def plot_inter_order_mask(data, inter_order_mask):
mask = data.T * inter_order_mask.T
# Plot:
fig, ax = plt.subplots(1, 1)
im = ax.imshow(linear(data.T), cmap='Blues', origin='lower', alpha=1.0)
im = ax.imshow(linear(mask), cmap='Blues', origin='lower', alpha=0.73)
# Settings:
ax.set_xlabel('Dispersion (pixels)')
ax.set_ylabel('Cross (pixels)')
# Colorbar:
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='1.5%', pad=0.0)
cbar = plt.colorbar(im, cax=cax)
plt.ylabel('Norm. Counts')
plt.show()
def plot_background_residuals(F_back, S_back, S):
F = F_back / np.max(F_back)
S = S_back / np.max(S_back)
I = F - S
fig, ax = plt.subplots(2, 1)
# Plot:
im0 = ax[0].imshow(linear(S.T), cmap='Blues', origin='lower')
im1 = ax[1].imshow(I.T,
vmin=np.min(I),
vmax=np.max(I),
cmap='Blues',
origin='lower')
# Settings:
fig.subplots_adjust(hspace=-0.50)
ax[0].set_xticklabels([])
ax[0].set_yticklabels(['', '', '200', '400'])
# Colorbar ax0:
cbar0 = fig.add_axes([0.9, 0.510, 0.015, 0.227])
cbar1 = fig.add_axes([0.9, 0.253, 0.015, 0.228])
fig.colorbar(im0, cax=cbar0)
fig.colorbar(im1, cax=cbar1)
# Labels:
ax[0].annotate('(a)', xy=(50, 280), fontsize=15)
ax[1].annotate('(b)', xy=(50, 280), fontsize=15)
ax[1].set_xlabel('Dispersion (pixels)')
ax[0].set_ylabel(r'Cross Dispersion (pixels)\qquad\qquad\qquad\tiny.')
cbar0.set_ylabel('Norm. Counts')
cbar1.set_ylabel('Residuals')
plt.show()
def FITS(img, sigma=2, xlab=None, ylab=None, colorbar=None):
""" Easy plot of pixel array data """
plt.figure()
plt.imshow(linear(img, sigma), cmap='Blues', origin='lower')
# Labels:
if xlab is None and ylab is None:
xlab, ylab = r'$x$ (pixel)', r'$y$ (pixel)'
plt.xlabel(xlab)
plt.ylabel(ylab)
plt.show()
def plot_arc_scale(l_obs0, l_teo0, l_obs1, l_teo1, l, obs_results):
"""FIGURE MADE TO SPANS ENTIRE SCREEN"""
# Unpack results from peak_finder:
text, img, COF, radius = obs_results[0], obs_results[1], obs_results[
2], obs_results[3]
#----------
# SUBPLOTS:
#----------
font = 17
fig = plt.figure()
# 1. subplot:
ax1 = fig.add_subplot(4, 1, (1, 3))
for i in range(len(l_obs0)):
ax1.axvline(x=l_obs0[i], ymax=0.85, color='r', linestyle='--', alpha=1)
for i in range(len(l_obs1)):
ax1.axvline(x=l_obs1[i], ymax=0.85, color='r', linestyle='-', alpha=1)
for i in range(len(l_teo0)):
ax1.axvline(x=l_teo0[i], ymax=0.85, color='b', linestyle='--', alpha=1)
for i in range(len(l_teo1)):
ax1.axvline(x=l_teo1[i], ymax=0.85, color='b', linestyle='-', alpha=1)
ax1.plot(l,
img.sum(axis=1) / np.max(img.sum(axis=1)),
'k-',
label='ThAr spectrum')
# 2. subplot:
ax2 = fig.add_subplot(313)
ax2.imshow(linear(img.T), cmap='Blues')
ax2.scatter(COF[:, 0],
COF[:, 1],
s=radius * 12,
facecolors='none',
edgecolors='r',
marker='s')
# Annotation:
for i in range(len(l_teo0)):
ax1.annotate(l_teo0[i], (l_teo0[i] - 0.5, 1.15),
rotation=45,
fontsize=11,
color='b')
# Labels:
ax1.set_ylabel('Normalized Counts', fontsize=font)
ax1.set_xlabel(r'$\lambda$ (Å)', fontsize=font)
ax1.tick_params(labelsize=font)
#------
#ax1.set_xticklabels([])
#ax2.set_yticklabels(['0', ''])
ax2.set_xlabel(r'$\lambda$ (pixel)', fontsize=font)
ax2.set_ylabel(r'$x$ (pixel)', fontsize=font)
ax2.tick_params(labelsize=font)
# Axes:
ax1.set_xlim((min(l), max(l)))
ax1.set_ylim((0, 1.2))
ax2.set_xlim((0, 2749))
ax2.invert_yaxis()
plt.show()
def plot_optimal_extraction(img):
# Prepare for scaled colorbar:
gs = gridspec.GridSpec(1, 3)
#------------
# 1. SUBPLOT:
#------------
ax1 = plt.subplot2grid((2, 3), (0, 0), colspan=2)
# Plot pixel image:
#im = FITS(img.T, ax=ax1)
im = ax1.imshow(linear(img.T), cmap='Blues', origin='lower')
# Plot red and green boxes (given as cornerpoint and height and width):
ax1.add_patch(
Rectangle((7.5, -0.4),
1,
len(img[:, 0]) - 0.3,
fill=None,
edgecolor='r',
linewidth=1.2))
ax1.add_patch(
Rectangle((29.5, -0.4),
1,
len(img[:, 0]) - 0.3,
fill=None,
edgecolor='g',
linewidth=1.2))
# Labels:
ax1.set_xlabel(r'$\lambda$ (pixel)')
ax1.set_ylabel(r'$x$ (pixel)')
#------------
# 2. SUBPLOT:
#------------
ax2 = plt.subplot2grid((9, 3), (0, 2), colspan=1, rowspan=5)
# Cross cut in the image:
l1 = img[:, 8]
l2 = img[:, 10] #+100*np.ones(len(l1))
# Plot step plots:
ax2.step(l1 / np.max(img), range(len(l1)), color='r')
ax2.step(l2 / np.max(img), range(len(l2)), color='g')
# Remove ticks and numbers from both axes:
ax2.set_xticklabels([])
ax2.set_yticklabels([])
# Plot equation inside plot:
ax2.text(-0.05, 6.5, r'$\sum\limits_x \text{P}_{x\lambda}=1$', fontsize=15)
# Put colorbar from first plot as the x label (as they are shared):
divider = make_axes_locatable(ax2)
cax = divider.append_axes('bottom', size='10%',
pad=0.0) # right, left, top, bottom
cbar = plt.colorbar(im, cax=cax, orientation='horizontal')
cbar.ax.set_xlabel('Normalized Counts')
# Plot figure with small ajustment:
plt.subplots_adjust(wspace=0, hspace=-0.03)
plt.show()
def plot_background(data):
# Plot:
fig, ax = plt.subplots()
im = ax.imshow(linear(data.T), cmap='Blues', origin='lower')
# Settings:
ax.set_xlabel('Dispersion (pixels)')
ax.set_xticklabels([])
ax.set_ylabel(r'Cross Dispersion (pixels)')
# Colorbar:
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='3%', pad=0.0)
cbar = plt.colorbar(im, cax=cax)
plt.ylabel(r'Counts')
plt.show()
def plot_arc_illustration(l_obs0, l_teo0, l_obs1, l_teo1, l, obs_results):
"""FIGURE MADE TO SPANS ENTIRE SCREEN"""
# Unpack results from peak_finder:
text, img, COF, radius = obs_results[0], obs_results[1], obs_results[
2], obs_results[3]
#----------
# SUBPLOTS:
#----------
font = 12
fig = plt.figure()
h = 0.85
# 1. subplot:
ax1 = fig.add_subplot(10, 1, (1, 9))
for i in range(len(l_obs0)):
ax1.axvline(x=l_obs0[i], ymax=h, color='r', linestyle=':', alpha=1)
#for i in range(len(l_obs1)): ax1.axvline(x=l_obs1[i], ymax=0.85, color='r', linestyle='-', alpha=1)
for i in range(len(l_teo0)):
ax1.axvline(x=l_teo0[i], ymax=h, color='b', linestyle=':', alpha=1)
for i in range(len(l_teo1)):
ax1.axvline(x=l_teo1[i],
ymax=h,
color='limegreen',
linestyle='-',
alpha=1)
ax1.plot(l,
img.sum(axis=1) / np.max(img.sum(axis=1)),
'k-',
label='ThAr spectrum')
# 2. subplot:
ax2 = fig.add_subplot(10, 1, 10)
ax2.imshow(linear(img.T), cmap='Blues')
ax2.scatter(COF[:, 0],
COF[:, 1],
s=radius * 12,
facecolors='none',
edgecolors='r',
marker='s')
#-----------------
# GLOBAL SETTINGS:
#-----------------
# Legend:
ax1.axvline(x=l_obs0[0],
ymax=h,
color='r',
linestyle='--',
alpha=0.4,
label='COF')
ax1.axvline(x=l_teo0[i],
ymax=h,
color='b',
linestyle=':',
alpha=1.0,
label='Atlas lines')
ax1.axvline(x=l_teo1[i],
ymax=h,
color='limegreen',
linestyle='-',
alpha=0.2,
label='Final lines')
ax1.legend(bbox_to_anchor=(0.93, 1.14), ncol=4, fontsize=font)
# Annotation:
for i in range(len(l_teo0)):
ax1.annotate(l_teo0[i], (l_teo0[i] - 0.5, 1.15),
rotation=45,
fontsize=8,
color='darkblue')
# Labels:
ax1.set_ylabel('Norm. Counts', fontsize=font)
ax1.tick_params(labelsize=font)
ax1.set_xticklabels([])
ax1.set_yticklabels(['', '', '0.2', '0.4', '0.6', '0.8', '1.0', '1.2'])
#ax2.set_yticklabels(['0', ''])
ax2.set_xlabel(r'$\lambda$ (pixel)', fontsize=font)
ax2.set_ylabel(r'$\Delta x$', fontsize=font)
ax2.tick_params(labelsize=font)
# Axes:
ax1.set_xlim((l[820], l[1850]))
ax2.set_xlim((820, 1850))
ax1.set_ylim((-0.02, 1.2))
ax2.invert_yaxis()
fig.subplots_adjust(hspace=-0.35)
plt.show()