def test_aggregate_region_with_other_method(simple_df, variable, data):
# use other method (max) both as string and passing the function
_df = data.copy()
if simple_df.time_col == 'time':
_df.year = _df.year.replace(DTS_MAPPING)
_df.rename({'year': 'time'}, axis='columns', inplace=True)
exp = IamDataFrame(_df).filter(region='World')
for m in ['max', np.max]:
assert_frame_equal(simple_df.aggregate_region(variable, method=m), exp)
def test_aggregate_region(simple_df, variable):
# check that `variable` is a a direct sum across regions
exp = simple_df.filter(variable=variable, region='World')
assert_frame_equal(simple_df.aggregate_region(variable), exp)
# check custom `region` (will include `World`, so double-count values)
foo = exp.rename(region={'World': 'foo'})
foo.data.value = foo.data.value * 2
assert_frame_equal(simple_df.aggregate_region(variable, region='foo'), foo)
def test_aggregate(simple_df, variable, data):
# check that `variable` is a a direct sum and matches given total
exp = simple_df.filter(variable=variable)
assert_frame_equal(simple_df.aggregate(variable), exp)
# use other method (max) both as string and passing the function
_df = data.copy()
if simple_df.time_col == 'time':
_df.year = _df.year.replace(DTS_MAPPING)
_df.rename({'year': 'time'}, axis='columns', inplace=True)
exp = IamDataFrame(_df)
for m in ['max', np.max]:
assert_frame_equal(simple_df.aggregate(variable, method=m), exp)
def test_downscale_region(simple_df, variable):
simple_df.set_meta([1], name='test')
regions = ['reg_a', 'reg_b']
# return as new IamDataFrame
obs = simple_df.downscale_region(variable, proxy='Population')
exp = simple_df.filter(variable=variable, region=regions)
assert_frame_equal(exp, obs)
# append to `self` (after removing to-be-downscaled timeseries)
inplace = simple_df.filter(variable=variable, region=regions, keep=False)
inplace.downscale_region(variable, proxy='Population', append=True)
assert_frame_equal(inplace, simple_df)
def test_aggregate_region_with_weights(simple_df):
# carbon price shouldn't be summed but be weighted by emissions
v = 'Price|Carbon'
w = 'Emissions|CO2'
assert simple_df.check_aggregate_region(v) is not None
assert simple_df.check_aggregate_region(v, weight=w) is None
exp = simple_df.filter(variable=v, region='World')
assert_frame_equal(simple_df.aggregate_region(v, weight=w), exp)
# inconsistent index of variable and weight raises an error
_df = simple_df.filter(variable=w, region='reg_b', keep=False)
pytest.raises(ValueError, _df.aggregate_region, v, weight=w)
# using weight and method other than 'sum' raises an error
pytest.raises(ValueError, simple_df.aggregate_region, v, method='max',
weight='bar')
def test_aggregate_region_with_subregions(simple_df, variable):
# check that custom `subregions` works (assumes only `reg_a` is in `World`)
exp = (
simple_df.filter(variable=variable, region='reg_a')
.rename(region={'reg_a': 'World'})
)
obs = simple_df.aggregate_region(variable, subregions='reg_a')
assert_frame_equal(obs, exp)
# check that both custom `region` and `subregions` work
foo = exp.rename(region={'World': 'foo'})
obs = simple_df.aggregate_region(variable, region='foo',
subregions='reg_a')
assert_frame_equal(obs, foo)
# check that invalid list of subregions returns empty
assert simple_df.aggregate_region(variable, subregions=['reg_c']).empty
def test_aggregate_region_append(simple_df, variable):
# remove `variable`, do aggregate and append, check equality to original
_df = simple_df.filter(variable=variable, region='World', keep=False)
_df.aggregate_region(variable, append=True)
assert_frame_equal(_df, simple_df)