###################################################################################################
# Repository: https://github.com/lgervasoni/urbansprawl
# MIT License
###################################################################################################
import osmnx as ox
import pandas as pd
import geopandas as gpd
import numpy as np
from .tags import height_tags
from ..settings import storage_folder
# Format for load/save the geo-data ['geojson','shp']
geo_format = 'geojson' # 'shp'
geo_driver = 'GeoJSON' # 'ESRI Shapefile'
###################################################
### I/O utils
###################################################
def get_dataframes_filenames(city_ref_file):
"""
Get data frame file names for input city
Parameters
----------
city_ref_file : string
name of input city
Returns
----------
[ string, string, string ]
returns filenames for buildings, building parts, and points of interest
"""
import os
if not(os.path.isdir(storage_folder)):
os.makedirs(storage_folder)
geo_poly_file = storage_folder+"/"+city_ref_file+"_buildings."+geo_format
geo_poly_parts_file = storage_folder+"/"+city_ref_file+"_building_parts."+geo_format
geo_point_file = storage_folder+"/"+city_ref_file+"_poi."+geo_format
return geo_poly_file, geo_poly_parts_file, geo_point_file
def load_geodataframe(geo_filename):
"""
Load input GeoDataFrame
Parameters
----------
geo_filename : string
input GeoDataFrame filename
Returns
----------
geopandas.GeoDataFrame
loaded data
"""
# Load using geopandas
df_osm_data = gpd.read_file(geo_filename)
# Set None as NaN
df_osm_data.fillna(value=np.nan, inplace=True)
# Replace empty string (Json NULL sometimes read as '') for NaN
df_osm_data.replace('', np.nan, inplace=True)
def list_int_from_string(x): # List of integers given input in string format
return [ int(id_) for id_ in x.split(",") ]
def list_str_from_string(x): # List of strings given input in string format
return x.split(",")
# Recover list
if ( "activity_category" in df_osm_data.columns):
df_osm_data[ "activity_category" ] = df_osm_data.activity_category.apply(lambda x: list_str_from_string(x) if pd.notnull(x) else np.nan )
if ( "containing_parts" in df_osm_data.columns):
df_osm_data[ "containing_parts" ] = df_osm_data.containing_parts.apply( lambda x: list_int_from_string(x) if pd.notnull(x) else np.nan )
if ( "containing_poi" in df_osm_data.columns):
df_osm_data[ "containing_poi" ] = df_osm_data.containing_poi.apply( lambda x: list_int_from_string(x) if pd.notnull(x) else np.nan )
# To UTM coordinates
return ox.project_gdf( df_osm_data )
def store_geodataframe(df_osm_data, geo_filename):
"""
Store input GeoDataFrame
Parameters
----------
df_osm_data : geopandas.GeoDataFrame
input OSM data frame
geo_filename : string
filename for GeoDataFrame storage
Returns
----------
"""
# To EPSG 4326 (GeoJSON does not store projection information)
df_osm_data = ox.project_gdf(df_osm_data, to_latlong=True)
# Lists to string (needed to save GeoJSON files)
if ( "activity_category" in df_osm_data.columns):
df_osm_data.activity_category = df_osm_data.activity_category.apply( lambda x: ','.join(str(e) for e in x) if isinstance(x,list) else np.nan )
if ( "containing_parts" in df_osm_data.columns):
df_osm_data.containing_parts = df_osm_data.containing_parts.apply( lambda x: ','.join(str(e) for e in x) if isinstance(x,list) else np.nan )
if ( "containing_poi" in df_osm_data.columns):
df_osm_data.containing_poi = df_osm_data.containing_poi.apply( lambda x: ','.join(str(e) for e in x) if isinstance(x,list) else np.nan )
# Save to file
df_osm_data.to_file(geo_filename, driver=geo_driver)
###################################################
### GeoDataFrame processing utils
###################################################
def sanity_check_height_tags(df_osm):
"""
Compute a sanity check for all height tags
If incorrectly tagged, try to replace with the correct tag
Any meter or level related string are replaced, and heights using the imperial units are converted to the metric system
Parameters
----------
df_osm : geopandas.GeoDataFrame
input OSM data frame
Returns
----------
"""
def sanity_check(value):
### Sanity check for height tags (sometimes wrongly-tagged)
if not( (value is np.nan) or (value is None) ): # Non-null value
try: # Can be read as float?
return float(value)
except:
try: # Try removing incorrectly tagged information: meters/levels
return float( value.replace('meters','').replace('meter','').replace('m','').replace('levels','').replace('level','').replace('l','') )
except:
try: # Feet and inch values? e.g.: 4'7''
split_value = value.split("'")
feet, inches = split_value[0], split_value[1]
if (inches is ''): # Non existent inches
inches = '0'
tot_inches = float(feet)*12 + float(inches)
# Return meters equivalent
return tot_inches * 0.0254
except: # None. Incorrect tag
return None
return value
# Available height tags
available_height_tags = [ col for col in height_tags if col in df_osm.columns ]
# Apply-map sanity check
df_osm[ available_height_tags ] = df_osm[ available_height_tags ].applymap(sanity_check)
def associate_structures(df_osm_encompassing_structures, df_osm_structures, operation='contains', column='containing_'):
"""
Associate input structure geometries to its encompassing structures
Structures are associated using the operation 'contains' or 'intersects'
A new column in the encompassing data frame is added, incorporating the indices of the containing structures
Parameters
----------
df_osm_encompassing_structures : geopandas.GeoDataFrame
encompassing data frame
df_osm_structures : geopandas.GeoDataFrame
structures data frame
operation : string
spatial join operation to associate structures
column : string
name of the column to add in encompassing data frame
Returns
----------
"""
# Find, for each geometry, all containing structures
sjoin = gpd.sjoin(df_osm_encompassing_structures[['geometry']], df_osm_structures[['geometry']], op=operation, rsuffix='cont')
# Group by: polygon_index -> list of containing points indices
group_indices = sjoin.groupby( sjoin.index, as_index=True )['index_cont'].apply(list)
# Create new column
df_osm_encompassing_structures.loc[ group_indices.index, column ] = group_indices.values
# Reset indices
df_osm_encompassing_structures.index.rename('',inplace=True)
df_osm_structures.index.rename('',inplace=True)
