plot_flight_borders: add flags for flexibility. tm_wheel_parser.py: plot data distribution

.gitignore

@@ -1,4 +1,5 @@
 *.csv
+*.xml
 *.png
 *.swp
 *.swo

README.markdown

@@ -28,6 +28,8 @@ This project contains tools for decoding, converting, and visualizing telemetry
 -> `/plots/` — folder where output plots are saved.    
 
 ## Requirements
+- git
+
 - Python version 3.10 or upper. For checking type:
 ```bash
 python3 --version

bin/asotr.py

@@ -445,7 +445,7 @@ def cut_norm_data(data, time_begin, duration_sec, channel='ch1',
     
     orig_data = pd.DataFrame({ 'timestamp': time_l, 'temp': temp_l })
     interp_data = orig_data.set_index('timestamp')
-    interp_data = interp_data.resample('S').mean().interpolate(method=interp["method"], 
+    interp_data = interp_data.resample('s').mean().interpolate(method=interp["method"], 
             order=interp["order"])
     interp_data = interp_data.reset_index(names=['timestamp'])
 
@@ -498,7 +498,7 @@ def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
             data_info['data'], thermocycle_info, channel=data_info['channel'],
             interp=interp, accuracy=data_info['find_accuracy'])
 
-    fig = plt.figure(figsize=(9, 6), dpi=200)
+    fig = plt.figure(figsize=(8, 7), dpi=200)
     fig.suptitle(title, fontsize=plot_info['font'])
     ax1 = fig.add_subplot(2,1,1)
     ax2 = fig.add_subplot(2,1,2)
@@ -510,6 +510,10 @@ def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
     
     idx = find_best_time_idx(step_interp_cycle.timestamp, step_begin, 
             accuracy=data_info['find_accuracy'])
+    if idx == -1:
+        print(f'index corresponding to time {step_begin} in times array not found!')
+        return
+
     ax1.axvline(x = step_interp_cycle.timestamp[idx], color='r', linestyle='-.', 
             label= thermocycle_info['type_ru'] + ' воздействие, начало')
     
@@ -531,8 +535,8 @@ def plot_step_response_in_thermocycle(data_info, thermocycle_info, interp,
             fancybox=True, framealpha=0.4)
     ax2.grid(True)
     ax2.tick_params(axis='both', width=1, labelsize=plot_info['font'])
-    ax2.set_xlabel('Время, ЧЧ:MM:CC', fontsize=plot_info['font']) 
+    ax2.set_xlabel('Время, ЧЧ:MM', fontsize=plot_info['font']) 
-    ax2.set_ylabel(r'$T_{norm}$, $^\circ$C', fontsize=plot_info['font'])
+    ax2.set_ylabel(r'$T_{норм}$, $^\circ$C', fontsize=plot_info['font'])
     
     fig.suptitle(title, fontsize=plot_info['font'])
     plt.tight_layout()
@@ -555,7 +559,7 @@ def plot_imp_response(data, data_info, plot_info, thermocycle_info):
     ax1.grid(True)
     ax1.tick_params(axis='both', width=1, labelsize=plot_info['font'])
     ax1.set_xlabel('время', fontsize=plot_info['font']) 
-    ax1.set_ylabel(r'$t_{norm}$, $^\circ$C', fontsize=plot_info['font'])
+    ax1.set_ylabel(r'$t_{норм}$, $^\circ$C', fontsize=plot_info['font'])
     
     fig.suptitle(title, fontsize=plot_info['font'])
     plt.tight_layout()

bin/asotr_kdi.py

@@ -0,0 +1,207 @@
+import sys
+from importlib import reload
+sys.path.append('./')
+import asotr
+reload(asotr)
+import matplotlib.pyplot as plt
+from matplotlib import dates
+import pandas as pd
+from datetime import datetime, timedelta
+import os
+import json
+
+def cmd_parse(asotr_data):
+    decode_list = []
+
+    for elem in asotr_data.itertuples():
+        elem_msg = asotr.cmd_decode(elem.cmd.strip())
+        if elem_msg != '':
+            str_ = f'{elem.timestamp};{elem_msg}'
+            decode_list.append(str_)
+
+    return decode_list
+
+def pow_temp_parse(elem, tstamp_format='%d.%m.%Y %H:%M:%S.%f'):
+    timestamp = pd.to_datetime(elem.timestamp, format=tstamp_format)
+    if "*1" not in elem.data:
+        return ('none', [])
+    l = []
+
+    data = elem.data[2:]
+    l = []
+    l.append(asotr.get_utc_seconds(elem.timestamp, tstamp_format))
+    l.append(timestamp)
+
+    if "status 3" in elem.cmd:
+        l.extend(list(map(float, data.split())))
+        return ('t', l)
+    elif "status 4" in elem.cmd:
+        l.extend(list(map(int, data.split())))
+        return ('p', l)
+    else:
+        return ('none', [])
+
+def get_cmd_data(fname, tstamp_format='%d.%m.%Y %H:%M:%S.%f'):
+    t = []
+    p = []
+    asotr_data = pd.read_csv(fname, delimiter=';', names=['timestamp', 'cmd', 'data'])
+
+    cmd_list = cmd_parse(asotr_data)
+
+    for elem in asotr_data.itertuples():
+        data = pow_temp_parse(elem, tstamp_format)
+        if data[0] == 't':
+            t.append(data[1])
+        elif data[0] == 'p':
+            p.append(data[1])
+    temp = pd.DataFrame(t, 
+            columns=['timestamp_sec', 'timestamp', 'ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6'])
+    power = pd.DataFrame(p, 
+            columns=['timestamp_sec', 'timestamp', 'ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6'])
+    return (cmd_list, temp, power)
+
+
+def decode_collect_data(uart_server_log_path, asotr_kit): 
+    files = os.listdir(uart_server_log_path)
+    files1 = [file for file in files if 'cmd_data' in file]
+    files1 = sorted(files1)
+    
+    cmd_list_itog = []
+    temp_itog = []
+    pow_itog = []
+    
+    cmd_list, temp, power = get_cmd_data(uart_server_log_path + files1[0])
+    cmd_list_itog.extend(cmd_list)
+    temp_itog = temp
+    pow_itog = power
+    print(files1[0])
+    
+    for i, file in enumerate(files1):
+        if i > 0:
+            print(file)
+            cmd_list, temp, power = get_cmd_data(uart_server_log_path + file)
+            cmd_list_itog.extend(cmd_list)
+            temp_itog = pd.concat([temp_itog, temp], ignore_index=True)
+            pow_itog = pd.concat([pow_itog, power], ignore_index=True)
+    
+    temp_itog = temp_itog.sort_values(by='timestamp_sec')
+    pow_itog = pow_itog.sort_values(by='timestamp_sec')
+    
+    temp_itog['timestamp'] = temp_itog['timestamp'].dt.strftime('%d.%m.%Y %H:%M:%S.%f') 
+    pow_itog['timestamp'] = pow_itog['timestamp'].dt.strftime('%d.%m.%Y %H:%M:%S.%f') 
+    
+    temp_itog.to_csv(f'./data/asotr0{asotr_kit}_data_T.csv', index=False, sep=';',
+            encoding='utf-8-sig', decimal='.') 
+    pow_itog.to_csv(f'./data/asotr0{asotr_kit}_data_P.csv', index=False, sep=';',
+            encoding='utf-8-sig', decimal='.') 
+
+    with open('./data/cmd_human.csv', 'w') as file:
+        for elem in cmd_list_itog:
+            file.write(f'{elem}\n')
+
+
+def load_ps_cmd_decode(fname):
+    with open(fname, 'r') as file:
+        ps_cmd_decode = json.load(file)
+    return ps_cmd_decode
+
+def get_ps_cmd_data(fname, ps_model):
+    decode = load_ps_cmd_decode('./decode_ps_cmd.json')
+    ps_data = pd.read_csv(fname, sep=';', names=['timestamp', 'cmd', 'cmd_answer'])
+    out = {'cmd_write': [], 'cmd_read': [], 'cmd_read_desc': []}
+
+    if ps_model == 'PSH-3630A':
+        for elem in decode:
+            if elem['ps_model'] == ps_model:
+                decode_cmd_write = elem['cmd_write']
+                decode_cmd_read = elem['cmd_read']
+                decode_cmd_read_desc = elem['cmd_read_desc']
+                break 
+        
+        cmd_write = []
+        cmd_read_desc = []
+        cmd_read = []
+        for row in ps_data.itertuples():
+            # check if current row is cmd_write:
+            cmd_str = row.cmd.split(' ')
+
+            timestamp_sec = asotr.get_utc_seconds(row.timestamp, '%d.%m.%Y %H:%M:%S.%f')
+            if (len(cmd_str) > 1):
+                elem = [timestamp_sec, row.timestamp,
+                        decode_cmd_write[cmd_str[0]], cmd_str[1]]
+                cmd_write.append(elem)
+            elif (len(cmd_str) == 1):
+                elem = [timestamp_sec, row.timestamp, 
+                        decode_cmd_read[cmd_str[0]], row.cmd_answer.strip()]
+                cmd_read.append(elem)
+                elem = [timestamp_sec, row.timestamp, 
+                        decode_cmd_read_desc[cmd_str[0]], row.cmd_answer.strip()]
+                cmd_read_desc.append(elem)
+
+        out['cmd_write'] = cmd_write
+        out['cmd_read'] = cmd_read 
+        out['cmd_read_desc'] = cmd_read_desc 
+
+    return out
+
+def convert_data_date_format(directory, fname_pattern, column_names,
+        old_date_fmt = "%Y.%m.%d %H:%M:%S.%f",
+        new_date_fmt = '%d.%m.%Y %H:%M:%S.%f'):
+
+    files = os.listdir(directory)
+    dateparse = lambda x: datetime.strptime(x, old_date_fmt)
+
+    for file in files:
+        fname = directory + file
+        if 'cmd_data' in fname:
+            print(fname)
+            df = pd.read_csv(fname, sep=';', decimal='.', 
+                    names=column_names, 
+                    parse_dates=['timestamp'], date_parser=dateparse)
+    
+            df['timestamp'] = df['timestamp'].dt.strftime(new_date_fmt) 
+    
+            df.to_csv(fname, header=False, index=False, sep=';',
+                    decimal='.', encoding='utf-8-sig')
+ 
+
+def plot_data(fname, title, ox_dtime_format, events={}, hlines={}, ch='ch1'):
+    cmd_list, temp, power = get_cmd_data(fname)
+
+    fig = plt.figure(figsize=(10, 8), dpi=150)
+    ax1 = fig.add_subplot(2,1,1)
+    ax2 = fig.add_subplot(2,1,2, sharex=ax1)
+
+    ax1.plot(temp.timestamp, temp[ch])
+
+    if len(hlines) > 0:
+        for label, value in hlines.items():
+            ax1.axhline(y = value, color='b', linestyle='--')
+            ax1.text(temp.timestamp[len(temp.timestamp) - 1], value, label, va='bottom')
+
+    if len(events) > 0:
+        for time, event in events.items():
+            idx = asotr.find_best_time_idx(temp.timestamp, time, accuracy='seconds')
+            ax1.axvline(x = temp.timestamp[idx], color='r', linestyle='-.')
+            ax1.text(temp.timestamp[idx], max(temp[ch]) + 1, event, rotation=45, va='bottom')
+    
+    ax2.plot(power.timestamp, power[ch])
+
+    date_formatter = dates.DateFormatter(ox_dtime_format)
+    
+    ax1.xaxis.set_major_formatter(date_formatter)
+    ax1.set_xlabel('время')
+    ax1.set_ylabel('Температура, $^\circ$C')
+    ax1.grid(True)
+
+    ax2.xaxis.set_major_formatter(date_formatter)
+    ax2.set_ylabel('Мощность, %')
+    ax2.grid(True)
+    
+    fig.suptitle(title)
+    plt.legend()
+    plt.tight_layout()
+    fig.savefig('plot_experim_data.png')
+    plt.show()
+
+

bin/kdi_noize.py

@@ -0,0 +1,32 @@
+import sys
+from importlib import reload
+sys.path.append('./')
+import asotr_kdi
+import asotr
+reload(asotr_kdi)
+reload(asotr)
+
+fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250314_asotr.log'
+
+# fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250324_asotr_step_var_cond.log'
+
+# fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250325_asotrPD_thermostat.log'
+
+# fname_save = '../data/experiments/noize_20250325_12.csv'
+# begin_time = '25.03.2025 11:55:00'
+# end_time = '25.03.2025 12:10:00'
+
+fname_save = '../data/experiments/noize_20250314_14.csv'
+begin_time = '14.03.2025 14:20:00'
+end_time = '14.03.2025 14:35:00'
+
+cmd_list, temp, power = asotr_kdi.get_cmd_data(fname)
+
+begin = asotr.find_best_time_idx(temp.timestamp, begin_time, accuracy='seconds')
+end = asotr.find_best_time_idx(temp.timestamp, end_time, accuracy='seconds')
+
+temp1 = temp[begin:end]
+
+print(temp1)
+temp1.to_csv(fname_save, date_format='%d.%m.%Y %H:%M:%S.%f', index=False, sep=';', columns=['timestamp', 'ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6'])
+

bin/plot_asotr_flight_all_for_Mikhail.py

@@ -0,0 +1,180 @@
+import matplotlib.pyplot as plt
+from  matplotlib import dates
+import pandas as pd
+from datetime import datetime
+import sys
+
+font = 14
+print_width = 15
+print_height = 8
+plot_windows = 2
+channels = [1, 0, 0, 0, 0, 0]
+asotr_kit = '01'
+
+xborders=False
+begin=0;
+end=0;
+
+path = '../data/asotr/'
+fname_B = f'{path}beta_2025.xlsx'
+
+fname = 'asotr' + asotr_kit + '_data_T.csv'
+fname_pow = 'asotr' + asotr_kit + '_data_P.csv'
+
+pict_name = '../plots/' + 'ASOTR' + asotr_kit + '_flight_T_P_all' 
+ox_dtime_format = '%Y.%m.%d'
+
+legend=['БРД1', 'БРД2', 'БРД3', 'БРД4', 'плита МУП МВН, датчик1', 'плита МУП МВН, датчик 2']
+width=[1, 2, 1, 1, 1, 1]
+
+marker = ['-', '--', '-', '-', '-.', '-'];
+width_arr = [1, 0.5, 0.2, 0.1, 1, 1]
+
+data_b = pd.read_excel(fname_B, 
+        sheet_name=0,
+        usecols=[0,1,2], 
+        header=4,
+        names=['turn_num', 'beta_angle', 'timestamp'],
+        parse_dates=['timestamp'],
+        date_format='%Y-%m-%d %H:%M:%S')
+
+
+fname = [path + fname, path + fname_pow]
+data = [pd.read_csv(fname[0], sep=';', parse_dates=['timestamp'], date_format="%d.%m.%Y %H:%M:%S.%f"),
+        pd.read_csv(fname[1], sep=';', parse_dates=['timestamp'], date_format="%d.%m.%Y %H:%M:%S.%f")]
+
+ch= [[], [], [], [], [], []]
+ch_signs = ["temp", "pow"]
+data_dict = {"temp": ch, "pow": ch, "time": []}
+data_dict["time"] = data[0]['timestamp']
+col=['ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6', 'ch7']
+
+for j in range(2):
+    for index, row, in data[j].iterrows():
+        for i in range(6):
+            ch[i].append(float(row[col[i]]))
+    data_dict[ch_signs[j]] = ch
+    ch= [[], [], [], [], [], []]
+
+len_data = [len(data_dict['temp'][0]), len(data_dict['pow'][0])]
+len_ = min(len_data)
+
+if xborders == False:
+    begin = 0
+    end = len_ - 1
+
+
+
+if plot_windows == 1:
+    fig, ax = plt.subplots(figsize=(print_width, print_height), dpi=300)
+
+    i = 0
+    for elem in data_dict['temp']:
+        if channels[i] == 1:
+            ax.plot(data_dict['time'][begin:end], elem[begin:end], marker[i], linewidth=width[i], label=legend[i])
+        i += 1
+
+    ax.tick_params(axis="both", width=1, labelsize=font)
+    ax.grid(visible=True, linestyle = 'dotted')
+    ax.set_ylabel(r"Температура, $^\circ$C", fontsize=font)
+    ax.set_xlabel('Время', fontsize=font)
+    ax.legend(fontsize=font)
+
+    date_formatter = dates.DateFormatter(ox_dtime_format)
+    ax.xaxis.set_major_formatter(date_formatter)
+
+    plt.tight_layout()
+    fig.savefig(pict_name)
+    plt.show()
+
+elif plot_windows == 2:
+
+    fig = plt.figure(figsize=(print_width, print_height), dpi=600)
+    ax1 = fig.add_subplot(1, 1, 1)
+    # ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
+
+    i = 0
+    for elem in data_dict['temp']:
+        if channels[i] == 1:
+            ax1.plot(data_dict['time'][begin:end], elem[begin:end], marker[i], linewidth=width[i], label=legend[i])
+        i += 1
+   
+    ax3 = ax1.twinx()
+    ax3.plot(data_b['timestamp'][0:3400], data_b['beta_angle'][0:3400], marker[1], color='r', linewidth=width[1], label='угол Бета')
+    ax3.set_ylabel('Угол Бета', fontsize=font)
+    ax3.tick_params(axis="y", width=1, labelsize=font)
+    ax3.legend(fontsize=font, loc='upper right')
+
+    # i = 0
+    # for elem in data_dict['pow']:
+    #     if channels[i] == 1:
+    #         ax2.plot(data_dict['time'][begin:end], elem[begin:end], marker[i], linewidth=width[i], label=legend[i])
+    #     i += 1
+
+    ax1.tick_params(axis="both", width=1, labelsize=font)
+    ax1.grid(visible=True, linestyle = 'dotted')
+    ax1.set_ylabel(r"Температура, $^\circ$C", fontsize=font)
+    ax1.set_xlabel('Время', fontsize=font)
+    ax1.legend(fontsize=font, loc='lower right')
+
+    date_formatter = dates.DateFormatter(ox_dtime_format)
+    ax1.xaxis.set_major_formatter(date_formatter)
+
+    # ax2.tick_params(axis="both", width=1, labelsize=font)
+    # ax2.grid(visible=True, linestyle = 'dotted')
+    # ax2.set_ylabel('Мощность, %', fontsize=font)
+    # ax2.set_xlabel('Время', fontsize=font)
+    # ax2.legend(fontsize=font, loc='lower right')
+
+    # date_formatter = dates.DateFormatter(ox_dtime_format)
+    # ax2.xaxis.set_major_formatter(date_formatter)
+
+    plt.title('АСОТР ' + asotr_kit, fontsize=font)
+    plt.tight_layout()
+    fig.savefig(pict_name)
+    plt.show()
+
+
+
+
+
+# asotr_kit2 = '02'
+# fname2 = 'asotr' + asotr_kit2 + '_data_T.csv'
+# fname_pow2 = 'asotr' + asotr_kit2 + '_data_P.csv'
+# legend2=['2 БРД1', '2 БРД2', '2 БРД3', '2 БРД4', '2 плита МУП МВН, датчик1', '2 плита МУП МВН, датчик 2']
+
+# fname2 = [path + fname2, path + fname_pow2]
+# data2 = [pd.read_csv(fname2[0], sep=';', parse_dates=['timestamp'], date_parser=dateparse),
+#         pd.read_csv(fname2[1], sep=';', parse_dates=['timestamp'], date_parser=dateparse)]
+
+# ch= [[], [], [], [], [], []]
+# ch_signs = ["temp", "pow"]
+# data_dict2 = {"temp": ch, "pow": ch, "time": []}
+# data_dict2["time"] = data2[0]['timestamp']
+# col=['ch1', 'ch2', 'ch3', 'ch4', 'ch5', 'ch6', 'ch7']
+
+# for j in range(2):
+#     for index, row, in data2[j].iterrows():
+#         for i in range(6):
+#             ch[i].append(float(row[col[i]]))
+#     data_dict2[ch_signs[j]] = ch
+#     ch= [[], [], [], [], [], []]
+
+# len_data2 = [len(data_dict2['temp'][0]), len(data_dict2['pow'][0])]
+# len_2 = min(len_data2)
+
+# if xborders == False:
+#     begin2 = 0
+#     end2 = len_2 - 1
+
+
+#     i = 0
+#     for elem in data_dict2['temp']:
+#         if channels[i] == 1:
+#             print('legend2: ' + legend2[i])
+#             ax1.plot(data_dict2['time'][begin2:end2], elem[begin2:end2], marker[i], linewidth=width[i], label=legend2[i])
+#         i += 1
+    
+    # ax2.plot(pd.Series(data_dict2['temp'][0]) - pd.Series(data_dict['temp'][0]))
+ 
+

bin/plot_data.py

@@ -0,0 +1,115 @@
+import sys
+from importlib import reload
+sys.path.append('./')
+import asotr_kdi
+reload(asotr_kdi)
+import matplotlib.pyplot as plt
+import pandas as pd
+import re
+
+ox_dtime_format = "%H:%M:%S"
+ch = 'ch4'
+# fname = '~/Danila/work/MVN/Soft/PID/rust/uart_server/log/cmd_data_20250311.log'
+# fname = '~/Danila/work/MVN/Soft/PID/rust/uart_server/log/cmd_data_20250311.log'
+
+# events = []
+# hlines = []
+# date = '17.03.2025'
+# fname = '~/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250317_asotrD_thermostat.log'
+# title = f'Эксперименты по настройке ПД-регулятора на канале {ch[2]} АСОТР КДИ СПИН-X ({date})'
+
+# hlines = {
+#         'Туст. = 27': 27,
+#         'Туст. = 29': 29,
+#         'Туст. = 31': 31,
+#         }
+
+# events = {
+#         '17.03.2025 12:50:14': 'Kp=0, Kd=2000, Ki=0, Туст.=29',
+#         '17.03.2025 13:20:18': 'запрет работы ПИД',
+#         '17.03.2025 14:15:14': 'Kp=0, Kd=2000, Ki=0, Туст.=31',
+#         '17.03.2025 14:45:18': 'запрет работы ПИД',
+#         '17.03.2025 15:41:14': 'Kp=50, Kd=2000, Ki=0, Туст.=31',
+#         '17.03.2025 16:11:17': 'запрет работы ПИД',
+#         '17.03.2025 17:05:13': 'Kp=50, Kd=2000, Ki=0, Туст.=27',
+#         '17.03.2025 17:50:17': 'запрет работы ПИД',
+#         '17.03.2025 19:30:10': 'Kp=100, Kd=2000, Ki=0, Туст.=27',
+#         '17.03.2025 20:15:13': 'запрет работы ПИД',
+#         }
+
+# date = '16.04.2025'
+# fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_20250416_asotrPID_thermostat.log'
+# title = f'Проверка работы ПИД-регулятора (Kp=615, Ki=0.000115, Kd=1300), канал {ch[2]} АСОТР КДИ СПИН-X ({date})'
+
+# hlines = {
+#         '27': 27,
+#         '27.5': 27.5,
+#         '28': 28,
+#         '29': 29
+#         }
+
+# events = {
+#         '16.04.2025 15:45:11': 'Туст=27',
+#         '16.04.2025 16:15:15': 'Туст=27.5',
+#         '16.04.2025 16:45:18': 'Туст=28',
+#         '16.04.2025 17:15:22': 'Туст=29',
+#         }
+
+
+events = []
+hlines = []
+
+date = '14.03.2025'
+title = f'Эксперименты с АСОТР КДИ СПИН-X ({date})'
+fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250314_asotr.log'
+
+
+# date = '20.03.2025'
+# title = f'Эксперименты с АСОТР КДИ СПИН-X ({date})'
+# fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250320_asotr_step_var_cond.log'
+
+# date = '21.03.2025'
+# title = f'Эксперименты с АСОТР КДИ СПИН-X ({date})'
+# fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250321_asotr_step_var_cond.log'
+
+# date = '24.03.2025'
+# title = f'Эксперименты с АСОТР КДИ СПИН-X ({date})'
+# fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250324_asotr_step_var_cond.log'
+
+# date = '01.04.2025'
+# title = f'Эксперименты с АСОТР КДИ СПИН-X ({date})'
+# fname = '/home/danila/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250401_asotr_step_var_cond.log'
+# title = f'эксперимент по подаче ступенчатого воздействия на канал {ch[2]} АСОТР КДИ СПИН-X в различных условиях ({date})'
+
+# hlines = {
+#         'Туст. = 27': 27,
+#         'Туст. = 26': 26
+#         }
+
+# events = {
+#         '25.03.2025 11:25:00': 'Kp=200, Kd=2000, Ki=0, Туст.=27',
+#         '25.03.2025 12:10:14': 'запрет работы ПИД',
+#         '25.03.2025 12:30:00': 'Kp=200, Kd=2000, Ki=0, Туст.=26',
+#         '25.03.2025 13:15:14': 'запрет работы ПИД',
+#         }
+
+# date = '25.03.2025'
+# title = f'Эксперименты по настройке ПД-регулятора на канале {ch[2]} АСОТР КДИ СПИН-X ({date})'
+# fname = '~/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250325_asotrPD_thermostat.log'
+
+# hlines = {
+#         'Туст. = 26': 26
+#         }
+
+# events = {
+#         # '26.03.2025 16:15:11': 'Kp=200, Kd=2000, Ki=0, Туст.=26',
+#         # '26.03.2025 18:45:30': 'запрет работы ПИД',
+#         }
+
+# date = '26.03.2025'
+# title = f'Эксперименты по настройке ПД-регулятора на канале {ch[2]} АСОТР КДИ СПИН-X ({date})'
+# fname = '~/Danila/work/MVN/Soft/PID/rust/uart_server/log_experim/cmd_data_asotr/asotr01/cmd_data_20250326_asotrPD_thermostat.log'
+
+asotr_kdi.plot_data(fname, title, ox_dtime_format, events, hlines, ch)
+
+

bin/plot_flight_borders.py

@@ -8,17 +8,17 @@ import asotr
 reload(asotr)
 import pandas as pd
 
-def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=True):
+def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=12, cmd=0, 
+        show_flag=False, plot_windows=2, width_=1, time_format='%d.%m.%Y'):
     print_width = 20
     print_height = 12
-    width = 1
-    plot_windows = 2
 
     channels = list(map(int, ch))
     pict_name = (f'../plots/reports/ASOTR{asotr_kit}_flight_T_P_{asotr.convert_to_str(channels)}_{begin[0:5].replace(".", "")}_{end[0:5].replace(".", "")}.png')
 
     plot_task = {"temp": 1, "temp_set": 1, "pow": 1}
-    ox_dtime_format = "%d.%m.%Y"
+    # ox_dtime_format = "%d.%m.%Y"
+    ox_dtime_format = time_format
 
     legend = [
         "канал 1 (БРД1)",
@@ -29,10 +29,10 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
         "канал 6 (плита МУП МВН)",
     ]
     legend_set = list(map(lambda x: x + " уставка", legend))
-    width = [1, 1, 1, 1, 1, 1]
+    width = [width_, width_, width_, width_, width_, width_]
     width_set = [3, 3, 3, 3, 3, 3]
 
-    marker = ["-", "--", "-.", "-", "-", "--"]
+    marker = ["-", "-", "-.", "-", "-", "--"]
     width_arr = [1, 0.5, 0.2, 0.1, 1, 1]
 
     # get from files and prepare data
@@ -44,7 +44,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
         return
     
     if plot_windows == 1:
-        fig, ax = plt.subplots(figsize=(print_width, print_height), dpi=200)
+        fig, ax = plt.subplots(figsize=(print_width, print_height), dpi=300)
 
         if plot_task["temp"] == 1:
             for i in range(len(channels)):
@@ -72,7 +72,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
 
     elif plot_windows == 2:
 
-        fig = plt.figure(figsize=(print_width, print_height), dpi=200)
+        fig = plt.figure(figsize=(print_width, print_height), dpi=300)
         ax1 = fig.add_subplot(2, 1, 1)
         ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
 
@@ -173,10 +173,20 @@ if __name__ == '__main__':
     argparser.add_argument('-f', '--font', required=False, 
             help='type font size (from 1 to 30)')
     argparser.add_argument('-d', '--cmd', required=False, 
-            help='type display commands flag (0/1)')
+            help='type display commands flag (0 or 1)')
     argparser.add_argument('-p', '--plot', required=False, 
-            help='display data in plot flag (0/1)')
+            help='display data in plot flag (0 or 1)')
+    argparser.add_argument('-w', '--plotwindows', required=False,
+            help='plot counts in window (1 or 2)')
+    argparser.add_argument('-l', '--linewidth', required=False,
+            help='line width in plot (1, 2, ...)')
+    argparser.add_argument('-t', '--timeformat', required=False,
+            help='specify the time format, for example: %d.%m.%Y')
+    
     args = argparser.parse_args()
     
-    plot_asotr_borders(args.source, args.channel, args.asotr, args.begin, args.end, 
+    # plot_asotr_borders(args.source, args.channel, args.asotr, args.begin, args.end, 
-            args.font, args.cmd, show_flag=args.plot)
+    #         args.font, args.cmd, show_flag=args.plot, plot_windows=int(args.plotwindows),
+    #         width_=int(args.linewidth), time_format = args.timeformat)
+    
+    plot_asotr_borders(args.source, args.channel, args.asotr, args.begin, args.end)

bin/step_response.py

@@ -11,11 +11,8 @@ import pandas as pd
 from datetime import datetime
 
 asotr_kit = 1
-# fname = f'../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
 fname = f'../data/asotr/asotr0{asotr_kit}_data_T.csv'
-
+data = pd.read_csv(fname, sep=';', parse_dates=["timestamp"], date_format="%d.%m.%Y %H:%M:%S.%f")
-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'
@@ -48,7 +45,6 @@ data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=datepa
 # name_fig = 'step_response_KDI_20242403.png'
 
 
-
 # interp = {'method': 'polynomial', 'order': 1}
 # thermocycle_info = {'date': '01.04.2025', 
 #         'time_begin': ['01.04.2025 16:27:00', '01.04.2025 18:00:00'], 
@@ -62,6 +58,8 @@ data = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=datepa
 #         'name_fig': f'{name}.png', 'font': 10}
 
 
+fname = f'../../python_cyclo/data/asotr0{asotr_kit}_data_T.csv'
+data = pd.read_csv(fname, sep=';', parse_dates=["timestamp"], date_format="%d.%m.%Y %H:%M:%S.%f")
 
 interp = {'method': 'polynomial', 'order': 1}
 
@@ -69,46 +67,12 @@ data_info_list = []
 thermocycle_info_list = []
 cut_step_resp_list = []
 
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
+data_info = {'data': data, 'device': 'КДИ', 'channel': 'ch1', 'period': '1 мин', 
         'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '25.04.2025', 
+thermocycle_info = {'date': '01.04.2025', 
-        'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'], 
+        'time_begin': ['01.04.2025 16:27:13', '01.04.2025 18:00:18'], 
         'duration_sec': 92*60, 'type': 'step', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
+cut_step_resp = {'time_step_begin': '01.04.2025 18:53:20', 'step_duration': 25*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '25.04.2025', 
-        'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'], 
-        'duration_sec': 92*60, 'type': 'step1_to2', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '25.04.2025', 
-        'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'], 
-        'duration_sec': 92*60, 'type': 'impulse', 'type_ru': 'импульсное'}
-cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 15*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '25.04.2025', 
-        'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'], 
-        'duration_sec': 92*60, 'type': 'impulse1_to2', 'type_ru': 'импульсное'}
-cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 20*60}
 
 data_info_list.append(data_info)
 thermocycle_info_list.append(thermocycle_info)
@@ -116,144 +80,199 @@ cut_step_resp_list.append(cut_step_resp)
 
 
 
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '30.04.2025', 
-        'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'], 
-        'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '30.04.2025', 
-        'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'], 
-        'duration_sec': 93*60, 'type': 'step2_to1', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '30.04.2025', 
-        'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'], 
-        'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
-cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 15*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '30.04.2025', 
-        'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'], 
-        'duration_sec': 93*60, 'type': 'impulse2_to1', 'type_ru': 'импульсное'}
-cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 20*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '02.05.2025', 
-        'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'], 
-        'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '02.05.2025', 
-        'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'], 
-        'duration_sec': 93*60, 'type': 'step4_to3', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '02.05.2025', 
-        'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'], 
-        'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
-cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 15*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
-
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '02.05.2025', 
-        'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'], 
-        'duration_sec': 93*60, 'type': 'impulse4_to3', 'type_ru': 'импульсное'}
-cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 20*60}
-
-data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
 
 
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
+# interp = {'method': 'polynomial', 'order': 1}
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '04.05.2025', 
-        'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'], 
-        'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 26*60}
 
-data_info_list.append(data_info)
+# data_info_list = []
-thermocycle_info_list.append(thermocycle_info)
+# thermocycle_info_list = []
-cut_step_resp_list.append(cut_step_resp)
+# cut_step_resp_list = []
 
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
+#         'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '04.05.2025', 
+# thermocycle_info = {'date': '25.04.2025', 
-        'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'], 
+#         'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'], 
-        'duration_sec': 93*60, 'type': 'step3_to4', 'type_ru': 'ступенчатое'}
+#         'duration_sec': 92*60, 'type': 'step', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 30*60}
+# cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
 
-data_info_list.append(data_info)
+# data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
+# thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
+# cut_step_resp_list.append(cut_step_resp)
 
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
+#         'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '04.05.2025', 
+# thermocycle_info = {'date': '25.04.2025', 
-        'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'], 
+#         'time_begin': ['24.04.2025 22:46:32', '25.04.2025 00:19:33'], 
-        'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
+#         'duration_sec': 92*60, 'type': 'step1_to2', 'type_ru': 'ступенчатое'}
-cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 15*60}
+# cut_step_resp = {'time_step_begin': '25.04.2025 01:18:01', 'step_duration': 30*60}
 
-data_info_list.append(data_info)
+# data_info_list.append(data_info)
-thermocycle_info_list.append(thermocycle_info)
+# thermocycle_info_list.append(thermocycle_info)
-cut_step_resp_list.append(cut_step_resp)
+# cut_step_resp_list.append(cut_step_resp)
 
-data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
-        'find_accuracy': 'seconds'}
-thermocycle_info = {'date': '04.05.2025', 
-        'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'], 
-        'duration_sec': 93*60, 'type': 'impulse3_to4', 'type_ru': 'импульсное'}
-cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 20*60}
 
-data_info_list.append(data_info)
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
-thermocycle_info_list.append(thermocycle_info)
+#         'find_accuracy': 'seconds'}
-cut_step_resp_list.append(cut_step_resp)
+# thermocycle_info = {'date': '25.04.2025', 
+#         'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'], 
+#         'duration_sec': 92*60, 'type': 'impulse', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 15*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
+#         'find_accuracy2: 'seconds'}
+# thermocycle_info = {'date': '25.04.2025', 
+#         'time_begin': ['25.04.2025 01:52:34', '25.04.2025 03:25:34'], 
+#         'duration_sec': 92*60, 'type': 'impulse1_to2', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '25.04.2025 04:24:00', 'step_duration': 20*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '30.04.2025', 
+#         'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'], 
+#         'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
+# cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '30.04.2025', 
+#         'time_begin': ['29.04.2025 22:02:54', '29.04.2025 23:35:54'], 
+#         'duration_sec': 93*60, 'type': 'step2_to1', 'type_ru': 'ступенчатое'}
+# cut_step_resp = {'time_step_begin': '30.04.2025 00:36:01', 'step_duration': 30*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch2', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '30.04.2025', 
+#         'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'], 
+#         'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 15*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch1', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '30.04.2025', 
+#         'time_begin': ['30.04.2025 01:09:55', '30.04.2025 02:41:54'], 
+#         'duration_sec': 93*60, 'type': 'impulse2_to1', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '30.04.2025 03:42:00', 'step_duration': 20*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '02.05.2025', 
+#         'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'], 
+#         'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
+# cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '02.05.2025', 
+#         'time_begin': ['01.05.2025 22:05:30', '01.05.2025 23:38:40'], 
+#         'duration_sec': 93*60, 'type': 'step4_to3', 'type_ru': 'ступенчатое'}
+# cut_step_resp = {'time_step_begin': '02.05.2025 00:39:00', 'step_duration': 30*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '02.05.2025', 
+#         'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'], 
+#         'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 15*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '02.05.2025', 
+#         'time_begin': ['02.05.2025 01:12:30', '02.05.2025 02:46:02'], 
+#         'duration_sec': 93*60, 'type': 'impulse4_to3', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '02.05.2025 03:45:02', 'step_duration': 20*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '04.05.2025', 
+#         'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'], 
+#         'duration_sec': 93*60, 'type': 'step', 'type_ru': 'ступенчатое'}
+# cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 26*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '04.05.2025', 
+#         'time_begin': ['03.05.2025 22:12:11', '03.05.2025 23:45:10'], 
+#         'duration_sec': 93*60, 'type': 'step3_to4', 'type_ru': 'ступенчатое'}
+# cut_step_resp = {'time_step_begin': '04.05.2025 00:42:01', 'step_duration': 30*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch3', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '04.05.2025', 
+#         'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'], 
+#         'duration_sec': 93*60, 'type': 'impulse', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 15*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
+
+# data_info = {'data': data, 'device': 'летный', 'channel': 'ch4', 'period': '1 мин', 
+#         'find_accuracy': 'seconds'}
+# thermocycle_info = {'date': '04.05.2025', 
+#         'time_begin': ['04.05.2025 01:19:10', '04.05.2025 02:52:11'], 
+#         'duration_sec': 93*60, 'type': 'impulse3_to4', 'type_ru': 'импульсное'}
+# cut_step_resp = {'time_step_begin': '04.05.2025 03:48:01', 'step_duration': 20*60}
+
+# data_info_list.append(data_info)
+# thermocycle_info_list.append(thermocycle_info)
+# cut_step_resp_list.append(cut_step_resp)
 
 
 def get_step_response(data_info, thermocycle_info, cut_step_resp):
     name = f'{data_info["channel"]}_{thermocycle_info["type"]}_response_{data_info["device"]}_{thermocycle_info["date"].replace(".","")}'
     plot_info = {'title': 'Реакция на ' + thermocycle_info['type_ru'] + ' воздействие', 
-            'ox_dtime_format': "%H:%M:%S", 'legend_pos': ['upper left', 'lower left'],
+            'ox_dtime_format': "%H:%M", 'legend_pos': ['upper left', 'lower left'],
             'name_fig': f'../plots/response/{name}.png', 'font': 10}
     
     asotr.plot_step_response_in_thermocycle(data_info, thermocycle_info, interp, 
@@ -270,7 +289,7 @@ def get_step_response(data_info, thermocycle_info, cut_step_resp):
     
     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"]})'
     
-    fig = plt.figure(figsize=(10, 6), dpi=200)
+    fig = plt.figure(figsize=(6, 10), dpi=200)
     fig.suptitle(title, fontsize=plot_info['font'])
     ax1 = fig.add_subplot(1,1,1)
     
@@ -282,8 +301,8 @@ def get_step_response(data_info, thermocycle_info, cut_step_resp):
     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_ylabel(r'$T_{норм}$, $^\circ$C', fontsize=plot_info['font'])
-    ax1.set_xlabel('Время, ЧЧ:MM:CC', fontsize=plot_info['font'])
+    ax1.set_xlabel('Время, ЧЧ:MM', fontsize=plot_info['font'])
     
     plt.tight_layout()
     fig.savefig(plot_info["name_fig"])

bin/step_response_diff.py

@@ -102,7 +102,7 @@ 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_ylabel(r'$T_{норм}$, $^\circ$C', fontsize=plot_info['font'])
 ax1.set_xlabel('Время', fontsize=plot_info['font'])
 
 plt.tight_layout()

bin/tm_brd_parser.py

@@ -15,7 +15,7 @@ path_itog_brd_data = '../data/brd_data/'
 class PathFileNotFound(Exception):
     pass
 
-
+# recursive search files by pattern in specified directory
 def find_required_files(root_dir, pattern):
     result = []
     for dirpath, _, filenames in os.walk(root_dir):
@@ -30,7 +30,7 @@ def find_required_files(root_dir, pattern):
 
     return sorted(result)
 
-
+# read all finded files into pandas dataFrame
 def read_files_into_df(fname_list, column_list, dtype_columns={}):
     data_itog = pd.DataFrame()
     epoch_start = pd.Timestamp('2000-01-01')
@@ -89,7 +89,7 @@ if __name__ == "__main__":
         column_list = ['TIME', 'PER_1Hz', 'ST_HV']
         column_list_itog = ['TIME', 'timestamp', 'PER_1Hz', 'ST_HV']
 
-        collect_tm_brd_files(root_dir_tm_data, column_list, column_list_itog)
+        # collect_tm_brd_files(root_dir_tm_data, column_list, column_list_itog)
 
         ## plot 'evolution' 1 Hz from tm brd data
         print('plot evolution 1 Hz from tm brd data')
@@ -97,5 +97,7 @@ if __name__ == "__main__":
         fname = path_itog_brd_data + 'mvn_tm_brd01.csv'
         df = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_format="%d.%m.%Y %H:%M:%S.%f")
 
+        # print(df)
         plt.plot(df['timestamp'], df['PER_1Hz'], '.')
+        plt.grid()
         plt.show()

bin/tm_wheel_parser.py

@@ -3,13 +3,18 @@ import os
 import re
 from pathlib import Path
 import matplotlib.pyplot as plt
+from matplotlib import dates
 from datetime import datetime, timedelta
 import sys
+import seaborn as sns
 
 tstamp_s = '%d.%m.%Y %H:%M:%S.%f'
-ox_dtime_format = '%d.%m.%Y %H:%M'
+ox_dtime_format = '%H:%M'
+ox_date_format = '%d.%m.%Y'
 
 path_itog_brd_data = '../data/brd_data/'
+pict_name = '../plots/' + 'MVN_wheel'
+font = 16
 
 
 class PathFileNotFound(Exception):
@@ -92,23 +97,64 @@ if __name__ == "__main__":
         column_list = ['TIME', 'STATE']
         column_list_itog = ['TIME', 'timestamp', 'STATE']
 
-        collect_tm_brd_wheel_data(root_dir_wheel_data, column_list, column_list_itog)
+        # collect_tm_brd_wheel_data(root_dir_wheel_data, column_list, column_list_itog)
 
         ## parse and plot wheel csv data
         print('parse and plot wheel csv data')
 
-        border_clr_wheel = 2
+        border_clr_wheel = 1
         fname = path_itog_brd_data + 'mvn_wheel_brd01.csv'
-        wheel_df = pd.read_csv(fname, sep=';')
+        wheel_df = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_format="%d.%m.%Y %H:%M:%S.%f")
+        ## diff between 0 and 0 - 30 sec
         wheel_df['TIME_diff'] = wheel_df['TIME'].diff()
-        median_tdiff = wheel_df['TIME_diff'].median()
 
-        wheel_df_clear = wheel_df[(wheel_df['TIME_diff'] > median_tdiff - border_clr_wheel) &
+        ## sampling decimation in order to get period 60 sec
-                                  (wheel_df['TIME_diff'] < median_tdiff + border_clr_wheel)]
+        wheel_df = wheel_df.iloc[1::2]
+        wheel_df['TIME_period'] = wheel_df['TIME'].diff()
+        # print(wheel_df)
+        median_tdiff = wheel_df['TIME_period'].median()
+        # print(median_tdiff)
 
-        wheel_df_peaks = wheel_df[(wheel_df['TIME_diff'] <= median_tdiff - border_clr_wheel) |
+        ## discard outliers of the measured wheel period
-                                  (wheel_df['TIME_diff'] >= median_tdiff + border_clr_wheel)]
+        wheel_df = wheel_df[
+                (wheel_df['TIME_period'] > median_tdiff - border_clr_wheel) &
+                (wheel_df['TIME_period'] < median_tdiff + border_clr_wheel)]
+
+        median = wheel_df['TIME_period'].median()
+        
+        rows, cols = wheel_df.shape
+        median = pd.Series([median] * rows) 
+
+        date_format = dates.DateFormatter(ox_date_format)
+        datetime_format = dates.DateFormatter(ox_dtime_format)
+
+        fig, axes = plt.subplots(3, 1, figsize=(18, 20), dpi=300, height_ratios=[1, 1, 1])
+        
+        axes[0].plot(wheel_df['timestamp'], wheel_df['TIME_period'], '.', 
+                markersize=5)
+        axes[0].plot(wheel_df['timestamp'], median)
+        axes[0].set_title("")
+        axes[0].set_xlabel("Время (ДД.MM.ГГГГ)", fontsize=font)
+        axes[0].set_ylabel("Полупериод, сек", fontsize=font)
+        axes[0].grid(True)
+        axes[0].xaxis.set_major_formatter(date_format)
+        axes[0].tick_params(axis="both", width=1, labelsize=font)
+
+        axes[1].plot(wheel_df['timestamp'][0:400], wheel_df['TIME_period'][0:400], '.', markersize=10)
+        axes[1].set_title("")
+        axes[1].set_xlabel("Время (ЧЧ:ММ)", fontsize=font)
+        axes[1].set_ylabel("Полупериод, сек", fontsize=font)
+        axes[1].grid(True)
+        axes[1].xaxis.set_major_formatter(datetime_format)
+        axes[1].tick_params(axis="both", width=1, labelsize=font)
+
+        sns.histplot(wheel_df['TIME_period'], kde=False, bins=300, ax=axes[2], color='red')
+        axes[2].set_title("")
+        axes[2].set_xlabel("Полупериод, сек", fontsize=font)
+        axes[2].set_ylabel("Частота встречаемости", fontsize=font)
+        axes[2].grid(True)
+        axes[2].tick_params(axis="both", width=1, labelsize=font)
+        
+        fig.savefig(pict_name)
 
-        plt.plot(wheel_df_clear['TIME'], wheel_df_clear['TIME_diff'], '-')
-        plt.plot(wheel_df_peaks['TIME'], wheel_df_peaks['TIME_diff'], '.')
         plt.show()