def get_template_numbers(patients, VERBOSE=0):
'''Collect template numbers from all patient samples'''
data = []
for pname, patient in patients.iterrows():
patient = Patient(patient)
if VERBOSE:
print pname, patient.code
samples = patient.samples
n_approx = samples['templates approx']
dils = [get_dilution(x) for x in samples['dilutions']]
n_dils = [2 * estimate_ntemplates_Poisson(x) for x in dils]
# Attach sample date info
age = np.array((datetime.datetime.now() - samples.date)) / 86400e9
data.append({
'n_approx': n_approx,
'n_dil': n_dils,
'age': age,
'pname': patient.code
})
return data
def loadK31(reg, filepath, fromHIV=False):
'''
Loading data for 31 additional patients
Input arguments:
reg: name of genetic region (gag or pol)
filepath: path to directory where the frequency data are to be stored/downloaded
fromHIV: download raw data and store them, if True; use stored data, if False
'''
data = {}
if fromHIV:
sys.path.append("/scicore/home/neher/neher/HIV/hivwholeseq")
from hivwholeseq.patients.patients import load_patients, Patient
pats = load_patients(csv=True)
fmt = "%d/%m/%Y"
fhandle = open(filepath + 'K31_info_{}.txt'.format(reg), 'w')
for pcode, pat in pats.iterrows():
try:
EDI = datetime.strptime(pat["infect date best"], fmt)
P = Patient(pat)
aft = P.get_allele_frequency_trajectories(reg, cov_min=500)[0]
for si, (scode, sample) in enumerate(P.samples.iterrows()):
try:
date = datetime.strptime(sample["date"], fmt)
af = aft[si]
TI = date.toordinal() - EDI.toordinal()
fhandle.write('{}\t{}\t{}\n'.format(pcode, scode, TI))
np.save(
filepath +
'{}_{}_{}_data.npy'.format(pcode, scode, reg),
af.data)
np.save(
filepath +
'{}_{}_{}_mask.npy'.format(pcode, scode, reg),
af.mask)
data['{}_{}'.format(pcode,
scode)] = (date.toordinal() -
EDI.toordinal(), af)
print(pcode, scode, "WORKED!!!")
except:
print(scode, "didn't work")
except:
print("skipping patient ", pcode)
fhandle.close()
else:
with open(filepath + 'K31_info_{}.txt'.format(reg), 'r') as fhandle:
for line in fhandle:
words = line.split()
pat_name = '_'.join(words[:2])
af_data = np.load(filepath +
'{}_{}_data.npy'.format(pat_name, reg))
af_mask = np.load(filepath +
'{}_{}_mask.npy'.format(pat_name, reg))
af = np.ma.masked_array(af_data, mask=af_mask)
data[pat_name] = (int(words[2]), af)
return data
args = parser.parse_args()
pnames = args.patients
regions = args.regions
VERBOSE = args.verbose
plot = args.plot
patients = load_patients()
if pnames is not None:
patients = patients.loc[pnames]
pnames = patients.index.tolist()
data = []
for pname, patient in patients.iterrows():
patient = Patient(patient)
patient.discard_nonsequenced_samples()
for ifr, region in enumerate(regions):
if VERBOSE >= 1:
print pname, region
try:
dg, ind = patient.get_divergence(region, cov_min=10)
except ValueError:
continue
times = patient.times[ind]
data.append({'pname': pname, 'region': region, 'dg': dg, 't': times})
if VERBOSE >= 1:
