bugged stringlengths 4 228k | fixed stringlengths 0 96.3M | __index_level_0__ int64 0 481k |
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def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | def fmin_bfgs(f, x0, fprime=None, args=(), maxgtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | 200 |
def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | 201 |
def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | 202 |
def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | 203 |
def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | 204 |
def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | 205 |
def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | def fmin_bfgs(f, x0, fprime=None, args=(), avegtol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0): """Minimize a function using the BFGS algorithm. Description: Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno ... | 206 |
def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | 207 |
def _scalarfunc(*params): params = squeeze(asarray(params)) return func(params,*args) | def_scalarfunc(*params):params=squeeze(asarray(params))returnfunc(params,*args) | 208 |
def tvar(a, limits=None, inclusive=(1,1)): """Returns the sample variance of values in an array, (i.e., using N-1), ignoring values strictly outside the sequence passed to 'limits'. Note: either limit in the sequence, or the value of limits itself, can be set to None. The inclusive list/tuple determines whether the l... | def tvar(a, limits=None, inclusive=(1,1)): """Returns the sample variance of values in an array, (i.e., using N-1), ignoring values strictly outside the sequence passed to 'limits'. Note: either limit in the sequence, or the value of limits itself, can be set to None. The inclusive list/tuple determines whether the l... | 209 |
def tsem(a, limits=None, inclusive=(True,True)): """Returns the standard error of the mean for the values in an array, (i.e., using N for the denominator), ignoring values strictly outside the sequence passed to 'limits'. Note: either limit in the sequence, or the value of limits itself, can be set to None. The incl... | def tsem(a, limits=None, inclusive=(True,True)): """Returns the standard error of the mean for the values in an array, (i.e., using N for the denominator), ignoring values strictly outside the sequence passed to 'limits'. Note: either limit in the sequence, or the value of limits itself, can be set to None. The incl... | 210 |
def variation(a, axis=0): """Computes the coefficient of variation, the ratio of the biased standard deviation to the mean. Parameters ---------- a : array axis : int or None References ---------- [CRCProbStat2000] section 2.2.20 """ a, axis = _chk_asarray(a, axis) n = a.shape[axis] correction = np.sqrt(float(n-1) / ... | def variation(a, axis=0): """Computes the coefficient of variation, the ratio of the biased standard deviation to the mean. Parameters ---------- a : array axis : int or None References ---------- [CRCProbStat2000] section 2.2.20 """ a, axis = _chk_asarray(a, axis) n = a.shape[axis] return a.std(axis)/a.mean(axis) | 211 |
def pointbiserialr(x, y): # comment: I am changing the semantics somewhat. The original function is # fairly general and accepts an x sequence that has any type of thing in it as # along as there are only two unique items. I am going to restrict this to # a boolean array for my sanity. """Calculates a point biserial co... | def pointbiserialr(x, y): # comment: I am changing the semantics somewhat. The original function is # fairly general and accepts an x sequence that has any type of thing in it as # along as there are only two unique items. I am going to restrict this to # a boolean array for my sanity. """Calculates a point biserial co... | 212 |
def __init__(self,file_name,permission='r',format='n'): if type(file_name) == type(''): if sys.platform=='win32' and 'b' not in permission: print "Warning: Generally fopen is used for opening binary\n" + \ "files, which on this system requires attaching a 'b' \n" + \ "to the permission flag." self.__dict__['fid'] = ope... | def __init__(self,file_name,permission='rb',format='n'): if 'b' not in permission: permission += 'b' if type(file_name) in (types.StringType, types.UnicodeType): self.__dict__['fid'] = open(file_name,permission) elif 'fileno' in file_name.__methods__: # first argument is an open file self.__dict__['fid'] = file_name i... | 213 |
def read(self,count,stype,rtype=None,bs=None): """Read data from file and return it in a Numeric array. | def read(self,count,stype,rtype=None,bs=None): """Read data from file and return it in a Numeric array. | 214 |
def fort_write(self,fmt,*args): """Write a Fortran binary record. | def fort_write(self,fmt,*args): """Write a Fortran binary record. | 215 |
def loadmat(name, dict=None, appendmat=1): """Load the MATLAB mat file saved in level 1.0 format. If name is a full path name load it in. Otherwise search for the file on the sys.path list and load the first one found (the current directory is searched first). Only Level 1.0 MAT files are supported so far. Inputs: ... | def loadmat(name, dict=None, appendmat=1): """Load the MATLAB mat file saved in level 1.0 format. If name is a full path name load it in. Otherwise search for the file on the sys.path list and load the first one found (the current directory is searched first). Only Level 1.0 MAT files are supported so far. Inputs: ... | 216 |
def loadmat(name, dict=None, appendmat=1): """Load the MATLAB mat file saved in level 1.0 format. If name is a full path name load it in. Otherwise search for the file on the sys.path list and load the first one found (the current directory is searched first). Only Level 1.0 MAT files are supported so far. Inputs: ... | def loadmat(name, dict=None, appendmat=1): """Load the MATLAB mat file saved in level 1.0 format. If name is a full path name load it in. Otherwise search for the file on the sys.path list and load the first one found (the current directory is searched first). Only Level 1.0 MAT files are supported so far. Inputs: ... | 217 |
fpedef = "-DFPU_HPUX" | fpedef = "-DFPU_HPUX" | 218 |
def plot(x,*args,**keywds): """Plot curves. Description: Plot one or more curves on the same graph. Inputs: There can be a variable number of inputs which consist of pairs or triples. The second variable is plotted against the first using the linetype specified by the optional third variable in the triple. If onl... | def plot(x,*args,**keywds): """Plot curves. Description: Plot one or more curves on the same graph. Inputs: There can be a variable number of inputs which consist of pairs or triples. The second variable is plotted against the first using the linetype specified by the optional third variable in the triple. If onl... | 219 |
def plot(x,*args,**keywds): """Plot curves. Description: Plot one or more curves on the same graph. Inputs: There can be a variable number of inputs which consist of pairs or triples. The second variable is plotted against the first using the linetype specified by the optional third variable in the triple. If onl... | def plot(x,*args,**keywds): """Plot curves. Description: Plot one or more curves on the same graph. Inputs: There can be a variable number of inputs which consist of pairs or triples. The second variable is plotted against the first using the linetype specified by the optional third variable in the triple. If onl... | 220 |
def mean(self, axis=None): """Average the matrix over the given axis. If the axis is None, average over both rows and columns, returning a scalar. """ if axis==0: mean = self.sum(0) mean *= 1.0 / self.shape[0] return mean elif axis==1: mean = self.sum(1) mean *= 1.0 / self.shape[1] return mean elif axis is None: retur... | def def setdiag(self, values, k=0): """Fills the diagonal elements {a_ii} with the values from the given sequence. If k != 0, fills the off-diagonal elements {a_{i,i+k}} instead. """ M, N = self.shape if len(values) > min(M, N+k): raise ValueError, "sequence of target values is too long" for i, v in enumerate(values):... | 221 |
def setdiag(self, values, k=0): M, N = self.shape assert len(values) >= max(M, N) for i in xrange(min(M, N-k)): self[i, i+k] = values[i] return | def setdiag(self, values, k=0): M, N = self.shape assert len(values) >= max(M, N) for i in xrange(min(M, N-k)): self[i, i+k] = values[i] return | 222 |
def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 223 |
def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 224 |
def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 225 |
def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 226 |
def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | def __setitem__(self, index, x): try: assert len(index) == 2 except (AssertionError, TypeError): raise IndexError, "invalid index" i, j = index if isinstance(i, int): if not (i>=0 and i<self.shape[0]): raise IndexError, "lil_matrix index out of range" else: if isinstance(i, slice): seq = xrange(i.start or 0, i.stop or ... | 227 |
def solve(A, b, permc_spec=2): if not hasattr(A, 'tocsr') and not hasattr(A, 'tocsc'): raise ValueError, "sparse matrix must be able to return CSC format--"\ "A.tocsc()--or CSR format--A.tocsr()" if not hasattr(A, 'shape'): raise ValueError, "sparse matrix must be able to return shape" \ " (rows, cols) = A.shape" M, N ... | def solve(A, b, permc_spec=2): if not hasattr(A, 'tocsr') and not hasattr(A, 'tocsc'): raise ValueError, "sparse matrix must be able to return CSC format--"\ "A.tocsc()--or CSR format--A.tocsr()" if not hasattr(A, 'shape'): raise ValueError, "sparse matrix must be able to return shape" \ " (rows, cols) = A.shape" M, N ... | 228 |
def _testme(): a = csc_matrix((arange(1, 9), numpy.transpose([[0, 1, 1, 2, 2, 3, 3, 4], [0, 1, 3, 0, 2, 3, 4, 4]]))) print "Representation of a matrix:" print repr(a) print "How a matrix prints:" print a print "Adding two matrices:" b = a+a print b print "Subtracting two matrices:" c = b - a print c print "Multiplying ... | def _testme(): a = csc_matrix((arange(1, 9), numpy.transpose([[0, 1, 1, 2, 2, 3, 3, 4], [0, 1, 3, 0, 2, 3, 4, 4]]))) print "Representation of a matrix:" print repr(a) print "How a matrix prints:" print a print "Adding two matrices:" b = a+a print b print "Subtracting two matrices:" c = b - a print c print "Multiplying ... | 229 |
def bilinear(b,a,fs=1.0): """Return a digital filter from an analog filter using the bilinear transform. The bilinear transform substitutes (z-1) / (z+1) for s """ fs =float(fs) a,b = map(atleast_1d,(a,b)) D = len(a) - 1 N = len(b) - 1 artype = Num.Float M = max([N,D]) Np = M Dp = M bprime = Num.zeros(Np+1,artype) apr... | def bilinear(b,a,fs=1.0): """Return a digital filter from an analog filter using the bilinear transform. The bilinear transform substitutes (z-1) / (z+1) for s """ fs =float(fs) a,b = map(atleast_1d,(a,b)) D = len(a) - 1 N = len(b) - 1 artype = Num.Float M = max([N,D]) Np = M Dp = M bprime = Num.zeros(Np+1,artype) apr... | 230 |
def bilinear(b,a,fs=1.0): """Return a digital filter from an analog filter using the bilinear transform. The bilinear transform substitutes (z-1) / (z+1) for s """ fs =float(fs) a,b = map(atleast_1d,(a,b)) D = len(a) - 1 N = len(b) - 1 artype = Num.Float M = max([N,D]) Np = M Dp = M bprime = Num.zeros(Np+1,artype) apr... | def bilinear(b,a,fs=1.0): """Return a digital filter from an analog filter using the bilinear transform. The bilinear transform substitutes (z-1) / (z+1) for s """ fs =float(fs) a,b = map(atleast_1d,(a,b)) D = len(a) - 1 N = len(b) - 1 artype = Num.Float M = max([N,D]) Np = M Dp = M bprime = Num.zeros(Np+1,artype) apr... | 231 |
def mannwhitneyu(x,y): """ | def mannwhitneyu(x,y): """ | 232 |
def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | 233 |
def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | 234 |
def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | 235 |
def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | def _parse_mimatrix(fid,bytes): dclass, cmplx, nzmax =_parse_array_flags(fid) dims = _get_element(fid)[0] name = ''.join(asarray(_get_element(fid)[0]).astype('c')) if dclass in mxArrays: result, unused =_get_element(fid) if type == mxCHAR_CLASS: result = ''.join(asarray(result).astype('c')) else: if cmplx: imag, unused... | 236 |
def check_linregressBIGX(self): """ W.II.F. Regress BIG on X. | def check_linregressBIGX(self): """ W.II.F. Regress BIG on X. | 237 |
def inv(a, overwrite_a=0): """Return inverse of square matrix a. """ a1 = asarray(a) if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]: raise ValueError, 'expected square matrix' overwrite_a = overwrite_a or a1 is not a #XXX: I found no advantage or disadvantage of using finv. | def inv(a, overwrite_a=0): """Return inverse of square matrix a. """ a1 = asarray(a) if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]: raise ValueError, 'expected square matrix' overwrite_a = overwrite_a or a1 is not a #XXX: I found no advantage or disadvantage of using finv. | 238 |
def configuration(parent_package='',parent_path=None): config = Configuration('integrate', parent_package, parent_path) blas_opt = get_info('blas_opt') if not blas_opt: raise NotFoundError,'no blas resources found' config.add_library('linpack_lite', sources=[join('linpack_lite','*.f')]) config.add_library('mach', sou... | blas_opt = get_info('blas_opt',notfound_action=2) config.add_library('linpack_lite', sources=[join('linpack_lite','*.f')]) config.add_library('mach', sources=[join('mach','*.f')]) config.add_library('quadpack', sources=[join('quadpack','*.f')]) config.add_library('odepack', sources=[join('odepack','*.f')]) # should we... | 239 |
def configuration(parent_package='',parent_path=None): config = Configuration('integrate', parent_package, parent_path) blas_opt = get_info('blas_opt') if not blas_opt: raise NotFoundError,'no blas resources found' config.add_library('linpack_lite', sources=[join('linpack_lite','*.f')]) config.add_library('mach', sou... | def configuration(parent_package='',parent_path=None): config = Configuration('integrate', parent_package, parent_path) blas_opt = get_info('blas_opt') if not blas_opt: raise NotFoundError,'no blas resources found' config.add_library('linpack_lite', sources=[join('linpack_lite','*.f')]) config.add_library('mach', sou... | 240 |
def configuration(parent_package='',parent_path=None): config = Configuration('integrate', parent_package, parent_path) blas_opt = get_info('blas_opt') if not blas_opt: raise NotFoundError,'no blas resources found' config.add_library('linpack_lite', sources=[join('linpack_lite','*.f')]) config.add_library('mach', sou... | def configuration(parent_package='',parent_path=None): config = Configuration('integrate', parent_package, parent_path) blas_opt = get_info('blas_opt') if not blas_opt: raise NotFoundError,'no blas resources found' config.add_library('linpack_lite', sources=[join('linpack_lite','*.f')]) config.add_library('mach', sou... | 241 |
def colex (listoflists,cnums): """\nExtracts from listoflists the columns specified in the list 'cnums' (cnums can be an integer, a sequence of integers, or an expression that | def colex (listoflists,cnums): """\nExtracts from listoflists the columns specified in the list 'cnums' (cnums can be an integer, a sequence of integers, or an expression that | 242 |
def lena(): import cPickle, os fname = os.path.join(__path__[0],'plt','lena.dat') f = open(fname,'rb') lena = scipy.array(cPickle.load(f)) f.close() return lena | def lena(): import cPickle, os fname = os.path.join(os.path.dirname(__file__),'plt','lena.dat') f = open(fname,'rb') lena = scipy.array(cPickle.load(f)) f.close() return lena | 243 |
def check_pro_ang1_cv(self): assert_equal(cephes.pro_ang1_cv(1,1,1,1,0),(1.0,0.0)) | def check_pro_ang1_cv(self): assert_equal(cephes.pro_ang1_cv(1,1,1,1,0),(1.0,0.0)) | 244 |
def configuration(parent_package='',parent_path=None): package = 'optimize' config = default_config_dict(package,parent_package) local_path = get_path(__name__,parent_path) minpack = glob(os.path.join(local_path,'minpack','*.f')) config['fortran_libraries'].append(('minpack',{'sources':minpack})) sources = ['_minpack... | def configuration(parent_package='',parent_path=None): package = 'optimize' config = default_config_dict(package,parent_package) local_path = get_path(__name__,parent_path) minpack = glob(os.path.join(local_path,'minpack','*.f')) config['fortran_libraries'].append(('minpack',{'sources':minpack})) sources = ['_minpack... | 245 |
def configuration(parent_package='',parent_path=None): package = 'optimize' config = default_config_dict(package,parent_package) local_path = get_path(__name__,parent_path) minpack = glob(os.path.join(local_path,'minpack','*.f')) config['fortran_libraries'].append(('minpack',{'sources':minpack})) sources = ['_minpack... | def configuration(parent_package='',parent_path=None): package = 'optimize' config = default_config_dict(package,parent_package) local_path = get_path(__name__,parent_path) minpack = glob(os.path.join(local_path,'minpack','*.f')) config['fortran_libraries'].append(('minpack',{'sources':minpack})) sources = ['_minpack... | 246 |
def errorbars(x,y,err,ptcolor='r',linecolor='b',pttype='o',linetype='-',fac=0.25): """Draw connected points with errorbars. Description: Plot connected points with errorbars. Inputs: x, y -- The points to plot. err -- The error in the y values. ptcolor -- The color for the points. linecolor -- The color of the conn... | def errorbars(x,y,err,ptcolor='r',linecolor='b',pttype='o',linetype='-',fac=0.25): """Draw connected points with errorbars. Description: Plot connected points with errorbars. Inputs: x, y -- The points to plot. err -- The error in the y values. ptcolor -- The color for the points. linecolor -- The color of the conn... | 247 |
def legend(text,linetypes=None,lleft=None,color=None,tfont='helvetica',fontsize=14,nobox=0): """Construct and place a legend. Description: Build a legend and place it on the current plot with an interactive prompt. Inputs: text -- A list of strings which document the curves. linetypes -- If not given, then the text... | defprint linetypes[k], text[k] print llx+width+deltax, ypos-deltay legend(text,linetypes=None,lleft=None,color=None,tfont='helvetica',fontsize=14,nobox=0):print linetypes[k], text[k] print llx+width+deltax, ypos-deltay """Constructprint linetypes[k], text[k] print llx+width+deltax, ypos-deltay andprint linetypes[k], te... | 248 |
def setup_package(ignore_packages=[]): old_path = os.getcwd() path = get_path(__name__) os.chdir(path) sys.path.insert(0,os.path.join(path,'Lib')) # setup files of subpackages require scipy_core: sys.path.insert(0,os.path.join(path,'scipy_core')) try: #sys.path.insert(0,os.path.join(path,'Lib')) from scipy_version impo... | def setup_package(ignore_packages=[]): old_path = os.getcwd() path = get_path(__name__) os.chdir(path) sys.path.insert(0,os.path.join(path,'Lib')) # setup files of subpackages require scipy_core: sys.path.insert(0,os.path.join(path,'scipy_core')) try: #sys.path.insert(0,os.path.join(path,'Lib')) from scipy_version impo... | 249 |
def _import_wx_core(wx_pth, pexec): """Imports the core modules for wx. This is necessary for wxPython-2.5.x. """ # Find the suffix. suffix = '.so' for x in [x[0] for x in imp.get_suffixes() if x[-1] is imp.C_EXTENSION]: if os.path.exists(os.path.join(wx_pth, '_core_' + x)): suffix = x break # Now import the modules m... | def _import_wx_core(wx_pth, pexec): """Imports the core modules for wx. This is necessary for wxPython-2.5.x. """ # Find the suffix. suffix = '.so' for x in [x[0] for x in imp.get_suffixes() if x[-1] is imp.C_EXTENSION]: if os.path.exists(os.path.join(wx_pth, '_core_' + x)): suffix = x break # Now import the modules m... | 250 |
def _import_wx_core(wx_pth, pexec): """Imports the core modules for wx. This is necessary for wxPython-2.5.x. """ # Find the suffix. suffix = '.so' for x in [x[0] for x in imp.get_suffixes() if x[-1] is imp.C_EXTENSION]: if os.path.exists(os.path.join(wx_pth, '_core_' + x)): suffix = x break # Now import the modules m... | def _import_wx_core(wx_pth, pexec): """Imports the core modules for wx. This is necessary for wxPython-2.5.x. """ # Find the suffix. suffix = '.so' for x in [x[0] for x in imp.get_suffixes() if x[-1] is imp.C_EXTENSION]: if os.path.exists(os.path.join(wx_pth, '_core_' + x)): suffix = x break # Now import the modules m... | 251 |
def _import_wx_core(wx_pth, pexec): """Imports the core modules for wx. This is necessary for wxPython-2.5.x. """ # Find the suffix. suffix = '.so' for x in [x[0] for x in imp.get_suffixes() if x[-1] is imp.C_EXTENSION]: if os.path.exists(os.path.join(wx_pth, '_core_' + x)): suffix = x break # Now import the modules m... | def _import_wx_core(wx_pth, pexec): """Imports the core modules for wx. This is necessary for wxPython-2.5.x. """ # Find the suffix. suffix = '.so' for x in [x[0] for x in imp.get_suffixes() if x[-1] is imp.C_EXTENSION]: if os.path.exists(os.path.join(wx_pth, '_core_' + x)): suffix = x break # Now import the modules m... | 252 |
def _import_wx_core(wx_pth, pexec): """Imports the core modules for wx. This is necessary for wxPython-2.5.x. """ # Find the suffix. suffix = '.so' for x in [x[0] for x in imp.get_suffixes() if x[-1] is imp.C_EXTENSION]: if os.path.exists(os.path.join(wx_pth, '_core_' + x)): suffix = x break # Now import the modules m... | defreturn 1 _import_wx_core(wx_pth,return 1 pexec):return 1 """Importsreturn 1 thereturn 1 corereturn 1 modulesreturn 1 forreturn 1 wx.return 1 return 1 Thisreturn 1 isreturn 1 necessaryreturn 1 forreturn 1 wxPython-2.5.x.return 1 """return 1 #return 1 Findreturn 1 thereturn 1 suffix.return 1 suffixreturn 1 =return 1 '... | 253 |
def ppf(self,q,*args,**kwds): loc,scale=map(kwds.get,['loc','scale']) args, loc, scale = self.__fix_loc_scale(args, loc, scale) q,loc,scale = map(arr,(q,loc,scale)) args = tuple(map(arr,args)) cond0 = self._argcheck(*args) & (scale > 0) & (loc==loc) cond1 = (q > 0) & (q < 1) cond2 = (q==1) & cond0 cond = cond0 & cond1 ... | def ppf(self,q,*args,**kwds): loc,scale=map(kwds.get,['loc','scale']) args, loc, scale = self.__fix_loc_scale(args, loc, scale) q,loc,scale = map(arr,(q,loc,scale)) args = tuple(map(arr,args)) cond0 = self._argcheck(*args) & (scale > 0) & (loc==loc) cond1 = (q > 0) & (q < 1) cond2 = (q==1) & cond0 cond = cond0 & cond1 ... | 254 |
def ppf(self,q,*args,**kwds): loc,scale=map(kwds.get,['loc','scale']) args, loc, scale = self.__fix_loc_scale(args, loc, scale) q,loc,scale = map(arr,(q,loc,scale)) args = tuple(map(arr,args)) cond0 = self._argcheck(*args) & (scale > 0) & (loc==loc) cond1 = (q > 0) & (q < 1) cond2 = (q==1) & cond0 cond = cond0 & cond1 ... | def ppf(self,q,*args,**kwds): loc,scale=map(kwds.get,['loc','scale']) args, loc, scale = self.__fix_loc_scale(args, loc, scale) q,loc,scale = map(arr,(q,loc,scale)) args = tuple(map(arr,args)) cond0 = self._argcheck(*args) & (scale > 0) & (loc==loc) cond1 = (q > 0) & (q < 1) cond2 = (q==1) & cond0 cond = cond0 & cond1 ... | 255 |
def _stats(self, x): return 0, 0.25, 0, -1.0 | def _stats(self, x): return 0, 0.25, 0, -1.0 | 256 |
def _stats(self, c): return c/3.0, (1.0-c+c*c)/18, sqrt(2)*(2*c-1)*(c+1)*(c-2) / \ (5*(1.0-c+c*c)**1.5), -3.0/5.0 | def _stats(self, c): return (c+1.0)/3.0, (1.0-c+c*c)/18, sqrt(2)*(2*c-1)*(c+1)*(c-2) / \ (5*(1.0-c+c*c)**1.5), -3.0/5.0 | 257 |
def config_toplevel(self): print " ============= begin top level configuration =============" | def config_toplevel(self): print " ============= begin top level configuration =============" | 258 |
def configuration(parent_package=''): #if parent_package: # parent_package += '.' local_path = get_path(__name__) test_path = os.path.join(local_path,'tests') config = default_config_dict() #config['packages'].append(dot_join(parent_package,'stats')) config['packages'].append(dot_join(parent_package,'stats.tests'))... | defconfig['packages'].append(dot_join(parent_package,'stats')) configuration(parent_package=''):config['packages'].append(dot_join(parent_package,'stats')) #ifconfig['packages'].append(dot_join(parent_package,'stats')) parent_package:config['packages'].append(dot_join(parent_package,'stats')) #config['packages'].append... | 259 |
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): """Minimize the sum of squares of a set of equations. Description: Return the point which minimizes the sum of squares of M (non-linear) equations in N unknowns given a s... | 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): """Minimize the sum of squares of a set of equations. Description: Return the point which minimizes the sum of squares of M (non-linear) equations in N unknowns given a s... | 260 |
def check_gradient(fcn,Dfcn,x0,col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x)) if 1 not in fvec.shape: raise ValueError, "Function does not return a 1-D array." m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = atleast_... | def check_gradient(fcn,Dfcn,x0,args=(),col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x)) if 1 not in fvec.shape: raise ValueError, "Function does not return a 1-D array." m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = ... | 261 |
def check_gradient(fcn,Dfcn,x0,col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x)) if 1 not in fvec.shape: raise ValueError, "Function does not return a 1-D array." m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = atleast_... | def check_gradient(fcn,Dfcn,x0,col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x,*args)) m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = atleast_1d(Dfcn(x)) fjac.shape = (m,n) if col_deriv == 0: fjac = transpose(fjac) xp... | 262 |
def check_gradient(fcn,Dfcn,x0,col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x)) if 1 not in fvec.shape: raise ValueError, "Function does not return a 1-D array." m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = atleast_... | def check_gradient(fcn,Dfcn,x0,col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x)) if 1 not in fvec.shape: raise ValueError, "Function does not return a 1-D array." m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = atleast_... | 263 |
def check_gradient(fcn,Dfcn,x0,col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x)) if 1 not in fvec.shape: raise ValueError, "Function does not return a 1-D array." m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = atleast_... | def check_gradient(fcn,Dfcn,x0,col_deriv=0): """Perform a simple check on the gradient for correctness. """ x = atleast_1d(x0) n = len(x) x.shape = (n,) fvec = atleast_1d(fcn(x)) if 1 not in fvec.shape: raise ValueError, "Function does not return a 1-D array." m = len(fvec) fvec.shape = (m,) ldfjac = m fjac = atleast_... | 264 |
def configuration(parent_package='', top_path=None): config = Configuration('montecarlo', parent_package, top_path) config.add_extension('_intsampler', include_dirs = [numpy.get_numpy_include()], sources = [join('src',f) for f in ['_intsamplermodule.c', 'sampler5tbl.c']] ) config.add_data_dir('tests') config.add_dat... | def configuration(parent_package='', top_path=None): config = Configuration('montecarlo', parent_package, top_path) config.add_extension('_intsampler', include_dirs = [numpy.get_numpy_include(), '/usr/include/python2.4/numpy/random/'], libraries=['randomkit'], sources = [join('src', f) for f in ['_intsamplermodule... | 265 |
def check_nrdtrimn(self): assert_equal(cephes.nrdtrimn(0.5,1,1),1.0) | def check_nrdtrimn(self): assert_equal(cephes.nrdtrimn(0.5,1,1),1.0) | 266 |
def check_bei_zeros(self): bi = bi_zeros(5) assert_array_almost_equal(bi[0],array([-1.173713222709127, -3.271093302836352, -4.830737841662016, -6.169852128310251, -7.376762079367764]),11) | def check_bei_zeros(self): bi = bi_zeros(5) assert_array_almost_equal(bi[0],array([-1.173713222709127, -3.271093302836352, -4.830737841662016, -6.169852128310251, -7.376762079367764]),11) | 267 |
def check_basic(self): x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46, 4.43,0.21,4.75,0.71,1.52,3.24, 0.93,0.42,4.97,9.53,4.55,0.47,6.66] w,pw = scipy.stats.shapiro(x1) assert_almost_equal(w,0.90047299861907959,7) assert_almost_equal(pw,0.042089745402336121,7) x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11, 3.48,1.10,0.88,-... | def check_basic(self): x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46, 4.43,0.21,4.75,0.71,1.52,3.24, 0.93,0.42,4.97,9.53,4.55,0.47,6.66] w,pw = scipy.stats.shapiro(x1) assert_almost_equal(w,0.90047299861907959,6) assert_almost_equal(pw,0.042089745402336121,6) x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11, 3.48,1.10,0.88,-... | 268 |
def check_basic(self): x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46, 4.43,0.21,4.75,0.71,1.52,3.24, 0.93,0.42,4.97,9.53,4.55,0.47,6.66] w,pw = scipy.stats.shapiro(x1) assert_almost_equal(w,0.90047299861907959,7) assert_almost_equal(pw,0.042089745402336121,7) x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11, 3.48,1.10,0.88,-... | def check_basic(self): x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46, 4.43,0.21,4.75,0.71,1.52,3.24, 0.93,0.42,4.97,9.53,4.55,0.47,6.66] w,pw = scipy.stats.shapiro(x1) assert_almost_equal(w,0.90047299861907959,7) assert_almost_equal(pw,0.042089745402336121,7) x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11, 3.48,1.10,0.88,-... | 269 |
def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0, callback=None): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vect... | def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0, callback=None): """Description: Minimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vect... | 270 |
def check_cbrt(self): assert_equal(cephes.cbrt(1),1.0) | def check_cbrt(self): assert_equal(cephes.cbrt(1),1.0) | 271 |
def check_exp10(self): assert_equal(cephes.exp10(2),100.0) | def check_exp10(self): assert_equal(cephes.exp10(2),100.0) | 272 |
def check_cbrt(self): cb = cbrt(27) cbrl = 27**(1.0/3.0) assert_equal(cb,cbrl) | def check_cbrt(self): cb = cbrt(27) cbrl = 27**(1.0/3.0) assert_equal(cb,cbrl) | 273 |
def check_exp10(self): ex = exp10(2) exrl = 10**2 assert_equal(ex,exrl) | def check_exp10(self): ex = exp10(2) exrl = 10**2 assert_equal(ex,exrl) | 274 |
def makestr (x): if type(x) <> StringType: x = str(x) return x | def makestr (x): if type(x) != StringType: x = str(x) return x | 275 |
def collapse (a,keepcols,collapsecols,stderr=0,ns=0,cfcn=None): """Averages data in collapsecol, keeping all unique items in keepcols (using unique, which keeps unique LISTS of column numbers), retaining the unique sets of values in keepcols, the mean for each. If the sterr or N of the mean are desired, set either or ... | def collapse (a,keepcols,collapsecols,stderr=0,ns=0,cfcn=None): """Averages data in collapsecol, keeping all unique items in keepcols (using unique, which keeps unique LISTS of column numbers), retaining the unique sets of values in keepcols, the mean for each. If the sterr or N of the mean are desired, set either or ... | 276 |
def collapse (a,keepcols,collapsecols,stderr=0,ns=0,cfcn=None): """Averages data in collapsecol, keeping all unique items in keepcols (using unique, which keeps unique LISTS of column numbers), retaining the unique sets of values in keepcols, the mean for each. If the sterr or N of the mean are desired, set either or ... | def collapse (a,keepcols,collapsecols,stderr=0,ns=0,cfcn=None): """Averages data in collapsecol, keeping all unique items in keepcols (using unique, which keeps unique LISTS of column numbers), retaining the unique sets of values in keepcols, the mean for each. If the sterr or N of the mean are desired, set either or ... | 277 |
def makestr (item): if type(item) <> StringType: item = str(item) return item | def makestr (item): if type(item) != StringType: item = str(item) return item | 278 |
def lineincustcols (inlist,colsizes): """\nReturns a string composed of elements in inlist, with each element | def lineincustcols (inlist,colsizes): """\nReturns a string composed of elements in inlist, with each element | 279 |
def list2string (inlist): """\nConverts a 1D list to a single long string for file output, using | def list2string (inlist): """\nConverts a 1D list to a single long string for file output, using | 280 |
def configuration(parent_package=''): if sys.platform == 'win32': from scipy_distutils.mingw32_support import * from scipy_distutils.core import Extension from scipy_distutils.misc_util import get_path, default_config_dict from scipy_distutils.misc_util import fortran_library_item, dot_join from scipy_distutils.syste... | def configuration(parent_package=''): if sys.platform == 'win32': import scipy_distutils.mingw32_support from scipy_distutils.core import Extension from scipy_distutils.misc_util import get_path, default_config_dict from scipy_distutils.misc_util import fortran_library_item, dot_join from scipy_distutils.system_info ... | 281 |
def legend(text,linetypes=None,lleft=None,color='black',tfont='helvetica',fontsize=14,nobox=0): """Construct and place a legend. Description: Build a legend and place it on the current plot with an interactive prompt. Inputs: text -- A list of strings which document the curves. linetypes -- If not given, then the t... | def legend(text,linetypes=None,lleft=None,color=None,tfont='helvetica',fontsize=14,nobox=0): """Construct and place a legend. Description: Build a legend and place it on the current plot with an interactive prompt. Inputs: text -- A list of strings which document the curves. linetypes -- If not given, then the text... | 282 |
def legend(text,linetypes=None,lleft=None,color='black',tfont='helvetica',fontsize=14,nobox=0): """Construct and place a legend. Description: Build a legend and place it on the current plot with an interactive prompt. Inputs: text -- A list of strings which document the curves. linetypes -- If not given, then the t... | def legend(text,linetypes=None,lleft=None,color='black',tfont='helvetica',fontsize=14,nobox=0): """Construct and place a legend. Description: Build a legend and place it on the current plot with an interactive prompt. Inputs: text -- A list of strings which document the curves. linetypes -- If not given, then the t... | 283 |
def title(text,color=None,font='helvetica',fontsize=18,deltax=0.0,deltay=0.0): """Set title for plot. To get symbol font for the next character precede by !. To get superscript enclose with ^^ To get subscript enclose with _<text>_ """ global _textcolor if color is None: color = _textcolor else: _textcolor = color i... | deftitle(text,color=None,font='helvetica',fontsize=18,deltax=0.0,deltay=0.0):"""Settitleforplot.Togetsymbolfontforthenextcharacterprecedeby!.Togetsuperscriptenclosewith^^Togetsubscriptenclosewith_<text>_"""global_textcolorifcolorisNone:color=_textcolorelse:_textcolor=colorifcolorisNone:color='black'vp=gist.viewport()xm... | 284 |
def _get_namespace(self): return self.__namespace or default_namespace | def _get_namespace(self): return self.__namespace or default_namespace | 285 |
def histogram2(a, bins): """ histogram2(a,bins) -- Compute histogram of a using divisions in bins Description: Count the number of times values from array a fall into numerical ranges defined by bins. Range x is given by bins[x] <= range_x < bins[x+1] where x =0,N and N is the length of the bins array. The last rang... | def histogram2(a, bins): """ histogram2(a,bins) -- Compute histogram of a using divisions in bins Description: Count the number of times values from array a fall into numerical ranges defined by bins. Range x is given by bins[x] <= range_x < bins[x+1] where x =0,N and N is the length of the bins array. The last rang... | 286 |
def wiener(im,mysize=None,noise=None): """Perform a wiener filter on an N-dimensional array. Description: Apply a wiener filter to the N-dimensional array in. Inputs: in -- an N-dimensional array. kernel_size -- A scalar or an N-length list giving the size of the median filter window in each dimension. Elements of... | def wiener(im,mysize=None,noise=None): """Perform a wiener filter on an N-dimensional array. Description: Apply a wiener filter to the N-dimensional array in. Inputs: in -- an N-dimensional array. kernel_size -- A scalar or an N-length list giving the size of the median filter window in each dimension. Elements of... | 287 |
def resample(x,num,t=None,axis=0,window=None): """Resample to num samples using Fourier method along the given axis. The resampled signal starts at the same value of x but is sampled with a spacing of len(x) / num * (spacing of x). Because a Fourier method is used, the signal is assumed periodic. Window controls a F... | def resample(x,num,t=None,axis=0,window=None): """Resample to num samples using Fourier method along the given axis. The resampled signal starts at the same value of x but is sampled with a spacing of len(x) / num * (spacing of x). Because a Fourier method is used, the signal is assumed periodic. Window controls a F... | 288 |
def makeExpressions(context): """Make private copy of the expressions module with a custom get_context(). An attempt was made to make this threadsafe, but I can't guarantee it's bulletproof. """ import sys, imp try: from scipy.sandbox.numexpr import expressions modname = 'scipy.sandbox.numexpr.expressions' except Impo... | def makeExpressions(context): """Make private copy of the expressions module with a custom get_context(). An attempt was made to make this threadsafe, but I can't guarantee it's bulletproof. """ import sys, imp modname = modname[__name__.rfind('.')-1:] + '.expressions' # get our own, private copy of expressions imp.ac... | 289 |
def sample(self, size, return_probs=0): """Generates a sample of the given size from the specified discrete distribution, optionally returning the probabilities under the distribution. | def sample(self, size, return_probs=0): """Generates a sample of the given size from the specified discrete distribution, optionally returning the probabilities under the distribution. | 290 |
def __init__(self, mydict): # We can't use this: # self.labels = numpy.array(mydict.keys(), object) # since numpy's construction of object arrays is dodgy. Instead, # create an empty object array and fill it: self.labels = numpy.empty(len(mydict), dtype=object) for i, label in enumerate(mydict): self.labels[i] = lab... | def __init__(self, mydict): # We can't use this: # self.labels = numpy.array(mydict.keys(), object) # since numpy's construction of object arrays is dodgy. Instead, # create an empty object array and fill it: self.labels = numpy.empty(len(mydict), dtype=object) for i, label ... | 291 |
def __radd__(self, other): """ Function supporting the operation: self + other. This does not currently work correctly for self + dense. Perhaps dense matrices need some hooks to support this. """ if isscalar(other) or (isdense(other) and rank(other)==0): raise NotImplementedError, 'adding a scalar to a CSC matrix is '... | def __radd__(self, other): """ Function supporting the operation: self + other. This does not currently work correctly for self + dense. Perhaps dense matrices need some hooks to support this. """ if isscalar(other) or (isdense(other) and rank(other)==0): raise NotImplementedError, 'adding a scalar to a CSC matrix is '... | 292 |
def __add__(self, other): if isscalar(other) or (isdense(other) and rank(other)==0): raise NotImplementedError, 'adding a scalar to a CSC matrix is ' \ 'not yet supported' elif isspmatrix(other): ocs = other.tocsc() if (ocs.shape != self.shape): raise ValueError, "inconsistent shapes" dtypechar = _coerce_rules[(self.dt... | def __add__(self, other): if isscalar(other) or (isdense(other) and rank(other)==0): raise NotImplementedError, 'adding a scalar to a CSC matrix is ' \ 'not yet supported' elif isspmatrix(other): ocs = other.tocsc() if (ocs.shape != self.shape): raise ValueError, "inconsistent shapes" dtypechar = _coerce_rules[(self.dt... | 293 |
def __add__(self, other): # First check if argument is a scalar if isscalar(other) or (isdense(other) and rank(other)==0): # Now we would add this scalar to every element. raise NotImplementedError, 'adding a scalar to a sparse matrix ' \ 'is not yet supported' elif isspmatrix(other): ocs = other.tocsr() if (ocs.shape ... | def __add__(self, other): # First check if argument is a scalar if isscalar(other) or (isdense(other) and rank(other)==0): # Now we would add this scalar to every element. raise NotImplementedError, 'adding a scalar to a sparse matrix ' \ 'is not yet supported' elif isspmatrix(other): ocs = other.tocsr() if (ocs.shape ... | 294 |
def __init__(self, A=None): """ Create a new dictionary-of-keys sparse matrix. An optional argument A is accepted, which initializes the dok_matrix with it. This can be a tuple of dimensions (m, n) or a (dense) array to copy. """ dict.__init__(self) spmatrix.__init__(self) self.shape = (0, 0) # If _validate is True, e... | def __init__(self, A=None): """ Create a new dictionary-of-keys sparse matrix. An optional argument A is accepted, which initializes the dok_matrix with it. This can be a tuple of dimensions (m, n) or a (dense) array to copy. """ dict.__init__(self) spmatrix.__init__(self) self.shape = (0, 0) # If _validate is True, e... | 295 |
def __init__(self, A=None): """ Create a new dictionary-of-keys sparse matrix. An optional argument A is accepted, which initializes the dok_matrix with it. This can be a tuple of dimensions (m, n) or a (dense) array to copy. """ dict.__init__(self) spmatrix.__init__(self) self.shape = (0, 0) # If _validate is True, e... | def __init__(self, A=None): """ Create a new dictionary-of-keys sparse matrix. An optional argument A is accepted, which initializes the dok_matrix with it. This can be a tuple of dimensions (m, n) or a (dense) array to copy. """ dict.__init__(self) spmatrix.__init__(self) self.shape = (0, 0) # If _validate is True, e... | 296 |
def setdiag(self, values, k=0): N = len(values) for n in range(N): self[n, n+k] = values[n] return | def setdiag(self, values, k=0): M, N = self.shape m = len(values) for i in range(min(M, N-k)): self[i, i+k] = values[i] return | 297 |
def configuration(parent_package='', top_path=None): config = Configuration('montecarlo', parent_package, top_path) # This code requires 'randomkit.c' and 'randomkit.h' to have been copied # to (or symlinked to) montecarlo/src/. config.add_extension('_intsampler', sources = [join('src', f) for f in ['_intsamplermodu... | def configuration(parent_package='', top_path=None): config = Configuration('montecarlo', parent_package, top_path) # This code requires 'randomkit.c' and 'randomkit.h' to have been copied # to (or symlinked to) montecarlo/src/. config.add_extension('_intsampler', sources = [join('src', f) for f in ['_intsamplermodu... | 298 |
def __init__(self,name,location,p_frame=None): | def __init__(self,name,location,p_frame=None): | 299 |
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