Move P_int, PA_int computation in pipeline, replot NGC1068, MKN463, IC5063
This commit is contained in:
Thibault Barnouin
@@ -18,12 +18,12 @@ from lib.deconvolve import from_file_psf
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def main():
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##### User inputs
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## Input and output locations
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globals()['data_folder'] = "../data/NGC1068_x274020/"
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infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
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'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
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'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
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psf_file = 'NGC1068_f253m00.fits'
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globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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# globals()['data_folder'] = "../data/NGC1068_x274020/"
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# infiles = ['x274020at.c0f.fits','x274020bt.c0f.fits','x274020ct.c0f.fits',
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# 'x274020dt.c0f.fits','x274020et.c0f.fits','x274020ft.c0f.fits',
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# 'x274020gt.c0f.fits','x274020ht.c0f.fits','x274020it.c0f.fits']
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# psf_file = 'NGC1068_f253m00.fits'
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# globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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# globals()['data_folder'] = "../data/NGC1068_x14w010/"
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# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
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@@ -62,10 +62,10 @@ def main():
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# 'x3995202r_c0f.fits','x3995206r_c0f.fits']
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# globals()['plots_folder'] = "../plots/PG1630+377_x39510/"
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# globals()['data_folder'] = "../data/IC5063_x3nl030/"
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# infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
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# psf_file = 'IC5063_f502m00.fits'
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# globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
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globals()['data_folder'] = "../data/IC5063_x3nl030/"
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infiles = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
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psf_file = 'IC5063_f502m00.fits'
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globals()['plots_folder'] = "../plots/IC5063_x3nl030/"
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# globals()['data_folder'] = "../data/MKN3_x3nl010/"
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# infiles = ['x3nl0101r_c0f.fits','x3nl0102r_c0f.fits','x3nl0103r_c0f.fits']
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@@ -116,8 +116,8 @@ def main():
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rotate_stokes = True #rotation to North convention can give erroneous results
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rotate_data = False #rotation to North convention can give erroneous results
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# Polarization map output
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figname = 'NGC1068_FOC' #target/intrument name
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figtype = '_combine_FWHM020_wae' #additionnal informations
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figname = 'IC5063_FOC' #target/intrument name
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figtype = '_combine_FWHM020' #additionnal informations
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SNRp_cut = 10. #P measurments with SNR>3
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SNRi_cut = 100. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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step_vec = 1 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
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@@ -127,41 +127,21 @@ def main():
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## Step 1:
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# Get data from fits files and translate to flux in erg/cm²/s/Angstrom.
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data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 1 : ", data)
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# Crop data to remove outside blank margins.
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print(">Crop")
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data_array, error_array, headers = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 2 : ", data)
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# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
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if deconvolve:
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print(">Deconvolve")
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data_array = proj_red.deconvolve_array(data_array, headers, psf=psf, FWHM=psf_FWHM, scale=psf_scale, shape=psf_shape, iterations=iterations)
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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print(">Get error")
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data_array, error_array, headers = proj_red.get_error(data_array, headers, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 3 : ", data)
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# Rebin data to desired pixel size.
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Dxy = np.ones(2)
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if rebin:
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print(">Rebin")
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data_array, error_array, headers, Dxy = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 4 : ", data)
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# Align and rescale images with oversampling.
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data_mask = np.zeros(data_array.shape[1:]).astype(bool)
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if px_scale.lower() not in ['full','integrate']:
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print(">Align")
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data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 5 : ", data)
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if px_scale.lower() not in ['full','integrate']:
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vertex = image_hull((1.-data_mask),step=5,null_val=0.,inside=True)
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@@ -173,15 +153,11 @@ def main():
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# Rotate data to have North up
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ref_header = deepcopy(headers[0])
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if rotate_data:
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print(">Rotate data")
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alpha = ref_header['orientat']
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mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)], [np.sin(-alpha), np.cos(-alpha)]])
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rectangle[0:2] = np.dot(mrot, np.asarray(rectangle[0:2]))+np.array(data_array.shape[1:])/2
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rectangle[4] = alpha
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data_array, error_array, headers, data_mask = proj_red.rotate_data(data_array, error_array, data_mask, headers, -ref_header['orientat'])
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for data in data_array:
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if (data < 0.).any():
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print("ETAPE 6 : ", data)
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#Plot array for checking output
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if display_data:
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proj_plots.plot_obs(data_array, headers, vmin=data_array.min(), vmax=data_array.max(), rectangle =[rectangle,]*data_array.shape[0], savename=figname+"_center_"+align_center, plots_folder=plots_folder)
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@@ -192,14 +168,12 @@ def main():
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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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print(">Compute Stokes")
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I_stokes, Q_stokes, U_stokes, Stokes_cov = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
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## Step 3:
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# Rotate images to have North up
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ref_header = deepcopy(headers[0])
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if rotate_stokes:
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print(">Rotate stokes")
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alpha = ref_header['orientat']
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mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
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[np.sin(-alpha), np.cos(-alpha)]])
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@@ -207,7 +181,6 @@ def main():
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rectangle[4] = alpha
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I_stokes, Q_stokes, U_stokes, Stokes_cov, headers, data_mask = proj_red.rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers, -ref_header['orientat'], SNRi_cut=None)
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# Compute polarimetric parameters (polarization degree and angle).
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print(">Compute Pol")
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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, headers)
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## Step 4:
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