我正在使用以下代码绘制一些数据的均值和方差的变化
import matplotlib.pyplot as pyplot
import numpy
vis_mv(data, ax = None):
if ax is None: ax = pyplot.gca()
cmap = pyplot.get_cmap()
colors = cmap(numpy.linspace(0, 1, len(data)))
xs = numpy.arange(len(data)) + 1
means = numpy.array([ numpy.mean(x) for x in data ])
varis = numpy.array([ numpy.var(x) for x in data ])
vlim = max(1, numpy.amax(varis))
# variance
ax.imshow([[0.,1.],[0.,1.]],
cmap = cmap, interpolation = 'bicubic',
extent = (1, len(data), -vlim, vlim), aspect = 'auto'
)
ax.fill_between(xs, -vlim, -varis, color = 'white')
ax.fill_between(xs, varis, vlim, color = 'white')
# mean
ax.plot(xs, means, color = 'white', zorder = 1)
ax.scatter(xs, means, color = colors, edgecolor = 'white', zorder = 2)
return ax
这工作得很好: 但现在我希望能够以垂直方式使用这种可视化,作为某种高级颜色条,类似于另一个情节。我希望可以旋转整个轴及其所有内容, 但我只能找到 this question ,这也没有真正可靠的答案。因此,我尝试自己做如下:
from matplotlib.transforms import Affine2D
ax = vis_mv()
r = Affine2D().rotate_deg(90) + ax.transData
for x in ax.images + ax.lines + ax.collections:
x.set_transform(r)
old = ax.axis()
ax.axis(old[2:4] + old[0:2])
这几乎 达到了目的(请注意过去位于白线上的散点如何被放大并且没有按预期旋转)。
不幸的是,保存 scatter
ing 结果的 PathCollection
没有按预期运行。在尝试了一些东西之后,我发现 scatter 有某种偏移变换,这似乎与其他集合中的正则变换是等价的。
x = numpy.arange(5)
ax = pyplot.gca()
p0, = ax.plot(x)
p1 = ax.scatter(x,x)
ax.transData == p0.get_transform() # True
ax.transData == p1.get_offset_transform() # True
似乎我可能想更改散点图的偏移变换,但我没有设法找到任何允许我更改 PathCollection
上的变换的方法。而且,做我真正想做的事情会变得更加不便。
有人知道是否可以更改偏移变换吗?
提前致谢
最佳答案
不幸的是,PathCollection
没有 .set_offset_transform()
方法,但可以访问私有(private) _transOffset
属性并设置旋转变换
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
from matplotlib.collections import PathCollection
import numpy as np; np.random.seed(3)
def vis_mv(data, ax = None):
if ax is None: ax = plt.gca()
cmap = plt.get_cmap()
colors = cmap(np.linspace(0, 1, len(data)))
xs = np.arange(len(data)) + 1
means = np.array([ np.mean(x) for x in data ])
varis = np.array([ np.var(x) for x in data ])
vlim = max(1, np.amax(varis))
# variance
ax.imshow([[0.,1.],[0.,1.]],
cmap = cmap, interpolation = 'bicubic',
extent = (1, len(data), -vlim, vlim), aspect = 'auto' )
ax.fill_between(xs, -vlim, -varis, color = 'white')
ax.fill_between(xs, varis, vlim, color = 'white')
# mean
ax.plot(xs, means, color = 'white', zorder = 1)
ax.scatter(xs, means, color = colors, edgecolor = 'white', zorder = 2)
return ax
data = np.random.normal(size=(9, 9))
ax = vis_mv(data)
r = Affine2D().rotate_deg(90)
for x in ax.images + ax.lines + ax.collections:
trans = x.get_transform()
x.set_transform(r+trans)
if isinstance(x, PathCollection):
transoff = x.get_offset_transform()
x._transOffset = r+transoff
old = ax.axis()
ax.axis(old[2:4] + old[0:2])
plt.show()
关于python - 在 matplotlib 中转换整个轴(或散点图),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/43892973/