"""
This file is part of CLIMADA.
Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS.
CLIMADA is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free
Software Foundation, version 3.
CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with CLIMADA. If not, see <https://www.gnu.org/licenses/>.
---
Define Uncertainty Impact class
"""
__all__ = ['CalcImpact']
import logging
import time
import pandas as pd
import numpy as np
from climada.engine import Impact
from climada.engine.unsequa import Calc, InputVar, UncImpactOutput
from climada.util import log_level
LOGGER = logging.getLogger(__name__)
[docs]class CalcImpact(Calc):
"""
Impact uncertainty caclulation class.
This is the class to perform uncertainty analysis on the outputs of a
climada.engine.impact.Impact() object.
Attributes
----------
rp : list(int)
List of the chosen return periods.
calc_eai_exp : bool
Compute eai_exp or not
calc_at_event : bool
Compute eai_exp or not
metric_names : tuple(str)
Names of the impact output metris
('aai_agg', 'freq_curve', 'at_event', 'eai_exp', 'tot_value')
value_unit : str
Unit of the exposures value
input_var_names : tuple(str)
Names of the required uncertainty input variables
('exp_input_var', 'impf_input_var', 'haz_input_var')
exp_input_var : climada.engine.uncertainty.input_var.InputVar
Exposure uncertainty variable
impf_input_var : climada.engine.uncertainty.input_var.InputVar
Impact function set uncertainty variable
haz_input_var: climada.engine.uncertainty.input_var.InputVar
Hazard uncertainty variable
"""
[docs] def __init__(self, exp_input_var, impf_input_var, haz_input_var):
"""Initialize UncCalcImpact
Sets the uncertainty input variables, the impact metric_names, and the
units.
Parameters
----------
exp_input_var : climada.engine.uncertainty.input_var.InputVar or climada.entity.Exposure
Exposure uncertainty variable or Exposure
impf_input_var : climada.engine.uncertainty.input_var.InputVar or climada.entity.ImpactFuncSet
Impact function set uncertainty variable or Impact function set
haz_input_var : climada.engine.uncertainty.input_var.InputVar or climada.hazard.Hazard
Hazard uncertainty variable or Hazard
"""
Calc.__init__(self)
self.input_var_names = ('exp_input_var', 'impf_input_var', 'haz_input_var')
self.exp_input_var = InputVar.var_to_inputvar(exp_input_var)
self.impf_input_var = InputVar.var_to_inputvar(impf_input_var)
self.haz_input_var = InputVar.var_to_inputvar(haz_input_var)
self.metric_names = ('aai_agg', 'freq_curve', 'at_event',
'eai_exp', 'tot_value')
self.value_unit = self.exp_input_var.evaluate().value_unit
self.check_distr()
[docs] def uncertainty(self,
unc_sample,
rp=None,
calc_eai_exp=False,
calc_at_event=False,
pool=None
):
"""
Computes the impact for each sample in unc_data.sample_df.
By default, the aggregated average annual impact
(impact.aai_agg) and the excees impact at return periods rp
(imppact.calc_freq_curve(self.rp).impact) is computed.
Optionally, eai_exp and at_event is computed (this may require
a larger amount of memory if the number of samples and/or the number
of centroids and/or exposures points is large).
This sets the attributes self.rp, self.calc_eai_exp,
self.calc_at_event, self.metrics.
This sets the attributes:
unc_output.aai_agg_unc_df,
unc_output.freq_curve_unc_df
unc_output.eai_exp_unc_df
unc_output.at_event_unc_df
unc_output.tot_value_unc_df
unc_output.unit
Parameters
----------
unc_sample : climada.engine.uncertainty.unc_output.UncOutput
Uncertainty data object with the input parameters samples
rp : list(int), optional
Return periods in years to be computed.
The default is [5, 10, 20, 50, 100, 250].
calc_eai_exp : boolean, optional
Toggle computation of the impact at each centroid location.
The default is False.
calc_at_event : boolean, optional
Toggle computation of the impact for each event.
The default is False.
pool : pathos.pools.ProcessPool, optional
Pool of CPUs for parralel computations.
The default is None.
Returns
-------
unc_output : climada.engine.uncertainty.unc_output.UncImpactOutput
Uncertainty data object with the impact outputs for each sample
and all the sample data copied over from unc_sample.
Raises
------
ValueError:
If no sampling parameters defined, the distribution cannot
be computed.
See Also
--------
climada.engine.impact: Compute risk.
"""
if unc_sample.samples_df.empty:
raise ValueError("No sample was found. Please create one first"
"using UncImpact.make_sample(N)")
samples_df = unc_sample.samples_df.copy(deep=True)
unit = self.value_unit
if rp is None:
rp=[5, 10, 20, 50, 100, 250]
self.rp = rp
self.calc_eai_exp = calc_eai_exp
self.calc_at_event = calc_at_event
start = time.time()
one_sample = samples_df.iloc[0:1].iterrows()
imp_metrics = map(self._map_impact_calc, one_sample)
[aai_agg_list, freq_curve_list,
eai_exp_list, at_event_list, tot_value_list] = list(zip(*imp_metrics))
elapsed_time = (time.time() - start)
self.est_comp_time(unc_sample.n_samples, elapsed_time, pool)
#Compute impact distributions
with log_level(level='ERROR', name_prefix='climada'):
if pool:
LOGGER.info('Using %s CPUs.', pool.ncpus)
chunksize = min(unc_sample.n_samples // pool.ncpus, 100)
imp_metrics = pool.map(self._map_impact_calc,
samples_df.iterrows(),
chunsize = chunksize)
else:
imp_metrics = map(self._map_impact_calc,
samples_df.iterrows())
#Perform the actual computation
with log_level(level='ERROR', name_prefix='climada'):
[aai_agg_list, freq_curve_list,
eai_exp_list, at_event_list,
tot_value_list] = list(zip(*imp_metrics))
# Assign computed impact distribution data to self
aai_agg_unc_df = pd.DataFrame(aai_agg_list,
columns = ['aai_agg'])
freq_curve_unc_df = pd.DataFrame(freq_curve_list,
columns=['rp' + str(n) for n in rp])
eai_exp_unc_df = pd.DataFrame(eai_exp_list)
# Setting to sparse dataframes is not compatible with .to_hdf5
# if np.count_nonzero(df_eai_exp.to_numpy()) / df_eai_exp.size < 0.5:
# df_eai_exp = df_eai_exp.astype(pd.SparseDtype("float", 0.0))
#eai_exp_unc_df = df_eai_exp
at_event_unc_df = pd.DataFrame(at_event_list)
# Setting to sparse dataframes is not compatible with .to_hdf5
# if np.count_nonzero(df_at_event.to_numpy()) / df_at_event.size < 0.5:
# df_at_event = df_at_event.astype(pd.SparseDtype("float", 0.0))
#at_event_unc_df = df_at_event
tot_value_unc_df = pd.DataFrame(tot_value_list,
columns = ['tot_value'])
if calc_eai_exp:
exp = self.exp_input_var.evaluate()
coord_df = exp.gdf[['latitude', 'longitude']]
else:
coord_df = pd.DataFrame([])
return UncImpactOutput(samples_df=samples_df,
unit=unit,
aai_agg_unc_df=aai_agg_unc_df,
freq_curve_unc_df=freq_curve_unc_df,
eai_exp_unc_df=eai_exp_unc_df,
at_event_unc_df=at_event_unc_df,
tot_value_unc_df=tot_value_unc_df,
coord_df=coord_df
)
def _map_impact_calc(self, sample_iterrows):
"""
Map to compute impact for all parameter samples in parrallel
Parameters
----------
sample_iterrows : pd.DataFrame.iterrows()
Generator of the parameter samples
Returns
-------
: list
impact metrics list for all samples containing aai_agg, rp_curve,
eai_exp (np.array([]) if self.calc_eai_exp=False) and at_event
(np.array([]) if self.calc_at_event=False).
"""
# [1] only the rows of the dataframe passed by pd.DataFrame.iterrows()
exp_samples = sample_iterrows[1][self.exp_input_var.labels].to_dict()
impf_samples = sample_iterrows[1][self.impf_input_var.labels].to_dict()
haz_samples = sample_iterrows[1][self.haz_input_var.labels].to_dict()
exp = self.exp_input_var.evaluate(**exp_samples)
impf = self.impf_input_var.evaluate(**impf_samples)
haz = self.haz_input_var.evaluate(**haz_samples)
imp = Impact()
imp.calc(exposures=exp, impact_funcs=impf, hazard=haz)
# Extract from climada.impact the chosen metrics
freq_curve = imp.calc_freq_curve(self.rp).impact
if self.calc_eai_exp:
eai_exp = imp.eai_exp
else:
eai_exp = np.array([])
if self.calc_at_event:
at_event= imp.at_event
else:
at_event = np.array([])
return [imp.aai_agg, freq_curve, eai_exp, at_event, imp.tot_value]