[i] reverted PinstMean for dry periods (research purposes) back to mean per link

FHSTP/prep.py → FHSTP/01_data_preparation/prep.py

@@ -2,8 +2,8 @@
 # Title: Data Preparation for LINK Configs and Transmissions
 # Author: Fabian Kovac <ds191008@fhstp.ac.at>
 # Team: University of Applied Sciences St. Pölten
-# Version: 2.4
-# Last changed: 2021-09-08
+# Version: 2.5
+# Last changed: 2021-09-27
 #
 
 import sys
@@ -495,44 +495,21 @@ def prep() -> None:
     _log('Merged config data to link dataframe')
     
     
-    _log('\n******************************** MERGE INCA ********************************')
-    
-    # set INCA RR data for each km of link path
-    df_link['RRpath'] = get_inca_data(
-        df_link['BEGINTIME'],
-        df_link['LONGITUDE_A'],
-        df_link['LATITUDE_A'],
-        df_link['LONGITUDE_B'],
-        df_link['LATITUDE_B'],
-        df_link['LENGTH']
-     )
-    _log('Merged INCA RR data to transmissions')
-    
-    
     _log('\n******************************** CALC POWER LEVELS *************************')
     
     # build df with differences of sending and receiving levels
-    df_diff = df_link[['LINKID', 'TXLEVEL', 'REMOTERXLEVEL', 'RRpath']].copy()
+    df_diff = df_link[['LINKID', 'TXLEVEL', 'REMOTERXLEVEL']].copy()
     df_diff['PINSTMEAN'] = df_diff['TXLEVEL'] - df_diff['REMOTERXLEVEL']
     _log('Built dataframe with mean link difference levels of TXLEVEL and REMOTERXLEVEL')
     
-    # get min rainrate of LINK path
-    df_diff['RRmin'] = np.array([np.array(x).min() for x in df_diff['RRpath'].values])
-    _log('Calculate min rainrates for LINK path')
+    # get mean of differences per link
+    df_diff = df_diff.groupby(['LINKID']).agg({'PINSTMEAN' : 'mean'}).reset_index()
     
-    # get mean of differences per link of dry periods
-    df_diff = df_diff[df_diff['RRmin'] == 0.0].groupby(['LINKID']).agg({'PINSTMEAN' : 'mean'}).reset_index()
-    _log('Calculate mean difference levels of dry periods')
     
     # merge differences to transmission dataframe
     df_link = pd.merge(df_link, df_diff, how = 'left', left_on = 'LINKID', right_on = 'LINKID')
     _log('Merged mean link difference levels back to link dataframe')
     
-    # if PINSTMEAN is NA, LINK only saw wet periods
-    # --> set to mean of all LINKS
-    df_link['PINSTMEAN'] = df_link['PINSTMEAN'].fillna(df_link['PINSTMEAN'].mean())
-    _log('Filled NAs of PINSTMEAN (exclusively wet periods) with daily mean of all LINKS')
-
     
     # calculate instantaneous values from transmitted and received signal strength
     df_link['PINST'] = df_link['TXLEVEL'] - df_link['REMOTERXLEVEL']
@@ -588,13 +565,24 @@ def prep() -> None:
             'TXFREQUENCY' : 'TxFrequency'
         })
     
-    # set RRpath as last column
-    name_cols.update({'RRpath' : 'RRpath'})
-    
     df_link = df_link.rename(columns = name_cols).reindex(columns = list(name_cols.values()))
     _log('Converted link dataframe to RAINLINK format')
     
     
+    _log('\n******************************** MERGE INCA ********************************')
+    
+    # set INCA RR data for each km of link path
+    df_link['RRpath'] = get_inca_data(
+        df_link['DateTime'],
+        df_link['XStart'],
+        df_link['YStart'],
+        df_link['XEnd'],
+        df_link['YEnd'],
+        df_link['PathLength']
+     )
+    _log('Merged INCA RR data to transmissions')
+    
+    
     _log('\n******************************** SAVE FILES ********************************')
     
     # build path for clean config and transmissions destination files