swutil/np_tools.py
def integral(A=None,dF=None,F=None,axis = 0,trapez = False,cumulative = False):
    '''
    Turns an array A of length N (the function values in N points)
    and an array dF of length N-1 (the masses of the N-1 intervals)
    into an array of length N (the integral \int A dF at N points, with first entry 0)
    
    :param A: Integrand (optional, default ones, length N)
    :param dF: Integrator (optional, default ones, length N-1)
    :param F: Alternative to dF (optional, length N)
    :param trapez: Use trapezoidal rule (else left point)
    '''
    ndim = max(v.ndim for v in (A,dF,F) if v is not None)
    def broadcast(x):
        new_shape = [1]*ndim
        new_shape[axis] = -1
        return np.reshape(x,new_shape)
    if F is not None:
        assert(dF is None)
        if F.ndim<ndim:
            F = broadcast(F)
        N = F.shape[axis]
        dF = F.take(indices = range(1,N),axis = axis)-F.take(indices = range(N-1),axis = axis)
    elif dF is not None:
        if dF.ndim<ndim:
            dF = broadcast(dF)
        N = dF.shape[axis]+1
    else:
        if A.ndim<ndim:
            A = broadcast(A)
        N = A.shape[axis]
    if A is not None:
        if trapez:
            midA = (A.take(indices = range(1,N),axis = axis)+A.take(indices = range(N-1),axis = axis))/2
        else:
            midA = A.take(indices=range(N-1),axis=axis)
        if dF is not None:
            dY = midA*dF
        else:
            dY = midA
    else:
        dY = dF
    pad_shape = list(dY.shape)
    pad_shape[axis] = 1
    pad = np.zeros(pad_shape)
    if cumulative:
        return np.concatenate((pad,np.cumsum(dY,axis = axis)),axis = axis)
    else:
        return np.sum(dY,axis = axis)
def integral(A=None,dF=None,F=None,axis = 0,trapez = False,cumulative = False):
    '''
    Turns an array A of length N (the function values in N points)
    and an array dF of length N-1 (the masses of the N-1 intervals)
    into an array of length N (the integral \int A dF at N points, with first entry 0)
    
    :param A: Integrand (optional, default ones, length N)
    :param dF: Integrator (optional, default ones, length N-1)
    :param F: Alternative to dF (optional, length N)
    :param trapez: Use trapezoidal rule (else left point)
    '''
    ndim = max(v.ndim for v in (A,dF,F) if v is not None)
    def broadcast(x):
        new_shape = [1]*ndim
        new_shape[axis] = -1
        return np.reshape(x,new_shape)
    if F is not None:
        assert(dF is None)
        if F.ndim<ndim:
            F = broadcast(F)
        N = F.shape[axis]
        dF = F.take(indices = range(1,N),axis = axis)-F.take(indices = range(N-1),axis = axis)
    elif dF is not None:
        if dF.ndim<ndim:
            dF = broadcast(dF)
        N = dF.shape[axis]+1
    else:
        if A.ndim<ndim:
            A = broadcast(A)
        N = A.shape[axis]
    if A is not None:
        if trapez:
            midA = (A.take(indices = range(1,N),axis = axis)+A.take(indices = range(N-1),axis = axis))/2
        else:
            midA = A.take(indices=range(N-1),axis=axis)
        if dF is not None:
            dY = midA*dF
        else:
            dY = midA
    else:
        dY = dF
    pad_shape = list(dY.shape)
    pad_shape[axis] = 1
    pad = np.zeros(pad_shape)
    if cumulative:
        return np.concatenate((pad,np.cumsum(dY,axis = axis)),axis = axis)
    else:
        return np.sum(dY,axis = axis)
def integral ( A = None , dF = None , F = None , axis = 0 , trapez = False , cumulative = False ) : ndim = max ( v . ndim for v in ( A , dF , F ) if v is not None ) def broadcast ( x ) : new_shape = [ 1 ] * ndim new_shape [ axis ] = - 1 return np . reshape ( x , new_shape ) if F is not None : assert ( dF is None ) if F . ndim < ndim : F = broadcast ( F ) N = F . shape [ axis ] dF = F . take ( indices = range ( 1 , N ) , axis = axis ) - F . take ( indices = range ( N - 1 ) , axis = axis ) elif dF is not None : if dF . ndim < ndim : dF = broadcast ( dF ) N = dF . shape [ axis ] + 1 else : if A . ndim < ndim : A = broadcast ( A ) N = A . shape [ axis ] if A is not None : if trapez : midA = ( A . take ( indices = range ( 1 , N ) , axis = axis ) + A . take ( indices = range ( N - 1 ) , axis = axis ) ) / 2 else : midA = A . take ( indices = range ( N - 1 ) , axis = axis ) if dF is not None : dY = midA * dF else : dY = midA else : dY = dF pad_shape = list ( dY . shape ) pad_shape [ axis ] = 1 pad = np . zeros ( pad_shape ) if cumulative : return np . concatenate ( ( pad , np . cumsum ( dY , axis = axis ) ) , axis = axis ) else : return np . sum ( dY , axis = axis )
Turns an array A of length N (the function values in N points)
    and an array dF of length N-1 (the masses of the N-1 intervals)
    into an array of length N (the integral \int A dF at N points, with first entry 0)
    
    :param A: Integrand (optional, default ones, length N)
    :param dF: Integrator (optional, default ones, length N-1)
    :param F: Alternative to dF (optional, length N)
    :param trapez: Use trapezoidal rule (else left point)
Turns an array A of length N ( the function values in N points ) and an array dF of length N - 1 ( the masses of the N - 1 intervals ) into an array of length N ( the integral \ int A dF at N points with first entry 0 ) : param A : Integrand ( optional default ones length N ) : param dF : Integrator ( optional default ones length N - 1 ) : param F : Alternative to dF ( optional length N ) : param trapez : Use trapezoidal rule ( else left point )