fio-l1-vienna-shapefile-recovery

Rerun

import geopandas as gpd
import pandas as pd
import json

def migrate_parcels(shapefile_path, column_map_path, output_path):
    # 1. Read the column map
    # The file seems to have a header like "1 truncated,original" or similar.
    # Looking at the read output: "1 truncated,original"
    # Let's check the structure carefully.
    column_map_df = pd.read_csv(column_map_path, sep='\t', skiprows=1, names=['id', 'truncated', 'original'])
    # Wait, the 'Read' output showed:
    # "     1	truncated,original"
    # "     2	KATASTRALG,KATASTRALGEMEINDE_NAME"
    # It looks like it's tab separated, but the header itself might be weird.
    # Let's try reading it with a more robust approach.
    
    # Re-reading column_map.csv logic:
    # It looks like it might be: 
    # index (tab) truncated (comma) original ? 
    # No, the "Read" output shows: "     2	KATASTRALG,KATASTRALGEMEINDE_NAME"
    # This implies the separator between the ID and the rest is a TAB, 
    # and the separator between truncated and original is a COMMA.
    
    mapping = {}
    with open(column_map_path, 'r', encoding='utf-8') as f:
        lines = f.readlines()
        # Skip the first line which looks like "1 truncated,original"
        for line in lines[1:]:
            parts = line.strip().split('\t')
            if len(parts) >= 2:
                cols = parts[1].split(',')
                if len(cols) == 2:
                    truncated, original = cols
                    mapping[truncated] = original

    print(f"Loaded mapping: {mapping}")

    # 2. Read the shapefile
    gdf = gpd.read_file(shapefile_path)
    print(f"Original columns: {gdf.columns.tolist()}")

    # 3. Rename columns
    # Only rename if the truncated name exists in the shapefile
    rename_dict = {k: v for k, v in mapping.items() if k in gdf.columns}
    gdf = gdf.rename(columns=rename_dict)
    print(f"Renamed columns: {gdf.columns.tolist()}")

    # 4. Ensure Geometry is Polygon or MultiPolygon
    # GeoPandas usually handles this, but we can filter/verify
    # Note: shapefiles can sometimes have weird geometry types.
    # We want to ensure they are valid.
    gdf['geometry'] = gdf['geometry'].buffer(0) # Small trick to fix some invalid geometries if needed, but maybe just keep as is.
    
    # 5. Write to GeoJSON
    # GeoJSON is typically UTF-8.
    gdf.to_file(output_path, driver='GeoJSON')
    print(f"Successfully saved to {output_path}")

if __name__ == "__main__":
    migrate_parcels('parcels.shp', 'column_map.csv', 'parcels.geojson')

import geopandas as gpd
import pandas as pd
import json

def migrate_parcels(shapefile_path, column_map_path, output_path):
    mapping = {}
    with open(column_map_path, 'r', encoding='utf-8') as f:
        lines = f.readlines()
        # The lines look like: "     2	KATASTRALG,KATASTRALGEMEINDE_NAME"
        for line in lines:
            if '\t' in line:
                parts = line.strip().split('\t')
                if len(parts) >= 2:
                    cols = parts[1].split(',')
                    if len(cols) == 2:
                        truncated, original = cols
                        mapping[truncated] = original

    print(f"Loaded mapping: {mapping}")

    gdf = gpd.read_file(shapefile_path)
    print(f"Original columns: {gdf.columns.tolist()}")

    # Rename columns
    rename_dict = {k: v for k, v in mapping.items() if k in gdf.columns}
    gdf = gdf.rename(columns=rename_dict)
    print(f"Renamed columns: {gdf.columns.tolist()}")

    # Ensure Geometry is Polygon or MultiPolygon
    # GeoPandas automatically handles the conversion to GeoJSON format
    # We just need to make sure we use driver='GeoJSON'
    gdf.to_file(output_path, driver='GeoJSON')
    print(f"Successfully saved to {output_path}")

if __name__ == "__main__":
    migrate_parcels('parcels.shp', 'column_map.csv', 'parcels.geojson')

import geopandas as gpd
import pandas as pd
import json

def migrate_parcels(shapefile_path, column_map_path, output_path):
    mapping = {}
    with open(column_map_path, 'r', encoding='utf-8') as f:
        lines = f.readlines()
        for line in lines:
            # Line looks like: "     2	KATASTRALG,KATASTRALGEMEINDE_NAME"
            # Let's try to split by TAB first.
            if '\t' in line:
                parts = line.strip().split('\t')
                # parts[0] is the ID (e.g., "2")
                # parts[1] is the content (e.g., "KATASTRALG,KATASTRALGEMEINDE_NAME")
                if len(parts) >= 2:
                    content = parts[1]
                    if ',' in content:
                        truncated, original = content.split(',', 1)
                        mapping[truncated.strip()] = original.strip()

    print(f"Loaded mapping: {mapping}")

    gdf = gpd.read_file(shapefile_path)
    print(f"Original columns: {gdf.columns.tolist()}")

    # Rename columns
    rename_dict = {k: v for k, v in mapping.items() if k in gdf.columns}
    gdf = gdf.rename(columns=rename_dict)
    print(f"Renamed columns: {gdf.columns.tolist()}")

    # Final check: Ensure all columns from mapping that WERE in the original file are renamed.
    # We already did that with rename_dict.

    # Write to GeoJSON
    gdf.to_file(output_path, driver='GeoJSON')
    print(f"Successfully saved to {output_path}")

if __name__ == "__main__":
    migrate_parcels('parcels.shp', 'column_map.csv', 'parcels.geojson')

import geopandas as gpd
import pandas as pd

def migrate_parcels(shapefile_path, column_map_path, output_path):
    # The cat -A output shows that the file is actually COMMA separated!
    # truncated,original
    # KATASTRALG,KATASTRALGEMEINDE_NAME
    
    # Let's use pandas to read it properly.
    column_map_df = pd.read_csv(column_map_path)
    
    # The columns in the CSV are 'truncated' and 'original'
    # We create a dictionary mapping truncated to original
    mapping = dict(zip(column_map_df['truncated'], column_map_df['original']))
    print(f"Loaded mapping: {mapping}")

    # Read the shapefile
    gdf = gpd.read_file(shapefile_path)
    print(f"Original columns: {gdf.columns.tolist()}")

    # Rename columns
    # We use a dictionary comprehension to only include columns that exist in gdf
    rename_dict = {k: v for k, v in mapping.items() if k in gdf.columns}
    gdf = gdf.rename(columns=rename_dict)
    print(f"Renamed columns: {gdf.columns.tolist()}")

    # Save as GeoJSON
    gdf.to_file(output_path, driver='GeoJSON')
    print(f"Successfully saved to {output_path}")

if __name__ == "__main__":
    migrate_parcels('parcels.shp', 'column_map.csv', 'parcels.geojson')