Source code for astroML.plotting.multiaxes

"""
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])