From 2cf5f4d4191b8c9b4a15395e07dd47b3e8507c7c Mon Sep 17 00:00:00 2001
From: Michael Szell <michael.szell@gmail.com>
Date: Thu, 18 Apr 2024 18:19:09 +0200
Subject: [PATCH] Polish mwe.py and make it more user-friendly

---
 scripts/examples/mwe.py | 45 +++++++++++++++++------------------------
 1 file changed, 19 insertions(+), 26 deletions(-)

diff --git a/scripts/examples/mwe.py b/scripts/examples/mwe.py
index 0a05da9..b4d2533 100644
--- a/scripts/examples/mwe.py
+++ b/scripts/examples/mwe.py
@@ -1,39 +1,32 @@
 """Minimum working example for superblockify."""
 
-from superblockify import ResidentialPartitioner
+# While this script is running, figures will be generated on screen. 
+# Close the figure windows to proceed with the script execution.
+
 
+from superblockify import ResidentialPartitioner
 
 if __name__ == "__main__":
-    part = ResidentialPartitioner(
-        name="Salerno_mwe",
-        city_name="Salerno",
-        search_str="Salern, Campania, Italy",
-        # reload_graph=True
+    # --- Initialize the partitioner ---
+    partitioner = ResidentialPartitioner(
+        name="Scheveningen_test",
+        city_name="Scheveningen",
+        search_str="Scheveningen, NL",
+        unit="time",  # "time", "distance", any other edge attribute, or None to count edges
     )
-    # Find search strings via https://nominatim.openstreetmap.org/
-    # The smaller the place, the quicker the partitioning
-    # For large places sufficient memory is required
+    # If you want to select a different city, find the corresponding search string
+    # (`search_str`) at https://nominatim.openstreetmap.org/. 
+    # The smaller the place, the quicker the partitioning.
+    # For large places sufficient memory is required.
 
-    # Save the preprocessed graph to disk:
-    # >>> part.save(save_graph_copy=True)
-    # If loading a previously saved graph, all previous steps are not needed:
-    # >>> part = ResidentialPartitioner.load("Salerno_mwe")
-
-    part.run(
+    # --- Run the partitioner ---
+    partitioner.run(
         calculate_metrics=True,
         make_plots=True,
         replace_max_speeds=False,
     )
 
-    # Save the partitioner to disk, with all attributes, without an extra copy of the
+    # --- Save the partitioner ---
+    # Save it to disk, with all attributes, without an extra copy of the
     # graph. The original graph has been cached in the data/graphs folder.
-    part.save()
-
-# or
-# >>> import superblockify as sb
-# >>> part = sb.ResidentialPartitioner(...)
-# >>> part.run(...)
-# >>> sb.save_to_gpkg(part)
-
-# If you have the whole GHS POP raster, set the path after importing like so:
-# >>> sb.config.Config.FULL_RASTER = ...
+    partitioner.save()