210 lines
9.2 KiB
Python
Executable File
210 lines
9.2 KiB
Python
Executable File
#!/usr/bin/python3
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#-*- coding:utf-8 -*-
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"""
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Library function for simplified fits handling.
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prototypes :
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- get_obs_data(infiles, data_folder) -> data_array, headers
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Extract the observationnal data from fits files
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- save_Stokes(I, Q, U, Stokes_cov, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P, headers, data_mask, filename, data_folder, return_hdul) -> ( HDUL_data )
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Save computed polarimetry parameters to a single fits file (and return HDUList)
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"""
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import numpy as np
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from os.path import join as path_join
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from astropy.io import fits
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from astropy.wcs import WCS
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from lib.convex_hull import image_hull, clean_ROI
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from lib.plots import princ_angle
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import matplotlib.pyplot as plt
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def get_obs_data(infiles, data_folder="", compute_flux=False):
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"""
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Extract the observationnal data from the given fits files.
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----------
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Inputs:
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infiles : strlist
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List of the fits file names to be added to the observation set.
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data_folder : str, optional
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Relative or absolute path to the folder containing the data.
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compute_flux : boolean, optional
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If True, return data_array will contain flux information, assuming
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raw data are counts and header have keywork EXPTIME and PHOTFLAM.
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Default to False.
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----------
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Returns:
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data_array : numpy.ndarray
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Array of images (2D floats) containing the observation data.
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headers : header list
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List of headers objects corresponding to each image in data_array.
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"""
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data_array, headers = [], []
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for i in range(len(infiles)):
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with fits.open(path_join(data_folder,infiles[i])) as f:
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headers.append(f[0].header)
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data_array.append(f[0].data)
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data_array = np.array(data_array,dtype=np.double)
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# Prevent negative count value in imported data
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for i in range(len(data_array)):
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data_array[i][data_array[i] < 0.] = 0.
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# force WCS to convention PCi_ja unitary, cdelt in deg
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for header in headers:
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new_wcs = WCS(header).deepcopy()
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if new_wcs.wcs.has_cd() or (new_wcs.wcs.cdelt == np.array([1., 1.])).all():
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# Update WCS with relevant information
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if new_wcs.wcs.has_cd():
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old_cd = new_wcs.wcs.cd
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del new_wcs.wcs.cd
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keys = list(new_wcs.to_header().keys())+['CD1_1','CD1_2','CD2_1','CD2_2']
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for key in keys:
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header.remove(key, ignore_missing=True)
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new_cdelt = np.linalg.eig(old_cd)[0]
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elif (new_wcs.wcs.cdelt == np.array([1., 1.])).all() and \
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(new_wcs.array_shape in [(512, 512),(1024,512),(512,1024),(1024,1024)]):
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old_cd = new_wcs.wcs.pc
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new_wcs.wcs.pc = np.dot(old_cd, np.diag(1./new_cdelt))
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new_wcs.wcs.cdelt = new_cdelt
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for key, val in new_wcs.to_header().items():
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header[key] = val
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header['orientat'] = princ_angle(float(header['orientat']))
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# force WCS for POL60 to have same pixel size as POL0 and POL120
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is_pol60 = np.array([head['filtnam1'].lower()=='pol60' for head in headers],dtype=bool)
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cdelt = np.array([WCS(head).wcs.cdelt for head in headers])
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if np.unique(cdelt[np.logical_not(is_pol60)],axis=0).size!=2:
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print(np.unique(cdelt[np.logical_not(is_pol60)],axis=0).size)
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raise ValueError("Not all images have same pixel size")
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else:
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for head in np.array(headers,dtype=object)[is_pol60]:
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head['cdelt1'],head['cdelt2'] = np.unique(cdelt[np.logical_not(is_pol60)],axis=0)[0]
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if compute_flux:
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for i in range(len(infiles)):
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# Compute the flux in counts/sec
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data_array[i] /= headers[i]['EXPTIME']
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return data_array, headers
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def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
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s_P_P, PA, s_PA, s_PA_P, headers, data_mask, filename, data_folder="",
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return_hdul=False):
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"""
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Save computed polarimetry parameters to a single fits file,
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updating header accordingly.
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----------
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Inputs:
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I_stokes, Q_stokes, U_stokes, P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P : numpy.ndarray
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Images (2D float arrays) containing the computed polarimetric data :
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Stokes parameters I, Q, U, Polarization degree and debieased,
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its error propagated and assuming Poisson noise, Polarization angle,
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its error propagated and assuming Poisson noise.
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Stokes_cov : numpy.ndarray
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Covariance matrix of the Stokes parameters I, Q, U.
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headers : header list
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Header of reference some keywords will be copied from (CRVAL, CDELT,
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INSTRUME, PROPOSID, TARGNAME, ORIENTAT, EXPTOT).
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data_mask : numpy.ndarray
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2D boolean array delimiting the data to work on.
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filename : str
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Name that will be given to the file on writing (will appear in header).
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data_folder : str, optional
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Relative or absolute path to the folder the fits file will be saved to.
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Defaults to current folder.
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return_hdul : boolean, optional
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If True, the function will return the created HDUList from the
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input arrays.
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Defaults to False.
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----------
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Return:
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hdul : astropy.io.fits.hdu.hdulist.HDUList
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HDUList containing I_stokes in the PrimaryHDU, then Q_stokes, U_stokes,
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P, s_P, PA, s_PA in this order. Headers have been updated to relevant
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informations (WCS, orientation, data_type).
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Only returned if return_hdul is True.
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"""
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#Create new WCS object given the modified images
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ref_header = headers[0]
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exp_tot = np.array([header['exptime'] for header in headers]).sum()
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new_wcs = WCS(ref_header).deepcopy()
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if data_mask.shape != (1,1):
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vertex = clean_ROI(data_mask)
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shape = vertex[1::2]-vertex[0::2]
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new_wcs.array_shape = shape
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new_wcs.wcs.crpix = np.array(new_wcs.wcs.crpix) - vertex[0::-2]
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header = new_wcs.to_header()
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header['instrume'] = (ref_header['instrume'], 'Instrument from which data is reduced')
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header['photplam'] = (ref_header['photplam'], 'Pivot Wavelength')
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header['photflam'] = (ref_header['photflam'], 'Inverse Sensitivity in DN/sec/cm**2/Angst')
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header['exptot'] = (exp_tot, 'Total exposure time in sec')
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header['proposid'] = (ref_header['proposid'], 'PEP proposal identifier for observation')
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header['targname'] = (ref_header['targname'], 'Target name')
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header['orientat'] = (ref_header['orientat'], 'Angle between North and the y-axis of the image')
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header['filename'] = (filename, 'Original filename')
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header['P_int'] = (ref_header['P_int'], 'Integrated polarization degree')
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header['P_int_err'] = (ref_header['P_int_err'], 'Integrated polarization degree error')
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header['PA_int'] = (ref_header['PA_int'], 'Integrated polarization angle')
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header['PA_int_err'] = (ref_header['PA_int_err'], 'Integrated polarization angle error')
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#Crop Data to mask
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if data_mask.shape != (1,1):
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I_stokes = I_stokes[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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Q_stokes = Q_stokes[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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U_stokes = U_stokes[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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P = P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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debiased_P = debiased_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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s_P = s_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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s_P_P = s_P_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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PA = PA[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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s_PA = s_PA[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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s_PA_P = s_PA_P[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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new_Stokes_cov = np.zeros((*Stokes_cov.shape[:-2],*shape[::-1]))
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for i in range(3):
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for j in range(3):
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Stokes_cov[i,j][(1-data_mask).astype(bool)] = 0.
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new_Stokes_cov[i,j] = Stokes_cov[i,j][vertex[2]:vertex[3],vertex[0]:vertex[1]]
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Stokes_cov = new_Stokes_cov
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data_mask = data_mask[vertex[2]:vertex[3],vertex[0]:vertex[1]]
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data_mask = data_mask.astype(float, copy=False)
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#Create HDUList object
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hdul = fits.HDUList([])
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#Add I_stokes as PrimaryHDU
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header['datatype'] = ('I_stokes', 'type of data stored in the HDU')
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I_stokes[(1-data_mask).astype(bool)] = 0.
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primary_hdu = fits.PrimaryHDU(data=I_stokes, header=header)
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primary_hdu.name = 'I_stokes'
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hdul.append(primary_hdu)
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#Add Q, U, Stokes_cov, P, s_P, PA, s_PA to the HDUList
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for data, name in [[Q_stokes,'Q_stokes'],[U_stokes,'U_stokes'],
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[Stokes_cov,'IQU_cov_matrix'],[P, 'Pol_deg'],
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[debiased_P, 'Pol_deg_debiased'],[s_P, 'Pol_deg_err'],
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[s_P_P, 'Pol_deg_err_Poisson_noise'],[PA, 'Pol_ang'],
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[s_PA, 'Pol_ang_err'],[s_PA_P, 'Pol_ang_err_Poisson_noise'],
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[data_mask, 'Data_mask']]:
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hdu_header = header.copy()
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hdu_header['datatype'] = name
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if not name == 'IQU_cov_matrix':
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data[(1-data_mask).astype(bool)] = 0.
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hdu = fits.ImageHDU(data=data,header=hdu_header)
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hdu.name = name
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hdul.append(hdu)
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#Save fits file to designated filepath
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hdul.writeto(path_join(data_folder,filename+".fits"), overwrite=True)
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if return_hdul:
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return hdul
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else:
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return 0
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