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