def test_emdat_impact_yearlysum_no_futurewarning(self):
"""Ensure that no FutureWarning is issued"""
with warnings.catch_warnings():
# Make sure that FutureWarning will cause an error
warnings.simplefilter("error", category=FutureWarning)
im_d.emdat_impact_yearlysum(
EMDAT_TEST_CSV,
countries=["Bangladesh", "USA"],
hazard="Flood",
year_range=(2015, 2017),
reference_year=None,
imp_str="Total Affected",
)
def 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:
df_out: DataFrame with recorded impact written to rows for each year
or event.
"""
if impact_data_source == 'emdat':
if yearly_impact:
em_data = emdat_impact_yearlysum(region_ids, hazard_type,
emdat_file_csv=source_file, year_range=year_range,
reference_year=reference_year)
else:
raise ValueError ('init_impact_data not yet implemented for yearly_impact = False.')
em_data = emdat_impact_event()
else:
raise ValueError('init_impact_data not yet implemented for other impact_data_sources than emdat.')
return em_data
def test_emdat_damage_yearlysum(self):
"""test emdat_impact_yearlysum yearly impact data extraction with scaling"""
df = im_d.emdat_impact_yearlysum(EMDAT_TEST_CSV, countries=['Bangladesh', 'USA'],
hazard='Flood', year_range=(2015, 2017),
reference_year=2000)
self.assertEqual(36, df.size)
self.assertAlmostEqual(df.impact.max(), 15150000000.0)
self.assertEqual(df.impact_scaled.min(), 10943000.0)
self.assertEqual(df["year"][5], 2017)
self.assertEqual(df["reference_year"].max(), 2000)
self.assertIn('USA', list(df['ISO']))
self.assertIn(50, list(df['region_id']))
def test_emdat_affected_yearlysum(self):
"""test emdat_impact_yearlysum yearly impact data extraction"""
df = im_d.emdat_impact_yearlysum(EMDAT_TEST_CSV, countries=['Bangladesh', 'USA'],
hazard='Flood', year_range=(2015, 2017),
reference_year=None, imp_str="Total Affected")
self.assertEqual(36, df.size)
self.assertEqual(df["impact"][1], 91000)
self.assertEqual(df.impact.sum(), 11517946)
self.assertEqual(df["year"][5], 2017)
self.assertIn('USA', list(df['ISO']))
self.assertIn('BGD', list(df['ISO']))
def test_emdat_impact_yearlysum(self):
"""test emdat_impact_yearlysum yearly impact data extraction"""
df = im_d.emdat_impact_yearlysum(['Bangladesh', 'USA'], 'Flood', \
EMDAT_TEST_CSV, [2015, 2017], \
imp_str = 'Total affected')
self.assertEqual(36, df.size)
self.assertEqual(df["impact"][1], 1900000)
self.assertEqual(df.impact.sum(), 11517946)
self.assertEqual(df["year"][5], 2017)
self.assertIn('USA', list(df['ISO3']))
self.assertIn('BGD', list(df['ISO3']))
self.assertEqual(0, df['reference_year'].max())
def test_emdat_impact_yearlysum(self):
"""test emdat_impact_yearlysum yearly impact data extraction"""
emdat_file_csv = os.path.join(DATA_FOLDER, \
'emdat_testdata_BGD_USA_1970-2017.csv')
df = im_d.emdat_impact_yearlysum(['Bangladesh', 'USA'], 'Flood', \
emdat_file_csv, [2015, 2017], \
imp_str = 'Total affected')
self.assertEqual(36, df.size)
self.assertEqual(df["impact"][1], 1900000)
self.assertEqual(df.impact.sum(), 11517946)
self.assertEqual(df["year"][5], 2017)
self.assertIn('USA', list(df['ISO3']))
self.assertIn('BGD', list(df['ISO3']))
self.assertEqual(0, df['reference_year'].max())
