我想复制here所描述的雷达图
主要区别是我的数据有两个不同的spoke_labels

import numpy as np

import matplotlib.pyplot as plt
from matplotlib.patches import Circle, RegularPolygon
from matplotlib.path import Path
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projection
from matplotlib.spines import Spine
from matplotlib.transforms import Affine2D

def radar_factory(num_vars, frame='circle'):
    """
    Create a radar chart with `num_vars` axes.

    This function creates a RadarAxes projection and registers it.

    Parameters
    ----------
    num_vars : int
        Number of variables for radar chart.
    frame : {'circle', 'polygon'}
        Shape of frame surrounding axes.

    """
    # calculate evenly-spaced axis angles
    theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)

    class RadarTransform(PolarAxes.PolarTransform):

        def transform_path_non_affine(self, path):
            # Paths with non-unit interpolation steps correspond to gridlines,
            # in which case we force interpolation (to defeat PolarTransform's
            # autoconversion to circular arcs).
            if path._interpolation_steps > 1:
                path = path.interpolated(num_vars)
            return Path(self.transform(path.vertices), path.codes)

    class RadarAxes(PolarAxes):

        name = 'radar'
        PolarTransform = RadarTransform

        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)
            # rotate plot such that the first axis is at the top
            self.set_theta_zero_location('N')

        def fill(self, *args, closed=True, **kwargs):
            """Override fill so that line is closed by default"""
            return super().fill(closed=closed, *args, **kwargs)

        def plot(self, *args, **kwargs):
            """Override plot so that line is closed by default"""
            lines = super().plot(*args, **kwargs)
            for line in lines:
                self._close_line(line)

        def _close_line(self, line):
            x, y = line.get_data()
            # FIXME: markers at x[0], y[0] get doubled-up
            if x[0] != x[-1]:
                x = np.append(x, x[0])
                y = np.append(y, y[0])
                line.set_data(x, y)

        def set_varlabels(self, labels):
            self.set_thetagrids(np.degrees(theta), labels)

        def _gen_axes_patch(self):
            # The Axes patch must be centered at (0.5, 0.5) and of radius 0.5
            # in axes coordinates.
            if frame == 'circle':
                return Circle((0.5, 0.5), 0.5)
            elif frame == 'polygon':
                return RegularPolygon((0.5, 0.5), num_vars,
                                      radius=.5, edgecolor="k")
            else:
                raise ValueError("Unknown value for 'frame': %s" % frame)

        def _gen_axes_spines(self):
            if frame == 'circle':
                return super()._gen_axes_spines()
            elif frame == 'polygon':
                # spine_type must be 'left'/'right'/'top'/'bottom'/'circle'.
                spine = Spine(axes=self,
                              spine_type='circle',
                              path=Path.unit_regular_polygon(num_vars))
                # unit_regular_polygon gives a polygon of radius 1 centered at
                # (0, 0) but we want a polygon of radius 0.5 centered at (0.5,
                # 0.5) in axes coordinates.
                spine.set_transform(Affine2D().scale(.5).translate(.5, .5)
                                    + self.transAxes)
                return {'polar': spine}
            else:
                raise ValueError("Unknown value for 'frame': %s" % frame)

    register_projection(RadarAxes)
    return theta

def example_data():
    data = [
        ['WW yield', 'WR yield', 'WB yield', 'SM yield','N leached','Net N minerilization',
        'Emitted soil $N_{2}$O','Organic soil N','Organic soil C','Emitted soil $CO_{2}$'],
        ('WW-WR-WB-SM', [
            [0.629053747,0.254076315,0.332256277,0.204105265,0.133944624,0.318652711,0.075673605,0.207815416,0.268688223,
             0.355527198],
            [0.638266936,0.256631724,0.34185792,0.212581768,0.120456481,0.335636918,0.075777949,0.2048961,0.265332129,
             0.363333365],
            [0.6216949,0.261880304,0.373231106,0.458953575,0.099304806,0.389855072,0.060179553,0.376827867,0.411683333,
             0.4480729]]),
        ['WR yield', 'WB yield', 'WW yield', 'SP yield','N leached','Net N minerilization',
        'Emitted soil N$_{2}$O','Organic soil N','Organic soil C','Emitted soil $CO_{2}$'], **#here I have different label**
        ('WR-WB-WW-SP', [
            [0.303873044,0.343198048,0.72326815,0.470654888,0.147377613,0.349091327,0.087892159,0.215422083,0.273716559,
             0.357139508],
            [0.344731808,0.336010854,0.714918073,0.52981827,0.102251601,0.378815911,0.062455782,0.205903907,0.265096764,
             0.377555353],
            [0.329971424,0.380316019,0.809793741,0.59992031,0.110443314,0.457303888,0.08134116,0.413050845,0.435613362,
             0.472325334]])
    ]
    return data

if __name__ == '__main__':
    N = 10
    theta = radar_factory(N, frame='polygon')

    data = example_data()
    spoke_labels = data.pop(0)

    fig, axs = plt.subplots(figsize=(20, 20), nrows=2, ncols=1,
                            subplot_kw=dict(projection='radar'))

    labels = ('Factor 1', 'Factor 2', 'Factor 3')
    num_vars = len(labels)
    colors = ['b', 'r', 'g']
    # Plot the two cases from the example data on separate axes
    for ax, (title, case_data) in zip(axs.flat, data):
        #ax.set_rgrids([0.2, 0.4, 0.6, 0.8],angle=360)
        ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
                     horizontalalignment='center', verticalalignment='center')
        for d, color in zip(case_data, colors):
            ax.plot(theta, d,'--', color=color)
        ax.set_varlabels(spoke_labels)
        ax.set_rgrids([0,0.1, 0.2, 0.3, 0.4, 0.5,0.6,0.7,0.8],angle=90)
        ax.grid(color='silver',linestyle='-', linewidth=0.5,dash_capstyle='projecting',ds='default')
        ax.spines['polar'].set_visible(False)
        ax.set_rlabel_position(90 / num_vars)
        ax.legend(['Factor 1', 'Factor 2', 'Factor 3'],bbox_to_anchor=(1.1, 1.05),loc='best',frameon=False)

    

    plt.show()

这个错误是因为zip（axs.flat，data）接收到太多的值而无法解包。如何解决？应该将每种情况的数据分开并在两个轴上绘图。如果是这样的话，欣赏任何相关的例子…

[![~\AppData\Local\Temp\ipykernel_17292\4071153916.py in <module>
    157     colors = \['b', 'r', 'g'\]
    158     # Plot the four cases from the example data on separate axes
--> 159     for ax, (title, case_data) in zip(axs.flat, data):
    160         #ax.set_rgrids(\[0.2, 0.4, 0.6, 0.8\],angle=360)
    161         ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),

ValueError: too many values to unpack (expected 2)][2]][2]