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        "author": "Thinking Machines Data Science",
        "author_email": "hello@thinkingmachin.es",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: GIS",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "![Geomancer Logo](https://storage.googleapis.com/tm-geomancer/assets/header.png)\n---\n\n[![PyPI](https://img.shields.io/pypi/v/geomancer.svg?color=brightgreen&style=flat-square)](https://pypi.org/project/geomancer/)\n[![Build Status](https://img.shields.io/badge/dynamic/json.svg?color=brightgreen&label=build&query=status&url=https%3A%2F%2Fcloud.drone.io%2Fapi%2Frepos%2Fthinkingmachines%2Fgeomancer%2Fbuilds%2Flatest%3Fref%3Drefs%2Fheads%2Fmaster&style=flat-square)](https://cloud.drone.io/thinkingmachines/geomancer)\n[![Read the Docs](https://img.shields.io/readthedocs/geomancer.svg?style=flat-square)](https://geomancer.readthedocs.io/en/latest/)\n[![Coveralls github](https://img.shields.io/coveralls/github/thinkingmachines/geomancer.svg?style=flat-square)](https://coveralls.io/github/thinkingmachines/geomancer)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square)](https://opensource.org/licenses/MIT)\n\nGeomancer is a geospatial feature engineering library. It leverages geospatial\ndata such as [OpenStreetMap (OSM)](https://www.openstreetmap.org/) alongside a\ndata warehouse like BigQuery. You can use this to create, share, and iterate\ngeospatial features for your downstream tasks (analysis, modelling,\nvisualization, etc.). \n\n## Features\n\nGeomancer can perform geospatial feature engineering for all types of vector data\n(i.e. points, lines, polygons).\n\n- Feature primitives for geospatial feature engineering\n- Ability to switch out data warehouses (BigQuery, SQLite, PostgreSQL (*In Progress*))\n- Compile and share your features using our SpellBook \n\n## Setup and Installation\n\n### Installing the library\n\nGeomancer can be installed using `pip`.\n\n```\n$ pip install geomancer\n```\n\nThis will install **all** dependencies for every data-warehouse we support. If\nyou wish to do this only for a specific warehouse, then you can add an\nidentifier:\n\n```\n$ pip install geomancer[bq] # For BigQuery\n$ pip install geomancer[sqlite] # For SQLite\n$ pip install geomancer[psql] # For PostgreSQL\n```\n\nAlternatively, you can also clone the repository then run `install`.\n\n```\n$ git clone https://github.com/thinkingmachines/geomancer.git\n$ cd geomancer\n$ python setup.py install\n```\n\n### Setting up your data warehouse\n\nGeomancer is powered by a geospatial data warehouse: we highly-recommend using\n[BigQuery](https://cloud.google.com/bigquery/) as your data warehouse and\n[Geofabrik's OSM catalog](https://www.geofabrik.de/data/download.html) as your\nsource of Points and Lines of interest. \n\n[![Geomancer architecture](https://storage.googleapis.com/tm-geomancer/assets/architecture.png\n)](https://github.com/thinkingmachines/geomancer)\n\nYou can see the set-up instructions in [this link](https://geomancer.readthedocs.io/en/latest/setup.html#setting-up-your-data-warehouse)\n\n## Basic Usage\n\nAll of the feature engineering functions in Geomancer are called \"spells\". For\nexample, you want to get the distance to the nearest supermarket for each\npoint.\n\n```python\nfrom geomancer.spells import DistanceToNearest\n\n# Load your dataset in a pandas dataframe\n# df = load_dataset()\n\ndist_spell = DistanceToNearest(\n    \"supermarket\",\n    source_table=\"ph_osm.gis_osm_pois_free_1\",\n    feature_name=\"dist_supermarket\",\n    dburl=\"bigquery://project-name\",\n).cast(df)\n```\n\nYou can specify the type of filter  using the format `{column}:{filter}`.  By\ndefault, the `column` value is `fclass`. For example, if you wish to look for\nroads on a bridge, then pass `bridge:T`:\n\n```python\nfrom geomancer.spells import DistanceToNearest\n\n# Load the dataset in a pandas dataframe\n# df = load_dataset()\n\ndist_spell = DistanceToNearest(\n    \"bridge:T\",\n    source_table=\"ph_osm.gis_osm_roads_free_1\",\n    feature_name=\"dist_road_bridges\",\n    dburl=\"bigquery://project-name\",\n).cast(df)\n```\n\nCompose multiple spells into a \"spell book\" which you can export as a JSON file.\n\n```python\nfrom geomancer.spells import DistanceToNearest\nfrom geomancer.spellbook import SpellBook\n\nspellbook = SpellBook([\n    DistanceToNearest(\n        \"supermarket\",\n        source_table=\"ph_osm.gis_osm_pois_free_1\",\n        feature_name=\"dist_supermarket\",\n        dburl=\"bigquery://project-name\",\n    ),\n    DistanceToNearest(\n        \"embassy\",\n        source_table=\"ph_osm.gis_osm_pois_free_1\",\n        feature_name=\"dist_embassy\",\n        dburl=\"bigquery://project-name\",\n    ),\n])\nspellbook.to_json(\"dist_supermarket_and_embassy.json\")\n```\n\nYou can share the generated file so other people can re-use your feature extractions\nwith their own datasets.\n\n```python\nfrom geomancer.spellbook import SpellBook\n\n# Load the dataset in a pandas dataframe\n# df = load_dataset()\n\nspellbook = SpellBook.read_json(\"dist_supermarket_and_embassy.json\")\ndist_supermarket_and_embassy = spellbook.cast(df)\n```\n\n## Contributing\n\nThis project is open for contributors! Contibutions can come in the form of\nfeature requests, bug fixes, documentation, tutorials and the like! We highly\nrecommend to file an Issue first before submitting a [Pull\nRequest](https://help.github.com/en/articles/creating-a-pull-request).\n\nSimply fork this repository and make a Pull Request! We'd definitely appreciate:\n\n- Implementation of new features\n- Bug Reports\n- Documentation\n- Testing\n\nAlso, we have a\n[CONTRIBUTING](https://github.com/thinkingmachines/geomancer/blob/master/CONTRIBUTING.rst)\nand a [CODE_OF_CONDUCT](https://github.com/thinkingmachines/geomancer/blob/master/CODE_OF_CONDUCT.rst),\nso please check that one out!\n\n## License\n\nMIT License \u00a9 2019, Thinking Machines Data Science",
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