Python core.zeros函数代码示例

def european_call(r, sigma, T, Smax, m, n, Smin=0.0, barrier=None):
    
    X = linspace(0.0, Smax, n+2)
    X = X[1:-1]
    
    Fp = clip(X-K, 0.0, 1e600)
    
    if barrier is None:
        Fu = Smax - K*exp(-r * linspace(0.0, T, m+1))
        Fl = zeros((m+1, ))
    elif barrier == 'up-and-out':
        Fu = Fl = zeros((m+1,))
    
    bss = BS_FDM_cn(r, sigma, T, Smin, Smax, Fl, Fu, Fp, m, n)
    return X, bss.solve()

 def test_vecself(self):
     """Ticket 844."""
     # Inner product of a vector with itself segfaults or give meaningless
     # result
     a = zeros(shape = (1, 80), dtype = float64)
     p = inner_(a, a)
     assert_almost_equal(p, 0, decimal = DECPREC)

def solve(a, b):
    """Return the solution of a*x = b
    """
    one_eq = len(b.shape) == 1
    if one_eq:
        b = b[:, newaxis]
    _assertRank2(a, b)
    _assertSquareness(a)
    n_eq = a.shape[0]
    n_rhs = b.shape[1]
    if n_eq != b.shape[0]:
        raise LinAlgError, 'Incompatible dimensions'
    t, result_t = _commonType(a, b)
#    lapack_routine = _findLapackRoutine('gesv', t)
    if isComplexType(t):
        lapack_routine = lapack_lite.zgesv
    else:
        lapack_routine = lapack_lite.dgesv
    a, b = _fastCopyAndTranspose(t, a, b)
    pivots = zeros(n_eq, fortran_int)
    results = lapack_routine(n_eq, n_rhs, a, n_eq, pivots, b, n_eq, 0)
    if results['info'] > 0:
        raise LinAlgError, 'Singular matrix'
    if one_eq:
        return b.ravel().astype(result_t)
    else:
        return b.transpose().astype(result_t)

def european_put(r, sigma, T, Bu, m, n, Bl=0.0, barrier=None, method=None):
  """Compute prices for a European-style put option."""

  X = linspace(0.0, B, n+2)
  X = X[1:-1]

  Fp = clip(K-X, 0.0, 1e600)
  
  if barrier is None:
    Fu = zeros((m+1,))
    Fl = K*exp(-r * linspace(0.0, T, m+1))
  elif barrier == 'up-and-out':
    Fu = Fl = zeros((m+1,))

  bss = BlackScholesSolver(r, sigma, T, Bl, Bu, Fl, Fu, Fp, m, n)
  return X, bss.solve(method)

def det(a):
    """Compute the determinant of a matrix

    Parameters
    ----------
    a : array-like, shape (M, M)

    Returns
    -------
    det : float or complex
        Determinant of a

    Notes
    -----
    The determinant is computed via LU factorization, LAPACK routine z/dgetrf.
    """
    a = asarray(a)
    _assertRank2(a)
    _assertSquareness(a)
    t, result_t = _commonType(a)
    a = _fastCopyAndTranspose(t, a)
    n = a.shape[0]
    if isComplexType(t):
        lapack_routine = lapack_lite.zgetrf
    else:
        lapack_routine = lapack_lite.dgetrf
    pivots = zeros((n,), fortran_int)
    results = lapack_routine(n, n, a, n, pivots, 0)
    info = results['info']
    if (info < 0):
        raise TypeError, "Illegal input to Fortran routine"
    elif (info > 0):
        return 0.0
    sign = add.reduce(pivots != arange(1, n+1)) % 2
    return (1.-2.*sign)*multiply.reduce(diagonal(a), axis=-1)

def _raw_fft(a, n=None, axis=-1, init_function=fftpack.cffti,
             work_function=fftpack.cfftf, fft_cache = _fft_cache ):
    a = asarray(a)

    if n == None: n = a.shape[axis]

    if n < 1: raise ValueError("Invalid number of FFT data points (%d) specified." % n)

    try:
        wsave = fft_cache[n]
    except(KeyError):
        wsave = init_function(n)
        fft_cache[n] = wsave

    if a.shape[axis] != n:
        s = list(a.shape)
        if s[axis] > n:
            index = [slice(None)]*len(s)
            index[axis] = slice(0,n)
            a = a[index]
        else:
            index = [slice(None)]*len(s)
            index[axis] = slice(0,s[axis])
            s[axis] = n
            z = zeros(s, a.dtype.char)
            z[index] = a
            a = z

    if axis != -1:
        a = swapaxes(a, axis, -1)
    r = work_function(a, wsave)
    if axis != -1:
        r = swapaxes(r, axis, -1)
    return r

 def test_log2(self):
     a = nx.array([4.5, 2.3, 6.5])
     out = nx.zeros(a.shape, float)
     tgt = nx.array([2.169925, 1.20163386, 2.70043972])
     res = ufl.log2(a)
     assert_almost_equal(res, tgt)
     res = ufl.log2(a, out)
     assert_almost_equal(res, tgt)
     assert_almost_equal(out, tgt)

    def test_isneginf(self):
        a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0])
        out = nx.zeros(a.shape, bool)
        tgt = nx.array([False, True, False, False, False, False])

        res = ufl.isneginf(a)
        assert_equal(res, tgt)
        res = ufl.isneginf(a, out)
        assert_equal(res, tgt)
        assert_equal(out, tgt)

    def test_fix(self):
        a = nx.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]])
        out = nx.zeros(a.shape, float)
        tgt = nx.array([[1., 1., 1., 1.], [-1., -1., -1., -1.]])

        res = ufl.fix(a)
        assert_equal(res, tgt)
        res = ufl.fix(a, out)
        assert_equal(res, tgt)
        assert_equal(out, tgt)
        assert_equal(ufl.fix(3.14), 3)

def rand(*args):
    """Returns an array of random numbers with the given shape.

    This only uses the standard library, so it is useful for testing purposes.
    """
    import random
    from numpy.core import zeros, float64
    results = zeros(args, float64)
    f = results.flat
    for i in range(len(f)):
        f[i] = random.random()
    return results

    def test_isneginf(self):
        a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0])
        out = nx.zeros(a.shape, bool)
        tgt = nx.array([False, True, False, False, False, False])

        res = ufl.isneginf(a)
        assert_equal(res, tgt)
        res = ufl.isneginf(a, out)
        assert_equal(res, tgt)
        assert_equal(out, tgt)

        a = a.astype(np.complex)
        with assert_raises(TypeError):
            ufl.isneginf(a)

def _raw_fft(a, n=None, axis=-1, init_function=fftpack.cffti,
             work_function=fftpack.cfftf, fft_cache=_fft_cache):
    a = asarray(a)
    axis = normalize_axis_index(axis, a.ndim)

    if n is None:
        n = a.shape[axis]

    if n < 1:
        raise ValueError("Invalid number of FFT data points (%d) specified."
                         % n)

    # We have to ensure that only a single thread can access a wsave array
    # at any given time. Thus we remove it from the cache and insert it
    # again after it has been used. Multiple threads might create multiple
    # copies of the wsave array. This is intentional and a limitation of
    # the current C code.
    wsave = fft_cache.pop_twiddle_factors(n)
    if wsave is None:
        wsave = init_function(n)

    if a.shape[axis] != n:
        s = list(a.shape)
        if s[axis] > n:
            index = [slice(None)]*len(s)
            index[axis] = slice(0, n)
            a = a[tuple(index)]
        else:
            index = [slice(None)]*len(s)
            index[axis] = slice(0, s[axis])
            s[axis] = n
            z = zeros(s, a.dtype.char)
            z[tuple(index)] = a
            a = z

    if axis != a.ndim - 1:
        a = swapaxes(a, axis, -1)
    r = work_function(a, wsave)
    if axis != a.ndim - 1:
        r = swapaxes(r, axis, -1)

    # As soon as we put wsave back into the cache, another thread could pick it
    # up and start using it, so we must not do this until after we're
    # completely done using it ourselves.
    fft_cache.put_twiddle_factors(n, wsave)

    return r

def det(a):
    """
    Compute the determinant of an array.

    Parameters
    ----------
    a : array_like, shape (M, M)
        Input array.

    Returns
    -------
    det : ndarray
        Determinant of `a`.

    Notes
    -----
    The determinant is computed via LU factorization using the LAPACK
    routine z/dgetrf.

    Examples
    --------
    The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:

    >>> a = np.array([[1, 2], [3, 4]])
    >>> np.linalg.det(a)
    -2.0

    """
    a = asarray(a)
    _assertRank2(a)
    _assertSquareness(a)
    t, result_t = _commonType(a)
    a = _fastCopyAndTranspose(t, a)
    n = a.shape[0]
    if isComplexType(t):
        lapack_routine = lapack_lite.zgetrf
    else:
        lapack_routine = lapack_lite.dgetrf
    pivots = zeros((n,), fortran_int)
    results = lapack_routine(n, n, a, n, pivots, 0)
    info = results['info']
    if (info < 0):
        raise TypeError, "Illegal input to Fortran routine"
    elif (info > 0):
        return 0.0
    sign = add.reduce(pivots != arange(1, n+1)) % 2
    return (1.-2.*sign)*multiply.reduce(diagonal(a), axis=-1)

def _raw_fft(a, n=None, axis=-1, init_function=fftpack.cffti,
             work_function=fftpack.cfftf, fft_cache=_fft_cache):
    a = asarray(a)

    if n is None:
        n = a.shape[axis]

    if n < 1:
        raise ValueError("Invalid number of FFT data points (%d) specified."
                         % n)

    try:
        # Thread-safety note: We rely on list.pop() here to atomically
        # retrieve-and-remove a wsave from the cache.  This ensures that no
        # other thread can get the same wsave while we're using it.
        wsave = fft_cache.setdefault(n, []).pop()
    except (IndexError):
        wsave = init_function(n)

    if a.shape[axis] != n:
        s = list(a.shape)
        if s[axis] > n:
            index = [slice(None)]*len(s)
            index[axis] = slice(0, n)
            a = a[index]
        else:
            index = [slice(None)]*len(s)
            index[axis] = slice(0, s[axis])
            s[axis] = n
            z = zeros(s, a.dtype.char)
            z[index] = a
            a = z

    if axis != -1:
        a = swapaxes(a, axis, -1)
    r = work_function(a, wsave)
    if axis != -1:
        r = swapaxes(r, axis, -1)

    # As soon as we put wsave back into the cache, another thread could pick it
    # up and start using it, so we must not do this until after we're
    # completely done using it ourselves.
    fft_cache[n].append(wsave)

    return r

def ifftpad(a, n, scale=True):
    """
    Pad the spectrum at high frequencies.

    The padding done by the `ifft` function appends zeros to the end of the
    spectrum which shifts the frequencies and can make the resulting signal
    differ from the non-padded version. This function pads the spectrum
    by putting the zeros in the middle where the highest frequencies are.
    Taking the `ifft` of this padded version result in a signal that is
    an interpolated version of the unpadded signal, which is what is expected.

    Parameters
    ----------
    a : array_like
        Input array, can be complex.
    n : int
        Length of the padded spectrum.
        `n` should be larger than the length of the input.
    scale : bool, optional
        Whether to scale the spectrum or not. The `ifft` function
        divides by the input length which will be the incorrect length
        for a padded spectrum. Setting this parameter will pre-scale
        the spectrum so that dividing by the padded length will be correct.

    Returns
    -------
    out : ndarray
        The spectrum padded to length `n`. Possibly scaled as well.

    Examples
    --------
    >>> spectrum = np.array([0, 1, 2, -3, -2, -1])
    >>> np.fft.ifftpad(spectrum, 10, scale=False)
    array([ 0.,  1.,  2.,  0.,  0.,  0.,  0., -3., -2., -1.])
    """
    spectrum = concatenate((a[:len(a) // 2],
                            zeros(n - len(a)),
                            a[len(a) // 2:]))
    if scale:
        spectrum *= (n / len(a))
    return spectrum