python - Matplotlib 中的一半或四分之一极坐标图？

Question

我正在尝试在 Matplotlib 中制作一个 180 度而不是 360 度的极坐标图，类似于MATLAB中的http://www.mathworks.com/matlabcentral/fileexchange/27230-half-polar-coordinates-figure-plot-function-halfpolar . 有任何想法吗？

Answer 1

以下适用于 matplotlib 2.1 或更高版本。matplotlib 页面上也有一个示例。
您可以使用通常的极坐标图，ax = fig.add_subplot(111, polar=True)并限制 theta 范围。对于半极坐标图

ax.set_thetamin(0)
ax.set_thetamax(180)

或四分之一极坐标图

ax.set_thetamin(0)
ax.set_thetamax(90)

完整示例：

import matplotlib.pyplot as plt
import numpy as np

theta = np.linspace(0,np.pi)
r = np.sin(theta)

fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
c = ax.scatter(theta, r, c=r, s=10, cmap='hsv', alpha=0.75)

ax.set_thetamin(0)
ax.set_thetamax(180)

plt.show()

Answer 2

如果有人只需要一个简单的四分之一半图，官方 matplotlib 文档中的示例代码可能会使事情变得有些模糊。我写了一个代码片段，可以帮助不熟悉AxisArtists 这里的人。

"""
Reference:
1. https://gist.github.com/ycopin/3342888
2. http://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html#axisartist
"""

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.projections import PolarAxes
from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear, FloatingSubplot
import mpl_toolkits.axisartist.grid_finder as gf


def generate_polar_axes():
    polar_trans = PolarAxes.PolarTransform()

    # Setup the axis, here we map angles in degrees to angles in radius
    phi_degree = np.arange(0, 90, 10)
    tlocs = phi_degree * np.pi / 180
    gl1 = gf.FixedLocator(tlocs)  # Positions
    tf1 = gf.DictFormatter(dict(zip(tlocs, map(str, phi_degree))))

    # Standard deviation axis extent
    radius_min = 0
    radius_max = 1

    # Set up the axes range in the parameter "extremes"
    ghelper = GridHelperCurveLinear(polar_trans, extremes=(0, np.pi / 2,  # 1st quadrant
                                                           radius_min, radius_max),
                                    grid_locator1=gl1,
                                    tick_formatter1=tf1,
                                    )

    figure = plt.figure()

    floating_ax = FloatingSubplot(figure, 111, grid_helper=ghelper)
    figure.add_subplot(floating_ax)

    # Adjust axes
    floating_ax.axis["top"].set_axis_direction("bottom")  # "Angle axis"
    floating_ax.axis["top"].toggle(ticklabels=True, label=True)
    floating_ax.axis["top"].major_ticklabels.set_axis_direction("top")
    floating_ax.axis["top"].label.set_axis_direction("top")
    floating_ax.axis["top"].label.set_text("angle (deg)")

    floating_ax.axis["left"].set_axis_direction("bottom")  # "X axis"
    floating_ax.axis["left"].label.set_text("radius")

    floating_ax.axis["right"].set_axis_direction("top")  # "Y axis"
    floating_ax.axis["right"].toggle(ticklabels=True)
    floating_ax.axis["right"].major_ticklabels.set_axis_direction("left")

    floating_ax.axis["bottom"].set_visible(False)  # Useless

    # Contours along standard deviations
    floating_ax.grid(True)
    floating_ax.set_title("Quarter polar plot")

    data_ax = floating_ax.get_aux_axes(polar_trans)  # return the axes that can be plotted on

    return figure, data_ax


if __name__ == "__main__":
    
    # Plot data onto the defined polar axes
    fig, ax = generate_polar_axes()

    theta = np.random.rand(10) * np.pi / 2

    radius = np.random.rand(10)

    ax.scatter(theta, radius)

    fig.savefig("test.png")