sfepy.base.compat module¶
This module contains functions that have different names or behavior depending on NumPy and Scipy versions.

sfepy.base.compat.
in1d
(ar1, ar2, assume_unique=False, invert=False)¶ Test whether each element of a 1D 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
isin()
instead of in1d for new code. Parameters
 ar1(M,) array_like
Input array.
 ar2array_like
The values against which to test each value of ar1.
 assume_uniquebool, optional
If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
 invertbool, 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))
.New in version 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 elementwise function version of the python keyword in, for 1D sequences.
in1d(a, b)
is roughly equivalent tonp.array([item in b for item in a])
. However, this idea fails if ar2 is a set, or similar (nonsequence) container: Asar2
is converted to an array, in those casesasarray(ar2)
is an object array rather than the expected array of contained values.New in version 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])

sfepy.base.compat.
unique
(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)¶ Find the unique elements of an array.
Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements:
the indices of the input array that give the unique values
the indices of the unique array that reconstruct the input array
the number of times each unique value comes up in the input array
 Parameters
 ararray_like
Input array. Unless axis is specified, this will be flattened if it is not already 1D.
 return_indexbool, optional
If True, also return the indices of ar (along the specified axis, if provided, or in the flattened array) that result in the unique array.
 return_inversebool, optional
If True, also return the indices of the unique array (for the specified axis, if provided) that can be used to reconstruct ar.
 return_countsbool, optional
If True, also return the number of times each unique item appears in ar.
New in version 1.9.0.
 axisint or None, optional
The axis to operate on. If None, ar will be flattened. If an integer, the subarrays indexed by the given axis will be flattened and treated as the elements of a 1D array with the dimension of the given axis, see the notes for more details. Object arrays or structured arrays that contain objects are not supported if the axis kwarg is used. The default is None.
New in version 1.13.0.
 Returns
 uniquendarray
The sorted unique values.
 unique_indicesndarray, optional
The indices of the first occurrences of the unique values in the original array. Only provided if return_index is True.
 unique_inversendarray, optional
The indices to reconstruct the original array from the unique array. Only provided if return_inverse is True.
 unique_countsndarray, optional
The number of times each of the unique values comes up in the original array. Only provided if return_counts is True.
New in version 1.9.0.
See also
numpy.lib.arraysetops
Module with a number of other functions for performing set operations on arrays.
Notes
When an axis is specified the subarrays indexed by the axis are sorted. This is done by making the specified axis the first dimension of the array and then flattening the subarrays in C order. The flattened subarrays are then viewed as a structured type with each element given a label, with the effect that we end up with a 1D array of structured types that can be treated in the same way as any other 1D array. The result is that the flattened subarrays are sorted in lexicographic order starting with the first element.
Examples
>>> np.unique([1, 1, 2, 2, 3, 3]) array([1, 2, 3]) >>> a = np.array([[1, 1], [2, 3]]) >>> np.unique(a) array([1, 2, 3])
Return the unique rows of a 2D array
>>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]]) >>> np.unique(a, axis=0) array([[1, 0, 0], [2, 3, 4]])
Return the indices of the original array that give the unique values:
>>> a = np.array(['a', 'b', 'b', 'c', 'a']) >>> u, indices = np.unique(a, return_index=True) >>> u array(['a', 'b', 'c'], dtype='S1') >>> indices array([0, 1, 3]) >>> a[indices] array(['a', 'b', 'c'], dtype='S1')
Reconstruct the input array from the unique values:
>>> a = np.array([1, 2, 6, 4, 2, 3, 2]) >>> u, indices = np.unique(a, return_inverse=True) >>> u array([1, 2, 3, 4, 6]) >>> indices array([0, 1, 4, 3, 1, 2, 1]) >>> u[indices] array([1, 2, 6, 4, 2, 3, 2])