Add ruff

.gitignore

@@ -5,4 +5,7 @@ __pycache__
 *.egg-info
 
 # The created deck
-*.apkg
+*.apkg
+
+# Ruff
+.ruff_cache

README.md

@@ -8,7 +8,7 @@ I've lived in Brisbane my whole life, and I don't know all the suburbs. That's b
 
 ## Creating the Deck
 
-```
+```bash
 # Install the repo
 pip install -e.
 

brisbane_suburbs_anki_cards/__init__.py

@@ -2,4 +2,4 @@
 
 current_file = os.path.dirname(os.path.abspath(__file__))
 
-DOC_KML = os.path.join(current_file, 'doc.kml')
+DOC_KML = os.path.join(current_file, "doc.kml")

brisbane_suburbs_anki_cards/constants.py

@@ -1 +1 @@
-LOCAL_GOVERNMENT_AREAS_WE_CARE_ABOUT: set[str] = {'Brisbane City'}
+LOCAL_GOVERNMENT_AREAS_WE_CARE_ABOUT: set[str] = {"Brisbane City"}

brisbane_suburbs_anki_cards/entry.py

@@ -1,8 +1,9 @@
 from brisbane_suburbs_anki_cards.generate_anki_deck import create_anki_deck
 from brisbane_suburbs_anki_cards.generate_images import create_and_save_images
 
+
 def main():
     # This is the entrypoint for the whole package. This is what's ran when you
     # call `create-deck` when the package is installed.
     create_and_save_images()
-    create_anki_deck()
+    create_anki_deck()

brisbane_suburbs_anki_cards/generate_anki_deck.py

@@ -15,18 +15,19 @@
     MODEL_ID,
     NOTE_MODEL_NAME,
     fields=[
-        {'name': 'Image'},
-        {'name': 'Suburb'},
+        {"name": "Image"},
+        {"name": "Suburb"},
     ],
     templates=[
         {
-            'name': 'ImageSuburbCard',
-            'qfmt': '{{Image}}',
-            'afmt': '''{{FrontSide}}<hr id="answer" style="border:none;"/><div style="text-align:center; font-size: 1.5em;">{{Suburb}}</div>''',
+            "name": "ImageSuburbCard",
+            "qfmt": "{{Image}}",
+            "afmt": """{{FrontSide}}<hr id="answer" style="border:none;"/><div style="text-align:center; font-size: 1.5em;">{{Suburb}}</div>""",
         }
-    ]
+    ],
 )
 
+
 def create_anki_deck() -> None:
     suburb_to_cbd_mapping = get_suburb_to_due_order_mapping()
 
@@ -35,32 +36,33 @@ def create_anki_deck() -> None:
     media_files = []
 
     # Loop over all images in the output folder and add them to the deck
-    for root, dirs, files in os.walk(os.path.join('output', 'Brisbane City')):
+    for root, dirs, files in os.walk(os.path.join("output", "Brisbane City")):
         for file in files:
             if file.endswith(".jpg"):
                 # The image path is the media file
                 image_path = os.path.join(root, file)
 
                 # Extract locality name from file name (remove .jpg extension)
                 locality = os.path.splitext(file)[0]
-                
+
                 # Add the image and locality to the Anki deck
                 my_note = genanki.Note(
                     model=my_model,
                     fields=[f'<img src="{file}">', locality],
-                    due=suburb_to_cbd_mapping[locality]
+                    due=suburb_to_cbd_mapping[locality],
                 )
                 my_deck.add_note(my_note)
                 media_files.append(image_path)
 
     # Generate the Anki package
-    output_anki_file = 'suburb_maps.apkg'
+    output_anki_file = "suburb_maps.apkg"
     my_package = genanki.Package(my_deck)
     my_package.media_files = media_files  # Add media files
     my_package.write_to_file(output_anki_file)
 
     print(f"Anki deck created and saved as {output_anki_file}")
 
+
 def get_suburb_to_due_order_mapping() -> dict[str, int]:
     """
     I have found that the default order that cards are introduced is
@@ -85,23 +87,39 @@ def get_suburb_to_due_order_mapping() -> dict[str, int]:
     """
     suburbs_df = get_localities_df(DOC_KML)
 
-    brisbane_city_point = suburbs_df[suburbs_df['locality'] == 'Brisbane City']['geometry'].to_crs(epsg=3857).centroid
+    brisbane_city_point = (
+        suburbs_df[suburbs_df["locality"] == "Brisbane City"]["geometry"]
+        .to_crs(epsg=3857)
+        .centroid
+    )
 
     # Reproject all geometries to the same CRS for distance calculations
-    suburbs_df['geometry'] = suburbs_df['geometry'].to_crs(epsg=3857)
+    suburbs_df["geometry"] = suburbs_df["geometry"].to_crs(epsg=3857)
 
     # Calculate the distance of each suburb's centroid to Brisbane City's centroid
-    suburbs_df['distance_to_cbd'] = suburbs_df['geometry'].centroid.apply(lambda x: x.distance(brisbane_city_point))
+    suburbs_df["distance_to_cbd"] = suburbs_df["geometry"].centroid.apply(
+        lambda x: x.distance(brisbane_city_point)
+    )
 
     # Sort the suburbs by distance to the CBD
-    sorted_suburbs = suburbs_df.sort_values(by='distance_to_cbd')
+    sorted_suburbs = suburbs_df.sort_values(by="distance_to_cbd")
 
     # Create a mapping of suburb names to their order in the sorted list
-    suburb_to_order_mapping = { (lga, locality): order for order, (lga, locality) in enumerate(zip(sorted_suburbs['lga'], sorted_suburbs['locality'])) }
+    suburb_to_order_mapping = {
+        (lga, locality): order
+        for order, (lga, locality) in enumerate(
+            zip(sorted_suburbs["lga"], sorted_suburbs["locality"])
+        )
+    }
 
     # Only keep the suburbs in the LGA's we care about
-    filtered_mapping = {locality: order for (lga, locality), order in suburb_to_order_mapping.items() if lga in LOCAL_GOVERNMENT_AREAS_WE_CARE_ABOUT}
+    filtered_mapping = {
+        locality: order
+        for (lga, locality), order in suburb_to_order_mapping.items()
+        if lga in LOCAL_GOVERNMENT_AREAS_WE_CARE_ABOUT
+    }
     return filtered_mapping
 
+
 if __name__ == "__main__":
-    create_anki_deck()
+    create_anki_deck()

brisbane_suburbs_anki_cards/generate_images.py

@@ -7,32 +7,35 @@
 from brisbane_suburbs_anki_cards.process_kml import get_localities_df
 from brisbane_suburbs_anki_cards.constants import LOCAL_GOVERNMENT_AREAS_WE_CARE_ABOUT
 
-ZOOM_OUT_FACTOR = 0.8 # 80%
-TILE_SET: Literal['google', 'openstreetmap'] = 'openstreetmap'
+ZOOM_OUT_FACTOR = 0.8  # 80%
+TILE_SET: Literal["google", "openstreetmap"] = "openstreetmap"
+
 
 def get_output_path(lga: str, locality: str) -> str:
-    save_directory = os.path.join('output', lga)
+    save_directory = os.path.join("output", lga)
     if not os.path.exists(save_directory):
         os.makedirs(save_directory)
 
-    save_path = os.path.join(save_directory, f'{locality}.jpg')
+    save_path = os.path.join(save_directory, f"{locality}.jpg")
     return save_path
 
+
 def get_zoom_level_from_distance(distance_meters: float) -> int:
     # Earth's circumference in meters (at the equator)
     earth_circumference_meters = 40075016.686
 
     # Loop through zoom levels from 0 to 21 (Google Maps max zoom)
     for zoom in range(0, 22):
         # Calculate tile width at this zoom level in meters
-        tile_width_meters = earth_circumference_meters / (2 ** zoom)
+        tile_width_meters = earth_circumference_meters / (2**zoom)
 
         # If the distance is smaller than the tile width, this zoom level is good
         if tile_width_meters < distance_meters:
             return zoom
 
     return 0  # Default to zoom level 0 if no match found
 
+
 def create_and_save_images() -> None:
     suburb_df = get_localities_df(DOC_KML)
 
@@ -47,15 +50,17 @@ def create_and_save_images() -> None:
         selected_suburb = selected_suburb.to_crs(epsg=3857)
 
         # Plot the selected suburb with a solid fill color
-        ax = selected_suburb.plot(figsize=(5, 5), alpha=1.0, edgecolor='k', color='white')
+        ax = selected_suburb.plot(
+            figsize=(5, 5), alpha=1.0, edgecolor="k", color="white"
+        )
 
         # Get current bounds and expand them to zoom out
         minx, miny, maxx, maxy = selected_suburb.total_bounds
-        
+
         # Calculate expanded bounds
         x_range = maxx - minx
         y_range = maxy - miny
-        
+
         # Expand the bounds by the zoom-out factor
         minx -= x_range * ZOOM_OUT_FACTOR
         maxx += x_range * ZOOM_OUT_FACTOR
@@ -74,20 +79,27 @@ def create_and_save_images() -> None:
         zoom_level = get_zoom_level_from_distance(maxx - minx)
 
         # Zoom out by reducing the zoom level
-        if TILE_SET == 'google':
+        if TILE_SET == "google":
             google_maps_tile_url = "http://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}"
             ctx.add_basemap(ax, source=google_maps_tile_url, zoom=zoom_level)
-        elif TILE_SET == 'openstreetmap':
-            ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, attribution='')
+        elif TILE_SET == "openstreetmap":
+            ctx.add_basemap(
+                ax, source=ctx.providers.OpenStreetMap.Mapnik, attribution=""
+            )
 
         # Hide axes
         ax.set_axis_off()
 
         plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
 
         # Save the plot to an image file
-        plt.savefig(get_output_path(lga, locality), bbox_inches='tight', pad_inches=0, dpi=150, transparent=False)
+        plt.savefig(
+            get_output_path(lga, locality),
+            bbox_inches="tight",
+            pad_inches=0,
+            dpi=150,
+            transparent=False,
+        )
         plt.close()
 
         print(f"Map for {locality}, {lga} created!")
-

brisbane_suburbs_anki_cards/process_kml.py

@@ -3,38 +3,42 @@
 import geopandas as gpd
 from brisbane_suburbs_anki_cards import DOC_KML
 
+
 def get_localities_df(filename: str) -> pd.DataFrame:
     suburb_df = gpd.read_file(filename)
-    
+
     # Apply the function to extract locality and LGA
-    suburb_df[['locality', 'lga']] = suburb_df['Description'].apply(lambda x: pd.Series(extract_locality_lga(x)))
-    suburb_df = suburb_df.drop(columns=['Name', 'Description'])
+    suburb_df[["locality", "lga"]] = suburb_df["Description"].apply(
+        lambda x: pd.Series(extract_locality_lga(x))
+    )
+    suburb_df = suburb_df.drop(columns=["Name", "Description"])
     return suburb_df
 
 
 # Function to extract locality and lga
 def extract_locality_lga(html_description):
-    soup = BeautifulSoup(html_description, 'html.parser')
-    
+    soup = BeautifulSoup(html_description, "html.parser")
+
     # Find all rows in the table
-    rows = soup.find_all('tr')
-    
+    rows = soup.find_all("tr")
+
     # Initialize variables
     locality = None
     lga = None
-    
+
     # Iterate over rows to find locality and lga
     for row in rows:
-        cells = row.find_all('td')
+        cells = row.find_all("td")
         if len(cells) == 2:
             header = cells[0].get_text(strip=True).lower()
             value = cells[1].get_text(strip=True)
-            if header == 'locality':
+            if header == "locality":
                 locality = value
-            elif header == 'lga':
+            elif header == "lga":
                 lga = value
-    
+
     return locality, lga
 
+
 if __name__ == "__main__":
-    get_localities_df(DOC_KML)
+    get_localities_df(DOC_KML)

pyproject.toml

@@ -22,5 +22,10 @@ dependencies = [
     "geopandas==1.0.1"
 ]
 
+[project.optional-dependencies]
+dev = [
+  "ruff==0.6.9"
+]
+
 [project.scripts]
 create-deck = "brisbane_suburbs_anki_cards.entry:main"

setup.py

@@ -1,5 +1,5 @@
 from setuptools import setup, find_packages
 
 setup(
-    packages=find_packages(exclude=['output']),
-)
+    packages=find_packages(exclude=["output"]),
+)