diff --git a/package/FOC_reduction.py b/package/FOC_reduction.py index 5dd6c89..c4d7272 100755 --- a/package/FOC_reduction.py +++ b/package/FOC_reduction.py @@ -41,12 +41,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop= # Background estimation error_sub_type = "scott" # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51)) - subtract_error = 1.50 + subtract_error = 0.50 display_bkg = True # Data binning - pxsize = 0.10 - pxscale = "arcsec" # pixel, arcsec or full + pxsize = 4 + pxscale = "px" # pixel, arcsec or full rebin_operation = "sum" # sum or average # Alignement @@ -59,8 +59,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop= # Smoothing smoothing_function = "combine" # gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine - smoothing_FWHM = 0.150 # If None, no smoothing is done - smoothing_scale = "arcsec" # pixel or arcsec + smoothing_FWHM = 1.5 # If None, no smoothing is done + smoothing_scale = "px" # pixel or arcsec # Rotation rotate_North = True @@ -216,29 +216,27 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop= # FWHM of FOC have been estimated at about 0.03" across 1500-5000 Angstrom band, which is about 2 detector pixels wide # see Jedrzejewski, R.; Nota, A.; Hack, W. J., A Comparison Between FOC and WFPC2 # Bibcode : 1995chst.conf...10J - I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes, sigma_flux = proj_red.compute_Stokes( + I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_stat = proj_red.compute_Stokes( data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=transmitcorr ) - I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, coeff_stokes, sigma_flux_bkg = proj_red.compute_Stokes( + I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, s_IQU_stat_bkg = proj_red.compute_Stokes( background, background_error, np.array(True).reshape(1, 1), headers, FWHM=None, scale=smoothing_scale, smoothing=smoothing_function, transmitcorr=False ) # Step 3: # Rotate images to have North up if rotate_North: - I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, sigma_flux = proj_red.rotate_Stokes( - I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, sigma_flux=sigma_flux, SNRi_cut=None + I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat = proj_red.rotate_Stokes( + I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat=s_IQU_stat, SNRi_cut=None ) - I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg, sigma_flux_bkg = proj_red.rotate_Stokes( - I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, sigma_flux=sigma_flux_bkg, SNRi_cut=None + I_bkg, Q_bkg, U_bkg, S_cov_bkg, data_mask_bkg, header_bkg, s_IQU_stat_bkg = proj_red.rotate_Stokes( + I_bkg, Q_bkg, U_bkg, S_cov_bkg, np.array(True).reshape(1, 1), header_bkg, s_IQU_stat=s_IQU_stat_bkg, SNRi_cut=None ) # Compute polarimetric parameters (polarization degree and angle). - P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol( - I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes=coeff_stokes, sigma_flux=sigma_flux - ) + P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P = proj_red.compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_stat=s_IQU_stat) P_bkg, debiased_P_bkg, s_P_bkg, s_P_P_bkg, PA_bkg, s_PA_bkg, s_PA_P_bkg = proj_red.compute_pol( - I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, coeff_stokes=coeff_stokes, sigma_flux=sigma_flux_bkg + I_bkg, Q_bkg, U_bkg, S_cov_bkg, header_bkg, s_IQU_stat=s_IQU_stat_bkg ) # Step 4: diff --git a/package/lib/reduction.py b/package/lib/reduction.py index badce29..87db179 100755 --- a/package/lib/reduction.py +++ b/package/lib/reduction.py @@ -1462,10 +1462,10 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle") header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error") - return I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes, sigma_flux + return I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_stat -def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_stokes=None, sigma_flux=None): +def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, s_IQU_stat=None): """ Compute the polarization degree (in %) and angle (in deg) and their respective errors from given Stokes parameters. @@ -1548,20 +1548,19 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_s s_P_P = np.ones(I_stokes.shape) * fmax s_PA_P = np.ones(I_stokes.shape) * fmax maskP = np.logical_and(mask, P > 0.0) - if coeff_stokes is not None and sigma_flux is not None: + if s_IQU_stat is not None: s_P_P[maskP] = ( P[maskP] / I_stokes[maskP] * np.sqrt( - np.sum( - [ - ((coeff_stokes[1, i] * Q_stokes[maskP] + coeff_stokes[2, i] * U_stokes[maskP]) / (I_stokes[maskP] * P[maskP] ** 2) - coeff_stokes[0, i]) - ** 2 - * sigma_flux[i][maskP] ** 2 - for i in range(sigma_flux.shape[0]) - ], - axis=0, - )[0] + s_IQU_stat[0, 0][maskP] + - 2.0 / (I_stokes[maskP] * P[maskP] ** 2) * (Q_stokes[maskP] * s_IQU_stat[0, 1][maskP] + U_stokes[maskP] * s_IQU_stat[0, 2][maskP]) + + 1.0 + / (I_stokes[maskP] ** 2 * P[maskP] ** 4) + * ( + Q_stokes[maskP] ** 2 * s_IQU_stat[1, 1][maskP] + + U_stokes[maskP] ** 2 * s_IQU_stat[2, 2][maskP] * Q_stokes[maskP] * U_stokes[maskP] * s_IQU_stat[1, 2][maskP] + ) ) ) else: @@ -1588,7 +1587,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, header_stokes, coeff_s return P, debiased_P, s_P, s_P_P, PA, s_PA, s_PA_P -def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, sigma_flux=None, SNRi_cut=None): +def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_stokes, s_IQU_stat=None, SNRi_cut=None): """ Use scipy.ndimage.rotate to rotate I_stokes to an angle, and a rotation matrix to rotate Q, U of a given angle in degrees and update header @@ -1681,8 +1680,16 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_st new_I_stokes[i, j], new_Q_stokes[i, j], new_U_stokes[i, j] = np.dot(mrot, np.array([new_I_stokes[i, j], new_Q_stokes[i, j], new_U_stokes[i, j]])).T new_Stokes_cov[:, :, i, j] = np.dot(mrot, np.dot(new_Stokes_cov[:, :, i, j], mrot.T)) - if sigma_flux is not None: - new_sigma_flux = sc_rotate(zeropad(sigma_flux, (sigma_flux.shape[0], *shape)), ang, order=1, reshape=False, cval=0.0) + if s_IQU_stat is not None: + s_IQU_stat = zeropad(s_IQU_stat, [*s_IQU_stat.shape[:-2], *shape]) + new_s_IQU_stat = np.zeros((*s_IQU_stat.shape[:-2], *shape)) + for i in range(3): + for j in range(3): + new_s_IQU_stat[i, j] = sc_rotate(s_IQU_stat[i, j], ang, order=1, reshape=False, cval=0.0) + new_s_IQU_stat[i, i] = np.abs(new_s_IQU_stat[i, i]) + for i in range(shape[0]): + for j in range(shape[1]): + new_s_IQU_stat[:, :, i, j] = np.dot(mrot, np.dot(new_s_IQU_stat[:, :, i, j], mrot.T)) # Update headers to new angle mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]]) @@ -1737,8 +1744,8 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, header_st new_header_stokes["PA_int"] = (PA_diluted, "Integrated polarization angle") new_header_stokes["sPA_int"] = (np.ceil(PA_diluted_err * 10.0) / 10.0, "Integrated polarization angle error") - if sigma_flux is not None: - return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes, new_sigma_flux + if s_IQU_stat is not None: + return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes, new_s_IQU_stat else: return new_I_stokes, new_Q_stokes, new_U_stokes, new_Stokes_cov, new_data_mask, new_header_stokes