Python numba.float32方法代碼示例

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def quadrilateral_intersection(pts1, pts2, int_pts):
    num_of_inter = 0
    for i in range(4):
        if point_in_quadrilateral(pts1[2 * i], pts1[2 * i + 1], pts2):
            int_pts[num_of_inter * 2] = pts1[2 * i]
            int_pts[num_of_inter * 2 + 1] = pts1[2 * i + 1]
            num_of_inter += 1
        if point_in_quadrilateral(pts2[2 * i], pts2[2 * i + 1], pts1):
            int_pts[num_of_inter * 2] = pts2[2 * i]
            int_pts[num_of_inter * 2 + 1] = pts2[2 * i + 1]
            num_of_inter += 1
    temp_pts = cuda.local.array((2, ), dtype=numba.float32)
    for i in range(4):
        for j in range(4):
            has_pts = line_segment_intersection(pts1, pts2, i, j, temp_pts)
            if has_pts:
                int_pts[num_of_inter * 2] = temp_pts[0]
                int_pts[num_of_inter * 2 + 1] = temp_pts[1]
                num_of_inter += 1

    return num_of_inter 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def rbbox_to_corners(corners, rbbox):
    # generate clockwise corners and rotate it clockwise
    angle = rbbox[4]
    a_cos = math.cos(angle)
    a_sin = math.sin(angle)
    center_x = rbbox[0]
    center_y = rbbox[1]
    x_d = rbbox[2]
    y_d = rbbox[3]
    corners_x = cuda.local.array((4, ), dtype=numba.float32)
    corners_y = cuda.local.array((4, ), dtype=numba.float32)
    corners_x[0] = -x_d / 2
    corners_x[1] = -x_d / 2
    corners_x[2] = x_d / 2
    corners_x[3] = x_d / 2
    corners_y[0] = -y_d / 2
    corners_y[1] = y_d / 2
    corners_y[2] = y_d / 2
    corners_y[3] = -y_d / 2
    for i in range(4):
        corners[2 *
                i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
        corners[2 * i
                + 1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def quadrilateral_intersection(pts1, pts2, int_pts):
    num_of_inter = 0
    for i in range(4):
        if point_in_quadrilateral(pts1[2 * i], pts1[2 * i + 1], pts2):
            int_pts[num_of_inter * 2] = pts1[2 * i]
            int_pts[num_of_inter * 2 + 1] = pts1[2 * i + 1]
            num_of_inter += 1
        if point_in_quadrilateral(pts2[2 * i], pts2[2 * i + 1], pts1):
            int_pts[num_of_inter * 2] = pts2[2 * i]
            int_pts[num_of_inter * 2 + 1] = pts2[2 * i + 1]
            num_of_inter += 1
    temp_pts = cuda.local.array((2,), dtype=numba.float32)
    for i in range(4):
        for j in range(4):
            has_pts = line_segment_intersection(pts1, pts2, i, j, temp_pts)
            if has_pts:
                int_pts[num_of_inter * 2] = temp_pts[0]
                int_pts[num_of_inter * 2 + 1] = temp_pts[1]
                num_of_inter += 1

    return num_of_inter 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def rbbox_to_corners(corners, rbbox):
    # generate clockwise corners and rotate it clockwise
    angle = rbbox[4]
    a_cos = math.cos(angle)
    a_sin = math.sin(angle)
    center_x = rbbox[0]
    center_y = rbbox[1]
    x_d = rbbox[2]
    y_d = rbbox[3]
    corners_x = cuda.local.array((4,), dtype=numba.float32)
    corners_y = cuda.local.array((4,), dtype=numba.float32)
    corners_x[0] = -x_d / 2
    corners_x[1] = -x_d / 2
    corners_x[2] = x_d / 2
    corners_x[3] = x_d / 2
    corners_y[0] = -y_d / 2
    corners_y[1] = y_d / 2
    corners_y[2] = y_d / 2
    corners_y[3] = -y_d / 2
    for i in range(4):
        corners[2 *
                i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
        corners[2 * i
                + 1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def map_neighbors(indices, similarity, labels, top_k, pad_ind, pad_val):
    m = indices.shape[0]
    point_labels = np.full(
        (m, top_k), pad_ind, dtype=np.int64)
    point_label_sims = np.full(
        (m, top_k), pad_val, dtype=np.float32)
    for i in nb.prange(m):
        unique_point_labels, point_label_sim = map_one(
            labels[indices[i]], similarity[i], pad_ind)
        if top_k < len(unique_point_labels):
            top_indices = np.argsort(
                point_label_sim)[-1 * top_k:][::-1]
            point_labels[i] = unique_point_labels[top_indices]
            point_label_sims[i] = point_label_sim[top_indices]
        else:
            point_labels[i, :len(unique_point_labels)] = unique_point_labels
            point_label_sims[i, :len(unique_point_labels)] = point_label_sim
    return point_labels, point_label_sims 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def rbbox_to_corners(corners, rbbox):
    # generate clockwise corners and rotate it clockwise
    angle = rbbox[4]
    a_cos = math.cos(angle)
    a_sin = math.sin(angle)
    center_x = rbbox[0]
    center_y = rbbox[1]
    x_d = rbbox[2]
    y_d = rbbox[3]
    corners_x = cuda.local.array((4,), dtype=numba.float32)
    corners_y = cuda.local.array((4,), dtype=numba.float32)
    corners_x[0] = -x_d / 2
    corners_x[1] = -x_d / 2
    corners_x[2] = x_d / 2
    corners_x[3] = x_d / 2
    corners_y[0] = -y_d / 2
    corners_y[1] = y_d / 2
    corners_y[2] = y_d / 2
    corners_y[3] = -y_d / 2
    for i in range(4):
        corners[2 * i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
        corners[2 * i + 1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def _process(
        self,
        im,
        learning_rate: Param(0.04, (0.0, 1.0)),
        learn_every: Param(400, (1, 10000)),
        only_darker: Param(True),
    ):
        messages = []
        if self.background_image is None:
            self.background_image = im.astype(np.float32)
            messages.append("I:New backgorund image set")
        elif self.i == 0:
            self.background_image[:, :] = im.astype(np.float32) * np.float32(
                learning_rate
            ) + self.background_image * np.float32(1 - learning_rate)

        self.i = (self.i + 1) % learn_every

        if only_darker:
            return NodeOutput(messages, negdif(self.background_image, im))
        else:
            return NodeOutput(messages, absdif(self.background_image, im)) 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def rbbox_to_corners(corners, rbbox):
    # generate clockwise corners and rotate it clockwise
    angle = rbbox[4]
    a_cos = math.cos(angle)
    a_sin = math.sin(angle)
    center_x = rbbox[0]
    center_y = rbbox[1]
    x_d = rbbox[2]
    y_d = rbbox[3]
    corners_x = cuda.local.array((4, ), dtype=numba.float32)
    corners_y = cuda.local.array((4, ), dtype=numba.float32)
    corners_x[0] = -x_d / 2
    corners_x[1] = -x_d / 2
    corners_x[2] = x_d / 2
    corners_x[3] = x_d / 2
    corners_y[0] = -y_d / 2
    corners_y[1] = y_d / 2
    corners_y[2] = y_d / 2
    corners_y[3] = -y_d / 2
    for i in range(4):
        corners[2 * i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
        corners[2 * i +
                1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def get_bio_span(exprs, ndim, svd_mode, var_subset=None, **kwargs):
    centred = exprs - np.mean(exprs, axis=0)
    if var_subset is not None:
        subsetter = [True] * centred.shape[1]
        keeper = [False] * centred.shape[1]
        for i in var_subset:
            subsetter[i] = False
            keeper[i] = True
        leftovers = centred[:, subsetter].T
        centred = centred[:, keeper]
    ndim = min(ndim, *centred.shape)
    singular = svd_internal(centred.T, ndim, svd_mode, **kwargs)
    if var_subset is None:
        return singular[0]
    output = np.zeros((exprs.shape[1], ndim), dtype=np.float32)
    output[keeper,] = singular[0]
    output[subsetter,] = np.divide(np.dot(leftovers, singular[2]), singular[1][range(ndim)])
    return output 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def linear_interp(iy, ix, yb, xb, f):
    """ 2d interpolation of f on grid of yb, xb into grid of iy, ix 
        assumes f is 3D and last two dimensions are yb,xb """
    fup = f.copy().astype(np.float32)
    Lax = [iy.size, ix.size]
    for n in range(2):
        fup = np.transpose(fup,(1,2,0)).copy()
        if n==0:
            ds  = np.abs(iy[:,np.newaxis] - yb[:,np.newaxis].T)
        else:
            ds  = np.abs(ix[:,np.newaxis] - xb[:,np.newaxis].T)
        im1 = np.argmin(ds, axis=1)
        w1  = ds[np.arange(0,Lax[n],1,int),im1]
        ds[np.arange(0,Lax[n],1,int),im1] = np.inf
        im2 = np.argmin(ds, axis=1)
        w2  = ds[np.arange(0,Lax[n],1,int),im2]
        wnorm = w1+w2
        w1 /= wnorm
        w2 /= wnorm
        fup = (1-w1[:,np.newaxis,np.newaxis]) * fup[im1] + (1-w2[:,np.newaxis,np.newaxis]) * fup[im2]
    fup = np.transpose(fup, (1,2,0))
    return fup 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def _get_scaled_intensity_root(intensity: np.ndarray, degree: int)\
        -> np.ndarray:
    """
    JIT helper function for `MsmsSpectrum.scale_intensity`.

    Parameters
    ----------
    intensity : np.ndarray
        The intensities of the spectrum fragment peaks.
    degree : int
        The degree of the root scaling.

    Returns
    -------
    np.ndarray
        The root-scaled intensities.
    """
    return np.power(intensity, 1 / degree).astype(np.float32) 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def _get_scaled_intensity_log(intensity: np.ndarray, base: int) -> np.ndarray:
    """
    JIT helper function for `MsmsSpectrum.scale_intensity`.

    Parameters
    ----------
    intensity : np.ndarray
        The intensities of the spectrum fragment peaks.
    base : int
        The base of the log scaling.

    Returns
    -------
    np.ndarray
        The log-scaled intensities.
    """
    return (np.log1p(intensity) / np.log(base)).astype(np.float32) 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def _get_scaled_intensity_rank(intensity: np.ndarray, max_rank: int)\
        -> np.ndarray:
    """
    JIT helper function for `MsmsSpectrum.scale_intensity`.

    Parameters
    ----------
    intensity : np.ndarray
        The intensities of the spectrum fragment peaks.
    max_rank : int
        The maximum rank of the rank scaling.

    Returns
    -------
    np.ndarray
        The rank-scaled intensities.
    """
    return ((max_rank - np.argsort(np.argsort(intensity)[::-1]))
            .astype(np.float32)) 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def sort_vertex_in_convex_polygon(int_pts, num_of_inter):
    if num_of_inter > 0:
        center = cuda.local.array((2, ), dtype=numba.float32)
        center[:] = 0.0
        for i in range(num_of_inter):
            center[0] += int_pts[2 * i]
            center[1] += int_pts[2 * i + 1]
        center[0] /= num_of_inter
        center[1] /= num_of_inter
        v = cuda.local.array((2, ), dtype=numba.float32)
        vs = cuda.local.array((16, ), dtype=numba.float32)
        for i in range(num_of_inter):
            v[0] = int_pts[2 * i] - center[0]
            v[1] = int_pts[2 * i + 1] - center[1]
            d = math.sqrt(v[0] * v[0] + v[1] * v[1])
            v[0] = v[0] / d
            v[1] = v[1] / d
            if v[1] < 0:
                v[0] = -2 - v[0]
            vs[i] = v[0]
        j = 0
        temp = 0
        for i in range(1, num_of_inter):
            if vs[i - 1] > vs[i]:
                temp = vs[i]
                tx = int_pts[2 * i]
                ty = int_pts[2 * i + 1]
                j = i
                while j > 0 and vs[j - 1] > temp:
                    vs[j] = vs[j - 1]
                    int_pts[j * 2] = int_pts[j * 2 - 2]
                    int_pts[j * 2 + 1] = int_pts[j * 2 - 1]
                    j -= 1

                vs[j] = temp
                int_pts[j * 2] = tx
                int_pts[j * 2 + 1] = ty 

# 需要導入模塊: import numba [as 別名]
# 或者: from numba import float32 [as 別名]
def line_segment_intersection(pts1, pts2, i, j, temp_pts):
    A = cuda.local.array((2, ), dtype=numba.float32)
    B = cuda.local.array((2, ), dtype=numba.float32)
    C = cuda.local.array((2, ), dtype=numba.float32)
    D = cuda.local.array((2, ), dtype=numba.float32)

    A[0] = pts1[2 * i]
    A[1] = pts1[2 * i + 1]

    B[0] = pts1[2 * ((i + 1) % 4)]
    B[1] = pts1[2 * ((i + 1) % 4) + 1]

    C[0] = pts2[2 * j]
    C[1] = pts2[2 * j + 1]

    D[0] = pts2[2 * ((j + 1) % 4)]
    D[1] = pts2[2 * ((j + 1) % 4) + 1]
    BA0 = B[0] - A[0]
    BA1 = B[1] - A[1]
    DA0 = D[0] - A[0]
    CA0 = C[0] - A[0]
    DA1 = D[1] - A[1]
    CA1 = C[1] - A[1]
    acd = DA1 * CA0 > CA1 * DA0
    bcd = (D[1] - B[1]) * (C[0] - B[0]) > (C[1] - B[1]) * (D[0] - B[0])
    if acd != bcd:
        abc = CA1 * BA0 > BA1 * CA0
        abd = DA1 * BA0 > BA1 * DA0
        if abc != abd:
            DC0 = D[0] - C[0]
            DC1 = D[1] - C[1]
            ABBA = A[0] * B[1] - B[0] * A[1]
            CDDC = C[0] * D[1] - D[0] * C[1]
            DH = BA1 * DC0 - BA0 * DC1
            Dx = ABBA * DC0 - BA0 * CDDC
            Dy = ABBA * DC1 - BA1 * CDDC
            temp_pts[0] = Dx / DH
            temp_pts[1] = Dy / DH
            return True
    return False