Tibeuleu/FOC_Reduction
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add Data dump to analysis tool, replot NGC1068

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Thibault Barnouin
2022-04-07 08:55:13 +02:00
parent f6df92a1c2
commit 7bbd2bc2e8
9 changed files with 80 additions and 10 deletions
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src/lib/reduction.py
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@@ -500,17 +500,41 @@ def get_error(data_array, headers, sub_shape=(15,15), display=False,
ax.set_title("Background flux and error computed for each image")
plt.legend()
fig2, ax2 = plt.subplots(figsize=(10,10))
data0 = data[0]*convert_flux
instr = headers[0]['instrume']
rootname = headers[0]['rootname']
exptime = headers[0]['exptime']
filt = headers[0]['filtnam1']
#plots
im = ax2.imshow(data0, vmin=data0.min(), vmax=data0.max(), origin='lower', cmap='gray')
x, y, width, height, angle, color = rectangle[0]
ax2.add_patch(Rectangle((x, y),width,height,edgecolor=color,fill=False))
ax2.annotate(instr+":"+rootname, color='white', fontsize=5,
xy=(0.02, 0.95), xycoords='axes fraction')
ax2.annotate(filt, color='white', fontsize=10, xy=(0.02, 0.02),
xycoords='axes fraction')
ax2.annotate(exptime, color='white', fontsize=5, xy=(0.80, 0.02),
xycoords='axes fraction')
ax2.set(title="Location of background computation.",
xlabel='pixel offset',
ylabel='pixel offset')
fig2.subplots_adjust(hspace=0, wspace=0, right=0.85)
cbar_ax = fig2.add_axes([0.9, 0.12, 0.02, 0.75])
fig2.colorbar(im, cax=cbar_ax, label=r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]")
if not(savename is None):
#fig.suptitle(savename+"_background_flux")
fig.savefig(plots_folder+savename+"_background_flux.png",
bbox_inches='tight')
fig2.savefig(plots_folder+savename+'_'+filt+'_background_location.png',
bbox_inches='tight')
vmin = np.min(np.log10(data[data > 0.]))
vmax = np.max(np.log10(data[data > 0.]))
plot_obs(data, headers, vmin=data.min(), vmax=data.max(),
rectangle=rectangle,
savename=savename+"_background_location",
plots_folder=plots_folder)
else:
vmin = np.min(np.log10(data[data > 0.]))
vmax = np.max(np.log10(data[data > 0.]))
@@ -1186,7 +1210,7 @@ def compute_Stokes(data_array, error_array, data_mask, headers,
Stokes_cov[0,0], Stokes_cov[1,1], Stokes_cov[2,2] = Stokes_error**2
#Compute integrated values for P, PA before any rotation
mask = deepcopy(data_mask).astype(bool)
mask = np.logical_and(data_mask.astype(bool), (I_stokes > 0.))
n_pix = I_stokes[mask].size
I_diluted = I_stokes[mask].sum()
Q_diluted = Q_stokes[mask].sum()