def make_osmexposure(highValueArea,
mode="default",
country=None,
save_path=None,
check_plot=1,
**kwargs):
"""
Generate climada-compatiple entity by assigning values to midpoints of
individual house shapes from OSM query, according to surface area and country.
Parameters:
highValueArea (str): absolute path for gdf of building features queried
from get_features_OSM()
mode (str): "LitPop" or "default": Default assigns a value of 5400 Chf to
each m2 of building, LitPop assigns total LitPop value for the region
proportionally to houses (by base area of house)
Country (str): ISO3 code or name of country in which entity is located.
Only if mode = LitPop
kwargs (dict): arguments for LitPop set_country method
Returns:
exp_building (Exposure): (CLIMADA-compatible) with allocated asset values.
Saved as exposure_buildings_mode_lat_lon.h5
Example:
buildings_47_8 = \
make_osmexposure(save_path + '/OSM_features_47_8.shp',
mode="default", save_path = save_path, check_plot=1)
"""
if save_path is None:
save_path = Path.cwd()
elif isinstance(save_path, str):
save_path = Path(save_path)
High_Value_Area_gdf = _get_midpoints(highValueArea)
High_Value_Area_gdf = _assign_values_exposure(High_Value_Area_gdf, mode,
country, **kwargs)
# put back into CLIMADA-compatible entity format and save as hdf5 file:
exp_buildings = Exposures(High_Value_Area_gdf)
exp_buildings.set_lat_lon()
exp_buildings.check()
exp_buildings.write_hdf5(
save_path.joinpath('exposure_buildings_' + mode + '_' +
str(int(min(High_Value_Area_gdf.bounds.miny))) +
'_' +
str(int(min(High_Value_Area_gdf.bounds.minx))) +
'.h5'))
# plotting
if check_plot == 1:
# normal hexagons
exp_buildings.plot_hexbin(pop_name=True)
# select the OSM background image from the available ctx.sources
# - returns connection error, left out for now:
#fig, ax = exp_buildings.plot_basemap(buffer=30000, url=ctx.sources.OSM_C, cmap='brg')
return exp_buildings
def get_osmstencil_litpop(bbox, country, mode, highValueArea=None, \
save_path=os.getcwd(), check_plot=1, **kwargs):
"""
Generate climada-compatible exposure by downloading LitPop exposure for a bounding box,
corrected for centroids which lie inside a certain high-value multipolygon area
from previous OSM query.
Parameters:
bbox (array): List of coordinates in format [South, West, North, East]
Country (str): ISO3 code or name of country in which bbox is located
highValueArea (str): path of gdf of high-value area from previous step.
If empty, searches for cwd/High_Value_Area_lat_lon.shp
mode (str): mode of re-assigning low-value points to high-value points.
"nearest", "even", or "proportional"
kwargs (dict): arguments for LitPop set_country method
Returns:
exp_sub_high_exp (Exposure): (CLIMADA-compatible) with re-allocated asset
values with name exposure_high_lat_lon
Example:
exposure_high_47_8 = get_osmstencil_litpop([47.16, 8.0, 47.3, 8.0712],\
'CHE',"proportional", highValueArea = \
save_path + '/High_Value_Area_47_8.shp' ,\
save_path = save_path)
"""
if highValueArea == None:
try:
High_Value_Area_gdf = \
geopandas.read_file(os.getcwd() + '/High_Value_Area_'+ str(int(bbox[0]))+'_'+
str(int(bbox[1]))+".shp")
except:
print('No file found of form %s. Please add or specify path.' \
%(os.getcwd() + 'High_Value_Area_'+str(int(bbox[0]))+'_'+\
str(int(bbox[1]))+".shp"))
else:
High_Value_Area_gdf = geopandas.read_file(highValueArea)
exp_sub = _get_litpop_bbox(country, High_Value_Area_gdf, **kwargs)
exp_sub_high = _split_exposure_highlow(exp_sub, mode, High_Value_Area_gdf)
###### how to "spread" centroids with value to e.g. hexagons? ###########
# put exp_sub_high back into CLIMADA-compatible exposure format and save as hdf5 file:
exp_sub_high_exp = Exposures(exp_sub_high)
exp_sub_high_exp.set_lat_lon()
exp_sub_high_exp.check()
exp_sub_high_exp.write_hdf5(save_path + '/exposure_high_'+str(int(bbox[0]))+\
'_'+str(int(bbox[1]))+'.h5')
# plotting
if check_plot == 1:
# normal hexagons
exp_sub_high_exp.plot_hexbin(pop_name=True)
# select the OSM background image from the available ctx.sources - doesnt work atm
#fig, ax = exp_sub_high_exp.plot_basemap(buffer=30000, url=ctx.sources.OSM_C, cmap='brg')
return exp_sub_high_exp
exposure_tmp = Exposures(exposure_tmp)
exposure_tmp.set_geometry_points() # set geometry attribute (shapely Points) from GeoDataFrame from latitude and longitude
exposure_tmp.check() # puts metadata that has not been assigned
exposure_data = exposure_data.append(exposure_tmp)
if fix_zeros:
exposure_tmp.value[exposure_tmp.value<plot_minimum] = plot_minimum
else:
print('\n' + '\x1b[1;03;30;30m' + 'ERROR Loading: %s_%ias.tiff' %(fi[0:-4]+fadd, res_target) + '\x1b[0m')
print('\n' + '\x1b[1;03;30;30m' + 'Checking combined data...' + '\x1b[0m')
exposure_data.check()
if write_to_tiff:
print('\n' + '\x1b[1;03;30;30m' + 'Writing combined data to TIFF...' + '\x1b[0m')
exposure_data.plot_raster(res=res_target/3600, raster_res=res_target/3600, save_tiff=\
os.path.join(RES_DIR, '%s_000_%ias.tiff' %(files[0][0:-8]+fadd, res_target)))
ax_exp = exposure_data.plot_hexbin(pop_name=False, cmap='plasma', norm=LogNorm(vmin=plot_minimum, vmax=0.1*exposure_data.value.max()))
if save_plots:
plt.savefig(os.path.join(RES_DIR, 'LitPop_pc_%iarcsec_%i_%s_world_map.png' % (res_target, REF_YEAR, fadd)), \
dpi=300, facecolor='w', edgecolor='w', \
orientation='portrait', papertype=None, format='png', \
transparent=False, bbox_inches=None, pad_inches=0.1, \
frameon=None, metadata=None)
plt.savefig(os.path.join(RES_DIR, 'LitPop_pc_%iarcsec_%i_%s_world_map.pdf' % (res_target, REF_YEAR, fadd)), \
dpi=300, facecolor='w', edgecolor='w', \
orientation='portrait', papertype=None, format='pdf', \
transparent=False, bbox_inches=None, pad_inches=0.1, \
frameon=None, metadata=None)
ax_exp_scatter = exposure_data.plot_scatter(pop_name=False, cmap='plasma', s=.01, shapes=False, \
norm=LogNorm(vmin=plot_minimum, vmax=np.max([0.1*exposure_data.value.max(), 10**9])))
