def create_marginalized_hist(ax, param, samples, percentiles=None, label=None,
color='navy', filled=False, linecolor='b', title=True):
"""Plots a 1D marginalized histogram of the given param from the given
samples.
Parameters
----------
ax : pyplot.Axes
The axes on which to draw the plot.
param : string
The parameter to plot.
samples : FieldArray
A field array of the samples to plot.
percentiles : {None, float or array}
What percentiles to draw lines at. If None, will draw lines at
`[5, 50, 95]` (i.e., the bounds on the upper 90th percentile and the
median).
label : {None, string}
A label for the parameter. If None, will use `param`.
color : {'navy', string}
What color to make the histogram; default is navy.
filled : {False, bool}
Whether to fill the histogram; default is False.
linecolor : {'b', string}
What color to use for the percentile lines.
title : {True, bool}
Add a title with the median value +/- uncertainty, with the
max(min) `percentile` used for the +(-) uncertainty.
"""
if label is None:
label = param
if filled:
htype = 'stepfilled'
else:
htype = 'step'
values = samples[param]
ax.hist(values, bins=50, color=color, histtype=htype)
if percentiles is None:
percentiles = [5., 50., 95.]
values = numpy.percentile(values, percentiles)
for val in values:
ax.axvline(x=val, ls='dashed', color=linecolor)
if title:
values_med = numpy.median(values)
values_min = values.min()
values_max = values.max()
negerror = values_med - values_min
poserror = values_max - values_med
fmt = '$' + str_utils.format_value(values_med, negerror,
plus_error=poserror, ndecs=2) + '$'
ax.set_title('{} = {}'.format(label, fmt))
# Remove y-ticks
ax.set_yticks([])
def create_marginalized_hist(ax, values, label, percentiles=None,
color='k', fillcolor='gray', linecolor='navy', title=True,
rotated=False, plot_min=None, plot_max=None):
"""Plots a 1D marginalized histogram of the given param from the given
samples.
Parameters
----------
ax : pyplot.Axes
The axes on which to draw the plot.
values : array
The parameter values to plot.
label : str
A label to use for the title.
percentiles : {None, float or array}
What percentiles to draw lines at. If None, will draw lines at
`[5, 50, 95]` (i.e., the bounds on the upper 90th percentile and the
median).
color : {'k', string}
What color to make the histogram; default is black.
fillcolor : {'gray', string, or None}
What color to fill the histogram with. Set to None to not fill the
histogram. Default is 'gray'.
linecolor : {'navy', string}
What color to use for the percentile lines. Default is 'navy'.
title : {True, bool}
Add a title with the median value +/- uncertainty, with the
max(min) `percentile` used for the +(-) uncertainty.
rotated : {False, bool}
Plot the histogram on the y-axis instead of the x. Default is False.
plot_min : {None, float}
The minimum value to plot. If None, will default to whatever `pyplot`
creates.
plot_max : {None, float}
The maximum value to plot. If None, will default to whatever `pyplot`
creates.
scalefac : {1., float}
Factor to scale the default font sizes by. Default is 1 (no scaling).
"""
if fillcolor is None:
htype = 'step'
else:
htype = 'stepfilled'
if rotated:
orientation = 'horizontal'
else:
orientation = 'vertical'
ax.hist(values, bins=50, histtype=htype, orientation=orientation,
facecolor=fillcolor, edgecolor=color, lw=2)
if percentiles is None:
percentiles = [5., 50., 95.]
values = numpy.percentile(values, percentiles)
for val in values:
if rotated:
ax.axhline(y=val, ls='dashed', color=linecolor, lw=2)
else:
ax.axvline(x=val, ls='dashed', color=linecolor, lw=2)
if title:
values_med = numpy.median(values)
values_min = values.min()
values_max = values.max()
negerror = values_med - values_min
poserror = values_max - values_med
fmt = '$' + str_utils.format_value(values_med, negerror,
plus_error=poserror, ndecs=2) + '$'
if rotated:
ax.yaxis.set_label_position("right")
ax.set_ylabel('{} = {}'.format(label, fmt), rotation=-90,
labelpad=26, fontsize=18)
# Remove x-ticks
ax.set_xticks([])
# turn off x-labels
ax.set_xlabel('')
# set limits
ymin, ymax = ax.get_ylim()
if plot_min is not None:
ymin = plot_min
if plot_max is not None:
ymax = plot_max
ax.set_ylim(ymin, ymax)
else:
ax.set_title('{} = {}'.format(label, fmt), fontsize=18, y=1.04)
# Remove y-ticks
ax.set_yticks([])
# turn off y-label
ax.set_ylabel('')
# set limits
xmin, xmax = ax.get_xlim()
if plot_min is not None:
xmin = plot_min
if plot_max is not None:
xmax = plot_max
ax.set_xlim(xmin, xmax)
def create_marginalized_hist(ax,
values,
label,
percentiles=None,
color='k',
fillcolor='gray',
linecolor='navy',
title=True,
expected_value=None,
expected_color='red',
rotated=False,
plot_min=None,
plot_max=None):
"""Plots a 1D marginalized histogram of the given param from the given
samples.
Parameters
----------
ax : pyplot.Axes
The axes on which to draw the plot.
values : array
The parameter values to plot.
label : str
A label to use for the title.
percentiles : {None, float or array}
What percentiles to draw lines at. If None, will draw lines at
`[5, 50, 95]` (i.e., the bounds on the upper 90th percentile and the
median).
color : {'k', string}
What color to make the histogram; default is black.
fillcolor : {'gray', string, or None}
What color to fill the histogram with. Set to None to not fill the
histogram. Default is 'gray'.
linecolor : {'navy', string}
What color to use for the percentile lines. Default is 'navy'.
title : {True, bool}
Add a title with the median value +/- uncertainty, with the
max(min) `percentile` used for the +(-) uncertainty.
rotated : {False, bool}
Plot the histogram on the y-axis instead of the x. Default is False.
plot_min : {None, float}
The minimum value to plot. If None, will default to whatever `pyplot`
creates.
plot_max : {None, float}
The maximum value to plot. If None, will default to whatever `pyplot`
creates.
scalefac : {1., float}
Factor to scale the default font sizes by. Default is 1 (no scaling).
"""
if fillcolor is None:
htype = 'step'
else:
htype = 'stepfilled'
if rotated:
orientation = 'horizontal'
else:
orientation = 'vertical'
ax.hist(values,
bins=50,
histtype=htype,
orientation=orientation,
facecolor=fillcolor,
edgecolor=color,
lw=2,
density=True)
if percentiles is None:
percentiles = [5., 50., 95.]
values = numpy.percentile(values, percentiles)
for val in values:
if rotated:
ax.axhline(y=val, ls='dashed', color=linecolor, lw=2, zorder=3)
else:
ax.axvline(x=val, ls='dashed', color=linecolor, lw=2, zorder=3)
# plot expected
if expected_value is not None:
if rotated:
ax.axhline(expected_value, color=expected_color, lw=1.5, zorder=2)
else:
ax.axvline(expected_value, color=expected_color, lw=1.5, zorder=2)
if title:
values_med = numpy.median(values)
values_min = values.min()
values_max = values.max()
negerror = values_med - values_min
poserror = values_max - values_med
fmt = '${0}$'.format(
str_utils.format_value(values_med,
negerror,
plus_error=poserror,
ndecs=2))
if rotated:
ax.yaxis.set_label_position("right")
# sets colored title for marginal histogram
set_marginal_histogram_title(ax,
fmt,
color,
label=label,
rotated=rotated)
# Remove x-ticks
ax.set_xticks([])
# turn off x-labels
ax.set_xlabel('')
# set limits
ymin, ymax = ax.get_ylim()
if plot_min is not None:
ymin = plot_min
if plot_max is not None:
ymax = plot_max
ax.set_ylim(ymin, ymax)
else:
# sets colored title for marginal histogram
set_marginal_histogram_title(ax, fmt, color, label=label)
# Remove y-ticks
ax.set_yticks([])
# turn off y-label
ax.set_ylabel('')
# set limits
xmin, xmax = ax.get_xlim()
if plot_min is not None:
xmin = plot_min
if plot_max is not None:
xmax = plot_max
ax.set_xlim(xmin, xmax)
def create_marginalized_hist(ax, values, label, percentiles=None,
color='k', fillcolor='gray', linecolor='navy',
linestyle='-',
title=True, expected_value=None,
expected_color='red', rotated=False,
plot_min=None, plot_max=None):
"""Plots a 1D marginalized histogram of the given param from the given
samples.
Parameters
----------
ax : pyplot.Axes
The axes on which to draw the plot.
values : array
The parameter values to plot.
label : str
A label to use for the title.
percentiles : {None, float or array}
What percentiles to draw lines at. If None, will draw lines at
`[5, 50, 95]` (i.e., the bounds on the upper 90th percentile and the
median).
color : {'k', string}
What color to make the histogram; default is black.
fillcolor : {'gray', string, or None}
What color to fill the histogram with. Set to None to not fill the
histogram. Default is 'gray'.
linestyle : str, optional
What line style to use for the histogram. Default is '-'.
linecolor : {'navy', string}
What color to use for the percentile lines. Default is 'navy'.
title : bool, optional
Add a title with a estimated value +/- uncertainty. The estimated value
is the pecentile halfway between the max/min of ``percentiles``, while
the uncertainty is given by the max/min of the ``percentiles``. If no
percentiles are specified, defaults to quoting the median +/- 95/5
percentiles.
rotated : {False, bool}
Plot the histogram on the y-axis instead of the x. Default is False.
plot_min : {None, float}
The minimum value to plot. If None, will default to whatever `pyplot`
creates.
plot_max : {None, float}
The maximum value to plot. If None, will default to whatever `pyplot`
creates.
scalefac : {1., float}
Factor to scale the default font sizes by. Default is 1 (no scaling).
"""
if fillcolor is None:
htype = 'step'
else:
htype = 'stepfilled'
if rotated:
orientation = 'horizontal'
else:
orientation = 'vertical'
ax.hist(values, bins=50, histtype=htype, orientation=orientation,
facecolor=fillcolor, edgecolor=color, ls=linestyle, lw=2,
density=True)
if percentiles is None:
percentiles = [5., 50., 95.]
if len(percentiles) > 0:
plotp = numpy.percentile(values, percentiles)
else:
plotp = []
for val in plotp:
if rotated:
ax.axhline(y=val, ls='dashed', color=linecolor, lw=2, zorder=3)
else:
ax.axvline(x=val, ls='dashed', color=linecolor, lw=2, zorder=3)
# plot expected
if expected_value is not None:
if rotated:
ax.axhline(expected_value, color=expected_color, lw=1.5, zorder=2)
else:
ax.axvline(expected_value, color=expected_color, lw=1.5, zorder=2)
if title:
if len(percentiles) > 0:
minp = min(percentiles)
maxp = max(percentiles)
medp = (maxp + minp) / 2.
else:
minp = 5
medp = 50
maxp = 95
values_min = numpy.percentile(values, minp)
values_med = numpy.percentile(values, medp)
values_max = numpy.percentile(values, maxp)
negerror = values_med - values_min
poserror = values_max - values_med
fmt = '${0}$'.format(str_utils.format_value(
values_med, negerror, plus_error=poserror))
if rotated:
ax.yaxis.set_label_position("right")
# sets colored title for marginal histogram
set_marginal_histogram_title(ax, fmt, color,
label=label, rotated=rotated)
else:
# sets colored title for marginal histogram
set_marginal_histogram_title(ax, fmt, color, label=label)
# remove ticks and set limits
if rotated:
# Remove x-ticks
ax.set_xticks([])
# turn off x-labels
ax.set_xlabel('')
# set limits
ymin, ymax = ax.get_ylim()
if plot_min is not None:
ymin = plot_min
if plot_max is not None:
ymax = plot_max
ax.set_ylim(ymin, ymax)
else:
# Remove y-ticks
ax.set_yticks([])
# turn off y-label
ax.set_ylabel('')
# set limits
xmin, xmax = ax.get_xlim()
if plot_min is not None:
xmin = plot_min
if plot_max is not None:
xmax = plot_max
ax.set_xlim(xmin, xmax)
def create_marginalized_hist(ax, values, label, percentiles=None,
color='k', fillcolor='gray', linecolor='navy',
linestyle='-',
title=True, expected_value=None,
expected_color='red', rotated=False,
plot_min=None, plot_max=None):
"""Plots a 1D marginalized histogram of the given param from the given
samples.
Parameters
----------
ax : pyplot.Axes
The axes on which to draw the plot.
values : array
The parameter values to plot.
label : str
A label to use for the title.
percentiles : {None, float or array}
What percentiles to draw lines at. If None, will draw lines at
`[5, 50, 95]` (i.e., the bounds on the upper 90th percentile and the
median).
color : {'k', string}
What color to make the histogram; default is black.
fillcolor : {'gray', string, or None}
What color to fill the histogram with. Set to None to not fill the
histogram. Default is 'gray'.
linestyle : str, optional
What line style to use for the histogram. Default is '-'.
linecolor : {'navy', string}
What color to use for the percentile lines. Default is 'navy'.
title : bool, optional
Add a title with a estimated value +/- uncertainty. The estimated value
is the pecentile halfway between the max/min of ``percentiles``, while
the uncertainty is given by the max/min of the ``percentiles``. If no
percentiles are specified, defaults to quoting the median +/- 95/5
percentiles.
rotated : {False, bool}
Plot the histogram on the y-axis instead of the x. Default is False.
plot_min : {None, float}
The minimum value to plot. If None, will default to whatever `pyplot`
creates.
plot_max : {None, float}
The maximum value to plot. If None, will default to whatever `pyplot`
creates.
scalefac : {1., float}
Factor to scale the default font sizes by. Default is 1 (no scaling).
"""
if fillcolor is None:
htype = 'step'
else:
htype = 'stepfilled'
if rotated:
orientation = 'horizontal'
else:
orientation = 'vertical'
ax.hist(values, bins=50, histtype=htype, orientation=orientation,
facecolor=fillcolor, edgecolor=color, ls=linestyle, lw=2,
density=True)
if percentiles is None:
percentiles = [5., 50., 95.]
if len(percentiles) > 0:
plotp = numpy.percentile(values, percentiles)
else:
plotp = []
for val in plotp:
if rotated:
ax.axhline(y=val, ls='dashed', color=linecolor, lw=2, zorder=3)
else:
ax.axvline(x=val, ls='dashed', color=linecolor, lw=2, zorder=3)
# plot expected
if expected_value is not None:
if rotated:
ax.axhline(expected_value, color=expected_color, lw=1.5, zorder=2)
else:
ax.axvline(expected_value, color=expected_color, lw=1.5, zorder=2)
if title:
if len(percentiles) > 0:
minp = min(percentiles)
maxp = max(percentiles)
medp = (maxp + minp) / 2.
else:
minp = 5
medp = 50
maxp = 95
values_min = numpy.percentile(values, minp)
values_med = numpy.percentile(values, medp)
values_max = numpy.percentile(values, maxp)
negerror = values_med - values_min
poserror = values_max - values_med
fmt = '${0}$'.format(str_utils.format_value(
values_med, negerror, plus_error=poserror))
if rotated:
ax.yaxis.set_label_position("right")
# sets colored title for marginal histogram
set_marginal_histogram_title(ax, fmt, color,
label=label, rotated=rotated)
# Remove x-ticks
ax.set_xticks([])
# turn off x-labels
ax.set_xlabel('')
# set limits
ymin, ymax = ax.get_ylim()
if plot_min is not None:
ymin = plot_min
if plot_max is not None:
ymax = plot_max
ax.set_ylim(ymin, ymax)
else:
# sets colored title for marginal histogram
set_marginal_histogram_title(ax, fmt, color, label=label)
# Remove y-ticks
ax.set_yticks([])
# turn off y-label
ax.set_ylabel('')
# set limits
xmin, xmax = ax.get_xlim()
if plot_min is not None:
xmin = plot_min
if plot_max is not None:
xmax = plot_max
ax.set_xlim(xmin, xmax)
