diff --git a/README.md b/README.md index ed30b6d9..74b486a6 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ A `Python` package for pedestrian-scale network-based urban analysis: network an - Documentation for `v3.x`: see documented code per tagged release [v3](https://github.com/benchmark-urbanism/cityseer-api/tree/v3.7.2) - Documentation for `v4+`: -Demo Notebooks: +Demo Notebooks: Issues: diff --git a/docs/astro.config.mjs b/docs/astro.config.mjs index 66847e24..40e9e6ea 100644 --- a/docs/astro.config.mjs +++ b/docs/astro.config.mjs @@ -42,6 +42,17 @@ function admonitionRemarkPlugin() { } } +function replaceUnderscoresInIds() { + return (tree) => { + visit(tree, 'element', (node) => { + if (node.tagName && /^h[1-6]$/.test(node.tagName) && node.properties && node.properties.id) { + // Replace underscores with dashes in the id property + node.properties.id = node.properties.id.replace(/_/g, '-') + } + }) + } +} + export default defineConfig({ root: '.', srcDir: './src', @@ -78,12 +89,7 @@ export default defineConfig({ ], rehypePlugins: [ rehypeSlug, - [ - rehypeKatex, - { - output: 'htmlAndMathml', - }, - ], + replaceUnderscoresInIds, [ rehypeAutolinkHeadings, { @@ -95,7 +101,7 @@ export default defineConfig({ 'svg', { xmlns: 'http://www.w3.org/2000/svg', - viewbox: '0 0 20 20', + viewBox: '0 0 20 20', ariaHidden: 'true', width: '15px', height: '15px', @@ -114,6 +120,12 @@ export default defineConfig({ }, }, ], + [ + rehypeKatex, + { + output: 'htmlAndMathml', + }, + ], [ rehypeCitation, // ESM, import manually else doesn't seem to work { diff --git a/docs/package-lock.json b/docs/package-lock.json index 45adfce6..728ba775 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -4,62 +4,61 @@ "requires": true, "packages": { "": { - "name": "docs", "dependencies": { "@vicons/fa": "0.12.0", - "@vueuse/core": "10.5.0", - "animejs": "3.2.1", - "astro-remote": "^0.2.4", - "date-fns": "2.30.0", - "eslint-plugin-astro": "^0.29.1", + "@vueuse/core": "10.9.0", + "animejs": "3.2.2", + "astro-remote": "^0.3.2", + "date-fns": "3.6.0", + "eslint-plugin-astro": "^0.32.0", "url-slug": "4.0.1", - "vue": "3.3.4" + "vue": "3.4.21" }, "devDependencies": { "@astrojs/markdown-component": "^1.0.5", - "@astrojs/markdown-remark": "3.3.0", + "@astrojs/markdown-remark": "4.3.1", "@astrojs/prefetch": "0.4.1", - "@astrojs/sitemap": "^3.0.3", - "@astrojs/tailwind": "5.0.2", - "@astrojs/vue": "^3.0.1", - "@fec/remark-a11y-emoji": "3.1.0", + "@astrojs/sitemap": "^3.1.1", + "@astrojs/tailwind": "5.1.0", + "@astrojs/vue": "^4.0.9", + "@fec/remark-a11y-emoji": "4.0.2", "@prettier/plugin-pug": "3.0.0", - "@rushstack/eslint-patch": "1.5.1", - "@typescript-eslint/eslint-plugin": "6.7.5", - "@typescript-eslint/parser": "^6.7.5", + "@rushstack/eslint-patch": "1.8.0", + "@typescript-eslint/eslint-plugin": "7.3.1", + "@typescript-eslint/parser": "^7.3.1", "@volar/vue-language-plugin-pug": "1.0.18", - "astro": "^3.6.4", - "autoprefixer": "10.4.16", - "eslint": 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"rehype-autolink-headings": "7.0.0", - "rehype-citation": "^1.0.3", + "rehype-autolink-headings": "7.1.0", + "rehype-citation": "^2.0.0", "rehype-katex": "7.0.0", "rehype-slug": "6.0.0", "remark-directive": "3.0.0", - "remark-emoji": "4.0.0", + "remark-emoji": "4.0.1", "remark-math": "6.0.0", - "remark-smartypants": "2.0.0", - "serialize-javascript": "6.0.1", + "remark-smartypants": "2.1.0", + "serialize-javascript": "6.0.2", "shorthash": "0.0.2", - "stylelint": "15.10.3", - "stylelint-config-standard": "34.0.0", - "tailwindcss": "3.3.3", + "stylelint": "16.2.1", + "stylelint-config-standard": "36.0.0", + "tailwindcss": "3.4.1", "unist-util-visit": "5.0.0" } }, @@ -97,14 +96,14 @@ } }, "node_modules/@astrojs/compiler": { - "version": "2.3.2", - "resolved": "https://registry.npmjs.org/@astrojs/compiler/-/compiler-2.3.2.tgz", - "integrity": "sha512-jkY7bCVxl27KeZsSxIZ+pqACe+g8VQUdTiSJRj/sXYdIaZlW3ZMq4qF2M17P/oDt3LBq0zLNwQr4Cb7fSpRGxQ==" + "version": "2.7.0", + "resolved": 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"16.2.1", + "stylelint-config-standard": "36.0.0", + "tailwindcss": "3.4.1", "unist-util-visit": "5.0.0" } } diff --git a/docs/plots/plots.py b/docs/plots/plots.py index 975c8a85..14e098ae 100644 --- a/docs/plots/plots.py +++ b/docs/plots/plots.py @@ -35,8 +35,8 @@ distances=[400, 800], ) # custom colourmap -segment_harmonic_vals = nodes_gdf["cc_metric_segment_harmonic_800"] -mixed_uses_vals = nodes_gdf["cc_metric_hill_wt_q0_800"] +segment_harmonic_vals = nodes_gdf["cc_seg_harmonic_800"] +mixed_uses_vals = nodes_gdf["cc_hill_q0_800_wt"] cmap = colors.LinearSegmentedColormap.from_list("cityseer", ["#64c1ff", "#d32f2f"]) segment_harmonic_vals = colors.Normalize()(segment_harmonic_vals) segment_harmonic_cols = cmap(segment_harmonic_vals) @@ -180,7 +180,7 @@ def simple_plot(_G, _path, plot_geoms=True): # let's extract and normalise the values vals = [] for node, data in G_after.nodes(data=True): - vals.append(data["cc_metric_node_beta_800"]) + vals.append(data["cc_beta_800"]) # let's create a custom colourmap using matplotlib cmap = colors.LinearSegmentedColormap.from_list( "cityseer", [(100 / 255, 193 / 255, 255 / 255, 255 / 255), (211 / 255, 47 / 255, 47 / 255, 1 / 255)] diff --git a/docs/public/images/assignment.png b/docs/public/images/assignment.png index 2f6e81fe..851839cf 100644 Binary files a/docs/public/images/assignment.png and b/docs/public/images/assignment.png differ diff --git a/docs/public/images/assignment_decomposed.png b/docs/public/images/assignment_decomposed.png index 030f7b4a..e024b9c9 100644 Binary files a/docs/public/images/assignment_decomposed.png and b/docs/public/images/assignment_decomposed.png differ diff --git a/docs/public/images/assignment_plot.png b/docs/public/images/assignment_plot.png index 2673f3e7..18289759 100644 Binary files a/docs/public/images/assignment_plot.png and b/docs/public/images/assignment_plot.png differ diff --git a/docs/public/images/betas.png b/docs/public/images/betas.png index b2ea797b..920100e6 100644 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a/docs/public/images/graph_simple.png and b/docs/public/images/graph_simple.png differ diff --git a/docs/public/images/intro_mixed_uses.png b/docs/public/images/intro_mixed_uses.png index f82f704b..b70bd693 100644 Binary files a/docs/public/images/intro_mixed_uses.png and b/docs/public/images/intro_mixed_uses.png differ diff --git a/docs/public/images/intro_segment_harmonic.png b/docs/public/images/intro_segment_harmonic.png index 812d6686..a557c54b 100644 Binary files a/docs/public/images/intro_segment_harmonic.png and b/docs/public/images/intro_segment_harmonic.png differ diff --git a/docs/src/layouts/PageLayout.astro b/docs/src/layouts/PageLayout.astro index c5ec33f4..a450327a 100644 --- a/docs/src/layouts/PageLayout.astro +++ b/docs/src/layouts/PageLayout.astro @@ -12,7 +12,6 @@ const currentPath = Astro.url.pathname const navPaths = [ '/intro', '/guide', - '/examples', '/tools/io', '/tools/graphs', '/tools/plot', diff --git a/docs/src/pages/examples.md b/docs/src/pages/examples.md deleted file mode 100644 index c6a37a6b..00000000 --- a/docs/src/pages/examples.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -layout: '@src/layouts/PageLayout.astro' ---- - -# Examples - -The following `Jupyter` notebooks provide some examples of how this package can be used. - -## Getting Started Guide - -The `Getting Started` guide from the [intro](/intro). - -Github: getting_started.ipynb - -## Graph Corrections Guide - -`Graph Corrections` guide. - -Github: graph_corrections.ipynb - -## Graph Cleaning Guide - -`Graph Cleaning` guide. - -Github: graph_cleaning.ipynb - -## Importing OSM data - -Examples showing how to import OSM data as discussed in [OSM and NetworkX](/guide#osm-and-networkx). - -Github: osm_to_cityseer.ipynb - -## Importing GeoPandas LineString data - -Example showing how to import `GeoPandas` LineString data. This approach is demonstrated for ingesting a `momepy` network which can then be used for centrality and landuse accessibility analysis. - -Github: momepy_to_cityseer.ipynb - -## Centralities for London - -Computing network centralities for London with OS Open data. - -Github: london_centralities.ipynb - -## Landuse Accessibilities for London - -Computing landuse acccessibilities to pubs and restaurants in London. - -Github: london_amenities.ipynb - -## Street Network continuity - -Computing street network continuities for street names, route identifiers, and highway types: - -- OS Open data: Github: open_roads_continuity.ipynb - -- OSM data: Github: osm_continuity.ipynb diff --git a/docs/src/pages/guide.md b/docs/src/pages/guide.md index 3089bb1e..d9f1f1ff 100644 --- a/docs/src/pages/guide.md +++ b/docs/src/pages/guide.md @@ -33,9 +33,9 @@ Good sources of street network data, such as the Ordnance Survey's [OS Open Road When a high-quality source is available, it is best not to attempt additional clean-up unless there is a particular reason. On the other hand, many indispensable sources of network information, particularly Open Street Map data, can be particularly messy for network analysis purposes. -`cityseer` uses customisable graph cleaning methods that reduce topological idiosyncrasies which may otherwise confound centrality measures. It can, for example, remove dual carriageways while merging nodes and roadways in a manner that is as 'tidy' as possible. These are demonstrated in the [`Graph Cleaning`](/examples#graph-cleaning-guide) example notebook. +`cityseer` uses customisable graph cleaning methods that reduce topological idiosyncrasies which may otherwise confound centrality measures. It can, for example, remove dual carriageways while merging nodes and roadways in a manner that is as 'tidy' as possible. These are demonstrated in the [`Graph Cleaning`](https://benchmark-urbanism.github.io/cityseer-examples/examples/graph_cleaning.html) guide. -There are still situations where it can be difficult to automate network cleaning, especially when using the same workflows across different cities. Each city tends to have its own quirks, and for this reason it can be preferable to retain the unsimplified representation but to control for algorithmic artefacts encountered for centrality computations by using other methods. These are demonstrated in the [`Graph Corrections`](/examples#graph-corrections-guide) example notebook. +There are still situations where it can be difficult to automate network cleaning, especially when using the same workflows across different cities. Each city tends to have its own quirks, and for this reason it can be preferable to retain the unsimplified representation but to control for algorithmic artefacts encountered for centrality computations by using other methods. These are demonstrated in the [`OSM Strategies`](https://benchmark-urbanism.github.io/cityseer-examples/examples/graph_corrections.html) guide. ## OSM and NetworkX @@ -50,7 +50,7 @@ The following points may be helpful when using `OSMnx` and `cityseer` together: - `cityseer` graph simplification and consolidation workflows will give different results to those employed in `OSMnx`. If you're using `OSMnx` to ingest networks from `OSM` but wish to simplify and consolidate the network as part of a `cityseer` workflow, set the `OSMnx` `simplify` argument to `False` so that the network is not automatically simplified. - `cityseer` uses internal validation workflows to check that the geometries associated with an edge remain connected to the coordinates of the nodes on either side. If performing graph manipulation outside of `cityseer` before conversion, the conversion function may complain of disconnected geometries. In these cases, you may need to relax the tolerance parameter used for error checking upon conversion to a `cityseer` `MultiGraph`, in which case geometries disconnected from their end-nodes (within the tolerance parameter) will be "snapped" to meet their endpoints as part of the conversion process. -See the [Importing OSM data](/examples#importing-osm-data) notebook for examples. +See the [OSM - cityseer](https://benchmark-urbanism.github.io/cityseer-examples/examples/osm_to_cityseer.html) guide. ## GeoPandas diff --git a/docs/src/pages/intro.md b/docs/src/pages/intro.md index 7fcb856b..87d66617 100644 --- a/docs/src/pages/intro.md +++ b/docs/src/pages/intro.md @@ -6,26 +6,16 @@ layout: '@src/layouts/PageLayout.astro' `cityseer` is a collection of computational tools for fine-grained street-network and land-use analysis, useful for assessing the morphological precursors to vibrant neighbourhoods. +The getting started guide and a growing collection of other examples is available via the [`Cityseer Examples`](https://benchmark-urbanism.github.io/cityseer-examples/) site. The example notebooks include workflows showing how to run graph cleaning, network centralities, and land-use accessibility analysis for some real-world situations. + `cityseer` is underpinned by network-based methods that have been developed from the ground-up for micro-morphological urban analysis at the pedestrian scale, with the intention of providing contextually specific metrics for any given streetfront location. Importantly, `cityseer` computes metrics directly over the street network and offers distance-weighted variants. The combination of these strategies makes `cityseer` more contextually sensitive than methods otherwise based on crow-flies aggregation methods that do not take the network structure and its affect on pedestrian walking distances into account. The use of `python` facilitates interaction with popular computational tools for network manipulation (e.g. [`networkX`](https://networkx.github.io/)), geospatial data processing (e.g. [`shapely`](https://shapely.readthedocs.io), etc.), Open Street Map workflows with [`OSMnx`](https://osmnx.readthedocs.io/), and interaction with the [`numpy`](http://www.numpy.org/), [`geopandas`](https://geopandas.org/en/stable/) (and [`momepy`](http://docs.momepy.org)) stack of packages. The underlying algorithms are parallelised and implemented in `rust` so that the methods can be scaled to large networks. In-out convenience methods are provided for interfacing with `networkX` and graph cleaning tools aid the incorporation of messier network representations such as those derived from [Open Street Map](https://www.openstreetmap.org). The github repository is available at [github.com/benchmark-urbanism/cityseer-api](https://github.com/benchmark-urbanism/cityseer-api). Please cite the [associated paper](https://journals.sagepub.com/doi/full/10.1177/23998083221133827) when using this package. Associated papers introducing the package and demonstrating the forms of analysis that can be done with it are [available at `arXiv`](https://arxiv.org/a/simons_g_1.html). -## Installation - -`cityseer` is a `python` package that can be installed with `pip`: - -```bash -pip install cityseer -``` - Code tests are run against Python versions `3.10` - `3.12`. -## Notebooks - -The getting started guide on this page, and a growing collection of other examples, is available as an Jupyter Notebooks which can be accessed via the [`Examples`](/examples) page. The examples include workflows showing how to run network centralities and land-use accessibility analysis for some real-world situations. - ## Old versions For documentations of older versions of `cityseer`, please refer to the docstrings which are directly embedded in the code for the respective version: @@ -33,246 +23,3 @@ For documentations of older versions of `cityseer`, please refer to the docstrin - Documentation for [`v1.x`](https://github.com/benchmark-urbanism/cityseer-api/tree/v1.2.1/cityseer) - Documentation for [`v2.x`](https://github.com/benchmark-urbanism/cityseer-api/tree/v2.0.0/cityseer) - Documentation for [`v3.x`](https://github.com/benchmark-urbanism/cityseer-api/tree/v3.7.2) - -## Quickstart - -`cityseer` revolves around networks (graphs). If you're comfortable with `numpy` and abstract data handling, then the underlying data structures can be created and manipulated directly. However, it is generally more convenient to sketch the graph using [`NetworkX`](https://networkx.github.io/) and to let `cityseer` take care of initialising and converting the graph for you. - -```python -# any networkX MultiGraph with 'x' and 'y' node attributes will do -# here we'll use the cityseer mock module to generate an example networkX graph -import networkx as nx -from cityseer.tools import mock, graphs, plot, io - -G = mock.mock_graph() -print(G) -# let's plot the network -plot.plot_nx(G, labels=True, node_size=80, dpi=200, figsize=(4, 4)) -``` - -![An example graph](/images/graph.png) -_An example graph._ - -## Graph Preparation - -The [`tools.graphs`](/tools/graphs) module contains a collection of convenience functions for the preparation and conversion of `networkX` `MultiGraphs`, i.e. undirected graphs allowing for parallel edges. The [`tools.graphs`](/tools/graphs) module is designed to work with raw `shapely` [`Linestring`](https://shapely.readthedocs.io/en/latest/manual.html#linestrings) geometries that have been assigned to the graph's edge (link) `geom` attributes. The benefit to this approach is that the geometry of the network is decoupled from the topology: the topology is consequently free from distortions which would otherwise confound centrality and other metrics. - -There are generally two scenarios when creating a street network graph: - -1. In the ideal case, if you have access to a high-quality street network dataset -- which keeps the topology of the network separate from the geometry of the streets -- then you would construct the network based on the topology while assigning the roadway geometries to the respective edges spanning the nodes. [OS Open Roads](https://www.ordnancesurvey.co.uk/business-and-government/products/os-open-roads.html) is a good example of this type of dataset. Assigning the geometries to an edge involves A) casting the geometry to a [`shapely`](https://shapely.readthedocs.io) `LineString`, and B) assigning this geometry to the respective edge by adding the `LineString` geometry as a `geom` attribute. e.g. `G.add_edge(start_node, end_node, geom=a_linestring_geom)`. - -2. In reality, most data-sources are not this refined and will represent roadway geometries by adding additional nodes to the network. For a variety of reasons, this is not ideal and you may want to follow the [`Graph Cleaning`](/guide#graph-cleaning) guide; in these cases, the [`graphs.nx_simple_geoms`](/tools/graphs#nx-simple-geoms) method can be used to generate the street geometries, after which several methods can be applied to clean and prepare the graph. For example, [`nx_wgs_to_utm`](/tools/graphs#nx-wgs-to-utm) aids coordinate conversions; [`nx_remove_dangling_nodes`](/tools/graphs#nx-remove-dangling-nodes) removes remove roadway stubs, [`nx_remove_filler_nodes`](/tools/graphs#nx-remove-filler-nodes) strips-out filler nodes, and [`nx_consolidate_nodes`](/tools/graphs#nx-consolidate-nodes) assists in cleaning-up the network. - -3. A third approach uses only minimal graph cleaning and compensates for messy network representations through algorithmic corrections. This is demonstrated in Graph Corrections example notebook on the [`Examples`](/examples#graph-corrections-guide) page. - -## Example - -Here, we'll walk through a high-level overview showing how to use `cityseer`. You can provide your own shapely geometries if available; else, you can auto-infer simple geometries from the start to end node of each network edge, which works well for graphs where nodes have been used to inscribe roadway geometries. - -```python -# use nx_simple_geoms to infer geoms for your edges -G = graphs.nx_simple_geoms(G) -plot.plot_nx(G, labels=True, node_size=80, plot_geoms=True, dpi=200, figsize=(4, 4)) -``` - -![An example graph](/images/graph_example.png) -_A graph with inferred geometries. In this case the geometries are all exactly straight._ - -We have now inferred geometries for each edge, meaning that each edge now has an associated `LineString` geometry. Any further manipulation of the graph using the `cityseer.graph` module will retain and further manipulate these geometries in-place. - -Once the geoms are readied, we can use tools such as [`nx_decompose`](/tools/graphs#nx-decompose) for generating granular graph representations and [`nx_to_dual`](/tools/graphs#nx-to-dual) for casting a primal graph representation to its dual. - -```python -# this will (optionally) decompose the graph -G_decomp = graphs.nx_decompose(G, 50) -plot.plot_nx(G_decomp, plot_geoms=True, labels=False, dpi=200, figsize=(4, 4)) -``` - -![An example decomposed graph](/images/graph_decomposed.png) -_A decomposed graph._ - -```python -# this will (optionally) cast to a dual network -G_dual = graphs.nx_to_dual(G) -# here we are plotting the newly decomposed graph (blue) against the original graph (red) -plot.plot_nx_primal_or_dual(G, G_dual, plot_geoms=False, dpi=200, figsize=(4, 4)) -``` - -![An example dual graph](/images/graph_dual.png) -_A dual graph (blue) plotted against the primal source graph (red). In this case, the true geometry has not been plotted so that the dual graph is easily delineated from the primal graph._ - -## Metrics - -After graph preparation and cleaning has been completed, the `networkX` graph can be transformed into data structures for efficiently computing centralities, land-use measures, or statistical aggregations. - -Use [network_structure_from_nx](/tools/graphs#network-structure-from-nx) to convert a `networkX` graph into GeoPandas [`GeoDataFrames`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html) and a [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure), the latter of which is used by `cityseer` for efficiently computing the measures with the underlying `rust` algorithms. - -### Network Centralities - -The [`networks.node_centrality_shortest`](/metrics/networks#node-centrality-shortest), [`networks.node_centrality_simplest`](/metrics/networks#node-centrality-simplest), and [`networks.segment_centrality`](/metrics/networks#segment-centrality) methods wrap underlying `rust` functions that compute the centrality methods. All selected measures and distance thresholds are computed simultaneously to reduce the amount of time required for multi-variable and multi-scalar workflows. The results of the computations will be written to the `GeoDataFrame`. - -```python -from cityseer.metrics import networks - -# create a Network layer from the networkX graph -# use a CRS EPSG code matching the projected coordinate reference system for your data -nodes_gdf, edges_gdf, network_structure = io.network_structure_from_nx(G_decomp, crs=3395) -# the underlying method allows the computation of various centralities simultaneously, e.g. -nodes_gdf = networks.segment_centrality( - # the network structure for which to compute the measures - network_structure=network_structure, - # the nodes GeoDataFrame, to which the results will be written - nodes_gdf=nodes_gdf, - # the distance thresholds for which to compute centralities - distances=[200, 400, 800, 1600], -) -# the results are now in the GeoDataFrame -nodes_gdf.head() -``` - -```python -# plot centrality -from matplotlib import colors - -# custom colourmap -cmap = colors.LinearSegmentedColormap.from_list("cityseer", ["#64c1ff", "#d32f2f"]) -# normalise the values -segment_harmonic_vals = nodes_gdf["cc_metric_segment_harmonic_800"] -segment_harmonic_vals = colors.Normalize()(segment_harmonic_vals) -# cast against the colour map -segment_harmonic_cols = cmap(segment_harmonic_vals) -# plot segment_harmonic -# cityseer's plot methods are used here and in tests for convenience -# that said, rather use plotting methods directly from networkX or GeoPandas where possible -plot.plot_nx(G_decomp, labels=False, node_colour=segment_harmonic_cols, dpi=200, figsize=(4, 4)) -``` - -![Example centrality plot](/images/intro_segment_harmonic.png) -_800m segmentised harmonic centrality._ - -### Land-use and statistical measures - -Landuse and statistical measures require a GeoPandas [`GeoDataFrame`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.html) consisting of `Point` geometries. Columns representing categorical landuse information ("pub", "shop", "school") can be passed to landuse methods, whereas columns representing numerical information can be used for statistical methods. - -When computing these measures, `cityseer` will assign each data point to the two closest network nodes — one in either direction — based on the closest adjacent street edge. This enables `cityseer` to use dynamic spatial aggregation methods that more accurately describe distances from the perspective of pedestrians travelling over the network, and relative to the direction of approach. - -[`layers.compute_landuses`](/metrics/layers#compute-landuses) and [`layers.compute_mixed_uses`](/metrics/layers#compute-mixed-uses) methods are used for the calculation of land-use accessibility and mixed-use measures whereas [`layers.compute_stats`](/metrics/layers#compute-stats) can be used for statistical aggregations. As with the centrality methods, the measures are computed over the network and are computed simultaneously for all measures and distances. - -```python -from cityseer.metrics import layers - -# a mock data dictionary representing categorical landuse data -# here randomly generated letters represent fictitious landuse categories -data_gdf = mock.mock_landuse_categorical_data(G_decomp, random_seed=25) -data_gdf.head() -``` - -```python -# example easy-wrapper method for computing mixed-uses -# this is a distance weighted form of hill diversity -nodes_gdf, data_gdf = layers.compute_mixed_uses( - # the source data - data_gdf, - # column in the dataframe which contains the landuse labels - landuse_column_label="categorical_landuses", - # nodes GeoDataFrame - the results are written here - nodes_gdf=nodes_gdf, - # measures will be computed relative to pedestrian distances over the network - network_structure=network_structure, - # distance thresholds for which you want to compute the measures - distances=[200, 400, 800, 1600], -) -# the GeoDataFrame will contain the results of the calculations -print(nodes_gdf.columns) -# which can be retrieved as needed -print(nodes_gdf["cc_metric_q0_800_hill"]) -``` - -```python -# for curiosity's sake: -# plot the assignments to see which edges the data points were assigned to -plot.plot_assignment(network_structure, G_decomp, data_gdf, dpi=200, figsize=(4, 4)) -``` - -![Data assigned to network](/images/assignment.png) -_Data points assigned to a Network Layer._ - -![Data assigned to a decomposed network](/images/assignment_decomposed.png) -_Data assignment becomes more precise on a decomposed Network Layer._ - -```python -# plot distance-weighted "hill" numbers mixed uses -mixed_uses_vals = nodes_gdf["cc_metric_q0_800_hill_weighted"] -mixed_uses_vals = colors.Normalize()(mixed_uses_vals) -mixed_uses_cols = cmap(mixed_uses_vals) -plot.plot_assignment( - network_structure, - G_decomp, - data_gdf, - node_colour=mixed_uses_cols, - data_labels=data_gdf["categorical_landuses"].values, - dpi=200, - figsize=(4, 4), -) -``` - -![Example mixed-use plot](/images/intro_mixed_uses.png) -_800m distance-weighted mixed-uses._ - -```python -# compute landuse accessibilities for land-use types a, b, c -nodes_gdf, data_gdf = layers.compute_accessibilities( - # the source data - data_gdf, - # column in the dataframe which contains the landuse labels - landuse_column_label="categorical_landuses", - # the landuse categories for which to compute accessibilities - accessibility_keys=["a", "b", "c"], - # nodes GeoDataFrame - the results are written here - nodes_gdf=nodes_gdf, - # measures will be computed relative to pedestrian distances over the network - network_structure=network_structure, - # distance thresholds for which you want to compute the measures - distances=[200, 400, 800, 1600], -) -# accessibilities are computed in both weighted and unweighted forms -# e.g. for "a" and "b" landuse codes in weighted and non weighted, respectively -print(nodes_gdf[["cc_metric_a_800_weighted", "cc_metric_b_1600_non_weighted"]]) -``` - -Aggregations can likewise be computed for numerical data. Let's generate some mock numerical data: - -```python -numerical_data_gdf = mock.mock_numerical_data(G_decomp, num_arrs=3) -numerical_data_gdf.head() -# compute stats for column mock_numerical_1 -nodes_gdf, numerical_data_gdf = layers.compute_stats( - # the source data - numerical_data_gdf, - # numerical column to compute stats for - stats_column_label="mock_numerical_1", - # nodes GeoDataFrame - the results are written here - nodes_gdf=nodes_gdf, - # measures will be computed relative to pedestrian distances over the network - network_structure=network_structure, - # distance thresholds for which you want to compute the measures - distances=[800, 1600], -) -# statistical aggregations are calculated for each requested column, -# and in the following forms: -# max, min, sum, sum_weighted, mean, mean_weighted, variance, variance_weighted -print(nodes_gdf["cc_metric_max_800"]) -print(nodes_gdf["cc_metric_mean_wt_800"]) -``` - -The landuse metrics and statistical aggregations are computed over the street network relative to the network, with results written to each node. The mixed-use, accessibility, and statistical aggregations can therefore be compared directly to centrality computations from the same locations, and can be correlated or otherwise compared. - -Data derived from metrics can be converted back into a `NetworkX` graph using the [nx_from_cityseer_geopandas](/tools/io#nx-from-cityseer-geopandas) method. - -```python -nx_multigraph_round_trip = io.nx_from_cityseer_geopandas( - nodes_gdf, - edges_gdf, -) -nx_multigraph_round_trip.nodes["0"] -``` diff --git a/docs/src/pages/metrics/layers.md b/docs/src/pages/metrics/layers.md index e762ef06..81760069 100644 --- a/docs/src/pages/metrics/layers.md +++ b/docs/src/pages/metrics/layers.md @@ -1,16 +1,15 @@ --- layout: ../../layouts/PageLayout.astro --- +
# layers -
## assign_gdf_to_network -
assign_gdf_to_network
( @@ -41,9 +40,10 @@ layout: ../../layouts/PageLayout.astro
+Assign a `GeoDataFrame` to a [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). A `NetworkStructure` provides the backbone for the calculation of land-use and statistical aggregations over the network. Data points will be assigned to the two closest network nodes — one in either direction — based on the closest adjacent street edge. This facilitates a dynamic spatial aggregation strategy which will select the shortest distance to a data point relative to either direction of approach. - Assign a `GeoDataFrame` to a [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). A `NetworkStructure` provides the backbone for the calculation of land-use and statistical aggregations over the network. Data points will be assigned to the two closest network nodes — one in either direction — based on the closest adjacent street edge. This facilitates a dynamic spatial aggregation strategy which will select the shortest distance to a data point relative to either direction of approach. ### Parameters +
data_gdf
@@ -51,7 +51,8 @@ layout: ../../layouts/PageLayout.astro
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+
@@ -61,7 +62,8 @@ layout: ../../layouts/PageLayout.astro
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure).
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). +
@@ -71,7 +73,8 @@ layout: ../../layouts/PageLayout.astro
- The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes.
+The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes. +
@@ -81,10 +84,12 @@ layout: ../../layouts/PageLayout.astro
- An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier.
+An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier. + ### Returns +
data_map
@@ -92,7 +97,8 @@ layout: ../../layouts/PageLayout.astro
- A [`rustalgos.DataMap`](/rustalgos#datamap) instance.
+A [`rustalgos.DataMap`](/rustalgos#datamap) instance.
+
@@ -102,7 +108,8 @@ layout: ../../layouts/PageLayout.astro
- The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_neareset_assign`.
+The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_neareset_assign`. + ### Notes @@ -130,12 +137,10 @@ representation of variations of metrics along street-fronts. -
## compute_accessibilities -
compute_accessibilities
( @@ -211,9 +216,10 @@ representation of variations of metrics along street-fronts.
+Compute land-use accessibilities for the specified land-use classification keys over the street network. The landuses are aggregated and computed over the street network relative to the network nodes, with the implication that the measures are generated from the same locations as those used for centrality computations. - Compute land-use accessibilities for the specified land-use classification keys over the street network. The landuses are aggregated and computed over the street network relative to the network nodes, with the implication that the measures are generated from the same locations as those used for centrality computations. ### Parameters +
data_gdf
@@ -221,7 +227,8 @@ representation of variations of metrics along street-fronts.
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+
@@ -231,7 +238,8 @@ representation of variations of metrics along street-fronts.
- The column label from which to take landuse categories, e.g. a column labelled "landuse_categories" might contain "shop", "pub", "school", etc.
+The column label from which to take landuse categories, e.g. a column labelled "landuse_categories" might contain "shop", "pub", "school", etc. +
@@ -241,7 +249,8 @@ representation of variations of metrics along street-fronts.
- Land-use keys for which to compute accessibilities. The keys should be selected from the same land-use schema used for the `landuse_labels` parameter, e.g. "pub". The calculations will be performed in both `weighted` and `non_weighted` variants.
+Land-use keys for which to compute accessibilities. The keys should be selected from the same land-use schema used for the `landuse_labels` parameter, e.g. "pub". The calculations will be performed in both `weighted` and `non_weighted` variants. +
@@ -251,7 +260,8 @@ representation of variations of metrics along street-fronts.
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the function.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the function. +
@@ -261,7 +271,8 @@ representation of variations of metrics along street-fronts.
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. +
@@ -271,7 +282,8 @@ representation of variations of metrics along street-fronts.
- The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes.
+The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes. +
@@ -281,7 +293,8 @@ representation of variations of metrics along street-fronts.
- Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead.
+Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead. +
@@ -291,7 +304,8 @@ representation of variations of metrics along street-fronts.
- A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead.
+A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead. +
@@ -301,7 +315,8 @@ representation of variations of metrics along street-fronts.
- An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier.
+An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier. +
@@ -311,7 +326,8 @@ representation of variations of metrics along street-fronts.
- Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances.
+Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances. +
@@ -321,7 +337,8 @@ representation of variations of metrics along street-fronts.
- Tolerance in metres indicating a spatial buffer for datapoint accuracy. Intended for situations where datapoint locations are not precise. If greater than zero, weighted functions will clip the spatial impedance curve above weights corresponding to the given spatial tolerance and normalises to the new range. For background, see [`rustalgos.clip_weights_curve`](/rustalgos#clip-weights-curve).
+Tolerance in metres indicating a spatial buffer for datapoint accuracy. Intended for situations where datapoint locations are not precise. If greater than zero, weighted functions will clip the spatial impedance curve above weights corresponding to the given spatial tolerance and normalises to the new range. For background, see [`rustalgos.clip_weights_curve`](/rustalgos#clip-weights-curve). +
@@ -331,7 +348,8 @@ representation of variations of metrics along street-fronts.
- The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information.
+The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information. +
@@ -341,10 +359,12 @@ representation of variations of metrics along street-fronts.
- The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees.
+The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees. + ### Returns +
nodes_gdf
@@ -352,7 +372,8 @@ representation of variations of metrics along street-fronts.
- The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics. Three columns will be returned for each input landuse class and distance combination; a simple count of reachable locations, a distance weighted count of reachable locations, and the smallest distance to the nearest location.
+The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics. Three columns will be returned for each input landuse class and distance combination; a simple count of reachable locations, a distance weighted count of reachable locations, and the smallest distance to the nearest location.
+
@@ -362,7 +383,8 @@ representation of variations of metrics along street-fronts.
- The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_neareset_assign`.
+The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_neareset_assign`. + ### Notes @@ -388,22 +410,19 @@ nodes_gdf, landuses_gdf = layers.compute_accessibilities( ) print(nodes_gdf.columns) # weighted form -print(nodes_gdf["cc_metric_c_400_weighted"]) +print(nodes_gdf["cc_400_wt"]) # non-weighted form -print(nodes_gdf["cc_metric_c_400_non_weighted"]) +print(nodes_gdf["cc_400_nw"]) # nearest distance to landuse -print(nodes_gdf["cc_metric_c_400_distance"]) +print(nodes_gdf["cc_distance"]) ``` - -
## compute_mixed_uses -
compute_mixed_uses
( @@ -494,13 +513,14 @@ print(nodes_gdf["cc_metric_c_400_distance"])
+Compute landuse metrics. This function wraps the underlying `rust` optimised functions for aggregating and computing various mixed-use. These are computed simultaneously for any required combinations of measures (and distances). By default, hill and hill weighted measures will be computed, by the available flags e.g. `compute_hill` or `compute_shannon` can be used to configure which classes of measures should run. - Compute landuse metrics. This function wraps the underlying `rust` optimised functions for aggregating and computing various mixed-use. These are computed simultaneously for any required combinations of measures (and distances). By default, hill and hill weighted measures will be computed, by the available flags e.g. `compute_hill` or `compute_shannon` can be used to configure which classes of measures should run. +See the accompanying paper on `arXiv` for additional information about methods for computing mixed-use measures at the pedestrian scale. - See the accompanying paper on `arXiv` for additional information about methods for computing mixed-use measures at the pedestrian scale. +The data is aggregated and computed over the street network, with the implication that mixed-use and land-use accessibility aggregations are generated from the same locations as for centrality computations, which can therefore be correlated or otherwise compared. The outputs of the calculations are written to the corresponding node indices in the same `node_gdf` `GeoDataFrame` used for centrality methods, and which will display the calculated metrics under correspondingly labelled columns. - The data is aggregated and computed over the street network, with the implication that mixed-use and land-use accessibility aggregations are generated from the same locations as for centrality computations, which can therefore be correlated or otherwise compared. The outputs of the calculations are written to the corresponding node indices in the same `node_gdf` `GeoDataFrame` used for centrality methods, and which will display the calculated metrics under correspondingly labelled columns. ### Parameters +
data_gdf
@@ -508,7 +528,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+
@@ -518,7 +539,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- The column label from which to take landuse categories, e.g. a column labelled "landuse_categories" might contain "shop", "pub", "school", etc., landuse categories.
+The column label from which to take landuse categories, e.g. a column labelled "landuse_categories" might contain "shop", "pub", "school", etc., landuse categories. +
@@ -528,7 +550,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the function.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the function. +
@@ -538,7 +561,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. +
@@ -548,7 +572,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes.
+The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes. +
@@ -558,7 +583,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- Compute Hill diversity. This is the recommended form of diversity index. Computed for q of 0, 1, and 2.
+Compute Hill diversity. This is the recommended form of diversity index. Computed for q of 0, 1, and 2. +
@@ -568,7 +594,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- Compute distance weighted Hill diversity. This is the recommended form of diversity index. Computed for q of 0, 1, and 2.
+Compute distance weighted Hill diversity. This is the recommended form of diversity index. Computed for q of 0, 1, and 2. +
@@ -578,7 +605,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- Compute shannon entropy. Hill diversity of q=1 is generally preferable.
+Compute shannon entropy. Hill diversity of q=1 is generally preferable. +
@@ -588,7 +616,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- Compute the gini form of diversity index. Hill diversity of q=2 is generally preferable.
+Compute the gini form of diversity index. Hill diversity of q=2 is generally preferable. +
@@ -598,7 +627,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead.
+Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead. +
@@ -608,7 +638,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead.
+A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead. +
@@ -618,7 +649,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier.
+An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier. +
@@ -628,7 +660,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances.
+Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances. +
@@ -638,7 +671,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- Tolerance in metres indicating a spatial buffer for datapoint accuracy. Intended for situations where datapoint locations are not precise. If greater than zero, weighted functions will clip the spatial impedance curve above weights corresponding to the given spatial tolerance and normalises to the new range. For background, see [`rustalgos.clip_weights_curve`](/rustalgos#clip-weights-curve).
+Tolerance in metres indicating a spatial buffer for datapoint accuracy. Intended for situations where datapoint locations are not precise. If greater than zero, weighted functions will clip the spatial impedance curve above weights corresponding to the given spatial tolerance and normalises to the new range. For background, see [`rustalgos.clip_weights_curve`](/rustalgos#clip-weights-curve). +
@@ -648,7 +682,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information.
+The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information. +
@@ -658,10 +693,12 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees.
+The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees. + ### Returns +
nodes_gdf
@@ -669,7 +706,8 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- The input `node_gdf` parameter is returned with additional columns populated with the calculated metrics.
+The input `node_gdf` parameter is returned with additional columns populated with the calculated metrics.
+
@@ -679,24 +717,26 @@ print(nodes_gdf["cc_metric_c_400_distance"])
- The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_nearest_assign`.
+The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_nearest_assign`. + ### Notes -| key | formula | notes | -|-----|:-------:|-------| -| hill | $$q\geq{0},\ q\neq{1} \\ \big(\sum_{i}^{S}p_{i}^q\big)^{1/(1-q)} \\ lim_{q\to1} \\ exp\big(-\sum_{i}^{S}\ p_{i}\ log\ p_{i}\big)$$ | Hill diversity: this is the preferred form of diversity metric because it adheres to the replication principle and uses units of effective species instead of measures of information or uncertainty. The `q` parameter controls the degree of emphasis on the _richness_ of species as opposed to the _balance_ of species. Over-emphasis on balance can be misleading in an urban context, for which reason research finds support for using `q=0`: this reduces to a simple count of distinct land-uses.| -| hill_wt | $$\big[\sum_{i}^{S}d_{i}\big(\frac{p_{i}}{\bar{T}}\big)^{q} \big]^{1/(1-q)} \\ \bar{T} = \sum_{i}^{S}d_{i}p_{i}$$ | This is a distance-weighted variant of Hill Diversity based on the distances from the point of computation to the nearest example of a particular land-use. It therefore gives a locally representative indication of the intensity of mixed-uses. $d_{i}$ is a negative exponential function where $\beta$ controls the strength of the decay. ($\beta$ is provided by the `Network Layer`, see [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas).)| -| shannon | $$ -\sum_{i}^{S}\ p_{i}\ log\ p_{i}$$ | Shannon diversity (or_information entropy_) is one of the classic diversity indices. Note that it is preferable to use Hill Diversity with `q=1`, which is effectively a transformation of Shannon diversity into units of effective species.| -| gini | $$ 1 - \sum_{i}^{S} p_{i}^2$$ | Gini-Simpson is another classic diversity index. It can behave problematically because it does not adhere to the replication principle and places emphasis on the balance of species, which can be counter-productive for purposes of measuring mixed-uses. Note that where an emphasis on balance is desired, it is preferable to use Hill Diversity with `q=2`, which is effectively a transformation of Gini-Simpson diversity into units of effective species.| +| key | formula | notes | +| ------- | :--------------------------------------------------------------------------------------------------------------------------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| hill | $$q\geq{0},\ q\neq{1} \\ \big(\sum_{i}^{S}p_{i}^q\big)^{1/(1-q)} \\ lim_{q\to1} \\ exp\big(-\sum_{i}^{S}\ p_{i}\ log\ p_{i}\big)$$ | Hill diversity: this is the preferred form of diversity metric because it adheres to the replication principle and uses units of effective species instead of measures of information or uncertainty. The `q` parameter controls the degree of emphasis on the _richness_ of species as opposed to the _balance_ of species. Over-emphasis on balance can be misleading in an urban context, for which reason research finds support for using `q=0`: this reduces to a simple count of distinct land-uses. | +| hill_wt | $$\big[\sum_{i}^{S}d_{i}\big(\frac{p_{i}}{\bar{T}}\big)^{q} \big]^{1/(1-q)} \\ \bar{T} = \sum_{i}^{S}d_{i}p_{i}$$ | This is a distance-weighted variant of Hill Diversity based on the distances from the point of computation to the nearest example of a particular land-use. It therefore gives a locally representative indication of the intensity of mixed-uses. $d_{i}$ is a negative exponential function where $\beta$ controls the strength of the decay. ($\beta$ is provided by the `Network Layer`, see [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas).) | +| shannon | $$ -\sum*{i}^{S}\ p*{i}\ log\ p\_{i}$$ | Shannon diversity (or*information entropy*) is one of the classic diversity indices. Note that it is preferable to use Hill Diversity with `q=1`, which is effectively a transformation of Shannon diversity into units of effective species. | +| gini | $$ 1 - \sum*{i}^{S} p*{i}^2$$ | Gini-Simpson is another classic diversity index. It can behave problematically because it does not adhere to the replication principle and places emphasis on the balance of species, which can be counter-productive for purposes of measuring mixed-uses. Note that where an emphasis on balance is desired, it is preferable to use Hill Diversity with `q=2`, which is effectively a transformation of Gini-Simpson diversity into units of effective species. | :::note `hill_branch_wt` at `q=0` is generally the best choice for granular landuse data, or else `q=1` or `q=2` for increasingly crude landuse classifications schemas. ::: - A worked example: +A worked example: + ```python from cityseer.metrics import networks, layers from cityseer.tools import mock, graphs @@ -718,7 +758,7 @@ nodes_gdf, landuses_gdf = layers.compute_mixed_uses( # the data is written to the GeoDataFrame print(nodes_gdf.columns) # access accordingly, e.g. hill diversity at q=0 and 800m -print(nodes_gdf["cc_metric_hill_q0_800"]) +print(nodes_gdf["cc_hill_q0_800"]) ``` :::warning @@ -729,12 +769,10 @@ been applied. -
## compute_stats -
compute_stats
( @@ -805,9 +843,10 @@ been applied.
+Compute numerical statistics over the street network. This function wraps the underlying `rust` optimised function for computing statistical measures. The data is aggregated and computed over the street network relative to the network nodes, with the implication that statistical aggregations are generated from the same locations as for centrality computations, which can therefore be correlated or otherwise compared. - Compute numerical statistics over the street network. This function wraps the underlying `rust` optimised function for computing statistical measures. The data is aggregated and computed over the street network relative to the network nodes, with the implication that statistical aggregations are generated from the same locations as for centrality computations, which can therefore be correlated or otherwise compared. ### Parameters +
data_gdf
@@ -815,7 +854,8 @@ been applied.
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing data points. The coordinates of data points should correspond as precisely as possible to the location of the feature in space; or, in the case of buildings, should ideally correspond to the location of the building entrance.
+
@@ -825,7 +865,8 @@ been applied.
- The column label corresponding to the column in `data_gdf` from which to take numerical information.
+The column label corresponding to the column in `data_gdf` from which to take numerical information. +
@@ -835,7 +876,8 @@ been applied.
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the function.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the function. +
@@ -845,7 +887,8 @@ been applied.
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) function. +
@@ -855,7 +898,8 @@ been applied.
- The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes.
+The maximum distance to consider when assigning respective data points to the nearest adjacent network nodes. +
@@ -865,7 +909,8 @@ been applied.
- Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead.
+Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead. +
@@ -875,7 +920,8 @@ been applied.
- A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead.
+A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead. +
@@ -885,7 +931,8 @@ been applied.
- An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier.
+An optional column name for data point keys. This is used for deduplicating points representing a shared source of information. For example, where a single greenspace is represented by many entrances as datapoints, only the nearest entrance (from a respective location) will be considered (during aggregations) when the points share a datapoint identifier. +
@@ -895,7 +942,8 @@ been applied.
- Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances.
+Whether to use a simplest-path heuristic in-lieu of a shortest-path heuristic when calculating aggregations and distances. +
@@ -905,7 +953,8 @@ been applied.
- Tolerance in metres indicating a spatial buffer for datapoint accuracy. Intended for situations where datapoint locations are not precise. If greater than zero, weighted functions will clip the spatial impedance curve above weights corresponding to the given spatial tolerance and normalises to the new range. For background, see [`rustalgos.clip_weights_curve`](/rustalgos#clip-weights-curve).
+Tolerance in metres indicating a spatial buffer for datapoint accuracy. Intended for situations where datapoint locations are not precise. If greater than zero, weighted functions will clip the spatial impedance curve above weights corresponding to the given spatial tolerance and normalises to the new range. For background, see [`rustalgos.clip_weights_curve`](/rustalgos#clip-weights-curve). +
@@ -915,7 +964,8 @@ been applied.
- The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information.
+The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information. +
@@ -925,10 +975,12 @@ been applied.
- The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees.
+The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees. + ### Returns +
nodes_gdf
@@ -936,7 +988,8 @@ been applied.
- The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+
@@ -946,12 +999,13 @@ been applied.
- The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_neareset_assign`.
+The input `data_gdf` is returned with two additional columns: `nearest_assigned` and `next_neareset_assign`. + ### Notes - A worked example: +A worked example: ```python from cityseer.metrics import networks, layers @@ -973,23 +1027,21 @@ nodes_gdf, numerical_gdf = layers.compute_stats( ) print(nodes_gdf.columns) # weighted form -print(nodes_gdf["cc_metric_mock_numerical_1_mean_wt_400"]) +print(nodes_gdf["cc_mock_numerical_1_mean_wt_400"]) # non-weighted form -print(nodes_gdf["cc_metric_mock_numerical_1_sum_200"]) +print(nodes_gdf["cc_mock_numerical_1_sum_200"]) ``` - :::note The following stat types will be available for each `stats_key` for each of the computed distances: + - `max` and `min` - `sum` and `sum_weighted` - `mean` and `mean_weighted` - `variance` and `variance_weighted` -::: + ::: - -
diff --git a/docs/src/pages/metrics/networks.md b/docs/src/pages/metrics/networks.md index 9a701442..f615bd57 100644 --- a/docs/src/pages/metrics/networks.md +++ b/docs/src/pages/metrics/networks.md @@ -1,63 +1,61 @@ --- layout: ../../layouts/PageLayout.astro --- +
# networks - - Compute network centralities. There are three network centrality methods available depending on whether you're using a node-based or segment-based approach, with the former available in both shortest and simplest (angular) variants. +Compute network centralities. There are three network centrality methods available depending on whether you're using a node-based or segment-based approach, with the former available in both shortest and simplest (angular) variants. - [`node_centrality_shortest`](#node-centrality-shortest) - [`node_centrality_simplest`](#node-centrality-simplest) - [`segment_centrality`](#segment-centrality) - These methods wrap the underlying `rust` optimised functions for computing centralities. Multiple classes of measures and distances are computed simultaneously to reduce the amount of time required for multi-variable and multi-scalar strategies. +These methods wrap the underlying `rust` optimised functions for computing centralities. Multiple classes of measures and distances are computed simultaneously to reduce the amount of time required for multi-variable and multi-scalar strategies. - See the accompanying paper on `arXiv` for additional information about methods for computing centrality measures. +See the accompanying paper on `arXiv` for additional information about methods for computing centrality measures. :::note The reasons for picking one approach over another are varied: - Node based centralities compute the measures relative to each reachable node within the threshold distances. For -this reason, they can be susceptible to distortions caused by messy graph topologies such redundant and varied -concentrations of degree=2 nodes (e.g. to describe roadway geometry) or needlessly complex representations of -street intersections. In these cases, the network should first be cleaned using methods such as those available in -the [`graph`](/tools/graphs) module (see the [graph cleaning guide](/guide#graph-cleaning) for examples). If a -network topology has varied intensities of nodes but the street segments are less spurious, then segmentised methods -can be preferable because they are based on segment distances: segment aggregations remain the same regardless of -the number of intervening nodes, however, are not immune from situations such as needlessly complex representations -of roadway intersections or a proliferation of walking paths in greenspaces; + this reason, they can be susceptible to distortions caused by messy graph topologies such redundant and varied + concentrations of degree=2 nodes (e.g. to describe roadway geometry) or needlessly complex representations of + street intersections. In these cases, the network should first be cleaned using methods such as those available in + the [`graph`](/tools/graphs) module (see the [Graph Cleaning](https://benchmark-urbanism.github.io/cityseer-examples/examples/graph_cleaning.html) guide for examples). If a + network topology has varied intensities of nodes but the street segments are less spurious, then segmentised methods + can be preferable because they are based on segment distances: segment aggregations remain the same regardless of + the number of intervening nodes, however, are not immune from situations such as needlessly complex representations + of roadway intersections or a proliferation of walking paths in greenspaces; - Node-based `harmonic` centrality can be problematic on graphs where nodes are erroneously placed too close -together or where impedances otherwise approach zero, as may be the case for simplest-path measures or small -distance thesholds. This happens because the outcome of the division step can balloon towards $\infty$ once -impedances decrease below 1. + together or where impedances otherwise approach zero, as may be the case for simplest-path measures or small + distance thesholds. This happens because the outcome of the division step can balloon towards $\infty$ once + impedances decrease below 1. - Note that `cityseer`'s implementation of simplest (angular) measures work on both primal and dual graphs (node only). - Measures should only be directly compared on the same topology because different topologies can otherwise affect -the expression of a measure. Accordingly, measures computed on dual graphs cannot be compared to measures computed -on primal graphs because this does not account for the impact of differing topologies. Dual graph representations -can have substantially greater numbers of nodes and edges for the same underlying street network; for example, a -four-way intersection consisting of one node with four edges translates to four nodes and six edges on the dual. -This effect is amplified for denser regions of the network. + the expression of a measure. Accordingly, measures computed on dual graphs cannot be compared to measures computed + on primal graphs because this does not account for the impact of differing topologies. Dual graph representations + can have substantially greater numbers of nodes and edges for the same underlying street network; for example, a + four-way intersection consisting of one node with four edges translates to four nodes and six edges on the dual. + This effect is amplified for denser regions of the network. - Segmentised versions of centrality measures should not be computed on dual graph topologies because street segment -lengths would be duplicated for each permutation of dual edge spanning street intersections. By way of example, -the contribution of a single edge segment at a four-way intersection would be duplicated three times. + lengths would be duplicated for each permutation of dual edge spanning street intersections. By way of example, + the contribution of a single edge segment at a four-way intersection would be duplicated three times. - The usual formulations of closeness or normalised closeness are discouraged because these do not behave -suitably for localised graphs. Harmonic closeness or Hillier normalisation (which resembles a simplified form of -Improved Closeness Centrality proposed by Wasserman and Faust) should be used instead. + suitably for localised graphs. Harmonic closeness or Hillier normalisation (which resembles a simplified form of + Improved Closeness Centrality proposed by Wasserman and Faust) should be used instead. - Network decomposition can be a useful strategy when working at small distance thresholds, and confers advantages -such as more regularly spaced snapshots and fewer artefacts at small distance thresholds where street edges -intersect distance thresholds. However, the regular spacing of the decomposed segments will introduce spikes in the -distributions of node-based centrality measures when working at very small distance thresholds. Segmentised versions -may therefore be preferable when working at small thresholds on decomposed networks. -::: - + such as more regularly spaced snapshots and fewer artefacts at small distance thresholds where street edges + intersect distance thresholds. However, the regular spacing of the decomposed segments will introduce spikes in the + distributions of node-based centrality measures when working at very small distance thresholds. Segmentised versions + may therefore be preferable when working at small thresholds on decomposed networks. + :::
## node_centrality_shortest -
node_centrality_shortest
( @@ -107,13 +105,14 @@ may therefore be preferable when working at small thresholds on decomposed netwo
- - Compute node-based network centrality using the shortest path heuristic. +Compute node-based network centrality using the shortest path heuristic. :::note Node weights are taken into account when computing centralities. These would typically be initialised at 1 unless manually specified. ::: + ### Parameters +
network_structure
@@ -121,7 +120,8 @@ manually specified.
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method.
+
@@ -131,7 +131,8 @@ manually specified.
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the method.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the method. +
@@ -141,7 +142,8 @@ manually specified.
- Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead.
+Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead. +
@@ -151,7 +153,8 @@ manually specified.
- A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead.
+A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead. +
@@ -161,7 +164,8 @@ manually specified.
- Compute closeness centralities. True by default.
+Compute closeness centralities. True by default. +
@@ -171,7 +175,8 @@ manually specified.
- Compute betweenness centralities. True by default.
+Compute betweenness centralities. True by default. +
@@ -181,7 +186,8 @@ manually specified.
- The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information.
+The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information. +
@@ -191,10 +197,12 @@ manually specified.
- The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees.
+The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees. + ### Returns +
nodes_gdf
@@ -202,32 +210,31 @@ manually specified.
- The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+ ### Notes - The following keys use the shortest-path heuristic: +The following keys use the shortest-path heuristic: -| key | formula | notes | -| ----------------------| :------:| ----- | -| node_density | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | -| node_harmonic | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form of closeness centrality for localised implementations constrained by the threshold $d_{max}$. | -| node_hillier | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by farness. This is also a simplified form of Improved Closeness Centrality. | -| node_beta | $$\sum_{j\neq{i}}^{n} \\ \exp(-\beta\cdot d[i,j])$$ | Also known as the gravity index. This is a spatial impedance metric differentiated from other closeness centralities by the use of an explicit $\beta$ parameter, which can be used to model the decay in walking tolerance as distances increase. | -| node_cycles | $$\sum_{j\neq{i}j=cycle}^{n}1$$ | A summation of network cycles. | -| node_farness | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | -| node_betweenness | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all shortest-paths traversing each node $i$. | -| node_betweenness_beta | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n} \\ \exp(-\beta\cdot d[j,k])$$ | Applies a spatial impedance decay function to betweenness centrality. $d$ represents the full distance from any $j$ to $k$ node pair passing through node $i$. | +| key | formula | notes | +| ---------------- | :---------------------------------------------------------------------------: | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| density | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | +| harmonic | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form of closeness centrality for localised implementations constrained by the threshold $d_{max}$. | +| hillier | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by farness. This is also a simplified form of Improved Closeness Centrality. | +| beta | $$\sum_{j\neq{i}}^{n} \\ \exp(-\beta\cdot d[i,j])$$ | Also known as the gravity index. This is a spatial impedance metric differentiated from other closeness centralities by the use of an explicit $\beta$ parameter, which can be used to model the decay in walking tolerance as distances increase. | +| cycles | $$\sum_{j\neq{i}j=cycle}^{n}1$$ | A summation of network cycles. | +| farness | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | +| betweenness | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all shortest-paths traversing each node $i$. | +| betweenness_beta | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n} \\ \exp(-\beta\cdot d[j,k])$$ | Applies a spatial impedance decay function to betweenness centrality. $d$ represents the full distance from any $j$ to $k$ node pair passing through node $i$. | -
## node_centrality_simplest -
node_centrality_simplest
( @@ -277,13 +284,14 @@ manually specified.
- - Compute node-based network centrality using the simplest path (angular) heuristic. +Compute node-based network centrality using the simplest path (angular) heuristic. :::note Node weights are taken into account when computing centralities. These would typically be initialised at 1 unless manually specified. ::: + ### Parameters +
network_structure
@@ -291,7 +299,8 @@ manually specified.
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method.
+
@@ -301,7 +310,8 @@ manually specified.
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the method.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the method. +
@@ -311,7 +321,8 @@ manually specified.
- Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead.
+Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead. +
@@ -321,7 +332,8 @@ manually specified.
- A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead.
+A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead. +
@@ -331,7 +343,8 @@ manually specified.
- Compute closeness centralities. True by default.
+Compute closeness centralities. True by default. +
@@ -341,7 +354,8 @@ manually specified.
- Compute betweenness centralities. True by default.
+Compute betweenness centralities. True by default. +
@@ -351,7 +365,8 @@ manually specified.
- The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information.
+The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information. +
@@ -361,10 +376,12 @@ manually specified.
- The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees.
+The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees. + ### Returns +
nodes_gdf
@@ -372,31 +389,30 @@ manually specified.
- The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+ ### Notes - The following keys use the simplest-path heuristic: +The following keys use the simplest-path heuristic: -| key | formula | notes | -| ----------------------| :------:| ----- | -| node_density_simplest | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | -| node_harmonic_simplest | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form of closeness centrality for localised implementations constrained by the threshold $d_{max}$. | -| node_hillier_simplest | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by farness. This is also a simplified form of Improved Closeness Centrality. | -| node_farness_simplest | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | -| node_betweenness_simplest | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all shortest-paths traversing each node $i$. | +| key | formula | notes | +| ------------------------- | :------------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------------------- | +| node_density_simplest | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | +| node_harmonic_simplest | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form of closeness centrality for localised implementations constrained by the threshold $d_{max}$. | +| node_hillier_simplest | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by farness. This is also a simplified form of Improved Closeness Centrality. | +| node_farness_simplest | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | +| node_betweenness_simplest | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all shortest-paths traversing each node $i$. | The following keys use the simplest-path (shortest-angular-path) heuristic, and are available when the `angular` parameter is explicitly set to `True`: -
## segment_centrality -
segment_centrality
( @@ -446,11 +462,12 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
+Compute segment-based network centrality using the shortest path heuristic. > Simplest path heuristics introduce conceptual and practical complications and support is deprecated since v4. - Compute segment-based network centrality using the shortest path heuristic. > Simplest path heuristics introduce conceptual and practical complications and support is deprecated since v4. +> For conceptual and practical reasons, segment based centralities are not weighted by node weights. - > For conceptual and practical reasons, segment based centralities are not weighted by node weights. ### Parameters +
network_structure
@@ -458,7 +475,8 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure). Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method.
+
@@ -468,7 +486,8 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the method.
+A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the [`io.network_structure_from_nx`](/tools/io#network-structure-from-nx) method. The outputs of calculations will be written to this `GeoDataFrame`, which is then returned from the method. +
@@ -478,7 +497,8 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead.
+Distances corresponding to the local $d_{max}$ thresholds to be used for calculations. The $\beta$ parameters (for distance-weighted metrics) will be determined implicitly. If the `distances` parameter is not provided, then the `beta` parameter must be provided instead. +
@@ -488,7 +508,8 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead.
+A $\beta$, or array of $\beta$ to be used for the exponential decay function for weighted metrics. The `distance` parameters for unweighted metrics will be determined implicitly. If the `betas` parameter is not provided, then the `distance` parameter must be provided instead. +
@@ -498,7 +519,8 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- Compute closeness centralities. True by default.
+Compute closeness centralities. True by default. +
@@ -508,7 +530,8 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- Compute betweenness centralities. True by default.
+Compute betweenness centralities. True by default. +
@@ -518,7 +541,8 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information.
+The default `min_threshold_wt` parameter can be overridden to generate custom mappings between the `distance` and `beta` parameters. See [`rustalgos.distances_from_beta`](/rustalgos#distances-from-betas) for more information. +
@@ -528,10 +552,12 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees.
+The scale of random jitter to add to shortest path calculations, useful for situations with highly rectilinear grids or for smoothing metrics on messy network representations. A random sample is drawn from a range of zero to one and is then multiplied by the specified `jitter_scale`. This random value is added to the shortest path calculations to provide random variation to the paths traced through the network. When working with shortest paths in metres, the random value represents distance in metres. When using a simplest path heuristic, the jitter will represent angular change in degrees. + ### Returns +
nodes_gdf
@@ -539,22 +565,21 @@ The following keys use the simplest-path (shortest-angular-path) heuristic, and
- The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+The input `node_gdf` parameter is returned with additional columns populated with the calcualted metrics.
+ ### Notes - Segment path centralities are available with the following keys: +Segment path centralities are available with the following keys: -| key | formula | notes | -| ------------------- | :-----: |------ | -| segment_density | $$\sum_{(a, b)}^{edges}d_{b} - d_{a}$$ | A summation of edge lengths. | -| segment_harmonic | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\ln(b) -\ln(a)$$ | A continuous form of harmonic closeness centrality applied to edge lengths. | -| segment_beta | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\frac{\exp(-\beta\cdot b) -\exp(-\beta\cdot a)}{-\beta}$$ | A # pylint: disable=line-too-long continuous form of beta-weighted (gravity index) centrality applied to edge lengths. | -| segment_betweenness | | A continuous form of betweenness: Resembles `segment_beta` applied to edges situated on shortest paths between all nodes $j$ and $k$ passing through $i$. | +| key | formula | notes | +| ------------------- | :------------------------------------------------------------------------------------------: | --------------------------------------------------------------------------------------------------------------------------------------------------------- | +| segment_density | $$\sum_{(a, b)}^{edges}d_{b} - d_{a}$$ | A summation of edge lengths. | +| segment_harmonic | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\ln(b) -\ln(a)$$ | A continuous form of harmonic closeness centrality applied to edge lengths. | +| segment_beta | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\frac{\exp(-\beta\cdot b) -\exp(-\beta\cdot a)}{-\beta}$$ | A # pylint: disable=line-too-long continuous form of beta-weighted (gravity index) centrality applied to edge lengths. | +| segment_betweenness | | A continuous form of betweenness: Resembles `segment_beta` applied to edges situated on shortest paths between all nodes $j$ and $k$ passing through $i$. | - -
diff --git a/docs/src/pages/tools/plot.md b/docs/src/pages/tools/plot.md index 9b9e00e3..2687a7f2 100644 --- a/docs/src/pages/tools/plot.md +++ b/docs/src/pages/tools/plot.md @@ -1,30 +1,23 @@ --- layout: ../../layouts/PageLayout.astro --- +
# plot - - Convenience methods for plotting graphs within the cityseer API context. Custom behaviour can be achieved by directly manipulating the underlying [`NetworkX`](https://networkx.github.io) and [`matplotlib`](https://matplotlib.org) figures. This module is predominately used for basic plots or visual verification of behaviour in code tests. Users are encouraged to use matplotlib or other plotting packages directly where possible. See the demos section for examples. - +Convenience methods for plotting graphs within the cityseer API context. Custom behaviour can be achieved by directly manipulating the underlying [`NetworkX`](https://networkx.github.io) and [`matplotlib`](https://matplotlib.org) figures. This module is predominately used for basic plots or visual verification of behaviour in code tests. Users are encouraged to use matplotlib or other plotting packages directly where possible.
- ## ColourMap - - - Specifies global colour presets. - - +Specifies global colour presets.
## ColourMap -
ColourMap
( @@ -34,15 +27,12 @@ layout: ../../layouts/PageLayout.astro
-
-
## plot_nx_primal_or_dual -
plot_nx_primal_or_dual
( @@ -121,9 +111,10 @@ layout: ../../layouts/PageLayout.astro
+Plot a primal or dual cityseer graph. - Plot a primal or dual cityseer graph. ### Parameters +
primal_graph
@@ -131,7 +122,8 @@ layout: ../../layouts/PageLayout.astro
- An optional `NetworkX` MultiGraph to plot in the primal representation. Defaults to None.
+An optional `NetworkX` MultiGraph to plot in the primal representation. Defaults to None.
+
@@ -141,7 +133,8 @@ layout: ../../layouts/PageLayout.astro
- An optional `NetworkX` MultiGraph to plot in the dual representation. Defaults to None.
+An optional `NetworkX` MultiGraph to plot in the dual representation. Defaults to None.
+
@@ -151,7 +144,8 @@ layout: ../../layouts/PageLayout.astro
- An optional filepath: if provided, the image will be saved to the path instead of being displayed. Defaults to None.
+An optional filepath: if provided, the image will be saved to the path instead of being displayed. Defaults to None. +
@@ -161,7 +155,8 @@ layout: ../../layouts/PageLayout.astro
- Whether to display node labels. Defaults to False.
+Whether to display node labels. Defaults to False. +
@@ -171,7 +166,8 @@ layout: ../../layouts/PageLayout.astro
- The diameter for the primal graph's nodes.
+The diameter for the primal graph's nodes. +
@@ -181,7 +177,8 @@ layout: ../../layouts/PageLayout.astro
- Primal node colour or colours. When passing an iterable of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None.
+Primal node colour or colours. When passing an iterable of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None. +
@@ -191,7 +188,8 @@ layout: ../../layouts/PageLayout.astro
- Primal edge colour or colours. When passing an iterable of colours, the number of colours should match the order and number of edges in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None.
+Primal edge colour or colours. When passing an iterable of colours, the number of colours should match the order and number of edges in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None. +
@@ -201,7 +199,8 @@ layout: ../../layouts/PageLayout.astro
- The diameter for the dual graph's nodes.
+The diameter for the dual graph's nodes. +
@@ -211,7 +210,8 @@ layout: ../../layouts/PageLayout.astro
- Dual node colour or colours. When passing a list of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None.
+Dual node colour or colours. When passing a list of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None. +
@@ -221,7 +221,8 @@ layout: ../../layouts/PageLayout.astro
- Dual edge colour or colours. When passing an iterable of colours, the number of colours should match the order and number of edges in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None.
+Dual edge colour or colours. When passing an iterable of colours, the number of colours should match the order and number of edges in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None. +
@@ -231,7 +232,8 @@ layout: ../../layouts/PageLayout.astro
- Linewidths for the primal edge. Defaults to None.
+Linewidths for the primal edge. Defaults to None. +
@@ -241,7 +243,8 @@ layout: ../../layouts/PageLayout.astro
- Linewidths for the dual edge. Defaults to None.
+Linewidths for the dual edge. Defaults to None. +
@@ -251,7 +254,8 @@ layout: ../../layouts/PageLayout.astro
- Whether to plot the edge geometries. If set to `False`, straight lines will be drawn from node-to-node to represent edges. Defaults to True.
+Whether to plot the edge geometries. If set to `False`, straight lines will be drawn from node-to-node to represent edges. Defaults to True. +
@@ -261,7 +265,8 @@ layout: ../../layouts/PageLayout.astro
- A tuple or list with the minimum and maxium `x` extents to be plotted. Defaults to None.
+A tuple or list with the minimum and maxium `x` extents to be plotted. Defaults to None. +
@@ -271,7 +276,8 @@ layout: ../../layouts/PageLayout.astro
- A tuple or list with the minimum and maxium `y` extents to be plotted. Defaults to None.
+A tuple or list with the minimum and maxium `y` extents to be plotted. Defaults to None. +
@@ -281,7 +287,8 @@ layout: ../../layouts/PageLayout.astro
- An optional `matplotlib` `ax` to which to plot. If not provided, a figure and ax will be generated.
+An optional `matplotlib` `ax` to which to plot. If not provided, a figure and ax will be generated. +
@@ -291,12 +298,13 @@ layout: ../../layouts/PageLayout.astro
- `kwargs` which will be passed to the `matplotlib` figure parameters. If provided, these will override the default figure size or dpi parameters.
+`kwargs` which will be passed to the `matplotlib` figure parameters. If provided, these will override the default figure size or dpi parameters. + ### Notes - Plot either or both primal and dual representations of a `networkX MultiGraph`. Only call this function directly if explicitly printing both primal and dual graphs. Otherwise, use the simplified [`plot_nx`](/tools/plot#plot-nx) method instead. +Plot either or both primal and dual representations of a `networkX MultiGraph`. Only call this function directly if explicitly printing both primal and dual graphs. Otherwise, use the simplified [`plot_nx`](/tools/plot#plot-nx) method instead. ```py from cityseer.tools import mock, graphs, plot @@ -312,12 +320,10 @@ plot.plot_nx_primal_or_dual(G_simple, -
## plot_nx -
plot_nx
( @@ -377,9 +383,10 @@ plot.plot_nx_primal_or_dual(G_simple,
+Plot a `networkX` MultiGraph. - Plot a `networkX` MultiGraph. ### Parameters +
nx_multigraph
@@ -387,7 +394,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- A `NetworkX` MultiGraph.
+A `NetworkX` MultiGraph.
+
@@ -397,7 +405,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- An optional filepath: if provided, the image will be saved to the path instead of being displayed. Defaults to None.
+An optional filepath: if provided, the image will be saved to the path instead of being displayed. Defaults to None. +
@@ -407,7 +416,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- Whether to display node labels. Defaults to False.
+Whether to display node labels. Defaults to False. +
@@ -417,7 +427,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- The diameter for the graph's nodes.
+The diameter for the graph's nodes. +
@@ -427,7 +438,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- Node colour or colours. When passing an iterable of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None.
+Node colour or colours. When passing an iterable of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None. +
@@ -437,7 +449,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- Edges colour as a `matplotlib` compatible colour string. Defaults to None.
+Edges colour as a `matplotlib` compatible colour string. Defaults to None. +
@@ -447,7 +460,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- Linewidths for edges. Defaults to None.
+Linewidths for edges. Defaults to None. +
@@ -457,7 +471,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- Whether to plot the edge geometries. If set to `False`, straight lines will be drawn from node-to-node to represent edges. Defaults to True.
+Whether to plot the edge geometries. If set to `False`, straight lines will be drawn from node-to-node to represent edges. Defaults to True. +
@@ -467,7 +482,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- A tuple or list with the minimum and maximum `x` extents to be plotted. Defaults to None.
+A tuple or list with the minimum and maximum `x` extents to be plotted. Defaults to None. +
@@ -477,7 +493,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- A tuple or list with the minimum and maximum `y` extents to be plotted. Defaults to None.
+A tuple or list with the minimum and maximum `y` extents to be plotted. Defaults to None. +
@@ -487,7 +504,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- An optional `matplotlib` `ax` to which to plot. If not provided, a figure and ax will be generated.
+An optional `matplotlib` `ax` to which to plot. If not provided, a figure and ax will be generated. +
@@ -497,7 +515,8 @@ plot.plot_nx_primal_or_dual(G_simple,
- `kwargs` which will be passed to the `matplotlib` figure parameters. If provided, these will override the default figure size or dpi parameters.
+`kwargs` which will be passed to the `matplotlib` figure parameters. If provided, these will override the default figure size or dpi parameters. + ### Notes @@ -519,7 +538,7 @@ G_after = graphs.nx_from_network_structure(nodes_gdf, network_structure, G) # let's extract and normalise the values vals = [] for node, data in G_after.nodes(data=True): - vals.append(data["cc_metric_node_beta_800"]) + vals.append(data["cc_node_beta_800"]) # let's create a custom colourmap using matplotlib cmap = colors.LinearSegmentedColormap.from_list( "cityseer", [(100 / 255, 193 / 255, 255 / 255, 255 / 255), (211 / 255, 47 / 255, 47 / 255, 1 / 255)] @@ -532,17 +551,14 @@ cols = cmap(vals) plot.plot_nx(G_after, node_colour=cols) ``` - ![Example Colour Plot.](/images/graph_colour.png) _Colour plot of 800m gravity index centrality on a 50m decomposed graph._ -
## plot_assignment -
plot_assignment
( @@ -582,12 +598,13 @@ plot.plot_nx(G_after, node_colour=cols)
- - Plot a `network_structure` and `data_gdf` for visualising assignment of data points to respective nodes. +Plot a `network_structure` and `data_gdf` for visualising assignment of data points to respective nodes. :::warning This method is primarily intended for package testing and development. ::: + ### Parameters +
network_structure
@@ -595,7 +612,8 @@ This method is primarily intended for package testing and development.
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure) instance.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure) instance.
+
@@ -605,7 +623,8 @@ This method is primarily intended for package testing and development.
- A `NetworkX` MultiGraph.
+A `NetworkX` MultiGraph. +
@@ -615,7 +634,8 @@ This method is primarily intended for package testing and development.
- A `data_gdf` `GeoDataFrame` with `nearest_assigned` and `next_neareset_assign` columns.
+A `data_gdf` `GeoDataFrame` with `nearest_assigned` and `next_neareset_assign` columns. +
@@ -625,7 +645,8 @@ This method is primarily intended for package testing and development.
- An optional filepath: if provided, the image will be saved to the path instead of being displayed. Defaults to None.
+An optional filepath: if provided, the image will be saved to the path instead of being displayed. Defaults to None. +
@@ -635,7 +656,8 @@ This method is primarily intended for package testing and development.
- Node colour or colours. When passing a list of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None.
+Node colour or colours. When passing a list of colours, the number of colours should match the order and number of nodes in the MultiGraph. The colours are passed to the underlying [`draw_networkx`](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#draw-networkx) # pylint: disable=line-too-long method and should be formatted accordingly. Defaults to None. +
@@ -645,7 +667,8 @@ This method is primarily intended for package testing and development.
- Whether to plot the node labels. Defaults to False.
+Whether to plot the node labels. Defaults to False. +
@@ -655,7 +678,8 @@ This method is primarily intended for package testing and development.
- An optional iterable of categorical data labels which will be mapped to colours. The number of labels should match the number of data points in `data_layer`. Defaults to None.
+An optional iterable of categorical data labels which will be mapped to colours. The number of labels should match the number of data points in `data_layer`. Defaults to None. +
@@ -665,7 +689,8 @@ This method is primarily intended for package testing and development.
- `kwargs` which will be passed to the `matplotlib` figure parameters. If provided, these will override the default figure size or dpi parameters.
+`kwargs` which will be passed to the `matplotlib` figure parameters. If provided, these will override the default figure size or dpi parameters. + ### Notes @@ -674,12 +699,10 @@ This method is primarily intended for package testing and development. -
## plot_network_structure -
plot_network_structure
( @@ -702,13 +725,14 @@ This method is primarily intended for package testing and development.
- - Plot a graph from raw `cityseer` network structure. +Plot a graph from raw `cityseer` network structure. :::note Note that this function is subject to frequent revision pending short-term development requirements. It is used mainly to visually confirm the correct behaviour of particular algorithms during the software development cycle. ::: + ### Parameters +
network_structure
@@ -716,7 +740,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure) instance.
+A [`rustalgos.NetworkStructure`](/rustalgos/rustalgos#networkstructure) instance.
+
@@ -726,7 +751,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A `data_gdf` `GeoDataFrame` with `nearest_assigned` and `next_neareset_assign` columns.
+A `data_gdf` `GeoDataFrame` with `nearest_assigned` and `next_neareset_assign` columns. +
@@ -736,18 +762,16 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- An optional polygon. Defaults to None.
- - +An optional polygon. Defaults to None. +
## plot_scatter -
plot_scatter
( @@ -809,9 +833,10 @@ mainly to visually confirm the correct behaviour of particular algorithms during
+Convenience plotting function for plotting outputs from examples in demo notebooks. - Convenience plotting function for plotting outputs from examples in demo notebooks. ### Parameters +
ax
@@ -819,7 +844,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A 'matplotlib' `Ax` to which to plot.
+A 'matplotlib' `Ax` to which to plot.
+
@@ -829,7 +855,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A numpy array of floats representing the `x` coordinates for points to plot.
+A numpy array of floats representing the `x` coordinates for points to plot. +
@@ -839,7 +866,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A numpy array of floats representing the `y` coordinates for points to plot.
+A numpy array of floats representing the `y` coordinates for points to plot. +
@@ -849,7 +877,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A numpy array of floats representing the data values for the provided points.
+A numpy array of floats representing the data values for the provided points. +
@@ -859,7 +888,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A tuple or list containing the `[s, w, n, e]` bounding box extents for clipping the plot.
+A tuple or list containing the `[s, w, n, e]` bounding box extents for clipping the plot. +
@@ -869,7 +899,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A tuple of two floats, representing the minimum and maximum percentiles at which to clip the data.
+A tuple of two floats, representing the minimum and maximum percentiles at which to clip the data. +
@@ -879,7 +910,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A `matplotlib` colour map key.
+A `matplotlib` colour map key. +
@@ -889,7 +921,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A float representing an exponential for reshaping the values distribution. Defaults to 1 which returns the values as provided. An exponential greater than or less than 1 will shape the values distribution accordingly.
+A float representing an exponential for reshaping the values distribution. Defaults to 1 which returns the values as provided. An exponential greater than or less than 1 will shape the values distribution accordingly. +
@@ -899,7 +932,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A float representing the minimum size for a plotted point.
+A float representing the minimum size for a plotted point. +
@@ -909,7 +943,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A float representing the maximum size for a plotted point.
+A float representing the maximum size for a plotted point. +
@@ -919,7 +954,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- Whether or not to rasterise the output. Recommended for plots with a large number of points.
+Whether or not to rasterise the output. Recommended for plots with a large number of points. +
@@ -929,18 +965,16 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A hex or other valid `matplotlib` colour value for the ax and figure faces (backgrounds).
- - +A hex or other valid `matplotlib` colour value for the ax and figure faces (backgrounds). +
## plot_nx_edges -
plot_nx_edges
( @@ -1023,9 +1057,10 @@ mainly to visually confirm the correct behaviour of particular algorithms during
+Convenience plotting function for plotting edge outputs from examples in demo notebooks. - Convenience plotting function for plotting edge outputs from examples in demo notebooks. ### Parameters +
ax
@@ -1033,7 +1068,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A 'matplotlib' `Ax` to which to plot.
+A 'matplotlib' `Ax` to which to plot.
+
@@ -1043,7 +1079,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A `NetworkX` MultiGraph.
+A `NetworkX` MultiGraph. +
@@ -1053,7 +1090,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- An edge key for the provided `nx_multigraph`. Plotted values will be retrieved from this edge key.
+An edge key for the provided `nx_multigraph`. Plotted values will be retrieved from this edge key. +
@@ -1063,7 +1101,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A tuple or list containing the `[s, w, n, e]` bounding box extents for clipping the plot.
+A tuple or list containing the `[s, w, n, e]` bounding box extents for clipping the plot. +
@@ -1073,7 +1112,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A tuple of two floats, representing the minimum and maximum percentiles at which to clip the data.
+A tuple of two floats, representing the minimum and maximum percentiles at which to clip the data. +
@@ -1083,7 +1123,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A `matplotlib` colour map key.
+A `matplotlib` colour map key. +
@@ -1093,7 +1134,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A float representing an exponential for reshaping the values distribution. Defaults to 1 which returns the values as provided. An exponential greater than or less than 1 will shape the values distribution accordingly.
+A float representing an exponential for reshaping the values distribution. Defaults to 1 which returns the values as provided. An exponential greater than or less than 1 will shape the values distribution accordingly. +
@@ -1103,7 +1145,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A float representing the minimum line width for a plotted edge.
+A float representing the minimum line width for a plotted edge. +
@@ -1113,7 +1156,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A float representing the maximum line width for a plotted edge.
+A float representing the maximum line width for a plotted edge. +
@@ -1123,7 +1167,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A key for retrieving categorical labels from edges.
+A key for retrieving categorical labels from edges. +
@@ -1133,7 +1178,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- Whether to plot colours by categorical. This requires an `edge_label_key` parameter.
+Whether to plot colours by categorical. This requires an `edge_label_key` parameter. +
@@ -1143,7 +1189,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- The number of categorical values (sorted in decreasing order) to plot.
+The number of categorical values (sorted in decreasing order) to plot. +
@@ -1153,7 +1200,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- Whether or not to rasterise the output. Recommended for plots with a large number of edges.
+Whether or not to rasterise the output. Recommended for plots with a large number of edges. +
@@ -1163,7 +1211,8 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- A hex or other valid `matplotlib` colour value for the ax and figure faces (backgrounds).
+A hex or other valid `matplotlib` colour value for the ax and figure faces (backgrounds). +
@@ -1173,12 +1222,10 @@ mainly to visually confirm the correct behaviour of particular algorithms during
- Whether to invert the plot order, e.g. if using an inverse colour map.
- - +Whether to invert the plot order, e.g. if using an inverse colour map. - +
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for network-based pedestrian-scale urban analysis" readme = "README.md" requires-python = ">=3.10, <3.13" diff --git a/pysrc/cityseer/config.py b/pysrc/cityseer/config.py index d1f6c5ad..02e1b19f 100644 --- a/pysrc/cityseer/config.py +++ b/pysrc/cityseer/config.py @@ -14,11 +14,18 @@ np.seterr(invalid="ignore") -def prep_gdf_key(key: str) -> str: +def prep_gdf_key(key: str, dist: int, angular: bool = False, weighted: bool | None = None) -> str: """Format a column label for GeoPandas.""" key = key.replace(".0", "") key = key.replace(".0_", "_") - return f"cc_metric_{key}" + key = f"cc_{key}_{dist}" + if angular is True: + key += "_ang" + if weighted is True: + key += "_wt" + elif weighted is False: + key += "_nw" + return key def check_quiet() -> bool: diff --git a/pysrc/cityseer/metrics/layers.py b/pysrc/cityseer/metrics/layers.py index a99adc16..464c2b6e 100644 --- a/pysrc/cityseer/metrics/layers.py +++ b/pysrc/cityseer/metrics/layers.py @@ -146,7 +146,7 @@ def compute_accessibilities( accessibility_keys: tuple[str] Land-use keys for which to compute accessibilities. The keys should be selected from the same land-use schema used for the `landuse_labels` parameter, e.g. "pub". The calculations will be performed in both - `weighted` and `non_weighted` variants. + weighted `wt` and non_weighted `nw` variants. nodes_gdf A [`GeoDataFrame`](https://geopandas.org/en/stable/docs/user_guide/data_structures.html#geodataframe) representing nodes. Best generated with the @@ -203,7 +203,7 @@ def compute_accessibilities( -------- ```python from cityseer.metrics import networks, layers - from cityseer.tools import mock, graphs + from cityseer.tools import mock, graphs, io # prepare a mock graph G = mock.mock_graph() @@ -222,11 +222,11 @@ def compute_accessibilities( ) print(nodes_gdf.columns) # weighted form - print(nodes_gdf["cc_metric_c_400_weighted"]) + print(nodes_gdf["cc_c_400_wt"]) # non-weighted form - print(nodes_gdf["cc_metric_c_400_non_weighted"]) + print(nodes_gdf["cc_c_400_nw"]) # nearest distance to landuse - print(nodes_gdf["cc_metric_c_400_distance"]) + print(nodes_gdf["cc_c_nearest_max_800"]) ``` """ @@ -256,12 +256,13 @@ def compute_accessibilities( distances, betas = rustalgos.pair_distances_and_betas(distances, betas) # pylint: disable=unpacking-non-sequence for acc_key in accessibility_keys: for dist_key in distances: - ac_nw_data_key = config.prep_gdf_key(f"{acc_key}_{dist_key}_non_weighted") + ac_nw_data_key = config.prep_gdf_key(acc_key, dist_key, angular, weighted=False) nodes_gdf[ac_nw_data_key] = result[acc_key].unweighted[dist_key] # type: ignore - ac_wt_data_key = config.prep_gdf_key(f"{acc_key}_{dist_key}_weighted") + ac_wt_data_key = config.prep_gdf_key(acc_key, dist_key, angular, weighted=True) nodes_gdf[ac_wt_data_key] = result[acc_key].weighted[dist_key] # type: ignore - ac_dist_data_key = config.prep_gdf_key(f"{acc_key}_{dist_key}_distance") - nodes_gdf[ac_dist_data_key] = result[acc_key].distance[dist_key] # type: ignore + if dist_key == max(distances): + ac_dist_data_key = config.prep_gdf_key(f"{acc_key}_nearest_max", dist_key, angular) + nodes_gdf[ac_dist_data_key] = result[acc_key].distance[dist_key] # type: ignore return nodes_gdf, data_gdf @@ -398,14 +399,14 @@ def compute_mixed_uses( transformation of Gini-Simpson diversity into units of effective species.| :::note - `hill_branch_wt` at `q=0` is generally the best choice for granular landuse data, or else `q=1` or + `hill_wt` at `q=0` is generally the best choice for granular landuse data, or else `q=1` or `q=2` for increasingly crude landuse classifications schemas. ::: A worked example: ```python from cityseer.metrics import networks, layers - from cityseer.tools import mock, graphs + from cityseer.tools import mock, graphs, io # prepare a mock graph G = mock.mock_graph() @@ -424,7 +425,7 @@ def compute_mixed_uses( # the data is written to the GeoDataFrame print(nodes_gdf.columns) # access accordingly, e.g. hill diversity at q=0 and 800m - print(nodes_gdf["cc_metric_hill_q0_800"]) + print(nodes_gdf["cc_hill_q0_800_nw"]) ``` :::warning Be cognisant that mixed-use and land-use accessibility measures are sensitive to the classification schema that @@ -462,16 +463,16 @@ def compute_mixed_uses( for dist_key in distances: for q_key in [0, 1, 2]: if compute_hill: - hill_nw_data_key = config.prep_gdf_key(f"hill_q{q_key}_{dist_key}") + hill_nw_data_key = config.prep_gdf_key(f"hill_q{q_key}", dist_key, angular, weighted=False) nodes_gdf[hill_nw_data_key] = result.hill[q_key][dist_key] # type: ignore if compute_hill_weighted: - hill_wt_data_key = config.prep_gdf_key(f"hill_wt_q{q_key}_{dist_key}") + hill_wt_data_key = config.prep_gdf_key(f"hill_q{q_key}", dist_key, angular, weighted=True) nodes_gdf[hill_wt_data_key] = result.hill_weighted[q_key][dist_key] # type: ignore if compute_shannon: - shannon_data_key = config.prep_gdf_key(f"shannon_{dist_key}") + shannon_data_key = config.prep_gdf_key("shannon", dist_key, angular) nodes_gdf[shannon_data_key] = result.shannon[dist_key] # type: ignore if compute_gini: - gini_data_key = config.prep_gdf_key(f"gini_{dist_key}") + gini_data_key = config.prep_gdf_key("gini", dist_key, angular) nodes_gdf[gini_data_key] = result.gini[dist_key] # type: ignore return nodes_gdf, data_gdf @@ -564,7 +565,7 @@ def compute_stats( ```python from cityseer.metrics import networks, layers - from cityseer.tools import mock, graphs + from cityseer.tools import mock, graphs, io # prepare a mock graph G = mock.mock_graph() @@ -582,18 +583,18 @@ def compute_stats( ) print(nodes_gdf.columns) # weighted form - print(nodes_gdf["cc_metric_mock_numerical_1_mean_wt_400"]) + print(nodes_gdf["cc_mock_numerical_1_mean_400_wt"]) # non-weighted form - print(nodes_gdf["cc_metric_mock_numerical_1_sum_200"]) + print(nodes_gdf["cc_mock_numerical_1_mean_400_nw"]) ``` :::note The following stat types will be available for each `stats_key` for each of the computed distances: - `max` and `min` - - `sum` and `sum_weighted` - - `mean` and `mean_weighted` - - `variance` and `variance_weighted` + - `sum` and `sum_wt` + - `mean` and `mean_wt` + - `variance` and `variance_wt` ::: """ @@ -621,27 +622,25 @@ def compute_stats( # unpack the numerical arrays distances, betas = rustalgos.pair_distances_and_betas(distances, betas) # pylint: disable=unpacking-non-sequence for dist_key in distances: - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_sum_{dist_key}")] = result.sum[dist_key] # type: ignore - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_sum_wt_{dist_key}")] = result.sum_wt[ # type: ignore - dist_key - ] - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_mean_{dist_key}")] = result.mean[dist_key] # type: ignore - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_mean_wt_{dist_key}")] = result.mean_wt[ # type: ignore - dist_key - ] - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_count_{dist_key}")] = result.count[ # type: ignore - dist_key - ] - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_count_wt_{dist_key}")] = result.count_wt[ # type: ignore - dist_key - ] - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_var_{dist_key}")] = result.variance[ # type: ignore - dist_key - ] - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_var_wt_{dist_key}")] = result.variance_wt[ # type: ignore - dist_key - ] - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_max_{dist_key}")] = result.max[dist_key] # type: ignore - nodes_gdf[config.prep_gdf_key(f"{stats_column_label}_min_{dist_key}")] = result.min[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_sum", dist_key, angular=angular, weighted=False) + nodes_gdf[k] = result.sum[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_sum", dist_key, angular=angular, weighted=True) + nodes_gdf[k] = result.sum_wt[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_mean", dist_key, angular=angular, weighted=False) + nodes_gdf[k] = result.mean[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_mean", dist_key, angular=angular, weighted=True) + nodes_gdf[k] = result.mean_wt[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_count", dist_key, angular=angular, weighted=False) + nodes_gdf[k] = result.count[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_count", dist_key, angular=angular, weighted=True) + nodes_gdf[k] = result.count_wt[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_var", dist_key, angular=angular, weighted=False) + nodes_gdf[k] = result.variance[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_var", dist_key, angular=angular, weighted=True) + nodes_gdf[k] = result.variance_wt[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_max", dist_key, angular=angular) + nodes_gdf[k] = result.max[dist_key] # type: ignore + k = config.prep_gdf_key(f"{stats_column_label}_min", dist_key, angular=angular) + nodes_gdf[k] = result.min[dist_key] # type: ignore return nodes_gdf, data_gdf diff --git a/pysrc/cityseer/metrics/networks.py b/pysrc/cityseer/metrics/networks.py index 12610f94..eeb12ff5 100644 --- a/pysrc/cityseer/metrics/networks.py +++ b/pysrc/cityseer/metrics/networks.py @@ -136,20 +136,20 @@ def node_centrality_shortest( | key | formula | notes | | ----------------------| :------:| ----- | - | node_density | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | - | node_harmonic | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form + | density | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | + | harmonic | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form of closeness centrality for localised implementations constrained by the threshold $d_{max}$. | - | node_hillier | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by + | hillier | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by farness. This is also a simplified form of Improved Closeness Centrality. | - | node_beta | $$\sum_{j\neq{i}}^{n} \\ \exp(-\beta\cdot d[i,j])$$ | Also known as the gravity index. + | beta | $$\sum_{j\neq{i}}^{n} \\ \exp(-\beta\cdot d[i,j])$$ | Also known as the gravity index. This is a spatial impedance metric differentiated from other closeness centralities by the use of an explicit $\beta$ parameter, which can be used to model the decay in walking tolerance as distances increase. | - | node_cycles | $$\sum_{j\neq{i}j=cycle}^{n}1$$ | A summation of network cycles. | - | node_farness | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | - | node_betweenness | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all + | cycles | $$\sum_{j\neq{i}j=cycle}^{n}1$$ | A summation of network cycles. | + | farness | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | + | betweenness | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all shortest-paths traversing each node $i$. | - | node_betweenness_beta | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n} \\ \exp(-\beta\cdot d[j,k])$$ | Applies a + | betweenness_beta | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n} \\ \exp(-\beta\cdot d[j,k])$$ | Applies a spatial impedance decay function to betweenness centrality. $d$ represents the full distance from any $j$ to $k$ node pair passing through node $i$. | @@ -172,18 +172,27 @@ def node_centrality_shortest( ) # unpack if compute_closeness is True: - for measure_name in ["node_beta", "node_cycles", "node_density", "node_farness", "node_harmonic"]: + for measure_key, attr_key in [ + ("beta", "node_beta"), + ("cycles", "node_cycles"), + ("density", "node_density"), + ("farness", "node_farness"), + ("harmonic", "node_harmonic"), + ]: for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") - nodes_gdf[data_key] = getattr(result, measure_name)[distance] + data_key = config.prep_gdf_key(measure_key, distance) + nodes_gdf[data_key] = getattr(result, attr_key)[distance] for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"node_hillier_{distance}") + data_key = config.prep_gdf_key("hillier", distance) nodes_gdf[data_key] = result.node_density[distance] ** 2 / result.node_farness[distance] # type: ignore if compute_betweenness is True: - for measure_name in ["node_betweenness", "node_betweenness_beta"]: + for measure_key, attr_key in [ + ("betweenness", "node_betweenness"), + ("betweenness_beta", "node_betweenness_beta"), + ]: for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") - nodes_gdf[data_key] = getattr(result, measure_name)[distance] + data_key = config.prep_gdf_key(measure_key, distance) + nodes_gdf[data_key] = getattr(result, attr_key)[distance] return nodes_gdf @@ -250,13 +259,13 @@ def node_centrality_simplest( | key | formula | notes | | ----------------------| :------:| ----- | - | node_density_simplest | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | - | node_harmonic_simplest | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form + | density_ang | $$\sum_{j\neq{i}}^{n}1$$ | A summation of nodes. | + | harmonic_ang | $$\sum_{j\neq{i}}^{n}\frac{1}{d_{(i,j)}}$$ | Harmonic closeness is an appropriate form of closeness centrality for localised implementations constrained by the threshold $d_{max}$. | - | node_hillier_simplest | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by + | hillier_ang | $$\frac{(n-1)^2}{\sum_{j \neq i}^{n} d_{(i,j)}}$$ | The square of node density divided by farness. This is also a simplified form of Improved Closeness Centrality. | - | node_farness_simplest | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | - | node_betweenness_simplest | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing + | farness_ang | $$\sum_{j\neq{i}}^{n}d_{(i,j)}$$ | A summation of distances in metres. | + | betweenness_ang | $$\sum_{j\neq{i}}^{n}\sum_{k\neq{j}\neq{i}}^{n}1$$ | Betweenness centrality summing all shortest-paths traversing each node $i$. | The following keys use the simplest-path (shortest-angular-path) heuristic, and are available when the `angular` @@ -280,20 +289,20 @@ def node_centrality_simplest( ) if compute_closeness is True: for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"node_density_simplest_{distance}") + data_key = config.prep_gdf_key("density", distance, angular=True) nodes_gdf[data_key] = result.node_density[distance] # type: ignore for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"node_harmonic_simplest_{distance}") + data_key = config.prep_gdf_key("harmonic", distance, angular=True) nodes_gdf[data_key] = result.node_harmonic[distance] # type: ignore for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"node_hillier_simplest_{distance}") + data_key = config.prep_gdf_key("hillier", distance, angular=True) nodes_gdf[data_key] = result.node_density[distance] ** 2 / result.node_farness[distance] # type: ignore for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"node_farness_simplest_{distance}") + data_key = config.prep_gdf_key("farness", distance, angular=True) nodes_gdf[data_key] = result.node_farness[distance] # type: ignore if compute_betweenness is True: for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"node_betweenness_simplest_{distance}") + data_key = config.prep_gdf_key("betweenness", distance, angular=True) nodes_gdf[data_key] = result.node_betweenness[distance] # type: ignore return nodes_gdf @@ -360,12 +369,12 @@ def segment_centrality( | key | formula | notes | | ------------------- | :-----: |------ | - | segment_density | $$\sum_{(a, b)}^{edges}d_{b} - d_{a}$$ | A summation of edge lengths. | - | segment_harmonic | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\ln(b) -\ln(a)$$ | A continuous form of + | seg_density | $$\sum_{(a, b)}^{edges}d_{b} - d_{a}$$ | A summation of edge lengths. | + | seg_harmonic | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\ln(b) -\ln(a)$$ | A continuous form of harmonic closeness centrality applied to edge lengths. | - | segment_beta | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\frac{\exp(-\beta\cdot b) -\exp(-\beta\cdot a)}{-\beta}$$ | A # pylint: disable=line-too-long + | seg_beta | $$\sum_{(a, b)}^{edges}\int_{a}^{b}\frac{\exp(-\beta\cdot b) -\exp(-\beta\cdot a)}{-\beta}$$ | A # pylint: disable=line-too-long continuous form of beta-weighted (gravity index) centrality applied to edge lengths. | - | segment_betweenness | | A continuous form of betweenness: Resembles `segment_beta` applied to edges situated + | seg_betweenness | | A continuous form of betweenness: Resembles `segment_beta` applied to edges situated on shortest paths between all nodes $j$ and $k$ passing through $i$. | """ @@ -385,13 +394,16 @@ def segment_centrality( total=network_structure.node_count(), rust_struct=network_structure, partial_func=partial_func ) if compute_closeness is True: - for measure_name in ["segment_density", "segment_harmonic", "segment_beta"]: + for measure_key, attr_key in [ + ("seg_density", "segment_density"), + ("seg_harmonic", "segment_harmonic"), + ("seg_beta", "segment_beta"), + ]: for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") - nodes_gdf[data_key] = getattr(result, measure_name)[distance] + data_key = config.prep_gdf_key(measure_key, distance) + nodes_gdf[data_key] = getattr(result, attr_key)[distance] if compute_betweenness is True: - for measure_name in ["segment_betweenness"]: - for distance in distances: # type: ignore - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") - nodes_gdf[data_key] = getattr(result, measure_name)[distance] + for distance in distances: # type: ignore + data_key = config.prep_gdf_key("seg_betweenness", distance) + nodes_gdf[data_key] = getattr(result, "segment_betweenness")[distance] return nodes_gdf diff --git a/pysrc/cityseer/tools/io.py b/pysrc/cityseer/tools/io.py index 622f4ece..acacf3bd 100644 --- a/pysrc/cityseer/tools/io.py +++ b/pysrc/cityseer/tools/io.py @@ -1038,7 +1038,7 @@ def nx_from_cityseer_geopandas( geom=row_data[geom_key], ) # unpack any metrics written to the nodes - metrics_column_labels: list[str] = [c for c in nodes_gdf.columns if c.startswith("cc_metric")] + metrics_column_labels: list[str] = [c for c in nodes_gdf.columns if c.startswith("cc_")] if metrics_column_labels: logger.info("Unpacking metrics to nodes.") for metrics_column_label in metrics_column_labels: diff --git a/pysrc/cityseer/tools/plot.py b/pysrc/cityseer/tools/plot.py index b1968fdd..764a8099 100644 --- a/pysrc/cityseer/tools/plot.py +++ b/pysrc/cityseer/tools/plot.py @@ -4,7 +4,6 @@ Custom behaviour can be achieved by directly manipulating the underlying [`NetworkX`](https://networkx.github.io) and [`matplotlib`](https://matplotlib.org) figures. This module is predominately used for basic plots or visual verification of behaviour in code tests. Users are encouraged to use matplotlib or other plotting packages directly where possible. -See the demos section for examples. """ # workaround until networkx adopts types @@ -374,7 +373,7 @@ def plot_nx( Examples -------- ```py - from cityseer.tools import mock, graphs, plot + from cityseer.tools import mock, graphs, plot, io from cityseer.metrics import networks from matplotlib import colors @@ -382,15 +381,17 @@ def plot_nx( G = mock.mock_graph() G = graphs.nx_simple_geoms(G) G = graphs.nx_decompose(G, 50) - nodes_gdf, network_structure = io.network_structure_from_nx(G, crs=3395) - networks.node_centrality( - measures=["node_beta"], network_structure=network_structure, nodes_gdf=nodes_gdf, distances=[800] + nodes_gdf, edges_gdf, network_structure = io.network_structure_from_nx(G, crs=3395) + networks.node_centrality_shortest( + network_structure=network_structure, + nodes_gdf=nodes_gdf, + distances=[800] ) - G_after = graphs.nx_from_network_structure(nodes_gdf, network_structure, G) + G_after = io.nx_from_cityseer_geopandas(nodes_gdf, edges_gdf) # let's extract and normalise the values vals = [] for node, data in G_after.nodes(data=True): - vals.append(data["cc_metric_node_beta_800"]) + vals.append(data["cc_beta_800"]) # let's create a custom colourmap using matplotlib cmap = colors.LinearSegmentedColormap.from_list( "cityseer", [(100 / 255, 193 / 255, 255 / 255, 255 / 255), (211 / 255, 47 / 255, 47 / 255, 1 / 255)] @@ -682,7 +683,8 @@ def plot_scatter( face_colour: str = "#111", ) -> Any: """ - Convenience plotting function for plotting outputs from examples in demo notebooks. + Convenience plotting function for plotting outputs from examples in the + [Cityseer Guide](https://benchmark-urbanism.github.io/cityseer-examples/). Parameters ---------- @@ -774,7 +776,8 @@ def plot_nx_edges( invert_plot_order: bool = False, ): """ - Convenience plotting function for plotting edge outputs from examples in demo notebooks. + Convenience plotting function for plotting outputs from examples in the + [Cityseer Guide](https://benchmark-urbanism.github.io/cityseer-examples/). Parameters ---------- diff --git a/pysrc/cityseer/tools/util.py b/pysrc/cityseer/tools/util.py index ba2cd143..e3ad8885 100644 --- a/pysrc/cityseer/tools/util.py +++ b/pysrc/cityseer/tools/util.py @@ -112,7 +112,6 @@ def measure_max_angle(linestring_coords: ListCoordsType) -> float: for c_idx in range(len(linestring_coords) - 2): angle = _measure_linestring_angle(linestring_coords, c_idx, c_idx + 1, c_idx + 2) max_angle = max(max_angle, angle) - return max_angle @@ -534,7 +533,7 @@ def blend_metrics( raise ValueError('Method should be one of "min", "max", or "avg"') merged_edges_gdf = edges_gdf.copy() for node_column in nodes_gdf.columns: - if not node_column.startswith("cc_metric"): + if not node_column.startswith("cc_"): continue # suffix is only applied for overlapping column names merged_edges_gdf = pd.merge( diff --git a/src/data.rs b/src/data.rs index 3359a511..7cc7d2c2 100644 --- a/src/data.rs +++ b/src/data.rs @@ -333,6 +333,7 @@ impl DataMap { ) }) .collect(); + let max_dist = distances.iter().max().copied().unwrap(); let dists: HashMap = accessibility_keys .clone() .into_iter() @@ -340,7 +341,8 @@ impl DataMap { ( acc_key, MetricResult::new( - distances.clone(), + // single dist + vec![max_dist], network_structure.node_count(), f32::NAN, ), @@ -391,11 +393,14 @@ impl DataMap { let val_wt = clipped_beta_wt(b, mcw, data_dist); metrics_wt[&lu_class].metric[i][*netw_src_idx] .fetch_add(val_wt.unwrap(), Ordering::Relaxed); - let current_dist = - dists[&lu_class].metric[i][*netw_src_idx].load(Ordering::Relaxed); - if current_dist.is_nan() || data_dist < current_dist { - dists[&lu_class].metric[i][*netw_src_idx] - .store(data_dist, Ordering::Relaxed); + if d == max_dist { + // there is a single max dist so use 0 for index + let current_dist = dists[&lu_class].metric[0][*netw_src_idx] + .load(Ordering::Relaxed); + if current_dist.is_nan() || data_dist < current_dist { + dists[&lu_class].metric[0][*netw_src_idx] + .store(data_dist, Ordering::Relaxed); + } } } } diff --git a/tests/metrics/test_layers.py b/tests/metrics/test_layers.py index dec10038..4c2a7374 100644 --- a/tests/metrics/test_layers.py +++ b/tests/metrics/test_layers.py @@ -68,7 +68,7 @@ def test_compute_accessibilities(primal_graph): ) for acc_key in key_set: for dist_key in distances: - acc_data_key_nw = config.prep_gdf_key(f"{acc_key}_{dist_key}_non_weighted") + acc_data_key_nw = config.prep_gdf_key(acc_key, dist_key, angular, weighted=False) assert np.allclose( nodes_gdf[acc_data_key_nw].values, accessibility_data[acc_key].unweighted[dist_key], @@ -76,7 +76,7 @@ def test_compute_accessibilities(primal_graph): rtol=config.RTOL, equal_nan=True, ) - acc_data_key_wt = config.prep_gdf_key(f"{acc_key}_{dist_key}_weighted") + acc_data_key_wt = config.prep_gdf_key(acc_key, dist_key, angular, weighted=True) assert np.allclose( nodes_gdf[acc_data_key_wt].values, accessibility_data[acc_key].weighted[dist_key], @@ -84,14 +84,17 @@ def test_compute_accessibilities(primal_graph): rtol=config.RTOL, equal_nan=True, ) - acc_data_key_dist = config.prep_gdf_key(f"{acc_key}_{dist_key}_distance") - assert np.allclose( - nodes_gdf[acc_data_key_dist].values, - accessibility_data[acc_key].distance[dist_key], - atol=config.ATOL, - rtol=config.RTOL, - equal_nan=True, - ) + acc_data_key_dist = config.prep_gdf_key(f"{acc_key}_nearest_max", dist_key, angular) + if dist_key == max(distances): + assert np.allclose( + nodes_gdf[acc_data_key_dist].values, + accessibility_data[acc_key].distance[dist_key], + atol=config.ATOL, + rtol=config.RTOL, + equal_nan=True, + ) + else: + assert acc_data_key_dist not in nodes_gdf.columns # most integrity checks happen in underlying method with pytest.raises(ValueError): nodes_gdf, data_gdf = layers.compute_accessibilities( @@ -145,28 +148,28 @@ def test_compute_mixed_uses(primal_graph): ) for dist_key in distances: for q_key in [0, 1, 2]: - hill_nw_data_key = config.prep_gdf_key(f"hill_q{q_key}_{dist_key}") + hill_nw_data_key = config.prep_gdf_key(f"hill_q{q_key}", dist_key, angular=angular, weighted=False) assert np.allclose( nodes_gdf[hill_nw_data_key].values, mu_data.hill[q_key][dist_key], atol=config.ATOL, rtol=config.RTOL, ) - hill_wt_data_key = config.prep_gdf_key(f"hill_wt_q{q_key}_{dist_key}") + hill_wt_data_key = config.prep_gdf_key(f"hill_q{q_key}", dist_key, angular=angular, weighted=True) assert np.allclose( nodes_gdf[hill_wt_data_key].values, mu_data.hill_weighted[q_key][dist_key], atol=config.ATOL, rtol=config.RTOL, ) - shannon_data_key = config.prep_gdf_key(f"shannon_{dist_key}") + shannon_data_key = config.prep_gdf_key("shannon", dist_key, angular=angular) assert np.allclose( nodes_gdf[shannon_data_key].values, mu_data.shannon[dist_key], atol=config.ATOL, rtol=config.RTOL, ) - gini_data_key = config.prep_gdf_key(f"gini_{dist_key}") + gini_data_key = config.prep_gdf_key("gini", dist_key, angular=angular) assert np.allclose( nodes_gdf[gini_data_key].values, mu_data.gini[dist_key], @@ -194,6 +197,7 @@ def test_compute_stats(primal_graph): network_structure, max_assign_dist, distances=distances, + angular=angular, ) # generate stats # compare to manual @@ -202,73 +206,74 @@ def test_compute_stats(primal_graph): network_structure, numerical_map=numerical_map, distances=distances, + angular=angular, ) for dist_key in distances: assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_sum_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_sum", dist_key, angular=angular, weighted=False)], stats_result.sum[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_sum_wt_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_sum", dist_key, angular=angular, weighted=True)], stats_result.sum_wt[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_mean_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_mean", dist_key, angular=angular, weighted=False)], stats_result.mean[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_mean_wt_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_mean", dist_key, angular=angular, weighted=True)], stats_result.mean_wt[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_count_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_count", dist_key, angular=angular, weighted=False)], stats_result.count[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_count_wt_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_count", dist_key, angular=angular, weighted=True)], stats_result.count_wt[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_var_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_var", dist_key, angular=angular, weighted=False)], stats_result.variance[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_var_wt_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_var", dist_key, angular=angular, weighted=True)], stats_result.variance_wt[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_max_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_max", dist_key, angular=angular)], stats_result.max[dist_key], atol=config.ATOL, rtol=config.RTOL, equal_nan=True, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"mock_numerical_1_min_{dist_key}")], + nodes_gdf[config.prep_gdf_key("mock_numerical_1_min", dist_key, angular=angular)], stats_result.min[dist_key], atol=config.ATOL, rtol=config.RTOL, diff --git a/tests/metrics/test_networks.py b/tests/metrics/test_networks.py index e45bbbb0..86c305a9 100644 --- a/tests/metrics/test_networks.py +++ b/tests/metrics/test_networks.py @@ -33,29 +33,38 @@ def test_node_centrality_shortest(primal_graph): node_result_short = network_structure.local_node_centrality_shortest( betas=betas, compute_closeness=_closeness, compute_betweenness=_betweenness ) - for distance in distances: + for dist_key in distances: if _closeness is True: - for measure_name in ["node_beta", "node_cycles", "node_density", "node_farness", "node_harmonic"]: - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") + for measure_key, attr_key in [ + ("beta", "node_beta"), + ("cycles", "node_cycles"), + ("density", "node_density"), + ("farness", "node_farness"), + ("harmonic", "node_harmonic"), + ]: + data_key = config.prep_gdf_key(measure_key, dist_key) assert np.allclose( nodes_gdf[data_key], - getattr(node_result_short, measure_name)[distance], + getattr(node_result_short, attr_key)[dist_key], atol=config.ATOL, rtol=config.RTOL, ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"node_hillier_{distance}")], - node_result_short.node_density[distance] ** 2 / node_result_short.node_farness[distance], + nodes_gdf[config.prep_gdf_key("hillier", dist_key)], + node_result_short.node_density[dist_key] ** 2 / node_result_short.node_farness[dist_key], equal_nan=True, atol=config.ATOL, rtol=config.RTOL, ) if _betweenness is True: - for measure_name in ["node_betweenness", "node_betweenness_beta"]: - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") + for measure_key, attr_key in [ + ("betweenness", "node_betweenness"), + ("betweenness_beta", "node_betweenness_beta"), + ]: + data_key = config.prep_gdf_key(measure_key, dist_key) assert np.allclose( nodes_gdf[data_key], - getattr(node_result_short, measure_name)[distance], + getattr(node_result_short, attr_key)[dist_key], atol=config.ATOL, rtol=config.RTOL, ) @@ -85,40 +94,31 @@ def test_node_centrality_simplest(primal_graph): node_result_simplest = network_structure.local_node_centrality_simplest( betas=betas, compute_closeness=_closeness, compute_betweenness=_betweenness ) - for distance in distances: + for dist_key in distances: if _closeness is True: + for measure_key, attr_key in [ + ("density", "node_density"), + ("farness", "node_farness"), + ("harmonic", "node_harmonic"), + ]: + assert np.allclose( + nodes_gdf[config.prep_gdf_key(measure_key, dist_key, angular=True)], + getattr(node_result_simplest, attr_key)[dist_key], + equal_nan=True, + atol=config.ATOL, + rtol=config.RTOL, + ) assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"node_density_simplest_{distance}")], - node_result_simplest.node_density[distance], - equal_nan=True, - atol=config.ATOL, - rtol=config.RTOL, - ) - assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"node_farness_simplest_{distance}")], - node_result_simplest.node_farness[distance], - equal_nan=True, - atol=config.ATOL, - rtol=config.RTOL, - ) - assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"node_hillier_simplest_{distance}")], - node_result_simplest.node_density[distance] ** 2 / node_result_simplest.node_farness[distance], - equal_nan=True, - atol=config.ATOL, - rtol=config.RTOL, - ) - assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"node_harmonic_simplest_{distance}")], - node_result_simplest.node_harmonic[distance], + nodes_gdf[config.prep_gdf_key("hillier", dist_key, angular=True)], + node_result_simplest.node_density[dist_key] ** 2 / node_result_simplest.node_farness[dist_key], equal_nan=True, atol=config.ATOL, rtol=config.RTOL, ) if _betweenness is True: assert np.allclose( - nodes_gdf[config.prep_gdf_key(f"node_betweenness_simplest_{distance}")], - node_result_simplest.node_betweenness[distance], + nodes_gdf[config.prep_gdf_key("betweenness", dist_key, angular=True)], + node_result_simplest.node_betweenness[dist_key], equal_nan=True, atol=config.ATOL, rtol=config.RTOL, @@ -149,12 +149,15 @@ def test_segment_centrality(primal_graph): segment_result = network_structure.local_segment_centrality( betas=betas, compute_closeness=_closeness, compute_betweenness=_betweenness ) - for distance in distances: + for dist_key in distances: if _closeness is True: - for measure_name in ["segment_density", "segment_harmonic", "segment_beta"]: - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") - assert np.allclose(nodes_gdf[data_key], getattr(segment_result, measure_name)[distance]) + for measure_key, attr_key in [ + ("seg_density", "segment_density"), + ("seg_harmonic", "segment_harmonic"), + ("seg_beta", "segment_beta"), + ]: + data_key = config.prep_gdf_key(measure_key, dist_key) + assert np.allclose(nodes_gdf[data_key], getattr(segment_result, attr_key)[dist_key]) if _betweenness is True: - for measure_name in ["segment_betweenness"]: - data_key = config.prep_gdf_key(f"{measure_name}_{distance}") - assert np.allclose(nodes_gdf[data_key], getattr(segment_result, measure_name)[distance]) + data_key = config.prep_gdf_key("seg_betweenness", dist_key) + assert np.allclose(nodes_gdf[data_key], getattr(segment_result, "segment_betweenness")[dist_key]) diff --git a/tests/rustalgos/test_data.py b/tests/rustalgos/test_data.py index 3a38687b..28b02478 100644 --- a/tests/rustalgos/test_data.py +++ b/tests/rustalgos/test_data.py @@ -158,22 +158,28 @@ def test_accessibility(primal_graph): assert accessibilities["b"].weighted[dist][src_idx] - b_wt < config.ATOL assert accessibilities["c"].weighted[dist][src_idx] - c_wt < config.ATOL assert accessibilities["z"].weighted[dist][src_idx] - z_wt < config.ATOL - if np.isfinite(a_dist): - assert accessibilities["a"].distance[dist][src_idx] - a_dist < config.ATOL + if dist == max(distances): + if np.isfinite(a_dist): + assert accessibilities["a"].distance[dist][src_idx] - a_dist < config.ATOL + else: + assert np.isnan(a_dist) and np.isnan(accessibilities["a"].distance[dist][src_idx]) + if np.isfinite(b_dist): + assert accessibilities["b"].distance[dist][src_idx] - b_dist < config.ATOL + else: + assert np.isnan(b_dist) and np.isnan(accessibilities["b"].distance[dist][src_idx]) + if np.isfinite(c_dist): + assert accessibilities["c"].distance[dist][src_idx] - c_dist < config.ATOL + else: + assert np.isnan(c_dist) and np.isnan(accessibilities["c"].distance[dist][src_idx]) + if np.isfinite(z_dist): + assert accessibilities["z"].distance[dist][src_idx] - z_dist < config.ATOL + else: + assert np.isnan(z_dist) and np.isnan(accessibilities["z"].distance[dist][src_idx]) else: - assert np.isnan(a_dist) and np.isnan(accessibilities["a"].distance[dist][src_idx]) - if np.isfinite(b_dist): - assert accessibilities["b"].distance[dist][src_idx] - b_dist < config.ATOL - else: - assert np.isnan(b_dist) and np.isnan(accessibilities["b"].distance[dist][src_idx]) - if np.isfinite(c_dist): - assert accessibilities["c"].distance[dist][src_idx] - c_dist < config.ATOL - else: - assert np.isnan(c_dist) and np.isnan(accessibilities["c"].distance[dist][src_idx]) - if np.isfinite(z_dist): - assert accessibilities["z"].distance[dist][src_idx] - z_dist < config.ATOL - else: - assert np.isnan(z_dist) and np.isnan(accessibilities["z"].distance[dist][src_idx]) + assert dist not in accessibilities["a"].distance + assert dist not in accessibilities["b"].distance + assert dist not in accessibilities["c"].distance + assert dist not in accessibilities["z"].distance # check for deduplication assert z_nw in [0, 1] assert z_wt <= 1 diff --git a/tests/tools/test_io.py b/tests/tools/test_io.py index 973c7c67..0ab7e503 100644 --- a/tests/tools/test_io.py +++ b/tests/tools/test_io.py @@ -705,14 +705,14 @@ def test_nx_from_cityseer_geopandas(primal_graph): distances=[500, 1000], ) column_labels: list[str] = [ - "cc_metric_a_500_non_weighted", - "cc_metric_a_1000_non_weighted", - "cc_metric_a_500_weighted", - "cc_metric_a_1000_weighted", - "cc_metric_c_500_non_weighted", - "cc_metric_c_1000_non_weighted", - "cc_metric_c_500_weighted", - "cc_metric_c_1000_weighted", + "cc_a_500_nw", + "cc_a_1000_nw", + "cc_a_500_wt", + "cc_a_1000_wt", + "cc_c_500_nw", + "cc_c_1000_nw", + "cc_c_500_wt", + "cc_c_1000_wt", ] # without backbone G_round_trip_nx = io.nx_from_cityseer_geopandas( diff --git a/tests/tools/test_util.py b/tests/tools/test_util.py index 22a1b117..bc13392f 100644 --- a/tests/tools/test_util.py +++ b/tests/tools/test_util.py @@ -135,7 +135,7 @@ def test_blend_metrics(primal_graph): # AVG merged_edges_gdf_avg = util.blend_metrics(nodes_gdf, edges_gdf, method="avg") for node_column in nodes_gdf.columns: - if not node_column.startswith("cc_metric"): + if not node_column.startswith("cc_"): continue for _edge_idx, edge_row in merged_edges_gdf_avg.iterrows(): start_val = nodes_gdf.loc[edge_row.nx_start_node_key, node_column] @@ -144,7 +144,7 @@ def test_blend_metrics(primal_graph): # MIN merged_edges_gdf_min = util.blend_metrics(nodes_gdf, edges_gdf, method="min") for node_column in nodes_gdf.columns: - if not node_column.startswith("cc_metric"): + if not node_column.startswith("cc_"): continue for _edge_idx, edge_row in merged_edges_gdf_min.iterrows(): start_val = nodes_gdf.loc[edge_row.nx_start_node_key, node_column] @@ -153,7 +153,7 @@ def test_blend_metrics(primal_graph): # MAX merged_edges_gdf_max = util.blend_metrics(nodes_gdf, edges_gdf, method="max") for node_column in nodes_gdf.columns: - if not node_column.startswith("cc_metric"): + if not node_column.startswith("cc_"): continue for _edge_idx, edge_row in merged_edges_gdf_max.iterrows(): start_val = nodes_gdf.loc[edge_row.nx_start_node_key, node_column]