args = parser.parse_args()
Analyser = HaseAnalyser()
print args
Analyser.DF = args.df
Analyser.result_path = args.r
Analyser.file_number = args.N
results = OrderedDict()
results['RSID'] = np.array([])
results['p_value'] = np.array([])
results['t-stat'] = np.array([])
results['phenotype'] = np.array([])
results['SE'] = np.array([])
results['MAF'] = np.array([])
results['BETA'] = np.array([])
while True:
Analyser.summary()
if Analyser.results is None:
break
print('Saving data...')
if not os.path.exists(os.path.join(args.out, 'results' + '.csv')):
df = pd.DataFrame.from_dict(results)
df.to_csv(os.path.join(args.out, 'results' + '.csv'),
sep=" ",
index=None)
df = pd.DataFrame.from_dict(Analyser.results)
with open(os.path.join(args.out, 'results' + '.csv'), 'a') as f:
df.to_csv(f, sep=" ", header=False, index=None)
#TODO (low) add reference panel
args = parser.parse_args()
Analyser=HaseAnalyser()
print args
Analyser.DF=args.df
Analyser.result_path=args.r
results={}
results['RSID']=np.array([])
results['p_value']=np.array([])
results['t-stat']=np.array([])
results['phenotype']=np.array([])
results['SE']=np.array([])
results['MAF']=np.array([])
results['BETA'] = np.array([])
while True:
Analyser.summary()
if Analyser.results is None:
break
print('Saving data...')
if not os.path.exists(os.path.join(args.out,'results'+'.csv')):
df=pd.DataFrame.from_dict(results)
df.to_csv( os.path.join(args.out,'results'+'.csv'), sep=" " )
df=pd.DataFrame.from_dict(Analyser.results)
with open(os.path.join(args.out,'results'+'.csv'), 'a') as f:
df.to_csv(f, sep=" ",header=False)
