# Tutorial

## Sample data

For the purpose of this, lets generate a test matrix and groups

```python
from mopsy import colsum
import random from rd
# generate a random sparse array with some density
from scipy.sparse import random
mat = random(10, 150, 0.25)

# generate random groups
ngrps = 15
gsets = [x for x in range(15)]
groups = [rd.choice(gsets) for x in range(mat.shape[axis])]
```

## apply a function along an axis

Methods are available to perform `sum`, `median`, `mean` along any axis.

To apply any of these methods

```python
colsum(mat, groups)
```

# Bring your own function

`mopsy` provides a generic `apply` method is also available for perform row-wise or column-wise operations.

lets define our own function to count the number of non-zero elements in the array

```python
import numpy as np

def nz_func(arr):
    return np.count_nonzero(arr)
```

now lets apply the function,

```python
from mopsy import multi_apply

apply(nz_func, mat, axis=1)
```

## Multiple functions

`mopsy` also supports applying multiple functions at the same time.

```python
from mopsy import multi_apply
import numpy as np

multi_apply([np.sum, np.mean], mat, axis=0)
```

## Append rows or columns to a sparse matrix

you can use append_row or append_col to add a new row or column to a sparse matrix. Usually helps save a few lines of code.

```python
from scipy.sparse import eye

mat = eye(5).tocsr()

tmat_wrow = append_row(mat, np.array([0, 0, 0, 0, 0]))

tmat_wcol = append_col(mat, np.array([[0], [0], [0], [0], [0]]))
```

That's all for today!