Chore/name docs parity gbn

docs/source/mwe.ipynb

@@ -590,7 +590,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.7"
+   "version": "3.14.8"
   }
  },
  "nbformat": 4,

docs/source/usage_01_gap_identification.ipynb

@@ -28,7 +28,7 @@
     "tags": []
    },
    "source": [
-    "**Parameters covered**: `numgaps`, `radius`, `mingap`, `maxgap`"
+    "**Parameters covered**: `numgaps`, `settings.radius`, `mingap`, `maxgap`"
    ]
   },
   {
@@ -92,7 +92,7 @@
    "source": [
     "FixBikeNet is based on the idea that any shortest path between two nodes on an existing bicycle network, where the entire path goes through the non-bicycle network, is a potential gap. Out of these potentially millions of gaps in a city, FixBikeNet identifies those that are most important to close in terms of providing the most cycled meters (per gap meter). It ranks the identified gaps and selects the top `numgaps` - the first parameter for running FixBikeNet. The ranking happens through betweenness centrality, a proxy for cyclist flow. This measure assumes that cyclists move from each intersection to each other intersection in the city.\n",
     "\n",
-    "The assumption of such a global all-to-all flow is relaxed in FixBikeNet to only consider local betweenness flows within a `radius` - the second parameter. This relaxation is done for several reasons: \n",
+    "The assumption of such a global all-to-all flow is relaxed in FixBikeNet to only consider local betweenness flows within a `settings.radius` (that is loaded from `fbn.settings`). This relaxation is done for several reasons: \n",
     "1) Typical cyclists are usually not traversing the whole city\n",
     "2) global betweenness is biased towards the city center while local betweenness allows identifying gaps on neighborhood levels\n",
     "3) calculations are much more effective, especially for large cities."

examples/development.py

@@ -21,10 +21,10 @@
 #             },
 #         )
 
+fbn.settings.radius = 1000
 
 fbn.fixbikenet(
     city_query="Frederiksberg Municipality",
-    radius = 1000,
     mingap = 0,
     maxgap = 100,
     numgaps = 20,

fixbikenet/fixbikenet.py

@@ -47,7 +47,6 @@
 
 def fixbikenet(
     city_query,
-    radius = 2500,
     mingap = 20,
     maxgap = 800,
     numgaps = 50,
@@ -56,26 +55,34 @@ def fixbikenet(
     export_plot=False,
     import_files={},
 ):
-    """Find gaps in bicycle networks and return the `numgaps` that are the most important to fill.
+    """Find gaps in bicycle networks and return the `numgaps` that are the most
+    important to fill.
 
     Parameters
     ----------
     city_query : str
-        name of the city that the analysis should be performed on
-    radius : int, default 2500
-        cut-off length for computation of local betweenness centrality, in meters
+        Name of the city that the analysis should be performed on.
     mingap : int, default 20
-        Minimum distance between node pairs to be considered as a potential gap, in meters.
+        Minimum distance between node pairs to be considered as a potential 
+        gap, in meters.
     maxgap : int, default 800
-        maximum distance between node pairs to be considered as a potential gap, in meters
+        Maximum distance between node pairs to be considered as a potential 
+        gap, in meters.
     numgaps : int, default 50
         Number of gaps to find.
-    export_data : bool, optional, default True
-        If set to True, data will be saved to a file. The filename is [slug].gpkg, where slug is a string id made out of city_name
+    export_data : bool, default True
+        If set to True, data will be saved to a file. The filename is 
+        [slug].[settings.export_file_format], where slug is a string id made 
+        out of `city_query` (or `city_id` if set).
     city_id : str | None, default None
-        If set, the slugified city_id is used in the filename of the data export. For example, a city_id "Athens" will slugify into "athens" in filenames. If set to None, the slugified city_query is used in the filename of the data export. It is useful to set a city_id for cities where the city_query is not the city name, for example to set for a city_query "Municipality of Athens" the city_id to "Athens".
+        If set, the slugified `city_id` is used in the filename of the data 
+        export. For example, a `city_id` "Athens" will slugify into "athens" 
+        in filenames. If set to None, the slugified `city_query` is used in the
+        filename of the data export. It is useful to set a `city_id` for cities
+        where the `city_query` is not the city name, for example to set for a 
+        `city_query` "Municipality of Athens" the `city_id` to "Athens".
     export_plot : bool, optional, default False
-        If set to True, plot will be saved to a file
+        If set to True, plot will be saved to a file.
     import_files: dict, default {}
         The following key:value entries can be set:
 
@@ -104,7 +111,7 @@ def fixbikenet(
     Returns
     -------
     gaps_ordered : geopandas.geodataframe.GeoDataFrame
-        ordered geodataframe with the `numgaps` most important gaps to fill
+        Ordered geodataframe with the `numgaps` most important gaps to fill.
 
     References
     ----------
@@ -115,7 +122,7 @@ def fixbikenet(
     starttime = time.time()
     np.random.seed(settings.random_seed) # Set random number generator seed for reproducibility
     setting_was_auto = _validate_settings()
-    import_files = _validate_parameters(city_query, radius, mingap, maxgap, export_data, import_files)
+    import_files = _validate_parameters(city_query, mingap, maxgap, export_data, import_files)
     _print_header(city_query)
 
     ### Import data files
@@ -187,7 +194,7 @@ def fixbikenet(
     found_gaps, found_gaps_nsp = find_actual_gaps(G, potential_gaps, mingap)
 
     # calculating local betweenness score dependent on radius
-    ebc = compute_local_betweenness_centrality(G, nodes_gdf, radius)
+    ebc = compute_local_betweenness_centrality(G, nodes_gdf)
 
     G_lengths = nx.get_edge_attributes(G, "length")
     nx.set_edge_attributes(G, G_lengths, "distance")