlZSElJaT5AQABSUlKQkZEhu09GRoYkPQCkpqYa0+fm5qKgoECSJjo6GklJScY0u3fvxtmzZxEQEIBrr70WnTp1wqhRoyS1PeZqa2tRUVEh+SEicgctDFeW42pMo9aV5JddwrNf7FfpaM6z6DNl5wL/d+0e92WG3MKhoKakpASNjY2IjY2VbI+NjUVBQYHsPgUFBTbTG/5vK82JEycAAPPnz8fs2bOxYcMGtGnTBjfffDNKS0tlz7tw4UJER0cbf7p16+bIpRJRC6TGkG5qYvrePfPFfpwoqWp+TSONOfbycaG63kM5IbX4xOgnvb5p2N1zzz2He++9F4mJiVi1ahV0Oh3WrVsnu8+sWbNQXl5u/Dl9+rQns0xEfuDguQqUKyjYtNr85Cq/D+rsXB+nE/A9DgU17du3R2BgIAoLCyXbCwsLERcXJ7tPXFyczfSG/9tK06lTJwDA1VdfbXw9NDQUvXr1Ql5enux5Q0NDERUVJfkhIrJF7pv7698fVrBfs5KLdXbTX9ezjSPZ8knm76RpgKDzYhho2jxoL2ZjTON7HApqQkJCkJiYiPT0dOM2vV6P9PR0JCcny+6TnJwsSQ8AmzdvNqaPj49HXFycJE1FRQUyMzONaRITExEaGorDh5sfLvX19Th58iR69OjhyCUQkY/xdm2Bo8sY3PnOz3bTtA4LdjY7HuWuZiKvNT8J4G+f7G7+1d9rolqgIEd3mDlzJiZOnIghQ4Zg6NChWLRoEaqqqjB58mQAwIQJE9ClSxcsXLgQADBjxgyMGDECb7zxBkaPHo01a9Zg165deO+99wAAOp0Ojz/+OF588UUkJCQgPj4ec+bMQefOnY3z0ERFReHhhx/GvHnz0K1bN/To0QOvvfYaAGDMmDFqvA9ERLKFnKMdfytrGxScx32lqSOz5FqysWCTo0cyu0at1Hp8m9Pc/9NecKVj+5PPcTioGTt2LIqLizF37lwUFBRg0KBB2LRpk7Gjb15eHgICmiuAhg0bhtWrV2P27Nl49tlnkZCQgPXr16Nfv37GNE899RSqqqowbdo0lJWVYfjw4di0aRPCwsKMaV577TUEBQXhL3/5Cy5duoSkpCRs3boVbdr4fzUuEXmPr32ZV9Jc5g3SEWJeyoRZjMKaGv/jcFADANOnT8f06dNlX9u2bZvFtjFjxtisUdHpdFiwYAEWLFhgNU1wcDBef/11vP766w7nl4hICbkyztdmkl2zU76foTLSUt+VK9fku+ZgRRTraXyPU0ENEZGneLsFQM3564QQ+P1cJeoaXWki8k2mt1ErAY/dZkBGNT6HQQ0R0WVyhZya/V8+23UaT3/u/UnolHLl0m3tq5XKL41kg1TkE/PUEBF5i5oF8P/tOKXewXyMaY2bVibfsztPjWdyQSpiUENEdJk/9KlRk2vBh/UlCbz1lpqfVjPBFamGQQ0RkQ2+FtP4WHa9ym6XGm936CKHMaghIrpMrpBTUlOjtN+Nr5WRavapKTJZpVsri36yn7D/YVBDRGSDNopf73DXtWskpnGpaXHJ1qNY+O3vKuaG1MDRT0SkaR4tAJ2cUZjNFJZsvWveimnM76XdeWqs3Fa9XuD1748AAP6c1APd2kaokDtSA2tqiIhsUDJPjf82P7lp7SeN1NQ4mw/TGp5L9Y0q5YbUwKCGiOgyudEwLXn0kytszlOjmUY9O2s/WelVY7oXPx/awqCGSEN2HC/Bt/vPeTsbZEJJmeWvzU/OFtcXaxuw0cbnWCtxgP3RT/LbTQOZRjWnnCaXMaghcgO9XuC7AwUorKhxaL8H/pWJRz7ZjdOl1W7KGdni7CrdWhnNoxUzPs3GBztOWn1dK++Ws/nQwpw7JI9BDZEbrPntNB76KAu3vL7Nqf1Nh7+Sd6lZZllrztAsJy8+/VCRneN6JxIwP62z2TDdjzU12sKghsgNtl5+qFfVsROhL+GMwp6hlXfU2b49pp8Jfj60hUENkRu4+qDz0y4aPknf8hbUNnJXh16txAHOzijMjsLaxaCGyA1crZJmTNPMkwGeXN+YllxouevStTL6ye48NVa2S2tqVMsOqYBBDZEbtOSC0Je5+66xBq6JZha0dDIjprvpGdVoCoMaIjdg50H/4XMBqorZdV9NjW+wFoSaBkONvvb58HMMaojcwJmC0PRB6a/znjjDk2WG/JBu9Y7Pu+pd5u+/GqOfGNNoC4MaIjdwpnMpH47apGiVbg/kwxv8bUFLi+YnezMKc/I9n8OghsgNnKmpMd2H3+i9Q66QY7CpPs10FLY3+knBMglsftIWBjVEbuDMg46PRm1SFKD66c1z20zJXnq/fjtZKs2GCgtaNjb66c33UQxqiNzAmRpp0wcsu9R4iRfKp5HXxHn+pF7mrTBg35lyh/JhvaNw878b2PykKQxqiNyg2ME1nwBplfzp0kvIOnVBzSyRk1TttC1zrMAA7Uaw7utTo41AQI0h3Q0teXZGDQrydgaI/FF+uRNBjcmD8tHVuwEA6f8Ygd4dWqmVLbLD6QUOXSj+tdK/RI7bWp80csnOZsO0+Ukr10JNWFNDpGG/n6uQ3d7QyG+H/qIlFoqauWS7HYXt76aZayEADGqINEPpiKlfjpXgytnf4qNfT7k5Ry2P3C1wd+OQp4Ka7LwLePfH43aGIFsOenYHrQRydmvJrDQ9chZh7WLzE5FGyBeolg/Vxz7Nhl4Ac9bn4C/X9/BAzsgepYW0XBHpqeanu5ftAAC0iQzBfUO6eeSc1milyc3+kG4lx9DGtVAT1tQQaYTco1ErD/+Wwhvvt6fLxGNFF228Ki3G2adGns8tndGCMKgh0gh+4/Ndrtw53nXvcX6eGtePQe7BoIZII5Q+Gxn8uI833lpPn9ORPkK+OKR77+kyDFuYjq/35rt8LCULWrI2VVsY1BBphOCAJr8nX0hqt1D0xQmFH/44C/nlNfj7p9kK8mE7J9l5ZbLbWVOjXQxqiDRC6Tc+PkPdR/a9VVC14UrBpnah6AufD3cGAnUN6nw7MF9SQcoX3uWWiUENkUbIPej5LdC/yI9+0kAmrHBX04qnmmwu1TU6ve+hgkqrr7GmRrsY1BBpBJ+N3ueN/kotsY+UOy/ZtInv19zzbjlHTX1zsNTy7p62Magh0gilhVsLLAM1z7VlEjxLbu6jZtLc+GKfGk+4c8kvxn+3xKBUyxjUEGkEJyn1PneXT3KLY3p89JONmKah0TOZcec1S47tgcvhn622MKgh0ggODfVdLnUUVi8bLmswi6zdlzctXbVrbC87QZ7GoIZIK+Q6CsslY3W3R7l/7Sft3M96Dy2UqqFLdtmsL/Z77H0j+xjUEGmEHz3nyUeZNz+5K+DyVFBjr/ZTrXwczK9Q50DkMgY1RBrhT99efZWz90DpbrJDujU0o3CD3kM1NX4WwgcFurs+j5RyKqhZunQpevbsibCwMCQlJWHnzp02069btw59+/ZFWFgY+vfvj40bN0peF0Jg7ty56NSpE8LDw5GSkoKjR4/KHqu2thaDBg2CTqfDnj17nMk+kSb524PeF8ndA7nOve4+p7fU+0FHYbXO48hdDw5k/YBWOHwn1q5di5kzZ2LevHnYvXs3Bg4ciNTUVBQVFcmm37FjB8aNG4cpU6YgOzsbaWlpSEtLQ05OjjHNq6++isWLF2PFihXIzMxEZGQkUlNTUVNTY3G8p556Cp07d3Y020Sax/6G2uTuPi9aqqEzr6nxxSHdah3bkeMEBrCmRiscDmrefPNNTJ06FZMnT8bVV1+NFStWICIiAitXrpRN//bbb2PkyJF48skncdVVV+GFF17A4MGDsWTJEgBND4xFixZh9uzZuOuuuzBgwAB8+OGHyM/Px/r16yXH+vbbb/H999/j9ddfd/xKiTROrvDUUifSlsDpt9uF+6SlId2eqqnxZZ/9dtpiW3AAa2q0wqE7UVdXh6ysLKSkpDQfICAAKSkpyMjIkN0nIyNDkh4AUlNTjelzc3NRUFAgSRMdHY2kpCTJMQsLCzF16lR89NFHiIiIsJvX2tpaVFRUSH6ItExp4cZix7NaVvOTWU2Nu5ZJ8IHmJzkXqurw1Of7LLa7+SNCDnAoqCkpKUFjYyNiY2Ml22NjY1FQUCC7T0FBgc30hv/bSiOEwKRJk/Dwww9jyJAhivK6cOFCREdHG3+6deumaD8iarmcrqhx5ZzaiWk8NueKJwM5NWs7q+oaVDsWuYdP1Jm98847qKysxKxZsxTvM2vWLJSXlxt/Tp+2rDIk0hItFW7UTM0v4XLf6LW0TIJ585PbPpMa+azbyoYj951/u9rhUFDTvn17BAYGorCwULK9sLAQcXFxsvvExcXZTG/4v600W7duRUZGBkJDQxEUFIQrrrgCADBkyBBMnDhR9ryhoaGIioqS/BBpmZ7tT17n7j5MsgGFhvrU6D1WU+PGYwt1AjP+mfkmh4KakJAQJCYmIj093bhNr9cjPT0dycnJsvskJydL0gPA5s2bjenj4+MRFxcnSVNRUYHMzExjmsWLF2Pv3r3Ys2cP9uzZYxwSvnbtWrz00kuOXAKRZvEh6rtcWyZBu3febaOf3Bg8OnRklfKh5XvY0gQ5usPMmTMxceJEDBkyBEOHDsWiRYtQVVWFyZMnAwAmTJiALl26YOHChQCAGTNmYMSIEXjjjTcwevRorFmzBrt27cJ7770HoKkT3uOPP44XX3wRCQkJiI+Px5w5c9C5c2ekpaUBALp37y7JQ6tWrQAAvXv3RteuXZ2+eCIt4Ugn7/PGHdDSbTcvnH2x9cn8/XT2XGx+8k0OBzVjx45FcXEx5s6di4KCAgwaNAibNm0ydvTNy8tDgMnwtmHDhmH16tWYPXs2nn32WSQkJGD9+vXo16+fMc1TTz2FqqoqTJs2DWVlZRg+fDg2bdqEsLAwFS6RyDcofS7y+elfPN+nxvvcu0p388HtncYbc9qQezkc1ADA9OnTMX36dNnXtm3bZrFtzJgxGDNmjNXj6XQ6LFiwAAsWLFB0/p49e/JbLfkdfqS9z+n+F0p3lIkoFPelcjIvQginh6W7be0ntxz18rHNa2psXAP/5vyPT4x+ImoZ+ITVInffFS0VrJ6bP8YzfWo89eWXX7K1g0ENkRs58gVZbuCJ3LOyJTxAvXeNzs3q7NI8NS7sq+j4LnVidg93XrN5zZetc6n1OfP/v0jfwaCGSCNaQKziMzw6Q6yG1knwWE7c2qfG9u/ucCC/AnUNnlnhnGxjUEOkERwW2sxbAZ7hvKbFvppZkQsn3F5T40hatYYO2TuPG6/a9Nie6ij890+z8cjHWSodjVzBoIZII1hTox0BprUZCu6LS008br7vWmyudO/oJ7PfPfRlIf1QkUfOQ7YxqCHSCA2WPV7j7R41pkGNkry4MoLJ0zV0tlrWzHPiiwtaOlYz5bZskJcwqCHSCLWH9pILTCtqFNwXl4Iad9fUmP1us7+Qh/qjuDWQMzm0ELavgX9x/odBDZGP4YPYfQwFYIBjrU+yI9fkyC5o2QJvqDuv2Z1fDlrivfI1DGqoRfp2/zlM+3AXyi/Vq37sf20/4dR+ssO3W2gI4+1+ILZWspbT6MJCkJ4e0m3r2iybn9zDrZPvOZKWUYrfYVBDLdIjn+zG9wcLsWjLEdWP/dLG353ar6UGMFpiuAeSmhpFHYVdaX7Szn33h8nq3Htstx2aVMKghlq08xfr3Hp8dyyK1xIerN6+RJ2ko7CSPjUKj+uBlZfU7Ozrrs/apfpG9xwYMtev4jXwi4f2Magh0gg+Lr3POE+NgzU1LjU/aejGeyorF2sa3HZs6fup9rpaqh6O3IBBDbVoWnpGcfRTM2+/FZLJ99zd/OTmgteiT42NyiJPzfFS6cagxpyta3D0tvEvVPsY1FCL5u4+BI6sjiy/zpPMNj5a3cY4+inAsWYiFypqVA/g5D4fWuq3AwAVngxqbA7pdux90dr7SJYY1BBpBh+YBt4O3Bzt+dKosLCTHdLt4Lmcobi/lllu3FWGV9d5JqhRP2AkrWNQQ6QR/BLofc2jn0w6Crt98j33Nz+Z5s/m3HueWfrJpT5IjrJ1Joebn/g3qnkMaqhFU/sZVd/o/Eq9SvPCB6v7yHYUdmA/p87p/K6Kj6e1z5an+o+5chb5iRL5x6d1DGqoZVP5GfWekxPvAUB9g/MBkb/xdtnhSF8owMWaB7cvkyCcfz/ddCM8VVOjF8JmIOJoLQ5DGu1jUEOkou8OFDi97wP/zrTY5u3CvaVydPST0poHj/SpsdO53MF4zS30wjO1HvZOweYn/8Oghlo0d3dI1UD5QU4w7VOjJGBxqflJ7T41Mp19TU9hqxbKPC/u/OvwRGWNgO1r+OlosdXX5ANQRjVax6CGiOgyuT41XdqE291PS2s/KZ0awFxRZQ3yy2tUzo11nmiCshcw7jh+3sHjuZIb8gQGNdSiqf2QYs2MOrRQeDx/5zUAgIiQQLtpXRv95PSu8seT+V1JDcPr3x22PJYb74MnOgub11KpcTzSNgY11KKp/pDyQIcFPlfdx1D46wDERAQr3k9xnxqZsFf9GYUtj6ekUqSqznI9Jnf2e/FITY3a7y3/+jQvyNsZICLrWuojVEuFh6KOwi4MXHP3t3+tDkNWOmGhK/R6qPpHpNG3kkywpoaI6LLmPjU6Y4daNUc/2TqnWuSbn9Q5lqsiTZry9B6pqaGWhkENtWhq1wh4pE8Nn9Sa48oyCWqT7SiskSmQEmJbG//d4KGOwmr+jbOmRvsY1FCLxoeUNnnrvpieVmfc5ltDui2P7/w8Ne6oRTKc3yM1NWp3FOY3Cs1jnxoiN9LCRGe+5vOsM9h/ttzb2TDeO7c3Pzm9p3KmtSIB3vxQCoFAnQ4NQnikT436nbBVPRy5AYMaatHc/Yxyx0PQ378t/mPdXsnvnuzoajiXo+W+S/PUuP1DCDQ0Np/E2vnkLln1OXQABAToAL3wyOgnvVD3Gvz7L88/sPmJWrTNBwtVPZ55YejqQ1CrI1f8nU7XPPxayR1wbUFL999jVxZaVZMQQFBA0/vqyogxR86n7vEcO6AnmthIikENkRsxKPEtsnfLx0Y/WRwf0loRR4IotT+/vTtEIvBy5O+p5icl1/Dyxt+x8udcBcdTbuuhQlwz7zt8s++cA3uRqxjUEKnIvArfHY9txknup4POoSYopU0pcsGPZ/rUNFeLePPz8/Sovk3NT3DP5Hu7TpZKfldyioP5FXhv+wks2HBQsj3zRKlFWnvv3V/ez8T3lxe1/esHu3CpvhGPrt5tPxOkGgY1RG7EAMR1thZgVJtk4UfDNgVhh9KaGtO+LXLndAchgHqZ83pDVFgwAg3NT2648F2nLkg3CPt3r6quwWJb1qlSfL0332K7vVqfn46WYNpHWSirrrOXVXITBjVEKvJkAUzuY3oblZS9Sudcka+dcH/AIWl+ciCYUH1tNF3z6CvPLJPg3Hl25l6Q3a70SH/7hLUz3sKghsjN2K/Glzg3n4vSglMu+HF/nxppR2FnzrftcBHuW5GB3JIql/Kigw6Bl0sddwQ15tem1wu7Aacj74fStI6u/k3qYVBD5GaMaXyPzuS/Sm6fXLOSbDqZIT/un1ZAWrA7cj5D482kVb9h58lSPPapazUQOh2MHYXd0fxk3tgkALz/k/0OwIqPzz9mzWNQQ+Rmrozy4CPUs2SXGFBw/+SCFdl0sn1q3H+XlcxTo0RhRa3LeXFnR2FzegGs/MXxoMZaLR3/HrWPQQ2RiuSehYcLKlU9Bx+srsk6dQG3vrEN248UW03TtKCl8mOa1oTERYVZTSdXkHtg7j1J0PVtzjlcrLXsHCu7r1nmbAUi58ovobKm3u4xAz0Y1DgaMNpLz4oa7WNQQ+RmE1bu9HYWyMQD//oVx4urZO+LaZmlk9lmjWkBHW6yErU5b/SpMT/voYJKPL5mj3PHsTKJX2FFDZIXbsXA57+3eM0QxADS5idP9KlRto9lLZa1eNbfZ/P2BwxqiFQk9+2+tIrDO12hdvNMbYP9piLT26ho9JPCPjXX92pnsc3V69t+pBivfXfIapAghGX+tvyubCZt8yNa63S7+/JQarmXg0yDGuiam588EM0p6bcjrPzbbmLSJK79RORj2FnRfSTz1DjQ/qR0yHRokOX3SFfvpqHGqVf7Vrg3satsGms1LPaYX4rSoevWSDoKa3CZhKZ7Z/2+8y9P+5yqqVm6dCl69uyJsLAwJCUlYedO29Xr69atQ9++fREWFob+/ftj48aNkteFEJg7dy46deqE8PBwpKSk4OjRo8bXT548iSlTpiA+Ph7h4eHo3bs35s2bh7o6fgMmbdHZeCA6hU9R79A51vyktKOwLJXu8dmyS1YOr96K2M40GZnGhjrArTU15gGlo2cwXJ7VjsL8e9Q8h4OatWvXYubMmZg3bx52796NgQMHIjU1FUVFRbLpd+zYgXHjxmHKlCnIzs5GWloa0tLSkJOTY0zz6quvYvHixVixYgUyMzMRGRmJ1NRU1NTUAAAOHToEvV6Pd999FwcOHMBbb72FFStW4Nlnn3XysolIi7xdCyV7fgV5alQ4ZFru+J64Ymf7r5j3IXG1H4xO1zxPjScWe1TU/GSSxF6fGfap0T6Hg5o333wTU6dOxeTJk3H11VdjxYoViIiIwMqVK2XTv/322xg5ciSefPJJXHXVVXjhhRcwePBgLFmyBEDTH/miRYswe/Zs3HXXXRgwYAA+/PBD5OfnY/369QCAkSNHYtWqVbjtttvQq1cv3HnnnXjiiSfwxRdfOH/lRD5A7iHqz49Vby9qbDi9Do5NvufKMgRuD+SEssJdaXPbloOFKKqoMdtXWVZ0AAIDmoodT3QUdrz5ybXXyfscCmrq6uqQlZWFlJSU5gMEBCAlJQUZGRmy+2RkZEjSA0BqaqoxfW5uLgoKCiRpoqOjkZSUZPWYAFBeXo62bdtafb22thYVFRWSHyLSNm/X1MhxdPSTrUuQe8kzNTXO7Sd3LQ9+uAs3v77NbjoD8ybZwMu/emSVboeHdNt53YW8kGc4FNSUlJSgsbERsbGxku2xsbEoKCiQ3aegoMBmesP/HTnmsWPH8M477+Chhx6ymteFCxciOjra+NOtWzfbF0ekArWrpzVYxruV3OV6Y0FL03lq1Fz7ydY53UVA/aae6rpGxWklfWp0zUO8PdH8pOS9Nf2bNfzbWt84LQbdJOVzQ7rPnj2LkSNHYsyYMZg6darVdLNmzUJ5ebnx5/Tp0x7MJbVUaj/zZL/Z+/FzVUvX5kin70aFHYXlggF399MQwjO1IkrodLrmBS3dskyC7d/tsRdnaeNdJFscCmrat2+PwMBAFBZK5zgoLCxEXFyc7D5xcXE20xv+r+SY+fn5+MMf/oBhw4bhvffes5nX0NBQREVFSX6I3E3th55GyiKPccd6QI5o/qZuuc0W6dpK8um3HS7Cf7LOWJ5TpUu2FYJ5YvZepdw5o7DFgpbKqmpM9r98/63Pvkca51BQExISgsTERKSnpxu36fV6pKenIzk5WXaf5ORkSXoA2Lx5szF9fHw84uLiJGkqKiqQmZkpOebZs2dx8803IzExEatWrUJAgM9VMhGRE7xW5e9A85OSppTnvsyR3a7W1Vk7joBwOlhU+t6bTmj4o9nyE+bxgbH5ySN9ahxMb+d1bwfdZJ/Dk+/NnDkTEydOxJAhQzB06FAsWrQIVVVVmDx5MgBgwoQJ6NKlCxYuXAgAmDFjBkaMGIE33ngDo0ePxpo1a7Br1y5jTYtOp8Pjjz+OF198EQkJCYiPj8ecOXPQuXNnpKWlAWgOaHr06IHXX38dxcXNfzTWaoiIlCqqqEFHG+v1OELtArilDSH1eplhMk+JIz15JLPSWrkGa3PZeCJoc3pIt8LdHl+7x/jviWbLT5gukwDA2PykdBZmR8it0t0pOgznymvkdzDf304rotc/n2SXw0HN2LFjUVxcjLlz56KgoACDBg3Cpk2bjB198/LyJLUow4YNw+rVqzF79mw8++yzSEhIwPr169GvXz9jmqeeegpVVVWYNm0aysrKMHz4cGzatAlhYU0FzebNm3Hs2DEcO3YMXbtKZ8xkxy1y1SvfHsKbYwepciy1a9Rb2se7stZyQURn34KGRj0CA3TGjsYH8sux53SZon1N+9MoasFQkMbWMgbuJISyoMZd3bHNgxp31tQczJeOchVCYHD3Nvhm/zmr+5jmwl6eWtifo09yapmE6dOnY/r06bKvbdu2zWLbmDFjMGbMGKvH0+l0WLBgARYsWCD7+qRJkzBp0iRnskpkV4WClYWVUr1PjcrH07oxK6xP4+CIipp63PTqD0ju1Q7L/5wIABi9+Ge7+5m+346MujItDK3X1FgJahSfxTZbuXU2fnhj8xFcGdfauZ0vs1ZT4+wwc2uyTl3A9welfTOFUDChnsnLg1/cjMduSUBUmGXR2L1thOpfoi9U1eHeFTtw18AumJGSoOqxWyp2TCGvq6ptwGOfZmOjjW9T7qXid1TVhz+1nLBmd94FnDpfbbHdmbfg2/3nUFZdj29z5KeFsMe0+UnJ6ZWksdbc0qgXKL+kXmBtTkDZSCNrKR76KMul8wfozGtqmv6v9uinbYctZ7UXEA59foQAFqcflX/NDY3B//rpBE4UV+GtLUdUPnLLxaCGvO717w/jv3vz8bdPdnvl/GpOg9JyQhD13bNsh7ezIFsAKvp2rmguG+tVE+//nGv/AC5kQa756WhhJUYv/gnfH3Au8FPKavOTym21cn/GeqHu9wI1j5VbUoVl246rd0ACwKCGNCDzRKlXz69mXwJPzFPjj2wFDu58D2wFE4ZgN6/UsvbInJL+IbY6xtar1BYjdxwhhGwAMX11Ng7kV2CaizUx9pjX1DQ3P3lm9JO9e3O4sNJim/WmR/XyfP976jS1khSDGvK6mgbls5O6g5qPVrU7P7aU1idvzaPywoaDkt9NGxgMnYWr6xpxoarO5nGko5/kr8XWrMPBAa6H1joAD/zrV4vt1ibfK7tk+5rUYj77hts6CssEIkLYbzIy/wxYI1Su9SmsqFXvYGTEoIa8r4UU3M5oKfNi2Opf4Y0Rjubf1E+er7KZ3tX7FBSozqP4t5MXZLfL1dSo9bbaa0YKMotqAj1ZUwPnrtNaiKmhOQzJCgY1pEkfZZzEL8dKvJ0Nj8kvuyS7/VK9d2uxPEXhKgNOq65rUJTOWgFo3oSidD+lggLdNaC6iVzQaL7F2RzY6/BrXgkVYJhRWOVgVS7/TQGx4+eR20NJUxZ5H4Ma8j6zp1HmifOY89UBjP93pkdOr+Zzytlvcn9+X/5aq2qVFca+zt1rE72z9ZhD6XWQ5sm8s6sp85okZ64kSIXmJ1vkuuyoVQNmr6A3DwgN17r2t9P47958VfJgjbMdha29NwxqtI9BTQtRfqkem3LOodbL/VdkmTwnDhVU4PQF+VoLd9nye6Hib/L2OFtQnCiWb964WNNCgho31+ufLLHdfGRgmgvTRSpt1dSoUc4FunHZF2s1DOo1P9l+3WKemsu/nzpfjcc+zVbt3svdoqZ5ahz3+veHZbd7OqbR6wU+23Uax4svevbEPoxBTQsxYeVOPPzxbrz+nfwfq6dt2JePrFOWo55GLvrJI7UT4cGBkt/f2qzNeSIqW0hNjdrDe805Omxfp5OOVrJZU2P+uxOXEuzu5ie5PjVqHdvOBVsM6Ta7GWrVGMmtqt40T43jx6+pl4/UPN2h/fPdZ/DUf/bh1jd+9Oh5fRmDmhZi7+Xp4b/Yfda7GQFwuKAS01dn497l8kMaiys9Pypg7+lyVY6j9jc5Nj85/p7q9QKVZjVccgWe/LmaT2ZagNnqx6tGoRyiUkdhOQJCPqhR6cNqr6A3r+U6ZTZEXrUZlVWsqZEjhPMLgzorW+GyHtSMQQ153Nky2/N+eGIRR3d1YZDLe7e24U4fz16B0aD2XPNeomZNzT/W7cWL3/wu3ehMTY0kT9YPYJ51JZ/fkMAAvHJPf+PvYWY1h2qTbX5S69h27p15TU2F2ezJ7gwUKi7VY9vhYvsJFWKXGu1jUNPCaOFv0nyIpxrV946ymFxLpSDHNO8v3HUNAKBPrPNr59gr60e+/ZPTx9YSmzU1DnxqhRD4MtuyNtLe6KXmc5nkSfLmq5M/gxkpCbh/aHfcmNAegHsLdmsLWqp1Skebn8xvRc5Z6SKUzpK7w1/IfBZc4emaGvc2SvonBjXkcabDV+VqGjzx2HDXw8I076GXv327Uglhr6bmWJF/dCBUq6+CtcM4er910Elqamxlz7ycc6TcMwRbaly+rbhNrjA2X2/K2eVC7BX05n1ozN27fIc2BzCYEXDvPDU1MtM3qLmES0vBoKaF8cZEZuZM+w/UyQQ1vjxs0vT9NTyPXLkeX34vHGFrBI0jb4G19ZUUFw4m59JLghr1+vyYMlRiqHGfrR1CQNk8QM5mwd6xzQd2ydWaVde6HtR4IgBw59/je9tPuO3YLQmDmhZGC0Wk6eypdQ1yE2h4IBPmD0CVzml6GMPD25XnoKMP0Zr6Rjz8URb+k3XG+ZN6gVrz1Fir8VHa/GRg3qfms9/OoKiiRjatefOTkisxZKf5M+LO5ifh0ird9jjc/KTiuT2paZkE9+X0UIFlM5zSDu7UjEFNC6OFL/6mzzi5oEbSk0ELGXaS4RuqK9/uHG2W+TDjJDYdKMAT6/Y6fU5vaLTxdd+Rd8Da+krKK2pMRz8152nlL7m428oq4q58RHUqNj/ZoqQjtrN/a/aObR5Qyo9Scv0NsL4IpXqcvU8VNfX2E8lg85PjGNSQx5k+GGob9JYzspr87raHvQeOq0pNjYODmy5UO/fw9Da1BnE1WlsJ29HRT7AMkM5aWcrCPGh1rE+N/DGcYa0AFFBWE+Z0TY2jQY3MzXjgX5l2Fw31NgHnh3T/7ePd9o8vc2jGNI5jUEMeZ/pgqJWrqTH543bXZFfuimlMr635W7gLNTUK9j1WVNl8TqfP5F227rNjfWqs1dQonadGWZ4k+yhKJZ8fNTsK26LoWpzMg73PqMUSEDK34nBhJRZt0eYEmKacvU8/K1jHTu454YnaJ3/DoKaF0UJzjmke5JqfGiU1Nd7PryN6tIs0/luNb+FK9k15c7vTx1fDRxknXS6QbF2nI++ftcJbadlgPJVOh8E92ijbx4VaJkMTpRp/l9YOMfXDXdiw75z9/Z2MauzlPUBBnxoAqHBxSRB3l/9CAIfOqTP83NrxyXVB3s4AeZYW/m5MmxrkCqxVv5y0+boa3BXcGSbaG9Uvzvht3JVv4e5ePkANc746AAC4c2Bn9OrQyqlj2KpJcOQ9kBv9JIRwquP04O7yQc2lukYEBAChQU1D9i2DAfv5NRTAxto8Fe7zG1aW+rC2rpha7DUdmg/pthZ8+EKdxDo3dsDX/l+6b2BNjZ8rqqiRFuAa+MsxDVTsVYt7qvlJrVmMDZfWJ661sabGlQDK0VFBnq6tNr226jrnh+Xauk5rTUqyx5FJe6TQ9lw+ptdgrKi5/H/z2aBr6htx9bxNSHo53WR/xdmz4KnmJ3ey9zdqsaCllQ+pqyPg3D1SyN23SLZPjS9EehrDoEajauobsfa3PBRaGUaqxH+yzmDoy+l4/r8HVcyZ6yRBjbAdTjjaUVYppc/P+kY9/v5pNj7dmafsuJf/r4PO+C3ckx2F3SHnbDm2H5Gfat60PNt7pszpc9iqqXCktk4uAKq0M/LEVmFiXgDnlVZDCKCsut6YZ2c6ChuOqmZHYVc5PU+NnR0tJu+2UlB/tSffuQx4iOz0E6qS6VPjE/VX2sKgRqNe/+4wnv58P9KW/mI3rRDyC9a9vLFp/ZsPdpxsTqtaDp1n+gy0V+2u1vwlzlqffRZf783HrC/2O7SfTtf8DdWRmgZz8p0HnT6cU25/52dMWLkTp0st1+wybe557sscp89h69u+I7V1zqxGbfoem9eqmQc1ppUOhvvqyic0wMXO5FroI+doTY27Cmp3/12Yz8CsNtbUqINBjYqqahuwO++CKg+a9ENFAIBz5fZrah5fuwdJL6c7PReCpznS/OS2PjUKi6IyB4dIm2bXMOpDSaEcFxUmu11u31ah1rvCqV1gmH6WC2RqDdWqSbIVvDoS1DTIDOm29xGSO7zO7P+WrzTny2KZBNunazqKTvp/Z+NeNf88nD2W3cn3FPap8VVXxjrXj8yc3LvoZ2+VRzCoUdG4f/2Ke5btUKUzmSOB0Vd78lFysRbfKBnhoIFvdqYPcHsPRL1e4HjxReRbmSPEWUrfBseDqqb0OjSvcaV2TU1EiOWKzrZqvPLOVyPzxHmrr3+26zRe+uag7Gejpr45ajFd3sJArZo0W8GRyzU19j5jMn1qDGw1nRhqqVz5mwo0qampb9Rj+5FiVNUqHwWkZtDvqdFP7uKtAGChyWrrrtDCs9kfMKhR0b4z5QCAdbtOezkn1qnxZ1NQXoM73vkZnzl5ndLqfttpS6vrcOsbP2LYK1udOpc15qf97eQF1Dfq8UnmKXy7/5zVdHaPe3kHSfOTgpnlrBUocuW53HtmCC7kvgXf9NoPGPverziYLz8c9an/7MO/fspFhkzgU13XXMCGBMkENdYmu3OQreBIrobIGtnRT3b2Mf08GoJDw72znDiuWaOV5iclhZP5PDVCAIu2HMGElTvx0EdZdvc3nktxSvex9/E2n6fG1ttT7pOTR6oTTsnW1LCqxmEMavyYu/4e/rnpEPafLcdT/9nn1P6mD/1GvbD5kDtZ0tyPw5VvMmXVdfj7p9lWO7wCQPrvhXjuyxw88knz7J+OfhNuDmp0CL5cs6GkpsFaErl95dLKpftqz1nJ71l5F2zmQa5AMR3RZCuYcpWtmiYlc6wYnL5gWaPnSPNT/eVfgi5PIGOrUKlvlG9+MualtBqbcs7Z/Nwa5qk5XnwRS384DkDZRG0G3v5yX9egx5QPfrOZxrymJtHG/D/XL0y3+po93goAnDlvcWWtxTb5PjW2D77jWAn2ni5zPANOOH+x1idqkxjUuIEa912Nj47cMdTIm+m3d4O6Bj1yS5TNh2H6ZdpeoWgeADnrn5sO4eu9+ZiwcuflA1umKVL4oCmqrMHdy36RrZEzrXExfNuvV9DxxHCdb40dKNkuf81ygY7lthlr9kjev3onRm9cqm8OauTOYW1VbEfZu7dKH6b//slypWN7zSqG66pv1BvfI0PToXlNjaTp1Nrop8v/v/HVH/Dwx7vx3YECq+c2FFpf7D5rNY0tak1FAEg/6/dc20XRPl9mn0GlneYy89anR27ubTWt6efNUb40UkjuGepon5qiyho88O9M3KVgMImrdhwvQeKLW/D42j1uP5erGNS4gRrt3FoOiM1HMwDApFU78YfXt+F7Gw9wg0aZ6n5rTF+11TfFXqF3xuwbvFxhUFVrUoAbO4Fapntt02Fk55XhSZmaKtPmp+DLX8OVNNEYLu2aztG4Y2Bn4/azZZcsRh3ZqqkxvzOmhUSdEwssmfbvMLz/F6rq8Jf3M/HVnrMe6SgMKA9oEzq2tthm72/p0LlK1DY04oZXtuIflxcCDZbpPwRI/7aNfWrs5GlnrmUNWfOQcfl99l9uyrbH0efE4ykJstvf/fE4vj9YaPxdaQ1cxSX7/X/MOwqHBVv2CTNVVFmDv32ShV8cqLECvFhT49Q+lnvJPsNsHLzAZBCJu2pQLtU1orSqDsu3NdUian3YPcCgxi08FY9cqmtEtp0mBXNqfLMLDLD82Ow43tQfY9pHWfjtZKntPJjVvuTJDBU2fd3AWlDz9d58XPdSOrJO2T6vNA+W20y/PZVU1V4eyWaZrtLGdO6G5DroTGpqlAQ1TWkCdMCZC9L343/e+tEs75bHs/aeXzTJa4GCkXTmTEd/NeoFauobcf97v+KnoyWYsWaPzcKvtKoOH/16SlE/CXvBrdLO1q3DLEeG2dt3wYYDyDlbLqmps9anxvTzaPh3xnHLvkimgaitwtbaRHR3LPlZUc2no2VZrw6tMPyK9hbbF357SPJ7vcIAWEnw42hH4flfH8DG/QUY/+9Mh/bzFqXrMxk+4xet1GydK5dOlPruj8fx7o/NNY+mr50tu4Q9Js1O7pq88fqF6Rj8wmaHR4F6E4MaN1Cn+cn+QSau2om7l+0w/u6pLyqBdk40ZkWGzddN/wAvVNtembfGpKbBWo3H3z/NRsnFWkz7UHkHSzmmD5s/vLYN9yzbgR9t9MGRY5iOv6FRb2zCUFLLYNoX56GbekleMx2BtDO3VHYl7ocNqwCbPWCf/bJ5fh1bwaM1pSYrJ+uFwP+u3YPDhc0LaNqqhXrk4yzMWZ+DGWuzZV837UBtr3BUWsskt0CqvY7agTqdxd9ssKH5yewJKRdkP3G5dsdACOAtk7WwbP25WAtqAGD3KftfWBz9klJT34iwYPuP/boGZcdVUittXlNjj3mtqqmcs+XY4WANjrspvbovss9i0ZYj6DfvO2z5vdDi9WNFF7H8x+PG380DTdPHyA2vbMXcy8uTAOo1A5szzM2z/6yymkMtYFDjBobP3qc78/C3T7JQ2+BYO7FeL3C61P4Q5p25ymsmjHmz8QwSQmDO+hws23bM5jHkamocUWQymqXkou2gxrT5w7RvSnl1Pd7afETybdaR5hW5t6HapPmp6nIH2V0KCpbqugZ8nnUGF0wCgC2/FxpHfSj51ttcU6PDyH6d8P7EIZZ5FgL3vycfMDY06vHVnrM4Yxa4bPm9yPhv03xU1tTjk8xTsh0WTZkGnQ2NAt/mSJsXbQUjmZc/n9sOWwaGRworMWjBZry95WjTcewEfvUNeqzOzMPvdhYUlJv19YfDRTIpmwUFBlh8Howdhc2KLPM5lqxW+5tstlWm23qtSqbfhcVpHPwCVdugR6id5h9AeSGp5PyB9r4FmbGV+vZ3fsYD/86UneLBkQ7W3vDEur1YdPnzvmCD/Czva3+zPqL0Un0jHvjXr1j6g+Xz2V3LyThCK52IuaClO1y+uYZZaIMC9mHfmTK8dHd/3CBT9WvuXzKdHVXLmo3XDuRX4KNfTwEA/nbzFVbTmQ/RdERRRQ3mmyzbYK/Af+vyQwCQ/uHO/+8BfJl9VvpeOfA3JfcH6EyfE6CpuvyzXWcwsFuMybGEsWB0pKbG8NZ2bxthkaa+UVitZtaLpo7Btpi+17O+2I8N+87h419tL/9g+q1Z7lu50ofpwfwKXBnbCkGX+6r889tDuFjbgLe2HMGMlAS73/i/2H0WL12eIfvkK6MBNN3DV787jG5tIvBAUncA8vfQ3jUGBVjW1Bg+48eLpetGmV5uQ6OQPZ8Qwm6ThOF1WzU11popTBkm6VSqtr4R4QqCGsXNTwruv6M1NUocKazEyZIqJPVqZ2wqlAue3W1MYldV+/LYmo/ri91nsOP4eWNTvylX5sIqKK/BZ7tO40hhJcZe1w2DusVg/L8zkXpNnDGNTmc7gK1r0GP04p9wZWxrLB0/2Om8qIE1NW5gfu+/3puPk+erFbcR/+unXPUzZXA5cw2Neot1pUxrRcwL/Zyz5Xhhw0FU1NQraiO39lA0nwvFtGlFjunU5KbH3HW5/4ySRRSFEPjpaPO3uK/2nJUNDuw9yOUCoUa9wGe7mpqcTIdWCiGaJ99T0FHYUIAZCjm5Ak1pQWONaT4Mw6Rt1Xzo9ULSbi9XgJlvO1Esv3jkHxf/hLlfN1eXmxfG9i5trcxIs5yzFVi+7bikia3OwVpRoKlTsHlQZbh35p8v8z419VburWlBZyvAket0b1Br528DaGp6dURggE5ZUKNi85OtwM0Rpv2u/vHZXjzw70zjl5o9HhrWbOqLvw3Dy/f099ioK9PmJnOuzBc1Y0023tx8BBv2ncNf3t+Jj3/Nw74z5Xjtu8PGNPau8NcT53G06CK+2a98+gV3YVDjBq6PfrK+/49HijHri32yQwLN2fogTli5E0kvp6PnM98Yh5yaPnzNC6vb3/kZ7/+ci5e/+d3mXC8GNVaGZpqPfFByHQZLfziOW97YhuLKWuPIIlOVtQ34JPOUxfbdeWWS363VaNhbsE6uUP98t/XZo4OMaz9ZHvfN7w/jvnczUFPfKPtA7tcl2mKbkuDIFke/zf1j3V5J3pZstV/tffs7P1s93urMphoT84CtqKIGjXaaO44VWQZLpiO7DP1mnFl0sGubcGMNpUGQldFP9y5v7sPWoNfLnq+ipsHYtwqQ/zs0bJObpdka0/5Nzhp7XTdFfWqUTENw5kK13Q7egOMdhfdaGfl12qQD/fnL78Vbm4/goY92KVojT22Du7dBcGCAqv1ZdNDhQlWd1eenNXvPlOHfP53ApbpGh2akBpqbiQ3kzm2v5lEbDU9N2PzkBiWV1h8+b20+gjMXLuH1MQOsflBsxUQTL8+z0qG15VpBSr4QNej1mLByp6QK86GPsoxV+s3pBOoaG5BbUoVPMpur7/eeKcdZkypSa1XtNfV6tA5r+gPZmVuKofFtcbbskkVtg+kwansMK2UvTj9q/CZt7rkvczB2SDdJoWTehGCN6ZBWOY1CWPzBmPdhMWXIg140fcs0fbgvvhwgbNx/TtJp0JDGfEixEAKVta6NQHD04ftltnTulJ0yI6w2mn0zU1JzVmRWQzj05XS8dHc/h/IGSGc4fjv9KDpFh+MHJ5ogYqPCsCb9qGSbkibWRr1QFkTZOFSozCzNcj769RTmrM/B3Nuvxl+Hxyvax1zPdhGICAlSrflpy8FCRaNuomRGpCm143gJrukUjf/uy8fs9ZYLptY26PHdAdt/t0qEBgXIdjK35uW7m5dGsFZb54y6Rj2ufWEzurUNd2i/SauaJkB88ZvfERYcgD1zbzN+gbxQVYeYiGDZ57T536I1jvTZUdL86k4MatygoKIGPZ/5Rva1ty8/PMdf3x2/5Zaic0w47hjYGTX1jThXXoP49pEW+5y/WIuQoAC0Dgs2blts9hBWSi+gqKalrlGPPy3fgSOF0oDA/GFX16hHaJDlQ9IQ7T/75X6bE4sdMRlJo1RlTb3VeUQMeWoUTTMVhwUHouSi7c6wSsnFBItlai+ApsDUNPA6UVKFKzpaLnxX36iXDLW2VpY+/fk+YzOXs1yt6ZGzRKbT4l1LfsYb9w20uo9cP5RSOx3G7XnHyn1QQi4/QQo6wzcoDGpW/XIST97WR7LN8MyXW3rCwHC3DuZXYM7lAn3BhoPGoMbR5kjD38ydg7pY/dwaKGl+iosOszlSyeBPiV2Rcfy8ov6E5h74VybaRASjzM0rZIcGBSCpVztFz0YAxj5cgOvNwnKUDBSxpqZej1Pnq9EnrjV+O1mKMSsycO/grrJ/k/P/a9mk5Uw8Ipnks1EgJMh7QQ2bn7wk80QpFn57CI99mg29XuDuZTvwh9e34ZdjJcaqVYPEF7eg//zv7R5z9vocResMWSOZWKxRWAQ0gGUzgLVvN9V1jThefNHuTKnODBWsbxRWmweApr4Idy/dgaEvbUF1XYPNmjNHbHWwU2Zrk9W0TxRfRHl1vUU/Jp1OJ/mmZ63/gasBDQAcKqi83A9E/p5tP1qCn44Wo7quAY852FfD1N4z5Uh5c7vV1+UCgTc2H5FJaZsrn3VThonFTAUrGLFT16BHXaP9mqm6Bj3mfS3fH8JWUGO4vj8u/kmyfcF/D6JRLzBp1U675zZlCGqu6NgKd5pM8ChHSUFd3yhQUWM/2AgM0OH9Sdc5XcN0oboeEQpqlxwx+Yaekt9DgwPxwaTrnDqWswMM3Mnw+TUE+5/vPoOZa/dY9AvML3N87io5b5r8/bojyHMEa2q85MOMk8Z/J7642Tj3iK3OxHaHvTYKfL03H/cM7grAsXbO2oamSdUMlBYYA+Z/j9yFf7TY/vTn+9zWea+ytsHmeiefZJ7CwcsdYLNOXUBplTo1NY+u3o03N0finIJJ7ASaqmCT4tsiM7cU00wWKTQtyN7afARtIkKMv7u71vbhj7MsAiuDT3fm4dOdeUi5KlZ2Hg21KKnduH1AJ6trPi3fdhyP3NzbrYWJrQ68BjX1jYqbLEybcE3ZCmqsFQ4rf8nFyl+sDyZ4f+IQxLePxC1vSCdtDDY5V1y0ZfO1KSXXVd+ox6Yc+zOIq9HlJDgoAFDQtKnU0yP7YtUvJ42/hwYFICBAh4xZtyB5oWOL5zqz/Ii7Gf42TIOYL7LPIiYiBNNvuQJtIoJxpPCibJO0vXLmn5sO4emRfY2/ny6tNi7mDDT1dYsM9V5owZoaLzEtGOUmU5OjZN6KwgrnCvC7lkg72imZBdegWKZ5x52jEexVEb/+ffO3hsKKWuOcM2o4XlylqN+IYUSEeSc8QFqonyuvkTTBuXskxeaDhZIHkBx3BjSAtMOnNYvGDsLPT/8BvTtYNsf+c1PTpGRq9mUw10pBP5DZ63PwiGHSQwcZ7rJc062Bs9c3uHsb9OrQyqLvTKhJ7aa9EUlK+lBU1TWiwsbs2gZR4a4XcI7OaLtqsvVal4xZt1gMWDD0beoUbb8vy7De7SS/uzKc2l0MzyjTUZ9AU0A8+IXN+DgzD6mLtiPnrOXoR3tNucu3HcelukY0NOqx41gJbnz1B8nrb3zveK2rmhjUqKhNRLD9RC6oVtCp9p+bDqG2oRErf851aLTEoQJp35Y9ZiOGbPn1hOOTAHrKPzcdwiUVgxqlXh9jvU+JOdOHopKBIp2iw/DEbVc6ky2ndGgdqtqxej7zDf537V676YICA9C1TYTVc2edKnX5G3KCTB8nA9PaM2uKKmstZmm+Z7CyhSANzae2amqqahskUxooFR4SKHvsYJN+DrYGXaVeE6uooD6roD/No3/o7ZVOozdf2cHqa3L31lbNnPmq4neYNd1FhKjbNKaGe5btwC4by9XMkel07Yir5m7CbYu2Y12WZbO4YUCHtzgV1CxduhQ9e/ZEWFgYkpKSsHOn7bbddevWoW/fvggLC0P//v2xceNGyetCCMydOxedOnVCeHg4UlJScPSotCNsaWkpxo8fj6ioKMTExGDKlCm4eFHZqBZPcXfEfv3CdEXphry4xeqMlUo99qnyb6COzpXhScWVtV6ZabR/V8sh2UoomdMjKFCH6bfIL0zoDkpGy7hLbJR8M8m3+wscGq0ix1ZBFhPu3BeUF9OUjeIyrFFla0j3uqwzGPi8/b50pv49YYixFsIiqDE5l60J8RI6tlY038+KHy37Ipnz1iSzcoFUj3YRuP+6brILalrLZ9bsFKx7KFmyzbxp/qaEDhg3tDseGiFd3sTb/mRnuRpXnSiuwrly5zs0u4vDQc3atWsxc+ZMzJs3D7t378bAgQORmpqKoiL5TpQ7duzAuHHjMGXKFGRnZyMtLQ1paWnIyWmOFF999VUsXrwYK1asQGZmJiIjI5GamoqamuYmmvHjx+PAgQPYvHkzNmzYgO3bt2PatGlOXLL7KJmzwRNsLbiolEYupUVbPTVJdruhGVxpAeoqNZoPnGUtqPn3z7l4dLVzTT8GtoZTt4m0X1MjJyJE2XsVdTloigxVN2C88crmEUbmAZPp77aaZCtq6lVr2ru6c5Qqx1HDG2MG4pV7B8i+Zm1usXatQhEQoENyr+YmJ/P3JiBAh4X39Mf4oT2cylfKVR2d2k8LtFhL73BQ8+abb2Lq1KmYPHkyrr76aqxYsQIRERFYuXKlbPq3334bI0eOxJNPPomrrroKL7zwAgYPHowlS5YAaKqlWbRoEWbPno277roLAwYMwIcffoj8/HysX78eAPD7779j06ZN+Pe//42kpCQMHz4c77zzDtasWYP8fPml0Gtra1FRUSH5cYdjRRfx/H8P4On/7FO17wa1TKZrKV0f3042jWGeoD9f3wNJ8W3dnqf5d1xj8/Vb+7rvoax0Hhdn2Kqp6RLj2DwhjjLM3RJnJWhzlmngYl5TE2PSPL5hn/xzEwDOX6xzaQRLYIAOt/TtiH/e2x+j+3dy+jhqszUNhL0apU4xzffp3ssDMcyZj4R7amQf2XTmljzg3WUF/I1DT4y6ujpkZWUhJSWl+QABAUhJSUFGhnxVV0ZGhiQ9AKSmphrT5+bmoqCgQJImOjoaSUlJxjQZGRmIiYnBkCHNi/ylpKQgICAAmZnyo4UWLlyI6Oho40+3bt0cuVTF8ssuYdUvJ2WncSdturlPU3u7GgVKR5k+H4/c3Nv47xE22vblmBbiAQE6zL39aos0/U1mGx7W2/G5PxyV0LE1ljxwrdXX3zF5Te0Ap2+c49/0+3WJkqzDZc3NfZrzem33GKyZdr3xd8PoIGfWObtGQe1Ex8uTZ3ZSMXj6Q58OkmYX8xqA/7m6eS2fUf2sBxu9O7bC/1wdCwC4qlOUcXjw0yP7ok9sa5t56NomHPvn34aVk67D2Ou6e6U/zbXdYwBY9nHsE2c97706WPav+vP1zXPRPPfHqzC6fyd8MPk6RFvpOxluUkt3/OU/4m83X4GPp8jXtpoKCw70WG2No5P6KWU6BYIjs2S7g0P1yiUlJWhsbERsbKxke2xsLA4dOiS7T0FBgWz6goIC4+uGbbbSdOwovelBQUFo27atMY25WbNmYebMmcbfKyoq3BLYdGsbgUf/0FSI6aDDNZ2j8O72E4iNCsVDI3rjrc1H0NAoEBUehKCAAHyz/xxG9YtDXHQYBnSNRn2jQOXladXbRASjY+tQxLdvhejwILRrFQqdrmlRwboGPXJLqtC9bQSCA3W4WNuIA/nliIkIQeuwIBSU12DroSJc0zkKnaLDEaADurQJx+5TFxAVHoziylok926H/LJLKK6sxfHiKrSJCMbQ+LbYd6YcQYE6TEzuiaxTF1BysQ6lVbUorKjF2bJLaB0WhLaRIbgpoQOCAnXYeqgIl+oaERUejCdu64OD+eXYfrQE+WWXEBwYgB7tIhAaFIAfDhejXWQI9JdnmGzUC/yxfyd0axuO2/t3xrqs0zh74RJ6X543Y+1vp3GuvAbR4cEY0rMNauobsW7XGfRoF4Go8GCMva4bhGgasn28uArBATqUVtdhcPc26NomHKdLL6FjVCjyyy6hqrYRp85XISYiGF3bRKBTdBi2Hy3G5GHxGNKzDdb+dho3XdkBFZfqsf9sOUou1iG/7BKq6xqx5/QF3NK3IyJCgnDmwiXEt49AUGAAfsstxZ+v74FfjpXg94IKTP/DFbiiY2ss2HAQF6rqEBIUgJfu7id58C8aOwhf783HxdoGFFyeXPHHI8UorarD9b3aokPrUDToBbq3jUBESKBkgkUA+EtyDwTomkZR/XKsBL07tsIHk4caX3/45l4oqKjB0cJKjOrfCSOubI+v9uSjqKIWsdFhaBsRjNuuiUP674X4Zv851NTr0Se2NcJDApFfdgld2oRj4/5zKKyoRd+41ri+V9NnJC46DMWVtUi7tguiI4Jx+4DOEAI4VFCBqzpFoaSyFjUNetx6+X367KFklFysxR/7d8LG/eew6+QFdGsbjrMXLqH8Uj3Oldfgmi5ROFFchak39sLv5ypw05Ud8MEvuaht0GNA1xh8d6AAN1zRThKo/bF/HB4a0QtnL1xCTb0efeNao1eHSHybU4CKS/VoHRaEa7u3wdHCSnRvF4n8skuYemMvhAcHYsGGg0ju3Q5J8W2RfbrMOLz1419P4U+JXTH1xl4QAjh4rhzz7rgGnWPCsWrSdQgLDjR+q9/6j5ux/Wgx0q7tgn//dALVdY0IDtShqKIWjUIgQKfDgK7RCA8ONBaaqyZfh09+zUNVbQOiw4PRoXUo5nyVg/atQnFVpyjcNagzul1esLRVaBAWj7sWL31zEJGhQbj5yo6IiQhG97YReP/nXMRGNX1uxyd1R8fWYaiua8CvJ86jrkGP266Jwy/HShAXFYbeHVvhz9dLmz/+cVsfxEaF4ccjxUjs0cYYqDS9diWKL9ZiSI82GNWvEzYfLEDfTlHIzruA+4d2hxBAcu92GNWvE/JKq7DndDnGJ3XHnxK74uNfT+HnYyXoEhOOC9V1+N//uRI/Hi5Gg16PB5J6KGqC2/DYcBzMr0DxxVrsP1OOdq1CMPyK9iioqMEtfTti3a4zqK5rRPHFWpy/WIu+cVE4c6EaAsC9g7vgcMFFXN+rLb7MPou9Z8rRvW04BnaLQWRIEG4f0BSwffP3G7Hl90JEhgShc0y4RV+adQ8n47ucArQOC8a9ic0dvLfMHIGduaUYe11zedGuVajdhRq7xIRj+fjBaBsZYqwFvOGKdnj1TwNwdaco/HS0BEeLKvH4rVfiy+yz2HqoEG+OHQQA+NeEIfhi91lU1tTjbNklfJtTgC4x4XhqZF/sOFaCi3UN2H3qAmrq9ejdIRJHCi8iPCQQoUEBCA4MwLGii+jSJhy3XR2LfWfK0bVNOHLyK/Bbbiku1TdicPcYJHRsjQdvjEdhRS0+2HESg3vEoPRiHTb/XohhvdsjMiQQn+8+g0v1jejQOhSP3nwFAgN0yDp1AeOTeuCTzFPGZSF+PVGKa7vH4HhxFe4a2BljhnTFuH/9ipyzFVh4T38b75IHCAecPXtWABA7duyQbH/yySfF0KFDZfcJDg4Wq1evlmxbunSp6NixoxBCiF9++UUAEPn5+ZI0Y8aMEffdd58QQoiXXnpJXHnllRbH7tChg1i2bJmivJeXlwsAory8XFF6IiIi8j5Hym+H6onat2+PwMBAFBZK57EoLCxEXFyc7D5xcXE20xv+by+NeUfkhoYGlJaWWj0vERERtSwOBTUhISFITExEenrz0GK9Xo/09HQkJyfL7pOcnCxJDwCbN282po+Pj0dcXJwkTUVFBTIzM41pkpOTUVZWhqys5llZt27dCr1ej6Qk+22WRERE5P8cHqs5c+ZMTJw4EUOGDMHQoUOxaNEiVFVVYfLkyQCACRMmoEuXLli4cCEAYMaMGRgxYgTeeOMNjB49GmvWrMGuXbvw3nvvAWiaT+Dxxx/Hiy++iISEBMTHx2POnDno3Lkz0tLSAABXXXUVRo4cialTp2LFihWor6/H9OnTcf/996NzZ9trmBAREVHL4HBQM3bsWBQXF2Pu3LkoKCjAoEGDsGnTJmNH37y8PASYrHA7bNgwrF69GrNnz8azzz6LhIQErF+/Hv36Nc+x8dRTT6GqqgrTpk1DWVkZhg8fjk2bNiEsrHl0yieffILp06fj1ltvRUBAAO69914sXrzYlWsnIiIiP6ITwltzPnpWRUUFoqOjUV5ejqgo7UwIRURERNY5Un5z7SciIiLyCwxqiIiIyC8wqCEiIiK/wKCGiIiI/AKDGiIiIvILDGqIiIjILzCoISIiIr/AoIaIiIj8gsMzCvsqwxyDFRUVXs4JERERKWUot5XMFdxigprKykoAQLdu3bycEyIiInJUZWUloqOjbaZpMcsk6PV65Ofno3Xr1tDpdKoeu6KiAt26dcPp06db5BIMvP6Wff0A3wNeP6+/JV8/4N73QAiByspKdO7cWbK2pJwWU1MTEBCArl27uvUcUVFRLfYDDfD6W/r1A3wPeP28/pZ8/YD73gN7NTQG7ChMREREfoFBDREREfkFBjUqCA0Nxbx58xAaGurtrHgFr79lXz/A94DXz+tvydcPaOc9aDEdhYmIiMi/saaGiIiI/AKDGiIiIvILDGqIiIjILzCoISIiIr/AoIaIiIj8AoMaFy1duhQ9e/ZEWFgYkpKSsHPnTm9nSRULFy7Eddddh9atW6Njx45IS0vD4cOHJWluvvlm6HQ6yc/DDz8sSZOXl4fRo0cjIiICHTt2xJNPPomGhgZPXopT5s+fb3Ftffv2Nb5eU1ODRx99FO3atUOrVq1w7733orCwUHIMX712g549e1q8BzqdDo8++igA/7v/27dvxx133IHOnTtDp9Nh/fr1kteFEJg7dy46deqE8PBwpKSk4OjRo5I0paWlGD9+PKKiohATE4MpU6bg4sWLkjT79u3DjTfeiLCwMHTr1g2vvvqquy9NEVvXX19fj6effhr9+/dHZGQkOnfujAkTJiA/P19yDLnPzCuvvCJJ44vXDwCTJk2yuLaRI0dK0vjy/QfsvwdyzwOdTofXXnvNmMbrnwFBTluzZo0ICQkRK1euFAcOHBBTp04VMTExorCw0NtZc1lqaqpYtWqVyMnJEXv27BF//OMfRffu3cXFixeNaUaMGCGmTp0qzp07Z/wpLy83vt7Q0CD69esnUlJSRHZ2tti4caNo3769mDVrljcuySHz5s0T11xzjeTaiouLja8//PDDolu3biI9PV3s2rVLXH/99WLYsGHG13352g2Kiook179582YBQPzwww9CCP+7/xs3bhTPPfec+OKLLwQA8eWXX0pef+WVV0R0dLRYv3692Lt3r7jzzjtFfHy8uHTpkjHNyJEjxcCBA8Wvv/4qfvrpJ3HFFVeIcePGGV8vLy8XsbGxYvz48SInJ0d8+umnIjw8XLz77rueukyrbF1/WVmZSElJEWvXrhWHDh0SGRkZYujQoSIxMVFyjB49eogFCxZIPhOmzwxfvX4hhJg4caIYOXKk5NpKS0slaXz5/gth/z0wvfZz586JlStXCp1OJ44fP25M4+3PAIMaFwwdOlQ8+uijxt8bGxtF586dxcKFC72YK/coKioSAMSPP/5o3DZixAgxY8YMq/ts3LhRBAQEiIKCAuO25cuXi6ioKFFbW+vO7Lps3rx5YuDAgbKvlZWVieDgYLFu3Trjtt9//10AEBkZGUII3752a2bMmCF69+4t9Hq9EMK/77/5A12v14u4uDjx2muvGbeVlZWJ0NBQ8emnnwohhDh48KAAIH777Tdjmm+//VbodDpx9uxZIYQQy5YtE23atJFc/9NPPy369Onj5ityjFyBZm7nzp0CgDh16pRxW48ePcRbb71ldR9fvv6JEyeKu+66y+o+/nT/hVD2GbjrrrvELbfcItnm7c8Am5+cVFdXh6ysLKSkpBi3BQQEICUlBRkZGV7MmXuUl5cDANq2bSvZ/sknn6B9+/bo168fZs2aherqauNrGRkZ6N+/P2JjY43bUlNTUVFRgQMHDngm4y44evQoOnfujF69emH8+PHIy8sDAGRlZaG+vl5y7/v27Yvu3bsb772vX7u5uro6fPzxx/jrX/8qWeXen++/qdzcXBQUFEjueXR0NJKSkiT3PCYmBkOGDDGmSUlJQUBAADIzM41pbrrpJoSEhBjTpKam4vDhw7hw4YKHrkYd5eXl0Ol0iImJkWx/5ZVX0K5dO1x77bV47bXXJM2Nvn7927ZtQ8eOHdGnTx888sgjOH/+vPG1lnb/CwsL8c0332DKlCkWr3nzM9BiVulWW0lJCRobGyUPbACIjY3FoUOHvJQr99Dr9Xj88cdxww03oF+/fsbtDzzwAHr06IHOnTtj3759ePrpp3H48GF88cUXAICCggLZ98fwmpYlJSXhgw8+QJ8+fXDu3Dk8//zzuPHGG5GTk4OCggKEhIRYPMxjY2ON1+XL1y5n/fr1KCsrw6RJk4zb/Pn+mzPkV+56TO95x44dJa8HBQWhbdu2kjTx8fEWxzC81qZNG7fkX201NTV4+umnMW7cOMmKzH//+98xePBgtG3bFjt27MCsWbNw7tw5vPnmmwB8+/pHjhyJe+65B/Hx8Th+/DieffZZjBo1ChkZGQgMDGxR9x8A/u///g+tW7fGPffcI9nu7c8Agxqy69FHH0VOTg5+/vlnyfZp06YZ/92/f3906tQJt956K44fP47evXt7OpuqGjVqlPHfAwYMQFJSEnr06IHPPvsM4eHhXsyZd7z//vsYNWoUOnfubNzmz/efrKuvr8d9990HIQSWL18ueW3mzJnGfw8YMAAhISF46KGHsHDhQq+vCeSq+++/3/jv/v37Y8CAAejduze2bduGW2+91Ys5846VK1di/PjxCAsLk2z39meAzU9Oat++PQIDAy1GvBQWFiIuLs5LuVLf9OnTsWHDBvzwww/o2rWrzbRJSUkAgGPHjgEA4uLiZN8fw2u+JCYmBldeeSWOHTuGuLg41NXVoaysTJLG9N7707WfOnUKW7ZswYMPPmgznT/ff0N+bf29x8XFoaioSPJ6Q0MDSktL/eZzYQhoTp06hc2bN0tqaeQkJSWhoaEBJ0+eBOD712+qV69eaN++veTz7u/33+Cnn37C4cOH7T4TAM9/BhjUOCkkJASJiYlIT083btPr9UhPT0dycrIXc6YOIQSmT5+OL7/8Elu3brWoLpSzZ88eAECnTp0AAMnJydi/f7/kD93wILz66qvdkm93uXjxIo4fP45OnTohMTERwcHBknt/+PBh5OXlGe+9P137qlWr0LFjR4wePdpmOn++//Hx8YiLi5Pc84qKCmRmZkrueVlZGbKysoxptm7dCr1ebwz4kpOTsX37dtTX1xvTbN68GX369NF804MhoDl69Ci2bNmCdu3a2d1nz549CAgIMDbL+PL1mztz5gzOnz8v+bz78/039f777yMxMREDBw60m9bjnwFVuhu3UGvWrBGhoaHigw8+EAcPHhTTpk0TMTExktEevuqRRx4R0dHRYtu2bZKhedXV1UIIIY4dOyYWLFggdu3aJXJzc8VXX30levXqJW666SbjMQxDem+77TaxZ88esWnTJtGhQwfNDuk19Y9//ENs27ZN5Obmil9++UWkpKSI9u3bi6KiIiFE05Du7t27i61bt4pdu3aJ5ORkkZycbNzfl6/dVGNjo+jevbt4+umnJdv98f5XVlaK7OxskZ2dLQCIN998U2RnZxtH97zyyisiJiZGfPXVV2Lfvn3irrvukh3Sfe2114rMzEzx888/i4SEBMmQ3rKyMhEbGyv+8pe/iJycHLFmzRoRERGhiSG9tq6/rq5O3HnnnaJr165iz549kmeCYRTLjh07xFtvvSX27Nkjjh8/Lj7++GPRoUMHMWHCBOM5fPX6KysrxRNPPCEyMjJEbm6u2LJlixg8eLBISEgQNTU1xmP48v0Xwv7fgBBNQ7IjIiLE8uXLLfbXwmeAQY2L3nnnHdG9e3cREhIihg4dKn799VdvZ0kVAGR/Vq1aJYQQIi8vT9x0002ibdu2IjQ0VFxxxRXiySeflMxTIoQQJ0+eFKNGjRLh4eGiffv24h//+Ieor6/3whU5ZuzYsaJTp04iJCREdOnSRYwdO1YcO3bM+PqlS5fE3/72N9GmTRsREREh7r77bnHu3DnJMXz12k199913AoA4fPiwZLs/3v8ffvhB9jM/ceJEIUTTsO45c+aI2NhYERoaKm699VaL9+X8+fNi3LhxolWrViIqKkpMnjxZVFZWStLs3btXDB8+XISGhoouXbqIV155xVOXaJOt68/NzbX6TDDMW5SVlSWSkpJEdHS0CAsLE1dddZV4+eWXJYW+EL55/dXV1eK2224THTp0EMHBwaJHjx5i6tSpFl9gffn+C2H/b0AIId59910RHh4uysrKLPbXwmdAJ4QQrtf3EBEREXkX+9QQERGRX2BQQ0RERH6BQQ0RERH5BQY1RERE5BcY1BAREZFfYFBDREREfoFBDREREfkFBjVERETkFxjUEBERkV9gUENERER+gUENERER+YX/BxkoPDH92sZzAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plt.plot(clf.feature_importances_)\n",
+    "_ = plt.title(\"Feature Importance\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:05:03.073580500Z",
+     "start_time": "2024-04-29T13:05:02.986578100Z"
+    }
+   },
+   "id": "22c926f0aff2cf9e",
+   "execution_count": 11
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "to_plot = []\n",
+    "params = best_params.copy()\n",
+    "for baseline_lam in range(2, 21):\n",
+    "    params['baseline_lam'] = baseline_lam\n",
+    "    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
+    "    to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
+    "_ = plt.plot(range(2, 21), to_plot)\n",
+    "_ = plt.title(\"Impact of varying baseline lambdas\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:07:28.464220800Z",
+     "start_time": "2024-04-29T13:05:03.075672100Z"
+    }
+   },
+   "id": "5e1397e0d62bdae5",
+   "execution_count": 13
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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uu+8iOjoarVq1wjfffINNmzZV+9zPY2dnB3Nzc5SUlDx3jE/+LJf/mUxKSsLUqVPRq1cvGBkZqUJe2c8b6Qe+JEQ6ZeXKlSgqKlJ9vWzZMhQXF6NPnz4AHv/WWv43+sLCQixdulTtOG3atEGTJk2wcOHCCk9g5fctu8Ogsie5JwUHB8PIyAjff/+92jFWr16NrKws9OvXr2qDrMTgwYMhk8nw888/Y/v27QgNDVW7+6GycQuCoLpFWxN9+vSBra0tvvjiCxw8eLDK3ZWqaNu2LWxsbLBq1SoUFxerlm/evFmj+RPHjh1Tm9uQkJCA33//Hb169VJ9L2QyWYXOzg8//FDh9vYn39TMzMwMnp6eqt/W7e3tERQUhBUrViApKalCLZXdBl4Tauq8O3fuVJvrcvLkSZw4cUL1byYvL091u3oZDw8PmJubq74HtUUmk2Hw4MH49ddfK9xqDaiPMTg4WO1RvuMyYcIEKJVKrF69GitXroSBgQHGjRsnSmePag87LKRTCgsL0aNHDwwdOhTXr1/H0qVL0alTJ7zyyisAgI4dO6JBgwYYM2YMpk2bBolEgo0bN1b4j0sqlWLZsmXo378/WrVqhbCwMDg5OeHatWu4fPky9u3bBwCqiZ3Tpk1DSEgIZDIZhg8fXmltdnZ2mD17NubPn4/evXvjlVdeUdXYrl27F3rit7e3R7du3fDtt98iJycHw4YNU1vv4+MDDw8PvPfee7h79y4sLCzw66+/VmsSpaGhIYYPH47FixdDJpNhxIgR1a77SUZGRpg3bx6mTp2K7t27Y+jQoYiPj8e6devg4eFR5Y5WixYtEBISonZbMwDMnz9ftU1oaCg2btwIS0tLNGvWDMeOHcP+/fthY2OjdqxmzZohKCgI/v7+sLa2xunTp/HLL7+o3dq9ZMkSdOrUCS1btsSECRPg7u6OlJQUHDt2DHfu3MGFCxdq4LtTUU2c19PTE506dcKkSZNQUFCAhQsXwsbGBh988AGA0m5L2b+pZs2awcDAAL/99htSUlKe+rNeFYcOHVK9N05aWhpyc3Px3//+F0Dpy3BlHdDPP/8cf//9NwICAjBhwgQ0a9YM9+7dw9mzZ7F//37cu3fvmedZu3Ytdu/ejXXr1qnmZf3www94/fXXsWzZMrz99tvVHgNpmTq/L4n03tNuazY1Na2wbdeuXYXmzZtXWO7q6qp2q2nZMQ8ePChMnDhRaNCggWBmZiaMHDlSyMjIUNv3yJEjQocOHQRjY2PB2dlZ+OCDD4R9+/ZVuN1UEATh8OHDQs+ePQVzc3PB1NRU8PX1FX744QfV+uLiYmHq1KmCnZ2dIJFIqnSL8+LFiwUfHx/B0NBQcHBwECZNmlTh1unq3Fq5atUqAYBgbm4uPHz4sML6K1euCMHBwYKZmZlga2srTJgwQbhw4YIAQFi7dq1qu6ddi/LKbpvu1atXpeufdlvz9u3b1bYru7W2/PkFQRC+//57wdXVVZDL5UL79u2FI0eOCP7+/kLv3r2f/U0QSm9rnjx5srBp0ybBy8tLkMvlQuvWrStc2/v37wthYWGCra2tYGZmJoSEhAjXrl0TXF1dhTFjxqi2++9//yu0b99esLKyEoyNjQUfHx/h008/Vbt9XhAEISYmRhg9erTg6OgoGBoaCg0bNhRCQ0OFX375pcL3oSq3NVd2GzGeuJ2/quetTPnzfPPNN4KLi4sgl8uFzp07CxcuXFBtl56eLkyePFnw8fERTE1NBUtLSyEgIEDYtm3bM48vCM++rbnsZ7yyx5NjTElJESZPniy4uLgIhoaGgqOjo9CjRw9h5cqVzzx/QkKCYGlpKfTv37/CukGDBgmmpqZCbGxspXXxtmbdIxEE9sxI+61btw5hYWE4deoU2rZtK3Y5eu/ChQto1aoVNmzYgFGjRtX6+ZRKJezs7PDqq69i1apVtX6++iA+Ph5NmjTBV199VeGDCWtKUFAQioqK8Pvvv8PIyEjtrjVtk5+fjwcPHuDLL7/EV199hbS0NNja2opdFmmAc1iIqIJVq1bBzMwMr776ao0fOz8/v8JLdBs2bMC9e/dE/0Rf0tzRo0dhZ2eH1157TexSnmn58uWws7NTe7dr0i2cw0JEKn/88QeuXLmClStXqj42oaYdP34cM2bMwJAhQ2BjY4OzZ89i9erVaNGiBYYMGVLj56Pa880336jmST35HkvaZvDgwWrvu2RpaSliNVQdDCxEpDJ16lSkpKSgb9++ahNYa5KbmxtcXFzw/fff4969e7C2tsbo0aPx+eef81OgdUz5dxvWdi4uLnBxcRG7DHoBnMNCREREWo9zWIiIiEjrMbAQERGR1tOLOSxKpRKJiYkwNzev9iftEhERUd0SBAE5OTlwdnau9PPNytOLwJKYmMjJVERERDoqISGh0k+QL08vAou5uTmA0gFr8xsXERER0WPZ2dlwcXFRPY8/i14ElrKXgSwsLBhYiIiIdExVpnNw0i0RERFpPQYWIiIi0noMLERERKT1GFiIiIhI6zGwEBERkdZjYCEiIiKtx8BCREREWo+BhYiIiLQeAwsRERFpPQYWIiIi0noMLERERKT1GFiIiIhI6zGwEJHWKixWIuJqCr6PiMb93EKxyyEiEenFpzUTkf4oKlHiaEwGdl1IxL7LycjOLwYARKc+wA8jWotcHRGJhYGFiERXohRwIjYDf1xMwt5LSbifV6RaZ2cuR1pOAXZdTMTU7p5o6mAuYqVEJBYGFiIShVIp4PSt+9h1MRF7opKR/qBAtc7G1Ah9Wjoi1NcZ7dysMXnzWey9nIxF+6OxZGQbEasmIrEwsBBRnREEAecSMrHrQhL2RCUhOTtftc7KxBC9m5eGlA7u1jCQPZ5iN72nF/ZeTsbuqCRMTc6Gj6OFGOUTkYgYWIioVgmCgKi7Wdh9MQm7LibhbuZD1TpzhQF6NXNEqJ8TOnnawlBW+X0APo4W6NfSCbujkrAwPBrLR/nXVflEpCUYWIioxgmCgKtJOdh1MRG7o5JwKyNPtc7USIaezRwQ6uuMzk1tITeQVemY7wR7Yc+lJOy9nIzLiVlo7mxZW+UTkRZiYCGiGhOdkoM/LiZh18VExKblqpYrDKXo8ZID+vs6IcjbHgrDqoWU8po6mCPU1xl/XEjEwv3RWDW6bU2WTkRajoGFiF5IXHoudl1IxK6LSbiekqNabmQgRTdvO4T6OqPHS/YwMXrx/27e6eGF3RcTEX4lBVF3stCyEbssRPUFAwsRaSzhXh52PeqkXE7MVi03lEnQxcsOoX5OCH7JAeYKwxo9r6e9GV7xc8bO84lYuP8GVo9tV6PHJyLtxcBCRFWSmPkQe6KS8MfFJFxIyFQtl0kleNnTFqG+Tghp5ghLk5oNKU+a1sML/7uQiIhrqTifkIlWLla1ej4i0g4MLET0VKnZ+dgTVXp3z+lb91XLpRKgg7sNQn2d0buFI6xNjeqsJnc7Mwxq3Qi/nr2DhftvYF1Y+zo7NxGJh4GFiNSkPyjAn5eSsftiIk7E3YMglC6XSIB2btYI9XVCnxZOsDOXi1bjtB6e2Hn+LiKvp+HMrfvwd20gWi1EVDcYWIgImXmF2HspGbsuJuFYbAZKlIJqXevGVujv64y+LZ3gaKkQscrHXG1MMbhNQ2w7Xdpl2TguQOySiKiWMbAQ1VPZ+UX463IKdl1MxOHodBSXCym+jSwR6uuEvi2d0KiBiYhVPt3U7l7YcfYu/olOx6n4e2jnZi12SURUixhYiOqRBwXFiLiagj8uJOHQjTQUlihV615yskCorxNCfZ3gamMqYpVV42JtgiFtG+Hnkwn4LvwGfprQQeySiKgWMbAQ6bmHhSU4cC0Vuy4m4sC1VBQUPw4pXvZmCPV1RqifEzzszESssnomd/PEL2fu4GhMBo7HZqCDu43YJRFRLWFgIdJD+UUlOHgjDbsuJiHiagryCktU65rYmj7qpDjD29FcxCpfXKMGJhja1gWbT9zGd+E3sPXNQLFLIqJawsBCpCcKi5U4fDMNuy4k4a8rKXhQUKxa16iBcWknxdcJzZ0tIJFIRKy0Zk3u5ontp+/gRNw9HI1JR0cPW7FLIqJawMBCpMOKSpQ4GpOB3RcTse9yCrIeFqnWOVkq0K+lE0L9nOHXyFKvQkp5zlbGGN7eBRuO3cJ34TcQ6G6jt2Mlqs8YWIh0TIlSwInYDPxxMQl7LyXhft7jkGJnLi8NKb5OaNO4AaTS+vHE/XaQJ7acSsCp+Ps4fDMdnb3sxC6JiGoYAwuRDlAqBZy+dR+7LiZiT1Qy0h8UqNbZmBqhdwtHhPo6o30Ta8jqSUgpz9FSgZEBjbH2SDy+C7+BTp627LIQ6RkGFiItJQgCziVkYteFJOyJSkJydr5qnZWJIXo3d0Q/XycEutvAQCYVsVLtMKmrB346cRtnb2fi4I00BHnbi10SEdUgBhYiLSIIAi7dzcaui4nYdTEJdzMfqtaZyw3Qq7kjQv2c0MnTFoYMKWrsLRQY1cEVPx6Ow3f7o9G1qR27LER6hIGFSGSCIOBaco4qpNzKyFOtMzWSIbiZA0J9ndGlqS3kBjIRK9V+b3b1wKYTt3AhIRN/X09Fdx8HsUsiohrCwEIkkpupOfjjQhJ2XUxETFquarnCUIoePg4I9XVCNx97KAwZUqrKzlyOMYFuWHEoFt+FR6Obtz27LER6goGFqA7Fpedi14VE7I5KwrXkHNVyIwMpgpraIdTPGT187GEq5z/N6prYxR0bj99C1N0s7L+aip7N2GUh0gf8X5GoliXcy8Oui6WdlMuJ2arlhjIJOnvZIdTXCT2bOcBcYShilfrDxkyOMR3dsCwyBt+F30DwS+yyEOkDBhaiWpCU9RC7Lybhj4tJuJCQqVouk0rQ0cMG/X2dEdLcEZYmDCm1YWJnd2w4Go8rSdnYdzkFvVs4il0SEb0gBhaiGpKanY89UUnYdTEJp2/dVy2XSoAO7jYI9XVGSHMH2JjJRayyfmhgaoSwl5tg8d83sXD/DfRq5lBv3kSPSF8xsBC9gIwHBfjzUjJ2XUzEibh7EITH69q7WSPUzwm9WzjC3lwhXpH11PjOTbD+aDyuJedg7+Vk9G3pJHZJRPQCGFiINJSZV4h9l5Ox62ISjsZkoET5OKW0bmyFUF9n9GvpBEdLhhQxWZkYIaxTE3wfEY3vwm8gpLljvXwXYCJ9wcBCVAXZ+UUIv5yCPy4m4nB0OorLhZSWDS0R6uuEfr5OaNTARMQq6UnjOjXB2iNxiE59gN1RSXjFz1nskoiomhhYiJ4it6AY+6+mYNfFJBy8nobCEqVqnY+jOfr7lXZS3GxNRaySnsXS2BATOrvj2/AbWLT/Bvq1dGKXhUhHMbAQlfOwsAQHrqVi18VEHLiWioLixyHF094Mob5OCPV1hqe9mYhVkibCXnbD6sNxiEnLxR8XEjGwdUOxSyKiaqjWh5EsWbIEbm5uUCgUCAgIwMmTJ5+6bVFRET7++GN4eHhAoVDAz88Pe/fuVdtm3rx5kEgkag8fH5/qlEaksfyiEuy7nIypP5+D/3/DMfmns/jzUjIKipVwszHBlG6e2Du9M8JndMH04KYMKzrGXGGIiV3cAQDfR0SjuFynjIh0h8Ydlq1bt2LmzJlYvnw5AgICsHDhQoSEhOD69euwt6/46agffvghNm3ahFWrVsHHxwf79u3DoEGDcPToUbRu3Vq1XfPmzbF///7HhRmw+UO1p7BYicM307DrQhLCr6Qgp6BYta5RA2P083VCf19nNHe24JuO6YExHd3w4z+xiE3Pxe/nEzHYv5HYJRGRhiSCUP5GzOcLCAhAu3btsHjxYgCAUqmEi4sLpk6dilmzZlXY3tnZGf/5z38wefJk1bLBgwfD2NgYmzZtAlDaYdm5cyfOnz9frUFkZ2fD0tISWVlZsLCwqNYxSP8VlyhxNCYDuy4mYt/lFGQ9LFKtc7RQoJ+vE0J9ndDKxYohRQ8ti4zBF3uvwdXGBBEzu8KAn3ZNJDpNnr81amMUFhbizJkzmD17tmqZVCpFcHAwjh07Vuk+BQUFUCjUb+80NjbG4cOH1ZZFR0fD2dkZCoUCgYGBWLBgARo3bvzUYxYUFKi+zs7OrnQ7qh8EQUBhiRL5RUoUFJUgv0iJ/OIS5D/6e/bDIhy4noq9l5JxL7dQtZ+duRz9WpaGlDaNG/CNxfTc6EBXrPonFrcy8rDj3F0MbesidklEpAGNAkt6ejpKSkrg4KD+YWIODg64du1apfuEhITg22+/RZcuXeDh4YGIiAjs2LEDJSUlqm0CAgKwbt06eHt7IykpCfPnz0fnzp1x6dIlmJubVzjmggULMH/+fE1KpzoiCAKKSgRVYCgoUpb+WaxUBYj8opJH68uWla4vKCpBfvHjZY+3fbyuoPy64sf7VrVPaG1qhD4tHBHq64z2Tax5x0g9Yio3wFtd3fHZnmv44UA0BrVuCEN2WYh0Rq1PFFm0aBEmTJgAHx8fSCQSeHh4ICwsDGvWrFFt06dPH9XffX19ERAQAFdXV2zbtg3jxo2rcMzZs2dj5syZqq+zs7Ph4sLflipTVPI4KBSUCwkFaoHhiVDxaF35cKDavrik0n0KygUNpUYvMtYsiQRQGMigMJRCYSiDwlAGuYEUfo2sEOrnhEB3G74UUI+N6uCGlYfikHDvIX49cwfD21fexSUi7aNRYLG1tYVMJkNKSora8pSUFDg6Vv7hYnZ2dti5cyfy8/ORkZEBZ2dnzJo1C+7u7k89j5WVFZo2bYqbN29Wul4ul0Mu173PYylRCo+7B090EgoqCQxPdiQKnggd6p2G0nUF5dcVK9XehVUMquDwKETIy/58FCYUBmXB4vG68kGj7O8V1pf7u7zsTwMpjGRSzj+hpzI2kuGtru747+6r+OHATbzaphGMDBhgiXSBRoHFyMgI/v7+iIiIwMCBAwGUTrqNiIjAlClTnrmvQqFAw4YNUVRUhF9//RVDhw596rYPHjxATEwMRo0apUl5Na6oRIndF5PUAkDBE12GgvIdiUrCRPllxSKHh7IAIC8XEsqe/OUVgoL0USgoHwzK7ftEmFA7zqM/5QYMD6R9Xu/gihWHYnE38yG2n0nAyABXsUsioirQ+CWhmTNnYsyYMWjbti3at2+PhQsXIjc3F2FhYQCA0aNHo2HDhliwYAEA4MSJE7h79y5atWqFu3fvYt68eVAqlfjggw9Ux3zvvffQv39/uLq6IjExEXPnzoVMJsOIESNqaJjVU6IUMH3r+Vo5tpGBVD04PBkODB51IdS6Do+3Kx8MFBW6FRVDiZFMykmlRAAUhjK8HeSB+X9cweIDN/Ev/0aQG8jELouInkPjwDJs2DCkpaVhzpw5SE5ORqtWrbB3717VRNzbt29DKn3cYs3Pz8eHH36I2NhYmJmZoW/fvti4cSOsrKxU29y5cwcjRoxARkYG7Ozs0KlTJxw/fhx2dnYvPsIXIDeQ4mVPG/WQUMnLG+pBQz10qHUhDB6HCYYHIvGMaN8Yyw/GICkrH1tPJWB0oJvYJRHRc2j8PizaiO/DQkSa2ngsHh/9fhkOFnIcfL8bFIbsshDVNU2evznbjIjqpaHtXOBsqUBKdgF+Pnlb7HKI6DkYWIioXpIbyDC5uycAYGlkDPKLSp6zBxGJiYGFiOqtIf4uaGhljLScAmw6fkvscojoGRhYiKjeMjKQYuqjLsvygzHIKyx+zh5EJBYGFiKq1wb7N4KLtTHSHxSyy0KkxRhYiKheM5RJMbW7FwBg+cFY5Bawy0KkjRhYiKjee7V1Q7jamOBebiE2HGOXhUgbMbAQUb1nIJNi2qMuy4pDMXjALguR1mFgISICMKCVM9xtTZGZV4T1R+PFLoeInsDAQkSER12WHqVdlpWHYpGdXyRyRURUHgMLEdEj/f2c4WFniqyHRVh7OF7scoioHAYWIqJHZFIJpgc3BQD8eDgWWQ/ZZSHSFgwsRETl9GvphKYOZsjJL8bqw3Fil0NEjzCwEBGVIy3XZVl7OA6ZeYUiV0REAAMLEVEFvZs7wsfRHDkFxfjxH3ZZiLQBAwsR0RPUuixH4nAvl10WIrExsBARVSKkuQOaO1sgt7AEq/6JFbsconqPgYWIqBISyeMuy/qj8ch4UCByRUT1GwMLEdFTBL9kj5YNLZFXWIKVh9hlIRITAwsR0VNIJBLM6Fn67rfrj8UjLYddFiKxMLAQET1DN297+LlYIb9IiRUHY8Quh6jeYmAhInoGiUSCmT1L57JsPH4Lqdn5IldEVD8xsBARPUcXL1u0aWyFgmIllkayy0IkBgYWIqLnKO2yeAMAfjp5G8lZ7LIQ1TUGFiKiKnjZ0wbt3BqgsFiJpZE3xS6HqN5hYCEiqoLSO4ZK57JsOZmAxMyHIldEVL8wsBARVVFHD1sENLFGYYkSS/5ml4WoLjGwEBFpoKzLsu10Au7czxO5GqL6g4GFiEgDHdxt8LKnDYpKBHZZiOoQAwsRkYZmPPqMoe2n7yDhHrssRHWBgYWISENt3azR2csWxUoBPxyIFrsconqBgYWIqBrK5rL8evYu4tNzRa6GSP8xsBARVUObxg0Q5G2HEqWAHw5wLgtRbWNgISKqprK5LL+du4PYtAciV0Ok3xhYiIiqyc/FCj187KEUgO8jOJeFqDYxsBARvYCyuSz/u5CIm6k5IldDpL8YWIiIXkCLhpbo2cwBSgFYFMG5LES1hYGFiOgFTQ/2AgDsupiIGynsshDVBgYWIqIX1NzZEr2bO0IQgEX7OZeFqDYwsBAR1YDpPUu7LLujknA1KVvkaoj0DwMLEVEN8HG0QD9fJwDsshDVBgYWIqIaMr2HFyQSYO/lZFxOzBK7HCK9wsBCRFRDvBzM0d/XGQCwkF0WohrFwEJEVIOm9fCCVAKEX0lB1B12WYhqCgMLEVEN8rQ3w4BWDQEAC/ffELkaIv3BwEJEVMOm9fCCTCpBxLVUnE/IFLscIr3AwEJEVMOa2Jpi4KMuy3fh7LIQ1QQGFiKiWjCthydkUgkO3kjDmVv3xS6HSOcxsBAR1QJXG1MMbsO5LEQ1hYGFiKiWTO3uBQOpBP9Ep+NU/D2xyyHSaQwsRES1xMXaBEPaugDgXBaiF8XAQkRUi6Z094ShTIKjMRk4HpshdjlEOouBhYioFjW0MsawdqVdlm/Db0AQBJErItJNDCxERLVscjdPGMmkOBl3D8di2GUhqg4GFiKiWuZkaYwR7R/NZdnPLgtRdTCwEBHVgbe7ecLIQIpT8fdx+Ga62OUQ6RwGFiKiOuBgocDIgMYASu8YYpeFSDMMLEREdWRSkAcUhlKcvZ2JgzfSxC6HSKdUK7AsWbIEbm5uUCgUCAgIwMmTJ5+6bVFRET7++GN4eHhAoVDAz88Pe/fufaFjEhHpIntzBV4PcAXALguRpjQOLFu3bsXMmTMxd+5cnD17Fn5+fggJCUFqamql23/44YdYsWIFfvjhB1y5cgVvvfUWBg0ahHPnzlX7mEREuurNrh4wNpThwp0s/H2d/8cRVZVE0DDiBwQEoF27dli8eDEAQKlUwsXFBVOnTsWsWbMqbO/s7Iz//Oc/mDx5smrZ4MGDYWxsjE2bNlXrmE/Kzs6GpaUlsrKyYGFhoclwiIjq3II9V7HiUCxaNrTE/6a8DIlEInZJRKLQ5Plbow5LYWEhzpw5g+Dg4McHkEoRHByMY8eOVbpPQUEBFAqF2jJjY2McPnz4hY6ZnZ2t9iAi0hUTu7jDxEiGqLtZ2H+VXRaiqtAosKSnp6OkpAQODg5qyx0cHJCcnFzpPiEhIfj2228RHR0NpVKJ8PBw7NixA0lJSdU+5oIFC2Bpaal6uLi4aDIMIiJR2ZjJMbajG4DSd79VKjmXheh5av0uoUWLFsHLyws+Pj4wMjLClClTEBYWBqm0+qeePXs2srKyVI+EhIQarJiIqPZN6OwOM7kBriZl468rlf9yRkSPaZQabG1tIZPJkJKSorY8JSUFjo6Ole5jZ2eHnTt3Ijc3F7du3cK1a9dgZmYGd3f3ah9TLpfDwsJC7UFEpEsamBoh7GU3AMDC/dHsshA9h0aBxcjICP7+/oiIiFAtUyqViIiIQGBg4DP3VSgUaNiwIYqLi/Hrr79iwIABL3xMIiJdNr6TO8zlBriWnIM/L7HLQvQsGr8uM3PmTKxatQrr16/H1atXMWnSJOTm5iIsLAwAMHr0aMyePVu1/YkTJ7Bjxw7Exsbin3/+Qe/evaFUKvHBBx9U+ZhERPrI0sQQb3RqAgBYuP8GSthlIXoqA013GDZsGNLS0jBnzhwkJyejVatW2Lt3r2rS7O3bt9Xmp+Tn5+PDDz9EbGwszMzM0LdvX2zcuBFWVlZVPiYRkb56o1MTrDkSh+jUB9gdlYRX/JzFLolIK2n8PizaiO/DQkS67PuIaHwbfgMedqb4a0ZXyKR8XxaqH2rtfViIiKjmhb3sBktjQ8Sk5eKPC4lil0OklRhYiIhEZq4wxMQupXdOLoqIRnGJUuSKiLQPAwsRkRYY09ENDUwMEZeei9/Ps8tC9CQGFiIiLWAmN8DELh4AgO8PsMtC9CQGFiIiLTE60BU2pka4lZGHHefuil0OkVZhYCEi0hKmcgO81fVRlyUiGkXsshCpMLAQEWmR1zu4wtZMjjv3H+KXM3fELodIazCwEBFpEWMjGSYFlXZZFh+4icJidlmIAAYWIiKtMzKgMezN5bib+RDbTvPT6IkABhYiIq2jMJTh7UddliV/30RBcYnIFRGJj4GFiEgLDW/fGI4WCiRl5WPrKXZZiBhYiIi0kMJQhsndHndZ8ovYZaH6jYGFiEhLDW3nAmdLBVKyC/Dzydtil0MkKgYWIiItJTeQYXJ3TwDA0sgYdlmoXmNgISLSYkP8XdDQyhhpOQXYdPyW2OUQiYaBhYhIixkZSDH1UZdl+cEY5BUWi1wRkTgYWIiItNxg/0ZwsTZG+oNCdlmo3mJgISLScoYyKaZ19wIALD8Yi9wCdlmo/mFgISLSAYNaN4SbjQnu5RZi/bF4scshqnMMLEREOsBAJsW0HqVdlpWHYpGTXyRyRUR1i4GFiEhHvOLnDHdbU2TmFWH90XixyyGqUwwsREQ6wkAmxTvBpV2WVf/EIZtdFqpHGFiIiHRIqK8zPO3NkPWwCGsPx4tdDlGdYWAhItIhMqkE7zyay/Lj4VhkPWSXheoHBhYiIh3Tr6UTmjqYISe/GKsPx4ldDlGdYGAhItIxUqkE04ObAgDWHI5DZl6hyBUR1T4GFiIiHdS7uSN8HM3xoKAYP/7DLgvpPwYWIiIdVL7LsvZIHO7lsstC+o2BhYhIR4U0d0BzZwvkFpZg1T+xYpdDVKsYWIiIdJREIsGMR12W9Ufjkf6gQOSKiGoPAwsRkQ7r8ZI9fBtZIq+wBCsPsctC+ouBhYhIh5Xvsmw4Fo+0HHZZSD8xsBAR6bggbzu0crFCfpESyw/GiF0OUa1gYCEi0nESiQQzepZ2WTYdv4XU7HyRKyKqeQwsRER6oIuXLfxdG6CgWImlkeyykP5hYCEi0gPl57L8dPI2krPYZSH9wsBCRKQnXva0QXs3axQWK7E08qbY5RDVKAYWIiI9IZFIML1n6Sc5bzmZgMTMhyJXRFRzGFiIiPRIRw9bdHC3RmGJEkv+ZpeF9AcDCxGRnimby7LtdALu3M8TuRqimsHAQkSkZwLcbfCypw2KSgR2WUhvMLAQEemhsi7L9tN3cDuDXRbSfQwsRER6qK2bNTp72aJYKeCHA9Fil0P0whhYiIj0VNm73+44dxfx6bkiV0P0YhhYiIj0VJvGDRDkbYcSpYDv2WUhHcfAQkSkx8rmsuw8dxexaQ9Eroao+hhYiIj0mJ+LFYJfsodSAL6PYJeFdBcDCxGRnpv+qMvyvwuJuJmaI3I1RNXDwEJEpOdaNLREr2YOUArAogi+LwvpJgYWIqJ6oKzLsutiIm6ksMtCuoeBhYioHmjmbIE+LRwhCMCi/ZzLQrqHgYWIqJ6YHtwUEgmwOyoJV5OyxS6HSCMMLERE9YS3ozn6tnQCwC4L6R4GFiKiemR6Dy9IJMDey8m4dDdL7HKIqoyBhYioHvFyMEd/X2cAwEJ2WUiHMLAQEdUz03p4QSoB9l9NQdQddllINzCwEBHVM572ZhjQqiEA4Lv9N0SuhqhqGFiIiOqhaT28IJNKcOBaKs4nZIpdDtFzVSuwLFmyBG5ublAoFAgICMDJkyefuf3ChQvh7e0NY2NjuLi4YMaMGcjPz1etnzdvHiQSidrDx8enOqUREVEVNLE1xaDWj7os4eyykPbTOLBs3boVM2fOxNy5c3H27Fn4+fkhJCQEqamplW7/008/YdasWZg7dy6uXr2K1atXY+vWrfj3v/+ttl3z5s2RlJSkehw+fLh6IyIioiqZ2t0TMqkEB2+k4cyt+2KXQ/RMGgeWb7/9FhMmTEBYWBiaNWuG5cuXw8TEBGvWrKl0+6NHj+Lll1/Ga6+9Bjc3N/Tq1QsjRoyo0JUxMDCAo6Oj6mFra1u9ERERUZW42pjiX20aAQAWci4LaTmNAkthYSHOnDmD4ODgxweQShEcHIxjx45Vuk/Hjh1x5swZVUCJjY3Fnj170LdvX7XtoqOj4ezsDHd3d4wcORK3b99+ah0FBQXIzs5WexARkeamdPeEgVSCf6LTcSr+ntjlED2VRoElPT0dJSUlcHBwUFvu4OCA5OTkSvd57bXX8PHHH6NTp04wNDSEh4cHgoKC1F4SCggIwLp167B3714sW7YMcXFx6Ny5M3JyKv+ArgULFsDS0lL1cHFx0WQYRET0iIu1CYa0Lf0/lHNZSJvV+l1CkZGR+Oyzz7B06VKcPXsWO3bswO7du/HJJ5+otunTpw+GDBkCX19fhISEYM+ePcjMzMS2bdsqPebs2bORlZWleiQkJNT2MIiI9NaU7p4wlElwNCYDx2MzxC6HqFIaBRZbW1vIZDKkpKSoLU9JSYGjo2Ol+3z00UcYNWoUxo8fj5YtW2LQoEH47LPPsGDBAiiVykr3sbKyQtOmTXHz5s1K18vlclhYWKg9iIioehpaGWNYu9Iuy7fhNyAIgsgVEVWkUWAxMjKCv78/IiIiVMuUSiUiIiIQGBhY6T55eXmQStVPI5PJAOCp/ygePHiAmJgYODk5aVIeERFV0+RunjCSSXEy7h6OxbDLQtpH45eEZs6ciVWrVmH9+vW4evUqJk2ahNzcXISFhQEARo8ejdmzZ6u279+/P5YtW4YtW7YgLi4O4eHh+Oijj9C/f39VcHnvvfdw8OBBxMfH4+jRoxg0aBBkMhlGjBhRQ8MkIqJncbI0xoj27LKQ9jLQdIdhw4YhLS0Nc+bMQXJyMlq1aoW9e/eqJuLevn1braPy4YcfQiKR4MMPP8Tdu3dhZ2eH/v3749NPP1Vtc+fOHYwYMQIZGRmws7NDp06dcPz4cdjZ2dXAEImIqCre7uaJn08l4PSt+zh8Mx2dvfh/MGkPiaAHMTo7OxuWlpbIysrifBYiohfw8R9XsOZIHFo3tsKOSR0hkUjELon0mCbP3/wsISIiUnkryB0KQynO3c7EwRtpYpdDpMLAQkREKvbmCozq4Aqg9H1Z9KAJT3qCgYWIiNS82dUDxoYyXLiThQPXKv+cOKK6xsBCRERqbM3kGN3xUZdlP7sspB0YWIiIqII3u3jAxEiGS3ezEX4l5fk7ENUyBhYiIqrA2tQIYzu6AQC+2x8NpZJdFhIXAwsREVVqQmd3mMkNcDUpG39dqfwDbonqCgMLERFVqoGpEcJedgMALGSXhUTGwEJERE81vpM7zOUGuJacgz8vsctC4mFgISKip7I0McQbnZoAABbuv4ESdllIJAwsRET0TG90agILhQGiUx9gd1SS2OVQPcXAQkREz2RpbIgJnd0BAIvYZSGRMLAQEdFzjX3ZDVYmhohJy8UfFxLFLofqIQYWIiJ6LnNFuS5LRDSKS5QiV0T1DQMLERFVyZiObmhgYoi49FzsPM8uC9UtBhYiIqoSM7kB3uzqAQD44UA0ithloTrEwEJERFU2OtAVNqZGuJWRh9/O3hW7HKpHGFiIiKjKTIwM8NajLsv37LJQHWJgISIijbzewRW2ZnLcuf8Qv5y5I3Y5VE8wsBARkUaMjWSYFFTaZVl84CYKi9llodrHwEJERBobGdAY9uZy3M18iG2nE8Quh+oBBhYiItKYwlCGtx91WZb8fRMFxSUiV0T6joGFiIiqZXj7xnC0UCApKx9bT7HLQrWLgYWIiKpFYSjD5O6eAEq7LPlF7LJQ7WFgISKiahvathGcLRVIyS7Azydvi10O6TEGFiIiqja5gQxTunsBAJZGxuBhIbssVDsYWIiI6IX8y78RGloZIy2nAJtP3BK7HNJTDCxERPRCjAykmNajdC7L8oMxyCssFrki0kcMLERE9MJebdMIja1NkP6gEBuPsctCNY+BhYiIXpihTIqpj+4YWnEoFrkF7LJQzWJgISKiGjGodUO42ZjgXm4h1h+LF7sc0jMMLEREVCMMZFJM61F6x9DKQ7HIyS8SuSLSJwwsRERUY17xc4a7nSky84qw/mi82OWQHmFgISKiGmMgk+Kdcl2WbHZZqIYwsBARUY0K9XWGp70ZsvOLsfZwvNjlkJ5gYCEioholk0owPbi0y/Lj4VhkPWSXhV4cAwsREdW4vi2c4O1gjpz8Yqw+HCd2OaQHGFiIiKjGSct1WdYcjkNmXqHIFZGuY2AhIqJaEdLcET6O5nhQUIxV/8SKXQ7pOAYWIiKqFVKpBDN6NgUArDsSj4R7eSJXRLqMgYWIiGpNr2YO8GtkidzCEoxYdRx37jO0UPUwsBARUa2RSCRYMaotmtia4s79hxi+8jjuZj4UuyzSQQwsRERUqxwtFfh5Qge42Zg8Ci3HGFpIYwwsRERU6xwtFfh5Yge42pgg4d5DjFh5HIkMLaQBBhYiIqoTTpbG+HlCBzS2NsHte3kYvvI4krIYWqhqGFiIiKjOOFsZY8tEhhbSHAMLERHVKWcrY/w8sQNcrI1xKyMPI1YeR3JWvthlkZZjYCEiojrX0MoYWyYGwsXaGPEZeRi+8hhDCz0TAwsREYmioVXpnJZGDUpDy4hVx5GSzdBClWNgISIi0TRqYIKfJ3RAQytjxKXnYsRKhhaqHAMLERGJysXaBFsmloaW2EehJZWhhZ7AwEJERKJ7MrQMX3UcqTkMLfQYAwsREWkFF+vSl4ecLRWITXvUaWFooUcYWIiISGs0tjHBlomBcLZUICYtF6+tOoG0nAKxyyItwMBCRERapbGNCX6e2AFOlgrcTH2A11YdZ2ghBhYiItI+rjam+HlCBzhaKBD9KLSkP2Boqc8YWIiISCu52Zpiy0SGFirFwEJERFrLzdYUP0/sAAcLOW6kPMDIVSeQwdBSL1UrsCxZsgRubm5QKBQICAjAyZMnn7n9woUL4e3tDWNjY7i4uGDGjBnIz1ef+a3pMYmIqH5oYlv68pC9uRzXU3Iw8keGlvpI48CydetWzJw5E3PnzsXZs2fh5+eHkJAQpKamVrr9Tz/9hFmzZmHu3Lm4evUqVq9eja1bt+Lf//53tY9JRET1i7udGbZMLA0t15JLQ8u93EKxy6I6JBEEQdBkh4CAALRr1w6LFy8GACiVSri4uGDq1KmYNWtWhe2nTJmCq1evIiIiQrXs3XffxYkTJ3D48OFqHfNJ2dnZsLS0RFZWFiwsLDQZDhER6ZCYtAcYvrL0riEfR3P8NKEDrE2NxC6LqkmT52+NOiyFhYU4c+YMgoODHx9AKkVwcDCOHTtW6T4dO3bEmTNnVC/xxMbGYs+ePejbt2+1j1lQUIDs7Gy1BxER6T8POzP8PKED7Mp1Wu6z01IvaBRY0tPTUVJSAgcHB7XlDg4OSE5OrnSf1157DR9//DE6deoEQ0NDeHh4ICgoSPWSUHWOuWDBAlhaWqoeLi4umgyDiIh0mKd9aWixNZPjalI2Q0s9Uet3CUVGRuKzzz7D0qVLcfbsWezYsQO7d+/GJ598Uu1jzp49G1lZWapHQkJCDVZMRETaztPeDFsmBsDWTI4rSdl4ffUJZOYxtOgzjQKLra0tZDIZUlJS1JanpKTA0dGx0n0++ugjjBo1CuPHj0fLli0xaNAgfPbZZ1iwYAGUSmW1jimXy2FhYaH2ICKi+sXT3hw/TwiArZkRLieWdloYWvSXRoHFyMgI/v7+ahNolUolIiIiEBgYWOk+eXl5kErVTyOTyQAAgiBU65hEREQA4OVQOvHWxrQ0tLy++gSy8orELotqgcYvCc2cOROrVq3C+vXrcfXqVUyaNAm5ubkICwsDAIwePRqzZ89Wbd+/f38sW7YMW7ZsQVxcHMLDw/HRRx+hf//+quDyvGMSERE9TVMHc/w8sTS0XLrL0KKvDDTdYdiwYUhLS8OcOXOQnJyMVq1aYe/evapJs7dv31brqHz44YeQSCT48MMPcffuXdjZ2aF///749NNPq3xMIiKiZ2n6qNMyYtVxRN3Nwqg1J7BxXAAsjQ3FLo1qiMbvw6KN+D4sREQEANeSs/HaqtI3lfNrZIkNDC1ardbeh4WIiEib+ThaYPP4ADQwMcSFO1kYveYksvP58pA+YGAhIiK98pKTBTaP71AaWhIyMWo1Q4s+YGAhIiK908y5NLRYPQoto1efRA5Di05jYCEiIr1UGloCYGViiPMJmRi9hqFFlzGwEBGR3mrubIlNjybenrudiTEMLTqLgYWIiPRai4aW2Dy+NLScvZ2JsWtP4UFBsdhlkYYYWIiISO+VhRYLhQHO3LqPsWtOMrToGAYWIiKqF0pDSwdYKAxw+tZ9hK1laNElDCxERFRvtGxkiU3jA2CuMMCp+NLQksvQohMYWIiIqF7xbWSFTePKh5ZTDC06gIGFiIjqHT8XK2wcFwBzuQFOxt9D2LpTyCtkaNFmDCxERFQvtXKxwoZx7UtDS9w9hK1laNFmDCxERFRvtW7cAOvHtYeZ3AAn4u7hjXWn8LCwROyyqBIMLEREVK+1adwAGx6FluOxDC3aioGFiIjqvTaNG2D9G6Wh5VhsBsatZ2jRNgwsREREAPxdG2D9G+1gaiTD0ZgMjN9wCvlFDC3agoGFiIjoEX9Xa6x/oz1MjWQ4cjMD49efZmjREgwsRERE5bR1s8a6N9rDxEiGwzfTMWEDQ4s2YGAhIiJ6Qjs3a6wLKw0t/0QztGgDBhYiIqJKtG9ijbVj26lCy8SNZxhaRMTAQkRE9BQB7jZYO7YdjA1lOHQjDW8ytIiGgYWIiOgZAtxtsDasNLQcvJGGtzYxtIiBgYWIiOg5OrjbYM3YdlAYShF5PQ2TNp1BQTFDS11iYCEiIqqCQI/HoeXv62mYtOksQ0sdYmAhIiKqoo4etlgzpjS0HLiWircZWuoMAwsREZEGOnraYvWYdpAbSBFxLRWTN59FYbFS7LL0HgMLERGRhl4uF1r2X03F2wwttY6BhYiIqBo6ednixzFtH4WWFEz+iaGlNjGwEBERVVNnLzusGt0WRgZShF9JwRSGllrDwEJERPQCujR9HFr+upKCqT+fRVEJQ0tNY2AhIiJ6QV2b2mHlKH8YGUix73IKpv50jqGlhjGwEBER1YAgb/vS0CKTYu/lZEz7maGlJjGwEBER1ZAgb3useBRa/ryUjHe2MLTUFAYWIiKiGtTNxx7LR7WBkUyKPVHJmL7lPIoZWl4YAwsREVEN6+7jgGWvt4GhTILdUUl4ZytDy4tiYCEiIqoFPV5ywLKR/qWh5WISZmy7wNDyAhhYiIiIaklwMwcsfRRa/riQiJkMLdXGwEJERFSLejZzwJLXSl8e+t+FRLy7naGlOhhYiIiIalmv5o5Y/FobGEgl+P18aWgpUQpil6VTGFiIiIjqQMgToeU9hhaNMLAQERHVkd4tHLH4tdYwkErw27m7eJ+hpcoYWIiIiOpQ7xZO+GFEa8ikEuw4dxfv/8LQUhUMLERERHWsT8tyoeXsXXzwy0WGlucwELsAIiKi+qhvSycIAjBtyzn8evYOpBLgi8G+kEolYpemlRhYiIiIRNLP1wkCBLyz5Ty2n7kDgKHlafiSEBERkYhCfZ2xcFgrSCXA9jN3MHtHFJR8eagCBhYiIiKR9fdzxsLhrSGVAFtPJ+DfvzG0PImBhYiISAu84ueM7x51WracSsB/djK0lMc5LERERFpiQKuGAIAZW8/j55MJACT4dGALzmkBOyxERERaZUCrhvhmqB+kEuDnk7fx4e+X2GkBAwsREZHWGdS6Eb4Z6geJBPjpxG3M+d8lCEL9Di0MLERERFpoUOtG+GZIaWjZdPw2Pvq9focWBhYiIiIt9WqbRvjqX49Dy9z/Xa63oYWBhYiISIv9y78RvhzsC4kE2HDsFubV09DCwEJERKTlhrR1wRePQsv6Y7cw/48r9S60MLAQERHpgKFtXfDFq74AgHVH4+tdaGFgISIi0hFD27ngi8EtAZSGlo931Z/QwsBCRESkQ4a1a4zPXy0NLWuPxOO/u6/Wi9BSrcCyZMkSuLm5QaFQICAgACdPnnzqtkFBQZBIJBUe/fr1U20zduzYCut79+5dndKIiIj03vD2jfHZoNLQsvpwHD6tB6FF47fm37p1K2bOnInly5cjICAACxcuREhICK5fvw57e/sK2+/YsQOFhYWqrzMyMuDn54chQ4aobde7d2+sXbtW9bVcLte0NCIionrjtYDGECDgP79dwo+H4yCRAP/u+xIkEv18G3+NOyzffvstJkyYgLCwMDRr1gzLly+HiYkJ1qxZU+n21tbWcHR0VD3Cw8NhYmJSIbDI5XK17Ro0aFC9EREREdUTIwNc8d+BLQAAq/6Jw+d/XtPbTotGgaWwsBBnzpxBcHDw4wNIpQgODsaxY8eqdIzVq1dj+PDhMDU1VVseGRkJe3t7eHt7Y9KkScjIyHjqMQoKCpCdna32ICIiqo9e7+CKTx6FlhWHYvH5Xv0MLRoFlvT0dJSUlMDBwUFtuYODA5KTk5+7/8mTJ3Hp0iWMHz9ebXnv3r2xYcMGRERE4IsvvsDBgwfRp08flJSUVHqcBQsWwNLSUvVwcXHRZBhERER6ZVQHV3wyoDkAYMXBWHyx97rehRaN57C8iNWrV6Nly5Zo37692vLhw4er/t6yZUv4+vrCw8MDkZGR6NGjR4XjzJ49GzNnzlR9nZ2dzdBCRET12qhANwgA5vx+GcsPxkAqAd4P8dabOS0adVhsbW0hk8mQkpKitjwlJQWOjo7P3Dc3NxdbtmzBuHHjnnsed3d32Nra4ubNm5Wul8vlsLCwUHsQERHVd6MD3TD/ldJOy9LIGHz9l/50WjQKLEZGRvD390dERIRqmVKpREREBAIDA5+57/bt21FQUIDXX3/9uee5c+cOMjIy4OTkpEl5RERE9d6Yjm6Y278ZAGDJ3zH45q8behFaNL5LaObMmVi1ahXWr1+Pq1evYtKkScjNzUVYWBgAYPTo0Zg9e3aF/VavXo2BAwfCxsZGbfmDBw/w/vvv4/jx44iPj0dERAQGDBgAT09PhISEVHNYRERE9VfYy00wJ7Q0tCz++ya+Ddf90KLxHJZhw4YhLS0Nc+bMQXJyMlq1aoW9e/eqJuLevn0bUql6Drp+/ToOHz6Mv/76q8LxZDIZLl68iPXr1yMzMxPOzs7o1asXPvnkE74XCxERUTW90akJBACf7LqCHw7chEQiwcyeTcUuq9okgq5HLpROurW0tERWVhbnsxAREZXz4z+x+O/uqwCAd3p4YYYWhRZNnr/5WUJERER6bHxnd/yn70sAgEUR0Vi4/4bIFVUPAwsREZGem9DFHf/u6wMAWLg/Gt9HRItckeYYWIiIiOqBiV08MLtPaWj5NvwGftCx0MLAQkREVE+82dUDsx6Flm/Cb2DxAd0JLQwsRERE9chbXT3wQW9vAMDXf93Akr8rf5NWbcPAQkREVM+8HeSJ90NKQ8tX+65jaaT2hxYGFiIionpocrfHoeXLvdexLDJG5IqejYGFiIionprczRPvPnpfli/2XsPyg9obWhhYiIiI6rGpPbxU74D7+Z/XsPKQdoYWBhYiIqJ6bloPL8wILg0tn+25hlWHYkWuqCIGFiIiIsI7wV6YHuwFAPh0z1X8+I92hRYGFiIiIgIATA9uimk9SkPLf3drV2hhYCEiIiKVGcFemNbdE0BpaFl9OE7kikoxsBAREZGKRCLBjJ5NMfVRaPlk1xWsPSJ+aGFgISIiIjUSiQQzezbF5G4eAID5f1zBOpFDCwMLERERVSCRSPBeL2+8HfQotOy6gpupOaLVYyDamYmIiEirSSQSvB/iDYkEcLU2hae9uWi1MLAQERHRU5WGFh+xy+BLQkRERKT9GFiIiIhI6zGwEBERkdZjYCEiIiKtx8BCREREWo+BhYiIiLQeAwsRERFpPQYWIiIi0noMLERERKT1GFiIiIhI6zGwEBERkdZjYCEiIiKtx8BCREREWk8vPq1ZEAQAQHZ2tsiVEBERUVWVPW+XPY8/i14ElpycHACAi4uLyJUQERGRpnJycmBpafnMbSRCVWKNllMqlUhMTIS5uTkkEkmNHjs7OxsuLi5ISEiAhYVFjR5bG+j7+AD9HyPHp/v0fYz6Pj5A/8dYW+MTBAE5OTlwdnaGVPrsWSp60WGRSqVo1KhRrZ7DwsJCL38Iy+j7+AD9HyPHp/v0fYz6Pj5A/8dYG+N7XmelDCfdEhERkdZjYCEiIiKtx8DyHHK5HHPnzoVcLhe7lFqh7+MD9H+MHJ/u0/cx6vv4AP0fozaMTy8m3RIREZF+Y4eFiIiItB4DCxEREWk9BhYiIiLSegwsREREpPUYWIiIiEjr1fvAcujQIfTv3x/Ozs6QSCTYuXPnc/eJjIxEmzZtIJfL4enpiXXr1tV6ndWl6fgiIyMhkUgqPJKTk+umYA0tWLAA7dq1g7m5Oezt7TFw4EBcv379uftt374dPj4+UCgUaNmyJfbs2VMH1WquOuNbt25dheunUCjqqGLNLVu2DL6+vqp30AwMDMSff/75zH105foBmo9P167fkz7//HNIJBJMnz79mdvp0jUsryrj07VrOG/evAr1+vj4PHMfMa5fvQ8subm58PPzw5IlS6q0fVxcHPr164du3brh/PnzmD59OsaPH499+/bVcqXVo+n4yly/fh1JSUmqh729fS1V+GIOHjyIyZMn4/jx4wgPD0dRURF69eqF3Nzcp+5z9OhRjBgxAuPGjcO5c+cwcOBADBw4EJcuXarDyqumOuMDSt8+u/z1u3XrVh1VrLlGjRrh888/x5kzZ3D69Gl0794dAwYMwOXLlyvdXpeuH6D5+ADdun7lnTp1CitWrICvr+8zt9O1a1imquMDdO8aNm/eXK3ew4cPP3Vb0a6fQCoAhN9+++2Z23zwwQdC8+bN1ZYNGzZMCAkJqcXKakZVxvf3338LAIT79+/XSU01LTU1VQAgHDx48KnbDB06VOjXr5/asoCAAOHNN9+s7fJeWFXGt3btWsHS0rLuiqoFDRo0EH788cdK1+ny9SvzrPHp6vXLyckRvLy8hPDwcKFr167CO++889RtdfEaajI+XbuGc+fOFfz8/Kq8vVjXr953WDR17NgxBAcHqy0LCQnBsWPHRKqodrRq1QpOTk7o2bMnjhw5InY5VZaVlQUAsLa2fuo2unwNqzI+AHjw4AFcXV3h4uLy3N/mtUlJSQm2bNmC3NxcBAYGVrqNLl+/qowP0M3rN3nyZPTr16/CtamMLl5DTcYH6N41jI6OhrOzM9zd3TFy5Ejcvn37qduKdf304tOa61JycjIcHBzUljk4OCA7OxsPHz6EsbGxSJXVDCcnJyxfvhxt27ZFQUEBfvzxRwQFBeHEiRNo06aN2OU9k1KpxPTp0/Hyyy+jRYsWT93uaddQW+fplKnq+Ly9vbFmzRr4+voiKysLX3/9NTp27IjLly/X+qeaV1dUVBQCAwORn58PMzMz/Pbbb2jWrFml2+ri9dNkfLp4/bZs2YKzZ8/i1KlTVdpe166hpuPTtWsYEBCAdevWwdvbG0lJSZg/fz46d+6MS5cuwdzcvML2Yl0/BhZS4+3tDW9vb9XXHTt2RExMDL777jts3LhRxMqeb/Lkybh06dIzX3vVZVUdX2BgoNpv7x07dsRLL72EFStW4JNPPqntMqvF29sb58+fR1ZWFn755ReMGTMGBw8efOqTuq7RZHy6dv0SEhLwzjvvIDw8XKsnllZXdcana9ewT58+qr/7+voiICAArq6u2LZtG8aNGydiZeoYWDTk6OiIlJQUtWUpKSmwsLDQ+e7K07Rv317rQ8CUKVOwa9cuHDp06Lm/wTztGjo6OtZmiS9Ek/E9ydDQEK1bt8bNmzdrqboXZ2RkBE9PTwCAv78/Tp06hUWLFmHFihUVttXF66fJ+J6k7dfvzJkzSE1NVevAlpSU4NChQ1i8eDEKCgogk8nU9tGla1id8T1J26/hk6ysrNC0adOn1ivW9eMcFg0FBgYiIiJCbVl4ePgzX4/WdefPn4eTk5PYZVRKEARMmTIFv/32Gw4cOIAmTZo8dx9duobVGd+TSkpKEBUVpbXXsDJKpRIFBQWVrtOl6/c0zxrfk7T9+vXo0QNRUVE4f/686tG2bVuMHDkS58+fr/TJXJeuYXXG9yRtv4ZPevDgAWJiYp5ar2jXr1an9OqAnJwc4dy5c8K5c+cEAMK3334rnDt3Trh165YgCIIwa9YsYdSoUartY2NjBRMTE+H9998Xrl69KixZskSQyWTC3r17xRrCM2k6vu+++07YuXOnEB0dLURFRQnvvPOOIJVKhf3794s1hGeaNGmSYGlpKURGRgpJSUmqR15enmqbUaNGCbNmzVJ9feTIEcHAwED4+uuvhatXrwpz584VDA0NhaioKDGG8EzVGd/8+fOFffv2CTExMcKZM2eE4cOHCwqFQrh8+bIYQ3iuWbNmCQcPHhTi4uKEixcvCrNmzRIkEonw119/CYKg29dPEDQfn65dv8o8eReNrl/DJz1vfLp2Dd99910hMjJSiIuLE44cOSIEBwcLtra2QmpqqiAI2nP96n1gKbuN98nHmDFjBEEQhDFjxghdu3atsE+rVq0EIyMjwd3dXVi7dm2d111Vmo7viy++EDw8PASFQiFYW1sLQUFBwoEDB8QpvgoqGxsAtWvStWtX1XjLbNu2TWjatKlgZGQkNG/eXNi9e3fdFl5F1Rnf9OnThcaNGwtGRkaCg4OD0LdvX+Hs2bN1X3wVvfHGG4Krq6tgZGQk2NnZCT169FA9mQuCbl8/QdB8fLp2/Srz5BO6rl/DJz1vfLp2DYcNGyY4OTkJRkZGQsOGDYVhw4YJN2/eVK3XlusnEQRBqN0eDhEREdGL4RwWIiIi0noMLERERKT1GFiIiIhI6zGwEBERkdZjYCEiIiKtx8BCREREWo+BhYiIiLQeAwsRERFpPQYWIiIi0noMLERERKT1GFiIiIhI6/0/kVpWycYTW4oAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "to_plot = []\n",
+    "params = best_params.copy()\n",
+    "for baseline_p in [1e-1, 1e-2, 1e-3, 1e-4, 1e-5]:\n",
+    "    params['baseline_p'] = baseline_p\n",
+    "    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
+    "    to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
+    "_ = plt.plot([1, 2, 3, 4, 5], to_plot)\n",
+    "_ = plt.title(\"Impact of varying baseline ps [1e-x]\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:08:10.487807400Z",
+     "start_time": "2024-04-29T13:07:28.454989200Z"
+    }
+   },
+   "id": "5562f44317c1d060",
    "execution_count": 14
   },
   {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "to_plot = []\n",
+    "params = best_params.copy()\n",
+    "for smooth_polyorder in range(3, 15):\n",
+    "    params['smooth_polyorder'] = smooth_polyorder\n",
+    "    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
+    "    to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
+    "_ = plt.plot(range(3, 15), to_plot)\n",
+    "_ = plt.title(\"Impact of varying smoothing polynomial order\")"
+   ],
    "metadata": {
+    "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-04-16T18:01:59.073661Z",
-     "start_time": "2024-04-16T18:01:57.766415Z"
+     "end_time": "2024-04-29T13:09:41.590715700Z",
+     "start_time": "2024-04-29T13:08:10.478203400Z"
     }
    },
+   "id": "e763853b27eb8b33",
+   "execution_count": 15
+  },
+  {
    "cell_type": "code",
-   "source": [
-    "clf = RandomForestClassifier()\n",
-    "print(np.mean(cross_val_score(clf, 0*X_train, truth_train.to_numpy().ravel(), cv=10)))"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
    ],
-   "id": "59fa3f4c120b9255",
+   "source": [
+    "to_plot = []\n",
+    "params = best_params.copy()\n",
+    "for smooth_window_length in range(7, 21):\n",
+    "    params['smooth_window_length'] = smooth_window_length\n",
+    "    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
+    "    to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10)[1])\n",
+    "_ = plt.plot(range(7, 21), to_plot)\n",
+    "_ = plt.title(\"Impact of varying smoothing window size\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:11:25.914335900Z",
+     "start_time": "2024-04-29T13:09:41.581935700Z"
+    }
+   },
+   "id": "710747d57aa84b92",
+   "execution_count": 16
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "to_plot = []\n",
+    "params = best_params.copy()\n",
+    "for max_depth in range(1, 21):\n",
+    "    params['max_depth'] = max_depth\n",
+    "    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
+    "    to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10))\n",
+    "_ = plt.plot(range(1, 21), to_plot)\n",
+    "_ = plt.title(\"Impact of varying tree depth\")\n",
+    "_ = plt.legend([\"training loss\", \"validation loss\"])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:13:40.774246600Z",
+     "start_time": "2024-04-29T13:11:25.903010900Z"
+    }
+   },
+   "id": "8b4cbe0b798df349",
+   "execution_count": 17
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "to_plot = []\n",
+    "params = best_params.copy()\n",
+    "for n_estimators in range(1, 201, 10):\n",
+    "    params['n_estimators'] = n_estimators\n",
+    "    process_params = {key: params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    classifier_params = {key: params[key] for key in best_params.keys() if key not in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "    X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=True)\n",
+    "    to_plot.append(evaluate_classifier_params(RandomForestClassifier, classifier_params, X_train, truth_train, X_test, truth_test, iters=10))\n",
+    "_ = plt.plot(range(1, 201, 10), to_plot)\n",
+    "_ = plt.title(\"Impact of varying number of trees\")\n",
+    "_ = plt.legend([\"training loss\", \"validation loss\"])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:16:03.256973700Z",
+     "start_time": "2024-04-29T13:13:40.758410700Z"
+    }
+   },
+   "id": "a12b68fb6514154c",
+   "execution_count": 18
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Removing data"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "cb73da091b1f3d9c"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "process_params = {key: best_params[key] for key in ['baseline_lam', 'baseline_p', 'smooth_window_length', 'smooth_polyorder']}\n",
+    "X_train, X_test = process_train_test(process_params, experiments_train, metadata_train, experiments_test, metadata_test, scale=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:16:05.098757100Z",
+     "start_time": "2024-04-29T13:16:03.242224700Z"
+    }
+   },
+   "id": "ea25ffb654d57354",
+   "execution_count": 19
+  },
+  {
+   "cell_type": "code",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.35420899854862115\n"
+      "0 1748 0.9809829059829058\n",
+      "0.05 1748 0.9810541310541309\n",
+      "0.1 1009 0.9829059829059827\n",
+      "0.15 455 0.9886752136752135\n",
+      "0.2 298 0.9849002849002849\n",
+      "0.25 202 0.9849002849002849\n",
+      "0.3 134 0.9867521367521366\n",
+      "0.35 62 0.9621082621082617\n",
+      "0.4 42 0.9544159544159543\n",
+      "0.45 31 0.9542735042735042\n",
+      "0.5 19 0.9467236467236466\n",
+      "0.55 9 0.9335470085470087\n"
      ]
     }
    ],
-   "execution_count": 15
+   "source": [
+    "param_grid = {\n",
+    "    'n_estimators': range(1, 201, 100),\n",
+    "    'max_depth': range(5, 21, 5)\n",
+    "}\n",
+    "results = []\n",
+    "X_size = []\n",
+    "for min_std in [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55]:\n",
+    "    to_drop = X_train.std() > min_std\n",
+    "    X_train_small = X_train.loc[:, to_drop]\n",
+    "    X_size.append(len(X_train_small.columns))\n",
+    "\n",
+    "    scaler = StandardScaler()\n",
+    "    scaler.fit(X_train_small)\n",
+    "    X_train_small = scaler.transform(X_train_small)\n",
+    "    \n",
+    "    hyper_results = []\n",
+    "    for params in ParameterGrid(param_grid):\n",
+    "        try:\n",
+    "            clf = RandomForestClassifier(**params)\n",
+    "            hyper_results.append([params, cross_validate(clf, X_train_small, truth_train.to_numpy().ravel(), cv=20, return_estimator=True)])\n",
+    "        except Exception as e:\n",
+    "            pass  # print(params, e)\n",
+    "        \n",
+    "    crossval_res = 0\n",
+    "    best_params = dict()\n",
+    "    for r in hyper_results:\n",
+    "        mean = np.mean(r[1]['test_score'])\n",
+    "        if mean > crossval_res:\n",
+    "            crossval_res = mean\n",
+    "            best_params = r[0]\n",
+    "    \n",
+    "    print(min_std, X_size[-1], crossval_res)\n",
+    "    results.append(crossval_res)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:26:42.086233700Z",
+     "start_time": "2024-04-29T13:22:45.050103200Z"
+    }
+   },
+   "id": "8e64395e456294ff",
+   "execution_count": 21
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55], X_size)\n",
+    "_ = plt.title(\"Amount of features\")\n",
+    "_ = plt.xlabel(\"minimum standard deviation\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:26:54.764759600Z",
+     "start_time": "2024-04-29T13:26:54.647378500Z"
+    }
+   },
+   "id": "6c47122fe8c7be5a",
+   "execution_count": 23
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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rVmLcuHEiPz9fNyYnJ0dERUUJFxcXoVQqRceOHUVMTIzecSqXNX/88cfV1pOeni7Gjh0r3N3dhaWlpfD09BRDhgwRGzZsuON5EElJJkQ9/TONiIiIyEg4h4WIiIhMHgMLERERmTwGFiIiIjJ5DCxERERk8hhYiIiIyOQxsBAREZHJazKN47RaLS5evAh7e3uDv4CNiIiIpCGEwNWrV+Hh4VHliz1v1WQCy8WLF+Hl5SV1GURERFQHmZmZaNWqVY3vN5nAUtmSOjMzEw4ODhJXQ0RERLVRVFQELy+vu361RJMJLJWPgRwcHBhYiIiIGpm7TefgpFsiIiIyeQwsREREZPLqFFiWLVsGHx8fqFQq9OjRA/v27atxbHl5OebMmQM/Pz+oVCoEBwcjISFBb8zVq1fxyiuvwNvbG9bW1ujVqxf2799fl9KIiIioCTI4sKxbtw5Tp07FrFmzcPDgQQQHByMiIgK5ubnVjo+OjsaKFSuwdOlSHDt2DJMmTUJkZCQOHTqkG/Pss89iy5YtiIuLw5EjR/Dwww8jPDwcFy5cqPuZERERUZMhE0IIQ3bo0aMHunXrhs8++wxARf8TLy8vvPjii5g+fXqV8R4eHnj77bcxefJk3bbhw4fD2toaa9aswfXr12Fvb48ff/wRgwcP1o0JCQnBI488gvfee69WdRUVFcHR0RGFhYWcdEtERNRI1Pbvb4PusJSVlSE5ORnh4eH/HkAuR3h4OJKSkqrdp7S0FCqVSm+btbU1du3aBQBQq9XQaDR3HFPTcYuKivReRERE1DQZFFjy8/Oh0Wjg5uamt93NzQ3Z2dnV7hMREYFFixYhNTUVWq0WW7ZsQXx8PLKysgBU9E/p2bMn5s6di4sXL0Kj0WDNmjVISkrSjanOvHnz4OjoqHuxaRwREVHTVe+rhJYsWYKAgAAEBQVBqVRiypQpiIqK0mu/GxcXByEEPD09YWVlhU8//RSjRo26Y4veGTNmoLCwUPfKzMys71MhIiIiiRgUWFxcXKBQKJCTk6O3PScnB+7u7tXu4+rqik2bNqG4uBhnz57FiRMnYGdnB19fX90YPz8/bN++HdeuXUNmZib27duH8vJyvTG3s7Ky0jWJY7M4IiKips2gwKJUKhESEoLExETdNq1Wi8TERPTs2fOO+6pUKnh6ekKtVmPjxo0YNmxYlTG2trZo2bIlLl++jM2bN1c7hoiIiMyPwa35p06dinHjxiE0NBTdu3fH4sWLUVxcjKioKADA2LFj4enpiXnz5gEA9u7diwsXLqBz5864cOECZs+eDa1Wi2nTpumOuXnzZgghcN999yEtLQ1vvPEGgoKCdMckIiIi82ZwYBk5ciTy8vIwc+ZMZGdno3PnzkhISNBNxD137pze3JMbN24gOjoaGRkZsLOzw6BBgxAXFwcnJyfdmMLCQsyYMQPnz59Hs2bNMHz4cLz//vuwtLS89zMkIiKiRs/gPiymin1Y6F6pNVqotQIqS4XUpRARmY3a/v3dZL6tmehenMkvxjOr9+PilRt4rq8vJvbzhY2S//cgIjIV/PJDMnv7z1xC5Oe7kZ5XjOvlGixJTMWABduw/kAmtNomcQOSiKjRY2Ahs/ZjygWMXrkXl0vKEdzKER8/3glezayRU1SKNzb8jaGf7cJf6flSl0lEZPY4h4XMkhACnyam4ZM/TgEABrZ3xycjO8NaqUCpWoPVf53B0sQ0XC1VAwDC27phxqAg+LnaSVk2EVGTU9u/vxlYyOyUqjWYsfEI4g9VfBv4xL6+eHNgEORymd64gmulWJKYirV7z0GjFbCQy/DU/d54+cEAONsqpSidiKjJYWAhqsaVkjI8F5eMfacvQSGXYe6wDniyR+s77pOWexXzfj2BxBO5AAAHlQVeejAAY3p6w8qCK4qIiO4FAwvRbc7kF+Pp2P3IyC+GvZUFlo3uir6BrrXef3daPt775TiOZ1V8M3jrZjaY8UgQBnZwh0wmu8veRERUHQYWolscOHMJE74+gMsl5fB0ssaq8d1wn7u9wcfRaAU2Jp/Hx7+fRN7VUgBANx9nRA9uh2AvJyNXTUTU9DGwEN30Y8oFvLH+b5RptAhu5YiV40LRwl51T8csLlVjxY4MfLkjHTfKtQCAxzp74I2BQfB0sjZG2UREZoGBhcyeEAKf/ZmGhVsqVgJFtHfD4pFdYK003ryTrMLr+HjzScQfrJjAa2Uhx7NhbfB8f3/YWbHxHBHR3TCwkFkrU2sxI/4INh48DwB4rq8vplezEshYjpwvxHu/HMPe05cAAC52Vnjt4UCMCPWCop4+k4ioKWBgIbN1paQMk9YkY09GxUqgOcPaY3QP73r/XCEEfj+Wg3m/HseZghIAQJC7Pd4a1Nagyb1Uv26Ua7D/zCXYqyzRmfOOiCTHwEJm6WxBMaJiKlYC2d1cCdSvgcNCmVqLNXvOYkliKgqvlwMA+t/nircGtUWgm+ETfeneCCFwMucqdp7Kx47UPOw9fQllai0s5DJsfb0/vJrZSF0ikVljYCGzc+DMJTwXl4xLxWXwdLLGV+NDEeQu3e+FKyVl+DQxDV8nnYFaKyCXAaO6t8arDwXCxc5KsrrMQcG1UuxKy8eOU/nYmZqH3JsruirJZIAQwHuPdcBT99f/3TciqhkDC5mVH1Mu4I0Nf6NMrUWnVo7439hQtHC4t5VAxnI6vxjzfzuOzUdzAAB2VhZ4YYAfnu7dBipLNp4zhjK1FgfPXcaOU3nYmZqPfy4W4tY/2VSWctzv2xxhAa7oF+iC345kY+GWU4ho74YVY0KlK5yIGFjIPAghsGxrGhb8/u9KoE9GdoaN0vRW6OzJKMD7vxzHkQuFAABPJ2u8+UgQhnZqycZzBhJC4HR+MXam5mPHqTwkZRSgpEyjN6ZtSwf0DXBB30BXhHg764XDlMwreGzZbtirLHDonYdgoeD3wBJJhYGFmrwytRZv/XAEG5IrVgJNCGuD6Y+0NelVOVqtwKaUC/go4SSyi24AALq0dkL04HYI8XaWuDrTVni9HEnp+dh+8zHP+cvX9d5vbqtE2M2A0sff5Y532DRaga5zt6Dwejk2Pt+L155IQrX9+9v0/hlKVAuFJeWYtCYZSRkFUMhlePfR9o1iLoJcLsN/urbCIx1a4n87M/DF9nQcOncFw7/4C4M7tcT0gUGcBHqTWqPF3xcKdY95UjKvQKP9999XlgoZQr2boW+gK8ICXNCupUOtl60r5DL08XfBL0eysDM1j4GFqBHgHRZqdM4VlGB87D5k5Em3EshYcotuYOHvp/B9ciaEAJQKOaJ6++CFAf5wtLaUurwGd+HK9ZsBJQ+7UvNRdEOt976vqy36Briib6AL7vdtfk+P/r7bdw7T448gxNsZG5/vda+lE1Ed8ZEQNUnJZy9hwtcVK4E8HFVYFdVN0pVAxnLsYhHe//UYdqcVAACa2SrxSngARnVvDcsmPL+iuFSNvacLsOPmkuOMvGK99x1UFugT4IK+Aa7oE+CCVs7Gu/t0/nIJ+ny4FQq5DAffecgsAyKRKWBgoSbn/w5fxGvrD6NMrUVHT0d8Nc50VgIZgxACW0/m4v1fjiP95l/cfq62eHtwWwy4r0WTmJir1QocyyrCjtQ87DyVjwNnL6Fc8+8fQQq5DJ29nNA3wBVhgS4IbuVUr3OSHli4DRl5xVj+VAgGdnCvt88hoppxDgs1GUIIfL4tHR9vPgkAeKidG5b81zRXAt0LmUyGB4LcEBbgiu/2ncMnf6QiPa8YT8ceQG//5nh7UDu082h8YTz36g3svDlRdldaPvKvlem938rZGn0DXdE3wAU9/Vwa9E5H3wBXZOQVY2dqHgMLkYnjHRYyabevBHq2TxvMGGTaK4GMpfB6OT7fmoaY3WdQptFCJgNGhHhhQl9f2BjxCxyNTQA4fTMEbD+VhxPZV/Xet1Eq0MuvoidK30BX+DS3kezu0R/HcvDs1wfQupkNdkwbIEkNROaOj4So0bt1JZBcBrw7rAPGNIKVQMaWeakE8xNO4Je/s6Qupc46ejrqlhx3be0MpYVpzMu5VqpG53d/h1orsP2N/vBubit1SURmh4+EqFE7V1CCqNh9SM8rhq1Sgc9Gd8WA+1pIXZYkvJrZYNmTXfF070uY/9sJHM4slLqku2pmq0Rvfxf0DXRBH38XNDfRryKws7JAV29n7Dt9CTtT8xlYiEwYAwuZnOSzl/Hc1wdQUFyGlo4qrBrfDW1b8q5ZiHczrJ/E5bfG1jfA5WZgyWsUvXyIzJVp3Jcluunnvy9i1Mo9KCguQwdPB2ya3JthhepVWEBFD5+/0gqg1mglroaIasLAQiah8juBpnxzCGVqLcLbuuH7iT3h1oSWLZNp6uDpCCcbS1wtVePw+StSl0NENWBgIcmVqbV4c+PfumXLz/RpgxVjQprcsmUyTQq5DL39XQAAO07lS1wNEdWEgYUkVXi9HONj9uH7A+chlwFzh7XHO0PamcWyZTIdYTcDy87UPIkrIaKa8J+wJJnMSyWIit2PtNxrFSuBnuyKAUHmuRKIpNUnoCKwHD5fiMLr5WzTT2SCeIeFJHHw3GU8tmw30nKvoaWjCusn9WJYIcm0craBr6stNFqBpPQCqcshomowsFCD++XvLIz6smIlUHuPipVAjbHlPDUtfW+uFuJjISLTxMBCDabiO4HSMPmbgyhVaxHetgVXApHJCAuonMfCibdEpohzWKhBlGu0iP7hH6w7kAkAiOrtg+jBnFxLpuN+3+awVMhw7lIJzhYUs+stkYlhYKF6c6Nco+sgmng8Fxn5xZDLgFlD22NcLx+pyyPSY2tlga6tnbH39CXsSM3HGAYWIpPCwEJGI4TAqZxr2HEqDztS87Dv9CWUqv/tHGpnZYFPR3XGA0FuElZJVLO+ga7Ye/oSdp7KM8sv2iQyZQwsdE8uFZdhZ2oedqbmY2dqHnKKSvXeb+mo0n1Lb5i/KxxtuFyUTFcffxd8vPkkktIr2vRbKDjNj8hUMLCQQcrUWhw8dxk7U/Ow41Q+/rlYCCH+fV9lKUePNs3RN9AVfQNc4N/CDjIZ56lQ41DZpv9KSTkOn7+CEO9mUpdERDcxsNAdCSFwpqDkZkDJQ1J6AYrLNHpj2rZ0QN+bd1FCvJ2hslRIVC3Rvals0//L31nYcSqfgYXIhDCwUBVFN8rxV1oBdqTmYWdqHjIvXdd7v7mtUveYp4+/C1pwWTI1IX0DKgLLztQ8vPpQoNTlENFNDCwEjVbg8Pkr2HmqYh7Kocwr0Gj/fc5jqZAh1LtZxTyUABe0a+kAOZcjUxPV52YDuZTMK2zTT2RCGFjM1IUr17Hz5mqe3WkFKLxerve+r6st+ga4om+gC3q0aQ5bK/5WIfPg6WQNP1dbpOcVIyk9HwM7tJS6JCICA4vZKClTY2/GJWw/VfGYJz2vWO99B5UF+gS4ICyg4i5KK2cbiSolkl5YgCvS84qxI5WBhchUMLA0UVqtwPHsIuy4+ZjnwJnLKNP82xNFLgO6tHbWzUXp5OnIJZxEN4UFuCD2rzP8XiEiE8LA0oTkXr2BXan5up4o+dfK9N73dLJG30BX9At0QU8/Fz6bJ6pBZZv+zEvX2aafyEQwsNzFoXOXUarWQisEtFpAI8TNXwtotDd/LXDLrwU02oo7HFohKsbrxuLm+7f/+uaYm8eqHK8RAuLmsSt+fXO7tmK5seaW/U/nl+B4VpFe7TZKBXr5NUdYgCv6BrrCp7kNe6IQ1QLb9BOZHgaWu5jwdTLyr5XefaCJ6OjpqHvM07W1M5QWfMxDVBds009kWhhY7sKnuQ0crC2gkMkgl8kgl8ugkKPi1zIZFHIZFDIZZLKKplMKuQwymQyKmz/fOk4ul0EuQ8Wxbu4nv3ks/bG4OVamG6u3382fb92vma0Svfyao7mdldSXjKhJCAv4t01/uUYLS87xIpIUA8tdbHi+l9QlEJEE2ns4wtnGEpdLynE48wpCfdj1lkhKdfonw7Jly+Dj4wOVSoUePXpg3759NY4tLy/HnDlz4OfnB5VKheDgYCQkJOiN0Wg0eOedd9CmTRtYW1vDz88Pc+fOhbj1S2qIiBpQZZt+ANiRmi9xNURkcGBZt24dpk6dilmzZuHgwYMIDg5GREQEcnNzqx0fHR2NFStWYOnSpTh27BgmTZqEyMhIHDp0SDfmww8/xBdffIHPPvsMx48fx4cffoiPPvoIS5curfuZERHdo7CAisDC5c1E0pMJA29j9OjRA926dcNnn30GANBqtfDy8sKLL76I6dOnVxnv4eGBt99+G5MnT9ZtGz58OKytrbFmzRoAwJAhQ+Dm5oavvvqqxjF3U1RUBEdHRxQWFsLBwcGQUyIiqtaFK9fRe/6fkMuAQzMfZisAonpQ27+/DbrDUlZWhuTkZISHh/97ALkc4eHhSEpKqnaf0tJSqFT6X45nbW2NXbt26X7u1asXEhMTcerUKQDA4cOHsWvXLjzyyCM11lJaWoqioiK9FxGRMVW26dcKICmdj4WIpGRQYMnPz4dGo4Gbm5vedjc3N2RnZ1e7T0REBBYtWoTU1FRotVps2bIF8fHxyMrK0o2ZPn06/vvf/yIoKAiWlpbo0qULXnnlFYwePbrGWubNmwdHR0fdy8vLy5BTISKqlbCbX4bIeSxE0qr3dXpLlixBQEAAgoKCoFQqMWXKFERFRUEu//ejv//+e6xduxbffPMNDh48iNWrV2PBggVYvXp1jcedMWMGCgsLda/MzMz6PhUiMkN9A29OvD2Vx4UARBIyaFmzi4sLFAoFcnJy9Lbn5OTA3d292n1cXV2xadMm3LhxAwUFBfDw8MD06dPh6+urG/PGG2/o7rIAQMeOHXH27FnMmzcP48aNq/a4VlZWsLJizxEiql892lS06T9/+TrOFpTAx4Vdb4mkYNAdFqVSiZCQECQmJuq2abVaJCYmomfPnnfcV6VSwdPTE2q1Ghs3bsSwYcN075WUlOjdcQEAhUIBrVZ7+2GIiBqUrZUFQrydAXC1EJGUDH4kNHXqVKxcuRKrV6/G8ePH8fzzz6O4uBhRUVEAgLFjx2LGjBm68Xv37kV8fDwyMjKwc+dODBw4EFqtFtOmTdONGTp0KN5//3388ssvOHPmDH744QcsWrQIkZGRRjhFIqJ7w3ksRNIzuNPtyJEjkZeXh5kzZyI7OxudO3dGQkKCbiLuuXPn9O6W3LhxA9HR0cjIyICdnR0GDRqEuLg4ODk56cYsXboU77zzDl544QXk5ubCw8MDEydOxMyZM+/9DImI7hHb9BNJz+A+LKaKfViIqL5otAKh723B5ZJybJjUk236iYyoXvqwEBGZI7bpJ5IeAwsRUS30vTmPhRNviaTBwEJEVAt9bn6v0OHMKygsKZe4GiLzw8BCRFQLHk7W8G9hB60A/mKbfqIGx8BCRFRLld/ezHksRA2PgYWIqJYqA8vOVLbpJ2poDCxERLV0e5t+Imo4DCxERLXENv1E0mFgISIyANv0E0mDgYWIyACV/Vgq2/QTUcNgYCEiMkB7Dwc421jiWqkaKZlXpC6HyGwwsBARGUAul6FPZdfbU5zHQtRQGFiIiAwUxu8VImpwDCxERAaqbNP/93m26SdqKAwsREQGYpt+oobHwEJEVAds00/UsBhYiIjqoHJ5845TbNNP1BAYWIiI6qCHbzNYKmS4cOU6zrBNP1G9Y2AhIqoDG6UFQr2bAWCbfqKGwMBCRFRHYYE357Gc4jwWovrGwEJEVEdh/hXzWPZksE0/UX1jYCEiqiO26SdqOAwsRER1xDb9RA2HgYWI6B6wHwtRw2BgISK6B2G3tOm/UlImcTVETRcDCxHRPWjpaI0AXZv+AqnLIWqyGFiIiO5RWOU8FvZjIao3DCxERPdIN4/lVD7b9BPVEwYWIqJ7dGub/tP5xVKXQ9QkMbAQEd2jW9v070rjaiGi+sDAQkRkBGzTT1S/GFiIiIyg782Jt0np+WzTT1QPGFiIiIygXUsHNLNVorhMg0PnrkhdDlGTw8BCRGQEcrkMffwrHgtxeTOR8TGwEBEZSR+26SeqNwwsRERGwjb9RPWHgYWIyEgq2/QLtuknMjoGFiIiI2KbfqL6wcBCRGREt/ZjYZt+IuNhYCEiMqIebZpBqZCzTT+RkTGwEBEZkY3SAqE+zgCAnVwtRGQ0DCxEREbGeSxExsfAQkRkZJXLm5PSC1CmZpt+ImNgYCEiMjL9Nv2XpS6HqElgYCEiMrJb2/TvSuM8FiJjYGAhIqoHYWzTT2RUDCxERPWgcuIt2/QTGQcDCxFRPXB3VCHQraJN/+40tuknulcMLERE9YTLm4mMp06BZdmyZfDx8YFKpUKPHj2wb9++GseWl5djzpw58PPzg0qlQnBwMBISEvTG+Pj4QCaTVXlNnjy5LuUREZmEPjfnsexMZZt+ontlcGBZt24dpk6dilmzZuHgwYMIDg5GREQEcnNzqx0fHR2NFStWYOnSpTh27BgmTZqEyMhIHDp0SDdm//79yMrK0r22bNkCAHjiiSfqeFpERNK7tU1/Btv0E90TmTAw9vfo0QPdunXDZ599BgDQarXw8vLCiy++iOnTp1cZ7+Hhgbffflvvbsnw4cNhbW2NNWvWVPsZr7zyCn7++WekpqZCJpPVqq6ioiI4OjqisLAQDg4OhpwSEVG9eXLlHvyVXoB3H22Pcb18pC6HyOTU9u9vg+6wlJWVITk5GeHh4f8eQC5HeHg4kpKSqt2ntLQUKpVKb5u1tTV27dpV42esWbMGTz/9dK3DChGRqeI8FiLjMCiw5OfnQ6PRwM3NTW+7m5sbsrOzq90nIiICixYtQmpqKrRaLbZs2YL4+HhkZWVVO37Tpk24cuUKxo8ff8daSktLUVRUpPciIjI1bNNPZBz1vkpoyZIlCAgIQFBQEJRKJaZMmYKoqCjI5dV/9FdffYVHHnkEHh4edzzuvHnz4OjoqHt5eXnVR/lERPekXUsHNGebfqJ7ZlBgcXFxgUKhQE5Ojt72nJwcuLu7V7uPq6srNm3ahOLiYpw9exYnTpyAnZ0dfH19q4w9e/Ys/vjjDzz77LN3rWXGjBkoLCzUvTIzMw05FSKiBiGXy/RWCxFR3RgUWJRKJUJCQpCYmKjbptVqkZiYiJ49e95xX5VKBU9PT6jVamzcuBHDhg2rMiYmJgYtWrTA4MGD71qLlZUVHBwc9F5ERKao8nuFOI+FqO4sDN1h6tSpGDduHEJDQ9G9e3csXrwYxcXFiIqKAgCMHTsWnp6emDdvHgBg7969uHDhAjp37owLFy5g9uzZ0Gq1mDZtmt5xtVotYmJiMG7cOFhYGFwWEZHJ0rXpv1CIy8VlcLZVSlwRUeNjcDIYOXIk8vLyMHPmTGRnZ6Nz585ISEjQTcQ9d+6c3vyUGzduIDo6GhkZGbCzs8OgQYMQFxcHJycnveP+8ccfOHfuHJ5++ul7OyMiIhNT2ab/VM417E7Px5BOd56jR0RVGdyHxVSxDwsRmbK5Px/DV7tO47/dvDB/eCepyyEyGfXSh4WIiOomjG36ie4JAwsRUQPo0aY52/QT3QMGFiKiBmCtVKBbG2cAwM5TXC1EZCgGFiKiBvJvm372YyEyFAMLEVEDqezHkpTBNv1EhmJgISJqIJVt+kvKNDjINv1EBmFgISJqILe26d/Fx0JEBmFgISJqQP/OY+HEWyJDMLAQETWgyn4slW36iah2GFiIiBqQm4MK97nZQwhgdzofCxHVFgMLEVED03W9PcXAQlRbDCxERA2sj65Nfx7b9BPVEgMLEVEDq2zTf7HwBtLz2KafqDYYWIiIGphem36uFiKqFQYWIiIJVC5vZj8WotphYCEikkDlxFu26SeqHQYWIiIJtHV3gIsd2/QT1RYDCxGRBORyme7LEDmPhejuGFiIiCTSR9emn/NYiO6GgYWISCKV81iOXCjEJbbpJ7ojBhYiIonotelP410WojthYCEiklDlXRYubya6MwYWIiIJhQVWzmNhm36iO2FgISKSUHefZlBasE0/0d0wsBARSchaqUB3n2YAuLyZ6E4YWIiIJBam+/ZmzmMhqgkDCxGRxPpUtulPL0CpWiNxNUSmiYGFiEhilW36r5drcPDsFanLITJJDCxERBK7tU3/rjTOYyGqDgMLEZEJCGObfqI7YmAhIjIBbNNPdGcMLEREJqCFgwpB7mzTT1QTBhYiIhPx7/JmzmMhuh0DCxGRiehzyzwWtukn0sfAQkRkIirb9GcV3kB63jWpyyEyKQwsREQm4tY2/TtOcR4L0a0YWIiITEjlPJZdnHhLpIeBhYjIhFT2Y2GbfiJ9DCxERCYkyN0eLnZWbNNPdBsGFiIiEyKXy7i8magaDCxERCam8nuF2Kaf6F8MLEREJqbyDss/FwuRknlF2mKITAQDCxGRiWnhoEJEezcIATy7ej/OFZRIXRKR5BhYiIhM0MIRndGupQPyr5VhfOw+XOYXIpKZY2AhIjJBdlYWiInqBg9HFTLyivFc3AHcKOcyZzJfDCxERCbKzUGF2Ke7w15lgf1nLuO19Yeh1fI7hsg8MbAQEZmwQDd7rHgqBJYKGX75OwsfJpyQuiQiSTCwEBGZuF7+Lvjo8U4AgBU7MhCXdEbagogkwMBCRNQIRHZphdceCgQAzPrpKP44liNxRUQNi4GFiKiRmPKAP0aGekErgBe/PYTD7NFCZqROgWXZsmXw8fGBSqVCjx49sG/fvhrHlpeXY86cOfDz84NKpUJwcDASEhKqjLtw4QKeeuopNG/eHNbW1ujYsSMOHDhQl/KIiJokmUyG9yI7oG+gK66Xa/DM6v3IvMQeLWQeDA4s69atw9SpUzFr1iwcPHgQwcHBiIiIQG5ubrXjo6OjsWLFCixduhTHjh3DpEmTEBkZiUOHDunGXL58Gb1794alpSV+++03HDt2DAsXLoSzs3Pdz4yIqAmyVMjx+eiuuh4t42L24UoJe7RQ0ycTQhi0Rq5Hjx7o1q0bPvvsMwCAVquFl5cXXnzxRUyfPr3KeA8PD7z99tuYPHmybtvw4cNhbW2NNWvWAACmT5+O3bt3Y+fOnXU+kaKiIjg6OqKwsBAODg51Pg4RUWOQU3QDkct242LhDXT3aYavn+kOlaVC6rKIDFbbv78NusNSVlaG5ORkhIeH/3sAuRzh4eFISkqqdp/S0lKoVCq9bdbW1ti1a5fu559++gmhoaF44okn0KJFC3Tp0gUrV640pDQiIrPi5qBCTFR32FtZYN+ZS3hjw9/s0UJNmkGBJT8/HxqNBm5ubnrb3dzckJ2dXe0+ERERWLRoEVJTU6HVarFlyxbEx8cjKytLNyYjIwNffPEFAgICsHnzZjz//PN46aWXsHr16hprKS0tRVFRkd6LiMic3Oduj+VjQmAhl+H/Dl/ER5tPSl0SUb2p91VCS5YsQUBAAIKCgqBUKjFlyhRERUVBLv/3o7VaLbp27YoPPvgAXbp0wXPPPYcJEyZg+fLlNR533rx5cHR01L28vLzq+1SIiExOb38XfDi8okfL8u3piNtzVuKKiOqHQYHFxcUFCoUCOTn66/9zcnLg7u5e7T6urq7YtGkTiouLcfbsWZw4cQJ2dnbw9fXVjWnZsiXatWunt1/btm1x7ty5GmuZMWMGCgsLda/MzExDToWIqMkYHtIKUyt7tPz4DxKPs0cLNT0GBRalUomQkBAkJibqtmm1WiQmJqJnz5533FelUsHT0xNqtRobN27EsGHDdO/17t0bJ0/q38o8deoUvL29azyelZUVHBwc9F5ERObqxQf8MSK0FbQCmPLNIfx9/orUJREZlcGPhKZOnYqVK1di9erVOH78OJ5//nkUFxcjKioKADB27FjMmDFDN37v3r2Ij49HRkYGdu7ciYEDB0Kr1WLatGm6Ma+++ir27NmDDz74AGlpafjmm2/w5Zdf6q0sIiKimslkMrwf2RFhAS64Xq7B07EH2KOFmhSDA8vIkSOxYMECzJw5E507d0ZKSgoSEhJ0E3HPnTunN6H2xo0biI6ORrt27RAZGQlPT0/s2rULTk5OujHdunXDDz/8gG+//RYdOnTA3LlzsXjxYowePfrez5CIyExU9mhp29IB+ddKMT5mHwpLyqUui8goDO7DYqrYh4WIqEJ24Q1Efr4bWYU30L1NM8Q90x1WFuzRQqapXvqwEBGR6XN3VCEmqltFj5bTl/D6evZoocaPgYWIqAkKcnfAF0/926Pl49/Zo4UaNwYWIqImqk+AC+bf7NHyxbZ0rGGPFmrEGFiIiJqwx0Na4dXwih4tM3/8B3+eYI8WapwYWIiImriXHvTHEyH/9mg5cr5Q6pKIDMbAQkTUxMlkMnzwn4oeLSVlGjy9ej97tFCjw8BCRGQGKnu0BLnbI+9qKaJi97NHCzUqDCxERGbCXmWJmKhucHdQIS33Gp6LO4BStUbqsohqhYGFiMiMtHS0RkxUN9hZWWDv6Ut4gz1aqJFgYCEiMjNtWzrgi6e6wkIuw0+HL2IBe7RQI8DAQkRkhsICXDHvPx0BAJ9vS8c3e89JXBHRnTGwEBGZqSdCvfDygwEAgHd+/AdbT+RKXBFRzRhYiIjM2CvhARjetRU0WoHJ3xzEPxfYo4VMEwMLEZEZk8lkmPefjujjX9GjJSp2P85fZo8WMj0MLEREZk5pIcfnT/3bo2V8DHu0kOlhYCEiIjjc1qNl4hr2aCHTwsBCREQAKnq0rBpf0aNlT8YlvLnhbwjBHi1kGhhYiIhIp52HAz4f3RUKuQybUi5i4e+npC6JCAADCxER3aZvoCvmRVb0aPlsaxq+3cceLSQ9BhYiIqpiRDcvvHSzR0v0pn+w9SR7tJC0GFiIiKhar4YH4D9dPSt6tKxljxaSFgMLERFVSyaTYf5/OqG3f3P2aCHJMbAQEVGNlBZyfPFUCO5zq+jREhWzH4XX2aOFGh4DCxER3VFljxY3Byuk5l7DpLhklKm1UpdFZoaBhYiI7srDqaJHi61SgaSMAry5kT1aqGExsBARUa2093DE50+FQCGX4YdDF7BoC3u0UMOxkLoAIiJqPPoFuuKDyA54c+MRLP0zDUqFHB08HWGpkMNCIYOlQg5LhQwWcjmUFhX/a2khh6VcdtsYORRymdSnQ40IAwsRERlkZLfWuHD5Oj79Mw0L7+Eui0yGivAil8HSQl4RbhT/BhvlrQFHrh+IKsbc/HUN7ykVMlgo5OjUyhG9/FyMeAVICgwsRERksFcfCoSlQo7EE7lQa7VQawTKNVqUawTUGi3KNAJqrRblai3KtRXv3T7lRQigTK1FGQCU1e8XLX40vBNGdPOq18+g+iUTTWTWVFFRERwdHVFYWAgHBwepyyEiottotJWh5paAo60IOJVh59b/VWsEym+GHrX29vdu+bVWoEytrQhIt+6r0SKr8Aa2n8qDQi7D/8aGYkBQC6kvA92mtn9/8w4LERE1CIVcBoVcAZWlosE+UwiB19YfRvzBC3hh7UF899z9CPZyarDPJ+PhKiEiImqyZDIZPhzeCWEBLrhersHTsftxJr9Y6rKoDhhYiIioSbNUVHTrbe/hgILiMoyL2Yf8a6VSl0UGYmAhIqImz87KAjFR3dDK2RpnC0rwdOx+FJeqpS6LDMDAQkREZqGFvQqrn+4OZxtL/H2+EJO/OYhyDb9ioLFgYCEiIrPh52qHr8Z3g8pSjm0n8/D2D0f4FQONBAMLERGZla6tnbF0VFfIZcD3B87jkz9SpS6JaoGBhYiIzM5D7dww97EOAIBPE1Pxzd5zEldEd8PAQkREZml0D2+89IA/ACB60xH8cSxH4oroThhYiIjIbL36UCBGhLaCVgBTvj2Ig+cuS10S1YCBhYiIzJZMJsP7kR0x4D5X3CjX4pnY/cjIuyZ1WVQNBhYiIjJrlgo5lo3uiuBWjrhcUo5xMfuQe/WG1GXRbRhYiIjI7NkoLfDV+G7wbm6DzEvX8XTsflxjYzmTwsBCREQEwMXOCqujuqO5rRL/XCjC82uS2VjOhDCwEBER3eTjYotV47vB2lKBnan5eHPj32wsZyIYWIiIiG4R7OWEz0d3hUIuQ/zBC1jw+0mpSyIwsBAREVUxIKgF5kV2BAAs25qOuKQz0hZEDCxERETVGdHNC1MfCgQAzPzpKBL+yZa4IvPGwEJERFSDFx/wx6jurSEE8PJ3h3DgzCWpSzJbDCxEREQ1kMlkmDusPcLbtkCpWotnVh9AWu5VqcsyS3UKLMuWLYOPjw9UKhV69OiBffv21Ti2vLwcc+bMgZ+fH1QqFYKDg5GQkKA3Zvbs2ZDJZHqvoKCgupRGRERkVBYKOZaO6orOXk4ovF6Ocav2I6eIjeUamsGBZd26dZg6dSpmzZqFgwcPIjg4GBEREcjNza12fHR0NFasWIGlS5fi2LFjmDRpEiIjI3Ho0CG9ce3bt0dWVpbutWvXrrqdERERkZFZKxVYNb4b2rjY4sKV6xgfsx9FN8qlLsusGBxYFi1ahAkTJiAqKgrt2rXD8uXLYWNjg1WrVlU7Pi4uDm+99RYGDRoEX19fPP/88xg0aBAWLlyoN87CwgLu7u66l4uLS93OiIiIqB40s1VidVR3uNhZ4XhWESbFJaNMzcZyDcWgwFJWVobk5GSEh4f/ewC5HOHh4UhKSqp2n9LSUqhUKr1t1tbWVe6gpKamwsPDA76+vhg9ejTOnTtnSGlERET1rnVzG8RGdYOtUoG/0gvwxobD0GrZWK4hGBRY8vPzodFo4Obmprfdzc0N2dnVL/eKiIjAokWLkJqaCq1Wiy1btiA+Ph5ZWVm6MT169EBsbCwSEhLwxRdf4PTp0wgLC8PVqzVPbCotLUVRUZHei4iIqL518HTEF0+FwEIuw48pF/FhwgmpSzIL9b5KaMmSJQgICEBQUBCUSiWmTJmCqKgoyOX/fvQjjzyCJ554Ap06dUJERAR+/fVXXLlyBd9//32Nx503bx4cHR11Ly8vr/o+FSIiIgBA30BXfDi8EwBgxY4MrNp1WuKKmj6DAouLiwsUCgVycnL0tufk5MDd3b3afVxdXbFp0yYUFxfj7NmzOHHiBOzs7ODr61vj5zg5OSEwMBBpaWk1jpkxYwYKCwt1r8zMTENOhYiI6J4MD2mFNyLuAwDM/eUYfvk76y570L0wKLAolUqEhIQgMTFRt02r1SIxMRE9e/a8474qlQqenp5Qq9XYuHEjhg0bVuPYa9euIT09HS1btqxxjJWVFRwcHPReREREDemF/n4Yc783hABeXZeCPRkFUpfUZBn8SGjq1KlYuXIlVq9ejePHj+P5559HcXExoqKiAABjx47FjBkzdOP37t2L+Ph4ZGRkYOfOnRg4cCC0Wi2mTZumG/P6669j+/btOHPmDP766y9ERkZCoVBg1KhRRjhFIiKi+iGTyTD70fZ4uJ0byjRaTPj6AE5ms7FcfbAwdIeRI0ciLy8PM2fORHZ2Njp37oyEhATdRNxz587pzU+5ceMGoqOjkZGRATs7OwwaNAhxcXFwcnLSjTl//jxGjRqFgoICuLq6ok+fPtizZw9cXV3v/QyJiIjqkUIuw6ejumD0//Yi+exljI/Zh/gXeqGlo7XUpTUpMiFEk1iPVVRUBEdHRxQWFvLxEBERNbgrJWUY/sVfSM8rxn1u9vh+Uk84WltKXZbJq+3f3/wuISIiIiNwslFi9dPd0cLeCidzruK5rw+gVK2Ruqwmg4GFiIjISFo52yA2qjvsrCyw9/QlTP2ejeWMhYGFiIjIiNp5OGDFmBBYKmT45e8svP/rcalLahIYWIiIiIyst78LFjwRDAD4atdp/G9nhsQVNX4MLERERPVgWGdPvDUoCADw3i/H8dPhixJX1LgxsBAREdWTCWG+iOrtAwB47fsU/JWWL21BjRgDCxERUT2RyWR4Z3A7DO7YEuUagYlxyTiexS/rrQsGFiIionokl8uwcEQwurdphqulaoyP2YcLV65LXVajw8BCRERUz1SWCqwcE4pANzvkFJVi3Kp9uFJSJnVZjQoDCxERUQNwtLFEbFR3uDuokJZ7DRO+PoAb5WwsV1sMLERERA3Ew8kasU93g73KAvvPXMar61KgYWO5WmFgISIiakBB7g74ckwolAo5fvsnG3P+7yiayNf61SsGFiIiogbW0685Fo2saCy3Oukslm9nY7m7YWAhIiKSwJBOHnhnSDsAwEebT3C5810wsBAREUnkmT5tMKijO4QAPtlySupyTBoDCxERkYSmPhQImQz4/VgOjpwvlLock8XAQkREJCH/FvYYFuwBAPjkD95lqQkDCxERkcReDg+EQi7DnydycfDcZanLMUkMLERERBJr42KL/3TxBMC5LDVhYCEiIjIBLz0YAAu5DDtT87Hv9CWpyzE5DCxEREQmwKuZDZ4I9QIALNpyUuJqTA8DCxERkYmY8oA/lAo59mRcwl9p+VKXY1IYWIiIiEyEp5M1/tu98i7LKbbsvwUDCxERkQmZPMAfVhZyHDh7GTtSeZelEgMLERGRCXFzUOGp+70BAIt+P8m7LDcxsBAREZmYSf38YG2pwOHzhfjzRK7U5ZgEBhYiIiIT42pvhbG9bt5l4VwWAAwsREREJmliXz/YKhU4erEIm49mS12O5BhYiIiITFAzWyWiercBAHyyJRVarXnfZWFgISIiMlETwnxhr7LAyZyr+OVIltTlSIqBhYiIyEQ52lji2T6+AIDFf5yCxozvsjCwEBERmbCoPj5wtLZEel4xfjp8QepyJMPAQkREZMIcVJZ4rm/FXZYlf6RCrdFKXJE0GFiIiIhM3PhePmhmq8SZghLEHzTPuywMLERERCbO1soCk/pV3GX59M9UlKnN7y4LAwsREVEjMOZ+H7jaW+H85etYn5wpdTkNjoGFiIioEbBWKvBCfz8AwGd/puFGuUbiihoWAwsREVEjMap7a7g7qJBVeAPr9pvXXRYGFiIiokZCZanA5Af8AQDLtprXXRYGFiIiokZkZKgXPJ2skXu1FGv2nJW6nAbDwEJERNSIKC3kePHmXZYvtqWjpEwtcUUNg4GFiIiokRke0gqtm9mgoLgMq/8yj7ssDCxERESNjKVCjpcfDAAArNiRjqs3yiWuqP4xsBARETVCwzp7wNfFFldKyhG7+4zU5dQ7BhYiIqJGyEIhx8vhFXdZVu7MQOH1pn2XhYGFiIiokRrSyQOBbnYouqHGVzszpC6nXjGwEBERNVIKuQyvhAcCAFbtPoPLxWUSV1R/GFiIiIgasYHt3dG2pQOularxZRO+y8LAQkRE1IjJ5TJMfajiLkvs7jPIv1YqcUX1g4GFiIiokQtv2wKdWjnierkGK7anS11OvahTYFm2bBl8fHygUqnQo0cP7Nu3r8ax5eXlmDNnDvz8/KBSqRAcHIyEhIQax8+fPx8ymQyvvPJKXUojIiIyOzKZDK/evMvyddJZ5BbdkLgi4zM4sKxbtw5Tp07FrFmzcPDgQQQHByMiIgK5ubnVjo+OjsaKFSuwdOlSHDt2DJMmTUJkZCQOHTpUZez+/fuxYsUKdOrUyfAzISIiMmP9A13RtbUTStVafL6t6d1lMTiwLFq0CBMmTEBUVBTatWuH5cuXw8bGBqtWrap2fFxcHN566y0MGjQIvr6+eP755zFo0CAsXLhQb9y1a9cwevRorFy5Es7OznU7GyIiIjMlk8kw9aH7AADf7D2HrMLrEldkXAYFlrKyMiQnJyM8PPzfA8jlCA8PR1JSUrX7lJaWQqVS6W2ztrbGrl279LZNnjwZgwcP1js2ERER1V5v/+bo3qYZyjRafPZnmtTlGJVBgSU/Px8ajQZubm56293c3JCdnV3tPhEREVi0aBFSU1Oh1WqxZcsWxMfHIysrSzfmu+++w8GDBzFv3rxa11JaWoqioiK9FxERkTmruMtSMZfl+wOZyLxUInFFxlPvq4SWLFmCgIAABAUFQalUYsqUKYiKioJcXvHRmZmZePnll7F27doqd2LuZN68eXB0dNS9vLy86usUiIiIGo37fZujt39zlGtEk7rLYlBgcXFxgUKhQE5Ojt72nJwcuLu7V7uPq6srNm3ahOLiYpw9exYnTpyAnZ0dfH19AQDJycnIzc1F165dYWFhAQsLC2zfvh2ffvopLCwsoNFoqj3ujBkzUFhYqHtlZmYacipERERNVuVclg0Hz+NMfrHE1RiHQYFFqVQiJCQEiYmJum1arRaJiYno2bPnHfdVqVTw9PSEWq3Gxo0bMWzYMADAgw8+iCNHjiAlJUX3Cg0NxejRo5GSkgKFQlHt8aysrODg4KD3IiIiIiDE2xn973OFRivwaWKq1OUYhYWhO0ydOhXjxo1DaGgounfvjsWLF6O4uBhRUVEAgLFjx8LT01M3H2Xv3r24cOECOnfujAsXLmD27NnQarWYNm0aAMDe3h4dOnTQ+wxbW1s0b968ynYiIiKqnVfDA7HtZB42pVzACwP84d/CTuqS7onBgWXkyJHIy8vDzJkzkZ2djc6dOyMhIUE3EffcuXO6+SkAcOPGDURHRyMjIwN2dnYYNGgQ4uLi4OTkZLSTICIiIn3BXk4Ib+uGP47nYEliKpaO6iJ1SfdEJoQQUhdhDEVFRXB0dERhYSEfDxEREQE4erEQgz/dBZkMSHi5L+5zt5e6pCpq+/c3v0uIiIioiWrv4YhHOrhDCGDxH6ekLueeMLAQERE1Ya8+FAiZDPjtn2wcvVgodTl1xsBCRETUhAW62WNoJw8AwCdbGu9dFgYWIiKiJu7l8ADIZcAfx3NxOPOK1OXUCQMLERFRE+fnaofHungCABY10rssDCxERERm4OUHA6CQy7D9VB6Sz16SuhyDMbAQERGZAe/mtni8aysAjfMuCwMLERGRmXjxQX9YKmTYnVaAPRkFUpdjEAYWIiIiM9HK2QYju3kBABb9fgqNqXcsAwsREZEZmTzAH0oLOfaduYTdaY3nLgsDCxERkRlp6WiNJ7u3BgAs3HKy0dxlYWAhIiIyMy8M8IPKUo5D565g28k8qcupFQYWIiIiM9PCXoUx93sDqFgx1BjusjCwEBERmaFJ/fxgo1TgyIVCbDmWI3U5d8XAQkREZIaa21lhfC8fABV3WbRa077LwsBCRERkpiaE+cLOygInsq8i4Wi21OXcEQMLERGRmXK2VeLpPm0AVHyTs8aE77IwsBAREZmxZ/q0gYPKAqm51/Dz3xelLqdGDCxERERmzNHaEhPCfAEAS/5IhVqjlbii6jGwEBERmbmoPm3gbGOJjPxibEoxzbssDCxERERmzs7KAhP7+QEAPk1MRbkJ3mVhYCEiIiKM7ekNFzslzl0qwcbk81KXUwUDCxEREcFGaYFJN++yLP0zDaVqjcQV6WNgISIiIgDAU/d7o4W9FS5cuY7v92dKXY4eBhYiIiICAKgsFZg8wB8A8NnWNNwoN527LAwsREREpPPf7l7wcFQhp6gU3+w9J3U5OgwsREREpGNlocCUBwIAAJ9vS8f1MtO4y8LAQkRERHoeD2mFVs7WyL9Wirg9Z6QuBwADCxEREd1GaSHHSw9W3GVZvj0D10rVElfEwEJERETV+E8XT/g0t8Gl4jKs/uuM1OUwsBAREVFVFgo5Xg6vuMvy5Y4MFN0ol7QeBhYiIiKq1qPBnvBvYYfC6+VYteu0pLUwsBAREVG1FHIZXrl5l+WrnadxpaRMsloYWIiIiKhGgzq0RJC7PVxudsCVioVkn0xEREQmTy6X4avx3eBmbwULhXT3ORhYiIiI6I48naylLoGPhIiIiMj0MbAQERGRyWNgISIiIpPHwEJEREQmj4GFiIiITB4DCxEREZk8BhYiIiIyeQwsREREZPIYWIiIiMjkMbAQERGRyWNgISIiIpPHwEJEREQmj4GFiIiITF6T+bZmIQQAoKioSOJKiIiIqLYq/96u/Hu8Jk0msFy9ehUA4OXlJXElREREZKirV6/C0dGxxvdl4m6RppHQarW4ePEi7O3tIZPJjHbcoqIieHl5ITMzEw4ODkY7rrnhdTQOXkfj4HU0Dl5H4zD36yiEwNWrV+Hh4QG5vOaZKk3mDotcLkerVq3q7fgODg5m+RvJ2HgdjYPX0Th4HY2D19E4zPk63unOSiVOuiUiIiKTx8BCREREJo+B5S6srKwwa9YsWFlZSV1Ko8braBy8jsbB62gcvI7GwetYO01m0i0RERE1XbzDQkRERCaPgYWIiIhMHgMLERERmTwGFiIiIjJ5DCwAli1bBh8fH6hUKvTo0QP79u274/j169cjKCgIKpUKHTt2xK+//tpAlZo2Q67j0aNHMXz4cPj4+EAmk2Hx4sUNV6iJM+Q6rly5EmFhYXB2doazszPCw8Pv+vvXXBhyHePj4xEaGgonJyfY2tqic+fOiIuLa8BqTZehfz5W+u677yCTyfDYY4/Vb4GNhCHXMTY2FjKZTO+lUqkasFoTJczcd999J5RKpVi1apU4evSomDBhgnBychI5OTnVjt+9e7dQKBTio48+EseOHRPR0dHC0tJSHDlypIErNy2GXsd9+/aJ119/XXz77bfC3d1dfPLJJw1bsIky9Do++eSTYtmyZeLQoUPi+PHjYvz48cLR0VGcP3++gSs3LYZex61bt4r4+Hhx7NgxkZaWJhYvXiwUCoVISEho4MpNi6HXsdLp06eFp6enCAsLE8OGDWuYYk2YodcxJiZGODg4iKysLN0rOzu7gas2PWYfWLp37y4mT56s+1mj0QgPDw8xb968asePGDFCDB48WG9bjx49xMSJE+u1TlNn6HW8lbe3NwPLTfdyHYUQQq1WC3t7e7F69er6KrFRuNfrKIQQXbp0EdHR0fVRXqNRl+uoVqtFr169xP/+9z8xbtw4BhZh+HWMiYkRjo6ODVRd42HWj4TKysqQnJyM8PBw3Ta5XI7w8HAkJSVVu09SUpLeeACIiIiocbw5qMt1pKqMcR1LSkpQXl6OZs2a1VeZJu9er6MQAomJiTh58iT69u1bn6WatLpexzlz5qBFixZ45plnGqJMk1fX63jt2jV4e3vDy8sLw4YNw9GjRxuiXJNm1oElPz8fGo0Gbm5uetvd3NyQnZ1d7T7Z2dkGjTcHdbmOVJUxruObb74JDw+PKqHanNT1OhYWFsLOzg5KpRKDBw/G0qVL8dBDD9V3uSarLtdx165d+Oqrr7By5cqGKLFRqMt1vO+++7Bq1Sr8+OOPWLNmDbRaLXr16oXz5883RMkmq8l8WzORuZs/fz6+++47bNu2jRP06sDe3h4pKSm4du0aEhMTMXXqVPj6+qJ///5Sl9YoXL16FWPGjMHKlSvh4uIidTmNWs+ePdGzZ0/dz7169ULbtm2xYsUKzJ07V8LKpGXWgcXFxQUKhQI5OTl623NycuDu7l7tPu7u7gaNNwd1uY5U1b1cxwULFmD+/Pn4448/0KlTp/os0+TV9TrK5XL4+/sDADp37ozjx49j3rx5ZhtYDL2O6enpOHPmDIYOHarbptVqAQAWFhY4efIk/Pz86rdoE2SMPx8tLS3RpUsXpKWl1UeJjYZZPxJSKpUICQlBYmKibptWq0ViYqJeur1Vz5499cYDwJYtW2ocbw7qch2pqrpex48++ghz585FQkICQkNDG6JUk2as349arRalpaX1UWKjYOh1DAoKwpEjR5CSkqJ7PfrooxgwYABSUlLg5eXVkOWbDGP8ftRoNDhy5AhatmxZX2U2DlLP+pXad999J6ysrERsbKw4duyYeO6554STk5NuCdmYMWPE9OnTdeN3794tLCwsxIIFC8Tx48fFrFmzuKxZGH4dS0tLxaFDh8ShQ4dEy5Ytxeuvvy4OHTokUlNTpToFk2DodZw/f75QKpViw4YNeksgr169KtUpmARDr+MHH3wgfv/9d5Geni6OHTsmFixYICwsLMTKlSulOgWTYOh1vB1XCVUw9Dq+++67YvPmzSI9PV0kJyeL//73v0KlUomjR49KdQomwewDixBCLF26VLRu3VoolUrRvXt3sWfPHt17/fr1E+PGjdMb//3334vAwEChVCpF+/btxS+//NLAFZsmQ67j6dOnBYAqr379+jV84SbGkOvo7e1d7XWcNWtWwxduYgy5jm+//bbw9/cXKpVKODs7i549e4rvvvtOgqpNj6F/Pt6KgeVfhlzHV155RTfWzc1NDBo0SBw8eFCCqk2LTAghpLq7Q0RERFQbZj2HhYiIiBoHBhYiIiIyeQwsREREZPIYWIiIiMjkMbAQERGRyWNgISIiIpPHwEJEREQmj4GFyIi2bdsGmUyGK1eu1Hqf8ePH47HHHqu3mpqy+rx2dTl2bGwsnJycjFpH//798corr5jMcYikYtZffkhkbL169UJWVhYcHR1rvc+SJUvQVPo3zp49G5s2bUJKSorUpTQZ8fHxsLS0rPX4bdu2YcCAAbh8+bJeeDL0OESmhoGFyIiUSqXB31BtSLgh4ysvLzfpv8ibNWtmUschkgofCRHVoH///njxxRfxyiuvwNnZGW5ubli5ciWKi4sRFRUFe3t7+Pv747ffftPtc/sjocpHBJs3b0bbtm1hZ2eHgQMHIisrS7fP7Y8e6vK51T2K2LRpE2Qyme7n2bNno3Pnzli1ahVat24NOzs7vPDCC9BoNPjoo4/g7u6OFi1a4P3337/jddm2bRu6d+8OW1tbODk5oXfv3jh79ixiY2Px7rvv4vDhw5DJZJDJZIiNjQUALFq0CB07doStrS28vLzwwgsv4Nq1a1Xqv9N10mg0mDp1KpycnNC8eXNMmzatyp2phIQE9OnTRzdmyJAhSE9P171/5swZyGQyrFu3Dv369YNKpcLatWtrdezqxMbGonXr1rCxsUFkZCQKCgqqjPnxxx/RtWtXqFQq+Pr64t1334VarQYAPPnkkxg5cqTe+PLycri4uODrr78GUPVRTlxcHEJDQ2Fvbw93d3c8+eSTyM3N1Z3fgAEDAADOzs6QyWQYP358tce5fPkyxo4dC2dnZ9jY2OCRRx5BamqqQf9NiBoSAwvRHaxevRouLi7Yt28fXnzxRTz//PN44okn0KtXLxw8eBAPP/wwxowZg5KSkhqPUVJSggULFiAuLg47duzAuXPn8Prrr9f751YnPT0dv/32GxISEvDtt9/iq6++wuDBg3H+/Hls374dH374IaKjo7F3795q91er1XjsscfQr18//P3330hKSsJzzz0HmUyGkSNH4rXXXkP79u2RlZWFrKws3V/Gcrkcn376KY4ePYrVq1fjzz//xLRp0wy6TgsXLkRsbCxWrVqFXbt24dKlS/jhhx/0jlFcXIypU6fiwIEDSExMhFwuR2RkJLRard646dOn4+WXX8bx48cRERFRq2Pfbu/evXjmmWcwZcoUpKSkYMCAAXjvvff0xuzcuRNjx47Fyy+/jGPHjmHFihWIjY3VhcLRo0fj//7v//TC2+bNm1FSUoLIyMhqP7e8vBxz587F4cOHsWnTJpw5c0YXSry8vLBx40YAwMmTJ5GVlYUlS5ZUe5zx48fjwIED+Omnn5CUlAQhBAYNGoTy8nLdmLr83iWqN1J+8yKRKevXr5/o06eP7me1Wi1sbW3FmDFjdNuysrIEAJGUlCSEEGLr1q0CgLh8+bIQQoiYmBgBQKSlpen2WbZsmXBzc9P9fPs32tblc2NiYoSjo6Ne/T/88IO49f/is2bNEjY2NqKoqEi3LSIiQvj4+AiNRqPbdt9994l58+ZVe00KCgoEALFt27Zq3581a5YIDg6u9r1brV+/XjRv3lz3c22uU8uWLcVHH32k+7m8vFy0atXqjt8GnJeXJwCII0eOCCH+/ZbwxYsX642ry7FHjRolBg0apLdt5MiRev8dHnzwQfHBBx/ojYmLixMtW7bUfY6Li4v4+uuv9Y47cuRI3c/9+vUTL7/8co117N+/XwAQV69eFUJU/T1Y3XFOnTolAIjdu3fr3s/PzxfW1tbi+++/F0LU7r8JUUPiHRaiO+jUqZPu1wqFAs2bN0fHjh1129zc3ABAd0u+OjY2NvDz89P93LJlyzuON9bnVsfHxwf29vZ6x2nXrh3kcrnetpqO26xZM4wfPx4REREYOnQolixZUqtHBH/88QcefPBBeHp6wt7eHmPGjEFBQYHeHaI7XafCwkJkZWWhR48euvctLCwQGhqq9zmpqakYNWoUfH194eDgAB8fHwDAuXPn9Mbdul9tj32748eP6+0DAD179tT7+fDhw5gzZw7s7Ox0rwkTJiArKwslJSWwsLDAiBEjsHbtWgAVd4h+/PFHjB49usbPTU5OxtChQ9G6dWvY29ujX79+1Z7j3Wq3sLDQq7958+a47777cPz4cd22uvzeJaovDCxEd3D7ZEyZTKa3rXKOyO2PHO52DHGX+RGGfq5cLq9yzFtv7df2uJXb7nQ+MTExSEpKQq9evbBu3ToEBgZiz549NY4/c+YMhgwZgk6dOmHjxo1ITk7GsmXLAABlZWV3rO1u1+l2Q4cOxaVLl7By5Urs3btX92jr1s8BAFtbW4OOW1fXrl3Du+++i5SUFN3ryJEjSE1NhUqlAlDxWCgxMRG5ubnYtGkTrK2tMXDgwGqPV1xcjIiICDg4OGDt2rXYv3+/7tHV7edoDMb4b0JkLAwsRE2Aq6srrl69iuLiYt22+lxa3KVLF8yYMQN//fUXOnTogG+++QZAxSopjUajNzY5ORlarRYLFy7E/fffj8DAQFy8eNGgz3N0dETLli315tao1WokJyfrfi4oKMDJkycRHR2NBx98EG3btsXly5eNcuzqtG3btspcn9uDW9euXXHy5En4+/tXeVXe1erVqxe8vLywbt06rF27Fk888USNq5ZOnDiBgoICzJ8/H2FhYQgKCqpyx0OpVAJAlf8Ot9euVqv16q+8fu3atbvjeRNJhcuaiZqAHj16wMbGBm+99RZeeukl7N27V7dCx5hOnz6NL7/8Eo8++ig8PDxw8uRJpKamYuzYsQAqHjmdPn0aKSkpaNWqlW5FU3l5OZYuXYqhQ4di9+7dWL58ucGf/fLLL2P+/PkICAhAUFAQFi1apNegz9nZGc2bN8eXX36Jli1b4ty5c5g+fbpRjl2dl156Cb1798aCBQswbNgwbN68GQkJCXpjZs6ciSFDhqB169Z4/PHHIZfLcfjwYfzzzz96E3SffPJJLF++HKdOncLWrVtr/MzWrVtDqVRi6dKlmDRpEv755x/MnTtXb4y3tzdkMhl+/vlnDBo0CNbW1rCzs9MbExAQgGHDhmHChAlYsWIF7O3tMX36dHh6emLYsGG1umZEDY13WIiagGbNmmHNmjX49ddf0bFjR3z77beYPXu20T/HxsYGJ06cwPDhwxEYGIjnnnsOkydPxsSJEwEAw4cPx8CBAzFgwAC4urri22+/RXBwMBYtWoQPP/wQHTp0wNq1azFv3jyDP/u1117DmDFjMG7cOPTs2RP29vZ6K2nkcjm+++47JCcno0OHDnj11Vfx8ccfG+XY1bn//vuxcuVKLFmyBMHBwfj9998RHR2tNyYiIgI///wzfv/9d3Tr1g33338/PvnkE3h7e+uNGz16NI4dOwZPT0/07t27xs90dXVFbGws1q9fj3bt2mH+/PlYsGCB3hhPT0+8++67mD59Otzc3DBlypRqjxUTE4OQkBAMGTIEPXv2hBACv/76q0n3pCHzJhN8IElEREQmjndYiIiIyOQxsBAREZHJY2AhIiIik8fAQkRERCaPgYWIiIhMHgMLERERmTwGFiIiIjJ5DCxERERk8hhYiIiIyOQxsBAREZHJY2AhIiIik8fAQkRERCbv/wGmJlJzw5JNLAAAAABJRU5ErkJggg=="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55], results)\n",
+    "_ = plt.title(\"Mean validation score\")\n",
+    "_ = plt.xlabel(\"minimum standard deviation\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:26:55.661004700Z",
+     "start_time": "2024-04-29T13:26:55.589740400Z"
+    }
+   },
+   "id": "b620fcf2b4b9f6f4",
+   "execution_count": 24
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "to_drop = X_train.std() > .15\n",
+    "X_train_small = X_train.loc[:, to_drop]\n",
+    "X_test_small = X_test.loc[:, to_drop]\n",
+    "_ = X_train.iloc[:,10:].transpose().plot(legend=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:28:49.137639600Z",
+     "start_time": "2024-04-29T13:28:48.167781600Z"
+    }
+   },
+   "id": "975356d69cc20c53",
+   "execution_count": 31
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = X_train_small.transpose().plot(legend=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:28:51.983978100Z",
+     "start_time": "2024-04-29T13:28:51.289055Z"
+    }
+   },
+   "id": "fdf20b15d00a8430",
+   "execution_count": 32
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(1.0, 0.9838983050847455)"
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_train_small)\n",
+    "X_train_small = scaler.transform(X_train_small)\n",
+    "X_test_small = scaler.transform(X_test_small)\n",
+    "evaluate_classifier_params(RandomForestClassifier, best_params, X_train_small, truth_train, X_test_small, truth_test, iters=20)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-04-29T13:29:05.925694400Z",
+     "start_time": "2024-04-29T13:28:59.691080400Z"
+    }
+   },
+   "id": "7661e709fa106edd",
+   "execution_count": 33
   },
   {
-   "metadata": {},
    "cell_type": "code",
    "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "8e54d640f33e81bc"
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "9737bff20459b9ff"
   }
  ],
  "metadata": {
diff --git a/data/data_exploration.ipynb b/data/data_exploration.ipynb
index 74792be..fe1326b 100644
--- a/data/data_exploration.ipynb
+++ b/data/data_exploration.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:44.085296Z",
-     "start_time": "2024-04-16T14:08:42.452131Z"
+     "end_time": "2024-04-26T09:59:56.875027100Z",
+     "start_time": "2024-04-26T09:59:52.262782800Z"
     }
    },
    "source": [
@@ -22,8 +22,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:44.345071Z",
-     "start_time": "2024-04-16T14:08:44.085296Z"
+     "end_time": "2024-04-26T09:59:57.051525800Z",
+     "start_time": "2024-04-26T09:59:56.876559800Z"
     }
    },
    "cell_type": "code",
@@ -35,215 +35,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "    strain  replica   phase  objective substrate  laser_power[%]  grating  \\\n",
-       "0     CHA0      3.0  liquid         50       alu             100     1800   \n",
-       "1     CHA0      NaN   solid         50       alu             100     1800   \n",
-       "2     F113      2.0   solid         50       alu             100     1800   \n",
-       "3     A390      3.0  liquid         50       alu             100     1800   \n",
-       "4     A390      2.0   solid         50       alu             100     1800   \n",
-       "..     ...      ...     ...        ...       ...             ...      ...   \n",
-       "523   F113      3.0  liquid         50       alu             100     1800   \n",
-       "524   CHA0      1.0  liquid         50       alu             100     1800   \n",
-       "525   F113      2.0   solid         50       alu             100     1800   \n",
-       "526   CHA0      3.0  liquid         50       alu             100     1800   \n",
-       "527   F113      2.0   solid         50       alu             100     1800   \n",
-       "\n",
-       "     exposition[sec]  confocalhigh  accumulations  \n",
-       "0                 20          True              2  \n",
-       "1                 20          True              2  \n",
-       "2                 20          True              2  \n",
-       "3                 20          True              2  \n",
-       "4                 20          True              2  \n",
-       "..               ...           ...            ...  \n",
-       "523               20          True              2  \n",
-       "524               20          True              2  \n",
-       "525               20          True              2  \n",
-       "526               20          True              2  \n",
-       "527               20          True              2  \n",
-       "\n",
-       "[528 rows x 10 columns]"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>strain</th>\n",
-       "      <th>replica</th>\n",
-       "      <th>phase</th>\n",
-       "      <th>objective</th>\n",
-       "      <th>substrate</th>\n",
-       "      <th>laser_power[%]</th>\n",
-       "      <th>grating</th>\n",
-       "      <th>exposition[sec]</th>\n",
-       "      <th>confocalhigh</th>\n",
-       "      <th>accumulations</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>CHA0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>CHA0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>A390</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>A390</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>523</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>524</th>\n",
-       "      <td>CHA0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>525</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>526</th>\n",
-       "      <td>CHA0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>liquid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>527</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>528 rows × 10 columns</p>\n",
-       "</div>"
-      ]
+      "text/plain": "    strain  replica   phase  objective substrate  laser_power[%]  grating  \\\n0     CHA0      3.0  liquid         50       alu             100     1800   \n1     CHA0      NaN   solid         50       alu             100     1800   \n2     F113      2.0   solid         50       alu             100     1800   \n3     A390      3.0  liquid         50       alu             100     1800   \n4     A390      2.0   solid         50       alu             100     1800   \n..     ...      ...     ...        ...       ...             ...      ...   \n523   F113      3.0  liquid         50       alu             100     1800   \n524   CHA0      1.0  liquid         50       alu             100     1800   \n525   F113      2.0   solid         50       alu             100     1800   \n526   CHA0      3.0  liquid         50       alu             100     1800   \n527   F113      2.0   solid         50       alu             100     1800   \n\n     exposition[sec]  confocalhigh  accumulations  \n0                 20          True              2  \n1                 20          True              2  \n2                 20          True              2  \n3                 20          True              2  \n4                 20          True              2  \n..               ...           ...            ...  \n523               20          True              2  \n524               20          True              2  \n525               20          True              2  \n526               20          True              2  \n527               20          True              2  \n\n[528 rows x 10 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>strain</th>\n      <th>replica</th>\n      <th>phase</th>\n      <th>objective</th>\n      <th>substrate</th>\n      <th>laser_power[%]</th>\n      <th>grating</th>\n      <th>exposition[sec]</th>\n      <th>confocalhigh</th>\n      <th>accumulations</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CHA0</td>\n      <td>3.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CHA0</td>\n      <td>NaN</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A390</td>\n      <td>3.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A390</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>523</th>\n      <td>F113</td>\n      <td>3.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>524</th>\n      <td>CHA0</td>\n      <td>1.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>525</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>526</th>\n      <td>CHA0</td>\n      <td>3.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>527</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>528 rows × 10 columns</p>\n</div>"
      },
      "execution_count": 2,
      "metadata": {},
@@ -255,8 +48,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:44.363409Z",
-     "start_time": "2024-04-16T14:08:44.345071Z"
+     "end_time": "2024-04-26T09:59:57.060741400Z",
+     "start_time": "2024-04-26T09:59:57.044734800Z"
     }
    },
    "cell_type": "code",
@@ -268,203 +61,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "     objective  laser_power[%]  grating  exposition[sec]  accumulations  \\\n",
-       "0           50             100     1800               20              2   \n",
-       "1           50             100     1800               20              2   \n",
-       "2           50             100     1800               20              2   \n",
-       "3           50             100     1800               20              2   \n",
-       "4           50             100     1800               20              2   \n",
-       "..         ...             ...      ...              ...            ...   \n",
-       "523         50             100     1800               20              2   \n",
-       "524         50             100     1800               20              2   \n",
-       "525         50             100     1800               20              2   \n",
-       "526         50             100     1800               20              2   \n",
-       "527         50             100     1800               20              2   \n",
-       "\n",
-       "     phase_liquid  phase_solid  substrate_alu  confocalhigh_True  \n",
-       "0               1            0              1                  1  \n",
-       "1               0            1              1                  1  \n",
-       "2               0            1              1                  1  \n",
-       "3               1            0              1                  1  \n",
-       "4               0            1              1                  1  \n",
-       "..            ...          ...            ...                ...  \n",
-       "523             1            0              1                  1  \n",
-       "524             1            0              1                  1  \n",
-       "525             0            1              1                  1  \n",
-       "526             1            0              1                  1  \n",
-       "527             0            1              1                  1  \n",
-       "\n",
-       "[528 rows x 9 columns]"
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-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
-       "      <th>524</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
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-       "      <th>525</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>528 rows × 9 columns</p>\n",
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-      ]
+      "text/plain": "     objective  laser_power[%]  grating  exposition[sec]  accumulations  \\\n0           50             100     1800               20              2   \n1           50             100     1800               20              2   \n2           50             100     1800               20              2   \n3           50             100     1800               20              2   \n4           50             100     1800               20              2   \n..         ...             ...      ...              ...            ...   \n523         50             100     1800               20              2   \n524         50             100     1800               20              2   \n525         50             100     1800               20              2   \n526         50             100     1800               20              2   \n527         50             100     1800               20              2   \n\n     phase_liquid  phase_solid  substrate_alu  confocalhigh_True  \n0               1            0              1                  1  \n1               0            1              1                  1  \n2               0            1              1                  1  \n3               1            0              1                  1  \n4               0            1              1                  1  \n..            ...          ...            ...                ...  \n523             1            0              1                  1  \n524             1            0              1                  1  \n525             0            1              1                  1  \n526             1            0              1                  1  \n527             0            1              1                  1  \n\n[528 rows x 9 columns]",
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      },
      "execution_count": 3,
      "metadata": {},
@@ -476,383 +74,20 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:44.390433Z",
-     "start_time": "2024-04-16T14:08:44.363409Z"
+     "end_time": "2024-04-26T09:59:57.084598300Z",
+     "start_time": "2024-04-26T09:59:57.060741400Z"
     }
    },
    "cell_type": "code",
-   "source": "experiments",
+   "source": [
+    "experiments"
+   ],
    "id": "58d62ec1ed5bf712",
    "outputs": [
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-       "      <td>17200.982284</td>\n",
-       "      <td>17151.063092</td>\n",
-       "      <td>17082.008985</td>\n",
-       "      <td>17024.961405</td>\n",
-       "      <td>17032.450041</td>\n",
-       "      <td>17168.732873</td>\n",
-       "      <td>17305.712763</td>\n",
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-       "      <td>3251.436367</td>\n",
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-       "      <td>3172.244319</td>\n",
-       "      <td>3249.871217</td>\n",
-       "      <td>3260.952953</td>\n",
-       "      <td>3216.461160</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>524</th>\n",
-       "      <td>7243.554940</td>\n",
-       "      <td>7232.422883</td>\n",
-       "      <td>7210.338436</td>\n",
-       "      <td>7152.476179</td>\n",
-       "      <td>7118.142932</td>\n",
-       "      <td>7139.018310</td>\n",
-       "      <td>7138.782155</td>\n",
-       "      <td>7101.544029</td>\n",
-       "      <td>7035.620040</td>\n",
-       "      <td>6931.622011</td>\n",
-       "      <td>...</td>\n",
-       "      <td>9995.081816</td>\n",
-       "      <td>9932.822669</td>\n",
-       "      <td>10030.510762</td>\n",
-       "      <td>10121.979900</td>\n",
-       "      <td>10067.985582</td>\n",
-       "      <td>9990.683366</td>\n",
-       "      <td>9914.801509</td>\n",
-       "      <td>9903.585600</td>\n",
-       "      <td>9918.220675</td>\n",
-       "      <td>9950.592656</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>525</th>\n",
-       "      <td>6290.164064</td>\n",
-       "      <td>6285.578797</td>\n",
-       "      <td>6260.533539</td>\n",
-       "      <td>6191.284547</td>\n",
-       "      <td>6167.541016</td>\n",
-       "      <td>6218.388989</td>\n",
-       "      <td>6221.357782</td>\n",
-       "      <td>6164.687474</td>\n",
-       "      <td>6142.045859</td>\n",
-       "      <td>6152.129107</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10824.400770</td>\n",
-       "      <td>10802.906442</td>\n",
-       "      <td>10733.806089</td>\n",
-       "      <td>10672.337683</td>\n",
-       "      <td>10644.521395</td>\n",
-       "      <td>10620.716495</td>\n",
-       "      <td>10611.719473</td>\n",
-       "      <td>10651.781342</td>\n",
-       "      <td>10684.854706</td>\n",
-       "      <td>10702.353518</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>526</th>\n",
-       "      <td>5327.836545</td>\n",
-       "      <td>5401.122331</td>\n",
-       "      <td>5456.359102</td>\n",
-       "      <td>5418.306128</td>\n",
-       "      <td>5383.273787</td>\n",
-       "      <td>5358.553291</td>\n",
-       "      <td>5334.995174</td>\n",
-       "      <td>5314.268889</td>\n",
-       "      <td>5303.749689</td>\n",
-       "      <td>5311.737819</td>\n",
-       "      <td>...</td>\n",
-       "      <td>9694.848556</td>\n",
-       "      <td>9704.488708</td>\n",
-       "      <td>9743.374621</td>\n",
-       "      <td>9762.006943</td>\n",
-       "      <td>9649.631146</td>\n",
-       "      <td>9593.338048</td>\n",
-       "      <td>9579.071631</td>\n",
-       "      <td>9666.939860</td>\n",
-       "      <td>9703.644841</td>\n",
-       "      <td>9697.622114</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>527</th>\n",
-       "      <td>8195.034277</td>\n",
-       "      <td>8192.678071</td>\n",
-       "      <td>8162.267898</td>\n",
-       "      <td>8071.247233</td>\n",
-       "      <td>8010.268057</td>\n",
-       "      <td>7998.532200</td>\n",
-       "      <td>7976.096146</td>\n",
-       "      <td>7940.331751</td>\n",
-       "      <td>7887.197385</td>\n",
-       "      <td>7817.358591</td>\n",
-       "      <td>...</td>\n",
-       "      <td>8722.159925</td>\n",
-       "      <td>8719.149837</td>\n",
-       "      <td>8676.622386</td>\n",
-       "      <td>8661.970062</td>\n",
-       "      <td>8722.639885</td>\n",
-       "      <td>8673.858473</td>\n",
-       "      <td>8616.453751</td>\n",
-       "      <td>8648.938599</td>\n",
-       "      <td>8698.230708</td>\n",
-       "      <td>8733.806260</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>528 rows × 1746 columns</p>\n",
-       "</div>"
-      ]
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<td>3649.184314</td>\n      <td>3619.540757</td>\n      <td>...</td>\n      <td>5361.827220</td>\n      <td>5296.784607</td>\n      <td>5312.611270</td>\n      <td>5343.476227</td>\n      <td>5316.028557</td>\n      <td>5315.315095</td>\n      <td>5325.196162</td>\n      <td>5292.517723</td>\n      <td>5337.812719</td>\n      <td>5448.225282</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>9873.391244</td>\n      <td>9896.587508</td>\n      <td>9884.553562</td>\n      <td>9796.404872</td>\n      <td>9705.894800</td>\n      <td>9611.514004</td>\n      <td>9593.564386</td>\n      <td>9670.818710</td>\n      <td>9651.071648</td>\n      <td>9538.039870</td>\n      <td>...</td>\n      <td>17184.220925</td>\n      <td>17169.826221</td>\n      <td>17194.941818</td>\n      <td>17200.982284</td>\n      <td>17151.063092</td>\n      <td>17082.008985</td>\n      <td>17024.961405</td>\n      <td>17032.450041</td>\n      <td>17168.732873</td>\n      <td>17305.712763</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>523</th>\n      <td>2084.041170</td>\n      <td>2021.638070</td>\n      <td>1978.476575</td>\n      <td>2034.768953</td>\n      <td>2063.089691</td>\n      <td>1995.920765</td>\n      <td>1958.470371</td>\n      <td>1993.421365</td>\n      <td>2005.578679</td>\n      <td>1976.406840</td>\n      <td>...</td>\n      <td>3254.670796</td>\n      <td>3261.366502</td>\n      <td>3272.676517</td>\n      <td>3278.115278</td>\n      <td>3251.436367</td>\n      <td>3210.008681</td>\n      <td>3172.244319</td>\n      <td>3249.871217</td>\n      <td>3260.952953</td>\n      <td>3216.461160</td>\n    </tr>\n    <tr>\n      <th>524</th>\n      <td>7243.554940</td>\n      <td>7232.422883</td>\n      <td>7210.338436</td>\n      <td>7152.476179</td>\n      <td>7118.142932</td>\n      <td>7139.018310</td>\n      <td>7138.782155</td>\n      <td>7101.544029</td>\n      <td>7035.620040</td>\n      <td>6931.622011</td>\n      <td>...</td>\n      <td>9995.081816</td>\n      <td>9932.822669</td>\n      <td>10030.510762</td>\n      <td>10121.979900</td>\n      <td>10067.985582</td>\n      <td>9990.683366</td>\n      <td>9914.801509</td>\n      <td>9903.585600</td>\n      <td>9918.220675</td>\n      <td>9950.592656</td>\n    </tr>\n    <tr>\n      <th>525</th>\n      <td>6290.164064</td>\n      <td>6285.578797</td>\n      <td>6260.533539</td>\n      <td>6191.284547</td>\n      <td>6167.541016</td>\n      <td>6218.388989</td>\n      <td>6221.357782</td>\n      <td>6164.687474</td>\n      <td>6142.045859</td>\n      <td>6152.129107</td>\n      <td>...</td>\n      <td>10824.400770</td>\n      <td>10802.906442</td>\n      <td>10733.806089</td>\n      <td>10672.337683</td>\n      <td>10644.521395</td>\n      <td>10620.716495</td>\n      <td>10611.719473</td>\n      <td>10651.781342</td>\n      <td>10684.854706</td>\n      <td>10702.353518</td>\n    </tr>\n    <tr>\n      <th>526</th>\n      <td>5327.836545</td>\n      <td>5401.122331</td>\n      <td>5456.359102</td>\n      <td>5418.306128</td>\n      <td>5383.273787</td>\n      <td>5358.553291</td>\n      <td>5334.995174</td>\n      <td>5314.268889</td>\n      <td>5303.749689</td>\n      <td>5311.737819</td>\n      <td>...</td>\n      <td>9694.848556</td>\n      <td>9704.488708</td>\n      <td>9743.374621</td>\n      <td>9762.006943</td>\n      <td>9649.631146</td>\n      <td>9593.338048</td>\n      <td>9579.071631</td>\n      <td>9666.939860</td>\n      <td>9703.644841</td>\n      <td>9697.622114</td>\n    </tr>\n    <tr>\n      <th>527</th>\n      <td>8195.034277</td>\n      <td>8192.678071</td>\n      <td>8162.267898</td>\n      <td>8071.247233</td>\n      <td>8010.268057</td>\n      <td>7998.532200</td>\n      <td>7976.096146</td>\n      <td>7940.331751</td>\n      <td>7887.197385</td>\n      <td>7817.358591</td>\n      <td>...</td>\n      <td>8722.159925</td>\n      <td>8719.149837</td>\n      <td>8676.622386</td>\n      <td>8661.970062</td>\n      <td>8722.639885</td>\n      <td>8673.858473</td>\n      <td>8616.453751</td>\n      <td>8648.938599</td>\n      <td>8698.230708</td>\n      <td>8733.806260</td>\n    </tr>\n  </tbody>\n</table>\n<p>528 rows × 1746 columns</p>\n</div>"
      },
      "execution_count": 4,
      "metadata": {},
@@ -864,20 +99,20 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:46.357612Z",
-     "start_time": "2024-04-16T14:08:44.390433Z"
+     "end_time": "2024-04-26T09:59:58.068786500Z",
+     "start_time": "2024-04-26T09:59:57.074092900Z"
     }
    },
    "cell_type": "code",
-   "source": "_ = experiments.transpose().plot(legend=False)",
+   "source": [
+    "_ = experiments.transpose().plot(legend=False)"
+   ],
    "id": "c99d1b6edb867192",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -888,8 +123,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:46.627354Z",
-     "start_time": "2024-04-16T14:08:46.357612Z"
+     "end_time": "2024-04-26T09:59:58.216395100Z",
+     "start_time": "2024-04-26T09:59:58.068786500Z"
     }
    },
    "cell_type": "code",
@@ -903,10 +138,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -917,8 +150,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:50.286796Z",
-     "start_time": "2024-04-16T14:08:46.627354Z"
+     "end_time": "2024-04-26T10:00:00.052464700Z",
+     "start_time": "2024-04-26T09:59:58.202126Z"
     }
    },
    "cell_type": "code",
@@ -931,10 +164,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -945,8 +176,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:52.805031Z",
-     "start_time": "2024-04-16T14:08:50.286796Z"
+     "end_time": "2024-04-26T10:00:01.430611400Z",
+     "start_time": "2024-04-26T10:00:00.037016100Z"
     }
    },
    "cell_type": "code",
@@ -959,10 +190,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -973,8 +202,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:52.995217Z",
-     "start_time": "2024-04-16T14:08:52.805031Z"
+     "end_time": "2024-04-26T10:00:01.562395300Z",
+     "start_time": "2024-04-26T10:00:01.421977800Z"
     }
    },
    "cell_type": "code",
@@ -987,10 +216,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1001,8 +228,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:08:55.533409Z",
-     "start_time": "2024-04-16T14:08:52.995217Z"
+     "end_time": "2024-04-26T10:00:02.845179100Z",
+     "start_time": "2024-04-26T10:00:01.548885700Z"
     }
    },
    "cell_type": "code",
@@ -1015,10 +242,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1029,8 +254,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:11:19.934584Z",
-     "start_time": "2024-04-16T14:11:19.893550Z"
+     "end_time": "2024-04-26T10:00:02.858586200Z",
+     "start_time": "2024-04-26T10:00:02.834690800Z"
     }
    },
    "cell_type": "code",
@@ -1042,386 +267,21 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "     objective  laser_power[%]  grating  exposition[sec]  accumulations  \\\n",
-       "0           50             100     1800               20              2   \n",
-       "1           50             100     1800               20              2   \n",
-       "2           50             100     1800               20              2   \n",
-       "3           50             100     1800               20              2   \n",
-       "4           50             100     1800               20              2   \n",
-       "..         ...             ...      ...              ...            ...   \n",
-       "523         50             100     1800               20              2   \n",
-       "524         50             100     1800               20              2   \n",
-       "525         50             100     1800               20              2   \n",
-       "526         50             100     1800               20              2   \n",
-       "527         50             100     1800               20              2   \n",
-       "\n",
-       "     phase_liquid  phase_solid  substrate_alu  confocalhigh_True       181  \\\n",
-       "0               1            0              1                  1 -0.299485   \n",
-       "1               0            1              1                  1 -0.071990   \n",
-       "2               0            1              1                  1 -0.129200   \n",
-       "3               1            0              1                  1 -0.281426   \n",
-       "4               0            1              1                  1 -0.232889   \n",
-       "..            ...          ...            ...                ...       ...   \n",
-       "523             1            0              1                  1 -0.090060   \n",
-       "524             1            0              1                  1  0.218434   \n",
-       "525             0            1              1                  1 -0.124807   \n",
-       "526             1            0              1                  1 -0.500839   \n",
-       "527             0            1              1                  1 -0.067152   \n",
-       "\n",
-       "     ...      1917      1918      1919      1920      1921      1922  \\\n",
-       "0    ... -0.099779 -0.133344 -0.233556 -0.353051 -0.412390 -0.375944   \n",
-       "1    ... -0.361364 -0.286732 -0.254597 -0.273810 -0.311510 -0.330628   \n",
-       "2    ... -0.229192 -0.202971 -0.191374 -0.181775 -0.191283 -0.314714   \n",
-       "3    ... -0.269852 -0.275944 -0.305332 -0.323362 -0.301384 -0.333912   \n",
-       "4    ... -0.309343 -0.328670 -0.326767 -0.332768 -0.361922 -0.411394   \n",
-       "..   ...       ...       ...       ...       ...       ...       ...   \n",
-       "523  ... -0.167459 -0.165322 -0.132145 -0.140044 -0.275117 -0.372081   \n",
-       "524  ... -0.396873 -0.336000 -0.216534 -0.114254 -0.136184 -0.355353   \n",
-       "525  ... -0.164972 -0.194494 -0.275090 -0.364688 -0.440014 -0.472264   \n",
-       "526  ... -0.204567 -0.197048 -0.175478 -0.219285 -0.366606 -0.494809   \n",
-       "527  ... -0.357148 -0.380659 -0.389910 -0.394442 -0.414922 -0.433362   \n",
-       "\n",
-       "         1923      1924      1925      1926  \n",
-       "0   -0.337513 -0.373616 -0.401737 -0.387572  \n",
-       "1   -0.354670 -0.391580 -0.375889 -0.265337  \n",
-       "2   -0.439199 -0.495830 -0.485446 -0.387097  \n",
-       "3   -0.379791 -0.376634 -0.287635 -0.072417  \n",
-       "4   -0.440068 -0.432535 -0.373218 -0.245863  \n",
-       "..        ...       ...       ...       ...  \n",
-       "523 -0.371779 -0.283918 -0.235794 -0.310222  \n",
-       "524 -0.594436 -0.707970 -0.702249 -0.540342  \n",
-       "525 -0.467227 -0.439868 -0.401022 -0.361123  \n",
-       "526 -0.517148 -0.394632 -0.281323 -0.263670  \n",
-       "527 -0.447434 -0.459646 -0.428600 -0.327952  \n",
-       "\n",
-       "[528 rows x 1755 columns]"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>objective</th>\n",
-       "      <th>laser_power[%]</th>\n",
-       "      <th>grating</th>\n",
-       "      <th>exposition[sec]</th>\n",
-       "      <th>accumulations</th>\n",
-       "      <th>phase_liquid</th>\n",
-       "      <th>phase_solid</th>\n",
-       "      <th>substrate_alu</th>\n",
-       "      <th>confocalhigh_True</th>\n",
-       "      <th>181</th>\n",
-       "      <th>...</th>\n",
-       "      <th>1917</th>\n",
-       "      <th>1918</th>\n",
-       "      <th>1919</th>\n",
-       "      <th>1920</th>\n",
-       "      <th>1921</th>\n",
-       "      <th>1922</th>\n",
-       "      <th>1923</th>\n",
-       "      <th>1924</th>\n",
-       "      <th>1925</th>\n",
-       "      <th>1926</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.299485</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.099779</td>\n",
-       "      <td>-0.133344</td>\n",
-       "      <td>-0.233556</td>\n",
-       "      <td>-0.353051</td>\n",
-       "      <td>-0.412390</td>\n",
-       "      <td>-0.375944</td>\n",
-       "      <td>-0.337513</td>\n",
-       "      <td>-0.373616</td>\n",
-       "      <td>-0.401737</td>\n",
-       "      <td>-0.387572</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.071990</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.361364</td>\n",
-       "      <td>-0.286732</td>\n",
-       "      <td>-0.254597</td>\n",
-       "      <td>-0.273810</td>\n",
-       "      <td>-0.311510</td>\n",
-       "      <td>-0.330628</td>\n",
-       "      <td>-0.354670</td>\n",
-       "      <td>-0.391580</td>\n",
-       "      <td>-0.375889</td>\n",
-       "      <td>-0.265337</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.129200</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.229192</td>\n",
-       "      <td>-0.202971</td>\n",
-       "      <td>-0.191374</td>\n",
-       "      <td>-0.181775</td>\n",
-       "      <td>-0.191283</td>\n",
-       "      <td>-0.314714</td>\n",
-       "      <td>-0.439199</td>\n",
-       "      <td>-0.495830</td>\n",
-       "      <td>-0.485446</td>\n",
-       "      <td>-0.387097</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.281426</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.269852</td>\n",
-       "      <td>-0.275944</td>\n",
-       "      <td>-0.305332</td>\n",
-       "      <td>-0.323362</td>\n",
-       "      <td>-0.301384</td>\n",
-       "      <td>-0.333912</td>\n",
-       "      <td>-0.379791</td>\n",
-       "      <td>-0.376634</td>\n",
-       "      <td>-0.287635</td>\n",
-       "      <td>-0.072417</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.232889</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.309343</td>\n",
-       "      <td>-0.328670</td>\n",
-       "      <td>-0.326767</td>\n",
-       "      <td>-0.332768</td>\n",
-       "      <td>-0.361922</td>\n",
-       "      <td>-0.411394</td>\n",
-       "      <td>-0.440068</td>\n",
-       "      <td>-0.432535</td>\n",
-       "      <td>-0.373218</td>\n",
-       "      <td>-0.245863</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>523</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.090060</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.167459</td>\n",
-       "      <td>-0.165322</td>\n",
-       "      <td>-0.132145</td>\n",
-       "      <td>-0.140044</td>\n",
-       "      <td>-0.275117</td>\n",
-       "      <td>-0.372081</td>\n",
-       "      <td>-0.371779</td>\n",
-       "      <td>-0.283918</td>\n",
-       "      <td>-0.235794</td>\n",
-       "      <td>-0.310222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>524</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.218434</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.396873</td>\n",
-       "      <td>-0.336000</td>\n",
-       "      <td>-0.216534</td>\n",
-       "      <td>-0.114254</td>\n",
-       "      <td>-0.136184</td>\n",
-       "      <td>-0.355353</td>\n",
-       "      <td>-0.594436</td>\n",
-       "      <td>-0.707970</td>\n",
-       "      <td>-0.702249</td>\n",
-       "      <td>-0.540342</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>525</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.124807</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.164972</td>\n",
-       "      <td>-0.194494</td>\n",
-       "      <td>-0.275090</td>\n",
-       "      <td>-0.364688</td>\n",
-       "      <td>-0.440014</td>\n",
-       "      <td>-0.472264</td>\n",
-       "      <td>-0.467227</td>\n",
-       "      <td>-0.439868</td>\n",
-       "      <td>-0.401022</td>\n",
-       "      <td>-0.361123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>526</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.500839</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.204567</td>\n",
-       "      <td>-0.197048</td>\n",
-       "      <td>-0.175478</td>\n",
-       "      <td>-0.219285</td>\n",
-       "      <td>-0.366606</td>\n",
-       "      <td>-0.494809</td>\n",
-       "      <td>-0.517148</td>\n",
-       "      <td>-0.394632</td>\n",
-       "      <td>-0.281323</td>\n",
-       "      <td>-0.263670</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>527</th>\n",
-       "      <td>50</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.067152</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.357148</td>\n",
-       "      <td>-0.380659</td>\n",
-       "      <td>-0.389910</td>\n",
-       "      <td>-0.394442</td>\n",
-       "      <td>-0.414922</td>\n",
-       "      <td>-0.433362</td>\n",
-       "      <td>-0.447434</td>\n",
-       "      <td>-0.459646</td>\n",
-       "      <td>-0.428600</td>\n",
-       "      <td>-0.327952</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>528 rows × 1755 columns</p>\n",
-       "</div>"
-      ]
+      "text/plain": "     objective  laser_power[%]  grating  exposition[sec]  accumulations  \\\n0           50             100     1800               20              2   \n1           50             100     1800               20              2   \n2           50             100     1800               20              2   \n3           50             100     1800               20              2   \n4           50             100     1800               20              2   \n..         ...             ...      ...              ...            ...   \n523         50             100     1800               20              2   \n524         50             100     1800               20              2   \n525         50             100     1800               20              2   \n526         50             100     1800               20              2   \n527         50             100     1800               20              2   \n\n     phase_liquid  phase_solid  substrate_alu  confocalhigh_True       181  \\\n0               1            0              1                  1 -0.299485   \n1               0            1              1                  1 -0.071990   \n2               0            1              1                  1 -0.129200   \n3               1            0              1                  1 -0.281426   \n4               0            1              1                  1 -0.232889   \n..            ...          ...            ...                ...       ...   \n523             1            0              1                  1 -0.090060   \n524             1            0              1                  1  0.218434   \n525             0            1              1                  1 -0.124807   \n526             1            0              1                  1 -0.500839   \n527             0            1              1                  1 -0.067152   \n\n     ...      1917      1918      1919      1920      1921      1922  \\\n0    ... -0.099779 -0.133344 -0.233556 -0.353051 -0.412390 -0.375944   \n1    ... -0.361364 -0.286732 -0.254597 -0.273810 -0.311510 -0.330628   \n2    ... -0.229192 -0.202971 -0.191374 -0.181775 -0.191283 -0.314714   \n3    ... -0.269852 -0.275944 -0.305332 -0.323362 -0.301384 -0.333912   \n4    ... -0.309343 -0.328670 -0.326767 -0.332768 -0.361922 -0.411394   \n..   ...       ...       ...       ...       ...       ...       ...   \n523  ... -0.167459 -0.165322 -0.132145 -0.140044 -0.275117 -0.372081   \n524  ... -0.396873 -0.336000 -0.216534 -0.114254 -0.136184 -0.355353   \n525  ... -0.164972 -0.194494 -0.275090 -0.364688 -0.440014 -0.472264   \n526  ... -0.204567 -0.197048 -0.175478 -0.219285 -0.366606 -0.494809   \n527  ... -0.357148 -0.380659 -0.389910 -0.394442 -0.414922 -0.433362   \n\n         1923      1924      1925      1926  \n0   -0.337513 -0.373616 -0.401737 -0.387572  \n1   -0.354670 -0.391580 -0.375889 -0.265337  \n2   -0.439199 -0.495830 -0.485446 -0.387097  \n3   -0.379791 -0.376634 -0.287635 -0.072417  \n4   -0.440068 -0.432535 -0.373218 -0.245863  \n..        ...       ...       ...       ...  \n523 -0.371779 -0.283918 -0.235794 -0.310222  \n524 -0.594436 -0.707970 -0.702249 -0.540342  \n525 -0.467227 -0.439868 -0.401022 -0.361123  \n526 -0.517148 -0.394632 -0.281323 -0.263670  \n527 -0.447434 -0.459646 -0.428600 -0.327952  \n\n[528 rows x 1755 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>objective</th>\n      <th>laser_power[%]</th>\n      <th>grating</th>\n      <th>exposition[sec]</th>\n      <th>accumulations</th>\n      <th>phase_liquid</th>\n      <th>phase_solid</th>\n      <th>substrate_alu</th>\n      <th>confocalhigh_True</th>\n      <th>181</th>\n      <th>...</th>\n      <th>1917</th>\n      <th>1918</th>\n      <th>1919</th>\n      <th>1920</th>\n      <th>1921</th>\n      <th>1922</th>\n      <th>1923</th>\n      <th>1924</th>\n      <th>1925</th>\n      <th>1926</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.299485</td>\n      <td>...</td>\n      <td>-0.099779</td>\n      <td>-0.133344</td>\n      <td>-0.233556</td>\n      <td>-0.353051</td>\n      <td>-0.412390</td>\n      <td>-0.375944</td>\n      <td>-0.337513</td>\n      <td>-0.373616</td>\n      <td>-0.401737</td>\n      <td>-0.387572</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.071990</td>\n      <td>...</td>\n      <td>-0.361364</td>\n      <td>-0.286732</td>\n      <td>-0.254597</td>\n      <td>-0.273810</td>\n      <td>-0.311510</td>\n      <td>-0.330628</td>\n      <td>-0.354670</td>\n      <td>-0.391580</td>\n      <td>-0.375889</td>\n      <td>-0.265337</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.129200</td>\n      <td>...</td>\n      <td>-0.229192</td>\n      <td>-0.202971</td>\n      <td>-0.191374</td>\n      <td>-0.181775</td>\n      <td>-0.191283</td>\n      <td>-0.314714</td>\n      <td>-0.439199</td>\n      <td>-0.495830</td>\n      <td>-0.485446</td>\n      <td>-0.387097</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.281426</td>\n      <td>...</td>\n      <td>-0.269852</td>\n      <td>-0.275944</td>\n      <td>-0.305332</td>\n      <td>-0.323362</td>\n      <td>-0.301384</td>\n      <td>-0.333912</td>\n      <td>-0.379791</td>\n      <td>-0.376634</td>\n      <td>-0.287635</td>\n      <td>-0.072417</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.232889</td>\n      <td>...</td>\n      <td>-0.309343</td>\n      <td>-0.328670</td>\n      <td>-0.326767</td>\n      <td>-0.332768</td>\n      <td>-0.361922</td>\n      <td>-0.411394</td>\n      <td>-0.440068</td>\n      <td>-0.432535</td>\n      <td>-0.373218</td>\n      <td>-0.245863</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>523</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.090060</td>\n      <td>...</td>\n      <td>-0.167459</td>\n      <td>-0.165322</td>\n      <td>-0.132145</td>\n      <td>-0.140044</td>\n      <td>-0.275117</td>\n      <td>-0.372081</td>\n      <td>-0.371779</td>\n      <td>-0.283918</td>\n      <td>-0.235794</td>\n      <td>-0.310222</td>\n    </tr>\n    <tr>\n      <th>524</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.218434</td>\n      <td>...</td>\n      <td>-0.396873</td>\n      <td>-0.336000</td>\n      <td>-0.216534</td>\n      <td>-0.114254</td>\n      <td>-0.136184</td>\n      <td>-0.355353</td>\n      <td>-0.594436</td>\n      <td>-0.707970</td>\n      <td>-0.702249</td>\n      <td>-0.540342</td>\n    </tr>\n    <tr>\n      <th>525</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.124807</td>\n      <td>...</td>\n      <td>-0.164972</td>\n      <td>-0.194494</td>\n      <td>-0.275090</td>\n      <td>-0.364688</td>\n      <td>-0.440014</td>\n      <td>-0.472264</td>\n      <td>-0.467227</td>\n      <td>-0.439868</td>\n      <td>-0.401022</td>\n      <td>-0.361123</td>\n    </tr>\n    <tr>\n      <th>526</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.500839</td>\n      <td>...</td>\n      <td>-0.204567</td>\n      <td>-0.197048</td>\n      <td>-0.175478</td>\n      <td>-0.219285</td>\n      <td>-0.366606</td>\n      <td>-0.494809</td>\n      <td>-0.517148</td>\n      <td>-0.394632</td>\n      <td>-0.281323</td>\n      <td>-0.263670</td>\n    </tr>\n    <tr>\n      <th>527</th>\n      <td>50</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.067152</td>\n      <td>...</td>\n      <td>-0.357148</td>\n      <td>-0.380659</td>\n      <td>-0.389910</td>\n      <td>-0.394442</td>\n      <td>-0.414922</td>\n      <td>-0.433362</td>\n      <td>-0.447434</td>\n      <td>-0.459646</td>\n      <td>-0.428600</td>\n      <td>-0.327952</td>\n    </tr>\n  </tbody>\n</table>\n<p>528 rows × 1755 columns</p>\n</div>"
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 13
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-16T14:11:40.003881Z",
-     "start_time": "2024-04-16T14:11:36.682919Z"
+     "end_time": "2024-04-26T10:00:04.727425700Z",
+     "start_time": "2024-04-26T10:00:02.850988200Z"
     }
    },
    "cell_type": "code",
@@ -1433,26 +293,50 @@
    ],
    "id": "3909f7be70d848b5",
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Edouard\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py:136: UserWarning: Could not find the number of physical cores for the following reason:\n",
+      "[WinError 2] The system cannot find the file specified\n",
+      "Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.\n",
+      "  warnings.warn(\n",
+      "  File \"C:\\Users\\Edouard\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 257, in _count_physical_cores\n",
+      "    cpu_info = subprocess.run(\n",
+      "               ^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\Edouard\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\subprocess.py\", line 548, in run\n",
+      "    with Popen(*popenargs, **kwargs) as process:\n",
+      "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\Edouard\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\subprocess.py\", line 1026, in __init__\n",
+      "    self._execute_child(args, executable, preexec_fn, close_fds,\n",
+      "  File \"C:\\Users\\Edouard\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\subprocess.py\", line 1538, in _execute_child\n",
+      "    hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n",
+      "                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n"
+     ]
+    },
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 14
+   "execution_count": 12
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-26T10:00:04.728023200Z",
+     "start_time": "2024-04-26T10:00:04.718062600Z"
+    }
+   },
    "cell_type": "code",
-   "source": "",
+   "source": [],
    "id": "e30a2902afa2a95",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 12
   }
  ],
  "metadata": {
diff --git a/data/data_loading.py b/data/data_loading.py
index 8ba4908..b84ea39 100644
--- a/data/data_loading.py
+++ b/data/data_loading.py
@@ -57,5 +57,5 @@ def load_raw_data() -> tuple[pd.DataFrame, list[pd.DataFrame]]:
 
 def load_data(name: str, path: os.path = os.path.join("data")) -> tuple[pd.DataFrame, pd.DataFrame]:
     metadata = pd.read_csv(os.path.join(path, name, "metadata.csv"))
-    experiments = pd.read_csv(os.path.join(path, name, "experiments.csv"))
+    experiments = pd.read_csv(os.path.join(path, name, "experiments.csv"), dtype=float)
     return metadata, experiments
diff --git a/data/data_processing.ipynb b/data/data_processing.ipynb
index 181a5b0..f4b2c91 100644
--- a/data/data_processing.ipynb
+++ b/data/data_processing.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2024-04-13T13:46:20.019128Z",
-     "start_time": "2024-04-13T13:46:15.068690Z"
+     "end_time": "2024-04-24T13:41:21.513552800Z",
+     "start_time": "2024-04-24T13:41:20.448753Z"
     }
    },
    "source": [
@@ -22,8 +22,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-13T13:46:22.737432Z",
-     "start_time": "2024-04-13T13:46:20.019128Z"
+     "end_time": "2024-04-24T13:41:22.904530200Z",
+     "start_time": "2024-04-24T13:41:21.514615400Z"
     }
    },
    "cell_type": "code",
@@ -36,215 +36,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "    strain replica   phase objective substrate laser_power[%] grating  \\\n",
-       "0     A390     1.0  liquid        50       alu            100    1800   \n",
-       "1     A390     1.0  liquid        50       alu            100    1800   \n",
-       "2     A390     1.0  liquid        50       alu            100    1800   \n",
-       "3     A390     1.0  liquid        50       alu            100    1800   \n",
-       "4     A390     1.0  liquid        50       alu            100    1800   \n",
-       "..     ...     ...     ...       ...       ...            ...     ...   \n",
-       "700   F113     2.0   solid        50       alu            100    1800   \n",
-       "701   F113     2.0   solid        50       alu            100    1800   \n",
-       "702   F113     2.0   solid        50       alu            100    1800   \n",
-       "703   F113     2.0   solid        50       alu            100    1800   \n",
-       "704   F113     2.0   solid        50       alu            100    1800   \n",
-       "\n",
-       "    exposition[sec] confocalhigh accumulations  \n",
-       "0                20         True             2  \n",
-       "1                20         True             2  \n",
-       "2                20         True             2  \n",
-       "3                20         True             2  \n",
-       "4                20         True             2  \n",
-       "..              ...          ...           ...  \n",
-       "700              20         True             2  \n",
-       "701              20         True             2  \n",
-       "702              20         True             2  \n",
-       "703              20         True             2  \n",
-       "704              20         True             2  \n",
-       "\n",
-       "[705 rows x 10 columns]"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>strain</th>\n",
-       "      <th>replica</th>\n",
-       "      <th>phase</th>\n",
-       "      <th>objective</th>\n",
-       "      <th>substrate</th>\n",
-       "      <th>laser_power[%]</th>\n",
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-       "      <th>confocalhigh</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>0</th>\n",
-       "      <td>A390</td>\n",
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-       "      <th>1</th>\n",
-       "      <td>A390</td>\n",
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-       "      <td>A390</td>\n",
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-       "      <td>A390</td>\n",
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-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
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-       "      <th>700</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
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-       "      <td>1800</td>\n",
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-       "      <td>50</td>\n",
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-       "      <td>1800</td>\n",
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-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
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-       "    <tr>\n",
-       "      <th>702</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>2.0</td>\n",
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-       "      <td>50</td>\n",
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-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>703</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
-       "      <td>100</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>704</th>\n",
-       "      <td>F113</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>solid</td>\n",
-       "      <td>50</td>\n",
-       "      <td>alu</td>\n",
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-       "      <td>20</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>705 rows × 10 columns</p>\n",
-       "</div>"
-      ]
+      "text/plain": "    strain replica   phase objective substrate laser_power[%] grating  \\\n0     A390     1.0  liquid        50       alu            100    1800   \n1     A390     1.0  liquid        50       alu            100    1800   \n2     A390     1.0  liquid        50       alu            100    1800   \n3     A390     1.0  liquid        50       alu            100    1800   \n4     A390     1.0  liquid        50       alu            100    1800   \n..     ...     ...     ...       ...       ...            ...     ...   \n700   F113     2.0   solid        50       alu            100    1800   \n701   F113     2.0   solid        50       alu            100    1800   \n702   F113     2.0   solid        50       alu            100    1800   \n703   F113     2.0   solid        50       alu            100    1800   \n704   F113     2.0   solid        50       alu            100    1800   \n\n    exposition[sec] confocalhigh accumulations  \n0                20         True             2  \n1                20         True             2  \n2                20         True             2  \n3                20         True             2  \n4                20         True             2  \n..              ...          ...           ...  \n700              20         True             2  \n701              20         True             2  \n702              20         True             2  \n703              20         True             2  \n704              20         True             2  \n\n[705 rows x 10 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>strain</th>\n      <th>replica</th>\n      <th>phase</th>\n      <th>objective</th>\n      <th>substrate</th>\n      <th>laser_power[%]</th>\n      <th>grating</th>\n      <th>exposition[sec]</th>\n      <th>confocalhigh</th>\n      <th>accumulations</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A390</td>\n      <td>1.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A390</td>\n      <td>1.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A390</td>\n      <td>1.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A390</td>\n      <td>1.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A390</td>\n      <td>1.0</td>\n      <td>liquid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>700</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>701</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>702</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>703</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>704</th>\n      <td>F113</td>\n      <td>2.0</td>\n      <td>solid</td>\n      <td>50</td>\n      <td>alu</td>\n      <td>100</td>\n      <td>1800</td>\n      <td>20</td>\n      <td>True</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>705 rows × 10 columns</p>\n</div>"
      },
      "execution_count": 2,
      "metadata": {},
@@ -256,109 +49,20 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-13T13:46:22.751007Z",
-     "start_time": "2024-04-13T13:46:22.739441Z"
+     "end_time": "2024-04-24T13:41:22.912710300Z",
+     "start_time": "2024-04-24T13:41:22.909549600Z"
     }
    },
    "cell_type": "code",
-   "source": "sliced_experiments[0]",
+   "source": [
+    "sliced_experiments[0]"
+   ],
    "id": "7332ba064f82e1ab",
    "outputs": [
     {
      "data": {
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      },
      "execution_count": 3,
      "metadata": {},
@@ -370,8 +74,8 @@
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-     "start_time": "2024-04-13T13:46:22.754522Z"
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@@ -1094,9878 +798,7 @@
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-       " 184   15217.462044\n",
-       " 185   15151.631602\n",
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-       " 1922  34619.811769\n",
-       " 1923  34739.902572\n",
-       " 1924  34481.281361\n",
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-       " 185   11497.515892\n",
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-       " 1922  23259.160077\n",
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-       " 1924  23237.842294\n",
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-       " 185   14589.036222\n",
-       " ...            ...\n",
-       " 1922  27759.794667\n",
-       " 1923  27701.766984\n",
-       " 1924  27707.542236\n",
-       " 1925  27766.287448\n",
-       " 1926  27838.199140\n",
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-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12962.544993\n",
-       " 182   12888.778360\n",
-       " 183   12819.365812\n",
-       " 184   12764.176613\n",
-       " 185   12737.702642\n",
-       " ...            ...\n",
-       " 1922  24519.211765\n",
-       " 1923  24573.630709\n",
-       " 1924  24534.071366\n",
-       " 1925  24543.687146\n",
-       " 1926  24582.879296\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
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-       " 181   11218.268657\n",
-       " 182   11174.286298\n",
-       " 183   11138.891151\n",
-       " 184   11131.547559\n",
-       " 185   11115.027596\n",
-       " ...            ...\n",
-       " 1922  21569.532670\n",
-       " 1923  21603.756959\n",
-       " 1924  21496.946353\n",
-       " 1925  21603.877186\n",
-       " 1926  21826.919220\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13797.517019\n",
-       " 182   13699.189902\n",
-       " 183   13624.446367\n",
-       " 184   13626.742532\n",
-       " 185   13609.690867\n",
-       " ...            ...\n",
-       " 1922  25599.870205\n",
-       " 1923  25666.872646\n",
-       " 1924  25828.025424\n",
-       " 1925  25977.235900\n",
-       " 1926  26088.341561\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9500.903430\n",
-       " 182    9395.398390\n",
-       " 183    9327.081873\n",
-       " 184    9380.247863\n",
-       " 185    9372.983056\n",
-       " ...            ...\n",
-       " 1922  17762.395943\n",
-       " 1923  17729.292606\n",
-       " 1924  17631.368364\n",
-       " 1925  17719.442114\n",
-       " 1926  17908.803279\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9737.872920\n",
-       " 182    9689.048324\n",
-       " 183    9644.117090\n",
-       " 184    9611.904176\n",
-       " 185    9580.085679\n",
-       " ...            ...\n",
-       " 1922  17024.965060\n",
-       " 1923  17124.649955\n",
-       " 1924  17120.660056\n",
-       " 1925  17098.382555\n",
-       " 1926  17060.351796\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9199.072282\n",
-       " 182    9192.839419\n",
-       " 183    9170.494344\n",
-       " 184    9095.516044\n",
-       " 185    9031.298566\n",
-       " ...            ...\n",
-       " 1922  16020.668347\n",
-       " 1923  16034.688033\n",
-       " 1924  16045.178564\n",
-       " 1925  16092.876008\n",
-       " 1926  16147.318945\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9137.087615\n",
-       " 182    9078.193966\n",
-       " 183    9020.844001\n",
-       " 184    8968.536739\n",
-       " 185    8928.190135\n",
-       " ...            ...\n",
-       " 1922  15542.194813\n",
-       " 1923  15555.297180\n",
-       " 1924  15536.474579\n",
-       " 1925  15569.354706\n",
-       " 1926  15617.797227\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6145.054156\n",
-       " 182    6165.116404\n",
-       " 183    6169.544779\n",
-       " 184    6122.902501\n",
-       " 185    6085.828147\n",
-       " ...            ...\n",
-       " 1922  11035.333397\n",
-       " 1923  10968.079272\n",
-       " 1924  11095.937226\n",
-       " 1925  11083.206052\n",
-       " 1926  10984.066406\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8894.198163\n",
-       " 182    8901.924978\n",
-       " 183    8894.795759\n",
-       " 184    8839.136833\n",
-       " 185    8780.877258\n",
-       " ...            ...\n",
-       " 1922  15173.260687\n",
-       " 1923  15153.206559\n",
-       " 1924  15049.716659\n",
-       " 1925  15105.108062\n",
-       " 1926  15250.442617\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8829.150525\n",
-       " 182    8758.889940\n",
-       " 183    8703.307294\n",
-       " 184    8695.672582\n",
-       " 185    8685.443706\n",
-       " ...            ...\n",
-       " 1922  14332.405612\n",
-       " 1923  14377.801442\n",
-       " 1924  14403.740836\n",
-       " 1925  14359.691425\n",
-       " 1926  14271.861800\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7178.607067\n",
-       " 182    7232.783983\n",
-       " 183    7253.352016\n",
-       " 184    7164.131032\n",
-       " 185    7100.628705\n",
-       " ...            ...\n",
-       " 1922  11843.338965\n",
-       " 1923  11811.407967\n",
-       " 1924  11831.782967\n",
-       " 1925  11845.856863\n",
-       " 1926  11848.067542\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6143.837135\n",
-       " 182    6201.889554\n",
-       " 183    6225.708811\n",
-       " 184    6137.699734\n",
-       " 185    6076.007874\n",
-       " ...            ...\n",
-       " 1922  10207.088285\n",
-       " 1923  10158.871636\n",
-       " 1924  10083.966332\n",
-       " 1925  10123.178132\n",
-       " 1926  10231.898635\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10073.133750\n",
-       " 182   10096.997157\n",
-       " 183   10095.374439\n",
-       " 184   10010.497048\n",
-       " 185    9932.389724\n",
-       " ...            ...\n",
-       " 1922  15416.241294\n",
-       " 1923  15427.569023\n",
-       " 1924  15279.197179\n",
-       " 1925  15328.791919\n",
-       " 1926  15491.223987\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8301.595537\n",
-       " 182    8279.259348\n",
-       " 183    8240.649036\n",
-       " 184    8148.876590\n",
-       " 185    8083.420983\n",
-       " ...            ...\n",
-       " 1922  12698.602092\n",
-       " 1923  12745.528900\n",
-       " 1924  12735.734077\n",
-       " 1925  12759.987996\n",
-       " 1926  12792.902776\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8492.640137\n",
-       " 182    8590.296387\n",
-       " 183    8655.297094\n",
-       " 184    8613.623031\n",
-       " 185    8555.587155\n",
-       " ...            ...\n",
-       " 1922  13423.558228\n",
-       " 1923  13446.976210\n",
-       " 1924  13374.597422\n",
-       " 1925  13452.279611\n",
-       " 1926  13608.855780\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9812.038102\n",
-       " 182    9766.555239\n",
-       " 183    9716.833528\n",
-       " 184    9653.264915\n",
-       " 185    9592.480136\n",
-       " ...            ...\n",
-       " 1922  14884.632169\n",
-       " 1923  14855.938156\n",
-       " 1924  14978.046111\n",
-       " 1925  15034.602855\n",
-       " 1926  15035.317227\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10147.072778\n",
-       " 182    9977.660983\n",
-       " 183    9839.941393\n",
-       " 184    9805.749670\n",
-       " 185    9761.580077\n",
-       " ...            ...\n",
-       " 1922  14533.548047\n",
-       " 1923  14592.073792\n",
-       " 1924  14646.246140\n",
-       " 1925  14671.424955\n",
-       " 1926  14674.249921\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9562.504347\n",
-       " 182    9544.984186\n",
-       " 183    9509.783964\n",
-       " 184    9416.828874\n",
-       " 185    9350.190397\n",
-       " ...            ...\n",
-       " 1922  13981.371647\n",
-       " 1923  13872.245264\n",
-       " 1924  13866.778724\n",
-       " 1925  13950.222968\n",
-       " 1926  14072.029141\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8353.312792\n",
-       " 182    8368.973578\n",
-       " 183    8369.624469\n",
-       " 184    8321.243040\n",
-       " 185    8257.895183\n",
-       " ...            ...\n",
-       " 1922  12488.254015\n",
-       " 1923  12418.294393\n",
-       " 1924  12273.700327\n",
-       " 1925  12315.092824\n",
-       " 1926  12467.393087\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6373.140976\n",
-       " 182    6413.225401\n",
-       " 183    6416.255066\n",
-       " 184    6298.239180\n",
-       " 185    6238.251482\n",
-       " ...            ...\n",
-       " 1922   9925.207265\n",
-       " 1923   9977.497268\n",
-       " 1924   9964.199920\n",
-       " 1925   9986.212068\n",
-       " 1926  10022.705471\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8765.868306\n",
-       " 182    8755.585040\n",
-       " 183    8729.031684\n",
-       " 184    8649.329368\n",
-       " 185    8584.775460\n",
-       " ...            ...\n",
-       " 1922  13001.207320\n",
-       " 1923  13020.540628\n",
-       " 1924  12927.213608\n",
-       " 1925  12944.214951\n",
-       " 1926  13018.686444\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8181.690564\n",
-       " 182    8175.336229\n",
-       " 183    8145.518533\n",
-       " 184    8039.053858\n",
-       " 185    7961.496945\n",
-       " ...            ...\n",
-       " 1922  12370.491355\n",
-       " 1923  12296.094409\n",
-       " 1924  12316.533802\n",
-       " 1925  12349.589500\n",
-       " 1926  12385.632734\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7991.522343\n",
-       " 182    7907.831314\n",
-       " 183    7844.757200\n",
-       " 184    7849.031670\n",
-       " 185    7841.937183\n",
-       " ...            ...\n",
-       " 1922  11183.674441\n",
-       " 1923  11191.079200\n",
-       " 1924  11308.521119\n",
-       " 1925  11296.090891\n",
-       " 1926  11196.005856\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9251.985139\n",
-       " 182    9223.097538\n",
-       " 183    9200.456259\n",
-       " 184    9198.219629\n",
-       " 185    9163.669322\n",
-       " ...            ...\n",
-       " 1922  13384.594164\n",
-       " 1923  13340.255746\n",
-       " 1924  13288.755114\n",
-       " 1925  13361.466763\n",
-       " 1926  13498.873008\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10096.986329\n",
-       " 182   10086.861328\n",
-       " 183   10050.456383\n",
-       " 184    9928.203615\n",
-       " 185    9845.627726\n",
-       " ...            ...\n",
-       " 1922  13867.200117\n",
-       " 1923  13983.292621\n",
-       " 1924  13813.836181\n",
-       " 1925  13793.785766\n",
-       " 1926  13860.126366\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11805.654384\n",
-       " 182   11776.404888\n",
-       " 183   11735.102049\n",
-       " 184   11654.424961\n",
-       " 185   11597.871824\n",
-       " ...            ...\n",
-       " 1922  15984.366345\n",
-       " 1923  16120.218554\n",
-       " 1924  15850.763377\n",
-       " 1925  15827.799528\n",
-       " 1926  15954.341484\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9702.360052\n",
-       " 182    9724.846948\n",
-       " 183    9726.382821\n",
-       " 184    9659.478688\n",
-       " 185    9591.767478\n",
-       " ...            ...\n",
-       " 1922  14312.556456\n",
-       " 1923  14329.040168\n",
-       " 1924  14310.054056\n",
-       " 1925  14299.891319\n",
-       " 1926  14288.552343\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9062.258149\n",
-       " 182    9064.006637\n",
-       " 183    9063.591886\n",
-       " 184    9051.996024\n",
-       " 185    9026.939491\n",
-       " ...            ...\n",
-       " 1922  15010.605538\n",
-       " 1923  15017.176423\n",
-       " 1924  14967.006179\n",
-       " 1925  15021.553385\n",
-       " 1926  15163.552753\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6973.791433\n",
-       " 182    6954.628882\n",
-       " 183    6932.324442\n",
-       " 184    6893.780549\n",
-       " 185    6854.930188\n",
-       " ...            ...\n",
-       " 1922  11493.754252\n",
-       " 1923  11587.649431\n",
-       " 1924  11524.503776\n",
-       " 1925  11522.792132\n",
-       " 1926  11572.385566\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6413.500129\n",
-       " 182    6433.594635\n",
-       " 183    6436.168381\n",
-       " 184    6348.182766\n",
-       " 185    6289.224067\n",
-       " ...            ...\n",
-       " 1922  10315.270892\n",
-       " 1923  10385.382531\n",
-       " 1924  10302.254530\n",
-       " 1925  10291.359914\n",
-       " 1926  10340.789207\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6090.062856\n",
-       " 182   6103.453733\n",
-       " 183   6110.681383\n",
-       " 184   6086.053231\n",
-       " 185   6063.539752\n",
-       " ...           ...\n",
-       " 1922  9748.502851\n",
-       " 1923  9765.762924\n",
-       " 1924  9725.327864\n",
-       " 1925  9744.639213\n",
-       " 1926  9813.846284\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5923.591866\n",
-       " 182   5989.617320\n",
-       " 183   6032.074690\n",
-       " 184   5952.715966\n",
-       " 185   5898.453102\n",
-       " ...           ...\n",
-       " 1922  9510.275174\n",
-       " 1923  9394.603593\n",
-       " 1924  9410.458575\n",
-       " 1925  9470.169013\n",
-       " 1926  9566.504240\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5552.502854\n",
-       " 182   5561.163892\n",
-       " 183   5561.172410\n",
-       " 184   5516.458740\n",
-       " 185   5471.891552\n",
-       " ...           ...\n",
-       " 1922  8470.514303\n",
-       " 1923  8439.335299\n",
-       " 1924  8398.724325\n",
-       " 1925  8453.729886\n",
-       " 1926  8588.587210\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4481.093592\n",
-       " 182   4491.626354\n",
-       " 183   4495.237918\n",
-       " 184   4463.075962\n",
-       " 185   4431.515559\n",
-       " ...           ...\n",
-       " 1922  6907.094830\n",
-       " 1923  6899.207871\n",
-       " 1924  6836.482456\n",
-       " 1925  6840.885156\n",
-       " 1926  6901.348225\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3778.243732\n",
-       " 182   3796.690683\n",
-       " 183   3805.730130\n",
-       " 184   3766.145117\n",
-       " 185   3734.873626\n",
-       " ...           ...\n",
-       " 1922  5978.312298\n",
-       " 1923  5919.357519\n",
-       " 1924  5848.155433\n",
-       " 1925  5842.365524\n",
-       " 1926  5891.202960\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3665.011590\n",
-       " 182   3635.456273\n",
-       " 183   3605.677536\n",
-       " 184   3574.744012\n",
-       " 185   3549.856445\n",
-       " ...           ...\n",
-       " 1922  5527.717428\n",
-       " 1923  5541.153450\n",
-       " 1924  5485.550671\n",
-       " 1925  5502.776271\n",
-       " 1926  5580.822673\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3553.386985\n",
-       " 182   3543.813649\n",
-       " 183   3534.995309\n",
-       " 184   3530.079316\n",
-       " 185   3526.221403\n",
-       " ...           ...\n",
-       " 1922  5439.653651\n",
-       " 1923  5461.690807\n",
-       " 1924  5437.377123\n",
-       " 1925  5486.489571\n",
-       " 1926  5596.922020\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3675.081951\n",
-       " 182   3668.855641\n",
-       " 183   3657.435079\n",
-       " 184   3619.167044\n",
-       " 185   3589.061442\n",
-       " ...           ...\n",
-       " 1922  5598.488833\n",
-       " 1923  5660.456365\n",
-       " 1924  5688.334677\n",
-       " 1925  5754.955345\n",
-       " 1926  5853.930724\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3778.538897\n",
-       " 182   3786.988241\n",
-       " 183   3783.647686\n",
-       " 184   3719.368805\n",
-       " 185   3671.718483\n",
-       " ...           ...\n",
-       " 1922  5749.793521\n",
-       " 1923  5741.197467\n",
-       " 1924  5682.491284\n",
-       " 1925  5704.012341\n",
-       " 1926  5792.533174\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3684.371084\n",
-       " 182   3628.824209\n",
-       " 183   3583.000492\n",
-       " 184   3587.432758\n",
-       " 185   3582.186781\n",
-       " ...           ...\n",
-       " 1922  5576.265018\n",
-       " 1923  5631.287710\n",
-       " 1924  5554.830579\n",
-       " 1925  5534.101732\n",
-       " 1926  5559.912970\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3399.753926\n",
-       " 182   3416.156143\n",
-       " 183   3423.042191\n",
-       " 184   3380.742119\n",
-       " 185   3344.335599\n",
-       " ...           ...\n",
-       " 1922  5226.515788\n",
-       " 1923  5226.788060\n",
-       " 1924  5212.876332\n",
-       " 1925  5221.156991\n",
-       " 1926  5247.971070\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3131.833418\n",
-       " 182   3141.538560\n",
-       " 183   3143.348671\n",
-       " 184   3104.351827\n",
-       " 185   3074.273489\n",
-       " ...           ...\n",
-       " 1922  4728.444859\n",
-       " 1923  4692.556546\n",
-       " 1924  4667.097213\n",
-       " 1925  4666.097303\n",
-       " 1926  4685.524072\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3471.296498\n",
-       " 182   3376.400203\n",
-       " 183   3304.956851\n",
-       " 184   3354.734472\n",
-       " 185   3390.906627\n",
-       " ...           ...\n",
-       " 1922  5035.247080\n",
-       " 1923  5060.136068\n",
-       " 1924  5026.379130\n",
-       " 1925  5023.337178\n",
-       " 1926  5045.946079\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3618.237115\n",
-       " 182   3598.673858\n",
-       " 183   3573.804978\n",
-       " 184   3521.512979\n",
-       " 185   3485.245869\n",
-       " ...           ...\n",
-       " 1922  5257.035191\n",
-       " 1923  5331.752792\n",
-       " 1924  5334.548189\n",
-       " 1925  5337.915201\n",
-       " 1926  5341.759582\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3676.144213\n",
-       " 182   3680.582217\n",
-       " 183   3679.503966\n",
-       " 184   3649.913908\n",
-       " 185   3622.135514\n",
-       " ...           ...\n",
-       " 1922  5315.315095\n",
-       " 1923  5325.196162\n",
-       " 1924  5292.517723\n",
-       " 1925  5337.812719\n",
-       " 1926  5448.225282\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3583.546907\n",
-       " 182   3578.642799\n",
-       " 183   3569.734750\n",
-       " 184   3540.131573\n",
-       " 185   3514.306048\n",
-       " ...           ...\n",
-       " 1922  5279.432173\n",
-       " 1923  5270.318780\n",
-       " 1924  5186.510765\n",
-       " 1925  5176.074536\n",
-       " 1926  5226.912920\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3189.099247\n",
-       " 182   3177.482815\n",
-       " 183   3167.378183\n",
-       " 184   3165.087567\n",
-       " 185   3158.713489\n",
-       " ...           ...\n",
-       " 1922  4623.336510\n",
-       " 1923  4678.747593\n",
-       " 1924  4678.326093\n",
-       " 1925  4653.990689\n",
-       " 1926  4609.684187\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3066.873394\n",
-       " 182   3046.577779\n",
-       " 183   3033.092820\n",
-       " 184   3054.810024\n",
-       " 185   3063.070047\n",
-       " ...           ...\n",
-       " 1922  4403.451902\n",
-       " 1923  4344.607166\n",
-       " 1924  4347.271100\n",
-       " 1925  4378.975121\n",
-       " 1926  4434.931245\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2976.370999\n",
-       " 182   2980.725460\n",
-       " 183   2978.914541\n",
-       " 184   2945.236693\n",
-       " 185   2916.243458\n",
-       " ...           ...\n",
-       " 1922  4289.904075\n",
-       " 1923  4271.421939\n",
-       " 1924  4200.000748\n",
-       " 1925  4198.546533\n",
-       " 1926  4255.523490\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3112.702936\n",
-       " 182   3137.735446\n",
-       " 183   3153.686607\n",
-       " 184   3122.699096\n",
-       " 185   3102.748083\n",
-       " ...           ...\n",
-       " 1922  4557.507369\n",
-       " 1923  4552.334480\n",
-       " 1924  4582.152263\n",
-       " 1925  4595.485513\n",
-       " 1926  4595.052116\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3257.808341\n",
-       " 182   3184.214843\n",
-       " 183   3119.691092\n",
-       " 184   3102.046038\n",
-       " 185   3087.121270\n",
-       " ...           ...\n",
-       " 1922  4614.982081\n",
-       " 1923  4676.393227\n",
-       " 1924  4719.790771\n",
-       " 1925  4696.042260\n",
-       " 1926  4616.218396\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3410.437257\n",
-       " 182   3391.515634\n",
-       " 183   3367.504931\n",
-       " 184   3317.190353\n",
-       " 185   3279.573792\n",
-       " ...           ...\n",
-       " 1922  4958.003226\n",
-       " 1923  5011.272758\n",
-       " 1924  5007.765507\n",
-       " 1925  5008.240413\n",
-       " 1926  5012.040918\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3214.485959\n",
-       " 182   3185.543419\n",
-       " 183   3167.087671\n",
-       " 184   3202.834894\n",
-       " 185   3224.067771\n",
-       " ...           ...\n",
-       " 1922  4703.267046\n",
-       " 1923  4679.179025\n",
-       " 1924  4769.526132\n",
-       " 1925  4778.535953\n",
-       " 1926  4719.618971\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3093.425539\n",
-       " 182   3080.464728\n",
-       " 183   3067.933548\n",
-       " 184   3057.623004\n",
-       " 185   3042.472009\n",
-       " ...           ...\n",
-       " 1922  4372.958994\n",
-       " 1923  4368.932284\n",
-       " 1924  4441.501647\n",
-       " 1925  4433.856403\n",
-       " 1926  4359.221933\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3193.171756\n",
-       " 182   3183.555324\n",
-       " 183   3169.609308\n",
-       " 184   3133.285017\n",
-       " 185   3102.552416\n",
-       " ...           ...\n",
-       " 1922  4512.075836\n",
-       " 1923  4456.867628\n",
-       " 1924  4467.893166\n",
-       " 1925  4466.402542\n",
-       " 1926  4454.459360\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3333.200276\n",
-       " 182   3303.605392\n",
-       " 183   3274.544340\n",
-       " 184   3248.242500\n",
-       " 185   3227.987105\n",
-       " ...           ...\n",
-       " 1922  4767.720999\n",
-       " 1923  4739.507476\n",
-       " 1924  4715.097638\n",
-       " 1925  4721.411281\n",
-       " 1926  4753.382872\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3421.695476\n",
-       " 182   3412.106388\n",
-       " 183   3395.699506\n",
-       " 184   3344.053571\n",
-       " 185   3304.046916\n",
-       " ...           ...\n",
-       " 1922  4754.158050\n",
-       " 1923  4699.103052\n",
-       " 1924  4812.405394\n",
-       " 1925  4862.611372\n",
-       " 1926  4860.123999\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9062.258149\n",
-       " 182    9064.006637\n",
-       " 183    9063.591886\n",
-       " 184    9051.996024\n",
-       " 185    9026.939491\n",
-       " ...            ...\n",
-       " 1922  15010.605538\n",
-       " 1923  15017.176423\n",
-       " 1924  14967.006179\n",
-       " 1925  15021.553385\n",
-       " 1926  15163.552753\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6973.791433\n",
-       " 182    6954.628882\n",
-       " 183    6932.324442\n",
-       " 184    6893.780549\n",
-       " 185    6854.930188\n",
-       " ...            ...\n",
-       " 1922  11493.754252\n",
-       " 1923  11587.649431\n",
-       " 1924  11524.503776\n",
-       " 1925  11522.792132\n",
-       " 1926  11572.385566\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6413.500129\n",
-       " 182    6433.594635\n",
-       " 183    6436.168381\n",
-       " 184    6348.182766\n",
-       " 185    6289.224067\n",
-       " ...            ...\n",
-       " 1922  10315.270892\n",
-       " 1923  10385.382531\n",
-       " 1924  10302.254530\n",
-       " 1925  10291.359914\n",
-       " 1926  10340.789207\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6090.062856\n",
-       " 182   6103.453733\n",
-       " 183   6110.681383\n",
-       " 184   6086.053231\n",
-       " 185   6063.539752\n",
-       " ...           ...\n",
-       " 1922  9748.502851\n",
-       " 1923  9765.762924\n",
-       " 1924  9725.327864\n",
-       " 1925  9744.639213\n",
-       " 1926  9813.846284\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5923.591866\n",
-       " 182   5989.617320\n",
-       " 183   6032.074690\n",
-       " 184   5952.715966\n",
-       " 185   5898.453102\n",
-       " ...           ...\n",
-       " 1922  9510.275174\n",
-       " 1923  9394.603593\n",
-       " 1924  9410.458575\n",
-       " 1925  9470.169013\n",
-       " 1926  9566.504240\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5552.502854\n",
-       " 182   5561.163892\n",
-       " 183   5561.172410\n",
-       " 184   5516.458740\n",
-       " 185   5471.891552\n",
-       " ...           ...\n",
-       " 1922  8470.514303\n",
-       " 1923  8439.335299\n",
-       " 1924  8398.724325\n",
-       " 1925  8453.729886\n",
-       " 1926  8588.587210\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4481.093592\n",
-       " 182   4491.626354\n",
-       " 183   4495.237918\n",
-       " 184   4463.075962\n",
-       " 185   4431.515559\n",
-       " ...           ...\n",
-       " 1922  6907.094830\n",
-       " 1923  6899.207871\n",
-       " 1924  6836.482456\n",
-       " 1925  6840.885156\n",
-       " 1926  6901.348225\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3778.243732\n",
-       " 182   3796.690683\n",
-       " 183   3805.730130\n",
-       " 184   3766.145117\n",
-       " 185   3734.873626\n",
-       " ...           ...\n",
-       " 1922  5978.312298\n",
-       " 1923  5919.357519\n",
-       " 1924  5848.155433\n",
-       " 1925  5842.365524\n",
-       " 1926  5891.202960\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3665.011590\n",
-       " 182   3635.456273\n",
-       " 183   3605.677536\n",
-       " 184   3574.744012\n",
-       " 185   3549.856445\n",
-       " ...           ...\n",
-       " 1922  5527.717428\n",
-       " 1923  5541.153450\n",
-       " 1924  5485.550671\n",
-       " 1925  5502.776271\n",
-       " 1926  5580.822673\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3553.386985\n",
-       " 182   3543.813649\n",
-       " 183   3534.995309\n",
-       " 184   3530.079316\n",
-       " 185   3526.221403\n",
-       " ...           ...\n",
-       " 1922  5439.653651\n",
-       " 1923  5461.690807\n",
-       " 1924  5437.377123\n",
-       " 1925  5486.489571\n",
-       " 1926  5596.922020\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3675.081951\n",
-       " 182   3668.855641\n",
-       " 183   3657.435079\n",
-       " 184   3619.167044\n",
-       " 185   3589.061442\n",
-       " ...           ...\n",
-       " 1922  5598.488833\n",
-       " 1923  5660.456365\n",
-       " 1924  5688.334677\n",
-       " 1925  5754.955345\n",
-       " 1926  5853.930724\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3778.538897\n",
-       " 182   3786.988241\n",
-       " 183   3783.647686\n",
-       " 184   3719.368805\n",
-       " 185   3671.718483\n",
-       " ...           ...\n",
-       " 1922  5749.793521\n",
-       " 1923  5741.197467\n",
-       " 1924  5682.491284\n",
-       " 1925  5704.012341\n",
-       " 1926  5792.533174\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3684.371084\n",
-       " 182   3628.824209\n",
-       " 183   3583.000492\n",
-       " 184   3587.432758\n",
-       " 185   3582.186781\n",
-       " ...           ...\n",
-       " 1922  5576.265018\n",
-       " 1923  5631.287710\n",
-       " 1924  5554.830579\n",
-       " 1925  5534.101732\n",
-       " 1926  5559.912970\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3399.753926\n",
-       " 182   3416.156143\n",
-       " 183   3423.042191\n",
-       " 184   3380.742119\n",
-       " 185   3344.335599\n",
-       " ...           ...\n",
-       " 1922  5226.515788\n",
-       " 1923  5226.788060\n",
-       " 1924  5212.876332\n",
-       " 1925  5221.156991\n",
-       " 1926  5247.971070\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3131.833418\n",
-       " 182   3141.538560\n",
-       " 183   3143.348671\n",
-       " 184   3104.351827\n",
-       " 185   3074.273489\n",
-       " ...           ...\n",
-       " 1922  4728.444859\n",
-       " 1923  4692.556546\n",
-       " 1924  4667.097213\n",
-       " 1925  4666.097303\n",
-       " 1926  4685.524072\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3471.296498\n",
-       " 182   3376.400203\n",
-       " 183   3304.956851\n",
-       " 184   3354.734472\n",
-       " 185   3390.906627\n",
-       " ...           ...\n",
-       " 1922  5035.247080\n",
-       " 1923  5060.136068\n",
-       " 1924  5026.379130\n",
-       " 1925  5023.337178\n",
-       " 1926  5045.946079\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3618.237115\n",
-       " 182   3598.673858\n",
-       " 183   3573.804978\n",
-       " 184   3521.512979\n",
-       " 185   3485.245869\n",
-       " ...           ...\n",
-       " 1922  5257.035191\n",
-       " 1923  5331.752792\n",
-       " 1924  5334.548189\n",
-       " 1925  5337.915201\n",
-       " 1926  5341.759582\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3676.144213\n",
-       " 182   3680.582217\n",
-       " 183   3679.503966\n",
-       " 184   3649.913908\n",
-       " 185   3622.135514\n",
-       " ...           ...\n",
-       " 1922  5315.315095\n",
-       " 1923  5325.196162\n",
-       " 1924  5292.517723\n",
-       " 1925  5337.812719\n",
-       " 1926  5448.225282\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3583.546907\n",
-       " 182   3578.642799\n",
-       " 183   3569.734750\n",
-       " 184   3540.131573\n",
-       " 185   3514.306048\n",
-       " ...           ...\n",
-       " 1922  5279.432173\n",
-       " 1923  5270.318780\n",
-       " 1924  5186.510765\n",
-       " 1925  5176.074536\n",
-       " 1926  5226.912920\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3189.099247\n",
-       " 182   3177.482815\n",
-       " 183   3167.378183\n",
-       " 184   3165.087567\n",
-       " 185   3158.713489\n",
-       " ...           ...\n",
-       " 1922  4623.336510\n",
-       " 1923  4678.747593\n",
-       " 1924  4678.326093\n",
-       " 1925  4653.990689\n",
-       " 1926  4609.684187\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3066.873394\n",
-       " 182   3046.577779\n",
-       " 183   3033.092820\n",
-       " 184   3054.810024\n",
-       " 185   3063.070047\n",
-       " ...           ...\n",
-       " 1922  4403.451902\n",
-       " 1923  4344.607166\n",
-       " 1924  4347.271100\n",
-       " 1925  4378.975121\n",
-       " 1926  4434.931245\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2976.370999\n",
-       " 182   2980.725460\n",
-       " 183   2978.914541\n",
-       " 184   2945.236693\n",
-       " 185   2916.243458\n",
-       " ...           ...\n",
-       " 1922  4289.904075\n",
-       " 1923  4271.421939\n",
-       " 1924  4200.000748\n",
-       " 1925  4198.546533\n",
-       " 1926  4255.523490\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3112.702936\n",
-       " 182   3137.735446\n",
-       " 183   3153.686607\n",
-       " 184   3122.699096\n",
-       " 185   3102.748083\n",
-       " ...           ...\n",
-       " 1922  4557.507369\n",
-       " 1923  4552.334480\n",
-       " 1924  4582.152263\n",
-       " 1925  4595.485513\n",
-       " 1926  4595.052116\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3257.808341\n",
-       " 182   3184.214843\n",
-       " 183   3119.691092\n",
-       " 184   3102.046038\n",
-       " 185   3087.121270\n",
-       " ...           ...\n",
-       " 1922  4614.982081\n",
-       " 1923  4676.393227\n",
-       " 1924  4719.790771\n",
-       " 1925  4696.042260\n",
-       " 1926  4616.218396\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3410.437257\n",
-       " 182   3391.515634\n",
-       " 183   3367.504931\n",
-       " 184   3317.190353\n",
-       " 185   3279.573792\n",
-       " ...           ...\n",
-       " 1922  4958.003226\n",
-       " 1923  5011.272758\n",
-       " 1924  5007.765507\n",
-       " 1925  5008.240413\n",
-       " 1926  5012.040918\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3214.485959\n",
-       " 182   3185.543419\n",
-       " 183   3167.087671\n",
-       " 184   3202.834894\n",
-       " 185   3224.067771\n",
-       " ...           ...\n",
-       " 1922  4703.267046\n",
-       " 1923  4679.179025\n",
-       " 1924  4769.526132\n",
-       " 1925  4778.535953\n",
-       " 1926  4719.618971\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3093.425539\n",
-       " 182   3080.464728\n",
-       " 183   3067.933548\n",
-       " 184   3057.623004\n",
-       " 185   3042.472009\n",
-       " ...           ...\n",
-       " 1922  4372.958994\n",
-       " 1923  4368.932284\n",
-       " 1924  4441.501647\n",
-       " 1925  4433.856403\n",
-       " 1926  4359.221933\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3193.171756\n",
-       " 182   3183.555324\n",
-       " 183   3169.609308\n",
-       " 184   3133.285017\n",
-       " 185   3102.552416\n",
-       " ...           ...\n",
-       " 1922  4512.075836\n",
-       " 1923  4456.867628\n",
-       " 1924  4467.893166\n",
-       " 1925  4466.402542\n",
-       " 1926  4454.459360\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3333.200276\n",
-       " 182   3303.605392\n",
-       " 183   3274.544340\n",
-       " 184   3248.242500\n",
-       " 185   3227.987105\n",
-       " ...           ...\n",
-       " 1922  4767.720999\n",
-       " 1923  4739.507476\n",
-       " 1924  4715.097638\n",
-       " 1925  4721.411281\n",
-       " 1926  4753.382872\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3421.695476\n",
-       " 182   3412.106388\n",
-       " 183   3395.699506\n",
-       " 184   3344.053571\n",
-       " 185   3304.046916\n",
-       " ...           ...\n",
-       " 1922  4754.158050\n",
-       " 1923  4699.103052\n",
-       " 1924  4812.405394\n",
-       " 1925  4862.611372\n",
-       " 1926  4860.123999\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   19959.902670\n",
-       " 182   19934.934963\n",
-       " 183   19926.002522\n",
-       " 184   19951.714215\n",
-       " 185   19912.638510\n",
-       " ...            ...\n",
-       " 1922  57282.226895\n",
-       " 1923  57384.770113\n",
-       " 1924  57419.565567\n",
-       " 1925  57646.858710\n",
-       " 1926  57887.832877\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   16102.243794\n",
-       " 182   16026.110601\n",
-       " 183   15963.490567\n",
-       " 184   15930.065668\n",
-       " 185   15897.621774\n",
-       " ...            ...\n",
-       " 1922  37304.424488\n",
-       " 1923  37321.521246\n",
-       " 1924  36956.700539\n",
-       " 1925  37072.652063\n",
-       " 1926  37354.943110\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15502.306853\n",
-       " 182   15484.144897\n",
-       " 183   15428.439601\n",
-       " 184   15291.622061\n",
-       " 185   15207.335193\n",
-       " ...            ...\n",
-       " 1922  35324.276805\n",
-       " 1923  35523.197676\n",
-       " 1924  35135.154747\n",
-       " 1925  35238.379492\n",
-       " 1926  35481.774664\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15055.132515\n",
-       " 182   14927.503360\n",
-       " 183   14827.528671\n",
-       " 184   14787.301352\n",
-       " 185   14752.352710\n",
-       " ...            ...\n",
-       " 1922  32098.396526\n",
-       " 1923  32196.758741\n",
-       " 1924  32054.799146\n",
-       " 1925  32207.484271\n",
-       " 1926  32421.510117\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13759.376945\n",
-       " 182   13633.593906\n",
-       " 183   13543.691777\n",
-       " 184   13531.310225\n",
-       " 185   13481.954050\n",
-       " ...            ...\n",
-       " 1922  29652.742532\n",
-       " 1923  29535.871067\n",
-       " 1924  29438.474602\n",
-       " 1925  29464.244820\n",
-       " 1926  29501.235889\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13074.080233\n",
-       " 182   12959.557536\n",
-       " 183   12852.226383\n",
-       " 184   12760.432532\n",
-       " 185   12718.679881\n",
-       " ...            ...\n",
-       " 1922  28638.917955\n",
-       " 1923  28538.743634\n",
-       " 1924  28454.795402\n",
-       " 1925  28573.112017\n",
-       " 1926  28736.495688\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13566.345427\n",
-       " 182   13407.181462\n",
-       " 183   13292.552487\n",
-       " 184   13274.141197\n",
-       " 185   13236.977767\n",
-       " ...            ...\n",
-       " 1922  26858.881715\n",
-       " 1923  26843.468947\n",
-       " 1924  26876.289654\n",
-       " 1925  26914.018893\n",
-       " 1926  26903.328789\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11615.153939\n",
-       " 182   11597.861811\n",
-       " 183   11560.322822\n",
-       " 184   11479.040564\n",
-       " 185   11411.859327\n",
-       " ...            ...\n",
-       " 1922  22887.661956\n",
-       " 1923  22819.495808\n",
-       " 1924  22720.161212\n",
-       " 1925  22901.485024\n",
-       " 1926  23136.639225\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11532.423064\n",
-       " 182   11489.102178\n",
-       " 183   11458.866633\n",
-       " 184   11456.901915\n",
-       " 185   11421.769941\n",
-       " ...            ...\n",
-       " 1922  21720.318834\n",
-       " 1923  21789.469840\n",
-       " 1924  21566.736254\n",
-       " 1925  21652.624399\n",
-       " 1926  21835.453343\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12678.054341\n",
-       " 182   12677.568468\n",
-       " 183   12659.575004\n",
-       " 184   12603.756456\n",
-       " 185   12564.594411\n",
-       " ...            ...\n",
-       " 1922  23606.881436\n",
-       " 1923  23635.010647\n",
-       " 1924  23459.528323\n",
-       " 1925  23514.985538\n",
-       " 1926  23626.891964\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11478.909078\n",
-       " 182   11474.276889\n",
-       " 183   11437.794108\n",
-       " 184   11332.498240\n",
-       " 185   11227.862941\n",
-       " ...            ...\n",
-       " 1922  21214.224856\n",
-       " 1923  21184.450752\n",
-       " 1924  21315.755045\n",
-       " 1925  21392.497912\n",
-       " 1926  21410.123952\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12954.456195\n",
-       " 182   12964.677688\n",
-       " 183   12935.030678\n",
-       " 184   12819.247917\n",
-       " 185   12716.520933\n",
-       " ...            ...\n",
-       " 1922  26642.053378\n",
-       " 1923  26665.629276\n",
-       " 1924  26727.675994\n",
-       " 1925  26914.331586\n",
-       " 1926  27075.807050\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12893.931537\n",
-       " 182   12807.407829\n",
-       " 183   12719.211514\n",
-       " 184   12627.401534\n",
-       " 185   12559.303179\n",
-       " ...            ...\n",
-       " 1922  27221.077844\n",
-       " 1923  27257.113594\n",
-       " 1924  27334.907787\n",
-       " 1925  27386.123330\n",
-       " 1926  27380.983248\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11361.945114\n",
-       " 182   11313.266005\n",
-       " 183   11262.064156\n",
-       " 184   11205.411939\n",
-       " 185   11136.301065\n",
-       " ...            ...\n",
-       " 1922  19614.170606\n",
-       " 1923  19695.029597\n",
-       " 1924  19670.490456\n",
-       " 1925  19810.435774\n",
-       " 1926  19962.577439\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11868.161704\n",
-       " 182   11830.218215\n",
-       " 183   11814.640337\n",
-       " 184   11847.383281\n",
-       " 185   11862.941302\n",
-       " ...            ...\n",
-       " 1922  20862.683152\n",
-       " 1923  20790.228453\n",
-       " 1924  20645.705726\n",
-       " 1925  20767.829718\n",
-       " 1926  20970.213070\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10359.260977\n",
-       " 182   10327.808406\n",
-       " 183   10279.697458\n",
-       " 184   10195.596147\n",
-       " 185   10141.036415\n",
-       " ...            ...\n",
-       " 1922  18551.847507\n",
-       " 1923  18633.867183\n",
-       " 1924  18536.065415\n",
-       " 1925  18624.426682\n",
-       " 1926  18751.369287\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11206.812719\n",
-       " 182   11106.077105\n",
-       " 183   11021.178929\n",
-       " 184   10970.497477\n",
-       " 185   10894.198210\n",
-       " ...            ...\n",
-       " 1922  19412.428673\n",
-       " 1923  19254.055806\n",
-       " 1924  19207.520452\n",
-       " 1925  19269.467633\n",
-       " 1926  19344.310188\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11602.632452\n",
-       " 182   11592.010858\n",
-       " 183   11550.383428\n",
-       " 184   11441.768005\n",
-       " 185   11378.114665\n",
-       " ...            ...\n",
-       " 1922  20869.325605\n",
-       " 1923  20925.941270\n",
-       " 1924  20781.436219\n",
-       " 1925  20928.776936\n",
-       " 1926  21149.377229\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11132.916511\n",
-       " 182   11082.209147\n",
-       " 183   11027.291768\n",
-       " 184   10963.278663\n",
-       " 185   10895.660778\n",
-       " ...            ...\n",
-       " 1922  19505.703737\n",
-       " 1923  19456.491892\n",
-       " 1924  19404.760832\n",
-       " 1925  19517.507465\n",
-       " 1926  19636.280310\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9909.448200\n",
-       " 182    9891.285235\n",
-       " 183    9841.695901\n",
-       " 184    9724.210014\n",
-       " 185    9657.500985\n",
-       " ...            ...\n",
-       " 1922  17168.805616\n",
-       " 1923  17100.018517\n",
-       " 1924  17207.797557\n",
-       " 1925  17305.107374\n",
-       " 1926  17356.864206\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9873.391244\n",
-       " 182    9896.587508\n",
-       " 183    9884.553562\n",
-       " 184    9796.404872\n",
-       " 185    9705.894800\n",
-       " ...            ...\n",
-       " 1922  17082.008985\n",
-       " 1923  17024.961405\n",
-       " 1924  17032.450041\n",
-       " 1925  17168.732873\n",
-       " 1926  17305.712763\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11462.393950\n",
-       " 182   11484.445916\n",
-       " 183   11455.658567\n",
-       " 184   11317.033063\n",
-       " 185   11219.037669\n",
-       " ...            ...\n",
-       " 1922  19707.102066\n",
-       " 1923  19741.312687\n",
-       " 1924  19520.394757\n",
-       " 1925  19648.697355\n",
-       " 1926  19889.478182\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11640.371173\n",
-       " 182   11595.061388\n",
-       " 183   11519.384699\n",
-       " 184   11378.100424\n",
-       " 185   11284.279268\n",
-       " ...            ...\n",
-       " 1922  18655.816318\n",
-       " 1923  18619.432372\n",
-       " 1924  18551.922271\n",
-       " 1925  18687.381619\n",
-       " 1926  18843.989917\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11122.674677\n",
-       " 182   11128.674172\n",
-       " 183   11108.938568\n",
-       " 184   11033.602379\n",
-       " 185   10970.853650\n",
-       " ...            ...\n",
-       " 1922  19796.204764\n",
-       " 1923  19760.667527\n",
-       " 1924  19658.836719\n",
-       " 1925  19828.563699\n",
-       " 1926  20049.101694\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11737.808016\n",
-       " 182   11720.530520\n",
-       " 183   11693.341981\n",
-       " 184   11644.740670\n",
-       " 185   11614.828342\n",
-       " ...            ...\n",
-       " 1922  20171.051655\n",
-       " 1923  20242.045051\n",
-       " 1924  20326.216768\n",
-       " 1925  20308.216921\n",
-       " 1926  20235.685734\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10732.658722\n",
-       " 182   10599.842888\n",
-       " 183   10502.065130\n",
-       " 184   10479.987005\n",
-       " 185   10471.306208\n",
-       " ...            ...\n",
-       " 1922  17617.036610\n",
-       " 1923  17555.790018\n",
-       " 1924  17679.298856\n",
-       " 1925  17753.844178\n",
-       " 1926  17788.675417\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11948.875429\n",
-       " 182   11928.954644\n",
-       " 183   11899.966982\n",
-       " 184   11851.390370\n",
-       " 185   11755.931553\n",
-       " ...            ...\n",
-       " 1922  19780.957966\n",
-       " 1923  19745.491544\n",
-       " 1924  19685.443564\n",
-       " 1925  19753.471644\n",
-       " 1926  19842.241406\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9889.820676\n",
-       " 182    9829.624413\n",
-       " 183    9781.250697\n",
-       " 184    9758.419548\n",
-       " 185    9752.529644\n",
-       " ...            ...\n",
-       " 1922  17065.980243\n",
-       " 1923  17144.940410\n",
-       " 1924  17161.845713\n",
-       " 1925  17238.252270\n",
-       " 1926  17301.911237\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9633.718687\n",
-       " 182    9607.516872\n",
-       " 183    9576.257091\n",
-       " 184    9534.069591\n",
-       " 185    9480.196528\n",
-       " ...            ...\n",
-       " 1922  16014.767339\n",
-       " 1923  16005.047718\n",
-       " 1924  16122.083072\n",
-       " 1925  16284.648169\n",
-       " 1926  16407.413390\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10116.220383\n",
-       " 182   10037.370237\n",
-       " 183    9954.526053\n",
-       " 184    9863.052767\n",
-       " 185    9813.519611\n",
-       " ...            ...\n",
-       " 1922  17101.048324\n",
-       " 1923  17075.044805\n",
-       " 1924  17082.539754\n",
-       " 1925  17157.233293\n",
-       " 1926  17230.815430\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   19959.902670\n",
-       " 182   19934.934963\n",
-       " 183   19926.002522\n",
-       " 184   19951.714215\n",
-       " 185   19912.638510\n",
-       " ...            ...\n",
-       " 1922  57282.226895\n",
-       " 1923  57384.770113\n",
-       " 1924  57419.565567\n",
-       " 1925  57646.858710\n",
-       " 1926  57887.832877\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   16102.243794\n",
-       " 182   16026.110601\n",
-       " 183   15963.490567\n",
-       " 184   15930.065668\n",
-       " 185   15897.621774\n",
-       " ...            ...\n",
-       " 1922  37304.424488\n",
-       " 1923  37321.521246\n",
-       " 1924  36956.700539\n",
-       " 1925  37072.652063\n",
-       " 1926  37354.943110\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15502.306853\n",
-       " 182   15484.144897\n",
-       " 183   15428.439601\n",
-       " 184   15291.622061\n",
-       " 185   15207.335193\n",
-       " ...            ...\n",
-       " 1922  35324.276805\n",
-       " 1923  35523.197676\n",
-       " 1924  35135.154747\n",
-       " 1925  35238.379492\n",
-       " 1926  35481.774664\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15055.132515\n",
-       " 182   14927.503360\n",
-       " 183   14827.528671\n",
-       " 184   14787.301352\n",
-       " 185   14752.352710\n",
-       " ...            ...\n",
-       " 1922  32098.396526\n",
-       " 1923  32196.758741\n",
-       " 1924  32054.799146\n",
-       " 1925  32207.484271\n",
-       " 1926  32421.510117\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13759.376945\n",
-       " 182   13633.593906\n",
-       " 183   13543.691777\n",
-       " 184   13531.310225\n",
-       " 185   13481.954050\n",
-       " ...            ...\n",
-       " 1922  29652.742532\n",
-       " 1923  29535.871067\n",
-       " 1924  29438.474602\n",
-       " 1925  29464.244820\n",
-       " 1926  29501.235889\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13074.080233\n",
-       " 182   12959.557536\n",
-       " 183   12852.226383\n",
-       " 184   12760.432532\n",
-       " 185   12718.679881\n",
-       " ...            ...\n",
-       " 1922  28638.917955\n",
-       " 1923  28538.743634\n",
-       " 1924  28454.795402\n",
-       " 1925  28573.112017\n",
-       " 1926  28736.495688\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13566.345427\n",
-       " 182   13407.181462\n",
-       " 183   13292.552487\n",
-       " 184   13274.141197\n",
-       " 185   13236.977767\n",
-       " ...            ...\n",
-       " 1922  26858.881715\n",
-       " 1923  26843.468947\n",
-       " 1924  26876.289654\n",
-       " 1925  26914.018893\n",
-       " 1926  26903.328789\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11615.153939\n",
-       " 182   11597.861811\n",
-       " 183   11560.322822\n",
-       " 184   11479.040564\n",
-       " 185   11411.859327\n",
-       " ...            ...\n",
-       " 1922  22887.661956\n",
-       " 1923  22819.495808\n",
-       " 1924  22720.161212\n",
-       " 1925  22901.485024\n",
-       " 1926  23136.639225\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11532.423064\n",
-       " 182   11489.102178\n",
-       " 183   11458.866633\n",
-       " 184   11456.901915\n",
-       " 185   11421.769941\n",
-       " ...            ...\n",
-       " 1922  21720.318834\n",
-       " 1923  21789.469840\n",
-       " 1924  21566.736254\n",
-       " 1925  21652.624399\n",
-       " 1926  21835.453343\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12678.054341\n",
-       " 182   12677.568468\n",
-       " 183   12659.575004\n",
-       " 184   12603.756456\n",
-       " 185   12564.594411\n",
-       " ...            ...\n",
-       " 1922  23606.881436\n",
-       " 1923  23635.010647\n",
-       " 1924  23459.528323\n",
-       " 1925  23514.985538\n",
-       " 1926  23626.891964\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11478.909078\n",
-       " 182   11474.276889\n",
-       " 183   11437.794108\n",
-       " 184   11332.498240\n",
-       " 185   11227.862941\n",
-       " ...            ...\n",
-       " 1922  21214.224856\n",
-       " 1923  21184.450752\n",
-       " 1924  21315.755045\n",
-       " 1925  21392.497912\n",
-       " 1926  21410.123952\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12954.456195\n",
-       " 182   12964.677688\n",
-       " 183   12935.030678\n",
-       " 184   12819.247917\n",
-       " 185   12716.520933\n",
-       " ...            ...\n",
-       " 1922  26642.053378\n",
-       " 1923  26665.629276\n",
-       " 1924  26727.675994\n",
-       " 1925  26914.331586\n",
-       " 1926  27075.807050\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12893.931537\n",
-       " 182   12807.407829\n",
-       " 183   12719.211514\n",
-       " 184   12627.401534\n",
-       " 185   12559.303179\n",
-       " ...            ...\n",
-       " 1922  27221.077844\n",
-       " 1923  27257.113594\n",
-       " 1924  27334.907787\n",
-       " 1925  27386.123330\n",
-       " 1926  27380.983248\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11361.945114\n",
-       " 182   11313.266005\n",
-       " 183   11262.064156\n",
-       " 184   11205.411939\n",
-       " 185   11136.301065\n",
-       " ...            ...\n",
-       " 1922  19614.170606\n",
-       " 1923  19695.029597\n",
-       " 1924  19670.490456\n",
-       " 1925  19810.435774\n",
-       " 1926  19962.577439\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11868.161704\n",
-       " 182   11830.218215\n",
-       " 183   11814.640337\n",
-       " 184   11847.383281\n",
-       " 185   11862.941302\n",
-       " ...            ...\n",
-       " 1922  20862.683152\n",
-       " 1923  20790.228453\n",
-       " 1924  20645.705726\n",
-       " 1925  20767.829718\n",
-       " 1926  20970.213070\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10359.260977\n",
-       " 182   10327.808406\n",
-       " 183   10279.697458\n",
-       " 184   10195.596147\n",
-       " 185   10141.036415\n",
-       " ...            ...\n",
-       " 1922  18551.847507\n",
-       " 1923  18633.867183\n",
-       " 1924  18536.065415\n",
-       " 1925  18624.426682\n",
-       " 1926  18751.369287\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11206.812719\n",
-       " 182   11106.077105\n",
-       " 183   11021.178929\n",
-       " 184   10970.497477\n",
-       " 185   10894.198210\n",
-       " ...            ...\n",
-       " 1922  19412.428673\n",
-       " 1923  19254.055806\n",
-       " 1924  19207.520452\n",
-       " 1925  19269.467633\n",
-       " 1926  19344.310188\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11602.632452\n",
-       " 182   11592.010858\n",
-       " 183   11550.383428\n",
-       " 184   11441.768005\n",
-       " 185   11378.114665\n",
-       " ...            ...\n",
-       " 1922  20869.325605\n",
-       " 1923  20925.941270\n",
-       " 1924  20781.436219\n",
-       " 1925  20928.776936\n",
-       " 1926  21149.377229\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11132.916511\n",
-       " 182   11082.209147\n",
-       " 183   11027.291768\n",
-       " 184   10963.278663\n",
-       " 185   10895.660778\n",
-       " ...            ...\n",
-       " 1922  19505.703737\n",
-       " 1923  19456.491892\n",
-       " 1924  19404.760832\n",
-       " 1925  19517.507465\n",
-       " 1926  19636.280310\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9909.448200\n",
-       " 182    9891.285235\n",
-       " 183    9841.695901\n",
-       " 184    9724.210014\n",
-       " 185    9657.500985\n",
-       " ...            ...\n",
-       " 1922  17168.805616\n",
-       " 1923  17100.018517\n",
-       " 1924  17207.797557\n",
-       " 1925  17305.107374\n",
-       " 1926  17356.864206\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9873.391244\n",
-       " 182    9896.587508\n",
-       " 183    9884.553562\n",
-       " 184    9796.404872\n",
-       " 185    9705.894800\n",
-       " ...            ...\n",
-       " 1922  17082.008985\n",
-       " 1923  17024.961405\n",
-       " 1924  17032.450041\n",
-       " 1925  17168.732873\n",
-       " 1926  17305.712763\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11462.393950\n",
-       " 182   11484.445916\n",
-       " 183   11455.658567\n",
-       " 184   11317.033063\n",
-       " 185   11219.037669\n",
-       " ...            ...\n",
-       " 1922  19707.102066\n",
-       " 1923  19741.312687\n",
-       " 1924  19520.394757\n",
-       " 1925  19648.697355\n",
-       " 1926  19889.478182\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11640.371173\n",
-       " 182   11595.061388\n",
-       " 183   11519.384699\n",
-       " 184   11378.100424\n",
-       " 185   11284.279268\n",
-       " ...            ...\n",
-       " 1922  18655.816318\n",
-       " 1923  18619.432372\n",
-       " 1924  18551.922271\n",
-       " 1925  18687.381619\n",
-       " 1926  18843.989917\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11122.674677\n",
-       " 182   11128.674172\n",
-       " 183   11108.938568\n",
-       " 184   11033.602379\n",
-       " 185   10970.853650\n",
-       " ...            ...\n",
-       " 1922  19796.204764\n",
-       " 1923  19760.667527\n",
-       " 1924  19658.836719\n",
-       " 1925  19828.563699\n",
-       " 1926  20049.101694\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11737.808016\n",
-       " 182   11720.530520\n",
-       " 183   11693.341981\n",
-       " 184   11644.740670\n",
-       " 185   11614.828342\n",
-       " ...            ...\n",
-       " 1922  20171.051655\n",
-       " 1923  20242.045051\n",
-       " 1924  20326.216768\n",
-       " 1925  20308.216921\n",
-       " 1926  20235.685734\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10732.658722\n",
-       " 182   10599.842888\n",
-       " 183   10502.065130\n",
-       " 184   10479.987005\n",
-       " 185   10471.306208\n",
-       " ...            ...\n",
-       " 1922  17617.036610\n",
-       " 1923  17555.790018\n",
-       " 1924  17679.298856\n",
-       " 1925  17753.844178\n",
-       " 1926  17788.675417\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11948.875429\n",
-       " 182   11928.954644\n",
-       " 183   11899.966982\n",
-       " 184   11851.390370\n",
-       " 185   11755.931553\n",
-       " ...            ...\n",
-       " 1922  19780.957966\n",
-       " 1923  19745.491544\n",
-       " 1924  19685.443564\n",
-       " 1925  19753.471644\n",
-       " 1926  19842.241406\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9889.820676\n",
-       " 182    9829.624413\n",
-       " 183    9781.250697\n",
-       " 184    9758.419548\n",
-       " 185    9752.529644\n",
-       " ...            ...\n",
-       " 1922  17065.980243\n",
-       " 1923  17144.940410\n",
-       " 1924  17161.845713\n",
-       " 1925  17238.252270\n",
-       " 1926  17301.911237\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9633.718687\n",
-       " 182    9607.516872\n",
-       " 183    9576.257091\n",
-       " 184    9534.069591\n",
-       " 185    9480.196528\n",
-       " ...            ...\n",
-       " 1922  16014.767339\n",
-       " 1923  16005.047718\n",
-       " 1924  16122.083072\n",
-       " 1925  16284.648169\n",
-       " 1926  16407.413390\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10116.220383\n",
-       " 182   10037.370237\n",
-       " 183    9954.526053\n",
-       " 184    9863.052767\n",
-       " 185    9813.519611\n",
-       " ...            ...\n",
-       " 1922  17101.048324\n",
-       " 1923  17075.044805\n",
-       " 1924  17082.539754\n",
-       " 1925  17157.233293\n",
-       " 1926  17230.815430\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4021.523293\n",
-       " 182   4006.673746\n",
-       " 183   3988.683363\n",
-       " 184   3954.458971\n",
-       " 185   3937.017488\n",
-       " ...           ...\n",
-       " 1922  7638.301906\n",
-       " 1923  7618.588415\n",
-       " 1924  7681.156517\n",
-       " 1925  7776.235417\n",
-       " 1926  7898.464899\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3708.059896\n",
-       " 182   3721.832830\n",
-       " 183   3729.983562\n",
-       " 184   3709.074885\n",
-       " 185   3694.213196\n",
-       " ...           ...\n",
-       " 1922  7036.442245\n",
-       " 1923  7043.672836\n",
-       " 1924  7091.558085\n",
-       " 1925  7107.032952\n",
-       " 1926  7095.441096\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3804.715337\n",
-       " 182   3777.842731\n",
-       " 183   3745.987319\n",
-       " 184   3688.377333\n",
-       " 185   3650.269970\n",
-       " ...           ...\n",
-       " 1922  7157.587447\n",
-       " 1923  7220.912860\n",
-       " 1924  7160.316702\n",
-       " 1925  7152.616443\n",
-       " 1926  7189.090874\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3907.536887\n",
-       " 182   3992.650168\n",
-       " 183   4055.281784\n",
-       " 184   4001.712665\n",
-       " 185   3961.446176\n",
-       " ...           ...\n",
-       " 1922  7647.248175\n",
-       " 1923  7666.480969\n",
-       " 1924  7619.507131\n",
-       " 1925  7615.762244\n",
-       " 1926  7648.118936\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4040.077335\n",
-       " 182   4081.883282\n",
-       " 183   4112.015510\n",
-       " 184   4081.809911\n",
-       " 185   4055.684342\n",
-       " ...           ...\n",
-       " 1922  7691.333107\n",
-       " 1923  7667.846672\n",
-       " 1924  7669.909093\n",
-       " 1925  7665.710115\n",
-       " 1926  7656.282085\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4161.758170\n",
-       " 182   4154.923492\n",
-       " 183   4151.549034\n",
-       " 184   4166.059377\n",
-       " 185   4166.960506\n",
-       " ...           ...\n",
-       " 1922  7939.551768\n",
-       " 1923  7915.775861\n",
-       " 1924  7874.051709\n",
-       " 1925  7858.480392\n",
-       " 1926  7864.749963\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3288.506569\n",
-       " 182   3250.908156\n",
-       " 183   3218.168990\n",
-       " 184   3210.545757\n",
-       " 185   3202.165562\n",
-       " ...           ...\n",
-       " 1922  6197.080221\n",
-       " 1923  6169.873706\n",
-       " 1924  6134.177612\n",
-       " 1925  6173.035934\n",
-       " 1926  6274.156516\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2477.706265\n",
-       " 182   2466.004904\n",
-       " 183   2459.926247\n",
-       " 184   2482.909596\n",
-       " 185   2502.719535\n",
-       " ...           ...\n",
-       " 1922  4561.913766\n",
-       " 1923  4563.596640\n",
-       " 1924  4575.679555\n",
-       " 1925  4570.122911\n",
-       " 1926  4549.835032\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2178.289523\n",
-       " 182   2232.537004\n",
-       " 183   2274.464588\n",
-       " 184   2252.714463\n",
-       " 185   2235.952674\n",
-       " ...           ...\n",
-       " 1922  3911.583812\n",
-       " 1923  3844.060659\n",
-       " 1924  3815.358734\n",
-       " 1925  3861.787018\n",
-       " 1926  3970.958419\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2109.567847\n",
-       " 182   2057.165440\n",
-       " 183   2014.162416\n",
-       " 184   2019.741881\n",
-       " 185   2022.448554\n",
-       " ...           ...\n",
-       " 1922  3346.361444\n",
-       " 1923  3366.716494\n",
-       " 1924  3347.340862\n",
-       " 1925  3339.334886\n",
-       " 1926  3340.823996\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1969.479775\n",
-       " 182   1962.383315\n",
-       " 183   1958.097912\n",
-       " 184   1968.341983\n",
-       " 185   1968.001036\n",
-       " ...           ...\n",
-       " 1922  3036.136339\n",
-       " 1923  3040.947491\n",
-       " 1924  3027.788443\n",
-       " 1925  3027.143398\n",
-       " 1926  3036.949111\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1924.527342\n",
-       " 182   1932.755290\n",
-       " 183   1932.871508\n",
-       " 184   1891.060713\n",
-       " 185   1861.797605\n",
-       " ...           ...\n",
-       " 1922  2819.423705\n",
-       " 1923  2790.424427\n",
-       " 1924  2810.618312\n",
-       " 1925  2839.371029\n",
-       " 1926  2875.271439\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1907.042499\n",
-       " 182   1895.273031\n",
-       " 183   1880.363464\n",
-       " 184   1849.223708\n",
-       " 185   1828.818000\n",
-       " ...           ...\n",
-       " 1922  2764.710390\n",
-       " 1923  2722.025100\n",
-       " 1924  2788.115742\n",
-       " 1925  2833.200027\n",
-       " 1926  2860.741378\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1953.514861\n",
-       " 182   1915.757048\n",
-       " 183   1881.343888\n",
-       " 184   1864.218195\n",
-       " 185   1857.374342\n",
-       " ...           ...\n",
-       " 1922  2854.149700\n",
-       " 1923  2857.204800\n",
-       " 1924  2865.930999\n",
-       " 1925  2893.454238\n",
-       " 1926  2936.675355\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2092.635235\n",
-       " 182   2103.549297\n",
-       " 183   2112.410483\n",
-       " 184   2110.660987\n",
-       " 185   2102.559665\n",
-       " ...           ...\n",
-       " 1922  3231.504863\n",
-       " 1923  3261.795386\n",
-       " 1924  3277.878458\n",
-       " 1925  3245.579978\n",
-       " 1926  3172.876852\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2436.317737\n",
-       " 182   2397.951054\n",
-       " 183   2359.899777\n",
-       " 184   2323.478740\n",
-       " 185   2303.084224\n",
-       " ...           ...\n",
-       " 1922  3666.852090\n",
-       " 1923  3723.185859\n",
-       " 1924  3663.168358\n",
-       " 1925  3651.492205\n",
-       " 1926  3680.187123\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2526.310101\n",
-       " 182   2535.942913\n",
-       " 183   2542.765279\n",
-       " 184   2535.061319\n",
-       " 185   2520.551178\n",
-       " ...           ...\n",
-       " 1922  4073.572411\n",
-       " 1923  4095.554712\n",
-       " 1924  4112.791945\n",
-       " 1925  4099.868475\n",
-       " 1926  4061.757049\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2812.209788\n",
-       " 182   2840.539048\n",
-       " 183   2866.278665\n",
-       " 184   2878.633231\n",
-       " 185   2884.032530\n",
-       " ...           ...\n",
-       " 1922  4513.765990\n",
-       " 1923  4559.835935\n",
-       " 1924  4480.106735\n",
-       " 1925  4482.293538\n",
-       " 1926  4552.890444\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2643.202259\n",
-       " 182   2646.850445\n",
-       " 183   2647.470378\n",
-       " 184   2632.438214\n",
-       " 185   2630.107787\n",
-       " ...           ...\n",
-       " 1922  4296.077839\n",
-       " 1923  4352.294124\n",
-       " 1924  4350.785612\n",
-       " 1925  4358.955136\n",
-       " 1926  4375.207030\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2133.389447\n",
-       " 182   2151.639952\n",
-       " 183   2164.593859\n",
-       " 184   2150.171307\n",
-       " 185   2131.512802\n",
-       " ...           ...\n",
-       " 1922  3498.535075\n",
-       " 1923  3505.073249\n",
-       " 1924  3479.456128\n",
-       " 1925  3457.801137\n",
-       " 1926  3439.455019\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1982.560266\n",
-       " 182   1990.128563\n",
-       " 183   1994.669565\n",
-       " 184   1983.563423\n",
-       " 185   1968.070440\n",
-       " ...           ...\n",
-       " 1922  3107.864806\n",
-       " 1923  3085.989257\n",
-       " 1924  3070.749659\n",
-       " 1925  3090.227431\n",
-       " 1926  3138.698549\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1779.496839\n",
-       " 182   1796.372028\n",
-       " 183   1812.169421\n",
-       " 184   1822.396022\n",
-       " 185   1822.793750\n",
-       " ...           ...\n",
-       " 1922  2703.170354\n",
-       " 1923  2756.105253\n",
-       " 1924  2772.121174\n",
-       " 1925  2760.822031\n",
-       " 1926  2726.711393\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1707.254767\n",
-       " 182   1698.794019\n",
-       " 183   1689.033062\n",
-       " 184   1672.551731\n",
-       " 185   1664.083846\n",
-       " ...           ...\n",
-       " 1922  2365.102093\n",
-       " 1923  2333.790015\n",
-       " 1924  2360.182987\n",
-       " 1925  2375.212678\n",
-       " 1926  2380.752609\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1636.768702\n",
-       " 182   1607.637716\n",
-       " 183   1580.771615\n",
-       " 184   1565.611992\n",
-       " 185   1555.138899\n",
-       " ...           ...\n",
-       " 1922  2227.600300\n",
-       " 1923  2237.786796\n",
-       " 1924  2222.434493\n",
-       " 1925  2220.732950\n",
-       " 1926  2230.431498\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1646.742950\n",
-       " 182   1652.938137\n",
-       " 183   1658.485707\n",
-       " 184   1660.685946\n",
-       " 185   1660.164283\n",
-       " ...           ...\n",
-       " 1922  2260.510952\n",
-       " 1923  2257.710607\n",
-       " 1924  2265.313855\n",
-       " 1925  2315.057206\n",
-       " 1926  2399.992812\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1769.884145\n",
-       " 182   1742.102076\n",
-       " 183   1720.804331\n",
-       " 184   1733.022040\n",
-       " 185   1737.528294\n",
-       " ...           ...\n",
-       " 1922  2472.606704\n",
-       " 1923  2517.090721\n",
-       " 1924  2534.118308\n",
-       " 1925  2519.276748\n",
-       " 1926  2477.820466\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1883.773122\n",
-       " 182   1884.664566\n",
-       " 183   1884.258037\n",
-       " 184   1877.142686\n",
-       " 185   1874.412246\n",
-       " ...           ...\n",
-       " 1922  2790.700844\n",
-       " 1923  2713.360232\n",
-       " 1924  2773.189682\n",
-       " 1925  2818.270168\n",
-       " 1926  2851.033425\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2085.346342\n",
-       " 182   2074.931145\n",
-       " 183   2065.487124\n",
-       " 184   2061.062805\n",
-       " 185   2058.452755\n",
-       " ...           ...\n",
-       " 1922  3161.879663\n",
-       " 1923  3195.167018\n",
-       " 1924  3172.638643\n",
-       " 1925  3142.687923\n",
-       " 1926  3106.538618\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2295.824251\n",
-       " 182   2272.770572\n",
-       " 183   2252.415954\n",
-       " 184   2246.011943\n",
-       " 185   2244.749074\n",
-       " ...           ...\n",
-       " 1922  3460.911444\n",
-       " 1923  3514.082288\n",
-       " 1924  3481.163149\n",
-       " 1925  3473.835448\n",
-       " 1926  3487.879798\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2519.522740\n",
-       " 182   2525.822136\n",
-       " 183   2535.584880\n",
-       " 184   2563.248602\n",
-       " 185   2577.002076\n",
-       " ...           ...\n",
-       " 1922  3907.798473\n",
-       " 1923  3851.592181\n",
-       " 1924  3903.226485\n",
-       " 1925  3908.219650\n",
-       " 1926  3874.261633\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11541.978798\n",
-       " 182   11513.363879\n",
-       " 183   11496.858613\n",
-       " 184   11542.944382\n",
-       " 185   11561.057396\n",
-       " ...            ...\n",
-       " 1922  36617.264638\n",
-       " 1923  36796.432577\n",
-       " 1924  36601.005071\n",
-       " 1925  36781.750284\n",
-       " 1926  37276.646748\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8407.273072\n",
-       " 182    8521.583556\n",
-       " 183    8594.811974\n",
-       " 184    8455.699975\n",
-       " 185    8353.322227\n",
-       " ...            ...\n",
-       " 1922  24257.348505\n",
-       " 1923  24232.055399\n",
-       " 1924  24193.542155\n",
-       " 1925  24277.977274\n",
-       " 1926  24465.089648\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7829.136962\n",
-       " 182    7855.399311\n",
-       " 183    7868.577822\n",
-       " 184    7814.130046\n",
-       " 185    7764.818318\n",
-       " ...            ...\n",
-       " 1922  20467.481731\n",
-       " 1923  20584.284988\n",
-       " 1924  20410.699854\n",
-       " 1925  20384.991844\n",
-       " 1926  20482.779723\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7213.440575\n",
-       " 182    7202.304739\n",
-       " 183    7188.245315\n",
-       " 184    7159.074782\n",
-       " 185    7141.092150\n",
-       " ...            ...\n",
-       " 1922  18180.735164\n",
-       " 1923  18151.337843\n",
-       " 1924  18117.233175\n",
-       " 1925  18136.618172\n",
-       " 1926  18200.673728\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    5229.433843\n",
-       " 182    5248.054054\n",
-       " 183    5255.860462\n",
-       " 184    5207.773684\n",
-       " 185    5169.663006\n",
-       " ...            ...\n",
-       " 1922  10986.899887\n",
-       " 1923  10990.944483\n",
-       " 1924  10939.196055\n",
-       " 1925  10946.045151\n",
-       " 1926  11001.830504\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4822.681182\n",
-       " 182   4749.941011\n",
-       " 183   4691.355361\n",
-       " 184   4705.930033\n",
-       " 185   4708.559098\n",
-       " ...           ...\n",
-       " 1922  9588.883917\n",
-       " 1923  9570.120429\n",
-       " 1924  9579.738336\n",
-       " 1925  9601.321903\n",
-       " 1926  9632.898292\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4858.543535\n",
-       " 182   4821.799079\n",
-       " 183   4784.288614\n",
-       " 184   4742.818890\n",
-       " 185   4709.511169\n",
-       " ...           ...\n",
-       " 1922  9736.392302\n",
-       " 1923  9730.404068\n",
-       " 1924  9795.339130\n",
-       " 1925  9792.578938\n",
-       " 1926  9733.284747\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    5274.379203\n",
-       " 182    5251.681119\n",
-       " 183    5230.383993\n",
-       " 184    5216.327990\n",
-       " 185    5204.690210\n",
-       " ...            ...\n",
-       " 1922  10675.677784\n",
-       " 1923  10833.203975\n",
-       " 1924  10753.265129\n",
-       " 1925  10725.065365\n",
-       " 1926  10740.074204\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4788.588490\n",
-       " 182   4736.506081\n",
-       " 183   4692.524937\n",
-       " 184   4690.416713\n",
-       " 185   4690.879146\n",
-       " ...           ...\n",
-       " 1922  8259.229344\n",
-       " 1923  8310.328812\n",
-       " 1924  8289.523640\n",
-       " 1925  8244.216378\n",
-       " 1926  8178.446807\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4549.374495\n",
-       " 182   4506.594757\n",
-       " 183   4466.184775\n",
-       " 184   4438.023324\n",
-       " 185   4412.278609\n",
-       " ...           ...\n",
-       " 1922  7752.028360\n",
-       " 1923  7858.638708\n",
-       " 1924  7845.076602\n",
-       " 1925  7867.941491\n",
-       " 1926  7921.227475\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4736.848813\n",
-       " 182   4696.757331\n",
-       " 183   4661.632692\n",
-       " 184   4652.180120\n",
-       " 185   4649.531839\n",
-       " ...           ...\n",
-       " 1922  8161.129311\n",
-       " 1923  8203.404143\n",
-       " 1924  8179.454582\n",
-       " 1925  8235.194396\n",
-       " 1926  8357.484800\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5023.243076\n",
-       " 182   4993.849931\n",
-       " 183   4971.048882\n",
-       " 184   4982.320324\n",
-       " 185   4979.528628\n",
-       " ...           ...\n",
-       " 1922  8610.948469\n",
-       " 1923  8673.253503\n",
-       " 1924  8680.665862\n",
-       " 1925  8696.056244\n",
-       " 1926  8718.109274\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3742.601635\n",
-       " 182   3805.033969\n",
-       " 183   3853.742010\n",
-       " 184   3831.513455\n",
-       " 185   3806.560407\n",
-       " ...           ...\n",
-       " 1922  6180.102529\n",
-       " 1923  6247.673558\n",
-       " 1924  6162.951924\n",
-       " 1925  6182.347054\n",
-       " 1926  6288.692698\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11541.978798\n",
-       " 182   11513.363879\n",
-       " 183   11496.858613\n",
-       " 184   11542.944382\n",
-       " 185   11561.057396\n",
-       " ...            ...\n",
-       " 1922  36617.264638\n",
-       " 1923  36796.432577\n",
-       " 1924  36601.005071\n",
-       " 1925  36781.750284\n",
-       " 1926  37276.646748\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8407.273072\n",
-       " 182    8521.583556\n",
-       " 183    8594.811974\n",
-       " 184    8455.699975\n",
-       " 185    8353.322227\n",
-       " ...            ...\n",
-       " 1922  24257.348505\n",
-       " 1923  24232.055399\n",
-       " 1924  24193.542155\n",
-       " 1925  24277.977274\n",
-       " 1926  24465.089648\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7829.136962\n",
-       " 182    7855.399311\n",
-       " 183    7868.577822\n",
-       " 184    7814.130046\n",
-       " 185    7764.818318\n",
-       " ...            ...\n",
-       " 1922  20467.481731\n",
-       " 1923  20584.284988\n",
-       " 1924  20410.699854\n",
-       " 1925  20384.991844\n",
-       " 1926  20482.779723\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7213.440575\n",
-       " 182    7202.304739\n",
-       " 183    7188.245315\n",
-       " 184    7159.074782\n",
-       " 185    7141.092150\n",
-       " ...            ...\n",
-       " 1922  18180.735164\n",
-       " 1923  18151.337843\n",
-       " 1924  18117.233175\n",
-       " 1925  18136.618172\n",
-       " 1926  18200.673728\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    5229.433843\n",
-       " 182    5248.054054\n",
-       " 183    5255.860462\n",
-       " 184    5207.773684\n",
-       " 185    5169.663006\n",
-       " ...            ...\n",
-       " 1922  10986.899887\n",
-       " 1923  10990.944483\n",
-       " 1924  10939.196055\n",
-       " 1925  10946.045151\n",
-       " 1926  11001.830504\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4822.681182\n",
-       " 182   4749.941011\n",
-       " 183   4691.355361\n",
-       " 184   4705.930033\n",
-       " 185   4708.559098\n",
-       " ...           ...\n",
-       " 1922  9588.883917\n",
-       " 1923  9570.120429\n",
-       " 1924  9579.738336\n",
-       " 1925  9601.321903\n",
-       " 1926  9632.898292\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4858.543535\n",
-       " 182   4821.799079\n",
-       " 183   4784.288614\n",
-       " 184   4742.818890\n",
-       " 185   4709.511169\n",
-       " ...           ...\n",
-       " 1922  9736.392302\n",
-       " 1923  9730.404068\n",
-       " 1924  9795.339130\n",
-       " 1925  9792.578938\n",
-       " 1926  9733.284747\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    5274.379203\n",
-       " 182    5251.681119\n",
-       " 183    5230.383993\n",
-       " 184    5216.327990\n",
-       " 185    5204.690210\n",
-       " ...            ...\n",
-       " 1922  10675.677784\n",
-       " 1923  10833.203975\n",
-       " 1924  10753.265129\n",
-       " 1925  10725.065365\n",
-       " 1926  10740.074204\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4788.588490\n",
-       " 182   4736.506081\n",
-       " 183   4692.524937\n",
-       " 184   4690.416713\n",
-       " 185   4690.879146\n",
-       " ...           ...\n",
-       " 1922  8259.229344\n",
-       " 1923  8310.328812\n",
-       " 1924  8289.523640\n",
-       " 1925  8244.216378\n",
-       " 1926  8178.446807\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4549.374495\n",
-       " 182   4506.594757\n",
-       " 183   4466.184775\n",
-       " 184   4438.023324\n",
-       " 185   4412.278609\n",
-       " ...           ...\n",
-       " 1922  7752.028360\n",
-       " 1923  7858.638708\n",
-       " 1924  7845.076602\n",
-       " 1925  7867.941491\n",
-       " 1926  7921.227475\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4736.848813\n",
-       " 182   4696.757331\n",
-       " 183   4661.632692\n",
-       " 184   4652.180120\n",
-       " 185   4649.531839\n",
-       " ...           ...\n",
-       " 1922  8161.129311\n",
-       " 1923  8203.404143\n",
-       " 1924  8179.454582\n",
-       " 1925  8235.194396\n",
-       " 1926  8357.484800\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5023.243076\n",
-       " 182   4993.849931\n",
-       " 183   4971.048882\n",
-       " 184   4982.320324\n",
-       " 185   4979.528628\n",
-       " ...           ...\n",
-       " 1922  8610.948469\n",
-       " 1923  8673.253503\n",
-       " 1924  8680.665862\n",
-       " 1925  8696.056244\n",
-       " 1926  8718.109274\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3742.601635\n",
-       " 182   3805.033969\n",
-       " 183   3853.742010\n",
-       " 184   3831.513455\n",
-       " 185   3806.560407\n",
-       " ...           ...\n",
-       " 1922  6180.102529\n",
-       " 1923  6247.673558\n",
-       " 1924  6162.951924\n",
-       " 1925  6182.347054\n",
-       " 1926  6288.692698\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12991.525272\n",
-       " 182   13003.740494\n",
-       " 183   12976.232399\n",
-       " 184   12818.961471\n",
-       " 185   12673.237787\n",
-       " ...            ...\n",
-       " 1922  15276.435855\n",
-       " 1923  15277.656249\n",
-       " 1924  15238.948547\n",
-       " 1925  15364.814019\n",
-       " 1926  15578.323791\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12997.537488\n",
-       " 182   13142.386278\n",
-       " 183   13225.347736\n",
-       " 184   13106.143909\n",
-       " 185   12991.112366\n",
-       " ...            ...\n",
-       " 1922  15923.284960\n",
-       " 1923  15865.672041\n",
-       " 1924  15729.894263\n",
-       " 1925  15778.212869\n",
-       " 1926  15932.028713\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11186.387034\n",
-       " 182   11159.729776\n",
-       " 183   11110.853567\n",
-       " 184   10989.395452\n",
-       " 185   10904.023604\n",
-       " ...            ...\n",
-       " 1922  13886.191330\n",
-       " 1923  13913.892538\n",
-       " 1924  13916.788371\n",
-       " 1925  13949.952971\n",
-       " 1926  13997.275703\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12070.027785\n",
-       " 182   12084.132624\n",
-       " 183   12086.198867\n",
-       " 184   12048.939031\n",
-       " 185   11978.961752\n",
-       " ...            ...\n",
-       " 1922  15409.915251\n",
-       " 1923  15283.557677\n",
-       " 1924  15262.610077\n",
-       " 1925  15277.599872\n",
-       " 1926  15305.017146\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12713.562350\n",
-       " 182   12648.430798\n",
-       " 183   12598.291764\n",
-       " 184   12597.128293\n",
-       " 185   12580.605939\n",
-       " ...            ...\n",
-       " 1922  15459.959758\n",
-       " 1923  15497.336239\n",
-       " 1924  15469.104547\n",
-       " 1925  15479.961565\n",
-       " 1926  15509.102383\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11308.186310\n",
-       " 182   11261.548710\n",
-       " 183   11207.074652\n",
-       " 184   11127.001498\n",
-       " 185   11035.946643\n",
-       " ...            ...\n",
-       " 1922  14855.025093\n",
-       " 1923  14831.001077\n",
-       " 1924  14701.352719\n",
-       " 1925  14750.127541\n",
-       " 1926  14898.727305\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10058.866014\n",
-       " 182   10014.745550\n",
-       " 183    9968.420018\n",
-       " 184    9914.891263\n",
-       " 185    9848.589496\n",
-       " ...            ...\n",
-       " 1922  14007.666051\n",
-       " 1923  13987.086492\n",
-       " 1924  13875.083967\n",
-       " 1925  13828.634557\n",
-       " 1926  13816.387618\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8995.027092\n",
-       " 182    8973.440398\n",
-       " 183    8951.689788\n",
-       " 184    8929.403714\n",
-       " 185    8920.478659\n",
-       " ...            ...\n",
-       " 1922  11661.100490\n",
-       " 1923  11665.460989\n",
-       " 1924  11585.532958\n",
-       " 1925  11656.294567\n",
-       " 1926  11807.856210\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11914.951345\n",
-       " 182   11935.702353\n",
-       " 183   11923.145803\n",
-       " 184   11801.784559\n",
-       " 185   11699.355621\n",
-       " ...            ...\n",
-       " 1922  14822.614592\n",
-       " 1923  14793.752740\n",
-       " 1924  14684.992007\n",
-       " 1925  14789.341385\n",
-       " 1926  15011.391561\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11810.455756\n",
-       " 182   11839.192651\n",
-       " 183   11836.814253\n",
-       " 184   11732.792564\n",
-       " 185   11649.899914\n",
-       " ...            ...\n",
-       " 1922  13093.729529\n",
-       " 1923  12999.159680\n",
-       " 1924  13032.227862\n",
-       " 1925  13107.553213\n",
-       " 1926  13196.752109\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9170.219278\n",
-       " 182    9131.324620\n",
-       " 183    9092.729284\n",
-       " 184    9055.111728\n",
-       " 185    9009.709719\n",
-       " ...            ...\n",
-       " 1922  10242.254131\n",
-       " 1923  10237.790710\n",
-       " 1924  10182.743362\n",
-       " 1925  10236.142962\n",
-       " 1926  10348.993009\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9238.105508\n",
-       " 182    9188.554601\n",
-       " 183    9124.284413\n",
-       " 184    9011.931242\n",
-       " 185    8916.516883\n",
-       " ...            ...\n",
-       " 1922  10214.447220\n",
-       " 1923  10117.943825\n",
-       " 1924  10093.174254\n",
-       " 1925  10116.323244\n",
-       " 1926  10163.419768\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8490.041890\n",
-       " 182   8452.399249\n",
-       " 183   8417.402340\n",
-       " 184   8391.048153\n",
-       " 185   8338.560465\n",
-       " ...           ...\n",
-       " 1922  9593.228069\n",
-       " 1923  9584.497859\n",
-       " 1924  9589.951142\n",
-       " 1925  9561.985107\n",
-       " 1926  9513.767149\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8974.325881\n",
-       " 182    8907.415094\n",
-       " 183    8853.306502\n",
-       " 184    8841.018410\n",
-       " 185    8819.154253\n",
-       " ...            ...\n",
-       " 1922  10418.214179\n",
-       " 1923  10481.456316\n",
-       " 1924  10381.523235\n",
-       " 1925  10410.204214\n",
-       " 1926  10506.730389\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9301.858666\n",
-       " 182    9247.649997\n",
-       " 183    9193.735371\n",
-       " 184    9140.781287\n",
-       " 185    9071.264415\n",
-       " ...            ...\n",
-       " 1922  10608.119773\n",
-       " 1923  10602.855156\n",
-       " 1924  10601.132303\n",
-       " 1925  10666.250370\n",
-       " 1926  10763.896559\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11490.249362\n",
-       " 182   11383.649060\n",
-       " 183   11294.842314\n",
-       " 184   11264.161186\n",
-       " 185   11215.523843\n",
-       " ...            ...\n",
-       " 1922  12560.591326\n",
-       " 1923  12479.469450\n",
-       " 1924  12537.538894\n",
-       " 1925  12556.526487\n",
-       " 1926  12546.015898\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9363.165118\n",
-       " 182    9327.625804\n",
-       " 183    9269.082256\n",
-       " 184    9135.391548\n",
-       " 185    9028.011875\n",
-       " ...            ...\n",
-       " 1922  10766.936855\n",
-       " 1923  10822.758095\n",
-       " 1924  10779.279560\n",
-       " 1925  10754.707763\n",
-       " 1926  10742.357384\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11732.708441\n",
-       " 182   11744.773461\n",
-       " 183   11733.537933\n",
-       " 184   11646.187283\n",
-       " 185   11566.204657\n",
-       " ...            ...\n",
-       " 1922  11175.267581\n",
-       " 1923  11224.011936\n",
-       " 1924  11044.786558\n",
-       " 1925  10999.831297\n",
-       " 1926  11035.782853\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10167.838426\n",
-       " 182   10111.733587\n",
-       " 183   10085.014050\n",
-       " 184   10154.286497\n",
-       " 185   10170.311176\n",
-       " ...            ...\n",
-       " 1922  16679.936897\n",
-       " 1923  16805.898271\n",
-       " 1924  16723.520746\n",
-       " 1925  16798.768341\n",
-       " 1926  16952.705234\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8373.333126\n",
-       " 182    8317.680404\n",
-       " 183    8270.298175\n",
-       " 184    8249.932889\n",
-       " 185    8228.168448\n",
-       " ...            ...\n",
-       " 1922  12842.227813\n",
-       " 1923  12826.105847\n",
-       " 1924  12750.511151\n",
-       " 1925  12765.070847\n",
-       " 1926  12829.825822\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7996.333716\n",
-       " 182    7940.636136\n",
-       " 183    7886.952633\n",
-       " 184    7839.848448\n",
-       " 185    7817.670995\n",
-       " ...            ...\n",
-       " 1922  12380.312925\n",
-       " 1923  12419.348951\n",
-       " 1924  12292.513724\n",
-       " 1925  12272.846022\n",
-       " 1926  12314.021484\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7510.675029\n",
-       " 182    7476.247358\n",
-       " 183    7461.515185\n",
-       " 184    7511.121640\n",
-       " 185    7525.828469\n",
-       " ...            ...\n",
-       " 1922  11024.894898\n",
-       " 1923  11016.072310\n",
-       " 1924  10990.377866\n",
-       " 1925  11058.634175\n",
-       " 1926  11174.917579\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6814.716726\n",
-       " 182   6877.514609\n",
-       " 183   6904.986149\n",
-       " 184   6817.058305\n",
-       " 185   6741.684552\n",
-       " ...           ...\n",
-       " 1922  9619.344669\n",
-       " 1923  9568.633876\n",
-       " 1924  9619.004457\n",
-       " 1925  9641.010547\n",
-       " 1926  9645.552578\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7243.554940\n",
-       " 182   7232.422883\n",
-       " 183   7210.338436\n",
-       " 184   7152.476179\n",
-       " 185   7118.142932\n",
-       " ...           ...\n",
-       " 1922  9990.683366\n",
-       " 1923  9914.801509\n",
-       " 1924  9903.585600\n",
-       " 1925  9918.220675\n",
-       " 1926  9950.592656\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6681.322407\n",
-       " 182   6591.476641\n",
-       " 183   6519.743062\n",
-       " 184   6507.175959\n",
-       " 185   6480.644603\n",
-       " ...           ...\n",
-       " 1922  8999.395912\n",
-       " 1923  8941.594748\n",
-       " 1924  9033.666086\n",
-       " 1925  9032.433622\n",
-       " 1926  8967.232228\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6611.501300\n",
-       " 182   6573.633860\n",
-       " 183   6541.227615\n",
-       " 184   6526.661268\n",
-       " 185   6518.276304\n",
-       " ...           ...\n",
-       " 1922  8664.315088\n",
-       " 1923  8651.698048\n",
-       " 1924  8567.190472\n",
-       " 1925  8596.167145\n",
-       " 1926  8688.034217\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10167.838426\n",
-       " 182   10111.733587\n",
-       " 183   10085.014050\n",
-       " 184   10154.286497\n",
-       " 185   10170.311176\n",
-       " ...            ...\n",
-       " 1922  16679.936897\n",
-       " 1923  16805.898271\n",
-       " 1924  16723.520746\n",
-       " 1925  16798.768341\n",
-       " 1926  16952.705234\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8373.333126\n",
-       " 182    8317.680404\n",
-       " 183    8270.298175\n",
-       " 184    8249.932889\n",
-       " 185    8228.168448\n",
-       " ...            ...\n",
-       " 1922  12842.227813\n",
-       " 1923  12826.105847\n",
-       " 1924  12750.511151\n",
-       " 1925  12765.070847\n",
-       " 1926  12829.825822\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7996.333716\n",
-       " 182    7940.636136\n",
-       " 183    7886.952633\n",
-       " 184    7839.848448\n",
-       " 185    7817.670995\n",
-       " ...            ...\n",
-       " 1922  12380.312925\n",
-       " 1923  12419.348951\n",
-       " 1924  12292.513724\n",
-       " 1925  12272.846022\n",
-       " 1926  12314.021484\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7510.675029\n",
-       " 182    7476.247358\n",
-       " 183    7461.515185\n",
-       " 184    7511.121640\n",
-       " 185    7525.828469\n",
-       " ...            ...\n",
-       " 1922  11024.894898\n",
-       " 1923  11016.072310\n",
-       " 1924  10990.377866\n",
-       " 1925  11058.634175\n",
-       " 1926  11174.917579\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6814.716726\n",
-       " 182   6877.514609\n",
-       " 183   6904.986149\n",
-       " 184   6817.058305\n",
-       " 185   6741.684552\n",
-       " ...           ...\n",
-       " 1922  9619.344669\n",
-       " 1923  9568.633876\n",
-       " 1924  9619.004457\n",
-       " 1925  9641.010547\n",
-       " 1926  9645.552578\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7243.554940\n",
-       " 182   7232.422883\n",
-       " 183   7210.338436\n",
-       " 184   7152.476179\n",
-       " 185   7118.142932\n",
-       " ...           ...\n",
-       " 1922  9990.683366\n",
-       " 1923  9914.801509\n",
-       " 1924  9903.585600\n",
-       " 1925  9918.220675\n",
-       " 1926  9950.592656\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6681.322407\n",
-       " 182   6591.476641\n",
-       " 183   6519.743062\n",
-       " 184   6507.175959\n",
-       " 185   6480.644603\n",
-       " ...           ...\n",
-       " 1922  8999.395912\n",
-       " 1923  8941.594748\n",
-       " 1924  9033.666086\n",
-       " 1925  9032.433622\n",
-       " 1926  8967.232228\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6611.501300\n",
-       " 182   6573.633860\n",
-       " 183   6541.227615\n",
-       " 184   6526.661268\n",
-       " 185   6518.276304\n",
-       " ...           ...\n",
-       " 1922  8664.315088\n",
-       " 1923  8651.698048\n",
-       " 1924  8567.190472\n",
-       " 1925  8596.167145\n",
-       " 1926  8688.034217\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7880.766744\n",
-       " 182    7855.047490\n",
-       " 183    7827.699341\n",
-       " 184    7791.931936\n",
-       " 185    7762.663613\n",
-       " ...            ...\n",
-       " 1922  16699.701152\n",
-       " 1923  16699.180958\n",
-       " 1924  16702.444515\n",
-       " 1925  16846.913230\n",
-       " 1926  17109.305906\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6267.223670\n",
-       " 182    6218.650334\n",
-       " 183    6174.500876\n",
-       " 184    6153.217073\n",
-       " 185    6146.902827\n",
-       " ...            ...\n",
-       " 1922  12583.381757\n",
-       " 1923  12611.360933\n",
-       " 1924  12610.950466\n",
-       " 1925  12669.183852\n",
-       " 1926  12776.392185\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    5615.987906\n",
-       " 182    5587.998743\n",
-       " 183    5564.654716\n",
-       " 184    5565.319953\n",
-       " 185    5568.707365\n",
-       " ...            ...\n",
-       " 1922  10769.670021\n",
-       " 1923  10799.269364\n",
-       " 1924  10788.184243\n",
-       " 1925  10777.089087\n",
-       " 1926  10765.985552\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4948.018924\n",
-       " 182   5005.929710\n",
-       " 183   5057.363319\n",
-       " 184   5075.318414\n",
-       " 185   5080.269680\n",
-       " ...           ...\n",
-       " 1922  9087.763405\n",
-       " 1923  9052.388641\n",
-       " 1924  9036.898728\n",
-       " 1925  9065.235825\n",
-       " 1926  9130.173957\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5372.399270\n",
-       " 182   5335.716305\n",
-       " 183   5310.708267\n",
-       " 184   5346.044290\n",
-       " 185   5369.285649\n",
-       " ...           ...\n",
-       " 1922  9538.203766\n",
-       " 1923  9534.576070\n",
-       " 1924  9544.189564\n",
-       " 1925  9545.271184\n",
-       " 1926  9539.227623\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5364.851755\n",
-       " 182   5330.167279\n",
-       " 183   5298.611837\n",
-       " 184   5283.229396\n",
-       " 185   5266.030569\n",
-       " ...           ...\n",
-       " 1922  9371.080749\n",
-       " 1923  9348.830091\n",
-       " 1924  9341.726055\n",
-       " 1925  9324.936165\n",
-       " 1926  9300.057377\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3989.983304\n",
-       " 182   4008.455455\n",
-       " 183   4014.815579\n",
-       " 184   3958.572394\n",
-       " 185   3919.109801\n",
-       " ...           ...\n",
-       " 1922  6914.388347\n",
-       " 1923  6905.714230\n",
-       " 1924  6895.155590\n",
-       " 1925  6884.587107\n",
-       " 1926  6874.010402\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3440.301783\n",
-       " 182   3436.047121\n",
-       " 183   3427.788665\n",
-       " 184   3398.835841\n",
-       " 185   3379.407453\n",
-       " ...           ...\n",
-       " 1922  6017.082337\n",
-       " 1923  5962.143190\n",
-       " 1924  5933.722633\n",
-       " 1925  5951.394827\n",
-       " 1926  6007.560231\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3227.610310\n",
-       " 182   3252.656555\n",
-       " 183   3273.056937\n",
-       " 184   3269.444298\n",
-       " 185   3265.226331\n",
-       " ...           ...\n",
-       " 1922  5760.509534\n",
-       " 1923  5701.100284\n",
-       " 1924  5645.518943\n",
-       " 1925  5649.670323\n",
-       " 1926  5703.705991\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3414.258114\n",
-       " 182   3446.658315\n",
-       " 183   3466.733432\n",
-       " 184   3423.104020\n",
-       " 185   3380.679858\n",
-       " ...           ...\n",
-       " 1922  5808.004049\n",
-       " 1923  5885.766665\n",
-       " 1924  5869.855882\n",
-       " 1925  5828.878232\n",
-       " 1926  5766.966616\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3509.128181\n",
-       " 182   3508.880701\n",
-       " 183   3506.036779\n",
-       " 184   3489.772652\n",
-       " 185   3475.472796\n",
-       " ...           ...\n",
-       " 1922  5712.236525\n",
-       " 1923  5773.209573\n",
-       " 1924  5780.236686\n",
-       " 1925  5763.356568\n",
-       " 1926  5726.510925\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3545.049988\n",
-       " 182   3555.470981\n",
-       " 183   3561.134426\n",
-       " 184   3542.207586\n",
-       " 185   3515.112305\n",
-       " ...           ...\n",
-       " 1922  6000.219056\n",
-       " 1923  6041.561508\n",
-       " 1924  5929.686341\n",
-       " 1925  5921.333889\n",
-       " 1926  5999.435846\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3334.631275\n",
-       " 182   3332.363382\n",
-       " 183   3326.524938\n",
-       " 184   3302.231423\n",
-       " 185   3284.286988\n",
-       " ...           ...\n",
-       " 1922  5481.702297\n",
-       " 1923  5484.269549\n",
-       " 1924  5373.363487\n",
-       " 1925  5368.258539\n",
-       " 1926  5451.510748\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3206.052965\n",
-       " 182   3209.101856\n",
-       " 183   3209.771445\n",
-       " 184   3198.143167\n",
-       " 185   3184.547700\n",
-       " ...           ...\n",
-       " 1922  5154.059486\n",
-       " 1923  5252.887584\n",
-       " 1924  5189.943695\n",
-       " 1925  5162.812128\n",
-       " 1926  5165.588328\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3078.736083\n",
-       " 182   3099.765569\n",
-       " 183   3114.417294\n",
-       " 184   3096.104354\n",
-       " 185   3078.243164\n",
-       " ...           ...\n",
-       " 1922  4988.034984\n",
-       " 1923  5118.801268\n",
-       " 1924  4952.551255\n",
-       " 1925  4915.395644\n",
-       " 1926  4986.050000\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3240.381378\n",
-       " 182   3238.102271\n",
-       " 183   3226.194379\n",
-       " 184   3164.518299\n",
-       " 185   3131.266953\n",
-       " ...           ...\n",
-       " 1922  5148.651017\n",
-       " 1923  5162.456666\n",
-       " 1924  5151.666312\n",
-       " 1925  5171.611733\n",
-       " 1926  5217.225370\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3207.469820\n",
-       " 182   3185.857925\n",
-       " 183   3172.246401\n",
-       " 184   3199.986308\n",
-       " 185   3212.303649\n",
-       " ...           ...\n",
-       " 1922  5097.427078\n",
-       " 1923  5072.706740\n",
-       " 1924  5155.802585\n",
-       " 1925  5176.343924\n",
-       " 1926  5144.644429\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3514.743152\n",
-       " 182   3467.174225\n",
-       " 183   3420.026653\n",
-       " 184   3375.056929\n",
-       " 185   3345.205475\n",
-       " ...           ...\n",
-       " 1922  5445.131781\n",
-       " 1923  5417.819591\n",
-       " 1924  5442.535861\n",
-       " 1925  5479.800621\n",
-       " 1926  5527.544939\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3258.035797\n",
-       " 182   3290.417350\n",
-       " 183   3310.041060\n",
-       " 184   3263.723454\n",
-       " 185   3227.835902\n",
-       " ...           ...\n",
-       " 1922  5088.134391\n",
-       " 1923  5147.528367\n",
-       " 1924  5098.133029\n",
-       " 1925  5115.308725\n",
-       " 1926  5188.079559\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2844.184913\n",
-       " 182   2856.521733\n",
-       " 183   2856.095939\n",
-       " 184   2789.704167\n",
-       " 185   2745.536250\n",
-       " ...           ...\n",
-       " 1922  4468.315800\n",
-       " 1923  4446.880236\n",
-       " 1924  4454.774625\n",
-       " 1925  4458.690391\n",
-       " 1926  4459.283510\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2822.760351\n",
-       " 182   2814.432855\n",
-       " 183   2803.398794\n",
-       " 184   2778.375334\n",
-       " 185   2759.398434\n",
-       " ...           ...\n",
-       " 1922  4417.654711\n",
-       " 1923  4440.074017\n",
-       " 1924  4461.530198\n",
-       " 1925  4475.034924\n",
-       " 1926  4481.899192\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2792.245824\n",
-       " 182   2814.574075\n",
-       " 183   2823.709390\n",
-       " 184   2764.654525\n",
-       " 185   2718.296761\n",
-       " ...           ...\n",
-       " 1922  4431.276012\n",
-       " 1923  4405.764738\n",
-       " 1924  4319.755273\n",
-       " 1925  4335.583781\n",
-       " 1926  4436.459727\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2975.434333\n",
-       " 182   2944.463693\n",
-       " 183   2922.356624\n",
-       " 184   2946.062601\n",
-       " 185   2953.135757\n",
-       " ...           ...\n",
-       " 1922  4749.674154\n",
-       " 1923  4723.253220\n",
-       " 1924  4697.797985\n",
-       " 1925  4655.238457\n",
-       " 1926  4598.394707\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3122.575441\n",
-       " 182   3052.298223\n",
-       " 183   3002.883475\n",
-       " 184   3061.300348\n",
-       " 185   3093.258101\n",
-       " ...           ...\n",
-       " 1922  4958.109799\n",
-       " 1923  4919.870134\n",
-       " 1924  4889.224482\n",
-       " 1925  4908.085790\n",
-       " 1926  4968.291598\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3030.855359\n",
-       " 182   3011.888499\n",
-       " 183   2994.972585\n",
-       " 184   2988.657374\n",
-       " 185   2992.171436\n",
-       " ...           ...\n",
-       " 1922  4824.623017\n",
-       " 1923  4836.788909\n",
-       " 1924  4798.828944\n",
-       " 1925  4828.588983\n",
-       " 1926  4914.903693\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2958.952548\n",
-       " 182   2943.976111\n",
-       " 183   2929.428834\n",
-       " 184   2917.099748\n",
-       " 185   2894.183255\n",
-       " ...           ...\n",
-       " 1922  4591.364219\n",
-       " 1923  4564.237456\n",
-       " 1924  4531.457819\n",
-       " 1925  4548.752676\n",
-       " 1926  4607.865997\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2948.918973\n",
-       " 182   2923.943671\n",
-       " 183   2897.231509\n",
-       " 184   2861.542056\n",
-       " 185   2835.225091\n",
-       " ...           ...\n",
-       " 1922  4399.111895\n",
-       " 1923  4354.685006\n",
-       " 1924  4430.382364\n",
-       " 1925  4477.680439\n",
-       " 1926  4501.261563\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2960.634981\n",
-       " 182   2923.663963\n",
-       " 183   2896.528720\n",
-       " 184   2920.231544\n",
-       " 185   2935.014288\n",
-       " ...           ...\n",
-       " 1922  4452.265748\n",
-       " 1923  4492.225996\n",
-       " 1924  4397.881411\n",
-       " 1925  4369.497096\n",
-       " 1926  4396.197853\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2852.853874\n",
-       " 182   2848.529025\n",
-       " 183   2853.398635\n",
-       " 184   2905.791543\n",
-       " 185   2926.885366\n",
-       " ...           ...\n",
-       " 1922  4414.508887\n",
-       " 1923  4476.258274\n",
-       " 1924  4452.856948\n",
-       " 1925  4427.156802\n",
-       " 1926  4399.536852\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3078.692341\n",
-       " 182   3060.397734\n",
-       " 183   3044.478749\n",
-       " 184   3040.838613\n",
-       " 185   3035.685843\n",
-       " ...           ...\n",
-       " 1922  4590.695081\n",
-       " 1923  4565.458291\n",
-       " 1924  4562.935277\n",
-       " 1925  4586.031150\n",
-       " 1926  4630.521996\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6547.212991\n",
-       " 182    6574.655279\n",
-       " 183    6591.394030\n",
-       " 184    6552.809522\n",
-       " 185    6521.782433\n",
-       " ...            ...\n",
-       " 1922  13735.629459\n",
-       " 1923  13617.264169\n",
-       " 1924  13558.561138\n",
-       " 1925  13623.357110\n",
-       " 1926  13791.290191\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    5534.029097\n",
-       " 182    5538.688875\n",
-       " 183    5538.481566\n",
-       " 184    5513.117794\n",
-       " 185    5487.146404\n",
-       " ...            ...\n",
-       " 1922  10541.039092\n",
-       " 1923  10465.900405\n",
-       " 1924  10569.535974\n",
-       " 1925  10614.051724\n",
-       " 1926  10609.195032\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5327.836545\n",
-       " 182   5401.122331\n",
-       " 183   5456.359102\n",
-       " 184   5418.306128\n",
-       " 185   5383.273787\n",
-       " ...           ...\n",
-       " 1922  9593.338048\n",
-       " 1923  9579.071631\n",
-       " 1924  9666.939860\n",
-       " 1925  9703.644841\n",
-       " 1926  9697.622114\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5143.099954\n",
-       " 182   5176.373139\n",
-       " 183   5198.727812\n",
-       " 184   5164.648098\n",
-       " 185   5145.234102\n",
-       " ...           ...\n",
-       " 1922  9114.020301\n",
-       " 1923  9108.250567\n",
-       " 1924  9085.464940\n",
-       " 1925  9045.007683\n",
-       " 1926  8989.792405\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5144.679433\n",
-       " 182   5141.961188\n",
-       " 183   5132.751308\n",
-       " 184   5089.988193\n",
-       " 185   5051.455108\n",
-       " ...           ...\n",
-       " 1922  8617.087783\n",
-       " 1923  8535.237244\n",
-       " 1924  8569.757423\n",
-       " 1925  8698.254642\n",
-       " 1926  8905.234440\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3676.690800\n",
-       " 182   3621.809499\n",
-       " 183   3570.593317\n",
-       " 184   3538.320993\n",
-       " 185   3507.557556\n",
-       " ...           ...\n",
-       " 1922  5811.856125\n",
-       " 1923  5904.074968\n",
-       " 1924  5911.119420\n",
-       " 1925  5895.964867\n",
-       " 1926  5862.271369\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2931.249101\n",
-       " 182   2936.264600\n",
-       " 183   2940.524029\n",
-       " 184   2940.875567\n",
-       " 185   2943.344382\n",
-       " ...           ...\n",
-       " 1922  4689.301703\n",
-       " 1923  4681.341455\n",
-       " 1924  4691.353436\n",
-       " 1925  4733.828361\n",
-       " 1926  4803.413904\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2807.038463\n",
-       " 182   2809.373141\n",
-       " 183   2816.468414\n",
-       " 184   2848.169723\n",
-       " 185   2868.834565\n",
-       " ...           ...\n",
-       " 1922  4302.091634\n",
-       " 1923  4276.649838\n",
-       " 1924  4312.690825\n",
-       " 1925  4372.677961\n",
-       " 1926  4452.663125\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2713.157303\n",
-       " 182   2732.143443\n",
-       " 183   2745.616831\n",
-       " 184   2730.596524\n",
-       " 185   2718.599225\n",
-       " ...           ...\n",
-       " 1922  4134.680635\n",
-       " 1923  4158.564040\n",
-       " 1924  4175.809627\n",
-       " 1925  4185.099832\n",
-       " 1926  4187.746296\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2650.255172\n",
-       " 182   2659.902728\n",
-       " 183   2665.225302\n",
-       " 184   2648.193391\n",
-       " 185   2630.101054\n",
-       " ...           ...\n",
-       " 1922  4021.698136\n",
-       " 1923  4012.385782\n",
-       " 1924  3917.977357\n",
-       " 1925  3914.012119\n",
-       " 1926  3985.578249\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2487.991261\n",
-       " 182   2436.967068\n",
-       " 183   2395.667120\n",
-       " 184   2404.628775\n",
-       " 185   2416.464751\n",
-       " ...           ...\n",
-       " 1922  3624.240940\n",
-       " 1923  3604.654872\n",
-       " 1924  3572.791219\n",
-       " 1925  3591.002690\n",
-       " 1926  3651.033152\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2456.819790\n",
-       " 182   2455.780223\n",
-       " 183   2459.608603\n",
-       " 184   2488.597879\n",
-       " 185   2509.575011\n",
-       " ...           ...\n",
-       " 1922  3674.174782\n",
-       " 1923  3689.352063\n",
-       " 1924  3664.802723\n",
-       " 1925  3672.684896\n",
-       " 1926  3707.651437\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2673.480807\n",
-       " 182   2631.143357\n",
-       " 183   2601.561364\n",
-       " 184   2637.908360\n",
-       " 185   2655.960328\n",
-       " ...           ...\n",
-       " 1922  3943.995914\n",
-       " 1923  3945.993433\n",
-       " 1924  3901.655157\n",
-       " 1925  3918.198783\n",
-       " 1926  3985.586410\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2819.683706\n",
-       " 182   2819.353943\n",
-       " 183   2815.778613\n",
-       " 184   2795.427958\n",
-       " 185   2778.402231\n",
-       " ...           ...\n",
-       " 1922  4135.493948\n",
-       " 1923  4122.828217\n",
-       " 1924  4103.553152\n",
-       " 1925  4134.934144\n",
-       " 1926  4208.619277\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3071.978322\n",
-       " 182   3077.010706\n",
-       " 183   3078.041530\n",
-       " 184   3058.389538\n",
-       " 185   3046.296997\n",
-       " ...           ...\n",
-       " 1922  4565.421801\n",
-       " 1923  4566.972984\n",
-       " 1924  4597.835848\n",
-       " 1925  4594.564793\n",
-       " 1926  4562.787647\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2715.089603\n",
-       " 182   2779.398701\n",
-       " 183   2823.817558\n",
-       " 184   2765.429948\n",
-       " 185   2729.720718\n",
-       " ...           ...\n",
-       " 1922  4241.585711\n",
-       " 1923  4193.622088\n",
-       " 1924  4171.226436\n",
-       " 1925  4173.862524\n",
-       " 1926  4197.403244\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2318.045510\n",
-       " 182   2323.654381\n",
-       " 183   2328.399132\n",
-       " 184   2328.677512\n",
-       " 185   2328.048530\n",
-       " ...           ...\n",
-       " 1922  3442.798207\n",
-       " 1923  3475.705519\n",
-       " 1924  3378.095644\n",
-       " 1925  3368.079329\n",
-       " 1926  3431.214588\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2217.864391\n",
-       " 182   2202.799497\n",
-       " 183   2190.111019\n",
-       " 184   2189.705494\n",
-       " 185   2190.358536\n",
-       " ...           ...\n",
-       " 1922  3174.048463\n",
-       " 1923  3166.571231\n",
-       " 1924  3183.685199\n",
-       " 1925  3200.814639\n",
-       " 1926  3217.957000\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2086.312607\n",
-       " 182   2098.558827\n",
-       " 183   2106.264317\n",
-       " 184   2090.500191\n",
-       " 185   2075.944232\n",
-       " ...           ...\n",
-       " 1922  3010.689690\n",
-       " 1923  3106.361717\n",
-       " 1924  3032.699195\n",
-       " 1925  2991.992649\n",
-       " 1926  2978.808464\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2030.861866\n",
-       " 182   2043.768053\n",
-       " 183   2055.920044\n",
-       " 184   2064.173827\n",
-       " 185   2064.413277\n",
-       " ...           ...\n",
-       " 1922  2844.638489\n",
-       " 1923  2864.405921\n",
-       " 1924  2829.313862\n",
-       " 1925  2822.088791\n",
-       " 1926  2838.136140\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   18928.731634\n",
-       " 182   18900.999779\n",
-       " 183   18864.186490\n",
-       " 184   18797.707180\n",
-       " 185   18731.617765\n",
-       " ...            ...\n",
-       " 1922  49307.100163\n",
-       " 1923  49400.729720\n",
-       " 1924  49137.790326\n",
-       " 1925  49253.767538\n",
-       " 1926  49576.939220\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   17025.541284\n",
-       " 182   16914.270114\n",
-       " 183   16837.213609\n",
-       " 184   16871.925010\n",
-       " 185   16842.213802\n",
-       " ...            ...\n",
-       " 1922  38007.208271\n",
-       " 1923  37940.229634\n",
-       " 1924  37884.691755\n",
-       " 1925  38026.511229\n",
-       " 1926  38263.434537\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15652.768634\n",
-       " 182   15621.979319\n",
-       " 183   15575.695054\n",
-       " 184   15478.793950\n",
-       " 185   15399.232860\n",
-       " ...            ...\n",
-       " 1922  33087.114682\n",
-       " 1923  33170.195432\n",
-       " 1924  33055.521190\n",
-       " 1925  33285.748722\n",
-       " 1926  33694.612815\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13414.418284\n",
-       " 182   13370.698526\n",
-       " 183   13328.371249\n",
-       " 184   13290.592745\n",
-       " 185   13238.247960\n",
-       " ...            ...\n",
-       " 1922  24937.305997\n",
-       " 1923  24929.249674\n",
-       " 1924  24998.377198\n",
-       " 1925  25048.604462\n",
-       " 1926  25070.839223\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13755.081511\n",
-       " 182   13755.390987\n",
-       " 183   13748.349416\n",
-       " 184   13717.294423\n",
-       " 185   13662.698057\n",
-       " ...            ...\n",
-       " 1922  24852.142723\n",
-       " 1923  24913.787578\n",
-       " 1924  24933.816619\n",
-       " 1925  25035.195349\n",
-       " 1926  25172.370858\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13943.540440\n",
-       " 182   13981.196186\n",
-       " 183   13996.498721\n",
-       " 184   13938.780763\n",
-       " 185   13878.062765\n",
-       " ...            ...\n",
-       " 1922  26876.593247\n",
-       " 1923  27001.322054\n",
-       " 1924  26797.035438\n",
-       " 1925  26836.734643\n",
-       " 1926  27006.633988\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12837.921883\n",
-       " 182   12730.149202\n",
-       " 183   12631.411009\n",
-       " 184   12562.185484\n",
-       " 185   12510.303742\n",
-       " ...            ...\n",
-       " 1922  23761.121517\n",
-       " 1923  23780.245287\n",
-       " 1924  23653.876600\n",
-       " 1925  23637.198471\n",
-       " 1926  23673.703048\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12291.718553\n",
-       " 182   12228.535590\n",
-       " 183   12148.438916\n",
-       " 184   12013.090792\n",
-       " 185   11929.784420\n",
-       " ...            ...\n",
-       " 1922  19738.179572\n",
-       " 1923  19796.192074\n",
-       " 1924  19766.443336\n",
-       " 1925  19843.885746\n",
-       " 1926  19966.683905\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10505.429569\n",
-       " 182   10498.019791\n",
-       " 183   10461.359161\n",
-       " 184   10329.145749\n",
-       " 185   10246.979992\n",
-       " ...            ...\n",
-       " 1922  17014.899494\n",
-       " 1923  17135.865076\n",
-       " 1924  17002.344874\n",
-       " 1925  17003.736406\n",
-       " 1926  17081.380858\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   18928.731634\n",
-       " 182   18900.999779\n",
-       " 183   18864.186490\n",
-       " 184   18797.707180\n",
-       " 185   18731.617765\n",
-       " ...            ...\n",
-       " 1922  49307.100163\n",
-       " 1923  49400.729720\n",
-       " 1924  49137.790326\n",
-       " 1925  49253.767538\n",
-       " 1926  49576.939220\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   17025.541284\n",
-       " 182   16914.270114\n",
-       " 183   16837.213609\n",
-       " 184   16871.925010\n",
-       " 185   16842.213802\n",
-       " ...            ...\n",
-       " 1922  38007.208271\n",
-       " 1923  37940.229634\n",
-       " 1924  37884.691755\n",
-       " 1925  38026.511229\n",
-       " 1926  38263.434537\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15652.768634\n",
-       " 182   15621.979319\n",
-       " 183   15575.695054\n",
-       " 184   15478.793950\n",
-       " 185   15399.232860\n",
-       " ...            ...\n",
-       " 1922  33087.114682\n",
-       " 1923  33170.195432\n",
-       " 1924  33055.521190\n",
-       " 1925  33285.748722\n",
-       " 1926  33694.612815\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13414.418284\n",
-       " 182   13370.698526\n",
-       " 183   13328.371249\n",
-       " 184   13290.592745\n",
-       " 185   13238.247960\n",
-       " ...            ...\n",
-       " 1922  24937.305997\n",
-       " 1923  24929.249674\n",
-       " 1924  24998.377198\n",
-       " 1925  25048.604462\n",
-       " 1926  25070.839223\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13755.081511\n",
-       " 182   13755.390987\n",
-       " 183   13748.349416\n",
-       " 184   13717.294423\n",
-       " 185   13662.698057\n",
-       " ...            ...\n",
-       " 1922  24852.142723\n",
-       " 1923  24913.787578\n",
-       " 1924  24933.816619\n",
-       " 1925  25035.195349\n",
-       " 1926  25172.370858\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13943.540440\n",
-       " 182   13981.196186\n",
-       " 183   13996.498721\n",
-       " 184   13938.780763\n",
-       " 185   13878.062765\n",
-       " ...            ...\n",
-       " 1922  26876.593247\n",
-       " 1923  27001.322054\n",
-       " 1924  26797.035438\n",
-       " 1925  26836.734643\n",
-       " 1926  27006.633988\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12837.921883\n",
-       " 182   12730.149202\n",
-       " 183   12631.411009\n",
-       " 184   12562.185484\n",
-       " 185   12510.303742\n",
-       " ...            ...\n",
-       " 1922  23761.121517\n",
-       " 1923  23780.245287\n",
-       " 1924  23653.876600\n",
-       " 1925  23637.198471\n",
-       " 1926  23673.703048\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12291.718553\n",
-       " 182   12228.535590\n",
-       " 183   12148.438916\n",
-       " 184   12013.090792\n",
-       " 185   11929.784420\n",
-       " ...            ...\n",
-       " 1922  19738.179572\n",
-       " 1923  19796.192074\n",
-       " 1924  19766.443336\n",
-       " 1925  19843.885746\n",
-       " 1926  19966.683905\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10505.429569\n",
-       " 182   10498.019791\n",
-       " 183   10461.359161\n",
-       " 184   10329.145749\n",
-       " 185   10246.979992\n",
-       " ...            ...\n",
-       " 1922  17014.899494\n",
-       " 1923  17135.865076\n",
-       " 1924  17002.344874\n",
-       " 1925  17003.736406\n",
-       " 1926  17081.380858\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5188.913131\n",
-       " 182   5189.533464\n",
-       " 183   5182.770925\n",
-       " 184   5160.057720\n",
-       " 185   5135.550864\n",
-       " ...           ...\n",
-       " 1922  8630.387094\n",
-       " 1923  8616.983782\n",
-       " 1924  8577.390979\n",
-       " 1925  8635.081931\n",
-       " 1926  8715.366764\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3888.887411\n",
-       " 182   3900.026790\n",
-       " 183   3900.617476\n",
-       " 184   3878.417754\n",
-       " 185   3863.539040\n",
-       " ...           ...\n",
-       " 1922  6249.510051\n",
-       " 1923  6221.797854\n",
-       " 1924  6215.313637\n",
-       " 1925  6314.521881\n",
-       " 1926  6431.248874\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3440.891179\n",
-       " 182   3421.287750\n",
-       " 183   3415.398496\n",
-       " 184   3439.138667\n",
-       " 185   3438.849973\n",
-       " ...           ...\n",
-       " 1922  5374.066701\n",
-       " 1923  5395.324341\n",
-       " 1924  5325.009632\n",
-       " 1925  5315.572181\n",
-       " 1926  5335.271448\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3268.106317\n",
-       " 182   3254.486616\n",
-       " 183   3238.759878\n",
-       " 184   3218.480896\n",
-       " 185   3204.512160\n",
-       " ...           ...\n",
-       " 1922  5023.469072\n",
-       " 1923  4989.107185\n",
-       " 1924  4975.146642\n",
-       " 1925  5022.079006\n",
-       " 1926  5088.518825\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3254.455088\n",
-       " 182   3263.517146\n",
-       " 183   3265.827727\n",
-       " 184   3253.551770\n",
-       " 185   3243.289544\n",
-       " ...           ...\n",
-       " 1922  4976.223431\n",
-       " 1923  4901.697853\n",
-       " 1924  4872.056440\n",
-       " 1925  4922.902728\n",
-       " 1926  4992.499125\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3544.447333\n",
-       " 182   3551.542564\n",
-       " 183   3541.128944\n",
-       " 184   3492.887512\n",
-       " 185   3452.702099\n",
-       " ...           ...\n",
-       " 1922  5345.679672\n",
-       " 1923  5314.587850\n",
-       " 1924  5289.180022\n",
-       " 1925  5362.459867\n",
-       " 1926  5452.650840\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3613.211626\n",
-       " 182   3621.649440\n",
-       " 183   3617.218729\n",
-       " 184   3584.985614\n",
-       " 185   3562.848970\n",
-       " ...           ...\n",
-       " 1922  5400.349211\n",
-       " 1923  5393.949247\n",
-       " 1924  5376.908842\n",
-       " 1925  5375.962849\n",
-       " 1926  5390.572673\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3645.159918\n",
-       " 182   3636.922532\n",
-       " 183   3625.100655\n",
-       " 184   3605.534501\n",
-       " 185   3588.481659\n",
-       " ...           ...\n",
-       " 1922  5577.100414\n",
-       " 1923  5571.201035\n",
-       " 1924  5499.864861\n",
-       " 1925  5489.405945\n",
-       " 1926  5503.838199\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3186.989014\n",
-       " 182   3179.364015\n",
-       " 183   3175.115302\n",
-       " 184   3178.161043\n",
-       " 185   3179.943799\n",
-       " ...           ...\n",
-       " 1922  4676.573348\n",
-       " 1923  4669.365496\n",
-       " 1924  4618.801418\n",
-       " 1925  4663.418284\n",
-       " 1926  4744.442800\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2837.109172\n",
-       " 182   2814.097695\n",
-       " 183   2793.410506\n",
-       " 184   2777.744931\n",
-       " 185   2782.571393\n",
-       " ...           ...\n",
-       " 1922  4159.115274\n",
-       " 1923  4198.312793\n",
-       " 1924  4143.507869\n",
-       " 1925  4106.886632\n",
-       " 1926  4083.914042\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2894.596830\n",
-       " 182   2858.916206\n",
-       " 183   2810.163758\n",
-       " 184   2733.169681\n",
-       " 185   2694.838227\n",
-       " ...           ...\n",
-       " 1922  4031.244548\n",
-       " 1923  4024.217795\n",
-       " 1924  3986.103054\n",
-       " 1925  3999.775102\n",
-       " 1926  4028.484252\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2631.918287\n",
-       " 182   2650.246111\n",
-       " 183   2653.172522\n",
-       " 184   2622.824236\n",
-       " 185   2601.308308\n",
-       " ...           ...\n",
-       " 1922  3638.065051\n",
-       " 1923  3629.279845\n",
-       " 1924  3654.833033\n",
-       " 1925  3679.693249\n",
-       " 1926  3693.224046\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2379.825346\n",
-       " 182   2381.444166\n",
-       " 183   2380.531460\n",
-       " 184   2374.149404\n",
-       " 185   2371.812068\n",
-       " ...           ...\n",
-       " 1922  3264.778338\n",
-       " 1923  3274.400655\n",
-       " 1924  3332.289228\n",
-       " 1925  3339.096355\n",
-       " 1926  3321.580180\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2540.654485\n",
-       " 182   2534.953678\n",
-       " 183   2527.355456\n",
-       " 184   2515.657875\n",
-       " 185   2505.721482\n",
-       " ...           ...\n",
-       " 1922  3404.502040\n",
-       " 1923  3407.033365\n",
-       " 1924  3428.378693\n",
-       " 1925  3444.130130\n",
-       " 1926  3451.412274\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2651.810144\n",
-       " 182   2626.107951\n",
-       " 183   2617.495932\n",
-       " 184   2645.807172\n",
-       " 185   2643.764598\n",
-       " ...           ...\n",
-       " 1922  3699.759342\n",
-       " 1923  3650.298905\n",
-       " 1924  3616.312794\n",
-       " 1925  3629.914829\n",
-       " 1926  3654.819938\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2728.764415\n",
-       " 182   2716.414642\n",
-       " 183   2709.340342\n",
-       " 184   2713.663678\n",
-       " 185   2723.054508\n",
-       " ...           ...\n",
-       " 1922  3804.342375\n",
-       " 1923  3851.362138\n",
-       " 1924  3753.682372\n",
-       " 1925  3756.770251\n",
-       " 1926  3799.032800\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2710.642579\n",
-       " 182   2677.608650\n",
-       " 183   2655.759979\n",
-       " 184   2658.077016\n",
-       " 185   2664.953318\n",
-       " ...           ...\n",
-       " 1922  3551.975926\n",
-       " 1923  3582.034425\n",
-       " 1924  3590.867758\n",
-       " 1925  3603.903224\n",
-       " 1926  3610.979281\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2467.741261\n",
-       " 182   2449.355793\n",
-       " 183   2435.403162\n",
-       " 184   2431.027658\n",
-       " 185   2417.280690\n",
-       " ...           ...\n",
-       " 1922  3285.960308\n",
-       " 1923  3274.718036\n",
-       " 1924  3311.714722\n",
-       " 1925  3287.824633\n",
-       " 1926  3241.080187\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2309.272860\n",
-       " 182   2292.202729\n",
-       " 183   2284.627632\n",
-       " 184   2297.566518\n",
-       " 185   2283.175187\n",
-       " ...           ...\n",
-       " 1922  2913.472165\n",
-       " 1923  2915.792800\n",
-       " 1924  2959.009341\n",
-       " 1925  2962.339723\n",
-       " 1926  2943.269218\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2122.385998\n",
-       " 182   2137.317253\n",
-       " 183   2147.605305\n",
-       " 184   2147.861731\n",
-       " 185   2143.814518\n",
-       " ...           ...\n",
-       " 1922  2853.520343\n",
-       " 1923  2857.450221\n",
-       " 1924  2832.760038\n",
-       " 1925  2843.761176\n",
-       " 1926  2868.058928\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2439.680458\n",
-       " 182   2407.281368\n",
-       " 183   2380.794006\n",
-       " 184   2367.078911\n",
-       " 185   2364.996781\n",
-       " ...           ...\n",
-       " 1922  3168.816541\n",
-       " 1923  3095.232185\n",
-       " 1924  3168.765960\n",
-       " 1925  3208.021637\n",
-       " 1926  3224.036182\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2540.035598\n",
-       " 182   2537.003433\n",
-       " 183   2541.776412\n",
-       " 184   2563.412375\n",
-       " 185   2568.865760\n",
-       " ...           ...\n",
-       " 1922  3398.893583\n",
-       " 1923  3390.772007\n",
-       " 1924  3381.821724\n",
-       " 1925  3388.268121\n",
-       " 1926  3401.602493\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2527.475056\n",
-       " 182   2495.086310\n",
-       " 183   2485.906083\n",
-       " 184   2526.867776\n",
-       " 185   2549.133396\n",
-       " ...           ...\n",
-       " 1922  3423.124757\n",
-       " 1923  3412.515154\n",
-       " 1924  3392.077180\n",
-       " 1925  3375.133353\n",
-       " 1926  3351.498997\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2615.134088\n",
-       " 182   2633.459894\n",
-       " 183   2635.118663\n",
-       " 184   2600.768361\n",
-       " 185   2588.664690\n",
-       " ...           ...\n",
-       " 1922  3517.489830\n",
-       " 1923  3508.275518\n",
-       " 1924  3437.709703\n",
-       " 1925  3446.920116\n",
-       " 1926  3494.481314\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2586.804669\n",
-       " 182   2564.430553\n",
-       " 183   2539.739967\n",
-       " 184   2510.044654\n",
-       " 185   2502.853576\n",
-       " ...           ...\n",
-       " 1922  3456.921849\n",
-       " 1923  3387.540704\n",
-       " 1924  3378.589946\n",
-       " 1925  3405.334950\n",
-       " 1926  3452.183668\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2432.223512\n",
-       " 182   2380.475912\n",
-       " 183   2362.908730\n",
-       " 184   2419.188215\n",
-       " 185   2438.051476\n",
-       " ...           ...\n",
-       " 1922  3138.322097\n",
-       " 1923  3170.881164\n",
-       " 1924  3146.232805\n",
-       " 1925  3177.574636\n",
-       " 1926  3222.052793\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2381.062909\n",
-       " 182   2377.344569\n",
-       " 183   2374.048718\n",
-       " 184   2371.665654\n",
-       " 185   2354.596486\n",
-       " ...           ...\n",
-       " 1922  3119.318821\n",
-       " 1923  3106.015539\n",
-       " 1924  3028.083879\n",
-       " 1925  3013.827479\n",
-       " 1926  3036.166570\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2198.903993\n",
-       " 182   2199.833988\n",
-       " 183   2205.615311\n",
-       " 184   2221.877912\n",
-       " 185   2224.737824\n",
-       " ...           ...\n",
-       " 1922  2935.797208\n",
-       " 1923  2954.749623\n",
-       " 1924  2954.098108\n",
-       " 1925  2900.949174\n",
-       " 1926  2838.856366\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1991.938757\n",
-       " 182   1986.172423\n",
-       " 183   1978.508654\n",
-       " 184   1966.745486\n",
-       " 185   1956.237124\n",
-       " ...           ...\n",
-       " 1922  2568.173585\n",
-       " 1923  2589.317379\n",
-       " 1924  2549.967158\n",
-       " 1925  2533.004872\n",
-       " 1926  2525.617604\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2128.337192\n",
-       " 182   2141.267564\n",
-       " 183   2145.547389\n",
-       " 184   2131.137736\n",
-       " 185   2121.777889\n",
-       " ...           ...\n",
-       " 1922  2686.407240\n",
-       " 1923  2691.795194\n",
-       " 1924  2707.822341\n",
-       " 1925  2697.955247\n",
-       " 1926  2674.385384\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2207.208570\n",
-       " 182   2215.478247\n",
-       " 183   2224.801195\n",
-       " 184   2236.399733\n",
-       " 185   2231.553063\n",
-       " ...           ...\n",
-       " 1922  2864.628353\n",
-       " 1923  2888.569653\n",
-       " 1924  2873.285089\n",
-       " 1925  2900.693989\n",
-       " 1926  2942.663221\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2339.222387\n",
-       " 182   2379.531416\n",
-       " 183   2401.271230\n",
-       " 184   2382.892323\n",
-       " 185   2355.128747\n",
-       " ...           ...\n",
-       " 1922  3037.459061\n",
-       " 1923  3063.283551\n",
-       " 1924  3049.066695\n",
-       " 1925  3039.745683\n",
-       " 1926  3034.201261\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2297.753199\n",
-       " 182   2306.700979\n",
-       " 183   2305.100406\n",
-       " 184   2280.710159\n",
-       " 185   2270.980883\n",
-       " ...           ...\n",
-       " 1922  2887.443888\n",
-       " 1923  2906.551883\n",
-       " 1924  2951.856492\n",
-       " 1925  2971.974740\n",
-       " 1926  2973.335127\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2126.731185\n",
-       " 182   2092.293120\n",
-       " 183   2071.571808\n",
-       " 184   2080.485493\n",
-       " 185   2078.774387\n",
-       " ...           ...\n",
-       " 1922  2733.889264\n",
-       " 1923  2748.463366\n",
-       " 1924  2735.249351\n",
-       " 1925  2728.331186\n",
-       " 1926  2726.013137\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2017.324092\n",
-       " 182   1984.207293\n",
-       " 183   1966.283560\n",
-       " 184   1981.184392\n",
-       " 185   1974.579746\n",
-       " ...           ...\n",
-       " 1922  2524.666451\n",
-       " 1923  2538.945772\n",
-       " 1924  2513.168625\n",
-       " 1925  2522.382532\n",
-       " 1926  2543.267626\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1929.569227\n",
-       " 182   1932.468508\n",
-       " 183   1925.664252\n",
-       " 184   1897.895540\n",
-       " 185   1893.645560\n",
-       " ...           ...\n",
-       " 1922  2384.616459\n",
-       " 1923  2366.088292\n",
-       " 1924  2392.518059\n",
-       " 1925  2429.454583\n",
-       " 1926  2458.157820\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2074.347091\n",
-       " 182   2062.586118\n",
-       " 183   2058.631796\n",
-       " 184   2071.543716\n",
-       " 185   2065.986367\n",
-       " ...           ...\n",
-       " 1922  2662.902036\n",
-       " 1923  2665.674195\n",
-       " 1924  2635.735591\n",
-       " 1925  2645.686527\n",
-       " 1926  2666.891777\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2226.514238\n",
-       " 182   2244.793627\n",
-       " 183   2249.148112\n",
-       " 184   2223.417895\n",
-       " 185   2207.790323\n",
-       " ...           ...\n",
-       " 1922  2904.505642\n",
-       " 1923  2886.094475\n",
-       " 1924  2799.776924\n",
-       " 1925  2835.229117\n",
-       " 1926  2911.091378\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2251.509658\n",
-       " 182   2235.766975\n",
-       " 183   2236.058091\n",
-       " 184   2270.990173\n",
-       " 185   2287.980047\n",
-       " ...           ...\n",
-       " 1922  2774.528824\n",
-       " 1923  2782.314641\n",
-       " 1924  2843.271554\n",
-       " 1925  2874.146791\n",
-       " 1926  2884.496731\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2384.789209\n",
-       " 182   2334.370191\n",
-       " 183   2305.897148\n",
-       " 184   2324.838302\n",
-       " 185   2325.569008\n",
-       " ...           ...\n",
-       " 1922  2946.633439\n",
-       " 1923  2940.118028\n",
-       " 1924  2961.401172\n",
-       " 1925  2981.290231\n",
-       " 1926  2993.501297\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2235.236887\n",
-       " 182   2206.148972\n",
-       " 183   2187.823833\n",
-       " 184   2192.751630\n",
-       " 185   2203.506965\n",
-       " ...           ...\n",
-       " 1922  2820.105504\n",
-       " 1923  2787.569983\n",
-       " 1924  2805.699087\n",
-       " 1925  2837.832373\n",
-       " 1926  2868.077233\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2154.637973\n",
-       " 182   2102.857073\n",
-       " 183   2075.131770\n",
-       " 184   2099.378493\n",
-       " 185   2104.153897\n",
-       " ...           ...\n",
-       " 1922  2638.725498\n",
-       " 1923  2630.704735\n",
-       " 1924  2573.601830\n",
-       " 1925  2607.478373\n",
-       " 1926  2671.483285\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2122.225022\n",
-       " 182   2121.144296\n",
-       " 183   2116.688824\n",
-       " 184   2104.942226\n",
-       " 185   2096.728929\n",
-       " ...           ...\n",
-       " 1922  2652.592598\n",
-       " 1923  2644.323294\n",
-       " 1924  2642.585920\n",
-       " 1925  2675.846261\n",
-       " 1926  2714.479125\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1954.221532\n",
-       " 182   1937.109021\n",
-       " 183   1922.108802\n",
-       " 184   1911.672184\n",
-       " 185   1903.504266\n",
-       " ...           ...\n",
-       " 1922  2373.943644\n",
-       " 1923  2371.388070\n",
-       " 1924  2399.907798\n",
-       " 1925  2387.837401\n",
-       " 1926  2357.913955\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1873.125350\n",
-       " 182   1815.973613\n",
-       " 183   1778.026863\n",
-       " 184   1781.572415\n",
-       " 185   1797.779799\n",
-       " ...           ...\n",
-       " 1922  2308.383366\n",
-       " 1923  2309.693067\n",
-       " 1924  2299.554336\n",
-       " 1925  2302.712119\n",
-       " 1926  2301.712773\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1955.720324\n",
-       " 182   1975.301680\n",
-       " 183   1981.168848\n",
-       " 184   1957.406562\n",
-       " 185   1953.369217\n",
-       " ...           ...\n",
-       " 1922  2417.850457\n",
-       " 1923  2374.212880\n",
-       " 1924  2333.789426\n",
-       " 1925  2373.150659\n",
-       " 1926  2439.956735\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1964.246626\n",
-       " 182   1997.172204\n",
-       " 183   2015.537549\n",
-       " 184   2002.445563\n",
-       " 185   1987.062705\n",
-       " ...           ...\n",
-       " 1922  2541.728205\n",
-       " 1923  2540.229722\n",
-       " 1924  2483.114823\n",
-       " 1925  2454.682651\n",
-       " 1926  2444.842182\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2105.966140\n",
-       " 182   2060.851108\n",
-       " 183   2025.446877\n",
-       " 184   2011.022797\n",
-       " 185   2005.699264\n",
-       " ...           ...\n",
-       " 1922  2571.005402\n",
-       " 1923  2552.445033\n",
-       " 1924  2499.510217\n",
-       " 1925  2517.256929\n",
-       " 1926  2555.903599\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2210.974317\n",
-       " 182   2208.570054\n",
-       " 183   2208.275506\n",
-       " 184   2212.538988\n",
-       " 185   2201.869472\n",
-       " ...           ...\n",
-       " 1922  2604.166677\n",
-       " 1923  2583.337951\n",
-       " 1924  2538.763929\n",
-       " 1925  2569.773730\n",
-       " 1926  2623.326773\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1938.546209\n",
-       " 182   1951.453940\n",
-       " 183   1949.804892\n",
-       " 184   1916.705976\n",
-       " 185   1903.323204\n",
-       " ...           ...\n",
-       " 1922  2347.625499\n",
-       " 1923  2312.576561\n",
-       " 1924  2330.642533\n",
-       " 1925  2339.613807\n",
-       " 1926  2340.723084\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1894.609357\n",
-       " 182   1871.485241\n",
-       " 183   1861.443113\n",
-       " 184   1879.664573\n",
-       " 185   1884.738308\n",
-       " ...           ...\n",
-       " 1922  2301.104922\n",
-       " 1923  2296.889284\n",
-       " 1924  2257.500237\n",
-       " 1925  2281.096969\n",
-       " 1926  2325.019860\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1779.223004\n",
-       " 182   1820.799502\n",
-       " 183   1839.798738\n",
-       " 184   1810.019883\n",
-       " 185   1795.403224\n",
-       " ...           ...\n",
-       " 1922  2243.603449\n",
-       " 1923  2250.549751\n",
-       " 1924  2198.619353\n",
-       " 1925  2219.470758\n",
-       " 1926  2264.704566\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1955.290871\n",
-       " 182   1932.174009\n",
-       " 183   1913.701725\n",
-       " 184   1905.264036\n",
-       " 185   1896.818382\n",
-       " ...           ...\n",
-       " 1922  2271.465132\n",
-       " 1923  2258.551187\n",
-       " 1924  2280.788402\n",
-       " 1925  2298.831733\n",
-       " 1926  2308.985844\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2026.132972\n",
-       " 182   2002.357431\n",
-       " 183   1987.867154\n",
-       " 184   1993.437656\n",
-       " 185   2007.114861\n",
-       " ...           ...\n",
-       " 1922  2424.274911\n",
-       " 1923  2460.686071\n",
-       " 1924  2439.077011\n",
-       " 1925  2462.259575\n",
-       " 1926  2499.372053\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2046.669046\n",
-       " 182   2042.910721\n",
-       " 183   2038.309314\n",
-       " 184   2031.886431\n",
-       " 185   2023.938463\n",
-       " ...           ...\n",
-       " 1922  2497.695063\n",
-       " 1923  2471.708332\n",
-       " 1924  2463.680555\n",
-       " 1925  2466.849758\n",
-       " 1926  2474.209916\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1959.593605\n",
-       " 182   1979.175402\n",
-       " 183   1994.535131\n",
-       " 184   2000.773097\n",
-       " 185   2001.953497\n",
-       " ...           ...\n",
-       " 1922  2455.911644\n",
-       " 1923  2487.547158\n",
-       " 1924  2500.413319\n",
-       " 1925  2504.883463\n",
-       " 1926  2502.218788\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13415.514602\n",
-       " 182   13445.775690\n",
-       " 183   13453.368214\n",
-       " 184   13386.910095\n",
-       " 185   13331.014384\n",
-       " ...            ...\n",
-       " 1922  34921.735881\n",
-       " 1923  35015.831974\n",
-       " 1924  34572.352175\n",
-       " 1925  34618.093725\n",
-       " 1926  34952.542032\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11829.528738\n",
-       " 182   11788.951621\n",
-       " 183   11751.175871\n",
-       " 184   11722.551251\n",
-       " 185   11669.587982\n",
-       " ...            ...\n",
-       " 1922  27066.340932\n",
-       " 1923  27124.336224\n",
-       " 1924  27018.260466\n",
-       " 1925  27089.352469\n",
-       " 1926  27254.262113\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11185.734147\n",
-       " 182   11230.391909\n",
-       " 183   11247.224596\n",
-       " 184   11173.162037\n",
-       " 185   11103.477565\n",
-       " ...            ...\n",
-       " 1922  25048.395352\n",
-       " 1923  25058.023623\n",
-       " 1924  24973.609481\n",
-       " 1925  25101.050793\n",
-       " 1926  25329.548750\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11793.284904\n",
-       " 182   11774.698210\n",
-       " 183   11724.667171\n",
-       " 184   11571.917938\n",
-       " 185   11463.030079\n",
-       " ...            ...\n",
-       " 1922  24691.427704\n",
-       " 1923  24750.108742\n",
-       " 1924  24508.962278\n",
-       " 1925  24514.944185\n",
-       " 1926  24671.317661\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11739.799246\n",
-       " 182   11740.534629\n",
-       " 183   11719.529600\n",
-       " 184   11627.505892\n",
-       " 185   11546.639384\n",
-       " ...            ...\n",
-       " 1922  23955.040888\n",
-       " 1923  23894.125797\n",
-       " 1924  23848.209130\n",
-       " 1925  23932.180232\n",
-       " 1926  24086.921329\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12114.593261\n",
-       " 182   12192.687012\n",
-       " 183   12236.085362\n",
-       " 184   12166.145407\n",
-       " 185   12082.432528\n",
-       " ...            ...\n",
-       " 1922  24286.214176\n",
-       " 1923  24388.569949\n",
-       " 1924  24214.230302\n",
-       " 1925  24183.620430\n",
-       " 1926  24235.949145\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11155.212025\n",
-       " 182   11089.894484\n",
-       " 183   11037.379751\n",
-       " 184   11026.687522\n",
-       " 185   10978.494190\n",
-       " ...            ...\n",
-       " 1922  20487.064713\n",
-       " 1923  20490.597429\n",
-       " 1924  20534.063338\n",
-       " 1925  20516.373721\n",
-       " 1926  20456.458828\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10179.854240\n",
-       " 182   10141.080047\n",
-       " 183   10096.036199\n",
-       " 184   10030.511481\n",
-       " 185    9977.144930\n",
-       " ...            ...\n",
-       " 1922  18403.556876\n",
-       " 1923  18390.849538\n",
-       " 1924  18416.277568\n",
-       " 1925  18432.883749\n",
-       " 1926  18426.215314\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8488.853626\n",
-       " 182    8399.223586\n",
-       " 183    8338.654358\n",
-       " 184    8373.017121\n",
-       " 185    8385.044528\n",
-       " ...            ...\n",
-       " 1922  15347.665103\n",
-       " 1923  15370.823286\n",
-       " 1924  15307.057504\n",
-       " 1925  15294.983399\n",
-       " 1926  15307.994529\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8721.327093\n",
-       " 182    8752.360864\n",
-       " 183    8759.162255\n",
-       " 184    8686.804540\n",
-       " 185    8616.431272\n",
-       " ...            ...\n",
-       " 1922  15478.891273\n",
-       " 1923  15464.624700\n",
-       " 1924  15605.615862\n",
-       " 1925  15623.047410\n",
-       " 1926  15552.026641\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8801.892113\n",
-       " 182    8738.292819\n",
-       " 183    8681.866417\n",
-       " 184    8648.871465\n",
-       " 185    8625.246624\n",
-       " ...            ...\n",
-       " 1922  15249.212289\n",
-       " 1923  15097.625586\n",
-       " 1924  15032.778995\n",
-       " 1925  15097.185795\n",
-       " 1926  15228.106366\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8623.402171\n",
-       " 182    8663.088663\n",
-       " 183    8685.896888\n",
-       " 184    8653.569443\n",
-       " 185    8616.051408\n",
-       " ...            ...\n",
-       " 1922  14941.387638\n",
-       " 1923  14845.274158\n",
-       " 1924  14778.305093\n",
-       " 1925  14761.765809\n",
-       " 1926  14771.439650\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8188.334823\n",
-       " 182    8220.637998\n",
-       " 183    8227.457873\n",
-       " 184    8151.032297\n",
-       " 185    8077.588217\n",
-       " ...            ...\n",
-       " 1922  14215.118960\n",
-       " 1923  14196.268288\n",
-       " 1924  14207.558692\n",
-       " 1925  14270.559444\n",
-       " 1926  14355.469923\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8764.691816\n",
-       " 182    8755.074880\n",
-       " 183    8722.619009\n",
-       " 184    8615.555948\n",
-       " 185    8539.594669\n",
-       " ...            ...\n",
-       " 1922  14260.879701\n",
-       " 1923  14184.416836\n",
-       " 1924  14095.422518\n",
-       " 1925  14209.454183\n",
-       " 1926  14427.168478\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7817.757073\n",
-       " 182    7774.701629\n",
-       " 183    7741.172239\n",
-       " 184    7738.761291\n",
-       " 185    7737.945649\n",
-       " ...            ...\n",
-       " 1922  13089.855577\n",
-       " 1923  13086.045114\n",
-       " 1924  13127.481351\n",
-       " 1925  13150.641036\n",
-       " 1926  13154.297070\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7345.780297\n",
-       " 182    7335.993502\n",
-       " 183    7308.842079\n",
-       " 184    7224.966202\n",
-       " 185    7156.836538\n",
-       " ...            ...\n",
-       " 1922  11627.188025\n",
-       " 1923  11585.713805\n",
-       " 1924  11566.362796\n",
-       " 1925  11584.211460\n",
-       " 1926  11618.007850\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8802.530329\n",
-       " 182    8810.246559\n",
-       " 183    8802.324636\n",
-       " 184    8743.318080\n",
-       " 185    8668.545560\n",
-       " ...            ...\n",
-       " 1922  14785.135383\n",
-       " 1923  14834.551170\n",
-       " 1924  14857.417331\n",
-       " 1925  14885.963935\n",
-       " 1926  14910.318397\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10437.293229\n",
-       " 182   10416.544741\n",
-       " 183   10379.053638\n",
-       " 184   10286.869990\n",
-       " 185   10214.420599\n",
-       " ...            ...\n",
-       " 1922  15636.774237\n",
-       " 1923  15634.706636\n",
-       " 1924  15520.834161\n",
-       " 1925  15543.770458\n",
-       " 1926  15640.902852\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9615.395516\n",
-       " 182    9552.560334\n",
-       " 183    9500.808220\n",
-       " 184    9485.260795\n",
-       " 185    9451.161006\n",
-       " ...            ...\n",
-       " 1922  14968.400697\n",
-       " 1923  15034.100944\n",
-       " 1924  14976.558015\n",
-       " 1925  15044.486976\n",
-       " 1926  15174.374454\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9215.875031\n",
-       " 182    9149.659306\n",
-       " 183    9101.407510\n",
-       " 184    9111.837882\n",
-       " 185    9117.881574\n",
-       " ...            ...\n",
-       " 1922  14381.580655\n",
-       " 1923  14477.447817\n",
-       " 1924  14285.075342\n",
-       " 1925  14305.792785\n",
-       " 1926  14451.953750\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7019.458886\n",
-       " 182    7071.617405\n",
-       " 183    7094.232886\n",
-       " 184    7020.341114\n",
-       " 185    6971.172835\n",
-       " ...            ...\n",
-       " 1922  11029.427806\n",
-       " 1923  11029.335032\n",
-       " 1924  11038.114704\n",
-       " 1925  11050.680067\n",
-       " 1926  11059.553555\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7677.885317\n",
-       " 182    7737.453865\n",
-       " 183    7762.320564\n",
-       " 184    7673.827885\n",
-       " 185    7607.264085\n",
-       " ...            ...\n",
-       " 1922  12073.952604\n",
-       " 1923  12148.576396\n",
-       " 1924  12058.266421\n",
-       " 1925  12031.623193\n",
-       " 1926  12043.519844\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8190.852015\n",
-       " 182    8155.172832\n",
-       " 183    8114.319856\n",
-       " 184    8056.565823\n",
-       " 185    8009.774345\n",
-       " ...            ...\n",
-       " 1922  12392.193690\n",
-       " 1923  12452.899807\n",
-       " 1924  12452.467357\n",
-       " 1925  12505.003724\n",
-       " 1926  12574.194142\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7715.388877\n",
-       " 182    7760.299159\n",
-       " 183    7772.066526\n",
-       " 184    7675.567037\n",
-       " 185    7600.995370\n",
-       " ...            ...\n",
-       " 1922  12042.823040\n",
-       " 1923  11983.540910\n",
-       " 1924  12063.481567\n",
-       " 1925  12111.281948\n",
-       " 1926  12132.017460\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7981.936654\n",
-       " 182    8000.218659\n",
-       " 183    8003.179029\n",
-       " 184    7956.088719\n",
-       " 185    7924.949145\n",
-       " ...            ...\n",
-       " 1922  12368.691191\n",
-       " 1923  12421.168244\n",
-       " 1924  12360.144255\n",
-       " 1925  12328.111512\n",
-       " 1926  12311.836016\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7573.907648\n",
-       " 182    7546.819191\n",
-       " 183    7513.465025\n",
-       " 184    7459.642879\n",
-       " 185    7430.148150\n",
-       " ...            ...\n",
-       " 1922  11467.618705\n",
-       " 1923  11470.129556\n",
-       " 1924  11466.432586\n",
-       " 1925  11549.755461\n",
-       " 1926  11673.234298\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7082.883498\n",
-       " 182    6985.753961\n",
-       " 183    6893.908830\n",
-       " 184    6819.326091\n",
-       " 185    6765.477369\n",
-       " ...            ...\n",
-       " 1922  11109.582606\n",
-       " 1923  11062.589822\n",
-       " 1924  11077.641590\n",
-       " 1925  11095.850005\n",
-       " 1926  11108.946876\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6745.084634\n",
-       " 182    6620.588414\n",
-       " 183    6532.179382\n",
-       " 184    6561.655162\n",
-       " 185    6573.581960\n",
-       " ...            ...\n",
-       " 1922  10331.155579\n",
-       " 1923  10313.027869\n",
-       " 1924  10231.875723\n",
-       " 1925  10251.596475\n",
-       " 1926  10332.715820\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7852.938583\n",
-       " 182    7789.226133\n",
-       " 183    7729.871034\n",
-       " 184    7684.749948\n",
-       " 185    7635.234416\n",
-       " ...            ...\n",
-       " 1922  11605.911636\n",
-       " 1923  11624.620016\n",
-       " 1924  11498.723551\n",
-       " 1925  11534.855892\n",
-       " 1926  11661.783201\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7867.097396\n",
-       " 182    7758.064634\n",
-       " 183    7677.773193\n",
-       " 184    7691.370064\n",
-       " 185    7687.216447\n",
-       " ...            ...\n",
-       " 1922  11866.991833\n",
-       " 1923  11849.036479\n",
-       " 1924  11797.337616\n",
-       " 1925  11852.194760\n",
-       " 1926  11960.986837\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7707.630391\n",
-       " 182    7699.886187\n",
-       " 183    7697.770000\n",
-       " 184    7714.038667\n",
-       " 185    7716.546396\n",
-       " ...            ...\n",
-       " 1922  11667.464633\n",
-       " 1923  11626.796116\n",
-       " 1924  11483.142076\n",
-       " 1925  11480.071829\n",
-       " 1926  11559.271365\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7766.887483\n",
-       " 182    7818.468632\n",
-       " 183    7837.534882\n",
-       " 184    7750.385796\n",
-       " 185    7696.335829\n",
-       " ...            ...\n",
-       " 1922  11876.436516\n",
-       " 1923  11805.820802\n",
-       " 1924  11810.525348\n",
-       " 1925  11892.162181\n",
-       " 1926  12006.378400\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6478.571297\n",
-       " 182   6456.017366\n",
-       " 183   6431.578784\n",
-       " 184   6400.983680\n",
-       " 185   6379.759216\n",
-       " ...           ...\n",
-       " 1922  9827.251078\n",
-       " 1923  9828.501097\n",
-       " 1924  9823.540870\n",
-       " 1925  9852.630908\n",
-       " 1926  9899.281991\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6640.759829\n",
-       " 182    6622.890877\n",
-       " 183    6616.746264\n",
-       " 184    6648.901156\n",
-       " 185    6661.712364\n",
-       " ...            ...\n",
-       " 1922  10472.430839\n",
-       " 1923  10435.592356\n",
-       " 1924  10406.687053\n",
-       " 1925  10438.310518\n",
-       " 1926  10491.570079\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6864.992227\n",
-       " 182    6807.160070\n",
-       " 183    6762.602600\n",
-       " 184    6761.409108\n",
-       " 185    6753.034699\n",
-       " ...            ...\n",
-       " 1922  10341.793506\n",
-       " 1923  10281.320651\n",
-       " 1924  10171.959540\n",
-       " 1925  10207.605340\n",
-       " 1926  10324.648828\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7078.130513\n",
-       " 182    7133.628497\n",
-       " 183    7153.953972\n",
-       " 184    7059.382585\n",
-       " 185    6980.555905\n",
-       " ...            ...\n",
-       " 1922  10380.968077\n",
-       " 1923  10384.115231\n",
-       " 1924  10302.972554\n",
-       " 1925  10337.206211\n",
-       " 1926  10441.004883\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13415.514602\n",
-       " 182   13445.775690\n",
-       " 183   13453.368214\n",
-       " 184   13386.910095\n",
-       " 185   13331.014384\n",
-       " ...            ...\n",
-       " 1922  34921.735881\n",
-       " 1923  35015.831974\n",
-       " 1924  34572.352175\n",
-       " 1925  34618.093725\n",
-       " 1926  34952.542032\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11829.528738\n",
-       " 182   11788.951621\n",
-       " 183   11751.175871\n",
-       " 184   11722.551251\n",
-       " 185   11669.587982\n",
-       " ...            ...\n",
-       " 1922  27066.340932\n",
-       " 1923  27124.336224\n",
-       " 1924  27018.260466\n",
-       " 1925  27089.352469\n",
-       " 1926  27254.262113\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11185.734147\n",
-       " 182   11230.391909\n",
-       " 183   11247.224596\n",
-       " 184   11173.162037\n",
-       " 185   11103.477565\n",
-       " ...            ...\n",
-       " 1922  25048.395352\n",
-       " 1923  25058.023623\n",
-       " 1924  24973.609481\n",
-       " 1925  25101.050793\n",
-       " 1926  25329.548750\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11793.284904\n",
-       " 182   11774.698210\n",
-       " 183   11724.667171\n",
-       " 184   11571.917938\n",
-       " 185   11463.030079\n",
-       " ...            ...\n",
-       " 1922  24691.427704\n",
-       " 1923  24750.108742\n",
-       " 1924  24508.962278\n",
-       " 1925  24514.944185\n",
-       " 1926  24671.317661\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11739.799246\n",
-       " 182   11740.534629\n",
-       " 183   11719.529600\n",
-       " 184   11627.505892\n",
-       " 185   11546.639384\n",
-       " ...            ...\n",
-       " 1922  23955.040888\n",
-       " 1923  23894.125797\n",
-       " 1924  23848.209130\n",
-       " 1925  23932.180232\n",
-       " 1926  24086.921329\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12114.593261\n",
-       " 182   12192.687012\n",
-       " 183   12236.085362\n",
-       " 184   12166.145407\n",
-       " 185   12082.432528\n",
-       " ...            ...\n",
-       " 1922  24286.214176\n",
-       " 1923  24388.569949\n",
-       " 1924  24214.230302\n",
-       " 1925  24183.620430\n",
-       " 1926  24235.949145\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11155.212025\n",
-       " 182   11089.894484\n",
-       " 183   11037.379751\n",
-       " 184   11026.687522\n",
-       " 185   10978.494190\n",
-       " ...            ...\n",
-       " 1922  20487.064713\n",
-       " 1923  20490.597429\n",
-       " 1924  20534.063338\n",
-       " 1925  20516.373721\n",
-       " 1926  20456.458828\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10179.854240\n",
-       " 182   10141.080047\n",
-       " 183   10096.036199\n",
-       " 184   10030.511481\n",
-       " 185    9977.144930\n",
-       " ...            ...\n",
-       " 1922  18403.556876\n",
-       " 1923  18390.849538\n",
-       " 1924  18416.277568\n",
-       " 1925  18432.883749\n",
-       " 1926  18426.215314\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8488.853626\n",
-       " 182    8399.223586\n",
-       " 183    8338.654358\n",
-       " 184    8373.017121\n",
-       " 185    8385.044528\n",
-       " ...            ...\n",
-       " 1922  15347.665103\n",
-       " 1923  15370.823286\n",
-       " 1924  15307.057504\n",
-       " 1925  15294.983399\n",
-       " 1926  15307.994529\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8721.327093\n",
-       " 182    8752.360864\n",
-       " 183    8759.162255\n",
-       " 184    8686.804540\n",
-       " 185    8616.431272\n",
-       " ...            ...\n",
-       " 1922  15478.891273\n",
-       " 1923  15464.624700\n",
-       " 1924  15605.615862\n",
-       " 1925  15623.047410\n",
-       " 1926  15552.026641\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8801.892113\n",
-       " 182    8738.292819\n",
-       " 183    8681.866417\n",
-       " 184    8648.871465\n",
-       " 185    8625.246624\n",
-       " ...            ...\n",
-       " 1922  15249.212289\n",
-       " 1923  15097.625586\n",
-       " 1924  15032.778995\n",
-       " 1925  15097.185795\n",
-       " 1926  15228.106366\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8623.402171\n",
-       " 182    8663.088663\n",
-       " 183    8685.896888\n",
-       " 184    8653.569443\n",
-       " 185    8616.051408\n",
-       " ...            ...\n",
-       " 1922  14941.387638\n",
-       " 1923  14845.274158\n",
-       " 1924  14778.305093\n",
-       " 1925  14761.765809\n",
-       " 1926  14771.439650\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8188.334823\n",
-       " 182    8220.637998\n",
-       " 183    8227.457873\n",
-       " 184    8151.032297\n",
-       " 185    8077.588217\n",
-       " ...            ...\n",
-       " 1922  14215.118960\n",
-       " 1923  14196.268288\n",
-       " 1924  14207.558692\n",
-       " 1925  14270.559444\n",
-       " 1926  14355.469923\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8764.691816\n",
-       " 182    8755.074880\n",
-       " 183    8722.619009\n",
-       " 184    8615.555948\n",
-       " 185    8539.594669\n",
-       " ...            ...\n",
-       " 1922  14260.879701\n",
-       " 1923  14184.416836\n",
-       " 1924  14095.422518\n",
-       " 1925  14209.454183\n",
-       " 1926  14427.168478\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7817.757073\n",
-       " 182    7774.701629\n",
-       " 183    7741.172239\n",
-       " 184    7738.761291\n",
-       " 185    7737.945649\n",
-       " ...            ...\n",
-       " 1922  13089.855577\n",
-       " 1923  13086.045114\n",
-       " 1924  13127.481351\n",
-       " 1925  13150.641036\n",
-       " 1926  13154.297070\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7345.780297\n",
-       " 182    7335.993502\n",
-       " 183    7308.842079\n",
-       " 184    7224.966202\n",
-       " 185    7156.836538\n",
-       " ...            ...\n",
-       " 1922  11627.188025\n",
-       " 1923  11585.713805\n",
-       " 1924  11566.362796\n",
-       " 1925  11584.211460\n",
-       " 1926  11618.007850\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8802.530329\n",
-       " 182    8810.246559\n",
-       " 183    8802.324636\n",
-       " 184    8743.318080\n",
-       " 185    8668.545560\n",
-       " ...            ...\n",
-       " 1922  14785.135383\n",
-       " 1923  14834.551170\n",
-       " 1924  14857.417331\n",
-       " 1925  14885.963935\n",
-       " 1926  14910.318397\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10437.293229\n",
-       " 182   10416.544741\n",
-       " 183   10379.053638\n",
-       " 184   10286.869990\n",
-       " 185   10214.420599\n",
-       " ...            ...\n",
-       " 1922  15636.774237\n",
-       " 1923  15634.706636\n",
-       " 1924  15520.834161\n",
-       " 1925  15543.770458\n",
-       " 1926  15640.902852\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9615.395516\n",
-       " 182    9552.560334\n",
-       " 183    9500.808220\n",
-       " 184    9485.260795\n",
-       " 185    9451.161006\n",
-       " ...            ...\n",
-       " 1922  14968.400697\n",
-       " 1923  15034.100944\n",
-       " 1924  14976.558015\n",
-       " 1925  15044.486976\n",
-       " 1926  15174.374454\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9215.875031\n",
-       " 182    9149.659306\n",
-       " 183    9101.407510\n",
-       " 184    9111.837882\n",
-       " 185    9117.881574\n",
-       " ...            ...\n",
-       " 1922  14381.580655\n",
-       " 1923  14477.447817\n",
-       " 1924  14285.075342\n",
-       " 1925  14305.792785\n",
-       " 1926  14451.953750\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7019.458886\n",
-       " 182    7071.617405\n",
-       " 183    7094.232886\n",
-       " 184    7020.341114\n",
-       " 185    6971.172835\n",
-       " ...            ...\n",
-       " 1922  11029.427806\n",
-       " 1923  11029.335032\n",
-       " 1924  11038.114704\n",
-       " 1925  11050.680067\n",
-       " 1926  11059.553555\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7677.885317\n",
-       " 182    7737.453865\n",
-       " 183    7762.320564\n",
-       " 184    7673.827885\n",
-       " 185    7607.264085\n",
-       " ...            ...\n",
-       " 1922  12073.952604\n",
-       " 1923  12148.576396\n",
-       " 1924  12058.266421\n",
-       " 1925  12031.623193\n",
-       " 1926  12043.519844\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8190.852015\n",
-       " 182    8155.172832\n",
-       " 183    8114.319856\n",
-       " 184    8056.565823\n",
-       " 185    8009.774345\n",
-       " ...            ...\n",
-       " 1922  12392.193690\n",
-       " 1923  12452.899807\n",
-       " 1924  12452.467357\n",
-       " 1925  12505.003724\n",
-       " 1926  12574.194142\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7715.388877\n",
-       " 182    7760.299159\n",
-       " 183    7772.066526\n",
-       " 184    7675.567037\n",
-       " 185    7600.995370\n",
-       " ...            ...\n",
-       " 1922  12042.823040\n",
-       " 1923  11983.540910\n",
-       " 1924  12063.481567\n",
-       " 1925  12111.281948\n",
-       " 1926  12132.017460\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7981.936654\n",
-       " 182    8000.218659\n",
-       " 183    8003.179029\n",
-       " 184    7956.088719\n",
-       " 185    7924.949145\n",
-       " ...            ...\n",
-       " 1922  12368.691191\n",
-       " 1923  12421.168244\n",
-       " 1924  12360.144255\n",
-       " 1925  12328.111512\n",
-       " 1926  12311.836016\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7573.907648\n",
-       " 182    7546.819191\n",
-       " 183    7513.465025\n",
-       " 184    7459.642879\n",
-       " 185    7430.148150\n",
-       " ...            ...\n",
-       " 1922  11467.618705\n",
-       " 1923  11470.129556\n",
-       " 1924  11466.432586\n",
-       " 1925  11549.755461\n",
-       " 1926  11673.234298\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7082.883498\n",
-       " 182    6985.753961\n",
-       " 183    6893.908830\n",
-       " 184    6819.326091\n",
-       " 185    6765.477369\n",
-       " ...            ...\n",
-       " 1922  11109.582606\n",
-       " 1923  11062.589822\n",
-       " 1924  11077.641590\n",
-       " 1925  11095.850005\n",
-       " 1926  11108.946876\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6745.084634\n",
-       " 182    6620.588414\n",
-       " 183    6532.179382\n",
-       " 184    6561.655162\n",
-       " 185    6573.581960\n",
-       " ...            ...\n",
-       " 1922  10331.155579\n",
-       " 1923  10313.027869\n",
-       " 1924  10231.875723\n",
-       " 1925  10251.596475\n",
-       " 1926  10332.715820\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7852.938583\n",
-       " 182    7789.226133\n",
-       " 183    7729.871034\n",
-       " 184    7684.749948\n",
-       " 185    7635.234416\n",
-       " ...            ...\n",
-       " 1922  11605.911636\n",
-       " 1923  11624.620016\n",
-       " 1924  11498.723551\n",
-       " 1925  11534.855892\n",
-       " 1926  11661.783201\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7867.097396\n",
-       " 182    7758.064634\n",
-       " 183    7677.773193\n",
-       " 184    7691.370064\n",
-       " 185    7687.216447\n",
-       " ...            ...\n",
-       " 1922  11866.991833\n",
-       " 1923  11849.036479\n",
-       " 1924  11797.337616\n",
-       " 1925  11852.194760\n",
-       " 1926  11960.986837\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7707.630391\n",
-       " 182    7699.886187\n",
-       " 183    7697.770000\n",
-       " 184    7714.038667\n",
-       " 185    7716.546396\n",
-       " ...            ...\n",
-       " 1922  11667.464633\n",
-       " 1923  11626.796116\n",
-       " 1924  11483.142076\n",
-       " 1925  11480.071829\n",
-       " 1926  11559.271365\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7766.887483\n",
-       " 182    7818.468632\n",
-       " 183    7837.534882\n",
-       " 184    7750.385796\n",
-       " 185    7696.335829\n",
-       " ...            ...\n",
-       " 1922  11876.436516\n",
-       " 1923  11805.820802\n",
-       " 1924  11810.525348\n",
-       " 1925  11892.162181\n",
-       " 1926  12006.378400\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6478.571297\n",
-       " 182   6456.017366\n",
-       " 183   6431.578784\n",
-       " 184   6400.983680\n",
-       " 185   6379.759216\n",
-       " ...           ...\n",
-       " 1922  9827.251078\n",
-       " 1923  9828.501097\n",
-       " 1924  9823.540870\n",
-       " 1925  9852.630908\n",
-       " 1926  9899.281991\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6640.759829\n",
-       " 182    6622.890877\n",
-       " 183    6616.746264\n",
-       " 184    6648.901156\n",
-       " 185    6661.712364\n",
-       " ...            ...\n",
-       " 1922  10472.430839\n",
-       " 1923  10435.592356\n",
-       " 1924  10406.687053\n",
-       " 1925  10438.310518\n",
-       " 1926  10491.570079\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6864.992227\n",
-       " 182    6807.160070\n",
-       " 183    6762.602600\n",
-       " 184    6761.409108\n",
-       " 185    6753.034699\n",
-       " ...            ...\n",
-       " 1922  10341.793506\n",
-       " 1923  10281.320651\n",
-       " 1924  10171.959540\n",
-       " 1925  10207.605340\n",
-       " 1926  10324.648828\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7078.130513\n",
-       " 182    7133.628497\n",
-       " 183    7153.953972\n",
-       " 184    7059.382585\n",
-       " 185    6980.555905\n",
-       " ...            ...\n",
-       " 1922  10380.968077\n",
-       " 1923  10384.115231\n",
-       " 1924  10302.972554\n",
-       " 1925  10337.206211\n",
-       " 1926  10441.004883\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12991.525272\n",
-       " 182   13003.740494\n",
-       " 183   12976.232399\n",
-       " 184   12818.961471\n",
-       " 185   12673.237787\n",
-       " ...            ...\n",
-       " 1922  15276.435855\n",
-       " 1923  15277.656249\n",
-       " 1924  15238.948547\n",
-       " 1925  15364.814019\n",
-       " 1926  15578.323791\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12997.537488\n",
-       " 182   13142.386278\n",
-       " 183   13225.347736\n",
-       " 184   13106.143909\n",
-       " 185   12991.112366\n",
-       " ...            ...\n",
-       " 1922  15923.284960\n",
-       " 1923  15865.672041\n",
-       " 1924  15729.894263\n",
-       " 1925  15778.212869\n",
-       " 1926  15932.028713\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11186.387034\n",
-       " 182   11159.729776\n",
-       " 183   11110.853567\n",
-       " 184   10989.395452\n",
-       " 185   10904.023604\n",
-       " ...            ...\n",
-       " 1922  13886.191330\n",
-       " 1923  13913.892538\n",
-       " 1924  13916.788371\n",
-       " 1925  13949.952971\n",
-       " 1926  13997.275703\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12070.027785\n",
-       " 182   12084.132624\n",
-       " 183   12086.198867\n",
-       " 184   12048.939031\n",
-       " 185   11978.961752\n",
-       " ...            ...\n",
-       " 1922  15409.915251\n",
-       " 1923  15283.557677\n",
-       " 1924  15262.610077\n",
-       " 1925  15277.599872\n",
-       " 1926  15305.017146\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12713.562350\n",
-       " 182   12648.430798\n",
-       " 183   12598.291764\n",
-       " 184   12597.128293\n",
-       " 185   12580.605939\n",
-       " ...            ...\n",
-       " 1922  15459.959758\n",
-       " 1923  15497.336239\n",
-       " 1924  15469.104547\n",
-       " 1925  15479.961565\n",
-       " 1926  15509.102383\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11308.186310\n",
-       " 182   11261.548710\n",
-       " 183   11207.074652\n",
-       " 184   11127.001498\n",
-       " 185   11035.946643\n",
-       " ...            ...\n",
-       " 1922  14855.025093\n",
-       " 1923  14831.001077\n",
-       " 1924  14701.352719\n",
-       " 1925  14750.127541\n",
-       " 1926  14898.727305\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10058.866014\n",
-       " 182   10014.745550\n",
-       " 183    9968.420018\n",
-       " 184    9914.891263\n",
-       " 185    9848.589496\n",
-       " ...            ...\n",
-       " 1922  14007.666051\n",
-       " 1923  13987.086492\n",
-       " 1924  13875.083967\n",
-       " 1925  13828.634557\n",
-       " 1926  13816.387618\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8995.027092\n",
-       " 182    8973.440398\n",
-       " 183    8951.689788\n",
-       " 184    8929.403714\n",
-       " 185    8920.478659\n",
-       " ...            ...\n",
-       " 1922  11661.100490\n",
-       " 1923  11665.460989\n",
-       " 1924  11585.532958\n",
-       " 1925  11656.294567\n",
-       " 1926  11807.856210\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11914.951345\n",
-       " 182   11935.702353\n",
-       " 183   11923.145803\n",
-       " 184   11801.784559\n",
-       " 185   11699.355621\n",
-       " ...            ...\n",
-       " 1922  14822.614592\n",
-       " 1923  14793.752740\n",
-       " 1924  14684.992007\n",
-       " 1925  14789.341385\n",
-       " 1926  15011.391561\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11810.455756\n",
-       " 182   11839.192651\n",
-       " 183   11836.814253\n",
-       " 184   11732.792564\n",
-       " 185   11649.899914\n",
-       " ...            ...\n",
-       " 1922  13093.729529\n",
-       " 1923  12999.159680\n",
-       " 1924  13032.227862\n",
-       " 1925  13107.553213\n",
-       " 1926  13196.752109\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9170.219278\n",
-       " 182    9131.324620\n",
-       " 183    9092.729284\n",
-       " 184    9055.111728\n",
-       " 185    9009.709719\n",
-       " ...            ...\n",
-       " 1922  10242.254131\n",
-       " 1923  10237.790710\n",
-       " 1924  10182.743362\n",
-       " 1925  10236.142962\n",
-       " 1926  10348.993009\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9238.105508\n",
-       " 182    9188.554601\n",
-       " 183    9124.284413\n",
-       " 184    9011.931242\n",
-       " 185    8916.516883\n",
-       " ...            ...\n",
-       " 1922  10214.447220\n",
-       " 1923  10117.943825\n",
-       " 1924  10093.174254\n",
-       " 1925  10116.323244\n",
-       " 1926  10163.419768\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8490.041890\n",
-       " 182   8452.399249\n",
-       " 183   8417.402340\n",
-       " 184   8391.048153\n",
-       " 185   8338.560465\n",
-       " ...           ...\n",
-       " 1922  9593.228069\n",
-       " 1923  9584.497859\n",
-       " 1924  9589.951142\n",
-       " 1925  9561.985107\n",
-       " 1926  9513.767149\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8974.325881\n",
-       " 182    8907.415094\n",
-       " 183    8853.306502\n",
-       " 184    8841.018410\n",
-       " 185    8819.154253\n",
-       " ...            ...\n",
-       " 1922  10418.214179\n",
-       " 1923  10481.456316\n",
-       " 1924  10381.523235\n",
-       " 1925  10410.204214\n",
-       " 1926  10506.730389\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9301.858666\n",
-       " 182    9247.649997\n",
-       " 183    9193.735371\n",
-       " 184    9140.781287\n",
-       " 185    9071.264415\n",
-       " ...            ...\n",
-       " 1922  10608.119773\n",
-       " 1923  10602.855156\n",
-       " 1924  10601.132303\n",
-       " 1925  10666.250370\n",
-       " 1926  10763.896559\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11490.249362\n",
-       " 182   11383.649060\n",
-       " 183   11294.842314\n",
-       " 184   11264.161186\n",
-       " 185   11215.523843\n",
-       " ...            ...\n",
-       " 1922  12560.591326\n",
-       " 1923  12479.469450\n",
-       " 1924  12537.538894\n",
-       " 1925  12556.526487\n",
-       " 1926  12546.015898\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9363.165118\n",
-       " 182    9327.625804\n",
-       " 183    9269.082256\n",
-       " 184    9135.391548\n",
-       " 185    9028.011875\n",
-       " ...            ...\n",
-       " 1922  10766.936855\n",
-       " 1923  10822.758095\n",
-       " 1924  10779.279560\n",
-       " 1925  10754.707763\n",
-       " 1926  10742.357384\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11732.708441\n",
-       " 182   11744.773461\n",
-       " 183   11733.537933\n",
-       " 184   11646.187283\n",
-       " 185   11566.204657\n",
-       " ...            ...\n",
-       " 1922  11175.267581\n",
-       " 1923  11224.011936\n",
-       " 1924  11044.786558\n",
-       " 1925  10999.831297\n",
-       " 1926  11035.782853\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3373.025023\n",
-       " 182   3341.435432\n",
-       " 183   3312.434662\n",
-       " 184   3296.814709\n",
-       " 185   3283.159082\n",
-       " ...           ...\n",
-       " 1922  5847.253694\n",
-       " 1923  5814.480493\n",
-       " 1924  5830.902626\n",
-       " 1925  5834.813859\n",
-       " 1926  5828.276927\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2947.096815\n",
-       " 182   2916.789229\n",
-       " 183   2892.532646\n",
-       " 184   2899.551818\n",
-       " 185   2905.210619\n",
-       " ...           ...\n",
-       " 1922  4999.020321\n",
-       " 1923  4991.598751\n",
-       " 1924  4916.100006\n",
-       " 1925  4932.769799\n",
-       " 1926  5026.411844\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3027.542992\n",
-       " 182   3001.244100\n",
-       " 183   2977.318949\n",
-       " 184   2965.662920\n",
-       " 185   2964.138057\n",
-       " ...           ...\n",
-       " 1922  5230.722035\n",
-       " 1923  5187.136065\n",
-       " 1924  5129.399932\n",
-       " 1925  5159.862911\n",
-       " 1926  5263.983176\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2451.994559\n",
-       " 182   2480.280723\n",
-       " 183   2501.758151\n",
-       " 184   2488.043344\n",
-       " 185   2464.950813\n",
-       " ...           ...\n",
-       " 1922  4063.277551\n",
-       " 1923  4094.435166\n",
-       " 1924  4048.030054\n",
-       " 1925  4042.576983\n",
-       " 1926  4071.323984\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2084.041170\n",
-       " 182   2021.638070\n",
-       " 183   1978.476575\n",
-       " 184   2034.768953\n",
-       " 185   2063.089691\n",
-       " ...           ...\n",
-       " 1922  3210.008681\n",
-       " 1923  3172.244319\n",
-       " 1924  3249.871217\n",
-       " 1925  3260.952953\n",
-       " 1926  3216.461160\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1869.172707\n",
-       " 182   1866.063395\n",
-       " 183   1861.222404\n",
-       " 184   1847.430906\n",
-       " 185   1828.949846\n",
-       " ...           ...\n",
-       " 1922  2906.607718\n",
-       " 1923  2892.694265\n",
-       " 1924  2952.547675\n",
-       " 1925  2988.542238\n",
-       " 1926  3004.611682\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1710.198630\n",
-       " 182   1692.407110\n",
-       " 183   1677.424079\n",
-       " 184   1676.957253\n",
-       " 185   1681.934661\n",
-       " ...           ...\n",
-       " 1922  2671.360510\n",
-       " 1923  2634.447727\n",
-       " 1924  2610.801602\n",
-       " 1925  2608.769758\n",
-       " 1926  2624.788539\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1913.740935\n",
-       " 182   1943.295874\n",
-       " 183   1962.039617\n",
-       " 184   1924.903645\n",
-       " 185   1897.896165\n",
-       " ...           ...\n",
-       " 1922  2930.476390\n",
-       " 1923  2873.553179\n",
-       " 1924  2918.797508\n",
-       " 1925  2949.849004\n",
-       " 1926  2969.047710\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2053.105652\n",
-       " 182   2056.016817\n",
-       " 183   2058.495948\n",
-       " 184   2058.742038\n",
-       " 185   2056.870938\n",
-       " ...           ...\n",
-       " 1922  3324.396132\n",
-       " 1923  3317.367330\n",
-       " 1924  3288.595417\n",
-       " 1925  3292.251080\n",
-       " 1926  3322.987823\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3373.025023\n",
-       " 182   3341.435432\n",
-       " 183   3312.434662\n",
-       " 184   3296.814709\n",
-       " 185   3283.159082\n",
-       " ...           ...\n",
-       " 1922  5847.253694\n",
-       " 1923  5814.480493\n",
-       " 1924  5830.902626\n",
-       " 1925  5834.813859\n",
-       " 1926  5828.276927\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2947.096815\n",
-       " 182   2916.789229\n",
-       " 183   2892.532646\n",
-       " 184   2899.551818\n",
-       " 185   2905.210619\n",
-       " ...           ...\n",
-       " 1922  4999.020321\n",
-       " 1923  4991.598751\n",
-       " 1924  4916.100006\n",
-       " 1925  4932.769799\n",
-       " 1926  5026.411844\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3027.542992\n",
-       " 182   3001.244100\n",
-       " 183   2977.318949\n",
-       " 184   2965.662920\n",
-       " 185   2964.138057\n",
-       " ...           ...\n",
-       " 1922  5230.722035\n",
-       " 1923  5187.136065\n",
-       " 1924  5129.399932\n",
-       " 1925  5159.862911\n",
-       " 1926  5263.983176\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2451.994559\n",
-       " 182   2480.280723\n",
-       " 183   2501.758151\n",
-       " 184   2488.043344\n",
-       " 185   2464.950813\n",
-       " ...           ...\n",
-       " 1922  4063.277551\n",
-       " 1923  4094.435166\n",
-       " 1924  4048.030054\n",
-       " 1925  4042.576983\n",
-       " 1926  4071.323984\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2084.041170\n",
-       " 182   2021.638070\n",
-       " 183   1978.476575\n",
-       " 184   2034.768953\n",
-       " 185   2063.089691\n",
-       " ...           ...\n",
-       " 1922  3210.008681\n",
-       " 1923  3172.244319\n",
-       " 1924  3249.871217\n",
-       " 1925  3260.952953\n",
-       " 1926  3216.461160\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1869.172707\n",
-       " 182   1866.063395\n",
-       " 183   1861.222404\n",
-       " 184   1847.430906\n",
-       " 185   1828.949846\n",
-       " ...           ...\n",
-       " 1922  2906.607718\n",
-       " 1923  2892.694265\n",
-       " 1924  2952.547675\n",
-       " 1925  2988.542238\n",
-       " 1926  3004.611682\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1710.198630\n",
-       " 182   1692.407110\n",
-       " 183   1677.424079\n",
-       " 184   1676.957253\n",
-       " 185   1681.934661\n",
-       " ...           ...\n",
-       " 1922  2671.360510\n",
-       " 1923  2634.447727\n",
-       " 1924  2610.801602\n",
-       " 1925  2608.769758\n",
-       " 1926  2624.788539\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   1913.740935\n",
-       " 182   1943.295874\n",
-       " 183   1962.039617\n",
-       " 184   1924.903645\n",
-       " 185   1897.896165\n",
-       " ...           ...\n",
-       " 1922  2930.476390\n",
-       " 1923  2873.553179\n",
-       " 1924  2918.797508\n",
-       " 1925  2949.849004\n",
-       " 1926  2969.047710\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2053.105652\n",
-       " 182   2056.016817\n",
-       " 183   2058.495948\n",
-       " 184   2058.742038\n",
-       " 185   2056.870938\n",
-       " ...           ...\n",
-       " 1922  3324.396132\n",
-       " 1923  3317.367330\n",
-       " 1924  3288.595417\n",
-       " 1925  3292.251080\n",
-       " 1926  3322.987823\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3970.152121\n",
-       " 182   3983.956304\n",
-       " 183   3991.489201\n",
-       " 184   3966.607770\n",
-       " 185   3955.485668\n",
-       " ...           ...\n",
-       " 1922  7573.962767\n",
-       " 1923  7554.695346\n",
-       " 1924  7642.294659\n",
-       " 1925  7681.007929\n",
-       " 1926  7678.895239\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3518.651712\n",
-       " 182   3601.052544\n",
-       " 183   3664.000198\n",
-       " 184   3626.400427\n",
-       " 185   3599.836467\n",
-       " ...           ...\n",
-       " 1922  6688.031289\n",
-       " 1923  6635.362180\n",
-       " 1924  6670.673343\n",
-       " 1925  6659.328352\n",
-       " 1926  6609.019640\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2739.377498\n",
-       " 182   2762.365906\n",
-       " 183   2774.866097\n",
-       " 184   2733.155958\n",
-       " 185   2705.052115\n",
-       " ...           ...\n",
-       " 1922  4799.349536\n",
-       " 1923  4837.911917\n",
-       " 1924  4829.165068\n",
-       " 1925  4822.687527\n",
-       " 1926  4818.105140\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2393.445957\n",
-       " 182   2352.408154\n",
-       " 183   2320.123352\n",
-       " 184   2333.080087\n",
-       " 185   2338.930352\n",
-       " ...           ...\n",
-       " 1922  4089.153434\n",
-       " 1923  4086.987673\n",
-       " 1924  4099.007220\n",
-       " 1925  4101.358456\n",
-       " 1926  4095.635445\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2356.705043\n",
-       " 182   2335.658042\n",
-       " 183   2321.854267\n",
-       " 184   2345.488473\n",
-       " 185   2359.748904\n",
-       " ...           ...\n",
-       " 1922  4049.829173\n",
-       " 1923  4040.293440\n",
-       " 1924  3933.368778\n",
-       " 1925  3899.225576\n",
-       " 1926  3925.863991\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2679.737037\n",
-       " 182   2635.400847\n",
-       " 183   2593.650475\n",
-       " 184   2565.265386\n",
-       " 185   2549.731834\n",
-       " ...           ...\n",
-       " 1922  4496.225734\n",
-       " 1923  4553.127086\n",
-       " 1924  4488.003499\n",
-       " 1925  4483.173180\n",
-       " 1926  4528.695275\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2599.194876\n",
-       " 182   2562.847220\n",
-       " 183   2537.092761\n",
-       " 184   2566.091245\n",
-       " 185   2574.071840\n",
-       " ...           ...\n",
-       " 1922  4396.856801\n",
-       " 1923  4302.800093\n",
-       " 1924  4273.115664\n",
-       " 1925  4303.756968\n",
-       " 1926  4384.777801\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2288.771338\n",
-       " 182   2259.718289\n",
-       " 183   2230.225545\n",
-       " 184   2198.460159\n",
-       " 185   2179.394792\n",
-       " ...           ...\n",
-       " 1922  3600.564337\n",
-       " 1923  3581.493152\n",
-       " 1924  3595.532087\n",
-       " 1925  3593.072194\n",
-       " 1926  3576.833716\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2312.844587\n",
-       " 182   2305.122560\n",
-       " 183   2302.591462\n",
-       " 184   2326.890652\n",
-       " 185   2337.884181\n",
-       " ...           ...\n",
-       " 1922  3753.129843\n",
-       " 1923  3731.003930\n",
-       " 1924  3823.311472\n",
-       " 1925  3832.575551\n",
-       " 1926  3772.487956\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2399.878944\n",
-       " 182   2397.499407\n",
-       " 183   2390.792022\n",
-       " 184   2361.715334\n",
-       " 185   2342.767980\n",
-       " ...           ...\n",
-       " 1922  4083.408861\n",
-       " 1923  4068.875226\n",
-       " 1924  4060.026076\n",
-       " 1925  4088.177631\n",
-       " 1926  4147.229399\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3970.152121\n",
-       " 182   3983.956304\n",
-       " 183   3991.489201\n",
-       " 184   3966.607770\n",
-       " 185   3955.485668\n",
-       " ...           ...\n",
-       " 1922  7573.962767\n",
-       " 1923  7554.695346\n",
-       " 1924  7642.294659\n",
-       " 1925  7681.007929\n",
-       " 1926  7678.895239\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3518.651712\n",
-       " 182   3601.052544\n",
-       " 183   3664.000198\n",
-       " 184   3626.400427\n",
-       " 185   3599.836467\n",
-       " ...           ...\n",
-       " 1922  6688.031289\n",
-       " 1923  6635.362180\n",
-       " 1924  6670.673343\n",
-       " 1925  6659.328352\n",
-       " 1926  6609.019640\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2739.377498\n",
-       " 182   2762.365906\n",
-       " 183   2774.866097\n",
-       " 184   2733.155958\n",
-       " 185   2705.052115\n",
-       " ...           ...\n",
-       " 1922  4799.349536\n",
-       " 1923  4837.911917\n",
-       " 1924  4829.165068\n",
-       " 1925  4822.687527\n",
-       " 1926  4818.105140\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2393.445957\n",
-       " 182   2352.408154\n",
-       " 183   2320.123352\n",
-       " 184   2333.080087\n",
-       " 185   2338.930352\n",
-       " ...           ...\n",
-       " 1922  4089.153434\n",
-       " 1923  4086.987673\n",
-       " 1924  4099.007220\n",
-       " 1925  4101.358456\n",
-       " 1926  4095.635445\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2356.705043\n",
-       " 182   2335.658042\n",
-       " 183   2321.854267\n",
-       " 184   2345.488473\n",
-       " 185   2359.748904\n",
-       " ...           ...\n",
-       " 1922  4049.829173\n",
-       " 1923  4040.293440\n",
-       " 1924  3933.368778\n",
-       " 1925  3899.225576\n",
-       " 1926  3925.863991\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2679.737037\n",
-       " 182   2635.400847\n",
-       " 183   2593.650475\n",
-       " 184   2565.265386\n",
-       " 185   2549.731834\n",
-       " ...           ...\n",
-       " 1922  4496.225734\n",
-       " 1923  4553.127086\n",
-       " 1924  4488.003499\n",
-       " 1925  4483.173180\n",
-       " 1926  4528.695275\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2599.194876\n",
-       " 182   2562.847220\n",
-       " 183   2537.092761\n",
-       " 184   2566.091245\n",
-       " 185   2574.071840\n",
-       " ...           ...\n",
-       " 1922  4396.856801\n",
-       " 1923  4302.800093\n",
-       " 1924  4273.115664\n",
-       " 1925  4303.756968\n",
-       " 1926  4384.777801\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2288.771338\n",
-       " 182   2259.718289\n",
-       " 183   2230.225545\n",
-       " 184   2198.460159\n",
-       " 185   2179.394792\n",
-       " ...           ...\n",
-       " 1922  3600.564337\n",
-       " 1923  3581.493152\n",
-       " 1924  3595.532087\n",
-       " 1925  3593.072194\n",
-       " 1926  3576.833716\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2312.844587\n",
-       " 182   2305.122560\n",
-       " 183   2302.591462\n",
-       " 184   2326.890652\n",
-       " 185   2337.884181\n",
-       " ...           ...\n",
-       " 1922  3753.129843\n",
-       " 1923  3731.003930\n",
-       " 1924  3823.311472\n",
-       " 1925  3832.575551\n",
-       " 1926  3772.487956\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2399.878944\n",
-       " 182   2397.499407\n",
-       " 183   2390.792022\n",
-       " 184   2361.715334\n",
-       " 185   2342.767980\n",
-       " ...           ...\n",
-       " 1922  4083.408861\n",
-       " 1923  4068.875226\n",
-       " 1924  4060.026076\n",
-       " 1925  4088.177631\n",
-       " 1926  4147.229399\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    5197.975692\n",
-       " 182    5185.932598\n",
-       " 183    5173.237584\n",
-       " 184    5157.173004\n",
-       " 185    5138.838171\n",
-       " ...            ...\n",
-       " 1922  11320.649582\n",
-       " 1923  11283.912250\n",
-       " 1924  11220.726242\n",
-       " 1925  11259.968588\n",
-       " 1926  11384.751416\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4291.804278\n",
-       " 182   4320.310074\n",
-       " 183   4333.028034\n",
-       " 184   4264.143578\n",
-       " 185   4226.556463\n",
-       " ...           ...\n",
-       " 1922  8610.748806\n",
-       " 1923  8615.395182\n",
-       " 1924  8522.641707\n",
-       " 1925  8560.188406\n",
-       " 1926  8706.552043\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4174.964141\n",
-       " 182   4158.133496\n",
-       " 183   4140.216986\n",
-       " 184   4116.687976\n",
-       " 185   4105.557352\n",
-       " ...           ...\n",
-       " 1922  7875.756333\n",
-       " 1923  7908.631919\n",
-       " 1924  7825.174472\n",
-       " 1925  7828.753899\n",
-       " 1926  7905.019997\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4208.436430\n",
-       " 182   4203.607045\n",
-       " 183   4197.694658\n",
-       " 184   4186.184568\n",
-       " 185   4170.136319\n",
-       " ...           ...\n",
-       " 1922  7892.360909\n",
-       " 1923  7846.068038\n",
-       " 1924  7715.653147\n",
-       " 1925  7695.576253\n",
-       " 1926  7767.645382\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4001.788107\n",
-       " 182   3970.273490\n",
-       " 183   3948.271405\n",
-       " 184   3975.436641\n",
-       " 185   3984.608367\n",
-       " ...           ...\n",
-       " 1922  7348.445234\n",
-       " 1923  7356.672093\n",
-       " 1924  7313.830665\n",
-       " 1925  7292.585837\n",
-       " 1926  7289.376872\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3813.568011\n",
-       " 182   3836.684694\n",
-       " 183   3857.318704\n",
-       " 184   3865.120545\n",
-       " 185   3868.235055\n",
-       " ...           ...\n",
-       " 1922  7178.443424\n",
-       " 1923  7269.094720\n",
-       " 1924  7136.516525\n",
-       " 1925  7115.413189\n",
-       " 1926  7187.405297\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   3834.565950\n",
-       " 182   3770.372401\n",
-       " 183   3724.770932\n",
-       " 184   3775.266136\n",
-       " 185   3811.097307\n",
-       " ...           ...\n",
-       " 1922  6903.635017\n",
-       " 1923  6896.944440\n",
-       " 1924  6947.940346\n",
-       " 1925  6961.973575\n",
-       " 1926  6945.138348\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2960.273187\n",
-       " 182   2932.633318\n",
-       " 183   2906.068667\n",
-       " 184   2885.061479\n",
-       " 185   2870.857438\n",
-       " ...           ...\n",
-       " 1922  5136.348845\n",
-       " 1923  5163.336976\n",
-       " 1924  5161.936598\n",
-       " 1925  5196.974162\n",
-       " 1926  5262.441965\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2469.428464\n",
-       " 182   2452.394064\n",
-       " 183   2441.413841\n",
-       " 184   2461.725772\n",
-       " 185   2466.614336\n",
-       " ...           ...\n",
-       " 1922  4298.143604\n",
-       " 1923  4279.591088\n",
-       " 1924  4278.073119\n",
-       " 1925  4270.290598\n",
-       " 1926  4257.276394\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2510.042951\n",
-       " 182   2473.018128\n",
-       " 183   2449.182731\n",
-       " 184   2493.519383\n",
-       " 185   2525.762288\n",
-       " ...           ...\n",
-       " 1922  4193.227367\n",
-       " 1923  4213.306648\n",
-       " 1924  4159.614021\n",
-       " 1925  4136.048845\n",
-       " 1926  4137.643858\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2424.734525\n",
-       " 182   2399.696723\n",
-       " 183   2382.225711\n",
-       " 184   2403.865089\n",
-       " 185   2418.550174\n",
-       " ...           ...\n",
-       " 1922  3971.295265\n",
-       " 1923  3979.921596\n",
-       " 1924  3935.587892\n",
-       " 1925  3933.346862\n",
-       " 1926  3966.258478\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2352.041974\n",
-       " 182   2348.990311\n",
-       " 183   2343.341542\n",
-       " 184   2324.269141\n",
-       " 185   2310.487770\n",
-       " ...           ...\n",
-       " 1922  3900.819721\n",
-       " 1923  3898.988313\n",
-       " 1924  3904.723023\n",
-       " 1925  3921.850257\n",
-       " 1926  3948.491670\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2390.450085\n",
-       " 182   2410.064248\n",
-       " 183   2420.487578\n",
-       " 184   2383.406349\n",
-       " 185   2355.848726\n",
-       " ...           ...\n",
-       " 1922  3983.749265\n",
-       " 1923  4036.862905\n",
-       " 1924  3962.280775\n",
-       " 1925  3960.509493\n",
-       " 1926  4019.544373\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2400.011726\n",
-       " 182   2398.965229\n",
-       " 183   2400.947615\n",
-       " 184   2418.585354\n",
-       " 185   2419.740844\n",
-       " ...           ...\n",
-       " 1922  3973.904876\n",
-       " 1923  3972.183878\n",
-       " 1924  3948.715560\n",
-       " 1925  3945.722950\n",
-       " 1926  3959.830115\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2215.186094\n",
-       " 182   2212.118050\n",
-       " 183   2211.862017\n",
-       " 184   2226.140397\n",
-       " 185   2247.981774\n",
-       " ...           ...\n",
-       " 1922  3818.921769\n",
-       " 1923  3796.617215\n",
-       " 1924  3796.078438\n",
-       " 1925  3810.342660\n",
-       " 1926  3836.969238\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2222.003054\n",
-       " 182   2209.604742\n",
-       " 183   2199.150754\n",
-       " 184   2198.746364\n",
-       " 185   2200.610368\n",
-       " ...           ...\n",
-       " 1922  3684.229697\n",
-       " 1923  3741.834429\n",
-       " 1924  3725.638653\n",
-       " 1925  3725.939705\n",
-       " 1926  3740.017672\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2202.009490\n",
-       " 182   2196.273855\n",
-       " 183   2186.642270\n",
-       " 184   2156.873729\n",
-       " 185   2140.107844\n",
-       " ...           ...\n",
-       " 1922  3670.341466\n",
-       " 1923  3703.693710\n",
-       " 1924  3711.489107\n",
-       " 1925  3716.444680\n",
-       " 1926  3719.028645\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2253.129228\n",
-       " 182   2268.061436\n",
-       " 183   2275.640799\n",
-       " 184   2245.215600\n",
-       " 185   2226.129475\n",
-       " ...           ...\n",
-       " 1922  3791.315836\n",
-       " 1923  3800.145333\n",
-       " 1924  3768.307531\n",
-       " 1925  3792.808034\n",
-       " 1926  3864.358066\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2308.115120\n",
-       " 182   2317.721848\n",
-       " 183   2324.409958\n",
-       " 184   2316.012633\n",
-       " 185   2303.833651\n",
-       " ...           ...\n",
-       " 1922  3851.229919\n",
-       " 1923  3829.614322\n",
-       " 1924  3836.383238\n",
-       " 1925  3892.123763\n",
-       " 1926  3988.761705\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2370.810895\n",
-       " 182   2361.762886\n",
-       " 183   2352.604534\n",
-       " 184   2342.875851\n",
-       " 185   2332.238747\n",
-       " ...           ...\n",
-       " 1922  3955.647606\n",
-       " 1923  3972.821492\n",
-       " 1924  3938.920008\n",
-       " 1925  3966.479189\n",
-       " 1926  4045.365710\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   2669.415486\n",
-       " 182   2651.071862\n",
-       " 183   2626.665657\n",
-       " 184   2570.923859\n",
-       " 185   2526.972144\n",
-       " ...           ...\n",
-       " 1922  4435.194765\n",
-       " 1923  4445.021787\n",
-       " 1924  4433.092719\n",
-       " 1925  4433.109499\n",
-       " 1926  4443.102554\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12135.328432\n",
-       " 182   12056.131356\n",
-       " 183   11980.504254\n",
-       " 184   11916.539072\n",
-       " 185   11853.761964\n",
-       " ...            ...\n",
-       " 1922  23531.235002\n",
-       " 1923  23679.804770\n",
-       " 1924  23465.675982\n",
-       " 1925  23503.726964\n",
-       " 1926  23686.381953\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10744.387491\n",
-       " 182   10767.664708\n",
-       " 183   10750.127633\n",
-       " 184   10599.263869\n",
-       " 185   10470.520010\n",
-       " ...            ...\n",
-       " 1922  17861.588939\n",
-       " 1923  17945.247268\n",
-       " 1924  17822.281359\n",
-       " 1925  17897.287749\n",
-       " 1926  18077.422497\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9203.134466\n",
-       " 182    9235.558861\n",
-       " 183    9229.518188\n",
-       " 184    9097.824958\n",
-       " 185    8996.033372\n",
-       " ...            ...\n",
-       " 1922  15157.619242\n",
-       " 1923  15180.739634\n",
-       " 1924  15099.190392\n",
-       " 1925  15138.692971\n",
-       " 1926  15235.888206\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8596.850373\n",
-       " 182    8587.212772\n",
-       " 183    8559.897802\n",
-       " 184    8474.836758\n",
-       " 185    8424.271500\n",
-       " ...            ...\n",
-       " 1922  14123.277331\n",
-       " 1923  14210.918817\n",
-       " 1924  14158.489524\n",
-       " 1925  14107.308412\n",
-       " 1926  14057.728439\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7857.855371\n",
-       " 182    7852.795138\n",
-       " 183    7844.917840\n",
-       " 184    7827.838124\n",
-       " 185    7799.386435\n",
-       " ...            ...\n",
-       " 1922  13349.731010\n",
-       " 1923  13322.620182\n",
-       " 1924  13314.990763\n",
-       " 1925  13316.187765\n",
-       " 1926  13327.521639\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9825.231295\n",
-       " 182    9796.412243\n",
-       " 183    9763.047282\n",
-       " 184    9714.832351\n",
-       " 185    9673.991173\n",
-       " ...            ...\n",
-       " 1922  15004.021141\n",
-       " 1923  14908.954181\n",
-       " 1924  14760.513525\n",
-       " 1925  14835.262534\n",
-       " 1926  15034.452070\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9361.043851\n",
-       " 182    9410.816029\n",
-       " 183    9425.100917\n",
-       " 184    9323.460661\n",
-       " 185    9222.803582\n",
-       " ...            ...\n",
-       " 1922  15410.135740\n",
-       " 1923  15451.644108\n",
-       " 1924  15227.018477\n",
-       " 1925  15224.933274\n",
-       " 1926  15353.250353\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8538.208701\n",
-       " 182    8517.900234\n",
-       " 183    8496.020719\n",
-       " 184    8469.009116\n",
-       " 185    8428.430089\n",
-       " ...            ...\n",
-       " 1922  13516.442289\n",
-       " 1923  13417.355622\n",
-       " 1924  13319.902339\n",
-       " 1925  13373.134505\n",
-       " 1926  13502.179649\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10464.183539\n",
-       " 182   10329.467309\n",
-       " 183   10206.278802\n",
-       " 184   10120.747527\n",
-       " 185   10059.339599\n",
-       " ...            ...\n",
-       " 1922  15662.107529\n",
-       " 1923  15623.307501\n",
-       " 1924  15530.326441\n",
-       " 1925  15548.935260\n",
-       " 1926  15628.190623\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12664.408014\n",
-       " 182   12621.264869\n",
-       " 183   12585.770805\n",
-       " 184   12575.263742\n",
-       " 185   12544.808630\n",
-       " ...            ...\n",
-       " 1922  18939.016912\n",
-       " 1923  19011.378848\n",
-       " 1924  18930.339707\n",
-       " 1925  19003.142281\n",
-       " 1926  19157.044061\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9480.976736\n",
-       " 182    9424.790244\n",
-       " 183    9367.489422\n",
-       " 184    9306.548457\n",
-       " 185    9236.224113\n",
-       " ...            ...\n",
-       " 1922  13700.046321\n",
-       " 1923  13766.444916\n",
-       " 1924  13755.741001\n",
-       " 1925  13791.697560\n",
-       " 1926  13841.396876\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7487.809933\n",
-       " 182    7448.047836\n",
-       " 183    7398.887500\n",
-       " 184    7319.026255\n",
-       " 185    7262.092517\n",
-       " ...            ...\n",
-       " 1922  11054.074490\n",
-       " 1923  11041.919857\n",
-       " 1924  10941.017079\n",
-       " 1925  10933.415055\n",
-       " 1926  10975.366756\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9242.353279\n",
-       " 182    9187.471223\n",
-       " 183    9135.229047\n",
-       " 184    9091.610483\n",
-       " 185    9049.382328\n",
-       " ...            ...\n",
-       " 1922  12201.624086\n",
-       " 1923  12262.903875\n",
-       " 1924  12148.583167\n",
-       " 1925  12149.630792\n",
-       " 1926  12219.750899\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9456.749520\n",
-       " 182    9439.883089\n",
-       " 183    9406.900866\n",
-       " 184    9321.273727\n",
-       " 185    9262.961607\n",
-       " ...            ...\n",
-       " 1922  11746.284289\n",
-       " 1923  11814.656836\n",
-       " 1924  11641.778048\n",
-       " 1925  11641.005716\n",
-       " 1926  11739.837659\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9149.081566\n",
-       " 182    9084.857272\n",
-       " 183    9013.572468\n",
-       " 184    8919.223332\n",
-       " 185    8845.007241\n",
-       " ...            ...\n",
-       " 1922  11595.732168\n",
-       " 1923  11683.946373\n",
-       " 1924  11436.882610\n",
-       " 1925  11335.422036\n",
-       " 1926  11323.672072\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11504.005269\n",
-       " 182   11447.393878\n",
-       " 183   11401.085736\n",
-       " 184   11388.434880\n",
-       " 185   11356.833172\n",
-       " ...            ...\n",
-       " 1922  13040.253051\n",
-       " 1923  12986.837905\n",
-       " 1924  12916.487527\n",
-       " 1925  12927.464739\n",
-       " 1926  12979.996445\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11023.384474\n",
-       " 182   10934.723688\n",
-       " 183   10864.957485\n",
-       " 184   10856.913591\n",
-       " 185   10838.496506\n",
-       " ...            ...\n",
-       " 1922  12739.867999\n",
-       " 1923  12779.293151\n",
-       " 1924  12542.820929\n",
-       " 1925  12574.919042\n",
-       " 1926  12770.697618\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11331.225263\n",
-       " 182   11193.280203\n",
-       " 183   11064.202739\n",
-       " 184   10964.092755\n",
-       " 185   10896.880838\n",
-       " ...            ...\n",
-       " 1922  12949.519928\n",
-       " 1923  12947.817106\n",
-       " 1924  12897.308899\n",
-       " 1925  12995.605868\n",
-       " 1926  13168.838712\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9501.844246\n",
-       " 182    9506.977815\n",
-       " 183    9499.444168\n",
-       " 184    9450.530952\n",
-       " 185    9396.424884\n",
-       " ...            ...\n",
-       " 1922  12516.687931\n",
-       " 1923  12553.987051\n",
-       " 1924  12527.452960\n",
-       " 1925  12577.843155\n",
-       " 1926  12664.471444\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11041.409928\n",
-       " 182   11020.067690\n",
-       " 183   10982.138789\n",
-       " 184   10890.026787\n",
-       " 185   10818.846011\n",
-       " ...            ...\n",
-       " 1922  13974.385550\n",
-       " 1923  13871.368669\n",
-       " 1924  13806.359199\n",
-       " 1925  13809.436164\n",
-       " 1926  13852.937265\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10968.108548\n",
-       " 182   10933.427601\n",
-       " 183   10871.050512\n",
-       " 184   10718.199361\n",
-       " 185   10612.201483\n",
-       " ...            ...\n",
-       " 1922  14341.326113\n",
-       " 1923  14318.647891\n",
-       " 1924  14198.332866\n",
-       " 1925  14242.570440\n",
-       " 1926  14382.436407\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10197.775170\n",
-       " 182   10195.660251\n",
-       " 183   10175.870569\n",
-       " 184   10098.343328\n",
-       " 185   10026.589861\n",
-       " ...            ...\n",
-       " 1922  12870.159516\n",
-       " 1923  12870.561848\n",
-       " 1924  12940.174222\n",
-       " 1925  13026.829826\n",
-       " 1926  13108.605352\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10483.658172\n",
-       " 182   10462.248898\n",
-       " 183   10432.386300\n",
-       " 184   10374.909512\n",
-       " 185   10319.618752\n",
-       " ...            ...\n",
-       " 1922  13648.013741\n",
-       " 1923  13582.800785\n",
-       " 1924  13521.930836\n",
-       " 1925  13576.442935\n",
-       " 1926  13689.787581\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9676.922797\n",
-       " 182    9664.081567\n",
-       " 183    9624.646737\n",
-       " 184    9498.339479\n",
-       " 185    9408.716297\n",
-       " ...            ...\n",
-       " 1922  13039.820516\n",
-       " 1923  13073.971596\n",
-       " 1924  13067.354172\n",
-       " 1925  13114.965175\n",
-       " 1926  13182.183635\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9656.648886\n",
-       " 182    9608.481043\n",
-       " 183    9571.088735\n",
-       " 184    9568.896505\n",
-       " 185    9557.528507\n",
-       " ...            ...\n",
-       " 1922  14532.208950\n",
-       " 1923  14507.140450\n",
-       " 1924  14512.204213\n",
-       " 1925  14529.250283\n",
-       " 1926  14543.702071\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10306.942281\n",
-       " 182   10218.862139\n",
-       " 183   10146.705171\n",
-       " 184   10126.563901\n",
-       " 185   10094.452022\n",
-       " ...            ...\n",
-       " 1922  13074.883634\n",
-       " 1923  13152.039194\n",
-       " 1924  13213.332123\n",
-       " 1925  13255.722505\n",
-       " 1926  13268.787460\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8885.575519\n",
-       " 182    8850.645579\n",
-       " 183    8809.447249\n",
-       " 184    8747.772177\n",
-       " 185    8705.436794\n",
-       " ...            ...\n",
-       " 1922  11630.797360\n",
-       " 1923  11557.222454\n",
-       " 1924  11778.129651\n",
-       " 1925  11887.477830\n",
-       " 1926  11913.859764\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9195.877362\n",
-       " 182    9223.635427\n",
-       " 183    9228.723862\n",
-       " 184    9159.758175\n",
-       " 185    9103.349446\n",
-       " ...            ...\n",
-       " 1922  12464.547037\n",
-       " 1923  12415.967295\n",
-       " 1924  12513.723608\n",
-       " 1925  12527.005969\n",
-       " 1926  12482.508280\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9616.049292\n",
-       " 182    9481.898587\n",
-       " 183    9384.457332\n",
-       " 184    9406.933617\n",
-       " 185    9374.759361\n",
-       " ...            ...\n",
-       " 1922  12833.156944\n",
-       " 1923  12836.001610\n",
-       " 1924  12751.006029\n",
-       " 1925  12761.207858\n",
-       " 1926  12827.640353\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9620.197691\n",
-       " 182    9615.347893\n",
-       " 183    9594.699340\n",
-       " 184    9522.441524\n",
-       " 185    9454.362325\n",
-       " ...            ...\n",
-       " 1922  12319.511097\n",
-       " 1923  12326.993575\n",
-       " 1924  12338.035242\n",
-       " 1925  12354.124116\n",
-       " 1926  12368.937971\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12473.808617\n",
-       " 182   12538.615573\n",
-       " 183   12551.517528\n",
-       " 184   12394.863148\n",
-       " 185   12269.503236\n",
-       " ...            ...\n",
-       " 1922  19119.780419\n",
-       " 1923  19189.518174\n",
-       " 1924  18896.444941\n",
-       " 1925  18962.731959\n",
-       " 1926  19233.062888\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12828.717324\n",
-       " 182   12704.947667\n",
-       " 183   12599.122061\n",
-       " 184   12551.913689\n",
-       " 185   12499.712236\n",
-       " ...            ...\n",
-       " 1922  17917.635832\n",
-       " 1923  18117.445242\n",
-       " 1924  18000.769737\n",
-       " 1925  18037.927564\n",
-       " 1926  18149.084377\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13871.243125\n",
-       " 182   13713.606532\n",
-       " 183   13592.514788\n",
-       " 184   13590.802880\n",
-       " 185   13546.006220\n",
-       " ...            ...\n",
-       " 1922  20516.352525\n",
-       " 1923  20494.544144\n",
-       " 1924  20446.084548\n",
-       " 1925  20518.684776\n",
-       " 1926  20647.344688\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15418.288424\n",
-       " 182   15352.813122\n",
-       " 183   15291.225472\n",
-       " 184   15242.337483\n",
-       " 185   15189.253841\n",
-       " ...            ...\n",
-       " 1922  21803.037524\n",
-       " 1923  21831.536000\n",
-       " 1924  21692.373121\n",
-       " 1925  21605.513438\n",
-       " 1926  21544.684611\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11417.180585\n",
-       " 182   11381.219900\n",
-       " 183   11338.364910\n",
-       " 184   11272.988527\n",
-       " 185   11207.802568\n",
-       " ...            ...\n",
-       " 1922  15572.286459\n",
-       " 1923  15697.310388\n",
-       " 1924  15527.037029\n",
-       " 1925  15609.585820\n",
-       " 1926  15825.443203\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   15459.302742\n",
-       " 182   15437.155061\n",
-       " 183   15395.448740\n",
-       " 184   15289.850866\n",
-       " 185   15202.389680\n",
-       " ...            ...\n",
-       " 1922  23589.105540\n",
-       " 1923  23648.017635\n",
-       " 1924  23727.420413\n",
-       " 1925  23893.863610\n",
-       " 1926  24084.878126\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13452.138719\n",
-       " 182   13342.440130\n",
-       " 183   13259.605694\n",
-       " 184   13264.527490\n",
-       " 185   13234.742874\n",
-       " ...            ...\n",
-       " 1922  19980.912574\n",
-       " 1923  20015.131579\n",
-       " 1924  19980.188397\n",
-       " 1925  19998.835454\n",
-       " 1926  20038.394218\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11251.963922\n",
-       " 182   11261.305152\n",
-       " 183   11264.236980\n",
-       " 184   11246.231432\n",
-       " 185   11176.960752\n",
-       " ...            ...\n",
-       " 1922  16725.838346\n",
-       " 1923  16888.968907\n",
-       " 1924  16800.333805\n",
-       " 1925  16864.908709\n",
-       " 1926  17012.847032\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11538.035086\n",
-       " 182   11566.739218\n",
-       " 183   11562.920500\n",
-       " 184   11452.860467\n",
-       " 185   11352.559082\n",
-       " ...            ...\n",
-       " 1922  17579.627256\n",
-       " 1923  17636.191766\n",
-       " 1924  17510.051615\n",
-       " 1925  17492.970310\n",
-       " 1926  17545.628910\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12409.383781\n",
-       " 182   12332.218962\n",
-       " 183   12258.937730\n",
-       " 184   12198.342881\n",
-       " 185   12151.103622\n",
-       " ...            ...\n",
-       " 1922  20137.357788\n",
-       " 1923  20090.656811\n",
-       " 1924  19924.102518\n",
-       " 1925  19971.905374\n",
-       " 1926  20135.644067\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10265.732461\n",
-       " 182   10208.306554\n",
-       " 183   10158.837590\n",
-       " 184   10135.361305\n",
-       " 185   10098.716988\n",
-       " ...            ...\n",
-       " 1922  14657.831782\n",
-       " 1923  14684.989938\n",
-       " 1924  14583.527186\n",
-       " 1925  14558.339483\n",
-       " 1926  14583.684220\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9740.205015\n",
-       " 182    9667.386467\n",
-       " 183    9608.619583\n",
-       " 184    9595.754800\n",
-       " 185    9545.588084\n",
-       " ...            ...\n",
-       " 1922  13479.801035\n",
-       " 1923  13427.971038\n",
-       " 1924  13379.616240\n",
-       " 1925  13462.410726\n",
-       " 1926  13607.354649\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10458.154698\n",
-       " 182   10333.435445\n",
-       " 183   10238.234145\n",
-       " 184   10239.458160\n",
-       " 185   10193.807947\n",
-       " ...            ...\n",
-       " 1922  14010.794233\n",
-       " 1923  13988.209689\n",
-       " 1924  13915.902871\n",
-       " 1925  13960.866756\n",
-       " 1926  14062.291015\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9972.715994\n",
-       " 182    9900.592002\n",
-       " 183    9817.807467\n",
-       " 184    9700.198494\n",
-       " 185    9623.862459\n",
-       " ...            ...\n",
-       " 1922  13828.415491\n",
-       " 1923  13796.672223\n",
-       " 1924  13858.051012\n",
-       " 1925  13910.616713\n",
-       " 1926  13948.765822\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11016.628221\n",
-       " 182   10971.289007\n",
-       " 183   10927.341774\n",
-       " 184   10887.941676\n",
-       " 185   10819.214672\n",
-       " ...            ...\n",
-       " 1922  15878.840716\n",
-       " 1923  16041.369774\n",
-       " 1924  15849.205632\n",
-       " 1925  15893.462788\n",
-       " 1926  16068.773634\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11940.064052\n",
-       " 182   11934.158306\n",
-       " 183   11889.148618\n",
-       " 184   11716.399386\n",
-       " 185   11583.519415\n",
-       " ...            ...\n",
-       " 1922  16663.741739\n",
-       " 1923  16486.611912\n",
-       " 1924  16451.200295\n",
-       " 1925  16501.538244\n",
-       " 1926  16593.645859\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11137.563784\n",
-       " 182   11082.241707\n",
-       " 183   11028.151738\n",
-       " 184   10978.086656\n",
-       " 185   10927.615547\n",
-       " ...            ...\n",
-       " 1922  15285.658070\n",
-       " 1923  15304.450781\n",
-       " 1924  15347.227296\n",
-       " 1925  15408.931506\n",
-       " 1926  15462.063557\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12283.284070\n",
-       " 182   12216.101610\n",
-       " 183   12134.350018\n",
-       " 184   12005.005930\n",
-       " 185   11924.911919\n",
-       " ...            ...\n",
-       " 1922  17857.890031\n",
-       " 1923  17698.867564\n",
-       " 1924  17688.688271\n",
-       " 1925  17753.544610\n",
-       " 1926  17853.714451\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12192.950802\n",
-       " 182   12171.079330\n",
-       " 183   12132.777516\n",
-       " 184   12040.803254\n",
-       " 185   11957.592703\n",
-       " ...            ...\n",
-       " 1922  17624.654725\n",
-       " 1923  17675.957255\n",
-       " 1924  17620.854982\n",
-       " 1925  17640.147487\n",
-       " 1926  17698.534999\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11802.592301\n",
-       " 182   11748.015688\n",
-       " 183   11691.230588\n",
-       " 184   11627.231097\n",
-       " 185   11556.839672\n",
-       " ...            ...\n",
-       " 1922  16359.892670\n",
-       " 1923  16415.074866\n",
-       " 1924  16333.689765\n",
-       " 1925  16378.401408\n",
-       " 1926  16487.247307\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10597.178466\n",
-       " 182   10515.131592\n",
-       " 183   10434.933218\n",
-       " 184   10360.773284\n",
-       " 185   10285.206958\n",
-       " ...            ...\n",
-       " 1922  16584.262469\n",
-       " 1923  16469.065724\n",
-       " 1924  16456.632643\n",
-       " 1925  16528.694088\n",
-       " 1926  16641.996484\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9793.774398\n",
-       " 182    9709.632261\n",
-       " 183    9630.153232\n",
-       " 184    9565.907020\n",
-       " 185    9489.883488\n",
-       " ...            ...\n",
-       " 1922  14279.167734\n",
-       " 1923  14210.557721\n",
-       " 1924  14091.273000\n",
-       " 1925  14152.305828\n",
-       " 1926  14307.961914\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9272.367534\n",
-       " 182    9114.182050\n",
-       " 183    9000.517957\n",
-       " 184    9032.290407\n",
-       " 185    9021.381718\n",
-       " ...            ...\n",
-       " 1922  13456.222362\n",
-       " 1923  13498.199907\n",
-       " 1924  13272.269983\n",
-       " 1925  13369.143012\n",
-       " 1926  13646.795667\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9842.368598\n",
-       " 182    9785.558618\n",
-       " 183    9729.198159\n",
-       " 184    9674.306134\n",
-       " 185    9640.150992\n",
-       " ...            ...\n",
-       " 1922  14246.410412\n",
-       " 1923  14332.536413\n",
-       " 1924  14175.662044\n",
-       " 1925  14131.622688\n",
-       " 1926  14151.366366\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8291.037125\n",
-       " 182    8267.366763\n",
-       " 183    8240.560087\n",
-       " 184    8203.508121\n",
-       " 185    8157.873972\n",
-       " ...            ...\n",
-       " 1922  11565.905139\n",
-       " 1923  11541.353218\n",
-       " 1924  11581.539456\n",
-       " 1925  11668.398740\n",
-       " 1926  11769.735469\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10930.896390\n",
-       " 182   10897.543567\n",
-       " 183   10838.997716\n",
-       " 184   10698.154637\n",
-       " 185   10583.820857\n",
-       " ...            ...\n",
-       " 1922  15381.534000\n",
-       " 1923  15431.097402\n",
-       " 1924  15478.069625\n",
-       " 1925  15601.984041\n",
-       " 1926  15750.348750\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7174.625457\n",
-       " 182    7115.497433\n",
-       " 183    7068.392331\n",
-       " 184    7060.562109\n",
-       " 185    7037.371948\n",
-       " ...            ...\n",
-       " 1922  10859.032116\n",
-       " 1923  10833.080077\n",
-       " 1924  10793.781466\n",
-       " 1925  10819.423310\n",
-       " 1926  10873.798517\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9192.000677\n",
-       " 182    9137.112572\n",
-       " 183    9086.428463\n",
-       " 184    9049.477406\n",
-       " 185    9005.739395\n",
-       " ...            ...\n",
-       " 1922  12876.248498\n",
-       " 1923  12906.103826\n",
-       " 1924  12900.508497\n",
-       " 1925  12981.932251\n",
-       " 1926  13096.635664\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9200.821572\n",
-       " 182    9197.255040\n",
-       " 183    9187.743622\n",
-       " 184    9158.812241\n",
-       " 185    9105.342741\n",
-       " ...            ...\n",
-       " 1922  13207.477970\n",
-       " 1923  13151.185966\n",
-       " 1924  13185.318543\n",
-       " 1925  13251.619588\n",
-       " 1926  13330.195587\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9495.875583\n",
-       " 182    9413.697155\n",
-       " 183    9335.869761\n",
-       " 184    9272.255738\n",
-       " 185    9226.784283\n",
-       " ...            ...\n",
-       " 1922  13879.248358\n",
-       " 1923  13935.158897\n",
-       " 1924  13724.956246\n",
-       " 1925  13724.055153\n",
-       " 1926  13844.278439\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11147.051033\n",
-       " 182   11124.387727\n",
-       " 183   11064.335423\n",
-       " 184   10882.145727\n",
-       " 185   10729.252029\n",
-       " ...            ...\n",
-       " 1922  14867.857084\n",
-       " 1923  14799.298242\n",
-       " 1924  14791.865792\n",
-       " 1925  14745.965675\n",
-       " 1926  14675.984492\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9430.624134\n",
-       " 182    9374.431594\n",
-       " 183    9342.619092\n",
-       " 184    9390.448049\n",
-       " 185    9374.853708\n",
-       " ...            ...\n",
-       " 1922  12784.110206\n",
-       " 1923  12833.890729\n",
-       " 1924  12713.378734\n",
-       " 1925  12739.762305\n",
-       " 1926  12847.699961\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9566.145767\n",
-       " 182    9464.647280\n",
-       " 183    9371.872581\n",
-       " 184    9307.595592\n",
-       " 185    9248.695245\n",
-       " ...            ...\n",
-       " 1922  12398.503722\n",
-       " 1923  12440.895607\n",
-       " 1924  12489.558486\n",
-       " 1925  12511.748959\n",
-       " 1926  12506.333869\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9763.147256\n",
-       " 182    9816.299474\n",
-       " 183    9838.657905\n",
-       " 184    9760.423299\n",
-       " 185    9692.151032\n",
-       " ...            ...\n",
-       " 1922  12875.815522\n",
-       " 1923  12947.833554\n",
-       " 1924  12704.071559\n",
-       " 1925  12703.023963\n",
-       " 1926  12850.208126\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11675.907124\n",
-       " 182   11575.326983\n",
-       " 183   11493.011873\n",
-       " 184   11470.362530\n",
-       " 185   11433.747427\n",
-       " ...            ...\n",
-       " 1922  15134.371258\n",
-       " 1923  15190.949661\n",
-       " 1924  15102.089181\n",
-       " 1925  15133.017482\n",
-       " 1926  15230.100196\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10213.370542\n",
-       " 182   10159.270744\n",
-       " 183   10106.872684\n",
-       " 184   10060.033635\n",
-       " 185   10005.409078\n",
-       " ...            ...\n",
-       " 1922  12725.297521\n",
-       " 1923  12705.430853\n",
-       " 1924  12740.548909\n",
-       " 1925  12865.218604\n",
-       " 1926  13031.875391\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10465.917181\n",
-       " 182   10476.497827\n",
-       " 183   10461.431221\n",
-       " 184   10362.583599\n",
-       " 185   10276.687652\n",
-       " ...            ...\n",
-       " 1922  13351.388834\n",
-       " 1923  13349.363500\n",
-       " 1924  13208.027641\n",
-       " 1925  13255.200995\n",
-       " 1926  13412.150858\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10886.658841\n",
-       " 182   10933.946643\n",
-       " 183   10941.679891\n",
-       " 184   10820.201594\n",
-       " 185   10719.650152\n",
-       " ...            ...\n",
-       " 1922  14019.714292\n",
-       " 1923  13964.246561\n",
-       " 1924  13979.684692\n",
-       " 1925  14015.935743\n",
-       " 1926  14052.793830\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9878.246101\n",
-       " 182    9774.785920\n",
-       " 183    9695.376620\n",
-       " 184    9694.533533\n",
-       " 185    9673.943222\n",
-       " ...            ...\n",
-       " 1922  13840.344433\n",
-       " 1923  13811.388760\n",
-       " 1924  13755.772598\n",
-       " 1925  13723.474100\n",
-       " 1926  13706.331602\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9244.009735\n",
-       " 182    9213.787960\n",
-       " 183    9164.786927\n",
-       " 184    9054.440314\n",
-       " 185    8962.628726\n",
-       " ...            ...\n",
-       " 1922  13022.823498\n",
-       " 1923  13090.955262\n",
-       " 1924  13003.904757\n",
-       " 1925  13037.904550\n",
-       " 1926  13139.435976\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10760.137475\n",
-       " 182   10694.772555\n",
-       " 183   10630.636305\n",
-       " 184   10570.513705\n",
-       " 185   10524.345408\n",
-       " ...            ...\n",
-       " 1922  15289.726398\n",
-       " 1923  15276.399478\n",
-       " 1924  15229.335084\n",
-       " 1925  15302.700326\n",
-       " 1926  15430.685274\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11938.435823\n",
-       " 182   11817.891972\n",
-       " 183   11719.516415\n",
-       " 184   11693.557284\n",
-       " 185   11644.257156\n",
-       " ...            ...\n",
-       " 1922  14943.537038\n",
-       " 1923  15033.739848\n",
-       " 1924  14909.341490\n",
-       " 1925  14867.518522\n",
-       " 1926  14876.458710\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11276.983879\n",
-       " 182   11281.662810\n",
-       " 183   11261.005399\n",
-       " 184   11157.582600\n",
-       " 185   11046.166832\n",
-       " ...            ...\n",
-       " 1922  15456.221678\n",
-       " 1923  15422.321242\n",
-       " 1924  15367.963918\n",
-       " 1925  15343.230315\n",
-       " 1926  15336.599765\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10040.822250\n",
-       " 182    9989.367613\n",
-       " 183    9948.687378\n",
-       " 184    9943.203523\n",
-       " 185    9906.801828\n",
-       " ...            ...\n",
-       " 1922  13777.138128\n",
-       " 1923  13745.419893\n",
-       " 1924  13777.542999\n",
-       " 1925  13855.706299\n",
-       " 1926  13942.588400\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10297.520358\n",
-       " 182   10244.763806\n",
-       " 183   10188.078751\n",
-       " 184   10118.560593\n",
-       " 185   10048.833465\n",
-       " ...            ...\n",
-       " 1922  14044.495749\n",
-       " 1923  14098.589571\n",
-       " 1924  14094.198157\n",
-       " 1925  14075.931971\n",
-       " 1926  14042.408006\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9594.280471\n",
-       " 182    9528.775934\n",
-       " 183    9467.784712\n",
-       " 184    9421.536986\n",
-       " 185    9373.088758\n",
-       " ...            ...\n",
-       " 1922  13302.619210\n",
-       " 1923  13283.730515\n",
-       " 1924  13189.393393\n",
-       " 1925  13265.290457\n",
-       " 1926  13435.643910\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   18371.696960\n",
-       " 182   18443.277177\n",
-       " 183   18458.736291\n",
-       " 184   18352.945969\n",
-       " 185   18281.740357\n",
-       " ...            ...\n",
-       " 1922  40512.791663\n",
-       " 1923  40470.756434\n",
-       " 1924  40570.155424\n",
-       " 1925  40634.583132\n",
-       " 1926  40612.976010\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   14902.126429\n",
-       " 182   14981.965476\n",
-       " 183   14975.518075\n",
-       " 184   14782.649124\n",
-       " 185   14624.789278\n",
-       " ...            ...\n",
-       " 1922  26603.719989\n",
-       " 1923  26572.906337\n",
-       " 1924  26564.951792\n",
-       " 1925  26659.888639\n",
-       " 1926  26750.060576\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10936.466628\n",
-       " 182   10961.791551\n",
-       " 183   10954.628140\n",
-       " 184   10877.273805\n",
-       " 185   10812.505004\n",
-       " ...            ...\n",
-       " 1922  17825.296277\n",
-       " 1923  17807.679850\n",
-       " 1924  17744.239193\n",
-       " 1925  17818.673898\n",
-       " 1926  17934.523917\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10623.963693\n",
-       " 182   10531.164506\n",
-       " 183   10468.544868\n",
-       " 184   10471.128033\n",
-       " 185   10445.358763\n",
-       " ...            ...\n",
-       " 1922  15357.548841\n",
-       " 1923  15450.346808\n",
-       " 1924  15475.383425\n",
-       " 1925  15484.327956\n",
-       " 1926  15462.719870\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9513.957774\n",
-       " 182    9422.359899\n",
-       " 183    9345.754179\n",
-       " 184    9301.538957\n",
-       " 185    9251.712511\n",
-       " ...            ...\n",
-       " 1922  12832.574313\n",
-       " 1923  12854.386911\n",
-       " 1924  12850.874285\n",
-       " 1925  12913.156965\n",
-       " 1926  12981.162971\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11211.498700\n",
-       " 182   11175.478016\n",
-       " 183   11110.776186\n",
-       " 184   10984.108847\n",
-       " 185   10941.121809\n",
-       " ...            ...\n",
-       " 1922  16340.753772\n",
-       " 1923  16254.491014\n",
-       " 1924  16218.003640\n",
-       " 1925  16396.394171\n",
-       " 1926  16605.824656\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12389.671629\n",
-       " 182   12310.426427\n",
-       " 183   12218.116796\n",
-       " 184   12097.581514\n",
-       " 185   12045.795251\n",
-       " ...            ...\n",
-       " 1922  16385.420389\n",
-       " 1923  16455.585204\n",
-       " 1924  16236.320683\n",
-       " 1925  16253.582191\n",
-       " 1926  16358.319600\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8041.078935\n",
-       " 182   7991.336002\n",
-       " 183   7936.117360\n",
-       " 184   7869.068469\n",
-       " 185   7810.311078\n",
-       " ...           ...\n",
-       " 1922  9265.369670\n",
-       " 1923  9256.920189\n",
-       " 1924  9292.624103\n",
-       " 1925  9280.739448\n",
-       " 1926  9247.183636\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8748.849301\n",
-       " 182   8643.817017\n",
-       " 183   8578.036788\n",
-       " 184   8597.060477\n",
-       " 185   8560.657707\n",
-       " ...           ...\n",
-       " 1922  9725.634349\n",
-       " 1923  9705.582522\n",
-       " 1924  9634.796537\n",
-       " 1925  9743.881649\n",
-       " 1926  9890.007907\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8521.170544\n",
-       " 182   8532.196275\n",
-       " 183   8513.056417\n",
-       " 184   8428.743918\n",
-       " 185   8352.200286\n",
-       " ...           ...\n",
-       " 1922  9695.322684\n",
-       " 1923  9725.890379\n",
-       " 1924  9711.515379\n",
-       " 1925  9793.031001\n",
-       " 1926  9899.135336\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10082.966582\n",
-       " 182   10051.481215\n",
-       " 183   10023.376157\n",
-       " 184   10002.574242\n",
-       " 185    9966.562467\n",
-       " ...            ...\n",
-       " 1922  11805.955657\n",
-       " 1923  11744.243215\n",
-       " 1924  11679.995741\n",
-       " 1925  11762.722978\n",
-       " 1926  11878.977375\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9139.558382\n",
-       " 182    9129.976142\n",
-       " 183    9133.473585\n",
-       " 184    9165.229634\n",
-       " 185    9163.597182\n",
-       " ...            ...\n",
-       " 1922  10638.649552\n",
-       " 1923  10585.055770\n",
-       " 1924  10487.227487\n",
-       " 1925  10523.765088\n",
-       " 1926  10602.679099\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9418.629067\n",
-       " 182    9476.463070\n",
-       " 183    9481.553331\n",
-       " 184    9372.690931\n",
-       " 185    9314.866509\n",
-       " ...            ...\n",
-       " 1922  10792.112330\n",
-       " 1923  10839.551984\n",
-       " 1924  10872.332287\n",
-       " 1925  10945.719421\n",
-       " 1926  11000.494867\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9998.973128\n",
-       " 182    9880.079594\n",
-       " 183    9808.034366\n",
-       " 184    9837.204729\n",
-       " 185    9811.970641\n",
-       " ...            ...\n",
-       " 1922  10860.486700\n",
-       " 1923  10865.537960\n",
-       " 1924  10921.303111\n",
-       " 1925  10998.081990\n",
-       " 1926  11044.408949\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8195.034277\n",
-       " 182   8192.678071\n",
-       " 183   8162.267898\n",
-       " 184   8071.247233\n",
-       " 185   8010.268057\n",
-       " ...           ...\n",
-       " 1922  8673.858473\n",
-       " 1923  8616.453751\n",
-       " 1924  8648.938599\n",
-       " 1925  8698.230708\n",
-       " 1926  8733.806260\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8193.825555\n",
-       " 182   8135.427728\n",
-       " 183   8067.758805\n",
-       " 184   7980.059712\n",
-       " 185   7928.481499\n",
-       " ...           ...\n",
-       " 1922  8899.234896\n",
-       " 1923  8894.222239\n",
-       " 1924  8844.220345\n",
-       " 1925  8832.002221\n",
-       " 1926  8831.442210\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7563.452908\n",
-       " 182   7585.705934\n",
-       " 183   7584.330416\n",
-       " 184   7531.905517\n",
-       " 185   7487.026265\n",
-       " ...           ...\n",
-       " 1922  8356.547901\n",
-       " 1923  8353.938384\n",
-       " 1924  8352.960480\n",
-       " 1925  8363.181777\n",
-       " 1926  8381.741260\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   11866.205149\n",
-       " 182   11810.248039\n",
-       " 183   11766.745732\n",
-       " 184   11750.151982\n",
-       " 185   11718.605537\n",
-       " ...            ...\n",
-       " 1922  13524.876069\n",
-       " 1923  13579.019017\n",
-       " 1924  13430.404118\n",
-       " 1925  13451.138649\n",
-       " 1926  13542.752242\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10456.386815\n",
-       " 182   10518.348465\n",
-       " 183   10529.674804\n",
-       " 184   10431.603735\n",
-       " 185   10331.921310\n",
-       " ...            ...\n",
-       " 1922  12191.547600\n",
-       " 1923  12179.207710\n",
-       " 1924  12139.044200\n",
-       " 1925  12197.564346\n",
-       " 1926  12285.223727\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6446.258296\n",
-       " 182   6477.050928\n",
-       " 183   6471.135507\n",
-       " 184   6385.912472\n",
-       " 185   6326.938842\n",
-       " ...           ...\n",
-       " 1922  6875.508255\n",
-       " 1923  6846.787459\n",
-       " 1924  6846.653936\n",
-       " 1925  6873.818889\n",
-       " 1926  6897.994240\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6857.695440\n",
-       " 182   6830.494382\n",
-       " 183   6810.047954\n",
-       " 184   6804.194880\n",
-       " 185   6786.943642\n",
-       " ...           ...\n",
-       " 1922  7147.242264\n",
-       " 1923  7099.065683\n",
-       " 1924  7152.240240\n",
-       " 1925  7161.331478\n",
-       " 1926  7146.913649\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   9007.248267\n",
-       " 182   9000.318903\n",
-       " 183   8966.180035\n",
-       " 184   8873.255136\n",
-       " 185   8816.192982\n",
-       " ...           ...\n",
-       " 1922  8932.034003\n",
-       " 1923  8831.723946\n",
-       " 1924  8880.950588\n",
-       " 1925  8968.688862\n",
-       " 1926  9045.557988\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10544.452546\n",
-       " 182   10505.016121\n",
-       " 183   10447.446134\n",
-       " 184   10350.698654\n",
-       " 185   10256.898036\n",
-       " ...            ...\n",
-       " 1922  11772.393797\n",
-       " 1923  11824.767442\n",
-       " 1924  11766.798376\n",
-       " 1925  11834.106839\n",
-       " 1926  11929.393193\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9492.807844\n",
-       " 182    9496.611073\n",
-       " 183    9504.210440\n",
-       " 184    9520.011347\n",
-       " 185    9473.293958\n",
-       " ...            ...\n",
-       " 1922   9950.920926\n",
-       " 1923   9892.675356\n",
-       " 1924   9954.578766\n",
-       " 1925  10026.297970\n",
-       " 1926  10074.791740\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   12242.472659\n",
-       " 182   12193.898999\n",
-       " 183   12130.146326\n",
-       " 184   12033.599451\n",
-       " 185   11957.721673\n",
-       " ...            ...\n",
-       " 1922  13769.439266\n",
-       " 1923  13722.263851\n",
-       " 1924  13705.287210\n",
-       " 1925  13777.900794\n",
-       " 1926  13856.891223\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10258.751546\n",
-       " 182   10166.575467\n",
-       " 183   10097.196463\n",
-       " 184   10077.070456\n",
-       " 185   10043.253983\n",
-       " ...            ...\n",
-       " 1922  11038.490544\n",
-       " 1923  11005.373474\n",
-       " 1924  11013.105166\n",
-       " 1925  11123.033797\n",
-       " 1926  11230.423961\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8712.173669\n",
-       " 182   8651.169132\n",
-       " 183   8589.542309\n",
-       " 184   8526.571039\n",
-       " 185   8477.718059\n",
-       " ...           ...\n",
-       " 1922  9931.535765\n",
-       " 1923  9925.835216\n",
-       " 1924  9894.773347\n",
-       " 1925  9901.500747\n",
-       " 1926  9906.886536\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8286.273126\n",
-       " 182   8241.229234\n",
-       " 183   8184.591882\n",
-       " 184   8102.906903\n",
-       " 185   8041.651705\n",
-       " ...           ...\n",
-       " 1922  8595.937002\n",
-       " 1923  8607.714678\n",
-       " 1924  8465.750032\n",
-       " 1925  8481.237779\n",
-       " 1926  8551.281693\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6225.891800\n",
-       " 182   6217.295686\n",
-       " 183   6188.029471\n",
-       " 184   6114.105580\n",
-       " 185   6045.175368\n",
-       " ...           ...\n",
-       " 1922  6291.945928\n",
-       " 1923  6289.579155\n",
-       " 1924  6245.498347\n",
-       " 1925  6211.204786\n",
-       " 1926  6179.574440\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5917.621123\n",
-       " 182   5987.045689\n",
-       " 183   6030.091366\n",
-       " 184   6016.145550\n",
-       " 185   5971.552982\n",
-       " ...           ...\n",
-       " 1922  7250.845762\n",
-       " 1923  7230.869190\n",
-       " 1924  7169.155491\n",
-       " 1925  7199.819764\n",
-       " 1926  7269.068701\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7154.472227\n",
-       " 182   7141.984085\n",
-       " 183   7098.701613\n",
-       " 184   6988.888105\n",
-       " 185   6914.921353\n",
-       " ...           ...\n",
-       " 1922  8226.425513\n",
-       " 1923  8220.959476\n",
-       " 1924  8172.957266\n",
-       " 1925  8290.833073\n",
-       " 1926  8450.193011\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6707.645791\n",
-       " 182   6684.429849\n",
-       " 183   6658.475724\n",
-       " 184   6626.605764\n",
-       " 185   6592.174080\n",
-       " ...           ...\n",
-       " 1922  6339.754364\n",
-       " 1923  6401.872421\n",
-       " 1924  6383.921348\n",
-       " 1925  6359.665649\n",
-       " 1926  6337.598884\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7079.964273\n",
-       " 182   7104.828297\n",
-       " 183   7100.157046\n",
-       " 184   7031.674939\n",
-       " 185   6973.761550\n",
-       " ...           ...\n",
-       " 1922  6962.214446\n",
-       " 1923  6910.898955\n",
-       " 1924  6923.308993\n",
-       " 1925  6995.219227\n",
-       " 1926  7071.519452\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8140.003114\n",
-       " 182   8057.581323\n",
-       " 183   7996.900058\n",
-       " 184   7983.189121\n",
-       " 185   7953.262379\n",
-       " ...           ...\n",
-       " 1922  7991.320120\n",
-       " 1923  7999.014340\n",
-       " 1924  7985.454365\n",
-       " 1925  8023.687735\n",
-       " 1926  8070.339057\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7292.518959\n",
-       " 182   7254.028296\n",
-       " 183   7233.473734\n",
-       " 184   7251.670055\n",
-       " 185   7229.123131\n",
-       " ...           ...\n",
-       " 1922  7543.870887\n",
-       " 1923  7512.497860\n",
-       " 1924  7661.830247\n",
-       " 1925  7643.912418\n",
-       " 1926  7553.104694\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   13851.193812\n",
-       " 182   13644.028840\n",
-       " 183   13508.828580\n",
-       " 184   13529.107814\n",
-       " 185   13519.785138\n",
-       " ...            ...\n",
-       " 1922  15456.217455\n",
-       " 1923  15387.654701\n",
-       " 1924  15391.791696\n",
-       " 1925  15391.729413\n",
-       " 1926  15381.632205\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9602.053049\n",
-       " 182    9588.523282\n",
-       " 183    9540.402406\n",
-       " 184    9417.547567\n",
-       " 185    9354.578548\n",
-       " ...            ...\n",
-       " 1922  11133.236549\n",
-       " 1923  11138.323110\n",
-       " 1924  11088.619827\n",
-       " 1925  11137.597662\n",
-       " 1926  11228.715759\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   8827.914142\n",
-       " 182   8785.604356\n",
-       " 183   8736.340754\n",
-       " 184   8672.053455\n",
-       " 185   8633.022892\n",
-       " ...           ...\n",
-       " 1922  9188.537060\n",
-       " 1923  9239.572686\n",
-       " 1924  9172.903494\n",
-       " 1925  9201.410595\n",
-       " 1926  9251.077887\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    9932.480105\n",
-       " 182    9877.625921\n",
-       " 183    9848.093579\n",
-       " 184    9873.268981\n",
-       " 185    9857.960522\n",
-       " ...            ...\n",
-       " 1922  11896.164142\n",
-       " 1923  11795.050872\n",
-       " 1924  11778.862109\n",
-       " 1925  11838.964944\n",
-       " 1926  11902.266401\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7046.559612\n",
-       " 182   6959.336862\n",
-       " 183   6904.824167\n",
-       " 184   6920.981426\n",
-       " 185   6907.026593\n",
-       " ...           ...\n",
-       " 1922  7178.551502\n",
-       " 1923  7199.628639\n",
-       " 1924  7157.758058\n",
-       " 1925  7201.271235\n",
-       " 1926  7267.066558\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6334.061331\n",
-       " 182   6329.481110\n",
-       " 183   6296.214500\n",
-       " 184   6200.971052\n",
-       " 185   6143.866699\n",
-       " ...           ...\n",
-       " 1922  6567.037761\n",
-       " 1923  6539.667332\n",
-       " 1924  6576.054011\n",
-       " 1925  6614.550267\n",
-       " 1926  6640.607357\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   7326.598999\n",
-       " 182   7274.769033\n",
-       " 183   7236.236994\n",
-       " 184   7226.435079\n",
-       " 185   7189.028390\n",
-       " ...           ...\n",
-       " 1922  8901.394353\n",
-       " 1923  8915.768602\n",
-       " 1924  8845.922011\n",
-       " 1925  8881.051013\n",
-       " 1926  8941.788026\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10100.087377\n",
-       " 182   10069.938034\n",
-       " 183   10051.817271\n",
-       " 184   10059.684206\n",
-       " 185   10034.899489\n",
-       " ...            ...\n",
-       " 1922  22802.009599\n",
-       " 1923  22694.969576\n",
-       " 1924  22736.640031\n",
-       " 1925  22937.213108\n",
-       " 1926  23137.925004\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8659.378039\n",
-       " 182    8660.413357\n",
-       " 183    8663.979593\n",
-       " 184    8673.013867\n",
-       " 185    8649.903929\n",
-       " ...            ...\n",
-       " 1922  16067.658100\n",
-       " 1923  16065.402152\n",
-       " 1924  16043.513895\n",
-       " 1925  16138.513070\n",
-       " 1926  16255.871832\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6773.173938\n",
-       " 182    6830.691846\n",
-       " 183    6854.450233\n",
-       " 184    6805.271302\n",
-       " 185    6778.831377\n",
-       " ...            ...\n",
-       " 1922  11706.687826\n",
-       " 1923  11780.979665\n",
-       " 1924  11707.336356\n",
-       " 1925  11699.468899\n",
-       " 1926  11709.547951\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6290.164064\n",
-       " 182    6285.578797\n",
-       " 183    6260.533539\n",
-       " 184    6191.284547\n",
-       " 185    6167.541016\n",
-       " ...            ...\n",
-       " 1922  10620.716495\n",
-       " 1923  10611.719473\n",
-       " 1924  10651.781342\n",
-       " 1925  10684.854706\n",
-       " 1926  10702.353518\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6658.738042\n",
-       " 182    6666.872754\n",
-       " 183    6670.154482\n",
-       " 184    6662.951357\n",
-       " 185    6632.703036\n",
-       " ...            ...\n",
-       " 1922  10330.705189\n",
-       " 1923  10289.291255\n",
-       " 1924  10161.130270\n",
-       " 1925  10210.724460\n",
-       " 1926  10329.478736\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5991.956220\n",
-       " 182   5910.611876\n",
-       " 183   5836.664349\n",
-       " 184   5778.697615\n",
-       " 185   5734.094329\n",
-       " ...           ...\n",
-       " 1922  8590.600624\n",
-       " 1923  8595.300898\n",
-       " 1924  8552.571731\n",
-       " 1925  8575.626875\n",
-       " 1926  8615.784260\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6267.435748\n",
-       " 182   6249.504367\n",
-       " 183   6232.419876\n",
-       " 184   6217.165087\n",
-       " 185   6208.731465\n",
-       " ...           ...\n",
-       " 1922  8456.684797\n",
-       " 1923  8519.140553\n",
-       " 1924  8475.356462\n",
-       " 1925  8460.258482\n",
-       " 1926  8454.784686\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5556.768391\n",
-       " 182   5546.758049\n",
-       " 183   5541.178988\n",
-       " 184   5545.173696\n",
-       " 185   5544.362035\n",
-       " ...           ...\n",
-       " 1922  7680.271497\n",
-       " 1923  7747.144852\n",
-       " 1924  7637.448198\n",
-       " 1925  7630.615868\n",
-       " 1926  7662.770641\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6370.319348\n",
-       " 182   6376.418238\n",
-       " 183   6359.734987\n",
-       " 184   6293.831005\n",
-       " 185   6276.473660\n",
-       " ...           ...\n",
-       " 1922  8589.947378\n",
-       " 1923  8577.589792\n",
-       " 1924  8520.233732\n",
-       " 1925  8613.353825\n",
-       " 1926  8744.477823\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7407.377273\n",
-       " 182    7476.312688\n",
-       " 183    7497.564910\n",
-       " 184    7415.797773\n",
-       " 185    7358.004753\n",
-       " ...            ...\n",
-       " 1922  10421.476711\n",
-       " 1923  10360.227350\n",
-       " 1924  10268.636441\n",
-       " 1925  10342.911706\n",
-       " 1926  10469.559160\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6694.473535\n",
-       " 182   6629.225053\n",
-       " 183   6573.901734\n",
-       " 184   6540.021694\n",
-       " 185   6543.578994\n",
-       " ...           ...\n",
-       " 1922  9200.354102\n",
-       " 1923  9204.739049\n",
-       " 1924  9133.886776\n",
-       " 1925  9176.408652\n",
-       " 1926  9263.585484\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5755.678564\n",
-       " 182   5689.650819\n",
-       " 183   5651.055991\n",
-       " 184   5671.729883\n",
-       " 185   5668.878440\n",
-       " ...           ...\n",
-       " 1922  7957.033983\n",
-       " 1923  7910.880957\n",
-       " 1924  7833.588659\n",
-       " 1925  7873.868729\n",
-       " 1926  7948.937094\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5257.477033\n",
-       " 182   5207.401355\n",
-       " 183   5167.248314\n",
-       " 184   5148.533092\n",
-       " 185   5122.205046\n",
-       " ...           ...\n",
-       " 1922  6870.625889\n",
-       " 1923  6835.660325\n",
-       " 1924  6791.652118\n",
-       " 1925  6784.029684\n",
-       " 1926  6788.695697\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5469.867981\n",
-       " 182   5468.520352\n",
-       " 183   5458.524536\n",
-       " 184   5429.844345\n",
-       " 185   5406.200595\n",
-       " ...           ...\n",
-       " 1922  7350.929222\n",
-       " 1923  7265.703247\n",
-       " 1924  7208.172313\n",
-       " 1925  7238.820919\n",
-       " 1926  7304.699291\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5113.257049\n",
-       " 182   5085.180361\n",
-       " 183   5057.319471\n",
-       " 184   5029.924813\n",
-       " 185   5014.656568\n",
-       " ...           ...\n",
-       " 1922  6797.895950\n",
-       " 1923  6767.320493\n",
-       " 1924  6728.526300\n",
-       " 1925  6762.517400\n",
-       " 1926  6811.176046\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5585.851872\n",
-       " 182   5563.168977\n",
-       " 183   5535.427551\n",
-       " 184   5496.757188\n",
-       " 185   5492.734680\n",
-       " ...           ...\n",
-       " 1922  7015.703941\n",
-       " 1923  7025.743908\n",
-       " 1924  7061.150474\n",
-       " 1925  7114.065730\n",
-       " 1926  7159.164159\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5020.666886\n",
-       " 182   5045.284695\n",
-       " 183   5037.414236\n",
-       " 184   4959.352997\n",
-       " 185   4927.801558\n",
-       " ...           ...\n",
-       " 1922  6026.547373\n",
-       " 1923  6002.526121\n",
-       " 1924  5998.101562\n",
-       " 1925  6034.973847\n",
-       " 1926  6079.624534\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4721.550573\n",
-       " 182   4691.425702\n",
-       " 183   4669.743439\n",
-       " 184   4666.301402\n",
-       " 185   4668.425788\n",
-       " ...           ...\n",
-       " 1922  5983.856360\n",
-       " 1923  5999.492690\n",
-       " 1924  5891.652728\n",
-       " 1925  5923.775712\n",
-       " 1926  5996.608393\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5620.407634\n",
-       " 182   5647.765856\n",
-       " 183   5643.268069\n",
-       " 184   5569.945488\n",
-       " 185   5512.250430\n",
-       " ...           ...\n",
-       " 1922  7099.287958\n",
-       " 1923  7074.439076\n",
-       " 1924  7110.896779\n",
-       " 1925  7183.762458\n",
-       " 1926  7259.022318\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6181.079496\n",
-       " 182   6209.838828\n",
-       " 183   6183.539128\n",
-       " 184   6038.284592\n",
-       " 185   5949.603493\n",
-       " ...           ...\n",
-       " 1922  8144.948006\n",
-       " 1923  8069.557980\n",
-       " 1924  8075.854697\n",
-       " 1925  8164.048745\n",
-       " 1926  8269.577696\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6110.029651\n",
-       " 182   6102.753919\n",
-       " 183   6075.229727\n",
-       " 184   6003.958810\n",
-       " 185   5950.342697\n",
-       " ...           ...\n",
-       " 1922  7797.416467\n",
-       " 1923  7797.077539\n",
-       " 1924  7723.948751\n",
-       " 1925  7773.293039\n",
-       " 1926  7857.913529\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5684.890374\n",
-       " 182   5624.858339\n",
-       " 183   5584.875594\n",
-       " 184   5588.209263\n",
-       " 185   5582.685017\n",
-       " ...           ...\n",
-       " 1922  7059.210831\n",
-       " 1923  7045.541522\n",
-       " 1924  6926.350235\n",
-       " 1925  6984.916659\n",
-       " 1926  7098.169463\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4800.032232\n",
-       " 182   4758.574868\n",
-       " 183   4737.163580\n",
-       " 184   4759.061766\n",
-       " 185   4754.144431\n",
-       " ...           ...\n",
-       " 1922  5918.278657\n",
-       " 1923  5887.092730\n",
-       " 1924  5834.601569\n",
-       " 1925  5860.491704\n",
-       " 1926  5902.921345\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4994.233513\n",
-       " 182   5006.171958\n",
-       " 183   4984.569713\n",
-       " 184   4890.502928\n",
-       " 185   4845.715783\n",
-       " ...           ...\n",
-       " 1922  6082.161050\n",
-       " 1923  6067.860953\n",
-       " 1924  6059.266888\n",
-       " 1925  6129.066173\n",
-       " 1926  6205.764646\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5794.322845\n",
-       " 182   5799.685609\n",
-       " 183   5793.867835\n",
-       " 184   5763.894549\n",
-       " 185   5733.639879\n",
-       " ...           ...\n",
-       " 1922  7255.818398\n",
-       " 1923  7295.524548\n",
-       " 1924  7226.507187\n",
-       " 1925  7263.865107\n",
-       " 1926  7330.247135\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4929.706587\n",
-       " 182   4880.259062\n",
-       " 183   4827.023180\n",
-       " 184   4765.602565\n",
-       " 185   4721.592934\n",
-       " ...           ...\n",
-       " 1922  6145.814954\n",
-       " 1923  6151.525304\n",
-       " 1924  6171.146516\n",
-       " 1925  6154.374977\n",
-       " 1926  6116.099923\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4976.683372\n",
-       " 182   4944.587511\n",
-       " 183   4915.028350\n",
-       " 184   4890.949721\n",
-       " 185   4863.050972\n",
-       " ...           ...\n",
-       " 1922  6301.021722\n",
-       " 1923  6298.939648\n",
-       " 1924  6335.294130\n",
-       " 1925  6372.358290\n",
-       " 1926  6384.340966\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4624.879736\n",
-       " 182   4610.755115\n",
-       " 183   4595.789124\n",
-       " 184   4579.005354\n",
-       " 185   4559.167826\n",
-       " ...           ...\n",
-       " 1922  5781.168717\n",
-       " 1923  5732.219513\n",
-       " 1924  5680.752341\n",
-       " 1925  5677.568191\n",
-       " 1926  5690.624174\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5815.276942\n",
-       " 182   5785.949748\n",
-       " 183   5748.612117\n",
-       " 184   5693.967964\n",
-       " 185   5640.791390\n",
-       " ...           ...\n",
-       " 1922  6902.897781\n",
-       " 1923  6888.642235\n",
-       " 1924  6925.075316\n",
-       " 1925  6934.919387\n",
-       " 1926  6916.892846\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5650.689017\n",
-       " 182   5624.010916\n",
-       " 183   5577.087592\n",
-       " 184   5486.424537\n",
-       " 185   5440.487382\n",
-       " ...           ...\n",
-       " 1922  7332.869062\n",
-       " 1923  7290.313640\n",
-       " 1924  7309.041860\n",
-       " 1925  7312.375830\n",
-       " 1926  7300.034115\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6322.280621\n",
-       " 182   6301.687030\n",
-       " 183   6273.081571\n",
-       " 184   6227.166501\n",
-       " 185   6183.233482\n",
-       " ...           ...\n",
-       " 1922  7966.108624\n",
-       " 1923  7987.508932\n",
-       " 1924  7965.590687\n",
-       " 1925  7957.665311\n",
-       " 1926  7954.704193\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5698.545706\n",
-       " 182   5728.581779\n",
-       " 183   5732.034873\n",
-       " 184   5678.055538\n",
-       " 185   5637.696455\n",
-       " ...           ...\n",
-       " 1922  7064.511451\n",
-       " 1923  7047.818826\n",
-       " 1924  7103.075455\n",
-       " 1925  7191.245598\n",
-       " 1926  7264.274756\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4721.482986\n",
-       " 182   4716.710030\n",
-       " 183   4714.047225\n",
-       " 184   4715.943395\n",
-       " 185   4687.460987\n",
-       " ...           ...\n",
-       " 1922  5943.253102\n",
-       " 1923  5994.795669\n",
-       " 1924  5962.166249\n",
-       " 1925  5984.124688\n",
-       " 1926  6020.282414\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4475.939129\n",
-       " 182   4495.821569\n",
-       " 183   4503.044538\n",
-       " 184   4482.916766\n",
-       " 185   4455.174896\n",
-       " ...           ...\n",
-       " 1922  5218.262111\n",
-       " 1923  5166.010745\n",
-       " 1924  5125.958977\n",
-       " 1925  5159.392028\n",
-       " 1926  5220.334329\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4236.825013\n",
-       " 182   4214.647920\n",
-       " 183   4207.240139\n",
-       " 184   4231.741399\n",
-       " 185   4222.847266\n",
-       " ...           ...\n",
-       " 1922  5102.206485\n",
-       " 1923  5106.338168\n",
-       " 1924  5096.571439\n",
-       " 1925  5085.402060\n",
-       " 1926  5072.943940\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4275.228424\n",
-       " 182   4236.377266\n",
-       " 183   4203.438897\n",
-       " 184   4183.275087\n",
-       " 185   4176.257179\n",
-       " ...           ...\n",
-       " 1922  5314.297172\n",
-       " 1923  5287.302024\n",
-       " 1924  5329.789713\n",
-       " 1925  5295.292834\n",
-       " 1926  5236.148053\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4362.354997\n",
-       " 182   4433.595406\n",
-       " 183   4451.034918\n",
-       " 184   4352.237791\n",
-       " 185   4266.765683\n",
-       " ...           ...\n",
-       " 1922  5099.248095\n",
-       " 1923  5071.851922\n",
-       " 1924  5029.698197\n",
-       " 1925  5049.829455\n",
-       " 1926  5095.167409\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181   10100.087377\n",
-       " 182   10069.938034\n",
-       " 183   10051.817271\n",
-       " 184   10059.684206\n",
-       " 185   10034.899489\n",
-       " ...            ...\n",
-       " 1922  22802.009599\n",
-       " 1923  22694.969576\n",
-       " 1924  22736.640031\n",
-       " 1925  22937.213108\n",
-       " 1926  23137.925004\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    8659.378039\n",
-       " 182    8660.413357\n",
-       " 183    8663.979593\n",
-       " 184    8673.013867\n",
-       " 185    8649.903929\n",
-       " ...            ...\n",
-       " 1922  16067.658100\n",
-       " 1923  16065.402152\n",
-       " 1924  16043.513895\n",
-       " 1925  16138.513070\n",
-       " 1926  16255.871832\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6773.173938\n",
-       " 182    6830.691846\n",
-       " 183    6854.450233\n",
-       " 184    6805.271302\n",
-       " 185    6778.831377\n",
-       " ...            ...\n",
-       " 1922  11706.687826\n",
-       " 1923  11780.979665\n",
-       " 1924  11707.336356\n",
-       " 1925  11699.468899\n",
-       " 1926  11709.547951\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6290.164064\n",
-       " 182    6285.578797\n",
-       " 183    6260.533539\n",
-       " 184    6191.284547\n",
-       " 185    6167.541016\n",
-       " ...            ...\n",
-       " 1922  10620.716495\n",
-       " 1923  10611.719473\n",
-       " 1924  10651.781342\n",
-       " 1925  10684.854706\n",
-       " 1926  10702.353518\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    6658.738042\n",
-       " 182    6666.872754\n",
-       " 183    6670.154482\n",
-       " 184    6662.951357\n",
-       " 185    6632.703036\n",
-       " ...            ...\n",
-       " 1922  10330.705189\n",
-       " 1923  10289.291255\n",
-       " 1924  10161.130270\n",
-       " 1925  10210.724460\n",
-       " 1926  10329.478736\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5991.956220\n",
-       " 182   5910.611876\n",
-       " 183   5836.664349\n",
-       " 184   5778.697615\n",
-       " 185   5734.094329\n",
-       " ...           ...\n",
-       " 1922  8590.600624\n",
-       " 1923  8595.300898\n",
-       " 1924  8552.571731\n",
-       " 1925  8575.626875\n",
-       " 1926  8615.784260\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6267.435748\n",
-       " 182   6249.504367\n",
-       " 183   6232.419876\n",
-       " 184   6217.165087\n",
-       " 185   6208.731465\n",
-       " ...           ...\n",
-       " 1922  8456.684797\n",
-       " 1923  8519.140553\n",
-       " 1924  8475.356462\n",
-       " 1925  8460.258482\n",
-       " 1926  8454.784686\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5556.768391\n",
-       " 182   5546.758049\n",
-       " 183   5541.178988\n",
-       " 184   5545.173696\n",
-       " 185   5544.362035\n",
-       " ...           ...\n",
-       " 1922  7680.271497\n",
-       " 1923  7747.144852\n",
-       " 1924  7637.448198\n",
-       " 1925  7630.615868\n",
-       " 1926  7662.770641\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6370.319348\n",
-       " 182   6376.418238\n",
-       " 183   6359.734987\n",
-       " 184   6293.831005\n",
-       " 185   6276.473660\n",
-       " ...           ...\n",
-       " 1922  8589.947378\n",
-       " 1923  8577.589792\n",
-       " 1924  8520.233732\n",
-       " 1925  8613.353825\n",
-       " 1926  8744.477823\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "         #Intensity\n",
-       " 181    7407.377273\n",
-       " 182    7476.312688\n",
-       " 183    7497.564910\n",
-       " 184    7415.797773\n",
-       " 185    7358.004753\n",
-       " ...            ...\n",
-       " 1922  10421.476711\n",
-       " 1923  10360.227350\n",
-       " 1924  10268.636441\n",
-       " 1925  10342.911706\n",
-       " 1926  10469.559160\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6694.473535\n",
-       " 182   6629.225053\n",
-       " 183   6573.901734\n",
-       " 184   6540.021694\n",
-       " 185   6543.578994\n",
-       " ...           ...\n",
-       " 1922  9200.354102\n",
-       " 1923  9204.739049\n",
-       " 1924  9133.886776\n",
-       " 1925  9176.408652\n",
-       " 1926  9263.585484\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5755.678564\n",
-       " 182   5689.650819\n",
-       " 183   5651.055991\n",
-       " 184   5671.729883\n",
-       " 185   5668.878440\n",
-       " ...           ...\n",
-       " 1922  7957.033983\n",
-       " 1923  7910.880957\n",
-       " 1924  7833.588659\n",
-       " 1925  7873.868729\n",
-       " 1926  7948.937094\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5257.477033\n",
-       " 182   5207.401355\n",
-       " 183   5167.248314\n",
-       " 184   5148.533092\n",
-       " 185   5122.205046\n",
-       " ...           ...\n",
-       " 1922  6870.625889\n",
-       " 1923  6835.660325\n",
-       " 1924  6791.652118\n",
-       " 1925  6784.029684\n",
-       " 1926  6788.695697\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5469.867981\n",
-       " 182   5468.520352\n",
-       " 183   5458.524536\n",
-       " 184   5429.844345\n",
-       " 185   5406.200595\n",
-       " ...           ...\n",
-       " 1922  7350.929222\n",
-       " 1923  7265.703247\n",
-       " 1924  7208.172313\n",
-       " 1925  7238.820919\n",
-       " 1926  7304.699291\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5113.257049\n",
-       " 182   5085.180361\n",
-       " 183   5057.319471\n",
-       " 184   5029.924813\n",
-       " 185   5014.656568\n",
-       " ...           ...\n",
-       " 1922  6797.895950\n",
-       " 1923  6767.320493\n",
-       " 1924  6728.526300\n",
-       " 1925  6762.517400\n",
-       " 1926  6811.176046\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5585.851872\n",
-       " 182   5563.168977\n",
-       " 183   5535.427551\n",
-       " 184   5496.757188\n",
-       " 185   5492.734680\n",
-       " ...           ...\n",
-       " 1922  7015.703941\n",
-       " 1923  7025.743908\n",
-       " 1924  7061.150474\n",
-       " 1925  7114.065730\n",
-       " 1926  7159.164159\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5020.666886\n",
-       " 182   5045.284695\n",
-       " 183   5037.414236\n",
-       " 184   4959.352997\n",
-       " 185   4927.801558\n",
-       " ...           ...\n",
-       " 1922  6026.547373\n",
-       " 1923  6002.526121\n",
-       " 1924  5998.101562\n",
-       " 1925  6034.973847\n",
-       " 1926  6079.624534\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4721.550573\n",
-       " 182   4691.425702\n",
-       " 183   4669.743439\n",
-       " 184   4666.301402\n",
-       " 185   4668.425788\n",
-       " ...           ...\n",
-       " 1922  5983.856360\n",
-       " 1923  5999.492690\n",
-       " 1924  5891.652728\n",
-       " 1925  5923.775712\n",
-       " 1926  5996.608393\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5620.407634\n",
-       " 182   5647.765856\n",
-       " 183   5643.268069\n",
-       " 184   5569.945488\n",
-       " 185   5512.250430\n",
-       " ...           ...\n",
-       " 1922  7099.287958\n",
-       " 1923  7074.439076\n",
-       " 1924  7110.896779\n",
-       " 1925  7183.762458\n",
-       " 1926  7259.022318\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6181.079496\n",
-       " 182   6209.838828\n",
-       " 183   6183.539128\n",
-       " 184   6038.284592\n",
-       " 185   5949.603493\n",
-       " ...           ...\n",
-       " 1922  8144.948006\n",
-       " 1923  8069.557980\n",
-       " 1924  8075.854697\n",
-       " 1925  8164.048745\n",
-       " 1926  8269.577696\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6110.029651\n",
-       " 182   6102.753919\n",
-       " 183   6075.229727\n",
-       " 184   6003.958810\n",
-       " 185   5950.342697\n",
-       " ...           ...\n",
-       " 1922  7797.416467\n",
-       " 1923  7797.077539\n",
-       " 1924  7723.948751\n",
-       " 1925  7773.293039\n",
-       " 1926  7857.913529\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5684.890374\n",
-       " 182   5624.858339\n",
-       " 183   5584.875594\n",
-       " 184   5588.209263\n",
-       " 185   5582.685017\n",
-       " ...           ...\n",
-       " 1922  7059.210831\n",
-       " 1923  7045.541522\n",
-       " 1924  6926.350235\n",
-       " 1925  6984.916659\n",
-       " 1926  7098.169463\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4800.032232\n",
-       " 182   4758.574868\n",
-       " 183   4737.163580\n",
-       " 184   4759.061766\n",
-       " 185   4754.144431\n",
-       " ...           ...\n",
-       " 1922  5918.278657\n",
-       " 1923  5887.092730\n",
-       " 1924  5834.601569\n",
-       " 1925  5860.491704\n",
-       " 1926  5902.921345\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4994.233513\n",
-       " 182   5006.171958\n",
-       " 183   4984.569713\n",
-       " 184   4890.502928\n",
-       " 185   4845.715783\n",
-       " ...           ...\n",
-       " 1922  6082.161050\n",
-       " 1923  6067.860953\n",
-       " 1924  6059.266888\n",
-       " 1925  6129.066173\n",
-       " 1926  6205.764646\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5794.322845\n",
-       " 182   5799.685609\n",
-       " 183   5793.867835\n",
-       " 184   5763.894549\n",
-       " 185   5733.639879\n",
-       " ...           ...\n",
-       " 1922  7255.818398\n",
-       " 1923  7295.524548\n",
-       " 1924  7226.507187\n",
-       " 1925  7263.865107\n",
-       " 1926  7330.247135\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4929.706587\n",
-       " 182   4880.259062\n",
-       " 183   4827.023180\n",
-       " 184   4765.602565\n",
-       " 185   4721.592934\n",
-       " ...           ...\n",
-       " 1922  6145.814954\n",
-       " 1923  6151.525304\n",
-       " 1924  6171.146516\n",
-       " 1925  6154.374977\n",
-       " 1926  6116.099923\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4976.683372\n",
-       " 182   4944.587511\n",
-       " 183   4915.028350\n",
-       " 184   4890.949721\n",
-       " 185   4863.050972\n",
-       " ...           ...\n",
-       " 1922  6301.021722\n",
-       " 1923  6298.939648\n",
-       " 1924  6335.294130\n",
-       " 1925  6372.358290\n",
-       " 1926  6384.340966\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4624.879736\n",
-       " 182   4610.755115\n",
-       " 183   4595.789124\n",
-       " 184   4579.005354\n",
-       " 185   4559.167826\n",
-       " ...           ...\n",
-       " 1922  5781.168717\n",
-       " 1923  5732.219513\n",
-       " 1924  5680.752341\n",
-       " 1925  5677.568191\n",
-       " 1926  5690.624174\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5815.276942\n",
-       " 182   5785.949748\n",
-       " 183   5748.612117\n",
-       " 184   5693.967964\n",
-       " 185   5640.791390\n",
-       " ...           ...\n",
-       " 1922  6902.897781\n",
-       " 1923  6888.642235\n",
-       " 1924  6925.075316\n",
-       " 1925  6934.919387\n",
-       " 1926  6916.892846\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5650.689017\n",
-       " 182   5624.010916\n",
-       " 183   5577.087592\n",
-       " 184   5486.424537\n",
-       " 185   5440.487382\n",
-       " ...           ...\n",
-       " 1922  7332.869062\n",
-       " 1923  7290.313640\n",
-       " 1924  7309.041860\n",
-       " 1925  7312.375830\n",
-       " 1926  7300.034115\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   6322.280621\n",
-       " 182   6301.687030\n",
-       " 183   6273.081571\n",
-       " 184   6227.166501\n",
-       " 185   6183.233482\n",
-       " ...           ...\n",
-       " 1922  7966.108624\n",
-       " 1923  7987.508932\n",
-       " 1924  7965.590687\n",
-       " 1925  7957.665311\n",
-       " 1926  7954.704193\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   5698.545706\n",
-       " 182   5728.581779\n",
-       " 183   5732.034873\n",
-       " 184   5678.055538\n",
-       " 185   5637.696455\n",
-       " ...           ...\n",
-       " 1922  7064.511451\n",
-       " 1923  7047.818826\n",
-       " 1924  7103.075455\n",
-       " 1925  7191.245598\n",
-       " 1926  7264.274756\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4721.482986\n",
-       " 182   4716.710030\n",
-       " 183   4714.047225\n",
-       " 184   4715.943395\n",
-       " 185   4687.460987\n",
-       " ...           ...\n",
-       " 1922  5943.253102\n",
-       " 1923  5994.795669\n",
-       " 1924  5962.166249\n",
-       " 1925  5984.124688\n",
-       " 1926  6020.282414\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4475.939129\n",
-       " 182   4495.821569\n",
-       " 183   4503.044538\n",
-       " 184   4482.916766\n",
-       " 185   4455.174896\n",
-       " ...           ...\n",
-       " 1922  5218.262111\n",
-       " 1923  5166.010745\n",
-       " 1924  5125.958977\n",
-       " 1925  5159.392028\n",
-       " 1926  5220.334329\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4236.825013\n",
-       " 182   4214.647920\n",
-       " 183   4207.240139\n",
-       " 184   4231.741399\n",
-       " 185   4222.847266\n",
-       " ...           ...\n",
-       " 1922  5102.206485\n",
-       " 1923  5106.338168\n",
-       " 1924  5096.571439\n",
-       " 1925  5085.402060\n",
-       " 1926  5072.943940\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4275.228424\n",
-       " 182   4236.377266\n",
-       " 183   4203.438897\n",
-       " 184   4183.275087\n",
-       " 185   4176.257179\n",
-       " ...           ...\n",
-       " 1922  5314.297172\n",
-       " 1923  5287.302024\n",
-       " 1924  5329.789713\n",
-       " 1925  5295.292834\n",
-       " 1926  5236.148053\n",
-       " \n",
-       " [1746 rows x 1 columns],\n",
-       "        #Intensity\n",
-       " 181   4362.354997\n",
-       " 182   4433.595406\n",
-       " 183   4451.034918\n",
-       " 184   4352.237791\n",
-       " 185   4266.765683\n",
-       " ...           ...\n",
-       " 1922  5099.248095\n",
-       " 1923  5071.851922\n",
-       " 1924  5029.698197\n",
-       " 1925  5049.829455\n",
-       " 1926  5095.167409\n",
-       " \n",
-       " [1746 rows x 1 columns]]"
-      ]
+      "text/plain": "[        #Intensity\n 181   15196.588457\n 182   15238.392893\n 183   15260.348015\n 184   15217.462044\n 185   15151.631602\n ...            ...\n 1922  34619.811769\n 1923  34739.902572\n 1924  34481.281361\n 1925  34600.945806\n 1926  34924.482502\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11541.323730\n 182   11514.042480\n 183   11496.918280\n 184   11512.973779\n 185   11497.515892\n ...            ...\n 1922  23259.160077\n 1923  23452.001384\n 1924  23237.842294\n 1925  23289.105272\n 1926  23490.181875\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   14841.336497\n 182   14791.788614\n 183   14735.185229\n 184   14655.533872\n 185   14589.036222\n ...            ...\n 1922  27759.794667\n 1923  27701.766984\n 1924  27707.542236\n 1925  27766.287448\n 1926  27838.199140\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12962.544993\n 182   12888.778360\n 183   12819.365812\n 184   12764.176613\n 185   12737.702642\n ...            ...\n 1922  24519.211765\n 1923  24573.630709\n 1924  24534.071366\n 1925  24543.687146\n 1926  24582.879296\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11218.268657\n 182   11174.286298\n 183   11138.891151\n 184   11131.547559\n 185   11115.027596\n ...            ...\n 1922  21569.532670\n 1923  21603.756959\n 1924  21496.946353\n 1925  21603.877186\n 1926  21826.919220\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13797.517019\n 182   13699.189902\n 183   13624.446367\n 184   13626.742532\n 185   13609.690867\n ...            ...\n 1922  25599.870205\n 1923  25666.872646\n 1924  25828.025424\n 1925  25977.235900\n 1926  26088.341561\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9500.903430\n 182    9395.398390\n 183    9327.081873\n 184    9380.247863\n 185    9372.983056\n ...            ...\n 1922  17762.395943\n 1923  17729.292606\n 1924  17631.368364\n 1925  17719.442114\n 1926  17908.803279\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9737.872920\n 182    9689.048324\n 183    9644.117090\n 184    9611.904176\n 185    9580.085679\n ...            ...\n 1922  17024.965060\n 1923  17124.649955\n 1924  17120.660056\n 1925  17098.382555\n 1926  17060.351796\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9199.072282\n 182    9192.839419\n 183    9170.494344\n 184    9095.516044\n 185    9031.298566\n ...            ...\n 1922  16020.668347\n 1923  16034.688033\n 1924  16045.178564\n 1925  16092.876008\n 1926  16147.318945\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9137.087615\n 182    9078.193966\n 183    9020.844001\n 184    8968.536739\n 185    8928.190135\n ...            ...\n 1922  15542.194813\n 1923  15555.297180\n 1924  15536.474579\n 1925  15569.354706\n 1926  15617.797227\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6145.054156\n 182    6165.116404\n 183    6169.544779\n 184    6122.902501\n 185    6085.828147\n ...            ...\n 1922  11035.333397\n 1923  10968.079272\n 1924  11095.937226\n 1925  11083.206052\n 1926  10984.066406\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8894.198163\n 182    8901.924978\n 183    8894.795759\n 184    8839.136833\n 185    8780.877258\n ...            ...\n 1922  15173.260687\n 1923  15153.206559\n 1924  15049.716659\n 1925  15105.108062\n 1926  15250.442617\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8829.150525\n 182    8758.889940\n 183    8703.307294\n 184    8695.672582\n 185    8685.443706\n ...            ...\n 1922  14332.405612\n 1923  14377.801442\n 1924  14403.740836\n 1925  14359.691425\n 1926  14271.861800\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7178.607067\n 182    7232.783983\n 183    7253.352016\n 184    7164.131032\n 185    7100.628705\n ...            ...\n 1922  11843.338965\n 1923  11811.407967\n 1924  11831.782967\n 1925  11845.856863\n 1926  11848.067542\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6143.837135\n 182    6201.889554\n 183    6225.708811\n 184    6137.699734\n 185    6076.007874\n ...            ...\n 1922  10207.088285\n 1923  10158.871636\n 1924  10083.966332\n 1925  10123.178132\n 1926  10231.898635\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10073.133750\n 182   10096.997157\n 183   10095.374439\n 184   10010.497048\n 185    9932.389724\n ...            ...\n 1922  15416.241294\n 1923  15427.569023\n 1924  15279.197179\n 1925  15328.791919\n 1926  15491.223987\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8301.595537\n 182    8279.259348\n 183    8240.649036\n 184    8148.876590\n 185    8083.420983\n ...            ...\n 1922  12698.602092\n 1923  12745.528900\n 1924  12735.734077\n 1925  12759.987996\n 1926  12792.902776\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8492.640137\n 182    8590.296387\n 183    8655.297094\n 184    8613.623031\n 185    8555.587155\n ...            ...\n 1922  13423.558228\n 1923  13446.976210\n 1924  13374.597422\n 1925  13452.279611\n 1926  13608.855780\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9812.038102\n 182    9766.555239\n 183    9716.833528\n 184    9653.264915\n 185    9592.480136\n ...            ...\n 1922  14884.632169\n 1923  14855.938156\n 1924  14978.046111\n 1925  15034.602855\n 1926  15035.317227\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10147.072778\n 182    9977.660983\n 183    9839.941393\n 184    9805.749670\n 185    9761.580077\n ...            ...\n 1922  14533.548047\n 1923  14592.073792\n 1924  14646.246140\n 1925  14671.424955\n 1926  14674.249921\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9562.504347\n 182    9544.984186\n 183    9509.783964\n 184    9416.828874\n 185    9350.190397\n ...            ...\n 1922  13981.371647\n 1923  13872.245264\n 1924  13866.778724\n 1925  13950.222968\n 1926  14072.029141\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8353.312792\n 182    8368.973578\n 183    8369.624469\n 184    8321.243040\n 185    8257.895183\n ...            ...\n 1922  12488.254015\n 1923  12418.294393\n 1924  12273.700327\n 1925  12315.092824\n 1926  12467.393087\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6373.140976\n 182    6413.225401\n 183    6416.255066\n 184    6298.239180\n 185    6238.251482\n ...            ...\n 1922   9925.207265\n 1923   9977.497268\n 1924   9964.199920\n 1925   9986.212068\n 1926  10022.705471\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8765.868306\n 182    8755.585040\n 183    8729.031684\n 184    8649.329368\n 185    8584.775460\n ...            ...\n 1922  13001.207320\n 1923  13020.540628\n 1924  12927.213608\n 1925  12944.214951\n 1926  13018.686444\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8181.690564\n 182    8175.336229\n 183    8145.518533\n 184    8039.053858\n 185    7961.496945\n ...            ...\n 1922  12370.491355\n 1923  12296.094409\n 1924  12316.533802\n 1925  12349.589500\n 1926  12385.632734\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7991.522343\n 182    7907.831314\n 183    7844.757200\n 184    7849.031670\n 185    7841.937183\n ...            ...\n 1922  11183.674441\n 1923  11191.079200\n 1924  11308.521119\n 1925  11296.090891\n 1926  11196.005856\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9251.985139\n 182    9223.097538\n 183    9200.456259\n 184    9198.219629\n 185    9163.669322\n ...            ...\n 1922  13384.594164\n 1923  13340.255746\n 1924  13288.755114\n 1925  13361.466763\n 1926  13498.873008\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10096.986329\n 182   10086.861328\n 183   10050.456383\n 184    9928.203615\n 185    9845.627726\n ...            ...\n 1922  13867.200117\n 1923  13983.292621\n 1924  13813.836181\n 1925  13793.785766\n 1926  13860.126366\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11805.654384\n 182   11776.404888\n 183   11735.102049\n 184   11654.424961\n 185   11597.871824\n ...            ...\n 1922  15984.366345\n 1923  16120.218554\n 1924  15850.763377\n 1925  15827.799528\n 1926  15954.341484\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9702.360052\n 182    9724.846948\n 183    9726.382821\n 184    9659.478688\n 185    9591.767478\n ...            ...\n 1922  14312.556456\n 1923  14329.040168\n 1924  14310.054056\n 1925  14299.891319\n 1926  14288.552343\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9062.258149\n 182    9064.006637\n 183    9063.591886\n 184    9051.996024\n 185    9026.939491\n ...            ...\n 1922  15010.605538\n 1923  15017.176423\n 1924  14967.006179\n 1925  15021.553385\n 1926  15163.552753\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6973.791433\n 182    6954.628882\n 183    6932.324442\n 184    6893.780549\n 185    6854.930188\n ...            ...\n 1922  11493.754252\n 1923  11587.649431\n 1924  11524.503776\n 1925  11522.792132\n 1926  11572.385566\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6413.500129\n 182    6433.594635\n 183    6436.168381\n 184    6348.182766\n 185    6289.224067\n ...            ...\n 1922  10315.270892\n 1923  10385.382531\n 1924  10302.254530\n 1925  10291.359914\n 1926  10340.789207\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6090.062856\n 182   6103.453733\n 183   6110.681383\n 184   6086.053231\n 185   6063.539752\n ...           ...\n 1922  9748.502851\n 1923  9765.762924\n 1924  9725.327864\n 1925  9744.639213\n 1926  9813.846284\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5923.591866\n 182   5989.617320\n 183   6032.074690\n 184   5952.715966\n 185   5898.453102\n ...           ...\n 1922  9510.275174\n 1923  9394.603593\n 1924  9410.458575\n 1925  9470.169013\n 1926  9566.504240\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5552.502854\n 182   5561.163892\n 183   5561.172410\n 184   5516.458740\n 185   5471.891552\n ...           ...\n 1922  8470.514303\n 1923  8439.335299\n 1924  8398.724325\n 1925  8453.729886\n 1926  8588.587210\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4481.093592\n 182   4491.626354\n 183   4495.237918\n 184   4463.075962\n 185   4431.515559\n ...           ...\n 1922  6907.094830\n 1923  6899.207871\n 1924  6836.482456\n 1925  6840.885156\n 1926  6901.348225\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3778.243732\n 182   3796.690683\n 183   3805.730130\n 184   3766.145117\n 185   3734.873626\n ...           ...\n 1922  5978.312298\n 1923  5919.357519\n 1924  5848.155433\n 1925  5842.365524\n 1926  5891.202960\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3665.011590\n 182   3635.456273\n 183   3605.677536\n 184   3574.744012\n 185   3549.856445\n ...           ...\n 1922  5527.717428\n 1923  5541.153450\n 1924  5485.550671\n 1925  5502.776271\n 1926  5580.822673\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3553.386985\n 182   3543.813649\n 183   3534.995309\n 184   3530.079316\n 185   3526.221403\n ...           ...\n 1922  5439.653651\n 1923  5461.690807\n 1924  5437.377123\n 1925  5486.489571\n 1926  5596.922020\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3675.081951\n 182   3668.855641\n 183   3657.435079\n 184   3619.167044\n 185   3589.061442\n ...           ...\n 1922  5598.488833\n 1923  5660.456365\n 1924  5688.334677\n 1925  5754.955345\n 1926  5853.930724\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3778.538897\n 182   3786.988241\n 183   3783.647686\n 184   3719.368805\n 185   3671.718483\n ...           ...\n 1922  5749.793521\n 1923  5741.197467\n 1924  5682.491284\n 1925  5704.012341\n 1926  5792.533174\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3684.371084\n 182   3628.824209\n 183   3583.000492\n 184   3587.432758\n 185   3582.186781\n ...           ...\n 1922  5576.265018\n 1923  5631.287710\n 1924  5554.830579\n 1925  5534.101732\n 1926  5559.912970\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3399.753926\n 182   3416.156143\n 183   3423.042191\n 184   3380.742119\n 185   3344.335599\n ...           ...\n 1922  5226.515788\n 1923  5226.788060\n 1924  5212.876332\n 1925  5221.156991\n 1926  5247.971070\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3131.833418\n 182   3141.538560\n 183   3143.348671\n 184   3104.351827\n 185   3074.273489\n ...           ...\n 1922  4728.444859\n 1923  4692.556546\n 1924  4667.097213\n 1925  4666.097303\n 1926  4685.524072\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3471.296498\n 182   3376.400203\n 183   3304.956851\n 184   3354.734472\n 185   3390.906627\n ...           ...\n 1922  5035.247080\n 1923  5060.136068\n 1924  5026.379130\n 1925  5023.337178\n 1926  5045.946079\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3618.237115\n 182   3598.673858\n 183   3573.804978\n 184   3521.512979\n 185   3485.245869\n ...           ...\n 1922  5257.035191\n 1923  5331.752792\n 1924  5334.548189\n 1925  5337.915201\n 1926  5341.759582\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3676.144213\n 182   3680.582217\n 183   3679.503966\n 184   3649.913908\n 185   3622.135514\n ...           ...\n 1922  5315.315095\n 1923  5325.196162\n 1924  5292.517723\n 1925  5337.812719\n 1926  5448.225282\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3583.546907\n 182   3578.642799\n 183   3569.734750\n 184   3540.131573\n 185   3514.306048\n ...           ...\n 1922  5279.432173\n 1923  5270.318780\n 1924  5186.510765\n 1925  5176.074536\n 1926  5226.912920\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3189.099247\n 182   3177.482815\n 183   3167.378183\n 184   3165.087567\n 185   3158.713489\n ...           ...\n 1922  4623.336510\n 1923  4678.747593\n 1924  4678.326093\n 1925  4653.990689\n 1926  4609.684187\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3066.873394\n 182   3046.577779\n 183   3033.092820\n 184   3054.810024\n 185   3063.070047\n ...           ...\n 1922  4403.451902\n 1923  4344.607166\n 1924  4347.271100\n 1925  4378.975121\n 1926  4434.931245\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2976.370999\n 182   2980.725460\n 183   2978.914541\n 184   2945.236693\n 185   2916.243458\n ...           ...\n 1922  4289.904075\n 1923  4271.421939\n 1924  4200.000748\n 1925  4198.546533\n 1926  4255.523490\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3112.702936\n 182   3137.735446\n 183   3153.686607\n 184   3122.699096\n 185   3102.748083\n ...           ...\n 1922  4557.507369\n 1923  4552.334480\n 1924  4582.152263\n 1925  4595.485513\n 1926  4595.052116\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3257.808341\n 182   3184.214843\n 183   3119.691092\n 184   3102.046038\n 185   3087.121270\n ...           ...\n 1922  4614.982081\n 1923  4676.393227\n 1924  4719.790771\n 1925  4696.042260\n 1926  4616.218396\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3410.437257\n 182   3391.515634\n 183   3367.504931\n 184   3317.190353\n 185   3279.573792\n ...           ...\n 1922  4958.003226\n 1923  5011.272758\n 1924  5007.765507\n 1925  5008.240413\n 1926  5012.040918\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3214.485959\n 182   3185.543419\n 183   3167.087671\n 184   3202.834894\n 185   3224.067771\n ...           ...\n 1922  4703.267046\n 1923  4679.179025\n 1924  4769.526132\n 1925  4778.535953\n 1926  4719.618971\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3093.425539\n 182   3080.464728\n 183   3067.933548\n 184   3057.623004\n 185   3042.472009\n ...           ...\n 1922  4372.958994\n 1923  4368.932284\n 1924  4441.501647\n 1925  4433.856403\n 1926  4359.221933\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3193.171756\n 182   3183.555324\n 183   3169.609308\n 184   3133.285017\n 185   3102.552416\n ...           ...\n 1922  4512.075836\n 1923  4456.867628\n 1924  4467.893166\n 1925  4466.402542\n 1926  4454.459360\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3333.200276\n 182   3303.605392\n 183   3274.544340\n 184   3248.242500\n 185   3227.987105\n ...           ...\n 1922  4767.720999\n 1923  4739.507476\n 1924  4715.097638\n 1925  4721.411281\n 1926  4753.382872\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3421.695476\n 182   3412.106388\n 183   3395.699506\n 184   3344.053571\n 185   3304.046916\n ...           ...\n 1922  4754.158050\n 1923  4699.103052\n 1924  4812.405394\n 1925  4862.611372\n 1926  4860.123999\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9062.258149\n 182    9064.006637\n 183    9063.591886\n 184    9051.996024\n 185    9026.939491\n ...            ...\n 1922  15010.605538\n 1923  15017.176423\n 1924  14967.006179\n 1925  15021.553385\n 1926  15163.552753\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6973.791433\n 182    6954.628882\n 183    6932.324442\n 184    6893.780549\n 185    6854.930188\n ...            ...\n 1922  11493.754252\n 1923  11587.649431\n 1924  11524.503776\n 1925  11522.792132\n 1926  11572.385566\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6413.500129\n 182    6433.594635\n 183    6436.168381\n 184    6348.182766\n 185    6289.224067\n ...            ...\n 1922  10315.270892\n 1923  10385.382531\n 1924  10302.254530\n 1925  10291.359914\n 1926  10340.789207\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6090.062856\n 182   6103.453733\n 183   6110.681383\n 184   6086.053231\n 185   6063.539752\n ...           ...\n 1922  9748.502851\n 1923  9765.762924\n 1924  9725.327864\n 1925  9744.639213\n 1926  9813.846284\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5923.591866\n 182   5989.617320\n 183   6032.074690\n 184   5952.715966\n 185   5898.453102\n ...           ...\n 1922  9510.275174\n 1923  9394.603593\n 1924  9410.458575\n 1925  9470.169013\n 1926  9566.504240\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5552.502854\n 182   5561.163892\n 183   5561.172410\n 184   5516.458740\n 185   5471.891552\n ...           ...\n 1922  8470.514303\n 1923  8439.335299\n 1924  8398.724325\n 1925  8453.729886\n 1926  8588.587210\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4481.093592\n 182   4491.626354\n 183   4495.237918\n 184   4463.075962\n 185   4431.515559\n ...           ...\n 1922  6907.094830\n 1923  6899.207871\n 1924  6836.482456\n 1925  6840.885156\n 1926  6901.348225\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3778.243732\n 182   3796.690683\n 183   3805.730130\n 184   3766.145117\n 185   3734.873626\n ...           ...\n 1922  5978.312298\n 1923  5919.357519\n 1924  5848.155433\n 1925  5842.365524\n 1926  5891.202960\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3665.011590\n 182   3635.456273\n 183   3605.677536\n 184   3574.744012\n 185   3549.856445\n ...           ...\n 1922  5527.717428\n 1923  5541.153450\n 1924  5485.550671\n 1925  5502.776271\n 1926  5580.822673\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3553.386985\n 182   3543.813649\n 183   3534.995309\n 184   3530.079316\n 185   3526.221403\n ...           ...\n 1922  5439.653651\n 1923  5461.690807\n 1924  5437.377123\n 1925  5486.489571\n 1926  5596.922020\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3675.081951\n 182   3668.855641\n 183   3657.435079\n 184   3619.167044\n 185   3589.061442\n ...           ...\n 1922  5598.488833\n 1923  5660.456365\n 1924  5688.334677\n 1925  5754.955345\n 1926  5853.930724\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3778.538897\n 182   3786.988241\n 183   3783.647686\n 184   3719.368805\n 185   3671.718483\n ...           ...\n 1922  5749.793521\n 1923  5741.197467\n 1924  5682.491284\n 1925  5704.012341\n 1926  5792.533174\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3684.371084\n 182   3628.824209\n 183   3583.000492\n 184   3587.432758\n 185   3582.186781\n ...           ...\n 1922  5576.265018\n 1923  5631.287710\n 1924  5554.830579\n 1925  5534.101732\n 1926  5559.912970\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3399.753926\n 182   3416.156143\n 183   3423.042191\n 184   3380.742119\n 185   3344.335599\n ...           ...\n 1922  5226.515788\n 1923  5226.788060\n 1924  5212.876332\n 1925  5221.156991\n 1926  5247.971070\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3131.833418\n 182   3141.538560\n 183   3143.348671\n 184   3104.351827\n 185   3074.273489\n ...           ...\n 1922  4728.444859\n 1923  4692.556546\n 1924  4667.097213\n 1925  4666.097303\n 1926  4685.524072\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3471.296498\n 182   3376.400203\n 183   3304.956851\n 184   3354.734472\n 185   3390.906627\n ...           ...\n 1922  5035.247080\n 1923  5060.136068\n 1924  5026.379130\n 1925  5023.337178\n 1926  5045.946079\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3618.237115\n 182   3598.673858\n 183   3573.804978\n 184   3521.512979\n 185   3485.245869\n ...           ...\n 1922  5257.035191\n 1923  5331.752792\n 1924  5334.548189\n 1925  5337.915201\n 1926  5341.759582\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3676.144213\n 182   3680.582217\n 183   3679.503966\n 184   3649.913908\n 185   3622.135514\n ...           ...\n 1922  5315.315095\n 1923  5325.196162\n 1924  5292.517723\n 1925  5337.812719\n 1926  5448.225282\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3583.546907\n 182   3578.642799\n 183   3569.734750\n 184   3540.131573\n 185   3514.306048\n ...           ...\n 1922  5279.432173\n 1923  5270.318780\n 1924  5186.510765\n 1925  5176.074536\n 1926  5226.912920\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3189.099247\n 182   3177.482815\n 183   3167.378183\n 184   3165.087567\n 185   3158.713489\n ...           ...\n 1922  4623.336510\n 1923  4678.747593\n 1924  4678.326093\n 1925  4653.990689\n 1926  4609.684187\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3066.873394\n 182   3046.577779\n 183   3033.092820\n 184   3054.810024\n 185   3063.070047\n ...           ...\n 1922  4403.451902\n 1923  4344.607166\n 1924  4347.271100\n 1925  4378.975121\n 1926  4434.931245\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2976.370999\n 182   2980.725460\n 183   2978.914541\n 184   2945.236693\n 185   2916.243458\n ...           ...\n 1922  4289.904075\n 1923  4271.421939\n 1924  4200.000748\n 1925  4198.546533\n 1926  4255.523490\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3112.702936\n 182   3137.735446\n 183   3153.686607\n 184   3122.699096\n 185   3102.748083\n ...           ...\n 1922  4557.507369\n 1923  4552.334480\n 1924  4582.152263\n 1925  4595.485513\n 1926  4595.052116\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3257.808341\n 182   3184.214843\n 183   3119.691092\n 184   3102.046038\n 185   3087.121270\n ...           ...\n 1922  4614.982081\n 1923  4676.393227\n 1924  4719.790771\n 1925  4696.042260\n 1926  4616.218396\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3410.437257\n 182   3391.515634\n 183   3367.504931\n 184   3317.190353\n 185   3279.573792\n ...           ...\n 1922  4958.003226\n 1923  5011.272758\n 1924  5007.765507\n 1925  5008.240413\n 1926  5012.040918\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3214.485959\n 182   3185.543419\n 183   3167.087671\n 184   3202.834894\n 185   3224.067771\n ...           ...\n 1922  4703.267046\n 1923  4679.179025\n 1924  4769.526132\n 1925  4778.535953\n 1926  4719.618971\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3093.425539\n 182   3080.464728\n 183   3067.933548\n 184   3057.623004\n 185   3042.472009\n ...           ...\n 1922  4372.958994\n 1923  4368.932284\n 1924  4441.501647\n 1925  4433.856403\n 1926  4359.221933\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3193.171756\n 182   3183.555324\n 183   3169.609308\n 184   3133.285017\n 185   3102.552416\n ...           ...\n 1922  4512.075836\n 1923  4456.867628\n 1924  4467.893166\n 1925  4466.402542\n 1926  4454.459360\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3333.200276\n 182   3303.605392\n 183   3274.544340\n 184   3248.242500\n 185   3227.987105\n ...           ...\n 1922  4767.720999\n 1923  4739.507476\n 1924  4715.097638\n 1925  4721.411281\n 1926  4753.382872\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3421.695476\n 182   3412.106388\n 183   3395.699506\n 184   3344.053571\n 185   3304.046916\n ...           ...\n 1922  4754.158050\n 1923  4699.103052\n 1924  4812.405394\n 1925  4862.611372\n 1926  4860.123999\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   19959.902670\n 182   19934.934963\n 183   19926.002522\n 184   19951.714215\n 185   19912.638510\n ...            ...\n 1922  57282.226895\n 1923  57384.770113\n 1924  57419.565567\n 1925  57646.858710\n 1926  57887.832877\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   16102.243794\n 182   16026.110601\n 183   15963.490567\n 184   15930.065668\n 185   15897.621774\n ...            ...\n 1922  37304.424488\n 1923  37321.521246\n 1924  36956.700539\n 1925  37072.652063\n 1926  37354.943110\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15502.306853\n 182   15484.144897\n 183   15428.439601\n 184   15291.622061\n 185   15207.335193\n ...            ...\n 1922  35324.276805\n 1923  35523.197676\n 1924  35135.154747\n 1925  35238.379492\n 1926  35481.774664\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15055.132515\n 182   14927.503360\n 183   14827.528671\n 184   14787.301352\n 185   14752.352710\n ...            ...\n 1922  32098.396526\n 1923  32196.758741\n 1924  32054.799146\n 1925  32207.484271\n 1926  32421.510117\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13759.376945\n 182   13633.593906\n 183   13543.691777\n 184   13531.310225\n 185   13481.954050\n ...            ...\n 1922  29652.742532\n 1923  29535.871067\n 1924  29438.474602\n 1925  29464.244820\n 1926  29501.235889\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13074.080233\n 182   12959.557536\n 183   12852.226383\n 184   12760.432532\n 185   12718.679881\n ...            ...\n 1922  28638.917955\n 1923  28538.743634\n 1924  28454.795402\n 1925  28573.112017\n 1926  28736.495688\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13566.345427\n 182   13407.181462\n 183   13292.552487\n 184   13274.141197\n 185   13236.977767\n ...            ...\n 1922  26858.881715\n 1923  26843.468947\n 1924  26876.289654\n 1925  26914.018893\n 1926  26903.328789\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11615.153939\n 182   11597.861811\n 183   11560.322822\n 184   11479.040564\n 185   11411.859327\n ...            ...\n 1922  22887.661956\n 1923  22819.495808\n 1924  22720.161212\n 1925  22901.485024\n 1926  23136.639225\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11532.423064\n 182   11489.102178\n 183   11458.866633\n 184   11456.901915\n 185   11421.769941\n ...            ...\n 1922  21720.318834\n 1923  21789.469840\n 1924  21566.736254\n 1925  21652.624399\n 1926  21835.453343\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12678.054341\n 182   12677.568468\n 183   12659.575004\n 184   12603.756456\n 185   12564.594411\n ...            ...\n 1922  23606.881436\n 1923  23635.010647\n 1924  23459.528323\n 1925  23514.985538\n 1926  23626.891964\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11478.909078\n 182   11474.276889\n 183   11437.794108\n 184   11332.498240\n 185   11227.862941\n ...            ...\n 1922  21214.224856\n 1923  21184.450752\n 1924  21315.755045\n 1925  21392.497912\n 1926  21410.123952\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12954.456195\n 182   12964.677688\n 183   12935.030678\n 184   12819.247917\n 185   12716.520933\n ...            ...\n 1922  26642.053378\n 1923  26665.629276\n 1924  26727.675994\n 1925  26914.331586\n 1926  27075.807050\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12893.931537\n 182   12807.407829\n 183   12719.211514\n 184   12627.401534\n 185   12559.303179\n ...            ...\n 1922  27221.077844\n 1923  27257.113594\n 1924  27334.907787\n 1925  27386.123330\n 1926  27380.983248\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11361.945114\n 182   11313.266005\n 183   11262.064156\n 184   11205.411939\n 185   11136.301065\n ...            ...\n 1922  19614.170606\n 1923  19695.029597\n 1924  19670.490456\n 1925  19810.435774\n 1926  19962.577439\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11868.161704\n 182   11830.218215\n 183   11814.640337\n 184   11847.383281\n 185   11862.941302\n ...            ...\n 1922  20862.683152\n 1923  20790.228453\n 1924  20645.705726\n 1925  20767.829718\n 1926  20970.213070\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10359.260977\n 182   10327.808406\n 183   10279.697458\n 184   10195.596147\n 185   10141.036415\n ...            ...\n 1922  18551.847507\n 1923  18633.867183\n 1924  18536.065415\n 1925  18624.426682\n 1926  18751.369287\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11206.812719\n 182   11106.077105\n 183   11021.178929\n 184   10970.497477\n 185   10894.198210\n ...            ...\n 1922  19412.428673\n 1923  19254.055806\n 1924  19207.520452\n 1925  19269.467633\n 1926  19344.310188\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11602.632452\n 182   11592.010858\n 183   11550.383428\n 184   11441.768005\n 185   11378.114665\n ...            ...\n 1922  20869.325605\n 1923  20925.941270\n 1924  20781.436219\n 1925  20928.776936\n 1926  21149.377229\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11132.916511\n 182   11082.209147\n 183   11027.291768\n 184   10963.278663\n 185   10895.660778\n ...            ...\n 1922  19505.703737\n 1923  19456.491892\n 1924  19404.760832\n 1925  19517.507465\n 1926  19636.280310\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9909.448200\n 182    9891.285235\n 183    9841.695901\n 184    9724.210014\n 185    9657.500985\n ...            ...\n 1922  17168.805616\n 1923  17100.018517\n 1924  17207.797557\n 1925  17305.107374\n 1926  17356.864206\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9873.391244\n 182    9896.587508\n 183    9884.553562\n 184    9796.404872\n 185    9705.894800\n ...            ...\n 1922  17082.008985\n 1923  17024.961405\n 1924  17032.450041\n 1925  17168.732873\n 1926  17305.712763\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11462.393950\n 182   11484.445916\n 183   11455.658567\n 184   11317.033063\n 185   11219.037669\n ...            ...\n 1922  19707.102066\n 1923  19741.312687\n 1924  19520.394757\n 1925  19648.697355\n 1926  19889.478182\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11640.371173\n 182   11595.061388\n 183   11519.384699\n 184   11378.100424\n 185   11284.279268\n ...            ...\n 1922  18655.816318\n 1923  18619.432372\n 1924  18551.922271\n 1925  18687.381619\n 1926  18843.989917\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11122.674677\n 182   11128.674172\n 183   11108.938568\n 184   11033.602379\n 185   10970.853650\n ...            ...\n 1922  19796.204764\n 1923  19760.667527\n 1924  19658.836719\n 1925  19828.563699\n 1926  20049.101694\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11737.808016\n 182   11720.530520\n 183   11693.341981\n 184   11644.740670\n 185   11614.828342\n ...            ...\n 1922  20171.051655\n 1923  20242.045051\n 1924  20326.216768\n 1925  20308.216921\n 1926  20235.685734\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10732.658722\n 182   10599.842888\n 183   10502.065130\n 184   10479.987005\n 185   10471.306208\n ...            ...\n 1922  17617.036610\n 1923  17555.790018\n 1924  17679.298856\n 1925  17753.844178\n 1926  17788.675417\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11948.875429\n 182   11928.954644\n 183   11899.966982\n 184   11851.390370\n 185   11755.931553\n ...            ...\n 1922  19780.957966\n 1923  19745.491544\n 1924  19685.443564\n 1925  19753.471644\n 1926  19842.241406\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9889.820676\n 182    9829.624413\n 183    9781.250697\n 184    9758.419548\n 185    9752.529644\n ...            ...\n 1922  17065.980243\n 1923  17144.940410\n 1924  17161.845713\n 1925  17238.252270\n 1926  17301.911237\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9633.718687\n 182    9607.516872\n 183    9576.257091\n 184    9534.069591\n 185    9480.196528\n ...            ...\n 1922  16014.767339\n 1923  16005.047718\n 1924  16122.083072\n 1925  16284.648169\n 1926  16407.413390\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10116.220383\n 182   10037.370237\n 183    9954.526053\n 184    9863.052767\n 185    9813.519611\n ...            ...\n 1922  17101.048324\n 1923  17075.044805\n 1924  17082.539754\n 1925  17157.233293\n 1926  17230.815430\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   19959.902670\n 182   19934.934963\n 183   19926.002522\n 184   19951.714215\n 185   19912.638510\n ...            ...\n 1922  57282.226895\n 1923  57384.770113\n 1924  57419.565567\n 1925  57646.858710\n 1926  57887.832877\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   16102.243794\n 182   16026.110601\n 183   15963.490567\n 184   15930.065668\n 185   15897.621774\n ...            ...\n 1922  37304.424488\n 1923  37321.521246\n 1924  36956.700539\n 1925  37072.652063\n 1926  37354.943110\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15502.306853\n 182   15484.144897\n 183   15428.439601\n 184   15291.622061\n 185   15207.335193\n ...            ...\n 1922  35324.276805\n 1923  35523.197676\n 1924  35135.154747\n 1925  35238.379492\n 1926  35481.774664\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15055.132515\n 182   14927.503360\n 183   14827.528671\n 184   14787.301352\n 185   14752.352710\n ...            ...\n 1922  32098.396526\n 1923  32196.758741\n 1924  32054.799146\n 1925  32207.484271\n 1926  32421.510117\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13759.376945\n 182   13633.593906\n 183   13543.691777\n 184   13531.310225\n 185   13481.954050\n ...            ...\n 1922  29652.742532\n 1923  29535.871067\n 1924  29438.474602\n 1925  29464.244820\n 1926  29501.235889\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13074.080233\n 182   12959.557536\n 183   12852.226383\n 184   12760.432532\n 185   12718.679881\n ...            ...\n 1922  28638.917955\n 1923  28538.743634\n 1924  28454.795402\n 1925  28573.112017\n 1926  28736.495688\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13566.345427\n 182   13407.181462\n 183   13292.552487\n 184   13274.141197\n 185   13236.977767\n ...            ...\n 1922  26858.881715\n 1923  26843.468947\n 1924  26876.289654\n 1925  26914.018893\n 1926  26903.328789\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11615.153939\n 182   11597.861811\n 183   11560.322822\n 184   11479.040564\n 185   11411.859327\n ...            ...\n 1922  22887.661956\n 1923  22819.495808\n 1924  22720.161212\n 1925  22901.485024\n 1926  23136.639225\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11532.423064\n 182   11489.102178\n 183   11458.866633\n 184   11456.901915\n 185   11421.769941\n ...            ...\n 1922  21720.318834\n 1923  21789.469840\n 1924  21566.736254\n 1925  21652.624399\n 1926  21835.453343\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12678.054341\n 182   12677.568468\n 183   12659.575004\n 184   12603.756456\n 185   12564.594411\n ...            ...\n 1922  23606.881436\n 1923  23635.010647\n 1924  23459.528323\n 1925  23514.985538\n 1926  23626.891964\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11478.909078\n 182   11474.276889\n 183   11437.794108\n 184   11332.498240\n 185   11227.862941\n ...            ...\n 1922  21214.224856\n 1923  21184.450752\n 1924  21315.755045\n 1925  21392.497912\n 1926  21410.123952\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12954.456195\n 182   12964.677688\n 183   12935.030678\n 184   12819.247917\n 185   12716.520933\n ...            ...\n 1922  26642.053378\n 1923  26665.629276\n 1924  26727.675994\n 1925  26914.331586\n 1926  27075.807050\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12893.931537\n 182   12807.407829\n 183   12719.211514\n 184   12627.401534\n 185   12559.303179\n ...            ...\n 1922  27221.077844\n 1923  27257.113594\n 1924  27334.907787\n 1925  27386.123330\n 1926  27380.983248\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11361.945114\n 182   11313.266005\n 183   11262.064156\n 184   11205.411939\n 185   11136.301065\n ...            ...\n 1922  19614.170606\n 1923  19695.029597\n 1924  19670.490456\n 1925  19810.435774\n 1926  19962.577439\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11868.161704\n 182   11830.218215\n 183   11814.640337\n 184   11847.383281\n 185   11862.941302\n ...            ...\n 1922  20862.683152\n 1923  20790.228453\n 1924  20645.705726\n 1925  20767.829718\n 1926  20970.213070\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10359.260977\n 182   10327.808406\n 183   10279.697458\n 184   10195.596147\n 185   10141.036415\n ...            ...\n 1922  18551.847507\n 1923  18633.867183\n 1924  18536.065415\n 1925  18624.426682\n 1926  18751.369287\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11206.812719\n 182   11106.077105\n 183   11021.178929\n 184   10970.497477\n 185   10894.198210\n ...            ...\n 1922  19412.428673\n 1923  19254.055806\n 1924  19207.520452\n 1925  19269.467633\n 1926  19344.310188\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11602.632452\n 182   11592.010858\n 183   11550.383428\n 184   11441.768005\n 185   11378.114665\n ...            ...\n 1922  20869.325605\n 1923  20925.941270\n 1924  20781.436219\n 1925  20928.776936\n 1926  21149.377229\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11132.916511\n 182   11082.209147\n 183   11027.291768\n 184   10963.278663\n 185   10895.660778\n ...            ...\n 1922  19505.703737\n 1923  19456.491892\n 1924  19404.760832\n 1925  19517.507465\n 1926  19636.280310\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9909.448200\n 182    9891.285235\n 183    9841.695901\n 184    9724.210014\n 185    9657.500985\n ...            ...\n 1922  17168.805616\n 1923  17100.018517\n 1924  17207.797557\n 1925  17305.107374\n 1926  17356.864206\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9873.391244\n 182    9896.587508\n 183    9884.553562\n 184    9796.404872\n 185    9705.894800\n ...            ...\n 1922  17082.008985\n 1923  17024.961405\n 1924  17032.450041\n 1925  17168.732873\n 1926  17305.712763\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11462.393950\n 182   11484.445916\n 183   11455.658567\n 184   11317.033063\n 185   11219.037669\n ...            ...\n 1922  19707.102066\n 1923  19741.312687\n 1924  19520.394757\n 1925  19648.697355\n 1926  19889.478182\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11640.371173\n 182   11595.061388\n 183   11519.384699\n 184   11378.100424\n 185   11284.279268\n ...            ...\n 1922  18655.816318\n 1923  18619.432372\n 1924  18551.922271\n 1925  18687.381619\n 1926  18843.989917\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11122.674677\n 182   11128.674172\n 183   11108.938568\n 184   11033.602379\n 185   10970.853650\n ...            ...\n 1922  19796.204764\n 1923  19760.667527\n 1924  19658.836719\n 1925  19828.563699\n 1926  20049.101694\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11737.808016\n 182   11720.530520\n 183   11693.341981\n 184   11644.740670\n 185   11614.828342\n ...            ...\n 1922  20171.051655\n 1923  20242.045051\n 1924  20326.216768\n 1925  20308.216921\n 1926  20235.685734\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10732.658722\n 182   10599.842888\n 183   10502.065130\n 184   10479.987005\n 185   10471.306208\n ...            ...\n 1922  17617.036610\n 1923  17555.790018\n 1924  17679.298856\n 1925  17753.844178\n 1926  17788.675417\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11948.875429\n 182   11928.954644\n 183   11899.966982\n 184   11851.390370\n 185   11755.931553\n ...            ...\n 1922  19780.957966\n 1923  19745.491544\n 1924  19685.443564\n 1925  19753.471644\n 1926  19842.241406\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9889.820676\n 182    9829.624413\n 183    9781.250697\n 184    9758.419548\n 185    9752.529644\n ...            ...\n 1922  17065.980243\n 1923  17144.940410\n 1924  17161.845713\n 1925  17238.252270\n 1926  17301.911237\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9633.718687\n 182    9607.516872\n 183    9576.257091\n 184    9534.069591\n 185    9480.196528\n ...            ...\n 1922  16014.767339\n 1923  16005.047718\n 1924  16122.083072\n 1925  16284.648169\n 1926  16407.413390\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10116.220383\n 182   10037.370237\n 183    9954.526053\n 184    9863.052767\n 185    9813.519611\n ...            ...\n 1922  17101.048324\n 1923  17075.044805\n 1924  17082.539754\n 1925  17157.233293\n 1926  17230.815430\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4021.523293\n 182   4006.673746\n 183   3988.683363\n 184   3954.458971\n 185   3937.017488\n ...           ...\n 1922  7638.301906\n 1923  7618.588415\n 1924  7681.156517\n 1925  7776.235417\n 1926  7898.464899\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3708.059896\n 182   3721.832830\n 183   3729.983562\n 184   3709.074885\n 185   3694.213196\n ...           ...\n 1922  7036.442245\n 1923  7043.672836\n 1924  7091.558085\n 1925  7107.032952\n 1926  7095.441096\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3804.715337\n 182   3777.842731\n 183   3745.987319\n 184   3688.377333\n 185   3650.269970\n ...           ...\n 1922  7157.587447\n 1923  7220.912860\n 1924  7160.316702\n 1925  7152.616443\n 1926  7189.090874\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3907.536887\n 182   3992.650168\n 183   4055.281784\n 184   4001.712665\n 185   3961.446176\n ...           ...\n 1922  7647.248175\n 1923  7666.480969\n 1924  7619.507131\n 1925  7615.762244\n 1926  7648.118936\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4040.077335\n 182   4081.883282\n 183   4112.015510\n 184   4081.809911\n 185   4055.684342\n ...           ...\n 1922  7691.333107\n 1923  7667.846672\n 1924  7669.909093\n 1925  7665.710115\n 1926  7656.282085\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4161.758170\n 182   4154.923492\n 183   4151.549034\n 184   4166.059377\n 185   4166.960506\n ...           ...\n 1922  7939.551768\n 1923  7915.775861\n 1924  7874.051709\n 1925  7858.480392\n 1926  7864.749963\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3288.506569\n 182   3250.908156\n 183   3218.168990\n 184   3210.545757\n 185   3202.165562\n ...           ...\n 1922  6197.080221\n 1923  6169.873706\n 1924  6134.177612\n 1925  6173.035934\n 1926  6274.156516\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2477.706265\n 182   2466.004904\n 183   2459.926247\n 184   2482.909596\n 185   2502.719535\n ...           ...\n 1922  4561.913766\n 1923  4563.596640\n 1924  4575.679555\n 1925  4570.122911\n 1926  4549.835032\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2178.289523\n 182   2232.537004\n 183   2274.464588\n 184   2252.714463\n 185   2235.952674\n ...           ...\n 1922  3911.583812\n 1923  3844.060659\n 1924  3815.358734\n 1925  3861.787018\n 1926  3970.958419\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2109.567847\n 182   2057.165440\n 183   2014.162416\n 184   2019.741881\n 185   2022.448554\n ...           ...\n 1922  3346.361444\n 1923  3366.716494\n 1924  3347.340862\n 1925  3339.334886\n 1926  3340.823996\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1969.479775\n 182   1962.383315\n 183   1958.097912\n 184   1968.341983\n 185   1968.001036\n ...           ...\n 1922  3036.136339\n 1923  3040.947491\n 1924  3027.788443\n 1925  3027.143398\n 1926  3036.949111\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1924.527342\n 182   1932.755290\n 183   1932.871508\n 184   1891.060713\n 185   1861.797605\n ...           ...\n 1922  2819.423705\n 1923  2790.424427\n 1924  2810.618312\n 1925  2839.371029\n 1926  2875.271439\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1907.042499\n 182   1895.273031\n 183   1880.363464\n 184   1849.223708\n 185   1828.818000\n ...           ...\n 1922  2764.710390\n 1923  2722.025100\n 1924  2788.115742\n 1925  2833.200027\n 1926  2860.741378\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1953.514861\n 182   1915.757048\n 183   1881.343888\n 184   1864.218195\n 185   1857.374342\n ...           ...\n 1922  2854.149700\n 1923  2857.204800\n 1924  2865.930999\n 1925  2893.454238\n 1926  2936.675355\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2092.635235\n 182   2103.549297\n 183   2112.410483\n 184   2110.660987\n 185   2102.559665\n ...           ...\n 1922  3231.504863\n 1923  3261.795386\n 1924  3277.878458\n 1925  3245.579978\n 1926  3172.876852\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2436.317737\n 182   2397.951054\n 183   2359.899777\n 184   2323.478740\n 185   2303.084224\n ...           ...\n 1922  3666.852090\n 1923  3723.185859\n 1924  3663.168358\n 1925  3651.492205\n 1926  3680.187123\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2526.310101\n 182   2535.942913\n 183   2542.765279\n 184   2535.061319\n 185   2520.551178\n ...           ...\n 1922  4073.572411\n 1923  4095.554712\n 1924  4112.791945\n 1925  4099.868475\n 1926  4061.757049\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2812.209788\n 182   2840.539048\n 183   2866.278665\n 184   2878.633231\n 185   2884.032530\n ...           ...\n 1922  4513.765990\n 1923  4559.835935\n 1924  4480.106735\n 1925  4482.293538\n 1926  4552.890444\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2643.202259\n 182   2646.850445\n 183   2647.470378\n 184   2632.438214\n 185   2630.107787\n ...           ...\n 1922  4296.077839\n 1923  4352.294124\n 1924  4350.785612\n 1925  4358.955136\n 1926  4375.207030\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2133.389447\n 182   2151.639952\n 183   2164.593859\n 184   2150.171307\n 185   2131.512802\n ...           ...\n 1922  3498.535075\n 1923  3505.073249\n 1924  3479.456128\n 1925  3457.801137\n 1926  3439.455019\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1982.560266\n 182   1990.128563\n 183   1994.669565\n 184   1983.563423\n 185   1968.070440\n ...           ...\n 1922  3107.864806\n 1923  3085.989257\n 1924  3070.749659\n 1925  3090.227431\n 1926  3138.698549\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1779.496839\n 182   1796.372028\n 183   1812.169421\n 184   1822.396022\n 185   1822.793750\n ...           ...\n 1922  2703.170354\n 1923  2756.105253\n 1924  2772.121174\n 1925  2760.822031\n 1926  2726.711393\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1707.254767\n 182   1698.794019\n 183   1689.033062\n 184   1672.551731\n 185   1664.083846\n ...           ...\n 1922  2365.102093\n 1923  2333.790015\n 1924  2360.182987\n 1925  2375.212678\n 1926  2380.752609\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1636.768702\n 182   1607.637716\n 183   1580.771615\n 184   1565.611992\n 185   1555.138899\n ...           ...\n 1922  2227.600300\n 1923  2237.786796\n 1924  2222.434493\n 1925  2220.732950\n 1926  2230.431498\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1646.742950\n 182   1652.938137\n 183   1658.485707\n 184   1660.685946\n 185   1660.164283\n ...           ...\n 1922  2260.510952\n 1923  2257.710607\n 1924  2265.313855\n 1925  2315.057206\n 1926  2399.992812\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1769.884145\n 182   1742.102076\n 183   1720.804331\n 184   1733.022040\n 185   1737.528294\n ...           ...\n 1922  2472.606704\n 1923  2517.090721\n 1924  2534.118308\n 1925  2519.276748\n 1926  2477.820466\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1883.773122\n 182   1884.664566\n 183   1884.258037\n 184   1877.142686\n 185   1874.412246\n ...           ...\n 1922  2790.700844\n 1923  2713.360232\n 1924  2773.189682\n 1925  2818.270168\n 1926  2851.033425\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2085.346342\n 182   2074.931145\n 183   2065.487124\n 184   2061.062805\n 185   2058.452755\n ...           ...\n 1922  3161.879663\n 1923  3195.167018\n 1924  3172.638643\n 1925  3142.687923\n 1926  3106.538618\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2295.824251\n 182   2272.770572\n 183   2252.415954\n 184   2246.011943\n 185   2244.749074\n ...           ...\n 1922  3460.911444\n 1923  3514.082288\n 1924  3481.163149\n 1925  3473.835448\n 1926  3487.879798\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2519.522740\n 182   2525.822136\n 183   2535.584880\n 184   2563.248602\n 185   2577.002076\n ...           ...\n 1922  3907.798473\n 1923  3851.592181\n 1924  3903.226485\n 1925  3908.219650\n 1926  3874.261633\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11541.978798\n 182   11513.363879\n 183   11496.858613\n 184   11542.944382\n 185   11561.057396\n ...            ...\n 1922  36617.264638\n 1923  36796.432577\n 1924  36601.005071\n 1925  36781.750284\n 1926  37276.646748\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8407.273072\n 182    8521.583556\n 183    8594.811974\n 184    8455.699975\n 185    8353.322227\n ...            ...\n 1922  24257.348505\n 1923  24232.055399\n 1924  24193.542155\n 1925  24277.977274\n 1926  24465.089648\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7829.136962\n 182    7855.399311\n 183    7868.577822\n 184    7814.130046\n 185    7764.818318\n ...            ...\n 1922  20467.481731\n 1923  20584.284988\n 1924  20410.699854\n 1925  20384.991844\n 1926  20482.779723\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7213.440575\n 182    7202.304739\n 183    7188.245315\n 184    7159.074782\n 185    7141.092150\n ...            ...\n 1922  18180.735164\n 1923  18151.337843\n 1924  18117.233175\n 1925  18136.618172\n 1926  18200.673728\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    5229.433843\n 182    5248.054054\n 183    5255.860462\n 184    5207.773684\n 185    5169.663006\n ...            ...\n 1922  10986.899887\n 1923  10990.944483\n 1924  10939.196055\n 1925  10946.045151\n 1926  11001.830504\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4822.681182\n 182   4749.941011\n 183   4691.355361\n 184   4705.930033\n 185   4708.559098\n ...           ...\n 1922  9588.883917\n 1923  9570.120429\n 1924  9579.738336\n 1925  9601.321903\n 1926  9632.898292\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4858.543535\n 182   4821.799079\n 183   4784.288614\n 184   4742.818890\n 185   4709.511169\n ...           ...\n 1922  9736.392302\n 1923  9730.404068\n 1924  9795.339130\n 1925  9792.578938\n 1926  9733.284747\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    5274.379203\n 182    5251.681119\n 183    5230.383993\n 184    5216.327990\n 185    5204.690210\n ...            ...\n 1922  10675.677784\n 1923  10833.203975\n 1924  10753.265129\n 1925  10725.065365\n 1926  10740.074204\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4788.588490\n 182   4736.506081\n 183   4692.524937\n 184   4690.416713\n 185   4690.879146\n ...           ...\n 1922  8259.229344\n 1923  8310.328812\n 1924  8289.523640\n 1925  8244.216378\n 1926  8178.446807\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4549.374495\n 182   4506.594757\n 183   4466.184775\n 184   4438.023324\n 185   4412.278609\n ...           ...\n 1922  7752.028360\n 1923  7858.638708\n 1924  7845.076602\n 1925  7867.941491\n 1926  7921.227475\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4736.848813\n 182   4696.757331\n 183   4661.632692\n 184   4652.180120\n 185   4649.531839\n ...           ...\n 1922  8161.129311\n 1923  8203.404143\n 1924  8179.454582\n 1925  8235.194396\n 1926  8357.484800\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5023.243076\n 182   4993.849931\n 183   4971.048882\n 184   4982.320324\n 185   4979.528628\n ...           ...\n 1922  8610.948469\n 1923  8673.253503\n 1924  8680.665862\n 1925  8696.056244\n 1926  8718.109274\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3742.601635\n 182   3805.033969\n 183   3853.742010\n 184   3831.513455\n 185   3806.560407\n ...           ...\n 1922  6180.102529\n 1923  6247.673558\n 1924  6162.951924\n 1925  6182.347054\n 1926  6288.692698\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11541.978798\n 182   11513.363879\n 183   11496.858613\n 184   11542.944382\n 185   11561.057396\n ...            ...\n 1922  36617.264638\n 1923  36796.432577\n 1924  36601.005071\n 1925  36781.750284\n 1926  37276.646748\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8407.273072\n 182    8521.583556\n 183    8594.811974\n 184    8455.699975\n 185    8353.322227\n ...            ...\n 1922  24257.348505\n 1923  24232.055399\n 1924  24193.542155\n 1925  24277.977274\n 1926  24465.089648\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7829.136962\n 182    7855.399311\n 183    7868.577822\n 184    7814.130046\n 185    7764.818318\n ...            ...\n 1922  20467.481731\n 1923  20584.284988\n 1924  20410.699854\n 1925  20384.991844\n 1926  20482.779723\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7213.440575\n 182    7202.304739\n 183    7188.245315\n 184    7159.074782\n 185    7141.092150\n ...            ...\n 1922  18180.735164\n 1923  18151.337843\n 1924  18117.233175\n 1925  18136.618172\n 1926  18200.673728\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    5229.433843\n 182    5248.054054\n 183    5255.860462\n 184    5207.773684\n 185    5169.663006\n ...            ...\n 1922  10986.899887\n 1923  10990.944483\n 1924  10939.196055\n 1925  10946.045151\n 1926  11001.830504\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4822.681182\n 182   4749.941011\n 183   4691.355361\n 184   4705.930033\n 185   4708.559098\n ...           ...\n 1922  9588.883917\n 1923  9570.120429\n 1924  9579.738336\n 1925  9601.321903\n 1926  9632.898292\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4858.543535\n 182   4821.799079\n 183   4784.288614\n 184   4742.818890\n 185   4709.511169\n ...           ...\n 1922  9736.392302\n 1923  9730.404068\n 1924  9795.339130\n 1925  9792.578938\n 1926  9733.284747\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    5274.379203\n 182    5251.681119\n 183    5230.383993\n 184    5216.327990\n 185    5204.690210\n ...            ...\n 1922  10675.677784\n 1923  10833.203975\n 1924  10753.265129\n 1925  10725.065365\n 1926  10740.074204\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4788.588490\n 182   4736.506081\n 183   4692.524937\n 184   4690.416713\n 185   4690.879146\n ...           ...\n 1922  8259.229344\n 1923  8310.328812\n 1924  8289.523640\n 1925  8244.216378\n 1926  8178.446807\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4549.374495\n 182   4506.594757\n 183   4466.184775\n 184   4438.023324\n 185   4412.278609\n ...           ...\n 1922  7752.028360\n 1923  7858.638708\n 1924  7845.076602\n 1925  7867.941491\n 1926  7921.227475\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4736.848813\n 182   4696.757331\n 183   4661.632692\n 184   4652.180120\n 185   4649.531839\n ...           ...\n 1922  8161.129311\n 1923  8203.404143\n 1924  8179.454582\n 1925  8235.194396\n 1926  8357.484800\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5023.243076\n 182   4993.849931\n 183   4971.048882\n 184   4982.320324\n 185   4979.528628\n ...           ...\n 1922  8610.948469\n 1923  8673.253503\n 1924  8680.665862\n 1925  8696.056244\n 1926  8718.109274\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3742.601635\n 182   3805.033969\n 183   3853.742010\n 184   3831.513455\n 185   3806.560407\n ...           ...\n 1922  6180.102529\n 1923  6247.673558\n 1924  6162.951924\n 1925  6182.347054\n 1926  6288.692698\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12991.525272\n 182   13003.740494\n 183   12976.232399\n 184   12818.961471\n 185   12673.237787\n ...            ...\n 1922  15276.435855\n 1923  15277.656249\n 1924  15238.948547\n 1925  15364.814019\n 1926  15578.323791\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12997.537488\n 182   13142.386278\n 183   13225.347736\n 184   13106.143909\n 185   12991.112366\n ...            ...\n 1922  15923.284960\n 1923  15865.672041\n 1924  15729.894263\n 1925  15778.212869\n 1926  15932.028713\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11186.387034\n 182   11159.729776\n 183   11110.853567\n 184   10989.395452\n 185   10904.023604\n ...            ...\n 1922  13886.191330\n 1923  13913.892538\n 1924  13916.788371\n 1925  13949.952971\n 1926  13997.275703\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12070.027785\n 182   12084.132624\n 183   12086.198867\n 184   12048.939031\n 185   11978.961752\n ...            ...\n 1922  15409.915251\n 1923  15283.557677\n 1924  15262.610077\n 1925  15277.599872\n 1926  15305.017146\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12713.562350\n 182   12648.430798\n 183   12598.291764\n 184   12597.128293\n 185   12580.605939\n ...            ...\n 1922  15459.959758\n 1923  15497.336239\n 1924  15469.104547\n 1925  15479.961565\n 1926  15509.102383\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11308.186310\n 182   11261.548710\n 183   11207.074652\n 184   11127.001498\n 185   11035.946643\n ...            ...\n 1922  14855.025093\n 1923  14831.001077\n 1924  14701.352719\n 1925  14750.127541\n 1926  14898.727305\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10058.866014\n 182   10014.745550\n 183    9968.420018\n 184    9914.891263\n 185    9848.589496\n ...            ...\n 1922  14007.666051\n 1923  13987.086492\n 1924  13875.083967\n 1925  13828.634557\n 1926  13816.387618\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8995.027092\n 182    8973.440398\n 183    8951.689788\n 184    8929.403714\n 185    8920.478659\n ...            ...\n 1922  11661.100490\n 1923  11665.460989\n 1924  11585.532958\n 1925  11656.294567\n 1926  11807.856210\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11914.951345\n 182   11935.702353\n 183   11923.145803\n 184   11801.784559\n 185   11699.355621\n ...            ...\n 1922  14822.614592\n 1923  14793.752740\n 1924  14684.992007\n 1925  14789.341385\n 1926  15011.391561\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11810.455756\n 182   11839.192651\n 183   11836.814253\n 184   11732.792564\n 185   11649.899914\n ...            ...\n 1922  13093.729529\n 1923  12999.159680\n 1924  13032.227862\n 1925  13107.553213\n 1926  13196.752109\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9170.219278\n 182    9131.324620\n 183    9092.729284\n 184    9055.111728\n 185    9009.709719\n ...            ...\n 1922  10242.254131\n 1923  10237.790710\n 1924  10182.743362\n 1925  10236.142962\n 1926  10348.993009\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9238.105508\n 182    9188.554601\n 183    9124.284413\n 184    9011.931242\n 185    8916.516883\n ...            ...\n 1922  10214.447220\n 1923  10117.943825\n 1924  10093.174254\n 1925  10116.323244\n 1926  10163.419768\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8490.041890\n 182   8452.399249\n 183   8417.402340\n 184   8391.048153\n 185   8338.560465\n ...           ...\n 1922  9593.228069\n 1923  9584.497859\n 1924  9589.951142\n 1925  9561.985107\n 1926  9513.767149\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8974.325881\n 182    8907.415094\n 183    8853.306502\n 184    8841.018410\n 185    8819.154253\n ...            ...\n 1922  10418.214179\n 1923  10481.456316\n 1924  10381.523235\n 1925  10410.204214\n 1926  10506.730389\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9301.858666\n 182    9247.649997\n 183    9193.735371\n 184    9140.781287\n 185    9071.264415\n ...            ...\n 1922  10608.119773\n 1923  10602.855156\n 1924  10601.132303\n 1925  10666.250370\n 1926  10763.896559\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11490.249362\n 182   11383.649060\n 183   11294.842314\n 184   11264.161186\n 185   11215.523843\n ...            ...\n 1922  12560.591326\n 1923  12479.469450\n 1924  12537.538894\n 1925  12556.526487\n 1926  12546.015898\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9363.165118\n 182    9327.625804\n 183    9269.082256\n 184    9135.391548\n 185    9028.011875\n ...            ...\n 1922  10766.936855\n 1923  10822.758095\n 1924  10779.279560\n 1925  10754.707763\n 1926  10742.357384\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11732.708441\n 182   11744.773461\n 183   11733.537933\n 184   11646.187283\n 185   11566.204657\n ...            ...\n 1922  11175.267581\n 1923  11224.011936\n 1924  11044.786558\n 1925  10999.831297\n 1926  11035.782853\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10167.838426\n 182   10111.733587\n 183   10085.014050\n 184   10154.286497\n 185   10170.311176\n ...            ...\n 1922  16679.936897\n 1923  16805.898271\n 1924  16723.520746\n 1925  16798.768341\n 1926  16952.705234\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8373.333126\n 182    8317.680404\n 183    8270.298175\n 184    8249.932889\n 185    8228.168448\n ...            ...\n 1922  12842.227813\n 1923  12826.105847\n 1924  12750.511151\n 1925  12765.070847\n 1926  12829.825822\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7996.333716\n 182    7940.636136\n 183    7886.952633\n 184    7839.848448\n 185    7817.670995\n ...            ...\n 1922  12380.312925\n 1923  12419.348951\n 1924  12292.513724\n 1925  12272.846022\n 1926  12314.021484\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7510.675029\n 182    7476.247358\n 183    7461.515185\n 184    7511.121640\n 185    7525.828469\n ...            ...\n 1922  11024.894898\n 1923  11016.072310\n 1924  10990.377866\n 1925  11058.634175\n 1926  11174.917579\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6814.716726\n 182   6877.514609\n 183   6904.986149\n 184   6817.058305\n 185   6741.684552\n ...           ...\n 1922  9619.344669\n 1923  9568.633876\n 1924  9619.004457\n 1925  9641.010547\n 1926  9645.552578\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7243.554940\n 182   7232.422883\n 183   7210.338436\n 184   7152.476179\n 185   7118.142932\n ...           ...\n 1922  9990.683366\n 1923  9914.801509\n 1924  9903.585600\n 1925  9918.220675\n 1926  9950.592656\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6681.322407\n 182   6591.476641\n 183   6519.743062\n 184   6507.175959\n 185   6480.644603\n ...           ...\n 1922  8999.395912\n 1923  8941.594748\n 1924  9033.666086\n 1925  9032.433622\n 1926  8967.232228\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6611.501300\n 182   6573.633860\n 183   6541.227615\n 184   6526.661268\n 185   6518.276304\n ...           ...\n 1922  8664.315088\n 1923  8651.698048\n 1924  8567.190472\n 1925  8596.167145\n 1926  8688.034217\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10167.838426\n 182   10111.733587\n 183   10085.014050\n 184   10154.286497\n 185   10170.311176\n ...            ...\n 1922  16679.936897\n 1923  16805.898271\n 1924  16723.520746\n 1925  16798.768341\n 1926  16952.705234\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8373.333126\n 182    8317.680404\n 183    8270.298175\n 184    8249.932889\n 185    8228.168448\n ...            ...\n 1922  12842.227813\n 1923  12826.105847\n 1924  12750.511151\n 1925  12765.070847\n 1926  12829.825822\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7996.333716\n 182    7940.636136\n 183    7886.952633\n 184    7839.848448\n 185    7817.670995\n ...            ...\n 1922  12380.312925\n 1923  12419.348951\n 1924  12292.513724\n 1925  12272.846022\n 1926  12314.021484\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7510.675029\n 182    7476.247358\n 183    7461.515185\n 184    7511.121640\n 185    7525.828469\n ...            ...\n 1922  11024.894898\n 1923  11016.072310\n 1924  10990.377866\n 1925  11058.634175\n 1926  11174.917579\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6814.716726\n 182   6877.514609\n 183   6904.986149\n 184   6817.058305\n 185   6741.684552\n ...           ...\n 1922  9619.344669\n 1923  9568.633876\n 1924  9619.004457\n 1925  9641.010547\n 1926  9645.552578\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7243.554940\n 182   7232.422883\n 183   7210.338436\n 184   7152.476179\n 185   7118.142932\n ...           ...\n 1922  9990.683366\n 1923  9914.801509\n 1924  9903.585600\n 1925  9918.220675\n 1926  9950.592656\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6681.322407\n 182   6591.476641\n 183   6519.743062\n 184   6507.175959\n 185   6480.644603\n ...           ...\n 1922  8999.395912\n 1923  8941.594748\n 1924  9033.666086\n 1925  9032.433622\n 1926  8967.232228\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6611.501300\n 182   6573.633860\n 183   6541.227615\n 184   6526.661268\n 185   6518.276304\n ...           ...\n 1922  8664.315088\n 1923  8651.698048\n 1924  8567.190472\n 1925  8596.167145\n 1926  8688.034217\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7880.766744\n 182    7855.047490\n 183    7827.699341\n 184    7791.931936\n 185    7762.663613\n ...            ...\n 1922  16699.701152\n 1923  16699.180958\n 1924  16702.444515\n 1925  16846.913230\n 1926  17109.305906\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6267.223670\n 182    6218.650334\n 183    6174.500876\n 184    6153.217073\n 185    6146.902827\n ...            ...\n 1922  12583.381757\n 1923  12611.360933\n 1924  12610.950466\n 1925  12669.183852\n 1926  12776.392185\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    5615.987906\n 182    5587.998743\n 183    5564.654716\n 184    5565.319953\n 185    5568.707365\n ...            ...\n 1922  10769.670021\n 1923  10799.269364\n 1924  10788.184243\n 1925  10777.089087\n 1926  10765.985552\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4948.018924\n 182   5005.929710\n 183   5057.363319\n 184   5075.318414\n 185   5080.269680\n ...           ...\n 1922  9087.763405\n 1923  9052.388641\n 1924  9036.898728\n 1925  9065.235825\n 1926  9130.173957\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5372.399270\n 182   5335.716305\n 183   5310.708267\n 184   5346.044290\n 185   5369.285649\n ...           ...\n 1922  9538.203766\n 1923  9534.576070\n 1924  9544.189564\n 1925  9545.271184\n 1926  9539.227623\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5364.851755\n 182   5330.167279\n 183   5298.611837\n 184   5283.229396\n 185   5266.030569\n ...           ...\n 1922  9371.080749\n 1923  9348.830091\n 1924  9341.726055\n 1925  9324.936165\n 1926  9300.057377\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3989.983304\n 182   4008.455455\n 183   4014.815579\n 184   3958.572394\n 185   3919.109801\n ...           ...\n 1922  6914.388347\n 1923  6905.714230\n 1924  6895.155590\n 1925  6884.587107\n 1926  6874.010402\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3440.301783\n 182   3436.047121\n 183   3427.788665\n 184   3398.835841\n 185   3379.407453\n ...           ...\n 1922  6017.082337\n 1923  5962.143190\n 1924  5933.722633\n 1925  5951.394827\n 1926  6007.560231\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3227.610310\n 182   3252.656555\n 183   3273.056937\n 184   3269.444298\n 185   3265.226331\n ...           ...\n 1922  5760.509534\n 1923  5701.100284\n 1924  5645.518943\n 1925  5649.670323\n 1926  5703.705991\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3414.258114\n 182   3446.658315\n 183   3466.733432\n 184   3423.104020\n 185   3380.679858\n ...           ...\n 1922  5808.004049\n 1923  5885.766665\n 1924  5869.855882\n 1925  5828.878232\n 1926  5766.966616\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3509.128181\n 182   3508.880701\n 183   3506.036779\n 184   3489.772652\n 185   3475.472796\n ...           ...\n 1922  5712.236525\n 1923  5773.209573\n 1924  5780.236686\n 1925  5763.356568\n 1926  5726.510925\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3545.049988\n 182   3555.470981\n 183   3561.134426\n 184   3542.207586\n 185   3515.112305\n ...           ...\n 1922  6000.219056\n 1923  6041.561508\n 1924  5929.686341\n 1925  5921.333889\n 1926  5999.435846\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3334.631275\n 182   3332.363382\n 183   3326.524938\n 184   3302.231423\n 185   3284.286988\n ...           ...\n 1922  5481.702297\n 1923  5484.269549\n 1924  5373.363487\n 1925  5368.258539\n 1926  5451.510748\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3206.052965\n 182   3209.101856\n 183   3209.771445\n 184   3198.143167\n 185   3184.547700\n ...           ...\n 1922  5154.059486\n 1923  5252.887584\n 1924  5189.943695\n 1925  5162.812128\n 1926  5165.588328\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3078.736083\n 182   3099.765569\n 183   3114.417294\n 184   3096.104354\n 185   3078.243164\n ...           ...\n 1922  4988.034984\n 1923  5118.801268\n 1924  4952.551255\n 1925  4915.395644\n 1926  4986.050000\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3240.381378\n 182   3238.102271\n 183   3226.194379\n 184   3164.518299\n 185   3131.266953\n ...           ...\n 1922  5148.651017\n 1923  5162.456666\n 1924  5151.666312\n 1925  5171.611733\n 1926  5217.225370\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3207.469820\n 182   3185.857925\n 183   3172.246401\n 184   3199.986308\n 185   3212.303649\n ...           ...\n 1922  5097.427078\n 1923  5072.706740\n 1924  5155.802585\n 1925  5176.343924\n 1926  5144.644429\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3514.743152\n 182   3467.174225\n 183   3420.026653\n 184   3375.056929\n 185   3345.205475\n ...           ...\n 1922  5445.131781\n 1923  5417.819591\n 1924  5442.535861\n 1925  5479.800621\n 1926  5527.544939\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3258.035797\n 182   3290.417350\n 183   3310.041060\n 184   3263.723454\n 185   3227.835902\n ...           ...\n 1922  5088.134391\n 1923  5147.528367\n 1924  5098.133029\n 1925  5115.308725\n 1926  5188.079559\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2844.184913\n 182   2856.521733\n 183   2856.095939\n 184   2789.704167\n 185   2745.536250\n ...           ...\n 1922  4468.315800\n 1923  4446.880236\n 1924  4454.774625\n 1925  4458.690391\n 1926  4459.283510\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2822.760351\n 182   2814.432855\n 183   2803.398794\n 184   2778.375334\n 185   2759.398434\n ...           ...\n 1922  4417.654711\n 1923  4440.074017\n 1924  4461.530198\n 1925  4475.034924\n 1926  4481.899192\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2792.245824\n 182   2814.574075\n 183   2823.709390\n 184   2764.654525\n 185   2718.296761\n ...           ...\n 1922  4431.276012\n 1923  4405.764738\n 1924  4319.755273\n 1925  4335.583781\n 1926  4436.459727\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2975.434333\n 182   2944.463693\n 183   2922.356624\n 184   2946.062601\n 185   2953.135757\n ...           ...\n 1922  4749.674154\n 1923  4723.253220\n 1924  4697.797985\n 1925  4655.238457\n 1926  4598.394707\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3122.575441\n 182   3052.298223\n 183   3002.883475\n 184   3061.300348\n 185   3093.258101\n ...           ...\n 1922  4958.109799\n 1923  4919.870134\n 1924  4889.224482\n 1925  4908.085790\n 1926  4968.291598\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3030.855359\n 182   3011.888499\n 183   2994.972585\n 184   2988.657374\n 185   2992.171436\n ...           ...\n 1922  4824.623017\n 1923  4836.788909\n 1924  4798.828944\n 1925  4828.588983\n 1926  4914.903693\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2958.952548\n 182   2943.976111\n 183   2929.428834\n 184   2917.099748\n 185   2894.183255\n ...           ...\n 1922  4591.364219\n 1923  4564.237456\n 1924  4531.457819\n 1925  4548.752676\n 1926  4607.865997\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2948.918973\n 182   2923.943671\n 183   2897.231509\n 184   2861.542056\n 185   2835.225091\n ...           ...\n 1922  4399.111895\n 1923  4354.685006\n 1924  4430.382364\n 1925  4477.680439\n 1926  4501.261563\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2960.634981\n 182   2923.663963\n 183   2896.528720\n 184   2920.231544\n 185   2935.014288\n ...           ...\n 1922  4452.265748\n 1923  4492.225996\n 1924  4397.881411\n 1925  4369.497096\n 1926  4396.197853\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2852.853874\n 182   2848.529025\n 183   2853.398635\n 184   2905.791543\n 185   2926.885366\n ...           ...\n 1922  4414.508887\n 1923  4476.258274\n 1924  4452.856948\n 1925  4427.156802\n 1926  4399.536852\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3078.692341\n 182   3060.397734\n 183   3044.478749\n 184   3040.838613\n 185   3035.685843\n ...           ...\n 1922  4590.695081\n 1923  4565.458291\n 1924  4562.935277\n 1925  4586.031150\n 1926  4630.521996\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6547.212991\n 182    6574.655279\n 183    6591.394030\n 184    6552.809522\n 185    6521.782433\n ...            ...\n 1922  13735.629459\n 1923  13617.264169\n 1924  13558.561138\n 1925  13623.357110\n 1926  13791.290191\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    5534.029097\n 182    5538.688875\n 183    5538.481566\n 184    5513.117794\n 185    5487.146404\n ...            ...\n 1922  10541.039092\n 1923  10465.900405\n 1924  10569.535974\n 1925  10614.051724\n 1926  10609.195032\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5327.836545\n 182   5401.122331\n 183   5456.359102\n 184   5418.306128\n 185   5383.273787\n ...           ...\n 1922  9593.338048\n 1923  9579.071631\n 1924  9666.939860\n 1925  9703.644841\n 1926  9697.622114\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5143.099954\n 182   5176.373139\n 183   5198.727812\n 184   5164.648098\n 185   5145.234102\n ...           ...\n 1922  9114.020301\n 1923  9108.250567\n 1924  9085.464940\n 1925  9045.007683\n 1926  8989.792405\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5144.679433\n 182   5141.961188\n 183   5132.751308\n 184   5089.988193\n 185   5051.455108\n ...           ...\n 1922  8617.087783\n 1923  8535.237244\n 1924  8569.757423\n 1925  8698.254642\n 1926  8905.234440\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3676.690800\n 182   3621.809499\n 183   3570.593317\n 184   3538.320993\n 185   3507.557556\n ...           ...\n 1922  5811.856125\n 1923  5904.074968\n 1924  5911.119420\n 1925  5895.964867\n 1926  5862.271369\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2931.249101\n 182   2936.264600\n 183   2940.524029\n 184   2940.875567\n 185   2943.344382\n ...           ...\n 1922  4689.301703\n 1923  4681.341455\n 1924  4691.353436\n 1925  4733.828361\n 1926  4803.413904\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2807.038463\n 182   2809.373141\n 183   2816.468414\n 184   2848.169723\n 185   2868.834565\n ...           ...\n 1922  4302.091634\n 1923  4276.649838\n 1924  4312.690825\n 1925  4372.677961\n 1926  4452.663125\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2713.157303\n 182   2732.143443\n 183   2745.616831\n 184   2730.596524\n 185   2718.599225\n ...           ...\n 1922  4134.680635\n 1923  4158.564040\n 1924  4175.809627\n 1925  4185.099832\n 1926  4187.746296\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2650.255172\n 182   2659.902728\n 183   2665.225302\n 184   2648.193391\n 185   2630.101054\n ...           ...\n 1922  4021.698136\n 1923  4012.385782\n 1924  3917.977357\n 1925  3914.012119\n 1926  3985.578249\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2487.991261\n 182   2436.967068\n 183   2395.667120\n 184   2404.628775\n 185   2416.464751\n ...           ...\n 1922  3624.240940\n 1923  3604.654872\n 1924  3572.791219\n 1925  3591.002690\n 1926  3651.033152\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2456.819790\n 182   2455.780223\n 183   2459.608603\n 184   2488.597879\n 185   2509.575011\n ...           ...\n 1922  3674.174782\n 1923  3689.352063\n 1924  3664.802723\n 1925  3672.684896\n 1926  3707.651437\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2673.480807\n 182   2631.143357\n 183   2601.561364\n 184   2637.908360\n 185   2655.960328\n ...           ...\n 1922  3943.995914\n 1923  3945.993433\n 1924  3901.655157\n 1925  3918.198783\n 1926  3985.586410\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2819.683706\n 182   2819.353943\n 183   2815.778613\n 184   2795.427958\n 185   2778.402231\n ...           ...\n 1922  4135.493948\n 1923  4122.828217\n 1924  4103.553152\n 1925  4134.934144\n 1926  4208.619277\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3071.978322\n 182   3077.010706\n 183   3078.041530\n 184   3058.389538\n 185   3046.296997\n ...           ...\n 1922  4565.421801\n 1923  4566.972984\n 1924  4597.835848\n 1925  4594.564793\n 1926  4562.787647\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2715.089603\n 182   2779.398701\n 183   2823.817558\n 184   2765.429948\n 185   2729.720718\n ...           ...\n 1922  4241.585711\n 1923  4193.622088\n 1924  4171.226436\n 1925  4173.862524\n 1926  4197.403244\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2318.045510\n 182   2323.654381\n 183   2328.399132\n 184   2328.677512\n 185   2328.048530\n ...           ...\n 1922  3442.798207\n 1923  3475.705519\n 1924  3378.095644\n 1925  3368.079329\n 1926  3431.214588\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2217.864391\n 182   2202.799497\n 183   2190.111019\n 184   2189.705494\n 185   2190.358536\n ...           ...\n 1922  3174.048463\n 1923  3166.571231\n 1924  3183.685199\n 1925  3200.814639\n 1926  3217.957000\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2086.312607\n 182   2098.558827\n 183   2106.264317\n 184   2090.500191\n 185   2075.944232\n ...           ...\n 1922  3010.689690\n 1923  3106.361717\n 1924  3032.699195\n 1925  2991.992649\n 1926  2978.808464\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2030.861866\n 182   2043.768053\n 183   2055.920044\n 184   2064.173827\n 185   2064.413277\n ...           ...\n 1922  2844.638489\n 1923  2864.405921\n 1924  2829.313862\n 1925  2822.088791\n 1926  2838.136140\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   18928.731634\n 182   18900.999779\n 183   18864.186490\n 184   18797.707180\n 185   18731.617765\n ...            ...\n 1922  49307.100163\n 1923  49400.729720\n 1924  49137.790326\n 1925  49253.767538\n 1926  49576.939220\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   17025.541284\n 182   16914.270114\n 183   16837.213609\n 184   16871.925010\n 185   16842.213802\n ...            ...\n 1922  38007.208271\n 1923  37940.229634\n 1924  37884.691755\n 1925  38026.511229\n 1926  38263.434537\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15652.768634\n 182   15621.979319\n 183   15575.695054\n 184   15478.793950\n 185   15399.232860\n ...            ...\n 1922  33087.114682\n 1923  33170.195432\n 1924  33055.521190\n 1925  33285.748722\n 1926  33694.612815\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13414.418284\n 182   13370.698526\n 183   13328.371249\n 184   13290.592745\n 185   13238.247960\n ...            ...\n 1922  24937.305997\n 1923  24929.249674\n 1924  24998.377198\n 1925  25048.604462\n 1926  25070.839223\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13755.081511\n 182   13755.390987\n 183   13748.349416\n 184   13717.294423\n 185   13662.698057\n ...            ...\n 1922  24852.142723\n 1923  24913.787578\n 1924  24933.816619\n 1925  25035.195349\n 1926  25172.370858\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13943.540440\n 182   13981.196186\n 183   13996.498721\n 184   13938.780763\n 185   13878.062765\n ...            ...\n 1922  26876.593247\n 1923  27001.322054\n 1924  26797.035438\n 1925  26836.734643\n 1926  27006.633988\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12837.921883\n 182   12730.149202\n 183   12631.411009\n 184   12562.185484\n 185   12510.303742\n ...            ...\n 1922  23761.121517\n 1923  23780.245287\n 1924  23653.876600\n 1925  23637.198471\n 1926  23673.703048\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12291.718553\n 182   12228.535590\n 183   12148.438916\n 184   12013.090792\n 185   11929.784420\n ...            ...\n 1922  19738.179572\n 1923  19796.192074\n 1924  19766.443336\n 1925  19843.885746\n 1926  19966.683905\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10505.429569\n 182   10498.019791\n 183   10461.359161\n 184   10329.145749\n 185   10246.979992\n ...            ...\n 1922  17014.899494\n 1923  17135.865076\n 1924  17002.344874\n 1925  17003.736406\n 1926  17081.380858\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   18928.731634\n 182   18900.999779\n 183   18864.186490\n 184   18797.707180\n 185   18731.617765\n ...            ...\n 1922  49307.100163\n 1923  49400.729720\n 1924  49137.790326\n 1925  49253.767538\n 1926  49576.939220\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   17025.541284\n 182   16914.270114\n 183   16837.213609\n 184   16871.925010\n 185   16842.213802\n ...            ...\n 1922  38007.208271\n 1923  37940.229634\n 1924  37884.691755\n 1925  38026.511229\n 1926  38263.434537\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15652.768634\n 182   15621.979319\n 183   15575.695054\n 184   15478.793950\n 185   15399.232860\n ...            ...\n 1922  33087.114682\n 1923  33170.195432\n 1924  33055.521190\n 1925  33285.748722\n 1926  33694.612815\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13414.418284\n 182   13370.698526\n 183   13328.371249\n 184   13290.592745\n 185   13238.247960\n ...            ...\n 1922  24937.305997\n 1923  24929.249674\n 1924  24998.377198\n 1925  25048.604462\n 1926  25070.839223\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13755.081511\n 182   13755.390987\n 183   13748.349416\n 184   13717.294423\n 185   13662.698057\n ...            ...\n 1922  24852.142723\n 1923  24913.787578\n 1924  24933.816619\n 1925  25035.195349\n 1926  25172.370858\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13943.540440\n 182   13981.196186\n 183   13996.498721\n 184   13938.780763\n 185   13878.062765\n ...            ...\n 1922  26876.593247\n 1923  27001.322054\n 1924  26797.035438\n 1925  26836.734643\n 1926  27006.633988\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12837.921883\n 182   12730.149202\n 183   12631.411009\n 184   12562.185484\n 185   12510.303742\n ...            ...\n 1922  23761.121517\n 1923  23780.245287\n 1924  23653.876600\n 1925  23637.198471\n 1926  23673.703048\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12291.718553\n 182   12228.535590\n 183   12148.438916\n 184   12013.090792\n 185   11929.784420\n ...            ...\n 1922  19738.179572\n 1923  19796.192074\n 1924  19766.443336\n 1925  19843.885746\n 1926  19966.683905\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10505.429569\n 182   10498.019791\n 183   10461.359161\n 184   10329.145749\n 185   10246.979992\n ...            ...\n 1922  17014.899494\n 1923  17135.865076\n 1924  17002.344874\n 1925  17003.736406\n 1926  17081.380858\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5188.913131\n 182   5189.533464\n 183   5182.770925\n 184   5160.057720\n 185   5135.550864\n ...           ...\n 1922  8630.387094\n 1923  8616.983782\n 1924  8577.390979\n 1925  8635.081931\n 1926  8715.366764\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3888.887411\n 182   3900.026790\n 183   3900.617476\n 184   3878.417754\n 185   3863.539040\n ...           ...\n 1922  6249.510051\n 1923  6221.797854\n 1924  6215.313637\n 1925  6314.521881\n 1926  6431.248874\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3440.891179\n 182   3421.287750\n 183   3415.398496\n 184   3439.138667\n 185   3438.849973\n ...           ...\n 1922  5374.066701\n 1923  5395.324341\n 1924  5325.009632\n 1925  5315.572181\n 1926  5335.271448\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3268.106317\n 182   3254.486616\n 183   3238.759878\n 184   3218.480896\n 185   3204.512160\n ...           ...\n 1922  5023.469072\n 1923  4989.107185\n 1924  4975.146642\n 1925  5022.079006\n 1926  5088.518825\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3254.455088\n 182   3263.517146\n 183   3265.827727\n 184   3253.551770\n 185   3243.289544\n ...           ...\n 1922  4976.223431\n 1923  4901.697853\n 1924  4872.056440\n 1925  4922.902728\n 1926  4992.499125\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3544.447333\n 182   3551.542564\n 183   3541.128944\n 184   3492.887512\n 185   3452.702099\n ...           ...\n 1922  5345.679672\n 1923  5314.587850\n 1924  5289.180022\n 1925  5362.459867\n 1926  5452.650840\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3613.211626\n 182   3621.649440\n 183   3617.218729\n 184   3584.985614\n 185   3562.848970\n ...           ...\n 1922  5400.349211\n 1923  5393.949247\n 1924  5376.908842\n 1925  5375.962849\n 1926  5390.572673\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3645.159918\n 182   3636.922532\n 183   3625.100655\n 184   3605.534501\n 185   3588.481659\n ...           ...\n 1922  5577.100414\n 1923  5571.201035\n 1924  5499.864861\n 1925  5489.405945\n 1926  5503.838199\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3186.989014\n 182   3179.364015\n 183   3175.115302\n 184   3178.161043\n 185   3179.943799\n ...           ...\n 1922  4676.573348\n 1923  4669.365496\n 1924  4618.801418\n 1925  4663.418284\n 1926  4744.442800\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2837.109172\n 182   2814.097695\n 183   2793.410506\n 184   2777.744931\n 185   2782.571393\n ...           ...\n 1922  4159.115274\n 1923  4198.312793\n 1924  4143.507869\n 1925  4106.886632\n 1926  4083.914042\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2894.596830\n 182   2858.916206\n 183   2810.163758\n 184   2733.169681\n 185   2694.838227\n ...           ...\n 1922  4031.244548\n 1923  4024.217795\n 1924  3986.103054\n 1925  3999.775102\n 1926  4028.484252\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2631.918287\n 182   2650.246111\n 183   2653.172522\n 184   2622.824236\n 185   2601.308308\n ...           ...\n 1922  3638.065051\n 1923  3629.279845\n 1924  3654.833033\n 1925  3679.693249\n 1926  3693.224046\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2379.825346\n 182   2381.444166\n 183   2380.531460\n 184   2374.149404\n 185   2371.812068\n ...           ...\n 1922  3264.778338\n 1923  3274.400655\n 1924  3332.289228\n 1925  3339.096355\n 1926  3321.580180\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2540.654485\n 182   2534.953678\n 183   2527.355456\n 184   2515.657875\n 185   2505.721482\n ...           ...\n 1922  3404.502040\n 1923  3407.033365\n 1924  3428.378693\n 1925  3444.130130\n 1926  3451.412274\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2651.810144\n 182   2626.107951\n 183   2617.495932\n 184   2645.807172\n 185   2643.764598\n ...           ...\n 1922  3699.759342\n 1923  3650.298905\n 1924  3616.312794\n 1925  3629.914829\n 1926  3654.819938\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2728.764415\n 182   2716.414642\n 183   2709.340342\n 184   2713.663678\n 185   2723.054508\n ...           ...\n 1922  3804.342375\n 1923  3851.362138\n 1924  3753.682372\n 1925  3756.770251\n 1926  3799.032800\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2710.642579\n 182   2677.608650\n 183   2655.759979\n 184   2658.077016\n 185   2664.953318\n ...           ...\n 1922  3551.975926\n 1923  3582.034425\n 1924  3590.867758\n 1925  3603.903224\n 1926  3610.979281\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2467.741261\n 182   2449.355793\n 183   2435.403162\n 184   2431.027658\n 185   2417.280690\n ...           ...\n 1922  3285.960308\n 1923  3274.718036\n 1924  3311.714722\n 1925  3287.824633\n 1926  3241.080187\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2309.272860\n 182   2292.202729\n 183   2284.627632\n 184   2297.566518\n 185   2283.175187\n ...           ...\n 1922  2913.472165\n 1923  2915.792800\n 1924  2959.009341\n 1925  2962.339723\n 1926  2943.269218\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2122.385998\n 182   2137.317253\n 183   2147.605305\n 184   2147.861731\n 185   2143.814518\n ...           ...\n 1922  2853.520343\n 1923  2857.450221\n 1924  2832.760038\n 1925  2843.761176\n 1926  2868.058928\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2439.680458\n 182   2407.281368\n 183   2380.794006\n 184   2367.078911\n 185   2364.996781\n ...           ...\n 1922  3168.816541\n 1923  3095.232185\n 1924  3168.765960\n 1925  3208.021637\n 1926  3224.036182\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2540.035598\n 182   2537.003433\n 183   2541.776412\n 184   2563.412375\n 185   2568.865760\n ...           ...\n 1922  3398.893583\n 1923  3390.772007\n 1924  3381.821724\n 1925  3388.268121\n 1926  3401.602493\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2527.475056\n 182   2495.086310\n 183   2485.906083\n 184   2526.867776\n 185   2549.133396\n ...           ...\n 1922  3423.124757\n 1923  3412.515154\n 1924  3392.077180\n 1925  3375.133353\n 1926  3351.498997\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2615.134088\n 182   2633.459894\n 183   2635.118663\n 184   2600.768361\n 185   2588.664690\n ...           ...\n 1922  3517.489830\n 1923  3508.275518\n 1924  3437.709703\n 1925  3446.920116\n 1926  3494.481314\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2586.804669\n 182   2564.430553\n 183   2539.739967\n 184   2510.044654\n 185   2502.853576\n ...           ...\n 1922  3456.921849\n 1923  3387.540704\n 1924  3378.589946\n 1925  3405.334950\n 1926  3452.183668\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2432.223512\n 182   2380.475912\n 183   2362.908730\n 184   2419.188215\n 185   2438.051476\n ...           ...\n 1922  3138.322097\n 1923  3170.881164\n 1924  3146.232805\n 1925  3177.574636\n 1926  3222.052793\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2381.062909\n 182   2377.344569\n 183   2374.048718\n 184   2371.665654\n 185   2354.596486\n ...           ...\n 1922  3119.318821\n 1923  3106.015539\n 1924  3028.083879\n 1925  3013.827479\n 1926  3036.166570\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2198.903993\n 182   2199.833988\n 183   2205.615311\n 184   2221.877912\n 185   2224.737824\n ...           ...\n 1922  2935.797208\n 1923  2954.749623\n 1924  2954.098108\n 1925  2900.949174\n 1926  2838.856366\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1991.938757\n 182   1986.172423\n 183   1978.508654\n 184   1966.745486\n 185   1956.237124\n ...           ...\n 1922  2568.173585\n 1923  2589.317379\n 1924  2549.967158\n 1925  2533.004872\n 1926  2525.617604\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2128.337192\n 182   2141.267564\n 183   2145.547389\n 184   2131.137736\n 185   2121.777889\n ...           ...\n 1922  2686.407240\n 1923  2691.795194\n 1924  2707.822341\n 1925  2697.955247\n 1926  2674.385384\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2207.208570\n 182   2215.478247\n 183   2224.801195\n 184   2236.399733\n 185   2231.553063\n ...           ...\n 1922  2864.628353\n 1923  2888.569653\n 1924  2873.285089\n 1925  2900.693989\n 1926  2942.663221\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2339.222387\n 182   2379.531416\n 183   2401.271230\n 184   2382.892323\n 185   2355.128747\n ...           ...\n 1922  3037.459061\n 1923  3063.283551\n 1924  3049.066695\n 1925  3039.745683\n 1926  3034.201261\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2297.753199\n 182   2306.700979\n 183   2305.100406\n 184   2280.710159\n 185   2270.980883\n ...           ...\n 1922  2887.443888\n 1923  2906.551883\n 1924  2951.856492\n 1925  2971.974740\n 1926  2973.335127\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2126.731185\n 182   2092.293120\n 183   2071.571808\n 184   2080.485493\n 185   2078.774387\n ...           ...\n 1922  2733.889264\n 1923  2748.463366\n 1924  2735.249351\n 1925  2728.331186\n 1926  2726.013137\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2017.324092\n 182   1984.207293\n 183   1966.283560\n 184   1981.184392\n 185   1974.579746\n ...           ...\n 1922  2524.666451\n 1923  2538.945772\n 1924  2513.168625\n 1925  2522.382532\n 1926  2543.267626\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1929.569227\n 182   1932.468508\n 183   1925.664252\n 184   1897.895540\n 185   1893.645560\n ...           ...\n 1922  2384.616459\n 1923  2366.088292\n 1924  2392.518059\n 1925  2429.454583\n 1926  2458.157820\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2074.347091\n 182   2062.586118\n 183   2058.631796\n 184   2071.543716\n 185   2065.986367\n ...           ...\n 1922  2662.902036\n 1923  2665.674195\n 1924  2635.735591\n 1925  2645.686527\n 1926  2666.891777\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2226.514238\n 182   2244.793627\n 183   2249.148112\n 184   2223.417895\n 185   2207.790323\n ...           ...\n 1922  2904.505642\n 1923  2886.094475\n 1924  2799.776924\n 1925  2835.229117\n 1926  2911.091378\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2251.509658\n 182   2235.766975\n 183   2236.058091\n 184   2270.990173\n 185   2287.980047\n ...           ...\n 1922  2774.528824\n 1923  2782.314641\n 1924  2843.271554\n 1925  2874.146791\n 1926  2884.496731\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2384.789209\n 182   2334.370191\n 183   2305.897148\n 184   2324.838302\n 185   2325.569008\n ...           ...\n 1922  2946.633439\n 1923  2940.118028\n 1924  2961.401172\n 1925  2981.290231\n 1926  2993.501297\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2235.236887\n 182   2206.148972\n 183   2187.823833\n 184   2192.751630\n 185   2203.506965\n ...           ...\n 1922  2820.105504\n 1923  2787.569983\n 1924  2805.699087\n 1925  2837.832373\n 1926  2868.077233\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2154.637973\n 182   2102.857073\n 183   2075.131770\n 184   2099.378493\n 185   2104.153897\n ...           ...\n 1922  2638.725498\n 1923  2630.704735\n 1924  2573.601830\n 1925  2607.478373\n 1926  2671.483285\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2122.225022\n 182   2121.144296\n 183   2116.688824\n 184   2104.942226\n 185   2096.728929\n ...           ...\n 1922  2652.592598\n 1923  2644.323294\n 1924  2642.585920\n 1925  2675.846261\n 1926  2714.479125\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1954.221532\n 182   1937.109021\n 183   1922.108802\n 184   1911.672184\n 185   1903.504266\n ...           ...\n 1922  2373.943644\n 1923  2371.388070\n 1924  2399.907798\n 1925  2387.837401\n 1926  2357.913955\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1873.125350\n 182   1815.973613\n 183   1778.026863\n 184   1781.572415\n 185   1797.779799\n ...           ...\n 1922  2308.383366\n 1923  2309.693067\n 1924  2299.554336\n 1925  2302.712119\n 1926  2301.712773\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1955.720324\n 182   1975.301680\n 183   1981.168848\n 184   1957.406562\n 185   1953.369217\n ...           ...\n 1922  2417.850457\n 1923  2374.212880\n 1924  2333.789426\n 1925  2373.150659\n 1926  2439.956735\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1964.246626\n 182   1997.172204\n 183   2015.537549\n 184   2002.445563\n 185   1987.062705\n ...           ...\n 1922  2541.728205\n 1923  2540.229722\n 1924  2483.114823\n 1925  2454.682651\n 1926  2444.842182\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2105.966140\n 182   2060.851108\n 183   2025.446877\n 184   2011.022797\n 185   2005.699264\n ...           ...\n 1922  2571.005402\n 1923  2552.445033\n 1924  2499.510217\n 1925  2517.256929\n 1926  2555.903599\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2210.974317\n 182   2208.570054\n 183   2208.275506\n 184   2212.538988\n 185   2201.869472\n ...           ...\n 1922  2604.166677\n 1923  2583.337951\n 1924  2538.763929\n 1925  2569.773730\n 1926  2623.326773\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1938.546209\n 182   1951.453940\n 183   1949.804892\n 184   1916.705976\n 185   1903.323204\n ...           ...\n 1922  2347.625499\n 1923  2312.576561\n 1924  2330.642533\n 1925  2339.613807\n 1926  2340.723084\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1894.609357\n 182   1871.485241\n 183   1861.443113\n 184   1879.664573\n 185   1884.738308\n ...           ...\n 1922  2301.104922\n 1923  2296.889284\n 1924  2257.500237\n 1925  2281.096969\n 1926  2325.019860\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1779.223004\n 182   1820.799502\n 183   1839.798738\n 184   1810.019883\n 185   1795.403224\n ...           ...\n 1922  2243.603449\n 1923  2250.549751\n 1924  2198.619353\n 1925  2219.470758\n 1926  2264.704566\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1955.290871\n 182   1932.174009\n 183   1913.701725\n 184   1905.264036\n 185   1896.818382\n ...           ...\n 1922  2271.465132\n 1923  2258.551187\n 1924  2280.788402\n 1925  2298.831733\n 1926  2308.985844\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2026.132972\n 182   2002.357431\n 183   1987.867154\n 184   1993.437656\n 185   2007.114861\n ...           ...\n 1922  2424.274911\n 1923  2460.686071\n 1924  2439.077011\n 1925  2462.259575\n 1926  2499.372053\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2046.669046\n 182   2042.910721\n 183   2038.309314\n 184   2031.886431\n 185   2023.938463\n ...           ...\n 1922  2497.695063\n 1923  2471.708332\n 1924  2463.680555\n 1925  2466.849758\n 1926  2474.209916\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1959.593605\n 182   1979.175402\n 183   1994.535131\n 184   2000.773097\n 185   2001.953497\n ...           ...\n 1922  2455.911644\n 1923  2487.547158\n 1924  2500.413319\n 1925  2504.883463\n 1926  2502.218788\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13415.514602\n 182   13445.775690\n 183   13453.368214\n 184   13386.910095\n 185   13331.014384\n ...            ...\n 1922  34921.735881\n 1923  35015.831974\n 1924  34572.352175\n 1925  34618.093725\n 1926  34952.542032\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11829.528738\n 182   11788.951621\n 183   11751.175871\n 184   11722.551251\n 185   11669.587982\n ...            ...\n 1922  27066.340932\n 1923  27124.336224\n 1924  27018.260466\n 1925  27089.352469\n 1926  27254.262113\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11185.734147\n 182   11230.391909\n 183   11247.224596\n 184   11173.162037\n 185   11103.477565\n ...            ...\n 1922  25048.395352\n 1923  25058.023623\n 1924  24973.609481\n 1925  25101.050793\n 1926  25329.548750\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11793.284904\n 182   11774.698210\n 183   11724.667171\n 184   11571.917938\n 185   11463.030079\n ...            ...\n 1922  24691.427704\n 1923  24750.108742\n 1924  24508.962278\n 1925  24514.944185\n 1926  24671.317661\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11739.799246\n 182   11740.534629\n 183   11719.529600\n 184   11627.505892\n 185   11546.639384\n ...            ...\n 1922  23955.040888\n 1923  23894.125797\n 1924  23848.209130\n 1925  23932.180232\n 1926  24086.921329\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12114.593261\n 182   12192.687012\n 183   12236.085362\n 184   12166.145407\n 185   12082.432528\n ...            ...\n 1922  24286.214176\n 1923  24388.569949\n 1924  24214.230302\n 1925  24183.620430\n 1926  24235.949145\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11155.212025\n 182   11089.894484\n 183   11037.379751\n 184   11026.687522\n 185   10978.494190\n ...            ...\n 1922  20487.064713\n 1923  20490.597429\n 1924  20534.063338\n 1925  20516.373721\n 1926  20456.458828\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10179.854240\n 182   10141.080047\n 183   10096.036199\n 184   10030.511481\n 185    9977.144930\n ...            ...\n 1922  18403.556876\n 1923  18390.849538\n 1924  18416.277568\n 1925  18432.883749\n 1926  18426.215314\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8488.853626\n 182    8399.223586\n 183    8338.654358\n 184    8373.017121\n 185    8385.044528\n ...            ...\n 1922  15347.665103\n 1923  15370.823286\n 1924  15307.057504\n 1925  15294.983399\n 1926  15307.994529\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8721.327093\n 182    8752.360864\n 183    8759.162255\n 184    8686.804540\n 185    8616.431272\n ...            ...\n 1922  15478.891273\n 1923  15464.624700\n 1924  15605.615862\n 1925  15623.047410\n 1926  15552.026641\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8801.892113\n 182    8738.292819\n 183    8681.866417\n 184    8648.871465\n 185    8625.246624\n ...            ...\n 1922  15249.212289\n 1923  15097.625586\n 1924  15032.778995\n 1925  15097.185795\n 1926  15228.106366\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8623.402171\n 182    8663.088663\n 183    8685.896888\n 184    8653.569443\n 185    8616.051408\n ...            ...\n 1922  14941.387638\n 1923  14845.274158\n 1924  14778.305093\n 1925  14761.765809\n 1926  14771.439650\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8188.334823\n 182    8220.637998\n 183    8227.457873\n 184    8151.032297\n 185    8077.588217\n ...            ...\n 1922  14215.118960\n 1923  14196.268288\n 1924  14207.558692\n 1925  14270.559444\n 1926  14355.469923\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8764.691816\n 182    8755.074880\n 183    8722.619009\n 184    8615.555948\n 185    8539.594669\n ...            ...\n 1922  14260.879701\n 1923  14184.416836\n 1924  14095.422518\n 1925  14209.454183\n 1926  14427.168478\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7817.757073\n 182    7774.701629\n 183    7741.172239\n 184    7738.761291\n 185    7737.945649\n ...            ...\n 1922  13089.855577\n 1923  13086.045114\n 1924  13127.481351\n 1925  13150.641036\n 1926  13154.297070\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7345.780297\n 182    7335.993502\n 183    7308.842079\n 184    7224.966202\n 185    7156.836538\n ...            ...\n 1922  11627.188025\n 1923  11585.713805\n 1924  11566.362796\n 1925  11584.211460\n 1926  11618.007850\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8802.530329\n 182    8810.246559\n 183    8802.324636\n 184    8743.318080\n 185    8668.545560\n ...            ...\n 1922  14785.135383\n 1923  14834.551170\n 1924  14857.417331\n 1925  14885.963935\n 1926  14910.318397\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10437.293229\n 182   10416.544741\n 183   10379.053638\n 184   10286.869990\n 185   10214.420599\n ...            ...\n 1922  15636.774237\n 1923  15634.706636\n 1924  15520.834161\n 1925  15543.770458\n 1926  15640.902852\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9615.395516\n 182    9552.560334\n 183    9500.808220\n 184    9485.260795\n 185    9451.161006\n ...            ...\n 1922  14968.400697\n 1923  15034.100944\n 1924  14976.558015\n 1925  15044.486976\n 1926  15174.374454\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9215.875031\n 182    9149.659306\n 183    9101.407510\n 184    9111.837882\n 185    9117.881574\n ...            ...\n 1922  14381.580655\n 1923  14477.447817\n 1924  14285.075342\n 1925  14305.792785\n 1926  14451.953750\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7019.458886\n 182    7071.617405\n 183    7094.232886\n 184    7020.341114\n 185    6971.172835\n ...            ...\n 1922  11029.427806\n 1923  11029.335032\n 1924  11038.114704\n 1925  11050.680067\n 1926  11059.553555\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7677.885317\n 182    7737.453865\n 183    7762.320564\n 184    7673.827885\n 185    7607.264085\n ...            ...\n 1922  12073.952604\n 1923  12148.576396\n 1924  12058.266421\n 1925  12031.623193\n 1926  12043.519844\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8190.852015\n 182    8155.172832\n 183    8114.319856\n 184    8056.565823\n 185    8009.774345\n ...            ...\n 1922  12392.193690\n 1923  12452.899807\n 1924  12452.467357\n 1925  12505.003724\n 1926  12574.194142\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7715.388877\n 182    7760.299159\n 183    7772.066526\n 184    7675.567037\n 185    7600.995370\n ...            ...\n 1922  12042.823040\n 1923  11983.540910\n 1924  12063.481567\n 1925  12111.281948\n 1926  12132.017460\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7981.936654\n 182    8000.218659\n 183    8003.179029\n 184    7956.088719\n 185    7924.949145\n ...            ...\n 1922  12368.691191\n 1923  12421.168244\n 1924  12360.144255\n 1925  12328.111512\n 1926  12311.836016\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7573.907648\n 182    7546.819191\n 183    7513.465025\n 184    7459.642879\n 185    7430.148150\n ...            ...\n 1922  11467.618705\n 1923  11470.129556\n 1924  11466.432586\n 1925  11549.755461\n 1926  11673.234298\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7082.883498\n 182    6985.753961\n 183    6893.908830\n 184    6819.326091\n 185    6765.477369\n ...            ...\n 1922  11109.582606\n 1923  11062.589822\n 1924  11077.641590\n 1925  11095.850005\n 1926  11108.946876\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6745.084634\n 182    6620.588414\n 183    6532.179382\n 184    6561.655162\n 185    6573.581960\n ...            ...\n 1922  10331.155579\n 1923  10313.027869\n 1924  10231.875723\n 1925  10251.596475\n 1926  10332.715820\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7852.938583\n 182    7789.226133\n 183    7729.871034\n 184    7684.749948\n 185    7635.234416\n ...            ...\n 1922  11605.911636\n 1923  11624.620016\n 1924  11498.723551\n 1925  11534.855892\n 1926  11661.783201\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7867.097396\n 182    7758.064634\n 183    7677.773193\n 184    7691.370064\n 185    7687.216447\n ...            ...\n 1922  11866.991833\n 1923  11849.036479\n 1924  11797.337616\n 1925  11852.194760\n 1926  11960.986837\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7707.630391\n 182    7699.886187\n 183    7697.770000\n 184    7714.038667\n 185    7716.546396\n ...            ...\n 1922  11667.464633\n 1923  11626.796116\n 1924  11483.142076\n 1925  11480.071829\n 1926  11559.271365\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7766.887483\n 182    7818.468632\n 183    7837.534882\n 184    7750.385796\n 185    7696.335829\n ...            ...\n 1922  11876.436516\n 1923  11805.820802\n 1924  11810.525348\n 1925  11892.162181\n 1926  12006.378400\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6478.571297\n 182   6456.017366\n 183   6431.578784\n 184   6400.983680\n 185   6379.759216\n ...           ...\n 1922  9827.251078\n 1923  9828.501097\n 1924  9823.540870\n 1925  9852.630908\n 1926  9899.281991\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6640.759829\n 182    6622.890877\n 183    6616.746264\n 184    6648.901156\n 185    6661.712364\n ...            ...\n 1922  10472.430839\n 1923  10435.592356\n 1924  10406.687053\n 1925  10438.310518\n 1926  10491.570079\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6864.992227\n 182    6807.160070\n 183    6762.602600\n 184    6761.409108\n 185    6753.034699\n ...            ...\n 1922  10341.793506\n 1923  10281.320651\n 1924  10171.959540\n 1925  10207.605340\n 1926  10324.648828\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7078.130513\n 182    7133.628497\n 183    7153.953972\n 184    7059.382585\n 185    6980.555905\n ...            ...\n 1922  10380.968077\n 1923  10384.115231\n 1924  10302.972554\n 1925  10337.206211\n 1926  10441.004883\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13415.514602\n 182   13445.775690\n 183   13453.368214\n 184   13386.910095\n 185   13331.014384\n ...            ...\n 1922  34921.735881\n 1923  35015.831974\n 1924  34572.352175\n 1925  34618.093725\n 1926  34952.542032\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11829.528738\n 182   11788.951621\n 183   11751.175871\n 184   11722.551251\n 185   11669.587982\n ...            ...\n 1922  27066.340932\n 1923  27124.336224\n 1924  27018.260466\n 1925  27089.352469\n 1926  27254.262113\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11185.734147\n 182   11230.391909\n 183   11247.224596\n 184   11173.162037\n 185   11103.477565\n ...            ...\n 1922  25048.395352\n 1923  25058.023623\n 1924  24973.609481\n 1925  25101.050793\n 1926  25329.548750\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11793.284904\n 182   11774.698210\n 183   11724.667171\n 184   11571.917938\n 185   11463.030079\n ...            ...\n 1922  24691.427704\n 1923  24750.108742\n 1924  24508.962278\n 1925  24514.944185\n 1926  24671.317661\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11739.799246\n 182   11740.534629\n 183   11719.529600\n 184   11627.505892\n 185   11546.639384\n ...            ...\n 1922  23955.040888\n 1923  23894.125797\n 1924  23848.209130\n 1925  23932.180232\n 1926  24086.921329\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12114.593261\n 182   12192.687012\n 183   12236.085362\n 184   12166.145407\n 185   12082.432528\n ...            ...\n 1922  24286.214176\n 1923  24388.569949\n 1924  24214.230302\n 1925  24183.620430\n 1926  24235.949145\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11155.212025\n 182   11089.894484\n 183   11037.379751\n 184   11026.687522\n 185   10978.494190\n ...            ...\n 1922  20487.064713\n 1923  20490.597429\n 1924  20534.063338\n 1925  20516.373721\n 1926  20456.458828\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10179.854240\n 182   10141.080047\n 183   10096.036199\n 184   10030.511481\n 185    9977.144930\n ...            ...\n 1922  18403.556876\n 1923  18390.849538\n 1924  18416.277568\n 1925  18432.883749\n 1926  18426.215314\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8488.853626\n 182    8399.223586\n 183    8338.654358\n 184    8373.017121\n 185    8385.044528\n ...            ...\n 1922  15347.665103\n 1923  15370.823286\n 1924  15307.057504\n 1925  15294.983399\n 1926  15307.994529\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8721.327093\n 182    8752.360864\n 183    8759.162255\n 184    8686.804540\n 185    8616.431272\n ...            ...\n 1922  15478.891273\n 1923  15464.624700\n 1924  15605.615862\n 1925  15623.047410\n 1926  15552.026641\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8801.892113\n 182    8738.292819\n 183    8681.866417\n 184    8648.871465\n 185    8625.246624\n ...            ...\n 1922  15249.212289\n 1923  15097.625586\n 1924  15032.778995\n 1925  15097.185795\n 1926  15228.106366\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8623.402171\n 182    8663.088663\n 183    8685.896888\n 184    8653.569443\n 185    8616.051408\n ...            ...\n 1922  14941.387638\n 1923  14845.274158\n 1924  14778.305093\n 1925  14761.765809\n 1926  14771.439650\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8188.334823\n 182    8220.637998\n 183    8227.457873\n 184    8151.032297\n 185    8077.588217\n ...            ...\n 1922  14215.118960\n 1923  14196.268288\n 1924  14207.558692\n 1925  14270.559444\n 1926  14355.469923\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8764.691816\n 182    8755.074880\n 183    8722.619009\n 184    8615.555948\n 185    8539.594669\n ...            ...\n 1922  14260.879701\n 1923  14184.416836\n 1924  14095.422518\n 1925  14209.454183\n 1926  14427.168478\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7817.757073\n 182    7774.701629\n 183    7741.172239\n 184    7738.761291\n 185    7737.945649\n ...            ...\n 1922  13089.855577\n 1923  13086.045114\n 1924  13127.481351\n 1925  13150.641036\n 1926  13154.297070\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7345.780297\n 182    7335.993502\n 183    7308.842079\n 184    7224.966202\n 185    7156.836538\n ...            ...\n 1922  11627.188025\n 1923  11585.713805\n 1924  11566.362796\n 1925  11584.211460\n 1926  11618.007850\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8802.530329\n 182    8810.246559\n 183    8802.324636\n 184    8743.318080\n 185    8668.545560\n ...            ...\n 1922  14785.135383\n 1923  14834.551170\n 1924  14857.417331\n 1925  14885.963935\n 1926  14910.318397\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10437.293229\n 182   10416.544741\n 183   10379.053638\n 184   10286.869990\n 185   10214.420599\n ...            ...\n 1922  15636.774237\n 1923  15634.706636\n 1924  15520.834161\n 1925  15543.770458\n 1926  15640.902852\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9615.395516\n 182    9552.560334\n 183    9500.808220\n 184    9485.260795\n 185    9451.161006\n ...            ...\n 1922  14968.400697\n 1923  15034.100944\n 1924  14976.558015\n 1925  15044.486976\n 1926  15174.374454\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9215.875031\n 182    9149.659306\n 183    9101.407510\n 184    9111.837882\n 185    9117.881574\n ...            ...\n 1922  14381.580655\n 1923  14477.447817\n 1924  14285.075342\n 1925  14305.792785\n 1926  14451.953750\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7019.458886\n 182    7071.617405\n 183    7094.232886\n 184    7020.341114\n 185    6971.172835\n ...            ...\n 1922  11029.427806\n 1923  11029.335032\n 1924  11038.114704\n 1925  11050.680067\n 1926  11059.553555\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7677.885317\n 182    7737.453865\n 183    7762.320564\n 184    7673.827885\n 185    7607.264085\n ...            ...\n 1922  12073.952604\n 1923  12148.576396\n 1924  12058.266421\n 1925  12031.623193\n 1926  12043.519844\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8190.852015\n 182    8155.172832\n 183    8114.319856\n 184    8056.565823\n 185    8009.774345\n ...            ...\n 1922  12392.193690\n 1923  12452.899807\n 1924  12452.467357\n 1925  12505.003724\n 1926  12574.194142\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7715.388877\n 182    7760.299159\n 183    7772.066526\n 184    7675.567037\n 185    7600.995370\n ...            ...\n 1922  12042.823040\n 1923  11983.540910\n 1924  12063.481567\n 1925  12111.281948\n 1926  12132.017460\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7981.936654\n 182    8000.218659\n 183    8003.179029\n 184    7956.088719\n 185    7924.949145\n ...            ...\n 1922  12368.691191\n 1923  12421.168244\n 1924  12360.144255\n 1925  12328.111512\n 1926  12311.836016\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7573.907648\n 182    7546.819191\n 183    7513.465025\n 184    7459.642879\n 185    7430.148150\n ...            ...\n 1922  11467.618705\n 1923  11470.129556\n 1924  11466.432586\n 1925  11549.755461\n 1926  11673.234298\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7082.883498\n 182    6985.753961\n 183    6893.908830\n 184    6819.326091\n 185    6765.477369\n ...            ...\n 1922  11109.582606\n 1923  11062.589822\n 1924  11077.641590\n 1925  11095.850005\n 1926  11108.946876\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6745.084634\n 182    6620.588414\n 183    6532.179382\n 184    6561.655162\n 185    6573.581960\n ...            ...\n 1922  10331.155579\n 1923  10313.027869\n 1924  10231.875723\n 1925  10251.596475\n 1926  10332.715820\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7852.938583\n 182    7789.226133\n 183    7729.871034\n 184    7684.749948\n 185    7635.234416\n ...            ...\n 1922  11605.911636\n 1923  11624.620016\n 1924  11498.723551\n 1925  11534.855892\n 1926  11661.783201\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7867.097396\n 182    7758.064634\n 183    7677.773193\n 184    7691.370064\n 185    7687.216447\n ...            ...\n 1922  11866.991833\n 1923  11849.036479\n 1924  11797.337616\n 1925  11852.194760\n 1926  11960.986837\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7707.630391\n 182    7699.886187\n 183    7697.770000\n 184    7714.038667\n 185    7716.546396\n ...            ...\n 1922  11667.464633\n 1923  11626.796116\n 1924  11483.142076\n 1925  11480.071829\n 1926  11559.271365\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7766.887483\n 182    7818.468632\n 183    7837.534882\n 184    7750.385796\n 185    7696.335829\n ...            ...\n 1922  11876.436516\n 1923  11805.820802\n 1924  11810.525348\n 1925  11892.162181\n 1926  12006.378400\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6478.571297\n 182   6456.017366\n 183   6431.578784\n 184   6400.983680\n 185   6379.759216\n ...           ...\n 1922  9827.251078\n 1923  9828.501097\n 1924  9823.540870\n 1925  9852.630908\n 1926  9899.281991\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6640.759829\n 182    6622.890877\n 183    6616.746264\n 184    6648.901156\n 185    6661.712364\n ...            ...\n 1922  10472.430839\n 1923  10435.592356\n 1924  10406.687053\n 1925  10438.310518\n 1926  10491.570079\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6864.992227\n 182    6807.160070\n 183    6762.602600\n 184    6761.409108\n 185    6753.034699\n ...            ...\n 1922  10341.793506\n 1923  10281.320651\n 1924  10171.959540\n 1925  10207.605340\n 1926  10324.648828\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7078.130513\n 182    7133.628497\n 183    7153.953972\n 184    7059.382585\n 185    6980.555905\n ...            ...\n 1922  10380.968077\n 1923  10384.115231\n 1924  10302.972554\n 1925  10337.206211\n 1926  10441.004883\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12991.525272\n 182   13003.740494\n 183   12976.232399\n 184   12818.961471\n 185   12673.237787\n ...            ...\n 1922  15276.435855\n 1923  15277.656249\n 1924  15238.948547\n 1925  15364.814019\n 1926  15578.323791\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12997.537488\n 182   13142.386278\n 183   13225.347736\n 184   13106.143909\n 185   12991.112366\n ...            ...\n 1922  15923.284960\n 1923  15865.672041\n 1924  15729.894263\n 1925  15778.212869\n 1926  15932.028713\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11186.387034\n 182   11159.729776\n 183   11110.853567\n 184   10989.395452\n 185   10904.023604\n ...            ...\n 1922  13886.191330\n 1923  13913.892538\n 1924  13916.788371\n 1925  13949.952971\n 1926  13997.275703\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12070.027785\n 182   12084.132624\n 183   12086.198867\n 184   12048.939031\n 185   11978.961752\n ...            ...\n 1922  15409.915251\n 1923  15283.557677\n 1924  15262.610077\n 1925  15277.599872\n 1926  15305.017146\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12713.562350\n 182   12648.430798\n 183   12598.291764\n 184   12597.128293\n 185   12580.605939\n ...            ...\n 1922  15459.959758\n 1923  15497.336239\n 1924  15469.104547\n 1925  15479.961565\n 1926  15509.102383\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11308.186310\n 182   11261.548710\n 183   11207.074652\n 184   11127.001498\n 185   11035.946643\n ...            ...\n 1922  14855.025093\n 1923  14831.001077\n 1924  14701.352719\n 1925  14750.127541\n 1926  14898.727305\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10058.866014\n 182   10014.745550\n 183    9968.420018\n 184    9914.891263\n 185    9848.589496\n ...            ...\n 1922  14007.666051\n 1923  13987.086492\n 1924  13875.083967\n 1925  13828.634557\n 1926  13816.387618\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8995.027092\n 182    8973.440398\n 183    8951.689788\n 184    8929.403714\n 185    8920.478659\n ...            ...\n 1922  11661.100490\n 1923  11665.460989\n 1924  11585.532958\n 1925  11656.294567\n 1926  11807.856210\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11914.951345\n 182   11935.702353\n 183   11923.145803\n 184   11801.784559\n 185   11699.355621\n ...            ...\n 1922  14822.614592\n 1923  14793.752740\n 1924  14684.992007\n 1925  14789.341385\n 1926  15011.391561\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11810.455756\n 182   11839.192651\n 183   11836.814253\n 184   11732.792564\n 185   11649.899914\n ...            ...\n 1922  13093.729529\n 1923  12999.159680\n 1924  13032.227862\n 1925  13107.553213\n 1926  13196.752109\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9170.219278\n 182    9131.324620\n 183    9092.729284\n 184    9055.111728\n 185    9009.709719\n ...            ...\n 1922  10242.254131\n 1923  10237.790710\n 1924  10182.743362\n 1925  10236.142962\n 1926  10348.993009\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9238.105508\n 182    9188.554601\n 183    9124.284413\n 184    9011.931242\n 185    8916.516883\n ...            ...\n 1922  10214.447220\n 1923  10117.943825\n 1924  10093.174254\n 1925  10116.323244\n 1926  10163.419768\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8490.041890\n 182   8452.399249\n 183   8417.402340\n 184   8391.048153\n 185   8338.560465\n ...           ...\n 1922  9593.228069\n 1923  9584.497859\n 1924  9589.951142\n 1925  9561.985107\n 1926  9513.767149\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8974.325881\n 182    8907.415094\n 183    8853.306502\n 184    8841.018410\n 185    8819.154253\n ...            ...\n 1922  10418.214179\n 1923  10481.456316\n 1924  10381.523235\n 1925  10410.204214\n 1926  10506.730389\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9301.858666\n 182    9247.649997\n 183    9193.735371\n 184    9140.781287\n 185    9071.264415\n ...            ...\n 1922  10608.119773\n 1923  10602.855156\n 1924  10601.132303\n 1925  10666.250370\n 1926  10763.896559\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11490.249362\n 182   11383.649060\n 183   11294.842314\n 184   11264.161186\n 185   11215.523843\n ...            ...\n 1922  12560.591326\n 1923  12479.469450\n 1924  12537.538894\n 1925  12556.526487\n 1926  12546.015898\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9363.165118\n 182    9327.625804\n 183    9269.082256\n 184    9135.391548\n 185    9028.011875\n ...            ...\n 1922  10766.936855\n 1923  10822.758095\n 1924  10779.279560\n 1925  10754.707763\n 1926  10742.357384\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11732.708441\n 182   11744.773461\n 183   11733.537933\n 184   11646.187283\n 185   11566.204657\n ...            ...\n 1922  11175.267581\n 1923  11224.011936\n 1924  11044.786558\n 1925  10999.831297\n 1926  11035.782853\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3373.025023\n 182   3341.435432\n 183   3312.434662\n 184   3296.814709\n 185   3283.159082\n ...           ...\n 1922  5847.253694\n 1923  5814.480493\n 1924  5830.902626\n 1925  5834.813859\n 1926  5828.276927\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2947.096815\n 182   2916.789229\n 183   2892.532646\n 184   2899.551818\n 185   2905.210619\n ...           ...\n 1922  4999.020321\n 1923  4991.598751\n 1924  4916.100006\n 1925  4932.769799\n 1926  5026.411844\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3027.542992\n 182   3001.244100\n 183   2977.318949\n 184   2965.662920\n 185   2964.138057\n ...           ...\n 1922  5230.722035\n 1923  5187.136065\n 1924  5129.399932\n 1925  5159.862911\n 1926  5263.983176\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2451.994559\n 182   2480.280723\n 183   2501.758151\n 184   2488.043344\n 185   2464.950813\n ...           ...\n 1922  4063.277551\n 1923  4094.435166\n 1924  4048.030054\n 1925  4042.576983\n 1926  4071.323984\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2084.041170\n 182   2021.638070\n 183   1978.476575\n 184   2034.768953\n 185   2063.089691\n ...           ...\n 1922  3210.008681\n 1923  3172.244319\n 1924  3249.871217\n 1925  3260.952953\n 1926  3216.461160\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1869.172707\n 182   1866.063395\n 183   1861.222404\n 184   1847.430906\n 185   1828.949846\n ...           ...\n 1922  2906.607718\n 1923  2892.694265\n 1924  2952.547675\n 1925  2988.542238\n 1926  3004.611682\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1710.198630\n 182   1692.407110\n 183   1677.424079\n 184   1676.957253\n 185   1681.934661\n ...           ...\n 1922  2671.360510\n 1923  2634.447727\n 1924  2610.801602\n 1925  2608.769758\n 1926  2624.788539\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1913.740935\n 182   1943.295874\n 183   1962.039617\n 184   1924.903645\n 185   1897.896165\n ...           ...\n 1922  2930.476390\n 1923  2873.553179\n 1924  2918.797508\n 1925  2949.849004\n 1926  2969.047710\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2053.105652\n 182   2056.016817\n 183   2058.495948\n 184   2058.742038\n 185   2056.870938\n ...           ...\n 1922  3324.396132\n 1923  3317.367330\n 1924  3288.595417\n 1925  3292.251080\n 1926  3322.987823\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3373.025023\n 182   3341.435432\n 183   3312.434662\n 184   3296.814709\n 185   3283.159082\n ...           ...\n 1922  5847.253694\n 1923  5814.480493\n 1924  5830.902626\n 1925  5834.813859\n 1926  5828.276927\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2947.096815\n 182   2916.789229\n 183   2892.532646\n 184   2899.551818\n 185   2905.210619\n ...           ...\n 1922  4999.020321\n 1923  4991.598751\n 1924  4916.100006\n 1925  4932.769799\n 1926  5026.411844\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3027.542992\n 182   3001.244100\n 183   2977.318949\n 184   2965.662920\n 185   2964.138057\n ...           ...\n 1922  5230.722035\n 1923  5187.136065\n 1924  5129.399932\n 1925  5159.862911\n 1926  5263.983176\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2451.994559\n 182   2480.280723\n 183   2501.758151\n 184   2488.043344\n 185   2464.950813\n ...           ...\n 1922  4063.277551\n 1923  4094.435166\n 1924  4048.030054\n 1925  4042.576983\n 1926  4071.323984\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2084.041170\n 182   2021.638070\n 183   1978.476575\n 184   2034.768953\n 185   2063.089691\n ...           ...\n 1922  3210.008681\n 1923  3172.244319\n 1924  3249.871217\n 1925  3260.952953\n 1926  3216.461160\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1869.172707\n 182   1866.063395\n 183   1861.222404\n 184   1847.430906\n 185   1828.949846\n ...           ...\n 1922  2906.607718\n 1923  2892.694265\n 1924  2952.547675\n 1925  2988.542238\n 1926  3004.611682\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1710.198630\n 182   1692.407110\n 183   1677.424079\n 184   1676.957253\n 185   1681.934661\n ...           ...\n 1922  2671.360510\n 1923  2634.447727\n 1924  2610.801602\n 1925  2608.769758\n 1926  2624.788539\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   1913.740935\n 182   1943.295874\n 183   1962.039617\n 184   1924.903645\n 185   1897.896165\n ...           ...\n 1922  2930.476390\n 1923  2873.553179\n 1924  2918.797508\n 1925  2949.849004\n 1926  2969.047710\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2053.105652\n 182   2056.016817\n 183   2058.495948\n 184   2058.742038\n 185   2056.870938\n ...           ...\n 1922  3324.396132\n 1923  3317.367330\n 1924  3288.595417\n 1925  3292.251080\n 1926  3322.987823\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3970.152121\n 182   3983.956304\n 183   3991.489201\n 184   3966.607770\n 185   3955.485668\n ...           ...\n 1922  7573.962767\n 1923  7554.695346\n 1924  7642.294659\n 1925  7681.007929\n 1926  7678.895239\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3518.651712\n 182   3601.052544\n 183   3664.000198\n 184   3626.400427\n 185   3599.836467\n ...           ...\n 1922  6688.031289\n 1923  6635.362180\n 1924  6670.673343\n 1925  6659.328352\n 1926  6609.019640\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2739.377498\n 182   2762.365906\n 183   2774.866097\n 184   2733.155958\n 185   2705.052115\n ...           ...\n 1922  4799.349536\n 1923  4837.911917\n 1924  4829.165068\n 1925  4822.687527\n 1926  4818.105140\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2393.445957\n 182   2352.408154\n 183   2320.123352\n 184   2333.080087\n 185   2338.930352\n ...           ...\n 1922  4089.153434\n 1923  4086.987673\n 1924  4099.007220\n 1925  4101.358456\n 1926  4095.635445\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2356.705043\n 182   2335.658042\n 183   2321.854267\n 184   2345.488473\n 185   2359.748904\n ...           ...\n 1922  4049.829173\n 1923  4040.293440\n 1924  3933.368778\n 1925  3899.225576\n 1926  3925.863991\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2679.737037\n 182   2635.400847\n 183   2593.650475\n 184   2565.265386\n 185   2549.731834\n ...           ...\n 1922  4496.225734\n 1923  4553.127086\n 1924  4488.003499\n 1925  4483.173180\n 1926  4528.695275\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2599.194876\n 182   2562.847220\n 183   2537.092761\n 184   2566.091245\n 185   2574.071840\n ...           ...\n 1922  4396.856801\n 1923  4302.800093\n 1924  4273.115664\n 1925  4303.756968\n 1926  4384.777801\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2288.771338\n 182   2259.718289\n 183   2230.225545\n 184   2198.460159\n 185   2179.394792\n ...           ...\n 1922  3600.564337\n 1923  3581.493152\n 1924  3595.532087\n 1925  3593.072194\n 1926  3576.833716\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2312.844587\n 182   2305.122560\n 183   2302.591462\n 184   2326.890652\n 185   2337.884181\n ...           ...\n 1922  3753.129843\n 1923  3731.003930\n 1924  3823.311472\n 1925  3832.575551\n 1926  3772.487956\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2399.878944\n 182   2397.499407\n 183   2390.792022\n 184   2361.715334\n 185   2342.767980\n ...           ...\n 1922  4083.408861\n 1923  4068.875226\n 1924  4060.026076\n 1925  4088.177631\n 1926  4147.229399\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3970.152121\n 182   3983.956304\n 183   3991.489201\n 184   3966.607770\n 185   3955.485668\n ...           ...\n 1922  7573.962767\n 1923  7554.695346\n 1924  7642.294659\n 1925  7681.007929\n 1926  7678.895239\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3518.651712\n 182   3601.052544\n 183   3664.000198\n 184   3626.400427\n 185   3599.836467\n ...           ...\n 1922  6688.031289\n 1923  6635.362180\n 1924  6670.673343\n 1925  6659.328352\n 1926  6609.019640\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2739.377498\n 182   2762.365906\n 183   2774.866097\n 184   2733.155958\n 185   2705.052115\n ...           ...\n 1922  4799.349536\n 1923  4837.911917\n 1924  4829.165068\n 1925  4822.687527\n 1926  4818.105140\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2393.445957\n 182   2352.408154\n 183   2320.123352\n 184   2333.080087\n 185   2338.930352\n ...           ...\n 1922  4089.153434\n 1923  4086.987673\n 1924  4099.007220\n 1925  4101.358456\n 1926  4095.635445\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2356.705043\n 182   2335.658042\n 183   2321.854267\n 184   2345.488473\n 185   2359.748904\n ...           ...\n 1922  4049.829173\n 1923  4040.293440\n 1924  3933.368778\n 1925  3899.225576\n 1926  3925.863991\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2679.737037\n 182   2635.400847\n 183   2593.650475\n 184   2565.265386\n 185   2549.731834\n ...           ...\n 1922  4496.225734\n 1923  4553.127086\n 1924  4488.003499\n 1925  4483.173180\n 1926  4528.695275\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2599.194876\n 182   2562.847220\n 183   2537.092761\n 184   2566.091245\n 185   2574.071840\n ...           ...\n 1922  4396.856801\n 1923  4302.800093\n 1924  4273.115664\n 1925  4303.756968\n 1926  4384.777801\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2288.771338\n 182   2259.718289\n 183   2230.225545\n 184   2198.460159\n 185   2179.394792\n ...           ...\n 1922  3600.564337\n 1923  3581.493152\n 1924  3595.532087\n 1925  3593.072194\n 1926  3576.833716\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2312.844587\n 182   2305.122560\n 183   2302.591462\n 184   2326.890652\n 185   2337.884181\n ...           ...\n 1922  3753.129843\n 1923  3731.003930\n 1924  3823.311472\n 1925  3832.575551\n 1926  3772.487956\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2399.878944\n 182   2397.499407\n 183   2390.792022\n 184   2361.715334\n 185   2342.767980\n ...           ...\n 1922  4083.408861\n 1923  4068.875226\n 1924  4060.026076\n 1925  4088.177631\n 1926  4147.229399\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    5197.975692\n 182    5185.932598\n 183    5173.237584\n 184    5157.173004\n 185    5138.838171\n ...            ...\n 1922  11320.649582\n 1923  11283.912250\n 1924  11220.726242\n 1925  11259.968588\n 1926  11384.751416\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4291.804278\n 182   4320.310074\n 183   4333.028034\n 184   4264.143578\n 185   4226.556463\n ...           ...\n 1922  8610.748806\n 1923  8615.395182\n 1924  8522.641707\n 1925  8560.188406\n 1926  8706.552043\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4174.964141\n 182   4158.133496\n 183   4140.216986\n 184   4116.687976\n 185   4105.557352\n ...           ...\n 1922  7875.756333\n 1923  7908.631919\n 1924  7825.174472\n 1925  7828.753899\n 1926  7905.019997\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4208.436430\n 182   4203.607045\n 183   4197.694658\n 184   4186.184568\n 185   4170.136319\n ...           ...\n 1922  7892.360909\n 1923  7846.068038\n 1924  7715.653147\n 1925  7695.576253\n 1926  7767.645382\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4001.788107\n 182   3970.273490\n 183   3948.271405\n 184   3975.436641\n 185   3984.608367\n ...           ...\n 1922  7348.445234\n 1923  7356.672093\n 1924  7313.830665\n 1925  7292.585837\n 1926  7289.376872\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3813.568011\n 182   3836.684694\n 183   3857.318704\n 184   3865.120545\n 185   3868.235055\n ...           ...\n 1922  7178.443424\n 1923  7269.094720\n 1924  7136.516525\n 1925  7115.413189\n 1926  7187.405297\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   3834.565950\n 182   3770.372401\n 183   3724.770932\n 184   3775.266136\n 185   3811.097307\n ...           ...\n 1922  6903.635017\n 1923  6896.944440\n 1924  6947.940346\n 1925  6961.973575\n 1926  6945.138348\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2960.273187\n 182   2932.633318\n 183   2906.068667\n 184   2885.061479\n 185   2870.857438\n ...           ...\n 1922  5136.348845\n 1923  5163.336976\n 1924  5161.936598\n 1925  5196.974162\n 1926  5262.441965\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2469.428464\n 182   2452.394064\n 183   2441.413841\n 184   2461.725772\n 185   2466.614336\n ...           ...\n 1922  4298.143604\n 1923  4279.591088\n 1924  4278.073119\n 1925  4270.290598\n 1926  4257.276394\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2510.042951\n 182   2473.018128\n 183   2449.182731\n 184   2493.519383\n 185   2525.762288\n ...           ...\n 1922  4193.227367\n 1923  4213.306648\n 1924  4159.614021\n 1925  4136.048845\n 1926  4137.643858\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2424.734525\n 182   2399.696723\n 183   2382.225711\n 184   2403.865089\n 185   2418.550174\n ...           ...\n 1922  3971.295265\n 1923  3979.921596\n 1924  3935.587892\n 1925  3933.346862\n 1926  3966.258478\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2352.041974\n 182   2348.990311\n 183   2343.341542\n 184   2324.269141\n 185   2310.487770\n ...           ...\n 1922  3900.819721\n 1923  3898.988313\n 1924  3904.723023\n 1925  3921.850257\n 1926  3948.491670\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2390.450085\n 182   2410.064248\n 183   2420.487578\n 184   2383.406349\n 185   2355.848726\n ...           ...\n 1922  3983.749265\n 1923  4036.862905\n 1924  3962.280775\n 1925  3960.509493\n 1926  4019.544373\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2400.011726\n 182   2398.965229\n 183   2400.947615\n 184   2418.585354\n 185   2419.740844\n ...           ...\n 1922  3973.904876\n 1923  3972.183878\n 1924  3948.715560\n 1925  3945.722950\n 1926  3959.830115\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2215.186094\n 182   2212.118050\n 183   2211.862017\n 184   2226.140397\n 185   2247.981774\n ...           ...\n 1922  3818.921769\n 1923  3796.617215\n 1924  3796.078438\n 1925  3810.342660\n 1926  3836.969238\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2222.003054\n 182   2209.604742\n 183   2199.150754\n 184   2198.746364\n 185   2200.610368\n ...           ...\n 1922  3684.229697\n 1923  3741.834429\n 1924  3725.638653\n 1925  3725.939705\n 1926  3740.017672\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2202.009490\n 182   2196.273855\n 183   2186.642270\n 184   2156.873729\n 185   2140.107844\n ...           ...\n 1922  3670.341466\n 1923  3703.693710\n 1924  3711.489107\n 1925  3716.444680\n 1926  3719.028645\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2253.129228\n 182   2268.061436\n 183   2275.640799\n 184   2245.215600\n 185   2226.129475\n ...           ...\n 1922  3791.315836\n 1923  3800.145333\n 1924  3768.307531\n 1925  3792.808034\n 1926  3864.358066\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2308.115120\n 182   2317.721848\n 183   2324.409958\n 184   2316.012633\n 185   2303.833651\n ...           ...\n 1922  3851.229919\n 1923  3829.614322\n 1924  3836.383238\n 1925  3892.123763\n 1926  3988.761705\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2370.810895\n 182   2361.762886\n 183   2352.604534\n 184   2342.875851\n 185   2332.238747\n ...           ...\n 1922  3955.647606\n 1923  3972.821492\n 1924  3938.920008\n 1925  3966.479189\n 1926  4045.365710\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   2669.415486\n 182   2651.071862\n 183   2626.665657\n 184   2570.923859\n 185   2526.972144\n ...           ...\n 1922  4435.194765\n 1923  4445.021787\n 1924  4433.092719\n 1925  4433.109499\n 1926  4443.102554\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12135.328432\n 182   12056.131356\n 183   11980.504254\n 184   11916.539072\n 185   11853.761964\n ...            ...\n 1922  23531.235002\n 1923  23679.804770\n 1924  23465.675982\n 1925  23503.726964\n 1926  23686.381953\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10744.387491\n 182   10767.664708\n 183   10750.127633\n 184   10599.263869\n 185   10470.520010\n ...            ...\n 1922  17861.588939\n 1923  17945.247268\n 1924  17822.281359\n 1925  17897.287749\n 1926  18077.422497\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9203.134466\n 182    9235.558861\n 183    9229.518188\n 184    9097.824958\n 185    8996.033372\n ...            ...\n 1922  15157.619242\n 1923  15180.739634\n 1924  15099.190392\n 1925  15138.692971\n 1926  15235.888206\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8596.850373\n 182    8587.212772\n 183    8559.897802\n 184    8474.836758\n 185    8424.271500\n ...            ...\n 1922  14123.277331\n 1923  14210.918817\n 1924  14158.489524\n 1925  14107.308412\n 1926  14057.728439\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7857.855371\n 182    7852.795138\n 183    7844.917840\n 184    7827.838124\n 185    7799.386435\n ...            ...\n 1922  13349.731010\n 1923  13322.620182\n 1924  13314.990763\n 1925  13316.187765\n 1926  13327.521639\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9825.231295\n 182    9796.412243\n 183    9763.047282\n 184    9714.832351\n 185    9673.991173\n ...            ...\n 1922  15004.021141\n 1923  14908.954181\n 1924  14760.513525\n 1925  14835.262534\n 1926  15034.452070\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9361.043851\n 182    9410.816029\n 183    9425.100917\n 184    9323.460661\n 185    9222.803582\n ...            ...\n 1922  15410.135740\n 1923  15451.644108\n 1924  15227.018477\n 1925  15224.933274\n 1926  15353.250353\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8538.208701\n 182    8517.900234\n 183    8496.020719\n 184    8469.009116\n 185    8428.430089\n ...            ...\n 1922  13516.442289\n 1923  13417.355622\n 1924  13319.902339\n 1925  13373.134505\n 1926  13502.179649\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10464.183539\n 182   10329.467309\n 183   10206.278802\n 184   10120.747527\n 185   10059.339599\n ...            ...\n 1922  15662.107529\n 1923  15623.307501\n 1924  15530.326441\n 1925  15548.935260\n 1926  15628.190623\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12664.408014\n 182   12621.264869\n 183   12585.770805\n 184   12575.263742\n 185   12544.808630\n ...            ...\n 1922  18939.016912\n 1923  19011.378848\n 1924  18930.339707\n 1925  19003.142281\n 1926  19157.044061\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9480.976736\n 182    9424.790244\n 183    9367.489422\n 184    9306.548457\n 185    9236.224113\n ...            ...\n 1922  13700.046321\n 1923  13766.444916\n 1924  13755.741001\n 1925  13791.697560\n 1926  13841.396876\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7487.809933\n 182    7448.047836\n 183    7398.887500\n 184    7319.026255\n 185    7262.092517\n ...            ...\n 1922  11054.074490\n 1923  11041.919857\n 1924  10941.017079\n 1925  10933.415055\n 1926  10975.366756\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9242.353279\n 182    9187.471223\n 183    9135.229047\n 184    9091.610483\n 185    9049.382328\n ...            ...\n 1922  12201.624086\n 1923  12262.903875\n 1924  12148.583167\n 1925  12149.630792\n 1926  12219.750899\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9456.749520\n 182    9439.883089\n 183    9406.900866\n 184    9321.273727\n 185    9262.961607\n ...            ...\n 1922  11746.284289\n 1923  11814.656836\n 1924  11641.778048\n 1925  11641.005716\n 1926  11739.837659\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9149.081566\n 182    9084.857272\n 183    9013.572468\n 184    8919.223332\n 185    8845.007241\n ...            ...\n 1922  11595.732168\n 1923  11683.946373\n 1924  11436.882610\n 1925  11335.422036\n 1926  11323.672072\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11504.005269\n 182   11447.393878\n 183   11401.085736\n 184   11388.434880\n 185   11356.833172\n ...            ...\n 1922  13040.253051\n 1923  12986.837905\n 1924  12916.487527\n 1925  12927.464739\n 1926  12979.996445\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11023.384474\n 182   10934.723688\n 183   10864.957485\n 184   10856.913591\n 185   10838.496506\n ...            ...\n 1922  12739.867999\n 1923  12779.293151\n 1924  12542.820929\n 1925  12574.919042\n 1926  12770.697618\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11331.225263\n 182   11193.280203\n 183   11064.202739\n 184   10964.092755\n 185   10896.880838\n ...            ...\n 1922  12949.519928\n 1923  12947.817106\n 1924  12897.308899\n 1925  12995.605868\n 1926  13168.838712\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9501.844246\n 182    9506.977815\n 183    9499.444168\n 184    9450.530952\n 185    9396.424884\n ...            ...\n 1922  12516.687931\n 1923  12553.987051\n 1924  12527.452960\n 1925  12577.843155\n 1926  12664.471444\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11041.409928\n 182   11020.067690\n 183   10982.138789\n 184   10890.026787\n 185   10818.846011\n ...            ...\n 1922  13974.385550\n 1923  13871.368669\n 1924  13806.359199\n 1925  13809.436164\n 1926  13852.937265\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10968.108548\n 182   10933.427601\n 183   10871.050512\n 184   10718.199361\n 185   10612.201483\n ...            ...\n 1922  14341.326113\n 1923  14318.647891\n 1924  14198.332866\n 1925  14242.570440\n 1926  14382.436407\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10197.775170\n 182   10195.660251\n 183   10175.870569\n 184   10098.343328\n 185   10026.589861\n ...            ...\n 1922  12870.159516\n 1923  12870.561848\n 1924  12940.174222\n 1925  13026.829826\n 1926  13108.605352\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10483.658172\n 182   10462.248898\n 183   10432.386300\n 184   10374.909512\n 185   10319.618752\n ...            ...\n 1922  13648.013741\n 1923  13582.800785\n 1924  13521.930836\n 1925  13576.442935\n 1926  13689.787581\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9676.922797\n 182    9664.081567\n 183    9624.646737\n 184    9498.339479\n 185    9408.716297\n ...            ...\n 1922  13039.820516\n 1923  13073.971596\n 1924  13067.354172\n 1925  13114.965175\n 1926  13182.183635\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9656.648886\n 182    9608.481043\n 183    9571.088735\n 184    9568.896505\n 185    9557.528507\n ...            ...\n 1922  14532.208950\n 1923  14507.140450\n 1924  14512.204213\n 1925  14529.250283\n 1926  14543.702071\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10306.942281\n 182   10218.862139\n 183   10146.705171\n 184   10126.563901\n 185   10094.452022\n ...            ...\n 1922  13074.883634\n 1923  13152.039194\n 1924  13213.332123\n 1925  13255.722505\n 1926  13268.787460\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8885.575519\n 182    8850.645579\n 183    8809.447249\n 184    8747.772177\n 185    8705.436794\n ...            ...\n 1922  11630.797360\n 1923  11557.222454\n 1924  11778.129651\n 1925  11887.477830\n 1926  11913.859764\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9195.877362\n 182    9223.635427\n 183    9228.723862\n 184    9159.758175\n 185    9103.349446\n ...            ...\n 1922  12464.547037\n 1923  12415.967295\n 1924  12513.723608\n 1925  12527.005969\n 1926  12482.508280\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9616.049292\n 182    9481.898587\n 183    9384.457332\n 184    9406.933617\n 185    9374.759361\n ...            ...\n 1922  12833.156944\n 1923  12836.001610\n 1924  12751.006029\n 1925  12761.207858\n 1926  12827.640353\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9620.197691\n 182    9615.347893\n 183    9594.699340\n 184    9522.441524\n 185    9454.362325\n ...            ...\n 1922  12319.511097\n 1923  12326.993575\n 1924  12338.035242\n 1925  12354.124116\n 1926  12368.937971\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12473.808617\n 182   12538.615573\n 183   12551.517528\n 184   12394.863148\n 185   12269.503236\n ...            ...\n 1922  19119.780419\n 1923  19189.518174\n 1924  18896.444941\n 1925  18962.731959\n 1926  19233.062888\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12828.717324\n 182   12704.947667\n 183   12599.122061\n 184   12551.913689\n 185   12499.712236\n ...            ...\n 1922  17917.635832\n 1923  18117.445242\n 1924  18000.769737\n 1925  18037.927564\n 1926  18149.084377\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13871.243125\n 182   13713.606532\n 183   13592.514788\n 184   13590.802880\n 185   13546.006220\n ...            ...\n 1922  20516.352525\n 1923  20494.544144\n 1924  20446.084548\n 1925  20518.684776\n 1926  20647.344688\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15418.288424\n 182   15352.813122\n 183   15291.225472\n 184   15242.337483\n 185   15189.253841\n ...            ...\n 1922  21803.037524\n 1923  21831.536000\n 1924  21692.373121\n 1925  21605.513438\n 1926  21544.684611\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11417.180585\n 182   11381.219900\n 183   11338.364910\n 184   11272.988527\n 185   11207.802568\n ...            ...\n 1922  15572.286459\n 1923  15697.310388\n 1924  15527.037029\n 1925  15609.585820\n 1926  15825.443203\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   15459.302742\n 182   15437.155061\n 183   15395.448740\n 184   15289.850866\n 185   15202.389680\n ...            ...\n 1922  23589.105540\n 1923  23648.017635\n 1924  23727.420413\n 1925  23893.863610\n 1926  24084.878126\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13452.138719\n 182   13342.440130\n 183   13259.605694\n 184   13264.527490\n 185   13234.742874\n ...            ...\n 1922  19980.912574\n 1923  20015.131579\n 1924  19980.188397\n 1925  19998.835454\n 1926  20038.394218\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11251.963922\n 182   11261.305152\n 183   11264.236980\n 184   11246.231432\n 185   11176.960752\n ...            ...\n 1922  16725.838346\n 1923  16888.968907\n 1924  16800.333805\n 1925  16864.908709\n 1926  17012.847032\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11538.035086\n 182   11566.739218\n 183   11562.920500\n 184   11452.860467\n 185   11352.559082\n ...            ...\n 1922  17579.627256\n 1923  17636.191766\n 1924  17510.051615\n 1925  17492.970310\n 1926  17545.628910\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12409.383781\n 182   12332.218962\n 183   12258.937730\n 184   12198.342881\n 185   12151.103622\n ...            ...\n 1922  20137.357788\n 1923  20090.656811\n 1924  19924.102518\n 1925  19971.905374\n 1926  20135.644067\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10265.732461\n 182   10208.306554\n 183   10158.837590\n 184   10135.361305\n 185   10098.716988\n ...            ...\n 1922  14657.831782\n 1923  14684.989938\n 1924  14583.527186\n 1925  14558.339483\n 1926  14583.684220\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9740.205015\n 182    9667.386467\n 183    9608.619583\n 184    9595.754800\n 185    9545.588084\n ...            ...\n 1922  13479.801035\n 1923  13427.971038\n 1924  13379.616240\n 1925  13462.410726\n 1926  13607.354649\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10458.154698\n 182   10333.435445\n 183   10238.234145\n 184   10239.458160\n 185   10193.807947\n ...            ...\n 1922  14010.794233\n 1923  13988.209689\n 1924  13915.902871\n 1925  13960.866756\n 1926  14062.291015\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9972.715994\n 182    9900.592002\n 183    9817.807467\n 184    9700.198494\n 185    9623.862459\n ...            ...\n 1922  13828.415491\n 1923  13796.672223\n 1924  13858.051012\n 1925  13910.616713\n 1926  13948.765822\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11016.628221\n 182   10971.289007\n 183   10927.341774\n 184   10887.941676\n 185   10819.214672\n ...            ...\n 1922  15878.840716\n 1923  16041.369774\n 1924  15849.205632\n 1925  15893.462788\n 1926  16068.773634\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11940.064052\n 182   11934.158306\n 183   11889.148618\n 184   11716.399386\n 185   11583.519415\n ...            ...\n 1922  16663.741739\n 1923  16486.611912\n 1924  16451.200295\n 1925  16501.538244\n 1926  16593.645859\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11137.563784\n 182   11082.241707\n 183   11028.151738\n 184   10978.086656\n 185   10927.615547\n ...            ...\n 1922  15285.658070\n 1923  15304.450781\n 1924  15347.227296\n 1925  15408.931506\n 1926  15462.063557\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12283.284070\n 182   12216.101610\n 183   12134.350018\n 184   12005.005930\n 185   11924.911919\n ...            ...\n 1922  17857.890031\n 1923  17698.867564\n 1924  17688.688271\n 1925  17753.544610\n 1926  17853.714451\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12192.950802\n 182   12171.079330\n 183   12132.777516\n 184   12040.803254\n 185   11957.592703\n ...            ...\n 1922  17624.654725\n 1923  17675.957255\n 1924  17620.854982\n 1925  17640.147487\n 1926  17698.534999\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11802.592301\n 182   11748.015688\n 183   11691.230588\n 184   11627.231097\n 185   11556.839672\n ...            ...\n 1922  16359.892670\n 1923  16415.074866\n 1924  16333.689765\n 1925  16378.401408\n 1926  16487.247307\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10597.178466\n 182   10515.131592\n 183   10434.933218\n 184   10360.773284\n 185   10285.206958\n ...            ...\n 1922  16584.262469\n 1923  16469.065724\n 1924  16456.632643\n 1925  16528.694088\n 1926  16641.996484\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9793.774398\n 182    9709.632261\n 183    9630.153232\n 184    9565.907020\n 185    9489.883488\n ...            ...\n 1922  14279.167734\n 1923  14210.557721\n 1924  14091.273000\n 1925  14152.305828\n 1926  14307.961914\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9272.367534\n 182    9114.182050\n 183    9000.517957\n 184    9032.290407\n 185    9021.381718\n ...            ...\n 1922  13456.222362\n 1923  13498.199907\n 1924  13272.269983\n 1925  13369.143012\n 1926  13646.795667\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9842.368598\n 182    9785.558618\n 183    9729.198159\n 184    9674.306134\n 185    9640.150992\n ...            ...\n 1922  14246.410412\n 1923  14332.536413\n 1924  14175.662044\n 1925  14131.622688\n 1926  14151.366366\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8291.037125\n 182    8267.366763\n 183    8240.560087\n 184    8203.508121\n 185    8157.873972\n ...            ...\n 1922  11565.905139\n 1923  11541.353218\n 1924  11581.539456\n 1925  11668.398740\n 1926  11769.735469\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10930.896390\n 182   10897.543567\n 183   10838.997716\n 184   10698.154637\n 185   10583.820857\n ...            ...\n 1922  15381.534000\n 1923  15431.097402\n 1924  15478.069625\n 1925  15601.984041\n 1926  15750.348750\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7174.625457\n 182    7115.497433\n 183    7068.392331\n 184    7060.562109\n 185    7037.371948\n ...            ...\n 1922  10859.032116\n 1923  10833.080077\n 1924  10793.781466\n 1925  10819.423310\n 1926  10873.798517\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9192.000677\n 182    9137.112572\n 183    9086.428463\n 184    9049.477406\n 185    9005.739395\n ...            ...\n 1922  12876.248498\n 1923  12906.103826\n 1924  12900.508497\n 1925  12981.932251\n 1926  13096.635664\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9200.821572\n 182    9197.255040\n 183    9187.743622\n 184    9158.812241\n 185    9105.342741\n ...            ...\n 1922  13207.477970\n 1923  13151.185966\n 1924  13185.318543\n 1925  13251.619588\n 1926  13330.195587\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9495.875583\n 182    9413.697155\n 183    9335.869761\n 184    9272.255738\n 185    9226.784283\n ...            ...\n 1922  13879.248358\n 1923  13935.158897\n 1924  13724.956246\n 1925  13724.055153\n 1926  13844.278439\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11147.051033\n 182   11124.387727\n 183   11064.335423\n 184   10882.145727\n 185   10729.252029\n ...            ...\n 1922  14867.857084\n 1923  14799.298242\n 1924  14791.865792\n 1925  14745.965675\n 1926  14675.984492\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9430.624134\n 182    9374.431594\n 183    9342.619092\n 184    9390.448049\n 185    9374.853708\n ...            ...\n 1922  12784.110206\n 1923  12833.890729\n 1924  12713.378734\n 1925  12739.762305\n 1926  12847.699961\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9566.145767\n 182    9464.647280\n 183    9371.872581\n 184    9307.595592\n 185    9248.695245\n ...            ...\n 1922  12398.503722\n 1923  12440.895607\n 1924  12489.558486\n 1925  12511.748959\n 1926  12506.333869\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9763.147256\n 182    9816.299474\n 183    9838.657905\n 184    9760.423299\n 185    9692.151032\n ...            ...\n 1922  12875.815522\n 1923  12947.833554\n 1924  12704.071559\n 1925  12703.023963\n 1926  12850.208126\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11675.907124\n 182   11575.326983\n 183   11493.011873\n 184   11470.362530\n 185   11433.747427\n ...            ...\n 1922  15134.371258\n 1923  15190.949661\n 1924  15102.089181\n 1925  15133.017482\n 1926  15230.100196\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10213.370542\n 182   10159.270744\n 183   10106.872684\n 184   10060.033635\n 185   10005.409078\n ...            ...\n 1922  12725.297521\n 1923  12705.430853\n 1924  12740.548909\n 1925  12865.218604\n 1926  13031.875391\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10465.917181\n 182   10476.497827\n 183   10461.431221\n 184   10362.583599\n 185   10276.687652\n ...            ...\n 1922  13351.388834\n 1923  13349.363500\n 1924  13208.027641\n 1925  13255.200995\n 1926  13412.150858\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10886.658841\n 182   10933.946643\n 183   10941.679891\n 184   10820.201594\n 185   10719.650152\n ...            ...\n 1922  14019.714292\n 1923  13964.246561\n 1924  13979.684692\n 1925  14015.935743\n 1926  14052.793830\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9878.246101\n 182    9774.785920\n 183    9695.376620\n 184    9694.533533\n 185    9673.943222\n ...            ...\n 1922  13840.344433\n 1923  13811.388760\n 1924  13755.772598\n 1925  13723.474100\n 1926  13706.331602\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9244.009735\n 182    9213.787960\n 183    9164.786927\n 184    9054.440314\n 185    8962.628726\n ...            ...\n 1922  13022.823498\n 1923  13090.955262\n 1924  13003.904757\n 1925  13037.904550\n 1926  13139.435976\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10760.137475\n 182   10694.772555\n 183   10630.636305\n 184   10570.513705\n 185   10524.345408\n ...            ...\n 1922  15289.726398\n 1923  15276.399478\n 1924  15229.335084\n 1925  15302.700326\n 1926  15430.685274\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11938.435823\n 182   11817.891972\n 183   11719.516415\n 184   11693.557284\n 185   11644.257156\n ...            ...\n 1922  14943.537038\n 1923  15033.739848\n 1924  14909.341490\n 1925  14867.518522\n 1926  14876.458710\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11276.983879\n 182   11281.662810\n 183   11261.005399\n 184   11157.582600\n 185   11046.166832\n ...            ...\n 1922  15456.221678\n 1923  15422.321242\n 1924  15367.963918\n 1925  15343.230315\n 1926  15336.599765\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10040.822250\n 182    9989.367613\n 183    9948.687378\n 184    9943.203523\n 185    9906.801828\n ...            ...\n 1922  13777.138128\n 1923  13745.419893\n 1924  13777.542999\n 1925  13855.706299\n 1926  13942.588400\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10297.520358\n 182   10244.763806\n 183   10188.078751\n 184   10118.560593\n 185   10048.833465\n ...            ...\n 1922  14044.495749\n 1923  14098.589571\n 1924  14094.198157\n 1925  14075.931971\n 1926  14042.408006\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9594.280471\n 182    9528.775934\n 183    9467.784712\n 184    9421.536986\n 185    9373.088758\n ...            ...\n 1922  13302.619210\n 1923  13283.730515\n 1924  13189.393393\n 1925  13265.290457\n 1926  13435.643910\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   18371.696960\n 182   18443.277177\n 183   18458.736291\n 184   18352.945969\n 185   18281.740357\n ...            ...\n 1922  40512.791663\n 1923  40470.756434\n 1924  40570.155424\n 1925  40634.583132\n 1926  40612.976010\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   14902.126429\n 182   14981.965476\n 183   14975.518075\n 184   14782.649124\n 185   14624.789278\n ...            ...\n 1922  26603.719989\n 1923  26572.906337\n 1924  26564.951792\n 1925  26659.888639\n 1926  26750.060576\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10936.466628\n 182   10961.791551\n 183   10954.628140\n 184   10877.273805\n 185   10812.505004\n ...            ...\n 1922  17825.296277\n 1923  17807.679850\n 1924  17744.239193\n 1925  17818.673898\n 1926  17934.523917\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10623.963693\n 182   10531.164506\n 183   10468.544868\n 184   10471.128033\n 185   10445.358763\n ...            ...\n 1922  15357.548841\n 1923  15450.346808\n 1924  15475.383425\n 1925  15484.327956\n 1926  15462.719870\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9513.957774\n 182    9422.359899\n 183    9345.754179\n 184    9301.538957\n 185    9251.712511\n ...            ...\n 1922  12832.574313\n 1923  12854.386911\n 1924  12850.874285\n 1925  12913.156965\n 1926  12981.162971\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11211.498700\n 182   11175.478016\n 183   11110.776186\n 184   10984.108847\n 185   10941.121809\n ...            ...\n 1922  16340.753772\n 1923  16254.491014\n 1924  16218.003640\n 1925  16396.394171\n 1926  16605.824656\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12389.671629\n 182   12310.426427\n 183   12218.116796\n 184   12097.581514\n 185   12045.795251\n ...            ...\n 1922  16385.420389\n 1923  16455.585204\n 1924  16236.320683\n 1925  16253.582191\n 1926  16358.319600\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8041.078935\n 182   7991.336002\n 183   7936.117360\n 184   7869.068469\n 185   7810.311078\n ...           ...\n 1922  9265.369670\n 1923  9256.920189\n 1924  9292.624103\n 1925  9280.739448\n 1926  9247.183636\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8748.849301\n 182   8643.817017\n 183   8578.036788\n 184   8597.060477\n 185   8560.657707\n ...           ...\n 1922  9725.634349\n 1923  9705.582522\n 1924  9634.796537\n 1925  9743.881649\n 1926  9890.007907\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8521.170544\n 182   8532.196275\n 183   8513.056417\n 184   8428.743918\n 185   8352.200286\n ...           ...\n 1922  9695.322684\n 1923  9725.890379\n 1924  9711.515379\n 1925  9793.031001\n 1926  9899.135336\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10082.966582\n 182   10051.481215\n 183   10023.376157\n 184   10002.574242\n 185    9966.562467\n ...            ...\n 1922  11805.955657\n 1923  11744.243215\n 1924  11679.995741\n 1925  11762.722978\n 1926  11878.977375\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9139.558382\n 182    9129.976142\n 183    9133.473585\n 184    9165.229634\n 185    9163.597182\n ...            ...\n 1922  10638.649552\n 1923  10585.055770\n 1924  10487.227487\n 1925  10523.765088\n 1926  10602.679099\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9418.629067\n 182    9476.463070\n 183    9481.553331\n 184    9372.690931\n 185    9314.866509\n ...            ...\n 1922  10792.112330\n 1923  10839.551984\n 1924  10872.332287\n 1925  10945.719421\n 1926  11000.494867\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9998.973128\n 182    9880.079594\n 183    9808.034366\n 184    9837.204729\n 185    9811.970641\n ...            ...\n 1922  10860.486700\n 1923  10865.537960\n 1924  10921.303111\n 1925  10998.081990\n 1926  11044.408949\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8195.034277\n 182   8192.678071\n 183   8162.267898\n 184   8071.247233\n 185   8010.268057\n ...           ...\n 1922  8673.858473\n 1923  8616.453751\n 1924  8648.938599\n 1925  8698.230708\n 1926  8733.806260\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8193.825555\n 182   8135.427728\n 183   8067.758805\n 184   7980.059712\n 185   7928.481499\n ...           ...\n 1922  8899.234896\n 1923  8894.222239\n 1924  8844.220345\n 1925  8832.002221\n 1926  8831.442210\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7563.452908\n 182   7585.705934\n 183   7584.330416\n 184   7531.905517\n 185   7487.026265\n ...           ...\n 1922  8356.547901\n 1923  8353.938384\n 1924  8352.960480\n 1925  8363.181777\n 1926  8381.741260\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   11866.205149\n 182   11810.248039\n 183   11766.745732\n 184   11750.151982\n 185   11718.605537\n ...            ...\n 1922  13524.876069\n 1923  13579.019017\n 1924  13430.404118\n 1925  13451.138649\n 1926  13542.752242\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10456.386815\n 182   10518.348465\n 183   10529.674804\n 184   10431.603735\n 185   10331.921310\n ...            ...\n 1922  12191.547600\n 1923  12179.207710\n 1924  12139.044200\n 1925  12197.564346\n 1926  12285.223727\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6446.258296\n 182   6477.050928\n 183   6471.135507\n 184   6385.912472\n 185   6326.938842\n ...           ...\n 1922  6875.508255\n 1923  6846.787459\n 1924  6846.653936\n 1925  6873.818889\n 1926  6897.994240\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6857.695440\n 182   6830.494382\n 183   6810.047954\n 184   6804.194880\n 185   6786.943642\n ...           ...\n 1922  7147.242264\n 1923  7099.065683\n 1924  7152.240240\n 1925  7161.331478\n 1926  7146.913649\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   9007.248267\n 182   9000.318903\n 183   8966.180035\n 184   8873.255136\n 185   8816.192982\n ...           ...\n 1922  8932.034003\n 1923  8831.723946\n 1924  8880.950588\n 1925  8968.688862\n 1926  9045.557988\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10544.452546\n 182   10505.016121\n 183   10447.446134\n 184   10350.698654\n 185   10256.898036\n ...            ...\n 1922  11772.393797\n 1923  11824.767442\n 1924  11766.798376\n 1925  11834.106839\n 1926  11929.393193\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9492.807844\n 182    9496.611073\n 183    9504.210440\n 184    9520.011347\n 185    9473.293958\n ...            ...\n 1922   9950.920926\n 1923   9892.675356\n 1924   9954.578766\n 1925  10026.297970\n 1926  10074.791740\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   12242.472659\n 182   12193.898999\n 183   12130.146326\n 184   12033.599451\n 185   11957.721673\n ...            ...\n 1922  13769.439266\n 1923  13722.263851\n 1924  13705.287210\n 1925  13777.900794\n 1926  13856.891223\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10258.751546\n 182   10166.575467\n 183   10097.196463\n 184   10077.070456\n 185   10043.253983\n ...            ...\n 1922  11038.490544\n 1923  11005.373474\n 1924  11013.105166\n 1925  11123.033797\n 1926  11230.423961\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8712.173669\n 182   8651.169132\n 183   8589.542309\n 184   8526.571039\n 185   8477.718059\n ...           ...\n 1922  9931.535765\n 1923  9925.835216\n 1924  9894.773347\n 1925  9901.500747\n 1926  9906.886536\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8286.273126\n 182   8241.229234\n 183   8184.591882\n 184   8102.906903\n 185   8041.651705\n ...           ...\n 1922  8595.937002\n 1923  8607.714678\n 1924  8465.750032\n 1925  8481.237779\n 1926  8551.281693\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6225.891800\n 182   6217.295686\n 183   6188.029471\n 184   6114.105580\n 185   6045.175368\n ...           ...\n 1922  6291.945928\n 1923  6289.579155\n 1924  6245.498347\n 1925  6211.204786\n 1926  6179.574440\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5917.621123\n 182   5987.045689\n 183   6030.091366\n 184   6016.145550\n 185   5971.552982\n ...           ...\n 1922  7250.845762\n 1923  7230.869190\n 1924  7169.155491\n 1925  7199.819764\n 1926  7269.068701\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7154.472227\n 182   7141.984085\n 183   7098.701613\n 184   6988.888105\n 185   6914.921353\n ...           ...\n 1922  8226.425513\n 1923  8220.959476\n 1924  8172.957266\n 1925  8290.833073\n 1926  8450.193011\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6707.645791\n 182   6684.429849\n 183   6658.475724\n 184   6626.605764\n 185   6592.174080\n ...           ...\n 1922  6339.754364\n 1923  6401.872421\n 1924  6383.921348\n 1925  6359.665649\n 1926  6337.598884\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7079.964273\n 182   7104.828297\n 183   7100.157046\n 184   7031.674939\n 185   6973.761550\n ...           ...\n 1922  6962.214446\n 1923  6910.898955\n 1924  6923.308993\n 1925  6995.219227\n 1926  7071.519452\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8140.003114\n 182   8057.581323\n 183   7996.900058\n 184   7983.189121\n 185   7953.262379\n ...           ...\n 1922  7991.320120\n 1923  7999.014340\n 1924  7985.454365\n 1925  8023.687735\n 1926  8070.339057\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7292.518959\n 182   7254.028296\n 183   7233.473734\n 184   7251.670055\n 185   7229.123131\n ...           ...\n 1922  7543.870887\n 1923  7512.497860\n 1924  7661.830247\n 1925  7643.912418\n 1926  7553.104694\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   13851.193812\n 182   13644.028840\n 183   13508.828580\n 184   13529.107814\n 185   13519.785138\n ...            ...\n 1922  15456.217455\n 1923  15387.654701\n 1924  15391.791696\n 1925  15391.729413\n 1926  15381.632205\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9602.053049\n 182    9588.523282\n 183    9540.402406\n 184    9417.547567\n 185    9354.578548\n ...            ...\n 1922  11133.236549\n 1923  11138.323110\n 1924  11088.619827\n 1925  11137.597662\n 1926  11228.715759\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   8827.914142\n 182   8785.604356\n 183   8736.340754\n 184   8672.053455\n 185   8633.022892\n ...           ...\n 1922  9188.537060\n 1923  9239.572686\n 1924  9172.903494\n 1925  9201.410595\n 1926  9251.077887\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    9932.480105\n 182    9877.625921\n 183    9848.093579\n 184    9873.268981\n 185    9857.960522\n ...            ...\n 1922  11896.164142\n 1923  11795.050872\n 1924  11778.862109\n 1925  11838.964944\n 1926  11902.266401\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7046.559612\n 182   6959.336862\n 183   6904.824167\n 184   6920.981426\n 185   6907.026593\n ...           ...\n 1922  7178.551502\n 1923  7199.628639\n 1924  7157.758058\n 1925  7201.271235\n 1926  7267.066558\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6334.061331\n 182   6329.481110\n 183   6296.214500\n 184   6200.971052\n 185   6143.866699\n ...           ...\n 1922  6567.037761\n 1923  6539.667332\n 1924  6576.054011\n 1925  6614.550267\n 1926  6640.607357\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   7326.598999\n 182   7274.769033\n 183   7236.236994\n 184   7226.435079\n 185   7189.028390\n ...           ...\n 1922  8901.394353\n 1923  8915.768602\n 1924  8845.922011\n 1925  8881.051013\n 1926  8941.788026\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10100.087377\n 182   10069.938034\n 183   10051.817271\n 184   10059.684206\n 185   10034.899489\n ...            ...\n 1922  22802.009599\n 1923  22694.969576\n 1924  22736.640031\n 1925  22937.213108\n 1926  23137.925004\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8659.378039\n 182    8660.413357\n 183    8663.979593\n 184    8673.013867\n 185    8649.903929\n ...            ...\n 1922  16067.658100\n 1923  16065.402152\n 1924  16043.513895\n 1925  16138.513070\n 1926  16255.871832\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6773.173938\n 182    6830.691846\n 183    6854.450233\n 184    6805.271302\n 185    6778.831377\n ...            ...\n 1922  11706.687826\n 1923  11780.979665\n 1924  11707.336356\n 1925  11699.468899\n 1926  11709.547951\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6290.164064\n 182    6285.578797\n 183    6260.533539\n 184    6191.284547\n 185    6167.541016\n ...            ...\n 1922  10620.716495\n 1923  10611.719473\n 1924  10651.781342\n 1925  10684.854706\n 1926  10702.353518\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6658.738042\n 182    6666.872754\n 183    6670.154482\n 184    6662.951357\n 185    6632.703036\n ...            ...\n 1922  10330.705189\n 1923  10289.291255\n 1924  10161.130270\n 1925  10210.724460\n 1926  10329.478736\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5991.956220\n 182   5910.611876\n 183   5836.664349\n 184   5778.697615\n 185   5734.094329\n ...           ...\n 1922  8590.600624\n 1923  8595.300898\n 1924  8552.571731\n 1925  8575.626875\n 1926  8615.784260\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6267.435748\n 182   6249.504367\n 183   6232.419876\n 184   6217.165087\n 185   6208.731465\n ...           ...\n 1922  8456.684797\n 1923  8519.140553\n 1924  8475.356462\n 1925  8460.258482\n 1926  8454.784686\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5556.768391\n 182   5546.758049\n 183   5541.178988\n 184   5545.173696\n 185   5544.362035\n ...           ...\n 1922  7680.271497\n 1923  7747.144852\n 1924  7637.448198\n 1925  7630.615868\n 1926  7662.770641\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6370.319348\n 182   6376.418238\n 183   6359.734987\n 184   6293.831005\n 185   6276.473660\n ...           ...\n 1922  8589.947378\n 1923  8577.589792\n 1924  8520.233732\n 1925  8613.353825\n 1926  8744.477823\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7407.377273\n 182    7476.312688\n 183    7497.564910\n 184    7415.797773\n 185    7358.004753\n ...            ...\n 1922  10421.476711\n 1923  10360.227350\n 1924  10268.636441\n 1925  10342.911706\n 1926  10469.559160\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6694.473535\n 182   6629.225053\n 183   6573.901734\n 184   6540.021694\n 185   6543.578994\n ...           ...\n 1922  9200.354102\n 1923  9204.739049\n 1924  9133.886776\n 1925  9176.408652\n 1926  9263.585484\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5755.678564\n 182   5689.650819\n 183   5651.055991\n 184   5671.729883\n 185   5668.878440\n ...           ...\n 1922  7957.033983\n 1923  7910.880957\n 1924  7833.588659\n 1925  7873.868729\n 1926  7948.937094\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5257.477033\n 182   5207.401355\n 183   5167.248314\n 184   5148.533092\n 185   5122.205046\n ...           ...\n 1922  6870.625889\n 1923  6835.660325\n 1924  6791.652118\n 1925  6784.029684\n 1926  6788.695697\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5469.867981\n 182   5468.520352\n 183   5458.524536\n 184   5429.844345\n 185   5406.200595\n ...           ...\n 1922  7350.929222\n 1923  7265.703247\n 1924  7208.172313\n 1925  7238.820919\n 1926  7304.699291\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5113.257049\n 182   5085.180361\n 183   5057.319471\n 184   5029.924813\n 185   5014.656568\n ...           ...\n 1922  6797.895950\n 1923  6767.320493\n 1924  6728.526300\n 1925  6762.517400\n 1926  6811.176046\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5585.851872\n 182   5563.168977\n 183   5535.427551\n 184   5496.757188\n 185   5492.734680\n ...           ...\n 1922  7015.703941\n 1923  7025.743908\n 1924  7061.150474\n 1925  7114.065730\n 1926  7159.164159\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5020.666886\n 182   5045.284695\n 183   5037.414236\n 184   4959.352997\n 185   4927.801558\n ...           ...\n 1922  6026.547373\n 1923  6002.526121\n 1924  5998.101562\n 1925  6034.973847\n 1926  6079.624534\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4721.550573\n 182   4691.425702\n 183   4669.743439\n 184   4666.301402\n 185   4668.425788\n ...           ...\n 1922  5983.856360\n 1923  5999.492690\n 1924  5891.652728\n 1925  5923.775712\n 1926  5996.608393\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5620.407634\n 182   5647.765856\n 183   5643.268069\n 184   5569.945488\n 185   5512.250430\n ...           ...\n 1922  7099.287958\n 1923  7074.439076\n 1924  7110.896779\n 1925  7183.762458\n 1926  7259.022318\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6181.079496\n 182   6209.838828\n 183   6183.539128\n 184   6038.284592\n 185   5949.603493\n ...           ...\n 1922  8144.948006\n 1923  8069.557980\n 1924  8075.854697\n 1925  8164.048745\n 1926  8269.577696\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6110.029651\n 182   6102.753919\n 183   6075.229727\n 184   6003.958810\n 185   5950.342697\n ...           ...\n 1922  7797.416467\n 1923  7797.077539\n 1924  7723.948751\n 1925  7773.293039\n 1926  7857.913529\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5684.890374\n 182   5624.858339\n 183   5584.875594\n 184   5588.209263\n 185   5582.685017\n ...           ...\n 1922  7059.210831\n 1923  7045.541522\n 1924  6926.350235\n 1925  6984.916659\n 1926  7098.169463\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4800.032232\n 182   4758.574868\n 183   4737.163580\n 184   4759.061766\n 185   4754.144431\n ...           ...\n 1922  5918.278657\n 1923  5887.092730\n 1924  5834.601569\n 1925  5860.491704\n 1926  5902.921345\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4994.233513\n 182   5006.171958\n 183   4984.569713\n 184   4890.502928\n 185   4845.715783\n ...           ...\n 1922  6082.161050\n 1923  6067.860953\n 1924  6059.266888\n 1925  6129.066173\n 1926  6205.764646\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5794.322845\n 182   5799.685609\n 183   5793.867835\n 184   5763.894549\n 185   5733.639879\n ...           ...\n 1922  7255.818398\n 1923  7295.524548\n 1924  7226.507187\n 1925  7263.865107\n 1926  7330.247135\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4929.706587\n 182   4880.259062\n 183   4827.023180\n 184   4765.602565\n 185   4721.592934\n ...           ...\n 1922  6145.814954\n 1923  6151.525304\n 1924  6171.146516\n 1925  6154.374977\n 1926  6116.099923\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4976.683372\n 182   4944.587511\n 183   4915.028350\n 184   4890.949721\n 185   4863.050972\n ...           ...\n 1922  6301.021722\n 1923  6298.939648\n 1924  6335.294130\n 1925  6372.358290\n 1926  6384.340966\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4624.879736\n 182   4610.755115\n 183   4595.789124\n 184   4579.005354\n 185   4559.167826\n ...           ...\n 1922  5781.168717\n 1923  5732.219513\n 1924  5680.752341\n 1925  5677.568191\n 1926  5690.624174\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5815.276942\n 182   5785.949748\n 183   5748.612117\n 184   5693.967964\n 185   5640.791390\n ...           ...\n 1922  6902.897781\n 1923  6888.642235\n 1924  6925.075316\n 1925  6934.919387\n 1926  6916.892846\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5650.689017\n 182   5624.010916\n 183   5577.087592\n 184   5486.424537\n 185   5440.487382\n ...           ...\n 1922  7332.869062\n 1923  7290.313640\n 1924  7309.041860\n 1925  7312.375830\n 1926  7300.034115\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6322.280621\n 182   6301.687030\n 183   6273.081571\n 184   6227.166501\n 185   6183.233482\n ...           ...\n 1922  7966.108624\n 1923  7987.508932\n 1924  7965.590687\n 1925  7957.665311\n 1926  7954.704193\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5698.545706\n 182   5728.581779\n 183   5732.034873\n 184   5678.055538\n 185   5637.696455\n ...           ...\n 1922  7064.511451\n 1923  7047.818826\n 1924  7103.075455\n 1925  7191.245598\n 1926  7264.274756\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4721.482986\n 182   4716.710030\n 183   4714.047225\n 184   4715.943395\n 185   4687.460987\n ...           ...\n 1922  5943.253102\n 1923  5994.795669\n 1924  5962.166249\n 1925  5984.124688\n 1926  6020.282414\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4475.939129\n 182   4495.821569\n 183   4503.044538\n 184   4482.916766\n 185   4455.174896\n ...           ...\n 1922  5218.262111\n 1923  5166.010745\n 1924  5125.958977\n 1925  5159.392028\n 1926  5220.334329\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4236.825013\n 182   4214.647920\n 183   4207.240139\n 184   4231.741399\n 185   4222.847266\n ...           ...\n 1922  5102.206485\n 1923  5106.338168\n 1924  5096.571439\n 1925  5085.402060\n 1926  5072.943940\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4275.228424\n 182   4236.377266\n 183   4203.438897\n 184   4183.275087\n 185   4176.257179\n ...           ...\n 1922  5314.297172\n 1923  5287.302024\n 1924  5329.789713\n 1925  5295.292834\n 1926  5236.148053\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4362.354997\n 182   4433.595406\n 183   4451.034918\n 184   4352.237791\n 185   4266.765683\n ...           ...\n 1922  5099.248095\n 1923  5071.851922\n 1924  5029.698197\n 1925  5049.829455\n 1926  5095.167409\n \n [1746 rows x 1 columns],\n         #Intensity\n 181   10100.087377\n 182   10069.938034\n 183   10051.817271\n 184   10059.684206\n 185   10034.899489\n ...            ...\n 1922  22802.009599\n 1923  22694.969576\n 1924  22736.640031\n 1925  22937.213108\n 1926  23137.925004\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    8659.378039\n 182    8660.413357\n 183    8663.979593\n 184    8673.013867\n 185    8649.903929\n ...            ...\n 1922  16067.658100\n 1923  16065.402152\n 1924  16043.513895\n 1925  16138.513070\n 1926  16255.871832\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6773.173938\n 182    6830.691846\n 183    6854.450233\n 184    6805.271302\n 185    6778.831377\n ...            ...\n 1922  11706.687826\n 1923  11780.979665\n 1924  11707.336356\n 1925  11699.468899\n 1926  11709.547951\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6290.164064\n 182    6285.578797\n 183    6260.533539\n 184    6191.284547\n 185    6167.541016\n ...            ...\n 1922  10620.716495\n 1923  10611.719473\n 1924  10651.781342\n 1925  10684.854706\n 1926  10702.353518\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    6658.738042\n 182    6666.872754\n 183    6670.154482\n 184    6662.951357\n 185    6632.703036\n ...            ...\n 1922  10330.705189\n 1923  10289.291255\n 1924  10161.130270\n 1925  10210.724460\n 1926  10329.478736\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5991.956220\n 182   5910.611876\n 183   5836.664349\n 184   5778.697615\n 185   5734.094329\n ...           ...\n 1922  8590.600624\n 1923  8595.300898\n 1924  8552.571731\n 1925  8575.626875\n 1926  8615.784260\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6267.435748\n 182   6249.504367\n 183   6232.419876\n 184   6217.165087\n 185   6208.731465\n ...           ...\n 1922  8456.684797\n 1923  8519.140553\n 1924  8475.356462\n 1925  8460.258482\n 1926  8454.784686\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5556.768391\n 182   5546.758049\n 183   5541.178988\n 184   5545.173696\n 185   5544.362035\n ...           ...\n 1922  7680.271497\n 1923  7747.144852\n 1924  7637.448198\n 1925  7630.615868\n 1926  7662.770641\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6370.319348\n 182   6376.418238\n 183   6359.734987\n 184   6293.831005\n 185   6276.473660\n ...           ...\n 1922  8589.947378\n 1923  8577.589792\n 1924  8520.233732\n 1925  8613.353825\n 1926  8744.477823\n \n [1746 rows x 1 columns],\n         #Intensity\n 181    7407.377273\n 182    7476.312688\n 183    7497.564910\n 184    7415.797773\n 185    7358.004753\n ...            ...\n 1922  10421.476711\n 1923  10360.227350\n 1924  10268.636441\n 1925  10342.911706\n 1926  10469.559160\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6694.473535\n 182   6629.225053\n 183   6573.901734\n 184   6540.021694\n 185   6543.578994\n ...           ...\n 1922  9200.354102\n 1923  9204.739049\n 1924  9133.886776\n 1925  9176.408652\n 1926  9263.585484\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5755.678564\n 182   5689.650819\n 183   5651.055991\n 184   5671.729883\n 185   5668.878440\n ...           ...\n 1922  7957.033983\n 1923  7910.880957\n 1924  7833.588659\n 1925  7873.868729\n 1926  7948.937094\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5257.477033\n 182   5207.401355\n 183   5167.248314\n 184   5148.533092\n 185   5122.205046\n ...           ...\n 1922  6870.625889\n 1923  6835.660325\n 1924  6791.652118\n 1925  6784.029684\n 1926  6788.695697\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5469.867981\n 182   5468.520352\n 183   5458.524536\n 184   5429.844345\n 185   5406.200595\n ...           ...\n 1922  7350.929222\n 1923  7265.703247\n 1924  7208.172313\n 1925  7238.820919\n 1926  7304.699291\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5113.257049\n 182   5085.180361\n 183   5057.319471\n 184   5029.924813\n 185   5014.656568\n ...           ...\n 1922  6797.895950\n 1923  6767.320493\n 1924  6728.526300\n 1925  6762.517400\n 1926  6811.176046\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5585.851872\n 182   5563.168977\n 183   5535.427551\n 184   5496.757188\n 185   5492.734680\n ...           ...\n 1922  7015.703941\n 1923  7025.743908\n 1924  7061.150474\n 1925  7114.065730\n 1926  7159.164159\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5020.666886\n 182   5045.284695\n 183   5037.414236\n 184   4959.352997\n 185   4927.801558\n ...           ...\n 1922  6026.547373\n 1923  6002.526121\n 1924  5998.101562\n 1925  6034.973847\n 1926  6079.624534\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4721.550573\n 182   4691.425702\n 183   4669.743439\n 184   4666.301402\n 185   4668.425788\n ...           ...\n 1922  5983.856360\n 1923  5999.492690\n 1924  5891.652728\n 1925  5923.775712\n 1926  5996.608393\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5620.407634\n 182   5647.765856\n 183   5643.268069\n 184   5569.945488\n 185   5512.250430\n ...           ...\n 1922  7099.287958\n 1923  7074.439076\n 1924  7110.896779\n 1925  7183.762458\n 1926  7259.022318\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6181.079496\n 182   6209.838828\n 183   6183.539128\n 184   6038.284592\n 185   5949.603493\n ...           ...\n 1922  8144.948006\n 1923  8069.557980\n 1924  8075.854697\n 1925  8164.048745\n 1926  8269.577696\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6110.029651\n 182   6102.753919\n 183   6075.229727\n 184   6003.958810\n 185   5950.342697\n ...           ...\n 1922  7797.416467\n 1923  7797.077539\n 1924  7723.948751\n 1925  7773.293039\n 1926  7857.913529\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5684.890374\n 182   5624.858339\n 183   5584.875594\n 184   5588.209263\n 185   5582.685017\n ...           ...\n 1922  7059.210831\n 1923  7045.541522\n 1924  6926.350235\n 1925  6984.916659\n 1926  7098.169463\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4800.032232\n 182   4758.574868\n 183   4737.163580\n 184   4759.061766\n 185   4754.144431\n ...           ...\n 1922  5918.278657\n 1923  5887.092730\n 1924  5834.601569\n 1925  5860.491704\n 1926  5902.921345\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4994.233513\n 182   5006.171958\n 183   4984.569713\n 184   4890.502928\n 185   4845.715783\n ...           ...\n 1922  6082.161050\n 1923  6067.860953\n 1924  6059.266888\n 1925  6129.066173\n 1926  6205.764646\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5794.322845\n 182   5799.685609\n 183   5793.867835\n 184   5763.894549\n 185   5733.639879\n ...           ...\n 1922  7255.818398\n 1923  7295.524548\n 1924  7226.507187\n 1925  7263.865107\n 1926  7330.247135\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4929.706587\n 182   4880.259062\n 183   4827.023180\n 184   4765.602565\n 185   4721.592934\n ...           ...\n 1922  6145.814954\n 1923  6151.525304\n 1924  6171.146516\n 1925  6154.374977\n 1926  6116.099923\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4976.683372\n 182   4944.587511\n 183   4915.028350\n 184   4890.949721\n 185   4863.050972\n ...           ...\n 1922  6301.021722\n 1923  6298.939648\n 1924  6335.294130\n 1925  6372.358290\n 1926  6384.340966\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4624.879736\n 182   4610.755115\n 183   4595.789124\n 184   4579.005354\n 185   4559.167826\n ...           ...\n 1922  5781.168717\n 1923  5732.219513\n 1924  5680.752341\n 1925  5677.568191\n 1926  5690.624174\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5815.276942\n 182   5785.949748\n 183   5748.612117\n 184   5693.967964\n 185   5640.791390\n ...           ...\n 1922  6902.897781\n 1923  6888.642235\n 1924  6925.075316\n 1925  6934.919387\n 1926  6916.892846\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5650.689017\n 182   5624.010916\n 183   5577.087592\n 184   5486.424537\n 185   5440.487382\n ...           ...\n 1922  7332.869062\n 1923  7290.313640\n 1924  7309.041860\n 1925  7312.375830\n 1926  7300.034115\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   6322.280621\n 182   6301.687030\n 183   6273.081571\n 184   6227.166501\n 185   6183.233482\n ...           ...\n 1922  7966.108624\n 1923  7987.508932\n 1924  7965.590687\n 1925  7957.665311\n 1926  7954.704193\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   5698.545706\n 182   5728.581779\n 183   5732.034873\n 184   5678.055538\n 185   5637.696455\n ...           ...\n 1922  7064.511451\n 1923  7047.818826\n 1924  7103.075455\n 1925  7191.245598\n 1926  7264.274756\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4721.482986\n 182   4716.710030\n 183   4714.047225\n 184   4715.943395\n 185   4687.460987\n ...           ...\n 1922  5943.253102\n 1923  5994.795669\n 1924  5962.166249\n 1925  5984.124688\n 1926  6020.282414\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4475.939129\n 182   4495.821569\n 183   4503.044538\n 184   4482.916766\n 185   4455.174896\n ...           ...\n 1922  5218.262111\n 1923  5166.010745\n 1924  5125.958977\n 1925  5159.392028\n 1926  5220.334329\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4236.825013\n 182   4214.647920\n 183   4207.240139\n 184   4231.741399\n 185   4222.847266\n ...           ...\n 1922  5102.206485\n 1923  5106.338168\n 1924  5096.571439\n 1925  5085.402060\n 1926  5072.943940\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4275.228424\n 182   4236.377266\n 183   4203.438897\n 184   4183.275087\n 185   4176.257179\n ...           ...\n 1922  5314.297172\n 1923  5287.302024\n 1924  5329.789713\n 1925  5295.292834\n 1926  5236.148053\n \n [1746 rows x 1 columns],\n        #Intensity\n 181   4362.354997\n 182   4433.595406\n 183   4451.034918\n 184   4352.237791\n 185   4266.765683\n ...           ...\n 1922  5099.248095\n 1923  5071.851922\n 1924  5029.698197\n 1925  5049.829455\n 1926  5095.167409\n \n [1746 rows x 1 columns]]"
      },
      "execution_count": 4,
      "metadata": {},
@@ -10977,8 +810,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-13T14:23:59.586141Z",
-     "start_time": "2024-04-13T14:23:59.505593Z"
+     "end_time": "2024-04-24T14:17:46.062196400Z",
+     "start_time": "2024-04-24T14:17:46.011299800Z"
     }
    },
    "cell_type": "code",
@@ -10990,373 +823,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "        #Intensity    #Intensity    #Intensity    #Intensity    #Intensity  \\\n",
-       "181   15196.588457  11541.323730  14841.336497  12962.544993  11218.268657   \n",
-       "182   15238.392893  11514.042480  14791.788614  12888.778360  11174.286298   \n",
-       "183   15260.348015  11496.918280  14735.185229  12819.365812  11138.891151   \n",
-       "184   15217.462044  11512.973779  14655.533872  12764.176613  11131.547559   \n",
-       "185   15151.631602  11497.515892  14589.036222  12737.702642  11115.027596   \n",
-       "...            ...           ...           ...           ...           ...   \n",
-       "1922  34619.811769  23259.160077  27759.794667  24519.211765  21569.532670   \n",
-       "1923  34739.902572  23452.001384  27701.766984  24573.630709  21603.756959   \n",
-       "1924  34481.281361  23237.842294  27707.542236  24534.071366  21496.946353   \n",
-       "1925  34600.945806  23289.105272  27766.287448  24543.687146  21603.877186   \n",
-       "1926  34924.482502  23490.181875  27838.199140  24582.879296  21826.919220   \n",
-       "\n",
-       "        #Intensity    #Intensity    #Intensity    #Intensity    #Intensity  \\\n",
-       "181   13797.517019   9500.903430   9737.872920   9199.072282   9137.087615   \n",
-       "182   13699.189902   9395.398390   9689.048324   9192.839419   9078.193966   \n",
-       "183   13624.446367   9327.081873   9644.117090   9170.494344   9020.844001   \n",
-       "184   13626.742532   9380.247863   9611.904176   9095.516044   8968.536739   \n",
-       "185   13609.690867   9372.983056   9580.085679   9031.298566   8928.190135   \n",
-       "...            ...           ...           ...           ...           ...   \n",
-       "1922  25599.870205  17762.395943  17024.965060  16020.668347  15542.194813   \n",
-       "1923  25666.872646  17729.292606  17124.649955  16034.688033  15555.297180   \n",
-       "1924  25828.025424  17631.368364  17120.660056  16045.178564  15536.474579   \n",
-       "1925  25977.235900  17719.442114  17098.382555  16092.876008  15569.354706   \n",
-       "1926  26088.341561  17908.803279  17060.351796  16147.318945  15617.797227   \n",
-       "\n",
-       "      ...   #Intensity   #Intensity   #Intensity   #Intensity   #Intensity  \\\n",
-       "181   ...  4624.879736  5815.276942  5650.689017  6322.280621  5698.545706   \n",
-       "182   ...  4610.755115  5785.949748  5624.010916  6301.687030  5728.581779   \n",
-       "183   ...  4595.789124  5748.612117  5577.087592  6273.081571  5732.034873   \n",
-       "184   ...  4579.005354  5693.967964  5486.424537  6227.166501  5678.055538   \n",
-       "185   ...  4559.167826  5640.791390  5440.487382  6183.233482  5637.696455   \n",
-       "...   ...          ...          ...          ...          ...          ...   \n",
-       "1922  ...  5781.168717  6902.897781  7332.869062  7966.108624  7064.511451   \n",
-       "1923  ...  5732.219513  6888.642235  7290.313640  7987.508932  7047.818826   \n",
-       "1924  ...  5680.752341  6925.075316  7309.041860  7965.590687  7103.075455   \n",
-       "1925  ...  5677.568191  6934.919387  7312.375830  7957.665311  7191.245598   \n",
-       "1926  ...  5690.624174  6916.892846  7300.034115  7954.704193  7264.274756   \n",
-       "\n",
-       "       #Intensity   #Intensity   #Intensity   #Intensity   #Intensity  \n",
-       "181   4721.482986  4475.939129  4236.825013  4275.228424  4362.354997  \n",
-       "182   4716.710030  4495.821569  4214.647920  4236.377266  4433.595406  \n",
-       "183   4714.047225  4503.044538  4207.240139  4203.438897  4451.034918  \n",
-       "184   4715.943395  4482.916766  4231.741399  4183.275087  4352.237791  \n",
-       "185   4687.460987  4455.174896  4222.847266  4176.257179  4266.765683  \n",
-       "...           ...          ...          ...          ...          ...  \n",
-       "1922  5943.253102  5218.262111  5102.206485  5314.297172  5099.248095  \n",
-       "1923  5994.795669  5166.010745  5106.338168  5287.302024  5071.851922  \n",
-       "1924  5962.166249  5125.958977  5096.571439  5329.789713  5029.698197  \n",
-       "1925  5984.124688  5159.392028  5085.402060  5295.292834  5049.829455  \n",
-       "1926  6020.282414  5220.334329  5072.943940  5236.148053  5095.167409  \n",
-       "\n",
-       "[1746 rows x 705 columns]"
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-      "text/html": [
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>...</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
-       "      <th>#Intensity</th>\n",
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-       "    <tr>\n",
-       "      <th>181</th>\n",
-       "      <td>15196.588457</td>\n",
-       "      <td>11541.323730</td>\n",
-       "      <td>14841.336497</td>\n",
-       "      <td>12962.544993</td>\n",
-       "      <td>11218.268657</td>\n",
-       "      <td>13797.517019</td>\n",
-       "      <td>9500.903430</td>\n",
-       "      <td>9737.872920</td>\n",
-       "      <td>9199.072282</td>\n",
-       "      <td>9137.087615</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4624.879736</td>\n",
-       "      <td>5815.276942</td>\n",
-       "      <td>5650.689017</td>\n",
-       "      <td>6322.280621</td>\n",
-       "      <td>5698.545706</td>\n",
-       "      <td>4721.482986</td>\n",
-       "      <td>4475.939129</td>\n",
-       "      <td>4236.825013</td>\n",
-       "      <td>4275.228424</td>\n",
-       "      <td>4362.354997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>182</th>\n",
-       "      <td>15238.392893</td>\n",
-       "      <td>11514.042480</td>\n",
-       "      <td>14791.788614</td>\n",
-       "      <td>12888.778360</td>\n",
-       "      <td>11174.286298</td>\n",
-       "      <td>13699.189902</td>\n",
-       "      <td>9395.398390</td>\n",
-       "      <td>9689.048324</td>\n",
-       "      <td>9192.839419</td>\n",
-       "      <td>9078.193966</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4610.755115</td>\n",
-       "      <td>5785.949748</td>\n",
-       "      <td>5624.010916</td>\n",
-       "      <td>6301.687030</td>\n",
-       "      <td>5728.581779</td>\n",
-       "      <td>4716.710030</td>\n",
-       "      <td>4495.821569</td>\n",
-       "      <td>4214.647920</td>\n",
-       "      <td>4236.377266</td>\n",
-       "      <td>4433.595406</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>183</th>\n",
-       "      <td>15260.348015</td>\n",
-       "      <td>11496.918280</td>\n",
-       "      <td>14735.185229</td>\n",
-       "      <td>12819.365812</td>\n",
-       "      <td>11138.891151</td>\n",
-       "      <td>13624.446367</td>\n",
-       "      <td>9327.081873</td>\n",
-       "      <td>9644.117090</td>\n",
-       "      <td>9170.494344</td>\n",
-       "      <td>9020.844001</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4595.789124</td>\n",
-       "      <td>5748.612117</td>\n",
-       "      <td>5577.087592</td>\n",
-       "      <td>6273.081571</td>\n",
-       "      <td>5732.034873</td>\n",
-       "      <td>4714.047225</td>\n",
-       "      <td>4503.044538</td>\n",
-       "      <td>4207.240139</td>\n",
-       "      <td>4203.438897</td>\n",
-       "      <td>4451.034918</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>184</th>\n",
-       "      <td>15217.462044</td>\n",
-       "      <td>11512.973779</td>\n",
-       "      <td>14655.533872</td>\n",
-       "      <td>12764.176613</td>\n",
-       "      <td>11131.547559</td>\n",
-       "      <td>13626.742532</td>\n",
-       "      <td>9380.247863</td>\n",
-       "      <td>9611.904176</td>\n",
-       "      <td>9095.516044</td>\n",
-       "      <td>8968.536739</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4579.005354</td>\n",
-       "      <td>5693.967964</td>\n",
-       "      <td>5486.424537</td>\n",
-       "      <td>6227.166501</td>\n",
-       "      <td>5678.055538</td>\n",
-       "      <td>4715.943395</td>\n",
-       "      <td>4482.916766</td>\n",
-       "      <td>4231.741399</td>\n",
-       "      <td>4183.275087</td>\n",
-       "      <td>4352.237791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>185</th>\n",
-       "      <td>15151.631602</td>\n",
-       "      <td>11497.515892</td>\n",
-       "      <td>14589.036222</td>\n",
-       "      <td>12737.702642</td>\n",
-       "      <td>11115.027596</td>\n",
-       "      <td>13609.690867</td>\n",
-       "      <td>9372.983056</td>\n",
-       "      <td>9580.085679</td>\n",
-       "      <td>9031.298566</td>\n",
-       "      <td>8928.190135</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4559.167826</td>\n",
-       "      <td>5640.791390</td>\n",
-       "      <td>5440.487382</td>\n",
-       "      <td>6183.233482</td>\n",
-       "      <td>5637.696455</td>\n",
-       "      <td>4687.460987</td>\n",
-       "      <td>4455.174896</td>\n",
-       "      <td>4222.847266</td>\n",
-       "      <td>4176.257179</td>\n",
-       "      <td>4266.765683</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1922</th>\n",
-       "      <td>34619.811769</td>\n",
-       "      <td>23259.160077</td>\n",
-       "      <td>27759.794667</td>\n",
-       "      <td>24519.211765</td>\n",
-       "      <td>21569.532670</td>\n",
-       "      <td>25599.870205</td>\n",
-       "      <td>17762.395943</td>\n",
-       "      <td>17024.965060</td>\n",
-       "      <td>16020.668347</td>\n",
-       "      <td>15542.194813</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5781.168717</td>\n",
-       "      <td>6902.897781</td>\n",
-       "      <td>7332.869062</td>\n",
-       "      <td>7966.108624</td>\n",
-       "      <td>7064.511451</td>\n",
-       "      <td>5943.253102</td>\n",
-       "      <td>5218.262111</td>\n",
-       "      <td>5102.206485</td>\n",
-       "      <td>5314.297172</td>\n",
-       "      <td>5099.248095</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1923</th>\n",
-       "      <td>34739.902572</td>\n",
-       "      <td>23452.001384</td>\n",
-       "      <td>27701.766984</td>\n",
-       "      <td>24573.630709</td>\n",
-       "      <td>21603.756959</td>\n",
-       "      <td>25666.872646</td>\n",
-       "      <td>17729.292606</td>\n",
-       "      <td>17124.649955</td>\n",
-       "      <td>16034.688033</td>\n",
-       "      <td>15555.297180</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5732.219513</td>\n",
-       "      <td>6888.642235</td>\n",
-       "      <td>7290.313640</td>\n",
-       "      <td>7987.508932</td>\n",
-       "      <td>7047.818826</td>\n",
-       "      <td>5994.795669</td>\n",
-       "      <td>5166.010745</td>\n",
-       "      <td>5106.338168</td>\n",
-       "      <td>5287.302024</td>\n",
-       "      <td>5071.851922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1924</th>\n",
-       "      <td>34481.281361</td>\n",
-       "      <td>23237.842294</td>\n",
-       "      <td>27707.542236</td>\n",
-       "      <td>24534.071366</td>\n",
-       "      <td>21496.946353</td>\n",
-       "      <td>25828.025424</td>\n",
-       "      <td>17631.368364</td>\n",
-       "      <td>17120.660056</td>\n",
-       "      <td>16045.178564</td>\n",
-       "      <td>15536.474579</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5680.752341</td>\n",
-       "      <td>6925.075316</td>\n",
-       "      <td>7309.041860</td>\n",
-       "      <td>7965.590687</td>\n",
-       "      <td>7103.075455</td>\n",
-       "      <td>5962.166249</td>\n",
-       "      <td>5125.958977</td>\n",
-       "      <td>5096.571439</td>\n",
-       "      <td>5329.789713</td>\n",
-       "      <td>5029.698197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1925</th>\n",
-       "      <td>34600.945806</td>\n",
-       "      <td>23289.105272</td>\n",
-       "      <td>27766.287448</td>\n",
-       "      <td>24543.687146</td>\n",
-       "      <td>21603.877186</td>\n",
-       "      <td>25977.235900</td>\n",
-       "      <td>17719.442114</td>\n",
-       "      <td>17098.382555</td>\n",
-       "      <td>16092.876008</td>\n",
-       "      <td>15569.354706</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5677.568191</td>\n",
-       "      <td>6934.919387</td>\n",
-       "      <td>7312.375830</td>\n",
-       "      <td>7957.665311</td>\n",
-       "      <td>7191.245598</td>\n",
-       "      <td>5984.124688</td>\n",
-       "      <td>5159.392028</td>\n",
-       "      <td>5085.402060</td>\n",
-       "      <td>5295.292834</td>\n",
-       "      <td>5049.829455</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1926</th>\n",
-       "      <td>34924.482502</td>\n",
-       "      <td>23490.181875</td>\n",
-       "      <td>27838.199140</td>\n",
-       "      <td>24582.879296</td>\n",
-       "      <td>21826.919220</td>\n",
-       "      <td>26088.341561</td>\n",
-       "      <td>17908.803279</td>\n",
-       "      <td>17060.351796</td>\n",
-       "      <td>16147.318945</td>\n",
-       "      <td>15617.797227</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5690.624174</td>\n",
-       "      <td>6916.892846</td>\n",
-       "      <td>7300.034115</td>\n",
-       "      <td>7954.704193</td>\n",
-       "      <td>7264.274756</td>\n",
-       "      <td>6020.282414</td>\n",
-       "      <td>5220.334329</td>\n",
-       "      <td>5072.943940</td>\n",
-       "      <td>5236.148053</td>\n",
-       "      <td>5095.167409</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1746 rows × 705 columns</p>\n",
-       "</div>"
-      ]
+      "text/plain": "        #Intensity    #Intensity    #Intensity    #Intensity    #Intensity  \\\n181   15196.588457  11541.323730  14841.336497  12962.544993  11218.268657   \n182   15238.392893  11514.042480  14791.788614  12888.778360  11174.286298   \n183   15260.348015  11496.918280  14735.185229  12819.365812  11138.891151   \n184   15217.462044  11512.973779  14655.533872  12764.176613  11131.547559   \n185   15151.631602  11497.515892  14589.036222  12737.702642  11115.027596   \n...            ...           ...           ...           ...           ...   \n1922  34619.811769  23259.160077  27759.794667  24519.211765  21569.532670   \n1923  34739.902572  23452.001384  27701.766984  24573.630709  21603.756959   \n1924  34481.281361  23237.842294  27707.542236  24534.071366  21496.946353   \n1925  34600.945806  23289.105272  27766.287448  24543.687146  21603.877186   \n1926  34924.482502  23490.181875  27838.199140  24582.879296  21826.919220   \n\n        #Intensity    #Intensity    #Intensity    #Intensity    #Intensity  \\\n181   13797.517019   9500.903430   9737.872920   9199.072282   9137.087615   \n182   13699.189902   9395.398390   9689.048324   9192.839419   9078.193966   \n183   13624.446367   9327.081873   9644.117090   9170.494344   9020.844001   \n184   13626.742532   9380.247863   9611.904176   9095.516044   8968.536739   \n185   13609.690867   9372.983056   9580.085679   9031.298566   8928.190135   \n...            ...           ...           ...           ...           ...   \n1922  25599.870205  17762.395943  17024.965060  16020.668347  15542.194813   \n1923  25666.872646  17729.292606  17124.649955  16034.688033  15555.297180   \n1924  25828.025424  17631.368364  17120.660056  16045.178564  15536.474579   \n1925  25977.235900  17719.442114  17098.382555  16092.876008  15569.354706   \n1926  26088.341561  17908.803279  17060.351796  16147.318945  15617.797227   \n\n      ...   #Intensity   #Intensity   #Intensity   #Intensity   #Intensity  \\\n181   ...  4624.879736  5815.276942  5650.689017  6322.280621  5698.545706   \n182   ...  4610.755115  5785.949748  5624.010916  6301.687030  5728.581779   \n183   ...  4595.789124  5748.612117  5577.087592  6273.081571  5732.034873   \n184   ...  4579.005354  5693.967964  5486.424537  6227.166501  5678.055538   \n185   ...  4559.167826  5640.791390  5440.487382  6183.233482  5637.696455   \n...   ...          ...          ...          ...          ...          ...   \n1922  ...  5781.168717  6902.897781  7332.869062  7966.108624  7064.511451   \n1923  ...  5732.219513  6888.642235  7290.313640  7987.508932  7047.818826   \n1924  ...  5680.752341  6925.075316  7309.041860  7965.590687  7103.075455   \n1925  ...  5677.568191  6934.919387  7312.375830  7957.665311  7191.245598   \n1926  ...  5690.624174  6916.892846  7300.034115  7954.704193  7264.274756   \n\n       #Intensity   #Intensity   #Intensity   #Intensity   #Intensity  \n181   4721.482986  4475.939129  4236.825013  4275.228424  4362.354997  \n182   4716.710030  4495.821569  4214.647920  4236.377266  4433.595406  \n183   4714.047225  4503.044538  4207.240139  4203.438897  4451.034918  \n184   4715.943395  4482.916766  4231.741399  4183.275087  4352.237791  \n185   4687.460987  4455.174896  4222.847266  4176.257179  4266.765683  \n...           ...          ...          ...          ...          ...  \n1922  5943.253102  5218.262111  5102.206485  5314.297172  5099.248095  \n1923  5994.795669  5166.010745  5106.338168  5287.302024  5071.851922  \n1924  5962.166249  5125.958977  5096.571439  5329.789713  5029.698197  \n1925  5984.124688  5159.392028  5085.402060  5295.292834  5049.829455  \n1926  6020.282414  5220.334329  5072.943940  5236.148053  5095.167409  \n\n[1746 rows x 705 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>...</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n      <th>#Intensity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>181</th>\n      <td>15196.588457</td>\n      <td>11541.323730</td>\n      <td>14841.336497</td>\n      <td>12962.544993</td>\n      <td>11218.268657</td>\n      <td>13797.517019</td>\n      <td>9500.903430</td>\n      <td>9737.872920</td>\n      <td>9199.072282</td>\n      <td>9137.087615</td>\n      <td>...</td>\n      <td>4624.879736</td>\n      <td>5815.276942</td>\n      <td>5650.689017</td>\n      <td>6322.280621</td>\n      <td>5698.545706</td>\n      <td>4721.482986</td>\n      <td>4475.939129</td>\n      <td>4236.825013</td>\n      <td>4275.228424</td>\n      <td>4362.354997</td>\n    </tr>\n    <tr>\n      <th>182</th>\n      <td>15238.392893</td>\n      <td>11514.042480</td>\n      <td>14791.788614</td>\n      <td>12888.778360</td>\n      <td>11174.286298</td>\n      <td>13699.189902</td>\n      <td>9395.398390</td>\n      <td>9689.048324</td>\n      <td>9192.839419</td>\n      <td>9078.193966</td>\n      <td>...</td>\n      <td>4610.755115</td>\n      <td>5785.949748</td>\n      <td>5624.010916</td>\n      <td>6301.687030</td>\n      <td>5728.581779</td>\n      <td>4716.710030</td>\n      <td>4495.821569</td>\n      <td>4214.647920</td>\n      <td>4236.377266</td>\n      <td>4433.595406</td>\n    </tr>\n    <tr>\n      <th>183</th>\n      <td>15260.348015</td>\n      <td>11496.918280</td>\n      <td>14735.185229</td>\n      <td>12819.365812</td>\n      <td>11138.891151</td>\n      <td>13624.446367</td>\n      <td>9327.081873</td>\n      <td>9644.117090</td>\n      <td>9170.494344</td>\n      <td>9020.844001</td>\n      <td>...</td>\n      <td>4595.789124</td>\n      <td>5748.612117</td>\n      <td>5577.087592</td>\n      <td>6273.081571</td>\n      <td>5732.034873</td>\n      <td>4714.047225</td>\n      <td>4503.044538</td>\n      <td>4207.240139</td>\n      <td>4203.438897</td>\n      <td>4451.034918</td>\n    </tr>\n    <tr>\n      <th>184</th>\n      <td>15217.462044</td>\n      <td>11512.973779</td>\n      <td>14655.533872</td>\n      <td>12764.176613</td>\n      <td>11131.547559</td>\n      <td>13626.742532</td>\n      <td>9380.247863</td>\n      <td>9611.904176</td>\n      <td>9095.516044</td>\n      <td>8968.536739</td>\n      <td>...</td>\n      <td>4579.005354</td>\n      <td>5693.967964</td>\n      <td>5486.424537</td>\n      <td>6227.166501</td>\n      <td>5678.055538</td>\n      <td>4715.943395</td>\n      <td>4482.916766</td>\n      <td>4231.741399</td>\n      <td>4183.275087</td>\n      <td>4352.237791</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>15151.631602</td>\n      <td>11497.515892</td>\n      <td>14589.036222</td>\n      <td>12737.702642</td>\n      <td>11115.027596</td>\n      <td>13609.690867</td>\n      <td>9372.983056</td>\n      <td>9580.085679</td>\n      <td>9031.298566</td>\n      <td>8928.190135</td>\n      <td>...</td>\n      <td>4559.167826</td>\n      <td>5640.791390</td>\n      <td>5440.487382</td>\n      <td>6183.233482</td>\n      <td>5637.696455</td>\n      <td>4687.460987</td>\n      <td>4455.174896</td>\n      <td>4222.847266</td>\n      <td>4176.257179</td>\n      <td>4266.765683</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1922</th>\n      <td>34619.811769</td>\n      <td>23259.160077</td>\n      <td>27759.794667</td>\n      <td>24519.211765</td>\n      <td>21569.532670</td>\n      <td>25599.870205</td>\n      <td>17762.395943</td>\n      <td>17024.965060</td>\n      <td>16020.668347</td>\n      <td>15542.194813</td>\n      <td>...</td>\n      <td>5781.168717</td>\n      <td>6902.897781</td>\n      <td>7332.869062</td>\n      <td>7966.108624</td>\n      <td>7064.511451</td>\n      <td>5943.253102</td>\n      <td>5218.262111</td>\n      <td>5102.206485</td>\n      <td>5314.297172</td>\n      <td>5099.248095</td>\n    </tr>\n    <tr>\n      <th>1923</th>\n      <td>34739.902572</td>\n      <td>23452.001384</td>\n      <td>27701.766984</td>\n      <td>24573.630709</td>\n      <td>21603.756959</td>\n      <td>25666.872646</td>\n      <td>17729.292606</td>\n      <td>17124.649955</td>\n      <td>16034.688033</td>\n      <td>15555.297180</td>\n      <td>...</td>\n      <td>5732.219513</td>\n      <td>6888.642235</td>\n      <td>7290.313640</td>\n      <td>7987.508932</td>\n      <td>7047.818826</td>\n      <td>5994.795669</td>\n      <td>5166.010745</td>\n      <td>5106.338168</td>\n      <td>5287.302024</td>\n      <td>5071.851922</td>\n    </tr>\n    <tr>\n      <th>1924</th>\n      <td>34481.281361</td>\n      <td>23237.842294</td>\n      <td>27707.542236</td>\n      <td>24534.071366</td>\n      <td>21496.946353</td>\n      <td>25828.025424</td>\n      <td>17631.368364</td>\n      <td>17120.660056</td>\n      <td>16045.178564</td>\n      <td>15536.474579</td>\n      <td>...</td>\n      <td>5680.752341</td>\n      <td>6925.075316</td>\n      <td>7309.041860</td>\n      <td>7965.590687</td>\n      <td>7103.075455</td>\n      <td>5962.166249</td>\n      <td>5125.958977</td>\n      <td>5096.571439</td>\n      <td>5329.789713</td>\n      <td>5029.698197</td>\n    </tr>\n    <tr>\n      <th>1925</th>\n      <td>34600.945806</td>\n      <td>23289.105272</td>\n      <td>27766.287448</td>\n      <td>24543.687146</td>\n      <td>21603.877186</td>\n      <td>25977.235900</td>\n      <td>17719.442114</td>\n      <td>17098.382555</td>\n      <td>16092.876008</td>\n      <td>15569.354706</td>\n      <td>...</td>\n      <td>5677.568191</td>\n      <td>6934.919387</td>\n      <td>7312.375830</td>\n      <td>7957.665311</td>\n      <td>7191.245598</td>\n      <td>5984.124688</td>\n      <td>5159.392028</td>\n      <td>5085.402060</td>\n      <td>5295.292834</td>\n      <td>5049.829455</td>\n    </tr>\n    <tr>\n      <th>1926</th>\n      <td>34924.482502</td>\n      <td>23490.181875</td>\n      <td>27838.199140</td>\n      <td>24582.879296</td>\n      <td>21826.919220</td>\n      <td>26088.341561</td>\n      <td>17908.803279</td>\n      <td>17060.351796</td>\n      <td>16147.318945</td>\n      <td>15617.797227</td>\n      <td>...</td>\n      <td>5690.624174</td>\n      <td>6916.892846</td>\n      <td>7300.034115</td>\n      <td>7954.704193</td>\n      <td>7264.274756</td>\n      <td>6020.282414</td>\n      <td>5220.334329</td>\n      <td>5072.943940</td>\n      <td>5236.148053</td>\n      <td>5095.167409</td>\n    </tr>\n  </tbody>\n</table>\n<p>1746 rows × 705 columns</p>\n</div>"
      },
      "execution_count": 5,
      "metadata": {},
@@ -11368,20 +836,20 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-13T14:24:07.950072Z",
-     "start_time": "2024-04-13T14:23:59.586141Z"
+     "end_time": "2024-04-24T14:17:49.916551700Z",
+     "start_time": "2024-04-24T14:17:46.052096600Z"
     }
    },
    "cell_type": "code",
-   "source": "_ = df_experiments.plot(legend=False)",
+   "source": [
+    "_ = df_experiments.plot(legend=False)"
+   ],
    "id": "3f4e5af390b7d068",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -11392,8 +860,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-13T14:24:09.890144Z",
-     "start_time": "2024-04-13T14:24:07.950072Z"
+     "end_time": "2024-04-24T14:17:50.717719200Z",
+     "start_time": "2024-04-24T14:17:49.916551700Z"
     }
    },
    "cell_type": "code",
@@ -11411,12 +879,12 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-13T14:24:09.894524Z",
-     "start_time": "2024-04-13T14:24:09.890144Z"
+     "end_time": "2024-04-24T14:17:50.724277200Z",
+     "start_time": "2024-04-24T14:17:50.717719200Z"
     }
    },
    "cell_type": "code",
-   "source": "",
+   "source": [],
    "id": "2fd78b0a5be4104f",
    "outputs": [],
    "execution_count": 7
diff --git a/data/data_processing.py b/data/data_processing.py
index 82b313e..5d03312 100644
--- a/data/data_processing.py
+++ b/data/data_processing.py
@@ -80,9 +80,26 @@ def categorize_metadata(metadata: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFr
     return truth, encoded
 
 
-def process_experiments(experiments: pd.DataFrame, baseline_lam=10, baseline_p=1e-2,
-                        smooth_window_length=7, smooth_polyorder=3) -> pd.DataFrame:
+def process_experiments(experiments: pd.DataFrame, baseline_lam: int = 10, baseline_p: float = 1e-2,
+                        smooth_window_length: int = 7, smooth_polyorder: int = 3) -> pd.DataFrame:
     experiments = adjust_all_baselines(experiments, lam=baseline_lam, p=baseline_p)
     experiments = scale_experiments(experiments)
     experiments = smooth_experiments(experiments, window_length=smooth_window_length, polyorder=smooth_polyorder)
     return experiments
+
+
+def process_train_test(params: dict, experiments_train: pd.DataFrame, metadata_train: pd.DataFrame, experiments_test: pd.DataFrame = None, metadata_test: pd.DataFrame = None, scale: bool=True) -> tuple[pd.DataFrame, pd.DataFrame]:
+    processed_train = process_experiments(experiments_train, **params)
+    X_train = pd.concat([metadata_train, processed_train], axis=1)
+    if experiments_test is not None:
+        processed_test = process_experiments(experiments_test, **params)
+        X_test = pd.concat([metadata_test, processed_test], axis=1)
+    else:
+        X_test = None
+    if scale:
+        scaler = StandardScaler()
+        scaler.fit(X_train)
+        X_train = scaler.transform(X_train)
+        if X_test is not None:
+            X_test = scaler.transform(X_test)
+    return X_train, X_test