def test_hist_plot_2d():
fig, ax = plt.subplots()
np.random.seed(0)
data_x, data_y = np.random.randn(2, 10000)
hist_plot_2d(ax, data_x, data_y)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
assert xmin > -3 and xmax < 3 and ymin > -3 and ymax < 3
hist_plot_2d(ax, data_x, data_y, xmin=-np.inf)
hist_plot_2d(ax, data_x, data_y, xmax=np.inf)
hist_plot_2d(ax, data_x, data_y, ymin=-np.inf)
hist_plot_2d(ax, data_x, data_y, ymax=np.inf)
assert xmin > -3 and xmax < 3 and ymin > -3 and ymax < 3
data_x, data_y = np.random.uniform(-10, 10, (2, 1000000))
weights = np.exp(-(data_x**2 + data_y**2) / 2)
hist_plot_2d(ax, data_x, data_y, weights=weights, bins=30)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
assert xmin > -3 and xmax < 3 and ymin > -3 and ymax < 3
def plot(self, ax, paramname_x, paramname_y=None, *args, **kwargs):
"""Interface for 2D and 1D plotting routines.
Produces a single 1D or 2D plot on an axis.
Parameters
----------
ax: matplotlib.axes.Axes
Axes to plot on
paramname_x: str
Choice of parameter to plot on x-coordinate from self.columns.
paramname_y: str
Choice of parameter to plot on y-coordinate from self.columns.
optional, if not provided, or the same as paramname_x, then 1D plot
produced.
plot_type: str
Must be in {'kde', 'scatter', 'hist', 'fastkde'} for 2D plots and
in {'kde', 'hist', 'fastkde', 'astropyhist'} for 1D plots.
optional, (Default: 'kde')
ncompress: int
Number of samples to use in plotting routines.
optional, Default dynamically chosen
q: str, float, (float, float)
Plot the `q` inner posterior quantiles in 1d 'kde' plots. To plot
the full range, set `q=0` or `q=1`.
* if str: any of {'1sigma', '2sigma', '3sigma', '4sigma', '5sigma'}
Plot within mean +/- #sigma of posterior.
* if float: Plot within the symmetric confidence interval
`(1-q, q)` or `(q, 1-q)`.
* if tuple: Plot within the (possibly asymmetric) confidence
interval `q`.
optional, (Default: '5sigma')
Returns
-------
fig: matplotlib.figure.Figure
New or original (if supplied) figure object
axes: pandas.DataFrame or pandas.Series of matplotlib.axes.Axes
Pandas array of axes objects
"""
self._set_automatic_limits()
plot_type = kwargs.pop('plot_type', 'kde')
do_1d_plot = paramname_y is None or paramname_x == paramname_y
kwargs['label'] = kwargs.get('label', self.label)
ncompress = kwargs.pop('ncompress', None)
if do_1d_plot:
if paramname_x in self and plot_type is not None:
xmin, xmax = self._limits(paramname_x)
kwargs['xmin'] = kwargs.get('xmin', xmin)
kwargs['xmax'] = kwargs.get('xmax', xmax)
if plot_type == 'kde':
if ncompress is None:
ncompress = 1000
return kde_plot_1d(ax,
self[paramname_x],
weights=self.weights,
ncompress=ncompress,
*args,
**kwargs)
elif plot_type == 'fastkde':
x = self[paramname_x].compress(ncompress)
return fastkde_plot_1d(ax, x, *args, **kwargs)
elif plot_type == 'hist':
return hist_plot_1d(ax,
self[paramname_x],
weights=self.weights,
*args,
**kwargs)
elif plot_type == 'astropyhist':
x = self[paramname_x].compress(ncompress)
return hist_plot_1d(ax,
x,
plotter='astropyhist',
*args,
**kwargs)
else:
raise NotImplementedError("plot_type is '%s', but must be"
" one of {'kde', 'fastkde', "
"'hist', 'astropyhist'}." %
plot_type)
else:
ax.plot([], [])
else:
if (paramname_x in self and paramname_y in self
and plot_type is not None):
xmin, xmax = self._limits(paramname_x)
kwargs['xmin'] = kwargs.get('xmin', xmin)
kwargs['xmax'] = kwargs.get('xmax', xmax)
ymin, ymax = self._limits(paramname_y)
kwargs['ymin'] = kwargs.get('ymin', ymin)
kwargs['ymax'] = kwargs.get('ymax', ymax)
if plot_type == 'kde':
if ncompress is None:
ncompress = 1000
x = self[paramname_x]
y = self[paramname_y]
return kde_contour_plot_2d(ax,
x,
y,
weights=self.weights,
ncompress=ncompress,
*args,
**kwargs)
elif plot_type == 'fastkde':
x = self[paramname_x].compress(ncompress)
y = self[paramname_y].compress(ncompress)
return fastkde_contour_plot_2d(ax, x, y, *args, **kwargs)
elif plot_type == 'scatter':
if ncompress is None:
ncompress = 500
x = self[paramname_x].compress(ncompress)
y = self[paramname_y].compress(ncompress)
return scatter_plot_2d(ax, x, y, *args, **kwargs)
elif plot_type == 'hist':
x = self[paramname_x]
y = self[paramname_y]
return hist_plot_2d(ax,
x,
y,
weights=self.weights,
*args,
**kwargs)
else:
raise NotImplementedError("plot_type is '%s', but must be"
"in {'kde', 'fastkde',"
"'scatter', 'hist'}." %
plot_type)
else:
ax.plot([], [])
def plot(self, ax, paramname_x, paramname_y=None, *args, **kwargs):
"""Interface for 2D and 1D plotting routines.
Produces a single 1D or 2D plot on an axis.
Parameters
----------
ax: matplotlib.axes.Axes
Axes to plot on
paramname_x: str
Choice of parameter to plot on x-coordinate from self.columns.
paramname_y: str
Choice of parameter to plot on y-coordinate from self.columns.
optional, if not provided, or the same as paramname_x, then 1D plot
produced.
plot_type: str
Must be in {'kde', 'scatter', 'hist', 'fastkde'} for 2D plots and
in {'kde', 'hist', 'fastkde', 'astropyhist'} for 1D plots.
optional, (Default: 'kde')
ncompress: int
Number of samples to use in plotting routines.
optional, Default dynamically chosen
Returns
-------
fig: matplotlib.figure.Figure
New or original (if supplied) figure object
axes: pandas.DataFrame or pandas.Series of matplotlib.axes.Axes
Pandas array of axes objects
"""
plot_type = kwargs.pop('plot_type', 'kde')
do_1d_plot = paramname_y is None or paramname_x == paramname_y
kwargs['label'] = kwargs.get('label', self.label)
ncompress = kwargs.pop('ncompress', None)
if do_1d_plot:
if paramname_x in self and plot_type is not None:
xmin, xmax = self._limits(paramname_x)
if plot_type == 'kde':
if ncompress is None:
ncompress = 1000
return kde_plot_1d(ax,
self[paramname_x],
weights=self.weight,
ncompress=ncompress,
xmin=xmin,
xmax=xmax,
*args,
**kwargs)
elif plot_type == 'fastkde':
x = self[paramname_x].compress(ncompress)
return fastkde_plot_1d(ax,
x,
xmin=xmin,
xmax=xmax,
*args,
**kwargs)
elif plot_type == 'hist':
return hist_plot_1d(ax,
self[paramname_x],
weights=self.weight,
xmin=xmin,
xmax=xmax,
*args,
**kwargs)
elif plot_type == 'astropyhist':
x = self[paramname_x].compress(ncompress)
return hist_plot_1d(ax,
x,
plotter='astropyhist',
xmin=xmin,
xmax=xmax,
*args,
**kwargs)
else:
raise NotImplementedError("plot_type is '%s', but must be"
" one of {'kde', 'fastkde', "
"'hist', 'astropyhist'}." %
plot_type)
else:
ax.plot([], [])
else:
if (paramname_x in self and paramname_y in self
and plot_type is not None):
xmin, xmax = self._limits(paramname_x)
ymin, ymax = self._limits(paramname_y)
if plot_type == 'kde':
if ncompress is None:
ncompress = 1000
x = self[paramname_x]
y = self[paramname_y]
return kde_contour_plot_2d(ax,
x,
y,
weights=self.weight,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
ncompress=ncompress,
*args,
**kwargs)
elif plot_type == 'fastkde':
x = self[paramname_x].compress(ncompress)
y = self[paramname_y].compress(ncompress)
return fastkde_contour_plot_2d(ax,
x,
y,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
*args,
**kwargs)
elif plot_type == 'scatter':
if ncompress is None:
ncompress = 500
x = self[paramname_x].compress(ncompress)
y = self[paramname_y].compress(ncompress)
return scatter_plot_2d(ax,
x,
y,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
*args,
**kwargs)
elif plot_type == 'hist':
x = self[paramname_x]
y = self[paramname_y]
return hist_plot_2d(ax,
x,
y,
weights=self.weight,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax,
*args,
**kwargs)
else:
raise NotImplementedError("plot_type is '%s', but must be"
"in {'kde', 'fastkde',"
"'scatter', 'hist'}." %
plot_type)
else:
ax.plot([], [])
