"""
Multi-panel plotting
"""
from copy import deepcopy
import numpy as np
[docs]class MultiAxes:
"""Visualize Multiple-dimensional data
This class enables the visualization of multi-dimensional data, using
a triangular grid of 2D plots.
Parameters
----------
ndim : integer
Number of data dimensions
inner_labels : bool
If true, then label the inner axes. If false, then only the outer
axes will be labeled
fig : matplotlib.Figure
if specified, draw the plot on this figure. Otherwise, use the
current active figure.
left, bottom, right, top, wspace, hspace : floats
these parameters control the layout of the plots. They behave have
an identical effect as the arguments to plt.subplots_adjust. If not
specified, default values from the rc file will be used.
Examples
--------
A grid of scatter plots can be created as follows::
x = np.random.normal((4, 1000))
R = np.random.random((4, 4)) # projection matrix
x = np.dot(R, x)
ax = MultiAxes(4)
ax.scatter(x)
ax.set_labels(['x1', 'x2', 'x3', 'x4'])
Alternatively, the scatter plot can be visualized as a density::
ax = MultiAxes(4)
ax.density(x, bins=[20, 20, 20, 20])
"""
[docs] def __init__(self, ndim, inner_labels=False,
fig=None,
left=None, bottom=None,
right=None, top=None,
wspace=None, hspace=None):
# Import here so that testing with Agg will work
from matplotlib import pyplot as plt
if fig is None:
fig = plt.gcf()
self.fig = fig
self.ndim = ndim
self.inner_labels = inner_labels
self._update('left', left)
self._update('bottom', bottom)
self._update('right', right)
self._update('top', top)
self._update('wspace', wspace)
self._update('hspace', hspace)
self.axes = self._draw_panels()
def _update(self, s, val):
# Import here so that testing with Agg will work
from matplotlib import rcParams
if val is None:
val = getattr(self, s, None)
if val is None:
key = 'figure.subplot.' + s
val = rcParams[key]
setattr(self, s, val)
def _check_data(self, data):
data = np.asarray(data)
if data.ndim != 2:
raise ValueError("data dimension should be 2")
if data.shape[1] != self.ndim:
raise ValueError("leading dimension of data should match ndim")
return data
def _draw_panels(self):
# Import here so that testing with Agg will work
from matplotlib import pyplot as plt
if self.top <= self.bottom:
raise ValueError('top must be larger than bottom')
if self.right <= self.left:
raise ValueError('right must be larger than left')
ndim = self.ndim
panel_width = ((self.right - self.left)
/ (ndim - 1 + self.wspace * (ndim - 2)))
panel_height = ((self.top - self.bottom)
/ (ndim - 1 + self.hspace * (ndim - 2)))
full_panel_width = (1 + self.wspace) * panel_width
full_panel_height = (1 + self.hspace) * panel_height
axes = np.empty((ndim, ndim), dtype=object)
axes.fill(None)
for j in range(1, ndim):
for i in range(j):
left = self.left + i * full_panel_width
right = self.bottom + (ndim - 1 - j) * full_panel_height
ax = self.fig.add_axes([left, right,
panel_width, panel_height])
axes[i, j] = ax
if not self.inner_labels:
# remove unneeded x labels
for i in range(ndim):
for j in range(ndim - 1):
ax = axes[i, j]
if ax is not None:
ax.xaxis.set_major_formatter(plt.NullFormatter())
# remove unneeded y labels
for i in range(1, ndim):
for j in range(ndim):
ax = axes[i, j]
if ax is not None:
ax.yaxis.set_major_formatter(plt.NullFormatter())
return np.asarray(axes, dtype=object)
def set_limits(self, limits):
"""Set the axes limits
Parameters
----------
limits : list of tuples
a list of plot limits for each dimension, each in the form
(xmin, xmax). The length of `limits` should match the data
dimension.
"""
if len(limits) != self.ndim:
raise ValueError("limits do not match number of dimensions")
for i in range(self.ndim):
for j in range(self.ndim):
ax = self.axes[i, j]
if ax is not None:
ax.set_xlim(limits[i])
ax.set_ylim(limits[j])
def set_labels(self, labels):
"""Set the axes labels
Parameters
----------
labels : list of strings
a list of plot limits for each dimension. The length of `labels`
should match the data dimension.
"""
if len(labels) != self.ndim:
raise ValueError("labels do not match number of dimensions")
for i in range(self.ndim):
ax = self.axes[i, self.ndim - 1]
if ax is not None:
ax.set_xlabel(labels[i])
for j in range(self.ndim):
ax = self.axes[0, j]
if ax is not None:
ax.set_ylabel(labels[j])
def set_locators(self, locators):
"""Set the tick locators for the plots
Parameters
----------
locators : list or plt.Locator object
If a list, then the length should match the data dimension. If
a single Locator instance, then each axes will be given the
same locator.
"""
# Import here so that testing with Agg will work
from matplotlib import pyplot as plt
if isinstance(locators, plt.Locator):
locators = [deepcopy(locators) for i in range(self.ndim)]
elif len(locators) != self.ndim:
raise ValueError("locators do not match number of dimensions")
for i in range(self.ndim):
for j in range(self.ndim):
ax = self.axes[i, j]
if ax is not None:
ax.xaxis.set_major_locator(locators[i])
ax.yaxis.set_major_locator(locators[j])
def set_formatters(self, formatters):
"""Set the tick formatters for the outer edge of plots
Parameters
----------
formatterss : list or plt.Formatter object
If a list, then the length should match the data dimension. If
a single Formatter instance, then each axes will be given the
same locator.
"""
# Import here so that testing with Agg will work
from matplotlib import pyplot as plt
if isinstance(formatters, plt.Formatter):
formatters = [deepcopy(formatters) for i in range(self.ndim)]
elif len(formatters) != self.ndim:
raise ValueError("formatters do not match number of dimensions")
for i in range(self.ndim):
ax = self.axes[i, self.ndim - 1]
if ax is not None:
ax.xaxis.set_major_formatter(formatters[i])
for j in range(self.ndim):
ax = self.axes[0, j]
if ax is not None:
ax.xaxis.set_major_formatter(formatters[i])
def plot(self, data, *args, **kwargs):
"""Plot data
This function calls plt.plot() on each axes. All arguments or
keyword arguments are passed to the plt.plot function.
Parameters
----------
data : ndarray
shape of data is [n_samples, ndim], and ndim should match that
passed to the MultiAxes constructor.
"""
data = self._check_data(data)
for i in range(self.ndim):
for j in range(self.ndim):
ax = self.axes[i, j]
if ax is None:
continue
ax.plot(data[:, i], data[:, j], *args, **kwargs)
def scatter(self, data, *args, **kwargs):
"""Scatter plot data
This function calls plt.scatter() on each axes. All arguments or
keyword arguments are passed to the plt.scatter function.
Parameters
----------
data : ndarray
shape of data is [n_samples, ndim], and ndim should match that
passed to the MultiAxes constructor.
"""
data = self._check_data(data)
for i in range(self.ndim):
for j in range(self.ndim):
ax = self.axes[i, j]
if ax is None:
continue
ax.scatter(data[:, i], data[:, j], *args, **kwargs)
def density(self, data, bins=20, **kwargs):
"""Density plot of data
This function calls np.histogram2D to bin the data in each axes, then
calls plt.imshow() on the result. All extra arguments or
keyword arguments are passed to the plt.imshow function.
Parameters
----------
data : ndarray
shape of data is [n_samples, ndim], and ndim should match that
passed to the MultiAxes constructor.
bins : int, array, list of ints, or list of arrays
specify the bins for each dimension. If bins is a list, then the
length must match the data dimension
"""
data = self._check_data(data)
if not hasattr(bins, '__len__'):
bins = [bins for i in range(self.ndim)]
elif len(bins) != self.ndim:
bins = [bins for i in range(self.ndim)]
for i in range(self.ndim):
for j in range(self.ndim):
ax = self.axes[i, j]
if ax is None:
continue
H, xbins, ybins = np.histogram2d(data[:, i], data[:, j],
(bins[i], bins[j]))
ax.imshow(H.T, origin='lower', aspect='auto',
extent=(xbins[0], xbins[-1], ybins[0], ybins[-1]),
**kwargs)
ax.set_xlim(xbins[0], xbins[-1])
ax.set_ylim(ybins[0], ybins[-1])