3. Utils

gmtra.utils.load_config()

Read config.json

gmtra.utils.clean_fluvial_dirs(hazard_path)

Remove all the data we do not use.

Arguments:

hazard_path : file path to location of all hazard data.

gmtra.utils.load_osm_data(data_path, country)

Load osm data for an entire country.

Arguments:

data_path : file path to location of all data.

country : unique ID of the country for which we want to extract data from OpenStreetMap. Must be matching with the country ID used for saving the .osm.pbf file.

gmtra.utils.load_osm_data_region(data_path, region)

Load osm data for a specific region.

Arguments:

data_path : file path to location of all data.

region : unique ID of the region for which we want to extract data from OpenStreetMap. Must be matching with the region ID used for saving the .osm.pbf file.

gmtra.utils.load_hazard_map(hzd_path)

Load specific hazard map.

Arguments:

hzd_path : file path to location of the hazard map.

gmtra.utils.load_ssbn_hazard(hazard_path, country_full, country_ISO2, flood_type, flood_type_abb, rp)

Function to load a SSBN hazard map.

Arguments:

hazard_path : Path to location of all hazard data.

country_full : Full name of country. Obtained from create_folder_lookup.

country_ISO2 : ISO2 country code of the country.

flood_type : Specifies whether it is a pluvial or fluvial flood.

flood_type_abb : Abbrevated code of the flood type. FU for river flooding, PU for surface flooding.

rp : Return period of the flood map we want to extract.

Returns:

array: NumPy Array with the raster values.

affine : Affine of the array.

gmtra.utils.gdf_clip(gdf, clip_geom)

Function to clip a GeoDataFrame with a shapely geometry.

Arguments:

gdf : geopandas GeoDataFrame that we want to clip.

clip_geom : shapely geometry of region for which we do the calculation.

Returns:

gdf : clipped geopandas GeoDataframe

gmtra.utils.sum_tuples(l)

Function to sum a list of tuples.

Arguments:

l : list of tuples.

Returns:

tuple : a tuple with the sum of the list of tuples.

gmtra.utils.set_prot_standard(x, prot_lookup, events)

Function to set all values to zero below protection standard.

Arguments:

x : row in a GeoDataFrame that represents an unique infrastructure asset.

prot_lookup : dictionary with protection standards for each region.

events : A list with the unique hazard events in row x.

Returns:

x : row in a GeoDataFrame that represents an unique infrastructure asset with zero values for no flooding.

gmtra.utils.sensitivity_risk(RPS, loss_list, events, param_length)

Function to estimate the monetary risk for a particular hazard within the sensitivity analysis.

Arguments:

RPS : list of return periods in floating probabilities (i.e. [1/10,1/20,1/50]).

loss_list : list of lists with a monetary value per return period within each inner list.

Returns:

collect_risks : a list of all risks for each inner list of the input list.

gmtra.utils.monetary_risk(RPS, x, events)

Function to estimate the monetary risk for a particular hazard.

Arguments:

RPS : list of return periods in floating probabilities (i.e. [1/10,1/20,1/50]).

x : list of lists with a monetary value per return period within each inner list.

events : list of events that correspond with the return periods in the inner lists of x.

Returns:

collect_risks : a list of all risks for each inner list of the input list.

gmtra.utils.exposed_length_risk(x, hzd, RPS)

Function to estimate risk in terms of exposed kilometers.

Arguments:

x : row in a GeoDataFrame that represents an unique infrastructure asset.

hzd : abbrevation of the hazard we want to intersect. EQ for earthquakes, Cyc for cyclones, FU for river flooding, PU for surface flooding and CF for coastal flooding.

RPS : list of return periods in floating probabilities (i.e. [1/10,1/20,1/50]). Should match with the hazard we are considering.

Returns:

risk value : a floating number which represents the annual exposed kilometers of infrastructure.

gmtra.utils.total_length_risk(x, RPS)

Function to estimate risk if all assets would have been exposed.

Arguments:

x : row in a GeoDataFrame that represents an unique infrastructure asset.

RPS : list of return periods in floating probabilities (i.e. [1/10,1/20,1/50]). Should match with the hazard we are considering.

gmtra.utils.square_m2_cost_range(x)

Function to specify the range of possible costs for a bridge.

Arguments:

x : row in a GeoDataFrame that represents an unique bridge asset.

Returns:

list: a list with the range of possible bridge costs.

gmtra.utils.extract_value_from_gdf(x, gdf_sindex, gdf, column_name)

Arguments:

x : row in a geopandas GeoDataFrame. gdf_sindex : spatial index of dataframe of which we want to extract the value.

gdf : GeoDataFrame of which we want to extract the value.

column_name : column that contains the value we want to extract.

Returns:

extracted value from other GeoDataFrame

gmtra.utils.create_folder_lookup()

Function to create a dictionary in which we can lookup the folder path where the surface and river flood maps are located for a country.

gmtra.utils.map_roads()

Mapping function to create a dictionary with an aggregated list of road types.

Returns:

dictionary : A dictionary with road types and their aggregated equivalent

gmtra.utils.map_railway()

Mapping function to create a dictionary with an aggregated list of railway types.

Returns:

dictionary : A dictionary with road types and their aggregated equivalent

gmtra.utils.line_length(line, ellipsoid='WGS-84')

Length of a line in meters, given in geographic coordinates

Adapted from https://gis.stackexchange.com/questions/4022/looking-for-a-pythonic-way-to-calculate-the-length-of-a-wkt-linestring#answer-115285

Arguments:

line : a shapely LineString object with WGS-84 coordinates

Optional Arguments:

ellipsoid : string name of an ellipsoid that geopy understands (see http://geopy.readthedocs.io/en/latest/#module-geopy.distance)

Returns:

Length of line in meters