def test_skips(self):
data = {1: {'exchanges': [
{'type': 'production'},
{'uncertainty type': sa.LognormalUncertainty.id}
]}}
# Doesn't raise KeyError for 'amount'
data = uncertainify(data)
def test_skip_zero_amounts(self):
data = {1: {"exchanges": [{"amount": 0.0}]}}
data = uncertainify(data)
new_dict = {
"amount": 0.0,
}
self.assertEqual(data[1]["exchanges"][0], new_dict)
def test_factors_valid(self):
with self.assertRaises(AssertionError):
uncertainify({}, bounds_factor=-1)
with self.assertRaises(TypeError):
uncertainify({}, bounds_factor="foo")
with self.assertRaises(AssertionError):
uncertainify({}, sd_factor=-1)
with self.assertRaises(TypeError):
uncertainify({}, sd_factor="foo")
def test_skip_zero_amounts(self):
data = {1: {'exchanges': [
{'amount': 0.}
]}}
data = uncertainify(data)
new_dict = {
'amount': 0.,
}
self.assertEqual(data[1]['exchanges'][0], new_dict)
def test_uniform(self):
data = {1: {"exchanges": [{"amount": 10.0}]}}
data = uncertainify(data)
new_dict = {
"amount": 10.0,
"minimum": 9.0,
"maximum": 11.0,
"uncertainty type": sa.UniformUncertainty.id,
}
self.assertEqual(data[1]["exchanges"][0], new_dict)
def test_bounds_flipped_negative_amount(self):
data = {1: {"exchanges": [{"amount": -10.0}]}}
data = uncertainify(data)
new_dict = {
"amount": -10.0,
"minimum": -11.0,
"maximum": -9.0,
"uncertainty type": sa.UniformUncertainty.id,
}
self.assertEqual(data[1]["exchanges"][0], new_dict)
def test_normal_unbounded(self):
data = {1: {"exchanges": [{"amount": 10.0}]}}
data = uncertainify(data, sa.NormalUncertainty, bounds_factor=None)
new_dict = {
"amount": 10.0,
"loc": 10.0,
"scale": 1.0,
"uncertainty type": sa.NormalUncertainty.id,
}
self.assertEqual(data[1]["exchanges"][0], new_dict)
def test_skips(self):
data = {
1: {
"exchanges": [
{"type": "production"},
{"uncertainty type": sa.LognormalUncertainty.id},
]
}
}
# Doesn't raise KeyError for 'amount'
data = uncertainify(data)
def test_normal_bounded(self):
data = {1: {"exchanges": [{"amount": 10.0}]}}
data = uncertainify(data, sa.NormalUncertainty)
new_dict = {
"amount": 10.0,
"loc": 10.0,
"scale": 1.0,
"minimum": 9.0,
"maximum": 11.0,
"uncertainty type": sa.NormalUncertainty.id,
}
self.assertEqual(data[1]["exchanges"][0], new_dict)
def test_uniform(self):
data = {1: {'exchanges': [
{'amount': 10.}
]}}
data = uncertainify(data)
new_dict = {
'amount': 10.,
'minimum': 9.,
'maximum': 11.,
'uncertainty type': sa.UniformUncertainty.id,
}
self.assertEqual(data[1]['exchanges'][0], new_dict)
def test_bounds_flipped_negative_amount(self):
data = {1: {'exchanges': [
{'amount': -10.}
]}}
data = uncertainify(data)
new_dict = {
'amount': -10.,
'minimum': -11.,
'maximum': -9.,
'uncertainty type': sa.UniformUncertainty.id,
}
self.assertEqual(data[1]['exchanges'][0], new_dict)
def test_normal_unbounded(self):
data = {1: {'exchanges': [
{'amount': 10.}
]}}
data = uncertainify(data, sa.NormalUncertainty, bounds_factor=None)
new_dict = {
'amount': 10.,
'loc': 10.,
'scale': 1.,
'uncertainty type': sa.NormalUncertainty.id,
}
self.assertEqual(data[1]['exchanges'][0], new_dict)
def test_normal_negative_amount(self):
data = {1: {'exchanges': [
{'amount': -10.}
]}}
data = uncertainify(data, sa.NormalUncertainty)
new_dict = {
'amount': -10.,
'loc': -10.,
'scale': 1.,
'minimum': -11.,
'maximum': -9.,
'uncertainty type': sa.NormalUncertainty.id,
}
self.assertEqual(data[1]['exchanges'][0], new_dict)
def test_bounds_factor_none_ok(self):
uncertainify({}, bounds_factor=None)
def test_wrong_distribution(self):
with self.assertRaises(AssertionError):
uncertainify({}, sa.LognormalUncertainty)