diff options
Diffstat (limited to 'wqflask')
| -rw-r--r-- | wqflask/base/data_set.py | 258 | ||||
| -rw-r--r-- | wqflask/wqflask/correlation/correlation_gn3_api.py | 144 |
2 files changed, 303 insertions, 99 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py index dc338971..7531ac81 100644 --- a/wqflask/base/data_set.py +++ b/wqflask/base/data_set.py @@ -166,7 +166,6 @@ class DatasetType: if t in ['pheno', 'other_pheno']: group_name = name.replace("Publish", "") - results = g.db.execute(sql_query_mapping[t] % group_name).fetchone() if results: self.datasets[name] = dataset_name_mapping[t] @@ -278,7 +277,7 @@ class Markers: filtered_markers = [] for marker in self.markers: if marker['name'] in p_values: - #logger.debug("marker {} IS in p_values".format(i)) + # logger.debug("marker {} IS in p_values".format(i)) marker['p_value'] = p_values[marker['name']] if math.isnan(marker['p_value']) or (marker['p_value'] <= 0): marker['lod_score'] = 0 @@ -299,7 +298,7 @@ class HumanMarkers(Markers): self.markers = [] for line in marker_data_fh: splat = line.strip().split() - #logger.debug("splat:", splat) + # logger.debug("splat:", splat) if len(specified_markers) > 0: if splat[1] in specified_markers: marker = {} @@ -441,7 +440,7 @@ class DatasetGroup: # genotype_1 is Dataset Object without parents and f1 # genotype_2 is Dataset Object with parents and f1 (not for intercross) - #genotype_1 = reaper.Dataset() + # genotype_1 = reaper.Dataset() # reaper barfs on unicode filenames, so here we ensure it's a string if self.genofile: @@ -662,9 +661,39 @@ class DataSet: - def get_trait_data(self, sample_list=None): + + def chunk_dataset(self, dataset, n): + + + results = {} + + query = """ + SELECT ProbeSetXRef.DataId,ProbeSet.Name + FROM ProbeSet, ProbeSetXRef, ProbeSetFreeze + WHERE ProbeSetFreeze.Name = '{}' AND + ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id AND + ProbeSetXRef.ProbeSetId = ProbeSet.Id + """.format(self.name) + + # should cache this + + traits_name_dict= dict(g.db.execute(query).fetchall()) + + + + + for i in range(0, len(dataset), n): + matrix = list(dataset[i:i + n]) + trait_name = traits_name_dict[matrix[0][0]] + + my_values = [value for (trait_name, strain, value) in matrix] + results[trait_name] = my_values + return results + + def get_probeset_data(self, sample_list=None, trait_ids=None): if sample_list: self.samplelist = sample_list + else: self.samplelist = self.group.samplelist @@ -678,27 +707,59 @@ class DataSet: and Strain.SpeciesId=Species.Id and Species.name = '{}' """.format(create_in_clause(self.samplelist), *mescape(self.group.species)) - logger.sql(query) results = dict(g.db.execute(query).fetchall()) sample_ids = [results[item] for item in self.samplelist] + query = """SELECT * from ProbeSetData + where StrainID in {} + and id in (SELECT ProbeSetXRef.DataId + FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze) + WHERE ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id + and ProbeSetFreeze.Name = '{}' + and ProbeSet.Id = ProbeSetXRef.ProbeSetId)""".format(create_in_clause(sample_ids),self.name) + + query_results=list(g.db.execute(query).fetchall()) + + data_results=self.chunk_dataset(query_results, len(sample_ids)) + self.trait_data=data_results + + def get_trait_data(self, sample_list=None): + if sample_list: + self.samplelist=sample_list + else: + self.samplelist=self.group.samplelist + + if self.group.parlist != None and self.group.f1list != None: + if (self.group.parlist + self.group.f1list) in self.samplelist: + self.samplelist += self.group.parlist + self.group.f1list + + query=""" + SELECT Strain.Name, Strain.Id FROM Strain, Species + WHERE Strain.Name IN {} + and Strain.SpeciesId=Species.Id + and Species.name = '{}' + """.format(create_in_clause(self.samplelist), *mescape(self.group.species)) + logger.sql(query) + results=dict(g.db.execute(query).fetchall()) + sample_ids=[results[item] for item in self.samplelist] + # MySQL limits the number of tables that can be used in a join to 61, # so we break the sample ids into smaller chunks # Postgres doesn't have that limit, so we can get rid of this after we transition - chunk_size = 50 - number_chunks = int(math.ceil(len(sample_ids) / chunk_size)) - trait_sample_data = [] + chunk_size=50 + number_chunks=int(math.ceil(len(sample_ids) / chunk_size)) + trait_sample_data=[] for sample_ids_step in chunks.divide_into_chunks(sample_ids, number_chunks): if self.type == "Publish": - dataset_type = "Phenotype" + dataset_type="Phenotype" else: - dataset_type = self.type - temp = ['T%s.value' % item for item in sample_ids_step] + dataset_type=self.type + temp=['T%s.value' % item for item in sample_ids_step] if self.type == "Publish": - query = "SELECT {}XRef.Id,".format(escape(self.type)) + query="SELECT {}XRef.Id,".format(escape(self.type)) else: - query = "SELECT {}.Name,".format(escape(dataset_type)) - data_start_pos = 1 + query="SELECT {}.Name,".format(escape(dataset_type)) + data_start_pos=1 query += ', '.join(temp) query += ' FROM ({}, {}XRef, {}Freeze) '.format(*mescape(dataset_type, self.type, @@ -727,27 +788,27 @@ class DataSet: """.format(*mescape(self.type, self.type, self.type, self.type, self.name, dataset_type, self.type, self.type, dataset_type)) - results = g.db.execute(query).fetchall() + results=g.db.execute(query).fetchall() trait_sample_data.append(results) - trait_count = len(trait_sample_data[0]) - self.trait_data = collections.defaultdict(list) + trait_count=len(trait_sample_data[0]) + self.trait_data=collections.defaultdict(list) # put all of the separate data together into a dictionary where the keys are # trait names and values are lists of sample values for trait_counter in range(trait_count): - trait_name = trait_sample_data[0][trait_counter][0] + trait_name=trait_sample_data[0][trait_counter][0] for chunk_counter in range(int(number_chunks)): self.trait_data[trait_name] += ( trait_sample_data[chunk_counter][trait_counter][data_start_pos:]) class PhenotypeDataSet(DataSet): - DS_NAME_MAP['Publish'] = 'PhenotypeDataSet' + DS_NAME_MAP['Publish']='PhenotypeDataSet' def setup(self): # Fields in the database table - self.search_fields = ['Phenotype.Post_publication_description', + self.search_fields=['Phenotype.Post_publication_description', 'Phenotype.Pre_publication_description', 'Phenotype.Pre_publication_abbreviation', 'Phenotype.Post_publication_abbreviation', @@ -760,7 +821,7 @@ class PhenotypeDataSet(DataSet): 'PublishXRef.Id'] # Figure out what display_fields is - self.display_fields = ['name', 'group_code', + self.display_fields=['name', 'group_code', 'pubmed_id', 'pre_publication_description', 'post_publication_description', @@ -778,7 +839,7 @@ class PhenotypeDataSet(DataSet): 'sequence', 'units', 'comments'] # Fields displayed in the search results table header - self.header_fields = ['Index', + self.header_fields=['Index', 'Record', 'Description', 'Authors', @@ -787,9 +848,9 @@ class PhenotypeDataSet(DataSet): 'Max LRS Location', 'Additive Effect'] - self.type = 'Publish' + self.type='Publish' - self.query_for_group = ''' + self.query_for_group=''' SELECT InbredSet.Name, InbredSet.Id, InbredSet.GeneticType FROM @@ -809,13 +870,13 @@ class PhenotypeDataSet(DataSet): if not this_trait.haveinfo: this_trait.retrieve_info(get_qtl_info=True) - description = this_trait.post_publication_description + description=this_trait.post_publication_description # If the dataset is confidential and the user has access to confidential # phenotype traits, then display the pre-publication description instead # of the post-publication description if this_trait.confidential: - this_trait.description_display = "" + this_trait.description_display="" continue # for now, because no authorization features if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait( @@ -823,46 +884,46 @@ class PhenotypeDataSet(DataSet): userName=self.userName, authorized_users=this_trait.authorized_users): - description = this_trait.pre_publication_description + description=this_trait.pre_publication_description if len(description) > 0: - this_trait.description_display = description.strip() + this_trait.description_display=description.strip() else: - this_trait.description_display = "" + this_trait.description_display="" if not this_trait.year.isdigit(): - this_trait.pubmed_text = "N/A" + this_trait.pubmed_text="N/A" else: - this_trait.pubmed_text = this_trait.year + this_trait.pubmed_text=this_trait.year if this_trait.pubmed_id: - this_trait.pubmed_link = webqtlConfig.PUBMEDLINK_URL % this_trait.pubmed_id + this_trait.pubmed_link=webqtlConfig.PUBMEDLINK_URL % this_trait.pubmed_id # LRS and its location - this_trait.LRS_score_repr = "N/A" - this_trait.LRS_location_repr = "N/A" + this_trait.LRS_score_repr="N/A" + this_trait.LRS_location_repr="N/A" if this_trait.lrs: - query = """ + query=""" select Geno.Chr, Geno.Mb from Geno, Species where Species.Name = '%s' and Geno.Name = '%s' and Geno.SpeciesId = Species.Id """ % (species, this_trait.locus) logger.sql(query) - result = g.db.execute(query).fetchone() + result=g.db.execute(query).fetchone() if result: if result[0] and result[1]: - LRS_Chr = result[0] - LRS_Mb = result[1] + LRS_Chr=result[0] + LRS_Mb=result[1] - this_trait.LRS_score_repr = LRS_score_repr = '%3.1f' % this_trait.lrs - this_trait.LRS_location_repr = LRS_location_repr = 'Chr%s: %.6f' % ( + this_trait.LRS_score_repr=LRS_score_repr='%3.1f' % this_trait.lrs + this_trait.LRS_location_repr=LRS_location_repr='Chr%s: %.6f' % ( LRS_Chr, float(LRS_Mb)) def retrieve_sample_data(self, trait): - query = """ + query=""" SELECT Strain.Name, PublishData.value, PublishSE.error, NStrain.count, Strain.Name2 FROM @@ -880,34 +941,34 @@ class PhenotypeDataSet(DataSet): Strain.Name """ logger.sql(query) - results = g.db.execute(query, (trait, self.id)).fetchall() + results=g.db.execute(query, (trait, self.id)).fetchall() return results class GenotypeDataSet(DataSet): - DS_NAME_MAP['Geno'] = 'GenotypeDataSet' + DS_NAME_MAP['Geno']='GenotypeDataSet' def setup(self): # Fields in the database table - self.search_fields = ['Name', + self.search_fields=['Name', 'Chr'] # Find out what display_fields is - self.display_fields = ['name', + self.display_fields=['name', 'chr', 'mb', 'source2', 'sequence'] # Fields displayed in the search results table header - self.header_fields = ['Index', + self.header_fields=['Index', 'ID', 'Location'] # Todo: Obsolete or rename this field - self.type = 'Geno' + self.type='Geno' - self.query_for_group = ''' + self.query_for_group=''' SELECT InbredSet.Name, InbredSet.Id, InbredSet.GeneticType FROM @@ -926,11 +987,11 @@ class GenotypeDataSet(DataSet): this_trait.retrieveInfo() if this_trait.chr and this_trait.mb: - this_trait.location_repr = 'Chr%s: %.6f' % ( + this_trait.location_repr='Chr%s: %.6f' % ( this_trait.chr, float(this_trait.mb)) def retrieve_sample_data(self, trait): - query = """ + query=""" SELECT Strain.Name, GenoData.value, GenoSE.error, "N/A", Strain.Name2 FROM @@ -947,7 +1008,7 @@ class GenotypeDataSet(DataSet): Strain.Name """ logger.sql(query) - results = g.db.execute(query, + results=g.db.execute(query, (webqtlDatabaseFunction.retrieve_species_id(self.group.name), trait, self.name)).fetchall() return results @@ -961,11 +1022,11 @@ class MrnaAssayDataSet(DataSet): platform and is far too specific. ''' - DS_NAME_MAP['ProbeSet'] = 'MrnaAssayDataSet' + DS_NAME_MAP['ProbeSet']='MrnaAssayDataSet' def setup(self): # Fields in the database table - self.search_fields = ['Name', + self.search_fields=['Name', 'Description', 'Probe_Target_Description', 'Symbol', @@ -975,7 +1036,7 @@ class MrnaAssayDataSet(DataSet): 'RefSeq_TranscriptId'] # Find out what display_fields is - self.display_fields = ['name', 'symbol', + self.display_fields=['name', 'symbol', 'description', 'probe_target_description', 'chr', 'mb', 'alias', 'geneid', @@ -995,7 +1056,7 @@ class MrnaAssayDataSet(DataSet): 'flag'] # Fields displayed in the search results table header - self.header_fields = ['Index', + self.header_fields=['Index', 'Record', 'Symbol', 'Description', @@ -1006,9 +1067,9 @@ class MrnaAssayDataSet(DataSet): 'Additive Effect'] # Todo: Obsolete or rename this field - self.type = 'ProbeSet' + self.type='ProbeSet' - self.query_for_group = ''' + self.query_for_group=''' SELECT InbredSet.Name, InbredSet.Id, InbredSet.GeneticType FROM @@ -1026,7 +1087,7 @@ class MrnaAssayDataSet(DataSet): # Note: setting trait_list to [] is probably not a great idea. if not trait_list: - trait_list = [] + trait_list=[] for this_trait in trait_list: @@ -1034,33 +1095,33 @@ class MrnaAssayDataSet(DataSet): this_trait.retrieveInfo(QTL=1) if not this_trait.symbol: - this_trait.symbol = "N/A" + this_trait.symbol="N/A" # XZ, 12/08/2008: description # XZ, 06/05/2009: Rob asked to add probe target description - description_string = str( + description_string=str( str(this_trait.description).strip(codecs.BOM_UTF8), 'utf-8') - target_string = str( + target_string=str( str(this_trait.probe_target_description).strip(codecs.BOM_UTF8), 'utf-8') if len(description_string) > 1 and description_string != 'None': - description_display = description_string + description_display=description_string else: - description_display = this_trait.symbol + description_display=this_trait.symbol if (len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None'): - description_display = description_display + '; ' + target_string.strip() + description_display=description_display + '; ' + target_string.strip() # Save it for the jinja2 template - this_trait.description_display = description_display + this_trait.description_display=description_display if this_trait.chr and this_trait.mb: - this_trait.location_repr = 'Chr%s: %.6f' % ( + this_trait.location_repr='Chr%s: %.6f' % ( this_trait.chr, float(this_trait.mb)) # Get mean expression value - query = ( + query=( """select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet where ProbeSetXRef.ProbeSetFreezeId = %s and ProbeSet.Id = ProbeSetXRef.ProbeSetId and @@ -1068,44 +1129,45 @@ class MrnaAssayDataSet(DataSet): """ % (escape(str(this_trait.dataset.id)), escape(this_trait.name))) - #logger.debug("query is:", pf(query)) + # logger.debug("query is:", pf(query)) logger.sql(query) - result = g.db.execute(query).fetchone() + result=g.db.execute(query).fetchone() - mean = result[0] if result else 0 + mean=result[0] if result else 0 if mean: - this_trait.mean = "%2.3f" % mean + this_trait.mean="%2.3f" % mean # LRS and its location - this_trait.LRS_score_repr = 'N/A' - this_trait.LRS_location_repr = 'N/A' + this_trait.LRS_score_repr='N/A' + this_trait.LRS_location_repr='N/A' # Max LRS and its Locus location if this_trait.lrs and this_trait.locus: - query = """ + query=""" select Geno.Chr, Geno.Mb from Geno, Species where Species.Name = '{}' and Geno.Name = '{}' and Geno.SpeciesId = Species.Id """.format(species, this_trait.locus) logger.sql(query) - result = g.db.execute(query).fetchone() + result=g.db.execute(query).fetchone() if result: - lrs_chr, lrs_mb = result - this_trait.LRS_score_repr = '%3.1f' % this_trait.lrs - this_trait.LRS_location_repr = 'Chr%s: %.6f' % ( + lrs_chr, lrs_mb=result + this_trait.LRS_score_repr='%3.1f' % this_trait.lrs + this_trait.LRS_location_repr='Chr%s: %.6f' % ( lrs_chr, float(lrs_mb)) return trait_list def retrieve_sample_data(self, trait): - query = """ + query=""" SELECT Strain.Name, ProbeSetData.value, ProbeSetSE.error, NStrain.count, Strain.Name2 FROM - (ProbeSetData, ProbeSetFreeze, Strain, ProbeSet, ProbeSetXRef) + (ProbeSetData, ProbeSetFreeze, + Strain, ProbeSet, ProbeSetXRef) left join ProbeSetSE on (ProbeSetSE.DataId = ProbeSetData.Id AND ProbeSetSE.StrainId = ProbeSetData.StrainId) left join NStrain on @@ -1121,19 +1183,19 @@ class MrnaAssayDataSet(DataSet): Strain.Name """ % (escape(trait), escape(self.name)) logger.sql(query) - results = g.db.execute(query).fetchall() - #logger.debug("RETRIEVED RESULTS HERE:", results) + results=g.db.execute(query).fetchall() + # logger.debug("RETRIEVED RESULTS HERE:", results) return results def retrieve_genes(self, column_name): - query = """ + query=""" select ProbeSet.Name, ProbeSet.%s from ProbeSet,ProbeSetXRef where ProbeSetXRef.ProbeSetFreezeId = %s and ProbeSetXRef.ProbeSetId=ProbeSet.Id; """ % (column_name, escape(str(self.id))) logger.sql(query) - results = g.db.execute(query).fetchall() + results=g.db.execute(query).fetchall() return dict(results) @@ -1141,40 +1203,40 @@ class MrnaAssayDataSet(DataSet): class TempDataSet(DataSet): '''Temporary user-generated data set''' - DS_NAME_MAP['Temp'] = 'TempDataSet' + DS_NAME_MAP['Temp']='TempDataSet' def setup(self): - self.search_fields = ['name', + self.search_fields=['name', 'description'] - self.display_fields = ['name', + self.display_fields=['name', 'description'] - self.header_fields = ['Name', + self.header_fields=['Name', 'Description'] - self.type = 'Temp' + self.type='Temp' # Need to double check later how these are used - self.id = 1 - self.fullname = 'Temporary Storage' - self.shortname = 'Temp' + self.id=1 + self.fullname='Temporary Storage' + self.shortname='Temp' def geno_mrna_confidentiality(ob): - dataset_table = ob.type + "Freeze" - #logger.debug("dataset_table [%s]: %s" % (type(dataset_table), dataset_table)) + dataset_table=ob.type + "Freeze" + # logger.debug("dataset_table [%s]: %s" % (type(dataset_table), dataset_table)) - query = '''SELECT Id, Name, FullName, confidentiality, + query='''SELECT Id, Name, FullName, confidentiality, AuthorisedUsers FROM %s WHERE Name = "%s"''' % (dataset_table, ob.name) logger.sql(query) - result = g.db.execute(query) + result=g.db.execute(query) (dataset_id, name, full_name, confidential, - authorized_users) = result.fetchall()[0] + authorized_users)=result.fetchall()[0] if confidential: return True diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py index df0afba6..9fbfee48 100644 --- a/wqflask/wqflask/correlation/correlation_gn3_api.py +++ b/wqflask/wqflask/correlation/correlation_gn3_api.py @@ -27,6 +27,34 @@ def create_target_this_trait(start_vars): return (this_dataset, this_trait, target_dataset, sample_data) + +def test_process_data(this_trait,dataset,start_vars): + """test function for bxd,all and other sample data""" + + corr_samples_group = start_vars["corr_samples_group"] + + + primary_samples = dataset.group.samplelist + if dataset.group.parlist != None: + primary_samples += dataset.group.parlist + if dataset.group.f1list != None: + primary_samples += dataset.group.f1list + + # If either BXD/whatever Only or All Samples, append all of that group's samplelist + if corr_samples_group != 'samples_other': + sample_data = process_samples(start_vars, primary_samples) + + # If either Non-BXD/whatever or All Samples, get all samples from this_trait.data and + # exclude the primary samples (because they would have been added in the previous + # if statement if the user selected All Samples) + if corr_samples_group != 'samples_primary': + if corr_samples_group == 'samples_other': + primary_samples = [x for x in primary_samples if x not in ( + dataset.group.parlist + dataset.group.f1list)] + sample_data = process_samples(start_vars, list(this_trait.data.keys()), primary_samples) + + return sample_data + def process_samples(start_vars, sample_names, excluded_samples=None): """process samples""" sample_data = {} @@ -118,13 +146,22 @@ def fetch_sample_data(start_vars, this_trait, this_dataset, target_dataset): sample_data = process_samples( start_vars, this_dataset.group.samplelist) - target_dataset.get_trait_data(list(sample_data.keys())) + + # sample_data = test_process_data(this_trait,this_dataset,start_vars) + + if target_dataset.type =="ProbeSet": + # pass + target_dataset.get_probeset_data(list(sample_data.keys())) + else: + target_dataset.get_trait_data(list(sample_data.keys())) this_trait = retrieve_sample_data(this_trait, this_dataset) this_trait_data = { "trait_sample_data": sample_data, "trait_id": start_vars["trait_id"] } + # should remove this len(samplelist) == len(strain_values) + results = map_shared_keys_to_values( target_dataset.samplelist, target_dataset.trait_data) @@ -205,6 +242,7 @@ def compute_correlation(start_vars, method="pearson", compute_all=False): "target_dataset": start_vars['corr_dataset'], "return_results": corr_return_results} + return correlation_data @@ -265,3 +303,107 @@ def get_tissue_correlation_input(this_trait, trait_symbol_dict): } return (primary_tissue_data, target_tissue_data) return None + + +def generate_corr_data(corr_results, target_dataset): + counter = 0 + results_list = [] + for (index, trait_corr) in enumerate(corr_results): + trait_name = list(trait_corr.keys())[0] + trait = create_trait(dataset=target_dataset, + name=trait_name) + + trait_corr_data = trait_corr[trait_name] + + if trait.view == False: + continue + results_dict = {} + results_dict['index'] = index + 1 + results_dict['trait_id'] = trait.name + results_dict['dataset'] = trait.dataset.name + # results_dict['hmac'] = hmac.data_hmac( + # '{}:{}'.format(trait.name, trait.dataset.name)) + if target_dataset.type == "ProbeSet": + results_dict['symbol'] = trait.symbol + results_dict['description'] = "N/A" + results_dict['location'] = trait.location_repr + results_dict['mean'] = "N/A" + results_dict['additive'] = "N/A" + if bool(trait.description_display): + results_dict['description'] = trait.description_display + if bool(trait.mean): + results_dict['mean'] = f"{float(trait.mean):.3f}" + try: + results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" + except: + results_dict['lod_score'] = "N/A" + results_dict['lrs_location'] = trait.LRS_location_repr + if bool(trait.additive): + results_dict['additive'] = f"{float(trait.additive):.3f}" + results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}" + results_dict['num_overlap'] = trait.num_overlap + results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}" + results_dict['lit_corr'] = "--" + results_dict['tissue_corr'] = "--" + results_dict['tissue_pvalue'] = "--" + tissue_corr = trait_corr_data.get('tissue_corr',0) + lit_corr = trait_corr_data.get('lit_corr',0) + if bool(lit_corr): + results_dict['lit_corr'] = f"{float(trait_corr_data.get('lit_corr',0)):.3f}" + if bool(tissue_corr): + results_dict['tissue_corr'] = f"{float(trait_corr_data.get('tissue_corr',0)):.3f}" + results_dict['tissue_pvalue'] = f"{float(trait_corr_data.get('tissue_pvalue',0)):.3e}" + elif target_dataset.type == "Publish": + results_dict['abbreviation_display'] = "N/A" + results_dict['description'] = "N/A" + results_dict['mean'] = "N/A" + results_dict['authors_display'] = "N/A" + results_dict['additive'] = "N/A" + if for_api: + results_dict['pubmed_id'] = "N/A" + results_dict['year'] = "N/A" + else: + results_dict['pubmed_link'] = "N/A" + results_dict['pubmed_text'] = "N/A" + + if bool(trait.abbreviation): + results_dict['abbreviation_display'] = trait.abbreviation + if bool(trait.description_display): + results_dict['description'] = trait.description_display + if bool(trait.mean): + results_dict['mean'] = f"{float(trait.mean):.3f}" + if bool(trait.authors): + authors_list = trait.authors.split(',') + if len(authors_list) > 6: + results_dict['authors_display'] = ", ".join( + authors_list[:6]) + ", et al." + else: + results_dict['authors_display'] = trait.authors + if bool(trait.pubmed_id): + if for_api: + results_dict['pubmed_id'] = trait.pubmed_id + results_dict['year'] = trait.pubmed_text + else: + results_dict['pubmed_link'] = trait.pubmed_link + results_dict['pubmed_text'] = trait.pubmed_text + try: + results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}" + except: + results_dict['lod_score'] = "N/A" + results_dict['lrs_location'] = trait.LRS_location_repr + if bool(trait.additive): + results_dict['additive'] = f"{float(trait.additive):.3f}" + results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}" + results_dict['num_overlap'] = trait.num_overlap + results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}" + else: + results_dict['location'] = trait.location_repr + results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}" + results_dict['num_overlap'] = trait.num_overlap + results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}" + + results_list.append(results_dict) + + return results_list + + |