fixed few minor issues in samples and updated items_metadata file (Esri#962)

* updated sample

* removed more

* changed path

* fixed

* edited yaml with new samples

* final done

Co-authored-by: priyankatuteja <priyanka.datascience@gmail.com>

Author: priyankatuteja

items_metadata.yaml

@@ -469,15 +469,15 @@ samples:
   description: This sample showcases two autoregressive methods. one using a deep learning and another using a machine learning framework to predict temperature of England.
   licenseInfo: ""
   tags: ["Data Science", "GIS", "Time Series", "Temperature", "Forecast"]
-- title: Plant species identification using a TensorFlow-Lite model within mobile devices
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=fc21cc2f4a014a8e88f72d846b5afff1
-  path: ./samples/04_gis_analysts_data_scientists/train_a_tensorflow-lite_model_for_identifying_plant_species.ipynb
-  thumbnail: ./static/thumbnails/train_a_tensorflow-lite_model_for_identifying_plant_species.png
-  snippet: Identify plant species using a tensorflow model
-  description: This notebook intends to showcase this capability to train a deep learning model that can be used in mobile applications for a real time inferencing using TensorFlow Lite framework.
-  licenseInfo: ""
-  runtime: advanced_gpu
-  tags: ["Data Science", "GIS", "TansorFlow Lite", "Plant Species", "Deep Learning"]
+# - title: Plant species identification using a TensorFlow-Lite model within mobile devices
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=fc21cc2f4a014a8e88f72d846b5afff1
+#   path: ./samples/04_gis_analysts_data_scientists/train_a_tensorflow-lite_model_for_identifying_plant_species.ipynb
+#   thumbnail: ./static/thumbnails/train_a_tensorflow-lite_model_for_identifying_plant_species.png
+#   snippet: Identify plant species using a tensorflow model
+#   description: This notebook intends to showcase this capability to train a deep learning model that can be used in mobile applications for a real time inferencing using TensorFlow Lite framework.
+#   licenseInfo: ""
+#   runtime: advanced_gpu
+#   tags: ["Data Science", "GIS", "TansorFlow Lite", "Plant Species", "Deep Learning"]
 - title: Vehicle detection and tracking using deep learning
   url: https://geosaurus.maps.arcgis.com/home/item.html?id=871057ea4c864343900f36f3bf64b675
   path: ./samples/04_gis_analysts_data_scientists/vehicle_detection_and_tracking.ipynb
@@ -575,14 +575,14 @@ samples:
   description:  The analysis below uses a geoprocessing tool to deduce the path that the debris of a crashed airplane would take if it went down at different places in the ocean.
   licenseInfo: ''
   tags: ['Data Science', 'GIS', "Geoprocessing tools"]
-- title: Hey GIS, Give me a map of the recent natural disasters
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=7eae3c9f586f4d7ab7494b0494c9a97c
-  path: ./samples/05_content_publishers/hey_gis_give_me_a_map_of_the_recent_natural_disasters.ipynb
-  thumbnail: ./static/thumbnails/hey_gis_give_me_a_map_of_the_recent_natural_disasters.png
-  snippet: hey gis give me a map of the recent natural disasters
-  description: The sample notebook takes advantage of NASA's Earth Observatory Natural Event Tracker (EONET) API to collect a curated and continuously updated set of natural event metadata, and transform them into ArcGIS FeatureCollection(s) and save them into Web Maps in your GIS.
-  licenseInfo: ''
-  tags: ['Data Science', 'GIS', "Natural Disasters", "Map"]
+# - title: Hey GIS, Give me a map of the recent natural disasters
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=7eae3c9f586f4d7ab7494b0494c9a97c
+#   path: ./samples/05_content_publishers/hey_gis_give_me_a_map_of_the_recent_natural_disasters.ipynb
+#   thumbnail: ./static/thumbnails/hey_gis_give_me_a_map_of_the_recent_natural_disasters.png
+#   snippet: hey gis give me a map of the recent natural disasters
+#   description: The sample notebook takes advantage of NASA's Earth Observatory Natural Event Tracker (EONET) API to collect a curated and continuously updated set of natural event metadata, and transform them into ArcGIS FeatureCollection(s) and save them into Web Maps in your GIS.
+#   licenseInfo: ''
+#   tags: ['Data Science', 'GIS', "Natural Disasters", "Map"]
 - title: HTML Table to Pandas Data Frame to Portal Item
   url: https://geosaurus.maps.arcgis.com/home/item.html?id=8bbc583569244b2daabe8079c5644fc2
   path: ./samples/05_content_publishers/html_table_to_pandas_data_frame_to_portal_item.ipynb
@@ -599,46 +599,46 @@ samples:
   description: This notebook will search through all WebMap/WebScene/App Items in a portal/organization, identifying the 'insecure' ones if one or more service URLs use http\://.
   licenseInfo: ''
   tags: ['Data Science', 'GIS', "Insecure URL"]
-- title: Overwriting feature layers
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=691578df03c04f88862bc61774501699
-  path: ./samples/05_content_publishers/overwriting_feature_layers.ipynb
-  thumbnail: ./static/thumbnails/overwriting_feature_layers.png
-  snippet: overwriting feature layers
-  description: In this sample, we edit individual features as updated datasets are available
-  licenseInfo: ''
-  tags: ['Data Science', 'GIS', "Overwrite", "Feature", "Layers"]
-- title: PDF Table to PDF Map
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=d4124579b757443fa0577a93ad1d07ab
-  path: ./samples/05_content_publishers/pdf_table_to_pdf_map.ipynb
-  thumbnail: ./static/thumbnails/pdf_table_to_pdf_map.png
-  snippet: pdf table to pdf map
-  description: This sample shows how Pandas can be used to extract data from a table within a PDF file into the GIS for further analysis and visualization
-  licenseInfo: ''
-  tags: ['Data Science', 'GIS', "pdf", "Table", "Map"]
-- title: Publishing packages as web layers
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=d759771e21344942b5b67cf34439c91c
-  path: ./samples/05_content_publishers/publishing_packages_as_web_layers.ipynb
-  thumbnail: ./static/thumbnails/publishing_packages_as_web_layers.png
-  snippet: publishing packages as web layers
-  description: In this sample, we will observe how to publish web layers from tile, vector tile and scene layer packages.
-  licenseInfo: ''
-  tags: ['Data Science', 'GIS', "Web", "Publish", "Layers"]
-- title: Publishing SDs, Shapefiles, and CSVs
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=a1db6db172bc49a8932daacc2ed3d3ac
-  path: ./samples/05_content_publishers/publishing_sd_shapefiles_and_csv.ipynb
-  thumbnail: ./static/thumbnails/publishing_sd_shapefiles_and_csv.png
-  snippet: publishing sd shapefiles and csv
-  description: This sample notebook shows how different types of GIS datasets can be added to the GIS, and published as web layers.
-  licenseInfo: ''
-  tags: ['Data Science', 'GIS', "Shapefiles", "Publish", "CSV"]
-- title: Publishing web maps and web scenes
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=9840ca386ee7480a880d84a497db41de
-  path: ./samples/05_content_publishers/publishing_web_maps_and_web_scenes.ipynb
-  thumbnail: ./static/thumbnails/publishing_web_maps_and_web_scenes.png
-  snippet: publishing web maps and web scenes
-  description: This sample demonstrates how to create and publish simple examples of web maps and scenes using the Python API.
-  licenseInfo: ''
-  tags: ['Data Science', 'GIS', "Maps", "Web Scenes", "Publish"]
+# - title: Overwriting feature layers
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=691578df03c04f88862bc61774501699
+#   path: ./samples/05_content_publishers/overwriting_feature_layers.ipynb
+#   thumbnail: ./static/thumbnails/overwriting_feature_layers.png
+#   snippet: overwriting feature layers
+#   description: In this sample, we edit individual features as updated datasets are available
+#   licenseInfo: ''
+#   tags: ['Data Science', 'GIS', "Overwrite", "Feature", "Layers"]
+# - title: PDF Table to PDF Map
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=d4124579b757443fa0577a93ad1d07ab
+#   path: ./samples/05_content_publishers/pdf_table_to_pdf_map.ipynb
+#   thumbnail: ./static/thumbnails/pdf_table_to_pdf_map.png
+#   snippet: pdf table to pdf map
+#   description: This sample shows how Pandas can be used to extract data from a table within a PDF file into the GIS for further analysis and visualization
+#   licenseInfo: ''
+#   tags: ['Data Science', 'GIS', "pdf", "Table", "Map"]
+# - title: Publishing packages as web layers
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=d759771e21344942b5b67cf34439c91c
+#   path: ./samples/05_content_publishers/publishing_packages_as_web_layers.ipynb
+#   thumbnail: ./static/thumbnails/publishing_packages_as_web_layers.png
+#   snippet: publishing packages as web layers
+#   description: In this sample, we will observe how to publish web layers from tile, vector tile and scene layer packages.
+#   licenseInfo: ''
+#   tags: ['Data Science', 'GIS', "Web", "Publish", "Layers"]
+# - title: Publishing SDs, Shapefiles, and CSVs
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=a1db6db172bc49a8932daacc2ed3d3ac
+#   path: ./samples/05_content_publishers/publishing_sd_shapefiles_and_csv.ipynb
+#   thumbnail: ./static/thumbnails/publishing_sd_shapefiles_and_csv.png
+#   snippet: publishing sd shapefiles and csv
+#   description: This sample notebook shows how different types of GIS datasets can be added to the GIS, and published as web layers.
+#   licenseInfo: ''
+#   tags: ['Data Science', 'GIS', "Shapefiles", "Publish", "CSV"]
+# - title: Publishing web maps and web scenes
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=9840ca386ee7480a880d84a497db41de
+#   path: ./samples/05_content_publishers/publishing_web_maps_and_web_scenes.ipynb
+#   thumbnail: ./static/thumbnails/publishing_web_maps_and_web_scenes.png
+#   snippet: publishing web maps and web scenes
+#   description: This sample demonstrates how to create and publish simple examples of web maps and scenes using the Python API.
+#   licenseInfo: ''
+#   tags: ['Data Science', 'GIS', "Maps", "Web Scenes", "Publish"]
 - title: Building a change detection app using Jupyter Dashboard
   url: https://geosaurus.maps.arcgis.com/home/item.html?id=e3a0e48329cf4213a15574dd4b6b7694
   path: ./samples/02_power_users_developers/building_a_change_detection_app_using_jupyter_dashboard.ipynb
@@ -700,14 +700,14 @@ samples:
   description: This sample shows you how to update the content of web maps and web scenes.
   licenseInfo: ""
   tags: ["Data Science", "GIS", "Text", "Classification"]
-- title: Updating features in a feature layer
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=e8e2b0ac54584d079420b35b011d3d50
-  path: ./samples/05_content_publishers/updating_features_in_a_feature_layer.ipynb
-  thumbnail: ./static/thumbnails/default.png
-  snippet: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
-  description: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
-  licenseInfo: ""
-  tags: ["Data Science", "GIS", "Text", "Classification"]
+# - title: Updating features in a feature layer
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=e8e2b0ac54584d079420b35b011d3d50
+#   path: ./samples/05_content_publishers/updating_features_in_a_feature_layer.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
+#   description: As content publishers, you may be required to keep certain web layers upto date. As new data arrives, you may have to append new features, update existing features etc.
+#   licenseInfo: ""
+#   tags: ["Data Science", "GIS", "Text", "Classification"]
 - title: Land Parcel Extraction using Edge Detection model
   url: https://geosaurus.maps.arcgis.com/home/item.html?id=e164250b748240b5909159602dee826a
   path: ./samples/04_gis_analysts_data_scientists/land_parcel_extraction_using_edge_detection_deep_learning_model.ipynb
@@ -726,15 +726,15 @@ samples:
   licenseInfo: ""
   runtime: advanced_gpu
   tags: ["Data Science", "GIS", "Change Detection", "Deep Learning"]
-- title: Generating rgb imagery from digital surface model using Pix2Pix
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=d2d58e9d0e624f4baddd983d8acea3da
-  path: ./samples/04_gis_analysts_data_scientists/generating_rgb_imagery_from_digital_surface_model_using_pix2pix.ipynb
-  thumbnail: ./static/thumbnails/default.png
-  snippet: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
-  description: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
-  licenseInfo: ""
-  runtime: advanced_gpu
-  tags: ["Data Science", "GIS", "Image Translation", "Deep Learning"]
+# - title: Generating rgb imagery from digital surface model using Pix2Pix
+#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=d2d58e9d0e624f4baddd983d8acea3da
+#   path: ./samples/04_gis_analysts_data_scientists/generating_rgb_imagery_from_digital_surface_model_using_pix2pix.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
+#   description: The aim of this notebook is to make use of arcgis.learn Pix2Pix model to translate or convert the gray-scale DSM to a RGB imagery.
+#   licenseInfo: ""
+#   runtime: advanced_gpu
+#   tags: ["Data Science", "GIS", "Image Translation", "Deep Learning"]
 # - title: Coastline extraction using Landsat-8 multispectral imagery and band ratio technique
 #   url: https://geosaurus.maps.arcgis.com/home/item.html?id=4d8d5789a5e045bbbd02a01903841439
 #   path: ./samples/04_gis_analysts_data_scientists/coastline_extraction-usa-landsat8_multispectral_imagery.ipynb
@@ -753,6 +753,42 @@ samples:
   licenseInfo: ""
   runtime: advanced_gpu
   tags: ["Data Science", "GIS", "Story Map", "Text Translation", "Deep Learning"]
+- title: Solar Energy prediction using Weather Variables
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=7c28e6fce0584127b6656a98d3577d01
+  path: ./samples/04_gis_analysts_data_scientists/solar-energy-prediction-using-weather-variables.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: The aim of this notebook is to make use of arcgis.learn tabular models to predict solar energy using weather variables.
+  description: The aim of this notebook is to make use of arcgis.learn tabular models to predict solar energy using weather variables.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Solar energy prediction", "Deep Learning"]
+- title: Forecast Monthly Rainfall using TimeSeriesModel
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=e2bf1e372de8495cbc3e66aed3aade3c
+  path: ./samples/04_gis_analysts_data_scientists/forecasting_monthly_rainfall_in_california_using_deeplearning_timeseries_model_from_arcgis_learn.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: The aim of this notebook is to make use of arcgis.learn TimeSeriesModel to forecast monthly rainfall in California.
+  description: The aim of this notebook is to make use of arcgis.learn TimeSeriesModel to forecast monthly rainfall in California.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Rainfall forecast", "Deep Learning"]
+- title: LandCover Classification using Hyperspectral Imagery and Deep Learning
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=f253e07e0d3142ea87b4e024393c8eb0
+  path: ./samples/04_gis_analysts_data_scientists/landcover_classification_using_hyperspectral_imagery_and_deep_learning.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: The aim of this notebook is to make use of arcgis.learn UnetClassifier model to extract subclasses of two LULC classes: developed areas and forests.
+  description: The aim of this notebook is to make use of arcgis.learn UnetClassifier model to extract subclasses of two LULC classes: developed areas and forests.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Hyperspectral", "Deep Learning"]
+- title: Streams Extraction using MultiTaskRoadExtractor
+  url: https://geosaurus.maps.arcgis.com/home/item.html?id=356899f6baad407b9db49bb526073ee1
+  path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_multi_task_road_extractor.ipynb
+  thumbnail: ./static/thumbnails/default.png
+  snippet: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
+  description: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
+  licenseInfo: ""
+  runtime: advanced_gpu
+  tags: ["Data Science", "GIS", "Stream Extraction", "Deep Learning"]
 guides: []
 labs:
 - title: Create Data

samples/04_gis_analysts_data_scientists/sar_to_rgb_image_translation_using_cyclegan.ipynb

@@ -397,7 +397,7 @@
     }
    ],
    "source": [
-    "model.lr_find()"
+    "lr = model.lr_find()"
    ]
   },
   {
@@ -724,7 +724,7 @@
     }
    ],
    "source": [
-    "model.fit(25, lr=2e-04)"
+    "model.fit(25, lr=lr)"
    ]
   },
   {
@@ -842,7 +842,8 @@
     }
    ],
    "source": [
-    "model.predict(r\"D:\\CycleGAN\\Data\\data_for_cyclegan_le_3Bands\\Images\\train_a\\000000005.jpg\", convert_to=\"b\")"
+    "#un-comment the cell to run predict over your desired image.\n",
+    "# model.predict(r\"D:\\CycleGAN\\Data\\data_for_cyclegan_le_3Bands\\Images\\train_a\\000000005.jpg\", convert_to=\"b\")"
    ]
   },
   {
@@ -870,7 +871,8 @@
     }
    ],
    "source": [
-    "model.predict(r\"D:\\CycleGAN\\Data\\data_for_cyclegan_le_3Bands\\Images\\train_b\\000000008.jpg\", convert_to=\"a\")"
+    "#un-comment the cell to run predict over your desired image.\n",
+    "# model.predict(r\"D:\\CycleGAN\\Data\\data_for_cyclegan_le_3Bands\\Images\\train_b\\000000008.jpg\", convert_to=\"a\")"
    ]
   },
   {