def max_add_feat(active_chl,rng,pf):
rng = rng
min_per=1
addfeat_data = 0
header = preproc.get_header(pf)
header_non_act = np.copy(header).tolist()
header_non_act.remove(active_chl)
header_non_act.remove('Elapsed time')
for chl in header_non_act:
sig_add = preproc.get_column(chl,pf)[2:]
sig_add = preproc.check(sig_add,return_series=1)[0]
chl_add = sig_add[rng[0]:rng[-1]]
if not discard_add_feat(sig_add,rng):
idx_isnan = np.isnan(chl_add)
idx = np.arange(len(chl_add))
idx_nan = idx[idx_isnan]
percent = len(idx_nan)/float(len(idx))
if min_per ==1:
min_per=percent
min_chl = sig_add
elif percent<=min_per:
min_per = percent
min_chl = sig_add
if min_per==0:
break
addfeat_data = min_chl[rng[0]-50000:rng[0]]
if addfeat_data!=0:
if np.isnan(addfeat_data).any():
idx = np.arange(len(addfeat_data))
idx_isnan = np.isnan(addfeat_data)
idx = idx[idx_isnan].tolist()
idx_start = idx[-1]
addfeat_data = addfeat_data[idx_start:]
return chl_add, addfeat_data
__author__ = 'aubrey9012'
import numpy as np
import pandas as pd
import pickle
import sys
sys.path.append('/Users/aubrey9012/Desktop')
import preproc
import interpolator
file_name = '3270954'
pf = '/Users/aubrey9012/Downloads/medical_ICU-features/data/mimic/'+str(file_name)+'.csv'
header = preproc.get_header(pf)
header.remove('Elapsed time')
for col in header:
if col != 'ICP':
si = interpolator.SignalInterpolator(col,pf,40,0)
else:
si = interpolator.SignalInterpolator(col,pf,40,1)
si_ = si.interpolate()
if si_!=0:
cpi = interpolator.CopyPasteInterpolator(si_)
si_full = cpi.interpolate()
dump_name= str(col)+'.p'
pickle.dump(si_,open(dump_name, "wb" ))
if si_==0:
pickle.dump(si_,open(dump_name,"wb"))
