project reorganization: 1. executable files in bin directory now. 2. add recursive_unpack_targz.py for recursive unpacking specified in this script archives tar.gz with MVN data. 3. add asotr_unzip_plot.sh bash file for unpacking MVN data, collect asotr data into csv files and plot asotr MVN data. 4. add brd_wheel_1Hz_parser.py for demonstrate how to work with brd telemetry data

bin/temp_peaks_forecast.py

@@ -0,0 +1,86 @@
+import sys
+import statistics
+from importlib import reload
+sys.path.append('./')
+import asotr
+reload(asotr)
+from datetime import datetime, timedelta
+import matplotlib.pyplot as plt
+from matplotlib import dates
+from datetime import timedelta
+
+path = '../data/asotr/'
+channel = 'ch1'
+asotr_kit = '01'
+start_date = '25.04.2025 00:00:00'
+end_date = '25.04.2025 08:00:00'
+forecast_days = 20
+
+
+timeformat = '%d.%m.%Y %H:%M:%S' 
+num_peaks_forecast = forecast_days * 20
+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']
+
+periods_t, periods, _ = asotr.find_periods(time1, data1, shift_flag=False, peaks='min')
+_, _, peaks = asotr.find_periods(time1, data1, shift_flag=False, peaks='max')
+
+peaks_forecast = asotr.get_peak_temp_forecast(time1.iloc[peaks[0]], num_peaks_forecast)
+
+with open('../data/asotr/peaks_forecast.txt', 'w') as file:
+    for elem in peaks_forecast:
+        file.write(f'{str(elem)}\n')
+
+delta_sec = []
+for idx, elem in enumerate(peaks):
+    if idx > 0:
+        delta = time1.iloc[elem] - peaks_forecast[idx-1]
+        # print(delta)
+        delta_sec.append(delta.total_seconds())
+
+delta_self_sec = []
+delta_self_sec1 = []
+for idx, elem in enumerate(periods_t):
+    delta1 = elem.iloc[len(elem)-1] - elem.iloc[0]
+    delta_self_sec.append(delta1.total_seconds())
+
+for idx, elem in enumerate(delta_self_sec):
+    if idx > 0:
+        delta_self_sec1.append(delta_self_sec[idx] - delta_self_sec[idx - 1])
+
+# print(delta_self_sec)
+print(statistics.median(delta_self_sec))
+
+ox_dtime_format = "%d.%m.%Y %H:%M"
+fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(8, 6))
+
+date_formatter = dates.DateFormatter(ox_dtime_format)
+ax1.xaxis.set_major_formatter(date_formatter)
+
+ax1.plot(time1, data1)
+for elem in peaks:
+    ax1.axvline(x = time1.iloc[elem], color='r', linewidth=0.5)
+    
+ax1.set_title(f'температура на орбите: АСОТР{asotr_kit}, канал {channel[2]}')
+
+ax2.set_title('Разница по времени между временем i-го пика и i-м предсказанием пика')
+ax2.set_ylabel(r'$\Delta$$t_{peak}$ = $timePeak_i$ - $timeForecast_i$, сек')
+ax2.plot(delta_sec)
+
+ax3.set_title('Разница по времени между первым и последующим периодами')
+ax3.set_ylabel(r'$\Delta$$t_{period}$ = $period_i$ - $period_0$, сек')
+ax3.plot(delta_self_sec1)
+
+ax1.set_ylabel('Температура, град.')
+
+ax1.grid(True)
+ax2.grid(True)
+ax3.grid(True)
+plt.show()