之前在legend的使用中，便已经提及了transforms,用来转换参考系，一般情况下，我们不会用到这个，但是还是了解一下比较好

Data coordinates

最为常见的便是通过set_xlim, 和set_ylim来控制数据坐标

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

x = np.arange(0, 10, 0.005)
y = np.exp(-x/2.) * np.sin(2*np.pi*x)

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)

plt.show()

你可以通过ax.transData来将你的数据坐标，转换成再显示窗口上的像素坐标，单个坐标，或者传入序列都是被允许的

type(ax.transData)

matplotlib.transforms.CompositeGenericTransform

ax.transData.transform((5, 0)) #数据坐标(5, 0) 转换为显示窗口的像素坐标(221.4, 144.72) 这个玩意儿不一定的

array([221.4 , 144.72])

ax.transData.transform(((5, 0), (2, 3)))

array([[221.4 , 144.72],
       [120.96, 470.88]])

你也可以通过使用inverted()来反转，获得数据坐标

inv = ax.transData.inverted()

type(inv)

matplotlib.transforms.CompositeGenericTransform

inv.transform((221.4, 144.72))

array([5., 0.])

下面是一个比较完整的例子

x = np.arange(0, 10, 0.005)
y = np.exp(-x/2.) * np.sin(2*np.pi*x)

fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlim(0, 10)
ax.set_ylim(-1, 1)

xdata, ydata = 5, 0
xdisplay, ydisplay = ax.transData.transform_point((xdata, ydata))

bbox = dict(boxstyle="round", fc="0.8")
arrowprops = dict(
    arrowstyle="->",
    connectionstyle="angle,angleA=0,angleB=90,rad=10")

offset = 72
ax.annotate(\'data = (%.1f, %.1f)\' % (xdata, ydata),
            (xdata, ydata), xytext=(-2*offset, offset), textcoords=\'offset points\',
            bbox=bbox, arrowprops=arrowprops)

disp = ax.annotate(\'display = (%.1f, %.1f)\' % (xdisplay, ydisplay),
                   (xdisplay, ydisplay), xytext=(0.5*offset, -offset), #xytext 好像是text离前面点的距离
                   xycoords=\'figure pixels\', #这个属性来变换坐标系
                   textcoords=\'offset points\',
                   bbox=bbox, arrowprops=arrowprops)

plt.show()

很显然的一点是，当我们改变xlim, ylim的时候，同样的数据点转换成显示窗口后发生变化

ax.transData.transform((5, 0))

array([221.4 , 144.72])

ax.set_ylim(-1, 2)

(-1, 2)

ax.transData.transform((5, 0))

array([221.4 , 108.48])

ax.set_xlim(10, 20)

(10, 20)

ax.transData.transform((5, 0))

array([-113.4 ,  108.48])

Axes coordinates

除了数据坐标系，Axes坐标系是第二常用的，就像在上表中提到的(0, 0)表示左下角，而(1, 1)表示右上角，(0.5, 0.5)则表示中心。我们也可以过分一点，使用(-0.1, 1.1)会显示在axes的外围左上角部分。

fig = plt.figure()
for i, label in enumerate((\'A\', \'B\', \'C\', \'D\')):
    ax = fig.add_subplot(2, 2, i+1)
    ax.text(0.05, 0.95, label, transform=ax.transAxes,
            fontsize=16, fontweight=\'bold\', va=\'top\')

plt.show()

从上面的例子中我们可以看到，想在多个axes中相同的位置放置相似的东西，用ax.transAxes时非常方便的

fig, ax = plt.subplots()
x, y = 10*np.random.rand(2, 1000)
ax.plot(x, y, \'go\', alpha=0.2)  # plot some data in data coordinates

circ = mpatches.Circle((0.5, 0.5), 0.25, transform=ax.transAxes,
                       facecolor=\'blue\', alpha=0.75)
ax.add_patch(circ)
plt.show()

可以看到，上面的椭圆与数据坐标无关，始终放置在中间

Blended transformations 混合坐标系统

import matplotlib.transforms as transforms

fig, ax = plt.subplots()
x = np.random.randn(1000)

ax.hist(x, 30)
ax.set_title(r\'$\sigma=1 \/ \dots \/ \sigma=2$\', fontsize=16)

# the x coords of this transformation are data, and the
# y coord are axes
trans = transforms.blended_transform_factory(
    ax.transData, ax.transAxes)

# highlight the 1..2 stddev region with a span.
# We want x to be in data coordinates and y to
# span from 0..1 in axes coords
rect = mpatches.Rectangle((1, 0), width=1, height=1,
                         transform=trans, color=\'yellow\',
                         alpha=0.5)

ax.add_patch(rect)

plt.show()

注意到，上面我们使用了混合坐标系统，x轴方向是数据坐标系，而y轴方向是axes的坐标系统，我们做一个反转试试

import matplotlib.transforms as transforms

fig, ax = plt.subplots()
x = np.random.randn(1000)

ax.hist(x, 30)
ax.set_title(r\'$\sigma=1 \/ \dots \/ \sigma=2$\', fontsize=16)

# the x coords of this transformation are data, and the
# y coord are axes
trans = transforms.blended_transform_factory(
    ax.transAxes, ax.transData) #调了一下

# highlight the 1..2 stddev region with a span.
# We want x to be in data coordinates and y to
# span from 0..1 in axes coords
rect = mpatches.Rectangle((0.5, 0), width=0.5, height=50,  #注意这里的区别
                         transform=trans, color=\'yellow\',
                         alpha=0.5)

ax.add_patch(rect)

plt.show()

plotting in physical units

fig, ax = plt.subplots(figsize=(5, 4))
x, y = 10*np.random.rand(2, 1000)
ax.plot(x, y*10., \'go\', alpha=0.2)  # plot some data in data coordinates
# add a circle in fixed-units
circ = mpatches.Circle((2.5, 2), 1.0, transform=fig.dpi_scale_trans,
                       facecolor=\'blue\', alpha=0.75)
ax.add_patch(circ)
plt.show()

上面的圆使用了transform=fig.dpi_scale_trans坐标系统，其圆心为(2.5, 2)，半径为1，显然这些都是以figsize为基准的，所以，这个圆会在图片中心，如果我们变换figsize，图片的位置(显示位置)会发生变化

fig, ax = plt.subplots(figsize=(7, 2))
x, y = 10*np.random.rand(2, 1000)
ax.plot(x, y*10., \'go\', alpha=0.2)  # plot some data in data coordinates
# add a circle in fixed-units
circ = mpatches.Circle((2.5, 2), 1.0, transform=fig.dpi_scale_trans,
                       facecolor=\'blue\', alpha=0.75)
ax.add_patch(circ)
plt.show()

再来看一个有趣的例子，虽然我不知道改如何解释

fig, ax = plt.subplots()
xdata, ydata = (0.2, 0.7), (0.5, 0.5)
ax.plot(xdata, ydata, "o")
ax.set_xlim((0, 1))

trans = (fig.dpi_scale_trans +
         transforms.ScaledTranslation(xdata[0], ydata[0], ax.transData))

# plot an ellipse around the point that is 150 x 130 points in diameter...
circle = mpatches.Ellipse((0, 0), 150/72, 130/72, angle=40,
                          fill=None, transform=trans)
ax.add_patch(circle)
plt.show()

注意上面trans后面有个+号，这个表示，显示用dpi_scale_trans，即在图片(0, 0)也就是左下角位置画一个大小合适的椭圆，然后将这个椭圆移动到(x[data][0], y[data][0])位置处,感觉实现是椭圆上点每个都加上(xdata[0], ydata[0])

使用offset transforms 创建阴影效果

fig, ax = plt.subplots()

# make a simple sine wave
x = np.arange(0., 2., 0.01)
y = np.sin(2*np.pi*x)
line, = ax.plot(x, y, lw=3, color=\'blue\')

# shift the object over 2 points, and down 2 points
dx, dy = 2/72., -2/72.
offset = transforms.ScaledTranslation(dx, dy, fig.dpi_scale_trans)
shadow_transform = ax.transData + offset

# now plot the same data with our offset transform;
# use the zorder to make sure we are below the line
ax.plot(x, y, lw=3, color=\'gray\',
        transform=shadow_transform,
        zorder=0.5*line.get_zorder())

ax.set_title(\'creating a shadow effect with an offset transform\')
plt.show()

函数链接

matplotlib.transforms

inverted()-转回来

ScaledTranslation