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Merge pull request #3 from Andreyousan/code_trimming

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Code trimming
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Андрей Мухин 2022-08-31 16:49:40 +03:00 committed by GitHub
commit 8615bdaf0c
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2 changed files with 34 additions and 87 deletions
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14
get_region_archive_table.py
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@ -12,16 +12,6 @@ import time
import os import os
warnings.filterwarnings('ignore') warnings.filterwarnings('ignore')
# %% # %%
def ellipse(array):
grid = np.indices(array.shape)[::-1]
center = [((curr_axis*array).sum()/array.sum()) for curr_axis in grid]
y,x = np.where(array)
return center, np.abs(x-center[0]).max()*2, np.abs(y-center[1]).max()*2
def mask_ellipse(array):
(center_x, center_y), len_x, len_y = ellipse(array)
x,y = np.indices(array.shape)[::-1]
radius = ((x-center_x)/(0.5*len_x))**2+((y-center_y)/(0.5*len_y))**2
return radius <= 1
def poisson_divider(array): def poisson_divider(array):
sub_arrays = np.zeros((4,180,180)) sub_arrays = np.zeros((4,180,180))
for i in range(2): for i in range(2):
@ -109,8 +99,8 @@ def process(argument):
#%% #%%
if __name__ == '__main__': if __name__ == '__main__':
start = time.perf_counter() start = time.perf_counter()
processing = True processing = False
start_new = True start_new = False
group_size = 50 group_size = 50
fits_folder = 'D:\Programms\Jupyter\Science\Source_mask\\\Archive\Processing_v8' fits_folder = 'D:\Programms\Jupyter\Science\Source_mask\\\Archive\Processing_v8'
region_folder = f'{fits_folder}\\Region' region_folder = f'{fits_folder}\\Region'

107
get_region_pack.py
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@ -1,16 +1,11 @@
# %% # %%
import import_ipynb
import numpy as np import numpy as np
import pandas as pd
import itertools import itertools
from os import listdir, mkdir, stat from os import stat
from scipy.signal import fftconvolve, convolve2d from scipy.signal import fftconvolve, convolve2d
import matplotlib.pyplot as plt
from matplotlib.colors import SymLogNorm as lognorm
from astropy.io import fits from astropy.io import fits
from astropy.wcs import WCS from astropy.wcs import WCS
@ -60,25 +55,12 @@ def atrous(level = 0, resize = False, max_size = 1001):
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[(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 return output
def gauss(level=0, resize=False, max_size = 1000): def gauss(level=0, resize=False, max_size = 1000):
size = min(5*2**level+1 if not resize else 5*2**(level+1)+1, max_size) size = min(5*2**(level+1)+1, max_size)
sigma = 2**(level) sigma = 2**(level)
if sigma < 1:
out = np.zeros((size+1,size+1))
out[int((size-1)/2)+1][int((size-1)/2)+1] = 1
return out
A = 1/(2*np.pi*sigma**2)**0.5 A = 1/(2*np.pi*sigma**2)**0.5
x = A*np.exp( (-(np.arange(size)-(size-1)//2)**2)/(2*sigma**2)) x = A*np.exp( (-(np.arange(size)-(size-1)//2)**2)/(2*sigma**2))
out = np.multiply.outer(x,x) out = np.multiply.outer(x,x)
return out return out
def sigma(mode='atrous',level=0):
if mode=='atrous':
sigma = [0.8908, 0.20066, 0.08551, 0.04122, 0.02042, 0.0114]
elif mode=='gauss':
sigma = [0.912579, 0.125101, 0.104892, 4.97810e-02, 2.46556e-02, 1.14364e-02]
if level < 6:
return sigma[level]
else:
return sigma[5]/2**(level-5)
def adjecent(array): def adjecent(array):
grid = np.array([ grid = np.array([
[1,1,1], [1,1,1],
@ -166,31 +148,10 @@ class Observation:
self.model = (fits.getdata(f'D:\Programms\Jupyter\Science\Source_mask\Model/det1_fpm{self.det}.cxb.fits',0) 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] *(180/np.pi/10150)**2)[shift:360+shift,shift:360+shift]
self.exposure = self.header['EXPOSURE'] self.exposure = self.header['EXPOSURE']
# def get_coeff(self,folder='D:\\Programms\\Jupyter\\Science\\Source_mask\\Archive\\OCC_observations'):
# try:
# # _occ_list = get_link_list('D:\\Programms\\Jupyter\\Science\\Source_mask\\Archive\\OCC_observations')
# _occ_list = get_link_list(folder)
# occ_list = dict()
# for name in _occ_list:
# occ_list[name[name.index('nu'):name.index('02_cl.evt')]] = name
# occ = Observation(occ_list[self.name[:-2]],E_borders=[10,20])
# output = np.zeros((360,360))
# for i in range(2):
# for j in range(2):
# _temp = occ.data[180*i:180*(i+1),180*j:180*(j+1)].mean()
# output[180*i:180*(i+1),180*j:180*(j+1)] = _temp if _temp != 0 else 1
# except KeyError:
# output = np.ones((360,360))
# out = output.min()/output
# if np.any(out<0.8):
# return np.ones((360,360))
# else:
# return out
def get_coeff(self): def get_coeff(self):
# coeff = np.array([0.972,0.895,1.16,1.02]) if self.det=='A' else np.array([0.991,1.047,1.012,0.956])
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]) 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])
test = (coeff).reshape(2,2).repeat(180,0).repeat(180,1) resized_coeff = (coeff).reshape(2,2).repeat(180,0).repeat(180,1)
return test return resized_coeff
def get_data(self, file, E_borders=[3,20]): def get_data(self, file, E_borders=[3,20]):
PI_min, PI_max = (np.array(E_borders)-1.6)/0.04 PI_min, PI_max = (np.array(E_borders)-1.6)/0.04
data = file[1].data.copy() data = file[1].data.copy()
@ -215,15 +176,14 @@ class Observation:
temp = np.logical_or(temp, np.equal(pixpos_file['rawx'],x)*np.equal(pixpos_file['rawy'],y)) temp = np.logical_or(temp, np.equal(pixpos_file['rawx'],x)*np.equal(pixpos_file['rawy'],y))
temp = pixpos_file[temp] temp = pixpos_file[temp]
output += np.histogram2d(temp['REF_DET1Y'],temp['REF_DET1X'], 360, [[0,360],[0,360]])[0] output += np.histogram2d(temp['REF_DET1Y'],temp['REF_DET1X'], 360, [[0,360],[0,360]])[0]
output = convolve2d(output, kernel, mode='same') >0 output = convolve2d(output, kernel, mode='same') > 0
return output return output
def wavdecomp(self, mode = 'atrous', thresh=False, iteration = 1,occ_coeff = False): def wavdecomp(self, mode = 'atrous', thresh=False,occ_coeff = False):
#THRESHOLD #THRESHOLD
if type(thresh) is int: thresh_max, thresh_add = thresh, thresh/2 if type(thresh) is int: thresh_max, thresh_add = thresh, thresh/2
elif type(thresh) is tuple: thresh_max, thresh_add = thresh elif type(thresh) is tuple: thresh_max, thresh_add = thresh
#INIT BASIC VARIABLES #INIT NEEDED VARIABLES
wavelet = globals()[mode] wavelet = globals()[mode]
# max_level = int(np.ceil(np.log2(self.data.shape[0])))+1
max_level = 8 max_level = 8
conv_out = np.zeros((max_level+1,self.data.shape[0],self.data.shape[1])) conv_out = np.zeros((max_level+1,self.data.shape[0],self.data.shape[1]))
size = self.data.shape[0] size = self.data.shape[0]
@ -234,36 +194,33 @@ class Observation:
data_bkg = data.copy() data_bkg = data.copy()
#ITERATIVELY CONDUCT WAVLET DECOMPOSITION #ITERATIVELY CONDUCT WAVLET DECOMPOSITION
for i in range(max_level): for i in range(max_level):
for num_iter in range(iteration): conv = fftconvolve(data,wavelet(i),mode='same')
conv = fftconvolve(data,wavelet(i),mode='same') temp_out = data-conv
temp_out = data-conv #ERRORMAP CALCULATION
#ERRORMAP CALCULATION if thresh_max != 0:
if thresh_max != 0: sig = ((wavelet(i)**2).sum())**0.5
sig = sigma(mode, i) bkg = fftconvolve(data_bkg, wavelet(i),mode='same')
bkg = fftconvolve(data_bkg, wavelet(i),mode='same') bkg[bkg<0] = 0
bkg[bkg<0] = 0 # err = (1+np.sqrt(bkg/sig**2 + 0.75))*sig**3
# err = (1+np.sqrt(bkg/sig**2 + 0.75))*sig**3 err = (1+np.sqrt(bkg+0.75))*sig
err = (1+np.sqrt(bkg+0.75))*sig # significant = (np.abs(temp_out)> thresh_max*err)[size:2*size,size:2*size]
significant = (np.abs(temp_out)> thresh_max*err)[size:2*size,size:2*size] significant = (temp_out > thresh_max*err)[size:2*size,size:2*size]
# significant = (temp_out > thresh_max*err)[size:2*size,size:2*size] if thresh_add != 0:
if thresh_add != 0: # add_significant = (np.abs(temp_out)> thresh_add*err)[size:2*size,size:2*size]
add_significant = (np.abs(temp_out)> thresh_add*err)[size:2*size,size:2*size] add_significant = (temp_out > thresh_add*err)[size:2*size,size:2*size]
# add_significant = (temp_out > thresh_add*err)[size:2*size,size:2*size] adj = adjecent(significant)
add_condition = np.logical_and(add_significant[adj[0],adj[1]],np.logical_not(significant[adj[0],adj[1]]))
while (add_condition).any():
to_add = adj[0][add_condition], adj[1][add_condition]
significant[to_add[0],to_add[1]] = True
adj = adjecent(significant) adj = adjecent(significant)
add_condition = np.logical_and(add_significant[adj[0],adj[1]],np.logical_not(significant[adj[0],adj[1]])) add_condition = np.logical_and(add_significant[adj[0],adj[1]],np.logical_not(significant[adj[0],adj[1]]))
while (add_condition).any(): # add_condition = np.logical_and(np.abs(temp_out)[adj[0],adj[1]] >= thresh_add*err[adj[0],adj[1]], np.logical_not(significant)[adj[0],adj[1]])
to_add = adj[0][add_condition], adj[1][add_condition] temp_out[size:2*size,size:2*size][np.logical_not(significant)] = 0
significant[to_add[0],to_add[1]] = True #WRITING THE WAVELET DECOMP LAYER
adj = adjecent(significant) conv_out[i] = +temp_out[size:2*size,size:2*size]
add_condition = np.logical_and(add_significant[adj[0],adj[1]],np.logical_not(significant[adj[0],adj[1]])) conv_out[i][conv_out[i]<0]=0 #leave only positive data to prevent problems while summing layers
# add_condition = np.logical_and(np.abs(temp_out)[adj[0],adj[1]] >= thresh_add*err[adj[0],adj[1]], np.logical_not(significant)[adj[0],adj[1]]) data = conv
temp_out[size:2*size,size:2*size][np.logical_not(significant)] = 0
#WRITING THE WAVELET DECOMP LAYER
if temp_out[size:2*size,size:2*size].sum() == 0: break
conv_out[i] = +temp_out[size:2*size,size:2*size]
conv_out[i][conv_out[i]<0]=0 #leave only positive data to prevent problems while summing layers
# conv_out[i] = significant
data = conv
conv_out[max_level] = conv[size:2*size,size:2*size] conv_out[max_level] = conv[size:2*size,size:2*size]
return conv_out return conv_out
# %% # %%