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main.py

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+from theropoda_v2 import run as theropoda_run
+from trend_analysis import run as trend_run
+import os
+import argparse
+import shutil
+import sqlite3
+import pandas as pd
+from skmap.misc import date_range, ttprint
+from skmap import parallel
+
+if __name__ == '__main__':
+
+  parser = argparse.ArgumentParser(description='Toolkit created to extract Time Series information from Sentinel 2 stored in Earth Engine, perform gap filling and trend analysis image.')
+
+  parser.add_argument('--asset', type=str, required=True, help='The asset name or path')
+  parser.add_argument('--id_field', type=str, required=True, help='The ID field name')
+  parser.add_argument('--output_name', type=str, required=True, help='The output file name')
+
+  args = parser.parse_args()
+
+  asset = args.asset #'users/vieiramesquita/LAPIG_FieldSamples/lapig_goias_fieldwork_2022_50m' #Earth Engine Vector Asset
+  id_field = args.id_field #'ID_POINTS' #Vector collumn used as ID (use unique identifiers!)
+
+  db = asset.split('/')[-1]
+
+  db_name = db + '.db'  
+
+  colab_folder = ''
+  output_name = args.output_name #db_name
+
+  conn = sqlite3.connect(db_name)
+  conn.close()
+
+  #Check if polygon list file exists
+  if os.path.exists(os.path.join(colab_folder,db + '_polygonList.txt')) is False:
+    build_id_list(asset,id_field,colab_folder)
+
+  theropoda_run(asset,id_field,output_name,colab_folder)
+
+  input_file = output_name
+  start_date_trend, end_date_trend= '2019-01-01', '2024-01-01'
+  output_file_trends = f'{output_name[:-3]}_trend_analysis.pq'
+
+  ################################
+  ## SQLITE access
+  ################################
+  ttprint(f"Preparing {output_name}")
+  con = sqlite3.connect(output_name)
+  cur = con.cursor()
+  res = cur.execute(f"CREATE INDEX IF NOT EXISTS restoration_id_pol ON restoration ({id_field})")
+  con.commit()
+
+  ################################
+  ## Common data structures
+  ################################
+  ttprint(f"Preparing polygon ids")
+
+  idx_sql = f"SELECT {id_field}, MIN(date) min_date, MAX(date) max_date, COUNT(*) count FROM restoration GROUP BY 1 ORDER BY 1"
+  idx =  pd.read_sql_query(idx_sql, con=con)
+
+  dt_5days = list(date_range(start_date_trend, end_date_trend, date_unit='days', date_step=5, ignore_29feb=True))
+  season_size = int(len(dt_5days) / 5)
+
+  args = [ (output_name, r[f'{id_field}'], dt_5days, season_size, id_field, output_file_trends) for _, r in idx.iterrows() ]
+
+  ttprint(f"Starting trend analysis on {len(args)} polygons")
+  for id_pol in parallel.job(trend_run, args, joblib_args={'backend': 'multiprocessing'}):
+    continue
+
+  df2conv = pd.read_parquet(output_file_trends)
+  df2conv.to_parquet(f'{output_name[:-3]}_trend_analysis.parquet')
+
+  shutil.rmtree(df2conv)