def plot_matched_separation_hist(d12):
'''d12 is array of distances in degress between matched objects'''
#pixscale to convert d12 into N pixels
pixscale = dict(decam=0.25, bokmos=0.45)
#sns.set_style('ticks',{"axes.facecolor": ".97"})
#sns.set_palette('colorblind')
#setup plot
fig, ax = plt.subplots()
#plot
ax.hist(d12 * 3600, bins=50, color='b', align='mid')
ax2 = ax.twiny()
ax2.hist(d12 * 3600. / pixscale['bokmos'],
bins=50,
color='g',
align='mid',
visible=False)
xlab = ax.set_xlabel("arcsec")
xlab = ax2.set_xlabel("pixels [BASS]")
ylab = ax.set_ylabel("Matched")
#save
#sns.despine()
plt.savefig(os.path.join(get_outdir('bmd'), "separation_hist.png"),
bbox_extra_artists=[xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_matched_decam_vs_bokmos_psf_fwhm(obj, type="psf"):
"""using matched sample, plot decam psf_fwhm vs. bokmos psf_fwhm
obj['m_decam'] is DECaLS() object"""
# indices
index = np.all(
(indices_for_type(b, inst="m_decam", type=type), indices_for_type(b, inst="m_bokmos", type=type)), axis=0
) # both bokmos and decam of same type
# setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
text_args = dict(verticalalignment="center", horizontalalignment="left", fontsize=10)
# plot
for cnt, band in zip(range(3), ["g", "r", "z"]):
ax[cnt].scatter(
obj["m_bokmos"].data[band + "_psf_fwhm"][index],
obj["m_decam"].data[band + "_psf_fwhm"][index],
edgecolor="b",
c="none",
lw=1.0,
)
ax[cnt].text(0.05, 0.95, band, transform=ax[cnt].transAxes, **text_args)
# finish
for cnt, band in zip(range(3), ["g", "r", "z"]):
ax[cnt].set_xlim(0, 3)
ax[cnt].set_ylim(0, 3)
xlab = ax[1].set_xlabel("PSF_FWHM (bokmos)", **laba)
ylab = ax[0].set_ylabel("PSF_FWHM (decam)", **laba)
ti = plt.suptitle("%s Objects, Matched" % type.upper())
plt.savefig(
os.path.join(get_outdir("bmd"), "decam_vs_bokmos_psf_fwhm_%s.png" % type),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_matched_decam_vs_bokmos_psf_fwhm(obj, type='psf'):
'''using matched sample, plot decam psf_fwhm vs. bokmos psf_fwhm
obj['m_decam'] is DECaLS() object'''
#indices
index= np.all((indices_for_type(b,inst='m_decam',type=type),\
indices_for_type(b,inst='m_bokmos',type=type)), axis=0) #both bokmos and decam of same type
#setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
text_args = dict(verticalalignment='center',
horizontalalignment='left',
fontsize=10)
#plot
for cnt, band in zip(range(3), ['g', 'r', 'z']):
ax[cnt].scatter(obj['m_bokmos'].data[band+'_psf_fwhm'][index], obj['m_decam'].data[band+'_psf_fwhm'][index],\
edgecolor='b',c='none',lw=1.)
ax[cnt].text(0.05,
0.95,
band,
transform=ax[cnt].transAxes,
**text_args)
#finish
for cnt, band in zip(range(3), ['g', 'r', 'z']):
ax[cnt].set_xlim(0, 3)
ax[cnt].set_ylim(0, 3)
xlab = ax[1].set_xlabel('PSF_FWHM (bokmos)', **laba)
ylab = ax[0].set_ylabel('PSF_FWHM (decam)', **laba)
ti = plt.suptitle('%s Objects, Matched' % type.upper())
plt.savefig(os.path.join(get_outdir('bmd'),
'decam_vs_bokmos_psf_fwhm_%s.png' % type),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_confusion_matrix(cm, ticknames, addname=''):
'''cm -- NxN array containing the Confusion Matrix values
ticknames -- list of strings of length == N, column and row names for cm plot'''
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues, vmin=0, vmax=1)
cbar = plt.colorbar()
plt.xticks(range(len(ticknames)), ticknames)
plt.yticks(range(len(ticknames)), ticknames)
ylab = plt.ylabel('True (DECaLS)')
xlab = plt.xlabel('Predicted (BASS/MzLS)')
for row in range(len(ticknames)):
for col in range(len(ticknames)):
if np.isnan(cm[row, col]):
plt.text(col, row, 'n/a', va='center', ha='center')
elif cm[row, col] > 0.5:
plt.text(col,
row,
'%.2f' % cm[row, col],
va='center',
ha='center',
color='yellow')
else:
plt.text(col,
row,
'%.2f' % cm[row, col],
va='center',
ha='center',
color='black')
plt.savefig(os.path.join(get_outdir('bmd'),
'confusion_matrix%s.png' % addname),
bbox_extra_artists=[xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_magRatio_vs_mag(b, type="psf", addname=""):
# join indices b/c matched
i_type = np.all(
(indices_for_type(b, inst="m_decam", type=type), indices_for_type(b, inst="m_bokmos", type=type)), axis=0
) # both bokmos and decam of same type
# plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
for cnt, band in zip(range(3), ["g", "r", "z"]):
magRatio = (
np.log10(b["m_bokmos"].data[band + "flux"][i_type]) / np.log10(b["m_decam"].data[band + "flux"][i_type])
- 1.0
)
mag = 22.5 - 2.5 * np.log10(b["m_decam"].data[band + "flux"][i_type])
ax[cnt].scatter(mag, magRatio, c="b", edgecolor="b", s=5) # ,c='none',lw=2.)
# labels
for cnt, band in zip(range(3), ["g", "r", "z"]):
xlab = ax[cnt].set_xlabel("%s AB" % band, **laba)
ax[cnt].set_ylim(-0.5, 0.5)
ax[cnt].set_xlim(18, 26)
ylab = ax[0].set_ylabel(r"$m_{bm}/m_d - 1$", **laba)
ti = ax[1].set_title("%s" % type, **laba)
# put stats in suptitle
plt.savefig(
os.path.join(get_outdir("bmd"), "magRatio_vs_mag_%s%s.png" % (type, addname)),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_radec(obj, addname=''):
'''obj[m_types] -- DECaLS() objects with matched OR unmatched indices'''
#set seaborn panel styles
#sns.set_style('ticks',{"axes.facecolor": ".97"})
#sns.set_palette('colorblind')
#setup plot
fig, ax = plt.subplots(1, 2, figsize=(9, 6), sharey=True, sharex=True)
plt.subplots_adjust(wspace=0.25)
#plt.subplots_adjust(wspace=0.5)
#plot
ax[0].scatter(obj['m_decam'].data['ra'], obj['m_decam'].data['dec'], \
edgecolor='b',c='none',lw=1.)
ax[1].scatter(obj['u_decam'].data['ra'], obj['u_decam'].data['dec'], \
edgecolor='b',c='none',lw=1.,label='DECaLS')
ax[1].scatter(obj['u_bokmos'].data['ra'], obj['u_bokmos'].data['dec'], \
edgecolor='g',c='none',lw=1.,label='BASS/MzLS')
for cnt, ti in zip(range(2), ['Matched', 'Unmatched']):
ti = ax[cnt].set_title(ti, **laba)
xlab = ax[cnt].set_xlabel('RA', **laba)
ylab = ax[0].set_ylabel('DEC', **laba)
ax[0].legend(loc='upper left', **leg_args)
#save
#sns.despine()
plt.savefig(os.path.join(get_outdir('bmd'), 'radec%s.png' % addname),
bbox_extra_artists=[xlab, ylab, ti],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_magRatio_vs_mag(b, type='psf', addname=''):
#join indices b/c matched
i_type= np.all((indices_for_type(b, inst='m_decam',type=type),\
indices_for_type(b, inst='m_bokmos',type=type)), axis=0) #both bokmos and decam of same type
#plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
for cnt, band in zip(range(3), ['g', 'r', 'z']):
magRatio = np.log10(
b['m_bokmos'].data[band + 'flux'][i_type]) / np.log10(
b['m_decam'].data[band + 'flux'][i_type]) - 1.
mag = 22.5 - 2.5 * np.log10(b['m_decam'].data[band + 'flux'][i_type])
ax[cnt].scatter(mag, magRatio, c='b', edgecolor='b',
s=5) #,c='none',lw=2.)
#labels
for cnt, band in zip(range(3), ['g', 'r', 'z']):
xlab = ax[cnt].set_xlabel('%s AB' % band, **laba)
ax[cnt].set_ylim(-0.5, 0.5)
ax[cnt].set_xlim(18, 26)
ylab = ax[0].set_ylabel(r'$m_{bm}/m_d - 1$', **laba)
ti = ax[1].set_title("%s" % type, **laba)
#put stats in suptitle
plt.savefig(os.path.join(get_outdir('bmd'),
'magRatio_vs_mag_%s%s.png' % (type, addname)),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_radec(obj, addname=""):
"""obj[m_types] -- DECaLS() objects with matched OR unmatched indices"""
# set seaborn panel styles
# sns.set_style('ticks',{"axes.facecolor": ".97"})
# sns.set_palette('colorblind')
# setup plot
fig, ax = plt.subplots(1, 2, figsize=(9, 6), sharey=True, sharex=True)
plt.subplots_adjust(wspace=0.25)
# plt.subplots_adjust(wspace=0.5)
# plot
ax[0].scatter(obj["m_decam"].data["ra"], obj["m_decam"].data["dec"], edgecolor="b", c="none", lw=1.0)
ax[1].scatter(
obj["u_decam"].data["ra"], obj["u_decam"].data["dec"], edgecolor="b", c="none", lw=1.0, label="DECaLS"
)
ax[1].scatter(
obj["u_bokmos"].data["ra"], obj["u_bokmos"].data["dec"], edgecolor="g", c="none", lw=1.0, label="BASS/MzLS"
)
for cnt, ti in zip(range(2), ["Matched", "Unmatched"]):
ti = ax[cnt].set_title(ti, **laba)
xlab = ax[cnt].set_xlabel("RA", **laba)
ylab = ax[0].set_ylabel("DEC", **laba)
ax[0].legend(loc="upper left", **leg_args)
# save
# sns.despine()
plt.savefig(
os.path.join(get_outdir("bmd"), "radec%s.png" % addname),
bbox_extra_artists=[xlab, ylab, ti],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_dflux_chisq(b, type='psf', low=-8., hi=8., addname=''):
#join indices b/c matched
i_type= np.all((indices_for_type(b, inst='m_decam',type=type),\
indices_for_type(b, inst='m_bokmos',type=type)), axis=0) #both bokmos and decam of same type
#get flux diff for each band
hist = dict(g=0, r=0, z=0)
binc = dict(g=0, r=0, z=0)
stats = dict(g=0, r=0, z=0)
#chi
sample, mag = {}, {}
for band in ['g', 'r', 'z']:
sample[band]= (b['m_decam'].data[band+'flux'][i_type]-b['m_bokmos'].data[band+'flux'][i_type])/np.sqrt(\
np.power(b['m_decam'].data[band+'flux_ivar'][i_type],-1)+np.power(b['m_bokmos'].data[band+'flux_ivar'][i_type],-1))
mag[band] = 22.5 - 2.5 * np.log10(
b['m_decam'].data[band + 'flux'][i_type])
#loop over mag bins, one 3 panel for each mag bin
for b_low, b_hi in zip([18, 19, 20, 21, 22, 23], [19, 20, 21, 22, 23, 24]):
#plot each filter
for band in ['g', 'r', 'z']:
imag = np.all((b_low <= mag[band], mag[band] < b_hi), axis=0)
#print("len(imag)=",len(imag),"len(sample)=",len(sample),"len(sample[imag])=",len(sample[imag]))
hist[band], bins, junk = plt.hist(sample[band][imag],
range=(low, hi),
bins=50,
normed=True)
db = (bins[1:] - bins[:-1]) / 2
binc[band] = bins[:-1] + db
plt.close() #b/c plt.hist above
#for drawing unit gaussian N(0,1)
G = sp_stats.norm(0, 1)
xvals = np.linspace(low, hi)
#plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
for cnt, band in zip(range(3), ['g', 'r', 'z']):
ax[cnt].step(binc[band], hist[band], where='mid', c='b', lw=2)
ax[cnt].plot(xvals, G.pdf(xvals))
#labels
for cnt, band in zip(range(3), ['g', 'r', 'z']):
if band == 'r':
xlab = ax[cnt].set_xlabel(
r'%s $(F_{d}-F_{bm})/\sqrt{\sigma^2_{d}+\sigma^2_{bm}}$' %
band, **laba)
else:
xlab = ax[cnt].set_xlabel('%s' % band, **laba)
#xlab=ax[cnt].set_xlabel('%s' % band, **laba)
ax[cnt].set_ylim(0, 0.6)
ax[cnt].set_xlim(low, hi)
ylab = ax[0].set_ylabel('PDF', **laba)
ti = ax[1].set_title(
"%s (%.1f <= %s < %.1f)" % (type, b_low, band, b_hi), **laba)
#put stats in suptitle
plt.savefig(os.path.join(
get_outdir('bmd'), 'dflux_chisq_%s_%.1f-%s-%.1f%s.png' %
(type, b_low, band, b_hi, addname)),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_SN_vs_mag(obj, found_by="matched", type="all", addname=""):
"""obj['m_decam'] is DECaLS() object
found_by -- 'matched' or 'unmatched'
type -- all,psf,lrg"""
# indices for type == all,psf, or lrg
assert found_by == "matched" or found_by == "unmatched"
prefix = found_by[0] + "_" # m_ or u_
index = {}
for key in ["decam", "bokmos"]:
index[key] = indices_for_type(obj, inst=prefix + key, type=type)
# bin up SN values
min, max = 18.0, 25.0
bin_SN = dict(decam={}, bokmos={})
for key in bin_SN.keys():
for band in ["g", "r", "z"]:
bin_SN[key][band] = {}
i = index[key]
bin_edges = np.linspace(min, max, num=30)
bin_SN[key][band]["binc"], count, bin_SN[key][band]["q25"], bin_SN[key][band]["q50"], bin_SN[key][band][
"q75"
] = bin_up(
obj[prefix + key].data[band + "mag"][i],
obj[prefix + key].data[band + "flux"][i] * np.sqrt(obj[prefix + key].data[band + "flux_ivar"][i]),
bin_edges=bin_edges,
)
# setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
# plot SN
for cnt, band in zip(range(3), ["g", "r", "z"]):
# horiz line at SN = 5
ax[cnt].plot([1, 40], [5, 5], "k--", lw=2)
# data
for inst, color, lab in zip(["decam", "bokmos"], ["b", "g"], ["DECaLS", "BASS/MzLS"]):
ax[cnt].plot(bin_SN[inst][band]["binc"], bin_SN[inst][band]["q50"], c=color, ls="-", lw=2, label=lab)
ax[cnt].fill_between(
bin_SN[inst][band]["binc"],
bin_SN[inst][band]["q25"],
bin_SN[inst][band]["q75"],
color=color,
alpha=0.25,
)
# labels
ax[2].legend(loc=1, **leg_args)
for cnt, band in zip(range(3), ["g", "r", "z"]):
ax[cnt].set_yscale("log")
xlab = ax[cnt].set_xlabel("%s" % band, **laba)
ax[cnt].set_ylim(1, 100)
ax[cnt].set_xlim(20.0, 26.0)
ylab = ax[0].set_ylabel("S/N", **laba)
text_args = dict(verticalalignment="bottom", horizontalalignment="right", fontsize=10)
ax[2].text(26, 5, "S/N = 5 ", **text_args)
plt.savefig(
os.path.join(get_outdir("bmd"), "sn_%s_%s%s.png" % (found_by, type, addname)),
bbox_extra_artists=[xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_dflux_chisq(b, type="psf", low=-8.0, hi=8.0, addname=""):
# join indices b/c matched
i_type = np.all(
(indices_for_type(b, inst="m_decam", type=type), indices_for_type(b, inst="m_bokmos", type=type)), axis=0
) # both bokmos and decam of same type
# get flux diff for each band
hist = dict(g=0, r=0, z=0)
binc = dict(g=0, r=0, z=0)
stats = dict(g=0, r=0, z=0)
# chi
sample, mag = {}, {}
for band in ["g", "r", "z"]:
sample[band] = (b["m_decam"].data[band + "flux"][i_type] - b["m_bokmos"].data[band + "flux"][i_type]) / np.sqrt(
np.power(b["m_decam"].data[band + "flux_ivar"][i_type], -1)
+ np.power(b["m_bokmos"].data[band + "flux_ivar"][i_type], -1)
)
mag[band] = 22.5 - 2.5 * np.log10(b["m_decam"].data[band + "flux"][i_type])
# loop over mag bins, one 3 panel for each mag bin
for b_low, b_hi in zip([18, 19, 20, 21, 22, 23], [19, 20, 21, 22, 23, 24]):
# plot each filter
for band in ["g", "r", "z"]:
imag = np.all((b_low <= mag[band], mag[band] < b_hi), axis=0)
# print("len(imag)=",len(imag),"len(sample)=",len(sample),"len(sample[imag])=",len(sample[imag]))
hist[band], bins, junk = plt.hist(sample[band][imag], range=(low, hi), bins=50, normed=True)
db = (bins[1:] - bins[:-1]) / 2
binc[band] = bins[:-1] + db
plt.close() # b/c plt.hist above
# for drawing unit gaussian N(0,1)
G = sp_stats.norm(0, 1)
xvals = np.linspace(low, hi)
# plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
for cnt, band in zip(range(3), ["g", "r", "z"]):
ax[cnt].step(binc[band], hist[band], where="mid", c="b", lw=2)
ax[cnt].plot(xvals, G.pdf(xvals))
# labels
for cnt, band in zip(range(3), ["g", "r", "z"]):
if band == "r":
xlab = ax[cnt].set_xlabel(r"%s $(F_{d}-F_{bm})/\sqrt{\sigma^2_{d}+\sigma^2_{bm}}$" % band, **laba)
else:
xlab = ax[cnt].set_xlabel("%s" % band, **laba)
# xlab=ax[cnt].set_xlabel('%s' % band, **laba)
ax[cnt].set_ylim(0, 0.6)
ax[cnt].set_xlim(low, hi)
ylab = ax[0].set_ylabel("PDF", **laba)
ti = ax[1].set_title("%s (%.1f <= %s < %.1f)" % (type, b_low, band, b_hi), **laba)
# put stats in suptitle
plt.savefig(
os.path.join(get_outdir("bmd"), "dflux_chisq_%s_%.1f-%s-%.1f%s.png" % (type, b_low, band, b_hi, addname)),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_N_per_deg2(obj, type="all", req_mags=[24.0, 23.4, 22.5], addname=""):
"""image requirements grz<=24,23.4,22.5
compute number density in each bin for each band mag [18,requirement]"""
# indices for type for matched and unmatched samples
index = {}
for inst in ["m_decam", "u_decam", "m_bokmos", "u_bokmos"]:
index[inst] = indices_for_type(obj, inst=inst, type=type)
bin_nd = dict(decam={}, bokmos={})
for inst in ["decam", "bokmos"]:
bin_nd[inst] = {}
for band, req in zip(["g", "r", "z"], req_mags):
bin_nd[inst][band] = {}
bin_edges = np.linspace(18.0, req, num=15)
i_m, i_u = index["m_" + inst], index["u_" + inst] # need m+u
# join m_decam,u_decam OR m_bokmos,u_bokmos and only with correct all,psf,lrg index
sample = np.ma.concatenate(
(obj["m_" + inst].data[band + "mag"][i_m], obj["u_" + inst].data[band + "mag"][i_u]), axis=0
)
bin_nd[inst][band]["binc"], bin_nd[inst][band]["cnt"], q25, q50, q75 = bin_up(
sample, sample, bin_edges=bin_edges
)
# plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
for cnt, band in zip(range(3), ["g", "r", "z"]):
for inst, color, lab in zip(["decam", "bokmos"], ["b", "g"], ["DECaLS", "BASS/MzLS"]):
ax[cnt].step(
bin_nd[inst][band]["binc"],
bin_nd[inst][band]["cnt"] / obj["deg2_" + inst],
where="mid",
c=color,
lw=2,
label=lab,
)
# labels
for cnt, band in zip(range(3), ["g", "r", "z"]):
xlab = ax[cnt].set_xlabel("%s" % band) # , **laba)
# ax[cnt].set_ylim(0,0.6)
# ax[cnt].set_xlim(maglow,maghi)
ax[0].legend(loc="upper left", **leg_args)
ylab = ax[0].set_ylabel("counts/deg2") # , **laba)
ti = plt.suptitle("%ss" % type.upper(), **laba)
# Make space for and rotate the x-axis tick labels
fig.autofmt_xdate()
# put stats in suptitle
plt.savefig(
os.path.join(get_outdir("bmd"), "n_per_deg2_%s%s.png" % (type, addname)),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_matched_dmag_vs_psf_fwhm(obj, type='psf'):
'''using matched sample, plot diff in mags vs. DECAM psf_fwhm in bins
obj['m_decam'] is DECaLS() object'''
#indices
index= np.all((indices_for_type(b,inst='m_decam',type=type),\
indices_for_type(b,inst='m_bokmos',type=type)), axis=0) #both bokmos and decam of same type
#bin up by DECAM psf_fwhm
bin_edges = np.linspace(0, 3, num=6)
vals = {}
for band in ['g', 'r', 'z']:
vals[band] = {}
vals[band]['binc'],count,vals[band]['q25'],vals[band]['q50'],vals[band]['q75']=\
bin_up(obj['m_decam'].data[band+'_psf_fwhm'][index], \
obj['m_bokmos'].data[band+'mag'][index]- obj['m_decam'].data[band+'mag'][index], \
bin_edges=bin_edges)
#setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
text_args = dict(verticalalignment='center',
horizontalalignment='left',
fontsize=10)
#plot
for cnt, band in zip(range(3), ['g', 'r', 'z']):
ax[cnt].plot(vals[band]['binc'],
vals[band]['q50'],
c='b',
ls='-',
lw=2)
ax[cnt].fill_between(vals[band]['binc'],
vals[band]['q25'],
vals[band]['q75'],
color='b',
alpha=0.25)
ax[cnt].text(0.05,
0.95,
band,
transform=ax[cnt].transAxes,
**text_args)
#finish
xlab = ax[1].set_xlabel('decam PSF_FWHM', **laba)
ylab = ax[0].set_ylabel(r'Median $\Delta \, m$ (decam - bokmos)', **laba)
ti = plt.suptitle('%s Objects, Matched' % type.upper())
plt.savefig(os.path.join(get_outdir('bmd'),
'dmag_vs_psf_fwhm_%s.png' % type),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_N_per_deg2(obj, type='all', req_mags=[24., 23.4, 22.5], addname=''):
'''image requirements grz<=24,23.4,22.5
compute number density in each bin for each band mag [18,requirement]'''
#indices for type for matched and unmatched samples
index = {}
for inst in ['m_decam', 'u_decam', 'm_bokmos', 'u_bokmos']:
index[inst] = indices_for_type(obj, inst=inst, type=type)
bin_nd = dict(decam={}, bokmos={})
for inst in ['decam', 'bokmos']:
bin_nd[inst] = {}
for band, req in zip(['g', 'r', 'z'], req_mags):
bin_nd[inst][band] = {}
bin_edges = np.linspace(18., req, num=15)
i_m, i_u = index['m_' + inst], index['u_' + inst] #need m+u
#join m_decam,u_decam OR m_bokmos,u_bokmos and only with correct all,psf,lrg index
sample = np.ma.concatenate(
(obj['m_' + inst].data[band + 'mag'][i_m],
obj['u_' + inst].data[band + 'mag'][i_u]),
axis=0)
bin_nd[inst][band]['binc'],bin_nd[inst][band]['cnt'],q25,q50,q75=\
bin_up(sample,sample,bin_edges=bin_edges)
#plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
for cnt, band in zip(range(3), ['g', 'r', 'z']):
for inst, color, lab in zip(['decam', 'bokmos'], ['b', 'g'],
['DECaLS', 'BASS/MzLS']):
ax[cnt].step(bin_nd[inst][band]['binc'],
bin_nd[inst][band]['cnt'] / obj['deg2_' + inst],
where='mid',
c=color,
lw=2,
label=lab)
#labels
for cnt, band in zip(range(3), ['g', 'r', 'z']):
xlab = ax[cnt].set_xlabel('%s' % band) #, **laba)
#ax[cnt].set_ylim(0,0.6)
#ax[cnt].set_xlim(maglow,maghi)
ax[0].legend(loc='upper left', **leg_args)
ylab = ax[0].set_ylabel('counts/deg2') #, **laba)
ti = plt.suptitle("%ss" % type.upper(), **laba)
# Make space for and rotate the x-axis tick labels
fig.autofmt_xdate()
#put stats in suptitle
plt.savefig(os.path.join(get_outdir('bmd'),
'n_per_deg2_%s%s.png' % (type, addname)),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_HistTypes(obj, m_types=['m_decam', 'm_bokmos'], addname=''):
'''decam,bokmos -- DECaLS() objects with matched OR unmatched indices'''
#matched or unmatched objects
if m_types[0].startswith('m_') and m_types[1].startswith('m_'):
matched = True
elif m_types[0].startswith('u_') and m_types[1].startswith('u_'):
matched = False
else:
raise ValueError
#sns.set_style("whitegrid")
#sns.set_palette('colorblind')
#c1=sns.color_palette()[2]
#c2=sns.color_palette()[0] #'b'
c1 = 'b'
c2 = 'r'
###
types = ['PSF', 'SIMP', 'EXP', 'DEV', 'COMP']
ind = np.arange(len(types)) # the x locations for the groups
width = 0.35 # the width of the bars
###
ht_decam, ht_bokmos = np.zeros(5, dtype=int), np.zeros(5, dtype=int)
for cnt, typ in enumerate(types):
ht_decam[cnt] = np.where(
obj[m_types[0]].data['type'] == typ)[0].shape[0] / float(
obj['deg2_decam'])
ht_bokmos[cnt] = np.where(
obj[m_types[1]].data['type'] == typ)[0].shape[0] / float(
obj['deg2_bokmos'])
###
fig, ax = plt.subplots()
rects1 = ax.bar(ind, ht_decam, width, color=c1)
rects2 = ax.bar(ind + width, ht_bokmos, width, color=c2)
ylab = ax.set_ylabel("counts/deg2")
if matched: ti = ax.set_title('Matched')
else: ti = ax.set_title('Unmatched')
ax.set_xticks(ind + width)
ax.set_xticklabels(types)
ax.legend((rects1[0], rects2[0]), ('DECaLS', 'BASS/MzLS'), **leg_args)
#save
if matched: name = 'hist_types_Matched%s.png' % addname
else: name = 'hist_types_Unmatched%s.png' % addname
plt.savefig(os.path.join(get_outdir('bmd'), name),
bbox_extra_artists=[ylab, ti],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_psf_hists(decam, bokmos, zoom=False):
'''decam,bokmos are DECaLS() objects matched to decam ra,dec'''
#divide into samples of 0.25 mag bins, store q50 of each
width = 0.25 #in mag
low_vals = np.arange(20., 26., width)
med = {}
for b in ['g', 'r', 'z']:
med[b] = np.zeros(low_vals.size) - 100
for i, low in enumerate(low_vals):
for band in ['g', 'r', 'z']:
ind = np.all((low <= decam[band + 'mag'],
decam[band + 'mag'] < low + width),
axis=0)
if np.where(ind)[0].size > 0:
med[band][i] = np.percentile(bokmos[band + 'mag'][ind] -
decam[band + 'mag'][ind],
q=50)
else:
med[band][i] = np.nan
#make plot
#set seaborn panel styles
#sns.set_style('ticks',{"axes.facecolor": ".97"})
#sns.set_palette('colorblind')
#setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3)) #,sharey=True)
plt.subplots_adjust(wspace=0.5)
#plot
for cnt, band in zip(range(3), ['r', 'g', 'z']):
ax[cnt].scatter(low_vals, med[band],\
edgecolor='b',c='none',lw=2.) #,label=m_type.split('_')[-1])
xlab = ax[cnt].set_xlabel('bins of %s (decam)' % band, **laba)
ylab = ax[cnt].set_ylabel('q50[%s bokmos - decam]' % band, **laba)
if zoom: ax[cnt].set_ylim(-0.25, 0.25)
# sup=plt.suptitle('decam with matching bokmos',**laba)
#save
#sns.despine()
if zoom: name = "median_color_diff_zoom.png"
else: name = "median_color_diff.png"
plt.savefig(os.path.join(get_outdir('bmd'), name),
bbox_extra_artists=[xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_HistTypes(obj, m_types=["m_decam", "m_bokmos"], addname=""):
"""decam,bokmos -- DECaLS() objects with matched OR unmatched indices"""
# matched or unmatched objects
if m_types[0].startswith("m_") and m_types[1].startswith("m_"):
matched = True
elif m_types[0].startswith("u_") and m_types[1].startswith("u_"):
matched = False
else:
raise ValueError
# sns.set_style("whitegrid")
# sns.set_palette('colorblind')
# c1=sns.color_palette()[2]
# c2=sns.color_palette()[0] #'b'
c1 = "b"
c2 = "r"
###
types = ["PSF", "SIMP", "EXP", "DEV", "COMP"]
ind = np.arange(len(types)) # the x locations for the groups
width = 0.35 # the width of the bars
###
ht_decam, ht_bokmos = np.zeros(5, dtype=int), np.zeros(5, dtype=int)
for cnt, typ in enumerate(types):
ht_decam[cnt] = np.where(obj[m_types[0]].data["type"] == typ)[0].shape[0] / float(obj["deg2_decam"])
ht_bokmos[cnt] = np.where(obj[m_types[1]].data["type"] == typ)[0].shape[0] / float(obj["deg2_bokmos"])
###
fig, ax = plt.subplots()
rects1 = ax.bar(ind, ht_decam, width, color=c1)
rects2 = ax.bar(ind + width, ht_bokmos, width, color=c2)
ylab = ax.set_ylabel("counts/deg2")
if matched:
ti = ax.set_title("Matched")
else:
ti = ax.set_title("Unmatched")
ax.set_xticks(ind + width)
ax.set_xticklabels(types)
ax.legend((rects1[0], rects2[0]), ("DECaLS", "BASS/MzLS"), **leg_args)
# save
if matched:
name = "hist_types_Matched%s.png" % addname
else:
name = "hist_types_Unmatched%s.png" % addname
plt.savefig(os.path.join(get_outdir("bmd"), name), bbox_extra_artists=[ylab, ti], bbox_inches="tight", dpi=150)
plt.close()
def plot_nobs(b):
"""make histograms of nobs so can compare depths of g,r,z between the two catalogues"""
hi = 0
for cam in ["m_decam", "m_bokmos"]:
for band in "grz":
hi = np.max((hi, b[cam].data[band + "_nobs"].max()))
bins = hi
for cam in ["m_decam", "m_bokmos"]:
for band in "grz":
junk = plt.hist(b[cam].data[band + "_nobs"], bins=bins, normed=True, cumulative=True, align="mid")
xlab = plt.xlabel("nobs %s" % band, **laba)
ylab = plt.ylabel("CDF", **laba)
plt.savefig(
os.path.join(get_outdir("bmd"), "hist_nobs_%s_%s.png" % (band, cam[2:])),
bbox_extra_artists=[xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_nobs(b):
'''make histograms of nobs so can compare depths of g,r,z between the two catalogues'''
hi = 0
for cam in ['m_decam', 'm_bokmos']:
for band in 'grz':
hi = np.max((hi, b[cam].data[band + '_nobs'].max()))
bins = hi
for cam in ['m_decam', 'm_bokmos']:
for band in 'grz':
junk = plt.hist(b[cam].data[band + '_nobs'],
bins=bins,
normed=True,
cumulative=True,
align='mid')
xlab = plt.xlabel('nobs %s' % band, **laba)
ylab = plt.ylabel('CDF', **laba)
plt.savefig(os.path.join(get_outdir('bmd'),
'hist_nobs_%s_%s.png' % (band, cam[2:])),
bbox_extra_artists=[xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
def plot_psf_hists(decam, bokmos, zoom=False):
"""decam,bokmos are DECaLS() objects matched to decam ra,dec"""
# divide into samples of 0.25 mag bins, store q50 of each
width = 0.25 # in mag
low_vals = np.arange(20.0, 26.0, width)
med = {}
for b in ["g", "r", "z"]:
med[b] = np.zeros(low_vals.size) - 100
for i, low in enumerate(low_vals):
for band in ["g", "r", "z"]:
ind = np.all((low <= decam[band + "mag"], decam[band + "mag"] < low + width), axis=0)
if np.where(ind)[0].size > 0:
med[band][i] = np.percentile(bokmos[band + "mag"][ind] - decam[band + "mag"][ind], q=50)
else:
med[band][i] = np.nan
# make plot
# set seaborn panel styles
# sns.set_style('ticks',{"axes.facecolor": ".97"})
# sns.set_palette('colorblind')
# setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3)) # ,sharey=True)
plt.subplots_adjust(wspace=0.5)
# plot
for cnt, band in zip(range(3), ["r", "g", "z"]):
ax[cnt].scatter(low_vals, med[band], edgecolor="b", c="none", lw=2.0) # ,label=m_type.split('_')[-1])
xlab = ax[cnt].set_xlabel("bins of %s (decam)" % band, **laba)
ylab = ax[cnt].set_ylabel("q50[%s bokmos - decam]" % band, **laba)
if zoom:
ax[cnt].set_ylim(-0.25, 0.25)
# sup=plt.suptitle('decam with matching bokmos',**laba)
# save
# sns.despine()
if zoom:
name = "median_color_diff_zoom.png"
else:
name = "median_color_diff.png"
plt.savefig(os.path.join(get_outdir("bmd"), name), bbox_extra_artists=[xlab, ylab], bbox_inches="tight", dpi=150)
plt.close()
def plot_matched_dmag_vs_psf_fwhm(obj, type="psf"):
"""using matched sample, plot diff in mags vs. DECAM psf_fwhm in bins
obj['m_decam'] is DECaLS() object"""
# indices
index = np.all(
(indices_for_type(b, inst="m_decam", type=type), indices_for_type(b, inst="m_bokmos", type=type)), axis=0
) # both bokmos and decam of same type
# bin up by DECAM psf_fwhm
bin_edges = np.linspace(0, 3, num=6)
vals = {}
for band in ["g", "r", "z"]:
vals[band] = {}
vals[band]["binc"], count, vals[band]["q25"], vals[band]["q50"], vals[band]["q75"] = bin_up(
obj["m_decam"].data[band + "_psf_fwhm"][index],
obj["m_bokmos"].data[band + "mag"][index] - obj["m_decam"].data[band + "mag"][index],
bin_edges=bin_edges,
)
# setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
text_args = dict(verticalalignment="center", horizontalalignment="left", fontsize=10)
# plot
for cnt, band in zip(range(3), ["g", "r", "z"]):
ax[cnt].plot(vals[band]["binc"], vals[band]["q50"], c="b", ls="-", lw=2)
ax[cnt].fill_between(vals[band]["binc"], vals[band]["q25"], vals[band]["q75"], color="b", alpha=0.25)
ax[cnt].text(0.05, 0.95, band, transform=ax[cnt].transAxes, **text_args)
# finish
xlab = ax[1].set_xlabel("decam PSF_FWHM", **laba)
ylab = ax[0].set_ylabel(r"Median $\Delta \, m$ (decam - bokmos)", **laba)
ti = plt.suptitle("%s Objects, Matched" % type.upper())
plt.savefig(
os.path.join(get_outdir("bmd"), "dmag_vs_psf_fwhm_%s.png" % type),
bbox_extra_artists=[ti, xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_matched_separation_hist(d12):
"""d12 is array of distances in degress between matched objects"""
# pixscale to convert d12 into N pixels
pixscale = dict(decam=0.25, bokmos=0.45)
# sns.set_style('ticks',{"axes.facecolor": ".97"})
# sns.set_palette('colorblind')
# setup plot
fig, ax = plt.subplots()
# plot
ax.hist(d12 * 3600, bins=50, color="b", align="mid")
ax2 = ax.twiny()
ax2.hist(d12 * 3600.0 / pixscale["bokmos"], bins=50, color="g", align="mid", visible=False)
xlab = ax.set_xlabel("arcsec")
xlab = ax2.set_xlabel("pixels [BASS]")
ylab = ax.set_ylabel("Matched")
# save
# sns.despine()
plt.savefig(
os.path.join(get_outdir("bmd"), "separation_hist.png"),
bbox_extra_artists=[xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_confusion_matrix(cm, ticknames, addname=""):
"""cm -- NxN array containing the Confusion Matrix values
ticknames -- list of strings of length == N, column and row names for cm plot"""
plt.imshow(cm, interpolation="nearest", cmap=plt.cm.Blues, vmin=0, vmax=1)
cbar = plt.colorbar()
plt.xticks(range(len(ticknames)), ticknames)
plt.yticks(range(len(ticknames)), ticknames)
ylab = plt.ylabel("True (DECaLS)")
xlab = plt.xlabel("Predicted (BASS/MzLS)")
for row in range(len(ticknames)):
for col in range(len(ticknames)):
if np.isnan(cm[row, col]):
plt.text(col, row, "n/a", va="center", ha="center")
elif cm[row, col] > 0.5:
plt.text(col, row, "%.2f" % cm[row, col], va="center", ha="center", color="yellow")
else:
plt.text(col, row, "%.2f" % cm[row, col], va="center", ha="center", color="black")
plt.savefig(
os.path.join(get_outdir("bmd"), "confusion_matrix%s.png" % addname),
bbox_extra_artists=[xlab, ylab],
bbox_inches="tight",
dpi=150,
)
plt.close()
def plot_SN_vs_mag(obj, found_by='matched', type='all', addname=''):
'''obj['m_decam'] is DECaLS() object
found_by -- 'matched' or 'unmatched'
type -- all,psf,lrg'''
#indices for type == all,psf, or lrg
assert (found_by == 'matched' or found_by == 'unmatched')
prefix = found_by[0] + '_' # m_ or u_
index = {}
for key in ['decam', 'bokmos']:
index[key] = indices_for_type(obj, inst=prefix + key, type=type)
#bin up SN values
min, max = 18., 25.
bin_SN = dict(decam={}, bokmos={})
for key in bin_SN.keys():
for band in ['g', 'r', 'z']:
bin_SN[key][band] = {}
i = index[key]
bin_edges = np.linspace(min, max, num=30)
bin_SN[key][band]['binc'],count,bin_SN[key][band]['q25'],bin_SN[key][band]['q50'],bin_SN[key][band]['q75']=\
bin_up(obj[prefix+key].data[band+'mag'][i], \
obj[prefix+key].data[band+'flux'][i]*np.sqrt(obj[prefix+key].data[band+'flux_ivar'][i]),\
bin_edges=bin_edges)
#setup plot
fig, ax = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
plt.subplots_adjust(wspace=0.25)
#plot SN
for cnt, band in zip(range(3), ['g', 'r', 'z']):
#horiz line at SN = 5
ax[cnt].plot([1, 40], [5, 5], 'k--', lw=2)
#data
for inst, color, lab in zip(['decam', 'bokmos'], ['b', 'g'],
['DECaLS', 'BASS/MzLS']):
ax[cnt].plot(bin_SN[inst][band]['binc'],
bin_SN[inst][band]['q50'],
c=color,
ls='-',
lw=2,
label=lab)
ax[cnt].fill_between(bin_SN[inst][band]['binc'],
bin_SN[inst][band]['q25'],
bin_SN[inst][band]['q75'],
color=color,
alpha=0.25)
#labels
ax[2].legend(loc=1, **leg_args)
for cnt, band in zip(range(3), ['g', 'r', 'z']):
ax[cnt].set_yscale('log')
xlab = ax[cnt].set_xlabel('%s' % band, **laba)
ax[cnt].set_ylim(1, 100)
ax[cnt].set_xlim(20., 26.)
ylab = ax[0].set_ylabel('S/N', **laba)
text_args = dict(verticalalignment='bottom',
horizontalalignment='right',
fontsize=10)
ax[2].text(26, 5, 'S/N = 5 ', **text_args)
plt.savefig(os.path.join(get_outdir('bmd'),
'sn_%s_%s%s.png' % (found_by, type, addname)),
bbox_extra_artists=[xlab, ylab],
bbox_inches='tight',
dpi=150)
plt.close()
import astropy.io.fits as fits
import astropy.cosmology as co
c1 = co.Planck15
import numpy as np
import glob
import os
import sys
import matplotlib.pyplot as plt
from scipy.spatial import KDTree
import scipy.stats as st
from legacyanalysis.pathnames import get_indir,get_outdir,make_dir
f = open(os.path.join(get_outdir('dr23'),"out-ELG-decals.txt"),'w')
f.write("field 242 \n")
cFile1 = os.path.join(get_indir('dr23'),"catalog-EDR-242.2-243.7-8-12.1-DR3.fits")
f.write("total primary any mask \n")
hdu = fits.open(cFile1)
dat = hdu[1].data
prim = (dat['brick_primary'])
noJunk = (dat['brick_primary']) & (dat['decam_anymask'].T[1]==0) & (dat['decam_anymask'].T[2]==0) & (dat['decam_anymask'].T[4]==0)
psf = (noJunk) & (dat['type'] == "PSF")
areaC1 = ( numpy.max(dat['ra']) - numpy.min(dat['ra']) ) * ( numpy.max(dat['dec']) - numpy.min(dat['dec']) )*numpy.cos( numpy.pi * numpy.mean(dat['dec']) / 180. )
f.write("DR3 : "+str(len(dat))+" "+str( len(prim.nonzero()[0]))+" "+str( len(noJunk.nonzero()[0]))+" "+str( len(psf.nonzero()[0]))+" "+str( areaC1)+"\n")
f.write("DR3 : "+str(numpy.round(len(dat)/areaC1))+" "+str( numpy.round(len(prim.nonzero()[0])/areaC1))+" "+str( numpy.round(len(noJunk.nonzero()[0]) /areaC1) )+" "+str( numpy.round(len(psf.nonzero()[0]) /areaC1) )+"\n")
cFile1 =os.path.join(get_indir('dr23'), "catalog-EDR-242.2-243.7-8-12.1-DR2.fits")
indir= get_indir('cosmos')
#CREATES THE CATALOG LIST
catList = ["catalog-R3-R4.fits",
"catalog-R2-R4.fits",
"catalog-R2-R3.fits",
"catalog-R1-R4.fits",
"catalog-R1-R3.fits",
"catalog-R1-R2.fits"]
for cnt,cat in enumerate(catList): catList[cnt]= os.path.join(indir,cat)
# EACH CATALOG NEEDS TO HAVE THE TYPICAL DECALS CATALOG ENTRIES WITH "_1" AND "_2" APPENDED FOR DR2 and DR3
# DEFINES THE GAUSSIAN FUNCTION
gfun = lambda x, m0, s0 : st.norm.pdf(x,loc=m0,scale=s0)
#OPENS A FILE TO WRITE OUTPUTS
f=open(os.path.join(get_outdir('cosmos'),"depth-comparisonp.txt"),"w")
f.write("20<g<21.5 \n")
# CREATES A FIGURE
plt.figure(2,(5,5))
plt.axes([0.17,0.15,0.75,0.75])
# PLOT THE EXPECTED NORMAL DISTRIBUTION
plt.plot(np.arange(-10,6,0.1), st.norm.pdf(np.arange(-10,6,0.1),loc=0,scale=1), 'k--', lw=2, label='N(0,1)')
# LOOPS OVER MATCHED CATALOGS
for ii, el in enumerate(catList):
hdu=fits.open(el)
dr2=hdu[1].data
# DEFINES MAGNITUDES TO SELECT A MAGNITUDE BIN
g_mag_dr2 = 22.5 - 2.5 * np.log10(dr2['decam_flux_2'].T[1] / dr2['decam_mw_transmission_2'].T[1])
r_mag_dr2 = 22.5 - 2.5 * np.log10(dr2['decam_flux_2'].T[2] / dr2['decam_mw_transmission_2'].T[2])
z_mag_dr2 = 22.5 - 2.5 * np.log10(dr2['decam_flux_2'].T[4] / dr2['decam_mw_transmission_2'].T[4])
from legacyanalysis.pathnames import get_indir, get_outdir, make_dir
indir = get_indir('cosmos')
#CREATES THE CATALOG LIST
catList = [
"catalog-R3-R4.fits", "catalog-R2-R4.fits", "catalog-R2-R3.fits",
"catalog-R1-R4.fits", "catalog-R1-R3.fits", "catalog-R1-R2.fits"
]
for cnt, cat in enumerate(catList):
catList[cnt] = os.path.join(indir, cat)
# EACH CATALOG NEEDS TO HAVE THE TYPICAL DECALS CATALOG ENTRIES WITH "_1" AND "_2" APPENDED FOR DR2 and DR3
# DEFINES THE GAUSSIAN FUNCTION
gfun = lambda x, m0, s0: st.norm.pdf(x, loc=m0, scale=s0)
#OPENS A FILE TO WRITE OUTPUTS
f = open(os.path.join(get_outdir('cosmos'), "depth-comparisonp.txt"), "w")
f.write("20<g<21.5 \n")
# CREATES A FIGURE
plt.figure(2, (5, 5))
plt.axes([0.17, 0.15, 0.75, 0.75])
# PLOT THE EXPECTED NORMAL DISTRIBUTION
plt.plot(np.arange(-10, 6, 0.1),
st.norm.pdf(np.arange(-10, 6, 0.1), loc=0, scale=1),
'k--',
lw=2,
label='N(0,1)')
# LOOPS OVER MATCHED CATALOGS
for ii, el in enumerate(catList):
hdu = fits.open(el)
dr2 = hdu[1].data
