some light refactoring

code/awac_datafile_read.py

@@ -1,6 +1,6 @@
-from datetime import datetime
-import glob
 import pandas as pd
+"""Loads in AWAC Datafiles (.csv) and builds a readable and workable Dataframe out of it"""
+
 
 
 def load_csv(file_path_in):
@@ -24,9 +24,6 @@ def load_csv(file_path_in):
     y=awac_data_list[0][40:-1]
 
 
-    print("")
-
-
 
 def main():
     file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\FINOII20_AWAC.csv"

code/endbericht_auswertung_microcats.py

@@ -1,47 +0,0 @@
-from datetime import datetime
-import glob
-import pandas as pd
-import plotly.graph_objects as go
-import plotly.express as px
-
-
-def load_D_csv_file_to_df(file_path_in):
-    with open(file_path_in) as f:
-        raw_data = f.readlines()
-    data_listlist= [i[1:-2].split("\",\"") for i in raw_data]
-
-    d_data_df_str = pd.DataFrame(data_listlist[1:], columns=data_listlist[0])
-    d_data_df =d_data_df_str.loc[:, 'WP (2.0m)':'WP (20.0m)'].apply(pd.to_numeric)
-    d_data_df['date']= d_data_df_str['date']
-    cols = d_data_df.columns.tolist()
-    cols = cols[-1:] + cols[:-1]
-    d_data_df = d_data_df[cols]
-
-    y=['WP (2.0m)', 'WP (4.0m)', 'WP (6.0m)', 'WP (8.0m)', 'WP (10.0m)', 'WP (12.0m)', 'WP (14.0m)', 'WP (16.0m)', 'WP (18.0m)', 'WP (20.0m)']
-    fig = px.line(d_data_df, x="date", y=y)
-    fig.show()
-
-    return d_data_df
-
-
-
-
-def eval_d_data(d_data_table):
-    data_percentage={'date':0,'WP (2.0m)':0, 'WP (4.0m)':0, 'WP (6.0m)':0, 'WP (8.0m)':0, 'WP (10.0m)':0, 'WP (12.0m)':0, 'WP (14.0m)':0, 'WP (16.0m)':0, 'WP (18.0m)':0,'WP (20.0m)':0}
-    data_counts={'date':0,'WP (2.0m)':0, 'WP (4.0m)':0, 'WP (6.0m)':0, 'WP (8.0m)':0, 'WP (10.0m)':0, 'WP (12.0m)':0, 'WP (14.0m)':0, 'WP (16.0m)':0, 'WP (18.0m)':0,'WP (20.0m)':0}
-
-
-    for col in d_data_table:
-        for element in d_data_table[col]:
-            if element == element:
-                data_counts[col] +=  1
-        data_percentage[col]=data_counts[col]/data_counts['date']
-    print("")
-
-def main():
-    file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\FINO2 D 2022-01-01-2022-11-09 1668002689349.csv"
-    d_data_table = load_D_csv_file_to_df(file_path_in)
-    eval_d_data(d_data_table)
-
-if __name__ == '__main__':
-    main()

code/plot_MC_raw_data.py

@@ -1,13 +1,10 @@
-
 import datetime
-import numpy as np
-import time
-import glob
 import pandas as pd
 import matplotlib.pyplot as plt
 
 
-def secondsSince2000_toDatetime(ss2000):
+def seconds_since2000_to_datetime(ss2000):
+    """Translates the timestamp of MC files to datetime"""
     return datetime.datetime(2000,1,1)+ datetime.timedelta(seconds =ss2000)
 
 
@@ -22,18 +19,15 @@ def load_cnv(file_path_in):
         CCC = [i.strip() for i in line]
         CTD_data.append([g for g in CCC if g.strip()])
 
-
     if len(CTD_data[0])==5:
         CTD_data_df= pd.DataFrame(CTD_data, columns=['C','T','D','JulianDays','unknown']).apply(pd.to_numeric)
     elif len(CTD_data[0])==8:
         CTD_data_df = pd.DataFrame(CTD_data, columns=['seconds_since_2000','T', 'C', 'D', 'sal', 'oxsat','density','flag']).apply(pd.to_numeric)
-        CTD_data_df['date']=CTD_data_df['seconds_since_2000'].apply(secondsSince2000_toDatetime)
+        CTD_data_df['date']=CTD_data_df['seconds_since_2000'].apply(seconds_since2000_to_datetime)
 
     return CTD_data_df
 
 
-
-
 def main():
     ctd_raw_data = {}
     file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\02m\SBE37-IM_03709795_2022_10_28.cnv"
@@ -57,21 +51,13 @@ def main():
     file_path_in = r"D:\Python\FINO2_data_evaluation\data\evaluation_data_2022\MCs_2022_okt\20m\SBE37-IM_03709804_2022_10_28.cnv"
     ctd_raw_data["20m"] = load_cnv(file_path_in)
 
-    #plotstuff(ctd_raw_data)
 
+    plot_string= ['2m', '4m', '6m', '8m', '10m', '12m', '14m', '16m', '18m', '20m']
     fig3 = plt.figure()
-    plt.plot(ctd_raw_data['2m']['date'], ctd_raw_data['2m']['D'], label="2m")
-    plt.plot(ctd_raw_data['4m']['date'], ctd_raw_data['4m']['D'], label="4m")
-    plt.plot(ctd_raw_data['6m']['date'], ctd_raw_data['6m']['D'], label="6m")
-    plt.plot(ctd_raw_data['8m']['date'], ctd_raw_data['8m']['D'], label="8m")
-    plt.plot(ctd_raw_data['10m']['date'], ctd_raw_data['10m']['D'], label="10m")
-    plt.plot(ctd_raw_data['12m']['date'], ctd_raw_data['12m']['D'], label="12m")
-    plt.plot(ctd_raw_data['14m']['date'], ctd_raw_data['14m']['D'], label="14m")
-    plt.plot(ctd_raw_data['16m']['date'], ctd_raw_data['16m']['D'], label="16m")
-    plt.plot(ctd_raw_data['18m']['date'], ctd_raw_data['18m']['D'], label="18m")
-    plt.plot(ctd_raw_data['20m']['date'], ctd_raw_data['20m']['D'], label="20m")
+    for st in plot_string:
+        plt.plot(ctd_raw_data[st]['date'], ctd_raw_data[st]['D'], label=st)
     plt.show()
 
 
 if __name__ == '__main__':
-    main()
+    main()

code/plot_insida_exports.py

@@ -3,20 +3,15 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import math
 import numpy as np
+"""Plot insida exports for WQM, MC and AWAC data"""
+
 
 
 def plot_MC_export(CTD_nurDruck_df):
-     fig = plt.figure()
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (2.0m)'], label="2m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (4.0m)'], label="4m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (6.0m)'], label="6m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (8.0m)'], label="8m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (10.0m)'], label="10m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (12.0m)'], label="12m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (14.0m)'], label="14m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (16.0m)'], label="16m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (18.0m)'], label="18m")
-     plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df['WP (20.0m)'], label="20m")
+     plot_string =  ['WP (2.0m)', 'WP (4.0m)', 'WP (6.0m)', 'WP (8.0m)', 'WP (10.0m)', 'WP (12.0m)', 'WP (14.0m)', 'WP (16.0m)', 'WP (18.0m)', 'WP (20.0m)']
+     fig3 = plt.figure()
+     for st in plot_string:
+          plt.plot(CTD_nurDruck_df['date'], CTD_nurDruck_df[st], label=st)
      plt.show()
 
 
@@ -105,9 +100,9 @@ def plot_seegang_export(AWAC_seegang_df):
 if __name__ == '__main__':
      AWAC_seegang_df,CTD_df,CTD_nurDruck_df,WQM_1H_df,WQM_10m_df = load_export_data()
 
-     plot_seegang_export(AWAC_seegang_df)
+     #plot_seegang_export(AWAC_seegang_df)
 
-     #plot_MC_export(CTD_nurDruck_df)
+     plot_MC_export(CTD_nurDruck_df)
 
      #plot_WQM_1h_export(WQM_1H_df)