123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184 |
- from __future__ import division
- from itertools import permutations
- import numpy as np
- import pytest
- from pandas._libs.interval import IntervalTree
- from pandas import compat
- import pandas.util.testing as tm
- def skipif_32bit(param):
- """
- Skip parameters in a parametrize on 32bit systems. Specifically used
- here to skip leaf_size parameters related to GH 23440.
- """
- marks = pytest.mark.skipif(compat.is_platform_32bit(),
- reason='GH 23440: int type mismatch on 32bit')
- return pytest.param(param, marks=marks)
- @pytest.fixture(
- scope='class', params=['int32', 'int64', 'float32', 'float64', 'uint64'])
- def dtype(request):
- return request.param
- @pytest.fixture(params=[skipif_32bit(1), skipif_32bit(2), 10])
- def leaf_size(request):
- """
- Fixture to specify IntervalTree leaf_size parameter; to be used with the
- tree fixture.
- """
- return request.param
- @pytest.fixture(params=[
- np.arange(5, dtype='int64'),
- np.arange(5, dtype='int32'),
- np.arange(5, dtype='uint64'),
- np.arange(5, dtype='float64'),
- np.arange(5, dtype='float32'),
- np.array([0, 1, 2, 3, 4, np.nan], dtype='float64'),
- np.array([0, 1, 2, 3, 4, np.nan], dtype='float32')])
- def tree(request, leaf_size):
- left = request.param
- return IntervalTree(left, left + 2, leaf_size=leaf_size)
- class TestIntervalTree(object):
- def test_get_loc(self, tree):
- result = tree.get_loc(1)
- expected = np.array([0], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- result = np.sort(tree.get_loc(2))
- expected = np.array([0, 1], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- with pytest.raises(KeyError):
- tree.get_loc(-1)
- def test_get_indexer(self, tree):
- result = tree.get_indexer(np.array([1.0, 5.5, 6.5]))
- expected = np.array([0, 4, -1], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- with pytest.raises(KeyError):
- tree.get_indexer(np.array([3.0]))
- def test_get_indexer_non_unique(self, tree):
- indexer, missing = tree.get_indexer_non_unique(
- np.array([1.0, 2.0, 6.5]))
- result = indexer[:1]
- expected = np.array([0], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- result = np.sort(indexer[1:3])
- expected = np.array([0, 1], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- result = np.sort(indexer[3:])
- expected = np.array([-1], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- result = missing
- expected = np.array([2], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- def test_duplicates(self, dtype):
- left = np.array([0, 0, 0], dtype=dtype)
- tree = IntervalTree(left, left + 1)
- result = np.sort(tree.get_loc(0.5))
- expected = np.array([0, 1, 2], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- with pytest.raises(KeyError):
- tree.get_indexer(np.array([0.5]))
- indexer, missing = tree.get_indexer_non_unique(np.array([0.5]))
- result = np.sort(indexer)
- expected = np.array([0, 1, 2], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- result = missing
- expected = np.array([], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- def test_get_loc_closed(self, closed):
- tree = IntervalTree([0], [1], closed=closed)
- for p, errors in [(0, tree.open_left),
- (1, tree.open_right)]:
- if errors:
- with pytest.raises(KeyError):
- tree.get_loc(p)
- else:
- result = tree.get_loc(p)
- expected = np.array([0], dtype='intp')
- tm.assert_numpy_array_equal(result, expected)
- @pytest.mark.parametrize('leaf_size', [
- skipif_32bit(1), skipif_32bit(10), skipif_32bit(100), 10000])
- def test_get_indexer_closed(self, closed, leaf_size):
- x = np.arange(1000, dtype='float64')
- found = x.astype('intp')
- not_found = (-1 * np.ones(1000)).astype('intp')
- tree = IntervalTree(x, x + 0.5, closed=closed, leaf_size=leaf_size)
- tm.assert_numpy_array_equal(found, tree.get_indexer(x + 0.25))
- expected = found if tree.closed_left else not_found
- tm.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.0))
- expected = found if tree.closed_right else not_found
- tm.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.5))
- @pytest.mark.parametrize('left, right, expected', [
- (np.array([0, 1, 4]), np.array([2, 3, 5]), True),
- (np.array([0, 1, 2]), np.array([5, 4, 3]), True),
- (np.array([0, 1, np.nan]), np.array([5, 4, np.nan]), True),
- (np.array([0, 2, 4]), np.array([1, 3, 5]), False),
- (np.array([0, 2, np.nan]), np.array([1, 3, np.nan]), False)])
- @pytest.mark.parametrize('order', map(list, permutations(range(3))))
- def test_is_overlapping(self, closed, order, left, right, expected):
- # GH 23309
- tree = IntervalTree(left[order], right[order], closed=closed)
- result = tree.is_overlapping
- assert result is expected
- @pytest.mark.parametrize('order', map(list, permutations(range(3))))
- def test_is_overlapping_endpoints(self, closed, order):
- """shared endpoints are marked as overlapping"""
- # GH 23309
- left, right = np.arange(3), np.arange(1, 4)
- tree = IntervalTree(left[order], right[order], closed=closed)
- result = tree.is_overlapping
- expected = closed is 'both'
- assert result is expected
- @pytest.mark.parametrize('left, right', [
- (np.array([], dtype='int64'), np.array([], dtype='int64')),
- (np.array([0], dtype='int64'), np.array([1], dtype='int64')),
- (np.array([np.nan]), np.array([np.nan])),
- (np.array([np.nan] * 3), np.array([np.nan] * 3))])
- def test_is_overlapping_trivial(self, closed, left, right):
- # GH 23309
- tree = IntervalTree(left, right, closed=closed)
- assert tree.is_overlapping is False
- @pytest.mark.skipif(compat.is_platform_32bit(), reason='GH 23440')
- def test_construction_overflow(self):
- # GH 25485
- left, right = np.arange(101), [np.iinfo(np.int64).max] * 101
- tree = IntervalTree(left, right)
- # pivot should be average of left/right medians
- result = tree.root.pivot
- expected = (50 + np.iinfo(np.int64).max) / 2
- assert result == expected
|