1. add README for windows. 2. rewrite some python scripts and bat-files according to windows requirements. 3. Create tm_brd_parser.py and tm_wheel_parser.py for demonstration how to work with data in python

bin/tm_wheel_parser.py

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+import pandas as pd
+import os
+import re
+from pathlib import Path
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+import sys
+
+tstamp_s = '%d.%m.%Y %H:%M:%S.%f'
+ox_dtime_format = '%d.%m.%Y %H:%M'
+
+path_itog_brd_data = '../data/brd_data/'
+
+
+class PathFileNotFound(Exception):
+    pass
+
+
+def find_required_files(root_dir, pattern):
+    result = []
+    for dirpath, _, filenames in os.walk(root_dir):
+        for filename in filenames:
+            match = re.match(pattern, filename)
+            if match:
+                result.append(dirpath + '/' + filename)
+
+    if len(result) == 0:
+        raise PathFileNotFound(
+            f'error: check that the path is correct ({root_dir}) or files pattern is correct ({pattern})')
+
+    return sorted(result)
+
+
+def read_files_into_df(fname_list, column_list, dtype_columns={}):
+    data_itog = pd.DataFrame()
+    epoch_start = pd.Timestamp('2000-01-01')
+
+    for fname in fname_list:
+        data = pd.read_csv(fname, sep=r'\s+', dtype=str)
+        data = data.dropna()
+        data = data[column_list]
+
+        if 'TIME' in column_list:
+            # convert TIME value to human-readable timestamp (sinse epoch 01.01.2000)
+            time = data['TIME'].astype(float)
+            tstamp = epoch_start + pd.to_timedelta(time, unit='s')
+            timestamp = tstamp.dt.strftime(tstamp_s)
+            data['timestamp'] = timestamp
+
+            # clear dataframe rows where time value == 0
+            data['time'] = time
+            data_clear = data.query('time != 0.0')
+
+        data_itog = pd.concat([data_itog, data_clear], ignore_index=True)
+
+    return data_itog
+
+
+def collect_tm_brd_wheel_data(root_dir_wheel_data, column_list, column_list_itog):
+    patterns_wheel = [r'mvn_wheel_brd01_(.*)', r'mvn_wheel_brd02_(.*)', r'mvn_wheel_brd03_(.*)',
+                      r'mvn_wheel_brd04_(.*)']
+
+    for pattern in patterns_wheel:
+        fname = path_itog_brd_data + pattern[:15] + '.csv'
+        try:
+            found_files = find_required_files(root_dir_wheel_data, pattern)
+            data = read_files_into_df(found_files, column_list, dtype_columns={0: float, 1: int})
+        except KeyError as e:
+            print(
+                f'error in collect_tm_brd_wheel_data: the specified column name was not found in the data file (path: {root_dir_tm_data}) ({e})')
+            break
+        except Exception as e:
+            print(f'error in collect_tm_brd_wheel_data: {e}')
+            break
+
+        mask = data['STATE'] == '0'
+        data = data[mask]
+        data.to_csv(fname, index=False, sep=';', columns=column_list_itog, encoding='utf-8-sig')
+        print('data saved: ' + fname)
+
+
+if __name__ == "__main__":
+
+    if len(sys.argv) != 2:
+        print("Usage: python tm_wheel_parser.py /path/to/tm_brd_data/")
+    else:
+        root_dir_wheel_data = sys.argv[1]
+
+        ### collect raw tm wheel data into one file for each brd ###
+        print('collect raw tm wheel data into one file for each brd')
+
+        column_list = ['TIME', 'STATE']
+        column_list_itog = ['TIME', 'timestamp', 'STATE']
+
+        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
+        fname = path_itog_brd_data + 'mvn_wheel_brd01.csv'
+        wheel_df = pd.read_csv(fname, sep=';')
+        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) &
+                                  (wheel_df['TIME_diff'] < median_tdiff + border_clr_wheel)]
+
+        wheel_df_peaks = wheel_df[(wheel_df['TIME_diff'] <= median_tdiff - border_clr_wheel) |
+                                  (wheel_df['TIME_diff'] >= median_tdiff + border_clr_wheel)]
+
+        plt.plot(wheel_df_clear['TIME'], wheel_df_clear['TIME_diff'], '-')
+        plt.plot(wheel_df_peaks['TIME'], wheel_df_peaks['TIME_diff'], '.')
+        plt.show()