def lnlike(p, visibilities, parameters, plot):
# Set up the params dictionary.
keys = []
for key in sorted(parameters.keys()):
if not parameters[key]["fixed"]:
keys.append(key)
params = dict(zip(keys, p))
# Run the model.
m = modeling.run_flared_model(visibilities, params, parameters, plot, \
ncpus=ncpus, source=source, plot_vis=args.plot_vis, nice=nice)
# A list to put all of the chisq into.
chisq = []
# Calculate the chisq for the visibilities.
for j in range(len(visibilities["file"])):
chisq.append(-0.5*(numpy.sum((visibilities["data"][j].real - \
m.visibilities[visibilities["lam"][j]].real)**2 * \
visibilities["data"][j].weights)) + \
-0.5*(numpy.sum((visibilities["data"][j].imag - \
m.visibilities[visibilities["lam"][j]].imag)**2 * \
visibilities["data"][j].weights)))
# Return the sum of the chisq.
return numpy.array(chisq).sum()
def lnlike(params, visibilities, parameters, plot):
m = modeling.run_flared_model(visibilities, params, parameters, plot, \
ncpus=ncpus, source=source, plot_vis=args.plot_vis, nice=nice)
# A list to put all of the chisq into.
chisq = []
# Calculate the chisq for the visibilities.
for j in range(len(visibilities["file"])):
chisq.append(-0.5*(numpy.sum((visibilities["data"][j].real - \
m.visibilities[visibilities["lam"][j]].real)**2 * \
visibilities["data"][j].weights)) + \
-0.5*(numpy.sum((visibilities["data"][j].imag - \
m.visibilities[visibilities["lam"][j]].imag)**2 * \
visibilities["data"][j].weights)))
# Return the sum of the chisq.
return numpy.array(chisq).sum()
plt.savefig("fit.pdf")
# Make a dictionary of the best fit parameters.
params = dict(zip(keys, params))
############################################################################
#
# Plot the results.
#
############################################################################
# Create a high resolution model for averaging.
m = modeling.run_flared_model(visibilities, params, config.parameters, \
plot=True, ncpus=ncpus, source=source, plot_vis=args.plot_vis, \
nice=nice)
# Open up a pdf file to plot into.
pdf = PdfPages("model.pdf")
# Loop through the visibilities and plot.
for j in range(len(visibilities["file"])):
# Plot the best fit model over the data.
fig, ax = plt.subplots(nrows=visibilities["nrows"][j], \
ncols=visibilities["ncols"][j], sharex=True, sharey=True)
# Make a plot of the channel maps.