Metadata-Version: 2.1
Name: prospectpredictor
Version: 0.1.1
Summary: prospectivity prediction based on GIS shapefiles
Home-page: https://github.com/tyleracorn/prospectPredictor
Author: Tyler Acorn
Author-email: tyler.acorn@gmail.com
License: MIT
Description: #################
        prospectPredictor
        #################
        
        This is a pretty simple prospectivity prediction package that predicts the 
        prospectivity of an element based on how close it is to different 
        bedrock units. 
        
        ********************
        Overview of Package
        ********************
        The package utilizes 3 main classes. 
        
        - PrepShapes
        
            - handles exploring and prepping a shapefile and determining the project boundary. It can read in a single shapefile (using geopandas) and then you can use it to select the polygons of interest. 
        
        - RasterTemplate
        
            - uses the previously initialized *PrepShapes()* class to help you setup the raster template used by the predictor to predict prospectivity at a specified location.
        
        - PredictByDistance
        
            - the predictor class that predicts the likelihood based on distance to shapes of interest and the weighting schema architect. The predictor class uses the previously initialized PrepShapes() and RasterTemplate() classes. Only one prediction architect is currently implemented.
        
        
        Prediction Weighting Schema
        ===========================
        
        Currently this package uses a pseudo variogram style weighting schema with the following model (for location *i* and as an example 2 distances)
        
        .. image:: CodeCogsEqn.gif
        
        Generating a prospectivity heat map
        ===================================
        
        Using the package you can generate a heat map with liklihood of finding the element (based on a distance from 2 or more shape categories). The predictor generates prediction values ranging from 0, being least likely (i.e. 0%), to 1, being most likely (i.e. 100%). As an example here is a heat map generated from the included dataset.
        
        .. image:: Data/predictionHeatMap_projectBoundary.png
            :width: 100
            :height: 100
            :align: center
        
        Example
        =======
        
        An example.ipynb is included in the repo to demostate how to use the package.
        
        Dataset 
        =======
        the included dataset is from the `British Columbia Geologica Survey <https://www2.gov.bc.ca/gov/content/industry/mineral-exploration-mining/british-columbia-geological-survey>`_.
        
Keywords: GIS prospectivity varriogram shapefiles raster
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/x-rst
