default="logL",
choices=params,
help='Choose column for yaxis \
Must be one of: "{}"'.format('", "'.join(params)),
)
parser.add_argument(
"--chi2min",
type=float,
default=20.0,
help="chi2min threshold for splitting plots",
)
args = parser.parse_args()
if args.tex:
plt.rc({"usetex": True})
basename = args.filename.replace(".fits", "_diagnostics")
set_params(lw=2, fontsize=16, usetex=False)
stats = Table.read(args.filename)
fig = make_good_bad_plots(
stats,
suffix=args.suffix,
xparam=args.xparam,
yparam=args.yparam,
chi2min=args.chi2min,
)
if args.savefig:
fig.savefig("{}.{}".format(basename, args.savefig))
else:
plt.show()
def plot_toothpick_details(asts_filename, seds_filename, savefig=False):
"""
Plot the details of the toothpick noisemodel creation for each filter.
These plots show the individual AST results as points as
(flux_in - flux_out)/flux_in. In addition, the binned values of these
points are plotted giving the bias term in the observation model.
Error bars around the binned bias values give the binned sigma term of
the observation model. Finally, as a separate column of plots the
binned completeness in each filter is plotted.
Parameters
----------
asts_filename : str
filename with the AST results
seds_filename : str
filename with the SED grid (used just for the filter information)
savefig : str (default=False)
to save the figure, set this to the file extension (e.g., 'png', 'pdf')
"""
sedgrid = SEDGrid(seds_filename, backend="cache")
# read in AST results
model = toothpick.MultiFilterASTs(asts_filename, sedgrid.filters)
# set the column mappings as the external file is BAND_VEGA or BAND_IN
model.set_data_mappings(upcase=True,
in_pair=("in", "in"),
out_pair=("out", "rate"))
# compute binned biases, uncertainties, and completeness as a function of band flux
model.fit_bins(nbins=50, completeness_mag_cut=-10)
nfilters = len(sedgrid.filters)
fig, ax = plt.subplots(nrows=nfilters,
ncols=2,
figsize=(14, 10),
sharex=True)
set_params()
for i, cfilter in enumerate(sedgrid.filters):
mag_in = model.data[model.filter_aliases[cfilter + "_in"]]
flux_out = model.data[model.filter_aliases[cfilter + "_out"]]
flux_in = (10**(-0.4 * mag_in)) * model.vega_flux[i]
flux_out *= model.vega_flux[i]
gvals = flux_out != 0.0
ax[i, 0].plot(
flux_in[gvals],
(flux_in[gvals] - flux_out[gvals]) / flux_in[gvals],
"ko",
alpha=0.1,
markersize=2,
)
# not all bins are filled with good data
ngbins = model._nasts[i]
ax[i, 0].plot(
model._fluxes[0:ngbins, i],
model._biases[0:ngbins, i] / model._fluxes[0:ngbins, i],
"b-",
)
ax[i, 0].errorbar(
model._fluxes[0:ngbins, i],
model._biases[0:ngbins, i] / model._fluxes[0:ngbins, i],
yerr=model._sigmas[0:ngbins, i] / model._fluxes[0:ngbins, i],
fmt="bo",
markersize=2,
alpha=0.5,
)
ax[i, 0].set_ylim(-5e0, 5e0)
ax[i, 0].set_xscale("log")
ax[i, 0].set_ylabel(r"$(F_i - F_o)/F_i$")
ax[i, 1].plot(
model._fluxes[0:ngbins, i],
model._compls[0:ngbins, i],
"b-",
)
ax[i, 1].yaxis.tick_right()
ax[i, 1].yaxis.set_label_position("right")
ax[i, 1].set_ylim(0, 1)
ax[i, 1].set_xscale("log")
sfilt = cfilter.split("_")[-1]
ax[i, 1].set_ylabel(f"C({sfilt})")
ax[nfilters - 1, 0].set_xlabel(r"$F_i$")
ax[nfilters - 1, 1].set_xlabel(r"$F_i$")
# add in the zero line
# do after all the data has been plotted to get the full x range
pxrange = ax[0, 0].get_xlim()
for i, cfilter in enumerate(sedgrid.filters):
ax[i, 0].plot(pxrange, [0.0, 0.0], "k--", alpha=0.5)
# figname
basename = asts_filename.replace(".fits", "_plot")
fig.tight_layout()
# save or show fig
if savefig:
fig.savefig("{}.{}".format(basename, savefig))
else:
plt.show()
def plot_cmd(
fitsfile,
mag1_filter="F475W",
mag2_filter="F814W",
mag3_filter="F475W",
savefig=False,
show_plot=True,
):
"""
Read in flux from real or simulated data in fitsfile and plot a
color-magnitude diagram based on specified filters.
Parameters
----------
fitsfile : str
input fitsfile (includes full path to file); format = .fits
mag1_filter : str (default='F475W')
1st color filter
mag2_filter : str (default='F814W')
2nd color filter
mag3_filter : str (default='F475W')
magnitude
savefig : str (default=False)
to save the figure, set this to the file extension (e.g., 'png', 'pdf')
show_plot : boolean
True, show the plot (to screen or a file)
False, return the fig
"""
fits_data = fits.open(fitsfile)
table = fits_data[1].data
fits_data.close()
# Read in band_rate
mag1_flux = table["%s" % (mag1_filter + "_rate")]
mag2_flux = table["%s" % (mag2_filter + "_rate")]
mag_flux = table["%s" % (mag3_filter + "_rate")]
# Exclude negative or 0 fluxes
m1_pos_inds = np.where(mag1_flux > 0.0)
m2_pos_inds = np.where(mag2_flux > 0.0)
m_pos_inds = np.where(mag_flux > 0.0)
pos_inds = reduce(np.intersect1d, (m1_pos_inds, m2_pos_inds, m_pos_inds))
mag1_flux_pos = mag1_flux[pos_inds]
mag2_flux_pos = mag2_flux[pos_inds]
mag_flux_pos = mag_flux[pos_inds]
# Convert from flux to mags
mag1 = (-2.5) * np.log10(mag1_flux_pos)
mag2 = (-2.5) * np.log10(mag2_flux_pos)
mag = (-2.5) * np.log10(mag_flux_pos)
col = mag1 - mag2
fig = plt.figure(figsize=(9, 9))
# sets up plots to have larger fonts, wider lines, etc.
set_params()
plt.plot(col, mag, "k.", ms=0.1)
plt.gca().invert_yaxis()
plt.xlabel("%s - %s" % (mag1_filter, mag2_filter))
plt.ylabel(mag3_filter)
plt.title(fitsfile)
fig.tight_layout()
# figname
basename = fitsfile.replace(".fits", "_plot")
# save or show fig
if show_plot:
if savefig:
fig.savefig("{}.{}".format(basename, savefig))
else:
plt.show()
else:
return fig