def _get_header_row(self):
header = []
header.extend(Study.flat_complete_header_row())
header.extend(models.MetaProtocol.flat_complete_header_row())
header.extend(models.MetaResult.flat_complete_header_row())
header.extend(models.SingleResult.flat_complete_header_row())
return header
def _get_header_row(self):
header = []
header.extend(Study.flat_complete_header_row())
header.extend(models.StudyPopulation.flat_complete_header_row())
header.extend(models.Exposure.flat_complete_header_row())
header.extend(models.AssessedOutcome.flat_complete_header_row())
header.extend(models.AssessedOutcomeGroup.flat_complete_header_row())
return header
def _get_header_row(self):
header = []
header.extend(Study.flat_complete_header_row())
header.extend(models.StudyPopulation.flat_complete_header_row())
header.extend(models.Outcome.flat_complete_header_row())
header.extend(models.Exposure.flat_complete_header_row())
header.extend(models.ComparisonSet.flat_complete_header_row())
header.extend(models.Result.flat_complete_header_row())
header.extend(models.Group.flat_complete_header_row())
header.extend(models.GroupResult.flat_complete_header_row())
return header
def _get_header_row(self):
header = []
header.extend(Study.flat_complete_header_row())
header.extend(models.StudyPopulation.flat_complete_header_row())
header.extend(models.Outcome.flat_complete_header_row())
header.extend(models.Exposure.flat_complete_header_row())
header.extend(models.ComparisonSet.flat_complete_header_row())
header.extend(models.Result.flat_complete_header_row())
header.extend(models.Group.flat_complete_header_row())
header.extend(models.GroupResult.flat_complete_header_row())
return header
def _get_header_row(self):
self.doses = DoseUnits.objects.get_animal_units(self.kwargs.get('assessment'))
header = []
header.extend(Study.flat_complete_header_row())
header.extend(models.Experiment.flat_complete_header_row())
header.extend(models.AnimalGroup.flat_complete_header_row())
header.extend(models.DosingRegime.flat_complete_header_row())
header.extend(models.Endpoint.flat_complete_header_row())
header.extend(['doses-{}'.format(d) for d in self.doses])
header.extend(models.EndpointGroup.flat_complete_header_row())
return header
def _get_header_row(self):
self.doses = DoseUnits.get_animal_units(self.kwargs.get('assessment'))
header = []
header.extend(Study.flat_complete_header_row())
header.extend(models.Experiment.flat_complete_header_row())
header.extend(models.AnimalGroup.flat_complete_header_row())
header.extend(models.DosingRegime.flat_complete_header_row())
header.extend(models.Endpoint.flat_complete_header_row())
header.extend([u'doses-{}'.format(d) for d in self.doses])
header.extend(models.EndpointGroup.flat_complete_header_row())
return header
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser))
for rob in ser.get('riskofbiases', []):
rob_data = models.RiskOfBias.flat_complete_data_row(rob)
for score in rob['scores']:
row_copy = list(row)
row_copy.extend(
models.RiskOfBiasScore.flat_complete_data_row(score))
row_copy.extend(rob_data)
rows.append(row_copy)
return rows
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser['exposure']['study_population']['study']))
row.extend(models.StudyPopulation.flat_complete_data_row(ser['exposure']['study_population']))
row.extend(models.Exposure.flat_complete_data_row(ser['exposure']))
row.extend(models.AssessedOutcome.flat_complete_data_row(ser))
# build a row for each aog
for aog in ser['groups']:
row_copy = list(row) # clone
row_copy.extend(models.AssessedOutcomeGroup.flat_complete_data_row(aog))
rows.append(row_copy)
return rows
def handle(self, source_assessment_id, destination_assessment_id,
*args, **options):
source_assessment = Assessment.objects.get(pk=source_assessment_id)
target_assessment = Assessment.objects.get(pk=destination_assessment_id)
source_studies = Study.objects.filter(assessment=source_assessment)
cw = Study.copy_across_assessment(source_studies, target_assessment)
copyRoB = not options['noRiskOfBias']
if copyRoB:
cw = RiskOfBiasDomain.copy_across_assessment(
cw, source_studies, target_assessment)
cw = RiskOfBias.copy_across_assessment(
cw, source_studies, target_assessment)
def getChoices(self, field_name):
assessment_id = self.form.instance.assessment_id
choices = None
if field_name == "systems":
choices = list(Endpoint.get_system_choices(assessment_id))
elif field_name == "effects":
choices = list(Endpoint.get_effect_choices(assessment_id))
elif field_name == "effect_tags":
choices = EffectTag.get_choices(assessment_id)
elif field_name == "studies":
choices = Study.get_choices(assessment_id)
else:
raise ValueError("Unknown field name: {}".format(field_name))
return choices
def handle(self, source_assessment_id, destination_assessment_id, *args,
**options):
source_assessment = Assessment.objects.get(pk=source_assessment_id)
target_assessment = Assessment.objects.get(
pk=destination_assessment_id)
source_studies = Study.objects.filter(assessment=source_assessment)
cw = Study.copy_across_assessment(source_studies, target_assessment)
copyRoB = not options['noRiskOfBias']
if copyRoB:
cw = RiskOfBiasDomain.copy_across_assessment(
cw, source_studies, target_assessment)
cw = RiskOfBias.copy_across_assessment(cw, source_studies,
target_assessment)
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser['study_population']['study']))
row.extend(models.StudyPopulation.flat_complete_data_row(ser['study_population']))
row.extend(models.Outcome.flat_complete_data_row(ser))
for res in ser['results']:
row_copy = list(row)
row_copy.extend(models.Exposure.flat_complete_data_row(res["comparison_set"]["exposure"]))
row_copy.extend(models.ComparisonSet.flat_complete_data_row(res["comparison_set"]))
row_copy.extend(models.Result.flat_complete_data_row(res))
for rg in res['results']:
row_copy2 = list(row_copy)
row_copy2.extend(models.Group.flat_complete_data_row(rg["group"]))
row_copy2.extend(models.GroupResult.flat_complete_data_row(rg))
rows.append(row_copy2)
return rows
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser['animal_group']['experiment']['study']))
row.extend(models.Experiment.flat_complete_data_row(ser['animal_group']['experiment']))
row.extend(models.AnimalGroup.flat_complete_data_row(ser['animal_group']))
row.extend(models.DosingRegime.flat_complete_data_row(ser['animal_group']['dosing_regime']))
row.extend(models.Endpoint.flat_complete_data_row(ser))
for i, eg in enumerate(ser['groups']):
row_copy = copy(row)
row_copy.extend(models.DoseGroup.flat_complete_data_row(
ser['animal_group']['dosing_regime']['doses'],
self.doses, i))
row_copy.extend(models.EndpointGroup.flat_complete_data_row(eg, ser))
rows.append(row_copy)
return rows
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser['study_population']['study']))
row.extend(models.StudyPopulation.flat_complete_data_row(ser['study_population']))
row.extend(models.Outcome.flat_complete_data_row(ser))
for res in ser['results']:
row_copy = list(row)
row_copy.extend(models.Exposure.flat_complete_data_row(res["comparison_set"]["exposure"]))
row_copy.extend(models.ComparisonSet.flat_complete_data_row(res["comparison_set"]))
row_copy.extend(models.Result.flat_complete_data_row(res))
for rg in res['results']:
row_copy2 = list(row_copy)
row_copy2.extend(models.Group.flat_complete_data_row(rg["group"]))
row_copy2.extend(models.GroupResult.flat_complete_data_row(rg))
rows.append(row_copy2)
return rows
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser['protocol']['study']))
row.extend(models.MetaProtocol.flat_complete_data_row(ser['protocol']))
row.extend(models.MetaResult.flat_complete_data_row(ser))
if len(ser['single_results'])==0:
# print one-row with no single-results
rows.append(row)
else:
# print each single-result as a new row
for sr in ser['single_results']:
row_copy = list(row) # clone
row_copy.extend(models.SingleResult.flat_complete_data_row(sr))
rows.append(row_copy)
return rows
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser['animal_group']['experiment']['study']))
row.extend(models.Experiment.flat_complete_data_row(ser['animal_group']['experiment']))
row.extend(models.AnimalGroup.flat_complete_data_row(ser['animal_group']))
row.extend(models.DosingRegime.flat_complete_data_row(ser['animal_group']['dosing_regime']))
row.extend(models.Endpoint.flat_complete_data_row(ser))
for i, eg in enumerate(ser['groups']):
row_copy = copy(row)
row_copy.extend(models.DoseGroup.flat_complete_data_row(
ser['animal_group']['dosing_regime']['doses'],
self.doses, i))
row_copy.extend(models.EndpointGroup.flat_complete_data_row(eg, ser))
rows.append(row_copy)
return rows
def getPrefilterQueryset(self, field_name):
assessment_id = self.instance.assessment_id
choices = None
if field_name == "systems":
choices = list(Endpoint.get_system_choices(assessment_id))
elif field_name == "organs":
choices = list(Endpoint.get_organ_choices(assessment_id))
elif field_name == "effects":
choices = list(Endpoint.get_effect_choices(assessment_id))
elif field_name == "iv_categories":
choices = IVEndpointCategory.get_choices(assessment_id)
elif field_name == "effect_tags":
choices = EffectTag.get_choices(assessment_id)
elif field_name == "studies":
choices = Study.get_choices(assessment_id)
else:
raise ValueError("Unknown field name: {}".format(field_name))
return choices
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(
Study.flat_complete_data_row(
ser['exposure']['study_population']['study']))
row.extend(
models.StudyPopulation.flat_complete_data_row(
ser['exposure']['study_population']))
row.extend(models.Exposure.flat_complete_data_row(ser['exposure']))
row.extend(models.AssessedOutcome.flat_complete_data_row(ser))
# build a row for each aog
for aog in ser['groups']:
row_copy = list(row) # clone
row_copy.extend(
models.AssessedOutcomeGroup.flat_complete_data_row(aog))
rows.append(row_copy)
return rows
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser['protocol']['study']))
row.extend(
models.MetaProtocol.flat_complete_data_row(ser['protocol']))
row.extend(models.MetaResult.flat_complete_data_row(ser))
if len(ser['single_results']) == 0:
# print one-row with no single-results
rows.append(row)
else:
# print each single-result as a new row
for sr in ser['single_results']:
row_copy = list(row) # clone
row_copy.extend(
models.SingleResult.flat_complete_data_row(sr))
rows.append(row_copy)
return rows
def _get_data_rows(self):
rows = []
for obj in self.queryset:
ser = obj.get_json(json_encode=False)
row = []
row.extend(Study.flat_complete_data_row(ser))
try:
scores = [
rob['scores'] for rob in ser.get('riskofbiases', [])
if rob['final'] and rob['active']
][0]
except IndexError:
scores = []
for score in scores:
row_copy = list(row) # clone
row_copy.extend(
models.RiskOfBiasScore.flat_complete_data_row(score))
rows.append(row_copy)
return rows
def delete_caches(cls, ids):
id_lists = [(score.riskofbias.id, score.riskofbias.study_id) for score in cls.objects.filter(id__in=ids)]
rob_ids, study_ids = list(zip(*id_lists))
RiskOfBias.delete_caches(rob_ids)
Study.delete_caches(study_ids)
def _get_header_row(self):
header = []
header.extend(Study.flat_complete_header_row())
header.extend(models.RiskOfBiasScore.flat_complete_header_row())
return header
def delete_caches(cls, ids):
id_lists = [(score.riskofbias.id, score.riskofbias.study_id)
for score in cls.objects.filter(id__in=ids)]
rob_ids, study_ids = list(zip(*id_lists))
RiskOfBias.delete_caches(rob_ids)
Study.delete_caches(study_ids)
