Python numpy.arange方法代码示例

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def expected_tmrca(demography, sampled_pops=None, sampled_n=None):
    """
    The expected time to most recent common ancestor of the sample.

    Parameters
    ----------
    demography : Demography

    Returns
    -------
    tmrca : float-like

    See Also
    --------
    expected_deme_tmrca : tmrca of subsample within a deme
    expected_sfs_tensor_prod : compute general class of summary statistics
    """
    vecs = [np.ones(n + 1) for n in demography.sampled_n]
    n0 = len(vecs[0]) - 1.0
    vecs[0] = np.arange(n0 + 1) / n0
    return np.squeeze(expected_sfs_tensor_prod(vecs, demography)) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def resample(self):
        """Create a new SFS by resampling blocks with replacement.

        Note the resampled SFS is assumed to have the same length in base pairs \
        as the original SFS, which may be a poor assumption if the blocks are not of equal length.

        :returns: Resampled SFS
        :rtype: :class:`Sfs`
        """
        loci = np.random.choice(
            np.arange(self.n_loci), size=self.n_loci, replace=True)
        mat = self.freqs_matrix[:, loci]
        to_keep = np.asarray(mat.sum(axis=1) > 0).squeeze()
        to_keep = np.arange(len(self.configs))[to_keep]
        mat = mat[to_keep, :]
        configs = _ConfigList_Subset(self.configs, to_keep)

        return self.from_matrix(mat, configs, self.folded, self.length) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def sfs(self, n):
        if n == 0:
            return np.array([0.])
        Et_jj = self.etjj(n)
        #assert np.all(Et_jj[:-1] - Et_jj[1:] >= 0.0) and np.all(Et_jj >= 0.0) and np.all(Et_jj <= self.tau)

        ret = np.sum(Et_jj[:, None] * Wmatrix(n), axis=0)

        before_tmrca = self.tau - np.sum(ret * np.arange(1, n) / n)
        # ignore branch length above untruncated TMRCA
        if self.tau == float('inf'):
            before_tmrca = 0.0

        ret = np.concatenate((np.array([0.0]), ret, np.array([before_tmrca])))
        return ret

    # def transition_prob(self, v, axis=0):
    #     return moran_model.moran_action(self.scaled_time, v, axis=axis) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def average_path_length(tree, X):
    """Compute average path length: cost of simulating the average
    example; this is used in the objective function.

    @param tree: DecisionTreeClassifier instance
    @param X: NumPy array (D x N)
              D := number of dimensions
              N := number of examples
    @return path_length: float
                         average path length
    """
    leaf_indices = tree.apply(X)
    leaf_counts = np.bincount(leaf_indices)
    leaf_i = np.arange(tree.tree_.node_count)
    path_length = np.dot(leaf_i, leaf_counts) / float(X.shape[0])
    return path_length 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def get_ith_minibatch_ixs_fences(b_i, batch_size, fences):
    """Split timeseries data of uneven sequence lengths into batches.
    This is how we handle different sized sequences.
    
    @param b_i: integer
                iteration index
    @param batch_size: integer
                       size of batch
    @param fences: list of integers
                   sequence of cutoff array
    @return idx: integer
    @return batch_slice: slice object
    """
    num_data = len(fences) - 1
    num_minibatches = num_data / batch_size + ((num_data % batch_size) > 0)
    b_i = b_i % num_minibatches
    idx = slice(b_i * batch_size, (b_i+1) * batch_size)
    batch_i = np.arange(num_data)[idx]
    batch_slice = np.concatenate([range(i, j) for i, j in 
                                  zip(fences[batch_i], fences[batch_i+1])])
    return idx, batch_slice 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def advect(f, vx, vy):
    """Move field f according to x and y velocities (u and v)
       using an implicit Euler integrator."""
    rows, cols = f.shape
    cell_xs, cell_ys = np.meshgrid(np.arange(cols), np.arange(rows))
    center_xs = (cell_xs - vx).ravel()
    center_ys = (cell_ys - vy).ravel()

    # Compute indices of source cells.
    left_ix = np.floor(center_ys).astype(int)
    top_ix  = np.floor(center_xs).astype(int)
    rw = center_ys - left_ix              # Relative weight of right-hand cells.
    bw = center_xs - top_ix               # Relative weight of bottom cells.
    left_ix  = np.mod(left_ix,     rows)  # Wrap around edges of simulation.
    right_ix = np.mod(left_ix + 1, rows)
    top_ix   = np.mod(top_ix,      cols)
    bot_ix   = np.mod(top_ix  + 1, cols)

    # A linearly-weighted sum of the 4 surrounding cells.
    flat_f = (1 - rw) * ((1 - bw)*f[left_ix,  top_ix] + bw*f[left_ix,  bot_ix]) \
                 + rw * ((1 - bw)*f[right_ix, top_ix] + bw*f[right_ix, bot_ix])
    return np.reshape(flat_f, (rows, cols)) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def advect(f, vx, vy):
    """Move field f according to x and y velocities (u and v)
       using an implicit Euler integrator."""
    rows, cols = f.shape
    cell_ys, cell_xs = np.meshgrid(np.arange(rows), np.arange(cols))
    center_xs = (cell_xs - vx).ravel()
    center_ys = (cell_ys - vy).ravel()

    # Compute indices of source cells.
    left_ix = np.floor(center_xs).astype(int)
    top_ix  = np.floor(center_ys).astype(int)
    rw = center_xs - left_ix              # Relative weight of right-hand cells.
    bw = center_ys - top_ix               # Relative weight of bottom cells.
    left_ix  = np.mod(left_ix,     rows)  # Wrap around edges of simulation.
    right_ix = np.mod(left_ix + 1, rows)
    top_ix   = np.mod(top_ix,      cols)
    bot_ix   = np.mod(top_ix  + 1, cols)

    # A linearly-weighted sum of the 4 surrounding cells.
    flat_f = (1 - rw) * ((1 - bw)*f[left_ix,  top_ix] + bw*f[left_ix,  bot_ix]) \
                 + rw * ((1 - bw)*f[right_ix, top_ix] + bw*f[right_ix, bot_ix])
    return np.reshape(flat_f, (rows, cols)) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def compute_f(theta, lambda0, dL, shape):
    """ Compute the 'vacuum' field vector """

    # get plane wave k vector components (in units of grid cells)
    k0 = 2 * npa.pi / lambda0 * dL
    kx =  k0 * npa.sin(theta)
    ky = -k0 * npa.cos(theta)  # negative because downwards

    # array to write into
    f_src = npa.zeros(shape, dtype=npa.complex128)

    # get coordinates
    Nx, Ny = shape
    xpoints = npa.arange(Nx)
    ypoints = npa.arange(Ny)
    xv, yv = npa.meshgrid(xpoints, ypoints, indexing='ij')

    # compute values and insert into array
    x_PW = npa.exp(1j * xpoints * kx)[:, None]
    y_PW = npa.exp(1j * ypoints * ky)[:, None]

    f_src[xv, yv] = npa.outer(x_PW, y_PW)

    return f_src.flatten() 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def make_IO_matrices(indices, N):
    """ Makes matrices that relate the sparse matrix entries to their locations in the matrix
            The kth column of I is a 'one hot' vector specifing the k-th entries row index into A
            The kth column of J is a 'one hot' vector specifing the k-th entries columnn index into A
            O = J^T is for notational convenience.
            Armed with a vector of M entries 'a', we can construct the sparse matrix 'A' as:
                A = I @ diag(a) @ O
            where 'diag(a)' is a (MxM) matrix with vector 'a' along its diagonal.
            In index notation:
                A_ij = I_ik * a_k * O_kj
            In an opposite way, given sparse matrix 'A' we can strip out the entries `a` using the IO matrices as follows:
                a = diag(I^T @ A @ O^T)
            In index notation:
                a_k = I_ik * A_ij * O_kj
    """
    M = indices.shape[1]                                 # number of indices in the matrix
    entries_1 = npa.ones(M)                              # M entries of all 1's
    ik, jk = indices                                     # separate i and j components of the indices
    indices_I = npa.vstack((ik, npa.arange(M)))          # indices into the I matrix
    indices_J = npa.vstack((jk, npa.arange(M)))          # indices into the J matrix
    I = make_sparse(entries_1, indices_I, shape=(N, M))  # construct the I matrix
    J = make_sparse(entries_1, indices_J, shape=(N, M))  # construct the J matrix
    O = J.T                                              # make O = J^T matrix for consistency with my notes.
    return I, O 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def _make_A(self, eps_vec):

        eps_vec_xx, eps_vec_yy = self._grid_average_2d(eps_vec)
        eps_vec_xx_inv = 1 / (eps_vec_xx + 1e-5)  # the 1e-5 is for numerical stability
        eps_vec_yy_inv = 1 / (eps_vec_yy + 1e-5)  # autograd throws 'divide by zero' errors.

        indices_diag = npa.vstack((npa.arange(self.N), npa.arange(self.N)))

        entries_DxEpsy,   indices_DxEpsy   = spsp_mult(self.entries_Dxb, self.indices_Dxb, eps_vec_yy_inv, indices_diag, self.N)
        entires_DxEpsyDx, indices_DxEpsyDx = spsp_mult(entries_DxEpsy, indices_DxEpsy, self.entries_Dxf, self.indices_Dxf, self.N)

        entries_DyEpsx,   indices_DyEpsx   = spsp_mult(self.entries_Dyb, self.indices_Dyb, eps_vec_xx_inv, indices_diag, self.N)
        entires_DyEpsxDy, indices_DyEpsxDy = spsp_mult(entries_DyEpsx, indices_DyEpsx, self.entries_Dyf, self.indices_Dyf, self.N)

        entries_d = 1 / EPSILON_0 * npa.hstack((entires_DxEpsyDx, entires_DyEpsxDy))
        indices_d = npa.hstack((indices_DxEpsyDx, indices_DyEpsxDy))

        entries_diag = MU_0 * self.omega**2 * npa.ones(self.N)

        entries_a = npa.hstack((entries_d, entries_diag))
        indices_a = npa.hstack((indices_d, indices_diag))

        return entries_a, indices_a 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def get_scale(n, scale_factor):
        return anp.power(anp.full(n, scale_factor), anp.arange(n)) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def _evaluate(self, x, out, *args, **kwargs):
        a = anp.sum(0.5 * anp.arange(1, self.n_var + 1) * x, axis=1)
        out["F"] = anp.sum(anp.square(x), axis=1) + anp.square(a) + anp.power(a, 4) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def __init__(self):
        super().__init__(n_var=5, n_obj=2, n_constr=19, type_var=anp.int)
        # ri, ro, t, F, Z
        # self.xl = anp.array([60, 90, 1, 600, 2])
        self.xl = anp.array([0, 0, 0, 0, 0])
        self.xu = anp.array([20, 20, 4, 400, 7])

        self.x1 = anp.arange(60, 81)
        self.x2 = anp.arange(90, 111)
        self.x3 = anp.arange(1, 3.5, 0.5)
        self.x4 = anp.arange(600, 1001)
        self.x5 = anp.arange(2, 11) 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def moffat(y, x, alpha=4.7, beta=1.5, bbox=None):
    """Symmetric 2D Moffat function

    .. math::

        (1+\frac{(x-x0)^2+(y-y0)^2}{\alpha^2})^{-\beta}

    Parameters
    ----------
    y: float
        Vertical coordinate of the center
    x: float
        Horizontal coordinate of the center
    alpha: float
        Core width
    beta: float
        Power-law index
    bbox: Box
        Bounding box over which to evaluate the function

    Returns
    -------
    result: array
        A 2D circular gaussian sampled at the coordinates `(y_i, x_j)`
        for all i and j in `shape`.
    """
    Y = np.arange(bbox.shape[1]) + bbox.origin[1]
    X = np.arange(bbox.shape[2]) + bbox.origin[2]
    X, Y = np.meshgrid(X, Y)
    # TODO: has no pixel-integration formula
    return ((1 + ((X - x) ** 2 + (Y - y) ** 2) / alpha ** 2) ** -beta)[None, :, :] 

# 需要导入模块: from autograd import numpy [as 别名]
# 或者: from autograd.numpy import arange [as 别名]
def gaussian(y, x, sigma=1, integrate=True, bbox=None):
    """Circular Gaussian Function

    Parameters
    ----------
    y: float
        Vertical coordinate of the center
    x: float
        Horizontal coordinate of the center
    sigma: float
        Standard deviation of the gaussian
    integrate: bool
        Whether pixel integration is performed
    bbox: Box
        Bounding box over which to evaluate the function

    Returns
    -------
    result: array
        A 2D circular gaussian sampled at the coordinates `(y_i, x_j)`
        for all i and j in `shape`.
    """
    Y = np.arange(bbox.shape[1]) + bbox.origin[1]
    X = np.arange(bbox.shape[2]) + bbox.origin[2]

    def f(X):
        if not integrate:
            return np.exp(-(X ** 2) / (2 * sigma ** 2))
        else:
            sqrt2 = np.sqrt(2)
            return (
                np.sqrt(np.pi / 2)
                * sigma
                * (
                    scipy.special.erf((0.5 - X) / (sqrt2 * sigma))
                    + scipy.special.erf((2 * X + 1) / (2 * sqrt2 * sigma))
                )
            )

    return (f(Y - y)[:, None] * f(X - x)[None, :])[None, :, :]