From a6f7182a3ff2aced603094ddd60fa20d42c5fd6b Mon Sep 17 00:00:00 2001 From: Sarah Hoffmann Date: Mon, 16 Sep 2024 00:00:33 +0200 Subject: [PATCH] add first version of mkdocs-based documentation --- .gitignore | 2 + docs/cookbooks.md | 13 + .../Adding-Node-Infos-To-Boundaries.ipynb | 283 +++++++ .../cookbooks/Adding-Route-Info-To-Ways.ipynb | 195 +++++ docs/cookbooks/Enhance-Tags.ipynb | 190 +++++ docs/cookbooks/Filter-Data-By-Geometry.ipynb | 180 +++++ docs/cookbooks/Filtering-Data-By-Tags.ipynb | 355 ++++++++ .../Visualizing-Data-With-Geopandas.ipynb | 762 ++++++++++++++++++ docs/css/extra.css | 8 + docs/css/mkdocstrings.css | 34 + docs/data/buildings.opl | 11 + docs/data/liechtenstein.osm.pbf | Bin 0 -> 2710768 bytes docs/index.md | 52 ++ docs/reference.md | 21 + docs/reference/Area.md | 4 + docs/reference/Data-Writers.md | 7 + docs/reference/Dataclasses.md | 44 + docs/reference/Exceptions.md | 3 + docs/reference/File-Processing.md | 8 + docs/reference/Filters.md | 9 + docs/reference/Geometry-Functions.md | 19 + docs/reference/Handler-Processing.md | 15 + docs/reference/IO.md | 8 + docs/reference/Indexes.md | 16 + docs/user_manual.md | 30 + docs/user_manual/01-First-Steps.md | 214 +++++ docs/user_manual/02-Extracting-Object-Data.md | 258 ++++++ .../user_manual/03-Working-with-Geometries.md | 267 ++++++ docs/user_manual/04-Working-with-Filters.md | 279 +++++++ docs/user_manual/05-Working-with-Handlers.md | 100 +++ docs/user_manual/06-Writing-Data.md | 78 ++ .../07-Input-Formats-And-Other-Sources.md | 92 +++ .../08-Working-With-Change-Files.md | 3 + .../09-Working-With-History-Files.md | 3 + docs/user_manual/10-Replication-Tools.md | 195 +++++ examples/osm_url_stats.py | 31 +- mkdocs.yaml | 93 +++ src/osmium/file_processor.py | 2 +- 38 files changed, 3860 insertions(+), 24 deletions(-) create mode 100644 docs/cookbooks.md create mode 100644 docs/cookbooks/Adding-Node-Infos-To-Boundaries.ipynb create mode 100644 docs/cookbooks/Adding-Route-Info-To-Ways.ipynb create mode 100644 docs/cookbooks/Enhance-Tags.ipynb create mode 100644 docs/cookbooks/Filter-Data-By-Geometry.ipynb create mode 100644 docs/cookbooks/Filtering-Data-By-Tags.ipynb create mode 100644 docs/cookbooks/Visualizing-Data-With-Geopandas.ipynb create mode 100644 docs/css/extra.css create mode 100644 docs/css/mkdocstrings.css create mode 100644 docs/data/buildings.opl create mode 100644 docs/data/liechtenstein.osm.pbf create mode 100644 docs/index.md create mode 100644 docs/reference.md create mode 100644 docs/reference/Area.md create mode 100644 docs/reference/Data-Writers.md create mode 100644 docs/reference/Dataclasses.md create mode 100644 docs/reference/Exceptions.md create mode 100644 docs/reference/File-Processing.md create mode 100644 docs/reference/Filters.md create mode 100644 docs/reference/Geometry-Functions.md create mode 100644 docs/reference/Handler-Processing.md create mode 100644 docs/reference/IO.md create mode 100644 docs/reference/Indexes.md create mode 100644 docs/user_manual.md create mode 100644 docs/user_manual/01-First-Steps.md create mode 100644 docs/user_manual/02-Extracting-Object-Data.md create mode 100644 docs/user_manual/03-Working-with-Geometries.md create mode 100644 docs/user_manual/04-Working-with-Filters.md create mode 100644 docs/user_manual/05-Working-with-Handlers.md create mode 100644 docs/user_manual/06-Writing-Data.md create mode 100644 docs/user_manual/07-Input-Formats-And-Other-Sources.md create mode 100644 docs/user_manual/08-Working-With-Change-Files.md create mode 100644 docs/user_manual/09-Working-With-History-Files.md create mode 100644 docs/user_manual/10-Replication-Tools.md create mode 100644 mkdocs.yaml diff --git a/.gitignore b/.gitignore index 7cdf031f..385c1bd0 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,5 @@ contrib osmium.egg-info *.pyc .ycm_extra_conf.py +.ipynb_checkpoints +docs/data/out diff --git a/docs/cookbooks.md b/docs/cookbooks.md new file mode 100644 index 00000000..e0736f41 --- /dev/null +++ b/docs/cookbooks.md @@ -0,0 +1,13 @@ +# Overview + +This section presents practical examples and use cases, where pyosmium can +come in handy. Each cookbook comes with three sections: + +* __Task__ explains what should be achieved +* __Quick Solution__ gives you a ready to use script that solves the task +* __Background__ explains in detail what happens in the script and why it + is done the way it is done. This should help you to write similar scripts + on your own. + +All cookbooks are available as Jupiter notebooks in the +[pyosmium source tree](https://github.com/osmcode/pyosmium/tree/master/docs/cookbooks/) diff --git a/docs/cookbooks/Adding-Node-Infos-To-Boundaries.ipynb b/docs/cookbooks/Adding-Node-Infos-To-Boundaries.ipynb new file mode 100644 index 00000000..38483733 --- /dev/null +++ b/docs/cookbooks/Adding-Node-Infos-To-Boundaries.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "403b95b7-e1db-4fec-8f7b-9e384a84a621", + "metadata": {}, + "source": [ + "# Adding Place Node Information to Boundary Relations\n", + "\n", + "How to merge information from different OSM objects.\n", + "\n", + "## Task\n", + "\n", + "Administrative areas often represented with two different objects in OSM: a node describes the central point and a relation that contains all the ways that make up the boundary. The task is to find all administrative boundaries and their matching place nodes and output both togther in a geojson file. Relations and place nodes should be matched when they have the same wikidata tag.\n", + "\n", + "## Quick solution" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "46ba251f-d3ba-41c1-98a1-92f7a56a8128", + "metadata": {}, + "outputs": [], + "source": [ + "import osmium\n", + "from dataclasses import dataclass\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ac6ecdca-5ba6-485f-9253-88b8f73f5041", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class PlaceInfo:\n", + " id: int\n", + " tags: dict[str, str]\n", + " coords: tuple[float, float]\n", + "\n", + "geojsonfab = osmium.geom.GeoJSONFactory()\n", + "\n", + "class BoundaryHandler(osmium.SimpleHandler):\n", + " def __init__(self, outfile):\n", + " self.places = {}\n", + " self.outfile = outfile\n", + " # write the header of the geojson file\n", + " self.outfile.write('{\"type\": \"FeatureCollection\", \"features\": [')\n", + " # This is just to make sure, we place the commas on the right place.\n", + " self.delim = ''\n", + "\n", + " def finish(self):\n", + " self.outfile.write(']}')\n", + "\n", + " def node(self, n):\n", + " self.places[n.tags['wikidata']] = PlaceInfo(n.id, dict(n.tags), (n.location.lon, n.location.lat))\n", + " \n", + " def area(self, a):\n", + " # Find the corresponding place node\n", + " place = self.places.get(a.tags.get('wikidata', 'not found'), None)\n", + " # Geojsonfab creates a string with the geojson geometry.\n", + " # Convert to a Python object to make it easier to add data.\n", + " geom = json.loads(geojsonfab.create_multipolygon(a))\n", + " if geom:\n", + " # print the array delimiter, if necessary\n", + " self.outfile.write(self.delim)\n", + " self.delim = ','\n", + "\n", + " tags = dict(a.tags)\n", + " # add the place information to the propoerties\n", + " if place is not None:\n", + " tags['place_node:id'] = str(place.id)\n", + " tags['place_node:lat'] = str(place.coords[1])\n", + " tags['place_node:lon'] = str(place.coords[0])\n", + " for k, v in place.tags.items():\n", + " tags['place_node:tags:' + k] = v\n", + " # And wrap everything in proper GeoJSON.\n", + " feature = {'type': 'Feature', 'geometry': geom, 'properties': dict(tags)}\n", + " self.outfile.write(json.dumps(feature))\n", + "\n", + "# We are interested in boundary relations that make up areas and not in ways at all.\n", + "filters = [osmium.filter.KeyFilter('place').enable_for(osmium.osm.NODE),\n", + " osmium.filter.KeyFilter('wikidata').enable_for(osmium.osm.NODE),\n", + " osmium.filter.EntityFilter(~osmium.osm.WAY),\n", + " osmium.filter.TagFilter(('boundary', 'administrative')).enable_for(osmium.osm.AREA | osmium.osm.RELATION)]\n", + "\n", + "with open('../data/out/boundaries.geojson', 'w') as outf:\n", + " handler = BoundaryHandler(outf)\n", + " handler.apply_file('../data/liechtenstein.osm.pbf', filters=filters)\n", + " handler.finish()" + ] + }, + { + "cell_type": "markdown", + "id": "8f8b0c34-a744-4f02-adcb-04ce5ba8526f", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "Whenever you want to look at more than one OSM object at the time, you need to cache objects. Before starting such a task, it is always worth taking a closer look at the objects of interest. Find out how many candidates there are for you to look at and save and how large these objects are. There are always multiple ways to cache your data. Sometimes, when the number of candidates is really large, it is even more sensible to reread the file instead of caching the information.\n", + "\n", + "For the boundary problem, the calculation is relatively straightforward. Boundary relations are huge, so we do not want to cache them if it can somehow be avoided. That means we need to cache the place nodes. A quick look at [TagInfo](https://taginfo.openstreetmap.org/keys/place) tells us that there are about 7 million place nodes in the OSM planet. That is not a lot in the grand scheme of things. We could just read them all into memory and be done with it. It is still worth to take a closer look. The place nodes are later matched up by their `wikidata` tag. Looking into the [TagInfo combinations table](https://taginfo.openstreetmap.org/keys/place#combinations), only 10% of the place nodes have such a tag. That leaves 850.000 nodes to cache. Much better!\n", + "\n", + "Next we need to consider what information actually needs caching. In our case we want it all: the ID, the tags and the coordinates of the node. This information needs to be copied out of the node. You cannot just cache the entire node. Pyosmium won't let you do this because it wants to get rid of it as soon as the handler has seen it. Lets create a dataclass to receive the information we need:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2e2c1a5d-f978-4626-96ce-6cb271ce5ccc", + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class PlaceInfo:\n", + " id: int\n", + " tags: dict[str, str]\n", + " coords: tuple[float, float]" + ] + }, + { + "cell_type": "markdown", + "id": "17f2ad09-f865-4c43-9aec-43887a27338e", + "metadata": {}, + "source": [ + "This class can now be filled from the OSM file:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b1ab37d1-5e1a-4627-9ad7-09c4f47ba3d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "29 places cached.\n" + ] + } + ], + "source": [ + "class PlaceNodeReader:\n", + "\n", + " def __init__(self):\n", + " self.places = {}\n", + "\n", + " def node(self, n):\n", + " self.places[n.tags['wikidata']] = PlaceInfo(n.id, dict(n.tags), (n.location.lon, n.location.lat))\n", + "\n", + "reader = PlaceNodeReader()\n", + "\n", + "osmium.apply('../data/liechtenstein.osm.pbf',\n", + " osmium.filter.KeyFilter('place').enable_for(osmium.osm.NODE),\n", + " osmium.filter.KeyFilter('wikidata').enable_for(osmium.osm.NODE),\n", + " reader)\n", + "\n", + "print(f\"{len(reader.places)} places cached.\")" + ] + }, + { + "cell_type": "markdown", + "id": "8060aea2-5417-4c20-864a-da289250be08", + "metadata": {}, + "source": [ + "We use the `osmium.apply()` function here with a handler instead of a FileProcessor. The two approaches are equivalent. Which one you choose, depends on your personal taste. FileProcessor loops are less verbose and quicker to write. Handlers tend to yield more readable code when you want to do very different things with the different kinds of objects.\n", + "\n", + "As you can see in the code, it is entirely possible to use filter functions with the apply() functions. In our case, the filters make sure that only objects pass which have a `place` tag _and_ a `wikidata` tag. This leaves exactly the objects we need already, so no further processing needed in the handler callback.\n", + "\n", + "Next the relations need to be read. Relations can be huge, so we don't want to cache them but write them directly out into a file. If we want to create a geojson file, then we need the geometry of the relation in geojson format. Getting geojson format itself is easy. Pyosmium has a converter built-in for this, the GeoJSONFactory:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2f911347-8b21-4dbe-bd3a-b62b1224680b", + "metadata": {}, + "outputs": [], + "source": [ + "geojsonfab = osmium.geom.GeoJSONFactory()" + ] + }, + { + "cell_type": "markdown", + "id": "690af49a-de2e-4568-aecc-c64576112398", + "metadata": {}, + "source": [ + "The factory only needs to be instantiated once and can then be used globally.\n", + "\n", + "To get the polygon from a relation, the special area handler is needed. It is easiest to invoke by writing a SimpleHandler class with an `area()` callback. When `apply_file()` is called on the handler, it will take the necessary steps in the background to build the polygon geometries." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5de0aef9-9662-4219-97cf-ca515cd16120", + "metadata": {}, + "outputs": [], + "source": [ + "class BoundaryHandler(osmium.SimpleHandler):\n", + " def __init__(self, places, outfile):\n", + " self.places = places\n", + " self.outfile = outfile\n", + " # write the header of the geojson file\n", + " self.outfile.write('{\"type\": \"FeatureCollection\", \"features\": [')\n", + " # This is just to make sure, we place the commas on the right place.\n", + " self.delim = ''\n", + "\n", + " def finish(self):\n", + " self.outfile.write(']}')\n", + "\n", + " def area(self, a):\n", + " # Find the corresponding place node\n", + " place = self.places.get(a.tags.get('wikidata', 'not found'), None)\n", + " # Geojsonfab creates a string with the geojson geometry.\n", + " # Convert to a Python object to make it easier to add data.\n", + " geom = json.loads(geojsonfab.create_multipolygon(a))\n", + " if geom:\n", + " # print the array delimiter, if necessary\n", + " self.outfile.write(self.delim)\n", + " self.delim = ','\n", + "\n", + " tags = dict(a.tags)\n", + " # add the place information to the propoerties\n", + " if place is not None:\n", + " tags['place_node:id'] = str(place.id)\n", + " tags['place_node:lat'] = str(place.coords[1])\n", + " tags['place_node:lon'] = str(place.coords[0])\n", + " for k, v in place.tags.items():\n", + " tags['place_node:tags:' + k] = v\n", + " # And wrap everything in proper GeoJSON.\n", + " feature = {'type': 'Feature', 'geometry': geom, 'properties': dict(tags)}\n", + " self.outfile.write(json.dumps(feature))\n", + "\n", + "# We are interested in boundary relations that make up areas and not in ways at all.\n", + "filters = [osmium.filter.EntityFilter(osmium.osm.RELATION | osmium.osm.AREA),\n", + " osmium.filter.TagFilter(('boundary', 'administrative'))]\n", + "\n", + "with open('../data/out/boundaries.geojson', 'w') as outf:\n", + " handler = BoundaryHandler(reader.places, outf)\n", + " handler.apply_file('../data/liechtenstein.osm.pbf', filters=filters)\n", + " handler.finish()" + ] + }, + { + "cell_type": "markdown", + "id": "28e12d41-ead3-47e8-8671-1fc46b764803", + "metadata": {}, + "source": [ + "There are two things you should keep in mind, when working with areas:\n", + "1. When the area handler is invoked, the input file is always read twice. The first pass checks the relations and find out which ways it contains. The second pass assembles all necessary ways and builds the geometries.\n", + "2. The area handler automatically enables caching of node locations. You don't need to worry about this when working with small files like our Liechtenstein example. For larger files of continent- or planet-size, the node cache can become quite large. You should read up about the alternative implementations that can write out the node cache on disk to save RAM.\n", + "\n", + "This is already it. In the long version, we have read the input file twice, once to get the nodes and in the second pass to get the relations. This is not really necessary because the nodes come always before the relations in the file. The quick solution shows how to combine both handlers to create the geojson file in a single pass. The only part to pay attention to is the use of filters. Given that we have very different filters for nodes and relations, it is important to call `enable_for()` with the correct OSM type." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/cookbooks/Adding-Route-Info-To-Ways.ipynb b/docs/cookbooks/Adding-Route-Info-To-Ways.ipynb new file mode 100644 index 00000000..6f2d3afa --- /dev/null +++ b/docs/cookbooks/Adding-Route-Info-To-Ways.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "89ba777b-4729-4030-a186-ac51220e2632", + "metadata": {}, + "source": [ + "# Adding Relation Information to Member Ways\n", + "\n", + "How to transfer information from a relation to its members.\n", + "\n", + "## Task\n", + "\n", + "Take the name and reference from all cycling routes and add it to the member ways of the route relation. Write out a new file with the added way information.\n", + "\n", + "## Quick Solution" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "867f6858-7ee6-4815-a676-6e58e5d4cde5", + "metadata": {}, + "outputs": [], + "source": [ + "import osmium\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c1872db8-11c1-485d-bb9b-0051567099ac", + "metadata": {}, + "outputs": [], + "source": [ + "fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf', osmium.osm.RELATION)\\\n", + " .with_filter(osmium.filter.TagFilter(('type', 'route')))\\\n", + " .with_filter(osmium.filter.TagFilter(('route', 'bicycle')))\n", + "\n", + "routes = {}\n", + "members = defaultdict(list)\n", + "for rel in fp:\n", + " routes[rel.id] = (rel.tags.get('name', ''), rel.tags.get('ref', ''))\n", + " \n", + " for member in rel.members:\n", + " if member.type == 'w':\n", + " members[member.ref].append(rel.id)\n", + "\n", + "with osmium.SimpleWriter('../data/out/cycling.osm.opl', overwrite=True) as writer:\n", + " fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf')\\\n", + " .with_filter(osmium.filter.IdFilter(members.keys()).enable_for(osmium.osm.WAY))\\\n", + " .handler_for_filtered(writer)\n", + "\n", + " for way in fp:\n", + " assert all(i in routes for i in members[way.id])\n", + " # To add tags, first convert the tags into a Python dictionary.\n", + " tags = dict(way.tags)\n", + " tags['cycle_route:name'] = '|'.join(routes[i][0] for i in members[way.id])[:255]\n", + " tags['cycle_route:ref'] = '|'.join(routes[i][1] for i in members[way.id])[:255]\n", + " writer.add(way.replace(tags=tags))" + ] + }, + { + "cell_type": "markdown", + "id": "efb7cc9f-76c9-428e-a196-fd0fba50301d", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "The objects in an OSM file are usually order by their type: first come nodes, then ways and finally relations. Given that pyosmium always scans files sequentially, it will be necessary to read the OSM file twice when you want to transfer information from relations to ways.\n", + "\n", + "The first pass is all about getting the information from the relations. There are two pieces of information to collect: the information about the relation itself and the information which relations a way belongs to. Lets start with collection the relation information:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9c105332-3f78-4ac6-8aec-f4b5c84782a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Found 13 routes.'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf', osmium.osm.RELATION)\\\n", + " .with_filter(osmium.filter.TagFilter(('type', 'route')))\\\n", + " .with_filter(osmium.filter.TagFilter(('route', 'bicycle')))\n", + "\n", + "routes = {}\n", + "for rel in fp:\n", + " routes[rel.id] = (rel.tags.get('name', ''), rel.tags.get('ref', ''))\n", + "\n", + "f\"Found {len(routes)} routes.\"" + ] + }, + { + "cell_type": "markdown", + "id": "16d01528-12f7-455e-97e3-a65d4fbe515b", + "metadata": {}, + "source": [ + "It is safe to restrict the FileProcessor to the RELATION type because we are only interested in relations and don't need geometry information. A cycling route comes with two mandatory tags in OSM, `type=route` and `route=bicycle`. To filter for relations that have _both_ tags in them, simply chain two TagFilters. Don't just use a single filter with two tags like this: `osmium.filter.TagFilter(('type', 'route'), ('route', 'bicycle'))`. This would filter for relation that have either the route tag or the type tag. Not exactly what we want.\n", + "\n", + "For each relation that goes through the filter, save the information needed. Resist the temptation to simply save the complete relation. For one thing, a single relation can become quite large. But more importantly, pyosmium will not allow you to access the object anymore once the end of the loop iteraton is reached. You only ever see a temporary view of an object within the processing loop. You need to make a full copy of what you want to keep.\n", + "\n", + "Next we need to save the way-relation membership. This can be done in a simple dictionary. Just keep in mind that a single way can be in multiple relations. The member lookup needs to point to a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cea978d9-fbf9-42a7-9159-85ef7031a187", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Found 1023 ways that are part of a cycling relation.'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "members = defaultdict(list)\n", + "for rel in fp:\n", + " for member in rel.members:\n", + " if member.type == 'w':\n", + " members[member.ref].append(rel.id)\n", + "\n", + "f\"Found {len(members)} ways that are part of a cycling relation.\"" + ] + }, + { + "cell_type": "markdown", + "id": "ee67f100-3585-42c0-94bf-6b4a7608d5db", + "metadata": {}, + "source": [ + "This is all the information needed to add the cycling information to the ways. Now we can write out the enhanced cycling info file. Only the ways with relations on them need to be modified. So we use an IdFilter to process only these ways and forward all other objects directly to the writer. This works just the same as in the [Enhance-Tags cookbook](../Enhance-Tags.md):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e0116080-f6e3-4911-9454-12159992a0d8", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.SimpleWriter('../data/out/cycling.osm.opl', overwrite=True) as writer:\n", + " fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf')\\\n", + " .with_filter(osmium.filter.IdFilter(members.keys()).enable_for(osmium.osm.WAY))\\\n", + " .handler_for_filtered(writer)\n", + "\n", + " for way in fp:\n", + " assert all(i in routes for i in members[way.id])\n", + " # To add tags, first convert the tags into a Python dictionary.\n", + " tags = dict(way.tags)\n", + " tags['cycle_route:name'] = '|'.join(routes[i][0] for i in members[way.id])[:255]\n", + " tags['cycle_route:ref'] = '|'.join(routes[i][1] for i in members[way.id])[:255]\n", + " writer.add(way.replace(tags=tags))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/cookbooks/Enhance-Tags.ipynb b/docs/cookbooks/Enhance-Tags.ipynb new file mode 100644 index 00000000..4fc2f0a0 --- /dev/null +++ b/docs/cookbooks/Enhance-Tags.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "44ce734c-4cff-41f3-8010-689a0e751957", + "metadata": {}, + "source": [ + "# Enhancing Tags in an OSM File\n", + "\n", + "How to modify selected objects in an OSM file.\n", + "\n", + "## Task\n", + "\n", + "Localise the OSM file for the French language: when a `name:fr` tag is available, replace the `name` tag with it and save the original name in `name:local`.\n", + "\n", + "## Quick solution" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1ab0f3a3-d3af-465b-8721-0cb7227f8c8d", + "metadata": {}, + "outputs": [], + "source": [ + "import osmium" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "907f5fc8-d95a-455b-9dfb-0055b5415fa6", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.SimpleWriter('../data/out/renamed.pbf', overwrite=True) as writer:\n", + " fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf')\\\n", + " .with_filter(osmium.filter.KeyFilter('name:fr'))\\\n", + " .handler_for_filtered(writer)\n", + "\n", + " for obj in fp:\n", + " # start with a set of tags without name:fr\n", + " tags = {k: v for k, v in obj.tags if k != 'name:fr'}\n", + " # replace the name tag with the French version\n", + " tags['name'] = obj.tags['name:fr']\n", + " # Save the original if it exists.\n", + " if 'name' in obj.tags:\n", + " tags['name:local'] = obj.tags['name']\n", + " # Write back the object with the modified tags\n", + " writer.add(obj.replace(tags=tags))" + ] + }, + { + "cell_type": "markdown", + "id": "49a73c9f-facc-453c-8769-f92f70095399", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "To change selected tags in a file, it is necessary to read the file object by object, make changes as necessary and write back the data into a new file. This could be done with a simple FileProcessor (for reading the input file) that is combined with a SimpleWriter (for writing the output file):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "39cc90e3-cd69-4973-85e5-9ad130ba40a3", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.SimpleWriter('../data/out/ele.osm.opl', overwrite=True) as writer:\n", + " for obj in osmium.FileProcessor('../data/liechtenstein.osm.pbf'):\n", + " if 'name:fr' in obj.tags:\n", + " tags = {k: v for k, v in obj.tags if k != 'name:fr'}\n", + " # ... do more stuff here\n", + " writer.add(obj.replace(tags=tags))\n", + " else:\n", + " writer.add(obj)" + ] + }, + { + "cell_type": "markdown", + "id": "e9a76a09-3f27-464d-b823-0e2ce5fde83e", + "metadata": {}, + "source": [ + "If you run this code snippet on a large OSM file, it will take a very long time to execute. Even though we only want to change a handful of objects (all objects that have a `name:fr` tag), the FileProcessor needs to present every single object to the Python code in the loop because every single objects needs to be written in the output file. We need a way to tell the FileProcessor to directly write out all the objects that we are not inspecting in the for loop. This can be done with the `handler_for_filtered()` function. It allows to define a handler for all the objects, the `with_filter()` handlers have rejected. The SimpleWriter class can itself function as a handler. By setting it as the handler for filtered objects, they will be directly passed to the writer.\n", + "\n", + "With the SimpleWriter as fallback in place, we can now create a FileProcessor that filters for objects with a `name:fr` tag:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c7c02ede-cb5f-42e0-8f4c-7b8ce9d9a4f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1932181216 has the French name Vaduz.\n", + "3696525426 has the French name Liechtenstein.\n", + "9798887324 has the French name Schweizerisches Generalkonsulat.\n", + "159018431 has the French name Rhein.\n", + "424375869 has the French name Rhein.\n", + "8497 has the French name Rhein-Route.\n", + "12464 has the French name Seen-Route.\n", + "16239 has the French name Österreich.\n", + "19664 has the French name Seen-Route - Etappe 9.\n", + "27939 has the French name Cycling in Switzerland.\n", + "51701 has the French name Schweiz/Suisse/Svizzera/Svizra.\n", + "74942 has the French name Vorarlberg.\n", + "102638 has the French name Rhein-Route - Etappe 3.\n", + "102666 has the French name Österreich - Schweiz.\n", + "102877 has the French name Österreich — Liechtenstein.\n", + "123924 has the French name Rhein.\n", + "302442 has the French name Schweizer Hauptstrassen.\n", + "1155955 has the French name Liechtenstein.\n", + "1550322 has the French name Österreich — Schweiz / Suisse / Svizzera.\n", + "1665395 has the French name Via Alpina Red.\n", + "1686631 has the French name Graubünden/Grischun/Grigioni.\n", + "1687006 has the French name Sankt Gallen.\n", + "2128682 has the French name Rätikon.\n", + "2171555 has the French name EuroVelo 15 - Rheinradweg.\n", + "2668952 has the French name European Union / Union Européenne / Europäische Union.\n", + "2698607 has the French name Alps.\n", + "11342353 has the French name Appenzeller Alpen.\n", + "12579662 has the French name Via Alpina Green.\n", + "12729625 has the French name Eurozone.\n", + "13376469 has the French name Member States of the European Union / États members de l'Union européenne / Mitgliedstaaten der Europäischen Union.\n" + ] + } + ], + "source": [ + "with osmium.SimpleWriter('../data/out/buildings.opl', overwrite=True) as writer:\n", + " fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf')\\\n", + " .with_filter(osmium.filter.KeyFilter('name:fr'))\\\n", + " .handler_for_filtered(writer)\n", + "\n", + " for obj in fp:\n", + " print(f\"{obj.id} has the French name {obj.tags['name']}.\")" + ] + }, + { + "cell_type": "markdown", + "id": "434eea63-8ddd-4c74-864e-908a2cf1fba2", + "metadata": {}, + "source": [ + "If you run this piece of code, you will notice that suddenly all objects with a French name are missing from output file. This happens because once a file is presented to Python, the SimpleWriter object doesn't see it anymore. You have to explicitly call one of the 'add' functions of the SimpleWriter to write the modified object. So the full code is:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d9f2ddc7-814d-460e-ad6e-8d9fd42475d2", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.SimpleWriter('../data/out/buildings.opl', overwrite=True) as writer:\n", + " fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf')\\\n", + " .with_filter(osmium.filter.KeyFilter('name:fr'))\\\n", + " .handler_for_filtered(writer)\n", + " for obj in fp:\n", + " tags = {k: v for k, v in obj.tags if k != 'name:fr'}\n", + " # ... do more stuff here\n", + " writer.add(obj.replace(tags=tags))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/cookbooks/Filter-Data-By-Geometry.ipynb b/docs/cookbooks/Filter-Data-By-Geometry.ipynb new file mode 100644 index 00000000..4abdc936 --- /dev/null +++ b/docs/cookbooks/Filter-Data-By-Geometry.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7b67f84a-968e-409f-8583-e576ac9ed265", + "metadata": {}, + "source": [ + "# Filtering by Geometry\n", + "\n", + "How to create geographic extracts from an OSM file.\n", + "\n", + "## Task\n", + "\n", + "Given the country extract of Liechtenstein, extract all data that is within 2km of the coordinates 47.13,9.52. All objects inside the geographic area should be complete, meaning that complete geometries can be created for them.\n", + "\n", + "## Quick solution" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "21fcac74-acf5-4d6d-bcf8-120c024d1a82", + "metadata": {}, + "outputs": [], + "source": [ + "import osmium" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a80342da-0d78-4ca3-bf87-ed36f54f14e6", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.ForwardReferenceWriter('../data/out/centre.osm.pbf',\n", + " '../data/liechtenstein.osm.pbf', overwrite=True) as writer:\n", + " for obj in osmium.FileProcessor('../data/liechtenstein.osm.pbf', osmium.osm.NODE):\n", + " if osmium.geom.haversine_distance(osmium.osm.Location(9.52, 47.13), obj.location) < 2000:\n", + " writer.add_node(obj)" + ] + }, + { + "cell_type": "markdown", + "id": "d3a1c198-d00e-4479-8232-5ceee1545ff3", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "OSM data is not a simple selection of geometries. In an OSM data file only the OSM nodes have a location. All other OSM object are made up of OSM nodes or other OSM objects. To find out where an OSM way or relation is located on the planet, it is necessary to go back to the nodes it references.\n", + "\n", + "For the task at hand this means that any filtering by geometry needs to start with the OSM nodes. Lets start with a simple script that writes out all the nodes within the circle defined in the task:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "012b6ac6-0589-49b8-8410-531dbd4db749", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.SimpleWriter('../data/out/centre.opl', overwrite=True) as writer:\n", + " for obj in osmium.FileProcessor('../data/liechtenstein.osm.pbf', osmium.osm.NODE):\n", + " if osmium.geom.haversine_distance(osmium.osm.Location(9.52, 47.13), obj.location) < 2000:\n", + " writer.add_node(obj)" + ] + }, + { + "cell_type": "markdown", + "id": "12e5b7e0-1851-4a5b-b402-4302fac031aa", + "metadata": {}, + "source": [ + "The FileProcessor reads the data and SimpleWriter writes the nodes out that we are interested in. Given that we are looking at nodes only, the FileProcessor can be restricted to that type. For one thing, this makes processing faster. For another it means, we don't have to explicitly check for the type of the object within the for loop. We can trust that only nodes will be returned. Checking if a node should be included in the output file is a simple matter of computing the distance between the target coordinates and the location of the node. pyosmium has a convenient function `haversine_distance()` for that. It computes the distance between two points in meters.\n", + "\n", + "This gives us a file with nodes. But what about the ways and relations? To find out which ones to include, we need to follow the _forward references_. Given the IDs of the nodes already included in the file, we need to find the ways which reference any of the nodes. And then we need to find relations which reference either nodes already included or one of the newly found ways. Luckily for us, OSM files are ordered by node, way and relations. So by the time the FileProcessor sees the first way, it will already have seen all the nodes and it can make an informed decision, if the way needs including or not. The same is true for relations. They are last in the file, so all the node and way members have been processed already. The situation is more complicated with relation members and nested relations. We leave those out for the moment.\n", + "\n", + "Given that nodes, ways and relations need to be handled differently and we need to carry quite a bit of state, it is easier to implement the forward referencing collector as a [handler class](../../user_manual/05-Working-with-Handlers.md):" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fd22c7b7-6c1c-4ee5-b314-3ce7eeb1cc83", + "metadata": {}, + "outputs": [], + "source": [ + "class CoordHandler:\n", + " def __init__(self, coord, dist, writer):\n", + " self.center = osmium.osm.Location(*coord)\n", + " self.dist = dist\n", + " self.writer = writer\n", + " self.id_tracker = osmium.IdTracker()\n", + " \n", + " def node(self, n):\n", + " if osmium.geom.haversine_distance(self.center, n.location) <= self.dist:\n", + " self.writer.add_node(n)\n", + " self.id_tracker.add_node(n.id)\n", + "\n", + " def way(self, w):\n", + " if self.id_tracker.contains_any_references(w):\n", + " self.writer.add_way(w)\n", + " self.id_tracker.add_way(w.id)\n", + "\n", + " def relation(self, r):\n", + " if self.id_tracker.contains_any_references(r):\n", + " self.writer.add_relation(r)" + ] + }, + { + "cell_type": "markdown", + "id": "05315006-d70f-40aa-9cd7-502aec5ecd49", + "metadata": {}, + "source": [ + "The `IdTracker` class helps to keep track of all the objects that appear in the file. Every time a node or way is written, its ID is recorded. Tracking relation IDs would only be necessary for nested relations. The IDTracker gives us also a convenient function `contains_any_reference()` which checks if any of the IDs it is tracking is needed by the given object. If that is the case, the object needs to be written out.\n", + "\n", + "This is almost it. To get a referentially complete output file, we also need to add the objects that are referenced by the ways and relations we have added. This can be easily achieved by using the `BackReferenceWriter` in place of the `SimpleWriter`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "626ee34e-2d85-420e-bbf6-e76b87ff5ba0", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.BackReferenceWriter('../data/out/centre.osm.pbf', ref_src='../data/liechtenstein.osm.pbf', overwrite=True) as writer:\n", + " osmium.apply('../data/liechtenstein.osm.pbf', CoordHandler((9.52, 47.13), 2000, writer))" + ] + }, + { + "cell_type": "markdown", + "id": "c5f95b13-c2b5-4419-8b40-05ad85ab80ce", + "metadata": {}, + "source": [ + "To learn more about adding backward references, have a look at the cookbook on [Filtering By Tags](../Filtering-Data-By-Tags.ipynb).\n", + "\n", + "The `ForwardReferenceWriter` helps to automate most of what we have just done manually. It is a replacement for the `SimpleWriter` which collects the forward references under the hood. It will first collects the OSM data that should be written in a temporary file. When the writer is closed, it adds the forward references from a reference file. This means, the `ForwardReferenceWriter` needs two mandatory parameters to be instantiated: the name of the file to write to and the name of the file to copy the referenced data from:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "553e9519-1699-41a8-8a8a-d8c3ab586922", + "metadata": {}, + "outputs": [], + "source": [ + "writer = osmium.ForwardReferenceWriter('../data/out/centre.osm.pbf', '../data/liechtenstein.osm.pbf', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "id": "08352ef4-34e7-4886-b2b8-187575b3d387", + "metadata": {}, + "source": [ + "The writer will by default also add the necessary objects to make the file reference-complete. The writer can now replace the SimpleWriter in the code with the first attempt, resulting in the final solution shown in the Quick Solution." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/cookbooks/Filtering-Data-By-Tags.ipynb b/docs/cookbooks/Filtering-Data-By-Tags.ipynb new file mode 100644 index 00000000..fa96a8b8 --- /dev/null +++ b/docs/cookbooks/Filtering-Data-By-Tags.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "de4a5409-76ba-4767-b151-ce13f6439669", + "metadata": {}, + "source": [ + "# Filtering an OSM File By Tags\n", + "\n", + "How to create a thematic extract from an OSM file.\n", + "\n", + "## Task\n", + "\n", + "Given the country extract of Liechtenstein, create a fully usable OSM file that only contains all the schools in the file.\n", + "\n", + "## Quick Solution" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "77c434f2-83c3-4b9c-b090-a80cde5df080", + "metadata": {}, + "outputs": [], + "source": [ + "import osmium" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a7baa48f-0cb8-4f9c-b716-cf1adaa59aee", + "metadata": {}, + "outputs": [], + "source": [ + "fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf').with_filter(osmium.filter.KeyFilter('amenity'))\n", + "\n", + "with osmium.BackReferenceWriter(\"../data/out/schools_full.osm.pbf\", ref_src='../data/liechtenstein.osm.pbf', overwrite=True) as writer:\n", + " for obj in fp:\n", + " if obj.tags['amenity'] == 'school':\n", + " writer.add(obj)" + ] + }, + { + "cell_type": "markdown", + "id": "1426f67d-8c7c-480b-af3e-9384c5d5f81e", + "metadata": {}, + "source": [ + "When filtering objects from a file, it is important, to include all objects that are referenced by the filtered objects. The `BackReferenceWriter` collects the references and writes out a complete file." + ] + }, + { + "cell_type": "markdown", + "id": "2ac5f104-24d2-4524-a49d-4e43dce58315", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "Filtering school objects from a file is fairly easy. We need a file processor for the target file which returns all objects with an `amenity` key:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2a9a196b-e9ba-4c2d-b0db-0a07b89a4ba6", + "metadata": {}, + "outputs": [], + "source": [ + "fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf').with_filter(osmium.filter.KeyFilter('amenity'))" + ] + }, + { + "cell_type": "markdown", + "id": "49c354af-5b08-4b11-9aa3-83b6ce3c924f", + "metadata": {}, + "source": [ + "The additional filtering for the `school` value can then be done in the processing loop.\n", + "\n", + "Lets first check how many school objects are there:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "825f6742-96dd-4555-ab02-50496df48a4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Nodes: 3 Ways: 19 Relations: 1'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import Counter\n", + "\n", + "cnt = Counter()\n", + "\n", + "for obj in fp:\n", + " if obj.tags['amenity'] == 'school':\n", + " cnt.update([obj.type_str()])\n", + "\n", + "f\"Nodes: {cnt['n']} Ways: {cnt['w']} Relations: {cnt['r']}\"" + ] + }, + { + "cell_type": "markdown", + "id": "d1639455-8923-4199-829e-d8ffadeff147", + "metadata": {}, + "source": [ + "The counter distinguishes by OSM object types. As we can see, schools exist as nodes (point geometries), ways (polygon geometries) and relations (multipolygon geometries). All of them need to appear in the output file.\n", + "\n", + "The simple solution seems to be to write them all out into a file:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "62cd2b0f-634c-4cbd-8060-2c12b1772827", + "metadata": {}, + "outputs": [], + "source": [ + "with osmium.SimpleWriter('../data/out/schools.opl', overwrite=True) as writer:\n", + " for obj in fp:\n", + " if obj.tags['amenity'] == 'school':\n", + " writer.add(obj)" + ] + }, + { + "cell_type": "markdown", + "id": "71608e44-0961-4995-8fac-19c3b7a4a8a2", + "metadata": {}, + "source": [ + "However, if you try to use the resulting file in another program, you may find that it complains that the data is incomplete. The schools that are saved as ways in the file reference nodes which are now missing. The school relation references ways which are missing. And these again reference nodes, which need to appear in the output file as well. The file needs to be made _referentially complete_.\n", + "\n", + "### Finding backward references manually\n", + "\n", + "Lets try to collect the IDs of the missing nodes and relation manually first. This helps to understand how the process works. In a first pass, we can simply collect all the IDs we encounter when processing the schools:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "caa2bf26-e86e-4461-873e-26ed9089d54a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Nodes: 325 Ways: 3 Relations: 0'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "references = {'n': set(), 'w': set(), 'r': set()} # save references by their object type\n", + "\n", + "for obj in fp:\n", + " if obj.tags['amenity'] == 'school':\n", + " if obj.is_way():\n", + " references['n'].update(n.ref for n in obj.nodes)\n", + " elif obj.is_relation():\n", + " for member in obj.members:\n", + " references[member.type].add(member.ref)\n", + "\n", + "f\"Nodes: {len(references['n'])} Ways: {len(references['w'])} Relations: {len(references['r'])}\"" + ] + }, + { + "cell_type": "markdown", + "id": "3da84764-9e73-450a-baa3-5005a4bdbad7", + "metadata": {}, + "source": [ + "This gives us a set of all the direct references: the nodes of the school ways and and the ways in the school relations. We are still missing the indirect references: the nodes from the ways of the school relations. It is not possible to collect those while scanning the file for the first time. By the time the relations are scanned and we know which additional ways are of interest, the ways have already been read. We could cache all the node locations when scanning the ways in the file for the first time but that can become quite a lot of data to remember. It is faster to simply scan the file again once we know which ways are of interest:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "378a3df8-4af1-4cef-9e8d-51c4c6f6eae9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Nodes: 395 Ways: 3 Relations: 0'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for obj in osmium.FileProcessor('../data/liechtenstein.osm.pbf', osmium.osm.WAY):\n", + " if obj.id in references['w']:\n", + " references['n'].update(n.ref for n in obj.nodes)\n", + "\n", + "f\"Nodes: {len(references['n'])} Ways: {len(references['w'])} Relations: {len(references['r'])}\"" + ] + }, + { + "cell_type": "markdown", + "id": "9d2a7d4b-0d7a-43c7-8214-33d6658e35cd", + "metadata": {}, + "source": [ + "This time it is not possible to use a key filter because the ways that are part of the relations are not necessarily tagged with `amenity=school`. They might not have any tags at all. However, we can use a different trick and tell the file processor to only scan the ways in the file. This is the second parameter in the `FileProcessor()` constructor.\n", + "\n", + "After this second scan of the file, we know the IDs of all the objects that need to go into the output file. The data we are interested in doesn't have nested relations. When relations contain other relations, then another scan of the file is required to collect the triple indirection. This part shall be left as an exercise to the reader for now.\n", + "\n", + "Once all the necessary ids are collected, the objects needs to be extracted from the original file. This can be done with the IdFilter. It gets a list of all object IDs it is supposed to let pass. Given that we need nodes and ways from the original file, two filters are necessary:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9b33d939-a2fc-43fd-bd85-b263b05aa05f", + "metadata": {}, + "outputs": [], + "source": [ + "ref_fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf', osmium.osm.NODE | osmium.osm.WAY)\\\n", + " .with_filter(osmium.filter.IdFilter(references['n']).enable_for(osmium.osm.NODE))\\\n", + " .with_filter(osmium.filter.IdFilter(references['w']).enable_for(osmium.osm.WAY)) " + ] + }, + { + "cell_type": "markdown", + "id": "841e0bc5-dec9-4632-8989-0ed27b7328dd", + "metadata": {}, + "source": [ + "The data from this FileProcessor needs to be merged with the filtered data originally written out. We cannot just concatenate the two files because the order of elements matters. Most applications that process OSM data expect the elements in a well defined order: first nodes, then ways, then relations, all sorted by ID. When the input files are ordered correctly already, then the `zip_processors()` function can be used to iterate over multiple FileProcessors in parallel and write out the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "81fd1ee2-a1c9-4694-9c0f-65b274971a0d", + "metadata": {}, + "outputs": [], + "source": [ + "filtered_fp = osmium.FileProcessor('../data/out/schools.opl')\n", + "\n", + "with osmium.SimpleWriter(f'../data/out/schools_full.osm.pbf', overwrite=True) as writer:\n", + " for filtered_obj, ref_obj in osmium.zip_processors(filtered_fp, ref_fp):\n", + " if filtered_obj:\n", + " writer.add(filtered_obj)\n", + " else:\n", + " writer.add(ref_obj.replace(tags={}))" + ] + }, + { + "cell_type": "markdown", + "id": "e8780aee-3c2c-4913-a445-e1a328aef676", + "metadata": {}, + "source": [ + "This writes the data from the filtered file, if any exists and otherwise takes the data from the original file. Objects from the original files have their tags removed. This avoids to have unwanted first-class objects in your file. All additionally added objects now exist for the sole purpose of completing the ones you have filtered." + ] + }, + { + "cell_type": "markdown", + "id": "b08aa3e9-a99e-4caf-b84a-5ce15faa34f3", + "metadata": {}, + "source": [ + "### Finding backward references with the IDTracker\n", + "\n", + "The `IDTracker` class will track backward references for you just like described in the last paragraph." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bbec92fe-8f4a-484c-859b-5d6a2d9add8c", + "metadata": {}, + "outputs": [], + "source": [ + "references = osmium.IdTracker()\n", + "\n", + "with osmium.SimpleWriter(f'../data/out/schools.opl', overwrite=True) as writer:\n", + " for obj in fp:\n", + " if obj.tags['amenity'] == 'school':\n", + " writer.add(obj)\n", + " references.add_references(obj)\n", + "\n", + "references.complete_backward_references('../data/liechtenstein.osm.pbf', relation_depth=10)" + ] + }, + { + "cell_type": "markdown", + "id": "e04c9ad7-87e9-4f04-9bd8-d68819e2c4ee", + "metadata": {}, + "source": [ + "The function `complete_backward_references()` repeatedly reads from the file to collect all referenced objects. In contrast to the more simple solution above, it can also collect references in nested relations. The `relation_depth` parameter controls how far the nesting should be followed. In this case, we have set it to 10 which should be sufficient even for the most complex relations in OSM. It is a good idea to not set this parameter too high because every level of depth requires an additional scan of the relations in the reference file.\n", + "\n", + "With all the IDs collected, the final file can be written out as above. IdTracker can directly pose as a filter to a FileProcessor, so that the code can be slightly simplified:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fd30c9b4-b196-4031-8bba-7d53edd691fe", + "metadata": {}, + "outputs": [], + "source": [ + "fp1 = osmium.FileProcessor('../data/out/schools.opl')\n", + "fp2 = osmium.FileProcessor('../data/liechtenstein.osm.pbf').with_filter(references.id_filter())\n", + "\n", + "with osmium.SimpleWriter('../data/out/schools_full.opl', overwrite=True) as writer:\n", + " for o1, o2 in osmium.zip_processors(fp1, fp2):\n", + " if o1:\n", + " writer.add(o1)\n", + " else:\n", + " writer.add(o2.replace(tags={})) " + ] + }, + { + "cell_type": "markdown", + "id": "7ead3b93-a1e7-4a99-be9a-626cdc3a638d", + "metadata": {}, + "source": [ + "### Using BackReferenceWriter to collect references\n", + "\n", + "The BackReferenceWriter encapsulates a SimpleWriter and IdTracker and writes out the referenced objects, when `close()` is called. This reduces the task of filtering schools to the simple solution shown in the beginning." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/cookbooks/Visualizing-Data-With-Geopandas.ipynb b/docs/cookbooks/Visualizing-Data-With-Geopandas.ipynb new file mode 100644 index 00000000..84e29232 --- /dev/null +++ b/docs/cookbooks/Visualizing-Data-With-Geopandas.ipynb @@ -0,0 +1,762 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dac77494-34b4-4ef2-94b3-20d39bdbf4b4", + "metadata": {}, + "source": [ + "# Visualising Data With GeoPandas\n", + "\n", + "How to convert OSM data into a GeoPandas frame for further processing.\n", + "\n", + "## Task\n", + "\n", + "Show the street network of the input data on a map with different colors for the different maximum speeds.\n", + "\n", + "## Solution\n", + "\n", + "[GeoPanadas](https://geopandas.org) is a useful tool to help you process and visualise geodata in a Jupyter notebook. It can read data from many sources. Among others, it can read geo features from a Python iterable where each element implements the [`__geo_interface__`](https://gist.github.com/sgillies/2217756). An 'osmium.FileProcessor' happens to behave like a Python iterable. To make it compatible with GeoPandas, the output objects need to be extended with a `__geo_interface__`. This can be done with the GeoInterfaceFilter. This filter adds a geometry to the object. Therefore, geometry processing needs to be enabled accordingly:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7b9e6b85-6112-46c8-b26e-3c9ced042a94", + "metadata": {}, + "outputs": [], + "source": [ + "import osmium\n", + "import geopandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4ed4dc91-b674-42da-a3d9-e22225f9de1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fp = osmium.FileProcessor('../data/buildings.opl')\\\n", + " .with_areas()\\\n", + " .with_filter(osmium.filter.GeoInterfaceFilter())\n", + "\n", + "features = geopandas.GeoDataFrame.from_features(fp)\n", + "len(features)" + ] + }, + { + "cell_type": "markdown", + "id": "2aac82bf-fd20-4fc4-8232-dbc3c281b842", + "metadata": {}, + "source": [ + "This will load every single OSM object into the GeoDataFrame as long as it can be converted into a geometry, including all untagged nodes. This is usually not what you want. Therefore it is important to carefully filter the data before giving it to the GeoHandler. In our case we are only interested in streets. That means, it must be linear ways with a tag 'highway'. Lets add the appropriate filters:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "36b562ff-a6d4-46d6-b28c-dfb07cd4da38", + "metadata": {}, + "outputs": [], + "source": [ + "fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf')\\\n", + " .with_locations()\\\n", + " .with_filter(osmium.filter.EntityFilter(osmium.osm.WAY))\\\n", + " .with_filter(osmium.filter.KeyFilter('highway'))\\\n", + " .with_filter(osmium.filter.GeoInterfaceFilter())\n", + "\n", + "features = geopandas.GeoDataFrame.from_features(fp)" + ] + }, + { + "cell_type": "markdown", + "id": "852d2604-fdc4-4744-9ac6-887200fff0aa", + "metadata": {}, + "source": [ + "The first filter restricts the selection to ways, the second filter only lets through highway objects. Let's have a look at the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9a04db5d-2da1-4ec9-857b-d4ffca093721", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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geometryhighwaynamecycleway:bothlane_markingsonewaysurfacelanesmaxspeedsmoothness...informalbenchoperatorsheltertraffic_calmingvehicle:conditionalagriculturalold_namecrossingbin
0LINESTRING (9.54963 47.18788, 9.54968 47.18792...residentialIn den ÄusserenNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1LINESTRING (9.54173 47.18223, 9.54212 47.18255...tertiaryDorfstrassenononoasphaltNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2LINESTRING (9.51716 47.17078, 9.51719 47.17074...tertiaryPlanknerstrasseNaNNaNnoasphalt250good...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3LINESTRING (9.55048 47.18784, 9.55042 47.18778...residentialOberplanknerstrasseNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4LINESTRING (9.54627 47.18504, 9.54649 47.18508...residentialIm HäldeleNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
7501LINESTRING (9.54458 47.18668, 9.54461 47.18667)footwayNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7502LINESTRING (9.54374 47.18631, 9.54395 47.18649)serviceUnterm RainNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7503LINESTRING (9.54434 47.18478, 9.54444 47.18474)stepsNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7504LINESTRING (9.54470 47.18497, 9.54471 47.18501)serviceNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7505LINESTRING (9.54445 47.18494, 9.54452 47.18490)footwayNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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7506 rows × 154 columns

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" + ], + "text/plain": [ + " geometry highway \\\n", + "0 LINESTRING (9.54963 47.18788, 9.54968 47.18792... residential \n", + "1 LINESTRING (9.54173 47.18223, 9.54212 47.18255... tertiary \n", + "2 LINESTRING (9.51716 47.17078, 9.51719 47.17074... tertiary \n", + "3 LINESTRING (9.55048 47.18784, 9.55042 47.18778... residential \n", + "4 LINESTRING (9.54627 47.18504, 9.54649 47.18508... residential \n", + "... ... ... \n", + "7501 LINESTRING (9.54458 47.18668, 9.54461 47.18667) footway \n", + "7502 LINESTRING (9.54374 47.18631, 9.54395 47.18649) service \n", + "7503 LINESTRING (9.54434 47.18478, 9.54444 47.18474) steps \n", + "7504 LINESTRING (9.54470 47.18497, 9.54471 47.18501) service \n", + "7505 LINESTRING (9.54445 47.18494, 9.54452 47.18490) footway \n", + "\n", + " name cycleway:both lane_markings oneway surface lanes \\\n", + "0 In den Äusseren NaN NaN NaN NaN NaN \n", + "1 Dorfstrasse no no no asphalt NaN \n", + "2 Planknerstrasse NaN NaN no asphalt 2 \n", + "3 Oberplanknerstrasse NaN NaN NaN NaN NaN \n", + "4 Im Häldele NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... \n", + "7501 NaN NaN NaN NaN NaN NaN \n", + "7502 Unterm Rain NaN NaN NaN NaN NaN \n", + "7503 NaN NaN NaN NaN NaN NaN \n", + "7504 NaN NaN NaN NaN NaN NaN \n", + "7505 NaN NaN NaN NaN NaN NaN \n", + "\n", + " maxspeed smoothness ... informal bench operator shelter traffic_calming \\\n", + "0 NaN NaN ... NaN NaN NaN NaN NaN \n", + "1 NaN NaN ... NaN NaN NaN NaN NaN \n", + "2 50 good ... NaN NaN NaN NaN NaN \n", + "3 NaN NaN ... NaN NaN NaN NaN NaN \n", + "4 NaN NaN ... NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... ... ... ... \n", + "7501 NaN NaN ... NaN NaN NaN NaN NaN \n", + "7502 NaN NaN ... NaN NaN NaN NaN NaN \n", + "7503 NaN NaN ... NaN NaN NaN NaN NaN \n", + "7504 NaN NaN ... NaN NaN NaN NaN NaN \n", + "7505 NaN NaN ... NaN NaN NaN NaN NaN \n", + "\n", + " vehicle:conditional agricultural old_name crossing bin \n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... \n", + "7501 NaN NaN NaN NaN NaN \n", + "7502 NaN NaN NaN NaN NaN \n", + "7503 NaN NaN NaN NaN NaN \n", + "7504 NaN NaN NaN NaN NaN \n", + "7505 NaN NaN NaN NaN NaN \n", + "\n", + "[7506 rows x 154 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features" + ] + }, + { + "cell_type": "markdown", + "id": "7b7ff175-18a2-4f6b-acc7-e91ea168b8f7", + "metadata": {}, + "source": [ + "It also contains all possible tags, that an OSM object can have. We are only interested in a selected number of tags. The GeoHandler can be instructed to restrict the tags, it adds to the properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6b618af9-0e90-427f-b03e-fd50a2768e7e", + "metadata": {}, + "outputs": [], + "source": [ + "fp = osmium.FileProcessor('../data/liechtenstein.osm.pbf')\\\n", + " .with_locations()\\\n", + " .with_filter(osmium.filter.EntityFilter(osmium.osm.WAY))\\\n", + " .with_filter(osmium.filter.KeyFilter('highway'))\\\n", + " .with_filter(osmium.filter.GeoInterfaceFilter(tags=['highway', 'name', 'maxspeed']))\n", + "\n", + "features = geopandas.GeoDataFrame.from_features(fp)" + ] + }, + { + "cell_type": "markdown", + "id": "2fbeb7c7-ccc1-48d3-b4c9-f58b5ab93e8c", + "metadata": {}, + "source": [ + "This leaves us with a more concise data frame:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a3094b2d-dece-4ea3-a8f2-304f7f05fe4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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geometryhighwaynamemaxspeed
0LINESTRING (9.54963 47.18788, 9.54968 47.18792...residentialIn den ÄusserenNaN
1LINESTRING (9.54173 47.18223, 9.54212 47.18255...tertiaryDorfstrasseNaN
2LINESTRING (9.51716 47.17078, 9.51719 47.17074...tertiaryPlanknerstrasse50
3LINESTRING (9.55048 47.18784, 9.55042 47.18778...residentialOberplanknerstrasseNaN
4LINESTRING (9.54627 47.18504, 9.54649 47.18508...residentialIm HäldeleNaN
...............
7501LINESTRING (9.54458 47.18668, 9.54461 47.18667)footwayNaNNaN
7502LINESTRING (9.54374 47.18631, 9.54395 47.18649)serviceUnterm RainNaN
7503LINESTRING (9.54434 47.18478, 9.54444 47.18474)stepsNaNNaN
7504LINESTRING (9.54470 47.18497, 9.54471 47.18501)serviceNaNNaN
7505LINESTRING (9.54445 47.18494, 9.54452 47.18490)footwayNaNNaN
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7506 rows × 4 columns

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" + ], + "text/plain": [ + " geometry highway \\\n", + "0 LINESTRING (9.54963 47.18788, 9.54968 47.18792... residential \n", + "1 LINESTRING (9.54173 47.18223, 9.54212 47.18255... tertiary \n", + "2 LINESTRING (9.51716 47.17078, 9.51719 47.17074... tertiary \n", + "3 LINESTRING (9.55048 47.18784, 9.55042 47.18778... residential \n", + "4 LINESTRING (9.54627 47.18504, 9.54649 47.18508... residential \n", + "... ... ... \n", + "7501 LINESTRING (9.54458 47.18668, 9.54461 47.18667) footway \n", + "7502 LINESTRING (9.54374 47.18631, 9.54395 47.18649) service \n", + "7503 LINESTRING (9.54434 47.18478, 9.54444 47.18474) steps \n", + "7504 LINESTRING (9.54470 47.18497, 9.54471 47.18501) service \n", + "7505 LINESTRING (9.54445 47.18494, 9.54452 47.18490) footway \n", + "\n", + " name maxspeed \n", + "0 In den Äusseren NaN \n", + "1 Dorfstrasse NaN \n", + "2 Planknerstrasse 50 \n", + "3 Oberplanknerstrasse NaN \n", + "4 Im Häldele NaN \n", + "... ... ... \n", + "7501 NaN NaN \n", + "7502 Unterm Rain NaN \n", + "7503 NaN NaN \n", + "7504 NaN NaN \n", + "7505 NaN NaN \n", + "\n", + "[7506 rows x 4 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features" + ] + }, + { + "cell_type": "markdown", + "id": "8d8881ab-b10a-4ef0-ae74-29561b478eca", + "metadata": {}, + "source": [ + "All that is left, to plot the ways according to their maxspeed:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "83616996-52b6-408a-b4ee-5778354053a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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