def plot_beast_ifit(filters, waves, stats, pdf1d_hdu, starnum):
# setup the plot grid
gridNrow, gridNcol = 5, 12
gs = gridspec.GridSpec(
gridNrow,
gridNcol,
height_ratios=[1.0] * gridNrow,
width_ratios=[1.0] * gridNcol,
)
ax = []
# axes for the big SED plot. Leave empty columns right of the plot to
# put the legend and values.
sed_height = 2
free_cols = 3
index_sedplot = len(ax)
ax.append(plt.subplot(gs[0:sed_height, 0:-1 - free_cols]))
# axes for the 1D PDFs
nprim = 4
nsec = 3
nderiv = 3
indices_1dpdf = []
rows = [sed_height + i for i in range(3)]
widths = [3, 4, 4]
naxes = [nprim, nsec, nderiv]
for r, w, n in zip(rows, widths, naxes):
for i in range(n):
indices_1dpdf.append(len(ax))
ax.append(plt.subplot(gs[r, i * w:(i + 1) * w]))
# plot the SED
# print(np.sort(stats.colnames))
n_filters = len(filters)
# get the observations
waves *= 1e-4
obs_flux = np.empty((n_filters), dtype=np.float)
mod_flux = np.empty((n_filters, 3), dtype=np.float)
mod_flux_nd = np.empty((n_filters, 3), dtype=np.float)
mod_flux_wbias = np.empty((n_filters, 3), dtype=np.float)
k = starnum
c = ap_SkyCoord(
ra=stats["RA"][k] * ap_units.degree,
dec=stats["DEC"][k] * ap_units.degree,
frame="icrs",
)
corname = ("PHAT J" + c.ra.to_string(unit=ap_units.hourangle,
sep="",
precision=2,
alwayssign=False,
pad=True) +
c.dec.to_string(sep="", precision=2, alwayssign=True, pad=True))
for i, cfilter in enumerate(filters):
obs_flux[i] = stats[cfilter][k]
fluxname = "log" + cfilter
mod_flux[i, 0] = np.power(10.0, stats[fluxname + "_wd_p50"][k])
mod_flux[i, 1] = np.power(10.0, stats[fluxname + "_wd_p16"][k])
mod_flux[i, 2] = np.power(10.0, stats[fluxname + "_wd_p84"][k])
mod_flux_nd[i, 0] = np.power(10.0, stats[fluxname + "_nd_p50"][k])
mod_flux_nd[i, 1] = np.power(10.0, stats[fluxname + "_nd_p16"][k])
mod_flux_nd[i, 2] = np.power(10.0, stats[fluxname + "_nd_p84"][k])
if "sym" + fluxname + "_wd_bias_p50" in stats.colnames:
mod_flux_wbias[i, 0] = inverse_symlog(stats["sym" + fluxname +
"_wd_bias_p50"][k])
mod_flux_wbias[i, 1] = inverse_symlog(stats["sym" + fluxname +
"_wd_bias_p16"][k])
mod_flux_wbias[i, 2] = inverse_symlog(stats["sym" + fluxname +
"_wd_bias_p84"][k])
sed_ax = ax[index_sedplot]
sed_ax.plot(waves, obs_flux, "ko", label="observed")
if "symlog" + filters[0] + "_wd_bias_p50" in stats.colnames:
sed_ax.plot(waves,
mod_flux_wbias[:, 0],
"b-",
label="stellar+dust+bias")
sed_ax.fill_between(waves,
mod_flux_wbias[:, 1],
mod_flux_wbias[:, 2],
color="b",
alpha=0.3)
sed_ax.plot(waves, mod_flux[:, 0], "r-", label="stellar+dust")
sed_ax.fill_between(waves,
mod_flux[:, 1],
mod_flux[:, 2],
color="r",
alpha=0.2)
sed_ax.plot(waves, mod_flux_nd[:, 0], "y-", label="stellar only")
sed_ax.fill_between(waves,
mod_flux_nd[:, 1],
mod_flux_nd[:, 2],
color="y",
alpha=0.1)
# sed_ax.legend(loc='upper right', bbox_to_anchor=(1.25, 1.025), fontsize=8)
sed_ax.legend(loc="lower right", fontsize=9)
sed_ax.set_ylabel(r"Flux [ergs s$^{-1}$ cm$^{-2}$ $\AA^{-1}$]")
sed_ax.set_yscale("log")
sed_ax.text(0.5,
-0.07,
r"$\lambda$ [$\AA$]",
transform=sed_ax.transAxes,
va="top")
sed_ax.set_xlim(0.2, 2.0)
sed_ax.set_xscale("log")
sed_ax.minorticks_off()
sed_ax.set_xticks([0.2, 0.3, 0.4, 0.5, 0.8, 1.0, 2.0])
sed_ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
sed_ax.get_xaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())
sed_ax.text(0.05,
0.95,
corname,
transform=sed_ax.transAxes,
va="top",
ha="left")
# add the text results
keys = [
"Av", "M_ini", "logA", "distance", "Rv", "f_A", "Z", "logT", "logg",
"logL"
]
dispnames = [
"A(V)",
"log(M)",
"log(t)",
"d(kpc)",
"R(V)",
r"f$_\mathcal{A}$",
"Z",
r"log(T$_\mathrm{eff})$",
"log(g)",
"log(L)",
]
startprim, stopprim = 0, nprim - 1 # 0 1 2 3
startsec, stopsec = stopprim + 1, stopprim + nsec # 4 5 6
startderiv, stopderiv = stopsec + 1, stopsec + nderiv # 7 8 9
laby = 0.96
ty = np.linspace(laby - 0.1, 0.1, num=len(keys))
ty[startsec:] -= 0.04
ty[startderiv:] -= 0.04
tx = [1.14, 1.3, 1.47]
for i in range(len(keys)):
sed_ax.text(tx[0],
ty[i],
dispnames[i],
ha="center",
transform=sed_ax.transAxes)
sed_ax.text(
tx[1],
ty[i],
disp_str(stats, starnum, keys[i]),
ha="center",
color="m",
transform=sed_ax.transAxes,
)
best_val = stats[keys[i] + "_Best"][k]
if keys[i] == "M_ini":
best_val = np.log10(best_val)
if keys[i] == "distance":
best_val /= 1000.0
dispnames[i] = dispnames[i].replace("pc", "kpc")
sed_ax.text(
tx[2],
ty[i],
"$" + "{0:.2f}".format(best_val) + "$",
ha="center",
color="c",
transform=sed_ax.transAxes,
)
sed_ax.text(tx[0],
laby,
"Param",
ha="center",
transform=sed_ax.transAxes,
fontsize=10)
sed_ax.text(
tx[1],
laby,
r"50$\pm$33%",
ha="center",
color="k",
transform=sed_ax.transAxes,
fontsize=10,
)
sed_ax.text(
tx[2],
laby,
"Best",
color="k",
ha="center",
transform=sed_ax.transAxes,
fontsize=10,
)
# now draw boxes around the different kinds of parameters
tax = sed_ax
left, right = tx[0], tx[-1]
def draw_box_around_values(start, stop, ls):
deltaline = ty[start] - ty[start + 1]
top = ty[start] + deltaline # Draw the top border ABOVE the text
bottom = ty[stop]
rec = Rectangle(
(left - 0.1, bottom - 0.02),
right - left + 0.15,
top - bottom + 0.01,
fill=False,
lw=2,
transform=tax.transAxes,
ls=ls,
)
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# primary
draw_box_around_values(startprim, stopprim, ls="dashed")
# secondary
draw_box_around_values(startsec, stopsec, ls="dotted")
# derived
draw_box_around_values(startderiv, stopderiv, ls="dashdot")
# Make these plots:
# A, M, t, dist,
# R, fA, Z
# logT, logg, logL
# This is done by iterating over the axes created at the start of
# this function, from left to right, line per line.
# plot the primary parameter 1D PDFs
ax_iter = (ax[i] for i in indices_1dpdf)
first_primary_ax = next(ax_iter)
plot_1dpdf(first_primary_ax, pdf1d_hdu, "Av", "A(V)", starnum, stats=stats)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
"M_ini",
"log(M)",
starnum,
logx=True,
stats=stats)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
"logA",
"log(t)",
starnum,
stats=stats)
last_primary_ax = next(ax_iter)
plot_1dpdf(last_primary_ax,
pdf1d_hdu,
"distance",
"d(kpc)",
starnum,
stats=stats)
# plot the secondary parameter 1D PDFs
first_secondary_ax = next(ax_iter)
plot_1dpdf(first_secondary_ax,
pdf1d_hdu,
"Rv",
"R(V)",
starnum,
stats=stats)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
"f_A",
r"f$_\mathcal{A}$",
starnum,
stats=stats)
last_secondary_ax = next(ax_iter)
plot_1dpdf(last_secondary_ax, pdf1d_hdu, "Z", "Z", starnum, stats=stats)
# plot the derived parameter 1D PDFs
first_derived_ax = next(ax_iter)
plot_1dpdf(
first_derived_ax,
pdf1d_hdu,
"logT",
r"log(T$_\mathrm{eff})$",
starnum,
stats=stats,
)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
"logg",
"log(g)",
starnum,
stats=stats)
last_derived_ax = next(ax_iter)
plot_1dpdf(last_derived_ax,
pdf1d_hdu,
"logL",
"log(L)",
starnum,
stats=stats)
# A more manual version of tight_layout
plt.subplots_adjust(top=0.95,
bottom=0.05,
left=0.125,
right=0.925,
wspace=0.5,
hspace=0.5)
# PLOT ALL THE BOXES AFTER CALLING TIGHT LAYOUT! Tight layout
# changes the coordinates of the axes a little, but leaves the boxes
# untouched. Therefore, we plot the boxes here by extracting the
# coordinates of the axes after they have been modified by
# tight_layout.
def rectangle_around_axes(bottomleft_ax, topright_ax, pad, ls, label=None):
"""
pad: tuple, (left, right, bottom, top)
"""
left, bottom = bottomleft_ax.get_position().get_points()[0]
right, top = topright_ax.get_position().get_points()[1]
left -= pad[0]
right += pad[1]
bottom -= pad[2]
top += pad[3]
transf = plt.gcf().transFigure
rec = Rectangle(
(left, bottom),
right - left,
top - bottom,
transform=transf,
fill=False,
lw=2,
ls=ls,
)
rec = bottomleft_ax.add_patch(rec)
rec.set_clip_on(False)
if label:
middle = (top + bottom) / 2.0
moreleft = left # pad[0]
bottomleft_ax.text(
moreleft,
middle,
label,
transform=transf,
rotation="vertical",
fontstyle="oblique",
va="center",
ha="right",
)
rectanglePadding = (0.03, 0.01, 0.03, 0.01)
# Box around primaries
tax = first_primary_ax
rectangle_around_axes(
first_primary_ax,
last_primary_ax,
pad=rectanglePadding,
ls="dashed",
label="Primary",
)
tax.text(
0.0,
0.5,
"Probability",
transform=tax.transAxes,
rotation="vertical",
va="center",
ha="right",
)
# Box around secondaries
tax = first_secondary_ax
rectangle_around_axes(
first_secondary_ax,
last_secondary_ax,
pad=rectanglePadding,
ls="dotted",
label="Secondary",
)
tax.text(
0.0,
0.5,
"Probability",
transform=tax.transAxes,
rotation="vertical",
va="center",
ha="right",
)
# Box around deriveds
tax = first_derived_ax
rectangle_around_axes(
first_derived_ax,
last_derived_ax,
pad=rectanglePadding,
ls="dashdot",
label="Derived",
)
tax.text(
0.0,
0.5,
"Probability",
transform=tax.transAxes,
rotation="vertical",
va="center",
ha="right",
)
def plot_beast_ifit(filters, waves, stats, pdf1d_hdu):
# setup the plot grid
gridNrow, gridNcol = 5, 12
gs = gridspec.GridSpec(gridNrow,
gridNcol,
height_ratios=[1.] * gridNrow,
width_ratios=[1.] * gridNcol)
ax = []
# axes for the big SED plot. Leave empty columns right of the plot to
# put the legend and values.
sed_height = 2
free_cols = 2
index_sedplot = len(ax)
ax.append(plt.subplot(gs[0:sed_height, 0:-1 - free_cols]))
# axes for the 1D PDFs
nprim = 4
nsec = 3
nderiv = 3
indices_1dpdf = []
rows = [sed_height + i for i in range(3)]
widths = [3, 4, 4]
naxes = [nprim, nsec, nderiv]
for r, w, n in zip(rows, widths, naxes):
for i in range(n):
indices_1dpdf.append(len(ax))
ax.append(plt.subplot(gs[r, i * w:(i + 1) * w]))
# plot the SED
# print(np.sort(stats.colnames))
n_filters = len(filters)
# get the observations
waves *= 1e-4
obs_flux = np.empty((n_filters), dtype=np.float)
mod_flux = np.empty((n_filters, 3), dtype=np.float)
mod_flux_nd = np.empty((n_filters, 3), dtype=np.float)
mod_flux_wbias = np.empty((n_filters, 3), dtype=np.float)
k = starnum
c = ap_SkyCoord(ra=stats['RA'][k] * ap_units.degree,
dec=stats['DEC'][k] * ap_units.degree,
frame='icrs')
corname = ('PHAT J' + c.ra.to_string(unit=ap_units.hourangle,
sep="",
precision=2,
alwayssign=False,
pad=True) +
c.dec.to_string(sep="", precision=2, alwayssign=True, pad=True))
for i, cfilter in enumerate(filters):
obs_flux[i] = stats[cfilter][k]
mod_flux[i, 0] = np.power(10.0, stats['log' + cfilter + '_wd_p50'][k])
mod_flux[i, 1] = np.power(10.0, stats['log' + cfilter + '_wd_p16'][k])
mod_flux[i, 2] = np.power(10.0, stats['log' + cfilter + '_wd_p84'][k])
mod_flux_nd[i, 0] = np.power(10.0,
stats['log' + cfilter + '_nd_p50'][k])
mod_flux_nd[i, 1] = np.power(10.0,
stats['log' + cfilter + '_nd_p16'][k])
mod_flux_nd[i, 2] = np.power(10.0,
stats['log' + cfilter + '_nd_p84'][k])
if 'log' + cfilter + '_wd_bias_p50' in stats.colnames:
mod_flux_wbias[i, 0] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p50'][k])
mod_flux_wbias[i, 1] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p16'][k])
mod_flux_wbias[i, 2] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p84'][k])
sed_ax = ax[index_sedplot]
sed_ax.plot(waves, obs_flux, 'ko', label='observed')
if 'log' + filters[0] + '_wd_bias_p50' in stats.colnames:
sed_ax.plot(waves,
mod_flux_wbias[:, 0],
'b-',
label='stellar+dust+bias')
sed_ax.fill_between(waves,
mod_flux_wbias[:, 1],
mod_flux_wbias[:, 2],
color='b',
alpha=0.3)
sed_ax.plot(waves, mod_flux[:, 0], 'r-', label='stellar+dust')
sed_ax.fill_between(waves,
mod_flux[:, 1],
mod_flux[:, 2],
color='r',
alpha=0.2)
sed_ax.plot(waves, mod_flux_nd[:, 0], 'y-', label='stellar only')
sed_ax.fill_between(waves,
mod_flux_nd[:, 1],
mod_flux_nd[:, 2],
color='y',
alpha=0.1)
sed_ax.legend(loc='upper right', bbox_to_anchor=(1.25, 1.025))
sed_ax.set_ylabel(r'Flux [ergs s$^{-1}$ cm$^{-2}$ $\AA^{-1}$]')
sed_ax.set_yscale('log')
sed_ax.set_xscale('log')
sed_ax.text(0.5,
-0.01,
r'$\lambda$ [$\AA$]',
transform=sed_ax.transAxes,
va='top')
sed_ax.set_xlim(0.2, 2.0)
sed_ax.set_xticks([0.2, 0.3, 0.4, 0.5, 0.8, 0.9, 1.0, 2.0])
sed_ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
sed_ax.text(0.05,
0.95,
corname,
transform=sed_ax.transAxes,
va='top',
ha='left')
# add the text results
keys = [
'Av', 'M_ini', 'logA', 'distance', 'Rv', 'f_A', 'Z', 'logT', 'logg',
'logL'
]
dispnames = [
'A(V)', 'log(M)', 'log(t)', 'distance(pc)', 'R(V)', r'f$_\mathcal{A}$',
'Z', r'log(T$_\mathrm{eff})$', 'log(g)', 'log(L)'
]
startprim, stopprim = 0, nprim - 1 # 0 1 2 3
startsec, stopsec = stopprim + 1, stopprim + nsec # 4 5 6
startderiv, stopderiv = stopsec + 1, stopsec + nderiv # 7 8 9
laby = 0.72
ty = np.linspace(laby - 0.07, 0.1, num=len(keys))
ty[startsec:] -= 0.025
ty[startderiv:] -= 0.025
tx = [1.12, 1.2, 1.3]
for i in range(len(keys)):
sed_ax.text(tx[0],
ty[i],
dispnames[i],
ha='right',
transform=sed_ax.transAxes)
sed_ax.text(tx[1],
ty[i],
disp_str(stats, starnum, keys[i]),
ha='center',
color='m',
transform=sed_ax.transAxes)
best_val = stats[keys[i] + '_Best'][k]
if keys[i] == 'M_ini':
best_val = np.log10(best_val)
if keys[i] == 'distance':
best_val /= 1000.
dispnames[i] = dispnames[i].replace('pc', 'kpc')
sed_ax.text(tx[2],
ty[i],
'$' + "{0:.2f}".format(best_val) + '$',
ha='center',
color='c',
transform=sed_ax.transAxes)
sed_ax.text(tx[0], laby, 'Param', ha='right', transform=sed_ax.transAxes)
sed_ax.text(tx[1],
laby,
'50%$\pm$33%',
ha='center',
color='k',
transform=sed_ax.transAxes)
sed_ax.text(tx[2],
laby,
'Best',
color='k',
ha='center',
transform=sed_ax.transAxes)
# now draw boxes around the different kinds of parameters
tax = sed_ax
left, right = tx[0], tx[-1]
def draw_box_around_values(start, stop, ls):
deltaline = ty[start] - ty[start + 1]
top = ty[start] + deltaline # Draw the top border ABOVE the text
bottom = ty[stop]
rec = Rectangle((left - 0.1, bottom - 0.02),
right - left + 0.15,
top - bottom + 0.01,
fill=False,
lw=2,
transform=tax.transAxes,
ls=ls)
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# primary
draw_box_around_values(startprim, stopprim, ls='dashed')
# secondary
draw_box_around_values(startsec, stopsec, ls='dotted')
# derived
draw_box_around_values(startderiv, stopderiv, ls='dashdot')
# padding for rectangles of 1D PDFs
pad = 0.1
# Make these plots:
# A, M, t, dist,
# R, fA, Z
# logT, logg, logL
# This is done by iterating over the axes created at the start of
# this function, from left to right, line per line.
# plot the primary parameter 1D PDFs
ax_iter = (ax[i] for i in indices_1dpdf)
first_primary_ax = next(ax_iter)
plot_1dpdf(first_primary_ax, pdf1d_hdu, 'Av', 'A(V)', starnum, stats=stats)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
'M_ini',
'log(M)',
starnum,
logx=True,
stats=stats)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
'logA',
'log(t)',
starnum,
stats=stats)
last_primary_ax = next(ax_iter)
plot_1dpdf(last_primary_ax,
pdf1d_hdu,
'distance',
'distance(pc)',
starnum,
stats=stats)
# plot the secondary parameter 1D PDFs
first_secondary_ax = next(ax_iter)
plot_1dpdf(first_secondary_ax,
pdf1d_hdu,
'Rv',
'R(V)',
starnum,
stats=stats)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
'f_A',
r'f$_\mathcal{A}$',
starnum,
stats=stats)
last_secondary_ax = next(ax_iter)
plot_1dpdf(last_secondary_ax, pdf1d_hdu, 'Z', 'Z', starnum, stats=stats)
# plot the derived parameter 1D PDFs
first_derived_ax = next(ax_iter)
plot_1dpdf(first_derived_ax,
pdf1d_hdu,
'logT',
r'log(T$_\mathrm{eff})$',
starnum,
stats=stats)
plot_1dpdf(next(ax_iter),
pdf1d_hdu,
'logg',
'log(g)',
starnum,
stats=stats)
last_derived_ax = next(ax_iter)
plot_1dpdf(last_derived_ax,
pdf1d_hdu,
'logL',
'log(L)',
starnum,
stats=stats)
# A more manual version of tight_layout
plt.subplots_adjust(top=.95,
bottom=.05,
left=.125,
right=.925,
wspace=.5,
hspace=.5)
# PLOT ALL THE BOXES AFTER CALLING TIGHT LAYOUT! Tight layout
# changes the coordinates of the axes a little, but leaves the boxes
# untouched. Therefore, we plot the boxes here by extracting the
# coordinates of the axes after they have been modified by
# tight_layout.
def rectangle_around_axes(bottomleft_ax, topright_ax, pad, ls, label=None):
"""
pad: tuple, (left, right, bottom, top)
"""
left, bottom = bottomleft_ax.get_position().get_points()[0]
right, top = topright_ax.get_position().get_points()[1]
left -= pad[0]
right += pad[1]
bottom -= pad[2]
top += pad[3]
transf = plt.gcf().transFigure
rec = Rectangle((left, bottom),
right - left,
top - bottom,
transform=transf,
fill=False,
lw=2,
ls=ls)
rec = bottomleft_ax.add_patch(rec)
rec.set_clip_on(False)
if label:
middle = (top + bottom) / 2.
moreleft = left # pad[0]
bottomleft_ax.text(moreleft,
middle,
label,
transform=transf,
rotation='vertical',
fontstyle='oblique',
va='center',
ha='right')
rectanglePadding = (0.03, 0.01, 0.03, 0.01)
# Box around primaries
tax = first_primary_ax
rectangle_around_axes(first_primary_ax,
last_primary_ax,
pad=rectanglePadding,
ls='dashed',
label='Primary')
tax.text(0.0,
0.5,
'Probability',
transform=tax.transAxes,
rotation='vertical',
va='center',
ha='right')
# Box around secondaries
tax = first_secondary_ax
rectangle_around_axes(first_secondary_ax,
last_secondary_ax,
pad=rectanglePadding,
ls='dotted',
label='Secondary')
tax.text(0.0,
0.5,
'Probability',
transform=tax.transAxes,
rotation='vertical',
va='center',
ha='right')
# Box around deriveds
tax = first_derived_ax
rectangle_around_axes(first_derived_ax,
last_derived_ax,
pad=rectanglePadding,
ls='dashdot',
label='Derived')
tax.text(0.0,
0.5,
'Probability',
transform=tax.transAxes,
rotation='vertical',
va='center',
ha='right')
def IAU_names_and_extra_info(obsdata, surveyname="PHAT", extraInfo=False):
"""
Generates IAU approved names for the data using RA & DEC
and extra information about the sources (ra, dec, photometry, etc.)
Parameters
----------
obsdata : class
observations data
surveyname : str
name of survey [default = 'PHAT']
extraInfo : bool
set to get the HST specific PHAT software reduced survey information
Returns
-------
r : dict
A dict with a (name, ndarray) pair
"""
r = {}
go_name = False
if "ra" in list(obsdata.data.keys()):
go_name = True
ra_str = "ra"
dec_str = "dec"
if "RA" in list(obsdata.data.keys()):
go_name = True
ra_str = "RA"
dec_str = "DEC"
if go_name:
# generate the IAU names
_tnames = []
for i in range(len(obsdata)):
c = ap_SkyCoord(
ra=obsdata.data[ra_str][i] * ap_units.degree,
dec=obsdata.data[dec_str][i] * ap_units.degree,
frame="icrs",
)
_tnames.append(surveyname + " J" + c.ra.to_string(
unit=ap_units.hourangle,
sep="",
precision=2,
alwayssign=False,
pad=True,
) + c.dec.to_string(sep="", precision=2, alwayssign=True, pad=True)
)
r["Name"] = _tnames
# other useful information
r["RA"] = obsdata.data[ra_str]
r["DEC"] = obsdata.data[dec_str]
if extraInfo:
r["field"] = obsdata.data["field"]
r["inside_brick"] = obsdata.data["inside_brick"]
r["inside_chipgap"] = obsdata.data["inside_chipgap"]
else:
r["Name"] = ["noname" for x in range(len(obsdata))]
# include the observed filter fluxes
for k, filtername in enumerate(obsdata.filters):
obsfiltname = obsdata.filter_aliases[filtername]
r[filtername] = (obsdata.data[obsfiltname] *
obsdata.vega_flux[k]).astype(float)
return r
def IAU_names_and_extra_info(obsdata, surveyname='PHAT',extraInfo=False):
"""
generates IAU approved names for the data using RA & DEC
and extra information about the sources (ra, dec, photometry, etc.)
keywords
--------
obsdata: Observations
surveyname: string
name of survey [default = 'PHAT']
extraInfo: bool
set to get the HST specific PHAT software reduced survey information
returns
-------
r: dict
returns a dict with a (name, ndarray) pair
"""
r = {}
go_name = False
if 'ra' in list(obsdata.data.keys()):
go_name = True
ra_str = 'ra'
dec_str = 'dec'
if 'RA' in list(obsdata.data.keys()):
go_name = True
ra_str = 'RA'
dec_str = 'DEC'
if go_name:
# generate the IAU names
_tnames = []
for i in range(len(obsdata)):
c = ap_SkyCoord(ra=obsdata.data[ra_str][i]*ap_units.degree,
dec=obsdata.data[dec_str][i]*ap_units.degree,
frame='icrs')
_tnames.append(surveyname + ' J' +
c.ra.to_string(unit=ap_units.hourangle,
sep="",precision=2,
alwayssign=False,pad=True) +
c.dec.to_string(sep="",precision=2,
alwayssign=True,pad=True))
r['Name'] = _tnames
# other useful information
r['RA'] = obsdata.data[ra_str]
r['DEC'] = obsdata.data[dec_str]
if extraInfo:
r['field'] = obsdata.data['field']
r['inside_brick'] = obsdata.data['inside_brick']
r['inside_chipgap'] = obsdata.data['inside_chipgap']
else:
r['Name'] = ["noname" for x in range(len(obsdata))]
# include the observed filter fluxes
for k, filtername in enumerate(obsdata.filters):
r[filtername] = (obsdata.data[filtername]*
obsdata.vega_flux[k]).astype(float)
return r
def plot_beast_ifit(filters, waves, stats, pdf1d_hdu):
# setup the plot grid
gs = gridspec.GridSpec(4,
4,
height_ratios=[1.0, 1.0, 1.0, 1.0],
width_ratios=[1.0, 1.0, 1.0, 1.0])
ax = []
# plots for the 1D PDFs
for j in range(2):
for i in range(4):
ax.append(plt.subplot(gs[j + 2, i]))
# now for the big SED plot
ax.append(plt.subplot(gs[0:2, 0:3]))
# plot the SED
#print(np.sort(stats.colnames))
n_filters = len(filters)
# get the observations
waves *= 1e-4
obs_flux = np.empty((n_filters), dtype=np.float)
mod_flux = np.empty((n_filters, 3), dtype=np.float)
mod_flux_nd = np.empty((n_filters, 3), dtype=np.float)
mod_flux_wbias = np.empty((n_filters, 3), dtype=np.float)
k = starnum
c = ap_SkyCoord(ra=stats['RA'][k] * ap_units.degree,
dec=stats['DEC'][k] * ap_units.degree,
frame='icrs')
corname = ('PHAT J' + c.ra.to_string(unit=ap_units.hourangle,
sep="",
precision=2,
alwayssign=False,
pad=True) +
c.dec.to_string(sep="", precision=2, alwayssign=True, pad=True))
for i, cfilter in enumerate(filters):
obs_flux[i] = stats[cfilter][k]
mod_flux[i, 0] = np.power(10.0, stats['log' + cfilter + '_wd_p50'][k])
mod_flux[i, 1] = np.power(10.0, stats['log' + cfilter + '_wd_p16'][k])
mod_flux[i, 2] = np.power(10.0, stats['log' + cfilter + '_wd_p84'][k])
mod_flux_nd[i, 0] = np.power(10.0,
stats['log' + cfilter + '_nd_p50'][k])
mod_flux_nd[i, 1] = np.power(10.0,
stats['log' + cfilter + '_nd_p16'][k])
mod_flux_nd[i, 2] = np.power(10.0,
stats['log' + cfilter + '_nd_p84'][k])
if 'log' + cfilter + '_wd_bias_p50' in stats.colnames:
mod_flux_wbias[i, 0] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p50'][k])
mod_flux_wbias[i, 1] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p16'][k])
mod_flux_wbias[i, 2] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p84'][k])
ax[8].plot(waves, obs_flux, 'ko', label='observed')
if 'log' + filters[0] + '_wd_bias_p50' in stats.colnames:
ax[8].plot(waves,
mod_flux_wbias[:, 0],
'b-',
label='stellar+dust+bias')
ax[8].fill_between(waves,
mod_flux_wbias[:, 1],
mod_flux_wbias[:, 2],
color='b',
alpha=0.3)
ax[8].plot(waves, mod_flux[:, 0], 'r-', label='stellar+dust')
ax[8].fill_between(waves,
mod_flux[:, 1],
mod_flux[:, 2],
color='r',
alpha=0.2)
ax[8].plot(waves, mod_flux_nd[:, 0], 'y-', label='stellar only')
ax[8].fill_between(waves,
mod_flux_nd[:, 1],
mod_flux_nd[:, 2],
color='y',
alpha=0.1)
ax[8].legend(loc='upper right', bbox_to_anchor=(1.25, 1.025))
ax[8].set_ylabel(r'Flux [ergs s$^{-1}$ cm$^{-2}$ $\AA^{-1}$]')
ax[8].set_yscale('log')
ax[8].set_xscale('log')
ax[8].text(0.5,
-0.01,
r'$\lambda$ [$\AA$]',
transform=ax[8].transAxes,
va='top')
ax[8].set_xlim(0.2, 2.0)
ax[8].set_xticks([0.2, 0.3, 0.4, 0.5, 0.8, 0.9, 1.0, 2.0])
ax[8].get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax[8].text(0.05,
0.95,
corname,
transform=ax[8].transAxes,
va='top',
ha='left')
# add the text results
keys = ['Av', 'M_ini', 'logA', 'Rv', 'f_A', 'Z', 'logT', 'logg', 'logL']
dispnames = [
'A(V)', 'log(M)', 'log(t)', 'R(V)', r'f$_\mathcal{A}$', 'Z',
r'log(T$_\mathrm{eff})$', 'log(g)', 'log(L)'
]
laby = 0.72
ty = np.linspace(laby - 0.07, 0.1, num=len(keys))
ty[3:] -= 0.025
ty[6:] -= 0.025
tx = [1.12, 1.2, 1.3]
for i in range(len(keys)):
ax[8].text(tx[0],
ty[i],
dispnames[i],
ha='right',
transform=ax[8].transAxes)
ax[8].text(tx[1],
ty[i],
disp_str(stats, starnum, keys[i]),
ha='center',
color='m',
transform=ax[8].transAxes)
best_val = stats[keys[i] + '_Best'][k]
if keys[i] == 'M_ini':
best_val = np.log10(best_val)
ax[8].text(tx[2],
ty[i],
'$' + "{0:.2f}".format(best_val) + '$',
ha='center',
color='c',
transform=ax[8].transAxes)
ax[8].text(tx[0], laby, 'Param', ha='right', transform=ax[8].transAxes)
ax[8].text(tx[1],
laby,
'50%$\pm$33%',
ha='center',
color='k',
transform=ax[8].transAxes)
ax[8].text(tx[2],
laby,
'Best',
color='k',
ha='center',
transform=ax[8].transAxes)
# now draw boxes around the different kinds of parameters
tax = ax[8]
# primary
rec = Rectangle((tx[0] - 0.1, ty[2] - 0.02),
tx[2] - tx[0] + 0.15, (ty[0] - ty[2]) * 1.5,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashed')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# secondary
rec = Rectangle((tx[0] - 0.1, ty[5] - 0.02),
tx[2] - tx[0] + 0.15, (ty[3] - ty[5]) * 1.5,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dotted')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# derived
rec = Rectangle((tx[0] - 0.1, ty[8] - 0.02),
tx[2] - tx[0] + 0.15, (ty[6] - ty[8]) * 1.5,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashdot')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# padding for rectangles of 1D PDFs
pad = 0.1
# plot the primary parameter 1D PDFs
plot_1dpdf(ax[0], pdf1d_hdu, 'Av', 'A(V)', starnum, stats=stats)
plot_1dpdf(ax[1],
pdf1d_hdu,
'M_ini',
'log(M)',
starnum,
logx=True,
stats=stats)
plot_1dpdf(ax[2], pdf1d_hdu, 'logA', 'log(t)', starnum, stats=stats)
# draw a box around them and label
tax = ax[0]
rec = Rectangle((-1.75 * pad, -pad),
3 * (1.0 + pad) + 1.5 * pad,
1.0 + 1.5 * pad,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashed')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
tax.text(-2. * pad,
0.5,
'Primary',
transform=tax.transAxes,
rotation='vertical',
fontstyle='oblique',
va='center',
ha='right')
tax.text(0.0,
0.5,
'Probability',
transform=tax.transAxes,
rotation='vertical',
va='center',
ha='right')
# plot the secondary parameter 1D PDFs
plot_1dpdf(ax[4], pdf1d_hdu, 'Rv', 'R(V)', starnum, stats=stats)
plot_1dpdf(ax[5],
pdf1d_hdu,
'f_A',
r'f$_\mathcal{A}$',
starnum,
stats=stats)
plot_1dpdf(ax[6], pdf1d_hdu, 'Z', 'Z', starnum, stats=stats)
# draw a box around them
tax = ax[4]
rec = Rectangle((-1.75 * pad, -pad),
3 * (1.0 + pad) + 1.5 * pad,
1.0 + 1.5 * pad,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dotted')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
tax.text(-2 * pad,
0.5,
'Secondary',
transform=tax.transAxes,
rotation='vertical',
fontstyle='oblique',
va='center',
ha='right')
tax.text(0.0,
0.5,
'Probability',
transform=tax.transAxes,
rotation='vertical',
va='center',
ha='right')
# plot the derived parameter 1D PDFs
plot_1dpdf(ax[3],
pdf1d_hdu,
'logT',
r'log(T$_\mathrm{eff})$',
starnum,
stats=stats)
plot_1dpdf(ax[7], pdf1d_hdu, 'logg', 'log(g)', starnum, stats=stats)
# draw a box around them
tax = ax[7]
rec = Rectangle((-0.25 * pad, -pad),
1.0 + 0.5 * pad,
2 * (1.0 + 2. * pad) - 0.125 * pad,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashdot')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
tax.text(0.5,
2 * (1.0 + 2 * pad) - 1.0 * pad,
'Derived',
transform=tax.transAxes,
rotation='horizontal',
fontstyle='oblique',
va='bottom',
ha='center')
# optimize the figure layout
plt.tight_layout(h_pad=2.0, w_pad=1.0)
