def histogram(data, name, nbins=None, datarange=(None, None), format='png', suffix='', path='./', rows=1, columns=1, num=1, last=True, fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}, verbose=1):
# Internal histogram specification for handling nested arrays
try:
# Stand-alone plot or subplot?
standalone = rows==1 and columns==1 and num==1
if standalone:
if verbose>0:
print 'Generating histogram of', name
figure()
subplot(rows, columns, num)
#Specify number of bins (10 as default)
uniquevals = len(unique(data))
nbins = nbins or uniquevals*(uniquevals<=25) or int(4 + 1.5*log(len(data)))
# Generate histogram
hist(data.tolist(), nbins, histtype='stepfilled')
xlim(datarange)
# Plot options
title('\n\n %s hist'%name, x=0., y=1., ha='left', va='top', fontsize='medium')
ylabel("Frequency", fontsize='x-small')
# Plot vertical lines for median and 95% HPD interval
quant = calc_quantiles(data)
axvline(x=quant[50], linewidth=2, color='black')
for q in hpd(data, 0.05):
axvline(x=q, linewidth=2, color='grey', linestyle='dotted')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[rows])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[rows])
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to file
savefig("%s%s%s.%s" % (path, name, suffix, format))
#close()
except OverflowError:
print '... cannot generate histogram'
ax.acorr(mcmc_trace[-1][ck],
color='crimson',
detrend=ml.detrend_mean,
linestyle='-',
linewidth=1.5,
maxlags=acorr_maxlags)
ax.set_xlim(-acorr_maxlags, acorr_maxlags)
ax.set_ylim(-0.1, 1.1)
ax.set_ylabel(C_str[ck], rotation='horizontal')
if (i == 0):
ax.set_title("Autocorrelation (detrended)")
ax = AxesArr[i, 2]
ax.grid(color='lightgrey', linestyle=':')
# Calculate the median and 95% Highest Probability Density (HPD) or minimum width Bayesian Confidence (BCI) interval
hist_quant = calc_quantiles(mcmc_trace[-1][ck])
hist_hpd = calc_hpd(mcmc_trace[-1][ck], hist_hpd_alpha)
(hist_n, hist_bins, hist_patches) = ax.hist(mcmc_trace[-1][ck],
bins=hist_num_bins,
color='steelblue',
histtype='stepfilled',
linewidth=0.0,
normed=True,
zorder=2)
ax.set_ylim(0.0, max(hist_n) * 1.1)
ax.axvspan(hist_hpd[0],
hist_hpd[1],
alpha=0.25,
facecolor='darkslategray',
linewidth=1.5)
ax.axvline(hist_quant[50],
def histogram(data,
name,
nbins=None,
datarange=(None, None),
format='png',
suffix='',
path='./',
rows=1,
columns=1,
num=1,
last=True,
fontmap={
1: 10,
2: 8,
3: 6,
4: 5,
5: 4
},
verbose=1):
# Internal histogram specification for handling nested arrays
try:
# Stand-alone plot or subplot?
standalone = rows == 1 and columns == 1 and num == 1
if standalone:
if verbose > 0:
print 'Generating histogram of', name
figure()
subplot(rows, columns, num)
#Specify number of bins (10 as default)
uniquevals = len(unique(data))
nbins = nbins or uniquevals * (uniquevals <= 25) or int(4 + 1.5 *
log(len(data)))
# Generate histogram
hist(data.tolist(), nbins, histtype='stepfilled')
xlim(datarange)
# Plot options
title('\n\n %s hist' % name,
x=0.,
y=1.,
ha='left',
va='top',
fontsize='medium')
ylabel("Frequency", fontsize='x-small')
# Plot vertical lines for median and 95% HPD interval
quant = calc_quantiles(data)
axvline(x=quant[50], linewidth=2, color='black')
for q in hpd(data, 0.05):
axvline(x=q, linewidth=2, color='grey', linestyle='dotted')
# Smaller tick labels
tlabels = gca().get_xticklabels()
setp(tlabels, 'fontsize', fontmap[rows])
tlabels = gca().get_yticklabels()
setp(tlabels, 'fontsize', fontmap[rows])
if standalone:
if not os.path.exists(path):
os.mkdir(path)
if not path.endswith('/'):
path += '/'
# Save to file
savefig("%s%s%s.%s" % (path, name, suffix, format))
#close()
except OverflowError:
print '... cannot generate histogram'
def summary_plot(pymc_obj,
name='model',
format='png',
suffix='-summary',
path='./',
alpha=0.05,
quartiles=True,
rhat=True,
main=None,
chain_spacing=0.05,
vline_pos=0):
"""
Model summary plot
:Arguments:
pymc_obj: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name (optional): string
The name of the object.
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
alpha (optional): float
Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
rhat (optional): bool
Flag for plotting Gelman-Rubin statistics. Requires 2 or more
chains (defaults to True).
main (optional): string
Title for main plot. Passing False results in titles being
suppressed; passing False (default) results in default titles.
chain_spacing (optional): float
Plot spacing between chains (defaults to 0.05).
vline_pos (optional): numeric
Location of vertical reference line (defaults to 0).
"""
if not gridspec:
print '\nYour installation of matplotlib is not recent enough to support summary_plot; this function is disabled until matplotlib is updated.'
return
# Quantiles to be calculated
quantiles = [100 * alpha / 2, 50, 100 * (1 - alpha / 2)]
if quartiles:
quantiles = [100 * alpha / 2, 25, 50, 75, 100 * (1 - alpha / 2)]
# Range for x-axis
plotrange = None
# Number of chains
chains = None
# Gridspec
gs = None
# Subplots
interval_plot = None
rhat_plot = None
try:
# First try Model type
vars = pymc_obj._variables_to_tally
except AttributeError:
try:
# Try a database object
vars = pymc_obj._traces
except AttributeError:
# Assume an iterable
vars = pymc_obj
# Empty list for y-axis labels
labels = []
# Counter for current variable
var = 1
# Make sure there is something to print
if all([v._plot == False for v in vars]):
print 'No variables to plot'
return
for variable in vars:
# If plot flag is off, do not print
if variable._plot == False:
continue
# Extract name
varname = variable.__name__
# Retrieve trace(s)
i = 0
traces = []
while True:
try:
#traces.append(pymc_obj.trace(varname, chain=i)[:])
traces.append(variable.trace(chain=i))
i += 1
except (KeyError, IndexError):
break
chains = len(traces)
if gs is None:
# Initialize plot
if rhat and chains > 1:
gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
else:
gs = gridspec.GridSpec(1, 1)
# Subplot for confidence intervals
interval_plot = subplot(gs[0])
# Get quantiles
data = [calc_quantiles(d, quantiles) for d in traces]
data = [[d[q] for q in quantiles] for d in data]
# Ensure x-axis contains range of current interval
if plotrange:
plotrange = [
min(plotrange[0], nmin(data)),
max(plotrange[1], nmax(data))
]
else:
plotrange = [nmin(data), nmax(data)]
try:
# First try missing-value stochastic
value = variable.get_stoch_value()
except AttributeError:
# All other variable types
value = variable.value
# Number of elements in current variable
k = size(value)
# Append variable name(s) to list
if k > 1:
names = var_str(varname, shape(value))
labels += names
else:
labels.append(varname)
#labels.append('\n'.join(varname.split('_')))
# Add spacing for each chain, if more than one
e = [0] + [(chain_spacing * ((i + 2) / 2)) * (-1)**i
for i in range(chains - 1)]
# Loop over chains
for j, quants in enumerate(data):
# Deal with multivariate nodes
if k > 1:
for i, q in enumerate(transpose(quants)):
# Y coordinate with jitter
y = -(var + i) + e[j]
if quartiles:
# Plot median
pyplot(q[2], y, 'bo', markersize=4)
# Plot quartile interval
errorbar(x=(q[1], q[3]),
y=(y, y),
linewidth=2,
color="blue")
else:
# Plot median
pyplot(q[1], y, 'bo', markersize=4)
# Plot outer interval
errorbar(x=(q[0], q[-1]),
y=(y, y),
linewidth=1,
color="blue")
else:
# Y coordinate with jitter
y = -var + e[j]
if quartiles:
# Plot median
pyplot(quants[2], y, 'bo', markersize=4)
# Plot quartile interval
errorbar(x=(quants[1], quants[3]),
y=(y, y),
linewidth=2,
color="blue")
else:
# Plot median
pyplot(quants[1], y, 'bo', markersize=4)
# Plot outer interval
errorbar(x=(quants[0], quants[-1]),
y=(y, y),
linewidth=1,
color="blue")
# Increment index
var += k
# Update margins
left_margin = max([len(x) for x in labels]) * 0.015
gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05)
# Define range of y-axis
ylim(-var + 0.5, -0.5)
datarange = plotrange[1] - plotrange[0]
xlim(plotrange[0] - 0.05 * datarange, plotrange[1] + 0.05 * datarange)
# Add variable labels
ylabels = yticks([-(l + 1) for l in range(len(labels))], labels)
# Add title
if main is not False:
plot_title = main or str(int(
(1 - alpha) * 100)) + "% Credible Intervals"
title(plot_title)
# Remove ticklines on y-axes
for ticks in interval_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in interval_plot.spines.iteritems():
if loc in ['bottom', 'top']:
pass
#spine.set_position(('outward',10)) # outward by 10 points
elif loc in ['left', 'right']:
spine.set_color('none') # don't draw spine
# Reference line
axvline(vline_pos, color='k', linestyle='--')
# Genenerate Gelman-Rubin plot
if rhat and chains > 1:
from diagnostics import gelman_rubin
# If there are multiple chains, calculate R-hat
rhat_plot = subplot(gs[1])
if main is not False:
title("R-hat")
# Set x range
xlim(0.9, 2.1)
# X axis labels
xticks((1.0, 1.5, 2.0), ("1", "1.5", "2+"))
yticks([-(l + 1) for l in range(len(labels))], "")
# Calculate diagnostic
try:
R = gelman_rubin(pymc_obj)
except ValueError:
R = {}
for variable in vars:
R[variable.__name__] = gelman_rubin(variable)
i = 1
for variable in vars:
if variable._plot == False:
continue
# Extract name
varname = variable.__name__
try:
value = variable.get_stoch_value()
except AttributeError:
value = variable.value
k = size(value)
if k > 1:
pyplot([min(r, 2) for r in R[varname]],
[-(j + i) for j in range(k)],
'bo',
markersize=4)
else:
pyplot(min(R[varname], 2), -i, 'bo', markersize=4)
i += k
# Define range of y-axis
ylim(-i + 0.5, -0.5)
# Remove ticklines on y-axes
for ticks in rhat_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in rhat_plot.spines.iteritems():
if loc in ['bottom', 'top']:
pass
#spine.set_position(('outward',10)) # outward by 10 points
elif loc in ['left', 'right']:
spine.set_color('none') # don't draw spine
savefig("%s%s%s.%s" % (path, name, suffix, format))
def summary_plot(pymc_obj, name='model', format='png', suffix='-summary', path='./', alpha=0.05, quartiles=True, rhat=True, main=None, chain_spacing=0.05, vline_pos=0):
"""
Model summary plot
:Arguments:
pymc_obj: PyMC object, trace or array
A trace from an MCMC sample or a PyMC object with one or more traces.
name (optional): string
The name of the object.
format (optional): string
Graphic output format (defaults to png).
suffix (optional): string
Filename suffix.
path (optional): string
Specifies location for saving plots (defaults to local directory).
alpha (optional): float
Alpha value for (1-alpha)*100% credible intervals (defaults to 0.05).
rhat (optional): bool
Flag for plotting Gelman-Rubin statistics. Requires 2 or more
chains (defaults to True).
main (optional): string
Title for main plot. Passing False results in titles being
suppressed; passing False (default) results in default titles.
chain_spacing (optional): float
Plot spacing between chains (defaults to 0.05).
vline_pos (optional): numeric
Location of vertical reference line (defaults to 0).
"""
if not gridspec:
print '\nYour installation of matplotlib is not recent enough to support summary_plot; this function is disabled until matplotlib is updated.'
return
# Quantiles to be calculated
quantiles = [100*alpha/2, 50, 100*(1-alpha/2)]
if quartiles:
quantiles = [100*alpha/2, 25, 50, 75, 100*(1-alpha/2)]
# Range for x-axis
plotrange = None
# Number of chains
chains = None
# Gridspec
gs = None
# Subplots
interval_plot = None
rhat_plot = None
try:
# First try Model type
vars = pymc_obj._variables_to_tally
except AttributeError:
try:
# Try a database object
vars = pymc_obj._traces
except AttributeError:
# Assume an iterable
vars = pymc_obj
# Empty list for y-axis labels
labels = []
# Counter for current variable
var = 1
# Make sure there is something to print
if all([v._plot==False for v in vars]):
print 'No variables to plot'
return
for variable in vars:
# If plot flag is off, do not print
if variable._plot==False:
continue
# Extract name
varname = variable.__name__
# Retrieve trace(s)
i = 0
traces = []
while True:
try:
#traces.append(pymc_obj.trace(varname, chain=i)[:])
traces.append(variable.trace(chain=i))
i+=1
except (KeyError, IndexError):
break
chains = len(traces)
if gs is None:
# Initialize plot
if rhat and chains>1:
gs = gridspec.GridSpec(1, 2, width_ratios=[3,1])
else:
gs = gridspec.GridSpec(1, 1)
# Subplot for confidence intervals
interval_plot = subplot(gs[0])
# Get quantiles
data = [calc_quantiles(d, quantiles) for d in traces]
data = [[d[q] for q in quantiles] for d in data]
# Ensure x-axis contains range of current interval
if plotrange:
plotrange = [min(plotrange[0], nmin(data)), max(plotrange[1], nmax(data))]
else:
plotrange = [nmin(data), nmax(data)]
try:
# First try missing-value stochastic
value = variable.get_stoch_value()
except AttributeError:
# All other variable types
value = variable.value
# Number of elements in current variable
k = size(value)
# Append variable name(s) to list
if k>1:
names = var_str(varname, shape(value))
labels += names
else:
labels.append(varname)
#labels.append('\n'.join(varname.split('_')))
# Add spacing for each chain, if more than one
e = [0] + [(chain_spacing * ((i+2)/2))*(-1)**i for i in range(chains-1)]
# Loop over chains
for j,quants in enumerate(data):
# Deal with multivariate nodes
if k>1:
for i,q in enumerate(transpose(quants)):
# Y coordinate with jitter
y = -(var+i) + e[j]
if quartiles:
# Plot median
pyplot(q[2], y, 'bo', markersize=4)
# Plot quartile interval
errorbar(x=(q[1],q[3]), y=(y,y), linewidth=2, color="blue")
else:
# Plot median
pyplot(q[1], y, 'bo', markersize=4)
# Plot outer interval
errorbar(x=(q[0],q[-1]), y=(y,y), linewidth=1, color="blue")
else:
# Y coordinate with jitter
y = -var + e[j]
if quartiles:
# Plot median
pyplot(quants[2], y, 'bo', markersize=4)
# Plot quartile interval
errorbar(x=(quants[1],quants[3]), y=(y,y), linewidth=2, color="blue")
else:
# Plot median
pyplot(quants[1], y, 'bo', markersize=4)
# Plot outer interval
errorbar(x=(quants[0],quants[-1]), y=(y,y), linewidth=1, color="blue")
# Increment index
var += k
# Update margins
left_margin = max([len(x) for x in labels])*0.015
gs.update(left=left_margin, right=0.95, top=0.9, bottom=0.05)
# Define range of y-axis
ylim(-var+0.5, -0.5)
datarange = plotrange[1] - plotrange[0]
xlim(plotrange[0] - 0.05*datarange, plotrange[1] + 0.05*datarange)
# Add variable labels
ylabels = yticks([-(l+1) for l in range(len(labels))], labels)
# Add title
if main is not False:
plot_title = main or str(int((1-alpha)*100)) + "% Credible Intervals"
title(plot_title)
# Remove ticklines on y-axes
for ticks in interval_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in interval_plot.spines.iteritems():
if loc in ['bottom','top']:
pass
#spine.set_position(('outward',10)) # outward by 10 points
elif loc in ['left','right']:
spine.set_color('none') # don't draw spine
# Reference line
axvline(vline_pos, color='k', linestyle='--')
# Genenerate Gelman-Rubin plot
if rhat and chains>1:
from diagnostics import gelman_rubin
# If there are multiple chains, calculate R-hat
rhat_plot = subplot(gs[1])
if main is not False:
title("R-hat")
# Set x range
xlim(0.9,2.1)
# X axis labels
xticks((1.0,1.5,2.0), ("1", "1.5", "2+"))
yticks([-(l+1) for l in range(len(labels))], "")
# Calculate diagnostic
try:
R = gelman_rubin(pymc_obj)
except ValueError:
R = {}
for variable in vars:
R[variable.__name__] = gelman_rubin(variable)
i = 1
for variable in vars:
if variable._plot==False:
continue
# Extract name
varname = variable.__name__
try:
value = variable.get_stoch_value()
except AttributeError:
value = variable.value
k = size(value)
if k>1:
pyplot([min(r, 2) for r in R[varname]], [-(j+i) for j in range(k)], 'bo', markersize=4)
else:
pyplot(min(R[varname], 2), -i, 'bo', markersize=4)
i += k
# Define range of y-axis
ylim(-i+0.5, -0.5)
# Remove ticklines on y-axes
for ticks in rhat_plot.yaxis.get_major_ticks():
ticks.tick1On = False
ticks.tick2On = False
for loc, spine in rhat_plot.spines.iteritems():
if loc in ['bottom','top']:
pass
#spine.set_position(('outward',10)) # outward by 10 points
elif loc in ['left','right']:
spine.set_color('none') # don't draw spine
savefig("%s%s%s.%s" % (path, name, suffix, format))
