def gen_search_result(self):
"""
Get the info displayed in the search result table from the set of results computed in
the "search" function
"""
self.trait_list = []
json_trait_list = []
species = webqtlDatabaseFunction.retrieve_species(self.dataset.group.name)
# result_set represents the results for each search term; a search of
# "shh grin2b" would have two sets of results, one for each term
logger.debug("self.results is:", pf(self.results))
for index, result in enumerate(self.results):
if not result:
continue
#### Excel file needs to be generated ####
#logger.debug("foo locals are:", locals())
trait_id = result[0]
this_trait = trait.GeneralTrait(dataset=self.dataset, name=trait_id, get_qtl_info=True, get_sample_info=False)
self.trait_list.append(this_trait)
json_trait_list.append(trait.jsonable_table_row(this_trait, self.dataset.name, index + 1))
self.json_trait_list = json.dumps(json_trait_list)
def gen_search_result(self):
"""
Get the info displayed in the search result table from the set of results computed in
the "search" function
"""
self.trait_list = []
json_trait_list = []
species = webqtlDatabaseFunction.retrieve_species(
self.dataset.group.name)
# result_set represents the results for each search term; a search of
# "shh grin2b" would have two sets of results, one for each term
logger.debug("self.results is:", pf(self.results))
for index, result in enumerate(self.results):
if not result:
continue
#### Excel file needs to be generated ####
#logger.debug("foo locals are:", locals())
trait_id = result[0]
this_trait = trait.GeneralTrait(dataset=self.dataset,
name=trait_id,
get_qtl_info=True,
get_sample_info=False)
self.trait_list.append(this_trait)
json_trait_list.append(
trait.jsonable_table_row(this_trait, self.dataset.name,
index + 1))
self.json_trait_list = json.dumps(json_trait_list)
def __init__(self, dataset, name=None):
"""This sets self.group and self.group_id"""
#logger.debug("DATASET NAME2:", dataset.name)
if name == None:
self.name, self.id, self.genetic_type = fetchone(
dataset.query_for_group)
else:
self.name, self.id, self.genetic_type = fetchone(
"SELECT InbredSet.Name, InbredSet.Id, InbredSet.GeneticType FROM InbredSet where Name='%s'"
% name)
if self.name == 'BXD300':
self.name = "BXD"
self.f1list = None
self.parlist = None
self.get_f1_parent_strains()
self.accession_id = self.get_accession_id()
self.mapping_id, self.mapping_names = self.get_mapping_methods()
self.species = webqtlDatabaseFunction.retrieve_species(self.name)
self.incparentsf1 = False
self.allsamples = None
self._datasets = None
self.genofile = None
def __init__(self, dataset):
"""This sets self.group and self.group_id"""
#logger.debug("DATASET NAME2:", dataset.name)
self.name, self.id = fetchone(dataset.query_for_group)
if self.name == 'BXD300':
self.name = "BXD"
self.f1list = None
self.parlist = None
self.get_f1_parent_strains()
#logger.debug("parents/f1s: {}:{}".format(self.parlist, self.f1list))
self.species = webqtlDatabaseFunction.retrieve_species(self.name)
self.incparentsf1 = False
self.allsamples = None
self._datasets = None
def __init__(self, dataset, name=None):
"""This sets self.group and self.group_id"""
#logger.debug("DATASET NAME2:", dataset.name)
if name == None:
self.name, self.id, self.genetic_type = fetchone(dataset.query_for_group)
else:
self.name, self.id, self.genetic_type = fetchone("SELECT InbredSet.Name, InbredSet.Id, InbredSet.GeneticType FROM InbredSet where Name='%s'" % name)
if self.name == 'BXD300':
self.name = "BXD"
self.f1list = None
self.parlist = None
self.get_f1_parent_strains()
self.accession_id = self.get_accession_id()
self.mapping_id, self.mapping_names = self.get_mapping_methods()
self.species = webqtlDatabaseFunction.retrieve_species(self.name)
self.incparentsf1 = False
self.allsamples = None
self._datasets = None
self.genofile = None
def gen_search_result(self):
"""
Get the info displayed in the search result table from the set of results computed in
the "search" function
"""
trait_list = []
json_trait_list = []
species = webqtlDatabaseFunction.retrieve_species(
self.dataset.group.name)
# result_set represents the results for each search term; a search of
# "shh grin2b" would have two sets of results, one for each term
logger.debug("self.results is:", pf(self.results))
for index, result in enumerate(self.results):
if not result:
continue
#### Excel file needs to be generated ####
trait_dict = {}
trait_id = result[0]
trait_dict['index'] = index + 1
this_trait = trait.GeneralTrait(dataset=self.dataset,
name=trait_id,
get_qtl_info=True,
get_sample_info=False)
trait_dict['name'] = this_trait.name
if this_trait.dataset.type == "Publish":
trait_dict['display_name'] = this_trait.display_name
else:
trait_dict['display_name'] = this_trait.name
trait_dict['dataset'] = this_trait.dataset.name
trait_dict['hmac'] = hmac.data_hmac('{}:{}'.format(
this_trait.name, this_trait.dataset.name))
if this_trait.dataset.type == "ProbeSet":
trait_dict['symbol'] = this_trait.symbol
trait_dict[
'description'] = this_trait.description_display.decode(
'utf-8', 'replace')
trait_dict['location'] = this_trait.location_repr
trait_dict['mean'] = "N/A"
trait_dict['additive'] = "N/A"
if this_trait.mean != "" and this_trait.mean != None:
trait_dict['mean'] = '%.3f' % this_trait.mean
trait_dict['lrs_score'] = this_trait.LRS_score_repr
trait_dict['lrs_location'] = this_trait.LRS_location_repr
if this_trait.additive != "":
trait_dict['additive'] = '%.3f' % this_trait.additive
elif this_trait.dataset.type == "Geno":
trait_dict['location'] = this_trait.location_repr
elif this_trait.dataset.type == "Publish":
trait_dict['description'] = this_trait.description_display
trait_dict['authors'] = this_trait.authors
trait_dict['pubmed_id'] = "N/A"
if this_trait.pubmed_id:
trait_dict['pubmed_id'] = this_trait.pubmed_id
trait_dict['pubmed_link'] = this_trait.pubmed_link
trait_dict['pubmed_text'] = this_trait.pubmed_text
trait_dict['mean'] = "N/A"
if this_trait.mean != "" and this_trait.mean != None:
trait_dict['mean'] = '%.3f' % this_trait.mean
trait_dict['lrs_score'] = this_trait.LRS_score_repr
trait_dict['lrs_location'] = this_trait.LRS_location_repr
trait_dict['additive'] = "N/A"
if this_trait.additive != "":
trait_dict['additive'] = '%.3f' % this_trait.additive
trait_list.append(trait_dict)
#json_trait_list.append(trait.jsonable_table_row(this_trait, self.dataset.name, index + 1))
self.trait_list = json.dumps(trait_list)
def gen_search_result(self):
"""
Get the info displayed in the search result table from the set of results computed in
the "search" function
"""
trait_list = []
json_trait_list = []
species = webqtlDatabaseFunction.retrieve_species(
self.dataset.group.name)
# result_set represents the results for each search term; a search of
# "shh grin2b" would have two sets of results, one for each term
logger.debug("self.results is:", pf(self.results))
for index, result in enumerate(self.results):
if not result:
continue
#### Excel file needs to be generated ####
trait_dict = {}
trait_id = result[0]
this_trait = create_trait(dataset=self.dataset,
name=trait_id,
get_qtl_info=True,
get_sample_info=False)
if this_trait:
trait_dict['index'] = index + 1
trait_dict['name'] = this_trait.name
if this_trait.dataset.type == "Publish":
trait_dict['display_name'] = this_trait.display_name
else:
trait_dict['display_name'] = this_trait.name
trait_dict['dataset'] = this_trait.dataset.name
trait_dict['hmac'] = hmac.data_hmac('{}:{}'.format(
this_trait.name, this_trait.dataset.name))
if this_trait.dataset.type == "ProbeSet":
trait_dict['symbol'] = this_trait.symbol
trait_dict['description'] = "N/A"
if this_trait.description_display:
trait_dict[
'description'] = this_trait.description_display
trait_dict['location'] = this_trait.location_repr
trait_dict['mean'] = "N/A"
trait_dict['additive'] = "N/A"
if this_trait.mean != "" and this_trait.mean != None:
trait_dict['mean'] = f"{this_trait.mean:.3f}"
try:
trait_dict[
'lod_score'] = f"{float(this_trait.LRS_score_repr) / 4.61:.1f}"
except:
trait_dict['lod_score'] = "N/A"
trait_dict['lrs_location'] = this_trait.LRS_location_repr
if this_trait.additive != "":
trait_dict['additive'] = f"{this_trait.additive:.3f}"
elif this_trait.dataset.type == "Geno":
trait_dict['location'] = this_trait.location_repr
elif this_trait.dataset.type == "Publish":
trait_dict['description'] = "N/A"
if this_trait.description_display:
trait_dict[
'description'] = this_trait.description_display
trait_dict['authors'] = this_trait.authors
trait_dict['pubmed_id'] = "N/A"
if this_trait.pubmed_id:
trait_dict['pubmed_id'] = this_trait.pubmed_id
trait_dict['pubmed_link'] = this_trait.pubmed_link
trait_dict['pubmed_text'] = this_trait.pubmed_text
trait_dict['mean'] = "N/A"
if this_trait.mean != "" and this_trait.mean != None:
trait_dict['mean'] = f"{this_trait.mean:.3f}"
try:
trait_dict[
'lod_score'] = f"{float(this_trait.LRS_score_repr) / 4.61:.1f}"
except:
trait_dict['lod_score'] = "N/A"
trait_dict['lrs_location'] = this_trait.LRS_location_repr
trait_dict['additive'] = "N/A"
if this_trait.additive != "":
trait_dict['additive'] = f"{this_trait.additive:.3f}"
# Convert any bytes in dict to a normal utf-8 string
for key in trait_dict.keys():
if isinstance(trait_dict[key], bytes):
trait_dict[key] = trait_dict[key].decode('utf-8')
trait_list.append(trait_dict)
self.trait_list = trait_list
if self.dataset.type == "ProbeSet":
self.header_data_names = [
'index', 'display_name', 'symbol', 'description', 'location',
'mean', 'lrs_score', 'lrs_location', 'additive'
]
elif self.dataset.type == "Publish":
self.header_data_names = [
'index', 'display_name', 'description', 'mean', 'authors',
'pubmed_text', 'lrs_score', 'lrs_location', 'additive'
]
elif self.dataset.type == "Geno":
self.header_data_names = ['index', 'display_name', 'location']
