fix global variables

src/FOC_reduction.py

@@ -35,7 +35,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data", crop=
     # Background estimation
     error_sub_type = 'freedman-diaconis'   # sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51, 51))
     subtract_error = 0.50
-    display_bkg = True
+    display_bkg = False
 
     # Data binning
     rebin = True

src/lib/plots.py

@@ -54,7 +54,6 @@ from astropy.wcs import WCS
 from astropy.io import fits
 from astropy.coordinates import SkyCoord
 from scipy.ndimage import zoom as sc_zoom
-from lib.fits import save_Stokes
 from lib.utils import rot2D, princ_angle, sci_not
 
 
@@ -416,7 +415,7 @@ def polarisation_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
         X, Y = np.meshgrid(np.arange(stkI.data.shape[1]), np.arange(stkI.data.shape[0]))
         U, V = poldata*np.cos(np.pi/2.+pangdata*np.pi/180.), poldata*np.sin(np.pi/2.+pangdata*np.pi/180.)
         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=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.1, linewidth=0.5, color='w', edgecolor='k')
+                  scale=1./vec_scale, scale_units='xy', pivot='mid', headwidth=0., headlength=0., headaxislength=0., width=0.2, linewidth=0.3, color='w', edgecolor='k')
         pol_sc = AnchoredSizeBar(ax.transData, vec_scale, r"$P$= 100 %", 4, pad=0.5, sep=5, borderpad=0.5, frameon=False, size_vertical=0.005, color='w')
 
         ax.add_artist(pol_sc)
@@ -448,7 +447,7 @@ def polarisation_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c
     if savename is not None:
         if savename[-4:] not in ['.png', '.jpg', '.pdf']:
             savename += '.pdf'
-        fig.savefig(path_join(plots_folder, savename), bbox_inches='tight', dpi=300)
+        fig.savefig(path_join(plots_folder, savename), bbox_inches='tight', dpi=200)
 
     plt.show()
     return fig, ax

src/lib/reduction.py

@@ -1101,16 +1101,16 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
         same_filt3 = np.array([filt3 == header['filtnam3'] for header in headers]).all()
         same_filt4 = np.array([filt4 == header['filtnam4'] for header in headers]).all()
         if (same_filt2 and same_filt3 and same_filt4):
-            transmit2, transmit3, transmit4 = globals["trans2"][filt2.lower()], globals["trans3"][filt3.lower()], globals["trans4"][filt4.lower()]
+            transmit2, transmit3, transmit4 = globals()["trans2"][filt2.lower()], globals()["trans3"][filt3.lower()], globals()["trans4"][filt4.lower()]
         else:
             print("WARNING : All images in data_array are not from the same \
                     band filter, the limiting transmittance will be taken.")
-            transmit2 = np.min([globals["trans2"][header['filtnam2'].lower()] for header in headers])
+            transmit2 = np.min([globals()["trans2"][header['filtnam2'].lower()] for header in headers])
-            transmit3 = np.min([globals["trans3"][header['filtnam3'].lower()] for header in headers])
+            transmit3 = np.min([globals()["trans3"][header['filtnam3'].lower()] for header in headers])
-            transmit4 = np.min([globals["trans4"][header['filtnam4'].lower()] for header in headers])
+            transmit4 = np.min([globals()["trans4"][header['filtnam4'].lower()] for header in headers])
         if transmitcorr:
             transmit *= transmit2*transmit3*transmit4
-        pol_eff = np.array([globals["pol_efficiency"]['pol0'], globals["pol_efficiency"]['pol60'], globals["pol_efficiency"]['pol120']])
+        pol_eff = np.array([globals()["pol_efficiency"]['pol0'], globals()["pol_efficiency"]['pol60'], globals()["pol_efficiency"]['pol120']])
 
         # Calculating correction factor
         corr = np.array([1.0*h['photflam']/h['exptime'] for h in pol_headers])*pol_headers[0]['exptime']/pol_headers[0]['photflam']
@@ -1122,11 +1122,11 @@ def compute_Stokes(data_array, error_array, data_mask, headers, FWHM=None, scale
         coeff_stokes = np.zeros((3, 3))
         # Coefficients linking each polarizer flux to each Stokes parameter
         for i in range(3):
-            coeff_stokes[0, i] = pol_eff[(i+1) % 3]*pol_eff[(i+2) % 3]*np.sin(-2.*globals["theta"][(i+1) % 3]+2.*globals["theta"][(i+2) % 3])*2./transmit[i]
+            coeff_stokes[0, i] = pol_eff[(i+1) % 3]*pol_eff[(i+2) % 3]*np.sin(-2.*globals()["theta"][(i+1) % 3]+2.*globals()["theta"][(i+2) % 3])*2./transmit[i]
-            coeff_stokes[1, i] = (-pol_eff[(i+1) % 3]*np.sin(2.*globals["theta"][(i+1) % 3]) +
+            coeff_stokes[1, i] = (-pol_eff[(i+1) % 3]*np.sin(2.*globals()["theta"][(i+1) % 3]) +
-                                  pol_eff[(i+2) % 3]*np.sin(2.*globals["theta"][(i+2) % 3]))*2./transmit[i]
+                                  pol_eff[(i+2) % 3]*np.sin(2.*globals()["theta"][(i+2) % 3]))*2./transmit[i]
-            coeff_stokes[2, i] = (pol_eff[(i+1) % 3]*np.cos(2.*globals["theta"][(i+1) % 3]) -
+            coeff_stokes[2, i] = (pol_eff[(i+1) % 3]*np.cos(2.*globals()["theta"][(i+1) % 3]) -
-                                  pol_eff[(i+2) % 3]*np.cos(2.*globals["theta"][(i+2) % 3]))*2./transmit[i]
+                                  pol_eff[(i+2) % 3]*np.cos(2.*globals()["theta"][(i+2) % 3]))*2./transmit[i]
 
         # Normalization parameter for Stokes parameters computation
         A = (coeff_stokes[0, :]*transmit/2.).sum()