def splrep(x, y, w=None, xb=None, xe=None, k=3, task=0, s=None, t=None,
           full_output=0, per=0, quiet=1):
    """
    Find the B-spline representation of 1-D curve.

    Given the set of data points ``(x[i], y[i])`` determine a smooth spline
    approximation of degree k on the interval ``xb <= x <= xe``.

    Parameters
    ----------
    x, y : array_like
        The data points defining a curve y = f(x).
    w : array_like, optional
        Strictly positive rank-1 array of weights the same length as x and y.
        The weights are used in computing the weighted least-squares spline
        fit. If the errors in the y values have standard-deviation given by the
        vector d, then w should be 1/d. Default is ones(len(x)).
    xb, xe : float, optional
        The interval to fit.  If None, these default to x[0] and x[-1]
        respectively.
    k : int, optional
        The degree of the spline fit. It is recommended to use cubic splines.
        Even values of k should be avoided especially with small s values.
        1 <= k <= 5
    task : {1, 0, -1}, optional
        If task==0 find t and c for a given smoothing factor, s.

        If task==1 find t and c for another value of the smoothing factor, s.
        There must have been a previous call with task=0 or task=1 for the same
        set of data (t will be stored an used internally)

        If task=-1 find the weighted least square spline for a given set of
        knots, t. These should be interior knots as knots on the ends will be
        added automatically.
    s : float, optional
        A smoothing condition. The amount of smoothness is determined by
        satisfying the conditions: sum((w * (y - g))**2,axis=0) <= s where g(x)
        is the smoothed interpolation of (x,y). The user can use s to control
        the tradeoff between closeness and smoothness of fit. Larger s means
        more smoothing while smaller values of s indicate less smoothing.
        Recommended values of s depend on the weights, w. If the weights
        represent the inverse of the standard-deviation of y, then a good s
        value should be found in the range (m-sqrt(2*m),m+sqrt(2*m)) where m is
        the number of datapoints in x, y, and w. default : s=m-sqrt(2*m) if
        weights are supplied. s = 0.0 (interpolating) if no weights are
        supplied.
    t : array_like, optional
        The knots needed for task=-1. If given then task is automatically set
        to -1.
    full_output : bool, optional
        If non-zero, then return optional outputs.
    per : bool, optional
        If non-zero, data points are considered periodic with period x[m-1] -
        x[0] and a smooth periodic spline approximation is returned. Values of
        y[m-1] and w[m-1] are not used.
    quiet : bool, optional
        Non-zero to suppress messages.
        This parameter is deprecated; use standard Python warning filters
        instead.

    Returns
    -------
    tck : tuple
        (t,c,k) a tuple containing the vector of knots, the B-spline
        coefficients, and the degree of the spline.
    fp : array, optional
        The weighted sum of squared residuals of the spline approximation.
    ier : int, optional
        An integer flag about splrep success. Success is indicated if ier<=0.
        If ier in [1,2,3] an error occurred but was not raised. Otherwise an
        error is raised.
    msg : str, optional
        A message corresponding to the integer flag, ier.

    Notes
    -----
    See splev for evaluation of the spline and its derivatives.

    The user is responsible for assuring that the values of *x* are unique.
    Otherwise, *splrep* will not return sensible results.

    See Also
    --------
    UnivariateSpline, BivariateSpline
    splprep, splev, sproot, spalde, splint
    bisplrep, bisplev

    Notes
    -----
    See splev for evaluation of the spline and its derivatives. Uses the
    FORTRAN routine curfit from FITPACK.

    If provided, knots `t` must satisfy the Schoenberg-Whitney conditions,
    i.e., there must be a subset of data points ``x[j]`` such that
    ``t[j] < x[j] < t[j+k+1]``, for ``j=0, 1,...,n-k-2``.

    References
    ----------
    Based on algorithms described in [1]_, [2]_, [3]_, and [4]_:

    .. [1] P. Dierckx, "An algorithm for smoothing, differentiation and
       integration of experimental data using spline functions",
       J.Comp.Appl.Maths 1 (1975) 165-184.
    .. [2] P. Dierckx, "A fast algorithm for smoothing data on a rectangular
       grid while using spline functions", SIAM J.Numer.Anal. 19 (1982)
       1286-1304.
    .. [3] P. Dierckx, "An improved algorithm for curve fitting with spline
       functions", report tw54, Dept. Computer Science,K.U. Leuven, 1981.
    .. [4] P. Dierckx, "Curve and surface fitting with splines", Monographs on
       Numerical Analysis, Oxford University Press, 1993.

    Examples
    --------

    >>> import matplotlib.pyplot as plt
    >>> from scipy.interpolate import splev, splrep
    >>> x = np.linspace(0, 10, 10)
    >>> y = np.sin(x)
    >>> tck = splrep(x, y)
    >>> x2 = np.linspace(0, 10, 200)
    >>> y2 = splev(x2, tck)
    >>> plt.plot(x, y, 'o', x2, y2)
    >>> plt.show()

    """
    if task <= 0:
        _curfit_cache = {}
    x, y = map(atleast_1d, [x, y])
    m = len(x)
    if w is None:
        w = ones(m, float)
        if s is None:
            s = 0.0
    else:
        w = atleast_1d(w)
        if s is None:
            s = m - sqrt(2*m)
    if not len(w) == m:
        raise TypeError('len(w)=%d is not equal to m=%d' % (len(w), m))
    if (m != len(y)) or (m != len(w)):
        raise TypeError('Lengths of the first three arguments (x,y,w) must '
                        'be equal')
    if not (1 <= k <= 5):
        raise TypeError('Given degree of the spline (k=%d) is not supported. '
                        '(1<=k<=5)' % k)
    if m <= k:
        raise TypeError('m > k must hold')
    if xb is None:
        xb = x[0]
    if xe is None:
        xe = x[-1]
    if not (-1 <= 1 <= task):
        raise TypeError('task must be -1, 0 or 1')
    if t is not None:
        task = -1
    if task == -1:
        if t is None:
            raise TypeError('Knots must be given for task=-1')
        numknots = len(t)
        _curfit_cache['t'] = empty((numknots + 2*k + 2,), float)
        _curfit_cache['t'][k+1:-k-1] = t
        nest = len(_curfit_cache['t'])
    elif task == 0:
        if per:
            nest = max(m + 2*k, 2*k + 3)
        else:
            nest = max(m + k + 1, 2*k + 3)
        t = empty((nest,), float)
        _curfit_cache['t'] = t
    if task <= 0:
        if per:
            _curfit_cache['wrk'] = empty((m*(k + 1) + nest*(8 + 5*k),), float)
        else:
            _curfit_cache['wrk'] = empty((m*(k + 1) + nest*(7 + 3*k),), float)
        _curfit_cache['iwrk'] = empty((nest,), intc)
    try:
        t = _curfit_cache['t']
        wrk = _curfit_cache['wrk']
        iwrk = _curfit_cache['iwrk']
    except KeyError:
        raise TypeError("must call with task=1 only after"
                        " call with task=0,-1")
    if not per:
        n, c, fp, ier = dfitpack.curfit(task, x, y, w, t, wrk, iwrk,
                                        xb, xe, k, s)
    else:
        n, c, fp, ier = dfitpack.percur(task, x, y, w, t, wrk, iwrk, k, s)
    tck = (t[:n], c[:n], k)
    if ier <= 0 and not quiet:
        _mess = (_iermess[ier][0] + "\tk=%d n=%d m=%d fp=%f s=%f" %
                 (k, len(t), m, fp, s))
        warnings.warn(RuntimeWarning(_mess))
    if ier > 0 and not full_output:
        if ier in [1, 2, 3]:
            warnings.warn(RuntimeWarning(_iermess[ier][0]))
        else:
            try:
                raise _iermess[ier][1](_iermess[ier][0])
            except KeyError:
                raise _iermess['unknown'][1](_iermess['unknown'][0])
    if full_output:
        try:
            return tck, fp, ier, _iermess[ier][0]
        except KeyError:
            return tck, fp, ier, _iermess['unknown'][0]
    else:
        return tck
dataset/ETHPy150Open scipy/scipy/scipy/interpolate/fitpack.py/splrep/swapped operands of `<=`