Merged two files into one package
This commit is contained in:
Andrey Mukhin
parent
5093159bfe
commit
a213e28232
@ -1,175 +0,0 @@
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# %%
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from get_region_pack import *
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import numpy as np
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from pandas import DataFrame, read_csv
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from astropy.table import Table
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from astropy.coordinates import SkyCoord
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from astropy import units as u
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from multiprocessing import get_context, cpu_count
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import warnings
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import time
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import os
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warnings.filterwarnings('ignore')
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# %%
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def poisson_divider(array):
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sub_arrays = np.zeros((4,180,180))
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for i in range(2):
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for j in range(2):
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sub_arrays[i+2*j] = array[180*i:180*(i+1),180*j:180*(j+1)].filled(-1000)
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pix_sum = 0
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for layer in sub_arrays:
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_masked_array = np.ma.masked_less(layer,0)
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pix_sum += ((_masked_array-_masked_array.mean())**2/_masked_array.mean()).sum()
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pix_sum /= np.logical_not(array.mask).sum()
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return pix_sum
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def process(argument):
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idx, obs_name = argument
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bin_num = 6
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try:
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obs = Observation(obs_name)
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sky_coord = SkyCoord(ra=obs.ra*u.deg,dec=obs.dec*u.deg,frame='fk5').transform_to('galactic')
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lon, lat = sky_coord.l.value, sky_coord.b.value
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rem_signal, rem_area, poiss_comp, rms = np.zeros((4,2**bin_num))
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region = np.zeros(obs.data.shape, dtype=bool)
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rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
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masked_obs = np.ma.masked_array(obs.data, mask = region)
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good_lvl = np.zeros(bin_num,dtype=bool)
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good_idx = 0
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if obs.exposure > 1000:
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wav_obs = obs.wavdecomp('gauss',(5,3),occ_coeff=True)
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occ_coeff = obs.get_coeff()
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for idx, lvl in enumerate(binary_array(bin_num)):
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try:
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# region = np.array([layer>0 for layer in wav_obs[2:-1][lvl]]).sum(0).astype(bool)
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region = wav_obs[2:-1][lvl].sum(0)>0
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except ValueError:
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region = np.zeros(obs.data.shape,dtype=bool)
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masked_obs = np.ma.masked_array(obs.data, mask = region)*occ_coeff
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rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
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rem_signal[idx] = 1-obs.data[region].sum()/obs.data.sum()
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rem_area[idx] = 1 - rem_region.sum()/np.logical_not(obs.data.mask).sum()
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# poiss_comp[idx] = poisson_divider(masked_obs)
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poiss_comp[idx] = np.mean((masked_obs-masked_obs.mean())**2/masked_obs.mean())
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rms[idx] = np.sqrt(((masked_obs-masked_obs.mean())**2).mean())
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parameter = lambda idx: ((poiss_comp[idx])**2+((1-rem_area[idx])*0.5)**2)
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if (parameter(idx)<parameter(good_idx)):
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good_idx = idx
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good_lvl = lvl
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try:
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# region = np.array([layer>0 for layer in wav_obs[2:-1][lvl]]).sum(0).astype(bool)
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region = wav_obs[2:-1][good_lvl].sum(0)>0
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except ValueError:
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region = np.zeros(obs.data.shape,dtype=bool)
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masked_obs = np.ma.masked_array(obs.data, mask = region)
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rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
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to_table = [obs.obs_id,
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obs.det,
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obs.ra,
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obs.dec,
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lon,
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lat,
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obs.time_start,
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obs.exposure,
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masked_obs.mean()/obs.exposure, #count rate
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1 - rem_region.sum()/np.logical_not(obs.data.mask).sum(), #rem_area
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poiss_comp[good_idx],
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poiss_comp[0],
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rms[good_idx]
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]
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else:
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to_table = [obs.obs_id,
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obs.det,
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obs.ra,
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obs.dec,
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lon,
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lat,
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obs.time_start,
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obs.exposure,
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-1,
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-1, #rem_signal
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-1, #rem_area
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-1,
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-1,
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-1
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]
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return to_table, region.astype(int)
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except TypeError:
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return obs_name, np.zeros((360,360))
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#%%
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if __name__ == '__main__':
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#DIALOGUE
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print('Enter path to the input folder')
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input_folder = input()
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obs_list = get_link_list(input_folder,sort_list = True)[:]
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print('Create new file for this processing? y/n')
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continue_old = input()
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if continue_old == 'y':
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start_new = True
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elif continue_old == 'n':
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start_new = False
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else:
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print('Cannot interprete input, closing script')
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raise SystemExit(0)
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print(f'Enter path to the output folder')
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fits_folder = input()
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region_folder = f'{fits_folder}\\Region'
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#INIT ALL NECESSARY FILES AND VARIBALES
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start = time.perf_counter()
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processing = True
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group_size = 50
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os.makedirs(region_folder,exist_ok = True)
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#FILTERING BY THE FILE SIZE
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print(f'Finished scanning folders. Found {len(obs_list)} observations.')
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table = {
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'obs_id':[], 'detector':[], 'ra':[], 'dec':[], 'lon':[], 'lat':[], 't_start':[], 'exposure':[],
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'count_rate':[], 'remaining_area':[], 'poisson_chi2':[], 'poisson_chi2_full':[], 'rms':[]
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}
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if start_new:
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out_table = DataFrame(table)
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out_table.to_csv(f'{fits_folder}\\test.csv')
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out_table.to_csv(f'{fits_folder}\\test_skipped.csv')
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#FILTERING OUT PROCESSED OBSERVATIONS
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already_processed_list = read_csv(f'{fits_folder}\\test.csv',index_col=0,dtype={'obs_id':str})
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already_skipped_list = read_csv(f'{fits_folder}\\test_skipped.csv',index_col=0,dtype={'obs_id':str})
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already_processed = (already_processed_list['obs_id'].astype(str)+already_processed_list['detector']).values
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already_skipped = (already_skipped_list['obs_id'].astype(str)+already_skipped_list['detector']).values
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obs_list_names = [curr[curr.index('nu')+2:curr.index('_cl.evt')-2] for curr in obs_list]
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not_processed = np.array([(curr not in already_processed) for curr in obs_list_names])
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not_skipped = np.array([(curr not in already_skipped) for curr in obs_list_names])
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obs_list = obs_list[np.logical_and(not_processed,not_skipped)]
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print(f'Removed already processed observations. {len(obs_list)} observations remain.')
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#START PROCESSING
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if processing:
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print('Started processing...')
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num = 0
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for group_idx in range(len(obs_list)//group_size+1):
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print(f'Started group {group_idx}')
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group_list = obs_list[group_size*group_idx:min(group_size*(group_idx+1),len(obs_list))]
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max_size = np.array([stat(file).st_size/2**20 for file in group_list]).max()
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process_num = cpu_count() if max_size<50 else (cpu_count()//2 if max_size<200 else (cpu_count()//4 if max_size<1000 else 1))
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print(f"Max file size in group is {max_size:.2f}Mb, create {process_num} processes")
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with get_context('spawn').Pool(processes=process_num) as pool:
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for result,region in pool.imap(process,enumerate(group_list)):
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if type(result) is np.str_:
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obs_id = result[result.index('nu'):result.index('_cl.evt')]
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print(f'{num:>3} is skipped. File {obs_id}')
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num +=1
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continue
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for key,value in zip(table.keys(),result):
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table[key] = [value]
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if table['exposure'][0] < 1000:
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print(f'{num:>3} {str(result[0])+result[1]} is skipped. Exposure < 1000')
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DataFrame(table).to_csv(f'{fits_folder}\\test_skipped.csv',mode='a',header=False)
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num +=1
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continue
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DataFrame(table).to_csv(f'{fits_folder}\\test.csv',mode='a',header=False)
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fits.writeto(f'{region_folder}\\{str(result[0])+result[1]}_region.fits', region, overwrite= True)
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print(f'{num:>3} {str(result[0])+result[1]} is written.')
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num +=1
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print('Converting generated csv to fits file...')
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print(f'Current time in: {(time.perf_counter()-start):.2f}')
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print(f'Processed {num/len(obs_list)*100:.2f} percent')
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csv_file = read_csv(f'{fits_folder}\\test.csv',index_col=0,dtype={'obs_id':str})
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Table.from_pandas(csv_file).write(f'{fits_folder}\\test.fits',overwrite=True)
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print(f'Finished writing: {time.perf_counter()-start}')
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# %%
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@ -1,16 +1,28 @@
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# %%
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import numpy as np
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import itertools
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from os import stat
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from pandas import DataFrame, read_csv
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from astropy.table import Table
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from astropy.coordinates import SkyCoord
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from astropy import units as u
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from multiprocessing import get_context, cpu_count
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from time import perf_counter
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from os import stat, makedirs
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from os.path import dirname
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from scipy.signal import fftconvolve, convolve2d
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from astropy.io import fits
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from astropy.wcs import WCS
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from glob import glob
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# %%
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from warnings import filterwarnings
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filterwarnings('ignore')
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def get_link_list(folder, sort_list=True):
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links = glob(f'{folder}\\**\\*_cl.evt',recursive=True)
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if sort_list:
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sorted_list = sorted(links, key=lambda x: stat(x).st_size)
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return np.array(sorted_list)
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else:
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return np.array(links)
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def binary_array(num):
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variants = [[0,1],]*num
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out = np.zeros((2**num,num),bool)
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@ -34,14 +46,7 @@ def get_wcs(file):
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'NAXIS1': header['TLMAX38'], 'NAXIS2': header['TLMAX39']
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})
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return wcs
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def get_link_list(folder, sort_list=True):
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links = glob(f'{folder}\\**\\*_cl.evt',recursive=True)
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if sort_list:
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sorted_list = sorted(links, key=lambda x: stat(x).st_size)
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return np.array(sorted_list)
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else:
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return np.array(links)
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def atrous(level = 0, resize = False, max_size = 1001):
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def atrous(level = 0, max_size = 1001):
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base = 1/256*np.array([
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[1, 4, 6, 4, 1],
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[4, 16, 24, 16, 4],
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@ -52,12 +57,10 @@ def atrous(level = 0, resize = False, max_size = 1001):
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size = 2**level * (base.shape[0]-1)+1
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output = np.zeros((size,size))
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output[::2**level, ::2**level] = base
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if resize:
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output = np.pad(output, pad_width=2**(level+1))
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if output.shape[0]>max_size:
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return output[(size-1)//2-(max_size-1)//2:(size-1)//2+(max_size-1)//2+1, (size-1)//2-(max_size-1)//2:(size-1)//2+(max_size-1)//2+1]
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return output
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def gauss(level=0, resize=False, max_size = 1000):
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def gauss(level=0, max_size = 1000):
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size = min(5*2**(level+1)+1, max_size)
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sigma = 2**(level)
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A = 1/(2*np.pi*sigma**2)**0.5
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@ -105,19 +108,7 @@ def fill_poisson(array, size_input=32):
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mask[idx] = False
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size *= 2
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return output
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def fill_mean(array,size_input=3):
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size = size_input
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if not(isinstance(array,np.ma.MaskedArray)):
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print('No mask found')
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return array
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output = array.filled(0)
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for i,j in zip(*np.where(array.mask)):
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output[i,j] = array[max(0,i-size):min(array.shape[0]-1,i+size+1),max(0,j-size):min(array.shape[1]-1,j+size+1)].mean()
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while np.isnan(output).any():
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size += 5
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for i,j in zip(*np.where(np.isnan(output))):
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output[i,j] = array[max(0,i-size):min(array.shape[0]-1,i+size+1),max(0,j-size):min(array.shape[1]-1,j+size+1)].mean()
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return output
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def mirror(array):
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size = array.shape[0]
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output = np.tile(array,(3,3))
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@ -140,9 +131,6 @@ class Observation:
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self.wcs = get_wcs(file)
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self.data = np.ma.masked_array(*self.get_data(file,E_borders))
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self.hard_mask = add_borders(self.data.data,middle=False)
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shift = shift = int((360-64)/2)
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self.model = (fits.getdata(f'D:\Programms\Jupyter\Science\Source_mask\Model/det1_fpm{self.det}.cxb.fits',0)
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*(180/np.pi/10150)**2)[shift:360+shift,shift:360+shift]
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self.exposure = self.header['EXPOSURE']
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def get_coeff(self):
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coeff = np.array([0.977,0.861,1.163,1.05]) if self.det=='A' else np.array([1.004,0.997,1.025,0.979])
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@ -165,7 +153,8 @@ class Observation:
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output = np.zeros((360,360))
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kernel = np.ones((5,5))
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for i in range(4):
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pixpos_file = fits.getdata(f'D:\\Programms\\Jupyter\\Science\\Source_mask\\Pixpos\\nu{self.det}pixpos20100101v007.fits',i+1)
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current_dir = dirname(__file__)
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pixpos_file = fits.getdata(f'{current_dir}\\pixpos\\nu{self.det}pixpos20100101v007.fits',i+1)
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bad_pix_file = file[3+i].data.copy()
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temp = np.zeros(len(pixpos_file),dtype=bool)
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for x,y in zip(bad_pix_file['rawx'],bad_pix_file['rawy']):
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@ -174,7 +163,7 @@ class Observation:
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output += np.histogram2d(temp['REF_DET1Y'],temp['REF_DET1X'], 360, [[0,360],[0,360]])[0]
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output = convolve2d(output, kernel, mode='same') > 0
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return output
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def wavdecomp(self, mode = 'atrous', thresh=False,occ_coeff = False):
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def wavdecomp(self, mode = 'gauss', thresh=False,occ_coeff = False):
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#THRESHOLD
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if type(thresh) is int: thresh_max, thresh_add = thresh, thresh/2
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elif type(thresh) is tuple: thresh_max, thresh_add = thresh
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@ -219,4 +208,149 @@ class Observation:
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data = conv
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conv_out[max_level] = conv[size:2*size,size:2*size]
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return conv_out
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# %%
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def process(obs_path):
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bin_num = 6
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try:
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obs = Observation(obs_path)
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sky_coord = SkyCoord(ra=obs.ra*u.deg,dec=obs.dec*u.deg,frame='fk5').transform_to('galactic')
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lon, lat = sky_coord.l.value, sky_coord.b.value
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rem_signal, rem_area, poiss_comp, rms = np.zeros((4,2**bin_num))
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region = np.zeros(obs.data.shape, dtype=bool)
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rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
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masked_obs = np.ma.masked_array(obs.data, mask = region)
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good_lvl = np.zeros(bin_num,dtype=bool)
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good_idx = 0
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if obs.exposure > 1000:
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wav_obs = obs.wavdecomp('gauss',(5,3),occ_coeff=True)
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occ_coeff = obs.get_coeff()
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for idx, lvl in enumerate(binary_array(bin_num)):
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try:
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region = wav_obs[2:-1][lvl].sum(0)>0
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except ValueError:
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region = np.zeros(obs.data.shape,dtype=bool)
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masked_obs = np.ma.masked_array(obs.data, mask = region)*occ_coeff
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rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
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rem_signal[idx] = 1-obs.data[region].sum()/obs.data.sum()
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rem_area[idx] = 1 - rem_region.sum()/np.logical_not(obs.data.mask).sum()
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poiss_comp[idx] = np.mean((masked_obs-masked_obs.mean())**2/masked_obs.mean())
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rms[idx] = np.sqrt(((masked_obs-masked_obs.mean())**2).mean())
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parameter = lambda idx: ((poiss_comp[idx])**2+((1-rem_area[idx])*0.5)**2)
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if (parameter(idx)<parameter(good_idx)):
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good_idx = idx
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good_lvl = lvl
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try:
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region = wav_obs[2:-1][good_lvl].sum(0)>0
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except ValueError:
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region = np.zeros(obs.data.shape,dtype=bool)
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masked_obs = np.ma.masked_array(obs.data, mask = region)
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rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
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to_table = [obs.obs_id,
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obs.det,
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obs.ra,
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obs.dec,
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lon,
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lat,
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obs.time_start,
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obs.exposure,
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masked_obs.mean()/obs.exposure, #count rate
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1 - rem_region.sum()/np.logical_not(obs.data.mask).sum(), #rem_area
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poiss_comp[good_idx],
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poiss_comp[0],
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rms[good_idx]
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]
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else:
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to_table = [obs.obs_id,
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obs.det,
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obs.ra,
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obs.dec,
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lon,
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lat,
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obs.time_start,
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obs.exposure,
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-1,
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-1, #rem_signal
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-1, #rem_area
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-1,
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-1,
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-1
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]
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return to_table, region.astype(int)
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except TypeError:
|
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return obs_path, np.zeros((360,360))
|
||||
def process_folder(input_folder = None, continue_old = None, fits_folder = None):
|
||||
#DIALOGUE
|
||||
if not(input_folder):
|
||||
print('Enter path to the input folder')
|
||||
input_folder = input()
|
||||
if not(continue_old):
|
||||
print('Create new file for this processing? y/n')
|
||||
continue_old = input()
|
||||
if continue_old == 'y':
|
||||
start_new = True
|
||||
elif continue_old == 'n':
|
||||
start_new = False
|
||||
else:
|
||||
print('Cannot interprete input, closing script')
|
||||
raise SystemExit(0)
|
||||
if not(fits_folder):
|
||||
print(f'Enter path to the output folder')
|
||||
fits_folder = input()
|
||||
region_folder = f'{fits_folder}\\Region'
|
||||
#CREATE ALL NECESSARY FILES AND VARIBALES
|
||||
obs_list = get_link_list(input_folder,sort_list = True)
|
||||
start = perf_counter()
|
||||
group_size = 50
|
||||
makedirs(region_folder,exist_ok = True)
|
||||
#FILTERING BY THE FILE SIZE
|
||||
print(f'Finished scanning folders. Found {len(obs_list)} observations.')
|
||||
table = {
|
||||
'obs_id':[], 'detector':[], 'ra':[], 'dec':[], 'lon':[], 'lat':[], 't_start':[], 'exposure':[],
|
||||
'count_rate':[], 'remaining_area':[], 'poisson_chi2':[], 'poisson_chi2_full':[], 'rms':[]
|
||||
}
|
||||
if start_new:
|
||||
out_table = DataFrame(table)
|
||||
out_table.to_csv(f'{fits_folder}\\test.csv')
|
||||
out_table.to_csv(f'{fits_folder}\\test_skipped.csv')
|
||||
#FILTERING OUT PROCESSED OBSERVATIONS
|
||||
already_processed_list = read_csv(f'{fits_folder}\\test.csv',index_col=0,dtype={'obs_id':str})
|
||||
already_skipped_list = read_csv(f'{fits_folder}\\test_skipped.csv',index_col=0,dtype={'obs_id':str})
|
||||
already_processed = (already_processed_list['obs_id'].astype(str)+already_processed_list['detector']).values
|
||||
already_skipped = (already_skipped_list['obs_id'].astype(str)+already_skipped_list['detector']).values
|
||||
obs_list_names = [curr[curr.index('nu')+2:curr.index('_cl.evt')-2] for curr in obs_list]
|
||||
not_processed = np.array([(curr not in already_processed) for curr in obs_list_names])
|
||||
not_skipped = np.array([(curr not in already_skipped) for curr in obs_list_names])
|
||||
obs_list = obs_list[np.logical_and(not_processed,not_skipped)]
|
||||
print(f'Removed already processed observations. {len(obs_list)} observations remain.')
|
||||
#START PROCESSING
|
||||
print('Started processing...')
|
||||
num = 0
|
||||
for group_idx in range(len(obs_list)//group_size+1):
|
||||
print(f'Started group {group_idx}')
|
||||
group_list = obs_list[group_size*group_idx:min(group_size*(group_idx+1),len(obs_list))]
|
||||
max_size = np.array([stat(file).st_size/2**20 for file in group_list]).max()
|
||||
process_num = cpu_count() if max_size<50 else (cpu_count()//2 if max_size<200 else (cpu_count()//4 if max_size<1000 else 1))
|
||||
print(f"Max file size in group is {max_size:.2f}Mb, create {process_num} processes")
|
||||
with get_context('spawn').Pool(processes=process_num) as pool:
|
||||
for result,region in pool.imap(process,group_list):
|
||||
if type(result) is np.str_:
|
||||
obs_id = result[result.index('nu'):result.index('_cl.evt')]
|
||||
print(f'{num:>3} is skipped. File {obs_id}')
|
||||
num +=1
|
||||
continue
|
||||
for key,value in zip(table.keys(),result):
|
||||
table[key] = [value]
|
||||
if table['exposure'][0] < 1000:
|
||||
print(f'{num:>3} {str(result[0])+result[1]} is skipped. Exposure < 1000')
|
||||
DataFrame(table).to_csv(f'{fits_folder}\\test_skipped.csv',mode='a',header=False)
|
||||
num +=1
|
||||
continue
|
||||
DataFrame(table).to_csv(f'{fits_folder}\\test.csv',mode='a',header=False)
|
||||
fits.writeto(f'{region_folder}\\{str(result[0])+result[1]}_region.fits', region, overwrite= True)
|
||||
print(f'{num:>3} {str(result[0])+result[1]} is written.')
|
||||
num +=1
|
||||
print('Converting generated csv to fits file...')
|
||||
print(f'Current time in: {(perf_counter()-start):.2f}')
|
||||
print(f'Processed {num/len(obs_list)*100:.2f} percent')
|
||||
csv_file = read_csv(f'{fits_folder}\\test.csv',index_col=0,dtype={'obs_id':str})
|
||||
Table.from_pandas(csv_file).write(f'{fits_folder}\\test.fits',overwrite=True)
|
||||
print(f'Finished writing: {perf_counter()-start}')
|
||||
Loading…
x
Reference in New Issue
Block a user