5_TrainingTableExporter.py

"""
MIT License

Copyright (c) 2022 Ian Housman and Leah Campbell

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.
"""


#Script to take LandTrendr stack outputs and summarize across iGDEs for training and applying a model


####################################################################################################

import SAGE_Initialize as sage
from geeViz import getImagesLib, changeDetectionLib, taskManagerLib, assetManagerLib
from geeViz.geeView import *

####################################################################################################
#Define user parameters:

# Options defined in SAGE_Initialize:
# Start and end year of training data
# AnnualDGWField (prefix in front of annual DGW values, in training GDEs)
# GDE ID Name (ID attribute for each GDE)
# Well ID Name (ID attribute for each well)
# TrainingGDEs - these are filtered and prepped in SAGE_Initialize
# DGW null, min, and max values
# Input and output paths and naming

####################################################################################################
#                     Prep
####################################################################################################
# If we want to view the output in the viewer, all the input needs to be changed to "all_users_can_read"
# (This can also be done in the playground using the "Share" function).
if sage.viewTrainingTable:

  for applyYear in range(sage.startApplyYear, sage.endApplyYear+1):
    assetManagerLib.updateACL(sage.applyTableDir + '/' + sage.applyTableName + '_' + str(applyYear), all_users_can_read = True)
  assetManagerLib.updateACL(sage.trainingGDECollection, all_users_can_read = True)
  assetManagerLib.updateACL(sage.applyGDECollection, all_users_can_read = True)

# Get initial training GDE collection and filter
trainingGDEs = ee.FeatureCollection(sage.trainingGDECollection)

# Only include GDEs greater than a minimum size.
trainingGDEs = trainingGDEs.filter(ee.Filter.gte(sage.gdeSizeAttribute, sage.minGDESize))

# Only include shallow wells, or some other defining characteristic
if sage.filterWellTypeByAttributeOrValue == 'attribute':
  trainingGDEs = trainingGDEs.filter(ee.Filter.stringContains(sage.wellDepthAttribute, sage.wellDepthFilterName))
elif sage.filterWellTypeByAttributeOrValue == 'value':
  trainingGDEs = trainingGDEs.filter(ee.Filter.And(ee.Filter.gt(sage.wellDepthAttribute, sage.wellDepthMin), ee.Filter.lt(sage.wellDepthAttribute, sage.wellDepthMax)))

# Create a Unique ID for each GDE / well combination
trainingGDEs = trainingGDEs.map(lambda f: f.set('unique_id', f.getNumber(sage.gdeIdName).format().cat('_').cat(f.getNumber(sage.wellIdName).format())))

####################################################################################################
#                     Get Training Table
####################################################################################################
# Since we have overlap between the training GDEs and the apply GDEs (all training GDEs are in the apply GDE collection),
# we just use the predictor tables created for the apply GDEs using 4applyTableExporter.py, and join it with the
# formatted training GDE tables.

years = range(sage.startTrainingYear, sage.endTrainingYear+1)
outTraining = []
for yr in years:
  print(yr)
  #Set up fields to select from training features
  #Will only need the DGW for that year and id
  #Will pull geometry and all other fields from that year's apply table
  yearDGWField = sage.annualDGWField + str(yr)
  fromFields = [yearDGWField, sage.gdeIdName, sage.wellIdName]
  toFields = ['dgw', sage.gdeIdName, sage.wellIdName]

  # get training GDEs for this year
  igdesYr = trainingGDEs.select(fromFields, toFields)
 
  igdesYr = igdesYr.map(lambda f: f.set('dgw',ee.Number(f.get('dgw')).float()))

  # Filter out any null DGW values
  igdesYr = igdesYr.filter(ee.Filter.neq('dgw', sage.dgwNullValue))
  
  # Filter out max and min DGW values
  igdesYr = igdesYr.filter(ee.Filter.lte('dgw', sage.maxDGW))
  igdesYr = igdesYr.filter(ee.Filter.gte('dgw', sage.minDGW))
  
  
  #Bring in apply training table to join to to get geometry and all other fields
  yrEnding = '_{}'.format(yr)
  applyTrainingTableYr = sage.applyTableDir + '/' + sage.applyTableName + yrEnding
  applyTrainingTableYr = ee.FeatureCollection(applyTrainingTableYr)

  # Join the apply Tables with the training GDEs. This will only retain the training GDEs
  igdesYr = sage.joinFeatureCollectionsReverse(igdesYr, applyTrainingTableYr, sage.gdeIdName)

  outTraining.append(igdesYr)
  
#Combine all years into a single collection
outTraining = ee.FeatureCollection(outTraining).flatten()

Map.addLayer(outTraining, {'strokeColor':'F0F'}, 'Training Features', False) #,'layerType':'geeVectorImage'

# Export
t = ee.batch.Export.table.toAsset(**{\
    'collection': outTraining, 
    'description': sage.trainingTableName,
    'assetId': sage.trainingTablePath})

print('Exporting:', sage.trainingTableName)
t.start()

####################################################################################################
#                   Visualize in geeView() if Selected
####################################################################################################
if sage.viewTrainingTable:

  Map.view()

else:

  taskManagerLib.trackTasks()