Metadata-Version: 2.1
Name: h3
Version: 3.7.3
Summary: Hierarchical hexagonal geospatial indexing system
Home-page: https://github.com/uber/h3-py
Author: Uber Technologies
Author-email: AJ Friend <ajfriend@gmail.com>
License: Apache 2.0 License
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Topic :: Scientific/Engineering :: GIS
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: all
Requires-Dist: numpy ; extra == 'all'
Requires-Dist: pytest ; extra == 'all'
Requires-Dist: pytest-cov ; extra == 'all'
Requires-Dist: flake8 ; extra == 'all'
Provides-Extra: numpy
Requires-Dist: numpy ; extra == 'numpy'
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'
Requires-Dist: flake8 ; extra == 'test'

<img align="right" src="https://uber.github.io/img/h3Logo-color.svg" alt="H3 Logo" width="200">

# h3-py

[![PyPI version](https://badge.fury.io/py/h3.svg)](https://badge.fury.io/py/h3)
[![PyPI downloads](https://pypip.in/d/h3/badge.png)](https://pypistats.org/packages/h3)
[![conda](https://img.shields.io/conda/vn/conda-forge/h3-py.svg)](https://anaconda.org/conda-forge/h3-py)
[![version](https://img.shields.io/badge/h3-v3.7.1-blue.svg)](https://github.com/uber/h3/releases/tag/v3.7.1)
[![version](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

[![Tests](https://github.com/uber/h3-py/workflows/tests/badge.svg)](https://github.com/uber/h3-py/actions)
[![codecov](https://codecov.io/gh/uber/h3-py/branch/master/graph/badge.svg)](https://codecov.io/gh/uber/h3-py)

Python bindings for the
[H3 Core Library](https://github.com/uber/h3).

For API reference, see the
[H3 Documentation](https://h3geo.org/).


## Installation

From [PyPI](https://pypi.org/project/h3/):

`pip install h3`

From [conda](https://github.com/conda-forge/h3-py-feedstock):

```sh
conda config --add channels conda-forge
conda install h3-py
```

**New since v3.6.1**: We upload pre-built
[Python Wheels to PyPI](https://pypi.org/project/h3) for Linux/Mac/Windows,
which should avoid many previous installation issues.


## Usage

```python
>>> import h3
>>> lat, lng = 0, 0
>>> resolution = 0
>>> h3.geo_to_h3(lat, lng, resolution)
'8075fffffffffff'
```

## Example gallery

Browse [a collection of example notebooks](https://github.com/uber/h3-py-notebooks),
and if you have examples or visualizations of your own, please feel free to contribute!

We also have a simple [walkthrough of the API](https://nbviewer.jupyter.org/github/uber/h3-py-notebooks/blob/master/notebooks/usage.ipynb).
For more information, please see the [H3 Documentation](https://h3geo.org/).


## APIs

We provide multiple APIs in `h3-py`.
All APIs have the same set of functions, but differ
in their input/output formats.

### `h3.api.basic_str`

H3 indexes are represented as Python `str`s, using `list` and `set` for collections.

This is the default API provided when you `import h3`.
That is, `import h3.api.basic_str as h3` and `import h3`
are basically equivalent.

```python
>>> import h3
>>> h = h3.geo_to_h3(0, 0, 0)
>>> h
'8075fffffffffff'

>>> h3.hex_ring(h, 1)
{'8055fffffffffff',
 '8059fffffffffff',
 '807dfffffffffff',
 '8083fffffffffff',
 '8099fffffffffff'}
```

### `h3.api.basic_int`

H3 indexes are represented as Python `int`s, using `list` and `set` for collections.

```python
>>> import h3.api.basic_int as h3
>>> h = h3.geo_to_h3(0, 0, 0)
>>> h
578536630256664575

>>> h3.hex_ring(h, 1)
{577973680303243263,
 578044049047420927,
 578677367745019903,
 578782920861286399,
 579169948954263551}
```

### `h3.api.numpy_int`

H3 indexes are represented as `uint64`s, using `numpy.ndarray`
for collections.

The intention is for this API to be faster and more memory-efficient by
not requiring `int` to `str` conversion and by using
no-copy `numpy` arrays instead of Python `list`s and `set`s.

```python
>>> import h3.api.numpy_int as h3
>>> h = h3.geo_to_h3(0, 0, 0)
>>> h
578536630256664575

>>> h3.hex_ring(h, 1)
array([578782920861286399, 578044049047420927, 577973680303243263,
       578677367745019903, 579169948954263551], dtype=uint64)
```

Note that `h3` has no runtime dependencies on other libraries, so a standard
`pip install` will install no additional libraries.
However, `h3.api.numpy_int` requires `numpy`. To have `numpy` installed (if it isn't already) along
with `h3`, run `pip install h3[numpy]`.


### `h3.api.memview_int`

H3 indexes are represented as `uint64`s, using Python
[`memoryview` objects](https://docs.python.org/dev/library/stdtypes.html#memoryview)
for collections.

This API has the same benefits as `numpy_int`, except it uses
(the less well-known but dependency-free) `memoryview`.

```python
>>> import h3.api.memview_int as h3
>>> h = h3.geo_to_h3(0, 0, 0)
>>> h
578536630256664575

>>> mv = h3.hex_ring(h, 1)
>>> mv
<MemoryView of 'array' at 0x11188c710>

>>> mv[0]
578782920861286399

>>> list(mv)
[578782920861286399,
 578044049047420927,
 577973680303243263,
 578677367745019903,
 579169948954263551]
```

When using this API with `numpy`, note that `numpy.array` **creates a copy**
of the data, while `numpy.asarray` **does not create a copy** and the
result points to the same memory location as the `memoryview` object.

Continuing from the example above,

```python
>>> mv = h3.hex_ring(h, 1)
>>> a = np.array(mv)
>>> mv[0] = 0
>>> a
array([578782920861286399, 578044049047420927, 577973680303243263,
       578677367745019903, 579169948954263551], dtype=uint64)

>>> mv = h3.hex_ring(h, 1)
>>> a = np.asarray(mv)
>>> mv[0] = 0
>>> a
array([                 0, 578044049047420927, 577973680303243263,
       578677367745019903, 579169948954263551], dtype=uint64)
```

## Versioning

`h3-py` wraps the [H3 Core Library](https://github.com/uber/h3),
which is written in C.
Both projects employ [semantic versioning](https://semver.org/),
with versions taking the form `X.Y.Z`.

`h3-py` will match the C library
in *major* and *minor* numbers (`X.Y`), but may be different on the
*patch* (`Z`) number.

Use `h3.versions()` to see the version numbers for both
`h3-py` and the C library. For example,

```python
>>> import h3
>>> h3.versions()
{'c': '3.6.3', 'python': '3.6.1'}
```


