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brightness_temperature_calibration.py
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brightness_temperature_calibration.py
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import logging
import re
import sys
from collections import Counter
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
from astropy.io import fits
from tqdm import tqdm
from analise_utils import (ArrayOperations, Monitoring, MplFunction,
OsOperations, ZirinTb)
from config import *
def brightness_temperature_calibration(mode : str = mode, folder_mode : str = folder_mode, postfix = 'calibrated_brightness', number_of_mode_values = 7, name_of_file = None):
"""Калибровка яркостной температуры изображений. Производится путем выборки некоторого количества наиболее встречаемых округленных до сотен значений яркостной температуры спокойного Солнца и домножением изображения на расчитанный поправочный коэффициент
Args:
mode (str, optional): режим работы, расчет или использование сохраненных настроек, устанавливается в 'config.py'
folder_mode (str, optional): режим работы с папками, устанавливается в 'config.py'
postfix (str, optional): что будет добавлено в названия к обработанным файлам, по умолчанию 'calibrated_brightness'.
number_of_mode_values (int, optional): количество усредняемых наиболее встречающихся значений яркостной температуры в изображении, по умолчанию 7
name_of_file (str, optional): имя файла с настройками при использовании режима `saved_settings`, по умолчанию None.
"""
zirin = ZirinTb()
MplFunction.set_mpl_rc()
Monitoring.start_log('logs')
logging.info(f'Start of the brightness temperature calibration program')
logging.info(f'Path to files: {directory}')
files, freqs = OsOperations.freq_sorted_files_in_folder(directory) if folder_mode == 'one_folder' else OsOperations.freq_sorted_1st_two_files_in_folders(directory)
logging.info(f'Find {len(files)} files')
logging.info(f'List files: \n {files}')
OsOperations.create_place(directory, 'calibrated_brightness')
if folder_mode == 'folder_with_folders':
for freq in tqdm(freqs, desc='Создание папок для частот'):
OsOperations.create_place(f'{directory}_{postfix}/{freq}')
if mode == 'saved_settings':
try:
correction_factor_brightness_array = ArrayOperations.read_from_json(name_of_file)
if len(files)/2 != len(correction_factor_brightness_array):
Monitoring.logprint('Ошибка! Количество коррекционных коэффициентов не совпадает с количеством частот')
sys.exit()
except TypeError as err:
Monitoring.logprint(f'\nОшибка! Выбран режим `saved_settings`, но не указан файл настроек в аргументах функции!\nПодробнее: {err}')
sys.exit()
elif mode == 'calculation':
correction_factor_brightness_array = []
else:
Monitoring.logprint('Ошибка, неверный параметр `mode`')
for index, image in enumerate(tqdm(files, desc='Общий прогресс выполнения')):
if index % 2 == 0:
data1 = fits.open(f'{directory}/{freqs[index//2] if folder_mode == "folder_with_folders" else ""}/{files[index]}', ignore_missing_simple=True)
data2 = fits.open(f'{directory}/{freqs[index//2] if folder_mode == "folder_with_folders" else ""}/{files[index + 1]}', ignore_missing_simple=True)
header1 = data1[0].header
header2 = data2[0].header
dateandtime = f'{header1['DATE-OBS']}' # T{header1['T-OBS']}
img1 = data1[0].data
img2 = data2[0].data
data1.close()
data2.close()
if mode == 'calculation':
current_frequency = int(re.search(r'(?<=[_.])\d{4,5}(?=[_.])', str(files[index])).group())
Tb = zirin.getTbAtFrequency(current_frequency/1000)
data_inside_circle1 = ArrayOperations.cut_sun_disk_data(img1)
data_inside_circle2 = ArrayOperations.cut_sun_disk_data(img2)
data_inside_circle1 = np.round(data_inside_circle1, -2)
data_inside_circle2 = np.round(data_inside_circle2, -2)
counter1 = Counter(data_inside_circle1)
counter2 = Counter(data_inside_circle2)
most_common_for_1 = counter1.most_common(number_of_mode_values)
most_common_for_2 = counter2.most_common(number_of_mode_values)
most_common_values_1 = [item[0] for item in most_common_for_1]
most_common_values_2 = [item[0] for item in most_common_for_2]
count_values_1 = [item[1] for item in most_common_for_1]
count_values_2 = [item[1] for item in most_common_for_2]
mode1 = np.mean(most_common_values_1)
mode2 = np.mean(most_common_values_2)
count_values_1 = np.sum(count_values_1)
count_values_2 = np.sum(count_values_2)
logging.info(f"For image {index+1} mode intensity: {str(mode1)} [standard:{int(Tb*1000)}], count: {count_values_1}")
logging.info(f"For image {index+2} mode intensity: {str(mode2)} [standard:{int(Tb*1000)}], count: {count_values_2}")
correction_factor_brightness = 1 / (((mode1 + mode2)/2) / (Tb * 1000))
correction_factor_brightness_array.append(correction_factor_brightness)
logging.info(f"For image {index + 1}, {index + 2} correction factor: {str(correction_factor_brightness)}")
if folder_mode == 'one_folder':
fits.writeto(f'{directory}_calibrated_brightness/{files[index][:-4]}_calibrated_brightness.fits', img1 * correction_factor_brightness_array[index//2], overwrite=True, header=header1)
logging.info(f"Image {index+1}: {files[index]} - saved")
fits.writeto(f'{directory}_calibrated_brightness/{files[index + 1][:-4]}_calibrated_brightness.fits', img2 * correction_factor_brightness_array[index//2], overwrite=True, header=header2)
logging.info(f"Image {index+2}: {files[index + 1]} - saved")
elif folder_mode == 'folder_with_folders':
all_files_in_freq, freq = OsOperations.freq_sorted_files_in_folder(f'{directory}/{freqs[index//2]}')
for file in tqdm(all_files_in_freq, desc=f'Обработка файлов частоты {freqs[index//2]}', leave=False):
hdul1 = fits.open(f'{directory}/{freqs[index//2]}/{file}', ignore_missing_simple=True)
img1 = hdul1[0].data
header1 = hdul1[0].header
hdul1.close()
fits.writeto(f'{directory}_{postfix}/{freqs[index//2]}/{file[:-4] if file[-1]== "t" else file[:-5]}_{postfix}.fits', img1 * correction_factor_brightness_array[index//2], overwrite=True, header=header1)
ArrayOperations.save_on_json(correction_factor_brightness_array, f'BC_{dateandtime}')
fig = plt.figure()
ax = fig.gca()
plt.plot(correction_factor_brightness_array)
plt.grid()
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.1))
plt.show()
if __name__ == "__main__":
brightness_temperature_calibration(mode = mode, folder_mode = folder_mode, postfix = 'calibrated_brightness', number_of_mode_values = 7, name_of_file = 'BC_20220113.json')