add python files (impulse_response.py, step_response.py, step_response_diff.py) for extract step and impulse response from experiments with KDI MVN

asotr.py

@@ -448,10 +448,8 @@ def cut_norm_data(data, time_begin, duration_sec, channel='ch1',
 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 = thermocycle_info['time_begin'][0]
-
+    time_begin_step = thermocycle_info['time_begin'][1]
-    time_begin_orig = date + ' ' + thermocycle_info['time_begin'][0]
-    time_begin_step = date + ' ' + thermocycle_info['time_begin'][1]
 
     duration_sec = thermocycle_info['duration_sec']
     
@@ -472,7 +470,7 @@ def get_step_response_diff(data, thermocycle_info, channel='ch1',
     step_response = pd.DataFrame({'timestamp': step_time, 'temp': step_temp})
 
     if len(cut_step_resp) > 0:
-        time_begin = date + ' ' + cut_step_resp['time_step_begin']
+        time_begin = cut_step_resp['time_step_begin']
         try:
             step_response = cut_data(step_response, time_begin, 
                 cut_step_resp['step_duration'], accuracy='seconds')
@@ -502,7 +500,7 @@ def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
     ax1.plot(step_resp['timestamp'], step_resp['temp'], 
         label='реакция на ступенчатое воздействие')
     
-    step_begin = thermocycle_info['date'] + ' ' + cut_step_resp['time_step_begin']
+    step_begin = cut_step_resp['time_step_begin']
     
     idx = find_best_time_idx(step_interp_cycle.timestamp, step_begin, 
             accuracy=data_info['find_accuracy'])
@@ -535,8 +533,6 @@ def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
     fig.savefig(plot_info["name_fig"])
     plt.show()
 
-
-
 def plot_imp_response(data, data_info, plot_info, thermocycle_info):
     title = f'{plot_info["title"]}, канал {data_info["channel"][2]} АСОТР КДИ СПИН-X, период опроса {data_info["period"]} ({thermocycle_info["date"]})'
 

data/.~lock.asotr01_data_T.csv#

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

data/.~lock.asotr01_min_T.csv#

@@ -0,0 +1 @@
+,danila,danila-IdeaPad,21.05.2025 11:32,file:///home/danila/.config/libreoffice/4;

impulse_response.py

@@ -0,0 +1,41 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import sys
+from importlib import reload
+sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
+import asotr
+reload(asotr)
+import matplotlib.pyplot as plt
+from matplotlib import dates
+import pandas as pd
+from datetime import datetime
+
+asotr_kit = 1
+fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv' 
+
+dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
+data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
+
+interp = {'method': 'polynomial', 'order': 2}
+thermocycle_info = {'date': '14.03.2025', 'type': 'impuse'}
+cut_step_resp = {'orig_time_step_begin': '14.03.2025 13:49:32', 'orig_step_duration': 20*60}
+data_info = {'data': data, 'channel': 'ch1', 'period': '1 сек', 'find_accuracy': 'seconds'}
+
+name = f'{thermocycle_info["type"]}_response_{thermocycle_info["date"].replace(".","")}'
+
+plot_info = {'title': 'Реакция на импульсное воздействие', 
+        'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper right', 'lower left'],
+        'name_fig': f'{name}.png', 
+        'font': 10}
+
+tstamp_orig_begin = cut_step_resp['orig_time_step_begin']
+
+_, interp_imp_resp = asotr.cut_norm_data(data_info['data'], tstamp_orig_begin, 
+        cut_step_resp['orig_step_duration'], channel='ch1', interp=interp, 
+        accuracy=data_info['find_accuracy'])
+
+
+interp_imp_resp.to_csv(f'./data/asotr0{asotr_kit}_{name}.csv', index=False, sep=';', 
+        encoding='utf-8-sig', decimal='.')
+
+asotr.plot_imp_response(interp_imp_resp, data_info, plot_info, thermocycle_info)

step_response.py

@@ -0,0 +1,67 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import sys
+from importlib import reload
+sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
+import asotr
+reload(asotr)
+import matplotlib.pyplot as plt
+from matplotlib import dates
+import pandas as pd
+from datetime import datetime
+
+asotr_kit = 1
+# fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
+fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
+
+dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
+data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
+
+# date = '20.03.2025'
+# period = '1 мин'
+# time_begin_orig = date + ' 17:10:11' 
+# time_begin1 = date + ' 18:10:17'
+# time_begin2 = date + ' 19:10:23'
+# step_begin = time_begin2
+# duration = 3600
+# accuracy = 'seconds'
+# name_fig = 'step_response_KDI_20242003.png'
+
+# date = '21.03.2025'
+# period = '1 мин'
+# time_begin_orig = date + ' 14:00:11' 
+# time_begin1 = date + ' 15:00:16'
+# time_begin2 = date + ' 16:00:16'
+# step_begin = time_begin2
+# duration = 3600
+# accuracy = 'seconds'
+# name_fig = 'step_response_KDI_20242103.png'
+
+# date = '24.03.2025'
+# period = '1 сек'
+# time_begin_orig = date + ' 19:45:11' 
+# time_begin1 = date + ' 20:45:13'
+# time_begin2 = date + ' 21:45:17'
+# step_begin = time_begin2
+# duration = 3600
+# accuracy = 'seconds'
+# name_fig = 'step_response_KDI_20242403.png'
+
+
+interp = {'method': 'polynomial', 'order': 2}
+thermocycle_info = {'date': '01.04.2025', 
+        'time_begin': ['01.04.2025 16:27:00', '01.04.2025 18:00:00'], 
+        'duration_sec': 92*60, 'type': 'step'}
+cut_step_resp = {'time_step_begin': '01.04.2025 18:53:21', 'step_duration': 25*60}
+data_info = {'data': data, 'device': 'KDI', 'channel': 'ch1', 'period': '1 мин', 
+        'find_accuracy': 'seconds'}
+name = f'{thermocycle_info["type"]}_response_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
+plot_info = {'title': 'Реакция на ступенчатое воздействие', 
+        'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
+        'name_fig': f'{name}.png', 'font': 10}
+
+asotr.plot_step_response_in_thermocycle(data_info, thermocycle_info, interp, 
+        cut_step_resp, plot_info)
+
+
+

step_response_diff.py

@@ -0,0 +1,111 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import sys
+from importlib import reload
+sys.path.append('/home/danila/Danila/work/MVN/Soft/PID/python/')
+import asotr
+reload(asotr)
+
+import matplotlib.pyplot as plt
+from matplotlib import dates
+import pandas as pd
+from datetime import datetime
+
+asotr_kit = 1
+fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv' 
+
+dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
+data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
+
+# date = '20.03.2025'
+# period = '1 мин'
+# time_begin_orig = date + ' 17:10:11' 
+# time_begin1 = date + ' 18:10:17'
+# time_begin2 = date + ' 19:10:23'
+# step_begin = time_begin2
+# duration = 3600
+# accuracy = 'seconds'
+# name_fig = 'step_response_KDI_20242003.png'
+
+# date = '21.03.2025'
+# period = '1 мин'
+# time_begin_orig = date + ' 14:00:11' 
+# time_begin1 = date + ' 15:00:16'
+# time_begin2 = date + ' 16:00:16'
+# step_begin = time_begin2
+# duration = 3600
+# accuracy = 'seconds'
+# name_fig = 'step_response_KDI_20242103.png'
+
+# date = '24.03.2025'
+# period = '1 сек'
+# time_begin_orig = date + ' 19:45:11' 
+# time_begin1 = date + ' 20:45:13'
+# time_begin2 = date + ' 21:45:17'
+# step_begin = time_begin2
+# duration = 3600
+# accuracy = 'seconds'
+# name_fig = 'step_response_KDI_20242403.png'
+
+
+# interp = {'method': 'polynomial', 'order': 2}
+# thermocycle_info = {'date': '24.03.2025', 'time_begin': ['20:45:00', '21:45:11'], 
+#         'duration_sec': 60*60, 'type': 'step'}
+# cut_step_resp = {'time_step_begin': '21:45:11', 'step_duration': 60*60,
+#         'orig_time_step_begin': '19:45:11', 'orig_step_duration': 25*60}
+# data_info = {'data': data, 'device': 'KDI', 'channel': 'ch1', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# name = f'{thermocycle_info["type"]}_response_diff_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
+# plot_info = {'title': 'Реакция на ступенчатое воздействие', 
+#         'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
+#         'name_fig': f'{name}.png', 'font': 10}
+
+
+interp = {'method': 'polynomial', 'order': 2}
+thermocycle_info = {'date': '01.04.2025', 
+        'time_begin': ['01.04.2025 16:27:00', '01.04.2025 18:00:00'], 
+        'duration_sec': 92*60, 'type': 'step'}
+cut_step_resp = {'time_step_begin': '01.04.2025 18:53:21', 'step_duration': 24*60,
+        'orig_time_step_begin': '01.04.2025 15:22:10', 'orig_step_duration': 24*60}
+data_info = {'data': data, 'channel': 'ch1', 'period': '1 мин', 
+        'find_accuracy': 'seconds'}
+plot_info = {'title': 'Реакция на ступенч. воздейств.', 
+        'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['lower right', 'lower left'],
+        'name_fig': 'step_response_diff_KDI_20240401.png', 'font': 10}
+
+step_resp_cut, _, _ = asotr.get_step_response_diff(data_info['data'], thermocycle_info, 
+        channel=data_info['channel'], interp=interp, accuracy=data_info['find_accuracy'], 
+        cut_step_resp=cut_step_resp)
+
+tstamp_orig_begin = cut_step_resp['orig_time_step_begin']
+
+_, interp_step_resp = asotr.cut_norm_data(data_info['data'], tstamp_orig_begin, 
+        cut_step_resp['orig_step_duration'], channel='ch1', interp=interp, 
+        accuracy=data_info['find_accuracy'])
+
+max_ = min(len(interp_step_resp), len(step_resp_cut))
+
+interp_step_resp.to_csv(f'./data/asotr0{asotr_kit}_{thermocycle_info["type"]}_{thermocycle_info["date"].replace(".","")}.csv', index=False, sep=';', encoding='utf-8-sig', decimal='.')
+
+title = f'{plot_info["title"]}, канал {data_info["channel"][2]} АСОТР КДИ СПИН-X1-МВН, период опроса {data_info["period"]} ({thermocycle_info["date"]})'
+
+fig = plt.figure(figsize=(6, 6), dpi=200)
+fig.suptitle(title, fontsize=plot_info['font'])
+ax1 = fig.add_subplot(1,1,1)
+
+ax1.plot(step_resp_cut['timestamp'].iloc[0:max_], step_resp_cut['temp'].iloc[0:max_], '--',
+        label='реакция на ступенчатое воздействие с термоциклом')
+ax1.plot(step_resp_cut['timestamp'].iloc[0:max_], interp_step_resp['temp'].iloc[0:max_],
+        label='реакция на ступенчатое воздействие')
+
+date_formatter = dates.DateFormatter(plot_info['ox_dtime_format'])
+ax1.xaxis.set_major_formatter(date_formatter)
+ax1.legend(loc=plot_info["legend_pos"][0], fontsize=plot_info['font'])
+ax1.grid(True)
+ax1.tick_params(axis='both', width=1, labelsize=plot_info['font'])
+ax1.set_ylabel(r'$T_{norm}$, $^\circ$C', fontsize=plot_info['font'])
+ax1.set_xlabel('Время', fontsize=plot_info['font'])
+
+plt.tight_layout()
+fig.savefig(plot_info["name_fig"])
+plt.show()