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plot_final_images.py
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from matplotlib.backends.backend_pdf import PdfPages
from astropy.visualization import MinMaxInterval, AsinhStretch, LinearStretch, \
ImageNormalize, ManualInterval
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.ticker as ticker
import astropy.io.fits as fits
import astropy.stats
import numpy
import numpy as np
import glob
import os
import sys
#source = "IRAS15398"
source = str(sys.argv[1])
print(source)
pdf = PdfPages(source+'.pdf')
# Get a list of sources.
# A few definitions.
arcsec = 4.84813681e-06
c_l = 29979245800.0
class Transform:
def __init__(self, xmin, xmax, dx, fmt):
self.xmin = xmin
self.xmax = xmax
self.dx = dx
self.fmt = fmt
def __call__(self, x, p):
return self.fmt% ((x-(self.xmax-self.xmin)/2)*self.dx)
################################################################################
### Plot the continuum observations.
################################################################################
# The list of robust parameters we used.
robust_list = [2.0,1.0,0.5,0.0,-0.5,-1.0,-2.0]
# The list of tick values to use.
ticks_list = [numpy.array([-15.0,-10.0,-5.0,0.0,5.0,10.0,15.0]), \
numpy.array([-2.0,-1.0,0.0,1.0,2.0])]
# Loop through and plot.
for ticks in ticks_list:
# Generate a figure to put the plots in.
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(16,8.25))
# Loop over robust parameter in the axes.
for i, ax in enumerate(axes.flatten()):
if i >= len(robust_list):
ax.set_axis_off()
continue
# Get the appropriate robust value
robust = robust_list[i]
# Load the continuum image.
try:
data, header = fits.getdata("{0:s}_SB_continuum_robust_{1:3.1f}."
"image.tt0.fits".format(source, robust), header=True)
except:
ax.set_axis_off()
continue
#check if continuum images are 2 axis or if they include stokes and frequency planes
ndim_cont=len(data.shape)
image = data[:,:]*1000
# Get the center of the source(s).
x0, y0 = 0., 0.
# Plot the image.
N = image.shape[0]
pixelsize = numpy.abs(header["CDELT2"])*numpy.pi/180. / \
arcsec
xmin, xmax = int(round(N/2-x0/pixelsize+ticks[0]/pixelsize)), \
int(round(N/2-x0/pixelsize+ticks[-1]/pixelsize))
ymin, ymax = int(round(N/2+y0/pixelsize+ticks[0]/pixelsize)), \
int(round(N/2+y0/pixelsize+ticks[-1]/pixelsize))
npix = min(xmax - xmin, N)
if xmin < 0:
xmin, xmax = 0, npix
if xmax > N:
xmin, xmax = N - npix, N
if ymin < 0:
ymin, ymax = 0, npix
if ymax > N:
ymin, ymax = N - npix, N
# Get the normalization.
snr = numpy.nanmax(image) / astropy.stats.mad_std(image, \
ignore_nan=True)
if snr > 100:
norm = ImageNormalize(image, stretch=AsinhStretch(), \
interval=MinMaxInterval())
else:
norm = ImageNormalize(image, stretch=LinearStretch(), \
interval=MinMaxInterval())
# Plot the data.
implot = ax.imshow(image[ymin:ymax,xmin:xmax], origin="lower",\
interpolation="nearest", cmap="inferno", norm=norm)
# Add labels to the x-axis.
transform = ticker.FuncFormatter(Transform(xmin, xmax, pixelsize, \
'%.1f"'))
ax.set_xticks((ticks[1:-1]-ticks[0])/pixelsize)
ax.set_yticks((ticks[1:-1]-ticks[0])/pixelsize)
ax.get_xaxis().set_major_formatter(transform)
ax.get_yaxis().set_major_formatter(transform)
if i >= axes.size - axes.shape[1]:
ax.set_xlabel('$\Delta$R.A. ["]', fontsize=20, labelpad=8)
else:
ax.set_xticklabels([])
if i%axes.shape[1] == 0:
ax.set_ylabel('$\Delta$Dec. ["]', fontsize=20, labelpad=12)
else:
ax.set_yticklabels([])
ax.tick_params(axis='both', direction='in', labelsize=20, color="white")
ax.annotate("Robust = {0:3.1f}".format(robust), fontsize=20, \
color="white", xy=(0.05,0.9), xycoords="axes fraction")
# Make the axes white as well.
for side in ["left","right","top","bottom"]:
ax.spines[side].set_color("white")
# Show the beam.
bmaj = header["BMAJ"]/abs(header["CDELT1"])
bmin = header["BMIN"]/abs(header["CDELT1"])
bpa = header["BPA"]
xy = ((xmax - xmin)*0.1, (ymax - ymin)*0.1)
ax.add_artist(patches.Ellipse(xy=xy, width=bmaj, \
height=bmin, angle=(bpa+90), facecolor="white", \
edgecolor="black"))
# Add a colorbar.
divider = make_axes_locatable(ax)
cax = divider.append_axes('top', size='5%', pad=0.05)
if i == 0:
cbar = plt.colorbar(implot, cax=cax, orientation="horizontal")
cbar.set_label("mJy beam$^{-1}$", size=20)
else:
cbar = plt.colorbar(implot, cax=cax, orientation="horizontal")
cax.xaxis.set_ticks_position('top')
cax.xaxis.set_label_position('top')
cax.tick_params(labelsize=20)
cax.locator_params(nbins=5)
# Adjust the spacing.
plt.tight_layout(pad=1.08, rect=(0.01,0.01,0.99,0.99))
# Save the figure.
pdf.savefig(fig)
# Clear the figure.
plt.close(fig)
################################################################################
### Plot the spectral line observations.
################################################################################
# Get a list of datasets to look for.
datasets = ["C18O","13CO","12CO","SO","H2CO_3_21-2_20_218.76GHz", \
"H2CO_3_03-2_02_218.22GHz","H2CO_3_03-2_02_218.47GHz", \
"c-C3H2_217.82","cC3H2_217.94","cC3H2_218.16","DCN","CH3OH", \
"SiO"]
line_centers = [219.56035410,220.39868420,230.538,219.94944200,218.76006600,\
218.22219200,218.47563200,217.82215,217.94005,218.16044,217.2386, \
218.44006300,217.10498000]
# Loop through the sources and plot all the datasets for that source.
for dataset, line_center in zip(datasets, line_centers):
# Check for which robust parameters are available.
robust_list = [f.split("_")[-1].split("image")[0][0:-1] for f in \
glob.glob("{0:s}_SB_{1:s}_robust_*.image.fits".format(source, \
dataset))]
# Now plot each of the images.
for robust in robust_list:
# Load in the image.
if 'taper' in robust:
continue
image, header = fits.getdata("{0:s}_SB_{1:s}_robust_{2:s}.image."
"fits".format(source, dataset, robust), header=True)
freq = numpy.arange(image.shape[0])*header["CDELT3"] + header["CRVAL3"]
# Load the continuum image.
data = fits.getdata("{0:s}_SB_continuum_robust_{1:s}.image."
"tt0.fits".format(source, robust))
ndim_cont=len(data.shape)
if ndim_cont == 4:
cont = fits.getdata("{0:s}_SB_continuum_robust_{1:s}.image."
"tt0.fits".format(source, robust))[0,0]
elif ndim_cont ==2:
cont = fits.getdata("{0:s}_SB_continuum_robust_{1:s}.image."
"tt0.fits".format(source, robust)) #[0,0]
# Get the center of the source(s).
x0, y0 = 0., 0.
# Check the number of rows and number of columns.
nrows, ncols = round(image.shape[0]**0.5), round(image.shape[0]**0.5)
fontsize = 20
# Loop over field of view.
ticks_list = [numpy.array([-15.0,-10.0,0.0,10.0,15.0]), \
numpy.array([-2.0,-1.0,0.0,1.0,2.0])]
for ticks in ticks_list:
# Plot the image.
N = image.shape[1]
pixelsize = numpy.abs(header["CDELT2"])*numpy.pi/180. / arcsec
xmin, xmax = int(round(N/2-x0/pixelsize+ticks[0]/pixelsize)), \
int(round(N/2-x0/pixelsize+ticks[-1]/pixelsize))
ymin, ymax = int(round(N/2+y0/pixelsize+ticks[0]/pixelsize)), \
int(round(N/2+y0/pixelsize+ticks[-1]/pixelsize))
npix = min(xmax - xmin, N)
if xmin < 0:
xmin, xmax = 0, npix
if xmax > N:
xmin, xmax = N - npix, N
if ymin < 0:
ymin, ymax = 0, npix
if ymax > N:
ymin, ymax = N - npix, N
# Make a figure to put it in.
fig, ax = plt.subplots(nrows=nrows, ncols=ncols, \
figsize=(2*ncols,2*nrows+0.5))
# Get the normalization.
vmin = -2*numpy.nanstd(image[0])
vmax = max(10*numpy.nanstd(image[0]), numpy.nanmax(image))
snr = vmax / astropy.stats.mad_std(image[0], ignore_nan=True)
if snr > 100:
norm = ImageNormalize(image, stretch=AsinhStretch(), \
interval=ManualInterval(vmin=vmin, vmax=vmax))
else:
norm = ImageNormalize(image, stretch=LinearStretch(), \
interval=ManualInterval(vmin=vmin, vmax=vmax))
# Plot all of the channels.
velocity = (line_center - freq/1e9) / line_center * c_l
for i in range(nrows):
for j in range(ncols):
ind = i*ncols + j
# If we are beyond the limits of the frequency range, turn
# off the axis.
if ind >= image.shape[0]:
ax[i,j].set_axis_off()
continue
# Plot the data.
ax[i,j].imshow(image[ind,ymin:ymax,xmin:xmax], \
origin="lower", interpolation="bilinear", \
norm=norm, cmap="inferno")
# Contour the continuum data.
ax[i,j].contour(cont[ymin:ymax,xmin:xmax], \
colors="white", levels=numpy.nanmax(cont)*\
(numpy.arange(5)+0.5)/5., linewidths=0.5, \
alpha=0.25)
# Add labels to the x-axis.
transform = ticker.FuncFormatter(Transform(xmin, xmax, \
pixelsize, '%.1f"'))
ax[i,j].set_xticks((ticks[1:-1]-ticks[0])/pixelsize)
ax[i,j].set_yticks((ticks[1:-1]-ticks[0])/pixelsize)
ax[i,j].get_xaxis().set_major_formatter(transform)
ax[i,j].get_yaxis().set_major_formatter(transform)
if i == nrows-1:
ax[i,j].set_xlabel('$\Delta$R.A. ["]', \
fontsize=fontsize/1.75, labelpad=8)
else:
ax[i,j].set_xticklabels([])
if j == 0:
ax[i,j].set_ylabel('$\Delta$Dec. ["]', \
fontsize=fontsize/1.75, labelpad=12)
else:
ax[i,j].set_yticklabels([])
ax[i,j].tick_params(axis='both', direction='in', \
labelsize=fontsize/1.75, color="white")
# Make the axes white as well.
for side in ["left","right","top","bottom"]:
ax[i,j].spines[side].set_color("white")
# Add the velocity to the image.
txt = ax[i,j].annotate(r"$v={0:3.2f}$".\
format(velocity[ind]/1e5), xy=(0.01,0.85), \
xycoords='axes fraction', fontsize=fontsize, \
color="white")
# Show the beam.
bmaj = header["BMAJ"]/abs(header["CDELT1"])
bmin = header["BMIN"]/abs(header["CDELT1"])
bpa = header["BPA"]
xy = ((xmax - xmin)*0.1, (ymax - ymin)*0.1)
ax[i,j].add_artist(patches.Ellipse(xy=xy, width=bmaj, \
height=bmin, angle=(bpa+90), facecolor="white", \
edgecolor="black"))
# Add a title to the figure.
if np.max(ticks) > 5.0:
if nrows*ncols > 30:
fig.suptitle("{0:s}, Robust = {1:s} wide".format(dataset, robust), fontsize=2*fontsize)
else:
fig.suptitle("{0:s}, Robust = {1:s} wide".format(dataset, robust), fontsize=fontsize)
else:
if nrows*ncols > 30:
fig.suptitle("{0:s}, Robust = {1:s} zoom".format(dataset, robust), fontsize=2*fontsize)
else:
fig.suptitle("{0:s}, Robust = {1:s} zoom".format(dataset, robust), fontsize=fontsize)
# Adjust the spacing.
plt.tight_layout(pad=0, rect=(0.01,0.01,0.99,0.95))
# Save the figure.
pdf.savefig(fig)
# Clear the figure.
plt.close(fig)
# Close the pdf.
pdf.close()