From 767cae0ed8c19fe91d8640841f369eefd4dc2969 Mon Sep 17 00:00:00 2001 From: "Per.Andreas.Brodtkorb" Date: Mon, 29 Nov 2010 11:14:01 +0000 Subject: [PATCH] Small updates --- pywafo/src/wafo/kdetools.py | 150 +++++++++++++++++++++++--- pywafo/src/wafo/test/test_kdetools.py | 1 - 2 files changed, 138 insertions(+), 13 deletions(-) diff --git a/pywafo/src/wafo/kdetools.py b/pywafo/src/wafo/kdetools.py index 1670a7a..ea86ad6 100644 --- a/pywafo/src/wafo/kdetools.py +++ b/pywafo/src/wafo/kdetools.py @@ -10,10 +10,12 @@ #------------------------------------------------------------------------------- #!/usr/bin/env python from __future__ import division +import copy import warnings import numpy as np from numpy import pi, sqrt, atleast_2d, exp, newaxis, array #@UnresolvedImport import scipy +from scipy import interpolate from scipy import linalg from scipy.special import gamma from misc import tranproc, trangood @@ -111,7 +113,15 @@ class TKDE(object): >>> f array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318, 0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882]) - + + >>> kde.eval_grid(x) + array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318, + 0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882]) + + >>> kde.eval_grid_fast(x) + array([ 0. , 1.16200356, 0.99256178, 0.81930973, 0.65479862, + 0.51021576, 0.3896221 , 0.29266142, 0. , 0. ]) + import pylab as plb h1 = plb.plot(x, f) # 1D probability density plot t = np.trapz(f, x) @@ -125,11 +135,13 @@ class TKDE(object): self.alpha = alpha self.xmin = xmin self.xmax = xmax + self.inc = inc self.L2 = L2 self.d, self.n = self.dataset.shape self.initialize() def initialize(self): + self._set_xlimits() tdataset = self._dat2gaus(self.dataset) xmin = self.xmin if xmin is not None: @@ -137,9 +149,27 @@ class TKDE(object): xmax = self.xmax if xmax is not None: xmax = self._dat2gaus(xmax) - self.kde = KDE(tdataset, self.hs, self.kernel, self.alpha, xmin, xmax, + self.tkde = KDE(tdataset, self.hs, self.kernel, self.alpha, xmin, xmax, self.inc) - + def _set_xlimits(self): + amin = self.dataset.min(axis=-1) + amax = self.dataset.max(axis=-1) + xyzrange = amax-amin + offset = xyzrange/4.0 + if self.xmin is None: + self.xmin = amin - offset + else: + self.xmin = self.xmin * np.ones(self.d) + if self.xmax is None: + self.xmax = amax + offset + else: + self.xmax = self.xmax * np.ones(self.d) + + if self.L2 is not None: + L2 = np.atleast_1d(self.L2) * np.ones(self.d) # default no transformation + self.xmin = np.where(L2!=1, np.maximum(self.xmin, amin/2.0), self.xmin) + + def _check_shape(self, points): points = atleast_2d(points) d, m = points.shape @@ -160,11 +190,22 @@ class TKDE(object): L2 = np.atleast_1d(self.L2) * np.ones(self.d) # default no transformation - tpoints = points.copy() + tpoints = copy.copy(points) for i, v2 in enumerate(L2.tolist()): - tpoints[i] = np.where(v2 == 0, np.log(points[i]), points[i] ** v2) + tpoints[i] = np.log(points[i]) if v2 == 0 else points[i] ** v2 return tpoints + + def _gaus2dat(self, tpoints): + if self.L2 is None: + return tpoints # default no transformation + L2 = np.atleast_1d(self.L2) * np.ones(self.d) # default no transformation + + points = copy.copy(tpoints) + for i, v2 in enumerate(L2.tolist()): + points[i] = np.exp(tpoints[i]) if v2 == 0 else tpoints[i] ** (1.0/v2) + return points + def _scale_pdf(self, pdf, points): if self.L2 is None: return pdf @@ -177,7 +218,70 @@ class TKDE(object): transformation. Check the KDE for spurious spikes''' warnings.warn(msg) return pdf - + def eval_grid_fast(self, *args): + """Evaluate the estimated pdf on a grid. + + Parameters + ---------- + arg_0,arg_1,... arg_d-1 : vectors + Alternatively, if no vectors is passed in then + arg_i = linspace(self.xmin[i], self.xmax[i], self.inc) + + Returns + ------- + values : array-like + The values evaluated at meshgrid(*args). + + """ + + if self.L2 is None: + f = self.tkde.eval_grid_fast(*args) + self.args = self.tkde.args + return f + #targs = self._dat2gaus(list(args)) if len(args) else args + tf = self.tkde.eval_grid_fast() + self.args = self._gaus2dat(list(self.tkde.args)) + points = meshgrid(*self.args) if self.d>1 else self.args + f = self._scale_pdf(tf, points) + if len(args): + if self.d==1: + pdf = interpolate.interp1d(points[0],f, bounds_error=False, fill_value=0.0) + elif self.d==2: + pdf = interpolate.interp2d(points[0],points[1], f, bounds_error=False, fill_value=0.0) + ipoints = meshgrid(*args) if self.d>1 else args + fi = pdf(*ipoints) + fi.shape = ipoints[0].shape + return fi + return f + def eval_grid(self, *args): + """Evaluate the estimated pdf on a grid. + + Parameters + ---------- + arg_0,arg_1,... arg_d-1 : vectors + Alternatively, if no vectors is passed in then + arg_i = linspace(self.xmin[i], self.xmax[i], self.inc) + + Returns + ------- + values : array-like + The values evaluated at meshgrid(*args). + + """ + if len(args)==0: + args = [] + for i in range(self.d): + args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc)) + self.args = args + if self.L2 is None: + return self.tkde.eval_grid(*args) + targs = self._dat2gaus(list(args)) + tf = self.tkde.eval_grid(*targs) + points = meshgrid(*args) if self.d>1 else self.args + f = self._scale_pdf(tf, points) + return f + + return self.tkde.eval_grid(*args) def evaluate(self, points): """Evaluate the estimated pdf on a set of points. @@ -198,10 +302,10 @@ class TKDE(object): the dimensionality of the KDE. """ if self.L2 is None: - return self.kde(points) + return self.tkde(points) points = self._check_shape(points) tpoints = self._dat2gaus(points) - tf = self.kde(tpoints) + tf = self.tkde(tpoints) f = self._scale_pdf(tf, points) return f @@ -258,6 +362,24 @@ class KDE(object): array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073, 0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164]) + >>> kde.eval_grid(x) + array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073, + 0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164]) + + >>> kde0 = wk.KDE(data, hs=0.5, alpha=0.0) + >>> kde0.evaluate(x) + array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 , + 0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821]) + + >>> kde0.eval_grid(x) + array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 , + 0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821]) + + >>> f = kde0.eval_grid_fast() + >>> np.interp(x, kde0.args[0], f) + array([ 0.54344 , 1.04793706, 1.38047458, 1.28324876, 0.943131 , + 0.63570524, 0.39404219, 0.19450807, 0.08505344, 0.08505344]) + import pylab as plb h1 = plb.plot(x, f) # 1D probability density plot t = np.trapz(f, x) @@ -345,8 +467,10 @@ class KDE(object): """ if len(args)==0: + args = [] for i in range(self.d): args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc)) + self.args = args return self._eval_grid_fast(*args) def _eval_grid_fast(self, *args): X = np.vstack(args) @@ -360,16 +484,16 @@ class KDE(object): for i in range(d): Xn.append(x0*dx[i]) - Xnc = meshgrid(*Xn) + Xnc = meshgrid(*Xn) if d>1 else Xn shape0 = Xnc[0].shape for i in range(d): Xnc[i].shape = (-1,) - Xn = np.dot(np.vstack(Xnc), self.inv_hs) + Xn = np.dot(self.inv_hs, np.vstack(Xnc)) # Obtain the kernel weights. - kw = self.kernel(Xn) + kw = self.kernel(Xn)/self._norm_factor kw.shape = shape0 kw = np.fft.ifftshift(kw) fftn = np.fft.fftn @@ -401,13 +525,15 @@ class KDE(object): """ if len(args)==0: + args = [] for i in range(self.d): args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc)) + self.args = args return self._eval_grid(*args) def _eval_grid(self, *args): - grd = meshgrid(*args) + grd = meshgrid(*args) if len(args)>1 else list(args) shape0 = grd[0].shape d = len(grd) for i in range(d): diff --git a/pywafo/src/wafo/test/test_kdetools.py b/pywafo/src/wafo/test/test_kdetools.py index 968758e..c092c94 100644 --- a/pywafo/src/wafo/test/test_kdetools.py +++ b/pywafo/src/wafo/test/test_kdetools.py @@ -166,7 +166,6 @@ def test_gridcount_2D(): [ 0.02063536, 0.31054405, 0.71865964, 0.13486633, 0. ], [ 0. , 0. , 0. , 0. , 0. ]]) - h = plb.plot(x, c, '.') # 1D histogram h1 = plb.plot(x, c / dx / N) # 1D probability density plot