Mean and std attenuation based on dry periods

FHSTP/01_data_preparation/prep.py

@@ -2,8 +2,8 @@
 # Title: Data Preparation for LINK Configs and Transmissions
 # Author: Fabian Kovac <fabian.kovac@fhstp.ac.at>
 # Team: University of Applied Sciences St. Pölten
-# Version: 3.3
-# Last changed: 2021-12-15
+# Version: 4.0
+# Last changed: 2022-01-14
 #
 
 import sys
@@ -579,18 +579,14 @@ def prep() -> None:
     
     _log('\n******************************** CALC POWER LEVELS *************************')
     
-    # build df with differences of sending and receiving levels
-    df_diff = df_link[['LINKID', 'TXLEVEL', 'REMOTERXLEVEL']].copy()
-    df_diff['ATTENUATIONMEAN'] = df_diff['REMOTERXLEVEL'] - df_diff['TXLEVEL']
-    _log('Built dataframe with mean link attenuations')
+    # load dry LINK attenuation statistics
+    df_stats = pd.read_csv('00_Link_dry_attnstats.csv', sep = ';')
     
-    # get mean of differences per link
-    df_diff = df_diff.groupby(['LINKID']).agg({'ATTENUATIONMEAN' : 'mean'}).reset_index()
-    
-    # merge differences to transmission dataframe
-    df_link = pd.merge(df_link, df_diff, how = 'left', left_on = 'LINKID', right_on = 'LINKID')
-    del df_diff
-    _log('Merged mean link attenuations back to link dataframe')
+    # merge mean and std attenuation to transmission dataframe
+    df_link = pd.merge(df_link, df_stats, how = 'left', left_on = 'LINKID', right_on = 'LINKID')
+    df_link = df_link.dropna(axis = 0, subset = ['ATTENUATIONMEANDRY', 'ATTENUATIONSTDDRY'])
+    del df_stats
+    _log('Merged mean and std of link attenuations to link dataframe')
     
     
     
@@ -599,8 +595,15 @@ def prep() -> None:
     _log('Calculated attenuation per transmission')
 
     # calculate differences from attenuations and mean attenuations
-    df_link['ATTENUATIONDIFF'] = df_link['ATTENUATION'] - df_link['ATTENUATIONMEAN']
-    _log('Calculated attenuation differences of daily means')
+    df_link['ATTENUATIONDIFF'] = df_link['ATTENUATION'] - df_link['ATTENUATIONMEANDRY']
+    
+    df_std = df_link.groupby(['LINKID']).agg({'ATTENUATION' : 'std'}).reset_index()
+    df_std = df_std.rename(columns = {'LINKID' : 'LINKID', 'ATTENUATION' : 'ATTENUATIONSTD'})
+    
+    df_link = pd.merge(df_link, df_std, how = 'left', left_on = 'LINKID', right_on = 'LINKID')
+    df_link['ATTENUATIONSTDDIFF'] = df_link['ATTENUATIONSTD'] - df_link['ATTENUATIONSTDDRY']
+    del(df_std)
+    _log('Calculated attenuation differences of dry means')
     
     # calculate extinction coefficients for links based on attenuation and lengths of the links based on beer-lambert law
     # Coeff = -ln(10) * (Attenuation/PathLength)
@@ -617,8 +620,10 @@ def prep() -> None:
         'PMAX' : 'RxLevelMax',
         'TXLEVEL' : 'TxLevel',
         'ATTENUATION' : 'Attn',
-        'ATTENUATIONMEAN' : 'AttnMean',
+        'ATTENUATIONMEANDRY' : 'AttnMeanDry',
+        'ATTENUATIONSTDDRY' : 'AttnStdDry',
         'ATTENUATIONDIFF' : 'AttnDiff',
+        'ATTENUATIONSTDDIFF' : 'AttnStdDiff',
         'EXTINCTIONCOEFF' : 'ExtinctionCoeff',
         'SPEED' : 'Speed',
         'CURRRXBITRATE' : 'RxBitrate',