def do_for_all_analyses(test_func):
@pytest.mark.parametrize("analysis,single_hypothesis,many_hypotheses",
zip(get_obj().values(),
get_test_hypothesis().values(),
get_hypothesis_lists().values()),
ids=get_id_list())
def wrapper(analysis, single_hypothesis, many_hypotheses):
test_func(analysis, single_hypothesis, many_hypotheses)
return wrapper
"""Integration tests that apply to all Analysis classes,
and check that they conform correctly to the
required interface"""
import pytest
import numpy as np
from tensorflow_probability import distributions as tfd
from jmctf import JointDistribution
from jmctf_tests.analysis_class_register import get_id_list, get_obj, get_test_hypothesis, get_hypothesis_lists
# Fixture to create analysis objects and provide test parameters to use with them
@pytest.fixture(
scope="module",
params=list(zip(get_obj().values(),
get_test_hypothesis().values())) +
list(zip(get_obj().values(),
get_hypothesis_lists().values())),
ids=[name + " (single hypothesis)" for name in get_id_list()] +
[name + " (hypothesis list)" for name in get_id_list()])
def analysis_and_parameters(request):
analysis, parameters = request.param
return (analysis, parameters)
@pytest.fixture(scope="module")
def analysis(analysis_and_parameters):
analy, pars = analysis_and_parameters
return analy
has_curve = [
"NormalAnalysis", "NormalTEAnalysis", "BinnedAnalysis_single",
"BinnedAnalysis"
]
#has_curve = ["BinnedAnalysis_single"]
# Number of samples to draw during tests
N = 2 # Doing plotting, so don't want tonnes of curves confusing things
@pytest.fixture(scope="module")
def Nsamples():
return N
pars_id = [(get_obj(name), ID, curve, "{0}_{1}".format(name, ID))
for name in has_curve
for ID, curve in get_hypothesis_curves(name).items()]
params = [x[0:3] for x in pars_id] # analysis and curve data
ids = [x[3] for x in pars_id]
# "Entry point" fixture providing the analysis objects and parameters for them.
@pytest.fixture(scope="module", params=params, ids=ids)
def analysis_ID_params(request):
analysis, IDpar, params = request.param
return analysis, IDpar, params
@pytest.fixture(scope="module")
def analysis_params(analysis_ID_params):
return hyp_gen
# Special version for single hypothesis case
def get_hyp_gen_1(hypothesis):
def hyp_gen():
hyp_list = c.deep_expand_dims(hypothesis, 0)
yield hyp_list, [0]
return
return hyp_gen
@pytest.fixture(scope="module",
params=[(get_obj(name), get_test_hypothesis(name),
get_hypothesis_lists(name))
for name in analysis_tests.keys()],
ids=[name for name in analysis_tests.keys()])
def analysis_params(request):
analysis, params1, paramsN = request.param
return analysis, params1, paramsN
@pytest.fixture(scope="module", params=N_test_events)
def Nevents(request):
return request.param
@pytest.fixture(scope="module")
def analysis(analysis_params):
"NormalTEAnalysis": (N, 2, 2),
"BinnedAnalysis": (N, 4, 4)
}
# @pytest.fixture(scope="module"
# ,params=[(get_obj(name), get_test_hypothesis(name), H) for name,H in Hessians_shape.items()]
# +[(get_obj(name), get_hypothesis_lists(name), H) for name,H in Hessians_shape.items()]
# ,ids = [name + " (single hypothesis)" for name in Hessians_shape.keys()]
# +[name + " (multiple hypotheses)" for name in Hessians_shape.keys()]
# )
# "Entry point" fixture providing the analysis objects and parameters for them.
@pytest.fixture(
scope="module",
params=[(get_obj(name), get_hypothesis_lists(name), H)
for name, H in Hessians_shape.items()],
ids=[name + " (multiple hypotheses)" for name in Hessians_shape.keys()])
def analysis_params_Hshape(request):
analysis, params, H_shape = request.param
return analysis, params, H_shape
@pytest.fixture(scope="module")
def analysis_params(analysis_params_Hshape):
analysis, params, H_shape = analysis_params_Hshape
return analysis, params
@pytest.fixture(scope="module")
def samples(joint0, Nsamples):