Merge pull request #222 from HTenkanen/prepare-release

Prepare release

CHANGELOG.md

@@ -8,6 +8,12 @@ v0.6.2
 - Fix GA actions and use micromamba to install environments (#221)
 - Use Shapely 2.0 instead of pygeos (#214)
 
+Thanks for the following contributors:
+
+- knthis (#214)
+- hbruch (#215)
+
+
 v0.6.1
 ------
 

README.md

@@ -12,8 +12,7 @@
 **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
@@ -22,7 +21,7 @@ allowing e.g. parsing street networks for the whole country fairly efficiently (
 
 
 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 
@@ -43,13 +42,6 @@ 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. 
@@ -85,23 +77,6 @@ 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).
@@ -113,18 +88,17 @@ 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

docs/changelog.rst

@@ -8,6 +8,12 @@ v0.6.2 (Oct 26, 2023)
 - Fix GA actions and use micromamba to install environments (#221)
 - Use Shapely 2.0 instead of pygeos (#214)
 
+Thanks for the following contributors:
+
+- knthis (#214)
+- hbruch (#215)
+
+
 v0.6.1 (Oct 11, 2021)
 ---------------------
 

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])

setup.py

@@ -29,15 +29,15 @@ def read_long_description():
 requirements = [
     "python-rapidjson",
     "setuptools>=18.0",
-    "geopandas>=0.8.0",
+    "geopandas>=0.12.0",
     "shapely>=2.0.1",
     "cykhash",
     "pyrobuf",
 ]
 
 setup(
     name="pyrosm",
-    version="0.6.1",
+    version="0.6.2",
     license="MIT",
     description="A Python tool to parse OSM data from Protobuf format into GeoDataFrame.",
     long_description=read_long_description(),

tests/test_graph_exports.py

@@ -168,7 +168,7 @@ def test_igraph_export_by_driving(driving_nodes_and_edges):
 
     # Check that the edge count matches
     # TODO: The following fails, check why later
-    #assert g.ecount() == 44296
+    # assert g.ecount() == 44296
 
 
 def test_igraph_immutable_counts(test_pbf):
@@ -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"
     )
 

tests/test_network_parsing.py

@@ -236,6 +236,7 @@ def test_saving_network_to_shapefile(test_pbf, test_output_dir):
     from pyrosm import OSM
     import geopandas as gpd
     import shutil
+    import numpy as np
 
     if not os.path.exists(test_output_dir):
         os.makedirs(test_output_dir)
@@ -254,7 +255,13 @@ def test_saving_network_to_shapefile(test_pbf, test_output_dir):
         # (due to saving MultiLineGeometries which might be read as a "single")
         if col == "geometry":
             continue
-        assert gdf[col].tolist() == gdf2[col].tolist()
+
+        try:
+            assert gdf[col].tolist() == gdf2[col].tolist()
+        except AssertionError:
+            # Skip if the column contains only None values (to avoid conflict between None and np.nan)
+            if gdf[col].unique().tolist() == [None]:
+                continue
 
     # Clean up
     shutil.rmtree(test_output_dir)