Div. updates
Per.Andreas.Brodtkorb
14 years ago
parent
bb7b1de823
commit
0b71a79872
@ -0,0 +1,49 @@
|
||||
'''
|
||||
Created on 20. jan. 2011
|
||||
|
||||
@author: pab
|
||||
'''
|
||||
import numpy as np
|
||||
from numpy import exp
|
||||
from wafo.misc import meshgrid
|
||||
__all__ = ['peaks', 'humps']
|
||||
|
||||
|
||||
def peaks(x=None, y=None, n=51):
|
||||
'''
|
||||
Return the "well" known MatLab (R) peaks function
|
||||
evaluated in the [-3,3] x,y range
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import pylab as plt
|
||||
>>> x,y,z = peaks()
|
||||
>>> h = plt.contourf(x,y,z)
|
||||
|
||||
'''
|
||||
if x is None:
|
||||
x = np.linspace(-3, 3, n)
|
||||
if y is None:
|
||||
y = np.linspace(-3, 3, n)
|
||||
|
||||
[x1, y1] = meshgrid(x, y)
|
||||
|
||||
z = (3 * (1 - x1) ** 2 * exp(-(x1 ** 2) - (y1 + 1) ** 2)
|
||||
- 10 * (x1 / 5 - x1 ** 3 - y1 ** 5) * exp(-x1 ** 2 - y1 ** 2)
|
||||
- 1. / 3 * exp(-(x1 + 1) ** 2 - y1 ** 2))
|
||||
|
||||
return x1, y1, z
|
||||
|
||||
def humps(x=None):
|
||||
'''
|
||||
Computes a function that has three roots, and some humps.
|
||||
'''
|
||||
if x is None:
|
||||
y = np.linspace(0, 1)
|
||||
else:
|
||||
y = np.asarray(x)
|
||||
|
||||
return 1.0 / ((y - 0.3) ** 2 + 0.01) + 1.0 / ((y - 0.9) ** 2 + 0.04) + 2 * y - 5.2
|
||||
|
||||
if __name__ == '__main__':
|
||||
pass
|
||||
@ -0,0 +1,110 @@
|
||||
'''
|
||||
Created on 20. jan. 2011
|
||||
|
||||
@author: pab
|
||||
|
||||
license BSD
|
||||
'''
|
||||
import warnings
|
||||
import numpy as np
|
||||
from wafo.plotbackend import plotbackend
|
||||
|
||||
__all__ = ['cltext']
|
||||
|
||||
def cltext(levels, percent=False, n=4, xs=0.036, ys=0.94, zs=0):
|
||||
'''
|
||||
Places contour level text in the current window
|
||||
|
||||
Parameters
|
||||
----------
|
||||
levels = vector of contour levels or the corresponding percent which the
|
||||
contour line encloses
|
||||
percent = 0 if levels are the actual contour levels (default)
|
||||
1 if levels are the corresponding percent which the
|
||||
contour line encloses
|
||||
n = maximum N digits of precision (default 4)
|
||||
Returns
|
||||
h = handles to the text objects.
|
||||
CLTEXT creates text objects in the current figure and prints
|
||||
"Level curves at:" if percent is False and
|
||||
"Level curves enclosing:" otherwise
|
||||
and the contour levels or percent.
|
||||
|
||||
NOTE:
|
||||
-The handles to the lines of text may also be found by
|
||||
h = findobj(gcf,'gid','CLTEXT','type','text');
|
||||
h = findobj(gca,'gid','CLTEXT','type','text');
|
||||
-To make the text objects follow the data in the axes set the units
|
||||
for the text objects 'data' by
|
||||
set(h,'unit','data')
|
||||
|
||||
Examples:
|
||||
>>> from wafo.integrate import peaks
|
||||
>>> import pylab as plt
|
||||
>>> x,y,z = peaks();
|
||||
>>> h = plt.contour(x,y,z)
|
||||
>>> h = cltext(h.levels)
|
||||
>>> plt.show()
|
||||
|
||||
data = rndray(1,2000,2); f = kdebin(data,{'kernel','epan','L2',.5,'inc',128});
|
||||
contour(f.x{:},f.f,f.cl),cltext(f.pl,1)
|
||||
|
||||
See also
|
||||
pdfplot
|
||||
'''
|
||||
# TODO : Make it work like legend does (but without the box): include position options etc...
|
||||
clevels = np.atleast_1d(levels)
|
||||
_CLTEXT_TAG = 'CLTEXT'
|
||||
cax = plotbackend.gca()
|
||||
axpos = cax.get_position()
|
||||
xint = axpos.intervalx
|
||||
yint = axpos.intervaly
|
||||
|
||||
xss = xint[0] + xs * (xint[1] - xint[0])
|
||||
yss = yint[0] + ys * (yint[1] - yint[0])
|
||||
|
||||
cf = plotbackend.gcf() # get current figure
|
||||
#% delete cltext object if it exists
|
||||
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
def matchfun(x):
|
||||
if hasattr(x, 'get_gid'):
|
||||
return x.get_gid() == _CLTEXT_TAG
|
||||
return False
|
||||
h_cltxts = plotbackend.findobj(cf, matchfun);
|
||||
if len(h_cltxts):
|
||||
for i in h_cltxts:
|
||||
try:
|
||||
cax.texts.remove(i)
|
||||
except:
|
||||
warnings.warn('Tried to delete a non-existing CL-text')
|
||||
|
||||
try:
|
||||
cf.texts.remove(i)
|
||||
except:
|
||||
warnings.warn('Tried to delete a non-existing CL-text')
|
||||
|
||||
charHeight = 1 / 33;
|
||||
delta_y = charHeight;
|
||||
|
||||
if percent:
|
||||
titletxt = 'Level curves enclosing:';
|
||||
else:
|
||||
titletxt = 'Level curves at:';
|
||||
|
||||
format = '%0.' + ('%d' % n) + 'g\n'
|
||||
|
||||
cltxt = ''.join([format % level for level in clevels.tolist()])
|
||||
|
||||
titleProp = dict(gid=_CLTEXT_TAG, horizontalalignment='left',
|
||||
verticalalignment='center', fontweight='bold', axes=cax) #
|
||||
ha1 = plotbackend.figtext(xss, yss, titletxt, **titleProp)
|
||||
|
||||
yss -= delta_y;
|
||||
txtProp = dict(gid=_CLTEXT_TAG, horizontalalignment='left',
|
||||
verticalalignment='top', axes=cax)
|
||||
|
||||
ha2 = plotbackend.figtext(xss, yss, cltxt, **txtProp)
|
||||
|
||||
return ha1, ha2
|
||||
if __name__ == '__main__':
|
||||
pass
|
||||
@ -0,0 +1,16 @@
|
||||
'''
|
||||
python setup.py build_src build_ext --inplace
|
||||
|
||||
See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
|
||||
'''
|
||||
# File setup.py
|
||||
def configuration(parent_package='',top_path=None):
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
config = Configuration('',parent_package,top_path)
|
||||
|
||||
config.add_extension('c_library',
|
||||
sources = ['c_library.pyf','c_functions.c'])
|
||||
return config
|
||||
if __name__ == "__main__":
|
||||
from numpy.distutils.core import setup
|
||||
setup(**configuration(top_path='').todict())
|
||||
@ -0,0 +1,30 @@
|
||||
'''
|
||||
python setup.py build_src build_ext --inplace
|
||||
|
||||
See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
|
||||
'''
|
||||
|
||||
# File setup.py
|
||||
def compile_all():
|
||||
import os
|
||||
files = ['mvnprd', 'mvnprodcorrprb']
|
||||
compile1_format = 'gfortran -fPIC -c %s.f'
|
||||
for file in files:
|
||||
os.system(compile1_format % file)
|
||||
file_objects = ['%s.o' % file for file in files]
|
||||
return file_objects
|
||||
|
||||
|
||||
def configuration(parent_package='',top_path=None):
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
libs = compile_all()
|
||||
config = Configuration('',parent_package,top_path)
|
||||
|
||||
config.add_extension('mvnprdmod',
|
||||
libraries = libs,
|
||||
sources = ['mvnprd_interface.f'])
|
||||
return config
|
||||
if __name__ == "__main__":
|
||||
|
||||
from numpy.distutils.core import setup
|
||||
setup(**configuration(top_path='').todict())
|
||||
@ -0,0 +1,32 @@
|
||||
'''
|
||||
Created on 19. juli 2010
|
||||
|
||||
@author: pab
|
||||
'''
|
||||
|
||||
from wafo.spectrum.dispersion_relation import w2k,k2w
|
||||
|
||||
def test_k2w():
|
||||
'''
|
||||
>>> from numpy import arange
|
||||
>>> k2w(arange(0.01,.5,0.2))[0]
|
||||
array([ 0.3132092 , 1.43530485, 2.00551739])
|
||||
>>> k2w(arange(0.01,.5,0.2),h=20)[0]
|
||||
array([ 0.13914927, 1.43498213, 2.00551724])
|
||||
'''
|
||||
|
||||
|
||||
def test_w2k():
|
||||
'''
|
||||
>>> w2k(range(4))[0]
|
||||
array([ 0. , 0.1019368 , 0.4077472 , 0.91743119])
|
||||
>>> w2k(range(4),h=20)[0]
|
||||
array([ 0. , 0.10503601, 0.40774726, 0.91743119])
|
||||
'''
|
||||
|
||||
def main():
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -0,0 +1,68 @@
|
||||
import numpy as np
|
||||
from wafo.spectrum.models import (Bretschneider, Jonswap, OchiHubble, Tmaspec,
|
||||
Torsethaugen, McCormick, Wallop)
|
||||
|
||||
def test_bretschneider():
|
||||
'''
|
||||
>>> S = Bretschneider(Hm0=6.5,Tp=10)
|
||||
>>> S((0,1,2,3))
|
||||
array([ 0. , 1.69350993, 0.06352698, 0.00844783])
|
||||
'''
|
||||
def test_jonswap():
|
||||
'''
|
||||
|
||||
>>> S = Jonswap(Hm0=7, Tp=11,gamma=1)
|
||||
>>> S((0,1,2,3))
|
||||
array([ 0. , 1.42694133, 0.05051648, 0.00669692])
|
||||
>>> w = np.linspace(0,5)
|
||||
>>> S2 = Bretschneider(Hm0=7, Tp=11)
|
||||
|
||||
JONSWAP with gamma=1 should be equal to Bretscneider:
|
||||
>>> np.all(np.abs(S(w)-S2(w))<1.e-7)
|
||||
True
|
||||
'''
|
||||
|
||||
def test_tmaspec():
|
||||
'''
|
||||
>>> S = Tmaspec(Hm0=7, Tp=11,gamma=1,h=10)
|
||||
>>> S((0,1,2,3))
|
||||
array([ 0. , 0.70106233, 0.05022433, 0.00669692])
|
||||
'''
|
||||
def test_torsethaugen():
|
||||
'''
|
||||
>>> S = Torsethaugen(Hm0=7, Tp=11,gamma=1,h=10)
|
||||
>>> S((0,1,2,3))
|
||||
array([ 0. , 1.19989709, 0.05819794, 0.0093541 ])
|
||||
>>> S.wind(range(4))
|
||||
array([ 0. , 1.13560528, 0.05529849, 0.00888989])
|
||||
>>> S.swell(range(4))
|
||||
array([ 0. , 0.0642918 , 0.00289946, 0.00046421])
|
||||
'''
|
||||
|
||||
def test_ochihubble():
|
||||
'''
|
||||
>>> S = OchiHubble(par=2)
|
||||
>>> S(range(4))
|
||||
array([ 0. , 0.90155636, 0.04185445, 0.00583207])
|
||||
'''
|
||||
def test_mccormick():
|
||||
'''
|
||||
>>> S = McCormick(Hm0=6.5,Tp=10)
|
||||
>>> S(range(4))
|
||||
array([ 0. , 1.87865908, 0.15050447, 0.02994663])
|
||||
'''
|
||||
def test_wallop():
|
||||
'''
|
||||
>>> S = Wallop(Hm0=6.5, Tp=10)
|
||||
>>> S(range(4))
|
||||
array([ 0.00000000e+00, 9.36921871e-01, 2.76991078e-03,
|
||||
7.72996150e-05])
|
||||
'''
|
||||
|
||||
|
||||
def main():
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -0,0 +1,200 @@
|
||||
import wafo.spectrum.models
|
||||
from wafo.spectrum import SpecData1D
|
||||
def test_tocovmatrix():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap()
|
||||
>>> S = Sj.tospecdata()
|
||||
>>> acfmat = S.tocov_matrix(nr=3, nt=256, dt=0.1)
|
||||
>>> acfmat[:2,:]
|
||||
array([[ 3.06075987, 0. , -1.67750289, 0. ],
|
||||
[ 3.05246132, -0.16662376, -1.66819445, 0.18634189]])
|
||||
'''
|
||||
def test_tocovdata():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap()
|
||||
>>> S = Sj.tospecdata()
|
||||
>>> Nt = len(S.data)-1
|
||||
>>> acf = S.tocovdata(nr=0, nt=Nt)
|
||||
>>> acf.data[:5]
|
||||
array([ 3.06093287, 2.23846752, 0.48630084, -1.1336035 , -2.03036854])
|
||||
'''
|
||||
|
||||
def test_to_t_pdf():
|
||||
'''
|
||||
The density of Tc is computed by:
|
||||
>>> from wafo.spectrum import models as sm
|
||||
>>> Sj = sm.Jonswap()
|
||||
>>> S = Sj.tospecdata()
|
||||
>>> f = S.to_t_pdf(pdef='Tc', paramt=(0, 10, 51), speed=7, seed=100)
|
||||
>>> ['%2.3f' % val for val in f.data[:10]]
|
||||
['0.000', '0.014', '0.027', '0.040', '0.050', '0.059', '0.067', '0.072', '0.077', '0.081']
|
||||
|
||||
estimated error bounds
|
||||
>>> ['%2.4f' % val for val in f.err[:10]]
|
||||
['0.0000', '0.0003', '0.0003', '0.0004', '0.0006', '0.0009', '0.0016', '0.0019', '0.0020', '0.0021']
|
||||
'''
|
||||
def test_sim():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap();S = Sj.tospecdata()
|
||||
>>> ns =100; dt = .2
|
||||
>>> x1 = S.sim(ns,dt=dt)
|
||||
|
||||
>>> import numpy as np
|
||||
>>> import scipy.stats as st
|
||||
>>> x2 = S.sim(20000,20)
|
||||
>>> truth1 = [0,np.sqrt(S.moment(1)[0]),0., 0.]
|
||||
>>> funs = [np.mean,np.std,st.skew,st.kurtosis]
|
||||
>>> for fun,trueval in zip(funs,truth1):
|
||||
... res = fun(x2[:,1::],axis=0)
|
||||
... m = res.mean()
|
||||
... sa = res.std()
|
||||
... #trueval, m, sa
|
||||
... np.abs(m-trueval)<sa
|
||||
True
|
||||
array([ True], dtype=bool)
|
||||
True
|
||||
True
|
||||
'''
|
||||
def test_sim_nl():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap();S = Sj.tospecdata()
|
||||
>>> ns =100; dt = .2
|
||||
>>> x1 = S.sim_nl(ns,dt=dt)
|
||||
|
||||
>>> import numpy as np
|
||||
>>> import scipy.stats as st
|
||||
>>> x2, x1 = S.sim_nl(ns=20000,cases=40)
|
||||
>>> truth1 = [0,np.sqrt(S.moment(1)[0][0])] + S.stats_nl(moments='sk')
|
||||
>>> truth1[-1] = truth1[-1]-3
|
||||
>>> truth1
|
||||
[0, 1.7495200310090633, 0.18673120577479801, 0.061988521262417606]
|
||||
|
||||
>>> funs = [np.mean,np.std,st.skew,st.kurtosis]
|
||||
>>> for fun,trueval in zip(funs,truth1):
|
||||
... res = fun(x2[:,1::], axis=0)
|
||||
... m = res.mean()
|
||||
... sa = res.std()
|
||||
... #trueval, m, sa
|
||||
... np.abs(m-trueval)<2*sa
|
||||
True
|
||||
True
|
||||
True
|
||||
True
|
||||
'''
|
||||
def test_stats_nl():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Hs = 7.
|
||||
>>> Sj = sm.Jonswap(Hm0=Hs, Tp=11)
|
||||
>>> S = Sj.tospecdata()
|
||||
>>> me, va, sk, ku = S.stats_nl(moments='mvsk')
|
||||
>>> me; va; sk; ku
|
||||
0.0
|
||||
3.0608203389019537
|
||||
0.18673120577479801
|
||||
3.0619885212624176
|
||||
'''
|
||||
def test_testgaussian():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> import wafo.transform.models as wtm
|
||||
>>> import wafo.objects as wo
|
||||
>>> Hs = 7
|
||||
>>> Sj = sm.Jonswap(Hm0=Hs)
|
||||
>>> S0 = Sj.tospecdata()
|
||||
>>> ns =100; dt = .2
|
||||
>>> x1 = S0.sim(ns, dt=dt)
|
||||
|
||||
>>> S = S0.copy()
|
||||
>>> me, va, sk, ku = S.stats_nl(moments='mvsk')
|
||||
>>> S.tr = wtm.TrHermite(mean=me, sigma=Hs/4, skew=sk, kurt=ku, ysigma=Hs/4)
|
||||
>>> ys = wo.mat2timeseries(S.sim(ns=2**13))
|
||||
>>> g0, gemp = ys.trdata()
|
||||
>>> t0 = g0.dist2gauss()
|
||||
>>> t1 = S0.testgaussian(ns=2**13, t0=t0, cases=50)
|
||||
>>> sum(t1>t0)<5
|
||||
True
|
||||
'''
|
||||
def test_moment():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap(Hm0=5)
|
||||
>>> S = Sj.tospecdata() #Make spectrum ob
|
||||
>>> S.moment()
|
||||
([1.5614600345079888, 0.95567089481941048], ['m0', 'm0tt'])
|
||||
'''
|
||||
|
||||
def test_nyquist_freq():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap(Hm0=5)
|
||||
>>> S = Sj.tospecdata() #Make spectrum ob
|
||||
>>> S.nyquist_freq()
|
||||
3.0
|
||||
'''
|
||||
def test_sampling_period():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap(Hm0=5)
|
||||
>>> S = Sj.tospecdata() #Make spectrum ob
|
||||
>>> S.sampling_period()
|
||||
1.0471975511965976
|
||||
'''
|
||||
def test_normalize():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap(Hm0=5)
|
||||
>>> S = Sj.tospecdata() #Make spectrum ob
|
||||
>>> S.moment(2)
|
||||
([1.5614600345079888, 0.95567089481941048], ['m0', 'm0tt'])
|
||||
|
||||
>>> Sn = S.copy(); Sn.normalize()
|
||||
|
||||
Now the moments should be one
|
||||
>>> Sn.moment(2)
|
||||
([1.0000000000000004, 0.99999999999999967], ['m0', 'm0tt'])
|
||||
|
||||
'''
|
||||
def test_characteristic():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap(Hm0=5)
|
||||
>>> S = Sj.tospecdata() #Make spectrum ob
|
||||
>>> S.characteristic(1)
|
||||
(array([ 8.59007646]), array([[ 0.03040216]]), ['Tm01'])
|
||||
|
||||
>>> [ch, R, txt] = S.characteristic([1,2,3]) # fact a vector of integers
|
||||
>>> ch; R; txt
|
||||
array([ 8.59007646, 8.03139757, 5.62484314])
|
||||
array([[ 0.03040216, 0.02834263, NaN],
|
||||
[ 0.02834263, 0.0274645 , NaN],
|
||||
[ NaN, NaN, 0.01500249]])
|
||||
['Tm01', 'Tm02', 'Tm24']
|
||||
|
||||
>>> S.characteristic('Ss') # fact a string
|
||||
(array([ 0.04963112]), array([[ 2.63624782e-06]]), ['Ss'])
|
||||
|
||||
>>> S.characteristic(['Hm0','Tm02']) # fact a list of strings
|
||||
(array([ 4.99833578, 8.03139757]), array([[ 0.05292989, 0.02511371],
|
||||
[ 0.02511371, 0.0274645 ]]), ['Hm0', 'Tm02'])
|
||||
'''
|
||||
def test_bandwidth():
|
||||
'''
|
||||
>>> import numpy as np
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> Sj = sm.Jonswap(Hm0=3)
|
||||
>>> w = np.linspace(0,4,256)
|
||||
>>> S = SpecData1D(Sj(w),w) #Make spectrum object from numerical values
|
||||
>>> S.bandwidth([0,1,2,3])
|
||||
array([ 0.65354446, 0.3975428 , 0.75688813, 2.00207912])
|
||||
'''
|
||||
def main():
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -0,0 +1,59 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Created on 5. aug. 2010
|
||||
|
||||
@author: pab
|
||||
"""
|
||||
import wafo.data
|
||||
import numpy as np
|
||||
|
||||
def test_timeseries():
|
||||
'''
|
||||
>>> import wafo.data
|
||||
>>> import wafo.objects as wo
|
||||
>>> x = wafo.data.sea()
|
||||
>>> ts = wo.mat2timeseries(x)
|
||||
>>> ts.sampling_period()
|
||||
0.25
|
||||
|
||||
Estimate spectrum
|
||||
>>> S = ts.tospecdata()
|
||||
>>> S.data[:10]
|
||||
array([ 0.01350817, 0.0050932 , 0.00182003, 0.00534806, 0.049407 ,
|
||||
0.25144845, 0.28264622, 0.21398405, 0.18563258, 0.25878918])
|
||||
|
||||
Estimated covariance function
|
||||
>>> rf = ts.tocovdata(lag=150)
|
||||
>>> rf.data[:10]
|
||||
array([ 0.22368637, 0.20838473, 0.17110733, 0.12237803, 0.07024054,
|
||||
0.02064859, -0.02218831, -0.0555993 , -0.07859847, -0.09166187])
|
||||
'''
|
||||
def test_timeseries_trdata():
|
||||
'''
|
||||
>>> import wafo.spectrum.models as sm
|
||||
>>> import wafo.transform.models as tm
|
||||
>>> from wafo.objects import mat2timeseries
|
||||
>>> Hs = 7.0
|
||||
>>> Sj = sm.Jonswap(Hm0=Hs)
|
||||
>>> S = Sj.tospecdata() #Make spectrum object from numerical values
|
||||
>>> S.tr = tm.TrOchi(mean=0, skew=0.16, kurt=0, sigma=Hs/4, ysigma=Hs/4)
|
||||
>>> xs = S.sim(ns=2**16)
|
||||
>>> ts = mat2timeseries(xs)
|
||||
>>> g0, gemp = ts.trdata(monitor=True) # Monitor the development
|
||||
>>> g1, gemp = ts.trdata(method='m', gvar=0.5 ) # Equal weight on all points
|
||||
>>> g2, gemp = ts.trdata(method='n', gvar=[3.5, 0.5, 3.5]) # Less weight on the ends
|
||||
>>> S.tr.dist2gauss()
|
||||
5.9322684525265501
|
||||
>>> np.round(gemp.dist2gauss())
|
||||
6.0
|
||||
>>> np.round(g0.dist2gauss())
|
||||
4.0
|
||||
>>> np.round(g1.dist2gauss())
|
||||
4.0
|
||||
>>> np.round(g2.dist2gauss())
|
||||
4.0
|
||||
|
||||
'''
|
||||
if __name__=='__main__':
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
Loading…
Reference in New Issue