Tibeuleu/FOC_Reduction
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reduce NGC1068 for comparison with Kishimoto's pipeline

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Tibeuleu
2022-10-19 16:22:09 +02:00
parent d3c11eb4af
commit 3e315783f2
14 changed files with 37 additions and 29 deletions
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14
src/lib/plots.py
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@@ -1703,12 +1703,14 @@ class pol_map(object):
ax.add_artist(self.north_dir)
def display(self, fig=None, ax=None):
norm = None
if self.display_selection is None:
self.display_selection = "total_flux"
if self.display_selection.lower() in ['total_flux']:
self.data = self.I*self.convert_flux
vmin, vmax = 0., np.max(self.data[self.data > 0.])
vmin, vmax = np.min(self.data[self.cut])/10., np.max(self.data[self.data > 0.])
norm = LogNorm(vmin, vmax)
label = r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
elif self.display_selection.lower() in ['pol_flux']:
self.data = self.I*self.convert_flux*self.P
@@ -1738,12 +1740,18 @@ class pol_map(object):
ax = self.ax
if hasattr(self, 'im'):
self.im.remove()
self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
if not norm is None:
self.im = ax.imshow(self.data, norm=norm, aspect='equal', cmap='inferno')
else:
self.im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
self.cbar = plt.colorbar(self.im, cax=self.cbar_ax, label=label)
fig.canvas.draw_idle()
return self.im
else:
im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
if not norm is None:
im = ax.imshow(self.data, norm=norm, aspect='equal', cmap='inferno')
else:
im = ax.imshow(self.data, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno')
ax.set_xlim(0,self.data.shape[1])
ax.set_ylim(0,self.data.shape[0])
plt.colorbar(im, pad=0.025, aspect=80, label=label)

19
src/lib/reduction.py
View File

@@ -1475,24 +1475,23 @@ def rotate_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, data_mask, headers,
new_U_stokes = np.zeros(shape)
new_Stokes_cov = np.zeros((*Stokes_cov.shape[:-2],*shape))
for i in range(shape[0]):
for j in range(shape[1]):
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
new_Stokes_cov[:,:,i,j] = np.dot(mrot, np.dot(Stokes_cov[:,:,i,j], mrot.T))
#Rotate original images using scipy.ndimage.rotate
new_I_stokes = sc_rotate(new_I_stokes, ang, order=1, reshape=False, cval=0.)
new_Q_stokes = sc_rotate(new_Q_stokes, ang, order=1, reshape=False, cval=0.)
new_U_stokes = sc_rotate(new_U_stokes, ang, order=1, reshape=False, cval=0.)
new_I_stokes = sc_rotate(I_stokes, ang, order=1, reshape=False, cval=0.)
new_Q_stokes = sc_rotate(Q_stokes, ang, order=1, reshape=False, cval=0.)
new_U_stokes = sc_rotate(U_stokes, ang, order=1, reshape=False, cval=0.)
new_data_mask = sc_rotate(data_mask.astype(float)*10., ang, order=1, reshape=False, cval=0.)
new_data_mask[new_data_mask < 2] = 0.
new_data_mask = new_data_mask.astype(bool)
for i in range(3):
for j in range(3):
new_Stokes_cov[i,j] = sc_rotate(new_Stokes_cov[i,j], ang, order=1,
reshape=False, cval=0.)
new_Stokes_cov[i,j] = sc_rotate(Stokes_cov[i,j], ang, order=1, reshape=False, cval=0.)
new_Stokes_cov[i,i] = np.abs(new_Stokes_cov[i,i])
for i in range(shape[0]):
for j in range(shape[1]):
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
new_Stokes_cov[:,:,i,j] = np.dot(mrot, np.dot(new_Stokes_cov[:,:,i,j], mrot.T))
#Update headers to new angle
new_headers = []
mrot = np.array([[np.cos(-alpha), -np.sin(-alpha)],