python类fmax()的实例源码

def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out)

def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)

def twoFactorGaussianExample(inds,t_max=1.0,tau_max=3.0,b0=0.0759,b1=-0.0439,k=0.4454,a2=0.5,s1=0.02,s2=0.01,K=0.5,verbose=False):

    '''
    Compute the two factor Gaussian Example in Beck-Tempone-Szepessy-Zouraris
    '''


    f0 = lambda tau: b0+b1*np.exp(-1.0*k*tau)

    F = lambda x: np.exp(-1.0*x)
    G = lambda x: np.fmax(np.exp(-1.0*x)-K)
    Psi = lambda x: 1.0*x
    U = lambda x: 0.0*x

    d1 = lambda s: s1*s1*s
    d20 = lambda s: np.exp(-0.5*a2*s)
    d2 = lambda s: 2*s2*s2/a2*d20(s)*(1.0-d20(s))

    drift = lambda s: d1(s)+d2(s) 

    v1 = lambda s: s1*np.ones(np.shape(s))
    v2 = lambda s: s2*d20(s)

    vols = [v1,v2]

    identifierString = 'Evaluating the Two Factor Gaussian example.\n'
    identifierString += 's1: %f, s2: %f, b0: %f, tau_max: %f, t_max: %f\n'%(s1,s2,b0,tau_max,t_max)
    identifierString += 'k: %f, a2: %f, K: %f, b1: %f'%(k,a2,K,b1)

    return multiLevelHjmModel(inds,F,G,U,Psi,drift,vols,f0,t_max=t_max,tau_max=tau_max,identifierString=identifierString,verbose=verbose)

def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)

def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out)

def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)

def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)

def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out)

def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)

def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)

def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out)

def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)

def clip_to_window(boxlist, window):
  """Clip bounding boxes to a window.

  This op clips input bounding boxes (represented by bounding box
  corners) to a window, optionally filtering out boxes that do not
  overlap at all with the window.

  Args:
    boxlist: BoxList holding M_in boxes
    window: a numpy array of shape [4] representing the
            [y_min, x_min, y_max, x_max] window to which the op
            should clip boxes.

  Returns:
    a BoxList holding M_out boxes where M_out <= M_in
  """
  y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
  win_y_min = window[0]
  win_x_min = window[1]
  win_y_max = window[2]
  win_x_max = window[3]
  y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min)
  y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min)
  x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min)
  x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min)
  clipped = np_box_list.BoxList(
      np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped]))
  clipped = _copy_extra_fields(clipped, boxlist)
  areas = area(clipped)
  nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)),
                                    [-1]).astype(np.int32)
  return gather(clipped, nonzero_area_indices)

def test_reduce_complex(self):
        assert_equal(np.fmax.reduce([1, 2j]), 1)
        assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)

def test_float_nans(self):
        nan = np.nan
        arg1 = np.array([0,   nan, nan])
        arg2 = np.array([nan, 0,   nan])
        out = np.array([0,   0,   nan])
        assert_equal(np.fmax(arg1, arg2), out)

def test_complex_nans(self):
        nan = np.nan
        for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
            arg1 = np.array([0, cnan, cnan], dtype=np.complex)
            arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
            out = np.array([0,    0, nan], dtype=np.complex)
            assert_equal(np.fmax(arg1, arg2), out)

def load_contour_data(fpath, normalize=True):
    """ Load contour data from vamp output csv file.
    Initializes DataFrame to have all future columns.

    Parameters
    ----------
    fpath : str
        Path to vamp output csv file.

    Returns
    -------
    contour_data : DataFrame
        Pandas data frame with all contour data.
    """
    try:
        contour_data = pd.read_csv(fpath, header=None, index_col=None,
                                   delimiter=',').astype(float)
        del contour_data[0]  # all zeros
        del contour_data[1]  # just an unnecessary  index
        headers = contour_data.columns.values.astype('str')
        headers[0:12] = ['onset', 'offset', 'duration', 'pitch mean', 'pitch std',
                         'salience mean', 'salience std', 'salience tot',
                         'vibrato', 'vib rate', 'vib extent', 'vib coverage']
        contour_data.columns = headers
    except:
        contour_data = loadpickle(fpath)
        # trying to load with pickle

    # Check if there is any column with all nans... it should not be considered
    df = contour_data.isnull().all()
    if np.where(df)[0]:
        contour_data = contour_data.drop(contour_data.columns[np.where(df)[0][0]], axis=1)

    #   To ensure the contour has a duration > 0
    contour_data['duration'] = np.fmax(contour_data['duration'].values,0.001)

    contour_data.num_end_cols = 0
    contour_data['overlap'] = -1  # overlaps are unset
    contour_data['labels'] = -1  # all labels are unset
    contour_data['melodiness'] = ""
    contour_data['mel prob'] = -1
    contour_data.num_end_cols = 4

    if normalize:
        contour_data = normalize_features(contour_data)

    return contour_data

def test_datetime_minmax(self):
        # The metadata of the result should become the GCD
        # of the operand metadata
        a = np.array('1999-03-12T13', dtype='M8[2m]')
        b = np.array('1999-03-12T12', dtype='M8[s]')
        assert_equal(np.minimum(a, b), b)
        assert_equal(np.minimum(a, b).dtype, np.dtype('M8[s]'))
        assert_equal(np.fmin(a, b), b)
        assert_equal(np.fmin(a, b).dtype, np.dtype('M8[s]'))
        assert_equal(np.maximum(a, b), a)
        assert_equal(np.maximum(a, b).dtype, np.dtype('M8[s]'))
        assert_equal(np.fmax(a, b), a)
        assert_equal(np.fmax(a, b).dtype, np.dtype('M8[s]'))
        # Viewed as integers, the comparison is opposite because
        # of the units chosen
        assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))

        # Interaction with NaT
        a = np.array('1999-03-12T13', dtype='M8[2m]')
        dtnat = np.array('NaT', dtype='M8[h]')
        assert_equal(np.minimum(a, dtnat), a)
        assert_equal(np.minimum(dtnat, a), a)
        assert_equal(np.maximum(a, dtnat), a)
        assert_equal(np.maximum(dtnat, a), a)

        # Also do timedelta
        a = np.array(3, dtype='m8[h]')
        b = np.array(3*3600 - 3, dtype='m8[s]')
        assert_equal(np.minimum(a, b), b)
        assert_equal(np.minimum(a, b).dtype, np.dtype('m8[s]'))
        assert_equal(np.fmin(a, b), b)
        assert_equal(np.fmin(a, b).dtype, np.dtype('m8[s]'))
        assert_equal(np.maximum(a, b), a)
        assert_equal(np.maximum(a, b).dtype, np.dtype('m8[s]'))
        assert_equal(np.fmax(a, b), a)
        assert_equal(np.fmax(a, b).dtype, np.dtype('m8[s]'))
        # Viewed as integers, the comparison is opposite because
        # of the units chosen
        assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))

        # should raise between datetime and timedelta
        #
        # TODO: Allowing unsafe casting by
        #       default in ufuncs strikes again... :(
        a = np.array(3, dtype='m8[h]')
        b = np.array('1999-03-12T12', dtype='M8[s]')
        #assert_raises(TypeError, np.minimum, a, b)
        #assert_raises(TypeError, np.maximum, a, b)
        #assert_raises(TypeError, np.fmin, a, b)
        #assert_raises(TypeError, np.fmax, a, b)
        assert_raises(TypeError, np.minimum, a, b, casting='same_kind')
        assert_raises(TypeError, np.maximum, a, b, casting='same_kind')
        assert_raises(TypeError, np.fmin, a, b, casting='same_kind')
        assert_raises(TypeError, np.fmax, a, b, casting='same_kind')

def test_datetime_minmax(self):
        # The metadata of the result should become the GCD
        # of the operand metadata
        a = np.array('1999-03-12T13', dtype='M8[2m]')
        b = np.array('1999-03-12T12', dtype='M8[s]')
        assert_equal(np.minimum(a, b), b)
        assert_equal(np.minimum(a, b).dtype, np.dtype('M8[s]'))
        assert_equal(np.fmin(a, b), b)
        assert_equal(np.fmin(a, b).dtype, np.dtype('M8[s]'))
        assert_equal(np.maximum(a, b), a)
        assert_equal(np.maximum(a, b).dtype, np.dtype('M8[s]'))
        assert_equal(np.fmax(a, b), a)
        assert_equal(np.fmax(a, b).dtype, np.dtype('M8[s]'))
        # Viewed as integers, the comparison is opposite because
        # of the units chosen
        assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))

        # Interaction with NaT
        a = np.array('1999-03-12T13', dtype='M8[2m]')
        dtnat = np.array('NaT', dtype='M8[h]')
        assert_equal(np.minimum(a, dtnat), a)
        assert_equal(np.minimum(dtnat, a), a)
        assert_equal(np.maximum(a, dtnat), a)
        assert_equal(np.maximum(dtnat, a), a)

        # Also do timedelta
        a = np.array(3, dtype='m8[h]')
        b = np.array(3*3600 - 3, dtype='m8[s]')
        assert_equal(np.minimum(a, b), b)
        assert_equal(np.minimum(a, b).dtype, np.dtype('m8[s]'))
        assert_equal(np.fmin(a, b), b)
        assert_equal(np.fmin(a, b).dtype, np.dtype('m8[s]'))
        assert_equal(np.maximum(a, b), a)
        assert_equal(np.maximum(a, b).dtype, np.dtype('m8[s]'))
        assert_equal(np.fmax(a, b), a)
        assert_equal(np.fmax(a, b).dtype, np.dtype('m8[s]'))
        # Viewed as integers, the comparison is opposite because
        # of the units chosen
        assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))

        # should raise between datetime and timedelta
        #
        # TODO: Allowing unsafe casting by
        #       default in ufuncs strikes again... :(
        a = np.array(3, dtype='m8[h]')
        b = np.array('1999-03-12T12', dtype='M8[s]')
        #assert_raises(TypeError, np.minimum, a, b)
        #assert_raises(TypeError, np.maximum, a, b)
        #assert_raises(TypeError, np.fmin, a, b)
        #assert_raises(TypeError, np.fmax, a, b)
        assert_raises(TypeError, np.minimum, a, b, casting='same_kind')
        assert_raises(TypeError, np.maximum, a, b, casting='same_kind')
        assert_raises(TypeError, np.fmin, a, b, casting='same_kind')
        assert_raises(TypeError, np.fmax, a, b, casting='same_kind')

def gen_anscombe_forward(signal, gauss_std, gauss_mean = 0, poisson_multi = 1):
    """
    Applies the generalized Anscombe variance-stabilization transform
    assuming a mixed Poisson-Gaussian noise model as:

    signal = poisson_multi*Poisson{signal0} + Gauss{gauss_mean, gauss_std},

    where Poisson{} and Gauss{} are generalized descriptions of Poisson and
    Gaussian noise.

    Parameters
    ----------
    signal : ndarray
        Noisy signal (1-,2-,3D)

    gauss_std : float, int
        Standard deviation of Gaussian noise

    poisson_multi : float or int, optional (default = 1)
        Effectively a multiplier that scales the effect of the Poisson
        noise

    gauss_mean : float or int, optional (default = 0)
        Mean Gaussian noise level

    Returns
    -------
    fsignal : ndarray (matched to signal shape)
        "Anscombe-transformed" signal with an approximate unity standard \
        deviation/variance (~ 1)

    Note
    ----
    This software is a direct translation (with minor alterations) of the
    original MATLAB software created by Alessandro Foi and Markku Mäkitalo
    (Tampere University of Technology - 2011-2012). Please cite the references
    below if using this software. http://www.cs.tut.fi/~foi/

    References
    ----------
    [1] J.L. Starck, F. Murtagh, and A. Bijaoui, Image  Processing  and
    Data Analysis, Cambridge University Press, Cambridge, 1998)

    """

    SMALL_VAL = 1

    fsignal = 2/poisson_multi * _np.sqrt(_np.fmax(SMALL_VAL,poisson_multi*signal +
                                    (3/8)*poisson_multi**2 +
                                    gauss_std**2 -
                                    poisson_multi*gauss_mean))
#    fsignal = _ne.evaluate('2/poisson_multi * sqrt(where(poisson_multi*signal + (3/8)*poisson_multi**2 +\
#                            gauss_std**2 - poisson_multi*gauss_mean > SMALL_VAL,\
#                            poisson_multi*signal + (3/8)*poisson_multi**2 +\
#                            gauss_std**2 - poisson_multi*gauss_mean, SMALL_VAL))')
    #fsignal = 2/poisson_multi * _np.sqrt(_np.fmax(SMALL_VAL,fsignal))
    return fsignal