Module Import alias identifier ImportFrom alias FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad NameLoad NameLoad Num body Expr Str If UnaryOpNot BoolOpOr NameLoad NameLoad body Raise Call NameLoad Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareIs NameLoad NameLoad body Assign NameStore IfExp NameLoad Call AttributeLoad NameLoad attr keyword NameLoad NameLoad Assign NameStore IfExp NameLoad Call AttributeLoad NameLoad attr keyword NameLoad keyword NameLoad NameLoad orelse Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword NameLoad keyword NameLoad If NameLoad body If CompareIs NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr BinOpPow BinOpSub NameLoad NameLoad Num keyword NameLoad orelse Assign NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr BinOpPow BinOpSub NameLoad NameLoad Num keyword Num keyword NameLoad If CompareNotEq NameLoad Num body If CompareIs NameLoad NameLoad body AugAssignMult NameStore BinOpDiv AttributeLoad NameLoad attr BinOpSub AttributeLoad NameLoad attr NameLoad orelse AugAssignMult NameStore BinOpDiv SubscriptLoad AttributeLoad NameLoad attr Index NameLoad BinOpSub SubscriptLoad AttributeLoad NameLoad attr Index NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad orelse Assign NameStore NameLoad Assign NameStore IfExp NameLoad NameLoad NameLoad Return TupleLoad NameLoad NameLoad decorator_list Call NameLoad keyword Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad NameLoad Num NameLoad body Expr Str If NameLoad body Assign NameStore Call AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr NameLoad keyword AttributeLoad NameLoad attr Assign TupleStore NameStore NameStore Call NameLoad NameLoad keyword Num keyword NameLoad keyword NameLoad keyword NameLoad keyword NameLoad If NameLoad body AugAssignSub NameStore NameLoad If NameLoad body Assign SubscriptStore NameLoad Index CompareEq NameLoad Num Num AugAssignDiv NameStore NameLoad Return NameLoad decorator_list Call NameLoad keyword Str FunctionDef arguments args NameParam NameParam NameParam defaults NameLoad body Expr Str If CompareIs NameLoad NameLoad body Assign NameStore BinOpMult BinOpMult Call AttributeLoad NameLoad attr Call NameLoad NameLoad AttributeLoad Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr attr Return Call AttributeLoad NameLoad attr CompareGt NameLoad NameLoad decorator_list Call NameLoad keyword Str FunctionDef arguments args NameParam NameParam defaults NameLoad body Expr Str Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareIsNot NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr NameLoad If CompareNotEq SubscriptLoad AttributeLoad NameLoad attr Index Num SubscriptLoad AttributeLoad NameLoad attr Index Num body Raise Call NameLoad Str Assign TupleStore NameStore NameStore TupleLoad Call NameLoad NameLoad Call NameLoad NameLoad orelse Assign NameStore Call NameLoad NameLoad Assign NameStore NameLoad Return BinOpDiv Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num decorator_list Call NameLoad keyword Str FunctionDef arguments args NameParam defaults body Expr Str Return BinOpDiv BinOpMult NameLoad NameLoad Num decorator_list Call NameLoad keyword Str FunctionDef arguments args NameParam defaults body Expr Str Assign NameStore Call AttributeLoad NameLoad attr keyword Num keyword NameLoad Assign NameStore Call AttributeLoad NameLoad attr keyword Num keyword NameLoad Assign NameStore Call AttributeLoad NameLoad attr Return BinOpAdd BinOpSub BinOpSub NameLoad NameLoad NameLoad NameLoad decorator_list
N/A N/A numpy np skbio.util._decorator experimental mean_and_std N/A N/A a axis weights with_mean with_std ddof N/A None None True True 0 N/A N/A Compute the weighted average and standard deviation along the
    specified axis.

    Parameters
    ----------
    a : array_like
        Calculate average and standard deviation of these values.
    axis : int, optional
        Axis along which the statistics are computed. The default is
        to compute them on the flattened array.
    weights : array_like, optional
        An array of weights associated with the values in `a`. Each
        value in `a` contributes to the average according to its
        associated weight. The weights array can either be 1-D (in
        which case its length must be the size of `a` along the given
        axis) or of the same shape as `a`. If `weights=None`, then all
        data in `a` are assumed to have a weight equal to one.
    with_mean : bool, optional, defaults to True
        Compute average if True.
    with_std : bool, optional, defaults to True
        Compute standard deviation if True.
    ddof : int, optional, defaults to 0
        It means delta degrees of freedom. Variance is calculated by
        dividing by `n - ddof` (where `n` is the number of
        elements). By default it computes the maximum likelyhood
        estimator.
    Returns
    -------
    average, std
        Return the average and standard deviation along the specified
        axis. If any of them was not required, returns `None` instead
     N/A N/A N/A with_mean with_std N/A N/A N/A ValueError Either the mean or standard deviation need to be computed. N/A a N/A N/A np asarray a N/A N/A weights None N/A N/A avg N/A with_mean N/A N/A a mean axis axis None N/A std N/A with_std N/A N/A a std axis axis ddof ddof None N/A N/A avg N/A N/A np average a axis axis weights weights N/A with_std N/A N/A N/A axis None N/A N/A variance N/A N/A np average N/A N/A a avg 2 weights weights N/A N/A a_rolled N/A N/A np rollaxis a axis N/A variance N/A N/A np average N/A N/A a_rolled avg 2 axis 0 weights weights N/A N/A ddof 0 N/A N/A N/A axis None N/A N/A variance N/A N/A a size N/A N/A a size ddof N/A N/A variance N/A N/A N/A a shape N/A axis N/A N/A N/A a shape N/A axis ddof N/A std N/A N/A np sqrt variance N/A N/A std None N/A avg N/A with_mean avg None N/A N/A avg std N/A N/A experimental as_of 0.4.0 scale N/A N/A a weights with_mean with_std ddof copy N/A None True True 0 True N/A N/A Scale array by columns to have weighted average 0 and standard
    deviation 1.

    Parameters
    ----------
    a : array_like
        2D array whose columns are standardized according to the
        weights.
    weights : array_like, optional
        Array of weights associated with the columns of `a`. By
        default, the scaling is unweighted.
    with_mean : bool, optional, defaults to True
        Center columns to have 0 weighted mean.
    with_std : bool, optional, defaults to True
        Scale columns to have unit weighted std.
    ddof : int, optional, defaults to 0
        If with_std is True, variance is calculated by dividing by `n
        - ddof` (where `n` is the number of elements). By default it
        computes the maximum likelyhood stimator.
    copy : bool, optional, defaults to True
        Whether to perform the standardization in place, or return a
        new copy of `a`.

    Returns
    -------
    2D ndarray
        Scaled array.

    Notes
    -----
    Wherever std equals 0, it is replaced by 1 in order to avoid
    division by zero.
     N/A copy N/A N/A a N/A N/A a copy N/A a N/A N/A np asarray a dtype N/A np float64 N/A N/A avg std N/A mean_and_std a axis 0 weights weights with_mean with_mean with_std with_std ddof ddof N/A with_mean N/A N/A a avg N/A with_std N/A N/A N/A std N/A N/A std 0 1.0 N/A a std N/A a N/A N/A experimental as_of 0.4.0 svd_rank N/A N/A M_shape S tol N/A None N/A N/A Matrix rank of `M` given its singular values `S`.

    See `np.linalg.matrix_rank` for a rationale on the tolerance
    (we're not using that function because it doesn't let us reuse a
    precomputed SVD). N/A N/A tol None N/A N/A tol N/A N/A N/A N/A S max N/A max M_shape N/A N/A N/A np finfo N/A S dtype eps N/A N/A N/A np sum N/A S tol N/A N/A experimental as_of 0.4.0 corr N/A N/A x y N/A None N/A N/A Computes correlation between columns of `x`, or `x` and `y`.

    Correlation is covariance of (columnwise) standardized matrices,
    so each matrix is first centered and scaled to have variance one,
    and then their covariance is computed.

    Parameters
    ----------
    x : 2D array_like
        Matrix of shape (n, p). Correlation between its columns will
        be computed.
    y : 2D array_like, optional
        Matrix of shape (n, q). If provided, the correlation is
        computed between the columns of `x` and the columns of
        `y`. Else, it's computed between the columns of `x`.

    Returns
    -------
    correlation
        Matrix of computed correlations. Has shape (p, p) if `y` is
        not provided, else has shape (p, q).
     N/A x N/A N/A np asarray x N/A N/A y None N/A N/A y N/A N/A np asarray y N/A N/A N/A N/A y shape N/A 0 N/A N/A x shape N/A 0 N/A N/A N/A ValueError Both matrices must have the same number of rows N/A N/A x y N/A N/A scale x N/A scale y N/A N/A x N/A scale x N/A y x N/A N/A N/A N/A N/A x T dot y N/A N/A x shape N/A 0 N/A N/A experimental as_of 0.4.0 e_matrix N/A N/A distance_matrix N/A N/A N/A Compute E matrix from a distance matrix.

    Squares and divides by -2 the input elementwise. Eq. 9.20 in
    Legendre & Legendre 1998. N/A N/A N/A distance_matrix distance_matrix -2 N/A N/A experimental as_of 0.4.0 f_matrix N/A N/A E_matrix N/A N/A N/A Compute F matrix from E matrix.

    Centring step: for each element, the mean of the corresponding
    row and column are substracted, and the mean of the whole
    matrix is added. Eq. 9.21 in Legendre & Legendre 1998. N/A row_means N/A N/A E_matrix mean axis 1 keepdims True N/A col_means N/A N/A E_matrix mean axis 0 keepdims True N/A matrix_mean N/A N/A E_matrix mean N/A N/A N/A N/A E_matrix row_means col_means matrix_mean N/A