Added wafo.stats + small fixes
Per.Andreas.Brodtkorb
15 years ago
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d0c62a08fa
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4d338e15ce
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"""
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Statistics package in WAFO Toolbox.
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Readme - Readme file for module STATS in WAFO Toolbox
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"""
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from scipy.stats import *
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from wafo.stats.core import *
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#from wafo.stats.distributions import *
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#from wafo.spectrum.core import SpecData1D
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#import wafo.spectrum.models
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#import wafo.spectrum.dispersion_relation
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#from wafo.data_structures import SpecData1D
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from __future__ import division
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from wafo.wafodata import WafoData
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import numpy as np
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from numpy import inf
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from numpy.lib.shape_base import atleast_1d
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from numpy.ma.core import arange, floor
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__all__ = ['edf']
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def edf(x, method=2):
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'''
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Returns EDF Empirical Distribution Function.
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Parameters
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----------
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x : array-like
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data vector
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method : integer scalar
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1. Interpolation so that F(X_(k)) == (k-0.5)/n.
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2. Interpolation so that F(X_(k)) == k/(n+1). (default)
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3. The empirical distribution. F(X_(k)) = k/n
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Example
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-------
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>>> import wafo.stats as ws
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>>> x = np.linspace(0,6,200)
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>>> R = ws.rayleigh.rvs(scale=2,size=100)
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>>> F = ws.edf(R)
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>>> F.plot()
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See also edf, pdfplot, cumtrapz
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'''
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z = atleast_1d(x)
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z.sort()
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N = len(z)
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if method == 1:
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Fz1 = arange(0.5, N) / N
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elif method == 3:
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Fz1 = arange(1, N + 1) / N
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else:
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Fz1 = arange(1, N + 1) / (N + 1)
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F = WafoData(Fz1, z, xlab='x', ylab='F(x)')
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F.setplotter('step')
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return F
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def edfcnd(x, c=None, method=2):
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'''
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EDFCND Empirical Distribution Function CoNDitioned that X>=c.
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Parameters
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----------
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x : array-like
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data vector
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method : integer scalar
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1. Interpolation so that F(X_(k)) == (k-0.5)/n.
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2. Interpolation so that F(X_(k)) == k/(n+1). (default)
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3. The empirical distribution. F(X_(k)) = k/n
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Example
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-------
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>>> import wafo.stats as ws
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>>> x = np.linspace(0,6,200)
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>>> R = ws.rayleigh.rvs(scale=2,size=100)
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>>> Fc = ws.edfcd(R, 1)
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>>> Fc.plot()
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>>> F = ws.edf(R)
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>>> F.plot()
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See also edf, pdfplot, cumtrapz
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'''
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z = atleast_1d(x)
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if c is None:
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c = floor(min(z.min(), 0))
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try:
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F = edf(z[c <= z], method=method)
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except:
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ValueError('No data points above c=%d' % int(c))
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if - inf < c:
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F.labels.ylab = 'F(x| X>=%g)' % c
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return F
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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from numpy import asarray, ndarray, reshape, repeat, nan, product
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def valarray(shape,value=nan,typecode=None):
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"""Return an array of all value.
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"""
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out = reshape(repeat([value],product(shape,axis=0),axis=0),shape)
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if typecode is not None:
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out = out.astype(typecode)
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if not isinstance(out, ndarray):
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out = asarray(out)
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return out
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class rv_frozen(object):
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''' Frozen continous or discrete 1D Random Variable object (RV)
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Methods
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-------
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RV.rvs(size=1)
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- random variates
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RV.pdf(x)
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- probability density function (continous case)
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RV.pmf(x)
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- probability mass function (discrete case)
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RV.cdf(x)
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- cumulative density function
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RV.sf(x)
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- survival function (1-cdf --- sometimes more accurate)
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RV.ppf(q)
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- percent point function (inverse of cdf --- percentiles)
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RV.isf(q)
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- inverse survival function (inverse of sf)
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RV.stats(moments='mv')
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- mean('m'), variance('v'), skew('s'), and/or kurtosis('k')
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RV.entropy()
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- (differential) entropy of the RV.
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Parameters
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----------
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x : array-like
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quantiles
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q : array-like
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lower or upper tail probability
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size : int or tuple of ints, optional, keyword
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shape of random variates
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moments : string, optional, keyword
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one or more of 'm' mean, 'v' variance, 's' skewness, 'k' kurtosis
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'''
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def __init__(self, dist, *args, **kwds):
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self.dist = dist
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loc0, scale0 = map(kwds.get, ['loc', 'scale'])
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if isinstance(dist,rv_continuous):
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args, loc0, scale0 = dist.fix_loc_scale(args, loc0, scale0)
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self.par = args + (loc0, scale0)
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else: # rv_discrete
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args, loc0 = dist.fix_loc(args, loc0)
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self.par = args + (loc0,)
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def pdf(self,x):
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''' Probability density function at x of the given RV.'''
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return self.dist.pdf(x,*self.par)
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def cdf(self,x):
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'''Cumulative distribution function at x of the given RV.'''
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return self.dist.cdf(x,*self.par)
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def ppf(self,q):
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'''Percent point function (inverse of cdf) at q of the given RV.'''
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return self.dist.ppf(q,*self.par)
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def isf(self,q):
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'''Inverse survival function at q of the given RV.'''
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return self.dist.isf(q,*self.par)
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def rvs(self, size=None):
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'''Random variates of given type.'''
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kwds = dict(size=size)
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return self.dist.rvs(*self.par,**kwds)
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def sf(self,x):
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'''Survival function (1-cdf) at x of the given RV.'''
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return self.dist.sf(x,*self.par)
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def stats(self,moments='mv'):
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''' Some statistics of the given RV'''
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kwds = dict(moments=moments)
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return self.dist.stats(*self.par,**kwds)
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def moment(self,n):
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par1 = self.par[:self.dist.numargs]
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return self.dist.moment(n,*par1)
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def entropy(self):
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return self.dist.entropy(*self.par)
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def pmf(self,k):
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'''Probability mass function at k of the given RV'''
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return self.dist.pmf(k,*self.par)
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@ -0,0 +1,9 @@
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# Set this to eg. pylab to be able to plot
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import numpy
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try:
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from matplotlib import pyplot as plotbackend
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#from matplotlib import pyplot
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numpy.disp('Scipy.stats: plotbackend is set to matplotlib.pyplot')
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except:
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numpy.disp('Scipy.stats: Unable to load matplotlib.pyplot as plotbackend')
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plotbackend = None
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