Metadata-Version: 2.1
Name: tablecrow
Version: 1.0.6
Summary: abstract interface with remote database table
Home-page: https://github.com/zacharyburnett/TableCrow.git
Author: Zach Burnett
Author-email: zrb@umd.edu
License: MIT
Description: # TableCrow 
        
        [![tests](https://github.com/zacharyburnett/TableCrow/workflows/tests/badge.svg)](https://github.com/zacharyburnett/TableCrow/actions?query=workflow%3Atests)
        [![build](https://github.com/zacharyburnett/TableCrow/workflows/build/badge.svg)](https://github.com/zacharyburnett/TableCrow/actions?query=workflow%3Abuild)
        [![version](https://img.shields.io/pypi/v/tablecrow)](https://pypi.org/project/tablecrow)
        [![license](https://img.shields.io/badge/license-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        
        `tablecrow` is an abstraction library over a generalized database table.
        Currently, `tablecrow` offers an abstraction for PostGreSQL tables with simple PostGIS operations. 
        
        ```bash
        pip install tablecrow
        ```
        
        ## Data Model:
        `tablecrow` sees a database record / row as a dictionary of field names to values:
        ```python
        record = {'id': 1, 'time': datetime(2020, 1, 1), 'length': 4.4, 'name': 'long boi'}
        ```
        
        Similarly, a database schema is seen as a dictionary of field names to Python types:
        ```python
        fields = {'id': int, 'time': datetime, 'length': float, 'name': str}
        ```
        
        This also includes [Shapely geometric types](https://shapely.readthedocs.io/en/stable/manual.html#geometric-objects):
        ```python
        fields = {'id': int, 'polygon': Polygon}
        ```
        
        ## Usage:
        ```python
        from datetime import datetime
        
        from tablecrow import PostGresTable
        
        table = PostGresTable(
            hostname='localhost:5432',
            database='postgres',
            name='testing',
            fields={'id': int, 'time': datetime, 'length': float, 'name': str},
            primary_key='id',
            username='postgres',
            password='<password>',
        )
        
        # you can add a list of records with `.insert()`
        table.insert([
            {'id': 1, 'time': datetime(2020, 1, 1), 'length': 4.4, 'name': 'long boi'},
            {'id': 3, 'time': datetime(2020, 1, 3), 'length': 2, 'name': 'short boi'},
            {'id': 2},
        ])
        
        # or alternatively set or access a primary key value with square bracket indexing
        table[4] = {'time': datetime(2020, 1, 4), 'length': 5, 'name': 'long'}
        record = table[3]
        
        # you can query the database with a filtering dictionary or a SQL `WHERE` clause
        records = table.records_where({'name': 'short boi'})
        records = table.records_where({'name': '%long%'})
        records = table.records_where("time <= '20200102'::date")
        records = table.records_where("length > 2 OR name ILIKE '%short%'")
        ```
        #### compound primary key
        ```python
        from datetime import datetime
        
        from tablecrow import PostGresTable
        
        table = PostGresTable(
            hostname='localhost:5432',
            database='postgres',
            name='testing',
            fields={'id': int, 'time': datetime, 'length': float, 'name': str},
            primary_key=('id', 'name'),
            username='postgres',
            password='<password>',
        )
        
        # a compound primary key allows more flexibility in ID
        table.insert([
            {'id': 1, 'time': datetime(2020, 1, 1), 'length': 4.4, 'name': 'long boi'},
            {'id': 1, 'time': datetime(2020, 1, 1), 'length': 3, 'name': 'short boi'},
            {'id': 3, 'time': datetime(2020, 1, 3), 'length': 2, 'name': 'short boi'},
            {'id': 3, 'time': datetime(2020, 1, 3), 'length': 6, 'name': 'long boi'},
            {'id': 2, 'name':'short boi'},
        ])
        
        # key accessors must include entire primary key
        table[4, 'long'] = {'time': datetime(2020, 1, 4), 'length': 5}
        record = table[3, 'long boi']
        ```
        
        #### geometries
        ```python
        from pyproj import CRS
        from shapely.geometry import MultiPolygon, Polygon, box
        
        from tablecrow import PostGresTable
        
        table = PostGresTable(
            hostname='localhost:5432',
            database='postgres',
            name='testing',
            fields={'id': int, 'polygon': Polygon, 'multipolygon': MultiPolygon},
            primary_key='id',
            username='postgres',
            password='<password>',
            crs=CRS.from_epsg(4326),
        )
        
        big_box = box(-77.4, 39.65, -77.1, 39.725)
        little_box_inside_big_box = box(-77.7, 39.725, -77.4, 39.8)
        little_box_touching_big_box = box(-77.1, 39.575, -76.8, 39.65)
        disparate_box = box(-77.7, 39.425, -77.4, 39.5)
        
        multi_box = MultiPolygon([little_box_inside_big_box, little_box_touching_big_box])
        
        table.insert([
            {'id': 1, 'polygon': little_box_inside_big_box},
            {'id': 2, 'polygon': little_box_touching_big_box},
            {'id': 3, 'polygon': disparate_box, 'multipolygon': multi_box},
        ])
        
        # find all records with any geometry intersecting the given geometry
        records = table.records_intersecting(big_box)
        
        # find all records with only specific geometry fields intersecting the given geometry
        records = table.records_intersecting(big_box, geometry_fields=['polygon'])
        
        # you can also provide geometries in a different CRS
        records = table.records_intersecting(box(268397.8, 4392279.8, 320292.0, 4407509.6), crs=CRS.from_epsg(32618),
                                             geometry_fields=['polygon'])
        ```
        
        ## Acknowledgements
        The original core code and methodology of `tablecrow` was developed for the National Bathymetric Source project under the [Office of Coast Survey of the National Oceanic and Atmospheric Administration (NOAA)](https://nauticalcharts.noaa.gov), a part of the United States Department of Commerce.
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
