Andre-Tan/2023Oct22-EvidentlyReport.ipynb

## 2023Oct22-EvidentlyReport.ipynb
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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Core-Concepts\" data-toc-modified-id=\"Core-Concepts-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span><a href=\"https://docs.evidentlyai.com/readme/core-concepts\" target=\"_blank\">Core Concepts</a></a></span></li><li><span><a href=\"#Data-Preparation\" data-toc-modified-id=\"Data-Preparation-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Data Preparation</a></span></li><li><span><a href=\"#Reports-and-Test-Suites\" data-toc-modified-id=\"Reports-and-Test-Suites-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Reports and Test Suites</a></span><ul class=\"toc-item\"><li><span><a href=\"#Report-Presets-and-Examples\" data-toc-modified-id=\"Report-Presets-and-Examples-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Report Presets and Examples</a></span></li><li><span><a href=\"#Test-Suite-Presets-and-Examples\" data-toc-modified-id=\"Test-Suite-Presets-and-Examples-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Test Suite Presets and Examples</a></span></li><li><span><a href=\"#Output-Artefacts\" data-toc-modified-id=\"Output-Artefacts-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Output Artefacts</a></span></li></ul></li><li><span><a href=\"#References\" data-toc-modified-id=\"References-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>References</a></span></li></ul></div>"
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    "# [Core Concepts](https://docs.evidentlyai.com/readme/core-concepts)\n",
    "\n",
    "* A [**Metric**](https://docs.evidentlyai.com/reference/all-metrics) is a core component of Evidently. \n",
    "    * You can combine multiple **Metrics** in a **Report**. \n",
    "    * Reports are best for visual analysis and debugging of your models and data.\n",
    "\n",
    "\n",
    "* A [**Test**](https://docs.evidentlyai.com/reference/all-tests) is a **Metric** with a condition. Each test returns a pass or fail result. \n",
    "    * You can combine multiple **Tests** in a **Test Suite**. \n",
    "    * Test Suites are best of automated model checks as part of an ML pipeline.\n",
    "    \n",
    "For both Tests and Metrics, Evidently has [**Presets**](https://docs.evidentlyai.com/presets/all-presets). These are pre-built combinations of Metrics or Tests that fit a specific use case.\n",
    "\n",
    "* A **Snapshot** is a JSON version of the Report or a **Test Suite** which contains measurements and test results for a specific period.\n",
    "    * You can log them over time and run an Evidently Monitoring Dashboard for continuous monitoring.\n",
    "    \n",
    "---"
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    "Evidently expects a certain dataset structure and input column names set in [**Column Mapping**](https://docs.evidentlyai.com/user-guide/input-data/column-mapping) object. You can pass the object to Test Suites and Reports.\n",
    "\n",
    "If the column_mapping is not specified or set as None, Evidently will use the default mapping strategy.\n",
    "\n",
    "Column types:\n",
    "\n",
    "1. All columns with numeric types (np.number) will be treated as Numerical.\n",
    "2. All columns with DateTime format (np.datetime64) will be treated as DateTime.\n",
    "3. All other columns will be treated as Categorical.\n",
    "\n",
    "Dataset structure:\n",
    "\n",
    "1. The column named `id` will be treated as an ID column.\n",
    "2. The column named `datetime` will be treated as a DateTime column.\n",
    "3. The column named `target` will be treated as a target function.\n",
    "4. The column named `prediction` will be treated as a model prediction.\n",
    "\n",
    "Data structure requirements depend on the type of analysis. Here are example requirements:\n",
    "\n",
    "| Report/Test Suite          | Feature columns | Prediction column                 | Target column                     | ID column | Datetime column |\n",
    "| -------------------------- | --------------- | --------------------------------- | --------------------------------- | --------- | --------------- |\n",
    "| Data Quality               | Required        | Optional                          | Optional                          | Optional  | Optional        |\n",
    "| Data Drift                 | Required        | Optional                          | Optional                          | Optional  | Optional        |\n",
    "| Target Drift               | Optional        | Target and/or prediction required | Target and/or prediction required | Optional  | Optional        |\n",
    "| Classification Performance | Optional        | Required                          | Required                          | Optional  | Optional        |\n",
    "| Regression Performance     | Optional        | Required                          | Required                          | Optional  | Optional        |\n",
    "\n",
    "---"
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    "# Data Preparation\n",
    "\n",
    "**Metrics** may require a reference dataset for testing purposes (e.g., for stability testing).\n",
    "\n",
    "We are going to use `df_train` as the reference dataset, and `df_test` as the simulation dataset."
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    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from evidently import ColumnMapping\n",
    "from evidently.test_suite import TestSuite\n",
    "from evidently.report import Report\n",
    "\n",
    "from evidently.metric_preset import DataDriftPreset, DataQualityPreset, RegressionPreset, ClassificationPreset, TargetDriftPreset, TextOverviewPreset\n",
    "from evidently.test_preset import NoTargetPerformanceTestPreset, DataQualityTestPreset, DataStabilityTestPreset, DataDriftTestPreset, RegressionTestPreset, \\\n",
    "    MulticlassClassificationTestPreset, BinaryClassificationTopKTestPreset, BinaryClassificationTestPreset\n",
    "\n",
    "SEED = 42"
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    "breast_cancer = load_breast_cancer()\n",
    "\n",
    "column_target = 'target'\n",
    "column_score = 'proba_cancer'\n",
    "columns_feature = list(breast_cancer.feature_names)\n",
    "\n",
    "df_data = pd.DataFrame(\n",
    "    data=np.column_stack(\n",
    "        (breast_cancer.data, breast_cancer.target)\n",
    "    ),\n",
    "    columns=columns_feature+[column_target]\n",
    ")\n",
    "\n",
    "df_data[column_target] = df_data[column_target].apply(int)\n",
    "df_data[\"prediction\"] = np.random.uniform(size=df_data.shape[0]) # Random model as placeholder\n",
    "\n",
    "df_train, df_test = train_test_split(\n",
    "    df_data, test_size=0.3, random_state=SEED\n",
    ")\n",
    "\n",
    "df_train = df_train.reset_index(drop=True)\n",
    "df_test = df_test.reset_index(drop=True)\n",
    "\n",
    "df_train.shape, df_test.shape"
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    "column_mapping = ColumnMapping()\n",
    "\n",
    "column_mapping.target = column_target\n",
    "column_mapping.prediction = 'prediction'\n",
    "column_mapping.pos_label = 1 # Needed if a column with probability, not the predicted class\n",
    "column_mapping.target_names = None # Label name\n",
    "\n",
    "column_mapping.numerical_features = columns_feature\n",
    "column_mapping.categorical_features = None\n",
    "column_mapping.task = 'classification' # can be regression, classification, or recsys (recommender-system)\n",
    "\n",
    "column_mapping.id = None #there is no ID column in the dataset\n",
    "column_mapping.datetime = None # column with datetime\n",
    "column_mapping.text_features = None # columns with raw text features\n",
    "column_mapping.datetime_features = None #list of DateTime/temporal features"
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    "---\n",
    "\n",
    "# Reports and Test Suites\n",
    "\n",
    "In Windows, we can use `test_suites.show(mode=\"inline\")` to show the result in Notebook.\n",
    "\n",
    "However, in Linux and Mac, you need to run two things:\n",
    "\n",
    "```\n",
    "jupyter nbextension install --sys-prefix --symlink --overwrite --py evidently\n",
    "jupyter nbextension enable evidently --py --sys-prefix\n",
    "```\n",
    "\n",
    "Alternatively, it is possible to get the result as a HTML file, or as a JSON / python Dictionary.\n",
    "\n",
    "```\n",
    "report.save_html(\"report.html\")\n",
    "test_suites.json()\n",
    "report.as_dict()\n",
    "```\n",
    "\n",
    "## Report Presets and Examples\n",
    "\n",
    "| DataDriftPreset       | DataQualityPreset                   | RegressionPreset                   | ClassificationPreset          | TargetDriftPreset                                                            | TextOverviewPreset                    |\n",
    "| --------------------- | ----------------------------------- | ---------------------------------- | ----------------------------- | ---------------------------------------------------------------------------- | ------------------------------------- |\n",
    "| DatasetDriftMetric    | DatasetSummaryMetric                | RegressionQualityMetric            | ClassificationQualityMetric   | ColumnDriftMetric - for target and prediction if present in datasets.        | ColumnSummaryMetric                   |\n",
    "| DataDriftTable        | ColumnSummaryMetric for each column | RegressionPredictedVsActualScatter | ClassificationClassBalance    | ColumnValuePlot - if task is regression.                                     | TextDescriptorsDistribution           |\n",
    "| EmbeddingsDriftMetric | DatasetMissingValuesMetric          | RegressionPredictedVsActualPlot    | ClassificationConfusionMatrix | ColumnCorrelationsMetric - for target and prediction if present in datasets. | TextDescriptorsCorrelation            |\n",
    "|                       | DatasetCorrelationsMetric           | RegressionErrorPlot                | ClassificationQualityByClass  | TargetByFeaturesTable                                                        | ColumnDriftMetric                     |\n",
    "|                       |                                     | RegressionAbsPercentageErrorPlot   |                               |                                                                              | TextDescriptorsDescriptorsDriftMetric |\n",
    "|                       |                                     | RegressionErrorDistribution        |                               |                                                                              |                                       |\n",
    "|                       |                                     | RegressionErrorNormality           |                               |                                                                              |                                       |\n",
    "|                       |                                     | RegressionTopErrorMetric           |                               |                                                                              |                                       |\n",
    "|                       |                                     | RegressionErrorBiasTable           |                               |                                                                              |"
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       "}\n",
       "\n",
       ".center-align {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".material-icons {\n",
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       "  font-weight: normal;\n",
       "  font-style: normal;\n",
       "  font-size: 24px;\n",
       "  line-height: 1;\n",
       "  letter-spacing: normal;\n",
       "  text-transform: none;\n",
       "  display: inline-block;\n",
       "  white-space: nowrap;\n",
       "  word-wrap: normal;\n",
       "  direction: ltr;\n",
       "  text-rendering: optimizeLegibility;\n",
       "  -webkit-font-smoothing: antialiased;\n",
       "}\n",
       "&lt;/style&gt;\n",
       "&lt;script&gt;\n",
       "    var evidently_dashboard_d6c0af891526482183ee027422628b59 = {&quot;name&quot;: &quot;Report&quot;, &quot;widgets&quot;: [{&quot;type&quot;: &quot;counter&quot;, &quot;title&quot;: &quot;&quot;, &quot;size&quot;: 2, &quot;id&quot;: &quot;f0f87a09-ec4b-4361-a513-2ba0ac679494&quot;, &quot;details&quot;: &quot;&quot;, &quot;alertsPosition&quot;: null, &quot;alertStats&quot;: null, &quot;params&quot;: {&quot;counters&quot;: [{&quot;value&quot;: &quot;Dataset Drift&quot;, &quot;label&quot;: &quot;Dataset Drift is NOT detected. Dataset drift detection threshold is 0.5&quot;}]}, &quot;insights&quot;: [], &quot;alerts&quot;: [], &quot;tabs&quot;: [], &quot;widgets&quot;: [], &quot;pageSize&quot;: 5}, {&quot;type&quot;: &quot;counter&quot;, &quot;title&quot;: &quot;&quot;, &quot;size&quot;: 2, &quot;id&quot;: &quot;93a39ab9-9f39-4b1b-81b9-c22409ba8116&quot;, &quot;details&quot;: &quot;&quot;, &quot;alertsPosition&quot;: null, &quot;alertStats&quot;: null, &quot;params&quot;: {&quot;counters&quot;: [{&quot;value&quot;: &quot;32&quot;, &quot;label&quot;: &quot;Columns&quot;}, {&quot;value&quot;: &quot;0&quot;, &quot;label&quot;: &quot;Drifted Columns&quot;}, {&quot;value&quot;: &quot;0.0&quot;, &quot;label&quot;: &quot;Share of Drifted Columns&quot;}]}, &quot;insights&quot;: [], &quot;alerts&quot;: [], &quot;tabs&quot;: [], &quot;widgets&quot;: [], &quot;pageSize&quot;: 5}, {&quot;type&quot;: &quot;counter&quot;, &quot;title&quot;: &quot;&quot;, &quot;size&quot;: 2, &quot;id&quot;: &quot;30abf00c-ece6-47e9-a9d8-b1f3e9717ae4&quot;, &quot;details&quot;: &quot;&quot;, &quot;alertsPosition&quot;: null, &quot;alertStats&quot;: null, &quot;params&quot;: {&quot;counters&quot;: [{&quot;value&quot;: &quot;&quot;, &quot;label&quot;: &quot;Data Drift Summary&quot;}]}, &quot;insights&quot;: [], &quot;alerts&quot;: [], &quot;tabs&quot;: [], &quot;widgets&quot;: [], &quot;pageSize&quot;: 5}, {&quot;type&quot;: &quot;big_table&quot;, &quot;title&quot;: &quot;Drift is detected for 0.0% of columns (0 out of 32).&quot;, &quot;size&quot;: 2, &quot;id&quot;: &quot;e6483ec7-2650-4a98-bca6-d1ddddc08c82&quot;, &quot;details&quot;: &quot;&quot;, &quot;alertsPosition&quot;: &quot;row&quot;, &quot;alertStats&quot;: null, &quot;params&quot;: {&quot;rowsPerPage&quot;: 10, &quot;columns&quot;: [{&quot;title&quot;: &quot;Column&quot;, &quot;field&quot;: &quot;column_name&quot;}, {&quot;title&quot;: &quot;Type&quot;, &quot;field&quot;: &quot;column_type&quot;}, {&quot;title&quot;: &quot;Reference Distribution&quot;, &quot;field&quot;: &quot;reference_distribution&quot;, &quot;type&quot;: &quot;histogram&quot;, &quot;options&quot;: {&quot;xField&quot;: &quot;x&quot;, &quot;yField&quot;: &quot;y&quot;, &quot;color&quot;: &quot;#ed0400&quot;}}, {&quot;title&quot;: &quot;Current Distribution&quot;, &quot;field&quot;: &quot;current_distribution&quot;, &quot;type&quot;: &quot;histogram&quot;, &quot;options&quot;: {&quot;xField&quot;: &quot;x&quot;, &quot;yField&quot;: &quot;y&quot;, &quot;color&quot;: &quot;#ed0400&quot;}}, {&quot;title&quot;: &quot;Data Drift&quot;, &quot;field&quot;: &quot;data_drift&quot;}, {&quot;title&quot;: &quot;Stat Test&quot;, &quot;field&quot;: &quot;stattest_name&quot;}, {&quot;title&quot;: &quot;Drift Score&quot;, &quot;field&quot;: &quot;drift_score&quot;}], &quot;data&quot;: [{&quot;details&quot;: {&quot;parts&quot;: [{&quot;title&quot;: &quot;DATA DISTRIBUTION&quot;, &quot;id&quot;: &quot;a9822d57-2b6d-4710-91b9-e90cfcf6bd71&quot;, &quot;type&quot;: &quot;widget&quot;}]}, &quot;column_name&quot;: &quot;target&quot;, &quot;column_type&quot;: &quot;cat&quot;, &quot;stattest_name&quot;: &quot;PSI&quot;, &quot;reference_distribution&quot;: {&quot;x&quot;: [0, 1], &quot;y&quot;: [149, 249]}, &quot;current_distribution&quot;: {&quot;x&quot;: [0, 1], &quot;y&quot;: [63, 108]}, &quot;data_drift&quot;: &quot;Not Detected&quot;, &quot;drift_score&quot;: 0.000152}, {&quot;details&quot;: {&quot;parts&quot;: [{&quot;title&quot;: &quot;DATA DRIFT&quot;, &quot;id&quot;: &quot;0f0afcb6-1350-44c3-9bb2-36bd60492d32&quot;, &quot;type&quot;: &quot;widget&quot;}, {&quot;title&quot;: &quot;DATA DISTRIBUTION&quot;, &quot;id&quot;: &quot;f69e4dd9-b48d-4ab9-ba0e-f6c49e159238&quot;, &quot;type&quot;: &quot;widget&quot;}]}, &quot;column_name&quot;: &quot;prediction&quot;, &quot;column_type&quot;: &quot;num&quot;, &quot;stattest_name&quot;: &quot;PSI&quot;, &quot;reference_distribution&quot;: {&quot;x&quot;: [4.026859853056841e-05, 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&quot;y&quot;: [62.562814070351756, 37.437185929648244], &quot;type&quot;: &quot;bar&quot;, &quot;xaxis&quot;: &quot;x2&quot;, &quot;yaxis&quot;: &quot;y2&quot;}], &quot;layout&quot;: {&quot;template&quot;: {&quot;data&quot;: {&quot;histogram2dcontour&quot;: [{&quot;type&quot;: &quot;histogram2dcontour&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;choropleth&quot;: [{&quot;type&quot;: &quot;choropleth&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], 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[0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;heatmapgl&quot;: [{&quot;type&quot;: &quot;heatmapgl&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;contourcarpet&quot;: [{&quot;type&quot;: &quot;contourcarpet&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], &quot;contour&quot;: [{&quot;type&quot;: &quot;contour&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;surface&quot;: [{&quot;type&quot;: &quot;surface&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;mesh3d&quot;: [{&quot;type&quot;: &quot;mesh3d&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], &quot;scatter&quot;: [{&quot;fillpattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}, &quot;type&quot;: &quot;scatter&quot;}], &quot;parcoords&quot;: [{&quot;type&quot;: &quot;parcoords&quot;, &quot;line&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterpolargl&quot;: [{&quot;type&quot;: &quot;scatterpolargl&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;bar&quot;: [{&quot;error_x&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;error_y&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, 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&quot;line&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattermapbox&quot;: [{&quot;type&quot;: &quot;scattermapbox&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterternary&quot;: [{&quot;type&quot;: &quot;scatterternary&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattercarpet&quot;: [{&quot;type&quot;: &quot;scattercarpet&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;carpet&quot;: [{&quot;aaxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;baxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;type&quot;: &quot;carpet&quot;}], &quot;table&quot;: [{&quot;cells&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#EBF0F8&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;header&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#C8D4E3&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;type&quot;: &quot;table&quot;}], &quot;barpolar&quot;: [{&quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, &quot;width&quot;: 0.5}, &quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;barpolar&quot;}], &quot;pie&quot;: [{&quot;automargin&quot;: true, &quot;type&quot;: &quot;pie&quot;}]}, &quot;layout&quot;: {&quot;autotypenumbers&quot;: &quot;strict&quot;, &quot;colorway&quot;: [&quot;#636efa&quot;, &quot;#EF553B&quot;, &quot;#00cc96&quot;, &quot;#ab63fa&quot;, &quot;#FFA15A&quot;, &quot;#19d3f3&quot;, &quot;#FF6692&quot;, &quot;#B6E880&quot;, &quot;#FF97FF&quot;, &quot;#FECB52&quot;], &quot;font&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;hovermode&quot;: &quot;closest&quot;, &quot;hoverlabel&quot;: {&quot;align&quot;: &quot;left&quot;}, &quot;paper_bgcolor&quot;: &quot;white&quot;, &quot;plot_bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;polar&quot;: {&quot;bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;angularaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;radialaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}}, &quot;ternary&quot;: {&quot;bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;aaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;baxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;caxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}}, &quot;coloraxis&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}, &quot;colorscale&quot;: {&quot;sequential&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, 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&quot;zerolinecolor&quot;: &quot;white&quot;, &quot;automargin&quot;: true, &quot;zerolinewidth&quot;: 2}, &quot;yaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;, &quot;title&quot;: {&quot;standoff&quot;: 15}, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;automargin&quot;: true, &quot;zerolinewidth&quot;: 2}, &quot;scene&quot;: {&quot;xaxis&quot;: {&quot;backgroundcolor&quot;: &quot;#E5ECF6&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;showbackground&quot;: true, &quot;ticks&quot;: &quot;&quot;, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;gridwidth&quot;: 2}, &quot;yaxis&quot;: {&quot;backgroundcolor&quot;: &quot;#E5ECF6&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;showbackground&quot;: true, &quot;ticks&quot;: &quot;&quot;, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;gridwidth&quot;: 2}, &quot;zaxis&quot;: {&quot;backgroundcolor&quot;: &quot;#E5ECF6&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;showbackground&quot;: true, &quot;ticks&quot;: &quot;&quot;, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;gridwidth&quot;: 2}}, &quot;shapedefaults&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}}, &quot;annotationdefaults&quot;: {&quot;arrowcolor&quot;: &quot;#2a3f5f&quot;, &quot;arrowhead&quot;: 0, &quot;arrowwidth&quot;: 1}, &quot;geo&quot;: {&quot;bgcolor&quot;: &quot;white&quot;, &quot;landcolor&quot;: &quot;#E5ECF6&quot;, &quot;subunitcolor&quot;: &quot;white&quot;, &quot;showland&quot;: true, &quot;showlakes&quot;: true, &quot;lakecolor&quot;: &quot;white&quot;}, &quot;title&quot;: {&quot;x&quot;: 0.05}, &quot;mapbox&quot;: {&quot;style&quot;: &quot;light&quot;}}}, &quot;xaxis&quot;: {&quot;anchor&quot;: &quot;y&quot;, &quot;domain&quot;: [0.0, 0.45], &quot;title&quot;: {&quot;text&quot;: &quot;Class&quot;}}, &quot;yaxis&quot;: {&quot;anchor&quot;: &quot;x&quot;, &quot;domain&quot;: [0.0, 1.0], &quot;title&quot;: {&quot;text&quot;: &quot;Number Of Objects&quot;}}, &quot;xaxis2&quot;: {&quot;anchor&quot;: &quot;y2&quot;, &quot;domain&quot;: [0.55, 1.0], &quot;title&quot;: {&quot;text&quot;: &quot;Class&quot;}}, &quot;yaxis2&quot;: {&quot;anchor&quot;: &quot;x2&quot;, &quot;domain&quot;: [0.0, 1.0], &quot;matches&quot;: &quot;y&quot;, &quot;showticklabels&quot;: false}, &quot;annotations&quot;: [{&quot;font&quot;: {&quot;size&quot;: 16}, &quot;showarrow&quot;: false, &quot;text&quot;: &quot;current&quot;, &quot;x&quot;: 0.225, &quot;xanchor&quot;: &quot;center&quot;, &quot;xref&quot;: &quot;paper&quot;, &quot;y&quot;: 1.0, &quot;yanchor&quot;: &quot;bottom&quot;, &quot;yref&quot;: &quot;paper&quot;}, {&quot;font&quot;: {&quot;size&quot;: 16}, &quot;showarrow&quot;: false, &quot;text&quot;: &quot;reference&quot;, &quot;x&quot;: 0.775, 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&quot;subunitcolor&quot;: &quot;white&quot;, &quot;showland&quot;: true, &quot;showlakes&quot;: true, &quot;lakecolor&quot;: &quot;white&quot;}, &quot;title&quot;: {&quot;x&quot;: 0.05}, &quot;mapbox&quot;: {&quot;style&quot;: &quot;light&quot;}}}, &quot;xaxis&quot;: {&quot;anchor&quot;: &quot;y&quot;, &quot;domain&quot;: [0.0, 0.45], &quot;title&quot;: {&quot;text&quot;: &quot;Recall&quot;}}, &quot;yaxis&quot;: {&quot;anchor&quot;: &quot;x&quot;, &quot;domain&quot;: [0.0, 1.0], &quot;title&quot;: {&quot;text&quot;: &quot;Precision&quot;}}, &quot;xaxis2&quot;: {&quot;anchor&quot;: &quot;y2&quot;, &quot;domain&quot;: [0.55, 1.0], &quot;title&quot;: {&quot;text&quot;: &quot;Recall&quot;}}, &quot;yaxis2&quot;: {&quot;anchor&quot;: &quot;x2&quot;, &quot;domain&quot;: [0.0, 1.0], &quot;matches&quot;: &quot;y&quot;, &quot;showticklabels&quot;: false}, &quot;annotations&quot;: [{&quot;font&quot;: {&quot;size&quot;: 16}, &quot;showarrow&quot;: false, &quot;text&quot;: &quot;current&quot;, 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&quot;insights&quot;: []}, &quot;f1&quot;: &quot;worst radius&quot;}, {&quot;details&quot;: {&quot;parts&quot;: [{&quot;title&quot;: &quot;All&quot;, &quot;id&quot;: &quot;All_worst smoothness&quot;}, {&quot;title&quot;: &quot;0&quot;, &quot;id&quot;: &quot;worst smoothness_0&quot;}, {&quot;title&quot;: &quot;1&quot;, &quot;id&quot;: &quot;worst smoothness_1&quot;}], &quot;insights&quot;: []}, &quot;f1&quot;: &quot;worst smoothness&quot;}, {&quot;details&quot;: {&quot;parts&quot;: [{&quot;title&quot;: &quot;All&quot;, &quot;id&quot;: &quot;All_worst symmetry&quot;}, {&quot;title&quot;: &quot;0&quot;, &quot;id&quot;: &quot;worst symmetry_0&quot;}, {&quot;title&quot;: &quot;1&quot;, &quot;id&quot;: &quot;worst symmetry_1&quot;}], &quot;insights&quot;: []}, &quot;f1&quot;: &quot;worst symmetry&quot;}, {&quot;details&quot;: {&quot;parts&quot;: [{&quot;title&quot;: &quot;All&quot;, &quot;id&quot;: &quot;All_worst texture&quot;}, {&quot;title&quot;: &quot;0&quot;, &quot;id&quot;: &quot;worst texture_0&quot;}, {&quot;title&quot;: &quot;1&quot;, &quot;id&quot;: &quot;worst texture_1&quot;}], &quot;insights&quot;: []}, &quot;f1&quot;: &quot;worst texture&quot;}]}, &quot;insights&quot;: [], &quot;alerts&quot;: [], &quot;tabs&quot;: [], &quot;widgets&quot;: [], &quot;pageSize&quot;: 5}]};\n",
       "    var additional_graphs_evidently_dashboard_d6c0af891526482183ee027422628b59 = {&quot;a9822d57-2b6d-4710-91b9-e90cfcf6bd71&quot;: {&quot;type&quot;: &quot;big_graph&quot;, &quot;title&quot;: &quot;&quot;, &quot;size&quot;: 2, &quot;id&quot;: &quot;a9822d57-2b6d-4710-91b9-e90cfcf6bd71&quot;, &quot;details&quot;: &quot;&quot;, &quot;alertsPosition&quot;: null, &quot;alertStats&quot;: null, &quot;params&quot;: {&quot;data&quot;: [{&quot;marker&quot;: {&quot;color&quot;: &quot;#ed0400&quot;}, &quot;name&quot;: &quot;current&quot;, &quot;visible&quot;: true, &quot;x&quot;: [1, 0], &quot;y&quot;: [108, 63], &quot;type&quot;: &quot;bar&quot;, &quot;xaxis&quot;: &quot;x&quot;, &quot;yaxis&quot;: &quot;y&quot;}, {&quot;marker&quot;: {&quot;color&quot;: &quot;#ed0400&quot;}, &quot;name&quot;: &quot;current&quot;, &quot;visible&quot;: false, &quot;x&quot;: [1, 0], &quot;y&quot;: [63.1578947368421, 36.84210526315789], &quot;type&quot;: &quot;bar&quot;, &quot;xaxis&quot;: &quot;x&quot;, &quot;yaxis&quot;: &quot;y&quot;}, {&quot;marker&quot;: {&quot;color&quot;: &quot;#4d4d4d&quot;}, &quot;name&quot;: &quot;reference&quot;, &quot;visible&quot;: true, &quot;x&quot;: [1, 0], &quot;y&quot;: [249, 149], &quot;type&quot;: &quot;bar&quot;, &quot;xaxis&quot;: &quot;x&quot;, &quot;yaxis&quot;: &quot;y&quot;}, {&quot;marker&quot;: {&quot;color&quot;: &quot;#4d4d4d&quot;}, &quot;name&quot;: &quot;reference&quot;, &quot;visible&quot;: false, &quot;x&quot;: [1, 0], &quot;y&quot;: [62.562814070351756, 37.437185929648244], &quot;type&quot;: &quot;bar&quot;, &quot;xaxis&quot;: &quot;x&quot;, &quot;yaxis&quot;: &quot;y&quot;}], &quot;layout&quot;: {&quot;template&quot;: {&quot;data&quot;: {&quot;histogram2dcontour&quot;: [{&quot;type&quot;: &quot;histogram2dcontour&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;choropleth&quot;: [{&quot;type&quot;: &quot;choropleth&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], &quot;histogram2d&quot;: [{&quot;type&quot;: &quot;histogram2d&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;heatmap&quot;: [{&quot;type&quot;: &quot;heatmap&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;heatmapgl&quot;: [{&quot;type&quot;: &quot;heatmapgl&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;contourcarpet&quot;: [{&quot;type&quot;: &quot;contourcarpet&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], &quot;contour&quot;: [{&quot;type&quot;: &quot;contour&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;surface&quot;: [{&quot;type&quot;: &quot;surface&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;mesh3d&quot;: [{&quot;type&quot;: &quot;mesh3d&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], &quot;scatter&quot;: [{&quot;fillpattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}, &quot;type&quot;: &quot;scatter&quot;}], &quot;parcoords&quot;: [{&quot;type&quot;: &quot;parcoords&quot;, &quot;line&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterpolargl&quot;: [{&quot;type&quot;: &quot;scatterpolargl&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;bar&quot;: [{&quot;error_x&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;error_y&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, &quot;width&quot;: 0.5}, &quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;bar&quot;}], &quot;scattergeo&quot;: [{&quot;type&quot;: &quot;scattergeo&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterpolar&quot;: [{&quot;type&quot;: &quot;scatterpolar&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;histogram&quot;: [{&quot;marker&quot;: {&quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;histogram&quot;}], &quot;scattergl&quot;: [{&quot;type&quot;: &quot;scattergl&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatter3d&quot;: [{&quot;type&quot;: &quot;scatter3d&quot;, &quot;line&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattermapbox&quot;: [{&quot;type&quot;: &quot;scattermapbox&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterternary&quot;: [{&quot;type&quot;: &quot;scatterternary&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattercarpet&quot;: [{&quot;type&quot;: &quot;scattercarpet&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;carpet&quot;: [{&quot;aaxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;baxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;type&quot;: &quot;carpet&quot;}], &quot;table&quot;: [{&quot;cells&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#EBF0F8&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;header&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#C8D4E3&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;type&quot;: &quot;table&quot;}], &quot;barpolar&quot;: [{&quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, &quot;width&quot;: 0.5}, &quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;barpolar&quot;}], &quot;pie&quot;: [{&quot;automargin&quot;: true, &quot;type&quot;: &quot;pie&quot;}]}, &quot;layout&quot;: {&quot;autotypenumbers&quot;: &quot;strict&quot;, &quot;colorway&quot;: [&quot;#636efa&quot;, &quot;#EF553B&quot;, &quot;#00cc96&quot;, &quot;#ab63fa&quot;, &quot;#FFA15A&quot;, &quot;#19d3f3&quot;, &quot;#FF6692&quot;, &quot;#B6E880&quot;, &quot;#FF97FF&quot;, &quot;#FECB52&quot;], &quot;font&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;hovermode&quot;: &quot;closest&quot;, &quot;hoverlabel&quot;: {&quot;align&quot;: &quot;left&quot;}, &quot;paper_bgcolor&quot;: &quot;white&quot;, &quot;plot_bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;polar&quot;: {&quot;bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;angularaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;radialaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}}, &quot;ternary&quot;: {&quot;bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;aaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;baxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;caxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}}, &quot;coloraxis&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}, &quot;colorscale&quot;: {&quot;sequential&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]], &quot;sequentialminus&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]], &quot;diverging&quot;: [[0, &quot;#8e0152&quot;], [0.1, &quot;#c51b7d&quot;], [0.2, &quot;#de77ae&quot;], [0.3, &quot;#f1b6da&quot;], [0.4, &quot;#fde0ef&quot;], [0.5, &quot;#f7f7f7&quot;], [0.6, &quot;#e6f5d0&quot;], [0.7, &quot;#b8e186&quot;], [0.8, &quot;#7fbc41&quot;], [0.9, &quot;#4d9221&quot;], [1, &quot;#276419&quot;]]}, &quot;xaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;, &quot;title&quot;: {&quot;standoff&quot;: 15}, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;automargin&quot;: true, &quot;zerolinewidth&quot;: 2}, &quot;yaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;, &quot;title&quot;: {&quot;standoff&quot;: 15}, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;automargin&quot;: true, &quot;zerolinewidth&quot;: 2}, &quot;scene&quot;: {&quot;xaxis&quot;: {&quot;backgroundcolor&quot;: &quot;#E5ECF6&quot;, &quot;gridcolor&quot;: &quot;white&quot;, 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[0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;heatmapgl&quot;: [{&quot;type&quot;: &quot;heatmapgl&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]]}], &quot;contourcarpet&quot;: [{&quot;type&quot;: &quot;contourcarpet&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], &quot;contour&quot;: [{&quot;type&quot;: &quot;contour&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}, &quot;colorscale&quot;: [[0.0, 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[{&quot;type&quot;: &quot;mesh3d&quot;, &quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}], &quot;scatter&quot;: [{&quot;fillpattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}, &quot;type&quot;: &quot;scatter&quot;}], &quot;parcoords&quot;: [{&quot;type&quot;: &quot;parcoords&quot;, &quot;line&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterpolargl&quot;: [{&quot;type&quot;: &quot;scatterpolargl&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;bar&quot;: [{&quot;error_x&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;error_y&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, &quot;width&quot;: 0.5}, &quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;bar&quot;}], &quot;scattergeo&quot;: [{&quot;type&quot;: &quot;scattergeo&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterpolar&quot;: [{&quot;type&quot;: &quot;scatterpolar&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;histogram&quot;: [{&quot;marker&quot;: {&quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;histogram&quot;}], &quot;scattergl&quot;: [{&quot;type&quot;: &quot;scattergl&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatter3d&quot;: [{&quot;type&quot;: &quot;scatter3d&quot;, &quot;line&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattermapbox&quot;: [{&quot;type&quot;: &quot;scattermapbox&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterternary&quot;: [{&quot;type&quot;: &quot;scatterternary&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattercarpet&quot;: [{&quot;type&quot;: &quot;scattercarpet&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;carpet&quot;: [{&quot;aaxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;baxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;type&quot;: &quot;carpet&quot;}], &quot;table&quot;: [{&quot;cells&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#EBF0F8&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;header&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#C8D4E3&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;type&quot;: &quot;table&quot;}], &quot;barpolar&quot;: [{&quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, &quot;width&quot;: 0.5}, &quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;barpolar&quot;}], &quot;pie&quot;: [{&quot;automargin&quot;: true, &quot;type&quot;: &quot;pie&quot;}]}, &quot;layout&quot;: {&quot;autotypenumbers&quot;: &quot;strict&quot;, &quot;colorway&quot;: [&quot;#636efa&quot;, &quot;#EF553B&quot;, &quot;#00cc96&quot;, &quot;#ab63fa&quot;, &quot;#FFA15A&quot;, &quot;#19d3f3&quot;, &quot;#FF6692&quot;, &quot;#B6E880&quot;, &quot;#FF97FF&quot;, &quot;#FECB52&quot;], &quot;font&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;hovermode&quot;: &quot;closest&quot;, &quot;hoverlabel&quot;: {&quot;align&quot;: &quot;left&quot;}, &quot;paper_bgcolor&quot;: &quot;white&quot;, &quot;plot_bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;polar&quot;: {&quot;bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;angularaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;radialaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}}, &quot;ternary&quot;: {&quot;bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;aaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;baxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;caxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}}, &quot;coloraxis&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}, &quot;colorscale&quot;: {&quot;sequential&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]], &quot;sequentialminus&quot;: [[0.0, &quot;#0d0887&quot;], [0.1111111111111111, &quot;#46039f&quot;], [0.2222222222222222, &quot;#7201a8&quot;], [0.3333333333333333, &quot;#9c179e&quot;], [0.4444444444444444, &quot;#bd3786&quot;], [0.5555555555555556, &quot;#d8576b&quot;], [0.6666666666666666, &quot;#ed7953&quot;], [0.7777777777777778, &quot;#fb9f3a&quot;], [0.8888888888888888, &quot;#fdca26&quot;], [1.0, &quot;#f0f921&quot;]], &quot;diverging&quot;: [[0, &quot;#8e0152&quot;], [0.1, &quot;#c51b7d&quot;], [0.2, &quot;#de77ae&quot;], [0.3, &quot;#f1b6da&quot;], [0.4, &quot;#fde0ef&quot;], [0.5, &quot;#f7f7f7&quot;], [0.6, &quot;#e6f5d0&quot;], [0.7, &quot;#b8e186&quot;], [0.8, &quot;#7fbc41&quot;], [0.9, &quot;#4d9221&quot;], [1, &quot;#276419&quot;]]}, &quot;xaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;, &quot;title&quot;: {&quot;standoff&quot;: 15}, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;automargin&quot;: true, &quot;zerolinewidth&quot;: 2}, &quot;yaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;, &quot;title&quot;: {&quot;standoff&quot;: 15}, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;automargin&quot;: true, &quot;zerolinewidth&quot;: 2}, &quot;scene&quot;: {&quot;xaxis&quot;: {&quot;backgroundcolor&quot;: &quot;#E5ECF6&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;showbackground&quot;: true, &quot;ticks&quot;: &quot;&quot;, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;gridwidth&quot;: 2}, &quot;yaxis&quot;: {&quot;backgroundcolor&quot;: &quot;#E5ECF6&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;showbackground&quot;: true, &quot;ticks&quot;: &quot;&quot;, &quot;zerolinecolor&quot;: &quot;white&quot;, &quot;gridwidth&quot;: 2}, &quot;zaxis&quot;: {&quot;backgroundcolor&quot;: &quot;#E5ECF6&quot;, &quot;gridcolor&quot;: &quot;white&quot;, 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{&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;carpet&quot;: [{&quot;aaxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;baxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;type&quot;: &quot;carpet&quot;}], &quot;table&quot;: [{&quot;cells&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#EBF0F8&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;header&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#C8D4E3&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;type&quot;: &quot;table&quot;}], &quot;barpolar&quot;: 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0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatter3d&quot;: [{&quot;type&quot;: &quot;scatter3d&quot;, &quot;line&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattermapbox&quot;: [{&quot;type&quot;: &quot;scattermapbox&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterternary&quot;: [{&quot;type&quot;: &quot;scatterternary&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scattercarpet&quot;: [{&quot;type&quot;: &quot;scattercarpet&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;carpet&quot;: [{&quot;aaxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;baxis&quot;: {&quot;endlinecolor&quot;: &quot;#2a3f5f&quot;, &quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;minorgridcolor&quot;: &quot;white&quot;, &quot;startlinecolor&quot;: &quot;#2a3f5f&quot;}, &quot;type&quot;: &quot;carpet&quot;}], &quot;table&quot;: [{&quot;cells&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#EBF0F8&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;header&quot;: {&quot;fill&quot;: {&quot;color&quot;: &quot;#C8D4E3&quot;}, &quot;line&quot;: {&quot;color&quot;: &quot;white&quot;}}, &quot;type&quot;: &quot;table&quot;}], &quot;barpolar&quot;: [{&quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, &quot;width&quot;: 0.5}, &quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;barpolar&quot;}], &quot;pie&quot;: [{&quot;automargin&quot;: true, &quot;type&quot;: &quot;pie&quot;}]}, &quot;layout&quot;: {&quot;autotypenumbers&quot;: &quot;strict&quot;, &quot;colorway&quot;: [&quot;#636efa&quot;, &quot;#EF553B&quot;, &quot;#00cc96&quot;, &quot;#ab63fa&quot;, &quot;#FFA15A&quot;, &quot;#19d3f3&quot;, &quot;#FF6692&quot;, &quot;#B6E880&quot;, &quot;#FF97FF&quot;, &quot;#FECB52&quot;], &quot;font&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;hovermode&quot;: &quot;closest&quot;, &quot;hoverlabel&quot;: {&quot;align&quot;: &quot;left&quot;}, &quot;paper_bgcolor&quot;: &quot;white&quot;, &quot;plot_bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;polar&quot;: {&quot;bgcolor&quot;: &quot;#E5ECF6&quot;, &quot;angularaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, &quot;linecolor&quot;: &quot;white&quot;, &quot;ticks&quot;: &quot;&quot;}, &quot;radialaxis&quot;: {&quot;gridcolor&quot;: &quot;white&quot;, 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[{&quot;error_x&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;error_y&quot;: {&quot;color&quot;: &quot;#2a3f5f&quot;}, &quot;marker&quot;: {&quot;line&quot;: {&quot;color&quot;: &quot;#E5ECF6&quot;, &quot;width&quot;: 0.5}, &quot;pattern&quot;: {&quot;fillmode&quot;: &quot;overlay&quot;, &quot;size&quot;: 10, &quot;solidity&quot;: 0.2}}, &quot;type&quot;: &quot;bar&quot;}], &quot;scattergeo&quot;: [{&quot;type&quot;: &quot;scattergeo&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;scatterpolar&quot;: [{&quot;type&quot;: &quot;scatterpolar&quot;, &quot;marker&quot;: {&quot;colorbar&quot;: {&quot;outlinewidth&quot;: 0, &quot;ticks&quot;: &quot;&quot;}}}], &quot;histogram&quot;: [{&quot;marker&quot;: {&quot;p

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