如何获取Matplotlib生成的散点图的像素坐标？

Question

我使用Matplotlib生成散点图的PNG文件.现在,每个散点图,除了PNG文件,我也想在生成散点图中各点的像素坐标列表.

我用来为散点图生成PNG文件的代码基本上是这样的:

from matplotlib.figure import Figure
from matplotlib.pyplot import setp
from matplotlib.backends.backend_agg import FigureCanvasAgg

...

fig = Figure(figsize=(3, 3), dpi=100)
ax = fig.gca()
for (x, y), m, c in zip(points, markers, colors):
    ax.scatter(x, y, marker=m, c=c, s=SIZE, vmin=VMIN, vmax=VMAX)

# several assorted tweaks like ax.spines['top'].set_color('none'), etc.

setp(fig, 'facecolor', 'none')

# FigureCanvasAgg(fig).print_png(FILEPATH)

...(UPPERCASE中的变量代表可设置的参数).

我怎样才能(px, py)在结果PNG中产生一对像素坐标对应的点points？

[编辑:删除了一些废话imshow.]

[编辑:

好的,这是我最终提出的,基于Joe Kington的建议.

# continued from above...

cnvs = FigureCanvasAgg(fig)
fig.set_canvas(cnvs)
_, ht = cnvs.get_width_height()
pcoords = [(int(round(t[0])), int(round(ht - t[1]))) for t in
           ax.transData.transform(points)]
fig.savefig(FILEPATH, dpi=fig.dpi)

得到的像素坐标(in pcoords)非常接近正确的值.事实上,y coords是完全正确的.x坐标是1或2像素关闭,这足以满足我的目的.

]

Answer 1

这样做很简单,但是为了理解发生了什么,你需要阅读matplotlib的变换.该转换教程是一个良好的开端.

无论如何,这是一个例子:

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
points, = ax.plot(range(10), 'ro')
ax.axis([-1, 10, -1, 10])

# Get the x and y data and transform it into pixel coordinates
x, y = points.get_data()
xy_pixels = ax.transData.transform(np.vstack([x,y]).T)
xpix, ypix = xy_pixels.T

# In matplotlib, 0,0 is the lower left corner, whereas it's usually the upper 
# right for most image software, so we'll flip the y-coords...
width, height = fig.canvas.get_width_height()
ypix = height - ypix

print 'Coordinates of the points in pixel coordinates...'
for xp, yp in zip(xpix, ypix):
    print '{x:0.2f}\t{y:0.2f}'.format(x=xp, y=yp)

# We have to be sure to save the figure with it's current DPI
# (savfig overrides the DPI of the figure, by default)
fig.savefig('test.png', dpi=fig.dpi)

这会产生:

Coordinates of the points in pixel coordinates...
125.09  397.09
170.18  362.18
215.27  327.27
260.36  292.36
305.45  257.45
350.55  222.55
395.64  187.64
440.73  152.73
485.82  117.82
530.91  82.91