Python numpy.hypot方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def get_hessian(ccm, hes_norm=True, hes_smth=False, **kwargs):
    """ Find Hessian of the input cross correlation matrix <ccm>

    Parameters
    ----------
    ccm : 2D numpy array, cross-correlation matrix
    hes_norm : bool, normalize Hessian by AVG and STD?
    hes_smth : bool, smooth Hessian?

    """
    if hes_smth:
        ccm2 = nd.filters.gaussian_filter(ccm, 1)
    else:
        ccm2 = ccm
    # Jacobian components
    dcc_dy, dcc_dx = np.gradient(ccm2)
    # Hessian components
    d2cc_dx2 = np.gradient(dcc_dx)[1]
    d2cc_dy2 = np.gradient(dcc_dy)[0]
    hes = np.hypot(d2cc_dx2, d2cc_dy2)
    if hes_norm:
        hes = (hes - np.median(hes)) / np.std(hes)

    return hes 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def facet_flow(self, e0, e1, e2, d1=1, d2=1):
        s1 = (e0 - e1)/d1
        s2 = (e1 - e2)/d2
        r = np.arctan2(s2, s1)
        s = np.hypot(s1, s2)
        diag_angle    = np.arctan2(d2, d1)
        diag_distance = np.hypot(d1, d2)
        b0 = (r < 0)
        b1 = (r > diag_angle)
        r[b0] = 0
        s[b0] = s1[b0]
        if isinstance(diag_angle, np.ndarray):
            r[b1] = diag_angle[b1]
        else:
            r[b1] = diag_angle
        s[b1] = ((e0 - e2)/diag_distance)[b1]
        return r, s 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def _vlines(lines, ctrs=None, lengths=None, vecs=None, angle_lo=20, angle_hi=160, ransac_options=RANSAC_OPTIONS):
    ctrs = ctrs if ctrs is not None else lines.mean(1)
    vecs = vecs if vecs is not None else lines[:, 1, :] - lines[:, 0, :]
    lengths = lengths if lengths is not None else np.hypot(vecs[:, 0], vecs[:, 1])

    angles = np.degrees(np.arccos(vecs[:, 0] / lengths))
    points = np.column_stack([ctrs[:, 0], angles])
    point_indices, = np.nonzero((angles > angle_lo) & (angles < angle_hi))
    points = points[point_indices]
    if len(points) > 2:
        model_ransac = linear_model.RANSACRegressor(**ransac_options)
        model_ransac.fit(points[:, 0].reshape(-1, 1), points[:, 1].reshape(-1, 1))
        inlier_mask = model_ransac.inlier_mask_
        valid_lines = lines[point_indices[inlier_mask], :, :]
    else:
        valid_lines = []
    return valid_lines 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def _hlines(lines, ctrs=None, lengths=None, vecs=None, angle_lo=20, angle_hi=160, ransac_options=RANSAC_OPTIONS):
    ctrs = ctrs if ctrs is not None else lines.mean(1)
    vecs = vecs if vecs is not None else lines[:, 1, :] - lines[:, 0, :]
    lengths = lengths if lengths is not None else np.hypot(vecs[:, 0], vecs[:, 1])

    angles = np.degrees(np.arccos(vecs[:, 1] / lengths))
    points = np.column_stack([ctrs[:, 1], angles])
    point_indices, = np.nonzero((angles > angle_lo) & (angles < angle_hi))
    points = points[point_indices]
    if len(points) > 2:
        model_ransac = linear_model.RANSACRegressor(**ransac_options)
        model_ransac.fit(points[:, 0].reshape(-1, 1), points[:, 1].reshape(-1, 1))
        inlier_mask = model_ransac.inlier_mask_
        valid_lines = lines[point_indices[inlier_mask], :, :]
    else:
        valid_lines = []
    return valid_lines 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def onpick(self, event):

        if event.artist != line:
            return True

        N = len(event.ind)
        if not N:
            return True

        # the click locations
        x = event.mouseevent.xdata
        y = event.mouseevent.ydata

        distances = np.hypot(x - xs[event.ind], y - ys[event.ind])
        indmin = distances.argmin()
        dataind = event.ind[indmin]

        self.lastind = dataind
        self.update() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def _create_swept_area_grid(self):
        # TODO: add validity check:
        # rotor points has a minimum in order to always include points inside
        # the disk ... 2?
        #
        # the grid consists of the y,z coordinates of the discrete points which
        # lie within the rotor area: [(y1,z1), (y2,z2), ... , (yN, zN)]

        # update:
        # using all the grid point because that how roald did it.
        # are the points outside of the rotor disk used later?

        # determine the dimensions of the square grid
        num_points = int(np.round(np.sqrt(self.grid_point_count)))
        # syntax: np.linspace(min, max, n points)
        horizontal = np.linspace(-self.rotor_radius, self.rotor_radius, num_points)
        vertical = np.linspace(-self.rotor_radius, self.rotor_radius, num_points)

        # build the grid with all of the points
        grid = [(h, vertical[i]) for i in range(num_points) for h in horizontal]

        # keep only the points in the swept area
        grid = [point for point in grid if np.hypot(point[0], point[1]) < self.rotor_radius]

        return grid 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def hypot(left, right):
    """Given the "legs" of a right triangle, returns its hypotenuse.

    Equivalent to :math:`\\sqrt(left^2 + right^2)`, element-wise.
    Both inputs can be Symbol or scalar number. Broadcasting is not supported.

    Parameters
    ---------
    left : Symbol or scalar
        First leg of the triangle(s).
    right : Symbol or scalar
        Second leg of the triangle(s).

    Returns
    -------
    Symbol or scalar
        The hypotenuse of the triangle(s)

    Examples
    --------
    >>> mx.sym.hypot(3, 4)
    5.0
    >>> x = mx.sym.Variable('x')
    >>> y = mx.sym.Variable('y')
    >>> z = mx.sym.hypot(x, 4)
    >>> z.eval(x=mx.nd.array([3,5,2]))[0].asnumpy()
    array([ 5.,  6.40312433,  4.47213602], dtype=float32)
    >>> z = mx.sym.hypot(x, y)
    >>> z.eval(x=mx.nd.array([3,4]), y=mx.nd.array([10,2]))[0].asnumpy()
    array([ 10.44030666,   4.47213602], dtype=float32)
    """
    if isinstance(left, Symbol) and isinstance(right, Symbol):
        return _internal._Hypot(left, right)
    if isinstance(left, Symbol) and isinstance(right, Number):
        return _internal._HypotScalar(left, scalar=right)
    if isinstance(left, Number) and isinstance(right, Symbol):
        return _internal._HypotScalar(right, scalar=left)
    if isinstance(left, Number) and isinstance(right, Number):
        return _numpy.hypot(left, right)
    else:
        raise TypeError('types (%s, %s) not supported' % (str(type(left)), str(type(right)))) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def cart2pol(x, y):
    """Convert Cartesian to polar coordinates"""
    theta = np.arctan2(y, x)
    rho = np.hypot(x, y)
    return theta, rho 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def _(artist, event):
    # No need to call `line.contains` as we're going to redo the work anyways
    # (also see matplotlib/matplotlib#6645, though that's fixed in mpl2.1).

    # Always work in screen coordinates, as this is how we need to compute
    # distances.  Note that the artist transform may be different from the axes
    # transform (e.g., for axvline).
    xy = event.x, event.y
    data_xy = artist.get_xydata()
    data_screen_xy = artist.get_transform().transform(data_xy)
    sels = []
    # If markers are visible, find the closest vertex.
    if artist.get_marker() not in ["None", "none", " ", "", None]:
        ds = np.hypot(*(xy - data_screen_xy).T)
        try:
            argmin = np.nanargmin(ds)
        except ValueError:  # Raised by nanargmin([nan]).
            pass
        else:
            target = _with_attrs(
                _untransform(  # More precise than transforming back.
                    data_xy[argmin], data_screen_xy[argmin], artist.axes),
                index=argmin)
            sels.append(Selection(artist, target, ds[argmin], None, None))
    # If lines are visible, find the closest projection.
    if (artist.get_linestyle() not in ["None", "none", " ", "", None]
            and len(artist.get_xydata()) > 1):
        sel = _compute_projection_pick(artist, artist.get_path(), xy)
        if sel is not None:
            sel.target.index = {
                "_draw_lines": lambda _, index: index,
                "_draw_steps_pre": Index.pre_index,
                "_draw_steps_mid": Index.mid_index,
                "_draw_steps_post": Index.post_index}[
                    Line2D.drawStyles[artist.get_drawstyle()]](
                        len(data_xy), sel.target.index)
            sels.append(sel)
    sel = min(sels, key=lambda sel: sel.dist, default=None)
    return sel if sel and sel.dist < artist.get_pickradius() else None 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def fbm(shape, p, lower=-np.inf, upper=np.inf):
  freqs = tuple(np.fft.fftfreq(n, d=1.0 / n) for n in shape)
  freq_radial = np.hypot(*np.meshgrid(*freqs))
  envelope = (np.power(freq_radial, p, where=freq_radial!=0) *
              (freq_radial > lower) * (freq_radial < upper))
  envelope[0][0] = 0.0
  phase_noise = np.exp(2j * np.pi * np.random.rand(*shape))
  return normalize(np.real(np.fft.ifft2(np.fft.fft2(phase_noise) * envelope)))


# Returns each value of `a` with coordinates offset by `offset` (via complex 
# values). The values at the new coordiantes are the linear interpolation of
# neighboring values in `a`. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def gaussian_blur(a, sigma=1.0):
  freqs = tuple(np.fft.fftfreq(n, d=1.0 / n) for n in a.shape)
  freq_radial = np.hypot(*np.meshgrid(*freqs))
  sigma2 = sigma**2
  g = lambda x: ((2 * np.pi * sigma2) ** -0.5) * np.exp(-0.5 * (x / sigma)**2)
  kernel = g(freq_radial)
  kernel /= kernel.sum()
  return np.fft.ifft2(np.fft.fft2(a) * np.fft.fft2(kernel)).real 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def bump(shape, sigma):
  [y, x] = np.meshgrid(*map(np.arange, shape))
  r = np.hypot(x - shape[0] / 2, y - shape[1] / 2)
  c = min(shape) / 2
  return np.tanh(np.maximum(c - r, 0.0) / sigma)


# Returns a list of heights for each point in `points`. 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import hypot [as 別名]
def cosine_peak(x, y):
    circle = np.hypot(80 * x - 40.0, 90 * y - 45.)
    f = np.exp(-0.04 * circle) * np.cos(0.15 * circle)
    return f