# NumPy-style histograms in PyTorch
The `torchist` package implements NumPy's [`histogram`](https://numpy.org/doc/stable/reference/generated/numpy.histogram.html) and [`histogramdd`](https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html) functions in PyTorch with CUDA support. The package also features implementations of [`ravel_multi_index`](https://numpy.org/doc/stable/reference/generated/numpy.ravel_multi_index.html), [`unravel_index`](https://numpy.org/doc/stable/reference/generated/numpy.unravel_index.html) and some useful functionals like `entropy` or `kl_divergence`.
## Installation
The `torchist` package is available on [PyPI](https://pypi.org/project/torchist), which means it is installable with `pip`.
```
pip install torchist
```
Alternatively, if you need the latest features, you can install it from the repository.
```
pip install git+https://github.com/francois-rozet/torchist
```
## Getting Started
```python
import torch
import torchist
x = torch.rand(100, 3).cuda()
hist = torchist.histogramdd(x, bins=10, low=0.0, upp=1.0)
print(hist.shape) # (10, 10, 10)
```
## Benchmark
The implementations of `torchist` are on par or faster than those of `numpy` on CPU and benefit greately from CUDA capabilities.
```console
$ python torchist/__init__.py
CPU
---
np.histogram : 1.2559 s
np.histogramdd : 20.7816 s
np.histogram (non-uniform) : 5.4878 s
np.histogramdd (non-uniform) : 17.3757 s
torchist.histogram : 1.3975 s
torchist.histogramdd : 9.6160 s
torchist.histogram (non-uniform) : 5.0883 s
torchist.histogramdd (non-uniform) : 17.2743 s
CUDA
----
torchist.histogram : 0.1363 s
torchist.histogramdd : 0.3754 s
torchist.histogram (non-uniform) : 0.1355 s
torchist.histogramdd (non-uniform) : 0.5137 s
```
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"description": "# NumPy-style histograms in PyTorch\n\nThe `torchist` package implements NumPy's [`histogram`](https://numpy.org/doc/stable/reference/generated/numpy.histogram.html) and [`histogramdd`](https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html) functions in PyTorch with CUDA support. The package also features implementations of [`ravel_multi_index`](https://numpy.org/doc/stable/reference/generated/numpy.ravel_multi_index.html), [`unravel_index`](https://numpy.org/doc/stable/reference/generated/numpy.unravel_index.html) and some useful functionals like `entropy` or `kl_divergence`.\n\n## Installation\n\nThe `torchist` package is available on [PyPI](https://pypi.org/project/torchist), which means it is installable with `pip`.\n\n```\npip install torchist\n```\n\nAlternatively, if you need the latest features, you can install it from the repository.\n\n```\npip install git+https://github.com/francois-rozet/torchist\n```\n\n## Getting Started\n\n```python\nimport torch\nimport torchist\n\nx = torch.rand(100, 3).cuda()\n\nhist = torchist.histogramdd(x, bins=10, low=0.0, upp=1.0)\n\nprint(hist.shape) # (10, 10, 10)\n```\n\n## Benchmark\n\nThe implementations of `torchist` are on par or faster than those of `numpy` on CPU and benefit greately from CUDA capabilities.\n\n```console\n$ python torchist/__init__.py\nCPU\n---\nnp.histogram : 1.2559 s\nnp.histogramdd : 20.7816 s\nnp.histogram (non-uniform) : 5.4878 s\nnp.histogramdd (non-uniform) : 17.3757 s\ntorchist.histogram : 1.3975 s\ntorchist.histogramdd : 9.6160 s\ntorchist.histogram (non-uniform) : 5.0883 s\ntorchist.histogramdd (non-uniform) : 17.2743 s\n\nCUDA\n----\ntorchist.histogram : 0.1363 s\ntorchist.histogramdd : 0.3754 s\ntorchist.histogram (non-uniform) : 0.1355 s\ntorchist.histogramdd (non-uniform) : 0.5137 s\n```\n",
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