For the following code

```
# Numerical operation
SN_map_final = (new_SN_map - mean_SN) / sigma_SN
# Plot figure
fig12 = plt.figure(12)
fig_SN_final = plt.imshow(SN_map_final, interpolation='nearest')
plt.colorbar()
fig12 = plt.savefig(outname12)
```

with `new_SN_map`

being a 1D array and `mean_SN`

and `sigma_SN`

being constants, I get the following error.

```
Traceback (most recent call last):
File "c:\Users\Valentin\Desktop\Stage M2\density_map_simple.py", line 546, in <module>
fig_SN_final = plt.imshow(SN_map_final, interpolation='nearest')
File "c:\users\valentin\appdata\local\enthought\canopy\user\lib\site-packages\matplotlib\pyplot.py", line 3022, in imshow
**kwargs)
File "c:\users\valentin\appdata\local\enthought\canopy\user\lib\site-packages\matplotlib\__init__.py", line 1812, in inner
return func(ax, *args, **kwargs)
File "c:\users\valentin\appdata\local\enthought\canopy\user\lib\site-packages\matplotlib\axes\_axes.py", line 4947, in imshow
im.set_data(X)
File "c:\users\valentin\appdata\local\enthought\canopy\user\lib\site-packages\matplotlib\image.py", line 453, in set_data
raise TypeError("Invalid dimensions for image data")
TypeError: Invalid dimensions for image data
```

What is the source of this error? I thought my numerical operations were allowed.

This question is related to
`python`

`arrays`

`numpy`

`matplotlib`

There is a (somewhat) related question on StackOverflow:

Here the problem was that an array of shape (nx,ny,1) is still considered a 3D array, and must be `squeeze`

d or sliced into a 2D array.

More generally, the reason for the Exception

TypeError: Invalid dimensions for image data

is shown here: `matplotlib.pyplot.imshow()`

needs a 2D array, or a 3D array with the third dimension being of shape 3 or 4!

You can easily check this with (these checks are done by `imshow`

, this function is only meant to give a more specific message in case it's not a valid input):

```
from __future__ import print_function
import numpy as np
def valid_imshow_data(data):
data = np.asarray(data)
if data.ndim == 2:
return True
elif data.ndim == 3:
if 3 <= data.shape[2] <= 4:
return True
else:
print('The "data" has 3 dimensions but the last dimension '
'must have a length of 3 (RGB) or 4 (RGBA), not "{}".'
''.format(data.shape[2]))
return False
else:
print('To visualize an image the data must be 2 dimensional or '
'3 dimensional, not "{}".'
''.format(data.ndim))
return False
```

In your case:

```
>>> new_SN_map = np.array([1,2,3])
>>> valid_imshow_data(new_SN_map)
To visualize an image the data must be 2 dimensional or 3 dimensional, not "1".
False
```

The `np.asarray`

is what is done internally by `matplotlib.pyplot.imshow`

so it's generally best you do it too. If you have a numpy array it's obsolete but if not (for example a `list`

) it's necessary.

In your specific case you got a 1D array, so you need to add a dimension with `np.expand_dims()`

```
import matplotlib.pyplot as plt
a = np.array([1,2,3,4,5])
a = np.expand_dims(a, axis=0) # or axis=1
plt.imshow(a)
plt.show()
```

or just use something that accepts 1D arrays like `plot`

:

```
a = np.array([1,2,3,4,5])
plt.plot(a)
plt.show()
```

Source: Stackoverflow.com