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Update README.md

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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -34,9 +34,8 @@ $$ n_{1}+N_{1}\cdot (n_{2}+N_{2}\cdot (n_{3}+N_{3}\cdot (\cdots +N_{d-1}n_{d})\c
[Illustration of difference between row- and column-major ordering](https://en.wikipedia.org/wiki/Row-_and_column-major_order#/media/File:Row_and_column_major_order.svg) (by CMG Lee. CC BY-SA 4.0) [Illustration of difference between row- and column-major ordering](https://en.wikipedia.org/wiki/Row-_and_column-major_order#/media/File:Row_and_column_major_order.svg) (by CMG Lee. CC BY-SA 4.0)
## Information about the inner-workings of the matrix
### [numpy.ndarray.flags](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html) ## [numpy.ndarray.flags](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html)
```python ```python
ndarray.flags ndarray.flags
@ -62,7 +61,7 @@ Attributes:
|FARRAY (FA)|BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.| |FARRAY (FA)|BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.|
#### 1d ### 1d
```python ```python
import numpy as np import numpy as np
@ -82,7 +81,7 @@ Output
WRITEBACKIFCOPY : False WRITEBACKIFCOPY : False
``` ```
#### 2d ### 2d
```python ```python
import numpy as np import numpy as np
@ -101,3 +100,42 @@ Output
ALIGNED : True ALIGNED : True
WRITEBACKIFCOPY : False WRITEBACKIFCOPY : False
``` ```
## C - contigousness
There are situations when you need a C_CONTIGUOUS matrix. Examples are PyBind11 and numba.
```python
import numpy as np
a = np.arange(1, 10)
print(a.flags["C_CONTIGUOUS"]) # -> True
print(a[::1].flags["C_CONTIGUOUS"]) # -> True
print(a[::2].flags["C_CONTIGUOUS"]) # -> False
print(a[::2].copy().flags["C_CONTIGUOUS"]) # -> True
```
**You may want to make a copy of B for PyBind11 and numba or...**
## [numpy.ascontiguousarray](https://numpy.org/doc/stable/reference/generated/numpy.ascontiguousarray.html)
```python
numpy.ascontiguousarray(a, dtype=None, *, like=None)
```
> Return a contiguous array (ndim >= 1) in memory (C order).
```python
import numpy as np
a = np.arange(1, 10)
print(a.flags["C_CONTIGUOUS"]) # -> True
print(a[::2].flags["C_CONTIGUOUS"]) # -> False
print(np.ascontiguousarray(a[::2]).flags["C_CONTIGUOUS"]) # -> True
```