Tibeuleu/FOC_Reduction
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general improvements to pipeline

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Tibeuleu
2023-01-31 17:39:25 +01:00
parent 5c0d08a44b
commit fc80bafc66
4 changed files with 35 additions and 26 deletions
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4
src/lib/background.py
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@@ -89,10 +89,10 @@ def display_bkg(data, background, std_bkg, headers, histograms=None, binning=Non
filt = headers[0]['filtnam1']
#plots
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)
bkg_im = ax2.imshow(bkg_data0, origin='lower', cmap='Reds', alpha=0.5)
if not(rectangle is None):
x, y, width, height, angle, color = rectangle[0]
ax2.add_patch(Rectangle((x, y),width,height,edgecolor=color,fill=False))
ax2.add_patch(Rectangle((x, y),width,height,edgecolor=color,fill=False,lw=2))
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')

2
src/lib/fits.py
View File

@@ -169,6 +169,7 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
header['datatype'] = ('I_stokes', 'type of data stored in the HDU')
I_stokes[(1-data_mask).astype(bool)] = 0.
primary_hdu = fits.PrimaryHDU(data=I_stokes, header=header)
primary_hdu.name = 'I_stokes'
hdul.append(primary_hdu)
#Add Q, U, Stokes_cov, P, s_P, PA, s_PA to the HDUList
@@ -183,6 +184,7 @@ def save_Stokes(I_stokes, Q_stokes, U_stokes, Stokes_cov, P, debiased_P, s_P,
if not name == 'IQU_cov_matrix':
data[(1-data_mask).astype(bool)] = 0.
hdu = fits.ImageHDU(data=data,header=hdu_header)
hdu.name = name
hdul.append(hdu)
#Save fits file to designated filepath

35
src/lib/plots.py
View File

@@ -67,18 +67,23 @@ def princ_angle(ang):
return A[0]
def sci_not(v,err,rnd=1):
def sci_not(v,err,rnd=1,out=str):
"""
Return the scientifque error notation as a string.
"""
power = - int(('%E' % v)[-3:])+1
output = r"({0}".format(round(v*10**power,rnd))
output = [r"({0}".format(round(v*10**power,rnd)),round(v*10**power,rnd)]
if type(err) == list:
for error in err:
output += r" $\pm$ {0}".format(round(error*10**power,rnd))
output[0] += r" $\pm$ {0}".format(round(error*10**power,rnd))
output.append(round(error*10**power,rnd))
else:
output += r" $\pm$ {0}".format(round(err*10**power,rnd))
return output+r")e{0}".format(-power)
output[0] += r" $\pm$ {0}".format(round(err*10**power,rnd))
output.append(round(err*10**power,rnd))
if out==str:
return output[0]+r")e{0}".format(-power)
else:
return *output[1:],-power
def plot_obs(data_array, headers, shape=None, vmin=0., vmax=6., rectangle=None,
@@ -309,7 +314,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
if display.lower() in ['intensity']:
# If no display selected, show intensity map
display='i'
vmin, vmax = np.max(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = ax.imshow(stkI.data*convert_flux, norm=LogNorm(vmin,vmax), aspect='equal', 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(vmax*0.01, vmax*0.99, 10)
@@ -320,7 +325,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
# Display polarisation flux
display='pf'
pf_mask = (stkI.data > 0.) * (pol.data > 0.)
vmin, vmax = np.max(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
im = ax.imshow(stkI.data*convert_flux*pol.data, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
levelsPf = np.linspace(vmax*0.01, vmax*0.99, 10)
@@ -350,13 +355,13 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
elif display.lower() in ['s_i','i_err']:
# Display intensity error map
display='s_i'
vmin, vmax = 0., np.max(np.sqrt(stk_cov.data[0,0][stk_cov.data[0,0] > 0.])*convert_flux)
vmin, vmax = np.min(np.sqrt(stk_cov.data[0,0][stk_cov.data[0,0] > 0.])*convert_flux), 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, vmin=vmin, vmax=vmax, aspect='equal', 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
display='snri'
vmin, vmax = 0., np.max(SNRi[SNRi > 0.])
vmin, vmax = 0., np.max(SNRi[np.isfinite(SNRi)])
im = ax.imshow(SNRi, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$I_{Stokes}/\sigma_{I}$")
levelsSNRi = np.linspace(SNRi_cut, vmax*0.99, 10)
@@ -366,7 +371,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
elif display.lower() in ['snrp']:
# Display polarization degree signal-to-noise map
display='snrp'
vmin, vmax = SNRp_cut, np.max(SNRp[SNRp > 0.])
vmin, vmax = 0., np.max(SNRp[np.isfinite(SNRp)])
im = ax.imshow(SNRp, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
cbar = plt.colorbar(im, cax=cbar_ax, label=r"$P/\sigma_{P}$")
levelsSNRp = np.linspace(SNRp_cut, vmax*0.99, 10)
@@ -375,7 +380,7 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
#ax.clabel(cont,inline=True,fontsize=6)
else:
# Defaults to intensity map
vmin, vmax = np.min(stkI.data[SNRi > SNRi_cut]*convert_flux)/5., np.max(stkI.data[SNRi > SNRi_cut]*convert_flux)
vmin, vmax = 3.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux)
#im = ax.imshow(stkI.data*convert_flux, vmin=vmin, vmax=vmax, aspect='equal', cmap='inferno', alpha=1.)
#cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA$]")
im = ax.imshow(stkI.data*convert_flux, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.)
@@ -1545,6 +1550,7 @@ class pol_map(object):
def submit_save(expression):
ax_text_save.set(visible=False)
if expression != '':
plt.rcParams.update({'font.size': 15})
save_fig = plt.figure(figsize=(15,15))
save_ax = save_fig.add_subplot(111, projection=self.wcs)
self.ax_cosmetics(ax=save_ax)
@@ -1560,6 +1566,7 @@ class pol_map(object):
ax_snr_reset.set(visible=True)
ax_save.set(visible=True)
ax_dump.set(visible=True)
plt.rcParams.update({'font.size': 10})
self.fig.canvas.draw_idle()
text_save.on_submit(submit_save)
@@ -1736,17 +1743,17 @@ class pol_map(object):
self.display_selection = "total_flux"
if self.display_selection.lower() in ['total_flux']:
self.data = self.I*self.convert_flux
vmin, vmax = np.max(np.sqrt(self.IQU_cov[0,0][self.cut])*self.convert_flux), np.max(self.data[self.data > 0.])
vmin, vmax = .5*np.mean(np.sqrt(self.IQU_cov[0,0][self.IQU_cov[0,0] < 3.*self.I])*self.convert_flux), 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
vmin, vmax = np.max(np.sqrt(self.IQU_cov[0,0][self.cut])*self.convert_flux), np.max(self.I[self.data > 0.]*self.convert_flux)
vmin, vmax = .5*np.mean(np.sqrt(self.IQU_cov[0,0][self.IQU_cov[0,0] < 3.*self.I])*self.convert_flux), 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.
vmin, vmax = 0., 100. #np.max(self.data[self.data > 0.])
vmin, vmax = 0., np.max(self.data[self.P > self.s_P])
label = r"$P$ [%]"
elif self.display_selection.lower() in ['pol_ang']:
self.data = princ_angle(self.PA)