165 lines
4.7 KiB
Markdown
165 lines
4.7 KiB
Markdown
# New matrices
|
||
{:.no_toc}
|
||
|
||
<nav markdown="1" class="toc-class">
|
||
* TOC
|
||
{:toc}
|
||
</nav>
|
||
|
||
## The goal
|
||
|
||
Making a new matrix...
|
||
|
||
Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
|
||
|
||
Using **import numpy as np** is the standard.
|
||
|
||
## Simple example -- new [np.zeros()](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html)
|
||
|
||
Define the size of your new matrix with a tuple, e.g.
|
||
|
||
```python
|
||
M = numpy.zeros((DIM_0, DIM_1, DIM_2, …))
|
||
```
|
||
|
||
### 1d
|
||
|
||
```python
|
||
import numpy as np
|
||
|
||
M = np.zeros((2))
|
||
print(M)
|
||
```
|
||
|
||
Output:
|
||
|
||
```python
|
||
[0. 0.]
|
||
```
|
||
|
||
### 2d
|
||
|
||
```python
|
||
import numpy as np
|
||
|
||
M = np.zeros((2, 3))
|
||
print(M)
|
||
```
|
||
|
||
Output:
|
||
|
||
```python
|
||
[[0. 0. 0.]
|
||
[0. 0. 0.]]
|
||
```
|
||
|
||
### 3d
|
||
|
||
```python
|
||
import numpy as np
|
||
|
||
M = np.zeros((2, 3, 4))
|
||
print(M)
|
||
```
|
||
|
||
Output:
|
||
|
||
```python
|
||
[[[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]]
|
||
|
||
[[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]]]
|
||
```
|
||
|
||
## Simple example -- recycle [np.zeros_like()](https://numpy.org/doc/stable/reference/generated/numpy.zeros_like.html)
|
||
|
||
If you have a matrix with the same size you want then you can use zeros_like. This will also copy other properties like the data type.
|
||
|
||
as a prototype use
|
||
|
||
N = numpy.zeros_like(M)
|
||
|
||
```python
|
||
import numpy as np
|
||
|
||
M = np.zeros((2, 3, 4))
|
||
|
||
N = np.zeros_like(M)
|
||
print(N)
|
||
```
|
||
|
||
Output:
|
||
|
||
```python
|
||
[[[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]]
|
||
|
||
[[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]
|
||
[0. 0. 0. 0.]]]
|
||
```
|
||
|
||
## Remember unpacking
|
||
|
||
{: .topic-optional}
|
||
This is an optional topic!
|
||
|
||
```python
|
||
import numpy as np
|
||
|
||
d = (3, 4)
|
||
M = np.zeros((2, *d))
|
||
|
||
print(M)
|
||
```
|
||
|
||
## np.empty is not np.zeros
|
||
|
||
If you are sure that you don’t care about what is inside the matrix in the beginning use
|
||
|
||
```python
|
||
M = numpy.empty((DIM_0, DIM_1, DIM_2,...))
|
||
```
|
||
|
||
Empty claims a region in the memory and uses it for a matrix. Zeros goes one step further. It fills the memory with zeros.
|
||
|
||
Thus random junk (i.e. data that was stored prior at that memory position) with be the content of a matrix if you use empty. However, np.empty() is faster than np.zeros().
|
||
|
||
```python
|
||
import numpy as np
|
||
M = np.empty((10, 4))
|
||
print(M)
|
||
```
|
||
|
||
```python
|
||
[[1.66706425e-316 0.00000000e+000 6.89933729e-310 6.89933730e-310]
|
||
[6.89933729e-310 6.89933730e-310 6.89933729e-310 6.89933730e-310]
|
||
[6.89933730e-310 6.89933730e-310 6.89933729e-310 6.89933729e-310]
|
||
[6.89933730e-310 6.89933729e-310 6.89933730e-310 6.89933729e-310]
|
||
[6.89933730e-310 4.30513389e-317 4.30321296e-317 6.89933825e-310]
|
||
[4.30389280e-317 6.89933822e-310 4.30366750e-317 6.89933822e-310]
|
||
[4.30311810e-317 4.30480583e-317 4.30462401e-317 4.30336316e-317]
|
||
[6.89933822e-310 4.30386513e-317 4.30358055e-317 4.30571886e-317]
|
||
[4.30568724e-317 4.30659237e-317 6.89933822e-310 6.89933822e-310]
|
||
[6.89933822e-310 6.89933822e-310 4.30289676e-317 6.89920336e-310]]
|
||
```
|
||
|
||
|
||
## [From shape or value](https://numpy.org/doc/stable/reference/routines.array-creation.html#from-shape-or-value)
|
||
|
||
|||
|
||
|---|---|
|
||
|[empty](https://numpy.org/doc/stable/reference/generated/numpy.empty.html#numpy.empty)(shape[, dtype, order, like])|Return a new array of given shape and type, without initializing entries.|
|
||
|[empty_like](https://numpy.org/doc/stable/reference/generated/numpy.empty_like.html#numpy.empty_like)(prototype[, dtype, order, subok, ...])|Return a new array with the same shape and type as a given array.|
|
||
|[eye](https://numpy.org/doc/stable/reference/generated/numpy.eye.html#numpy.eye)(N[, M, k, dtype, order, like])|Return a 2-D array with ones on the diagonal and zeros elsewhere.|
|
||
|[identity](https://numpy.org/doc/stable/reference/generated/numpy.identity.html#numpy.identity)(n[, dtype, like])|Return the identity array.|
|
||
|[ones](https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones)(shape[, dtype, order, like])|Return a new array of given shape and type, filled with ones.|
|
||
|[ones_like](https://numpy.org/doc/stable/reference/generated/numpy.ones_like.html#numpy.ones_like)(a[, dtype, order, subok, shape])|Return an array of ones with the same shape and type as a given array.|
|
||
|[zeros](https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros)(shape[, dtype, order, like])|Return a new array of given shape and type, filled with zeros.|
|
||
|[zeros_like](https://numpy.org/doc/stable/reference/generated/numpy.zeros_like.html#numpy.zeros_like)(a[, dtype, order, subok, shape])|Return an array of zeros with the same shape and type as a given array.|
|
||
|[full](https://numpy.org/doc/stable/reference/generated/numpy.full.html#numpy.full)(shape, fill_value[, dtype, order, like])|Return a new array of given shape and type, filled with fill_value.|
|
||
|[full_like](https://numpy.org/doc/stable/reference/generated/numpy.full_like.html#numpy.full_like)(a, fill_value[, dtype, order, ...])|Return a full array with the same shape and type as a given array.|
|