tensorly
.copy
- copy(a, order='K', subok=False)
Return an array copy of the given object.
- Parameters:
- aarray_like
Input data.
- order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and
ndarray.copy
are very similar, but have different default values for their order= arguments.)- subokbool, optional
If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (defaults to False).
Added in version 1.19.0.
- Returns:
- arrndarray
Array interpretation of a.
See also
ndarray.copy
Preferred method for creating an array copy
Notes
This is equivalent to:
>>> np.array(a, copy=True)
The copy made of the data is shallow, i.e., for arrays with object dtype, the new array will point to the same objects. See Examples from ndarray.copy.
Examples
Create an array x, with a reference y and a copy z:
>>> x = np.array([1, 2, 3]) >>> y = x >>> z = np.copy(x)
Note that, when we modify x, y changes, but not z:
>>> x[0] = 10 >>> x[0] == y[0] True >>> x[0] == z[0] False
Note that, np.copy clears previously set WRITEABLE=False flag.
>>> a = np.array([1, 2, 3]) >>> a.flags["WRITEABLE"] = False >>> b = np.copy(a) >>> b.flags["WRITEABLE"] True >>> b[0] = 3 >>> b array([3, 2, 3])