import csv
import datetime
import io
import itertools
import re
import xlsxwriter
from pprint import pformat as pf
from zipfile import ZipFile, ZIP_DEFLATED
import simplejson as json
from gn3.computations.gemma import generate_hash_of_string
from gn2.base.trait import create_trait, retrieve_trait_info
def export_traits(targs, export_type):
if export_type == "collection":
return export_collection(targs)
else:
return export_traitlist(targs)
def export_collection(targs):
table_data = json.loads(targs['export_data'])
table_rows = table_data['rows']
buff = io.StringIO()
writer = csv.writer(buff)
now = datetime.datetime.now()
time_str = now.strftime('%H:%M (UTC) %m/%d/%y')
metadata_rows = [
["# Collection Name: " + targs['collection_name_export']],
["# User E-mail: " + targs['user_email_export']],
["# Time/Date: " + time_str]
]
for row in metadata_rows:
writer.writerow(row)
for trait in table_rows:
writer.writerow([trait])
csv_data = buff.getvalue()
buff.close()
if 'collection_name_export' in targs:
file_name = re.sub('\s+', '_', targs['collection_name_export']) # replace whitespace with underscore
else:
file_name = generate_hash_of_string("".join(table_rows))
return [file_name, csv_data]
def export_traitlist(targs):
table_data = json.loads(targs['export_data'])
table_rows = table_data['rows']
now = datetime.datetime.now()
time_str = now.strftime('%H:%M_%d%B%Y')
if 'file_name' in targs:
zip_file_name = targs['file_name'] + "_export_" + time_str
else:
zip_file_name = "export_" + time_str
metadata = []
if 'database_name' in targs:
if targs['database_name'] != "None":
metadata.append(["Data Set: " + targs['database_name']])
if 'accession_id' in targs:
if targs['accession_id'] != "None":
metadata.append(
["Metadata Link: http://genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=" + targs['accession_id']])
metadata.append(
["Export Date: " + datetime.datetime.now().strftime("%B %d, %Y")])
metadata.append(
["Export Time: " + datetime.datetime.now().strftime("%H:%M GMT")])
if 'search_string' in targs:
if targs['search_string'] != "None":
metadata.append(["Search Query: " + targs['search_string']])
if 'filter_term' in targs:
if targs['filter_term'] != "None":
metadata.append(["Search Filter Terms: " + targs['filter_term']])
metadata.append(["Exported Row Number: " + str(len(table_rows))])
metadata.append(["Funding for The GeneNetwork: NIGMS (R01 GM123489, 2017-2026), NIDA (P30 DA044223, 2017-2022), NIA (R01AG043930, 2013-2018), NIAAA (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017), NIDA/NIMH/NIAAA (P20-DA 21131, 2001-2012), NCI MMHCC (U01CA105417), NCRR/BIRN (U24 RR021760)"])
metadata.append([])
trait_list = []
for trait in table_rows:
trait_name, dataset_name, _hash = trait.split(":")
trait_ob = create_trait(name=trait_name, dataset_name=dataset_name)
trait_ob = retrieve_trait_info(
trait_ob, trait_ob.dataset, get_qtl_info=True)
trait_list.append(trait_ob)
table_headers = ['Index', 'URL', 'Species', 'Group', 'Dataset', 'Record ID', 'Symbol', 'Description', 'ProbeTarget', 'PubMed_ID', 'Chr', 'Mb', 'Alias', 'Gene_ID', 'Homologene_ID', 'UniGene_ID',
'Strand_Probe', 'Probe_set_specificity', 'Probe_set_BLAT_score', 'Probe_set_BLAT_Mb_start', 'Probe_set_BLAT_Mb_end', 'QTL_Chr', 'QTL_Mb', 'Locus_at_Peak', 'Max_LRS', 'P_value_of_MAX', 'Mean_Expression']
traits_by_group = sort_traits_by_group(trait_list)
file_list = []
for group in traits_by_group:
group_traits = traits_by_group[group]
samplelist = group_traits[0].dataset.group.all_samples_ordered()
if not samplelist:
continue
buff = io.StringIO()
writer = csv.writer(buff)
csv_rows = []
sample_headers = []
for sample in samplelist:
sample_headers.append(sample)
sample_headers.append(sample + "_SE")
full_headers = table_headers + sample_headers
for metadata_row in metadata:
writer.writerow(metadata_row)
csv_rows.append(full_headers)
for i, trait in enumerate(group_traits):
if getattr(trait, "symbol", None):
trait_symbol = getattr(trait, "symbol")
elif getattr(trait, "abbreviation", None):
trait_symbol = getattr(trait, "abbreviation")
else:
trait_symbol = "N/A"
row_contents = [
i + 1,
"https://genenetwork.org/show_trait?trait_id=" + \
str(trait.name) + "&dataset=" + str(trait.dataset.name),
trait.dataset.group.species,
trait.dataset.group.name,
trait.dataset.name,
trait.name,
trait_symbol,
getattr(trait, "description_display", "N/A"),
getattr(trait, "probe_target_description", "N/A"),
getattr(trait, "pubmed_id", "N/A"),
getattr(trait, "chr", "N/A"),
getattr(trait, "mb", "N/A"),
trait.alias_fmt,
getattr(trait, "geneid", "N/A"),
getattr(trait, "homologeneid", "N/A"),
getattr(trait, "unigeneid", "N/A"),
getattr(trait, "strand_probe", "N/A"),
getattr(trait, "probe_set_specificity", "N/A"),
getattr(trait, "probe_set_blat_score", "N/A"),
getattr(trait, "probe_set_blat_mb_start", "N/A"),
getattr(trait, "probe_set_blat_mb_end", "N/A"),
getattr(trait, "locus_chr", "N/A"),
getattr(trait, "locus_mb", "N/A"),
getattr(trait, "locus", "N/A"),
getattr(trait, "lrs", "N/A"),
getattr(trait, "pvalue", "N/A"),
getattr(trait, "mean", "N/A")
]
for sample in samplelist:
if sample in trait.data:
row_contents += [trait.data[sample].value,
trait.data[sample].variance]
else:
row_contents += ["x", "x"]
csv_rows.append(row_contents)
writer.writerows(csv_rows)
csv_data = buff.getvalue()
buff.close()
file_name = group + "_traits.csv"
file_list.append([file_name, csv_data])
return file_list
def sort_traits_by_group(trait_list=[]):
traits_by_group = {}
for trait in trait_list:
if trait.dataset.group.name not in list(traits_by_group.keys()):
traits_by_group[trait.dataset.group.name] = []
traits_by_group[trait.dataset.group.name].append(trait)
return traits_by_group
