Skip to content

Instantly share code, notes, and snippets.

@pplonski

pplonski/automl-example.ipynb

Last active March 13, 2025 09:17
Show Gist options
  • Save pplonski/b12fa41aed052fdbe60ac32b5c536961 to your computer and use it in GitHub Desktop.
Save pplonski/b12fa41aed052fdbe60ac32b5c536961 to your computer and use it in GitHub Desktop.
Download ZIP
Automated Machine Learning example on Adults income dataset
Display the source blob
Display the rendered blob
Raw
automl-example.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "70f0c856-7273-4437-880d-64bd0a152494",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded data shape (32561, 15)\n"
]
},
{
"data": {
"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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>39</td>\n",
" <td>State-gov</td>\n",
" <td>77516</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Never-married</td>\n",
" <td>Adm-clerical</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>2174</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>Self-emp-not-inc</td>\n",
" <td>83311</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Exec-managerial</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>38</td>\n",
" <td>Private</td>\n",
" <td>215646</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Divorced</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>53</td>\n",
" <td>Private</td>\n",
" <td>234721</td>\n",
" <td>11th</td>\n",
" <td>7</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Husband</td>\n",
" <td>Black</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>28</td>\n",
" <td>Private</td>\n",
" <td>338409</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Prof-specialty</td>\n",
" <td>Wife</td>\n",
" <td>Black</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>Cuba</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education-num \\\n",
"0 39 State-gov 77516 Bachelors 13 \n",
"1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
"2 38 Private 215646 HS-grad 9 \n",
"3 53 Private 234721 11th 7 \n",
"4 28 Private 338409 Bachelors 13 \n",
"\n",
" marital-status occupation relationship race sex \\\n",
"0 Never-married Adm-clerical Not-in-family White Male \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Divorced Handlers-cleaners Not-in-family White Male \n",
"3 Married-civ-spouse Handlers-cleaners Husband Black Male \n",
"4 Married-civ-spouse Prof-specialty Wife Black Female \n",
"\n",
" capital-gain capital-loss hours-per-week native-country income \n",
"0 2174 0 40 United-States <=50K \n",
"1 0 0 13 United-States <=50K \n",
"2 0 0 40 United-States <=50K \n",
"3 0 0 40 United-States <=50K \n",
"4 0 0 40 Cuba <=50K "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"# load example dataset\n",
"df = pd.read_csv(\"https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv\", skipinitialspace=True)\n",
"# display DataFrame shape\n",
"print(f\"Loaded data shape {df.shape}\")\n",
"# display first rows\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "743b04d9-17fb-4067-bd76-41c17368ddbf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All data shape (32561, 15)\n",
"Train shape (24420, 15)\n",
"Test shape (8141, 15)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"# split data\n",
"train, test = train_test_split(df, train_size=0.75, shuffle=True, random_state=42)\n",
"# display data shapes\n",
"print(f\"All data shape {df.shape}\")\n",
"print(f\"Train shape {train.shape}\")\n",
"print(f\"Test shape {test.shape}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "398164a0-e99b-4c84-9309-403ec1c92c22",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X shape is (24420, 14)\n",
"y shape is (24420,)\n"
]
}
],
"source": [
"import pandas as pd\n",
"# create X columns list and set y column\n",
"x_cols = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education-num\", \"marital-status\", \"occupation\", \"relationship\", \"race\", \"sex\", \"capital-gain\", \"capital-loss\", \"hours-per-week\", \"native-country\"]\n",
"y_col = \"income\"\n",
"# set input matrix\n",
"X = train[x_cols]\n",
"# set target vector\n",
"y = train[y_col]\n",
"# display data shapes\n",
"print(f\"X shape is {X.shape}\")\n",
"print(f\"y shape is {y.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "54712771-64b1-45a1-9048-2003ef45b89d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear algorithm was disabled.\n",
"AutoML directory: AutoML_1\n",
"The task is binary_classification with evaluation metric logloss\n",
"AutoML will use algorithms: ['Baseline', 'Decision Tree', 'Random Forest', 'Xgboost', 'Neural Network']\n",
"AutoML will ensemble available models\n",
"AutoML steps: ['simple_algorithms', 'default_algorithms', 'ensemble']\n",
"* Step simple_algorithms will try to check up to 2 models\n",
"1_Baseline logloss 0.553672 trained in 0.4 seconds\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/piotr/.config/mljar-studio/jlab_server/lib/python3.11/site-packages/supervised/utils/shap.py:123: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
"/home/piotr/.config/mljar-studio/jlab_server/lib/python3.11/site-packages/shap/plots/_beeswarm.py:718: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
"/home/piotr/.config/mljar-studio/jlab_server/lib/python3.11/site-packages/shap/plots/_beeswarm.py:738: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2_DecisionTree logloss 0.371593 trained in 7.7 seconds\n",
"* Step default_algorithms will try to check up to 3 models\n",
"3_Default_Xgboost logloss 0.277715 trained in 5.54 seconds\n",
"4_Default_NeuralNetwork logloss 0.351514 trained in 7.72 seconds\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/piotr/.config/mljar-studio/jlab_server/lib/python3.11/site-packages/supervised/utils/shap.py:123: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
"/home/piotr/.config/mljar-studio/jlab_server/lib/python3.11/site-packages/shap/plots/_beeswarm.py:718: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n",
"/home/piotr/.config/mljar-studio/jlab_server/lib/python3.11/site-packages/shap/plots/_beeswarm.py:738: FutureWarning: The NumPy global RNG was seeded by calling `np.random.seed`. In a future version this function will no longer use the global RNG. Pass `rng` explicitly to opt-in to the new behaviour and silence this warning.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"5_Default_RandomForest logloss 0.341413 trained in 4.44 seconds\n",
"* Step ensemble will try to check up to 1 model\n",
"Ensemble logloss 0.277715 trained in 2.2 seconds\n",
"AutoML fit time: 34.29 seconds\n",
"AutoML best model: 3_Default_Xgboost\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: #000;\n",
" --sklearn-color-text-muted: #666;\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: flex;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
" align-items: start;\n",
" justify-content: space-between;\n",
" gap: 0.5em;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label .caption {\n",
" font-size: 0.6rem;\n",
" font-weight: lighter;\n",
" color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
" text-align: center;\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>AutoML(total_time_limit=300)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AutoML</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>AutoML(total_time_limit=300)</pre></div> </div></div></div></div>"
],
"text/plain": [
"AutoML(total_time_limit=300)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from supervised import AutoML\n",
"# create automl object\n",
"automl = AutoML(total_time_limit=300, mode=\"Explain\")\n",
"# train automl\n",
"automl.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0e1f71be-8b58-48f9-a03f-a2d89bf69d3a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<!DOCTYPE html>\n",
"<html>\n",
"<head>\n",
" <style>\n",
" \n",
".styled-table {\n",
" border-collapse: collapse;\n",
" font-size: 0.9em;\n",
" font-family: Courier New;\n",
"}\n",
"\n",
".styled-table td, .styled-table th {\n",
" border: 1px solid #ddd;\n",
" padding: 8px;\n",
"}\n",
"\n",
".styled-table tr:nth-child(even){background-color: #f2f2f2;}\n",
"\n",
".styled-table tr:hover {background-color: #e0ecf5;}\n",
"\n",
".styled-table thead {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" text-align: left;\n",
" background-color: #0099cc;\n",
" color: white;\n",
"}\n",
"\n",
".mljar-automl-report {\n",
" font-family: ui-sans-serif, system-ui, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\";\n",
" background-color: rgba(236, 243, 249, 0.15);\n",
"\n",
"\n",
"h1 {\n",
" color: #004666;\n",
" border-bottom: 1px solid rgba(0,70,102,0.3)\n",
"}\n",
"h2 {\n",
" color: #004666;\n",
" padding-bottom: 5px;\n",
" margin-bottom: 0px;\n",
"}\n",
"\n",
"ul {\n",
" margin-top: 0px;\n",
"}\n",
"\n",
"p {\n",
" margin-top: 5px;\n",
"}\n",
"\n",
"h3 {\n",
" color: #004666;\n",
" padding-bottom: 5px;\n",
" margin-bottom: 0px;\n",
"}\n",
"a {\n",
" font-weight: bold;\n",
" color: #004666;\n",
"}\n",
"\n",
"a:hover {\n",
" cursor: pointer;\n",
" color: #0099CC;\n",
"}\n",
"}\n",
"\n",
"\n",
" </style>\n",
"</head>\n",
"<body>\n",
" <div class=\"mljar-automl-report-d140ed16-775d-4009-a92b-60d809402bbe\">\n",
" <div id=\"automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe\">\n",
"<img src=\"https://raw.githubusercontent.com/mljar/visual-identity/main/media/mljar_AutomatedML.png\" style=\"height:128px; margin-left: auto;\n",
"margin-right: auto;display: block;\"/>\n",
"\n",
"<h1>AutoML Leaderboard</h1>\n",
"<table class=\"styled-table\">\n",
"<thead>\n",
"<tr style=\"text-align: right;\">\n",
"<th style=\"text-align: left;\">Best model</th>\n",
"<th style=\"text-align: left;\">name</th>\n",
"<th style=\"text-align: left;\">model_type</th>\n",
"<th style=\"text-align: left;\">metric_type</th>\n",
"<th style=\"text-align: right;\">metric_value</th>\n",
"<th style=\"text-align: right;\">train_time</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\"></td>\n",
"<td style=\"text-align: left;\"><a onclick=\"toggleShow('1_Baseline-d140ed16-775d-4009-a92b-60d809402bbe');toggleShow('automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe')\" >1_Baseline</a></td>\n",
"<td style=\"text-align: left;\">Baseline</td>\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.553672</td>\n",
"<td style=\"text-align: right;\">1</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\"></td>\n",
"<td style=\"text-align: left;\"><a onclick=\"toggleShow('2_DecisionTree-d140ed16-775d-4009-a92b-60d809402bbe');toggleShow('automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe')\" >2_DecisionTree</a></td>\n",
"<td style=\"text-align: left;\">Decision Tree</td>\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.371593</td>\n",
"<td style=\"text-align: right;\">8.42</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\"><strong>the best</strong></td>\n",
"<td style=\"text-align: left;\"><a onclick=\"toggleShow('3_Default_Xgboost-d140ed16-775d-4009-a92b-60d809402bbe');toggleShow('automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe')\" >3_Default_Xgboost</a></td>\n",
"<td style=\"text-align: left;\">Xgboost</td>\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.277715</td>\n",
"<td style=\"text-align: right;\">6.3</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\"></td>\n",
"<td style=\"text-align: left;\"><a onclick=\"toggleShow('4_Default_NeuralNetwork-d140ed16-775d-4009-a92b-60d809402bbe');toggleShow('automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe')\" >4_Default_NeuralNetwork</a></td>\n",
"<td style=\"text-align: left;\">Neural Network</td>\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.351514</td>\n",
"<td style=\"text-align: right;\">8.46</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\"></td>\n",
"<td style=\"text-align: left;\"><a onclick=\"toggleShow('5_Default_RandomForest-d140ed16-775d-4009-a92b-60d809402bbe');toggleShow('automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe')\" >5_Default_RandomForest</a></td>\n",
"<td style=\"text-align: left;\">Random Forest</td>\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.341413</td>\n",
"<td style=\"text-align: right;\">5.19</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\"></td>\n",
"<td style=\"text-align: left;\"><a onclick=\"toggleShow('Ensemble-d140ed16-775d-4009-a92b-60d809402bbe');toggleShow('automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe')\" >Ensemble</a></td>\n",
"<td style=\"text-align: left;\">Ensemble</td>\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.277715</td>\n",
"<td style=\"text-align: right;\">2.2</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<h3>AutoML Performance</h3>\n",
"<p><img style=\"width:750px\" alt=\"AutoML Performance\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h3>AutoML Performance Boxplot</h3>\n",
"<p><img style=\"width:750px\" alt=\"AutoML Performance Boxplot\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h3>Features Importance</h3>\n",
"<p><img style=\"width:750px\" alt=\"features importance across models\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h3>Spearman Correlation of Models</h3>\n",
"<p><img style=\"width:750px\" alt=\"models spearman correlation\" src=\"data:image/png;base64, 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\" /></p>\n",
"\n",
"</div>\n",
"\n",
"<div id=\"2_DecisionTree-d140ed16-775d-4009-a92b-60d809402bbe\" style=\"display: none\">\n",
"<h1>Summary of 2_DecisionTree</h1>\n",
"<p><a onclick=\"toggleShow('2_DecisionTree-d140ed16-775d-4009-a92b-60d809402bbe');toggleShow('automl-report-main-d140ed16-775d-4009-a92b-60d809402bbe')\" >&lt;&lt; Go back</a></p>\n",
"<h2>Decision Tree</h2>\n",
"<ul>\n",
"<li><strong>n_jobs</strong>: -1</li>\n",
"<li><strong>criterion</strong>: gini</li>\n",
"<li><strong>max_depth</strong>: 3</li>\n",
"<li><strong>explain_level</strong>: 2</li>\n",
"</ul>\n",
"<h2>Validation</h2>\n",
"<ul>\n",
"<li><strong>validation_type</strong>: split</li>\n",
"<li><strong>train_ratio</strong>: 0.75</li>\n",
"<li><strong>shuffle</strong>: True</li>\n",
"<li><strong>stratify</strong>: True</li>\n",
"</ul>\n",
"<h2>Optimized metric</h2>\n",
"<p>logloss</p>\n",
"<h2>Training time</h2>\n",
"<p>7.7 seconds</p>\n",
"<h2>Metric details</h2>\n",
"<table class=\"styled-table\">\n",
"<thead>\n",
"<tr style=\"text-align: right;\">\n",
"<th style=\"text-align: left;\"></th>\n",
"<th style=\"text-align: right;\">score</th>\n",
"<th style=\"text-align: right;\">threshold</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.371593</td>\n",
"<td style=\"text-align: right;\">nan</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">auc</td>\n",
"<td style=\"text-align: right;\">0.848229</td>\n",
"<td style=\"text-align: right;\">nan</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">f1</td>\n",
"<td style=\"text-align: right;\">0.618236</td>\n",
"<td style=\"text-align: right;\">0.296602</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">accuracy</td>\n",
"<td style=\"text-align: right;\">0.839312</td>\n",
"<td style=\"text-align: right;\">0.458281</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">precision</td>\n",
"<td style=\"text-align: right;\">0.990909</td>\n",
"<td style=\"text-align: right;\">0.980159</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">recall</td>\n",
"<td style=\"text-align: right;\">0.999324</td>\n",
"<td style=\"text-align: right;\">0</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">mcc</td>\n",
"<td style=\"text-align: right;\">0.520572</td>\n",
"<td style=\"text-align: right;\">0.458281</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<h2>Metric details with threshold from accuracy metric</h2>\n",
"<table class=\"styled-table\">\n",
"<thead>\n",
"<tr style=\"text-align: right;\">\n",
"<th style=\"text-align: left;\"></th>\n",
"<th style=\"text-align: right;\">score</th>\n",
"<th style=\"text-align: right;\">threshold</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">logloss</td>\n",
"<td style=\"text-align: right;\">0.371593</td>\n",
"<td style=\"text-align: right;\">nan</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">auc</td>\n",
"<td style=\"text-align: right;\">0.848229</td>\n",
"<td style=\"text-align: right;\">nan</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">f1</td>\n",
"<td style=\"text-align: right;\">0.59513</td>\n",
"<td style=\"text-align: right;\">0.458281</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">accuracy</td>\n",
"<td style=\"text-align: right;\">0.839312</td>\n",
"<td style=\"text-align: right;\">0.458281</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">precision</td>\n",
"<td style=\"text-align: right;\">0.763771</td>\n",
"<td style=\"text-align: right;\">0.458281</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">recall</td>\n",
"<td style=\"text-align: right;\">0.487492</td>\n",
"<td style=\"text-align: right;\">0.458281</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">mcc</td>\n",
"<td style=\"text-align: right;\">0.520572</td>\n",
"<td style=\"text-align: right;\">0.458281</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<h2>Confusion matrix (at threshold=0.458281)</h2>\n",
"<table class=\"styled-table\">\n",
"<thead>\n",
"<tr style=\"text-align: right;\">\n",
"<th style=\"text-align: left;\"></th>\n",
"<th style=\"text-align: right;\">Predicted as &lt;=50K</th>\n",
"<th style=\"text-align: right;\">Predicted as &gt;50K</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">Labeled as &lt;=50K</td>\n",
"<td style=\"text-align: right;\">4403</td>\n",
"<td style=\"text-align: right;\">223</td>\n",
"</tr>\n",
"<tr style=\"text-align: right;\">\n",
"<td style=\"text-align: left;\">Labeled as &gt;50K</td>\n",
"<td style=\"text-align: right;\">758</td>\n",
"<td style=\"text-align: right;\">721</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<h2>Learning curves</h2>\n",
"<p><img style=\"width:750px\" alt=\"Learning curves\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h2>Decision Tree</h2>\n",
"<h3>Tree #1</h3>\n",
"<p><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:ns2=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"714.0\" height=\"411.0\" viewBox=\"0.0 0.0 714.0 411.0\">\n",
"<g id=\"graph0\" class=\"graph\" transform=\"scale(1.0 1.0) rotate(0) translate(4 407)\">\n",
"<title>G</title>\n",
"<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-407 710,-407 710,4 -4,4\" />\n",
"<g id=\"clust8\" class=\"cluster\">\n",
"<title>cluster_legend</title>\n",
"</g>\n",
"\n",
"<g id=\"node1\" class=\"node\">\n",
"<title>node2</title>\n",
"<svg width=\"159px\" height=\"66px\" viewBox=\"0 0 211.5 88.946333\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"16\" y=\"-171\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:46.565860</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 88.946333 L 211.5 88.946333 L 211.5 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 27.36 54.090083 L 211.5 54.090083 L 211.5 0.48684 L 27.36 0.48684 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 43.372174 54.090083 L 51.378261 54.090083 L 51.378261 18.453408 L 43.372174 18.453408 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 51.378261 54.090083 L 59.384348 54.090083 L 59.384348 54.069708 L 51.378261 54.069708 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 59.384348 54.090083 L 67.390435 54.090083 L 67.390435 54.05952 L 59.384348 54.05952 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 67.390435 54.090083 L 75.396522 54.090083 L 75.396522 54.090083 L 67.390435 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_7\">\n",
" <path d=\"M 75.396522 54.090083 L 83.402609 54.090083 L 83.402609 54.090083 L 75.396522 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_8\">\n",
" <path d=\"M 83.402609 54.090083 L 91.408696 54.090083 L 91.408696 54.090083 L 83.402609 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_9\">\n",
" <path d=\"M 91.408696 54.090083 L 99.414783 54.090083 L 99.414783 54.090083 L 91.408696 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_10\">\n",
" <path d=\"M 99.414783 54.090083 L 107.42087 54.090083 L 107.42087 54.090083 L 99.414783 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_11\">\n",
" <path d=\"M 107.42087 54.090083 L 115.426957 54.090083 L 115.426957 54.090083 L 107.42087 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_12\">\n",
" <path d=\"M 115.426957 54.090083 L 123.433043 54.090083 L 123.433043 54.090083 L 115.426957 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_13\">\n",
" <path d=\"M 123.433043 54.090083 L 131.43913 54.090083 L 131.43913 54.090083 L 123.433043 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_14\">\n",
" <path d=\"M 131.43913 54.090083 L 139.445217 54.090083 L 139.445217 54.090083 L 131.43913 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_15\">\n",
" <path d=\"M 139.445217 54.090083 L 147.451304 54.090083 L 147.451304 54.090083 L 139.445217 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_16\">\n",
" <path d=\"M 147.451304 54.090083 L 155.457391 54.090083 L 155.457391 54.090083 L 147.451304 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_17\">\n",
" <path d=\"M 155.457391 54.090083 L 163.463478 54.090083 L 163.463478 54.090083 L 155.457391 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_18\">\n",
" <path d=\"M 163.463478 54.090083 L 171.469565 54.090083 L 171.469565 54.090083 L 163.463478 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_19\">\n",
" <path d=\"M 171.469565 54.090083 L 179.475652 54.090083 L 179.475652 54.090083 L 171.469565 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_20\">\n",
" <path d=\"M 179.475652 54.090083 L 187.481739 54.090083 L 187.481739 54.090083 L 179.475652 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_21\">\n",
" <path d=\"M 187.481739 54.090083 L 195.487826 54.090083 L 195.487826 54.090083 L 187.481739 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_22\">\n",
" <path d=\"M 43.372174 18.453408 L 51.378261 18.453408 L 51.378261 3.039375 L 43.372174 3.039375 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_23\">\n",
" <path d=\"M 51.378261 54.069708 L 59.384348 54.069708 L 59.384348 52.561924 L 51.378261 52.561924 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_24\">\n",
" <path d=\"M 59.384348 54.05952 L 67.390435 54.05952 L 67.390435 53.570509 L 59.384348 53.570509 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_25\">\n",
" <path d=\"M 67.390435 54.090083 L 75.396522 54.090083 L 75.396522 54.028957 L 67.390435 54.028957 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_26\">\n",
" <path d=\"M 75.396522 54.090083 L 83.402609 54.090083 L 83.402609 54.090083 L 75.396522 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_27\">\n",
" <path d=\"M 83.402609 54.090083 L 91.408696 54.090083 L 91.408696 54.090083 L 83.402609 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_28\">\n",
" <path d=\"M 91.408696 54.090083 L 99.414783 54.090083 L 99.414783 54.090083 L 91.408696 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_29\">\n",
" <path d=\"M 99.414783 54.090083 L 107.42087 54.090083 L 107.42087 54.090083 L 99.414783 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_30\">\n",
" <path d=\"M 107.42087 54.090083 L 115.426957 54.090083 L 115.426957 54.090083 L 107.42087 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_31\">\n",
" <path d=\"M 115.426957 54.090083 L 123.433043 54.090083 L 123.433043 54.090083 L 115.426957 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_32\">\n",
" <path d=\"M 123.433043 54.090083 L 131.43913 54.090083 L 131.43913 54.090083 L 123.433043 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_33\">\n",
" <path d=\"M 131.43913 54.090083 L 139.445217 54.090083 L 139.445217 54.090083 L 131.43913 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_34\">\n",
" <path d=\"M 139.445217 54.090083 L 147.451304 54.090083 L 147.451304 54.090083 L 139.445217 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_35\">\n",
" <path d=\"M 147.451304 54.090083 L 155.457391 54.090083 L 155.457391 54.090083 L 147.451304 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_36\">\n",
" <path d=\"M 155.457391 54.090083 L 163.463478 54.090083 L 163.463478 54.090083 L 155.457391 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_37\">\n",
" <path d=\"M 163.463478 54.090083 L 171.469565 54.090083 L 171.469565 54.090083 L 163.463478 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_38\">\n",
" <path d=\"M 171.469565 54.090083 L 179.475652 54.090083 L 179.475652 54.090083 L 171.469565 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_39\">\n",
" <path d=\"M 179.475652 54.090083 L 187.481739 54.090083 L 187.481739 54.090083 L 179.475652 54.090083 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_40\">\n",
" <path d=\"M 187.481739 54.090083 L 195.487826 54.090083 L 195.487826 54.018769 L 187.481739 54.018769 z \" clip-path=\"url(#p565f94354d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_41\">\n",
" <path d=\"M 51.123304 54.921683 L 47.808784 63.237683 L 54.437824 63.237683 z \" style=\"fill: #444443\" />\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <defs>\n",
" <path id=\"m4429d55adf\" d=\"M 0 0 L 0 3.5 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m4429d55adf\" x=\"43.372174\" y=\"54.090083\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(40.827174 71.668833) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_2\">\n",
" <g>\n",
" <use xlink:href=\"#m4429d55adf\" x=\"195.487826\" y=\"54.090083\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(182.762826 71.668833) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-39\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(190.869141 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(254.492188 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(84.162187 86.450708) scale(0.12 -0.12)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-63\" d=\"M 3122 3366 L 3122 2828 Q 2878 2963 2633 3030 Q 2388 3097 2138 3097 Q 1578 3097 1268 2742 Q 959 2388 959 1747 Q 959 1106 1268 751 Q 1578 397 2138 397 Q 2388 397 2633 464 Q 2878 531 3122 666 L 3122 134 Q 2881 22 2623 -34 Q 2366 -91 2075 -91 Q 1284 -91 818 406 Q 353 903 353 1747 Q 353 2603 823 3093 Q 1294 3584 2113 3584 Q 2378 3584 2631 3529 Q 2884 3475 3122 3366 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-61\" d=\"M 2194 1759 Q 1497 1759 1228 1600 Q 959 1441 959 1056 Q 959 750 1161 570 Q 1363 391 1709 391 Q 2188 391 2477 730 Q 2766 1069 2766 1631 L 2766 1759 L 2194 1759 z M 3341 1997 L 3341 0 L 2766 0 L 2766 531 Q 2569 213 2275 61 Q 1981 -91 1556 -91 Q 1019 -91 701 211 Q 384 513 384 1019 Q 384 1609 779 1909 Q 1175 2209 1959 2209 L 2766 2209 L 2766 2266 Q 2766 2663 2505 2880 Q 2244 3097 1772 3097 Q 1472 3097 1187 3025 Q 903 2953 641 2809 L 641 3341 Q 956 3463 1253 3523 Q 1550 3584 1831 3584 Q 2591 3584 2966 3190 Q 3341 2797 3341 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-70\" d=\"M 1159 525 L 1159 -1331 L 581 -1331 L 581 3500 L 1159 3500 L 1159 2969 Q 1341 3281 1617 3432 Q 1894 3584 2278 3584 Q 2916 3584 3314 3078 Q 3713 2572 3713 1747 Q 3713 922 3314 415 Q 2916 -91 2278 -91 Q 1894 -91 1617 61 Q 1341 213 1159 525 z M 3116 1747 Q 3116 2381 2855 2742 Q 2594 3103 2138 3103 Q 1681 3103 1420 2742 Q 1159 2381 1159 1747 Q 1159 1113 1420 752 Q 1681 391 2138 391 Q 2594 391 2855 752 Q 3116 1113 3116 1747 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-69\" d=\"M 603 3500 L 1178 3500 L 1178 0 L 603 0 L 603 3500 z M 603 4863 L 1178 4863 L 1178 4134 L 603 4134 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-74\" d=\"M 1172 4494 L 1172 3500 L 2356 3500 L 2356 3053 L 1172 3053 L 1172 1153 Q 1172 725 1289 603 Q 1406 481 1766 481 L 2356 481 L 2356 0 L 1766 0 Q 1100 0 847 248 Q 594 497 594 1153 L 594 3053 L 172 3053 L 172 3500 L 594 3500 L 594 4494 L 1172 4494 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6c\" d=\"M 603 4863 L 1178 4863 L 1178 0 L 603 0 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-2d\" d=\"M 313 2009 L 1997 2009 L 1997 1497 L 313 1497 L 313 2009 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-67\" d=\"M 2906 1791 Q 2906 2416 2648 2759 Q 2391 3103 1925 3103 Q 1463 3103 1205 2759 Q 947 2416 947 1791 Q 947 1169 1205 825 Q 1463 481 1925 481 Q 2391 481 2648 825 Q 2906 1169 2906 1791 z M 3481 434 Q 3481 -459 3084 -895 Q 2688 -1331 1869 -1331 Q 1566 -1331 1297 -1286 Q 1028 -1241 775 -1147 L 775 -588 Q 1028 -725 1275 -790 Q 1522 -856 1778 -856 Q 2344 -856 2625 -561 Q 2906 -266 2906 331 L 2906 616 Q 2728 306 2450 153 Q 2172 0 1784 0 Q 1141 0 747 490 Q 353 981 353 1791 Q 353 2603 747 3093 Q 1141 3584 1784 3584 Q 2172 3584 2450 3431 Q 2728 3278 2906 2969 L 2906 3500 L 3481 3500 L 3481 434 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-63\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(54.980469 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-70\" transform=\"translate(116.259766 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(179.736328 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-74\" transform=\"translate(207.519531 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(246.728516 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6c\" transform=\"translate(308.007812 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-2d\" transform=\"translate(335.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-67\" transform=\"translate(371.875 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(435.351562 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(496.630859 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6e\" transform=\"translate(524.414062 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_3\">\n",
" <defs>\n",
" <path id=\"m0b6d8df8cd\" d=\"M 0 0 L -3.5 0 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m0b6d8df8cd\" x=\"27.36\" y=\"54.090083\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(15.27 57.129458) scale(0.08 -0.08)\">\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_4\">\n",
" <g>\n",
" <use xlink:href=\"#m0b6d8df8cd\" x=\"27.36\" y=\"3.039375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(0 6.07875) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-31\" d=\"M 794 531 L 1825 531 L 1825 4091 L 703 3866 L 703 4441 L 1819 4666 L 2450 4666 L 2450 531 L 3481 531 L 3481 0 L 794 0 L 794 531 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-35\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-31\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-31\" transform=\"translate(190.869141 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_42\">\n",
" <path d=\"M 27.36 54.090083 L 27.36 0.48684 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" <g id=\"patch_43\">\n",
" <path d=\"M 27.36 54.090083 L 211.5 54.090083 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p565f94354d\">\n",
" <rect x=\"27.36\" y=\"0.48684\" width=\"184.14\" height=\"53.603244\" />\n",
" </clipPath>\n",
" </defs>\n",
"</svg></g>\n",
"\n",
"<g id=\"node2\" class=\"node\">\n",
"<title>node5</title>\n",
"<svg width=\"159px\" height=\"55px\" viewBox=\"0 0 211.5 73.661991\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"196\" y=\"-165\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:46.634197</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 73.661991 L 211.5 73.661991 L 211.5 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 27.36 38.805741 L 211.5 38.805741 L 211.5 1.251057 L 27.36 1.251057 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 43.372174 38.805741 L 51.378261 38.805741 L 51.378261 27.367955 L 43.372174 27.367955 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 51.378261 38.805741 L 59.384348 38.805741 L 59.384348 38.805741 L 51.378261 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 59.384348 38.805741 L 67.390435 38.805741 L 67.390435 38.787112 L 59.384348 38.787112 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 67.390435 38.805741 L 75.396522 38.805741 L 75.396522 38.805741 L 67.390435 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_7\">\n",
" <path d=\"M 75.396522 38.805741 L 83.402609 38.805741 L 83.402609 38.805741 L 75.396522 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_8\">\n",
" <path d=\"M 83.402609 38.805741 L 91.408696 38.805741 L 91.408696 38.805741 L 83.402609 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_9\">\n",
" <path d=\"M 91.408696 38.805741 L 99.414783 38.805741 L 99.414783 38.805741 L 91.408696 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_10\">\n",
" <path d=\"M 99.414783 38.805741 L 107.42087 38.805741 L 107.42087 38.805741 L 99.414783 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_11\">\n",
" <path d=\"M 107.42087 38.805741 L 115.426957 38.805741 L 115.426957 38.805741 L 107.42087 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_12\">\n",
" <path d=\"M 115.426957 38.805741 L 123.433043 38.805741 L 123.433043 38.805741 L 115.426957 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_13\">\n",
" <path d=\"M 123.433043 38.805741 L 131.43913 38.805741 L 131.43913 38.805741 L 123.433043 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_14\">\n",
" <path d=\"M 131.43913 38.805741 L 139.445217 38.805741 L 139.445217 38.805741 L 131.43913 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_15\">\n",
" <path d=\"M 139.445217 38.805741 L 147.451304 38.805741 L 147.451304 38.805741 L 139.445217 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_16\">\n",
" <path d=\"M 147.451304 38.805741 L 155.457391 38.805741 L 155.457391 38.805741 L 147.451304 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_17\">\n",
" <path d=\"M 155.457391 38.805741 L 163.463478 38.805741 L 163.463478 38.805741 L 155.457391 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_18\">\n",
" <path d=\"M 163.463478 38.805741 L 171.469565 38.805741 L 171.469565 38.805741 L 163.463478 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_19\">\n",
" <path d=\"M 171.469565 38.805741 L 179.475652 38.805741 L 179.475652 38.805741 L 171.469565 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_20\">\n",
" <path d=\"M 179.475652 38.805741 L 187.481739 38.805741 L 187.481739 38.805741 L 179.475652 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_21\">\n",
" <path d=\"M 187.481739 38.805741 L 195.487826 38.805741 L 195.487826 38.805741 L 187.481739 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_22\">\n",
" <path d=\"M 43.372174 27.367955 L 51.378261 27.367955 L 51.378261 3.039375 L 43.372174 3.039375 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_23\">\n",
" <path d=\"M 51.378261 38.805741 L 59.384348 38.805741 L 59.384348 36.402688 L 51.378261 36.402688 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_24\">\n",
" <path d=\"M 59.384348 38.787112 L 67.390435 38.787112 L 67.390435 36.179148 L 59.384348 36.179148 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_25\">\n",
" <path d=\"M 67.390435 38.805741 L 75.396522 38.805741 L 75.396522 38.470431 L 67.390435 38.470431 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_26\">\n",
" <path d=\"M 75.396522 38.805741 L 83.402609 38.805741 L 83.402609 38.805741 L 75.396522 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_27\">\n",
" <path d=\"M 83.402609 38.805741 L 91.408696 38.805741 L 91.408696 38.805741 L 83.402609 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_28\">\n",
" <path d=\"M 91.408696 38.805741 L 99.414783 38.805741 L 99.414783 38.805741 L 91.408696 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_29\">\n",
" <path d=\"M 99.414783 38.805741 L 107.42087 38.805741 L 107.42087 38.805741 L 99.414783 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_30\">\n",
" <path d=\"M 107.42087 38.805741 L 115.426957 38.805741 L 115.426957 38.805741 L 107.42087 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_31\">\n",
" <path d=\"M 115.426957 38.805741 L 123.433043 38.805741 L 123.433043 38.805741 L 115.426957 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_32\">\n",
" <path d=\"M 123.433043 38.805741 L 131.43913 38.805741 L 131.43913 38.805741 L 123.433043 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_33\">\n",
" <path d=\"M 131.43913 38.805741 L 139.445217 38.805741 L 139.445217 38.805741 L 131.43913 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_34\">\n",
" <path d=\"M 139.445217 38.805741 L 147.451304 38.805741 L 147.451304 38.805741 L 139.445217 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_35\">\n",
" <path d=\"M 147.451304 38.805741 L 155.457391 38.805741 L 155.457391 38.805741 L 147.451304 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_36\">\n",
" <path d=\"M 155.457391 38.805741 L 163.463478 38.805741 L 163.463478 38.805741 L 155.457391 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_37\">\n",
" <path d=\"M 163.463478 38.805741 L 171.469565 38.805741 L 171.469565 38.805741 L 163.463478 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_38\">\n",
" <path d=\"M 171.469565 38.805741 L 179.475652 38.805741 L 179.475652 38.805741 L 171.469565 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_39\">\n",
" <path d=\"M 179.475652 38.805741 L 187.481739 38.805741 L 187.481739 38.805741 L 179.475652 38.805741 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_40\">\n",
" <path d=\"M 187.481739 38.805741 L 195.487826 38.805741 L 195.487826 37.948838 L 187.481739 37.948838 z \" clip-path=\"url(#p6d3ea15490)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_41\">\n",
" <path d=\"M 51.123304 39.637341 L 47.808784 47.953341 L 54.437824 47.953341 z \" style=\"fill: #444443\" />\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <defs>\n",
" <path id=\"m122277b9f9\" d=\"M 0 0 L 0 3.5 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m122277b9f9\" x=\"43.372174\" y=\"38.805741\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(40.827174 56.384491) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_2\">\n",
" <g>\n",
" <use xlink:href=\"#m122277b9f9\" x=\"195.487826\" y=\"38.805741\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(182.762826 56.384491) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-39\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(190.869141 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(254.492188 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(84.162187 71.166366) scale(0.12 -0.12)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-63\" d=\"M 3122 3366 L 3122 2828 Q 2878 2963 2633 3030 Q 2388 3097 2138 3097 Q 1578 3097 1268 2742 Q 959 2388 959 1747 Q 959 1106 1268 751 Q 1578 397 2138 397 Q 2388 397 2633 464 Q 2878 531 3122 666 L 3122 134 Q 2881 22 2623 -34 Q 2366 -91 2075 -91 Q 1284 -91 818 406 Q 353 903 353 1747 Q 353 2603 823 3093 Q 1294 3584 2113 3584 Q 2378 3584 2631 3529 Q 2884 3475 3122 3366 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-61\" d=\"M 2194 1759 Q 1497 1759 1228 1600 Q 959 1441 959 1056 Q 959 750 1161 570 Q 1363 391 1709 391 Q 2188 391 2477 730 Q 2766 1069 2766 1631 L 2766 1759 L 2194 1759 z M 3341 1997 L 3341 0 L 2766 0 L 2766 531 Q 2569 213 2275 61 Q 1981 -91 1556 -91 Q 1019 -91 701 211 Q 384 513 384 1019 Q 384 1609 779 1909 Q 1175 2209 1959 2209 L 2766 2209 L 2766 2266 Q 2766 2663 2505 2880 Q 2244 3097 1772 3097 Q 1472 3097 1187 3025 Q 903 2953 641 2809 L 641 3341 Q 956 3463 1253 3523 Q 1550 3584 1831 3584 Q 2591 3584 2966 3190 Q 3341 2797 3341 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-70\" d=\"M 1159 525 L 1159 -1331 L 581 -1331 L 581 3500 L 1159 3500 L 1159 2969 Q 1341 3281 1617 3432 Q 1894 3584 2278 3584 Q 2916 3584 3314 3078 Q 3713 2572 3713 1747 Q 3713 922 3314 415 Q 2916 -91 2278 -91 Q 1894 -91 1617 61 Q 1341 213 1159 525 z M 3116 1747 Q 3116 2381 2855 2742 Q 2594 3103 2138 3103 Q 1681 3103 1420 2742 Q 1159 2381 1159 1747 Q 1159 1113 1420 752 Q 1681 391 2138 391 Q 2594 391 2855 752 Q 3116 1113 3116 1747 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-69\" d=\"M 603 3500 L 1178 3500 L 1178 0 L 603 0 L 603 3500 z M 603 4863 L 1178 4863 L 1178 4134 L 603 4134 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-74\" d=\"M 1172 4494 L 1172 3500 L 2356 3500 L 2356 3053 L 1172 3053 L 1172 1153 Q 1172 725 1289 603 Q 1406 481 1766 481 L 2356 481 L 2356 0 L 1766 0 Q 1100 0 847 248 Q 594 497 594 1153 L 594 3053 L 172 3053 L 172 3500 L 594 3500 L 594 4494 L 1172 4494 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6c\" d=\"M 603 4863 L 1178 4863 L 1178 0 L 603 0 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-2d\" d=\"M 313 2009 L 1997 2009 L 1997 1497 L 313 1497 L 313 2009 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-67\" d=\"M 2906 1791 Q 2906 2416 2648 2759 Q 2391 3103 1925 3103 Q 1463 3103 1205 2759 Q 947 2416 947 1791 Q 947 1169 1205 825 Q 1463 481 1925 481 Q 2391 481 2648 825 Q 2906 1169 2906 1791 z M 3481 434 Q 3481 -459 3084 -895 Q 2688 -1331 1869 -1331 Q 1566 -1331 1297 -1286 Q 1028 -1241 775 -1147 L 775 -588 Q 1028 -725 1275 -790 Q 1522 -856 1778 -856 Q 2344 -856 2625 -561 Q 2906 -266 2906 331 L 2906 616 Q 2728 306 2450 153 Q 2172 0 1784 0 Q 1141 0 747 490 Q 353 981 353 1791 Q 353 2603 747 3093 Q 1141 3584 1784 3584 Q 2172 3584 2450 3431 Q 2728 3278 2906 2969 L 2906 3500 L 3481 3500 L 3481 434 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-63\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(54.980469 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-70\" transform=\"translate(116.259766 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(179.736328 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-74\" transform=\"translate(207.519531 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(246.728516 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6c\" transform=\"translate(308.007812 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-2d\" transform=\"translate(335.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-67\" transform=\"translate(371.875 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(435.351562 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(496.630859 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6e\" transform=\"translate(524.414062 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_3\">\n",
" <defs>\n",
" <path id=\"m1402678c0a\" d=\"M 0 0 L -3.5 0 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m1402678c0a\" x=\"27.36\" y=\"38.805741\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(15.27 41.845116) scale(0.08 -0.08)\">\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_4\">\n",
" <g>\n",
" <use xlink:href=\"#m1402678c0a\" x=\"27.36\" y=\"3.039375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(0 6.07875) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-31\" d=\"M 794 531 L 1825 531 L 1825 4091 L 703 3866 L 703 4441 L 1819 4666 L 2450 4666 L 2450 531 L 3481 531 L 3481 0 L 794 0 L 794 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-32\" d=\"M 1228 531 L 3431 531 L 3431 0 L 469 0 L 469 531 Q 828 903 1448 1529 Q 2069 2156 2228 2338 Q 2531 2678 2651 2914 Q 2772 3150 2772 3378 Q 2772 3750 2511 3984 Q 2250 4219 1831 4219 Q 1534 4219 1204 4116 Q 875 4013 500 3803 L 500 4441 Q 881 4594 1212 4672 Q 1544 4750 1819 4750 Q 2544 4750 2975 4387 Q 3406 4025 3406 3419 Q 3406 3131 3298 2873 Q 3191 2616 2906 2266 Q 2828 2175 2409 1742 Q 1991 1309 1228 531 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-31\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-32\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(190.869141 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_42\">\n",
" <path d=\"M 27.36 38.805741 L 27.36 1.251057 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" <g id=\"patch_43\">\n",
" <path d=\"M 27.36 38.805741 L 211.5 38.805741 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p6d3ea15490\">\n",
" <rect x=\"27.36\" y=\"1.251057\" width=\"184.14\" height=\"37.554684\" />\n",
" </clipPath>\n",
" </defs>\n",
"</svg></g>\n",
"\n",
"\n",
"<g id=\"node8\" class=\"node\">\n",
"<title>leaf3</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"68,-84 0,-84 0,0 68,0 68,-84\" />\n",
"<svg width=\"60px\" height=\"76px\" viewBox=\"0 0 80.079911 102.071523\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"4\" y=\"-80\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.042804</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 102.071523 L 80.079911 102.071523 L 80.079911 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 80.079911 40.039979 C 80.079911 32.620087 78.017418 25.344219 74.123663 19.028091 C 70.229907 12.711962 64.656051 7.600774 58.02699 4.267552 C 51.39793 0.93433 43.971015 -0.491525 36.578894 0.149843 C 29.186773 0.791211 22.116418 3.474903 16.160502 7.900044 C 10.204586 12.325186 5.594326 18.319986 2.84659 25.212355 C 0.098853 32.104724 -0.67969 39.62709 0.598244 46.936105 C 1.876179 54.245119 5.160981 61.057035 10.084382 66.608154 C 15.007784 72.159274 21.378653 76.234093 28.482737 78.375776 L 40.039911 40.039979 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 28.482737 78.375776 C 34.463087 80.178685 40.78317 80.557436 46.936036 79.481646 C 53.088902 78.405856 58.905518 75.905082 63.91932 72.179915 C 68.933122 68.454748 73.00637 63.607527 75.812338 58.027059 C 78.618307 52.446592 80.079911 46.286185 80.079911 40.039979 L 40.039911 40.039979 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(21.964676 90.121773) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-34\" d=\"M 2419 4116 L 825 1625 L 2419 1625 L 2419 4116 z M 2253 4666 L 3047 4666 L 3047 1625 L 3713 1625 L 3713 1100 L 3047 1100 L 3047 0 L 2419 0 L 2419 1100 L 313 1100 L 313 1709 L 2253 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-37\" d=\"M 525 4666 L 3525 4666 L 3525 4397 L 1831 0 L 1172 0 L 2766 4134 L 525 4134 L 525 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-33\" d=\"M 2597 2516 Q 3050 2419 3304 2112 Q 3559 1806 3559 1356 Q 3559 666 3084 287 Q 2609 -91 1734 -91 Q 1441 -91 1130 -33 Q 819 25 488 141 L 488 750 Q 750 597 1062 519 Q 1375 441 1716 441 Q 2309 441 2620 675 Q 2931 909 2931 1356 Q 2931 1769 2642 2001 Q 2353 2234 1838 2234 L 1294 2234 L 1294 2753 L 1863 2753 Q 2328 2753 2575 2939 Q 2822 3125 2822 3475 Q 2822 3834 2567 4026 Q 2313 4219 1838 4219 Q 1578 4219 1281 4162 Q 984 4106 628 3988 L 628 4550 Q 988 4650 1302 4700 Q 1616 4750 1894 4750 Q 2613 4750 3031 4423 Q 3450 4097 3450 3541 Q 3450 3153 3228 2886 Q 3006 2619 2597 2516 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-34\" transform=\"translate(147.167969 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(210.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-37\" transform=\"translate(274.414062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-33\" transform=\"translate(338.037109 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(23.820926 100.199804) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3c\" d=\"M 4684 3150 L 1459 2003 L 4684 863 L 4684 294 L 678 1747 L 678 2266 L 4684 3719 L 4684 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3c\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(167.578125 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(231.201172 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(294.824219 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge1\" class=\"edge\">\n",
"<title>node2-&gt;leaf3</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M71.59,-100.93C68.75,-96.55 65.83,-92.05 62.92,-87.57\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"64.06,-86.75 60.71,-84.15 61.71,-88.27 64.06,-86.75\" />\n",
"</g>\n",
"\n",
"<g id=\"node9\" class=\"node\">\n",
"<title>leaf4</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"150,-84 82,-84 82,0 150,0 150,-84\" />\n",
"<svg width=\"60px\" height=\"76px\" viewBox=\"0 0 80.079997 102.071537\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"86\" y=\"-80\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.065393</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 102.071537 L 80.079997 102.071537 L 80.079997 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 80.079997 40.039994 C 80.079997 39.207986 80.054065 38.37618 80.002236 37.545789 C 79.950408 36.715397 79.87271 35.886822 79.769255 35.061272 L 40.039997 40.039994 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 79.769255 35.061272 C 79.128508 29.948228 77.506692 25.006626 74.993015 20.508271 C 72.479337 16.009916 69.120745 12.038824 65.101973 8.813419 C 61.083201 5.588013 56.479307 3.168535 51.543521 1.688064 C 46.607736 0.207592 41.432245 -0.306221 36.301717 0.174885 C 31.17119 0.655991 26.18145 2.123031 21.60695 4.495313 C 17.032449 6.867594 12.958627 10.10081 9.609556 14.017122 C 6.260486 17.933434 3.698719 22.459697 2.06516 27.346952 C 0.431601 32.234207 -0.243239 37.391173 0.077759 42.534201 C 0.398756 47.677228 1.709596 52.710261 3.938176 57.356463 C 6.166757 62.002666 9.271456 66.175262 13.081504 69.644744 C 16.891552 73.114226 21.335789 75.815795 26.169766 77.600855 C 31.003743 79.385914 36.137176 80.221126 41.287709 80.060549 C 46.438242 79.899972 51.509678 78.746606 56.22307 76.66389 C 60.936462 74.581174 65.203779 71.608007 68.790303 67.907924 C 72.376827 64.20784 75.215573 59.849947 77.1504 55.073943 C 79.085227 50.297938 80.079998 45.193025 80.079997 40.039989 L 40.039997 40.039994 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(24.827888 90.121787) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-32\" d=\"M 1228 531 L 3431 531 L 3431 0 L 469 0 L 469 531 Q 828 903 1448 1529 Q 2069 2156 2228 2338 Q 2531 2678 2651 2914 Q 2772 3150 2772 3378 Q 2772 3750 2511 3984 Q 2250 4219 1831 4219 Q 1534 4219 1204 4116 Q 875 4013 500 3803 L 500 4441 Q 881 4594 1212 4672 Q 1544 4750 1819 4750 Q 2544 4750 2975 4387 Q 3406 4025 3406 3419 Q 3406 3131 3298 2873 Q 3191 2616 2906 2266 Q 2828 2175 2409 1742 Q 1991 1309 1228 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-32\" transform=\"translate(147.167969 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(210.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-32\" transform=\"translate(274.414062 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(27.591872 100.199819) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3e\" d=\"M 678 3150 L 678 3719 L 4684 2266 L 4684 1747 L 678 294 L 678 863 L 3897 2003 L 678 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3e\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(147.412109 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(211.035156 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge2\" class=\"edge\">\n",
"<title>node2-&gt;leaf4</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M103.06,-100.93C104,-96.72 104.96,-92.4 105.93,-88.1\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"107.3,-88.36 106.81,-84.15 104.57,-87.75 107.3,-88.36\" />\n",
"</g>\n",
"\n",
"<g id=\"node10\" class=\"node\">\n",
"<title>leaf6</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"259,-84 191,-84 191,0 259,0 259,-84\" />\n",
"<svg width=\"60px\" height=\"76px\" viewBox=\"0 0 80.079995 102.071539\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"195\" y=\"-80\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.086723</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M -0 102.071539 L 80.079995 102.071539 L 80.079995 0 L -0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 80.079995 40.039995 C 80.079995 33.257724 78.356748 26.585359 75.072545 20.651286 C 71.788342 14.717214 67.049623 9.713757 61.30267 6.112127 C 55.555717 2.510498 48.986787 0.427425 42.214525 0.059087 C 35.442263 -0.309251 28.686157 1.049084 22.582481 4.006167 L 40.039995 40.039995 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 22.582481 4.006167 C 16.147044 7.123979 10.662826 11.909599 6.702236 17.863533 C 2.741646 23.817468 0.447445 30.725104 0.059086 37.865467 C -0.329272 45.00583 1.202211 52.121542 4.493689 58.46991 C 7.785167 64.818278 12.717997 70.170473 18.777322 73.967865 C 24.836647 77.765258 31.804055 79.87097 38.952333 80.06522 C 46.10061 80.25947 53.172095 78.535257 59.428708 75.072543 C 65.685322 71.609829 70.901543 66.533429 74.532936 60.373185 C 78.164328 54.212942 80.079996 47.190904 80.079995 40.039988 L 40.039995 40.039995 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(21.96476 90.121789) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-31\" d=\"M 794 531 L 1825 531 L 1825 4091 L 703 3866 L 703 4441 L 1819 4666 L 2450 4666 L 2450 531 L 3481 531 L 3481 0 L 794 0 L 794 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-38\" d=\"M 2034 2216 Q 1584 2216 1326 1975 Q 1069 1734 1069 1313 Q 1069 891 1326 650 Q 1584 409 2034 409 Q 2484 409 2743 651 Q 3003 894 3003 1313 Q 3003 1734 2745 1975 Q 2488 2216 2034 2216 z M 1403 2484 Q 997 2584 770 2862 Q 544 3141 544 3541 Q 544 4100 942 4425 Q 1341 4750 2034 4750 Q 2731 4750 3128 4425 Q 3525 4100 3525 3541 Q 3525 3141 3298 2862 Q 3072 2584 2669 2484 Q 3125 2378 3379 2068 Q 3634 1759 3634 1313 Q 3634 634 3220 271 Q 2806 -91 2034 -91 Q 1263 -91 848 271 Q 434 634 434 1313 Q 434 1759 690 2068 Q 947 2378 1403 2484 z M 1172 3481 Q 1172 3119 1398 2916 Q 1625 2713 2034 2713 Q 2441 2713 2670 2916 Q 2900 3119 2900 3481 Q 2900 3844 2670 4047 Q 2441 4250 2034 4250 Q 1625 4250 1398 4047 Q 1172 3844 1172 3481 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-31\" transform=\"translate(147.167969 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(210.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(274.414062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-38\" transform=\"translate(338.037109 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(27.59187 100.19982) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3e\" d=\"M 678 3150 L 678 3719 L 4684 2266 L 4684 1747 L 678 294 L 678 863 L 3897 2003 L 678 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3e\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(147.412109 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(211.035156 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge4\" class=\"edge\">\n",
"<title>node5-&gt;leaf6</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M258.77,-106.48C255.65,-100.62 252.32,-94.37 249.02,-88.16\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"250.09,-87.18 246.97,-84.3 247.61,-88.49 250.09,-87.18\" />\n",
"</g>\n",
"\n",
"<g id=\"node11\" class=\"node\">\n",
"<title>leaf7</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"341,-84 273,-84 273,0 341,0 341,-84\" />\n",
"<svg width=\"60px\" height=\"76px\" viewBox=\"0 0 80.08 102.071544\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"277\" y=\"-80\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.108902</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 102.071544 L 80.08 102.071544 L 80.08 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 80.08 40.04 C 80.08 39.918815 80.07945 39.797631 80.07835 39.676452 C 80.077249 39.555272 80.075599 39.434098 80.073398 39.312934 L 40.04 40.04 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 80.073398 39.312934 C 79.978198 34.071086 78.854392 28.89861 76.765928 24.089833 C 74.677463 19.281055 71.664718 14.92895 67.899126 11.281163 C 64.133535 7.633377 59.687901 4.760436 54.815193 2.825819 C 49.942485 0.891201 44.736913 -0.067688 39.494687 0.003713 C 34.252461 0.075115 29.074937 1.175427 24.256727 3.242039 C 19.438518 5.308652 15.07278 8.301607 11.407936 12.0506 C 7.743092 15.799592 4.849998 20.232138 2.893279 25.096013 C 0.936561 29.959888 -0.045951 35.161053 0.00165 40.403549 C 0.049252 45.646044 1.126048 50.828511 3.170765 55.656053 C 5.215482 60.483595 8.188587 64.862875 11.920903 68.544701 C 15.653219 72.226527 20.072584 75.139714 24.927526 77.118494 C 29.782468 79.097274 34.979119 80.103389 40.221777 80.079587 C 45.464434 80.055786 50.651736 79.002529 55.488511 76.97975 C 60.325286 74.95697 64.718018 72.003777 68.416751 68.288214 C 72.115483 64.572652 75.048703 60.166557 77.049504 55.320649 C 79.050304 50.474741 80.08 45.282711 80.08 40.039999 L 40.04 40.04 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(24.827891 90.121794) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-33\" d=\"M 2597 2516 Q 3050 2419 3304 2112 Q 3559 1806 3559 1356 Q 3559 666 3084 287 Q 2609 -91 1734 -91 Q 1441 -91 1130 -33 Q 819 25 488 141 L 488 750 Q 750 597 1062 519 Q 1375 441 1716 441 Q 2309 441 2620 675 Q 2931 909 2931 1356 Q 2931 1769 2642 2001 Q 2353 2234 1838 2234 L 1294 2234 L 1294 2753 L 1863 2753 Q 2328 2753 2575 2939 Q 2822 3125 2822 3475 Q 2822 3834 2567 4026 Q 2313 4219 1838 4219 Q 1578 4219 1281 4162 Q 984 4106 628 3988 L 628 4550 Q 988 4650 1302 4700 Q 1616 4750 1894 4750 Q 2613 4750 3031 4423 Q 3450 4097 3450 3541 Q 3450 3153 3228 2886 Q 3006 2619 2597 2516 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-34\" d=\"M 2419 4116 L 825 1625 L 2419 1625 L 2419 4116 z M 2253 4666 L 3047 4666 L 3047 1625 L 3713 1625 L 3713 1100 L 3047 1100 L 3047 0 L 2419 0 L 2419 1100 L 313 1100 L 313 1709 L 2253 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-36\" d=\"M 2113 2584 Q 1688 2584 1439 2293 Q 1191 2003 1191 1497 Q 1191 994 1439 701 Q 1688 409 2113 409 Q 2538 409 2786 701 Q 3034 994 3034 1497 Q 3034 2003 2786 2293 Q 2538 2584 2113 2584 z M 3366 4563 L 3366 3988 Q 3128 4100 2886 4159 Q 2644 4219 2406 4219 Q 1781 4219 1451 3797 Q 1122 3375 1075 2522 Q 1259 2794 1537 2939 Q 1816 3084 2150 3084 Q 2853 3084 3261 2657 Q 3669 2231 3669 1497 Q 3669 778 3244 343 Q 2819 -91 2113 -91 Q 1303 -91 875 529 Q 447 1150 447 2328 Q 447 3434 972 4092 Q 1497 4750 2381 4750 Q 2619 4750 2861 4703 Q 3103 4656 3366 4563 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-33\" transform=\"translate(147.167969 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-34\" transform=\"translate(210.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-36\" transform=\"translate(274.414062 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(27.591875 100.199825) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3e\" d=\"M 678 3150 L 678 3719 L 4684 2266 L 4684 1747 L 678 294 L 678 863 L 3897 2003 L 678 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3e\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(147.412109 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(211.035156 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge5\" class=\"edge\">\n",
"<title>node5-&gt;leaf7</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M285.39,-106.48C287.38,-100.62 289.51,-94.37 291.63,-88.16\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"292.98,-88.54 292.94,-84.3 290.32,-87.64 292.98,-88.54\" />\n",
"</g>\n",
"\n",
"<g id=\"node3\" class=\"node\">\n",
"<title>node1</title>\n",
"<svg width=\"159px\" height=\"57px\" viewBox=\"0 0 211.5 76.427729\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"191\" y=\"-268\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:46.704354</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 76.427729 L 211.5 76.427729 L 211.5 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 27.36 41.571479 L 211.5 41.571479 L 211.5 1.11277 L 27.36 1.11277 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 43.372174 41.571479 L 51.378261 41.571479 L 51.378261 41.445711 L 43.372174 41.445711 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 51.378261 41.571479 L 59.384348 41.571479 L 59.384348 40.926919 L 51.378261 40.926919 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 59.384348 41.571479 L 67.390435 41.571479 L 67.390435 40.360964 L 59.384348 40.360964 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 67.390435 41.571479 L 75.396522 41.571479 L 75.396522 39.16617 L 67.390435 39.16617 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_7\">\n",
" <path d=\"M 75.396522 41.571479 L 83.402609 41.571479 L 83.402609 41.571479 L 75.396522 41.571479 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_8\">\n",
" <path d=\"M 83.402609 41.571479 L 91.408696 41.571479 L 91.408696 40.015103 L 83.402609 40.015103 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_9\">\n",
" <path d=\"M 91.408696 41.571479 L 99.414783 41.571479 L 99.414783 39.449148 L 91.408696 39.449148 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_10\">\n",
" <path d=\"M 99.414783 41.571479 L 107.42087 41.571479 L 107.42087 39.16617 L 99.414783 39.16617 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_11\">\n",
" <path d=\"M 107.42087 41.571479 L 115.426957 41.571479 L 115.426957 41.005524 L 107.42087 41.005524 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_12\">\n",
" <path d=\"M 115.426957 41.571479 L 123.433043 41.571479 L 123.433043 41.571479 L 115.426957 41.571479 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_13\">\n",
" <path d=\"M 123.433043 41.571479 L 131.43913 41.571479 L 131.43913 14.955872 L 123.433043 14.955872 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_14\">\n",
" <path d=\"M 131.43913 41.571479 L 139.445217 41.571479 L 139.445217 29.010422 L 131.43913 29.010422 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_15\">\n",
" <path d=\"M 139.445217 41.571479 L 147.451304 41.571479 L 147.451304 38.52161 L 139.445217 38.52161 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_16\">\n",
" <path d=\"M 147.451304 41.571479 L 155.457391 41.571479 L 155.457391 39.763567 L 147.451304 39.763567 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_17\">\n",
" <path d=\"M 155.457391 41.571479 L 163.463478 41.571479 L 163.463478 41.571479 L 155.457391 41.571479 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_18\">\n",
" <path d=\"M 163.463478 41.571479 L 171.469565 41.571479 L 171.469565 34.63853 L 163.463478 34.63853 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_19\">\n",
" <path d=\"M 171.469565 41.571479 L 179.475652 41.571479 L 179.475652 39.747846 L 171.469565 39.747846 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_20\">\n",
" <path d=\"M 179.475652 41.571479 L 187.481739 41.571479 L 187.481739 41.131292 L 179.475652 41.131292 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_21\">\n",
" <path d=\"M 187.481739 41.571479 L 195.487826 41.571479 L 195.487826 41.09985 L 187.481739 41.09985 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_22\">\n",
" <path d=\"M 43.372174 41.445711 L 51.378261 41.445711 L 51.378261 41.445711 L 43.372174 41.445711 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_23\">\n",
" <path d=\"M 51.378261 40.926919 L 59.384348 40.926919 L 59.384348 40.879756 L 51.378261 40.879756 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_24\">\n",
" <path d=\"M 59.384348 40.360964 L 67.390435 40.360964 L 67.390435 40.282359 L 59.384348 40.282359 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_25\">\n",
" <path d=\"M 67.390435 39.16617 L 75.396522 39.16617 L 75.396522 38.851751 L 67.390435 38.851751 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_26\">\n",
" <path d=\"M 75.396522 41.571479 L 83.402609 41.571479 L 83.402609 41.571479 L 75.396522 41.571479 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_27\">\n",
" <path d=\"M 83.402609 40.015103 L 91.408696 40.015103 L 91.408696 39.873614 L 83.402609 39.873614 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_28\">\n",
" <path d=\"M 91.408696 39.449148 L 99.414783 39.449148 L 99.414783 39.00896 L 91.408696 39.00896 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_29\">\n",
" <path d=\"M 99.414783 39.16617 L 107.42087 39.16617 L 107.42087 38.757425 L 99.414783 38.757425 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_30\">\n",
" <path d=\"M 107.42087 41.005524 L 115.426957 41.005524 L 115.426957 40.738267 L 107.42087 40.738267 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_31\">\n",
" <path d=\"M 115.426957 41.571479 L 123.433043 41.571479 L 123.433043 41.571479 L 115.426957 41.571479 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_32\">\n",
" <path d=\"M 123.433043 14.955872 L 131.43913 14.955872 L 131.43913 3.039375 L 123.433043 3.039375 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_33\">\n",
" <path d=\"M 131.43913 29.010422 L 139.445217 29.010422 L 139.445217 19.200535 L 131.43913 19.200535 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_34\">\n",
" <path d=\"M 139.445217 38.52161 L 147.451304 38.52161 L 147.451304 36.446442 L 139.445217 36.446442 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_35\">\n",
" <path d=\"M 147.451304 39.763567 L 155.457391 39.763567 L 155.457391 38.19147 L 147.451304 38.19147 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_36\">\n",
" <path d=\"M 155.457391 41.571479 L 163.463478 41.571479 L 163.463478 41.571479 L 155.457391 41.571479 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_37\">\n",
" <path d=\"M 163.463478 34.63853 L 171.469565 34.63853 L 171.469565 19.687885 L 163.463478 19.687885 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_38\">\n",
" <path d=\"M 171.469565 39.747846 L 179.475652 39.747846 L 179.475652 33.773876 L 171.469565 33.773876 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_39\">\n",
" <path d=\"M 179.475652 41.131292 L 187.481739 41.131292 L 187.481739 38.395842 L 179.475652 38.395842 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_40\">\n",
" <path d=\"M 187.481739 41.09985 L 195.487826 41.09985 L 195.487826 38.993239 L 187.481739 38.993239 z \" clip-path=\"url(#p3a2d44ad0d)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_41\">\n",
" <path d=\"M 159.994174 42.403079 L 156.679654 50.719079 L 163.308694 50.719079 z \" style=\"fill: #444443\" />\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <defs>\n",
" <path id=\"m25ffdd17ee\" d=\"M 0 0 L 0 3.5 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m25ffdd17ee\" x=\"43.372174\" y=\"41.571479\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(37.010924 59.150229) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-31\" d=\"M 794 531 L 1825 531 L 1825 4091 L 703 3866 L 703 4441 L 1819 4666 L 2450 4666 L 2450 531 L 3481 531 L 3481 0 L 794 0 L 794 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-2e\" d=\"M 684 794 L 1344 794 L 1344 0 L 684 0 L 684 794 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-31\" />\n",
" <use xlink:href=\"#DejaVuSans-2e\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(95.410156 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_2\">\n",
" <g>\n",
" <use xlink:href=\"#m25ffdd17ee\" x=\"159.994174\" y=\"41.571479\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(151.087924 59.150229) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-32\" d=\"M 1228 531 L 3431 531 L 3431 0 L 469 0 L 469 531 Q 828 903 1448 1529 Q 2069 2156 2228 2338 Q 2531 2678 2651 2914 Q 2772 3150 2772 3378 Q 2772 3750 2511 3984 Q 2250 4219 1831 4219 Q 1534 4219 1204 4116 Q 875 4013 500 3803 L 500 4441 Q 881 4594 1212 4672 Q 1544 4750 1819 4750 Q 2544 4750 2975 4387 Q 3406 4025 3406 3419 Q 3406 3131 3298 2873 Q 3191 2616 2906 2266 Q 2828 2175 2409 1742 Q 1991 1309 1228 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-31\" />\n",
" <use xlink:href=\"#DejaVuSans-32\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-2e\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(159.033203 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_3\">\n",
" <g id=\"line2d_3\">\n",
" <g>\n",
" <use xlink:href=\"#m25ffdd17ee\" x=\"195.487826\" y=\"41.571479\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(186.581576 59.150229) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-36\" d=\"M 2113 2584 Q 1688 2584 1439 2293 Q 1191 2003 1191 1497 Q 1191 994 1439 701 Q 1688 409 2113 409 Q 2538 409 2786 701 Q 3034 994 3034 1497 Q 3034 2003 2786 2293 Q 2538 2584 2113 2584 z M 3366 4563 L 3366 3988 Q 3128 4100 2886 4159 Q 2644 4219 2406 4219 Q 1781 4219 1451 3797 Q 1122 3375 1075 2522 Q 1259 2794 1537 2939 Q 1816 3084 2150 3084 Q 2853 3084 3261 2657 Q 3669 2231 3669 1497 Q 3669 778 3244 343 Q 2819 -91 2113 -91 Q 1303 -91 875 529 Q 447 1150 447 2328 Q 447 3434 972 4092 Q 1497 4750 2381 4750 Q 2619 4750 2861 4703 Q 3103 4656 3366 4563 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-31\" />\n",
" <use xlink:href=\"#DejaVuSans-36\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-2e\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(159.033203 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(74.04375 73.932104) scale(0.12 -0.12)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-65\" d=\"M 3597 1894 L 3597 1613 L 953 1613 Q 991 1019 1311 708 Q 1631 397 2203 397 Q 2534 397 2845 478 Q 3156 559 3463 722 L 3463 178 Q 3153 47 2828 -22 Q 2503 -91 2169 -91 Q 1331 -91 842 396 Q 353 884 353 1716 Q 353 2575 817 3079 Q 1281 3584 2069 3584 Q 2775 3584 3186 3129 Q 3597 2675 3597 1894 z M 3022 2063 Q 3016 2534 2758 2815 Q 2500 3097 2075 3097 Q 1594 3097 1305 2825 Q 1016 2553 972 2059 L 3022 2063 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-64\" d=\"M 2906 2969 L 2906 4863 L 3481 4863 L 3481 0 L 2906 0 L 2906 525 Q 2725 213 2448 61 Q 2172 -91 1784 -91 Q 1150 -91 751 415 Q 353 922 353 1747 Q 353 2572 751 3078 Q 1150 3584 1784 3584 Q 2172 3584 2448 3432 Q 2725 3281 2906 2969 z M 947 1747 Q 947 1113 1208 752 Q 1469 391 1925 391 Q 2381 391 2643 752 Q 2906 1113 2906 1747 Q 2906 2381 2643 2742 Q 2381 3103 1925 3103 Q 1469 3103 1208 2742 Q 947 2381 947 1747 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-75\" d=\"M 544 1381 L 544 3500 L 1119 3500 L 1119 1403 Q 1119 906 1312 657 Q 1506 409 1894 409 Q 2359 409 2629 706 Q 2900 1003 2900 1516 L 2900 3500 L 3475 3500 L 3475 0 L 2900 0 L 2900 538 Q 2691 219 2414 64 Q 2138 -91 1772 -91 Q 1169 -91 856 284 Q 544 659 544 1381 z M 1991 3584 L 1991 3584 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-63\" d=\"M 3122 3366 L 3122 2828 Q 2878 2963 2633 3030 Q 2388 3097 2138 3097 Q 1578 3097 1268 2742 Q 959 2388 959 1747 Q 959 1106 1268 751 Q 1578 397 2138 397 Q 2388 397 2633 464 Q 2878 531 3122 666 L 3122 134 Q 2881 22 2623 -34 Q 2366 -91 2075 -91 Q 1284 -91 818 406 Q 353 903 353 1747 Q 353 2603 823 3093 Q 1294 3584 2113 3584 Q 2378 3584 2631 3529 Q 2884 3475 3122 3366 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-61\" d=\"M 2194 1759 Q 1497 1759 1228 1600 Q 959 1441 959 1056 Q 959 750 1161 570 Q 1363 391 1709 391 Q 2188 391 2477 730 Q 2766 1069 2766 1631 L 2766 1759 L 2194 1759 z M 3341 1997 L 3341 0 L 2766 0 L 2766 531 Q 2569 213 2275 61 Q 1981 -91 1556 -91 Q 1019 -91 701 211 Q 384 513 384 1019 Q 384 1609 779 1909 Q 1175 2209 1959 2209 L 2766 2209 L 2766 2266 Q 2766 2663 2505 2880 Q 2244 3097 1772 3097 Q 1472 3097 1187 3025 Q 903 2953 641 2809 L 641 3341 Q 956 3463 1253 3523 Q 1550 3584 1831 3584 Q 2591 3584 2966 3190 Q 3341 2797 3341 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-74\" d=\"M 1172 4494 L 1172 3500 L 2356 3500 L 2356 3053 L 1172 3053 L 1172 1153 Q 1172 725 1289 603 Q 1406 481 1766 481 L 2356 481 L 2356 0 L 1766 0 Q 1100 0 847 248 Q 594 497 594 1153 L 594 3053 L 172 3053 L 172 3500 L 594 3500 L 594 4494 L 1172 4494 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-69\" d=\"M 603 3500 L 1178 3500 L 1178 0 L 603 0 L 603 3500 z M 603 4863 L 1178 4863 L 1178 4134 L 603 4134 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6f\" d=\"M 1959 3097 Q 1497 3097 1228 2736 Q 959 2375 959 1747 Q 959 1119 1226 758 Q 1494 397 1959 397 Q 2419 397 2687 759 Q 2956 1122 2956 1747 Q 2956 2369 2687 2733 Q 2419 3097 1959 3097 z M 1959 3584 Q 2709 3584 3137 3096 Q 3566 2609 3566 1747 Q 3566 888 3137 398 Q 2709 -91 1959 -91 Q 1206 -91 779 398 Q 353 888 353 1747 Q 353 2609 779 3096 Q 1206 3584 1959 3584 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-2d\" d=\"M 313 2009 L 1997 2009 L 1997 1497 L 313 1497 L 313 2009 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6d\" d=\"M 3328 2828 Q 3544 3216 3844 3400 Q 4144 3584 4550 3584 Q 5097 3584 5394 3201 Q 5691 2819 5691 2113 L 5691 0 L 5113 0 L 5113 2094 Q 5113 2597 4934 2840 Q 4756 3084 4391 3084 Q 3944 3084 3684 2787 Q 3425 2491 3425 1978 L 3425 0 L 2847 0 L 2847 2094 Q 2847 2600 2669 2842 Q 2491 3084 2119 3084 Q 1678 3084 1418 2786 Q 1159 2488 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1356 3278 1631 3431 Q 1906 3584 2284 3584 Q 2666 3584 2933 3390 Q 3200 3197 3328 2828 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-65\" />\n",
" <use xlink:href=\"#DejaVuSans-64\" transform=\"translate(61.523438 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-75\" transform=\"translate(125 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-63\" transform=\"translate(188.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(243.359375 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-74\" transform=\"translate(304.638672 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(343.847656 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6f\" transform=\"translate(371.630859 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6e\" transform=\"translate(432.8125 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-2d\" transform=\"translate(496.191406 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6e\" transform=\"translate(532.275391 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-75\" transform=\"translate(595.654297 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6d\" transform=\"translate(659.033203 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_4\">\n",
" <defs>\n",
" <path id=\"m85a4716b28\" d=\"M 0 0 L -3.5 0 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m85a4716b28\" x=\"27.36\" y=\"41.571479\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(15.27 44.610854) scale(0.08 -0.08)\">\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_5\">\n",
" <g>\n",
" <use xlink:href=\"#m85a4716b28\" x=\"27.36\" y=\"3.039375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_6\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(0 6.07875) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-34\" d=\"M 2419 4116 L 825 1625 L 2419 1625 L 2419 4116 z M 2253 4666 L 3047 4666 L 3047 1625 L 3713 1625 L 3713 1100 L 3047 1100 L 3047 0 L 2419 0 L 2419 1100 L 313 1100 L 313 1709 L 2253 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-32\" />\n",
" <use xlink:href=\"#DejaVuSans-34\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-31\" transform=\"translate(190.869141 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_42\">\n",
" <path d=\"M 27.36 41.571479 L 27.36 1.11277 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" <g id=\"patch_43\">\n",
" <path d=\"M 27.36 41.571479 L 211.5 41.571479 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p3a2d44ad0d\">\n",
" <rect x=\"27.36\" y=\"1.11277\" width=\"184.14\" height=\"40.458709\" />\n",
" </clipPath>\n",
" </defs>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge7\" class=\"edge\">\n",
"<title>node1-&gt;node2</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M214.63,-207.36C197.82,-197.76 179.18,-187.11 161.72,-177.12\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"162.41,-175.91 158.24,-175.14 161.02,-178.34 162.41,-175.91\" />\n",
"</g>\n",
"\n",
"<g id=\"edge8\" class=\"edge\">\n",
"<title>node1-&gt;node5</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M271.58,-207.36C272.11,-196.75 272.71,-184.86 273.25,-174.01\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"274.66,-173.82 273.46,-169.75 271.86,-173.68 274.66,-173.82\" />\n",
"</g>\n",
"\n",
"<g id=\"node6\" class=\"node\">\n",
"<title>node8</title>\n",
"<svg width=\"162px\" height=\"88px\" viewBox=\"0 0 216.59 118.01625\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"371\" y=\"-284\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:46.926381</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 118.01625 L 216.59 118.01625 L 216.59 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 32.45 83.16 L 216.59 83.16 L 216.59 -0 L 32.45 -0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 48.462174 83.16 L 56.468261 83.16 L 56.468261 10.33138 L 48.462174 10.33138 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 56.468261 83.16 L 64.474348 83.16 L 64.474348 82.852675 L 56.468261 82.852675 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 64.474348 83.16 L 72.480435 83.16 L 72.480435 83.16 L 64.474348 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 72.480435 83.16 L 80.486522 83.16 L 80.486522 83.16 L 72.480435 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_7\">\n",
" <path d=\"M 80.486522 83.16 L 88.492609 83.16 L 88.492609 83.152504 L 80.486522 83.152504 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_8\">\n",
" <path d=\"M 88.492609 83.16 L 96.498696 83.16 L 96.498696 83.16 L 88.492609 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_9\">\n",
" <path d=\"M 96.498696 83.16 L 104.504783 83.16 L 104.504783 83.130017 L 96.498696 83.130017 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_10\">\n",
" <path d=\"M 104.504783 83.16 L 112.51087 83.16 L 112.51087 83.16 L 104.504783 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_11\">\n",
" <path d=\"M 112.51087 83.16 L 120.516957 83.16 L 120.516957 83.16 L 112.51087 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_12\">\n",
" <path d=\"M 120.516957 83.16 L 128.523043 83.16 L 128.523043 83.16 L 120.516957 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_13\">\n",
" <path d=\"M 128.523043 83.16 L 136.52913 83.16 L 136.52913 83.16 L 128.523043 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_14\">\n",
" <path d=\"M 136.52913 83.16 L 144.535217 83.16 L 144.535217 83.16 L 136.52913 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_15\">\n",
" <path d=\"M 144.535217 83.16 L 152.541304 83.16 L 152.541304 83.16 L 144.535217 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_16\">\n",
" <path d=\"M 152.541304 83.16 L 160.547391 83.16 L 160.547391 83.16 L 152.541304 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_17\">\n",
" <path d=\"M 160.547391 83.16 L 168.553478 83.16 L 168.553478 83.16 L 160.547391 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_18\">\n",
" <path d=\"M 168.553478 83.16 L 176.559565 83.16 L 176.559565 83.16 L 168.553478 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_19\">\n",
" <path d=\"M 176.559565 83.16 L 184.565652 83.16 L 184.565652 83.16 L 176.559565 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_20\">\n",
" <path d=\"M 184.565652 83.16 L 192.571739 83.16 L 192.571739 83.16 L 184.565652 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_21\">\n",
" <path d=\"M 192.571739 83.16 L 200.577826 83.16 L 200.577826 83.16 L 192.571739 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_22\">\n",
" <path d=\"M 48.462174 10.33138 L 56.468261 10.33138 L 56.468261 3.96 L 48.462174 3.96 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_23\">\n",
" <path d=\"M 56.468261 82.852675 L 64.474348 82.852675 L 64.474348 82.118092 L 56.468261 82.118092 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_24\">\n",
" <path d=\"M 64.474348 83.16 L 72.480435 83.16 L 72.480435 82.560341 L 64.474348 82.560341 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_25\">\n",
" <path d=\"M 72.480435 83.16 L 80.486522 83.16 L 80.486522 83.100034 L 72.480435 83.100034 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_26\">\n",
" <path d=\"M 80.486522 83.152504 L 88.492609 83.152504 L 88.492609 83.092538 L 80.486522 83.092538 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_27\">\n",
" <path d=\"M 88.492609 83.16 L 96.498696 83.16 L 96.498696 83.040068 L 88.492609 83.040068 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_28\">\n",
" <path d=\"M 96.498696 83.130017 L 104.504783 83.130017 L 104.504783 83.130017 L 96.498696 83.130017 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_29\">\n",
" <path d=\"M 104.504783 83.16 L 112.51087 83.16 L 112.51087 83.16 L 104.504783 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_30\">\n",
" <path d=\"M 112.51087 83.16 L 120.516957 83.16 L 120.516957 83.16 L 112.51087 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_31\">\n",
" <path d=\"M 120.516957 83.16 L 128.523043 83.16 L 128.523043 83.16 L 120.516957 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_32\">\n",
" <path d=\"M 128.523043 83.16 L 136.52913 83.16 L 136.52913 83.16 L 128.523043 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_33\">\n",
" <path d=\"M 136.52913 83.16 L 144.535217 83.16 L 144.535217 83.16 L 136.52913 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_34\">\n",
" <path d=\"M 144.535217 83.16 L 152.541304 83.16 L 152.541304 83.16 L 144.535217 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_35\">\n",
" <path d=\"M 152.541304 83.16 L 160.547391 83.16 L 160.547391 83.16 L 152.541304 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_36\">\n",
" <path d=\"M 160.547391 83.16 L 168.553478 83.16 L 168.553478 83.16 L 160.547391 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_37\">\n",
" <path d=\"M 168.553478 83.16 L 176.559565 83.16 L 176.559565 83.16 L 168.553478 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_38\">\n",
" <path d=\"M 176.559565 83.16 L 184.565652 83.16 L 184.565652 83.16 L 176.559565 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_39\">\n",
" <path d=\"M 184.565652 83.16 L 192.571739 83.16 L 192.571739 83.16 L 184.565652 83.16 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_40\">\n",
" <path d=\"M 192.571739 83.16 L 200.577826 83.16 L 200.577826 83.05506 L 192.571739 83.05506 z \" clip-path=\"url(#pc1904ecbdb)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_41\">\n",
" <path d=\"M 59.222182 83.9916 L 55.907662 92.3076 L 62.536702 92.3076 z \" style=\"fill: #444443\" />\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <defs>\n",
" <path id=\"m2e89a2d615\" d=\"M 0 0 L 0 3.5 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m2e89a2d615\" x=\"48.462174\" y=\"83.16\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(45.917174 100.73875) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_2\">\n",
" <g>\n",
" <use xlink:href=\"#m2e89a2d615\" x=\"200.577826\" y=\"83.16\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(187.852826 100.73875) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-39\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(190.869141 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(254.492188 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(89.252188 115.520625) scale(0.12 -0.12)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-63\" d=\"M 3122 3366 L 3122 2828 Q 2878 2963 2633 3030 Q 2388 3097 2138 3097 Q 1578 3097 1268 2742 Q 959 2388 959 1747 Q 959 1106 1268 751 Q 1578 397 2138 397 Q 2388 397 2633 464 Q 2878 531 3122 666 L 3122 134 Q 2881 22 2623 -34 Q 2366 -91 2075 -91 Q 1284 -91 818 406 Q 353 903 353 1747 Q 353 2603 823 3093 Q 1294 3584 2113 3584 Q 2378 3584 2631 3529 Q 2884 3475 3122 3366 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-61\" d=\"M 2194 1759 Q 1497 1759 1228 1600 Q 959 1441 959 1056 Q 959 750 1161 570 Q 1363 391 1709 391 Q 2188 391 2477 730 Q 2766 1069 2766 1631 L 2766 1759 L 2194 1759 z M 3341 1997 L 3341 0 L 2766 0 L 2766 531 Q 2569 213 2275 61 Q 1981 -91 1556 -91 Q 1019 -91 701 211 Q 384 513 384 1019 Q 384 1609 779 1909 Q 1175 2209 1959 2209 L 2766 2209 L 2766 2266 Q 2766 2663 2505 2880 Q 2244 3097 1772 3097 Q 1472 3097 1187 3025 Q 903 2953 641 2809 L 641 3341 Q 956 3463 1253 3523 Q 1550 3584 1831 3584 Q 2591 3584 2966 3190 Q 3341 2797 3341 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-70\" d=\"M 1159 525 L 1159 -1331 L 581 -1331 L 581 3500 L 1159 3500 L 1159 2969 Q 1341 3281 1617 3432 Q 1894 3584 2278 3584 Q 2916 3584 3314 3078 Q 3713 2572 3713 1747 Q 3713 922 3314 415 Q 2916 -91 2278 -91 Q 1894 -91 1617 61 Q 1341 213 1159 525 z M 3116 1747 Q 3116 2381 2855 2742 Q 2594 3103 2138 3103 Q 1681 3103 1420 2742 Q 1159 2381 1159 1747 Q 1159 1113 1420 752 Q 1681 391 2138 391 Q 2594 391 2855 752 Q 3116 1113 3116 1747 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-69\" d=\"M 603 3500 L 1178 3500 L 1178 0 L 603 0 L 603 3500 z M 603 4863 L 1178 4863 L 1178 4134 L 603 4134 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-74\" d=\"M 1172 4494 L 1172 3500 L 2356 3500 L 2356 3053 L 1172 3053 L 1172 1153 Q 1172 725 1289 603 Q 1406 481 1766 481 L 2356 481 L 2356 0 L 1766 0 Q 1100 0 847 248 Q 594 497 594 1153 L 594 3053 L 172 3053 L 172 3500 L 594 3500 L 594 4494 L 1172 4494 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6c\" d=\"M 603 4863 L 1178 4863 L 1178 0 L 603 0 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-2d\" d=\"M 313 2009 L 1997 2009 L 1997 1497 L 313 1497 L 313 2009 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-67\" d=\"M 2906 1791 Q 2906 2416 2648 2759 Q 2391 3103 1925 3103 Q 1463 3103 1205 2759 Q 947 2416 947 1791 Q 947 1169 1205 825 Q 1463 481 1925 481 Q 2391 481 2648 825 Q 2906 1169 2906 1791 z M 3481 434 Q 3481 -459 3084 -895 Q 2688 -1331 1869 -1331 Q 1566 -1331 1297 -1286 Q 1028 -1241 775 -1147 L 775 -588 Q 1028 -725 1275 -790 Q 1522 -856 1778 -856 Q 2344 -856 2625 -561 Q 2906 -266 2906 331 L 2906 616 Q 2728 306 2450 153 Q 2172 0 1784 0 Q 1141 0 747 490 Q 353 981 353 1791 Q 353 2603 747 3093 Q 1141 3584 1784 3584 Q 2172 3584 2450 3431 Q 2728 3278 2906 2969 L 2906 3500 L 3481 3500 L 3481 434 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-63\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(54.980469 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-70\" transform=\"translate(116.259766 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(179.736328 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-74\" transform=\"translate(207.519531 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(246.728516 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6c\" transform=\"translate(308.007812 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-2d\" transform=\"translate(335.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-67\" transform=\"translate(371.875 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(435.351562 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(496.630859 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6e\" transform=\"translate(524.414062 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_3\">\n",
" <defs>\n",
" <path id=\"m1e20436fae\" d=\"M 0 0 L -3.5 0 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m1e20436fae\" x=\"32.45\" y=\"83.16\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(20.36 86.199375) scale(0.08 -0.08)\">\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_4\">\n",
" <g>\n",
" <use xlink:href=\"#m1e20436fae\" x=\"32.45\" y=\"3.96\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(-0 6.999375) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-31\" d=\"M 794 531 L 1825 531 L 1825 4091 L 703 3866 L 703 4441 L 1819 4666 L 2450 4666 L 2450 531 L 3481 531 L 3481 0 L 794 0 L 794 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-36\" d=\"M 2113 2584 Q 1688 2584 1439 2293 Q 1191 2003 1191 1497 Q 1191 994 1439 701 Q 1688 409 2113 409 Q 2538 409 2786 701 Q 3034 994 3034 1497 Q 3034 2003 2786 2293 Q 2538 2584 2113 2584 z M 3366 4563 L 3366 3988 Q 3128 4100 2886 4159 Q 2644 4219 2406 4219 Q 1781 4219 1451 3797 Q 1122 3375 1075 2522 Q 1259 2794 1537 2939 Q 1816 3084 2150 3084 Q 2853 3084 3261 2657 Q 3669 2231 3669 1497 Q 3669 778 3244 343 Q 2819 -91 2113 -91 Q 1303 -91 875 529 Q 447 1150 447 2328 Q 447 3434 972 4092 Q 1497 4750 2381 4750 Q 2619 4750 2861 4703 Q 3103 4656 3366 4563 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-31\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-36\" transform=\"translate(190.869141 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-36\" transform=\"translate(254.492188 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_42\">\n",
" <path d=\"M 32.45 83.16 L 32.45 -0 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" <g id=\"patch_43\">\n",
" <path d=\"M 32.45 83.16 L 216.59 83.16 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"pc1904ecbdb\">\n",
" <rect x=\"32.45\" y=\"-0\" width=\"184.14\" height=\"83.16\" />\n",
" </clipPath>\n",
" </defs>\n",
"</svg></g>\n",
"\n",
"\n",
"<g id=\"node4\" class=\"node\">\n",
"<title>node9</title>\n",
"<svg width=\"159px\" height=\"65px\" viewBox=\"0 0 211.5 86.888343\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"372\" y=\"-170\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:46.788696</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 86.888343 L 211.5 86.888343 L 211.5 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 27.36 52.032093 L 211.5 52.032093 L 211.5 0.589739 L 27.36 0.589739 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 43.372174 52.032093 L 51.378261 52.032093 L 51.378261 52.032093 L 43.372174 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 51.378261 52.032093 L 59.384348 52.032093 L 59.384348 52.032093 L 51.378261 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 59.384348 52.032093 L 67.390435 52.032093 L 67.390435 52.032093 L 59.384348 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 67.390435 52.032093 L 75.396522 52.032093 L 75.396522 6.744888 L 67.390435 6.744888 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_7\">\n",
" <path d=\"M 75.396522 52.032093 L 83.402609 52.032093 L 83.402609 52.032093 L 75.396522 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_8\">\n",
" <path d=\"M 83.402609 52.032093 L 91.408696 52.032093 L 91.408696 52.032093 L 83.402609 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_9\">\n",
" <path d=\"M 91.408696 52.032093 L 99.414783 52.032093 L 99.414783 52.032093 L 91.408696 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_10\">\n",
" <path d=\"M 99.414783 52.032093 L 107.42087 52.032093 L 107.42087 45.777014 L 99.414783 45.777014 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_11\">\n",
" <path d=\"M 107.42087 52.032093 L 115.426957 52.032093 L 115.426957 52.032093 L 107.42087 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_12\">\n",
" <path d=\"M 115.426957 52.032093 L 123.433043 52.032093 L 123.433043 52.032093 L 115.426957 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_13\">\n",
" <path d=\"M 123.433043 52.032093 L 131.43913 52.032093 L 131.43913 52.032093 L 123.433043 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_14\">\n",
" <path d=\"M 131.43913 52.032093 L 139.445217 52.032093 L 139.445217 22.139513 L 131.43913 22.139513 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_15\">\n",
" <path d=\"M 139.445217 52.032093 L 147.451304 52.032093 L 147.451304 52.032093 L 139.445217 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_16\">\n",
" <path d=\"M 147.451304 52.032093 L 155.457391 52.032093 L 155.457391 52.032093 L 147.451304 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_17\">\n",
" <path d=\"M 155.457391 52.032093 L 163.463478 52.032093 L 163.463478 52.032093 L 155.457391 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_18\">\n",
" <path d=\"M 163.463478 52.032093 L 171.469565 52.032093 L 171.469565 32.813119 L 163.463478 32.813119 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_19\">\n",
" <path d=\"M 171.469565 52.032093 L 179.475652 52.032093 L 179.475652 52.032093 L 171.469565 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_20\">\n",
" <path d=\"M 179.475652 52.032093 L 187.481739 52.032093 L 187.481739 52.032093 L 179.475652 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_21\">\n",
" <path d=\"M 187.481739 52.032093 L 195.487826 52.032093 L 195.487826 47.332681 L 187.481739 47.332681 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_22\">\n",
" <path d=\"M 43.372174 52.032093 L 51.378261 52.032093 L 51.378261 52.032093 L 43.372174 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_23\">\n",
" <path d=\"M 51.378261 52.032093 L 59.384348 52.032093 L 59.384348 52.032093 L 51.378261 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_24\">\n",
" <path d=\"M 59.384348 52.032093 L 67.390435 52.032093 L 67.390435 52.032093 L 59.384348 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_25\">\n",
" <path d=\"M 67.390435 6.744888 L 75.396522 6.744888 L 75.396522 3.039375 L 67.390435 3.039375 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_26\">\n",
" <path d=\"M 75.396522 52.032093 L 83.402609 52.032093 L 83.402609 52.032093 L 75.396522 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_27\">\n",
" <path d=\"M 83.402609 52.032093 L 91.408696 52.032093 L 91.408696 52.032093 L 83.402609 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_28\">\n",
" <path d=\"M 91.408696 52.032093 L 99.414783 52.032093 L 99.414783 52.032093 L 91.408696 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_29\">\n",
" <path d=\"M 99.414783 45.777014 L 107.42087 45.777014 L 107.42087 45.604162 L 99.414783 45.604162 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_30\">\n",
" <path d=\"M 107.42087 52.032093 L 115.426957 52.032093 L 115.426957 52.032093 L 107.42087 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_31\">\n",
" <path d=\"M 115.426957 52.032093 L 123.433043 52.032093 L 123.433043 52.032093 L 115.426957 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_32\">\n",
" <path d=\"M 123.433043 52.032093 L 131.43913 52.032093 L 131.43913 52.032093 L 123.433043 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_33\">\n",
" <path d=\"M 131.43913 22.139513 L 139.445217 22.139513 L 139.445217 21.880235 L 131.43913 21.880235 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_34\">\n",
" <path d=\"M 139.445217 52.032093 L 147.451304 52.032093 L 147.451304 52.032093 L 139.445217 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_35\">\n",
" <path d=\"M 147.451304 52.032093 L 155.457391 52.032093 L 155.457391 52.032093 L 147.451304 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_36\">\n",
" <path d=\"M 155.457391 52.032093 L 163.463478 52.032093 L 163.463478 52.032093 L 155.457391 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_37\">\n",
" <path d=\"M 163.463478 32.813119 L 171.469565 32.813119 L 171.469565 31.732795 L 163.463478 31.732795 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_38\">\n",
" <path d=\"M 171.469565 52.032093 L 179.475652 52.032093 L 179.475652 52.032093 L 171.469565 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_39\">\n",
" <path d=\"M 179.475652 52.032093 L 187.481739 52.032093 L 187.481739 52.032093 L 179.475652 52.032093 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_40\">\n",
" <path d=\"M 187.481739 47.332681 L 195.487826 47.332681 L 195.487826 43.357087 L 187.481739 43.357087 z \" clip-path=\"url(#pd59f9d21fc)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_41\">\n",
" <path d=\"M 180.276261 52.863693 L 176.961741 61.179693 L 183.590781 61.179693 z \" style=\"fill: #444443\" />\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <defs>\n",
" <path id=\"maee9701d73\" d=\"M 0 0 L 0 3.5 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#maee9701d73\" x=\"43.372174\" y=\"52.032093\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(40.827174 69.610843) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_2\">\n",
" <g>\n",
" <use xlink:href=\"#maee9701d73\" x=\"195.487826\" y=\"52.032093\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(192.942826 69.610843) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-35\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(84.165937 84.392718) scale(0.12 -0.12)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-72\" d=\"M 2631 2963 Q 2534 3019 2420 3045 Q 2306 3072 2169 3072 Q 1681 3072 1420 2755 Q 1159 2438 1159 1844 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1341 3275 1631 3429 Q 1922 3584 2338 3584 Q 2397 3584 2469 3576 Q 2541 3569 2628 3553 L 2631 2963 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-65\" d=\"M 3597 1894 L 3597 1613 L 953 1613 Q 991 1019 1311 708 Q 1631 397 2203 397 Q 2534 397 2845 478 Q 3156 559 3463 722 L 3463 178 Q 3153 47 2828 -22 Q 2503 -91 2169 -91 Q 1331 -91 842 396 Q 353 884 353 1716 Q 353 2575 817 3079 Q 1281 3584 2069 3584 Q 2775 3584 3186 3129 Q 3597 2675 3597 1894 z M 3022 2063 Q 3016 2534 2758 2815 Q 2500 3097 2075 3097 Q 1594 3097 1305 2825 Q 1016 2553 972 2059 L 3022 2063 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6c\" d=\"M 603 4863 L 1178 4863 L 1178 0 L 603 0 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-61\" d=\"M 2194 1759 Q 1497 1759 1228 1600 Q 959 1441 959 1056 Q 959 750 1161 570 Q 1363 391 1709 391 Q 2188 391 2477 730 Q 2766 1069 2766 1631 L 2766 1759 L 2194 1759 z M 3341 1997 L 3341 0 L 2766 0 L 2766 531 Q 2569 213 2275 61 Q 1981 -91 1556 -91 Q 1019 -91 701 211 Q 384 513 384 1019 Q 384 1609 779 1909 Q 1175 2209 1959 2209 L 2766 2209 L 2766 2266 Q 2766 2663 2505 2880 Q 2244 3097 1772 3097 Q 1472 3097 1187 3025 Q 903 2953 641 2809 L 641 3341 Q 956 3463 1253 3523 Q 1550 3584 1831 3584 Q 2591 3584 2966 3190 Q 3341 2797 3341 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-74\" d=\"M 1172 4494 L 1172 3500 L 2356 3500 L 2356 3053 L 1172 3053 L 1172 1153 Q 1172 725 1289 603 Q 1406 481 1766 481 L 2356 481 L 2356 0 L 1766 0 Q 1100 0 847 248 Q 594 497 594 1153 L 594 3053 L 172 3053 L 172 3500 L 594 3500 L 594 4494 L 1172 4494 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-69\" d=\"M 603 3500 L 1178 3500 L 1178 0 L 603 0 L 603 3500 z M 603 4863 L 1178 4863 L 1178 4134 L 603 4134 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6f\" d=\"M 1959 3097 Q 1497 3097 1228 2736 Q 959 2375 959 1747 Q 959 1119 1226 758 Q 1494 397 1959 397 Q 2419 397 2687 759 Q 2956 1122 2956 1747 Q 2956 2369 2687 2733 Q 2419 3097 1959 3097 z M 1959 3584 Q 2709 3584 3137 3096 Q 3566 2609 3566 1747 Q 3566 888 3137 398 Q 2709 -91 1959 -91 Q 1206 -91 779 398 Q 353 888 353 1747 Q 353 2609 779 3096 Q 1206 3584 1959 3584 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-73\" d=\"M 2834 3397 L 2834 2853 Q 2591 2978 2328 3040 Q 2066 3103 1784 3103 Q 1356 3103 1142 2972 Q 928 2841 928 2578 Q 928 2378 1081 2264 Q 1234 2150 1697 2047 L 1894 2003 Q 2506 1872 2764 1633 Q 3022 1394 3022 966 Q 3022 478 2636 193 Q 2250 -91 1575 -91 Q 1294 -91 989 -36 Q 684 19 347 128 L 347 722 Q 666 556 975 473 Q 1284 391 1588 391 Q 1994 391 2212 530 Q 2431 669 2431 922 Q 2431 1156 2273 1281 Q 2116 1406 1581 1522 L 1381 1569 Q 847 1681 609 1914 Q 372 2147 372 2553 Q 372 3047 722 3315 Q 1072 3584 1716 3584 Q 2034 3584 2315 3537 Q 2597 3491 2834 3397 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-68\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 4863 L 1159 4863 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-70\" d=\"M 1159 525 L 1159 -1331 L 581 -1331 L 581 3500 L 1159 3500 L 1159 2969 Q 1341 3281 1617 3432 Q 1894 3584 2278 3584 Q 2916 3584 3314 3078 Q 3713 2572 3713 1747 Q 3713 922 3314 415 Q 2916 -91 2278 -91 Q 1894 -91 1617 61 Q 1341 213 1159 525 z M 3116 1747 Q 3116 2381 2855 2742 Q 2594 3103 2138 3103 Q 1681 3103 1420 2742 Q 1159 2381 1159 1747 Q 1159 1113 1420 752 Q 1681 391 2138 391 Q 2594 391 2855 752 Q 3116 1113 3116 1747 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-72\" />\n",
" <use xlink:href=\"#DejaVuSans-65\" transform=\"translate(38.863281 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6c\" transform=\"translate(100.386719 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(128.169922 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-74\" transform=\"translate(189.449219 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(228.658203 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6f\" transform=\"translate(256.441406 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6e\" transform=\"translate(317.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-73\" transform=\"translate(381.001953 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-68\" transform=\"translate(433.101562 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(496.480469 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-70\" transform=\"translate(524.263672 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_3\">\n",
" <defs>\n",
" <path id=\"m7df102d1d7\" d=\"M 0 0 L -3.5 0 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m7df102d1d7\" x=\"27.36\" y=\"52.032093\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(15.27 55.071468) scale(0.08 -0.08)\">\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_4\">\n",
" <g>\n",
" <use xlink:href=\"#m7df102d1d7\" x=\"27.36\" y=\"3.039375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(0 6.07875) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-34\" d=\"M 2419 4116 L 825 1625 L 2419 1625 L 2419 4116 z M 2253 4666 L 3047 4666 L 3047 1625 L 3713 1625 L 3713 1100 L 3047 1100 L 3047 0 L 2419 0 L 2419 1100 L 313 1100 L 313 1709 L 2253 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-33\" d=\"M 2597 2516 Q 3050 2419 3304 2112 Q 3559 1806 3559 1356 Q 3559 666 3084 287 Q 2609 -91 1734 -91 Q 1441 -91 1130 -33 Q 819 25 488 141 L 488 750 Q 750 597 1062 519 Q 1375 441 1716 441 Q 2309 441 2620 675 Q 2931 909 2931 1356 Q 2931 1769 2642 2001 Q 2353 2234 1838 2234 L 1294 2234 L 1294 2753 L 1863 2753 Q 2328 2753 2575 2939 Q 2822 3125 2822 3475 Q 2822 3834 2567 4026 Q 2313 4219 1838 4219 Q 1578 4219 1281 4162 Q 984 4106 628 3988 L 628 4550 Q 988 4650 1302 4700 Q 1616 4750 1894 4750 Q 2613 4750 3031 4423 Q 3450 4097 3450 3541 Q 3450 3153 3228 2886 Q 3006 2619 2597 2516 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-34\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-33\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(190.869141 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_42\">\n",
" <path d=\"M 27.36 52.032093 L 27.36 0.589739 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" <g id=\"patch_43\">\n",
" <path d=\"M 27.36 52.032093 L 211.5 52.032093 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"pd59f9d21fc\">\n",
" <rect x=\"27.36\" y=\"0.589739\" width=\"184.14\" height=\"51.442354\" />\n",
" </clipPath>\n",
" </defs>\n",
"</svg></g>\n",
"\n",
"<g id=\"node5\" class=\"node\">\n",
"<title>node12</title>\n",
"<svg width=\"150px\" height=\"48px\" viewBox=\"0 0 201.32 64.295625\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"552\" y=\"-162\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:46.856696</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 64.295625 L 201.32 64.295625 L 201.32 -0 L 0 -0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 17.18 29.439375 L 201.32 29.439375 L 201.32 1.719375 L 17.18 1.719375 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 33.192174 29.439375 L 41.198261 29.439375 L 41.198261 27.459375 L 33.192174 27.459375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 41.198261 29.439375 L 49.204348 29.439375 L 49.204348 29.439375 L 41.198261 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 49.204348 29.439375 L 57.210435 29.439375 L 57.210435 29.439375 L 49.204348 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 57.210435 29.439375 L 65.216522 29.439375 L 65.216522 29.439375 L 57.210435 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_7\">\n",
" <path d=\"M 65.216522 29.439375 L 73.222609 29.439375 L 73.222609 27.459375 L 65.216522 27.459375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_8\">\n",
" <path d=\"M 73.222609 29.439375 L 81.228696 29.439375 L 81.228696 28.779375 L 73.222609 28.779375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_9\">\n",
" <path d=\"M 81.228696 29.439375 L 89.234783 29.439375 L 89.234783 28.779375 L 81.228696 28.779375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_10\">\n",
" <path d=\"M 89.234783 29.439375 L 97.24087 29.439375 L 97.24087 29.439375 L 89.234783 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_11\">\n",
" <path d=\"M 97.24087 29.439375 L 105.246957 29.439375 L 105.246957 29.439375 L 97.24087 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_12\">\n",
" <path d=\"M 105.246957 29.439375 L 113.253043 29.439375 L 113.253043 28.779375 L 105.246957 28.779375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_13\">\n",
" <path d=\"M 113.253043 29.439375 L 121.25913 29.439375 L 121.25913 29.439375 L 113.253043 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_14\">\n",
" <path d=\"M 121.25913 29.439375 L 129.265217 29.439375 L 129.265217 28.779375 L 121.25913 28.779375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_15\">\n",
" <path d=\"M 129.265217 29.439375 L 137.271304 29.439375 L 137.271304 29.439375 L 129.265217 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_16\">\n",
" <path d=\"M 137.271304 29.439375 L 145.277391 29.439375 L 145.277391 29.439375 L 137.271304 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_17\">\n",
" <path d=\"M 145.277391 29.439375 L 153.283478 29.439375 L 153.283478 29.439375 L 145.277391 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_18\">\n",
" <path d=\"M 153.283478 29.439375 L 161.289565 29.439375 L 161.289565 29.439375 L 153.283478 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_19\">\n",
" <path d=\"M 161.289565 29.439375 L 169.295652 29.439375 L 169.295652 29.439375 L 161.289565 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_20\">\n",
" <path d=\"M 169.295652 29.439375 L 177.301739 29.439375 L 177.301739 29.439375 L 169.295652 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_21\">\n",
" <path d=\"M 177.301739 29.439375 L 185.307826 29.439375 L 185.307826 29.439375 L 177.301739 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_22\">\n",
" <path d=\"M 33.192174 27.459375 L 41.198261 27.459375 L 41.198261 27.459375 L 33.192174 27.459375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_23\">\n",
" <path d=\"M 41.198261 29.439375 L 49.204348 29.439375 L 49.204348 24.819375 L 41.198261 24.819375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_24\">\n",
" <path d=\"M 49.204348 29.439375 L 57.210435 29.439375 L 57.210435 20.199375 L 49.204348 20.199375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_25\">\n",
" <path d=\"M 57.210435 29.439375 L 65.216522 29.439375 L 65.216522 14.919375 L 57.210435 14.919375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_26\">\n",
" <path d=\"M 65.216522 27.459375 L 73.222609 27.459375 L 73.222609 8.319375 L 65.216522 8.319375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_27\">\n",
" <path d=\"M 73.222609 28.779375 L 81.228696 28.779375 L 81.228696 3.039375 L 73.222609 3.039375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_28\">\n",
" <path d=\"M 81.228696 28.779375 L 89.234783 28.779375 L 89.234783 14.259375 L 81.228696 14.259375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_29\">\n",
" <path d=\"M 89.234783 29.439375 L 97.24087 29.439375 L 97.24087 15.579375 L 89.234783 15.579375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_30\">\n",
" <path d=\"M 97.24087 29.439375 L 105.246957 29.439375 L 105.246957 16.239375 L 97.24087 16.239375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_31\">\n",
" <path d=\"M 105.246957 28.779375 L 113.253043 28.779375 L 113.253043 18.879375 L 105.246957 18.879375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_32\">\n",
" <path d=\"M 113.253043 29.439375 L 121.25913 29.439375 L 121.25913 20.859375 L 113.253043 20.859375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_33\">\n",
" <path d=\"M 121.25913 28.779375 L 129.265217 28.779375 L 129.265217 22.179375 L 121.25913 22.179375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_34\">\n",
" <path d=\"M 129.265217 29.439375 L 137.271304 29.439375 L 137.271304 28.119375 L 129.265217 28.119375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_35\">\n",
" <path d=\"M 137.271304 29.439375 L 145.277391 29.439375 L 145.277391 25.479375 L 137.271304 25.479375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_36\">\n",
" <path d=\"M 145.277391 29.439375 L 153.283478 29.439375 L 153.283478 27.459375 L 145.277391 27.459375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_37\">\n",
" <path d=\"M 153.283478 29.439375 L 161.289565 29.439375 L 161.289565 29.439375 L 153.283478 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_38\">\n",
" <path d=\"M 161.289565 29.439375 L 169.295652 29.439375 L 169.295652 29.439375 L 161.289565 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_39\">\n",
" <path d=\"M 169.295652 29.439375 L 177.301739 29.439375 L 177.301739 29.439375 L 169.295652 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_40\">\n",
" <path d=\"M 177.301739 29.439375 L 185.307826 29.439375 L 185.307826 29.439375 L 177.301739 29.439375 z \" clip-path=\"url(#p293f594222)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_41\">\n",
" <path d=\"M 39.443502 30.270975 L 36.128982 38.586975 L 42.758022 38.586975 z \" style=\"fill: #444443\" />\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <defs>\n",
" <path id=\"m99cdaa0197\" d=\"M 0 0 L 0 3.5 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m99cdaa0197\" x=\"33.192174\" y=\"29.439375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(28.102174 47.018125) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-31\" d=\"M 794 531 L 1825 531 L 1825 4091 L 703 3866 L 703 4441 L 1819 4666 L 2450 4666 L 2450 531 L 3481 531 L 3481 0 L 794 0 L 794 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-37\" d=\"M 525 4666 L 3525 4666 L 3525 4397 L 1831 0 L 1172 0 L 2766 4134 L 525 4134 L 525 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-31\" />\n",
" <use xlink:href=\"#DejaVuSans-37\" transform=\"translate(63.623047 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_2\">\n",
" <g>\n",
" <use xlink:href=\"#m99cdaa0197\" x=\"185.307826\" y=\"29.439375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(180.217826 47.018125) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-39\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(63.623047 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(98.072187 61.8) scale(0.12 -0.12)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-61\" d=\"M 2194 1759 Q 1497 1759 1228 1600 Q 959 1441 959 1056 Q 959 750 1161 570 Q 1363 391 1709 391 Q 2188 391 2477 730 Q 2766 1069 2766 1631 L 2766 1759 L 2194 1759 z M 3341 1997 L 3341 0 L 2766 0 L 2766 531 Q 2569 213 2275 61 Q 1981 -91 1556 -91 Q 1019 -91 701 211 Q 384 513 384 1019 Q 384 1609 779 1909 Q 1175 2209 1959 2209 L 2766 2209 L 2766 2266 Q 2766 2663 2505 2880 Q 2244 3097 1772 3097 Q 1472 3097 1187 3025 Q 903 2953 641 2809 L 641 3341 Q 956 3463 1253 3523 Q 1550 3584 1831 3584 Q 2591 3584 2966 3190 Q 3341 2797 3341 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-67\" d=\"M 2906 1791 Q 2906 2416 2648 2759 Q 2391 3103 1925 3103 Q 1463 3103 1205 2759 Q 947 2416 947 1791 Q 947 1169 1205 825 Q 1463 481 1925 481 Q 2391 481 2648 825 Q 2906 1169 2906 1791 z M 3481 434 Q 3481 -459 3084 -895 Q 2688 -1331 1869 -1331 Q 1566 -1331 1297 -1286 Q 1028 -1241 775 -1147 L 775 -588 Q 1028 -725 1275 -790 Q 1522 -856 1778 -856 Q 2344 -856 2625 -561 Q 2906 -266 2906 331 L 2906 616 Q 2728 306 2450 153 Q 2172 0 1784 0 Q 1141 0 747 490 Q 353 981 353 1791 Q 353 2603 747 3093 Q 1141 3584 1784 3584 Q 2172 3584 2450 3431 Q 2728 3278 2906 2969 L 2906 3500 L 3481 3500 L 3481 434 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-65\" d=\"M 3597 1894 L 3597 1613 L 953 1613 Q 991 1019 1311 708 Q 1631 397 2203 397 Q 2534 397 2845 478 Q 3156 559 3463 722 L 3463 178 Q 3153 47 2828 -22 Q 2503 -91 2169 -91 Q 1331 -91 842 396 Q 353 884 353 1716 Q 353 2575 817 3079 Q 1281 3584 2069 3584 Q 2775 3584 3186 3129 Q 3597 2675 3597 1894 z M 3022 2063 Q 3016 2534 2758 2815 Q 2500 3097 2075 3097 Q 1594 3097 1305 2825 Q 1016 2553 972 2059 L 3022 2063 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-61\" />\n",
" <use xlink:href=\"#DejaVuSans-67\" transform=\"translate(61.279297 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-65\" transform=\"translate(124.755859 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_3\">\n",
" <defs>\n",
" <path id=\"m13ab9f8ab1\" d=\"M 0 0 L -3.5 0 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m13ab9f8ab1\" x=\"17.18\" y=\"29.439375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(5.09 32.47875) scale(0.08 -0.08)\">\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_4\">\n",
" <g>\n",
" <use xlink:href=\"#m13ab9f8ab1\" x=\"17.18\" y=\"3.039375\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(0 6.07875) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-34\" d=\"M 2419 4116 L 825 1625 L 2419 1625 L 2419 4116 z M 2253 4666 L 3047 4666 L 3047 1625 L 3713 1625 L 3713 1100 L 3047 1100 L 3047 0 L 2419 0 L 2419 1100 L 313 1100 L 313 1709 L 2253 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-34\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(63.623047 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_42\">\n",
" <path d=\"M 17.18 29.439375 L 17.18 1.719375 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" <g id=\"patch_43\">\n",
" <path d=\"M 17.18 29.439375 L 201.32 29.439375 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p293f594222\">\n",
" <rect x=\"17.18\" y=\"1.719375\" width=\"184.14\" height=\"27.72\" />\n",
" </clipPath>\n",
" </defs>\n",
"</svg></g>\n",
"\n",
"\n",
"<g id=\"node12\" class=\"node\">\n",
"<title>leaf10</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"443,-84 375,-84 375,0 443,0 443,-84\" />\n",
"<svg width=\"60px\" height=\"76px\" viewBox=\"0 0 80.079988 102.07154\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"379\" y=\"-80\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.132508</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 102.07154 L 80.079988 102.07154 L 80.079988 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 80.079988 40.039996 C 80.079988 35.042665 79.144424 30.089218 77.321739 25.436139 C 75.499054 20.783061 72.821259 16.512073 69.427009 12.844324 C 66.032759 9.176575 61.981665 6.17648 57.483492 3.999383 C 52.985318 1.822286 48.119064 0.506423 43.136701 0.119927 C 38.154339 -0.26657 33.143371 0.28309 28.363263 1.740444 C 23.583154 3.197798 19.117857 5.537252 15.198581 8.63767 C 11.279304 11.738088 7.974883 15.545018 5.456415 19.861341 C 2.937948 24.177664 1.249667 28.927572 0.478991 33.86512 C -0.291686 38.802667 -0.131224 43.841137 0.952069 48.71964 C 2.035362 53.598143 4.02246 58.231 6.810473 62.378325 C 9.598486 66.52565 13.138448 70.114604 17.247062 72.959354 C 21.355677 75.804105 25.960786 77.854689 30.823939 79.00493 C 35.687093 80.155171 40.722881 80.384867 45.670555 79.682124 C 50.618228 78.979382 55.390891 77.356543 59.74142 74.897638 C 64.091949 72.438732 67.943936 69.186945 71.097928 65.310651 C 74.251919 61.434357 76.652523 57.001635 78.175437 52.242008 L 40.039988 40.039996 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 78.175437 52.242008 C 78.805521 50.272783 79.282129 48.257709 79.600985 46.214872 C 79.919841 44.172035 80.079988 42.107567 80.079988 40.039995 L 40.039988 40.039996 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(21.964753 90.12179) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-38\" d=\"M 2034 2216 Q 1584 2216 1326 1975 Q 1069 1734 1069 1313 Q 1069 891 1326 650 Q 1584 409 2034 409 Q 2484 409 2743 651 Q 3003 894 3003 1313 Q 3003 1734 2745 1975 Q 2488 2216 2034 2216 z M 1403 2484 Q 997 2584 770 2862 Q 544 3141 544 3541 Q 544 4100 942 4425 Q 1341 4750 2034 4750 Q 2731 4750 3128 4425 Q 3525 4100 3525 3541 Q 3525 3141 3298 2862 Q 3072 2584 2669 2484 Q 3125 2378 3379 2068 Q 3634 1759 3634 1313 Q 3634 634 3220 271 Q 2806 -91 2034 -91 Q 1263 -91 848 271 Q 434 634 434 1313 Q 434 1759 690 2068 Q 947 2378 1403 2484 z M 1172 3481 Q 1172 3119 1398 2916 Q 1625 2713 2034 2713 Q 2441 2713 2670 2916 Q 2900 3119 2900 3481 Q 2900 3844 2670 4047 Q 2441 4250 2034 4250 Q 1625 4250 1398 4047 Q 1172 3844 1172 3481 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(147.167969 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-38\" transform=\"translate(210.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(274.414062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(338.037109 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(23.821003 100.199821) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3c\" d=\"M 4684 3150 L 1459 2003 L 4684 863 L 4684 294 L 678 1747 L 678 2266 L 4684 3719 L 4684 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3c\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(167.578125 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(231.201172 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(294.824219 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge10\" class=\"edge\">\n",
"<title>node9-&gt;leaf10</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M435.12,-101.46C433.16,-97.08 431.14,-92.55 429.13,-88.04\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"430.35,-87.35 427.44,-84.27 427.79,-88.49 430.35,-87.35\" />\n",
"</g>\n",
"\n",
"<g id=\"node13\" class=\"node\">\n",
"<title>leaf11</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"525,-84 457,-84 457,0 525,0 525,-84\" />\n",
"<svg width=\"60px\" height=\"76px\" viewBox=\"0 0 80.079975 102.071526\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"461\" y=\"-80\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.152785</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 102.071526 L 80.079975 102.071526 L 80.079975 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 80.079975 40.039982 C 80.079975 34.340117 78.862875 28.705411 76.51029 23.513707 C 74.157704 18.322002 70.72342 13.691998 66.437729 9.934165 C 62.152037 6.176332 57.112923 3.376587 51.65829 1.722671 C 46.203658 0.068754 40.458217 -0.401521 34.807237 0.343382 C 29.156257 1.088285 23.728936 3.031333 18.889212 6.042235 C 14.049488 9.053136 9.908011 13.063051 6.742495 17.803089 C 3.576979 22.543127 1.459796 27.904916 0.532918 33.528914 C -0.39396 39.152912 -0.109343 44.910537 1.367685 50.415702 L 40.039975 40.039982 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 1.367685 50.415702 C 3.883951 59.794307 9.727949 67.944686 17.803082 73.337462 C 25.878216 78.730239 35.645699 81.005599 45.272714 79.736582 C 54.899728 78.467565 63.743945 73.738843 70.145792 66.437735 C 76.547639 59.136628 80.079975 49.750276 80.079975 40.039982 L 40.039975 40.039982 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(24.827866 90.121776) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-38\" d=\"M 2034 2216 Q 1584 2216 1326 1975 Q 1069 1734 1069 1313 Q 1069 891 1326 650 Q 1584 409 2034 409 Q 2484 409 2743 651 Q 3003 894 3003 1313 Q 3003 1734 2745 1975 Q 2488 2216 2034 2216 z M 1403 2484 Q 997 2584 770 2862 Q 544 3141 544 3541 Q 544 4100 942 4425 Q 1341 4750 2034 4750 Q 2731 4750 3128 4425 Q 3525 4100 3525 3541 Q 3525 3141 3298 2862 Q 3072 2584 2669 2484 Q 3125 2378 3379 2068 Q 3634 1759 3634 1313 Q 3634 634 3220 271 Q 2806 -91 2034 -91 Q 1263 -91 848 271 Q 434 634 434 1313 Q 434 1759 690 2068 Q 947 2378 1403 2484 z M 1172 3481 Q 1172 3119 1398 2916 Q 1625 2713 2034 2713 Q 2441 2713 2670 2916 Q 2900 3119 2900 3481 Q 2900 3844 2670 4047 Q 2441 4250 2034 4250 Q 1625 4250 1398 4047 Q 1172 3844 1172 3481 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-33\" d=\"M 2597 2516 Q 3050 2419 3304 2112 Q 3559 1806 3559 1356 Q 3559 666 3084 287 Q 2609 -91 1734 -91 Q 1441 -91 1130 -33 Q 819 25 488 141 L 488 750 Q 750 597 1062 519 Q 1375 441 1716 441 Q 2309 441 2620 675 Q 2931 909 2931 1356 Q 2931 1769 2642 2001 Q 2353 2234 1838 2234 L 1294 2234 L 1294 2753 L 1863 2753 Q 2328 2753 2575 2939 Q 2822 3125 2822 3475 Q 2822 3834 2567 4026 Q 2313 4219 1838 4219 Q 1578 4219 1281 4162 Q 984 4106 628 3988 L 628 4550 Q 988 4650 1302 4700 Q 1616 4750 1894 4750 Q 2613 4750 3031 4423 Q 3450 4097 3450 3541 Q 3450 3153 3228 2886 Q 3006 2619 2597 2516 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-38\" transform=\"translate(147.167969 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(210.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-33\" transform=\"translate(274.414062 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(23.820991 100.199807) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3c\" d=\"M 4684 3150 L 1459 2003 L 4684 863 L 4684 294 L 678 1747 L 678 2266 L 4684 3719 L 4684 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3c\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(167.578125 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(231.201172 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(294.824219 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge11\" class=\"edge\">\n",
"<title>node9-&gt;leaf11</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M466.12,-101.46C467.99,-97.08 469.92,-92.55 471.83,-88.04\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"473.16,-88.5 473.44,-84.27 470.58,-87.4 473.16,-88.5\" />\n",
"</g>\n",
"\n",
"<g id=\"node14\" class=\"node\">\n",
"<title>leaf13</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"611,-58 579,-58 579,-26 611,-26 611,-58\" />\n",
"<svg width=\"24px\" height=\"24px\" viewBox=\"0 0 32.437969 32.241784\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"583\" y=\"-54\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.173500</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 32.241784 L 32.437969 32.241784 L 32.437969 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 22.686984 6.468 C 22.686984 5.61863 22.519677 4.777519 22.194637 3.992804 C 21.869597 3.208088 21.393146 2.495029 20.792551 1.894433 C 20.191956 1.293838 19.478896 0.817387 18.694181 0.492347 C 17.909465 0.167307 17.068354 0 16.218984 0 C 15.369614 0 14.528503 0.167307 13.743788 0.492347 C 12.959072 0.817387 12.246013 1.293838 11.645418 1.894433 C 11.044822 2.495029 10.568371 3.208088 10.243332 3.992804 C 9.918292 4.777519 9.750984 5.61863 9.750984 6.468 C 9.750984 7.31737 9.918292 8.158481 10.243332 8.943196 C 10.568371 9.727912 11.044822 10.440971 11.645418 11.041567 C 12.246013 11.642162 12.959072 12.118613 13.743788 12.443653 C 14.528503 12.768693 15.369614 12.936 16.218984 12.936 C 17.068354 12.936 17.909465 12.768693 18.694181 12.443653 C 19.478896 12.118613 20.191956 11.642162 20.792551 11.041567 C 21.393146 10.440971 21.869597 9.727912 22.194637 8.943196 C 22.519677 8.158481 22.686984 7.31737 22.686984 6.468 M 16.218984 6.468 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(6.733125 20.292034) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-33\" d=\"M 2597 2516 Q 3050 2419 3304 2112 Q 3559 1806 3559 1356 Q 3559 666 3084 287 Q 2609 -91 1734 -91 Q 1441 -91 1130 -33 Q 819 25 488 141 L 488 750 Q 750 597 1062 519 Q 1375 441 1716 441 Q 2309 441 2620 675 Q 2931 909 2931 1356 Q 2931 1769 2642 2001 Q 2353 2234 1838 2234 L 1294 2234 L 1294 2753 L 1863 2753 Q 2328 2753 2575 2939 Q 2822 3125 2822 3475 Q 2822 3834 2567 4026 Q 2313 4219 1838 4219 Q 1578 4219 1281 4162 Q 984 4106 628 3988 L 628 4550 Q 988 4650 1302 4700 Q 1616 4750 1894 4750 Q 2613 4750 3031 4423 Q 3450 4097 3450 3541 Q 3450 3153 3228 2886 Q 3006 2619 2597 2516 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-33\" transform=\"translate(147.167969 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(0 30.370065) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3c\" d=\"M 4684 3150 L 1459 2003 L 4684 863 L 4684 294 L 678 1747 L 678 2266 L 4684 3719 L 4684 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3c\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(167.578125 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(231.201172 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(294.824219 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge13\" class=\"edge\">\n",
"<title>node12-&gt;leaf13</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M617.74,-109.81C612.56,-94.58 606.22,-75.95 601.55,-62.23\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"602.77,-61.48 600.16,-58.14 600.12,-62.38 602.77,-61.48\" />\n",
"</g>\n",
"\n",
"<g id=\"node15\" class=\"node\">\n",
"<title>leaf14</title>\n",
"<polygon fill=\"none\" stroke=\"#444443\" stroke-width=\"0\" points=\"693,-84 625,-84 625,0 693,0 693,-84\" />\n",
"<svg width=\"60px\" height=\"76px\" viewBox=\"0 0 80.079994 102.071531\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"629\" y=\"-80\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:47.197116</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 102.071531 L 80.079994 102.071531 L 80.079994 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 80.079994 40.039988 C 80.079994 38.763621 80.018963 37.487983 79.897111 36.217446 C 79.775258 34.946909 79.592724 33.682925 79.350132 32.429824 L 40.039994 40.039988 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 79.350132 32.429824 C 78.381375 27.425727 76.465952 22.652329 73.706975 18.366595 C 70.947998 14.08086 67.395881 10.361098 63.241842 7.407552 C 59.087802 4.454006 54.407744 2.320645 49.453608 1.122312 C 44.499471 -0.076021 39.361777 -0.317429 34.317103 0.411083 C 29.272428 1.139594 24.412948 2.824715 20.000262 5.37573 C 15.587577 7.926745 11.702313 11.297042 8.553626 15.305189 C 5.404939 19.313336 3.050362 23.886096 1.616647 28.777306 C 0.182931 33.668516 -0.303725 38.788803 0.182877 43.862528 C 0.669478 48.936254 2.120445 53.870711 4.457669 58.400265 C 6.794893 62.929819 9.975668 66.971705 13.828764 70.308338 C 17.68186 73.644972 22.136874 76.215385 26.95398 77.881207 C 31.771087 79.547028 36.862267 80.27782 41.95345 80.034241 C 47.044632 79.790662 52.042791 78.577163 56.678863 76.459071 C 61.314934 74.340979 65.504207 71.356994 69.021163 67.667753 C 72.538118 63.978513 75.318494 59.651425 77.212615 54.91943 C 79.106736 50.187435 80.079993 45.136995 80.079994 40.039989 L 40.039994 40.039988 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(24.827884 90.121781) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-32\" d=\"M 1228 531 L 3431 531 L 3431 0 L 469 0 L 469 531 Q 828 903 1448 1529 Q 2069 2156 2228 2338 Q 2531 2678 2651 2914 Q 2772 3150 2772 3378 Q 2772 3750 2511 3984 Q 2250 4219 1831 4219 Q 1534 4219 1204 4116 Q 875 4013 500 3803 L 500 4441 Q 881 4594 1212 4672 Q 1544 4750 1819 4750 Q 2544 4750 2975 4387 Q 3406 4025 3406 3419 Q 3406 3131 3298 2873 Q 3191 2616 2906 2266 Q 2828 2175 2409 1742 Q 1991 1309 1228 531 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-33\" d=\"M 2597 2516 Q 3050 2419 3304 2112 Q 3559 1806 3559 1356 Q 3559 666 3084 287 Q 2609 -91 1734 -91 Q 1441 -91 1130 -33 Q 819 25 488 141 L 488 750 Q 750 597 1062 519 Q 1375 441 1716 441 Q 2309 441 2620 675 Q 2931 909 2931 1356 Q 2931 1769 2642 2001 Q 2353 2234 1838 2234 L 1294 2234 L 1294 2753 L 1863 2753 Q 2328 2753 2575 2939 Q 2822 3125 2822 3475 Q 2822 3834 2567 4026 Q 2313 4219 1838 4219 Q 1578 4219 1281 4162 Q 984 4106 628 3988 L 628 4550 Q 988 4650 1302 4700 Q 1616 4750 1894 4750 Q 2613 4750 3031 4423 Q 3450 4097 3450 3541 Q 3450 3153 3228 2886 Q 3006 2619 2597 2516 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-6e\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(63.378906 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-32\" transform=\"translate(147.167969 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-33\" transform=\"translate(210.791016 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(274.414062 0)\" />\n",
" </g>\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(27.591869 100.199813) scale(0.09 -0.09)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3e\" d=\"M 678 3150 L 678 3719 L 4684 2266 L 4684 1747 L 678 294 L 678 863 L 3897 2003 L 678 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3e\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(147.412109 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(211.035156 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge14\" class=\"edge\">\n",
"<title>node12-&gt;leaf14</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M636.26,-109.81C638.56,-103.04 641.09,-95.6 643.61,-88.21\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"645.04,-88.36 645,-84.12 642.39,-87.46 645.04,-88.36\" />\n",
"</g>\n",
"\n",
"<g id=\"edge16\" class=\"edge\">\n",
"<title>node8-&gt;node9</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M451.53,-191.93C451.49,-187.6 451.44,-183.24 451.4,-178.98\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"452.8,-178.74 451.36,-174.76 450,-178.77 452.8,-178.74\" />\n",
"</g>\n",
"\n",
"<g id=\"edge17\" class=\"edge\">\n",
"<title>node8-&gt;node12</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M534.38,-191.93C548.48,-183.87 562.75,-175.71 575.74,-168.29\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"576.59,-169.42 579.36,-166.22 575.2,-166.99 576.59,-169.42\" />\n",
"</g>\n",
"\n",
"<g id=\"node7\" class=\"node\">\n",
"<title>node0</title>\n",
"<svg width=\"159px\" height=\"76px\" viewBox=\"0 0 211.5 101.814847\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"282\" y=\"-399\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:46.998675</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 101.814847 L 211.5 101.814847 L 211.5 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 27.36 66.958597 L 211.5 66.958597 L 211.5 0 L 27.36 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 43.372174 66.958597 L 51.378261 66.958597 L 51.378261 31.84625 L 43.372174 31.84625 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 51.378261 66.958597 L 59.384348 66.958597 L 59.384348 66.958597 L 51.378261 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"M 59.384348 66.958597 L 67.390435 66.958597 L 67.390435 66.958597 L 59.384348 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"M 67.390435 66.958597 L 75.396522 66.958597 L 75.396522 31.198232 L 67.390435 31.198232 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_7\">\n",
" <path d=\"M 75.396522 66.958597 L 83.402609 66.958597 L 83.402609 66.958597 L 75.396522 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_8\">\n",
" <path d=\"M 83.402609 66.958597 L 91.408696 66.958597 L 91.408696 66.958597 L 83.402609 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_9\">\n",
" <path d=\"M 91.408696 66.958597 L 99.414783 66.958597 L 99.414783 66.958597 L 91.408696 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_10\">\n",
" <path d=\"M 99.414783 66.958597 L 107.42087 66.958597 L 107.42087 62.021722 L 99.414783 62.021722 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_11\">\n",
" <path d=\"M 107.42087 66.958597 L 115.426957 66.958597 L 115.426957 66.958597 L 107.42087 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_12\">\n",
" <path d=\"M 115.426957 66.958597 L 123.433043 66.958597 L 123.433043 66.958597 L 115.426957 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_13\">\n",
" <path d=\"M 123.433043 66.958597 L 131.43913 66.958597 L 131.43913 66.958597 L 123.433043 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_14\">\n",
" <path d=\"M 131.43913 66.958597 L 139.445217 66.958597 L 139.445217 43.340044 L 131.43913 43.340044 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_15\">\n",
" <path d=\"M 139.445217 66.958597 L 147.451304 66.958597 L 147.451304 66.958597 L 139.445217 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_16\">\n",
" <path d=\"M 147.451304 66.958597 L 155.457391 66.958597 L 155.457391 66.958597 L 147.451304 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_17\">\n",
" <path d=\"M 155.457391 66.958597 L 163.463478 66.958597 L 163.463478 66.958597 L 155.457391 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_18\">\n",
" <path d=\"M 163.463478 66.958597 L 171.469565 66.958597 L 171.469565 51.755752 L 163.463478 51.755752 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_19\">\n",
" <path d=\"M 171.469565 66.958597 L 179.475652 66.958597 L 179.475652 66.958597 L 171.469565 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_20\">\n",
" <path d=\"M 179.475652 66.958597 L 187.481739 66.958597 L 187.481739 66.958597 L 179.475652 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_21\">\n",
" <path d=\"M 187.481739 66.958597 L 195.487826 66.958597 L 195.487826 63.24102 L 187.481739 63.24102 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_22\">\n",
" <path d=\"M 43.372174 31.84625 L 51.378261 31.84625 L 51.378261 3.188505 L 43.372174 3.188505 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_23\">\n",
" <path d=\"M 51.378261 66.958597 L 59.384348 66.958597 L 59.384348 66.958597 L 51.378261 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_24\">\n",
" <path d=\"M 59.384348 66.958597 L 67.390435 66.958597 L 67.390435 66.958597 L 59.384348 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_25\">\n",
" <path d=\"M 67.390435 31.198232 L 75.396522 31.198232 L 75.396522 27.293071 L 67.390435 27.293071 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_26\">\n",
" <path d=\"M 75.396522 66.958597 L 83.402609 66.958597 L 83.402609 66.958597 L 75.396522 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_27\">\n",
" <path d=\"M 83.402609 66.958597 L 91.408696 66.958597 L 91.408696 66.958597 L 83.402609 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_28\">\n",
" <path d=\"M 91.408696 66.958597 L 99.414783 66.958597 L 99.414783 66.958597 L 91.408696 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_29\">\n",
" <path d=\"M 99.414783 62.021722 L 107.42087 62.021722 L 107.42087 61.868244 L 99.414783 61.868244 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_30\">\n",
" <path d=\"M 107.42087 66.958597 L 115.426957 66.958597 L 115.426957 66.958597 L 107.42087 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_31\">\n",
" <path d=\"M 115.426957 66.958597 L 123.433043 66.958597 L 123.433043 66.958597 L 115.426957 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_32\">\n",
" <path d=\"M 123.433043 66.958597 L 131.43913 66.958597 L 131.43913 66.958597 L 123.433043 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_33\">\n",
" <path d=\"M 131.43913 43.340044 L 139.445217 43.340044 L 139.445217 43.024562 L 131.43913 43.024562 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_34\">\n",
" <path d=\"M 139.445217 66.958597 L 147.451304 66.958597 L 147.451304 66.958597 L 139.445217 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_35\">\n",
" <path d=\"M 147.451304 66.958597 L 155.457391 66.958597 L 155.457391 66.958597 L 147.451304 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_36\">\n",
" <path d=\"M 155.457391 66.958597 L 163.463478 66.958597 L 163.463478 66.958597 L 155.457391 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_37\">\n",
" <path d=\"M 163.463478 51.755752 L 171.469565 51.755752 L 171.469565 50.621721 L 163.463478 50.621721 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_38\">\n",
" <path d=\"M 171.469565 66.958597 L 179.475652 66.958597 L 179.475652 66.958597 L 171.469565 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_39\">\n",
" <path d=\"M 179.475652 66.958597 L 187.481739 66.958597 L 187.481739 66.958597 L 179.475652 66.958597 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_40\">\n",
" <path d=\"M 187.481739 63.24102 L 195.487826 63.24102 L 195.487826 59.591655 L 187.481739 59.591655 z \" clip-path=\"url(#p429bd6c29a)\" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"patch_41\">\n",
" <path d=\"M 58.583739 67.790197 L 55.269219 76.106197 L 61.898259 76.106197 z \" style=\"fill: #444443\" />\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_1\">\n",
" <defs>\n",
" <path id=\"m2d2e3442a4\" d=\"M 0 0 L 0 3.5 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m2d2e3442a4\" x=\"43.372174\" y=\"66.958597\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(40.827174 84.537347) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_2\">\n",
" <g>\n",
" <use xlink:href=\"#m2d2e3442a4\" x=\"195.487826\" y=\"66.958597\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(192.942826 84.537347) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-35\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(84.165937 99.319222) scale(0.12 -0.12)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-72\" d=\"M 2631 2963 Q 2534 3019 2420 3045 Q 2306 3072 2169 3072 Q 1681 3072 1420 2755 Q 1159 2438 1159 1844 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1341 3275 1631 3429 Q 1922 3584 2338 3584 Q 2397 3584 2469 3576 Q 2541 3569 2628 3553 L 2631 2963 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-65\" d=\"M 3597 1894 L 3597 1613 L 953 1613 Q 991 1019 1311 708 Q 1631 397 2203 397 Q 2534 397 2845 478 Q 3156 559 3463 722 L 3463 178 Q 3153 47 2828 -22 Q 2503 -91 2169 -91 Q 1331 -91 842 396 Q 353 884 353 1716 Q 353 2575 817 3079 Q 1281 3584 2069 3584 Q 2775 3584 3186 3129 Q 3597 2675 3597 1894 z M 3022 2063 Q 3016 2534 2758 2815 Q 2500 3097 2075 3097 Q 1594 3097 1305 2825 Q 1016 2553 972 2059 L 3022 2063 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6c\" d=\"M 603 4863 L 1178 4863 L 1178 0 L 603 0 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-61\" d=\"M 2194 1759 Q 1497 1759 1228 1600 Q 959 1441 959 1056 Q 959 750 1161 570 Q 1363 391 1709 391 Q 2188 391 2477 730 Q 2766 1069 2766 1631 L 2766 1759 L 2194 1759 z M 3341 1997 L 3341 0 L 2766 0 L 2766 531 Q 2569 213 2275 61 Q 1981 -91 1556 -91 Q 1019 -91 701 211 Q 384 513 384 1019 Q 384 1609 779 1909 Q 1175 2209 1959 2209 L 2766 2209 L 2766 2266 Q 2766 2663 2505 2880 Q 2244 3097 1772 3097 Q 1472 3097 1187 3025 Q 903 2953 641 2809 L 641 3341 Q 956 3463 1253 3523 Q 1550 3584 1831 3584 Q 2591 3584 2966 3190 Q 3341 2797 3341 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-74\" d=\"M 1172 4494 L 1172 3500 L 2356 3500 L 2356 3053 L 1172 3053 L 1172 1153 Q 1172 725 1289 603 Q 1406 481 1766 481 L 2356 481 L 2356 0 L 1766 0 Q 1100 0 847 248 Q 594 497 594 1153 L 594 3053 L 172 3053 L 172 3500 L 594 3500 L 594 4494 L 1172 4494 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-69\" d=\"M 603 3500 L 1178 3500 L 1178 0 L 603 0 L 603 3500 z M 603 4863 L 1178 4863 L 1178 4134 L 603 4134 L 603 4863 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6f\" d=\"M 1959 3097 Q 1497 3097 1228 2736 Q 959 2375 959 1747 Q 959 1119 1226 758 Q 1494 397 1959 397 Q 2419 397 2687 759 Q 2956 1122 2956 1747 Q 2956 2369 2687 2733 Q 2419 3097 1959 3097 z M 1959 3584 Q 2709 3584 3137 3096 Q 3566 2609 3566 1747 Q 3566 888 3137 398 Q 2709 -91 1959 -91 Q 1206 -91 779 398 Q 353 888 353 1747 Q 353 2609 779 3096 Q 1206 3584 1959 3584 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-6e\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 3500 L 1159 3500 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-73\" d=\"M 2834 3397 L 2834 2853 Q 2591 2978 2328 3040 Q 2066 3103 1784 3103 Q 1356 3103 1142 2972 Q 928 2841 928 2578 Q 928 2378 1081 2264 Q 1234 2150 1697 2047 L 1894 2003 Q 2506 1872 2764 1633 Q 3022 1394 3022 966 Q 3022 478 2636 193 Q 2250 -91 1575 -91 Q 1294 -91 989 -36 Q 684 19 347 128 L 347 722 Q 666 556 975 473 Q 1284 391 1588 391 Q 1994 391 2212 530 Q 2431 669 2431 922 Q 2431 1156 2273 1281 Q 2116 1406 1581 1522 L 1381 1569 Q 847 1681 609 1914 Q 372 2147 372 2553 Q 372 3047 722 3315 Q 1072 3584 1716 3584 Q 2034 3584 2315 3537 Q 2597 3491 2834 3397 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-68\" d=\"M 3513 2113 L 3513 0 L 2938 0 L 2938 2094 Q 2938 2591 2744 2837 Q 2550 3084 2163 3084 Q 1697 3084 1428 2787 Q 1159 2491 1159 1978 L 1159 0 L 581 0 L 581 4863 L 1159 4863 L 1159 2956 Q 1366 3272 1645 3428 Q 1925 3584 2291 3584 Q 2894 3584 3203 3211 Q 3513 2838 3513 2113 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-70\" d=\"M 1159 525 L 1159 -1331 L 581 -1331 L 581 3500 L 1159 3500 L 1159 2969 Q 1341 3281 1617 3432 Q 1894 3584 2278 3584 Q 2916 3584 3314 3078 Q 3713 2572 3713 1747 Q 3713 922 3314 415 Q 2916 -91 2278 -91 Q 1894 -91 1617 61 Q 1341 213 1159 525 z M 3116 1747 Q 3116 2381 2855 2742 Q 2594 3103 2138 3103 Q 1681 3103 1420 2742 Q 1159 2381 1159 1747 Q 1159 1113 1420 752 Q 1681 391 2138 391 Q 2594 391 2855 752 Q 3116 1113 3116 1747 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-72\" />\n",
" <use xlink:href=\"#DejaVuSans-65\" transform=\"translate(38.863281 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6c\" transform=\"translate(100.386719 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-61\" transform=\"translate(128.169922 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-74\" transform=\"translate(189.449219 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(228.658203 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6f\" transform=\"translate(256.441406 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-6e\" transform=\"translate(317.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-73\" transform=\"translate(381.001953 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-68\" transform=\"translate(433.101562 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-69\" transform=\"translate(496.480469 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-70\" transform=\"translate(524.263672 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_3\">\n",
" <defs>\n",
" <path id=\"m0c1c84d1b1\" d=\"M 0 0 L -3.5 0 \" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </defs>\n",
" <g>\n",
" <use xlink:href=\"#m0c1c84d1b1\" x=\"27.36\" y=\"66.958597\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(15.27 69.997972) scale(0.08 -0.08)\">\n",
" <use xlink:href=\"#DejaVuSans-30\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_4\">\n",
" <g>\n",
" <use xlink:href=\"#m0c1c84d1b1\" x=\"27.36\" y=\"3.188505\" style=\"stroke: #000000; stroke-width: 0.3\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(0 6.22788) scale(0.08 -0.08)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-37\" d=\"M 525 4666 L 3525 4666 L 3525 4397 L 1831 0 L 1172 0 L 2766 4134 L 525 4134 L 525 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-34\" d=\"M 2419 4116 L 825 1625 L 2419 1625 L 2419 4116 z M 2253 4666 L 3047 4666 L 3047 1625 L 3713 1625 L 3713 1100 L 3047 1100 L 3047 0 L 2419 0 L 2419 1100 L 313 1100 L 313 1709 L 2253 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-39\" d=\"M 703 97 L 703 672 Q 941 559 1184 500 Q 1428 441 1663 441 Q 2288 441 2617 861 Q 2947 1281 2994 2138 Q 2813 1869 2534 1725 Q 2256 1581 1919 1581 Q 1219 1581 811 2004 Q 403 2428 403 3163 Q 403 3881 828 4315 Q 1253 4750 1959 4750 Q 2769 4750 3195 4129 Q 3622 3509 3622 2328 Q 3622 1225 3098 567 Q 2575 -91 1691 -91 Q 1453 -91 1209 -44 Q 966 3 703 97 z M 1959 2075 Q 2384 2075 2632 2365 Q 2881 2656 2881 3163 Q 2881 3666 2632 3958 Q 2384 4250 1959 4250 Q 1534 4250 1286 3958 Q 1038 3666 1038 3163 Q 1038 2656 1286 2365 Q 1534 2075 1959 2075 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-37\" />\n",
" <use xlink:href=\"#DejaVuSans-34\" transform=\"translate(63.623047 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-37\" transform=\"translate(127.246094 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-39\" transform=\"translate(190.869141 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_42\">\n",
" <path d=\"M 27.36 66.958597 L 27.36 0 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" <g id=\"patch_43\">\n",
" <path d=\"M 27.36 66.958597 L 211.5 66.958597 \" style=\"fill: none; stroke: #444443; stroke-width: 0.3; stroke-linejoin: miter; stroke-linecap: square\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p429bd6c29a\">\n",
" <rect x=\"27.36\" y=\"0\" width=\"184.14\" height=\"66.958597\" />\n",
" </clipPath>\n",
" </defs>\n",
"</svg></g>\n",
"\n",
"<g id=\"edge19\" class=\"edge\">\n",
"<title>node0-&gt;node1</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M329.43,-318.71C318.83,-304.85 307.11,-289.53 296.9,-276.18\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"297.88,-275.16 294.34,-272.83 295.66,-276.86 297.88,-275.16\" />\n",
"<text text-anchor=\"start\" x=\"321\" y=\"-299.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#444443\"> &#160;&#8804;</text>\n",
"</g>\n",
"\n",
"<g id=\"edge20\" class=\"edge\">\n",
"<title>node0-&gt;node8</title>\n",
"<path fill=\"none\" stroke=\"#444443\" stroke-width=\"0.3\" d=\"M392.57,-318.71C399.25,-309.98 406.36,-300.68 413.27,-291.65\" />\n",
"<polygon fill=\"#444443\" stroke=\"#444443\" stroke-width=\"0.3\" points=\"414.48,-292.36 415.8,-288.34 412.26,-290.66 414.48,-292.36\" />\n",
"<text text-anchor=\"start\" x=\"410\" y=\"-299.8\" font-family=\"Arial\" font-size=\"14.00\" fill=\"#444443\"> &#160;&gt;</text>\n",
"</g>\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"<g id=\"node16\" class=\"node\">\n",
"<title>legend</title>\n",
"<polygon fill=\"none\" stroke=\"black\" stroke-width=\"0\" points=\"515,-385.5 451,-385.5 451,-336.5 515,-336.5 515,-385.5\" />\n",
"<svg width=\"60px\" height=\"45px\" viewBox=\"0 0 80.542188 60.1125\" version=\"1.1\" preserveAspectRatio=\"xMinYMin meet\" x=\"453\" y=\"-383\">\n",
" <metadata>\n",
" <rdf:RDF>\n",
" <ns2:Work>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\" />\n",
" <dc:date>2025-03-12T12:45:45.155475</dc:date>\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:creator>\n",
" <ns2:Agent>\n",
" <dc:title>Matplotlib v3.10.0, https://matplotlib.org/</dc:title>\n",
" </ns2:Agent>\n",
" </dc:creator>\n",
" </ns2:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <defs>\n",
" <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"M 0 60.1125 L 80.542188 60.1125 L 80.542188 0 L 0 0 z \" style=\"fill: #ffffff\" />\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"legend_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"M 2 60.1125 L 73.542188 60.1125 Q 75.542188 60.1125 75.542188 58.1125 L 75.542188 7 Q 75.542188 5 73.542188 5 L 2 5 Q 0 5 0 7 L 0 58.1125 Q 0 60.1125 2 60.1125 z \" style=\"fill: #ffffff; opacity: 0.8; stroke: #444443; stroke-width: 0.5; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_1\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(20.18125 20.598438) scale(0.1 -0.1)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-Bold-74\" d=\"M 1759 4494 L 1759 3500 L 2913 3500 L 2913 2700 L 1759 2700 L 1759 1216 Q 1759 972 1856 886 Q 1953 800 2241 800 L 2816 800 L 2816 0 L 1856 0 Q 1194 0 917 276 Q 641 553 641 1216 L 641 2700 L 84 2700 L 84 3500 L 641 3500 L 641 4494 L 1759 4494 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-Bold-61\" d=\"M 2106 1575 Q 1756 1575 1579 1456 Q 1403 1338 1403 1106 Q 1403 894 1545 773 Q 1688 653 1941 653 Q 2256 653 2472 879 Q 2688 1106 2688 1447 L 2688 1575 L 2106 1575 z M 3816 1997 L 3816 0 L 2688 0 L 2688 519 Q 2463 200 2181 54 Q 1900 -91 1497 -91 Q 953 -91 614 226 Q 275 544 275 1050 Q 275 1666 698 1953 Q 1122 2241 2028 2241 L 2688 2241 L 2688 2328 Q 2688 2594 2478 2717 Q 2269 2841 1825 2841 Q 1466 2841 1156 2769 Q 847 2697 581 2553 L 581 3406 Q 941 3494 1303 3539 Q 1666 3584 2028 3584 Q 2975 3584 3395 3211 Q 3816 2838 3816 1997 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-Bold-72\" d=\"M 3138 2547 Q 2991 2616 2845 2648 Q 2700 2681 2553 2681 Q 2122 2681 1889 2404 Q 1656 2128 1656 1613 L 1656 0 L 538 0 L 538 3500 L 1656 3500 L 1656 2925 Q 1872 3269 2151 3426 Q 2431 3584 2822 3584 Q 2878 3584 2943 3579 Q 3009 3575 3134 3559 L 3138 2547 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-Bold-67\" d=\"M 2919 594 Q 2688 288 2409 144 Q 2131 0 1766 0 Q 1125 0 706 504 Q 288 1009 288 1791 Q 288 2575 706 3076 Q 1125 3578 1766 3578 Q 2131 3578 2409 3434 Q 2688 3291 2919 2981 L 2919 3500 L 4044 3500 L 4044 353 Q 4044 -491 3511 -936 Q 2978 -1381 1966 -1381 Q 1638 -1381 1331 -1331 Q 1025 -1281 716 -1178 L 716 -306 Q 1009 -475 1290 -558 Q 1572 -641 1856 -641 Q 2406 -641 2662 -400 Q 2919 -159 2919 353 L 2919 594 z M 2181 2772 Q 1834 2772 1640 2515 Q 1447 2259 1447 1791 Q 1447 1309 1634 1061 Q 1822 813 2181 813 Q 2531 813 2725 1069 Q 2919 1325 2919 1791 Q 2919 2259 2725 2515 Q 2531 2772 2181 2772 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-Bold-65\" d=\"M 4031 1759 L 4031 1441 L 1416 1441 Q 1456 1047 1700 850 Q 1944 653 2381 653 Q 2734 653 3104 758 Q 3475 863 3866 1075 L 3866 213 Q 3469 63 3072 -14 Q 2675 -91 2278 -91 Q 1328 -91 801 392 Q 275 875 275 1747 Q 275 2603 792 3093 Q 1309 3584 2216 3584 Q 3041 3584 3536 3087 Q 4031 2591 4031 1759 z M 2881 2131 Q 2881 2450 2695 2645 Q 2509 2841 2209 2841 Q 1884 2841 1681 2658 Q 1478 2475 1428 2131 L 2881 2131 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-Bold-74\" />\n",
" <use xlink:href=\"#DejaVuSans-Bold-61\" transform=\"translate(47.802734 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-Bold-72\" transform=\"translate(115.283203 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-Bold-67\" transform=\"translate(164.599609 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-Bold-65\" transform=\"translate(236.181641 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-Bold-74\" transform=\"translate(304.003906 0)\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"M 8 35.354688 L 28 35.354688 L 28 28.354688 L 8 28.354688 z \" style=\"fill: #fefebb; stroke: #444443; stroke-width: 0.4; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_2\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(31.5 35.354688) scale(0.1 -0.1)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3c\" d=\"M 4684 3150 L 1459 2003 L 4684 863 L 4684 294 L 678 1747 L 678 2266 L 4684 3719 L 4684 3150 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-3d\" d=\"M 678 2906 L 4684 2906 L 4684 2381 L 678 2381 L 678 2906 z M 678 1631 L 4684 1631 L 4684 1100 L 678 1100 L 678 1631 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-35\" d=\"M 691 4666 L 3169 4666 L 3169 4134 L 1269 4134 L 1269 2991 Q 1406 3038 1543 3061 Q 1681 3084 1819 3084 Q 2600 3084 3056 2656 Q 3513 2228 3513 1497 Q 3513 744 3044 326 Q 2575 -91 1722 -91 Q 1428 -91 1123 -41 Q 819 9 494 109 L 494 744 Q 775 591 1075 516 Q 1375 441 1709 441 Q 2250 441 2565 725 Q 2881 1009 2881 1497 Q 2881 1984 2565 2268 Q 2250 2553 1709 2553 Q 1456 2553 1204 2497 Q 953 2441 691 2322 L 691 4666 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-30\" d=\"M 2034 4250 Q 1547 4250 1301 3770 Q 1056 3291 1056 2328 Q 1056 1369 1301 889 Q 1547 409 2034 409 Q 2525 409 2770 889 Q 3016 1369 3016 2328 Q 3016 3291 2770 3770 Q 2525 4250 2034 4250 z M 2034 4750 Q 2819 4750 3233 4129 Q 3647 3509 3647 2328 Q 3647 1150 3233 529 Q 2819 -91 2034 -91 Q 1250 -91 836 529 Q 422 1150 422 2328 Q 422 3509 836 4129 Q 1250 4750 2034 4750 z \" transform=\"scale(0.015625)\" />\n",
" <path id=\"DejaVuSans-4b\" d=\"M 628 4666 L 1259 4666 L 1259 2694 L 3353 4666 L 4166 4666 L 1850 2491 L 4331 0 L 3500 0 L 1259 2247 L 1259 0 L 628 0 L 628 4666 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3c\" />\n",
" <use xlink:href=\"#DejaVuSans-3d\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(167.578125 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(231.201172 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(294.824219 0)\" />\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"M 8 50.032813 L 28 50.032813 L 28 43.032813 L 8 43.032813 z \" style=\"fill: #a1dab4; stroke: #444443; stroke-width: 0.4; stroke-linejoin: miter\" />\n",
" </g>\n",
" <g id=\"text_3\">\n",
" \n",
" <g style=\"fill: #444443\" transform=\"translate(31.5 50.032813) scale(0.1 -0.1)\">\n",
" <defs>\n",
" <path id=\"DejaVuSans-3e\" d=\"M 678 3150 L 678 3719 L 4684 2266 L 4684 1747 L 678 294 L 678 863 L 3897 2003 L 678 3150 z \" transform=\"scale(0.015625)\" />\n",
" </defs>\n",
" <use xlink:href=\"#DejaVuSans-3e\" />\n",
" <use xlink:href=\"#DejaVuSans-35\" transform=\"translate(83.789062 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-30\" transform=\"translate(147.412109 0)\" />\n",
" <use xlink:href=\"#DejaVuSans-4b\" transform=\"translate(211.035156 0)\" />\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</svg></g>\n",
"</g>\n",
"</svg></p>\n",
"<h3>Rules</h3>\n",
"<p>if (relationship &gt; 0.5) and (capital-gain &lt;= 7073.5) and (relationship &lt;= 4.5) then class: &lt;=50K (proba: 95.07%) | based on 9,800 samples</p>\n",
"<p>if (relationship &lt;= 0.5) and (education-num &lt;= 12.5) and (capital-gain &lt;= 5095.5) then class: &lt;=50K (proba: 70.34%) | based on 4,973 samples</p>\n",
"<p>if (relationship &lt;= 0.5) and (education-num &gt; 12.5) and (capital-gain &lt;= 5095.5) then class: &gt;50K (proba: 67.82%) | based on 1,908 samples</p>\n",
"<p>if (relationship &gt; 0.5) and (capital-gain &lt;= 7073.5) and (relationship &gt; 4.5) then class: &lt;=50K (proba: 54.17%) | based on 803 samples</p>\n",
"<p>if (relationship &lt;= 0.5) and (education-num &gt; 12.5) and (capital-gain &gt; 5095.5) then class: &gt;50K (proba: 99.71%) | based on 346 samples</p>\n",
"<p>if (relationship &lt;= 0.5) and (education-num &lt;= 12.5) and (capital-gain &gt; 5095.5) then class: &gt;50K (proba: 98.02%) | based on 252 samples</p>\n",
"<p>if (relationship &gt; 0.5) and (capital-gain &gt; 7073.5) and (age &gt; 20.0) then class: &gt;50K (proba: 96.96%) | based on 230 samples</p>\n",
"<p>if (relationship &gt; 0.5) and (capital-gain &gt; 7073.5) and (age &lt;= 20.0) then class: &lt;=50K (proba: 100.0%) | based on 3 samples</p>\n",
"<h2>Permutation-based Importance</h2>\n",
"<p><img style=\"width:750px\" alt=\"Permutation-based Importance\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h2>Confusion Matrix</h2>\n",
"<p><img style=\"width:750px\" alt=\"Confusion Matrix\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h2>Normalized Confusion Matrix</h2>\n",
"<p><img style=\"width:750px\" alt=\"Normalized Confusion Matrix\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h2>ROC Curve</h2>\n",
"<p><img style=\"width:750px\" alt=\"ROC Curve\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h2>Kolmogorov-Smirnov Statistic</h2>\n",
"<p><img style=\"width:750px\" alt=\"Kolmogorov-Smirnov Statistic\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h2>Precision-Recall Curve</h2>\n",
"<p><img style=\"width:750px\" alt=\"Precision-Recall Curve\" src=\"data:image/png;base64, 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\" /></p>\n",
"<h2>Calibration Curve</h2>\n",
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment