From scipy-svn at scipy.org Mon Feb 1 22:35:23 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 1 Feb 2010 21:35:23 -0600 (CST) Subject: [Scipy-svn] r6212 - trunk/scipy/stats Message-ID: <20100202033523.5127839CAEF@scipy.org> Author: oliphant Date: 2010-02-01 21:35:23 -0600 (Mon, 01 Feb 2010) New Revision: 6212 Modified: trunk/scipy/stats/stats.py Log: Fix stats.histogram to not use new= keyword of np.histogram. Modified: trunk/scipy/stats/stats.py =================================================================== --- trunk/scipy/stats/stats.py 2010-01-29 02:20:05 UTC (rev 6211) +++ trunk/scipy/stats/stats.py 2010-02-02 03:35:23 UTC (rev 6212) @@ -1403,7 +1403,7 @@ defaultlimits = (data_min - s, data_max + s) # use numpy's histogram method to compute bins hist, bin_edges = np.histogram(a, bins=numbins, range=defaultlimits, - weights=weights, new=True) + weights=weights) # hist are not always floats, convert to keep with old output hist = np.array(hist, dtype=float) # fixed width for bins is assumed, as numpy's histogram gives From scipy-svn at scipy.org Wed Feb 3 00:14:38 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 2 Feb 2010 23:14:38 -0600 (CST) Subject: [Scipy-svn] r6213 - in branches/0.7.x/scipy: lib/lapack sparse/linalg/dsolve sparse/linalg/eigen/arpack Message-ID: <20100203051438.56507C7C018@scipy.org> Author: cdavid Date: 2010-02-02 23:14:38 -0600 (Tue, 02 Feb 2010) New Revision: 6213 Modified: branches/0.7.x/scipy/lib/lapack/SConscript branches/0.7.x/scipy/sparse/linalg/dsolve/SConscript branches/0.7.x/scipy/sparse/linalg/eigen/arpack/SConscript Log: BUG: update scons scripts to last numscons. Modified: branches/0.7.x/scipy/lib/lapack/SConscript =================================================================== --- branches/0.7.x/scipy/lib/lapack/SConscript 2010-02-02 03:35:23 UTC (rev 6212) +++ branches/0.7.x/scipy/lib/lapack/SConscript 2010-02-03 05:14:38 UTC (rev 6213) @@ -53,7 +53,7 @@ #========== # Build #========== -env.AppendUnique(CPPPATH = env['F2PYINCLUDEDIR']) +env.AppendUnique(CPPPATH=[env['F2PYINCLUDEDIR']]) env.AppendUnique(F2PYOPTIONS = '--quiet') env['BUILDERS']['GenerateFakePyf'] = Builder(action = do_generate_fake_interface, Modified: branches/0.7.x/scipy/sparse/linalg/dsolve/SConscript =================================================================== --- branches/0.7.x/scipy/sparse/linalg/dsolve/SConscript 2010-02-02 03:35:23 UTC (rev 6212) +++ branches/0.7.x/scipy/sparse/linalg/dsolve/SConscript 2010-02-03 05:14:38 UTC (rev 6213) @@ -4,6 +4,7 @@ from numscons import GetNumpyEnvironment from numscons import CheckF77LAPACK from numscons import write_info +from numscons.core.misc import built_with_mstools, built_with_gnu_f77 env = GetNumpyEnvironment(ARGUMENTS) @@ -24,10 +25,10 @@ # Build superlu lib superlu_env = env.Clone() -superlu_def = {} +superlu_def = [] if sys.platform == 'win32': - superlu_def['NO_TIMER'] = 1 -superlu_def['USE_VENDOR_BLAS'] = 2 + superlu_def.append((('NO_TIMER'), 1)) +superlu_def.append((('USE_VENDOR_BLAS'), 2)) superlu_env.Append(CPPDEFINES = superlu_def) superlu_src = [pjoin('SuperLU', 'SRC', s) for s in [ "ccolumn_bmod.c", @@ -42,7 +43,7 @@ "dpanel_bmod.c", "dpanel_dfs.c", "dpivotL.c", "dpivotgrowth.c", "dpruneL.c", "dreadhb.c", "dsnode_bmod.c", "dsnode_dfs.c", "dsp_blas2.c", "dsp_blas3.c", "dutil.c", "dzsum1.c", "get_perm_c.c", "heap_relax_snode.c", "icmax1.c", -"izmax1.c", "lsame.c", "memory.c", "mmd.c", "relax_snode.c", "scolumn_bmod.c", +"izmax1.c", "memory.c", "mmd.c", "relax_snode.c", "scolumn_bmod.c", "scolumn_dfs.c", "scomplex.c", "scopy_to_ucol.c", "scsum1.c", "sgscon.c", "sgsequ.c", "sgsrfs.c", "sgssv.c", "sgssvx.c", "sgstrf.c", "sgstrs.c", "slacon.c", "slamch.c", "slangs.c", "slaqgs.c", "smemory.c", "sp_coletree.c", @@ -54,6 +55,11 @@ "zlacon.c", "zlangs.c", "zlaqgs.c", "zmemory.c", "zpanel_bmod.c", "zpanel_dfs.c", "zpivotL.c", "zpivotgrowth.c", "zpruneL.c", "zreadhb.c", "zsnode_bmod.c", "zsnode_dfs.c", "zsp_blas2.c", "zsp_blas3.c", "zutil.c"]] + +# XXX: we should detect whether lsame is already defined in BLAS/LAPACK. Here, +# when using MSVC + MKL, lsame is already in MKL +if not (built_with_mstools(env) and (not built_with_gnu_f77(env))): + superlu_src.append(pjoin("SuperLU", "SRC", "lsame.c")) superlu = superlu_env.DistutilsStaticExtLibrary('superlu_src', source = superlu_src) # Build python extensions Modified: branches/0.7.x/scipy/sparse/linalg/eigen/arpack/SConscript =================================================================== --- branches/0.7.x/scipy/sparse/linalg/eigen/arpack/SConscript 2010-02-02 03:35:23 UTC (rev 6212) +++ branches/0.7.x/scipy/sparse/linalg/eigen/arpack/SConscript 2010-02-03 05:14:38 UTC (rev 6213) @@ -53,9 +53,9 @@ src = [str(s) for s in arpack_src] -env.AppendUnique(CPPPATH = pjoin('ARPACK', 'SRC')) -env.AppendUnique(F77PATH = pjoin('ARPACK', 'SRC')) -env.AppendUnique(LIBPATH = '.') +env.AppendUnique(CPPPATH = [pjoin('ARPACK', 'SRC')]) +env.AppendUnique(F77PATH = [pjoin('ARPACK', 'SRC')]) +env.AppendUnique(LIBPATH = ['.']) arpack_lib = env.DistutilsStaticExtLibrary('arpack', source = src) # Build _arpack extension From scipy-svn at scipy.org Thu Feb 4 23:31:54 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Thu, 4 Feb 2010 22:31:54 -0600 (CST) Subject: [Scipy-svn] r6214 - trunk/scipy/sparse/linalg/eigen/arpack Message-ID: <20100205043154.E6E1C39CAE9@scipy.org> Author: cdavid Date: 2010-02-04 22:31:54 -0600 (Thu, 04 Feb 2010) New Revision: 6214 Modified: trunk/scipy/sparse/linalg/eigen/arpack/arpack.py Log: BUG: fix error message for non-square input to arpack.eigen. Modified: trunk/scipy/sparse/linalg/eigen/arpack/arpack.py =================================================================== --- trunk/scipy/sparse/linalg/eigen/arpack/arpack.py 2010-02-03 05:14:38 UTC (rev 6213) +++ trunk/scipy/sparse/linalg/eigen/arpack/arpack.py 2010-02-05 04:31:54 UTC (rev 6214) @@ -129,7 +129,7 @@ """ A = aslinearoperator(A) if A.shape[0] != A.shape[1]: - raise ValueError('expected square matrix (shape=%s)' % shape) + raise ValueError('expected square matrix (shape=%s)' % A.shape) n = A.shape[0] # guess type From scipy-svn at scipy.org Sun Feb 7 02:13:04 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 7 Feb 2010 01:13:04 -0600 (CST) Subject: [Scipy-svn] r6215 - trunk/scipy/interpolate Message-ID: <20100207071304.7214839C674@scipy.org> Author: oliphant Date: 2010-02-07 01:13:04 -0600 (Sun, 07 Feb 2010) New Revision: 6215 Modified: trunk/scipy/interpolate/rbf.py Log: Add ability to use arbitrary basis function to Rbf constructor for radial basis function interpolation. Modified: trunk/scipy/interpolate/rbf.py =================================================================== --- trunk/scipy/interpolate/rbf.py 2010-02-05 04:31:54 UTC (rev 6214) +++ trunk/scipy/interpolate/rbf.py 2010-02-07 07:13:04 UTC (rev 6215) @@ -3,6 +3,7 @@ Written by John Travers , February 2007 Based closely on Matlab code by Alex Chirokov Additional, large, improvements by Robert Hetland +Some additional alterations by Travis Oliphant Permission to use, modify, and distribute this software is given under the terms of the SciPy (BSD style) license. See LICENSE.txt that came with @@ -42,10 +43,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ -from numpy import (sqrt, log, asarray, newaxis, all, dot, float64, exp, eye, - isnan, float_) +from numpy import (sqrt, log, asarray, newaxis, all, dot, exp, eye, + float_) from scipy import linalg + class Rbf(object): """ Rbf(*args) @@ -58,18 +60,22 @@ *args : arrays x, y, z, ..., d, where x, y, z, ... are the coordinates of the nodes and d is the array of values at the nodes - function : str, optional + function : str or callable, optional The radial basis function, based on the radius, r, given by the norm (defult is Euclidean distance); the default is 'multiquadric':: 'multiquadric': sqrt((r/self.epsilon)**2 + 1) - 'inverse multiquadric': 1.0/sqrt((r/self.epsilon)**2 + 1) + 'inverse': 1.0/sqrt((r/self.epsilon)**2 + 1) 'gaussian': exp(-(r/self.epsilon)**2) 'linear': r 'cubic': r**3 'quintic': r**5 - 'thin-plate': r**2 * log(r) + 'thin_plate': r**2 * log(r) + If callable, then it must take 2 arguments (self, r). The epsilon parameter + will be available as self.epsilon. Other keyword arguments passed in will + be available as well. + epsilon : float, optional Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is @@ -99,26 +105,67 @@ def _euclidean_norm(self, x1, x2): return sqrt( ((x1 - x2)**2).sum(axis=0) ) - def _function(self, r): - if self.function.lower() == 'multiquadric': + def _h_multiquadric(self, r): return sqrt((1.0/self.epsilon*r)**2 + 1) - elif self.function.lower() == 'inverse multiquadric': + def _h_inverse_multiquadric(self, r): return 1.0/sqrt((1.0/self.epsilon*r)**2 + 1) - elif self.function.lower() == 'gaussian': + def _h_gaussian(self, r): return exp(-(1.0/self.epsilon*r)**2) - elif self.function.lower() == 'linear': - return r - elif self.function.lower() == 'cubic': - return r**3 - elif self.function.lower() == 'quintic': - return r**5 - elif self.function.lower() == 'thin-plate': - result = r**2 * log(r) - result[r == 0] = 0 # the spline is zero at zero - return result - else: - raise ValueError, 'Invalid basis function name' + def _h_linear(self, r): + return r + def _h_cubic(self, r): + return r**3 + def _h_quintic(self, r): + return r**5 + def _h_thin_plate(self, r): + result = r**2 * log(r) + result[r == 0] = 0 # the spline is zero at zero + return result + # Setup self._function and do smoke test on initial r + def _init_function(self, r): + if isinstance(self.function, str): + self.function = self.function.lower() + _mapped = {'inverse': 'inverse_multiquadric', + 'inverse multiquadric': 'inverse_multiquadric', + 'thin-plate': 'thin_plate'} + if self.function in _mapped: + self.function = _mapped[self.function] + + func_name = "_h_" + self.function + if hasattr(self, func_name): + self._function = getattr(self, func_name) + else: + functionlist = [x[3:] for x in dir(self) if x.startswith('_h_')] + raise ValueError, "function must be a callable or one of ", \ + ", ".join(functionlist) + self._function = getattr(self, "_h_"+self.function) + elif callable(self.function): + import new + allow_one = False + if hasattr(self.function, 'func_code'): + val = self.function + allow_one = True + elif hasattr(self.function, "im_func"): + val = self.function.im_func + elif hasattr(self.function, "__call__"): + val = self.function.__call__.im_func + else: + raise ValueError, "Cannot determine number of arguments to function" + + argcount = val.func_code.co_argcount + if allow_one and argcount == 1: + self._function = self.function + elif argcount == 2: + self._function = new.instancemethod(self.function, self, Rbf) + else: + raise ValueError, "Function argument must take 1 or 2 arguments." + + a0 = self._function(r) + if a0.shape != r.shape: + raise ValueError, "Callable must take array and return array of the same shape" + return a0 + def __init__(self, *args, **kwargs): self.xi = asarray([asarray(a, dtype=float_).flatten() for a in args[:-1]]) @@ -131,10 +178,17 @@ self.norm = kwargs.pop('norm', self._euclidean_norm) r = self._call_norm(self.xi, self.xi) self.epsilon = kwargs.pop('epsilon', r.mean()) - self.function = kwargs.pop('function', 'multiquadric') self.smooth = kwargs.pop('smooth', 0.0) - self.A = self._function(r) - eye(self.N)*self.smooth + self.function = kwargs.pop('function', self._h_multiquadric) + + # attach anything left in kwargs to self + # for use by any user-callable function or + # to save on the object returned. + for item, value in kwargs.items(): + setattr(self, item, value) + + self.A = self._init_function(r) - eye(self.N)*self.smooth self.nodes = linalg.solve(self.A, self.di) def _call_norm(self, x1, x2): From scipy-svn at scipy.org Mon Feb 8 04:19:23 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 8 Feb 2010 03:19:23 -0600 (CST) Subject: [Scipy-svn] r6216 - branches/0.7.x/scipy/sparse/tests Message-ID: <20100208091923.2739039CAE9@scipy.org> Author: cdavid Date: 2010-02-08 03:19:23 -0600 (Mon, 08 Feb 2010) New Revision: 6216 Modified: branches/0.7.x/scipy/sparse/tests/test_base.py branches/0.7.x/scipy/sparse/tests/test_construct.py Log: TEST: fix sparse unit tests failures. With recent changes to numpy test functions, assert_equal(dtype(str), str) is wrong, so we fix it here. (cherry picked from commit 8f4a65b53717722d774d6cd06ab0003445909e8c) Modified: branches/0.7.x/scipy/sparse/tests/test_base.py =================================================================== --- branches/0.7.x/scipy/sparse/tests/test_base.py 2010-02-07 07:13:04 UTC (rev 6215) +++ branches/0.7.x/scipy/sparse/tests/test_base.py 2010-02-08 09:19:23 UTC (rev 6216) @@ -65,7 +65,9 @@ """Test manipulating empty matrices. Fails in SciPy SVN <= r1768 """ shape = (5, 5) - for mytype in ['int32', 'float32', 'float64', 'complex64', 'complex128']: + for mytype in [np.dtype('int32'), np.dtype('float32'), + np.dtype('float64'), np.dtype('complex64'), + np.dtype('complex128')]: a = self.spmatrix(shape, dtype=mytype) b = a + a c = 2 * a @@ -239,11 +241,11 @@ def test_asfptype(self): A = self.spmatrix( arange(6,dtype='int32').reshape(2,3) ) - assert_equal( A.dtype , 'int32' ) - assert_equal( A.asfptype().dtype, 'float64' ) + assert_equal( A.dtype , np.dtype('int32') ) + assert_equal( A.asfptype().dtype, np.dtype('float64') ) assert_equal( A.asfptype().format, A.format ) - assert_equal( A.astype('int16').asfptype().dtype , 'float32' ) - assert_equal( A.astype('complex128').asfptype().dtype , 'complex128' ) + assert_equal( A.astype('int16').asfptype().dtype , np.dtype('float32') ) + assert_equal( A.astype('complex128').asfptype().dtype , np.dtype('complex128') ) B = A.asfptype() C = B.asfptype() Modified: branches/0.7.x/scipy/sparse/tests/test_construct.py =================================================================== --- branches/0.7.x/scipy/sparse/tests/test_construct.py 2010-02-07 07:13:04 UTC (rev 6215) +++ branches/0.7.x/scipy/sparse/tests/test_construct.py 2010-02-08 09:19:23 UTC (rev 6216) @@ -67,7 +67,7 @@ assert_equal(identity(2).toarray(), [[1,0],[0,1]]) I = identity(3, dtype='int8', format='dia') - assert_equal( I.dtype, 'int8' ) + assert_equal( I.dtype, np.dtype('int8') ) assert_equal( I.format, 'dia' ) for fmt in sparse_formats: @@ -81,7 +81,7 @@ assert_equal(eye(3,2).toarray(), [[1,0],[0,1],[0,0]]) assert_equal(eye(3,3).toarray(), [[1,0,0],[0,1,0],[0,0,1]]) - assert_equal(eye(3,3,dtype='int16').dtype, 'int16') + assert_equal(eye(3,3,dtype='int16').dtype, np.dtype('int16')) for m in [3, 5]: for n in [3, 5]: From scipy-svn at scipy.org Mon Feb 8 04:19:40 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 8 Feb 2010 03:19:40 -0600 (CST) Subject: [Scipy-svn] r6217 - in branches/0.7.x/scipy: spatial stats Message-ID: <20100208091940.D727D39CAE9@scipy.org> Author: cdavid Date: 2010-02-08 03:19:40 -0600 (Mon, 08 Feb 2010) New Revision: 6217 Modified: branches/0.7.x/scipy/spatial/ckdtree.c branches/0.7.x/scipy/stats/vonmises_cython.c Log: BUG: regenerate cython files with Cython 0.12.1. Modified: branches/0.7.x/scipy/spatial/ckdtree.c =================================================================== --- branches/0.7.x/scipy/spatial/ckdtree.c 2010-02-08 09:19:23 UTC (rev 6216) +++ branches/0.7.x/scipy/spatial/ckdtree.c 2010-02-08 09:19:40 UTC (rev 6217) @@ -1,8 +1,12 @@ -/* Generated by Cython 0.10 on Mon Nov 10 22:15:02 2008 */ +/* Generated by Cython 0.12.1 on Mon Feb 8 18:11:01 2010 */ #define PY_SSIZE_T_CLEAN #include "Python.h" #include "structmember.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#else + #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif @@ -11,16 +15,22 @@ #endif #if PY_VERSION_HEX < 0x02040000 #define METH_COEXIST 0 + #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type) + #define PyDict_Contains(d,o) PySequence_Contains(d,o) #endif + #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN + #define PY_FORMAT_SIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) PyInt_AsLong(o) #define PyNumber_Index(o) PyNumber_Int(o) #define PyIndex_Check(o) PyNumber_Check(o) + #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) #endif + #if PY_VERSION_HEX < 0x02060000 #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) @@ -30,22 +40,21 @@ #define PyType_Modified(t) typedef struct { - void *buf; - PyObject *obj; - Py_ssize_t len; - Py_ssize_t itemsize; - int readonly; - int ndim; - char *format; - Py_ssize_t *shape; - Py_ssize_t *strides; - Py_ssize_t *suboffsets; - void *internal; + void *buf; + PyObject *obj; + Py_ssize_t len; + Py_ssize_t itemsize; + int readonly; + int ndim; + char *format; + Py_ssize_t *shape; + Py_ssize_t *strides; + Py_ssize_t *suboffsets; + void *internal; } Py_buffer; #define PyBUF_SIMPLE 0 #define PyBUF_WRITABLE 0x0001 - #define PyBUF_LOCK 0x0002 #define PyBUF_FORMAT 0x0004 #define PyBUF_ND 0x0008 #define PyBUF_STRIDES (0x0010 | PyBUF_ND) @@ -55,21 +64,32 @@ #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) #endif + #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #endif + #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #endif + #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif + #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type - #define PyString_Type PyBytes_Type + #define PyString_Type PyUnicode_Type + #define PyString_CheckExact PyUnicode_CheckExact +#else + #define PyBytes_Type PyString_Type + #define PyBytes_CheckExact PyString_CheckExact +#endif + +#if PY_MAJOR_VERSION >= 3 #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) @@ -84,13 +104,17 @@ #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define PyBytes_Type PyString_Type + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) + #endif + #if PY_MAJOR_VERSION >= 3 #define PyMethod_New(func, self, klass) PyInstanceMethod_New(func) #endif + #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall @@ -98,9 +122,30 @@ #ifndef __cdecl #define __cdecl #endif + #ifndef __fastcall + #define __fastcall + #endif #else #define _USE_MATH_DEFINES #endif + +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) +#else + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) +#endif + +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_NAMESTR(n) ((char *)(n)) + #define __Pyx_DOCSTR(n) ((char *)(n)) +#else + #define __Pyx_NAMESTR(n) (n) + #define __Pyx_DOCSTR(n) (n) +#endif #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else @@ -109,54 +154,111 @@ #include #define __PYX_HAVE_API__scipy__spatial__ckdtree #include "stdlib.h" +#include "stdio.h" #include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#ifndef CYTHON_INLINE + #if defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #else + #define CYTHON_INLINE + #endif +#endif -#ifdef __GNUC__ -#define INLINE __inline__ -#elif _WIN32 -#define INLINE __inline +typedef struct {PyObject **p; char *s; const long n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ + + +/* Type Conversion Predeclarations */ + +#if PY_MAJOR_VERSION < 3 +#define __Pyx_PyBytes_FromString PyString_FromString +#define __Pyx_PyBytes_FromStringAndSize PyString_FromStringAndSize +#define __Pyx_PyBytes_AsString PyString_AsString #else -#define INLINE +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +#define __Pyx_PyBytes_AsString PyBytes_AsString #endif -typedef struct {PyObject **p; char *s; long n; char is_unicode; char intern; char is_identifier;} __Pyx_StringTabEntry; /*proto*/ +#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s) +#define __Pyx_PyBytes_AsUString(s) ((unsigned char*) __Pyx_PyBytes_AsString(s)) +#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +#if !defined(T_PYSSIZET) +#if PY_VERSION_HEX < 0x02050000 +#define T_PYSSIZET T_INT +#elif !defined(T_LONGLONG) +#define T_PYSSIZET \ + ((sizeof(Py_ssize_t) == sizeof(int)) ? T_INT : \ + ((sizeof(Py_ssize_t) == sizeof(long)) ? T_LONG : -1)) +#else +#define T_PYSSIZET \ + ((sizeof(Py_ssize_t) == sizeof(int)) ? T_INT : \ + ((sizeof(Py_ssize_t) == sizeof(long)) ? T_LONG : \ + ((sizeof(Py_ssize_t) == sizeof(PY_LONG_LONG)) ? T_LONGLONG : -1))) +#endif +#endif -static int __pyx_skip_dispatch = 0; +#if !defined(T_ULONGLONG) +#define __Pyx_T_UNSIGNED_INT(x) \ + ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \ + ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \ + ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : -1)))) +#else +#define __Pyx_T_UNSIGNED_INT(x) \ + ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \ + ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \ + ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : \ + ((sizeof(x) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1))))) +#endif +#if !defined(T_LONGLONG) +#define __Pyx_T_SIGNED_INT(x) \ + ((sizeof(x) == sizeof(char)) ? T_BYTE : \ + ((sizeof(x) == sizeof(short)) ? T_SHORT : \ + ((sizeof(x) == sizeof(int)) ? T_INT : \ + ((sizeof(x) == sizeof(long)) ? T_LONG : -1)))) +#else +#define __Pyx_T_SIGNED_INT(x) \ + ((sizeof(x) == sizeof(char)) ? T_BYTE : \ + ((sizeof(x) == sizeof(short)) ? T_SHORT : \ + ((sizeof(x) == sizeof(int)) ? T_INT : \ + ((sizeof(x) == sizeof(long)) ? T_LONG : \ + ((sizeof(x) == sizeof(PY_LONG_LONG)) ? T_LONGLONG : -1))))) +#endif -/* Type Conversion Predeclarations */ +#define __Pyx_T_FLOATING(x) \ + ((sizeof(x) == sizeof(float)) ? T_FLOAT : \ + ((sizeof(x) == sizeof(double)) ? T_DOUBLE : -1)) -#if PY_MAJOR_VERSION < 3 -#define __Pyx_PyBytes_FromString PyString_FromString -#define __Pyx_PyBytes_AsString PyString_AsString +#if !defined(T_SIZET) +#if !defined(T_ULONGLONG) +#define T_SIZET \ + ((sizeof(size_t) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(size_t) == sizeof(unsigned long)) ? T_ULONG : -1)) #else -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_AsString PyBytes_AsString +#define T_SIZET \ + ((sizeof(size_t) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(size_t) == sizeof(unsigned long)) ? T_ULONG : \ + ((sizeof(size_t) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1))) #endif +#endif -#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) -static INLINE int __Pyx_PyObject_IsTrue(PyObject* x); -static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x); -static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x); -static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*); -#define __pyx_PyInt_AsLong(x) (PyInt_CheckExact(x) ? PyInt_AS_LONG(x) : PyInt_AsLong(x)) #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x); -static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x); -static INLINE char __pyx_PyInt_char(PyObject* x); -static INLINE short __pyx_PyInt_short(PyObject* x); -static INLINE int __pyx_PyInt_int(PyObject* x); -static INLINE long __pyx_PyInt_long(PyObject* x); -static INLINE signed char __pyx_PyInt_signed_char(PyObject* x); -static INLINE signed short __pyx_PyInt_signed_short(PyObject* x); -static INLINE signed int __pyx_PyInt_signed_int(PyObject* x); -static INLINE signed long __pyx_PyInt_signed_long(PyObject* x); -static INLINE long double __pyx_PyInt_long_double(PyObject* x); + #ifdef __GNUC__ /* Test for GCC > 2.95 */ #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) @@ -174,103 +276,37 @@ static PyObject *__pyx_m; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; static const char **__pyx_f; -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, PyObject* kw_name); /*proto*/ -static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, - Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif -static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name); /*proto*/ -static INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); -static INLINE void __Pyx_ZeroBuffer(Py_buffer* buf); /*proto*/ -static INLINE const char* __Pyx_ConsumeWhitespace(const char* ts); /*proto*/ -static void __Pyx_BufferNdimError(Py_buffer* buffer, int expected_ndim); /*proto*/ -static const char* __Pyx_DescribeTokenInFormatString(const char* ts); /*proto*/ -static const char* __Pyx_CheckTypestring_double(const char* ts); /*proto*/ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif -static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast); /*proto*/ - -static void __Pyx_RaiseBufferFallbackError(void); /*proto*/ -static const char* __Pyx_CheckTypestring_nn___pyx_t_5numpy_int32_t(const char* ts); /*proto*/ - -static int __Pyx_GetBuffer_nn___pyx_t_5numpy_int32_t(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast); /*proto*/ - -static INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ -static INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ - -static INLINE PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i, int is_unsigned) { - PyObject *r; - if (PyList_CheckExact(o) && 0 <= i && i < PyList_GET_SIZE(o)) { - r = PyList_GET_ITEM(o, i); - Py_INCREF(r); - } - else if (PyTuple_CheckExact(o) && 0 <= i && i < PyTuple_GET_SIZE(o)) { - r = PyTuple_GET_ITEM(o, i); - Py_INCREF(r); - } - else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0) || !is_unsigned)) - r = PySequence_GetItem(o, i); - else { - PyObject *j = (likely(i >= 0) || !is_unsigned) ? PyInt_FromLong(i) : PyLong_FromUnsignedLongLong((sizeof(unsigned long long) > sizeof(Py_ssize_t) ? (1ULL << (sizeof(Py_ssize_t)*8)) : 0) + i); - if (!j) - return 0; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - } - return r; -} -static const char* __Pyx_CheckTypestring_int(const char* ts); /*proto*/ - -static int __Pyx_GetBuffer_int(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast); /*proto*/ -static void __Pyx_RaiseBufferIndexError(int axis); /*proto*/ -#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); -static void __Pyx_ReleaseBuffer(Py_buffer *view); -#else -#define __Pyx_GetBuffer PyObject_GetBuffer -#define __Pyx_ReleaseBuffer PyBuffer_Release +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj #endif -Py_ssize_t __Pyx_zeros[] = {0, 0}; -Py_ssize_t __Pyx_minusones[] = {-1, -1}; - -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/ - -static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/ - -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ - -static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ -static int __Pyx_EndUnpack(PyObject *); /*proto*/ - -static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ - -static INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ -static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ - -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ - -static void __Pyx_WriteUnraisable(const char *name); /*proto*/ - -static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/ - -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, long size); /*proto*/ - -static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ - -static void __Pyx_AddTraceback(const char *funcname); /*proto*/ - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ - -/* Type declarations */ - typedef npy_int8 __pyx_t_5numpy_int8_t; typedef npy_int16 __pyx_t_5numpy_int16_t; @@ -295,6 +331,10 @@ typedef npy_longlong __pyx_t_5numpy_long_t; +typedef npy_intp __pyx_t_5numpy_intp_t; + +typedef npy_uintp __pyx_t_5numpy_uintp_t; + typedef npy_ulong __pyx_t_5numpy_uint_t; typedef npy_ulonglong __pyx_t_5numpy_ulong_t; @@ -305,13 +345,37 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif + +/* Type declarations */ + typedef npy_cfloat __pyx_t_5numpy_cfloat_t; typedef npy_cdouble __pyx_t_5numpy_cdouble_t; typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":15 +typedef npy_cdouble __pyx_t_5numpy_complex_t; + +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":15 * * # priority queue * cdef union heapcontents: # <<<<<<<<<<<<<< @@ -324,7 +388,7 @@ char *ptrdata; }; -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":19 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":19 * char* ptrdata * * cdef struct heapitem: # <<<<<<<<<<<<<< @@ -337,7 +401,7 @@ union __pyx_t_5scipy_7spatial_7ckdtree_heapcontents contents; }; -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":23 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":23 * heapcontents contents * * cdef struct heap: # <<<<<<<<<<<<<< @@ -351,7 +415,7 @@ int space; }; -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":139 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":139 * * # Tree structure * cdef struct innernode: # <<<<<<<<<<<<<< @@ -367,7 +431,7 @@ struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *greater; }; -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":145 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":145 * innernode* less * innernode* greater * cdef struct leafnode: # <<<<<<<<<<<<<< @@ -382,7 +446,7 @@ int end_idx; }; -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":153 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":153 * # this is the standard trick for variable-size arrays: * # malloc sizeof(nodeinfo)+self.m*sizeof(double) bytes. * cdef struct nodeinfo: # <<<<<<<<<<<<<< @@ -395,7 +459,7 @@ double side_distances[0]; }; -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":157 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":157 * double side_distances[0] * * cdef class cKDTree: # <<<<<<<<<<<<<< @@ -427,157 +491,569 @@ void (*__query)(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *, double *, int *, double *, int, double, double, double); }; static struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *__pyx_vtabptr_5scipy_7spatial_7ckdtree_cKDTree; + +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif + +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct * __Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); + end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; + } + #define __Pyx_RefNannySetupContext(name) void *__pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) + #define __Pyx_RefNannyFinishContext() __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r);} } while(0) +#else + #define __Pyx_RefNannySetupContext(name) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) +#endif /* CYTHON_REFNANNY */ +#define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);} } while(0) +#define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r);} } while(0) + +static CYTHON_INLINE long __Pyx_div_long(long, long); /* proto */ + +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, PyObject* kw_name); /*proto*/ + +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/ + +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name); /*proto*/ + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(void); + +static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ +static int __Pyx_EndUnpack(PyObject *); /*proto*/ + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ + +/* Run-time type information about structs used with buffers */ +struct __Pyx_StructField_; + +typedef struct { + const char* name; /* for error messages only */ + struct __Pyx_StructField_* fields; + size_t size; /* sizeof(type) */ + char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject */ +} __Pyx_TypeInfo; + +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; + +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; + + +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); +static int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); + +static void __Pyx_RaiseBufferFallbackError(void); /*proto*/ + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ + + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} + + +#define __Pyx_GetItemInt_List(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \ + __Pyx_GetItemInt_List_Fast(o, i, size <= sizeof(long)) : \ + __Pyx_GetItemInt_Generic(o, to_py_func(i))) + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int fits_long) { + if (likely(o != Py_None)) { + if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) { + PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i); + Py_INCREF(r); + return r; + } + } + return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i)); +} + +#define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \ + __Pyx_GetItemInt_Tuple_Fast(o, i, size <= sizeof(long)) : \ + __Pyx_GetItemInt_Generic(o, to_py_func(i))) + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int fits_long) { + if (likely(o != Py_None)) { + if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) { + PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i); + Py_INCREF(r); + return r; + } + } + return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i)); +} + + +#define __Pyx_GetItemInt(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \ + __Pyx_GetItemInt_Fast(o, i, size <= sizeof(long)) : \ + __Pyx_GetItemInt_Generic(o, to_py_func(i))) + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int fits_long) { + PyObject *r; + if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) { + r = PyList_GET_ITEM(o, i); + Py_INCREF(r); + } + else if (PyTuple_CheckExact(o) && ((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { + r = PyTuple_GET_ITEM(o, i); + Py_INCREF(r); + } + else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0))) { + r = PySequence_GetItem(o, i); + } + else { + r = __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i)); + } + return r; +} +static void __Pyx_RaiseBufferIndexError(int axis); /*proto*/ +#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index); /*proto*/ +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); +static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else +#define __Pyx_GetBuffer PyObject_GetBuffer +#define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + +Py_ssize_t __Pyx_zeros[] = {0, 0}; +Py_ssize_t __Pyx_minusones[] = {-1, -1}; + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/ + +static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/ + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif + +#if defined(_WIN32) && defined(__cplusplus) && CYTHON_CCOMPLEX + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eqf(a, b) ((a)==(b)) + #define __Pyx_c_sumf(a, b) ((a)+(b)) + #define __Pyx_c_difff(a, b) ((a)-(b)) + #define __Pyx_c_prodf(a, b) ((a)*(b)) + #define __Pyx_c_quotf(a, b) ((a)/(b)) + #define __Pyx_c_negf(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zerof(z) ((z)==(float)0) + #define __Pyx_c_conjf(z) (::std::conj(z)) + /*#define __Pyx_c_absf(z) (::std::abs(z))*/ + #else + #define __Pyx_c_is_zerof(z) ((z)==0) + #define __Pyx_c_conjf(z) (conjf(z)) + /*#define __Pyx_c_absf(z) (cabsf(z))*/ + #endif +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + /*static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex);*/ +#endif + +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + /*#define __Pyx_c_abs(z) (::std::abs(z))*/ + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + /*#define __Pyx_c_abs(z) (cabs(z))*/ + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); + /*static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex);*/ +#endif + +static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *); + +static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *); + +static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject *); + +static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject *); + +static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject *); + +static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject *); + +static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject *); + +static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *); + +static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *); + +static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *); + +static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *); + +static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject *); + +static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject *); + +static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *); + +static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *); + +static void __Pyx_WriteUnraisable(const char *name); /*proto*/ + +static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/ + +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, long size, int strict); /*proto*/ + +static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ + +static void __Pyx_AddTraceback(const char *funcname); /*proto*/ + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ /* Module declarations from python_buffer */ +/* Module declarations from python_ref */ + /* Module declarations from stdlib */ +/* Module declarations from stdio */ + /* Module declarations from numpy */ /* Module declarations from numpy */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *, PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *, PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ +static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *); /*proto*/ /* Module declarations from scipy.spatial.ckdtree */ static PyTypeObject *__pyx_ptype_5scipy_7spatial_7ckdtree_cKDTree = 0; static double __pyx_v_5scipy_7spatial_7ckdtree_infinity; -static double __pyx_k_24; -static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapcreate(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, int); /*proto*/ -static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapdestroy(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ -static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapresize(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, int); /*proto*/ -static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heappush(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapcreate(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, int); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapdestroy(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapresize(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, int); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heappush(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *, struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem); /*proto*/ static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappeek(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapremove(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ static struct __pyx_t_5scipy_7spatial_7ckdtree_heapitem __pyx_f_5scipy_7spatial_7ckdtree_heappop(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *); /*proto*/ -static double __pyx_f_5scipy_7spatial_7ckdtree_dmax(double, double); /*proto*/ -static double __pyx_f_5scipy_7spatial_7ckdtree_dabs(double); /*proto*/ -static double __pyx_f_5scipy_7spatial_7ckdtree__distance_p(double *, double *, double, int, double); /*proto*/ +static CYTHON_INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dmax(double, double); /*proto*/ +static CYTHON_INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dabs(double); /*proto*/ +static CYTHON_INLINE double __pyx_f_5scipy_7spatial_7ckdtree__distance_p(double *, double *, double, int, double); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), 'R' }; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int32_t = { "numpy.int32_t", NULL, sizeof(__pyx_t_5numpy_int32_t), 'I' }; +static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), 'I' }; +#define __Pyx_MODULE_NAME "scipy.spatial.ckdtree" +int __pyx_module_is_main_scipy__spatial__ckdtree = 0; - /* Implementation of scipy.spatial.ckdtree */ -static PyObject *__pyx_int_0; -static char __pyx_k___init__[] = "__init__"; -static PyObject *__pyx_kp___init__; -static char __pyx_k___dealloc__[] = "__dealloc__"; -static PyObject *__pyx_kp___dealloc__; -static char __pyx_k_query[] = "query"; -static PyObject *__pyx_kp_query; -static char __pyx_k_data[] = "data"; -static PyObject *__pyx_kp_data; -static char __pyx_k_leafsize[] = "leafsize"; -static PyObject *__pyx_kp_leafsize; -static char __pyx_k_x[] = "x"; -static PyObject *__pyx_kp_x; -static char __pyx_k_k[] = "k"; -static PyObject *__pyx_kp_k; -static char __pyx_k_eps[] = "eps"; -static PyObject *__pyx_kp_eps; -static char __pyx_k_p[] = "p"; -static PyObject *__pyx_kp_p; -static char __pyx_k_23[] = "distance_upper_bound"; -static PyObject *__pyx_kp_23; -static char __pyx_k_numpy[] = "numpy"; -static PyObject *__pyx_kp_numpy; -static char __pyx_k_np[] = "np"; -static PyObject *__pyx_kp_np; -static char __pyx_k_kdtree[] = "kdtree"; -static PyObject *__pyx_kp_kdtree; -static char __pyx_k_inf[] = "inf"; -static PyObject *__pyx_kp_inf; -static char __pyx_k_ValueError[] = "ValueError"; -static PyObject *__pyx_kp_ValueError; -static char __pyx_k_ascontiguousarray[] = "ascontiguousarray"; -static PyObject *__pyx_kp_ascontiguousarray; -static char __pyx_k_dtype[] = "dtype"; -static PyObject *__pyx_kp_dtype; -static char __pyx_k_float[] = "float"; -static PyObject *__pyx_kp_float; -static char __pyx_k_shape[] = "shape"; -static PyObject *__pyx_kp_shape; -static char __pyx_k_amax[] = "amax"; -static PyObject *__pyx_kp_amax; -static char __pyx_k_axis[] = "axis"; -static PyObject *__pyx_kp_axis; -static char __pyx_k_amin[] = "amin"; -static PyObject *__pyx_kp_amin; -static char __pyx_k_arange[] = "arange"; -static PyObject *__pyx_kp_arange; -static char __pyx_k_27[] = "int32"; -static PyObject *__pyx_kp_27; -static char __pyx_k_asarray[] = "asarray"; -static PyObject *__pyx_kp_asarray; -static char __pyx_k_astype[] = "astype"; -static PyObject *__pyx_kp_astype; -static char __pyx_k_newaxis[] = "newaxis"; -static PyObject *__pyx_kp_newaxis; -static char __pyx_k_prod[] = "prod"; -static PyObject *__pyx_kp_prod; -static char __pyx_k_reshape[] = "reshape"; -static PyObject *__pyx_kp_reshape; -static char __pyx_k_empty[] = "empty"; -static PyObject *__pyx_kp_empty; -static char __pyx_k_fill[] = "fill"; -static PyObject *__pyx_kp_fill; -static char __pyx_k_30[] = "i"; -static PyObject *__pyx_kp_30; static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_kp_25; -static char __pyx_k_25[] = "Heap containing %d items cannot be resized to %d"; -static PyObject *__pyx_kp_26; -static char __pyx_k_26[] = "leafsize must be at least 1"; -static PyObject *__pyx_kp_28; -static PyObject *__pyx_kp_29; -static char __pyx_k_28[] = "x must consist of vectors of length %d but has shape %s"; -static char __pyx_k_29[] = "Only p-norms with 1<=p<=infinity permitted"; -static char __pyx_k___getbuffer__[] = "__getbuffer__"; -static PyObject *__pyx_kp___getbuffer__; -static char __pyx_k___releasebuffer__[] = "__releasebuffer__"; -static PyObject *__pyx_kp___releasebuffer__; -static char __pyx_k_info[] = "info"; -static PyObject *__pyx_kp_info; -static char __pyx_k_flags[] = "flags"; -static PyObject *__pyx_kp_flags; -static char __pyx_k_iteritems[] = "iteritems"; -static PyObject *__pyx_kp_iteritems; -static char __pyx_k_next[] = "next"; -static PyObject *__pyx_kp_next; -static char __pyx_k_StopIteration[] = "StopIteration"; -static PyObject *__pyx_kp_StopIteration; -static char __pyx_k_pop[] = "pop"; -static PyObject *__pyx_kp_pop; -static char __pyx_k_RuntimeError[] = "RuntimeError"; -static PyObject *__pyx_kp_RuntimeError; -static PyObject *__pyx_kp_1; -static PyObject *__pyx_kp_2; -static PyObject *__pyx_kp_20; -static PyObject *__pyx_kp_21; -static PyObject *__pyx_kp_22; -static PyObject *__pyx_builtin_StopIteration; +static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_RuntimeError; -static char __pyx_k_1[] = "ndarray is not C contiguous"; -static char __pyx_k_2[] = "ndarray is not Fortran contiguous"; -static char __pyx_k_3[] = "b"; -static char __pyx_k_4[] = "B"; -static char __pyx_k_5[] = "h"; -static char __pyx_k_6[] = "H"; -static char __pyx_k_7[] = "i"; -static char __pyx_k_8[] = "I"; -static char __pyx_k_9[] = "l"; -static char __pyx_k_10[] = "L"; -static char __pyx_k_11[] = "q"; -static char __pyx_k_12[] = "Q"; -static char __pyx_k_13[] = "f"; -static char __pyx_k_14[] = "d"; -static char __pyx_k_15[] = "g"; -static char __pyx_k_16[] = "Zf"; -static char __pyx_k_17[] = "Zd"; -static char __pyx_k_18[] = "Zg"; -static char __pyx_k_19[] = "O"; -static char __pyx_k_20[] = "unknown dtype code in numpy.pxd (%d)"; -static char __pyx_k_21[] = "Format string allocated too short."; -static char __pyx_k_22[] = "unknown dtype code in numpy.pxd (%d)"; +static char __pyx_k_1[] = "Heap containing %d items cannot be resized to %d"; +static char __pyx_k_2[] = "leafsize must be at least 1"; +static char __pyx_k_3[] = "distance_upper_bound"; +static char __pyx_k_5[] = "x must consist of vectors of length %d but has shape %s"; +static char __pyx_k_6[] = "Only p-norms with 1<=p<=infinity permitted"; +static char __pyx_k_7[] = "ndarray is not C contiguous"; +static char __pyx_k_8[] = "ndarray is not Fortran contiguous"; +static char __pyx_k_9[] = "Non-native byte order not supported"; +static char __pyx_k_10[] = "unknown dtype code in numpy.pxd (%d)"; +static char __pyx_k_11[] = "Format string allocated too short, see comment in numpy.pxd"; +static char __pyx_k_12[] = "Format string allocated too short."; +static char __pyx_k_13[] = "cKDTree.__init__ (line 195)"; +static char __pyx_k_14[] = "cKDTree.query (line 516)"; +static char __pyx_k__B[] = "B"; +static char __pyx_k__H[] = "H"; +static char __pyx_k__I[] = "I"; +static char __pyx_k__L[] = "L"; +static char __pyx_k__O[] = "O"; +static char __pyx_k__Q[] = "Q"; +static char __pyx_k__b[] = "b"; 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- /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":31 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":31 * self.space = initial_size * self.heap = stdlib.malloc(sizeof(heapitem)*self.space) * self.n=0 # <<<<<<<<<<<<<< @@ -615,11 +1092,13 @@ */ __pyx_v_self->n = 0; - __pyx_r = Py_None; Py_INCREF(Py_None); + __pyx_r = Py_None; __Pyx_INCREF(Py_None); + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); return __pyx_r; } -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":33 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":33 * self.n=0 * * cdef inline heapdestroy(heap* self): # <<<<<<<<<<<<<< @@ -627,10 +1106,11 @@ * */ -static INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapdestroy(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { - PyObject *__pyx_r; +static CYTHON_INLINE PyObject *__pyx_f_5scipy_7spatial_7ckdtree_heapdestroy(struct __pyx_t_5scipy_7spatial_7ckdtree_heap *__pyx_v_self) { + PyObject *__pyx_r = NULL; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":92 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":92 * it = heappeek(self) * heapremove(self) * return it # <<<<<<<<<<<<<< @@ -1156,13 +1684,14 @@ goto __pyx_L0; __pyx_L1_error:; - Py_XDECREF(__pyx_1); + __Pyx_XDECREF(__pyx_t_1); __Pyx_WriteUnraisable("scipy.spatial.ckdtree.heappop"); __pyx_L0:; + __Pyx_RefNannyFinishContext(); return __pyx_r; } -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":99 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":99 * * # utility functions * cdef inline double dmax(double x, double y): # <<<<<<<<<<<<<< @@ -1170,21 +1699,22 @@ * return x */ -static INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dmax(double __pyx_v_x, double __pyx_v_y) { +static CYTHON_INLINE double __pyx_f_5scipy_7spatial_7ckdtree_dmax(double __pyx_v_x, double __pyx_v_y) { double __pyx_r; 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- /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":122 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":122 * for i in range(k): * r = dmax(r,dabs(x[i]-y[i])) * if r>upperbound: # <<<<<<<<<<<<<< * return r * elif p==1: */ - __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); - if (__pyx_1) { + __pyx_t_1 = (__pyx_v_r > __pyx_v_upperbound); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":123 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":123 * r = dmax(r,dabs(x[i]-y[i])) * if r>upperbound: * return r # <<<<<<<<<<<<<< @@ -1343,26 +1881,28 @@ goto __pyx_L3; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":124 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":124 * if r>upperbound: * return r * elif p==1: # <<<<<<<<<<<<<< * for i in range(k): * r += dabs(x[i]-y[i]) */ - __pyx_1 = (__pyx_v_p == 1); - if (__pyx_1) { + __pyx_t_1 = (__pyx_v_p == 1); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":125 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":125 * return r * elif p==1: * for i in range(k): # <<<<<<<<<<<<<< * r += dabs(x[i]-y[i]) * if r>upperbound: */ - for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_k; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_k; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":126 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":126 * elif p==1: * for i in range(k): * r += dabs(x[i]-y[i]) # <<<<<<<<<<<<<< @@ -1371,17 +1911,17 @@ */ __pyx_v_r += __pyx_f_5scipy_7spatial_7ckdtree_dabs(((__pyx_v_x[__pyx_v_i]) - (__pyx_v_y[__pyx_v_i]))); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":127 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":127 * for i in range(k): * r += dabs(x[i]-y[i]) * if r>upperbound: # <<<<<<<<<<<<<< * return r * else: */ - __pyx_1 = (__pyx_v_r > __pyx_v_upperbound); - if (__pyx_1) { + __pyx_t_1 = (__pyx_v_r > __pyx_v_upperbound); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":128 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":128 * r += dabs(x[i]-y[i]) * if r>upperbound: * return r # <<<<<<<<<<<<<< @@ -1398,16 +1938,18 @@ } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":130 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":130 * return r * else: * for i in range(k): # <<<<<<<<<<<<<< * r += dabs(x[i]-y[i])**p * if r>upperbound: */ - for (__pyx_v_i = 0; 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- /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":243 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":243 * n = stdlib.malloc(sizeof(leafnode)) * n.split_dim = -1 * n.start_idx = start_idx # <<<<<<<<<<<<<< @@ -2021,7 +2648,7 @@ */ __pyx_v_n->start_idx = __pyx_v_start_idx; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":244 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":244 * n.split_dim = -1 * n.start_idx = start_idx * n.end_idx = end_idx # <<<<<<<<<<<<<< @@ -2030,7 +2657,7 @@ */ __pyx_v_n->end_idx = __pyx_v_end_idx; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":245 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":245 * n.start_idx = start_idx * n.end_idx = end_idx * return n # <<<<<<<<<<<<<< @@ -2043,7 +2670,7 @@ } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":247 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":247 * return n * else: * d = 0 # <<<<<<<<<<<<<< @@ -2052,7 +2679,7 @@ */ __pyx_v_d = 0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":248 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":248 * else: * d = 0 * size = 0 # <<<<<<<<<<<<<< @@ -2061,27 +2688,28 @@ */ __pyx_v_size = 0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":249 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":249 * d = 0 * size = 0 * for i in range(self.m): # <<<<<<<<<<<<<< * if maxes[i]-mins[i] > size: * d = i */ - __pyx_2 = __pyx_v_self->m; - for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_self->m; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":250 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":250 * size = 0 * for i in range(self.m): * if maxes[i]-mins[i] > size: # <<<<<<<<<<<<<< * d = i * size = maxes[i]-mins[i] */ - __pyx_1 = (((__pyx_v_maxes[__pyx_v_i]) - (__pyx_v_mins[__pyx_v_i])) > __pyx_v_size); - if (__pyx_1) { + __pyx_t_1 = (((__pyx_v_maxes[__pyx_v_i]) - (__pyx_v_mins[__pyx_v_i])) > __pyx_v_size); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":251 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":251 * for i in range(self.m): * if maxes[i]-mins[i] > size: * d = i # <<<<<<<<<<<<<< @@ -2090,7 +2718,7 @@ */ __pyx_v_d = __pyx_v_i; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":252 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":252 * if maxes[i]-mins[i] > size: * d = i * size = maxes[i]-mins[i] # <<<<<<<<<<<<<< @@ -2103,7 +2731,7 @@ __pyx_L6:; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":253 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":253 * d = i * size = maxes[i]-mins[i] * maxval = maxes[d] # <<<<<<<<<<<<<< @@ -2112,7 +2740,7 @@ */ __pyx_v_maxval = (__pyx_v_maxes[__pyx_v_d]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":254 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":254 * size = maxes[i]-mins[i] * maxval = maxes[d] * minval = mins[d] # <<<<<<<<<<<<<< @@ -2121,17 +2749,17 @@ */ __pyx_v_minval = (__pyx_v_mins[__pyx_v_d]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":255 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":255 * maxval = maxes[d] * minval = mins[d] * if maxval==minval: # <<<<<<<<<<<<<< * # all points are identical; warn user? * n = stdlib.malloc(sizeof(leafnode)) */ - __pyx_1 = (__pyx_v_maxval == __pyx_v_minval); - if (__pyx_1) { + __pyx_t_1 = (__pyx_v_maxval == __pyx_v_minval); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":257 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":257 * if maxval==minval: * # all points are identical; warn user? * n = stdlib.malloc(sizeof(leafnode)) # <<<<<<<<<<<<<< @@ -2140,7 +2768,7 @@ */ __pyx_v_n = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode)))); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":258 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":258 * # all points are identical; warn user? * n = stdlib.malloc(sizeof(leafnode)) * n.split_dim = -1 # <<<<<<<<<<<<<< @@ -2149,7 +2777,7 @@ */ __pyx_v_n->split_dim = -1; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":259 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":259 * n = stdlib.malloc(sizeof(leafnode)) * n.split_dim = -1 * n.start_idx = start_idx # <<<<<<<<<<<<<< @@ -2158,7 +2786,7 @@ */ __pyx_v_n->start_idx = __pyx_v_start_idx; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":260 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":260 * n.split_dim = -1 * n.start_idx = start_idx * n.end_idx = end_idx # <<<<<<<<<<<<<< @@ -2167,7 +2795,7 @@ */ __pyx_v_n->end_idx = __pyx_v_end_idx; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":261 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":261 * n.start_idx = start_idx * n.end_idx = end_idx * return n # <<<<<<<<<<<<<< @@ -2180,7 +2808,7 @@ } __pyx_L7:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":263 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":263 * return n * * split = (maxval+minval)/2 # <<<<<<<<<<<<<< @@ -2189,7 +2817,7 @@ */ __pyx_v_split = ((__pyx_v_maxval + __pyx_v_minval) / 2); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":265 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":265 * split = (maxval+minval)/2 * * p = start_idx # <<<<<<<<<<<<<< @@ -2198,7 +2826,7 @@ */ __pyx_v_p = __pyx_v_start_idx; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":266 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":266 * * p = start_idx * q = end_idx-1 # <<<<<<<<<<<<<< @@ -2207,7 +2835,7 @@ */ __pyx_v_q = (__pyx_v_end_idx - 1); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":267 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":267 * p = start_idx * q = end_idx-1 * while p<=q: # <<<<<<<<<<<<<< @@ -2215,20 +2843,20 @@ * p+=1 */ while (1) { - __pyx_1 = (__pyx_v_p <= __pyx_v_q); - if (!__pyx_1) break; + __pyx_t_1 = (__pyx_v_p <= __pyx_v_q); + if (!__pyx_t_1) break; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":268 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":268 * q = end_idx-1 * while p<=q: * if self.raw_data[self.raw_indices[p]*self.m+d]=split: */ - __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_p]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); - if (__pyx_1) { + __pyx_t_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_p]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":269 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":269 * while p<=q: * if self.raw_data[self.raw_indices[p]*self.m+d]=split: # <<<<<<<<<<<<<< * q-=1 * else: */ - __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_q]) * __pyx_v_self->m) + __pyx_v_d)]) >= __pyx_v_split); - if (__pyx_1) { + __pyx_t_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_q]) * __pyx_v_self->m) + __pyx_v_d)]) >= __pyx_v_split); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":271 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":271 * p+=1 * elif self.raw_data[self.raw_indices[q]*self.m+d]>=split: * q-=1 # <<<<<<<<<<<<<< @@ -2261,7 +2889,7 @@ } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":273 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":273 * q-=1 * else: * t = self.raw_indices[p] # <<<<<<<<<<<<<< @@ -2270,7 +2898,7 @@ */ __pyx_v_t = (__pyx_v_self->raw_indices[__pyx_v_p]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":274 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":274 * else: * t = self.raw_indices[p] * self.raw_indices[p] = self.raw_indices[q] # <<<<<<<<<<<<<< @@ -2279,7 +2907,7 @@ */ (__pyx_v_self->raw_indices[__pyx_v_p]) = (__pyx_v_self->raw_indices[__pyx_v_q]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":275 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":275 * t = self.raw_indices[p] * self.raw_indices[p] = self.raw_indices[q] * self.raw_indices[q] = t # <<<<<<<<<<<<<< @@ -2288,7 +2916,7 @@ */ (__pyx_v_self->raw_indices[__pyx_v_q]) = __pyx_v_t; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":276 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":276 * self.raw_indices[p] = self.raw_indices[q] * self.raw_indices[q] = t * p+=1 # <<<<<<<<<<<<<< @@ -2297,7 +2925,7 @@ */ __pyx_v_p += 1; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":277 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":277 * self.raw_indices[q] = t * p+=1 * q-=1 # <<<<<<<<<<<<<< @@ -2309,17 +2937,17 @@ __pyx_L10:; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":280 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":280 * * # slide midpoint if necessary * if p==start_idx: # <<<<<<<<<<<<<< * # no points less than split * j = start_idx */ - __pyx_1 = (__pyx_v_p == __pyx_v_start_idx); - if (__pyx_1) { + __pyx_t_1 = (__pyx_v_p == __pyx_v_start_idx); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":282 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":282 * if p==start_idx: * # no points less than split * j = start_idx # <<<<<<<<<<<<<< @@ -2328,7 +2956,7 @@ */ __pyx_v_j = __pyx_v_start_idx; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":283 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":283 * # no points less than split * j = start_idx * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< @@ -2337,26 +2965,28 @@ */ __pyx_v_split = (__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":284 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":284 * j = start_idx * split = self.raw_data[self.raw_indices[j]*self.m+d] * for i in range(start_idx+1, end_idx): # <<<<<<<<<<<<<< * if self.raw_data[self.raw_indices[i]*self.m+d]raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); - if (__pyx_1) { + __pyx_t_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) < __pyx_v_split); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":286 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":286 * for i in range(start_idx+1, end_idx): * if self.raw_data[self.raw_indices[i]*self.m+d]raw_indices[__pyx_v_start_idx]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":289 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":289 * split = self.raw_data[self.raw_indices[j]*self.m+d] * t = self.raw_indices[start_idx] * self.raw_indices[start_idx] = self.raw_indices[j] # <<<<<<<<<<<<<< @@ -2396,7 +3026,7 @@ */ (__pyx_v_self->raw_indices[__pyx_v_start_idx]) = (__pyx_v_self->raw_indices[__pyx_v_j]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":290 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":290 * t = self.raw_indices[start_idx] * self.raw_indices[start_idx] = self.raw_indices[j] * self.raw_indices[j] = t # <<<<<<<<<<<<<< @@ -2405,7 +3035,7 @@ */ (__pyx_v_self->raw_indices[__pyx_v_j]) = __pyx_v_t; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":291 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":291 * self.raw_indices[start_idx] = self.raw_indices[j] * self.raw_indices[j] = t * p = start_idx+1 # <<<<<<<<<<<<<< @@ -2414,7 +3044,7 @@ */ __pyx_v_p = (__pyx_v_start_idx + 1); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":292 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":292 * self.raw_indices[j] = t * p = start_idx+1 * q = start_idx # <<<<<<<<<<<<<< @@ -2425,17 +3055,17 @@ goto __pyx_L11; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":293 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":293 * p = start_idx+1 * q = start_idx * elif p==end_idx: # <<<<<<<<<<<<<< * # no points greater than split * j = end_idx-1 */ - __pyx_1 = (__pyx_v_p == __pyx_v_end_idx); - if (__pyx_1) { + __pyx_t_1 = (__pyx_v_p == __pyx_v_end_idx); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":295 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":295 * elif p==end_idx: * # no points greater than split * j = end_idx-1 # <<<<<<<<<<<<<< @@ -2444,7 +3074,7 @@ */ __pyx_v_j = (__pyx_v_end_idx - 1); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":296 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":296 * # no points greater than split * j = end_idx-1 * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< @@ -2453,27 +3083,28 @@ */ __pyx_v_split = (__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_j]) * __pyx_v_self->m) + __pyx_v_d)]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":297 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":297 * j = end_idx-1 * split = self.raw_data[self.raw_indices[j]*self.m+d] * for i in range(start_idx, end_idx-1): # <<<<<<<<<<<<<< * if self.raw_data[self.raw_indices[i]*self.m+d]>split: * j = i */ - __pyx_3 = (__pyx_v_end_idx - 1); - for (__pyx_v_i = __pyx_v_start_idx; __pyx_v_i < __pyx_3; __pyx_v_i+=1) { + __pyx_t_4 = (__pyx_v_end_idx - 1); + for (__pyx_t_2 = __pyx_v_start_idx; __pyx_t_2 < __pyx_t_4; __pyx_t_2+=1) { + __pyx_v_i = __pyx_t_2; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":298 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":298 * split = self.raw_data[self.raw_indices[j]*self.m+d] * for i in range(start_idx, end_idx-1): * if self.raw_data[self.raw_indices[i]*self.m+d]>split: # <<<<<<<<<<<<<< * j = i * split = self.raw_data[self.raw_indices[j]*self.m+d] */ - __pyx_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) > __pyx_v_split); - if (__pyx_1) { + __pyx_t_1 = ((__pyx_v_self->raw_data[(((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m) + __pyx_v_d)]) > __pyx_v_split); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":299 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":299 * for i in range(start_idx, end_idx-1): * if self.raw_data[self.raw_indices[i]*self.m+d]>split: * j = i # <<<<<<<<<<<<<< @@ -2482,7 +3113,7 @@ */ __pyx_v_j = __pyx_v_i; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":300 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":300 * if self.raw_data[self.raw_indices[i]*self.m+d]>split: * j = i * split = self.raw_data[self.raw_indices[j]*self.m+d] # <<<<<<<<<<<<<< @@ -2495,7 +3126,7 @@ __pyx_L17:; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":301 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":301 * j = i * split = self.raw_data[self.raw_indices[j]*self.m+d] * t = self.raw_indices[end_idx-1] # <<<<<<<<<<<<<< @@ -2504,7 +3135,7 @@ */ __pyx_v_t = (__pyx_v_self->raw_indices[(__pyx_v_end_idx - 1)]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":302 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":302 * split = self.raw_data[self.raw_indices[j]*self.m+d] * t = self.raw_indices[end_idx-1] * self.raw_indices[end_idx-1] = self.raw_indices[j] # <<<<<<<<<<<<<< @@ -2513,7 +3144,7 @@ */ (__pyx_v_self->raw_indices[(__pyx_v_end_idx - 1)]) = (__pyx_v_self->raw_indices[__pyx_v_j]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":303 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":303 * t = self.raw_indices[end_idx-1] * self.raw_indices[end_idx-1] = self.raw_indices[j] * self.raw_indices[j] = t # <<<<<<<<<<<<<< @@ -2522,7 +3153,7 @@ */ (__pyx_v_self->raw_indices[__pyx_v_j]) = __pyx_v_t; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":304 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":304 * self.raw_indices[end_idx-1] = self.raw_indices[j] * self.raw_indices[j] = t * p = end_idx-1 # <<<<<<<<<<<<<< @@ -2531,7 +3162,7 @@ */ __pyx_v_p = (__pyx_v_end_idx - 1); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":305 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":305 * self.raw_indices[j] = t * p = end_idx-1 * q = end_idx-2 # <<<<<<<<<<<<<< @@ -2543,7 +3174,7 @@ } __pyx_L11:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":308 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":308 * * # construct new node representation * ni = stdlib.malloc(sizeof(innernode)) # <<<<<<<<<<<<<< @@ -2552,7 +3183,7 @@ */ __pyx_v_ni = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)malloc((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_innernode)))); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":310 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":310 * ni = stdlib.malloc(sizeof(innernode)) * * mids = stdlib.malloc(sizeof(double)*self.m) # <<<<<<<<<<<<<< @@ -2561,17 +3192,18 @@ */ __pyx_v_mids = ((double *)malloc(((sizeof(double)) * __pyx_v_self->m))); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":311 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":311 * * mids = stdlib.malloc(sizeof(double)*self.m) * for i in range(self.m): # <<<<<<<<<<<<<< * mids[i] = maxes[i] * mids[d] = split */ - __pyx_2 = __pyx_v_self->m; - for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_self->m; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":312 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":312 * mids = stdlib.malloc(sizeof(double)*self.m) * for i in range(self.m): * mids[i] = maxes[i] # <<<<<<<<<<<<<< @@ -2581,7 +3213,7 @@ (__pyx_v_mids[__pyx_v_i]) = (__pyx_v_maxes[__pyx_v_i]); } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":313 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":313 * for i in range(self.m): * mids[i] = maxes[i] * mids[d] = split # <<<<<<<<<<<<<< @@ -2590,7 +3222,7 @@ */ (__pyx_v_mids[__pyx_v_d]) = __pyx_v_split; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":314 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":314 * mids[i] = maxes[i] * mids[d] = split * ni.less = self.__build(start_idx,p,mids,mins) # <<<<<<<<<<<<<< @@ -2599,17 +3231,18 @@ */ __pyx_v_ni->less = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__build(__pyx_v_self, __pyx_v_start_idx, __pyx_v_p, __pyx_v_mids, __pyx_v_mins); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":316 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":316 * ni.less = self.__build(start_idx,p,mids,mins) * * for i in range(self.m): # <<<<<<<<<<<<<< * mids[i] = mins[i] * mids[d] = split */ - __pyx_2 = __pyx_v_self->m; - for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_self->m; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":317 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":317 * * for i in range(self.m): * mids[i] = mins[i] # <<<<<<<<<<<<<< @@ -2619,7 +3252,7 @@ (__pyx_v_mids[__pyx_v_i]) = (__pyx_v_mins[__pyx_v_i]); } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":318 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":318 * for i in range(self.m): * mids[i] = mins[i] * mids[d] = split # <<<<<<<<<<<<<< @@ -2628,7 +3261,7 @@ */ (__pyx_v_mids[__pyx_v_d]) = __pyx_v_split; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":319 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":319 * mids[i] = mins[i] * mids[d] = split * ni.greater = self.__build(p,end_idx,maxes,mids) # <<<<<<<<<<<<<< @@ -2637,7 +3270,7 @@ */ __pyx_v_ni->greater = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__build(__pyx_v_self, __pyx_v_p, __pyx_v_end_idx, __pyx_v_maxes, __pyx_v_mids); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":321 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":321 * ni.greater = self.__build(p,end_idx,maxes,mids) * * stdlib.free(mids) # <<<<<<<<<<<<<< @@ -2646,7 +3279,7 @@ */ free(__pyx_v_mids); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":323 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":323 * stdlib.free(mids) * * ni.split_dim = d # <<<<<<<<<<<<<< @@ -2655,7 +3288,7 @@ */ __pyx_v_ni->split_dim = __pyx_v_d; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":324 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":324 * * ni.split_dim = d * ni.split = split # <<<<<<<<<<<<<< @@ -2664,7 +3297,7 @@ */ __pyx_v_ni->split = __pyx_v_split; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":326 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":326 * ni.split = split * * return ni # <<<<<<<<<<<<<< @@ -2678,10 +3311,12 @@ __pyx_r = 0; __pyx_L0:; + __Pyx_DECREF((PyObject *)__pyx_v_self); + __Pyx_RefNannyFinishContext(); return __pyx_r; } -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":328 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":328 * return ni * * cdef __free_tree(cKDTree self, innernode* node): # <<<<<<<<<<<<<< @@ -2690,44 +3325,48 @@ */ static PyObject *__pyx_f_5scipy_7spatial_7ckdtree_7cKDTree___free_tree(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *__pyx_v_self, struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_node) { - PyObject *__pyx_r; 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_t_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self->__pyx_vtab)->__free_tree(__pyx_v_self, __pyx_v_node->greater); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; goto __pyx_L3; } __pyx_L3:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":332 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":332 * self.__free_tree(node.less) * self.__free_tree(node.greater) * stdlib.free(node) # <<<<<<<<<<<<<< @@ -2736,17 +3375,20 @@ */ free(__pyx_v_node); - __pyx_r = Py_None; Py_INCREF(Py_None); + __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; - Py_XDECREF(__pyx_2); + __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__free_tree"); __pyx_r = 0; __pyx_L0:; 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- if (__pyx_1) { + __pyx_t_1 = (((int)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree) == 0); + if (__pyx_t_1) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":337 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":337 * if (self.tree) == 0: * # should happen only if __init__ was never called * return # <<<<<<<<<<<<<< @@ -2781,24 +3425,27 @@ } __pyx_L5:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":338 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":338 * # should happen only if __init__ was never called * return * self.__free_tree(self.tree) # <<<<<<<<<<<<<< * * cdef void __query(cKDTree self, */ - __pyx_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__free_tree(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 338; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(__pyx_2); __pyx_2 = 0; + __pyx_t_2 = ((struct __pyx_vtabstruct_5scipy_7spatial_7ckdtree_cKDTree *)((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->__pyx_vtab)->__free_tree(((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self), ((struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *)__pyx_v_self)->tree); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 338; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; goto __pyx_L0; __pyx_L1_error:; - Py_XDECREF(__pyx_2); + __Pyx_XDECREF(__pyx_t_2); __Pyx_AddTraceback("scipy.spatial.ckdtree.cKDTree.__dealloc__"); __pyx_L0:; + __Pyx_DECREF((PyObject *)__pyx_v_self); + __Pyx_RefNannyFinishContext(); } -/* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":340 +/* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":340 * self.__free_tree(self.tree) * * cdef void __query(cKDTree self, # <<<<<<<<<<<<<< @@ -2824,31 +3471,41 @@ struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_inode; struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_near; struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *__pyx_v_far; - PyObject *__pyx_1 = 0; - int __pyx_2; - int __pyx_3; + PyObject *__pyx_t_1 = NULL; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + int __pyx_t_5; + int __pyx_t_6; + double __pyx_t_7; + Py_ssize_t __pyx_t_8; + PyObject *__pyx_t_9 = NULL; + __Pyx_RefNannySetupContext("__query"); + __Pyx_INCREF((PyObject *)__pyx_v_self); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":371 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":371 * # distances between the nearest side of the cell and the target * # the head node of the cell * heapcreate(&q,12) # <<<<<<<<<<<<<< * * # priority queue for the nearest neighbors */ - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_q), 12); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 371; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_t_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_q), 12); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 371; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":376 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":376 * # furthest known neighbor first * # entries are (-distance**p, i) * heapcreate(&neighbors,k) # <<<<<<<<<<<<<< * * # set up first nodeinfo */ - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_neighbors), __pyx_v_k); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 376; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_t_1 = __pyx_f_5scipy_7spatial_7ckdtree_heapcreate((&__pyx_v_neighbors), __pyx_v_k); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 376; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":379 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":379 * * # set up first nodeinfo * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) # <<<<<<<<<<<<<< @@ -2857,7 +3514,7 @@ */ __pyx_v_inf = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo)) + (__pyx_v_self->m * (sizeof(double)))))); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":380 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":380 * # set up first nodeinfo * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) * inf.node = self.tree # <<<<<<<<<<<<<< @@ -2866,17 +3523,18 @@ */ __pyx_v_inf->node = __pyx_v_self->tree; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":381 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":381 * inf = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) * inf.node = self.tree * for i in range(self.m): # <<<<<<<<<<<<<< * inf.side_distances[i] = 0 * t = x[i]-self.raw_maxes[i] */ - __pyx_2 = __pyx_v_self->m; - for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_self->m; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":382 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":382 * inf.node = self.tree * for i in range(self.m): * inf.side_distances[i] = 0 # <<<<<<<<<<<<<< @@ -2885,7 +3543,7 @@ */ (__pyx_v_inf->side_distances[__pyx_v_i]) = 0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":383 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":383 * for i in range(self.m): * inf.side_distances[i] = 0 * t = x[i]-self.raw_maxes[i] # <<<<<<<<<<<<<< @@ -2894,17 +3552,17 @@ */ __pyx_v_t = ((__pyx_v_x[__pyx_v_i]) - (__pyx_v_self->raw_maxes[__pyx_v_i])); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":384 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":384 * inf.side_distances[i] = 0 * t = x[i]-self.raw_maxes[i] * if t>inf.side_distances[i]: # <<<<<<<<<<<<<< * inf.side_distances[i] = t * else: */ - __pyx_3 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); - if (__pyx_3) { + __pyx_t_4 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); + if (__pyx_t_4) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":385 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":385 * t = x[i]-self.raw_maxes[i] * if t>inf.side_distances[i]: * inf.side_distances[i] = t # <<<<<<<<<<<<<< @@ -2916,7 +3574,7 @@ } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":387 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":387 * inf.side_distances[i] = t * else: * t = self.raw_mins[i]-x[i] # <<<<<<<<<<<<<< @@ -2925,17 +3583,17 @@ */ __pyx_v_t = ((__pyx_v_self->raw_mins[__pyx_v_i]) - (__pyx_v_x[__pyx_v_i])); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":388 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":388 * else: * t = self.raw_mins[i]-x[i] * if t>inf.side_distances[i]: # <<<<<<<<<<<<<< * inf.side_distances[i] = t * if p!=1 and p!=infinity: */ - __pyx_3 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); - if (__pyx_3) { + __pyx_t_4 = (__pyx_v_t > (__pyx_v_inf->side_distances[__pyx_v_i])); + if (__pyx_t_4) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":389 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":389 * t = self.raw_mins[i]-x[i] * if t>inf.side_distances[i]: * inf.side_distances[i] = t # <<<<<<<<<<<<<< @@ -2949,20 +3607,23 @@ } __pyx_L5:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":390 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":390 * if t>inf.side_distances[i]: * inf.side_distances[i] = t * if p!=1 and p!=infinity: # <<<<<<<<<<<<<< * inf.side_distances[i]=inf.side_distances[i]**p * */ - __pyx_3 = (__pyx_v_p != 1); - if (__pyx_3) { - __pyx_3 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + __pyx_t_4 = (__pyx_v_p != 1); + if (__pyx_t_4) { + __pyx_t_5 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + __pyx_t_6 = __pyx_t_5; + } else { + __pyx_t_6 = __pyx_t_4; } - if (__pyx_3) { + if (__pyx_t_6) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":391 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":391 * inf.side_distances[i] = t * if p!=1 and p!=infinity: * inf.side_distances[i]=inf.side_distances[i]**p # <<<<<<<<<<<<<< @@ -2975,36 +3636,37 @@ __pyx_L7:; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":394 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":394 * * # compute first distance * min_distance = 0. # <<<<<<<<<<<<<< * for i in range(self.m): * if p==infinity: */ - __pyx_v_min_distance = 0.; + __pyx_v_min_distance = 0.0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":395 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":395 * # compute first distance * min_distance = 0. * for i in range(self.m): # <<<<<<<<<<<<<< * if p==infinity: * min_distance = dmax(min_distance,inf.side_distances[i]) */ - __pyx_2 = __pyx_v_self->m; - for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_self->m; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":396 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":396 * min_distance = 0. * for i in range(self.m): * if p==infinity: # <<<<<<<<<<<<<< * min_distance = dmax(min_distance,inf.side_distances[i]) * else: */ - __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); - if (__pyx_3) { + __pyx_t_6 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_t_6) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":397 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":397 * for i in range(self.m): * if p==infinity: * min_distance = dmax(min_distance,inf.side_distances[i]) # <<<<<<<<<<<<<< @@ -3016,7 +3678,7 @@ } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":399 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":399 * min_distance = dmax(min_distance,inf.side_distances[i]) * else: * min_distance += inf.side_distances[i] # <<<<<<<<<<<<<< @@ -3028,17 +3690,17 @@ __pyx_L10:; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":402 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":402 * * # fiddle approximation factor * if eps==0: # <<<<<<<<<<<<<< * epsfac=1 * elif p==infinity: */ - __pyx_3 = (__pyx_v_eps == 0); - if (__pyx_3) { + __pyx_t_6 = (__pyx_v_eps == 0); + if (__pyx_t_6) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":403 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":403 * # fiddle approximation factor * if eps==0: * epsfac=1 # <<<<<<<<<<<<<< @@ -3049,53 +3711,66 @@ goto __pyx_L11; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":404 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":404 * if eps==0: * epsfac=1 * elif p==infinity: # <<<<<<<<<<<<<< * epsfac = 1/(1+eps) * else: */ - __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); - if (__pyx_3) { + __pyx_t_6 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_t_6) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":405 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":405 * epsfac=1 * elif p==infinity: * epsfac = 1/(1+eps) # <<<<<<<<<<<<<< * else: * epsfac = 1/(1+eps)**p */ - __pyx_v_epsfac = (1 / (1 + __pyx_v_eps)); + __pyx_t_7 = (1 + __pyx_v_eps); + if (unlikely(__pyx_t_7 == 0)) { + PyErr_Format(PyExc_ZeroDivisionError, "float division"); + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 405; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + } + __pyx_v_epsfac = (1 / __pyx_t_7); goto __pyx_L11; } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":407 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":407 * epsfac = 1/(1+eps) * else: * epsfac = 1/(1+eps)**p # <<<<<<<<<<<<<< * * # internally we represent all distances as distance**p */ - __pyx_v_epsfac = (1 / pow((1 + __pyx_v_eps), __pyx_v_p)); + __pyx_t_7 = pow((1 + __pyx_v_eps), __pyx_v_p); + if (unlikely(__pyx_t_7 == 0)) { + PyErr_Format(PyExc_ZeroDivisionError, "float division"); + {__pyx_filename = __pyx_f[0]; __pyx_lineno = 407; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + } + __pyx_v_epsfac = (1 / __pyx_t_7); } __pyx_L11:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":410 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":410 * * # internally we represent all distances as distance**p * if p!=infinity and distance_upper_bound!=infinity: # <<<<<<<<<<<<<< * distance_upper_bound = distance_upper_bound**p * */ - __pyx_3 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); - if (__pyx_3) { - __pyx_3 = (__pyx_v_distance_upper_bound != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + __pyx_t_6 = (__pyx_v_p != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_t_6) { + __pyx_t_4 = (__pyx_v_distance_upper_bound != __pyx_v_5scipy_7spatial_7ckdtree_infinity); + __pyx_t_5 = __pyx_t_4; + } else { + __pyx_t_5 = __pyx_t_6; } - if (__pyx_3) { + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":411 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":411 * # internally we represent all distances as distance**p * if p!=infinity and distance_upper_bound!=infinity: * distance_upper_bound = distance_upper_bound**p # <<<<<<<<<<<<<< @@ -3107,7 +3782,7 @@ } __pyx_L12:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":413 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":413 * distance_upper_bound = distance_upper_bound**p * * while True: # <<<<<<<<<<<<<< @@ -3115,20 +3790,20 @@ * node = inf.node */ while (1) { - __pyx_3 = 1; - if (!__pyx_3) break; + __pyx_t_5 = 1; + if (!__pyx_t_5) break; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":414 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":414 * * while True: * if inf.node.split_dim==-1: # <<<<<<<<<<<<<< * node = inf.node * */ - __pyx_3 = (__pyx_v_inf->node->split_dim == -1); - if (__pyx_3) { + __pyx_t_5 = (__pyx_v_inf->node->split_dim == -1); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":415 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":415 * while True: * if inf.node.split_dim==-1: * node = inf.node # <<<<<<<<<<<<<< @@ -3137,17 +3812,18 @@ */ __pyx_v_node = ((struct __pyx_t_5scipy_7spatial_7ckdtree_leafnode *)__pyx_v_inf->node); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":418 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":418 * * # brute-force * for i in range(node.start_idx,node.end_idx): # <<<<<<<<<<<<<< * d = _distance_p( * self.raw_data+self.raw_indices[i]*self.m, */ - __pyx_2 = __pyx_v_node->end_idx; - for (__pyx_v_i = __pyx_v_node->start_idx; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_node->end_idx; + for (__pyx_t_3 = __pyx_v_node->start_idx; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":421 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":421 * d = _distance_p( * self.raw_data+self.raw_indices[i]*self.m, * x,p,self.m,distance_upper_bound) # <<<<<<<<<<<<<< @@ -3156,40 +3832,41 @@ */ __pyx_v_d = __pyx_f_5scipy_7spatial_7ckdtree__distance_p((__pyx_v_self->raw_data + ((__pyx_v_self->raw_indices[__pyx_v_i]) * __pyx_v_self->m)), __pyx_v_x, __pyx_v_p, __pyx_v_self->m, __pyx_v_distance_upper_bound); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":423 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":423 * x,p,self.m,distance_upper_bound) * * if draw_indices[__pyx_v_i]); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":429 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":429 * neighbor.priority = -d * neighbor.contents.intdata = self.raw_indices[i] * heappush(&neighbors,neighbor) # <<<<<<<<<<<<<< * * # adjust upper bound for efficiency */ - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_neighbors), __pyx_v_neighbor); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 429; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_t_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_neighbors), __pyx_v_neighbor); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 429; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":432 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":432 * * # adjust upper bound for efficiency * if neighbors.n==k: # <<<<<<<<<<<<<< * distance_upper_bound = -heappeek(&neighbors).priority * # done with this node, get another */ - __pyx_3 = (__pyx_v_neighbors.n == __pyx_v_k); - if (__pyx_3) { + __pyx_t_5 = (__pyx_v_neighbors.n == __pyx_v_k); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":433 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":433 * # adjust upper bound for efficiency * if neighbors.n==k: * distance_upper_bound = -heappeek(&neighbors).priority # <<<<<<<<<<<<<< @@ -3243,7 +3921,7 @@ __pyx_L18:; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":435 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":435 * distance_upper_bound = -heappeek(&neighbors).priority * # done with this node, get another * stdlib.free(inf) # <<<<<<<<<<<<<< @@ -3252,29 +3930,29 @@ */ free(__pyx_v_inf); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":436 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":436 * # done with this node, get another * stdlib.free(inf) * if q.n==0: # <<<<<<<<<<<<<< * # no more nodes to visit * break */ - __pyx_3 = (__pyx_v_q.n == 0); - if (__pyx_3) { + __pyx_t_5 = (__pyx_v_q.n == 0); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":438 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":438 * if q.n==0: * # no more nodes to visit * break # <<<<<<<<<<<<<< * else: * it = heappop(&q) */ - goto __pyx_L14; + goto __pyx_L14_break; goto __pyx_L21; } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":440 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":440 * break * else: * it = heappop(&q) # <<<<<<<<<<<<<< @@ -3283,7 +3961,7 @@ */ __pyx_v_it = __pyx_f_5scipy_7spatial_7ckdtree_heappop((&__pyx_v_q)); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":441 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":441 * else: * it = heappop(&q) * inf = it.contents.ptrdata # <<<<<<<<<<<<<< @@ -3292,7 +3970,7 @@ */ __pyx_v_inf = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)__pyx_v_it.contents.ptrdata); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":442 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":442 * it = heappop(&q) * inf = it.contents.ptrdata * min_distance = it.priority # <<<<<<<<<<<<<< @@ -3306,7 +3984,7 @@ } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":444 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":444 * min_distance = it.priority * else: * inode = inf.node # <<<<<<<<<<<<<< @@ -3315,17 +3993,17 @@ */ __pyx_v_inode = ((struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *)__pyx_v_inf->node); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":449 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":449 * # but since the distance_upper_bound decreases, we might get * # here even if the cell's too far * if min_distance>distance_upper_bound*epsfac: # <<<<<<<<<<<<<< * # since this is the nearest cell, we're done, bail out * stdlib.free(inf) */ - __pyx_3 = (__pyx_v_min_distance > (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); - if (__pyx_3) { + __pyx_t_5 = (__pyx_v_min_distance > (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":451 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":451 * if min_distance>distance_upper_bound*epsfac: * # since this is the nearest cell, we're done, bail out * stdlib.free(inf) # <<<<<<<<<<<<<< @@ -3334,17 +4012,18 @@ */ free(__pyx_v_inf); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":453 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":453 * stdlib.free(inf) * # free all the nodes still on the heap * for i in range(q.n): # <<<<<<<<<<<<<< * stdlib.free(q.heap[i].contents.ptrdata) * break */ - __pyx_2 = __pyx_v_q.n; - for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_q.n; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":454 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":454 * # free all the nodes still on the heap * for i in range(q.n): * stdlib.free(q.heap[i].contents.ptrdata) # <<<<<<<<<<<<<< @@ -3354,29 +4033,29 @@ free((__pyx_v_q.heap[__pyx_v_i]).contents.ptrdata); } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":455 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":455 * for i in range(q.n): * stdlib.free(q.heap[i].contents.ptrdata) * break # <<<<<<<<<<<<<< * * # set up children for searching */ - goto __pyx_L14; + goto __pyx_L14_break; goto __pyx_L22; } __pyx_L22:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":458 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":458 * * # set up children for searching * if x[inode.split_dim]split_dim]) < __pyx_v_inode->split); - if (__pyx_3) { + __pyx_t_5 = ((__pyx_v_x[__pyx_v_inode->split_dim]) < __pyx_v_inode->split); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":459 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":459 * # set up children for searching * if x[inode.split_dim]less; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":460 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":460 * if x[inode.split_dim]greater; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":463 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":463 * else: * near = inode.greater * far = inode.less # <<<<<<<<<<<<<< @@ -3417,7 +4096,7 @@ } __pyx_L25:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":468 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":468 * # we're going here next, so no point pushing it on the queue * # no need to recompute the distance or the side_distances * inf.node = near # <<<<<<<<<<<<<< @@ -3426,7 +4105,7 @@ */ __pyx_v_inf->node = __pyx_v_near; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":473 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":473 * # on the split value; compute its distance and side_distances * # and push it on the queue if it's near enough * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) # <<<<<<<<<<<<<< @@ -3435,7 +4114,7 @@ */ __pyx_v_inf2 = ((struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo *)malloc(((sizeof(struct __pyx_t_5scipy_7spatial_7ckdtree_nodeinfo)) + (__pyx_v_self->m * (sizeof(double)))))); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":474 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":474 * # and push it on the queue if it's near enough * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) * it2.contents.ptrdata = inf2 # <<<<<<<<<<<<<< @@ -3444,7 +4123,7 @@ */ __pyx_v_it2.contents.ptrdata = ((char *)__pyx_v_inf2); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":475 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":475 * inf2 = stdlib.malloc(sizeof(nodeinfo)+self.m*sizeof(double)) * it2.contents.ptrdata = inf2 * inf2.node = far # <<<<<<<<<<<<<< @@ -3453,17 +4132,18 @@ */ __pyx_v_inf2->node = __pyx_v_far; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":477 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":477 * inf2.node = far * # most side distances unchanged * for i in range(self.m): # <<<<<<<<<<<<<< * inf2.side_distances[i] = inf.side_distances[i] * */ - __pyx_2 = __pyx_v_self->m; - for (__pyx_v_i = 0; __pyx_v_i < __pyx_2; __pyx_v_i+=1) { + __pyx_t_2 = __pyx_v_self->m; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":478 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":478 * # most side distances unchanged * for i in range(self.m): * inf2.side_distances[i] = inf.side_distances[i] # <<<<<<<<<<<<<< @@ -3473,17 +4153,17 @@ (__pyx_v_inf2->side_distances[__pyx_v_i]) = (__pyx_v_inf->side_distances[__pyx_v_i]); } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":482 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":482 * # one side distance changes * # we can adjust the minimum distance without recomputing * if p == infinity: # <<<<<<<<<<<<<< * # we never use side_distances in the l_infinity case * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) */ - __pyx_3 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); - if (__pyx_3) { + __pyx_t_5 = (__pyx_v_p == __pyx_v_5scipy_7spatial_7ckdtree_infinity); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":485 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":485 * # we never use side_distances in the l_infinity case * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) # <<<<<<<<<<<<<< @@ -3494,17 +4174,17 @@ goto __pyx_L28; } - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":486 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":486 * # inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) * elif p == 1: # <<<<<<<<<<<<<< * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] */ - __pyx_3 = (__pyx_v_p == 1); - if (__pyx_3) { + __pyx_t_5 = (__pyx_v_p == 1); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":487 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":487 * far_min_distance = dmax(min_distance, dabs(inode.split-x[inode.split_dim])) * elif p == 1: * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) # <<<<<<<<<<<<<< @@ -3513,7 +4193,7 @@ */ (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim]) = __pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim]))); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":488 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":488 * elif p == 1: * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim]) * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] # <<<<<<<<<<<<<< @@ -3525,7 +4205,7 @@ } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":490 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":490 * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] * else: * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p # <<<<<<<<<<<<<< @@ -3534,7 +4214,7 @@ */ (__pyx_v_inf2->side_distances[__pyx_v_inode->split_dim]) = pow(__pyx_f_5scipy_7spatial_7ckdtree_dabs((__pyx_v_inode->split - (__pyx_v_x[__pyx_v_inode->split_dim]))), __pyx_v_p); - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":491 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":491 * else: * inf2.side_distances[inode.split_dim] = dabs(inode.split-x[inode.split_dim])**p * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] # <<<<<<<<<<<<<< @@ -3545,7 +4225,7 @@ } __pyx_L28:; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":493 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":493 * far_min_distance = min_distance - inf.side_distances[inode.split_dim] + inf2.side_distances[inode.split_dim] * * it2.priority = far_min_distance # <<<<<<<<<<<<<< @@ -3554,30 +4234,31 @@ */ __pyx_v_it2.priority = __pyx_v_far_min_distance; - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":497 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":497 * * # far child might be too far, if so, don't bother pushing it * if far_min_distance<=distance_upper_bound*epsfac: # <<<<<<<<<<<<<< * heappush(&q,it2) * else: */ - __pyx_3 = (__pyx_v_far_min_distance <= (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); - if (__pyx_3) { + __pyx_t_5 = (__pyx_v_far_min_distance <= (__pyx_v_distance_upper_bound * __pyx_v_epsfac)); + if (__pyx_t_5) { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":498 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":498 * # far child might be too far, if so, don't bother pushing it * if far_min_distance<=distance_upper_bound*epsfac: * heappush(&q,it2) # <<<<<<<<<<<<<< * else: * stdlib.free(inf2) */ - __pyx_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_q), __pyx_v_it2); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 498; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - Py_DECREF(__pyx_1); __pyx_1 = 0; + __pyx_t_1 = __pyx_f_5scipy_7spatial_7ckdtree_heappush((&__pyx_v_q), __pyx_v_it2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 498; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; goto __pyx_L29; } /*else*/ { - /* "/usr/data/david/src/dsp/scipy/trunk/scipy/spatial/ckdtree.pyx":500 + /* "/home/david/src/numeric/scipy/scipy-git/scipy/spatial/ckdtree.pyx":500 * heappush(&q,it2) * else: * stdlib.free(inf2) # <<<<<<<<<<<<<< @@ -3586,7 +4267,7 @@ */ free(__pyx_v_inf2); 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__pyx_clineno = __LINE__; goto __pyx_L3_error;} + } else { + __pyx_v_p = ((double)2); } if (values[4]) { - __pyx_v_distance_upper_bound = __pyx_PyFloat_AsDouble(values[4]); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 517; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __pyx_v_distance_upper_bound = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_distance_upper_bound == (double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 517; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } else { + __pyx_v_distance_upper_bound = __pyx_k_4; } } else { + __pyx_v_k = ((int)1); + __pyx_v_eps = ((double)0); + __pyx_v_p = ((double)2); + __pyx_v_distance_upper_bound = __pyx_k_4; switch (PyTuple_GET_SIZE(__pyx_args)) { - case 5: __pyx_v_distance_upper_bound = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 4)); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 517; __pyx_clineno = __LINE__; goto __pyx_L3_error;} - case 4: __pyx_v_p = __pyx_PyFloat_AsDouble(PyTuple_GET_ITEM(__pyx_args, 3)); 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- break; - case NPY_LONGLONG: + __pyx_t_1 = (__pyx_v_t == NPY_ULONG); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__L; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":120 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":256 * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" */ - __pyx_v_f = __pyx_k_11; - break; - case NPY_ULONGLONG: + __pyx_t_1 = (__pyx_v_t == NPY_LONGLONG); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__q; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":121 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":257 * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" */ - __pyx_v_f = __pyx_k_12; - break; - case NPY_FLOAT: + __pyx_t_1 = (__pyx_v_t == NPY_ULONGLONG); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__Q; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":122 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":258 * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" # <<<<<<<<<<<<<< * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" */ - __pyx_v_f = __pyx_k_13; - break; - case NPY_DOUBLE: + __pyx_t_1 = (__pyx_v_t == NPY_FLOAT); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__f; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":123 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":259 * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" # <<<<<<<<<<<<<< * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" */ - __pyx_v_f = __pyx_k_14; - break; - case NPY_LONGDOUBLE: + __pyx_t_1 = (__pyx_v_t == NPY_DOUBLE); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__d; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":124 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":260 * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" # <<<<<<<<<<<<<< * elif t == NPY_CFLOAT: f = "Zf" * elif t == NPY_CDOUBLE: f = "Zd" */ - __pyx_v_f = __pyx_k_15; - break; - case NPY_CFLOAT: + __pyx_t_1 = (__pyx_v_t == NPY_LONGDOUBLE); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__g; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":125 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":261 * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" # <<<<<<<<<<<<<< * elif t == NPY_CDOUBLE: f = "Zd" * elif t == NPY_CLONGDOUBLE: f = "Zg" */ - __pyx_v_f = __pyx_k_16; 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High-dimensional nearest-neighbor\n queries are a substantial open problem in computer science.\n ", /*tp_doc*/ + __Pyx_DOCSTR("kd-tree for quick nearest-neighbor lookup\n\n This class provides an index into a set of k-dimensional points\n which can be used to rapidly look up the nearest neighbors of any\n point. \n\n The algorithm used is described in Maneewongvatana and Mount 1999. \n The general idea is that the kd-tree is a binary trie, each of whose\n nodes represents an axis-aligned hyperrectangle. Each node specifies\n an axis and splits the set of points based on whether their coordinate\n along that axis is greater than or less than a particular value. \n\n During construction, the axis and splitting point are chosen by the \n \"sliding midpoint\" rule, which ensures that the cells do not all\n become long and thin. \n\n The tree can be queried for the r closest neighbors of any given point \n (optionally returning only those within some maximum distance of the \n point). 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High-dimensional nearest-neighbor\n queries are a substantial open problem in computer science.\n "), /*tp_doc*/ __pyx_tp_traverse_5scipy_7spatial_7ckdtree_cKDTree, /*tp_traverse*/ __pyx_tp_clear_5scipy_7spatial_7ckdtree_cKDTree, /*tp_clear*/ 0, /*tp_richcompare*/ @@ -5948,6 +7579,10 @@ 0, /*tp_cache*/ 0, /*tp_subclasses*/ 0, /*tp_weaklist*/ + 0, /*tp_del*/ + #if PY_VERSION_HEX >= 0x02060000 + 0, /*tp_version_tag*/ + #endif }; static struct PyMethodDef __pyx_methods[] = { @@ -5959,7 +7594,7 @@ #if PY_MAJOR_VERSION >= 3 static struct PyModuleDef __pyx_moduledef = { PyModuleDef_HEAD_INIT, - "ckdtree", + __Pyx_NAMESTR("ckdtree"), 0, /* m_doc */ -1, /* m_size */ __pyx_methods /* m_methods */, @@ -5971,70 +7606,111 @@ #endif static __Pyx_StringTabEntry __pyx_string_tab[] = { - {&__pyx_kp___init__, __pyx_k___init__, sizeof(__pyx_k___init__), 1, 1, 1}, - {&__pyx_kp___dealloc__, __pyx_k___dealloc__, sizeof(__pyx_k___dealloc__), 1, 1, 1}, - {&__pyx_kp_query, __pyx_k_query, 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sizeof(__pyx_k__x), 0, 0, 1, 1}, + {0, 0, 0, 0, 0, 0, 0} }; static int __Pyx_InitCachedBuiltins(void) { - __pyx_builtin_ValueError = __Pyx_GetName(__pyx_b, __pyx_kp_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_builtin_StopIteration = __Pyx_GetName(__pyx_b, __pyx_kp_StopIteration); if (!__pyx_builtin_StopIteration) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 144; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_builtin_RuntimeError = __Pyx_GetName(__pyx_b, __pyx_kp_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_ValueError = __Pyx_GetName(__pyx_b, __pyx_n_s__ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_range = __Pyx_GetName(__pyx_b, __pyx_n_s__range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 120; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_RuntimeError = __Pyx_GetName(__pyx_b, __pyx_n_s__RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 786; __pyx_clineno = __LINE__; goto __pyx_L1_error;} return 0; __pyx_L1_error:; return -1; } static int __Pyx_InitGlobals(void) { + if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; - if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_15 = PyInt_FromLong(15); if (unlikely(!__pyx_int_15)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; return 0; __pyx_L1_error:; return -1; @@ -6048,17 +7724,38 @@ PyMODINIT_FUNC PyInit_ckdtree(void) #endif { - PyObject *__pyx_1 = 0; - PyObject *__pyx_2 = 0; - double __pyx_3; + PyObject *__pyx_t_1 = NULL; + PyObject *__pyx_t_2 = NULL; + double __pyx_t_3; + PyObject *__pyx_t_4 = NULL; + #if CYTHON_REFNANNY + void* __pyx_refnanny = NULL; + __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); + if (!__Pyx_RefNanny) { + PyErr_Clear(); + __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny"); + if (!__Pyx_RefNanny) + Py_FatalError("failed to import 'refnanny' module"); + } + __pyx_refnanny = __Pyx_RefNanny->SetupContext("PyMODINIT_FUNC PyInit_ckdtree(void)", __LINE__, __FILE__); + #endif + __pyx_init_filenames(); __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #if PY_MAJOR_VERSION < 3 + __pyx_empty_bytes = PyString_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #else + __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #endif /*--- Library function declarations ---*/ - __pyx_init_filenames(); - /*--- Initialize various global constants etc. ---*/ - if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + /*--- Threads initialization code ---*/ + #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS + #ifdef WITH_THREAD /* Python build with threading support? */ + PyEval_InitThreads(); + #endif + #endif /*--- 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(unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (__pyx_module_is_main_scipy__spatial__ckdtree) { + if (__Pyx_SetAttrString(__pyx_m, "__name__", __pyx_n_s____main__) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + } /*--- Builtin init code ---*/ if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_skip_dispatch = 0; /*--- Global init code ---*/ /*--- Function export code ---*/ /*--- Type init code ---*/ __pyx_vtabptr_5scipy_7spatial_7ckdtree_cKDTree = &__pyx_vtable_5scipy_7spatial_7ckdtree_cKDTree; + #if PY_MAJOR_VERSION >= 3 + __pyx_vtable_5scipy_7spatial_7ckdtree_cKDTree.__build = (struct __pyx_t_5scipy_7spatial_7ckdtree_innernode *(*)(struct __pyx_obj_5scipy_7spatial_7ckdtree_cKDTree *, int, int, double *, double 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Archibald 2008 # <<<<<<<<<<<<<< + * # Released under the scipy license + * import numpy as np + */ + __pyx_t_2 = PyDict_New(); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + __pyx_t_1 = PyObject_GetAttr(__pyx_m, __pyx_n_s__cKDTree); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s____init__); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_1 = __Pyx_GetAttrString(__pyx_t_4, "__doc__"); + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + if (PyDict_SetItem(__pyx_t_2, ((PyObject *)__pyx_kp_u_13), __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_1 = PyObject_GetAttr(__pyx_m, __pyx_n_s__cKDTree); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_4 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__query); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_1 = __Pyx_GetAttrString(__pyx_t_4, "__doc__"); + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + if (PyDict_SetItem(__pyx_t_2, ((PyObject *)__pyx_kp_u_14), __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (PyObject_SetAttr(__pyx_m, __pyx_n_s____test__, ((PyObject *)__pyx_t_2)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; + + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/stdlib.pxd":2 * - * cdef extern from "stdlib.h": # <<<<<<<<<<<<<< - * ctypedef unsigned long size_t + * cdef extern from "stdlib.h" nogil: # <<<<<<<<<<<<<< * void free(void *ptr) + * void *malloc(size_t size) */ + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + __Pyx_XDECREF(__pyx_t_4); + if (__pyx_m) { + __Pyx_AddTraceback("init scipy.spatial.ckdtree"); + Py_DECREF(__pyx_m); __pyx_m = 0; + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init scipy.spatial.ckdtree"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); #if PY_MAJOR_VERSION < 3 return; #else return __pyx_m; #endif - __pyx_L1_error:; - Py_XDECREF(__pyx_1); - Py_XDECREF(__pyx_2); - __Pyx_AddTraceback("scipy.spatial.ckdtree"); - #if PY_MAJOR_VERSION >= 3 - return NULL; - #endif } static const char *__pyx_filenames[] = { @@ -6165,6 +7915,13 @@ __pyx_f = __pyx_filenames; } +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) @@ -6222,18 +7979,17 @@ PyObject*** first_kw_arg = argnames + num_pos_args; while (PyDict_Next(kwds, &pos, &key, &value)) { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) { - #else - if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) { - #endif - goto invalid_keyword_type; + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; } else { - name = argnames; - while (*name && (**name != key)) name++; - if (*name) { - if (name < first_kw_arg) goto arg_passed_twice; - values[name-argnames] = value; + #if PY_MAJOR_VERSION < 3 + if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) { + #else + if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) { + #endif + goto invalid_keyword_type; } else { for (name = first_kw_arg; *name; name++) { #if PY_MAJOR_VERSION >= 3 @@ -6241,8 +7997,7 @@ PyUnicode_Compare(**name, key) == 0) break; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && - strcmp(PyString_AS_STRING(**name), - PyString_AS_STRING(key)) == 0) break; + _PyString_Eq(**name, key)) break; #endif } if (*name) { @@ -6256,8 +8011,7 @@ PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice; #else if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && - strcmp(PyString_AS_STRING(**name), - PyString_AS_STRING(key)) == 0) goto arg_passed_twice; + _PyString_Eq(**name, key)) goto arg_passed_twice; #endif } if (kwds2) { @@ -6290,154 +8044,461 @@ return -1; } -static INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + #if PY_VERSION_HEX < 0x02050000 + "need more than %d value%s to unpack", (int)index, + #else + "need more than %zd value%s to unpack", index, + #endif + (index == 1) ? "" : "s"); } -static INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(void) { + PyErr_SetString(PyExc_ValueError, "too many values to unpack"); } -static INLINE const char* __Pyx_ConsumeWhitespace(const char* ts) { - while (1) { - switch (*ts) { - case '@': - case 10: - case 13: - case ' ': - ++ts; - break; - case '=': - case '<': - case '>': - case '!': - PyErr_SetString(PyExc_ValueError, "Buffer acquisition error: Only native byte order, size and alignment supported."); - return NULL; - default: - return ts; +static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) { + PyObject *item; + if (!(item = PyIter_Next(iter))) { + if (!PyErr_Occurred()) { + __Pyx_RaiseNeedMoreValuesError(index); + } } + return item; +} + +static int __Pyx_EndUnpack(PyObject *iter) { + PyObject *item; + if ((item = PyIter_Next(iter))) { + Py_DECREF(item); + __Pyx_RaiseTooManyValuesError(); + return -1; + } + else if (!PyErr_Occurred()) + return 0; + else + return -1; +} + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_Format(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} + +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + int new_count, enc_count; + int is_complex; + char enc_type; + char packmode; +} __Pyx_BufFmt_Context; + +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; } } -static void __Pyx_BufferNdimError(Py_buffer* buffer, int expected_ndim) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - expected_ndim, buffer->ndim); +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; } -static const char* __Pyx_DescribeTokenInFormatString(const char* ts) { - switch (*ts) { - case 'b': return "char"; - case 'B': return "unsigned char"; - case 'h': return "short"; - case 'H': return "unsigned short"; - case 'i': return "int"; - case 'I': return "unsigned int"; - case 'l': return "long"; - case 'L': return "unsigned long"; - case 'q': return "long long"; - case 'Q': return "unsigned long long"; - case 'f': return "float"; - case 'd': return "double"; - case 'g': return "long double"; - case 'Z': switch (*(ts+1)) { - case 'f': return "complex float"; - case 'd': return "complex double"; - case 'g': return "complex long double"; - default: return "unparseable format string"; - } +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + char msg[] = {ch, 0}; + PyErr_Format(PyExc_ValueError, "Unexpected format string character: '%s'", msg); +} + +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'b': return "'char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; + case 0: return "end"; default: return "unparseable format string"; } } -static const char* __Pyx_CheckTypestring_double(const char* ts) { - int ok; - ts = __Pyx_ConsumeWhitespace(ts); if (!ts) return NULL; - if (*ts == '1') ++ts; - ok = (*ts == 'd'); - if (!(ok)) { - PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch (expected double, got %s)", __Pyx_DescribeTokenInFormatString(ts)); - return NULL; +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; } - ++ts; - return ts; + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} + +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } } - -static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast) { - const char* ts; - if (obj == Py_None) { - __Pyx_ZeroBuffer(buf); - return 0; +} + +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_s_long_long; +#endif + +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_s_long_long) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} + +static size_t __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'h': case 'i': case 'l': case 'q': return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; + case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); + case 'O': return 'O'; + case 'P': return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - __Pyx_BufferNdimError(buf, nd); - goto fail; +} + +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); } - if (!cast) { - ts = buf->format; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - ts = __Pyx_CheckTypestring_double(ts); - if (!ts) goto fail; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - if (*ts != 0) { +} + +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset; + if (ctx->enc_type == 0) return 0; + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + + if (ctx->packmode == '@' || ctx->packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->packmode == '@') { + int align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + int align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + } + + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + /* special case -- treat as struct rather than complex number */ + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch (expected end, got %s)", - __Pyx_DescribeTokenInFormatString(ts)); - goto fail; + "Buffer dtype mismatch; next field is at offset %"PY_FORMAT_SIZE_T"d " + "but %"PY_FORMAT_SIZE_T"d expected", ctx->fmt_offset, offset); + return -1; } - } else { - if (buf->itemsize != sizeof(double)) { - PyErr_SetString(PyExc_ValueError, - "Attempted cast of buffer to datatype of different size."); - goto fail; + + ctx->fmt_offset += size; + + --ctx->enc_count; /* Consume from buffer string */ + + /* Done checking, move to next field, pushing or popping struct stack if needed */ + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; /* breaks both loops as ctx->enc_count == 0 */ + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; /* empty struct */ + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} + +static int __Pyx_BufFmt_FirstPack(__Pyx_BufFmt_Context* ctx) { + if (ctx->enc_type != 0 || ctx->packmode != '@') { + PyErr_SetString(PyExc_ValueError, "Buffer packing mode currently only allowed at beginning of format string (this is a defect)"); + return -1; } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; } -static void __Pyx_RaiseBufferFallbackError(void) { - PyErr_Format(PyExc_ValueError, - "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); -} -static const char* __Pyx_CheckTypestring_nn___pyx_t_5numpy_int32_t(const char* ts) { - int ok; - ts = __Pyx_ConsumeWhitespace(ts); if (!ts) return NULL; - if (*ts == '1') ++ts; - switch (*ts) { - case 'b': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(char) && (__pyx_t_5numpy_int32_t)-1 < 0); break; - case 'h': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(short) && (__pyx_t_5numpy_int32_t)-1 < 0); break; - case 'i': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(int) && (__pyx_t_5numpy_int32_t)-1 < 0); break; - case 'l': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(long) && (__pyx_t_5numpy_int32_t)-1 < 0); break; - case 'q': ok = (sizeof(__pyx_t_5numpy_int32_t) == sizeof(long long) && (__pyx_t_5numpy_int32_t)-1 < 0); break; - default: ok = 0; +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case 10: + case 13: + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + if (__Pyx_BufFmt_FirstPack(ctx) == -1) return NULL; + ctx->packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + if (__Pyx_BufFmt_FirstPack(ctx) == -1) return NULL; + ctx->packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + if (__Pyx_BufFmt_FirstPack(ctx) == -1) return NULL; + ctx->packmode = *ts++; + break; + case 'T': /* substruct */ + { + int i; + const char* ts_after_sub; + int struct_count = ctx->new_count; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + } + break; + case '}': /* end of substruct; either repeat or move on */ + ++ts; + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } /* fall through */ + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex) { + /* Continue pooling same type */ + ctx->enc_count += ctx->new_count; + } else { + /* New type */ + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + } + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + default: + { + ctx->new_count = __Pyx_BufFmt_ParseNumber(&ts); + if (ctx->new_count == -1) { /* First char was not a digit */ + char msg[2] = { *ts, 0 }; + PyErr_Format(PyExc_ValueError, + "Does not understand character buffer dtype format string ('%s')", msg); + return NULL; + } + } + } - if (!(ok)) { - PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch (expected numpy.int32_t, got %s)", __Pyx_DescribeTokenInFormatString(ts)); - return NULL; - } - ++ts; - return ts; } - -static int __Pyx_GetBuffer_nn___pyx_t_5numpy_int32_t(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast) { - const char* ts; +} + +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} + +static int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None) { __Pyx_ZeroBuffer(buf); return 0; @@ -6445,37 +8506,44 @@ buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { - __Pyx_BufferNdimError(buf, nd); + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); goto fail; } if (!cast) { - ts = buf->format; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - ts = __Pyx_CheckTypestring_nn___pyx_t_5numpy_int32_t(ts); - if (!ts) goto fail; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - if (*ts != 0) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch (expected end, got %s)", - __Pyx_DescribeTokenInFormatString(ts)); - goto fail; - } - } else { - if (buf->itemsize != sizeof(__pyx_t_5numpy_int32_t)) { - PyErr_SetString(PyExc_ValueError, - "Attempted cast of buffer to datatype of different size."); - goto fail; - } + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%"PY_FORMAT_SIZE_T"d byte%s) does not match size of '%s' (%"PY_FORMAT_SIZE_T"d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } -static INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { + +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_Format(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); @@ -6490,7 +8558,7 @@ Py_XDECREF(tmp_tb); } -static INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; @@ -6502,63 +8570,26 @@ } -static const char* __Pyx_CheckTypestring_int(const char* ts) { - int ok; - ts = __Pyx_ConsumeWhitespace(ts); if (!ts) return NULL; - if (*ts == '1') ++ts; - ok = (*ts == 'i'); - if (!(ok)) { - PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch (expected int, got %s)", __Pyx_DescribeTokenInFormatString(ts)); - return NULL; - } - ++ts; - return ts; - } - -static int __Pyx_GetBuffer_int(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast) { - const char* ts; - if (obj == Py_None) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - __Pyx_BufferNdimError(buf, nd); - goto fail; - } - if (!cast) { - ts = buf->format; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - ts = __Pyx_CheckTypestring_int(ts); - if (!ts) goto fail; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - if (*ts != 0) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch (expected end, got %s)", - __Pyx_DescribeTokenInFormatString(ts)); - goto fail; - } - } else { - if (buf->itemsize != sizeof(int)) { - PyErr_SetString(PyExc_ValueError, - "Attempted cast of buffer to datatype of different size."); - goto fail; - } - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -}static void __Pyx_RaiseBufferIndexError(int axis) { +static void __Pyx_RaiseBufferIndexError(int axis) { PyErr_Format(PyExc_IndexError, "Out of bounds on buffer access (axis %d)", axis); } +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { + if (t == Py_None) { + __Pyx_RaiseNoneNotIterableError(); + } else if (PyTuple_GET_SIZE(t) < index) { + __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); + } else { + __Pyx_RaiseTooManyValuesError(); + } +} + #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 @@ -6590,7 +8621,7 @@ PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; - __import__ = PyObject_GetAttrString(__pyx_b, "__import__"); + __import__ = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!__import__) goto bad; if (from_list) @@ -6607,8 +8638,8 @@ empty_dict = PyDict_New(); if (!empty_dict) goto bad; - module = PyObject_CallFunction(__import__, "OOOO", - name, global_dict, empty_dict, list); + module = PyObject_CallFunctionObjArgs(__import__, + name, global_dict, empty_dict, list, NULL); bad: Py_XDECREF(empty_list); Py_XDECREF(__import__); @@ -6624,6 +8655,7 @@ return result; } +#if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { Py_XINCREF(type); Py_XINCREF(value); @@ -6679,6 +8711,7 @@ } #endif } + __Pyx_ErrRestore(type, value, tb); return; raise_error: @@ -6688,100 +8721,581 @@ return; } -static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) { - PyObject *item; - if (!(item = PyIter_Next(iter))) { - if (!PyErr_Occurred()) { - PyErr_Format(PyExc_ValueError, - #if PY_VERSION_HEX < 0x02050000 - "need more than %d values to unpack", (int)index); - #else - "need more than %zd values to unpack", index); - #endif +#else /* Python 3+ */ + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (!PyExceptionClass_Check(type)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; } - return item; + + PyErr_SetObject(type, value); + + if (tb) { + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } + } + +bad: + return; } +#endif -static int __Pyx_EndUnpack(PyObject *iter) { - PyObject *item; - if ((item = PyIter_Next(iter))) { - Py_DECREF(item); - PyErr_SetString(PyExc_ValueError, "too many values to unpack"); - return -1; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); } - else if (!PyErr_Occurred()) - return 0; - else - return -1; + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } +/* + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { +#if HAVE_HYPOT + return hypotf(z.real, z.imag); +#else + return sqrtf(z.real*z.real + z.imag*z.imag); +#endif + } +*/ +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } +/* + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { +#if HAVE_HYPOT + return hypot(z.real, z.imag); +#else + return sqrt(z.real*z.real + z.imag*z.imag); +#endif + } +*/ +#endif + +static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { + const unsigned char neg_one = (unsigned char)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(unsigned char) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(unsigned char)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to unsigned char" : + "value too large to convert to unsigned char"); + } + return (unsigned char)-1; + } + return (unsigned char)val; + } + return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); } -static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (!type) { - PyErr_Format(PyExc_SystemError, "Missing type object"); - return 0; +static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { + const unsigned short neg_one = (unsigned short)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(unsigned short) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(unsigned short)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to unsigned short" : + "value too large to convert to unsigned short"); + } + return (unsigned short)-1; + } + return (unsigned short)val; } - if (obj == Py_None || PyObject_TypeCheck(obj, type)) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; + return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); } -static INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); +static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { + const unsigned int neg_one = (unsigned int)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(unsigned int) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(unsigned int)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to unsigned int" : + "value too large to convert to unsigned int"); + } + return (unsigned int)-1; + } + return (unsigned int)val; + } + return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); } -static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); +static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { + const char neg_one = (char)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(char) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(char)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to char" : + "value too large to convert to char"); + } + return (char)-1; + } + return (char)val; + } + return (char)__Pyx_PyInt_AsLong(x); } -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - __Pyx_ErrFetch(type, value, tb); - PyErr_NormalizeException(type, value, tb); - if (PyErr_Occurred()) - goto bad; - Py_INCREF(*type); - Py_INCREF(*value); - Py_INCREF(*tb); - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; - /* Make sure tstate is in a consistent state when we XDECREF - these objects (XDECREF may run arbitrary code). */ - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); - return 0; -bad: - Py_XDECREF(*type); - Py_XDECREF(*value); - Py_XDECREF(*tb); - return -1; +static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { + const short neg_one = (short)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(short) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(short)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to short" : + "value too large to convert to short"); + } + return (short)-1; + } + return (short)val; + } + return (short)__Pyx_PyInt_AsLong(x); } +static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { + const int neg_one = (int)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(int) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(int)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to int" : + "value too large to convert to int"); + } + return (int)-1; + } + return (int)val; + } + return (int)__Pyx_PyInt_AsLong(x); +} +static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { + const signed char neg_one = (signed char)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(signed char) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(signed char)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to signed char" : + "value too large to convert to signed char"); + } + return (signed char)-1; + } + return (signed char)val; + } + return (signed char)__Pyx_PyInt_AsSignedLong(x); +} + +static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { + const signed short neg_one = (signed short)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(signed short) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(signed short)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to signed short" : + "value too large to convert to signed short"); + } + return (signed short)-1; + } + return (signed short)val; + } + return (signed short)__Pyx_PyInt_AsSignedLong(x); +} + +static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { + const signed int neg_one = (signed int)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(signed int) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(signed int)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to signed int" : + "value too large to convert to signed int"); + } + return (signed int)-1; + } + return (signed int)val; + } + return (signed int)__Pyx_PyInt_AsSignedLong(x); +} + +static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { + const unsigned long neg_one = (unsigned long)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned long"); + return (unsigned long)-1; + } + return (unsigned long)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned long"); + return (unsigned long)-1; + } + return PyLong_AsUnsignedLong(x); + } else { + return PyLong_AsLong(x); + } + } else { + unsigned long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (unsigned long)-1; + val = __Pyx_PyInt_AsUnsignedLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { + const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned PY_LONG_LONG"); + return (unsigned PY_LONG_LONG)-1; + } + return (unsigned PY_LONG_LONG)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned PY_LONG_LONG"); + return (unsigned PY_LONG_LONG)-1; + } + return PyLong_AsUnsignedLongLong(x); + } else { + return PyLong_AsLongLong(x); + } + } else { + unsigned PY_LONG_LONG val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (unsigned PY_LONG_LONG)-1; + val = __Pyx_PyInt_AsUnsignedLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { + const long neg_one = (long)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long)-1; + } + return (long)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long)-1; + } + return PyLong_AsUnsignedLong(x); + } else { + return PyLong_AsLong(x); + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long)-1; + val = __Pyx_PyInt_AsLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { + const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to PY_LONG_LONG"); + return (PY_LONG_LONG)-1; + } + return (PY_LONG_LONG)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to PY_LONG_LONG"); + return (PY_LONG_LONG)-1; + } + return PyLong_AsUnsignedLongLong(x); + } else { + return PyLong_AsLongLong(x); + } + } else { + PY_LONG_LONG val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (PY_LONG_LONG)-1; + val = __Pyx_PyInt_AsLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { + const signed long neg_one = (signed long)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed long"); + return (signed long)-1; + } + return (signed long)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed long"); + return (signed long)-1; + } + return PyLong_AsUnsignedLong(x); + } else { + return PyLong_AsLong(x); + } + } else { + signed long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (signed long)-1; + val = __Pyx_PyInt_AsSignedLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { + const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed PY_LONG_LONG"); + return (signed PY_LONG_LONG)-1; + } + return (signed PY_LONG_LONG)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed PY_LONG_LONG"); + return (signed PY_LONG_LONG)-1; + } + return PyLong_AsUnsignedLongLong(x); + } else { + return PyLong_AsLongLong(x); + } + } else { + signed PY_LONG_LONG val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (signed PY_LONG_LONG)-1; + val = __Pyx_PyInt_AsSignedLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + static void __Pyx_WriteUnraisable(const char *name) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; @@ -6801,32 +9315,31 @@ } static int __Pyx_SetVtable(PyObject *dict, void *vtable) { - PyObject *pycobj = 0; - int result; - - pycobj = PyCObject_FromVoidPtr(vtable, 0); - if (!pycobj) +#if PY_VERSION_HEX < 0x03010000 + PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); +#else + PyObject *ob = PyCapsule_New(vtable, 0, 0); +#endif + if (!ob) goto bad; - if (PyDict_SetItemString(dict, "__pyx_vtable__", pycobj) < 0) + if (PyDict_SetItemString(dict, "__pyx_vtable__", ob) < 0) goto bad; - result = 0; - goto done; - + Py_DECREF(ob); + return 0; bad: - result = -1; -done: - Py_XDECREF(pycobj); - return result; + Py_XDECREF(ob); + return -1; } #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - long size) + long size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; + char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) @@ -6851,9 +9364,15 @@ module_name, class_name); goto bad; } - if (((PyTypeObject *)result)->tp_basicsize != size) { + if (!strict && ((PyTypeObject *)result)->tp_basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + PyErr_WarnEx(NULL, warning, 0); + } + else if (((PyTypeObject *)result)->tp_basicsize != size) { PyErr_Format(PyExc_ValueError, - "%s.%s does not appear to be the correct type object", + "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } @@ -6895,7 +9414,6 @@ PyObject *py_srcfile = 0; PyObject *py_funcname = 0; PyObject *py_globals = 0; - PyObject *empty_string = 0; PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; @@ -6922,12 +9440,6 @@ if (!py_funcname) goto bad; py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; - #if PY_MAJOR_VERSION < 3 - empty_string = PyString_FromStringAndSize("", 0); - #else - empty_string = PyBytes_FromStringAndSize("", 0); - #endif - if (!empty_string) goto bad; py_code = PyCode_New( 0, /*int argcount,*/ #if PY_MAJOR_VERSION >= 3 @@ -6936,7 +9448,7 @@ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ - empty_string, /*PyObject *code,*/ + __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ @@ -6945,7 +9457,7 @@ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ __pyx_lineno, /*int firstlineno,*/ - empty_string /*PyObject *lnotab*/ + __pyx_empty_bytes /*PyObject *lnotab*/ ); if (!py_code) goto bad; py_frame = PyFrame_New( @@ -6960,7 +9472,6 @@ bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); - Py_XDECREF(empty_string); Py_XDECREF(py_code); Py_XDECREF(py_frame); } @@ -6968,7 +9479,7 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 - if (t->is_unicode && (!t->is_identifier)) { + if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); @@ -6976,10 +9487,14 @@ *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ - if (t->is_identifier || (t->is_unicode && t->intern)) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->is_unicode) { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } @@ -6993,221 +9508,92 @@ /* Type Conversion Functions */ -static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject* x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} - -static INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { if (x == Py_True) return 1; - else if (x == Py_False) return 0; + else if ((x == Py_False) | (x == Py_None)) return 0; else return PyObject_IsTrue(x); } -static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x) { - if (PyInt_CheckExact(x)) { - return PyInt_AS_LONG(x); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_VERSION_HEX < 0x03000000 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return Py_INCREF(x), x; + m = Py_TYPE(x)->tp_as_number; +#if PY_VERSION_HEX < 0x03000000 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_VERSION_HEX < 0x03000000 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%s__ returned non-%s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; } - else if (PyLong_CheckExact(x)) { - return PyLong_AsLongLong(x); - } - else { - PY_LONG_LONG val; - PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; - val = __pyx_PyInt_AsLongLong(tmp); - Py_DECREF(tmp); - return val; - } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; } -static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x) { - if (PyInt_CheckExact(x)) { - long val = PyInt_AS_LONG(x); - if (unlikely(val < 0)) { - PyErr_SetString(PyExc_TypeError, "Negative assignment to unsigned type."); - return (unsigned PY_LONG_LONG)-1; - } - return val; - } - else if (PyLong_CheckExact(x)) { - return PyLong_AsUnsignedLongLong(x); - } - else { - PY_LONG_LONG val; - PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; - val = __pyx_PyInt_AsUnsignedLongLong(tmp); - Py_DECREF(tmp); - return val; - } +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject* x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; } - -static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x) { - if (sizeof(unsigned char) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - unsigned char val = (unsigned char)long_val; - if (unlikely((val != long_val) || (long_val < 0))) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned char"); - return (unsigned char)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { +#if PY_VERSION_HEX < 0x02050000 + if (ival <= LONG_MAX) + return PyInt_FromLong((long)ival); + else { + unsigned char *bytes = (unsigned char *) &ival; + int one = 1; int little = (int)*(unsigned char*)&one; + return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); + } +#else + return PyInt_FromSize_t(ival); +#endif } -static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x) { - if (sizeof(unsigned short) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - unsigned short val = (unsigned short)long_val; - if (unlikely((val != long_val) || (long_val < 0))) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned short"); - return (unsigned short)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } +static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { + unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); + if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { + return (size_t)-1; + } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to size_t"); + return (size_t)-1; + } + return (size_t)val; } -static INLINE char __pyx_PyInt_char(PyObject* x) { - if (sizeof(char) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - char val = (char)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); - return (char)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} -static INLINE short __pyx_PyInt_short(PyObject* x) { - if (sizeof(short) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - short val = (short)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to short"); - return (short)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE int __pyx_PyInt_int(PyObject* x) { - if (sizeof(int) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - int val = (int)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); - return (int)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE long __pyx_PyInt_long(PyObject* x) { - if (sizeof(long) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - long val = (long)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); - return (long)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed char __pyx_PyInt_signed_char(PyObject* x) { - if (sizeof(signed char) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed char val = (signed char)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed char"); - return (signed char)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed short __pyx_PyInt_signed_short(PyObject* x) { - if (sizeof(signed short) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed short val = (signed short)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed short"); - return (signed short)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed int __pyx_PyInt_signed_int(PyObject* x) { - if (sizeof(signed int) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed int val = (signed int)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed int"); - return (signed int)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed long __pyx_PyInt_signed_long(PyObject* x) { - if (sizeof(signed long) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed long val = (signed long)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed long"); - return (signed long)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE long double __pyx_PyInt_long_double(PyObject* x) { - if (sizeof(long double) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - long double val = (long double)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to long double"); - return (long double)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - +#endif /* Py_PYTHON_H */ Modified: branches/0.7.x/scipy/stats/vonmises_cython.c =================================================================== --- branches/0.7.x/scipy/stats/vonmises_cython.c 2010-02-08 09:19:23 UTC (rev 6216) +++ branches/0.7.x/scipy/stats/vonmises_cython.c 2010-02-08 09:19:40 UTC (rev 6217) @@ -1,8 +1,12 @@ -/* Generated by Cython 0.10 on Mon Nov 10 22:16:41 2008 */ +/* Generated by Cython 0.12.1 on Mon Feb 8 18:10:52 2010 */ #define PY_SSIZE_T_CLEAN #include "Python.h" #include "structmember.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#else + #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif @@ -11,16 +15,22 @@ #endif #if PY_VERSION_HEX < 0x02040000 #define METH_COEXIST 0 + #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type) + #define PyDict_Contains(d,o) PySequence_Contains(d,o) #endif + #if PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN + #define PY_FORMAT_SIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) #define PyInt_AsSsize_t(o) PyInt_AsLong(o) #define PyNumber_Index(o) PyNumber_Int(o) #define PyIndex_Check(o) PyNumber_Check(o) + #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) #endif + #if PY_VERSION_HEX < 0x02060000 #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) @@ -30,22 +40,21 @@ #define PyType_Modified(t) typedef struct { - void *buf; - PyObject *obj; - Py_ssize_t len; - Py_ssize_t itemsize; - int readonly; - int ndim; - char *format; - Py_ssize_t *shape; - Py_ssize_t *strides; - Py_ssize_t *suboffsets; - void *internal; + void *buf; + PyObject *obj; + Py_ssize_t len; + Py_ssize_t itemsize; + int readonly; + int ndim; + char *format; + Py_ssize_t *shape; + Py_ssize_t *strides; + Py_ssize_t *suboffsets; + void *internal; } Py_buffer; #define PyBUF_SIMPLE 0 #define PyBUF_WRITABLE 0x0001 - #define PyBUF_LOCK 0x0002 #define PyBUF_FORMAT 0x0004 #define PyBUF_ND 0x0008 #define PyBUF_STRIDES (0x0010 | PyBUF_ND) @@ -55,21 +64,32 @@ #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES) #endif + #if PY_MAJOR_VERSION < 3 #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" #else #define __Pyx_BUILTIN_MODULE_NAME "builtins" #endif + #if PY_MAJOR_VERSION >= 3 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #endif + #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif + #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type - #define PyString_Type PyBytes_Type + #define PyString_Type PyUnicode_Type + #define PyString_CheckExact PyUnicode_CheckExact +#else + #define PyBytes_Type PyString_Type + #define PyBytes_CheckExact PyString_CheckExact +#endif + +#if PY_MAJOR_VERSION >= 3 #define PyInt_Type PyLong_Type #define PyInt_Check(op) PyLong_Check(op) #define PyInt_CheckExact(op) PyLong_CheckExact(op) @@ -84,13 +104,17 @@ #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) #else #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define PyBytes_Type PyString_Type + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) + #endif + #if PY_MAJOR_VERSION >= 3 #define PyMethod_New(func, self, klass) PyInstanceMethod_New(func) #endif + #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall @@ -98,9 +122,30 @@ #ifndef __cdecl #define __cdecl #endif + #ifndef __fastcall + #define __fastcall + #endif #else #define _USE_MATH_DEFINES #endif + +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) +#else + #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) + #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) + #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) +#endif + +#if PY_VERSION_HEX < 0x02050000 + #define __Pyx_NAMESTR(n) ((char *)(n)) + #define __Pyx_DOCSTR(n) ((char *)(n)) +#else + #define __Pyx_NAMESTR(n) (n) + #define __Pyx_DOCSTR(n) (n) +#endif #ifdef __cplusplus #define __PYX_EXTERN_C extern "C" #else @@ -108,56 +153,113 @@ #endif #include #define __PYX_HAVE_API__scipy__stats__vonmises_cython -#include "math.h" #include "stdlib.h" +#include "stdio.h" #include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "math.h" +#ifndef CYTHON_INLINE + #if defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #else + #define CYTHON_INLINE + #endif +#endif -#ifdef __GNUC__ -#define INLINE __inline__ -#elif _WIN32 -#define INLINE __inline +typedef struct {PyObject **p; char *s; const long n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ + + +/* Type Conversion Predeclarations */ + +#if PY_MAJOR_VERSION < 3 +#define __Pyx_PyBytes_FromString PyString_FromString +#define __Pyx_PyBytes_FromStringAndSize PyString_FromStringAndSize +#define __Pyx_PyBytes_AsString PyString_AsString #else -#define INLINE +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +#define __Pyx_PyBytes_AsString PyBytes_AsString #endif -typedef struct {PyObject **p; char *s; long n; char is_unicode; char intern; char is_identifier;} __Pyx_StringTabEntry; /*proto*/ +#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s) +#define __Pyx_PyBytes_AsUString(s) ((unsigned char*) __Pyx_PyBytes_AsString(s)) +#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +#if !defined(T_PYSSIZET) +#if PY_VERSION_HEX < 0x02050000 +#define T_PYSSIZET T_INT +#elif !defined(T_LONGLONG) +#define T_PYSSIZET \ + ((sizeof(Py_ssize_t) == sizeof(int)) ? T_INT : \ + ((sizeof(Py_ssize_t) == sizeof(long)) ? T_LONG : -1)) +#else +#define T_PYSSIZET \ + ((sizeof(Py_ssize_t) == sizeof(int)) ? T_INT : \ + ((sizeof(Py_ssize_t) == sizeof(long)) ? T_LONG : \ + ((sizeof(Py_ssize_t) == sizeof(PY_LONG_LONG)) ? T_LONGLONG : -1))) +#endif +#endif -static int __pyx_skip_dispatch = 0; +#if !defined(T_ULONGLONG) +#define __Pyx_T_UNSIGNED_INT(x) \ + ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \ + ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \ + ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : -1)))) +#else +#define __Pyx_T_UNSIGNED_INT(x) \ + ((sizeof(x) == sizeof(unsigned char)) ? T_UBYTE : \ + ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \ + ((sizeof(x) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(x) == sizeof(unsigned long)) ? T_ULONG : \ + ((sizeof(x) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1))))) +#endif +#if !defined(T_LONGLONG) +#define __Pyx_T_SIGNED_INT(x) \ + ((sizeof(x) == sizeof(char)) ? T_BYTE : \ + ((sizeof(x) == sizeof(short)) ? T_SHORT : \ + ((sizeof(x) == sizeof(int)) ? T_INT : \ + ((sizeof(x) == sizeof(long)) ? T_LONG : -1)))) +#else +#define __Pyx_T_SIGNED_INT(x) \ + ((sizeof(x) == sizeof(char)) ? T_BYTE : \ + ((sizeof(x) == sizeof(short)) ? T_SHORT : \ + ((sizeof(x) == sizeof(int)) ? T_INT : \ + ((sizeof(x) == sizeof(long)) ? T_LONG : \ + ((sizeof(x) == sizeof(PY_LONG_LONG)) ? T_LONGLONG : -1))))) +#endif -/* Type Conversion Predeclarations */ +#define __Pyx_T_FLOATING(x) \ + ((sizeof(x) == sizeof(float)) ? T_FLOAT : \ + ((sizeof(x) == sizeof(double)) ? T_DOUBLE : -1)) -#if PY_MAJOR_VERSION < 3 -#define __Pyx_PyBytes_FromString PyString_FromString -#define __Pyx_PyBytes_AsString PyString_AsString +#if !defined(T_SIZET) +#if !defined(T_ULONGLONG) +#define T_SIZET \ + ((sizeof(size_t) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(size_t) == sizeof(unsigned long)) ? T_ULONG : -1)) #else -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_AsString PyBytes_AsString +#define T_SIZET \ + ((sizeof(size_t) == sizeof(unsigned int)) ? T_UINT : \ + ((sizeof(size_t) == sizeof(unsigned long)) ? T_ULONG : \ + ((sizeof(size_t) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1))) #endif +#endif -#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) -static INLINE int __Pyx_PyObject_IsTrue(PyObject* x); -static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x); -static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x); -static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*); -#define __pyx_PyInt_AsLong(x) (PyInt_CheckExact(x) ? PyInt_AS_LONG(x) : PyInt_AsLong(x)) #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x); -static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x); -static INLINE char __pyx_PyInt_char(PyObject* x); -static INLINE short __pyx_PyInt_short(PyObject* x); -static INLINE int __pyx_PyInt_int(PyObject* x); -static INLINE long __pyx_PyInt_long(PyObject* x); -static INLINE signed char __pyx_PyInt_signed_char(PyObject* x); -static INLINE signed short __pyx_PyInt_signed_short(PyObject* x); -static INLINE signed int __pyx_PyInt_signed_int(PyObject* x); -static INLINE signed long __pyx_PyInt_signed_long(PyObject* x); -static INLINE long double __pyx_PyInt_long_double(PyObject* x); + #ifdef __GNUC__ /* Test for GCC > 2.95 */ #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) @@ -175,12 +277,151 @@ static PyObject *__pyx_m; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; static const char **__pyx_f; + +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif + +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + +typedef npy_int8 __pyx_t_5numpy_int8_t; + +typedef npy_int16 __pyx_t_5numpy_int16_t; + +typedef npy_int32 __pyx_t_5numpy_int32_t; + +typedef npy_int64 __pyx_t_5numpy_int64_t; + +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +typedef npy_float32 __pyx_t_5numpy_float32_t; + +typedef npy_float64 __pyx_t_5numpy_float64_t; + +typedef npy_long __pyx_t_5numpy_int_t; + +typedef npy_longlong __pyx_t_5numpy_long_t; + +typedef npy_intp __pyx_t_5numpy_intp_t; + +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +typedef npy_ulong __pyx_t_5numpy_uint_t; + +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +typedef npy_double __pyx_t_5numpy_float_t; + +typedef npy_double __pyx_t_5numpy_double_t; + +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif + +/* Type declarations */ + +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +typedef npy_cdouble __pyx_t_5numpy_complex_t; + +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif + +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct * __Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); + end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; + } + #define __Pyx_RefNannySetupContext(name) void *__pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) + #define __Pyx_RefNannyFinishContext() __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r);} } while(0) +#else + #define __Pyx_RefNannySetupContext(name) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) +#endif /* CYTHON_REFNANNY */ +#define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);} } while(0) +#define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r);} } while(0) + static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name); /*proto*/ @@ -188,42 +429,124 @@ Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name); /*proto*/ -static INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); -static INLINE void __Pyx_ZeroBuffer(Py_buffer* buf); /*proto*/ -static INLINE const char* __Pyx_ConsumeWhitespace(const char* ts); /*proto*/ -static void __Pyx_BufferNdimError(Py_buffer* buffer, int expected_ndim); /*proto*/ -static const char* __Pyx_DescribeTokenInFormatString(const char* ts); /*proto*/ -static const char* __Pyx_CheckTypestring_double(const char* ts); /*proto*/ -static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast); /*proto*/ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(void); + +static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ +static int __Pyx_EndUnpack(PyObject *); /*proto*/ + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ + +/* Run-time type information about structs used with buffers */ +struct __Pyx_StructField_; + +typedef struct { + const char* name; /* for error messages only */ + struct __Pyx_StructField_* fields; + size_t size; /* sizeof(type) */ + char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject */ +} __Pyx_TypeInfo; + +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; + +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; + + +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); +static int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); + static void __Pyx_RaiseBufferFallbackError(void); /*proto*/ #define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) -static INLINE PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i, int is_unsigned) { + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; - if (PyList_CheckExact(o) && 0 <= i && i < PyList_GET_SIZE(o)) { + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} + + +#define __Pyx_GetItemInt_List(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \ + __Pyx_GetItemInt_List_Fast(o, i, size <= sizeof(long)) : \ + __Pyx_GetItemInt_Generic(o, to_py_func(i))) + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int fits_long) { + if (likely(o != Py_None)) { + if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) { + PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i); + Py_INCREF(r); + return r; + } + } + return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i)); +} + +#define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \ + __Pyx_GetItemInt_Tuple_Fast(o, i, size <= sizeof(long)) : \ + __Pyx_GetItemInt_Generic(o, to_py_func(i))) + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int fits_long) { + if (likely(o != Py_None)) { + if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) { + PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i); + Py_INCREF(r); + return r; + } + } + return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i)); +} + + +#define __Pyx_GetItemInt(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \ + __Pyx_GetItemInt_Fast(o, i, size <= sizeof(long)) : \ + __Pyx_GetItemInt_Generic(o, to_py_func(i))) + +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int fits_long) { + PyObject *r; + if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) { r = PyList_GET_ITEM(o, i); Py_INCREF(r); } - else if (PyTuple_CheckExact(o) && 0 <= i && i < PyTuple_GET_SIZE(o)) { + else if (PyTuple_CheckExact(o) && ((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { r = PyTuple_GET_ITEM(o, i); Py_INCREF(r); } - else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0) || !is_unsigned)) + else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0))) { r = PySequence_GetItem(o, i); + } else { - PyObject *j = (likely(i >= 0) || !is_unsigned) ? PyInt_FromLong(i) : PyLong_FromUnsignedLongLong((sizeof(unsigned long long) > sizeof(Py_ssize_t) ? (1ULL << (sizeof(Py_ssize_t)*8)) : 0) + i); - if (!j) - return 0; - r = PyObject_GetItem(o, j); - Py_DECREF(j); + r = __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i)); } return r; } -static INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ -static INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index); /*proto*/ #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); static void __Pyx_ReleaseBuffer(Py_buffer *view); @@ -239,201 +562,297 @@ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/ -static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ -static int __Pyx_EndUnpack(PyObject *); /*proto*/ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif -static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/ +#if defined(_WIN32) && defined(__cplusplus) && CYTHON_CCOMPLEX + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif -static INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ -static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eqf(a, b) ((a)==(b)) + #define __Pyx_c_sumf(a, b) ((a)+(b)) + #define __Pyx_c_difff(a, b) ((a)-(b)) + #define __Pyx_c_prodf(a, b) ((a)*(b)) + #define __Pyx_c_quotf(a, b) ((a)/(b)) + #define __Pyx_c_negf(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zerof(z) ((z)==(float)0) + #define __Pyx_c_conjf(z) (::std::conj(z)) + /*#define __Pyx_c_absf(z) (::std::abs(z))*/ + #else + #define __Pyx_c_is_zerof(z) ((z)==0) + #define __Pyx_c_conjf(z) (conjf(z)) + /*#define __Pyx_c_absf(z) (cabsf(z))*/ + #endif +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + /*static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex);*/ +#endif -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); -static void __Pyx_WriteUnraisable(const char *name); /*proto*/ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + /*#define __Pyx_c_abs(z) (::std::abs(z))*/ + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + /*#define __Pyx_c_abs(z) (cabs(z))*/ + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); + /*static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex);*/ +#endif -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, long size); /*proto*/ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ -static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ +static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *); -static void __Pyx_AddTraceback(const char *funcname); /*proto*/ +static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *); -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ +static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject *); -/* Type declarations */ +static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject *); -typedef npy_int8 __pyx_t_5numpy_int8_t; +static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject *); -typedef npy_int16 __pyx_t_5numpy_int16_t; +static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject *); -typedef npy_int32 __pyx_t_5numpy_int32_t; +static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject *); -typedef npy_int64 __pyx_t_5numpy_int64_t; +static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *); -typedef npy_uint8 __pyx_t_5numpy_uint8_t; +static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *); -typedef npy_uint16 __pyx_t_5numpy_uint16_t; +static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *); -typedef npy_uint32 __pyx_t_5numpy_uint32_t; +static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *); -typedef npy_uint64 __pyx_t_5numpy_uint64_t; +static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject *); -typedef npy_float32 __pyx_t_5numpy_float32_t; +static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject *); -typedef npy_float64 __pyx_t_5numpy_float64_t; +static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *); -typedef npy_long __pyx_t_5numpy_int_t; +static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *); -typedef npy_longlong __pyx_t_5numpy_long_t; +static void __Pyx_WriteUnraisable(const char *name); /*proto*/ -typedef npy_ulong __pyx_t_5numpy_uint_t; +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, long size, int strict); /*proto*/ -typedef npy_ulonglong __pyx_t_5numpy_ulong_t; +static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ -typedef npy_double __pyx_t_5numpy_float_t; +static void __Pyx_AddTraceback(const char *funcname); /*proto*/ -typedef npy_double __pyx_t_5numpy_double_t; +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ +/* Module declarations from python_buffer */ -typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +/* Module declarations from python_ref */ -typedef npy_cfloat __pyx_t_5numpy_cfloat_t; +/* Module declarations from stdlib */ -typedef npy_cdouble __pyx_t_5numpy_cdouble_t; +/* Module declarations from stdio */ -typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; -/* Module declarations from python_buffer */ - -/* Module declarations from stdlib */ - /* Module declarations from numpy */ /* Module declarations from numpy */ static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *, PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *, PyObject *, PyObject *, PyObject *, PyObject *); /*proto*/ +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ +static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *, PyObject *); /*proto*/ +static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *); /*proto*/ /* Module declarations from cython */ /* Module declarations from scipy.stats.vonmises_cython */ static double __pyx_f_5scipy_5stats_15vonmises_cython_von_mises_cdf_series(double, double, unsigned int); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), 'R' }; +#define __Pyx_MODULE_NAME "scipy.stats.vonmises_cython" +int __pyx_module_is_main_scipy__stats__vonmises_cython = 0; - /* Implementation of scipy.stats.vonmises_cython */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static char __pyx_k_1[] = "von_mises_cdf_normalapprox"; +static char __pyx_k_2[] = "ndarray is not C contiguous"; +static char __pyx_k_3[] = "ndarray is not Fortran contiguous"; +static char __pyx_k_4[] = "Non-native byte order not supported"; +static char __pyx_k_5[] = "unknown dtype code in numpy.pxd (%d)"; +static char __pyx_k_6[] = "Format string allocated too short, see comment in numpy.pxd"; +static char __pyx_k_7[] = "Format string allocated too short."; +static char __pyx_k_8[] = "scipy.stats"; +static char __pyx_k_9[] = "scipy.special"; +static char __pyx_k_10[] = "numpy.testing"; +static char __pyx_k__B[] = "B"; +static char __pyx_k__H[] = "H"; +static char __pyx_k__I[] = "I"; +static char __pyx_k__L[] = "L"; +static char __pyx_k__O[] = "O"; +static char __pyx_k__Q[] = "Q"; +static char __pyx_k__b[] = "b"; +static char __pyx_k__d[] = "d"; +static char __pyx_k__f[] = "f"; +static char __pyx_k__g[] = "g"; +static char __pyx_k__h[] = "h"; +static char __pyx_k__i[] = "i"; +static char __pyx_k__k[] = "k"; +static char __pyx_k__l[] = "l"; +static char __pyx_k__q[] = "q"; +static char __pyx_k__x[] = "x"; +static char __pyx_k__C1[] = "C1"; +static char __pyx_k__Zd[] = "Zd"; +static char __pyx_k__Zf[] = "Zf"; +static char __pyx_k__Zg[] = "Zg"; +static char __pyx_k__i0[] = "i0"; +static char __pyx_k__np[] = "np"; +static char __pyx_k__pi[] = "pi"; +static char __pyx_k__buf[] = "buf"; +static char __pyx_k__cdf[] = "cdf"; +static char __pyx_k__exp[] = "exp"; +static char __pyx_k__obj[] = "obj"; +static char __pyx_k__sin[] = "sin"; +static char __pyx_k__base[] = "base"; +static char __pyx_k__ndim[] = "ndim"; +static char __pyx_k__norm[] = "norm"; +static char __pyx_k__sqrt[] = "sqrt"; +static char __pyx_k__descr[] = "descr"; +static char __pyx_k__dtype[] = "dtype"; +static char __pyx_k__empty[] = "empty"; +static char __pyx_k__float[] = "float"; +static char __pyx_k__names[] = "names"; +static char __pyx_k__numpy[] = "numpy"; +static char __pyx_k__range[] = "range"; +static char __pyx_k__round[] = "round"; +static char __pyx_k__scipy[] = "scipy"; +static char __pyx_k__shape[] = "shape"; +static char __pyx_k__stats[] = "stats"; +static char __pyx_k__astype[] = "astype"; +static char __pyx_k__fields[] = "fields"; +static char __pyx_k__format[] = "format"; +static char __pyx_k__asarray[] = "asarray"; +static char __pyx_k__strides[] = "strides"; +static char __pyx_k____main__[] = "__main__"; +static char __pyx_k____test__[] = "__test__"; +static char __pyx_k__itemsize[] = "itemsize"; +static char __pyx_k__readonly[] = "readonly"; +static char __pyx_k__type_num[] = "type_num"; +static char __pyx_k__byteorder[] = "byteorder"; +static char __pyx_k__ValueError[] = "ValueError"; +static char __pyx_k__atleast_1d[] = "atleast_1d"; +static char __pyx_k__suboffsets[] = "suboffsets"; +static char __pyx_k__RuntimeError[] = "RuntimeError"; +static char __pyx_k__broadcast_arrays[] = "broadcast_arrays"; +static PyObject *__pyx_n_s_1; +static PyObject *__pyx_n_s_10; +static PyObject *__pyx_kp_u_2; +static PyObject *__pyx_kp_u_3; +static PyObject *__pyx_kp_u_4; +static PyObject *__pyx_kp_u_5; +static PyObject *__pyx_kp_u_6; +static PyObject *__pyx_kp_u_7; +static PyObject *__pyx_n_s_8; +static PyObject *__pyx_n_s_9; +static PyObject *__pyx_n_s__C1; +static PyObject *__pyx_n_s__RuntimeError; +static PyObject *__pyx_n_s__ValueError; +static PyObject *__pyx_n_s____main__; +static PyObject *__pyx_n_s____test__; +static PyObject *__pyx_n_s__asarray; +static PyObject *__pyx_n_s__astype; +static PyObject *__pyx_n_s__atleast_1d; +static PyObject *__pyx_n_s__base; +static PyObject *__pyx_n_s__broadcast_arrays; +static PyObject *__pyx_n_s__buf; +static PyObject *__pyx_n_s__byteorder; +static PyObject *__pyx_n_s__cdf; +static PyObject *__pyx_n_s__descr; +static PyObject *__pyx_n_s__dtype; +static PyObject *__pyx_n_s__empty; +static PyObject *__pyx_n_s__exp; +static PyObject *__pyx_n_s__fields; +static PyObject *__pyx_n_s__float; +static PyObject *__pyx_n_s__format; +static PyObject *__pyx_n_s__i0; +static PyObject *__pyx_n_s__itemsize; +static PyObject *__pyx_n_s__k; +static PyObject *__pyx_n_s__names; +static PyObject *__pyx_n_s__ndim; +static PyObject *__pyx_n_s__norm; +static PyObject *__pyx_n_s__np; +static PyObject *__pyx_n_s__numpy; +static PyObject *__pyx_n_s__obj; +static PyObject *__pyx_n_s__pi; +static PyObject *__pyx_n_s__range; +static PyObject *__pyx_n_s__readonly; +static PyObject *__pyx_n_s__round; +static PyObject *__pyx_n_s__scipy; +static PyObject *__pyx_n_s__shape; +static PyObject *__pyx_n_s__sin; +static PyObject *__pyx_n_s__sqrt; +static PyObject *__pyx_n_s__stats; +static PyObject *__pyx_n_s__strides; +static PyObject *__pyx_n_s__suboffsets; +static PyObject *__pyx_n_s__type_num; +static PyObject *__pyx_n_s__x; +static PyObject *__pyx_int_0; static PyObject *__pyx_int_2; -static PyObject *__pyx_int_24; static PyObject *__pyx_int_3; +static PyObject *__pyx_int_4; +static PyObject *__pyx_int_neg_1; +static PyObject *__pyx_int_15; static PyObject *__pyx_int_16; -static PyObject *__pyx_int_4; -static PyObject *__pyx_int_0; -static char __pyx_k_k[] = "k"; -static PyObject *__pyx_kp_k; -static char __pyx_k_x[] = "x"; -static PyObject *__pyx_kp_x; -static char __pyx_k_23[] = "C1"; -static PyObject *__pyx_kp_23; -static char __pyx_k_numpy[] = "numpy"; -static PyObject *__pyx_kp_numpy; -static char __pyx_k_np[] = "np"; -static PyObject *__pyx_kp_np; -static char __pyx_k_24[] = "scipy.stats"; -static PyObject *__pyx_kp_24; -static char __pyx_k_scipy[] = "scipy"; -static PyObject *__pyx_kp_scipy; -static char __pyx_k_25[] = "scipy.special"; -static PyObject *__pyx_kp_25; -static char __pyx_k_i0[] = "i0"; -static PyObject *__pyx_kp_i0; -static char __pyx_k_26[] = "numpy.testing"; -static PyObject *__pyx_kp_26; -static char __pyx_k_pi[] = "pi"; -static PyObject *__pyx_kp_pi; -static char __pyx_k_sqrt[] = "sqrt"; -static PyObject *__pyx_kp_sqrt; -static char __pyx_k_exp[] = "exp"; -static PyObject *__pyx_kp_exp; -static char __pyx_k_sin[] = "sin"; -static PyObject *__pyx_kp_sin; -static char __pyx_k_stats[] = "stats"; -static PyObject *__pyx_kp_stats; -static char __pyx_k_norm[] = "norm"; -static PyObject *__pyx_kp_norm; -static char __pyx_k_cdf[] = "cdf"; -static PyObject *__pyx_kp_cdf; -static char __pyx_k_asarray[] = "asarray"; -static PyObject *__pyx_kp_asarray; -static char __pyx_k_ndim[] = "ndim"; -static PyObject *__pyx_kp_ndim; -static char __pyx_k_27[] = "atleast_1d"; -static PyObject *__pyx_kp_27; -static char __pyx_k_round[] = "round"; -static PyObject *__pyx_kp_round; -static char __pyx_k_broadcast_arrays[] = "broadcast_arrays"; -static PyObject *__pyx_kp_broadcast_arrays; -static char __pyx_k_empty[] = "empty"; 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__pyx_v_i < __pyx_v_ndim; __pyx_v_i+=1) { + __pyx_t_6 = __pyx_v_ndim; + for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) { + __pyx_v_i = __pyx_t_7; - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":79 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":219 * info.shape = info.strides + ndim * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] # <<<<<<<<<<<<<< @@ -1794,7 +2488,7 @@ */ (__pyx_v_info->strides[__pyx_v_i]) = (PyArray_STRIDES(((PyArrayObject *)__pyx_v_self))[__pyx_v_i]); - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":80 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":220 * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] * info.shape[i] = PyArray_DIMS(self)[i] # <<<<<<<<<<<<<< @@ -1807,7 +2501,7 @@ } /*else*/ { - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":82 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":222 * info.shape[i] = PyArray_DIMS(self)[i] * else: * info.strides = PyArray_STRIDES(self) # <<<<<<<<<<<<<< @@ -1816,7 +2510,7 @@ */ __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(((PyArrayObject *)__pyx_v_self))); - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":83 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":223 * else: * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) # <<<<<<<<<<<<<< @@ -1827,7 +2521,7 @@ } __pyx_L8:; - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":84 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":224 * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL # <<<<<<<<<<<<<< @@ -1836,7 +2530,7 @@ */ __pyx_v_info->suboffsets = NULL; - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":85 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":225 * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<< @@ -1845,7 +2539,7 @@ */ __pyx_v_info->itemsize = PyArray_ITEMSIZE(((PyArrayObject *)__pyx_v_self)); - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":86 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":226 * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) * info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<< @@ -1854,7 +2548,7 @@ */ __pyx_v_info->readonly = (!PyArray_ISWRITEABLE(((PyArrayObject *)__pyx_v_self))); - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":89 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":229 * * cdef int t * cdef char* f = NULL # <<<<<<<<<<<<<< @@ -1863,308 +2557,411 @@ */ __pyx_v_f = NULL; - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":90 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":230 * cdef int t * cdef char* f = NULL * cdef dtype descr = self.descr # <<<<<<<<<<<<<< * cdef list stack - * + * cdef int offset */ - Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_self)->descr)); 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- Py_DECREF(__pyx_v_info->obj); + __Pyx_INCREF(__pyx_v_self); + __Pyx_GIVEREF(__pyx_v_self); + __Pyx_GOTREF(__pyx_v_info->obj); + __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = __pyx_v_self; } __pyx_L11:; - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":110 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":243 * info.obj = self * * if not hasfields: # <<<<<<<<<<<<<< * t = descr.type_num - * if t == NPY_BYTE: f = "b" + * if ((descr.byteorder == '>' and little_endian) or */ - __pyx_1 = (!__pyx_v_hasfields); - if (__pyx_1) { + __pyx_t_1 = (!__pyx_v_hasfields); + if (__pyx_t_1) { - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":111 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":244 * * if not hasfields: * t = descr.type_num # <<<<<<<<<<<<<< - * if t == NPY_BYTE: f = "b" - * elif t == NPY_UBYTE: f = "B" + * if ((descr.byteorder == '>' and little_endian) or + * (descr.byteorder == '<' and not little_endian)): */ __pyx_v_t = __pyx_v_descr->type_num; 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+ __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __Pyx_Raise(__pyx_t_5, 0, 0); + __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 247; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + goto __pyx_L13; + } + __pyx_L13:; + + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":248 + * (descr.byteorder == '<' and not little_endian)): + * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" # <<<<<<<<<<<<<< * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" */ - switch (__pyx_v_t) { - case NPY_BYTE: - __pyx_v_f = __pyx_k_3; - break; - case NPY_UBYTE: + __pyx_t_1 = (__pyx_v_t == NPY_BYTE); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__b; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":113 - * t = descr.type_num + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":249 + * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" # <<<<<<<<<<<<<< * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" */ - __pyx_v_f = __pyx_k_4; - break; - case NPY_SHORT: + __pyx_t_1 = (__pyx_v_t == NPY_UBYTE); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__B; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":114 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":250 * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" # <<<<<<<<<<<<<< * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" */ - __pyx_v_f = __pyx_k_5; - break; - case NPY_USHORT: + __pyx_t_1 = (__pyx_v_t == NPY_SHORT); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__h; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":115 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":251 * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" # <<<<<<<<<<<<<< * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" */ - __pyx_v_f = __pyx_k_6; - break; - case NPY_INT: + __pyx_t_1 = (__pyx_v_t == NPY_USHORT); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__H; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":116 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":252 * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" # <<<<<<<<<<<<<< * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" */ - __pyx_v_f = __pyx_k_7; - break; - case NPY_UINT: + __pyx_t_1 = (__pyx_v_t == NPY_INT); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__i; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":117 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":253 * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" # <<<<<<<<<<<<<< * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" */ - __pyx_v_f = __pyx_k_8; 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- break; - case NPY_LONGLONG: + __pyx_t_1 = (__pyx_v_t == NPY_ULONG); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__L; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":120 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":256 * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" */ - __pyx_v_f = __pyx_k_11; - break; - case NPY_ULONGLONG: + __pyx_t_1 = (__pyx_v_t == NPY_LONGLONG); + if (__pyx_t_1) { + __pyx_v_f = __pyx_k__q; + goto __pyx_L14; + } - /* "/usr/data/david/local/lib64/python2.4/site-packages/Cython/Includes/numpy.pxd":121 + /* "/home/david/local/lib/python2.6/site-packages/Cython/Includes/numpy.pxd":257 * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" */ - __pyx_v_f = __pyx_k_12; 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__pyx_builtin_ValueError = __Pyx_GetName(__pyx_b, __pyx_kp_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_builtin_StopIteration = __Pyx_GetName(__pyx_b, __pyx_kp_StopIteration); if (!__pyx_builtin_StopIteration) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 144; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_builtin_RuntimeError = __Pyx_GetName(__pyx_b, __pyx_kp_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_range = __Pyx_GetName(__pyx_b, __pyx_n_s__range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_ValueError = __Pyx_GetName(__pyx_b, __pyx_n_s__ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 205; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_builtin_RuntimeError = __Pyx_GetName(__pyx_b, __pyx_n_s__RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 786; __pyx_clineno = __LINE__; goto __pyx_L1_error;} return 0; __pyx_L1_error:; return -1; } static int __Pyx_InitGlobals(void) { + if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; __pyx_int_2 = PyInt_FromLong(2); if (unlikely(!__pyx_int_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; - 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__pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; - if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; + __pyx_int_24 = PyInt_FromLong(24); if (unlikely(!__pyx_int_24)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; return 0; __pyx_L1_error:; return -1; @@ -2909,16 +4275,36 @@ PyMODINIT_FUNC PyInit_vonmises_cython(void) #endif { - PyObject *__pyx_1 = 0; - PyObject *__pyx_2 = 0; + PyObject *__pyx_t_1 = NULL; + PyObject *__pyx_t_2 = NULL; + #if CYTHON_REFNANNY + void* __pyx_refnanny = NULL; + __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); + if (!__Pyx_RefNanny) { + PyErr_Clear(); + __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny"); + if (!__Pyx_RefNanny) + Py_FatalError("failed to import 'refnanny' module"); + } + __pyx_refnanny = __Pyx_RefNanny->SetupContext("PyMODINIT_FUNC PyInit_vonmises_cython(void)", __LINE__, __FILE__); + #endif + __pyx_init_filenames(); __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #if PY_MAJOR_VERSION < 3 + __pyx_empty_bytes = PyString_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #else + __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #endif /*--- Library function declarations ---*/ - 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"'complex long double'" : "'long double'"); case 'T': return "a struct"; case 'O': return "Python object"; case 'P': return "a pointer"; + case 0: return "end"; default: return "unparseable format string"; } } -static const char* __Pyx_CheckTypestring_double(const char* ts) { - int ok; - ts = __Pyx_ConsumeWhitespace(ts); if (!ts) return NULL; - if (*ts == '1') ++ts; - ok = (*ts == 'd'); - if (!(ok)) { - PyErr_Format(PyExc_ValueError, "Buffer dtype mismatch (expected double, got %s)", __Pyx_DescribeTokenInFormatString(ts)); - return NULL; +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; } - ++ts; - return ts; + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} + +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } } +} + +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_s_long_long; +#endif + +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_s_long_long) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} + +static size_t __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'h': case 'i': case 'l': case 'q': return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': return 'U'; + case 'f': case 'd': case 'g': return (is_complex ? 'C' : 'R'); + case 'O': return 'O'; + case 'P': return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} + +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} + +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset; + if (ctx->enc_type == 0) return 0; + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; -static int __Pyx_GetBuffer_double(PyObject* obj, Py_buffer* buf, int flags, int nd, int cast) { - const char* ts; + if (ctx->packmode == '@' || ctx->packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->packmode == '@') { + int align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + int align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + } + + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + /* special case -- treat as struct rather than complex number */ + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %"PY_FORMAT_SIZE_T"d " + "but %"PY_FORMAT_SIZE_T"d expected", ctx->fmt_offset, offset); + return -1; + } + + ctx->fmt_offset += size; + + --ctx->enc_count; /* Consume from buffer string */ + + /* Done checking, move to next field, pushing or popping struct stack if needed */ + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; /* breaks both loops as ctx->enc_count == 0 */ + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; /* empty struct */ + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} + +static int __Pyx_BufFmt_FirstPack(__Pyx_BufFmt_Context* ctx) { + if (ctx->enc_type != 0 || ctx->packmode != '@') { + PyErr_SetString(PyExc_ValueError, "Buffer packing mode currently only allowed at beginning of format string (this is a defect)"); + return -1; + } + return 0; +} + +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case 10: + case 13: + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + if (__Pyx_BufFmt_FirstPack(ctx) == -1) return NULL; + ctx->packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + if (__Pyx_BufFmt_FirstPack(ctx) == -1) return NULL; + ctx->packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + if (__Pyx_BufFmt_FirstPack(ctx) == -1) return NULL; + ctx->packmode = *ts++; + break; + case 'T': /* substruct */ + { + int i; + const char* ts_after_sub; + int struct_count = ctx->new_count; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + } + break; + case '}': /* end of substruct; either repeat or move on */ + ++ts; + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } /* fall through */ + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex) { + /* Continue pooling same type */ + ctx->enc_count += ctx->new_count; + } else { + /* New type */ + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + } + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + default: + { + ctx->new_count = __Pyx_BufFmt_ParseNumber(&ts); + if (ctx->new_count == -1) { /* First char was not a digit */ + char msg[2] = { *ts, 0 }; + PyErr_Format(PyExc_ValueError, + "Does not understand character buffer dtype format string ('%s')", msg); + return NULL; + } + } + + } + } +} + +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} + +static int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack) { if (obj == Py_None) { __Pyx_ZeroBuffer(buf); return 0; @@ -3235,36 +5018,37 @@ buf->buf = NULL; if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; if (buf->ndim != nd) { - __Pyx_BufferNdimError(buf, nd); + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); goto fail; } if (!cast) { - ts = buf->format; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - ts = __Pyx_CheckTypestring_double(ts); - if (!ts) goto fail; - ts = __Pyx_ConsumeWhitespace(ts); - if (!ts) goto fail; - if (*ts != 0) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch (expected end, got %s)", - __Pyx_DescribeTokenInFormatString(ts)); - goto fail; - } - } else { - if (buf->itemsize != sizeof(double)) { - PyErr_SetString(PyExc_ValueError, - "Attempted cast of buffer to datatype of different size."); - goto fail; - } + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%"PY_FORMAT_SIZE_T"d byte%s) does not match size of '%s' (%"PY_FORMAT_SIZE_T"d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; return 0; fail:; __Pyx_ZeroBuffer(buf); return -1; } + +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + static void __Pyx_RaiseBufferFallbackError(void) { PyErr_Format(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); @@ -3272,7 +5056,7 @@ -static INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); @@ -3287,7 +5071,7 @@ Py_XDECREF(tmp_tb); } -static INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; @@ -3299,6 +5083,20 @@ } +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +static void __Pyx_UnpackTupleError(PyObject *t, Py_ssize_t index) { + if (t == Py_None) { + __Pyx_RaiseNoneNotIterableError(); + } else if (PyTuple_GET_SIZE(t) < index) { + __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(t)); + } else { + __Pyx_RaiseTooManyValuesError(); + } +} + #if PY_MAJOR_VERSION < 3 static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { #if PY_VERSION_HEX >= 0x02060000 @@ -3330,7 +5128,7 @@ PyObject *global_dict = 0; PyObject *empty_dict = 0; PyObject *list; - __import__ = PyObject_GetAttrString(__pyx_b, "__import__"); + __import__ = __Pyx_GetAttrString(__pyx_b, "__import__"); if (!__import__) goto bad; if (from_list) @@ -3347,8 +5145,8 @@ empty_dict = PyDict_New(); if (!empty_dict) goto bad; - module = PyObject_CallFunction(__import__, "OOOO", - name, global_dict, empty_dict, list); + module = PyObject_CallFunctionObjArgs(__import__, + name, global_dict, empty_dict, list, NULL); bad: Py_XDECREF(empty_list); Py_XDECREF(__import__); @@ -3364,70 +5162,157 @@ return result; } -static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) { - PyObject *item; - if (!(item = PyIter_Next(iter))) { - if (!PyErr_Occurred()) { - PyErr_Format(PyExc_ValueError, - #if PY_VERSION_HEX < 0x02050000 - "need more than %d values to unpack", (int)index); - #else - "need more than %zd values to unpack", index); - #endif - } +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); } - return item; -} + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif -static int __Pyx_EndUnpack(PyObject *iter) { - PyObject *item; - if ((item = PyIter_Next(iter))) { - Py_DECREF(item); - PyErr_SetString(PyExc_ValueError, "too many values to unpack"); - return -1; +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); } - else if (!PyErr_Occurred()) - return 0; - else - return -1; -} + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } +/* + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { +#if HAVE_HYPOT + return hypotf(z.real, z.imag); +#else + return sqrtf(z.real*z.real + z.imag*z.imag); +#endif + } +*/ +#endif -static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (!type) { - PyErr_Format(PyExc_SystemError, "Missing type object"); - return 0; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); } - if (obj == Py_None || PyObject_TypeCheck(obj, type)) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif -static INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } +/* + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { +#if HAVE_HYPOT + return hypot(z.real, z.imag); +#else + return sqrt(z.real*z.real + z.imag*z.imag); +#endif + } +*/ +#endif -static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} - +#if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { Py_XINCREF(type); Py_XINCREF(value); @@ -3483,6 +5368,7 @@ } #endif } + __Pyx_ErrRestore(type, value, tb); return; raise_error: @@ -3492,36 +5378,431 @@ return; } -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - __Pyx_ErrFetch(type, value, tb); - PyErr_NormalizeException(type, value, tb); - if (PyErr_Occurred()) +#else /* Python 3+ */ + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); goto bad; - Py_INCREF(*type); - Py_INCREF(*value); - Py_INCREF(*tb); - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; - /* Make sure tstate is in a consistent state when we XDECREF - these objects (XDECREF may run arbitrary code). */ - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); - return 0; + } + if (value == Py_None) + value = 0; + + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (!PyExceptionClass_Check(type)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + + PyErr_SetObject(type, value); + + if (tb) { + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } + } + bad: - Py_XDECREF(*type); - Py_XDECREF(*value); - Py_XDECREF(*tb); - return -1; + return; } +#endif +static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { + const unsigned char neg_one = (unsigned char)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(unsigned char) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(unsigned char)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to unsigned char" : + "value too large to convert to unsigned char"); + } + return (unsigned char)-1; + } + return (unsigned char)val; + } + return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); +} +static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { + const unsigned short neg_one = (unsigned short)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(unsigned short) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(unsigned short)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to unsigned short" : + "value too large to convert to unsigned short"); + } + return (unsigned short)-1; + } + return (unsigned short)val; + } + return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); +} + +static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { + const unsigned int neg_one = (unsigned int)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(unsigned int) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(unsigned int)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to unsigned int" : + "value too large to convert to unsigned int"); + } + return (unsigned int)-1; + } + return (unsigned int)val; + } + return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); +} + +static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { + const char neg_one = (char)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(char) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(char)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to char" : + "value too large to convert to char"); + } + return (char)-1; + } + return (char)val; + } + return (char)__Pyx_PyInt_AsLong(x); +} + +static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { + const short neg_one = (short)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(short) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(short)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to short" : + "value too large to convert to short"); + } + return (short)-1; + } + return (short)val; + } + return (short)__Pyx_PyInt_AsLong(x); +} + +static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { + const int neg_one = (int)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(int) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(int)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to int" : + "value too large to convert to int"); + } + return (int)-1; + } + return (int)val; + } + return (int)__Pyx_PyInt_AsLong(x); +} + +static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { + const signed char neg_one = (signed char)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(signed char) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(signed char)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to signed char" : + "value too large to convert to signed char"); + } + return (signed char)-1; + } + return (signed char)val; + } + return (signed char)__Pyx_PyInt_AsSignedLong(x); +} + +static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { + const signed short neg_one = (signed short)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(signed short) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(signed short)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to signed short" : + "value too large to convert to signed short"); + } + return (signed short)-1; + } + return (signed short)val; + } + return (signed short)__Pyx_PyInt_AsSignedLong(x); +} + +static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { + const signed int neg_one = (signed int)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; + if (sizeof(signed int) < sizeof(long)) { + long val = __Pyx_PyInt_AsLong(x); + if (unlikely(val != (long)(signed int)val)) { + if (!unlikely(val == -1 && PyErr_Occurred())) { + PyErr_SetString(PyExc_OverflowError, + (is_unsigned && unlikely(val < 0)) ? + "can't convert negative value to signed int" : + "value too large to convert to signed int"); + } + return (signed int)-1; + } + return (signed int)val; + } + return (signed int)__Pyx_PyInt_AsSignedLong(x); +} + +static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { + const unsigned long neg_one = (unsigned long)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned long"); + return (unsigned long)-1; + } + return (unsigned long)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned long"); + return (unsigned long)-1; + } + return PyLong_AsUnsignedLong(x); + } else { + return PyLong_AsLong(x); + } + } else { + unsigned long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (unsigned long)-1; + val = __Pyx_PyInt_AsUnsignedLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { + const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned PY_LONG_LONG"); + return (unsigned PY_LONG_LONG)-1; + } + return (unsigned PY_LONG_LONG)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned PY_LONG_LONG"); + return (unsigned PY_LONG_LONG)-1; + } + return PyLong_AsUnsignedLongLong(x); + } else { + return PyLong_AsLongLong(x); + } + } else { + unsigned PY_LONG_LONG val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (unsigned PY_LONG_LONG)-1; + val = __Pyx_PyInt_AsUnsignedLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { + const long neg_one = (long)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long)-1; + } + return (long)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long)-1; + } + return PyLong_AsUnsignedLong(x); + } else { + return PyLong_AsLong(x); + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long)-1; + val = __Pyx_PyInt_AsLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { + const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to PY_LONG_LONG"); + return (PY_LONG_LONG)-1; + } + return (PY_LONG_LONG)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to PY_LONG_LONG"); + return (PY_LONG_LONG)-1; + } + return PyLong_AsUnsignedLongLong(x); + } else { + return PyLong_AsLongLong(x); + } + } else { + PY_LONG_LONG val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (PY_LONG_LONG)-1; + val = __Pyx_PyInt_AsLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { + const signed long neg_one = (signed long)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed long"); + return (signed long)-1; + } + return (signed long)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed long"); + return (signed long)-1; + } + return PyLong_AsUnsignedLong(x); + } else { + return PyLong_AsLong(x); + } + } else { + signed long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (signed long)-1; + val = __Pyx_PyInt_AsSignedLong(tmp); + Py_DECREF(tmp); + return val; + } +} + +static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { + const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; + const int is_unsigned = neg_one > const_zero; +#if PY_VERSION_HEX < 0x03000000 + if (likely(PyInt_Check(x))) { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed PY_LONG_LONG"); + return (signed PY_LONG_LONG)-1; + } + return (signed PY_LONG_LONG)val; + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { + if (unlikely(Py_SIZE(x) < 0)) { + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to signed PY_LONG_LONG"); + return (signed PY_LONG_LONG)-1; + } + return PyLong_AsUnsignedLongLong(x); + } else { + return PyLong_AsLongLong(x); + } + } else { + signed PY_LONG_LONG val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (signed PY_LONG_LONG)-1; + val = __Pyx_PyInt_AsSignedLongLong(tmp); + Py_DECREF(tmp); + return val; + } +} + static void __Pyx_WriteUnraisable(const char *name) { PyObject *old_exc, *old_val, *old_tb; PyObject *ctx; @@ -3543,11 +5824,12 @@ #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - long size) + long size, int strict) { PyObject *py_module = 0; PyObject *result = 0; PyObject *py_name = 0; + char warning[200]; py_module = __Pyx_ImportModule(module_name); if (!py_module) @@ -3572,9 +5854,15 @@ module_name, class_name); goto bad; } - if (((PyTypeObject *)result)->tp_basicsize != size) { + if (!strict && ((PyTypeObject *)result)->tp_basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + PyErr_WarnEx(NULL, warning, 0); + } + else if (((PyTypeObject *)result)->tp_basicsize != size) { PyErr_Format(PyExc_ValueError, - "%s.%s does not appear to be the correct type object", + "%s.%s has the wrong size, try recompiling", module_name, class_name); goto bad; } @@ -3616,7 +5904,6 @@ PyObject *py_srcfile = 0; PyObject *py_funcname = 0; PyObject *py_globals = 0; - PyObject *empty_string = 0; PyCodeObject *py_code = 0; PyFrameObject *py_frame = 0; @@ -3643,12 +5930,6 @@ if (!py_funcname) goto bad; py_globals = PyModule_GetDict(__pyx_m); if (!py_globals) goto bad; - #if PY_MAJOR_VERSION < 3 - empty_string = PyString_FromStringAndSize("", 0); - #else - empty_string = PyBytes_FromStringAndSize("", 0); - #endif - if (!empty_string) goto bad; py_code = PyCode_New( 0, /*int argcount,*/ #if PY_MAJOR_VERSION >= 3 @@ -3657,7 +5938,7 @@ 0, /*int nlocals,*/ 0, /*int stacksize,*/ 0, /*int flags,*/ - empty_string, /*PyObject *code,*/ + __pyx_empty_bytes, /*PyObject *code,*/ __pyx_empty_tuple, /*PyObject *consts,*/ __pyx_empty_tuple, /*PyObject *names,*/ __pyx_empty_tuple, /*PyObject *varnames,*/ @@ -3666,7 +5947,7 @@ py_srcfile, /*PyObject *filename,*/ py_funcname, /*PyObject *name,*/ __pyx_lineno, /*int firstlineno,*/ - empty_string /*PyObject *lnotab*/ + __pyx_empty_bytes /*PyObject *lnotab*/ ); if (!py_code) goto bad; py_frame = PyFrame_New( @@ -3681,7 +5962,6 @@ bad: Py_XDECREF(py_srcfile); Py_XDECREF(py_funcname); - Py_XDECREF(empty_string); Py_XDECREF(py_code); Py_XDECREF(py_frame); } @@ -3689,7 +5969,7 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 - if (t->is_unicode && (!t->is_identifier)) { + if (t->is_unicode) { *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); } else if (t->intern) { *t->p = PyString_InternFromString(t->s); @@ -3697,10 +5977,14 @@ *t->p = PyString_FromStringAndSize(t->s, t->n - 1); } #else /* Python 3+ has unicode identifiers */ - if (t->is_identifier || (t->is_unicode && t->intern)) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->is_unicode) { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } } else { *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); } @@ -3714,221 +5998,92 @@ /* Type Conversion Functions */ -static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject* x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} - -static INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { if (x == Py_True) return 1; - else if (x == Py_False) return 0; + else if ((x == Py_False) | (x == Py_None)) return 0; else return PyObject_IsTrue(x); } -static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x) { - if (PyInt_CheckExact(x)) { - return PyInt_AS_LONG(x); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_VERSION_HEX < 0x03000000 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return Py_INCREF(x), x; + m = Py_TYPE(x)->tp_as_number; +#if PY_VERSION_HEX < 0x03000000 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_VERSION_HEX < 0x03000000 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%s__ returned non-%s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; } - else if (PyLong_CheckExact(x)) { - return PyLong_AsLongLong(x); - } - else { - PY_LONG_LONG val; - PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; - val = __pyx_PyInt_AsLongLong(tmp); - Py_DECREF(tmp); - return val; - } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; } -static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x) { - if (PyInt_CheckExact(x)) { - long val = PyInt_AS_LONG(x); - if (unlikely(val < 0)) { - PyErr_SetString(PyExc_TypeError, "Negative assignment to unsigned type."); - return (unsigned PY_LONG_LONG)-1; - } - return val; - } - else if (PyLong_CheckExact(x)) { - return PyLong_AsUnsignedLongLong(x); - } - else { - PY_LONG_LONG val; - PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1; - val = __pyx_PyInt_AsUnsignedLongLong(tmp); - Py_DECREF(tmp); - return val; - } +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject* x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; } - -static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x) { - if (sizeof(unsigned char) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - unsigned char val = (unsigned char)long_val; - if (unlikely((val != long_val) || (long_val < 0))) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned char"); - return (unsigned char)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { +#if PY_VERSION_HEX < 0x02050000 + if (ival <= LONG_MAX) + return PyInt_FromLong((long)ival); + else { + unsigned char *bytes = (unsigned char *) &ival; + int one = 1; int little = (int)*(unsigned char*)&one; + return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); + } +#else + return PyInt_FromSize_t(ival); +#endif } -static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x) { - if (sizeof(unsigned short) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - unsigned short val = (unsigned short)long_val; - if (unlikely((val != long_val) || (long_val < 0))) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned short"); - return (unsigned short)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } +static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { + unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); + if (unlikely(val == (unsigned PY_LONG_LONG)-1 && PyErr_Occurred())) { + return (size_t)-1; + } else if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to size_t"); + return (size_t)-1; + } + return (size_t)val; } -static INLINE char __pyx_PyInt_char(PyObject* x) { - if (sizeof(char) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - char val = (char)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to char"); - return (char)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} -static INLINE short __pyx_PyInt_short(PyObject* x) { - if (sizeof(short) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - short val = (short)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to short"); - return (short)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE int __pyx_PyInt_int(PyObject* x) { - if (sizeof(int) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - int val = (int)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to int"); - return (int)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE long __pyx_PyInt_long(PyObject* x) { - if (sizeof(long) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - long val = (long)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to long"); - return (long)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed char __pyx_PyInt_signed_char(PyObject* x) { - if (sizeof(signed char) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed char val = (signed char)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed char"); - return (signed char)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed short __pyx_PyInt_signed_short(PyObject* x) { - if (sizeof(signed short) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed short val = (signed short)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed short"); - return (signed short)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed int __pyx_PyInt_signed_int(PyObject* x) { - if (sizeof(signed int) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed int val = (signed int)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed int"); - return (signed int)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE signed long __pyx_PyInt_signed_long(PyObject* x) { - if (sizeof(signed long) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - signed long val = (signed long)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed long"); - return (signed long)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - -static INLINE long double __pyx_PyInt_long_double(PyObject* x) { - if (sizeof(long double) < sizeof(long)) { - long long_val = __pyx_PyInt_AsLong(x); - long double val = (long double)long_val; - if (unlikely((val != long_val) )) { - PyErr_SetString(PyExc_OverflowError, "value too large to convert to long double"); - return (long double)-1; - } - return val; - } - else { - return __pyx_PyInt_AsLong(x); - } -} - +#endif /* Py_PYTHON_H */ From scipy-svn at scipy.org Mon Feb 8 04:46:08 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 8 Feb 2010 03:46:08 -0600 (CST) Subject: [Scipy-svn] r6218 - trunk/scipy/special Message-ID: <20100208094608.D8D3439CAE6@scipy.org> Author: stefan Date: 2010-02-08 03:46:08 -0600 (Mon, 08 Feb 2010) New Revision: 6218 Modified: trunk/scipy/special/SConscript Log: bug: Avoid accidentally using other numpy installations by prepending include path. Modified: trunk/scipy/special/SConscript =================================================================== --- trunk/scipy/special/SConscript 2010-02-08 09:19:40 UTC (rev 6217) +++ trunk/scipy/special/SConscript 2010-02-08 09:46:08 UTC (rev 6218) @@ -22,8 +22,7 @@ config.CheckF77Mangling() config.Finish() -env.AppendUnique(CPPPATH = env["PYEXTCPPPATH"]) -env.AppendUnique(CPPPATH = get_numpy_include_dirs()) +env.PrependUnique(CPPPATH=[get_numpy_include_dirs(), env["PYEXTCPPPATH"]]) def build_lib(name, ext, libname = None): """ext should be .f or .c""" if not libname: @@ -47,7 +46,7 @@ math_info = get_pkg_info("npymath") env.MergeFlags(math_info.cflags()) env.MergeFlags(math_info.libs()) -env.AppendUnique(LIBPATH = ['.']) +env.PrependUnique(LIBPATH = ['.']) # orthogonal_eval extension env.NumpyPythonExtension('orthogonal_eval', source = 'orthogonal_eval.c') From scipy-svn at scipy.org Mon Feb 8 08:18:36 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 8 Feb 2010 07:18:36 -0600 (CST) Subject: [Scipy-svn] r6219 - in trunk/scipy/signal: . tests Message-ID: <20100208131836.DBB91C7C018@scipy.org> Author: stefan Date: 2010-02-08 07:18:36 -0600 (Mon, 08 Feb 2010) New Revision: 6219 Added: trunk/scipy/signal/tests/test_ltisys.py Modified: trunk/scipy/signal/ltisys.py Log: BUG: Remove syntax error in little used branch of ss2tf. Modified: trunk/scipy/signal/ltisys.py =================================================================== --- trunk/scipy/signal/ltisys.py 2010-02-08 09:46:08 UTC (rev 6218) +++ trunk/scipy/signal/ltisys.py 2010-02-08 13:18:36 UTC (rev 6219) @@ -136,13 +136,15 @@ if D.shape[-1] != 0: D = D[:,input] - den = poly(A) + try: + den = poly(A) + except ValueError: + den = 1 if (product(B.shape,axis=0) == 0) and (product(C.shape,axis=0) == 0): num = numpy.ravel(D) if (product(D.shape,axis=0) == 0) and (product(A.shape,axis=0) == 0): den = [] - end return num, den num_states = A.shape[0] Added: trunk/scipy/signal/tests/test_ltisys.py =================================================================== --- trunk/scipy/signal/tests/test_ltisys.py (rev 0) +++ trunk/scipy/signal/tests/test_ltisys.py 2010-02-08 13:18:36 UTC (rev 6219) @@ -0,0 +1,21 @@ +import numpy as np +from numpy.testing import * + +from scipy.signal.ltisys import ss2tf + +class TestSS2TF: + def tst_matrix_shapes(self, p, q, r): + ss2tf(np.zeros((p, p)), + np.zeros((p, q)), + np.zeros((r, p)), + np.zeros((r, q)), 0) + + def test_basic(self): + for p, q, r in [ + (3, 3, 3), + (0, 3, 3), + (1, 1, 1)]: + yield self.tst_matrix_shapes, p, q, r + +if __name__ == "__main__": + run_module_suite() From scipy-svn at scipy.org Mon Feb 8 10:18:27 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 8 Feb 2010 09:18:27 -0600 (CST) Subject: [Scipy-svn] r6220 - in trunk/scipy/fftpack: . tests Message-ID: <20100208151827.A730A39CAE6@scipy.org> Author: cdavid Date: 2010-02-08 09:18:27 -0600 (Mon, 08 Feb 2010) New Revision: 6220 Modified: trunk/scipy/fftpack/basic.py trunk/scipy/fftpack/tests/test_basic.py Log: BUG: fftn did not call single precision implementation if input was single precision. Modified: trunk/scipy/fftpack/basic.py =================================================================== --- trunk/scipy/fftpack/basic.py 2010-02-08 13:18:36 UTC (rev 6219) +++ trunk/scipy/fftpack/basic.py 2010-02-08 15:18:27 UTC (rev 6220) @@ -358,10 +358,13 @@ hasattr(x,'__array__')) work_function = fftpack.zfftnd elif istype(tmp, numpy.complex64): - raise NotImplementedError + work_function = fftpack.cfftnd else: overwrite_x = 1 - work_function = fftpack.zfftnd + if istype(tmp, numpy.float32): + work_function = fftpack.cfftnd + else: + work_function = fftpack.zfftnd return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) Modified: trunk/scipy/fftpack/tests/test_basic.py =================================================================== --- trunk/scipy/fftpack/tests/test_basic.py 2010-02-08 13:18:36 UTC (rev 6219) +++ trunk/scipy/fftpack/tests/test_basic.py 2010-02-08 15:18:27 UTC (rev 6220) @@ -321,6 +321,17 @@ y_r = numpy.fft.fftn(x, s=(8, 8), axes=(-3, -2)) assert_array_almost_equal(y, y_r) +class TestFftnSingle(TestCase): + def test_definition(self): + x = [[1,2,3],[4,5,6],[7,8,9]] + y = fftn(np.array(x, np.float32)) + if not y.dtype == np.complex64: + raise ValueError("double precision output with single precision") + + y_r = np.array(fftn(x), np.complex64) + assert_array_almost_equal_nulp(np.real(y), np.real(y_r)) + assert_array_almost_equal_nulp(np.imag(y), np.imag(y_r)) + class TestFftn(TestCase): def test_definition(self): From scipy-svn at scipy.org Tue Feb 9 03:41:34 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 9 Feb 2010 02:41:34 -0600 (CST) Subject: [Scipy-svn] r6221 - trunk/scipy/optimize Message-ID: <20100209084134.F1AAEC7C01F@scipy.org> Author: dmitrey.kroshko Date: 2010-02-09 02:41:34 -0600 (Tue, 09 Feb 2010) New Revision: 6221 Modified: trunk/scipy/optimize/anneal.py trunk/scipy/optimize/cobyla.py trunk/scipy/optimize/lbfgsb.py trunk/scipy/optimize/minpack.py trunk/scipy/optimize/nnls.py trunk/scipy/optimize/optimize.py trunk/scipy/optimize/slsqp.py trunk/scipy/optimize/tnc.py Log: scikits.openopt replaced by mere openopt Modified: trunk/scipy/optimize/anneal.py =================================================================== --- trunk/scipy/optimize/anneal.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/anneal.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -217,6 +217,8 @@ fixed_point -- scalar fixed-point finder + OpenOpt -- Python package with more optimization solvers + """ x0 = asarray(x0) lower = asarray(lower) Modified: trunk/scipy/optimize/cobyla.py =================================================================== --- trunk/scipy/optimize/cobyla.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/cobyla.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -46,8 +46,6 @@ See also: - scikits.openopt, which offers a unified syntax to call this and other solvers - fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg -- multivariate local optimizers leastsq -- nonlinear least squares minimizer @@ -65,6 +63,9 @@ fixed_point -- scalar fixed-point finder + OpenOpt -- a tool which offers a unified syntax to call this and + other solvers with possibility of automatic differentiation + """ err = "cons must be a sequence of callable functions or a single"\ " callable function." Modified: trunk/scipy/optimize/lbfgsb.py =================================================================== --- trunk/scipy/optimize/lbfgsb.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/lbfgsb.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -119,8 +119,6 @@ ACM Transactions on Mathematical Software, Vol 23, Num. 4, pp. 550 - 560. See also: - scikits.openopt, which offers a unified syntax to call this and other solvers - fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg -- multivariate local optimizers leastsq -- nonlinear least squares minimizer @@ -138,6 +136,9 @@ fixed_point -- scalar fixed-point finder + OpenOpt -- a tool which offers a unified syntax to call this and + other solvers with possibility of automatic differentiation + """ n = len(x0) Modified: trunk/scipy/optimize/minpack.py =================================================================== --- trunk/scipy/optimize/minpack.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/minpack.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -102,8 +102,6 @@ See Also -------- - scikits.openopt : offers a unified syntax to call this and other solvers - fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg : multivariate local optimizers leastsq : nonlinear least squares minimizer @@ -118,6 +116,9 @@ fixed_point : scalar and vector fixed-point finder + OpenOpt : a tool which offers a unified syntax to call this and + other solvers with possibility of automatic differentiation + """ if not warning : msg = "The warning keyword is deprecated. Use the warnings module." @@ -263,7 +264,6 @@ See Also -------- - scikits.openopt: offers a unified syntax to call this and other solvers fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg: multivariate local optimizers fmin_l_bfgs_b, fmin_tnc, fmin_cobyla: constrained multivariate optimizers anneal, brute: global optimizers @@ -272,6 +272,9 @@ brentq, brenth, ridder, bisect, newton: one-dimensional root-finding fixed_point: scalar and vector fixed-point finder curve_fit: find parameters for a curve-fitting problem. + OpenOpt : a tool which offers a unified syntax to call this and + other solvers with possibility of automatic differentiation + """ if not warning : msg = "The warning keyword is deprecated. Use the warnings module." Modified: trunk/scipy/optimize/nnls.py =================================================================== --- trunk/scipy/optimize/nnls.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/nnls.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -16,6 +16,8 @@ wrapper around NNLS.F code below nnls/ directory + Check OpenOpt for more LLSP solvers + """ A,b = map(asarray_chkfinite, (A,b)) Modified: trunk/scipy/optimize/optimize.py =================================================================== --- trunk/scipy/optimize/optimize.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/optimize.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -156,7 +156,8 @@ Uses a Nelder-Mead simplex algorithm to find the minimum of function of one or more variables. - + Check OpenOpt - a tool which offers a unified syntax to call + this and other solvers with possibility of automatic differentiation. """ fcalls, func = wrap_function(func, args) x0 = asfarray(x0).flatten() @@ -694,8 +695,8 @@ *See Also*: - scikits.openopt : SciKit which offers a unified syntax to call - this and other solvers. + OpenOpt : a tool which offers a unified syntax to call + this and other solvers with possibility of automatic differentiation. """ x0 = asarray(x0).squeeze() @@ -862,7 +863,8 @@ using the nonlinear conjugate gradient algorithm of Polak and Ribiere See Wright, and Nocedal 'Numerical Optimization', 1999, pg. 120-122. - + Check OpenOpt - a tool which offers a unified syntax to call + this and other solvers with possibility of automatic differentiation. """ x0 = asarray(x0).flatten() if maxiter is None: @@ -1018,8 +1020,7 @@ If True, return a list of results at each iteration. :Notes: - 1. scikits.openopt offers a unified syntax to call this and other solvers. - 2. Only one of `fhess_p` or `fhess` need to be given. If `fhess` + 1. Only one of `fhess_p` or `fhess` need to be given. If `fhess` is provided, then `fhess_p` will be ignored. If neither `fhess` nor `fhess_p` is provided, then the hessian product will be approximated using finite differences on `fprime`. `fhess_p` @@ -1027,6 +1028,8 @@ given, finite-differences on `fprime` are used to compute it. See Wright, and Nocedal 'Numerical Optimization', 1999, pg. 140. + 2. Check OpenOpt - a tool which offers a unified syntax to call + this and other solvers with possibility of automatic differentiation. """ x0 = asarray(x0).flatten() @@ -1179,8 +1182,9 @@ Finds a local minimizer of the scalar function `func` in the interval x1 < xopt < x2 using Brent's method. (See `brent` for auto-bracketing). + Check OpenOpt - a tool which offers a unified syntax to call + this and other solvers with possibility of automatic differentiation. - """ # Test bounds are of correct form @@ -1722,7 +1726,8 @@ Uses a modification of Powell's method to find the minimum of a function of N variables. - + Check OpenOpt - a tool which offers a unified syntax to call + this and other solvers with possibility of automatic differentiation. """ # we need to use a mutable object here that we can update in the # wrapper function Modified: trunk/scipy/optimize/slsqp.py =================================================================== --- trunk/scipy/optimize/slsqp.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/slsqp.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -146,6 +146,11 @@ for examples see :ref:`in the tutorial ` + See also + -------- + OpenOpt - a tool which offers a unified syntax to call this + and other solvers with possibility of automatic differentiation. + """ exit_modes = { -1 : "Gradient evaluation required (g & a)", Modified: trunk/scipy/optimize/tnc.py =================================================================== --- trunk/scipy/optimize/tnc.py 2010-02-08 15:18:27 UTC (rev 6220) +++ trunk/scipy/optimize/tnc.py 2010-02-09 08:41:34 UTC (rev 6221) @@ -164,8 +164,6 @@ Return code as defined in the RCSTRINGS dict. :SeeAlso: - - scikits.openopt, which offers a unified syntax to call this and other solvers - - fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg : multivariate local optimizers @@ -184,6 +182,9 @@ - fixed_point : scalar fixed-point finder + - OpenOpt : a tool which offers a unified syntax to call this and + other solvers with possibility of automatic differentiation. + """ x0 = asarray(x0, dtype=float).tolist() n = len(x0) From scipy-svn at scipy.org Tue Feb 9 20:50:10 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 9 Feb 2010 19:50:10 -0600 (CST) Subject: [Scipy-svn] r6222 - trunk/scipy/fftpack/tests Message-ID: <20100210015010.B7E8D39C4B4@scipy.org> Author: cdavid Date: 2010-02-09 19:50:09 -0600 (Tue, 09 Feb 2010) New Revision: 6222 Modified: trunk/scipy/fftpack/tests/test_basic.py Log: BUG: comparing real/imag parts does not work for assert_array_almost_equal_nulp. I fixed assert_array_almost_equal_nulp to handle complex type in numpy 1.4.0.1 and in trunk Modified: trunk/scipy/fftpack/tests/test_basic.py =================================================================== --- trunk/scipy/fftpack/tests/test_basic.py 2010-02-09 08:41:34 UTC (rev 6221) +++ trunk/scipy/fftpack/tests/test_basic.py 2010-02-10 01:50:09 UTC (rev 6222) @@ -329,8 +329,7 @@ raise ValueError("double precision output with single precision") y_r = np.array(fftn(x), np.complex64) - assert_array_almost_equal_nulp(np.real(y), np.real(y_r)) - assert_array_almost_equal_nulp(np.imag(y), np.imag(y_r)) + assert_array_almost_equal_nulp(y, y_r) class TestFftn(TestCase): From scipy-svn at scipy.org Tue Feb 9 20:50:25 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 9 Feb 2010 19:50:25 -0600 (CST) Subject: [Scipy-svn] r6223 - trunk/scipy/fftpack/tests Message-ID: <20100210015025.7380039C4B4@scipy.org> Author: cdavid Date: 2010-02-09 19:50:25 -0600 (Tue, 09 Feb 2010) New Revision: 6223 Modified: trunk/scipy/fftpack/tests/test_basic.py Log: TST: add single precision test for ifftn. (single prec ifftn is not implemented yet). Modified: trunk/scipy/fftpack/tests/test_basic.py =================================================================== --- trunk/scipy/fftpack/tests/test_basic.py 2010-02-10 01:50:09 UTC (rev 6222) +++ trunk/scipy/fftpack/tests/test_basic.py 2010-02-10 01:50:25 UTC (rev 6223) @@ -486,22 +486,34 @@ except ValueError: pass -class TestIfftn(TestCase): - +class _TestIfftn(TestCase): + dtype = None + cdtype = None def test_definition(self): - x = [[1,2,3],[4,5,6],[7,8,9]] + x = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype=self.dtype) y = ifftn(x) - assert_array_almost_equal(y,direct_idftn(x)) + assert y.dtype == self.cdtype + assert_array_almost_equal_nulp(y,direct_idftn(x),self.maxnlp) x = random((20,26)) - assert_array_almost_equal(ifftn(x),direct_idftn(x)) + assert_array_almost_equal_nulp(ifftn(x),direct_idftn(x),self.maxnlp) x = random((5,4,3,20)) - assert_array_almost_equal(ifftn(x),direct_idftn(x)) + assert_array_almost_equal_nulp(ifftn(x),direct_idftn(x),self.maxnlp) def test_random_complex(self): for size in [1,2,51,32,64,92]: x = random([size,size]) + 1j*random([size,size]) - assert_array_almost_equal (ifftn(fftn(x)),x) - assert_array_almost_equal (fftn(ifftn(x)),x) + assert_array_almost_equal_nulp(ifftn(fftn(x)),x,self.maxnlp) + assert_array_almost_equal_nulp(fftn(ifftn(x)),x,self.maxnlp) +class TestIfftnDouble(_TestIfftn): + dtype = np.float64 + cdtype = np.complex128 + maxnlp = 2000 + +class TestIfftnSingle(_TestIfftn): + dtype = np.float32 + cdtype = np.complex64 + maxnlp = 2000 + if __name__ == "__main__": run_module_suite() From scipy-svn at scipy.org Tue Feb 9 20:50:37 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 9 Feb 2010 19:50:37 -0600 (CST) Subject: [Scipy-svn] r6224 - trunk/scipy/fftpack Message-ID: <20100210015037.C9EA939C4B4@scipy.org> Author: cdavid Date: 2010-02-09 19:50:37 -0600 (Tue, 09 Feb 2010) New Revision: 6224 Modified: trunk/scipy/fftpack/basic.py Log: BUG: fix single precision handling for ifftn. Modified: trunk/scipy/fftpack/basic.py =================================================================== --- trunk/scipy/fftpack/basic.py 2010-02-10 01:50:25 UTC (rev 6223) +++ trunk/scipy/fftpack/basic.py 2010-02-10 01:50:37 UTC (rev 6224) @@ -263,7 +263,6 @@ work_function = fftpack.drfft return _raw_fft(tmp,n,axis,-1,overwrite_x,work_function) - def _raw_fftnd(x, s, axes, direction, overwrite_x, work_function): """ Internal auxiliary function for fftnd, ifftnd.""" if s is None: @@ -352,6 +351,9 @@ Notes: y == fftn(ifftn(y)) within numerical accuracy. """ + return _raw_fftn_dispatch(x, shape, axes, overwrite_x, 1) + +def _raw_fftn_dispatch(x, shape, axes, overwrite_x, direction): tmp = asarray(x) if istype(tmp, numpy.complex128): overwrite_x = overwrite_x or (tmp is not x and not \ @@ -365,7 +367,7 @@ work_function = fftpack.cfftnd else: work_function = fftpack.zfftnd - return _raw_fftnd(tmp,shape,axes,1,overwrite_x,work_function) + return _raw_fftnd(tmp,shape,axes,direction,overwrite_x,work_function) def ifftn(x, shape=None, axes=None, overwrite_x=0): @@ -383,19 +385,8 @@ Optional input: see fftn.__doc__ """ - tmp = asarray(x) - if istype(tmp, numpy.complex128): - overwrite_x = overwrite_x or (tmp is not x and not \ - hasattr(x,'__array__')) - work_function = fftpack.zfftnd - elif istype(tmp, numpy.complex64): - raise NotImplementedError - else: - overwrite_x = 1 - work_function = fftpack.zfftnd - return _raw_fftnd(tmp,shape,axes,-1,overwrite_x,work_function) + return _raw_fftn_dispatch(x, shape, axes, overwrite_x, -1) - def fft2(x, shape=None, axes=(-2,-1), overwrite_x=0): """ 2-D discrete Fourier transform. From scipy-svn at scipy.org Wed Feb 10 02:41:40 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:41:40 -0600 (CST) Subject: [Scipy-svn] r6225 - trunk/scipy/optimize Message-ID: <20100210074140.A317939CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:41:40 -0600 (Wed, 10 Feb 2010) New Revision: 6225 Modified: trunk/scipy/optimize/slsqp.py Log: DOC: Reformat approx_jacobian docstring. Modified: trunk/scipy/optimize/slsqp.py =================================================================== --- trunk/scipy/optimize/slsqp.py 2010-02-10 01:50:37 UTC (rev 6224) +++ trunk/scipy/optimize/slsqp.py 2010-02-10 07:41:40 UTC (rev 6225) @@ -17,20 +17,28 @@ _epsilon = sqrt(finfo(float).eps) def approx_jacobian(x,func,epsilon,*args): - """Approximate the Jacobian matrix of callable function func + """Approximate the Jacobian matrix of a callable function. - *Parameters*: - x - The state vector at which the Jacobian matrix is desired - func - A vector-valued function of the form f(x,*args) - epsilon - The peturbation used to determine the partial derivatives - *args - Additional arguments passed to func + Parameters + ---------- + x : array_like + The state vector at which to compute the Jacobian matrix. + func : callable f(x, *args) + The vector-valued function. + epsilon : float\ + The peturbation used to determine the partial derivatives. + *args : tuple + Additional arguments passed to func. - *Returns*: - An array of dimensions (lenf, lenx) where lenf is the length - of the outputs of func, and lenx is the number of + Returns + ------- + An array of dimensions ``(lenf, lenx)`` where ``lenf`` is the length + of the outputs of `func`, and ``lenx`` is the number of elements in + `x`. - *Notes*: - The approximation is done using forward differences + Notes + ----- + The approximation is done using forward differences. """ x0 = asfarray(x) @@ -44,8 +52,6 @@ return jac.transpose() - - def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_eqcons=None, fprime_ieqcons=None, args = (), iter = 100, acc = 1.0E-6, From scipy-svn at scipy.org Wed Feb 10 02:42:25 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:42:25 -0600 (CST) Subject: [Scipy-svn] r6226 - trunk/scipy/optimize Message-ID: <20100210074225.DD0FD39CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:42:25 -0600 (Wed, 10 Feb 2010) New Revision: 6226 Modified: trunk/scipy/optimize/tnc.py Log: DOC: Reformat TNC docstring. Modified: trunk/scipy/optimize/tnc.py =================================================================== --- trunk/scipy/optimize/tnc.py 2010-02-10 07:41:40 UTC (rev 6225) +++ trunk/scipy/optimize/tnc.py 2010-02-10 07:42:25 UTC (rev 6226) @@ -86,106 +86,86 @@ """Minimize a function with variables subject to bounds, using gradient information. - :Parameters: - func : callable func(x, *args) - Function to minimize. Should return f and g, where f is - the value of the function and g its gradient (a list of - floats). If the function returns None, the minimization - is aborted. - x0 : list of floats - Initial estimate of minimum. - fprime : callable fprime(x, *args) - Gradient of func. If None, then func must return the - function value and the gradient (f,g = func(x, *args)). - args : tuple - Arguments to pass to function. - approx_grad : bool - If true, approximate the gradient numerically. - bounds : list - (min, max) pairs for each element in x, defining the - bounds on that parameter. Use None or +/-inf for one of - min or max when there is no bound in that direction. - scale : list of floats - Scaling factors to apply to each variable. If None, the - factors are up-low for interval bounded variables and - 1+|x] fo the others. Defaults to None - offset : float - Value to substract from each variable. If None, the - offsets are (up+low)/2 for interval bounded variables - and x for the others. - messages : - Bit mask used to select messages display during - minimization values defined in the MSGS dict. Defaults to - MGS_ALL. - maxCGit : int - Maximum number of hessian*vector evaluations per main - iteration. If maxCGit == 0, the direction chosen is - -gradient if maxCGit < 0, maxCGit is set to - max(1,min(50,n/2)). Defaults to -1. - maxfun : int - Maximum number of function evaluation. if None, maxfun is - set to max(100, 10*len(x0)). Defaults to None. - eta : float - Severity of the line search. if < 0 or > 1, set to 0.25. - Defaults to -1. - stepmx : float - Maximum step for the line search. May be increased during - call. If too small, it will be set to 10.0. Defaults to 0. - accuracy : float - Relative precision for finite difference calculations. If - <= machine_precision, set to sqrt(machine_precision). - Defaults to 0. - fmin : float - Minimum function value estimate. Defaults to 0. - ftol : float - Precision goal for the value of f in the stoping criterion. - If ftol < 0.0, ftol is set to 0.0 defaults to -1. - xtol : float - Precision goal for the value of x in the stopping - criterion (after applying x scaling factors). If xtol < - 0.0, xtol is set to sqrt(machine_precision). Defaults to - -1. - pgtol : float - Precision goal for the value of the projected gradient in - the stopping criterion (after applying x scaling factors). - If pgtol < 0.0, pgtol is set to 1e-2 * sqrt(accuracy). - Setting it to 0.0 is not recommended. Defaults to -1. - rescale : float - Scaling factor (in log10) used to trigger f value - rescaling. If 0, rescale at each iteration. If a large - value, never rescale. If < 0, rescale is set to 1.3. + Parameters + ---------- + func : callable func(x, *args) + Function to minimize. Should return f and g, where f is + the value of the function and g its gradient (a list of + floats). If the function returns None, the minimization + is aborted. + x0 : list of floats + Initial estimate of minimum. + fprime : callable fprime(x, *args) + Gradient of func. If None, then func must return the + function value and the gradient (f,g = func(x, *args)). + args : tuple + Arguments to pass to function. + approx_grad : bool + If true, approximate the gradient numerically. + bounds : list + (min, max) pairs for each element in x, defining the + bounds on that parameter. Use None or +/-inf for one of + min or max when there is no bound in that direction. + scale : list of floats + Scaling factors to apply to each variable. If None, the + factors are up-low for interval bounded variables and + 1+|x] fo the others. Defaults to None + offset : float + Value to substract from each variable. If None, the + offsets are (up+low)/2 for interval bounded variables + and x for the others. + messages : + Bit mask used to select messages display during + minimization values defined in the MSGS dict. Defaults to + MGS_ALL. + maxCGit : int + Maximum number of hessian*vector evaluations per main + iteration. If maxCGit == 0, the direction chosen is + -gradient if maxCGit < 0, maxCGit is set to + max(1,min(50,n/2)). Defaults to -1. + maxfun : int + Maximum number of function evaluation. if None, maxfun is + set to max(100, 10*len(x0)). Defaults to None. + eta : float + Severity of the line search. if < 0 or > 1, set to 0.25. + Defaults to -1. + stepmx : float + Maximum step for the line search. May be increased during + call. If too small, it will be set to 10.0. Defaults to 0. + accuracy : float + Relative precision for finite difference calculations. If + <= machine_precision, set to sqrt(machine_precision). + Defaults to 0. + fmin : float + Minimum function value estimate. Defaults to 0. + ftol : float + Precision goal for the value of f in the stoping criterion. + If ftol < 0.0, ftol is set to 0.0 defaults to -1. + xtol : float + Precision goal for the value of x in the stopping + criterion (after applying x scaling factors). If xtol < + 0.0, xtol is set to sqrt(machine_precision). Defaults to + -1. + pgtol : float + Precision goal for the value of the projected gradient in + the stopping criterion (after applying x scaling factors). + If pgtol < 0.0, pgtol is set to 1e-2 * sqrt(accuracy). + Setting it to 0.0 is not recommended. Defaults to -1. + rescale : float + Scaling factor (in log10) used to trigger f value + rescaling. If 0, rescale at each iteration. If a large + value, never rescale. If < 0, rescale is set to 1.3. - :Returns: - x : list of floats - The solution. - nfeval : int - The number of function evaluations. - rc : - Return code as defined in the RCSTRINGS dict. + Returns + ------- + x : list of floats + The solution. + nfeval : int + The number of function evaluations. + rc : + Return code as defined in the RCSTRINGS dict. - :SeeAlso: - - fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg : - multivariate local optimizers - - - leastsq : nonlinear least squares minimizer - - - fmin_l_bfgs_b, fmin_tnc, fmin_cobyla : constrained - multivariate optimizers - - - anneal, brute : global optimizers - - - fminbound, brent, golden, bracket : local scalar minimizers - - - fsolve : n-dimensional root-finding - - - brentq, brenth, ridder, bisect, newton : one-dimensional root-finding - - - fixed_point : scalar fixed-point finder - - - OpenOpt : a tool which offers a unified syntax to call this and - other solvers with possibility of automatic differentiation. - -""" + """ x0 = asarray(x0, dtype=float).tolist() n = len(x0) From scipy-svn at scipy.org Wed Feb 10 02:43:05 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:43:05 -0600 (CST) Subject: [Scipy-svn] r6227 - trunk/scipy/optimize Message-ID: <20100210074305.0C1C839CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:43:04 -0600 (Wed, 10 Feb 2010) New Revision: 6227 Modified: trunk/scipy/optimize/optimize.py trunk/scipy/optimize/slsqp.py Log: DOC: Reformat docstrings in optimize and slsqp. Modified: trunk/scipy/optimize/optimize.py =================================================================== --- trunk/scipy/optimize/optimize.py 2010-02-10 07:42:25 UTC (rev 6226) +++ trunk/scipy/optimize/optimize.py 2010-02-10 07:43:04 UTC (rev 6227) @@ -107,57 +107,56 @@ full_output=0, disp=1, retall=0, callback=None): """Minimize a function using the downhill simplex algorithm. - :Parameters: + Parameters + ---------- + func : callable func(x,*args) + The objective function to be minimized. + x0 : ndarray + Initial guess. + args : tuple + Extra arguments passed to func, i.e. ``f(x,*args)``. + callback : callable + Called after each iteration, as callback(xk), where xk is the + current parameter vector. - func : callable func(x,*args) - The objective function to be minimized. - x0 : ndarray - Initial guess. - args : tuple - Extra arguments passed to func, i.e. ``f(x,*args)``. - callback : callable - Called after each iteration, as callback(xk), where xk is the - current parameter vector. + Returns + ------- + xopt : ndarray + Parameter that minimizes function. + fopt : float + Value of function at minimum: ``fopt = func(xopt)``. + iter : int + Number of iterations performed. + funcalls : int + Number of function calls made. + warnflag : int + 1 : Maximum number of function evaluations made. + 2 : Maximum number of iterations reached. + allvecs : list + Solution at each iteration. - :Returns: (xopt, {fopt, iter, funcalls, warnflag}) + Other Parameters + ---------------- + xtol : float + Relative error in xopt acceptable for convergence. + ftol : number + Relative error in func(xopt) acceptable for convergence. + maxiter : int + Maximum number of iterations to perform. + maxfun : number + Maximum number of function evaluations to make. + full_output : bool + Set to True if fval and warnflag outputs are desired. + disp : bool + Set to True to print convergence messages. + retall : bool + Set to True to return list of solutions at each iteration. - xopt : ndarray - Parameter that minimizes function. - fopt : float - Value of function at minimum: ``fopt = func(xopt)``. - iter : int - Number of iterations performed. - funcalls : int - Number of function calls made. - warnflag : int - 1 : Maximum number of function evaluations made. - 2 : Maximum number of iterations reached. - allvecs : list - Solution at each iteration. + Notes + ----- + Uses a Nelder-Mead simplex algorithm to find the minimum of + a function of one or more variables. - *Other Parameters*: - - xtol : float - Relative error in xopt acceptable for convergence. - ftol : number - Relative error in func(xopt) acceptable for convergence. - maxiter : int - Maximum number of iterations to perform. - maxfun : number - Maximum number of function evaluations to make. - full_output : bool - Set to True if fval and warnflag outputs are desired. - disp : bool - Set to True to print convergence messages. - retall : bool - Set to True to return list of solutions at each iteration. - - :Notes: - - Uses a Nelder-Mead simplex algorithm to find the minimum of - function of one or more variables. - Check OpenOpt - a tool which offers a unified syntax to call - this and other solvers with possibility of automatic differentiation. """ fcalls, func = wrap_function(func, args) x0 = asfarray(x0).flatten() @@ -347,10 +346,12 @@ phi_rec = phi0 a_rec = 0 while 1: - # interpolate to find a trial step length between a_lo and a_hi - # Need to choose interpolation here. Use cubic interpolation and then if the - # result is within delta * dalpha or outside of the interval bounded by a_lo or a_hi - # then use quadratic interpolation, if the result is still too close, then use bisection + # interpolate to find a trial step length between a_lo and + # a_hi Need to choose interpolation here. Use cubic + # interpolation and then if the result is within delta * + # dalpha or outside of the interval bounded by a_lo or a_hi + # then use quadratic interpolation, if the result is still too + # close, then use bisection dalpha = a_hi-a_lo; if dalpha < 0: a,b = a_hi,a_lo @@ -358,10 +359,11 @@ # minimizer of cubic interpolant # (uses phi_lo, derphi_lo, phi_hi, and the most recent value of phi) - # if the result is too close to the end points (or out of the interval) - # then use quadratic interpolation with phi_lo, derphi_lo and phi_hi - # if the result is stil too close to the end points (or out of the interval) - # then use bisection + # if the result is too close to the end points (or out of + # the interval) then use quadratic interpolation with + # phi_lo, derphi_lo and phi_hi + # if the result is stil too close to the end points (or + # out of the interval) then use bisection if (i > 0): cchk = delta1*dalpha @@ -413,43 +415,43 @@ args=(), c1=1e-4, c2=0.9, amax=50): """Find alpha that satisfies strong Wolfe conditions. - :Parameters: + Parameters + ---------- + f : callable f(x,*args) + Objective function. + myfprime : callable f'(x,*args) + Objective function gradient (can be None). + xk : ndarray + Starting point. + pk : ndarray + Search direction. + gfk : ndarray + Gradient value for x=xk (xk being the current parameter + estimate). + args : tuple + Additional arguments passed to objective function. + c1 : float + Parameter for Armijo condition rule. + c2 : float + Parameter for curvature condition rule. - f : callable f(x,*args) - Objective function. - myfprime : callable f'(x,*args) - Objective function gradient (can be None). - xk : ndarray - Starting point. - pk : ndarray - Search direction. - gfk : ndarray - Gradient value for x=xk (xk being the current parameter - estimate). - args : tuple - Additional arguments passed to objective function. - c1 : float - Parameter for Armijo condition rule. - c2 : float - Parameter for curvature condition rule. + Returns + ------- + alpha0 : float + Alpha for which ``x_new = x0 + alpha * pk``. + fc : int + Number of function evaluations made. + gc : int + Number of gradient evaluations made. - :Returns: + Notes + ----- + Uses the line search algorithm to enforce strong Wolfe + conditions. See Wright and Nocedal, 'Numerical Optimization', + 1999, pg. 59-60. - alpha0 : float - Alpha for which ``x_new = x0 + alpha * pk``. - fc : int - Number of function evaluations made. - gc : int - Number of gradient evaluations made. + For the zoom phase it uses an algorithm by [...]. - :Notes: - - Uses the line search algorithm to enforce strong Wolfe - conditions. See Wright and Nocedal, 'Numerical Optimization', - 1999, pg. 59-60. - - For the zoom phase it uses an algorithm by [...]. - """ global _ls_fc, _ls_gc, _ls_ingfk @@ -634,70 +636,66 @@ retall=0, callback=None): """Minimize a function using the BFGS algorithm. - :Parameters: + Parameters + ---------- + f : callable f(x,*args) + Objective function to be minimized. + x0 : ndarray + Initial guess. + fprime : callable f'(x,*args) + Gradient of f. + args : tuple + Extra arguments passed to f and fprime. + gtol : float + Gradient norm must be less than gtol before succesful termination. + norm : float + Order of norm (Inf is max, -Inf is min) + epsilon : int or ndarray + If fprime is approximated, use this value for the step size. + callback : callable + An optional user-supplied function to call after each + iteration. Called as callback(xk), where xk is the + current parameter vector. - f : callable f(x,*args) - Objective function to be minimized. - x0 : ndarray - Initial guess. - fprime : callable f'(x,*args) - Gradient of f. - args : tuple - Extra arguments passed to f and fprime. - gtol : float - Gradient norm must be less than gtol before succesful termination. - norm : float - Order of norm (Inf is max, -Inf is min) - epsilon : int or ndarray - If fprime is approximated, use this value for the step size. - callback : callable - An optional user-supplied function to call after each - iteration. Called as callback(xk), where xk is the - current parameter vector. + Returns + ------- + xopt : ndarray + Parameters which minimize f, i.e. f(xopt) == fopt. + fopt : float + Minimum value. + gopt : ndarray + Value of gradient at minimum, f'(xopt), which should be near 0. + Bopt : ndarray + Value of 1/f''(xopt), i.e. the inverse hessian matrix. + func_calls : int + Number of function_calls made. + grad_calls : int + Number of gradient calls made. + warnflag : integer + 1 : Maximum number of iterations exceeded. + 2 : Gradient and/or function calls not changing. + allvecs : list + Results at each iteration. Only returned if retall is True. - :Returns: (xopt, {fopt, gopt, Hopt, func_calls, grad_calls, warnflag}, ) + Other Parameters + ---------------- + maxiter : int + Maximum number of iterations to perform. + full_output : bool + If True,return fopt, func_calls, grad_calls, and warnflag + in addition to xopt. + disp : bool + Print convergence message if True. + retall : bool + Return a list of results at each iteration if True. - xopt : ndarray - Parameters which minimize f, i.e. f(xopt) == fopt. - fopt : float - Minimum value. - gopt : ndarray - Value of gradient at minimum, f'(xopt), which should be near 0. - Bopt : ndarray - Value of 1/f''(xopt), i.e. the inverse hessian matrix. - func_calls : int - Number of function_calls made. - grad_calls : int - Number of gradient calls made. - warnflag : integer - 1 : Maximum number of iterations exceeded. - 2 : Gradient and/or function calls not changing. - allvecs : list - Results at each iteration. Only returned if retall is True. + Notes + ----- + Optimize the function, f, whose gradient is given by fprime + using the quasi-Newton method of Broyden, Fletcher, Goldfarb, + and Shanno (BFGS) See Wright, and Nocedal 'Numerical + Optimization', 1999, pg. 198. - *Other Parameters*: - maxiter : int - Maximum number of iterations to perform. - full_output : bool - If True,return fopt, func_calls, grad_calls, and warnflag - in addition to xopt. - disp : bool - Print convergence message if True. - retall : bool - Return a list of results at each iteration if True. - - :Notes: - - Optimize the function, f, whose gradient is given by fprime - using the quasi-Newton method of Broyden, Fletcher, Goldfarb, - and Shanno (BFGS) See Wright, and Nocedal 'Numerical - Optimization', 1999, pg. 198. - - *See Also*: - - OpenOpt : a tool which offers a unified syntax to call - this and other solvers with possibility of automatic differentiation. - """ x0 = asarray(x0).squeeze() if x0.ndim == 0: @@ -808,63 +806,64 @@ maxiter=None, full_output=0, disp=1, retall=0, callback=None): """Minimize a function using a nonlinear conjugate gradient algorithm. - :Parameters: - f : callable f(x,*args) - Objective function to be minimized. - x0 : ndarray - Initial guess. - fprime : callable f'(x,*args) - Function which computes the gradient of f. - args : tuple - Extra arguments passed to f and fprime. - gtol : float - Stop when norm of gradient is less than gtol. - norm : float - Order of vector norm to use. -Inf is min, Inf is max. - epsilon : float or ndarray - If fprime is approximated, use this value for the step - size (can be scalar or vector). - callback : callable - An optional user-supplied function, called after each - iteration. Called as callback(xk), where xk is the - current parameter vector. + Parameters + ---------- + f : callable f(x,*args) + Objective function to be minimized. + x0 : ndarray + Initial guess. + fprime : callable f'(x,*args) + Function which computes the gradient of f. + args : tuple + Extra arguments passed to f and fprime. + gtol : float + Stop when norm of gradient is less than gtol. + norm : float + Order of vector norm to use. -Inf is min, Inf is max. + epsilon : float or ndarray + If fprime is approximated, use this value for the step + size (can be scalar or vector). + callback : callable + An optional user-supplied function, called after each + iteration. Called as callback(xk), where xk is the + current parameter vector. - :Returns: (xopt, {fopt, func_calls, grad_calls, warnflag}, {allvecs}) + Returns + ------- + xopt : ndarray + Parameters which minimize f, i.e. f(xopt) == fopt. + fopt : float + Minimum value found, f(xopt). + func_calls : int + The number of function_calls made. + grad_calls : int + The number of gradient calls made. + warnflag : int + 1 : Maximum number of iterations exceeded. + 2 : Gradient and/or function calls not changing. + allvecs : ndarray + If retall is True (see other parameters below), then this + vector containing the result at each iteration is returned. - xopt : ndarray - Parameters which minimize f, i.e. f(xopt) == fopt. - fopt : float - Minimum value found, f(xopt). - func_calls : int - The number of function_calls made. - grad_calls : int - The number of gradient calls made. - warnflag : int - 1 : Maximum number of iterations exceeded. - 2 : Gradient and/or function calls not changing. - allvecs : ndarray - If retall is True (see other parameters below), then this - vector containing the result at each iteration is returned. + Other Parameters + ---------------- + maxiter : int + Maximum number of iterations to perform. + full_output : bool + If True then return fopt, func_calls, grad_calls, and + warnflag in addition to xopt. + disp : bool + Print convergence message if True. + retall : bool + Return a list of results at each iteration if True. - *Other Parameters*: - maxiter : int - Maximum number of iterations to perform. - full_output : bool - If True then return fopt, func_calls, grad_calls, and - warnflag in addition to xopt. - disp : bool - Print convergence message if True. - retall : bool - return a list of results at each iteration if True. + Notes + ----- + Optimize the function, f, whose gradient is given by fprime + using the nonlinear conjugate gradient algorithm of Polak and + Ribiere. See Wright & Nocedal, 'Numerical Optimization', + 1999, pg. 120-122. - :Notes: - - Optimize the function, f, whose gradient is given by fprime - using the nonlinear conjugate gradient algorithm of Polak and - Ribiere See Wright, and Nocedal 'Numerical Optimization', - 1999, pg. 120-122. - Check OpenOpt - a tool which offers a unified syntax to call - this and other solvers with possibility of automatic differentiation. """ x0 = asarray(x0).flatten() if maxiter is None: @@ -963,74 +962,73 @@ callback=None): """Minimize a function using the Newton-CG method. - :Parameters: + Parameters + ---------- + f : callable f(x,*args) + Objective function to be minimized. + x0 : ndarray + Initial guess. + fprime : callable f'(x,*args) + Gradient of f. + fhess_p : callable fhess_p(x,p,*args) + Function which computes the Hessian of f times an + arbitrary vector, p. + fhess : callable fhess(x,*args) + Function to compute the Hessian matrix of f. + args : tuple + Extra arguments passed to f, fprime, fhess_p, and fhess + (the same set of extra arguments is supplied to all of + these functions). + epsilon : float or ndarray + If fhess is approximated, use this value for the step size. + callback : callable + An optional user-supplied function which is called after + each iteration. Called as callback(xk), where xk is the + current parameter vector. - f : callable f(x,*args) - Objective function to be minimized. - x0 : ndarray - Initial guess. - fprime : callable f'(x,*args) - Gradient of f. - fhess_p : callable fhess_p(x,p,*args) - Function which computes the Hessian of f times an - arbitrary vector, p. - fhess : callable fhess(x,*args) - Function to compute the Hessian matrix of f. - args : tuple - Extra arguments passed to f, fprime, fhess_p, and fhess - (the same set of extra arguments is supplied to all of - these functions). - epsilon : float or ndarray - If fhess is approximated, use this value for the step size. - callback : callable - An optional user-supplied function which is called after - each iteration. Called as callback(xk), where xk is the - current parameter vector. + Returns + ------- + xopt : ndarray + Parameters which minimizer f, i.e. ``f(xopt) == fopt``. + fopt : float + Value of the function at xopt, i.e. ``fopt = f(xopt)``. + fcalls : int + Number of function calls made. + gcalls : int + Number of gradient calls made. + hcalls : int + Number of hessian calls made. + warnflag : int + Warnings generated by the algorithm. + 1 : Maximum number of iterations exceeded. + allvecs : list + The result at each iteration, if retall is True (see below). - :Returns: (xopt, {fopt, fcalls, gcalls, hcalls, warnflag},{allvecs}) + Other Parameters + ---------------- + avextol : float + Convergence is assumed when the average relative error in + the minimizer falls below this amount. + maxiter : int + Maximum number of iterations to perform. + full_output : bool + If True, return the optional outputs. + disp : bool + If True, print convergence message. + retall : bool + If True, return a list of results at each iteration. - xopt : ndarray - Parameters which minimizer f, i.e. ``f(xopt) == fopt``. - fopt : float - Value of the function at xopt, i.e. ``fopt = f(xopt)``. - fcalls : int - Number of function calls made. - gcalls : int - Number of gradient calls made. - hcalls : int - Number of hessian calls made. - warnflag : int - Warnings generated by the algorithm. - 1 : Maximum number of iterations exceeded. - allvecs : list - The result at each iteration, if retall is True (see below). + Notes + ----- + Only one of `fhess_p` or `fhess` need to be given. If `fhess` + is provided, then `fhess_p` will be ignored. If neither `fhess` + nor `fhess_p` is provided, then the hessian product will be + approximated using finite differences on `fprime`. `fhess_p` + must compute the hessian times an arbitrary vector. If it is not + given, finite-differences on `fprime` are used to compute + it. See Wright & Nocedal, 'Numerical Optimization', 1999, + pg. 140. - *Other Parameters*: - - avextol : float - Convergence is assumed when the average relative error in - the minimizer falls below this amount. - maxiter : int - Maximum number of iterations to perform. - full_output : bool - If True, return the optional outputs. - disp : bool - If True, print convergence message. - retall : bool - If True, return a list of results at each iteration. - - :Notes: - 1. Only one of `fhess_p` or `fhess` need to be given. If `fhess` - is provided, then `fhess_p` will be ignored. If neither `fhess` - nor `fhess_p` is provided, then the hessian product will be - approximated using finite differences on `fprime`. `fhess_p` - must compute the hessian times an arbitrary vector. If it is not - given, finite-differences on `fprime` are used to compute - it. See Wright, and Nocedal 'Numerical Optimization', 1999, - pg. 140. - 2. Check OpenOpt - a tool which offers a unified syntax to call - this and other solvers with possibility of automatic differentiation. - """ x0 = asarray(x0).flatten() fcalls, f = wrap_function(f, args) @@ -1141,50 +1139,47 @@ full_output=0, disp=1): """Bounded minimization for scalar functions. - :Parameters: + Parameters + ---------- + func : callable f(x,*args) + Objective function to be minimized (must accept and return scalars). + x1, x2 : float or array scalar + The optimization bounds. + args : tuple + Extra arguments passed to function. + xtol : float + The convergence tolerance. + maxfun : int + Maximum number of function evaluations allowed. + full_output : bool + If True, return optional outputs. + disp : int + If non-zero, print messages. + 0 : no message printing. + 1 : non-convergence notification messages only. + 2 : print a message on convergence too. + 3 : print iteration results. - func : callable f(x,*args) - Objective function to be minimized (must accept and return scalars). - x1, x2 : float or array scalar - The optimization bounds. - args : tuple - Extra arguments passed to function. - xtol : float - The convergence tolerance. - maxfun : int - Maximum number of function evaluations allowed. - full_output : bool - If True, return optional outputs. - disp : int - If non-zero, print messages. - 0 : no message printing. - 1 : non-convergence notification messages only. - 2 : print a message on convergence too. - 3 : print iteration results. + Returns + ------- + xopt : ndarray + Parameters (over given interval) which minimize the + objective function. + fval : number + The function value at the minimum point. + ierr : int + An error flag (0 if converged, 1 if maximum number of + function calls reached). + numfunc : int + The number of function calls made. - :Returns: (xopt, {fval, ierr, numfunc}) + Notes + ----- + Finds a local minimizer of the scalar function `func` in the + interval x1 < xopt < x2 using Brent's method. (See `brent` + for auto-bracketing). - xopt : ndarray - Parameters (over given interval) which minimize the - objective function. - fval : number - The function value at the minimum point. - ierr : int - An error flag (0 if converged, 1 if maximum number of - function calls reached). - numfunc : int - The number of function calls made. - - - :Notes: - - Finds a local minimizer of the scalar function `func` in the - interval x1 < xopt < x2 using Brent's method. (See `brent` - for auto-bracketing). - Check OpenOpt - a tool which offers a unified syntax to call - this and other solvers with possibility of automatic differentiation. - """ # Test bounds are of correct form @@ -1333,7 +1328,8 @@ if brack is None: xa,xb,xc,fa,fb,fc,funcalls = bracket(func, args=args) elif len(brack) == 2: - xa,xb,xc,fa,fb,fc,funcalls = bracket(func, xa=brack[0], xb=brack[1], args=args) + xa,xb,xc,fa,fb,fc,funcalls = bracket(func, xa=brack[0], + xb=brack[1], args=args) elif len(brack) == 3: xa,xb,xc = brack if (xa > xc): # swap so xa < xc can be assumed @@ -1345,7 +1341,8 @@ assert ((fb f(xb) < f(xc). It doesn't always mean that obtained solution will satisfy xa<=x<=xb - :Parameters: + Parameters + ---------- + func : callable f(x,*args) + Objective function to minimize. + xa, xb : float + Bracketing interval. + args : tuple + Additional arguments (if present), passed to `func`. + grow_limit : float + Maximum grow limit. + maxiter : int + Maximum number of iterations to perform. - func : callable f(x,*args) - Objective function to minimize. - xa, xb : float - Bracketing interval. - args : tuple - Additional arguments (if present), passed to `func`. - grow_limit : float - Maximum grow limit. - maxiter : int - Maximum number of iterations to perform. + Returns + ------- + xa, xb, xc : float + Bracket. + fa, fb, fc : float + Objective function values in bracket. + funcalls : int + Number of function evaluations made. - :Returns: xa, xb, xc, fa, fb, fc, funcalls - - xa, xb, xc : float - Bracket. - fa, fb, fc : float - Objective function values in bracket. - funcalls : int - Number of function evaluations made. - """ _gold = 1.618034 _verysmall_num = 1e-21 @@ -1669,65 +1665,63 @@ direc=None): """Minimize a function using modified Powell's method. - :Parameters: + Parameters + ---------- + func : callable f(x,*args) + Objective function to be minimized. + x0 : ndarray + Initial guess. + args : tuple + Eextra arguments passed to func. + callback : callable + An optional user-supplied function, called after each + iteration. Called as ``callback(xk)``, where ``xk`` is the + current parameter vector. + direc : ndarray + Initial direction set. - func : callable f(x,*args) - Objective function to be minimized. - x0 : ndarray - Initial guess. - args : tuple - Eextra arguments passed to func. - callback : callable - An optional user-supplied function, called after each - iteration. Called as ``callback(xk)``, where ``xk`` is the - current parameter vector. - direc : ndarray - Initial direction set. + Returns + ------- + xopt : ndarray + Parameter which minimizes `func`. + fopt : number + Value of function at minimum: ``fopt = func(xopt)``. + direc : ndarray + Current direction set. + iter : int + Number of iterations. + funcalls : int + Number of function calls made. + warnflag : int + Integer warning flag: + 1 : Maximum number of function evaluations. + 2 : Maximum number of iterations. + allvecs : list + List of solutions at each iteration. - :Returns: (xopt, {fopt, xi, direc, iter, funcalls, warnflag}, {allvecs}) + Other Parameters + ---------------- + xtol : float + Line-search error tolerance. + ftol : float + Relative error in ``func(xopt)`` acceptable for convergence. + maxiter : int + Maximum number of iterations to perform. + maxfun : int + Maximum number of function evaluations to make. + full_output : bool + If True, fopt, xi, direc, iter, funcalls, and + warnflag are returned. + disp : bool + If True, print convergence messages. + retall : bool + If True, return a list of the solution at each iteration. - xopt : ndarray - Parameter which minimizes `func`. - fopt : number - Value of function at minimum: ``fopt = func(xopt)``. - direc : ndarray - Current direction set. - iter : int - Number of iterations. - funcalls : int - Number of function calls made. - warnflag : int - Integer warning flag: - 1 : Maximum number of function evaluations. - 2 : Maximum number of iterations. - allvecs : list - List of solutions at each iteration. + Notes + ----- + Uses a modification of Powell's method to find the minimum of + a function of N variables. - *Other Parameters*: - - xtol : float - Line-search error tolerance. - ftol : float - Relative error in ``func(xopt)`` acceptable for convergence. - maxiter : int - Maximum number of iterations to perform. - maxfun : int - Maximum number of function evaluations to make. - full_output : bool - If True, fopt, xi, direc, iter, funcalls, and - warnflag are returned. - disp : bool - If True, print convergence messages. - retall : bool - If True, return a list of the solution at each iteration. - - - :Notes: - - Uses a modification of Powell's method to find the minimum of - a function of N variables. - Check OpenOpt - a tool which offers a unified syntax to call - this and other solvers with possibility of automatic differentiation. """ # we need to use a mutable object here that we can update in the # wrapper function @@ -1840,37 +1834,37 @@ def brute(func, ranges, args=(), Ns=20, full_output=0, finish=fmin): """Minimize a function over a given range by brute force. - :Parameters: + Parameters + ---------- + func : callable ``f(x,*args)`` + Objective function to be minimized. + ranges : tuple + Each element is a tuple of parameters or a slice object to + be handed to ``numpy.mgrid``. + args : tuple + Extra arguments passed to function. + Ns : int + Default number of samples, if those are not provided. + full_output : bool + If True, return the evaluation grid. - func : callable ``f(x,*args)`` - Objective function to be minimized. - ranges : tuple - Each element is a tuple of parameters or a slice object to - be handed to ``numpy.mgrid``. - args : tuple - Extra arguments passed to function. - Ns : int - Default number of samples, if those are not provided. - full_output : bool - If True, return the evaluation grid. + Returns + ------- + x0 : ndarray + Value of arguments to `func`, giving minimum over the grid. + fval : int + Function value at minimum. + grid : tuple + Representation of the evaluation grid. It has the same + length as x0. + Jout : ndarray + Function values over grid: ``Jout = func(*grid)``. - :Returns: (x0, fval, {grid, Jout}) + Notes + ----- + Find the minimum of a function evaluated on a grid given by + the tuple ranges. - x0 : ndarray - Value of arguments to `func`, giving minimum over the grid. - fval : int - Function value at minimum. - grid : tuple - Representation of the evaluation grid. It has the same - length as x0. - Jout : ndarray - Function values over grid: ``Jout = func(*grid)``. - - :Notes: - - Find the minimum of a function evaluated on a grid given by - the tuple ranges. - """ N = len(ranges) if N > 40: Modified: trunk/scipy/optimize/slsqp.py =================================================================== --- trunk/scipy/optimize/slsqp.py 2010-02-10 07:42:25 UTC (rev 6226) +++ trunk/scipy/optimize/slsqp.py 2010-02-10 07:43:04 UTC (rev 6227) @@ -149,14 +149,8 @@ Examples -------- + Examples are given :ref:`in the tutorial `. - for examples see :ref:`in the tutorial ` - - See also - -------- - OpenOpt - a tool which offers a unified syntax to call this - and other solvers with possibility of automatic differentiation. - """ exit_modes = { -1 : "Gradient evaluation required (g & a)", From scipy-svn at scipy.org Wed Feb 10 02:43:44 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:43:44 -0600 (CST) Subject: [Scipy-svn] r6228 - trunk/scipy/optimize Message-ID: <20100210074344.5F10839CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:43:44 -0600 (Wed, 10 Feb 2010) New Revision: 6228 Modified: trunk/scipy/optimize/nnls.py Log: DOC: Reformat NNLS docstring. Modified: trunk/scipy/optimize/nnls.py =================================================================== --- trunk/scipy/optimize/nnls.py 2010-02-10 07:43:04 UTC (rev 6227) +++ trunk/scipy/optimize/nnls.py 2010-02-10 07:43:44 UTC (rev 6228) @@ -2,22 +2,27 @@ from numpy import asarray_chkfinite, zeros, double def nnls(A,b): - """ - Solve || Ax - b ||_2 -> min with x>=0 + """ + Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``. - Inputs: - A -- matrix as above - b -- vector as above + Parameters + ---------- + A : ndarray + Matrix ``A`` as shown above. + b : ndarray + Right-hand side vector. - Outputs: - x -- solution vector - rnorm -- residual || Ax-b ||_2 + Returns + ------- + x : ndarray + Solution vector. + rnorm : float + The residual, ``|| Ax-b ||_2``. + Notes + ----- + This is a wrapper for ``NNLS.F``. - wrapper around NNLS.F code below nnls/ directory - - Check OpenOpt for more LLSP solvers - """ A,b = map(asarray_chkfinite, (A,b)) @@ -26,7 +31,7 @@ raise ValueError, "expected matrix" if len(b.shape)!=1: raise ValueError, "expected vector" - + m,n = A.shape if m != b.shape[0]: From scipy-svn at scipy.org Wed Feb 10 02:44:24 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:44:24 -0600 (CST) Subject: [Scipy-svn] r6229 - trunk/scipy/optimize Message-ID: <20100210074424.2891739CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:44:24 -0600 (Wed, 10 Feb 2010) New Revision: 6229 Modified: trunk/scipy/optimize/minpack.py Log: DOC: Clean up minpack docstrings. Modified: trunk/scipy/optimize/minpack.py =================================================================== --- trunk/scipy/optimize/minpack.py 2010-02-10 07:43:44 UTC (rev 6228) +++ trunk/scipy/optimize/minpack.py 2010-02-10 07:44:24 UTC (rev 6229) @@ -7,7 +7,8 @@ error = _minpack.error -__all__ = ['fsolve', 'leastsq', 'newton', 'fixed_point','bisection', 'curve_fit'] +__all__ = ['fsolve', 'leastsq', 'newton', 'fixed_point', + 'bisection', 'curve_fit'] def check_func(thefunc, x0, args, numinputs, output_shape=None): res = atleast_1d(thefunc(*((x0[:numinputs],)+args))) @@ -16,109 +17,96 @@ if len(output_shape) > 1: if output_shape[1] == 1: return shape(res) - msg = "There is a mismatch between the input and output shape of %s." % thefunc.func_name + msg = "There is a mismatch between the input and output " \ + "shape of %s." % thefunc.func_name raise TypeError(msg) return shape(res) -def fsolve(func,x0,args=(),fprime=None,full_output=0,col_deriv=0,xtol=1.49012e-8,maxfev=0,band=None,epsfcn=0.0,factor=100,diag=None, warning=True): +def fsolve(func, x0, args=(), fprime=None, full_output=0, + col_deriv=0, xtol=1.49012e-8, maxfev=0, band=None, + epsfcn=0.0, factor=100, diag=None, warning=True): """ Find the roots of a function. - Return the roots of the (non-linear) equations defined by func(x)=0 given a - starting estimate. + Return the roots of the (non-linear) equations defined by + ``func(x) = 0`` given a starting estimate. Parameters ---------- - func - A Python function or method which takes at least one (possibly vector) - argument. - x0 - The starting estimate for the roots of func(x)=0. - args - Any extra arguments to func are placed in this tuple. - fprime - A function or method to compute the Jacobian of func with derivatives - across the rows. If this is None, the Jacobian will be estimated. - full_output - Non-zero to return the optional outputs. - col_deriv - Non-zero to specify that the Jacobian function computes derivatives down + func : callable f(x, *args) + A function that takes at least one (possibly vector) argument. + x0 : ndarray + The starting estimate for the roots of ``func(x) = 0``. + args : tuple + Any extra arguments to `func`. + fprime : callable(x) + A function to compute the Jacobian of `func` with derivatives + across the rows. By default, the Jacobian will be estimated. + full_output : bool + If True, return optional outputs. + col_deriv : bool + Specify whether the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation). - warning - True to print a warning message when the call is unsuccessful; False to - suppress the warning message. Deprecated, use the warnings module - instead. + warning : bool + Whether to print a warning message when the call is unsuccessful. + This option is deprecated, use the warnings module instead. Returns ------- - x - The solution (or the result of the last iteration for an unsuccessful call. - infodict - A dictionary of optional outputs with the keys: + x : ndarray + The solution (or the result of the last iteration for + an unsuccessful call). + infodict : dict + A dictionary of optional outputs with the keys:: - * 'nfev': number of function calls - * 'njev': number of Jacobian calls - * 'fvec': function evaluated at the output - * 'fjac': the orthogonal matrix, q, produced by the QR factorization of - the final approximate Jacobian matrix, stored column wise - * 'r': upper triangular matrix produced by QR factorization of same - matrix - * 'qtf': the vector (transpose(q) * fvec) + * 'nfev': number of function calls + * 'njev': number of Jacobian calls + * 'fvec': function evaluated at the output + * 'fjac': the orthogonal matrix, q, produced by the QR + factorization of the final approximate Jacobian + matrix, stored column wise + * 'r': upper triangular matrix produced by QR factorization of same + matrix + * 'qtf': the vector (transpose(q) * fvec) - ier - An integer flag. If it is 1, the solution was found. If it is 1, the solution was - not found and the following message gives more information. - mesg - A string message giving information about the cause of failure. + ier : int + An integer flag. Set to 1 if a solution was found, otherwise refer + to `mesg` for more information. + mesg : str + If no solution is found, `mesg` details the cause of failure. Other Parameters ---------------- - xtol + xtol : float The calculation will terminate if the relative error between two consecutive iterates is at most `xtol`. - maxfev - The maximum number of calls to the function. If zero, then 100*(N+1) is - the maximum where N is the number of elements in x0. - band - If set to a two-sequence containing the number of sub- and super-diagonals - within the band of the Jacobi matrix, the Jacobi matrix is considered - banded (only for fprime=None). - epsfcn - A suitable step length for the forward-difference approximation of the - Jacobian (for fprime=None). If `epsfcn` is less than the machine - precision, it is assumed that the relative errors in the functions are of - the order of the machine precision. - factor - A parameter determining the initial step bound (`factor` * || `diag` * x||). - Should be in the interval (0.1,100). - diag - A sequency of N positive entries that serve as a scale factors for the + maxfev : int + The maximum number of calls to the function. If zero, then + ``100*(N+1)`` is the maximum where N is the number of elements + in `x0`. + band : tuple + If set to a two-sequence containing the number of sub- and + super-diagonals within the band of the Jacobi matrix, the + Jacobi matrix is considered banded (only for ``fprime=None``). + epsfcn : float + A suitable step length for the forward-difference + approximation of the Jacobian (for ``fprime=None``). If + `epsfcn` is less than the machine precision, it is assumed + that the relative errors in the functions are of the order of + the machine precision. + factor : float + A parameter determining the initial step bound + (``factor * || diag * x||``). Should be in the interval + ``(0.1, 100)``. + diag : sequence + N positive entries that serve as a scale factors for the variables. Notes ----- - "fsolve" is a wrapper around MINPACK's hybrd and hybrj algorithms. + ``fsolve`` is a wrapper around MINPACK's hybrd and hybrj algorithms. - See Also - -------- - fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg : multivariate local optimizers - - leastsq : nonlinear least squares minimizer - - fmin_l_bfgs_b, fmin_tnc, fmin_cobyla : constrained multivariate optimizers - - anneal, brute : global optimizers - - fminbound, brent, golden, bracket : local scalar minimizers - - brentq, brenth, ridder, bisect, newton : one-dimensional root-finding - - fixed_point : scalar and vector fixed-point finder - - OpenOpt : a tool which offers a unified syntax to call this and - other solvers with possibility of automatic differentiation - """ if not warning : msg = "The warning keyword is deprecated. Use the warnings module." @@ -135,19 +123,28 @@ ml,mu = band[:2] if (maxfev == 0): maxfev = 200*(n+1) - retval = _minpack._hybrd(func,x0,args,full_output,xtol,maxfev,ml,mu,epsfcn,factor,diag) + retval = _minpack._hybrd(func, x0, args, full_output, xtol, + maxfev, ml, mu, epsfcn, factor, diag) else: check_func(Dfun,x0,args,n,(n,n)) if (maxfev == 0): maxfev = 100*(n+1) - retval = _minpack._hybrj(func,Dfun,x0,args,full_output,col_deriv,xtol,maxfev,factor,diag) + retval = _minpack._hybrj(func, Dfun, x0, args, full_output, + col_deriv, xtol, maxfev, factor,diag) errors = {0:["Improper input parameters were entered.",TypeError], - 1:["The solution converged.",None], - 2:["The number of calls to function has reached maxfev = %d." % maxfev, ValueError], - 3:["xtol=%f is too small, no further improvement in the approximate\n solution is possible." % xtol, ValueError], - 4:["The iteration is not making good progress, as measured by the \n improvement from the last five Jacobian evaluations.", ValueError], - 5:["The iteration is not making good progress, as measured by the \n improvement from the last ten iterations.", ValueError], + 1:["The solution converged.", None], + 2:["The number of calls to function has " + "reached maxfev = %d." % maxfev, ValueError], + 3:["xtol=%f is too small, no further improvement " + "in the approximate\n solution " + "is possible." % xtol, ValueError], + 4:["The iteration is not making good progress, as measured " + "by the \n improvement from the last five " + "Jacobian evaluations.", ValueError], + 5:["The iteration is not making good progress, " + "as measured by the \n improvement from the last " + "ten iterations.", ValueError], 'unknown': ["An error occurred.", TypeError]} info = retval[-1] # The FORTRAN return value @@ -173,9 +170,13 @@ return retval[0] -def leastsq(func,x0,args=(),Dfun=None,full_output=0,col_deriv=0,ftol=1.49012e-8,xtol=1.49012e-8,gtol=0.0,maxfev=0,epsfcn=0.0,factor=100,diag=None,warning=True): +def leastsq(func, x0, args=(), Dfun=None, full_output=0, + col_deriv=0, ftol=1.49012e-8, xtol=1.49012e-8, + gtol=0.0, maxfev=0, epsfcn=0.0, factor=100, diag=None,warning=True): """Minimize the sum of squares of a set of equations. + :: + x = arg min(sum(func(y)**2,axis=0)) y @@ -184,58 +185,56 @@ func : callable should take at least one (possibly length N vector) argument and returns M floating point numbers. - x0 : + x0 : ndarray The starting estimate for the minimization. - args : + args : tuple Any extra arguments to func are placed in this tuple. Dfun : callable A function or method to compute the Jacobian of func with derivatives across the rows. If this is None, the Jacobian will be estimated. - full_output : + full_output : bool non-zero to return all optional outputs. - col_deriv : + col_deriv : bool non-zero to specify that the Jacobian function computes derivatives down the columns (faster, because there is no transpose operation). - ftol : + ftol : float Relative error desired in the sum of squares. - xtol : + xtol : float Relative error desired in the approximate solution. - gtol : + gtol : float Orthogonality desired between the function vector and the columns of the Jacobian. - maxfev : + maxfev : int The maximum number of calls to the function. If zero, then 100*(N+1) is the maximum where N is the number of elements in x0. - epsfcn : + epsfcn : float A suitable step length for the forward-difference approximation of the Jacobian (for Dfun=None). If epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision. - factor : - A parameter determining the initial step bound (factor * || diag * - x||). Should be in interval (0.1,100). - diag : - A sequency of N positive entries that serve as a scale factors for the - variables. + factor : float + A parameter determining the initial step bound + (``factor * || diag * x||``). Should be in interval ``(0.1, 100)``. + diag : sequence + N positive entries that serve as a scale factors for the variables. warning : bool - True to print a warning message when the call is unsuccessful; False to - suppress the warning message. Deprecated, use the warnings module - instead. + Whether to print a warning message when the call is unsuccessful. + Deprecated, use the warnings module instead. Returns ------- - x : - the solution (or the result of the last iteration for an unsuccessful - call. - - cov_x : - uses the fjac and ipvt optional outputs to construct an estimate of the - jacobian around the solution. None if a singular matrix encountered - (indicates very flat curvature in some direction). This matrix must be - multiplied by the residual standard deviation to get the covariance of - the parameter estimates -- see curve_fit. + x : ndarray + The solution (or the result of the last iteration for an unsuccessful + call). + cov_x : ndarray + Uses the fjac and ipvt optional outputs to construct an + estimate of the jacobian around the solution. ``None`` if a + singular matrix encountered (indicates very flat curvature in + some direction). This matrix must be multiplied by the + residual standard deviation to get the covariance of the + parameter estimates -- see curve_fit. infodict : dict - a dictionary of optional outputs with the keys: + a dictionary of optional outputs with the keys:: - 'nfev' : the number of function calls - 'fvec' : the function evaluated at the output @@ -251,10 +250,10 @@ magnitude. Column j of p is column ipvt(j) of the identity matrix. - 'qtf' : the vector (transpose(q) * fvec). - mesg : - a string message giving information about the cause of failure. - ier : - an integer flag. If it is equal to 1, 2, 3 or 4, the solution was + mesg : str + A string message giving information about the cause of failure. + ier : int + An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. Otherwise, the solution was not found. In either case, the optional output variable 'mesg' gives more information. @@ -262,19 +261,6 @@ ----- "leastsq" is a wrapper around MINPACK's lmdif and lmder algorithms. - See Also - -------- - fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg: multivariate local optimizers - fmin_l_bfgs_b, fmin_tnc, fmin_cobyla: constrained multivariate optimizers - anneal, brute: global optimizers - fminbound, brent, golden, bracket: local scalar minimizers - fsolve: n-dimensional root-finding - brentq, brenth, ridder, bisect, newton: one-dimensional root-finding - fixed_point: scalar and vector fixed-point finder - curve_fit: find parameters for a curve-fitting problem. - OpenOpt : a tool which offers a unified syntax to call this and - other solvers with possibility of automatic differentiation - """ if not warning : msg = "The warning keyword is deprecated. Use the warnings module." @@ -288,7 +274,9 @@ if Dfun is None: if (maxfev == 0): maxfev = 200*(n+1) - retval = _minpack._lmdif(func,x0,args,full_output,ftol,xtol,gtol,maxfev,epsfcn,factor,diag) + retval = _minpack._lmdif(func, x0, args, full_output, + ftol, xtol, gtol, + maxfev, epsfcn, factor, diag) else: if col_deriv: check_func(Dfun,x0,args,n,(n,m)) @@ -299,14 +287,26 @@ retval = _minpack._lmder(func,Dfun,x0,args,full_output,col_deriv,ftol,xtol,gtol,maxfev,factor,diag) errors = {0:["Improper input parameters.", TypeError], - 1:["Both actual and predicted relative reductions in the sum of squares\n are at most %f" % ftol, None], - 2:["The relative error between two consecutive iterates is at most %f" % xtol, None], - 3:["Both actual and predicted relative reductions in the sum of squares\n are at most %f and the relative error between two consecutive iterates is at \n most %f" % (ftol,xtol), None], - 4:["The cosine of the angle between func(x) and any column of the\n Jacobian is at most %f in absolute value" % gtol, None], - 5:["Number of calls to function has reached maxfev = %d." % maxfev, ValueError], - 6:["ftol=%f is too small, no further reduction in the sum of squares\n is possible.""" % ftol, ValueError], - 7:["xtol=%f is too small, no further improvement in the approximate\n solution is possible." % xtol, ValueError], - 8:["gtol=%f is too small, func(x) is orthogonal to the columns of\n the Jacobian to machine precision." % gtol, ValueError], + 1:["Both actual and predicted relative reductions " + "in the sum of squares\n are at most %f" % ftol, None], + 2:["The relative error between two consecutive " + "iterates is at most %f" % xtol, None], + 3:["Both actual and predicted relative reductions in " + "the sum of squares\n are at most %f and the " + "relative error between two consecutive " + "iterates is at \n most %f" % (ftol,xtol), None], + 4:["The cosine of the angle between func(x) and any " + "column of the\n Jacobian is at most %f in " + "absolute value" % gtol, None], + 5:["Number of calls to function has reached " + "maxfev = %d." % maxfev, ValueError], + 6:["ftol=%f is too small, no further reduction " + "in the sum of squares\n is possible.""" % ftol, ValueError], + 7:["xtol=%f is too small, no further improvement in " + "the approximate\n solution is possible." % xtol, ValueError], + 8:["gtol=%f is too small, func(x) is orthogonal to the " + "columns of\n the Jacobian to machine " + "precision." % gtol, ValueError], 'unknown':["Unknown error.", TypeError]} info = retval[-1] # The FORTRAN return value @@ -437,6 +437,7 @@ def check_gradient(fcn,Dfcn,x0,args=(),col_deriv=0): """Perform a simple check on the gradient for correctness. + """ x = atleast_1d(x0) @@ -580,23 +581,6 @@ >>> fixed_point(func, [1.2, 1.3], args=(c1,c2)) array([ 1.4920333 , 1.37228132]) - See also: - - fmin, fmin_powell, fmin_cg, - fmin_bfgs, fmin_ncg -- multivariate local optimizers - leastsq -- nonlinear least squares minimizer - - fmin_l_bfgs_b, fmin_tnc, - fmin_cobyla -- constrained multivariate optimizers - - anneal, brute -- global optimizers - - fminbound, brent, golden, bracket -- local scalar minimizers - - fsolve -- n-dimensional root-finding - - brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding - """ if not isscalar(x0): x0 = asarray(x0) @@ -635,25 +619,6 @@ """Bisection root-finding method. Given a function and an interval with func(a) * func(b) < 0, find the root between a and b. - See also: - - fmin, fmin_powell, fmin_cg, - fmin_bfgs, fmin_ncg -- multivariate local optimizers - leastsq -- nonlinear least squares minimizer - - fmin_l_bfgs_b, fmin_tnc, - fmin_cobyla -- constrained multivariate optimizers - - anneal, brute -- global optimizers - - fminbound, brent, golden, bracket -- local scalar minimizers - - fsolve -- n-dimensional root-finding - - brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding - - fixed_point -- scalar and vector fixed-point finder - """ i = 1 eva = func(a,*args) From scipy-svn at scipy.org Wed Feb 10 02:45:00 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:45:00 -0600 (CST) Subject: [Scipy-svn] r6230 - trunk/scipy/optimize Message-ID: <20100210074500.B51C539CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:45:00 -0600 (Wed, 10 Feb 2010) New Revision: 6230 Modified: trunk/scipy/optimize/lbfgsb.py Log: DOC: Reformat lbfgsb docs. Modified: trunk/scipy/optimize/lbfgsb.py =================================================================== --- trunk/scipy/optimize/lbfgsb.py 2010-02-10 07:44:24 UTC (rev 6229) +++ trunk/scipy/optimize/lbfgsb.py 2010-02-10 07:45:00 UTC (rev 6230) @@ -40,57 +40,58 @@ """ Minimize a function func using the L-BFGS-B algorithm. - Arguments: + Parameters + ---------- + func : callable f(x, *args) + Function to minimise. + x0 : ndarray + Initial guess. + fprime : callable fprime(x, *args) + The gradient of `func`. If None, then `func` returns the function + value and the gradient (``f, g = func(x, *args)``), unless + `approx_grad` is True in which case `func` returns only ``f``. + args : tuple + Arguments to pass to `func` and `fprime`. + approx_grad : bool + Whether to approximate the gradient numerically (in which case + `func` returns only the function value). + bounds : list + ``(min, max)`` pairs for each element in ``x``, defining + the bounds on that parameter. Use None for one of ``min`` or + ``max`` when there is no bound in that direction. + m : int + The maximum number of variable metric corrections + used to define the limited memory matrix. (The limited memory BFGS + method does not store the full hessian but uses this many terms in an + approximation to it.) + factr : float + The iteration stops when + ``(f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr * eps``, + where ``eps`` is the machine precision, which is automatically + generated by the code. Typical values for `factr` are: 1e12 for + low accuracy; 1e7 for moderate accuracy; 10.0 for extremely + high accuracy. + pgtol : float + The iteration will stop when + ``max{|proj g_i | i = 1, ..., n} <= pgtol`` + where ``pg_i`` is the i-th component of the projected gradient. + epsilon : float + Step size used when `approx_grad` is True, for numerically + calculating the gradient + iprint : int + Controls the frequency of output. ``iprint < 0`` means no output. + maxfun : int + Maximum number of function evaluations. - func -- function to minimize. Called as func(x, *args) + Returns + ------- + x : ndarray + Estimated position of the minimum. + f : float + Value of `func` at the minimum. + d : dict + Information dictionary. - x0 -- initial guess to minimum - - fprime -- gradient of func. If None, then func returns the function - value and the gradient ( f, g = func(x, *args) ), unless - approx_grad is True then func returns only f. - Called as fprime(x, *args) - - args -- arguments to pass to function - - approx_grad -- if true, approximate the gradient numerically and func returns - only function value. - - bounds -- a list of (min, max) pairs for each element in x, defining - the bounds on that parameter. Use None for one of min or max - when there is no bound in that direction - - m -- the maximum number of variable metric corrections - used to define the limited memory matrix. (the limited memory BFGS - method does not store the full hessian but uses this many terms in an - approximation to it). - - factr -- The iteration stops when - (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr*epsmch - - where epsmch is the machine precision, which is automatically - generated by the code. Typical values for factr: 1e12 for - low accuracy; 1e7 for moderate accuracy; 10.0 for extremely - high accuracy. - - pgtol -- The iteration will stop when - max{|proj g_i | i = 1, ..., n} <= pgtol - where pg_i is the ith component of the projected gradient. - - epsilon -- step size used when approx_grad is true, for numerically - calculating the gradient - - iprint -- controls the frequency of output. <0 means no output. - - maxfun -- maximum number of function evaluations. - - - Returns: - x, f, d = fmin_lbfgs_b(func, x0, ...) - - x -- position of the minimum - f -- value of func at the minimum - d -- dictionary of information from routine d['warnflag'] is 0 if converged, 1 if too many function evaluations, @@ -99,17 +100,20 @@ d['funcalls'] is the number of function calls made. - License of L-BFGS-B (Fortran code) - ================================== + Notes + ----- - The version included here (in fortran code) is 2.1 (released in 1997). It was - written by Ciyou Zhu, Richard Byrd, and Jorge Nocedal . It - carries the following condition for use: + License of L-BFGS-B (Fortran code): - This software is freely available, but we expect that all publications - describing work using this software , or all commercial products using it, - quote at least one of the references given below. + The version included here (in fortran code) is 2.1 (released in + 1997). It was written by Ciyou Zhu, Richard Byrd, and Jorge Nocedal + . It carries the following condition for use: + This software is freely available, but we expect that all + publications describing work using this software, or all + commercial products using it, quote at least one of the references + given below. + References * R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and @@ -118,27 +122,6 @@ FORTRAN routines for large scale bound constrained optimization (1997), ACM Transactions on Mathematical Software, Vol 23, Num. 4, pp. 550 - 560. - See also: - fmin, fmin_powell, fmin_cg, - fmin_bfgs, fmin_ncg -- multivariate local optimizers - leastsq -- nonlinear least squares minimizer - - fmin_l_bfgs_b, fmin_tnc, - fmin_cobyla -- constrained multivariate optimizers - - anneal, brute -- global optimizers - - fminbound, brent, golden, bracket -- local scalar minimizers - - fsolve -- n-dimensional root-finding - - brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding - - fixed_point -- scalar fixed-point finder - - OpenOpt -- a tool which offers a unified syntax to call this and - other solvers with possibility of automatic differentiation - """ n = len(x0) From scipy-svn at scipy.org Wed Feb 10 02:45:41 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:45:41 -0600 (CST) Subject: [Scipy-svn] r6231 - trunk/scipy/optimize Message-ID: <20100210074541.2386539CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:45:41 -0600 (Wed, 10 Feb 2010) New Revision: 6231 Modified: trunk/scipy/optimize/cobyla.py Log: DOC: Reformat COBYLA docstring. Modified: trunk/scipy/optimize/cobyla.py =================================================================== --- trunk/scipy/optimize/cobyla.py 2010-02-10 07:45:00 UTC (rev 6230) +++ trunk/scipy/optimize/cobyla.py 2010-02-10 07:45:41 UTC (rev 6231) @@ -14,61 +14,40 @@ iprint=1, maxfun=1000): """ Minimize a function using the Constrained Optimization BY Linear - Approximation (COBYLA) method + Approximation (COBYLA) method. - Arguments: + Parameters + ---------- + func : callable f(x, *args) + Function to minimize. + x0 : ndarray + Initial guess. + cons : sequence + Constraint functions; must all be ``>=0`` (a single function + if only 1 constraint). + args : tuple + Extra arguments to pass to function. + consargs : tuple + Extra arguments to pass to constraint functions (default of None means + use same extra arguments as those passed to func). + Use ``()`` for no extra arguments. + rhobeg : + Reasonable initial changes to the variables. + rhoend : + Final accuracy in the optimization (not precisely guaranteed). + iprint : {0, 1, 2, 3} + Controls the frequency of output; 0 implies no output. + maxfun : int + Maximum number of function evaluations. - func -- function to minimize. Called as func(x, *args) + Returns + ------- + x : ndarray + The argument that minimises `f`. - x0 -- initial guess to minimum - - cons -- a sequence of functions that all must be >=0 (a single function - if only 1 constraint) - - args -- extra arguments to pass to function - - consargs -- extra arguments to pass to constraints (default of None means - use same extra arguments as those passed to func). - Use () for no extra arguments. - - rhobeg -- reasonable initial changes to the variables - - rhoend -- final accuracy in the optimization (not precisely guaranteed) - - iprint -- controls the frequency of output: 0 (no output),1,2,3 - - maxfun -- maximum number of function evaluations. - - - Returns: - - x -- the minimum - - See also: - - fmin, fmin_powell, fmin_cg, - fmin_bfgs, fmin_ncg -- multivariate local optimizers - leastsq -- nonlinear least squares minimizer - - fmin_l_bfgs_b, fmin_tnc, - fmin_cobyla -- constrained multivariate optimizers - - anneal, brute -- global optimizers - - fminbound, brent, golden, bracket -- local scalar minimizers - - fsolve -- n-dimenstional root-finding - - brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding - - fixed_point -- scalar fixed-point finder - - OpenOpt -- a tool which offers a unified syntax to call this and - other solvers with possibility of automatic differentiation - """ err = "cons must be a sequence of callable functions or a single"\ - " callable function." + " callable function." try: m = len(cons) except TypeError: @@ -93,7 +72,7 @@ k += 1 return f - xopt = _cobyla.minimize(calcfc, m=m, x=copy(x0), rhobeg=rhobeg, rhoend=rhoend, - iprint=iprint, maxfun=maxfun) + xopt = _cobyla.minimize(calcfc, m=m, x=copy(x0), rhobeg=rhobeg, + rhoend=rhoend, iprint=iprint, maxfun=maxfun) return xopt From scipy-svn at scipy.org Wed Feb 10 02:46:19 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:46:19 -0600 (CST) Subject: [Scipy-svn] r6232 - trunk/scipy/optimize Message-ID: <20100210074619.54734C7C018@scipy.org> Author: stefan Date: 2010-02-10 01:46:19 -0600 (Wed, 10 Feb 2010) New Revision: 6232 Modified: trunk/scipy/optimize/anneal.py Log: DOC: Reformat anneal docs. Modified: trunk/scipy/optimize/anneal.py =================================================================== --- trunk/scipy/optimize/anneal.py 2010-02-10 07:45:41 UTC (rev 6231) +++ trunk/scipy/optimize/anneal.py 2010-02-10 07:46:19 UTC (rev 6232) @@ -160,65 +160,68 @@ Schedule is a schedule class implementing the annealing schedule. Available ones are 'fast', 'cauchy', 'boltzmann' - Inputs: + Parameters + ---------- + func : callable f(x, *args) + Function to be optimized. + x0 : ndarray + Initial guess. + args : tuple + Extra parameters to `func`. + schedule : base_schedule + Annealing schedule to use (a class). + full_output : bool + Whether to return optional outputs. + T0 : float + Initial Temperature (estimated as 1.2 times the largest + cost-function deviation over random points in the range). + Tf : float + Final goal temperature. + maxeval : int + Maximum function evaluations. + maxaccept : int + Maximum changes to accept. + maxiter : int + Maximum cooling iterations. + learn_rate : float + Scale constant for adjusting guesses. + boltzmann : float + Boltzmann constant in acceptance test + (increase for less stringent test at each temperature). + feps : float + Stopping relative error tolerance for the function value in + last four coolings. + quench, m, n : float + Parameters to alter fast_sa schedule. + lower, upper : float or ndarray + Lower and upper bounds on `x`. + dwell : int + The number of times to search the space at each temperature. - func -- Function to be optimized - x0 -- Parameters to be optimized over - args -- Extra parameters to function - schedule -- Annealing schedule to use (a class) - full_output -- Return optional outputs - T0 -- Initial Temperature (estimated as 1.2 times the largest - cost-function deviation over random points in the range) - Tf -- Final goal temperature - maxeval -- Maximum function evaluations - maxaccept -- Maximum changes to accept - maxiter -- Maximum cooling iterations - learn_rate -- scale constant for adjusting guesses - boltzmann -- Boltzmann constant in acceptance test - (increase for less stringent test at each temperature). - feps -- Stopping relative error tolerance for the function value in - last four coolings. - quench, m, n -- Parameters to alter fast_sa schedule - lower, upper -- lower and upper bounds on x0 (scalar or array). - dwell -- The number of times to search the space at each temperature. + Outputs + ------- + xmin : ndarray + Point giving smallest value found. + retval : int + Flag indicating stopping condition:: - Outputs: (xmin, {Jmin, T, feval, iters, accept,} retval) - - xmin -- Point giving smallest value found - retval -- Flag indicating stopping condition: 0 : Cooled to global optimum 1 : Cooled to final temperature 2 : Maximum function evaluations 3 : Maximum cooling iterations reached 4 : Maximum accepted query locations reached - Jmin -- Minimum value of function found - T -- final temperature - feval -- Number of function evaluations - iters -- Number of cooling iterations - accept -- Number of tests accepted. + Jmin : float + Minimum value of function found. + T : float + Final temperature. + feval : int + Number of function evaluations. + iters : int + Number of cooling iterations. + accept : int + Number of tests accepted. - See also: - - fmin, fmin_powell, fmin_cg, - fmin_bfgs, fmin_ncg -- multivariate local optimizers - leastsq -- nonlinear least squares minimizer - - fmin_l_bfgs_b, fmin_tnc, - fmin_cobyla -- constrained multivariate optimizers - - anneal, brute -- global optimizers - - fminbound, brent, golden, bracket -- local scalar minimizers - - fsolve -- n-dimensional root-finding - - brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding - - fixed_point -- scalar fixed-point finder - - OpenOpt -- Python package with more optimization solvers - """ x0 = asarray(x0) lower = asarray(lower) From scipy-svn at scipy.org Wed Feb 10 02:46:53 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 10 Feb 2010 01:46:53 -0600 (CST) Subject: [Scipy-svn] r6233 - trunk/scipy/optimize Message-ID: <20100210074653.8364939CAE6@scipy.org> Author: stefan Date: 2010-02-10 01:46:53 -0600 (Wed, 10 Feb 2010) New Revision: 6233 Modified: trunk/scipy/optimize/info.py Log: DOC: Add link to OpenOpt in optimize module docstring. Modified: trunk/scipy/optimize/info.py =================================================================== --- trunk/scipy/optimize/info.py 2010-02-10 07:46:19 UTC (rev 6232) +++ trunk/scipy/optimize/info.py 2010-02-10 07:46:53 UTC (rev 6233) @@ -88,6 +88,12 @@ line_search -- Return a step that satisfies the strong Wolfe conditions. check_grad -- Check the supplied derivative using finite difference techniques. + +External Libraries:: + + OpenOpt -- A BSD-licensed optimisation framework which includes a number of + constrained and unconstrained solvers (see http://openopt.org). + """ postpone_import = 1 From scipy-svn at scipy.org Sun Feb 14 15:40:27 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 14 Feb 2010 14:40:27 -0600 (CST) Subject: [Scipy-svn] r6234 - scipy.org/www Message-ID: <20100214204027.DB3D039CAE7@scipy.org> Author: stefan Date: 2010-02-14 14:40:27 -0600 (Sun, 14 Feb 2010) New Revision: 6234 Modified: scipy.org/www/getting-started.rst Log: Fix link to topical software. Modified: scipy.org/www/getting-started.rst =================================================================== --- scipy.org/www/getting-started.rst 2010-02-10 07:46:53 UTC (rev 6233) +++ scipy.org/www/getting-started.rst 2010-02-14 20:40:27 UTC (rev 6234) @@ -68,7 +68,7 @@ There is a sizeable collection of both generic and application-specific numerical code written in or using numpy and scipy. See the - :ref:`topical-software` index for a partial list. Python has many advanced + :doc:`topical-software` index for a partial list. Python has many advanced modules to build interactive applications (for instance Traits_ or wxPython_). Using SciPy with these is the quickest way to build a fully-fledged scientific application. From scipy-svn at scipy.org Sun Feb 14 15:41:10 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 14 Feb 2010 14:41:10 -0600 (CST) Subject: [Scipy-svn] r6235 - in scipy.org: . source source/_static source/_static/images www/_theme/scipy www/_theme/scipy/static Message-ID: <20100214204110.D64EE39CAE7@scipy.org> Author: stefan Date: 2010-02-14 14:41:10 -0600 (Sun, 14 Feb 2010) New Revision: 6235 Added: scipy.org/source/ scipy.org/source/_static/ scipy.org/source/_static/images/ scipy.org/source/_static/images/cython-logo.png Modified: scipy.org/www/_theme/scipy/index.html scipy.org/www/_theme/scipy/static/scipy.css Log: Add Cython to front page. Adjust vertical adjustment to display better. Added: scipy.org/source/_static/images/cython-logo.png =================================================================== --- scipy.org/source/_static/images/cython-logo.png (rev 0) +++ scipy.org/source/_static/images/cython-logo.png 2010-02-14 20:41:10 UTC (rev 6235) @@ -0,0 +1,27 @@ +?PNG + + +IHDR72?C?sBIT|d? pHYs??????tEXtSoftwarewww.inkscape.org??<?IDATh???}?\U?ovf??????-_-?X,??gL???P ?V??!?bE??Ph??!??B?D?h?&% +-,m?D*I?"??Z??J??[??;???ofvv???????????????;??{???K?s??l?T?0`|??,??=?^?u?9?9h +F???g?9?I at f??K?,?FD?9????s?????G??aD????Dds=??9?\\ QO?u???Jya$?R?9??~K?-}T??D??4????sK????vf??p?????xMd???k?E"?????p??V?a??? +{? xxBD?N?97???Ez???D??????s??z??? +>,?WS?W?wphZY)"7&e??9??????)?.?u#?T?8???LYd????Q????? ST? +8WDv??H*q?u?i)?=?PD???????I?p???}????? +???s'Wf ??sn6? ????i"?)Ia?]?f*,?^??? ?:?T??????x?oe?K??/'?????hK??-? "?? %8?\l??@??""7'????U????Dw_?v????%`~?p??~?S??>d?O7?KT??d +?lSS?b +A????y??Y??eI????????\{?e2_???`?t?gG?'?? #??/@?{??N??????pn?a}0"I??)???????? +pV?????2u???d?Y??????A?|?[h,_??`a3???Ad????? ??z,U?gq???>?\?h?????}q?K????X?4??!;?.?}?[?:??`h;.V?z?????^?R?!? +??U???NG?K?:+?d `?h1Y?U?4pG8?D??o?dk=???/.??1???h?V&V?aLZ`6??????R???%????H????];?1X? +Z?@5Pe?a?f??? .Cd??2 @??Z??\p?-??k?g??L?????OHL@??4Z7??? +???g???~G?8??3??q ?ab?*?<\Y.??BQ&lz?y?z[???T??!(????^m5??Z???S? +?z` ?~???z??+S}???3?????bB?B?f??????P?3???7?p>??R,???C??'??? ?2m????????Z?jU-0?{???w]yB?.?|???4?/??s?~n>w????? ?????` ???4\??????)????`M?0?A?B}OSl?h? + ??6n?@??YK?pSe?Y?bE????|?ro1 +??o??M$??KD??$?Z?9????j4T???L??????uvv~????800??????\.`f> ??{????????????ax? ????]?|yy8??G5?E???a&?Y????????????w[[[?kii????i???m;p?@[???\3;???[???E??Z?#9d?U??|t?lH{ 'I??)^"??h*\?8?&?Nc?i???R????N`???y??A +R)??IEND?B`? \ No newline at end of file Modified: scipy.org/www/_theme/scipy/index.html =================================================================== --- scipy.org/www/_theme/scipy/index.html 2010-02-14 20:40:27 UTC (rev 6234) +++ scipy.org/www/_theme/scipy/index.html 2010-02-14 20:41:10 UTC (rev 6235) @@ -85,13 +85,22 @@ + + + + + Cython
Rapid C Extensions + + + - + Chaco
2D Plotting Widgets - + Traits
Typed/Validated Attributes and Widgets Modified: scipy.org/www/_theme/scipy/static/scipy.css =================================================================== --- scipy.org/www/_theme/scipy/static/scipy.css 2010-02-14 20:40:27 UTC (rev 6234) +++ scipy.org/www/_theme/scipy/static/scipy.css 2010-02-14 20:41:10 UTC (rev 6235) @@ -205,7 +205,11 @@ td.package { vertical-align: top; } + td.packageicon { vertical-align: middle; } +td.middle { + vertical-align: middle; +} From scipy-svn at scipy.org Sun Feb 14 15:41:51 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 14 Feb 2010 14:41:51 -0600 (CST) Subject: [Scipy-svn] r6236 - in scipy.org: source/_static/images www/_theme/scipy/static/images Message-ID: <20100214204151.71D0639CAE7@scipy.org> Author: stefan Date: 2010-02-14 14:41:51 -0600 (Sun, 14 Feb 2010) New Revision: 6236 Added: scipy.org/www/_theme/scipy/static/images/cython-logo.png Removed: scipy.org/source/_static/images/cython-logo.png Log: Update file locations to match repository. Deleted: scipy.org/source/_static/images/cython-logo.png =================================================================== --- scipy.org/source/_static/images/cython-logo.png 2010-02-14 20:41:10 UTC (rev 6235) +++ scipy.org/source/_static/images/cython-logo.png 2010-02-14 20:41:51 UTC (rev 6236) @@ -1,27 +0,0 @@ -?PNG - - -IHDR72?C?sBIT|d? pHYs??????tEXtSoftwarewww.inkscape.org??<?IDATh???}?\U?ovf??????-_-?X,??gL???P ?V??!?bE??Ph??!??B?D?h?&% --,m?D*I?"??Z??J??[??;???ofvv???????????????;??{???K?s??l?T?0`|??,??=?^?u?9?9h -F???g?9?I at f??K?,?FD?9????s?????G??aD????Dds=??9?\\ QO?u???Jya$?R?9??~K?-}T??D??4????sK????vf??p?????xMd???k?E"?????p??V?a??? -{? xxBD?N?97???Ez???D??????s??z??? ->,?WS?W?wphZY)"7&e??9??????)?.?u#?T?8???LYd????Q????? ST? -8WDv??H*q?u?i)?=?PD???????I?p???}????? -???s'Wf ??sn6? ????i"?)Ia?]?f*,?^??? ?:?T??????x?oe?K??/'?????hK??-? "?? %8?\l??@??""7'????U????Dw_?v????%`~?p??~?S??>d?O7?KT??d -?lSS?b -A????y??Y??eI????????\{?e2_???`?t?gG?'?? #??/@?{??N??????pn?a}0"I??)???????? -pV?????2u???d?Y??????A?|?[h,_??`a3???Ad????? ??z,U?gq???>?\?h?????}q?K????X?4??!;?.?}?[?:??`h;.V?z?????^?R?!? -??U???NG?K?:+?d `?h1Y?U?4pG8?D??o?dk=???/.??1???h?V&V?aLZ`6??????R???%????H????];?1X? -Z?@5Pe?a?f??? .Cd??2 @??Z??\p?-??k?g??L?????OHL@??4Z7??? -???g???~G?8??3??q ?ab?*?<\Y.??BQ&lz?y?z[???T??!(????^m5??Z???S? -?z` ?~???z??+S}???3?????bB?B?f??????P?3???7?p>??R,???C??'??? ?2m????????Z?jU-0?{???w]yB?.?|???4?/??s?~n>w????? ?????` ???4\??????)????`M?0?A?B}OSl?h? - ??6n?@??YK?pSe?Y?bE????|?ro1 -??o??M$??KD??$?Z?9????j4T???L??????uvv~????800??????\.`f> ??{????????????ax? ????]?|yy8??G5?E???a&?Y????????????w[[[?kii????i???m;p?@[???\3;???[???E??Z?#9d?U??|t?lH{ 'I??)^"??h*\?8?&?Nc?i???R????N`???y??A -R)??IEND?B`? \ No newline at end of file Copied: scipy.org/www/_theme/scipy/static/images/cython-logo.png (from rev 6235, scipy.org/source/_static/images/cython-logo.png) =================================================================== --- scipy.org/www/_theme/scipy/static/images/cython-logo.png (rev 0) +++ scipy.org/www/_theme/scipy/static/images/cython-logo.png 2010-02-14 20:41:51 UTC (rev 6236) @@ -0,0 +1,27 @@ +?PNG + + +IHDR72?C?sBIT|d? pHYs??????tEXtSoftwarewww.inkscape.org??<?IDATh???}?\U?ovf??????-_-?X,??gL???P ?V??!?bE??Ph??!??B?D?h?&% +-,m?D*I?"??Z??J??[??;???ofvv???????????????;??{???K?s??l?T?0`|??,??=?^?u?9?9h +F???g?9?I at f??K?,?FD?9????s?????G??aD????Dds=??9?\\ QO?u???Jya$?R?9??~K?-}T??D??4????sK????vf??p?????xMd???k?E"?????p??V?a??? +{? xxBD?N?97???Ez???D??????s??z??? +>,?WS?W?wphZY)"7&e??9??????)?.?u#?T?8???LYd????Q????? ST? +8WDv??H*q?u?i)?=?PD???????I?p???}????? +???s'Wf ??sn6? ????i"?)Ia?]?f*,?^??? ?:?T??????x?oe?K??/'?????hK??-? "?? %8?\l??@??""7'????U????Dw_?v????%`~?p??~?S??>d?O7?KT??d +?lSS?b +A????y??Y??eI????????\{?e2_???`?t?gG?'?? #??/@?{??N??????pn?a}0"I??)???????? +pV?????2u???d?Y??????A?|?[h,_??`a3???Ad????? ??z,U?gq???>?\?h?????}q?K????X?4??!;?.?}?[?:??`h;.V?z?????^?R?!? +??U???NG?K?:+?d `?h1Y?U?4pG8?D??o?dk=???/.??1???h?V&V?aLZ`6??????R???%????H????];?1X? +Z?@5Pe?a?f??? .Cd??2 @??Z??\p?-??k?g??L?????OHL@??4Z7??? +???g???~G?8??3??q ?ab?*?<\Y.??BQ&lz?y?z[???T??!(????^m5??Z???S? +?z` ?~???z??+S}???3?????bB?B?f??????P?3???7?p>??R,???C??'??? ?2m????????Z?jU-0?{???w]yB?.?|???4?/??s?~n>w????? ?????` ???4\??????)????`M?0?A?B}OSl?h? + ??6n?@??YK?pSe?Y?bE????|?ro1 +??o??M$??KD??$?Z?9????j4T???L??????uvv~????800??????\.`f> ??{????????????ax? ????]?|yy8??G5?E???a&?Y????????????w[[[?kii????i???m;p?@[???\3;???[???E??Z?#9d?U??|t?lH{ 'I??)^"??h*\?8?&?Nc?i???R????N`???y??A +R)??IEND?B`? \ No newline at end of file From scipy-svn at scipy.org Sun Feb 14 15:45:01 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 14 Feb 2010 14:45:01 -0600 (CST) Subject: [Scipy-svn] r6237 - scipy.org Message-ID: <20100214204501.0E57C39CAE7@scipy.org> Author: stefan Date: 2010-02-14 14:45:01 -0600 (Sun, 14 Feb 2010) New Revision: 6237 Removed: scipy.org/source/ Log: Remove empty directory created by git-svn. From scipy-svn at scipy.org Mon Feb 15 04:40:52 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 15 Feb 2010 03:40:52 -0600 (CST) Subject: [Scipy-svn] r6238 - trunk/scipy/optimize Message-ID: <20100215094052.A0898C7C01F@scipy.org> Author: dmitrey.kroshko Date: 2010-02-15 03:40:52 -0600 (Mon, 15 Feb 2010) New Revision: 6238 Modified: trunk/scipy/optimize/info.py Log: minor doc update Modified: trunk/scipy/optimize/info.py =================================================================== --- trunk/scipy/optimize/info.py 2010-02-14 20:45:01 UTC (rev 6237) +++ trunk/scipy/optimize/info.py 2010-02-15 09:40:52 UTC (rev 6238) @@ -89,10 +89,13 @@ check_grad -- Check the supplied derivative using finite difference techniques. -External Libraries:: +Related Software:: - OpenOpt -- A BSD-licensed optimisation framework which includes a number of - constrained and unconstrained solvers (see http://openopt.org). + OpenOpt -- A BSD-licensed optimisation framework (see http://openopt.org), + which includes a number of constrained and unconstrained + solvers from and beyond scipy.optimize module, + unified text and graphical output of convergence + and automatic differentiation. """ From scipy-svn at scipy.org Tue Feb 16 17:54:01 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 16 Feb 2010 16:54:01 -0600 (CST) Subject: [Scipy-svn] r6239 - trunk/scipy/optimize Message-ID: <20100216225401.492A739C4B4@scipy.org> Author: oliphant Date: 2010-02-16 16:54:01 -0600 (Tue, 16 Feb 2010) New Revision: 6239 Modified: trunk/scipy/optimize/minpack.py Log: Fix typo from #1115. Modified: trunk/scipy/optimize/minpack.py =================================================================== --- trunk/scipy/optimize/minpack.py 2010-02-15 09:40:52 UTC (rev 6238) +++ trunk/scipy/optimize/minpack.py 2010-02-16 22:54:01 UTC (rev 6239) @@ -313,7 +313,7 @@ if (info not in [1,2,3,4] and not full_output): if info in [5,6,7,8]: - warning.warn(errors[info][0], RuntimeWarning) + warnings.warn(errors[info][0], RuntimeWarning) else: try: raise errors[info][1](errors[info][0]) From scipy-svn at scipy.org Tue Feb 16 22:25:50 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Tue, 16 Feb 2010 21:25:50 -0600 (CST) Subject: [Scipy-svn] r6240 - in trunk/scipy/signal: . tests Message-ID: <20100217032550.2C2C439C4B4@scipy.org> Author: cdavid Date: 2010-02-16 21:25:50 -0600 (Tue, 16 Feb 2010) New Revision: 6240 Modified: trunk/scipy/signal/filter_design.py trunk/scipy/signal/tests/test_filter_design.py Log: BUG: #1116, thanks to Warren. Modified: trunk/scipy/signal/filter_design.py =================================================================== --- trunk/scipy/signal/filter_design.py 2010-02-16 22:54:01 UTC (rev 6239) +++ trunk/scipy/signal/filter_design.py 2010-02-17 03:25:50 UTC (rev 6240) @@ -220,7 +220,7 @@ outb = b * (1.0) / a[0] outa = a * (1.0) / a[0] if allclose(outb[:,0], 0, rtol=1e-14): - warnings.warn("Badly conditionned filter coefficients (numerator): the " + warnings.warn("Badly conditioned filter coefficients (numerator): the " "results may be meaningless", BadCoefficients) while allclose(outb[:,0], 0, rtol=1e-14) and (outb.shape[-1] > 1): outb = outb[:,1:] Modified: trunk/scipy/signal/tests/test_filter_design.py =================================================================== --- trunk/scipy/signal/tests/test_filter_design.py 2010-02-16 22:54:01 UTC (rev 6239) +++ trunk/scipy/signal/tests/test_filter_design.py 2010-02-17 03:25:50 UTC (rev 6240) @@ -23,7 +23,7 @@ assert_array_almost_equal(p, p_r) def test_bad_filter(self): - """Regression test for #651: better handling of badly conditionned + """Regression test for #651: better handling of badly conditioned filter coefficients.""" warnings.simplefilter("error", BadCoefficients) try: From scipy-svn at scipy.org Fri Feb 19 06:01:32 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 19 Feb 2010 05:01:32 -0600 (CST) Subject: [Scipy-svn] r6241 - in trunk/scipy/signal: . tests Message-ID: <20100219110132.0DEAB39CAED@scipy.org> Author: stefan Date: 2010-02-19 05:01:31 -0600 (Fri, 19 Feb 2010) New Revision: 6241 Modified: trunk/scipy/signal/tests/test_waveforms.py trunk/scipy/signal/waveforms.py Log: Rewrite of chirp by Warren Weckesser. Modified: trunk/scipy/signal/tests/test_waveforms.py =================================================================== --- trunk/scipy/signal/tests/test_waveforms.py 2010-02-17 03:25:50 UTC (rev 6240) +++ trunk/scipy/signal/tests/test_waveforms.py 2010-02-19 11:01:31 UTC (rev 6241) @@ -1,14 +1,246 @@ -#this program corresponds to special.py import numpy as np -from numpy.testing import * +from numpy.testing import TestCase, assert_almost_equal, assert_equal, assert_, \ + assert_raises, run_module_suite -import scipy.signal as signal +import scipy.signal.waveforms as waveforms + +# These chirp_* functions are the instantaneous frequencies of the signals +# returned by chirp(). + +def chirp_linear(t, f0, f1, t1): + f = f0 + (f1 - f0) * t / t1 + return f + +def chirp_quadratic(t, f0, f1, t1, vertex_zero=True): + if vertex_zero: + f = f0 + (f1 - f0) * t**2 / t1**2 + else: + f = f1 - (f1 - f0) * (t1 - t)**2 / t1**2 + return f + +def chirp_geometric(t, f0, f1, t1): + f = f0 * (f1/f0)**(t/t1) + return f + +def chirp_hyperbolic(t, f0, f1, t1): + f = f0*f1*t1 / ((f0 - f1)*t + f1*t1) + return f + + +def compute_frequency(t, theta): + """Compute theta'(t)/(2*pi), where theta'(t) is the derivative of theta(t).""" + # Assume theta and t are 1D numpy arrays. + # Assume that t is uniformly spaced. + dt = t[1] - t[0] + f = np.diff(theta)/(2*np.pi) / dt + tf = 0.5*(t[1:] + t[:-1]) + return tf, f + + class TestChirp(TestCase): - def test_log_chirp_at_zero(self): - assert_almost_equal(signal.waveforms.chirp(t=0, method='log'), - 1.0) + def test_linear_at_zero(self): + w = waveforms.chirp(t=0, f0=1.0, f1=2.0, t1=1.0, method='linear') + assert_almost_equal(w, 1.0) + + def test_linear_freq_01(self): + method = 'linear' + f0 = 1.0 + f1 = 2.0 + t1 = 1.0 + t = np.linspace(0, t1, 100) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_linear(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_linear_freq_02(self): + method = 'linear' + f0 = 200.0 + f1 = 100.0 + t1 = 10.0 + t = np.linspace(0, t1, 100) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_linear(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_quadratic_at_zero(self): + w = waveforms.chirp(t=0, f0=1.0, f1=2.0, t1=1.0, method='quadratic') + assert_almost_equal(w, 1.0) + + def test_quadratic_at_zero2(self): + w = waveforms.chirp(t=0, f0=1.0, f1=2.0, t1=1.0, method='quadratic', + vertex_zero=False) + assert_almost_equal(w, 1.0) + + def test_quadratic_freq_01(self): + method = 'quadratic' + f0 = 1.0 + f1 = 2.0 + t1 = 1.0 + t = np.linspace(0, t1, 2000) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_quadratic(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_quadratic_freq_02(self): + method = 'quadratic' + f0 = 20.0 + f1 = 10.0 + t1 = 10.0 + t = np.linspace(0, t1, 2000) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_quadratic(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_logarithmic_at_zero(self): + w = waveforms.chirp(t=0, f0=1.0, f1=2.0, t1=1.0, method='logarithmic') + assert_almost_equal(w, 1.0) + + def test_logarithmic_freq_01(self): + method = 'logarithmic' + f0 = 1.0 + f1 = 2.0 + t1 = 1.0 + t = np.linspace(0, t1, 10000) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_geometric(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_logarithmic_freq_02(self): + method = 'logarithmic' + f0 = 200.0 + f1 = 100.0 + t1 = 10.0 + t = np.linspace(0, t1, 10000) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_geometric(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_logarithmic_freq_03(self): + method = 'logarithmic' + f0 = 100.0 + f1 = 100.0 + t1 = 10.0 + t = np.linspace(0, t1, 10000) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_geometric(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_hyperbolic_at_zero(self): + w = waveforms.chirp(t=0, f0=10.0, f1=1.0, t1=1.0, method='hyperbolic') + assert_almost_equal(w, 1.0) + + def test_hyperbolic_freq_01(self): + method = 'hyperbolic' + f0 = 10.0 + f1 = 1.0 + t1 = 1.0 + t = np.linspace(0, t1, 10000) + phase = waveforms._chirp_phase(t, f0, t1, f1, method) + tf, f = compute_frequency(t, phase) + abserr = np.max(np.abs(f - chirp_hyperbolic(tf, f0, f1, t1))) + assert_(abserr < 1e-6) + + def test_hyperbolic_freq_02(self): + method = 'hyperbolic' + f0 = 10.0 + f1 = 100.0 + t1 = 1.0 + t = np.linspace(0, t1, 10) + assert_raises(ValueError, waveforms.chirp, t, f0, t1, f1, method) + + def test_hyperbolic_freq_03(self): + method = 'hyperbolic' + f0 = -10.0 + f1 = 0.0 + t1 = 1.0 + t = np.linspace(0, t1, 10) + assert_raises(ValueError, waveforms.chirp, t, f0, t1, f1, method) + + def test_unknown_method(self): + method = "foo" + f0 = 10.0 + f1 = 20.0 + t1 = 1.0 + t = np.linspace(0, t1, 10) + assert_raises(ValueError, waveforms.chirp, t, f0, t1, f1, method) + + +class TestSweepPoly(TestCase): + + def test_sweep_poly_quad1(self): + p = np.poly1d([1.0, 0.0, 1.0]) + t = np.linspace(0, 3.0, 10000) + phase = waveforms._sweep_poly_phase(t, p) + tf, f = compute_frequency(t, phase) + expected = p(tf) + abserr = np.max(np.abs(f - expected)) + assert_(abserr < 1e-6) + + def test_sweep_poly_const(self): + p = np.poly1d(2.0) + t = np.linspace(0, 3.0, 10000) + phase = waveforms._sweep_poly_phase(t, p) + tf, f = compute_frequency(t, phase) + expected = p(tf) + abserr = np.max(np.abs(f - expected)) + assert_(abserr < 1e-6) + + def test_sweep_poly_linear(self): + p = np.poly1d([-1.0, 10.0]) + t = np.linspace(0, 3.0, 10000) + phase = waveforms._sweep_poly_phase(t, p) + tf, f = compute_frequency(t, phase) + expected = p(tf) + abserr = np.max(np.abs(f - expected)) + assert_(abserr < 1e-6) + + def test_sweep_poly_quad2(self): + p = np.poly1d([1.0, 0.0, -2.0]) + t = np.linspace(0, 3.0, 10000) + phase = waveforms._sweep_poly_phase(t, p) + tf, f = compute_frequency(t, phase) + expected = p(tf) + abserr = np.max(np.abs(f - expected)) + assert_(abserr < 1e-6) + + def test_sweep_poly_cubic(self): + p = np.poly1d([2.0, 1.0, 0.0, -2.0]) + t = np.linspace(0, 2.0, 10000) + phase = waveforms._sweep_poly_phase(t, p) + tf, f = compute_frequency(t, phase) + expected = p(tf) + abserr = np.max(np.abs(f - expected)) + assert_(abserr < 1e-6) + + def test_sweep_poly_cubic2(self): + """Use an array of coefficients instead of a poly1d.""" + p = np.array([2.0, 1.0, 0.0, -2.0]) + t = np.linspace(0, 2.0, 10000) + phase = waveforms._sweep_poly_phase(t, p) + tf, f = compute_frequency(t, phase) + expected = np.poly1d(p)(tf) + abserr = np.max(np.abs(f - expected)) + assert_(abserr < 1e-6) + + def test_sweep_poly_cubic3(self): + """Use a list of coefficients instead of a poly1d.""" + p = [2.0, 1.0, 0.0, -2.0] + t = np.linspace(0, 2.0, 10000) + phase = waveforms._sweep_poly_phase(t, p) + tf, f = compute_frequency(t, phase) + expected = np.poly1d(p)(tf) + abserr = np.max(np.abs(f - expected)) + assert_(abserr < 1e-6) + if __name__ == "__main__": run_module_suite() Modified: trunk/scipy/signal/waveforms.py =================================================================== --- trunk/scipy/signal/waveforms.py 2010-02-17 03:25:50 UTC (rev 6240) +++ trunk/scipy/signal/waveforms.py 2010-02-19 11:01:31 UTC (rev 6241) @@ -1,8 +1,12 @@ # Author: Travis Oliphant # 2003 +# +# Feb. 2010: Updated by Warren Weckesser: +# Rewrote much of chirp() +# Added sweep_poly() from numpy import asarray, zeros, place, nan, mod, pi, extract, log, sqrt, \ - exp, cos, sin, size, polyval, polyint, log10 + exp, cos, sin, polyval, polyint def sawtooth(t,width=1): """Returns a periodic sawtooth waveform with period 2*pi @@ -135,67 +139,174 @@ if retquad and retenv: return yI, yQ, yenv -def chirp(t, f0=0, t1=1, f1=100, method='linear', phi=0, qshape=None): + +def chirp(t, f0, t1, f1, method='linear', phi=0, vertex_zero=True): """Frequency-swept cosine generator. + In the following, 'Hz' should be interpreted as 'cycles per time unit'; there is + no assumption here that the time unit is one second. The important distinction + is that the units of rotation are cycles, not radians. + Parameters ---------- t : ndarray Times at which to evaluate the waveform. - f0 : float or ndarray, optional - Frequency (in Hz) of the waveform at time 0. If `f0` is an - ndarray, it specifies the frequency change as a polynomial in - `t` (see Notes below). - t1 : float, optional + f0 : float + Frequency (in Hz) at time t=0. + t1 : float Time at which `f1` is specified. - f1 : float, optional + f1 : float Frequency (in Hz) of the waveform at time `t1`. - method : {'linear', 'quadratic', 'logarithmic'}, optional - Kind of frequency sweep. - phi : float - Phase offset, in degrees. - qshape : {'convex', 'concave'} - If method is 'quadratic', `qshape` specifies its shape. + method : {'linear', 'quadratic', 'logarithmic', 'hyperbolic'}, optional + Kind of frequency sweep. If not given, `linear` is assumed. See Notes below + for more details. + phi : float, optional + Phase offset, in degrees. Default is 0. + vertex_zero : bool, optional + This parameter is only used when `method` is 'quadratic'. + It determines whether the vertex of the parabola that is the graph of the + frequency is at t=0 or t=t1. + Returns + ------- + A numpy array containing the signal evaluated at 't' with the requested + time-varying frequency. More precisely, the function returns + cos(phase + (pi/180)*phi) + where `phase` is the integral (from 0 to t) of 2*pi*f(t). f(t) is defined below. + Notes ----- - If `f0` is an array, it forms the coefficients of a polynomial in - `t` (see `numpy.polval`). The polynomial determines the waveform - frequency change in time. In this case, the values of `f1`, `t1`, - `method`, and `qshape` are ignored. + There are four options for the `method`. The following formulas give the + instantaneous frequency (in Hz) of the signal generated by `chirp()`. + For convenience, the shorter names shown below may also be used. + + linear, lin, li: + f(t) = f0 + (f1 - f0) * t / t1 + + quadratic, quad, q: + + The graph of the frequency f(t) is a parabola through (0, f0) and (t1, f1). + By default, the vertex of the parabola is at (0, f0). If `vertex_zero` + is False, then the vertex is at (t1, f1). The formula is: + + if vertex_zero is True:: + f(t) = f0 + (f1 - f0) * t**2 / t1**2 + else: + f(t) = f1 - (f1 - f0) * (t1 - t)**2 / t1**2 + + To use a more general quadratic function, or an arbitrary polynomial, + use the function `scipy.signal.waveforms.sweep_poly`. + + logarithmic, log, lo: + + f(t) = f0 * (f1/f0)**(t/t1) + + f0 and f1 must be nonzero and have the same sign. + + This signal is also known as a geometric or exponential chirp. + + hyperbolic, hyp: + + f(t) = f0*f1*t1 / ((f0 - f1)*t + f1*t1) + + f1 must be positive, and f0 must be greater than f1. + + See Also + -------- + scipy.signal.waveforms.sweep_poly """ - # Convert to radians. + # 'phase' is computed in _chirp_phase, to make testing easier. + phase = _chirp_phase(t, f0, t1, f1, method, vertex_zero) + # Convert phi to radians. phi *= pi / 180 - if size(f0) > 1: - # We were given a polynomial. - return cos(2*pi*polyval(polyint(f0),t)+phi) - if method in ['linear','lin','li']: - beta = (f1-f0)/t1 - phase_angle = 2*pi * (f0*t + 0.5*beta*t*t) + return cos(phase + phi) + + +def _chirp_phase(t, f0, t1, f1, method='linear', vertex_zero=True): + """ + Calculate the phase used by chirp_phase to generate its output. See + chirp_phase for a description of the arguments. + """ + if method in ['linear', 'lin', 'li']: + beta = (f1 - f0) / t1 + phase = 2*pi * (f0*t + 0.5*beta*t*t) + elif method in ['quadratic','quad','q']: - if qshape == 'concave': - mxf = max(f0,f1) - mnf = min(f0,f1) - f1,f0 = mxf, mnf - elif qshape == 'convex': - mxf = max(f0,f1) - mnf = min(f0,f1) - f1,f0 = mnf, mxf + beta = (f1 - f0)/(t1**2) + if vertex_zero: + phase = 2*pi * (f0*t + beta * t**3/3) else: - raise ValueError("qshape must be either 'concave' or 'convex' but " - "a value of %r was given." % qshape) - beta = (f1-f0)/t1/t1 - phase_angle = 2*pi * (f0*t + beta*t*t*t/3) - elif method in ['logarithmic','log','lo']: - if f1 <= f0: - raise ValueError( - "For a logarithmic sweep, f1=%f must be larger than f0=%f." - % (f1, f0)) - beta = log10(f1-f0)/t1 - phase_angle = 2*pi * (f0*t + (pow(10,beta*t)-1)/(beta*log(10))) + phase = 2*pi * (f1*t + beta * ((t1 - t)**3 - t1**3)/3) + + elif method in ['logarithmic', 'log', 'lo']: + if f0*f1 <= 0.0: + raise ValueError("For a geometric chirp, f0 and f1 must be nonzero " \ + "and have the same sign.") + if f0 == f1: + phase = 2*pi * f0 * t + else: + beta = t1 / log(f1/f0) + phase = 2*pi * beta * f0 * (pow(f1/f0, t/t1) - 1.0) + + elif method in ['hyperbolic', 'hyp']: + if f1 <= 0.0 or f0 <= f1: + raise ValueError("hyperbolic chirp requires f0 > f1 > 0.0.") + c = f1*t1 + df = f0 - f1 + phase = 2*pi * (f0 * c / df) * log((df*t + c)/c) + else: - raise ValueError("method must be 'linear', 'quadratic', or " - "'logarithmic' but a value of %r was given." % method) + raise ValueError("method must be 'linear', 'quadratic', 'logarithmic', " + "or 'hyperbolic', but a value of %r was given." % method) - return cos(phase_angle + phi) + return phase + + +def sweep_poly(t, poly, phi=0): + """Frequency-swept cosine generator, with a time-dependent frequency specified + as a polynomial. + + This function generates a sinusoidal function whose instantaneous frequency + varies with time. The frequency at time `t` is given by the polynomial `poly`. + + Parameters + ---------- + t : ndarray + Times at which to evaluate the waveform. + poly : 1D ndarray (or array-like), or instance of numpy.poly1d + The desired frequency expressed as a polynomial. If `poly` is a list or + ndarray of length n, then the elements of `poly` are the coefficients of the + polynomial, and the instantaneous frequency is + f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1] + If `poly` is an instance of numpy.poly1d, then the instantaneous frequency is + f(t) = poly(t) + phi : float, optional + Phase offset, in degrees. Default is 0. + + Returns + ------- + A numpy array containing the signal evaluated at 't' with the requested + time-varying frequency. More precisely, the function returns + cos(phase + (pi/180)*phi) + where `phase` is the integral (from 0 to t) of 2*pi*f(t); f(t) is defined above. + + See Also + -------- + scipy.signal.waveforms.chirp + """ + # 'phase' is computed in _sweep_poly_phase, to make testing easier. + phase = _sweep_poly_phase(t, poly) + # Convert to radians. + phi *= pi / 180 + return cos(phase + phi) + +def _sweep_poly_phase(t, poly): + """ + Calculate the phase used by sweep_poly to generate its output. See + sweep_poly for a description of the arguments. + """ + # polyint handles lists, ndarrays and instances of poly1d automatically. + intpoly = polyint(poly) + phase = 2*pi * polyval(intpoly, t) + return phase From scipy-svn at scipy.org Fri Feb 19 06:02:13 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 19 Feb 2010 05:02:13 -0600 (CST) Subject: [Scipy-svn] r6242 - trunk/scipy/signal Message-ID: <20100219110213.6087939CAED@scipy.org> Author: stefan Date: 2010-02-19 05:02:12 -0600 (Fri, 19 Feb 2010) New Revision: 6242 Modified: trunk/scipy/signal/waveforms.py Log: DOC: Waveforms: format math in monospace. Modified: trunk/scipy/signal/waveforms.py =================================================================== --- trunk/scipy/signal/waveforms.py 2010-02-19 11:01:31 UTC (rev 6241) +++ trunk/scipy/signal/waveforms.py 2010-02-19 11:02:12 UTC (rev 6242) @@ -13,6 +13,7 @@ which rises from -1 to 1 on the interval 0 to width*2*pi and drops from 1 to -1 on the interval width*2*pi to 2*pi width must be in the interval [0,1] + """ t,w = asarray(t), asarray(width) w = asarray(w + (t-t)) @@ -51,6 +52,7 @@ """Returns a periodic square-wave waveform with period 2*pi which is +1 from 0 to 2*pi*duty and -1 from 2*pi*duty to 2*pi duty must be in the interval [0,1] + """ t,w = asarray(t), asarray(duty) w = asarray(w + (t-t)) @@ -102,6 +104,7 @@ pulse amplitude falls below tpr (in dB). retquad -- Return the quadrature (imaginary) as well as the real part of the signal retenv -- Return the envelope of th signal. + """ if fc < 0: raise ValueError, "Center frequency (fc=%.2f) must be >=0." % fc @@ -152,7 +155,7 @@ t : ndarray Times at which to evaluate the waveform. f0 : float - Frequency (in Hz) at time t=0. + Frequency (in Hz) at time t=0. t1 : float Time at which `f1` is specified. f1 : float @@ -171,50 +174,55 @@ ------- A numpy array containing the signal evaluated at 't' with the requested time-varying frequency. More precisely, the function returns - cos(phase + (pi/180)*phi) - where `phase` is the integral (from 0 to t) of 2*pi*f(t). f(t) is defined below. + ``cos(phase + (pi/180)*phi)`` + where `phase` is the integral (from 0 to t) of ``2*pi*f(t)``. + ``f(t)`` is defined below. Notes ----- There are four options for the `method`. The following formulas give the instantaneous frequency (in Hz) of the signal generated by `chirp()`. For convenience, the shorter names shown below may also be used. - + linear, lin, li: - f(t) = f0 + (f1 - f0) * t / t1 - + ``f(t) = f0 + (f1 - f0) * t / t1`` + quadratic, quad, q: The graph of the frequency f(t) is a parabola through (0, f0) and (t1, f1). By default, the vertex of the parabola is at (0, f0). If `vertex_zero` is False, then the vertex is at (t1, f1). The formula is: - if vertex_zero is True:: - f(t) = f0 + (f1 - f0) * t**2 / t1**2 - else: - f(t) = f1 - (f1 - f0) * (t1 - t)**2 / t1**2 + if vertex_zero is True: + ``f(t) = f0 + (f1 - f0) * t**2 / t1**2`` + + else + + ``f(t) = f1 - (f1 - f0) * (t1 - t)**2 / t1**2`` + To use a more general quadratic function, or an arbitrary polynomial, use the function `scipy.signal.waveforms.sweep_poly`. logarithmic, log, lo: - f(t) = f0 * (f1/f0)**(t/t1) - + ``f(t) = f0 * (f1/f0)**(t/t1)`` + f0 and f1 must be nonzero and have the same sign. This signal is also known as a geometric or exponential chirp. hyperbolic, hyp: - f(t) = f0*f1*t1 / ((f0 - f1)*t + f1*t1) - + ``f(t) = f0*f1*t1 / ((f0 - f1)*t + f1*t1)`` + f1 must be positive, and f0 must be greater than f1. See Also -------- scipy.signal.waveforms.sweep_poly + """ # 'phase' is computed in _chirp_phase, to make testing easier. phase = _chirp_phase(t, f0, t1, f1, method, vertex_zero) @@ -222,11 +230,12 @@ phi *= pi / 180 return cos(phase + phi) - + def _chirp_phase(t, f0, t1, f1, method='linear', vertex_zero=True): """ Calculate the phase used by chirp_phase to generate its output. See chirp_phase for a description of the arguments. + """ if method in ['linear', 'lin', 'li']: beta = (f1 - f0) / t1 @@ -237,7 +246,7 @@ if vertex_zero: phase = 2*pi * (f0*t + beta * t**3/3) else: - phase = 2*pi * (f1*t + beta * ((t1 - t)**3 - t1**3)/3) + phase = 2*pi * (f1*t + beta * ((t1 - t)**3 - t1**3)/3) elif method in ['logarithmic', 'log', 'lo']: if f0*f1 <= 0.0: @@ -266,7 +275,7 @@ def sweep_poly(t, poly, phi=0): """Frequency-swept cosine generator, with a time-dependent frequency specified as a polynomial. - + This function generates a sinusoidal function whose instantaneous frequency varies with time. The frequency at time `t` is given by the polynomial `poly`. @@ -275,12 +284,14 @@ t : ndarray Times at which to evaluate the waveform. poly : 1D ndarray (or array-like), or instance of numpy.poly1d - The desired frequency expressed as a polynomial. If `poly` is a list or - ndarray of length n, then the elements of `poly` are the coefficients of the - polynomial, and the instantaneous frequency is - f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1] - If `poly` is an instance of numpy.poly1d, then the instantaneous frequency is - f(t) = poly(t) + The desired frequency expressed as a polynomial. If `poly` is + a list or ndarray of length n, then the elements of `poly` are + the coefficients of the polynomial, and the instantaneous + frequency is + ``f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1]`` + If `poly` is an instance of numpy.poly1d, then the + instantaneous frequency is + ``f(t) = poly(t)`` phi : float, optional Phase offset, in degrees. Default is 0. @@ -288,12 +299,14 @@ ------- A numpy array containing the signal evaluated at 't' with the requested time-varying frequency. More precisely, the function returns - cos(phase + (pi/180)*phi) - where `phase` is the integral (from 0 to t) of 2*pi*f(t); f(t) is defined above. - + ``cos(phase + (pi/180)*phi)`` + where `phase` is the integral (from 0 to t) of ``2 * pi * f(t)``; + ``f(t)`` is defined above. + See Also -------- scipy.signal.waveforms.chirp + """ # 'phase' is computed in _sweep_poly_phase, to make testing easier. phase = _sweep_poly_phase(t, poly) @@ -305,6 +318,7 @@ """ Calculate the phase used by sweep_poly to generate its output. See sweep_poly for a description of the arguments. + """ # polyint handles lists, ndarrays and instances of poly1d automatically. intpoly = polyint(poly) From scipy-svn at scipy.org Sun Feb 21 10:38:35 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 21 Feb 2010 09:38:35 -0600 (CST) Subject: [Scipy-svn] r6243 - trunk/scipy/signal Message-ID: <20100221153835.68225C7C12B@scipy.org> Author: ptvirtan Date: 2010-02-21 09:38:35 -0600 (Sun, 21 Feb 2010) New Revision: 6243 Modified: trunk/scipy/signal/lfilter.c.src trunk/scipy/signal/sigtools.h trunk/scipy/signal/sigtoolsmodule.c Log: BUG: signaltools: make lfilter work also after Numpy ABI change (value of NPY_OBJECT should not be hardcoded) Modified: trunk/scipy/signal/lfilter.c.src =================================================================== --- trunk/scipy/signal/lfilter.c.src 2010-02-19 11:02:12 UTC (rev 6242) +++ trunk/scipy/signal/lfilter.c.src 2010-02-21 15:38:35 UTC (rev 6243) @@ -33,13 +33,24 @@ typedef void (BasicFilterFunction) (char *, char *, char *, char *, char *, intp, uintp, intp, intp); -static BasicFilterFunction *BasicFilterFunctions[] = - { NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, - FLOAT_filt, DOUBLE_filt, EXTENDED_filt, - CFLOAT_filt, CDOUBLE_filt, CEXTENDED_filt, - OBJECT_filt, NULL, NULL, NULL -}; +static BasicFilterFunction *BasicFilterFunctions[256]; +void +scipy_signal_sigtools_linear_filter_module_init() +{ + int k; + for (k = 0; k < 256; ++k) { + BasicFilterFunctions[k] = NULL; + } + BasicFilterFunctions[NPY_FLOAT] = FLOAT_filt; + BasicFilterFunctions[NPY_DOUBLE] = DOUBLE_filt; + BasicFilterFunctions[NPY_LONGDOUBLE] = EXTENDED_filt; + BasicFilterFunctions[NPY_CFLOAT] = CFLOAT_filt; + BasicFilterFunctions[NPY_CDOUBLE] = CDOUBLE_filt; + BasicFilterFunctions[NPY_CLONGDOUBLE] = CEXTENDED_filt; + BasicFilterFunctions[NPY_OBJECT] = OBJECT_filt; +} + /* There is the start of an OBJECT_filt, but it may need work */ static int @@ -114,7 +125,12 @@ typenum); } - basic_filter = BasicFilterFunctions[(int) (arX->descr->type_num)]; + if (arX->descr->type_num < 256) { + basic_filter = BasicFilterFunctions[(int) (arX->descr->type_num)]; + } + else { + basic_filter = NULL; + } if (basic_filter == NULL) { PyObject *msg, *str; char *s; Modified: trunk/scipy/signal/sigtools.h =================================================================== --- trunk/scipy/signal/sigtools.h 2010-02-19 11:02:12 UTC (rev 6242) +++ trunk/scipy/signal/sigtools.h 2010-02-21 15:38:35 UTC (rev 6243) @@ -55,6 +55,9 @@ PyObject* scipy_signal_sigtools_correlateND(PyObject *NPY_UNUSED(dummy), PyObject *args); +void +scipy_signal_sigtools_linear_filter_module_init(); + /* static int index_out_of_bounds(int *, int *, int ); static long compute_offsets (unsigned long *, long *, int *, int *, int *, int *, int); Modified: trunk/scipy/signal/sigtoolsmodule.c =================================================================== --- trunk/scipy/signal/sigtoolsmodule.c 2010-02-19 11:02:12 UTC (rev 6242) +++ trunk/scipy/signal/sigtoolsmodule.c 2010-02-21 15:38:35 UTC (rev 6243) @@ -1338,6 +1338,7 @@ PyDict_SetItemString(d,"HILBERT", PyInt_FromLong((long) HILBERT)); */ + scipy_signal_sigtools_linear_filter_module_init(); /* Check for errors */ if (PyErr_Occurred()) { From scipy-svn at scipy.org Sun Feb 21 16:04:04 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Sun, 21 Feb 2010 15:04:04 -0600 (CST) Subject: [Scipy-svn] r6244 - trunk/scipy/integrate Message-ID: <20100221210404.D8DD939CB11@scipy.org> Author: stefan Date: 2010-02-21 15:04:04 -0600 (Sun, 21 Feb 2010) New Revision: 6244 Modified: trunk/scipy/integrate/odepack.py Log: DOC: Note default value for rtol/atol in odeint. Modified: trunk/scipy/integrate/odepack.py =================================================================== --- trunk/scipy/integrate/odepack.py 2010-02-21 15:38:35 UTC (rev 6243) +++ trunk/scipy/integrate/odepack.py 2010-02-21 21:04:04 UTC (rev 6244) @@ -105,6 +105,7 @@ where ewt is a vector of positive error weights computed as: ``ewt = rtol * abs(y) + atol`` rtol and atol can be either vectors the same length as y or scalars. + Defaults to 1.49012e-8. tcrit : array Vector of critical points (e.g. singularities) where integration care should be taken. From scipy-svn at scipy.org Mon Feb 22 04:02:18 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 22 Feb 2010 03:02:18 -0600 (CST) Subject: [Scipy-svn] r6245 - in trunk/scipy/sparse/linalg/eigen/arpack: . tests Message-ID: <20100222090218.6897139CB0A@scipy.org> Author: cdavid Date: 2010-02-22 03:02:18 -0600 (Mon, 22 Feb 2010) New Revision: 6245 Modified: trunk/scipy/sparse/linalg/eigen/arpack/arpack.py trunk/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py Log: ENH: implement naive SVD for sparse real matrices. Modified: trunk/scipy/sparse/linalg/eigen/arpack/arpack.py =================================================================== --- trunk/scipy/sparse/linalg/eigen/arpack/arpack.py 2010-02-21 21:04:04 UTC (rev 6244) +++ trunk/scipy/sparse/linalg/eigen/arpack/arpack.py 2010-02-22 09:02:18 UTC (rev 6245) @@ -39,13 +39,14 @@ __docformat__ = "restructuredtext en" -__all___=['eigen','eigen_symmetric'] +__all___=['eigen','eigen_symmetric', 'svd'] import warnings import _arpack import numpy as np from scipy.sparse.linalg.interface import aslinearoperator +from scipy.sparse import csc_matrix, csr_matrix _type_conv = {'f':'s', 'd':'d', 'F':'c', 'D':'z'} _ndigits = {'f':5, 'd':12, 'F':5, 'D':12} @@ -488,3 +489,58 @@ if return_eigenvectors: return d,z return d + +def svd(A, k=6): + """Compute a few singular values/vectors for a sparse matrix using ARPACK. + + Parameters + ---------- + A: sparse matrix + Array to compute the SVD on. + k: int + Number of singular values and vectors to compute. + + Note + ---- + This is a naive implementation using the symmetric eigensolver on A.T * A + or A * A.T, depending on which one is more efficient. + + Complex support is not implemented yet + """ + # TODO: implement complex support once ARPACK-based eigen_hermitian is + # available + n, m = A.shape + + if np.iscomplexobj(A): + raise NotImplementedError("Complex support for sparse SVD not " \ + "implemented yet") + op = lambda x: x.T.conjugate() + else: + op = lambda x: x.T + + def _left(x): + x = csc_matrix(x) + m = op(x) * x + + eigvals, eigvec = eigen_symmetric(m, k) + s = np.sqrt(eigvals) + + v = eigvec + u = (x * v) / s + return u, s, op(v) + + def _right(x): + x = csr_matrix(x) + m = x * op(x) + + eigvals, eigvec = eigen_symmetric(m, k) + s = np.sqrt(eigvals) + + u = eigvec + vh = (op(u) * x) / s[:, None] + return u, s, vh + + if n > m: + return _left(A) + else: + return _right(A) Modified: trunk/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py =================================================================== --- trunk/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py 2010-02-21 21:04:04 UTC (rev 6244) +++ trunk/scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py 2010-02-22 09:02:18 UTC (rev 6245) @@ -4,12 +4,15 @@ python tests/test_arpack.py [-l] [-v] """ +import numpy as np from numpy.testing import * from numpy import array, finfo, argsort, dot, round, conj, random -from scipy.sparse.linalg.eigen.arpack import eigen_symmetric, eigen +from scipy.sparse.linalg.eigen.arpack import eigen_symmetric, eigen, svd +from scipy.linalg import svd as dsvd + def assert_almost_equal_cc(actual,desired,decimal=7,err_msg='',verbose=True): # almost equal or complex conjugates almost equal try: @@ -263,5 +266,51 @@ for m in self.nonsymmetric: self.eval_evec(m,typ,k,which) +def sorted_svd(m, k): + """Compute svd of a dense matrix m, and return singular vectors/values + sorted.""" + u, s, vh = dsvd(m) + ii = np.argsort(s)[-k:] + + return u[:, ii], s[ii], vh[ii] + +def svd_estimate(u, s, vh): + return np.dot(u, np.dot(np.diag(s), vh)) + +class TestSparseSvd(TestCase): + def test_simple_real(self): + x = np.array([[1, 2, 3], + [3, 4, 3], + [1, 0, 2], + [0, 0, 1]], np.float) + + for m in [x.T, x]: + for k in range(1, 3): + u, s, vh = sorted_svd(m, k) + su, ss, svh = svd(m, k) + + m_hat = svd_estimate(u, s, vh) + sm_hat = svd_estimate(su, ss, svh) + + assert_array_almost_equal_nulp(m_hat, sm_hat, nulp=1000) + + @dec.knownfailureif(True, "Complex sparse SVD not implemented (depends on " + "Hermitian support in eigen_symmetric") + def test_simple_complex(self): + x = np.array([[1, 2, 3], + [3, 4, 3], + [1+1j, 0, 2], + [0, 0, 1]], np.complex) + + for m in [x, x.T.conjugate()]: + for k in range(1, 3): + u, s, vh = sorted_svd(m, k) + su, ss, svh = svd(m, k) + + m_hat = svd_estimate(u, s, vh) + sm_hat = svd_estimate(su, ss, svh) + + assert_array_almost_equal_nulp(m_hat, sm_hat, nulp=1000) + if __name__ == "__main__": run_module_suite() From scipy-svn at scipy.org Mon Feb 22 04:07:04 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 22 Feb 2010 03:07:04 -0600 (CST) Subject: [Scipy-svn] r6246 - trunk/doc/release Message-ID: <20100222090704.A326839CB0A@scipy.org> Author: cdavid Date: 2010-02-22 03:07:04 -0600 (Mon, 22 Feb 2010) New Revision: 6246 Modified: trunk/doc/release/0.8.0-notes.rst Log: DOC: mention sparse SVD in release notes. Modified: trunk/doc/release/0.8.0-notes.rst =================================================================== --- trunk/doc/release/0.8.0-notes.rst 2010-02-22 09:02:18 UTC (rev 6245) +++ trunk/doc/release/0.8.0-notes.rst 2010-02-22 09:07:04 UTC (rev 6246) @@ -83,6 +83,13 @@ the slided sum-products, which correspond to the usual definition of correlation +ARPACK-based sparse SVD +----------------------- + +A naive implementation of SVD for sparse matrices is available in +scipy.sparse.linalg.eigen.arpack. It is based on using an symmetric solver on +, and as such may not be very precise. + Removed features ================ From scipy-svn at scipy.org Mon Feb 22 15:28:01 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 22 Feb 2010 14:28:01 -0600 (CST) Subject: [Scipy-svn] r6247 - trunk/scipy/special Message-ID: <20100222202801.08C8439CB12@scipy.org> Author: stefan Date: 2010-02-22 14:28:00 -0600 (Mon, 22 Feb 2010) New Revision: 6247 Modified: trunk/scipy/special/SConscript Log: BUG: Prepend path in SConscript. Modified: trunk/scipy/special/SConscript =================================================================== --- trunk/scipy/special/SConscript 2010-02-22 09:07:04 UTC (rev 6246) +++ trunk/scipy/special/SConscript 2010-02-22 20:28:00 UTC (rev 6247) @@ -14,7 +14,7 @@ if sys.platform=='win32': # define_macros.append(('NOINFINITIES',None)) # define_macros.append(('NONANS',None)) - env.AppendUnique(CPPDEFINES = '_USE_MATH_DEFINES') + env.PrependUnique(CPPDEFINES = '_USE_MATH_DEFINES') config = env.NumpyConfigure(custom_tests = {'CheckF77Clib' : CheckF77Clib}) if not config.CheckF77Clib(): From scipy-svn at scipy.org Mon Feb 22 15:28:54 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 22 Feb 2010 14:28:54 -0600 (CST) Subject: [Scipy-svn] r6248 - in trunk/scipy/signal: . tests Message-ID: <20100222202854.31D4F39CB12@scipy.org> Author: stefan Date: 2010-02-22 14:28:54 -0600 (Mon, 22 Feb 2010) New Revision: 6248 Modified: trunk/scipy/signal/ltisys.py trunk/scipy/signal/tests/test_ltisys.py Log: ENH: Rewrite of lsim2 and new impulse2 by Warren Weckesser. Modified: trunk/scipy/signal/ltisys.py =================================================================== --- trunk/scipy/signal/ltisys.py 2010-02-22 20:28:00 UTC (rev 6247) +++ trunk/scipy/signal/ltisys.py 2010-02-22 20:28:54 UTC (rev 6248) @@ -1,17 +1,26 @@ +""" +ltisys -- a collection of classes and functions for modeling linear time invariant +systems. +""" + # # Author: Travis Oliphant 2001 # +# Feb 2010: Warren Weckesser +# Rewrote lsim2 and added impulse2. +# from filter_design import tf2zpk, zpk2tf, normalize import numpy from numpy import product, zeros, array, dot, transpose, arange, ones, \ - nan_to_num + nan_to_num, zeros_like, linspace import scipy.interpolate as interpolate import scipy.integrate as integrate import scipy.linalg as linalg from numpy import r_, eye, real, atleast_1d, atleast_2d, poly, \ squeeze, diag, asarray + def tf2ss(num, den): """Transfer function to state-space representation. @@ -271,71 +280,95 @@ return lsim(self, U, T, X0=X0) -def lsim2(system, U, T, X0=None): - """Simulate output of a continuous-time linear system, using ODE solver. +def lsim2(system, U=None, T=None, X0=None, **kwargs): + """Simulate output of a continuous-time linear system, by using the ODE solver + `scipy.integrate.odeint`. - Inputs: + Parameters + ---------- + system : an instance of the LTI class or a tuple describing the system. + The following gives the number of elements in the tuple and the interpretation. + 2 (num, den) + 3 (zeros, poles, gain) + 4 (A, B, C, D) + U : ndarray or array-like (1D or 2D), optional + An input array describing the input at each time T. Linear interpolation + is used between given times. If there are multiple inputs, then each column + of the rank-2 array represents an input. If U is not given, the input is + assumed to be zero. + T : ndarray or array-like (1D or 2D), optional + The time steps at which the input is defined and at which the output is + desired. The default is 101 evenly spaced points on the interval [0,10.0]. + X0 : ndarray or array-like (1D), optional + The initial condition of the state vector. If `X0` is not given, the initial + conditions are assumed to be 0. + **kwargs : + Additional keyword arguments are passed on to the function odeint. See the + notes below for more details. - system -- an instance of the LTI class or a tuple describing the - system. The following gives the number of elements in - the tuple and the interpretation. - 2 (num, den) - 3 (zeros, poles, gain) - 4 (A, B, C, D) - U -- an input array describing the input at each time T - (linear interpolation is assumed between given times). - If there are multiple inputs, then each column of the - rank-2 array represents an input. - T -- the time steps at which the input is defined and at which - the output is desired. - X0 -- (optional, default=0) the initial conditions on the state vector. + Returns: (T, yout, xout) + ------------------------ + T : 1D ndarray + The time values for the output. + yout : ndarray + The response of the system. + xout : ndarray + The time-evolution of the state-vector. - Outputs: (T, yout, xout) - - T -- the time values for the output. - yout -- the response of the system. - xout -- the time-evolution of the state-vector. + Notes + ----- + This function uses :func:`scipy.integrate.odeint` to solve the system's differential + equations. Additional keyword arguments given to `lsim2` are passed on to `odeint`. + See the documentation for :func:`scipy.integrate.odeint` for the full list of + arguments. """ - # system is an lti system or a sequence - # with 2 (num, den) - # 3 (zeros, poles, gain) - # 4 (A, B, C, D) - # describing the system - # U is an input vector at times T - # if system describes multiple outputs - # then U can be a rank-2 array with the number of columns - # being the number of inputs - # rather than use lsim, use direct integration and matrix-exponential. if isinstance(system, lti): sys = system else: sys = lti(*system) - U = atleast_1d(U) - T = atleast_1d(T) - if len(U.shape) == 1: - U = U.reshape((U.shape[0],1)) - sU = U.shape - if len(T.shape) != 1: - raise ValueError, "T must be a rank-1 array." - if sU[0] != len(T): - raise ValueError, "U must have the same number of rows as elements in T." - if sU[1] != sys.inputs: - raise ValueError, "System does not define that many inputs." if X0 is None: X0 = zeros(sys.B.shape[0],sys.A.dtype) - # for each output point directly integrate assume zero-order hold - # or linear interpolation. + if T is None: + # XXX T should really be a required argument, but U was changed from a required + # positional argument to a keyword, and T is after U in the argument list. + # So we either: change the API and move T in front of U; check here for T being + # None and raise an excpetion; or assign a default value to T here. This code + # implements the latter. + T = linspace(0, 10.0, 101) - ufunc = interpolate.interp1d(T, U, kind='linear', axis=0, bounds_error=False) + T = atleast_1d(T) + if len(T.shape) != 1: + raise ValueError, "T must be a rank-1 array." - def fprime(x, t, sys, ufunc): - return dot(sys.A,x) + squeeze(dot(sys.B,nan_to_num(ufunc([t])))) + if U is not None: + U = atleast_1d(U) + if len(U.shape) == 1: + U = U.reshape(-1,1) + sU = U.shape + if sU[0] != len(T): + raise ValueError, "U must have the same number of rows as elements in T." + if sU[1] != sys.inputs: + raise ValueError("The number of inputs in U (%d) is not compatible with " + "the number of system inputs (%d)" % (sU[1], sys.inputs)) + # Create a callable that uses linear interpolation to calculate the input at + # any time. + ufunc = interpolate.interp1d(T, U, kind='linear', axis=0, bounds_error=False) - xout = integrate.odeint(fprime, X0, T, args=(sys, ufunc)) - yout = dot(sys.C,transpose(xout)) + dot(sys.D,transpose(U)) + def fprime(x, t, sys, ufunc): + """The vector field of the linear system.""" + return dot(sys.A,x) + squeeze(dot(sys.B,nan_to_num(ufunc([t])))) + xout = integrate.odeint(fprime, X0, T, args=(sys, ufunc), **kwargs) + yout = dot(sys.C,transpose(xout)) + dot(sys.D,transpose(U)) + else: + def fprime(x, t, sys): + """The vector field of the linear system.""" + return dot(sys.A,x) + xout = integrate.odeint(fprime, X0, T, args=(sys,), **kwargs) + yout = dot(sys.C,transpose(xout)) + return T, squeeze(transpose(yout)), xout @@ -469,6 +502,75 @@ h[k] = squeeze(dot(dot(C,eA),B)) return T, h + +def impulse2(system, X0=None, T=None, N=None, **kwargs): + """Impulse response of a single-input continuous-time linear system. + + The solution is generated by calling `scipy.soignal.lsim2`, which uses the + differential equation solver `scipy.integrate.odeint`. + + Parameters + ---------- + system : an instance of the LTI class or a tuple describing the system. + The following gives the number of elements in the tuple and the interpretation. + 2 (num, den) + 3 (zeros, poles, gain) + 4 (A, B, C, D) + T : 1D ndarray or array-like, optional + The time steps at which the input is defined and at which the output is + desired. If `T` is not given, the function will generate a set of time + samples automatically. + X0 : 1D ndarray or array-like, optional + The initial condition of the state vector. If X0 is None, the initial + conditions are assumed to be 0. + N : int, optional + Number of time points to compute. If `N` is not given, 100 points are used. + **kwargs : + Additional keyword arguments are passed on the function `scipy.signal.lsim2`, + which in turn passes them on to :func:`scipy.integrate.odeint`. See the + documation for :func:`scipy.integrate.odeint` for information about these + arguments. + + Returns: (T, yout, xout) + ------------------------ + T : 1D ndarray + The time values for the output. + yout : ndarray + The output response of the system. + + See Also + -------- + scipy.signal.impulse + """ + if isinstance(system, lti): + sys = system + else: + sys = lti(*system) + B = sys.B + if B.shape[-1] != 1: + raise ValueError, "impulse2() requires a single-input system." + B = B.squeeze() + if X0 is None: + X0 = zeros_like(B) + if N is None: + N = 100 + if T is None: + # Create a reasonable time interval. This could use some more work. + # For example, what is expected when the system is unstable? + vals = linalg.eigvals(sys.A) + r = min(abs(real(vals))) + if r == 0.0: + r = 1.0 + tc = 1.0/r + T = arange(0, 7*tc, 7*tc / float(N)) + # Move the impulse in the input to the initial conditions, and then + # solve using lsim2(). + U = zeros_like(T) + ic = B + X0 + Tr, Yr, Xr = lsim2(sys, U, T, ic, **kwargs) + return Tr, Yr + + def step(system, X0=None, T=None, N=None): """Step response of continuous-time system. Modified: trunk/scipy/signal/tests/test_ltisys.py =================================================================== --- trunk/scipy/signal/tests/test_ltisys.py 2010-02-22 20:28:00 UTC (rev 6247) +++ trunk/scipy/signal/tests/test_ltisys.py 2010-02-22 20:28:54 UTC (rev 6248) @@ -1,7 +1,7 @@ import numpy as np -from numpy.testing import * +from numpy.testing import assert_almost_equal, assert_equal, run_module_suite -from scipy.signal.ltisys import ss2tf +from scipy.signal.ltisys import ss2tf, lsim2, impulse2, lti class TestSS2TF: def tst_matrix_shapes(self, p, q, r): @@ -17,5 +17,136 @@ (1, 1, 1)]: yield self.tst_matrix_shapes, p, q, r + +class Test_lsim2(object): + + def test_01(self): + t = np.linspace(0,10,1001) + u = np.zeros_like(t) + # First order system: x'(t) + x(t) = u(t), x(0) = 1. + # Exact solution is x(t) = exp(-t). + system = ([1.0],[1.0,1.0]) + tout, y, x = lsim2(system, u, t, X0=[1.0]) + expected_x = np.exp(-tout) + assert_almost_equal(x[:,0], expected_x) + + def test_02(self): + t = np.array([0.0, 1.0, 1.0, 3.0]) + u = np.array([0.0, 0.0, 1.0, 1.0]) + # Simple integrator: x'(t) = u(t) + system = ([1.0],[1.0,0.0]) + tout, y, x = lsim2(system, u, t, X0=[1.0]) + expected_x = np.maximum(1.0, tout) + assert_almost_equal(x[:,0], expected_x) + + def test_03(self): + t = np.array([0.0, 1.0, 1.0, 1.1, 1.1, 2.0]) + u = np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0]) + # Simple integrator: x'(t) = u(t) + system = ([1.0],[1.0, 0.0]) + tout, y, x = lsim2(system, u, t, hmax=0.01) + expected_x = np.array([0.0, 0.0, 0.0, 0.1, 0.1, 0.1]) + assert_almost_equal(x[:,0], expected_x) + + def test_04(self): + t = np.linspace(0, 10, 1001) + u = np.zeros_like(t) + # Second order system with a repeated root: x''(t) + 2*x(t) + x(t) = 0. + # With initial conditions x(0)=1.0 and x'(t)=0.0, the exact solution + # is (1-t)*exp(-t). + system = ([1.0], [1.0, 2.0, 1.0]) + tout, y, x = lsim2(system, u, t, X0=[1.0, 0.0]) + expected_x = (1.0 - tout) * np.exp(-tout) + assert_almost_equal(x[:,0], expected_x) + + def test_05(self): + # This test triggers a "BadCoefficients" warning from scipy.signal.filter_design, + # but the test passes. I think the warning is related to the incomplete handling + # of multi-input systems in scipy.signal. + + # A system with two state variables, two inputs, and one output. + A = np.array([[-1.0, 0.0], [0.0, -2.0]]) + B = np.array([[1.0, 0.0], [0.0, 1.0]]) + C = np.array([1.0, 0.0]) + D = np.zeros((1,2)) + + t = np.linspace(0, 10.0, 101) + tout, y, x = lsim2((A,B,C,D), T=t, X0=[1.0, 1.0]) + expected_y = np.exp(-tout) + expected_x0 = np.exp(-tout) + expected_x1 = np.exp(-2.0*tout) + assert_almost_equal(y, expected_y) + assert_almost_equal(x[:,0], expected_x0) + assert_almost_equal(x[:,1], expected_x1) + + def test_06(self): + """Test use of the default values of the arguments `T` and `U`.""" + # Second order system with a repeated root: x''(t) + 2*x(t) + x(t) = 0. + # With initial conditions x(0)=1.0 and x'(t)=0.0, the exact solution + # is (1-t)*exp(-t). + system = ([1.0], [1.0, 2.0, 1.0]) + tout, y, x = lsim2(system, X0=[1.0, 0.0]) + expected_x = (1.0 - tout) * np.exp(-tout) + assert_almost_equal(x[:,0], expected_x) + +class Test_impulse2(object): + + def test_01(self): + # First order system: x'(t) + x(t) = u(t) + # Exact impulse response is x(t) = exp(-t). + system = ([1.0],[1.0,1.0]) + tout, y = impulse2(system) + expected_y = np.exp(-tout) + assert_almost_equal(y, expected_y) + + def test_02(self): + """Specify the desired time values for the output.""" + + # First order system: x'(t) + x(t) = u(t) + # Exact impulse response is x(t) = exp(-t). + system = ([1.0],[1.0,1.0]) + n = 21 + t = np.linspace(0, 2.0, n) + tout, y = impulse2(system, T=t) + assert_equal(tout.shape, (n,)) + assert_almost_equal(tout, t) + expected_y = np.exp(-t) + assert_almost_equal(y, expected_y) + + def test_03(self): + """Specify an initial condition as a scalar.""" + + # First order system: x'(t) + x(t) = u(t), x(0)=3.0 + # Exact impulse response is x(t) = 4*exp(-t). + system = ([1.0],[1.0,1.0]) + tout, y = impulse2(system, X0=3.0) + expected_y = 4.0*np.exp(-tout) + assert_almost_equal(y, expected_y) + + def test_04(self): + """Specify an initial condition as a list.""" + + # First order system: x'(t) + x(t) = u(t), x(0)=3.0 + # Exact impulse response is x(t) = 4*exp(-t). + system = ([1.0],[1.0,1.0]) + tout, y = impulse2(system, X0=[3.0]) + expected_y = 4.0*np.exp(-tout) + assert_almost_equal(y, expected_y) + + def test_05(self): + # Simple integrator: x'(t) = u(t) + system = ([1.0],[1.0,0.0]) + tout, y = impulse2(system) + expected_y = np.ones_like(tout) + assert_almost_equal(y, expected_y) + + def test_06(self): + # Second order system with a repeated root: x''(t) + 2*x(t) + x(t) = u(t) + # The exact impulse response is t*exp(-t). + system = ([1.0], [1.0, 2.0, 1.0]) + tout, y = impulse2(system) + expected_y = tout * np.exp(-tout) + assert_almost_equal(y, expected_y) + if __name__ == "__main__": run_module_suite() From scipy-svn at scipy.org Mon Feb 22 15:29:36 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Mon, 22 Feb 2010 14:29:36 -0600 (CST) Subject: [Scipy-svn] r6249 - trunk/scipy/signal Message-ID: <20100222202936.215F739CB12@scipy.org> Author: stefan Date: 2010-02-22 14:29:35 -0600 (Mon, 22 Feb 2010) New Revision: 6249 Modified: trunk/scipy/signal/ltisys.py Log: DOC: Reformat ltisys docs. Modified: trunk/scipy/signal/ltisys.py =================================================================== --- trunk/scipy/signal/ltisys.py 2010-02-22 20:28:54 UTC (rev 6248) +++ trunk/scipy/signal/ltisys.py 2010-02-22 20:29:35 UTC (rev 6249) @@ -1,6 +1,6 @@ """ -ltisys -- a collection of classes and functions for modeling linear time invariant -systems. +ltisys -- a collection of classes and functions for modeling linear +time invariant systems. """ # @@ -24,13 +24,17 @@ def tf2ss(num, den): """Transfer function to state-space representation. - Inputs: + Parameters + ---------- + num, den : array_like + Sequences representing the numerator and denominator + polynomials. - num, den -- sequences representing the numerator and denominator polynomials. + Returns + ------- + A, B, C, D : ndarray + State space representation of the system. - Outputs: - - A, B, C, D -- state space representation of the system. """ # Controller canonical state-space representation. # if M+1 = len(num) and K+1 = len(den) then we must have M <= K @@ -68,7 +72,7 @@ C = num[:,1:] - num[:,0] * den[1:] return A, B, C, D -def none_to_empty(arg): +def _none_to_empty(arg): if arg is None: return [] else: @@ -76,8 +80,9 @@ def abcd_normalize(A=None, B=None, C=None, D=None): """Check state-space matrices and ensure they are rank-2. + """ - A, B, C, D = map(none_to_empty, (A, B, C, D)) + A, B, C, D = map(_none_to_empty, (A, B, C, D)) A, B, C, D = map(atleast_2d, (A, B, C, D)) if ((len(A.shape) > 2) or (len(B.shape) > 2) or \ @@ -118,15 +123,19 @@ def ss2tf(A, B, C, D, input=0): """State-space to transfer function. - Inputs: + Parameters + ---------- + A, B, C, D : ndarray + State-space representation of linear system. + input : int + For multiple-input systems, the input to use. - A, B, C, D -- state-space representation of linear system. - input -- For multiple-input systems, the input to use. + Returns + ------- + num, den : 1D ndarray + Numerator and denominator polynomials (as sequences) + respectively. - Outputs: - - num, den -- Numerator and denominator polynomials (as sequences) - respectively. """ # transfer function is C (sI - A)**(-1) B + D A, B, C, D = map(asarray, (A, B, C, D)) @@ -168,13 +177,18 @@ def zpk2ss(z,p,k): """Zero-pole-gain representation to state-space representation - Inputs: + Parameters + ---------- + z, p : sequence + Zeros and poles. + k : float + System gain. - z, p, k -- zeros, poles (sequences), and gain of system + Returns + ------- + A, B, C, D : ndarray + State-space matrices. - Outputs: - - A, B, C, D -- state-space matrices. """ return tf2ss(*zpk2tf(z,p,k)) @@ -281,33 +295,36 @@ def lsim2(system, U=None, T=None, X0=None, **kwargs): - """Simulate output of a continuous-time linear system, by using the ODE solver - `scipy.integrate.odeint`. + """Simulate output of a continuous-time linear system, by using + the ODE solver `scipy.integrate.odeint`. Parameters ---------- system : an instance of the LTI class or a tuple describing the system. - The following gives the number of elements in the tuple and the interpretation. + The following gives the number of elements in the tuple and + the interpretation. 2 (num, den) 3 (zeros, poles, gain) 4 (A, B, C, D) U : ndarray or array-like (1D or 2D), optional - An input array describing the input at each time T. Linear interpolation - is used between given times. If there are multiple inputs, then each column - of the rank-2 array represents an input. If U is not given, the input is - assumed to be zero. + An input array describing the input at each time T. Linear + interpolation is used between given times. If there are + multiple inputs, then each column of the rank-2 array + represents an input. If U is not given, the input is assumed + to be zero. T : ndarray or array-like (1D or 2D), optional - The time steps at which the input is defined and at which the output is - desired. The default is 101 evenly spaced points on the interval [0,10.0]. + The time steps at which the input is defined and at which the + output is desired. The default is 101 evenly spaced points on + the interval [0,10.0]. X0 : ndarray or array-like (1D), optional - The initial condition of the state vector. If `X0` is not given, the initial - conditions are assumed to be 0. + The initial condition of the state vector. If `X0` is not + given, the initial conditions are assumed to be 0. **kwargs : - Additional keyword arguments are passed on to the function odeint. See the - notes below for more details. + Additional keyword arguments are passed on to the function + odeint. See the notes below for more details. - Returns: (T, yout, xout) - ------------------------ + Returns + ------- T : 1D ndarray The time values for the output. yout : ndarray @@ -317,12 +334,12 @@ Notes ----- - This function uses :func:`scipy.integrate.odeint` to solve the system's differential - equations. Additional keyword arguments given to `lsim2` are passed on to `odeint`. - See the documentation for :func:`scipy.integrate.odeint` for the full list of - arguments. + This function uses :func:`scipy.integrate.odeint` to solve the + system's differential equations. Additional keyword arguments + given to `lsim2` are passed on to `odeint`. See the documentation + for :func:`scipy.integrate.odeint` for the full list of arguments. + """ - if isinstance(system, lti): sys = system else: @@ -332,11 +349,12 @@ X0 = zeros(sys.B.shape[0],sys.A.dtype) if T is None: - # XXX T should really be a required argument, but U was changed from a required - # positional argument to a keyword, and T is after U in the argument list. - # So we either: change the API and move T in front of U; check here for T being - # None and raise an excpetion; or assign a default value to T here. This code - # implements the latter. + # XXX T should really be a required argument, but U was + # changed from a required positional argument to a keyword, + # and T is after U in the argument list. So we either: change + # the API and move T in front of U; check here for T being + # None and raise an excpetion; or assign a default value to T + # here. This code implements the latter. T = linspace(0, 10.0, 101) T = atleast_1d(T) @@ -349,22 +367,26 @@ U = U.reshape(-1,1) sU = U.shape if sU[0] != len(T): - raise ValueError, "U must have the same number of rows as elements in T." + raise ValueError("U must have the same number of rows " + "as elements in T.") + if sU[1] != sys.inputs: - raise ValueError("The number of inputs in U (%d) is not compatible with " - "the number of system inputs (%d)" % (sU[1], sys.inputs)) - # Create a callable that uses linear interpolation to calculate the input at - # any time. - ufunc = interpolate.interp1d(T, U, kind='linear', axis=0, bounds_error=False) + raise ValueError("The number of inputs in U (%d) is not " + "compatible with the number of system " + "inputs (%d)" % (sU[1], sys.inputs)) + # Create a callable that uses linear interpolation to + # calculate the input at any time. + ufunc = interpolate.interp1d(T, U, kind='linear', + axis=0, bounds_error=False) def fprime(x, t, sys, ufunc): - """The vector field of the linear system.""" + """The vector field of the linear system.""" return dot(sys.A,x) + squeeze(dot(sys.B,nan_to_num(ufunc([t])))) xout = integrate.odeint(fprime, X0, T, args=(sys, ufunc), **kwargs) yout = dot(sys.C,transpose(xout)) + dot(sys.D,transpose(U)) else: def fprime(x, t, sys): - """The vector field of the linear system.""" + """The vector field of the linear system.""" return dot(sys.A,x) xout = integrate.odeint(fprime, X0, T, args=(sys,), **kwargs) yout = dot(sys.C,transpose(xout)) @@ -375,28 +397,36 @@ def lsim(system, U, T, X0=None, interp=1): """Simulate output of a continuous-time linear system. - Inputs: + Parameters + ---------- + system : an instance of the LTI class or a tuple describing the system. + The following gives the number of elements in the tuple and + the interpretation. + 2 (num, den) + 3 (zeros, poles, gain) + 4 (A, B, C, D) + U : array_like + An input array describing the input at each time `T` + (interpolation is assumed between given times). If there are + multiple inputs, then each column of the rank-2 array + represents an input. + T : array_like + The time steps at which the input is defined and at which the + output is desired. + X0 : + The initial conditions on the state vector (zero by default). + interp : {1, 0} + Whether to use linear (1) or zero-order hold (0) interpolation. - system -- an instance of the LTI class or a tuple describing the - system. The following gives the number of elements in - the tuple and the interpretation. - 2 (num, den) - 3 (zeros, poles, gain) - 4 (A, B, C, D) - U -- an input array describing the input at each time T - (interpolation is assumed between given times). - If there are multiple inputs, then each column of the - rank-2 array represents an input. - T -- the time steps at which the input is defined and at which - the output is desired. - X0 -- (optional, default=0) the initial conditions on the state vector. - interp -- linear (1) or zero-order hold (0) interpolation + Returns + ------- + T : 1D ndarray + Time values for the output. + yout : 1D ndarray + System response. + xout : ndarray + Time-evolution of the state-vector. - Outputs: (T, yout, xout) - - T -- the time values for the output. - yout -- the response of the system. - xout -- the time-evolution of the state-vector. """ # system is an lti system or a sequence # with 2 (num, den) @@ -419,7 +449,8 @@ if len(T.shape) != 1: raise ValueError, "T must be a rank-1 array." if sU[0] != len(T): - raise ValueError, "U must have the same number of rows as elements in T." + raise ValueError("U must have the same number of rows " + "as elements in T.") if sU[1] != sys.inputs: raise ValueError, "System does not define that many inputs." @@ -464,19 +495,25 @@ def impulse(system, X0=None, T=None, N=None): """Impulse response of continuous-time system. - Inputs: + Parameters + ---------- + system : LTI class or tuple + If specified as a tuple, the system is described as + ``(num, den)``, ``(zero, pole, gain)``, or ``(A, B, C, D)``. + X0 : array_like, optional + Initial state-vector. Defaults to zero. + T : array_like, optional + Time points. Computed if not given. + N : int, optional + The number of time points to compute (if `T` is not given). - system -- an instance of the LTI class or a tuple with 2, 3, or 4 - elements representing (num, den), (zero, pole, gain), or - (A, B, C, D) representation of the system. - X0 -- (optional, default = 0) inital state-vector. - T -- (optional) time points (autocomputed if not given). - N -- (optional) number of time points to autocompute (100 if not given). + Returns + ------- + T : 1D ndarray + Time points. + yout : 1D ndarray + Impulse response of the system (except for singularities at zero). - Ouptuts: (T, yout) - - T -- output time points, - yout -- impulse response of system (except possible singularities at 0). """ if isinstance(system, lti): sys = system @@ -512,27 +549,30 @@ Parameters ---------- system : an instance of the LTI class or a tuple describing the system. - The following gives the number of elements in the tuple and the interpretation. + The following gives the number of elements in the tuple and + the interpretation. 2 (num, den) 3 (zeros, poles, gain) 4 (A, B, C, D) T : 1D ndarray or array-like, optional - The time steps at which the input is defined and at which the output is - desired. If `T` is not given, the function will generate a set of time - samples automatically. + The time steps at which the input is defined and at which the + output is desired. If `T` is not given, the function will + generate a set of time samples automatically. X0 : 1D ndarray or array-like, optional - The initial condition of the state vector. If X0 is None, the initial - conditions are assumed to be 0. + The initial condition of the state vector. If X0 is None, the + initial conditions are assumed to be 0. N : int, optional - Number of time points to compute. If `N` is not given, 100 points are used. + Number of time points to compute. If `N` is not given, 100 + points are used. **kwargs : - Additional keyword arguments are passed on the function `scipy.signal.lsim2`, - which in turn passes them on to :func:`scipy.integrate.odeint`. See the - documation for :func:`scipy.integrate.odeint` for information about these + Additional keyword arguments are passed on the function + `scipy.signal.lsim2`, which in turn passes them on to + :func:`scipy.integrate.odeint`. See the documation for + :func:`scipy.integrate.odeint` for information about these arguments. - Returns: (T, yout, xout) - ------------------------ + Returns + ------- T : 1D ndarray The time values for the output. yout : ndarray @@ -541,6 +581,7 @@ See Also -------- scipy.signal.impulse + """ if isinstance(system, lti): sys = system @@ -574,19 +615,28 @@ def step(system, X0=None, T=None, N=None): """Step response of continuous-time system. - Inputs: + Parameters + ---------- + system : an instance of the LTI class or a tuple describing the system. + The following gives the number of elements in the tuple and + the interpretation. + 2 (num, den) + 3 (zeros, poles, gain) + 4 (A, B, C, D) + X0 : array_like, optional + Initial state-vector (default is zero). + T : array_like, optional + Time points (computed if not given). + N : int + Number of time points to compute if `T` is not given. - system -- an instance of the LTI class or a tuple with 2, 3, or 4 - elements representing (num, den), (zero, pole, gain), or - (A, B, C, D) representation of the system. - X0 -- (optional, default = 0) inital state-vector. - T -- (optional) time points (autocomputed if not given). - N -- (optional) number of time points to autocompute (100 if not given). + Returns + ------- + T : 1D ndarray + Output time points. + yout : 1D ndarray + Step response of system. - Ouptuts: (T, yout) - - T -- output time points, - yout -- step response of system. """ if isinstance(system, lti): sys = system From scipy-svn at scipy.org Wed Feb 24 07:27:43 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Wed, 24 Feb 2010 06:27:43 -0600 (CST) Subject: [Scipy-svn] r6250 - in trunk/scipy/signal: . tests Message-ID: <20100224122743.903D039CAED@scipy.org> Author: stefan Date: 2010-02-24 06:27:43 -0600 (Wed, 24 Feb 2010) New Revision: 6250 Modified: trunk/scipy/signal/signaltools.py trunk/scipy/signal/tests/test_signaltools.py Log: BUG: Medfilt segfaults on None input. Closes #1124. Modified: trunk/scipy/signal/signaltools.py =================================================================== --- trunk/scipy/signal/signaltools.py 2010-02-22 20:29:35 UTC (rev 6249) +++ trunk/scipy/signal/signaltools.py 2010-02-24 12:27:43 UTC (rev 6250) @@ -291,7 +291,7 @@ result. """ - volume = asarray(volume) + volume = atleast_1d(volume) if kernel_size is None: kernel_size = [3] * len(volume.shape) kernel_size = asarray(kernel_size) Modified: trunk/scipy/signal/tests/test_signaltools.py =================================================================== --- trunk/scipy/signal/tests/test_signaltools.py 2010-02-22 20:29:35 UTC (rev 6249) +++ trunk/scipy/signal/tests/test_signaltools.py 2010-02-24 12:27:43 UTC (rev 6250) @@ -280,6 +280,10 @@ [ 0, 7, 11, 7, 4, 4, 19, 19, 24, 0]]) assert_array_equal(d, e) + def test_none(self): + """Ticket #1124.""" + signal.medfilt(None) + class TestWiener(TestCase): def test_basic(self): g = array([[5,6,4,3],[3,5,6,2],[2,3,5,6],[1,6,9,7]],'d') @@ -781,8 +785,6 @@ 9.444121133484362e-17-0.79252210110103j ]) yield assert_almost_equal, aan[0], a0hilb, 14, 'N regression' - - if __name__ == "__main__": run_module_suite() From scipy-svn at scipy.org Fri Feb 26 05:21:34 2010 From: scipy-svn at scipy.org (scipy-svn at scipy.org) Date: Fri, 26 Feb 2010 11:21:34 +0100 Subject: [Scipy-svn] Your love life spice-uppers Message-ID: <000d01cab6cd$78c94e10$6400a8c0@somberlyn59> Haven't got time to go and purchase products for managing diseases? With our web store ordered products for fighting diseases will be shipped to your house soon after submitting your order! By the way you will pay one of the most moderate prices on-line! Order and let diseases go away! http://razor066.spaces.live.com From scipy-svn at scipy.org Sat Feb 27 10:10:30 2010 From: scipy-svn at scipy.org (Homer) Date: Sat, 27 Feb 2010 09:10:30 -0600 (CST) Subject: [Scipy-svn] Order status #047901 Message-ID: <20100227151030.B18BB39CC0D@scipy.org> An HTML attachment was scrubbed... URL: From scipy-svn at scipy.org Sat Feb 27 11:48:19 2010 From: scipy-svn at scipy.org (Earnest) Date: Sat, 27 Feb 2010 10:48:19 -0600 (CST) Subject: [Scipy-svn] Your order #489945 Message-ID: <20100227164819.D688F39CAF7@scipy.org> An HTML attachment was scrubbed... URL: