Pcolormesh没有得到正确的位置matplotlib(Pcolormesh not getting correct position matplotlib)

我正在使用以下代码在png图像上绘制数据：

x, y = np.genfromtxt('pogba_t1314.csv', delimiter=',', unpack=True) print(x[1], y[1]) y = y[np.logical_not(np.isnan(y))] x = x[np.logical_not(np.isnan(x))] k = gaussian_kde(np.vstack([x, y])) xi, yi = np.mgrid[x.min():x.max():x.size**0.5*1j,y.min():y.max():y.size**0.5*1j] zi = k(np.vstack([xi.flatten(), yi.flatten()])) fig = plt.figure(figsize=(9,10)) ax1 = fig.add_subplot(211) ax1.pcolormesh(xi, yi, zi.reshape(xi.shape), alpha=0.5) ax1.plot(y,x, "o") ax1.set_xlim(0, 740) ax1.set_ylim(515, 0) im = plt.imread('statszone_football_pitch.png') ax1.imshow(im, extent=[0, 740, 0, 515], aspect='auto')

结果如下：

Pcolormesh应该在整个图像上，但事实并非如此。我该如何设置正确的位置？为什么我得到更小的pcolormesh尊重整个音高？我应该有这样的东西（即使这指的是contourf）：

I'm plotting data on top of a png image with this code:

x, y = np.genfromtxt('pogba_t1314.csv', delimiter=',', unpack=True) print(x[1], y[1]) y = y[np.logical_not(np.isnan(y))] x = x[np.logical_not(np.isnan(x))] k = gaussian_kde(np.vstack([x, y])) xi, yi = np.mgrid[x.min():x.max():x.size**0.5*1j,y.min():y.max():y.size**0.5*1j] zi = k(np.vstack([xi.flatten(), yi.flatten()])) fig = plt.figure(figsize=(9,10)) ax1 = fig.add_subplot(211) ax1.pcolormesh(xi, yi, zi.reshape(xi.shape), alpha=0.5) ax1.plot(y,x, "o") ax1.set_xlim(0, 740) ax1.set_ylim(515, 0) im = plt.imread('statszone_football_pitch.png') ax1.imshow(im, extent=[0, 740, 0, 515], aspect='auto')

Here is the result:

Pcolormesh should be over the whole image, but it is not. How can i set the correct position? Why i get smaller pcolormesh respect of whole pitch? I should have something like this (even if this refers to contourf):

最满意答案

你的代码似乎工作正常。问题在于，图像中代表x轴的不是你在图中给出的x轴。也就是说，如果你将x改为y（反之亦然），你将获得正确的情节：

from scipy.stats.kde import gaussian_kde import matplotlib.pyplot as plt import numpy as np x, y = np.genfromtxt('pogba_t1314.csv', delimiter=',', unpack=True) print(x[1], y[1]) y = y[np.logical_not(np.isnan(y))] x = x[np.logical_not(np.isnan(x))] k = gaussian_kde(np.vstack([x, y])) #xi, yi = np.mgrid[x.min():x.max():x.size**0.5*1j,y.min():y.max():y.size**0.5*1j] xi, yi = np.mgrid[y.min():y.max():y.size**0.5*1j,x.min():x.max():x.size**0.5*1j] # Changed this. #zi = k(np.vstack([xi.flatten(), yi.flatten()])) zi = k(np.vstack([yi.flatten(),xi.flatten()])) # And this. fig = plt.figure(figsize=(9,10)) ax1 = fig.add_subplot(111) ax1.pcolormesh(xi, yi, zi.reshape(xi.shape), alpha=0.5) ax1.plot(y,x, "o") ax1.set_xlim(0, 740) ax1.set_ylim(515, 0) im = plt.imread('statszone_football_pitch.jpg') ax1.imshow(im, extent=[0, 740, 0, 515], aspect='auto') plt.show()

结果如下：

pcolormesh在图像上

我猜这是你散乱点的一种密度图。

Your code seems to work fine. The problem is that what represents your x-axis in the image is not what you giving as x-axis in the plot. That said if you change x to y (and vice-versa) you'll obtain the correct plot:

from scipy.stats.kde import gaussian_kde import matplotlib.pyplot as plt import numpy as np x, y = np.genfromtxt('pogba_t1314.csv', delimiter=',', unpack=True) print(x[1], y[1]) y = y[np.logical_not(np.isnan(y))] x = x[np.logical_not(np.isnan(x))] k = gaussian_kde(np.vstack([x, y])) #xi, yi = np.mgrid[x.min():x.max():x.size**0.5*1j,y.min():y.max():y.size**0.5*1j] xi, yi = np.mgrid[y.min():y.max():y.size**0.5*1j,x.min():x.max():x.size**0.5*1j] # Changed this. #zi = k(np.vstack([xi.flatten(), yi.flatten()])) zi = k(np.vstack([yi.flatten(),xi.flatten()])) # And this. fig = plt.figure(figsize=(9,10)) ax1 = fig.add_subplot(111) ax1.pcolormesh(xi, yi, zi.reshape(xi.shape), alpha=0.5) ax1.plot(y,x, "o") ax1.set_xlim(0, 740) ax1.set_ylim(515, 0) im = plt.imread('statszone_football_pitch.jpg') ax1.imshow(im, extent=[0, 740, 0, 515], aspect='auto') plt.show()

The result is this:

pcolormesh over image

I guess it's a kind of density plot for your scattered points.

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