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): 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['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 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