Merged two files into one package

get_region_archive_table.py

@@ -1,175 +0,0 @@
-# %%
-from get_region_pack import *
-import numpy as np
-from pandas import DataFrame, read_csv
-from astropy.table import Table
-from astropy.coordinates import SkyCoord
-from astropy import units as u
-from multiprocessing import get_context, cpu_count
-import warnings
-import time
-import os
-warnings.filterwarnings('ignore')
-# %%
-def poisson_divider(array):
-    sub_arrays = np.zeros((4,180,180))
-    for i in range(2):
-        for j in range(2):
-            sub_arrays[i+2*j] = array[180*i:180*(i+1),180*j:180*(j+1)].filled(-1000)
-    pix_sum = 0
-    for layer in sub_arrays:
-        _masked_array = np.ma.masked_less(layer,0)
-        pix_sum += ((_masked_array-_masked_array.mean())**2/_masked_array.mean()).sum()
-    pix_sum /= np.logical_not(array.mask).sum()
-    return pix_sum
-def process(argument):
-    idx, obs_name = argument
-    bin_num = 6
-    try:
-        obs = Observation(obs_name)
-        sky_coord = SkyCoord(ra=obs.ra*u.deg,dec=obs.dec*u.deg,frame='fk5').transform_to('galactic')
-        lon, lat = sky_coord.l.value, sky_coord.b.value
-        rem_signal, rem_area, poiss_comp, rms = np.zeros((4,2**bin_num))
-        region = np.zeros(obs.data.shape, dtype=bool)
-        rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
-        masked_obs = np.ma.masked_array(obs.data, mask = region)
-        good_lvl = np.zeros(bin_num,dtype=bool)
-        good_idx = 0
-        if obs.exposure > 1000:
-            wav_obs = obs.wavdecomp('gauss',(5,3),occ_coeff=True)
-            occ_coeff = obs.get_coeff()
-            for idx, lvl in enumerate(binary_array(bin_num)):
-                try:
-                    # region = np.array([layer>0 for layer in wav_obs[2:-1][lvl]]).sum(0).astype(bool)
-                    region = wav_obs[2:-1][lvl].sum(0)>0
-                except ValueError:
-                    region = np.zeros(obs.data.shape,dtype=bool)
-                masked_obs = np.ma.masked_array(obs.data, mask = region)*occ_coeff
-                rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
-                rem_signal[idx] = 1-obs.data[region].sum()/obs.data.sum()
-                rem_area[idx] = 1 - rem_region.sum()/np.logical_not(obs.data.mask).sum()
-                # poiss_comp[idx] = poisson_divider(masked_obs)
-                poiss_comp[idx] = np.mean((masked_obs-masked_obs.mean())**2/masked_obs.mean())
-                rms[idx] = np.sqrt(((masked_obs-masked_obs.mean())**2).mean())
-                parameter = lambda idx: ((poiss_comp[idx])**2+((1-rem_area[idx])*0.5)**2)
-                if (parameter(idx)<parameter(good_idx)):
-                    good_idx = idx
-                    good_lvl = lvl
-            try:
-                # region = np.array([layer>0 for layer in wav_obs[2:-1][lvl]]).sum(0).astype(bool)
-                region = wav_obs[2:-1][good_lvl].sum(0)>0
-            except ValueError:
-                region = np.zeros(obs.data.shape,dtype=bool)
-            masked_obs = np.ma.masked_array(obs.data, mask = region)
-            rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
-            to_table = [obs.obs_id,
-                        obs.det,
-                        obs.ra,
-                        obs.dec,
-                        lon,
-                        lat,
-                        obs.time_start, 
-                        obs.exposure,
-                        masked_obs.mean()/obs.exposure, #count rate
-                        1 - rem_region.sum()/np.logical_not(obs.data.mask).sum(), #rem_area
-                        poiss_comp[good_idx],
-                        poiss_comp[0],
-                        rms[good_idx]
-                        ]
-        else:
-            to_table = [obs.obs_id,
-                        obs.det,
-                        obs.ra,
-                        obs.dec,
-                        lon,
-                        lat,
-                        obs.time_start, 
-                        obs.exposure,
-                        -1,
-                        -1, #rem_signal
-                        -1, #rem_area
-                        -1,
-                        -1,
-                        -1
-                        ]
-        return to_table, region.astype(int)
-    except TypeError:
-        return obs_name, np.zeros((360,360))
-#%%  
-if __name__ == '__main__':
-    #DIALOGUE
-    print('Enter path to the input folder')
-    input_folder = input()
-    obs_list = get_link_list(input_folder,sort_list = True)[:]
-    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)
-    print(f'Enter path to the output folder')
-    fits_folder = input()
-    region_folder = f'{fits_folder}\\Region'
-    #INIT ALL NECESSARY FILES AND VARIBALES
-    start = time.perf_counter()
-    processing = True
-    group_size = 50
-    os.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
-    if 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,enumerate(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: {(time.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: {time.perf_counter()-start}')
-# %%

wavsource_nustar.py

@@ -1,16 +1,28 @@
 # %%
 import numpy as np
 import itertools
-
+from pandas import DataFrame, read_csv
-from os import stat
+from astropy.table import Table
-
+from astropy.coordinates import SkyCoord
+from astropy import units as u
+from multiprocessing import get_context, cpu_count
+from time import perf_counter
+from os import stat, makedirs
+from os.path import dirname
 from scipy.signal import fftconvolve, convolve2d
-
 from astropy.io import fits
 from astropy.wcs import WCS
-
 from glob import glob
-# %%
+from warnings import filterwarnings
+filterwarnings('ignore')
+
+def get_link_list(folder, sort_list=True):
+    links = glob(f'{folder}\\**\\*_cl.evt',recursive=True)
+    if sort_list:
+        sorted_list = sorted(links, key=lambda x: stat(x).st_size) 
+        return np.array(sorted_list)
+    else:
+        return np.array(links)
 def binary_array(num):
     variants = [[0,1],]*num
     out = np.zeros((2**num,num),bool)
@@ -34,14 +46,7 @@ def get_wcs(file):
         'NAXIS1': header['TLMAX38'], 'NAXIS2': header['TLMAX39']
     })
     return wcs
-def get_link_list(folder, sort_list=True):
+def atrous(level = 0, max_size = 1001):
-    links = glob(f'{folder}\\**\\*_cl.evt',recursive=True)
-    if sort_list:
-        sorted_list = sorted(links, key=lambda x: stat(x).st_size) 
-        return np.array(sorted_list)
-    else:
-        return np.array(links)
-def atrous(level = 0, resize = False, max_size = 1001):
     base = 1/256*np.array([
         [1,  4,  6,  4, 1],
         [4, 16, 24, 16, 4],
@@ -52,12 +57,10 @@ def atrous(level = 0, resize = False, max_size = 1001):
     size = 2**level * (base.shape[0]-1)+1
     output = np.zeros((size,size))
     output[::2**level, ::2**level] = base
-    if resize:
-        output = np.pad(output, pad_width=2**(level+1))
     if output.shape[0]>max_size:
         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]
     return output
-def gauss(level=0, resize=False, max_size = 1000):
+def gauss(level=0, max_size = 1000):
     size = min(5*2**(level+1)+1, max_size)
     sigma = 2**(level)
     A = 1/(2*np.pi*sigma**2)**0.5
@@ -105,19 +108,7 @@ def fill_poisson(array, size_input=32):
         mask[idx] = False
         size *= 2
     return output
-def fill_mean(array,size_input=3):
+
-    size = size_input
-    if not(isinstance(array,np.ma.MaskedArray)):
-        print('No mask found')
-        return array
-    output = array.filled(0)
-    for i,j in zip(*np.where(array.mask)):
-        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()
-    while np.isnan(output).any():
-        size += 5
-        for i,j in zip(*np.where(np.isnan(output))):
-            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()
-    return output
 def mirror(array):
     size = array.shape[0]
     output = np.tile(array,(3,3))
@@ -140,9 +131,6 @@ class Observation:
             self.wcs = get_wcs(file)
             self.data = np.ma.masked_array(*self.get_data(file,E_borders))
             self.hard_mask = add_borders(self.data.data,middle=False)
-        shift = shift = int((360-64)/2)
-        self.model = (fits.getdata(f'D:\Programms\Jupyter\Science\Source_mask\Model/det1_fpm{self.det}.cxb.fits',0)
-                *(180/np.pi/10150)**2)[shift:360+shift,shift:360+shift]
         self.exposure = self.header['EXPOSURE']
     def get_coeff(self):
         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])
@@ -165,7 +153,8 @@ class Observation:
         output = np.zeros((360,360))
         kernel = np.ones((5,5))
         for i in range(4):
-            pixpos_file = fits.getdata(f'D:\\Programms\\Jupyter\\Science\\Source_mask\\Pixpos\\nu{self.det}pixpos20100101v007.fits',i+1)
+            current_dir = dirname(__file__)
+            pixpos_file = fits.getdata(f'{current_dir}\\pixpos\\nu{self.det}pixpos20100101v007.fits',i+1)
             bad_pix_file = file[3+i].data.copy()
             temp = np.zeros(len(pixpos_file),dtype=bool)
             for x,y in zip(bad_pix_file['rawx'],bad_pix_file['rawy']):    
@@ -174,7 +163,7 @@ class Observation:
             output += np.histogram2d(temp['REF_DET1Y'],temp['REF_DET1X'], 360, [[0,360],[0,360]])[0]
         output = convolve2d(output, kernel, mode='same') > 0
         return output
-    def wavdecomp(self, mode = 'atrous', thresh=False,occ_coeff = False):
+    def wavdecomp(self, mode = 'gauss', thresh=False,occ_coeff = False):
         #THRESHOLD
         if type(thresh) is int: thresh_max, thresh_add = thresh, thresh/2
         elif type(thresh) is tuple: thresh_max, thresh_add = thresh
@@ -219,4 +208,149 @@ class Observation:
             data = conv
         conv_out[max_level] = conv[size:2*size,size:2*size]
         return conv_out
-# %%
+def process(obs_path):
+    bin_num = 6
+    try:
+        obs = Observation(obs_path)
+        sky_coord = SkyCoord(ra=obs.ra*u.deg,dec=obs.dec*u.deg,frame='fk5').transform_to('galactic')
+        lon, lat = sky_coord.l.value, sky_coord.b.value
+        rem_signal, rem_area, poiss_comp, rms = np.zeros((4,2**bin_num))
+        region = np.zeros(obs.data.shape, dtype=bool)
+        rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
+        masked_obs = np.ma.masked_array(obs.data, mask = region)
+        good_lvl = np.zeros(bin_num,dtype=bool)
+        good_idx = 0
+        if obs.exposure > 1000:
+            wav_obs = obs.wavdecomp('gauss',(5,3),occ_coeff=True)
+            occ_coeff = obs.get_coeff()
+            for idx, lvl in enumerate(binary_array(bin_num)):
+                try:
+                    region = wav_obs[2:-1][lvl].sum(0)>0
+                except ValueError:
+                    region = np.zeros(obs.data.shape,dtype=bool)
+                masked_obs = np.ma.masked_array(obs.data, mask = region)*occ_coeff
+                rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
+                rem_signal[idx] = 1-obs.data[region].sum()/obs.data.sum()
+                rem_area[idx] = 1 - rem_region.sum()/np.logical_not(obs.data.mask).sum()
+                poiss_comp[idx] = np.mean((masked_obs-masked_obs.mean())**2/masked_obs.mean())
+                rms[idx] = np.sqrt(((masked_obs-masked_obs.mean())**2).mean())
+                parameter = lambda idx: ((poiss_comp[idx])**2+((1-rem_area[idx])*0.5)**2)
+                if (parameter(idx)<parameter(good_idx)):
+                    good_idx = idx
+                    good_lvl = lvl
+            try:
+                region = wav_obs[2:-1][good_lvl].sum(0)>0
+            except ValueError:
+                region = np.zeros(obs.data.shape,dtype=bool)
+            masked_obs = np.ma.masked_array(obs.data, mask = region)
+            rem_region = np.logical_and(region, np.logical_not(obs.data.mask))
+            to_table = [obs.obs_id,
+                        obs.det,
+                        obs.ra,
+                        obs.dec,
+                        lon,
+                        lat,
+                        obs.time_start, 
+                        obs.exposure,
+                        masked_obs.mean()/obs.exposure, #count rate
+                        1 - rem_region.sum()/np.logical_not(obs.data.mask).sum(), #rem_area
+                        poiss_comp[good_idx],
+                        poiss_comp[0],
+                        rms[good_idx]
+                        ]
+        else:
+            to_table = [obs.obs_id,
+                        obs.det,
+                        obs.ra,
+                        obs.dec,
+                        lon,
+                        lat,
+                        obs.time_start, 
+                        obs.exposure,
+                        -1,
+                        -1, #rem_signal
+                        -1, #rem_area
+                        -1,
+                        -1,
+                        -1
+                        ]
+        return to_table, region.astype(int)
+    except TypeError:
+        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}')