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 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.

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)).

New in version 1.8.0.

Returns:

in1d : (M,) ndarray, bool

The values ar1[in1d] are in ar2.

See also

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.

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], dtype=bool)
>>> test[mask]
array([0, 2, 0])
>>> mask = np.in1d(test, states, invert=True)
>>> mask
array([False,  True, False,  True, False], dtype=bool)
>>> test[mask]
array([1, 5])

sfepy.base.compat.unique(ar, return_index=False, return_inverse=False, return_counts=False)

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, and the number of times each unique value comes up in the input array.

Parameters:

ar : array_like

Input array. This will be flattened if it is not already 1-D.

return_index : bool, optional

If True, also return the indices of ar that result in the unique array.

return_inverse : bool, optional

If True, also return the indices of the unique array that can be used to reconstruct ar.

return_counts : bool, optional

If True, also return the number of times each unique value comes up in ar.

New in version 1.9.0.

Returns:

unique : ndarray

The sorted unique values.

unique_indices : ndarray, optional

The indices of the first occurrences of the unique values in the (flattened) original array. Only provided if return_index is True.

unique_inverse : ndarray, optional

The indices to reconstruct the (flattened) original array from the unique array. Only provided if return_inverse is True.

unique_counts : ndarray, 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.

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 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])