def plotconditional(
s, dists, add, mult, parameters, cube, plotinds, n_sec, n_dims, nicenames, modecolors=["g", "m", "y", "c"]
):
import matplotlib.cm as cm
mp = multipanel(dims=(n_dims - 1, n_dims - 1), diagonal=True, figID=4, panel_size=(3, 3))
for i in plotinds[0]:
for j in range(i):
ind = (n_dims - 1) * (i - 1) + j
mp.grid[ind].contourf(
dists["all"][i, j][:, :, 1] * mult[j] + add[j],
dists["all"][i, j][:, :, 0] * mult[i] + add[i],
dists["all"][i, j][:, :, 2],
5,
cmap=cm.gray_r,
alpha=0.8,
origin="lower",
) # NEed to transpose?????
# mp.grid[ind].scatter(datsep['all'][:,j+2]*mult[j]+add[j],datsep['all'][:,i+2]*mult[i]+add[i], color='k',marker='+',s=1, alpha=0.4)
if i == n_dims - 1:
mp.grid[ind].set_xlabel(parameters[j])
if j == 0:
mp.grid[ind].set_ylabel(parameters[i])
for m, mode in enumerate(s["modes"]):
mp.grid[ind].axvline(x=mode["mean"][j] * mult[j] + add[j], color=modecolors[m], label="Mode")
mp.grid[ind].axhline(y=mode["mean"][i] * mult[i] + add[i], color=modecolors[m])
mp.grid[ind].axvline(x=cube[j] * mult[j] + add[j], color="k", linestyle="--", label="4D Max")
mp.grid[ind].axhline(y=cube[i] * mult[i] + add[i], color="k", linestyle="--")
###### Still need to get everything aligned right. Fix ticks moves the outer boxes
mp.fix_ticks()
for i in plotinds[0]:
for j in range(i):
ind = (n_dims - 1) * (i - 1) + j
mp.grid[ind].set_xlim(
[
numpy.min(dists["all"][i, j][:, :, 1] * mult[j] + add[j]),
numpy.max(dists["all"][i, j][:, :, 1] * mult[j] + add[j]),
]
)
mp.grid[ind].set_ylim(
[
numpy.min(dists["all"][i, j][:, :, 0] * mult[i] + add[i]),
numpy.max(dists["all"][i, j][:, :, 0] * mult[i] + add[i]),
]
)
plt.legend(bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), loc=3)
plt.draw()
plt.savefig("fig_conditional.png")
print "Saved fig_conditional.png"
def plotconditional2(
s, dists, add, mult, parameters, cube, plotinds, n_sec, n_dims, nicenames, modecolors=["g", "m", "y", "c"]
):
import matplotlib.cm as cm
if n_dims > 7:
nplot = n_sec[0] - 1
else:
nplot = n_sec - 1
mp = multipanel(dims=(nplot, nplot), diagonal=True, figID=5, panel_size=(3, 3))
for i in plotinds[1]:
for j in range(n_dims, i):
ind = (nplot) * (i - n_dims - 1) + j - n_dims
mp.grid[ind].contourf(
dists["all"][i, j][:, :, 1] + add[j],
dists["all"][i, j][:, :, 0] + add[i],
dists["all"][i, j][:, :, 2],
5,
cmap=cm.gray_r,
alpha=0.8,
origin="lower",
) # NEed to transpose?????
if i == plotinds[1][nplot]:
mp.grid[ind].set_xlabel(parameters[j])
if j == plotinds[1][0]:
mp.grid[ind].set_ylabel(parameters[i])
for m, mode in enumerate(s["modes"]):
mp.grid[ind].axvline(x=mode["mean"][j] + add[j], color=modecolors[m], label="Mode")
mp.grid[ind].axhline(y=mode["mean"][i] + add[i], color=modecolors[m])
mp.grid[ind].axvline(x=cube[j] + add[j], color="k", linestyle="--", label="4D Max")
mp.grid[ind].axhline(y=cube[i] + add[i], color="k", linestyle="--")
###### Still need to get everything aligned right.
mp.fix_ticks()
for i in plotinds[1]:
for j in range(n_dims, i):
ind = (nplot) * (i - n_dims - 1) + j - n_dims
mp.grid[ind].set_xlim(
[numpy.min(dists["all"][i, j][:, :, 1] + add[j]), numpy.max(dists["all"][i, j][:, :, 1] + add[j])]
)
mp.grid[ind].set_ylim(
[numpy.min(dists["all"][i, j][:, :, 0] + add[i]), numpy.max(dists["all"][i, j][:, :, 0] + add[i])]
)
plt.legend(bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), loc=3)
plt.draw()
plt.savefig("fig_conditional2.png")
print "Saved fig_conditional2.png"
pl.savefig(fpath("electron_temperature_77vs111.pdf"))
keys = [r"$S_{5 GHz}$", r"$S_{15 GHz}$", r"H112$\alpha$", r"H77$\alpha$"]
data = {r"H77$\alpha$": h77a, r"$S_{15 GHz}$": h77c, r"H112$\alpha$": h112a, r"$S_{5 GHz}$": h112c}
combs = list(itertools.combinations(keys, 2))
xpos = {r"$S_{5 GHz}$": 0, r"$S_{15 GHz}$": 1, r"H112$\alpha$": 2}
ypos = {r"$S_{15 GHz}$": 0, r"H112$\alpha$": 1, r"H77$\alpha$": 2}
pl.figure(ii + 4)
pl.clf()
mp = multiplot.multipanel(dims=(3, 3), diagonal=False, figID=ii + 4)
for ii, (xd, yd) in zip(mp.grid.keys(), combs):
if not (yd in ypos and xd in xpos):
continue
print mp.axis_number(ypos[yd], xpos[xd]), xpos[xd], ypos[yd]
ax = mp.grid[mp.axis_number(ypos[yd], xpos[xd])]
# ax.plot(data[xd][rrlmask],data[yd][rrlmask],'.')
mpl_plot_templates.adaptive_param_plot(
data[xd][rrlmask].to(u.Jy).value,
data[yd][rrlmask].to(u.Jy).value,
bins=30,
threshold=5,
fill=False,
alpha=0.8,
axis=ax,
cmap=pl.mpl.cm.spectral,
def docontours_multi(mc, start=6000, end=None, skip=None, dosave=False,
parnames=(), bins=30, fignum=35,
savename='multipanel.png'):
if isinstance(mc, dict):
traces = mc
elif isinstance(mc, astropy.io.fits.fitsrec.FITS_rec):
traces = {n:mc[n] for n in mc.names}
else:
mcnames = mc.db._traces.keys()
traces = {k:np.concatenate([v.squeeze()
for v in mc.trace(k)._trace.values()])[start:end:skip]
for k in mcnames
if k in parnames}
pl.figure(fignum)
pl.clf()
npars = len(traces.keys())
mp = multiplot.multipanel(dims=(npars,npars), padding=(0,0), diagonal=True, figID=fignum)
parnumbers = {p:i for i,p in enumerate(traces.keys())}
for par1,par2 in itertools.combinations(traces.keys(),2):
# coordinates are -y, x
axno = mp.axis_number(parnumbers[par2],parnumbers[par1])
if axno is None:
continue
ax = mp.grid[axno]
try:
agpy.pymc_plotting.hist2d(traces, par1, par2,
varslice=(None,None,None), axis=ax,
colorbar=False, clear=True,
doerrellipse=False, bins=bins)
except ValueError as E:
print E
continue
# if x > 0, hide y labels.
if parnumbers[par1] > 0:
ax.set_ylabel("")
ax.set_yticks([])
else:
ax.set_ylabel(ax.get_ylabel(), fontsize=12)
ax.tick_params(axis='both', which='major', labelsize=12)
ax.yaxis.get_major_locator().set_params(nbins=4)
if parnumbers[par2] == npars-1:
ax.set_xlabel(ax.get_xlabel(), fontsize=12)
ax.xaxis.get_major_locator().set_params(nbins=4)
ax.tick_params(axis='both', which='major', labelsize=12)
else:
ax.set_xlabel('')
ax.set_xticks([])
for par in traces.keys():
ax = mp.grid[mp.axis_number(parnumbers[par],parnumbers[par])]
try:
agpy.pymc_plotting.plot_mc_hist(traces, par,
varslice=(None,None,None),
onesided=False, axis=ax,
legend=False)
except Exception as E:
print E
continue
# if x > 0, hide y labels.
if parnumbers[par] > 0:
ax.set_ylabel("")
ax.set_yticks([])
else:
ax.set_ylabel(par, fontsize=12)
ax.tick_params(axis='both', which='major', labelsize=12)
ax.yaxis.get_major_locator().set_params(nbins=4)
if npars-1 == parnumbers[par]: # label bottom panel
ax.set_xlabel(par, fontsize=12)
ax.xaxis.get_major_locator().set_params(nbins=4)
ax.tick_params(axis='both', which='major', labelsize=12)
if dosave:
savefig(savename,bbox_inches='tight')
def plotmarginal2(
s,
dists,
add,
mult,
parameters,
cube,
plotinds,
n_sec,
n_dims,
nicenames,
title="",
norm1=True,
modecolors=["g", "m", "y", "c"],
colors=["b", "b", "b", "b", "r", "r", "r", "r", "b", "b", "b", "r", "r", "r", "k", "k", "k"],
meas=0,
):
mp = multipanel(dims=(1, 3), figID=2, padding=(0, 0), panel_size=(3, 1))
mp.title(title)
for j in plotinds[1]:
# The filling was not adding up right; don't plot each individual mode.
for m, mode in enumerate(s["modes"]):
# yplot=dists[m][j][1,:]
# yplot*=numpy.exp(mode['local evidence'])/numpy.exp(s['global evidence'])
# if norm1: yplot/=numpy.max(dists['all'][j][1,:])
# #mp.grid[numpy.mod(j,4)].plot(dists[m][j][0,:]*mult[j]+add[j],yplot,':',color=modecolors[m],label='Mode',drawstyle='steps')
# dx=(dists[m][j][0,1]-dists[m][j][0,0])*mult[j]
# xx= numpy.ravel(zip(dists[m][j][0,:]*mult[j]+add[j], dists[m][j][0,:]*mult[j]+add[j] + dx))
# yy =numpy.ravel(zip(yplot, yplot))
# mp.grid[numpy.mod(j-n_dims,3)].fill_between(xx-dx,0,yy,color=modecolors[m],alpha=0.2)
mp.grid[numpy.mod(j - n_dims, 3)].axvline(
x=mode["mean"][j] * mult[j] + add[j], color=modecolors[m], label="Mode"
)
mp.grid[numpy.mod(j - n_dims, 3)].axvline(
x=mode["mean"][j] * mult[j] + add[j] + mode["sigma"][j],
color=modecolors[m],
label="Mode",
linestyle="--",
)
mp.grid[numpy.mod(j - n_dims, 3)].axvline(
x=mode["mean"][j] * mult[j] + add[j] - mode["sigma"][j],
color=modecolors[m],
label="Mode",
linestyle="--",
)
yplot = dists["all"][j][1, :]
if norm1:
yplot /= numpy.max(yplot)
mp.grid[numpy.mod(j - n_dims, 3)].plot(
dists["all"][j][0, :] * mult[j] + add[j], yplot, "-", color=colors[j], drawstyle="steps"
)
mp.grid[numpy.mod(j - n_dims, 3)].axvline(x=cube[j] * mult[j] + add[j], color=colors[j])
if norm1:
mp.grid[numpy.mod(j - n_dims, 3)].set_ylim(0, 1)
if j == n_dims:
ax2a = mp.grid[numpy.mod(j - n_dims, 3)].twiny()
newx = dists["all"][j][0, :] - numpy.log10(units.solLum.to("erg/s"))
ax2a.plot(newx, yplot, "-", color=colors[j], drawstyle="steps")
if j == n_dims + 2:
ax2b = mp.grid[numpy.mod(j - n_dims, 3)].twiny()
newx = dists["all"][j][0, :] + add[j] + meas["addmass"]
ax2b.plot(newx, yplot, "-", color=colors[j], drawstyle="steps")
# Add a secondary x-axis for BACD --> Mass
# log(mass) = log(BACD) + log(area) + log(mu)+log(m_H2) - log(X)
# meas['areacm'] + meas['head']['mol_weight'] + log10(2.0*units.M_p.to('kg')/units.solMass.to('kg')) - meas['head']['abundance']
# Makes sure aligned
ax2a.set_xlim(mp.grid[0].set_xlim() - numpy.log10(units.solLum.to("erg/s")))
ax2a.set_xlabel(r"log(L [L$_{sol}$])")
ax2b.set_xlim(mp.grid[2].set_xlim() + meas["addmass"])
ax2b.set_xlabel(r"log(M(H$_{2}$ [M$_{sol}$])")
mp.grid[0].set_ylabel("Probability")
[mp.grid[i - 4].set_xlabel(nicenames[i]) for i in [4, 5, 6]]
mp.fix_ticks()
plt.savefig("fig_marginalized2.png")
print "Saved fig_marginalized2.png"
if n_dims == 8:
mp = multipanel(dims=(1, 3), figID=3, padding=(0, 0), panel_size=(3, 1))
mp.title(title)
for j in plotinds[2]:
for m, mode in enumerate(s["modes"]):
yplot = dists[m][j][1, :]
yplot *= numpy.exp(mode["local evidence"]) / numpy.exp(s["global evidence"])
if norm1:
yplot /= numpy.max(dists["all"][j][1, :])
# mp.grid[numpy.mod(j,4)].plot(dists[m][j][0,:]*mult[j]+add[j],yplot,':',color=modecolors[m],label='Mode',drawstyle='steps')
dx = (dists[m][j][0, 1] - dists[m][j][0, 0]) * mult[j]
xx = numpy.ravel(zip(dists[m][j][0, :] * mult[j] + add[j], dists[m][j][0, :] * mult[j] + add[j] + dx))
yy = numpy.ravel(zip(yplot, yplot))
mp.grid[numpy.mod(j - n_dims, 3)].fill_between(xx - dx, 0, yy, color=modecolors[m], alpha=0.2)
# mp.grid[numpy.mod(j-n_dims,3)].axvline(x=mode['mean'][j]+add[j],color=modecolors[m],label='Mode')
# mp.grid[numpy.mod(j-n_dims,3)].axvline(x=mode['mean'][j]+add[j]+mode['sigma'][j],color=modecolors[m],label='Mode',linestyle='--')
# mp.grid[numpy.mod(j-n_dims,3)].axvline(x=mode['mean'][j]+add[j]-mode['sigma'][j],color=modecolors[m],label='Mode',linestyle='--')
yplot = dists["all"][j][1, :]
if norm1:
yplot /= numpy.max(yplot)
mp.grid[numpy.mod(j - n_dims, 3)].plot(
dists["all"][j][0, :] + add[j], yplot, "-", color=colors[j], drawstyle="steps"
)
mp.grid[numpy.mod(j - n_dims, 3)].axvline(x=cube[j] + add[j], color=colors[j])
mp.grid[0].set_ylabel("Probability")
[mp.grid[i - 7].set_xlabel(nicenames[i]) for i in [7, 8, 9]]
mp.fix_ticks()
plt.savefig("fig_marginalized2ratio.png")
print "Saved fig_marginalized2ratio.png"
def plotmarginal(
s,
dists,
add,
mult,
parameters,
cube,
plotinds,
n_sec,
n_dims,
nicenames,
title="",
norm1=True,
modecolors=["g", "m", "y", "c"],
colors=["b", "b", "b", "b", "r", "r", "r", "r", "b", "b", "b", "r", "r", "r", "k", "k", "k"],
dists2={},
colors2=["g", "g", "g", "g", "m", "m", "m", "m", "g", "g", "g", "m", "m", "m", "gray", "gray", "gray"],
cube2=[],
):
mp = multipanel(dims=(2, 2), figID=1, padding=(0, 0.2))
mp.title(title)
for j in plotinds[0]:
# The filling was not adding up right; don't plot each individual mode. FUTURE NOTE: normalization is messed up
# because numpy arrays are pointers, and I've modified the value of dists.
for m, mode in enumerate(s["modes"]):
# yplot=dists[m][j][1,:]
# yplot*=numpy.exp(mode['local evidence'])/numpy.exp(s['global evidence'])
# if norm1: yplot/=numpy.max(dists['all'][j][1,:])
# #mp.grid[numpy.mod(j,4)].plot(dists[m][j][0,:]*mult[j]+add[j],yplot,':',color=modecolors[m],label='Mode',drawstyle='steps')
# dx=(dists[m][j][0,1]-dists[m][j][0,0])*mult[j]
# xx= numpy.ravel(zip(dists[m][j][0,:]*mult[j]+add[j], dists[m][j][0,:]*mult[j]+add[j] + dx))
# yy =numpy.ravel(zip(yplot, yplot))
# #mp.grid[numpy.mod(j,4)].fill_between(xx-dx,0,yy,color=modecolors[m],alpha=0.2)
#
mp.grid[numpy.mod(j, 4)].axvline(x=mode["mean"][j] * mult[j] + add[j], color=modecolors[m], label="Mode")
mp.grid[numpy.mod(j, 4)].axvline(
x=mode["mean"][j] * mult[j] + add[j] + mode["sigma"][j],
color=modecolors[m],
label="Mode",
linestyle="--",
)
mp.grid[numpy.mod(j, 4)].axvline(
x=mode["mean"][j] * mult[j] + add[j] - mode["sigma"][j],
color=modecolors[m],
label="Mode",
linestyle="--",
)
##print mode['maximum'][j]+add[j],mode['sigma'][j]
yplot = dists["all"][j][1, :] # WHY DOES DISTS CHANGE SOMEWHERE HERE?
if norm1:
yplot /= numpy.max(dists["all"][j][1, :])
mp.grid[numpy.mod(j, 4)].plot(
dists["all"][j][0, :] * mult[j] + add[j], yplot, "-", color=colors[j], drawstyle="steps"
)
mp.grid[numpy.mod(j, 4)].axvline(x=cube[j] * mult[j] + add[j], color=colors[j], linestyle="-", label="4D Max")
if dists2:
yplot2 = dists2["all"][j][1, :]
if norm1:
yplot2 /= numpy.max(dists2["all"][j][1, :])
mp.grid[numpy.mod(j, 4)].plot(
dists2["all"][j][0, :] * mult[j] + add[j], yplot2, "-", color=colors2[j], drawstyle="steps"
)
mp.grid[numpy.mod(j, 4)].axvline(
x=cube2[j] * mult[j] + add[j], color=colors2[j], linestyle="-", label="4D Max, Comparison"
)
if norm1:
mp.grid[numpy.mod(j, 4)].set_ylim(0, 1)
mp.grid[0].set_ylabel("Probability")
mp.grid[2].set_ylabel("Probability")
if parameters[0] == "h2den1": # CO likelihood
mp.grid[0].set_xticks([2, 3, 4, 5, 6])
mp.grid[0].set_xticks([2.5, 3.5, 4.5, 5.5], minor=True)
mp.grid[1].set_xticks([1, 1.5, 2, 2.5, 3.0])
mp.grid[3].set_xticks([-2.0, -1.0])
mp.grid[3].set_xticks([-2.5, -1.5, -0.5], minor=True)
# [mp.grid[i].set_xlim(xrange[i]) for i in range(4)]
[mp.grid[i].set_xlabel(nicenames[i]) for i in range(4)]
else:
[mp.grid[i].set_xlabel(nicenames[i]) for i in plotinds[0]]
mp.grid[0].text(4, 1, "ln(like):")
for m, mode in enumerate(s["modes"]):
mp.grid[0].text(4, 0.9 - 0.1 * m, "%.2f" % mode["local evidence"], color=modecolors[m])
mp.fix_ticks()
plt.savefig("fig_marginalized.png")
print "Saved fig_marginalized.png"