144 lines
6.0 KiB
Python
Executable File
144 lines
6.0 KiB
Python
Executable File
"""
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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, ref_header, 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 astropy.io import fits
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from astropy import wcs
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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(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)
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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 ergs/cm²/s.angstrom
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data_array[i] *= headers[i]['PHOTFLAM']/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, 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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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.WCS(ref_header).deepcopy()
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if new_wcs.wcs.has_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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ref_header.remove(key, ignore_missing=True)
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new_wcs.wcs.cdelt = 3600.*np.sqrt(np.sum(new_wcs.wcs.get_pc()**2,axis=1))
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if (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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# Update WCS with relevant information
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HST_aper = 2400. # HST aperture in mm
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f_ratio = ref_header['f_ratio']
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px_dim = np.array([25., 25.]) # Pixel dimension in µm
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if ref_header['pxformt'].lower() == 'zoom':
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px_dim[0] = 50.
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new_wcs.wcs.cdelt = 206.3*px_dim/(f_ratio*HST_aper)
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new_wcs.wcs.crpix = [I_stokes.shape[0]/2, I_stokes.shape[1]/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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#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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primary_hdu = fits.PrimaryHDU(data=I_stokes, header=header)
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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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hdu_header = header.copy()
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hdu_header['datatype'] = name
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hdu = fits.ImageHDU(data=data,header=hdu_header)
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hdul.append(hdu)
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#Save fits file to designated filepath
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hdul.writeto(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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