def generate(self, realnums, parameters, seedvalues, corrdict):
"""Generates parameter values by drawing from
defined distributions.
Args:
realnums (list): list of intergers with realization numbers
parameters (OrderedDict):
dictionary of parameters and distributions
seeds (str): default or None
corrdict(OrderedDict): correlation info
"""
self.sensvalues = pd.DataFrame(columns=parameters.keys(),
index=realnums)
numreals = len(realnums)
if corrdict is None:
for key in parameters.keys():
dist_name = parameters[key][0].lower()
dist_params = parameters[key][1]
try:
self.sensvalues[key] = design_dist.draw_values(
dist_name, dist_params, numreals)
except ValueError as error:
raise ValueError(
"Problem with parameter {} in sensitivity "
"with sensname {}: {}.".format(key, self.sensname,
error.args[0]))
else: # Some or all parameters are correlated
df_params = pd.DataFrame.from_dict(
parameters,
orient="index",
columns=["dist_name", "dist_params", "corr_sheet"],
)
df_params["corr_sheet"].fillna("nocorr", inplace=True)
df_params.reset_index(inplace=True)
df_params.rename(columns={"index": "param_name"}, inplace=True)
param_groups = df_params.groupby(["corr_sheet"])
for correl, group in param_groups:
param_dict = OrderedDict()
for index, row in group.iterrows():
param_dict[row["param_name"]] = [
row["dist_name"],
row["dist_params"],
]
if not correl == "nocorr":
if len(group) == 1:
_printwarning(correl)
df_correlations = design_dist.read_correlations(
corrdict, correl)
multivariate_parameters = df_correlations.index.values
cov_matrix = design_dist.make_covariance_matrix(
df_correlations)
normalscoremeans = len(multivariate_parameters) * [0]
normalscoresamples = numpy.random.multivariate_normal(
normalscoremeans, cov_matrix, size=numreals)
normalscoresamples_df = pd.DataFrame(
data=normalscoresamples,
columns=multivariate_parameters)
for key in param_dict.keys():
dist_name = param_dict[key][0].lower()
dist_parameters = param_dict[key][1]
if key in multivariate_parameters:
try:
self.sensvalues[key] = design_dist.draw_values(
dist_name,
dist_parameters,
numreals,
normalscoresamples_df[key],
)
except ValueError as error:
raise ValueError("Problem in sensitivity "
"with sensname {} for "
"parameter {}: {}.".format(
self.sensname, key,
error.args[0]))
else:
raise ValueError(
"Parameter{} specified with correlation "
"matrix {} but is not listed in "
"that sheet".format(key, correl))
else: # group nocorr where correlation matrix is not defined
for key in param_dict.keys():
dist_name = param_dict[key][0].lower()
dist_parameters = param_dict[key][1]
try:
self.sensvalues[key] = design_dist.draw_values(
dist_name, dist_parameters, numreals)
except ValueError as error:
raise ValueError("Problem in sensitivity "
"with sensname {} for parameter "
"{}: {}.".format(
self.sensname, key,
error.args[0]))
if self.sensname != "background":
self.sensvalues["REAL"] = realnums
self.sensvalues["SENSNAME"] = self.sensname
self.sensvalues["SENSCASE"] = "p10_p90"
if "RMS_SEED" not in self.sensvalues.keys() and seedvalues:
self.sensvalues["RMS_SEED"] = seedvalues[:len(realnums)]
def test_draw_values():
"""Test the wrapper function for drawing values"""
values = dists.draw_values("unif", [0, 1], 10)
assert len(values) == 10
assert all([isinstance(value, numbers.Number) for value in values])
assert all([0 <= value <= 1 for value in values])
values = dists.draw_values("UNIF", [0, 1], 10)
assert len(values) == 10
values = dists.draw_values("unifORM", [0, 1], 10)
assert len(values) == 10
values = dists.draw_values("UnifORMgarbagestillworks", [0, 1], 10)
assert len(values) == 10
with pytest.raises(ValueError):
dists.draw_values("non-existing-distribution", [0, 10], 100)
values = dists.draw_values("NORMAL", [0, 1], 10)
assert len(values) == 10
values = dists.draw_values("LOGNORMAL", [0.1, 1], 10)
assert len(values) == 10
values = dists.draw_values("Pert", [0.1, 1, 10], 10)
assert len(values) == 10
values = dists.draw_values("triangular", [0.1, 1, 10], 10)
assert len(values) == 10
values = dists.draw_values("logunif", [0.1, 1], 10)
assert len(values) == 10