我正在编写一些代码，希望在其中进行绘图。 为此，我使用了matplotlib（v 2.2.3）的tricontourf函数，因为数据是非结构化的。我要绘制的每个数组都包含1000个float32值。
在某些情况下，一切正常，情节按预期出炉。但是，对于其中的某些数组，我会收到以下错误消息：
TypeError：最后的轮廓线必须给出级别；查看帮助（contourf） 异常RuntimeError：RuntimeError（“无法加入当前线程”，）被忽略
仅当我将轮廓级别指定为整数时才会出现错误。如果我改为将“ levels” arg指定为具有相同数量级别的np.linspace，则不会有问题。 但是由于我不知道为什么错误会首先出现（因为在某些情况下它会起作用），因此我有点犹豫，只是用那个替换参数。
希望有人对此有经验并能回答我的问题。
程序中对应的代码行是：
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 22})
x = [0.001,0.002,0.003,0.004,0.004,0.005,0.007,0.007,0.008,0.008,0.008,0.009,0.009,0.009,0.009,0.011,0.012,0.013,0.014,0.014,0.015,0.015,0.015,0.019,0.019,0.020,0.020,0.024,0.024,0.025,0.026,0.027,0.028,0.030,0.032,0.034,0.036,0.039,0.040,0.041,0.041,0.042,0.044,0.045,0.046,0.046,0.047,0.049,0.050,0.051,0.051,0.052,0.052,0.055,0.058,0.060,0.062,0.064,0.064,0.065,0.065,0.065,0.066,0.067,0.068,0.068,0.069,0.070,0.070,0.073,0.075,0.076,0.076,0.076,0.076,0.077,0.080,0.082,0.083,0.084,0.084,0.087,0.089,0.090,0.090,0.091,0.092,0.092,0.092,0.093,0.093,0.093,0.095,0.095,0.096,0.096,0.096,0.097,0.097,0.097,0.098,0.098,0.098,0.098,0.099,0.099,0.102,0.104,0.104,0.104,0.105,0.105,0.105,0.109,0.110,0.111,0.113,0.115,0.115,0.116,0.118,0.121,0.121,0.122,0.124,0.124,0.125,0.125,0.126,0.127,0.127,0.128,0.128,0.130,0.131,0.132,0.132,0.132,0.133,0.134,0.135,0.137,0.138,0.138,0.140,0.140,0.140,0.141,0.142,0.146,0.146,0.148,0.150,0.150,0.151,0.152,0.154,0.156,0.157,0.158,0.158,0.158,0.158,0.158,0.159,0.159,0.161,0.161,0.162,0.164,0.167,0.169,0.170,0.171,0.173,0.173,0.173,0.173,0.173,0.176,0.177,0.178,0.179,0.179,0.180,0.181,0.182,0.183,0.184,0.184,0.185,0.186,0.186,0.193,0.194,0.195,0.195,0.195,0.196,0.196,0.197,0.199,0.200,0.201,0.201,0.201,0.202,0.203,0.203,0.204,0.205,0.205,0.206,0.206,0.212,0.214,0.217,0.218,0.221,0.222,0.222,0.223,0.224,0.227,0.227,0.228,0.228,0.228,0.232,0.233,0.234,0.234,0.234,0.236,0.237,0.237,0.237,0.237,0.239,0.242,0.242,0.243,0.243,0.244,0.246,0.246,0.247,0.247,0.247,0.248,0.248,0.249,0.250,0.252,0.253,0.256,0.256,0.256,0.258,0.258,0.261,0.266,0.269,0.270,0.271,0.271,0.271,0.272,0.272,0.275,0.275,0.277,0.278,0.280,0.283,0.283,0.284,0.284,0.286,0.287,0.289,0.289,0.290,0.292,0.295,0.295,0.297,0.299,0.301,0.302,0.303,0.304,0.304,0.305,0.305,0.305,0.306,0.306,0.307,0.308,0.310,0.311,0.312,0.312,0.313,0.313,0.314,0.314,0.314,0.316,0.317,0.318,0.318,0.319,0.319,0.319,0.321,0.322,0.322,0.322,0.323,0.324,0.325,0.325,0.326,0.326,0.328,0.328,0.330,0.333,0.336,0.336,0.337,0.338,0.338,0.339,0.340,0.340,0.341,0.342,0.345,0.345,0.348,0.349,0.350,0.351,0.351,0.352,0.352,0.353,0.354,0.356,0.357,0.359,0.360,0.361,0.362,0.363,0.363,0.363,0.363,0.364,0.365,0.365,0.366,0.366,0.367,0.367,0.368,0.369,0.370,0.374,0.374,0.375,0.376,0.377,0.377,0.378,0.379,0.380,0.381,0.382,0.382,0.384,0.385,0.385,0.386,0.386,0.386,0.387,0.387,0.387,0.388,0.388,0.389,0.390,0.391,0.393,0.393,0.394,0.395,0.396,0.397,0.398,0.398,0.398,0.398,0.399,0.400,0.402,0.402,0.404,0.405,0.405,0.407,0.407,0.407,0.407,0.407,0.408,0.408,0.409,0.410,0.411,0.412,0.412,0.413,0.413,0.414,0.414,0.414,0.415,0.415,0.416,0.417,0.417,0.418,0.418,0.418,0.418,0.418,0.422,0.422,0.424,0.424,0.427,0.427,0.430,0.431,0.431,0.432,0.433,0.433,0.433,0.434,0.436,0.437,0.439,0.440,0.440,0.440,0.442,0.446,0.446,0.448,0.450,0.452,0.453,0.456,0.456,0.456,0.457,0.458,0.459,0.459,0.460,0.460,0.461,0.461,0.462,0.462,0.463,0.463,0.464,0.465,0.465,0.466,0.466,0.470,0.470,0.473,0.474,0.477,0.479,0.480,0.481,0.482,0.482,0.483,0.483,0.484,0.485,0.487,0.487,0.487,0.487,0.488,0.488,0.492,0.492,0.493,0.495,0.496,0.497,0.498,0.499,0.501,0.503,0.504,0.504,0.504,0.506,0.507,0.510,0.511,0.511,0.512,0.512,0.513,0.513,0.514,0.515,0.516,0.516,0.517,0.517,0.518,0.520,0.520,0.521,0.523,0.525,0.525,0.529,0.529,0.530,0.532,0.532,0.532,0.533,0.534,0.534,0.535,0.535,0.536,0.536,0.538,0.539,0.540,0.540,0.540,0.540,0.542,0.544,0.544,0.544,0.545,0.547,0.547,0.547,0.547,0.549,0.549,0.550,0.550,0.550,0.553,0.554,0.555,0.557,0.557,0.558,0.559,0.562,0.562,0.563,0.563,0.565,0.567,0.567,0.567,0.568,0.569,0.570,0.570,0.570,0.572,0.572,0.573,0.575,0.575,0.577,0.578,0.579,0.579,0.582,0.583,0.584,0.584,0.585,0.586,0.586,0.586,0.586,0.587,0.587,0.590,0.592,0.596,0.596,0.596,0.597,0.597,0.598,0.603,0.606,0.607,0.610,0.611,0.612,0.612,0.612,0.614,0.614,0.614,0.614,0.615,0.615,0.615,0.617,0.619,0.620,0.620,0.623,0.623,0.624,0.624,0.625,0.626,0.626,0.629,0.629,0.629,0.631,0.631,0.633,0.638,0.639,0.639,0.640,0.640,0.641,0.641,0.642,0.643,0.644,0.644,0.644,0.645,0.645,0.645,0.646,0.648,0.652,0.652,0.653,0.654,0.657,0.657,0.658,0.658,0.658,0.659,0.659,0.659,0.659,0.662,0.664,0.665,0.665,0.665,0.666,0.668,0.668,0.669,0.670,0.670,0.671,0.671,0.672,0.674,0.676,0.677,0.677,0.678,0.680,0.680,0.683,0.683,0.683,0.686,0.686,0.687,0.687,0.687,0.688,0.688,0.689,0.690,0.692,0.692,0.692,0.693,0.693,0.694,0.694,0.695,0.695,0.696,0.696,0.697,0.699,0.699,0.700,0.700,0.700,0.701,0.701,0.702,0.703,0.703,0.704,0.705,0.706,0.708,0.710,0.711,0.711,0.712,0.712,0.713,0.713,0.713,0.715,0.716,0.718,0.718,0.718,0.718,0.718,0.718,0.720,0.721,0.721,0.723,0.724,0.724,0.726,0.728,0.729,0.729,0.734,0.735,0.736,0.740,0.740,0.741,0.741,0.742,0.742,0.742,0.742,0.743,0.746,0.747,0.748,0.748,0.750,0.750,0.753,0.755,0.755,0.755,0.758,0.760,0.760,0.763,0.763,0.764,0.767,0.768,0.770,0.776,0.781,0.781,0.783,0.785,0.786,0.786,0.786,0.789,0.790,0.790,0.793,0.794,0.795,0.796,0.797,0.799,0.799,0.800,0.801,0.802,0.802,0.803,0.805,0.807,0.809,0.809,0.811,0.812,0.814,0.815,0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y = 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z1 = 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z2 = 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def plot_function1(x, y, z):
print('plotting evil plans')
fig = plt.figure(figsize=(36, 32))
ax = fig.add_subplot(111)
ax.tricontourf(x, y, z, np.linspace(min(z), max(z), 1000), cmap='magma', antialiased=False)
plt.close(fig)
def plot_function2(x, y, z):
print('plotting')
fig = plt.figure(figsize=(36, 32))
ax = fig.add_subplot(111)
ax.tricontourf(x, y, z, 1000, cmap='magma', antialiased=False)
plt.close(fig)
x, y, z1, z2 = np.asarray(x), np.asarray(y), np.asarray(z1), np.asarray(z2)
print('shape of x1 = {} and dtype = {}'.format(np.shape(x), x.dtype))
print('shape of y1 = {} and dtype = {}'.format(np.shape(y), y.dtype))
print('shape of z1 = {} and dtype = {}'.format(np.shape(z1), z1.dtype))
print('shape of z2 = {} and dtype = {}'.format(np.shape(z2), z2.dtype))
plot_function1(x, y, z1)
plot_function2(x, y, z1)
plot_function1(x, y, z2)
plot_function2(x, y, z2)
如果位置x和y处的值由z1给出，则上面的示例可以很好地工作，但是无法使用第二版的plot函数进行绘制，即以“ levels”作为整数的参数。
以上代码的完整输出为：
x1的形状=（1000，）和dtype = float64
形状y1 =（1000，）和dtype = float64
z1 =（1000，）和dtype = float64的形状
z2 =（1000，）和dtype = float64的形状
制定邪恶计划
密谋
制定邪恶计划
密谋
追溯（最近一次通话）：
中的文件“ stack_example.py”，第40行 plot_function2（x，y，z2）
plot_function2中第27行的文件“ stack_example.py”
ax.tricontourf（x，y，z，1000，cmap ='magma'，antialiased = False）
在tricontourf中的文件“ /home/patrik/.local/lib/python2.7/site-packages/matplotlib/axes/_axes.py”，行8144
return mtri.tricontourf（self，* args，** kwargs）
在tricontourf中，文件“ /home/patrik/.local/lib/python2.7/site-packages/matplotlib/tri/tricontour.py”，第282行，
返回TriContourSet（ax，* args，** kwargs）
__init__
中的第40行的文件“ /home/patrik/.local/lib/python2.7/site-packages/matplotlib/tri/tricontour.py” ContourSet。初始化（自我，斧头，* args，** kwargs）
__init__
中的第890行的文件“ /home/patrik/.local/lib/python2.7/site-packages/matplotlib/contour.py” kwargs = self._process_args（* args，** kwargs）
_process_args中第51行的文件“ /home/patrik/.local/lib/python2.7/site-packages/matplotlib/tri/tricontour.py”
tri，z = self._contour_args（args，kwargs）
_contour_args中的文件“ /home/patrik/.local/lib/python2.7/site-packages/matplotlib/tri/tricontour.py”，第94行，
self._contour_level_args（z，args [1：]）
_contour_level_args中的文件“ /home/patrik/.local/lib/python2.7/site-packages/matplotlib/contour.py”，行1220
.format（fn））
类型错误：最后的轮廓参数必须给出级别；参见help（contourf）