def test_making_membership(self):
""""Can we add a dummy user to the demo study?"""
study = Study.objects.get(slug='demo-study')
user = make_user({'username': "******", 'email': "*****@*****.**", 'password': "******"})
membership = Membership(study=study, user=user)
membership.save()
assert membership.condition is None # has not auto-randomised
allocate(membership)
membership.add_observations()
return membership
def test_autorandomisation(self):
study = Study.objects.get(slug='demo-study')
study.auto_randomise = False
study.save()
user = make_user({'username': "******", 'email': "*****@*****.**", 'password': "******"})
membership = Membership(study=study, user=user)
membership.save()
assert membership.condition is None
allocate(membership)
assert membership.condition is not None
def test_making_membership(self):
""""Can we add a dummy user to the demo study?"""
study = Study.objects.get(slug='demo-study')
user = make_user({
'username': "******",
'email': "*****@*****.**",
'password': "******"
})
membership = Membership(study=study, user=user)
membership.save()
assert membership.condition is None # has not auto-randomised
allocate(membership)
membership.add_observations()
return membership
def test_auto_add_observations(self):
study = Study.objects.get(slug='demo-study')
study.auto_add_observations = False
study.save()
user = make_user({'username': "******", 'email': "*****@*****.**", 'password': "******"})
membership = Membership(study=study, user=user)
membership.save()
assert membership.condition is None
allocate(membership)
assert membership.condition is not None
assert membership.observation_set.all().count() is 0
membership.add_observations()
assert membership.observation_set.all().count() is 1
def _get_p_for_allocation(study, users, ratio):
SAMPLESIZE = len(users)
groups = list(study.studycondition_set.all())
study.membership_set.all().delete()
[setattr(i, 'weight', w) for i, w in zip(groups, ratio)]
[i.save() for i in groups]
normalisedweights = [i / sum(ratio) for i in ratio]
expected = dict([(j, int(SAMPLESIZE * i))
for i, j in zip(normalisedweights, ratio)])
mems = [Membership(study=study, user=user) for user in users]
[allocate(i) for i in mems]
# make each group min = 1 because otherwise chisq can blow up
observed = {
i.weight: min([1, i.membership_set.all().count()])
for i in groups
}
ex_ob = [(expected[i], observed[i]) for i in list(observed.keys())]
# XXX TODO reinstatewith python 3 functionality
# chi, p = stats.lchisquare(*list(zip(*ex_ob)))
return p
def test_autorandomisation(self):
study = Study.objects.get(slug='demo-study')
study.auto_randomise = False
study.save()
user = make_user({
'username': "******",
'email': "*****@*****.**",
'password': "******"
})
membership = Membership(study=study, user=user)
membership.save()
assert membership.condition is None
allocate(membership)
assert membership.condition is not None
def randomise_membership(request, membership_id):
"""View to assign Membership to Condition"""
mem = get_object_or_404(Membership, id=int(membership_id))
was_allocated = allocate(mem)
if was_allocated[0] is True:
messages.success(request, was_allocated[1])
else:
messages.error(request, was_allocated[1],)
return HttpResponseRedirect(reverse('admin:signalbox_membership_change', args=(mem.id,)))
def test_auto_add_observations(self):
study = Study.objects.get(slug='demo-study')
study.auto_add_observations = False
study.save()
user = make_user({
'username': "******",
'email': "*****@*****.**",
'password': "******"
})
membership = Membership(study=study, user=user)
membership.save()
assert membership.condition is None
allocate(membership)
assert membership.condition is not None
assert membership.observation_set.all().count() is 0
membership.add_observations()
assert membership.observation_set.all().count() is 1
def allocate_new_membership(sender, created, instance, **kwargs):
"""When a participant joins a study, we need to randomise them and add observations
if the Study settings say so."""
if created and instance.study.auto_randomise:
_, _ = allocate(instance)
if created and instance.study.auto_add_observations and instance.study.auto_randomise:
instance.add_observations()
# we don't execute at this point if we are testing because it makes unit
# tests awkward (we can't test the intermediate states of some functions.)
if not settings.TESTING:
execute_the_todo_list()
def allocate_new_membership(sender, created, instance, **kwargs):
"""When a participant joins a study, we need to randomise them and add observations
if the Study settings say so."""
if created and instance.study.auto_randomise:
_, _ = allocate(instance)
if created and instance.study.auto_add_observations and instance.study.auto_randomise:
instance.add_observations()
# we don't execute at this point if we are testing because it makes unit
# tests awkward (we can't test the intermediate states of some functions.)
if not settings.TESTING:
execute_the_todo_list()
def randomise_membership(request, membership_id):
"""View to assign Membership to Condition"""
mem = get_object_or_404(Membership, id=int(membership_id))
was_allocated = allocate(mem)
if was_allocated[0] is True:
messages.success(request, was_allocated[1])
else:
messages.error(
request,
was_allocated[1],
)
return HttpResponseRedirect(
reverse('admin:signalbox_membership_change', args=(mem.id, )))
def _get_p_for_allocation(study, users, ratio):
SAMPLESIZE = len(users)
groups = list(study.studycondition_set.all())
study.membership_set.all().delete()
[setattr(i, 'weight', w) for i, w in zip(groups, ratio)]
[i.save() for i in groups]
normalisedweights = [i / sum(ratio) for i in ratio]
expected = dict([(j, int(SAMPLESIZE * i)) for i, j in zip(normalisedweights, ratio)])
mems = [Membership(study=study, user=user) for user in users]
[allocate(i) for i in mems]
# make each group min = 1 because otherwise chisq can blow up
observed = {i.weight: min([1, i.membership_set.all().count()]) for i in groups}
ex_ob = [(expected[i], observed[i]) for i in observed.keys()]
chi, p = stats.lchisquare(*zip(*ex_ob))
return p
