Break tutorial calls one argument per line past 90 characters.

Author: Nathaniel Henry

docs/getting_started.md

@@ -114,7 +114,10 @@ A block summary, with the readable category names merged on and sorted by time:
 
 ```{code-cell} python
 walk_times = close.block_summary(geoid = "440070008001068", mode = "walk")
-walk_times = walk_times.merge(amenity_types[["dest_type_id", "name"]], on = "dest_type_id")
+walk_times = walk_times.merge(
+    amenity_types[["dest_type_id", "name"]],
+    on = "dest_type_id"
+)
 walk_times.sort_values("travel_time")[["name", "travel_time"]]
 ```
 

docs/token_use.md

@@ -34,8 +34,11 @@ when you want the etag:
 ```python
 close.output = "raw"
 first = close.block_summary(geoid = "440070008001068", mode = "walk")
-again = close.block_summary(geoid = "440070008001068", mode = "walk",
-                            if_none_match = first.etag)
+again = close.block_summary(
+    geoid = "440070008001068",
+    mode = "walk",
+    if_none_match = first.etag
+)
 again.not_modified   # True, and nothing was charged
 ```
 
@@ -60,8 +63,12 @@ returns every reachable block with its minutes. A whole walkshed for 10 tokens,
 where a catchment or block query charges per block:
 
 ```python
-shed = close.isochrone(block = "440070008001068", minutes = 30, mode = "walk",
-                       format = "blocks")
+shed = close.isochrone(
+    block = "440070008001068",
+    minutes = 30,
+    mode = "walk",
+    format = "blocks"
+)
 ```
 
 ## Watch what you spend
@@ -76,8 +83,12 @@ types = close.destination_types()
 supermarket_dest_type = types.loc[types["label"] == "grocery_stores",
                                   "dest_type_id"].iloc[0]
 
-supermarkets = close.pois_search(lat = 41.823, lon = -71.412, radius_m = 1200,
-                                 type = supermarket_dest_type)
+supermarkets = close.pois_search(
+    lat = 41.823,
+    lon = -71.412,
+    radius_m = 1200,
+    type = supermarket_dest_type
+)
 supermarkets.attrs["tokens_charged"], supermarkets.attrs["tokens_remaining"]
 ```
 

docs/tutorials/amenity_basket.md

@@ -67,8 +67,12 @@ same way (at a higher token cost).
 
 ```{code-cell} python
 blocks = close.blocks_query(
-    center = {"lon": city["lon"], "lat": city["lat"]}, radius_m = 2500,
-    mode = "walk", type = list(basket.values()), include_population = True)
+    center = {"lon": city["lon"], "lat": city["lat"]},
+    radius_m = 2500,
+    mode = "walk",
+    type = list(basket.values()),
+    include_population = True
+)
 
 one_per_block = blocks.drop_duplicates("geoid").reset_index(drop = True)
 total_pop = one_per_block["population"].sum()
@@ -94,8 +98,12 @@ highlighted, the city boundary behind.
 near_library = set(blocks.loc[(blocks.dest_type_id == basket["library"]) &
                               (blocks.travel_time <= 15), "geoid"])
 one_per_block["has_library"] = one_per_block.geoid.isin(near_library)
-close_map(one_per_block, highlight = "has_library", color = "#058040",
-          boundary = city_boundary)
+close_map(
+    one_per_block,
+    highlight = "has_library",
+    color = "#058040",
+    boundary = city_boundary
+)
 ```
 
 ## The 15-minute-city score
@@ -127,8 +135,12 @@ basket_pop = one_per_block.loc[one_per_block.geoid.isin(covered_all),
 print(f"All six amenities: {100 * basket_pop / total_pop:.0f}% of residents")
 
 one_per_block["full_basket"] = one_per_block.geoid.isin(covered_all)
-close_map(one_per_block, highlight = "full_basket", color = "#f36e21",
-          boundary = city_boundary)
+close_map(
+    one_per_block,
+    highlight = "full_basket",
+    color = "#f36e21",
+    boundary = city_boundary
+)
 ```
 
 ## Which amenity to add first
@@ -170,8 +182,10 @@ gap = np.array([" + ".join(n for n, m in zip(names, row) if m) for row in missin
 
 almost = np.isin(n_missing, [1, 2])
 almost_pop = pops[almost].sum()
-print(f"{100 * almost_pop / total_pop:.0f}% of residents are one or two amenities "
-      f"short of the full basket.\n")
+print(
+    f"{100 * almost_pop / total_pop:.0f}% of residents are one or two amenities "
+    f"short of the full basket.\n"
+)
 
 by_gap = (pd.Series(pops[almost], index = gap[almost])
           .groupby(level = 0).sum().sort_values(ascending = False))
@@ -188,8 +202,14 @@ isochrone gives exactly the blocks that could reach the site on foot in 15 minut
 
 ```{code-cell} python
 site_lon, site_lat = -77.437, 37.548
-reachable = close.isochrone(lon = site_lon, lat = site_lat, mode = "walk",
-                            direction = "to", minutes = 15, format = "blocks")
+reachable = close.isochrone(
+    lon = site_lon,
+    lat = site_lat,
+    mode = "walk",
+    direction = "to",
+    minutes = 15,
+    format = "blocks"
+)
 
 near_supermarket = set(blocks.loc[(blocks.dest_type_id == basket["supermarket"]) &
                                   (blocks.travel_time <= 15), "geoid"])
@@ -203,6 +223,10 @@ Map the whole city and highlight the blocks that would newly gain access.
 
 ```{code-cell} python
 one_per_block["newly_served"] = one_per_block.geoid.isin(newly_served)
-close_map(one_per_block, highlight = "newly_served", color = "#e8590c",
-          boundary = city_boundary)
+close_map(
+    one_per_block,
+    highlight = "newly_served",
+    color = "#e8590c",
+    boundary = city_boundary
+)
 ```

docs/tutorials/competitor_walksheds.md

@@ -59,17 +59,25 @@ ours = cafes.iloc[0]
 print(ours["name"])
 cafes["is_ours"] = cafes["dest_id"] == ours["dest_id"]
 
-our_shed = close.poi_catchment(dest_id = int(ours["dest_id"]), mode = "walk",
-                               max_minutes = 10)
+our_shed = close.poi_catchment(
+    dest_id = int(ours["dest_id"]),
+    mode = "walk",
+    max_minutes = 10
+)
 walkshed = our_shed.dissolve()
 ```
 
 Draw the walkshed, with the cafes on top and our shop in orange.
 
 ```{code-cell} python
-close_map(cafes, color = ["#f36e21" if o else "#202a5b" for o in cafes["is_ours"]],
-          label = "name", background = walkshed, background_color = "#74b9ff",
-          boundary = city_boundary)
+close_map(
+    cafes,
+    color = ["#f36e21" if o else "#202a5b" for o in cafes["is_ours"]],
+    label = "name",
+    background = walkshed,
+    background_color = "#74b9ff",
+    boundary = city_boundary
+)
 ```
 
 ## Who else those blocks can reach
@@ -88,8 +96,11 @@ nearest = others.nsmallest(min(15, len(others)), "dist")
 
 shared = {}
 for _, cafe_row in nearest.iterrows():
-    their_shed = close.poi_catchment(dest_id = int(cafe_row["dest_id"]), mode = "walk",
-                                     max_minutes = 10)
+    their_shed = close.poi_catchment(
+        dest_id = int(cafe_row["dest_id"]),
+        mode = "walk",
+        max_minutes = 10
+    )
     shared[cafe_row["dest_id"]] = len(our_geoids & set(their_shed["geoid"]))
 
 for dest_id, n in sorted(shared.items(), key = lambda kv: -kv[1]):
@@ -114,8 +125,14 @@ def cafe_color(row):
         return "#f36e21"
     return "#e03131" if row["is_competitor"] else "#202a5b"
 
-close_map(cafes, color = [cafe_color(r) for _, r in cafes.iterrows()], label = "name",
-          background = walkshed, background_color = "#74b9ff", boundary = city_boundary)
+close_map(
+    cafes,
+    color = [cafe_color(r) for _, r in cafes.iterrows()],
+    label = "name",
+    background = walkshed,
+    background_color = "#74b9ff",
+    boundary = city_boundary
+)
 ```
 
 The same recipe scales up: raise the cafe count you check, or compare whole cities by

docs/tutorials/first_map.md

@@ -56,7 +56,10 @@ on top.
 
 ```{code-cell} python
 walk_times = close.point_summary(lat = start_lat, lon = start_lon, mode = "walk")
-walk_times = walk_times.merge(amenity_types[["dest_type_id", "name"]], on = "dest_type_id")
+walk_times = walk_times.merge(
+    amenity_types[["dest_type_id", "name"]],
+    on = "dest_type_id"
+)
 walk_times.sort_values("travel_time")[["name", "travel_time"]]
 ```
 
@@ -69,12 +72,21 @@ within `max_minutes`, each carrying its walk time — no isochrone to overlay.
 boundary behind it for context.
 
 ```{code-cell} python
-nearby_supermarkets = close.point_pois(lat = start_lat, lon = start_lon,
-                                       mode = "walk", type = supermarket_type,
-                                       max_minutes = 30)
+nearby_supermarkets = close.point_pois(
+    lat = start_lat,
+    lon = start_lon,
+    mode = "walk",
+    type = supermarket_type,
+    max_minutes = 30
+)
 
 city_boundary = close.place_boundary(geoid = providence_ri["geoid"])
-close_map(nearby_supermarkets, color = "#e8590c", boundary = city_boundary, label = "name")
+close_map(
+    nearby_supermarkets,
+    color = "#e8590c",
+    boundary = city_boundary,
+    label = "name"
+)
 ```
 
 ## Draw how far you can walk
@@ -84,8 +96,14 @@ minutes. Shade it by the `contour` minutes; blue marks the nearest, most-reachab
 ring.
 
 ```{code-cell} python
-rings = close.isochrone(lon = start_lon, lat = start_lat, mode = "walk",
-                        direction = "from", contours = [10, 20, 30], format = "geojson")
+rings = close.isochrone(
+    lon = start_lon,
+    lat = start_lat,
+    mode = "walk",
+    direction = "from",
+    contours = [10, 20, 30],
+    format = "geojson"
+)
 close_map(rings, fill = "contour", reverse = True)
 ```
 
@@ -95,10 +113,22 @@ The same starting point and the same 30-minute budget, on foot and by bus — th
 clearest way to see what transit buys you.
 
 ```{code-cell} python
-walk = close.isochrone(lon = start_lon, lat = start_lat, mode = "walk",
-                       direction = "from", minutes = 30, format = "geojson")
-transit = close.isochrone(lon = start_lon, lat = start_lat, mode = "transit",
-                          direction = "from", minutes = 30, format = "geojson")
+walk = close.isochrone(
+    lon = start_lon,
+    lat = start_lat,
+    mode = "walk",
+    direction = "from",
+    minutes = 30,
+    format = "geojson"
+)
+transit = close.isochrone(
+    lon = start_lon,
+    lat = start_lat,
+    mode = "transit",
+    direction = "from",
+    minutes = 30,
+    format = "geojson"
+)
 
 close_map(walk, color = "#058040")
 ```