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This commit is contained in:
Andrey Mukhin
parent
2a759d92ef
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9e2c094b0a
@ -12,16 +12,6 @@ import time
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import os
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warnings.filterwarnings('ignore')
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# %%
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def ellipse(array):
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grid = np.indices(array.shape)[::-1]
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center = [((curr_axis*array).sum()/array.sum()) for curr_axis in grid]
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y,x = np.where(array)
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return center, np.abs(x-center[0]).max()*2, np.abs(y-center[1]).max()*2
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def mask_ellipse(array):
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(center_x, center_y), len_x, len_y = ellipse(array)
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x,y = np.indices(array.shape)[::-1]
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radius = ((x-center_x)/(0.5*len_x))**2+((y-center_y)/(0.5*len_y))**2
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return radius <= 1
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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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@ -109,8 +99,8 @@ def process(argument):
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#%%
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if __name__ == '__main__':
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start = time.perf_counter()
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processing = True
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start_new = True
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processing = False
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start_new = False
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group_size = 50
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fits_folder = 'D:\Programms\Jupyter\Science\Source_mask\\\Archive\Processing_v8'
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region_folder = f'{fits_folder}\\Region'
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@ -60,25 +60,12 @@ def atrous(level = 0, resize = False, max_size = 1001):
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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 if not resize else 5*2**(level+1)+1, max_size)
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size = min(5*2**(level+1)+1, max_size)
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sigma = 2**(level)
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if sigma < 1:
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out = np.zeros((size+1,size+1))
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out[int((size-1)/2)+1][int((size-1)/2)+1] = 1
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return out
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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 sigma(mode='atrous',level=0):
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if mode=='atrous':
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sigma = [0.8908, 0.20066, 0.08551, 0.04122, 0.02042, 0.0114]
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elif mode=='gauss':
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sigma = [0.912579, 0.125101, 0.104892, 4.97810e-02, 2.46556e-02, 1.14364e-02]
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if level < 6:
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return sigma[level]
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else:
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return sigma[5]/2**(level-5)
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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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@ -166,31 +153,10 @@ class Observation:
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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,folder='D:\\Programms\\Jupyter\\Science\\Source_mask\\Archive\\OCC_observations'):
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# try:
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# # _occ_list = get_link_list('D:\\Programms\\Jupyter\\Science\\Source_mask\\Archive\\OCC_observations')
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# _occ_list = get_link_list(folder)
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# occ_list = dict()
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# for name in _occ_list:
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# occ_list[name[name.index('nu'):name.index('02_cl.evt')]] = name
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# occ = Observation(occ_list[self.name[:-2]],E_borders=[10,20])
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# output = np.zeros((360,360))
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# for i in range(2):
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# for j in range(2):
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# _temp = occ.data[180*i:180*(i+1),180*j:180*(j+1)].mean()
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# output[180*i:180*(i+1),180*j:180*(j+1)] = _temp if _temp != 0 else 1
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# except KeyError:
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# output = np.ones((360,360))
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# out = output.min()/output
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# if np.any(out<0.8):
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# return np.ones((360,360))
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# else:
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# return out
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def get_coeff(self):
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# 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])
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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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test = (coeff).reshape(2,2).repeat(180,0).repeat(180,1)
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return test
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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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@ -215,15 +181,14 @@ class Observation:
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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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output = convolve2d(output, kernel, mode='same') > 0
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return output
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def wavdecomp(self, mode = 'atrous', thresh=False, iteration = 1,occ_coeff = False):
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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 BASIC VARIABLES
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#INIT NEEDED VARIABLES
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wavelet = globals()[mode]
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# max_level = int(np.ceil(np.log2(self.data.shape[0])))+1
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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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@ -234,36 +199,34 @@ class Observation:
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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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for num_iter in range(iteration):
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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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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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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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# conv_out[i] = significant
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data = conv
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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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