def get_editing_dataset(self, request):
# Generate a pseudo-return when editing or creating a dataset.
# Do not include the settings field; this will be set from the
# input-form. Should approximately mirror the Visual API from rest-framework.
dose_units = None
try:
dose_units = int(request.POST.get("dose_units"))
except (TypeError, ValueError) as e:
# TypeError if dose_units is None; ValueError if dose_units is ""
pass
data = {
"title": request.POST.get("title"),
"slug": request.POST.get("slug"),
"caption": request.POST.get("caption"),
"dose_units": dose_units,
"created": datetime.now().isoformat(),
"last_updated": datetime.now().isoformat()
}
data["endpoints"] = [
SerializerHelper.get_serialized(e, json=False)
for e in self.get_endpoints(request)
]
data["studies"] = [
SerializerHelper.get_serialized(s, json=False)
for s in self.get_studies(request)
]
return json.dumps(data)
def get_editing_dataset(self, request):
# Generate a pseudo-return when editing or creating a dataset.
# Do not include the settings field; this will be set from the
# input-form. Should approximately mirror the Visual API from rest-framework.
dose_units = None
try:
dose_units = int(request.POST.get("dose_units"))
except (TypeError, ValueError) as e:
# TypeError if dose_units is None; ValueError if dose_units is ""
pass
data = {
"title": request.POST.get("title"),
"slug": request.POST.get("slug"),
"caption": request.POST.get("caption"),
"dose_units": dose_units,
"created": datetime.now().isoformat(),
"last_updated": datetime.now().isoformat()
}
data["endpoints"] = [
SerializerHelper.get_serialized(e, json=False)
for e in self.get_endpoints(request)
]
data["studies"] = [
SerializerHelper.get_serialized(s, json=False)
for s in self.get_studies(request)
]
return json.dumps(data)
def get_docx_template_context(assessment, queryset):
"""
Given a queryset of endpoints, invert the cached results to build
a top-down data hierarchy from study to endpoint. We use this
approach since our endpoints are cached, so while it may require
more computation, its close to free on database access.
"""
endpoints = [
SerializerHelper.get_serialized(obj, json=False)
for obj in queryset
]
studies = {}
# flip dictionary nesting
for thisEp in endpoints:
thisAG = thisEp["animal_group"]
thisExp = thisEp["animal_group"]["experiment"]
thisStudy = thisEp["animal_group"]["experiment"]["study"]
study = studies.get(thisStudy["id"])
if study is None:
study = thisStudy
study["exps"] = {}
studies[study["id"]] = study
exp = study["exps"].get(thisExp["id"])
if exp is None:
exp = thisExp
exp["ags"] = {}
study["exps"][exp["id"]] = exp
ag = exp["ags"].get(thisAG["id"])
if ag is None:
ag = thisAG
ag["eps"] = {}
exp["ags"][ag["id"]] = ag
ep = ag["eps"].get(thisEp["id"])
if ep is None:
ep = thisEp
ag["eps"][ep["id"]] = ep
# convert value dictionaries to lists
studies = sorted(list(studies.values()),
key=lambda obj: (obj["short_citation"].lower()))
for study in studies:
study["exps"] = sorted(list(study["exps"].values()),
key=lambda obj: (obj["name"].lower()))
for exp in study["exps"]:
exp["ags"] = sorted(list(exp["ags"].values()),
key=lambda obj: (obj["name"].lower()))
for ag in exp["ags"]:
ag["eps"] = sorted(list(ag["eps"].values()),
key=lambda obj: (obj["name"].lower()))
return {
"assessment": AssessmentSerializer().to_representation(assessment),
"studies": studies
}
def to_representation(self, instance):
ret = super().to_representation(instance)
ret['url_update'] = instance.get_update_url()
ret['url_delete'] = instance.get_delete_url()
ret["endpoints"] = [
SerializerHelper.get_serialized(d, json=False)
for d in instance.get_endpoints()
]
ret["studies"] = [
SerializerHelper.get_serialized(d, json=False)
for d in instance.get_studies()
]
return ret
def to_representation(self, instance):
ret = super().to_representation(instance)
ret['url_update'] = instance.get_update_url()
ret['url_delete'] = instance.get_delete_url()
ret["endpoints"] = [
SerializerHelper.get_serialized(d, json=False)
for d in instance.get_endpoints()
]
ret["studies"] = [
SerializerHelper.get_serialized(d, json=False)
for d in instance.get_studies()
]
return ret
def get_docx_template_context(cls, assessment, queryset):
studies = [
SerializerHelper.get_serialized(study, json=False)
for study in queryset
]
return {
"assessment": AssessmentSerializer().to_representation(assessment),
"studies": studies
}
def get_json(self, json_encode=True):
return SerializerHelper.get_serialized(self, json=json_encode)
def get_json(self, json_encode=True):
return SerializerHelper.get_serialized(self, json=json_encode)
def get_docx_template_context(cls, assessment, queryset):
"""
Given a queryset of endpoints, invert the cached results to build
a top-down data hierarchy from study to endpoint. We use this
approach since our endpoints are cached, so while it may require
more computation, its close to free on database access.
"""
endpoints = [
SerializerHelper.get_serialized(obj, json=False)
for obj in queryset
]
studies = {}
# flip dictionary nesting
for thisEp in endpoints:
thisAG = thisEp["animal_group"]
thisExp = thisEp["animal_group"]["experiment"]
thisStudy = thisEp["animal_group"]["experiment"]["study"]
study = studies.get(thisStudy["id"])
if study is None:
study = thisStudy
study["exps"] = {}
studies[study["id"]] = study
exp = study["exps"].get(thisExp["id"])
if exp is None:
exp = thisExp
exp["ags"] = {}
study["exps"][exp["id"]] = exp
ag = exp["ags"].get(thisAG["id"])
if ag is None:
ag = thisAG
ag["eps"] = {}
exp["ags"][ag["id"]] = ag
ep = ag["eps"].get(thisEp["id"])
if ep is None:
ep = thisEp
ag["eps"][ep["id"]] = ep
# convert value dictionaries to lists
studies = sorted(
studies.values(),
key=lambda obj: (obj["short_citation"].lower()))
for study in studies:
study["exps"] = sorted(
study["exps"].values(),
key=lambda obj: (obj["name"].lower()))
for exp in study["exps"]:
exp["ags"] = sorted(
exp["ags"].values(),
key=lambda obj: (obj["name"].lower()))
for ag in exp["ags"]:
ag["eps"] = sorted(
ag["eps"].values(),
key=lambda obj: (obj["name"].lower()))
return {
"assessment": AssessmentSerializer().to_representation(assessment),
"studies": studies
}
def d_response(self, json_encode=True):
return SerializerHelper.get_serialized(self, json=json_encode)
def get_docx_template_context(assessment, queryset):
studies = [SerializerHelper.get_serialized(study, json=False) for study in queryset]
return {
"assessment": AssessmentSerializer().to_representation(assessment),
"studies": studies
}
def get_json(self, json_encode=True):
return SerializerHelper.get_serialized(self, json=json_encode, from_cache=False)
def get_docx_template_context(assessment, queryset):
"""
Given a queryset of meta-results, invert the cached results to build
a top-down data hierarchy from study to meta-result. We use this
approach since our meta-results are cached, so while it may require
more computation, its close to free on database access.
"""
def getStatMethods(mr):
key = "{}|{}".format(
mr["adjustments_list"],
mr["statistical_notes"]
)
return key, mr
results = [
SerializerHelper.get_serialized(obj, json=False)
for obj in queryset
]
studies = {}
# flip dictionary nesting
for thisMr in results:
thisPro = thisMr["protocol"]
thisStudy = thisMr["protocol"]["study"]
study = studies.get(thisStudy["id"])
if study is None:
study = thisStudy
study["protocols"] = {}
studies[study["id"]] = study
pro = study["protocols"].get(thisPro["id"])
if pro is None:
pro = thisPro
pro["inclusion_list"] = ', '.join(pro["inclusion_criteria"])
pro["exclusion_list"] = ', '.join(pro["exclusion_criteria"])
pro["results"] = {}
pro["statistical_methods"] = {}
study["protocols"][pro["id"]] = pro
mr = pro["results"].get(thisMr["id"])
if mr is None:
mr = thisMr
mr["ci_percent"] = int(mr["ci_units"] * 100.)
mr["adjustments_list"] = ', '.join(
sorted(mr["adjustment_factors"]))
pro["results"][mr["id"]] = mr
statKey, statVal = getStatMethods(thisMr)
stats = pro["statistical_methods"].get(statKey)
if stats is None:
stats = statVal
pro["statistical_methods"][statKey] = statVal
stats["sm_endpoints"] = []
stats["sm_endpoints"].append(thisMr)
# convert value dictionaries to lists
studies = sorted(
list(studies.values()),
key=lambda obj: (obj["short_citation"].lower()))
for study in studies:
study["protocols"] = sorted(
list(study["protocols"].values()),
key=lambda obj: (obj["name"].lower()))
for pro in study["protocols"]:
pro["results"] = sorted(
list(pro["results"].values()),
key=lambda obj: (obj["label"].lower()))
pro["statistical_methods"] = list(pro["statistical_methods"].values())
for obj in pro["statistical_methods"]:
obj["sm_endpoints"] = "; ".join([
d["label"] for d in obj["sm_endpoints"]
])
return {
"assessment": AssessmentSerializer().to_representation(assessment),
"studies": studies
}
def get_json(self, json_encode=True):
return SerializerHelper.get_serialized(self, json=json_encode, from_cache=False)
def get_docx_template_context(assessment, queryset):
"""
Given a queryset of meta-results, invert the cached results to build
a top-down data hierarchy from study to meta-result. We use this
approach since our meta-results are cached, so while it may require
more computation, its close to free on database access.
"""
def getStatMethods(mr):
key = "{}|{}".format(
mr["adjustments_list"],
mr["statistical_notes"]
)
return key, mr
results = [
SerializerHelper.get_serialized(obj, json=False)
for obj in queryset
]
studies = {}
# flip dictionary nesting
for thisMr in results:
thisPro = thisMr["protocol"]
thisStudy = thisMr["protocol"]["study"]
study = studies.get(thisStudy["id"])
if study is None:
study = thisStudy
study["protocols"] = {}
studies[study["id"]] = study
pro = study["protocols"].get(thisPro["id"])
if pro is None:
pro = thisPro
pro["inclusion_list"] = ', '.join(pro["inclusion_criteria"])
pro["exclusion_list"] = ', '.join(pro["exclusion_criteria"])
pro["results"] = {}
pro["statistical_methods"] = {}
study["protocols"][pro["id"]] = pro
mr = pro["results"].get(thisMr["id"])
if mr is None:
mr = thisMr
mr["ci_percent"] = int(mr["ci_units"] * 100.)
mr["adjustments_list"] = ', '.join(
sorted(mr["adjustment_factors"]))
pro["results"][mr["id"]] = mr
statKey, statVal = getStatMethods(thisMr)
stats = pro["statistical_methods"].get(statKey)
if stats is None:
stats = statVal
pro["statistical_methods"][statKey] = statVal
stats["sm_endpoints"] = []
stats["sm_endpoints"].append(thisMr)
# convert value dictionaries to lists
studies = sorted(
list(studies.values()),
key=lambda obj: (obj["short_citation"].lower()))
for study in studies:
study["protocols"] = sorted(
list(study["protocols"].values()),
key=lambda obj: (obj["name"].lower()))
for pro in study["protocols"]:
pro["results"] = sorted(
list(pro["results"].values()),
key=lambda obj: (obj["label"].lower()))
pro["statistical_methods"] = list(pro["statistical_methods"].values())
for obj in pro["statistical_methods"]:
obj["sm_endpoints"] = "; ".join([
d["label"] for d in obj["sm_endpoints"]
])
return {
"assessment": AssessmentSerializer().to_representation(assessment),
"studies": studies
}
def d_response(self, json_encode=True):
return SerializerHelper.get_serialized(self, json=json_encode)
