Colored dots depending on their outline color

Question

Is there a way to colorize a point according to the color scheme used by the contour function? I understand that I can specify colormap, but presumably the contour function does some scaling and / or normalization of the data?

Here is an example:

 import numpy as np import scipy.stats as ss def plot_2d_probsurface(data, resolution=20, ax = None, xlim=None, ylim=None): # create a function to calcualte the density at a particular location kde = ss.gaussian_kde(data.T) # calculate the limits if there are no values passed in # passed in values are useful if calling this function # systematically with different sets of data whose limits # aren't consistent if xlim is None: xlim = (min(data[:,0]), max(data[:,0])) if ylim is None: ylim = (min(data[:,1]), max(data[:,1])) # create some tick marks that will be used to create a grid xs = np.linspace(xlim[0], xlim[1], resolution) ys = np.linspace(ylim[0], ylim[1], resolution) # wrap the KDE function and vectorize it so that we can call it on # the entire grid at once def calc_prob(x,y): return kde([x,y])[0] calc_prob = vectorize(calc_prob) # check if we've received a plotting surface if ax is None: fig = plt.figure(figsize=(6,6)) ax = fig.add_subplot(1,1,1) # create the grid and calculate the density at each point X,Y = np.meshgrid(xs, ys) Z = calc_prob(X,Y) # the values according to which the points should be colored point_values = kde(data.T) # plot the contour cont = ax.contour(X,Y,Z) #print cont ax.plot(data[:,0], data[:,1], 'o') return (None, None) data_x = np.random.random((50,2)) cont = plot_2d_probsurface(data_x) 

So, in the graph below, the points with the highest density will be colored brown, next orange, next yellow, etc. The values ​​according to which points should be colored are already in point_values . It just needs to be converted to colors and passed to the plot function. But how do I scale them, like on a contour graph?

Answer 1

It looks as simple as changing plot to scatter and passing the point values ​​as a parameter to c=point_values :

 import numpy as np import scipy.stats as ss def plot_2d_probsurface(data, resolution=20, ax = None, xlim=None, ylim=None): # create a function to calcualte the density at a particular location kde = ss.gaussian_kde(data.T) # calculate the limits if there are no values passed in # passed in values are useful if calling this function # systematically with different sets of data whose limits # aren't consistent if xlim is None: xlim = (min(data[:,0]), max(data[:,0])) if ylim is None: ylim = (min(data[:,1]), max(data[:,1])) # create some tick marks that will be used to create a grid xs = np.linspace(xlim[0], xlim[1], resolution) ys = np.linspace(ylim[0], ylim[1], resolution) # wrap the KDE function and vectorize it so that we can call it on # the entire grid at once def calc_prob(x,y): return kde([x,y])[0] calc_prob = vectorize(calc_prob) # check if we've received a plotting surface if ax is None: fig = plt.figure(figsize=(6,6)) ax = fig.add_subplot(1,1,1) # create the grid and calculate the density at each point X,Y = np.meshgrid(xs, ys) Z = calc_prob(X,Y) # plot the contour cont = ax.contour(X,Y,Z) point_values = kde(data.T) print point_values #print cont ax.scatter(data[:,0], data[:,1], c=point_values) return (None, None) data_x = np.random.random((50,2)) cont = plot_2d_probsurface(data_x) 

With this result: