def test_reporter_from_dantzig(test_mp, test_data_path):
scen = make_dantzig(test_mp, solve=test_data_path)
# 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 len(d) == 1
assert np.isclose(d.iloc[0], 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)
exp = (rep.get('d:i-j') * weights).sum(dim=['j']) / weights.sum(dim=['j'])
# TODO: attrs has to be explicitly copied here because math is done which
# returns a pd.Series
exp = Quantity(exp, attrs=rep.get('d:i-j').attrs)
assert_series_equal(obs.sort_index(), exp.sort_index())
# 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=[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 demand demand-margin z x'.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)
def test_reporting_units():
"""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',
Quantity(xr.DataArray([1., 3, 8], attrs={'_unit': 'MJ'}, **dims)))
r.add('time',
Quantity(xr.DataArray([5., 6, 8], attrs={'_unit': 'hour'}, **dims)))
r.add('efficiency', 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_assert_qty():
# tests without `attr` property, in which case direct pd.Series and
# xr.DataArray comparisons are possible
a = xr.DataArray([0.8, 0.2], coords=[['oil', 'water']], dims=['p'])
b = a.to_series()
assert_qty_equal(a, b)
assert_qty_equal(b, a)
assert_qty_allclose(a, b)
assert_qty_allclose(b, a)
c = Quantity(a)
assert_qty_equal(a, c)
assert_qty_equal(c, a)
assert_qty_allclose(a, c)
assert_qty_allclose(c, a)
def test_assert_qty_attrs():
# tests *with* `attr` property, in which case direct pd.Series and
# xr.DataArray comparisons *not* are possible
a = xr.DataArray([0.8, 0.2], coords=[['oil', 'water']], dims=['p'])
attrs = {'foo': 'bar'}
a.attrs = attrs
b = Quantity(a)
# make sure it has the correct property
assert a.attrs == attrs
assert b.attrs == attrs
assert_qty_equal(a, b)
assert_qty_equal(b, a)
assert_qty_allclose(a, b)
assert_qty_allclose(b, a)
a.attrs = {'bar': 'foo'}
assert_qty_equal(a, b, check_attrs=False)
def test_assert(self, a):
"""Test assertions about Quantity.
These are tests without `attr` property, in which case direct pd.Series
and xr.DataArray comparisons are possible.
"""
# Convert to pd.Series
b = a.to_series()
assert_qty_equal(a, b)
assert_qty_equal(b, a)
assert_qty_allclose(a, b)
assert_qty_allclose(b, a)
c = Quantity(a)
assert_qty_equal(a, c)
assert_qty_equal(c, a)
assert_qty_allclose(a, c)
assert_qty_allclose(c, a)
def test_assert_with_attrs(self, a):
"""Test assertions about Quantity with attrs.
Here direct pd.Series and xr.DataArray comparisons are *not* possible.
"""
attrs = {'foo': 'bar'}
a.attrs = attrs
b = Quantity(a)
# make sure it has the correct property
assert a.attrs == attrs
assert b.attrs == attrs
assert_qty_equal(a, b)
assert_qty_equal(b, a)
assert_qty_allclose(a, b)
assert_qty_allclose(b, a)
# check_attrs=False allows a successful equals assertion even when the
# attrs are different
a.attrs = {'bar': 'foo'}
assert_qty_equal(a, b, check_attrs=False)
