semvec/pypi-packages huggingface dataset:
autoaicluster 0.2.0
Auto AI Clustering: end-to-end segmentation via Python API or local web UI.
License: MIT
Requires Python: >=3.9
Keywords: clustering, automl, segmentation, fastapi, pipeline, visualization

# Auto AI Cluster

End-to-end clustering (segmentation) via Python API or local web UI.

## Install

```bash
pip install autoaicluster
```

## One-liner usage

```python
from autoaicluster import autoaicluster

# Launch web UI
url = autoaicluster(ui=True)
print(url)

# Or run AutoML clustering directly on a DataFrame or CSV path
import pandas as pd

df = pd.DataFrame({
    'age':[25,37,29,41,33,52,47,23,39,31],
    'income':[40,72,50,90,60,120,95,35,70,55],
    'city':['A','B','A','B','A','B','B','A','B','A']
})
res = autoaicluster(df)
print(res['algorithm'], res['silhouette'], res['label_counts'])
```

## Advanced (Python API)
- Full control via `enterprise_cluster_solution.auto_segment` and `run_pipeline` for preprocessing and algorithm selection.

## Notes
- To access UI from other machines, set env vars before launching: `ECS_HOST=0.0.0.0 ECS_PORT=8000`.

## License
MIT
fitstoolz 0.0.1b1
Python libraries for interfacing with FITS data in an intuitive way
License: MIT
Requires Python: <3.13,>=3.11

==========
fitstools
==========

Python libraries for interfacing with FITS images in an intuitive way

Features
--------

* Image and coordinate data indexing is consistent
* Simple API for adding, transposing and expanding axes (or dimensions)
* `Xarray support <https://docs.xarray.dev/en/stable/index.html>`_
* `Zarr support <https://zarr.readthedocs.io/en/stable/index.html>`_ 

Example Usage
-------------

.. code-block:: ipython

    In[1]: from fitstools.reader import FitsData

    In[2]: myfits = FitsData("example-image.fits")
           myfits.coord_names

    Out[2]: ['STOKES', 'FREQ', 'DEC', 'RA']

    In[3]: myfits.dshape
    Out[3]: (1, 504, 100, 100) # these dimensions match the labels above

    In[4]: myfits.coords

    Out[4]: 
    Coordinates:
    STOKES   (stokes) int32 4B dask.array<chunksize=(1,), meta=np.ndarray>
    FREQ     (spectral) float64 4kB 8.803e+08 8.804e+08 ... 9.328e+08 9.329e+08
    RA       (celestial.ra) float64 800B 53.16 53.16 53.16 ... 53.1 53.1 53.1
    DEC      (celesstial.dec) float64 800B -28.16 -28.16 ... -28.11 -28.11