def testSinReturnSaveSpaces(self):
space = self.workspace(self.n)
table = self.wavetable(self.n)
x = numx.arange(self.n) * ((2 + 0j) * numx.pi / self.n)
for i in range(1, self.n / 2):
y = numx.sin(x * i)
tmp = self.convert(y)
f = self.transform(tmp, space, table, tmp)
self._CheckSinResult(f, i)
def testSinReturnSaveSpaces(self):
space = self.workspace(self.n)
table = self.wavetable(self.n)
x = numx.arange(self.n) * ((2+0j) * numx.pi / self.n)
for i in range(1,self.n/2):
y = numx.sin(x * i)
tmp = self.convert(y)
f = self.transform(tmp, space, table, tmp)
self._CheckSinResult(f, i)
def EFunc(self):
x = self._data
t = x-1.0
t2 = t*t
# Necessary as my python does not handle the exp of big numbers
# correctly
if t2 > 700:
tmp = 0
else:
tmp = numx.exp(-t2)
return tmp*numx.sin(8*x)
def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(10, nbreak)
b.knots_uniform(x[0], x[-1])
X = numx.zeros((N, ncoeffs))
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
res_x = x[::N / 64]
X = b.eval_vector(res_x)
res_y, res_y_err = multifit.linear_est_matrix(X, c, cov)
pylab.plot(x, y, '-')
pylab.errorbar(res_x, res_y, fmt='g-', xerr=res_y_err)
def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(10, nbreak)
b.knots_uniform(x[0], x[-1])
X = numx.zeros((N, ncoeffs))
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
res_x = x[::N/64]
X = b.eval_vector(res_x)
res_y, res_y_err = multifit.linear_est_matrix(X, c, cov)
pylab.plot(x,y, '-')
pylab.errorbar(res_x, res_y, fmt='g-', xerr=res_y_err)
def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(4, nbreak)
k = b.get_internal_knots()
pygsl.set_debug_level(10)
b.knots(k)
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
b.set_coefficients_and_covariance_matrix(c, cov)
res_x = x[:: N / 64]
res_y, res_y_err = b.eval_dep_yerr_vector(res_x)
# res_y = b.eval_dep_vector(res_x)
print res_y
pylab.plot(x, y, "-")
pylab.errorbar(res_x, res_y, fmt="g-", xerr=res_y_err)
def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(4, nbreak)
k = b.get_internal_knots()
pygsl.set_debug_level(10)
b.knots(k)
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
b.set_coefficients_and_covariance_matrix(c, cov)
res_x = x[::N / 64]
res_y, res_y_err = b.eval_dep_yerr_vector(res_x)
#res_y = b.eval_dep_vector(res_x)
print(res_y)
pylab.plot(x, y, '-')
pylab.errorbar(res_x, res_y, fmt='g-', xerr=res_y_err)
'graph' program. e.g
$ 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 polydd():
xa = numx.arange(100) / 100.0 * 2. * numx.pi
ya = numx.sin(xa)
dd = poly.poly_dd(xa, ya)
dd.eval(0)
c = dd.taylor(0.0)
import pygsl
def run(array):
# Initalise the wavelet and the workspace
w = wavelet.daubechies(4)
ws = wavelet.workspace(len(array))
# Transform forward
result = w.transform_forward(array, ws)
# Select the largest 20 coefficients
abscoeff = numx.absolute(result)
indices = numx.argsort(abscoeff) # ascending order
tmp = numx.zeros(result.shape, numx.float_)
for i in indices[-20:]:
tmp[i] = result[i] # Set all others to zero
# And back
result2 = w.transform_inverse(tmp, ws)
#print result2
#print result2 - array
if __name__ == '__main__':
a = numx.arange(256)
b = numx.sin(a*(2*numx.pi / 16.))
#b = a * 0.0
run(b)
def polydd():
xa = numx.arange(100) / 100.0 * 2. * numx.pi
ya = numx.sin(xa)
dd = poly.poly_dd(xa, ya)
dd.eval(0)
c = dd.taylor(0.0)
def f2(x,y):
return numx.sin(x) / x
def SinOne(self, x, l, args=()):
y = numx.sin(x * l)
tmp = self.convert(y)
f = self.transform(*((tmp,) + args))
self._CheckSinResult(f, l)
def setUp(self):
xa = Numeric.arange(100) / 100.0 * 2. * Numeric.pi
ya = Numeric.sin(xa)
self.dd = poly.poly_dd(xa, ya)
def setUp(self):
xa = Numeric.arange(100) / 100.0 * 2. * Numeric.pi
ya = Numeric.sin(xa)
self.dd = poly.poly_dd(xa, ya)
def SinOne(self, x, l, args=()):
y = numx.sin(x * l)
tmp = self.convert(y)
f = self.transform(*((tmp, ) + args))
self._CheckSinResult(f, l)
def f1(x,y):
return Numeric.sin(x)
def f1(x, y):
return Numeric.sin(x)
def f2(x, y):
return numx.sin(x) / x
'graph' program. e.g
$ 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)
def f2(x,y):
return Numeric.sin(x) / x
def f2(x, y):
return Numeric.sin(x) / x
def run(array):
# Initalise the wavelet and the workspace
w = wavelet.daubechies(4)
ws = wavelet.workspace(len(array))
# Transform forward
result = w.transform_forward(array, ws)
# Select the largest 20 coefficients
abscoeff = numx.absolute(result)
indices = numx.argsort(abscoeff) # ascending order
tmp = numx.zeros(result.shape, numx.float_)
for i in indices[-20:]:
tmp[i] = result[i] # Set all others to zero
# And back
result2 = w.transform_inverse(tmp, ws)
#print result2
#print result2 - array
if __name__ == '__main__':
a = numx.arange(256)
b = numx.sin(a * (2 * numx.pi / 16.))
#b = a * 0.0
run(b)
