reduce NGC1068 for comparison with Kishimoto's pipeline
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Tibeuleu
@@ -18,12 +18,13 @@ from astropy.wcs import WCS
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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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#globals()['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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#globals()['infiles'] = ['xn1c400.fits','xn2c400.fits','xn3c400.fits']
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globals()['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/IC5063_x3nl030/"
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#globals()['infiles'] = ['x3nl0301r_c0f.fits','x3nl0302r_c0f.fits','x3nl0303r_c0f.fits']
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@@ -90,13 +91,13 @@ from astropy.wcs import WCS
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#globals()['plots_folder'] = "../plots/3C273_x0u20/"
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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()['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'] = ['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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#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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@@ -128,28 +129,28 @@ def main():
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# Data binning
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rebin = True
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if rebin:
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pxsize = 0.05
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px_scale = 'arcsec' #pixel, arcsec or full
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pxsize = 10
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px_scale = 'pixel' #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 = 0.10 #If None, no smoothing is done
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smoothing_FWHM = None #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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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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final_display = True
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final_display = False
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# Polarization map output
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figname = 'M87_POS1_1999_FOC' #target/intrument name
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figtype = '_combine_FWHM010' #additionnal informations
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figname = 'NGC1068_K_FOC' #target/intrument name
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figtype = '_bin10px' #additionnal informations
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SNRp_cut = 3. #P measurments with SNR>3
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SNRi_cut = 30. #I measurments with SNR>30, which implies an uncertainty in P of 4.7%.
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step_vec = 0 #plot all vectors in the array. if step_vec = 2, then every other vector will be plotted
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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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##### Pipeline start
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@@ -1703,12 +1703,14 @@ class pol_map(object):
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ax.add_artist(self.north_dir)
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def display(self, fig=None, ax=None):
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norm = None
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if self.display_selection is None:
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self.display_selection = "total_flux"
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if self.display_selection.lower() in ['total_flux']:
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self.data = self.I*self.convert_flux
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vmin, vmax = 0., np.max(self.data[self.data > 0.])
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vmin, vmax = np.min(self.data[self.cut])/10., np.max(self.data[self.data > 0.])
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norm = LogNorm(vmin, vmax)
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label = r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
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elif self.display_selection.lower() in ['pol_flux']:
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self.data = self.I*self.convert_flux*self.P
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@@ -1738,12 +1740,18 @@ class pol_map(object):
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ax = self.ax
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if hasattr(self, 'im'):
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self.im.remove()
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self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
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if not norm is None:
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self.im = ax.imshow(self.data, norm=norm, aspect='equal', cmap='inferno')
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else:
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self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
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self.cbar = plt.colorbar(self.im, cax=self.cbar_ax, label=label)
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fig.canvas.draw_idle()
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return self.im
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else:
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im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
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if not norm is None:
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im = ax.imshow(self.data, norm=norm, aspect='equal', cmap='inferno')
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else:
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im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
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ax.set_xlim(0,self.data.shape[1])
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ax.set_ylim(0,self.data.shape[0])
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plt.colorbar(im, pad=0.025, aspect=80, label=label)
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@@ -1475,24 +1475,23 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
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new_U_stokes = np.zeros(shape)
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new_Stokes_cov = np.zeros((*Stokes_cov.shape[:-2],*shape))
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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_I_stokes[i,j], new_Q_stokes[i,j], new_U_stokes[i,j] = np.dot(mrot, np.array([I_stokes[i,j], Q_stokes[i,j], U_stokes[i,j]])).T
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new_Stokes_cov[:,:,i,j] = np.dot(mrot, np.dot(Stokes_cov[:,:,i,j], mrot.T))
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#Rotate original images using scipy.ndimage.rotate
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new_I_stokes = sc_rotate(new_I_stokes, ang, order=1, reshape=False, cval=0.)
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new_Q_stokes = sc_rotate(new_Q_stokes, ang, order=1, reshape=False, cval=0.)
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new_U_stokes = sc_rotate(new_U_stokes, ang, order=1, reshape=False, cval=0.)
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new_I_stokes = sc_rotate(I_stokes, ang, order=1, reshape=False, cval=0.)
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new_Q_stokes = sc_rotate(Q_stokes, ang, order=1, reshape=False, cval=0.)
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new_U_stokes = sc_rotate(U_stokes, ang, order=1, reshape=False, cval=0.)
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new_data_mask = sc_rotate(data_mask.astype(float)*10., ang, order=1, reshape=False, cval=0.)
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new_data_mask[new_data_mask < 2] = 0.
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new_data_mask = new_data_mask.astype(bool)
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for i in range(3):
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for j in range(3):
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new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang, order=1,
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reshape=False, cval=0.)
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new_Stokes_cov[i,j] = sc_rotate(Stokes_cov[i,j], ang, order=1, reshape=False, cval=0.)
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new_Stokes_cov[i,i] = np.abs(new_Stokes_cov[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_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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#Update headers to new angle
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new_headers = []
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mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],
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