42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
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('/home/danila/Danila/work/MVN/Soft/PID/python/')
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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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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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interp = {'method': 'polynomial', 'order': 2}
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thermocycle_info = {'date': '14.03.2025', 'type': 'impuse'}
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cut_step_resp = {'orig_time_step_begin': '14.03.2025 13:49:32', 'orig_step_duration': 20*60}
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data_info = {'data': data, 'channel': 'ch1', 'period': '1 сек', 'find_accuracy': 'seconds'}
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name = f'{thermocycle_info["type"]}_response_{thermocycle_info["date"].replace(".","")}'
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plot_info = {'title': 'Реакция на импульсное воздействие',
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'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper right', 'lower left'],
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'name_fig': f'{name}.png',
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'font': 10}
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tstamp_orig_begin = cut_step_resp['orig_time_step_begin']
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_, interp_imp_resp = asotr.cut_norm_data(data_info['data'], tstamp_orig_begin,
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cut_step_resp['orig_step_duration'], channel='ch1', interp=interp,
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accuracy=data_info['find_accuracy'])
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interp_imp_resp.to_csv(f'./data/asotr0{asotr_kit}_{name}.csv', index=False, sep=';',
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encoding='utf-8-sig', decimal='.')
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asotr.plot_imp_response(interp_imp_resp, data_info, plot_info, thermocycle_info)
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