def run():
# Here should go some meaningful data sample to test the transform.
# Can you write one?
n = 512
data = numx.zeros((n,))
x = numx.arange(n) * (2 * numx.pi / n)
y = numx.cos(x*5)
#result = transform(y)
wavefreq, resampled = compress(y)
pylab.figure(1)
pylab.hold(0)
pylab.plot(x,y,x,resampled)
pylab.grid(1)
pylab.figure(2)
pylab.hold(0)
pylab.plot(wavefreq[:30], '.-')
pylab.grid(1)
pylab.figure(3)
pylab.hold(0)
pylab.plot(wavefreq[:50], '.-')
pylab.grid(1)
def real_example_simple1():
n = 32
data = numx.cos(1 * (numx.arange(n) * (2 * numx.pi / n))).astype(
numx.Float32)
print(data)
result = fft.real_transform_float(data)
print(result)
def run():
# Here should go some meaningful data sample to test the transform.
# Can you write one?
n = 512
data = numx.zeros((n, ))
x = numx.arange(n) * (2 * numx.pi / n)
y = numx.cos(x * 5)
#result = transform(y)
wavefreq, resampled = compress(y)
pylab.figure(1)
pylab.hold(0)
pylab.plot(x, y, x, resampled)
pylab.grid(1)
pylab.figure(2)
pylab.hold(0)
pylab.plot(wavefreq[:30], '.-')
pylab.grid(1)
pylab.figure(3)
pylab.hold(0)
pylab.plot(wavefreq[:50], '.-')
pylab.grid(1)
def complex_example_simple():
n = 16
data = numx.arange(n) * (2 * numx.pi / n)
data = numx.cos(data) +0j
space = fft.complex_workspace(n)
wavetable = fft.complex_wavetable(n)
print data
#data[:11] = 1.0
#data[11:] = 1.0
tmp = fft.complex_backward(data, space, wavetable)
print "tmp", tmp
def complex_example_simple():
n = 16
data = numx.arange(n) * (2 * numx.pi / n)
data = numx.cos(data) + 0j
space = fft.complex_workspace(n)
wavetable = fft.complex_wavetable(n)
print(data)
#data[:11] = 1.0
#data[11:] = 1.0
pygsl.init.set_debug_level(0)
tmp = fft.complex_backward(data, space, wavetable)
print("tmp", tmp)
def run():
r = rng.rng()
bw = bspline(4, nbreak)
# Data to be fitted
x = 15. / (N - 1) * numx.arange(N)
y = numx.cos(x) * numx.exp(0.1 * x)
sigma = .1
w = 1.0 / sigma**2 * numx.ones(N)
dy = r.gaussian(sigma, N)
y = y + dy
# use uniform breakpoints on [0, 15]
bw.knots_uniform(0.0, 15.0)
X = numx.zeros((N, ncoeffs))
for i in range(N):
B = bw.eval(x[i])
X[i, :] = B
# do the fit
c, cov, chisq = multifit.wlinear(X, w, y,
multifit.linear_workspace(N, ncoeffs))
# output the smoothed curve
res_y = []
res_y_err = []
for i in range(N):
B = bw.eval(x[i])
yi, yi_err = multifit.linear_est(B, c, cov)
res_y.append(yi)
res_y_err.append(yi_err)
#print yi, yerr
res_y = numx.array(res_y)
res_y_err = numx.array(res_y_err)
return (
x,
y,
), (x, res_y), res_y_err
def run():
r = rng.rng()
bw = bspline(4, nbreak)
# Data to be fitted
x = 15. / (N-1) * numx.arange(N)
y = numx.cos(x) * numx.exp(0.1 * x)
sigma = .1
w = 1.0 / sigma**2 * numx.ones(N)
dy = r.gaussian(sigma, N)
y = y + dy
# use uniform breakpoints on [0, 15]
bw.knots_uniform(0.0, 15.0)
X = numx.zeros((N, ncoeffs))
for i in range(N):
B = bw.eval(x[i])
X[i,:] = B
# do the fit
c, cov, chisq = multifit.wlinear(X, w, y, multifit.linear_workspace(N, ncoeffs))
# output the smoothed curve
res_y = []
res_y_err = []
for i in range(N):
B = bw.eval(x[i])
yi, yi_err = multifit.linear_est(B, c, cov)
res_y.append(yi)
res_y_err.append(yi_err)
#print yi, yerr
res_y = numx.array(res_y)
res_y_err = numx.array(res_y_err)
return (x, y,), (x, res_y), res_y_err
def fn1(x, params):
return Numeric.cos(x) + 1.0
def CosOne(self, x, l, args=()):
y = numx.cos(x * l)
tmp = self.convert(y)
f = self.transform(*((tmp, ) + args))
self._CheckCosResult(f, l)
def fn1(x, params):
return Numeric.cos(x) + 1.0
def CosOne(self, x, l, args=()):
y = numx.cos(x * l)
tmp = self.convert(y)
f = self.transform(*((tmp,) + args))
self._CheckCosResult(f, l)
def g(k, params):
numx.cos(k,k)
return A / (1.0 - numx.multiply.reduce(k))
def fn1(x, params):
return numx.cos(x) + 1.0
$ python ./interpolation.py > interp.dat
$ graph -T ps < interp.dat > interp.ps
The result shows a smooth interpolation of the original points. The
interpolation method can changed simply by varing spline. .
"""
from pygsl import spline, errors
from pygsl import _numobj as numx
print "#m=0,S=2"
n = 10
a = numx.arange(n)
x = a + 0.5 * numx.sin(a)
y = a + numx.cos(a**2)
for i in a:
print x[i], y[i]
print "#m=1,S=0"
# Generation of the spline object ... Acceleration is handled internally
myspline = spline.cspline(n)
myspline = spline.linear(n)
acc = myspline._object.get_accel_object()
#print "Accel object", dir(acc)
# initalise with the vector of the independent and the dependent
myspline.init(x,y)
x1 = numx.arange(n * 20) / 20.
for xi in x1:
#print xi, myspline.eval(xi)
pass
def real_example_simple1():
n = 32
data = numx.cos(1*(numx.arange(n) * (2 * numx.pi / n))).astype(numx.Float32)
print data
result = fft.real_transform_float(data)
print result
def g(k, params):
numx.cos(k, k)
return A / (1.0 - numx.multiply.reduce(k))
$ python ./interpolation.py > interp.dat
$ graph -T ps < interp.dat > interp.ps
The result shows a smooth interpolation of the original points. The
interpolation method can changed simply by varing spline. .
"""
from pygsl import spline, errors, init
from pygsl import _numobj as numx
print ("#m=0,S=2")
n = 10
a = numx.arange(n)
x = a + 0.5 * numx.sin(a)
y = a + numx.cos(a**2)
for i in a:
print (x[i], y[i])
print ("#m=1,S=0")
# Generation of the spline object ... Acceleration is handled internally
myspline = spline.cspline(n)
myspline = spline.linear(n)
#acc = myspline._object.get_accel_object()
#print "Accel object", dir(acc)
# initalise with the vector of the independent and the dependent
init.add_c_traceback_frames(1)
#init.set_debug_level(10)
myspline.init(x, y[:3])
#print("Saved error state", init.error_handler_state_get())
def fn1(x, params):
return numx.cos(x) + 1.0
