correct find_best_time_interval function. Create Readme
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Danila Gamkov
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README.markdown
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README.markdown
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# asotr_flight
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The asotr_flight python scripts is designed to analyze ASOTR flight data
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## Contents
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- **Setup**
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- **Using**
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- Plot ASOTR data in specified date borders (for MVN reports)
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- **Contacts**
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**Note**: \<PATH_TO_ASOTR_FLIGHT\> - path where is asotr_flight scripts is cloned from heagit
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**Note**: \<PATH_TO_ASOTR_CSV\> - path where is asotr_csv program is cloned from heagit
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## Setup
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1. Install python 3.10 or newest.
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2. Clone the repo to your computer:
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```
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git clone http://heagit.cosmos.ru/gamkov/asotr_flight.git
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```
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3. Enter the repo:
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```
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cd <PATH_TO_ASOTR_FLIGHT>
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```
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## Using
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### Plot ASOTR data in specified date borders (for MVN reports)
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1. Donwload data from science data server to directory \<PATH_TO_ASOTR_DATA\>.
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If you don't have MVN data, you might download it from server with science SRG data (IP: 193.232.11.95).
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For questions about downloading science data contact Shtykovsky A. (a.shtykovsky@cosmos.ru) or Chelovekov I. (chelovekov@cosmos.ru)
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2. Parse all raw data from ASOTR into csv files and plot all csv data:
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```
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cd <PATH_TO_ASOTR_CSV>
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./asotr_all_unzip_auto.sh <PATH_TO_ASOTR_DATA>/
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```
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csv data will be in directory:
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```
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<PATH_TO_ASOTR_CSV>/data/
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```
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3. plot ASOTR data in specified data borders
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You might plot data by using shell-script (for MVN reports), for example:
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```
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cd <PATH_TO_ASOTR_FLIGHT>
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./plot_flight_borders.sh <PATH_TO_ASOTR_CSV>/ 10.03.2025 23.04.2025 14 0
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```
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where:
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10.03.2025 - start date
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23.04.2025 - end date
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14 - font size for plot
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0 - display commands flag (not display)
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Or you might plot data by using python script directly, for example:
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```
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cd <PATH_TO_ASOTR_FLIGHT>
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python3 plot_flight_borders.py -s <PATH_TO_ASOTR_CSV> -c 100000 -a 01 -b 10.03.2025 -e 15.03.2025 -f 14 -d 0 -p 0
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```
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Graphs with data are in the directory:
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```
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<PATH_TO_ASOTR_FLIGHT>/reports
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```
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## Contatcs
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For questions about the program, please contact Danila Gamkov, email: danila_gamkov@cosmos.ru
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16
asotr.py
16
asotr.py
@ -146,12 +146,12 @@ def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
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elif accuracy == 'seconds':
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delta = timedelta(seconds=30)
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low = 0
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high = len(time_arr) - 1
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mid = len(time_arr) // 2
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low = time_arr.idxmin()
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high = time_arr.idxmax()
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mid = time_arr.idxmin() + (time_arr.idxmax() - time_arr.idxmin()) // 2
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if mid not in time_arr.index:
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# print(f'mid not in time_arr: {mid}')
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# print(f'mid not in time_arr: {mid}, {time_arr.index}, {time_arr.idxmin()}')
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return -1
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a = time_arr[mid]
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@ -160,7 +160,7 @@ def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
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low = mid + 1
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else:
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high = mid - 1
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mid = (low + high) // 2
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mid = low + (high - low) // 2
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# print(f'mid: (low + high)/2: {mid}')
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if mid not in time_arr.index:
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@ -186,8 +186,14 @@ def find_best_time_idx(time_arr, user_time, accuracy='minutes') -> int:
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# print(f'{time_arr[j]} > {tstamp}: {j}')
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return j
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if mid <= low + 1 or mid >= high - 1:
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return -1
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return mid
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class TimeIndexNotFound(Exception):
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pass
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def find_time_idx(data_list, keys_list, timestamp, accuracy):
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out_dict = dict.fromkeys(keys_list, -1)
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@ -16,7 +16,7 @@ def convert_to_str(lst):
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res += f"_{idx + 1}"
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return res
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def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=False):
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def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0, show_flag=True):
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print_width = 20
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print_height = 12
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width = 1
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@ -74,7 +74,8 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
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plt.tight_layout()
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fig.savefig(pict_name)
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plt.show()
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if show_flag == True:
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plt.show()
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elif plot_windows == 2:
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@ -85,22 +86,29 @@ def plot_asotr_borders(path_with_data, ch, asotr_kit, begin, end, font=14, cmd=0
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if cmd == '1':
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fname = './flight_cmd_human.txt'
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try:
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cmd_human = pd.read_csv('./flight_cmd_human.txt',
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cmd_human = pd.read_csv('./data/cmd_human.csv',
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delimiter=';', names=['timestamp', 'cmd'])
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except Exception as e:
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print(f'Error parsing file: {e}')
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return
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max_temp = max(data_dict['temp'].iloc[:,1])
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min_temp = min(data_dict['temp'].iloc[:,1])
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# print(cmd_human)
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step = 0
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for i, row in cmd_human.iterrows():
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if i > 20:
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row_time = row['timestamp'][0:len(row['timestamp']) - 4]
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idx = asotr.find_best_time_idx(data_dict['time_temp'],
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row_time, accuracy='minutes')
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if idx != -1:
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ax1.axvline(x = data_dict['time_temp'][idx], color='r',
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linestyle='-.')
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ax1.text(data_dict['time_temp'][idx], 30, row['cmd'],
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rotation=45, va='bottom', fontsize=font)
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row_time = row['timestamp'][0:len(row['timestamp']) - 4]
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# print(row_time)
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idx = asotr.find_best_time_idx(data_dict['time_temp'],
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row_time, accuracy='minutes')
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# print(idx)
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if idx != -1:
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ax1.axvline(x = data_dict['time_temp'][idx], color='r',
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linestyle='-.')
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ax1.text(data_dict['time_temp'][idx], max_temp - step, row['cmd'],
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rotation=45, va='bottom', fontsize=font)
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step += (max_temp - min_temp)/20
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if plot_task["temp"] == 1:
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for i in range(len(channels)):
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@ -176,6 +184,6 @@ if __name__ == '__main__':
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argparser.add_argument('-p', '--plot', required=False,
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help='display data in plot flag (0/1)')
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args = argparser.parse_args()
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plot_asotr_borders(args.source, args.channel, args.asotr, args.begin, args.end,
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args.font, args.cmd, args.plot)
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@ -12,19 +12,19 @@ from datetime import timedelta
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path = '/home/danila/Danila/work/MVN/Soft/asotr_csv/data/'
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channel = 'ch1'
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asotr_kit = '01'
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start_date = '23.04.2025 00:00:00'
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start_date = '22.04.2025 00:00:00'
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end_date = '23.04.2025 01:20:00'
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forecast_days = 20
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# end_date = '26.03.2025 01:20:00'
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timeformat = '%d.%m.%Y %H:%M:%S'
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delta_date = datetime.strptime(start_date, timeformat) + timedelta(days=forecast_days)
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end_date = delta_date.strftime(timeformat)
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num_peaks_forecast = forecast_days * 20
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# start_date = '06.01.2025 22:40:00'
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# end_date = '21.01.2025 01:20:00'
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shift = True
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raw_data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
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try:
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raw_data, data_dict = asotr.get_data(path, asotr_kit, start_date, end_date, 'minutes')
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except Exception as e:
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sys.exit()
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data1 = data_dict['temp'][channel]
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time1 = data_dict['time_temp']
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