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https://github.com/douglasdavis/pygram11
Simple and fast histogramming in Python accelerated with OpenMP.
https://github.com/douglasdavis/pygram11
calculating-histograms conda-forge cpp14 histogram histograms macos numpy openmp pybind11 pypi python python3 weights
Last synced: 2 months ago
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Simple and fast histogramming in Python accelerated with OpenMP.
- Host: GitHub
- URL: https://github.com/douglasdavis/pygram11
- Owner: douglasdavis
- License: mit
- Created: 2019-02-01T22:19:44.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2024-03-12T00:32:31.000Z (10 months ago)
- Last Synced: 2024-09-30T00:04:26.592Z (3 months ago)
- Topics: calculating-histograms, conda-forge, cpp14, histogram, histograms, macos, numpy, openmp, pybind11, pypi, python, python3, weights
- Language: Python
- Homepage: https://pygram11.readthedocs.io
- Size: 1.28 MB
- Stars: 28
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# pygram11
[](https://pygram11.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/douglasdavis/pygram11/actions)
[](https://pypi.org/project/pygram11/)
[](https://anaconda.org/conda-forge/pygram11)
[](https://pypi.org/project/pygram11/)
Simple and fast histogramming in Python accelerated with
[OpenMP](https://www.openmp.org/) with help from
[pybind11](https://github.com/pybind/pybind11).
`pygram11` provides functions for very fast histogram calculations
(and the variance in each bin) in one and two dimensions. The API is
very simple; documentation can be [found
here](https://pygram11.readthedocs.io/) (you'll also find [some
benchmarks](https://pygram11.readthedocs.io/en/stable/bench.html)
there).
## Installing
### From PyPI
Binary wheels are provided for Linux, macOS, and Windows. They can be
installed from [PyPI](https://pypi.org/project/pygram11/) via pip:
```
pip install pygram11
```
### From conda-forge
For installation via the `conda` package manager [pygram11 is part of
conda-forge](https://anaconda.org/conda-forge/pygram11).
```none
conda install pygram11 -c conda-forge
```
### From Source
You need is a C++14 compiler and OpenMP. If you are using a relatively
modern GCC release on Linux then you probably don't have to worry
about the OpenMP dependency. If you are on macOS, you can install
`libomp` from Homebrew (pygram11 does compile on Apple Silicon devices
with Python version `>= 3.9` and `libomp` installed from Homebrew).
With those dependencies met, simply run:
```none
git clone https://github.com/douglasdavis/pygram11.git --recurse-submodules
cd pygram11
pip install .
```
Or let pip handle the cloning procedure:
```none
pip install git+https://github.com/douglasdavis/pygram11.git@main
```
Tests are run on Python versions `>= 3.8` and binary wheels are
provided for those versions.
## In Action
A histogram (with fixed bin width) of weighted data in one dimension:
```python
>>> rng = np.random.default_rng(123)
>>> x = rng.standard_normal(10000)
>>> w = rng.uniform(0.8, 1.2, x.shape[0])
>>> h, err = pygram11.histogram(x, bins=40, range=(-4, 4), weights=w)
```
A histogram with fixed bin width which saves the under and overflow in
the first and last bins:
```python
>>> x = rng.standard_normal(1000000)
>>> h, __ = pygram11.histogram(x, bins=20, range=(-3, 3), flow=True)
```
where we've used `__` to catch the `None` returned when weights are
absent. A histogram in two dimensions with variable width bins:
```python
>>> x = rng.standard_normal(1000)
>>> y = rng.standard_normal(1000)
>>> xbins = [-2.0, -1.0, -0.5, 1.5, 2.0, 3.1]
>>> ybins = [-3.0, -1.5, -0.1, 0.8, 2.0, 2.8]
>>> h, err = pygram11.histogram2d(x, y, bins=[xbins, ybins])
```
Manually controlling OpenMP acceleration with context managers:
```python
>>> with pygram11.omp_disabled(): # disable all thresholds.
... result, _ = pygram11.histogram(x, bins=10, range=(-3, 3))
...
>>> with pygram11.omp_forced(key="thresholds.var1d"): # force a single threshold.
... result, _ = pygram11.histogram(x, bins=[-3, -2, 0, 2, 3])
...
```
Histogramming multiple weight variations for the same data, then
putting the result in a DataFrame (the input pandas DataFrame will be
interpreted as a NumPy array):
```python
>>> N = 10000
>>> weights = pd.DataFrame({"weight_a": np.abs(rng.standard_normal(N)),
... "weight_b": rng.uniform(0.5, 0.8, N),
... "weight_c": rng.uniform(0.0, 1.0, N)})
>>> data = rng.standard_normal(N)
>>> count, err = pygram11.histogram(data, bins=20, range=(-3, 3), weights=weights, flow=True)
>>> count_df = pd.DataFrame(count, columns=weights.columns)
>>> err_df = pd.DataFrame(err, columns=weights.columns)
```
I also wrote a [blog
post](https://ddavis.io/posts/introducing-pygram11/) with some simple
examples.
## Other Libraries
- [boost-histogram](https://github.com/scikit-hep/boost-histogram)
provides Pythonic object oriented histograms.
- Simple and fast histogramming in Python using the NumPy C API:
[fast-histogram](https://github.com/astrofrog/fast-histogram) (no
variance or overflow support).
- To calculate histograms in Python on a GPU, see
[cupy.histogram](https://docs.cupy.dev/en/stable/reference/generated/cupy.histogram.html).
---
If there is something you'd like to see in pygram11, please open an
issue or pull request.