text: """
  Multi-panel plotting
  """
  from copy import deepcopy
  import numpy as np
  from matplotlib import pyplot as plt
  from matplotlib import rcParams
  
  
  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])
      """
      def __init__(self, ndim, inner_labels=False,
                   fig=None,
                   left=None, bottom=None,
                   right=None, top=None,
                   wspace=None, hspace=None):
          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):
          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):
          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.
          """
          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.
          """
          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])
  
  if __name__ == '__main__':
      ma = MultiAxes(4, hspace=0, wspace=0)
      data = np.random.normal(0.5, 0.2, (4, 1000))
      data += np.arange(4)[:, None]
      labels = ['x1', 'x2', 'x3', 'x4']
      locators = [plt.MultipleLocator(0.2) for i in range(4)]
  
      #ma.scatter(data, c=np.random.random(1000), s=1, lw=0)
      ma.density(data, bins=20)
  
      ma.set_labels(labels)
      ma.set_locators(locators)
  
      plt.show()
  