def twinhist(X, Y, ax=None, xbins=10, ybins=10, fmt='o',
color='b', histalpha=0.5, xlabel=None, ylabel=None,
xlim=None, ylim=None, **kwargs):
""" Plot a twin histogram."""
from barak.plot import hist_xedge, hist_yedge
if ax is None:
ax = pl.gca()
ax.plot(X,Y, fmt, color=color, **kwargs)
hist_xedge(X,ax, bins=xbins,fill=True, color=color, alpha=histalpha)
hist_yedge(Y,ax, bins=ybins,fill=True, color=color, alpha=histalpha)
if xlim is not None:
ax.set_xlim(*xlim)
if ylim is not None:
ax.set_ylim(*ylim)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
return ax
def plot_posteriors(chain, P, nplot='all'):
""" Plot the posterior distributions for a series of parameter
samples. chain has shape (nsample, nparameters).
"""
if nplot == 'all':
nplot = chain.shape[-1]
#nrows, ncols = get_nrows_ncols(nplot)
#fig,axes = get_fig_axes(nrows, ncols, nplot)
fig = pl.figure(figsize=(8.4, 8.4))
fig.subplots_adjust(left=0.05, bottom=0.05, hspace=0.001, wspace=0.001)
axes = []
pl.rc('xtick', labelsize=8)
pl.rc('ytick', labelsize=8)
for i0 in xrange(nplot):
for i1 in xrange(nplot):
if i0 == i1:# or i1 < i0:
continue
ax = fig.add_subplot(nplot,nplot, i0 * nplot + i1 + 1)
y,x = chain[:,i0], chain[:,i1]
ax.plot(x,y,'r.', ms=1, mew=0)#, alpha=0.5)
#y,x = chain[:,i0][P['ijoint_sig'][1]], chain[:,i1][P['ijoint_sig'][1]]
#ax.plot(x,y,'g.', ms=1.5, mew=0)
#y,x = chain[:,i0][P['ijoint_sig'][0]], chain[:,i1][P['ijoint_sig'][0]]
#ax.plot(x,y,'r.', ms=1.5, mew=0)
ax.plot(P['ml'][i1], P['ml'][i0], 'xk', ms=8, mew=2)
ax.plot(P['ml'][i1], P['ml'][i0], 'xr', ms=6, mew=1)
puttext(0.05, 0.95, P['names'][i0], ax, fontsize=12, va='top')
puttext(0.95, 0.05, P['names'][i1], ax, fontsize=12, ha='right')
y0, y1 = np.percentile(chain[:,i0], [5, 95])
dy = y1 - y0
ax.set_ylim(y0 - dy, y1 + dy)
x0, x1 = np.percentile(chain[:,i1], [5, 95])
dx = x1 - x0
ax.set_xlim(x0 - dx, x1 + dx)
c = 'crimson'
if i0 == 0:
ax.xaxis.set_tick_params(labeltop='on')
ax.xaxis.set_tick_params(labelbottom='off')
for t in ax.get_xticklabels():
t.set_rotation(60)
elif i0 == nplot-1 or (i0 == nplot-2 and i1 == nplot-1):
hist_xedge(chain[:, i1], ax, fmt='b', bins=P['bins'][i1], loc='bottom')
ax.axvline(P['p1sig'][i1][0], ymax=0.2, color=c, lw=0.5)
ax.axvline(P['p1sig'][i1][1], ymax=0.2, color=c, lw=0.5)
ax.axvline(P['median'][i1], ymax=0.2, color=c, lw=1.5)
for t in ax.get_xticklabels():
t.set_rotation(60)
else:
ax.set_xticklabels('')
if not (i1 == 0 or (i0 == 0 and i1 == 1) or i1 == nplot-1):
ax.set_yticklabels('')
if (i0 == 0 and i1 == 1) or i1 == 0:
hist_yedge(chain[:, i0], ax, fmt='b', bins=P['bins'][i0], loc='left')
ax.axhline(P['p1sig'][i0][0], xmax=0.2, color=c, lw=0.5)
ax.axhline(P['p1sig'][i0][1], xmax=0.2, color=c, lw=0.5)
ax.axhline(P['median'][i0], xmax=0.2, color=c, lw=1.5)
if i1 == nplot - 1:
ax.yaxis.set_tick_params(labelright='on')
ax.yaxis.set_tick_params(labelleft='off')
axes.append(ax)
return fig, axes
def plot_posteriors(chain, P, nplot='all'):
""" Plot the posterior distributions for a series of parameter
samples. chain has shape (nsample, nparameters).
"""
if nplot == 'all':
nplot = chain.shape[-1]
#nrows, ncols = get_nrows_ncols(nplot)
#fig,axes = get_fig_axes(nrows, ncols, nplot)
fig = pl.figure(figsize=(8.4, 8.4))
fig.subplots_adjust(left=0.05, bottom=0.06, hspace=0.001, wspace=0.001)
axes = []
pl.rc('xtick', labelsize=8)
pl.rc('ytick', labelsize=8)
for i0 in xrange(nplot):
for i1 in xrange(nplot):
if i0 == i1: # or i1 < i0: # uncomment to keep just one triangle.
continue
ax = fig.add_subplot(nplot, nplot, i0 * nplot + i1 + 1)
y, x = chain[:, i0], chain[:, i1]
ax.plot(x, y, 'r.', ms=1, mew=0) #, alpha=0.5)
#y,x = chain[:,i0][P['ijoint_sig'][1]], chain[:,i1][P['ijoint_sig'][1]]
#ax.plot(x,y,'g.', ms=1.5, mew=0)
#y,x = chain[:,i0][P['ijoint_sig'][0]], chain[:,i1][P['ijoint_sig'][0]]
#ax.plot(x,y,'r.', ms=1.5, mew=0)
ax.plot(P['ml'][i1], P['ml'][i0], 'xk', ms=8, mew=2)
ax.plot(P['ml'][i1], P['ml'][i0], 'xr', ms=6, mew=1)
puttext(0.05, 0.95, P['names'][i0], ax, fontsize=12, va='top')
puttext(0.95, 0.05, P['names'][i1], ax, fontsize=12, ha='right')
y0, y1 = np.percentile(chain[:, i0], [5, 95])
dy = y1 - y0
ax.set_ylim(y0 - dy, y1 + dy)
x0, x1 = np.percentile(chain[:, i1], [5, 95])
dx = x1 - x0
ax.set_xlim(x0 - dx, x1 + dx)
c = 'crimson'
if i0 == 0:
ax.xaxis.set_tick_params(labeltop='on')
ax.xaxis.set_tick_params(labelbottom='off')
for t in ax.get_xticklabels():
t.set_rotation(60)
elif i0 == nplot - 1 or (i0 == nplot - 2 and i1 == nplot - 1):
hist_xedge(chain[:, i1],
ax,
fmt='b',
bins=P['bins'][i1],
loc='bottom')
ax.axvline(P['p1sig'][i1][0], ymax=0.2, color=c, lw=0.5)
ax.axvline(P['p1sig'][i1][1], ymax=0.2, color=c, lw=0.5)
ax.axvline(P['median'][i1], ymax=0.2, color=c, lw=1.5)
for t in ax.get_xticklabels():
t.set_rotation(60)
else:
ax.set_xticklabels('')
if not (i1 == 0 or (i0 == 0 and i1 == 1) or i1 == nplot - 1):
ax.set_yticklabels('')
if (i0 == 0 and i1 == 1) or i1 == 0:
hist_yedge(chain[:, i0],
ax,
fmt='b',
bins=P['bins'][i0],
loc='left')
ax.axhline(P['p1sig'][i0][0], xmax=0.2, color=c, lw=0.5)
ax.axhline(P['p1sig'][i0][1], xmax=0.2, color=c, lw=0.5)
ax.axhline(P['median'][i0], xmax=0.2, color=c, lw=1.5)
if i1 == nplot - 1:
ax.yaxis.set_tick_params(labelright='on')
ax.yaxis.set_tick_params(labelleft='off')
axes.append(ax)
return fig, axes
def plot_posteriors(chain, P, nplot="all"):
""" Plot the posterior distributions for a series of parameter
samples. chain has shape (nsample, nparameters).
"""
if nplot == "all":
nplot = chain.shape[-1]
# nrows, ncols = get_nrows_ncols(nplot)
# fig,axes = get_fig_axes(nrows, ncols, nplot)
fig = pl.figure(figsize=(8.4, 8.4))
fig.subplots_adjust(left=0.05, bottom=0.06, hspace=0.001, wspace=0.001)
axes = []
pl.rc("xtick", labelsize=8)
pl.rc("ytick", labelsize=8)
for i0 in xrange(nplot):
for i1 in xrange(nplot):
if i0 == i1: # or i1 < i0: # uncomment to keep just one triangle.
continue
ax = fig.add_subplot(nplot, nplot, i0 * nplot + i1 + 1)
y, x = chain[:, i0], chain[:, i1]
ax.plot(x, y, "r.", ms=1, mew=0) # , alpha=0.5)
# y,x = chain[:,i0][P['ijoint_sig'][1]], chain[:,i1][P['ijoint_sig'][1]]
# ax.plot(x,y,'g.', ms=1.5, mew=0)
# y,x = chain[:,i0][P['ijoint_sig'][0]], chain[:,i1][P['ijoint_sig'][0]]
# ax.plot(x,y,'r.', ms=1.5, mew=0)
ax.plot(P["ml"][i1], P["ml"][i0], "xk", ms=8, mew=2)
ax.plot(P["ml"][i1], P["ml"][i0], "xr", ms=6, mew=1)
puttext(0.05, 0.95, P["names"][i0], ax, fontsize=12, va="top")
puttext(0.95, 0.05, P["names"][i1], ax, fontsize=12, ha="right")
y0, y1 = np.percentile(chain[:, i0], [5, 95])
dy = y1 - y0
ax.set_ylim(y0 - dy, y1 + dy)
x0, x1 = np.percentile(chain[:, i1], [5, 95])
dx = x1 - x0
ax.set_xlim(x0 - dx, x1 + dx)
c = "crimson"
if i0 == 0:
ax.xaxis.set_tick_params(labeltop="on")
ax.xaxis.set_tick_params(labelbottom="off")
for t in ax.get_xticklabels():
t.set_rotation(60)
elif i0 == nplot - 1 or (i0 == nplot - 2 and i1 == nplot - 1):
hist_xedge(chain[:, i1], ax, fmt="b", bins=P["bins"][i1], loc="bottom")
ax.axvline(P["p1sig"][i1][0], ymax=0.2, color=c, lw=0.5)
ax.axvline(P["p1sig"][i1][1], ymax=0.2, color=c, lw=0.5)
ax.axvline(P["median"][i1], ymax=0.2, color=c, lw=1.5)
for t in ax.get_xticklabels():
t.set_rotation(60)
else:
ax.set_xticklabels("")
if not (i1 == 0 or (i0 == 0 and i1 == 1) or i1 == nplot - 1):
ax.set_yticklabels("")
if (i0 == 0 and i1 == 1) or i1 == 0:
hist_yedge(chain[:, i0], ax, fmt="b", bins=P["bins"][i0], loc="left")
ax.axhline(P["p1sig"][i0][0], xmax=0.2, color=c, lw=0.5)
ax.axhline(P["p1sig"][i0][1], xmax=0.2, color=c, lw=0.5)
ax.axhline(P["median"][i0], xmax=0.2, color=c, lw=1.5)
if i1 == nplot - 1:
ax.yaxis.set_tick_params(labelright="on")
ax.yaxis.set_tick_params(labelleft="off")
axes.append(ax)
return fig, axes
def triplot(names, vals, sigvals, fig, indirect={}, labels=None, fontsize=14):
from barak.plot import hist_yedge, hist_xedge, puttext
npar = len(names)
bins = {}
for n in names:
x0, x1 = vals[n].min(), vals[n].max()
dx = x1 - x0
lo = x0 - 0.1*dx
hi = x1 + 0.1*dx
bins[n] = np.linspace(lo, hi, 20)
axes = {}
for i0,n0 in enumerate(names):
for i1,n1 in enumerate(names):
if i0 == i1:# or i1 < i0: # uncomment to keep just one triangle.
continue
ax = fig.add_subplot(npar,npar, i0 * npar + i1 + 1)
ax.locator_params(tight=True, nbins=8)
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
axes[(n0 + ' ' + n1)] = ax
y,x = vals[n0], vals[n1]
if USE_HEXBIN:
ax.hexbin(x,y,cmap=CM, gridsize=40,linewidths=0.1)
else:
ax.plot(x,y,'r.', ms=0.5, mew=0)#, alpha=0.5)
color = 'k' if n0 not in indirect else 'g'
text = labels[n0] if labels is not None else n0
puttext(0.05, 0.95, text, ax, color=color ,fontsize=fontsize, va='top')
color = 'k' if n1 not in indirect else 'g'
text = labels[n1] if labels is not None else n1
puttext(0.95, 0.08, text, ax, color=color ,fontsize=fontsize, ha='right')
# set limits
y0, y1 = np.percentile(vals[n0], [5, 95])
dy = y1 - y0
ax.set_ylim(y0 - dy, y1 + dy)
x0, x1 = np.percentile(vals[n1], [5, 95])
dx = x1 - x0
ax.set_xlim(x0 - dx, x1 + dx)
c = 'k'
if i0 == 0:
ax.xaxis.set_tick_params(labeltop='on')
ax.xaxis.set_tick_params(labelbottom='off')
for t in ax.get_xticklabels():
t.set_rotation(60)
elif i0 == npar-1 or (i0 == npar-2 and i1 == npar-1):
hist_xedge(vals[n1], ax, color='forestgreen',
fill=dict(color='forestgreen',alpha=0.3),
bins=bins[n1], loc='bottom')
ax.axvline(sigvals[n1][0], ymax=0.2, color=c, lw=0.5)
ax.axvline(sigvals[n1][1], ymax=0.2, color=c, lw=0.5)
cen = sum(sigvals[n1]) / 2.
ax.axvline(cen, ymax=0.2, color=c, lw=1.5)
for t in ax.get_xticklabels():
t.set_rotation(60)
else:
ax.set_xticklabels('')
if not (i1 == 0 or (i0 == 0 and i1 == 1) or i1 == npar-1):
ax.set_yticklabels('')
if (i0 == 0 and i1 == 1) or i1 == 0:
hist_yedge(vals[n0], ax, color='forestgreen',
fill=dict(color='forestgreen',alpha=0.3),
bins=bins[n0], loc='left')
ax.axhline(sigvals[n0][0], xmax=0.2, color=c, lw=0.5)
ax.axhline(sigvals[n0][1], xmax=0.2, color=c, lw=0.5)
cen = sum(sigvals[n0]) / 2.
ax.axhline(cen, xmax=0.2, color=c, lw=1.5)
if i1 == npar - 1:
ax.yaxis.set_tick_params(labelright='on')
ax.yaxis.set_tick_params(labelleft='off')
#ax.minorticks_on()
return axes