'''correlation plots
Author: Josef Perktold
License: BSD-3
example for usage with different options in
statsmodels/sandbox/examples/thirdparty/ex_ratereturn.py
'''
import numpy as np
from . import utils
[docs]def plot_corr(dcorr, xnames=None, ynames=None, title=None, normcolor=False,
ax=None, cmap='RdYlBu_r'):
"""Plot correlation of many variables in a tight color grid.
Parameters
----------
dcorr : ndarray
Correlation matrix, square 2-D array.
xnames : list of str, optional
Labels for the horizontal axis. If not given (None), then the
matplotlib defaults (integers) are used. If it is an empty list, [],
then no ticks and labels are added.
ynames : list of str, optional
Labels for the vertical axis. Works the same way as `xnames`.
If not given, the same names as for `xnames` are re-used.
title : str, optional
The figure title. If None, the default ('Correlation Matrix') is used.
If ``title=''``, then no title is added.
normcolor : bool or tuple of scalars, optional
If False (default), then the color coding range corresponds to the
range of `dcorr`. If True, then the color range is normalized to
(-1, 1). If this is a tuple of two numbers, then they define the range
for the color bar.
ax : Matplotlib AxesSubplot instance, optional
If `ax` is None, then a figure is created. If an axis instance is
given, then only the main plot but not the colorbar is created.
cmap : str or Matplotlib Colormap instance, optional
The colormap for the plot. Can be any valid Matplotlib Colormap
instance or name.
Returns
-------
fig : Matplotlib figure instance
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import statsmodels.graphics.api as smg
>>> hie_data = sm.datasets.randhie.load_pandas()
>>> corr_matrix = np.corrcoef(hie_data.data.T)
>>> smg.plot_corr(corr_matrix, xnames=hie_data.names)
>>> plt.show()
..plot :: plots/graphics_correlation_plot_corr.py
"""
if ax is None:
create_colorbar = True
else:
create_colorbar = False
fig, ax = utils.create_mpl_ax(ax)
nvars = dcorr.shape[0]
if ynames is None:
ynames = xnames
if title is None:
title = 'Correlation Matrix'
if isinstance(normcolor, tuple):
vmin, vmax = normcolor
elif normcolor:
vmin, vmax = -1.0, 1.0
else:
vmin, vmax = None, None
axim = ax.imshow(dcorr, cmap=cmap, interpolation='nearest',
extent=(0,nvars,0,nvars), vmin=vmin, vmax=vmax)
# create list of label positions
labelPos = np.arange(0, nvars) + 0.5
if ynames is not None:
ax.set_yticks(labelPos)
ax.set_yticks(labelPos[:-1]+0.5, minor=True)
ax.set_yticklabels(ynames[::-1], fontsize='small',
horizontalalignment='right')
elif ynames == []:
ax.set_yticks([])
if xnames is not None:
ax.set_xticks(labelPos)
ax.set_xticks(labelPos[:-1]+0.5, minor=True)
ax.set_xticklabels(xnames, fontsize='small', rotation=45,
horizontalalignment='right')
elif xnames == []:
ax.set_xticks([])
if not title == '':
ax.set_title(title)
if create_colorbar:
fig.colorbar(axim, use_gridspec=True)
fig.tight_layout()
ax.tick_params(which='minor', length=0)
ax.tick_params(direction='out', top=False, right=False)
try:
ax.grid(True, which='minor', linestyle='-', color='w', lw=1)
except AttributeError:
# Seems to fail for axes created with AxesGrid. MPL bug?
pass
return fig
[docs]def plot_corr_grid(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None,
ynames=None, fig=None, cmap='RdYlBu_r'):
"""Create a grid of correlation plots.
The individual correlation plots are assumed to all have the same
variables, axis labels can be specified only once.
Parameters
----------
dcorrs : list or iterable of ndarrays
List of correlation matrices.
titles : list of str, optional
List of titles for the subplots. By default no title are shown.
ncols : int, optional
Number of columns in the subplot grid. If not given, the number of
columns is determined automatically.
normcolor : bool or tuple, optional
If False (default), then the color coding range corresponds to the
range of `dcorr`. If True, then the color range is normalized to
(-1, 1). If this is a tuple of two numbers, then they define the range
for the color bar.
xnames : list of str, optional
Labels for the horizontal axis. If not given (None), then the
matplotlib defaults (integers) are used. If it is an empty list, [],
then no ticks and labels are added.
ynames : list of str, optional
Labels for the vertical axis. Works the same way as `xnames`.
If not given, the same names as for `xnames` are re-used.
fig : Matplotlib figure instance, optional
If given, this figure is simply returned. Otherwise a new figure is
created.
cmap : str or Matplotlib Colormap instance, optional
The colormap for the plot. Can be any valid Matplotlib Colormap
instance or name.
Returns
-------
fig : Matplotlib figure instance
If `ax` is None, the created figure. Otherwise the figure to which
`ax` is connected.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import statsmodels.api as sm
In this example we just reuse the same correlation matrix several times.
Of course in reality one would show a different correlation (measuring a
another type of correlation, for example Pearson (linear) and Spearman,
Kendall (nonlinear) correlations) for the same variables.
>>> hie_data = sm.datasets.randhie.load_pandas()
>>> corr_matrix = np.corrcoef(hie_data.data.T)
>>> sm.graphics.plot_corr_grid([corr_matrix] * 8, xnames=hie_data.names)
>>> plt.show()
..plot :: plots/graphics_correlation_plot_corr_grid.py
"""
if ynames is None:
ynames = xnames
if not titles:
titles = ['']*len(dcorrs)
n_plots = len(dcorrs)
if ncols is not None:
nrows = int(np.ceil(n_plots / float(ncols)))
else:
# Determine number of rows and columns, square if possible, otherwise
# prefer a wide (more columns) over a high layout.
if n_plots < 4:
nrows, ncols = 1, n_plots
else:
nrows = int(np.sqrt(n_plots))
ncols = int(np.ceil(n_plots / float(nrows)))
# Create a figure with the correct size
aspect = min(ncols / float(nrows), 1.8)
vsize = np.sqrt(nrows) * 5
fig = utils.create_mpl_fig(fig, figsize=(vsize * aspect + 1, vsize))
for i, c in enumerate(dcorrs):
ax = fig.add_subplot(nrows, ncols, i+1)
# Ensure to only plot labels on bottom row and left column
_xnames = xnames if nrows * ncols - (i+1) < ncols else []
_ynames = ynames if (i+1) % ncols == 1 else []
plot_corr(c, xnames=_xnames, ynames=_ynames, title=titles[i],
normcolor=normcolor, ax=ax, cmap=cmap)
# Adjust figure margins and add a colorbar
fig.subplots_adjust(bottom=0.1, left=0.09, right=0.9, top=0.9)
cax = fig.add_axes([0.92, 0.1, 0.025, 0.8])
fig.colorbar(fig.axes[0].images[0], cax=cax)
return fig