Update theropoda.py

theropoda.py

@@ -49,11 +49,6 @@
 from joblib import Parallel, delayed
 from loguru import logger
 import sqlite3
-from skmap.misc import date_range, ttprint
-from trend_analysis import run as trend_run
-from skmap import parallel
-import argparse
-import shutil
 
 logger.add("log_do_.log", rotation="500 MB")
 
@@ -181,11 +176,11 @@ def reduceData(img):
       .set('satelite',img.get('satelite')) #Sapacraft plataform name (i.e. Sentinel 2A or 2B)
       .set('MGRS_TILE',img.get('MGRS_TILE')) #Reference tile grid
       .set('AREA_HA',ee.Feature(geometry).area(1).divide(10000)) #Choosed polygon ID Field
-      #.set('NDVI_mean',ee.Number(ee.Dictionary(series).get('NDVI_mean'))) #NDVI pixel average for the polygon
+      .set('NDVI_mean',ee.Number(ee.Dictionary(series).get('NDVI_mean'))) #NDVI pixel average for the polygon
       .set('NDVI_median',ee.Number(ee.Dictionary(series).get('NDVI_median'))) #NDVI pixel median for the polygon
-      #.set('NDVI_min',ee.Number(ee.Dictionary(series).get('NDVI_min'))) #NDVI pixel minimum value for the polygon
-      #.set('NDVI_max',ee.Number(ee.Dictionary(series).get('NDVI_max'))) #NDVI pixel maximum value for the polygon
-      #.set('NDVI_stdDev',ee.Number(ee.Dictionary(series).get('NDVI_stdDev'))) #NDVI pixel Standard Deviation for the polygon
+      .set('NDVI_min',ee.Number(ee.Dictionary(series).get('NDVI_min'))) #NDVI pixel minimum value for the polygon
+      .set('NDVI_max',ee.Number(ee.Dictionary(series).get('NDVI_max'))) #NDVI pixel maximum value for the polygon
+      .set('NDVI_stdDev',ee.Number(ee.Dictionary(series).get('NDVI_stdDev'))) #NDVI pixel Standard Deviation for the polygon
       .set('Pixel_Count',ee.Number(ee.Dictionary(series).get('NDVI_count'))) #Number of pixels cloudless and shadowless used for estimatives
       .set('Total_Pixels',ee.Number(ee.Dictionary(series).get('full_count'))) #Total number of pixels inside the polygon
       .set('Pixel_Size',pixel_size) #Size of the pixel used
@@ -496,70 +491,3 @@ def run(asset,id_field,output_name,colab_folder):
 
   logger.success(f'The average processing time was {round(pd.DataFrame(time_list).mean()[0],2)} seconds')
   logger.success(f'Processing finished. All the work took {round(time.time() - start_time,3)} seconds to complete')
-
-#from google.colab import drive
-#drive.mount('/content/drive/')
-
-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)
-
-  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')
-  df2conv = None
-  shutil.rmtree(df2conv)