def _check_axes_shape(self,
axes,
axes_num=None,
layout=None,
figsize=None):
"""
Check expected number of axes is drawn in expected layout
Parameters
----------
axes : matplotlib Axes object, or its list-like
axes_num : number
expected number of axes. Unnecessary axes should be set to
invisible.
layout : tuple
expected layout, (expected number of rows , columns)
figsize : tuple
expected figsize. default is matplotlib default
"""
if figsize is None:
figsize = self.default_figsize
visible_axes = self._flatten_visible(axes)
if axes_num is not None:
self.assertEqual(len(visible_axes), axes_num)
for ax in visible_axes:
# check something drawn on visible axes
self.assertTrue(len(ax.get_children()) > 0)
if layout is not None:
result = self._get_axes_layout(plotting._flatten(axes))
self.assertEqual(result, layout)
self.assert_numpy_array_equal(visible_axes[0].figure.get_size_inches(),
np.array(figsize, dtype=np.float64))
def _check_axes_shape(self, axes, axes_num=None, layout=None,
figsize=None):
"""
Check expected number of axes is drawn in expected layout
Parameters
----------
axes : matplotlib Axes object, or its list-like
axes_num : number
expected number of axes. Unnecessary axes should be set to
invisible.
layout : tuple
expected layout, (expected number of rows , columns)
figsize : tuple
expected figsize. default is matplotlib default
"""
if figsize is None:
figsize = self.default_figsize
visible_axes = self._flatten_visible(axes)
if axes_num is not None:
self.assertEqual(len(visible_axes), axes_num)
for ax in visible_axes:
# check something drawn on visible axes
self.assertTrue(len(ax.get_children()) > 0)
if layout is not None:
result = self._get_axes_layout(plotting._flatten(axes))
self.assertEqual(result, layout)
self.assert_numpy_array_equal(
visible_axes[0].figure.get_size_inches(),
np.array(figsize, dtype=np.float64))
def _flatten_visible(self, axes):
"""
Flatten axes, and filter only visible
Parameters
----------
axes : matplotlib Axes object, or its list-like
"""
axes = plotting._flatten(axes)
axes = [ax for ax in axes if ax.get_visible()]
return axes
def _flatten_visible(self, axes):
"""
Flatten axes, and filter only visible
Parameters
----------
axes : matplotlib Axes object, or its list-like
"""
axes = plotting._flatten(axes)
axes = [ax for ax in axes if ax.get_visible()]
return axes
def compare_variables(self, cuts= None, **kwargs):
# data1: DataFrame
# data2: DataFrame
# column: string or sequence
# If passed, will be used to limit data to a subset of columns
if 'alpha' not in self.kwargs:
kwargs['alpha'] = 0.5
# if column is not None:
# if not isinstance(column, (list, np.ndarray, Index)):
# column = [column]
# data1 = data1[column]
# data2 = data2[column]
if cuts != None:
if passed_cut (evt, cuts)
self.data1 = self.data1._get_numeric_data()
self.data2 = self.data2._get_numeric_data()
naxes = len(self.data1.columns)
fig, axes = plotting._subplots(naxes=naxes, ax=ax, squeeze=False,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout)
_axes = plotting._flatten(axes)
for i, col in enumerate(com._try_sort(self.data1.columns)):
ax = _axes[i]
low = min(data1[col].min(), data2[col].min())
high = max(data1[col].max(), data2[col].max())
ax.hist(data1[col].dropna().values,
bins=bins, range=(low,high), **kwds)
ax.hist(data2[col].dropna().values,
bins=bins, range=(low,high), **kwds)
ax.set_title(col)
ax.grid(grid)
plotting._set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
ylabelsize=ylabelsize, yrot=yrot)
fig.subplots_adjust(wspace=0.3, hspace=0.7)
return axes