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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -96,3 +96,30 @@ print(type(np.array(z))) # -> <class 'numpy.ndarray'>
print(type(z)) # -> <class 'int'> print(type(z)) # -> <class 'int'>
print(z.shape) # -> AttributeError: 'int' object has no attribute 'shape' print(z.shape) # -> AttributeError: 'int' object has no attribute 'shape'
``` ```
## Stop vanishing dimensions
One way to do stop vanishing dimensions is to use slices of thickness 1. If you want the nth element, then use **n:n+1**:
```python
import numpy as np
data = np.zeros((5, 3, 2))
# All the same dimensionwise
print(data.shape) # -> (5, 3, 2)
print(data[0:1, :, :].shape) # -> (1, 3, 2)
print(data[:, 0:1, :].shape) # -> (5, 1, 2)
print(data[:, :, 0:1].shape) # -> (5, 3, 1)
print(data[:, 0:1, 0:1].shape) # -> (5, 1, 1)
print(data[0:1, :, 0:1].shape) # -> (1, 3, 1)
print(data[0:1, 0:1, :].shape) # -> (1, 1, 2)
print(data[0:1, 0:1, 0:1].shape) # -> (1, 1, 1)
print(type(data[0:1, 0:1, 0:1])) # -> <class 'numpy.ndarray'>
```
**Please understand this creates a view which is connected to original data.** If necessary make a **copy()**.