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force convention on WCS (unitary PCi_ja for given initial cdelt)

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This commit is contained in:
Thibault Barnouin
2022-03-11 17:31:22 +01:00
parent d700f46738
commit da15353318
27 changed files with 152 additions and 144 deletions
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108
src/lib/plots.py
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@@ -263,7 +263,7 @@ class crop_map(object):
return Stokes_crop, data_mask
def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30.,
def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_cut=30.,
step_vec=1, savename=None, plots_folder="", display=None):
"""
Plots polarization map from Stokes HDUList.
@@ -301,6 +301,10 @@ def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30
degree ('p','pol','pol_deg') or polarization degree error ('s_p',
'pol_err','pol_deg_err').
Defaults to None (intensity).
----------
Returns:
fig, ax : matplotlib.pyplot object
The figure and ax created for interactive contour maps.
"""
#Get data
stkI = Stokes[np.argmax([Stokes[i].header['datatype']=='I_stokes' for i in range(len(Stokes))])]
@@ -318,6 +322,10 @@ def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30
convert_flux = Stokes[0].header['photflam']
wcs = WCS(Stokes[0]).deepcopy()
#Get image mask
if data_mask is None:
data_mask = np.ones(stkI.shape).astype(bool)
#Plot Stokes parameters map
if display is None:
plot_Stokes(Stokes, savename=savename, plots_folder=plots_folder)
@@ -354,64 +362,62 @@ def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30
if display is None:
# If no display selected, show intensity map
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = ax.imshow(stkI.data*convert_flux,extent=[-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
levelsI = np.linspace(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
cont = ax.contour(SNRi, levels=levelsI, colors='grey', linewidths=0.5)
elif display.lower() in ['pol_flux']:
# Display polarisation flux
pf_mask = (stkI.data > 0.) * (pol.data > 0.)
vmin, vmax = 0., np.max(stkI.data[pf_mask]*convert_flux*pol.data[pf_mask])
im = ax.imshow(stkI.data*convert_flux*pol.data,extent=[-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(stkI.data*convert_flux*pol.data, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
levelsI = np.linspace(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
elif display.lower() in ['p','pol','pol_deg']:
# Display polarization degree map
vmin, vmax = 0., 100.
im = ax.imshow(pol.data*100.,extent=[-pol.data.shape[1]/2.,pol.data.shape[1]/2.,-pol.data.shape[0]/2.,pol.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(pol.data*100., vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$P$ [%]")
elif display.lower() in ['s_p','pol_err','pol_deg_err']:
# Display polarization degree error map
vmin, vmax = 0., 10.
p_err = pol_err.data.copy()
p_err[p_err > vmax/100.] = np.nan
im = ax.imshow(p_err*100.,extent=[-pol_err.data.shape[1]/2.,pol_err.data.shape[1]/2.,-pol_err.data.shape[0]/2.,pol_err.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(p_err*100., vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\sigma_P$ [%]")
elif display.lower() in ['s_i','i_err']:
# Display intensity error map
vmin, vmax = 0., np.max(np.sqrt(stk_cov.data[0,0][stk_cov.data[0,0] > 0.])*convert_flux)
im = ax.imshow(np.sqrt(stk_cov.data[0,0])*convert_flux,extent=[-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(np.sqrt(stk_cov.data[0,0])*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$\sigma_I$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
elif display.lower() in ['snr','snri']:
# Display I_stokes signal-to-noise map
vmin, vmax = 0., np.max(SNRi[SNRi > 0.])
im = ax.imshow(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(SNRi, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$I_{Stokes}/\sigma_{I}$")
levelsI = np.linspace(SNRi_cut, np.max(SNRi[SNRi > 0.]), 10)
#print(levelsI)
cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
cont = ax.contour(SNRi, levels=levelsI, colors='grey', linewidths=0.5)
elif display.lower() in ['snrp']:
# Display polarization degree signal-to-noise map
vmin, vmax = SNRp_cut, np.max(SNRp[SNRp > 0.])
im = ax.imshow(SNRp, extent=[-SNRp.shape[1]/2.,SNRp.shape[1]/2.,-SNRp.shape[0]/2.,SNRp.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$P/\sigma_{P}$")
levelsP = np.linspace(SNRp_cut, np.max(SNRp[SNRp > 0.]), 10)
cont = ax.contour(SNRp, extent=[-SNRp.shape[1]/2.,SNRp.shape[1]/2.,-SNRp.shape[0]/2.,SNRp.shape[0]/2.], levels=levelsP, colors='grey', linewidths=0.5)
cont = ax.contour(SNRp, levels=levelsP, colors='grey', linewidths=0.5)
else:
# Defaults to intensity map
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = ax.imshow(stkI.data*convert_flux,extent=[-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.], vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA$]")
levelsI = np.linspace(SNRi_cut, SNRi.max(), 10)
cont = ax.contour(SNRi, extent=[-SNRi.shape[1]/2.,SNRi.shape[1]/2.,-SNRi.shape[0]/2.,SNRi.shape[0]/2.], levels=levelsI, colors='grey', linewidths=0.5)
cont = ax.contour(SNRi, levels=levelsI, colors='grey', linewidths=0.5)
fontprops = fm.FontProperties(size=16)
px_size = wcs.wcs.get_cdelt()[0]
px_size = wcs.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(ax.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
ax.add_artist(px_sc)
X, Y = np.meshgrid(np.linspace(-stkI.data.shape[0]/2.,stkI.data.shape[0]/2.,stkI.data.shape[0]), np.linspace(-stkI.data.shape[1]/2.,stkI.data.shape[1]/2.,stkI.data.shape[1]))
#pol.data[np.isfinite(pol.data)] = 1./2.
X, Y = np.meshgrid(np.linspace(0,stkI.data.shape[0],stkI.data.shape[0]), np.linspace(0,stkI.data.shape[1],stkI.data.shape[1]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
Q = ax.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
pol_sc = AnchoredSizeBar(ax.transData, 2., r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
@@ -481,4 +487,68 @@ def polarization_map(Stokes, data_mask, rectangle=None, SNRp_cut=3., SNRi_cut=30
fig.savefig(plots_folder+savename+".png",bbox_inches='tight',dpi=200)
plt.show()
return 0
return fig, ax
class align_maps(object):
"""
Class to interactively align maps with different WCS.
"""
def __init__(self, Stokes, other_map, SNRp_cut=3., SNRi_cut=30.):
#Get data
stkI = Stokes[np.argmax([Stokes[i].header['datatype']=='I_stokes' for i in range(len(Stokes))])]
stk_cov = Stokes[np.argmax([Stokes[i].header['datatype']=='IQU_cov_matrix' for i in range(len(Stokes))])]
pol = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_debiased' for i in range(len(Stokes))])]
pol_err = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_deg_err' for i in range(len(Stokes))])]
pang = Stokes[np.argmax([Stokes[i].header['datatype']=='Pol_ang' for i in range(len(Stokes))])]
wcs1 = WCS(Stokes[0]).deepcopy()
convert_flux = Stokes[0].header['photflam']
wcs2 = WCS(other_map).deepcopy()
#Compute SNR and apply cuts
pol.data[pol.data == 0.] = np.nan
SNRp = pol.data/pol_err.data
SNRp[np.isnan(SNRp)] = 0.
pol.data[SNRp < SNRp_cut] = np.nan
SNRi = stkI.data/np.sqrt(stk_cov.data[0,0])
SNRi[np.isnan(SNRi)] = 0.
pol.data[SNRi < SNRi_cut] = np.nan
plt.rcParams.update({'font.size': 16})
self.fig = plt.figure(figsize=(25,15))
#Plot the UV map
self.ax1 = self.fig.add_subplot(121, projection=wcs1)
self.ax1.set_facecolor('k')
vmin, vmax = 0., np.max(stkI.data[stkI.data > 0.]*convert_flux)
im1 = self.ax1.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
fontprops = fm.FontProperties(size=16)
px_size = wcs1.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(self.ax1.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
self.ax1.add_artist(px_sc)
north_dir1 = AnchoredDirectionArrows(self.ax1.transAxes, "E", "N", length=-0.08, fontsize=0.03, loc=1, aspect_ratio=-1, sep_y=0.01, sep_x=0.01, angle=-Stokes[0].header['orientat'], color='w', arrow_props={'ec': 'w', 'fc': 'w', 'alpha': 1,'lw': 2})
self.ax1.add_artist(north_dir1)
pol.data[np.isfinite(pol.data)] = 1./2.
step_vec = 1
X, Y = np.meshgrid(np.linspace(0,stkI.data.shape[0],stkI.data.shape[0]), np.linspace(0,stkI.data.shape[1],stkI.data.shape[1]))
U, V = pol.data*np.cos(np.pi/2.+pang.data*np.pi/180.), pol.data*np.sin(np.pi/2.+pang.data*np.pi/180.)
Q = self.ax1.quiver(X[::step_vec,::step_vec],Y[::step_vec,::step_vec],U[::step_vec,::step_vec],V[::step_vec,::step_vec],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='w')
self.ax1.set_title("Click on selected point of reference.")
#Plot the other map
self.ax2 = self.fig.add_subplot(122, projection=wcs2)
self.ax2.set_facecolor('k')
vmin, vmax = 0., np.max(other_map.data[other_map.data > 0.])
im2 = self.ax2.imshow(other_map.data, vmin=vmin, vmax=vmax, aspect='auto', cmap='inferno', alpha=1.)
fontprops = fm.FontProperties(size=16)
px_size = wcs2.wcs.get_cdelt()[0]*3600.
px_sc = AnchoredSizeBar(self.ax2.transData, 1./px_size, '1 arcsec', 3, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w', fontproperties=fontprops)
self.ax2.add_artist(px_sc)
self.ax2.set_title("Click on selected point of reference.")