correct find_best_time_interval function. Create Readme

README.markdown

@@ -0,0 +1,68 @@
+# asotr_flight
+
+The asotr_flight python scripts is designed to analyze ASOTR flight data
+
+## Contents
+
+- **Setup**
+- **Using**
+- Plot ASOTR data in specified date borders (for MVN reports)
+- **Contacts**
+
+**Note**: \<PATH_TO_ASOTR_FLIGHT\> - path where is asotr_flight scripts is cloned from heagit
+**Note**: \<PATH_TO_ASOTR_CSV\> - path where is asotr_csv program is cloned from heagit
+
+## Setup
+1. Install python 3.10 or newest.  
+
+2. Clone the repo to your computer:  
+```
+git clone http://heagit.cosmos.ru/gamkov/asotr_flight.git
+```
+
+3. Enter the repo:  
+```
+cd <PATH_TO_ASOTR_FLIGHT>
+```
+
+## Using
+### Plot ASOTR data in specified date borders (for MVN reports)
+1. Donwload data from science data server to directory \<PATH_TO_ASOTR_DATA\>.  
+If you don't have MVN data, you might download it from server with science SRG data (IP: 193.232.11.95).   
+For questions about downloading science data contact Shtykovsky A. (a.shtykovsky@cosmos.ru) or Chelovekov I. (chelovekov@cosmos.ru)
+
+2. Parse all raw data from ASOTR into csv files and plot all csv data:  
+```
+cd <PATH_TO_ASOTR_CSV>  
+./asotr_all_unzip_auto.sh <PATH_TO_ASOTR_DATA>/
+```
+csv data will be in directory:  
+```
+<PATH_TO_ASOTR_CSV>/data/  
+```
+
+3. plot ASOTR data in specified data borders  
+You might plot data by using shell-script (for MVN reports), for example:  
+```
+cd <PATH_TO_ASOTR_FLIGHT>
+./plot_flight_borders.sh <PATH_TO_ASOTR_CSV>/ 10.03.2025 23.04.2025 14 0
+```
+where:  
+10.03.2025 - start date  
+23.04.2025 - end date  
+14 - font size for plot  
+0 - display commands flag (not display)  
+
+Or you might plot data by using python script directly, for example:  
+```
+cd <PATH_TO_ASOTR_FLIGHT>
+python3 plot_flight_borders.py -s <PATH_TO_ASOTR_CSV> -c 100000 -a 01 -b 10.03.2025 -e 15.03.2025 -f 14 -d 0 -p 0
+```
+
+Graphs with data are in the directory:  
+```
+<PATH_TO_ASOTR_FLIGHT>/reports
+```
+
+## Contatcs
+For questions about the program, please contact Danila Gamkov, email: danila_gamkov@cosmos.ru

asotr.py

@@ -146,12 +146,12 @@ def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
     elif accuracy == 'seconds':
         delta = timedelta(seconds=30)
 
-    low = 0
+    low = time_arr.idxmin()
-    high = len(time_arr) - 1
+    high = time_arr.idxmax()
-    mid = len(time_arr) // 2
+    mid = time_arr.idxmin() + (time_arr.idxmax() - time_arr.idxmin()) // 2
      
     if mid not in time_arr.index:
-        # print(f'mid not in time_arr: {mid}')
+        # print(f'mid not in time_arr: {mid}, {time_arr.index}, {time_arr.idxmin()}')
         return -1
 
     a = time_arr[mid]
@@ -160,7 +160,7 @@ def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
             low = mid + 1
         else:
             high = mid - 1
-        mid = (low + high) // 2
+        mid = low + (high - low) // 2
         # print(f'mid: (low + high)/2: {mid}')
 
         if mid not in time_arr.index:
@@ -186,8 +186,14 @@ def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
                 # print(f'{time_arr[j]} > {tstamp}: {j}')
                 return j
 
+    if mid <= low + 1 or mid >= high - 1:
+        return -1
+    
     return mid
 
+class TimeIndexNotFound(Exception):
+    pass
+
 def find_time_idx(data_list, keys_list, timestamp, accuracy):
     out_dict = dict.fromkeys(keys_list, -1)
 

plot_flight_borders.py

@@ -16,7 +16,7 @@ def convert_to_str(lst):
         res += f"_{idx + 1}"
     return res
 
-def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=False):
+def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=True):
     print_width = 20
     print_height = 12
     width = 1
@@ -74,6 +74,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
 
             plt.tight_layout()
             fig.savefig(pict_name)
+            if show_flag == True: 
                 plt.show()
 
     elif plot_windows == 2:
@@ -85,22 +86,29 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
         if cmd == '1':
             fname = './flight_cmd_human.txt'
             try:
-                cmd_human = pd.read_csv('./flight_cmd_human.txt', 
+                cmd_human = pd.read_csv('./data/cmd_human.csv', 
                     delimiter=';', names=['timestamp', 'cmd'])
             except Exception as e:
                 print(f'Error parsing file: {e}')
                 return
             
+            max_temp = max(data_dict['temp'].iloc[:,1])
+            min_temp = min(data_dict['temp'].iloc[:,1])
+            # print(cmd_human)
+            step = 0
             for i, row in cmd_human.iterrows():
-                if i > 20:
                 row_time = row['timestamp'][0:len(row['timestamp']) - 4]
+                # print(row_time)
                 idx = asotr.find_best_time_idx(data_dict['time_temp'], 
                     row_time, accuracy='minutes')
+
+                # print(idx)
                 if idx != -1:
                     ax1.axvline(x = data_dict['time_temp'][idx], color='r', 
                             linestyle='-.')
-                        ax1.text(data_dict['time_temp'][idx], 30, row['cmd'], 
+                    ax1.text(data_dict['time_temp'][idx], max_temp - step, row['cmd'], 
                             rotation=45, va='bottom', fontsize=font)
+                    step += (max_temp - min_temp)/20 
         
         if plot_task["temp"] == 1:
             for i in range(len(channels)):

temp_peaks_forecast.py

@@ -12,19 +12,19 @@ from datetime import timedelta
 path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
 channel = 'ch1'
 asotr_kit = '01'
-start_date = '23.04.2025 00:00:00'
+start_date = '22.04.2025 00:00:00'
+end_date = '23.04.2025 01:20:00'
 forecast_days = 20
-# end_date = '26.03.2025 01:20:00'
+
 
 timeformat = '%d.%m.%Y %H:%M:%S' 
-delta_date = datetime.strptime(start_date, timeformat) + timedelta(days=forecast_days)
-end_date = delta_date.strftime(timeformat)
 num_peaks_forecast = forecast_days * 20
-# start_date = '06.01.2025 22:40:00'
-# end_date = '21.01.2025 01:20:00'
 shift = True 
 
+try:
     raw_data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
+except Exception as e:
+    sys.exit()
 
 data1 = data_dict['temp'][channel]
 time1 = data_dict['time_temp']