Sample notebook update for test automation

Author: SurajBaloni

samples/04_gis_analysts_data_scientists/automate_building_footprint_extraction_using_instance_segmentation.ipynb

@@ -236,9 +236,9 @@
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "import os\n",
+    "from datetime import datetime\n",
     "from pathlib import Path\n",
-    "from arcgis.learn import MaskRCNN, prepare_data\n",
-    "from arcgis.gis import GIS"
+    "from arcgis.learn import MaskRCNN, prepare_data"
    ]
   },
   {
@@ -617,6 +617,16 @@
     "As we can see, with 10 epochs, we are already seeing reasonable results. Further improvments can be achieved through more sophisticated hyperparameter tuning. Let's save the model, so it can be used for inference, or further training subsequently. By default, it will be saved into your data_path that you specified in the very beginning of this notebook, in the `prepare_data` call."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timestamp = datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n",
+    "model_name = \"Building_footprint_10epochs_\" + timestamp"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -677,7 +687,7 @@
     }
    ],
    "source": [
-    "model.save('Building_footprint_10epochs', publish=True)"
+    "model.save(model_name, publish=True)"
    ]
   },
   {
@@ -752,9 +762,9 @@
    "notebookRuntimeVersion": ""
   },
   "kernelspec": {
-   "display_name": "Python [conda env:conda-dl] *",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "conda-env-conda-dl-py"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -766,7 +776,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/automatic_road_extraction_using_deep_learning.ipynb

@@ -826,9 +826,9 @@
    "notebookRuntimeVersion": ""
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -840,7 +840,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.7"
   },
   "toc": {
    "base_numbering": 1,

samples/04_gis_analysts_data_scientists/building_reconstruction_using_mask_rcnn.ipynb

@@ -1719,9 +1719,9 @@
    "notebookRuntimeVersion": ""
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1733,7 +1733,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/change_detection_of_buildings_from_satellite_imagery.ipynb

@@ -58,15 +58,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import pandas as pd\n",
     "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
-    "\n",
-    "from arcgis.gis import GIS\n",
     "from arcgis.learn import MLModel, prepare_tabulardata"
    ]
   },
@@ -97,6 +94,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from arcgis.gis import GIS\n",
     "gis = GIS(\"home\")"
    ]
   },
@@ -2178,9 +2176,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pro3.6_certify2.4.2_24Sep2025 [conda env:conda-pro3.6_certify2.4.2_24Sep2025] *",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "conda-env-conda-pro3.6_certify2.4.2_24Sep2025-pro3.6_certify2.4.2_24sep2025"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {

samples/04_gis_analysts_data_scientists/classifying_human_activity_using_tabPFN_classifier.ipynb

@@ -62,17 +62,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.metrics import r2_score\n",
     "import sklearn.metrics as metrics\n",
-    "\n",
-    "from arcgis.gis import GIS\n",
     "from arcgis.learn import TimeSeriesModel, prepare_tabulardata"
    ]
   },
@@ -91,6 +87,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from arcgis.gis import GIS\n",
     "gis = GIS(\"home\")"
    ]
   },
@@ -1903,9 +1900,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1917,7 +1914,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/coastline_classification_using_feature_classifier.ipynb

@@ -111,16 +111,6 @@
     "## Export training data"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ad42be61",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from arcgis.gis import GIS"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -129,6 +119,7 @@
    "outputs": [],
    "source": [
     "# Connect to GIS\n",
+    "from arcgis.gis import GIS\n",
     "gis = GIS('home')"
    ]
   },
@@ -1134,9 +1125,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1148,7 +1139,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/covid_case_forecasting_for_alabama_state_using_timeseriesmodel_from_arcgis_learn.ipynb

@@ -239,6 +239,7 @@
     "import os\n",
     "import zipfile\n",
     "from pathlib import Path\n",
+    "from datetime import datetime\n",
     "from arcgis.learn import prepare_data, DETReg"
    ]
   },
@@ -439,9 +440,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "f41c1a79",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "detreg_model = DETReg(data)"
@@ -451,9 +450,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "ef1115d7",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1216,9 +1213,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "64810d9b",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1282,6 +1277,17 @@
     "We will use the `save()` method to save the trained model. By default, it will be saved to the 'models' sub-folder within our training data folder."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5f19663-d37a-44af-8031-1e0ec0f5da52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timestamp = datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n",
+    "model_name = \"palm_e100_\" + timestamp"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 24,
@@ -1308,7 +1314,7 @@
     }
    ],
    "source": [
-    "detreg_model.save('palm_e100', publish=True)"
+    "detreg_model.save(model_name, publish=True)"
    ]
   },
   {
@@ -1402,9 +1408,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1416,7 +1422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/detecting_mussel_farms_using_deep_learning.ipynb

@@ -57,7 +57,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import numpy as np\n",
@@ -77,7 +76,6 @@
     "from sklearn.metrics import r2_score\n",
     "import sklearn.metrics as metrics\n",
     "\n",
-    "from arcgis.gis import GIS\n",
     "from arcgis.learn import TimeSeriesModel, prepare_tabulardata\n",
     "from arcgis.features import FeatureLayer, FeatureLayerCollection"
    ]
@@ -95,6 +93,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from arcgis.gis import GIS\n",
     "gis = GIS(\"home\")"
    ]
   },
@@ -4079,9 +4078,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "condapac_18March2024",
+   "display_name": "Python [conda env:dlenv_30_9] *",
    "language": "python",
-   "name": "condapac_18march2024"
+   "name": "conda-env-dlenv_30_9-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -4093,7 +4092,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,