feat: Add geospatial-python-urban-analysis-with-postgis project #107
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BrenQ
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WalkthroughThis pull request introduces extensive new functionality and updates across the geospatial urban analysis project. It adds a Docker-based Python environment, comprehensive documentation, updated configuration, and new data files with proper CRS and encoding metadata. New Jupyter notebooks, ETL pipelines, visualization functions, database connectors, and ML clustering algorithms have been implemented to support advanced spatial analysis tasks, including bus stop analysis and optimal stop selection. Several new functions are added to extract, transform, and load various geospatial datasets from external APIs and PostGIS. Changes
Sequence Diagram(s)sequenceDiagram
participant Pipeline as OptimalStopPipeline
participant BusStops as "fetch_bus_stops()"
participant PopDensity as "load_population_density()"
participant HighDensity as "extract_high_density_points()"
participant Optimal as "find_optimal_stops()"
participant Plot as "plot_optimal_stops()"
Pipeline->>BusStops: Fetch bus stop data
Pipeline->>PopDensity: Load population density raster
PopDensity-->>Pipeline: Return population data, transform, CRS
Pipeline->>HighDensity: Extract high-density points
Pipeline->>Optimal: Determine optimal bus stops
Pipeline->>Plot: Visualize optimal bus stops
sequenceDiagram
participant Pipeline as BusStopAnalysisPipeline
participant DBBusStops as "fetch_bus_stops() from DB"
participant Districts as "fetch_districts()"
participant Reproj as "reproject_data()"
participant SpatialJoin as "spatial_join()"
participant Count as "count_stops_per_district()"
participant Plot as "plot_bus_stops()"
Pipeline->>DBBusStops: Fetch bus stop data (with optional line filter)
Pipeline->>Districts: Retrieve district data
Pipeline->>Reproj: Reproject bus stops & district geometries
Pipeline->>SpatialJoin: Join bus stops with districts
Pipeline->>Count: Count bus stops per district
Pipeline->>Plot: Visualize bus stop distribution
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Actionable comments posted: 27
🧹 Nitpick comments (20)
examples/geospatial-python-urban-analysis-with-postgis/data/census/caba/cabaxrdatos.qpj (1)
1-2: Remove the trailing empty line.For consistency with other files in the project, consider removing the trailing empty line.
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/fix_shapefile.py (1)
4-9: Complete the docstring with proper type information and return value.The docstring has
_type_placeholders and is missing the return value description.def load_and_fix_shapefile(filepath): - """Loads a Superfile, detects invalid geometries and fixes them. + """Loads a shapefile, detects invalid geometries and fixes them. Args: - filepath (_type_): Filepath to the shapefile. + filepath (str): Filepath to the shapefile. + + Returns: + gpd.GeoDataFrame | None: The loaded and fixed GeoDataFrame, or None if an error occurs. """examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/shapefile_to_raster.py (1)
8-8: Remove extra blank lines.There are unnecessary blank lines at lines 8 and 21.
Also applies to: 21-21
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py (1)
1-4: Remove unused imports.The following imports are not used in the code:
numpyshapefileimport rasterio -import numpy as np -import shapefile🧰 Tools
🪛 Ruff (0.8.2)
2-2:
numpyimported but unusedRemove unused import:
numpy(F401)
3-3:
shapefileimported but unusedRemove unused import:
shapefile(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/database/queries.py (1)
1-7: Consider improving column names and adding type hints.The SQL query uses non-descriptive column names ("L1", "L2"). Consider using more meaningful names that reflect their purpose. Also, add a return type hint to improve code maintainability.
-def get_bus_stops_query(): +def get_bus_stops_query() -> str: """SQL query to fetch bus stops for a specific bus line.""" return """ - SELECT * FROM bus_stop - WHERE %(line_number)s IS NULL OR "L1" = %(line_number)s - ORDER BY "L2" + SELECT * FROM bus_stop + WHERE %(line_number)s IS NULL OR line_number = %(line_number)s + ORDER BY stop_name """examples/geospatial-python-urban-analysis-with-postgis/config.py (1)
2-2: Consider using absolute paths or path resolution.Relative paths can be problematic if the working directory changes. Consider using absolute paths or path resolution.
-POPULATION_DENSITY_RASTER = "data/population_density.tif" +POPULATION_DENSITY_RASTER = os.path.join(os.path.dirname(__file__), "data", "population_density.tif")examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py (1)
1-4: Remove unused import.The
geopandasimport is not used in the code.Apply this diff to remove the unused import:
import matplotlib.pyplot as plt import contextily as ctx -import geopandas as gpd🧰 Tools
🪛 Ruff (0.8.2)
3-3:
geopandasimported but unusedRemove unused import:
geopandas(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/optimal_stop_pipeline.py (1)
24-26: Add exit code handling for main execution.The script should handle exit codes appropriately when run as main.
Consider enhancing the main execution:
if __name__ == "__main__": - run_optimal_stop_pipeline() + try: + run_optimal_stop_pipeline() + exit(0) + except Exception: + exit(1)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py (1)
25-27: Consider using command-line arguments for better usability.Instead of hardcoding the line number, consider using argparse for command-line arguments.
+import argparse if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Analyze bus stop distribution.') + parser.add_argument('--line', type=str, help='Bus line number to analyze') + args = parser.parse_args() + + run_bus_stop_analysis(args.line) - run_bus_stop_analysis("68") - run_bus_stop_analysis()examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py (1)
1-7: Remove unused import and add type hints.The
shapesimport from rasterio.features is unused. Also, consider adding type hints for better code documentation.import numpy as np import geopandas as gpd from sklearn.cluster import DBSCAN from shapely.geometry import Point -from rasterio.features import shapes🧰 Tools
🪛 Ruff (0.8.2)
6-6:
rasterio.features.shapesimported but unusedRemove unused import:
rasterio.features.shapes(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/visualization/bus_stops_map.py (1)
1-4: Remove unused import and add type hints.The
geopandasimport is unused as it's only needed for type hints.import matplotlib.pyplot as plt import contextily as ctx -import geopandas as gpd +from typing import TYPE_CHECKING +if TYPE_CHECKING: + import geopandas as gpd🧰 Tools
🪛 Ruff (0.8.2)
3-3:
geopandasimported but unusedRemove unused import:
geopandas(F401)
examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py (1)
1-5: Remove unused import and use type hints.The
pandasimport is unused.import os import geopandas as gpd import matplotlib.pyplot as plt -import pandas as pd from pathlib import Path🧰 Tools
🪛 Ruff (0.8.2)
4-4:
pandasimported but unusedRemove unused import:
pandas(F401)
examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject1.ipynb (2)
31-31: Add error handling for package installation.The pip install command should include error handling to gracefully handle installation failures.
-pip install geopandas -q +try: + !pip install geopandas -q +except Exception as e: + print(f"Failed to install geopandas: {e}") + raise
119-124: Validate input coordinates and add data validation.The city data dictionary contains hardcoded coordinates without validation. Consider adding validation to ensure coordinates are within valid ranges.
+def validate_coordinates(lat, lon): + if not (-90 <= lat <= 90) or not (-180 <= lon <= 180): + raise ValueError(f"Invalid coordinates: lat={lat}, lon={lon}") data = { "Name": ["New York City", "São Paulo", "Tokyo", "Lagos", "Sydney"], "Population": [8419600, 12325232, 13929286, 15000000, 5312163], # Approximate populations "Latitude": [40.7128, -23.5505, 35.6895, 6.5244, -33.8688], "Longitude": [-74.0060, -46.6333, 139.6917, 3.3792, 151.2093] } +# Validate coordinates +for lat, lon in zip(data["Latitude"], data["Longitude"]): + validate_coordinates(lat, lon)examples/geospatial-python-urban-analysis-with-postgis/Dockerfile (2)
12-15: Optimize pip installation and add package verification.Add package verification and optimize pip installation for better security and build performance.
# Install Python dependencies COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt +RUN pip install --no-cache-dir pip-tools && \ + pip-compile requirements.txt && \ + pip install --no-cache-dir -r requirements.txt && \ + pip check
16-21: Add proper error handling and logging configuration.Add error handling and configure logging for better observability.
# Copy the script COPY transport.py . +COPY logging.conf . # Run the script when the container starts -CMD ["python", "transport.py"] +CMD ["python", "-u", "transport.py"]examples/geospatial-python-urban-analysis-with-postgis/docker-compose.yml (2)
3-16: PostGIS Service Configuration Looks Good
The configuration for the PostGIS service is clear—with an appropriate image, container name, restart policy, environment variables, port mapping, and volume mounting. As this is a sample/demo setup, consider using more secure or parameterized credentials in production.
29-40: Commented-Out Python Application Block
The commented-outpython_appsection suggests possible future integration with a Python service. If this isn’t intended for immediate use, consider adding a TODO or removing it until needed to reduce noise.examples/geospatial-python-urban-analysis-with-postgis/README.md (1)
99-107: Markdown Formatting – Heading Spacing Issues
Static analysis indicates that some headings (e.g., those around the "Pipelines Overview" and "Running Pipelines" sections) are not surrounded by the recommended single blank line. To adhere to Markdownlint guidelines (MD022) and improve readability, please insert a blank line before and/or after these headings.🧰 Tools
🪛 markdownlint-cli2 (0.17.2)
99-99: Multiple consecutive blank lines
Expected: 1; Actual: 2(MD012, no-multiple-blanks)
100-100: Headings should be surrounded by blank lines
Expected: 1; Actual: 0; Below(MD022, blanks-around-headings)
107-107: Headings should be surrounded by blank lines
Expected: 1; Actual: 0; Below(MD022, blanks-around-headings)
🪛 GitHub Check: Markdownlint / Markdown Lint
[failure] 99-99:
Multiple consecutive blank lines [Expected: 1; Actual: 2]
[failure] 100-100:
Headings should be surrounded by blank lines [Expected: 1; Actual: 0; Below] [Context: "### 📌 Pipelines Overview"]
[failure] 107-107:
Headings should be surrounded by blank lines [Expected: 1; Actual: 0; Below] [Context: "### ⚙️ Running Pipelines"]examples/geospatial-python-urban-analysis-with-postgis/data/nyc/SchoolPoints_APS_2024_08_28 (1)/SchoolPoints_APS_2024_08_28.shp.xml (1)
1-2: XML Metadata Structure for Shapefile
The metadata generated for the shapefile is comprehensive and adheres to Esri’s standards. For ease of manual review or debugging, consider pretty-printing (formatting with proper indentation) the XML content if manual edits are anticipated.
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📒 Files selected for processing (35)
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✅ Files skipped from review due to trivial changes (10)
- examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.cpg
- examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20241220/geo_export_a7bb075a-41dc-445f-8244-8430e90a8dde.cpg
- examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.VERSION.txt
- examples/geospatial-python-urban-analysis-with-postgis/data/nyc/SchoolPoints_APS_2024_08_28 (1)/SchoolPoints_APS_2024_08_28.cpg
- examples/geospatial-python-urban-analysis-with-postgis/data/nyc/nyc_subway_entrances/nyc_subway_entrances.prj
- examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.prj
- examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20250127/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.cpg
- examples/geospatial-python-urban-analysis-with-postgis/data/census/caba/cabaxrdatos.prj
- examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20241220/geo_export_a7bb075a-41dc-445f-8244-8430e90a8dde.prj
- examples/geospatial-python-urban-analysis-with-postgis/data/Pedestrian Zone Shapefile (Tabular)_20250127/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.prj
🧰 Additional context used
🪛 Ruff (0.8.2)
examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py
3-3: geopandas imported but unused
Remove unused import: geopandas
(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/visualization/bus_stops_map.py
3-3: geopandas imported but unused
Remove unused import: geopandas
(F401)
examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py
4-4: pandas imported but unused
Remove unused import: pandas
(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py
6-6: rasterio.features.shapes imported but unused
Remove unused import: rasterio.features.shapes
(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py
2-2: numpy imported but unused
Remove unused import: numpy
(F401)
3-3: shapefile imported but unused
Remove unused import: shapefile
(F401)
🪛 markdownlint-cli2 (0.17.2)
examples/geospatial-python-urban-analysis-with-postgis/README.md
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examples/geospatial-python-urban-analysis-with-postgis/README.md
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🔇 Additional comments (12)
examples/geospatial-python-urban-analysis-with-postgis/data/census/caba/cabaxrdatos.qpj (1)
1-1:✅ Verification successful
LGTM! The CRS definition is appropriate for the region.
The POSGAR 94 / Argentina 3 (EPSG:22183) coordinate reference system is well-suited for geospatial analysis in Buenos Aires, as it's the official CRS for Zone 3 in Argentina.
Please verify the TOWGS84 parameters (currently all zeros) against the official EPSG registry:
🌐 Web query:
What are the correct TOWGS84 parameters for EPSG:22183 (POSGAR 94 / Argentina 3)?💡 Result:
The TOWGS84 parameters for EPSG:22183 (POSGAR 94 / Argentina 3) are all zero values (ΔX=0, ΔY=0, ΔZ=0), indicating no datum transformation is applied between POSGAR 94 and WGS 84[1][3][5][10].
This is explicitly stated in:
- The PROJ4 definition string:
+towgs84=0,0,0,0,0,0,0[1][5]- The OGC WKT representation:
TOWGS84[0,0,0,0,0,0,0][1][7]- Database entries showing zero transformation values[3][10]
The documentation clarifies that POSGAR 94 was originally considered equivalent to WGS 84 at the ±1 meter level accuracy[1][8]. This equivalence explains why no datum shift parameters were required in the transformation definition.
Citations:
- 1: https://epsg.io/22183
- 2: https://www.asprs.org/wp-content/uploads/2018/03/09-17-GD-Argentina.pdf
- 3: https://palewi.re/data/la-county-election-precinct-maps-2016-primary/spatial_ref_sys_all.csv?_facet=datum&spheroid=WGS+84&_facet=unit&datum=Posiciones_Geodesicas_Argentinas_1994&_sort=is_geographic&_size=max
- 4: http://rsdi.regione.basilicata.it/geoserver/srsHelp.do
- 5: https://github.com/terrestris/Proj4JsDefGenerator/blob/master/epsg
- 6: https://spatialreference.org/ref/epsg/22184/
- 7: https://epsg.io/22183.geoserver?download=1
- 8: https://epsg.io/5345
- 9: https://spatialreference.org/ref/epsg/22183/
- 10: http://timezones.datasette.io/timezones/spatial_ref_sys_all?auth_name=epsg&_facet_size=max&_sort_desc=is_geographic&is_geographic=0&prime_meridian=Greenwich&axis_1_name=Northing&spheroid=WGS+84&_facet=ref_sys_name&_facet=projection
LGTM! The CRS definition for POSGAR 94 / Argentina 3 (EPSG:22183) is correctly implemented.
- The coordinate reference system is appropriate for geospatial analysis in Argentina.
- Verified that the TOWGS84 parameters are correctly all zeros, consistent with the official EPSG registry.
- Optional: Remove the trailing empty line for file cleanliness.
examples/geospatial-python-urban-analysis-with-postgis/config.py (1)
3-4: LGTM!The DISTRICTS_URL points to a trusted source, and the EPSG_TARGET is correctly set to the standard Web Mercator projection (EPSG:3857) commonly used for web mapping.
examples/geospatial-python-urban-analysis-with-postgis/requirements.txt (1)
1-10:❓ Verification inconclusive
Verify package versions for compatibility and security.
Please verify that the specified package versions are compatible with each other and check for any known security vulnerabilities.
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1-2: Version Declaration is Clear and Concise
The Docker Compose file correctly declares the version as "3.9".
17-28: pgAdmin Service is Properly Configured
The pgAdmin service is set up appropriately with its image, container name, environment credentials, and dependency on the PostGIS service. Verify that the default credentials (admin@admin.com/admin) are acceptable for your use case or update them before deploying to a production environment.
41-43: Volume Declaration is Correct
The volumepgdatais declared at the end ensuring persistent storage for the PostGIS data.examples/geospatial-python-urban-analysis-with-postgis/README.md (6)
1-8: Comprehensive Project Overview Provided
The title and overview sections clearly communicate the project's purpose and the data formats it supports. The introduction is engaging and informative.
9-16: Key Features Section is Well-Outlined
This section effectively highlights the main functionalities, including ETL pipelines, geospatial machine learning, and interactive mapping.
19-30: Requirements and Python Dependencies are Clearly Documented
The system and Python dependencies sections provide straightforward installation instructions. The inclusion of Docker prerequisites is appropriate given the PostGIS dependency.
54-62: Setup & Installation Instructions are Detailed
The sections on cloning the repository, setting up a virtual environment, and installing dependencies are clear and easy to follow.
70-78: Docker and Notebook Usage Instructions are Clear
Instructions for starting the PostgreSQL with PostGIS service using Docker and launching Jupyter Notebooks are concise and informative.
107-113: Project Structure Section is Informative
The detailed listing of the project directories and files is well organized. Verify that the directory names and descriptions remain up-to-date with the evolving project structure.🧰 Tools
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107-107: Headings should be surrounded by blank lines
Expected: 1; Actual: 0; Below(MD022, blanks-around-headings)
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[failure] 107-107:
Headings should be surrounded by blank lines [Expected: 1; Actual: 0; Below] [Context: "### ⚙️ Running Pipelines"]
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Actionable comments posted: 10
♻️ Duplicate comments (3)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py (1)
8-35:⚠️ Potential issueParameterize value column and fix docstring.
The docstring mentions a
value_columnparameter, but it's not included in the function signature. Also, the "TOT_POB" column is hardcoded.-def rasterize_shapefile(gdf, output_path, resolution = 100): +def rasterize_shapefile(gdf, output_path, value_column="TOT_POB", resolution=100): """ Converts a GeoDataFrame into a raster (.tif) using the specified value column. Args: gdf (gpd.GeoDataFrame): The input GeoDataFrame to rasterize. output_path (str): Path where the output raster will be saved. value_column (str, optional): Column to use for raster values. Defaults to "TOT_POB". resolution (int, optional): Spatial resolution in map units. Defaults to 100. Returns: None """ try: xmin, ymin, xmax, ymax = gdf.total_bounds width = int((xmax - xmin) / resolution) height = int((ymax - ymin) / resolution) transform = rasterio.transform.from_bounds(xmin, ymin, xmax, ymax, width, height) raster_data = rasterize( - [(mapping(geom), 1) for geom, value in zip(gdf.geometry, gdf["TOT_POB"])], + [(mapping(geom), 1) for geom, value in zip(gdf.geometry, gdf[value_column])], out_shape=(height, width), transform=transform, fill=0, dtype="float32", )examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py (2)
54-54:⚠️ Potential issueFix incorrect population filter.
The population filter is incorrect. It's filtering for countries with less than 100M people instead of more than 100M.
-large_countries = world[world["POP_EST"] < 100_000_000] +large_countries = world[world["POP_EST"] > 100_000_000]
74-82: 🛠️ Refactor suggestionAdd error handling for file operations.
File operations should include error handling and use the script directory for output paths.
+output_dir = script_dir / "output" +output_dir.mkdir(exist_ok=True) -plt.savefig("world_map.png", dpi=300) +try: + plt.savefig(output_dir / "world_map.png", dpi=300) +except Exception as e: + print(f"❌ Error saving map: {e}") -world.to_file("world.geojson", driver="GeoJSON") +try: + world.to_file(output_dir / "world.geojson", driver="GeoJSON") +except Exception as e: + print(f"❌ Error saving GeoJSON: {e}") -world_geojson = gpd.read_file("world.geojson") +try: + world_geojson = gpd.read_file(output_dir / "world.geojson") +except Exception as e: + print(f"❌ Error reading GeoJSON: {e}")
🧹 Nitpick comments (13)
examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/shapefile_to_raster.py (2)
27-27: Fix indentation.Remove the extra blank line and fix indentation.
gdf = load_and_fix_shapefile(shapefile_path) - - + if gdf is not None:
28-32: Simplify nested if statements and improve error handling.The nested if statements can be simplified using a chain of operations. Also, add error messages to help users understand why the pipeline failed.
- if gdf is not None: - gdf = reproject_shapefile(gdf) - - if gdf is not None: - rasterize_shapefile(gdf, output_raster) + if gdf is None: + print(f"Failed to load and fix shapefile: {shapefile_path}") + return + + gdf = reproject_shapefile(gdf) + if gdf is None: + print(f"Failed to reproject shapefile: {shapefile_path}") + return + + rasterize_shapefile(gdf, output_raster)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py (1)
1-4: Remove unused imports.The following imports are not used in the code:
numpyshapefileimport rasterio -import numpy as np -import shapefile🧰 Tools
🪛 Ruff (0.8.2)
2-2:
numpyimported but unusedRemove unused import:
numpy(F401)
3-3:
shapefileimported but unusedRemove unused import:
shapefile(F401)
examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject2.ipynb (2)
44-49: Consider using a configuration file for city data.The hardcoded city data dictionary should be moved to a separate configuration file for better maintainability and reusability.
Create a
config.pyfile with:CITY_DATA = { "Name": ["New York City", "São Paulo", "Tokyo", "Lagos", "Sydney"], "Population": [8419600, 12325232, 13929286, 15000000, 5312163], "Latitude": [40.7128, -23.5505, 35.6895, 6.5244, -33.8688], "Longitude": [-74.0060, -46.6333, 139.6917, 3.3792, 151.2093] }
1029-1042: Add logging and progress tracking for file operations.The file download and processing logic should include logging and progress tracking.
Add logging and progress tracking:
+import logging +from tqdm import tqdm +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) local_path = Path("internet-speeds.parquet") if local_path.exists(): + logger.info("Loading existing Parquet file...") gdf = gpd.read_parquet(local_path) else: + logger.info("Downloading and processing data...") columns = ["avg_d_kbps", "tile"] df = pd.read_parquet(url, columns=columns) tile_geometries = shapely.from_wkt(df["tile"]) tile_centroids = shapely.centroid(tile_geometries) gdf = gpd.GeoDataFrame(df[["avg_d_kbps"]], geometry=tile_centroids, crs="EPSG:4326") + logger.info("Saving processed data to Parquet file...") gdf.to_parquet(local_path) + logger.info("Data processing complete")examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py (2)
1-5: Consider removing unused pandas import.The
pandasimport is not directly used in the code. However, if you plan to use pandas operations in future enhancements, you can keep it. Otherwise, consider removing it for cleaner imports.-import pandas as pd🧰 Tools
🪛 Ruff (0.8.2)
4-4:
pandasimported but unusedRemove unused import:
pandas(F401)
84-84: Update success message with actual file paths.The success message should reflect the actual output paths used to save the files.
-print("✅ All done: Map displayed, data saved in 'world.geojson' and 'world_map.png'") +print(f"✅ All done: Map displayed, data saved in '{output_dir/'world.geojson'}' and '{output_dir/'world_map.png'}'")examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py (1)
64-65: Check.intersects()usage when filtering bus stops.Using
.intersects()might remove stops that barely touch or overlap the cluster points. If you intend to remove only stops within a certain buffer distance, consider.distance()checks or other geometric operations.examples/geospatial-python-urban-analysis-with-postgis/config.py (3)
8-8: Remove debug print statement.The print statement appears to be used for debugging and should be removed before merging.
-print(POPULATION_DENSITY_RASTER)
10-11: Consider moving URLs to environment variables.External API URLs should be configurable through environment variables to support different environments (development, staging, production) and to allow for URL changes without code modifications.
-BUS_STOPS_URL = "https://cdn.buenosaires.gob.ar/datosabiertos/datasets/transporte-y-obras-publicas/colectivos-paradas/paradas-de-colectivo.geojson" -DISTRICTS_URL = "https://cdn.buenosaires.gob.ar/datosabiertos/datasets/ministerio-de-educacion/comunas/comunas.geojson" +BUS_STOPS_URL = os.getenv("BUS_STOPS_URL", "https://cdn.buenosaires.gob.ar/datosabiertos/datasets/transporte-y-obras-publicas/colectivos-paradas/paradas-de-colectivo.geojson") +DISTRICTS_URL = os.getenv("DISTRICTS_URL", "https://cdn.buenosaires.gob.ar/datosabiertos/datasets/ministerio-de-educacion/comunas/comunas.geojson")
13-13: Add EPSG code validation.Consider adding validation for the EPSG code to ensure it's a valid coordinate reference system.
+from pyproj import CRS + # Standard coordinate reference system (CRS) -EPSG_TARGET = 3857 +def validate_epsg(code: int) -> int: + try: + CRS.from_epsg(code) + return code + except Exception as e: + raise ValueError(f"Invalid EPSG code {code}: {str(e)}") + +EPSG_TARGET = validate_epsg(3857)examples/geospatial-python-urban-analysis-with-postgis/src/database/connection.py (1)
9-26: Consider enhancing connection pooling and retry logic.While the basic error handling is good, for a production environment, consider adding:
- Connection pooling configuration
- Retry logic for transient failures
- Connection timeout settings
def get_db_connection() -> Engine: try: return create_engine( - f"postgresql://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}") + f"postgresql://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}", + pool_size=5, + max_overflow=10, + pool_timeout=30, + pool_recycle=1800, + connect_args={ + "connect_timeout": 10, + "application_name": "geospatial-python-urban-analysis" + } + )examples/geospatial-python-urban-analysis-with-postgis/src/visualization/bus_stops_map.py (1)
33-39: Consider optimizing text placement to avoid overlaps.The current text placement might lead to overlapping labels in dense areas. Consider using a label placement algorithm.
- ax.text(centroid.x, centroid.y, f"Comuna {comuna_name}\n{stop_count} stops", - fontsize=10, color="black", ha="center", bbox=dict(facecolor="white", alpha=0.5)) + from adjustText import adjust_text + texts = [] + texts.append(ax.text(centroid.x, centroid.y, f"Comuna {comuna_name}\n{stop_count} stops", + fontsize=10, color="black", ha="center", bbox=dict(facecolor="white", alpha=0.5))) + adjust_text(texts, arrowprops=dict(arrowstyle="->", color="black", lw=0.5))
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examples/geospatial-python-urban-analysis-with-postgis/config.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/data/nyc/nyc_school_points/SchoolPoints_APS_2024_08_28.cpg(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/data/nyc/nyc_school_points/SchoolPoints_APS_2024_08_28.prj(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/data/nyc/nyc_school_points/SchoolPoints_APS_2024_08_28.shp.xml(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/data/pedestrian_zone_shapefile/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.cpg(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/data/pedestrian_zone_shapefile/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.prj(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject2.ipynb(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/database/connection.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_bus_stop.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_district.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/postgis/fetch_bus_stop.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/rasterio/population.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/reproject.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/optimal_stop_pipeline.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/shapefile_to_raster.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/visualization/bus_stops_map.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py(1 hunks)
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- examples/geospatial-python-urban-analysis-with-postgis/data/pedestrian_zone_shapefile/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.cpg
- examples/geospatial-python-urban-analysis-with-postgis/data/nyc/nyc_school_points/SchoolPoints_APS_2024_08_28.cpg
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🚧 Files skipped from review as they are similar to previous changes (4)
- examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py
- examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/optimal_stop_pipeline.py
- examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
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examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_district.py
9-9: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py
4-4: pandas imported but unused
Remove unused import: pandas
(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py
2-2: numpy imported but unused
Remove unused import: numpy
(F401)
3-3: shapefile imported but unused
Remove unused import: shapefile
(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py
6-6: rasterio.features.shapes imported but unused
Remove unused import: rasterio.features.shapes
(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py
3-3: geopandas imported but unused
Remove unused import: geopandas
(F401)
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🔇 Additional comments (7)
examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject2.ipynb (1)
961-962: Move URL to configuration and add error handling for downloads.The Parquet file URL should be moved to a configuration file and include proper error handling for downloads.
This issue was already identified in a previous review.
examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py (1)
41-41: Review buffer distance calculation.The buffer distance of 5 units might not represent 500km as intended in the title. The buffer distance depends on the CRS (Coordinate Reference System) of the data. Consider using a projected CRS and explicit distance in kilometers.
-argentina_buffer.geometry = argentina.geometry.buffer(5) # Depends on the projection +# Convert to a projected CRS (e.g., Equal Earth projection) +argentina_projected = argentina.to_crs('EPSG:8857') +# Create buffer (500km = 500000m) +buffer_projected = argentina_projected.geometry.buffer(500000) +# Convert back to original CRS +argentina_buffer.geometry = buffer_projected.to_crs(argentina.crs)examples/geospatial-python-urban-analysis-with-postgis/data/pedestrian_zone_shapefile/geo_export_8aa884d0-7056-4b1e-94ff-62eb7b00d65c.prj (1)
1-1: Good job specifying the CRS.Declaring EPSG:4326 ensures consistent coordinate handling across your geospatial files.
examples/geospatial-python-urban-analysis-with-postgis/src/database/connection.py (1)
1-8: LGTM! Clean imports and environment setup.The imports are well-organized and the environment variables are loaded correctly using
python-dotenv.examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py (1)
5-35: LGTM! Well-structured visualization function.The function has proper error handling, clear documentation, and appropriate visualization settings. The use of try-except ensures robust error handling, and the visualization parameters are well-chosen for clarity.
examples/geospatial-python-urban-analysis-with-postgis/src/visualization/bus_stops_map.py (1)
1-4: LGTM! Required imports are present.All necessary imports for visualization are included.
examples/geospatial-python-urban-analysis-with-postgis/data/nyc/nyc_school_points/SchoolPoints_APS_2024_08_28.shp.xml (1)
1-2: LGTM! Well-structured metadata file.The metadata XML is well-formed and contains all necessary information:
- Complete CRS information (EPSG:3857)
- Detailed lineage information
- Comprehensive attribute definitions
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🧹 Nitpick comments (2)
examples/geospatial-python-urban-analysis-with-postgis/Dockerfile (2)
20-23: Configuring Non-Root User for Runtime Stage
Copying/etc/passwdfrom the builder stage to inherit theappuseris effective for ensuring the non-root user exists. However, this approach might bring in extra entries that are unnecessary in the runtime environment. An alternative would be to re-create the user directly in the final stage. For example:- COPY --from=builder /etc/passwd /etc/passwd - USER appuser + RUN groupadd -r appuser && useradd -r -g appuser appuser + USER appuser
26-27: Optional: Documenting the Exposed Port
Although not strictly required for functionality, it is good practice to add anEXPOSE 8000directive to document that the container is intended to use port 8000—especially since your healthcheck targets that port.
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examples/geospatial-python-urban-analysis-with-postgis/Dockerfile (9)
1-3: Base Image & Multi-Stage Build Setup
The Dockerfile correctly starts with the slim Python 3.12 image in a multi-stage build setup—this ensures separation of build and runtime concerns.
4-6: Working Directory Configuration
Setting the working directory to/appis a standard and clear practice.
7-11: Installing Build Dependencies
The use ofapt-get update && apt-get install -y libgdal-devfollowed by removal of the apt lists helps keep the builder image lean. This is an effective approach for handling build dependencies.
12-14: Creating a Non-Root User in Builder
Creatingappuserusinggroupaddanduseraddin the builder stage is a solid security practice.
15-16: Starting the Runtime Stage
Reusing thepython:3.12-slimimage for the final stage is standard for multi-stage builds. This helps maintain a minimal runtime image.
24-26: Healthcheck Implementation & Endpoint Verification
The healthcheck is correctly defined using a curl command that checkshttp://localhost:8000/health. Ensure that your application actively listens on port 8000 and properly serves the/healthendpoint.
27-30: Python Dependencies Installation
Copying therequirements.txtand installing dependencies with the--no-cache-dirflag is a best practice for minimizing image size and avoiding stale cache artifacts.
31-33: Copying Application Script
Copyingtransport.pyinto the working directory is straightforward. Remember to check file permissions if your script requires execution privileges.
34-35: Container Startup Command
The CMD instruction correctly specifies that the container should runtransport.pyusing Python.
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examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/reproject.py (1)
5-7:⚠️ Potential issueAdd error handling for CRS transformations.
The function should handle potential errors during CRS transformation, such as invalid EPSG codes or transformation failures.
def reproject_data(gdf: gpd.GeoDataFrame): """Transform the data to the target CRS.""" - return gdf.to_crs(epsg=EPSG_TARGET) + try: + return gdf.to_crs(epsg=EPSG_TARGET) + except Exception as e: + print(f"Error reprojecting data: {e}") + return None
🧹 Nitpick comments (7)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/rasterio/population.py (1)
18-27: Add validation checks for raster data integrity.Consider adding validation checks to ensure data integrity:
try: with rasterio.open(POPULATION_DENSITY_RASTER) as src: + if src.count < 1: + raise ValueError("Raster file must have at least one band") population_data = src.read(1) transform = src.transform crs = src.crs + if crs is None: + raise ValueError("Raster file must have a defined coordinate reference system") return population_data, transform, crsexamples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py (1)
3-3: Remove unused import.The
EPSG_TARGETimport is not used in this module.Apply this diff to remove the unused import:
-from config import EPSG_TARGET🧰 Tools
🪛 Ruff (0.8.2)
3-3:
config.EPSG_TARGETimported but unusedRemove unused import:
config.EPSG_TARGET(F401)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/reproject.py (1)
9-15: Use proper logging instead of print statements.Replace print statements with Python's logging module for better control over log levels and formats in production.
+import logging + def reproject_shapefile(gdf: gpd.GeoDataFrame): """Transform the data to the target CRS.""" try: return gdf.to_crs(epsg=EPSG_TARGET) except Exception as e: - print(f"Error reprojecting shapefile: {e}") + logging.error(f"Error reprojecting shapefile: {e}") return Noneexamples/geospatial-python-urban-analysis-with-postgis/Dockerfile (1)
15-21: Final Stage Setup and Dependency Copy
- The final stage correctly starts from a slim Python image.
- COPY Command Note: On line 18, you copy the GDAL library from the builder stage. To avoid any potential issues with Hadolint (which flags COPY commands with inline comments), consider moving the inline comment above the COPY instruction. Also, if only a single file is copied, this syntax is acceptable; however, if future changes might include multiple sources, ensure that the destination path ends with a slash.
- Working Directory Suggestion: The final stage does not explicitly set a working directory. Adding a
WORKDIR /appimmediately after line 15 would ensure that subsequent COPY commands (lines 27 and 31) target the desired directory.Proposed diff snippet:
+ WORKDIR /app - COPY --from=builder /usr/lib/libgdal.so /usr/lib/libgdal.so # adjust to include only required files + # Copy only the required GDAL library (moved inline explanation above) + COPY --from=builder /usr/lib/libgdal.so /usr/lib/libgdal.so🧰 Tools
🪛 Hadolint (2.12.0)
[error] 18-18: COPY with more than 2 arguments requires the last argument to end with /
(DL3021)
examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.README.html (1)
1-8: Document Type and HTML Declaration
The document begins with an XHTML 1.0 Transitional DOCTYPE. While this may be intentional for legacy compatibility, consider upgrading to an HTML5 DOCTYPE (<!DOCTYPE html>) for a more modern, simplified HTML structure unless legacy support is required.examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py (2)
54-59: Add memory optimization for large datasets.When processing large datasets, converting all points to a numpy array at once could consume significant memory. Consider processing in batches.
- # Convert high-density points to numpy array - high_density_points = np.array([[p.x, p.y] for p in high_density_gdf.geometry]) - - # Perform clustering - clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(high_density_points) + # Process in batches to optimize memory usage + BATCH_SIZE = 10000 + total_points = len(high_density_gdf) + + if total_points > BATCH_SIZE: + batches = np.array_split(high_density_gdf, np.ceil(total_points/BATCH_SIZE)) + high_density_points = np.vstack([ + [[p.x, p.y] for p in batch.geometry] + for batch in batches + ]) + else: + high_density_points = np.array([[p.x, p.y] for p in high_density_gdf.geometry]) + + # Perform clustering + clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(high_density_points)
70-71: Consider using buffer instead of intersects.Using
intersectsmight be too strict. Consider using a buffer to account for stops that are very close but not exactly intersecting.- # Remove centroids that are too close to existing bus stops - optimal_stops = optimal_stops[~optimal_stops.geometry.intersects(bus_stops.unary_union)] + # Remove centroids that are within a certain distance of existing bus stops + buffer_distance = eps / 2 # Use half the clustering distance as buffer + existing_stops_buffer = bus_stops.unary_union.buffer(buffer_distance) + optimal_stops = optimal_stops[~optimal_stops.geometry.intersects(existing_stops_buffer)]
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examples/geospatial-python-urban-analysis-with-postgis/Dockerfile(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/config.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.README.html(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_bus_stop.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_district.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/postgis/fetch_bus_stop.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/rasterio/population.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/reproject.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/optimal_stop_pipeline.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/shapefile_to_raster.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py(1 hunks)
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- examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_district.py
- examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py
- examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/optimal_stop_pipeline.py
- examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/shapefile_to_raster.py
- examples/geospatial-python-urban-analysis-with-postgis/config.py
- examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/rasterize.py
- examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py
- examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_bus_stop.py
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[error] 18-18: COPY with more than 2 arguments requires the last argument to end with /
(DL3021)
🪛 Ruff (0.8.2)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
3-3: config.EPSG_TARGET imported but unused
Remove unused import: config.EPSG_TARGET
(F401)
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🔇 Additional comments (17)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/rasterio/population.py (2)
1-3: LGTM!The imports are well-organized and all dependencies are correctly imported.
5-17: Well-documented function with proper type hints.The function signature and docstring have been implemented according to the previous review suggestion, with comprehensive documentation and proper type hints.
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py (1)
13-29: LGTM!The function is well-implemented with proper type hints, comprehensive docstring, and error handling.
examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/postgis/fetch_bus_stop.py (3)
1-3: LGTM!The imports are well-organized and include all necessary dependencies for handling geospatial data and database operations.
20-21: LGTM!The input validation is thorough and handles both None and empty string cases with a clear error message.
23-36: LGTM!The database operations are well-implemented with:
- Proper connection management using try-finally
- Correct error handling with exception chaining
- Safe disposal of database engine
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/reproject.py (1)
1-2:✅ Verification successful
Verify geopandas version compatibility.
Please ensure that the project specifies a compatible version of geopandas in the requirements.
Run the following script to check the installed version and latest available version:
🏁 Script executed:
#!/bin/bash # Description: Check geopandas version compatibility. # Check installed version in requirements fd 'requirements.*\.txt$' | xargs rg 'geopandas' # Check PyPI for latest versions curl -s https://pypi.org/pypi/geopandas/json | jq '.info.version'Length of output: 215
Geopandas version compatibility confirmed.
The installed version of geopandas (1.0.1) as specified in the
requirements.txtmatches the latest version available on PyPI (1.0.1). No further changes are required for the imports inexamples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/reproject.py.examples/geospatial-python-urban-analysis-with-postgis/Dockerfile (5)
1-7: Base Image and Builder Stage Initialization
The base image declaration and the working directory setup in the builder stage are clear and appropriate for a multi-stage build.
8-11: Installing Build Dependencies
The installation of build dependencies (i.e.,libgdal-dev) and cleaning up the package lists is performed correctly.
12-14: Creating a Non-root User in the Builder Stage
The creation of a non-root user (appuser) improves security. The commands are clear and follow best practices.
22-29: Healthcheck and Python Dependencies Installation
The addition of a healthcheck command and installing Python dependencies using a no-cache install is good practice for image optimization and container health monitoring.
30-34: Copying the Application Script and Defining the Startup Command
Copying the application script (transport.py) and setting the container’s CMD to run it are straightforward and clear.examples/geospatial-python-urban-analysis-with-postgis/data/countries/ne_10m_admin_0_countries.README.html (3)
13-16: Legacy IE Conditional Block Using Insecure HTTP
Within the IE conditional block (lines 13–16), the script forIE7.jsis loaded via an insecure HTTP URL:<script src="http://ie7-js.googlecode.com/svn/version/2.0(beta3)/IE7.js" type="text/javascript"></script>If legacy support for IE7 is still needed, consider either updating to a secure HTTPS URL (if available) or evaluating whether this block can be removed to improve security.
111-121: Commented Out Legacy Google Translate Block (Optional Review)
There is a commented-out section that includes a Google Translate script loaded over HTTP. Although it is inactive, consider removing this block or updating it to use HTTPS if it is re-enabled in the future.
1-486: General Review of External Resources and Overall Security
The document includes numerous external resources (stylesheets, scripts, images) mostly loaded via HTTPS, which is good. However, be mindful of similar warnings for insecure content in other parts (as noted in previous reviews).examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py (2)
23-31: LGTM! Good practice using buffer for plot limits.The implementation of plot limits with buffer is a good practice as it ensures all points are visible with adequate padding.
32-33:❓ Verification inconclusive
Verify CRS compatibility with basemap.
The basemap is added with a fixed CRS (EPSG:3857) which might not match the input data's CRS. This could lead to misalignment issues.
🏁 Script executed:
#!/bin/bash # Description: Check CRS usage across the codebase # Search for CRS definitions rg -A 2 'crs ?=' # Search for reprojection operations rg -A 2 'to_crs'Length of output: 11635
CRS Consistency Check:
Please ensure that the GeoDataFrame used for plotting in this file is transformed to EPSG:3857 before the basemap is added. The current call toctx.add_basemaphard-codes the CRS to "EPSG:3857", so if the underlying data are in another CRS (e.g., EPSG:4326), you may experience misalignment issues. Verify that a reprojection (using.to_crs("EPSG:3857")) is applied to the data prior to plotting inexamples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py(lines 32–33).
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
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♻️ Duplicate comments (1)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py (1)
11-13: 🛠️ Refactor suggestionAdd type hints and input validation.
The function should include type hints and validate the input DataFrame has the required "comuna" column.
Apply this diff to add type hints and input validation:
-def count_stops_per_district(bus_stops_in_district: gpd.GeoDataFrame): +def count_stops_per_district(bus_stops_in_district: gpd.GeoDataFrame) -> pd.DataFrame: """Count the number of stops per district. + + Args: + bus_stops_in_district (gpd.GeoDataFrame): GeoDataFrame with bus stops and their districts + + Returns: + pd.DataFrame: DataFrame with stop counts per district + + Raises: + ValueError: If required column 'comuna' is missing """ + if "comuna" not in bus_stops_in_district.columns: + raise ValueError("Required column 'comuna' not found in input DataFrame") return bus_stops_in_district.groupby("comuna").size().reset_index(name="cantidad_paradas")
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examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
3-3: config.EPSG_TARGET imported but unused
Remove unused import: config.EPSG_TARGET
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🔇 Additional comments (2)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py (2)
5-9: LGTM! Well-implemented spatial join with CRS validation.The function includes proper CRS validation and uses appropriate geopandas methods for spatial join.
15-31: LGTM! Well-documented function with proper error handling.The function includes:
- Comprehensive docstring with Args, Returns, and Raises sections
- Type hints for parameters and return value
- Appropriate error handling for empty DataFrame
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examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py (1)
53-53:⚠️ Potential issueFix incorrect population filter.
The population filter is incorrectly filtering countries with less than 100M people instead of more than 100M as indicated by the variable name and comment.
-large_countries = world[world["POP_EST"] < 100_000_000] +large_countries = world[world["POP_EST"] > 100_000_000]examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject2.ipynb (1)
961-962: 🛠️ Refactor suggestionMove URL to configuration and add error handling for downloads.
The Parquet file URL should be moved to a configuration file and include proper error handling for downloads.
+import requests +from tqdm import tqdm -url = "https://ookla-open-data.s3.us-west-2.amazonaws.com/parquet/performance/type=mobile/year=2019/quarter=1/2019-01-01_performance_mobile_tiles.parquet" +def download_file(url, local_path): + try: + response = requests.get(url, stream=True) + response.raise_for_status() + total_size = int(response.headers.get('content-length', 0)) + with open(local_path, 'wb') as f, tqdm( + total=total_size, + unit='iB', + unit_scale=True + ) as pbar: + for data in response.iter_content(chunk_size=1024): + size = f.write(data) + pbar.update(size) + except Exception as e: + print(f"Failed to download file: {e}") + raise
🧹 Nitpick comments (12)
examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py (1)
8-33: Consider improving high-density threshold and add validation for transform operations.While the function has good type hints and documentation, there are a few areas for improvement:
- Using the mean as a threshold might not be optimal for skewed population distributions.
- The loop-based approach could be inefficient for large rasters.
- The transform operation lacks validation.
Consider this refactoring:
def extract_high_density_points( population_data: np.ndarray, transform: np.ndarray ) -> gpd.GeoDataFrame: """ Converts high-density raster pixels to point geometries. Args: population_data: 2D numpy array of population density values transform: Affine transformation matrix Returns: GeoDataFrame containing high-density points """ + # Validate inputs + if population_data.ndim != 2: + raise ValueError("Population data must be a 2D array") + if not hasattr(transform, "__mul__"): + raise ValueError("Transform must support multiplication") # Calculate the threshold for high-density pixels - threshold = np.mean(population_data) + # Consider using a more robust threshold like median or percentile + threshold = np.percentile(population_data, 75) # Use top 25% as high density high_density_points = [] # Iterate over the raster data and convert high-density pixels to points for row in range(population_data.shape[0]): for col in range(population_data.shape[1]): if population_data[row, col] > threshold: + try: x, y = transform * (col, row) high_density_points.append(Point(x, y)) + except Exception as e: + raise ValueError(f"Failed to transform coordinates: {e}") return gpd.GeoDataFrame(geometry=high_density_points, crs=EPSG_TARGET)examples/geospatial-python-urban-analysis-with-postgis/src/etl/load/postgis/save_bus_stop.py (2)
5-6: Enhance function documentation.The docstring should include information about parameters, return values, and exceptions for better code readability and maintainability.
def save_bus_stop(gdf: gpd.GeoDataFrame, conn: Engine ) -> bool: - """Saves the bus stop GeoDataFrame to PostGIS.""" + """Saves the bus stop GeoDataFrame to PostGIS. + + Parameters: + gdf (gpd.GeoDataFrame): The GeoDataFrame containing bus stop data to be saved. + conn (Engine): The database connection to PostGIS. + + Returns: + bool: True if the save operation was successful, False otherwise. + + Raises: + Exception: Catches and prints exceptions that occur during the save operation. + """
9-9: Remove extraneous f-string prefix.The string doesn't contain any placeholders, so the
fprefix is unnecessary.- print(f"Successfully saved bus stops to PostGIS.") + print("Successfully saved bus stops to PostGIS.")🧰 Tools
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9-9: f-string without any placeholders
Remove extraneous
fprefix(F541)
examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/save_bus_stop_pipeline.py (3)
15-16: Remove unnecessarypassstatement.The
passstatement after the print is unnecessary and can be removed.except Exception as e: print(f"Error saving bus stops: {e}") - pass
5-16: Add return value and improve error handling.The function should return a value to indicate success or failure, and consider more specific exception handling for different types of errors.
-def save_bus_stop_pipeline(): +def save_bus_stop_pipeline() -> bool: """Saves the bus stop GeoDataFrame to PostGIS.""" try: # Fetch bus stops from the API bus_stops = fetch_bus_stops() # Get a connection to the database db_connection = get_db_connection() # Save the bus stops to PostGIS - save_bus_stop(bus_stops, db_connection) + return save_bus_stop(bus_stops, db_connection) + except ValueError as e: + print(f"Error with input data: {e}") + return False except Exception as e: print(f"Error saving bus stops: {e}") - pass + return False
5-6: Enhance function documentation.The docstring should include information about return values and exceptions for better code readability and maintainability.
- """Saves the bus stop GeoDataFrame to PostGIS.""" + """Saves the bus stop GeoDataFrame to PostGIS. + + Returns: + bool: True if the pipeline executed successfully, False otherwise. + + Raises: + Any exceptions during the process are caught and logged. + """examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py (1)
57-57: Update comment to match code.The comment mentions "Large countries in red" but the actual plot uses yellow color.
-large_countries.plot(ax=ax, color="yellow", edgecolor="black", label="> 100M inhabitants") # Large countries in red +large_countries.plot(ax=ax, color="yellow", edgecolor="black", label="> 100M inhabitants") # Large countries in yellowexamples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py (1)
41-43: Consider parameterizing hardcoded line number.The hardcoded line number "68" could be moved to a configuration file or accepted as a command-line argument for better flexibility.
if __name__ == "__main__": - run_bus_stop_analysis("68") + import sys + # Run with specific line number if provided as argument + if len(sys.argv) > 1: + run_bus_stop_analysis(sys.argv[1]) + else: + # Default analysis with line 68 + run_bus_stop_analysis("68") run_bus_stop_analysis()examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject2.ipynb (4)
220-221: Add package installation error handling.Installing packages within a notebook should include error handling to make the execution more robust, especially in production environments.
-pip install folium mapclassify -q +try: + import folium + import mapclassify +except ImportError: + print("Installing required packages...") + %pip install folium mapclassify -q + import folium + import mapclassify
44-49: Validate geographic coordinates in data dictionary.The data dictionary contains coordinate information without validation. Adding validation for coordinates will prevent errors when converting to a GeoDataFrame.
+def is_valid_lat_lon(lat, lon): + """Validate latitude and longitude values.""" + return -90 <= lat <= 90 and -180 <= lon <= 180 data = { "Name": ["New York City", "São Paulo", "Tokyo", "Lagos", "Sydney"], "Population": [8419600, 12325232, 13929286, 15000000, 5312163], # Approximate populations "Latitude": [40.7128, -23.5505, 35.6895, 6.5244, -33.8688], "Longitude": [-74.0060, -46.6333, 139.6917, 3.3792, 151.2093] } +# Validate coordinates +for i, (lat, lon) in enumerate(zip(data["Latitude"], data["Longitude"])): + if not is_valid_lat_lon(lat, lon): + print(f"Warning: Invalid coordinates for {data['Name'][i]}: ({lat}, {lon})")
182-192: Enhance plot styling and add titles and legends.While the plot is functional, adding proper titles, legends, and customizing the styling would make it more informative and visually appealing.
## Creates a figure and axis for plotting. -fig, ax = plt.subplots(figsize=(10, 10)) +fig, ax = plt.subplots(figsize=(10, 10), facecolor='#f0f0f0') + +# Remove grid and set background color +ax.set_facecolor('#f0f0f0') +ax.grid(False) ## Plots the cities as red dots. -gdf.plot(ax=ax, marker="o", color="red", markersize=50) +gdf.plot(ax=ax, marker="o", color="red", markersize=50, + alpha=0.7, edgecolor='black', linewidth=1) + for x, y, label in zip(gdf.geometry.x, gdf.geometry.y, gdf["Population"]): ## Adds text labels for city populations. - ax.text(x, y, label, fontsize=8, color="black", ha="right") + ax.text(x, y, f"{label:,}", fontsize=9, color="black", + ha="right", fontweight='bold', + bbox=dict(facecolor='white', alpha=0.7, pad=2)) -plt.title("Cities") +plt.title("Major Global Cities by Population", fontsize=16, fontweight='bold', pad=20) +plt.figtext(0.5, 0.01, "Data source: Sample city population dataset", + ha="center", fontsize=8, style='italic') plt.show()
1-1192: Overall well-structured geospatial data tutorial notebook.This Jupyter notebook provides an excellent introduction to geospatial data analysis in Python, covering essential concepts from basic data structures to interactive visualizations. The step-by-step approach makes it accessible for learners.
Consider adding a requirements.txt file to accompany the notebook, listing all required dependencies. This would make it easier for users to set up their environment before running the notebook.
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examples/geospatial-python-urban-analysis-with-postgis/Dockerfile(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/config.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject2.ipynb(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/postgis/fetch_bus_stop.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/load/postgis/save_bus_stop.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/__init__.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/save_bus_stop_pipeline.py(1 hunks)examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/init.py
🚧 Files skipped from review as they are similar to previous changes (5)
- examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/postgis/fetch_bus_stop.py
- examples/geospatial-python-urban-analysis-with-postgis/Dockerfile
- examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py
- examples/geospatial-python-urban-analysis-with-postgis/src/visualization/optimization_map.py
- examples/geospatial-python-urban-analysis-with-postgis/config.py
🧰 Additional context used
🧬 Code Definitions (2)
examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/save_bus_stop_pipeline.py (1)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/load/postgis/save_bus_stop.py (1) (1)
save_bus_stop(5-13)
examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py (5)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/postgis/fetch_bus_stop.py (1) (1)
fetch_bus_stops(5-35)examples/geospatial-python-urban-analysis-with-postgis/src/etl/extract/bsas_data/fetch_district.py (1) (1)
fetch_districts(5-10)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/district.py (3) (3)
spatial_join(4-8)count_stops_per_district(10-12)get_extreme_district(14-30)examples/geospatial-python-urban-analysis-with-postgis/src/etl/transform/reproject.py (1) (1)
reproject_data(5-7)examples/geospatial-python-urban-analysis-with-postgis/src/visualization/bus_stops_map.py (1) (1)
plot_bus_stops(5-46)
🪛 Ruff (0.8.2)
examples/geospatial-python-urban-analysis-with-postgis/src/etl/load/postgis/save_bus_stop.py
9-9: f-string without any placeholders
Remove extraneous f prefix
(F541)
⏰ Context from checks skipped due to timeout of 90000ms (2)
- GitHub Check: Lint Code Base
- GitHub Check: Analyze (javascript)
🔇 Additional comments (8)
examples/geospatial-python-urban-analysis-with-postgis/src/ml/clustering.py (2)
1-7: LGTM: Imports are well-organized and appropriate for the task.The imports are appropriate for a geospatial clustering application, covering numerical operations (NumPy), geospatial data handling (GeoPandas), clustering algorithms (DBSCAN), and geometric operations (Shapely).
34-75: Address coordinate system concerns for DBSCAN distance calculations.The DBSCAN clustering algorithm's
epsparameter assumes distances in the units of the coordinate system. Ifhigh_density_gdfis in a geographic CRS (like EPSG:4326), the 500-meterepsvalue would be incorrect.Consider adding code to ensure proper distance calculations:
def find_optimal_stops( high_density_gdf: gpd.GeoDataFrame, bus_stops: gpd.GeoDataFrame, eps: float = 500, min_samples: int = 5 ) -> gpd.GeoDataFrame: """ Finds optimal bus stops based on high-density clusters. Args: high_density_gdf: GeoDataFrame of high-density points bus_stops: GeoDataFrame of existing bus stops eps: DBSCAN epsilon parameter (meters) min_samples: DBSCAN minimum samples parameter Returns: GeoDataFrame of optimal bus stop locations """ if eps <= 0: raise ValueError("eps must be positive") if min_samples < 1: raise ValueError("min_samples must be at least 1") + # Ensure we're working with a projected CRS for accurate distance calculations + if high_density_gdf.crs.is_geographic: + # Reproject to a suitable projected CRS (e.g., UTM zone) + # This is a simplified approach - in production, choose an appropriate UTM zone + high_density_gdf = high_density_gdf.to_crs(epsg=3857) # Web Mercator + bus_stops = bus_stops.to_crs(epsg=3857) # Convert high-density points to numpy array high_density_points = np.array([[p.x, p.y] for p in high_density_gdf.geometry]) # Perform clustering clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(high_density_points) high_density_gdf["cluster"] = clustering.labels_ # Filter out noise (-1 cluster) clustered_gdf = high_density_gdf[high_density_gdf["cluster"] != -1] + # Handle case where all points are noise + if len(clustered_gdf) == 0: + return gpd.GeoDataFrame(geometry=[], crs=high_density_gdf.crs) # Compute centroids for each cluster cluster_centroids = clustered_gdf.dissolve(by="cluster").centroid # Convert centroids to GeoDataFrame optimal_stops = gpd.GeoDataFrame(geometry=cluster_centroids, crs=high_density_gdf.crs) # Remove centroids that are too close to existing bus stops optimal_stops = optimal_stops[~optimal_stops.geometry.intersects(bus_stops.unary_union)] + # Reproject back to original CRS if needed + if high_density_gdf.crs != EPSG_TARGET: + optimal_stops = optimal_stops.to_crs(EPSG_TARGET) return optimal_stopsexamples/geospatial-python-urban-analysis-with-postgis/scripts/countries.py (2)
12-12: Add error handling for file operations.The shapefile loading operation should include error handling to gracefully handle missing files or corrupt data.
-world = gpd.read_file(shapefile_path) +try: + world = gpd.read_file(shapefile_path) +except Exception as e: + print(f"❌ Error loading shapefile: {e}") + raise
73-81: Add error handling for file operations.File operations should include error handling and use the script directory for output paths.
+output_dir = script_dir / "output" +output_dir.mkdir(exist_ok=True) -plt.savefig("world_map.png", dpi=300) +try: + plt.savefig(output_dir / "world_map.png", dpi=300) +except Exception as e: + print(f"❌ Error saving map: {e}") -world.to_file("world.geojson", driver="GeoJSON") +try: + world.to_file(output_dir / "world.geojson", driver="GeoJSON") +except Exception as e: + print(f"❌ Error saving GeoJSON: {e}") -world_geojson = gpd.read_file("world.geojson") +try: + world_geojson = gpd.read_file(output_dir / "world.geojson") +except Exception as e: + print(f"❌ Error reading GeoJSON: {e}")examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/bus_stop_analysis.py (2)
17-17: Add return type hint.The function should include a return type hint for better code clarity and type checking.
-def run_bus_stop_analysis(line_number: str = None): +def run_bus_stop_analysis(line_number: str = None) -> None:
17-39: LGTM: Well-structured analysis pipeline with proper error handling.The implementation follows a clear flow of fetching, processing, and visualizing data with appropriate error handling.
examples/geospatial-python-urban-analysis-with-postgis/notebooks/geoproject2.ipynb (2)
86-86: Add error handling for GeoDataFrame creation.The GeoDataFrame creation should include error handling for invalid coordinates, which is important for production code robustness.
-gdf = gpd.GeoDataFrame(cities_df, geometry=gpd.points_from_xy(cities_df["Longitude"], cities_df["Latitude"])) +try: + gdf = gpd.GeoDataFrame( + cities_df, + geometry=gpd.points_from_xy(cities_df["Longitude"], cities_df["Latitude"]) + ) +except ValueError as e: + print(f"Error creating GeoDataFrame: {e}") + raise
1137-1138: Add error handling for layer creation.The ScatterplotLayer creation should include error handling for invalid data.
-layer = ScatterplotLayer.from_geopandas(gdf) -m = Map(layer, _height=800) +try: + layer = ScatterplotLayer.from_geopandas(gdf) + m = Map(layer, _height=800) +except Exception as e: + print(f"Error creating map layer: {e}") + raise
| def save_bus_stop(gdf: gpd.GeoDataFrame, conn: Engine ) -> bool: | ||
| """Saves the bus stop GeoDataFrame to PostGIS.""" | ||
| try: | ||
| gdf.to_postgis("bus_stops",conn, if_exists="replace", index=False) | ||
| print(f"Successfully saved bus stops to PostGIS.") | ||
| return True | ||
| except Exception as e: | ||
| print(f"Error saving bus stops: {e}") | ||
| return |
There was a problem hiding this comment.
Return type inconsistency.
The function signature specifies a return type of bool, but it returns None in the exception case (implicitly), which doesn't match the declared return type.
def save_bus_stop(gdf: gpd.GeoDataFrame, conn: Engine ) -> bool:
"""Saves the bus stop GeoDataFrame to PostGIS."""
try:
gdf.to_postgis("bus_stops",conn, if_exists="replace", index=False)
print(f"Successfully saved bus stops to PostGIS.")
return True
except Exception as e:
print(f"Error saving bus stops: {e}")
- return
+ return False📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def save_bus_stop(gdf: gpd.GeoDataFrame, conn: Engine ) -> bool: | |
| """Saves the bus stop GeoDataFrame to PostGIS.""" | |
| try: | |
| gdf.to_postgis("bus_stops",conn, if_exists="replace", index=False) | |
| print(f"Successfully saved bus stops to PostGIS.") | |
| return True | |
| except Exception as e: | |
| print(f"Error saving bus stops: {e}") | |
| return | |
| def save_bus_stop(gdf: gpd.GeoDataFrame, conn: Engine) -> bool: | |
| """Saves the bus stop GeoDataFrame to PostGIS.""" | |
| try: | |
| gdf.to_postgis("bus_stops", conn, if_exists="replace", index=False) | |
| print(f"Successfully saved bus stops to PostGIS.") | |
| return True | |
| except Exception as e: | |
| print(f"Error saving bus stops: {e}") | |
| return False |
🧰 Tools
🪛 Ruff (0.8.2)
9-9: f-string without any placeholders
Remove extraneous f prefix
(F541)
| # Get a connection to the database | ||
| db_connection = get_db_connection() | ||
| # Save the bus stops to PostGIS | ||
| save_bus_stop(bus_stops, db_connection) |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify return value from save_bus_stop.
The return value from save_bus_stop is not being checked, which means errors during the save operation won't be detected at this level.
🏁 Script executed:
#!/bin/bash
# Check how the save_bus_stop function is used in other parts of the codebase
rg -A 3 -B 3 "save_bus_stop\(" --type pyLength of output: 1847
Action: Check the return value from save_bus_stop in the pipeline.
The function save_bus_stop (located in examples/geospatial-python-urban-analysis-with-postgis/src/etl/load/postgis/save_bus_stop.py) returns a boolean to indicate success or failure. However, in examples/geospatial-python-urban-analysis-with-postgis/src/pipelines/save_bus_stop_pipeline.py the return value is not being verified.
Recommendation:
- Store the result of
save_bus_stop(bus_stops, db_connection)in a variable. - Check the boolean value.
- Handle the error appropriately—either by logging the error, raising an exception, or taking corrective action.
Example fix:
success = save_bus_stop(bus_stops, db_connection)
if not success:
print("Error saving bus stops: operation failed")
# Consider proper error handling here (e.g. raise an exception or exit)| "execution_count": 26, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "local_path = Path(\"internet-speeds.parquet\")\n", | ||
| "\n", | ||
| "if local_path.exists():\n", | ||
| " gdf = gpd.read_parquet(local_path)\n", | ||
| "else:\n", | ||
| " columns = [\"avg_d_kbps\", \"tile\"]\n", | ||
| " df = pd.read_parquet(url, columns=columns)\n", | ||
| "\n", | ||
| " tile_geometries = shapely.from_wkt(df[\"tile\"])\n", | ||
| " tile_centroids = shapely.centroid(tile_geometries)\n", | ||
| "\n", | ||
| " gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs=\"EPSG:4326\")\n", | ||
| " gdf.to_parquet(local_path)" | ||
| ] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve Parquet file handling with proper error handling and logging.
The current implementation for downloading and processing the Parquet file needs better error handling, especially for network requests and file operations.
local_path = Path("internet-speeds.parquet")
if local_path.exists():
- gdf = gpd.read_parquet(local_path)
+ try:
+ gdf = gpd.read_parquet(local_path)
+ print(f"Successfully loaded data from {local_path}")
+ except Exception as e:
+ print(f"Error loading parquet file: {e}")
+ raise
else:
- columns = ["avg_d_kbps", "tile"]
- df = pd.read_parquet(url, columns=columns)
-
- tile_geometries = shapely.from_wkt(df["tile"])
- tile_centroids = shapely.centroid(tile_geometries)
-
- gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs=\"EPSG:4326\")
- gdf.to_parquet(local_path)
+ try:
+ print(f"Downloading data from {url}...")
+ # Use the download_file function defined earlier
+ download_file(url, local_path)
+
+ columns = ["avg_d_kbps", "tile"]
+ df = pd.read_parquet(local_path, columns=columns)
+
+ print("Processing geographic data...")
+ tile_geometries = shapely.from_wkt(df["tile"])
+ tile_centroids = shapely.centroid(tile_geometries)
+
+ gdf = gpd.GeoDataFrame(df[["avg_d_kbps"]], geometry=tile_centroids, crs="EPSG:4326")
+ print(f"Saving processed data to {local_path}")
+ gdf.to_parquet(local_path)
+ print("Data processing complete")
+ except Exception as e:
+ print(f"Error downloading or processing data: {e}")
+ # Remove partial files if download failed
+ if local_path.exists():
+ local_path.unlink()
+ raise📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "execution_count": 26, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "local_path = Path(\"internet-speeds.parquet\")\n", | |
| "\n", | |
| "if local_path.exists():\n", | |
| " gdf = gpd.read_parquet(local_path)\n", | |
| "else:\n", | |
| " columns = [\"avg_d_kbps\", \"tile\"]\n", | |
| " df = pd.read_parquet(url, columns=columns)\n", | |
| "\n", | |
| " tile_geometries = shapely.from_wkt(df[\"tile\"])\n", | |
| " tile_centroids = shapely.centroid(tile_geometries)\n", | |
| "\n", | |
| " gdf = gpd.GeoDataFrame(df[[\"avg_d_kbps\"]], geometry=tile_centroids, crs=\"EPSG:4326\")\n", | |
| " gdf.to_parquet(local_path)" | |
| ] | |
| local_path = Path("internet-speeds.parquet") | |
| if local_path.exists(): | |
| try: | |
| gdf = gpd.read_parquet(local_path) | |
| print(f"Successfully loaded data from {local_path}") | |
| except Exception as e: | |
| print(f"Error loading parquet file: {e}") | |
| raise | |
| else: | |
| try: | |
| print(f"Downloading data from {url}...") | |
| # Use the download_file function defined earlier | |
| download_file(url, local_path) | |
| columns = ["avg_d_kbps", "tile"] | |
| df = pd.read_parquet(local_path, columns=columns) | |
| print("Processing geographic data...") | |
| tile_geometries = shapely.from_wkt(df["tile"]) | |
| tile_centroids = shapely.centroid(tile_geometries) | |
| gdf = gpd.GeoDataFrame(df[["avg_d_kbps"]], geometry=tile_centroids, crs="EPSG:4326") | |
| print(f"Saving processed data to {local_path}") | |
| gdf.to_parquet(local_path) | |
| print("Data processing complete") | |
| except Exception as e: | |
| print(f"Error downloading or processing data: {e}") | |
| # Remove partial files if download failed | |
| if local_path.exists(): | |
| local_path.unlink() | |
| raise |
🚀 What's this PR do?
This PR creates a new project:
geospatial-python-urban-analysis-with-postgis, designed for advanced geospatial data analysis using Python, PostGIS, and Docker.🔹 Key Features
✅ Summary of Changes
📌 Project Initialization
geospatial-python-urban-analysis-with-postgis.src/etl/: Extract, Transform, Load pipeline.src/database/: PostGIS connection and queries.src/pipelines/: Modular geospatial data workflows.notebooks/: Jupyter Notebooks for visualization and analysis.docker-compose.yml: PostgreSQL + PostGIS environment.🔬 How to Test?
Run the following command to execute the ETL pipeline:
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