diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/_import_tools.py | 4 | ||||
| -rw-r--r-- | numpy/core/defmatrix.py | 2 | ||||
| -rw-r--r-- | numpy/core/ma.py | 2 | ||||
| -rw-r--r-- | numpy/dft/fftpack.py | 12 | ||||
| -rw-r--r-- | numpy/dual.py | 9 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 104 | ||||
| -rw-r--r-- | numpy/lib/mlab.py | 9 | ||||
| -rw-r--r-- | numpy/lib/polynomial.py | 2 | ||||
| -rw-r--r-- | numpy/random/mtrand/mtrand.c | 4 | ||||
| -rw-r--r-- | numpy/random/mtrand/mtrand.pyx | 4 |
10 files changed, 121 insertions, 31 deletions
diff --git a/numpy/_import_tools.py b/numpy/_import_tools.py index 3139c1d63..6a08e00b0 100644 --- a/numpy/_import_tools.py +++ b/numpy/_import_tools.py @@ -264,12 +264,12 @@ class PackageLoader: This function is intended to shorten the need to import many of numpy's submodules constantly with statements such as - import numpy.linalg, numpy.fft, numpy.etc... + import numpy.linalg, numpy.dft, numpy.etc... Instead, you can say: import numpy - numpy.pkgload('linalg','fft',...) + numpy.pkgload('linalg','dft',...) or diff --git a/numpy/core/defmatrix.py b/numpy/core/defmatrix.py index 547bfe493..a3f161384 100644 --- a/numpy/core/defmatrix.py +++ b/numpy/core/defmatrix.py @@ -199,7 +199,7 @@ class matrix(N.ndarray): return self.transpose() def getI(self): - from numpy.linalg import inv + from numpy.dual import inv return matrix(inv(self)) A = property(getA, None, doc="base array") diff --git a/numpy/core/ma.py b/numpy/core/ma.py index f32c21d48..ded2be698 100644 --- a/numpy/core/ma.py +++ b/numpy/core/ma.py @@ -822,7 +822,7 @@ array(data = %(data)s, """ # XXX: This changes bool conversion logic from MA. - # XXX: In MA bool(a) == len(a) != 0, but in scipy + # XXX: In MA bool(a) == len(a) != 0, but in numpy # XXX: scalars do not have len m = self._mask d = self._data diff --git a/numpy/dft/fftpack.py b/numpy/dft/fftpack.py index 2cd3d8f8a..c5bb085e2 100644 --- a/numpy/dft/fftpack.py +++ b/numpy/dft/fftpack.py @@ -19,12 +19,12 @@ inverse_fft2d(a, s=None, axes=(-2, -1)) real_fft2d(a, s=None, axes=(-2,-1)) inverse_real_fft2d(a, s=None, axes=(-2, -1)) """ -__all__ = ['fft','inverse_fft', 'ifft', 'real_fft', 'refft', 'inverse_real_fft', - 'irefft', 'hfft', 'ihfft', 'refftn', 'irefftn', 'refft2', 'irefft2', - 'fft2', 'ifft2', - 'hermite_fft','inverse_hermite_fft','fftnd','inverse_fftnd','fft2d', - 'inverse_fft2d', 'real_fftnd', 'real_fft2d', 'inverse_real_fftnd', - 'inverse_real_fft2d',] +__all__ = ['fft','inverse_fft', 'ifft', 'real_fft', 'refft', + 'inverse_real_fft', 'irefft', 'hfft', 'ihfft', 'refftn', + 'irefftn', 'refft2', 'irefft2', 'fft2', 'ifft2', + 'hermite_fft','inverse_hermite_fft','fftnd','inverse_fftnd', + 'fft2d', 'inverse_fft2d', 'real_fftnd', 'real_fft2d', + 'inverse_real_fftnd', 'inverse_real_fft2d','fftn','ifftn'] from numpy.core import * import fftpack_lite as fftpack diff --git a/numpy/dual.py b/numpy/dual.py index ad516e7a4..32baa020e 100644 --- a/numpy/dual.py +++ b/numpy/dual.py @@ -5,7 +5,7 @@ __all__ = ['fft','ifft','fftn','ifftn','fft2','ifft2', 'inv','svd','solve','det','eig','eigvals','lstsq', - 'pinv','cholesky'] + 'pinv','cholesky','i0'] try: import scipy.linalg as linpkg @@ -17,6 +17,10 @@ try: except ImportError: import numpy.dft as fftpkg +try: + from scipy.special import i0 +except ImportError: + from numpy.lib import i0 fft = fftpkg.fft ifft = fftpkg.ifft @@ -35,6 +39,3 @@ lstsq = linpkg.lstsq pinv = linpkg.pinv cholesky = linpkg.cholesky -del linpkg -del fftpkg - diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 71d46e843..d1166d2c7 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -7,16 +7,17 @@ l__all__ = ['logspace', 'linspace', 'nanargmin', 'nanmin', 'vectorize', 'asarray_chkfinite', 'average', 'histogram', 'bincount', 'digitize', 'cov', 'corrcoef', 'msort', 'median', 'sinc', 'hamming', 'hanning', 'bartlett', 'blackman', - 'kaiser', 'trapz' + 'kaiser', 'trapz', 'i0' ] import types import math import numpy.core.numeric as _nx -from numpy.core.numeric import ones, zeros, arange, concatenate, array, asarray, empty +from numpy.core.numeric import ones, zeros, arange, concatenate, array, \ + asarray, empty, empty_like from numpy.core.numeric import ScalarType, dot, where, newaxis, isscalar -from numpy.core.umath import pi, multiply, add, arctan2, maximum, minimum, frompyfunc, \ - isnan, absolute, cos, less_equal, sqrt, sin, mod +from numpy.core.umath import pi, multiply, add, arctan2, maximum, minimum, \ + frompyfunc, isnan, absolute, cos, less_equal, sqrt, sin, mod, exp from numpy.core.oldnumeric import ravel, nonzero, choose, \ sometrue, alltrue, reshape, any, all, typecodes, ArrayType, squeeze,\ sort @@ -167,9 +168,6 @@ def asarray_chkfinite(a): raise ValueError, "array must not contain infs or NaNs" return a - - - def piecewise(x, condlist, funclist, *args, **kw): """Return a piecewise-defined function. @@ -694,12 +692,102 @@ def hamming(M): n = arange(0,M) return 0.54-0.46*cos(2.0*pi*n/(M-1)) +## Code from cephes for i0 + +_i0A = [ +-4.41534164647933937950E-18, + 3.33079451882223809783E-17, +-2.43127984654795469359E-16, + 1.71539128555513303061E-15, +-1.16853328779934516808E-14, + 7.67618549860493561688E-14, +-4.85644678311192946090E-13, + 2.95505266312963983461E-12, +-1.72682629144155570723E-11, + 9.67580903537323691224E-11, +-5.18979560163526290666E-10, + 2.65982372468238665035E-9, +-1.30002500998624804212E-8, + 6.04699502254191894932E-8, +-2.67079385394061173391E-7, + 1.11738753912010371815E-6, +-4.41673835845875056359E-6, + 1.64484480707288970893E-5, +-5.75419501008210370398E-5, + 1.88502885095841655729E-4, +-5.76375574538582365885E-4, + 1.63947561694133579842E-3, +-4.32430999505057594430E-3, + 1.05464603945949983183E-2, +-2.37374148058994688156E-2, + 4.93052842396707084878E-2, +-9.49010970480476444210E-2, + 1.71620901522208775349E-1, +-3.04682672343198398683E-1, + 6.76795274409476084995E-1] + +_i0B = [ +-7.23318048787475395456E-18, +-4.83050448594418207126E-18, + 4.46562142029675999901E-17, + 3.46122286769746109310E-17, +-2.82762398051658348494E-16, +-3.42548561967721913462E-16, + 1.77256013305652638360E-15, + 3.81168066935262242075E-15, +-9.55484669882830764870E-15, +-4.15056934728722208663E-14, + 1.54008621752140982691E-14, + 3.85277838274214270114E-13, + 7.18012445138366623367E-13, +-1.79417853150680611778E-12, +-1.32158118404477131188E-11, +-3.14991652796324136454E-11, + 1.18891471078464383424E-11, + 4.94060238822496958910E-10, + 3.39623202570838634515E-9, + 2.26666899049817806459E-8, + 2.04891858946906374183E-7, + 2.89137052083475648297E-6, + 6.88975834691682398426E-5, + 3.36911647825569408990E-3, + 8.04490411014108831608E-1] + +def _chbevl(x, vals): + b0 = vals[0] + b1 = 0.0 + + for i in xrange(1,len(vals)): + b2 = b1 + b1 = b0 + b0 = x*b1 - b2 + vals[i] + + return 0.5*(b0 - b2) + + +def _i0_1(x): + return exp(x) * _chbevl(x/2.0-2, _i0A) + +def _i0_2(x): + return exp(x) * _chbevl(32.0/x - 2.0, _i0B) / sqrt(x) + +def i0(x): + x = atleast_1d(x) + y = empty_like(x) + ind = (x<0) + x[ind] = -x[ind] + ind = (x<=8.0) + y[ind] = _i0_1(x[ind]) + ind2 = ~ind + y[ind2] = _i0_2(x[ind2]) + return y.squeeze() + def kaiser(M,beta): """kaiser(M, beta) returns a Kaiser window of length M with shape parameter beta. It depends on numpy.special (in full numpy) for the modified bessel function i0. """ - from numpy.special import i0 + from numpy.dual import i0 n = arange(0,M) alpha = (M-1)/2.0 return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(beta) diff --git a/numpy/lib/mlab.py b/numpy/lib/mlab.py index 5a04fe589..cb634b826 100644 --- a/numpy/lib/mlab.py +++ b/numpy/lib/mlab.py @@ -5,10 +5,11 @@ from numpy.core.numeric import * from twodim_base import eye, tri, diag, fliplr, flipud, rot90, tril, triu from numpy.core.oldnumeric import amax as max from numpy.core.oldnumeric import amin as min -from function_base import msort, median, trapz, diff, cov, corrcoef, kaiser, blackman, \ - bartlett, hanning, hamming, sinc, angle -from numpy.core.oldnumeric import cumsum, ptp, mean, std, prod, cumprod, squeeze +from function_base import msort, median, trapz, diff, cov, corrcoef, \ + kaiser, blackman, bartlett, hanning, hamming, sinc, angle +from numpy.core.oldnumeric import cumsum, ptp, mean, std, prod, cumprod, \ + squeeze from polynomial import roots from numpy.random import rand, randn -from numpy.linalg import eig, svd +from numpy.dual import eig, svd diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py index 62d195c8a..83e919263 100644 --- a/numpy/lib/polynomial.py +++ b/numpy/lib/polynomial.py @@ -19,7 +19,7 @@ lstsq = None def get_linalg_funcs(): "Look for linear algebra functions in numpy" global eigvals, lstsq - from numpy.linalg import eigvals, lstsq + from numpy.dual import eigvals, lstsq return def _eigvals(arg): diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c index a6038c069..2ea2049b8 100644 --- a/numpy/random/mtrand/mtrand.c +++ b/numpy/random/mtrand/mtrand.c @@ -1,4 +1,4 @@ -/* Generated by Pyrex 0.9.3.1 on Wed Jan 4 20:21:15 2006 */ +/* Generated by Pyrex 0.9.3.1 on Thu Jan 5 00:40:10 2006 */ #include "Python.h" #include "structmember.h" @@ -3942,7 +3942,7 @@ static PyObject *__pyx_k115p; static char (__pyx_k112[]) = "mean must be 1 dimensional"; static char (__pyx_k113[]) = "cov must be 2 dimensional and square"; static char (__pyx_k114[]) = "mean and cov must have same length"; -static char (__pyx_k115[]) = "numpy.linalg"; +static char (__pyx_k115[]) = "numpy.dual"; static PyObject *__pyx_f_6mtrand_11RandomState_multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static char __pyx_doc_6mtrand_11RandomState_multivariate_normal[] = "Return an array containing multivariate normally distributed random numbers\n with specified mean and covariance.\n\n multivariate_normal(mean, cov) -> random values\n multivariate_normal(mean, cov, [m, n, ...]) -> random values\n\n mean must be a 1 dimensional array. cov must be a square two dimensional\n array with the same number of rows and columns as mean has elements.\n\n The first form returns a single 1-D array containing a multivariate\n normal.\n\n The second form returns an array of shape (m, n, ..., cov.shape[0]).\n In this case, output[i,j,...,:] is a 1-D array containing a multivariate\n normal.\n "; diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index 89256fcf6..4f8bd3057 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -832,8 +832,8 @@ cdef class RandomState: # Then the matrix products of the rows of x and A has the desired # covariance. Note that sqrt(s)*v where (u,s,v) is the singular value # decomposition of cov is such an A. - - from numpy.linalg import svd + + from numpy.dual import svd # XXX: we really should be doing this by Cholesky decomposition (u,s,v) = svd(cov) x = _sp.matrixmultiply(x*_sp.sqrt(s),v) |