def test_var_returns_a_float():
var = Var([50, 17], [1990, 2000, 2010])
var[:, :] = 34.7
assert isinstance(var[50, 2000], float)
var[17, 1990] = 204.3
assert var[17, 1990] == 204.3
def test_mulstd_read_failure():
onet = Var([50, 60], [2000, 2010])
with pytest.raises(ValueError):
onet.get_mulstd("else")
# Here the key is good, but there is nothing there.
assert isnan(onet.get_mulstd('dage'))
def test_mulstd(name, value):
onet = Var([50, 60], [2000, 2010])
onet.set_mulstd(name, value)
assert onet.get_mulstd(name) == value
onet.set_mulstd("dage", 2.4)
onet.set_mulstd("dtime", -7.3)
assert onet.get_mulstd("dage") == 2.4
assert onet.get_mulstd("dtime") == -7.3
def var_from_mean(self):
"""Given a prior grid, create a Var from the mean of the value priors.
Returns:
Var: A new Var object with the same ages and times and value
equal to the mean.
"""
var = Var(self.ages, self.times)
for age, time in self.age_time():
var[age, time] = self.value[age, time].mean
return var
def _ensure_weights(self):
"""If weights weren't made, then make them constant. Must be done after
there is data in the Model."""
# Find an age and time already in the model because adding an
# age and time outside the model can change the integration ranges.
arbitrary_grid = next(iter(self.rate.values()))
one_age_time = (arbitrary_grid.ages[0:1], arbitrary_grid.times[0:1])
for kind in (weight.name for weight in WeightEnum):
if kind not in self.weights:
self.weights[kind] = Var(*one_age_time)
self.weights[kind].grid.loc[:, "mean"] = 1.0
def get_weights(self):
"""
Gets the weights to be written for the model.
"""
weights = self.weights.copy()
arbitrary_grid = next(iter(self.rate.values()))
one_age_time = (arbitrary_grid.ages[0:1], arbitrary_grid.times[0:1])
for kind in (weight.name for weight in WeightEnum):
if kind not in self.weights:
weights[kind] = Var(*one_age_time)
weights[kind].grid.loc[:, "mean"] = 1.0
return weights
def rectangular_data_to_var(gridded_data):
"""Using this very regular data, where every age and time is present,
construct an initial guess as a Var object. Very regular means that there
is a complete set of ages-cross-times."""
gridded_data = gridded_data.copy()
try:
gridded_data['age'] = gridded_data[['age_lower', 'age_upper']].mean(axis=1)
gridded_data['time'] = gridded_data[['time_lower', 'time_upper']].mean(axis=1)
except AttributeError:
LOG.error(f"Data to make a var has columns {gridded_data.columns}")
raise RuntimeError(
f"Wrong columns in rectangular_data_to_var {gridded_data.columns}")
gridded_data = gridded_data.sort_values(by=['age', 'time'])
guess = Var(ages=sorted(gridded_data['age'].unique()), times=sorted(gridded_data['time'].unique()))
assert guess.variable_count() == len(gridded_data), \
"Number of age/time points exceed number of unique age/time points"
guess[:, :] = gridded_data['mean'].values.reshape((-1, 1))
return guess
def test_time_dimension(a, t, v):
times = [1990, 1995, 2000, 2005]
onet = Var([50], times)
for i in range(4):
onet[50, times[i]] = 0.3 * i
assert isclose(onet(a, t), v)
def test_age_dimension(a, t, v):
onea = Var([0, 1, 2, 3], [2000])
for i in range(4):
onea[i, :] = 0.2 * i
assert isclose(onea(a, t), v)
def test_warp(a, t, v):
warp = Var([0, 1], [5, 10, 15])
warp[:, :] = 0.5
warp[1, 10] = 6
assert isclose(warp(a, t), v)
assert warp(0.9, 10) > 5
def test_const_square(a, t, v):
two = Var([0, 1], [5, 10])
two[:, :] = 36.9
assert isclose(two(a, t), v)
def test_works_as_a_function():
const = Var([0], [0])
const[0, 0] = 21.2
assert const(0, 0) == 21.2
assert const(365, -29) == 21.2
def test_mulstd_failure(name, value):
onet = Var([50, 60], [2000, 2010])
with pytest.raises(ValueError):
onet.set_mulstd(name, value)