jwass

jwass

import functools

import fiona
import geojson
import pyproj
import shapely.geometry
import shapely.ops

omit = ['SHAPE_AREA', 'SHAPE_LEN']

jwass

import fiona
import fiona.crs

def convert(f_in, f_out):
    with fiona.open(f_in) as source:
        with fiona.open(
                f_out,
                'w',
                driver='GeoJSON',
                crs = fiona.crs.from_epsg(4326),

jwass

import matplotlib.pyplot as plt
import numpy as np

from shapely.geometry import LineString
from shapely.ops import triangulate

def uniform_sample(poly, n=100):
    """
    Uniformly sample the Delaunay triangulation of a polygon. If the polygon
    is convex, this will uniformly sample its area.

jwass

jwass

Thinking about the Turf tornado analysis from https://www.mapbox.com/blog/60-years-of-tornadoes-with-turf/ and what the similar approacoh is in GeoPandas.

The two programs take slightly different approaches to the counting. Turf loops over the counties, counting how many tornadoes fall inside its borders. GeoPandas performs a spatial join - first forming a spatial index on the tornadoes. The joined GeoDataFrame combines the columns (properties) of both sets. Then a groupby operation is performed counting the number of entries for each county.

The Turf version is significantly faster - most likely due to the slow spatial join operation in GeoPandas and that all columns are included resulting in a large final DataFrame - and probably just that node is much faster than Python here. Overall, Turf's speed is impressive.

jwass

import difflib
import json
import re

import bs4
import pandas as pd
import requests

regex = re.compile('\((.*)\)')

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jwass

Quick idea to understand what modules get imported.

Example:

>>> import importlog

# Run a bunch of code
>>> import shapely.geometry
>>> p = shapely.geometry.Point(0.0, 0.0)
>>> b = p.buffer(1.0)

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{
 "type": "network",
 "nodes": [
   {"coordinates": [-71.13568, 42.38196], "properties": {}},
   {"coordinates": [-71.13555, 42.382523], "properties": {"highway": "traffic_signals"}},
   {"coordinates": [-71.134087, 42.38384], "properties": {"railway": "level_crossing"}},
   {"coordinates": [-71.141007, 42.386862], "properties": {"highway": "crossing"}}
 ],
 "geometries": [
   {"type": "LineString", "nodes": [0, 1, 2], "properties": {"highway": "primary", "name": "Main Street"}},