"""Compatibility functions for numpy versions in lib np_new_unique ------------- Optionally provides the count of the number of occurences of each unique element. Copied from Numpy source, under license: Copyright (c) 2005-2015, NumPy Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the NumPy Developers nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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""" from __future__ import absolute_import import numpy as np from .python import PY3 from .scipy import NumpyVersion np_matrix_rank = np.linalg.matrix_rank if NumpyVersion(np.__version__) >= '1.9.0': np_new_unique = np.unique else: def np_new_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`. .. versionadded:: 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. .. versionadded:: 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]) """ ar = np.asanyarray(ar).flatten() optional_indices = return_index or return_inverse optional_returns = optional_indices or return_counts if ar.size == 0: if not optional_returns: ret = ar else: ret = (ar,) if return_index: ret += (np.empty(0, np.bool),) if return_inverse: ret += (np.empty(0, np.bool),) if return_counts: ret += (np.empty(0, np.intp),) return ret if optional_indices: perm = ar.argsort(kind='mergesort' if return_index else 'quicksort') aux = ar[perm] else: ar.sort() aux = ar flag = np.concatenate(([True], aux[1:] != aux[:-1])) if not optional_returns: ret = aux[flag] else: ret = (aux[flag],) if return_index: ret += (perm[flag],) if return_inverse: iflag = np.cumsum(flag) - 1 inv_idx = np.empty(ar.shape, dtype=np.intp) inv_idx[perm] = iflag ret += (inv_idx,) if return_counts: idx = np.concatenate(np.nonzero(flag) + ([ar.size],)) ret += (np.diff(idx),) return ret def recarray_select(recarray, fields): """" Work-around for changes in NumPy 1.13 that return views for recarray multiple column selection """ from pandas import DataFrame fields = [fields] if not isinstance(fields, (tuple, list)) else fields if len(fields) == len(recarray.dtype): selection = recarray else: recarray = DataFrame.from_records(recarray) selection = recarray[fields].to_records(index=False) _bytelike_dtype_names(selection) return selection def _bytelike_dtype_names(arr): # See # 3658 if not PY3: dtype = arr.dtype names = dtype.names names = [bytes(name) if isinstance(name, unicode) else name for name in names] dtype.names = names