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
This commit is contained in:
Danila Gamkov
294
bin/step_response.py
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294
bin/step_response.py
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import pandas as pd
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import matplotlib.pyplot as plt
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import sys
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from importlib import reload
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sys.path.append('./')
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import asotr
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reload(asotr)
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import matplotlib.pyplot as plt
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from matplotlib import dates
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import pandas as pd
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from datetime import datetime
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asotr_kit = 1
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# fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
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fname = f'../data/asotr/asotr0{asotr_kit}_data_T.csv'
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dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
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data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
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# date = '20.03.2025'
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# period = '1 мин'
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# time_begin_orig = date + ' 17:10:11'
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# time_begin1 = date + ' 18:10:17'
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# time_begin2 = date + ' 19:10:23'
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# step_begin = time_begin2
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# duration = 3600
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# accuracy = 'seconds'
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# name_fig = 'step_response_KDI_20242003.png'
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# date = '21.03.2025'
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# period = '1 мин'
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# time_begin_orig = date + ' 14:00:11'
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# time_begin1 = date + ' 15:00:16'
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# time_begin2 = date + ' 16:00:16'
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# step_begin = time_begin2
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# duration = 3600
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# accuracy = 'seconds'
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# name_fig = 'step_response_KDI_20242103.png'
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# date = '24.03.2025'
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# period = '1 сек'
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# time_begin_orig = date + ' 19:45:11'
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# time_begin1 = date + ' 20:45:13'
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# time_begin2 = date + ' 21:45:17'
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# step_begin = time_begin2
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# duration = 3600
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# accuracy = 'seconds'
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# name_fig = 'step_response_KDI_20242403.png'
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# interp = {'method': 'polynomial', 'order': 1}
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# thermocycle_info = {'date': '01.04.2025',
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# 'time_begin': ['01.04.2025 16:27:00', '01.04.2025 18:00:00'],
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# 'duration_sec': 92*60, 'type': 'step'}
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# cut_step_resp = {'time_step_begin': '01.04.2025 18:53:21', 'step_duration': 25*60}
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# data_info = {'data': data, 'device': 'KDI', 'channel': 'ch1', 'period': '1 мин',
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# 'find_accuracy': 'seconds'}
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# name = f'{thermocycle_info["type"]}_response_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
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# plot_info = {'title': 'Реакция на ступенчатое воздействие',
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# 'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
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# 'name_fig': f'{name}.png', 'font': 10}
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interp = {'method': 'polynomial', 'order': 1}
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data_info_list = []
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thermocycle_info_list = []
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cut_step_resp_list = []
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '25.04.2025',
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'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'],
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'duration_sec': 92*60, 'type': 'step', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '25.04.2025',
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'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'],
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'duration_sec': 92*60, 'type': 'step1_to2', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '25.04.2025',
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'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'],
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'duration_sec': 92*60, 'type': 'impulse', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 15*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '25.04.2025',
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'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'],
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'duration_sec': 92*60, 'type': 'impulse1_to2', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 20*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '30.04.2025',
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'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'],
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'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '30.04.2025',
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'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'],
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'duration_sec': 93*60, 'type': 'step2_to1', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '30.04.2025',
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'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'],
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'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 15*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '30.04.2025',
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'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'],
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'duration_sec': 93*60, 'type': 'impulse2_to1', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 20*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '02.05.2025',
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'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'],
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'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '02.05.2025',
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'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'],
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'duration_sec': 93*60, 'type': 'step4_to3', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '02.05.2025',
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'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'],
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'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 15*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '02.05.2025',
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'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'],
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'duration_sec': 93*60, 'type': 'impulse4_to3', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 20*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '04.05.2025',
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'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'],
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'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 26*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '04.05.2025',
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'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'],
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'duration_sec': 93*60, 'type': 'step3_to4', 'type_ru': 'ступенчатое'}
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cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 30*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '04.05.2025',
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'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'],
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'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 15*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин',
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'find_accuracy': 'seconds'}
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thermocycle_info = {'date': '04.05.2025',
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'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'],
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'duration_sec': 93*60, 'type': 'impulse3_to4', 'type_ru': 'импульсное'}
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cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 20*60}
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data_info_list.append(data_info)
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thermocycle_info_list.append(thermocycle_info)
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cut_step_resp_list.append(cut_step_resp)
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def get_step_response(data_info, thermocycle_info, cut_step_resp):
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name = f'{data_info["channel"]}_{thermocycle_info["type"]}_response_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
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plot_info = {'title': 'Реакция на ' + thermocycle_info['type_ru'] + ' воздействие',
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'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
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'name_fig': f'../plots/response/{name}.png', 'font': 10}
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asotr.plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
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cut_step_resp, plot_info)
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step_resp_cut, _, _ = asotr.get_step_response_diff(data_info['data'], thermocycle_info,
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channel=data_info['channel'], interp=interp, accuracy=data_info['find_accuracy'],
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cut_step_resp=cut_step_resp)
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max_ = len(step_resp_cut)
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step_resp_cut.to_csv(f'../data/asotr/response/asotr0{asotr_kit}_{data_info["channel"]}_{thermocycle_info["type"]}_{thermocycle_info["date"].replace(".","")}.csv', index=False, sep=';', encoding='utf-8-sig', decimal='.')
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title = f'{plot_info["title"]}, канал {data_info["channel"][2]} АСОТР, {data_info["device"]} СПИН-X1-МВН, период опроса {data_info["period"]} ({thermocycle_info["date"]})'
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fig = plt.figure(figsize=(10, 6), dpi=200)
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fig.suptitle(title, fontsize=plot_info['font'])
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ax1 = fig.add_subplot(1,1,1)
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ax1.plot(step_resp_cut['timestamp'].iloc[0:max_], step_resp_cut['temp'].iloc[0:max_], '-',
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label='реакция на ' + thermocycle_info['type_ru'] + ' воздействие с термоциклом')
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date_formatter = dates.DateFormatter(plot_info['ox_dtime_format'])
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ax1.xaxis.set_major_formatter(date_formatter)
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ax1.legend(loc=plot_info["legend_pos"][0], fontsize=plot_info['font'])
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ax1.grid(True)
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ax1.tick_params(axis='both', width=1, labelsize=plot_info['font'])
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ax1.set_ylabel(r'$T_{norm}$, $^\circ$C', fontsize=plot_info['font'])
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ax1.set_xlabel('Время, ЧЧ:MM:CC', fontsize=plot_info['font'])
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plt.tight_layout()
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fig.savefig(plot_info["name_fig"])
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plt.show()
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for i, elem in enumerate(data_info_list):
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get_step_response(data_info_list[i], thermocycle_info_list[i], cut_step_resp_list[i])
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