Dominik Weckmüller do-me

How to set up your own OSRM backend with Docker on Windows

The OSRM docker quick start provides a great explanation on how to set up the container: https://hub.docker.com/r/osrm/osrm-backend/

Yet - for Docker on Windows minor changes were necessary for me (otherwise I'd get "File not found" or "Permission" errors).

This is how it worked for me:

	# Mkdocs routine for continuous deployment
	# This file must be on main branch under .github/workflows/ci.yml

	name: ci
	on:
	push:
	branches:
	- main
	jobs:
	deploy:

	# Reads hdf file, filters by bounding box, plots image with datashader
	# Best in Jupyter. Script executed with 66.000.000 points in 7 sec on a laptop.

	import datashader as ds, pandas as pd, colorcet

	df = pd.read_hdf('output.hdf')

	# Filter USA Contiguous 48
	df = df[df['latitude'].between(22.547351, 51.327674)]
	df = df[df['longitude'].between(-129.9599164,-53.274208)]

	import pandas as pd
	import json
	import folium
	from folium.plugins import MarkerCluster

	# Load the JSON data, e.g. from https://cordis.europa.eu/datalab
	with open("lat_long_input.json") as f:
	data = json.load(f)

	# Extract the fields of interest from the 'content' section of the JSON

	function papa_load_default_csv(){
	fetch('data/test.csv.gz')
	.then(response => response.arrayBuffer())
	.then(buffer => pako.inflate(new Uint8Array(buffer)))
	.then(decompressed => {
	// convert binary data to string
	const decoder = new TextDecoder('utf-8');
	const csv = decoder.decode(decompressed);
	// parse csv with Papa Parse
	Papa.parse(csv, {

	import pandas as pd
	df = pd.read_csv("your_file.csv")
	df = df.groupby(["all","columns","you","need_for_grouping"])['value_for_sum'].sum().reset_index()
	df

	import pandas as pd

	df = pd.read_excel("adressen.xlsx")
	grouped = df.groupby(["Straße", "Hausnummer", "PLZ", "Ort"]).agg({'Servicezeit in Sekunden': 'sum'}).reset_index()

	### originale Reihenfolge ableiten

	grouped['Reihenfolge'] = df.groupby(["Straße", "Hausnummer", "PLZ", "Ort"]).apply(lambda x: list(x.index)).reset_index(drop=True)
	grouped['Reihenfolge'] = grouped['Reihenfolge'] .apply(lambda x: min([int(i) for i in x]))
	grouped = grouped.sort_values("Reihenfolge").reset_index(drop=True)

	import geopandas as gpd
	import pydeck as pdk

	gdf = gpd.read_parquet("points.parquet") # lat lon columns in EPSG:4326 included
	polys = gpd.read_parquet("polys.parquet") # load polygons/multipolygons
	polys['geometry'] = polys['geometry'].buffer(0) # a trick in geopandas to fix invalid geometries, holes etc.
	polys = polys.to_crs("4326") # IMPORTANT: Must be in 4326, else map won't display

	def hex_to_rgb(hex_color):
	hex_color = hex_color.lstrip('#')

	from tqdm.contrib.concurrent import process_map

	# ONLY WORKING ON UBUNTU NOT ON WINDOWS - tested on Python 3.9 - 3.11

	# import your pandas df here
	# test_array = df["your_colum"].to_list() # to_list is crucial as it must be picklable

	def test_function(x):
	return x*x