538 lines
21 KiB
Python
538 lines
21 KiB
Python
# %%
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import numpy as np
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import itertools
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from pandas import DataFrame, read_csv
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from astropy.table import Table, unique
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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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from warnings import filterwarnings
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filterwarnings('ignore')
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def get_link_list(folder: str, sort_list: bool = True) -> list[str]:
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"""
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Returns array of paths to all *_cl.evt files in the directory recursively.
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"""
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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: int) -> list[list[bool]]:
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"""
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Returns list of all possible combinations of num of bool values.
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"""
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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(file_path: str, mode: str = 'Sky') -> list[int]:
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"""
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Returns a 2d array of counts for given observation file.
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Modes 'Sky' and 'Det' return arrays in (X,Y) and DET1 respectively.
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"""
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temp = fits.getdata(file_path, 1)
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if mode == 'Sky':
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return np.histogram2d(temp['Y'],
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temp['X'],
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1000,
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[[0, 1000], [0, 1000]])[0]
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if mode == 'Det':
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return np.histogram2d(temp['DET1Y'],
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temp['DET1X'],
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360,
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[[0, 360], [0, 360]])[0]
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def get_wcs(file):
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"""
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Returns WCS for given observation.
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Note that argument here is an opened fits file, not a path.
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"""
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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 atrous(level: int = 0, max_size: int = 1001) -> list[list[float]]:
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"""
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Returns a trou kernel with the size 2**level and corresponding shape.
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"""
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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 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,
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(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 atrous_sig(level: int = 0) -> float:
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sig_values = [0.8908, 0.20066, 0.08551, 0.04122, 0.02042]
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if level < 5:
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return sig_values[level]
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else:
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return sig_values[4]/2**(level-4)
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def gauss(level: int = 0, max_size: int = 1000) -> list[list[float]]:
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"""
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Returns gaussian kernel with sigma = 2**level
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"""
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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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"""
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Returns two lists of indices of cells adjecent or diagonal to non-zero cells of given array
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"""
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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)),
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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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return output
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def add_borders(array, middle=True):
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"""
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Returns border mask for an DET1 observation array
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"""
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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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# Add border masks
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x_min, y_min = np.argmax(datax), np.argmax(datay)
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x_max = len(datax) - np.argmax(datax[::-1])
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y_max = len(datay) - np.argmax(datay[::-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 = 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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"""
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Fills all masked elements of an array with poisson signal with local expected value.
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"""
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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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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 mirror(array):
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"""
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Returns 3 times bigger array with mirrored elements.
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Original array is located in center.
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"""
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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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"""
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Main class, contains information about the observation given.
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"""
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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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self.exposure = self.header['EXPOSURE']
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def get_coeff(self):
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"""
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Returns normalalizing coefficients for different chips of the observation detector.
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Coefficients are obtained from stacked observations in OCC mode.
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"""
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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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"""
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Returns masked array with DET1 image data for given energy band.
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Mask is created from observations badpix tables and to mask the border and gaps.
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"""
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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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"""
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Creates a mask for observation based on badpix tables.
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"""
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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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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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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='gauss', thresh=False, occ_coeff=False):
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"""
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Performs a wavelet decomposition of image.
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"""
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# THRESHOLD
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if type(thresh) is int:
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thresh_max, thresh_add = thresh, thresh/2
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elif type(thresh) is tuple:
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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(
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(max_level+1, self.data.shape[0], self.data.shape[1])
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)
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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:
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data = data*self.get_coeff()
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data = mirror(data)
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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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conv[conv < 0] = 0
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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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if mode == 'gauss':
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sig = ((wavelet(i)**2).sum())**0.5
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if mode == 'atrous':
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sig = atrous_sig(i)
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bkg = fftconvolve(data, wavelet(i), mode='same')
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bkg[bkg < 0] = 0
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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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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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adj = adjecent(significant)
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add_condition = np.logical_and(adj, add_significant)
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while (add_condition).any():
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significant = np.logical_or(significant, add_condition)
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adj = adjecent(significant)
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add_condition = np.logical_and(adj, add_significant)
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significant = mirror(significant)
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temp_out[np.logical_not(significant)] = 0
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# WRITING THE WAVELET DECOMP LAYER
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conv_out[i] = +temp_out[size:2*size, size:2*size]
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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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def region_to_raw(self, region):
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"""
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Returns a hdu_list with positions of masked pixels in RAW coordinates.
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"""
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x_region, y_region = np.where(region)
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tables = []
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for i in range(4):
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current_dir = dirname(__file__)
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pixpos = Table(fits.getdata(f'{current_dir}\\pixpos\\nu{self.det}pixpos20100101v007.fits', i+1))
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pixpos = pixpos[pixpos['REF_DET1X'] != -1]
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test = np.zeros(len(pixpos['REF_DET1X']), dtype=bool)
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for idx, (x, y) in enumerate(zip(pixpos['REF_DET1X'], pixpos['REF_DET1Y'])):
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test[idx] = np.logical_and(np.equal(x, x_region), np.equal(y, y_region)).any()
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table = Table({'RAWX': pixpos['RAWX'][test], 'RAWY': pixpos['RAWY'][test]})
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if not table:
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tables.append(table)
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else:
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tables.append(unique(table))
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hdu_list = fits.HDUList([
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fits.PrimaryHDU(),
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fits.table_to_hdu(tables[0]),
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fits.table_to_hdu(tables[1]),
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fits.table_to_hdu(tables[2]),
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fits.table_to_hdu(tables[3]),
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])
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return hdu_list
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def process(args):
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"""
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Creates a mask using wavelet decomposition and produces some statistical and metadata about the passed observation.
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args must contain two arguments: path to the file of interest and threshold, e.g. ('D:\Data\obs_cl.evt',(5,2))
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"""
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obs_path, thresh = args
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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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region_raw = -1
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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('atrous', thresh, occ_coeff=True)
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wav_obs[wav_obs < 0] = 0
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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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# region = fftconvolve(wav_obs[2:-1][lvl].sum(0)>0, gauss(3),mode='same') > 0.5
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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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# region = fftconvolve(wav_obs[2:-1][good_lvl].sum(0)>0, gauss(2),mode='same')>0.2
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if region.sum() > 0:
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region_raw = obs.region_to_raw(region.astype(int))
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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), region_raw
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except TypeError:
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return obs_path, -1, -1
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def process_folder(input_folder=None, start_new_file=None, fits_folder=None, thresh=None):
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"""
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Generates a fits-table of parameters, folder with mask images in DET1 and BADPIX tables in RAW for all observations in given folder.
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Note that observations with exposure < 1000 sec a skipped.
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start_new_file can be either 'y' or 'n'.
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thresh must be a tuple, e.g. (5,2).
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"""
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# DIALOGUE
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if not (input_folder):
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print('Enter path to the input folder')
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input_folder = input()
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if not (start_new_file):
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print('Create new file for this processing? y/n')
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start_new_file = input()
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if start_new_file == 'y':
|
|
start_new = True
|
|
elif start_new_file == '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'
|
|
region_raw_folder = f'{fits_folder}\\Region_raw'
|
|
if not thresh:
|
|
print('Enter threshold values for wavelet decomposition:')
|
|
print('General threshold:')
|
|
_thresh_max = float(input())
|
|
print('Additional threshold:')
|
|
_thresh_add = float(input())
|
|
thresh = (_thresh_max, _thresh_add)
|
|
# 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)
|
|
makedirs(region_raw_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:
|
|
packed_args = map(lambda _: (_, thresh), group_list)
|
|
for result, region, region_raw in pool.imap(process, packed_args):
|
|
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)
|
|
if region_raw != -1:
|
|
region_raw.writeto(f'{region_raw_folder}\\{str(result[0])+result[1]}_reg_raw.fits', 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}')
|