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This is the API documentation for the spector library.
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## Classes


indices

A sparse boolean array, i.e., set of indices.

Provides a memory efficient set interface, with optimized conversion between numpy arrays.

Args:
    keys (Iterable): optional iterable of keys

vector

A sparse array of index keys mapped to numeric values.

Provides a memory efficient Counter interface, with optimized conversion between numpy arrays.

Args:
    keys (Iterable[int]): optional iterable of keys
    values: optional scalar or iterable of values

matrix(data=(), copy=True)

A sparse vector of vectors.

Args:
    data (Iterable):


## Functions


groupby(keys: collections.abc.Iterable, *arrays) -> collections.abc.Iterator[tuple]

Generate unique keys with associated groups.

Args:
    keys:
    *arrays (Iterable):


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This is the User Guide documentation for the package.
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## indices
```{python}
import numpy as np

from spector import indices

ind = indices([0, 2])
ind
```

```{python}
np.array(ind)
```

```{python}
1 in ind
```

```{python}
ind.add(1)
```

```{python}
ind.todense()
```

```{python}
indices.fromdense([True, False, True])
```

## vector
```{python}
from spector import vector

vec = vector({0: 1.0, 2: 2.0, 4: 1.0})
vec
```

```{python}
np.array(vec)
```

```{python}
vec[2] += 1.0
vec[2]
```

```{python}
vec.sum()
```

```{python}
vec.todense()
```

```{python}
vector.fromdense([1, 0, 2, 0, 1])
```

## matrix
```{python}
from spector import matrix

mat = matrix({0: {1: 2.0}})
mat
```

```{python}
mat.row, mat.col, mat.data
```