233 lines
11 KiB
Python
233 lines
11 KiB
Python
# %%
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import import_ipynb
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import numpy as np
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import pandas as pd
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import itertools
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from os import listdir, mkdir, stat
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from scipy.signal import fftconvolve, convolve2d
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import matplotlib.pyplot as plt
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from matplotlib.colors import SymLogNorm as lognorm
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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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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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for idx, level in enumerate(itertools.product(*variants)):
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out[idx] = np.array(level,dtype=bool)
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return out
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def create_array(filename,mode='Sky'):
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temp = fits.getdata(filename,1)
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if mode == 'Sky':
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return np.histogram2d(temp['Y'],temp['X'],1000,[[0, 1000], [0, 1000]])[0]
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if mode == 'Det':
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return np.histogram2d(temp['DET1Y'],temp['DET1X'],360,[[0, 360], [0, 360]])[0]
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def get_wcs(file):
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header = file[1].header
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wcs = WCS({
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'CTYPE1': header['TCTYP38'], 'CTYPE2': header['TCTYP39'],
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'CUNIT1': header['TCUNI38'], 'CUNIT2': header['TCUNI39'],
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'CDELT1': header['TCDLT38'], 'CDELT2': header['TCDLT39'],
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'CRPIX1': header['TCRPX38'], 'CRPIX2': header['TCRPX39'],
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'CRVAL1': header['TCRVL38'], 'CRVAL2': header['TCRVL39'],
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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=False):
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links = glob(f'{folder}\\**\\*_cl.evt',recursive=True)
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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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def atrous(level = 0, resize = False, 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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[6, 24, 36, 24, 6],
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[4, 16, 24, 16, 4],
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[1, 4, 6, 4, 1],
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])
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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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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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x = A*np.exp( (-(np.arange(size)-(size-1)//2)**2)/(2*sigma**2))
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out = np.multiply.outer(x,x)
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return out
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def adjecent(array):
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grid = np.array([
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[1,1,1],
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[1,0,1],
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[1,1,1]
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])
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output = fftconvolve(array,grid,mode='same') >= 0.5
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try:
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output = np.logical_and(np.logical_and(output, np.logical_not(array)),np.logical_not(array.mask))
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except AttributeError:
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output = np.logical_and(output, np.logical_not(array))
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output = np.argwhere(output == True)
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return output[:,0], output[:,1]
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def add_borders(array,middle=True):
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# array_blurred = convolve2d(array,np.ones((5,5)),mode='same')
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mask = np.zeros(array.shape)
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datax, datay = np.any(array>0,0), np.any(array>0,1)
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# datax, datay = np.any(array_blurred>0,0), np.any(array_blurred>0,1)
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#Add border masks
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x_min, y_min = np.argmax(datax), np.argmax(datay)
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x_max, y_max = len(datax) - np.argmax(datax[::-1]), len(datay) - np.argmax(datay[::-1])
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# x_mid_min, y_mid_min = x_min+10+np.argmin(datax[x_min+10:]), y_min+10+np.argmin(datay[y_min+10:])
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# x_mid_max, y_mid_max = x_max-10-np.argmin(datax[x_max-11::-1]), y_max-10-np.argmin(datay[y_max-11::-1])
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mask[y_min:y_max,x_min:x_max] = True
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if middle is True:
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mask[176:191,:] = False
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mask[:,176:191] = False
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# mask[y_mid_min:y_mid_max,:] = False
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# mask[:,x_mid_min:x_mid_max] = False
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mask = np.logical_not(mask)
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return mask
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def fill_poisson(array, size_input=32):
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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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size = size_input
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output = array.data.copy()
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mask = array.mask.copy()
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while mask.sum()>1:
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kernel = np.ones((size,size))/size**2
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# coeff = fftconvolve(np.logical_not(mask), kernel, mode='same')
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coeff = fftconvolve(np.logical_not(mask),kernel,mode='same')
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mean = fftconvolve(output,kernel,mode='same')
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idx = np.where(np.logical_and(mask,coeff>0.1))
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output[idx] = np.random.poisson(np.abs(mean[idx]/coeff[idx]))
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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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output[0:size] = np.flipud(output[0:size])
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output[2*size:3*size] = np.flipud(output[2*size:3*size])
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output[:,0:size] = np.fliplr(output[:,0:size])
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output[:,2*size:3*size] = np.fliplr(output[:,2*size:3*size])
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return output
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class Observation:
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def __init__(self, file_name, E_borders=[3,20]):
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self.filename = file_name
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self.name = file_name[file_name.find('nu'):].replace('_cl.evt','')
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with fits.open(file_name) as file:
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self.obs_id = file[0].header['OBS_ID']
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self.ra = file[0].header['RA_NOM']
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self.dec = file[0].header['DEC_NOM']
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self.time_start = file[0].header['TSTART']
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self.header = file[0].header
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self.det = self.header['INSTRUME'][-1]
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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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resized_coeff = (coeff).reshape(2,2).repeat(180,0).repeat(180,1)
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return resized_coeff
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def get_data(self, file, E_borders=[3,20]):
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PI_min, PI_max = (np.array(E_borders)-1.6)/0.04
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data = file[1].data.copy()
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idx_mask = (data['STATUS'].sum(1) == 0)
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idx_output = np.logical_and(idx_mask,np.logical_and((data['PI']>PI_min),(data['PI']<PI_max)))
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data_output = data[idx_output]
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data_mask = data[np.logical_not(idx_mask)]
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build_hist = lambda array: np.histogram2d(array['DET1Y'],array['DET1X'],360,[[0,360],[0,360]])[0]
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output = build_hist(data_output)
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mask = build_hist(data_mask)
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mask = np.logical_or(mask,add_borders(output))
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mask = np.logical_or(mask, self.get_bad_pix(file))
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return output, mask
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def get_bad_pix(self, file):
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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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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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temp = np.logical_or(temp, np.equal(pixpos_file['rawx'],x)*np.equal(pixpos_file['rawy'],y))
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temp = pixpos_file[temp]
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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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#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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#INIT NEEDED VARIABLES
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wavelet = globals()[mode]
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max_level = 8
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conv_out = np.zeros((max_level+1,self.data.shape[0],self.data.shape[1]))
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size = self.data.shape[0]
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#PREPARE ORIGINAL DATA FOR ANALYSIS: FILL THE HOLES + MIRROR + DETECTOR CORRECTION
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data = fill_poisson(self.data)
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if occ_coeff: data = data*self.get_coeff()
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data = mirror(data)
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data_bkg = data.copy()
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#ITERATIVELY CONDUCT WAVLET DECOMPOSITION
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for i in range(max_level):
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conv = fftconvolve(data,wavelet(i),mode='same')
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temp_out = data-conv
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#ERRORMAP CALCULATION
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if thresh_max != 0:
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sig = sigma(mode, i)
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bkg = fftconvolve(data_bkg, wavelet(i),mode='same')
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bkg[bkg<0] = 0
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# err = (1+np.sqrt(bkg/sig**2 + 0.75))*sig**3
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err = (1+np.sqrt(bkg+0.75))*sig
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significant = (np.abs(temp_out)> thresh_max*err)[size:2*size,size:2*size]
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# significant = (temp_out > thresh_max*err)[size:2*size,size:2*size]
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if thresh_add != 0:
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add_significant = (np.abs(temp_out)> thresh_add*err)[size:2*size,size:2*size]
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# add_significant = (temp_out > thresh_add*err)[size:2*size,size:2*size]
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adj = adjecent(significant)
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add_condition = np.logical_and(add_significant[adj[0],adj[1]],np.logical_not(significant[adj[0],adj[1]]))
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while (add_condition).any():
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to_add = adj[0][add_condition], adj[1][add_condition]
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significant[to_add[0],to_add[1]] = True
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adj = adjecent(significant)
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add_condition = np.logical_and(add_significant[adj[0],adj[1]],np.logical_not(significant[adj[0],adj[1]]))
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# 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]])
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temp_out[size:2*size,size:2*size][np.logical_not(significant)] = 0
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#WRITING THE WAVELET DECOMP LAYER
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if temp_out[size:2*size,size:2*size].sum() == 0: break
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conv_out[i] = +temp_out[size:2*size,size:2*size]
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conv_out[i][conv_out[i]<0]=0 #leave only positive data to prevent problems while summing layers
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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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