def test_units(ureg):
"""Test handling of units within Reporter computations."""
r = Reporter()
# Create some dummy data
dims = dict(coords=['a b c'.split()], dims=['x'])
r.add('energy:x', as_quantity(xr.DataArray([1., 3, 8], **dims),
units='MJ'))
r.add('time', as_quantity(xr.DataArray([5., 6, 8], **dims), units='hour'))
r.add('efficiency', as_quantity(xr.DataArray([0.9, 0.8, 0.95], **dims)))
# Aggregation preserves units
r.add('energy', (computations.sum, 'energy:x', None, ['x']))
assert r.get('energy').attrs['_unit'] == ureg.parse_units('MJ')
# Units are derived for a ratio of two quantities
r.add('power', (computations.ratio, 'energy:x', 'time'))
assert r.get('power').attrs['_unit'] == ureg.parse_units('MJ/hour')
# Product of dimensioned and dimensionless quantities keeps the former
r.add('energy2', (computations.product, 'energy:x', 'efficiency'))
assert r.get('energy2').attrs['_unit'] == ureg.parse_units('MJ')
def test_reporter_add_product(test_mp, ureg):
scen = ixmp.Scenario(test_mp, 'reporter_add_product',
'reporter_add_product', 'new')
*_, x = add_test_data(scen)
rep = Reporter.from_scenario(scen)
# add_product() works
key = rep.add_product('x squared', 'x', 'x', sums=True)
# Product has the expected dimensions
assert key == 'x squared:t-y'
# Product has the expected value
exp = as_quantity(x * x)
exp.attrs['_unit'] = ureg('kilogram ** 2').units
assert_qty_equal(exp, rep.get(key))
def test_aggregate(test_mp):
scen = ixmp.Scenario(test_mp, 'Group reporting', 'group reporting', 'new')
t, t_foo, t_bar, x = add_test_data(scen)
# Reporter
rep = Reporter.from_scenario(scen)
# Define some groups
t_groups = {'foo': t_foo, 'bar': t_bar, 'baz': ['foo1', 'bar5', 'bar6']}
# Use the computation directly
agg1 = computations.aggregate(as_quantity(x), {'t': t_groups}, True)
# Expected set of keys along the aggregated dimension
assert set(agg1.coords['t'].values) == set(t) | set(t_groups.keys())
# Sums are as expected
assert_qty_allclose(agg1.sel(t='foo', drop=True), x.sel(t=t_foo).sum('t'))
assert_qty_allclose(agg1.sel(t='bar', drop=True), x.sel(t=t_bar).sum('t'))
assert_qty_allclose(agg1.sel(t='baz', drop=True),
x.sel(t=['foo1', 'bar5', 'bar6']).sum('t'))
# Use Reporter convenience method
key2 = rep.aggregate('x:t-y', 'agg2', {'t': t_groups}, keep=True)
# Group has expected key and contents
assert key2 == 'x:t-y:agg2'
# Aggregate is computed without error
agg2 = rep.get(key2)
assert_qty_equal(agg1, agg2)
# Add aggregates, without keeping originals
key3 = rep.aggregate('x:t-y', 'agg3', {'t': t_groups}, keep=False)
# Distinct keys
assert key3 != key2
# Only the aggregated and no original keys along the aggregated dimension
agg3 = rep.get(key3)
assert set(agg3.coords['t'].values) == set(t_groups.keys())
with pytest.raises(NotImplementedError):
# Not yet supported; requires two separate operations
rep.aggregate('x:t-y', 'agg3', {'t': t_groups, 'y': [2000, 2010]})
def test_file_formats(test_data_path, tmp_path):
r = Reporter()
expected = as_quantity(pd.read_csv(test_data_path / 'report-input0.csv',
index_col=['i', 'j'])['value'],
units='km')
# CSV file is automatically parsed to xr.DataArray
p1 = test_data_path / 'report-input0.csv'
k = r.add_file(p1, units=pint.Unit('km'))
assert_qty_equal(r.get(k), expected)
# Dimensions can be specified
p2 = test_data_path / 'report-input1.csv'
k2 = r.add_file(p2, dims=dict(i='i', j_dim='j'))
assert_qty_equal(r.get(k), r.get(k2))
# Units are loaded from a column
assert r.get(k2).attrs['_unit'] == pint.Unit('km')
# Specifying units that do not match file contents → ComputationError
r.add_file(p2, key='bad', dims=dict(i='i', j_dim='j'), units='kg')
with pytest.raises(ComputationError):
r.get('bad')
# Write to CSV
p3 = tmp_path / 'report-output.csv'
r.write(k, p3)
# Output is identical to input file, except for order
assert (sorted(p1.read_text().split('\n')) == sorted(
p3.read_text().split('\n')))
# Write to Excel
p4 = tmp_path / 'report-output.xlsx'
r.write(k, p4)
def test_reporter_from_dantzig(test_mp, ureg):
scen = make_dantzig(test_mp, solve=True)
# Reporter.from_scenario can handle the Dantzig problem
rep = Reporter.from_scenario(scen)
# Partial sums are available automatically (d is defined over i and j)
d_i = rep.get('d:i')
# Units pass through summation
assert d_i.attrs['_unit'] == ureg.parse_units('km')
# Summation across all dimensions results a 1-element Quantity
d = rep.get('d:')
assert d.shape == ((1, ) if Quantity is AttrSeries else tuple())
assert d.size == 1
assert np.isclose(d.values, 11.7)
# Weighted sum
weights = Quantity(
xr.DataArray([1, 2, 3],
coords=['chicago new-york topeka'.split()],
dims=['j']))
new_key = rep.aggregate('d:i-j', 'weighted', 'j', weights)
# ...produces the expected new key with the summed dimension removed and
# tag added
assert new_key == 'd:i:weighted'
# ...produces the expected new value
obs = rep.get(new_key)
d_ij = rep.get('d:i-j')
exp = (d_ij * weights).sum(dim=['j']) / weights.sum(dim=['j'])
# FIXME attrs has to be explicitly copied here because math is done which
# returns a pd.Series
exp.attrs = d_ij.attrs
assert_qty_equal(exp, obs)
# Disaggregation with explicit data
# (cases of canned food 'p'acked in oil or water)
shares = xr.DataArray([0.8, 0.2], coords=[['oil', 'water']], dims=['p'])
new_key = rep.disaggregate('b:j', 'p', args=[as_quantity(shares)])
# ...produces the expected key with new dimension added
assert new_key == 'b:j-p'
b_jp = rep.get('b:j-p')
# Units pass through disaggregation
assert b_jp.attrs['_unit'] == 'cases'
# Set elements are available
assert rep.get('j') == ['new-york', 'chicago', 'topeka']
# 'all' key retrieves all quantities
obs = {da.name for da in rep.get('all')}
exp = set(('a b d f x z cost cost-margin demand demand-margin supply '
'supply-margin').split())
assert obs == exp
# Shorthand for retrieving a full key name
assert rep.full_key('d') == 'd:i-j' and isinstance(rep.full_key('d'), Key)
