根據輪廓顏色的顏色點

Question

有沒有辦法根據contour函數使用的顏色表着色點？ 我意識到我可以指定一個顏色映射表，但大概contour函數會執行一些 縮放和/或數據規範化？

下面是一個例子：......根據該值

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) 

所以在下面的情節，最高密度的點會變成棕色，未來橙，未來黃等點數應該是有色的已經在point_values。這隻需要轉換爲顏色並傳遞給plot函數。但是，如何在contour情節中對它們進行縮放？

Answer 1

這似乎是因爲改變plot到scatter並傳遞點值作爲c=point_values參數作爲簡單：

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) 

有了這個結果：