'''create scatterplot with confidence ellipsis
Author: Josef Perktold
License: BSD-3
TODO: update script to use sharex, sharey, and visible=False
see https://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label
for sharex I need to have the ax of the last_row when editing the earlier
rows. Or you axes_grid1, imagegrid
http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html
'''
import numpy as np
from scipy import stats
from . import utils
__all__ = ['scatter_ellipse']
def _make_ellipse(mean, cov, ax, level=0.95, color=None):
"""Support function for scatter_ellipse."""
from matplotlib.patches import Ellipse
v, w = np.linalg.eigh(cov)
u = w[0] / np.linalg.norm(w[0])
angle = np.arctan(u[1]/u[0])
angle = 180 * angle / np.pi # convert to degrees
v = 2 * np.sqrt(v * stats.chi2.ppf(level, 2)) #get size corresponding to level
ell = Ellipse(mean[:2], v[0], v[1], 180 + angle, facecolor='none',
edgecolor=color,
#ls='dashed', #for debugging
lw=1.5)
ell.set_clip_box(ax.bbox)
ell.set_alpha(0.5)
ax.add_artist(ell)
[docs]def scatter_ellipse(data, level=0.9, varnames=None, ell_kwds=None,
plot_kwds=None, add_titles=False, keep_ticks=False,
fig=None):
"""Create a grid of scatter plots with confidence ellipses.
ell_kwds, plot_kdes not used yet
looks ok with 5 or 6 variables, too crowded with 8, too empty with 1
Parameters
----------
data : array_like
Input data.
level : scalar, optional
Default is 0.9.
varnames : list[str], optional
Variable names. Used for y-axis labels, and if `add_titles` is True
also for titles. If not given, integers 1..data.shape[1] are used.
ell_kwds : dict, optional
UNUSED
plot_kwds : dict, optional
UNUSED
add_titles : bool, optional
Whether or not to add titles to each subplot. Default is False.
Titles are constructed from `varnames`.
keep_ticks : bool, optional
If False (default), remove all axis ticks.
fig : Figure, optional
If given, this figure is simply returned. Otherwise a new figure is
created.
Returns
-------
Figure
If `fig` is None, the created figure. Otherwise `fig` itself.
Examples
--------
>>> import statsmodels.api as sm
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from statsmodels.graphics.plot_grids import scatter_ellipse
>>> data = sm.datasets.statecrime.load_pandas().data
>>> fig = plt.figure(figsize=(8,8))
>>> scatter_ellipse(data, varnames=data.columns, fig=fig)
>>> plt.show()
..plot :: plots/graphics_correlation_plot_corr_grid.py
"""
fig = utils.create_mpl_fig(fig)
import matplotlib.ticker as mticker
data = np.asanyarray(data) #needs mean and cov
nvars = data.shape[1]
if varnames is None:
#assuming single digit, nvars<=10 else use 'var%2d'
varnames = ['var%d' % i for i in range(nvars)]
plot_kwds_ = dict(ls='none', marker='.', color='k', alpha=0.5)
if plot_kwds:
plot_kwds_.update(plot_kwds)
ell_kwds_= dict(color='k')
if ell_kwds:
ell_kwds_.update(ell_kwds)
dmean = data.mean(0)
dcov = np.cov(data, rowvar=0)
for i in range(1, nvars):
#print '---'
ax_last=None
for j in range(i):
#print i,j, i*(nvars-1)+j+1
ax = fig.add_subplot(nvars-1, nvars-1, (i-1)*(nvars-1)+j+1)
## #sharey=ax_last) #sharey does not allow empty ticks?
## if j == 0:
## print 'new ax_last', j
## ax_last = ax
## ax.set_ylabel(varnames[i])
#TODO: make sure we have same xlim and ylim
formatter = mticker.FormatStrFormatter('% 3.1f')
ax.yaxis.set_major_formatter(formatter)
ax.xaxis.set_major_formatter(formatter)
idx = np.array([j,i])
ax.plot(*data[:,idx].T, **plot_kwds_)
if np.isscalar(level):
level = [level]
for alpha in level:
_make_ellipse(dmean[idx], dcov[idx[:,None], idx], ax, level=alpha,
**ell_kwds_)
if add_titles:
ax.set_title('%s-%s' % (varnames[i], varnames[j]))
if not ax.is_first_col():
if not keep_ticks:
ax.set_yticks([])
else:
ax.yaxis.set_major_locator(mticker.MaxNLocator(3))
else:
ax.set_ylabel(varnames[i])
if ax.is_last_row():
ax.set_xlabel(varnames[j])
else:
if not keep_ticks:
ax.set_xticks([])
else:
ax.xaxis.set_major_locator(mticker.MaxNLocator(3))
dcorr = np.corrcoef(data, rowvar=0)
dc = dcorr[idx[:,None], idx]
xlim = ax.get_xlim()
ylim = ax.get_ylim()
## xt = xlim[0] + 0.1 * (xlim[1] - xlim[0])
## yt = ylim[0] + 0.1 * (ylim[1] - ylim[0])
## if dc[1,0] < 0 :
## yt = ylim[0] + 0.1 * (ylim[1] - ylim[0])
## else:
## yt = ylim[1] - 0.2 * (ylim[1] - ylim[0])
yrangeq = ylim[0] + 0.4 * (ylim[1] - ylim[0])
if dc[1,0] < -0.25 or (dc[1,0] < 0.25 and dmean[idx][1] > yrangeq):
yt = ylim[0] + 0.1 * (ylim[1] - ylim[0])
else:
yt = ylim[1] - 0.2 * (ylim[1] - ylim[0])
xt = xlim[0] + 0.1 * (xlim[1] - xlim[0])
ax.text(xt, yt, '$\\rho=%0.2f$'% dc[1,0])
for ax in fig.axes:
if ax.is_last_row(): # or ax.is_first_col():
ax.xaxis.set_major_locator(mticker.MaxNLocator(3))
if ax.is_first_col():
ax.yaxis.set_major_locator(mticker.MaxNLocator(3))
return fig