Div. updates
Per.Andreas.Brodtkorb
14 years ago
parent
bb7b1de823
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0b71a79872
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'''
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Created on 20. jan. 2011
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@author: pab
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'''
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import numpy as np
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from numpy import exp
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from wafo.misc import meshgrid
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__all__ = ['peaks', 'humps']
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def peaks(x=None, y=None, n=51):
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'''
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Return the "well" known MatLab (R) peaks function
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evaluated in the [-3,3] x,y range
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Example
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-------
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>>> import pylab as plt
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>>> x,y,z = peaks()
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>>> h = plt.contourf(x,y,z)
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'''
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if x is None:
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x = np.linspace(-3, 3, n)
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if y is None:
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y = np.linspace(-3, 3, n)
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[x1, y1] = meshgrid(x, y)
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z = (3 * (1 - x1) ** 2 * exp(-(x1 ** 2) - (y1 + 1) ** 2)
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- 10 * (x1 / 5 - x1 ** 3 - y1 ** 5) * exp(-x1 ** 2 - y1 ** 2)
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- 1. / 3 * exp(-(x1 + 1) ** 2 - y1 ** 2))
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return x1, y1, z
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def humps(x=None):
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'''
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Computes a function that has three roots, and some humps.
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'''
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if x is None:
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y = np.linspace(0, 1)
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else:
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y = np.asarray(x)
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return 1.0 / ((y - 0.3) ** 2 + 0.01) + 1.0 / ((y - 0.9) ** 2 + 0.04) + 2 * y - 5.2
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if __name__ == '__main__':
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pass
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'''
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Created on 20. jan. 2011
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@author: pab
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license BSD
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'''
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import warnings
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import numpy as np
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from wafo.plotbackend import plotbackend
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__all__ = ['cltext']
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def cltext(levels, percent=False, n=4, xs=0.036, ys=0.94, zs=0):
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'''
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Places contour level text in the current window
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Parameters
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----------
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levels = vector of contour levels or the corresponding percent which the
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contour line encloses
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percent = 0 if levels are the actual contour levels (default)
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1 if levels are the corresponding percent which the
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contour line encloses
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n = maximum N digits of precision (default 4)
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Returns
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h = handles to the text objects.
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CLTEXT creates text objects in the current figure and prints
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"Level curves at:" if percent is False and
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"Level curves enclosing:" otherwise
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and the contour levels or percent.
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NOTE:
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-The handles to the lines of text may also be found by
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h = findobj(gcf,'gid','CLTEXT','type','text');
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h = findobj(gca,'gid','CLTEXT','type','text');
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-To make the text objects follow the data in the axes set the units
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for the text objects 'data' by
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set(h,'unit','data')
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Examples:
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>>> from wafo.integrate import peaks
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>>> import pylab as plt
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>>> x,y,z = peaks();
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>>> h = plt.contour(x,y,z)
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>>> h = cltext(h.levels)
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>>> plt.show()
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data = rndray(1,2000,2); f = kdebin(data,{'kernel','epan','L2',.5,'inc',128});
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contour(f.x{:},f.f,f.cl),cltext(f.pl,1)
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See also
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pdfplot
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'''
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# TODO : Make it work like legend does (but without the box): include position options etc...
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clevels = np.atleast_1d(levels)
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_CLTEXT_TAG = 'CLTEXT'
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cax = plotbackend.gca()
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axpos = cax.get_position()
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xint = axpos.intervalx
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yint = axpos.intervaly
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xss = xint[0] + xs * (xint[1] - xint[0])
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yss = yint[0] + ys * (yint[1] - yint[0])
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cf = plotbackend.gcf() # get current figure
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#% delete cltext object if it exists
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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def matchfun(x):
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if hasattr(x, 'get_gid'):
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return x.get_gid() == _CLTEXT_TAG
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return False
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h_cltxts = plotbackend.findobj(cf, matchfun);
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if len(h_cltxts):
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for i in h_cltxts:
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try:
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cax.texts.remove(i)
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except:
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warnings.warn('Tried to delete a non-existing CL-text')
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try:
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cf.texts.remove(i)
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except:
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warnings.warn('Tried to delete a non-existing CL-text')
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charHeight = 1 / 33;
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delta_y = charHeight;
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if percent:
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titletxt = 'Level curves enclosing:';
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else:
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titletxt = 'Level curves at:';
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format = '%0.' + ('%d' % n) + 'g\n'
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cltxt = ''.join([format % level for level in clevels.tolist()])
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titleProp = dict(gid=_CLTEXT_TAG, horizontalalignment='left',
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verticalalignment='center', fontweight='bold', axes=cax) #
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ha1 = plotbackend.figtext(xss, yss, titletxt, **titleProp)
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yss -= delta_y;
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txtProp = dict(gid=_CLTEXT_TAG, horizontalalignment='left',
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verticalalignment='top', axes=cax)
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ha2 = plotbackend.figtext(xss, yss, cltxt, **txtProp)
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return ha1, ha2
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if __name__ == '__main__':
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pass
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'''
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python setup.py build_src build_ext --inplace
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See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
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'''
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# File setup.py
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def configuration(parent_package='',top_path=None):
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from numpy.distutils.misc_util import Configuration
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config = Configuration('',parent_package,top_path)
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config.add_extension('c_library',
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sources = ['c_library.pyf','c_functions.c'])
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return config
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if __name__ == "__main__":
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from numpy.distutils.core import setup
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setup(**configuration(top_path='').todict())
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'''
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python setup.py build_src build_ext --inplace
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See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
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'''
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# File setup.py
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def compile_all():
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import os
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files = ['mvnprd', 'mvnprodcorrprb']
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compile1_format = 'gfortran -fPIC -c %s.f'
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for file in files:
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os.system(compile1_format % file)
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file_objects = ['%s.o' % file for file in files]
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return file_objects
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def configuration(parent_package='',top_path=None):
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from numpy.distutils.misc_util import Configuration
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libs = compile_all()
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config = Configuration('',parent_package,top_path)
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config.add_extension('mvnprdmod',
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libraries = libs,
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sources = ['mvnprd_interface.f'])
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return config
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if __name__ == "__main__":
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from numpy.distutils.core import setup
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setup(**configuration(top_path='').todict())
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'''
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Created on 19. juli 2010
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@author: pab
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'''
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from wafo.spectrum.dispersion_relation import w2k,k2w
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def test_k2w():
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'''
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>>> from numpy import arange
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>>> k2w(arange(0.01,.5,0.2))[0]
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array([ 0.3132092 , 1.43530485, 2.00551739])
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>>> k2w(arange(0.01,.5,0.2),h=20)[0]
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array([ 0.13914927, 1.43498213, 2.00551724])
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'''
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def test_w2k():
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'''
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>>> w2k(range(4))[0]
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array([ 0. , 0.1019368 , 0.4077472 , 0.91743119])
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>>> w2k(range(4),h=20)[0]
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array([ 0. , 0.10503601, 0.40774726, 0.91743119])
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'''
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def main():
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import doctest
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doctest.testmod()
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if __name__ == '__main__':
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main()
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import numpy as np
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from wafo.spectrum.models import (Bretschneider, Jonswap, OchiHubble, Tmaspec,
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Torsethaugen, McCormick, Wallop)
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def test_bretschneider():
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'''
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>>> S = Bretschneider(Hm0=6.5,Tp=10)
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>>> S((0,1,2,3))
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array([ 0. , 1.69350993, 0.06352698, 0.00844783])
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'''
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def test_jonswap():
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'''
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>>> S = Jonswap(Hm0=7, Tp=11,gamma=1)
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>>> S((0,1,2,3))
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array([ 0. , 1.42694133, 0.05051648, 0.00669692])
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>>> w = np.linspace(0,5)
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>>> S2 = Bretschneider(Hm0=7, Tp=11)
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JONSWAP with gamma=1 should be equal to Bretscneider:
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>>> np.all(np.abs(S(w)-S2(w))<1.e-7)
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True
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'''
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def test_tmaspec():
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'''
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>>> S = Tmaspec(Hm0=7, Tp=11,gamma=1,h=10)
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>>> S((0,1,2,3))
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array([ 0. , 0.70106233, 0.05022433, 0.00669692])
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'''
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def test_torsethaugen():
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'''
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>>> S = Torsethaugen(Hm0=7, Tp=11,gamma=1,h=10)
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>>> S((0,1,2,3))
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array([ 0. , 1.19989709, 0.05819794, 0.0093541 ])
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>>> S.wind(range(4))
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array([ 0. , 1.13560528, 0.05529849, 0.00888989])
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>>> S.swell(range(4))
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array([ 0. , 0.0642918 , 0.00289946, 0.00046421])
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'''
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def test_ochihubble():
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'''
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>>> S = OchiHubble(par=2)
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>>> S(range(4))
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array([ 0. , 0.90155636, 0.04185445, 0.00583207])
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'''
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def test_mccormick():
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'''
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>>> S = McCormick(Hm0=6.5,Tp=10)
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>>> S(range(4))
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array([ 0. , 1.87865908, 0.15050447, 0.02994663])
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'''
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def test_wallop():
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'''
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>>> S = Wallop(Hm0=6.5, Tp=10)
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>>> S(range(4))
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array([ 0.00000000e+00, 9.36921871e-01, 2.76991078e-03,
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7.72996150e-05])
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'''
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def main():
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import doctest
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doctest.testmod()
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if __name__ == '__main__':
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main()
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import wafo.spectrum.models
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from wafo.spectrum import SpecData1D
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def test_tocovmatrix():
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'''
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>>> import wafo.spectrum.models as sm
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>>> Sj = sm.Jonswap()
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>>> S = Sj.tospecdata()
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>>> acfmat = S.tocov_matrix(nr=3, nt=256, dt=0.1)
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>>> acfmat[:2,:]
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array([[ 3.06075987, 0. , -1.67750289, 0. ],
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[ 3.05246132, -0.16662376, -1.66819445, 0.18634189]])
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'''
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def test_tocovdata():
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'''
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>>> import wafo.spectrum.models as sm
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>>> Sj = sm.Jonswap()
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>>> S = Sj.tospecdata()
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>>> Nt = len(S.data)-1
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>>> acf = S.tocovdata(nr=0, nt=Nt)
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>>> acf.data[:5]
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array([ 3.06093287, 2.23846752, 0.48630084, -1.1336035 , -2.03036854])
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'''
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def test_to_t_pdf():
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'''
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The density of Tc is computed by:
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>>> from wafo.spectrum import models as sm
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>>> Sj = sm.Jonswap()
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>>> S = Sj.tospecdata()
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>>> f = S.to_t_pdf(pdef='Tc', paramt=(0, 10, 51), speed=7, seed=100)
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>>> ['%2.3f' % val for val in f.data[:10]]
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['0.000', '0.014', '0.027', '0.040', '0.050', '0.059', '0.067', '0.072', '0.077', '0.081']
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estimated error bounds
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>>> ['%2.4f' % val for val in f.err[:10]]
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['0.0000', '0.0003', '0.0003', '0.0004', '0.0006', '0.0009', '0.0016', '0.0019', '0.0020', '0.0021']
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'''
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def test_sim():
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'''
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>>> import wafo.spectrum.models as sm
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>>> Sj = sm.Jonswap();S = Sj.tospecdata()
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>>> ns =100; dt = .2
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>>> x1 = S.sim(ns,dt=dt)
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>>> import numpy as np
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>>> import scipy.stats as st
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>>> x2 = S.sim(20000,20)
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>>> truth1 = [0,np.sqrt(S.moment(1)[0]),0., 0.]
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>>> funs = [np.mean,np.std,st.skew,st.kurtosis]
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>>> for fun,trueval in zip(funs,truth1):
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... res = fun(x2[:,1::],axis=0)
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... m = res.mean()
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... sa = res.std()
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... #trueval, m, sa
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... np.abs(m-trueval)<sa
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True
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array([ True], dtype=bool)
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True
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True
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'''
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def test_sim_nl():
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'''
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>>> import wafo.spectrum.models as sm
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>>> Sj = sm.Jonswap();S = Sj.tospecdata()
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>>> ns =100; dt = .2
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>>> 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