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
def load_exp_agr(force_new_hdf5_generation, name_hdf5_file, input_folder, haz_real):
"""
Parameters
----------
Load generate Exposure of agriculture if forced or if hdf5 file not present.
Otherwise load hdf5 file.
Parameters
----------
force_new_hdf5_generation : dict of bool
contains bool wether new Exposure should be forcefully generated.
name_hdf5_file : str
name of hdf5 file from wich Exposure is loaded.
input_folder : str
Path to input folder containing hdf5 file.
haz_real : climada.hazard.base.Hazard
CLIMADA hazard.
Returns
-------
exp_infr : climada.entity.exposures.base.Exposures
CLIMADA Exposure of Exposure.
"""
file1 = Path(input_folder + "/" + name_hdf5_file["exp_agr"])
file2 = Path(input_folder + "/" + "exp_agr_no_centr.hdf5")
if not file2.exists() and not file1.exists():
print("Please use import_agrar_exposure to create the hdf5 file!" +
" and move it to the input folder")
sys.exit()
elif force_new_hdf5_generation["exp_agr"]: #be carefull, this step will take ages when you do both at once
if not file2.exists():
print("Please use import_agrar_exposure to create the hdf5 file!" +
" and move it to the input folder")
sys.exit()
exp_agr = Exposures()
exp_agr.read_hdf5(input_folder + "/exp_agr_no_centr.hdf5")
exp_agr.check()
exp_agr.assign_centroids(haz_real, method = "NN", distance = "haversine", threshold = 2)
exp_agr.check()
exp_agr.write_hdf5(input_folder + "/exp_agr.hdf5")
else:
#Agrar Exposure
exp_agr = Exposures()
exp_agr.read_hdf5(input_folder + "/exp_agr.hdf5")
exp_agr.check()
return exp_agr
exposure_tmp = Exposures()
if os.path.exists(os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4]+fadd, res_target))):
print('\n' + '\x1b[1;03;30;30m' + 'TIFF exists already, skipping: %s_%ias.tiff' %(fi[0:-4], res_target) + '\x1b[0m')
continue
else:
print('\n' + '\x1b[1;03;30;30m' + 'Loading: %s ...' %(fi) + '\x1b[0m')
exposure_tmp = exposure_tmp.from_csv(os.path.join(ENTITY_DIR, fi), index_col=None)
if np.isnan(exposure_tmp.value.max()):
continue
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
if write_to_hdf5:
exposure_tmp.write_hdf5(os.path.join(ENTITY_DIR_HDF5, '%s.hdf5' %(fi[0:-4]+fadd)))
if write_to_tiff:
if fix_zeros:
exposure_tmp.value[exposure_tmp.value<1] = 1
# exposure_tmp.plot_raster(res=RES_ARCSEC/3600, save_tiff=\
# os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4], RES_ARCSEC)))
exposure_tmp.plot_raster(res=RES_ARCSEC/3600, raster_res=res_target/3600, save_tiff=\
os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4]+fadd, res_target)))
"""COMBINE AND PLOT EXPOSURE AT TARGET RESOLUTION:"""
print('\n' + '\x1b[1;03;30;30m' + 'COMBINE AND PLOT EXPOSURE AT TARGET RESOLUTION' + '\x1b[0m')
for res_target in res_targets:
exposure_data = Exposures()
for idx, fi in enumerate(files):
exposure_tmp = Exposures()
if try_read_from_tiff and os.path.exists(os.path.join(RES_DIR, '%s_%ias.tiff' %(fi[0:-4]+fadd, res_target))):
avg_value_ackerbau = value_ackerbau / exp_hail_agr[exp_hail_agr["region_id"] ==
221].shape[0]
a = exp_hail_agr[exp_hail_agr["region_id"] == 201].assign(value=avg_value_obst)
b = exp_hail_agr[exp_hail_agr["region_id"] == 202].assign(
value=avg_value_rebbau)
c = exp_hail_agr[exp_hail_agr["region_id"] == 221].assign(
value=avg_value_ackerbau)
exp_hail_agr = pd.concat([a, b, c]).sort_index()
exp_hail_agr = Exposures(exp_hail_agr)
exp_hail_agr.loc[exp_hail_agr["region_id"] == 201, "if_"] = int(3)
exp_hail_agr.loc[exp_hail_agr["region_id"] == 202, "if_"] = int(2)
exp_hail_agr.loc[exp_hail_agr["region_id"] == 221, "if_"] = int(4)
exp_hail_agr = exp_hail_agr.rename(columns={'if_': 'if_HL'})
exp_hail_agr.check()
exp_hail_agr.head()
exp_hail_agr.value_unit = "CHF"
exp_hail_agr.write_hdf5(input_folder + "/exp_agr_no_centr.hdf5")
test = exp_hail_agr[exp_hail_agr["value"] > 0]
plt.scatter(test[test["region_id"].isin([221, 222, 223])]["longitude"],
test[test["region_id"].isin([221, 222, 223])]["latitude"])
plt.scatter(test[test["region_id"] == 201]["longitude"],
test[test["region_id"] == 201]["latitude"])
plt.scatter(test[test["region_id"] == 202]["longitude"],
test[test["region_id"] == 202]["latitude"])
plt.legend(labels=["Acker - Futterbau", "Obstbau", "Rebbau"])
