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    Parameters
    ----------
    t : ndarray, shape (nt + k + 1,)
        sorted 1D array of knots
    k : int
        spline order
    xval: float
        argument at which to evaluate the B-splines
    m : int
        index of the left edge of the evaluation interval, ``t[m] <= x < t[m+1]``
    nu : int, optional
        Evaluate derivatives order `nu`. Default is zero.

    Returns
    -------
    ndarray, shape (k+1,)
        The values of B-splines :math:`[B_{m-k}(xval), ..., B_{m}(xval)]` if
        `nu` is zero, otherwise the derivatives of order `nu`.

    Examples
    --------

    A textbook use of this sort of routine is plotting the ``k+1`` polynomial
    pieces which make up a B-spline of order `k`.

    Consider a cubic spline

    >>> k = 3
    >>> t = [0., 2., 2., 3., 4.]   # internal knots
    >>> a, b = t[0], t[-1]    # base interval is [a, b)
    >>> t = [a]*k + t + [b]*k  # add boundary knots

    >>> import matplotlib.pyplot as plt
    >>> xx = np.linspace(a, b, 100)
    >>> plt.plot(xx, BSpline.basis_element(k:-k)(xx),
    ...          'r-', lw=5, alpha=0.5)
    >>> c = ['b', 'g', 'c', 'k']

    Now we use slide an interval ``t[m]..t[m+1]`` along the base interval
    ``a..b`` and use `evaluate_all_bspl` to compute the restriction of
    the B-spline of interest to this interval:

    >>> for i in range(k+1):
    ...    x1, x2 = t[2*k - i], t[2*k - i + 1]
    ...    xx = np.linspace(x1 - 0.5, x2 + 0.5)
    ...    yy = [evaluate_all_bspl(t, k, x, 2*k - i)[i] for x in xx]
    ...    plt.plot(xx, yy, c[i] + '--', lw=3, label=str(i))
    ...
    >>> plt.grid(True)
    >>> plt.legend()
    >>> plt.show()

    Empty shape tuple for cython.arrayCannot create writable memory view from read-only memoryviewCannot assign to read-only memoryviewCan only create a buffer that is contiguous in memory.Buffer view does not expose stridesunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to import
Routines for evaluating and manipulating B-splines.

<strided and direct or indirect>unable to allocate array data.Cannot do derivative order %s.ndarray is not C contiguousitemsize <= 0 for cython.arrayNo matching signature foundevaluate_all_bspl (line 163)Invalid shape in axis %d: %d.Cannot index with type '%s'scipy.interpolate._bspl<contiguous and indirect><MemoryView of %r at 0x%x>_handle_lhs_derivatives<MemoryView of %r object><contiguous and direct><strided and indirect>NotImplementedError<strided and direct>cline_in_traceback__pyx_unpickle_Enumevaluate_all_bspl__setstate_cython____pyx_PickleErrorevaluate_splinedtype_is_objectallocate_bufferView.MemoryViewdouble complex__reduce_cython____pyx_getbufferstringsource__pyx_checksumRuntimeError_norm_eq_lsqextrapolatePickleErrorMemoryErrorImportErrorsignatures__pyx_vtable____pyx_resultderiv_ordsValueErrorIndexError__reduce_ex____pyx_stateenumerateTypeError__setstate____pyx_typeitemsizeinterval__getstate__defaults_bspl.pyxEllipsismemviewfortranupdateunpackstruct__reduce__pickleoffset__name__kwargs__import__formatencodedouble_collocstripstartsplitshaperangenumpyfloat_flagserroremptydtype__class__ASCIIxvalwvalwork__test__stopstepsizepackndimnamemode__main__leftkind__dict__clmnbaseargswrkrowrhsoutobj__new__bbbxpnuntnpkukljpipidciab|yxwtsrnmkjicaO()Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported!%.200s() takes no keyword arguments%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingUnable to initialize pickling for %s__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict object__defaults__ must be set to a tuple objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__qualname__ must be set to a string object__name__ must be set to a string objectDoes not understand character buffer dtype format string ('%c')Cannot convert %.200s to %.200sord() expected string of length 1, but %.200s foundord() expected a character, but string of length %zd foundNeed at least one argument, 0 given.First argument should be of type %.200s, got %.200s.View.MemoryView.memoryview.__str__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.setitem_indexedView.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__getitem__View.MemoryView.array.__setitem__Subscript deletion not supported by %.200s__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes %.8s %zd positional argument%.1s (%zd given)'NoneType' object is not iterableneed more than %zd value%.1s to unpacktoo many values to unpack (expected %zd)View.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_objectcalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionView.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.strides.__get__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__PyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer__View.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectUnexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Acquisition count is %d (line %d)View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperBuffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer is not indirectly contiguous in dimension %d.Buffer and memoryview are not contiguous in the same dimension.C-contiguous buffer is not contiguous in dimension %dC-contiguous buffer is not indirect in dimension %dBuffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.Buffer is not indirectly accessible in dimension %d.Buffer not fortran contiguous.View.MemoryView.memoryview.is_slicevalue too large to convert to intView.MemoryView.Enum.__reduce_cython__View.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_sliceView.MemoryView.memoryview.__repr__c-string too long to convert to PythonView.MemoryView.get_slice_from_memviewView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarobject of type 'NoneType' has no len()only single character unicode strings can be converted to Py_UCS4, got length %zdscipy.interpolate._bspl.__pyx_fused_cpdefView.MemoryView.memoryview.__cinit__'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__Cannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copyView.MemoryView.memoryview.setitem_slice_assignmentView.MemoryView.__pyx_unpickle_Enumscipy.interpolate._bspl._handle_lhs_derivativesinteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointerAll dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)local variable '%s' referenced before assignmentscipy.interpolate._bspl.evaluate_all_bsplOut of bounds on buffer access (axis %d)scipy.interpolate._bspl.evaluate_splinescipy.interpolate._bspl._norm_eq_lsqView.MemoryView.memoryview.__setitem__View.MemoryView.memoryview.__getitem__scipy.interpolate._bspl._collocView.MemoryView.array.__cinit__Argument '%.200s' must not be Noneexpected bytes, NoneType foundArgument '%.200s' has incorrect type (expected %.200s, got %.200s)compiletime version %s of module '%.100s' does not match runtime version %sscipy.interpolate._bspl._memoryviewsliceInternal class for passing memoryview slices to Pythonscipy.interpolate._bspl.memoryviewð¿ð?ø
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    The observation equations are ``A @ c = y``, and the normal equations are
    ``A.T @ A @ c = A.T @ y``. This routine fills in the rhs and lhs for the
    latter.

    The B-spline collocation matrix is defined as :math:`A_{j,l} = B_l(x_j)`,
    so that row ``j`` contains all the B-splines which are non-zero
    at ``x_j``.

    The normal eq matrix has at most `2k+1` bands and is constructed in the
    LAPACK symmetrix banded storage: ``A[i, j] == ab[i-j, j]`` with `i >= j`.
    See the doctsring for `scipy.linalg.cholesky_banded` for more info.

    This routine is not supposed to be called directly, and
    does no error checking.

    Parameters
    ----------
    x : ndarray, shape (n,)
        sorted 1D array of x values
    t : ndarray, shape (nt + k + 1,)
        sorted 1D array of knots
    k : int
        spline order
    y : ndarray, shape (n, s)
        a 2D array of y values. The second dimension contains all trailing
        dimensions of the original array of ordinates.
    w : ndarray, shape(n,)
        Weights.
    ab : ndarray, shape (k+1, n), in Fortran order.
        This parameter is modified in-place.
        On entry: should be zeroed out.
        On exit: LHS of the normal equations.
    rhs : ndarray, shape (n, s), in Fortran order.
        This parameter is modified in-place.
        On entry: should be zeroed out.
        On exit: RHS of the normal equations.

     Fill in the entries of the collocation matrix corresponding to known
    derivatives at xval.

    The collocation matrix is in the banded storage, as prepared by _colloc.
    No error checking.

    Parameters
    ----------
    t : ndarray, shape (nt + k + 1,)
        knots
    k : integer
        B-spline order
    xval : float
        The value at which to evaluate the derivatives at.
    ab : ndarray, shape(2*kl + ku + 1, nt), Fortran order
        B-spline collocation matrix.
        This argument is modified *in-place*.
    kl : integer
        Number of lower diagonals of ab.
    ku : integer
        Number of upper diagonals of ab.
    deriv_ords : 1D ndarray
        Orders of derivatives known at xval
    offset : integer, optional
        Skip this many rows of the matrix ab.

    Build the B-spline collocation matrix.

    The collocation matrix is defined as :math:`B_{j,l} = B_l(x_j)`,
    so that row ``j`` contains all the B-splines which are non-zero
    at ``x_j``.

    The matrix is constructed in the LAPACK banded storage.
    Basically, for an N-by-N matrix A with ku upper diagonals and
    kl lower diagonals, the shape of the array Ab is (2*kl + ku +1, N),
    where the last kl+ku+1 rows of Ab contain the diagonals of A, and
    the first kl rows of Ab are not referenced.
    For more info see, e.g. the docs for the ``*gbsv`` routine.

    This routine is not supposed to be called directly, and
    does no error checking.

    Parameters
    ----------
    x : ndarray, shape (n,)
        sorted 1D array of x values
    t : ndarray, shape (nt + k + 1,)
        sorted 1D array of knots
    k : int
        spline order
    ab : ndarray, shape (2*kl + ku + 1, nt), F-order
        This parameter is modified in-place.
        On exit: zeroed out.
        On exit: B-spline collocation matrix in the band storage with
        ``ku`` upper diagonals and ``kl`` lower diagonals.
        Here ``kl = ku = k``.
    offset : int, optional
        skip this many rows

    Evaluate the ``k+1`` B-splines which are non-zero on interval ``m``.

    Parameters
    ----------
    t : ndarray, shape (nt + k + 1,)
        sorted 1D array of knots
    k : int
        spline order
    xval: float
        argument at which to evaluate the B-splines
    m : int
        index of the left edge of the evaluation interval, ``t[m] <= x < t[m+1]``
    nu : int, optional
        Evaluate derivatives order `nu`. Default is zero.

    Returns
    -------
    ndarray, shape (k+1,)
        The values of B-splines :math:`[B_{m-k}(xval), ..., B_{m}(xval)]` if
        `nu` is zero, otherwise the derivatives of order `nu`.

    Examples
    --------

    A textbook use of this sort of routine is plotting the ``k+1`` polynomial
    pieces which make up a B-spline of order `k`.

    Consider a cubic spline

    >>> k = 3
    >>> t = [0., 2., 2., 3., 4.]   # internal knots
    >>> a, b = t[0], t[-1]    # base interval is [a, b)
    >>> t = [a]*k + t + [b]*k  # add boundary knots

    >>> import matplotlib.pyplot as plt
    >>> xx = np.linspace(a, b, 100)
    >>> plt.plot(xx, BSpline.basis_element(k:-k)(xx),
    ...          'r-', lw=5, alpha=0.5)
    >>> c = ['b', 'g', 'c', 'k']

    Now we use slide an interval ``t[m]..t[m+1]`` along the base interval
    ``a..b`` and use `evaluate_all_bspl` to compute the restriction of
    the B-spline of interest to this interval:

    >>> for i in range(k+1):
    ...    x1, x2 = t[2*k - i], t[2*k - i + 1]
    ...    xx = np.linspace(x1 - 0.5, x2 + 0.5)
    ...    yy = [evaluate_all_bspl(t, k, x, 2*k - i)[i] for x in xx]
    ...    plt.plot(xx, yy, c[i] + '--', lw=3, label=str(i))
    ...
    >>> plt.grid(True)
    >>> plt.legend()
    >>> plt.show()

    
    Evaluate a spline in the B-spline basis.

    Parameters
    ----------
    t : ndarray, shape (n+k+1)
        knots
    c : ndarray, shape (n, m)
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        Points to evaluate the spline at.
    nu : int
        Order of derivative to evaluate.
    extrapolate : int, optional
        Whether to extrapolate to ouf-of-bounds points, or to return NaNs.
    out : ndarray, shape (s, m)
        Computed values of the spline at each of the input points.
        This argument is modified in-place.

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