def test_change_all_exposures_pass(self):
"""Test _change_all_exposures method"""
meas = Measure()
meas.exposures_set = EXP_DEMO_H5
ref_exp = Exposures()
ref_exp.read_hdf5(EXP_DEMO_H5)
exposures = Exposures()
exposures.gdf['latitude'] = np.ones(10)
exposures.gdf['longitude'] = np.ones(10)
new_exp = meas._change_all_exposures(exposures)
self.assertEqual(new_exp.ref_year, ref_exp.ref_year)
self.assertEqual(new_exp.value_unit, ref_exp.value_unit)
self.assertEqual(new_exp.tag.file_name, ref_exp.tag.file_name)
self.assertEqual(new_exp.tag.description, ref_exp.tag.description)
self.assertTrue(
np.array_equal(new_exp.gdf.value.values, ref_exp.gdf.value.values))
self.assertTrue(
np.array_equal(new_exp.gdf.latitude.values,
ref_exp.gdf.latitude.values))
self.assertTrue(
np.array_equal(new_exp.gdf.longitude.values,
ref_exp.gdf.longitude.values))
def test_error_logs_wrong_crs(self):
"""Ambiguous crs definition"""
expo = good_exposures()
expo.set_geometry_points() # sets crs to 4326
# all good
_expo = Exposures(expo.gdf, meta={'crs': 4326}, crs=DEF_CRS)
with self.assertRaises(ValueError) as cm:
_expo = Exposures(expo.gdf, meta={'crs': 4230}, crs=4326)
self.assertIn(
"Inconsistent CRS definition, crs and meta arguments don't match",
str(cm.exception))
with self.assertRaises(ValueError) as cm:
_expo = Exposures(expo.gdf, meta={'crs': 4230})
self.assertIn(
"Inconsistent CRS definition, data doesn't match meta or crs argument",
str(cm.exception))
with self.assertRaises(ValueError) as cm:
_expo = Exposures(expo.gdf, crs='epsg:4230')
self.assertIn(
"Inconsistent CRS definition, data doesn't match meta or crs argument",
str(cm.exception))
_expo = Exposures(expo.gdf)
_expo.meta['crs'] = 'epsg:4230'
with self.assertRaises(ValueError) as cm:
_expo.check()
self.assertIn(
"Inconsistent CRS definition, gdf (EPSG:4326) attribute doesn't match "
"meta (epsg:4230) attribute.", str(cm.exception))
def test_io_hdf5_pass(self):
"""write and read hdf5"""
exp_df = Exposures(pd.read_excel(ENT_TEMPLATE_XLS))
exp_df.set_geometry_points()
exp_df.check()
# set metadata
exp_df.ref_year = 2020
exp_df.tag = Tag(ENT_TEMPLATE_XLS, 'ENT_TEMPLATE_XLS')
exp_df.value_unit = 'XSD'
file_name = DATA_DIR.joinpath('test_hdf5_exp.h5')
exp_df.write_hdf5(file_name)
exp_read = Exposures()
exp_read.read_hdf5(file_name)
self.assertEqual(exp_df.ref_year, exp_read.ref_year)
self.assertEqual(exp_df.value_unit, exp_read.value_unit)
self.assertEqual(exp_df.crs, exp_read.crs)
self.assertEqual(exp_df.tag.file_name, exp_read.tag.file_name)
self.assertEqual(exp_df.tag.description, exp_read.tag.description)
self.assertTrue(
np.array_equal(exp_df.gdf.latitude.values,
exp_read.gdf.latitude.values))
self.assertTrue(
np.array_equal(exp_df.gdf.longitude.values,
exp_read.gdf.longitude.values))
self.assertTrue(
np.array_equal(exp_df.gdf.value.values, exp_read.gdf.value.values))
self.assertTrue(
np.array_equal(exp_df.gdf.deductible.values,
exp_read.gdf.deductible.values))
self.assertTrue(
np.array_equal(exp_df.gdf.cover.values, exp_read.gdf.cover.values))
self.assertTrue(
np.array_equal(exp_df.gdf.region_id.values,
exp_read.gdf.region_id.values))
self.assertTrue(
np.array_equal(exp_df.gdf.category_id.values,
exp_read.gdf.category_id.values))
self.assertTrue(
np.array_equal(exp_df.gdf.if_TC.values, exp_read.gdf.if_TC.values))
self.assertTrue(
np.array_equal(exp_df.gdf.centr_TC.values,
exp_read.gdf.centr_TC.values))
self.assertTrue(
np.array_equal(exp_df.gdf.if_FL.values, exp_read.gdf.if_FL.values))
self.assertTrue(
np.array_equal(exp_df.gdf.centr_FL.values,
exp_read.gdf.centr_FL.values))
for point_df, point_read in zip(exp_df.gdf.geometry.values,
exp_read.gdf.geometry.values):
self.assertEqual(point_df.x, point_read.x)
self.assertEqual(point_df.y, point_read.y)
def test_no_coord_fail(self):
"""Error if no coordinates."""
new_var_names = copy.deepcopy(DEF_VAR_MAT)
new_var_names['var_name']['lat'] = 'no valid Latitude'
exp = Exposures()
with self.assertRaises(KeyError):
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
new_var_names['var_name']['lat'] = 'nLatitude'
new_var_names['var_name']['lon'] = 'no valid Longitude'
exp = Exposures()
with self.assertRaises(KeyError):
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
def test_calc_sector_total_impact(self):
"""Test running total impact calculations."""
sup = SupplyChain()
sup.read_wiod16(year='test',
range_rows=(5, 117),
range_cols=(4, 116),
col_iso3=2,
col_sectors=1)
# Tropical cyclone over Florida and Caribbean
hazard = Hazard('TC')
hazard.read_mat(HAZ_TEST_MAT)
# Read demo entity values
# Set the entity default file to the demo one
exp = Exposures()
exp.read_hdf5(EXP_DEMO_H5)
exp.check()
exp.gdf.region_id = 840 #assign right id for USA
exp.assign_centroids(hazard)
impf_tc = IFTropCyclone()
impf_tc.set_emanuel_usa()
impf_set = ImpactFuncSet()
impf_set.append(impf_tc)
impf_set.check()
sup.calc_sector_direct_impact(hazard, exp, impf_set)
sup.calc_indirect_impact(io_approach='ghosh')
sup.calc_total_impact()
self.assertAlmostEqual((sup.years.shape[0], sup.mriot_data.shape[0]),
sup.total_impact.shape)
self.assertAlmostEqual((sup.mriot_data.shape[0], ),
sup.total_aai_agg.shape)
def test_cutoff_hazard_pass(self):
"""Test _cutoff_hazard_damage"""
meas = MeasureSet()
meas.read_mat(ENT_TEST_MAT)
act_1 = meas.get_measure(name='Seawall')[0]
haz = Hazard('TC')
haz.read_mat(HAZ_TEST_MAT)
exp = Exposures()
exp.read_mat(ENT_TEST_MAT)
exp.gdf.rename(columns={'if_': 'if_TC'}, inplace=True)
exp.check()
imp_set = ImpactFuncSet()
imp_set.read_mat(ENT_TEST_MAT)
new_haz = act_1._cutoff_hazard_damage(exp, imp_set, haz)
self.assertFalse(id(new_haz) == id(haz))
pos_no_null = np.array([6249, 7697, 9134, 13500, 13199, 5944, 9052, 9050, 2429,
5139, 9053, 7102, 4096, 1070, 5948, 1076, 5947, 7432,
5949, 11694, 5484, 6246, 12147, 778, 3326, 7199, 12498,
11698, 6245, 5327, 4819, 8677, 5970, 7101, 779, 3894,
9051, 5976, 3329, 5978, 4282, 11697, 7193, 5351, 7310,
7478, 5489, 5526, 7194, 4283, 7191, 5328, 4812, 5528,
5527, 5488, 7475, 5529, 776, 5758, 4811, 6223, 7479,
7470, 5480, 5325, 7477, 7318, 7317, 11696, 7313, 13165,
6221])
all_haz = np.arange(haz.intensity.shape[0])
all_haz[pos_no_null] = -1
pos_null = np.argwhere(all_haz > 0).reshape(-1)
for i_ev in pos_null:
self.assertEqual(new_haz.intensity[i_ev, :].max(), 0)
def _cutoff_hazard_damage(self, exposures, if_set, hazard):
"""Cutoff of hazard events which generate damage with a frequency higher
than hazard_freq_cutoff.
Parameters:
exposures (Exposures): exposures instance
imp_set (ImpactFuncSet): impact functions instance
hazard (Hazard): hazard instance
Returns:
ImpactFuncSet
"""
if self.hazard_freq_cutoff == 0:
return hazard
LOGGER.debug('Cutting events whose damage have a frequency > %s.',
self.hazard_freq_cutoff)
from climada.engine.impact import Impact
imp = Impact()
exp_imp = exposures
if self.exp_region_id != 0:
# compute impact only in selected region
exp_imp = exposures[exposures.region_id == self.exp_region_id]
exp_imp = Exposures(exp_imp)
imp.calc(exp_imp, if_set, hazard)
new_haz = copy.deepcopy(hazard)
sort_idxs = np.argsort(imp.at_event)[::-1]
exceed_freq = np.cumsum(imp.frequency[sort_idxs])
cutoff = exceed_freq > self.hazard_freq_cutoff
sel_haz = sort_idxs[cutoff]
new_haz_inten = new_haz.intensity.tolil()
new_haz_inten[sel_haz, :] = np.zeros((sel_haz.size, new_haz.intensity.shape[1]))
new_haz.intensity = new_haz_inten.tocsr()
return new_haz
def _change_all_exposures(self, exposures):
"""Change exposures to provided exposures_set.
Parameters:
exposures (Exposures): exposures instance
Returns:
Exposures
"""
if isinstance(self.exposures_set,
str) and self.exposures_set == NULL_STR:
return exposures
if isinstance(self.exposures_set, str):
LOGGER.debug('Setting new exposures %s', self.exposures_set)
new_exp = Exposures()
new_exp.read_hdf5(self.exposures_set)
new_exp.check()
elif isinstance(self.exposures_set, Exposures):
LOGGER.debug('Setting new exposures. ')
new_exp = copy.deepcopy(self.exposures_set)
new_exp.check()
else:
LOGGER.error('Wrong input exposures.')
raise ValueError
if not np.array_equal(np.unique(exposures.latitude.values),
np.unique(new_exp.latitude.values)) or \
not np.array_equal(np.unique(exposures.longitude.values),
np.unique(new_exp.longitude.values)):
LOGGER.warning('Exposures locations have changed.')
return new_exp
def from_excel(cls, file_name, description=''):
"""Read csv or xls or xlsx file following climada's template.
Parameters
----------
file_name : str, optional
file name(s) or folder name
containing the files to read
description : str or list(str), optional
one description of the
data or a description of each data file
Returns
-------
ent : climada.entity.Entity
The entity from excel file
"""
exp = Exposures(pd.read_excel(file_name))
exp.tag = Tag()
exp.tag.file_name = str(file_name)
exp.tag.description = description
dr = DiscRates.from_excel(file_name, description)
impf_set = ImpactFuncSet.from_excel(file_name, description)
meas_set = MeasureSet.from_excel(file_name, description)
return cls(exposures=exp,
disc_rates=dr,
impact_func_set=impf_set,
measure_set=meas_set)
def test_assign_pass(self):
"""Check that attribute `assigned` is correctly set."""
np_rand = np.random.RandomState(123456789)
haz = Hazard('FL')
haz.set_raster([HAZ_DEMO_FL], window=Window(10, 20, 50, 60))
haz.raster_to_vector()
ncentroids = haz.centroids.size
exp = Exposures()
exp.gdf.crs = haz.centroids.crs
# some are matching exactly, some are geographically close
exp.gdf['longitude'] = np.concatenate([
haz.centroids.lon,
haz.centroids.lon + 0.001 * (-0.5 + np_rand.rand(ncentroids))
])
exp.gdf['latitude'] = np.concatenate([
haz.centroids.lat,
haz.centroids.lat + 0.001 * (-0.5 + np_rand.rand(ncentroids))
])
expected_result = np.concatenate(
[np.arange(ncentroids),
np.arange(ncentroids)])
# make sure that it works for both float32 and float64
for test_dtype in [np.float64, np.float32]:
haz.centroids.lat = haz.centroids.lat.astype(test_dtype)
haz.centroids.lon = haz.centroids.lon.astype(test_dtype)
exp.assign_centroids(haz)
self.assertEqual(exp.gdf.shape[0],
len(exp.gdf[INDICATOR_CENTR + 'FL']))
np.testing.assert_array_equal(
exp.gdf[INDICATOR_CENTR + 'FL'].values, expected_result)
def test_no_impact_fail(self):
"""Error if no impact ids."""
new_var_names = copy.deepcopy(DEF_VAR_MAT)
new_var_names['var_name']['imp'] = 'no valid value'
exp = Exposures()
with self.assertRaises(KeyError):
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
def test_set_gdf(self):
"""Test setting the GeoDataFrame"""
empty_gdf = gpd.GeoDataFrame()
gdf_without_geometry = good_exposures().gdf
good_exp = good_exposures()
good_exp.set_crs(crs='epsg:3395')
good_exp.set_geometry_points()
gdf_with_geometry = good_exp.gdf
probe = Exposures()
self.assertRaises(ValueError, probe.set_gdf, pd.DataFrame())
probe.set_gdf(empty_gdf)
self.assertTrue(probe.gdf.equals(gpd.GeoDataFrame()))
self.assertTrue(u_coord.equal_crs(DEF_CRS, probe.crs))
self.assertIsNone(probe.gdf.crs)
probe.set_gdf(gdf_with_geometry)
self.assertTrue(probe.gdf.equals(gdf_with_geometry))
self.assertTrue(u_coord.equal_crs('epsg:3395', probe.crs))
self.assertTrue(u_coord.equal_crs('epsg:3395', probe.gdf.crs))
probe.set_gdf(gdf_without_geometry)
self.assertTrue(probe.gdf.equals(good_exposures().gdf))
self.assertTrue(u_coord.equal_crs(DEF_CRS, probe.crs))
self.assertIsNone(probe.gdf.crs)
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 read_excel(self, file_name, description=''):
"""Read csv or xls or xlsx file following climada's template.
Parameters:
file_name (str, optional): file name(s) or folder name
containing the files to read
description (str or list(str), optional): one description of the
data or a description of each data file
Raises:
ValueError
"""
self.exposures = Exposures(pd.read_excel(file_name))
self.exposures.tag = Tag()
self.exposures.tag.file_name = file_name
self.exposures.tag.description = description
self.disc_rates = DiscRates()
self.disc_rates.read_excel(file_name, description)
self.impact_funcs = ImpactFuncSet()
self.impact_funcs.read_excel(file_name, description)
self.measures = MeasureSet()
self.measures.read_excel(file_name, description)
def test_constructoer_pass(self):
""" Test initialization with input GeiDataFrame """
in_gpd = gpd.GeoDataFrame()
in_gpd['value'] = np.zeros(10)
in_gpd.ref_year = 2015
in_exp = Exposures(in_gpd)
self.assertEqual(in_exp.ref_year, 2015)
self.assertTrue(np.array_equal(in_exp.value, np.zeros(10)))
def test_read_template_pass(self):
"""Wrong exposures definition"""
df = pd.read_excel(ENT_TEMPLATE_XLS)
exp_df = Exposures(df)
# set metadata
exp_df.ref_year = 2020
exp_df.tag = Tag(ENT_TEMPLATE_XLS, 'ENT_TEMPLATE_XLS')
exp_df.value_unit = 'XSD'
exp_df.check()
def test_no_refyear_pass(self):
"""Not error if no value unit."""
new_var_names = copy.deepcopy(DEF_VAR_MAT)
new_var_names['var_name']['ref'] = 'no valid ref'
exp = Exposures()
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
# Check results
self.assertEqual(2018, exp.ref_year)
def test_no_unit_pass(self):
"""Not error if no value unit."""
new_var_names = copy.deepcopy(DEF_VAR_MAT)
new_var_names['var_name']['uni'] = 'no valid unit'
exp = Exposures()
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
# Check results
self.assertEqual('USD', exp.value_unit)
def test_no_assigned_pass(self):
"""Not error if no value unit."""
new_var_names = copy.deepcopy(DEF_VAR_MAT)
new_var_names['var_name']['ass'] = 'no valid assign'
exp = Exposures()
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
# Check results
self.assertTrue('centr_' not in exp.gdf)
def test_no_category_pass(self):
"""Not error if no category id."""
new_var_names = copy.deepcopy(DEF_VAR_MAT)
new_var_names['var_name']['cat'] = 'no valid category'
exp = Exposures()
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
# Check results
self.assertTrue('category_id' not in exp.gdf)
def test_no_region_pass(self):
"""Not error if no region id."""
new_var_names = copy.deepcopy(DEF_VAR_MAT)
new_var_names['var_name']['reg'] = 'no valid region'
exp = Exposures()
exp.read_mat(ENT_TEST_MAT, var_names=new_var_names)
# Check results
self.assertTrue('region_id' not in exp.gdf)
def _build_exp(self):
eai_exp = Exposures()
eai_exp['value'] = self.eai_exp
eai_exp['latitude'] = self.coord_exp[:, 0]
eai_exp['longitude'] = self.coord_exp[:, 1]
eai_exp.crs = self.crs
eai_exp.value_unit = self.unit
eai_exp.check()
return eai_exp
def test_io_hdf5_pass(self):
"""write and read hdf5"""
exp_df = Exposures(pd.read_excel(ENT_TEMPLATE_XLS), crs="epsg:32632")
exp_df.set_geometry_points()
exp_df.check()
# set metadata
exp_df.ref_year = 2020
exp_df.tag = Tag(ENT_TEMPLATE_XLS, 'ENT_TEMPLATE_XLS')
exp_df.value_unit = 'XSD'
file_name = DATA_DIR.joinpath('test_hdf5_exp.h5')
# pd.errors.PerformanceWarning should be suppressed. Therefore, make sure that
# PerformanceWarning would result in test failure here
import warnings
with warnings.catch_warnings():
warnings.simplefilter("error",
category=pd.errors.PerformanceWarning)
exp_df.write_hdf5(file_name)
exp_read = Exposures.from_hdf5(file_name)
self.assertEqual(exp_df.ref_year, exp_read.ref_year)
self.assertEqual(exp_df.value_unit, exp_read.value_unit)
self.assertDictEqual(exp_df.meta, exp_read.meta)
self.assertTrue(u_coord.equal_crs(exp_df.crs, exp_read.crs))
self.assertTrue(u_coord.equal_crs(exp_df.gdf.crs, exp_read.gdf.crs))
self.assertEqual(exp_df.tag.file_name, exp_read.tag.file_name)
self.assertEqual(exp_df.tag.description, exp_read.tag.description)
np.testing.assert_array_equal(exp_df.gdf.latitude.values,
exp_read.gdf.latitude.values)
np.testing.assert_array_equal(exp_df.gdf.longitude.values,
exp_read.gdf.longitude.values)
np.testing.assert_array_equal(exp_df.gdf.value.values,
exp_read.gdf.value.values)
np.testing.assert_array_equal(exp_df.gdf.deductible.values,
exp_read.gdf.deductible.values)
np.testing.assert_array_equal(exp_df.gdf.cover.values,
exp_read.gdf.cover.values)
np.testing.assert_array_equal(exp_df.gdf.region_id.values,
exp_read.gdf.region_id.values)
np.testing.assert_array_equal(exp_df.gdf.category_id.values,
exp_read.gdf.category_id.values)
np.testing.assert_array_equal(exp_df.gdf.impf_TC.values,
exp_read.gdf.impf_TC.values)
np.testing.assert_array_equal(exp_df.gdf.centr_TC.values,
exp_read.gdf.centr_TC.values)
np.testing.assert_array_equal(exp_df.gdf.impf_FL.values,
exp_read.gdf.impf_FL.values)
np.testing.assert_array_equal(exp_df.gdf.centr_FL.values,
exp_read.gdf.centr_FL.values)
for point_df, point_read in zip(exp_df.gdf.geometry.values,
exp_read.gdf.geometry.values):
self.assertEqual(point_df.x, point_read.x)
self.assertEqual(point_df.y, point_read.y)
def test_assign_raster_same_pass(self):
"""Test assign_centroids with raster hazard"""
exp = Exposures()
exp.set_from_raster(HAZ_DEMO_FL, window=Window(10, 20, 50, 60))
exp.check()
haz = Hazard('FL')
haz.set_raster([HAZ_DEMO_FL], window=Window(10, 20, 50, 60))
exp.assign_centroids(haz)
np.testing.assert_array_equal(exp.gdf[INDICATOR_CENTR + 'FL'].values,
np.arange(haz.centroids.size, dtype=int))
def test_set_crs(self):
"""Test setting the CRS"""
empty_gdf = gpd.GeoDataFrame()
gdf_without_geometry = good_exposures().gdf
good_exp = good_exposures()
good_exp.set_geometry_points()
gdf_with_geometry = good_exp.gdf
probe = Exposures(gdf_without_geometry)
self.assertTrue(u_coord.equal_crs(DEF_CRS, probe.crs))
probe.set_crs('epsg:3395')
self.assertTrue(u_coord.equal_crs('epsg:3395', probe.crs))
probe = Exposures(gdf_with_geometry)
self.assertTrue(u_coord.equal_crs(DEF_CRS, probe.crs))
probe.set_crs(DEF_CRS)
self.assertTrue(u_coord.equal_crs(DEF_CRS, probe.crs))
self.assertRaises(ValueError, probe.set_crs, 'epsg:3395')
self.assertEqual('EPSG:4326', probe.meta.get('crs'))
def test_wrongDisc_fail(self):
"""Wrong discount rates"""
ent = Entity.from_excel(ENT_TEMPLATE_XLS)
ent.disc_rates.rates = np.array([1, 2])
with self.assertRaises(ValueError) as cm:
ent.check()
self.assertIn('DiscRates.rates', str(cm.exception))
with self.assertRaises(ValueError) as cm:
ent.disc_rates = Exposures()
self.assertIn('DiscRates', str(cm.exception))
def _build_exp(self):
eai_exp = Exposures()
eai_exp['value'] = self.eai_exp
eai_exp['latitude'] = self.coord_exp[:, 0]
eai_exp['longitude'] = self.coord_exp[:, 1]
eai_exp.crs = self.crs
eai_exp.value_unit = self.unit
eai_exp.ref_year = 0
eai_exp.tag = Tag()
eai_exp.meta = None
return eai_exp
def test_wrongImpFun_fail(self):
"""Wrong impact functions"""
ent = Entity.from_excel(ENT_TEMPLATE_XLS)
ent.impact_funcs.get_func('TC', 1).paa = np.array([1, 2])
with self.assertRaises(ValueError) as cm:
ent.check()
self.assertIn('ImpactFunc.paa', str(cm.exception))
with self.assertRaises(ValueError) as cm:
ent.impact_funcs = Exposures()
self.assertIn('ImpactFuncSet', str(cm.exception))
def test_exposures_value_pass(self):
"""Plot exposures values."""
myexp = pd.read_excel(ENT_DEMO_TODAY)
myexp = Exposures(myexp)
myexp.check()
myexp.tag.description = 'demo_today'
myax = myexp.plot_hexbin()
self.assertIn('demo_today', myax.get_title())
myexp.tag.description = ''
myax = myexp.plot_hexbin()
self.assertIn('', myax.get_title())
def test_ctx_osm_pass(self):
""" Test basemap function using osm images """
myexp = Exposures()
myexp['latitude'] = np.array([30, 40, 50])
myexp['longitude'] = np.array([0, 0, 0])
myexp['value'] = np.array([1, 1, 1])
myexp.check()
try:
myexp.plot_basemap(url=ctx.sources.OSM_A)
except urllib.error.HTTPError:
self.assertEqual(1, 0)
