def test_change_exposures_if_pass(self):
"""Test _change_exposures_if"""
meas = Measure()
meas.imp_fun_map = '1to3'
meas.haz_type = 'TC'
imp_set = ImpactFuncSet()
imp_tc = ImpactFunc()
imp_tc.haz_type = 'TC'
imp_tc.id = 1
imp_tc.intensity = np.arange(10, 100, 10)
imp_tc.mdd = np.arange(10, 100, 10)
imp_tc.paa = np.arange(10, 100, 10)
imp_set.append(imp_tc)
imp_tc = ImpactFunc()
imp_tc.haz_type = 'TC'
imp_tc.id = 3
imp_tc.intensity = np.arange(10, 100, 10)
imp_tc.mdd = np.arange(10, 100, 10) * 2
imp_tc.paa = np.arange(10, 100, 10) * 2
exp = Exposures()
exp.read_hdf5(EXP_DEMO_H5)
new_exp = meas._change_exposures_if(exp)
self.assertEqual(new_exp.ref_year, exp.ref_year)
self.assertEqual(new_exp.value_unit, exp.value_unit)
self.assertEqual(new_exp.tag.file_name, exp.tag.file_name)
self.assertEqual(new_exp.tag.description, exp.tag.description)
self.assertTrue(np.array_equal(new_exp.gdf.value.values, exp.gdf.value.values))
self.assertTrue(np.array_equal(new_exp.gdf.latitude.values, exp.gdf.latitude.values))
self.assertTrue(np.array_equal(new_exp.gdf.longitude.values, exp.gdf.longitude.values))
self.assertTrue(np.array_equal(exp.gdf[INDICATOR_IF + 'TC'].values, np.ones(new_exp.gdf.shape[0])))
self.assertTrue(np.array_equal(new_exp.gdf[INDICATOR_IF + 'TC'].values, np.ones(new_exp.gdf.shape[0]) * 3))
def test_change_imp_func_pass(self):
"""Test _change_imp_func"""
meas = MeasureSet()
meas.read_mat(ENT_TEST_MAT)
act_1 = meas.get_measure(name='Mangroves')[0]
imp_set = ImpactFuncSet()
imp_tc = ImpactFunc()
imp_tc.haz_type = 'XX'
imp_tc.id = 1
imp_tc.intensity = np.arange(10, 100, 10)
imp_tc.intensity[0] = 0.
imp_tc.intensity[-1] = 100.
imp_tc.mdd = np.array([0.0, 0.0, 0.021857142857143, 0.035887500000000,
0.053977415307403, 0.103534246575342, 0.180414000000000,
0.410796000000000, 0.410796000000000])
imp_tc.paa = np.array([0, 0.005000000000000, 0.042000000000000, 0.160000000000000,
0.398500000000000, 0.657000000000000, 1.000000000000000,
1.000000000000000, 1.000000000000000])
imp_set.append(imp_tc)
new_imp = act_1._change_imp_func(imp_set).get_func('XX')[0]
self.assertTrue(np.array_equal(new_imp.intensity, np.array([4., 24., 34., 44.,
54., 64., 74., 84., 104.])))
self.assertTrue(np.array_equal(new_imp.mdd, np.array([0, 0, 0.021857142857143, 0.035887500000000,
0.053977415307403, 0.103534246575342, 0.180414000000000, 0.410796000000000, 0.410796000000000])))
self.assertTrue(np.array_equal(new_imp.paa, np.array([0, 0.005000000000000, 0.042000000000000,
0.160000000000000, 0.398500000000000, 0.657000000000000, 1.000000000000000,
1.000000000000000, 1.000000000000000])))
self.assertFalse(id(new_imp) == id(imp_tc))
def test_calc_mdr_pass(self):
"""Compute mdr interpolating values."""
imp_fun = ImpactFunc()
imp_fun.intensity = np.arange(0, 100, 10)
imp_fun.paa = np.arange(0, 1, 0.1)
imp_fun.mdd = np.arange(0, 1, 0.1)
new_inten = 17.2
self.assertEqual(imp_fun.calc_mdr(new_inten), 0.029583999999999996)
def _fill_dfr(self, dfr, var_names):
def _get_xls_funcs(dfr, var_names):
"""Parse individual impact functions."""
dist_func = []
for (haz_type,
imp_id) in zip(dfr[var_names['col_name']['peril']],
dfr[var_names['col_name']['func_id']]):
if (haz_type, imp_id) not in dist_func:
dist_func.append((haz_type, imp_id))
return dist_func
try:
dist_func = _get_xls_funcs(dfr, var_names)
for haz_type, imp_id in dist_func:
df_func = dfr[dfr[var_names['col_name']['peril']] == haz_type]
df_func = df_func[df_func[var_names['col_name']['func_id']] ==
imp_id]
func = ImpactFunc()
func.haz_type = haz_type
func.id = imp_id
# check that the unit of the intensity is the same
try:
if len(df_func[var_names['col_name']
['name']].unique()) != 1:
raise ValueError(
'Impact function with two different names.')
func.name = df_func[var_names['col_name']
['name']].values[0]
except KeyError:
func.name = str(func.id)
# check that the unit of the intensity is the same, if provided
try:
if len(df_func[var_names['col_name']
['unit']].unique()) != 1:
raise ValueError('Impact function with two different \
intensity units.')
func.intensity_unit = \
df_func[var_names['col_name']['unit']].values[0]
except KeyError:
pass
func.intensity = df_func[var_names['col_name']['inten']].values
func.mdd = df_func[var_names['col_name']['mdd']].values
func.paa = df_func[var_names['col_name']['paa']].values
self.append(func)
except KeyError as err:
LOGGER.error("Not existing variable: %s", str(err))
raise err
def read_mat(self, file_name, description='', var_names=DEF_VAR_MAT):
"""Read MATLAB file generated with previous MATLAB CLIMADA version.
Parameters:
file_name (str): absolute file name
description (str, optional): description of the data
var_names (dict, optional): name of the variables in the file
"""
def _get_hdf5_funcs(imp, file_name, var_names):
"""Get rows that fill every impact function and its name."""
func_pos = dict()
for row, (fun_id, fun_type) in enumerate(
zip(imp[var_names['var_name']['fun_id']].squeeze(),
imp[var_names['var_name']['peril']].squeeze())):
type_str = u_hdf5.get_str_from_ref(file_name, fun_type)
key = (type_str, int(fun_id))
if key not in func_pos:
func_pos[key] = list()
func_pos[key].append(row)
return func_pos
def _get_hdf5_str(imp, idxs, file_name, var_name):
"""Get rows with same string in var_name."""
prev_str = ""
for row in idxs:
cur_str = u_hdf5.get_str_from_ref(file_name, imp[var_name][row][0])
if prev_str == "":
prev_str = cur_str
elif prev_str != cur_str:
LOGGER.error("Impact function with two different %s.", var_name)
raise ValueError
return prev_str
imp = u_hdf5.read(file_name)
self.clear()
self.tag.file_name = str(file_name)
self.tag.description = description
try:
imp = imp[var_names['sup_field_name']]
except KeyError:
pass
try:
imp = imp[var_names['field_name']]
funcs_idx = _get_hdf5_funcs(imp, file_name, var_names)
for imp_key, imp_rows in funcs_idx.items():
func = ImpactFunc()
func.haz_type = imp_key[0]
func.id = imp_key[1]
# check that this function only has one intensity unit, if provided
try:
func.intensity_unit = _get_hdf5_str(imp, imp_rows,
file_name,
var_names['var_name']['unit'])
except KeyError:
pass
# check that this function only has one name
try:
func.name = _get_hdf5_str(imp, imp_rows, file_name,
var_names['var_name']['name'])
except KeyError:
func.name = str(func.id)
func.intensity = np.take(imp[var_names['var_name']['inten']], imp_rows)
func.mdd = np.take(imp[var_names['var_name']['mdd']], imp_rows)
func.paa = np.take(imp[var_names['var_name']['paa']], imp_rows)
self.append(func)
except KeyError as err:
LOGGER.error("Not existing variable: %s", str(err))
raise err
def from_mat(cls, file_name, description='', var_names=None):
"""Read MATLAB file generated with previous MATLAB CLIMADA version.
Parameters
----------
file_name : str
absolute file name
description : str, optional
description of the data
var_names : dict, optional
name of the variables in the file
Return
------
impf_set : climada.entity.impact_func_set.ImpactFuncSet
Impact func set as defined in matlab file.
"""
if var_names is None:
var_names = DEF_VAR_MAT
def _get_hdf5_funcs(imp, file_name, var_names):
"""Get rows that fill every impact function and its name."""
func_pos = dict()
for row, (fun_id, fun_type) in enumerate(
zip(imp[var_names['var_name']['fun_id']].squeeze(),
imp[var_names['var_name']['peril']].squeeze())):
type_str = u_hdf5.get_str_from_ref(file_name, fun_type)
key = (type_str, int(fun_id))
if key not in func_pos:
func_pos[key] = list()
func_pos[key].append(row)
return func_pos
def _get_hdf5_str(imp, idxs, file_name, var_name):
"""Get rows with same string in var_name."""
prev_str = ""
for row in idxs:
cur_str = u_hdf5.get_str_from_ref(file_name,
imp[var_name][row][0])
if prev_str == "":
prev_str = cur_str
elif prev_str != cur_str:
raise ValueError("Impact function with two different %s." %
var_name)
return prev_str
imp = u_hdf5.read(file_name)
impf_set = cls()
impf_set.tag.file_name = str(file_name)
impf_set.tag.description = description
try:
imp = imp[var_names['sup_field_name']]
except KeyError:
pass
try:
imp = imp[var_names['field_name']]
funcs_idx = _get_hdf5_funcs(imp, file_name, var_names)
for imp_key, imp_rows in funcs_idx.items():
func = ImpactFunc()
func.haz_type = imp_key[0]
func.id = imp_key[1]
# check that this function only has one intensity unit, if provided
try:
func.intensity_unit = _get_hdf5_str(
imp, imp_rows, file_name,
var_names['var_name']['unit'])
except KeyError:
pass
# check that this function only has one name
try:
func.name = _get_hdf5_str(imp, imp_rows, file_name,
var_names['var_name']['name'])
except KeyError:
func.name = str(func.id)
func.intensity = np.take(imp[var_names['var_name']['inten']],
imp_rows)
func.mdd = np.take(imp[var_names['var_name']['mdd']], imp_rows)
func.paa = np.take(imp[var_names['var_name']['paa']], imp_rows)
impf_set.append(func)
except KeyError as err:
raise KeyError("Not existing variable: %s" % str(err)) from err
return impf_set