plot.py 文件源码

def plot_embedding_contours(Y, contours = 10, cmap = plt.cm.plasma, xmin = None, xmax = None, ymin = None, ymax = None, npts = 100, density = False):
  """Plot a 2d density map of the embedding Y"""

  if xmin is None:
    xmin = np.min(Y[:,0]);
  if xmax is None:
    xmax = np.max(Y[:,0]);
  if ymin is None:
    ymin = np.min(Y[:,1]);
  if ymax is None:
    ymax = np.max(Y[:,1]);   

  #print xmin,xmax,ymin,ymax
  dx = float(xmax-xmin) / npts;
  dy = float(xmax-xmin) / npts;
  xx, yy = np.mgrid[xmin:xmax:dx, ymin:ymax:dy]
  positions = np.vstack([xx.ravel(), yy.ravel()])
  kernel = st.gaussian_kde(Y.T);
  #print xx.shape
  #print positions.shape
  #print Y.shape
  #print kernel(positions).shape
  f = kernel(positions)
  f = np.reshape(f, xx.shape)
  #print f.shape

  ax = plt.gca()
  ax.set_xlim(xmin, xmax)
  ax.set_ylim(ymin, ymax)
  # Contourf plot
  if density:
    cfset = ax.contourf(xx, yy, f, cmap=cmap)
  ## Or kernel density estimate plot instead of the contourf plot
  #ax.imshow(np.rot90(f), cmap='Blues', extent=[xmin, xmax, ymin, ymax])
  # Contour plot
  if contours is not None:
    cset = ax.contour(xx, yy, f, contours, cmap=cmap)
  # Label plot
  #ax.clabel(cset, inline=1, fontsize=10)
  ax.set_xlabel('Y0')
  ax.set_ylabel('Y1')