Merge remote-tracking branch 'origin/master'

README.md

@@ -11,16 +11,15 @@
 **Pyrosm** is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (`*.osm.pbf`) into Geopandas GeoDataFrames. 
 Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, 
 Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible 
-to parse the data from OSM with more specific filters. 
-
+to parse the data from OSM with more specific filters.
 
 **Pyrosm** is easy to use and it provides a somewhat similar user interface as [OSMnx](https://github.com/gboeing/osmnx).
 The main difference between pyrosm and OSMnx is that OSMnx reads the data over internet using OverPass API, whereas pyrosm reads the data from local OSM data dumps
 that can be downloaded e.g. from [GeoFabrik's website](http://download.geofabrik.de/). This makes it possible to read data faster thus 
 allowing e.g. parsing street networks for the whole country fairly efficiently (however, see [caveats](#caveats)).
 
 The library has been developed by keeping performance in mind, hence, it is mainly written in Cython (*Python with C-like performance*) 
-which makes it probably faster than any other Python alternatives for parsing OpenStreetMap data.
+which makes it fast to parse OpenStreetMap data from PBF files.
 Pyrosm is built on top of another Cython library called [Pyrobuf](https://github.com/appnexus/pyrobuf) which is a faster Cython alternative 
 to Google's Protobuf library: It provides 2-4x boost in performance for deserializing the protocol buffer messages compared to 
 Google's version with C++ backend. Google's Protocol Buffers is a commonly used and efficient method to serialize and compress structured data 
@@ -41,13 +40,6 @@ which is also used by OpenStreetMap contributors to distribute the OSM data in P
  - filter data based on bounding box
  - export networks as a directed graph to `igraph`, `networkx` and `pandana`
 
-## Roadmap
-
- - add possibility to optimize memory usage (see #87)
- - add possibility to simplify graph (see #89)
- - add possibility to crop PBF and save a subset into new PBF.
- - add Cython specific tests
-
 ## Install
 
 Pyrosm is distributed via PyPi and conda-forge. 
@@ -83,23 +75,6 @@ That being said, it is also possible to extract neighborhood level information w
 Using `pyrosm` is straightforward. See [docs](https://pyrosm.readthedocs.io/en/latest/basics.html) 
 for instructions how to use the library.
 
-## Performance
-
-See [docs](https://pyrosm.readthedocs.io/en/latest/benchmarking.html) for more comprehensive benchmarking tests. Reading all drivable roads in Helsinki Region (approx. 85,000 roads) 
-takes approximately **12 seconds** (laptop with 16GB memory, SSD drive, and Intel Core i5-8250U CPU 1.6 GHZ). And the result looks something like:
-
-![Helsinki_driving_net](resources/img/Helsinki_driving_net.PNG)
-
-Parsing all buildings from the same area (approx. 180,000) takes approximately **17 seconds**. And the result looks something like:
-
-![Helsinki_building_footprints](resources/img/Helsinki_building_footprints.png)
-
-Parsing all Points of Interest (POIs) with defaults elements (amenities, shops and tourism) 
-takes approximately **14 seconds** (approx. 32,000 features). 
-And the result looks something like:
-
-![Helsinki_POIs](resources/img/Helsinki_POIs_amenity_shop_tourism.png)
-
 ## Get in touch + contributions
 
 If you find a bug from the tool, have question, or would like to suggest a new feature to it, you can [make a new issue here](https://github.com/HTenkanen/pyrosm/issues).
@@ -111,18 +86,17 @@ please check the [contribution guidelines](https://pyrosm.readthedocs.io/en/late
 
 You can install a local development version of the tool by 1) installing necessary packages with conda and 2) building pyrosm from source:
 
- 1. install conda-environment for Python 3.7 or 3.8 by:
+ 1. install conda-environment for Python 3.12 by:
 
-    - Python 3.7 (you might want to modify the env-name which is `test` by default): `$ conda env create -f ci/37-conda.yaml`
-    - Python 3.8: `$ conda env create -f ci/38-conda.yaml`
+    - Python 3.12 (you might want to modify the env-name which is `test` by default): `$ conda env create -f ci/312-conda.yaml`
 
  2. build pyrosm development version from master (activate the environment first):
 
     - `pip install -e .`
 
 You can run tests with `pytest` by executing:
 
-  `$ pytest -v` 
+  `$ pytest . -v` 
 
 
 ## License and copyright

pyrosm/frames.pyx

@@ -98,7 +98,7 @@ cpdef prepare_way_gdf(node_coordinates, ways, parse_network, calculate_seg_lengt
             way_gdf["v"] = v
 
             # Calculate the length of the geometries
-            way_gdf["length"] = calculate_geom_array_length(way_gdf.geometry.values.data)
+            way_gdf["length"] = calculate_geom_array_length(way_gdf.geometry.values.to_numpy())
 
         # For cases not related to networks
         else:
@@ -126,7 +126,7 @@ cpdef prepare_relation_gdf(node_coordinates, relations, relation_ways, tags_as_c
                                       node_coordinates,
                                       tags_as_columns)
 
-        relation_gdf = gpd.GeoDataFrame(relations)
+        relation_gdf = gpd.GeoDataFrame(relations, crs="epsg:4326")
         relation_gdf['osm_type'] = "relation"
 
     else:
@@ -154,7 +154,7 @@ cpdef prepare_geodataframe(nodes, node_coordinates, ways,
         # Prepare nodes
         node_gdf = prepare_node_gdf(nodes)
     else:
-        node_gdf = pd.DataFrame()
+        node_gdf = gpd.GeoDataFrame()
 
     # Merge all
     gdf = pd.concat([node_gdf, way_gdf, relation_gdf])

tests/test_graph_exports.py

@@ -334,7 +334,7 @@ def test_igraph_connectivity(immutable_nodes_and_edges):
 
     # Test that finding shortest paths works for all nodes
     N = g.vcount()
-    shortest_paths = g.shortest_paths_dijkstra(
+    shortest_paths = g.distances(
         source=5, target=[i for i in range(N)], weights="length"
     )