<p align="center">
<img src="https://gvinciguerra.github.io/PGM-index/images/pygm.svg" alt="pygm" style="width: 300px" />
</p>
<p align="center">PyGM is a Python library that enables fast query operations on sorted lists of numbers (like integers and floats) with a tiny memory overhead. Internally, it features the <a href="https://github.com/gvinciguerra/PGM-index">PGM-index</a>, a state-of-the-art learned data structure that robustly scales to billions of elements in just a few tens of megabytes.</p>
<p align="center">
<a href="https://github.com/gvinciguerra/PyGM/actions/workflows/build.yml"><img src="https://github.com/gvinciguerra/PyGM/actions/workflows/build.yml/badge.svg" alt="Build status" /></a>
<a href="https://codecov.io/gh/gvinciguerra/PyGM"><img src="https://codecov.io/gh/gvinciguerra/PyGM/branch/master/graph/badge.svg?token=G20KV3DLAN" alt="Code coverage"/></a>
<a href="https://pypi.org/project/pygm/"><img src="https://img.shields.io/pypi/v/pygm" alt="PyPI"/></a>
<a href="https://github.com/gvinciguerra/PyGM/blob/master/LICENSE"><img src="https://img.shields.io/github/license/gvinciguerra/PyGM" alt="License" /></a>
<a href="https://github.com/gvinciguerra/PyGM/stargazers"><img src="https://img.shields.io/github/stars/gvinciguerra/PyGM" alt="GitHub stars" /></a>
<a href="https://github.com/gvinciguerra/PyGM/network/members"><img alt="GitHub forks" src="https://img.shields.io/github/forks/gvinciguerra/PyGM" /></a>
</p>
## Quick start
Install with pip:
```bash
pip install pygm
```
PyGM supports both standard and other useful list and set operations:
```python
>>> from pygm import SortedList, SortedSet
>>> sl = SortedList([0, 1, 34, 144, 1, 55, 233, 2, 3, 21, 89, 5, 8, 13])
>>> sl
SortedList([0, 1, 1, ..., 144, 233])
>>> sl.find_gt(9) # smallest element > 9
13
>>> sl.count(1) # frequency of 1
2
>>> 42 in sl # membership test
False
>>> list(sl.range(0, 21, inclusive=(False, True))) # elements 0 < x <= 21
[1, 1, 2, 3, 5, 8, 13, 21]
>>> sl[2:10:3] # slicing syntax support
SortedList([1, 5, 21])
>>> (sl + [-3, -2, -1]).rank(0) # number of elements <= 0
4
>>> ss = SortedSet([1, 2, 3, 4]) ^ {3, 4, 5} # set symmetric difference
>>> ss.find_lt(5)
2
```
The full documentation is available [online](https://pgm.di.unipi.it/docs/python-reference/) and in the Python interpreter via the `help()` built-in function.
## Installation
PyGM is compatible with Python 3.3+. The easiest way to install it is through PyPI:
```bash
pip install pygm
```
Otherwise, you can clone the repo, build it from source and install it as follows:
```bash
if [[ "$(uname)" == "Darwin" ]]; then brew install libomp; fi
git clone https://github.com/gvinciguerra/PyGM.git
cd PyGM
git submodule update --init --recursive
pip install .
```
Remember to leave the source directory `PyGM/` and its parent before running Python.
## Performance
Here are some plots that compare the performance of PyGM with two popular libraries, [sortedcontainers](https://github.com/grantjenks/python-sortedcontainers) and [blist](http://github.com/DanielStutzbach/blist), on synthetic data.
<p align="center">
<img src="https://gvinciguerra.github.io/PGM-index/images/pygm-lists-time.svg" alt="Query performance of Python packages implementing sorted lists" style="width: 700px" />
</p>
All the graphs are log-log and show, for increasing data sizes, the average time it takes to perform each operation (lower is better). In particular, the `__init__` plot shows the construction time, `__contains__` measures membership queries, and `__getitem__` shows the cost of accessing an element given its position.
The interesting operations on sorted lists are: (i) `index`, which returns the position of the first occurrence of a given element; (ii) `count`, which returns the number of occurrences of a given element; (iii) `bisect_left`, which returns the insertion point for a given value in the list to maintain the sorted order (and is used to implement `find_[ge|gt|le|lt]`).
You can run and plot the experiments on your computer and your data with the notebook in [tests/benchmark.ipynb](https://github.com/gvinciguerra/PyGM/blob/master/tests/benchmark.ipynb).
## License
This project is licensed under the terms of the Apache License 2.0.
If you use the library in an academic setting, please cite the following paper:
> Paolo Ferragina and Giorgio Vinciguerra. The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. PVLDB, 13(8): 1162-1175, 2020.
```tex
@article{Ferragina:2020pgm,
Author = {Paolo Ferragina and Giorgio Vinciguerra},
Title = {The {PGM-index}: a fully-dynamic compressed learned index with provable worst-case bounds},
Year = {2020},
Volume = {13},
Number = {8},
Pages = {1162--1175},
Doi = {10.14778/3389133.3389135},
Url = {https://pgm.di.unipi.it},
Issn = {2150-8097},
Journal = {{PVLDB}}}
```
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"description": "<p align=\"center\">\n <img src=\"https://gvinciguerra.github.io/PGM-index/images/pygm.svg\" alt=\"pygm\" style=\"width: 300px\" />\n</p>\n\n<p align=\"center\">PyGM is a Python library that enables fast query operations on sorted lists of numbers (like integers and floats) with a tiny memory overhead. Internally, it features the <a href=\"https://github.com/gvinciguerra/PGM-index\">PGM-index</a>, a state-of-the-art learned data structure that robustly scales to billions of elements in just a few tens of megabytes.</p>\n\n<p align=\"center\">\n <a href=\"https://github.com/gvinciguerra/PyGM/actions/workflows/build.yml\"><img src=\"https://github.com/gvinciguerra/PyGM/actions/workflows/build.yml/badge.svg\" alt=\"Build status\" /></a>\n <a href=\"https://codecov.io/gh/gvinciguerra/PyGM\"><img src=\"https://codecov.io/gh/gvinciguerra/PyGM/branch/master/graph/badge.svg?token=G20KV3DLAN\" alt=\"Code coverage\"/></a>\n <a href=\"https://pypi.org/project/pygm/\"><img src=\"https://img.shields.io/pypi/v/pygm\" alt=\"PyPI\"/></a>\n <a href=\"https://github.com/gvinciguerra/PyGM/blob/master/LICENSE\"><img src=\"https://img.shields.io/github/license/gvinciguerra/PyGM\" alt=\"License\" /></a>\n <a href=\"https://github.com/gvinciguerra/PyGM/stargazers\"><img src=\"https://img.shields.io/github/stars/gvinciguerra/PyGM\" alt=\"GitHub stars\" /></a>\n <a href=\"https://github.com/gvinciguerra/PyGM/network/members\"><img alt=\"GitHub forks\" src=\"https://img.shields.io/github/forks/gvinciguerra/PyGM\" /></a>\n</p>\n\n## Quick start\n\nInstall with pip:\n\n```bash\npip install pygm\n```\n\nPyGM supports both standard and other useful list and set operations:\n\n```python\n>>> from pygm import SortedList, SortedSet\n>>> sl = SortedList([0, 1, 34, 144, 1, 55, 233, 2, 3, 21, 89, 5, 8, 13])\n>>> sl\nSortedList([0, 1, 1, ..., 144, 233])\n>>> sl.find_gt(9) # smallest element > 9\n13\n>>> sl.count(1) # frequency of 1\n2\n>>> 42 in sl # membership test\nFalse\n>>> list(sl.range(0, 21, inclusive=(False, True))) # elements 0 < x <= 21\n[1, 1, 2, 3, 5, 8, 13, 21]\n>>> sl[2:10:3] # slicing syntax support\nSortedList([1, 5, 21])\n>>> (sl + [-3, -2, -1]).rank(0) # number of elements <= 0\n4\n>>> ss = SortedSet([1, 2, 3, 4]) ^ {3, 4, 5} # set symmetric difference\n>>> ss.find_lt(5)\n2\n```\n\nThe full documentation is available [online](https://pgm.di.unipi.it/docs/python-reference/) and in the Python interpreter via the `help()` built-in function.\n\n## Installation\n\nPyGM is compatible with Python 3.3+. The easiest way to install it is through PyPI:\n\n```bash\npip install pygm\n```\n\nOtherwise, you can clone the repo, build it from source and install it as follows:\n\n```bash\nif [[ \"$(uname)\" == \"Darwin\" ]]; then brew install libomp; fi\ngit clone https://github.com/gvinciguerra/PyGM.git\ncd PyGM\ngit submodule update --init --recursive\npip install .\n```\n\nRemember to leave the source directory `PyGM/` and its parent before running Python. \n\n## Performance\n\nHere are some plots that compare the performance of PyGM with two popular libraries, [sortedcontainers](https://github.com/grantjenks/python-sortedcontainers) and [blist](http://github.com/DanielStutzbach/blist), on synthetic data.\n\n<p align=\"center\">\n <img src=\"https://gvinciguerra.github.io/PGM-index/images/pygm-lists-time.svg\" alt=\"Query performance of Python packages implementing sorted lists\" style=\"width: 700px\" />\n</p>\n\nAll the graphs are log-log and show, for increasing data sizes, the average time it takes to perform each operation (lower is better). 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