BioLawCom.De

#!/usr/bin/python
 
import numpy
import pylab
 
# Numbers of coefficients
N = 5
 
# Random-point of polynom
x = numpy.arange(0,N*32,32)
y = numpy.random.randn(N)
 
# Find polynom-coefficients
p = numpy.polyfit(x,y,N-1)
 
# Interpolate polynom
mypolynom  = lambda x : numpy.polyval(p,x)
vecpolyval = numpy.vectorize(mypolynom)
newx = numpy.arange(0,N*32)
newy = vecpolyval(newx)
 
# Plot
pylab.grid(True)
pylab.plot(x,y,'o')
pylab.plot(newx, newy, linewidth=2)
pylab.show()

#!/usr/bin/python
 
import numpy
import pylab
from scipy.signal import cspline1d, cspline1d_eval
 
# Dimension of signal
N = 40
 
# Random-point of polynom
x = numpy.arange(0,N*32,32)
y = numpy.random.randn(N)
 
# Cubic-Spline-Interpolation
cs = cspline1d(y)
newx = numpy.arange(0,N*32)
newy = cspline1d_eval(cs, newx, dx=32, x0=0)
 
# Plot
pylab.grid(True)
pylab.plot(x,y,'o')
pylab.plot(newx, newy, linewidth=2)
pylab.show()

import numpy
import scipy.signal
 
def spline_pyrex(sigIn, int samp_pos, int samp) :
	cdef int i, j, block_length, nblock, dimen
	dimen = sigIn.shape[0]
	block_length = 32*512
	nblock = dimen/block_length
	sigOut = numpy.zeros(dimen, dtype=numpy.complex64)
 
	for i from 0 <= i < nblock :
		to_spline = sigIn[i*block_length + samp_pos : (i+1) * block_length + samp_pos : samp]
		cs = scipy.signal.cspline1d(to_spline.real)
		interp_re = 1*scipy.signal.cspline1d_eval(cs, numpy.arange(block_length, dtype=numpy.int16), dx=samp,x0=samp_pos)
		cs = scipy.signal.cspline1d(to_spline.imag)
		interp_im = 1*scipy.signal.cspline1d_eval(cs, numpy.arange(block_length, dtype=numpy.int16), dx=samp,x0=samp_pos)
		sigOut[i*block_length : (i+1) * block_length] = interp_re + 1j * interp_im
 
	return sigOut
 
 
def spline(sigIn,  samp_pos,  samp) :
	dimen = sigIn.shape[0]
	block_length = 32*512
	nblock = dimen/block_length
	sigOut = numpy.zeros(dimen, dtype=numpy.complex64)
 
	for i in xrange(nblock) :
		to_spline = sigIn[i*block_length + samp_pos : (i+1) * block_length + samp_pos : samp]
		cs = scipy.signal.cspline1d(to_spline.real)
		interp_re = 1*scipy.signal.cspline1d_eval(cs, numpy.arange(block_length, dtype=numpy.int16), dx=samp,x0=samp_pos)
		cs = scipy.signal.cspline1d(to_spline.imag)
		interp_im = 1*scipy.signal.cspline1d_eval(cs, numpy.arange(block_length, dtype=numpy.int16), dx=samp,x0=samp_pos)
		sigOut[i*block_length : (i+1) * block_length] = interp_re + 1j * interp_im
 
	return sigOut