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

.gitignore

@@ -6,8 +6,8 @@
 *.log
 *.txt
 *.xls
-*.xlsx
 *.csv#
+*.exe
 *.doc
 *.docx
 /bin/__pycache__

README.markdown

@@ -6,24 +6,26 @@ This project contains tools for decoding, converting, and visualizing telemetry
 
 - **Project structure**
 - **Requirements**
-- ** Linux Setup**  
+- **Linux Setup**  
-- ** Windows 10 Setup**
+- **Windows 10 Setup**
 - **Using the tools**
     - *Plot all ASOTR data*
     - *Plot ASOTR data in specified date borders (for MVN reports)*
 - **Contacts**
 
 **Note 1**: \<PATH_TO_MVN_FLIGHT\> - path where is mvn_flight scripts is cloned from heagit  
-**Note 2**: \<PATH_TO_ASOTR_DATA\> - path with raw MVN ASOTR data  
+**Note 2**: \<PATH_TO_ASOTR_DATA\> - path with raw MVN ASOTR data   
-**Note 3**: \<PATH_TO_MVN_DATA\> - path with raw MVN data  
 
 ## Project structure
 -> `/bin/` - ready-to-use binary files and python scripts for assembling, extracting and plotting telemetry data     
---> `/bin/asotr_unzip_plot.sh` or `/bin/asotr_unzip_plot.bat` — scripts to automate the unpack-convert-plot chain for Linux/Windows.  
+--> `/bin/asotr_unzip_plot.sh` or `/bin/asotr_unzip_plot.bat` — scripts to automate the unpack-convert-plot chain ASOTR data (Linux/Windows).  
 --> `/bin/plot_flight_borders.py` — Python script to generate plots from CSV data.  
--> `/asotr_csv/` — binary parser to collect data from raw ASOTR data to CSV. This parser written using the Rust language. For more informatin see documentation in `/asotr_csv/README.markdown`).   
+--> `/bin/plot_flight_borders.sh` or `/bin/plot_flight_borders.bat` - scripts to automate plot generations for MVN reports  
--> `/data/` — folder containing processed CSV files.  
+--> `/bin/tm_brd_parser.py` - python script for demostration processing BRD telemetry data (1 Hz evolution)  
--> `/plots/` — folder where output plots are saved.  
+--> `/bin/tm_wheel_parser.py` - python script for demostration processing BRD wheel telemetry data  
+-> `/asotr_csv/` — binary parser to collect data from raw ASOTR data to CSV. This parser   written using the Rust language. For more informatin see documentation in `/asotr_csv/README.markdown`).     
+-> `/data/` — folder containing processed CSV files.    
+-> `/plots/` — folder where output plots are saved.    
 
 ## Requirements
 - Python version 3.10 or upper. For checking type:
@@ -31,21 +33,11 @@ This project contains tools for decoding, converting, and visualizing telemetry
 python3 --version
 ```
 
-Python dependencies:
-```bash
-pip install matplotlib pandas
-```
-
 - Rust compiler version 1.83.0 or upper. For checking type:
 ```bash
 rustc --version
 ```
 
-Additional tools (Linux only):
-```bash
-sudo apt install unzip git
-```
-
 ## Linux Setup
 1. Clone the repo to your computer:  
 ```bash
@@ -56,7 +48,7 @@ git clone http://heagit.cosmos.ru/gamkov/mvn_flight.git
 ```bash
 sudo apt update
 sudo apt install python3 python3-pip
-pip3 install matplotlib pandas
+pip3 install matplotlib pandas openpyxl scipy
 ```
 
 1. Install Rust compiler (if you do not have):
@@ -74,7 +66,7 @@ git clone http://heagit.cosmos.ru/gamkov/mvn_flight.git
 
 3. Open CMD and install python dependencies:
 ```cmd
-pip install matplotlib pandas
+pip install matplotlib pandas openpyxl scipy
 ```
 
 4. Install Rust compiler (if you do not have):  
@@ -87,23 +79,41 @@ For more detailed information you can go to: https://doc.rust-lang.ru/book/ch01-
 ### Plot all ASOTR data
 1. Donwload data from science data server to directory \<PATH_TO_ASOTR_DATA\>.  
 If you don't have MVN data, you might download it from server with science SRG data (IP: 193.232.11.95).   
-For questions about downloading science data contact Shtykovsky A. (a.shtykovsky@cosmos.ru) or Chelovekov I. (chelovekov@cosmos.ru)
+For questions about downloading science data contact Shtykovsky A. (a.shtykovsky@cosmos.ru) or Chelovekov I. (chelovekov@cosmos.ru)  
+
+Linux (for example):
+```
+sftp username@IP_address
+cd mvn/data/data/2025
+get -r 20250101*
+```
+
+Windows:  
+```
+Open filezilla
+connect to sft server
+get data from: /export/home/user_name/mvn/data/data/2025
+```
+
+
 
 2. Parse all raw data from ASOTR into csv files and plot all csv data:  
 
 Linux:
 ```bash  
-./bin/asotr_unzip_plot.sh <PATH_TO_ASOTR_DATA>/
+cd bin
+./asotr_unzip_plot.sh <PATH_TO_ASOTR_DATA>/
 ```
 
 Windows:
 ```cmd
-.\bin\asotr_unzip_plot.bat <PATH_TO_ASOTR_DATA>\
+cd bin
+asotr_unzip_plot.bat <PATH_TO_ASOTR_DATA>\
 ```
 
 csv data will be in directory:  
 ```cmd
-./data/asotr/  
+/data/asotr/  
 ```
 
 ### Plot ASOTR data in specified date borders (for MVN reports)
@@ -113,12 +123,14 @@ csv data will be in directory:
 
 Linux:
 ```bash
-./bin/plot_flight_borders.sh ../data/asotr/ 10.03.2025_00:00:00 23.04.2025_23:59:59
+cd bin
+./plot_flight_borders.sh ../data/asotr/ 10.03.2025_00:00:00 23.04.2025_23:59:59
 ```
 
 Windows:
 ```cmd
-.\bin\plot_flight_borders.bat ..\data\asotr\ 10.03.2025_00:00:00 23.04.2025_23:59:59
+cd bin
+plot_flight_borders.bat ..\data\asotr\ 10.03.2025_00:00:00 23.04.2025_23:59:59
 ```
 
 where:  

asotr_csv/src/main.rs

@@ -174,7 +174,7 @@ pub mod asotr_data {
         let mut fname = String::new();
         let msg_prev = format!("Error parsing filename {}:", filename_full);
 
-        match filename_full.rfind('/') {
+        match filename_full.rfind('/').or_else(|| filename_full.rfind('\\')) {
             Some(val) => { fname = (filename_full[val+1..filename_full.len()]).to_string(); }
             _ => { fname = filename_full.clone();  }
         }

asotr_csv/target/.rustc_info.json

@@ -1 +1 @@
-{"rustc_fingerprint":2742313010855374649,"outputs":{"15729799797837862367":{"success":true,"status":"","code":0,"stdout":"___\nlib___.rlib\nlib___.so\nlib___.so\nlib___.a\nlib___.so\n/home/danila/.rustup/toolchains/stable-x86_64-unknown-linux-gnu\noff\npacked\nunpacked\n___\ndebug_assertions\npanic=\"unwind\"\nproc_macro\ntarget_abi=\"\"\ntarget_arch=\"x86_64\"\ntarget_endian=\"little\"\ntarget_env=\"gnu\"\ntarget_family=\"unix\"\ntarget_feature=\"fxsr\"\ntarget_feature=\"sse\"\ntarget_feature=\"sse2\"\ntarget_has_atomic=\"16\"\ntarget_has_atomic=\"32\"\ntarget_has_atomic=\"64\"\ntarget_has_atomic=\"8\"\ntarget_has_atomic=\"ptr\"\ntarget_os=\"linux\"\ntarget_pointer_width=\"64\"\ntarget_vendor=\"unknown\"\nunix\n","stderr":""},"4614504638168534921":{"success":true,"status":"","code":0,"stdout":"rustc 1.83.0 (90b35a623 2024-11-26)\nbinary: rustc\ncommit-hash: 90b35a6239c3d8bdabc530a6a0816f7ff89a0aaf\ncommit-date: 2024-11-26\nhost: x86_64-unknown-linux-gnu\nrelease: 1.83.0\nLLVM version: 19.1.1\n","stderr":""}},"successes":{}}
+{"rustc_fingerprint":14808889899039181664,"outputs":{"7971740275564407648":{"success":true,"status":"","code":0,"stdout":"___.exe\nlib___.rlib\n___.dll\n___.dll\n___.lib\n___.dll\nC:\\Users\\danil\\.rustup\\toolchains\\stable-x86_64-pc-windows-msvc\npacked\n___\ndebug_assertions\npanic=\"unwind\"\nproc_macro\ntarget_abi=\"\"\ntarget_arch=\"x86_64\"\ntarget_endian=\"little\"\ntarget_env=\"msvc\"\ntarget_family=\"windows\"\ntarget_feature=\"cmpxchg16b\"\ntarget_feature=\"fxsr\"\ntarget_feature=\"sse\"\ntarget_feature=\"sse2\"\ntarget_feature=\"sse3\"\ntarget_has_atomic=\"128\"\ntarget_has_atomic=\"16\"\ntarget_has_atomic=\"32\"\ntarget_has_atomic=\"64\"\ntarget_has_atomic=\"8\"\ntarget_has_atomic=\"ptr\"\ntarget_os=\"windows\"\ntarget_pointer_width=\"64\"\ntarget_vendor=\"pc\"\nwindows\n","stderr":""},"17747080675513052775":{"success":true,"status":"","code":0,"stdout":"rustc 1.87.0 (17067e9ac 2025-05-09)\nbinary: rustc\ncommit-hash: 17067e9ac6d7ecb70e50f92c1944e545188d2359\ncommit-date: 2025-05-09\nhost: x86_64-pc-windows-msvc\nrelease: 1.87.0\nLLVM version: 20.1.1\n","stderr":""}},"successes":{}}

bin/asotr.py

@@ -232,12 +232,10 @@ def get_data(path, asotr_kit, start_date, end_date, time_accuracy):
 
     fname = [path + fname_temp, path + fname_tempSet, path + fname_pow]
 
-    dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
-
     try:
-        data = [ pd.read_csv(fname[0], sep=";", parse_dates=["timestamp"], date_parser=dateparse),
+        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_parser=dateparse),
+            pd.read_csv(fname[1], sep=";", parse_dates=["timestamp"], date_format="%d.%m.%Y %H:%M:%S.%f"),
-            pd.read_csv(fname[2], sep=";", parse_dates=["timestamp"], date_parser=dateparse),]
+            pd.read_csv(fname[2], sep=";", parse_dates=["timestamp"], date_format="%d.%m.%Y %H:%M:%S.%f"),]
     except FileNotFoundError:
         print(f'Error opening file: one (or all) file not found in directory: \n{fname}')
         return

bin/asotr_unzip_plot.bat

@@ -12,10 +12,10 @@ copy "..\asotr_csv\target\release\asotr_csv.exe" .\
 set "path_=%~1"
 
 REM unpacking recursively archive using Python script
-python recursive_unpack_targz.py "%path_%"
+python recursive_unpack_targz.py %path_%
 
 REM run parser
-asotr_csv.exe -d "%path_%"
+asotr_csv.exe -d %path_%
 
 REM plot data
 python plot_asotr_flight_all.py

bin/brd_wheel_1Hz_parser.py

@@ -1,140 +0,0 @@
-import pandas as pd
-import os
-import re
-from pathlib import Path
-import matplotlib.pyplot as plt
-from datetime import datetime, timedelta
-
-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_files(root_dir_tm_data, column_list, column_list_itog):
-    patterns_tm = [r'mvn_tm_brd01_(.*)', r'mvn_tm_brd02_(.*)', r'mvn_tm_brd03_(.*)', 
-        r'mvn_tm_brd04_(.*)']
-
-    for pattern in patterns_tm:
-        fname = path_itog_brd_data + pattern[:12] + '.csv'
-        try:
-            found_files = find_required_files(root_dir_tm_data, pattern)
-            data = read_files_into_df(found_files, column_list, dtype_columns={11: float})
-        except KeyError as e:
-            print(f'error in collect_tm_brd_files: 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_files: {e}')
-            break
-
-        data.to_csv(fname, index=False, sep=';', columns=column_list_itog, encoding='utf-8-sig')
-        print('data saved: ' + fname)
-
-
-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)
-
-
-### collect raw tm brd data into one file for each brd ###
-
-root_dir_tm_data = '/home/danila/Danila/work/MVN/flight/brd_data/arch_for_MB/archive_tm_data_txt/'
-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 raw tm wheel data into one file for each brd ###
-
-root_dir_wheel_data = '/home/danila/Danila/work/MVN/flight/brd_data/arch_for_MB/archive_wheel_data_txt/'
-column_list = ['TIME', 'STATE']
-column_list_itog = ['TIME', 'timestamp', 'STATE']
-
-collect_tm_brd_wheel_data(root_dir_wheel_data, column_list, column_list_itog)
-
-
-## plot 'evolution' 1 Hz from tm brd data
-
-fname = path_itog_brd_data + 'mvn_tm_brd01.csv'
-dateparse = lambda x: datetime.strptime(x, tstamp_s)
-df = pd.read_csv(fname, sep=';', parse_dates=['timestamp'], date_parser=dateparse)
-
-plt.plot(df['timestamp'], df['PER_1Hz'], '.')
-plt.show()
-
-
-## 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.show()

bin/plot_asotr_flight_all.py

@@ -31,21 +31,18 @@ width=[1, 1, 1, 1, 1, 1]
 marker = ['-', '-', '-', '-', '--', '-'];
 width_arr = [1, 0.5, 0.2, 0.1, 1, 1]
 
-dateparse = lambda x: datetime.strptime(x, "%d.%m.%Y %H:%M:%S.%f")
-dparse_b = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
-
 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_parser=dparse_b)
+        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_parser=dateparse),
+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_parser=dateparse)]
+        pd.read_csv(fname[1], sep=';', parse_dates=['timestamp'], date_format="%d.%m.%Y %H:%M:%S.%f")]
 
 ch= [[], [], [], [], [], []]
 ch_signs = ["temp", "pow"]
@@ -80,7 +77,7 @@ if plot_windows == 1:
 
     ax.tick_params(axis="both", width=1, labelsize=font)
     ax.grid(visible=True, linestyle = 'dotted')
-    ax.set_ylabel('Температура, $^\circ$C', fontsize=font)
+    ax.set_ylabel(r"Температура, $^\circ$C", fontsize=font)
     ax.set_xlabel('Время', fontsize=font)
     ax.legend(fontsize=font)
 
@@ -117,7 +114,7 @@ elif plot_windows == 2:
 
     ax1.tick_params(axis="both", width=1, labelsize=font)
     ax1.grid(visible=True, linestyle = 'dotted')
-    ax1.set_ylabel('Температура, $^\circ$C', fontsize=font)
+    ax1.set_ylabel(r"Температура, $^\circ$C", fontsize=font)
     ax1.set_xlabel('Время', fontsize=font)
     ax1.legend(fontsize=font, loc='lower right')
 

bin/plot_flight_borders.bat

@@ -6,16 +6,16 @@ IF "%~2"=="" (
     goto :EOF
 )
 
-set "path_csv_data=..\data\asotr"
+set "path_csv_data=..\data\asotr\"
 set "begin=%~1"
 set "end=%~2"
 
 REM run Python-script with parameters
-python plot_flight_borders.py -s "%path_csv_data%" -c 111100 -a 01 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 111100 -a 01 -b %begin% -e %end%
-python plot_flight_borders.py -s "%path_csv_data%" -c 001000 -a 01 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 001000 -a 01 -b %begin% -e %end%
-python plot_flight_borders.py -s "%path_csv_data%" -c 000011 -a 01 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 000011 -a 01 -b %begin% -e %end%
-python plot_flight_borders.py -s "%path_csv_data%" -c 111100 -a 02 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 111100 -a 02 -b %begin% -e %end%
-python plot_flight_borders.py -s "%path_csv_data%" -c 010100 -a 02 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 010100 -a 02 -b %begin% -e %end%
-python plot_flight_borders.py -s "%path_csv_data%" -c 010000 -a 02 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 010000 -a 02 -b %begin% -e %end%
-python plot_flight_borders.py -s "%path_csv_data%" -c 000100 -a 02 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 000100 -a 02 -b %begin% -e %end%
-python plot_flight_borders.py -s "%path_csv_data%" -c 000011 -a 02 -b %begin% -e %end%
+python plot_flight_borders.py -s %path_csv_data% -c 000011 -a 02 -b %begin% -e %end%

bin/plot_flight_borders.py

@@ -15,7 +15,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
     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(".", "")}_{end[6:]}.png')
+    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"
@@ -57,7 +57,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
             
             ax.tick_params(axis="both", width=1, labelsize=font)
             ax.grid(visible=True, linestyle="dotted")
-            ax.set_ylabel("Температура, $^\circ$C", fontsize=font)
+            ax.set_ylabel(r"Температура, $^\circ$C", fontsize=font)
             ax.set_xlabel("Время", fontsize=font)
             ax.legend(fontsize=font)
 
@@ -131,7 +131,7 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
 
         ax1.tick_params(axis="both", width=1, labelsize=font)
         ax1.grid(visible=True, linestyle="dotted")
-        ax1.set_ylabel("Температура, $^\circ$C", fontsize=font)
+        ax1.set_ylabel(r"Температура, $^\circ$C", fontsize=font)
         ax1.set_xlabel("Время", fontsize=font)
         ax1.legend(fontsize=font)   
 

bin/tm_brd_parser.py

@@ -0,0 +1,101 @@
+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_files(root_dir_tm_data, column_list, column_list_itog):
+    patterns_tm = [r'mvn_tm_brd01_(.*)', r'mvn_tm_brd02_(.*)', r'mvn_tm_brd03_(.*)',
+                   r'mvn_tm_brd04_(.*)']
+
+    for pattern in patterns_tm:
+        fname = path_itog_brd_data + pattern[:12] + '.csv'
+        try:
+            found_files = find_required_files(root_dir_tm_data, pattern)
+            data = read_files_into_df(found_files, column_list, dtype_columns={11: float})
+        except KeyError as e:
+            print(
+                f'error in collect_tm_brd_files: 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_files: {e}')
+            break
+
+        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_brd_parser.py /path/to/tm_brd_data/")
+    else:
+        root_dir_tm_data = sys.argv[1]
+
+        print('collect raw brd tm data into one file for each brd')
+
+        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)
+
+        ## plot 'evolution' 1 Hz from tm brd data
+        print('plot evolution 1 Hz from tm brd data')
+
+        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")
+
+        plt.plot(df['timestamp'], df['PER_1Hz'], '.')
+        plt.show()

bin/tm_wheel_parser.py

@@ -0,0 +1,114 @@
+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()