dd=args.VAL[0]
if dd=='no':
#print 'no data willbe plotted...'
plotdata=dd
elif (dd=='yes') or (args.VAL==[]):
#print 'plot everything...'
plotdata='yes'
else:
sys.exit("Error (-plotdata): Only 'yes' or 'no' values are admisible.")
#######################
### MAIN ##############
#######################
sys.stdout.write("\a")
#loading data points ('N' points for each one of the 'DIM' CVs)
N=file_lines(args.INPUT)
DIM=file_cols(args.INPUT)
P1=load_data(args.INPUT,N,DIM)
dK1=[]
dK2=[]
conv_all=[]
# estimate the initial string (P2) of size Min
P2 = string_ini(P1,DIM,args.K)
Pini=string_ini(P1,DIM,args.K)
PiniT=zip(*Pini)
# maximun distances of the data to the images of the string
maxdistances=maxvec(P1,P2)
# Estimate the mean of the voronoid cells
#######################################################
###### Parsing arguments from the command line ########
#######################################################
def _make_parser():
parser = argparse.ArgumentParser(description="This tool performs a mean reversion test, that is, the tendency of a time series to drift towards its long-term mean. As input file this tool expects a time series for each column; any number of columns and rows can be processes. The use of the test here was inspired by applications in financial mathematics; in our implementation the Augmented Dickey-Fuller test (ADFT) was used. With ADFT we will optain for each columns a '1' ---> mean reversal (stationarity) or '0' ---> NO time reversal (no stationarity) of the time series. Further explanations and examples can be found at https://gist.github.com/r-hong/82515ca44fb35090b8c8 ",epilog="Thanks for using meanReversalTest.py!!")
parser.add_argument('IN_file', help="Input file")
return parser
#------------------------------
_p = _make_parser()
__doc__ += _p.format_help()
######################################################
if __name__ == '__main__':
args = _p.parse_args()
#Read filename
nc = file_cols(args.IN_file)
for i in range(nc):
exec "c%s=[]" % (i)
with open(args.IN_file) as file:
reader = csv.reader(file, delimiter=' ',skipinitialspace=True)
for row in reader:
for i in range(nc):
exec "c%s.append(float(row[%s]))" % (i,i)
#perform test
for i in range(nc):
exec "t%s = ts.adfuller(c%s,1)" % (i,i)
final=[]
a="'1%'"
b="'5%'"
args = _p.parse_args()
#preprocessing the input file:
# saving every args.stride value of args.IN_file and saving to tmp.dat
ss="'"
ss+='0~'
ss+=`args.S`
ss+='p'
ss+="'"
cmd='sed -n '
cmd+=ss
cmd+=' '
cmd+=args.fileName
cmd+=' > tmp.dat'
os.system(cmd)
##########################################
#get Number of rows and number of columns from the file and read from the file into a matrix 'vecT'
nCol = file_cols('tmp.dat')
vecT = readFile('tmp.dat',nCol)
#print len(vecT[0])
#calculate the max likelihood estimators
results = estValB(vecT,args.K,args.N)
#print results[0]
#format the results to 6 decimal points before printing
myList=formatRes(results,6)
print ' '.join(str(p) for p in myList)
