public static ndarray ediff1d(ndarray ary, ndarray to_end = null, ndarray to_begin = null)
{
/*
* The differences between consecutive elements of an array.
*
* Parameters
* ----------
* ary : array_like
* If necessary, will be flattened before the differences are taken.
* to_end : array_like, optional
* Number(s) to append at the end of the returned differences.
* to_begin : array_like, optional
* Number(s) to prepend at the beginning of the returned differences.
*
* Returns
* -------
* ediff1d : ndarray
* The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.
*
* See Also
* --------
* diff, gradient
*
* Notes
* -----
* When applied to masked arrays, this function drops the mask information
* if the `to_begin` and/or `to_end` parameters are used.
*
* Examples
* --------
* >>> x = np.array([1, 2, 4, 7, 0])
* >>> np.ediff1d(x)
* array([ 1, 2, 3, -7])
*
* >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
* array([-99, 1, 2, 3, -7, 88, 99])
*
* The returned array is always 1D.
*
* >>> y = [[1, 2, 4], [1, 6, 24]]
* >>> np.ediff1d(y)
* array([ 1, 2, -3, 5, 18])
*/
// force a 1d array
ary = np.asanyarray(ary).ravel();
// fast track default case
if (to_begin is null && to_end is null)
{
return(ary.A("1:") - ary.A(":-1"));
}
int l_begin = 0;
int l_end = 0;
if (to_begin is null)
{
l_begin = 0;
}
else
{
to_begin = np.asanyarray(to_begin).ravel();
l_begin = len(to_begin);
}
if (to_end == null)
{
l_end = 0;
}
else
{
to_end = np.asanyarray(to_end).ravel();
l_end = len(to_end);
}
// do the calculation in place and copy to_begin and to_end
int l_diff = Math.Max(len(ary) - 1, 0);
ndarray result = np.empty(new shape(l_diff + l_begin + l_end), dtype: ary.Dtype);
result = ary.__array_wrap__(result);
if (l_begin > 0)
{
result[":" + l_begin.ToString()] = to_begin;
}
if (l_end > 0)
{
result[(l_begin + l_diff).ToString() + ":"] = to_end;
}
ndarray _out = result.A(l_begin.ToString() + ":" + (l_begin + l_diff).ToString());
_out = np.subtract(ary.A("1:"), ary.A(":-1"));
result[l_begin.ToString() + ":" + (l_begin + l_diff).ToString()] = _out;
return(result);
}
private static uniqueData _unique1d(ndarray ar1, bool return_index = false,
bool return_inverse = false, bool return_counts = false)
{
//Find the unique elements of an array, ignoring shape.
var ar = np.asanyarray(ar1).flatten();
bool optional_indices = return_index || return_inverse;
ndarray perm = null;
ndarray aux = null;
if (optional_indices)
{
if (return_index)
{
perm = ar.ArgSort(kind: NPY_SORTKIND.NPY_QUICKSORT);
}
else
{
perm = ar.ArgSort(kind: NPY_SORTKIND.NPY_MERGESORT);
}
aux = ar.A(perm);
}
else
{
ar = ar.Sort();
aux = ar;
}
ndarray mask = np.empty(aux.shape, dtype: np.Bool);
mask[":1"] = true;
ndarray T1 = aux.A("1:");
ndarray T2 = aux.A(":-1");
mask["1:"] = T1.NotEquals(T2);
var ret = new uniqueData();
ret.data = aux.A(mask);
if (return_index)
{
ret.indices = perm.A(mask);
}
if (return_inverse)
{
ndarray imask = np.cumsum(mask) - 1;
ndarray inv_idx = np.empty(mask.shape, dtype: np.intp);
inv_idx[perm] = imask;
ret.inverse = inv_idx;
}
if (return_counts)
{
List <ndarray> parts = new List <ndarray>();
parts.AddRange(np.nonzero(mask));
parts.Add(np.array(new npy_intp[] { mask.size }));
ndarray idx = np.concatenate(parts);
ret.counts = np.diff(idx);
}
return(ret);
}
public static ndarray in1d(ndarray ar1, ndarray ar2, bool assume_unique = false, bool invert = false)
{
/*
* Test whether each element of a 1-D array is also present in a second array.
*
* Returns a boolean array the same length as `ar1` that is True
* where an element of `ar1` is in `ar2` and False otherwise.
*
* We recommend using :func:`isin` instead of `in1d` for new code.
*
* Parameters
* ----------
* ar1 : (M,) array_like
* Input array.
* ar2 : array_like
* The values against which to test each value of `ar1`.
* assume_unique : bool, optional
* If True, the input arrays are both assumed to be unique, which
* can speed up the calculation. Default is False.
* invert : bool, optional
* If True, the values in the returned array are inverted (that is,
* False where an element of `ar1` is in `ar2` and True otherwise).
* Default is False. ``np.in1d(a, b, invert=True)`` is equivalent
* to (but is faster than) ``np.invert(in1d(a, b))``.
*
* .. versionadded:: 1.8.0
*
* Returns
* -------
* in1d : (M,) ndarray, bool
* The values `ar1[in1d]` are in `ar2`.
*
* See Also
* --------
* isin : Version of this function that preserves the
* shape of ar1.
* numpy.lib.arraysetops : Module with a number of other functions for
* performing set operations on arrays.
*
* Notes
* -----
* `in1d` can be considered as an element-wise function version of the
* python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly
* equivalent to ``np.array([item in b for item in a])``.
* However, this idea fails if `ar2` is a set, or similar (non-sequence)
* container: As ``ar2`` is converted to an array, in those cases
* ``asarray(ar2)`` is an object array rather than the expected array of
* contained values.
*
* .. versionadded:: 1.4.0
*
* Examples
* --------
* >>> test = np.array([0, 1, 2, 5, 0])
* >>> states = [0, 2]
* >>> mask = np.in1d(test, states)
* >>> mask
* array([ True, False, True, False, True])
* >>> test[mask]
* array([0, 2, 0])
* >>> mask = np.in1d(test, states, invert=True)
* >>> mask
* array([False, True, False, True, False])
* >>> test[mask]
* array([1, 5])
*/
// Ravel both arrays, behavior for the first array could be different
ar1 = np.asarray(ar1).ravel();
ar2 = np.asarray(ar2).ravel();
//Check if one of the arrays may contain arbitrary objects
bool contains_object = ar1.Dtype.hasobject || ar2.Dtype.hasobject;
// This code is run when
// a) the first condition is true, making the code significantly faster
// b) the second condition is true (i.e. `ar1` or `ar2` may contain
// arbitrary objects), since then sorting is not guaranteed to work
if (len(ar2) < 10 * Math.Pow(len(ar1), 0.145) || contains_object)
{
ndarray mask;
if (invert)
{
mask = np.ones(new shape(len(ar1)), dtype: np.Bool);
foreach (dynamic a in ar2)
{
ndarray temp = ar1 != a;
mask &= temp;
}
}
else
{
mask = np.zeros(new shape(len(ar1)), dtype: np.Bool);
foreach (dynamic a in ar2)
{
ndarray temp = ar1 == a;
mask |= temp;
}
}
return(mask);
}
// Otherwise use sorting
ndarray rev_idx = null;
if (!assume_unique)
{
var temp = np.unique(ar1, return_inverse: true);
ar1 = temp.data;
rev_idx = temp.indices;
ar2 = np.unique(ar2).data;
}
ndarray ar = np.concatenate(ar1, ar2);
// We need this to be a stable sort, so always use 'mergesort'
// here. The values from the first array should always come before
// the values from the second array.
ndarray order = ar.ArgSort(kind: NPY_SORTKIND.NPY_MERGESORT);
ndarray bool_ar;
ndarray sar = ar.A(order);
if (invert)
{
bool_ar = (sar.A("1:")).NotEquals(sar.A(":-1"));
}
else
{
bool_ar = (sar.A("1:")).Equals(sar.A(":-1"));
}
ndarray flag = np.concatenate(new ndarray[] { bool_ar, np.array(new bool[] { invert }) });
ndarray ret = np.empty(ar.shape, dtype: np.Bool);
ret[order] = flag;
if (assume_unique)
{
return(ret.A(":" + len(ar1).ToString()));
}
else
{
return(ret.A(rev_idx));
}
}
/// <summary>
/// Return a 2-D array with ones on the diagonal and zeros elsewhere
/// </summary>
/// <param name="N">Number of rows in the output</param>
/// <param name="M">(optional) Number of columns in the output. If None, defaults to N</param>
/// <param name="k">(optional) Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.</param>
/// <param name="dtype">(optional) Data-type of the returned array</param>
/// <param name="order">(optional)</param>
/// <returns>An array where all elements are equal to zero, except for the k-th diagonal, whose values are equal to one</returns>
public static ndarray eye(int N, int?M = null, int k = 0, dtype dtype = null, NPY_ORDER order = NPY_ORDER.NPY_CORDER)
{
/*
* Return a 2-D array with ones on the diagonal and zeros elsewhere.
*
* Parameters
* ----------
* N : int
* Number of rows in the output.
* M : int, optional
* Number of columns in the output. If None, defaults to `N`.
* k : int, optional
* Index of the diagonal: 0 (the default) refers to the main diagonal,
* a positive value refers to an upper diagonal, and a negative value
* to a lower diagonal.
* dtype : data-type, optional
* Data-type of the returned array.
* order : {'C', 'F'}, optional
* Whether the output should be stored in row-major (C-style) or
* column-major (Fortran-style) order in memory.
*
* .. versionadded:: 1.14.0
*
* Returns
* -------
* I : ndarray of shape (N,M)
* An array where all elements are equal to zero, except for the `k`-th
* diagonal, whose values are equal to one.
*
* See Also
* --------
* identity : (almost) equivalent function
* diag : diagonal 2-D array from a 1-D array specified by the user.
*
* Examples
* --------
* >>> np.eye(2, dtype=int)
* array([[1, 0],
* [0, 1]])
* >>> np.eye(3, k=1)
* array([[ 0., 1., 0.],
* [ 0., 0., 1.],
* [ 0., 0., 0.]])
*
*/
int i;
if (M == null)
{
M = N;
}
ndarray m = zeros(new shape(N, (int)M), dtype: dtype, order: order);
if (k >= M)
{
return(m);
}
if (k >= 0)
{
i = k;
}
else
{
i = (-k) * (int)M;
}
m.A(":" + (M - k).ToString()).Flat[i.ToString() + "::" + (M + 1).ToString()] = 1;
return(m);
}
