因此，我正在尝试制作一个正方形的图，该正方形根据存储在7x7矩阵“ nprob”中的概率密度进行着色。
nprob = prob/sum
print(nprob.todense())
x,y = np.meshgrid(np.arange(0,7,1),np.arange(0,7,1))
fig, dens = plt.subplots()
dens.set_title('probability density for...')
dens.set_xlabel('i')
dens.set_ylabel('t')
m = dens.pcolormesh(x, y, nprob[x,y], cmap = 'Blues', shading='auto')
cbar=plt.colorbar(m)
我收到以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-132-6d9dfcd16dcc> in <module>
9 dens.set_xlabel('i')
10 dens.set_ylabel('t')
---> 11 m = dens.pcolormesh(x, y, nprob[x,y], cmap = 'Blues', shading='auto')
12 cbar=plt.colorbar(m)
/opt/miniconda3/lib/python3.8/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1445 def inner(ax, *args, data=None, **kwargs):
1446 if data is None:
-> 1447 return func(ax, *map(sanitize_sequence, args), **kwargs)
1448
1449 bound = new_sig.bind(ax, *args, **kwargs)
/opt/miniconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py in pcolormesh(self, alpha, norm, cmap, vmin, vmax, shading, antialiased, *args, **kwargs)
6090 kwargs.setdefault('edgecolors', 'None')
6091
-> 6092 X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
6093 shading=shading, kwargs=kwargs)
6094 Ny, Nx = X.shape
/opt/miniconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py in _pcolorargs(self, funcname, shading, *args, **kwargs)
5583 if isinstance(Y, np.ma.core.MaskedArray):
5584 Y = Y.data
-> 5585 nrows, ncols = C.shape
5586 else:
5587 raise TypeError(f'{funcname}() takes 1 or 3 positional arguments '
ValueError: not enough values to unpack (expected 2, got 0)
老实说，我经常会遇到这个错误，而且我通常会重新整理一下，直到发现一个我更了解的错误为止，因此可能是时候了解它的含义了。不清楚的是什么？我希望它绘制网格上49个指定点处的概率密度图。
制作一个样本稀疏矩阵（您可能已经提供了一个:(）：
In [31]: from scipy import sparse
In [32]: nprob = sparse.csr_matrix(np.eye(7))
In [33]: nprob
Out[33]:
<7x7 sparse matrix of type '<class 'numpy.float64'>'
with 7 stored elements in Compressed Sparse Row format>
In [34]: nprob.A
Out[34]:
array([[1., 0., 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0.],
[0., 0., 0., 1., 0., 0., 0.],
[0., 0., 0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 1.]])
In [35]: x,y = np.meshgrid(np.arange(0,7,1),np.arange(0,7,1))
注意索引的作用-不多-它仍然是csr矩阵：
In [36]: nprob[x,y]
Out[36]:
<7x7 sparse matrix of type '<class 'numpy.float64'>'
with 7 stored elements in Compressed Sparse Row format>
现在您的情节：
In [37]: fig, dens = plt.subplots()
...: dens.set_title('probability density for...')
...: dens.set_xlabel('i')
...: dens.set_ylabel('t')
Out[37]: Text(0, 0.5, 't')
In [38]: m = dens.pcolormesh(x, y, nprob[x,y], cmap = 'Blues', shading='auto')
Traceback (most recent call last):
File "<ipython-input-38-62cf80a40eaf>", line 1, in <module>
m = dens.pcolormesh(x, y, nprob[x,y], cmap = 'Blues', shading='auto')
File "/usr/local/lib/python3.8/dist-packages/matplotlib/__init__.py", line 1438, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_axes.py", line 6093, in pcolormesh
X, Y, C, shading = self._pcolorargs('pcolormesh', *args,
File "/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_axes.py", line 5582, in _pcolorargs
nrows, ncols = C.shape
ValueError: not enough values to unpack (expected 2, got 0)
但是，如果我们绘制该矩阵的密集版本，该怎么办：
In [39]: m = dens.pcolormesh(x, y, nprob[x,y].A, cmap = 'Blues', shading='auto')
有用。
plt对稀疏矩阵一无所知。我怀疑它只是在做：
In [41]: np.array(nprob)
Out[41]:
array(<7x7 sparse matrix of type '<class 'numpy.float64'>'
with 7 stored elements in Compressed Sparse Row format>, dtype=object)
In [42]: _.shape
Out[42]: ()
那是一个0d对象dtype数组，而不是plot函数期望的2d数组。
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