def test_main(resource, capsys):
in_file = resource('cat.jpg')
main(in_file)
stdout, _ = capsys.readouterr()
assert re.match(r'Found label:.*cat', stdout)
def test_main(capsys):
in_file = os.path.join(RESOURCES, 'cat.jpg')
main(in_file)
stdout, _ = capsys.readouterr()
assert re.match(r'Found label:.*cat', stdout)
def _check_labelling(infile, labelfile):
## simple check
with open(infile) as f:
for l in f:
info = l.strip().split('\t')[8].split(';')
label = get_value_from_keycolonvalue_list('mirna_label', info)
if label == '':
isLabelled = False
else:
isLabelled = True
break
if isLabelled:
return infile
else:
print '## No labelling is found, proceed with labelling...'
outfile = '%s.label' % infile
lb.main(infile, labelfile, outfile)
return outfile
def main(f_config, gff_infile, outdir, has_mirna, make_plots):
ensure_dir(outdir)
cparser = SafeConfigParser()
cparser.read(f_config)
f_params = cparser.get('promi2', 'params')
listoffeatures = cparser.get('promi2', 'features').split(',')
labelfile = cparser.get('configs', 'labelfile')
if 'corr' in listoffeatures:
is_consider_corr = True
else:
is_consider_corr = False
## Make sure no chrM in infile
_verify_infile(gff_infile)
## Extract features
gff_allfeatures = extractFeatures_given_gff(f_config, gff_infile, outdir,
has_mirna, is_consider_corr)
## Don't consider TSS which does not have a partner miRNA
gff_allfeatures = _filter_keepValidPairs(gff_allfeatures)
## Run Promirna
fo_predictions = os.path.join(
outdir, 'Predictions.%s.txt' % os.path.basename(gff_infile))
promi2.promi2(f_params, listoffeatures, gff_allfeatures, fo_predictions)
## Label predictions
fo_labelledpredictions = fo_predictions + '.label'
label.main(fo_predictions, labelfile, fo_labelledpredictions)
## Generate plots
if make_plots:
import plots
outdir_plt = os.path.join(outdir, 'plots')
plots.main(fo_labelledpredictions, outdir_plt, f_config)
return fo_labelledpredictions
def main(f_config, gff_infile, outdir, has_mirna, make_plots):
ensure_dir(outdir)
cparser = SafeConfigParser()
cparser.read(f_config)
f_params = cparser.get('promi2', 'params')
listoffeatures = cparser.get('promi2', 'features').split(',')
labelfile = cparser.get('configs', 'labelfile')
if 'corr' in listoffeatures:
is_consider_corr = True
else:
is_consider_corr = False
## Make sure no chrM in infile
_verify_infile(gff_infile)
## Extract features
gff_allfeatures = extractFeatures_given_gff(f_config, gff_infile, outdir, has_mirna, is_consider_corr)
## Don't consider TSS which does not have a partner miRNA
gff_allfeatures = _filter_keepValidPairs(gff_allfeatures)
## Run Promirna
fo_predictions = os.path.join(outdir,
'Predictions.%s.txt' % os.path.basename(gff_infile))
promi2.promi2(f_params, listoffeatures, gff_allfeatures, fo_predictions)
## Label predictions
fo_labelledpredictions = fo_predictions + '.label'
label.main(fo_predictions, labelfile, fo_labelledpredictions)
## Generate plots
if make_plots:
import plots
outdir_plt = os.path.join(outdir, 'plots')
plots.main(fo_labelledpredictions, outdir_plt, f_config)
return fo_labelledpredictions
def main(lat, lng):
start_time = time.time()
#Define our connection string
options = config.main()
conn_string = "host='" + options['host'] + "' dbname='" + options[
'db_name'] + "' user='******'db_user'] + "' password='******'db_pass'] + "'"
# print the connection string we will use to connect
print("Connecting to database") # % (conn_string)
# get a connection, if a connect cannot be made an exception will be raised here
conn = psycopg2.connect(conn_string)
# conn.cursor will return a cursor object, you can use this cursor to perform queries
cur = conn.cursor()
lat = float(lat)
lng = float(lng)
print("Connected!\n")
sql = (
"SELECT label, CHAR_LENGTH(label),lng,lat, ST_Distance(geog_def, poi) AS distance_m"
" FROM " + options['db_prefix'] + "road,"
" (select ST_MakePoint(%(lng)f, %(lat)f)::geography as poi) as poi"
" WHERE ST_DWithin(geog_def, poi, 100000)"
" AND CHAR_LENGTH(label) >=6 "
" AND label LIKE '%%+%%' "
" ORDER BY ST_Distance(geog_def, poi)"
" LIMIT 1;" % {
'lat': lat,
'lng': lng
})
print(sql)
cur.execute(sql)
rows = cur.fetchall()
print(time.time() - start_time, "seconds")
if len(rows) == 0:
print("Cannot find closest Distance")
return {'label': '0', 'distance': '0'}
else:
for row in rows:
# convert label STA or - to KM
km = label.main(row[0])
print("\nGet Closest Distance:", km['km'])
return {'label': str(km['km']), 'distance': round(row[4], 2)}
def label(self):
self.labels=label.main(self.path)
label.label_parse(self.labels)
Hello, world!
"""
)
#url = "http://localhost:8000/?%s"
#params = urllib.parse.urlencode({'spam': 1, 'eggs': 2, 'bacon': 0})
#try:
# urlopen(url % params)
#except error.URLError as e: print("URL Error:",e.read() , url)
#except error.HTTPError as e: print("HTTP Error:",e.read() , url)
#sys.exit(0)
lng = 115.719
lat = -0.493033
data = '1,2,3';
x , y,z = data.split(',')
print(x)
km = label.main('STA 29+000')
print(km['km'])
d =distance.main(lat,lng)
time.sleep(0.5)
val = {"lat": lat, "distance" : d['distance'], "lng" : lng}
re.sub(r'\W+', '', d['label'])
print('get label is=',d['label'])
#sys.exit(0)
print("type", type(d['label']), type(d['distance']))
print(str(d['label']), d['distance'], val['distance'])
print("<p>KM is =", d['label'],"</p>")