examples.py

import pandas as pd
import json
from toolbox import Handler, Indicator

class ShellIndicator(Indicator):
	'''
	Example of an empty-shell indicator that always returns one.
	'''

	def setup(self):
		self.fitted_model = None

	def load_module(self):
		self.fitted_model = 1

	def return_indicator(self, geogrid_data):
		return self.fitted_model

from numpy import log
from collections import Counter
class Diversity(Indicator):
	'''
	Example of a diversity of land use indicator
	'''
	def setup(self):
		self.name = 'Entropy'

	def load_module(self):
		pass

	def return_indicator(self, geogrid_data):
		uses = [cell['land_use'] for cell in geogrid_data]
		uses = [use for use in uses if use != 'None']

		frequencies = Counter(uses)
		total = sum(frequencies.values(), 0.0)
		entropy = 0
		for key in frequencies:
			p = frequencies[key]/total
			entropy += -p*log(p)

		return entropy


from toolbox import Indicator
import random
class RandomIndicator(Indicator):
	def setup(self):
		pass

	def return_indicator(self, geogrid_data):
		result=[{'name': 'Social Wellbeing', 'value': random.random()},
			    {'name': 'Environmental Impact', 'value': random.random()},
			    {'name': 'Mobility Impact', 'value': random.random()},
			    {'name': 'Economic Impact', 'value': random.random()},
			    {'name': 'Innovation Potential', 'value': random.random()}]
		return result


from numpy import mean
class Noise(Indicator):
    '''
    Example of Noise heatmap indicator for points centered in each grid cell.
    The main difference between a heatmap and a numeric indicator is that indicator_type is set to either 'heatmap' or 'access'.

    Note that this class requires the geometry of the table as input, which is why it sets:
    requires_geometry = True
    in the setup.

    '''
    def setup(self,name=None):
        self.indicator_type = 'heatmap'
        self.name = ('noise' if name is None else name)
        self.requires_geometry = True

    def load_module(self):
        pass

    def return_indicator(self, geogrid_data):
        features = []
        for cell in geogrid_data:
            feature = {}
            lat,lon = zip(*cell['geometry']['coordinates'][0])
            lat,lon = mean(lat),mean(lon)
            feature['geometry'] = {'coordinates': [lat,lon],'type': 'Point'}
            feature['properties'] = {self.name:random.random()}
            features.append(feature)
        out = {'type':'FeatureCollection','features':features}
        return out

def main():
	seashell = ShellIndicator(name='seashell')
	div = Diversity(name="land_use_diversity")

	H = Handler('corktown', quietly=False)
	H.add_indicator(seashell)
	H.add_indicator(div)

	print(H.geogrid_data())

	print(H.list_indicators())
	print(H.update_package())

	H.listen()

if __name__ == '__main__':
	main()