Change background estimation to max of histogram
This commit is contained in:
Tibeuleu
+30
-30
@@ -40,11 +40,11 @@ globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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#globals()['plots_folder'] = "../plots/3C405_x136060/"
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#globals()['data_folder'] = "../data/CygnusA_x43w0/"
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##globals()['infiles'] = ['x43w0101r_c0f.fits', 'x43w0102r_c0f.fits', 'x43w0103r_c0f.fits',
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## 'x43w0104r_c0f.fits', 'x43w0105r_c0f.fits', 'x43w0106r_c0f.fits',
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## 'x43w0107r_c0f.fits', 'x43w0108r_c0f.fits', 'x43w0109r_c0f.fits']
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#globals()['infiles'] = ['x43w0201r_c0f.fits', 'x43w0202r_c0f.fits', 'x43w0203r_c0f.fits',
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# 'x43w0204r_c0f.fits', 'x43w0205r_c0f.fits', 'x43w0206r_c0f.fits']
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#globals()['infiles'] = ['x43w0101r_c0f.fits', 'x43w0102r_c0f.fits', 'x43w0103r_c0f.fits',
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# 'x43w0104r_c0f.fits', 'x43w0105r_c0f.fits', 'x43w0106r_c0f.fits',
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# 'x43w0107r_c0f.fits', 'x43w0108r_c0f.fits', 'x43w0109r_c0f.fits'] #F342W
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##globals()['infiles'] = ['x43w0201r_c0f.fits', 'x43w0202r_c0f.fits', 'x43w0203r_c0f.fits',
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## 'x43w0204r_c0f.fits', 'x43w0205r_c0f.fits', 'x43w0206r_c0f.fits'] #F275W
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#globals()['plots_folder'] = "../plots/CygnusA_x43w0/"
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#globals()['data_folder'] = "../data/3C109_x3mc010/"
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@@ -72,8 +72,8 @@ globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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#globals()['plots_folder'] = "../plots/MKN3_x3nl010/"
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#globals()['data_folder'] = "../data/MKN3_x3md010/"
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##globals()['infiles'] = ['x3md0101r_c0f.fits', 'x3md0102r_c0f.fits', 'x3md0103r_c0f.fits']
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#globals()['infiles'] = ['x3md0104r_c0f.fits', 'x3md0105r_c0f.fits', 'x3md0106r_c0f.fits']
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#globals()['infiles'] = ['x3md0101r_c0f.fits', 'x3md0102r_c0f.fits', 'x3md0103r_c0f.fits'] #F275W
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##globals()['infiles'] = ['x3md0104r_c0f.fits', 'x3md0105r_c0f.fits', 'x3md0106r_c0f.fits'] #F342W
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#globals()['plots_folder'] = "../plots/MKN3_x3md010/"
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#globals()['data_folder'] = "../data/MKN78_x3nl020/"
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@@ -91,19 +91,19 @@ globals()['plots_folder'] = "../plots/NGC1068_x274020/"
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#BEWARE: 5 observations separated by 1 year each (1995, 1996, 1997, 1998, 1999)
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#globals()['data_folder'] = "../data/M87/POS1/"
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#globals()['infiles'] = ['x2py010ct_c0f.fits','x2py010dt_c0f.fits','x2py010et_c0f.fits','x2py010ft_c0f.fits'] #1995
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#globals()['infiles'] = ['x3be010ct_c0f.fits','x3be010dt_c0f.fits','x3be010et_c0f.fits','x3be010ft_c0f.fits'] #1996
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#globals()['infiles'] = ['x43r010km_c0f.fits','x43r010mm_c0f.fits','x43r010om_c0f.fits','x43r010rm_c0f.fits'] #1997
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#globals()['infiles'] = ['x43r110kr_c0f.fits','x43r110mr_c0f.fits','x43r110or_c0f.fits','x43r110rr_c0f.fits'] #1998
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##globals()['infiles'] = ['x2py010ct_c0f.fits','x2py010dt_c0f.fits','x2py010et_c0f.fits','x2py010ft_c0f.fits'] #1995
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##globals()['infiles'] = ['x3be010ct_c0f.fits','x3be010dt_c0f.fits','x3be010et_c0f.fits','x3be010ft_c0f.fits'] #1996
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##globals()['infiles'] = ['x43r010km_c0f.fits','x43r010mm_c0f.fits','x43r010om_c0f.fits','x43r010rm_c0f.fits'] #1997
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##globals()['infiles'] = ['x43r110kr_c0f.fits','x43r110mr_c0f.fits','x43r110or_c0f.fits','x43r110rr_c0f.fits'] #1998
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#globals()['infiles'] = ['x43r210kr_c0f.fits','x43r210mr_c0f.fits','x43r210or_c0f.fits','x43r210rr_c0f.fits'] #1999
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#globals()['plots_folder'] = "../plots/M87/POS1/"
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#BEWARE: 5 observations separated by 1 year each (1995, 1996, 1997, 1998, 1999)
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#globals()['data_folder'] = "../data/M87/POS3/"
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#globals()['infiles'] = ['x2py030at_c0f.fits','x2py030bt_c0f.fits','x2py030ct_c0f.fits','x2py0309t_c0f.fits'] #1995
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#globals()['infiles'] = ['x3be030at_c0f.fits','x3be030bt_c0f.fits','x3be030ct_c0f.fits','x3be0309t_c0f.fits'] #1996
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#globals()['infiles'] = ['x43r030em_c0f.fits','x43r030gm_c0f.fits','x43r030im_c0f.fits','x43r030lm_c0f.fits'] #1997
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#globals()['infiles'] = ['x43r130er_c0f.fits','x43r130fr_c0f.fits','x43r130ir_c0f.fits','x43r130lr_c0f.fits'] #1998
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##globals()['infiles'] = ['x2py030at_c0f.fits','x2py030bt_c0f.fits','x2py030ct_c0f.fits','x2py0309t_c0f.fits'] #1995
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##globals()['infiles'] = ['x3be030at_c0f.fits','x3be030bt_c0f.fits','x3be030ct_c0f.fits','x3be0309t_c0f.fits'] #1996
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##globals()['infiles'] = ['x43r030em_c0f.fits','x43r030gm_c0f.fits','x43r030im_c0f.fits','x43r030lm_c0f.fits'] #1997
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##globals()['infiles'] = ['x43r130er_c0f.fits','x43r130fr_c0f.fits','x43r130ir_c0f.fits','x43r130lr_c0f.fits'] #1998
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#globals()['infiles'] = ['x43r230er_c0f.fits','x43r230fr_c0f.fits','x43r230ir_c0f.fits','x43r230lr_c0f.fits'] #1999
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#globals()['plots_folder'] = "../plots/M87/POS3/"
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@@ -123,20 +123,20 @@ def main():
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# Initial crop
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display_crop = False
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# Error estimation
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error_sub_shape = (10,10)
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error_sub_shape = (80,80)
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display_error = False
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# Data binning
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rebin = True
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if rebin:
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pxsize = 10
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px_scale = 'pixel' #pixel, arcsec or full
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pxsize = 0.05
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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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# Smoothing
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smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine
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smoothing_FWHM = None #If None, no smoothing is done
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smoothing_FWHM = 0.10 #If None, no smoothing is done
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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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@@ -146,7 +146,7 @@ def main():
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final_display = False
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# Polarization map output
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figname = 'NGC1068_FOC' #target/intrument name
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figtype = '_bin10px' #additionnal informations
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figtype = '_combine_FWHM010' #additionnal informations
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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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@@ -163,15 +163,13 @@ def main():
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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, algo=algo)
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# Rotate data to have North up
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if rotate_data:
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data_mask = np.ones(data_array.shape[1:]).astype(bool)
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alpha = headers[0]['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, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
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# Estimate error from data background, estimated from sub-image of desired sub_shape.
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if px_scale.lower() not in ['full','integrate']:
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#data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, sub_shape=error_sub_shape, display=display_error, savename=figname+"_errors", plots_folder=plots_folder, return_background=True)
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data_array, error_array, headers, background = proj_red.get_error2(data_array, headers, error_array, display=display_error, savename=figname+"_errors", plots_folder=plots_folder, return_background=True)
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# Align and rescale images with oversampling.
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data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, upsample_factor=10, 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, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False)
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# Rebin data to desired pixel size.
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if rebin:
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@@ -179,9 +177,11 @@ def main():
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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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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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if px_scale.lower() not in ['full','integrate']:
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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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# Rotate data to have North up
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if rotate_data:
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data_mask = np.ones(data_array.shape[1:]).astype(bool)
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alpha = headers[0]['orientat']
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data_array, error_array, data_mask, headers = proj_red.rotate_data(data_array, error_array, data_mask, headers, -alpha)
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#Plot array for checking output
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if display_data and px_scale.lower() not in ['full','integrate']:
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@@ -44,8 +44,8 @@ for d in data_K:
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for d in [data_S, data_K]:
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for i in d:
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d[i][np.isnan(d[i])] = 0.
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d['P'] = np.where(d['I']>0.,np.sqrt(d['Q']**2+d['U']**2)/d['I'],0.)
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d['sP'] = np.where(d['I']>0.,np.sqrt((d['Q']**2*d['sQ']**2+d['U']**2*d['sU']**2)/(d['Q']**2+d['U']**2)+((d['Q']/d['I'])**2+(d['U']/d['I'])**2)*d['sI']**2)/d['I'],0.)
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d['P'] = np.where(np.logical_and(np.isfinite(d['I']),d['I']>0.),np.sqrt(d['Q']**2+d['U']**2)/d['I'],0.)
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d['sP'] = np.where(np.logical_and(np.isfinite(d['I']),d['I']>0.),np.sqrt((d['Q']**2*d['sQ']**2+d['U']**2*d['sU']**2)/(d['Q']**2+d['U']**2)+((d['Q']/d['I'])**2+(d['U']/d['I'])**2)*d['sI']**2)/d['I'],0.)
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d['PA'] = princ_angle((90./np.pi)*np.arctan2(d['U'],d['Q'])+180.)
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d['SNRp'] = np.zeros(d['P'].shape)
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d['SNRp'][d['sP']>0.] = d['P'][d['sP']>0.]/d['sP'][d['sP']>0.]
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+5
-5
@@ -337,7 +337,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
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# Display polarization degree map
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display='pa'
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vmin, vmax = 0., 360.
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im = ax.imshow(pang.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
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im = ax.imshow(princ_angle(pang.data), vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
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cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\theta_P$ [°]")
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elif display.lower() in ['s_p','pol_err','pol_deg_err']:
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# Display polarization degree error map
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@@ -1743,7 +1743,7 @@ class pol_map(object):
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vmin, vmax = 0., np.max(self.data[self.data > 0.])
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label = r"$P$ [%]"
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elif self.display_selection.lower() in ['pol_ang']:
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self.data = self.PA
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self.data = princ_angle(self.PA)
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vmin, vmax = 0, 360.
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label = r"$\theta_{P}$ [°]"
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elif self.display_selection.lower() in ['snri']:
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@@ -1833,7 +1833,7 @@ class pol_map(object):
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P_cut = np.sqrt(Q_cut**2+U_cut**2)/I_cut
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P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) + ((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
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PA_cut = princ_angle(np.degrees((1./2.)*np.arctan2(U_cut,Q_cut))+180.)
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PA_cut = 360.-princ_angle(np.degrees((1./2.)*np.arctan2(U_cut,Q_cut)))
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PA_cut_err = princ_angle(np.degrees((1./(2.*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)))
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else:
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@@ -1858,7 +1858,7 @@ class pol_map(object):
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P_reg = np.sqrt(Q_reg**2+U_reg**2)/I_reg
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P_reg_err = np.sqrt((Q_reg**2*Q_reg_err**2 + U_reg**2*U_reg_err**2 + 2.*Q_reg*U_reg*QU_reg_err)/(Q_reg**2 + U_reg**2) + ((Q_reg/I_reg)**2 + (U_reg/I_reg)**2)*I_reg_err**2 - 2.*(Q_reg/I_reg)*IQ_reg_err - 2.*(U_reg/I_reg)*IU_reg_err)/I_reg
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PA_reg = princ_angle((90./np.pi)*np.arctan2(U_reg,Q_reg)+180.)
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PA_reg = 360.-princ_angle((90./np.pi)*np.arctan2(U_reg,Q_reg))
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PA_reg_err = (90./(np.pi*(Q_reg**2+U_reg**2)))*np.sqrt(U_reg**2*Q_reg_err**2 + Q_reg**2*U_reg_err**2 - 2.*Q_reg*U_reg*QU_reg_err)
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new_cut = np.logical_and(self.region, self.cut)
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@@ -1875,7 +1875,7 @@ class pol_map(object):
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P_cut = np.sqrt(Q_cut**2+U_cut**2)/I_cut
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P_cut_err = np.sqrt((Q_cut**2*Q_cut_err**2 + U_cut**2*U_cut_err**2 + 2.*Q_cut*U_cut*QU_cut_err)/(Q_cut**2 + U_cut**2) + ((Q_cut/I_cut)**2 + (U_cut/I_cut)**2)*I_cut_err**2 - 2.*(Q_cut/I_cut)*IQ_cut_err - 2.*(U_cut/I_cut)*IU_cut_err)/I_cut
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PA_cut = princ_angle((90./np.pi)*np.arctan2(U_cut,Q_cut)+180.)
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PA_cut = 360.-princ_angle((90./np.pi)*np.arctan2(U_cut,Q_cut))
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PA_cut_err = (90./(np.pi*(Q_cut**2+U_cut**2)))*np.sqrt(U_cut**2*Q_cut_err**2 + Q_cut**2*U_cut_err**2 - 2.*Q_cut*U_cut*QU_cut_err)
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if hasattr(self, 'cont'):
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+229
-31
@@ -407,6 +407,200 @@ def deconvolve_array(data_array, headers, psf='gaussian', FWHM=1., scale='px',
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return deconv_array
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def get_error2(data_array, headers, error_array=None, data_mask=None,
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display=False, savename=None, plots_folder="",
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return_background=False):
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"""
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----------
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Inputs:
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data_array : numpy.ndarray
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Array containing the data to study (2D float arrays).
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headers : header list
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Headers associated with the images in data_array.
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error_array : numpy.ndarray, optional
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Array of images (2D floats, aligned and of the same shape) containing
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the error in each pixel of the observation images in data_array.
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If None, will be initialized to zeros.
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Defaults to None.
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data_mask : numpy.ndarray, optional
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2D boolean array delimiting the data to work on.
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If None, will be initialized with a full true mask.
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Defaults to None.
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display : boolean, optional
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If True, data_array will be displayed with a rectangle around the
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sub-image selected for background computation.
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Defaults to False.
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savename : str, optional
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Name of the figure the map should be saved to. If None, the map won't
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be saved (only displayed). Only used if display is True.
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Defaults to None.
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plots_folder : str, optional
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Relative (or absolute) filepath to the folder in wich the map will
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be saved. Not used if savename is None.
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Defaults to current folder.
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return_background : boolean, optional
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If True, the pixel background value for each image in data_array is
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returned.
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Defaults to False.
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----------
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Returns:
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data_array : numpy.ndarray
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Array containing the data to study minus the background.
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headers : header list
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Updated headers associated with the images in data_array.
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error_array : numpy.ndarray
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Array containing the background values associated to the images in
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data_array.
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background : numpy.ndarray
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Array containing the pixel background value for each image in
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data_array.
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Only returned if return_background is True.
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"""
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# Crop out any null edges
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if error_array is None:
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error_array = np.zeros(data_array.shape)
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n_data_array, n_error_array = deepcopy(data_array), deepcopy(error_array)
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if not data_mask is None:
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data, mask = n_data_array, deepcopy(data_mask)
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else:
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data, _, _ = crop_array(n_data_array, headers, step=5, null_val=0., inside=False)
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data_mask = np.ones(data_array[0].shape, dtype=bool)
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mask = np.ones(data[0].shape, dtype=bool)
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error_bkg = np.ones(n_data_array.shape)
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std_bkg = np.zeros((data.shape[0]))
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background = np.zeros((data.shape[0]))
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if display:
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fig_h, ax_h = plt.subplots(figsize=(10,6), constrained_layout=True)
|
||||
date_time = np.array([headers[i]['date-obs']+';'+headers[i]['time-obs']
|
||||
for i in range(len(headers))])
|
||||
date_time = np.array([datetime.strptime(d,'%Y-%m-%d;%H:%M:%S')
|
||||
for d in date_time])
|
||||
for i, image in enumerate(data):
|
||||
#Compute the Count-rate histogram for the image
|
||||
n_mask = np.logical_and(mask,image>0.)
|
||||
|
||||
#n_bins = np.fix(np.sqrt(image[n_mask].size)).astype(int) # Square-root
|
||||
#n_bins = np.ceil(np.log2(image[n_mask].size)).astype(int)+1 # Sturges
|
||||
#n_bins = 2*np.fix(np.power(image[n_mask].size,1/3)).astype(int) # Rice
|
||||
#n_bins = np.fix((image[n_mask].max()-image[n_mask].min())/(3.5*image[n_mask].std()/np.power(image[n_mask].size,1/3))).astype(int) # Scott
|
||||
n_bins = np.fix((image[n_mask].max()-image[n_mask].min())/(2*np.subtract(*np.percentile(image[n_mask], [75, 25]))/np.power(image[n_mask].size,1/3))).astype(int) # Freedman-Diaconis
|
||||
|
||||
hist, bin_edges = np.histogram(np.log(image[n_mask]),bins=n_bins)
|
||||
bin_centers = np.exp((bin_edges[:-1]+bin_edges[1:])/2)
|
||||
#Take the background as the count-rate with the maximum number of pixels
|
||||
hist_max = bin_centers[np.argmax(hist)]
|
||||
bkg = np.sqrt(np.sum(image[np.abs(image-hist_max)/hist_max<0.5]**2)/image[np.abs(image-hist_max)/hist_max<0.5].size)
|
||||
#bkg = np.sum(image[n_mask]*1/np.abs(image[n_mask]-hist_max))/np.sum(1/np.abs(image[n_mask]-hist_max))
|
||||
#bkg = np.percentile(image[image<hist_max],25.)
|
||||
#bkg = 0.95*hist_max
|
||||
if display:
|
||||
ax_h.plot(bin_centers,hist,'+',color="C{0:d}".format(i),alpha=0.8,label=headers[i]['filtnam1']+' ('+str(date_time[i])+") with n_bins = {0:d}".format(n_bins))
|
||||
ax_h.plot([bkg,bkg],[hist.min(), hist.max()],'x--',color="C{0:d}".format(i),alpha=0.8)
|
||||
error_bkg[i] *= bkg
|
||||
|
||||
# Quadratically add uncertainties in the "correction factors" (see Kishimoto 1999)
|
||||
#wavelength dependence of the polariser filters
|
||||
#estimated to less than 1%
|
||||
err_wav = data_array[i]*0.01
|
||||
#difference in PSFs through each polarizers
|
||||
#estimated to less than 3%
|
||||
err_psf = data_array[i]*0.03
|
||||
#flatfielding uncertainties
|
||||
#estimated to less than 3%
|
||||
err_flat = data_array[i]*0.03
|
||||
|
||||
n_error_array[i] = np.sqrt(n_error_array[i]**2 + error_bkg[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
|
||||
|
||||
#Substract background
|
||||
n_data_array[i][data_mask] = n_data_array[i][data_mask] - bkg
|
||||
n_data_array[i][np.logical_and(data_mask,n_data_array[i] <= 0.01*bkg)] = 0.01*bkg#n_data_array[i][np.logical_and(data_mask,n_data_array[i] > 0.)].min()
|
||||
|
||||
std_bkg[i] = image[image<2*bkg].std()
|
||||
background[i] = bkg
|
||||
|
||||
if (data_array[i] < 0.).any():
|
||||
print(data_array[i])
|
||||
#if i==0:
|
||||
#np.savetxt("output/s_bg.txt",error_bkg[i])
|
||||
#np.savetxt("output/s_wav.txt",err_wav)
|
||||
#np.savetxt("output/s_psf.txt",err_psf)
|
||||
#np.savetxt("output/s_flat.txt",err_flat)
|
||||
if display:
|
||||
ax_h.set_xscale('log')
|
||||
ax_h.set_xlim([background.mean()*1e-2,background.mean()*1e2])
|
||||
ax_h.set_xlabel(r"Count rate [$s^{-1}$]")
|
||||
#ax_h.set_yscale('log')
|
||||
ax_h.set_ylabel(r"Number of pixels in bin")
|
||||
ax_h.set_title("Histogram for each observation")
|
||||
plt.legend()
|
||||
|
||||
plt.rcParams.update({'font.size': 15})
|
||||
convert_flux = np.array([head['photflam'] for head in headers])
|
||||
filt = np.array([headers[i]['filtnam1'] for i in range(len(headers))])
|
||||
dict_filt = {"POL0":'r', "POL60":'g', "POL120":'b'}
|
||||
c_filt = np.array([dict_filt[f] for f in filt])
|
||||
|
||||
fig,ax = plt.subplots(figsize=(10,6), constrained_layout=True)
|
||||
for f in np.unique(filt):
|
||||
mask = [fil==f for fil in filt]
|
||||
ax.scatter(date_time[mask], background[mask]*convert_flux[mask],
|
||||
color=dict_filt[f],label="{0:s}".format(f))
|
||||
ax.errorbar(date_time, background*convert_flux,
|
||||
yerr=std_bkg*convert_flux, fmt='+k',
|
||||
markersize=0, ecolor=c_filt)
|
||||
# Date handling
|
||||
locator = mdates.AutoDateLocator()
|
||||
formatter = mdates.ConciseDateFormatter(locator)
|
||||
ax.xaxis.set_major_locator(locator)
|
||||
ax.xaxis.set_major_formatter(formatter)
|
||||
ax.set_xlabel("Observation date and time")
|
||||
ax.set_ylabel(r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
||||
#ax.set_title("Background flux and error computed for each image")
|
||||
plt.legend()
|
||||
|
||||
plt.rcParams.update({'font.size': 15})
|
||||
fig2, ax2 = plt.subplots(figsize=(10,10))
|
||||
data0 = data[0]*convert_flux[0]
|
||||
bkg_data0 = data0 <= background[0]*convert_flux[0]
|
||||
instr = headers[0]['instrume']
|
||||
rootname = headers[0]['rootname']
|
||||
exptime = headers[0]['exptime']
|
||||
filt = headers[0]['filtnam1']
|
||||
#plots
|
||||
#im = ax2.imshow(data0, vmin=data0.min(), vmax=data0.max(), origin='lower', cmap='gray')
|
||||
im = ax2.imshow(data0, norm=LogNorm(data0[data0>0.].mean()/10.,data0.max()), origin='lower', cmap='gray')
|
||||
bkg_im = ax2.imshow(bkg_data0, origin='lower', cmap='Reds', alpha=0.7)
|
||||
ax2.annotate(instr+":"+rootname, color='white', fontsize=10,
|
||||
xy=(0.02, 0.95), xycoords='axes fraction')
|
||||
ax2.annotate(filt, color='white', fontsize=14, xy=(0.02, 0.02),
|
||||
xycoords='axes fraction')
|
||||
ax2.annotate(str(exptime)+" s", color='white', fontsize=10, xy=(0.80, 0.02),
|
||||
xycoords='axes fraction')
|
||||
ax2.set(#title="Location of background computation.",
|
||||
xlabel='pixel offset',
|
||||
ylabel='pixel offset')
|
||||
|
||||
fig2.subplots_adjust(hspace=0, wspace=0, right=0.85)
|
||||
cbar_ax = fig2.add_axes([0.9, 0.12, 0.02, 0.75])
|
||||
fig2.colorbar(im, cax=cbar_ax, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
|
||||
|
||||
if not(savename is None):
|
||||
fig.savefig(plots_folder+savename+"_background_flux.png",
|
||||
bbox_inches='tight')
|
||||
fig2.savefig(plots_folder+savename+'_'+filt+'_background_location.png',
|
||||
bbox_inches='tight')
|
||||
fig_h.savefig(plots_folder+savename+'_histograms.png', bbox_inches='tight')
|
||||
|
||||
plt.show()
|
||||
|
||||
if return_background:
|
||||
return n_data_array, n_error_array, headers, background #np.array([n_error_array[i][0,0] for i in range(n_error_array.shape[0])])
|
||||
else:
|
||||
return n_data_array, n_error_array, headers
|
||||
|
||||
|
||||
def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
sub_shape=None, display=False, savename=None, plots_folder="",
|
||||
return_background=False):
|
||||
@@ -465,10 +659,12 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
# Crop out any null edges
|
||||
if error_array is None:
|
||||
error_array = np.zeros(data_array.shape)
|
||||
n_data_array, n_error_array = deepcopy(data_array), deepcopy(error_array)
|
||||
if not data_mask is None:
|
||||
data, error, mask = data_array, error_array, data_mask
|
||||
data, error, mask = n_data_array, n_error_array, deepcopy(data_mask)
|
||||
else:
|
||||
data, error, _ = crop_array(data_array, headers, error_array, step=5, null_val=0., inside=False)
|
||||
data, _, _ = crop_array(n_data_array, headers, n_error_array, step=5, null_val=0., inside=False)
|
||||
data_mask = np.ones(n_data_array[0].shape, dtype=bool)
|
||||
mask = np.ones(data[0].shape, dtype=bool)
|
||||
if sub_shape is None:
|
||||
sub_shape = (np.array(data_array.shape[1:])/10).astype(int)
|
||||
@@ -481,8 +677,9 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
diff = (sub_shape-1).astype(int)
|
||||
temp = np.zeros((shape[0],shape[1]-diff[0],shape[2]-diff[1]))
|
||||
error_bkg = np.ones(data_array.shape)
|
||||
rectangle = []
|
||||
std_bkg = np.zeros((data.shape[0]))
|
||||
background = np.zeros((shape[0]))
|
||||
rectangle = []
|
||||
|
||||
for i,image in enumerate(data):
|
||||
# Find the sub-image of smallest integrated flux (suppose no source)
|
||||
@@ -518,9 +715,12 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
error_array[i] = np.sqrt(error_array[i]**2 + error_bkg[i]**2 + err_wav**2 + err_psf**2 + err_flat**2)
|
||||
|
||||
#Substract background
|
||||
data_array[i] = np.abs(data_array[i] - sub_image.mean())
|
||||
n_data_array[i][data_mask] = n_data_array[i][data_mask] - bkg
|
||||
n_data_array[i][np.logical_and(data_mask,n_data_array[i] <= 0.)] = n_data_array[i][np.logical_and(data_mask,n_data_array[i] > 0.)].min()
|
||||
|
||||
background[i] = sub_image.sum()
|
||||
std_bkg[i] = image[image<2*bkg].std()
|
||||
background[i] = bkg
|
||||
|
||||
if (data_array[i] < 0.).any():
|
||||
print(data_array[i])
|
||||
#if i==0:
|
||||
@@ -531,7 +731,7 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
|
||||
if display:
|
||||
plt.rcParams.update({'font.size': 15})
|
||||
convert_flux = headers[0]['photflam']
|
||||
convert_flux = np.array([head['photflam'] for head in headers])
|
||||
date_time = np.array([headers[i]['date-obs']+';'+headers[i]['time-obs']
|
||||
for i in range(len(headers))])
|
||||
date_time = np.array([datetime.strptime(d,'%Y-%m-%d;%H:%M:%S')
|
||||
@@ -543,10 +743,10 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
fig,ax = plt.subplots(figsize=(10,6), constrained_layout=True)
|
||||
for f in np.unique(filt):
|
||||
mask = [fil==f for fil in filt]
|
||||
ax.scatter(date_time[mask], background[mask]*convert_flux,
|
||||
ax.scatter(date_time[mask], background[mask]*convert_flux[mask],
|
||||
color=dict_filt[f],label="{0:s}".format(f))
|
||||
ax.errorbar(date_time, background*convert_flux,
|
||||
yerr=error_array[:,0,0]*convert_flux, fmt='+k',
|
||||
yerr=std_bkg*convert_flux, fmt='+k',
|
||||
markersize=0, ecolor=c_filt)
|
||||
# Date handling
|
||||
locator = mdates.AutoDateLocator()
|
||||
@@ -560,7 +760,7 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
|
||||
plt.rcParams.update({'font.size': 15})
|
||||
fig2, ax2 = plt.subplots(figsize=(10,10))
|
||||
data0 = data[0]*convert_flux
|
||||
data0 = data[0]*convert_flux[0]
|
||||
instr = headers[0]['instrume']
|
||||
rootname = headers[0]['rootname']
|
||||
exptime = headers[0]['exptime']
|
||||
@@ -589,16 +789,12 @@ def get_error(data_array, headers, error_array=None, data_mask=None,
|
||||
bbox_inches='tight')
|
||||
fig2.savefig(plots_folder+savename+'_'+filt+'_background_location.png',
|
||||
bbox_inches='tight')
|
||||
vmin = np.min(np.log10(data[data > 0.]))
|
||||
vmax = np.max(np.log10(data[data > 0.]))
|
||||
plot_obs(data, headers, vmin=data.min(), vmax=data.max(),
|
||||
rectangle=rectangle,
|
||||
savename=savename+"_background_location",
|
||||
plots_folder=plots_folder)
|
||||
else:
|
||||
vmin = np.min(np.log10(data[data > 0.]))
|
||||
vmax = np.max(np.log10(data[data > 0.]))
|
||||
plot_obs(np.log10(data), headers, vmin=vmin, vmax=vmax,
|
||||
plot_obs(data, headers, vmin=vmin, vmax=vmax,
|
||||
rectangle=rectangle)
|
||||
|
||||
plt.show()
|
||||
@@ -696,13 +892,17 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
|
||||
mask = sum_image > 0.
|
||||
new_error = np.zeros(rms_image.shape)
|
||||
if operation.lower() in ["mean", "average", "avg"]:
|
||||
new_error[mask] = np.sqrt(bin_ndarray(error**2*image,
|
||||
new_shape=new_shape, operation='average')[mask]/sum_image[mask])
|
||||
new_error = np.sqrt(bin_ndarray(error**2,
|
||||
new_shape=new_shape, operation='average'))
|
||||
#new_error[mask] = np.sqrt(bin_ndarray(error**2*image,
|
||||
# new_shape=new_shape, operation='average')[mask]/sum_image[mask])
|
||||
#new_error[mask] = np.sqrt(bin_ndarray(error**2,
|
||||
# new_shape=new_shape, operation='average')[mask])
|
||||
else:
|
||||
new_error[mask] = np.sqrt(bin_ndarray(error**2*image,
|
||||
new_shape=new_shape, operation='sum')[mask]/sum_image[mask])
|
||||
new_error = np.sqrt(bin_ndarray(error**2,
|
||||
new_shape=new_shape, operation='sum'))
|
||||
#new_error[mask] = np.sqrt(bin_ndarray(error**2*image,
|
||||
# new_shape=new_shape, operation='sum')[mask]/sum_image[mask])
|
||||
#new_error[mask] = np.sqrt(bin_ndarray(error**2,
|
||||
# new_shape=new_shape, operation='sum')[mask])
|
||||
rebinned_error.append(np.sqrt(rms_image**2 + new_error**2))
|
||||
@@ -724,8 +924,8 @@ def rebin_array(data_array, error_array, headers, pxsize, scale,
|
||||
return rebinned_data, rebinned_error, rebinned_headers, Dxy, data_mask
|
||||
|
||||
|
||||
def align_data(data_array, headers, error_array=None, upsample_factor=1.,
|
||||
ref_data=None, ref_center=None, return_shifts=False):
|
||||
def align_data(data_array, headers, error_array=None, background=None,
|
||||
upsample_factor=1., ref_data=None, ref_center=None, return_shifts=False):
|
||||
"""
|
||||
Align images in data_array using cross correlation, and rescale them to
|
||||
wider images able to contain any rotation of the reference image.
|
||||
@@ -789,10 +989,8 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
|
||||
if not same:
|
||||
raise ValueError("All images in data_array must have same shape as\
|
||||
ref_data")
|
||||
if error_array is None:
|
||||
_, error_array, headers, background = get_error(data_array, headers, return_background=True)
|
||||
else:
|
||||
_, _, headers, background = get_error(data_array, headers, error_array=error_array, return_background=True)
|
||||
if (error_array is None) or (background is None):
|
||||
_, error_array, headers, background = get_error2(data_array, headers, return_background=True)
|
||||
|
||||
# Crop out any null edges
|
||||
#(ref_data must be cropped as well)
|
||||
@@ -801,8 +999,8 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
|
||||
full_headers.append(headers[0])
|
||||
err_array = np.concatenate((error_array,[np.zeros(ref_data.shape)]),axis=0)
|
||||
|
||||
full_array, err_array, full_headers = crop_array(full_array, full_headers, err_array, step=5,
|
||||
inside=False)
|
||||
full_array, err_array, full_headers = crop_array(full_array, full_headers,
|
||||
err_array, step=5, inside=False, null_val=0.)
|
||||
|
||||
data_array, ref_data, headers = full_array[:-1], full_array[-1], full_headers[:-1]
|
||||
error_array = err_array[:-1]
|
||||
@@ -833,7 +1031,7 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
|
||||
shifts, errors = [], []
|
||||
for i,image in enumerate(data_array):
|
||||
# Initialize rescaled images to background values
|
||||
rescaled_error[i] *= background[i]
|
||||
rescaled_error[i] *= 0.01*background[i]
|
||||
# Get shifts and error by cross-correlation to ref_data
|
||||
shift, error, phase_diff = phase_cross_correlation(ref_data/ref_data.max(), image/image.max(),
|
||||
upsample_factor=upsample_factor)
|
||||
@@ -876,7 +1074,7 @@ def align_data(data_array, headers, error_array=None, upsample_factor=1.,
|
||||
headers[i].update(headers_wcs[i].to_header())
|
||||
|
||||
data_mask = rescaled_mask.all(axis=0)
|
||||
data_array, error_array, data_mask, headers = crop_array(rescaled_image, headers, rescaled_error, data_mask)
|
||||
data_array, error_array, data_mask, headers = crop_array(rescaled_image, headers, rescaled_error, data_mask, null_val=0.01*background)
|
||||
|
||||
if return_shifts:
|
||||
return data_array, error_array, headers, data_mask, shifts, errors
|
||||
@@ -1316,7 +1514,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
|
||||
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
|
||||
P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
|
||||
|
||||
PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted)+180.)
|
||||
PA_diluted = 360.-princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted))
|
||||
PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
|
||||
|
||||
for header in headers:
|
||||
@@ -1376,7 +1574,7 @@ def compute_pol(I_stokes, Q_stokes, U_stokes, Stokes_cov, headers):
|
||||
P = np.zeros(I_stokes.shape)
|
||||
P[mask] = I_pol[mask]/I_stokes[mask]
|
||||
PA = np.zeros(I_stokes.shape)
|
||||
PA[mask] = princ_angle((90./np.pi)*np.arctan2(U_stokes[mask],Q_stokes[mask])+180.)
|
||||
PA[mask] = (90./np.pi)*np.arctan2(U_stokes[mask],Q_stokes[mask])
|
||||
|
||||
if (P>1).any():
|
||||
print("WARNING : found {0:d} pixels for which P > 1".format(P[P>1.].size))
|
||||
@@ -1575,7 +1773,7 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
|
||||
P_diluted = np.sqrt(Q_diluted**2+U_diluted**2)/I_diluted
|
||||
P_diluted_err = (1./I_diluted)*np.sqrt((Q_diluted**2*Q_diluted_err**2 + U_diluted**2*U_diluted_err**2 + 2.*Q_diluted*U_diluted*QU_diluted_err)/(Q_diluted**2 + U_diluted**2) + ((Q_diluted/I_diluted)**2 + (U_diluted/I_diluted)**2)*I_diluted_err**2 - 2.*(Q_diluted/I_diluted)*IQ_diluted_err - 2.*(U_diluted/I_diluted)*IU_diluted_err)
|
||||
|
||||
PA_diluted = princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted)+180.)
|
||||
PA_diluted = 360.-princ_angle((90./np.pi)*np.arctan2(U_diluted,Q_diluted))
|
||||
PA_diluted_err = (90./(np.pi*(Q_diluted**2 + U_diluted**2)))*np.sqrt(U_diluted**2*Q_diluted_err**2 + Q_diluted**2*U_diluted_err**2 - 2.*Q_diluted*U_diluted*QU_diluted_err)
|
||||
|
||||
for header in new_headers:
|
||||
|
||||
+7
-7
@@ -20,30 +20,30 @@ levelsMorganti = np.array([1.,2.,3.,8.,16.,32.,64.,128.])
|
||||
#levels18GHz = np.array([0.6, 1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_18GHz[0].data.max()
|
||||
levels18GHz = levelsMorganti*0.28*1e-3
|
||||
A = overplot_radio(Stokes_UV, Stokes_18GHz)
|
||||
A.plot(levels=levels18GHz, SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/18GHz_overplot_forced.png')
|
||||
A.plot(levels=levels18GHz, SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/18GHz_overplot.png')
|
||||
|
||||
#levels24GHz = np.array([1.,1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_24GHz[0].data.max()
|
||||
levels24GHz = levelsMorganti*0.46*1e-3
|
||||
B = overplot_radio(Stokes_UV, Stokes_24GHz)
|
||||
B.plot(levels=levels24GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/24GHz_overplot_forced.png')
|
||||
B.plot(levels=levels24GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/24GHz_overplot.png')
|
||||
|
||||
levels103GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_103GHz[0].data[Stokes_103GHz[0].data > 0.]))
|
||||
C = overplot_radio(Stokes_UV, Stokes_103GHz)
|
||||
C.plot(levels=levels103GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/103GHz_overplot_forced.png')
|
||||
C.plot(levels=levels103GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/103GHz_overplot.png')
|
||||
|
||||
levels229GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_229GHz[0].data[Stokes_229GHz[0].data > 0.]))
|
||||
D = overplot_radio(Stokes_UV, Stokes_229GHz)
|
||||
D.plot(levels=levels229GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/229GHz_overplot_forced.png')
|
||||
D.plot(levels=levels229GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/229GHz_overplot.png')
|
||||
|
||||
levels357GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_357GHz[0].data[Stokes_357GHz[0].data > 0.]))
|
||||
E = overplot_radio(Stokes_UV, Stokes_357GHz)
|
||||
E.plot(levels=levels357GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/357GHz_overplot_forced.png')
|
||||
E.plot(levels=levels357GHz, SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/357GHz_overplot.png')
|
||||
|
||||
#F = overplot_pol(Stokes_UV, Stokes_S2)
|
||||
#F.plot(SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/S2_overplot_forced.png', norm=LogNorm(vmin=5e-20,vmax=5e-18))
|
||||
#F.plot(SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/S2_overplot.png', norm=LogNorm(vmin=5e-20,vmax=5e-18))
|
||||
|
||||
G = overplot_pol(Stokes_UV, Stokes_IR, cmap='inferno')
|
||||
G.plot(SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/IR_overplot_forced.png', norm=LogNorm(vmin=1e-17,vmax=5e-15), cmap='inferno_r')
|
||||
G.plot(SNRp_cut=3.0, SNRi_cut=60.0, savename='../plots/IC5063_x3nl030/IR_overplot.png', norm=LogNorm(vmin=1e-17,vmax=5e-15), cmap='inferno_r')
|
||||
|
||||
#data_folder1 = "../data/M87/POS1/"
|
||||
#plots_folder1 = "../plots/M87/POS1/"
|
||||
|
||||
Reference in New Issue
Block a user