sfepy.base.compat module¶
This module contains functions that have different names or behavior depending on NumPy and Scipy versions.
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sfepy.base.compat.
in1d
(ar1, ar2, assume_unique=False, invert=False)[source]¶ 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 tonp.array([item in b for item in a])
.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])
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sfepy.base.compat.
unique
(ar, return_index=False, return_inverse=False)[source]¶ Find the unique elements of an array.
Returns the sorted unique elements of an array. There are two optional outputs in addition to the unique elements: the indices of the input array that give the unique values, and the indices of the unique array that reconstruct 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.
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.
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])