asotr.py: fix bug with decoding command to human-readable format (cmd_decode), add exception if index not finded for specified time

.gitignore

@@ -5,4 +5,6 @@
 *.zip
 *.log
 *.txt
+*.xls
+*.xlsx
 /__pycache__

asotr.py

@@ -17,6 +17,13 @@ danila_gamkov@cosmos.ru
 """
 __author__ = 'Danila Gamkov'
 
+
+class IdxNotFound(Exception):
+    pass
+
+class TimeIndexNotFound(Exception):
+    pass
+
 fname_json_decode = './decode_asotr_cmd.json'
 
 def get_utc_seconds(timestamp_str, timestamp_format):
@@ -66,11 +73,9 @@ def cmd_decode(cmd_string):
     if 'OK' in cmd_string:
         return out
 
-    cmd = cmd_string.split(' ')
-    if len(cmd) > 5:
-        return out
+    cmd = cmd_string.split()
     
-    if cmd[1] == '':
+    if len(cmd) > 5:
         return out
     
     if '1' in cmd[0]:
@@ -191,9 +196,6 @@ def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
     
     return mid
 
-class TimeIndexNotFound(Exception):
-    pass
-
 def find_time_idx(data_list, keys_list, timestamp, accuracy):
     out_dict = dict.fromkeys(keys_list, -1)
 
@@ -412,15 +414,25 @@ def cut_data(data, time_begin, duration_sec, accuracy='seconds'):
     time_end = tstamp_end.strftime(time_format)
 
     idx_begin = find_best_time_idx(data['timestamp'], time_begin, accuracy)
+
     idx_end = find_best_time_idx(data['timestamp'], time_end, accuracy)
     
+    if idx_begin == -1 or idx_end == -1:
+        raise IdxNotFound(f"error finding array index corresponding to timestamp: check time_begin ({time_begin}) or time_end ({time_end})")
+
+    idx_end = idx_end - 1
     out = data.loc[idx_begin : idx_end]  
     return out
 
 
 def cut_norm_data(data, time_begin, duration_sec, channel='ch1', 
         interp={'method': 'cubic', 'order': 2}, accuracy='seconds'):
+
+    try:
         data_period = cut_data(data, time_begin, duration_sec, accuracy)
+    except IdxNotFound as e:
+        print(f'{e}')
+
     temp_norm = data_period[channel].values - data_period[channel].iloc[0]
     time_l = list(data_period['timestamp'])
     temp_l = list(temp_norm)
@@ -433,13 +445,14 @@ def cut_norm_data(data, time_begin, duration_sec, channel='ch1',
 
     return orig_data, interp_data
 
-
 def get_step_response_diff(data, thermocycle_info, channel='ch1', 
         interp={'method': 'cubic', 'order': 2}, accuracy='seconds', cut_step_resp={}):
 
     date = thermocycle_info['date']
+
     time_begin_orig = date + ' ' + thermocycle_info['time_begin'][0]
     time_begin_step = date + ' ' + thermocycle_info['time_begin'][1]
+
     duration_sec = thermocycle_info['duration_sec']
     
     _, orig_interp_cycle = cut_norm_data(data, time_begin_orig, duration_sec, channel,
@@ -460,8 +473,12 @@ def get_step_response_diff(data, thermocycle_info, channel='ch1',
 
     if len(cut_step_resp) > 0:
         time_begin = date + ' ' + cut_step_resp['time_step_begin']
+        try:
             step_response = cut_data(step_response, time_begin, 
                 cut_step_resp['step_duration'], accuracy='seconds')
+        except IdxNotFound as e:
+            print(f'{e}')
+
         first = step_response['temp'].iloc[0]
         step_response['temp'] = step_response['temp'] - first
         

data/.~lock.asotr01_data_T.csv#

@@ -0,0 +1 @@
+,danila,danila-IdeaPad,20.05.2025 18:07,file:///home/danila/.config/libreoffice/4;

decode_cmd_data.py

@@ -7,12 +7,14 @@ import pandas as pd
 from datetime import datetime, timedelta
 
 path_data = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
-fname = '/home/danila/Danila/work/MVN/Soft/PID/data/flight/cmd_asotr/all_flight_cmd_asotr.csv'
+fname_cmd_flight = '/home/danila/Danila/work/MVN/Soft/PID/data/flight/cmd_asotr/all_flight_cmd_asotr.csv'
 fname_cmd_temp = './data/flight_cmd_temp.csv'
+timeformat = '%d.%m.%Y %H:%M:%S' 
+prev_days = 25
 
 ## get flight commands file (generated by mvn_log_viewer) 
 ## Translate to human-readeble format and take temperatures from flight commands file
-cmd_list, temperature_list = asotr.get_cmd_data(fname)
+cmd_list, temperature_list = asotr.get_cmd_data(fname_cmd_flight)
 with open('./data/cmd_human.csv', 'w') as file:
     for elem in cmd_list:
         file.write(f'{elem}\n')
@@ -26,22 +28,18 @@ with open(fname_cmd_temp, 'w') as file:
 ## insert temperatures from flight commands file to main asotr temperatures data files
 df_asotr_ = asotr.insert_temp_data_from_flight_cmd(fname_cmd_temp, path_data)
 
-## form timestamp file where minimum of temperatures were registered 
+## form timestamp file where minimum of temperatures registered 
 end_date = ''
 for i, data in enumerate(df_asotr_):
     end_date = data['timestamp'].iloc[len(data) - 1][0:18]
     data.to_csv(f'./data/asotr0{i+1}_data_T.csv', index=False, sep=';', 
             encoding='utf-8-sig', decimal='.')
 
-timeformat = '%d.%m.%Y %H:%M:%S' 
-prev_days = 14
-
 delta_date = datetime.strptime(end_date, timeformat) - timedelta(days=prev_days)
 start_date = delta_date.strftime(timeformat)
 
 for kit in range(1,3):
     asotr_kit = f'0{kit}'
-    print(asotr_kit)
     
     _, data_dict = asotr.get_data(path_data, asotr_kit, start_date, end_date, 'minutes')
 
@@ -59,12 +57,14 @@ for kit in range(1,3):
         
         min_temp_ch.append(min_temp_period)
 
+    fname = f'./data/asotr{asotr_kit}_min_T.csv'
+
     df = pd.DataFrame(min_temp_ch).transpose()
-    df.to_csv(f'./data/asotr{asotr_kit}_min_T.csv', header=False, index=False, sep=';', 
+    df.to_csv(fname, header=False, index=False, sep=';', 
             encoding='utf-8-sig', decimal='.')
-    df1 = pd.read_csv(f'./data/asotr{asotr_kit}_min_T.csv', sep=';', 
+    df1 = pd.read_csv(fname, sep=';', 
             names=['ch1','ch2','ch3','ch4','ch5','ch6'])
-    df1.to_csv(f'./data/asotr{asotr_kit}_min_T.csv', index=False, sep=';', 
+    df1.to_csv(fname, index=False, sep=';', 
             encoding='utf-8-sig', decimal='.')
 
 

plot_flight_borders.py

@@ -186,4 +186,4 @@ if __name__ == '__main__':
     args = argparser.parse_args()
     
     plot_asotr_borders(args.source, args.channel, args.asotr, args.begin, args.end, 
-            args.font, args.cmd, args.plot)
+            args.font, args.cmd, show_flag=args.plot)

plot_periods_profile.py

@@ -12,11 +12,11 @@ pict_name = 'periods_profile_10042025.png'
 path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
 channel = 'ch1'
 asotr_kit = '01'
-start_date = '10.04.2025 04:00:00'
-end_date = '10.04.2025 12:00:00'
+start_date = '24.04.2025 22:30:00'
+end_date = '25.04.2025 02:00:00'
 # start_date = '06.01.2025 22:40:00'
 # end_date = '21.01.2025 01:20:00'
-shift = True 
+shift = False 
 
 raw_data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
 

prepare_flight_cyclo.py

@@ -8,12 +8,12 @@ from datetime import datetime, timedelta
 
 path = './data/experiments/'
 
-timestamp = '03.05.2025 01:27:00'
-cyclogram_file = 'cyclogram_step_ident_ch1.xls'
+timestamp = '04.05.2025 00:42:00'
+cyclogram_file = 'cyclogram_step_ident_ch3.xls'
 asotr.insert_data_cyclo(timestamp, cyclogram_file, path)
 
-timestamp = '03.05.2025 04:33:00'
-cyclogram_file = 'cyclogram_imp_ident_ch1.xls'
+timestamp = '04.05.2025 03:48:00'
+cyclogram_file = 'cyclogram_imp_ident_ch3.xls'
 asotr.insert_data_cyclo(timestamp, cyclogram_file, path)
 
 

temp_peaks_forecast.py

@@ -12,8 +12,8 @@ from datetime import timedelta
 path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
 channel = 'ch1'
 asotr_kit = '01'
-start_date = '22.04.2025 00:00:00'
-end_date = '23.04.2025 01:20:00'
+start_date = '25.04.2025 00:00:00'
+end_date = '25.04.2025 08:00:00'
 forecast_days = 20