使 matplotlib 自动缩放忽略一些图

Question

我使用 matplotib 的 Axes API 来绘制一些图形。我绘制的其中一条线代表理论预期线。它在原始 y 和 x 限制之外没有任何意义。我想要的是 matlplotlib 在自动缩放限制时忽略它。我以前做的是检查当前限制是多少，然后绘制并重置限制。问题是，当我绘制第三张图时，限制与理论线一起重新计算，这确实扩大了图表。

# Boilerplate
from matplotlib.figure import Figure
from matplotlib.backends.backend_pdf import FigureCanvasPdf
from numpy import sin, linspace


fig = Figure()
ax = fig.add_subplot(1,1,1)

x1 = linspace(-1,1,100)
ax.plot(x1, sin(x1))
ax.plot(x1, 3*sin(x1))
# I wish matplotlib would not consider the second plot when rescaling
ax.plot(x1, sin(x1/2.0))
# But would consider the first and last

canvas_pdf = FigureCanvasPdf(fig)
canvas_pdf.print_figure("test.pdf")

Answer 1

显而易见的方法是手动设置您想要的限制。 （例如ax.axis([xmin, xmax, ymin, ymax])）

如果您不想手动找出限制，您有几个选择...

正如几个人（tillsten、Yann 和 Vorticity）所提到的，如果您可以绘制最后要忽略的函数，那么您可以在绘制之前禁用自动缩放或将 scaley=False kwarg 传递给 plot

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
x1 = np.linspace(-1,1,100)

ax.plot(x1, np.sin(x1))
ax.plot(x1, np.sin(x1 / 2.0))
ax.autoscale(False)         #You could skip this line and use scalex=False on
ax.plot(x1, 3 * np.sin(x1)) #the "theoretical" plot. It has to be last either way

fig.savefig('test.pdf')

请注意，如果您想控制它，您可以调整最后一个图的 zorder，使其绘制在“中间”。

如果您不想依赖顺序，并且只想指定要自动缩放的行列表，那么您可以执行以下操作：（注意：这是一个简化版本，假设您重新处理 Line2D 对象，而不是一般的 matplotlib 艺术家。）

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms

def main():
    fig, ax = plt.subplots()
    x1 = np.linspace(-1,1,100)

    line1, = ax.plot(x1, np.sin(x1))
    line2, = ax.plot(x1, 3 * np.sin(x1))
    line3, = ax.plot(x1, np.sin(x1 / 2.0))
    autoscale_based_on(ax, [line1, line3])

    plt.show()

def autoscale_based_on(ax, lines):
    ax.dataLim = mtransforms.Bbox.unit()
    for line in lines:
        xy = np.vstack(line.get_data()).T
        ax.dataLim.update_from_data_xy(xy, ignore=False)
    ax.autoscale_view()

if __name__ == '__main__':
    main()

Answer 2

使用 scalex/scaley kw 参数：

plot(x1, 3*sin(x1), scaley=False)

Answer 3

LineCollection objects 可以通过使用autolim=False 参数来忽略：

from matplotlib.collections import LineCollection

fig, ax = plt.subplots()
x1 = np.linspace(-1,1,100)

# Will update limits
ax.plot(x1, np.sin(x1))

# Will not update limits
col = LineCollection([np.column_stack((x1, 3 * np.sin(x1)))], colors='g')
ax.add_collection(col, autolim=False)

# Will still update limits
ax.plot(x1, np.sin(x1 / 2.0))

Answer 4

无论绘图顺序如何，都可以通过创建另一个轴来完成此操作。

在这个版本中，我们创建了一个双轴并禁用该双轴上的自动缩放。通过这种方式，绘图会根据原始坐标轴中绘制的任何内容进行缩放，但不会根据放置在双轴上的任何内容进行缩放。

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
x1 = np.linspace(-1,1,100)
twin_ax = ax.twinx()  # Create a twin axes.
twin_ax.autoscale(False)  # Turn off autoscaling on the twin axes.
twin_ax.set_yticks([])  # Remove the extra tick numbers from the twin axis.

ax.plot(x1, np.sin(x1))
twin_ax.plot(x1, 3 * np.sin(x1), c='green')  # Plotting the thing we don't want to scale on in the twin axes.
ax.plot(x1, np.sin(x1 / 2.0))

twin_ax.set_ylim(ax.get_ylim())  # Make sure the y limits of the twin matches the autoscaled of the original.

fig.savefig('test.pdf')

注意，以上仅防止未缠绕的轴自动缩放（在上述情况下为 y）。为了让它对 x 和 y 都起作用，我们可以对 x 和 y 执行孪生过程（或从头开始创建新轴）：

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
x1 = np.linspace(-1,1,100)
x2 = np.linspace(-2,2,100)  # Would extend the x limits if auto scaled
twin_ax = ax.twinx().twiny()  # Create a twin axes.
twin_ax.autoscale(False)  # Turn off autoscaling on the twin axes.
twin_ax.set_yticks([])  # Remove the extra tick numbers from the twin axis.
twin_ax.set_xticks([])  # Remove the extra tick numbers from the twin axis.

ax.plot(x1, np.sin(x1))
twin_ax.plot(x2, 3 * np.sin(x2), c='green')  # Plotting the thing we don't want to scale on in the twin axes.
ax.plot(x1, np.sin(x1 / 2.0))

twin_ax.set_ylim(ax.get_ylim())  # Make sure the y limits of the twin matches the autoscaled of the original.
twin_ax.set_xlim(ax.get_xlim())  # Make sure the x limits of the twin matches the autoscaled of the original.

fig.savefig('test.png')

Answer 5

作为jam's answer的概括，可以从matplotlib的任何绘图函数中获取一个集合对象，然后用autolim=False重新添加。例如，

fig, ax = plt.subplots()
x1 = np.linspace(-1,1,100)

# Get hold of collection
collection = ax.plot(x1, np.sin(x1))

# Remove collection from the plot
collection.remove()

# Rescale
ax.relim()

# Add the collection without autoscaling
ax.add_collection(collection, autolim=False)