wildfire risk forecasting using climax

[moonlanderr]

<insert pull request description here>

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Checklist

Please go through each entry in the below checklist and mark an 'X' if that condition has been met. Every entry should be marked with an 'X' to be get the Pull Request approved.

• All imports are in the first cell?
  • First block of imports are standard libraries
  • Second block are 3rd party libraries
  • Third block are all arcgis imports? Note that in some cases, for samples, it is a good idea to keep the imports next to where they are used, particularly for uncommonly used features that we want to highlight.
• All GIS object instantiations are one of the following?
  • gis = GIS()
  • gis = GIS('home') or gis = GIS('pro')
  • gis = GIS(profile="your_online_portal")
  • gis = GIS(profile="your_enterprise_portal")
• If this notebook requires setup or teardown, did you add the appropriate code to ./misc/setup.py and/or ./misc/teardown.py?
• If this notebook references any portal items that need to be staged on AGOL/Python API playground, did you coordinate with a Python API team member to stage the item the correct way with the api_data_owner user?
• If the notebook requires working with local data (such as CSV, FGDB, SHP, Raster files), upload the files as items to the Geosaurus Online Org using api_data_owner account and change the notebook to first download and unpack the files.
• Code simplified & split out across multiple cells, useful comments?
• Consistent voice/tense/narrative style? Thoroughly checked for typos?
• All images used like <img src="base64str_here"> instead of <img src="https://some.url">? All map widgets contain a static image preview? (Call mapview_inst.take_screenshot() to do so)
• All file paths are constructed in an OS-agnostic fashion with os.path.join()? (Instead of r"\foo\bar", os.path.join(os.path.sep, "foo", "bar"), etc.)
• Is your code formatted using Jupyter Black? You can use Jupyter Black to format your code in the notebook.
• If this notebook showcases deep learning capabilities, please go through the following checklist:
  • Are the inputs required for Export Training Data Using Deep Learning tool published on geosaurus org (api data owner account) and added in the notebook using gis.content.get function?
  • Is training data zipped and published as Image Collection? Note: Whole folder is zipped with name same as the notebook name.
  • Are the inputs required for model inferencing published on geosaurus org (api data owner account) and added in the notebook using gis.content.get function? Note: This includes providing test raster and trained model.
  • Are the inferenced results displayed using a webmap widget?
• IF YOU WANT THIS SAMPLE TO BE DISPLAYED ON THE DEVELOPERS.ARCGIS.COM WEBSITE, ping @jyaistMap so he can add it to the list for the next deploy.

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