Metadata-Version: 2.1
Name: region-estimators
Version: 1.1.4
Summary: Make estimations for geographic regions, based on actual data (e.g. from sensors)
Home-page: https://github.com/UoMResearchIT/region_estimators
Author: Ann Gledson
Author-email: ann.gledson@manchester.ac.uk
License: UNKNOWN
Description: # region_estimators package
        
        [![Build Status](https://travis-ci.org/UoMResearchIT/region_estimators.svg?branch=master)](https://travis-ci.org/UoMResearchIT/region_estimators)
        
        region_estimators is a Python library to calculate regional estimations of scalar quantities, based on some known scalar quantities at specific locations.
        For example, estimating the NO2 (pollution) level of a postcode/zip region, based on site data nearby.
        This first version of the package is initialised with 2 estimation methods:
        1. ConcentricRegions: look for actual data points in gradually wider rings, starting with sites within the region, and then working in rings outwards, until sites are found. If more than one site is found at the final stage, it takes the mean.
        2. Simple Distance measure: This is a very basic implementation... Find the nearest site to the region and use that value.
        If sites exist within the region, take the mean.
           
        The sections below are:
        - [Repository Structure](#repository-structure)
        - [Requirements](#requirements)
        - [Usage](#usage) 
        - [Unit Testing](#unit-testing)
        - [Contributing](#contributing)  
        - [Copyright and licensing](#copyright--licensing)
        
        
        <!-- toc -->
        
        ## Repository Structure
        
        The `region_estimators` directory contains the python modules used by the tools. 
        The `sample_input_files` directory contains examples of the input files required to test installation.
        Operational scripts are stored within the `scripts` directory. 
        A set of python unittest test files can be found in the `test` directory.
        
        
        ```
        .
        ├── region_estimations
        ├── sample_input_files
        ├── scripts
        │   └── outputs
        ├── test
        
        ```
        
        ## Requirements
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install region_estimators.
        ```bash
        pip install shapely
        pip install pandas
        pip install geopandas
        pip install region_estimators
        ```
        
        ## Usage
        An example python script that uses the region_estimators package can be found in the `scripts` directory.
        The required parts are highlighted in the following shortened excerpt:
        
        ```python
        from shapely import wkt
        import pandas as pd
        from region_estimators import RegionEstimatorFactory, EstimationData
        
        
        if __name__ == '__main__':
            # obtain inputs arguments 
            #... [See scripts for obtaining via commandline arguments (argparse)]    
        
            # Prepare input files  (For sample input files, see the 'sample_input_files' folder)
            df_regions = pd.read_csv(regions_filespec, index_col='region_id')
            df_sites = pd.read_csv(sites_filespec, index_col='site_id')
            df_actuals = pd.read_csv(actuals_filespec)
        
            # Convert the regions geometry column from string to wkt format using wkt
            df_regions['geometry'] = df_regions.apply(lambda row: wkt.loads(row.geometry), axis=1)
        
            # Create estimator, the first parameter is the estimation method.
        
            estimation_data = EstimationData(df_sites, df_regions, df_actuals)
        
            estimator = RegionEstimatorFactory.region_estimator(method, estimation_data, verbose, max_processors)
        
            # Make estimations
            if method == 'concentric-regions':
                estimator.max_ring_count = max_rings
            df_estimates = estimator.get_estimations(measurement, region_id, timestamp)
        
            print(df_estimates)
        
            # Convert dataframe result to (for example) a csv file:
            if args.save_to_csv:
                df_estimates.to_csv(os.path.join(outdir_name, 'estimates_{}.csv'.format(outfile_suffix)))
        
        ```
        
        ##### Details of estimation_data class creation / parameters:
        ```
        ├── Constructor
        │   ├── 3 pandas.Dataframe objects:  
        │   │   └── regions (metadata)
        │   │   │    └── required columns
        │   │   │   │    └── 'region_id' (INDEX): identifier for region (must be unique to each region)
        │   │   │   │    └── 'geometry' (shapely.wkt/geom.wkt):  Multi-polygon representing regions location and shape.
        
        │   │   └── sites (metadata)
        │   │   │    └── required columns
        │   │   │   │    └── 'site_id' (INDEX): identifier for site (must be unique to each site)
        │   │   │   │    └── 'latitude' (numeric): latitude of site location
        │   │   │   │    └── 'longitude' (numeric): longitude of site location
        │   │   │    └── optional columns
        │   │   │   │    └── 'name' (string): Human readable name of site
        
        │   │   └── actuals (data)
        │   │   │    └── required columns
        │   │   │   │    └── 'timestamp' (string): timestamp of actual reading
        │   │   │   │    └── 'site_id': (string) ID of site which took actua in sites (in value and type))
        │   │   │   │    └── [one or more value columns] (float):    value of actual measurement readings.
        
        ├── Returns
        │   ├── Initialised instance of EstimationData class
        ```
        
        ##### Details of region_estimators factory class parameters: #####
        ```
        ├── Required inputs
        │   ├── method_name (string): The estimation method to be uesed
                                      In the first version the options are 'concentric-regions' or 'distance-simple'
        │   ├── estimation_data (EstimationData instance - see above): data required to make estimations
        
        ├── Optional inputs
        │   ├── verbose: (int) Verbosity of output level. zero or less => No debug output. Default=0
        │   ├── max_processors (int) Maximum number of processors to use. Default=1
        │                     (Maximum: Number of processor available)
        
        ├── Returns
        │   ├── Initialised instance of subclass of RegionEstimator class
        ```
        
        ##### Details of RegionEstimator (subclass) get_estimations method parameters
        ```
        ├── Required inputs
        │   ├── measurement: which measurement to be estimated (e.g. 'urtica')
        
        ├── Optional inputs
        │   ├── region_id: region identifier (string (or None to get all regions))
        │   ├── timestamp: timestamp identifier (string (or None to get all timestamps))
        │   ├── ignore_site_ids: (list of str) Site IDs to be ignored. Default=[]
        
        ├── Returns
        │   ├── pandas dataframe, with columns:
        │   │   └── measurement
        │   │   └── region_id
        │   │   └── timestamp
        │   │   └── value: (float or empty) The estimated value
        │   │   └── extra_data: (dict string) Extra info about the estimation calculation
        
        WARNING! - estimator.get_estimates('urtica', None, None) will calculate every region at every timestamp.
        ```
        
        ## Unit testing
        A set of python unittest test files can be found in the `test` directory, and can be run from the shell 
        (once the necessary requirements are installed) with the command:
        ```bash
        python -m unittest
        ```
        
        ## Contributing
        ### Improvements to code
        Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
        
        ### Extending the RegionEstimator class 
        with further method classes:
        * Add the new class name (str) to the available_methods list in region_estimator_factory.py:
           ```python
           available_methods = {'concentric-regions': ConcentricRegionsEstimator,
                                 'distance-simple': DistanceSimpleEstimator}
           ```
        * Create the new RegionEstimator subclass, following the layout in other current subclasses. 
          E.g. ConcentricRegionsEstimator (concentric_regions_estimator.py) and
             DistanceSimpleEstimator (distance_simple_estimator.py)
        
        ## Copyright & Licensing
        
        This software has been developed by the [Research IT](https://research-it.manchester.ac.uk/) group at the [University of Manchester](https://www.manchester.ac.uk/) for an [Alan Turing Institute](https://www.turing.ac.uk/) project.
        
        (c) 2019-2021 University of Manchester.
        Licensed under the MIT license (https://opensource.org/licenses/MIT)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
