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Recap: Understanding the "Greengraph" Example¶

We now know enough to understand everything we did in the initial example chapter on the "Greengraph" (notebook). Go back to that part of the notes, and re-read the code.

Now, we can even write it up into a class, and save it as a module. Remember that it is generally a better idea to create files in an editor or integrated development environment (IDE) rather than through the notebook!

Classes for Greengraph¶

The original example was written as a collection of functions. Alternatively, we can rewrite it in an object-oriented style, using classes to group related functionality.

In [1]:

%%bash
mkdir -p greengraph  # Create the folder for the module (on mac or linux)

In [2]:

%%writefile greengraph/graph.py
import numpy as np
import geopy
from matplotlib import pyplot as plt

from .map import Map


class Greengraph(object):
    def __init__(self, start, end):
        self.start = start
        self.end = end
        self.geocoder = geopy.geocoders.Nominatim(user_agent="comp0023")

    def geolocate(self, place):
        return self.geocoder.geocode(place, exactly_one=False)[0][1]

    def location_sequence(self, start, end, steps):
        lats = np.linspace(start[0], end[0], steps)
        longs = np.linspace(start[1], end[1], steps)
        return np.vstack([lats, longs]).transpose()

    def green_between(self, steps):
        """Count the amount of green space along a linear path between two locations."""
        self.steps = steps

        sequence = self.location_sequence(
            start=self.geolocate(self.start),
            end=self.geolocate(self.end),
            steps=steps,
        )
        maps = [Map(*location) for location in sequence]
        self.green_at_each_location = [current_map.count_green() for current_map in maps]

        return self.green_at_each_location

    def plot_green_between(self, steps):
        """ount the amount of green space along a linear path between two locations"""
        if not hasattr(self, 'green_at_each_location') or steps != self.steps:
            green_between_locations = self.green_between(steps)
        else:
            green_between_locations = self.green_at_each_location
        plt.plot(green_between_locations)
        xticks_steps = 5 if steps > 10 else 1
        plt.xticks(range(0, steps, xticks_steps))
        plt.xlabel("Sequence step")
        plt.ylabel(r"$N_{green}$")
        plt.title(f"{self.start} -- {self.end}")

Writing greengraph/graph.py

In [3]:

%%writefile greengraph/map.py
import math
from io import BytesIO

import numpy as np
import imageio.v3 as iio
import requests

class Map(object):
    def __init__(self, latitude, longitude, satellite=True, zoom=10,
                 sensor=False):
        base = "https://mt0.google.com/vt?"
        x_coord, y_coord = self.deg2num(latitude, longitude, zoom)

        params = dict(
            x=x_coord,
            y=y_coord,
            z=zoom,
        )
        if satellite:
            params['lyrs'] = 's'

        self.image = requests.get(
            base, params=params).content  # Fetch our PNG image data
        content = BytesIO(self.image)
        self.pixels = iio.imread(content) # Parse our PNG image as a numpy array

    def deg2num(self, latitude, longitude, zoom):
        """Convert latitude and longitude to XY tiles coordinates."""

        lat_rad = math.radians(latitude)
        n = 2.0 ** zoom
        x_tiles_coord = int((longitude + 180.0) / 360.0 * n)
        y_tiles_coord = int((1.0 - math.asinh(math.tan(lat_rad)) / math.pi) / 2.0 * n)

        return (x_tiles_coord, y_tiles_coord)

    def green(self, threshold):
        """Determine if each pixel in an image array is green."""

        # RGB indices
        red, green, blue = range(3)

        # Use NumPy to build an element-by-element logical array
        greener_than_red = self.pixels[:, :, green] > threshold * self.pixels[:, :, red]
        greener_than_blue = self.pixels[:, :, green] > threshold * self.pixels[:, :, blue]
        green = np.logical_and(greener_than_red, greener_than_blue)
        return green

    def count_green(self, threshold=1.1):
        return np.sum(self.green(threshold))

    def show_green(data, threshold=1.1):
        green = self.green(threshold)
        out = green[:, :, np.newaxis] * array([0, 1, 0])[np.newaxis, np.newaxis, :]
        buffer = BytesIO()
        result = iio.imwrite(buffer, out, extension='.png')
        return buffer.getvalue()

Writing greengraph/map.py

In [4]:

%%writefile greengraph/__init__.py
from .graph import Greengraph

Writing greengraph/__init__.py

Invoking our code and making a plot¶

In [5]:

from matplotlib import pyplot as plt
from greengraph import Greengraph
%matplotlib inline

mygraph = Greengraph('New York', 'Chicago')
data = mygraph.green_between(20)
mygraph.plot_green_between(20)

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