move alignement before rebinning, before background computation
This commit is contained in:
Thibault Barnouin
@@ -14,6 +14,7 @@ import lib.plots as proj_plots #Functions for plotting data
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from lib.convex_hull import image_hull
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from lib.deconvolve import from_file_psf
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import matplotlib.pyplot as plt
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from astropy.wcs import WCS
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def main():
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@@ -26,6 +27,11 @@ def main():
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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/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/NGC1068_x14w010/"
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# infiles = ['x14w0101t_c0f.fits','x14w0102t_c0f.fits','x14w0103t_c0f.fits',
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# 'x14w0104t_c0f.fits','x14w0105p_c0f.fits','x14w0106t_c0f.fits']
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@@ -63,11 +69,6 @@ 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/MKN3_x3nl010/"
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# infiles = ['x3nl0101r_c0f.fits','x3nl0102r_c0f.fits','x3nl0103r_c0f.fits']
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# globals()['plots_folder'] = "../plots/MKN3_x3nl010/"
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@@ -93,13 +94,14 @@ def main():
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#psf = from_file_psf(data_folder+psf_file)
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psf_FWHM = 0.15
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psf_scale = 'arcsec'
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psf_shape=(9,9)
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iterations = 10
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psf_shape=(25,25)
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iterations = 5
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algo="richardson"
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# Initial crop
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display_crop = False
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display_crop = True
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# Error estimation
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error_sub_shape = (75,75)
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display_error = False
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error_sub_shape = (10,10)
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display_error = True
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# Data binning
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rebin = True
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if rebin:
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@@ -107,22 +109,23 @@ def main():
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px_scale = 'arcsec' #pixel, arcsec or full
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rebin_operation = 'sum' #sum or average
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# Alignement
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align_center = 'image' #If None will align image to image center
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display_data = False
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align_center = 'image' #If None will align image to image center
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display_data = True
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# Smoothing
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smoothing_function = 'combine' #gaussian_after, gaussian or combine
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smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
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smoothing_FWHM = 0.20 #If None, no smoothing is done
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smoothing_scale = 'arcsec' #pixel or arcsec
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smoothing_scale = 'arcsec' #pixel or arcsec
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# Rotation
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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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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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# Final crop
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crop = False #Crop to desired ROI
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crop = False #Crop to desired ROI
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final_display = True
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# Polarization map output
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figname = 'NGC1068_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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SNRp_cut = 5. #P measurments with SNR>3
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SNRi_cut = 50. #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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# if step_vec = 0 then all vectors are displayed at full length
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@@ -134,17 +137,41 @@ def main():
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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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# Deconvolve data using Richardson-Lucy iterative algorithm with a gaussian PSF of given FWHM.
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if 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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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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# Rebin data to desired pixel size.
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Dxy = np.ones(2)
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if 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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# Align and rescale images with oversampling.
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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, algo=algo)
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Dxy = np.ones(2)*10
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data_mask = np.ones(data_array.shape[1:]).astype(bool)
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# Align and rescale images with oversampling.
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if px_scale.lower() not in ['full','integrate']:
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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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data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, upsample_factor=int(Dxy.min()), ref_center=align_center, return_shifts=False)
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im = plt.imshow(error_array[0]/data_array[0]*100, origin='lower', vmin=0, vmax=100)
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plt.colorbar(im)
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wcs = WCS(headers[0])
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plt.plot(*wcs.wcs.crpix,'r+')
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plt.title("Align error")
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plt.show()
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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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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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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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im = plt.imshow(error_array[0]/data_array[0]*100, origin='lower', vmin=0, vmax=100)
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plt.colorbar(im)
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wcs = WCS(headers[0])
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plt.plot(*wcs.wcs.crpix,'r+')
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plt.title("Rotate error")
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plt.show()
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# Rebin data to desired pixel size.
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if rebin:
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data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask)
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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data_array, error_array, headers = proj_red.get_error(data_array, headers, error_array, data_mask, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder)
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im = plt.imshow(error_array[0]/data_array[0]*100, origin='lower', vmin=0, vmax=100)
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plt.colorbar(im)
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wcs = WCS(headers[0])
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plt.plot(*wcs.wcs.crpix,'r+')
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plt.title("Background error")
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plt.show()
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if px_scale.lower() not in ['full','integrate']:
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vertex = image_hull(data_mask,step=5,null_val=0.,inside=True)
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@@ -153,14 +180,6 @@ def main():
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shape = np.array([vertex[1]-vertex[0],vertex[3]-vertex[2]])
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rectangle = [vertex[2], vertex[0], shape[1], shape[0], 0., 'g']
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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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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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#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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@@ -172,6 +191,13 @@ def main():
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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 = proj_red.compute_Stokes(data_array, error_array, data_mask, headers, FWHM=smoothing_FWHM, scale=smoothing_scale, smoothing=smoothing_function)
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im = plt.imshow(np.sqrt(Stokes_cov[0,0])/I_stokes*100, origin='lower', vmin=0, vmax=100)
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plt.colorbar(im)
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wcs = WCS(headers[0])
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plt.plot(*wcs.wcs.crpix,'r+')
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plt.title("Stokes error")
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plt.show()
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## Step 3:
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# Rotate images to have North up
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@@ -183,6 +209,13 @@ def main():
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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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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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im = plt.imshow(np.sqrt(Stokes_cov[0,0])/I_stokes*100, origin='lower', vmin=0, vmax=100)
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plt.colorbar(im)
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wcs = WCS(headers[0])
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plt.plot(*wcs.wcs.crpix,'r+')
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plt.title("Rotate Stokes error")
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plt.show()
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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(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers)
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@@ -201,7 +234,7 @@ def main():
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Stokes_test, data_mask = stokescrop.hdul_crop, stokescrop.data_mask
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# Plot polarization map (Background is either total Flux, Polarization degree or Polarization degree error).
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if px_scale.lower() not in ['full','integrate']:
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if px_scale.lower() not in ['full','integrate'] and final_display:
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display=None)
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_flux", plots_folder=plots_folder, display='Pol_Flux')
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P", plots_folder=plots_folder, display='Pol_deg')
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@@ -209,7 +242,7 @@ def main():
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_P_err", plots_folder=plots_folder, display='Pol_deg_err')
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRi", plots_folder=plots_folder, display='SNRi')
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype+"_SNRp", plots_folder=plots_folder, display='SNRp')
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else:
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elif final_display:
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proj_plots.polarization_map(deepcopy(Stokes_test), data_mask, rectangle=None, SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, step_vec=step_vec, savename=figname+figtype, plots_folder=plots_folder, display='default')
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return 0
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