class_docstr: 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])
docstr: 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.
docstr_tok: ["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","."]
code: 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)
  
file_path: astroML/astroML/plotting/multiaxes.py
imports: from copy import deepcopy
  import numpy
label_desc: Use image node_id 10 for calling the MultiAxes obj's underlying member method code with example usage: obj.scatter(data) without return types