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# vim: et sts=4 ts=4
import argparse
import numpy as np
from supersvd import supersvd
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--type", choices=['real', 'double'], default='real',
help="Data type, default is '%(default)s'")
parser.add_argument("-x", metavar="X.STD", required=True, help="X data input file name")
parser.add_argument("-y", metavar="Y.STD", required=True, help="Y data input file name")
parser.add_argument("-t", "--time", type=int, required=True, help="Length of the time interval")
parser.add_argument("-k", type=int, default=3,
help="Number of singular values, default is %(default)d")
parser.add_argument("-xv", help="X singular vectors output file name, if necessary")
parser.add_argument("-yv", help="Y singular vectors output file name, if necessary")
parser.add_argument("-xc", help="X time coefficients output file name, if necessary")
parser.add_argument("-yc", help="Y time coefficients output file name, if necessary")
parser.add_argument("-stat", help="Correlation and variance values in CSV, if necessary")
parser.add_argument("--dont-subtract-mean", dest="elim_mean", help="Disable subtracting of the time mean from input", action="store_false")
args = parser.parse_args()
if args.type == 'real':
dtype = np.float32
else:
dtype = np.float64
t = args.time
X = np.fromfile(args.x, dtype=dtype).reshape(t, -1)
Y = np.fromfile(args.y, dtype=dtype).reshape(t, -1)
svd = supersvd(X, Y, args.k, args.elim_mean)
if args.xv is not None:
svd.x_vect.tofile(args.xv)
if args.yv is not None:
svd.y_vect.tofile(args.yv)
if args.xc is not None:
svd.x_coeff.tofile(args.xc)
if args.yc is not None:
svd.y_coeff.tofile(args.yc)
if args.stat is not None:
f = open(args.stat, 'w')
f.write('number,corrcoeff,x_varfrac,y_varfrac,covfrac\n')
for i in range(args.k):
print('Singular value number:', i+1)
corrcoeff = 100 * svd.corrcoeff[i]
x_varfrac = 100 * svd.x_variance_fraction[i]
y_varfrac = 100 * svd.y_variance_fraction[i]
covfrac = 100 * svd.eigenvalue_fraction[i]
if args.stat is not None:
f.write('%d,%f,%f,%f,%f\n' % (i+1, corrcoeff, x_varfrac, y_varfrac, covfrac))
print('Time series correlation coefficient:', '%8.4f%%' % corrcoeff)
print(args.x, 'variance fraction:', '%8.4f%%' % x_varfrac)
print(args.y, 'variance fraction:', '%8.4f%%' % y_varfrac)
print('Covariance fraction:', '%8.4f%%' % covfrac)
if args.stat is not None:
f.close()
if __name__ == "__main__":
main()