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, *, kind=None)¶

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 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)).

kind{None, ‘sort’, ‘table’}, optional

The algorithm to use. This will not affect the final result, but will affect the speed and memory use. The default, None, will select automatically based on memory considerations.

If ‘sort’, will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of ar1 and ar2, not accounting for size of dtypes.
If ‘table’, will use a lookup table approach similar to a counting sort. This is only available for boolean and integer arrays. This will have a memory usage of the size of ar1 plus the max-min value of ar2. assume_unique has no effect when the ‘table’ option is used.
If None, will automatically choose ‘table’ if the required memory allocation is less than or equal to 6 times the sum of the sizes of ar1 and ar2, otherwise will use ‘sort’. This is done to not use a large amount of memory by default, even though ‘table’ may be faster in most cases. If ‘table’ is chosen, assume_unique will have no effect.

Added 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 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.

Using kind='table' tends to be faster than kind=’sort’ if the following relationship is true: log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927, but may use greater memory. The default value for kind will be automatically selected based only on memory usage, so one may manually set kind='table' if memory constraints can be relaxed.

Added 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, *, equal_nan=True)¶

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 1-D.
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.
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 1-D 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.

Added in version 1.13.0.
equal_nanbool, optional: If True, collapses multiple NaN values in the return array into one.

Added in version 1.24.

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.

Added in version 1.9.0.

See also

numpy.lib.arraysetops: Module with a number of other functions for performing set operations on arrays.
repeat: Repeat elements of an array.

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 (move the axis to the first dimension to keep the order of the other axes) 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 1-D array of structured types that can be treated in the same way as any other 1-D 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='<U1')
>>> indices
array([0, 1, 3])
>>> a[indices]
array(['a', 'b', 'c'], dtype='<U1')

Reconstruct the input array from the unique values and inverse:

>>> 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])

Reconstruct the input values from the unique values and counts:

>>> a = np.array([1, 2, 6, 4, 2, 3, 2])
>>> values, counts = np.unique(a, return_counts=True)
>>> values
array([1, 2, 3, 4, 6])
>>> counts
array([1, 3, 1, 1, 1])
>>> np.repeat(values, counts)
array([1, 2, 2, 2, 3, 4, 6])    # original order not preserved