climada.engine package

• climada.engine.uncertainty package
  • climada.engine.uncertainty.base module
  • climada.engine.uncertainty.unc_cost_benefit module
  • climada.engine.uncertainty.unc_impact module
  • climada.engine.uncertainty.unc_robustness module

climada.engine.calibration_opt module

climada.engine.calibration_opt.calib_instance(hazard, exposure, impact_func, df_out=Empty DataFrame Columns: [] Index: [], yearly_impact=False, return_cost='False')

calculate one impact instance for the calibration algorithm and write to given DataFrame

Parameters

• hazard – hazard set instance

• exposure – exposure set instance

• impact_func – impact function instance

Optional Parameters:

df_out: Output DataFrame with headers of columns defined and optionally with

first row (index=0) defined with values. If columns “impact”, “event_id”, or “year” are not included, they are created here. Data like reported impacts or impact function parameters can be given here; values are preserved.

yearly_impact (boolean): if set True, impact is returned per year,

not per event

return_cost: if not ‘False’ but any of ‘R2’, ‘logR2’,

cost is returned instead of df_out

Returns

DataFrame with modelled impact written to rows for each year

or event.

Return type

df_out

climada.engine.calibration_opt.init_if(if_name_or_instance, param_dict, df_out=Empty DataFrame Columns: [] Index: [0])

create an ImpactFunc based on the parameters in param_dict using the method specified in if_parameterisation_name and document it in df_out.

Parameters

• if_name_or_instance (str or ImpactFunc) – method of impact function parameterisation e.g. ‘emanuel’ or an instance of ImpactFunc

• param_dict (dict of parameter_names and values) – e.g. {‘v_thresh’: 25.7, ‘v_half’: 70, ‘scale’: 1} or {‘mdd_shift’: 1.05, ‘mdd_scale’: 0.8, ‘paa_shift’: 1, paa_scale’: 1}

Returns

• imp_fun (ImpactFunc) – The Impact function based on the parameterisation

• df_out (DataFrame) – Output DataFrame with headers of columns defined and with first row (index=0) defined with values. The impact function parameters from param_dict are represented here.

climada.engine.calibration_opt.change_if(if_instance, param_dict)

apply a shifting or a scaling defined in param_dict to the impact function in if_istance and return it as a new ImpactFunc object. :Parameters: * if_instance (ImpactFunc) – an instance of ImpactFunc

• param_dict – dict of parameter_names and values (interpreted as

• factors, 1 = neutral) –

  e.g. {‘mdd_shift’: 1.05, ‘mdd_scale’: 0.8,

  ‘paa_shift’: 1, paa_scale’: 1}

Returns

The Impact function based on the parameterisation

Return type

ImpactFunc

climada.engine.calibration_opt.init_impact_data(hazard_type, region_ids, year_range, source_file, reference_year, impact_data_source='emdat', yearly_impact=True)

creates a dataframe containing the recorded impact data for one hazard type and one area (countries, country or local split) :Parameters: * hazard_type – default = ‘TC’, type of hazard ‘WS’,’FL’ etc.

• region_ids – name the region_ids or country names

• year_range (list) – list containting start and end year. e.g. [1980, 2017]

• reference_year – impacts will be scaled to this year

• impact_data_source – default ‘emdat’, others maybe possible

Optional Parameters:

yearly_impact (boolean): if set True, impact is returned per year,

not per event

Returns

DataFrame with recorded impact written to rows for each year

or event.

Return type

df_out

climada.engine.calibration_opt.calib_cost_calc(df_out, cost_function)

calculate the cost function of the modelled impact impact_CLIMADA and the reported impact impact_scaled in df_out :Parameters: * df_out (pd.Dataframe) – DataFrame as created in calib_instance

• cost_function (str) – chooses the cost function e.g. ‘R2’ or ‘logR2’

Returns

the results of the cost function when comparing modelled and

reported impact

Return type

cost

climada.engine.calibration_opt.calib_all(hazard, exposure, if_name_or_instance, param_full_dict, impact_data_source, year_range, yearly_impact=True)

portrait the difference between modelled and reported impacts for all impact functions described in param_full_dict and if_name_or_instance :Parameters: * hazard – list or instance of hazard

• exposure – list or instance of exposure of full countries

• if_name_or_instance (string or ImpactFunc) – the name of a parameterisation or an instance of class ImpactFunc e.g. ‘emanuel’

• param_full_dict (dict) – a dict containing keys used for if_name_or_instance and values which are iterable (lists) e.g. {‘v_thresh’: [25.7, 20], ‘v_half’: [70], ‘scale’: [1, 0.8]}

• impact_data_source (dict or dataframe) – with name of impact data source and file location or dataframe

• year_range

• yearly_impact

Returns

DataFrame with modelled impact written to rows for each year

or event.

Return type

df_result

climada.engine.calibration_opt.calib_optimize(hazard, exposure, if_name_or_instance, param_dict, impact_data_source, year_range, yearly_impact=True, cost_fucntion='R2', show_details=False)

portrait the difference between modelled and reported impacts for all impact functions described in param_full_dict and if_name_or_instance :Parameters: * hazard – list or instance of hazard

• exposure – list or instance of exposure of full countries

• if_name_or_instance (string or ImpactFunc) – the name of a parameterisation or an instance of class ImpactFunc e.g. ‘emanuel’

• param_dict (dict) – a dict containing keys used for if_name_or_instance and one set of values e.g. {‘v_thresh’: 25.7, ‘v_half’: 70, ‘scale’: 1}

• impact_data_source (dict or dataframe) – with name of impact data source and file location or dataframe

• year_range

• yearly_impact

• cost_function (string) – the argument for function calib_cost_calc, default ‘R2’

• show_details (bool) – if True, return a tuple with the parameters AND the details of the optimization like success, status, number of iterations etc

Returns

the parameters with the best calibration results

(or a tuple with (1) the parameters and (2) the optimization output)

Return type

param_dict_result

climada.engine.cost_benefit module

class climada.engine.cost_benefit.CostBenefit

Bases: object

Impact definition. Compute from an entity (exposures and impact functions) and hazard.

present_year

present reference year

Type

int

future_year

future year

Type

int

tot_climate_risk

total climate risk without measures

Type

float

unit

unit used for impact

Type

str

color_rgb

color code RGB for each measure. Key: measure name (‘no measure’ used for case without measure), Value: np.array

Type

dict

benefit

benefit of each measure. Key: measure name, Value: float benefit

Type

dict

cost_ben_ratio

cost benefit ratio of each measure. Key: measure name, Value: float cost benefit ratio

Type

dict

imp_meas_future

impact of each measure at future or default. Key: measure name (‘no measure’ used for case without measure), Value: dict with:

‘cost’ (tuple): (cost measure, cost factor insurance), ‘risk’ (float): risk measurement, ‘risk_transf’ (float): annual expected risk transfer, ‘efc’ (ImpactFreqCurve): impact exceedance freq

(optional) ‘impact’ (Impact): impact instance

Type

dict

imp_meas_present

impact of each measure at present. Key: measure name (‘no measure’ used for case without measure), Value: dict with:

‘cost’ (tuple): (cost measure, cost factor insurance), ‘risk’ (float): risk measurement, ‘risk_transf’ (float): annual expected risk transfer, ‘efc’ (ImpactFreqCurve): impact exceedance freq

(optional) ‘impact’ (Impact): impact instance

Type

dict

__init__()

Initilization

calc(hazard, entity, haz_future=None, ent_future=None, future_year=None, risk_func=<function risk_aai_agg>, imp_time_depen=None, save_imp=False)

Compute cost-benefit ratio for every measure provided current and, optionally, future conditions. Present and future measures need to have the same name. The measures costs need to be discounted by the user. If future entity provided, only the costs of the measures of the future and the discount rates of the present will be used.

Parameters

• hazard (Hazard) – hazard

• entity (Entity) – entity

• haz_future (Hazard, optional) – hazard in the future (future year provided at ent_future)

• ent_future (Entity, optional) – entity in the future

• future_year (int, optional) – future year to consider if no ent_future provided. The benefits are added from the entity.exposures.ref_year until ent_future.exposures.ref_year, or until future_year if no ent_future given. Default: entity.exposures.ref_year+1

• risk_func (func, optional) – function describing risk measure to use to compute the annual benefit from the Impact. Default: average annual impact (aggregated).

• imp_time_depen (float, optional) – parameter which represents time evolution of impact (super- or sublinear). If None: all years count the same when there is no future hazard nor entity and 1 (linear annual change) when there is future hazard or entity. Default: None.

• save_imp (bool, optional) – True if Impact of each measure is saved. Default: False.

combine_measures(in_meas_names, new_name, new_color, disc_rates, imp_time_depen=None, risk_func=<function risk_aai_agg>)

Compute cost-benefit of the combination of measures previously computed by calc with save_imp=True. The benefits of the measures per event are added. To combine with risk transfer options use apply_risk_transfer.

Parameters

• in_meas_names (list(str)) – list with names of measures to combine

• new_name (str) – name to give to the new resulting measure

• new_color (np.array) – color code RGB for new measure, e.g. np.array([0.1, 0.1, 0.1])

• disc_rates (DiscRates) – discount rates instance

• imp_time_depen (float, optional) – parameter which represents time evolution of impact (super- or sublinear). If None: all years count the same when there is no future hazard nor entity and 1 (linear annual change) when there is future hazard or entity. Default: None.

• risk_func (func, optional) – function describing risk measure given an Impact. Default: average annual impact (aggregated).

Returns

CostBenefit

apply_risk_transfer(meas_name, attachment, cover, disc_rates, cost_fix=0, cost_factor=1, imp_time_depen=None, risk_func=<function risk_aai_agg>)

Applies risk transfer to given measure computed before with saved impact and compares it to when no measure is applied. Appended to dictionaries of measures.

Paramters:

meas_name (str): name of measure where to apply risk transfer attachment (float): risk transfer values attachment (deductible) cover (float): risk transfer cover cost_fix (float): fixed cost of implemented innsurance, e.g.

transaction costs

cost_factor (float): factor to which to multiply the insurance layer

to compute its cost. Default: 1

imp_time_depen (float, optional): parameter which represents time

evolution of impact (super- or sublinear). If None: all years count the same when there is no future hazard nor entity and 1 (linear annual change) when there is future hazard or entity. Default: None.

risk_func (func, optional): function describing risk measure given

an Impact. Default: average annual impact (aggregated).

remove_measure(meas_name)

Remove computed values of given measure

Parameters

meas_name (str) – name of measure to remove

plot_cost_benefit(cb_list=None, axis=None, **kwargs)

Plot cost-benefit graph. Call after calc().

Parameters

• cb_list (list(CostBenefit), optional) – if other CostBenefit provided, overlay them all. Used for uncertainty visualization.

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for Rectangle matplotlib, e.g. alpha=0.5 (color is set by measures color attribute)

Returns

matplotlib.axes._subplots.AxesSubplot

plot_event_view(return_per=(10, 25, 100), axis=None, **kwargs)

Plot averted damages for return periods. Call after calc().

Parameters

• return_per (list, optional) – years to visualize. Default 10, 25, 100

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for bar matplotlib function, e.g. alpha=0.5 (color is set by measures color attribute)

Returns

matplotlib.axes._subplots.AxesSubplot

static plot_waterfall(hazard, entity, haz_future, ent_future, risk_func=<function risk_aai_agg>, axis=None, **kwargs)

Plot waterfall graph at future with given risk metric. Can be called before and after calc().

Parameters

• hazard (Hazard) – hazard

• entity (Entity) – entity

• haz_future (Hazard) – hazard in the future (future year provided at ent_future)

• ent_future (Entity) – entity in the future

• risk_func (func, optional) – function describing risk measure given an Impact. Default: average annual impact (aggregated).

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for bar matplotlib function, e.g. alpha=0.5

Returns

matplotlib.axes._subplots.AxesSubplot

plot_arrow_averted(axis, in_meas_names=None, accumulate=False, combine=False, risk_func=<function risk_aai_agg>, disc_rates=None, imp_time_depen=1, **kwargs)

Plot waterfall graph with accumulated values from present to future year. Call after calc() with save_imp=True.

Parameters

• axis (matplotlib.axes._subplots.AxesSubplot) – axis from plot_waterfall or plot_waterfall_accumulated where arrow will be added to last bar

• in_meas_names (list(str), optional) – list with names of measures to represented total averted damage. Default: all measures

• accumulate (bool, optional) – accumulated averted damage (True) or averted damage in future (False). Default: False

• combine (bool, optional) – use combine_measures to compute total averted damage (True) or just add benefits (False). Default: False

• risk_func (func, optional) – function describing risk measure given an Impact used in combine_measures. Default: average annual impact (aggregated).

• disc_rates (DiscRates, optional) – discount rates used in combine_measures

• imp_time_depen (float, optional) – parameter which represent time evolution of impact used in combine_measures. Default: 1 (linear).

• kwargs (optional) – arguments for bar matplotlib function, e.g. alpha=0.5

plot_waterfall_accumulated(hazard, entity, ent_future, risk_func=<function risk_aai_agg>, imp_time_depen=1, axis=None, **kwargs)

Plot waterfall graph with accumulated values from present to future year. Call after calc() with save_imp=True. Provide same inputs as in calc.

Parameters

• hazard (Hazard) – hazard

• entity (Entity) – entity

• ent_future (Entity) – entity in the future

• risk_func (func, optional) – function describing risk measure given an Impact. Default: average annual impact (aggregated).

• imp_time_depen (float, optional) – parameter which represent time evolution of impact. Default: 1 (linear).

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for bar matplotlib function, e.g. alpha=0.5

Returns

matplotlib.axes._subplots.AxesSubplot

climada.engine.cost_benefit.risk_aai_agg(impact)

Risk measurement as average annual impact aggregated.

Parameters

impact (Impact) – an Impact instance

Returns

float

climada.engine.cost_benefit.risk_rp_100(impact)

Risk measurement as exceedance impact at 100 years return period.

Parameters

impact (Impact) – an Impact instance

Returns

float

climada.engine.cost_benefit.risk_rp_250(impact)

Risk measurement as exceedance impact at 250 years return period.

Parameters

impact (Impact) – an Impact instance

Returns

float

climada.engine.forecast module

class climada.engine.forecast.Forecast(hazard_dict, exposure, impact_funcs, haz_model='NWP', exposure_name=None)

Bases: object

Forecast definition. Compute an impact forecast with predefined hazard originating from a forecast (like numerical weather prediction models), exposure and impact. Use the calc() method to calculate a forecasted impact. Then use the plotting methods to illustrate the forecasted impacts. By default plots are saved under in a ‘/forecast/plots’ folder in the configurable save_dir in local_data (see climada.util.config) under a name summarizing the Hazard type, haz model name, initialization time of the forecast run, event date, exposure name and the plot title. As the class is relatively new, there might be future changes to the attributes, the methods, and the parameters used to call the methods. It was discovered at some point, that there might be a memory leak in matplotlib even when figures are closed (https://github.com/matplotlib/matplotlib/issues/8519). Due to this reason the plotting functions in this module have the flag close_fig, to close figures within the function scope, which might mitigate that problem if a script runs this plotting functions many times.

run_datetime

initialization time of the forecast model run used to create the Hazard

Type

list of datetime.datetime

event_date

Date on which the Hazard event takes place

Type

datetime.datetime

hazard

List of the hazard forecast with different lead times.

Type

list of CLIMADA Hazard

haz_model

Short string specifying the model used to create the hazard, if possible three big letters.

Type

str

exposure

an CLIMADA Exposures containg values at risk

Type

Exposure

exposure_name

string specifying the exposure (e.g. ‘EU’), which is used to name output files.

Type

str

vulnerability

Set of impact functions used in the impact calculation.

Type

ImpactFuncSet

__init__(hazard_dict, exposure, impact_funcs, haz_model='NWP', exposure_name=None)

Initialization with hazard, exposure and vulnerability.

Parameters

• hazard_dict (dict) – Dictionary of the format {run_datetime: Hazard} with run_datetime being the initialization time of a weather forecast run and Hazard being a CLIMADA Hazard derived from that forecast for one event. A probabilistic representation of that one event is possible, as long as the attribute Hazard.date is the same for all events. Several run_datetime:Hazard combinations for the same event can be provided.

• exposure (Exposure)

• impact_funcs (ImpactFuncSet)

• haz_model (str, optional) – Short string specifying the model used to create the hazard, if possible three big letters. Default is ‘NWP’ for numerical weather prediction.

• exposure_name (str, optional) – string specifying the exposure (e.g. ‘EU’), which is used to name output files.

ei_exp(run_datetime=None)

Expected impact per exposure

Parameters

run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

Returns

Return type

float

ai_agg(run_datetime=None)

average impact aggregated over all exposures

Parameters

run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

Returns

Return type

float

haz_summary_str(run_datetime=None)

provide a summary string for the hazard part of the forecast

Parameters

run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

Returns

summarizing the most important information about the hazard

Return type

str

summary_str(run_datetime=None)

provide a summary string for the impact forecast

Parameters

run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

Returns

summarizing the most important information about the impact forecast

Return type

str

lead_time(run_datetime=None)

provide the lead time for the impact forecast

Parameters

• run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime,

  default is first element of attribute run_datetime.

  Returns

• ——-

• datetime.timedelta – the difference between the initialization time of the forecast model run and the date of the event, commenly named lead time

calc(force_reassign=False)

calculate the impacts for all lead times using exposure, all hazards of all run_datetime, and ImpactFunctionSet.

Parameters

force_reassign (bool, optional) – Reassign hazard centroids to the exposure for all hazards, default is false.

plot_imp_map(run_datetime=None, save_fig=True, close_fig=False, polygon_file=None, polygon_file_crs='epsg:4326', proj=<cartopy.crs.PlateCarree object>, figsize=(9, 13))

plot a map of the impacts

Parameters

• run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

• save_fig (bool, optional) – Figure is saved if True, folder is within your configurable save_dir and filename is derived from the method summary_str() (for more details see class docstring). Default is True.

• close_fig (bool, optional) – Figure not drawn if True. Default is False.

• polygon_file (str, optional) – Points to a .shp-file with polygons do be drawn as outlines on the plot, default is None to not draw the lines. please also specify the crs in the parameter polygon_file_crs.

• polygon_file_crs (str, optional) – String of pattern <provider>:<code> specifying the crs. has to be readable by pyproj.Proj. Default is ‘epsg:4326’.

• proj (ccrs) – coordinate reference system used in coordinates The default is ccrs.PlateCarree()

• figsize (tuple) – figure size for plt.subplots, width, height in inches The default is (9, 13)

Returns

axes

Return type

cartopy.mpl.geoaxes.GeoAxesSubplot

plot_hist(run_datetime=None, save_fig=True, close_fig=False, figsize=(9, 8))

plot histogram of the forecasted impacts all ensemble members

Parameters

• run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

• save_fig (bool, optional) – Figure is saved if True, folder is within your configurable save_dir and filename is derived from the method summary_str() (for more details see class docstring). Default is True.

• close_fig (bool, optional) – Figure is not drawn if True. Default is False.

• figsize (tuple) – figure size for plt.subplots, width, height in inches The default is (9, 8)

Returns

axes

Return type

matplotlib.axes.Axes

plot_exceedence_prob(threshold, explain_str=None, run_datetime=None, save_fig=True, close_fig=False, polygon_file=None, polygon_file_crs='epsg:4326', proj=<cartopy.crs.PlateCarree object>, figsize=(9, 13))

plot exceedence map

Parameters

• threshold (float) – Threshold of impact unit for which exceedence probability should be plotted.

• explain_str (str, optional) – Short str which explains threshold, explain_str is included in the title of the figure.

• run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

• save_fig (bool, optional) – Figure is saved if True, folder is within your configurable save_dir and filename is derived from the method summary_str() (for more details see class docstring). Default is True.

• close_fig (bool, optional) – Figure not drawn if True. Default is False.

• polygon_file (str, optional) – Points to a .shp-file with polygons do be drawn as outlines on the plot, default is None to not draw the lines. please also specify the crs in the parameter polygon_file_crs.

• polygon_file_crs (str, optional) – String of pattern <provider>:<code> specifying the crs. has to be readable by pyproj.Proj. Default is ‘epsg:4326’.

• proj (ccrs) – coordinate reference system used in coordinates The default is ccrs.PlateCarree()

• figsize (tuple) – figure size for plt.subplots, width, height in inches The default is (9, 13)

Returns

axes

Return type

cartopy.mpl.geoaxes.GeoAxesSubplot

plot_warn_map(polygon_file=None, polygon_file_crs='epsg:4326', thresholds='default', decision_level='exposure_point', probability_aggregation=0.5, area_aggregation=0.5, title='WARNINGS', explain_text='warn level based on thresholds', run_datetime=None, proj=<cartopy.crs.PlateCarree object>, figsize=(9, 13), save_fig=True, close_fig=False)

plot map colored with 5 warning colors for all regions in provided shape file.

Parameters

• polygon_file (str, optional) – path to shp-file containing warning region polygons

• polygon_file_crs (str, optional) – String of pattern <provider>:<code> specifying the crs. has to be readable by pyproj.Proj. Default is ‘epsg:4326’.

• thresholds (list of 4 floats, optional) – Thresholds for coloring region in second, third, forth and fifth warning color.

• decision_level (str, optional) – Either ‘exposure_point’ or ‘polygon’. Default value is ‘exposure_point’.

• probability_aggregation (float or str, optional) – Either a float between [0..1] spezifying a quantile or ‘mean’ or ‘sum’. Default value is 0.5.

• area_aggregation (float or str.) – Either a float between [0..1] specifying a quantile or ‘mean’ or ‘sum’. Default value is 0.5.

• run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

• title (str, optional) – Default is ‘WARNINGS’.

• explain_text (str, optional) – Defaut is ‘warn level based on thresholds’.

• proj (ccrs) – coordinate reference system used in coordinates

• figsize (tuple) – figure size for plt.subplots, width, height in inches The default is (9, 13)

• save_fig (bool, optional) – Figure is saved if True, folder is within your configurable save_dir and filename is derived from the method summary_str() (for more details see class docstring). Default is True.

• close_fig (bool, optional) – Figure is not drawn if True. The default is False.

Returns

axes

Return type

cartopy.mpl.geoaxes.GeoAxesSubplot

plot_hexbin_ei_exposure(run_datetime=None, figsize=(9, 13))

plot the expected impact

Parameters

• run_datetime (datetime.datetime, optional) – Select the used hazard by the run_datetime, default is first element of attribute run_datetime.

• figsize (tuple) – figure size for plt.subplots, width, height in inches The default is (9, 13)

Returns

axes

Return type

cartopy.mpl.geoaxes.GeoAxesSubplot

climada.engine.impact module

class climada.engine.impact.ImpactFreqCurve

Bases: object

Impact exceedence frequency curve.

tag

dictionary of tags of exposures, impact functions set and hazard: {‘exp’: Tag(), ‘if_set’: Tag(), ‘haz’: TagHazard()}

Type

dict

return_per

return period

Type

np.array

impact

impact exceeding frequency

Type

np.array

unit

value unit used (given by exposures unit)

Type

str

label

string describing source data

Type

str

__init__()

Initialize self. See help(type(self)) for accurate signature.

plot(axis=None, log_frequency=False, **kwargs)

Plot impact frequency curve.

Parameters

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• log_frequency (boolean) – plot logarithmioc exceedance frequency on x-axis

• kwargs (optional) – arguments for plot matplotlib function, e.g. color=’b’

Returns

matplotlib.axes._subplots.AxesSubplot

class climada.engine.impact.Impact

Bases: object

Impact definition. Compute from an entity (exposures and impact functions) and hazard.

tag

dictionary of tags of exposures, impact functions set and hazard: {‘exp’: Tag(), ‘if_set’: Tag(), ‘haz’: TagHazard()}

Type

dict

event_id

id (>0) of each hazard event

Type

np.array

event_name

name of each hazard event

Type

list

date

date if events as integer date corresponding to the proleptic Gregorian ordinal, where January 1 of year 1 has ordinal 1 (ordinal format of datetime library)

Type

np.array

coord_exp

exposures coordinates [lat, lon] (in degrees)

Type

np.ndarray

eai_exp

expected annual impact for each exposure

Type

np.array

at_event

impact for each hazard event

Type

np.array

frequency

annual frequency of event

Type

np.arrray

tot_value

total exposure value affected

Type

float

aai_agg

average annual impact (aggregated)

Type

float

unit

value unit used (given by exposures unit)

Type

str

imp_mat

matrix num_events x num_exp with impacts. only filled if save_mat is True in calc()

Type

sparse.csr_matrix

__init__()

Empty initialization.

calc_freq_curve(return_per=None)

Compute impact exceedance frequency curve.

Parameters

return_per (np.array, optional) – return periods where to compute the exceedance impact. Use impact’s frequencies if not provided

Returns

ImpactFreqCurve

calc(exposures, impact_funcs, hazard, save_mat=False)

Compute impact of an hazard to exposures.

Parameters

• exposures (Exposures) – exposures

• impact_funcs (ImpactFuncSet) – impact functions

• hazard (Hazard) – hazard

• self_mat (bool) – self impact matrix: events x exposures

Examples

Use Entity class:

>>> haz = Hazard('TC') # Set hazard
>>> haz.read_mat(HAZ_DEMO_MAT)
>>> haz.check()
>>> ent = Entity() # Load entity with default values
>>> ent.read_excel(ENT_TEMPLATE_XLS) # Set exposures
>>> ent.check()
>>> imp = Impact()
>>> imp.calc(ent.exposures, ent.impact_funcs, haz)
>>> imp.calc_freq_curve().plot()

Specify only exposures and impact functions:

>>> haz = Hazard('TC') # Set hazard
>>> haz.read_mat(HAZ_DEMO_MAT)
>>> haz.check()
>>> funcs = ImpactFuncSet()
>>> funcs.read_excel(ENT_TEMPLATE_XLS) # Set impact functions
>>> funcs.check()
>>> exp = Exposures(pd.read_excel(ENT_TEMPLATE_XLS)) # Set exposures
>>> exp.check()
>>> imp = Impact()
>>> imp.calc(exp, funcs, haz)
>>> imp.aai_agg

calc_risk_transfer(attachment, cover)

Compute traaditional risk transfer over impact. Returns new impact with risk transfer applied and the insurance layer resulting Impact metrics.

Parameters

• attachment (float) – attachment (deductible)

• cover (float) – cover

Returns

Impact, Impact

plot_hexbin_eai_exposure(mask=None, ignore_zero=True, pop_name=True, buffer=0.0, extend='neither', axis=None, **kwargs)

Plot hexbin expected annual impact of each exposure.

Parameters

• mask (np.array, optional) – mask to apply to eai_exp plotted.

• ignore_zero (bool, optional) – flag to indicate if zero and negative values are ignored in plot. Default: False

• pop_name (bool, optional) – add names of the populated places

• buffer (float, optional) – border to add to coordinates. Default: 1.0.

• extend (str, optional) – extend border colorbar with arrows. [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for hexbin matplotlib function

Returns

cartopy.mpl.geoaxes.GeoAxesSubplot

plot_scatter_eai_exposure(mask=None, ignore_zero=True, pop_name=True, buffer=0.0, extend='neither', axis=None, **kwargs)

Plot scatter expected annual impact of each exposure.

Parameters

• mask (np.array, optional) – mask to apply to eai_exp plotted.

• ignore_zero (bool, optional) – flag to indicate if zero and negative values are ignored in plot. Default: False

• pop_name (bool, optional) – add names of the populated places

• buffer (float, optional) – border to add to coordinates. Default: 1.0.

• extend (str, optional) – extend border colorbar with arrows. [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for hexbin matplotlib function

Returns

cartopy.mpl.geoaxes.GeoAxesSubplot

plot_raster_eai_exposure(res=None, raster_res=None, save_tiff=None, raster_f=<function Impact.<lambda>>, label='value (log10)', axis=None, **kwargs)

Plot raster expected annual impact of each exposure.

Parameters

• res (float, optional) – resolution of current data in units of latitude and longitude, approximated if not provided.

• raster_res (float, optional) – desired resolution of the raster

• save_tiff (str, optional) – file name to save the raster in tiff format, if provided

• raster_f (lambda function) – transformation to use to data. Default: log10 adding 1.

• label (str) – colorbar label

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for imshow matplotlib function

Returns

cartopy.mpl.geoaxes.GeoAxesSubplot

plot_basemap_eai_exposure(mask=None, ignore_zero=False, pop_name=True, buffer=0.0, extend='neither', zoom=10, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png', axis=None, **kwargs)

Plot basemap expected annual impact of each exposure.

Parameters

• mask (np.array, optional) – mask to apply to eai_exp plotted.

• ignore_zero (bool, optional) – flag to indicate if zero and negative values are ignored in plot. Default: False

• pop_name (bool, optional) – add names of the populated places

• buffer (float, optional) – border to add to coordinates. Default: 0.0.

• extend (str, optional) – extend border colorbar with arrows. [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]

• zoom (int, optional) – zoom coefficient used in the satellite image

• url (str, optional) – image source, e.g. ctx.sources.OSM_C

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for scatter matplotlib function, e.g. cmap=’Greys’. Default: ‘Wistia’

Returns

cartopy.mpl.geoaxes.GeoAxesSubplot

plot_hexbin_impact_exposure(event_id=1, mask=None, ignore_zero=True, pop_name=True, buffer=0.0, extend='neither', axis=None, **kwargs)

Plot hexbin impact of an event at each exposure. Requires attribute imp_mat.

Parameters

• event_id (int, optional) – id of the event for which to plot the impact. Default: 1.

• mask (np.array, optional) – mask to apply to impact plotted.

• ignore_zero (bool, optional) – flag to indicate if zero and negative values are ignored in plot. Default: False

• pop_name (bool, optional) – add names of the populated places

• buffer (float, optional) – border to add to coordinates. Default: 1.0.

• extend (str, optional) – extend border colorbar with arrows. [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]

• kwargs (optional) – arguments for hexbin matplotlib function

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

Returns

matplotlib.figure.Figure, cartopy.mpl.geoaxes.GeoAxesSubplot

plot_basemap_impact_exposure(event_id=1, mask=None, ignore_zero=True, pop_name=True, buffer=0.0, extend='neither', zoom=10, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png', axis=None, **kwargs)

Plot basemap impact of an event at each exposure. Requires attribute imp_mat.

Parameters

• event_id (int, optional) – id of the event for which to plot the impact. Default: 1.

• mask (np.array, optional) – mask to apply to impact plotted.

• ignore_zero (bool, optional) – flag to indicate if zero and negative values are ignored in plot. Default: False

• pop_name (bool, optional) – add names of the populated places

• buffer (float, optional) – border to add to coordinates. Default: 0.0.

• extend (str, optional) – extend border colorbar with arrows. [ ‘neither’ | ‘both’ | ‘min’ | ‘max’ ]

• zoom (int, optional) – zoom coefficient used in the satellite image

• url (str, optional) – image source, e.g. ctx.sources.OSM_C

• axis (matplotlib.axes._subplots.AxesSubplot, optional) – axis to use

• kwargs (optional) – arguments for scatter matplotlib function, e.g. cmap=’Greys’. Default: ‘Wistia’

Returns

cartopy.mpl.geoaxes.GeoAxesSubplot

write_csv(file_name)

Write data into csv file. imp_mat is not saved.

Parameters

file_name (str) – absolute path of the file

write_excel(file_name)

Write data into Excel file. imp_mat is not saved.

Parameters

file_name (str) – absolute path of the file

write_sparse_csr(file_name)

Write imp_mat matrix in numpy’s npz format.

calc_impact_year_set(all_years=True, year_range=None)

Calculate yearly impact from impact data.

Parameters

• all_years (boolean) – return values for all years between first and

• last year with event, including years without any events.

• year_range (tuple or list with integers) – start and end year

Returns

Impact year set of type numpy.ndarray with summed impact per year.

local_exceedance_imp(return_periods=(25, 50, 100, 250))

Compute exceedance impact map for given return periods. Requires attribute imp_mat.

Parameters

return_periods (np.array) – return periods to consider

Returns

np.array

plot_rp_imp(return_periods=(25, 50, 100, 250), log10_scale=True, smooth=True, axis=None, **kwargs)

Compute and plot exceedance impact maps for different return periods. Calls local_exceedance_imp.

Parameters

• return_periods (tuple(int), optional) – return periods to consider

• log10_scale (boolean, optional) – plot impact as log10(impact)

• smooth (bool, optional) – smooth plot to plot.RESOLUTIONxplot.RESOLUTION

• kwargs (optional) – arguments for pcolormesh matplotlib function used in event plots

Returns

matplotlib.axes._subplots.AxesSubplot, np.ndarray (return_periods.size x num_centroids)

static read_sparse_csr(file_name)

Read imp_mat matrix from numpy’s npz format.

Parameters

file_name (str) – file name

Returns

sparse.csr_matrix

read_csv(file_name)

Read csv file containing impact data generated by write_csv.

Parameters

file_name (str) – absolute path of the file

read_excel(file_name)

Read excel file containing impact data generated by write_excel.

Parameters

file_name (str) – absolute path of the file

static video_direct_impact(exp, if_set, haz_list, file_name='', writer=<matplotlib.animation.PillowWriter object>, imp_thresh=0, args_exp=None, args_imp=None)

Computes and generates video of accumulated impact per input events over exposure.

Parameters

• exp (Exposures) – exposures instance, constant during all video

• if_set (ImpactFuncSet) – impact functions

• haz_list (list(Hazard)) – every Hazard contains an event; all hazards use the same centroids

• file_name (str, optional) – file name to save video, if provided

• writer = (matplotlib.animation., optional*) – video writer. Default: pillow with bitrate=500

• imp_thresh (float) – represent damages greater than threshold

• args_exp (optional) – arguments for scatter (points) or hexbin (raster) matplotlib function used in exposures

• args_imp (optional) – arguments for scatter (points) or hexbin (raster) matplotlib function used in impact

Returns

list(Impact)

select(event_ids=None, event_names=None, dates=None, coord_exp=None)

Select a subset of events and/or exposure points from the impact. If multiple input variables are not None, it returns all the impacts matching at least one of the conditions.

Note

the frequencies are NOT adjusted. Method to adjust frequencies

and obtain correct eai_exp:

1- Select subset of impact according to your choice imp = impact.select(…) 2- Adjust manually the frequency of the subset of impact imp.frequency = […] 3- Use select without arguments to select all events and recompute the eai_exp with the updated frequencies. imp = imp.select()

Parameters

• event_ids (list[int], optional) – Selection of events by their id. The default is None.

• event_names (list[str], optional) – Selection of events by their name. The default is None.

• dates (tuple(), optional) – (start-date, end-date), events are selected if they are >= than start-date and <= than end-date. Dates in same format as impact.date (ordinal format of datetime library) The default is None.

• coord_exp ((np.ndarray), optional]) – Selection of exposures coordinates [lat, lon] (in degrees) The default is None.

Raises

ValueError – If the impact matrix is missing, the eai_exp and aai_agg cannot be updated for a selection of events and/or exposures.

Returns

imp – A new impact object with a selection of events and/or exposures

Return type

climada.engine.Impact

climada.engine.impact_data module

climada.engine.impact_data.assign_hazard_to_emdat(certainty_level, intensity_path_haz, names_path_haz, reg_id_path_haz, date_path_haz, emdat_data, start_time, end_time, keep_checks=False)

assign_hazard_to_emdat: link EMdat event to hazard

Parameters:

input files (paths):

intensity: sparse matrix with hazards as rows and grid points as cols, values only at location with impacts names: identifier for each hazard (i.e. IBtracID) (rows of the matrix) reg_id: ISO country ID of each grid point (cols of the matrix) date: start date of each hazard (rows of the matrix) emdat_data: pd.dataframe with EMdat data start: start date of events to be assigned ‘yyyy-mm-dd’ end: end date of events to be assigned ‘yyyy-mm-dd’ disaster_subtype: EMdat disaster subtype

Returns

pd.dataframe with EMdat entries linked to a hazard

climada.engine.impact_data.hit_country_per_hazard(intensity_path, names_path, reg_id_path, date_path)

hit_country_per_hazard: create list of hit countries from hazard set

Parameters:

input files:

intensity: sparse matrix with hazards as rows and grid points as cols, values only at location with impacts names: identifier for each hazard (i.e. IBtracID) (rows of the matrix) reg_id: ISO country ID of each grid point (cols of the matrix) date: start date of each hazard (rows of the matrix)

Returns

pd.dataframe with all hit countries per hazard

climada.engine.impact_data.create_lookup(emdat_data, start, end, disaster_subtype='Tropical cyclone')

create_lookup: prepare a lookup table of EMdat events to which hazards can be assigned

Parameters

• emdat_data – pd.dataframe with EMdat data

• start – start date of events to be assigned ‘yyyy-mm-dd’

• end – end date of events to be assigned ‘yyyy-mm-dd’

• disaster_subtype – EMdat disaster subtype

Returns

pd.dataframe lookup

climada.engine.impact_data.emdat_possible_hit(lookup, hit_countries, delta_t)

relate EM disaster to hazard using hit countries and time

Parameters

input files – lookup: pd.dataframe to relate EMdatID to hazard tracks: pd.dataframe with all hit countries per hazard delta_t: max time difference of start of EMdat event and hazard hit_countries: start: start date of events to be assigned end: end date of events to be assigned disaster_subtype: EMdat disaster subtype

Returns

list with possible hits

climada.engine.impact_data.match_em_id(lookup, poss_hit)

function to check if EM_ID has been assigned already and combine possible hits

Parameters

• lookup – pd.dataframe to relate EMdatID to hazard

• poss_hit – list with possible hits

Returns

list with all possible hits per EMdat ID

climada.engine.impact_data.assign_track_to_em(lookup, possible_tracks_1, possible_tracks_2, level)

function to assign a hazard to an EMdat event to get some confidene into the procedure, hazards get only assigned if there is no other hazard occuring at a bigger time interval in that country Thus a track of possible_tracks_1 gets only assigned if there are no other tracks in possible_tracks_2. The confidence can be expressed with a certainty level

Parameters

• lookup – pd.dataframe to relate EMdatID to hazard

• possible_tracks_1 – list of possible hits with smaller time horizon

• possible_tracks_2 – list of possible hits with larger time horizon

• level – level of confidence

Returns

pd.dataframe lookup with assigend tracks and possible hits

climada.engine.impact_data.check_assigned_track(lookup, checkset)

compare lookup with assigned tracks to a set with checked sets

Parameters

• lookup – pd.dataframe to relate EMdatID to hazard

• checkset – pd.dataframe with already checked hazards

Returns

error scores

climada.engine.impact_data.clean_emdat_df(emdat_file, countries=None, hazard=None, year_range=None, target_version=2020)

Get a clean and standardized DataFrame from EM-DAT-CSV-file (1) load EM-DAT data from CSV to DataFrame and remove header/footer, (2) handle version, clean up, and add columns, and (3) filter by country, hazard type and year range (if any given)

Parameters

emdat_file (str, Path, or DataFrame) – Either string with full path to CSV-file or pandas.DataFrame loaded from EM-DAT CSV

Optional parameters:

countries (list of str): country ISO3-codes or names, e.g. [‘JAM’, ‘CUB’].

countries=None for all countries (default)

hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.:

Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire; Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, Tsunami, etc.; OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc.

year_range (list or tuple): Year range to be extracted, e.g. (2000, 2015);

(only min and max are considered)

target_version (int): required EM-DAT data format version (i.e. year of download),

changes naming of columns/variables (default: 2020)

Returns

DataFrame containing cleaned and filtered EM-DAT impact data

Return type

df_data (pandas.DataFrame)

climada.engine.impact_data.emdat_countries_by_hazard(emdat_file_csv, hazard=None, year_range=None)

return list of all countries exposed to a chosen hazard type from EMDAT data as CSV.

Parameters

emdat_file (str or DataFrame) – Either string with full path to CSV-file or pandas.DataFrame loaded from EM-DAT CSV

Optional Parameters:

hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.:

Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire; Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, Tsunami, etc.; OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc.:

year_range (tuple of integers or None):

range of years to consider, i.e. (1950, 2000) default is None, i.e. consider all years

Returns

list

List of ISO3-codes of countries impacted by the disaster (sub-)types

countries_nameslist

List of names of countries impacted by the disaster (sub-)types

Return type

countries_iso3a

climada.engine.impact_data.scale_impact2refyear(impact_values, year_values, iso3a_values, reference_year=None)

Scale give impact values proportional to GDP to the according value in a reference year (for normalization of monetary values)

Parameters

• impact_values (list or array) – Impact values to be scaled.

• year_values (list or array) – Year of each impact (same length as impact_values)

• iso3a_values (list or array) – ISO3alpha code of country for each impact (same length as impact_values)

Optional Parameters:

reference_year (int):

Impact is scaled proportional to GDP to the value of the reference year. No scaling for reference_year=None (default)

climada.engine.impact_data.emdat_impact_yearlysum(emdat_file_csv, countries=None, hazard=None, year_range=None, reference_year=None, imp_str="Total Damages ('000 US$)", version=2020)

function to load EM-DAT data and sum impact per year :Parameters: emdat_file (str or DataFrame) – Either string with full path to CSV-file or

pandas.DataFrame loaded from EM-DAT CSV

Optional parameters:

countries (list of str): country ISO3-codes or names, e.g. [‘JAM’, ‘CUB’].

countries=None for all countries (default)

hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.:

Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire; Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, Tsunami, etc.; OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc.

year_range (list or tuple): Year range to be extracted, e.g. (2000, 2015);

(only min and max are considered)

version (int): given EM-DAT data format version (i.e. year of download),

changes naming of columns/variables (default: 2020)

reference_year (int): reference year of exposures. Impact is scaled

proportional to GDP to the value of the reference year. No scaling for 0 (default)

imp_str (str): Column name of impact metric in EMDAT CSV,

default = “Total Damages (‘000 US$)”

Returns

DataFrame with summed impact and scaled impact per

year and country.

Return type

out (pd.DataFrame)

climada.engine.impact_data.emdat_impact_event(emdat_file_csv, countries=None, hazard=None, year_range=None, reference_year=None, imp_str="Total Damages ('000 US$)", version=2020)

function to load EM-DAT data return impact per event

Parameters

emdat_file_csv (str) – Full path to EMDAT-file (CSV), i.e.: emdat_file_csv = SYSTEM_DIR.joinpath(‘emdat_201810.csv’)

Optional parameters:

countries (list of str): country ISO3-codes or names, e.g. [‘JAM’, ‘CUB’].

countries=None for all countries (default)

hazard (list or str): List of Disaster (sub-)type accordung EMDAT terminology, i.e.:

Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire; Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, Tsunami, etc.; OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc.

year_range (list or tuple): Year range to be extracted, e.g. (2000, 2015);

(only min and max are considered)

reference_year (int): reference year of exposures. Impact is scaled

proportional to GDP to the value of the reference year. No scaling for 0 (default)

imp_str (str): Column name of impact metric in EMDAT CSV,

default = “Total Damages (‘000 US$)”

version (int): EM-DAT version to take variable/column names from (defaul: 2020)

Returns

EMDAT DataFrame with new columns “year”,

”region_id”, and “impact” and +impact_scaled” total impact per event with same unit as chosen impact, but multiplied by 1000 if impact is given as 1000 US$ (e.g. imp_str=”Total Damages (‘000 US$) scaled”).

Return type

out (pandas DataFrame)

climada.engine.impact_data.emdat_to_impact(emdat_file_csv, hazard_type_climada, year_range=None, countries=None, hazard_type_emdat=None, reference_year=None, imp_str='Total Damages')

function to load EM-DAT data return impact per event

Parameters

• emdat_file_csv (str) – Full path to EMDAT-file (CSV), i.e.: emdat_file_csv = SYSTEM_DIR.joinpath(‘emdat_201810.csv’)

• hazard_type_climada (str) – Hazard type CLIMADA abbreviation, i.e. ‘TC’ for tropical cyclone

Optional parameters:

hazard_type_emdat (list or str): List of Disaster (sub-)type accordung

EMDAT terminology, e.g.: Animal accident, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire; Coastal Flooding, Convective Storm, Riverine Flood, Tropical cyclone, Tsunami, etc.; OR CLIMADA hazard type abbreviations, e.g. TC, BF, etc. If not given, it is deducted from hazard_type_climada

year_range (list with 2 integers): start and end year e.g. [1980, 2017]

default: None –> take year range from EM-DAT file

countries (list of str): country ISO3-codes or names, e.g. [‘JAM’].

Set to None or [‘all’] for all countries (default)

reference_year (int): reference year of exposures. Impact is scaled

proportional to GDP to the value of the reference year. No scaling for reference_year=0 (default)

imp_str (str): Column name of impact metric in EMDAT CSV,

default = “Total Damages (‘000 US$)”

Returns

impact object of same format as output from CLIMADA

impact computation. Values scaled with GDP to reference_year if reference_year is given. i.e. current US$ for imp_str=”Total Damages (‘000 US$) scaled” (factor 1000 is applied) impact_instance.eai_exp holds expected annual impact for each country. impact_instance.coord_exp holds rough central coordinates for each country.

countries (list): ISO3-codes of countries in same order as in impact_instance.eai_exp

Return type

impact_instance (instance of climada.engine.Impact)

climada.engine.supplychain module

class climada.engine.supplychain.SupplyChain

Bases: object

SupplyChain class.

The SupplyChain class provides methods for loading Multi-Regional Input-Output Tables (MRIOT) and computing direct, indirect and total impacts.

mriot_data

The input-output table data.

Type

np.array

mriot_reg_names

Names of regions considered in the input-output table.

Type

np.array

sectors

Sectors considered in the input-output table.

Type

np.array

total_prod

Countries’ total production.

Type

np.array

mriot_type

Type of the adopted input-output table.

Type

str

reg_pos

Regions’ positions within the input-output table and impact arrays.

Type

dict

reg_dir_imp

Regions undergoing direct impacts.

Type

list

years

Years of the considered hazard events for which impact is calculated.

Type

np.array

direct_impact

Direct impact array.

Type

np.array

direct_aai_agg

Average annual direct impact array.

Type

np.array

indirect_impact

Indirect impact array.

Type

np.array

indirect_aai_agg

Average annual indirect impact array.

Type

np.array

total_impact

Total impact array.

Type

np.array

total_aai_agg

Average annual total impact array.

Type

np.array

io_data

Dictionary with the coefficients, inverse and risk_structure matrixes and the selected input-output modeling approach.

Type

dict

__init__()

Initialize SupplyChain.

read_wiod16(year=2014, range_rows=(5, 2469), range_cols=(4, 2468), col_iso3=2, col_sectors=1)

Read multi-regional input-output tables of the 2016 release of the WIOD project: http://www.wiod.org/database/wiots16

Parameters

• year (int) – Year of WIOD table to use. Valid years go from 2000 to 2014. Default year is 2014.

• range_rows (tuple) – initial and end positions of data along rows. Default is (5,2469).

• range_cols (tuple) – initial and end positions of data along columns. Default is (4,2468).

• col_iso3 (int) – column with countries names in ISO3 codes. Default is 2.

• col_sectors (int) – column with sector names. Default is 1.

References

[1] Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. (2015), “An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production”, Review of International Economics., 23: 575–605

calc_sector_direct_impact(hazard, exposure, imp_fun_set, selected_subsec='service')

Calculate direct impacts.

hazardHazard

Hazard object for impact calculation.

exposureExposures

Exposures object for impact calculation. For WIOD tables, exposure.region_id must be country names following ISO3 codes.

imp_fun_setImpactFuncSet

Set of impact functions.

selected_subsecstr or list

Positions of the selected sectors. These positions can be either defined by the user by passing a list of values, or by using built-in sectors’ aggregations for the WIOD data passing a string with possible values being “service”, “manufacturing”, “agriculture” or “mining”. Default is “service”.

calc_indirect_impact(io_approach='ghosh')

Calculate indirect impacts according to the specified input-output appraoch. This function needs to be run after calc_sector_direct_impact.

Parameters

io_approach (str) – The adopted input-output modeling approach. Possible approaches are ‘leontief’, ‘ghosh’ and ‘eeioa’. Default is ‘gosh’.

References

[1] W. W. Leontief, Output, employment, consumption, and investment, The Quarterly Journal of Economics 58, 1944. [2] Ghosh, A., Input-Output Approach in an Allocation System, Economica, New Series, 25, no. 97: 58-64. doi:10.2307/2550694, 1958. [3] Kitzes, J., An Introduction to Environmentally-Extended Input-Output Analysis, Resources, 2, 489-503; doi:10.3390/resources2040489, 2013.

calc_total_impact()

Calculate total impacts summing direct and indirect impacts.