Tibeuleu/FOC_Reduction
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overplot IC5063 with Radio and IR

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Tibeuleu
2022-11-08 13:32:11 +01:00
parent a5d02fc0c0
commit 359e5b401f
22 changed files with 53 additions and 50 deletions
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35
src/lib/plots.py
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@@ -449,21 +449,21 @@ class align_maps(object):
self.map = map1
self.other_map = other_map
self.wcs_map = WCS(self.map[0]).deepcopy()
if self.wcs_map.naxis == 4:
self.wcs_map = WCS(self.map[0],naxis=[1,2]).deepcopy()
self.map[0].data = self.map[0].data[0,0]
elif self.wcs_map.naxis == 3:
self.wcs_map = WCS(self.map[0],naxis=[1,2]).deepcopy()
self.map[0].data = self.map[0].data[1]
self.wcs_map = deepcopy(WCS(self.map[0])).celestial
# if self.wcs_map.naxis == 4:
# self.wcs_map = WCS(self.map[0],naxis=[1,2]).deepcopy()
# self.map[0].data = self.map[0].data[0,0]
# elif self.wcs_map.naxis == 3:
# self.wcs_map = WCS(self.map[0],naxis=[1,2]).deepcopy()
# self.map[0].data = self.map[0].data[1]
self.wcs_other = WCS(self.other_map[0]).deepcopy()
if self.wcs_other.naxis == 4:
self.wcs_other = WCS(self.other_map[0],naxis=[1,2]).deepcopy()
self.other_map[0].data = self.other_map[0].data[0,0]
elif self.wcs_other.naxis == 3:
self.wcs_other = WCS(self.other_map[0],naxis=[1,2]).deepcopy()
self.other_map[0].data = self.other_map[0].data[1]
self.wcs_other = deepcopy(WCS(self.other_map[0])).celestial
# if self.wcs_other.naxis == 4:
# self.wcs_other = WCS(self.other_map[0],naxis=[1,2]).deepcopy()
# self.other_map[0].data = self.other_map[0].data[0,0]
# elif self.wcs_other.naxis == 3:
# self.wcs_other = WCS(self.other_map[0],naxis=[1,2]).deepcopy()
# self.other_map[0].data = self.other_map[0].data[1]
try:
convert_flux = self.map[0].header['photflam']
@@ -773,7 +773,7 @@ class overplot_pol(align_maps):
self.fig2.canvas.draw()
def plot(self, SNRp_cut=3., SNRi_cut=30., savename=None, **kwargs) -> None:
while not self.aligned():
while not self.aligned:
self.align()
self.overplot(SNRp_cut=SNRp_cut, SNRi_cut=SNRi_cut, savename=savename, **kwargs)
plt.show(block=True)
@@ -1739,7 +1739,8 @@ class pol_map(object):
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
vmin, vmax = 0., np.max(self.data[self.data > 0.])
vmin, vmax = np.min(self.I[self.cut]*self.convert_flux)/10., np.max(self.I[self.data > 0.]*self.convert_flux)
norm = LogNorm(vmin, vmax)
label = r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]"
elif self.display_selection.lower() in ['pol_deg']:
self.data = self.P*100.
@@ -1894,12 +1895,14 @@ class pol_map(object):
ax = self.ax
if hasattr(self, 'an_int'):
self.an_int.remove()
#self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.93), xycoords='axes fraction')
self.an_int = ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.85), xycoords='axes fraction')
if not self.region is None:
self.cont = ax.contour(self.region.astype(float),levels=[0.5], colors='white', linewidths=0.8)
fig.canvas.draw_idle()
return self.an_int
else:
#ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.94), xycoords='axes fraction')
ax.annotate(r"$F_{{\lambda}}^{{int}}$({0:.0f} $\AA$) = {1} $ergs \cdot cm^{{-2}} \cdot s^{{-1}} \cdot \AA^{{-1}}$".format(self.pivot_wav,sci_not(I_reg*self.convert_flux,I_reg_err*self.convert_flux,2))+"\n"+r"$P^{{int}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_reg*100.,np.ceil(P_reg_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{int}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_reg,np.ceil(PA_reg_err*10.)/10.)+"\n"+r"$P^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} %".format(P_cut*100.,np.ceil(P_cut_err*1000.)/10.)+"\n"+r"$\theta_{{P}}^{{cut}}$ = {0:.1f} $\pm$ {1:.1f} °".format(PA_cut,np.ceil(PA_cut_err*10.)/10.), color='white', fontsize=12, xy=(0.01, 0.90), xycoords='axes fraction')
if not self.region is None:
ax.contour(self.region.astype(float),levels=[0.5], colors='white', linewidths=0.8)

14
src/lib/reduction.py
View File

@@ -246,10 +246,10 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5,
Returns:
cropped_array : numpy.ndarray
Array containing the observationnal data homogeneously cropped.
headers : header list
Updated headers associated with the images in data_array.
cropped_error : numpy.ndarray
Array containing the error on the observationnal data homogeneously cropped.
headers : header list
Updated headers associated with the images in data_array.
"""
if error_array is None:
error_array = np.zeros(data_array.shape)
@@ -284,7 +284,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5,
crop_error_array[i] = error_array[i][v_array[0]:v_array[1],v_array[2]:v_array[3]]
#Update CRPIX value in the associated header
curr_wcs = deepcopy(WCS(crop_headers[i]))
curr_wcs.wcs.crpix = curr_wcs.wcs.crpix - np.array([v_array[2], v_array[0]])
curr_wcs.wcs.crpix[:2] = curr_wcs.wcs.crpix[:2] - np.array([v_array[2], v_array[0]])
crop_headers[i].update(curr_wcs.to_header())
crop_headers[i]['naxis1'], crop_headers[i]['naxis2'] = crop_array[i].shape
@@ -1606,11 +1606,11 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
Updated array containing the rotated images.
new_error_array : numpy.ndarray
Updated array containing the rotated errors.
new_data_mask : numpy.ndarray
Updated 2D boolean array delimiting the data to work on.
new_headers : header list
Updated list of headers corresponding to the reduced images accounting
for the new orientation angle.
new_data_mask : numpy.ndarray
Updated 2D boolean array delimiting the data to work on.
"""
#Rotate I_stokes, Q_stokes, U_stokes using rotation matrix
alpha = ang*np.pi/180.
@@ -1649,8 +1649,8 @@ def rotate_data(data_array, error_array, data_mask, headers, ang):
new_wcs = WCS(header).deepcopy()
new_wcs.wcs.pc = np.dot(mrot, new_wcs.wcs.pc)
new_wcs.wcs.crpix = np.dot(mrot, new_wcs.wcs.crpix - old_center[::-1]) + new_center[::-1]
new_wcs.wcs.pc[:2,:2] = np.dot(mrot, new_wcs.wcs.pc[:2,:2])
new_wcs.wcs.crpix[:2] = np.dot(mrot, new_wcs.wcs.crpix[:2] - old_center[::-1]) + new_center[::-1]
new_wcs.wcs.set()
for key, val in new_wcs.to_header().items():
new_header[key] = val