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| import csv | |
| from data_processing.age_distribution_by_id import get_age_distribution | |
| from data_processing.get_smallAreaInfo import get_smallAreas | |
| from data_processing.get_density import get_density | |
| from data_processing.income_decile_by_id import get_income_decile | |
| import os | |
| import pandas as pd | |
| import geopandas as gpd | |
| def get_feature_df(): | |
| ''' | |
| # TODO | |
| ''' | |
| # Specify file paths here | |
| csv_ibuafjoldi = os.path.join('given_data', 'ibuafjoldi.csv') | |
| csv_tekjutiundir = os.path.join('given_data', 'tekjutiundir.csv') | |
| json_ibuafjoldi = os.path.join('given_data', 'smasvaedi_2021.json') | |
| smallareas = gpd.read_file("given_data/smasvaedi_2021.json") | |
| # Small area id: id of the small area | |
| # Density: current density of the small area | |
| # Income distribution: the distribution of income in the small area per year (dictionary, keys: years, values: income distribution [buckets]) | |
| # Age distribution: distibution of age in the small area (age buckets of 5 years starting at 0-4) | |
| # Geometry: the lat and long coordinates for the small area polygon | |
| # Projected dwellings: | |
| columns = ["smallAreaId", "density", "income_distribution_per_year", "age_distribution", "geometry", "projected_dwellings"] | |
| # get list of smsv, each represented as {"id": smsv_id, "geometry": [(long, lat), ...]} | |
| smsv_id_geom = get_smallAreas() | |
| smsv_ids = [smsv["id"] for smsv in smsv_id_geom] # list of smsv ids | |
| # for each smsv_id get the age distribution for several years if required | |
| years = [2023, 2024] # Example years for age distribution | |
| age_distribution = get_age_distribution(years, smsv_ids, csv_ibuafjoldi) # Dict with age data | |
| # for each smsv_id get the income distribution (distributed in deciles) for several years if required | |
| years = [2023, 2024] # Example years for age distribution | |
| income_distribution = get_income_decile(years, smsv_ids, csv_tekjutiundir) # Dict with income data | |
| # Populate pandas dataframe | |
| data = [] | |
| for smsv in smsv_id_geom: | |
| smsv_id = smsv["id"] | |
| geometry = smsv["geometry"] | |
| # Calculate total population for density calculation | |
| population = sum(age_distribution.get(smsv_id, {}).get(2024, {}).values()) | |
| # Calculate density | |
| try: | |
| density = get_density(geometry, population) | |
| except ValueError as e: | |
| print(f"Density calculation failed for {smsv_id}: {e}") | |
| density = None | |
| # Age distribution | |
| age_dist = age_distribution.get(smsv_id, {}) | |
| # Income distribution | |
| income_dist = income_distribution.get(smsv_id, {}) | |
| # Add row to data | |
| data.append({ | |
| "smallAreaId": smsv_id, | |
| "density": density, | |
| "income_distribution_per_year": income_dist, | |
| "age_distribution": age_dist, | |
| "geometry": geometry, | |
| "projected_dwellings": None # Placeholder for now | |
| }) | |
| # Convert to Pandas DataFrame | |
| df = pd.DataFrame(data, columns=columns) | |
| # Display or save the DataFrame | |
| # print(df.head()) | |
| # df.to_csv('output.csv', index=False, encoding='utf-8') # Save to CSV | |
| return df |