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📄 test__datasource.py import os import pytest from tempfile import mkdtemp, mkstemp, NamedTemporaryFile from shutil import rmtree import numpy.lib._datasource as datasource from numpy.testing import assert_, assert_equal, assert_raises import urllib.request as urllib_request from urllib.parse import urlparse from urllib.error import URLError def urlopen_stub(url, data=None): '''Stub to replace urlopen for testing.''' if url == valid_httpurl(): tmpfile = NamedTemporaryFile(prefix='urltmp_') return tmpfile else: raise URLError('Name or service not known') # setup and teardown old_urlopen = None def setup_module(): global old_urlopen old_urlopen = urllib_request.urlopen urllib_request.urlopen = urlopen_stub def teardown_module(): urllib_request.urlopen = old_urlopen # A valid website for more robust testing http_path = 'http://www.google.com/' http_file = 'index.html' http_fakepath = 'http://fake.abc.web/site/' http_fakefile = 'fake.txt' malicious_files = ['/etc/shadow', '../../shadow', '..\\system.dat', 'c:\\windows\\system.dat'] magic_line = b'three is the magic number' # Utility functions used by many tests def valid_textfile(filedir): # Generate and return a valid temporary file. fd, path = mkstemp(suffix='.txt', prefix='dstmp_', dir=filedir, text=True) os.close(fd) return path def invalid_textfile(filedir): # Generate and return an invalid filename. fd, path = mkstemp(suffix='.txt', prefix='dstmp_', dir=filedir) os.close(fd) os.remove(path) return path def valid_httpurl(): return http_path+http_file def invalid_httpurl(): return http_fakepath+http_fakefile def valid_baseurl(): return http_path def invalid_baseurl(): return http_fakepath def valid_httpfile(): return http_file def invalid_httpfile(): return http_fakefile class TestDataSourceOpen: def setup_method(self): self.tmpdir = mkdtemp() self.ds = datasource.DataSource(self.tmpdir) def teardown_method(self): rmtree(self.tmpdir) del self.ds def test_ValidHTTP(self): fh = self.ds.open(valid_httpurl()) assert_(fh) fh.close() def test_InvalidHTTP(self): url = invalid_httpurl() assert_raises(OSError, self.ds.open, url) try: self.ds.open(url) except OSError as e: # Regression test for bug fixed in r4342. assert_(e.errno is None) def test_InvalidHTTPCacheURLError(self): assert_raises(URLError, self.ds._cache, invalid_httpurl()) def test_ValidFile(self): local_file = valid_textfile(self.tmpdir) fh = self.ds.open(local_file) assert_(fh) fh.close() def test_InvalidFile(self): invalid_file = invalid_textfile(self.tmpdir) assert_raises(OSError, self.ds.open, invalid_file) def test_ValidGzipFile(self): try: import gzip except ImportError: # We don't have the gzip capabilities to test. pytest.skip() # Test datasource's internal file_opener for Gzip files. filepath = os.path.join(self.tmpdir, 'foobar.txt.gz') fp = gzip.open(filepath, 'w') fp.write(magic_line) fp.close() fp = self.ds.open(filepath) result = fp.readline() fp.close() assert_equal(magic_line, result) def test_ValidBz2File(self): try: import bz2 except ImportError: # We don't have the bz2 capabilities to test. pytest.skip() # Test datasource's internal file_opener for BZip2 files. filepath = os.path.join(self.tmpdir, 'foobar.txt.bz2') fp = bz2.BZ2File(filepath, 'w') fp.write(magic_line) fp.close() fp = self.ds.open(filepath) result = fp.readline() fp.close() assert_equal(magic_line, result) class TestDataSourceExists: def setup_method(self): self.tmpdir = mkdtemp() self.ds = datasource.DataSource(self.tmpdir) def teardown_method(self): rmtree(self.tmpdir) del self.ds def test_ValidHTTP(self): assert_(self.ds.exists(valid_httpurl())) def test_InvalidHTTP(self): assert_equal(self.ds.exists(invalid_httpurl()), False) def test_ValidFile(self): # Test valid file in destpath tmpfile = valid_textfile(self.tmpdir) assert_(self.ds.exists(tmpfile)) # Test valid local file not in destpath localdir = mkdtemp() tmpfile = valid_textfile(localdir) assert_(self.ds.exists(tmpfile)) rmtree(localdir) def test_InvalidFile(self): tmpfile = invalid_textfile(self.tmpdir) assert_equal(self.ds.exists(tmpfile), False) class TestDataSourceAbspath: def setup_method(self): self.tmpdir = os.path.abspath(mkdtemp()) self.ds = datasource.DataSource(self.tmpdir) def teardown_method(self): rmtree(self.tmpdir) del self.ds def test_ValidHTTP(self): scheme, netloc, upath, pms, qry, frg = urlparse(valid_httpurl()) local_path = os.path.join(self.tmpdir, netloc, upath.strip(os.sep).strip('/')) assert_equal(local_path, self.ds.abspath(valid_httpurl())) def test_ValidFile(self): tmpfile = valid_textfile(self.tmpdir) tmpfilename = os.path.split(tmpfile)[-1] # Test with filename only assert_equal(tmpfile, self.ds.abspath(tmpfilename)) # Test filename with complete path assert_equal(tmpfile, self.ds.abspath(tmpfile)) def test_InvalidHTTP(self): scheme, netloc, upath, pms, qry, frg = urlparse(invalid_httpurl()) invalidhttp = os.path.join(self.tmpdir, netloc, upath.strip(os.sep).strip('/')) assert_(invalidhttp != self.ds.abspath(valid_httpurl())) def test_InvalidFile(self): invalidfile = valid_textfile(self.tmpdir) tmpfile = valid_textfile(self.tmpdir) tmpfilename = os.path.split(tmpfile)[-1] # Test with filename only assert_(invalidfile != self.ds.abspath(tmpfilename)) # Test filename with complete path assert_(invalidfile != self.ds.abspath(tmpfile)) def test_sandboxing(self): tmpfile = valid_textfile(self.tmpdir) tmpfilename = os.path.split(tmpfile)[-1] tmp_path = lambda x: os.path.abspath(self.ds.abspath(x)) assert_(tmp_path(valid_httpurl()).startswith(self.tmpdir)) assert_(tmp_path(invalid_httpurl()).startswith(self.tmpdir)) assert_(tmp_path(tmpfile).startswith(self.tmpdir)) assert_(tmp_path(tmpfilename).startswith(self.tmpdir)) for fn in malicious_files: assert_(tmp_path(http_path+fn).startswith(self.tmpdir)) assert_(tmp_path(fn).startswith(self.tmpdir)) def test_windows_os_sep(self): orig_os_sep = os.sep try: os.sep = '\\' self.test_ValidHTTP() self.test_ValidFile() self.test_InvalidHTTP() self.test_InvalidFile() self.test_sandboxing() finally: os.sep = orig_os_sep class TestRepositoryAbspath: def setup_method(self): self.tmpdir = os.path.abspath(mkdtemp()) self.repos = datasource.Repository(valid_baseurl(), self.tmpdir) def teardown_method(self): rmtree(self.tmpdir) del self.repos def test_ValidHTTP(self): scheme, netloc, upath, pms, qry, frg = urlparse(valid_httpurl()) local_path = os.path.join(self.repos._destpath, netloc, upath.strip(os.sep).strip('/')) filepath = self.repos.abspath(valid_httpfile()) assert_equal(local_path, filepath) def test_sandboxing(self): tmp_path = lambda x: os.path.abspath(self.repos.abspath(x)) assert_(tmp_path(valid_httpfile()).startswith(self.tmpdir)) for fn in malicious_files: assert_(tmp_path(http_path+fn).startswith(self.tmpdir)) assert_(tmp_path(fn).startswith(self.tmpdir)) def test_windows_os_sep(self): orig_os_sep = os.sep try: os.sep = '\\' self.test_ValidHTTP() self.test_sandboxing() finally: os.sep = orig_os_sep class TestRepositoryExists: def setup_method(self): self.tmpdir = mkdtemp() self.repos = datasource.Repository(valid_baseurl(), self.tmpdir) def teardown_method(self): rmtree(self.tmpdir) del self.repos def test_ValidFile(self): # Create local temp file tmpfile = valid_textfile(self.tmpdir) assert_(self.repos.exists(tmpfile)) def test_InvalidFile(self): tmpfile = invalid_textfile(self.tmpdir) assert_equal(self.repos.exists(tmpfile), False) def test_RemoveHTTPFile(self): assert_(self.repos.exists(valid_httpurl())) def test_CachedHTTPFile(self): localfile = valid_httpurl() # Create a locally cached temp file with an URL based # directory structure. This is similar to what Repository.open # would do. scheme, netloc, upath, pms, qry, frg = urlparse(localfile) local_path = os.path.join(self.repos._destpath, netloc) os.mkdir(local_path, 0o0700) tmpfile = valid_textfile(local_path) assert_(self.repos.exists(tmpfile)) class TestOpenFunc: def setup_method(self): self.tmpdir = mkdtemp() def teardown_method(self): rmtree(self.tmpdir) def test_DataSourceOpen(self): local_file = valid_textfile(self.tmpdir) # Test case where destpath is passed in fp = datasource.open(local_file, destpath=self.tmpdir) assert_(fp) fp.close() # Test case where default destpath is used fp = datasource.open(local_file) assert_(fp) fp.close() def test_del_attr_handling(): # DataSource __del__ can be called # even if __init__ fails when the # Exception object is caught by the # caller as happens in refguide_check # is_deprecated() function ds = datasource.DataSource() # simulate failed __init__ by removing key attribute # produced within __init__ and expected by __del__ del ds._istmpdest # should not raise an AttributeError if __del__ # gracefully handles failed __init__: ds.__del__()	10.32 KB	2026-01-20 12:58
📄 test__iotools.py import time from datetime import date import numpy as np from numpy.testing import ( assert_, assert_equal, assert_allclose, assert_raises, ) from numpy.lib._iotools import ( LineSplitter, NameValidator, StringConverter, has_nested_fields, easy_dtype, flatten_dtype ) class TestLineSplitter: "Tests the LineSplitter class." def test_no_delimiter(self): "Test LineSplitter w/o delimiter" strg = " 1 2 3 4 5 # test" test = LineSplitter()(strg) assert_equal(test, ['1', '2', '3', '4', '5']) test = LineSplitter('')(strg) assert_equal(test, ['1', '2', '3', '4', '5']) def test_space_delimiter(self): "Test space delimiter" strg = " 1 2 3 4 5 # test" test = LineSplitter(' ')(strg) assert_equal(test, ['1', '2', '3', '4', '', '5']) test = LineSplitter(' ')(strg) assert_equal(test, ['1 2 3 4', '5']) def test_tab_delimiter(self): "Test tab delimiter" strg = " 1\t 2\t 3\t 4\t 5 6" test = LineSplitter('\t')(strg) assert_equal(test, ['1', '2', '3', '4', '5 6']) strg = " 1 2\t 3 4\t 5 6" test = LineSplitter('\t')(strg) assert_equal(test, ['1 2', '3 4', '5 6']) def test_other_delimiter(self): "Test LineSplitter on delimiter" strg = "1,2,3,4,,5" test = LineSplitter(',')(strg) assert_equal(test, ['1', '2', '3', '4', '', '5']) # strg = " 1,2,3,4,,5 # test" test = LineSplitter(',')(strg) assert_equal(test, ['1', '2', '3', '4', '', '5']) # gh-11028 bytes comment/delimiters should get encoded strg = b" 1,2,3,4,,5 % test" test = LineSplitter(delimiter=b',', comments=b'%')(strg) assert_equal(test, ['1', '2', '3', '4', '', '5']) def test_constant_fixed_width(self): "Test LineSplitter w/ fixed-width fields" strg = " 1 2 3 4 5 # test" test = LineSplitter(3)(strg) assert_equal(test, ['1', '2', '3', '4', '', '5', '']) # strg = " 1 3 4 5 6# test" test = LineSplitter(20)(strg) assert_equal(test, ['1 3 4 5 6']) # strg = " 1 3 4 5 6# test" test = LineSplitter(30)(strg) assert_equal(test, ['1 3 4 5 6']) def test_variable_fixed_width(self): strg = " 1 3 4 5 6# test" test = LineSplitter((3, 6, 6, 3))(strg) assert_equal(test, ['1', '3', '4 5', '6']) # strg = " 1 3 4 5 6# test" test = LineSplitter((6, 6, 9))(strg) assert_equal(test, ['1', '3 4', '5 6']) # ----------------------------------------------------------------------------- class TestNameValidator: def test_case_sensitivity(self): "Test case sensitivity" names = ['A', 'a', 'b', 'c'] test = NameValidator().validate(names) assert_equal(test, ['A', 'a', 'b', 'c']) test = NameValidator(case_sensitive=False).validate(names) assert_equal(test, ['A', 'A_1', 'B', 'C']) test = NameValidator(case_sensitive='upper').validate(names) assert_equal(test, ['A', 'A_1', 'B', 'C']) test = NameValidator(case_sensitive='lower').validate(names) assert_equal(test, ['a', 'a_1', 'b', 'c']) # check exceptions assert_raises(ValueError, NameValidator, case_sensitive='foobar') def test_excludelist(self): "Test excludelist" names = ['dates', 'data', 'Other Data', 'mask'] validator = NameValidator(excludelist=['dates', 'data', 'mask']) test = validator.validate(names) assert_equal(test, ['dates_', 'data_', 'Other_Data', 'mask_']) def test_missing_names(self): "Test validate missing names" namelist = ('a', 'b', 'c') validator = NameValidator() assert_equal(validator(namelist), ['a', 'b', 'c']) namelist = ('', 'b', 'c') assert_equal(validator(namelist), ['f0', 'b', 'c']) namelist = ('a', 'b', '') assert_equal(validator(namelist), ['a', 'b', 'f0']) namelist = ('', 'f0', '') assert_equal(validator(namelist), ['f1', 'f0', 'f2']) def test_validate_nb_names(self): "Test validate nb names" namelist = ('a', 'b', 'c') validator = NameValidator() assert_equal(validator(namelist, nbfields=1), ('a',)) assert_equal(validator(namelist, nbfields=5, defaultfmt="g%i"), ['a', 'b', 'c', 'g0', 'g1']) def test_validate_wo_names(self): "Test validate no names" namelist = None validator = NameValidator() assert_(validator(namelist) is None) assert_equal(validator(namelist, nbfields=3), ['f0', 'f1', 'f2']) # ----------------------------------------------------------------------------- def _bytes_to_date(s): return date(time.strptime(s, "%Y-%m-%d")[:3]) class TestStringConverter: "Test StringConverter" def test_creation(self): "Test creation of a StringConverter" converter = StringConverter(int, -99999) assert_equal(converter._status, 1) assert_equal(converter.default, -99999) def test_upgrade(self): "Tests the upgrade method." converter = StringConverter() assert_equal(converter._status, 0) # test int assert_equal(converter.upgrade('0'), 0) assert_equal(converter._status, 1) # On systems where long defaults to 32-bit, the statuses will be # offset by one, so we check for this here. import numpy.core.numeric as nx status_offset = int(nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize) # test int > 232 assert_equal(converter.upgrade('17179869184'), 17179869184) assert_equal(converter._status, 1 + status_offset) # test float assert_allclose(converter.upgrade('0.'), 0.0) assert_equal(converter._status, 2 + status_offset) # test complex assert_equal(converter.upgrade('0j'), complex('0j')) assert_equal(converter._status, 3 + status_offset) # test str # note that the longdouble type has been skipped, so the # _status increases by 2. Everything should succeed with # unicode conversion (8). for s in ['a', b'a']: res = converter.upgrade(s) assert_(type(res) is str) assert_equal(res, 'a') assert_equal(converter._status, 8 + status_offset) def test_missing(self): "Tests the use of missing values." converter = StringConverter(missing_values=('missing', 'missed')) converter.upgrade('0') assert_equal(converter('0'), 0) assert_equal(converter(''), converter.default) assert_equal(converter('missing'), converter.default) assert_equal(converter('missed'), converter.default) try: converter('miss') except ValueError: pass def test_upgrademapper(self): "Tests updatemapper" dateparser = _bytes_to_date _original_mapper = StringConverter._mapper[:] try: StringConverter.upgrade_mapper(dateparser, date(2000, 1, 1)) convert = StringConverter(dateparser, date(2000, 1, 1)) test = convert('2001-01-01') assert_equal(test, date(2001, 1, 1)) test = convert('2009-01-01') assert_equal(test, date(2009, 1, 1)) test = convert('') assert_equal(test, date(2000, 1, 1)) finally: StringConverter._mapper = _original_mapper def test_string_to_object(self): "Make sure that string-to-object functions are properly recognized" old_mapper = StringConverter._mapper[:] # copy of list conv = StringConverter(_bytes_to_date) assert_equal(conv._mapper, old_mapper) assert_(hasattr(conv, 'default')) def test_keep_default(self): "Make sure we don't lose an explicit default" converter = StringConverter(None, missing_values='', default=-999) converter.upgrade('3.14159265') assert_equal(converter.default, -999) assert_equal(converter.type, np.dtype(float)) # converter = StringConverter( None, missing_values='', default=0) converter.upgrade('3.14159265') assert_equal(converter.default, 0) assert_equal(converter.type, np.dtype(float)) def test_keep_default_zero(self): "Check that we don't lose a default of 0" converter = StringConverter(int, default=0, missing_values="N/A") assert_equal(converter.default, 0) def test_keep_missing_values(self): "Check that we're not losing missing values" converter = StringConverter(int, default=0, missing_values="N/A") assert_equal( converter.missing_values, {'', 'N/A'}) def test_int64_dtype(self): "Check that int64 integer types can be specified" converter = StringConverter(np.int64, default=0) val = "-9223372036854775807" assert_(converter(val) == -9223372036854775807) val = "9223372036854775807" assert_(converter(val) == 9223372036854775807) def test_uint64_dtype(self): "Check that uint64 integer types can be specified" converter = StringConverter(np.uint64, default=0) val = "9223372043271415339" assert_(converter(val) == 9223372043271415339) class TestMiscFunctions: def test_has_nested_dtype(self): "Test has_nested_dtype" ndtype = np.dtype(float) assert_equal(has_nested_fields(ndtype), False) ndtype = np.dtype([('A', '\|S3'), ('B', float)]) assert_equal(has_nested_fields(ndtype), False) ndtype = np.dtype([('A', int), ('B', [('BA', float), ('BB', '\|S1')])]) assert_equal(has_nested_fields(ndtype), True) def test_easy_dtype(self): "Test ndtype on dtypes" # Simple case ndtype = float assert_equal(easy_dtype(ndtype), np.dtype(float)) # As string w/o names ndtype = "i4, f8" assert_equal(easy_dtype(ndtype), np.dtype([('f0', "i4"), ('f1', "f8")])) # As string w/o names but different default format assert_equal(easy_dtype(ndtype, defaultfmt="field_%03i"), np.dtype([('field_000', "i4"), ('field_001', "f8")])) # As string w/ names ndtype = "i4, f8" assert_equal(easy_dtype(ndtype, names="a, b"), np.dtype([('a', "i4"), ('b', "f8")])) # As string w/ names (too many) ndtype = "i4, f8" assert_equal(easy_dtype(ndtype, names="a, b, c"), np.dtype([('a', "i4"), ('b', "f8")])) # As string w/ names (not enough) ndtype = "i4, f8" assert_equal(easy_dtype(ndtype, names=", b"), np.dtype([('f0', "i4"), ('b', "f8")])) # ... (with different default format) assert_equal(easy_dtype(ndtype, names="a", defaultfmt="f%02i"), np.dtype([('a', "i4"), ('f00', "f8")])) # As list of tuples w/o names ndtype = [('A', int), ('B', float)] assert_equal(easy_dtype(ndtype), np.dtype([('A', int), ('B', float)])) # As list of tuples w/ names assert_equal(easy_dtype(ndtype, names="a,b"), np.dtype([('a', int), ('b', float)])) # As list of tuples w/ not enough names assert_equal(easy_dtype(ndtype, names="a"), np.dtype([('a', int), ('f0', float)])) # As list of tuples w/ too many names assert_equal(easy_dtype(ndtype, names="a,b,c"), np.dtype([('a', int), ('b', float)])) # As list of types w/o names ndtype = (int, float, float) assert_equal(easy_dtype(ndtype), np.dtype([('f0', int), ('f1', float), ('f2', float)])) # As list of types w names ndtype = (int, float, float) assert_equal(easy_dtype(ndtype, names="a, b, c"), np.dtype([('a', int), ('b', float), ('c', float)])) # As simple dtype w/ names ndtype = np.dtype(float) assert_equal(easy_dtype(ndtype, names="a, b, c"), np.dtype([(_, float) for _ in ('a', 'b', 'c')])) # As simple dtype w/o names (but multiple fields) ndtype = np.dtype(float) assert_equal( easy_dtype(ndtype, names=['', '', ''], defaultfmt="f%02i"), np.dtype([(_, float) for _ in ('f00', 'f01', 'f02')])) def test_flatten_dtype(self): "Testing flatten_dtype" # Standard dtype dt = np.dtype([("a", "f8"), ("b", "f8")]) dt_flat = flatten_dtype(dt) assert_equal(dt_flat, [float, float]) # Recursive dtype dt = np.dtype([("a", [("aa", '\|S1'), ("ab", '\|S2')]), ("b", int)]) dt_flat = flatten_dtype(dt) assert_equal(dt_flat, [np.dtype('\|S1'), np.dtype('\|S2'), int]) # dtype with shaped fields dt = np.dtype([("a", (float, 2)), ("b", (int, 3))]) dt_flat = flatten_dtype(dt) assert_equal(dt_flat, [float, int]) dt_flat = flatten_dtype(dt, True) assert_equal(dt_flat, [float] 2 + [int] * 3) # dtype w/ titles dt = np.dtype([(("a", "A"), "f8"), (("b", "B"), "f8")]) dt_flat = flatten_dtype(dt) assert_equal(dt_flat, [float, float])	13.42 KB	2026-01-20 12:58
📄 test__version.py """Tests for the NumpyVersion class. """ from numpy.testing import assert_, assert_raises from numpy.lib import NumpyVersion def test_main_versions(): assert_(NumpyVersion('1.8.0') == '1.8.0') for ver in ['1.9.0', '2.0.0', '1.8.1', '10.0.1']: assert_(NumpyVersion('1.8.0') < ver) for ver in ['1.7.0', '1.7.1', '0.9.9']: assert_(NumpyVersion('1.8.0') > ver) def test_version_1_point_10(): # regression test for gh-2998. assert_(NumpyVersion('1.9.0') < '1.10.0') assert_(NumpyVersion('1.11.0') < '1.11.1') assert_(NumpyVersion('1.11.0') == '1.11.0') assert_(NumpyVersion('1.99.11') < '1.99.12') def test_alpha_beta_rc(): assert_(NumpyVersion('1.8.0rc1') == '1.8.0rc1') for ver in ['1.8.0', '1.8.0rc2']: assert_(NumpyVersion('1.8.0rc1') < ver) for ver in ['1.8.0a2', '1.8.0b3', '1.7.2rc4']: assert_(NumpyVersion('1.8.0rc1') > ver) assert_(NumpyVersion('1.8.0b1') > '1.8.0a2') def test_dev_version(): assert_(NumpyVersion('1.9.0.dev-Unknown') < '1.9.0') for ver in ['1.9.0', '1.9.0a1', '1.9.0b2', '1.9.0b2.dev-ffffffff']: assert_(NumpyVersion('1.9.0.dev-f16acvda') < ver) assert_(NumpyVersion('1.9.0.dev-f16acvda') == '1.9.0.dev-11111111') def test_dev_a_b_rc_mixed(): assert_(NumpyVersion('1.9.0a2.dev-f16acvda') == '1.9.0a2.dev-11111111') assert_(NumpyVersion('1.9.0a2.dev-6acvda54') < '1.9.0a2') def test_dev0_version(): assert_(NumpyVersion('1.9.0.dev0+Unknown') < '1.9.0') for ver in ['1.9.0', '1.9.0a1', '1.9.0b2', '1.9.0b2.dev0+ffffffff']: assert_(NumpyVersion('1.9.0.dev0+f16acvda') < ver) assert_(NumpyVersion('1.9.0.dev0+f16acvda') == '1.9.0.dev0+11111111') def test_dev0_a_b_rc_mixed(): assert_(NumpyVersion('1.9.0a2.dev0+f16acvda') == '1.9.0a2.dev0+11111111') assert_(NumpyVersion('1.9.0a2.dev0+6acvda54') < '1.9.0a2') def test_raises(): for ver in ['1.9', '1,9.0', '1.7.x']: assert_raises(ValueError, NumpyVersion, ver)	1.95 KB	2026-01-20 12:58
📄 test_arraypad.py """Tests for the array padding functions. """ import pytest import numpy as np from numpy.testing import assert_array_equal, assert_allclose, assert_equal from numpy.lib.arraypad import _as_pairs _numeric_dtypes = ( np.sctypes["uint"] + np.sctypes["int"] + np.sctypes["float"] + np.sctypes["complex"] ) _all_modes = { 'constant': {'constant_values': 0}, 'edge': {}, 'linear_ramp': {'end_values': 0}, 'maximum': {'stat_length': None}, 'mean': {'stat_length': None}, 'median': {'stat_length': None}, 'minimum': {'stat_length': None}, 'reflect': {'reflect_type': 'even'}, 'symmetric': {'reflect_type': 'even'}, 'wrap': {}, 'empty': {} } class TestAsPairs: def test_single_value(self): """Test casting for a single value.""" expected = np.array([[3, 3]] * 10) for x in (3, [3], [[3]]): result = _as_pairs(x, 10) assert_equal(result, expected) # Test with dtype=object obj = object() assert_equal( _as_pairs(obj, 10), np.array([[obj, obj]] * 10) ) def test_two_values(self): """Test proper casting for two different values.""" # Broadcasting in the first dimension with numbers expected = np.array([[3, 4]] * 10) for x in ([3, 4], [[3, 4]]): result = _as_pairs(x, 10) assert_equal(result, expected) # and with dtype=object obj = object() assert_equal( _as_pairs(["a", obj], 10), np.array([["a", obj]] * 10) ) # Broadcasting in the second / last dimension with numbers assert_equal( _as_pairs([[3], [4]], 2), np.array([[3, 3], [4, 4]]) ) # and with dtype=object assert_equal( _as_pairs([["a"], [obj]], 2), np.array([["a", "a"], [obj, obj]]) ) def test_with_none(self): expected = ((None, None), (None, None), (None, None)) assert_equal( _as_pairs(None, 3, as_index=False), expected ) assert_equal( _as_pairs(None, 3, as_index=True), expected ) def test_pass_through(self): """Test if `x` already matching desired output are passed through.""" expected = np.arange(12).reshape((6, 2)) assert_equal( _as_pairs(expected, 6), expected ) def test_as_index(self): """Test results if `as_index=True`.""" assert_equal( _as_pairs([2.6, 3.3], 10, as_index=True), np.array([[3, 3]] * 10, dtype=np.intp) ) assert_equal( _as_pairs([2.6, 4.49], 10, as_index=True), np.array([[3, 4]] * 10, dtype=np.intp) ) for x in (-3, [-3], [[-3]], [-3, 4], [3, -4], [[-3, 4]], [[4, -3]], [[1, 2]] * 9 + [[1, -2]]): with pytest.raises(ValueError, match="negative values"): _as_pairs(x, 10, as_index=True) def test_exceptions(self): """Ensure faulty usage is discovered.""" with pytest.raises(ValueError, match="more dimensions than allowed"): _as_pairs([[[3]]], 10) with pytest.raises(ValueError, match="could not be broadcast"): _as_pairs([[1, 2], [3, 4]], 3) with pytest.raises(ValueError, match="could not be broadcast"): _as_pairs(np.ones((2, 3)), 3) class TestConditionalShortcuts: @pytest.mark.parametrize("mode", _all_modes.keys()) def test_zero_padding_shortcuts(self, mode): test = np.arange(120).reshape(4, 5, 6) pad_amt = [(0, 0) for _ in test.shape] assert_array_equal(test, np.pad(test, pad_amt, mode=mode)) @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',]) def test_shallow_statistic_range(self, mode): test = np.arange(120).reshape(4, 5, 6) pad_amt = [(1, 1) for _ in test.shape] assert_array_equal(np.pad(test, pad_amt, mode='edge'), np.pad(test, pad_amt, mode=mode, stat_length=1)) @pytest.mark.parametrize("mode", ['maximum', 'mean', 'median', 'minimum',]) def test_clip_statistic_range(self, mode): test = np.arange(30).reshape(5, 6) pad_amt = [(3, 3) for _ in test.shape] assert_array_equal(np.pad(test, pad_amt, mode=mode), np.pad(test, pad_amt, mode=mode, stat_length=30)) class TestStatistic: def test_check_mean_stat_length(self): a = np.arange(100).astype('f') a = np.pad(a, ((25, 20), ), 'mean', stat_length=((2, 3), )) b = np.array( [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98., 98. ]) assert_array_equal(a, b) def test_check_maximum_1(self): a = np.arange(100) a = np.pad(a, (25, 20), 'maximum') b = np.array( [99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99] ) assert_array_equal(a, b) def test_check_maximum_2(self): a = np.arange(100) + 1 a = np.pad(a, (25, 20), 'maximum') b = np.array( [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] ) assert_array_equal(a, b) def test_check_maximum_stat_length(self): a = np.arange(100) + 1 a = np.pad(a, (25, 20), 'maximum', stat_length=10) b = np.array( [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] ) assert_array_equal(a, b) def test_check_minimum_1(self): a = np.arange(100) a = np.pad(a, (25, 20), 'minimum') b = np.array( [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ) assert_array_equal(a, b) def test_check_minimum_2(self): a = np.arange(100) + 2 a = np.pad(a, (25, 20), 'minimum') b = np.array( [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] ) assert_array_equal(a, b) def test_check_minimum_stat_length(self): a = np.arange(100) + 1 a = np.pad(a, (25, 20), 'minimum', stat_length=10) b = np.array( [ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91] ) assert_array_equal(a, b) def test_check_median(self): a = np.arange(100).astype('f') a = np.pad(a, (25, 20), 'median') b = np.array( [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5] ) assert_array_equal(a, b) def test_check_median_01(self): a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]]) a = np.pad(a, 1, 'median') b = np.array( [[4, 4, 5, 4, 4], [3, 3, 1, 4, 3], [5, 4, 5, 9, 5], [8, 9, 8, 2, 8], [4, 4, 5, 4, 4]] ) assert_array_equal(a, b) def test_check_median_02(self): a = np.array([[3, 1, 4], [4, 5, 9], [9, 8, 2]]) a = np.pad(a.T, 1, 'median').T b = np.array( [[5, 4, 5, 4, 5], [3, 3, 1, 4, 3], [5, 4, 5, 9, 5], [8, 9, 8, 2, 8], [5, 4, 5, 4, 5]] ) assert_array_equal(a, b) def test_check_median_stat_length(self): a = np.arange(100).astype('f') a[1] = 2. a[97] = 96. a = np.pad(a, (25, 20), 'median', stat_length=(3, 5)) b = np.array( [ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 0., 2., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 96., 98., 99., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96., 96.] ) assert_array_equal(a, b) def test_check_mean_shape_one(self): a = [[4, 5, 6]] a = np.pad(a, (5, 7), 'mean', stat_length=2) b = np.array( [[4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6], [4, 4, 4, 4, 4, 4, 5, 6, 6, 6, 6, 6, 6, 6, 6]] ) assert_array_equal(a, b) def test_check_mean_2(self): a = np.arange(100).astype('f') a = np.pad(a, (25, 20), 'mean') b = np.array( [49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98., 99., 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5, 49.5] ) assert_array_equal(a, b) @pytest.mark.parametrize("mode", [ "mean", "median", "minimum", "maximum" ]) def test_same_prepend_append(self, mode): """ Test that appended and prepended values are equal """ # This test is constructed to trigger floating point rounding errors in # a way that caused gh-11216 for mode=='mean' a = np.array([-1, 2, -1]) + np.array([0, 1e-12, 0], dtype=np.float64) a = np.pad(a, (1, 1), mode) assert_equal(a[0], a[-1]) @pytest.mark.parametrize("mode", ["mean", "median", "minimum", "maximum"]) @pytest.mark.parametrize( "stat_length", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))] ) def test_check_negative_stat_length(self, mode, stat_length): arr = np.arange(30).reshape((6, 5)) match = "index can't contain negative values" with pytest.raises(ValueError, match=match): np.pad(arr, 2, mode, stat_length=stat_length) def test_simple_stat_length(self): a = np.arange(30) a = np.reshape(a, (6, 5)) a = np.pad(a, ((2, 3), (3, 2)), mode='mean', stat_length=(3,)) b = np.array( [[6, 6, 6, 5, 6, 7, 8, 9, 8, 8], [6, 6, 6, 5, 6, 7, 8, 9, 8, 8], [1, 1, 1, 0, 1, 2, 3, 4, 3, 3], [6, 6, 6, 5, 6, 7, 8, 9, 8, 8], [11, 11, 11, 10, 11, 12, 13, 14, 13, 13], [16, 16, 16, 15, 16, 17, 18, 19, 18, 18], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23], [26, 26, 26, 25, 26, 27, 28, 29, 28, 28], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23], [21, 21, 21, 20, 21, 22, 23, 24, 23, 23]] ) assert_array_equal(a, b) @pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning") @pytest.mark.filterwarnings( "ignore:invalid value encountered in( scalar)? divide:RuntimeWarning" ) @pytest.mark.parametrize("mode", ["mean", "median"]) def test_zero_stat_length_valid(self, mode): arr = np.pad([1., 2.], (1, 2), mode, stat_length=0) expected = np.array([np.nan, 1., 2., np.nan, np.nan]) assert_equal(arr, expected) @pytest.mark.parametrize("mode", ["minimum", "maximum"]) def test_zero_stat_length_invalid(self, mode): match = "stat_length of 0 yields no value for padding" with pytest.raises(ValueError, match=match): np.pad([1., 2.], 0, mode, stat_length=0) with pytest.raises(ValueError, match=match): np.pad([1., 2.], 0, mode, stat_length=(1, 0)) with pytest.raises(ValueError, match=match): np.pad([1., 2.], 1, mode, stat_length=0) with pytest.raises(ValueError, match=match): np.pad([1., 2.], 1, mode, stat_length=(1, 0)) class TestConstant: def test_check_constant(self): a = np.arange(100) a = np.pad(a, (25, 20), 'constant', constant_values=(10, 20)) b = np.array( [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20] ) assert_array_equal(a, b) def test_check_constant_zeros(self): a = np.arange(100) a = np.pad(a, (25, 20), 'constant') b = np.array( [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ) assert_array_equal(a, b) def test_check_constant_float(self): # If input array is int, but constant_values are float, the dtype of # the array to be padded is kept arr = np.arange(30).reshape(5, 6) test = np.pad(arr, (1, 2), mode='constant', constant_values=1.1) expected = np.array( [[ 1, 1, 1, 1, 1, 1, 1, 1, 1], [ 1, 0, 1, 2, 3, 4, 5, 1, 1], [ 1, 6, 7, 8, 9, 10, 11, 1, 1], [ 1, 12, 13, 14, 15, 16, 17, 1, 1], [ 1, 18, 19, 20, 21, 22, 23, 1, 1], [ 1, 24, 25, 26, 27, 28, 29, 1, 1], [ 1, 1, 1, 1, 1, 1, 1, 1, 1], [ 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) assert_allclose(test, expected) def test_check_constant_float2(self): # If input array is float, and constant_values are float, the dtype of # the array to be padded is kept - here retaining the float constants arr = np.arange(30).reshape(5, 6) arr_float = arr.astype(np.float64) test = np.pad(arr_float, ((1, 2), (1, 2)), mode='constant', constant_values=1.1) expected = np.array( [[ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1], [ 1.1, 0. , 1. , 2. , 3. , 4. , 5. , 1.1, 1.1], [ 1.1, 6. , 7. , 8. , 9. , 10. , 11. , 1.1, 1.1], [ 1.1, 12. , 13. , 14. , 15. , 16. , 17. , 1.1, 1.1], [ 1.1, 18. , 19. , 20. , 21. , 22. , 23. , 1.1, 1.1], [ 1.1, 24. , 25. , 26. , 27. , 28. , 29. , 1.1, 1.1], [ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1], [ 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]] ) assert_allclose(test, expected) def test_check_constant_float3(self): a = np.arange(100, dtype=float) a = np.pad(a, (25, 20), 'constant', constant_values=(-1.1, -1.2)) b = np.array( [-1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, -1.1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2, -1.2] ) assert_allclose(a, b) def test_check_constant_odd_pad_amount(self): arr = np.arange(30).reshape(5, 6) test = np.pad(arr, ((1,), (2,)), mode='constant', constant_values=3) expected = np.array( [[ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [ 3, 3, 0, 1, 2, 3, 4, 5, 3, 3], [ 3, 3, 6, 7, 8, 9, 10, 11, 3, 3], [ 3, 3, 12, 13, 14, 15, 16, 17, 3, 3], [ 3, 3, 18, 19, 20, 21, 22, 23, 3, 3], [ 3, 3, 24, 25, 26, 27, 28, 29, 3, 3], [ 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]] ) assert_allclose(test, expected) def test_check_constant_pad_2d(self): arr = np.arange(4).reshape(2, 2) test = np.lib.pad(arr, ((1, 2), (1, 3)), mode='constant', constant_values=((1, 2), (3, 4))) expected = np.array( [[3, 1, 1, 4, 4, 4], [3, 0, 1, 4, 4, 4], [3, 2, 3, 4, 4, 4], [3, 2, 2, 4, 4, 4], [3, 2, 2, 4, 4, 4]] ) assert_allclose(test, expected) def test_check_large_integers(self): uint64_max = 2 64 - 1 arr = np.full(5, uint64_max, dtype=np.uint64) test = np.pad(arr, 1, mode="constant", constant_values=arr.min()) expected = np.full(7, uint64_max, dtype=np.uint64) assert_array_equal(test, expected) int64_max = 2 63 - 1 arr = np.full(5, int64_max, dtype=np.int64) test = np.pad(arr, 1, mode="constant", constant_values=arr.min()) expected = np.full(7, int64_max, dtype=np.int64) assert_array_equal(test, expected) def test_check_object_array(self): arr = np.empty(1, dtype=object) obj_a = object() arr[0] = obj_a obj_b = object() obj_c = object() arr = np.pad(arr, pad_width=1, mode='constant', constant_values=(obj_b, obj_c)) expected = np.empty((3,), dtype=object) expected[0] = obj_b expected[1] = obj_a expected[2] = obj_c assert_array_equal(arr, expected) def test_pad_empty_dimension(self): arr = np.zeros((3, 0, 2)) result = np.pad(arr, [(0,), (2,), (1,)], mode="constant") assert result.shape == (3, 4, 4) class TestLinearRamp: def test_check_simple(self): a = np.arange(100).astype('f') a = np.pad(a, (25, 20), 'linear_ramp', end_values=(4, 5)) b = np.array( [4.00, 3.84, 3.68, 3.52, 3.36, 3.20, 3.04, 2.88, 2.72, 2.56, 2.40, 2.24, 2.08, 1.92, 1.76, 1.60, 1.44, 1.28, 1.12, 0.96, 0.80, 0.64, 0.48, 0.32, 0.16, 0.00, 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 94.3, 89.6, 84.9, 80.2, 75.5, 70.8, 66.1, 61.4, 56.7, 52.0, 47.3, 42.6, 37.9, 33.2, 28.5, 23.8, 19.1, 14.4, 9.7, 5.] ) assert_allclose(a, b, rtol=1e-5, atol=1e-5) def test_check_2d(self): arr = np.arange(20).reshape(4, 5).astype(np.float64) test = np.pad(arr, (2, 2), mode='linear_ramp', end_values=(0, 0)) expected = np.array( [[0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0.5, 1., 1.5, 2., 1., 0.], [0., 0., 0., 1., 2., 3., 4., 2., 0.], [0., 2.5, 5., 6., 7., 8., 9., 4.5, 0.], [0., 5., 10., 11., 12., 13., 14., 7., 0.], [0., 7.5, 15., 16., 17., 18., 19., 9.5, 0.], [0., 3.75, 7.5, 8., 8.5, 9., 9.5, 4.75, 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0.]]) assert_allclose(test, expected) @pytest.mark.xfail(exceptions=(AssertionError,)) def test_object_array(self): from fractions import Fraction arr = np.array([Fraction(1, 2), Fraction(-1, 2)]) actual = np.pad(arr, (2, 3), mode='linear_ramp', end_values=0) # deliberately chosen to have a non-power-of-2 denominator such that # rounding to floats causes a failure. expected = np.array([ Fraction( 0, 12), Fraction( 3, 12), Fraction( 6, 12), Fraction(-6, 12), Fraction(-4, 12), Fraction(-2, 12), Fraction(-0, 12), ]) assert_equal(actual, expected) def test_end_values(self): """Ensure that end values are exact.""" a = np.pad(np.ones(10).reshape(2, 5), (223, 123), mode="linear_ramp") assert_equal(a[:, 0], 0.) assert_equal(a[:, -1], 0.) assert_equal(a[0, :], 0.) assert_equal(a[-1, :], 0.) @pytest.mark.parametrize("dtype", _numeric_dtypes) def test_negative_difference(self, dtype): """ Check correct behavior of unsigned dtypes if there is a negative difference between the edge to pad and `end_values`. Check both cases to be independent of implementation. Test behavior for all other dtypes in case dtype casting interferes with complex dtypes. See gh-14191. """ x = np.array([3], dtype=dtype) result = np.pad(x, 3, mode="linear_ramp", end_values=0) expected = np.array([0, 1, 2, 3, 2, 1, 0], dtype=dtype) assert_equal(result, expected) x = np.array([0], dtype=dtype) result = np.pad(x, 3, mode="linear_ramp", end_values=3) expected = np.array([3, 2, 1, 0, 1, 2, 3], dtype=dtype) assert_equal(result, expected) class TestReflect: def test_check_simple(self): a = np.arange(100) a = np.pad(a, (25, 20), 'reflect') b = np.array( [25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79] ) assert_array_equal(a, b) def test_check_odd_method(self): a = np.arange(100) a = np.pad(a, (25, 20), 'reflect', reflect_type='odd') b = np.array( [-25, -24, -23, -22, -21, -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119] ) assert_array_equal(a, b) def test_check_large_pad(self): a = [[4, 5, 6], [6, 7, 8]] a = np.pad(a, (5, 7), 'reflect') b = np.array( [[7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7, 8, 7, 6, 7], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]] ) assert_array_equal(a, b) def test_check_shape(self): a = [[4, 5, 6]] a = np.pad(a, (5, 7), 'reflect') b = np.array( [[5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5], [5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5, 6, 5, 4, 5]] ) assert_array_equal(a, b) def test_check_01(self): a = np.pad([1, 2, 3], 2, 'reflect') b = np.array([3, 2, 1, 2, 3, 2, 1]) assert_array_equal(a, b) def test_check_02(self): a = np.pad([1, 2, 3], 3, 'reflect') b = np.array([2, 3, 2, 1, 2, 3, 2, 1, 2]) assert_array_equal(a, b) def test_check_03(self): a = np.pad([1, 2, 3], 4, 'reflect') b = np.array([1, 2, 3, 2, 1, 2, 3, 2, 1, 2, 3]) assert_array_equal(a, b) class TestEmptyArray: """Check how padding behaves on arrays with an empty dimension.""" @pytest.mark.parametrize( # Keep parametrization ordered, otherwise pytest-xdist might believe # that different tests were collected during parallelization "mode", sorted(_all_modes.keys() - {"constant", "empty"}) ) def test_pad_empty_dimension(self, mode): match = ("can't extend empty axis 0 using modes other than 'constant' " "or 'empty'") with pytest.raises(ValueError, match=match): np.pad([], 4, mode=mode) with pytest.raises(ValueError, match=match): np.pad(np.ndarray(0), 4, mode=mode) with pytest.raises(ValueError, match=match): np.pad(np.zeros((0, 3)), ((1,), (0,)), mode=mode) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_pad_non_empty_dimension(self, mode): result = np.pad(np.ones((2, 0, 2)), ((3,), (0,), (1,)), mode=mode) assert result.shape == (8, 0, 4) class TestSymmetric: def test_check_simple(self): a = np.arange(100) a = np.pad(a, (25, 20), 'symmetric') b = np.array( [24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80] ) assert_array_equal(a, b) def test_check_odd_method(self): a = np.arange(100) a = np.pad(a, (25, 20), 'symmetric', reflect_type='odd') b = np.array( [-24, -23, -22, -21, -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118] ) assert_array_equal(a, b) def test_check_large_pad(self): a = [[4, 5, 6], [6, 7, 8]] a = np.pad(a, (5, 7), 'symmetric') b = np.array( [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [7, 8, 8, 7, 6, 6, 7, 8, 8, 7, 6, 6, 7, 8, 8], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]] ) assert_array_equal(a, b) def test_check_large_pad_odd(self): a = [[4, 5, 6], [6, 7, 8]] a = np.pad(a, (5, 7), 'symmetric', reflect_type='odd') b = np.array( [[-3, -2, -2, -1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6], [-3, -2, -2, -1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6], [-1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8], [-1, 0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8], [ 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10], [ 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10], [ 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12], [ 3, 4, 4, 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12], [ 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14], [ 5, 6, 6, 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14], [ 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16], [ 7, 8, 8, 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16], [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18], [ 9, 10, 10, 11, 12, 12, 13, 14, 14, 15, 16, 16, 17, 18, 18]] ) assert_array_equal(a, b) def test_check_shape(self): a = [[4, 5, 6]] a = np.pad(a, (5, 7), 'symmetric') b = np.array( [[5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6], [5, 6, 6, 5, 4, 4, 5, 6, 6, 5, 4, 4, 5, 6, 6]] ) assert_array_equal(a, b) def test_check_01(self): a = np.pad([1, 2, 3], 2, 'symmetric') b = np.array([2, 1, 1, 2, 3, 3, 2]) assert_array_equal(a, b) def test_check_02(self): a = np.pad([1, 2, 3], 3, 'symmetric') b = np.array([3, 2, 1, 1, 2, 3, 3, 2, 1]) assert_array_equal(a, b) def test_check_03(self): a = np.pad([1, 2, 3], 6, 'symmetric') b = np.array([1, 2, 3, 3, 2, 1, 1, 2, 3, 3, 2, 1, 1, 2, 3]) assert_array_equal(a, b) class TestWrap: def test_check_simple(self): a = np.arange(100) a = np.pad(a, (25, 20), 'wrap') b = np.array( [75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] ) assert_array_equal(a, b) def test_check_large_pad(self): a = np.arange(12) a = np.reshape(a, (3, 4)) a = np.pad(a, (10, 12), 'wrap') b = np.array( [[10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11], [2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], [6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7, 4, 5, 6, 7], [10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11, 8, 9, 10, 11]] ) assert_array_equal(a, b) def test_check_01(self): a = np.pad([1, 2, 3], 3, 'wrap') b = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3]) assert_array_equal(a, b) def test_check_02(self): a = np.pad([1, 2, 3], 4, 'wrap') b = np.array([3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1]) assert_array_equal(a, b) def test_pad_with_zero(self): a = np.ones((3, 5)) b = np.pad(a, (0, 5), mode="wrap") assert_array_equal(a, b[:-5, :-5]) def test_repeated_wrapping(self): """ Check wrapping on each side individually if the wrapped area is longer than the original array. """ a = np.arange(5) b = np.pad(a, (12, 0), mode="wrap") assert_array_equal(np.r_[a, a, a, a][3:], b) a = np.arange(5) b = np.pad(a, (0, 12), mode="wrap") assert_array_equal(np.r_[a, a, a, a][:-3], b) def test_repeated_wrapping_multiple_origin(self): """ Assert that 'wrap' pads only with multiples of the original area if the pad width is larger than the original array. """ a = np.arange(4).reshape(2, 2) a = np.pad(a, [(1, 3), (3, 1)], mode='wrap') b = np.array( [[3, 2, 3, 2, 3, 2], [1, 0, 1, 0, 1, 0], [3, 2, 3, 2, 3, 2], [1, 0, 1, 0, 1, 0], [3, 2, 3, 2, 3, 2], [1, 0, 1, 0, 1, 0]] ) assert_array_equal(a, b) class TestEdge: def test_check_simple(self): a = np.arange(12) a = np.reshape(a, (4, 3)) a = np.pad(a, ((2, 3), (3, 2)), 'edge') b = np.array( [[0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [3, 3, 3, 3, 4, 5, 5, 5], [6, 6, 6, 6, 7, 8, 8, 8], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11]] ) assert_array_equal(a, b) def test_check_width_shape_1_2(self): # Check a pad_width of the form ((1, 2),). # Regression test for issue gh-7808. a = np.array([1, 2, 3]) padded = np.pad(a, ((1, 2),), 'edge') expected = np.array([1, 1, 2, 3, 3, 3]) assert_array_equal(padded, expected) a = np.array([[1, 2, 3], [4, 5, 6]]) padded = np.pad(a, ((1, 2),), 'edge') expected = np.pad(a, ((1, 2), (1, 2)), 'edge') assert_array_equal(padded, expected) a = np.arange(24).reshape(2, 3, 4) padded = np.pad(a, ((1, 2),), 'edge') expected = np.pad(a, ((1, 2), (1, 2), (1, 2)), 'edge') assert_array_equal(padded, expected) class TestEmpty: def test_simple(self): arr = np.arange(24).reshape(4, 6) result = np.pad(arr, [(2, 3), (3, 1)], mode="empty") assert result.shape == (9, 10) assert_equal(arr, result[2:-3, 3:-1]) def test_pad_empty_dimension(self): arr = np.zeros((3, 0, 2)) result = np.pad(arr, [(0,), (2,), (1,)], mode="empty") assert result.shape == (3, 4, 4) def test_legacy_vector_functionality(): def _padwithtens(vector, pad_width, iaxis, kwargs): vector[:pad_width[0]] = 10 vector[-pad_width[1]:] = 10 a = np.arange(6).reshape(2, 3) a = np.pad(a, 2, _padwithtens) b = np.array( [[10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 0, 1, 2, 10, 10], [10, 10, 3, 4, 5, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10]] ) assert_array_equal(a, b) def test_unicode_mode(): a = np.pad([1], 2, mode='constant') b = np.array([0, 0, 1, 0, 0]) assert_array_equal(a, b) @pytest.mark.parametrize("mode", ["edge", "symmetric", "reflect", "wrap"]) def test_object_input(mode): # Regression test for issue gh-11395. a = np.full((4, 3), fill_value=None) pad_amt = ((2, 3), (3, 2)) b = np.full((9, 8), fill_value=None) assert_array_equal(np.pad(a, pad_amt, mode=mode), b) class TestPadWidth: @pytest.mark.parametrize("pad_width", [ (4, 5, 6, 7), ((1,), (2,), (3,)), ((1, 2), (3, 4), (5, 6)), ((3, 4, 5), (0, 1, 2)), ]) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_misshaped_pad_width(self, pad_width, mode): arr = np.arange(30).reshape((6, 5)) match = "operands could not be broadcast together" with pytest.raises(ValueError, match=match): np.pad(arr, pad_width, mode) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_misshaped_pad_width_2(self, mode): arr = np.arange(30).reshape((6, 5)) match = ("input operand has more dimensions than allowed by the axis " "remapping") with pytest.raises(ValueError, match=match): np.pad(arr, (((3,), (4,), (5,)), ((0,), (1,), (2,))), mode) @pytest.mark.parametrize( "pad_width", [-2, (-2,), (3, -1), ((5, 2), (-2, 3)), ((-4,), (2,))]) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_negative_pad_width(self, pad_width, mode): arr = np.arange(30).reshape((6, 5)) match = "index can't contain negative values" with pytest.raises(ValueError, match=match): np.pad(arr, pad_width, mode) @pytest.mark.parametrize("pad_width, dtype", [ ("3", None), ("word", None), (None, None), (object(), None), (3.4, None), (((2, 3, 4), (3, 2)), object), (complex(1, -1), None), (((-2.1, 3), (3, 2)), None), ]) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_bad_type(self, pad_width, dtype, mode): arr = np.arange(30).reshape((6, 5)) match = "`pad_width` must be of integral type." if dtype is not None: # avoid DeprecationWarning when not specifying dtype with pytest.raises(TypeError, match=match): np.pad(arr, np.array(pad_width, dtype=dtype), mode) else: with pytest.raises(TypeError, match=match): np.pad(arr, pad_width, mode) with pytest.raises(TypeError, match=match): np.pad(arr, np.array(pad_width), mode) def test_pad_width_as_ndarray(self): a = np.arange(12) a = np.reshape(a, (4, 3)) a = np.pad(a, np.array(((2, 3), (3, 2))), 'edge') b = np.array( [[0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [0, 0, 0, 0, 1, 2, 2, 2], [3, 3, 3, 3, 4, 5, 5, 5], [6, 6, 6, 6, 7, 8, 8, 8], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11], [9, 9, 9, 9, 10, 11, 11, 11]] ) assert_array_equal(a, b) @pytest.mark.parametrize("pad_width", [0, (0, 0), ((0, 0), (0, 0))]) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_zero_pad_width(self, pad_width, mode): arr = np.arange(30).reshape(6, 5) assert_array_equal(arr, np.pad(arr, pad_width, mode=mode)) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_kwargs(mode): """Test behavior of pad's kwargs for the given mode.""" allowed = _all_modes[mode] not_allowed = {} for kwargs in _all_modes.values(): if kwargs != allowed: not_allowed.update(kwargs) # Test if allowed keyword arguments pass np.pad([1, 2, 3], 1, mode, allowed) # Test if prohibited keyword arguments of other modes raise an error for key, value in not_allowed.items(): match = "unsupported keyword arguments for mode '{}'".format(mode) with pytest.raises(ValueError, match=match): np.pad([1, 2, 3], 1, mode, {key: value}) def test_constant_zero_default(): arr = np.array([1, 1]) assert_array_equal(np.pad(arr, 2), [0, 0, 1, 1, 0, 0]) @pytest.mark.parametrize("mode", [1, "const", object(), None, True, False]) def test_unsupported_mode(mode): match= "mode '{}' is not supported".format(mode) with pytest.raises(ValueError, match=match): np.pad([1, 2, 3], 4, mode=mode) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_non_contiguous_array(mode): arr = np.arange(24).reshape(4, 6)[::2, ::2] result = np.pad(arr, (2, 3), mode) assert result.shape == (7, 8) assert_equal(result[2:-3, 2:-3], arr) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_memory_layout_persistence(mode): """Test if C and F order is preserved for all pad modes.""" x = np.ones((5, 10), order='C') assert np.pad(x, 5, mode).flags["C_CONTIGUOUS"] x = np.ones((5, 10), order='F') assert np.pad(x, 5, mode).flags["F_CONTIGUOUS"] @pytest.mark.parametrize("dtype", _numeric_dtypes) @pytest.mark.parametrize("mode", _all_modes.keys()) def test_dtype_persistence(dtype, mode): arr = np.zeros((3, 2, 1), dtype=dtype) result = np.pad(arr, 1, mode=mode) assert result.dtype == dtype	53.54 KB	2026-01-20 12:58
📄 test_arraysetops.py """Test functions for 1D array set operations. """ import numpy as np from numpy.testing import (assert_array_equal, assert_equal, assert_raises, assert_raises_regex) from numpy.lib.arraysetops import ( ediff1d, intersect1d, setxor1d, union1d, setdiff1d, unique, in1d, isin ) import pytest class TestSetOps: def test_intersect1d(self): # unique inputs a = np.array([5, 7, 1, 2]) b = np.array([2, 4, 3, 1, 5]) ec = np.array([1, 2, 5]) c = intersect1d(a, b, assume_unique=True) assert_array_equal(c, ec) # non-unique inputs a = np.array([5, 5, 7, 1, 2]) b = np.array([2, 1, 4, 3, 3, 1, 5]) ed = np.array([1, 2, 5]) c = intersect1d(a, b) assert_array_equal(c, ed) assert_array_equal([], intersect1d([], [])) def test_intersect1d_array_like(self): # See gh-11772 class Test: def __array__(self): return np.arange(3) a = Test() res = intersect1d(a, a) assert_array_equal(res, a) res = intersect1d([1, 2, 3], [1, 2, 3]) assert_array_equal(res, [1, 2, 3]) def test_intersect1d_indices(self): # unique inputs a = np.array([1, 2, 3, 4]) b = np.array([2, 1, 4, 6]) c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True) ee = np.array([1, 2, 4]) assert_array_equal(c, ee) assert_array_equal(a[i1], ee) assert_array_equal(b[i2], ee) # non-unique inputs a = np.array([1, 2, 2, 3, 4, 3, 2]) b = np.array([1, 8, 4, 2, 2, 3, 2, 3]) c, i1, i2 = intersect1d(a, b, return_indices=True) ef = np.array([1, 2, 3, 4]) assert_array_equal(c, ef) assert_array_equal(a[i1], ef) assert_array_equal(b[i2], ef) # non1d, unique inputs a = np.array([[2, 4, 5, 6], [7, 8, 1, 15]]) b = np.array([[3, 2, 7, 6], [10, 12, 8, 9]]) c, i1, i2 = intersect1d(a, b, assume_unique=True, return_indices=True) ui1 = np.unravel_index(i1, a.shape) ui2 = np.unravel_index(i2, b.shape) ea = np.array([2, 6, 7, 8]) assert_array_equal(ea, a[ui1]) assert_array_equal(ea, b[ui2]) # non1d, not assumed to be uniqueinputs a = np.array([[2, 4, 5, 6, 6], [4, 7, 8, 7, 2]]) b = np.array([[3, 2, 7, 7], [10, 12, 8, 7]]) c, i1, i2 = intersect1d(a, b, return_indices=True) ui1 = np.unravel_index(i1, a.shape) ui2 = np.unravel_index(i2, b.shape) ea = np.array([2, 7, 8]) assert_array_equal(ea, a[ui1]) assert_array_equal(ea, b[ui2]) def test_setxor1d(self): a = np.array([5, 7, 1, 2]) b = np.array([2, 4, 3, 1, 5]) ec = np.array([3, 4, 7]) c = setxor1d(a, b) assert_array_equal(c, ec) a = np.array([1, 2, 3]) b = np.array([6, 5, 4]) ec = np.array([1, 2, 3, 4, 5, 6]) c = setxor1d(a, b) assert_array_equal(c, ec) a = np.array([1, 8, 2, 3]) b = np.array([6, 5, 4, 8]) ec = np.array([1, 2, 3, 4, 5, 6]) c = setxor1d(a, b) assert_array_equal(c, ec) assert_array_equal([], setxor1d([], [])) def test_ediff1d(self): zero_elem = np.array([]) one_elem = np.array([1]) two_elem = np.array([1, 2]) assert_array_equal([], ediff1d(zero_elem)) assert_array_equal([0], ediff1d(zero_elem, to_begin=0)) assert_array_equal([0], ediff1d(zero_elem, to_end=0)) assert_array_equal([-1, 0], ediff1d(zero_elem, to_begin=-1, to_end=0)) assert_array_equal([], ediff1d(one_elem)) assert_array_equal([1], ediff1d(two_elem)) assert_array_equal([7, 1, 9], ediff1d(two_elem, to_begin=7, to_end=9)) assert_array_equal([5, 6, 1, 7, 8], ediff1d(two_elem, to_begin=[5, 6], to_end=[7, 8])) assert_array_equal([1, 9], ediff1d(two_elem, to_end=9)) assert_array_equal([1, 7, 8], ediff1d(two_elem, to_end=[7, 8])) assert_array_equal([7, 1], ediff1d(two_elem, to_begin=7)) assert_array_equal([5, 6, 1], ediff1d(two_elem, to_begin=[5, 6])) @pytest.mark.parametrize("ary, prepend, append, expected", [ # should fail because trying to cast # np.nan standard floating point value # into an integer array: (np.array([1, 2, 3], dtype=np.int64), None, np.nan, 'to_end'), # should fail because attempting # to downcast to int type: (np.array([1, 2, 3], dtype=np.int64), np.array([5, 7, 2], dtype=np.float32), None, 'to_begin'), # should fail because attempting to cast # two special floating point values # to integers (on both sides of ary), # `to_begin` is in the error message as the impl checks this first: (np.array([1., 3., 9.], dtype=np.int8), np.nan, np.nan, 'to_begin'), ]) def test_ediff1d_forbidden_type_casts(self, ary, prepend, append, expected): # verify resolution of gh-11490 # specifically, raise an appropriate # Exception when attempting to append or # prepend with an incompatible type msg = 'dtype of `{}` must be compatible'.format(expected) with assert_raises_regex(TypeError, msg): ediff1d(ary=ary, to_end=append, to_begin=prepend) @pytest.mark.parametrize( "ary,prepend,append,expected", [ (np.array([1, 2, 3], dtype=np.int16), 216, # will be cast to int16 under same kind rule. 216 + 4, np.array([0, 1, 1, 4], dtype=np.int16)), (np.array([1, 2, 3], dtype=np.float32), np.array([5], dtype=np.float64), None, np.array([5, 1, 1], dtype=np.float32)), (np.array([1, 2, 3], dtype=np.int32), 0, 0, np.array([0, 1, 1, 0], dtype=np.int32)), (np.array([1, 2, 3], dtype=np.int64), 3, -9, np.array([3, 1, 1, -9], dtype=np.int64)), ] ) def test_ediff1d_scalar_handling(self, ary, prepend, append, expected): # maintain backwards-compatibility # of scalar prepend / append behavior # in ediff1d following fix for gh-11490 actual = np.ediff1d(ary=ary, to_end=append, to_begin=prepend) assert_equal(actual, expected) assert actual.dtype == expected.dtype @pytest.mark.parametrize("kind", [None, "sort", "table"]) def test_isin(self, kind): # the tests for in1d cover most of isin's behavior # if in1d is removed, would need to change those tests to test # isin instead. def _isin_slow(a, b): b = np.asarray(b).flatten().tolist() return a in b isin_slow = np.vectorize(_isin_slow, otypes=[bool], excluded={1}) def assert_isin_equal(a, b): x = isin(a, b, kind=kind) y = isin_slow(a, b) assert_array_equal(x, y) # multidimensional arrays in both arguments a = np.arange(24).reshape([2, 3, 4]) b = np.array([[10, 20, 30], [0, 1, 3], [11, 22, 33]]) assert_isin_equal(a, b) # array-likes as both arguments c = [(9, 8), (7, 6)] d = (9, 7) assert_isin_equal(c, d) # zero-d array: f = np.array(3) assert_isin_equal(f, b) assert_isin_equal(a, f) assert_isin_equal(f, f) # scalar: assert_isin_equal(5, b) assert_isin_equal(a, 6) assert_isin_equal(5, 6) # empty array-like: if kind != "table": # An empty list will become float64, # which is invalid for kind="table" x = [] assert_isin_equal(x, b) assert_isin_equal(a, x) assert_isin_equal(x, x) # empty array with various types: for dtype in [bool, np.int64, np.float64]: if kind == "table" and dtype == np.float64: continue if dtype in {np.int64, np.float64}: ar = np.array([10, 20, 30], dtype=dtype) elif dtype in {bool}: ar = np.array([True, False, False]) empty_array = np.array([], dtype=dtype) assert_isin_equal(empty_array, ar) assert_isin_equal(ar, empty_array) assert_isin_equal(empty_array, empty_array) @pytest.mark.parametrize("kind", [None, "sort", "table"]) def test_in1d(self, kind): # we use two different sizes for the b array here to test the # two different paths in in1d(). for mult in (1, 10): # One check without np.array to make sure lists are handled correct a = [5, 7, 1, 2] b = [2, 4, 3, 1, 5] * mult ec = np.array([True, False, True, True]) c = in1d(a, b, assume_unique=True, kind=kind) assert_array_equal(c, ec) a[0] = 8 ec = np.array([False, False, True, True]) c = in1d(a, b, assume_unique=True, kind=kind) assert_array_equal(c, ec) a[0], a[3] = 4, 8 ec = np.array([True, False, True, False]) c = in1d(a, b, assume_unique=True, kind=kind) assert_array_equal(c, ec) a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5]) b = [2, 3, 4] * mult ec = [False, True, False, True, True, True, True, True, True, False, True, False, False, False] c = in1d(a, b, kind=kind) assert_array_equal(c, ec) b = b + [5, 5, 4] * mult ec = [True, True, True, True, True, True, True, True, True, True, True, False, True, True] c = in1d(a, b, kind=kind) assert_array_equal(c, ec) a = np.array([5, 7, 1, 2]) b = np.array([2, 4, 3, 1, 5] * mult) ec = np.array([True, False, True, True]) c = in1d(a, b, kind=kind) assert_array_equal(c, ec) a = np.array([5, 7, 1, 1, 2]) b = np.array([2, 4, 3, 3, 1, 5] * mult) ec = np.array([True, False, True, True, True]) c = in1d(a, b, kind=kind) assert_array_equal(c, ec) a = np.array([5, 5]) b = np.array([2, 2] * mult) ec = np.array([False, False]) c = in1d(a, b, kind=kind) assert_array_equal(c, ec) a = np.array([5]) b = np.array([2]) ec = np.array([False]) c = in1d(a, b, kind=kind) assert_array_equal(c, ec) if kind in {None, "sort"}: assert_array_equal(in1d([], [], kind=kind), []) def test_in1d_char_array(self): a = np.array(['a', 'b', 'c', 'd', 'e', 'c', 'e', 'b']) b = np.array(['a', 'c']) ec = np.array([True, False, True, False, False, True, False, False]) c = in1d(a, b) assert_array_equal(c, ec) @pytest.mark.parametrize("kind", [None, "sort", "table"]) def test_in1d_invert(self, kind): "Test in1d's invert parameter" # We use two different sizes for the b array here to test the # two different paths in in1d(). for mult in (1, 10): a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5]) b = [2, 3, 4] * mult assert_array_equal(np.invert(in1d(a, b, kind=kind)), in1d(a, b, invert=True, kind=kind)) # float: if kind in {None, "sort"}: for mult in (1, 10): a = np.array([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5], dtype=np.float32) b = [2, 3, 4] * mult b = np.array(b, dtype=np.float32) assert_array_equal(np.invert(in1d(a, b, kind=kind)), in1d(a, b, invert=True, kind=kind)) @pytest.mark.parametrize("kind", [None, "sort", "table"]) def test_in1d_ravel(self, kind): # Test that in1d ravels its input arrays. This is not documented # behavior however. The test is to ensure consistentency. a = np.arange(6).reshape(2, 3) b = np.arange(3, 9).reshape(3, 2) long_b = np.arange(3, 63).reshape(30, 2) ec = np.array([False, False, False, True, True, True]) assert_array_equal(in1d(a, b, assume_unique=True, kind=kind), ec) assert_array_equal(in1d(a, b, assume_unique=False, kind=kind), ec) assert_array_equal(in1d(a, long_b, assume_unique=True, kind=kind), ec) assert_array_equal(in1d(a, long_b, assume_unique=False, kind=kind), ec) def test_in1d_hit_alternate_algorithm(self): """Hit the standard isin code with integers""" # Need extreme range to hit standard code # This hits it without the use of kind='table' a = np.array([5, 4, 5, 3, 4, 4, 1e9], dtype=np.int64) b = np.array([2, 3, 4, 1e9], dtype=np.int64) expected = np.array([0, 1, 0, 1, 1, 1, 1], dtype=bool) assert_array_equal(expected, in1d(a, b)) assert_array_equal(np.invert(expected), in1d(a, b, invert=True)) a = np.array([5, 7, 1, 2], dtype=np.int64) b = np.array([2, 4, 3, 1, 5, 1e9], dtype=np.int64) ec = np.array([True, False, True, True]) c = in1d(a, b, assume_unique=True) assert_array_equal(c, ec) @pytest.mark.parametrize("kind", [None, "sort", "table"]) def test_in1d_boolean(self, kind): """Test that in1d works for boolean input""" a = np.array([True, False]) b = np.array([False, False, False]) expected = np.array([False, True]) assert_array_equal(expected, in1d(a, b, kind=kind)) assert_array_equal(np.invert(expected), in1d(a, b, invert=True, kind=kind)) @pytest.mark.parametrize("kind", [None, "sort"]) def test_in1d_timedelta(self, kind): """Test that in1d works for timedelta input""" rstate = np.random.RandomState(0) a = rstate.randint(0, 100, size=10) b = rstate.randint(0, 100, size=10) truth = in1d(a, b) a_timedelta = a.astype("timedelta64[s]") b_timedelta = b.astype("timedelta64[s]") assert_array_equal(truth, in1d(a_timedelta, b_timedelta, kind=kind)) def test_in1d_table_timedelta_fails(self): a = np.array([0, 1, 2], dtype="timedelta64[s]") b = a # Make sure it raises a value error: with pytest.raises(ValueError): in1d(a, b, kind="table") @pytest.mark.parametrize( "dtype1,dtype2", [ (np.int8, np.int16), (np.int16, np.int8), (np.uint8, np.uint16), (np.uint16, np.uint8), (np.uint8, np.int16), (np.int16, np.uint8), ] ) @pytest.mark.parametrize("kind", [None, "sort", "table"]) def test_in1d_mixed_dtype(self, dtype1, dtype2, kind): """Test that in1d works as expected for mixed dtype input.""" is_dtype2_signed = np.issubdtype(dtype2, np.signedinteger) ar1 = np.array([0, 0, 1, 1], dtype=dtype1) if is_dtype2_signed: ar2 = np.array([-128, 0, 127], dtype=dtype2) else: ar2 = np.array([127, 0, 255], dtype=dtype2) expected = np.array([True, True, False, False]) expect_failure = kind == "table" and any(( dtype1 == np.int8 and dtype2 == np.int16, dtype1 == np.int16 and dtype2 == np.int8 )) if expect_failure: with pytest.raises(RuntimeError, match="exceed the maximum"): in1d(ar1, ar2, kind=kind) else: assert_array_equal(in1d(ar1, ar2, kind=kind), expected) @pytest.mark.parametrize("kind", [None, "sort", "table"]) def test_in1d_mixed_boolean(self, kind): """Test that in1d works as expected for bool/int input.""" for dtype in np.typecodes["AllInteger"]: a = np.array([True, False, False], dtype=bool) b = np.array([0, 0, 0, 0], dtype=dtype) expected = np.array([False, True, True], dtype=bool) assert_array_equal(in1d(a, b, kind=kind), expected) a, b = b, a expected = np.array([True, True, True, True], dtype=bool) assert_array_equal(in1d(a, b, kind=kind), expected) def test_in1d_first_array_is_object(self): ar1 = [None] ar2 = np.array([1]10) expected = np.array([False]) result = np.in1d(ar1, ar2) assert_array_equal(result, expected) def test_in1d_second_array_is_object(self): ar1 = 1 ar2 = np.array([None]10) expected = np.array([False]) result = np.in1d(ar1, ar2) assert_array_equal(result, expected) def test_in1d_both_arrays_are_object(self): ar1 = [None] ar2 = np.array([None]10) expected = np.array([True]) result = np.in1d(ar1, ar2) assert_array_equal(result, expected) def test_in1d_both_arrays_have_structured_dtype(self): # Test arrays of a structured data type containing an integer field # and a field of dtype `object` allowing for arbitrary Python objects dt = np.dtype([('field1', int), ('field2', object)]) ar1 = np.array([(1, None)], dtype=dt) ar2 = np.array([(1, None)]10, dtype=dt) expected = np.array([True]) result = np.in1d(ar1, ar2) assert_array_equal(result, expected) def test_in1d_with_arrays_containing_tuples(self): ar1 = np.array([(1,), 2], dtype=object) ar2 = np.array([(1,), 2], dtype=object) expected = np.array([True, True]) result = np.in1d(ar1, ar2) assert_array_equal(result, expected) result = np.in1d(ar1, ar2, invert=True) assert_array_equal(result, np.invert(expected)) # An integer is added at the end of the array to make sure # that the array builder will create the array with tuples # and after it's created the integer is removed. # There's a bug in the array constructor that doesn't handle # tuples properly and adding the integer fixes that. ar1 = np.array([(1,), (2, 1), 1], dtype=object) ar1 = ar1[:-1] ar2 = np.array([(1,), (2, 1), 1], dtype=object) ar2 = ar2[:-1] expected = np.array([True, True]) result = np.in1d(ar1, ar2) assert_array_equal(result, expected) result = np.in1d(ar1, ar2, invert=True) assert_array_equal(result, np.invert(expected)) ar1 = np.array([(1,), (2, 3), 1], dtype=object) ar1 = ar1[:-1] ar2 = np.array([(1,), 2], dtype=object) expected = np.array([True, False]) result = np.in1d(ar1, ar2) assert_array_equal(result, expected) result = np.in1d(ar1, ar2, invert=True) assert_array_equal(result, np.invert(expected)) def test_in1d_errors(self): """Test that in1d raises expected errors.""" # Error 1: `kind` is not one of 'sort' 'table' or None. ar1 = np.array([1, 2, 3, 4, 5]) ar2 = np.array([2, 4, 6, 8, 10]) assert_raises(ValueError, in1d, ar1, ar2, kind='quicksort') # Error 2: `kind="table"` does not work for non-integral arrays. obj_ar1 = np.array([1, 'a', 3, 'b', 5], dtype=object) obj_ar2 = np.array([1, 'a', 3, 'b', 5], dtype=object) assert_raises(ValueError, in1d, obj_ar1, obj_ar2, kind='table') for dtype in [np.int32, np.int64]: ar1 = np.array([-1, 2, 3, 4, 5], dtype=dtype) # The range of this array will overflow: overflow_ar2 = np.array([-1, np.iinfo(dtype).max], dtype=dtype) # Error 3: `kind="table"` will trigger a runtime error # if there is an integer overflow expected when computing the # range of ar2 assert_raises( RuntimeError, in1d, ar1, overflow_ar2, kind='table' ) # Non-error: `kind=None` will not trigger a runtime error # if there is an integer overflow, it will switch to # the `sort` algorithm. result = np.in1d(ar1, overflow_ar2, kind=None) assert_array_equal(result, [True] + [False] * 4) result = np.in1d(ar1, overflow_ar2, kind='sort') assert_array_equal(result, [True] + [False] * 4) def test_union1d(self): a = np.array([5, 4, 7, 1, 2]) b = np.array([2, 4, 3, 3, 2, 1, 5]) ec = np.array([1, 2, 3, 4, 5, 7]) c = union1d(a, b) assert_array_equal(c, ec) # Tests gh-10340, arguments to union1d should be # flattened if they are not already 1D x = np.array([[0, 1, 2], [3, 4, 5]]) y = np.array([0, 1, 2, 3, 4]) ez = np.array([0, 1, 2, 3, 4, 5]) z = union1d(x, y) assert_array_equal(z, ez) assert_array_equal([], union1d([], [])) def test_setdiff1d(self): a = np.array([6, 5, 4, 7, 1, 2, 7, 4]) b = np.array([2, 4, 3, 3, 2, 1, 5]) ec = np.array([6, 7]) c = setdiff1d(a, b) assert_array_equal(c, ec) a = np.arange(21) b = np.arange(19) ec = np.array([19, 20]) c = setdiff1d(a, b) assert_array_equal(c, ec) assert_array_equal([], setdiff1d([], [])) a = np.array((), np.uint32) assert_equal(setdiff1d(a, []).dtype, np.uint32) def test_setdiff1d_unique(self): a = np.array([3, 2, 1]) b = np.array([7, 5, 2]) expected = np.array([3, 1]) actual = setdiff1d(a, b, assume_unique=True) assert_equal(actual, expected) def test_setdiff1d_char_array(self): a = np.array(['a', 'b', 'c']) b = np.array(['a', 'b', 's']) assert_array_equal(setdiff1d(a, b), np.array(['c'])) def test_manyways(self): a = np.array([5, 7, 1, 2, 8]) b = np.array([9, 8, 2, 4, 3, 1, 5]) c1 = setxor1d(a, b) aux1 = intersect1d(a, b) aux2 = union1d(a, b) c2 = setdiff1d(aux2, aux1) assert_array_equal(c1, c2) class TestUnique: def test_unique_1d(self): def check_all(a, b, i1, i2, c, dt): base_msg = 'check {0} failed for type {1}' msg = base_msg.format('values', dt) v = unique(a) assert_array_equal(v, b, msg) msg = base_msg.format('return_index', dt) v, j = unique(a, True, False, False) assert_array_equal(v, b, msg) assert_array_equal(j, i1, msg) msg = base_msg.format('return_inverse', dt) v, j = unique(a, False, True, False) assert_array_equal(v, b, msg) assert_array_equal(j, i2, msg) msg = base_msg.format('return_counts', dt) v, j = unique(a, False, False, True) assert_array_equal(v, b, msg) assert_array_equal(j, c, msg) msg = base_msg.format('return_index and return_inverse', dt) v, j1, j2 = unique(a, True, True, False) assert_array_equal(v, b, msg) assert_array_equal(j1, i1, msg) assert_array_equal(j2, i2, msg) msg = base_msg.format('return_index and return_counts', dt) v, j1, j2 = unique(a, True, False, True) assert_array_equal(v, b, msg) assert_array_equal(j1, i1, msg) assert_array_equal(j2, c, msg) msg = base_msg.format('return_inverse and return_counts', dt) v, j1, j2 = unique(a, False, True, True) assert_array_equal(v, b, msg) assert_array_equal(j1, i2, msg) assert_array_equal(j2, c, msg) msg = base_msg.format(('return_index, return_inverse ' 'and return_counts'), dt) v, j1, j2, j3 = unique(a, True, True, True) assert_array_equal(v, b, msg) assert_array_equal(j1, i1, msg) assert_array_equal(j2, i2, msg) assert_array_equal(j3, c, msg) a = [5, 7, 1, 2, 1, 5, 7]10 b = [1, 2, 5, 7] i1 = [2, 3, 0, 1] i2 = [2, 3, 0, 1, 0, 2, 3]10 c = np.multiply([2, 1, 2, 2], 10) # test for numeric arrays types = [] types.extend(np.typecodes['AllInteger']) types.extend(np.typecodes['AllFloat']) types.append('datetime64[D]') types.append('timedelta64[D]') for dt in types: aa = np.array(a, dt) bb = np.array(b, dt) check_all(aa, bb, i1, i2, c, dt) # test for object arrays dt = 'O' aa = np.empty(len(a), dt) aa[:] = a bb = np.empty(len(b), dt) bb[:] = b check_all(aa, bb, i1, i2, c, dt) # test for structured arrays dt = [('', 'i'), ('', 'i')] aa = np.array(list(zip(a, a)), dt) bb = np.array(list(zip(b, b)), dt) check_all(aa, bb, i1, i2, c, dt) # test for ticket #2799 aa = [1. + 0.j, 1 - 1.j, 1] assert_array_equal(np.unique(aa), [1. - 1.j, 1. + 0.j]) # test for ticket #4785 a = [(1, 2), (1, 2), (2, 3)] unq = [1, 2, 3] inv = [0, 1, 0, 1, 1, 2] a1 = unique(a) assert_array_equal(a1, unq) a2, a2_inv = unique(a, return_inverse=True) assert_array_equal(a2, unq) assert_array_equal(a2_inv, inv) # test for chararrays with return_inverse (gh-5099) a = np.chararray(5) a[...] = '' a2, a2_inv = np.unique(a, return_inverse=True) assert_array_equal(a2_inv, np.zeros(5)) # test for ticket #9137 a = [] a1_idx = np.unique(a, return_index=True)[1] a2_inv = np.unique(a, return_inverse=True)[1] a3_idx, a3_inv = np.unique(a, return_index=True, return_inverse=True)[1:] assert_equal(a1_idx.dtype, np.intp) assert_equal(a2_inv.dtype, np.intp) assert_equal(a3_idx.dtype, np.intp) assert_equal(a3_inv.dtype, np.intp) # test for ticket 2111 - float a = [2.0, np.nan, 1.0, np.nan] ua = [1.0, 2.0, np.nan] ua_idx = [2, 0, 1] ua_inv = [1, 2, 0, 2] ua_cnt = [1, 1, 2] assert_equal(np.unique(a), ua) assert_equal(np.unique(a, return_index=True), (ua, ua_idx)) assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv)) assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt)) # test for ticket 2111 - complex a = [2.0-1j, np.nan, 1.0+1j, complex(0.0, np.nan), complex(1.0, np.nan)] ua = [1.0+1j, 2.0-1j, complex(0.0, np.nan)] ua_idx = [2, 0, 3] ua_inv = [1, 2, 0, 2, 2] ua_cnt = [1, 1, 3] assert_equal(np.unique(a), ua) assert_equal(np.unique(a, return_index=True), (ua, ua_idx)) assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv)) assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt)) # test for ticket 2111 - datetime64 nat = np.datetime64('nat') a = [np.datetime64('2020-12-26'), nat, np.datetime64('2020-12-24'), nat] ua = [np.datetime64('2020-12-24'), np.datetime64('2020-12-26'), nat] ua_idx = [2, 0, 1] ua_inv = [1, 2, 0, 2] ua_cnt = [1, 1, 2] assert_equal(np.unique(a), ua) assert_equal(np.unique(a, return_index=True), (ua, ua_idx)) assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv)) assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt)) # test for ticket 2111 - timedelta nat = np.timedelta64('nat') a = [np.timedelta64(1, 'D'), nat, np.timedelta64(1, 'h'), nat] ua = [np.timedelta64(1, 'h'), np.timedelta64(1, 'D'), nat] ua_idx = [2, 0, 1] ua_inv = [1, 2, 0, 2] ua_cnt = [1, 1, 2] assert_equal(np.unique(a), ua) assert_equal(np.unique(a, return_index=True), (ua, ua_idx)) assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv)) assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt)) # test for gh-19300 all_nans = [np.nan] * 4 ua = [np.nan] ua_idx = [0] ua_inv = [0, 0, 0, 0] ua_cnt = [4] assert_equal(np.unique(all_nans), ua) assert_equal(np.unique(all_nans, return_index=True), (ua, ua_idx)) assert_equal(np.unique(all_nans, return_inverse=True), (ua, ua_inv)) assert_equal(np.unique(all_nans, return_counts=True), (ua, ua_cnt)) def test_unique_axis_errors(self): assert_raises(TypeError, self._run_axis_tests, object) assert_raises(TypeError, self._run_axis_tests, [('a', int), ('b', object)]) assert_raises(np.AxisError, unique, np.arange(10), axis=2) assert_raises(np.AxisError, unique, np.arange(10), axis=-2) def test_unique_axis_list(self): msg = "Unique failed on list of lists" inp = [[0, 1, 0], [0, 1, 0]] inp_arr = np.asarray(inp) assert_array_equal(unique(inp, axis=0), unique(inp_arr, axis=0), msg) assert_array_equal(unique(inp, axis=1), unique(inp_arr, axis=1), msg) def test_unique_axis(self): types = [] types.extend(np.typecodes['AllInteger']) types.extend(np.typecodes['AllFloat']) types.append('datetime64[D]') types.append('timedelta64[D]') types.append([('a', int), ('b', int)]) types.append([('a', int), ('b', float)]) for dtype in types: self._run_axis_tests(dtype) msg = 'Non-bitwise-equal booleans test failed' data = np.arange(10, dtype=np.uint8).reshape(-1, 2).view(bool) result = np.array([[False, True], [True, True]], dtype=bool) assert_array_equal(unique(data, axis=0), result, msg) msg = 'Negative zero equality test failed' data = np.array([[-0.0, 0.0], [0.0, -0.0], [-0.0, 0.0], [0.0, -0.0]]) result = np.array([[-0.0, 0.0]]) assert_array_equal(unique(data, axis=0), result, msg) @pytest.mark.parametrize("axis", [0, -1]) def test_unique_1d_with_axis(self, axis): x = np.array([4, 3, 2, 3, 2, 1, 2, 2]) uniq = unique(x, axis=axis) assert_array_equal(uniq, [1, 2, 3, 4]) def test_unique_axis_zeros(self): # issue 15559 single_zero = np.empty(shape=(2, 0), dtype=np.int8) uniq, idx, inv, cnt = unique(single_zero, axis=0, return_index=True, return_inverse=True, return_counts=True) # there's 1 element of shape (0,) along axis 0 assert_equal(uniq.dtype, single_zero.dtype) assert_array_equal(uniq, np.empty(shape=(1, 0))) assert_array_equal(idx, np.array([0])) assert_array_equal(inv, np.array([0, 0])) assert_array_equal(cnt, np.array([2])) # there's 0 elements of shape (2,) along axis 1 uniq, idx, inv, cnt = unique(single_zero, axis=1, return_index=True, return_inverse=True, return_counts=True) assert_equal(uniq.dtype, single_zero.dtype) assert_array_equal(uniq, np.empty(shape=(2, 0))) assert_array_equal(idx, np.array([])) assert_array_equal(inv, np.array([])) assert_array_equal(cnt, np.array([])) # test a "complicated" shape shape = (0, 2, 0, 3, 0, 4, 0) multiple_zeros = np.empty(shape=shape) for axis in range(len(shape)): expected_shape = list(shape) if shape[axis] == 0: expected_shape[axis] = 0 else: expected_shape[axis] = 1 assert_array_equal(unique(multiple_zeros, axis=axis), np.empty(shape=expected_shape)) def test_unique_masked(self): # issue 8664 x = np.array([64, 0, 1, 2, 3, 63, 63, 0, 0, 0, 1, 2, 0, 63, 0], dtype='uint8') y = np.ma.masked_equal(x, 0) v = np.unique(y) v2, i, c = np.unique(y, return_index=True, return_counts=True) msg = 'Unique returned different results when asked for index' assert_array_equal(v.data, v2.data, msg) assert_array_equal(v.mask, v2.mask, msg) def test_unique_sort_order_with_axis(self): # These tests fail if sorting along axis is done by treating subarrays # as unsigned byte strings. See gh-10495. fmt = "sort order incorrect for integer type '%s'" for dt in 'bhilq': a = np.array([[-1], [0]], dt) b = np.unique(a, axis=0) assert_array_equal(a, b, fmt % dt) def _run_axis_tests(self, dtype): data = np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0]]).astype(dtype) msg = 'Unique with 1d array and axis=0 failed' result = np.array([0, 1]) assert_array_equal(unique(data), result.astype(dtype), msg) msg = 'Unique with 2d array and axis=0 failed' result = np.array([[0, 1, 0, 0], [1, 0, 0, 0]]) assert_array_equal(unique(data, axis=0), result.astype(dtype), msg) msg = 'Unique with 2d array and axis=1 failed' result = np.array([[0, 0, 1], [0, 1, 0], [0, 0, 1], [0, 1, 0]]) assert_array_equal(unique(data, axis=1), result.astype(dtype), msg) msg = 'Unique with 3d array and axis=2 failed' data3d = np.array([[[1, 1], [1, 0]], [[0, 1], [0, 0]]]).astype(dtype) result = np.take(data3d, [1, 0], axis=2) assert_array_equal(unique(data3d, axis=2), result, msg) uniq, idx, inv, cnt = unique(data, axis=0, return_index=True, return_inverse=True, return_counts=True) msg = "Unique's return_index=True failed with axis=0" assert_array_equal(data[idx], uniq, msg) msg = "Unique's return_inverse=True failed with axis=0" assert_array_equal(uniq[inv], data) msg = "Unique's return_counts=True failed with axis=0" assert_array_equal(cnt, np.array([2, 2]), msg) uniq, idx, inv, cnt = unique(data, axis=1, return_index=True, return_inverse=True, return_counts=True) msg = "Unique's return_index=True failed with axis=1" assert_array_equal(data[:, idx], uniq) msg = "Unique's return_inverse=True failed with axis=1" assert_array_equal(uniq[:, inv], data) msg = "Unique's return_counts=True failed with axis=1" assert_array_equal(cnt, np.array([2, 1, 1]), msg) def test_unique_nanequals(self): # issue 20326 a = np.array([1, 1, np.nan, np.nan, np.nan]) unq = np.unique(a) not_unq = np.unique(a, equal_nan=False) assert_array_equal(unq, np.array([1, np.nan])) assert_array_equal(not_unq, np.array([1, np.nan, np.nan, np.nan]))	35.07 KB	2026-01-20 12:58
📄 test_arrayterator.py from operator import mul from functools import reduce import numpy as np from numpy.random import randint from numpy.lib import Arrayterator from numpy.testing import assert_ def test(): np.random.seed(np.arange(10)) # Create a random array ndims = randint(5)+1 shape = tuple(randint(10)+1 for dim in range(ndims)) els = reduce(mul, shape) a = np.arange(els) a.shape = shape buf_size = randint(2els) b = Arrayterator(a, buf_size) # Check that each block has at most ``buf_size`` elements for block in b: assert_(len(block.flat) <= (buf_size or els)) # Check that all elements are iterated correctly assert_(list(b.flat) == list(a.flat)) # Slice arrayterator start = [randint(dim) for dim in shape] stop = [randint(dim)+1 for dim in shape] step = [randint(dim)+1 for dim in shape] slice_ = tuple(slice(t) for t in zip(start, stop, step)) c = b[slice_] d = a[slice_] # Check that each block has at most ``buf_size`` elements for block in c: assert_(len(block.flat) <= (buf_size or els)) # Check that the arrayterator is sliced correctly assert_(np.all(c.__array__() == d)) # Check that all elements are iterated correctly assert_(list(c.flat) == list(d.flat))	1.26 KB	2026-01-20 12:58
📄 test_financial_expired.py import sys import pytest import numpy as np def test_financial_expired(): match = 'NEP 32' with pytest.warns(DeprecationWarning, match=match): func = np.fv with pytest.raises(RuntimeError, match=match): func(1, 2, 3)	247 B	2026-01-20 12:58
📄 test_format.py # doctest r''' Test the .npy file format. Set up: >>> import sys >>> from io import BytesIO >>> from numpy.lib import format >>> >>> scalars = [ ... np.uint8, ... np.int8, ... np.uint16, ... np.int16, ... np.uint32, ... np.int32, ... np.uint64, ... np.int64, ... np.float32, ... np.float64, ... np.complex64, ... np.complex128, ... object, ... ] >>> >>> basic_arrays = [] >>> >>> for scalar in scalars: ... for endian in '<>': ... dtype = np.dtype(scalar).newbyteorder(endian) ... basic = np.arange(15).astype(dtype) ... basic_arrays.extend([ ... np.array([], dtype=dtype), ... np.array(10, dtype=dtype), ... basic, ... basic.reshape((3,5)), ... basic.reshape((3,5)).T, ... basic.reshape((3,5))[::-1,::2], ... ]) ... >>> >>> Pdescr = [ ... ('x', 'i4', (2,)), ... ('y', 'f8', (2, 2)), ... ('z', 'u1')] >>> >>> >>> PbufferT = [ ... ([3,2], [[6.,4.],[6.,4.]], 8), ... ([4,3], [[7.,5.],[7.,5.]], 9), ... ] >>> >>> >>> Ndescr = [ ... ('x', 'i4', (2,)), ... ('Info', [ ... ('value', 'c16'), ... ('y2', 'f8'), ... ('Info2', [ ... ('name', 'S2'), ... ('value', 'c16', (2,)), ... ('y3', 'f8', (2,)), ... ('z3', 'u4', (2,))]), ... ('name', 'S2'), ... ('z2', 'b1')]), ... ('color', 'S2'), ... ('info', [ ... ('Name', 'U8'), ... ('Value', 'c16')]), ... ('y', 'f8', (2, 2)), ... ('z', 'u1')] >>> >>> >>> NbufferT = [ ... ([3,2], (6j, 6., ('nn', [6j,4j], [6.,4.], [1,2]), 'NN', True), 'cc', ('NN', 6j), [[6.,4.],[6.,4.]], 8), ... ([4,3], (7j, 7., ('oo', [7j,5j], [7.,5.], [2,1]), 'OO', False), 'dd', ('OO', 7j), [[7.,5.],[7.,5.]], 9), ... ] >>> >>> >>> record_arrays = [ ... np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('<')), ... np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('<')), ... np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('>')), ... np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('>')), ... ] Test the magic string writing. >>> format.magic(1, 0) '\x93NUMPY\x01\x00' >>> format.magic(0, 0) '\x93NUMPY\x00\x00' >>> format.magic(255, 255) '\x93NUMPY\xff\xff' >>> format.magic(2, 5) '\x93NUMPY\x02\x05' Test the magic string reading. >>> format.read_magic(BytesIO(format.magic(1, 0))) (1, 0) >>> format.read_magic(BytesIO(format.magic(0, 0))) (0, 0) >>> format.read_magic(BytesIO(format.magic(255, 255))) (255, 255) >>> format.read_magic(BytesIO(format.magic(2, 5))) (2, 5) Test the header writing. >>> for arr in basic_arrays + record_arrays: ... f = BytesIO() ... format.write_array_header_1_0(f, arr) # XXX: arr is not a dict, items gets called on it ... print(repr(f.getvalue())) ... "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '\|u1', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '\|u1', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '\|u1', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '\|i1', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '\|i1', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '\|i1', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<u2', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<u2', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<u2', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<u2', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<u2', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<u2', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>u2', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>u2', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<i2', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<i2', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<i2', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<i2', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<i2', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<i2', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>i2', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>i2', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<u4', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<u4', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<u4', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<u4', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<u4', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<u4', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>u4', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>u4', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<i4', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<i4', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<i4', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<i4', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<i4', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<i4', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>i4', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>i4', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<u8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<u8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<u8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<u8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<u8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<u8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>u8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>u8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<i8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<i8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<i8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<i8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<i8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<i8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>i8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>i8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<f4', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<f4', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<f4', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<f4', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<f4', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<f4', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>f4', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>f4', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<f8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<f8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<f8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<f8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<f8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<f8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>f8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>f8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<c8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<c8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<c8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<c8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<c8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<c8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>c8', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>c8', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '<c16', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '<c16', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '<c16', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '<c16', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '<c16', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '<c16', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': '>c16', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': '>c16', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': 'O', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 3)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (0,)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': ()} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (15,)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 5)} \n" "F\x00{'descr': 'O', 'fortran_order': True, 'shape': (5, 3)} \n" "F\x00{'descr': 'O', 'fortran_order': False, 'shape': (3, 3)} \n" "v\x00{'descr': [('x', '<i4', (2,)), ('y', '<f8', (2, 2)), ('z', '\|u1')],\n 'fortran_order': False,\n 'shape': (2,)} \n" "\x16\x02{'descr': [('x', '<i4', (2,)),\n ('Info',\n [('value', '<c16'),\n ('y2', '<f8'),\n ('Info2',\n [('name', '\|S2'),\n ('value', '<c16', (2,)),\n ('y3', '<f8', (2,)),\n ('z3', '<u4', (2,))]),\n ('name', '\|S2'),\n ('z2', '\|b1')]),\n ('color', '\|S2'),\n ('info', [('Name', '<U8'), ('Value', '<c16')]),\n ('y', '<f8', (2, 2)),\n ('z', '\|u1')],\n 'fortran_order': False,\n 'shape': (2,)} \n" "v\x00{'descr': [('x', '>i4', (2,)), ('y', '>f8', (2, 2)), ('z', '\|u1')],\n 'fortran_order': False,\n 'shape': (2,)} \n" "\x16\x02{'descr': [('x', '>i4', (2,)),\n ('Info',\n [('value', '>c16'),\n ('y2', '>f8'),\n ('Info2',\n [('name', '\|S2'),\n ('value', '>c16', (2,)),\n ('y3', '>f8', (2,)),\n ('z3', '>u4', (2,))]),\n ('name', '\|S2'),\n ('z2', '\|b1')]),\n ('color', '\|S2'),\n ('info', [('Name', '>U8'), ('Value', '>c16')]),\n ('y', '>f8', (2, 2)),\n ('z', '\|u1')],\n 'fortran_order': False,\n 'shape': (2,)} \n" ''' import sys import os import warnings import pytest from io import BytesIO import numpy as np from numpy.testing import ( assert_, assert_array_equal, assert_raises, assert_raises_regex, assert_warns, IS_PYPY, IS_WASM ) from numpy.testing._private.utils import requires_memory from numpy.lib import format # Generate some basic arrays to test with. scalars = [ np.uint8, np.int8, np.uint16, np.int16, np.uint32, np.int32, np.uint64, np.int64, np.float32, np.float64, np.complex64, np.complex128, object, ] basic_arrays = [] for scalar in scalars: for endian in '<>': dtype = np.dtype(scalar).newbyteorder(endian) basic = np.arange(1500).astype(dtype) basic_arrays.extend([ # Empty np.array([], dtype=dtype), # Rank-0 np.array(10, dtype=dtype), # 1-D basic, # 2-D C-contiguous basic.reshape((30, 50)), # 2-D F-contiguous basic.reshape((30, 50)).T, # 2-D non-contiguous basic.reshape((30, 50))[::-1, ::2], ]) # More complicated record arrays. # This is the structure of the table used for plain objects: # # +-+-+-+ # \|x\|y\|z\| # +-+-+-+ # Structure of a plain array description: Pdescr = [ ('x', 'i4', (2,)), ('y', 'f8', (2, 2)), ('z', 'u1')] # A plain list of tuples with values for testing: PbufferT = [ # x y z ([3, 2], [[6., 4.], [6., 4.]], 8), ([4, 3], [[7., 5.], [7., 5.]], 9), ] # This is the structure of the table used for nested objects (DON'T PANIC!): # # +-+---------------------------------+-----+----------+-+-+ # \|x\|Info \|color\|info \|y\|z\| # \| +-----+--+----------------+----+--+ +----+-----+ \| \| # \| \|value\|y2\|Info2 \|name\|z2\| \|Name\|Value\| \| \| # \| \| \| +----+-----+--+--+ \| \| \| \| \| \| \| # \| \| \| \|name\|value\|y3\|z3\| \| \| \| \| \| \| \| # +-+-----+--+----+-----+--+--+----+--+-----+----+-----+-+-+ # # The corresponding nested array description: Ndescr = [ ('x', 'i4', (2,)), ('Info', [ ('value', 'c16'), ('y2', 'f8'), ('Info2', [ ('name', 'S2'), ('value', 'c16', (2,)), ('y3', 'f8', (2,)), ('z3', 'u4', (2,))]), ('name', 'S2'), ('z2', 'b1')]), ('color', 'S2'), ('info', [ ('Name', 'U8'), ('Value', 'c16')]), ('y', 'f8', (2, 2)), ('z', 'u1')] NbufferT = [ # x Info color info y z # value y2 Info2 name z2 Name Value # name value y3 z3 ([3, 2], (6j, 6., ('nn', [6j, 4j], [6., 4.], [1, 2]), 'NN', True), 'cc', ('NN', 6j), [[6., 4.], [6., 4.]], 8), ([4, 3], (7j, 7., ('oo', [7j, 5j], [7., 5.], [2, 1]), 'OO', False), 'dd', ('OO', 7j), [[7., 5.], [7., 5.]], 9), ] record_arrays = [ np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('<')), np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('<')), np.array(PbufferT, dtype=np.dtype(Pdescr).newbyteorder('>')), np.array(NbufferT, dtype=np.dtype(Ndescr).newbyteorder('>')), np.zeros(1, dtype=[('c', ('<f8', (5,)), (2,))]) ] #BytesIO that reads a random number of bytes at a time class BytesIOSRandomSize(BytesIO): def read(self, size=None): import random size = random.randint(1, size) return super().read(size) def roundtrip(arr): f = BytesIO() format.write_array(f, arr) f2 = BytesIO(f.getvalue()) arr2 = format.read_array(f2, allow_pickle=True) return arr2 def roundtrip_randsize(arr): f = BytesIO() format.write_array(f, arr) f2 = BytesIOSRandomSize(f.getvalue()) arr2 = format.read_array(f2) return arr2 def roundtrip_truncated(arr): f = BytesIO() format.write_array(f, arr) #BytesIO is one byte short f2 = BytesIO(f.getvalue()[0:-1]) arr2 = format.read_array(f2) return arr2 def assert_equal_(o1, o2): assert_(o1 == o2) def test_roundtrip(): for arr in basic_arrays + record_arrays: arr2 = roundtrip(arr) assert_array_equal(arr, arr2) def test_roundtrip_randsize(): for arr in basic_arrays + record_arrays: if arr.dtype != object: arr2 = roundtrip_randsize(arr) assert_array_equal(arr, arr2) def test_roundtrip_truncated(): for arr in basic_arrays: if arr.dtype != object: assert_raises(ValueError, roundtrip_truncated, arr) def test_long_str(): # check items larger than internal buffer size, gh-4027 long_str_arr = np.ones(1, dtype=np.dtype((str, format.BUFFER_SIZE + 1))) long_str_arr2 = roundtrip(long_str_arr) assert_array_equal(long_str_arr, long_str_arr2) @pytest.mark.skipif(IS_WASM, reason="memmap doesn't work correctly") @pytest.mark.slow def test_memmap_roundtrip(tmpdir): for i, arr in enumerate(basic_arrays + record_arrays): if arr.dtype.hasobject: # Skip these since they can't be mmap'ed. continue # Write it out normally and through mmap. nfn = os.path.join(tmpdir, f'normal{i}.npy') mfn = os.path.join(tmpdir, f'memmap{i}.npy') with open(nfn, 'wb') as fp: format.write_array(fp, arr) fortran_order = ( arr.flags.f_contiguous and not arr.flags.c_contiguous) ma = format.open_memmap(mfn, mode='w+', dtype=arr.dtype, shape=arr.shape, fortran_order=fortran_order) ma[...] = arr ma.flush() # Check that both of these files' contents are the same. with open(nfn, 'rb') as fp: normal_bytes = fp.read() with open(mfn, 'rb') as fp: memmap_bytes = fp.read() assert_equal_(normal_bytes, memmap_bytes) # Check that reading the file using memmap works. ma = format.open_memmap(nfn, mode='r') ma.flush() def test_compressed_roundtrip(tmpdir): arr = np.random.rand(200, 200) npz_file = os.path.join(tmpdir, 'compressed.npz') np.savez_compressed(npz_file, arr=arr) with np.load(npz_file) as npz: arr1 = npz['arr'] assert_array_equal(arr, arr1) # aligned dt1 = np.dtype('i1, i4, i1', align=True) # non-aligned, explicit offsets dt2 = np.dtype({'names': ['a', 'b'], 'formats': ['i4', 'i4'], 'offsets': [1, 6]}) # nested struct-in-struct dt3 = np.dtype({'names': ['c', 'd'], 'formats': ['i4', dt2]}) # field with '' name dt4 = np.dtype({'names': ['a', '', 'b'], 'formats': ['i4']3}) # titles dt5 = np.dtype({'names': ['a', 'b'], 'formats': ['i4', 'i4'], 'offsets': [1, 6], 'titles': ['aa', 'bb']}) # empty dt6 = np.dtype({'names': [], 'formats': [], 'itemsize': 8}) @pytest.mark.parametrize("dt", [dt1, dt2, dt3, dt4, dt5, dt6]) def test_load_padded_dtype(tmpdir, dt): arr = np.zeros(3, dt) for i in range(3): arr[i] = i + 5 npz_file = os.path.join(tmpdir, 'aligned.npz') np.savez(npz_file, arr=arr) with np.load(npz_file) as npz: arr1 = npz['arr'] assert_array_equal(arr, arr1) @pytest.mark.xfail(IS_WASM, reason="Emscripten NODEFS has a buggy dup") def test_python2_python3_interoperability(): fname = 'win64python2.npy' path = os.path.join(os.path.dirname(__file__), 'data', fname) with pytest.warns(UserWarning, match="Reading.this warning\\."): data = np.load(path) assert_array_equal(data, np.ones(2)) def test_pickle_python2_python3(): # Test that loading object arrays saved on Python 2 works both on # Python 2 and Python 3 and vice versa data_dir = os.path.join(os.path.dirname(__file__), 'data') expected = np.array([None, range, '\u512a\u826f', b'\xe4\xb8\x8d\xe8\x89\xaf'], dtype=object) for fname in ['py2-objarr.npy', 'py2-objarr.npz', 'py3-objarr.npy', 'py3-objarr.npz']: path = os.path.join(data_dir, fname) for encoding in ['bytes', 'latin1']: data_f = np.load(path, allow_pickle=True, encoding=encoding) if fname.endswith('.npz'): data = data_f['x'] data_f.close() else: data = data_f if encoding == 'latin1' and fname.startswith('py2'): assert_(isinstance(data[3], str)) assert_array_equal(data[:-1], expected[:-1]) # mojibake occurs assert_array_equal(data[-1].encode(encoding), expected[-1]) else: assert_(isinstance(data[3], bytes)) assert_array_equal(data, expected) if fname.startswith('py2'): if fname.endswith('.npz'): data = np.load(path, allow_pickle=True) assert_raises(UnicodeError, data.__getitem__, 'x') data.close() data = np.load(path, allow_pickle=True, fix_imports=False, encoding='latin1') assert_raises(ImportError, data.__getitem__, 'x') data.close() else: assert_raises(UnicodeError, np.load, path, allow_pickle=True) assert_raises(ImportError, np.load, path, allow_pickle=True, fix_imports=False, encoding='latin1') def test_pickle_disallow(tmpdir): data_dir = os.path.join(os.path.dirname(__file__), 'data') path = os.path.join(data_dir, 'py2-objarr.npy') assert_raises(ValueError, np.load, path, allow_pickle=False, encoding='latin1') path = os.path.join(data_dir, 'py2-objarr.npz') with np.load(path, allow_pickle=False, encoding='latin1') as f: assert_raises(ValueError, f.__getitem__, 'x') path = os.path.join(tmpdir, 'pickle-disabled.npy') assert_raises(ValueError, np.save, path, np.array([None], dtype=object), allow_pickle=False) @pytest.mark.parametrize('dt', [ np.dtype(np.dtype([('a', np.int8), ('b', np.int16), ('c', np.int32), ], align=True), (3,)), np.dtype([('x', np.dtype({'names':['a','b'], 'formats':['i1','i1'], 'offsets':[0,4], 'itemsize':8, }, (3,)), (4,), )]), np.dtype([('x', ('<f8', (5,)), (2,), )]), np.dtype([('x', np.dtype(( np.dtype(( np.dtype({'names':['a','b'], 'formats':['i1','i1'], 'offsets':[0,4], 'itemsize':8}), (3,) )), (4,) ))) ]), np.dtype([ ('a', np.dtype(( np.dtype(( np.dtype(( np.dtype([ ('a', int), ('b', np.dtype({'names':['a','b'], 'formats':['i1','i1'], 'offsets':[0,4], 'itemsize':8})), ]), (3,), )), (4,), )), (5,), ))) ]), ]) def test_descr_to_dtype(dt): dt1 = format.descr_to_dtype(dt.descr) assert_equal_(dt1, dt) arr1 = np.zeros(3, dt) arr2 = roundtrip(arr1) assert_array_equal(arr1, arr2) def test_version_2_0(): f = BytesIO() # requires more than 2 byte for header dt = [(("%d" % i) * 100, float) for i in range(500)] d = np.ones(1000, dtype=dt) format.write_array(f, d, version=(2, 0)) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', UserWarning) format.write_array(f, d) assert_(w[0].category is UserWarning) # check alignment of data portion f.seek(0) header = f.readline() assert_(len(header) % format.ARRAY_ALIGN == 0) f.seek(0) n = format.read_array(f, max_header_size=200000) assert_array_equal(d, n) # 1.0 requested but data cannot be saved this way assert_raises(ValueError, format.write_array, f, d, (1, 0)) @pytest.mark.skipif(IS_WASM, reason="memmap doesn't work correctly") def test_version_2_0_memmap(tmpdir): # requires more than 2 byte for header dt = [(("%d" % i) * 100, float) for i in range(500)] d = np.ones(1000, dtype=dt) tf1 = os.path.join(tmpdir, f'version2_01.npy') tf2 = os.path.join(tmpdir, f'version2_02.npy') # 1.0 requested but data cannot be saved this way assert_raises(ValueError, format.open_memmap, tf1, mode='w+', dtype=d.dtype, shape=d.shape, version=(1, 0)) ma = format.open_memmap(tf1, mode='w+', dtype=d.dtype, shape=d.shape, version=(2, 0)) ma[...] = d ma.flush() ma = format.open_memmap(tf1, mode='r', max_header_size=200000) assert_array_equal(ma, d) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', UserWarning) ma = format.open_memmap(tf2, mode='w+', dtype=d.dtype, shape=d.shape, version=None) assert_(w[0].category is UserWarning) ma[...] = d ma.flush() ma = format.open_memmap(tf2, mode='r', max_header_size=200000) assert_array_equal(ma, d) @pytest.mark.parametrize("mmap_mode", ["r", None]) def test_huge_header(tmpdir, mmap_mode): f = os.path.join(tmpdir, f'large_header.npy') arr = np.array(1, dtype="i,"10000+"i") with pytest.warns(UserWarning, match=".format 2.0"): np.save(f, arr) with pytest.raises(ValueError, match="Header.large"): np.load(f, mmap_mode=mmap_mode) with pytest.raises(ValueError, match="Header.large"): np.load(f, mmap_mode=mmap_mode, max_header_size=20000) res = np.load(f, mmap_mode=mmap_mode, allow_pickle=True) assert_array_equal(res, arr) res = np.load(f, mmap_mode=mmap_mode, max_header_size=180000) assert_array_equal(res, arr) def test_huge_header_npz(tmpdir): f = os.path.join(tmpdir, f'large_header.npz') arr = np.array(1, dtype="i,"10000+"i") with pytest.warns(UserWarning, match=".format 2.0"): np.savez(f, arr=arr) # Only getting the array from the file actually reads it with pytest.raises(ValueError, match="Header.large"): np.load(f)["arr"] with pytest.raises(ValueError, match="Header.large"): np.load(f, max_header_size=20000)["arr"] res = np.load(f, allow_pickle=True)["arr"] assert_array_equal(res, arr) res = np.load(f, max_header_size=180000)["arr"] assert_array_equal(res, arr) def test_write_version(): f = BytesIO() arr = np.arange(1) # These should pass. format.write_array(f, arr, version=(1, 0)) format.write_array(f, arr) format.write_array(f, arr, version=None) format.write_array(f, arr) format.write_array(f, arr, version=(2, 0)) format.write_array(f, arr) # These should all fail. bad_versions = [ (1, 1), (0, 0), (0, 1), (2, 2), (255, 255), ] for version in bad_versions: with assert_raises_regex(ValueError, 'we only support format version.'): format.write_array(f, arr, version=version) bad_version_magic = [ b'\x93NUMPY\x01\x01', b'\x93NUMPY\x00\x00', b'\x93NUMPY\x00\x01', b'\x93NUMPY\x02\x00', b'\x93NUMPY\x02\x02', b'\x93NUMPY\xff\xff', ] malformed_magic = [ b'\x92NUMPY\x01\x00', b'\x00NUMPY\x01\x00', b'\x93numpy\x01\x00', b'\x93MATLB\x01\x00', b'\x93NUMPY\x01', b'\x93NUMPY', b'', ] def test_read_magic(): s1 = BytesIO() s2 = BytesIO() arr = np.ones((3, 6), dtype=float) format.write_array(s1, arr, version=(1, 0)) format.write_array(s2, arr, version=(2, 0)) s1.seek(0) s2.seek(0) version1 = format.read_magic(s1) version2 = format.read_magic(s2) assert_(version1 == (1, 0)) assert_(version2 == (2, 0)) assert_(s1.tell() == format.MAGIC_LEN) assert_(s2.tell() == format.MAGIC_LEN) def test_read_magic_bad_magic(): for magic in malformed_magic: f = BytesIO(magic) assert_raises(ValueError, format.read_array, f) def test_read_version_1_0_bad_magic(): for magic in bad_version_magic + malformed_magic: f = BytesIO(magic) assert_raises(ValueError, format.read_array, f) def test_bad_magic_args(): assert_raises(ValueError, format.magic, -1, 1) assert_raises(ValueError, format.magic, 256, 1) assert_raises(ValueError, format.magic, 1, -1) assert_raises(ValueError, format.magic, 1, 256) def test_large_header(): s = BytesIO() d = {'shape': tuple(), 'fortran_order': False, 'descr': '<i8'} format.write_array_header_1_0(s, d) s = BytesIO() d['descr'] = [('x'256256, '<i8')] assert_raises(ValueError, format.write_array_header_1_0, s, d) def test_read_array_header_1_0(): s = BytesIO() arr = np.ones((3, 6), dtype=float) format.write_array(s, arr, version=(1, 0)) s.seek(format.MAGIC_LEN) shape, fortran, dtype = format.read_array_header_1_0(s) assert_(s.tell() % format.ARRAY_ALIGN == 0) assert_((shape, fortran, dtype) == ((3, 6), False, float)) def test_read_array_header_2_0(): s = BytesIO() arr = np.ones((3, 6), dtype=float) format.write_array(s, arr, version=(2, 0)) s.seek(format.MAGIC_LEN) shape, fortran, dtype = format.read_array_header_2_0(s) assert_(s.tell() % format.ARRAY_ALIGN == 0) assert_((shape, fortran, dtype) == ((3, 6), False, float)) def test_bad_header(): # header of length less than 2 should fail s = BytesIO() assert_raises(ValueError, format.read_array_header_1_0, s) s = BytesIO(b'1') assert_raises(ValueError, format.read_array_header_1_0, s) # header shorter than indicated size should fail s = BytesIO(b'\x01\x00') assert_raises(ValueError, format.read_array_header_1_0, s) # headers without the exact keys required should fail # d = {"shape": (1, 2), # "descr": "x"} s = BytesIO( b"\x93NUMPY\x01\x006\x00{'descr': 'x', 'shape': (1, 2), }" + b" \n" ) assert_raises(ValueError, format.read_array_header_1_0, s) d = {"shape": (1, 2), "fortran_order": False, "descr": "x", "extrakey": -1} s = BytesIO() format.write_array_header_1_0(s, d) assert_raises(ValueError, format.read_array_header_1_0, s) def test_large_file_support(tmpdir): if (sys.platform == 'win32' or sys.platform == 'cygwin'): pytest.skip("Unknown if Windows has sparse filesystems") # try creating a large sparse file tf_name = os.path.join(tmpdir, 'sparse_file') try: # seek past end would work too, but linux truncate somewhat # increases the chances that we have a sparse filesystem and can # avoid actually writing 5GB import subprocess as sp sp.check_call(["truncate", "-s", "5368709120", tf_name]) except Exception: pytest.skip("Could not create 5GB large file") # write a small array to the end with open(tf_name, "wb") as f: f.seek(5368709120) d = np.arange(5) np.save(f, d) # read it back with open(tf_name, "rb") as f: f.seek(5368709120) r = np.load(f) assert_array_equal(r, d) @pytest.mark.skipif(IS_PYPY, reason="flaky on PyPy") @pytest.mark.skipif(np.dtype(np.intp).itemsize < 8, reason="test requires 64-bit system") @pytest.mark.slow @requires_memory(free_bytes=2 230) def test_large_archive(tmpdir): # Regression test for product of saving arrays with dimensions of array # having a product that doesn't fit in int32. See gh-7598 for details. shape = (230, 2) try: a = np.empty(shape, dtype=np.uint8) except MemoryError: pytest.skip("Could not create large file") fname = os.path.join(tmpdir, "large_archive") with open(fname, "wb") as f: np.savez(f, arr=a) del a with open(fname, "rb") as f: new_a = np.load(f)["arr"] assert new_a.shape == shape def test_empty_npz(tmpdir): # Test for gh-9989 fname = os.path.join(tmpdir, "nothing.npz") np.savez(fname) with np.load(fname) as nps: pass def test_unicode_field_names(tmpdir): # gh-7391 arr = np.array([ (1, 3), (1, 2), (1, 3), (1, 2) ], dtype=[ ('int', int), ('\N{CJK UNIFIED IDEOGRAPH-6574}\N{CJK UNIFIED IDEOGRAPH-5F62}', int) ]) fname = os.path.join(tmpdir, "unicode.npy") with open(fname, 'wb') as f: format.write_array(f, arr, version=(3, 0)) with open(fname, 'rb') as f: arr2 = format.read_array(f) assert_array_equal(arr, arr2) # notifies the user that 3.0 is selected with open(fname, 'wb') as f: with assert_warns(UserWarning): format.write_array(f, arr, version=None) def test_header_growth_axis(): for is_fortran_array, dtype_space, expected_header_length in [ [False, 22, 128], [False, 23, 192], [True, 23, 128], [True, 24, 192] ]: for size in [10*i for i in range(format.GROWTH_AXIS_MAX_DIGITS)]: fp = BytesIO() format.write_array_header_1_0(fp, { 'shape': (2, size) if is_fortran_array else (size, 2), 'fortran_order': is_fortran_array, 'descr': np.dtype([(' 'dtype_space, int)]) }) assert len(fp.getvalue()) == expected_header_length @pytest.mark.parametrize('dt, fail', [ (np.dtype({'names': ['a', 'b'], 'formats': [float, np.dtype('S3', metadata={'some': 'stuff'})]}), True), (np.dtype(int, metadata={'some': 'stuff'}), False), (np.dtype([('subarray', (int, (2,)))], metadata={'some': 'stuff'}), False), # recursive: metadata on the field of a dtype (np.dtype({'names': ['a', 'b'], 'formats': [ float, np.dtype({'names': ['c'], 'formats': [np.dtype(int, metadata={})]}) ]}), False) ]) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_metadata_dtype(dt, fail): # gh-14142 arr = np.ones(10, dtype=dt) buf = BytesIO() with assert_warns(UserWarning): np.save(buf, arr) buf.seek(0) if fail: with assert_raises(ValueError): np.load(buf) else: arr2 = np.load(buf) # BUG: assert_array_equal does not check metadata from numpy.lib.utils import drop_metadata assert_array_equal(arr, arr2) assert drop_metadata(arr.dtype) is not arr.dtype assert drop_metadata(arr2.dtype) is arr2.dtype	40 KB	2026-01-20 12:58
📄 test_function_base.py import operator import warnings import sys import decimal from fractions import Fraction import math import pytest import hypothesis from hypothesis.extra.numpy import arrays import hypothesis.strategies as st from functools import partial import numpy as np from numpy import ma from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_raises, assert_allclose, IS_PYPY, assert_warns, assert_raises_regex, suppress_warnings, HAS_REFCOUNT, IS_WASM ) import numpy.lib.function_base as nfb from numpy.random import rand from numpy.lib import ( add_newdoc_ufunc, angle, average, bartlett, blackman, corrcoef, cov, delete, diff, digitize, extract, flipud, gradient, hamming, hanning, i0, insert, interp, kaiser, meshgrid, msort, piecewise, place, rot90, select, setxor1d, sinc, trapz, trim_zeros, unwrap, unique, vectorize ) from numpy.core.numeric import normalize_axis_tuple def get_mat(n): data = np.arange(n) data = np.add.outer(data, data) return data def _make_complex(real, imag): """ Like real + 1j * imag, but behaves as expected when imag contains non-finite values """ ret = np.zeros(np.broadcast(real, imag).shape, np.complex_) ret.real = real ret.imag = imag return ret class TestRot90: def test_basic(self): assert_raises(ValueError, rot90, np.ones(4)) assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(0,1,2)) assert_raises(ValueError, rot90, np.ones((2,2)), axes=(0,2)) assert_raises(ValueError, rot90, np.ones((2,2)), axes=(1,1)) assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(-2,1)) a = [[0, 1, 2], [3, 4, 5]] b1 = [[2, 5], [1, 4], [0, 3]] b2 = [[5, 4, 3], [2, 1, 0]] b3 = [[3, 0], [4, 1], [5, 2]] b4 = [[0, 1, 2], [3, 4, 5]] for k in range(-3, 13, 4): assert_equal(rot90(a, k=k), b1) for k in range(-2, 13, 4): assert_equal(rot90(a, k=k), b2) for k in range(-1, 13, 4): assert_equal(rot90(a, k=k), b3) for k in range(0, 13, 4): assert_equal(rot90(a, k=k), b4) assert_equal(rot90(rot90(a, axes=(0,1)), axes=(1,0)), a) assert_equal(rot90(a, k=1, axes=(1,0)), rot90(a, k=-1, axes=(0,1))) def test_axes(self): a = np.ones((50, 40, 3)) assert_equal(rot90(a).shape, (40, 50, 3)) assert_equal(rot90(a, axes=(0,2)), rot90(a, axes=(0,-1))) assert_equal(rot90(a, axes=(1,2)), rot90(a, axes=(-2,-1))) def test_rotation_axes(self): a = np.arange(8).reshape((2,2,2)) a_rot90_01 = [[[2, 3], [6, 7]], [[0, 1], [4, 5]]] a_rot90_12 = [[[1, 3], [0, 2]], [[5, 7], [4, 6]]] a_rot90_20 = [[[4, 0], [6, 2]], [[5, 1], [7, 3]]] a_rot90_10 = [[[4, 5], [0, 1]], [[6, 7], [2, 3]]] assert_equal(rot90(a, axes=(0, 1)), a_rot90_01) assert_equal(rot90(a, axes=(1, 0)), a_rot90_10) assert_equal(rot90(a, axes=(1, 2)), a_rot90_12) for k in range(1,5): assert_equal(rot90(a, k=k, axes=(2, 0)), rot90(a_rot90_20, k=k-1, axes=(2, 0))) class TestFlip: def test_axes(self): assert_raises(np.AxisError, np.flip, np.ones(4), axis=1) assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=2) assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=-3) assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=(0, 3)) def test_basic_lr(self): a = get_mat(4) b = a[:, ::-1] assert_equal(np.flip(a, 1), b) a = [[0, 1, 2], [3, 4, 5]] b = [[2, 1, 0], [5, 4, 3]] assert_equal(np.flip(a, 1), b) def test_basic_ud(self): a = get_mat(4) b = a[::-1, :] assert_equal(np.flip(a, 0), b) a = [[0, 1, 2], [3, 4, 5]] b = [[3, 4, 5], [0, 1, 2]] assert_equal(np.flip(a, 0), b) def test_3d_swap_axis0(self): a = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) b = np.array([[[4, 5], [6, 7]], [[0, 1], [2, 3]]]) assert_equal(np.flip(a, 0), b) def test_3d_swap_axis1(self): a = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) b = np.array([[[2, 3], [0, 1]], [[6, 7], [4, 5]]]) assert_equal(np.flip(a, 1), b) def test_3d_swap_axis2(self): a = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) b = np.array([[[1, 0], [3, 2]], [[5, 4], [7, 6]]]) assert_equal(np.flip(a, 2), b) def test_4d(self): a = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5) for i in range(a.ndim): assert_equal(np.flip(a, i), np.flipud(a.swapaxes(0, i)).swapaxes(i, 0)) def test_default_axis(self): a = np.array([[1, 2, 3], [4, 5, 6]]) b = np.array([[6, 5, 4], [3, 2, 1]]) assert_equal(np.flip(a), b) def test_multiple_axes(self): a = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) assert_equal(np.flip(a, axis=()), a) b = np.array([[[5, 4], [7, 6]], [[1, 0], [3, 2]]]) assert_equal(np.flip(a, axis=(0, 2)), b) c = np.array([[[3, 2], [1, 0]], [[7, 6], [5, 4]]]) assert_equal(np.flip(a, axis=(1, 2)), c) class TestAny: def test_basic(self): y1 = [0, 0, 1, 0] y2 = [0, 0, 0, 0] y3 = [1, 0, 1, 0] assert_(np.any(y1)) assert_(np.any(y3)) assert_(not np.any(y2)) def test_nd(self): y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]] assert_(np.any(y1)) assert_array_equal(np.any(y1, axis=0), [1, 1, 0]) assert_array_equal(np.any(y1, axis=1), [0, 1, 1]) class TestAll: def test_basic(self): y1 = [0, 1, 1, 0] y2 = [0, 0, 0, 0] y3 = [1, 1, 1, 1] assert_(not np.all(y1)) assert_(np.all(y3)) assert_(not np.all(y2)) assert_(np.all(~np.array(y2))) def test_nd(self): y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]] assert_(not np.all(y1)) assert_array_equal(np.all(y1, axis=0), [0, 0, 1]) assert_array_equal(np.all(y1, axis=1), [0, 0, 1]) class TestCopy: def test_basic(self): a = np.array([[1, 2], [3, 4]]) a_copy = np.copy(a) assert_array_equal(a, a_copy) a_copy[0, 0] = 10 assert_equal(a[0, 0], 1) assert_equal(a_copy[0, 0], 10) def test_order(self): # It turns out that people rely on np.copy() preserving order by # default; changing this broke scikit-learn: # github.com/scikit-learn/scikit-learn/commit/7842748cf777412c506a8c0ed28090711d3a3783 # noqa a = np.array([[1, 2], [3, 4]]) assert_(a.flags.c_contiguous) assert_(not a.flags.f_contiguous) a_fort = np.array([[1, 2], [3, 4]], order="F") assert_(not a_fort.flags.c_contiguous) assert_(a_fort.flags.f_contiguous) a_copy = np.copy(a) assert_(a_copy.flags.c_contiguous) assert_(not a_copy.flags.f_contiguous) a_fort_copy = np.copy(a_fort) assert_(not a_fort_copy.flags.c_contiguous) assert_(a_fort_copy.flags.f_contiguous) def test_subok(self): mx = ma.ones(5) assert_(not ma.isMaskedArray(np.copy(mx, subok=False))) assert_(ma.isMaskedArray(np.copy(mx, subok=True))) # Default behavior assert_(not ma.isMaskedArray(np.copy(mx))) class TestAverage: def test_basic(self): y1 = np.array([1, 2, 3]) assert_(average(y1, axis=0) == 2.) y2 = np.array([1., 2., 3.]) assert_(average(y2, axis=0) == 2.) y3 = [0., 0., 0.] assert_(average(y3, axis=0) == 0.) y4 = np.ones((4, 4)) y4[0, 1] = 0 y4[1, 0] = 2 assert_almost_equal(y4.mean(0), average(y4, 0)) assert_almost_equal(y4.mean(1), average(y4, 1)) y5 = rand(5, 5) assert_almost_equal(y5.mean(0), average(y5, 0)) assert_almost_equal(y5.mean(1), average(y5, 1)) @pytest.mark.parametrize( 'x, axis, expected_avg, weights, expected_wavg, expected_wsum', [([1, 2, 3], None, [2.0], [3, 4, 1], [1.75], [8.0]), ([[1, 2, 5], [1, 6, 11]], 0, [[1.0, 4.0, 8.0]], [1, 3], [[1.0, 5.0, 9.5]], [[4, 4, 4]])], ) def test_basic_keepdims(self, x, axis, expected_avg, weights, expected_wavg, expected_wsum): avg = np.average(x, axis=axis, keepdims=True) assert avg.shape == np.shape(expected_avg) assert_array_equal(avg, expected_avg) wavg = np.average(x, axis=axis, weights=weights, keepdims=True) assert wavg.shape == np.shape(expected_wavg) assert_array_equal(wavg, expected_wavg) wavg, wsum = np.average(x, axis=axis, weights=weights, returned=True, keepdims=True) assert wavg.shape == np.shape(expected_wavg) assert_array_equal(wavg, expected_wavg) assert wsum.shape == np.shape(expected_wsum) assert_array_equal(wsum, expected_wsum) def test_weights(self): y = np.arange(10) w = np.arange(10) actual = average(y, weights=w) desired = (np.arange(10) ** 2).sum() * 1. / np.arange(10).sum() assert_almost_equal(actual, desired) y1 = np.array([[1, 2, 3], [4, 5, 6]]) w0 = [1, 2] actual = average(y1, weights=w0, axis=0) desired = np.array([3., 4., 5.]) assert_almost_equal(actual, desired) w1 = [0, 0, 1] actual = average(y1, weights=w1, axis=1) desired = np.array([3., 6.]) assert_almost_equal(actual, desired) # This should raise an error. Can we test for that ? # assert_equal(average(y1, weights=w1), 9./2.) # 2D Case w2 = [[0, 0, 1], [0, 0, 2]] desired = np.array([3., 6.]) assert_array_equal(average(y1, weights=w2, axis=1), desired) assert_equal(average(y1, weights=w2), 5.) y3 = rand(5).astype(np.float32) w3 = rand(5).astype(np.float64) assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3)) # test weights with `keepdims=False` and `keepdims=True` x = np.array([2, 3, 4]).reshape(3, 1) w = np.array([4, 5, 6]).reshape(3, 1) actual = np.average(x, weights=w, axis=1, keepdims=False) desired = np.array([2., 3., 4.]) assert_array_equal(actual, desired) actual = np.average(x, weights=w, axis=1, keepdims=True) desired = np.array([[2.], [3.], [4.]]) assert_array_equal(actual, desired) def test_returned(self): y = np.array([[1, 2, 3], [4, 5, 6]]) # No weights avg, scl = average(y, returned=True) assert_equal(scl, 6.) avg, scl = average(y, 0, returned=True) assert_array_equal(scl, np.array([2., 2., 2.])) avg, scl = average(y, 1, returned=True) assert_array_equal(scl, np.array([3., 3.])) # With weights w0 = [1, 2] avg, scl = average(y, weights=w0, axis=0, returned=True) assert_array_equal(scl, np.array([3., 3., 3.])) w1 = [1, 2, 3] avg, scl = average(y, weights=w1, axis=1, returned=True) assert_array_equal(scl, np.array([6., 6.])) w2 = [[0, 0, 1], [1, 2, 3]] avg, scl = average(y, weights=w2, axis=1, returned=True) assert_array_equal(scl, np.array([1., 6.])) def test_subclasses(self): class subclass(np.ndarray): pass a = np.array([[1,2],[3,4]]).view(subclass) w = np.array([[1,2],[3,4]]).view(subclass) assert_equal(type(np.average(a)), subclass) assert_equal(type(np.average(a, weights=w)), subclass) def test_upcasting(self): typs = [('i4', 'i4', 'f8'), ('i4', 'f4', 'f8'), ('f4', 'i4', 'f8'), ('f4', 'f4', 'f4'), ('f4', 'f8', 'f8')] for at, wt, rt in typs: a = np.array([[1,2],[3,4]], dtype=at) w = np.array([[1,2],[3,4]], dtype=wt) assert_equal(np.average(a, weights=w).dtype, np.dtype(rt)) def test_object_dtype(self): a = np.array([decimal.Decimal(x) for x in range(10)]) w = np.array([decimal.Decimal(1) for _ in range(10)]) w /= w.sum() assert_almost_equal(a.mean(0), average(a, weights=w)) def test_average_class_without_dtype(self): # see gh-21988 a = np.array([Fraction(1, 5), Fraction(3, 5)]) assert_equal(np.average(a), Fraction(2, 5)) class TestSelect: choices = [np.array([1, 2, 3]), np.array([4, 5, 6]), np.array([7, 8, 9])] conditions = [np.array([False, False, False]), np.array([False, True, False]), np.array([False, False, True])] def _select(self, cond, values, default=0): output = [] for m in range(len(cond)): output += [V[m] for V, C in zip(values, cond) if C[m]] or [default] return output def test_basic(self): choices = self.choices conditions = self.conditions assert_array_equal(select(conditions, choices, default=15), self._select(conditions, choices, default=15)) assert_equal(len(choices), 3) assert_equal(len(conditions), 3) def test_broadcasting(self): conditions = [np.array(True), np.array([False, True, False])] choices = [1, np.arange(12).reshape(4, 3)] assert_array_equal(select(conditions, choices), np.ones((4, 3))) # default can broadcast too: assert_equal(select([True], [0], default=[0]).shape, (1,)) def test_return_dtype(self): assert_equal(select(self.conditions, self.choices, 1j).dtype, np.complex_) # But the conditions need to be stronger then the scalar default # if it is scalar. choices = [choice.astype(np.int8) for choice in self.choices] assert_equal(select(self.conditions, choices).dtype, np.int8) d = np.array([1, 2, 3, np.nan, 5, 7]) m = np.isnan(d) assert_equal(select([m], [d]), [0, 0, 0, np.nan, 0, 0]) def test_deprecated_empty(self): assert_raises(ValueError, select, [], [], 3j) assert_raises(ValueError, select, [], []) def test_non_bool_deprecation(self): choices = self.choices conditions = self.conditions[:] conditions[0] = conditions[0].astype(np.int_) assert_raises(TypeError, select, conditions, choices) conditions[0] = conditions[0].astype(np.uint8) assert_raises(TypeError, select, conditions, choices) assert_raises(TypeError, select, conditions, choices) def test_many_arguments(self): # This used to be limited by NPY_MAXARGS == 32 conditions = [np.array([False])] * 100 choices = [np.array([1])] * 100 select(conditions, choices) class TestInsert: def test_basic(self): a = [1, 2, 3] assert_equal(insert(a, 0, 1), [1, 1, 2, 3]) assert_equal(insert(a, 3, 1), [1, 2, 3, 1]) assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3]) assert_equal(insert(a, 1, [1, 2, 3]), [1, 1, 2, 3, 2, 3]) assert_equal(insert(a, [1, -1, 3], 9), [1, 9, 2, 9, 3, 9]) assert_equal(insert(a, slice(-1, None, -1), 9), [9, 1, 9, 2, 9, 3]) assert_equal(insert(a, [-1, 1, 3], [7, 8, 9]), [1, 8, 2, 7, 3, 9]) b = np.array([0, 1], dtype=np.float64) assert_equal(insert(b, 0, b[0]), [0., 0., 1.]) assert_equal(insert(b, [], []), b) # Bools will be treated differently in the future: # assert_equal(insert(a, np.array([True]4), 9), [9, 1, 9, 2, 9, 3, 9]) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', FutureWarning) assert_equal( insert(a, np.array([True] 4), 9), [1, 9, 9, 9, 9, 2, 3]) assert_(w[0].category is FutureWarning) def test_multidim(self): a = [[1, 1, 1]] r = [[2, 2, 2], [1, 1, 1]] assert_equal(insert(a, 0, [1]), [1, 1, 1, 1]) assert_equal(insert(a, 0, [2, 2, 2], axis=0), r) assert_equal(insert(a, 0, 2, axis=0), r) assert_equal(insert(a, 2, 2, axis=1), [[1, 1, 2, 1]]) a = np.array([[1, 1], [2, 2], [3, 3]]) b = np.arange(1, 4).repeat(3).reshape(3, 3) c = np.concatenate( (a[:, 0:1], np.arange(1, 4).repeat(3).reshape(3, 3).T, a[:, 1:2]), axis=1) assert_equal(insert(a, [1], [[1], [2], [3]], axis=1), b) assert_equal(insert(a, [1], [1, 2, 3], axis=1), c) # scalars behave differently, in this case exactly opposite: assert_equal(insert(a, 1, [1, 2, 3], axis=1), b) assert_equal(insert(a, 1, [[1], [2], [3]], axis=1), c) a = np.arange(4).reshape(2, 2) assert_equal(insert(a[:, :1], 1, a[:, 1], axis=1), a) assert_equal(insert(a[:1,:], 1, a[1,:], axis=0), a) # negative axis value a = np.arange(24).reshape((2, 3, 4)) assert_equal(insert(a, 1, a[:,:, 3], axis=-1), insert(a, 1, a[:,:, 3], axis=2)) assert_equal(insert(a, 1, a[:, 2,:], axis=-2), insert(a, 1, a[:, 2,:], axis=1)) # invalid axis value assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=3) assert_raises(np.AxisError, insert, a, 1, a[:, 2, :], axis=-4) # negative axis value a = np.arange(24).reshape((2, 3, 4)) assert_equal(insert(a, 1, a[:, :, 3], axis=-1), insert(a, 1, a[:, :, 3], axis=2)) assert_equal(insert(a, 1, a[:, 2, :], axis=-2), insert(a, 1, a[:, 2, :], axis=1)) def test_0d(self): a = np.array(1) with pytest.raises(np.AxisError): insert(a, [], 2, axis=0) with pytest.raises(TypeError): insert(a, [], 2, axis="nonsense") def test_subclass(self): class SubClass(np.ndarray): pass a = np.arange(10).view(SubClass) assert_(isinstance(np.insert(a, 0, [0]), SubClass)) assert_(isinstance(np.insert(a, [], []), SubClass)) assert_(isinstance(np.insert(a, [0, 1], [1, 2]), SubClass)) assert_(isinstance(np.insert(a, slice(1, 2), [1, 2]), SubClass)) assert_(isinstance(np.insert(a, slice(1, -2, -1), []), SubClass)) # This is an error in the future: a = np.array(1).view(SubClass) assert_(isinstance(np.insert(a, 0, [0]), SubClass)) def test_index_array_copied(self): x = np.array([1, 1, 1]) np.insert([0, 1, 2], x, [3, 4, 5]) assert_equal(x, np.array([1, 1, 1])) def test_structured_array(self): a = np.array([(1, 'a'), (2, 'b'), (3, 'c')], dtype=[('foo', 'i'), ('bar', 'a1')]) val = (4, 'd') b = np.insert(a, 0, val) assert_array_equal(b[0], np.array(val, dtype=b.dtype)) val = [(4, 'd')] * 2 b = np.insert(a, [0, 2], val) assert_array_equal(b[[0, 3]], np.array(val, dtype=b.dtype)) def test_index_floats(self): with pytest.raises(IndexError): np.insert([0, 1, 2], np.array([1.0, 2.0]), [10, 20]) with pytest.raises(IndexError): np.insert([0, 1, 2], np.array([], dtype=float), []) @pytest.mark.parametrize('idx', [4, -4]) def test_index_out_of_bounds(self, idx): with pytest.raises(IndexError, match='out of bounds'): np.insert([0, 1, 2], [idx], [3, 4]) class TestAmax: def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.amax(a), 10.0) b = [[3, 6.0, 9.0], [4, 10.0, 5.0], [8, 3.0, 2.0]] assert_equal(np.amax(b, axis=0), [8.0, 10.0, 9.0]) assert_equal(np.amax(b, axis=1), [9.0, 10.0, 8.0]) class TestAmin: def test_basic(self): a = [3, 4, 5, 10, -3, -5, 6.0] assert_equal(np.amin(a), -5.0) b = [[3, 6.0, 9.0], [4, 10.0, 5.0], [8, 3.0, 2.0]] assert_equal(np.amin(b, axis=0), [3.0, 3.0, 2.0]) assert_equal(np.amin(b, axis=1), [3.0, 4.0, 2.0]) class TestPtp: def test_basic(self): a = np.array([3, 4, 5, 10, -3, -5, 6.0]) assert_equal(a.ptp(axis=0), 15.0) b = np.array([[3, 6.0, 9.0], [4, 10.0, 5.0], [8, 3.0, 2.0]]) assert_equal(b.ptp(axis=0), [5.0, 7.0, 7.0]) assert_equal(b.ptp(axis=-1), [6.0, 6.0, 6.0]) assert_equal(b.ptp(axis=0, keepdims=True), [[5.0, 7.0, 7.0]]) assert_equal(b.ptp(axis=(0,1), keepdims=True), [[8.0]]) class TestCumsum: def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32, np.uint32, np.float32, np.float64, np.complex64, np.complex128]: a = np.array(ba, ctype) a2 = np.array(ba2, ctype) tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype) assert_array_equal(np.cumsum(a, axis=0), tgt) tgt = np.array( [[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype) assert_array_equal(np.cumsum(a2, axis=0), tgt) tgt = np.array( [[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype) assert_array_equal(np.cumsum(a2, axis=1), tgt) class TestProd: def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] for ctype in [np.int16, np.uint16, np.int32, np.uint32, np.float32, np.float64, np.complex64, np.complex128]: a = np.array(ba, ctype) a2 = np.array(ba2, ctype) if ctype in ['1', 'b']: assert_raises(ArithmeticError, np.prod, a) assert_raises(ArithmeticError, np.prod, a2, 1) else: assert_equal(a.prod(axis=0), 26400) assert_array_equal(a2.prod(axis=0), np.array([50, 36, 84, 180], ctype)) assert_array_equal(a2.prod(axis=-1), np.array([24, 1890, 600], ctype)) class TestCumprod: def test_basic(self): ba = [1, 2, 10, 11, 6, 5, 4] ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]] for ctype in [np.int16, np.uint16, np.int32, np.uint32, np.float32, np.float64, np.complex64, np.complex128]: a = np.array(ba, ctype) a2 = np.array(ba2, ctype) if ctype in ['1', 'b']: assert_raises(ArithmeticError, np.cumprod, a) assert_raises(ArithmeticError, np.cumprod, a2, 1) assert_raises(ArithmeticError, np.cumprod, a) else: assert_array_equal(np.cumprod(a, axis=-1), np.array([1, 2, 20, 220, 1320, 6600, 26400], ctype)) assert_array_equal(np.cumprod(a2, axis=0), np.array([[1, 2, 3, 4], [5, 12, 21, 36], [50, 36, 84, 180]], ctype)) assert_array_equal(np.cumprod(a2, axis=-1), np.array([[1, 2, 6, 24], [5, 30, 210, 1890], [10, 30, 120, 600]], ctype)) class TestDiff: def test_basic(self): x = [1, 4, 6, 7, 12] out = np.array([3, 2, 1, 5]) out2 = np.array([-1, -1, 4]) out3 = np.array([0, 5]) assert_array_equal(diff(x), out) assert_array_equal(diff(x, n=2), out2) assert_array_equal(diff(x, n=3), out3) x = [1.1, 2.2, 3.0, -0.2, -0.1] out = np.array([1.1, 0.8, -3.2, 0.1]) assert_almost_equal(diff(x), out) x = [True, True, False, False] out = np.array([False, True, False]) out2 = np.array([True, True]) assert_array_equal(diff(x), out) assert_array_equal(diff(x, n=2), out2) def test_axis(self): x = np.zeros((10, 20, 30)) x[:, 1::2, :] = 1 exp = np.ones((10, 19, 30)) exp[:, 1::2, :] = -1 assert_array_equal(diff(x), np.zeros((10, 20, 29))) assert_array_equal(diff(x, axis=-1), np.zeros((10, 20, 29))) assert_array_equal(diff(x, axis=0), np.zeros((9, 20, 30))) assert_array_equal(diff(x, axis=1), exp) assert_array_equal(diff(x, axis=-2), exp) assert_raises(np.AxisError, diff, x, axis=3) assert_raises(np.AxisError, diff, x, axis=-4) x = np.array(1.11111111111, np.float64) assert_raises(ValueError, diff, x) def test_nd(self): x = 20 * rand(10, 20, 30) out1 = x[:, :, 1:] - x[:, :, :-1] out2 = out1[:, :, 1:] - out1[:, :, :-1] out3 = x[1:, :, :] - x[:-1, :, :] out4 = out3[1:, :, :] - out3[:-1, :, :] assert_array_equal(diff(x), out1) assert_array_equal(diff(x, n=2), out2) assert_array_equal(diff(x, axis=0), out3) assert_array_equal(diff(x, n=2, axis=0), out4) def test_n(self): x = list(range(3)) assert_raises(ValueError, diff, x, n=-1) output = [diff(x, n=n) for n in range(1, 5)] expected = [[1, 1], [0], [], []] assert_(diff(x, n=0) is x) for n, (expected, out) in enumerate(zip(expected, output), start=1): assert_(type(out) is np.ndarray) assert_array_equal(out, expected) assert_equal(out.dtype, np.int_) assert_equal(len(out), max(0, len(x) - n)) def test_times(self): x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64) expected = [ np.array([1, 1], dtype='timedelta64[D]'), np.array([0], dtype='timedelta64[D]'), ] expected.extend([np.array([], dtype='timedelta64[D]')] * 3) for n, exp in enumerate(expected, start=1): out = diff(x, n=n) assert_array_equal(out, exp) assert_equal(out.dtype, exp.dtype) def test_subclass(self): x = ma.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]], mask=[[False, False], [True, False], [False, True], [True, True], [False, False]]) out = diff(x) assert_array_equal(out.data, [[1], [1], [1], [1], [1]]) assert_array_equal(out.mask, [[False], [True], [True], [True], [False]]) assert_(type(out) is type(x)) out3 = diff(x, n=3) assert_array_equal(out3.data, [[], [], [], [], []]) assert_array_equal(out3.mask, [[], [], [], [], []]) assert_(type(out3) is type(x)) def test_prepend(self): x = np.arange(5) + 1 assert_array_equal(diff(x, prepend=0), np.ones(5)) assert_array_equal(diff(x, prepend=[0]), np.ones(5)) assert_array_equal(np.cumsum(np.diff(x, prepend=0)), x) assert_array_equal(diff(x, prepend=[-1, 0]), np.ones(6)) x = np.arange(4).reshape(2, 2) result = np.diff(x, axis=1, prepend=0) expected = [[0, 1], [2, 1]] assert_array_equal(result, expected) result = np.diff(x, axis=1, prepend=[[0], [0]]) assert_array_equal(result, expected) result = np.diff(x, axis=0, prepend=0) expected = [[0, 1], [2, 2]] assert_array_equal(result, expected) result = np.diff(x, axis=0, prepend=[[0, 0]]) assert_array_equal(result, expected) assert_raises(ValueError, np.diff, x, prepend=np.zeros((3,3))) assert_raises(np.AxisError, diff, x, prepend=0, axis=3) def test_append(self): x = np.arange(5) result = diff(x, append=0) expected = [1, 1, 1, 1, -4] assert_array_equal(result, expected) result = diff(x, append=[0]) assert_array_equal(result, expected) result = diff(x, append=[0, 2]) expected = expected + [2] assert_array_equal(result, expected) x = np.arange(4).reshape(2, 2) result = np.diff(x, axis=1, append=0) expected = [[1, -1], [1, -3]] assert_array_equal(result, expected) result = np.diff(x, axis=1, append=[[0], [0]]) assert_array_equal(result, expected) result = np.diff(x, axis=0, append=0) expected = [[2, 2], [-2, -3]] assert_array_equal(result, expected) result = np.diff(x, axis=0, append=[[0, 0]]) assert_array_equal(result, expected) assert_raises(ValueError, np.diff, x, append=np.zeros((3,3))) assert_raises(np.AxisError, diff, x, append=0, axis=3) class TestDelete: def setup_method(self): self.a = np.arange(5) self.nd_a = np.arange(5).repeat(2).reshape(1, 5, 2) def _check_inverse_of_slicing(self, indices): a_del = delete(self.a, indices) nd_a_del = delete(self.nd_a, indices, axis=1) msg = 'Delete failed for obj: %r' % indices assert_array_equal(setxor1d(a_del, self.a[indices, ]), self.a, err_msg=msg) xor = setxor1d(nd_a_del[0,:, 0], self.nd_a[0, indices, 0]) assert_array_equal(xor, self.nd_a[0,:, 0], err_msg=msg) def test_slices(self): lims = [-6, -2, 0, 1, 2, 4, 5] steps = [-3, -1, 1, 3] for start in lims: for stop in lims: for step in steps: s = slice(start, stop, step) self._check_inverse_of_slicing(s) def test_fancy(self): self._check_inverse_of_slicing(np.array([[0, 1], [2, 1]])) with pytest.raises(IndexError): delete(self.a, [100]) with pytest.raises(IndexError): delete(self.a, [-100]) self._check_inverse_of_slicing([0, -1, 2, 2]) self._check_inverse_of_slicing([True, False, False, True, False]) # not legal, indexing with these would change the dimension with pytest.raises(ValueError): delete(self.a, True) with pytest.raises(ValueError): delete(self.a, False) # not enough items with pytest.raises(ValueError): delete(self.a, [False]4) def test_single(self): self._check_inverse_of_slicing(0) self._check_inverse_of_slicing(-4) def test_0d(self): a = np.array(1) with pytest.raises(np.AxisError): delete(a, [], axis=0) with pytest.raises(TypeError): delete(a, [], axis="nonsense") def test_subclass(self): class SubClass(np.ndarray): pass a = self.a.view(SubClass) assert_(isinstance(delete(a, 0), SubClass)) assert_(isinstance(delete(a, []), SubClass)) assert_(isinstance(delete(a, [0, 1]), SubClass)) assert_(isinstance(delete(a, slice(1, 2)), SubClass)) assert_(isinstance(delete(a, slice(1, -2)), SubClass)) def test_array_order_preserve(self): # See gh-7113 k = np.arange(10).reshape(2, 5, order='F') m = delete(k, slice(60, None), axis=1) # 'k' is Fortran ordered, and 'm' should have the # same ordering as 'k' and NOT become C ordered assert_equal(m.flags.c_contiguous, k.flags.c_contiguous) assert_equal(m.flags.f_contiguous, k.flags.f_contiguous) def test_index_floats(self): with pytest.raises(IndexError): np.delete([0, 1, 2], np.array([1.0, 2.0])) with pytest.raises(IndexError): np.delete([0, 1, 2], np.array([], dtype=float)) @pytest.mark.parametrize("indexer", [np.array([1]), [1]]) def test_single_item_array(self, indexer): a_del_int = delete(self.a, 1) a_del = delete(self.a, indexer) assert_equal(a_del_int, a_del) nd_a_del_int = delete(self.nd_a, 1, axis=1) nd_a_del = delete(self.nd_a, np.array([1]), axis=1) assert_equal(nd_a_del_int, nd_a_del) def test_single_item_array_non_int(self): # Special handling for integer arrays must not affect non-integer ones. # If `False` was cast to `0` it would delete the element: res = delete(np.ones(1), np.array([False])) assert_array_equal(res, np.ones(1)) # Test the more complicated (with axis) case from gh-21840 x = np.ones((3, 1)) false_mask = np.array([False], dtype=bool) true_mask = np.array([True], dtype=bool) res = delete(x, false_mask, axis=-1) assert_array_equal(res, x) res = delete(x, true_mask, axis=-1) assert_array_equal(res, x[:, :0]) # Object or e.g. timedeltas should not* be allowed with pytest.raises(IndexError): delete(np.ones(2), np.array([0], dtype=object)) with pytest.raises(IndexError): # timedeltas are sometimes "integral, but clearly not allowed: delete(np.ones(2), np.array([0], dtype="m8[ns]")) class TestGradient: def test_basic(self): v = [[1, 1], [3, 4]] x = np.array(v) dx = [np.array([[2., 3.], [2., 3.]]), np.array([[0., 0.], [1., 1.]])] assert_array_equal(gradient(x), dx) assert_array_equal(gradient(v), dx) def test_args(self): dx = np.cumsum(np.ones(5)) dx_uneven = [1., 2., 5., 9., 11.] f_2d = np.arange(25).reshape(5, 5) # distances must be scalars or have size equal to gradient[axis] gradient(np.arange(5), 3.) gradient(np.arange(5), np.array(3.)) gradient(np.arange(5), dx) # dy is set equal to dx because scalar gradient(f_2d, 1.5) gradient(f_2d, np.array(1.5)) gradient(f_2d, dx_uneven, dx_uneven) # mix between even and uneven spaces and # mix between scalar and vector gradient(f_2d, dx, 2) # 2D but axis specified gradient(f_2d, dx, axis=1) # 2d coordinate arguments are not yet allowed assert_raises_regex(ValueError, '.scalars or 1d', gradient, f_2d, np.stack([dx]2, axis=-1), 1) def test_badargs(self): f_2d = np.arange(25).reshape(5, 5) x = np.cumsum(np.ones(5)) # wrong sizes assert_raises(ValueError, gradient, f_2d, x, np.ones(2)) assert_raises(ValueError, gradient, f_2d, 1, np.ones(2)) assert_raises(ValueError, gradient, f_2d, np.ones(2), np.ones(2)) # wrong number of arguments assert_raises(TypeError, gradient, f_2d, x) assert_raises(TypeError, gradient, f_2d, x, axis=(0,1)) assert_raises(TypeError, gradient, f_2d, x, x, x) assert_raises(TypeError, gradient, f_2d, 1, 1, 1) assert_raises(TypeError, gradient, f_2d, x, x, axis=1) assert_raises(TypeError, gradient, f_2d, 1, 1, axis=1) def test_datetime64(self): # Make sure gradient() can handle special types like datetime64 x = np.array( ['1910-08-16', '1910-08-11', '1910-08-10', '1910-08-12', '1910-10-12', '1910-12-12', '1912-12-12'], dtype='datetime64[D]') dx = np.array( [-5, -3, 0, 31, 61, 396, 731], dtype='timedelta64[D]') assert_array_equal(gradient(x), dx) assert_(dx.dtype == np.dtype('timedelta64[D]')) def test_masked(self): # Make sure that gradient supports subclasses like masked arrays x = np.ma.array([[1, 1], [3, 4]], mask=[[False, False], [False, False]]) out = gradient(x)[0] assert_equal(type(out), type(x)) # And make sure that the output and input don't have aliased mask # arrays assert_(x._mask is not out._mask) # Also check that edge_order=2 doesn't alter the original mask x2 = np.ma.arange(5) x2[2] = np.ma.masked np.gradient(x2, edge_order=2) assert_array_equal(x2.mask, [False, False, True, False, False]) def test_second_order_accurate(self): # Testing that the relative numerical error is less that 3% for # this example problem. This corresponds to second order # accurate finite differences for all interior and boundary # points. x = np.linspace(0, 1, 10) dx = x[1] - x[0] y = 2 * x ** 3 + 4 * x ** 2 + 2 * x analytical = 6 * x ** 2 + 8 * x + 2 num_error = np.abs((np.gradient(y, dx, edge_order=2) / analytical) - 1) assert_(np.all(num_error < 0.03) == True) # test with unevenly spaced np.random.seed(0) x = np.sort(np.random.random(10)) y = 2 * x ** 3 + 4 * x ** 2 + 2 * x analytical = 6 * x ** 2 + 8 * x + 2 num_error = np.abs((np.gradient(y, x, edge_order=2) / analytical) - 1) assert_(np.all(num_error < 0.03) == True) def test_spacing(self): f = np.array([0, 2., 3., 4., 5., 5.]) f = np.tile(f, (6,1)) + f.reshape(-1, 1) x_uneven = np.array([0., 0.5, 1., 3., 5., 7.]) x_even = np.arange(6.) fdx_even_ord1 = np.tile([2., 1.5, 1., 1., 0.5, 0.], (6,1)) fdx_even_ord2 = np.tile([2.5, 1.5, 1., 1., 0.5, -0.5], (6,1)) fdx_uneven_ord1 = np.tile([4., 3., 1.7, 0.5, 0.25, 0.], (6,1)) fdx_uneven_ord2 = np.tile([5., 3., 1.7, 0.5, 0.25, -0.25], (6,1)) # evenly spaced for edge_order, exp_res in [(1, fdx_even_ord1), (2, fdx_even_ord2)]: res1 = gradient(f, 1., axis=(0,1), edge_order=edge_order) res2 = gradient(f, x_even, x_even, axis=(0,1), edge_order=edge_order) res3 = gradient(f, x_even, x_even, axis=None, edge_order=edge_order) assert_array_equal(res1, res2) assert_array_equal(res2, res3) assert_almost_equal(res1[0], exp_res.T) assert_almost_equal(res1[1], exp_res) res1 = gradient(f, 1., axis=0, edge_order=edge_order) res2 = gradient(f, x_even, axis=0, edge_order=edge_order) assert_(res1.shape == res2.shape) assert_almost_equal(res2, exp_res.T) res1 = gradient(f, 1., axis=1, edge_order=edge_order) res2 = gradient(f, x_even, axis=1, edge_order=edge_order) assert_(res1.shape == res2.shape) assert_array_equal(res2, exp_res) # unevenly spaced for edge_order, exp_res in [(1, fdx_uneven_ord1), (2, fdx_uneven_ord2)]: res1 = gradient(f, x_uneven, x_uneven, axis=(0,1), edge_order=edge_order) res2 = gradient(f, x_uneven, x_uneven, axis=None, edge_order=edge_order) assert_array_equal(res1, res2) assert_almost_equal(res1[0], exp_res.T) assert_almost_equal(res1[1], exp_res) res1 = gradient(f, x_uneven, axis=0, edge_order=edge_order) assert_almost_equal(res1, exp_res.T) res1 = gradient(f, x_uneven, axis=1, edge_order=edge_order) assert_almost_equal(res1, exp_res) # mixed res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=1) res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=1) assert_array_equal(res1[0], res2[1]) assert_array_equal(res1[1], res2[0]) assert_almost_equal(res1[0], fdx_even_ord1.T) assert_almost_equal(res1[1], fdx_uneven_ord1) res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=2) res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=2) assert_array_equal(res1[0], res2[1]) assert_array_equal(res1[1], res2[0]) assert_almost_equal(res1[0], fdx_even_ord2.T) assert_almost_equal(res1[1], fdx_uneven_ord2) def test_specific_axes(self): # Testing that gradient can work on a given axis only v = [[1, 1], [3, 4]] x = np.array(v) dx = [np.array([[2., 3.], [2., 3.]]), np.array([[0., 0.], [1., 1.]])] assert_array_equal(gradient(x, axis=0), dx[0]) assert_array_equal(gradient(x, axis=1), dx[1]) assert_array_equal(gradient(x, axis=-1), dx[1]) assert_array_equal(gradient(x, axis=(1, 0)), [dx[1], dx[0]]) # test axis=None which means all axes assert_almost_equal(gradient(x, axis=None), [dx[0], dx[1]]) # and is the same as no axis keyword given assert_almost_equal(gradient(x, axis=None), gradient(x)) # test vararg order assert_array_equal(gradient(x, 2, 3, axis=(1, 0)), [dx[1]/2.0, dx[0]/3.0]) # test maximal number of varargs assert_raises(TypeError, gradient, x, 1, 2, axis=1) assert_raises(np.AxisError, gradient, x, axis=3) assert_raises(np.AxisError, gradient, x, axis=-3) # assert_raises(TypeError, gradient, x, axis=[1,]) def test_timedelta64(self): # Make sure gradient() can handle special types like timedelta64 x = np.array( [-5, -3, 10, 12, 61, 321, 300], dtype='timedelta64[D]') dx = np.array( [2, 7, 7, 25, 154, 119, -21], dtype='timedelta64[D]') assert_array_equal(gradient(x), dx) assert_(dx.dtype == np.dtype('timedelta64[D]')) def test_inexact_dtypes(self): for dt in [np.float16, np.float32, np.float64]: # dtypes should not be promoted in a different way to what diff does x = np.array([1, 2, 3], dtype=dt) assert_equal(gradient(x).dtype, np.diff(x).dtype) def test_values(self): # needs at least 2 points for edge_order ==1 gradient(np.arange(2), edge_order=1) # needs at least 3 points for edge_order ==1 gradient(np.arange(3), edge_order=2) assert_raises(ValueError, gradient, np.arange(0), edge_order=1) assert_raises(ValueError, gradient, np.arange(0), edge_order=2) assert_raises(ValueError, gradient, np.arange(1), edge_order=1) assert_raises(ValueError, gradient, np.arange(1), edge_order=2) assert_raises(ValueError, gradient, np.arange(2), edge_order=2) @pytest.mark.parametrize('f_dtype', [np.uint8, np.uint16, np.uint32, np.uint64]) def test_f_decreasing_unsigned_int(self, f_dtype): f = np.array([5, 4, 3, 2, 1], dtype=f_dtype) g = gradient(f) assert_array_equal(g, [-1]len(f)) @pytest.mark.parametrize('f_dtype', [np.int8, np.int16, np.int32, np.int64]) def test_f_signed_int_big_jump(self, f_dtype): maxint = np.iinfo(f_dtype).max x = np.array([1, 3]) f = np.array([-1, maxint], dtype=f_dtype) dfdx = gradient(f, x) assert_array_equal(dfdx, [(maxint + 1) // 2]2) @pytest.mark.parametrize('x_dtype', [np.uint8, np.uint16, np.uint32, np.uint64]) def test_x_decreasing_unsigned(self, x_dtype): x = np.array([3, 2, 1], dtype=x_dtype) f = np.array([0, 2, 4]) dfdx = gradient(f, x) assert_array_equal(dfdx, [-2]len(x)) @pytest.mark.parametrize('x_dtype', [np.int8, np.int16, np.int32, np.int64]) def test_x_signed_int_big_jump(self, x_dtype): minint = np.iinfo(x_dtype).min maxint = np.iinfo(x_dtype).max x = np.array([-1, maxint], dtype=x_dtype) f = np.array([minint // 2, 0]) dfdx = gradient(f, x) assert_array_equal(dfdx, [0.5, 0.5]) def test_return_type(self): res = np.gradient(([1, 2], [2, 3])) if np._using_numpy2_behavior(): assert type(res) is tuple else: assert type(res) is list class TestAngle: def test_basic(self): x = [1 + 3j, np.sqrt(2) / 2.0 + 1j np.sqrt(2) / 2, 1, 1j, -1, -1j, 1 - 3j, -1 + 3j] y = angle(x) yo = [ np.arctan(3.0 / 1.0), np.arctan(1.0), 0, np.pi / 2, np.pi, -np.pi / 2.0, -np.arctan(3.0 / 1.0), np.pi - np.arctan(3.0 / 1.0)] z = angle(x, deg=True) zo = np.array(yo) * 180 / np.pi assert_array_almost_equal(y, yo, 11) assert_array_almost_equal(z, zo, 11) def test_subclass(self): x = np.ma.array([1 + 3j, 1, np.sqrt(2)/2 * (1 + 1j)]) x[1] = np.ma.masked expected = np.ma.array([np.arctan(3.0 / 1.0), 0, np.arctan(1.0)]) expected[1] = np.ma.masked actual = angle(x) assert_equal(type(actual), type(expected)) assert_equal(actual.mask, expected.mask) assert_equal(actual, expected) class TestTrimZeros: a = np.array([0, 0, 1, 0, 2, 3, 4, 0]) b = a.astype(float) c = a.astype(complex) d = a.astype(object) def values(self): attr_names = ('a', 'b', 'c', 'd') return (getattr(self, name) for name in attr_names) def test_basic(self): slc = np.s_[2:-1] for arr in self.values(): res = trim_zeros(arr) assert_array_equal(res, arr[slc]) def test_leading_skip(self): slc = np.s_[:-1] for arr in self.values(): res = trim_zeros(arr, trim='b') assert_array_equal(res, arr[slc]) def test_trailing_skip(self): slc = np.s_[2:] for arr in self.values(): res = trim_zeros(arr, trim='F') assert_array_equal(res, arr[slc]) def test_all_zero(self): for _arr in self.values(): arr = np.zeros_like(_arr, dtype=_arr.dtype) res1 = trim_zeros(arr, trim='B') assert len(res1) == 0 res2 = trim_zeros(arr, trim='f') assert len(res2) == 0 def test_size_zero(self): arr = np.zeros(0) res = trim_zeros(arr) assert_array_equal(arr, res) @pytest.mark.parametrize( 'arr', [np.array([0, 262, 0]), np.array([0, 263, 0]), np.array([0, 2*64, 0])] ) def test_overflow(self, arr): slc = np.s_[1:2] res = trim_zeros(arr) assert_array_equal(res, arr[slc]) def test_no_trim(self): arr = np.array([None, 1, None]) res = trim_zeros(arr) assert_array_equal(arr, res) def test_list_to_list(self): res = trim_zeros(self.a.tolist()) assert isinstance(res, list) class TestExtins: def test_basic(self): a = np.array([1, 3, 2, 1, 2, 3, 3]) b = extract(a > 1, a) assert_array_equal(b, [3, 2, 2, 3, 3]) def test_place(self): # Make sure that non-np.ndarray objects # raise an error instead of doing nothing assert_raises(TypeError, place, [1, 2, 3], [True, False], [0, 1]) a = np.array([1, 4, 3, 2, 5, 8, 7]) place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6]) assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7]) place(a, np.zeros(7), []) assert_array_equal(a, np.arange(1, 8)) place(a, [1, 0, 1, 0, 1, 0, 1], [8, 9]) assert_array_equal(a, [8, 2, 9, 4, 8, 6, 9]) assert_raises_regex(ValueError, "Cannot insert from an empty array", lambda: place(a, [0, 0, 0, 0, 0, 1, 0], [])) # See Issue #6974 a = np.array(['12', '34']) place(a, [0, 1], '9') assert_array_equal(a, ['12', '9']) def test_both(self): a = rand(10) mask = a > 0.5 ac = a.copy() c = extract(mask, a) place(a, mask, 0) place(a, mask, c) assert_array_equal(a, ac) # _foo1 and _foo2 are used in some tests in TestVectorize. def _foo1(x, y=1.0): return ymath.floor(x) def _foo2(x, y=1.0, z=0.0): return ymath.floor(x) + z class TestVectorize: def test_simple(self): def addsubtract(a, b): if a > b: return a - b else: return a + b f = vectorize(addsubtract) r = f([0, 3, 6, 9], [1, 3, 5, 7]) assert_array_equal(r, [1, 6, 1, 2]) def test_scalar(self): def addsubtract(a, b): if a > b: return a - b else: return a + b f = vectorize(addsubtract) r = f([0, 3, 6, 9], 5) assert_array_equal(r, [5, 8, 1, 4]) def test_large(self): x = np.linspace(-3, 2, 10000) f = vectorize(lambda x: x) y = f(x) assert_array_equal(y, x) def test_ufunc(self): f = vectorize(math.cos) args = np.array([0, 0.5 np.pi, np.pi, 1.5 * np.pi, 2 * np.pi]) r1 = f(args) r2 = np.cos(args) assert_array_almost_equal(r1, r2) def test_keywords(self): def foo(a, b=1): return a + b f = vectorize(foo) args = np.array([1, 2, 3]) r1 = f(args) r2 = np.array([2, 3, 4]) assert_array_equal(r1, r2) r1 = f(args, 2) r2 = np.array([3, 4, 5]) assert_array_equal(r1, r2) def test_keywords_with_otypes_order1(self): # gh-1620: The second call of f would crash with # `ValueError: invalid number of arguments`. f = vectorize(_foo1, otypes=[float]) # We're testing the caching of ufuncs by vectorize, so the order # of these function calls is an important part of the test. r1 = f(np.arange(3.0), 1.0) r2 = f(np.arange(3.0)) assert_array_equal(r1, r2) def test_keywords_with_otypes_order2(self): # gh-1620: The second call of f would crash with # `ValueError: non-broadcastable output operand with shape () # doesn't match the broadcast shape (3,)`. f = vectorize(_foo1, otypes=[float]) # We're testing the caching of ufuncs by vectorize, so the order # of these function calls is an important part of the test. r1 = f(np.arange(3.0)) r2 = f(np.arange(3.0), 1.0) assert_array_equal(r1, r2) def test_keywords_with_otypes_order3(self): # gh-1620: The third call of f would crash with # `ValueError: invalid number of arguments`. f = vectorize(_foo1, otypes=[float]) # We're testing the caching of ufuncs by vectorize, so the order # of these function calls is an important part of the test. r1 = f(np.arange(3.0)) r2 = f(np.arange(3.0), y=1.0) r3 = f(np.arange(3.0)) assert_array_equal(r1, r2) assert_array_equal(r1, r3) def test_keywords_with_otypes_several_kwd_args1(self): # gh-1620 Make sure different uses of keyword arguments # don't break the vectorized function. f = vectorize(_foo2, otypes=[float]) # We're testing the caching of ufuncs by vectorize, so the order # of these function calls is an important part of the test. r1 = f(10.4, z=100) r2 = f(10.4, y=-1) r3 = f(10.4) assert_equal(r1, _foo2(10.4, z=100)) assert_equal(r2, _foo2(10.4, y=-1)) assert_equal(r3, _foo2(10.4)) def test_keywords_with_otypes_several_kwd_args2(self): # gh-1620 Make sure different uses of keyword arguments # don't break the vectorized function. f = vectorize(_foo2, otypes=[float]) # We're testing the caching of ufuncs by vectorize, so the order # of these function calls is an important part of the test. r1 = f(z=100, x=10.4, y=-1) r2 = f(1, 2, 3) assert_equal(r1, _foo2(z=100, x=10.4, y=-1)) assert_equal(r2, _foo2(1, 2, 3)) def test_keywords_no_func_code(self): # This needs to test a function that has keywords but # no func_code attribute, since otherwise vectorize will # inspect the func_code. import random try: vectorize(random.randrange) # Should succeed except Exception: raise AssertionError() def test_keywords2_ticket_2100(self): # Test kwarg support: enhancement ticket 2100 def foo(a, b=1): return a + b f = vectorize(foo) args = np.array([1, 2, 3]) r1 = f(a=args) r2 = np.array([2, 3, 4]) assert_array_equal(r1, r2) r1 = f(b=1, a=args) assert_array_equal(r1, r2) r1 = f(args, b=2) r2 = np.array([3, 4, 5]) assert_array_equal(r1, r2) def test_keywords3_ticket_2100(self): # Test excluded with mixed positional and kwargs: ticket 2100 def mypolyval(x, p): _p = list(p) res = _p.pop(0) while _p: res = res * x + _p.pop(0) return res vpolyval = np.vectorize(mypolyval, excluded=['p', 1]) ans = [3, 6] assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3])) assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3])) assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3])) def test_keywords4_ticket_2100(self): # Test vectorizing function with no positional args. @vectorize def f(*kw): res = 1.0 for _k in kw: res = kw[_k] return res assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8]) def test_keywords5_ticket_2100(self): # Test vectorizing function with no kwargs args. @vectorize def f(v): return np.prod(v) assert_array_equal(f([1, 2], [3, 4]), [3, 8]) def test_coverage1_ticket_2100(self): def foo(): return 1 f = vectorize(foo) assert_array_equal(f(), 1) def test_assigning_docstring(self): def foo(x): """Original documentation""" return x f = vectorize(foo) assert_equal(f.__doc__, foo.__doc__) doc = "Provided documentation" f = vectorize(foo, doc=doc) assert_equal(f.__doc__, doc) def test_UnboundMethod_ticket_1156(self): # Regression test for issue 1156 class Foo: b = 2 def bar(self, a): return a * self.b assert_array_equal(vectorize(Foo().bar)(np.arange(9)), np.arange(9) 2) assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)), np.arange(9) 2) def test_execution_order_ticket_1487(self): # Regression test for dependence on execution order: issue 1487 f1 = vectorize(lambda x: x) res1a = f1(np.arange(3)) res1b = f1(np.arange(0.1, 3)) f2 = vectorize(lambda x: x) res2b = f2(np.arange(0.1, 3)) res2a = f2(np.arange(3)) assert_equal(res1a, res2a) assert_equal(res1b, res2b) def test_string_ticket_1892(self): # Test vectorization over strings: issue 1892. f = np.vectorize(lambda x: x) s = '0123456789' * 10 assert_equal(s, f(s)) def test_cache(self): # Ensure that vectorized func called exactly once per argument. _calls = [0] @vectorize def f(x): _calls[0] += 1 return x ** 2 f.cache = True x = np.arange(5) assert_array_equal(f(x), x * x) assert_equal(_calls[0], len(x)) def test_otypes(self): f = np.vectorize(lambda x: x) f.otypes = 'i' x = np.arange(5) assert_array_equal(f(x), x) def test_parse_gufunc_signature(self): assert_equal(nfb._parse_gufunc_signature('(x)->()'), ([('x',)], [()])) assert_equal(nfb._parse_gufunc_signature('(x,y)->()'), ([('x', 'y')], [()])) assert_equal(nfb._parse_gufunc_signature('(x),(y)->()'), ([('x',), ('y',)], [()])) assert_equal(nfb._parse_gufunc_signature('(x)->(y)'), ([('x',)], [('y',)])) assert_equal(nfb._parse_gufunc_signature('(x)->(y),()'), ([('x',)], [('y',), ()])) assert_equal(nfb._parse_gufunc_signature('(),(a,b,c),(d)->(d,e)'), ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')])) # Tests to check if whitespaces are ignored assert_equal(nfb._parse_gufunc_signature('(x )->()'), ([('x',)], [()])) assert_equal(nfb._parse_gufunc_signature('( x , y )->( )'), ([('x', 'y')], [()])) assert_equal(nfb._parse_gufunc_signature('(x),( y) ->()'), ([('x',), ('y',)], [()])) assert_equal(nfb._parse_gufunc_signature('( x)-> (y ) '), ([('x',)], [('y',)])) assert_equal(nfb._parse_gufunc_signature(' (x)->( y),( )'), ([('x',)], [('y',), ()])) assert_equal(nfb._parse_gufunc_signature( '( ), ( a, b,c ) ,( d) -> (d , e)'), ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')])) with assert_raises(ValueError): nfb._parse_gufunc_signature('(x)(y)->()') with assert_raises(ValueError): nfb._parse_gufunc_signature('(x),(y)->') with assert_raises(ValueError): nfb._parse_gufunc_signature('((x))->(x)') def test_signature_simple(self): def addsubtract(a, b): if a > b: return a - b else: return a + b f = vectorize(addsubtract, signature='(),()->()') r = f([0, 3, 6, 9], [1, 3, 5, 7]) assert_array_equal(r, [1, 6, 1, 2]) def test_signature_mean_last(self): def mean(a): return a.mean() f = vectorize(mean, signature='(n)->()') r = f([[1, 3], [2, 4]]) assert_array_equal(r, [2, 3]) def test_signature_center(self): def center(a): return a - a.mean() f = vectorize(center, signature='(n)->(n)') r = f([[1, 3], [2, 4]]) assert_array_equal(r, [[-1, 1], [-1, 1]]) def test_signature_two_outputs(self): f = vectorize(lambda x: (x, x), signature='()->(),()') r = f([1, 2, 3]) assert_(isinstance(r, tuple) and len(r) == 2) assert_array_equal(r[0], [1, 2, 3]) assert_array_equal(r[1], [1, 2, 3]) def test_signature_outer(self): f = vectorize(np.outer, signature='(a),(b)->(a,b)') r = f([1, 2], [1, 2, 3]) assert_array_equal(r, [[1, 2, 3], [2, 4, 6]]) r = f([[[1, 2]]], [1, 2, 3]) assert_array_equal(r, [[[[1, 2, 3], [2, 4, 6]]]]) r = f([[1, 0], [2, 0]], [1, 2, 3]) assert_array_equal(r, [[[1, 2, 3], [0, 0, 0]], [[2, 4, 6], [0, 0, 0]]]) r = f([1, 2], [[1, 2, 3], [0, 0, 0]]) assert_array_equal(r, [[[1, 2, 3], [2, 4, 6]], [[0, 0, 0], [0, 0, 0]]]) def test_signature_computed_size(self): f = vectorize(lambda x: x[:-1], signature='(n)->(m)') r = f([1, 2, 3]) assert_array_equal(r, [1, 2]) r = f([[1, 2, 3], [2, 3, 4]]) assert_array_equal(r, [[1, 2], [2, 3]]) def test_signature_excluded(self): def foo(a, b=1): return a + b f = vectorize(foo, signature='()->()', excluded={'b'}) assert_array_equal(f([1, 2, 3]), [2, 3, 4]) assert_array_equal(f([1, 2, 3], b=0), [1, 2, 3]) def test_signature_otypes(self): f = vectorize(lambda x: x, signature='(n)->(n)', otypes=['float64']) r = f([1, 2, 3]) assert_equal(r.dtype, np.dtype('float64')) assert_array_equal(r, [1, 2, 3]) def test_signature_invalid_inputs(self): f = vectorize(operator.add, signature='(n),(n)->(n)') with assert_raises_regex(TypeError, 'wrong number of positional'): f([1, 2]) with assert_raises_regex( ValueError, 'does not have enough dimensions'): f(1, 2) with assert_raises_regex( ValueError, 'inconsistent size for core dimension'): f([1, 2], [1, 2, 3]) f = vectorize(operator.add, signature='()->()') with assert_raises_regex(TypeError, 'wrong number of positional'): f(1, 2) def test_signature_invalid_outputs(self): f = vectorize(lambda x: x[:-1], signature='(n)->(n)') with assert_raises_regex( ValueError, 'inconsistent size for core dimension'): f([1, 2, 3]) f = vectorize(lambda x: x, signature='()->(),()') with assert_raises_regex(ValueError, 'wrong number of outputs'): f(1) f = vectorize(lambda x: (x, x), signature='()->()') with assert_raises_regex(ValueError, 'wrong number of outputs'): f([1, 2]) def test_size_zero_output(self): # see issue 5868 f = np.vectorize(lambda x: x) x = np.zeros([0, 5], dtype=int) with assert_raises_regex(ValueError, 'otypes'): f(x) f.otypes = 'i' assert_array_equal(f(x), x) f = np.vectorize(lambda x: x, signature='()->()') with assert_raises_regex(ValueError, 'otypes'): f(x) f = np.vectorize(lambda x: x, signature='()->()', otypes='i') assert_array_equal(f(x), x) f = np.vectorize(lambda x: x, signature='(n)->(n)', otypes='i') assert_array_equal(f(x), x) f = np.vectorize(lambda x: x, signature='(n)->(n)') assert_array_equal(f(x.T), x.T) f = np.vectorize(lambda x: [x], signature='()->(n)', otypes='i') with assert_raises_regex(ValueError, 'new output dimensions'): f(x) def test_subclasses(self): class subclass(np.ndarray): pass m = np.array([[1., 0., 0.], [0., 0., 1.], [0., 1., 0.]]).view(subclass) v = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]).view(subclass) # generalized (gufunc) matvec = np.vectorize(np.matmul, signature='(m,m),(m)->(m)') r = matvec(m, v) assert_equal(type(r), subclass) assert_equal(r, [[1., 3., 2.], [4., 6., 5.], [7., 9., 8.]]) # element-wise (ufunc) mult = np.vectorize(lambda x, y: xy) r = mult(m, v) assert_equal(type(r), subclass) assert_equal(r, m v) def test_name(self): #See gh-23021 @np.vectorize def f2(a, b): return a + b assert f2.__name__ == 'f2' def test_decorator(self): @vectorize def addsubtract(a, b): if a > b: return a - b else: return a + b r = addsubtract([0, 3, 6, 9], [1, 3, 5, 7]) assert_array_equal(r, [1, 6, 1, 2]) def test_docstring(self): @vectorize def f(x): """Docstring""" return x if sys.flags.optimize < 2: assert f.__doc__ == "Docstring" def test_partial(self): def foo(x, y): return x + y bar = partial(foo, 3) vbar = np.vectorize(bar) assert vbar(1) == 4 def test_signature_otypes_decorator(self): @vectorize(signature='(n)->(n)', otypes=['float64']) def f(x): return x r = f([1, 2, 3]) assert_equal(r.dtype, np.dtype('float64')) assert_array_equal(r, [1, 2, 3]) assert f.__name__ == 'f' def test_bad_input(self): with assert_raises(TypeError): A = np.vectorize(pyfunc = 3) def test_no_keywords(self): with assert_raises(TypeError): @np.vectorize("string") def foo(): return "bar" def test_positional_regression_9477(self): # This supplies the first keyword argument as a positional, # to ensure that they are still properly forwarded after the # enhancement for #9477 f = vectorize((lambda x: x), ['float64']) r = f([2]) assert_equal(r.dtype, np.dtype('float64')) class TestLeaks: class A: iters = 20 def bound(self, args): return 0 @staticmethod def unbound(args): return 0 @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts") @pytest.mark.parametrize('name, incr', [ ('bound', A.iters), ('unbound', 0), ]) def test_frompyfunc_leaks(self, name, incr): # exposed in gh-11867 as np.vectorized, but the problem stems from # frompyfunc. # class.attribute = np.frompyfunc(<method>) creates a # reference cycle if <method> is a bound class method. It requires a # gc collection cycle to break the cycle (on CPython 3) import gc A_func = getattr(self.A, name) gc.disable() try: refcount = sys.getrefcount(A_func) for i in range(self.A.iters): a = self.A() a.f = np.frompyfunc(getattr(a, name), 1, 1) out = a.f(np.arange(10)) a = None # A.func is part of a reference cycle if incr is non-zero assert_equal(sys.getrefcount(A_func), refcount + incr) for i in range(5): gc.collect() assert_equal(sys.getrefcount(A_func), refcount) finally: gc.enable() class TestDigitize: def test_forward(self): x = np.arange(-6, 5) bins = np.arange(-5, 5) assert_array_equal(digitize(x, bins), np.arange(11)) def test_reverse(self): x = np.arange(5, -6, -1) bins = np.arange(5, -5, -1) assert_array_equal(digitize(x, bins), np.arange(11)) def test_random(self): x = rand(10) bin = np.linspace(x.min(), x.max(), 10) assert_(np.all(digitize(x, bin) != 0)) def test_right_basic(self): x = [1, 5, 4, 10, 8, 11, 0] bins = [1, 5, 10] default_answer = [1, 2, 1, 3, 2, 3, 0] assert_array_equal(digitize(x, bins), default_answer) right_answer = [0, 1, 1, 2, 2, 3, 0] assert_array_equal(digitize(x, bins, True), right_answer) def test_right_open(self): x = np.arange(-6, 5) bins = np.arange(-6, 4) assert_array_equal(digitize(x, bins, True), np.arange(11)) def test_right_open_reverse(self): x = np.arange(5, -6, -1) bins = np.arange(4, -6, -1) assert_array_equal(digitize(x, bins, True), np.arange(11)) def test_right_open_random(self): x = rand(10) bins = np.linspace(x.min(), x.max(), 10) assert_(np.all(digitize(x, bins, True) != 10)) def test_monotonic(self): x = [-1, 0, 1, 2] bins = [0, 0, 1] assert_array_equal(digitize(x, bins, False), [0, 2, 3, 3]) assert_array_equal(digitize(x, bins, True), [0, 0, 2, 3]) bins = [1, 1, 0] assert_array_equal(digitize(x, bins, False), [3, 2, 0, 0]) assert_array_equal(digitize(x, bins, True), [3, 3, 2, 0]) bins = [1, 1, 1, 1] assert_array_equal(digitize(x, bins, False), [0, 0, 4, 4]) assert_array_equal(digitize(x, bins, True), [0, 0, 0, 4]) bins = [0, 0, 1, 0] assert_raises(ValueError, digitize, x, bins) bins = [1, 1, 0, 1] assert_raises(ValueError, digitize, x, bins) def test_casting_error(self): x = [1, 2, 3 + 1.j] bins = [1, 2, 3] assert_raises(TypeError, digitize, x, bins) x, bins = bins, x assert_raises(TypeError, digitize, x, bins) def test_return_type(self): # Functions returning indices should always return base ndarrays class A(np.ndarray): pass a = np.arange(5).view(A) b = np.arange(1, 3).view(A) assert_(not isinstance(digitize(b, a, False), A)) assert_(not isinstance(digitize(b, a, True), A)) def test_large_integers_increasing(self): # gh-11022 x = 254 # loses precision in a float assert_equal(np.digitize(x, [x - 1, x + 1]), 1) @pytest.mark.xfail( reason="gh-11022: np.core.multiarray._monoticity loses precision") def test_large_integers_decreasing(self): # gh-11022 x = 254 # loses precision in a float assert_equal(np.digitize(x, [x + 1, x - 1]), 1) class TestUnwrap: def test_simple(self): # check that unwrap removes jumps greater that 2pi assert_array_equal(unwrap([1, 1 + 2 np.pi]), [1, 1]) # check that unwrap maintains continuity assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi)) def test_period(self): # check that unwrap removes jumps greater that 255 assert_array_equal(unwrap([1, 1 + 256], period=255), [1, 2]) # check that unwrap maintains continuity assert_(np.all(diff(unwrap(rand(10) * 1000, period=255)) < 255)) # check simple case simple_seq = np.array([0, 75, 150, 225, 300]) wrap_seq = np.mod(simple_seq, 255) assert_array_equal(unwrap(wrap_seq, period=255), simple_seq) # check custom discont value uneven_seq = np.array([0, 75, 150, 225, 300, 430]) wrap_uneven = np.mod(uneven_seq, 250) no_discont = unwrap(wrap_uneven, period=250) assert_array_equal(no_discont, [0, 75, 150, 225, 300, 180]) sm_discont = unwrap(wrap_uneven, period=250, discont=140) assert_array_equal(sm_discont, [0, 75, 150, 225, 300, 430]) assert sm_discont.dtype == wrap_uneven.dtype @pytest.mark.parametrize( "dtype", "O" + np.typecodes["AllInteger"] + np.typecodes["Float"] ) @pytest.mark.parametrize("M", [0, 1, 10]) class TestFilterwindows: def test_hanning(self, dtype: str, M: int) -> None: scalar = np.array(M, dtype=dtype)[()] w = hanning(scalar) if dtype == "O": ref_dtype = np.float64 else: ref_dtype = np.result_type(scalar.dtype, np.float64) assert w.dtype == ref_dtype # check symmetry assert_equal(w, flipud(w)) # check known value if scalar < 1: assert_array_equal(w, np.array([])) elif scalar == 1: assert_array_equal(w, np.ones(1)) else: assert_almost_equal(np.sum(w, axis=0), 4.500, 4) def test_hamming(self, dtype: str, M: int) -> None: scalar = np.array(M, dtype=dtype)[()] w = hamming(scalar) if dtype == "O": ref_dtype = np.float64 else: ref_dtype = np.result_type(scalar.dtype, np.float64) assert w.dtype == ref_dtype # check symmetry assert_equal(w, flipud(w)) # check known value if scalar < 1: assert_array_equal(w, np.array([])) elif scalar == 1: assert_array_equal(w, np.ones(1)) else: assert_almost_equal(np.sum(w, axis=0), 4.9400, 4) def test_bartlett(self, dtype: str, M: int) -> None: scalar = np.array(M, dtype=dtype)[()] w = bartlett(scalar) if dtype == "O": ref_dtype = np.float64 else: ref_dtype = np.result_type(scalar.dtype, np.float64) assert w.dtype == ref_dtype # check symmetry assert_equal(w, flipud(w)) # check known value if scalar < 1: assert_array_equal(w, np.array([])) elif scalar == 1: assert_array_equal(w, np.ones(1)) else: assert_almost_equal(np.sum(w, axis=0), 4.4444, 4) def test_blackman(self, dtype: str, M: int) -> None: scalar = np.array(M, dtype=dtype)[()] w = blackman(scalar) if dtype == "O": ref_dtype = np.float64 else: ref_dtype = np.result_type(scalar.dtype, np.float64) assert w.dtype == ref_dtype # check symmetry assert_equal(w, flipud(w)) # check known value if scalar < 1: assert_array_equal(w, np.array([])) elif scalar == 1: assert_array_equal(w, np.ones(1)) else: assert_almost_equal(np.sum(w, axis=0), 3.7800, 4) def test_kaiser(self, dtype: str, M: int) -> None: scalar = np.array(M, dtype=dtype)[()] w = kaiser(scalar, 0) if dtype == "O": ref_dtype = np.float64 else: ref_dtype = np.result_type(scalar.dtype, np.float64) assert w.dtype == ref_dtype # check symmetry assert_equal(w, flipud(w)) # check known value if scalar < 1: assert_array_equal(w, np.array([])) elif scalar == 1: assert_array_equal(w, np.ones(1)) else: assert_almost_equal(np.sum(w, axis=0), 10, 15) class TestTrapz: def test_simple(self): x = np.arange(-10, 10, .1) r = trapz(np.exp(-.5 * x ** 2) / np.sqrt(2 * np.pi), dx=0.1) # check integral of normal equals 1 assert_almost_equal(r, 1, 7) def test_ndim(self): x = np.linspace(0, 1, 3) y = np.linspace(0, 2, 8) z = np.linspace(0, 3, 13) wx = np.ones_like(x) * (x[1] - x[0]) wx[0] /= 2 wx[-1] /= 2 wy = np.ones_like(y) * (y[1] - y[0]) wy[0] /= 2 wy[-1] /= 2 wz = np.ones_like(z) * (z[1] - z[0]) wz[0] /= 2 wz[-1] /= 2 q = x[:, None, None] + y[None,:, None] + z[None, None,:] qx = (q * wx[:, None, None]).sum(axis=0) qy = (q * wy[None, :, None]).sum(axis=1) qz = (q * wz[None, None, :]).sum(axis=2) # n-d `x` r = trapz(q, x=x[:, None, None], axis=0) assert_almost_equal(r, qx) r = trapz(q, x=y[None,:, None], axis=1) assert_almost_equal(r, qy) r = trapz(q, x=z[None, None,:], axis=2) assert_almost_equal(r, qz) # 1-d `x` r = trapz(q, x=x, axis=0) assert_almost_equal(r, qx) r = trapz(q, x=y, axis=1) assert_almost_equal(r, qy) r = trapz(q, x=z, axis=2) assert_almost_equal(r, qz) def test_masked(self): # Testing that masked arrays behave as if the function is 0 where # masked x = np.arange(5) y = x * x mask = x == 2 ym = np.ma.array(y, mask=mask) r = 13.0 # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16)) assert_almost_equal(trapz(ym, x), r) xm = np.ma.array(x, mask=mask) assert_almost_equal(trapz(ym, xm), r) xm = np.ma.array(x, mask=mask) assert_almost_equal(trapz(y, xm), r) class TestSinc: def test_simple(self): assert_(sinc(0) == 1) w = sinc(np.linspace(-1, 1, 100)) # check symmetry assert_array_almost_equal(w, flipud(w), 7) def test_array_like(self): x = [0, 0.5] y1 = sinc(np.array(x)) y2 = sinc(list(x)) y3 = sinc(tuple(x)) assert_array_equal(y1, y2) assert_array_equal(y1, y3) class TestUnique: def test_simple(self): x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0]) assert_(np.all(unique(x) == [0, 1, 2, 3, 4])) assert_(unique(np.array([1, 1, 1, 1, 1])) == np.array([1])) x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham'] assert_(np.all(unique(x) == ['bar', 'foo', 'ham', 'widget'])) x = np.array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j]) assert_(np.all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10])) class TestCheckFinite: def test_simple(self): a = [1, 2, 3] b = [1, 2, np.inf] c = [1, 2, np.nan] np.lib.asarray_chkfinite(a) assert_raises(ValueError, np.lib.asarray_chkfinite, b) assert_raises(ValueError, np.lib.asarray_chkfinite, c) def test_dtype_order(self): # Regression test for missing dtype and order arguments a = [1, 2, 3] a = np.lib.asarray_chkfinite(a, order='F', dtype=np.float64) assert_(a.dtype == np.float64) class TestCorrCoef: A = np.array( [[0.15391142, 0.18045767, 0.14197213], [0.70461506, 0.96474128, 0.27906989], [0.9297531, 0.32296769, 0.19267156]]) B = np.array( [[0.10377691, 0.5417086, 0.49807457], [0.82872117, 0.77801674, 0.39226705], [0.9314666, 0.66800209, 0.03538394]]) res1 = np.array( [[1., 0.9379533, -0.04931983], [0.9379533, 1., 0.30007991], [-0.04931983, 0.30007991, 1.]]) res2 = np.array( [[1., 0.9379533, -0.04931983, 0.30151751, 0.66318558, 0.51532523], [0.9379533, 1., 0.30007991, -0.04781421, 0.88157256, 0.78052386], [-0.04931983, 0.30007991, 1., -0.96717111, 0.71483595, 0.83053601], [0.30151751, -0.04781421, -0.96717111, 1., -0.51366032, -0.66173113], [0.66318558, 0.88157256, 0.71483595, -0.51366032, 1., 0.98317823], [0.51532523, 0.78052386, 0.83053601, -0.66173113, 0.98317823, 1.]]) def test_non_array(self): assert_almost_equal(np.corrcoef([0, 1, 0], [1, 0, 1]), [[1., -1.], [-1., 1.]]) def test_simple(self): tgt1 = corrcoef(self.A) assert_almost_equal(tgt1, self.res1) assert_(np.all(np.abs(tgt1) <= 1.0)) tgt2 = corrcoef(self.A, self.B) assert_almost_equal(tgt2, self.res2) assert_(np.all(np.abs(tgt2) <= 1.0)) def test_ddof(self): # ddof raises DeprecationWarning with suppress_warnings() as sup: warnings.simplefilter("always") assert_warns(DeprecationWarning, corrcoef, self.A, ddof=-1) sup.filter(DeprecationWarning) # ddof has no or negligible effect on the function assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1) assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2) assert_almost_equal(corrcoef(self.A, ddof=3), self.res1) assert_almost_equal(corrcoef(self.A, self.B, ddof=3), self.res2) def test_bias(self): # bias raises DeprecationWarning with suppress_warnings() as sup: warnings.simplefilter("always") assert_warns(DeprecationWarning, corrcoef, self.A, self.B, 1, 0) assert_warns(DeprecationWarning, corrcoef, self.A, bias=0) sup.filter(DeprecationWarning) # bias has no or negligible effect on the function assert_almost_equal(corrcoef(self.A, bias=1), self.res1) def test_complex(self): x = np.array([[1, 2, 3], [1j, 2j, 3j]]) res = corrcoef(x) tgt = np.array([[1., -1.j], [1.j, 1.]]) assert_allclose(res, tgt) assert_(np.all(np.abs(res) <= 1.0)) def test_xy(self): x = np.array([[1, 2, 3]]) y = np.array([[1j, 2j, 3j]]) assert_allclose(np.corrcoef(x, y), np.array([[1., -1.j], [1.j, 1.]])) def test_empty(self): with warnings.catch_warnings(record=True): warnings.simplefilter('always', RuntimeWarning) assert_array_equal(corrcoef(np.array([])), np.nan) assert_array_equal(corrcoef(np.array([]).reshape(0, 2)), np.array([]).reshape(0, 0)) assert_array_equal(corrcoef(np.array([]).reshape(2, 0)), np.array([[np.nan, np.nan], [np.nan, np.nan]])) def test_extreme(self): x = [[1e-100, 1e100], [1e100, 1e-100]] with np.errstate(all='raise'): c = corrcoef(x) assert_array_almost_equal(c, np.array([[1., -1.], [-1., 1.]])) assert_(np.all(np.abs(c) <= 1.0)) @pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble]) def test_corrcoef_dtype(self, test_type): cast_A = self.A.astype(test_type) res = corrcoef(cast_A, dtype=test_type) assert test_type == res.dtype class TestCov: x1 = np.array([[0, 2], [1, 1], [2, 0]]).T res1 = np.array([[1., -1.], [-1., 1.]]) x2 = np.array([0.0, 1.0, 2.0], ndmin=2) frequencies = np.array([1, 4, 1]) x2_repeats = np.array([[0.0], [1.0], [1.0], [1.0], [1.0], [2.0]]).T res2 = np.array([[0.4, -0.4], [-0.4, 0.4]]) unit_frequencies = np.ones(3, dtype=np.int_) weights = np.array([1.0, 4.0, 1.0]) res3 = np.array([[2. / 3., -2. / 3.], [-2. / 3., 2. / 3.]]) unit_weights = np.ones(3) x3 = np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964]) def test_basic(self): assert_allclose(cov(self.x1), self.res1) def test_complex(self): x = np.array([[1, 2, 3], [1j, 2j, 3j]]) res = np.array([[1., -1.j], [1.j, 1.]]) assert_allclose(cov(x), res) assert_allclose(cov(x, aweights=np.ones(3)), res) def test_xy(self): x = np.array([[1, 2, 3]]) y = np.array([[1j, 2j, 3j]]) assert_allclose(cov(x, y), np.array([[1., -1.j], [1.j, 1.]])) def test_empty(self): with warnings.catch_warnings(record=True): warnings.simplefilter('always', RuntimeWarning) assert_array_equal(cov(np.array([])), np.nan) assert_array_equal(cov(np.array([]).reshape(0, 2)), np.array([]).reshape(0, 0)) assert_array_equal(cov(np.array([]).reshape(2, 0)), np.array([[np.nan, np.nan], [np.nan, np.nan]])) def test_wrong_ddof(self): with warnings.catch_warnings(record=True): warnings.simplefilter('always', RuntimeWarning) assert_array_equal(cov(self.x1, ddof=5), np.array([[np.inf, -np.inf], [-np.inf, np.inf]])) def test_1D_rowvar(self): assert_allclose(cov(self.x3), cov(self.x3, rowvar=False)) y = np.array([0.0780, 0.3107, 0.2111, 0.0334, 0.8501]) assert_allclose(cov(self.x3, y), cov(self.x3, y, rowvar=False)) def test_1D_variance(self): assert_allclose(cov(self.x3, ddof=1), np.var(self.x3, ddof=1)) def test_fweights(self): assert_allclose(cov(self.x2, fweights=self.frequencies), cov(self.x2_repeats)) assert_allclose(cov(self.x1, fweights=self.frequencies), self.res2) assert_allclose(cov(self.x1, fweights=self.unit_frequencies), self.res1) nonint = self.frequencies + 0.5 assert_raises(TypeError, cov, self.x1, fweights=nonint) f = np.ones((2, 3), dtype=np.int_) assert_raises(RuntimeError, cov, self.x1, fweights=f) f = np.ones(2, dtype=np.int_) assert_raises(RuntimeError, cov, self.x1, fweights=f) f = -1 * np.ones(3, dtype=np.int_) assert_raises(ValueError, cov, self.x1, fweights=f) def test_aweights(self): assert_allclose(cov(self.x1, aweights=self.weights), self.res3) assert_allclose(cov(self.x1, aweights=3.0 * self.weights), cov(self.x1, aweights=self.weights)) assert_allclose(cov(self.x1, aweights=self.unit_weights), self.res1) w = np.ones((2, 3)) assert_raises(RuntimeError, cov, self.x1, aweights=w) w = np.ones(2) assert_raises(RuntimeError, cov, self.x1, aweights=w) w = -1.0 * np.ones(3) assert_raises(ValueError, cov, self.x1, aweights=w) def test_unit_fweights_and_aweights(self): assert_allclose(cov(self.x2, fweights=self.frequencies, aweights=self.unit_weights), cov(self.x2_repeats)) assert_allclose(cov(self.x1, fweights=self.frequencies, aweights=self.unit_weights), self.res2) assert_allclose(cov(self.x1, fweights=self.unit_frequencies, aweights=self.unit_weights), self.res1) assert_allclose(cov(self.x1, fweights=self.unit_frequencies, aweights=self.weights), self.res3) assert_allclose(cov(self.x1, fweights=self.unit_frequencies, aweights=3.0 * self.weights), cov(self.x1, aweights=self.weights)) assert_allclose(cov(self.x1, fweights=self.unit_frequencies, aweights=self.unit_weights), self.res1) @pytest.mark.parametrize("test_type", [np.half, np.single, np.double, np.longdouble]) def test_cov_dtype(self, test_type): cast_x1 = self.x1.astype(test_type) res = cov(cast_x1, dtype=test_type) assert test_type == res.dtype class Test_I0: def test_simple(self): assert_almost_equal( i0(0.5), np.array(1.0634833707413234)) # need at least one test above 8, as the implementation is piecewise A = np.array([0.49842636, 0.6969809, 0.22011976, 0.0155549, 10.0]) expected = np.array([1.06307822, 1.12518299, 1.01214991, 1.00006049, 2815.71662847]) assert_almost_equal(i0(A), expected) assert_almost_equal(i0(-A), expected) B = np.array([[0.827002, 0.99959078], [0.89694769, 0.39298162], [0.37954418, 0.05206293], [0.36465447, 0.72446427], [0.48164949, 0.50324519]]) assert_almost_equal( i0(B), np.array([[1.17843223, 1.26583466], [1.21147086, 1.03898290], [1.03633899, 1.00067775], [1.03352052, 1.13557954], [1.05884290, 1.06432317]])) # Regression test for gh-11205 i0_0 = np.i0([0.]) assert_equal(i0_0.shape, (1,)) assert_array_equal(np.i0([0.]), np.array([1.])) def test_non_array(self): a = np.arange(4) class array_like: __array_interface__ = a.__array_interface__ def __array_wrap__(self, arr): return self # E.g. pandas series survive ufunc calls through array-wrap: assert isinstance(np.abs(array_like()), array_like) exp = np.i0(a) res = np.i0(array_like()) assert_array_equal(exp, res) def test_complex(self): a = np.array([0, 1 + 2j]) with pytest.raises(TypeError, match="i0 not supported for complex values"): res = i0(a) class TestKaiser: def test_simple(self): assert_(np.isfinite(kaiser(1, 1.0))) assert_almost_equal(kaiser(0, 1.0), np.array([])) assert_almost_equal(kaiser(2, 1.0), np.array([0.78984831, 0.78984831])) assert_almost_equal(kaiser(5, 1.0), np.array([0.78984831, 0.94503323, 1., 0.94503323, 0.78984831])) assert_almost_equal(kaiser(5, 1.56789), np.array([0.58285404, 0.88409679, 1., 0.88409679, 0.58285404])) def test_int_beta(self): kaiser(3, 4) class TestMsort: def test_simple(self): A = np.array([[0.44567325, 0.79115165, 0.54900530], [0.36844147, 0.37325583, 0.96098397], [0.64864341, 0.52929049, 0.39172155]]) with pytest.warns(DeprecationWarning, match="msort is deprecated"): assert_almost_equal( msort(A), np.array([[0.36844147, 0.37325583, 0.39172155], [0.44567325, 0.52929049, 0.54900530], [0.64864341, 0.79115165, 0.96098397]])) class TestMeshgrid: def test_simple(self): [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7]) assert_array_equal(X, np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])) assert_array_equal(Y, np.array([[4, 4, 4], [5, 5, 5], [6, 6, 6], [7, 7, 7]])) def test_single_input(self): [X] = meshgrid([1, 2, 3, 4]) assert_array_equal(X, np.array([1, 2, 3, 4])) def test_no_input(self): args = [] assert_array_equal([], meshgrid(args)) assert_array_equal([], meshgrid(args, copy=False)) def test_indexing(self): x = [1, 2, 3] y = [4, 5, 6, 7] [X, Y] = meshgrid(x, y, indexing='ij') assert_array_equal(X, np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]])) assert_array_equal(Y, np.array([[4, 5, 6, 7], [4, 5, 6, 7], [4, 5, 6, 7]])) # Test expected shapes: z = [8, 9] assert_(meshgrid(x, y)[0].shape == (4, 3)) assert_(meshgrid(x, y, indexing='ij')[0].shape == (3, 4)) assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2)) assert_(meshgrid(x, y, z, indexing='ij')[0].shape == (3, 4, 2)) assert_raises(ValueError, meshgrid, x, y, indexing='notvalid') def test_sparse(self): [X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7], sparse=True) assert_array_equal(X, np.array([[1, 2, 3]])) assert_array_equal(Y, np.array([[4], [5], [6], [7]])) def test_invalid_arguments(self): # Test that meshgrid complains about invalid arguments # Regression test for issue #4755: # https://github.com/numpy/numpy/issues/4755 assert_raises(TypeError, meshgrid, [1, 2, 3], [4, 5, 6, 7], indices='ij') def test_return_type(self): # Test for appropriate dtype in returned arrays. # Regression test for issue #5297 # https://github.com/numpy/numpy/issues/5297 x = np.arange(0, 10, dtype=np.float32) y = np.arange(10, 20, dtype=np.float64) X, Y = np.meshgrid(x,y) assert_(X.dtype == x.dtype) assert_(Y.dtype == y.dtype) # copy X, Y = np.meshgrid(x,y, copy=True) assert_(X.dtype == x.dtype) assert_(Y.dtype == y.dtype) # sparse X, Y = np.meshgrid(x,y, sparse=True) assert_(X.dtype == x.dtype) assert_(Y.dtype == y.dtype) def test_writeback(self): # Issue 8561 X = np.array([1.1, 2.2]) Y = np.array([3.3, 4.4]) x, y = np.meshgrid(X, Y, sparse=False, copy=True) x[0, :] = 0 assert_equal(x[0, :], 0) assert_equal(x[1, :], X) def test_nd_shape(self): a, b, c, d, e = np.meshgrid(([0] i for i in range(1, 6))) expected_shape = (2, 1, 3, 4, 5) assert_equal(a.shape, expected_shape) assert_equal(b.shape, expected_shape) assert_equal(c.shape, expected_shape) assert_equal(d.shape, expected_shape) assert_equal(e.shape, expected_shape) def test_nd_values(self): a, b, c = np.meshgrid([0], [1, 2], [3, 4, 5]) assert_equal(a, [[[0, 0, 0]], [[0, 0, 0]]]) assert_equal(b, [[[1, 1, 1]], [[2, 2, 2]]]) assert_equal(c, [[[3, 4, 5]], [[3, 4, 5]]]) def test_nd_indexing(self): a, b, c = np.meshgrid([0], [1, 2], [3, 4, 5], indexing='ij') assert_equal(a, [[[0, 0, 0], [0, 0, 0]]]) assert_equal(b, [[[1, 1, 1], [2, 2, 2]]]) assert_equal(c, [[[3, 4, 5], [3, 4, 5]]]) class TestPiecewise: def test_simple(self): # Condition is single bool list x = piecewise([0, 0], [True, False], [1]) assert_array_equal(x, [1, 0]) # List of conditions: single bool list x = piecewise([0, 0], [[True, False]], [1]) assert_array_equal(x, [1, 0]) # Conditions is single bool array x = piecewise([0, 0], np.array([True, False]), [1]) assert_array_equal(x, [1, 0]) # Condition is single int array x = piecewise([0, 0], np.array([1, 0]), [1]) assert_array_equal(x, [1, 0]) # List of conditions: int array x = piecewise([0, 0], [np.array([1, 0])], [1]) assert_array_equal(x, [1, 0]) x = piecewise([0, 0], [[False, True]], [lambda x:-1]) assert_array_equal(x, [0, -1]) assert_raises_regex(ValueError, '1 or 2 functions are expected', piecewise, [0, 0], [[False, True]], []) assert_raises_regex(ValueError, '1 or 2 functions are expected', piecewise, [0, 0], [[False, True]], [1, 2, 3]) def test_two_conditions(self): x = piecewise([1, 2], [[True, False], [False, True]], [3, 4]) assert_array_equal(x, [3, 4]) def test_scalar_domains_three_conditions(self): x = piecewise(3, [True, False, False], [4, 2, 0]) assert_equal(x, 4) def test_default(self): # No value specified for x[1], should be 0 x = piecewise([1, 2], [True, False], [2]) assert_array_equal(x, [2, 0]) # Should set x[1] to 3 x = piecewise([1, 2], [True, False], [2, 3]) assert_array_equal(x, [2, 3]) def test_0d(self): x = np.array(3) y = piecewise(x, x > 3, [4, 0]) assert_(y.ndim == 0) assert_(y == 0) x = 5 y = piecewise(x, [True, False], [1, 0]) assert_(y.ndim == 0) assert_(y == 1) # With 3 ranges (It was failing, before) y = piecewise(x, [False, False, True], [1, 2, 3]) assert_array_equal(y, 3) def test_0d_comparison(self): x = 3 y = piecewise(x, [x <= 3, x > 3], [4, 0]) # Should succeed. assert_equal(y, 4) # With 3 ranges (It was failing, before) x = 4 y = piecewise(x, [x <= 3, (x > 3) * (x <= 5), x > 5], [1, 2, 3]) assert_array_equal(y, 2) assert_raises_regex(ValueError, '2 or 3 functions are expected', piecewise, x, [x <= 3, x > 3], [1]) assert_raises_regex(ValueError, '2 or 3 functions are expected', piecewise, x, [x <= 3, x > 3], [1, 1, 1, 1]) def test_0d_0d_condition(self): x = np.array(3) c = np.array(x > 3) y = piecewise(x, [c], [1, 2]) assert_equal(y, 2) def test_multidimensional_extrafunc(self): x = np.array([[-2.5, -1.5, -0.5], [0.5, 1.5, 2.5]]) y = piecewise(x, [x < 0, x >= 2], [-1, 1, 3]) assert_array_equal(y, np.array([[-1., -1., -1.], [3., 3., 1.]])) def test_subclasses(self): class subclass(np.ndarray): pass x = np.arange(5.).view(subclass) r = piecewise(x, [x<2., x>=4], [-1., 1., 0.]) assert_equal(type(r), subclass) assert_equal(r, [-1., -1., 0., 0., 1.]) class TestBincount: def test_simple(self): y = np.bincount(np.arange(4)) assert_array_equal(y, np.ones(4)) def test_simple2(self): y = np.bincount(np.array([1, 5, 2, 4, 1])) assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1])) def test_simple_weight(self): x = np.arange(4) w = np.array([0.2, 0.3, 0.5, 0.1]) y = np.bincount(x, w) assert_array_equal(y, w) def test_simple_weight2(self): x = np.array([1, 2, 4, 5, 2]) w = np.array([0.2, 0.3, 0.5, 0.1, 0.2]) y = np.bincount(x, w) assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1])) def test_with_minlength(self): x = np.array([0, 1, 0, 1, 1]) y = np.bincount(x, minlength=3) assert_array_equal(y, np.array([2, 3, 0])) x = [] y = np.bincount(x, minlength=0) assert_array_equal(y, np.array([])) def test_with_minlength_smaller_than_maxvalue(self): x = np.array([0, 1, 1, 2, 2, 3, 3]) y = np.bincount(x, minlength=2) assert_array_equal(y, np.array([1, 2, 2, 2])) y = np.bincount(x, minlength=0) assert_array_equal(y, np.array([1, 2, 2, 2])) def test_with_minlength_and_weights(self): x = np.array([1, 2, 4, 5, 2]) w = np.array([0.2, 0.3, 0.5, 0.1, 0.2]) y = np.bincount(x, w, 8) assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0])) def test_empty(self): x = np.array([], dtype=int) y = np.bincount(x) assert_array_equal(x, y) def test_empty_with_minlength(self): x = np.array([], dtype=int) y = np.bincount(x, minlength=5) assert_array_equal(y, np.zeros(5, dtype=int)) def test_with_incorrect_minlength(self): x = np.array([], dtype=int) assert_raises_regex(TypeError, "'str' object cannot be interpreted", lambda: np.bincount(x, minlength="foobar")) assert_raises_regex(ValueError, "must not be negative", lambda: np.bincount(x, minlength=-1)) x = np.arange(5) assert_raises_regex(TypeError, "'str' object cannot be interpreted", lambda: np.bincount(x, minlength="foobar")) assert_raises_regex(ValueError, "must not be negative", lambda: np.bincount(x, minlength=-1)) @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts") def test_dtype_reference_leaks(self): # gh-6805 intp_refcount = sys.getrefcount(np.dtype(np.intp)) double_refcount = sys.getrefcount(np.dtype(np.double)) for j in range(10): np.bincount([1, 2, 3]) assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount) assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount) for j in range(10): np.bincount([1, 2, 3], [4, 5, 6]) assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount) assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount) @pytest.mark.parametrize("vals", [[[2, 2]], 2]) def test_error_not_1d(self, vals): # Test that values has to be 1-D (both as array and nested list) vals_arr = np.asarray(vals) with assert_raises(ValueError): np.bincount(vals_arr) with assert_raises(ValueError): np.bincount(vals) class TestInterp: def test_exceptions(self): assert_raises(ValueError, interp, 0, [], []) assert_raises(ValueError, interp, 0, [0], [1, 2]) assert_raises(ValueError, interp, 0, [0, 1], [1, 2], period=0) assert_raises(ValueError, interp, 0, [], [], period=360) assert_raises(ValueError, interp, 0, [0], [1, 2], period=360) def test_basic(self): x = np.linspace(0, 1, 5) y = np.linspace(0, 1, 5) x0 = np.linspace(0, 1, 50) assert_almost_equal(np.interp(x0, x, y), x0) def test_right_left_behavior(self): # Needs range of sizes to test different code paths. # size ==1 is special cased, 1 < size < 5 is linear search, and # size >= 5 goes through local search and possibly binary search. for size in range(1, 10): xp = np.arange(size, dtype=np.double) yp = np.ones(size, dtype=np.double) incpts = np.array([-1, 0, size - 1, size], dtype=np.double) decpts = incpts[::-1] incres = interp(incpts, xp, yp) decres = interp(decpts, xp, yp) inctgt = np.array([1, 1, 1, 1], dtype=float) dectgt = inctgt[::-1] assert_equal(incres, inctgt) assert_equal(decres, dectgt) incres = interp(incpts, xp, yp, left=0) decres = interp(decpts, xp, yp, left=0) inctgt = np.array([0, 1, 1, 1], dtype=float) dectgt = inctgt[::-1] assert_equal(incres, inctgt) assert_equal(decres, dectgt) incres = interp(incpts, xp, yp, right=2) decres = interp(decpts, xp, yp, right=2) inctgt = np.array([1, 1, 1, 2], dtype=float) dectgt = inctgt[::-1] assert_equal(incres, inctgt) assert_equal(decres, dectgt) incres = interp(incpts, xp, yp, left=0, right=2) decres = interp(decpts, xp, yp, left=0, right=2) inctgt = np.array([0, 1, 1, 2], dtype=float) dectgt = inctgt[::-1] assert_equal(incres, inctgt) assert_equal(decres, dectgt) def test_scalar_interpolation_point(self): x = np.linspace(0, 1, 5) y = np.linspace(0, 1, 5) x0 = 0 assert_almost_equal(np.interp(x0, x, y), x0) x0 = .3 assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.float32(.3) assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.float64(.3) assert_almost_equal(np.interp(x0, x, y), x0) x0 = np.nan assert_almost_equal(np.interp(x0, x, y), x0) def test_non_finite_behavior_exact_x(self): x = [1, 2, 2.5, 3, 4] xp = [1, 2, 3, 4] fp = [1, 2, np.inf, 4] assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.inf, np.inf, 4]) fp = [1, 2, np.nan, 4] assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.nan, np.nan, 4]) @pytest.fixture(params=[ lambda x: np.float_(x), lambda x: _make_complex(x, 0), lambda x: _make_complex(0, x), lambda x: _make_complex(x, np.multiply(x, -2)) ], ids=[ 'real', 'complex-real', 'complex-imag', 'complex-both' ]) def sc(self, request): """ scale function used by the below tests """ return request.param def test_non_finite_any_nan(self, sc): """ test that nans are propagated """ assert_equal(np.interp(0.5, [np.nan, 1], sc([ 0, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, np.nan], sc([ 0, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, 1], sc([np.nan, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, 1], sc([ 0, np.nan])), sc(np.nan)) def test_non_finite_inf(self, sc): """ Test that interp between opposite infs gives nan """ assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([ 0, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, 1], sc([-np.inf, +np.inf])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, 1], sc([+np.inf, -np.inf])), sc(np.nan)) # unless the y values are equal assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([ 10, 10])), sc(10)) def test_non_finite_half_inf_xf(self, sc): """ Test that interp where both axes have a bound at inf gives nan """ assert_equal(np.interp(0.5, [-np.inf, 1], sc([-np.inf, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [-np.inf, 1], sc([+np.inf, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [-np.inf, 1], sc([ 0, -np.inf])), sc(np.nan)) assert_equal(np.interp(0.5, [-np.inf, 1], sc([ 0, +np.inf])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, +np.inf], sc([-np.inf, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, +np.inf], sc([+np.inf, 10])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, +np.inf], sc([ 0, -np.inf])), sc(np.nan)) assert_equal(np.interp(0.5, [ 0, +np.inf], sc([ 0, +np.inf])), sc(np.nan)) def test_non_finite_half_inf_x(self, sc): """ Test interp where the x axis has a bound at inf """ assert_equal(np.interp(0.5, [-np.inf, -np.inf], sc([0, 10])), sc(10)) assert_equal(np.interp(0.5, [-np.inf, 1 ], sc([0, 10])), sc(10)) assert_equal(np.interp(0.5, [ 0, +np.inf], sc([0, 10])), sc(0)) assert_equal(np.interp(0.5, [+np.inf, +np.inf], sc([0, 10])), sc(0)) def test_non_finite_half_inf_f(self, sc): """ Test interp where the f axis has a bound at inf """ assert_equal(np.interp(0.5, [0, 1], sc([ 0, -np.inf])), sc(-np.inf)) assert_equal(np.interp(0.5, [0, 1], sc([ 0, +np.inf])), sc(+np.inf)) assert_equal(np.interp(0.5, [0, 1], sc([-np.inf, 10])), sc(-np.inf)) assert_equal(np.interp(0.5, [0, 1], sc([+np.inf, 10])), sc(+np.inf)) assert_equal(np.interp(0.5, [0, 1], sc([-np.inf, -np.inf])), sc(-np.inf)) assert_equal(np.interp(0.5, [0, 1], sc([+np.inf, +np.inf])), sc(+np.inf)) def test_complex_interp(self): # test complex interpolation x = np.linspace(0, 1, 5) y = np.linspace(0, 1, 5) + (1 + np.linspace(0, 1, 5))1.0j x0 = 0.3 y0 = x0 + (1+x0)1.0j assert_almost_equal(np.interp(x0, x, y), y0) # test complex left and right x0 = -1 left = 2 + 3.0j assert_almost_equal(np.interp(x0, x, y, left=left), left) x0 = 2.0 right = 2 + 3.0j assert_almost_equal(np.interp(x0, x, y, right=right), right) # test complex non finite x = [1, 2, 2.5, 3, 4] xp = [1, 2, 3, 4] fp = [1, 2+1j, np.inf, 4] y = [1, 2+1j, np.inf+0.5j, np.inf, 4] assert_almost_equal(np.interp(x, xp, fp), y) # test complex periodic x = [-180, -170, -185, 185, -10, -5, 0, 365] xp = [190, -190, 350, -350] fp = [5+1.0j, 10+2j, 3+3j, 4+4j] y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j, 3.5+3.5j, 3.75+3.75j] assert_almost_equal(np.interp(x, xp, fp, period=360), y) def test_zero_dimensional_interpolation_point(self): x = np.linspace(0, 1, 5) y = np.linspace(0, 1, 5) x0 = np.array(.3) assert_almost_equal(np.interp(x0, x, y), x0) xp = np.array([0, 2, 4]) fp = np.array([1, -1, 1]) actual = np.interp(np.array(1), xp, fp) assert_equal(actual, 0) assert_(isinstance(actual, np.float64)) actual = np.interp(np.array(4.5), xp, fp, period=4) assert_equal(actual, 0.5) assert_(isinstance(actual, np.float64)) def test_if_len_x_is_small(self): xp = np.arange(0, 10, 0.0001) fp = np.sin(xp) assert_almost_equal(np.interp(np.pi, xp, fp), 0.0) def test_period(self): x = [-180, -170, -185, 185, -10, -5, 0, 365] xp = [190, -190, 350, -350] fp = [5, 10, 3, 4] y = [7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75] assert_almost_equal(np.interp(x, xp, fp, period=360), y) x = np.array(x, order='F').reshape(2, -1) y = np.array(y, order='C').reshape(2, -1) assert_almost_equal(np.interp(x, xp, fp, period=360), y) class TestPercentile: def test_basic(self): x = np.arange(8) * 0.5 assert_equal(np.percentile(x, 0), 0.) assert_equal(np.percentile(x, 100), 3.5) assert_equal(np.percentile(x, 50), 1.75) x[1] = np.nan assert_equal(np.percentile(x, 0), np.nan) assert_equal(np.percentile(x, 0, method='nearest'), np.nan) def test_fraction(self): x = [Fraction(i, 2) for i in range(8)] p = np.percentile(x, Fraction(0)) assert_equal(p, Fraction(0)) assert_equal(type(p), Fraction) p = np.percentile(x, Fraction(100)) assert_equal(p, Fraction(7, 2)) assert_equal(type(p), Fraction) p = np.percentile(x, Fraction(50)) assert_equal(p, Fraction(7, 4)) assert_equal(type(p), Fraction) p = np.percentile(x, [Fraction(50)]) assert_equal(p, np.array([Fraction(7, 4)])) assert_equal(type(p), np.ndarray) def test_api(self): d = np.ones(5) np.percentile(d, 5, None, None, False) np.percentile(d, 5, None, None, False, 'linear') o = np.ones((1,)) np.percentile(d, 5, None, o, False, 'linear') def test_complex(self): arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') assert_raises(TypeError, np.percentile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') assert_raises(TypeError, np.percentile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') assert_raises(TypeError, np.percentile, arr_c, 0.5) def test_2D(self): x = np.array([[1, 1, 1], [1, 1, 1], [4, 4, 3], [1, 1, 1], [1, 1, 1]]) assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1]) @pytest.mark.parametrize("dtype", np.typecodes["Float"]) def test_linear_nan_1D(self, dtype): # METHOD 1 of H&F arr = np.asarray([15.0, np.NAN, 35.0, 40.0, 50.0], dtype=dtype) res = np.percentile( arr, 40.0, method="linear") np.testing.assert_equal(res, np.NAN) np.testing.assert_equal(res.dtype, arr.dtype) H_F_TYPE_CODES = [(int_type, np.float64) for int_type in np.typecodes["AllInteger"] ] + [(np.float16, np.float16), (np.float32, np.float32), (np.float64, np.float64), (np.longdouble, np.longdouble), (np.dtype("O"), np.float64)] @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES) @pytest.mark.parametrize(["method", "expected"], [("inverted_cdf", 20), ("averaged_inverted_cdf", 27.5), ("closest_observation", 20), ("interpolated_inverted_cdf", 20), ("hazen", 27.5), ("weibull", 26), ("linear", 29), ("median_unbiased", 27), ("normal_unbiased", 27.125), ]) def test_linear_interpolation(self, method, expected, input_dtype, expected_dtype): expected_dtype = np.dtype(expected_dtype) if np._get_promotion_state() == "legacy": expected_dtype = np.promote_types(expected_dtype, np.float64) arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=input_dtype) actual = np.percentile(arr, 40.0, method=method) np.testing.assert_almost_equal( actual, expected_dtype.type(expected), 14) if method in ["inverted_cdf", "closest_observation"]: if input_dtype == "O": np.testing.assert_equal(np.asarray(actual).dtype, np.float64) else: np.testing.assert_equal(np.asarray(actual).dtype, np.dtype(input_dtype)) else: np.testing.assert_equal(np.asarray(actual).dtype, np.dtype(expected_dtype)) TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["Float"] + "O" @pytest.mark.parametrize("dtype", TYPE_CODES) def test_lower_higher(self, dtype): assert_equal(np.percentile(np.arange(10, dtype=dtype), 50, method='lower'), 4) assert_equal(np.percentile(np.arange(10, dtype=dtype), 50, method='higher'), 5) @pytest.mark.parametrize("dtype", TYPE_CODES) def test_midpoint(self, dtype): assert_equal(np.percentile(np.arange(10, dtype=dtype), 51, method='midpoint'), 4.5) assert_equal(np.percentile(np.arange(9, dtype=dtype) + 1, 50, method='midpoint'), 5) assert_equal(np.percentile(np.arange(11, dtype=dtype), 51, method='midpoint'), 5.5) assert_equal(np.percentile(np.arange(11, dtype=dtype), 50, method='midpoint'), 5) @pytest.mark.parametrize("dtype", TYPE_CODES) def test_nearest(self, dtype): assert_equal(np.percentile(np.arange(10, dtype=dtype), 51, method='nearest'), 5) assert_equal(np.percentile(np.arange(10, dtype=dtype), 49, method='nearest'), 4) def test_linear_interpolation_extrapolation(self): arr = np.random.rand(5) actual = np.percentile(arr, 100) np.testing.assert_equal(actual, arr.max()) actual = np.percentile(arr, 0) np.testing.assert_equal(actual, arr.min()) def test_sequence(self): x = np.arange(8) * 0.5 assert_equal(np.percentile(x, [0, 100, 50]), [0, 3.5, 1.75]) def test_axis(self): x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, (25, 50, 100)), [2.75, 5.5, 11.0]) r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]] assert_equal(np.percentile(x, (25, 50, 100), axis=0), r0) r1 = [[0.75, 1.5, 3], [4.75, 5.5, 7], [8.75, 9.5, 11]] assert_equal(np.percentile(x, (25, 50, 100), axis=1), np.array(r1).T) # ensure qth axis is always first as with np.array(old_percentile(..)) x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6) assert_equal(np.percentile(x, (25, 50)).shape, (2,)) assert_equal(np.percentile(x, (25, 50, 75)).shape, (3,)) assert_equal(np.percentile(x, (25, 50), axis=0).shape, (2, 4, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=1).shape, (2, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=2).shape, (2, 3, 4, 6)) assert_equal(np.percentile(x, (25, 50), axis=3).shape, (2, 3, 4, 5)) assert_equal( np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), method="higher").shape, (2,)) assert_equal(np.percentile(x, (25, 50, 75), method="higher").shape, (3,)) assert_equal(np.percentile(x, (25, 50), axis=0, method="higher").shape, (2, 4, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=1, method="higher").shape, (2, 3, 5, 6)) assert_equal(np.percentile(x, (25, 50), axis=2, method="higher").shape, (2, 3, 4, 6)) assert_equal(np.percentile(x, (25, 50), axis=3, method="higher").shape, (2, 3, 4, 5)) assert_equal(np.percentile(x, (25, 50, 75), axis=1, method="higher").shape, (3, 3, 5, 6)) def test_scalar_q(self): # test for no empty dimensions for compatibility with old percentile x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, 50), 5.5) assert_(np.isscalar(np.percentile(x, 50))) r0 = np.array([4., 5., 6., 7.]) assert_equal(np.percentile(x, 50, axis=0), r0) assert_equal(np.percentile(x, 50, axis=0).shape, r0.shape) r1 = np.array([1.5, 5.5, 9.5]) assert_almost_equal(np.percentile(x, 50, axis=1), r1) assert_equal(np.percentile(x, 50, axis=1).shape, r1.shape) out = np.empty(1) assert_equal(np.percentile(x, 50, out=out), 5.5) assert_equal(out, 5.5) out = np.empty(4) assert_equal(np.percentile(x, 50, axis=0, out=out), r0) assert_equal(out, r0) out = np.empty(3) assert_equal(np.percentile(x, 50, axis=1, out=out), r1) assert_equal(out, r1) # test for no empty dimensions for compatibility with old percentile x = np.arange(12).reshape(3, 4) assert_equal(np.percentile(x, 50, method='lower'), 5.) assert_(np.isscalar(np.percentile(x, 50))) r0 = np.array([4., 5., 6., 7.]) c0 = np.percentile(x, 50, method='lower', axis=0) assert_equal(c0, r0) assert_equal(c0.shape, r0.shape) r1 = np.array([1., 5., 9.]) c1 = np.percentile(x, 50, method='lower', axis=1) assert_almost_equal(c1, r1) assert_equal(c1.shape, r1.shape) out = np.empty((), dtype=x.dtype) c = np.percentile(x, 50, method='lower', out=out) assert_equal(c, 5) assert_equal(out, 5) out = np.empty(4, dtype=x.dtype) c = np.percentile(x, 50, method='lower', axis=0, out=out) assert_equal(c, r0) assert_equal(out, r0) out = np.empty(3, dtype=x.dtype) c = np.percentile(x, 50, method='lower', axis=1, out=out) assert_equal(c, r1) assert_equal(out, r1) def test_exception(self): assert_raises(ValueError, np.percentile, [1, 2], 56, method='foobar') assert_raises(ValueError, np.percentile, [1], 101) assert_raises(ValueError, np.percentile, [1], -1) assert_raises(ValueError, np.percentile, [1], list(range(50)) + [101]) assert_raises(ValueError, np.percentile, [1], list(range(50)) + [-0.1]) def test_percentile_list(self): assert_equal(np.percentile([1, 2, 3], 0), 1) def test_percentile_out(self): x = np.array([1, 2, 3]) y = np.zeros((3,)) p = (1, 2, 3) np.percentile(x, p, out=y) assert_equal(np.percentile(x, p), y) x = np.array([[1, 2, 3], [4, 5, 6]]) y = np.zeros((3, 3)) np.percentile(x, p, axis=0, out=y) assert_equal(np.percentile(x, p, axis=0), y) y = np.zeros((3, 2)) np.percentile(x, p, axis=1, out=y) assert_equal(np.percentile(x, p, axis=1), y) x = np.arange(12).reshape(3, 4) # q.dim > 1, float r0 = np.array([[2., 3., 4., 5.], [4., 5., 6., 7.]]) out = np.empty((2, 4)) assert_equal(np.percentile(x, (25, 50), axis=0, out=out), r0) assert_equal(out, r0) r1 = np.array([[0.75, 4.75, 8.75], [1.5, 5.5, 9.5]]) out = np.empty((2, 3)) assert_equal(np.percentile(x, (25, 50), axis=1, out=out), r1) assert_equal(out, r1) # q.dim > 1, int r0 = np.array([[0, 1, 2, 3], [4, 5, 6, 7]]) out = np.empty((2, 4), dtype=x.dtype) c = np.percentile(x, (25, 50), method='lower', axis=0, out=out) assert_equal(c, r0) assert_equal(out, r0) r1 = np.array([[0, 4, 8], [1, 5, 9]]) out = np.empty((2, 3), dtype=x.dtype) c = np.percentile(x, (25, 50), method='lower', axis=1, out=out) assert_equal(c, r1) assert_equal(out, r1) def test_percentile_empty_dim(self): # empty dims are preserved d = np.arange(11 * 2).reshape(11, 1, 2, 1) assert_array_equal(np.percentile(d, 50, axis=0).shape, (1, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=1).shape, (11, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=2).shape, (11, 1, 1)) assert_array_equal(np.percentile(d, 50, axis=3).shape, (11, 1, 2)) assert_array_equal(np.percentile(d, 50, axis=-1).shape, (11, 1, 2)) assert_array_equal(np.percentile(d, 50, axis=-2).shape, (11, 1, 1)) assert_array_equal(np.percentile(d, 50, axis=-3).shape, (11, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=-4).shape, (1, 2, 1)) assert_array_equal(np.percentile(d, 50, axis=2, method='midpoint').shape, (11, 1, 1)) assert_array_equal(np.percentile(d, 50, axis=-2, method='midpoint').shape, (11, 1, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=0)).shape, (2, 1, 2, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=1)).shape, (2, 11, 2, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=2)).shape, (2, 11, 1, 1)) assert_array_equal(np.array(np.percentile(d, [10, 50], axis=3)).shape, (2, 11, 1, 2)) def test_percentile_no_overwrite(self): a = np.array([2, 3, 4, 1]) np.percentile(a, [50], overwrite_input=False) assert_equal(a, np.array([2, 3, 4, 1])) a = np.array([2, 3, 4, 1]) np.percentile(a, [50]) assert_equal(a, np.array([2, 3, 4, 1])) def test_no_p_overwrite(self): p = np.linspace(0., 100., num=5) np.percentile(np.arange(100.), p, method="midpoint") assert_array_equal(p, np.linspace(0., 100., num=5)) p = np.linspace(0., 100., num=5).tolist() np.percentile(np.arange(100.), p, method="midpoint") assert_array_equal(p, np.linspace(0., 100., num=5).tolist()) def test_percentile_overwrite(self): a = np.array([2, 3, 4, 1]) b = np.percentile(a, [50], overwrite_input=True) assert_equal(b, np.array([2.5])) b = np.percentile([2, 3, 4, 1], [50], overwrite_input=True) assert_equal(b, np.array([2.5])) def test_extended_axis(self): o = np.random.normal(size=(71, 23)) x = np.dstack([o] * 10) assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30)) x = np.moveaxis(x, -1, 0) assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30)) x = x.swapaxes(0, 1).copy() assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30)) x = x.swapaxes(0, 1).copy() assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)), np.percentile(x, [25, 60], axis=None)) assert_equal(np.percentile(x, [25, 60], axis=(0,)), np.percentile(x, [25, 60], axis=0)) d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11)) np.random.shuffle(d.ravel()) assert_equal(np.percentile(d, 25, axis=(0, 1, 2))[0], np.percentile(d[:,:,:, 0].flatten(), 25)) assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1], np.percentile(d[:,:, 1,:].flatten(), [10, 90])) assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2], np.percentile(d[:,:, 2,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2], np.percentile(d[2,:,:,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1], np.percentile(d[2, 1,:,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1], np.percentile(d[2,:,:, 1].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2], np.percentile(d[2,:, 2,:].flatten(), 25)) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) assert_raises(np.AxisError, np.percentile, d, axis=-5, q=25) assert_raises(np.AxisError, np.percentile, d, axis=(0, -5), q=25) assert_raises(np.AxisError, np.percentile, d, axis=4, q=25) assert_raises(np.AxisError, np.percentile, d, axis=(0, 4), q=25) # each of these refers to the same axis twice assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25) assert_raises(ValueError, np.percentile, d, axis=(-1, -1), q=25) assert_raises(ValueError, np.percentile, d, axis=(3, -1), q=25) def test_keepdims(self): d = np.ones((3, 5, 7, 11)) assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape, (1, 1, 7, 11)) assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape, (1, 5, 7, 1)) assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape, (3, 1, 7, 11)) assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape, (1, 1, 7, 1)) assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3), keepdims=True).shape, (2, 1, 1, 7, 1)) assert_equal(np.percentile(d, [1, 7], axis=(0, 3), keepdims=True).shape, (2, 1, 5, 7, 1)) @pytest.mark.parametrize('q', [7, [1, 7]]) @pytest.mark.parametrize( argnames='axis', argvalues=[ None, 1, (1,), (0, 1), (-3, -1), ] ) def test_keepdims_out(self, q, axis): d = np.ones((3, 5, 7, 11)) if axis is None: shape_out = (1,) * d.ndim else: axis_norm = normalize_axis_tuple(axis, d.ndim) shape_out = tuple( 1 if i in axis_norm else d.shape[i] for i in range(d.ndim)) shape_out = np.shape(q) + shape_out out = np.empty(shape_out) result = np.percentile(d, q, axis=axis, keepdims=True, out=out) assert result is out assert_equal(result.shape, shape_out) def test_out(self): o = np.zeros((4,)) d = np.ones((3, 4)) assert_equal(np.percentile(d, 0, 0, out=o), o) assert_equal(np.percentile(d, 0, 0, method='nearest', out=o), o) o = np.zeros((3,)) assert_equal(np.percentile(d, 1, 1, out=o), o) assert_equal(np.percentile(d, 1, 1, method='nearest', out=o), o) o = np.zeros(()) assert_equal(np.percentile(d, 2, out=o), o) assert_equal(np.percentile(d, 2, method='nearest', out=o), o) def test_out_nan(self): with warnings.catch_warnings(record=True): warnings.filterwarnings('always', '', RuntimeWarning) o = np.zeros((4,)) d = np.ones((3, 4)) d[2, 1] = np.nan assert_equal(np.percentile(d, 0, 0, out=o), o) assert_equal( np.percentile(d, 0, 0, method='nearest', out=o), o) o = np.zeros((3,)) assert_equal(np.percentile(d, 1, 1, out=o), o) assert_equal( np.percentile(d, 1, 1, method='nearest', out=o), o) o = np.zeros(()) assert_equal(np.percentile(d, 1, out=o), o) assert_equal( np.percentile(d, 1, method='nearest', out=o), o) def test_nan_behavior(self): a = np.arange(24, dtype=float) a[2] = np.nan assert_equal(np.percentile(a, 0.3), np.nan) assert_equal(np.percentile(a, 0.3, axis=0), np.nan) assert_equal(np.percentile(a, [0.3, 0.6], axis=0), np.array([np.nan] * 2)) a = np.arange(24, dtype=float).reshape(2, 3, 4) a[1, 2, 3] = np.nan a[1, 1, 2] = np.nan # no axis assert_equal(np.percentile(a, 0.3), np.nan) assert_equal(np.percentile(a, 0.3).ndim, 0) # axis0 zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0) b[2, 3] = np.nan b[1, 2] = np.nan assert_equal(np.percentile(a, 0.3, 0), b) # axis0 not zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 0) b[:, 2, 3] = np.nan b[:, 1, 2] = np.nan assert_equal(np.percentile(a, [0.3, 0.6], 0), b) # axis1 zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1) b[1, 3] = np.nan b[1, 2] = np.nan assert_equal(np.percentile(a, 0.3, 1), b) # axis1 not zerod b = np.percentile( np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1) b[:, 1, 3] = np.nan b[:, 1, 2] = np.nan assert_equal(np.percentile(a, [0.3, 0.6], 1), b) # axis02 zerod b = np.percentile( np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2)) b[1] = np.nan b[2] = np.nan assert_equal(np.percentile(a, 0.3, (0, 2)), b) # axis02 not zerod b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], (0, 2)) b[:, 1] = np.nan b[:, 2] = np.nan assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b) # axis02 not zerod with method='nearest' b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], (0, 2), method='nearest') b[:, 1] = np.nan b[:, 2] = np.nan assert_equal(np.percentile( a, [0.3, 0.6], (0, 2), method='nearest'), b) def test_nan_q(self): # GH18830 with pytest.raises(ValueError, match="Percentiles must be in"): np.percentile([1, 2, 3, 4.0], np.nan) with pytest.raises(ValueError, match="Percentiles must be in"): np.percentile([1, 2, 3, 4.0], [np.nan]) q = np.linspace(1.0, 99.0, 16) q[0] = np.nan with pytest.raises(ValueError, match="Percentiles must be in"): np.percentile([1, 2, 3, 4.0], q) @pytest.mark.parametrize("dtype", ["m8[D]", "M8[s]"]) @pytest.mark.parametrize("pos", [0, 23, 10]) def test_nat_basic(self, dtype, pos): # TODO: Note that times have dubious rounding as of fixing NaTs! # NaT and NaN should behave the same, do basic tests for NaT: a = np.arange(0, 24, dtype=dtype) a[pos] = "NaT" res = np.percentile(a, 30) assert res.dtype == dtype assert np.isnat(res) res = np.percentile(a, [30, 60]) assert res.dtype == dtype assert np.isnat(res).all() a = np.arange(0, 243, dtype=dtype).reshape(-1, 3) a[pos, 1] = "NaT" res = np.percentile(a, 30, axis=0) assert_array_equal(np.isnat(res), [False, True, False]) quantile_methods = [ 'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation', 'interpolated_inverted_cdf', 'hazen', 'weibull', 'linear', 'median_unbiased', 'normal_unbiased', 'nearest', 'lower', 'higher', 'midpoint'] class TestQuantile: # most of this is already tested by TestPercentile def V(self, x, y, alpha): # Identification function used in several tests. return (x >= y) - alpha def test_max_ulp(self): x = [0.0, 0.2, 0.4] a = np.quantile(x, 0.45) # The default linear method would result in 0 + 0.2 (0.45/2) = 0.18. # 0.18 is not exactly representable and the formula leads to a 1 ULP # different result. Ensure it is this exact within 1 ULP, see gh-20331. np.testing.assert_array_max_ulp(a, 0.18, maxulp=1) def test_basic(self): x = np.arange(8) * 0.5 assert_equal(np.quantile(x, 0), 0.) assert_equal(np.quantile(x, 1), 3.5) assert_equal(np.quantile(x, 0.5), 1.75) def test_correct_quantile_value(self): a = np.array([True]) tf_quant = np.quantile(True, False) assert_equal(tf_quant, a[0]) assert_equal(type(tf_quant), a.dtype) a = np.array([False, True, True]) quant_res = np.quantile(a, a) assert_array_equal(quant_res, a) assert_equal(quant_res.dtype, a.dtype) def test_fraction(self): # fractional input, integral quantile x = [Fraction(i, 2) for i in range(8)] q = np.quantile(x, 0) assert_equal(q, 0) assert_equal(type(q), Fraction) q = np.quantile(x, 1) assert_equal(q, Fraction(7, 2)) assert_equal(type(q), Fraction) q = np.quantile(x, Fraction(1, 2)) assert_equal(q, Fraction(7, 4)) assert_equal(type(q), Fraction) q = np.quantile(x, [Fraction(1, 2)]) assert_equal(q, np.array([Fraction(7, 4)])) assert_equal(type(q), np.ndarray) q = np.quantile(x, [[Fraction(1, 2)]]) assert_equal(q, np.array([[Fraction(7, 4)]])) assert_equal(type(q), np.ndarray) # repeat with integral input but fractional quantile x = np.arange(8) assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2)) def test_complex(self): #See gh-22652 arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') assert_raises(TypeError, np.quantile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') assert_raises(TypeError, np.quantile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') assert_raises(TypeError, np.quantile, arr_c, 0.5) def test_no_p_overwrite(self): # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) p = p0.copy() np.quantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) p0 = p0.tolist() p = p.tolist() np.quantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"]) def test_quantile_preserve_int_type(self, dtype): res = np.quantile(np.array([1, 2], dtype=dtype), [0.5], method="nearest") assert res.dtype == dtype @pytest.mark.parametrize("method", quantile_methods) def test_quantile_monotonic(self, method): # GH 14685 # test that the return value of quantile is monotonic if p0 is ordered # Also tests that the boundary values are not mishandled. p0 = np.linspace(0, 1, 101) quantile = np.quantile(np.array([0, 1, 1, 2, 2, 3, 3, 4, 5, 5, 1, 1, 9, 9, 9, 8, 8, 7]) * 0.1, p0, method=method) assert_equal(np.sort(quantile), quantile) # Also test one where the number of data points is clearly divisible: quantile = np.quantile([0., 1., 2., 3.], p0, method=method) assert_equal(np.sort(quantile), quantile) @hypothesis.given( arr=arrays(dtype=np.float64, shape=st.integers(min_value=3, max_value=1000), elements=st.floats(allow_infinity=False, allow_nan=False, min_value=-1e300, max_value=1e300))) def test_quantile_monotonic_hypo(self, arr): p0 = np.arange(0, 1, 0.01) quantile = np.quantile(arr, p0) assert_equal(np.sort(quantile), quantile) def test_quantile_scalar_nan(self): a = np.array([[10., 7., 4.], [3., 2., 1.]]) a[0][1] = np.nan actual = np.quantile(a, 0.5) assert np.isscalar(actual) assert_equal(np.quantile(a, 0.5), np.nan) @pytest.mark.parametrize("method", quantile_methods) @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9]) def test_quantile_identification_equation(self, method, alpha): # Test that the identification equation holds for the empirical # CDF: # E[V(x, Y)] = 0 <=> x is quantile # with Y the random variable for which we have observed values and # V(x, y) the canonical identification function for the quantile (at # level alpha), see # https://doi.org/10.48550/arXiv.0912.0902 rng = np.random.default_rng(4321) # We choose n and alpha such that we cover 3 cases: # - n * alpha is an integer # - n * alpha is a float that gets rounded down # - n * alpha is a float that gest rounded up n = 102 # n * alpha = 20.4, 51. , 91.8 y = rng.random(n) x = np.quantile(y, alpha, method=method) if method in ("higher",): # These methods do not fulfill the identification equation. assert np.abs(np.mean(self.V(x, y, alpha))) > 0.1 / n elif int(n * alpha) == n * alpha: # We can expect exact results, up to machine precision. assert_allclose(np.mean(self.V(x, y, alpha)), 0, atol=1e-14) else: # V = (x >= y) - alpha cannot sum to zero exactly but within # "sample precision". assert_allclose(np.mean(self.V(x, y, alpha)), 0, atol=1 / n / np.amin([alpha, 1 - alpha])) @pytest.mark.parametrize("method", quantile_methods) @pytest.mark.parametrize("alpha", [0.2, 0.5, 0.9]) def test_quantile_add_and_multiply_constant(self, method, alpha): # Test that # 1. quantile(c + x) = c + quantile(x) # 2. quantile(c * x) = c * quantile(x) # 3. quantile(-x) = -quantile(x, 1 - alpha) # On empirical quantiles, this equation does not hold exactly. # Koenker (2005) "Quantile Regression" Chapter 2.2.3 calls these # properties equivariance. rng = np.random.default_rng(4321) # We choose n and alpha such that we have cases for # - n * alpha is an integer # - n * alpha is a float that gets rounded down # - n * alpha is a float that gest rounded up n = 102 # n * alpha = 20.4, 51. , 91.8 y = rng.random(n) q = np.quantile(y, alpha, method=method) c = 13.5 # 1 assert_allclose(np.quantile(c + y, alpha, method=method), c + q) # 2 assert_allclose(np.quantile(c * y, alpha, method=method), c * q) # 3 q = -np.quantile(-y, 1 - alpha, method=method) if method == "inverted_cdf": if ( n * alpha == int(n * alpha) or np.round(n * alpha) == int(n * alpha) + 1 ): assert_allclose(q, np.quantile(y, alpha, method="higher")) else: assert_allclose(q, np.quantile(y, alpha, method="lower")) elif method == "closest_observation": if n * alpha == int(n * alpha): assert_allclose(q, np.quantile(y, alpha, method="higher")) elif np.round(n * alpha) == int(n * alpha) + 1: assert_allclose( q, np.quantile(y, alpha + 1/n, method="higher")) else: assert_allclose(q, np.quantile(y, alpha, method="lower")) elif method == "interpolated_inverted_cdf": assert_allclose(q, np.quantile(y, alpha + 1/n, method=method)) elif method == "nearest": if n * alpha == int(n * alpha): assert_allclose(q, np.quantile(y, alpha + 1/n, method=method)) else: assert_allclose(q, np.quantile(y, alpha, method=method)) elif method == "lower": assert_allclose(q, np.quantile(y, alpha, method="higher")) elif method == "higher": assert_allclose(q, np.quantile(y, alpha, method="lower")) else: # "averaged_inverted_cdf", "hazen", "weibull", "linear", # "median_unbiased", "normal_unbiased", "midpoint" assert_allclose(q, np.quantile(y, alpha, method=method)) class TestLerp: @hypothesis.given(t0=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1), t1=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1), a = st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300), b = st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) def test_linear_interpolation_formula_monotonic(self, t0, t1, a, b): l0 = nfb._lerp(a, b, t0) l1 = nfb._lerp(a, b, t1) if t0 == t1 or a == b: assert l0 == l1 # uninteresting elif (t0 < t1) == (a < b): assert l0 <= l1 else: assert l0 >= l1 @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1), a=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300), b=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) def test_linear_interpolation_formula_bounded(self, t, a, b): if a <= b: assert a <= nfb._lerp(a, b, t) <= b else: assert b <= nfb._lerp(a, b, t) <= a @hypothesis.given(t=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1), a=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300), b=st.floats(allow_nan=False, allow_infinity=False, min_value=-1e300, max_value=1e300)) def test_linear_interpolation_formula_symmetric(self, t, a, b): # double subtraction is needed to remove the extra precision of t < 0.5 left = nfb._lerp(a, b, 1 - (1 - t)) right = nfb._lerp(b, a, 1 - t) assert_allclose(left, right) def test_linear_interpolation_formula_0d_inputs(self): a = np.array(2) b = np.array(5) t = np.array(0.2) assert nfb._lerp(a, b, t) == 2.6 class TestMedian: def test_basic(self): a0 = np.array(1) a1 = np.arange(2) a2 = np.arange(6).reshape(2, 3) assert_equal(np.median(a0), 1) assert_allclose(np.median(a1), 0.5) assert_allclose(np.median(a2), 2.5) assert_allclose(np.median(a2, axis=0), [1.5, 2.5, 3.5]) assert_equal(np.median(a2, axis=1), [1, 4]) assert_allclose(np.median(a2, axis=None), 2.5) a = np.array([0.0444502, 0.0463301, 0.141249, 0.0606775]) assert_almost_equal((a[1] + a[3]) / 2., np.median(a)) a = np.array([0.0463301, 0.0444502, 0.141249]) assert_equal(a[0], np.median(a)) a = np.array([0.0444502, 0.141249, 0.0463301]) assert_equal(a[-1], np.median(a)) # check array scalar result assert_equal(np.median(a).ndim, 0) a[1] = np.nan assert_equal(np.median(a).ndim, 0) def test_axis_keyword(self): a3 = np.array([[2, 3], [0, 1], [6, 7], [4, 5]]) for a in [a3, np.random.randint(0, 100, size=(2, 3, 4))]: orig = a.copy() np.median(a, axis=None) for ax in range(a.ndim): np.median(a, axis=ax) assert_array_equal(a, orig) assert_allclose(np.median(a3, axis=0), [3, 4]) assert_allclose(np.median(a3.T, axis=1), [3, 4]) assert_allclose(np.median(a3), 3.5) assert_allclose(np.median(a3, axis=None), 3.5) assert_allclose(np.median(a3.T), 3.5) def test_overwrite_keyword(self): a3 = np.array([[2, 3], [0, 1], [6, 7], [4, 5]]) a0 = np.array(1) a1 = np.arange(2) a2 = np.arange(6).reshape(2, 3) assert_allclose(np.median(a0.copy(), overwrite_input=True), 1) assert_allclose(np.median(a1.copy(), overwrite_input=True), 0.5) assert_allclose(np.median(a2.copy(), overwrite_input=True), 2.5) assert_allclose(np.median(a2.copy(), overwrite_input=True, axis=0), [1.5, 2.5, 3.5]) assert_allclose( np.median(a2.copy(), overwrite_input=True, axis=1), [1, 4]) assert_allclose( np.median(a2.copy(), overwrite_input=True, axis=None), 2.5) assert_allclose( np.median(a3.copy(), overwrite_input=True, axis=0), [3, 4]) assert_allclose(np.median(a3.T.copy(), overwrite_input=True, axis=1), [3, 4]) a4 = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5)) np.random.shuffle(a4.ravel()) assert_allclose(np.median(a4, axis=None), np.median(a4.copy(), axis=None, overwrite_input=True)) assert_allclose(np.median(a4, axis=0), np.median(a4.copy(), axis=0, overwrite_input=True)) assert_allclose(np.median(a4, axis=1), np.median(a4.copy(), axis=1, overwrite_input=True)) assert_allclose(np.median(a4, axis=2), np.median(a4.copy(), axis=2, overwrite_input=True)) def test_array_like(self): x = [1, 2, 3] assert_almost_equal(np.median(x), 2) x2 = [x] assert_almost_equal(np.median(x2), 2) assert_allclose(np.median(x2, axis=0), x) def test_subclass(self): # gh-3846 class MySubClass(np.ndarray): def __new__(cls, input_array, info=None): obj = np.asarray(input_array).view(cls) obj.info = info return obj def mean(self, axis=None, dtype=None, out=None): return -7 a = MySubClass([1, 2, 3]) assert_equal(np.median(a), -7) @pytest.mark.parametrize('arr', ([1., 2., 3.], [1., np.nan, 3.], np.nan, 0.)) def test_subclass2(self, arr): """Check that we return subclasses, even if a NaN scalar.""" class MySubclass(np.ndarray): pass m = np.median(np.array(arr).view(MySubclass)) assert isinstance(m, MySubclass) def test_out(self): o = np.zeros((4,)) d = np.ones((3, 4)) assert_equal(np.median(d, 0, out=o), o) o = np.zeros((3,)) assert_equal(np.median(d, 1, out=o), o) o = np.zeros(()) assert_equal(np.median(d, out=o), o) def test_out_nan(self): with warnings.catch_warnings(record=True): warnings.filterwarnings('always', '', RuntimeWarning) o = np.zeros((4,)) d = np.ones((3, 4)) d[2, 1] = np.nan assert_equal(np.median(d, 0, out=o), o) o = np.zeros((3,)) assert_equal(np.median(d, 1, out=o), o) o = np.zeros(()) assert_equal(np.median(d, out=o), o) def test_nan_behavior(self): a = np.arange(24, dtype=float) a[2] = np.nan assert_equal(np.median(a), np.nan) assert_equal(np.median(a, axis=0), np.nan) a = np.arange(24, dtype=float).reshape(2, 3, 4) a[1, 2, 3] = np.nan a[1, 1, 2] = np.nan # no axis assert_equal(np.median(a), np.nan) assert_equal(np.median(a).ndim, 0) # axis0 b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0) b[2, 3] = np.nan b[1, 2] = np.nan assert_equal(np.median(a, 0), b) # axis1 b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1) b[1, 3] = np.nan b[1, 2] = np.nan assert_equal(np.median(a, 1), b) # axis02 b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2)) b[1] = np.nan b[2] = np.nan assert_equal(np.median(a, (0, 2)), b) @pytest.mark.skipif(IS_WASM, reason="fp errors don't work correctly") def test_empty(self): # mean(empty array) emits two warnings: empty slice and divide by 0 a = np.array([], dtype=float) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.median(a), np.nan) assert_(w[0].category is RuntimeWarning) assert_equal(len(w), 2) # multiple dimensions a = np.array([], dtype=float, ndmin=3) # no axis with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.median(a), np.nan) assert_(w[0].category is RuntimeWarning) # axis 0 and 1 b = np.array([], dtype=float, ndmin=2) assert_equal(np.median(a, axis=0), b) assert_equal(np.median(a, axis=1), b) # axis 2 b = np.array(np.nan, dtype=float, ndmin=2) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', RuntimeWarning) assert_equal(np.median(a, axis=2), b) assert_(w[0].category is RuntimeWarning) def test_object(self): o = np.arange(7.) assert_(type(np.median(o.astype(object))), float) o[2] = np.nan assert_(type(np.median(o.astype(object))), float) def test_extended_axis(self): o = np.random.normal(size=(71, 23)) x = np.dstack([o] * 10) assert_equal(np.median(x, axis=(0, 1)), np.median(o)) x = np.moveaxis(x, -1, 0) assert_equal(np.median(x, axis=(-2, -1)), np.median(o)) x = x.swapaxes(0, 1).copy() assert_equal(np.median(x, axis=(0, -1)), np.median(o)) assert_equal(np.median(x, axis=(0, 1, 2)), np.median(x, axis=None)) assert_equal(np.median(x, axis=(0, )), np.median(x, axis=0)) assert_equal(np.median(x, axis=(-1, )), np.median(x, axis=-1)) d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11)) np.random.shuffle(d.ravel()) assert_equal(np.median(d, axis=(0, 1, 2))[0], np.median(d[:,:,:, 0].flatten())) assert_equal(np.median(d, axis=(0, 1, 3))[1], np.median(d[:,:, 1,:].flatten())) assert_equal(np.median(d, axis=(3, 1, -4))[2], np.median(d[:,:, 2,:].flatten())) assert_equal(np.median(d, axis=(3, 1, 2))[2], np.median(d[2,:,:,:].flatten())) assert_equal(np.median(d, axis=(3, 2))[2, 1], np.median(d[2, 1,:,:].flatten())) assert_equal(np.median(d, axis=(1, -2))[2, 1], np.median(d[2,:,:, 1].flatten())) assert_equal(np.median(d, axis=(1, 3))[2, 2], np.median(d[2,:, 2,:].flatten())) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) assert_raises(np.AxisError, np.median, d, axis=-5) assert_raises(np.AxisError, np.median, d, axis=(0, -5)) assert_raises(np.AxisError, np.median, d, axis=4) assert_raises(np.AxisError, np.median, d, axis=(0, 4)) assert_raises(ValueError, np.median, d, axis=(1, 1)) def test_keepdims(self): d = np.ones((3, 5, 7, 11)) assert_equal(np.median(d, axis=None, keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.median(d, axis=(0, 1), keepdims=True).shape, (1, 1, 7, 11)) assert_equal(np.median(d, axis=(0, 3), keepdims=True).shape, (1, 5, 7, 1)) assert_equal(np.median(d, axis=(1,), keepdims=True).shape, (3, 1, 7, 11)) assert_equal(np.median(d, axis=(0, 1, 2, 3), keepdims=True).shape, (1, 1, 1, 1)) assert_equal(np.median(d, axis=(0, 1, 3), keepdims=True).shape, (1, 1, 7, 1)) @pytest.mark.parametrize( argnames='axis', argvalues=[ None, 1, (1, ), (0, 1), (-3, -1), ] ) def test_keepdims_out(self, axis): d = np.ones((3, 5, 7, 11)) if axis is None: shape_out = (1,) * d.ndim else: axis_norm = normalize_axis_tuple(axis, d.ndim) shape_out = tuple( 1 if i in axis_norm else d.shape[i] for i in range(d.ndim)) out = np.empty(shape_out) result = np.median(d, axis=axis, keepdims=True, out=out) assert result is out assert_equal(result.shape, shape_out) @pytest.mark.parametrize("dtype", ["m8[s]"]) @pytest.mark.parametrize("pos", [0, 23, 10]) def test_nat_behavior(self, dtype, pos): # TODO: Median does not support Datetime, due to `mean`. # NaT and NaN should behave the same, do basic tests for NaT. a = np.arange(0, 24, dtype=dtype) a[pos] = "NaT" res = np.median(a) assert res.dtype == dtype assert np.isnat(res) res = np.percentile(a, [30, 60]) assert res.dtype == dtype assert np.isnat(res).all() a = np.arange(0, 24*3, dtype=dtype).reshape(-1, 3) a[pos, 1] = "NaT" res = np.median(a, axis=0) assert_array_equal(np.isnat(res), [False, True, False]) class TestAdd_newdoc_ufunc: def test_ufunc_arg(self): assert_raises(TypeError, add_newdoc_ufunc, 2, "blah") assert_raises(ValueError, add_newdoc_ufunc, np.add, "blah") def test_string_arg(self): assert_raises(TypeError, add_newdoc_ufunc, np.add, 3) class TestAdd_newdoc: @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO") @pytest.mark.xfail(IS_PYPY, reason="PyPy does not modify tp_doc") def test_add_doc(self): # test that np.add_newdoc did attach a docstring successfully: tgt = "Current flat index into the array." assert_equal(np.core.flatiter.index.__doc__[:len(tgt)], tgt) assert_(len(np.core.ufunc.identity.__doc__) > 300) assert_(len(np.lib.index_tricks.mgrid.__doc__) > 300) @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO") def test_errors_are_ignored(self): prev_doc = np.core.flatiter.index.__doc__ # nothing changed, but error ignored, this should probably # give a warning (or even error) in the future. np.add_newdoc("numpy.core", "flatiter", ("index", "bad docstring")) assert prev_doc == np.core.flatiter.index.__doc__ class TestAddDocstring(): # Test should possibly be moved, but it also fits to be close to # the newdoc tests... @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO") @pytest.mark.skipif(IS_PYPY, reason="PyPy does not modify tp_doc") def test_add_same_docstring(self): # test for attributes (which are C-level defined) np.add_docstring(np.ndarray.flat, np.ndarray.flat.__doc__) # And typical functions: def func(): """docstring""" return np.add_docstring(func, func.__doc__) @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO") def test_different_docstring_fails(self): # test for attributes (which are C-level defined) with assert_raises(RuntimeError): np.add_docstring(np.ndarray.flat, "different docstring") # And typical functions: def func(): """docstring""" return with assert_raises(RuntimeError): np.add_docstring(func, "different docstring") class TestSortComplex: @pytest.mark.parametrize("type_in, type_out", [ ('l', 'D'), ('h', 'F'), ('H', 'F'), ('b', 'F'), ('B', 'F'), ('g', 'G'), ]) def test_sort_real(self, type_in, type_out): # sort_complex() type casting for real input types a = np.array([5, 3, 6, 2, 1], dtype=type_in) actual = np.sort_complex(a) expected = np.sort(a).astype(type_out) assert_equal(actual, expected) assert_equal(actual.dtype, expected.dtype) def test_sort_complex(self): # sort_complex() handling of complex input a = np.array([2 + 3j, 1 - 2j, 1 - 3j, 2 + 1j], dtype='D') expected = np.array([1 - 3j, 1 - 2j, 2 + 1j, 2 + 3j], dtype='D') actual = np.sort_complex(a) assert_equal(actual, expected) assert_equal(actual.dtype, expected.dtype)	154.03 KB	2026-01-20 12:58
📄 test_histograms.py import numpy as np from numpy.lib.histograms import histogram, histogramdd, histogram_bin_edges from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_raises, assert_allclose, assert_array_max_ulp, assert_raises_regex, suppress_warnings, ) from numpy.testing._private.utils import requires_memory import pytest class TestHistogram: def setup_method(self): pass def teardown_method(self): pass def test_simple(self): n = 100 v = np.random.rand(n) (a, b) = histogram(v) # check if the sum of the bins equals the number of samples assert_equal(np.sum(a, axis=0), n) # check that the bin counts are evenly spaced when the data is from # a linear function (a, b) = histogram(np.linspace(0, 10, 100)) assert_array_equal(a, 10) def test_one_bin(self): # Ticket 632 hist, edges = histogram([1, 2, 3, 4], [1, 2]) assert_array_equal(hist, [2, ]) assert_array_equal(edges, [1, 2]) assert_raises(ValueError, histogram, [1, 2], bins=0) h, e = histogram([1, 2], bins=1) assert_equal(h, np.array([2])) assert_allclose(e, np.array([1., 2.])) def test_density(self): # Check that the integral of the density equals 1. n = 100 v = np.random.rand(n) a, b = histogram(v, density=True) area = np.sum(a * np.diff(b)) assert_almost_equal(area, 1) # Check with non-constant bin widths v = np.arange(10) bins = [0, 1, 3, 6, 10] a, b = histogram(v, bins, density=True) assert_array_equal(a, .1) assert_equal(np.sum(a * np.diff(b)), 1) # Test that passing False works too a, b = histogram(v, bins, density=False) assert_array_equal(a, [1, 2, 3, 4]) # Variable bin widths are especially useful to deal with # infinities. v = np.arange(10) bins = [0, 1, 3, 6, np.inf] a, b = histogram(v, bins, density=True) assert_array_equal(a, [.1, .1, .1, 0.]) # Taken from a bug report from N. Becker on the numpy-discussion # mailing list Aug. 6, 2010. counts, dmy = np.histogram( [1, 2, 3, 4], [0.5, 1.5, np.inf], density=True) assert_equal(counts, [.25, 0]) def test_outliers(self): # Check that outliers are not tallied a = np.arange(10) + .5 # Lower outliers h, b = histogram(a, range=[0, 9]) assert_equal(h.sum(), 9) # Upper outliers h, b = histogram(a, range=[1, 10]) assert_equal(h.sum(), 9) # Normalization h, b = histogram(a, range=[1, 9], density=True) assert_almost_equal((h * np.diff(b)).sum(), 1, decimal=15) # Weights w = np.arange(10) + .5 h, b = histogram(a, range=[1, 9], weights=w, density=True) assert_equal((h * np.diff(b)).sum(), 1) h, b = histogram(a, bins=8, range=[1, 9], weights=w) assert_equal(h, w[1:-1]) def test_arr_weights_mismatch(self): a = np.arange(10) + .5 w = np.arange(11) + .5 with assert_raises_regex(ValueError, "same shape as"): h, b = histogram(a, range=[1, 9], weights=w, density=True) def test_type(self): # Check the type of the returned histogram a = np.arange(10) + .5 h, b = histogram(a) assert_(np.issubdtype(h.dtype, np.integer)) h, b = histogram(a, density=True) assert_(np.issubdtype(h.dtype, np.floating)) h, b = histogram(a, weights=np.ones(10, int)) assert_(np.issubdtype(h.dtype, np.integer)) h, b = histogram(a, weights=np.ones(10, float)) assert_(np.issubdtype(h.dtype, np.floating)) def test_f32_rounding(self): # gh-4799, check that the rounding of the edges works with float32 x = np.array([276.318359, -69.593948, 21.329449], dtype=np.float32) y = np.array([5005.689453, 4481.327637, 6010.369629], dtype=np.float32) counts_hist, xedges, yedges = np.histogram2d(x, y, bins=100) assert_equal(counts_hist.sum(), 3.) def test_bool_conversion(self): # gh-12107 # Reference integer histogram a = np.array([1, 1, 0], dtype=np.uint8) int_hist, int_edges = np.histogram(a) # Should raise an warning on booleans # Ensure that the histograms are equivalent, need to suppress # the warnings to get the actual outputs with suppress_warnings() as sup: rec = sup.record(RuntimeWarning, 'Converting input from .') hist, edges = np.histogram([True, True, False]) # A warning should be issued assert_equal(len(rec), 1) assert_array_equal(hist, int_hist) assert_array_equal(edges, int_edges) def test_weights(self): v = np.random.rand(100) w = np.ones(100) 5 a, b = histogram(v) na, nb = histogram(v, density=True) wa, wb = histogram(v, weights=w) nwa, nwb = histogram(v, weights=w, density=True) assert_array_almost_equal(a * 5, wa) assert_array_almost_equal(na, nwa) # Check weights are properly applied. v = np.linspace(0, 10, 10) w = np.concatenate((np.zeros(5), np.ones(5))) wa, wb = histogram(v, bins=np.arange(11), weights=w) assert_array_almost_equal(wa, w) # Check with integer weights wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1]) assert_array_equal(wa, [4, 5, 0, 1]) wa, wb = histogram( [1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], density=True) assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4) # Check weights with non-uniform bin widths a, b = histogram( np.arange(9), [0, 1, 3, 6, 10], weights=[2, 1, 1, 1, 1, 1, 1, 1, 1], density=True) assert_almost_equal(a, [.2, .1, .1, .075]) def test_exotic_weights(self): # Test the use of weights that are not integer or floats, but e.g. # complex numbers or object types. # Complex weights values = np.array([1.3, 2.5, 2.3]) weights = np.array([1, -1, 2]) + 1j * np.array([2, 1, 2]) # Check with custom bins wa, wb = histogram(values, bins=[0, 2, 3], weights=weights) assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2, 3])) # Check with even bins wa, wb = histogram(values, bins=2, range=[1, 3], weights=weights) assert_array_almost_equal(wa, np.array([1, 1]) + 1j * np.array([2, 3])) # Decimal weights from decimal import Decimal values = np.array([1.3, 2.5, 2.3]) weights = np.array([Decimal(1), Decimal(2), Decimal(3)]) # Check with custom bins wa, wb = histogram(values, bins=[0, 2, 3], weights=weights) assert_array_almost_equal(wa, [Decimal(1), Decimal(5)]) # Check with even bins wa, wb = histogram(values, bins=2, range=[1, 3], weights=weights) assert_array_almost_equal(wa, [Decimal(1), Decimal(5)]) def test_no_side_effects(self): # This is a regression test that ensures that values passed to # ``histogram`` are unchanged. values = np.array([1.3, 2.5, 2.3]) np.histogram(values, range=[-10, 10], bins=100) assert_array_almost_equal(values, [1.3, 2.5, 2.3]) def test_empty(self): a, b = histogram([], bins=([0, 1])) assert_array_equal(a, np.array([0])) assert_array_equal(b, np.array([0, 1])) def test_error_binnum_type (self): # Tests if right Error is raised if bins argument is float vals = np.linspace(0.0, 1.0, num=100) histogram(vals, 5) assert_raises(TypeError, histogram, vals, 2.4) def test_finite_range(self): # Normal ranges should be fine vals = np.linspace(0.0, 1.0, num=100) histogram(vals, range=[0.25,0.75]) assert_raises(ValueError, histogram, vals, range=[np.nan,0.75]) assert_raises(ValueError, histogram, vals, range=[0.25,np.inf]) def test_invalid_range(self): # start of range must be < end of range vals = np.linspace(0.0, 1.0, num=100) with assert_raises_regex(ValueError, "max must be larger than"): np.histogram(vals, range=[0.1, 0.01]) def test_bin_edge_cases(self): # Ensure that floating-point computations correctly place edge cases. arr = np.array([337, 404, 739, 806, 1007, 1811, 2012]) hist, edges = np.histogram(arr, bins=8296, range=(2, 2280)) mask = hist > 0 left_edges = edges[:-1][mask] right_edges = edges[1:][mask] for x, left, right in zip(arr, left_edges, right_edges): assert_(x >= left) assert_(x < right) def test_last_bin_inclusive_range(self): arr = np.array([0., 0., 0., 1., 2., 3., 3., 4., 5.]) hist, edges = np.histogram(arr, bins=30, range=(-0.5, 5)) assert_equal(hist[-1], 1) def test_bin_array_dims(self): # gracefully handle bins object > 1 dimension vals = np.linspace(0.0, 1.0, num=100) bins = np.array([[0, 0.5], [0.6, 1.0]]) with assert_raises_regex(ValueError, "must be 1d"): np.histogram(vals, bins=bins) def test_unsigned_monotonicity_check(self): # Ensures ValueError is raised if bins not increasing monotonically # when bins contain unsigned values (see #9222) arr = np.array([2]) bins = np.array([1, 3, 1], dtype='uint64') with assert_raises(ValueError): hist, edges = np.histogram(arr, bins=bins) def test_object_array_of_0d(self): # gh-7864 assert_raises(ValueError, histogram, [np.array(0.4) for i in range(10)] + [-np.inf]) assert_raises(ValueError, histogram, [np.array(0.4) for i in range(10)] + [np.inf]) # these should not crash np.histogram([np.array(0.5) for i in range(10)] + [.500000000000001]) np.histogram([np.array(0.5) for i in range(10)] + [.5]) def test_some_nan_values(self): # gh-7503 one_nan = np.array([0, 1, np.nan]) all_nan = np.array([np.nan, np.nan]) # the internal comparisons with NaN give warnings sup = suppress_warnings() sup.filter(RuntimeWarning) with sup: # can't infer range with nan assert_raises(ValueError, histogram, one_nan, bins='auto') assert_raises(ValueError, histogram, all_nan, bins='auto') # explicit range solves the problem h, b = histogram(one_nan, bins='auto', range=(0, 1)) assert_equal(h.sum(), 2) # nan is not counted h, b = histogram(all_nan, bins='auto', range=(0, 1)) assert_equal(h.sum(), 0) # nan is not counted # as does an explicit set of bins h, b = histogram(one_nan, bins=[0, 1]) assert_equal(h.sum(), 2) # nan is not counted h, b = histogram(all_nan, bins=[0, 1]) assert_equal(h.sum(), 0) # nan is not counted def test_datetime(self): begin = np.datetime64('2000-01-01', 'D') offsets = np.array([0, 0, 1, 1, 2, 3, 5, 10, 20]) bins = np.array([0, 2, 7, 20]) dates = begin + offsets date_bins = begin + bins td = np.dtype('timedelta64[D]') # Results should be the same for integer offsets or datetime values. # For now, only explicit bins are supported, since linspace does not # work on datetimes or timedeltas d_count, d_edge = histogram(dates, bins=date_bins) t_count, t_edge = histogram(offsets.astype(td), bins=bins.astype(td)) i_count, i_edge = histogram(offsets, bins=bins) assert_equal(d_count, i_count) assert_equal(t_count, i_count) assert_equal((d_edge - begin).astype(int), i_edge) assert_equal(t_edge.astype(int), i_edge) assert_equal(d_edge.dtype, dates.dtype) assert_equal(t_edge.dtype, td) def do_signed_overflow_bounds(self, dtype): exponent = 8 * np.dtype(dtype).itemsize - 1 arr = np.array([-2exponent + 4, 2exponent - 4], dtype=dtype) hist, e = histogram(arr, bins=2) assert_equal(e, [-2exponent + 4, 0, 2exponent - 4]) assert_equal(hist, [1, 1]) def test_signed_overflow_bounds(self): self.do_signed_overflow_bounds(np.byte) self.do_signed_overflow_bounds(np.short) self.do_signed_overflow_bounds(np.intc) self.do_signed_overflow_bounds(np.int_) self.do_signed_overflow_bounds(np.longlong) def do_precision_lower_bound(self, float_small, float_large): eps = np.finfo(float_large).eps arr = np.array([1.0], float_small) range = np.array([1.0 + eps, 2.0], float_large) # test is looking for behavior when the bounds change between dtypes if range.astype(float_small)[0] != 1: return # previously crashed count, x_loc = np.histogram(arr, bins=1, range=range) assert_equal(count, [1]) # gh-10322 means that the type comes from arr - this may change assert_equal(x_loc.dtype, float_small) def do_precision_upper_bound(self, float_small, float_large): eps = np.finfo(float_large).eps arr = np.array([1.0], float_small) range = np.array([0.0, 1.0 - eps], float_large) # test is looking for behavior when the bounds change between dtypes if range.astype(float_small)[-1] != 1: return # previously crashed count, x_loc = np.histogram(arr, bins=1, range=range) assert_equal(count, [1]) # gh-10322 means that the type comes from arr - this may change assert_equal(x_loc.dtype, float_small) def do_precision(self, float_small, float_large): self.do_precision_lower_bound(float_small, float_large) self.do_precision_upper_bound(float_small, float_large) def test_precision(self): # not looping results in a useful stack trace upon failure self.do_precision(np.half, np.single) self.do_precision(np.half, np.double) self.do_precision(np.half, np.longdouble) self.do_precision(np.single, np.double) self.do_precision(np.single, np.longdouble) self.do_precision(np.double, np.longdouble) def test_histogram_bin_edges(self): hist, e = histogram([1, 2, 3, 4], [1, 2]) edges = histogram_bin_edges([1, 2, 3, 4], [1, 2]) assert_array_equal(edges, e) arr = np.array([0., 0., 0., 1., 2., 3., 3., 4., 5.]) hist, e = histogram(arr, bins=30, range=(-0.5, 5)) edges = histogram_bin_edges(arr, bins=30, range=(-0.5, 5)) assert_array_equal(edges, e) hist, e = histogram(arr, bins='auto', range=(0, 1)) edges = histogram_bin_edges(arr, bins='auto', range=(0, 1)) assert_array_equal(edges, e) @requires_memory(free_bytes=1e10) @pytest.mark.slow def test_big_arrays(self): sample = np.zeros([100000000, 3]) xbins = 400 ybins = 400 zbins = np.arange(16000) hist = np.histogramdd(sample=sample, bins=(xbins, ybins, zbins)) assert_equal(type(hist), type((1, 2))) class TestHistogramOptimBinNums: """ Provide test coverage when using provided estimators for optimal number of bins """ def test_empty(self): estimator_list = ['fd', 'scott', 'rice', 'sturges', 'doane', 'sqrt', 'auto', 'stone'] # check it can deal with empty data for estimator in estimator_list: a, b = histogram([], bins=estimator) assert_array_equal(a, np.array([0])) assert_array_equal(b, np.array([0, 1])) def test_simple(self): """ Straightforward testing with a mixture of linspace data (for consistency). All test values have been precomputed and the values shouldn't change """ # Some basic sanity checking, with some fixed data. # Checking for the correct number of bins basic_test = {50: {'fd': 4, 'scott': 4, 'rice': 8, 'sturges': 7, 'doane': 8, 'sqrt': 8, 'auto': 7, 'stone': 2}, 500: {'fd': 8, 'scott': 8, 'rice': 16, 'sturges': 10, 'doane': 12, 'sqrt': 23, 'auto': 10, 'stone': 9}, 5000: {'fd': 17, 'scott': 17, 'rice': 35, 'sturges': 14, 'doane': 17, 'sqrt': 71, 'auto': 17, 'stone': 20}} for testlen, expectedResults in basic_test.items(): # Create some sort of non uniform data to test with # (2 peak uniform mixture) x1 = np.linspace(-10, -1, testlen // 5 * 2) x2 = np.linspace(1, 10, testlen // 5 * 3) x = np.concatenate((x1, x2)) for estimator, numbins in expectedResults.items(): a, b = np.histogram(x, estimator) assert_equal(len(a), numbins, err_msg="For the {0} estimator " "with datasize of {1}".format(estimator, testlen)) def test_small(self): """ Smaller datasets have the potential to cause issues with the data adaptive methods, especially the FD method. All bin numbers have been precalculated. """ small_dat = {1: {'fd': 1, 'scott': 1, 'rice': 1, 'sturges': 1, 'doane': 1, 'sqrt': 1, 'stone': 1}, 2: {'fd': 2, 'scott': 1, 'rice': 3, 'sturges': 2, 'doane': 1, 'sqrt': 2, 'stone': 1}, 3: {'fd': 2, 'scott': 2, 'rice': 3, 'sturges': 3, 'doane': 3, 'sqrt': 2, 'stone': 1}} for testlen, expectedResults in small_dat.items(): testdat = np.arange(testlen) for estimator, expbins in expectedResults.items(): a, b = np.histogram(testdat, estimator) assert_equal(len(a), expbins, err_msg="For the {0} estimator " "with datasize of {1}".format(estimator, testlen)) def test_incorrect_methods(self): """ Check a Value Error is thrown when an unknown string is passed in """ check_list = ['mad', 'freeman', 'histograms', 'IQR'] for estimator in check_list: assert_raises(ValueError, histogram, [1, 2, 3], estimator) def test_novariance(self): """ Check that methods handle no variance in data Primarily for Scott and FD as the SD and IQR are both 0 in this case """ novar_dataset = np.ones(100) novar_resultdict = {'fd': 1, 'scott': 1, 'rice': 1, 'sturges': 1, 'doane': 1, 'sqrt': 1, 'auto': 1, 'stone': 1} for estimator, numbins in novar_resultdict.items(): a, b = np.histogram(novar_dataset, estimator) assert_equal(len(a), numbins, err_msg="{0} estimator, " "No Variance test".format(estimator)) def test_limited_variance(self): """ Check when IQR is 0, but variance exists, we return the sturges value and not the fd value. """ lim_var_data = np.ones(1000) lim_var_data[:3] = 0 lim_var_data[-4:] = 100 edges_auto = histogram_bin_edges(lim_var_data, 'auto') assert_equal(edges_auto, np.linspace(0, 100, 12)) edges_fd = histogram_bin_edges(lim_var_data, 'fd') assert_equal(edges_fd, np.array([0, 100])) edges_sturges = histogram_bin_edges(lim_var_data, 'sturges') assert_equal(edges_sturges, np.linspace(0, 100, 12)) def test_outlier(self): """ Check the FD, Scott and Doane with outliers. The FD estimates a smaller binwidth since it's less affected by outliers. Since the range is so (artificially) large, this means more bins, most of which will be empty, but the data of interest usually is unaffected. The Scott estimator is more affected and returns fewer bins, despite most of the variance being in one area of the data. The Doane estimator lies somewhere between the other two. """ xcenter = np.linspace(-10, 10, 50) outlier_dataset = np.hstack((np.linspace(-110, -100, 5), xcenter)) outlier_resultdict = {'fd': 21, 'scott': 5, 'doane': 11, 'stone': 6} for estimator, numbins in outlier_resultdict.items(): a, b = np.histogram(outlier_dataset, estimator) assert_equal(len(a), numbins) def test_scott_vs_stone(self): """Verify that Scott's rule and Stone's rule converges for normally distributed data""" def nbins_ratio(seed, size): rng = np.random.RandomState(seed) x = rng.normal(loc=0, scale=2, size=size) a, b = len(np.histogram(x, 'stone')[0]), len(np.histogram(x, 'scott')[0]) return a / (a + b) ll = [[nbins_ratio(seed, size) for size in np.geomspace(start=10, stop=100, num=4).round().astype(int)] for seed in range(10)] # the average difference between the two methods decreases as the dataset size increases. avg = abs(np.mean(ll, axis=0) - 0.5) assert_almost_equal(avg, [0.15, 0.09, 0.08, 0.03], decimal=2) def test_simple_range(self): """ Straightforward testing with a mixture of linspace data (for consistency). Adding in a 3rd mixture that will then be completely ignored. All test values have been precomputed and the shouldn't change. """ # some basic sanity checking, with some fixed data. # Checking for the correct number of bins basic_test = { 50: {'fd': 8, 'scott': 8, 'rice': 15, 'sturges': 14, 'auto': 14, 'stone': 8}, 500: {'fd': 15, 'scott': 16, 'rice': 32, 'sturges': 20, 'auto': 20, 'stone': 80}, 5000: {'fd': 33, 'scott': 33, 'rice': 69, 'sturges': 27, 'auto': 33, 'stone': 80} } for testlen, expectedResults in basic_test.items(): # create some sort of non uniform data to test with # (3 peak uniform mixture) x1 = np.linspace(-10, -1, testlen // 5 * 2) x2 = np.linspace(1, 10, testlen // 5 * 3) x3 = np.linspace(-100, -50, testlen) x = np.hstack((x1, x2, x3)) for estimator, numbins in expectedResults.items(): a, b = np.histogram(x, estimator, range = (-20, 20)) msg = "For the {0} estimator".format(estimator) msg += " with datasize of {0}".format(testlen) assert_equal(len(a), numbins, err_msg=msg) @pytest.mark.parametrize("bins", ['auto', 'fd', 'doane', 'scott', 'stone', 'rice', 'sturges']) def test_signed_integer_data(self, bins): # Regression test for gh-14379. a = np.array([-2, 0, 127], dtype=np.int8) hist, edges = np.histogram(a, bins=bins) hist32, edges32 = np.histogram(a.astype(np.int32), bins=bins) assert_array_equal(hist, hist32) assert_array_equal(edges, edges32) def test_simple_weighted(self): """ Check that weighted data raises a TypeError """ estimator_list = ['fd', 'scott', 'rice', 'sturges', 'auto'] for estimator in estimator_list: assert_raises(TypeError, histogram, [1, 2, 3], estimator, weights=[1, 2, 3]) class TestHistogramdd: def test_simple(self): x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], [.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]]) H, edges = histogramdd(x, (2, 3, 3), range=[[-1, 1], [0, 3], [0, 3]]) answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]], [[0, 1, 0], [0, 0, 1], [0, 0, 1]]]) assert_array_equal(H, answer) # Check normalization ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]] H, edges = histogramdd(x, bins=ed, density=True) assert_(np.all(H == answer / 12.)) # Check that H has the correct shape. H, edges = histogramdd(x, (2, 3, 4), range=[[-1, 1], [0, 3], [0, 4]], density=True) answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]], [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]]) assert_array_almost_equal(H, answer / 6., 4) # Check that a sequence of arrays is accepted and H has the correct # shape. z = [np.squeeze(y) for y in np.split(x, 3, axis=1)] H, edges = histogramdd( z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]]) answer = np.array([[[0, 0], [0, 0], [0, 0]], [[0, 1], [0, 0], [1, 0]], [[0, 1], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]]]) assert_array_equal(H, answer) Z = np.zeros((5, 5, 5)) Z[list(range(5)), list(range(5)), list(range(5))] = 1. H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5) assert_array_equal(H, Z) def test_shape_3d(self): # All possible permutations for bins of different lengths in 3D. bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4), (4, 5, 6)) r = np.random.rand(10, 3) for b in bins: H, edges = histogramdd(r, b) assert_(H.shape == b) def test_shape_4d(self): # All possible permutations for bins of different lengths in 4D. bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4), (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6), (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7), (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5), (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5), (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4)) r = np.random.rand(10, 4) for b in bins: H, edges = histogramdd(r, b) assert_(H.shape == b) def test_weights(self): v = np.random.rand(100, 2) hist, edges = histogramdd(v) n_hist, edges = histogramdd(v, density=True) w_hist, edges = histogramdd(v, weights=np.ones(100)) assert_array_equal(w_hist, hist) w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, density=True) assert_array_equal(w_hist, n_hist) w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2) assert_array_equal(w_hist, 2 * hist) def test_identical_samples(self): x = np.zeros((10, 2), int) hist, edges = histogramdd(x, bins=2) assert_array_equal(edges[0], np.array([-0.5, 0., 0.5])) def test_empty(self): a, b = histogramdd([[], []], bins=([0, 1], [0, 1])) assert_array_max_ulp(a, np.array([[0.]])) a, b = np.histogramdd([[], [], []], bins=2) assert_array_max_ulp(a, np.zeros((2, 2, 2))) def test_bins_errors(self): # There are two ways to specify bins. Check for the right errors # when mixing those. x = np.arange(8).reshape(2, 4) assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5]) assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1]) assert_raises( ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]]) assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]])) def test_inf_edges(self): # Test using +/-inf bin edges works. See #1788. with np.errstate(invalid='ignore'): x = np.arange(6).reshape(3, 2) expected = np.array([[1, 0], [0, 1], [0, 1]]) h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]]) assert_allclose(h, expected) h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])]) assert_allclose(h, expected) h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]]) assert_allclose(h, expected) def test_rightmost_binedge(self): # Test event very close to rightmost binedge. See Github issue #4266 x = [0.9999999995] bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0] bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 1.) x = [1.0000000001] bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 0.0) x = [1.0001] bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) assert_(hist[1] == 0.0) def test_finite_range(self): vals = np.random.random((100, 3)) histogramdd(vals, range=[[0.0, 1.0], [0.25, 0.75], [0.25, 0.5]]) assert_raises(ValueError, histogramdd, vals, range=[[0.0, 1.0], [0.25, 0.75], [0.25, np.inf]]) assert_raises(ValueError, histogramdd, vals, range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]]) def test_equal_edges(self): """ Test that adjacent entries in an edge array can be equal """ x = np.array([0, 1, 2]) y = np.array([0, 1, 2]) x_edges = np.array([0, 2, 2]) y_edges = 1 hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) hist_expected = np.array([ [2.], [1.], # x == 2 falls in the final bin ]) assert_equal(hist, hist_expected) def test_edge_dtype(self): """ Test that if an edge array is input, its type is preserved """ x = np.array([0, 10, 20]) y = x / 10 x_edges = np.array([0, 5, 15, 20]) y_edges = x_edges / 10 hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) assert_equal(edges[0].dtype, x_edges.dtype) assert_equal(edges[1].dtype, y_edges.dtype) def test_large_integers(self): big = 2*60 # Too large to represent with a full precision float x = np.array([0], np.int64) x_edges = np.array([-1, +1], np.int64) y = big + x y_edges = big + x_edges hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) assert_equal(hist[0, 0], 1) def test_density_non_uniform_2d(self): # Defines the following grid: # # 0 2 8 # 0+-+-----+ # + \| + # + \| + # 6+-+-----+ # 8+-+-----+ x_edges = np.array([0, 2, 8]) y_edges = np.array([0, 6, 8]) relative_areas = np.array([ [3, 9], [1, 3]]) # ensure the number of points in each region is proportional to its area x = np.array([1] + [1]3 + [7]3 + [7]9) y = np.array([7] + [1]3 + [7]3 + [1]9) # sanity check that the above worked as intended hist, edges = histogramdd((y, x), bins=(y_edges, x_edges)) assert_equal(hist, relative_areas) # resulting histogram should be uniform, since counts and areas are proportional hist, edges = histogramdd((y, x), bins=(y_edges, x_edges), density=True) assert_equal(hist, 1 / (88)) def test_density_non_uniform_1d(self): # compare to histogram to show the results are the same v = np.arange(10) bins = np.array([0, 1, 3, 6, 10]) hist, edges = histogram(v, bins, density=True) hist_dd, edges_dd = histogramdd((v,), (bins,), density=True) assert_equal(hist, hist_dd) assert_equal(edges, edges_dd[0])	31.69 KB	2026-01-20 12:58
📄 test_index_tricks.py import pytest import numpy as np from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_raises, assert_raises_regex, ) from numpy.lib.index_tricks import ( mgrid, ogrid, ndenumerate, fill_diagonal, diag_indices, diag_indices_from, index_exp, ndindex, r_, s_, ix_ ) class TestRavelUnravelIndex: def test_basic(self): assert_equal(np.unravel_index(2, (2, 2)), (1, 0)) # test that new shape argument works properly assert_equal(np.unravel_index(indices=2, shape=(2, 2)), (1, 0)) # test that an invalid second keyword argument # is properly handled, including the old name `dims`. with assert_raises(TypeError): np.unravel_index(indices=2, hape=(2, 2)) with assert_raises(TypeError): np.unravel_index(2, hape=(2, 2)) with assert_raises(TypeError): np.unravel_index(254, ims=(17, 94)) with assert_raises(TypeError): np.unravel_index(254, dims=(17, 94)) assert_equal(np.ravel_multi_index((1, 0), (2, 2)), 2) assert_equal(np.unravel_index(254, (17, 94)), (2, 66)) assert_equal(np.ravel_multi_index((2, 66), (17, 94)), 254) assert_raises(ValueError, np.unravel_index, -1, (2, 2)) assert_raises(TypeError, np.unravel_index, 0.5, (2, 2)) assert_raises(ValueError, np.unravel_index, 4, (2, 2)) assert_raises(ValueError, np.ravel_multi_index, (-3, 1), (2, 2)) assert_raises(ValueError, np.ravel_multi_index, (2, 1), (2, 2)) assert_raises(ValueError, np.ravel_multi_index, (0, -3), (2, 2)) assert_raises(ValueError, np.ravel_multi_index, (0, 2), (2, 2)) assert_raises(TypeError, np.ravel_multi_index, (0.1, 0.), (2, 2)) assert_equal(np.unravel_index((23 + 1)6 + 4, (4, 3, 6)), [2, 1, 4]) assert_equal( np.ravel_multi_index([2, 1, 4], (4, 3, 6)), (23 + 1)6 + 4) arr = np.array([[3, 6, 6], [4, 5, 1]]) assert_equal(np.ravel_multi_index(arr, (7, 6)), [22, 41, 37]) assert_equal( np.ravel_multi_index(arr, (7, 6), order='F'), [31, 41, 13]) assert_equal( np.ravel_multi_index(arr, (4, 6), mode='clip'), [22, 23, 19]) assert_equal(np.ravel_multi_index(arr, (4, 4), mode=('clip', 'wrap')), [12, 13, 13]) assert_equal(np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9)), 1621) assert_equal(np.unravel_index(np.array([22, 41, 37]), (7, 6)), [[3, 6, 6], [4, 5, 1]]) assert_equal( np.unravel_index(np.array([31, 41, 13]), (7, 6), order='F'), [[3, 6, 6], [4, 5, 1]]) assert_equal(np.unravel_index(1621, (6, 7, 8, 9)), [3, 1, 4, 1]) def test_empty_indices(self): msg1 = 'indices must be integral: the provided empty sequence was' msg2 = 'only int indices permitted' assert_raises_regex(TypeError, msg1, np.unravel_index, [], (10, 3, 5)) assert_raises_regex(TypeError, msg1, np.unravel_index, (), (10, 3, 5)) assert_raises_regex(TypeError, msg2, np.unravel_index, np.array([]), (10, 3, 5)) assert_equal(np.unravel_index(np.array([],dtype=int), (10, 3, 5)), [[], [], []]) assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], []), (10, 3)) assert_raises_regex(TypeError, msg1, np.ravel_multi_index, ([], ['abc']), (10, 3)) assert_raises_regex(TypeError, msg2, np.ravel_multi_index, (np.array([]), np.array([])), (5, 3)) assert_equal(np.ravel_multi_index( (np.array([], dtype=int), np.array([], dtype=int)), (5, 3)), []) assert_equal(np.ravel_multi_index(np.array([[], []], dtype=int), (5, 3)), []) def test_big_indices(self): # ravel_multi_index for big indices (issue #7546) if np.intp == np.int64: arr = ([1, 29], [3, 5], [3, 117], [19, 2], [2379, 1284], [2, 2], [0, 1]) assert_equal( np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)), [5627771580, 117259570957]) # test unravel_index for big indices (issue #9538) assert_raises(ValueError, np.unravel_index, 1, (232-1, 231+1)) # test overflow checking for too big array (issue #7546) dummy_arr = ([0],[0]) half_max = np.iinfo(np.intp).max // 2 assert_equal( np.ravel_multi_index(dummy_arr, (half_max, 2)), [0]) assert_raises(ValueError, np.ravel_multi_index, dummy_arr, (half_max+1, 2)) assert_equal( np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0]) assert_raises(ValueError, np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F') def test_dtypes(self): # Test with different data types for dtype in [np.int16, np.uint16, np.int32, np.uint32, np.int64, np.uint64]: coords = np.array( [[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0]], dtype=dtype) shape = (5, 8) uncoords = 8coords[0]+coords[1] assert_equal(np.ravel_multi_index(coords, shape), uncoords) assert_equal(coords, np.unravel_index(uncoords, shape)) uncoords = coords[0]+5coords[1] assert_equal( np.ravel_multi_index(coords, shape, order='F'), uncoords) assert_equal(coords, np.unravel_index(uncoords, shape, order='F')) coords = np.array( [[1, 0, 1, 2, 3, 4], [1, 6, 1, 3, 2, 0], [1, 3, 1, 0, 9, 5]], dtype=dtype) shape = (5, 8, 10) uncoords = 10(8coords[0]+coords[1])+coords[2] assert_equal(np.ravel_multi_index(coords, shape), uncoords) assert_equal(coords, np.unravel_index(uncoords, shape)) uncoords = coords[0]+5(coords[1]+8coords[2]) assert_equal( np.ravel_multi_index(coords, shape, order='F'), uncoords) assert_equal(coords, np.unravel_index(uncoords, shape, order='F')) def test_clipmodes(self): # Test clipmodes assert_equal( np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12), mode='wrap'), np.ravel_multi_index([1, 1, 6, 2], (4, 3, 7, 12))) assert_equal(np.ravel_multi_index([5, 1, -1, 2], (4, 3, 7, 12), mode=( 'wrap', 'raise', 'clip', 'raise')), np.ravel_multi_index([1, 1, 0, 2], (4, 3, 7, 12))) assert_raises( ValueError, np.ravel_multi_index, [5, 1, -1, 2], (4, 3, 7, 12)) def test_writeability(self): # See gh-7269 x, y = np.unravel_index([1, 2, 3], (4, 5)) assert_(x.flags.writeable) assert_(y.flags.writeable) def test_0d(self): # gh-580 x = np.unravel_index(0, ()) assert_equal(x, ()) assert_raises_regex(ValueError, "0d array", np.unravel_index, [0], ()) assert_raises_regex( ValueError, "out of bounds", np.unravel_index, [1], ()) @pytest.mark.parametrize("mode", ["clip", "wrap", "raise"]) def test_empty_array_ravel(self, mode): res = np.ravel_multi_index( np.zeros((3, 0), dtype=np.intp), (2, 1, 0), mode=mode) assert(res.shape == (0,)) with assert_raises(ValueError): np.ravel_multi_index( np.zeros((3, 1), dtype=np.intp), (2, 1, 0), mode=mode) def test_empty_array_unravel(self): res = np.unravel_index(np.zeros(0, dtype=np.intp), (2, 1, 0)) # res is a tuple of three empty arrays assert(len(res) == 3) assert(all(a.shape == (0,) for a in res)) with assert_raises(ValueError): np.unravel_index([1], (2, 1, 0)) class TestGrid: def test_basic(self): a = mgrid[-1:1:10j] b = mgrid[-1:1:0.1] assert_(a.shape == (10,)) assert_(b.shape == (20,)) assert_(a[0] == -1) assert_almost_equal(a[-1], 1) assert_(b[0] == -1) assert_almost_equal(b[1]-b[0], 0.1, 11) assert_almost_equal(b[-1], b[0]+190.1, 11) assert_almost_equal(a[1]-a[0], 2.0/9.0, 11) def test_linspace_equivalence(self): y, st = np.linspace(2, 10, retstep=True) assert_almost_equal(st, 8/49.0) assert_array_almost_equal(y, mgrid[2:10:50j], 13) def test_nd(self): c = mgrid[-1:1:10j, -2:2:10j] d = mgrid[-1:1:0.1, -2:2:0.2] assert_(c.shape == (2, 10, 10)) assert_(d.shape == (2, 20, 20)) assert_array_equal(c[0][0, :], -np.ones(10, 'd')) assert_array_equal(c[1][:, 0], -2np.ones(10, 'd')) assert_array_almost_equal(c[0][-1, :], np.ones(10, 'd'), 11) assert_array_almost_equal(c[1][:, -1], 2np.ones(10, 'd'), 11) assert_array_almost_equal(d[0, 1, :] - d[0, 0, :], 0.1np.ones(20, 'd'), 11) assert_array_almost_equal(d[1, :, 1] - d[1, :, 0], 0.2np.ones(20, 'd'), 11) def test_sparse(self): grid_full = mgrid[-1:1:10j, -2:2:10j] grid_sparse = ogrid[-1:1:10j, -2:2:10j] # sparse grids can be made dense by broadcasting grid_broadcast = np.broadcast_arrays(grid_sparse) for f, b in zip(grid_full, grid_broadcast): assert_equal(f, b) @pytest.mark.parametrize("start, stop, step, expected", [ (None, 10, 10j, (200, 10)), (-10, 20, None, (1800, 30)), ]) def test_mgrid_size_none_handling(self, start, stop, step, expected): # regression test None value handling for # start and step values used by mgrid; # internally, this aims to cover previously # unexplored code paths in nd_grid() grid = mgrid[start:stop:step, start:stop:step] # need a smaller grid to explore one of the # untested code paths grid_small = mgrid[start:stop:step] assert_equal(grid.size, expected[0]) assert_equal(grid_small.size, expected[1]) def test_accepts_npfloating(self): # regression test for #16466 grid64 = mgrid[0.1:0.33:0.1, ] grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1), ] assert_(grid32.dtype == np.float64) assert_array_almost_equal(grid64, grid32) # different code path for single slice grid64 = mgrid[0.1:0.33:0.1] grid32 = mgrid[np.float32(0.1):np.float32(0.33):np.float32(0.1)] assert_(grid32.dtype == np.float64) assert_array_almost_equal(grid64, grid32) def test_accepts_longdouble(self): # regression tests for #16945 grid64 = mgrid[0.1:0.33:0.1, ] grid128 = mgrid[ np.longdouble(0.1):np.longdouble(0.33):np.longdouble(0.1), ] assert_(grid128.dtype == np.longdouble) assert_array_almost_equal(grid64, grid128) grid128c_a = mgrid[0:np.longdouble(1):3.4j] grid128c_b = mgrid[0:np.longdouble(1):3.4j, ] assert_(grid128c_a.dtype == grid128c_b.dtype == np.longdouble) assert_array_equal(grid128c_a, grid128c_b[0]) # different code path for single slice grid64 = mgrid[0.1:0.33:0.1] grid128 = mgrid[ np.longdouble(0.1):np.longdouble(0.33):np.longdouble(0.1) ] assert_(grid128.dtype == np.longdouble) assert_array_almost_equal(grid64, grid128) def test_accepts_npcomplexfloating(self): # Related to #16466 assert_array_almost_equal( mgrid[0.1:0.3:3j, ], mgrid[0.1:0.3:np.complex64(3j), ] ) # different code path for single slice assert_array_almost_equal( mgrid[0.1:0.3:3j], mgrid[0.1:0.3:np.complex64(3j)] ) # Related to #16945 grid64_a = mgrid[0.1:0.3:3.3j] grid64_b = mgrid[0.1:0.3:3.3j, ][0] assert_(grid64_a.dtype == grid64_b.dtype == np.float64) assert_array_equal(grid64_a, grid64_b) grid128_a = mgrid[0.1:0.3:np.clongdouble(3.3j)] grid128_b = mgrid[0.1:0.3:np.clongdouble(3.3j), ][0] assert_(grid128_a.dtype == grid128_b.dtype == np.longdouble) assert_array_equal(grid64_a, grid64_b) class TestConcatenator: def test_1d(self): assert_array_equal(r_[1, 2, 3, 4, 5, 6], np.array([1, 2, 3, 4, 5, 6])) b = np.ones(5) c = r_[b, 0, 0, b] assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1]) def test_mixed_type(self): g = r_[10.1, 1:10] assert_(g.dtype == 'f8') def test_more_mixed_type(self): g = r_[-10.1, np.array([1]), np.array([2, 3, 4]), 10.0] assert_(g.dtype == 'f8') def test_complex_step(self): # Regression test for #12262 g = r_[0:36:100j] assert_(g.shape == (100,)) # Related to #16466 g = r_[0:36:np.complex64(100j)] assert_(g.shape == (100,)) def test_2d(self): b = np.random.rand(5, 5) c = np.random.rand(5, 5) d = r_['1', b, c] # append columns assert_(d.shape == (5, 10)) assert_array_equal(d[:, :5], b) assert_array_equal(d[:, 5:], c) d = r_[b, c] assert_(d.shape == (10, 5)) assert_array_equal(d[:5, :], b) assert_array_equal(d[5:, :], c) def test_0d(self): assert_equal(r_[0, np.array(1), 2], [0, 1, 2]) assert_equal(r_[[0, 1, 2], np.array(3)], [0, 1, 2, 3]) assert_equal(r_[np.array(0), [1, 2, 3]], [0, 1, 2, 3]) class TestNdenumerate: def test_basic(self): a = np.array([[1, 2], [3, 4]]) assert_equal(list(ndenumerate(a)), [((0, 0), 1), ((0, 1), 2), ((1, 0), 3), ((1, 1), 4)]) class TestIndexExpression: def test_regression_1(self): # ticket #1196 a = np.arange(2) assert_equal(a[:-1], a[s_[:-1]]) assert_equal(a[:-1], a[index_exp[:-1]]) def test_simple_1(self): a = np.random.rand(4, 5, 6) assert_equal(a[:, :3, [1, 2]], a[index_exp[:, :3, [1, 2]]]) assert_equal(a[:, :3, [1, 2]], a[s_[:, :3, [1, 2]]]) class TestIx_: def test_regression_1(self): # Test empty untyped inputs create outputs of indexing type, gh-5804 a, = np.ix_(range(0)) assert_equal(a.dtype, np.intp) a, = np.ix_([]) assert_equal(a.dtype, np.intp) # but if the type is specified, don't change it a, = np.ix_(np.array([], dtype=np.float32)) assert_equal(a.dtype, np.float32) def test_shape_and_dtype(self): sizes = (4, 5, 3, 2) # Test both lists and arrays for func in (range, np.arange): arrays = np.ix_([func(sz) for sz in sizes]) for k, (a, sz) in enumerate(zip(arrays, sizes)): assert_equal(a.shape[k], sz) assert_(all(sh == 1 for j, sh in enumerate(a.shape) if j != k)) assert_(np.issubdtype(a.dtype, np.integer)) def test_bool(self): bool_a = [True, False, True, True] int_a, = np.nonzero(bool_a) assert_equal(np.ix_(bool_a)[0], int_a) def test_1d_only(self): idx2d = [[1, 2, 3], [4, 5, 6]] assert_raises(ValueError, np.ix_, idx2d) def test_repeated_input(self): length_of_vector = 5 x = np.arange(length_of_vector) out = ix_(x, x) assert_equal(out[0].shape, (length_of_vector, 1)) assert_equal(out[1].shape, (1, length_of_vector)) # check that input shape is not modified assert_equal(x.shape, (length_of_vector,)) def test_c_(): a = np.c_[np.array([[1, 2, 3]]), 0, 0, np.array([[4, 5, 6]])] assert_equal(a, [[1, 2, 3, 0, 0, 4, 5, 6]]) class TestFillDiagonal: def test_basic(self): a = np.zeros((3, 3), int) fill_diagonal(a, 5) assert_array_equal( a, np.array([[5, 0, 0], [0, 5, 0], [0, 0, 5]]) ) def test_tall_matrix(self): a = np.zeros((10, 3), int) fill_diagonal(a, 5) assert_array_equal( a, np.array([[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]) ) def test_tall_matrix_wrap(self): a = np.zeros((10, 3), int) fill_diagonal(a, 5, True) assert_array_equal( a, np.array([[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0], [5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0], [5, 0, 0], [0, 5, 0]]) ) def test_wide_matrix(self): a = np.zeros((3, 10), int) fill_diagonal(a, 5) assert_array_equal( a, np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5, 0, 0, 0, 0, 0, 0, 0]]) ) def test_operate_4d_array(self): a = np.zeros((3, 3, 3, 3), int) fill_diagonal(a, 4) i = np.array([0, 1, 2]) assert_equal(np.where(a != 0), (i, i, i, i)) def test_low_dim_handling(self): # raise error with low dimensionality a = np.zeros(3, int) with assert_raises_regex(ValueError, "at least 2-d"): fill_diagonal(a, 5) def test_hetero_shape_handling(self): # raise error with high dimensionality and # shape mismatch a = np.zeros((3,3,7,3), int) with assert_raises_regex(ValueError, "equal length"): fill_diagonal(a, 2) def test_diag_indices(): di = diag_indices(4) a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) a[di] = 100 assert_array_equal( a, np.array([[100, 2, 3, 4], [5, 100, 7, 8], [9, 10, 100, 12], [13, 14, 15, 100]]) ) # Now, we create indices to manipulate a 3-d array: d3 = diag_indices(2, 3) # And use it to set the diagonal of a zeros array to 1: a = np.zeros((2, 2, 2), int) a[d3] = 1 assert_array_equal( a, np.array([[[1, 0], [0, 0]], [[0, 0], [0, 1]]]) ) class TestDiagIndicesFrom: def test_diag_indices_from(self): x = np.random.random((4, 4)) r, c = diag_indices_from(x) assert_array_equal(r, np.arange(4)) assert_array_equal(c, np.arange(4)) def test_error_small_input(self): x = np.ones(7) with assert_raises_regex(ValueError, "at least 2-d"): diag_indices_from(x) def test_error_shape_mismatch(self): x = np.zeros((3, 3, 2, 3), int) with assert_raises_regex(ValueError, "equal length"): diag_indices_from(x) def test_ndindex(): x = list(ndindex(1, 2, 3)) expected = [ix for ix, e in ndenumerate(np.zeros((1, 2, 3)))] assert_array_equal(x, expected) x = list(ndindex((1, 2, 3))) assert_array_equal(x, expected) # Test use of scalars and tuples x = list(ndindex((3,))) assert_array_equal(x, list(ndindex(3))) # Make sure size argument is optional x = list(ndindex()) assert_equal(x, [()]) x = list(ndindex(())) assert_equal(x, [()]) # Make sure 0-sized ndindex works correctly x = list(ndindex([0])) assert_equal(x, [])	19.78 KB	2026-01-20 12:58
📄 test_io.py import sys import gc import gzip import os import threading import time import warnings import io import re import pytest from pathlib import Path from tempfile import NamedTemporaryFile from io import BytesIO, StringIO from datetime import datetime import locale from multiprocessing import Value, get_context from ctypes import c_bool import numpy as np import numpy.ma as ma from numpy.lib._iotools import ConverterError, ConversionWarning from numpy.compat import asbytes from numpy.ma.testutils import assert_equal from numpy.testing import ( assert_warns, assert_, assert_raises_regex, assert_raises, assert_allclose, assert_array_equal, temppath, tempdir, IS_PYPY, HAS_REFCOUNT, suppress_warnings, assert_no_gc_cycles, assert_no_warnings, break_cycles, IS_WASM ) from numpy.testing._private.utils import requires_memory class TextIO(BytesIO): """Helper IO class. Writes encode strings to bytes if needed, reads return bytes. This makes it easier to emulate files opened in binary mode without needing to explicitly convert strings to bytes in setting up the test data. """ def __init__(self, s=""): BytesIO.__init__(self, asbytes(s)) def write(self, s): BytesIO.write(self, asbytes(s)) def writelines(self, lines): BytesIO.writelines(self, [asbytes(s) for s in lines]) IS_64BIT = sys.maxsize > 2*32 try: import bz2 HAS_BZ2 = True except ImportError: HAS_BZ2 = False try: import lzma HAS_LZMA = True except ImportError: HAS_LZMA = False def strptime(s, fmt=None): """ This function is available in the datetime module only from Python >= 2.5. """ if type(s) == bytes: s = s.decode("latin1") return datetime(time.strptime(s, fmt)[:3]) class RoundtripTest: def roundtrip(self, save_func, args, kwargs): """ save_func : callable Function used to save arrays to file. file_on_disk : bool If true, store the file on disk, instead of in a string buffer. save_kwds : dict Parameters passed to `save_func`. load_kwds : dict Parameters passed to `numpy.load`. args : tuple of arrays Arrays stored to file. """ save_kwds = kwargs.get('save_kwds', {}) load_kwds = kwargs.get('load_kwds', {"allow_pickle": True}) file_on_disk = kwargs.get('file_on_disk', False) if file_on_disk: target_file = NamedTemporaryFile(delete=False) load_file = target_file.name else: target_file = BytesIO() load_file = target_file try: arr = args save_func(target_file, arr, save_kwds) target_file.flush() target_file.seek(0) if sys.platform == 'win32' and not isinstance(target_file, BytesIO): target_file.close() arr_reloaded = np.load(load_file, load_kwds) self.arr = arr self.arr_reloaded = arr_reloaded finally: if not isinstance(target_file, BytesIO): target_file.close() # holds an open file descriptor so it can't be deleted on win if 'arr_reloaded' in locals(): if not isinstance(arr_reloaded, np.lib.npyio.NpzFile): os.remove(target_file.name) def check_roundtrips(self, a): self.roundtrip(a) self.roundtrip(a, file_on_disk=True) self.roundtrip(np.asfortranarray(a)) self.roundtrip(np.asfortranarray(a), file_on_disk=True) if a.shape[0] > 1: # neither C nor Fortran contiguous for 2D arrays or more self.roundtrip(np.asfortranarray(a)[1:]) self.roundtrip(np.asfortranarray(a)[1:], file_on_disk=True) def test_array(self): a = np.array([], float) self.check_roundtrips(a) a = np.array([[1, 2], [3, 4]], float) self.check_roundtrips(a) a = np.array([[1, 2], [3, 4]], int) self.check_roundtrips(a) a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.csingle) self.check_roundtrips(a) a = np.array([[1 + 5j, 2 + 6j], [3 + 7j, 4 + 8j]], dtype=np.cdouble) self.check_roundtrips(a) def test_array_object(self): a = np.array([], object) self.check_roundtrips(a) a = np.array([[1, 2], [3, 4]], object) self.check_roundtrips(a) def test_1D(self): a = np.array([1, 2, 3, 4], int) self.roundtrip(a) @pytest.mark.skipif(sys.platform == 'win32', reason="Fails on Win32") def test_mmap(self): a = np.array([[1, 2.5], [4, 7.3]]) self.roundtrip(a, file_on_disk=True, load_kwds={'mmap_mode': 'r'}) a = np.asfortranarray([[1, 2.5], [4, 7.3]]) self.roundtrip(a, file_on_disk=True, load_kwds={'mmap_mode': 'r'}) def test_record(self): a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) self.check_roundtrips(a) @pytest.mark.slow def test_format_2_0(self): dt = [(("%d" % i) * 100, float) for i in range(500)] a = np.ones(1000, dtype=dt) with warnings.catch_warnings(record=True): warnings.filterwarnings('always', '', UserWarning) self.check_roundtrips(a) class TestSaveLoad(RoundtripTest): def roundtrip(self, args, kwargs): RoundtripTest.roundtrip(self, np.save, args, *kwargs) assert_equal(self.arr[0], self.arr_reloaded) assert_equal(self.arr[0].dtype, self.arr_reloaded.dtype) assert_equal(self.arr[0].flags.fnc, self.arr_reloaded.flags.fnc) class TestSavezLoad(RoundtripTest): def roundtrip(self, args, *kwargs): RoundtripTest.roundtrip(self, np.savez, args, *kwargs) try: for n, arr in enumerate(self.arr): reloaded = self.arr_reloaded['arr_%d' % n] assert_equal(arr, reloaded) assert_equal(arr.dtype, reloaded.dtype) assert_equal(arr.flags.fnc, reloaded.flags.fnc) finally: # delete tempfile, must be done here on windows if self.arr_reloaded.fid: self.arr_reloaded.fid.close() os.remove(self.arr_reloaded.fid.name) @pytest.mark.skipif(IS_PYPY, reason="Hangs on PyPy") @pytest.mark.skipif(not IS_64BIT, reason="Needs 64bit platform") @pytest.mark.slow def test_big_arrays(self): L = (1 << 31) + 100000 a = np.empty(L, dtype=np.uint8) with temppath(prefix="numpy_test_big_arrays_", suffix=".npz") as tmp: np.savez(tmp, a=a) del a npfile = np.load(tmp) a = npfile['a'] # Should succeed npfile.close() del a # Avoid pyflakes unused variable warning. def test_multiple_arrays(self): a = np.array([[1, 2], [3, 4]], float) b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex) self.roundtrip(a, b) def test_named_arrays(self): a = np.array([[1, 2], [3, 4]], float) b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex) c = BytesIO() np.savez(c, file_a=a, file_b=b) c.seek(0) l = np.load(c) assert_equal(a, l['file_a']) assert_equal(b, l['file_b']) def test_tuple_getitem_raises(self): # gh-23748 a = np.array([1, 2, 3]) f = BytesIO() np.savez(f, a=a) f.seek(0) l = np.load(f) with pytest.raises(KeyError, match="(1, 2)"): l[1, 2] def test_BagObj(self): a = np.array([[1, 2], [3, 4]], float) b = np.array([[1 + 2j, 2 + 7j], [3 - 6j, 4 + 12j]], complex) c = BytesIO() np.savez(c, file_a=a, file_b=b) c.seek(0) l = np.load(c) assert_equal(sorted(dir(l.f)), ['file_a','file_b']) assert_equal(a, l.f.file_a) assert_equal(b, l.f.file_b) @pytest.mark.skipif(IS_WASM, reason="Cannot start thread") def test_savez_filename_clashes(self): # Test that issue #852 is fixed # and savez functions in multithreaded environment def writer(error_list): with temppath(suffix='.npz') as tmp: arr = np.random.randn(500, 500) try: np.savez(tmp, arr=arr) except OSError as err: error_list.append(err) errors = [] threads = [threading.Thread(target=writer, args=(errors,)) for j in range(3)] for t in threads: t.start() for t in threads: t.join() if errors: raise AssertionError(errors) def test_not_closing_opened_fid(self): # Test that issue #2178 is fixed: # verify could seek on 'loaded' file with temppath(suffix='.npz') as tmp: with open(tmp, 'wb') as fp: np.savez(fp, data='LOVELY LOAD') with open(tmp, 'rb', 10000) as fp: fp.seek(0) assert_(not fp.closed) np.load(fp)['data'] # fp must not get closed by .load assert_(not fp.closed) fp.seek(0) assert_(not fp.closed) @pytest.mark.slow_pypy def test_closing_fid(self): # Test that issue #1517 (too many opened files) remains closed # It might be a "weak" test since failed to get triggered on # e.g. Debian sid of 2012 Jul 05 but was reported to # trigger the failure on Ubuntu 10.04: # http://projects.scipy.org/numpy/ticket/1517#comment:2 with temppath(suffix='.npz') as tmp: np.savez(tmp, data='LOVELY LOAD') # We need to check if the garbage collector can properly close # numpy npz file returned by np.load when their reference count # goes to zero. Python 3 running in debug mode raises a # ResourceWarning when file closing is left to the garbage # collector, so we catch the warnings. with suppress_warnings() as sup: sup.filter(ResourceWarning) # TODO: specify exact message for i in range(1, 1025): try: np.load(tmp)["data"] except Exception as e: msg = "Failed to load data from a file: %s" % e raise AssertionError(msg) finally: if IS_PYPY: gc.collect() def test_closing_zipfile_after_load(self): # Check that zipfile owns file and can close it. This needs to # pass a file name to load for the test. On windows failure will # cause a second error will be raised when the attempt to remove # the open file is made. prefix = 'numpy_test_closing_zipfile_after_load_' with temppath(suffix='.npz', prefix=prefix) as tmp: np.savez(tmp, lab='place holder') data = np.load(tmp) fp = data.zip.fp data.close() assert_(fp.closed) @pytest.mark.parametrize("count, expected_repr", [ (1, "NpzFile {fname!r} with keys: arr_0"), (5, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4"), # _MAX_REPR_ARRAY_COUNT is 5, so files with more than 5 keys are # expected to end in '...' (6, "NpzFile {fname!r} with keys: arr_0, arr_1, arr_2, arr_3, arr_4..."), ]) def test_repr_lists_keys(self, count, expected_repr): a = np.array([[1, 2], [3, 4]], float) with temppath(suffix='.npz') as tmp: np.savez(tmp, [a]count) l = np.load(tmp) assert repr(l) == expected_repr.format(fname=tmp) l.close() class TestSaveTxt: def test_array(self): a = np.array([[1, 2], [3, 4]], float) fmt = "%.18e" c = BytesIO() np.savetxt(c, a, fmt=fmt) c.seek(0) assert_equal(c.readlines(), [asbytes((fmt + ' ' + fmt + '\n') % (1, 2)), asbytes((fmt + ' ' + fmt + '\n') % (3, 4))]) a = np.array([[1, 2], [3, 4]], int) c = BytesIO() np.savetxt(c, a, fmt='%d') c.seek(0) assert_equal(c.readlines(), [b'1 2\n', b'3 4\n']) def test_1D(self): a = np.array([1, 2, 3, 4], int) c = BytesIO() np.savetxt(c, a, fmt='%d') c.seek(0) lines = c.readlines() assert_equal(lines, [b'1\n', b'2\n', b'3\n', b'4\n']) def test_0D_3D(self): c = BytesIO() assert_raises(ValueError, np.savetxt, c, np.array(1)) assert_raises(ValueError, np.savetxt, c, np.array([[[1], [2]]])) def test_structured(self): a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) c = BytesIO() np.savetxt(c, a, fmt='%d') c.seek(0) assert_equal(c.readlines(), [b'1 2\n', b'3 4\n']) def test_structured_padded(self): # gh-13297 a = np.array([(1, 2, 3),(4, 5, 6)], dtype=[ ('foo', 'i4'), ('bar', 'i4'), ('baz', 'i4') ]) c = BytesIO() np.savetxt(c, a[['foo', 'baz']], fmt='%d') c.seek(0) assert_equal(c.readlines(), [b'1 3\n', b'4 6\n']) def test_multifield_view(self): a = np.ones(1, dtype=[('x', 'i4'), ('y', 'i4'), ('z', 'f4')]) v = a[['x', 'z']] with temppath(suffix='.npy') as path: path = Path(path) np.save(path, v) data = np.load(path) assert_array_equal(data, v) def test_delimiter(self): a = np.array([[1., 2.], [3., 4.]]) c = BytesIO() np.savetxt(c, a, delimiter=',', fmt='%d') c.seek(0) assert_equal(c.readlines(), [b'1,2\n', b'3,4\n']) def test_format(self): a = np.array([(1, 2), (3, 4)]) c = BytesIO() # Sequence of formats np.savetxt(c, a, fmt=['%02d', '%3.1f']) c.seek(0) assert_equal(c.readlines(), [b'01 2.0\n', b'03 4.0\n']) # A single multiformat string c = BytesIO() np.savetxt(c, a, fmt='%02d : %3.1f') c.seek(0) lines = c.readlines() assert_equal(lines, [b'01 : 2.0\n', b'03 : 4.0\n']) # Specify delimiter, should be overridden c = BytesIO() np.savetxt(c, a, fmt='%02d : %3.1f', delimiter=',') c.seek(0) lines = c.readlines() assert_equal(lines, [b'01 : 2.0\n', b'03 : 4.0\n']) # Bad fmt, should raise a ValueError c = BytesIO() assert_raises(ValueError, np.savetxt, c, a, fmt=99) def test_header_footer(self): # Test the functionality of the header and footer keyword argument. c = BytesIO() a = np.array([(1, 2), (3, 4)], dtype=int) test_header_footer = 'Test header / footer' # Test the header keyword argument np.savetxt(c, a, fmt='%1d', header=test_header_footer) c.seek(0) assert_equal(c.read(), asbytes('# ' + test_header_footer + '\n1 2\n3 4\n')) # Test the footer keyword argument c = BytesIO() np.savetxt(c, a, fmt='%1d', footer=test_header_footer) c.seek(0) assert_equal(c.read(), asbytes('1 2\n3 4\n# ' + test_header_footer + '\n')) # Test the commentstr keyword argument used on the header c = BytesIO() commentstr = '% ' np.savetxt(c, a, fmt='%1d', header=test_header_footer, comments=commentstr) c.seek(0) assert_equal(c.read(), asbytes(commentstr + test_header_footer + '\n' + '1 2\n3 4\n')) # Test the commentstr keyword argument used on the footer c = BytesIO() commentstr = '% ' np.savetxt(c, a, fmt='%1d', footer=test_header_footer, comments=commentstr) c.seek(0) assert_equal(c.read(), asbytes('1 2\n3 4\n' + commentstr + test_header_footer + '\n')) def test_file_roundtrip(self): with temppath() as name: a = np.array([(1, 2), (3, 4)]) np.savetxt(name, a) b = np.loadtxt(name) assert_array_equal(a, b) def test_complex_arrays(self): ncols = 2 nrows = 2 a = np.zeros((ncols, nrows), dtype=np.complex128) re = np.pi im = np.e a[:] = re + 1.0j im # One format only c = BytesIO() np.savetxt(c, a, fmt=' %+.3e') c.seek(0) lines = c.readlines() assert_equal( lines, [b' ( +3.142e+00+ +2.718e+00j) ( +3.142e+00+ +2.718e+00j)\n', b' ( +3.142e+00+ +2.718e+00j) ( +3.142e+00+ +2.718e+00j)\n']) # One format for each real and imaginary part c = BytesIO() np.savetxt(c, a, fmt=' %+.3e' * 2 * ncols) c.seek(0) lines = c.readlines() assert_equal( lines, [b' +3.142e+00 +2.718e+00 +3.142e+00 +2.718e+00\n', b' +3.142e+00 +2.718e+00 +3.142e+00 +2.718e+00\n']) # One format for each complex number c = BytesIO() np.savetxt(c, a, fmt=['(%.3e%+.3ej)'] * ncols) c.seek(0) lines = c.readlines() assert_equal( lines, [b'(3.142e+00+2.718e+00j) (3.142e+00+2.718e+00j)\n', b'(3.142e+00+2.718e+00j) (3.142e+00+2.718e+00j)\n']) def test_complex_negative_exponent(self): # Previous to 1.15, some formats generated x+-yj, gh 7895 ncols = 2 nrows = 2 a = np.zeros((ncols, nrows), dtype=np.complex128) re = np.pi im = np.e a[:] = re - 1.0j * im c = BytesIO() np.savetxt(c, a, fmt='%.3e') c.seek(0) lines = c.readlines() assert_equal( lines, [b' (3.142e+00-2.718e+00j) (3.142e+00-2.718e+00j)\n', b' (3.142e+00-2.718e+00j) (3.142e+00-2.718e+00j)\n']) def test_custom_writer(self): class CustomWriter(list): def write(self, text): self.extend(text.split(b'\n')) w = CustomWriter() a = np.array([(1, 2), (3, 4)]) np.savetxt(w, a) b = np.loadtxt(w) assert_array_equal(a, b) def test_unicode(self): utf8 = b'\xcf\x96'.decode('UTF-8') a = np.array([utf8], dtype=np.str_) with tempdir() as tmpdir: # set encoding as on windows it may not be unicode even on py3 np.savetxt(os.path.join(tmpdir, 'test.csv'), a, fmt=['%s'], encoding='UTF-8') def test_unicode_roundtrip(self): utf8 = b'\xcf\x96'.decode('UTF-8') a = np.array([utf8], dtype=np.str_) # our gz wrapper support encoding suffixes = ['', '.gz'] if HAS_BZ2: suffixes.append('.bz2') if HAS_LZMA: suffixes.extend(['.xz', '.lzma']) with tempdir() as tmpdir: for suffix in suffixes: np.savetxt(os.path.join(tmpdir, 'test.csv' + suffix), a, fmt=['%s'], encoding='UTF-16-LE') b = np.loadtxt(os.path.join(tmpdir, 'test.csv' + suffix), encoding='UTF-16-LE', dtype=np.str_) assert_array_equal(a, b) def test_unicode_bytestream(self): utf8 = b'\xcf\x96'.decode('UTF-8') a = np.array([utf8], dtype=np.str_) s = BytesIO() np.savetxt(s, a, fmt=['%s'], encoding='UTF-8') s.seek(0) assert_equal(s.read().decode('UTF-8'), utf8 + '\n') def test_unicode_stringstream(self): utf8 = b'\xcf\x96'.decode('UTF-8') a = np.array([utf8], dtype=np.str_) s = StringIO() np.savetxt(s, a, fmt=['%s'], encoding='UTF-8') s.seek(0) assert_equal(s.read(), utf8 + '\n') @pytest.mark.parametrize("fmt", ["%f", b"%f"]) @pytest.mark.parametrize("iotype", [StringIO, BytesIO]) def test_unicode_and_bytes_fmt(self, fmt, iotype): # string type of fmt should not matter, see also gh-4053 a = np.array([1.]) s = iotype() np.savetxt(s, a, fmt=fmt) s.seek(0) if iotype is StringIO: assert_equal(s.read(), "%f\n" % 1.) else: assert_equal(s.read(), b"%f\n" % 1.) @pytest.mark.skipif(sys.platform=='win32', reason="files>4GB may not work") @pytest.mark.slow @requires_memory(free_bytes=7e9) def test_large_zip(self): def check_large_zip(memoryerror_raised): memoryerror_raised.value = False try: # The test takes at least 6GB of memory, writes a file larger # than 4GB. This tests the ``allowZip64`` kwarg to ``zipfile`` test_data = np.asarray([np.random.rand( np.random.randint(50,100),4) for i in range(800000)], dtype=object) with tempdir() as tmpdir: np.savez(os.path.join(tmpdir, 'test.npz'), test_data=test_data) except MemoryError: memoryerror_raised.value = True raise # run in a subprocess to ensure memory is released on PyPy, see gh-15775 # Use an object in shared memory to re-raise the MemoryError exception # in our process if needed, see gh-16889 memoryerror_raised = Value(c_bool) # Since Python 3.8, the default start method for multiprocessing has # been changed from 'fork' to 'spawn' on macOS, causing inconsistency # on memory sharing model, lead to failed test for check_large_zip ctx = get_context('fork') p = ctx.Process(target=check_large_zip, args=(memoryerror_raised,)) p.start() p.join() if memoryerror_raised.value: raise MemoryError("Child process raised a MemoryError exception") # -9 indicates a SIGKILL, probably an OOM. if p.exitcode == -9: pytest.xfail("subprocess got a SIGKILL, apparently free memory was not sufficient") assert p.exitcode == 0 class LoadTxtBase: def check_compressed(self, fopen, suffixes): # Test that we can load data from a compressed file wanted = np.arange(6).reshape((2, 3)) linesep = ('\n', '\r\n', '\r') for sep in linesep: data = '0 1 2' + sep + '3 4 5' for suffix in suffixes: with temppath(suffix=suffix) as name: with fopen(name, mode='wt', encoding='UTF-32-LE') as f: f.write(data) res = self.loadfunc(name, encoding='UTF-32-LE') assert_array_equal(res, wanted) with fopen(name, "rt", encoding='UTF-32-LE') as f: res = self.loadfunc(f) assert_array_equal(res, wanted) def test_compressed_gzip(self): self.check_compressed(gzip.open, ('.gz',)) @pytest.mark.skipif(not HAS_BZ2, reason="Needs bz2") def test_compressed_bz2(self): self.check_compressed(bz2.open, ('.bz2',)) @pytest.mark.skipif(not HAS_LZMA, reason="Needs lzma") def test_compressed_lzma(self): self.check_compressed(lzma.open, ('.xz', '.lzma')) def test_encoding(self): with temppath() as path: with open(path, "wb") as f: f.write('0.\n1.\n2.'.encode("UTF-16")) x = self.loadfunc(path, encoding="UTF-16") assert_array_equal(x, [0., 1., 2.]) def test_stringload(self): # umlaute nonascii = b'\xc3\xb6\xc3\xbc\xc3\xb6'.decode("UTF-8") with temppath() as path: with open(path, "wb") as f: f.write(nonascii.encode("UTF-16")) x = self.loadfunc(path, encoding="UTF-16", dtype=np.str_) assert_array_equal(x, nonascii) def test_binary_decode(self): utf16 = b'\xff\xfeh\x04 \x00i\x04 \x00j\x04' v = self.loadfunc(BytesIO(utf16), dtype=np.str_, encoding='UTF-16') assert_array_equal(v, np.array(utf16.decode('UTF-16').split())) def test_converters_decode(self): # test converters that decode strings c = TextIO() c.write(b'\xcf\x96') c.seek(0) x = self.loadfunc(c, dtype=np.str_, converters={0: lambda x: x.decode('UTF-8')}) a = np.array([b'\xcf\x96'.decode('UTF-8')]) assert_array_equal(x, a) def test_converters_nodecode(self): # test native string converters enabled by setting an encoding utf8 = b'\xcf\x96'.decode('UTF-8') with temppath() as path: with io.open(path, 'wt', encoding='UTF-8') as f: f.write(utf8) x = self.loadfunc(path, dtype=np.str_, converters={0: lambda x: x + 't'}, encoding='UTF-8') a = np.array([utf8 + 't']) assert_array_equal(x, a) class TestLoadTxt(LoadTxtBase): loadfunc = staticmethod(np.loadtxt) def setup_method(self): # lower chunksize for testing self.orig_chunk = np.lib.npyio._loadtxt_chunksize np.lib.npyio._loadtxt_chunksize = 1 def teardown_method(self): np.lib.npyio._loadtxt_chunksize = self.orig_chunk def test_record(self): c = TextIO() c.write('1 2\n3 4') c.seek(0) x = np.loadtxt(c, dtype=[('x', np.int32), ('y', np.int32)]) a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) assert_array_equal(x, a) d = TextIO() d.write('M 64 75.0\nF 25 60.0') d.seek(0) mydescriptor = {'names': ('gender', 'age', 'weight'), 'formats': ('S1', 'i4', 'f4')} b = np.array([('M', 64.0, 75.0), ('F', 25.0, 60.0)], dtype=mydescriptor) y = np.loadtxt(d, dtype=mydescriptor) assert_array_equal(y, b) def test_array(self): c = TextIO() c.write('1 2\n3 4') c.seek(0) x = np.loadtxt(c, dtype=int) a = np.array([[1, 2], [3, 4]], int) assert_array_equal(x, a) c.seek(0) x = np.loadtxt(c, dtype=float) a = np.array([[1, 2], [3, 4]], float) assert_array_equal(x, a) def test_1D(self): c = TextIO() c.write('1\n2\n3\n4\n') c.seek(0) x = np.loadtxt(c, dtype=int) a = np.array([1, 2, 3, 4], int) assert_array_equal(x, a) c = TextIO() c.write('1,2,3,4\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',') a = np.array([1, 2, 3, 4], int) assert_array_equal(x, a) def test_missing(self): c = TextIO() c.write('1,2,3,,5\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', converters={3: lambda s: int(s or - 999)}) a = np.array([1, 2, 3, -999, 5], int) assert_array_equal(x, a) def test_converters_with_usecols(self): c = TextIO() c.write('1,2,3,,5\n6,7,8,9,10\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', converters={3: lambda s: int(s or - 999)}, usecols=(1, 3,)) a = np.array([[2, -999], [7, 9]], int) assert_array_equal(x, a) def test_comments_unicode(self): c = TextIO() c.write('# comment\n1,2,3,5\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', comments='#') a = np.array([1, 2, 3, 5], int) assert_array_equal(x, a) def test_comments_byte(self): c = TextIO() c.write('# comment\n1,2,3,5\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', comments=b'#') a = np.array([1, 2, 3, 5], int) assert_array_equal(x, a) def test_comments_multiple(self): c = TextIO() c.write('# comment\n1,2,3\n@ comment2\n4,5,6 // comment3') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', comments=['#', '@', '//']) a = np.array([[1, 2, 3], [4, 5, 6]], int) assert_array_equal(x, a) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_comments_multi_chars(self): c = TextIO() c.write('/* comment\n1,2,3,5\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', comments='/') a = np.array([1, 2, 3, 5], int) assert_array_equal(x, a) # Check that '/' is not transformed to ['/', ''] c = TextIO() c.write('/ comment\n1,2,3,5\n') c.seek(0) assert_raises(ValueError, np.loadtxt, c, dtype=int, delimiter=',', comments='/') def test_skiprows(self): c = TextIO() c.write('comment\n1,2,3,5\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', skiprows=1) a = np.array([1, 2, 3, 5], int) assert_array_equal(x, a) c = TextIO() c.write('# comment\n1,2,3,5\n') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', skiprows=1) a = np.array([1, 2, 3, 5], int) assert_array_equal(x, a) def test_usecols(self): a = np.array([[1, 2], [3, 4]], float) c = BytesIO() np.savetxt(c, a) c.seek(0) x = np.loadtxt(c, dtype=float, usecols=(1,)) assert_array_equal(x, a[:, 1]) a = np.array([[1, 2, 3], [3, 4, 5]], float) c = BytesIO() np.savetxt(c, a) c.seek(0) x = np.loadtxt(c, dtype=float, usecols=(1, 2)) assert_array_equal(x, a[:, 1:]) # Testing with arrays instead of tuples. c.seek(0) x = np.loadtxt(c, dtype=float, usecols=np.array([1, 2])) assert_array_equal(x, a[:, 1:]) # Testing with an integer instead of a sequence for int_type in [int, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64]: to_read = int_type(1) c.seek(0) x = np.loadtxt(c, dtype=float, usecols=to_read) assert_array_equal(x, a[:, 1]) # Testing with some crazy custom integer type class CrazyInt: def __index__(self): return 1 crazy_int = CrazyInt() c.seek(0) x = np.loadtxt(c, dtype=float, usecols=crazy_int) assert_array_equal(x, a[:, 1]) c.seek(0) x = np.loadtxt(c, dtype=float, usecols=(crazy_int,)) assert_array_equal(x, a[:, 1]) # Checking with dtypes defined converters. data = '''JOE 70.1 25.3 BOB 60.5 27.9 ''' c = TextIO(data) names = ['stid', 'temp'] dtypes = ['S4', 'f8'] arr = np.loadtxt(c, usecols=(0, 2), dtype=list(zip(names, dtypes))) assert_equal(arr['stid'], [b"JOE", b"BOB"]) assert_equal(arr['temp'], [25.3, 27.9]) # Testing non-ints in usecols c.seek(0) bogus_idx = 1.5 assert_raises_regex( TypeError, '^usecols must be.%s' % type(bogus_idx).__name__, np.loadtxt, c, usecols=bogus_idx ) assert_raises_regex( TypeError, '^usecols must be.%s' % type(bogus_idx).__name__, np.loadtxt, c, usecols=[0, bogus_idx, 0] ) def test_bad_usecols(self): with pytest.raises(OverflowError): np.loadtxt(["1\n"], usecols=[264], delimiter=",") with pytest.raises((ValueError, OverflowError)): # Overflow error on 32bit platforms np.loadtxt(["1\n"], usecols=[262], delimiter=",") with pytest.raises(TypeError, match="If a structured dtype .. But 1 usecols were given and " "the number of fields is 3."): np.loadtxt(["1,1\n"], dtype="i,(2)i", usecols=[0], delimiter=",") def test_fancy_dtype(self): c = TextIO() c.write('1,2,3.0\n4,5,6.0\n') c.seek(0) dt = np.dtype([('x', int), ('y', [('t', int), ('s', float)])]) x = np.loadtxt(c, dtype=dt, delimiter=',') a = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dt) assert_array_equal(x, a) def test_shaped_dtype(self): c = TextIO("aaaa 1.0 8.0 1 2 3 4 5 6") dt = np.dtype([('name', 'S4'), ('x', float), ('y', float), ('block', int, (2, 3))]) x = np.loadtxt(c, dtype=dt) a = np.array([('aaaa', 1.0, 8.0, [[1, 2, 3], [4, 5, 6]])], dtype=dt) assert_array_equal(x, a) def test_3d_shaped_dtype(self): c = TextIO("aaaa 1.0 8.0 1 2 3 4 5 6 7 8 9 10 11 12") dt = np.dtype([('name', 'S4'), ('x', float), ('y', float), ('block', int, (2, 2, 3))]) x = np.loadtxt(c, dtype=dt) a = np.array([('aaaa', 1.0, 8.0, [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])], dtype=dt) assert_array_equal(x, a) def test_str_dtype(self): # see gh-8033 c = ["str1", "str2"] for dt in (str, np.bytes_): a = np.array(["str1", "str2"], dtype=dt) x = np.loadtxt(c, dtype=dt) assert_array_equal(x, a) def test_empty_file(self): with pytest.warns(UserWarning, match="input contained no data"): c = TextIO() x = np.loadtxt(c) assert_equal(x.shape, (0,)) x = np.loadtxt(c, dtype=np.int64) assert_equal(x.shape, (0,)) assert_(x.dtype == np.int64) def test_unused_converter(self): c = TextIO() c.writelines(['1 21\n', '3 42\n']) c.seek(0) data = np.loadtxt(c, usecols=(1,), converters={0: lambda s: int(s, 16)}) assert_array_equal(data, [21, 42]) c.seek(0) data = np.loadtxt(c, usecols=(1,), converters={1: lambda s: int(s, 16)}) assert_array_equal(data, [33, 66]) def test_dtype_with_object(self): # Test using an explicit dtype with an object data = """ 1; 2001-01-01 2; 2002-01-31 """ ndtype = [('idx', int), ('code', object)] func = lambda s: strptime(s.strip(), "%Y-%m-%d") converters = {1: func} test = np.loadtxt(TextIO(data), delimiter=";", dtype=ndtype, converters=converters) control = np.array( [(1, datetime(2001, 1, 1)), (2, datetime(2002, 1, 31))], dtype=ndtype) assert_equal(test, control) def test_uint64_type(self): tgt = (9223372043271415339, 9223372043271415853) c = TextIO() c.write("%s %s" % tgt) c.seek(0) res = np.loadtxt(c, dtype=np.uint64) assert_equal(res, tgt) def test_int64_type(self): tgt = (-9223372036854775807, 9223372036854775807) c = TextIO() c.write("%s %s" % tgt) c.seek(0) res = np.loadtxt(c, dtype=np.int64) assert_equal(res, tgt) def test_from_float_hex(self): # IEEE doubles and floats only, otherwise the float32 # conversion may fail. tgt = np.logspace(-10, 10, 5).astype(np.float32) tgt = np.hstack((tgt, -tgt)).astype(float) inp = '\n'.join(map(float.hex, tgt)) c = TextIO() c.write(inp) for dt in [float, np.float32]: c.seek(0) res = np.loadtxt( c, dtype=dt, converters=float.fromhex, encoding="latin1") assert_equal(res, tgt, err_msg="%s" % dt) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_default_float_converter_no_default_hex_conversion(self): """ Ensure that fromhex is only used for values with the correct prefix and is not called by default. Regression test related to gh-19598. """ c = TextIO("a b c") with pytest.raises(ValueError, match=".convert string 'a' to float64 at row 0, column 1"): np.loadtxt(c) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_default_float_converter_exception(self): """ Ensure that the exception message raised during failed floating point conversion is correct. Regression test related to gh-19598. """ c = TextIO("qrs tuv") # Invalid values for default float converter with pytest.raises(ValueError, match="could not convert string 'qrs' to float64"): np.loadtxt(c) def test_from_complex(self): tgt = (complex(1, 1), complex(1, -1)) c = TextIO() c.write("%s %s" % tgt) c.seek(0) res = np.loadtxt(c, dtype=complex) assert_equal(res, tgt) def test_complex_misformatted(self): # test for backward compatibility # some complex formats used to generate x+-yj a = np.zeros((2, 2), dtype=np.complex128) re = np.pi im = np.e a[:] = re - 1.0j im c = BytesIO() np.savetxt(c, a, fmt='%.16e') c.seek(0) txt = c.read() c.seek(0) # misformat the sign on the imaginary part, gh 7895 txt_bad = txt.replace(b'e+00-', b'e00+-') assert_(txt_bad != txt) c.write(txt_bad) c.seek(0) res = np.loadtxt(c, dtype=complex) assert_equal(res, a) def test_universal_newline(self): with temppath() as name: with open(name, 'w') as f: f.write('1 21\r3 42\r') data = np.loadtxt(name) assert_array_equal(data, [[1, 21], [3, 42]]) def test_empty_field_after_tab(self): c = TextIO() c.write('1 \t2 \t3\tstart \n4\t5\t6\t \n7\t8\t9.5\t') c.seek(0) dt = {'names': ('x', 'y', 'z', 'comment'), 'formats': ('<i4', '<i4', '<f4', '\|S8')} x = np.loadtxt(c, dtype=dt, delimiter='\t') a = np.array([b'start ', b' ', b'']) assert_array_equal(x['comment'], a) def test_unpack_structured(self): txt = TextIO("M 21 72\nF 35 58") dt = {'names': ('a', 'b', 'c'), 'formats': ('\|S1', '<i4', '<f4')} a, b, c = np.loadtxt(txt, dtype=dt, unpack=True) assert_(a.dtype.str == '\|S1') assert_(b.dtype.str == '<i4') assert_(c.dtype.str == '<f4') assert_array_equal(a, np.array([b'M', b'F'])) assert_array_equal(b, np.array([21, 35])) assert_array_equal(c, np.array([72., 58.])) def test_ndmin_keyword(self): c = TextIO() c.write('1,2,3\n4,5,6') c.seek(0) assert_raises(ValueError, np.loadtxt, c, ndmin=3) c.seek(0) assert_raises(ValueError, np.loadtxt, c, ndmin=1.5) c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', ndmin=1) a = np.array([[1, 2, 3], [4, 5, 6]]) assert_array_equal(x, a) d = TextIO() d.write('0,1,2') d.seek(0) x = np.loadtxt(d, dtype=int, delimiter=',', ndmin=2) assert_(x.shape == (1, 3)) d.seek(0) x = np.loadtxt(d, dtype=int, delimiter=',', ndmin=1) assert_(x.shape == (3,)) d.seek(0) x = np.loadtxt(d, dtype=int, delimiter=',', ndmin=0) assert_(x.shape == (3,)) e = TextIO() e.write('0\n1\n2') e.seek(0) x = np.loadtxt(e, dtype=int, delimiter=',', ndmin=2) assert_(x.shape == (3, 1)) e.seek(0) x = np.loadtxt(e, dtype=int, delimiter=',', ndmin=1) assert_(x.shape == (3,)) e.seek(0) x = np.loadtxt(e, dtype=int, delimiter=',', ndmin=0) assert_(x.shape == (3,)) # Test ndmin kw with empty file. with pytest.warns(UserWarning, match="input contained no data"): f = TextIO() assert_(np.loadtxt(f, ndmin=2).shape == (0, 1,)) assert_(np.loadtxt(f, ndmin=1).shape == (0,)) def test_generator_source(self): def count(): for i in range(10): yield "%d" % i res = np.loadtxt(count()) assert_array_equal(res, np.arange(10)) def test_bad_line(self): c = TextIO() c.write('1 2 3\n4 5 6\n2 3') c.seek(0) # Check for exception and that exception contains line number assert_raises_regex(ValueError, "3", np.loadtxt, c) def test_none_as_string(self): # gh-5155, None should work as string when format demands it c = TextIO() c.write('100,foo,200\n300,None,400') c.seek(0) dt = np.dtype([('x', int), ('a', 'S10'), ('y', int)]) np.loadtxt(c, delimiter=',', dtype=dt, comments=None) # Should succeed @pytest.mark.skipif(locale.getpreferredencoding() == 'ANSI_X3.4-1968', reason="Wrong preferred encoding") def test_binary_load(self): butf8 = b"5,6,7,\xc3\x95scarscar\r\n15,2,3,hello\r\n"\ b"20,2,3,\xc3\x95scar\r\n" sutf8 = butf8.decode("UTF-8").replace("\r", "").splitlines() with temppath() as path: with open(path, "wb") as f: f.write(butf8) with open(path, "rb") as f: x = np.loadtxt(f, encoding="UTF-8", dtype=np.str_) assert_array_equal(x, sutf8) # test broken latin1 conversion people now rely on with open(path, "rb") as f: x = np.loadtxt(f, encoding="UTF-8", dtype="S") x = [b'5,6,7,\xc3\x95scarscar', b'15,2,3,hello', b'20,2,3,\xc3\x95scar'] assert_array_equal(x, np.array(x, dtype="S")) def test_max_rows(self): c = TextIO() c.write('1,2,3,5\n4,5,7,8\n2,1,4,5') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', max_rows=1) a = np.array([1, 2, 3, 5], int) assert_array_equal(x, a) def test_max_rows_with_skiprows(self): c = TextIO() c.write('comments\n1,2,3,5\n4,5,7,8\n2,1,4,5') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', skiprows=1, max_rows=1) a = np.array([1, 2, 3, 5], int) assert_array_equal(x, a) c = TextIO() c.write('comment\n1,2,3,5\n4,5,7,8\n2,1,4,5') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', skiprows=1, max_rows=2) a = np.array([[1, 2, 3, 5], [4, 5, 7, 8]], int) assert_array_equal(x, a) def test_max_rows_with_read_continuation(self): c = TextIO() c.write('1,2,3,5\n4,5,7,8\n2,1,4,5') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', max_rows=2) a = np.array([[1, 2, 3, 5], [4, 5, 7, 8]], int) assert_array_equal(x, a) # test continuation x = np.loadtxt(c, dtype=int, delimiter=',') a = np.array([2,1,4,5], int) assert_array_equal(x, a) def test_max_rows_larger(self): #test max_rows > num rows c = TextIO() c.write('comment\n1,2,3,5\n4,5,7,8\n2,1,4,5') c.seek(0) x = np.loadtxt(c, dtype=int, delimiter=',', skiprows=1, max_rows=6) a = np.array([[1, 2, 3, 5], [4, 5, 7, 8], [2, 1, 4, 5]], int) assert_array_equal(x, a) @pytest.mark.parametrize(["skip", "data"], [ (1, ["ignored\n", "1,2\n", "\n", "3,4\n"]), # "Bad" lines that do not end in newlines: (1, ["ignored", "1,2", "", "3,4"]), (1, StringIO("ignored\n1,2\n\n3,4")), # Same as above, but do not skip any lines: (0, ["-1,0\n", "1,2\n", "\n", "3,4\n"]), (0, ["-1,0", "1,2", "", "3,4"]), (0, StringIO("-1,0\n1,2\n\n3,4"))]) def test_max_rows_empty_lines(self, skip, data): with pytest.warns(UserWarning, match=f"Input line 3.max_rows={3-skip}"): res = np.loadtxt(data, dtype=int, skiprows=skip, delimiter=",", max_rows=3-skip) assert_array_equal(res, [[-1, 0], [1, 2], [3, 4]][skip:]) if isinstance(data, StringIO): data.seek(0) with warnings.catch_warnings(): warnings.simplefilter("error", UserWarning) with pytest.raises(UserWarning): np.loadtxt(data, dtype=int, skiprows=skip, delimiter=",", max_rows=3-skip) class Testfromregex: def test_record(self): c = TextIO() c.write('1.312 foo\n1.534 bar\n4.444 qux') c.seek(0) dt = [('num', np.float64), ('val', 'S3')] x = np.fromregex(c, r"([0-9.]+)\s+(...)", dt) a = np.array([(1.312, 'foo'), (1.534, 'bar'), (4.444, 'qux')], dtype=dt) assert_array_equal(x, a) def test_record_2(self): c = TextIO() c.write('1312 foo\n1534 bar\n4444 qux') c.seek(0) dt = [('num', np.int32), ('val', 'S3')] x = np.fromregex(c, r"(\d+)\s+(...)", dt) a = np.array([(1312, 'foo'), (1534, 'bar'), (4444, 'qux')], dtype=dt) assert_array_equal(x, a) def test_record_3(self): c = TextIO() c.write('1312 foo\n1534 bar\n4444 qux') c.seek(0) dt = [('num', np.float64)] x = np.fromregex(c, r"(\d+)\s+...", dt) a = np.array([(1312,), (1534,), (4444,)], dtype=dt) assert_array_equal(x, a) @pytest.mark.parametrize("path_type", [str, Path]) def test_record_unicode(self, path_type): utf8 = b'\xcf\x96' with temppath() as str_path: path = path_type(str_path) with open(path, 'wb') as f: f.write(b'1.312 foo' + utf8 + b' \n1.534 bar\n4.444 qux') dt = [('num', np.float64), ('val', 'U4')] x = np.fromregex(path, r"(?u)([0-9.]+)\s+(\w+)", dt, encoding='UTF-8') a = np.array([(1.312, 'foo' + utf8.decode('UTF-8')), (1.534, 'bar'), (4.444, 'qux')], dtype=dt) assert_array_equal(x, a) regexp = re.compile(r"([0-9.]+)\s+(\w+)", re.UNICODE) x = np.fromregex(path, regexp, dt, encoding='UTF-8') assert_array_equal(x, a) def test_compiled_bytes(self): regexp = re.compile(b'(\\d)') c = BytesIO(b'123') dt = [('num', np.float64)] a = np.array([1, 2, 3], dtype=dt) x = np.fromregex(c, regexp, dt) assert_array_equal(x, a) def test_bad_dtype_not_structured(self): regexp = re.compile(b'(\\d)') c = BytesIO(b'123') with pytest.raises(TypeError, match='structured datatype'): np.fromregex(c, regexp, dtype=np.float64) #####-------------------------------------------------------------------------- class TestFromTxt(LoadTxtBase): loadfunc = staticmethod(np.genfromtxt) def test_record(self): # Test w/ explicit dtype data = TextIO('1 2\n3 4') test = np.genfromtxt(data, dtype=[('x', np.int32), ('y', np.int32)]) control = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) assert_equal(test, control) # data = TextIO('M 64.0 75.0\nF 25.0 60.0') descriptor = {'names': ('gender', 'age', 'weight'), 'formats': ('S1', 'i4', 'f4')} control = np.array([('M', 64.0, 75.0), ('F', 25.0, 60.0)], dtype=descriptor) test = np.genfromtxt(data, dtype=descriptor) assert_equal(test, control) def test_array(self): # Test outputting a standard ndarray data = TextIO('1 2\n3 4') control = np.array([[1, 2], [3, 4]], dtype=int) test = np.genfromtxt(data, dtype=int) assert_array_equal(test, control) # data.seek(0) control = np.array([[1, 2], [3, 4]], dtype=float) test = np.loadtxt(data, dtype=float) assert_array_equal(test, control) def test_1D(self): # Test squeezing to 1D control = np.array([1, 2, 3, 4], int) # data = TextIO('1\n2\n3\n4\n') test = np.genfromtxt(data, dtype=int) assert_array_equal(test, control) # data = TextIO('1,2,3,4\n') test = np.genfromtxt(data, dtype=int, delimiter=',') assert_array_equal(test, control) def test_comments(self): # Test the stripping of comments control = np.array([1, 2, 3, 5], int) # Comment on its own line data = TextIO('# comment\n1,2,3,5\n') test = np.genfromtxt(data, dtype=int, delimiter=',', comments='#') assert_equal(test, control) # Comment at the end of a line data = TextIO('1,2,3,5# comment\n') test = np.genfromtxt(data, dtype=int, delimiter=',', comments='#') assert_equal(test, control) def test_skiprows(self): # Test row skipping control = np.array([1, 2, 3, 5], int) kwargs = dict(dtype=int, delimiter=',') # data = TextIO('comment\n1,2,3,5\n') test = np.genfromtxt(data, skip_header=1, kwargs) assert_equal(test, control) # data = TextIO('# comment\n1,2,3,5\n') test = np.loadtxt(data, skiprows=1, kwargs) assert_equal(test, control) def test_skip_footer(self): data = ["# %i" % i for i in range(1, 6)] data.append("A, B, C") data.extend(["%i,%3.1f,%03s" % (i, i, i) for i in range(51)]) data[-1] = "99,99" kwargs = dict(delimiter=",", names=True, skip_header=5, skip_footer=10) test = np.genfromtxt(TextIO("\n".join(data)), kwargs) ctrl = np.array([("%f" % i, "%f" % i, "%f" % i) for i in range(41)], dtype=[(_, float) for _ in "ABC"]) assert_equal(test, ctrl) def test_skip_footer_with_invalid(self): with suppress_warnings() as sup: sup.filter(ConversionWarning) basestr = '1 1\n2 2\n3 3\n4 4\n5 \n6 \n7 \n' # Footer too small to get rid of all invalid values assert_raises(ValueError, np.genfromtxt, TextIO(basestr), skip_footer=1) # except ValueError: # pass a = np.genfromtxt( TextIO(basestr), skip_footer=1, invalid_raise=False) assert_equal(a, np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]])) # a = np.genfromtxt(TextIO(basestr), skip_footer=3) assert_equal(a, np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]])) # basestr = '1 1\n2 \n3 3\n4 4\n5 \n6 6\n7 7\n' a = np.genfromtxt( TextIO(basestr), skip_footer=1, invalid_raise=False) assert_equal(a, np.array([[1., 1.], [3., 3.], [4., 4.], [6., 6.]])) a = np.genfromtxt( TextIO(basestr), skip_footer=3, invalid_raise=False) assert_equal(a, np.array([[1., 1.], [3., 3.], [4., 4.]])) def test_header(self): # Test retrieving a header data = TextIO('gender age weight\nM 64.0 75.0\nF 25.0 60.0') with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(data, dtype=None, names=True) assert_(w[0].category is np.VisibleDeprecationWarning) control = {'gender': np.array([b'M', b'F']), 'age': np.array([64.0, 25.0]), 'weight': np.array([75.0, 60.0])} assert_equal(test['gender'], control['gender']) assert_equal(test['age'], control['age']) assert_equal(test['weight'], control['weight']) def test_auto_dtype(self): # Test the automatic definition of the output dtype data = TextIO('A 64 75.0 3+4j True\nBCD 25 60.0 5+6j False') with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(data, dtype=None) assert_(w[0].category is np.VisibleDeprecationWarning) control = [np.array([b'A', b'BCD']), np.array([64, 25]), np.array([75.0, 60.0]), np.array([3 + 4j, 5 + 6j]), np.array([True, False]), ] assert_equal(test.dtype.names, ['f0', 'f1', 'f2', 'f3', 'f4']) for (i, ctrl) in enumerate(control): assert_equal(test['f%i' % i], ctrl) def test_auto_dtype_uniform(self): # Tests whether the output dtype can be uniformized data = TextIO('1 2 3 4\n5 6 7 8\n') test = np.genfromtxt(data, dtype=None) control = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) assert_equal(test, control) def test_fancy_dtype(self): # Check that a nested dtype isn't MIA data = TextIO('1,2,3.0\n4,5,6.0\n') fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])]) test = np.genfromtxt(data, dtype=fancydtype, delimiter=',') control = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=fancydtype) assert_equal(test, control) def test_names_overwrite(self): # Test overwriting the names of the dtype descriptor = {'names': ('g', 'a', 'w'), 'formats': ('S1', 'i4', 'f4')} data = TextIO(b'M 64.0 75.0\nF 25.0 60.0') names = ('gender', 'age', 'weight') test = np.genfromtxt(data, dtype=descriptor, names=names) descriptor['names'] = names control = np.array([('M', 64.0, 75.0), ('F', 25.0, 60.0)], dtype=descriptor) assert_equal(test, control) def test_bad_fname(self): with pytest.raises(TypeError, match='fname must be a string,'): np.genfromtxt(123) def test_commented_header(self): # Check that names can be retrieved even if the line is commented out. data = TextIO(""" #gender age weight M 21 72.100000 F 35 58.330000 M 33 21.99 """) # The # is part of the first name and should be deleted automatically. with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(data, names=True, dtype=None) assert_(w[0].category is np.VisibleDeprecationWarning) ctrl = np.array([('M', 21, 72.1), ('F', 35, 58.33), ('M', 33, 21.99)], dtype=[('gender', '\|S1'), ('age', int), ('weight', float)]) assert_equal(test, ctrl) # Ditto, but we should get rid of the first element data = TextIO(b""" # gender age weight M 21 72.100000 F 35 58.330000 M 33 21.99 """) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(data, names=True, dtype=None) assert_(w[0].category is np.VisibleDeprecationWarning) assert_equal(test, ctrl) def test_names_and_comments_none(self): # Tests case when names is true but comments is None (gh-10780) data = TextIO('col1 col2\n 1 2\n 3 4') test = np.genfromtxt(data, dtype=(int, int), comments=None, names=True) control = np.array([(1, 2), (3, 4)], dtype=[('col1', int), ('col2', int)]) assert_equal(test, control) def test_file_is_closed_on_error(self): # gh-13200 with tempdir() as tmpdir: fpath = os.path.join(tmpdir, "test.csv") with open(fpath, "wb") as f: f.write('\N{GREEK PI SYMBOL}'.encode()) # ResourceWarnings are emitted from a destructor, so won't be # detected by regular propagation to errors. with assert_no_warnings(): with pytest.raises(UnicodeDecodeError): np.genfromtxt(fpath, encoding="ascii") def test_autonames_and_usecols(self): # Tests names and usecols data = TextIO('A B C D\n aaaa 121 45 9.1') with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(data, usecols=('A', 'C', 'D'), names=True, dtype=None) assert_(w[0].category is np.VisibleDeprecationWarning) control = np.array(('aaaa', 45, 9.1), dtype=[('A', '\|S4'), ('C', int), ('D', float)]) assert_equal(test, control) def test_converters_with_usecols(self): # Test the combination user-defined converters and usecol data = TextIO('1,2,3,,5\n6,7,8,9,10\n') test = np.genfromtxt(data, dtype=int, delimiter=',', converters={3: lambda s: int(s or - 999)}, usecols=(1, 3,)) control = np.array([[2, -999], [7, 9]], int) assert_equal(test, control) def test_converters_with_usecols_and_names(self): # Tests names and usecols data = TextIO('A B C D\n aaaa 121 45 9.1') with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(data, usecols=('A', 'C', 'D'), names=True, dtype=None, converters={'C': lambda s: 2 int(s)}) assert_(w[0].category is np.VisibleDeprecationWarning) control = np.array(('aaaa', 90, 9.1), dtype=[('A', '\|S4'), ('C', int), ('D', float)]) assert_equal(test, control) def test_converters_cornercases(self): # Test the conversion to datetime. converter = { 'date': lambda s: strptime(s, '%Y-%m-%d %H:%M:%SZ')} data = TextIO('2009-02-03 12:00:00Z, 72214.0') test = np.genfromtxt(data, delimiter=',', dtype=None, names=['date', 'stid'], converters=converter) control = np.array((datetime(2009, 2, 3), 72214.), dtype=[('date', np.object_), ('stid', float)]) assert_equal(test, control) def test_converters_cornercases2(self): # Test the conversion to datetime64. converter = { 'date': lambda s: np.datetime64(strptime(s, '%Y-%m-%d %H:%M:%SZ'))} data = TextIO('2009-02-03 12:00:00Z, 72214.0') test = np.genfromtxt(data, delimiter=',', dtype=None, names=['date', 'stid'], converters=converter) control = np.array((datetime(2009, 2, 3), 72214.), dtype=[('date', 'datetime64[us]'), ('stid', float)]) assert_equal(test, control) def test_unused_converter(self): # Test whether unused converters are forgotten data = TextIO("1 21\n 3 42\n") test = np.genfromtxt(data, usecols=(1,), converters={0: lambda s: int(s, 16)}) assert_equal(test, [21, 42]) # data.seek(0) test = np.genfromtxt(data, usecols=(1,), converters={1: lambda s: int(s, 16)}) assert_equal(test, [33, 66]) def test_invalid_converter(self): strip_rand = lambda x: float((b'r' in x.lower() and x.split()[-1]) or (b'r' not in x.lower() and x.strip() or 0.0)) strip_per = lambda x: float((b'%' in x.lower() and x.split()[0]) or (b'%' not in x.lower() and x.strip() or 0.0)) s = TextIO("D01N01,10/1/2003 ,1 %,R 75,400,600\r\n" "L24U05,12/5/2003, 2 %,1,300, 150.5\r\n" "D02N03,10/10/2004,R 1,,7,145.55") kwargs = dict( converters={2: strip_per, 3: strip_rand}, delimiter=",", dtype=None) assert_raises(ConverterError, np.genfromtxt, s, *kwargs) def test_tricky_converter_bug1666(self): # Test some corner cases s = TextIO('q1,2\nq3,4') cnv = lambda s: float(s[1:]) test = np.genfromtxt(s, delimiter=',', converters={0: cnv}) control = np.array([[1., 2.], [3., 4.]]) assert_equal(test, control) def test_dtype_with_converters(self): dstr = "2009; 23; 46" test = np.genfromtxt(TextIO(dstr,), delimiter=";", dtype=float, converters={0: bytes}) control = np.array([('2009', 23., 46)], dtype=[('f0', '\|S4'), ('f1', float), ('f2', float)]) assert_equal(test, control) test = np.genfromtxt(TextIO(dstr,), delimiter=";", dtype=float, converters={0: float}) control = np.array([2009., 23., 46],) assert_equal(test, control) def test_dtype_with_converters_and_usecols(self): dstr = "1,5,-1,1:1\n2,8,-1,1:n\n3,3,-2,m:n\n" dmap = {'1:1':0, '1:n':1, 'm:1':2, 'm:n':3} dtyp = [('e1','i4'),('e2','i4'),('e3','i2'),('n', 'i1')] conv = {0: int, 1: int, 2: int, 3: lambda r: dmap[r.decode()]} test = np.recfromcsv(TextIO(dstr,), dtype=dtyp, delimiter=',', names=None, converters=conv) control = np.rec.array([(1,5,-1,0), (2,8,-1,1), (3,3,-2,3)], dtype=dtyp) assert_equal(test, control) dtyp = [('e1','i4'),('e2','i4'),('n', 'i1')] test = np.recfromcsv(TextIO(dstr,), dtype=dtyp, delimiter=',', usecols=(0,1,3), names=None, converters=conv) control = np.rec.array([(1,5,0), (2,8,1), (3,3,3)], dtype=dtyp) assert_equal(test, control) def test_dtype_with_object(self): # Test using an explicit dtype with an object data = """ 1; 2001-01-01 2; 2002-01-31 """ ndtype = [('idx', int), ('code', object)] func = lambda s: strptime(s.strip(), "%Y-%m-%d") converters = {1: func} test = np.genfromtxt(TextIO(data), delimiter=";", dtype=ndtype, converters=converters) control = np.array( [(1, datetime(2001, 1, 1)), (2, datetime(2002, 1, 31))], dtype=ndtype) assert_equal(test, control) ndtype = [('nest', [('idx', int), ('code', object)])] with assert_raises_regex(NotImplementedError, 'Nested fields. not supported.'): test = np.genfromtxt(TextIO(data), delimiter=";", dtype=ndtype, converters=converters) # nested but empty fields also aren't supported ndtype = [('idx', int), ('code', object), ('nest', [])] with assert_raises_regex(NotImplementedError, 'Nested fields. not supported.'): test = np.genfromtxt(TextIO(data), delimiter=";", dtype=ndtype, converters=converters) def test_dtype_with_object_no_converter(self): # Object without a converter uses bytes: parsed = np.genfromtxt(TextIO("1"), dtype=object) assert parsed[()] == b"1" parsed = np.genfromtxt(TextIO("string"), dtype=object) assert parsed[()] == b"string" def test_userconverters_with_explicit_dtype(self): # Test user_converters w/ explicit (standard) dtype data = TextIO('skip,skip,2001-01-01,1.0,skip') test = np.genfromtxt(data, delimiter=",", names=None, dtype=float, usecols=(2, 3), converters={2: bytes}) control = np.array([('2001-01-01', 1.)], dtype=[('', '\|S10'), ('', float)]) assert_equal(test, control) def test_utf8_userconverters_with_explicit_dtype(self): utf8 = b'\xcf\x96' with temppath() as path: with open(path, 'wb') as f: f.write(b'skip,skip,2001-01-01' + utf8 + b',1.0,skip') test = np.genfromtxt(path, delimiter=",", names=None, dtype=float, usecols=(2, 3), converters={2: np.compat.unicode}, encoding='UTF-8') control = np.array([('2001-01-01' + utf8.decode('UTF-8'), 1.)], dtype=[('', '\|U11'), ('', float)]) assert_equal(test, control) def test_spacedelimiter(self): # Test space delimiter data = TextIO("1 2 3 4 5\n6 7 8 9 10") test = np.genfromtxt(data) control = np.array([[1., 2., 3., 4., 5.], [6., 7., 8., 9., 10.]]) assert_equal(test, control) def test_integer_delimiter(self): # Test using an integer for delimiter data = " 1 2 3\n 4 5 67\n890123 4" test = np.genfromtxt(TextIO(data), delimiter=3) control = np.array([[1, 2, 3], [4, 5, 67], [890, 123, 4]]) assert_equal(test, control) def test_missing(self): data = TextIO('1,2,3,,5\n') test = np.genfromtxt(data, dtype=int, delimiter=',', converters={3: lambda s: int(s or - 999)}) control = np.array([1, 2, 3, -999, 5], int) assert_equal(test, control) def test_missing_with_tabs(self): # Test w/ a delimiter tab txt = "1\t2\t3\n\t2\t\n1\t\t3" test = np.genfromtxt(TextIO(txt), delimiter="\t", usemask=True,) ctrl_d = np.array([(1, 2, 3), (np.nan, 2, np.nan), (1, np.nan, 3)],) ctrl_m = np.array([(0, 0, 0), (1, 0, 1), (0, 1, 0)], dtype=bool) assert_equal(test.data, ctrl_d) assert_equal(test.mask, ctrl_m) def test_usecols(self): # Test the selection of columns # Select 1 column control = np.array([[1, 2], [3, 4]], float) data = TextIO() np.savetxt(data, control) data.seek(0) test = np.genfromtxt(data, dtype=float, usecols=(1,)) assert_equal(test, control[:, 1]) # control = np.array([[1, 2, 3], [3, 4, 5]], float) data = TextIO() np.savetxt(data, control) data.seek(0) test = np.genfromtxt(data, dtype=float, usecols=(1, 2)) assert_equal(test, control[:, 1:]) # Testing with arrays instead of tuples. data.seek(0) test = np.genfromtxt(data, dtype=float, usecols=np.array([1, 2])) assert_equal(test, control[:, 1:]) def test_usecols_as_css(self): # Test giving usecols with a comma-separated string data = "1 2 3\n4 5 6" test = np.genfromtxt(TextIO(data), names="a, b, c", usecols="a, c") ctrl = np.array([(1, 3), (4, 6)], dtype=[(_, float) for _ in "ac"]) assert_equal(test, ctrl) def test_usecols_with_structured_dtype(self): # Test usecols with an explicit structured dtype data = TextIO("JOE 70.1 25.3\nBOB 60.5 27.9") names = ['stid', 'temp'] dtypes = ['S4', 'f8'] test = np.genfromtxt( data, usecols=(0, 2), dtype=list(zip(names, dtypes))) assert_equal(test['stid'], [b"JOE", b"BOB"]) assert_equal(test['temp'], [25.3, 27.9]) def test_usecols_with_integer(self): # Test usecols with an integer test = np.genfromtxt(TextIO(b"1 2 3\n4 5 6"), usecols=0) assert_equal(test, np.array([1., 4.])) def test_usecols_with_named_columns(self): # Test usecols with named columns ctrl = np.array([(1, 3), (4, 6)], dtype=[('a', float), ('c', float)]) data = "1 2 3\n4 5 6" kwargs = dict(names="a, b, c") test = np.genfromtxt(TextIO(data), usecols=(0, -1), kwargs) assert_equal(test, ctrl) test = np.genfromtxt(TextIO(data), usecols=('a', 'c'), kwargs) assert_equal(test, ctrl) def test_empty_file(self): # Test that an empty file raises the proper warning. with suppress_warnings() as sup: sup.filter(message="genfromtxt: Empty input file:") data = TextIO() test = np.genfromtxt(data) assert_equal(test, np.array([])) # when skip_header > 0 test = np.genfromtxt(data, skip_header=1) assert_equal(test, np.array([])) def test_fancy_dtype_alt(self): # Check that a nested dtype isn't MIA data = TextIO('1,2,3.0\n4,5,6.0\n') fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])]) test = np.genfromtxt(data, dtype=fancydtype, delimiter=',', usemask=True) control = ma.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=fancydtype) assert_equal(test, control) def test_shaped_dtype(self): c = TextIO("aaaa 1.0 8.0 1 2 3 4 5 6") dt = np.dtype([('name', 'S4'), ('x', float), ('y', float), ('block', int, (2, 3))]) x = np.genfromtxt(c, dtype=dt) a = np.array([('aaaa', 1.0, 8.0, [[1, 2, 3], [4, 5, 6]])], dtype=dt) assert_array_equal(x, a) def test_withmissing(self): data = TextIO('A,B\n0,1\n2,N/A') kwargs = dict(delimiter=",", missing_values="N/A", names=True) test = np.genfromtxt(data, dtype=None, usemask=True, kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', int), ('B', int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) # data.seek(0) test = np.genfromtxt(data, usemask=True, kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', float), ('B', float)]) assert_equal(test, control) assert_equal(test.mask, control.mask) def test_user_missing_values(self): data = "A, B, C\n0, 0., 0j\n1, N/A, 1j\n-9, 2.2, N/A\n3, -99, 3j" basekwargs = dict(dtype=None, delimiter=",", names=True,) mdtype = [('A', int), ('B', float), ('C', complex)] # test = np.genfromtxt(TextIO(data), missing_values="N/A", basekwargs) control = ma.array([(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j), (3, -99, 3j)], mask=[(0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 0)], dtype=mdtype) assert_equal(test, control) # basekwargs['dtype'] = mdtype test = np.genfromtxt(TextIO(data), missing_values={0: -9, 1: -99, 2: -999j}, usemask=True, basekwargs) control = ma.array([(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j), (3, -99, 3j)], mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)], dtype=mdtype) assert_equal(test, control) # test = np.genfromtxt(TextIO(data), missing_values={0: -9, 'B': -99, 'C': -999j}, usemask=True, basekwargs) control = ma.array([(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j), (3, -99, 3j)], mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)], dtype=mdtype) assert_equal(test, control) def test_user_filling_values(self): # Test with missing and filling values ctrl = np.array([(0, 3), (4, -999)], dtype=[('a', int), ('b', int)]) data = "N/A, 2, 3\n4, ,???" kwargs = dict(delimiter=",", dtype=int, names="a,b,c", missing_values={0: "N/A", 'b': " ", 2: "???"}, filling_values={0: 0, 'b': 0, 2: -999}) test = np.genfromtxt(TextIO(data), kwargs) ctrl = np.array([(0, 2, 3), (4, 0, -999)], dtype=[(_, int) for _ in "abc"]) assert_equal(test, ctrl) # test = np.genfromtxt(TextIO(data), usecols=(0, -1), kwargs) ctrl = np.array([(0, 3), (4, -999)], dtype=[(_, int) for _ in "ac"]) assert_equal(test, ctrl) data2 = "1,2,,4\n5,,7,8\n" test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int, missing_values="", filling_values=0) ctrl = np.array([[1, 2, 0, 4], [5, 0, 7, 8]]) assert_equal(test, ctrl) test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int, missing_values="", filling_values=-1) ctrl = np.array([[1, 2, -1, 4], [5, -1, 7, 8]]) assert_equal(test, ctrl) def test_withmissing_float(self): data = TextIO('A,B\n0,1.5\n2,-999.00') test = np.genfromtxt(data, dtype=None, delimiter=',', missing_values='-999.0', names=True, usemask=True) control = ma.array([(0, 1.5), (2, -1.)], mask=[(False, False), (False, True)], dtype=[('A', int), ('B', float)]) assert_equal(test, control) assert_equal(test.mask, control.mask) def test_with_masked_column_uniform(self): # Test masked column data = TextIO('1 2 3\n4 5 6\n') test = np.genfromtxt(data, dtype=None, missing_values='2,5', usemask=True) control = ma.array([[1, 2, 3], [4, 5, 6]], mask=[[0, 1, 0], [0, 1, 0]]) assert_equal(test, control) def test_with_masked_column_various(self): # Test masked column data = TextIO('True 2 3\nFalse 5 6\n') test = np.genfromtxt(data, dtype=None, missing_values='2,5', usemask=True) control = ma.array([(1, 2, 3), (0, 5, 6)], mask=[(0, 1, 0), (0, 1, 0)], dtype=[('f0', bool), ('f1', bool), ('f2', int)]) assert_equal(test, control) def test_invalid_raise(self): # Test invalid raise data = ["1, 1, 1, 1, 1"] 50 for i in range(5): data[10 * i] = "2, 2, 2, 2 2" data.insert(0, "a, b, c, d, e") mdata = TextIO("\n".join(data)) kwargs = dict(delimiter=",", dtype=None, names=True) def f(): return np.genfromtxt(mdata, invalid_raise=False, *kwargs) mtest = assert_warns(ConversionWarning, f) assert_equal(len(mtest), 45) assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'abcde'])) # mdata.seek(0) assert_raises(ValueError, np.genfromtxt, mdata, delimiter=",", names=True) def test_invalid_raise_with_usecols(self): # Test invalid_raise with usecols data = ["1, 1, 1, 1, 1"] 50 for i in range(5): data[10 * i] = "2, 2, 2, 2 2" data.insert(0, "a, b, c, d, e") mdata = TextIO("\n".join(data)) kwargs = dict(delimiter=",", dtype=None, names=True, invalid_raise=False) def f(): return np.genfromtxt(mdata, usecols=(0, 4), kwargs) mtest = assert_warns(ConversionWarning, f) assert_equal(len(mtest), 45) assert_equal(mtest, np.ones(45, dtype=[(_, int) for _ in 'ae'])) # mdata.seek(0) mtest = np.genfromtxt(mdata, usecols=(0, 1), kwargs) assert_equal(len(mtest), 50) control = np.ones(50, dtype=[(_, int) for _ in 'ab']) control[[10 * _ for _ in range(5)]] = (2, 2) assert_equal(mtest, control) def test_inconsistent_dtype(self): # Test inconsistent dtype data = ["1, 1, 1, 1, -1.1"] * 50 mdata = TextIO("\n".join(data)) converters = {4: lambda x: "(%s)" % x.decode()} kwargs = dict(delimiter=",", converters=converters, dtype=[(_, int) for _ in 'abcde'],) assert_raises(ValueError, np.genfromtxt, mdata, kwargs) def test_default_field_format(self): # Test default format data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.genfromtxt(TextIO(data), delimiter=",", dtype=None, defaultfmt="f%02i") ctrl = np.array([(0, 1, 2.3), (4, 5, 6.7)], dtype=[("f00", int), ("f01", int), ("f02", float)]) assert_equal(mtest, ctrl) def test_single_dtype_wo_names(self): # Test single dtype w/o names data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.genfromtxt(TextIO(data), delimiter=",", dtype=float, defaultfmt="f%02i") ctrl = np.array([[0., 1., 2.3], [4., 5., 6.7]], dtype=float) assert_equal(mtest, ctrl) def test_single_dtype_w_explicit_names(self): # Test single dtype w explicit names data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.genfromtxt(TextIO(data), delimiter=",", dtype=float, names="a, b, c") ctrl = np.array([(0., 1., 2.3), (4., 5., 6.7)], dtype=[(_, float) for _ in "abc"]) assert_equal(mtest, ctrl) def test_single_dtype_w_implicit_names(self): # Test single dtype w implicit names data = "a, b, c\n0, 1, 2.3\n4, 5, 6.7" mtest = np.genfromtxt(TextIO(data), delimiter=",", dtype=float, names=True) ctrl = np.array([(0., 1., 2.3), (4., 5., 6.7)], dtype=[(_, float) for _ in "abc"]) assert_equal(mtest, ctrl) def test_easy_structured_dtype(self): # Test easy structured dtype data = "0, 1, 2.3\n4, 5, 6.7" mtest = np.genfromtxt(TextIO(data), delimiter=",", dtype=(int, float, float), defaultfmt="f_%02i") ctrl = np.array([(0, 1., 2.3), (4, 5., 6.7)], dtype=[("f_00", int), ("f_01", float), ("f_02", float)]) assert_equal(mtest, ctrl) def test_autostrip(self): # Test autostrip data = "01/01/2003 , 1.3, abcde" kwargs = dict(delimiter=",", dtype=None) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) mtest = np.genfromtxt(TextIO(data), kwargs) assert_(w[0].category is np.VisibleDeprecationWarning) ctrl = np.array([('01/01/2003 ', 1.3, ' abcde')], dtype=[('f0', '\|S12'), ('f1', float), ('f2', '\|S8')]) assert_equal(mtest, ctrl) with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) mtest = np.genfromtxt(TextIO(data), autostrip=True, kwargs) assert_(w[0].category is np.VisibleDeprecationWarning) ctrl = np.array([('01/01/2003', 1.3, 'abcde')], dtype=[('f0', '\|S10'), ('f1', float), ('f2', '\|S5')]) assert_equal(mtest, ctrl) def test_replace_space(self): # Test the 'replace_space' option txt = "A.A, B (B), C:C\n1, 2, 3.14" # Test default: replace ' ' by '_' and delete non-alphanum chars test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=None) ctrl_dtype = [("AA", int), ("B_B", int), ("CC", float)] ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no replace, no delete test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=None, replace_space='', deletechars='') ctrl_dtype = [("A.A", int), ("B (B)", int), ("C:C", float)] ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no delete (spaces are replaced by _) test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=None, deletechars='') ctrl_dtype = [("A.A", int), ("B_(B)", int), ("C:C", float)] ctrl = np.array((1, 2, 3.14), dtype=ctrl_dtype) assert_equal(test, ctrl) def test_replace_space_known_dtype(self): # Test the 'replace_space' (and related) options when dtype != None txt = "A.A, B (B), C:C\n1, 2, 3" # Test default: replace ' ' by '_' and delete non-alphanum chars test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=int) ctrl_dtype = [("AA", int), ("B_B", int), ("CC", int)] ctrl = np.array((1, 2, 3), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no replace, no delete test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=int, replace_space='', deletechars='') ctrl_dtype = [("A.A", int), ("B (B)", int), ("C:C", int)] ctrl = np.array((1, 2, 3), dtype=ctrl_dtype) assert_equal(test, ctrl) # Test: no delete (spaces are replaced by _) test = np.genfromtxt(TextIO(txt), delimiter=",", names=True, dtype=int, deletechars='') ctrl_dtype = [("A.A", int), ("B_(B)", int), ("C:C", int)] ctrl = np.array((1, 2, 3), dtype=ctrl_dtype) assert_equal(test, ctrl) def test_incomplete_names(self): # Test w/ incomplete names data = "A,,C\n0,1,2\n3,4,5" kwargs = dict(delimiter=",", names=True) # w/ dtype=None ctrl = np.array([(0, 1, 2), (3, 4, 5)], dtype=[(_, int) for _ in ('A', 'f0', 'C')]) test = np.genfromtxt(TextIO(data), dtype=None, kwargs) assert_equal(test, ctrl) # w/ default dtype ctrl = np.array([(0, 1, 2), (3, 4, 5)], dtype=[(_, float) for _ in ('A', 'f0', 'C')]) test = np.genfromtxt(TextIO(data), kwargs) def test_names_auto_completion(self): # Make sure that names are properly completed data = "1 2 3\n 4 5 6" test = np.genfromtxt(TextIO(data), dtype=(int, float, int), names="a") ctrl = np.array([(1, 2, 3), (4, 5, 6)], dtype=[('a', int), ('f0', float), ('f1', int)]) assert_equal(test, ctrl) def test_names_with_usecols_bug1636(self): # Make sure we pick up the right names w/ usecols data = "A,B,C,D,E\n0,1,2,3,4\n0,1,2,3,4\n0,1,2,3,4" ctrl_names = ("A", "C", "E") test = np.genfromtxt(TextIO(data), dtype=(int, int, int), delimiter=",", usecols=(0, 2, 4), names=True) assert_equal(test.dtype.names, ctrl_names) # test = np.genfromtxt(TextIO(data), dtype=(int, int, int), delimiter=",", usecols=("A", "C", "E"), names=True) assert_equal(test.dtype.names, ctrl_names) # test = np.genfromtxt(TextIO(data), dtype=int, delimiter=",", usecols=("A", "C", "E"), names=True) assert_equal(test.dtype.names, ctrl_names) def test_fixed_width_names(self): # Test fix-width w/ names data = " A B C\n 0 1 2.3\n 45 67 9." kwargs = dict(delimiter=(5, 5, 4), names=True, dtype=None) ctrl = np.array([(0, 1, 2.3), (45, 67, 9.)], dtype=[('A', int), ('B', int), ('C', float)]) test = np.genfromtxt(TextIO(data), kwargs) assert_equal(test, ctrl) # kwargs = dict(delimiter=5, names=True, dtype=None) ctrl = np.array([(0, 1, 2.3), (45, 67, 9.)], dtype=[('A', int), ('B', int), ('C', float)]) test = np.genfromtxt(TextIO(data), kwargs) assert_equal(test, ctrl) def test_filling_values(self): # Test missing values data = b"1, 2, 3\n1, , 5\n0, 6, \n" kwargs = dict(delimiter=",", dtype=None, filling_values=-999) ctrl = np.array([[1, 2, 3], [1, -999, 5], [0, 6, -999]], dtype=int) test = np.genfromtxt(TextIO(data), kwargs) assert_equal(test, ctrl) def test_comments_is_none(self): # Github issue 329 (None was previously being converted to 'None'). with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(TextIO("test1,testNonetherestofthedata"), dtype=None, comments=None, delimiter=',') assert_(w[0].category is np.VisibleDeprecationWarning) assert_equal(test[1], b'testNonetherestofthedata') with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(TextIO("test1, testNonetherestofthedata"), dtype=None, comments=None, delimiter=',') assert_(w[0].category is np.VisibleDeprecationWarning) assert_equal(test[1], b' testNonetherestofthedata') def test_latin1(self): latin1 = b'\xf6\xfc\xf6' norm = b"norm1,norm2,norm3\n" enc = b"test1,testNonethe" + latin1 + b",test3\n" s = norm + enc + norm with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(TextIO(s), dtype=None, comments=None, delimiter=',') assert_(w[0].category is np.VisibleDeprecationWarning) assert_equal(test[1, 0], b"test1") assert_equal(test[1, 1], b"testNonethe" + latin1) assert_equal(test[1, 2], b"test3") test = np.genfromtxt(TextIO(s), dtype=None, comments=None, delimiter=',', encoding='latin1') assert_equal(test[1, 0], "test1") assert_equal(test[1, 1], "testNonethe" + latin1.decode('latin1')) assert_equal(test[1, 2], "test3") with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(TextIO(b"0,testNonethe" + latin1), dtype=None, comments=None, delimiter=',') assert_(w[0].category is np.VisibleDeprecationWarning) assert_equal(test['f0'], 0) assert_equal(test['f1'], b"testNonethe" + latin1) def test_binary_decode_autodtype(self): utf16 = b'\xff\xfeh\x04 \x00i\x04 \x00j\x04' v = self.loadfunc(BytesIO(utf16), dtype=None, encoding='UTF-16') assert_array_equal(v, np.array(utf16.decode('UTF-16').split())) def test_utf8_byte_encoding(self): utf8 = b"\xcf\x96" norm = b"norm1,norm2,norm3\n" enc = b"test1,testNonethe" + utf8 + b",test3\n" s = norm + enc + norm with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(TextIO(s), dtype=None, comments=None, delimiter=',') assert_(w[0].category is np.VisibleDeprecationWarning) ctl = np.array([ [b'norm1', b'norm2', b'norm3'], [b'test1', b'testNonethe' + utf8, b'test3'], [b'norm1', b'norm2', b'norm3']]) assert_array_equal(test, ctl) def test_utf8_file(self): utf8 = b"\xcf\x96" with temppath() as path: with open(path, "wb") as f: f.write((b"test1,testNonethe" + utf8 + b",test3\n") * 2) test = np.genfromtxt(path, dtype=None, comments=None, delimiter=',', encoding="UTF-8") ctl = np.array([ ["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"], ["test1", "testNonethe" + utf8.decode("UTF-8"), "test3"]], dtype=np.str_) assert_array_equal(test, ctl) # test a mixed dtype with open(path, "wb") as f: f.write(b"0,testNonethe" + utf8) test = np.genfromtxt(path, dtype=None, comments=None, delimiter=',', encoding="UTF-8") assert_equal(test['f0'], 0) assert_equal(test['f1'], "testNonethe" + utf8.decode("UTF-8")) def test_utf8_file_nodtype_unicode(self): # bytes encoding with non-latin1 -> unicode upcast utf8 = '\u03d6' latin1 = '\xf6\xfc\xf6' # skip test if cannot encode utf8 test string with preferred # encoding. The preferred encoding is assumed to be the default # encoding of io.open. Will need to change this for PyTest, maybe # using pytest.mark.xfail(raises=*). try: encoding = locale.getpreferredencoding() utf8.encode(encoding) except (UnicodeError, ImportError): pytest.skip('Skipping test_utf8_file_nodtype_unicode, ' 'unable to encode utf8 in preferred encoding') with temppath() as path: with io.open(path, "wt") as f: f.write("norm1,norm2,norm3\n") f.write("norm1," + latin1 + ",norm3\n") f.write("test1,testNonethe" + utf8 + ",test3\n") with warnings.catch_warnings(record=True) as w: warnings.filterwarnings('always', '', np.VisibleDeprecationWarning) test = np.genfromtxt(path, dtype=None, comments=None, delimiter=',') # Check for warning when encoding not specified. assert_(w[0].category is np.VisibleDeprecationWarning) ctl = np.array([ ["norm1", "norm2", "norm3"], ["norm1", latin1, "norm3"], ["test1", "testNonethe" + utf8, "test3"]], dtype=np.str_) assert_array_equal(test, ctl) def test_recfromtxt(self): # data = TextIO('A,B\n0,1\n2,3') kwargs = dict(delimiter=",", missing_values="N/A", names=True) test = np.recfromtxt(data, kwargs) control = np.array([(0, 1), (2, 3)], dtype=[('A', int), ('B', int)]) assert_(isinstance(test, np.recarray)) assert_equal(test, control) # data = TextIO('A,B\n0,1\n2,N/A') test = np.recfromtxt(data, dtype=None, usemask=True, kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', int), ('B', int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) assert_equal(test.A, [0, 2]) def test_recfromcsv(self): # data = TextIO('A,B\n0,1\n2,3') kwargs = dict(missing_values="N/A", names=True, case_sensitive=True) test = np.recfromcsv(data, dtype=None, kwargs) control = np.array([(0, 1), (2, 3)], dtype=[('A', int), ('B', int)]) assert_(isinstance(test, np.recarray)) assert_equal(test, control) # data = TextIO('A,B\n0,1\n2,N/A') test = np.recfromcsv(data, dtype=None, usemask=True, kwargs) control = ma.array([(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[('A', int), ('B', int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) assert_equal(test.A, [0, 2]) # data = TextIO('A,B\n0,1\n2,3') test = np.recfromcsv(data, missing_values='N/A',) control = np.array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)]) assert_(isinstance(test, np.recarray)) assert_equal(test, control) # data = TextIO('A,B\n0,1\n2,3') dtype = [('a', int), ('b', float)] test = np.recfromcsv(data, missing_values='N/A', dtype=dtype) control = np.array([(0, 1), (2, 3)], dtype=dtype) assert_(isinstance(test, np.recarray)) assert_equal(test, control) #gh-10394 data = TextIO('color\n"red"\n"blue"') test = np.recfromcsv(data, converters={0: lambda x: x.strip(b'\"')}) control = np.array([('red',), ('blue',)], dtype=[('color', (bytes, 4))]) assert_equal(test.dtype, control.dtype) assert_equal(test, control) def test_max_rows(self): # Test the `max_rows` keyword argument. data = '1 2\n3 4\n5 6\n7 8\n9 10\n' txt = TextIO(data) a1 = np.genfromtxt(txt, max_rows=3) a2 = np.genfromtxt(txt) assert_equal(a1, [[1, 2], [3, 4], [5, 6]]) assert_equal(a2, [[7, 8], [9, 10]]) # max_rows must be at least 1. assert_raises(ValueError, np.genfromtxt, TextIO(data), max_rows=0) # An input with several invalid rows. data = '1 1\n2 2\n0 \n3 3\n4 4\n5 \n6 \n7 \n' test = np.genfromtxt(TextIO(data), max_rows=2) control = np.array([[1., 1.], [2., 2.]]) assert_equal(test, control) # Test keywords conflict assert_raises(ValueError, np.genfromtxt, TextIO(data), skip_footer=1, max_rows=4) # Test with invalid value assert_raises(ValueError, np.genfromtxt, TextIO(data), max_rows=4) # Test with invalid not raise with suppress_warnings() as sup: sup.filter(ConversionWarning) test = np.genfromtxt(TextIO(data), max_rows=4, invalid_raise=False) control = np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]]) assert_equal(test, control) test = np.genfromtxt(TextIO(data), max_rows=5, invalid_raise=False) control = np.array([[1., 1.], [2., 2.], [3., 3.], [4., 4.]]) assert_equal(test, control) # Structured array with field names. data = 'a b\n#c d\n1 1\n2 2\n#0 \n3 3\n4 4\n5 5\n' # Test with header, names and comments txt = TextIO(data) test = np.genfromtxt(txt, skip_header=1, max_rows=3, names=True) control = np.array([(1.0, 1.0), (2.0, 2.0), (3.0, 3.0)], dtype=[('c', '<f8'), ('d', '<f8')]) assert_equal(test, control) # To continue reading the same "file", don't use skip_header or # names, and use the previously determined dtype. test = np.genfromtxt(txt, max_rows=None, dtype=test.dtype) control = np.array([(4.0, 4.0), (5.0, 5.0)], dtype=[('c', '<f8'), ('d', '<f8')]) assert_equal(test, control) def test_gft_using_filename(self): # Test that we can load data from a filename as well as a file # object tgt = np.arange(6).reshape((2, 3)) linesep = ('\n', '\r\n', '\r') for sep in linesep: data = '0 1 2' + sep + '3 4 5' with temppath() as name: with open(name, 'w') as f: f.write(data) res = np.genfromtxt(name) assert_array_equal(res, tgt) def test_gft_from_gzip(self): # Test that we can load data from a gzipped file wanted = np.arange(6).reshape((2, 3)) linesep = ('\n', '\r\n', '\r') for sep in linesep: data = '0 1 2' + sep + '3 4 5' s = BytesIO() with gzip.GzipFile(fileobj=s, mode='w') as g: g.write(asbytes(data)) with temppath(suffix='.gz2') as name: with open(name, 'w') as f: f.write(data) assert_array_equal(np.genfromtxt(name), wanted) def test_gft_using_generator(self): # gft doesn't work with unicode. def count(): for i in range(10): yield asbytes("%d" % i) res = np.genfromtxt(count()) assert_array_equal(res, np.arange(10)) def test_auto_dtype_largeint(self): # Regression test for numpy/numpy#5635 whereby large integers could # cause OverflowErrors. # Test the automatic definition of the output dtype # # 266 = 73786976294838206464 => should convert to float # 234 = 17179869184 => should convert to int64 # 210 = 1024 => should convert to int (int32 on 32-bit systems, # int64 on 64-bit systems) data = TextIO('73786976294838206464 17179869184 1024') test = np.genfromtxt(data, dtype=None) assert_equal(test.dtype.names, ['f0', 'f1', 'f2']) assert_(test.dtype['f0'] == float) assert_(test.dtype['f1'] == np.int64) assert_(test.dtype['f2'] == np.int_) assert_allclose(test['f0'], 73786976294838206464.) assert_equal(test['f1'], 17179869184) assert_equal(test['f2'], 1024) def test_unpack_float_data(self): txt = TextIO("1,2,3\n4,5,6\n7,8,9\n0.0,1.0,2.0") a, b, c = np.loadtxt(txt, delimiter=",", unpack=True) assert_array_equal(a, np.array([1.0, 4.0, 7.0, 0.0])) assert_array_equal(b, np.array([2.0, 5.0, 8.0, 1.0])) assert_array_equal(c, np.array([3.0, 6.0, 9.0, 2.0])) def test_unpack_structured(self): # Regression test for gh-4341 # Unpacking should work on structured arrays txt = TextIO("M 21 72\nF 35 58") dt = {'names': ('a', 'b', 'c'), 'formats': ('S1', 'i4', 'f4')} a, b, c = np.genfromtxt(txt, dtype=dt, unpack=True) assert_equal(a.dtype, np.dtype('S1')) assert_equal(b.dtype, np.dtype('i4')) assert_equal(c.dtype, np.dtype('f4')) assert_array_equal(a, np.array([b'M', b'F'])) assert_array_equal(b, np.array([21, 35])) assert_array_equal(c, np.array([72., 58.])) def test_unpack_auto_dtype(self): # Regression test for gh-4341 # Unpacking should work when dtype=None txt = TextIO("M 21 72.\nF 35 58.") expected = (np.array(["M", "F"]), np.array([21, 35]), np.array([72., 58.])) test = np.genfromtxt(txt, dtype=None, unpack=True, encoding="utf-8") for arr, result in zip(expected, test): assert_array_equal(arr, result) assert_equal(arr.dtype, result.dtype) def test_unpack_single_name(self): # Regression test for gh-4341 # Unpacking should work when structured dtype has only one field txt = TextIO("21\n35") dt = {'names': ('a',), 'formats': ('i4',)} expected = np.array([21, 35], dtype=np.int32) test = np.genfromtxt(txt, dtype=dt, unpack=True) assert_array_equal(expected, test) assert_equal(expected.dtype, test.dtype) def test_squeeze_scalar(self): # Regression test for gh-4341 # Unpacking a scalar should give zero-dim output, # even if dtype is structured txt = TextIO("1") dt = {'names': ('a',), 'formats': ('i4',)} expected = np.array((1,), dtype=np.int32) test = np.genfromtxt(txt, dtype=dt, unpack=True) assert_array_equal(expected, test) assert_equal((), test.shape) assert_equal(expected.dtype, test.dtype) @pytest.mark.parametrize("ndim", [0, 1, 2]) def test_ndmin_keyword(self, ndim: int): # lets have the same behaviour of ndmin as loadtxt # as they should be the same for non-missing values txt = "42" a = np.loadtxt(StringIO(txt), ndmin=ndim) b = np.genfromtxt(StringIO(txt), ndmin=ndim) assert_array_equal(a, b) class TestPathUsage: # Test that pathlib.Path can be used def test_loadtxt(self): with temppath(suffix='.txt') as path: path = Path(path) a = np.array([[1.1, 2], [3, 4]]) np.savetxt(path, a) x = np.loadtxt(path) assert_array_equal(x, a) def test_save_load(self): # Test that pathlib.Path instances can be used with save. with temppath(suffix='.npy') as path: path = Path(path) a = np.array([[1, 2], [3, 4]], int) np.save(path, a) data = np.load(path) assert_array_equal(data, a) def test_save_load_memmap(self): # Test that pathlib.Path instances can be loaded mem-mapped. with temppath(suffix='.npy') as path: path = Path(path) a = np.array([[1, 2], [3, 4]], int) np.save(path, a) data = np.load(path, mmap_mode='r') assert_array_equal(data, a) # close the mem-mapped file del data if IS_PYPY: break_cycles() break_cycles() @pytest.mark.xfail(IS_WASM, reason="memmap doesn't work correctly") def test_save_load_memmap_readwrite(self): # Test that pathlib.Path instances can be written mem-mapped. with temppath(suffix='.npy') as path: path = Path(path) a = np.array([[1, 2], [3, 4]], int) np.save(path, a) b = np.load(path, mmap_mode='r+') a[0][0] = 5 b[0][0] = 5 del b # closes the file if IS_PYPY: break_cycles() break_cycles() data = np.load(path) assert_array_equal(data, a) def test_savez_load(self): # Test that pathlib.Path instances can be used with savez. with temppath(suffix='.npz') as path: path = Path(path) np.savez(path, lab='place holder') with np.load(path) as data: assert_array_equal(data['lab'], 'place holder') def test_savez_compressed_load(self): # Test that pathlib.Path instances can be used with savez. with temppath(suffix='.npz') as path: path = Path(path) np.savez_compressed(path, lab='place holder') data = np.load(path) assert_array_equal(data['lab'], 'place holder') data.close() def test_genfromtxt(self): with temppath(suffix='.txt') as path: path = Path(path) a = np.array([(1, 2), (3, 4)]) np.savetxt(path, a) data = np.genfromtxt(path) assert_array_equal(a, data) def test_recfromtxt(self): with temppath(suffix='.txt') as path: path = Path(path) with path.open('w') as f: f.write('A,B\n0,1\n2,3') kwargs = dict(delimiter=",", missing_values="N/A", names=True) test = np.recfromtxt(path, kwargs) control = np.array([(0, 1), (2, 3)], dtype=[('A', int), ('B', int)]) assert_(isinstance(test, np.recarray)) assert_equal(test, control) def test_recfromcsv(self): with temppath(suffix='.txt') as path: path = Path(path) with path.open('w') as f: f.write('A,B\n0,1\n2,3') kwargs = dict(missing_values="N/A", names=True, case_sensitive=True) test = np.recfromcsv(path, dtype=None, kwargs) control = np.array([(0, 1), (2, 3)], dtype=[('A', int), ('B', int)]) assert_(isinstance(test, np.recarray)) assert_equal(test, control) def test_gzip_load(): a = np.random.random((5, 5)) s = BytesIO() f = gzip.GzipFile(fileobj=s, mode="w") np.save(f, a) f.close() s.seek(0) f = gzip.GzipFile(fileobj=s, mode="r") assert_array_equal(np.load(f), a) # These next two classes encode the minimal API needed to save()/load() arrays. # The `test_ducktyping` ensures they work correctly class JustWriter: def __init__(self, base): self.base = base def write(self, s): return self.base.write(s) def flush(self): return self.base.flush() class JustReader: def __init__(self, base): self.base = base def read(self, n): return self.base.read(n) def seek(self, off, whence=0): return self.base.seek(off, whence) def test_ducktyping(): a = np.random.random((5, 5)) s = BytesIO() f = JustWriter(s) np.save(f, a) f.flush() s.seek(0) f = JustReader(s) assert_array_equal(np.load(f), a) def test_gzip_loadtxt(): # Thanks to another windows brokenness, we can't use # NamedTemporaryFile: a file created from this function cannot be # reopened by another open call. So we first put the gzipped string # of the test reference array, write it to a securely opened file, # which is then read from by the loadtxt function s = BytesIO() g = gzip.GzipFile(fileobj=s, mode='w') g.write(b'1 2 3\n') g.close() s.seek(0) with temppath(suffix='.gz') as name: with open(name, 'wb') as f: f.write(s.read()) res = np.loadtxt(name) s.close() assert_array_equal(res, [1, 2, 3]) def test_gzip_loadtxt_from_string(): s = BytesIO() f = gzip.GzipFile(fileobj=s, mode="w") f.write(b'1 2 3\n') f.close() s.seek(0) f = gzip.GzipFile(fileobj=s, mode="r") assert_array_equal(np.loadtxt(f), [1, 2, 3]) def test_npzfile_dict(): s = BytesIO() x = np.zeros((3, 3)) y = np.zeros((3, 3)) np.savez(s, x=x, y=y) s.seek(0) z = np.load(s) assert_('x' in z) assert_('y' in z) assert_('x' in z.keys()) assert_('y' in z.keys()) for f, a in z.items(): assert_(f in ['x', 'y']) assert_equal(a.shape, (3, 3)) assert_(len(z.items()) == 2) for f in z: assert_(f in ['x', 'y']) assert_('x' in z.keys()) @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts") def test_load_refcount(): # Check that objects returned by np.load are directly freed based on # their refcount, rather than needing the gc to collect them. f = BytesIO() np.savez(f, [1, 2, 3]) f.seek(0) with assert_no_gc_cycles(): np.load(f) f.seek(0) dt = [("a", 'u1', 2), ("b", 'u1', 2)] with assert_no_gc_cycles(): x = np.loadtxt(TextIO("0 1 2 3"), dtype=dt) assert_equal(x, np.array([((0, 1), (2, 3))], dtype=dt)) def test_load_multiple_arrays_until_eof(): f = BytesIO() np.save(f, 1) np.save(f, 2) f.seek(0) assert np.load(f) == 1 assert np.load(f) == 2 with pytest.raises(EOFError): np.load(f)	105.36 KB	2026-01-20 12:58
📄 test_loadtxt.py """ Tests specific to `np.loadtxt` added during the move of loadtxt to be backed by C code. These tests complement those found in `test_io.py`. """ import sys import os import pytest from tempfile import NamedTemporaryFile, mkstemp from io import StringIO import numpy as np from numpy.ma.testutils import assert_equal from numpy.testing import assert_array_equal, HAS_REFCOUNT, IS_PYPY def test_scientific_notation(): """Test that both 'e' and 'E' are parsed correctly.""" data = StringIO( ( "1.0e-1,2.0E1,3.0\n" "4.0e-2,5.0E-1,6.0\n" "7.0e-3,8.0E1,9.0\n" "0.0e-4,1.0E-1,2.0" ) ) expected = np.array( [[0.1, 20., 3.0], [0.04, 0.5, 6], [0.007, 80., 9], [0, 0.1, 2]] ) assert_array_equal(np.loadtxt(data, delimiter=","), expected) @pytest.mark.parametrize("comment", ["..", "//", "@-", "this is a comment:"]) def test_comment_multiple_chars(comment): content = "# IGNORE\n1.5, 2.5# ABC\n3.0,4.0# XXX\n5.5,6.0\n" txt = StringIO(content.replace("#", comment)) a = np.loadtxt(txt, delimiter=",", comments=comment) assert_equal(a, [[1.5, 2.5], [3.0, 4.0], [5.5, 6.0]]) @pytest.fixture def mixed_types_structured(): """ Fixture providing hetergeneous input data with a structured dtype, along with the associated structured array. """ data = StringIO( ( "1000;2.4;alpha;-34\n" "2000;3.1;beta;29\n" "3500;9.9;gamma;120\n" "4090;8.1;delta;0\n" "5001;4.4;epsilon;-99\n" "6543;7.8;omega;-1\n" ) ) dtype = np.dtype( [('f0', np.uint16), ('f1', np.float64), ('f2', 'S7'), ('f3', np.int8)] ) expected = np.array( [ (1000, 2.4, "alpha", -34), (2000, 3.1, "beta", 29), (3500, 9.9, "gamma", 120), (4090, 8.1, "delta", 0), (5001, 4.4, "epsilon", -99), (6543, 7.8, "omega", -1) ], dtype=dtype ) return data, dtype, expected @pytest.mark.parametrize('skiprows', [0, 1, 2, 3]) def test_structured_dtype_and_skiprows_no_empty_lines( skiprows, mixed_types_structured): data, dtype, expected = mixed_types_structured a = np.loadtxt(data, dtype=dtype, delimiter=";", skiprows=skiprows) assert_array_equal(a, expected[skiprows:]) def test_unpack_structured(mixed_types_structured): data, dtype, expected = mixed_types_structured a, b, c, d = np.loadtxt(data, dtype=dtype, delimiter=";", unpack=True) assert_array_equal(a, expected["f0"]) assert_array_equal(b, expected["f1"]) assert_array_equal(c, expected["f2"]) assert_array_equal(d, expected["f3"]) def test_structured_dtype_with_shape(): dtype = np.dtype([("a", "u1", 2), ("b", "u1", 2)]) data = StringIO("0,1,2,3\n6,7,8,9\n") expected = np.array([((0, 1), (2, 3)), ((6, 7), (8, 9))], dtype=dtype) assert_array_equal(np.loadtxt(data, delimiter=",", dtype=dtype), expected) def test_structured_dtype_with_multi_shape(): dtype = np.dtype([("a", "u1", (2, 2))]) data = StringIO("0 1 2 3\n") expected = np.array([(((0, 1), (2, 3)),)], dtype=dtype) assert_array_equal(np.loadtxt(data, dtype=dtype), expected) def test_nested_structured_subarray(): # Test from gh-16678 point = np.dtype([('x', float), ('y', float)]) dt = np.dtype([('code', int), ('points', point, (2,))]) data = StringIO("100,1,2,3,4\n200,5,6,7,8\n") expected = np.array( [ (100, [(1., 2.), (3., 4.)]), (200, [(5., 6.), (7., 8.)]), ], dtype=dt ) assert_array_equal(np.loadtxt(data, dtype=dt, delimiter=","), expected) def test_structured_dtype_offsets(): # An aligned structured dtype will have additional padding dt = np.dtype("i1, i4, i1, i4, i1, i4", align=True) data = StringIO("1,2,3,4,5,6\n7,8,9,10,11,12\n") expected = np.array([(1, 2, 3, 4, 5, 6), (7, 8, 9, 10, 11, 12)], dtype=dt) assert_array_equal(np.loadtxt(data, delimiter=",", dtype=dt), expected) @pytest.mark.parametrize("param", ("skiprows", "max_rows")) def test_exception_negative_row_limits(param): """skiprows and max_rows should raise for negative parameters.""" with pytest.raises(ValueError, match="argument must be nonnegative"): np.loadtxt("foo.bar", {param: -3}) @pytest.mark.parametrize("param", ("skiprows", "max_rows")) def test_exception_noninteger_row_limits(param): with pytest.raises(TypeError, match="argument must be an integer"): np.loadtxt("foo.bar", {param: 1.0}) @pytest.mark.parametrize( "data, shape", [ ("1 2 3 4 5\n", (1, 5)), # Single row ("1\n2\n3\n4\n5\n", (5, 1)), # Single column ] ) def test_ndmin_single_row_or_col(data, shape): arr = np.array([1, 2, 3, 4, 5]) arr2d = arr.reshape(shape) assert_array_equal(np.loadtxt(StringIO(data), dtype=int), arr) assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=0), arr) assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=1), arr) assert_array_equal(np.loadtxt(StringIO(data), dtype=int, ndmin=2), arr2d) @pytest.mark.parametrize("badval", [-1, 3, None, "plate of shrimp"]) def test_bad_ndmin(badval): with pytest.raises(ValueError, match="Illegal value of ndmin keyword"): np.loadtxt("foo.bar", ndmin=badval) @pytest.mark.parametrize( "ws", ( " ", # space "\t", # tab "\u2003", # em "\u00A0", # non-break "\u3000", # ideographic space ) ) def test_blank_lines_spaces_delimit(ws): txt = StringIO( f"1 2{ws}30\n\n{ws}\n" f"4 5 60{ws}\n {ws} \n" f"7 8 {ws} 90\n # comment\n" f"3 2 1" ) # NOTE: It is unclear that the ` # comment` should succeed. Except # for delimiter=None, which should use any whitespace (and maybe # should just be implemented closer to Python expected = np.array([[1, 2, 30], [4, 5, 60], [7, 8, 90], [3, 2, 1]]) assert_equal( np.loadtxt(txt, dtype=int, delimiter=None, comments="#"), expected ) def test_blank_lines_normal_delimiter(): txt = StringIO('1,2,30\n\n4,5,60\n\n7,8,90\n# comment\n3,2,1') expected = np.array([[1, 2, 30], [4, 5, 60], [7, 8, 90], [3, 2, 1]]) assert_equal( np.loadtxt(txt, dtype=int, delimiter=',', comments="#"), expected ) @pytest.mark.parametrize("dtype", (float, object)) def test_maxrows_no_blank_lines(dtype): txt = StringIO("1.5,2.5\n3.0,4.0\n5.5,6.0") res = np.loadtxt(txt, dtype=dtype, delimiter=",", max_rows=2) assert_equal(res.dtype, dtype) assert_equal(res, np.array([["1.5", "2.5"], ["3.0", "4.0"]], dtype=dtype)) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") @pytest.mark.parametrize("dtype", (np.dtype("f8"), np.dtype("i2"))) def test_exception_message_bad_values(dtype): txt = StringIO("1,2\n3,XXX\n5,6") msg = f"could not convert string 'XXX' to {dtype} at row 1, column 2" with pytest.raises(ValueError, match=msg): np.loadtxt(txt, dtype=dtype, delimiter=",") def test_converters_negative_indices(): txt = StringIO('1.5,2.5\n3.0,XXX\n5.5,6.0') conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)} expected = np.array([[1.5, 2.5], [3.0, np.nan], [5.5, 6.0]]) res = np.loadtxt( txt, dtype=np.float64, delimiter=",", converters=conv, encoding=None ) assert_equal(res, expected) def test_converters_negative_indices_with_usecols(): txt = StringIO('1.5,2.5,3.5\n3.0,4.0,XXX\n5.5,6.0,7.5\n') conv = {-1: lambda s: np.nan if s == 'XXX' else float(s)} expected = np.array([[1.5, 3.5], [3.0, np.nan], [5.5, 7.5]]) res = np.loadtxt( txt, dtype=np.float64, delimiter=",", converters=conv, usecols=[0, -1], encoding=None, ) assert_equal(res, expected) # Second test with variable number of rows: res = np.loadtxt(StringIO('''0,1,2\n0,1,2,3,4'''), delimiter=",", usecols=[0, -1], converters={-1: (lambda x: -1)}) assert_array_equal(res, [[0, -1], [0, -1]]) def test_ragged_error(): rows = ["1,2,3", "1,2,3", "4,3,2,1"] with pytest.raises(ValueError, match="the number of columns changed from 3 to 4 at row 3"): np.loadtxt(rows, delimiter=",") def test_ragged_usecols(): # usecols, and negative ones, work even with varying number of columns. txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n") expected = np.array([[0, 0], [0, 0], [0, 0]]) res = np.loadtxt(txt, dtype=float, delimiter=",", usecols=[0, -2]) assert_equal(res, expected) txt = StringIO("0,0,XXX\n0\n0,XXX,XXX,0,XXX\n") with pytest.raises(ValueError, match="invalid column index -2 at row 2 with 1 columns"): # There is no -2 column in the second row: np.loadtxt(txt, dtype=float, delimiter=",", usecols=[0, -2]) def test_empty_usecols(): txt = StringIO("0,0,XXX\n0,XXX,0,XXX\n0,XXX,XXX,0,XXX\n") res = np.loadtxt(txt, dtype=np.dtype([]), delimiter=",", usecols=[]) assert res.shape == (3,) assert res.dtype == np.dtype([]) @pytest.mark.parametrize("c1", ["a", "の", "🫕"]) @pytest.mark.parametrize("c2", ["a", "の", "🫕"]) def test_large_unicode_characters(c1, c2): # c1 and c2 span ascii, 16bit and 32bit range. txt = StringIO(f"a,{c1},c,1.0\ne,{c2},2.0,g") res = np.loadtxt(txt, dtype=np.dtype('U12'), delimiter=",") expected = np.array( [f"a,{c1},c,1.0".split(","), f"e,{c2},2.0,g".split(",")], dtype=np.dtype('U12') ) assert_equal(res, expected) def test_unicode_with_converter(): txt = StringIO("cat,dog\nαβγ,δεζ\nabc,def\n") conv = {0: lambda s: s.upper()} res = np.loadtxt( txt, dtype=np.dtype("U12"), converters=conv, delimiter=",", encoding=None ) expected = np.array([['CAT', 'dog'], ['ΑΒΓ', 'δεζ'], ['ABC', 'def']]) assert_equal(res, expected) def test_converter_with_structured_dtype(): txt = StringIO('1.5,2.5,Abc\n3.0,4.0,dEf\n5.5,6.0,ghI\n') dt = np.dtype([('m', np.int32), ('r', np.float32), ('code', 'U8')]) conv = {0: lambda s: int(10float(s)), -1: lambda s: s.upper()} res = np.loadtxt(txt, dtype=dt, delimiter=",", converters=conv) expected = np.array( [(15, 2.5, 'ABC'), (30, 4.0, 'DEF'), (55, 6.0, 'GHI')], dtype=dt ) assert_equal(res, expected) def test_converter_with_unicode_dtype(): """ With the default 'bytes' encoding, tokens are encoded prior to being passed to the converter. This means that the output of the converter may be bytes instead of unicode as expected by `read_rows`. This test checks that outputs from the above scenario are properly decoded prior to parsing by `read_rows`. """ txt = StringIO('abc,def\nrst,xyz') conv = bytes.upper res = np.loadtxt( txt, dtype=np.dtype("U3"), converters=conv, delimiter=",") expected = np.array([['ABC', 'DEF'], ['RST', 'XYZ']]) assert_equal(res, expected) def test_read_huge_row(): row = "1.5, 2.5," 50000 row = row[:-1] + "\n" txt = StringIO(row * 2) res = np.loadtxt(txt, delimiter=",", dtype=float) assert_equal(res, np.tile([1.5, 2.5], (2, 50000))) @pytest.mark.parametrize("dtype", "edfgFDG") def test_huge_float(dtype): # Covers a non-optimized path that is rarely taken: field = "0" * 1000 + ".123456789" dtype = np.dtype(dtype) value = np.loadtxt([field], dtype=dtype)[()] assert value == dtype.type("0.123456789") @pytest.mark.parametrize( ("given_dtype", "expected_dtype"), [ ("S", np.dtype("S5")), ("U", np.dtype("U5")), ], ) def test_string_no_length_given(given_dtype, expected_dtype): """ The given dtype is just 'S' or 'U' with no length. In these cases, the length of the resulting dtype is determined by the longest string found in the file. """ txt = StringIO("AAA,5-1\nBBBBB,0-3\nC,4-9\n") res = np.loadtxt(txt, dtype=given_dtype, delimiter=",") expected = np.array( [['AAA', '5-1'], ['BBBBB', '0-3'], ['C', '4-9']], dtype=expected_dtype ) assert_equal(res, expected) assert_equal(res.dtype, expected_dtype) def test_float_conversion(): """ Some tests that the conversion to float64 works as accurately as the Python built-in `float` function. In a naive version of the float parser, these strings resulted in values that were off by an ULP or two. """ strings = [ '0.9999999999999999', '9876543210.123456', '5.43215432154321e+300', '0.901', '0.333', ] txt = StringIO('\n'.join(strings)) res = np.loadtxt(txt) expected = np.array([float(s) for s in strings]) assert_equal(res, expected) def test_bool(): # Simple test for bool via integer txt = StringIO("1, 0\n10, -1") res = np.loadtxt(txt, dtype=bool, delimiter=",") assert res.dtype == bool assert_array_equal(res, [[True, False], [True, True]]) # Make sure we use only 1 and 0 on the byte level: assert_array_equal(res.view(np.uint8), [[1, 0], [1, 1]]) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"]) @pytest.mark.filterwarnings("error:.integer via a float.:DeprecationWarning") def test_integer_signs(dtype): dtype = np.dtype(dtype) assert np.loadtxt(["+2"], dtype=dtype) == 2 if dtype.kind == "u": with pytest.raises(ValueError): np.loadtxt(["-1\n"], dtype=dtype) else: assert np.loadtxt(["-2\n"], dtype=dtype) == -2 for sign in ["++", "+-", "--", "-+"]: with pytest.raises(ValueError): np.loadtxt([f"{sign}2\n"], dtype=dtype) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"]) @pytest.mark.filterwarnings("error:.integer via a float.:DeprecationWarning") def test_implicit_cast_float_to_int_fails(dtype): txt = StringIO("1.0, 2.1, 3.7\n4, 5, 6") with pytest.raises(ValueError): np.loadtxt(txt, dtype=dtype, delimiter=",") @pytest.mark.parametrize("dtype", (np.complex64, np.complex128)) @pytest.mark.parametrize("with_parens", (False, True)) def test_complex_parsing(dtype, with_parens): s = "(1.0-2.5j),3.75,(7+-5.0j)\n(4),(-19e2j),(0)" if not with_parens: s = s.replace("(", "").replace(")", "") res = np.loadtxt(StringIO(s), dtype=dtype, delimiter=",") expected = np.array( [[1.0-2.5j, 3.75, 7-5j], [4.0, -1900j, 0]], dtype=dtype ) assert_equal(res, expected) def test_read_from_generator(): def gen(): for i in range(4): yield f"{i},{2i},{i2}" res = np.loadtxt(gen(), dtype=int, delimiter=",") expected = np.array([[0, 0, 0], [1, 2, 1], [2, 4, 4], [3, 6, 9]]) assert_equal(res, expected) def test_read_from_generator_multitype(): def gen(): for i in range(3): yield f"{i} {i / 4}" res = np.loadtxt(gen(), dtype="i, d", delimiter=" ") expected = np.array([(0, 0.0), (1, 0.25), (2, 0.5)], dtype="i, d") assert_equal(res, expected) def test_read_from_bad_generator(): def gen(): for entry in ["1,2", b"3, 5", 12738]: yield entry with pytest.raises( TypeError, match=r"non-string returned while reading data"): np.loadtxt(gen(), dtype="i, i", delimiter=",") @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts") def test_object_cleanup_on_read_error(): sentinel = object() already_read = 0 def conv(x): nonlocal already_read if already_read > 4999: raise ValueError("failed half-way through!") already_read += 1 return sentinel txt = StringIO("x\n" 10000) with pytest.raises(ValueError, match="at row 5000, column 1"): np.loadtxt(txt, dtype=object, converters={0: conv}) assert sys.getrefcount(sentinel) == 2 @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_character_not_bytes_compatible(): """Test exception when a character cannot be encoded as 'S'.""" data = StringIO("–") # == \u2013 with pytest.raises(ValueError): np.loadtxt(data, dtype="S5") @pytest.mark.parametrize("conv", (0, [float], "")) def test_invalid_converter(conv): msg = ( "converters must be a dictionary mapping columns to converter " "functions or a single callable." ) with pytest.raises(TypeError, match=msg): np.loadtxt(StringIO("1 2\n3 4"), converters=conv) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_converters_dict_raises_non_integer_key(): with pytest.raises(TypeError, match="keys of the converters dict"): np.loadtxt(StringIO("1 2\n3 4"), converters={"a": int}) with pytest.raises(TypeError, match="keys of the converters dict"): np.loadtxt(StringIO("1 2\n3 4"), converters={"a": int}, usecols=0) @pytest.mark.parametrize("bad_col_ind", (3, -3)) def test_converters_dict_raises_non_col_key(bad_col_ind): data = StringIO("1 2\n3 4") with pytest.raises(ValueError, match="converter specified for column"): np.loadtxt(data, converters={bad_col_ind: int}) def test_converters_dict_raises_val_not_callable(): with pytest.raises(TypeError, match="values of the converters dictionary must be callable"): np.loadtxt(StringIO("1 2\n3 4"), converters={0: 1}) @pytest.mark.parametrize("q", ('"', "'", "`")) def test_quoted_field(q): txt = StringIO( f"{q}alpha, x{q}, 2.5\n{q}beta, y{q}, 4.5\n{q}gamma, z{q}, 5.0\n" ) dtype = np.dtype([('f0', 'U8'), ('f1', np.float64)]) expected = np.array( [("alpha, x", 2.5), ("beta, y", 4.5), ("gamma, z", 5.0)], dtype=dtype ) res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar=q) assert_array_equal(res, expected) @pytest.mark.parametrize("q", ('"', "'", "`")) def test_quoted_field_with_whitepace_delimiter(q): txt = StringIO( f"{q}alpha, x{q} 2.5\n{q}beta, y{q} 4.5\n{q}gamma, z{q} 5.0\n" ) dtype = np.dtype([('f0', 'U8'), ('f1', np.float64)]) expected = np.array( [("alpha, x", 2.5), ("beta, y", 4.5), ("gamma, z", 5.0)], dtype=dtype ) res = np.loadtxt(txt, dtype=dtype, delimiter=None, quotechar=q) assert_array_equal(res, expected) def test_quote_support_default(): """Support for quoted fields is disabled by default.""" txt = StringIO('"lat,long", 45, 30\n') dtype = np.dtype([('f0', 'U24'), ('f1', np.float64), ('f2', np.float64)]) with pytest.raises(ValueError, match="the dtype passed requires 3 columns but 4 were"): np.loadtxt(txt, dtype=dtype, delimiter=",") # Enable quoting support with non-None value for quotechar param txt.seek(0) expected = np.array([("lat,long", 45., 30.)], dtype=dtype) res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar='"') assert_array_equal(res, expected) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_quotechar_multichar_error(): txt = StringIO("1,2\n3,4") msg = r".must be a single unicode character or None" with pytest.raises(TypeError, match=msg): np.loadtxt(txt, delimiter=",", quotechar="''") def test_comment_multichar_error_with_quote(): txt = StringIO("1,2\n3,4") msg = ( "when multiple comments or a multi-character comment is given, " "quotes are not supported." ) with pytest.raises(ValueError, match=msg): np.loadtxt(txt, delimiter=",", comments="123", quotechar='"') with pytest.raises(ValueError, match=msg): np.loadtxt(txt, delimiter=",", comments=["#", "%"], quotechar='"') # A single character string in a tuple is unpacked though: res = np.loadtxt(txt, delimiter=",", comments=("#",), quotechar="'") assert_equal(res, [[1, 2], [3, 4]]) def test_structured_dtype_with_quotes(): data = StringIO( ( "1000;2.4;'alpha';-34\n" "2000;3.1;'beta';29\n" "3500;9.9;'gamma';120\n" "4090;8.1;'delta';0\n" "5001;4.4;'epsilon';-99\n" "6543;7.8;'omega';-1\n" ) ) dtype = np.dtype( [('f0', np.uint16), ('f1', np.float64), ('f2', 'S7'), ('f3', np.int8)] ) expected = np.array( [ (1000, 2.4, "alpha", -34), (2000, 3.1, "beta", 29), (3500, 9.9, "gamma", 120), (4090, 8.1, "delta", 0), (5001, 4.4, "epsilon", -99), (6543, 7.8, "omega", -1) ], dtype=dtype ) res = np.loadtxt(data, dtype=dtype, delimiter=";", quotechar="'") assert_array_equal(res, expected) def test_quoted_field_is_not_empty(): txt = StringIO('1\n\n"4"\n""') expected = np.array(["1", "4", ""], dtype="U1") res = np.loadtxt(txt, delimiter=",", dtype="U1", quotechar='"') assert_equal(res, expected) def test_quoted_field_is_not_empty_nonstrict(): # Same as test_quoted_field_is_not_empty but check that we are not strict # about missing closing quote (this is the `csv.reader` default also) txt = StringIO('1\n\n"4"\n"') expected = np.array(["1", "4", ""], dtype="U1") res = np.loadtxt(txt, delimiter=",", dtype="U1", quotechar='"') assert_equal(res, expected) def test_consecutive_quotechar_escaped(): txt = StringIO('"Hello, my name is ""Monty""!"') expected = np.array('Hello, my name is "Monty"!', dtype="U40") res = np.loadtxt(txt, dtype="U40", delimiter=",", quotechar='"') assert_equal(res, expected) @pytest.mark.parametrize("data", ("", "\n\n\n", "# 1 2 3\n# 4 5 6\n")) @pytest.mark.parametrize("ndmin", (0, 1, 2)) @pytest.mark.parametrize("usecols", [None, (1, 2, 3)]) def test_warn_on_no_data(data, ndmin, usecols): """Check that a UserWarning is emitted when no data is read from input.""" if usecols is not None: expected_shape = (0, 3) elif ndmin == 2: expected_shape = (0, 1) # guess a single column?! else: expected_shape = (0,) txt = StringIO(data) with pytest.warns(UserWarning, match="input contained no data"): res = np.loadtxt(txt, ndmin=ndmin, usecols=usecols) assert res.shape == expected_shape with NamedTemporaryFile(mode="w") as fh: fh.write(data) fh.seek(0) with pytest.warns(UserWarning, match="input contained no data"): res = np.loadtxt(txt, ndmin=ndmin, usecols=usecols) assert res.shape == expected_shape @pytest.mark.parametrize("skiprows", (2, 3)) def test_warn_on_skipped_data(skiprows): data = "1 2 3\n4 5 6" txt = StringIO(data) with pytest.warns(UserWarning, match="input contained no data"): np.loadtxt(txt, skiprows=skiprows) @pytest.mark.parametrize(["dtype", "value"], [ ("i2", 0x0001), ("u2", 0x0001), ("i4", 0x00010203), ("u4", 0x00010203), ("i8", 0x0001020304050607), ("u8", 0x0001020304050607), # The following values are constructed to lead to unique bytes: ("float16", 3.07e-05), ("float32", 9.2557e-41), ("complex64", 9.2557e-41+2.8622554e-29j), ("float64", -1.758571353180402e-24), # Here and below, the repr side-steps a small loss of precision in # complex `str` in PyPy (which is probably fine, as repr works): ("complex128", repr(5.406409232372729e-29-1.758571353180402e-24j)), # Use integer values that fit into double. Everything else leads to # problems due to longdoubles going via double and decimal strings # causing rounding errors. ("longdouble", 0x01020304050607), ("clongdouble", repr(0x01020304050607 + (0x00121314151617 1j))), ("U2", "\U00010203\U000a0b0c")]) @pytest.mark.parametrize("swap", [True, False]) def test_byteswapping_and_unaligned(dtype, value, swap): # Try to create "interesting" values within the valid unicode range: dtype = np.dtype(dtype) data = [f"x,{value}\n"] # repr as PyPy `str` truncates some if swap: dtype = dtype.newbyteorder() full_dt = np.dtype([("a", "S1"), ("b", dtype)], align=False) # The above ensures that the interesting "b" field is unaligned: assert full_dt.fields["b"][1] == 1 res = np.loadtxt(data, dtype=full_dt, delimiter=",", encoding=None, max_rows=1) # max-rows prevents over-allocation assert res["b"] == dtype.type(value) @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"] + "efdFD" + "?") def test_unicode_whitespace_stripping(dtype): # Test that all numeric types (and bool) strip whitespace correctly # \u202F is a narrow no-break space, `\n` is just a whitespace if quoted. # Currently, skip float128 as it did not always support this and has no # "custom" parsing: txt = StringIO(' 3 ,"\u202F2\n"') res = np.loadtxt(txt, dtype=dtype, delimiter=",", quotechar='"') assert_array_equal(res, np.array([3, 2]).astype(dtype)) @pytest.mark.parametrize("dtype", "FD") def test_unicode_whitespace_stripping_complex(dtype): # Complex has a few extra cases since it has two components and # parentheses line = " 1 , 2+3j , ( 4+5j ), ( 6+-7j ) , 8j , ( 9j ) \n" data = [line, line.replace(" ", "\u202F")] res = np.loadtxt(data, dtype=dtype, delimiter=',') assert_array_equal(res, np.array([[1, 2+3j, 4+5j, 6-7j, 8j, 9j]] * 2)) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") @pytest.mark.parametrize("dtype", "FD") @pytest.mark.parametrize("field", ["1 +2j", "1+ 2j", "1+2 j", "1+-+3", "(1j", "(1", "(1+2j", "1+2j)"]) def test_bad_complex(dtype, field): with pytest.raises(ValueError): np.loadtxt([field + "\n"], dtype=dtype, delimiter=",") @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"] + "efgdFDG" + "?") def test_nul_character_error(dtype): # Test that a \0 character is correctly recognized as an error even if # what comes before is valid (not everything gets parsed internally). if dtype.lower() == "g": pytest.xfail("longdouble/clongdouble assignment may misbehave.") with pytest.raises(ValueError): np.loadtxt(["1\000"], dtype=dtype, delimiter=",", quotechar='"') @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") @pytest.mark.parametrize("dtype", np.typecodes["AllInteger"] + "efgdFDG" + "?") def test_no_thousands_support(dtype): # Mainly to document behaviour, Python supports thousands like 1_1. # (e and G may end up using different conversion and support it, this is # a bug but happens...) if dtype == "e": pytest.skip("half assignment currently uses Python float converter") if dtype in "eG": pytest.xfail("clongdouble assignment is buggy (uses `complex`?).") assert int("1_1") == float("1_1") == complex("1_1") == 11 with pytest.raises(ValueError): np.loadtxt(["1_1\n"], dtype=dtype) @pytest.mark.parametrize("data", [ ["1,2\n", "2\n,3\n"], ["1,2\n", "2\r,3\n"]]) def test_bad_newline_in_iterator(data): # In NumPy <=1.22 this was accepted, because newlines were completely # ignored when the input was an iterable. This could be changed, but right # now, we raise an error. msg = "Found an unquoted embedded newline within a single line" with pytest.raises(ValueError, match=msg): np.loadtxt(data, delimiter=",") @pytest.mark.parametrize("data", [ ["1,2\n", "2,3\r\n"], # a universal newline ["1,2\n", "'2\n',3\n"], # a quoted newline ["1,2\n", "'2\r',3\n"], ["1,2\n", "'2\r\n',3\n"], ]) def test_good_newline_in_iterator(data): # The quoted newlines will be untransformed here, but are just whitespace. res = np.loadtxt(data, delimiter=",", quotechar="'") assert_array_equal(res, [[1., 2.], [2., 3.]]) @pytest.mark.parametrize("newline", ["\n", "\r", "\r\n"]) def test_universal_newlines_quoted(newline): # Check that universal newline support within the tokenizer is not applied # to quoted fields. (note that lines must end in newline or quoted # fields will not include a newline at all) data = ['1,"2\n"\n', '3,"4\n', '1"\n'] data = [row.replace("\n", newline) for row in data] res = np.loadtxt(data, dtype=object, delimiter=",", quotechar='"') assert_array_equal(res, [['1', f'2{newline}'], ['3', f'4{newline}1']]) def test_null_character(): # Basic tests to check that the NUL character is not special: res = np.loadtxt(["1\0002\0003\n", "4\0005\0006"], delimiter="\000") assert_array_equal(res, [[1, 2, 3], [4, 5, 6]]) # Also not as part of a field (avoid unicode/arrays as unicode strips \0) res = np.loadtxt(["1\000,2\000,3\n", "4\000,5\000,6"], delimiter=",", dtype=object) assert res.tolist() == [["1\000", "2\000", "3"], ["4\000", "5\000", "6"]] def test_iterator_fails_getting_next_line(): class BadSequence: def __len__(self): return 100 def __getitem__(self, item): if item == 50: raise RuntimeError("Bad things happened!") return f"{item}, {item+1}" with pytest.raises(RuntimeError, match="Bad things happened!"): np.loadtxt(BadSequence(), dtype=int, delimiter=",") class TestCReaderUnitTests: # These are internal tests for path that should not be possible to hit # unless things go very very wrong somewhere. def test_not_an_filelike(self): with pytest.raises(AttributeError, match=".read"): np.core._multiarray_umath._load_from_filelike( object(), dtype=np.dtype("i"), filelike=True) def test_filelike_read_fails(self): # Can only be reached if loadtxt opens the file, so it is hard to do # via the public interface (although maybe not impossible considering # the current "DataClass" backing). class BadFileLike: counter = 0 def read(self, size): self.counter += 1 if self.counter > 20: raise RuntimeError("Bad bad bad!") return "1,2,3\n" with pytest.raises(RuntimeError, match="Bad bad bad!"): np.core._multiarray_umath._load_from_filelike( BadFileLike(), dtype=np.dtype("i"), filelike=True) def test_filelike_bad_read(self): # Can only be reached if loadtxt opens the file, so it is hard to do # via the public interface (although maybe not impossible considering # the current "DataClass" backing). class BadFileLike: counter = 0 def read(self, size): return 1234 # not a string! with pytest.raises(TypeError, match="non-string returned while reading data"): np.core._multiarray_umath._load_from_filelike( BadFileLike(), dtype=np.dtype("i"), filelike=True) def test_not_an_iter(self): with pytest.raises(TypeError, match="error reading from object, expected an iterable"): np.core._multiarray_umath._load_from_filelike( object(), dtype=np.dtype("i"), filelike=False) def test_bad_type(self): with pytest.raises(TypeError, match="internal error: dtype must"): np.core._multiarray_umath._load_from_filelike( object(), dtype="i", filelike=False) def test_bad_encoding(self): with pytest.raises(TypeError, match="encoding must be a unicode"): np.core._multiarray_umath._load_from_filelike( object(), dtype=np.dtype("i"), filelike=False, encoding=123) @pytest.mark.parametrize("newline", ["\r", "\n", "\r\n"]) def test_manual_universal_newlines(self, newline): # This is currently not available to users, because we should always # open files with universal newlines enabled `newlines=None`. # (And reading from an iterator uses slightly different code paths.) # We have no real support for `newline="\r"` or `newline="\n" as the # user cannot specify those options. data = StringIO('0\n1\n"2\n"\n3\n4 #\n'.replace("\n", newline), newline="") res = np.core._multiarray_umath._load_from_filelike( data, dtype=np.dtype("U10"), filelike=True, quote='"', comment="#", skiplines=1) assert_array_equal(res[:, 0], ["1", f"2{newline}", "3", "4 "]) def test_delimiter_comment_collision_raises(): with pytest.raises(TypeError, match=".control characters.incompatible"): np.loadtxt(StringIO("1, 2, 3"), delimiter=",", comments=",") def test_delimiter_quotechar_collision_raises(): with pytest.raises(TypeError, match=".control characters.incompatible"): np.loadtxt(StringIO("1, 2, 3"), delimiter=",", quotechar=",") def test_comment_quotechar_collision_raises(): with pytest.raises(TypeError, match=".control characters.incompatible"): np.loadtxt(StringIO("1 2 3"), comments="#", quotechar="#") def test_delimiter_and_multiple_comments_collision_raises(): with pytest.raises( TypeError, match="Comment characters.cannot include the delimiter" ): np.loadtxt(StringIO("1, 2, 3"), delimiter=",", comments=["#", ","]) @pytest.mark.parametrize( "ws", ( " ", # space "\t", # tab "\u2003", # em "\u00A0", # non-break "\u3000", # ideographic space ) ) def test_collision_with_default_delimiter_raises(ws): with pytest.raises(TypeError, match=".control characters.incompatible"): np.loadtxt(StringIO(f"1{ws}2{ws}3\n4{ws}5{ws}6\n"), comments=ws) with pytest.raises(TypeError, match=".control characters.incompatible"): np.loadtxt(StringIO(f"1{ws}2{ws}3\n4{ws}5{ws}6\n"), quotechar=ws) @pytest.mark.parametrize("nl", ("\n", "\r")) def test_control_character_newline_raises(nl): txt = StringIO(f"1{nl}2{nl}3{nl}{nl}4{nl}5{nl}6{nl}{nl}") msg = "control character.cannot be a newline" with pytest.raises(TypeError, match=msg): np.loadtxt(txt, delimiter=nl) with pytest.raises(TypeError, match=msg): np.loadtxt(txt, comments=nl) with pytest.raises(TypeError, match=msg): np.loadtxt(txt, quotechar=nl) @pytest.mark.parametrize( ("generic_data", "long_datum", "unitless_dtype", "expected_dtype"), [ ("2012-03", "2013-01-15", "M8", "M8[D]"), # Datetimes ("spam-a-lot", "tis_but_a_scratch", "U", "U17"), # str ], ) @pytest.mark.parametrize("nrows", (10, 50000, 60000)) # lt, eq, gt chunksize def test_parametric_unit_discovery( generic_data, long_datum, unitless_dtype, expected_dtype, nrows ): """Check that the correct unit (e.g. month, day, second) is discovered from the data when a user specifies a unitless datetime.""" # Unit should be "D" (days) due to last entry data = [generic_data] 50000 + [long_datum] expected = np.array(data, dtype=expected_dtype) # file-like path txt = StringIO("\n".join(data)) a = np.loadtxt(txt, dtype=unitless_dtype) assert a.dtype == expected.dtype assert_equal(a, expected) # file-obj path fd, fname = mkstemp() os.close(fd) with open(fname, "w") as fh: fh.write("\n".join(data)) a = np.loadtxt(fname, dtype=unitless_dtype) os.remove(fname) assert a.dtype == expected.dtype assert_equal(a, expected) def test_str_dtype_unit_discovery_with_converter(): data = ["spam-a-lot"] * 60000 + ["XXXtis_but_a_scratch"] expected = np.array( ["spam-a-lot"] * 60000 + ["tis_but_a_scratch"], dtype="U17" ) conv = lambda s: s.strip("XXX") # file-like path txt = StringIO("\n".join(data)) a = np.loadtxt(txt, dtype="U", converters=conv, encoding=None) assert a.dtype == expected.dtype assert_equal(a, expected) # file-obj path fd, fname = mkstemp() os.close(fd) with open(fname, "w") as fh: fh.write("\n".join(data)) a = np.loadtxt(fname, dtype="U", converters=conv, encoding=None) os.remove(fname) assert a.dtype == expected.dtype assert_equal(a, expected) @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8), reason="PyPy bug in error formatting") def test_control_character_empty(): with pytest.raises(TypeError, match="Text reading control character must"): np.loadtxt(StringIO("1 2 3"), delimiter="") with pytest.raises(TypeError, match="Text reading control character must"): np.loadtxt(StringIO("1 2 3"), quotechar="") with pytest.raises(ValueError, match="comments cannot be an empty string"): np.loadtxt(StringIO("1 2 3"), comments="") with pytest.raises(ValueError, match="comments cannot be an empty string"): np.loadtxt(StringIO("1 2 3"), comments=["#", ""]) def test_control_characters_as_bytes(): """Byte control characters (comments, delimiter) are supported.""" a = np.loadtxt(StringIO("#header\n1,2,3"), comments=b"#", delimiter=b",") assert_equal(a, [1, 2, 3]) @pytest.mark.filterwarnings('ignore::UserWarning') def test_field_growing_cases(): # Test empty field appending/growing (each field still takes 1 character) # to see if the final field appending does not create issues. res = np.loadtxt([""], delimiter=",", dtype=bytes) assert len(res) == 0 for i in range(1, 1024): res = np.loadtxt(["," * i], delimiter=",", dtype=bytes) assert len(res) == i+1	37.66 KB	2026-01-20 12:58
📄 test_mixins.py import numbers import operator import numpy as np from numpy.testing import assert_, assert_equal, assert_raises # NOTE: This class should be kept as an exact copy of the example from the # docstring for NDArrayOperatorsMixin. class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin): def __init__(self, value): self.value = np.asarray(value) # One might also consider adding the built-in list type to this # list, to support operations like np.add(array_like, list) _HANDLED_TYPES = (np.ndarray, numbers.Number) def __array_ufunc__(self, ufunc, method, inputs, kwargs): out = kwargs.get('out', ()) for x in inputs + out: # Only support operations with instances of _HANDLED_TYPES. # Use ArrayLike instead of type(self) for isinstance to # allow subclasses that don't override __array_ufunc__ to # handle ArrayLike objects. if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)): return NotImplemented # Defer to the implementation of the ufunc on unwrapped values. inputs = tuple(x.value if isinstance(x, ArrayLike) else x for x in inputs) if out: kwargs['out'] = tuple( x.value if isinstance(x, ArrayLike) else x for x in out) result = getattr(ufunc, method)(inputs, kwargs) if type(result) is tuple: # multiple return values return tuple(type(self)(x) for x in result) elif method == 'at': # no return value return None else: # one return value return type(self)(result) def __repr__(self): return '%s(%r)' % (type(self).__name__, self.value) def wrap_array_like(result): if type(result) is tuple: return tuple(ArrayLike(r) for r in result) else: return ArrayLike(result) def _assert_equal_type_and_value(result, expected, err_msg=None): assert_equal(type(result), type(expected), err_msg=err_msg) if isinstance(result, tuple): assert_equal(len(result), len(expected), err_msg=err_msg) for result_item, expected_item in zip(result, expected): _assert_equal_type_and_value(result_item, expected_item, err_msg) else: assert_equal(result.value, expected.value, err_msg=err_msg) assert_equal(getattr(result.value, 'dtype', None), getattr(expected.value, 'dtype', None), err_msg=err_msg) _ALL_BINARY_OPERATORS = [ operator.lt, operator.le, operator.eq, operator.ne, operator.gt, operator.ge, operator.add, operator.sub, operator.mul, operator.truediv, operator.floordiv, operator.mod, divmod, pow, operator.lshift, operator.rshift, operator.and_, operator.xor, operator.or_, ] class TestNDArrayOperatorsMixin: def test_array_like_add(self): def check(result): _assert_equal_type_and_value(result, ArrayLike(0)) check(ArrayLike(0) + 0) check(0 + ArrayLike(0)) check(ArrayLike(0) + np.array(0)) check(np.array(0) + ArrayLike(0)) check(ArrayLike(np.array(0)) + 0) check(0 + ArrayLike(np.array(0))) check(ArrayLike(np.array(0)) + np.array(0)) check(np.array(0) + ArrayLike(np.array(0))) def test_inplace(self): array_like = ArrayLike(np.array([0])) array_like += 1 _assert_equal_type_and_value(array_like, ArrayLike(np.array([1]))) array = np.array([0]) array += ArrayLike(1) _assert_equal_type_and_value(array, ArrayLike(np.array([1]))) def test_opt_out(self): class OptOut: """Object that opts out of __array_ufunc__.""" __array_ufunc__ = None def __add__(self, other): return self def __radd__(self, other): return self array_like = ArrayLike(1) opt_out = OptOut() # supported operations assert_(array_like + opt_out is opt_out) assert_(opt_out + array_like is opt_out) # not supported with assert_raises(TypeError): # don't use the Python default, array_like = array_like + opt_out array_like += opt_out with assert_raises(TypeError): array_like - opt_out with assert_raises(TypeError): opt_out - array_like def test_subclass(self): class SubArrayLike(ArrayLike): """Should take precedence over ArrayLike.""" x = ArrayLike(0) y = SubArrayLike(1) _assert_equal_type_and_value(x + y, y) _assert_equal_type_and_value(y + x, y) def test_object(self): x = ArrayLike(0) obj = object() with assert_raises(TypeError): x + obj with assert_raises(TypeError): obj + x with assert_raises(TypeError): x += obj def test_unary_methods(self): array = np.array([-1, 0, 1, 2]) array_like = ArrayLike(array) for op in [operator.neg, operator.pos, abs, operator.invert]: _assert_equal_type_and_value(op(array_like), ArrayLike(op(array))) def test_forward_binary_methods(self): array = np.array([-1, 0, 1, 2]) array_like = ArrayLike(array) for op in _ALL_BINARY_OPERATORS: expected = wrap_array_like(op(array, 1)) actual = op(array_like, 1) err_msg = 'failed for operator {}'.format(op) _assert_equal_type_and_value(expected, actual, err_msg=err_msg) def test_reflected_binary_methods(self): for op in _ALL_BINARY_OPERATORS: expected = wrap_array_like(op(2, 1)) actual = op(2, ArrayLike(1)) err_msg = 'failed for operator {}'.format(op) _assert_equal_type_and_value(expected, actual, err_msg=err_msg) def test_matmul(self): array = np.array([1, 2], dtype=np.float64) array_like = ArrayLike(array) expected = ArrayLike(np.float64(5)) _assert_equal_type_and_value(expected, np.matmul(array_like, array)) _assert_equal_type_and_value( expected, operator.matmul(array_like, array)) _assert_equal_type_and_value( expected, operator.matmul(array, array_like)) def test_ufunc_at(self): array = ArrayLike(np.array([1, 2, 3, 4])) assert_(np.negative.at(array, np.array([0, 1])) is None) _assert_equal_type_and_value(array, ArrayLike([-1, -2, 3, 4])) def test_ufunc_two_outputs(self): mantissa, exponent = np.frexp(2 -3) expected = (ArrayLike(mantissa), ArrayLike(exponent)) _assert_equal_type_and_value( np.frexp(ArrayLike(2 -3)), expected) _assert_equal_type_and_value( np.frexp(ArrayLike(np.array(2 -3))), expected)	6.87 KB	2026-01-20 12:58
📄 test_nanfunctions.py import warnings import pytest import inspect import numpy as np from numpy.core.numeric import normalize_axis_tuple from numpy.lib.nanfunctions import _nan_mask, _replace_nan from numpy.testing import ( assert_, assert_equal, assert_almost_equal, assert_raises, assert_array_equal, suppress_warnings ) # Test data _ndat = np.array([[0.6244, np.nan, 0.2692, 0.0116, np.nan, 0.1170], [0.5351, -0.9403, np.nan, 0.2100, 0.4759, 0.2833], [np.nan, np.nan, np.nan, 0.1042, np.nan, -0.5954], [0.1610, np.nan, np.nan, 0.1859, 0.3146, np.nan]]) # Rows of _ndat with nans removed _rdat = [np.array([0.6244, 0.2692, 0.0116, 0.1170]), np.array([0.5351, -0.9403, 0.2100, 0.4759, 0.2833]), np.array([0.1042, -0.5954]), np.array([0.1610, 0.1859, 0.3146])] # Rows of _ndat with nans converted to ones _ndat_ones = np.array([[0.6244, 1.0, 0.2692, 0.0116, 1.0, 0.1170], [0.5351, -0.9403, 1.0, 0.2100, 0.4759, 0.2833], [1.0, 1.0, 1.0, 0.1042, 1.0, -0.5954], [0.1610, 1.0, 1.0, 0.1859, 0.3146, 1.0]]) # Rows of _ndat with nans converted to zeros _ndat_zeros = np.array([[0.6244, 0.0, 0.2692, 0.0116, 0.0, 0.1170], [0.5351, -0.9403, 0.0, 0.2100, 0.4759, 0.2833], [0.0, 0.0, 0.0, 0.1042, 0.0, -0.5954], [0.1610, 0.0, 0.0, 0.1859, 0.3146, 0.0]]) class TestSignatureMatch: NANFUNCS = { np.nanmin: np.amin, np.nanmax: np.amax, np.nanargmin: np.argmin, np.nanargmax: np.argmax, np.nansum: np.sum, np.nanprod: np.prod, np.nancumsum: np.cumsum, np.nancumprod: np.cumprod, np.nanmean: np.mean, np.nanmedian: np.median, np.nanpercentile: np.percentile, np.nanquantile: np.quantile, np.nanvar: np.var, np.nanstd: np.std, } IDS = [k.__name__ for k in NANFUNCS] @staticmethod def get_signature(func, default="..."): """Construct a signature and replace all default parameter-values.""" prm_list = [] signature = inspect.signature(func) for prm in signature.parameters.values(): if prm.default is inspect.Parameter.empty: prm_list.append(prm) else: prm_list.append(prm.replace(default=default)) return inspect.Signature(prm_list) @pytest.mark.parametrize("nan_func,func", NANFUNCS.items(), ids=IDS) def test_signature_match(self, nan_func, func): # Ignore the default parameter-values as they can sometimes differ # between the two functions (e.g. one has `False` while the other # has `np._NoValue`) signature = self.get_signature(func) nan_signature = self.get_signature(nan_func) np.testing.assert_equal(signature, nan_signature) def test_exhaustiveness(self): """Validate that all nan functions are actually tested.""" np.testing.assert_equal( set(self.IDS), set(np.lib.nanfunctions.__all__) ) class TestNanFunctions_MinMax: nanfuncs = [np.nanmin, np.nanmax] stdfuncs = [np.min, np.max] def test_mutation(self): # Check that passed array is not modified. ndat = _ndat.copy() for f in self.nanfuncs: f(ndat) assert_equal(ndat, _ndat) def test_keepdims(self): mat = np.eye(3) for nf, rf in zip(self.nanfuncs, self.stdfuncs): for axis in [None, 0, 1]: tgt = rf(mat, axis=axis, keepdims=True) res = nf(mat, axis=axis, keepdims=True) assert_(res.ndim == tgt.ndim) def test_out(self): mat = np.eye(3) for nf, rf in zip(self.nanfuncs, self.stdfuncs): resout = np.zeros(3) tgt = rf(mat, axis=1) res = nf(mat, axis=1, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) def test_dtype_from_input(self): codes = 'efdgFDG' for nf, rf in zip(self.nanfuncs, self.stdfuncs): for c in codes: mat = np.eye(3, dtype=c) tgt = rf(mat, axis=1).dtype.type res = nf(mat, axis=1).dtype.type assert_(res is tgt) # scalar case tgt = rf(mat, axis=None).dtype.type res = nf(mat, axis=None).dtype.type assert_(res is tgt) def test_result_values(self): for nf, rf in zip(self.nanfuncs, self.stdfuncs): tgt = [rf(d) for d in _rdat] res = nf(_ndat, axis=1) assert_almost_equal(res, tgt) @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), ], ids=["0d", "2d"]) def test_allnans(self, axis, dtype, array): if axis is not None and array.ndim == 0: pytest.skip(f"`axis != None` not supported for 0d arrays") array = array.astype(dtype) match = "All-NaN slice encountered" for func in self.nanfuncs: with pytest.warns(RuntimeWarning, match=match): out = func(array, axis=axis) assert np.isnan(out).all() assert out.dtype == array.dtype def test_masked(self): mat = np.ma.fix_invalid(_ndat) msk = mat._mask.copy() for f in [np.nanmin]: res = f(mat, axis=1) tgt = f(_ndat, axis=1) assert_equal(res, tgt) assert_equal(mat._mask, msk) assert_(not np.isinf(mat).any()) def test_scalar(self): for f in self.nanfuncs: assert_(f(0.) == 0.) def test_subclass(self): class MyNDArray(np.ndarray): pass # Check that it works and that type and # shape are preserved mine = np.eye(3).view(MyNDArray) for f in self.nanfuncs: res = f(mine, axis=0) assert_(isinstance(res, MyNDArray)) assert_(res.shape == (3,)) res = f(mine, axis=1) assert_(isinstance(res, MyNDArray)) assert_(res.shape == (3,)) res = f(mine) assert_(res.shape == ()) # check that rows of nan are dealt with for subclasses (#4628) mine[1] = np.nan for f in self.nanfuncs: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') res = f(mine, axis=0) assert_(isinstance(res, MyNDArray)) assert_(not np.any(np.isnan(res))) assert_(len(w) == 0) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') res = f(mine, axis=1) assert_(isinstance(res, MyNDArray)) assert_(np.isnan(res[1]) and not np.isnan(res[0]) and not np.isnan(res[2])) assert_(len(w) == 1, 'no warning raised') assert_(issubclass(w[0].category, RuntimeWarning)) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') res = f(mine) assert_(res.shape == ()) assert_(res != np.nan) assert_(len(w) == 0) def test_object_array(self): arr = np.array([[1.0, 2.0], [np.nan, 4.0], [np.nan, np.nan]], dtype=object) assert_equal(np.nanmin(arr), 1.0) assert_equal(np.nanmin(arr, axis=0), [1.0, 2.0]) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') # assert_equal does not work on object arrays of nan assert_equal(list(np.nanmin(arr, axis=1)), [1.0, 4.0, np.nan]) assert_(len(w) == 1, 'no warning raised') assert_(issubclass(w[0].category, RuntimeWarning)) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_initial(self, dtype): class MyNDArray(np.ndarray): pass ar = np.arange(9).astype(dtype) ar[:5] = np.nan for f in self.nanfuncs: initial = 100 if f is np.nanmax else 0 ret1 = f(ar, initial=initial) assert ret1.dtype == dtype assert ret1 == initial ret2 = f(ar.view(MyNDArray), initial=initial) assert ret2.dtype == dtype assert ret2 == initial @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_where(self, dtype): class MyNDArray(np.ndarray): pass ar = np.arange(9).reshape(3, 3).astype(dtype) ar[0, :] = np.nan where = np.ones_like(ar, dtype=np.bool_) where[:, 0] = False for f in self.nanfuncs: reference = 4 if f is np.nanmin else 8 ret1 = f(ar, where=where, initial=5) assert ret1.dtype == dtype assert ret1 == reference ret2 = f(ar.view(MyNDArray), where=where, initial=5) assert ret2.dtype == dtype assert ret2 == reference class TestNanFunctions_ArgminArgmax: nanfuncs = [np.nanargmin, np.nanargmax] def test_mutation(self): # Check that passed array is not modified. ndat = _ndat.copy() for f in self.nanfuncs: f(ndat) assert_equal(ndat, _ndat) def test_result_values(self): for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]): for row in _ndat: with suppress_warnings() as sup: sup.filter(RuntimeWarning, "invalid value encountered in") ind = f(row) val = row[ind] # comparing with NaN is tricky as the result # is always false except for NaN != NaN assert_(not np.isnan(val)) assert_(not fcmp(val, row).any()) assert_(not np.equal(val, row[:ind]).any()) @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), ], ids=["0d", "2d"]) def test_allnans(self, axis, dtype, array): if axis is not None and array.ndim == 0: pytest.skip(f"`axis != None` not supported for 0d arrays") array = array.astype(dtype) for func in self.nanfuncs: with pytest.raises(ValueError, match="All-NaN slice encountered"): func(array, axis=axis) def test_empty(self): mat = np.zeros((0, 3)) for f in self.nanfuncs: for axis in [0, None]: assert_raises(ValueError, f, mat, axis=axis) for axis in [1]: res = f(mat, axis=axis) assert_equal(res, np.zeros(0)) def test_scalar(self): for f in self.nanfuncs: assert_(f(0.) == 0.) def test_subclass(self): class MyNDArray(np.ndarray): pass # Check that it works and that type and # shape are preserved mine = np.eye(3).view(MyNDArray) for f in self.nanfuncs: res = f(mine, axis=0) assert_(isinstance(res, MyNDArray)) assert_(res.shape == (3,)) res = f(mine, axis=1) assert_(isinstance(res, MyNDArray)) assert_(res.shape == (3,)) res = f(mine) assert_(res.shape == ()) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_keepdims(self, dtype): ar = np.arange(9).astype(dtype) ar[:5] = np.nan for f in self.nanfuncs: reference = 5 if f is np.nanargmin else 8 ret = f(ar, keepdims=True) assert ret.ndim == ar.ndim assert ret == reference @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_out(self, dtype): ar = np.arange(9).astype(dtype) ar[:5] = np.nan for f in self.nanfuncs: out = np.zeros((), dtype=np.intp) reference = 5 if f is np.nanargmin else 8 ret = f(ar, out=out) assert ret is out assert ret == reference _TEST_ARRAYS = { "0d": np.array(5), "1d": np.array([127, 39, 93, 87, 46]) } for _v in _TEST_ARRAYS.values(): _v.setflags(write=False) @pytest.mark.parametrize( "dtype", np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O", ) @pytest.mark.parametrize("mat", _TEST_ARRAYS.values(), ids=_TEST_ARRAYS.keys()) class TestNanFunctions_NumberTypes: nanfuncs = { np.nanmin: np.min, np.nanmax: np.max, np.nanargmin: np.argmin, np.nanargmax: np.argmax, np.nansum: np.sum, np.nanprod: np.prod, np.nancumsum: np.cumsum, np.nancumprod: np.cumprod, np.nanmean: np.mean, np.nanmedian: np.median, np.nanvar: np.var, np.nanstd: np.std, } nanfunc_ids = [i.__name__ for i in nanfuncs] @pytest.mark.parametrize("nanfunc,func", nanfuncs.items(), ids=nanfunc_ids) @np.errstate(over="ignore") def test_nanfunc(self, mat, dtype, nanfunc, func): mat = mat.astype(dtype) tgt = func(mat) out = nanfunc(mat) assert_almost_equal(out, tgt) if dtype == "O": assert type(out) is type(tgt) else: assert out.dtype == tgt.dtype @pytest.mark.parametrize( "nanfunc,func", [(np.nanquantile, np.quantile), (np.nanpercentile, np.percentile)], ids=["nanquantile", "nanpercentile"], ) def test_nanfunc_q(self, mat, dtype, nanfunc, func): mat = mat.astype(dtype) if mat.dtype.kind == "c": assert_raises(TypeError, func, mat, q=1) assert_raises(TypeError, nanfunc, mat, q=1) else: tgt = func(mat, q=1) out = nanfunc(mat, q=1) assert_almost_equal(out, tgt) if dtype == "O": assert type(out) is type(tgt) else: assert out.dtype == tgt.dtype @pytest.mark.parametrize( "nanfunc,func", [(np.nanvar, np.var), (np.nanstd, np.std)], ids=["nanvar", "nanstd"], ) def test_nanfunc_ddof(self, mat, dtype, nanfunc, func): mat = mat.astype(dtype) tgt = func(mat, ddof=0.5) out = nanfunc(mat, ddof=0.5) assert_almost_equal(out, tgt) if dtype == "O": assert type(out) is type(tgt) else: assert out.dtype == tgt.dtype class SharedNanFunctionsTestsMixin: def test_mutation(self): # Check that passed array is not modified. ndat = _ndat.copy() for f in self.nanfuncs: f(ndat) assert_equal(ndat, _ndat) def test_keepdims(self): mat = np.eye(3) for nf, rf in zip(self.nanfuncs, self.stdfuncs): for axis in [None, 0, 1]: tgt = rf(mat, axis=axis, keepdims=True) res = nf(mat, axis=axis, keepdims=True) assert_(res.ndim == tgt.ndim) def test_out(self): mat = np.eye(3) for nf, rf in zip(self.nanfuncs, self.stdfuncs): resout = np.zeros(3) tgt = rf(mat, axis=1) res = nf(mat, axis=1, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) def test_dtype_from_dtype(self): mat = np.eye(3) codes = 'efdgFDG' for nf, rf in zip(self.nanfuncs, self.stdfuncs): for c in codes: with suppress_warnings() as sup: if nf in {np.nanstd, np.nanvar} and c in 'FDG': # Giving the warning is a small bug, see gh-8000 sup.filter(np.ComplexWarning) tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type assert_(res is tgt) # scalar case tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type assert_(res is tgt) def test_dtype_from_char(self): mat = np.eye(3) codes = 'efdgFDG' for nf, rf in zip(self.nanfuncs, self.stdfuncs): for c in codes: with suppress_warnings() as sup: if nf in {np.nanstd, np.nanvar} and c in 'FDG': # Giving the warning is a small bug, see gh-8000 sup.filter(np.ComplexWarning) tgt = rf(mat, dtype=c, axis=1).dtype.type res = nf(mat, dtype=c, axis=1).dtype.type assert_(res is tgt) # scalar case tgt = rf(mat, dtype=c, axis=None).dtype.type res = nf(mat, dtype=c, axis=None).dtype.type assert_(res is tgt) def test_dtype_from_input(self): codes = 'efdgFDG' for nf, rf in zip(self.nanfuncs, self.stdfuncs): for c in codes: mat = np.eye(3, dtype=c) tgt = rf(mat, axis=1).dtype.type res = nf(mat, axis=1).dtype.type assert_(res is tgt, "res %s, tgt %s" % (res, tgt)) # scalar case tgt = rf(mat, axis=None).dtype.type res = nf(mat, axis=None).dtype.type assert_(res is tgt) def test_result_values(self): for nf, rf in zip(self.nanfuncs, self.stdfuncs): tgt = [rf(d) for d in _rdat] res = nf(_ndat, axis=1) assert_almost_equal(res, tgt) def test_scalar(self): for f in self.nanfuncs: assert_(f(0.) == 0.) def test_subclass(self): class MyNDArray(np.ndarray): pass # Check that it works and that type and # shape are preserved array = np.eye(3) mine = array.view(MyNDArray) for f in self.nanfuncs: expected_shape = f(array, axis=0).shape res = f(mine, axis=0) assert_(isinstance(res, MyNDArray)) assert_(res.shape == expected_shape) expected_shape = f(array, axis=1).shape res = f(mine, axis=1) assert_(isinstance(res, MyNDArray)) assert_(res.shape == expected_shape) expected_shape = f(array).shape res = f(mine) assert_(isinstance(res, MyNDArray)) assert_(res.shape == expected_shape) class TestNanFunctions_SumProd(SharedNanFunctionsTestsMixin): nanfuncs = [np.nansum, np.nanprod] stdfuncs = [np.sum, np.prod] @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), ], ids=["0d", "2d"]) def test_allnans(self, axis, dtype, array): if axis is not None and array.ndim == 0: pytest.skip(f"`axis != None` not supported for 0d arrays") array = array.astype(dtype) for func, identity in zip(self.nanfuncs, [0, 1]): out = func(array, axis=axis) assert np.all(out == identity) assert out.dtype == array.dtype def test_empty(self): for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]): mat = np.zeros((0, 3)) tgt = [tgt_value]3 res = f(mat, axis=0) assert_equal(res, tgt) tgt = [] res = f(mat, axis=1) assert_equal(res, tgt) tgt = tgt_value res = f(mat, axis=None) assert_equal(res, tgt) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_initial(self, dtype): ar = np.arange(9).astype(dtype) ar[:5] = np.nan for f in self.nanfuncs: reference = 28 if f is np.nansum else 3360 ret = f(ar, initial=2) assert ret.dtype == dtype assert ret == reference @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_where(self, dtype): ar = np.arange(9).reshape(3, 3).astype(dtype) ar[0, :] = np.nan where = np.ones_like(ar, dtype=np.bool_) where[:, 0] = False for f in self.nanfuncs: reference = 26 if f is np.nansum else 2240 ret = f(ar, where=where, initial=2) assert ret.dtype == dtype assert ret == reference class TestNanFunctions_CumSumProd(SharedNanFunctionsTestsMixin): nanfuncs = [np.nancumsum, np.nancumprod] stdfuncs = [np.cumsum, np.cumprod] @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan) ], ids=["0d", "2d"]) def test_allnans(self, axis, dtype, array): if axis is not None and array.ndim == 0: pytest.skip(f"`axis != None` not supported for 0d arrays") array = array.astype(dtype) for func, identity in zip(self.nanfuncs, [0, 1]): out = func(array) assert np.all(out == identity) assert out.dtype == array.dtype def test_empty(self): for f, tgt_value in zip(self.nanfuncs, [0, 1]): mat = np.zeros((0, 3)) tgt = tgt_valuenp.ones((0, 3)) res = f(mat, axis=0) assert_equal(res, tgt) tgt = mat res = f(mat, axis=1) assert_equal(res, tgt) tgt = np.zeros((0)) res = f(mat, axis=None) assert_equal(res, tgt) def test_keepdims(self): for f, g in zip(self.nanfuncs, self.stdfuncs): mat = np.eye(3) for axis in [None, 0, 1]: tgt = f(mat, axis=axis, out=None) res = g(mat, axis=axis, out=None) assert_(res.ndim == tgt.ndim) for f in self.nanfuncs: d = np.ones((3, 5, 7, 11)) # Randomly set some elements to NaN: rs = np.random.RandomState(0) d[rs.rand(d.shape) < 0.5] = np.nan res = f(d, axis=None) assert_equal(res.shape, (1155,)) for axis in np.arange(4): res = f(d, axis=axis) assert_equal(res.shape, (3, 5, 7, 11)) def test_result_values(self): for axis in (-2, -1, 0, 1, None): tgt = np.cumprod(_ndat_ones, axis=axis) res = np.nancumprod(_ndat, axis=axis) assert_almost_equal(res, tgt) tgt = np.cumsum(_ndat_zeros,axis=axis) res = np.nancumsum(_ndat, axis=axis) assert_almost_equal(res, tgt) def test_out(self): mat = np.eye(3) for nf, rf in zip(self.nanfuncs, self.stdfuncs): resout = np.eye(3) for axis in (-2, -1, 0, 1): tgt = rf(mat, axis=axis) res = nf(mat, axis=axis, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) class TestNanFunctions_MeanVarStd(SharedNanFunctionsTestsMixin): nanfuncs = [np.nanmean, np.nanvar, np.nanstd] stdfuncs = [np.mean, np.var, np.std] def test_dtype_error(self): for f in self.nanfuncs: for dtype in [np.bool_, np.int_, np.object_]: assert_raises(TypeError, f, _ndat, axis=1, dtype=dtype) def test_out_dtype_error(self): for f in self.nanfuncs: for dtype in [np.bool_, np.int_, np.object_]: out = np.empty(_ndat.shape[0], dtype=dtype) assert_raises(TypeError, f, _ndat, axis=1, out=out) def test_ddof(self): nanfuncs = [np.nanvar, np.nanstd] stdfuncs = [np.var, np.std] for nf, rf in zip(nanfuncs, stdfuncs): for ddof in [0, 1]: tgt = [rf(d, ddof=ddof) for d in _rdat] res = nf(_ndat, axis=1, ddof=ddof) assert_almost_equal(res, tgt) def test_ddof_too_big(self): nanfuncs = [np.nanvar, np.nanstd] stdfuncs = [np.var, np.std] dsize = [len(d) for d in _rdat] for nf, rf in zip(nanfuncs, stdfuncs): for ddof in range(5): with suppress_warnings() as sup: sup.record(RuntimeWarning) sup.filter(np.ComplexWarning) tgt = [ddof >= d for d in dsize] res = nf(_ndat, axis=1, ddof=ddof) assert_equal(np.isnan(res), tgt) if any(tgt): assert_(len(sup.log) == 1) else: assert_(len(sup.log) == 0) @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), ], ids=["0d", "2d"]) def test_allnans(self, axis, dtype, array): if axis is not None and array.ndim == 0: pytest.skip(f"`axis != None` not supported for 0d arrays") array = array.astype(dtype) match = "(Degrees of freedom <= 0 for slice.)\|(Mean of empty slice)" for func in self.nanfuncs: with pytest.warns(RuntimeWarning, match=match): out = func(array, axis=axis) assert np.isnan(out).all() # `nanvar` and `nanstd` convert complex inputs to their # corresponding floating dtype if func is np.nanmean: assert out.dtype == array.dtype else: assert out.dtype == np.abs(array).dtype def test_empty(self): mat = np.zeros((0, 3)) for f in self.nanfuncs: for axis in [0, None]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_(np.isnan(f(mat, axis=axis)).all()) assert_(len(w) == 1) assert_(issubclass(w[0].category, RuntimeWarning)) for axis in [1]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_equal(f(mat, axis=axis), np.zeros([])) assert_(len(w) == 0) @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) def test_where(self, dtype): ar = np.arange(9).reshape(3, 3).astype(dtype) ar[0, :] = np.nan where = np.ones_like(ar, dtype=np.bool_) where[:, 0] = False for f, f_std in zip(self.nanfuncs, self.stdfuncs): reference = f_std(ar[where][2:]) dtype_reference = dtype if f is np.nanmean else ar.real.dtype ret = f(ar, where=where) assert ret.dtype == dtype_reference np.testing.assert_allclose(ret, reference) _TIME_UNITS = ( "Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as" ) # All `inexact` + `timdelta64` type codes _TYPE_CODES = list(np.typecodes["AllFloat"]) _TYPE_CODES += [f"m8[{unit}]" for unit in _TIME_UNITS] class TestNanFunctions_Median: def test_mutation(self): # Check that passed array is not modified. ndat = _ndat.copy() np.nanmedian(ndat) assert_equal(ndat, _ndat) def test_keepdims(self): mat = np.eye(3) for axis in [None, 0, 1]: tgt = np.median(mat, axis=axis, out=None, overwrite_input=False) res = np.nanmedian(mat, axis=axis, out=None, overwrite_input=False) assert_(res.ndim == tgt.ndim) d = np.ones((3, 5, 7, 11)) # Randomly set some elements to NaN: w = np.random.random((4, 200)) np.array(d.shape)[:, None] w = w.astype(np.intp) d[tuple(w)] = np.nan with suppress_warnings() as sup: sup.filter(RuntimeWarning) res = np.nanmedian(d, axis=None, keepdims=True) assert_equal(res.shape, (1, 1, 1, 1)) res = np.nanmedian(d, axis=(0, 1), keepdims=True) assert_equal(res.shape, (1, 1, 7, 11)) res = np.nanmedian(d, axis=(0, 3), keepdims=True) assert_equal(res.shape, (1, 5, 7, 1)) res = np.nanmedian(d, axis=(1,), keepdims=True) assert_equal(res.shape, (3, 1, 7, 11)) res = np.nanmedian(d, axis=(0, 1, 2, 3), keepdims=True) assert_equal(res.shape, (1, 1, 1, 1)) res = np.nanmedian(d, axis=(0, 1, 3), keepdims=True) assert_equal(res.shape, (1, 1, 7, 1)) @pytest.mark.parametrize( argnames='axis', argvalues=[ None, 1, (1, ), (0, 1), (-3, -1), ] ) @pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning") def test_keepdims_out(self, axis): d = np.ones((3, 5, 7, 11)) # Randomly set some elements to NaN: w = np.random.random((4, 200)) * np.array(d.shape)[:, None] w = w.astype(np.intp) d[tuple(w)] = np.nan if axis is None: shape_out = (1,) * d.ndim else: axis_norm = normalize_axis_tuple(axis, d.ndim) shape_out = tuple( 1 if i in axis_norm else d.shape[i] for i in range(d.ndim)) out = np.empty(shape_out) result = np.nanmedian(d, axis=axis, keepdims=True, out=out) assert result is out assert_equal(result.shape, shape_out) def test_out(self): mat = np.random.rand(3, 3) nan_mat = np.insert(mat, [0, 2], np.nan, axis=1) resout = np.zeros(3) tgt = np.median(mat, axis=1) res = np.nanmedian(nan_mat, axis=1, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) # 0-d output: resout = np.zeros(()) tgt = np.median(mat, axis=None) res = np.nanmedian(nan_mat, axis=None, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) res = np.nanmedian(nan_mat, axis=(0, 1), out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) def test_small_large(self): # test the small and large code paths, current cutoff 400 elements for s in [5, 20, 51, 200, 1000]: d = np.random.randn(4, s) # Randomly set some elements to NaN: w = np.random.randint(0, d.size, size=d.size // 5) d.ravel()[w] = np.nan d[:,0] = 1. # ensure at least one good value # use normal median without nans to compare tgt = [] for x in d: nonan = np.compress(~np.isnan(x), x) tgt.append(np.median(nonan, overwrite_input=True)) assert_array_equal(np.nanmedian(d, axis=-1), tgt) def test_result_values(self): tgt = [np.median(d) for d in _rdat] res = np.nanmedian(_ndat, axis=1) assert_almost_equal(res, tgt) @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", _TYPE_CODES) def test_allnans(self, dtype, axis): mat = np.full((3, 3), np.nan).astype(dtype) with suppress_warnings() as sup: sup.record(RuntimeWarning) output = np.nanmedian(mat, axis=axis) assert output.dtype == mat.dtype assert np.isnan(output).all() if axis is None: assert_(len(sup.log) == 1) else: assert_(len(sup.log) == 3) # Check scalar scalar = np.array(np.nan).astype(dtype)[()] output_scalar = np.nanmedian(scalar) assert output_scalar.dtype == scalar.dtype assert np.isnan(output_scalar) if axis is None: assert_(len(sup.log) == 2) else: assert_(len(sup.log) == 4) def test_empty(self): mat = np.zeros((0, 3)) for axis in [0, None]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_(np.isnan(np.nanmedian(mat, axis=axis)).all()) assert_(len(w) == 1) assert_(issubclass(w[0].category, RuntimeWarning)) for axis in [1]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_equal(np.nanmedian(mat, axis=axis), np.zeros([])) assert_(len(w) == 0) def test_scalar(self): assert_(np.nanmedian(0.) == 0.) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) assert_raises(np.AxisError, np.nanmedian, d, axis=-5) assert_raises(np.AxisError, np.nanmedian, d, axis=(0, -5)) assert_raises(np.AxisError, np.nanmedian, d, axis=4) assert_raises(np.AxisError, np.nanmedian, d, axis=(0, 4)) assert_raises(ValueError, np.nanmedian, d, axis=(1, 1)) def test_float_special(self): with suppress_warnings() as sup: sup.filter(RuntimeWarning) for inf in [np.inf, -np.inf]: a = np.array([[inf, np.nan], [np.nan, np.nan]]) assert_equal(np.nanmedian(a, axis=0), [inf, np.nan]) assert_equal(np.nanmedian(a, axis=1), [inf, np.nan]) assert_equal(np.nanmedian(a), inf) # minimum fill value check a = np.array([[np.nan, np.nan, inf], [np.nan, np.nan, inf]]) assert_equal(np.nanmedian(a), inf) assert_equal(np.nanmedian(a, axis=0), [np.nan, np.nan, inf]) assert_equal(np.nanmedian(a, axis=1), inf) # no mask path a = np.array([[inf, inf], [inf, inf]]) assert_equal(np.nanmedian(a, axis=1), inf) a = np.array([[inf, 7, -inf, -9], [-10, np.nan, np.nan, 5], [4, np.nan, np.nan, inf]], dtype=np.float32) if inf > 0: assert_equal(np.nanmedian(a, axis=0), [4., 7., -inf, 5.]) assert_equal(np.nanmedian(a), 4.5) else: assert_equal(np.nanmedian(a, axis=0), [-10., 7., -inf, -9.]) assert_equal(np.nanmedian(a), -2.5) assert_equal(np.nanmedian(a, axis=-1), [-1., -2.5, inf]) for i in range(0, 10): for j in range(1, 10): a = np.array([([np.nan] * i) + ([inf] * j)] * 2) assert_equal(np.nanmedian(a), inf) assert_equal(np.nanmedian(a, axis=1), inf) assert_equal(np.nanmedian(a, axis=0), ([np.nan] * i) + [inf] * j) a = np.array([([np.nan] * i) + ([-inf] * j)] * 2) assert_equal(np.nanmedian(a), -inf) assert_equal(np.nanmedian(a, axis=1), -inf) assert_equal(np.nanmedian(a, axis=0), ([np.nan] * i) + [-inf] * j) class TestNanFunctions_Percentile: def test_mutation(self): # Check that passed array is not modified. ndat = _ndat.copy() np.nanpercentile(ndat, 30) assert_equal(ndat, _ndat) def test_keepdims(self): mat = np.eye(3) for axis in [None, 0, 1]: tgt = np.percentile(mat, 70, axis=axis, out=None, overwrite_input=False) res = np.nanpercentile(mat, 70, axis=axis, out=None, overwrite_input=False) assert_(res.ndim == tgt.ndim) d = np.ones((3, 5, 7, 11)) # Randomly set some elements to NaN: w = np.random.random((4, 200)) * np.array(d.shape)[:, None] w = w.astype(np.intp) d[tuple(w)] = np.nan with suppress_warnings() as sup: sup.filter(RuntimeWarning) res = np.nanpercentile(d, 90, axis=None, keepdims=True) assert_equal(res.shape, (1, 1, 1, 1)) res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True) assert_equal(res.shape, (1, 1, 7, 11)) res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True) assert_equal(res.shape, (1, 5, 7, 1)) res = np.nanpercentile(d, 90, axis=(1,), keepdims=True) assert_equal(res.shape, (3, 1, 7, 11)) res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True) assert_equal(res.shape, (1, 1, 1, 1)) res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True) assert_equal(res.shape, (1, 1, 7, 1)) @pytest.mark.parametrize('q', [7, [1, 7]]) @pytest.mark.parametrize( argnames='axis', argvalues=[ None, 1, (1,), (0, 1), (-3, -1), ] ) @pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning") def test_keepdims_out(self, q, axis): d = np.ones((3, 5, 7, 11)) # Randomly set some elements to NaN: w = np.random.random((4, 200)) * np.array(d.shape)[:, None] w = w.astype(np.intp) d[tuple(w)] = np.nan if axis is None: shape_out = (1,) * d.ndim else: axis_norm = normalize_axis_tuple(axis, d.ndim) shape_out = tuple( 1 if i in axis_norm else d.shape[i] for i in range(d.ndim)) shape_out = np.shape(q) + shape_out out = np.empty(shape_out) result = np.nanpercentile(d, q, axis=axis, keepdims=True, out=out) assert result is out assert_equal(result.shape, shape_out) def test_out(self): mat = np.random.rand(3, 3) nan_mat = np.insert(mat, [0, 2], np.nan, axis=1) resout = np.zeros(3) tgt = np.percentile(mat, 42, axis=1) res = np.nanpercentile(nan_mat, 42, axis=1, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) # 0-d output: resout = np.zeros(()) tgt = np.percentile(mat, 42, axis=None) res = np.nanpercentile(nan_mat, 42, axis=None, out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) res = np.nanpercentile(nan_mat, 42, axis=(0, 1), out=resout) assert_almost_equal(res, resout) assert_almost_equal(res, tgt) def test_complex(self): arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') assert_raises(TypeError, np.nanpercentile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') assert_raises(TypeError, np.nanpercentile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') assert_raises(TypeError, np.nanpercentile, arr_c, 0.5) def test_result_values(self): tgt = [np.percentile(d, 28) for d in _rdat] res = np.nanpercentile(_ndat, 28, axis=1) assert_almost_equal(res, tgt) # Transpose the array to fit the output convention of numpy.percentile tgt = np.transpose([np.percentile(d, (28, 98)) for d in _rdat]) res = np.nanpercentile(_ndat, (28, 98), axis=1) assert_almost_equal(res, tgt) @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", np.typecodes["Float"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), ], ids=["0d", "2d"]) def test_allnans(self, axis, dtype, array): if axis is not None and array.ndim == 0: pytest.skip(f"`axis != None` not supported for 0d arrays") array = array.astype(dtype) with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"): out = np.nanpercentile(array, 60, axis=axis) assert np.isnan(out).all() assert out.dtype == array.dtype def test_empty(self): mat = np.zeros((0, 3)) for axis in [0, None]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all()) assert_(len(w) == 1) assert_(issubclass(w[0].category, RuntimeWarning)) for axis in [1]: with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([])) assert_(len(w) == 0) def test_scalar(self): assert_equal(np.nanpercentile(0., 100), 0.) a = np.arange(6) r = np.nanpercentile(a, 50, axis=0) assert_equal(r, 2.5) assert_(np.isscalar(r)) def test_extended_axis_invalid(self): d = np.ones((3, 5, 7, 11)) assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=-5) assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=(0, -5)) assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=4) assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=(0, 4)) assert_raises(ValueError, np.nanpercentile, d, q=5, axis=(1, 1)) def test_multiple_percentiles(self): perc = [50, 100] mat = np.ones((4, 3)) nan_mat = np.nan * mat # For checking consistency in higher dimensional case large_mat = np.ones((3, 4, 5)) large_mat[:, 0:2:4, :] = 0 large_mat[:, :, 3:] = 2 for axis in [None, 0, 1]: for keepdim in [False, True]: with suppress_warnings() as sup: sup.filter(RuntimeWarning, "All-NaN slice encountered") val = np.percentile(mat, perc, axis=axis, keepdims=keepdim) nan_val = np.nanpercentile(nan_mat, perc, axis=axis, keepdims=keepdim) assert_equal(nan_val.shape, val.shape) val = np.percentile(large_mat, perc, axis=axis, keepdims=keepdim) nan_val = np.nanpercentile(large_mat, perc, axis=axis, keepdims=keepdim) assert_equal(nan_val, val) megamat = np.ones((3, 4, 5, 6)) assert_equal(np.nanpercentile(megamat, perc, axis=(1, 2)).shape, (2, 3, 6)) class TestNanFunctions_Quantile: # most of this is already tested by TestPercentile def test_regression(self): ar = np.arange(24).reshape(2, 3, 4).astype(float) ar[0][1] = np.nan assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50)) assert_equal(np.nanquantile(ar, q=0.5, axis=0), np.nanpercentile(ar, q=50, axis=0)) assert_equal(np.nanquantile(ar, q=0.5, axis=1), np.nanpercentile(ar, q=50, axis=1)) assert_equal(np.nanquantile(ar, q=[0.5], axis=1), np.nanpercentile(ar, q=[50], axis=1)) assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1), np.nanpercentile(ar, q=[25, 50, 75], axis=1)) def test_basic(self): x = np.arange(8) 0.5 assert_equal(np.nanquantile(x, 0), 0.) assert_equal(np.nanquantile(x, 1), 3.5) assert_equal(np.nanquantile(x, 0.5), 1.75) def test_complex(self): arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') assert_raises(TypeError, np.nanquantile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') assert_raises(TypeError, np.nanquantile, arr_c, 0.5) arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') assert_raises(TypeError, np.nanquantile, arr_c, 0.5) def test_no_p_overwrite(self): # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) p = p0.copy() np.nanquantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) p0 = p0.tolist() p = p.tolist() np.nanquantile(np.arange(100.), p, method="midpoint") assert_array_equal(p, p0) @pytest.mark.parametrize("axis", [None, 0, 1]) @pytest.mark.parametrize("dtype", np.typecodes["Float"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), ], ids=["0d", "2d"]) def test_allnans(self, axis, dtype, array): if axis is not None and array.ndim == 0: pytest.skip(f"`axis != None` not supported for 0d arrays") array = array.astype(dtype) with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"): out = np.nanquantile(array, 1, axis=axis) assert np.isnan(out).all() assert out.dtype == array.dtype @pytest.mark.parametrize("arr, expected", [ # array of floats with some nans (np.array([np.nan, 5.0, np.nan, np.inf]), np.array([False, True, False, True])), # int64 array that can't possibly have nans (np.array([1, 5, 7, 9], dtype=np.int64), True), # bool array that can't possibly have nans (np.array([False, True, False, True]), True), # 2-D complex array with nans (np.array([[np.nan, 5.0], [np.nan, np.inf]], dtype=np.complex64), np.array([[False, True], [False, True]])), ]) def test__nan_mask(arr, expected): for out in [None, np.empty(arr.shape, dtype=np.bool_)]: actual = _nan_mask(arr, out=out) assert_equal(actual, expected) # the above won't distinguish between True proper # and an array of True values; we want True proper # for types that can't possibly contain NaN if type(expected) is not np.ndarray: assert actual is True def test__replace_nan(): """ Test that _replace_nan returns the original array if there are no NaNs, not a copy. """ for dtype in [np.bool_, np.int32, np.int64]: arr = np.array([0, 1], dtype=dtype) result, mask = _replace_nan(arr, 0) assert mask is None # do not make a copy if there are no nans assert result is arr for dtype in [np.float32, np.float64]: arr = np.array([0, 1], dtype=dtype) result, mask = _replace_nan(arr, 2) assert (mask == False).all() # mask is not None, so we make a copy assert result is not arr assert_equal(result, arr) arr_nan = np.array([0, 1, np.nan], dtype=dtype) result_nan, mask_nan = _replace_nan(arr_nan, 2) assert_equal(mask_nan, np.array([False, False, True])) assert result_nan is not arr_nan assert_equal(result_nan, np.array([0, 1, 2])) assert np.isnan(arr_nan[-1])	46.49 KB	2026-01-20 12:58
📄 test_packbits.py import numpy as np from numpy.testing import assert_array_equal, assert_equal, assert_raises import pytest from itertools import chain def test_packbits(): # Copied from the docstring. a = [[[1, 0, 1], [0, 1, 0]], [[1, 1, 0], [0, 0, 1]]] for dt in '?bBhHiIlLqQ': arr = np.array(a, dtype=dt) b = np.packbits(arr, axis=-1) assert_equal(b.dtype, np.uint8) assert_array_equal(b, np.array([[[160], [64]], [[192], [32]]])) assert_raises(TypeError, np.packbits, np.array(a, dtype=float)) def test_packbits_empty(): shapes = [ (0,), (10, 20, 0), (10, 0, 20), (0, 10, 20), (20, 0, 0), (0, 20, 0), (0, 0, 20), (0, 0, 0), ] for dt in '?bBhHiIlLqQ': for shape in shapes: a = np.empty(shape, dtype=dt) b = np.packbits(a) assert_equal(b.dtype, np.uint8) assert_equal(b.shape, (0,)) def test_packbits_empty_with_axis(): # Original shapes and lists of packed shapes for different axes. shapes = [ ((0,), [(0,)]), ((10, 20, 0), [(2, 20, 0), (10, 3, 0), (10, 20, 0)]), ((10, 0, 20), [(2, 0, 20), (10, 0, 20), (10, 0, 3)]), ((0, 10, 20), [(0, 10, 20), (0, 2, 20), (0, 10, 3)]), ((20, 0, 0), [(3, 0, 0), (20, 0, 0), (20, 0, 0)]), ((0, 20, 0), [(0, 20, 0), (0, 3, 0), (0, 20, 0)]), ((0, 0, 20), [(0, 0, 20), (0, 0, 20), (0, 0, 3)]), ((0, 0, 0), [(0, 0, 0), (0, 0, 0), (0, 0, 0)]), ] for dt in '?bBhHiIlLqQ': for in_shape, out_shapes in shapes: for ax, out_shape in enumerate(out_shapes): a = np.empty(in_shape, dtype=dt) b = np.packbits(a, axis=ax) assert_equal(b.dtype, np.uint8) assert_equal(b.shape, out_shape) @pytest.mark.parametrize('bitorder', ('little', 'big')) def test_packbits_large(bitorder): # test data large enough for 16 byte vectorization a = np.array([1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0]) a = a.repeat(3) for dtype in '?bBhHiIlLqQ': arr = np.array(a, dtype=dtype) b = np.packbits(arr, axis=None, bitorder=bitorder) assert_equal(b.dtype, np.uint8) r = [252, 127, 192, 3, 254, 7, 252, 0, 7, 31, 240, 0, 28, 1, 255, 252, 113, 248, 3, 255, 192, 28, 15, 192, 28, 126, 0, 224, 127, 255, 227, 142, 7, 31, 142, 63, 28, 126, 56, 227, 240, 0, 227, 128, 63, 224, 14, 56, 252, 112, 56, 255, 241, 248, 3, 240, 56, 224, 112, 63, 255, 255, 199, 224, 14, 0, 31, 143, 192, 3, 255, 199, 0, 1, 255, 224, 1, 255, 252, 126, 63, 0, 1, 192, 252, 14, 63, 0, 15, 199, 252, 113, 255, 3, 128, 56, 252, 14, 7, 0, 113, 255, 255, 142, 56, 227, 129, 248, 227, 129, 199, 31, 128] if bitorder == 'big': assert_array_equal(b, r) # equal for size being multiple of 8 assert_array_equal(np.unpackbits(b, bitorder=bitorder)[:-4], a) # check last byte of different remainders (16 byte vectorization) b = [np.packbits(arr[:-i], axis=None)[-1] for i in range(1, 16)] assert_array_equal(b, [128, 128, 128, 31, 30, 28, 24, 16, 0, 0, 0, 199, 198, 196, 192]) arr = arr.reshape(36, 25) b = np.packbits(arr, axis=0) assert_equal(b.dtype, np.uint8) assert_array_equal(b, [[190, 186, 178, 178, 150, 215, 87, 83, 83, 195, 199, 206, 204, 204, 140, 140, 136, 136, 8, 40, 105, 107, 75, 74, 88], [72, 216, 248, 241, 227, 195, 202, 90, 90, 83, 83, 119, 127, 109, 73, 64, 208, 244, 189, 45, 41, 104, 122, 90, 18], [113, 120, 248, 216, 152, 24, 60, 52, 182, 150, 150, 150, 146, 210, 210, 246, 255, 255, 223, 151, 21, 17, 17, 131, 163], [214, 210, 210, 64, 68, 5, 5, 1, 72, 88, 92, 92, 78, 110, 39, 181, 149, 220, 222, 218, 218, 202, 234, 170, 168], [0, 128, 128, 192, 80, 112, 48, 160, 160, 224, 240, 208, 144, 128, 160, 224, 240, 208, 144, 144, 176, 240, 224, 192, 128]]) b = np.packbits(arr, axis=1) assert_equal(b.dtype, np.uint8) assert_array_equal(b, [[252, 127, 192, 0], [ 7, 252, 15, 128], [240, 0, 28, 0], [255, 128, 0, 128], [192, 31, 255, 128], [142, 63, 0, 0], [255, 240, 7, 0], [ 7, 224, 14, 0], [126, 0, 224, 0], [255, 255, 199, 0], [ 56, 28, 126, 0], [113, 248, 227, 128], [227, 142, 63, 0], [ 0, 28, 112, 0], [ 15, 248, 3, 128], [ 28, 126, 56, 0], [ 56, 255, 241, 128], [240, 7, 224, 0], [227, 129, 192, 128], [255, 255, 254, 0], [126, 0, 224, 0], [ 3, 241, 248, 0], [ 0, 255, 241, 128], [128, 0, 255, 128], [224, 1, 255, 128], [248, 252, 126, 0], [ 0, 7, 3, 128], [224, 113, 248, 0], [ 0, 252, 127, 128], [142, 63, 224, 0], [224, 14, 63, 0], [ 7, 3, 128, 0], [113, 255, 255, 128], [ 28, 113, 199, 0], [ 7, 227, 142, 0], [ 14, 56, 252, 0]]) arr = arr.T.copy() b = np.packbits(arr, axis=0) assert_equal(b.dtype, np.uint8) assert_array_equal(b, [[252, 7, 240, 255, 192, 142, 255, 7, 126, 255, 56, 113, 227, 0, 15, 28, 56, 240, 227, 255, 126, 3, 0, 128, 224, 248, 0, 224, 0, 142, 224, 7, 113, 28, 7, 14], [127, 252, 0, 128, 31, 63, 240, 224, 0, 255, 28, 248, 142, 28, 248, 126, 255, 7, 129, 255, 0, 241, 255, 0, 1, 252, 7, 113, 252, 63, 14, 3, 255, 113, 227, 56], [192, 15, 28, 0, 255, 0, 7, 14, 224, 199, 126, 227, 63, 112, 3, 56, 241, 224, 192, 254, 224, 248, 241, 255, 255, 126, 3, 248, 127, 224, 63, 128, 255, 199, 142, 252], [0, 128, 0, 128, 128, 0, 0, 0, 0, 0, 0, 128, 0, 0, 128, 0, 128, 0, 128, 0, 0, 0, 128, 128, 128, 0, 128, 0, 128, 0, 0, 0, 128, 0, 0, 0]]) b = np.packbits(arr, axis=1) assert_equal(b.dtype, np.uint8) assert_array_equal(b, [[190, 72, 113, 214, 0], [186, 216, 120, 210, 128], [178, 248, 248, 210, 128], [178, 241, 216, 64, 192], [150, 227, 152, 68, 80], [215, 195, 24, 5, 112], [ 87, 202, 60, 5, 48], [ 83, 90, 52, 1, 160], [ 83, 90, 182, 72, 160], [195, 83, 150, 88, 224], [199, 83, 150, 92, 240], [206, 119, 150, 92, 208], [204, 127, 146, 78, 144], [204, 109, 210, 110, 128], [140, 73, 210, 39, 160], [140, 64, 246, 181, 224], [136, 208, 255, 149, 240], [136, 244, 255, 220, 208], [ 8, 189, 223, 222, 144], [ 40, 45, 151, 218, 144], [105, 41, 21, 218, 176], [107, 104, 17, 202, 240], [ 75, 122, 17, 234, 224], [ 74, 90, 131, 170, 192], [ 88, 18, 163, 168, 128]]) # result is the same if input is multiplied with a nonzero value for dtype in 'bBhHiIlLqQ': arr = np.array(a, dtype=dtype) rnd = np.random.randint(low=np.iinfo(dtype).min, high=np.iinfo(dtype).max, size=arr.size, dtype=dtype) rnd[rnd == 0] = 1 arr = rnd.astype(dtype) b = np.packbits(arr, axis=-1) assert_array_equal(np.unpackbits(b)[:-4], a) assert_raises(TypeError, np.packbits, np.array(a, dtype=float)) def test_packbits_very_large(): # test some with a larger arrays gh-8637 # code is covered earlier but larger array makes crash on bug more likely for s in range(950, 1050): for dt in '?bBhHiIlLqQ': x = np.ones((200, s), dtype=bool) np.packbits(x, axis=1) def test_unpackbits(): # Copied from the docstring. a = np.array([[2], [7], [23]], dtype=np.uint8) b = np.unpackbits(a, axis=1) assert_equal(b.dtype, np.uint8) assert_array_equal(b, np.array([[0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 0, 1, 1, 1]])) def test_pack_unpack_order(): a = np.array([[2], [7], [23]], dtype=np.uint8) b = np.unpackbits(a, axis=1) assert_equal(b.dtype, np.uint8) b_little = np.unpackbits(a, axis=1, bitorder='little') b_big = np.unpackbits(a, axis=1, bitorder='big') assert_array_equal(b, b_big) assert_array_equal(a, np.packbits(b_little, axis=1, bitorder='little')) assert_array_equal(b[:,::-1], b_little) assert_array_equal(a, np.packbits(b_big, axis=1, bitorder='big')) assert_raises(ValueError, np.unpackbits, a, bitorder='r') assert_raises(TypeError, np.unpackbits, a, bitorder=10) def test_unpackbits_empty(): a = np.empty((0,), dtype=np.uint8) b = np.unpackbits(a) assert_equal(b.dtype, np.uint8) assert_array_equal(b, np.empty((0,))) def test_unpackbits_empty_with_axis(): # Lists of packed shapes for different axes and unpacked shapes. shapes = [ ([(0,)], (0,)), ([(2, 24, 0), (16, 3, 0), (16, 24, 0)], (16, 24, 0)), ([(2, 0, 24), (16, 0, 24), (16, 0, 3)], (16, 0, 24)), ([(0, 16, 24), (0, 2, 24), (0, 16, 3)], (0, 16, 24)), ([(3, 0, 0), (24, 0, 0), (24, 0, 0)], (24, 0, 0)), ([(0, 24, 0), (0, 3, 0), (0, 24, 0)], (0, 24, 0)), ([(0, 0, 24), (0, 0, 24), (0, 0, 3)], (0, 0, 24)), ([(0, 0, 0), (0, 0, 0), (0, 0, 0)], (0, 0, 0)), ] for in_shapes, out_shape in shapes: for ax, in_shape in enumerate(in_shapes): a = np.empty(in_shape, dtype=np.uint8) b = np.unpackbits(a, axis=ax) assert_equal(b.dtype, np.uint8) assert_equal(b.shape, out_shape) def test_unpackbits_large(): # test all possible numbers via comparison to already tested packbits d = np.arange(277, dtype=np.uint8) assert_array_equal(np.packbits(np.unpackbits(d)), d) assert_array_equal(np.packbits(np.unpackbits(d[::2])), d[::2]) d = np.tile(d, (3, 1)) assert_array_equal(np.packbits(np.unpackbits(d, axis=1), axis=1), d) d = d.T.copy() assert_array_equal(np.packbits(np.unpackbits(d, axis=0), axis=0), d) class TestCount(): x = np.array([ [1, 0, 1, 0, 0, 1, 0], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 1], [1, 1, 0, 0, 0, 1, 1], [1, 0, 1, 0, 1, 0, 1], [0, 0, 1, 1, 1, 0, 0], [0, 1, 0, 1, 0, 1, 0], ], dtype=np.uint8) padded1 = np.zeros(57, dtype=np.uint8) padded1[:49] = x.ravel() padded1b = np.zeros(57, dtype=np.uint8) padded1b[:49] = x[::-1].copy().ravel() padded2 = np.zeros((9, 9), dtype=np.uint8) padded2[:7, :7] = x @pytest.mark.parametrize('bitorder', ('little', 'big')) @pytest.mark.parametrize('count', chain(range(58), range(-1, -57, -1))) def test_roundtrip(self, bitorder, count): if count < 0: # one extra zero of padding cutoff = count - 1 else: cutoff = count # test complete invertibility of packbits and unpackbits with count packed = np.packbits(self.x, bitorder=bitorder) unpacked = np.unpackbits(packed, count=count, bitorder=bitorder) assert_equal(unpacked.dtype, np.uint8) assert_array_equal(unpacked, self.padded1[:cutoff]) @pytest.mark.parametrize('kwargs', [ {}, {'count': None}, ]) def test_count(self, kwargs): packed = np.packbits(self.x) unpacked = np.unpackbits(packed, kwargs) assert_equal(unpacked.dtype, np.uint8) assert_array_equal(unpacked, self.padded1[:-1]) @pytest.mark.parametrize('bitorder', ('little', 'big')) # delta==-1 when count<0 because one extra zero of padding @pytest.mark.parametrize('count', chain(range(8), range(-1, -9, -1))) def test_roundtrip_axis(self, bitorder, count): if count < 0: # one extra zero of padding cutoff = count - 1 else: cutoff = count packed0 = np.packbits(self.x, axis=0, bitorder=bitorder) unpacked0 = np.unpackbits(packed0, axis=0, count=count, bitorder=bitorder) assert_equal(unpacked0.dtype, np.uint8) assert_array_equal(unpacked0, self.padded2[:cutoff, :self.x.shape[1]]) packed1 = np.packbits(self.x, axis=1, bitorder=bitorder) unpacked1 = np.unpackbits(packed1, axis=1, count=count, bitorder=bitorder) assert_equal(unpacked1.dtype, np.uint8) assert_array_equal(unpacked1, self.padded2[:self.x.shape[0], :cutoff]) @pytest.mark.parametrize('kwargs', [ {}, {'count': None}, {'bitorder' : 'little'}, {'bitorder': 'little', 'count': None}, {'bitorder' : 'big'}, {'bitorder': 'big', 'count': None}, ]) def test_axis_count(self, kwargs): packed0 = np.packbits(self.x, axis=0) unpacked0 = np.unpackbits(packed0, axis=0, kwargs) assert_equal(unpacked0.dtype, np.uint8) if kwargs.get('bitorder', 'big') == 'big': assert_array_equal(unpacked0, self.padded2[:-1, :self.x.shape[1]]) else: assert_array_equal(unpacked0[::-1, :], self.padded2[:-1, :self.x.shape[1]]) packed1 = np.packbits(self.x, axis=1) unpacked1 = np.unpackbits(packed1, axis=1, *kwargs) assert_equal(unpacked1.dtype, np.uint8) if kwargs.get('bitorder', 'big') == 'big': assert_array_equal(unpacked1, self.padded2[:self.x.shape[0], :-1]) else: assert_array_equal(unpacked1[:, ::-1], self.padded2[:self.x.shape[0], :-1]) def test_bad_count(self): packed0 = np.packbits(self.x, axis=0) assert_raises(ValueError, np.unpackbits, packed0, axis=0, count=-9) packed1 = np.packbits(self.x, axis=1) assert_raises(ValueError, np.unpackbits, packed1, axis=1, count=-9) packed = np.packbits(self.x) assert_raises(ValueError, np.unpackbits, packed, count=-57)	17.13 KB	2026-01-20 12:58
📄 test_polynomial.py import numpy as np from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_almost_equal, assert_array_almost_equal, assert_raises, assert_allclose ) import pytest # `poly1d` has some support for `bool_` and `timedelta64`, # but it is limited and they are therefore excluded here TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" class TestPolynomial: def test_poly1d_str_and_repr(self): p = np.poly1d([1., 2, 3]) assert_equal(repr(p), 'poly1d([1., 2., 3.])') assert_equal(str(p), ' 2\n' '1 x + 2 x + 3') q = np.poly1d([3., 2, 1]) assert_equal(repr(q), 'poly1d([3., 2., 1.])') assert_equal(str(q), ' 2\n' '3 x + 2 x + 1') r = np.poly1d([1.89999 + 2j, -3j, -5.12345678, 2 + 1j]) assert_equal(str(r), ' 3 2\n' '(1.9 + 2j) x - 3j x - 5.123 x + (2 + 1j)') assert_equal(str(np.poly1d([-3, -2, -1])), ' 2\n' '-3 x - 2 x - 1') def test_poly1d_resolution(self): p = np.poly1d([1., 2, 3]) q = np.poly1d([3., 2, 1]) assert_equal(p(0), 3.0) assert_equal(p(5), 38.0) assert_equal(q(0), 1.0) assert_equal(q(5), 86.0) def test_poly1d_math(self): # here we use some simple coeffs to make calculations easier p = np.poly1d([1., 2, 4]) q = np.poly1d([4., 2, 1]) assert_equal(p/q, (np.poly1d([0.25]), np.poly1d([1.5, 3.75]))) assert_equal(p.integ(), np.poly1d([1/3, 1., 4., 0.])) assert_equal(p.integ(1), np.poly1d([1/3, 1., 4., 0.])) p = np.poly1d([1., 2, 3]) q = np.poly1d([3., 2, 1]) assert_equal(p * q, np.poly1d([3., 8., 14., 8., 3.])) assert_equal(p + q, np.poly1d([4., 4., 4.])) assert_equal(p - q, np.poly1d([-2., 0., 2.])) assert_equal(p ** 4, np.poly1d([1., 8., 36., 104., 214., 312., 324., 216., 81.])) assert_equal(p(q), np.poly1d([9., 12., 16., 8., 6.])) assert_equal(q(p), np.poly1d([3., 12., 32., 40., 34.])) assert_equal(p.deriv(), np.poly1d([2., 2.])) assert_equal(p.deriv(2), np.poly1d([2.])) assert_equal(np.polydiv(np.poly1d([1, 0, -1]), np.poly1d([1, 1])), (np.poly1d([1., -1.]), np.poly1d([0.]))) @pytest.mark.parametrize("type_code", TYPE_CODES) def test_poly1d_misc(self, type_code: str) -> None: dtype = np.dtype(type_code) ar = np.array([1, 2, 3], dtype=dtype) p = np.poly1d(ar) # `__eq__` assert_equal(np.asarray(p), ar) assert_equal(np.asarray(p).dtype, dtype) assert_equal(len(p), 2) # `__getitem__` comparison_dct = {-1: 0, 0: 3, 1: 2, 2: 1, 3: 0} for index, ref in comparison_dct.items(): scalar = p[index] assert_equal(scalar, ref) if dtype == np.object_: assert isinstance(scalar, int) else: assert_equal(scalar.dtype, dtype) def test_poly1d_variable_arg(self): q = np.poly1d([1., 2, 3], variable='y') assert_equal(str(q), ' 2\n' '1 y + 2 y + 3') q = np.poly1d([1., 2, 3], variable='lambda') assert_equal(str(q), ' 2\n' '1 lambda + 2 lambda + 3') def test_poly(self): assert_array_almost_equal(np.poly([3, -np.sqrt(2), np.sqrt(2)]), [1, -3, -2, 6]) # From matlab docs A = [[1, 2, 3], [4, 5, 6], [7, 8, 0]] assert_array_almost_equal(np.poly(A), [1, -6, -72, -27]) # Should produce real output for perfect conjugates assert_(np.isrealobj(np.poly([+1.082j, +2.613j, -2.613j, -1.082j]))) assert_(np.isrealobj(np.poly([0+1j, -0+-1j, 1+2j, 1-2j, 1.+3.5j, 1-3.5j]))) assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j, 1+3j, 1-3.j]))) assert_(np.isrealobj(np.poly([1j, -1j, 1+2j, 1-2j]))) assert_(np.isrealobj(np.poly([1j, -1j, 2j, -2j]))) assert_(np.isrealobj(np.poly([1j, -1j]))) assert_(np.isrealobj(np.poly([1, -1]))) assert_(np.iscomplexobj(np.poly([1j, -1.0000001j]))) np.random.seed(42) a = np.random.randn(100) + 1jnp.random.randn(100) assert_(np.isrealobj(np.poly(np.concatenate((a, np.conjugate(a)))))) def test_roots(self): assert_array_equal(np.roots([1, 0, 0]), [0, 0]) def test_str_leading_zeros(self): p = np.poly1d([4, 3, 2, 1]) p[3] = 0 assert_equal(str(p), " 2\n" "3 x + 2 x + 1") p = np.poly1d([1, 2]) p[0] = 0 p[1] = 0 assert_equal(str(p), " \n0") def test_polyfit(self): c = np.array([3., 2., 1.]) x = np.linspace(0, 2, 7) y = np.polyval(c, x) err = [1, -1, 1, -1, 1, -1, 1] weights = np.arange(8, 1, -1)2/7.0 # Check exception when too few points for variance estimate. Note that # the estimate requires the number of data points to exceed # degree + 1 assert_raises(ValueError, np.polyfit, [1], [1], deg=0, cov=True) # check 1D case m, cov = np.polyfit(x, y+err, 2, cov=True) est = [3.8571, 0.2857, 1.619] assert_almost_equal(est, m, decimal=4) val0 = [[ 1.4694, -2.9388, 0.8163], [-2.9388, 6.3673, -2.1224], [ 0.8163, -2.1224, 1.161 ]] assert_almost_equal(val0, cov, decimal=4) m2, cov2 = np.polyfit(x, y+err, 2, w=weights, cov=True) assert_almost_equal([4.8927, -1.0177, 1.7768], m2, decimal=4) val = [[ 4.3964, -5.0052, 0.4878], [-5.0052, 6.8067, -0.9089], [ 0.4878, -0.9089, 0.3337]] assert_almost_equal(val, cov2, decimal=4) m3, cov3 = np.polyfit(x, y+err, 2, w=weights, cov="unscaled") assert_almost_equal([4.8927, -1.0177, 1.7768], m3, decimal=4) val = [[ 0.1473, -0.1677, 0.0163], [-0.1677, 0.228 , -0.0304], [ 0.0163, -0.0304, 0.0112]] assert_almost_equal(val, cov3, decimal=4) # check 2D (n,1) case y = y[:, np.newaxis] c = c[:, np.newaxis] assert_almost_equal(c, np.polyfit(x, y, 2)) # check 2D (n,2) case yy = np.concatenate((y, y), axis=1) cc = np.concatenate((c, c), axis=1) assert_almost_equal(cc, np.polyfit(x, yy, 2)) m, cov = np.polyfit(x, yy + np.array(err)[:, np.newaxis], 2, cov=True) assert_almost_equal(est, m[:, 0], decimal=4) assert_almost_equal(est, m[:, 1], decimal=4) assert_almost_equal(val0, cov[:, :, 0], decimal=4) assert_almost_equal(val0, cov[:, :, 1], decimal=4) # check order 1 (deg=0) case, were the analytic results are simple np.random.seed(123) y = np.random.normal(size=(4, 10000)) mean, cov = np.polyfit(np.zeros(y.shape[0]), y, deg=0, cov=True) # Should get sigma_mean = sigma/sqrt(N) = 1./sqrt(4) = 0.5. assert_allclose(mean.std(), 0.5, atol=0.01) assert_allclose(np.sqrt(cov.mean()), 0.5, atol=0.01) # Without scaling, since reduced chi2 is 1, the result should be the same. mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=np.ones(y.shape[0]), deg=0, cov="unscaled") assert_allclose(mean.std(), 0.5, atol=0.01) assert_almost_equal(np.sqrt(cov.mean()), 0.5) # If we estimate our errors wrong, no change with scaling: w = np.full(y.shape[0], 1./0.5) mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=w, deg=0, cov=True) assert_allclose(mean.std(), 0.5, atol=0.01) assert_allclose(np.sqrt(cov.mean()), 0.5, atol=0.01) # But if we do not scale, our estimate for the error in the mean will # differ. mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=w, deg=0, cov="unscaled") assert_allclose(mean.std(), 0.5, atol=0.01) assert_almost_equal(np.sqrt(cov.mean()), 0.25) def test_objects(self): from decimal import Decimal p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')]) p2 = p Decimal('1.333333333333333') assert_(p2[1] == Decimal("3.9999999999999990")) p2 = p.deriv() assert_(p2[1] == Decimal('8.0')) p2 = p.integ() assert_(p2[3] == Decimal("1.333333333333333333333333333")) assert_(p2[2] == Decimal('1.5')) assert_(np.issubdtype(p2.coeffs.dtype, np.object_)) p = np.poly([Decimal(1), Decimal(2)]) assert_equal(np.poly([Decimal(1), Decimal(2)]), [1, Decimal(-3), Decimal(2)]) def test_complex(self): p = np.poly1d([3j, 2j, 1j]) p2 = p.integ() assert_((p2.coeffs == [1j, 1j, 1j, 0]).all()) p2 = p.deriv() assert_((p2.coeffs == [6j, 2j]).all()) def test_integ_coeffs(self): p = np.poly1d([3, 2, 1]) p2 = p.integ(3, k=[9, 7, 6]) assert_( (p2.coeffs == [1/4./5., 1/3./4., 1/2./3., 9/1./2., 7, 6]).all()) def test_zero_dims(self): try: np.poly(np.zeros((0, 0))) except ValueError: pass def test_poly_int_overflow(self): """ Regression test for gh-5096. """ v = np.arange(1, 21) assert_almost_equal(np.poly(v), np.poly(np.diag(v))) def test_zero_poly_dtype(self): """ Regression test for gh-16354. """ z = np.array([0, 0, 0]) p = np.poly1d(z.astype(np.int64)) assert_equal(p.coeffs.dtype, np.int64) p = np.poly1d(z.astype(np.float32)) assert_equal(p.coeffs.dtype, np.float32) p = np.poly1d(z.astype(np.complex64)) assert_equal(p.coeffs.dtype, np.complex64) def test_poly_eq(self): p = np.poly1d([1, 2, 3]) p2 = np.poly1d([1, 2, 4]) assert_equal(p == None, False) assert_equal(p != None, True) assert_equal(p == p, True) assert_equal(p == p2, False) assert_equal(p != p2, True) def test_polydiv(self): b = np.poly1d([2, 6, 6, 1]) a = np.poly1d([-1j, (1+2j), -(2+1j), 1]) q, r = np.polydiv(b, a) assert_equal(q.coeffs.dtype, np.complex128) assert_equal(r.coeffs.dtype, np.complex128) assert_equal(q*a + r, b) c = [1, 2, 3] d = np.poly1d([1, 2, 3]) s, t = np.polydiv(c, d) assert isinstance(s, np.poly1d) assert isinstance(t, np.poly1d) u, v = np.polydiv(d, c) assert isinstance(u, np.poly1d) assert isinstance(v, np.poly1d) def test_poly_coeffs_mutable(self): """ Coefficients should be modifiable """ p = np.poly1d([1, 2, 3]) p.coeffs += 1 assert_equal(p.coeffs, [2, 3, 4]) p.coeffs[2] += 10 assert_equal(p.coeffs, [2, 3, 14]) # this never used to be allowed - let's not add features to deprecated # APIs assert_raises(AttributeError, setattr, p, 'coeffs', np.array(1))	11.13 KB	2026-01-20 12:58
📄 test_recfunctions.py import pytest import numpy as np import numpy.ma as ma from numpy.ma.mrecords import MaskedRecords from numpy.ma.testutils import assert_equal from numpy.testing import assert_, assert_raises from numpy.lib.recfunctions import ( drop_fields, rename_fields, get_fieldstructure, recursive_fill_fields, find_duplicates, merge_arrays, append_fields, stack_arrays, join_by, repack_fields, unstructured_to_structured, structured_to_unstructured, apply_along_fields, require_fields, assign_fields_by_name) get_fieldspec = np.lib.recfunctions._get_fieldspec get_names = np.lib.recfunctions.get_names get_names_flat = np.lib.recfunctions.get_names_flat zip_descr = np.lib.recfunctions._zip_descr zip_dtype = np.lib.recfunctions._zip_dtype class TestRecFunctions: # Misc tests def setup_method(self): x = np.array([1, 2, ]) y = np.array([10, 20, 30]) z = np.array([('A', 1.), ('B', 2.)], dtype=[('A', '\|S3'), ('B', float)]) w = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) self.data = (w, x, y, z) def test_zip_descr(self): # Test zip_descr (w, x, y, z) = self.data # Std array test = zip_descr((x, x), flatten=True) assert_equal(test, np.dtype([('', int), ('', int)])) test = zip_descr((x, x), flatten=False) assert_equal(test, np.dtype([('', int), ('', int)])) # Std & flexible-dtype test = zip_descr((x, z), flatten=True) assert_equal(test, np.dtype([('', int), ('A', '\|S3'), ('B', float)])) test = zip_descr((x, z), flatten=False) assert_equal(test, np.dtype([('', int), ('', [('A', '\|S3'), ('B', float)])])) # Standard & nested dtype test = zip_descr((x, w), flatten=True) assert_equal(test, np.dtype([('', int), ('a', int), ('ba', float), ('bb', int)])) test = zip_descr((x, w), flatten=False) assert_equal(test, np.dtype([('', int), ('', [('a', int), ('b', [('ba', float), ('bb', int)])])])) def test_drop_fields(self): # Test drop_fields a = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) # A basic field test = drop_fields(a, 'a') control = np.array([((2, 3.0),), ((5, 6.0),)], dtype=[('b', [('ba', float), ('bb', int)])]) assert_equal(test, control) # Another basic field (but nesting two fields) test = drop_fields(a, 'b') control = np.array([(1,), (4,)], dtype=[('a', int)]) assert_equal(test, control) # A nested sub-field test = drop_fields(a, ['ba', ]) control = np.array([(1, (3.0,)), (4, (6.0,))], dtype=[('a', int), ('b', [('bb', int)])]) assert_equal(test, control) # All the nested sub-field from a field: zap that field test = drop_fields(a, ['ba', 'bb']) control = np.array([(1,), (4,)], dtype=[('a', int)]) assert_equal(test, control) # dropping all fields results in an array with no fields test = drop_fields(a, ['a', 'b']) control = np.array([(), ()], dtype=[]) assert_equal(test, control) def test_rename_fields(self): # Test rename fields a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))], dtype=[('a', int), ('b', [('ba', float), ('bb', (float, 2))])]) test = rename_fields(a, {'a': 'A', 'bb': 'BB'}) newdtype = [('A', int), ('b', [('ba', float), ('BB', (float, 2))])] control = a.view(newdtype) assert_equal(test.dtype, newdtype) assert_equal(test, control) def test_get_names(self): # Test get_names ndtype = np.dtype([('A', '\|S3'), ('B', float)]) test = get_names(ndtype) assert_equal(test, ('A', 'B')) ndtype = np.dtype([('a', int), ('b', [('ba', float), ('bb', int)])]) test = get_names(ndtype) assert_equal(test, ('a', ('b', ('ba', 'bb')))) ndtype = np.dtype([('a', int), ('b', [])]) test = get_names(ndtype) assert_equal(test, ('a', ('b', ()))) ndtype = np.dtype([]) test = get_names(ndtype) assert_equal(test, ()) def test_get_names_flat(self): # Test get_names_flat ndtype = np.dtype([('A', '\|S3'), ('B', float)]) test = get_names_flat(ndtype) assert_equal(test, ('A', 'B')) ndtype = np.dtype([('a', int), ('b', [('ba', float), ('bb', int)])]) test = get_names_flat(ndtype) assert_equal(test, ('a', 'b', 'ba', 'bb')) ndtype = np.dtype([('a', int), ('b', [])]) test = get_names_flat(ndtype) assert_equal(test, ('a', 'b')) ndtype = np.dtype([]) test = get_names_flat(ndtype) assert_equal(test, ()) def test_get_fieldstructure(self): # Test get_fieldstructure # No nested fields ndtype = np.dtype([('A', '\|S3'), ('B', float)]) test = get_fieldstructure(ndtype) assert_equal(test, {'A': [], 'B': []}) # One 1-nested field ndtype = np.dtype([('A', int), ('B', [('BA', float), ('BB', '\|S1')])]) test = get_fieldstructure(ndtype) assert_equal(test, {'A': [], 'B': [], 'BA': ['B', ], 'BB': ['B']}) # One 2-nested fields ndtype = np.dtype([('A', int), ('B', [('BA', int), ('BB', [('BBA', int), ('BBB', int)])])]) test = get_fieldstructure(ndtype) control = {'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'], 'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']} assert_equal(test, control) # 0 fields ndtype = np.dtype([]) test = get_fieldstructure(ndtype) assert_equal(test, {}) def test_find_duplicates(self): # Test find_duplicates a = ma.array([(2, (2., 'B')), (1, (2., 'B')), (2, (2., 'B')), (1, (1., 'B')), (2, (2., 'B')), (2, (2., 'C'))], mask=[(0, (0, 0)), (0, (0, 0)), (0, (0, 0)), (0, (0, 0)), (1, (0, 0)), (0, (1, 0))], dtype=[('A', int), ('B', [('BA', float), ('BB', '\|S1')])]) test = find_duplicates(a, ignoremask=False, return_index=True) control = [0, 2] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='A', return_index=True) control = [0, 1, 2, 3, 5] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='B', return_index=True) control = [0, 1, 2, 4] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='BA', return_index=True) control = [0, 1, 2, 4] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, key='BB', return_index=True) control = [0, 1, 2, 3, 4] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) def test_find_duplicates_ignoremask(self): # Test the ignoremask option of find_duplicates ndtype = [('a', int)] a = ma.array([1, 1, 1, 2, 2, 3, 3], mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype) test = find_duplicates(a, ignoremask=True, return_index=True) control = [0, 1, 3, 4] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) test = find_duplicates(a, ignoremask=False, return_index=True) control = [0, 1, 2, 3, 4, 6] assert_equal(sorted(test[-1]), control) assert_equal(test[0], a[test[-1]]) def test_repack_fields(self): dt = np.dtype('u1,f4,i8', align=True) a = np.zeros(2, dtype=dt) assert_equal(repack_fields(dt), np.dtype('u1,f4,i8')) assert_equal(repack_fields(a).itemsize, 13) assert_equal(repack_fields(repack_fields(dt), align=True), dt) # make sure type is preserved dt = np.dtype((np.record, dt)) assert_(repack_fields(dt).type is np.record) def test_structured_to_unstructured(self, tmp_path): a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)]) out = structured_to_unstructured(a) assert_equal(out, np.zeros((4,5), dtype='f8')) b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)], dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')]) out = np.mean(structured_to_unstructured(b[['x', 'z']]), axis=-1) assert_equal(out, np.array([ 3. , 5.5, 9. , 11. ])) out = np.mean(structured_to_unstructured(b[['x']]), axis=-1) assert_equal(out, np.array([ 1. , 4. , 7. , 10. ])) c = np.arange(20).reshape((4,5)) out = unstructured_to_structured(c, a.dtype) want = np.array([( 0, ( 1., 2), [ 3., 4.]), ( 5, ( 6., 7), [ 8., 9.]), (10, (11., 12), [13., 14.]), (15, (16., 17), [18., 19.])], dtype=[('a', 'i4'), ('b', [('f0', 'f4'), ('f1', 'u2')]), ('c', 'f4', (2,))]) assert_equal(out, want) d = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)], dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')]) assert_equal(apply_along_fields(np.mean, d), np.array([ 8.0/3, 16.0/3, 26.0/3, 11. ])) assert_equal(apply_along_fields(np.mean, d[['x', 'z']]), np.array([ 3. , 5.5, 9. , 11. ])) # check that for uniform field dtypes we get a view, not a copy: d = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)], dtype=[('x', 'i4'), ('y', 'i4'), ('z', 'i4')]) dd = structured_to_unstructured(d) ddd = unstructured_to_structured(dd, d.dtype) assert_(np.shares_memory(dd, d)) assert_(np.shares_memory(ddd, d)) # check that reversing the order of attributes works dd_attrib_rev = structured_to_unstructured(d[['z', 'x']]) assert_equal(dd_attrib_rev, [[5, 1], [7, 4], [11, 7], [12, 10]]) assert_(np.shares_memory(dd_attrib_rev, d)) # including uniform fields with subarrays unpacked d = np.array([(1, [2, 3], [[ 4, 5], [ 6, 7]]), (8, [9, 10], [[11, 12], [13, 14]])], dtype=[('x0', 'i4'), ('x1', ('i4', 2)), ('x2', ('i4', (2, 2)))]) dd = structured_to_unstructured(d) ddd = unstructured_to_structured(dd, d.dtype) assert_(np.shares_memory(dd, d)) assert_(np.shares_memory(ddd, d)) # check that reversing with sub-arrays works as expected d_rev = d[::-1] dd_rev = structured_to_unstructured(d_rev) assert_equal(dd_rev, [[8, 9, 10, 11, 12, 13, 14], [1, 2, 3, 4, 5, 6, 7]]) # check that sub-arrays keep the order of their values d_attrib_rev = d[['x2', 'x1', 'x0']] dd_attrib_rev = structured_to_unstructured(d_attrib_rev) assert_equal(dd_attrib_rev, [[4, 5, 6, 7, 2, 3, 1], [11, 12, 13, 14, 9, 10, 8]]) # with ignored field at the end d = np.array([(1, [2, 3], [[4, 5], [6, 7]], 32), (8, [9, 10], [[11, 12], [13, 14]], 64)], dtype=[('x0', 'i4'), ('x1', ('i4', 2)), ('x2', ('i4', (2, 2))), ('ignored', 'u1')]) dd = structured_to_unstructured(d[['x0', 'x1', 'x2']]) assert_(np.shares_memory(dd, d)) assert_equal(dd, [[1, 2, 3, 4, 5, 6, 7], [8, 9, 10, 11, 12, 13, 14]]) # test that nested fields with identical names don't break anything point = np.dtype([('x', int), ('y', int)]) triangle = np.dtype([('a', point), ('b', point), ('c', point)]) arr = np.zeros(10, triangle) res = structured_to_unstructured(arr, dtype=int) assert_equal(res, np.zeros((10, 6), dtype=int)) # test nested combinations of subarrays and structured arrays, gh-13333 def subarray(dt, shape): return np.dtype((dt, shape)) def structured(*dts): return np.dtype([('x{}'.format(i), dt) for i, dt in enumerate(dts)]) def inspect(dt, dtype=None): arr = np.zeros((), dt) ret = structured_to_unstructured(arr, dtype=dtype) backarr = unstructured_to_structured(ret, dt) return ret.shape, ret.dtype, backarr.dtype dt = structured(subarray(structured(np.int32, np.int32), 3)) assert_equal(inspect(dt), ((6,), np.int32, dt)) dt = structured(subarray(subarray(np.int32, 2), 2)) assert_equal(inspect(dt), ((4,), np.int32, dt)) dt = structured(np.int32) assert_equal(inspect(dt), ((1,), np.int32, dt)) dt = structured(np.int32, subarray(subarray(np.int32, 2), 2)) assert_equal(inspect(dt), ((5,), np.int32, dt)) dt = structured() assert_raises(ValueError, structured_to_unstructured, np.zeros(3, dt)) # these currently don't work, but we may make it work in the future assert_raises(NotImplementedError, structured_to_unstructured, np.zeros(3, dt), dtype=np.int32) assert_raises(NotImplementedError, unstructured_to_structured, np.zeros((3,0), dtype=np.int32)) # test supported ndarray subclasses d_plain = np.array([(1, 2), (3, 4)], dtype=[('a', 'i4'), ('b', 'i4')]) dd_expected = structured_to_unstructured(d_plain, copy=True) # recarray d = d_plain.view(np.recarray) dd = structured_to_unstructured(d, copy=False) ddd = structured_to_unstructured(d, copy=True) assert_(np.shares_memory(d, dd)) assert_(type(dd) is np.recarray) assert_(type(ddd) is np.recarray) assert_equal(dd, dd_expected) assert_equal(ddd, dd_expected) # memmap d = np.memmap(tmp_path / 'memmap', mode='w+', dtype=d_plain.dtype, shape=d_plain.shape) d[:] = d_plain dd = structured_to_unstructured(d, copy=False) ddd = structured_to_unstructured(d, copy=True) assert_(np.shares_memory(d, dd)) assert_(type(dd) is np.memmap) assert_(type(ddd) is np.memmap) assert_equal(dd, dd_expected) assert_equal(ddd, dd_expected) def test_unstructured_to_structured(self): # test if dtype is the args of np.dtype a = np.zeros((20, 2)) test_dtype_args = [('x', float), ('y', float)] test_dtype = np.dtype(test_dtype_args) field1 = unstructured_to_structured(a, dtype=test_dtype_args) # now field2 = unstructured_to_structured(a, dtype=test_dtype) # before assert_equal(field1, field2) def test_field_assignment_by_name(self): a = np.ones(2, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')]) newdt = [('b', 'f4'), ('c', 'u1')] assert_equal(require_fields(a, newdt), np.ones(2, newdt)) b = np.array([(1,2), (3,4)], dtype=newdt) assign_fields_by_name(a, b, zero_unassigned=False) assert_equal(a, np.array([(1,1,2),(1,3,4)], dtype=a.dtype)) assign_fields_by_name(a, b) assert_equal(a, np.array([(0,1,2),(0,3,4)], dtype=a.dtype)) # test nested fields a = np.ones(2, dtype=[('a', [('b', 'f8'), ('c', 'u1')])]) newdt = [('a', [('c', 'u1')])] assert_equal(require_fields(a, newdt), np.ones(2, newdt)) b = np.array([((2,),), ((3,),)], dtype=newdt) assign_fields_by_name(a, b, zero_unassigned=False) assert_equal(a, np.array([((1,2),), ((1,3),)], dtype=a.dtype)) assign_fields_by_name(a, b) assert_equal(a, np.array([((0,2),), ((0,3),)], dtype=a.dtype)) # test unstructured code path for 0d arrays a, b = np.array(3), np.array(0) assign_fields_by_name(b, a) assert_equal(b[()], 3) class TestRecursiveFillFields: # Test recursive_fill_fields. def test_simple_flexible(self): # Test recursive_fill_fields on flexible-array a = np.array([(1, 10.), (2, 20.)], dtype=[('A', int), ('B', float)]) b = np.zeros((3,), dtype=a.dtype) test = recursive_fill_fields(a, b) control = np.array([(1, 10.), (2, 20.), (0, 0.)], dtype=[('A', int), ('B', float)]) assert_equal(test, control) def test_masked_flexible(self): # Test recursive_fill_fields on masked flexible-array a = ma.array([(1, 10.), (2, 20.)], mask=[(0, 1), (1, 0)], dtype=[('A', int), ('B', float)]) b = ma.zeros((3,), dtype=a.dtype) test = recursive_fill_fields(a, b) control = ma.array([(1, 10.), (2, 20.), (0, 0.)], mask=[(0, 1), (1, 0), (0, 0)], dtype=[('A', int), ('B', float)]) assert_equal(test, control) class TestMergeArrays: # Test merge_arrays def setup_method(self): x = np.array([1, 2, ]) y = np.array([10, 20, 30]) z = np.array( [('A', 1.), ('B', 2.)], dtype=[('A', '\|S3'), ('B', float)]) w = np.array( [(1, (2, 3.0, ())), (4, (5, 6.0, ()))], dtype=[('a', int), ('b', [('ba', float), ('bb', int), ('bc', [])])]) self.data = (w, x, y, z) def test_solo(self): # Test merge_arrays on a single array. (_, x, _, z) = self.data test = merge_arrays(x) control = np.array([(1,), (2,)], dtype=[('f0', int)]) assert_equal(test, control) test = merge_arrays((x,)) assert_equal(test, control) test = merge_arrays(z, flatten=False) assert_equal(test, z) test = merge_arrays(z, flatten=True) assert_equal(test, z) def test_solo_w_flatten(self): # Test merge_arrays on a single array w & w/o flattening w = self.data[0] test = merge_arrays(w, flatten=False) assert_equal(test, w) test = merge_arrays(w, flatten=True) control = np.array([(1, 2, 3.0), (4, 5, 6.0)], dtype=[('a', int), ('ba', float), ('bb', int)]) assert_equal(test, control) def test_standard(self): # Test standard & standard # Test merge arrays (_, x, y, _) = self.data test = merge_arrays((x, y), usemask=False) control = np.array([(1, 10), (2, 20), (-1, 30)], dtype=[('f0', int), ('f1', int)]) assert_equal(test, control) test = merge_arrays((x, y), usemask=True) control = ma.array([(1, 10), (2, 20), (-1, 30)], mask=[(0, 0), (0, 0), (1, 0)], dtype=[('f0', int), ('f1', int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) def test_flatten(self): # Test standard & flexible (_, x, _, z) = self.data test = merge_arrays((x, z), flatten=True) control = np.array([(1, 'A', 1.), (2, 'B', 2.)], dtype=[('f0', int), ('A', '\|S3'), ('B', float)]) assert_equal(test, control) test = merge_arrays((x, z), flatten=False) control = np.array([(1, ('A', 1.)), (2, ('B', 2.))], dtype=[('f0', int), ('f1', [('A', '\|S3'), ('B', float)])]) assert_equal(test, control) def test_flatten_wflexible(self): # Test flatten standard & nested (w, x, _, _) = self.data test = merge_arrays((x, w), flatten=True) control = np.array([(1, 1, 2, 3.0), (2, 4, 5, 6.0)], dtype=[('f0', int), ('a', int), ('ba', float), ('bb', int)]) assert_equal(test, control) test = merge_arrays((x, w), flatten=False) controldtype = [('f0', int), ('f1', [('a', int), ('b', [('ba', float), ('bb', int), ('bc', [])])])] control = np.array([(1., (1, (2, 3.0, ()))), (2, (4, (5, 6.0, ())))], dtype=controldtype) assert_equal(test, control) def test_wmasked_arrays(self): # Test merge_arrays masked arrays (_, x, _, _) = self.data mx = ma.array([1, 2, 3], mask=[1, 0, 0]) test = merge_arrays((x, mx), usemask=True) control = ma.array([(1, 1), (2, 2), (-1, 3)], mask=[(0, 1), (0, 0), (1, 0)], dtype=[('f0', int), ('f1', int)]) assert_equal(test, control) test = merge_arrays((x, mx), usemask=True, asrecarray=True) assert_equal(test, control) assert_(isinstance(test, MaskedRecords)) def test_w_singlefield(self): # Test single field test = merge_arrays((np.array([1, 2]).view([('a', int)]), np.array([10., 20., 30.])),) control = ma.array([(1, 10.), (2, 20.), (-1, 30.)], mask=[(0, 0), (0, 0), (1, 0)], dtype=[('a', int), ('f1', float)]) assert_equal(test, control) def test_w_shorter_flex(self): # Test merge_arrays w/ a shorter flexndarray. z = self.data[-1] # Fixme, this test looks incomplete and broken #test = merge_arrays((z, np.array([10, 20, 30]).view([('C', int)]))) #control = np.array([('A', 1., 10), ('B', 2., 20), ('-1', -1, 20)], # dtype=[('A', '\|S3'), ('B', float), ('C', int)]) #assert_equal(test, control) # Hack to avoid pyflakes warnings about unused variables merge_arrays((z, np.array([10, 20, 30]).view([('C', int)]))) np.array([('A', 1., 10), ('B', 2., 20), ('-1', -1, 20)], dtype=[('A', '\|S3'), ('B', float), ('C', int)]) def test_singlerecord(self): (_, x, y, z) = self.data test = merge_arrays((x[0], y[0], z[0]), usemask=False) control = np.array([(1, 10, ('A', 1))], dtype=[('f0', int), ('f1', int), ('f2', [('A', '\|S3'), ('B', float)])]) assert_equal(test, control) class TestAppendFields: # Test append_fields def setup_method(self): x = np.array([1, 2, ]) y = np.array([10, 20, 30]) z = np.array( [('A', 1.), ('B', 2.)], dtype=[('A', '\|S3'), ('B', float)]) w = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) self.data = (w, x, y, z) def test_append_single(self): # Test simple case (_, x, _, _) = self.data test = append_fields(x, 'A', data=[10, 20, 30]) control = ma.array([(1, 10), (2, 20), (-1, 30)], mask=[(0, 0), (0, 0), (1, 0)], dtype=[('f0', int), ('A', int)],) assert_equal(test, control) def test_append_double(self): # Test simple case (_, x, _, _) = self.data test = append_fields(x, ('A', 'B'), data=[[10, 20, 30], [100, 200]]) control = ma.array([(1, 10, 100), (2, 20, 200), (-1, 30, -1)], mask=[(0, 0, 0), (0, 0, 0), (1, 0, 1)], dtype=[('f0', int), ('A', int), ('B', int)],) assert_equal(test, control) def test_append_on_flex(self): # Test append_fields on flexible type arrays z = self.data[-1] test = append_fields(z, 'C', data=[10, 20, 30]) control = ma.array([('A', 1., 10), ('B', 2., 20), (-1, -1., 30)], mask=[(0, 0, 0), (0, 0, 0), (1, 1, 0)], dtype=[('A', '\|S3'), ('B', float), ('C', int)],) assert_equal(test, control) def test_append_on_nested(self): # Test append_fields on nested fields w = self.data[0] test = append_fields(w, 'C', data=[10, 20, 30]) control = ma.array([(1, (2, 3.0), 10), (4, (5, 6.0), 20), (-1, (-1, -1.), 30)], mask=[( 0, (0, 0), 0), (0, (0, 0), 0), (1, (1, 1), 0)], dtype=[('a', int), ('b', [('ba', float), ('bb', int)]), ('C', int)],) assert_equal(test, control) class TestStackArrays: # Test stack_arrays def setup_method(self): x = np.array([1, 2, ]) y = np.array([10, 20, 30]) z = np.array( [('A', 1.), ('B', 2.)], dtype=[('A', '\|S3'), ('B', float)]) w = np.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=[('a', int), ('b', [('ba', float), ('bb', int)])]) self.data = (w, x, y, z) def test_solo(self): # Test stack_arrays on single arrays (_, x, _, _) = self.data test = stack_arrays((x,)) assert_equal(test, x) assert_(test is x) test = stack_arrays(x) assert_equal(test, x) assert_(test is x) def test_unnamed_fields(self): # Tests combinations of arrays w/o named fields (_, x, y, _) = self.data test = stack_arrays((x, x), usemask=False) control = np.array([1, 2, 1, 2]) assert_equal(test, control) test = stack_arrays((x, y), usemask=False) control = np.array([1, 2, 10, 20, 30]) assert_equal(test, control) test = stack_arrays((y, x), usemask=False) control = np.array([10, 20, 30, 1, 2]) assert_equal(test, control) def test_unnamed_and_named_fields(self): # Test combination of arrays w/ & w/o named fields (_, x, _, z) = self.data test = stack_arrays((x, z)) control = ma.array([(1, -1, -1), (2, -1, -1), (-1, 'A', 1), (-1, 'B', 2)], mask=[(0, 1, 1), (0, 1, 1), (1, 0, 0), (1, 0, 0)], dtype=[('f0', int), ('A', '\|S3'), ('B', float)]) assert_equal(test, control) assert_equal(test.mask, control.mask) test = stack_arrays((z, x)) control = ma.array([('A', 1, -1), ('B', 2, -1), (-1, -1, 1), (-1, -1, 2), ], mask=[(0, 0, 1), (0, 0, 1), (1, 1, 0), (1, 1, 0)], dtype=[('A', '\|S3'), ('B', float), ('f2', int)]) assert_equal(test, control) assert_equal(test.mask, control.mask) test = stack_arrays((z, z, x)) control = ma.array([('A', 1, -1), ('B', 2, -1), ('A', 1, -1), ('B', 2, -1), (-1, -1, 1), (-1, -1, 2), ], mask=[(0, 0, 1), (0, 0, 1), (0, 0, 1), (0, 0, 1), (1, 1, 0), (1, 1, 0)], dtype=[('A', '\|S3'), ('B', float), ('f2', int)]) assert_equal(test, control) def test_matching_named_fields(self): # Test combination of arrays w/ matching field names (_, x, _, z) = self.data zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)], dtype=[('A', '\|S3'), ('B', float), ('C', float)]) test = stack_arrays((z, zz)) control = ma.array([('A', 1, -1), ('B', 2, -1), ( 'a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)], dtype=[('A', '\|S3'), ('B', float), ('C', float)], mask=[(0, 0, 1), (0, 0, 1), (0, 0, 0), (0, 0, 0), (0, 0, 0)]) assert_equal(test, control) assert_equal(test.mask, control.mask) test = stack_arrays((z, zz, x)) ndtype = [('A', '\|S3'), ('B', float), ('C', float), ('f3', int)] control = ma.array([('A', 1, -1, -1), ('B', 2, -1, -1), ('a', 10., 100., -1), ('b', 20., 200., -1), ('c', 30., 300., -1), (-1, -1, -1, 1), (-1, -1, -1, 2)], dtype=ndtype, mask=[(0, 0, 1, 1), (0, 0, 1, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (1, 1, 1, 0), (1, 1, 1, 0)]) assert_equal(test, control) assert_equal(test.mask, control.mask) def test_defaults(self): # Test defaults: no exception raised if keys of defaults are not fields. (_, _, _, z) = self.data zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)], dtype=[('A', '\|S3'), ('B', float), ('C', float)]) defaults = {'A': '???', 'B': -999., 'C': -9999., 'D': -99999.} test = stack_arrays((z, zz), defaults=defaults) control = ma.array([('A', 1, -9999.), ('B', 2, -9999.), ( 'a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)], dtype=[('A', '\|S3'), ('B', float), ('C', float)], mask=[(0, 0, 1), (0, 0, 1), (0, 0, 0), (0, 0, 0), (0, 0, 0)]) assert_equal(test, control) assert_equal(test.data, control.data) assert_equal(test.mask, control.mask) def test_autoconversion(self): # Tests autoconversion adtype = [('A', int), ('B', bool), ('C', float)] a = ma.array([(1, 2, 3)], mask=[(0, 1, 0)], dtype=adtype) bdtype = [('A', int), ('B', float), ('C', float)] b = ma.array([(4, 5, 6)], dtype=bdtype) control = ma.array([(1, 2, 3), (4, 5, 6)], mask=[(0, 1, 0), (0, 0, 0)], dtype=bdtype) test = stack_arrays((a, b), autoconvert=True) assert_equal(test, control) assert_equal(test.mask, control.mask) with assert_raises(TypeError): stack_arrays((a, b), autoconvert=False) def test_checktitles(self): # Test using titles in the field names adtype = [(('a', 'A'), int), (('b', 'B'), bool), (('c', 'C'), float)] a = ma.array([(1, 2, 3)], mask=[(0, 1, 0)], dtype=adtype) bdtype = [(('a', 'A'), int), (('b', 'B'), bool), (('c', 'C'), float)] b = ma.array([(4, 5, 6)], dtype=bdtype) test = stack_arrays((a, b)) control = ma.array([(1, 2, 3), (4, 5, 6)], mask=[(0, 1, 0), (0, 0, 0)], dtype=bdtype) assert_equal(test, control) assert_equal(test.mask, control.mask) def test_subdtype(self): z = np.array([ ('A', 1), ('B', 2) ], dtype=[('A', '\|S3'), ('B', float, (1,))]) zz = np.array([ ('a', [10.], 100.), ('b', [20.], 200.), ('c', [30.], 300.) ], dtype=[('A', '\|S3'), ('B', float, (1,)), ('C', float)]) res = stack_arrays((z, zz)) expected = ma.array( data=[ (b'A', [1.0], 0), (b'B', [2.0], 0), (b'a', [10.0], 100.0), (b'b', [20.0], 200.0), (b'c', [30.0], 300.0)], mask=[ (False, [False], True), (False, [False], True), (False, [False], False), (False, [False], False), (False, [False], False) ], dtype=zz.dtype ) assert_equal(res.dtype, expected.dtype) assert_equal(res, expected) assert_equal(res.mask, expected.mask) class TestJoinBy: def setup_method(self): self.a = np.array(list(zip(np.arange(10), np.arange(50, 60), np.arange(100, 110))), dtype=[('a', int), ('b', int), ('c', int)]) self.b = np.array(list(zip(np.arange(5, 15), np.arange(65, 75), np.arange(100, 110))), dtype=[('a', int), ('b', int), ('d', int)]) def test_inner_join(self): # Basic test of join_by a, b = self.a, self.b test = join_by('a', a, b, jointype='inner') control = np.array([(5, 55, 65, 105, 100), (6, 56, 66, 106, 101), (7, 57, 67, 107, 102), (8, 58, 68, 108, 103), (9, 59, 69, 109, 104)], dtype=[('a', int), ('b1', int), ('b2', int), ('c', int), ('d', int)]) assert_equal(test, control) def test_join(self): a, b = self.a, self.b # Fixme, this test is broken #test = join_by(('a', 'b'), a, b) #control = np.array([(5, 55, 105, 100), (6, 56, 106, 101), # (7, 57, 107, 102), (8, 58, 108, 103), # (9, 59, 109, 104)], # dtype=[('a', int), ('b', int), # ('c', int), ('d', int)]) #assert_equal(test, control) # Hack to avoid pyflakes unused variable warnings join_by(('a', 'b'), a, b) np.array([(5, 55, 105, 100), (6, 56, 106, 101), (7, 57, 107, 102), (8, 58, 108, 103), (9, 59, 109, 104)], dtype=[('a', int), ('b', int), ('c', int), ('d', int)]) def test_join_subdtype(self): # tests the bug in https://stackoverflow.com/q/44769632/102441 foo = np.array([(1,)], dtype=[('key', int)]) bar = np.array([(1, np.array([1,2,3]))], dtype=[('key', int), ('value', 'uint16', 3)]) res = join_by('key', foo, bar) assert_equal(res, bar.view(ma.MaskedArray)) def test_outer_join(self): a, b = self.a, self.b test = join_by(('a', 'b'), a, b, 'outer') control = ma.array([(0, 50, 100, -1), (1, 51, 101, -1), (2, 52, 102, -1), (3, 53, 103, -1), (4, 54, 104, -1), (5, 55, 105, -1), (5, 65, -1, 100), (6, 56, 106, -1), (6, 66, -1, 101), (7, 57, 107, -1), (7, 67, -1, 102), (8, 58, 108, -1), (8, 68, -1, 103), (9, 59, 109, -1), (9, 69, -1, 104), (10, 70, -1, 105), (11, 71, -1, 106), (12, 72, -1, 107), (13, 73, -1, 108), (14, 74, -1, 109)], mask=[(0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 1, 0), (0, 0, 0, 1), (0, 0, 1, 0), (0, 0, 0, 1), (0, 0, 1, 0), (0, 0, 0, 1), (0, 0, 1, 0), (0, 0, 0, 1), (0, 0, 1, 0), (0, 0, 1, 0), (0, 0, 1, 0), (0, 0, 1, 0), (0, 0, 1, 0), (0, 0, 1, 0)], dtype=[('a', int), ('b', int), ('c', int), ('d', int)]) assert_equal(test, control) def test_leftouter_join(self): a, b = self.a, self.b test = join_by(('a', 'b'), a, b, 'leftouter') control = ma.array([(0, 50, 100, -1), (1, 51, 101, -1), (2, 52, 102, -1), (3, 53, 103, -1), (4, 54, 104, -1), (5, 55, 105, -1), (6, 56, 106, -1), (7, 57, 107, -1), (8, 58, 108, -1), (9, 59, 109, -1)], mask=[(0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1), (0, 0, 0, 1)], dtype=[('a', int), ('b', int), ('c', int), ('d', int)]) assert_equal(test, control) def test_different_field_order(self): # gh-8940 a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u1')]) b = np.ones(3, dtype=[('c', 'u1'), ('b', 'f4'), ('a', 'i4')]) # this should not give a FutureWarning: j = join_by(['c', 'b'], a, b, jointype='inner', usemask=False) assert_equal(j.dtype.names, ['b', 'c', 'a1', 'a2']) def test_duplicate_keys(self): a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u1')]) b = np.ones(3, dtype=[('c', 'u1'), ('b', 'f4'), ('a', 'i4')]) assert_raises(ValueError, join_by, ['a', 'b', 'b'], a, b) def test_same_name_different_dtypes_key(self): a_dtype = np.dtype([('key', 'S5'), ('value', '<f4')]) b_dtype = np.dtype([('key', 'S10'), ('value', '<f4')]) expected_dtype = np.dtype([ ('key', 'S10'), ('value1', '<f4'), ('value2', '<f4')]) a = np.array([('Sarah', 8.0), ('John', 6.0)], dtype=a_dtype) b = np.array([('Sarah', 10.0), ('John', 7.0)], dtype=b_dtype) res = join_by('key', a, b) assert_equal(res.dtype, expected_dtype) def test_same_name_different_dtypes(self): # gh-9338 a_dtype = np.dtype([('key', 'S10'), ('value', '<f4')]) b_dtype = np.dtype([('key', 'S10'), ('value', '<f8')]) expected_dtype = np.dtype([ ('key', '\|S10'), ('value1', '<f4'), ('value2', '<f8')]) a = np.array([('Sarah', 8.0), ('John', 6.0)], dtype=a_dtype) b = np.array([('Sarah', 10.0), ('John', 7.0)], dtype=b_dtype) res = join_by('key', a, b) assert_equal(res.dtype, expected_dtype) def test_subarray_key(self): a_dtype = np.dtype([('pos', int, 3), ('f', '<f4')]) a = np.array([([1, 1, 1], np.pi), ([1, 2, 3], 0.0)], dtype=a_dtype) b_dtype = np.dtype([('pos', int, 3), ('g', '<f4')]) b = np.array([([1, 1, 1], 3), ([3, 2, 1], 0.0)], dtype=b_dtype) expected_dtype = np.dtype([('pos', int, 3), ('f', '<f4'), ('g', '<f4')]) expected = np.array([([1, 1, 1], np.pi, 3)], dtype=expected_dtype) res = join_by('pos', a, b) assert_equal(res.dtype, expected_dtype) assert_equal(res, expected) def test_padded_dtype(self): dt = np.dtype('i1,f4', align=True) dt.names = ('k', 'v') assert_(len(dt.descr), 3) # padding field is inserted a = np.array([(1, 3), (3, 2)], dt) b = np.array([(1, 1), (2, 2)], dt) res = join_by('k', a, b) # no padding fields remain expected_dtype = np.dtype([ ('k', 'i1'), ('v1', 'f4'), ('v2', 'f4') ]) assert_equal(res.dtype, expected_dtype) class TestJoinBy2: @classmethod def setup_method(cls): cls.a = np.array(list(zip(np.arange(10), np.arange(50, 60), np.arange(100, 110))), dtype=[('a', int), ('b', int), ('c', int)]) cls.b = np.array(list(zip(np.arange(10), np.arange(65, 75), np.arange(100, 110))), dtype=[('a', int), ('b', int), ('d', int)]) def test_no_r1postfix(self): # Basic test of join_by no_r1postfix a, b = self.a, self.b test = join_by( 'a', a, b, r1postfix='', r2postfix='2', jointype='inner') control = np.array([(0, 50, 65, 100, 100), (1, 51, 66, 101, 101), (2, 52, 67, 102, 102), (3, 53, 68, 103, 103), (4, 54, 69, 104, 104), (5, 55, 70, 105, 105), (6, 56, 71, 106, 106), (7, 57, 72, 107, 107), (8, 58, 73, 108, 108), (9, 59, 74, 109, 109)], dtype=[('a', int), ('b', int), ('b2', int), ('c', int), ('d', int)]) assert_equal(test, control) def test_no_postfix(self): assert_raises(ValueError, join_by, 'a', self.a, self.b, r1postfix='', r2postfix='') def test_no_r2postfix(self): # Basic test of join_by no_r2postfix a, b = self.a, self.b test = join_by( 'a', a, b, r1postfix='1', r2postfix='', jointype='inner') control = np.array([(0, 50, 65, 100, 100), (1, 51, 66, 101, 101), (2, 52, 67, 102, 102), (3, 53, 68, 103, 103), (4, 54, 69, 104, 104), (5, 55, 70, 105, 105), (6, 56, 71, 106, 106), (7, 57, 72, 107, 107), (8, 58, 73, 108, 108), (9, 59, 74, 109, 109)], dtype=[('a', int), ('b1', int), ('b', int), ('c', int), ('d', int)]) assert_equal(test, control) def test_two_keys_two_vars(self): a = np.array(list(zip(np.tile([10, 11], 5), np.repeat(np.arange(5), 2), np.arange(50, 60), np.arange(10, 20))), dtype=[('k', int), ('a', int), ('b', int), ('c', int)]) b = np.array(list(zip(np.tile([10, 11], 5), np.repeat(np.arange(5), 2), np.arange(65, 75), np.arange(0, 10))), dtype=[('k', int), ('a', int), ('b', int), ('c', int)]) control = np.array([(10, 0, 50, 65, 10, 0), (11, 0, 51, 66, 11, 1), (10, 1, 52, 67, 12, 2), (11, 1, 53, 68, 13, 3), (10, 2, 54, 69, 14, 4), (11, 2, 55, 70, 15, 5), (10, 3, 56, 71, 16, 6), (11, 3, 57, 72, 17, 7), (10, 4, 58, 73, 18, 8), (11, 4, 59, 74, 19, 9)], dtype=[('k', int), ('a', int), ('b1', int), ('b2', int), ('c1', int), ('c2', int)]) test = join_by( ['a', 'k'], a, b, r1postfix='1', r2postfix='2', jointype='inner') assert_equal(test.dtype, control.dtype) assert_equal(test, control) class TestAppendFieldsObj: """ Test append_fields with arrays containing objects """ # https://github.com/numpy/numpy/issues/2346 def setup_method(self): from datetime import date self.data = dict(obj=date(2000, 1, 1)) def test_append_to_objects(self): "Test append_fields when the base array contains objects" obj = self.data['obj'] x = np.array([(obj, 1.), (obj, 2.)], dtype=[('A', object), ('B', float)]) y = np.array([10, 20], dtype=int) test = append_fields(x, 'C', data=y, usemask=False) control = np.array([(obj, 1.0, 10), (obj, 2.0, 20)], dtype=[('A', object), ('B', float), ('C', int)]) assert_equal(test, control)	42.97 KB	2026-01-20 12:58
📄 test_regression.py import os import numpy as np from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_array_almost_equal, assert_raises, _assert_valid_refcount, ) class TestRegression: def test_poly1d(self): # Ticket #28 assert_equal(np.poly1d([1]) - np.poly1d([1, 0]), np.poly1d([-1, 1])) def test_cov_parameters(self): # Ticket #91 x = np.random.random((3, 3)) y = x.copy() np.cov(x, rowvar=True) np.cov(y, rowvar=False) assert_array_equal(x, y) def test_mem_digitize(self): # Ticket #95 for i in range(100): np.digitize([1, 2, 3, 4], [1, 3]) np.digitize([0, 1, 2, 3, 4], [1, 3]) def test_unique_zero_sized(self): # Ticket #205 assert_array_equal([], np.unique(np.array([]))) def test_mem_vectorise(self): # Ticket #325 vt = np.vectorize(lambda args: args) vt(np.zeros((1, 2, 1)), np.zeros((2, 1, 1)), np.zeros((1, 1, 2))) vt(np.zeros((1, 2, 1)), np.zeros((2, 1, 1)), np.zeros((1, 1, 2)), np.zeros((2, 2))) def test_mgrid_single_element(self): # Ticket #339 assert_array_equal(np.mgrid[0:0:1j], [0]) assert_array_equal(np.mgrid[0:0], []) def test_refcount_vectorize(self): # Ticket #378 def p(x, y): return 123 v = np.vectorize(p) _assert_valid_refcount(v) def test_poly1d_nan_roots(self): # Ticket #396 p = np.poly1d([np.nan, np.nan, 1], r=False) assert_raises(np.linalg.LinAlgError, getattr, p, "r") def test_mem_polymul(self): # Ticket #448 np.polymul([], [1.]) def test_mem_string_concat(self): # Ticket #469 x = np.array([]) np.append(x, 'asdasd\tasdasd') def test_poly_div(self): # Ticket #553 u = np.poly1d([1, 2, 3]) v = np.poly1d([1, 2, 3, 4, 5]) q, r = np.polydiv(u, v) assert_equal(qv + r, u) def test_poly_eq(self): # Ticket #554 x = np.poly1d([1, 2, 3]) y = np.poly1d([3, 4]) assert_(x != y) assert_(x == x) def test_polyfit_build(self): # Ticket #628 ref = [-1.06123820e-06, 5.70886914e-04, -1.13822012e-01, 9.95368241e+00, -3.14526520e+02] x = [90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176] y = [9.0, 3.0, 7.0, 4.0, 4.0, 8.0, 6.0, 11.0, 9.0, 8.0, 11.0, 5.0, 6.0, 5.0, 9.0, 8.0, 6.0, 10.0, 6.0, 10.0, 7.0, 6.0, 6.0, 6.0, 13.0, 4.0, 9.0, 11.0, 4.0, 5.0, 8.0, 5.0, 7.0, 7.0, 6.0, 12.0, 7.0, 7.0, 9.0, 4.0, 12.0, 6.0, 6.0, 4.0, 3.0, 9.0, 8.0, 8.0, 6.0, 7.0, 9.0, 10.0, 6.0, 8.0, 4.0, 7.0, 7.0, 10.0, 8.0, 8.0, 6.0, 3.0, 8.0, 4.0, 5.0, 7.0, 8.0, 6.0, 6.0, 4.0, 12.0, 9.0, 8.0, 8.0, 8.0, 6.0, 7.0, 4.0, 4.0, 5.0, 7.0] tested = np.polyfit(x, y, 4) assert_array_almost_equal(ref, tested) def test_polydiv_type(self): # Make polydiv work for complex types msg = "Wrong type, should be complex" x = np.ones(3, dtype=complex) q, r = np.polydiv(x, x) assert_(q.dtype == complex, msg) msg = "Wrong type, should be float" x = np.ones(3, dtype=int) q, r = np.polydiv(x, x) assert_(q.dtype == float, msg) def test_histogramdd_too_many_bins(self): # Ticket 928. assert_raises(ValueError, np.histogramdd, np.ones((1, 10)), bins=2*10) def test_polyint_type(self): # Ticket #944 msg = "Wrong type, should be complex" x = np.ones(3, dtype=complex) assert_(np.polyint(x).dtype == complex, msg) msg = "Wrong type, should be float" x = np.ones(3, dtype=int) assert_(np.polyint(x).dtype == float, msg) def test_ndenumerate_crash(self): # Ticket 1140 # Shouldn't crash: list(np.ndenumerate(np.array([[]]))) def test_asfarray_none(self): # Test for changeset r5065 assert_array_equal(np.array([np.nan]), np.asfarray([None])) def test_large_fancy_indexing(self): # Large enough to fail on 64-bit. nbits = np.dtype(np.intp).itemsize 8 thesize = int((2nbits)(1.0/5.0)+1) def dp(): n = 3 a = np.ones((n,)5) i = np.random.randint(0, n, size=thesize) a[np.ix_(i, i, i, i, i)] = 0 def dp2(): n = 3 a = np.ones((n,)5) i = np.random.randint(0, n, size=thesize) a[np.ix_(i, i, i, i, i)] assert_raises(ValueError, dp) assert_raises(ValueError, dp2) def test_void_coercion(self): dt = np.dtype([('a', 'f4'), ('b', 'i4')]) x = np.zeros((1,), dt) assert_(np.r_[x, x].dtype == dt) def test_who_with_0dim_array(self): # ticket #1243 import os import sys oldstdout = sys.stdout sys.stdout = open(os.devnull, 'w') try: try: np.who({'foo': np.array(1)}) except Exception: raise AssertionError("ticket #1243") finally: sys.stdout.close() sys.stdout = oldstdout def test_include_dirs(self): # As a sanity check, just test that get_include # includes something reasonable. Somewhat # related to ticket #1405. include_dirs = [np.get_include()] for path in include_dirs: assert_(isinstance(path, str)) assert_(path != '') def test_polyder_return_type(self): # Ticket #1249 assert_(isinstance(np.polyder(np.poly1d([1]), 0), np.poly1d)) assert_(isinstance(np.polyder([1], 0), np.ndarray)) assert_(isinstance(np.polyder(np.poly1d([1]), 1), np.poly1d)) assert_(isinstance(np.polyder([1], 1), np.ndarray)) def test_append_fields_dtype_list(self): # Ticket #1676 from numpy.lib.recfunctions import append_fields base = np.array([1, 2, 3], dtype=np.int32) names = ['a', 'b', 'c'] data = np.eye(3).astype(np.int32) dlist = [np.float64, np.int32, np.int32] try: append_fields(base, names, data, dlist) except Exception: raise AssertionError() def test_loadtxt_fields_subarrays(self): # For ticket #1936 from io import StringIO dt = [("a", 'u1', 2), ("b", 'u1', 2)] x = np.loadtxt(StringIO("0 1 2 3"), dtype=dt) assert_equal(x, np.array([((0, 1), (2, 3))], dtype=dt)) dt = [("a", [("a", 'u1', (1, 3)), ("b", 'u1')])] x = np.loadtxt(StringIO("0 1 2 3"), dtype=dt) assert_equal(x, np.array([(((0, 1, 2), 3),)], dtype=dt)) dt = [("a", 'u1', (2, 2))] x = np.loadtxt(StringIO("0 1 2 3"), dtype=dt) assert_equal(x, np.array([(((0, 1), (2, 3)),)], dtype=dt)) dt = [("a", 'u1', (2, 3, 2))] x = np.loadtxt(StringIO("0 1 2 3 4 5 6 7 8 9 10 11"), dtype=dt) data = [((((0, 1), (2, 3), (4, 5)), ((6, 7), (8, 9), (10, 11))),)] assert_equal(x, np.array(data, dtype=dt)) def test_nansum_with_boolean(self): # gh-2978 a = np.zeros(2, dtype=bool) try: np.nansum(a) except Exception: raise AssertionError() def test_py3_compat(self): # gh-2561 # Test if the oldstyle class test is bypassed in python3 class C(): """Old-style class in python2, normal class in python3""" pass out = open(os.devnull, 'w') try: np.info(C(), output=out) except AttributeError: raise AssertionError() finally: out.close()	8.06 KB	2026-01-20 12:58
📄 test_shape_base.py import numpy as np import functools import sys import pytest from numpy.lib.shape_base import ( apply_along_axis, apply_over_axes, array_split, split, hsplit, dsplit, vsplit, dstack, column_stack, kron, tile, expand_dims, take_along_axis, put_along_axis ) from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_raises, assert_warns ) IS_64BIT = sys.maxsize > 232 def _add_keepdims(func): """ hack in keepdims behavior into a function taking an axis """ @functools.wraps(func) def wrapped(a, axis, kwargs): res = func(a, axis=axis, kwargs) if axis is None: axis = 0 # res is now a scalar, so we can insert this anywhere return np.expand_dims(res, axis=axis) return wrapped class TestTakeAlongAxis: def test_argequivalent(self): """ Test it translates from arg<func> to <func> """ from numpy.random import rand a = rand(3, 4, 5) funcs = [ (np.sort, np.argsort, dict()), (_add_keepdims(np.min), _add_keepdims(np.argmin), dict()), (_add_keepdims(np.max), _add_keepdims(np.argmax), dict()), (np.partition, np.argpartition, dict(kth=2)), ] for func, argfunc, kwargs in funcs: for axis in list(range(a.ndim)) + [None]: a_func = func(a, axis=axis, kwargs) ai_func = argfunc(a, axis=axis, *kwargs) assert_equal(a_func, take_along_axis(a, ai_func, axis=axis)) def test_invalid(self): """ Test it errors when indices has too few dimensions """ a = np.ones((10, 10)) ai = np.ones((10, 2), dtype=np.intp) # sanity check take_along_axis(a, ai, axis=1) # not enough indices assert_raises(ValueError, take_along_axis, a, np.array(1), axis=1) # bool arrays not allowed assert_raises(IndexError, take_along_axis, a, ai.astype(bool), axis=1) # float arrays not allowed assert_raises(IndexError, take_along_axis, a, ai.astype(float), axis=1) # invalid axis assert_raises(np.AxisError, take_along_axis, a, ai, axis=10) def test_empty(self): """ Test everything is ok with empty results, even with inserted dims """ a = np.ones((3, 4, 5)) ai = np.ones((3, 0, 5), dtype=np.intp) actual = take_along_axis(a, ai, axis=1) assert_equal(actual.shape, ai.shape) def test_broadcast(self): """ Test that non-indexing dimensions are broadcast in both directions """ a = np.ones((3, 4, 1)) ai = np.ones((1, 2, 5), dtype=np.intp) actual = take_along_axis(a, ai, axis=1) assert_equal(actual.shape, (3, 2, 5)) class TestPutAlongAxis: def test_replace_max(self): a_base = np.array([[10, 30, 20], [60, 40, 50]]) for axis in list(range(a_base.ndim)) + [None]: # we mutate this in the loop a = a_base.copy() # replace the max with a small value i_max = _add_keepdims(np.argmax)(a, axis=axis) put_along_axis(a, i_max, -99, axis=axis) # find the new minimum, which should max i_min = _add_keepdims(np.argmin)(a, axis=axis) assert_equal(i_min, i_max) def test_broadcast(self): """ Test that non-indexing dimensions are broadcast in both directions """ a = np.ones((3, 4, 1)) ai = np.arange(10, dtype=np.intp).reshape((1, 2, 5)) % 4 put_along_axis(a, ai, 20, axis=1) assert_equal(take_along_axis(a, ai, axis=1), 20) class TestApplyAlongAxis: def test_simple(self): a = np.ones((20, 10), 'd') assert_array_equal( apply_along_axis(len, 0, a), len(a)np.ones(a.shape[1])) def test_simple101(self): a = np.ones((10, 101), 'd') assert_array_equal( apply_along_axis(len, 0, a), len(a)np.ones(a.shape[1])) def test_3d(self): a = np.arange(27).reshape((3, 3, 3)) assert_array_equal(apply_along_axis(np.sum, 0, a), [[27, 30, 33], [36, 39, 42], [45, 48, 51]]) def test_preserve_subclass(self): def double(row): return row 2 class MyNDArray(np.ndarray): pass m = np.array([[0, 1], [2, 3]]).view(MyNDArray) expected = np.array([[0, 2], [4, 6]]).view(MyNDArray) result = apply_along_axis(double, 0, m) assert_(isinstance(result, MyNDArray)) assert_array_equal(result, expected) result = apply_along_axis(double, 1, m) assert_(isinstance(result, MyNDArray)) assert_array_equal(result, expected) def test_subclass(self): class MinimalSubclass(np.ndarray): data = 1 def minimal_function(array): return array.data a = np.zeros((6, 3)).view(MinimalSubclass) assert_array_equal( apply_along_axis(minimal_function, 0, a), np.array([1, 1, 1]) ) def test_scalar_array(self, cls=np.ndarray): a = np.ones((6, 3)).view(cls) res = apply_along_axis(np.sum, 0, a) assert_(isinstance(res, cls)) assert_array_equal(res, np.array([6, 6, 6]).view(cls)) def test_0d_array(self, cls=np.ndarray): def sum_to_0d(x): """ Sum x, returning a 0d array of the same class """ assert_equal(x.ndim, 1) return np.squeeze(np.sum(x, keepdims=True)) a = np.ones((6, 3)).view(cls) res = apply_along_axis(sum_to_0d, 0, a) assert_(isinstance(res, cls)) assert_array_equal(res, np.array([6, 6, 6]).view(cls)) res = apply_along_axis(sum_to_0d, 1, a) assert_(isinstance(res, cls)) assert_array_equal(res, np.array([3, 3, 3, 3, 3, 3]).view(cls)) def test_axis_insertion(self, cls=np.ndarray): def f1to2(x): """produces an asymmetric non-square matrix from x""" assert_equal(x.ndim, 1) return (x[::-1] * x[1:,None]).view(cls) a2d = np.arange(63).reshape((6, 3)) # 2d insertion along first axis actual = apply_along_axis(f1to2, 0, a2d) expected = np.stack([ f1to2(a2d[:,i]) for i in range(a2d.shape[1]) ], axis=-1).view(cls) assert_equal(type(actual), type(expected)) assert_equal(actual, expected) # 2d insertion along last axis actual = apply_along_axis(f1to2, 1, a2d) expected = np.stack([ f1to2(a2d[i,:]) for i in range(a2d.shape[0]) ], axis=0).view(cls) assert_equal(type(actual), type(expected)) assert_equal(actual, expected) # 3d insertion along middle axis a3d = np.arange(653).reshape((6, 5, 3)) actual = apply_along_axis(f1to2, 1, a3d) expected = np.stack([ np.stack([ f1to2(a3d[i,:,j]) for i in range(a3d.shape[0]) ], axis=0) for j in range(a3d.shape[2]) ], axis=-1).view(cls) assert_equal(type(actual), type(expected)) assert_equal(actual, expected) def test_subclass_preservation(self): class MinimalSubclass(np.ndarray): pass self.test_scalar_array(MinimalSubclass) self.test_0d_array(MinimalSubclass) self.test_axis_insertion(MinimalSubclass) def test_axis_insertion_ma(self): def f1to2(x): """produces an asymmetric non-square matrix from x""" assert_equal(x.ndim, 1) res = x[::-1] x[1:,None] return np.ma.masked_where(res%5==0, res) a = np.arange(63).reshape((6, 3)) res = apply_along_axis(f1to2, 0, a) assert_(isinstance(res, np.ma.masked_array)) assert_equal(res.ndim, 3) assert_array_equal(res[:,:,0].mask, f1to2(a[:,0]).mask) assert_array_equal(res[:,:,1].mask, f1to2(a[:,1]).mask) assert_array_equal(res[:,:,2].mask, f1to2(a[:,2]).mask) def test_tuple_func1d(self): def sample_1d(x): return x[1], x[0] res = np.apply_along_axis(sample_1d, 1, np.array([[1, 2], [3, 4]])) assert_array_equal(res, np.array([[2, 1], [4, 3]])) def test_empty(self): # can't apply_along_axis when there's no chance to call the function def never_call(x): assert_(False) # should never be reached a = np.empty((0, 0)) assert_raises(ValueError, np.apply_along_axis, never_call, 0, a) assert_raises(ValueError, np.apply_along_axis, never_call, 1, a) # but it's sometimes ok with some non-zero dimensions def empty_to_1(x): assert_(len(x) == 0) return 1 a = np.empty((10, 0)) actual = np.apply_along_axis(empty_to_1, 1, a) assert_equal(actual, np.ones(10)) assert_raises(ValueError, np.apply_along_axis, empty_to_1, 0, a) def test_with_iterable_object(self): # from issue 5248 d = np.array([ [{1, 11}, {2, 22}, {3, 33}], [{4, 44}, {5, 55}, {6, 66}] ]) actual = np.apply_along_axis(lambda a: set.union(a), 0, d) expected = np.array([{1, 11, 4, 44}, {2, 22, 5, 55}, {3, 33, 6, 66}]) assert_equal(actual, expected) # issue 8642 - assert_equal doesn't detect this! for i in np.ndindex(actual.shape): assert_equal(type(actual[i]), type(expected[i])) class TestApplyOverAxes: def test_simple(self): a = np.arange(24).reshape(2, 3, 4) aoa_a = apply_over_axes(np.sum, a, [0, 2]) assert_array_equal(aoa_a, np.array([[[60], [92], [124]]])) class TestExpandDims: def test_functionality(self): s = (2, 3, 4, 5) a = np.empty(s) for axis in range(-5, 4): b = expand_dims(a, axis) assert_(b.shape[axis] == 1) assert_(np.squeeze(b).shape == s) def test_axis_tuple(self): a = np.empty((3, 3, 3)) assert np.expand_dims(a, axis=(0, 1, 2)).shape == (1, 1, 1, 3, 3, 3) assert np.expand_dims(a, axis=(0, -1, -2)).shape == (1, 3, 3, 3, 1, 1) assert np.expand_dims(a, axis=(0, 3, 5)).shape == (1, 3, 3, 1, 3, 1) assert np.expand_dims(a, axis=(0, -3, -5)).shape == (1, 1, 3, 1, 3, 3) def test_axis_out_of_range(self): s = (2, 3, 4, 5) a = np.empty(s) assert_raises(np.AxisError, expand_dims, a, -6) assert_raises(np.AxisError, expand_dims, a, 5) a = np.empty((3, 3, 3)) assert_raises(np.AxisError, expand_dims, a, (0, -6)) assert_raises(np.AxisError, expand_dims, a, (0, 5)) def test_repeated_axis(self): a = np.empty((3, 3, 3)) assert_raises(ValueError, expand_dims, a, axis=(1, 1)) def test_subclasses(self): a = np.arange(10).reshape((2, 5)) a = np.ma.array(a, mask=a%3 == 0) expanded = np.expand_dims(a, axis=1) assert_(isinstance(expanded, np.ma.MaskedArray)) assert_equal(expanded.shape, (2, 1, 5)) assert_equal(expanded.mask.shape, (2, 1, 5)) class TestArraySplit: def test_integer_0_split(self): a = np.arange(10) assert_raises(ValueError, array_split, a, 0) def test_integer_split(self): a = np.arange(10) res = array_split(a, 1) desired = [np.arange(10)] compare_results(res, desired) res = array_split(a, 2) desired = [np.arange(5), np.arange(5, 10)] compare_results(res, desired) res = array_split(a, 3) desired = [np.arange(4), np.arange(4, 7), np.arange(7, 10)] compare_results(res, desired) res = array_split(a, 4) desired = [np.arange(3), np.arange(3, 6), np.arange(6, 8), np.arange(8, 10)] compare_results(res, desired) res = array_split(a, 5) desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6), np.arange(6, 8), np.arange(8, 10)] compare_results(res, desired) res = array_split(a, 6) desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6), np.arange(6, 8), np.arange(8, 9), np.arange(9, 10)] compare_results(res, desired) res = array_split(a, 7) desired = [np.arange(2), np.arange(2, 4), np.arange(4, 6), np.arange(6, 7), np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)] compare_results(res, desired) res = array_split(a, 8) desired = [np.arange(2), np.arange(2, 4), np.arange(4, 5), np.arange(5, 6), np.arange(6, 7), np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)] compare_results(res, desired) res = array_split(a, 9) desired = [np.arange(2), np.arange(2, 3), np.arange(3, 4), np.arange(4, 5), np.arange(5, 6), np.arange(6, 7), np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)] compare_results(res, desired) res = array_split(a, 10) desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3), np.arange(3, 4), np.arange(4, 5), np.arange(5, 6), np.arange(6, 7), np.arange(7, 8), np.arange(8, 9), np.arange(9, 10)] compare_results(res, desired) res = array_split(a, 11) desired = [np.arange(1), np.arange(1, 2), np.arange(2, 3), np.arange(3, 4), np.arange(4, 5), np.arange(5, 6), np.arange(6, 7), np.arange(7, 8), np.arange(8, 9), np.arange(9, 10), np.array([])] compare_results(res, desired) def test_integer_split_2D_rows(self): a = np.array([np.arange(10), np.arange(10)]) res = array_split(a, 3, axis=0) tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]), np.zeros((0, 10))] compare_results(res, tgt) assert_(a.dtype.type is res[-1].dtype.type) # Same thing for manual splits: res = array_split(a, [0, 1], axis=0) tgt = [np.zeros((0, 10)), np.array([np.arange(10)]), np.array([np.arange(10)])] compare_results(res, tgt) assert_(a.dtype.type is res[-1].dtype.type) def test_integer_split_2D_cols(self): a = np.array([np.arange(10), np.arange(10)]) res = array_split(a, 3, axis=-1) desired = [np.array([np.arange(4), np.arange(4)]), np.array([np.arange(4, 7), np.arange(4, 7)]), np.array([np.arange(7, 10), np.arange(7, 10)])] compare_results(res, desired) def test_integer_split_2D_default(self): """ This will fail if we change default axis """ a = np.array([np.arange(10), np.arange(10)]) res = array_split(a, 3) tgt = [np.array([np.arange(10)]), np.array([np.arange(10)]), np.zeros((0, 10))] compare_results(res, tgt) assert_(a.dtype.type is res[-1].dtype.type) # perhaps should check higher dimensions @pytest.mark.skipif(not IS_64BIT, reason="Needs 64bit platform") def test_integer_split_2D_rows_greater_max_int32(self): a = np.broadcast_to([0], (1 << 32, 2)) res = array_split(a, 4) chunk = np.broadcast_to([0], (1 << 30, 2)) tgt = [chunk] * 4 for i in range(len(tgt)): assert_equal(res[i].shape, tgt[i].shape) def test_index_split_simple(self): a = np.arange(10) indices = [1, 5, 7] res = array_split(a, indices, axis=-1) desired = [np.arange(0, 1), np.arange(1, 5), np.arange(5, 7), np.arange(7, 10)] compare_results(res, desired) def test_index_split_low_bound(self): a = np.arange(10) indices = [0, 5, 7] res = array_split(a, indices, axis=-1) desired = [np.array([]), np.arange(0, 5), np.arange(5, 7), np.arange(7, 10)] compare_results(res, desired) def test_index_split_high_bound(self): a = np.arange(10) indices = [0, 5, 7, 10, 12] res = array_split(a, indices, axis=-1) desired = [np.array([]), np.arange(0, 5), np.arange(5, 7), np.arange(7, 10), np.array([]), np.array([])] compare_results(res, desired) class TestSplit: # The split function is essentially the same as array_split, # except that it test if splitting will result in an # equal split. Only test for this case. def test_equal_split(self): a = np.arange(10) res = split(a, 2) desired = [np.arange(5), np.arange(5, 10)] compare_results(res, desired) def test_unequal_split(self): a = np.arange(10) assert_raises(ValueError, split, a, 3) class TestColumnStack: def test_non_iterable(self): assert_raises(TypeError, column_stack, 1) def test_1D_arrays(self): # example from docstring a = np.array((1, 2, 3)) b = np.array((2, 3, 4)) expected = np.array([[1, 2], [2, 3], [3, 4]]) actual = np.column_stack((a, b)) assert_equal(actual, expected) def test_2D_arrays(self): # same as hstack 2D docstring example a = np.array([[1], [2], [3]]) b = np.array([[2], [3], [4]]) expected = np.array([[1, 2], [2, 3], [3, 4]]) actual = np.column_stack((a, b)) assert_equal(actual, expected) def test_generator(self): with pytest.raises(TypeError, match="arrays to stack must be"): column_stack((np.arange(3) for _ in range(2))) class TestDstack: def test_non_iterable(self): assert_raises(TypeError, dstack, 1) def test_0D_array(self): a = np.array(1) b = np.array(2) res = dstack([a, b]) desired = np.array([[[1, 2]]]) assert_array_equal(res, desired) def test_1D_array(self): a = np.array([1]) b = np.array([2]) res = dstack([a, b]) desired = np.array([[[1, 2]]]) assert_array_equal(res, desired) def test_2D_array(self): a = np.array([[1], [2]]) b = np.array([[1], [2]]) res = dstack([a, b]) desired = np.array([[[1, 1]], [[2, 2, ]]]) assert_array_equal(res, desired) def test_2D_array2(self): a = np.array([1, 2]) b = np.array([1, 2]) res = dstack([a, b]) desired = np.array([[[1, 1], [2, 2]]]) assert_array_equal(res, desired) def test_generator(self): with pytest.raises(TypeError, match="arrays to stack must be"): dstack((np.arange(3) for _ in range(2))) # array_split has more comprehensive test of splitting. # only do simple test on hsplit, vsplit, and dsplit class TestHsplit: """Only testing for integer splits. """ def test_non_iterable(self): assert_raises(ValueError, hsplit, 1, 1) def test_0D_array(self): a = np.array(1) try: hsplit(a, 2) assert_(0) except ValueError: pass def test_1D_array(self): a = np.array([1, 2, 3, 4]) res = hsplit(a, 2) desired = [np.array([1, 2]), np.array([3, 4])] compare_results(res, desired) def test_2D_array(self): a = np.array([[1, 2, 3, 4], [1, 2, 3, 4]]) res = hsplit(a, 2) desired = [np.array([[1, 2], [1, 2]]), np.array([[3, 4], [3, 4]])] compare_results(res, desired) class TestVsplit: """Only testing for integer splits. """ def test_non_iterable(self): assert_raises(ValueError, vsplit, 1, 1) def test_0D_array(self): a = np.array(1) assert_raises(ValueError, vsplit, a, 2) def test_1D_array(self): a = np.array([1, 2, 3, 4]) try: vsplit(a, 2) assert_(0) except ValueError: pass def test_2D_array(self): a = np.array([[1, 2, 3, 4], [1, 2, 3, 4]]) res = vsplit(a, 2) desired = [np.array([[1, 2, 3, 4]]), np.array([[1, 2, 3, 4]])] compare_results(res, desired) class TestDsplit: # Only testing for integer splits. def test_non_iterable(self): assert_raises(ValueError, dsplit, 1, 1) def test_0D_array(self): a = np.array(1) assert_raises(ValueError, dsplit, a, 2) def test_1D_array(self): a = np.array([1, 2, 3, 4]) assert_raises(ValueError, dsplit, a, 2) def test_2D_array(self): a = np.array([[1, 2, 3, 4], [1, 2, 3, 4]]) try: dsplit(a, 2) assert_(0) except ValueError: pass def test_3D_array(self): a = np.array([[[1, 2, 3, 4], [1, 2, 3, 4]], [[1, 2, 3, 4], [1, 2, 3, 4]]]) res = dsplit(a, 2) desired = [np.array([[[1, 2], [1, 2]], [[1, 2], [1, 2]]]), np.array([[[3, 4], [3, 4]], [[3, 4], [3, 4]]])] compare_results(res, desired) class TestSqueeze: def test_basic(self): from numpy.random import rand a = rand(20, 10, 10, 1, 1) b = rand(20, 1, 10, 1, 20) c = rand(1, 1, 20, 10) assert_array_equal(np.squeeze(a), np.reshape(a, (20, 10, 10))) assert_array_equal(np.squeeze(b), np.reshape(b, (20, 10, 20))) assert_array_equal(np.squeeze(c), np.reshape(c, (20, 10))) # Squeezing to 0-dim should still give an ndarray a = [[[1.5]]] res = np.squeeze(a) assert_equal(res, 1.5) assert_equal(res.ndim, 0) assert_equal(type(res), np.ndarray) class TestKron: def test_basic(self): # Using 0-dimensional ndarray a = np.array(1) b = np.array([[1, 2], [3, 4]]) k = np.array([[1, 2], [3, 4]]) assert_array_equal(np.kron(a, b), k) a = np.array([[1, 2], [3, 4]]) b = np.array(1) assert_array_equal(np.kron(a, b), k) # Using 1-dimensional ndarray a = np.array([3]) b = np.array([[1, 2], [3, 4]]) k = np.array([[3, 6], [9, 12]]) assert_array_equal(np.kron(a, b), k) a = np.array([[1, 2], [3, 4]]) b = np.array([3]) assert_array_equal(np.kron(a, b), k) # Using 3-dimensional ndarray a = np.array([[[1]], [[2]]]) b = np.array([[1, 2], [3, 4]]) k = np.array([[[1, 2], [3, 4]], [[2, 4], [6, 8]]]) assert_array_equal(np.kron(a, b), k) a = np.array([[1, 2], [3, 4]]) b = np.array([[[1]], [[2]]]) k = np.array([[[1, 2], [3, 4]], [[2, 4], [6, 8]]]) assert_array_equal(np.kron(a, b), k) def test_return_type(self): class myarray(np.ndarray): __array_priority__ = 1.0 a = np.ones([2, 2]) ma = myarray(a.shape, a.dtype, a.data) assert_equal(type(kron(a, a)), np.ndarray) assert_equal(type(kron(ma, ma)), myarray) assert_equal(type(kron(a, ma)), myarray) assert_equal(type(kron(ma, a)), myarray) @pytest.mark.parametrize( "array_class", [np.asarray, np.mat] ) def test_kron_smoke(self, array_class): a = array_class(np.ones([3, 3])) b = array_class(np.ones([3, 3])) k = array_class(np.ones([9, 9])) assert_array_equal(np.kron(a, b), k) def test_kron_ma(self): x = np.ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]]) k = np.ma.array(np.diag([1, 4, 4, 16]), mask=~np.array(np.identity(4), dtype=bool)) assert_array_equal(k, np.kron(x, x)) @pytest.mark.parametrize( "shape_a,shape_b", [ ((1, 1), (1, 1)), ((1, 2, 3), (4, 5, 6)), ((2, 2), (2, 2, 2)), ((1, 0), (1, 1)), ((2, 0, 2), (2, 2)), ((2, 0, 0, 2), (2, 0, 2)), ]) def test_kron_shape(self, shape_a, shape_b): a = np.ones(shape_a) b = np.ones(shape_b) normalised_shape_a = (1,) * max(0, len(shape_b)-len(shape_a)) + shape_a normalised_shape_b = (1,) * max(0, len(shape_a)-len(shape_b)) + shape_b expected_shape = np.multiply(normalised_shape_a, normalised_shape_b) k = np.kron(a, b) assert np.array_equal( k.shape, expected_shape), "Unexpected shape from kron" class TestTile: def test_basic(self): a = np.array([0, 1, 2]) b = [[1, 2], [3, 4]] assert_equal(tile(a, 2), [0, 1, 2, 0, 1, 2]) assert_equal(tile(a, (2, 2)), [[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]]) assert_equal(tile(a, (1, 2)), [[0, 1, 2, 0, 1, 2]]) assert_equal(tile(b, 2), [[1, 2, 1, 2], [3, 4, 3, 4]]) assert_equal(tile(b, (2, 1)), [[1, 2], [3, 4], [1, 2], [3, 4]]) assert_equal(tile(b, (2, 2)), [[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]]) def test_tile_one_repetition_on_array_gh4679(self): a = np.arange(5) b = tile(a, 1) b += 2 assert_equal(a, np.arange(5)) def test_empty(self): a = np.array([[[]]]) b = np.array([[], []]) c = tile(b, 2).shape d = tile(a, (3, 2, 5)).shape assert_equal(c, (2, 0)) assert_equal(d, (3, 2, 0)) def test_kroncompare(self): from numpy.random import randint reps = [(2,), (1, 2), (2, 1), (2, 2), (2, 3, 2), (3, 2)] shape = [(3,), (2, 3), (3, 4, 3), (3, 2, 3), (4, 3, 2, 4), (2, 2)] for s in shape: b = randint(0, 10, size=s) for r in reps: a = np.ones(r, b.dtype) large = tile(b, r) klarge = kron(a, b) assert_equal(large, klarge) class TestMayShareMemory: def test_basic(self): d = np.ones((50, 60)) d2 = np.ones((30, 60, 6)) assert_(np.may_share_memory(d, d)) assert_(np.may_share_memory(d, d[::-1])) assert_(np.may_share_memory(d, d[::2])) assert_(np.may_share_memory(d, d[1:, ::-1])) assert_(not np.may_share_memory(d[::-1], d2)) assert_(not np.may_share_memory(d[::2], d2)) assert_(not np.may_share_memory(d[1:, ::-1], d2)) assert_(np.may_share_memory(d2[1:, ::-1], d2)) # Utility def compare_results(res, desired): """Compare lists of arrays.""" if len(res) != len(desired): raise ValueError("Iterables have different lengths") # See also PEP 618 for Python 3.10 for x, y in zip(res, desired): assert_array_equal(x, y)	26.19 KB	2026-01-20 12:58
📄 test_stride_tricks.py import numpy as np from numpy.core._rational_tests import rational from numpy.testing import ( assert_equal, assert_array_equal, assert_raises, assert_, assert_raises_regex, assert_warns, ) from numpy.lib.stride_tricks import ( as_strided, broadcast_arrays, _broadcast_shape, broadcast_to, broadcast_shapes, sliding_window_view, ) import pytest def assert_shapes_correct(input_shapes, expected_shape): # Broadcast a list of arrays with the given input shapes and check the # common output shape. inarrays = [np.zeros(s) for s in input_shapes] outarrays = broadcast_arrays(inarrays) outshapes = [a.shape for a in outarrays] expected = [expected_shape] len(inarrays) assert_equal(outshapes, expected) def assert_incompatible_shapes_raise(input_shapes): # Broadcast a list of arrays with the given (incompatible) input shapes # and check that they raise a ValueError. inarrays = [np.zeros(s) for s in input_shapes] assert_raises(ValueError, broadcast_arrays, inarrays) def assert_same_as_ufunc(shape0, shape1, transposed=False, flipped=False): # Broadcast two shapes against each other and check that the data layout # is the same as if a ufunc did the broadcasting. x0 = np.zeros(shape0, dtype=int) # Note that multiply.reduce's identity element is 1.0, so when shape1==(), # this gives the desired n==1. n = int(np.multiply.reduce(shape1)) x1 = np.arange(n).reshape(shape1) if transposed: x0 = x0.T x1 = x1.T if flipped: x0 = x0[::-1] x1 = x1[::-1] # Use the add ufunc to do the broadcasting. Since we're adding 0s to x1, the # result should be exactly the same as the broadcasted view of x1. y = x0 + x1 b0, b1 = broadcast_arrays(x0, x1) assert_array_equal(y, b1) def test_same(): x = np.arange(10) y = np.arange(10) bx, by = broadcast_arrays(x, y) assert_array_equal(x, bx) assert_array_equal(y, by) def test_broadcast_kwargs(): # ensure that a TypeError is appropriately raised when # np.broadcast_arrays() is called with any keyword # argument other than 'subok' x = np.arange(10) y = np.arange(10) with assert_raises_regex(TypeError, 'got an unexpected keyword'): broadcast_arrays(x, y, dtype='float64') def test_one_off(): x = np.array([[1, 2, 3]]) y = np.array([[1], [2], [3]]) bx, by = broadcast_arrays(x, y) bx0 = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]) by0 = bx0.T assert_array_equal(bx0, bx) assert_array_equal(by0, by) def test_same_input_shapes(): # Check that the final shape is just the input shape. data = [ (), (1,), (3,), (0, 1), (0, 3), (1, 0), (3, 0), (1, 3), (3, 1), (3, 3), ] for shape in data: input_shapes = [shape] # Single input. assert_shapes_correct(input_shapes, shape) # Double input. input_shapes2 = [shape, shape] assert_shapes_correct(input_shapes2, shape) # Triple input. input_shapes3 = [shape, shape, shape] assert_shapes_correct(input_shapes3, shape) def test_two_compatible_by_ones_input_shapes(): # Check that two different input shapes of the same length, but some have # ones, broadcast to the correct shape. data = [ [[(1,), (3,)], (3,)], [[(1, 3), (3, 3)], (3, 3)], [[(3, 1), (3, 3)], (3, 3)], [[(1, 3), (3, 1)], (3, 3)], [[(1, 1), (3, 3)], (3, 3)], [[(1, 1), (1, 3)], (1, 3)], [[(1, 1), (3, 1)], (3, 1)], [[(1, 0), (0, 0)], (0, 0)], [[(0, 1), (0, 0)], (0, 0)], [[(1, 0), (0, 1)], (0, 0)], [[(1, 1), (0, 0)], (0, 0)], [[(1, 1), (1, 0)], (1, 0)], [[(1, 1), (0, 1)], (0, 1)], ] for input_shapes, expected_shape in data: assert_shapes_correct(input_shapes, expected_shape) # Reverse the input shapes since broadcasting should be symmetric. assert_shapes_correct(input_shapes[::-1], expected_shape) def test_two_compatible_by_prepending_ones_input_shapes(): # Check that two different input shapes (of different lengths) broadcast # to the correct shape. data = [ [[(), (3,)], (3,)], [[(3,), (3, 3)], (3, 3)], [[(3,), (3, 1)], (3, 3)], [[(1,), (3, 3)], (3, 3)], [[(), (3, 3)], (3, 3)], [[(1, 1), (3,)], (1, 3)], [[(1,), (3, 1)], (3, 1)], [[(1,), (1, 3)], (1, 3)], [[(), (1, 3)], (1, 3)], [[(), (3, 1)], (3, 1)], [[(), (0,)], (0,)], [[(0,), (0, 0)], (0, 0)], [[(0,), (0, 1)], (0, 0)], [[(1,), (0, 0)], (0, 0)], [[(), (0, 0)], (0, 0)], [[(1, 1), (0,)], (1, 0)], [[(1,), (0, 1)], (0, 1)], [[(1,), (1, 0)], (1, 0)], [[(), (1, 0)], (1, 0)], [[(), (0, 1)], (0, 1)], ] for input_shapes, expected_shape in data: assert_shapes_correct(input_shapes, expected_shape) # Reverse the input shapes since broadcasting should be symmetric. assert_shapes_correct(input_shapes[::-1], expected_shape) def test_incompatible_shapes_raise_valueerror(): # Check that a ValueError is raised for incompatible shapes. data = [ [(3,), (4,)], [(2, 3), (2,)], [(3,), (3,), (4,)], [(1, 3, 4), (2, 3, 3)], ] for input_shapes in data: assert_incompatible_shapes_raise(input_shapes) # Reverse the input shapes since broadcasting should be symmetric. assert_incompatible_shapes_raise(input_shapes[::-1]) def test_same_as_ufunc(): # Check that the data layout is the same as if a ufunc did the operation. data = [ [[(1,), (3,)], (3,)], [[(1, 3), (3, 3)], (3, 3)], [[(3, 1), (3, 3)], (3, 3)], [[(1, 3), (3, 1)], (3, 3)], [[(1, 1), (3, 3)], (3, 3)], [[(1, 1), (1, 3)], (1, 3)], [[(1, 1), (3, 1)], (3, 1)], [[(1, 0), (0, 0)], (0, 0)], [[(0, 1), (0, 0)], (0, 0)], [[(1, 0), (0, 1)], (0, 0)], [[(1, 1), (0, 0)], (0, 0)], [[(1, 1), (1, 0)], (1, 0)], [[(1, 1), (0, 1)], (0, 1)], [[(), (3,)], (3,)], [[(3,), (3, 3)], (3, 3)], [[(3,), (3, 1)], (3, 3)], [[(1,), (3, 3)], (3, 3)], [[(), (3, 3)], (3, 3)], [[(1, 1), (3,)], (1, 3)], [[(1,), (3, 1)], (3, 1)], [[(1,), (1, 3)], (1, 3)], [[(), (1, 3)], (1, 3)], [[(), (3, 1)], (3, 1)], [[(), (0,)], (0,)], [[(0,), (0, 0)], (0, 0)], [[(0,), (0, 1)], (0, 0)], [[(1,), (0, 0)], (0, 0)], [[(), (0, 0)], (0, 0)], [[(1, 1), (0,)], (1, 0)], [[(1,), (0, 1)], (0, 1)], [[(1,), (1, 0)], (1, 0)], [[(), (1, 0)], (1, 0)], [[(), (0, 1)], (0, 1)], ] for input_shapes, expected_shape in data: assert_same_as_ufunc(input_shapes[0], input_shapes[1], "Shapes: %s %s" % (input_shapes[0], input_shapes[1])) # Reverse the input shapes since broadcasting should be symmetric. assert_same_as_ufunc(input_shapes[1], input_shapes[0]) # Try them transposed, too. assert_same_as_ufunc(input_shapes[0], input_shapes[1], True) # ... and flipped for non-rank-0 inputs in order to test negative # strides. if () not in input_shapes: assert_same_as_ufunc(input_shapes[0], input_shapes[1], False, True) assert_same_as_ufunc(input_shapes[0], input_shapes[1], True, True) def test_broadcast_to_succeeds(): data = [ [np.array(0), (0,), np.array(0)], [np.array(0), (1,), np.zeros(1)], [np.array(0), (3,), np.zeros(3)], [np.ones(1), (1,), np.ones(1)], [np.ones(1), (2,), np.ones(2)], [np.ones(1), (1, 2, 3), np.ones((1, 2, 3))], [np.arange(3), (3,), np.arange(3)], [np.arange(3), (1, 3), np.arange(3).reshape(1, -1)], [np.arange(3), (2, 3), np.array([[0, 1, 2], [0, 1, 2]])], # test if shape is not a tuple [np.ones(0), 0, np.ones(0)], [np.ones(1), 1, np.ones(1)], [np.ones(1), 2, np.ones(2)], # these cases with size 0 are strange, but they reproduce the behavior # of broadcasting with ufuncs (see test_same_as_ufunc above) [np.ones(1), (0,), np.ones(0)], [np.ones((1, 2)), (0, 2), np.ones((0, 2))], [np.ones((2, 1)), (2, 0), np.ones((2, 0))], ] for input_array, shape, expected in data: actual = broadcast_to(input_array, shape) assert_array_equal(expected, actual) def test_broadcast_to_raises(): data = [ [(0,), ()], [(1,), ()], [(3,), ()], [(3,), (1,)], [(3,), (2,)], [(3,), (4,)], [(1, 2), (2, 1)], [(1, 1), (1,)], [(1,), -1], [(1,), (-1,)], [(1, 2), (-1, 2)], ] for orig_shape, target_shape in data: arr = np.zeros(orig_shape) assert_raises(ValueError, lambda: broadcast_to(arr, target_shape)) def test_broadcast_shape(): # tests internal _broadcast_shape # _broadcast_shape is already exercised indirectly by broadcast_arrays # _broadcast_shape is also exercised by the public broadcast_shapes function assert_equal(_broadcast_shape(), ()) assert_equal(_broadcast_shape([1, 2]), (2,)) assert_equal(_broadcast_shape(np.ones((1, 1))), (1, 1)) assert_equal(_broadcast_shape(np.ones((1, 1)), np.ones((3, 4))), (3, 4)) assert_equal(_broadcast_shape(([np.ones((1, 2))] * 32)), (1, 2)) assert_equal(_broadcast_shape(([np.ones((1, 2))] 100)), (1, 2)) # regression tests for gh-5862 assert_equal(_broadcast_shape(([np.ones(2)] 32 + [1])), (2,)) bad_args = [np.ones(2)] * 32 + [np.ones(3)] * 32 assert_raises(ValueError, lambda: _broadcast_shape(bad_args)) def test_broadcast_shapes_succeeds(): # tests public broadcast_shapes data = [ [[], ()], [[()], ()], [[(7,)], (7,)], [[(1, 2), (2,)], (1, 2)], [[(1, 1)], (1, 1)], [[(1, 1), (3, 4)], (3, 4)], [[(6, 7), (5, 6, 1), (7,), (5, 1, 7)], (5, 6, 7)], [[(5, 6, 1)], (5, 6, 1)], [[(1, 3), (3, 1)], (3, 3)], [[(1, 0), (0, 0)], (0, 0)], [[(0, 1), (0, 0)], (0, 0)], [[(1, 0), (0, 1)], (0, 0)], [[(1, 1), (0, 0)], (0, 0)], [[(1, 1), (1, 0)], (1, 0)], [[(1, 1), (0, 1)], (0, 1)], [[(), (0,)], (0,)], [[(0,), (0, 0)], (0, 0)], [[(0,), (0, 1)], (0, 0)], [[(1,), (0, 0)], (0, 0)], [[(), (0, 0)], (0, 0)], [[(1, 1), (0,)], (1, 0)], [[(1,), (0, 1)], (0, 1)], [[(1,), (1, 0)], (1, 0)], [[(), (1, 0)], (1, 0)], [[(), (0, 1)], (0, 1)], [[(1,), (3,)], (3,)], [[2, (3, 2)], (3, 2)], ] for input_shapes, target_shape in data: assert_equal(broadcast_shapes(input_shapes), target_shape) assert_equal(broadcast_shapes(([(1, 2)] 32)), (1, 2)) assert_equal(broadcast_shapes(([(1, 2)] 100)), (1, 2)) # regression tests for gh-5862 assert_equal(broadcast_shapes(([(2,)] 32)), (2,)) def test_broadcast_shapes_raises(): # tests public broadcast_shapes data = [ [(3,), (4,)], [(2, 3), (2,)], [(3,), (3,), (4,)], [(1, 3, 4), (2, 3, 3)], [(1, 2), (3,1), (3,2), (10, 5)], [2, (2, 3)], ] for input_shapes in data: assert_raises(ValueError, lambda: broadcast_shapes(input_shapes)) bad_args = [(2,)] 32 + [(3,)] * 32 assert_raises(ValueError, lambda: broadcast_shapes(bad_args)) def test_as_strided(): a = np.array([None]) a_view = as_strided(a) expected = np.array([None]) assert_array_equal(a_view, np.array([None])) a = np.array([1, 2, 3, 4]) a_view = as_strided(a, shape=(2,), strides=(2 a.itemsize,)) expected = np.array([1, 3]) assert_array_equal(a_view, expected) a = np.array([1, 2, 3, 4]) a_view = as_strided(a, shape=(3, 4), strides=(0, 1 * a.itemsize)) expected = np.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]]) assert_array_equal(a_view, expected) # Regression test for gh-5081 dt = np.dtype([('num', 'i4'), ('obj', 'O')]) a = np.empty((4,), dtype=dt) a['num'] = np.arange(1, 5) a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize)) expected_num = [[1, 2, 3, 4]] * 3 expected_obj = [[None]4]3 assert_equal(a_view.dtype, dt) assert_array_equal(expected_num, a_view['num']) assert_array_equal(expected_obj, a_view['obj']) # Make sure that void types without fields are kept unchanged a = np.empty((4,), dtype='V4') a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize)) assert_equal(a.dtype, a_view.dtype) # Make sure that the only type that could fail is properly handled dt = np.dtype({'names': [''], 'formats': ['V4']}) a = np.empty((4,), dtype=dt) a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize)) assert_equal(a.dtype, a_view.dtype) # Custom dtypes should not be lost (gh-9161) r = [rational(i) for i in range(4)] a = np.array(r, dtype=rational) a_view = as_strided(a, shape=(3, 4), strides=(0, a.itemsize)) assert_equal(a.dtype, a_view.dtype) assert_array_equal([r] * 3, a_view) class TestSlidingWindowView: def test_1d(self): arr = np.arange(5) arr_view = sliding_window_view(arr, 2) expected = np.array([[0, 1], [1, 2], [2, 3], [3, 4]]) assert_array_equal(arr_view, expected) def test_2d(self): i, j = np.ogrid[:3, :4] arr = 10i + j shape = (2, 2) arr_view = sliding_window_view(arr, shape) expected = np.array([[[[0, 1], [10, 11]], [[1, 2], [11, 12]], [[2, 3], [12, 13]]], [[[10, 11], [20, 21]], [[11, 12], [21, 22]], [[12, 13], [22, 23]]]]) assert_array_equal(arr_view, expected) def test_2d_with_axis(self): i, j = np.ogrid[:3, :4] arr = 10i + j arr_view = sliding_window_view(arr, 3, 0) expected = np.array([[[0, 10, 20], [1, 11, 21], [2, 12, 22], [3, 13, 23]]]) assert_array_equal(arr_view, expected) def test_2d_repeated_axis(self): i, j = np.ogrid[:3, :4] arr = 10i + j arr_view = sliding_window_view(arr, (2, 3), (1, 1)) expected = np.array([[[[0, 1, 2], [1, 2, 3]]], [[[10, 11, 12], [11, 12, 13]]], [[[20, 21, 22], [21, 22, 23]]]]) assert_array_equal(arr_view, expected) def test_2d_without_axis(self): i, j = np.ogrid[:4, :4] arr = 10i + j shape = (2, 3) arr_view = sliding_window_view(arr, shape) expected = np.array([[[[0, 1, 2], [10, 11, 12]], [[1, 2, 3], [11, 12, 13]]], [[[10, 11, 12], [20, 21, 22]], [[11, 12, 13], [21, 22, 23]]], [[[20, 21, 22], [30, 31, 32]], [[21, 22, 23], [31, 32, 33]]]]) assert_array_equal(arr_view, expected) def test_errors(self): i, j = np.ogrid[:4, :4] arr = 10i + j with pytest.raises(ValueError, match='cannot contain negative values'): sliding_window_view(arr, (-1, 3)) with pytest.raises( ValueError, match='must provide window_shape for all dimensions of `x`'): sliding_window_view(arr, (1,)) with pytest.raises( ValueError, match='Must provide matching length window_shape and axis'): sliding_window_view(arr, (1, 3, 4), axis=(0, 1)) with pytest.raises( ValueError, match='window shape cannot be larger than input array'): sliding_window_view(arr, (5, 5)) def test_writeable(self): arr = np.arange(5) view = sliding_window_view(arr, 2, writeable=False) assert_(not view.flags.writeable) with pytest.raises( ValueError, match='assignment destination is read-only'): view[0, 0] = 3 view = sliding_window_view(arr, 2, writeable=True) assert_(view.flags.writeable) view[0, 1] = 3 assert_array_equal(arr, np.array([0, 3, 2, 3, 4])) def test_subok(self): class MyArray(np.ndarray): pass arr = np.arange(5).view(MyArray) assert_(not isinstance(sliding_window_view(arr, 2, subok=False), MyArray)) assert_(isinstance(sliding_window_view(arr, 2, subok=True), MyArray)) # Default behavior assert_(not isinstance(sliding_window_view(arr, 2), MyArray)) def as_strided_writeable(): arr = np.ones(10) view = as_strided(arr, writeable=False) assert_(not view.flags.writeable) # Check that writeable also is fine: view = as_strided(arr, writeable=True) assert_(view.flags.writeable) view[...] = 3 assert_array_equal(arr, np.full_like(arr, 3)) # Test that things do not break down for readonly: arr.flags.writeable = False view = as_strided(arr, writeable=False) view = as_strided(arr, writeable=True) assert_(not view.flags.writeable) class VerySimpleSubClass(np.ndarray): def __new__(cls, args, *kwargs): return np.array(args, subok=True, *kwargs).view(cls) class SimpleSubClass(VerySimpleSubClass): def __new__(cls, args, *kwargs): self = np.array(args, subok=True, *kwargs).view(cls) self.info = 'simple' return self def __array_finalize__(self, obj): self.info = getattr(obj, 'info', '') + ' finalized' def test_subclasses(): # test that subclass is preserved only if subok=True a = VerySimpleSubClass([1, 2, 3, 4]) assert_(type(a) is VerySimpleSubClass) a_view = as_strided(a, shape=(2,), strides=(2 a.itemsize,)) assert_(type(a_view) is np.ndarray) a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True) assert_(type(a_view) is VerySimpleSubClass) # test that if a subclass has __array_finalize__, it is used a = SimpleSubClass([1, 2, 3, 4]) a_view = as_strided(a, shape=(2,), strides=(2 * a.itemsize,), subok=True) assert_(type(a_view) is SimpleSubClass) assert_(a_view.info == 'simple finalized') # similar tests for broadcast_arrays b = np.arange(len(a)).reshape(-1, 1) a_view, b_view = broadcast_arrays(a, b) assert_(type(a_view) is np.ndarray) assert_(type(b_view) is np.ndarray) assert_(a_view.shape == b_view.shape) a_view, b_view = broadcast_arrays(a, b, subok=True) assert_(type(a_view) is SimpleSubClass) assert_(a_view.info == 'simple finalized') assert_(type(b_view) is np.ndarray) assert_(a_view.shape == b_view.shape) # and for broadcast_to shape = (2, 4) a_view = broadcast_to(a, shape) assert_(type(a_view) is np.ndarray) assert_(a_view.shape == shape) a_view = broadcast_to(a, shape, subok=True) assert_(type(a_view) is SimpleSubClass) assert_(a_view.info == 'simple finalized') assert_(a_view.shape == shape) def test_writeable(): # broadcast_to should return a readonly array original = np.array([1, 2, 3]) result = broadcast_to(original, (2, 3)) assert_equal(result.flags.writeable, False) assert_raises(ValueError, result.__setitem__, slice(None), 0) # but the result of broadcast_arrays needs to be writeable, to # preserve backwards compatibility for is_broadcast, results in [(False, broadcast_arrays(original,)), (True, broadcast_arrays(0, original))]: for result in results: # This will change to False in a future version if is_broadcast: with assert_warns(FutureWarning): assert_equal(result.flags.writeable, True) with assert_warns(DeprecationWarning): result[:] = 0 # Warning not emitted, writing to the array resets it assert_equal(result.flags.writeable, True) else: # No warning: assert_equal(result.flags.writeable, True) for results in [broadcast_arrays(original), broadcast_arrays(0, original)]: for result in results: # resets the warn_on_write DeprecationWarning result.flags.writeable = True # check: no warning emitted assert_equal(result.flags.writeable, True) result[:] = 0 # keep readonly input readonly original.flags.writeable = False _, result = broadcast_arrays(0, original) assert_equal(result.flags.writeable, False) # regression test for GH6491 shape = (2,) strides = [0] tricky_array = as_strided(np.array(0), shape, strides) other = np.zeros((1,)) first, second = broadcast_arrays(tricky_array, other) assert_(first.shape == second.shape) def test_writeable_memoryview(): # The result of broadcast_arrays exports as a non-writeable memoryview # because otherwise there is no good way to opt in to the new behaviour # (i.e. you would need to set writeable to False explicitly). # See gh-13929. original = np.array([1, 2, 3]) for is_broadcast, results in [(False, broadcast_arrays(original,)), (True, broadcast_arrays(0, original))]: for result in results: # This will change to False in a future version if is_broadcast: # memoryview(result, writable=True) will give warning but cannot # be tested using the python API. assert memoryview(result).readonly else: assert not memoryview(result).readonly def test_reference_types(): input_array = np.array('a', dtype=object) expected = np.array(['a'] * 3, dtype=object) actual = broadcast_to(input_array, (3,)) assert_array_equal(expected, actual) actual, _ = broadcast_arrays(input_array, np.ones(3)) assert_array_equal(expected, actual)	22.31 KB	2026-01-20 12:58
📄 test_twodim_base.py """Test functions for matrix module """ from numpy.testing import ( assert_equal, assert_array_equal, assert_array_max_ulp, assert_array_almost_equal, assert_raises, assert_ ) from numpy import ( arange, add, fliplr, flipud, zeros, ones, eye, array, diag, histogram2d, tri, mask_indices, triu_indices, triu_indices_from, tril_indices, tril_indices_from, vander, ) import numpy as np import pytest def get_mat(n): data = arange(n) data = add.outer(data, data) return data class TestEye: def test_basic(self): assert_equal(eye(4), array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])) assert_equal(eye(4, dtype='f'), array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], 'f')) assert_equal(eye(3) == 1, eye(3, dtype=bool)) def test_uint64(self): # Regression test for gh-9982 assert_equal(eye(np.uint64(2), dtype=int), array([[1, 0], [0, 1]])) assert_equal(eye(np.uint64(2), M=np.uint64(4), k=np.uint64(1)), array([[0, 1, 0, 0], [0, 0, 1, 0]])) def test_diag(self): assert_equal(eye(4, k=1), array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]])) assert_equal(eye(4, k=-1), array([[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])) def test_2d(self): assert_equal(eye(4, 3), array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 0]])) assert_equal(eye(3, 4), array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])) def test_diag2d(self): assert_equal(eye(3, 4, k=2), array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]])) assert_equal(eye(4, 3, k=-2), array([[0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 0]])) def test_eye_bounds(self): assert_equal(eye(2, 2, 1), [[0, 1], [0, 0]]) assert_equal(eye(2, 2, -1), [[0, 0], [1, 0]]) assert_equal(eye(2, 2, 2), [[0, 0], [0, 0]]) assert_equal(eye(2, 2, -2), [[0, 0], [0, 0]]) assert_equal(eye(3, 2, 2), [[0, 0], [0, 0], [0, 0]]) assert_equal(eye(3, 2, 1), [[0, 1], [0, 0], [0, 0]]) assert_equal(eye(3, 2, -1), [[0, 0], [1, 0], [0, 1]]) assert_equal(eye(3, 2, -2), [[0, 0], [0, 0], [1, 0]]) assert_equal(eye(3, 2, -3), [[0, 0], [0, 0], [0, 0]]) def test_strings(self): assert_equal(eye(2, 2, dtype='S3'), [[b'1', b''], [b'', b'1']]) def test_bool(self): assert_equal(eye(2, 2, dtype=bool), [[True, False], [False, True]]) def test_order(self): mat_c = eye(4, 3, k=-1) mat_f = eye(4, 3, k=-1, order='F') assert_equal(mat_c, mat_f) assert mat_c.flags.c_contiguous assert not mat_c.flags.f_contiguous assert not mat_f.flags.c_contiguous assert mat_f.flags.f_contiguous class TestDiag: def test_vector(self): vals = (100 * arange(5)).astype('l') b = zeros((5, 5)) for k in range(5): b[k, k] = vals[k] assert_equal(diag(vals), b) b = zeros((7, 7)) c = b.copy() for k in range(5): b[k, k + 2] = vals[k] c[k + 2, k] = vals[k] assert_equal(diag(vals, k=2), b) assert_equal(diag(vals, k=-2), c) def test_matrix(self, vals=None): if vals is None: vals = (100 * get_mat(5) + 1).astype('l') b = zeros((5,)) for k in range(5): b[k] = vals[k, k] assert_equal(diag(vals), b) b = b * 0 for k in range(3): b[k] = vals[k, k + 2] assert_equal(diag(vals, 2), b[:3]) for k in range(3): b[k] = vals[k + 2, k] assert_equal(diag(vals, -2), b[:3]) def test_fortran_order(self): vals = array((100 * get_mat(5) + 1), order='F', dtype='l') self.test_matrix(vals) def test_diag_bounds(self): A = [[1, 2], [3, 4], [5, 6]] assert_equal(diag(A, k=2), []) assert_equal(diag(A, k=1), [2]) assert_equal(diag(A, k=0), [1, 4]) assert_equal(diag(A, k=-1), [3, 6]) assert_equal(diag(A, k=-2), [5]) assert_equal(diag(A, k=-3), []) def test_failure(self): assert_raises(ValueError, diag, [[[1]]]) class TestFliplr: def test_basic(self): assert_raises(ValueError, fliplr, ones(4)) a = get_mat(4) b = a[:, ::-1] assert_equal(fliplr(a), b) a = [[0, 1, 2], [3, 4, 5]] b = [[2, 1, 0], [5, 4, 3]] assert_equal(fliplr(a), b) class TestFlipud: def test_basic(self): a = get_mat(4) b = a[::-1, :] assert_equal(flipud(a), b) a = [[0, 1, 2], [3, 4, 5]] b = [[3, 4, 5], [0, 1, 2]] assert_equal(flipud(a), b) class TestHistogram2d: def test_simple(self): x = array( [0.41702200, 0.72032449, 1.1437481e-4, 0.302332573, 0.146755891]) y = array( [0.09233859, 0.18626021, 0.34556073, 0.39676747, 0.53881673]) xedges = np.linspace(0, 1, 10) yedges = np.linspace(0, 1, 10) H = histogram2d(x, y, (xedges, yedges))[0] answer = array( [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]) assert_array_equal(H.T, answer) H = histogram2d(x, y, xedges)[0] assert_array_equal(H.T, answer) H, xedges, yedges = histogram2d(list(range(10)), list(range(10))) assert_array_equal(H, eye(10, 10)) assert_array_equal(xedges, np.linspace(0, 9, 11)) assert_array_equal(yedges, np.linspace(0, 9, 11)) def test_asym(self): x = array([1, 1, 2, 3, 4, 4, 4, 5]) y = array([1, 3, 2, 0, 1, 2, 3, 4]) H, xed, yed = histogram2d( x, y, (6, 5), range=[[0, 6], [0, 5]], density=True) answer = array( [[0., 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [1, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 1]]) assert_array_almost_equal(H, answer/8., 3) assert_array_equal(xed, np.linspace(0, 6, 7)) assert_array_equal(yed, np.linspace(0, 5, 6)) def test_density(self): x = array([1, 2, 3, 1, 2, 3, 1, 2, 3]) y = array([1, 1, 1, 2, 2, 2, 3, 3, 3]) H, xed, yed = histogram2d( x, y, [[1, 2, 3, 5], [1, 2, 3, 5]], density=True) answer = array([[1, 1, .5], [1, 1, .5], [.5, .5, .25]])/9. assert_array_almost_equal(H, answer, 3) def test_all_outliers(self): r = np.random.rand(100) + 1. + 1e6 # histogramdd rounds by decimal=6 H, xed, yed = histogram2d(r, r, (4, 5), range=([0, 1], [0, 1])) assert_array_equal(H, 0) def test_empty(self): a, edge1, edge2 = histogram2d([], [], bins=([0, 1], [0, 1])) assert_array_max_ulp(a, array([[0.]])) a, edge1, edge2 = histogram2d([], [], bins=4) assert_array_max_ulp(a, np.zeros((4, 4))) def test_binparameter_combination(self): x = array( [0, 0.09207008, 0.64575234, 0.12875982, 0.47390599, 0.59944483, 1]) y = array( [0, 0.14344267, 0.48988575, 0.30558665, 0.44700682, 0.15886423, 1]) edges = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) H, xe, ye = histogram2d(x, y, (edges, 4)) answer = array( [[2., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 1., 0., 0.], [1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 1.]]) assert_array_equal(H, answer) assert_array_equal(ye, array([0., 0.25, 0.5, 0.75, 1])) H, xe, ye = histogram2d(x, y, (4, edges)) answer = array( [[1., 1., 0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.], [0., 1., 0., 0., 1., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]) assert_array_equal(H, answer) assert_array_equal(xe, array([0., 0.25, 0.5, 0.75, 1])) def test_dispatch(self): class ShouldDispatch: def __array_function__(self, function, types, args, kwargs): return types, args, kwargs xy = [1, 2] s_d = ShouldDispatch() r = histogram2d(s_d, xy) # Cannot use assert_equal since that dispatches... assert_(r == ((ShouldDispatch,), (s_d, xy), {})) r = histogram2d(xy, s_d) assert_(r == ((ShouldDispatch,), (xy, s_d), {})) r = histogram2d(xy, xy, bins=s_d) assert_(r, ((ShouldDispatch,), (xy, xy), dict(bins=s_d))) r = histogram2d(xy, xy, bins=[s_d, 5]) assert_(r, ((ShouldDispatch,), (xy, xy), dict(bins=[s_d, 5]))) assert_raises(Exception, histogram2d, xy, xy, bins=[s_d]) r = histogram2d(xy, xy, weights=s_d) assert_(r, ((ShouldDispatch,), (xy, xy), dict(weights=s_d))) @pytest.mark.parametrize(("x_len", "y_len"), [(10, 11), (20, 19)]) def test_bad_length(self, x_len, y_len): x, y = np.ones(x_len), np.ones(y_len) with pytest.raises(ValueError, match='x and y must have the same length.'): histogram2d(x, y) class TestTri: def test_dtype(self): out = array([[1, 0, 0], [1, 1, 0], [1, 1, 1]]) assert_array_equal(tri(3), out) assert_array_equal(tri(3, dtype=bool), out.astype(bool)) def test_tril_triu_ndim2(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.ones((2, 2), dtype=dtype) b = np.tril(a) c = np.triu(a) assert_array_equal(b, [[1, 0], [1, 1]]) assert_array_equal(c, b.T) # should return the same dtype as the original array assert_equal(b.dtype, a.dtype) assert_equal(c.dtype, a.dtype) def test_tril_triu_ndim3(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.array([ [[1, 1], [1, 1]], [[1, 1], [1, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_tril_desired = np.array([ [[1, 0], [1, 1]], [[1, 0], [1, 0]], [[1, 0], [0, 0]], ], dtype=dtype) a_triu_desired = np.array([ [[1, 1], [0, 1]], [[1, 1], [0, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_triu_observed = np.triu(a) a_tril_observed = np.tril(a) assert_array_equal(a_triu_observed, a_triu_desired) assert_array_equal(a_tril_observed, a_tril_desired) assert_equal(a_triu_observed.dtype, a.dtype) assert_equal(a_tril_observed.dtype, a.dtype) def test_tril_triu_with_inf(): # Issue 4859 arr = np.array([[1, 1, np.inf], [1, 1, 1], [np.inf, 1, 1]]) out_tril = np.array([[1, 0, 0], [1, 1, 0], [np.inf, 1, 1]]) out_triu = out_tril.T assert_array_equal(np.triu(arr), out_triu) assert_array_equal(np.tril(arr), out_tril) def test_tril_triu_dtype(): # Issue 4916 # tril and triu should return the same dtype as input for c in np.typecodes['All']: if c == 'V': continue arr = np.zeros((3, 3), dtype=c) assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) # check special cases arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'], ['2004-01-01T12:00', '2003-01-03T13:45']], dtype='datetime64') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) arr = np.zeros((3, 3), dtype='f4,f4') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) def test_mask_indices(): # simple test without offset iu = mask_indices(3, np.triu) a = np.arange(9).reshape(3, 3) assert_array_equal(a[iu], array([0, 1, 2, 4, 5, 8])) # Now with an offset iu1 = mask_indices(3, np.triu, 1) assert_array_equal(a[iu1], array([1, 2, 5])) def test_tril_indices(): # indices without and with offset il1 = tril_indices(4) il2 = tril_indices(4, k=2) il3 = tril_indices(4, m=5) il4 = tril_indices(4, k=2, m=5) a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) b = np.arange(1, 21).reshape(4, 5) # indexing: assert_array_equal(a[il1], array([1, 5, 6, 9, 10, 11, 13, 14, 15, 16])) assert_array_equal(b[il3], array([1, 6, 7, 11, 12, 13, 16, 17, 18, 19])) # And for assigning values: a[il1] = -1 assert_array_equal(a, array([[-1, 2, 3, 4], [-1, -1, 7, 8], [-1, -1, -1, 12], [-1, -1, -1, -1]])) b[il3] = -1 assert_array_equal(b, array([[-1, 2, 3, 4, 5], [-1, -1, 8, 9, 10], [-1, -1, -1, 14, 15], [-1, -1, -1, -1, 20]])) # These cover almost the whole array (two diagonals right of the main one): a[il2] = -10 assert_array_equal(a, array([[-10, -10, -10, 4], [-10, -10, -10, -10], [-10, -10, -10, -10], [-10, -10, -10, -10]])) b[il4] = -10 assert_array_equal(b, array([[-10, -10, -10, 4, 5], [-10, -10, -10, -10, 10], [-10, -10, -10, -10, -10], [-10, -10, -10, -10, -10]])) class TestTriuIndices: def test_triu_indices(self): iu1 = triu_indices(4) iu2 = triu_indices(4, k=2) iu3 = triu_indices(4, m=5) iu4 = triu_indices(4, k=2, m=5) a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) b = np.arange(1, 21).reshape(4, 5) # Both for indexing: assert_array_equal(a[iu1], array([1, 2, 3, 4, 6, 7, 8, 11, 12, 16])) assert_array_equal(b[iu3], array([1, 2, 3, 4, 5, 7, 8, 9, 10, 13, 14, 15, 19, 20])) # And for assigning values: a[iu1] = -1 assert_array_equal(a, array([[-1, -1, -1, -1], [5, -1, -1, -1], [9, 10, -1, -1], [13, 14, 15, -1]])) b[iu3] = -1 assert_array_equal(b, array([[-1, -1, -1, -1, -1], [6, -1, -1, -1, -1], [11, 12, -1, -1, -1], [16, 17, 18, -1, -1]])) # These cover almost the whole array (two diagonals right of the # main one): a[iu2] = -10 assert_array_equal(a, array([[-1, -1, -10, -10], [5, -1, -1, -10], [9, 10, -1, -1], [13, 14, 15, -1]])) b[iu4] = -10 assert_array_equal(b, array([[-1, -1, -10, -10, -10], [6, -1, -1, -10, -10], [11, 12, -1, -1, -10], [16, 17, 18, -1, -1]])) class TestTrilIndicesFrom: def test_exceptions(self): assert_raises(ValueError, tril_indices_from, np.ones((2,))) assert_raises(ValueError, tril_indices_from, np.ones((2, 2, 2))) # assert_raises(ValueError, tril_indices_from, np.ones((2, 3))) class TestTriuIndicesFrom: def test_exceptions(self): assert_raises(ValueError, triu_indices_from, np.ones((2,))) assert_raises(ValueError, triu_indices_from, np.ones((2, 2, 2))) # assert_raises(ValueError, triu_indices_from, np.ones((2, 3))) class TestVander: def test_basic(self): c = np.array([0, 1, -2, 3]) v = vander(c) powers = np.array([[0, 0, 0, 0, 1], [1, 1, 1, 1, 1], [16, -8, 4, -2, 1], [81, 27, 9, 3, 1]]) # Check default value of N: assert_array_equal(v, powers[:, 1:]) # Check a range of N values, including 0 and 5 (greater than default) m = powers.shape[1] for n in range(6): v = vander(c, N=n) assert_array_equal(v, powers[:, m-n:m]) def test_dtypes(self): c = array([11, -12, 13], dtype=np.int8) v = vander(c) expected = np.array([[121, 11, 1], [144, -12, 1], [169, 13, 1]]) assert_array_equal(v, expected) c = array([1.0+1j, 1.0-1j]) v = vander(c, N=3) expected = np.array([[2j, 1+1j, 1], [-2j, 1-1j, 1]]) # The data is floating point, but the values are small integers, # so assert_array_equal should be safe here (rather than, say, # assert_array_almost_equal). assert_array_equal(v, expected)	18.4 KB	2026-01-20 12:58
📄 test_type_check.py import numpy as np from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_raises ) from numpy.lib.type_check import ( common_type, mintypecode, isreal, iscomplex, isposinf, isneginf, nan_to_num, isrealobj, iscomplexobj, asfarray, real_if_close ) def assert_all(x): assert_(np.all(x), x) class TestCommonType: def test_basic(self): ai32 = np.array([[1, 2], [3, 4]], dtype=np.int32) af16 = np.array([[1, 2], [3, 4]], dtype=np.float16) af32 = np.array([[1, 2], [3, 4]], dtype=np.float32) af64 = np.array([[1, 2], [3, 4]], dtype=np.float64) acs = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.csingle) acd = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.cdouble) assert_(common_type(ai32) == np.float64) assert_(common_type(af16) == np.float16) assert_(common_type(af32) == np.float32) assert_(common_type(af64) == np.float64) assert_(common_type(acs) == np.csingle) assert_(common_type(acd) == np.cdouble) class TestMintypecode: def test_default_1(self): for itype in '1bcsuwil': assert_equal(mintypecode(itype), 'd') assert_equal(mintypecode('f'), 'f') assert_equal(mintypecode('d'), 'd') assert_equal(mintypecode('F'), 'F') assert_equal(mintypecode('D'), 'D') def test_default_2(self): for itype in '1bcsuwil': assert_equal(mintypecode(itype+'f'), 'f') assert_equal(mintypecode(itype+'d'), 'd') assert_equal(mintypecode(itype+'F'), 'F') assert_equal(mintypecode(itype+'D'), 'D') assert_equal(mintypecode('ff'), 'f') assert_equal(mintypecode('fd'), 'd') assert_equal(mintypecode('fF'), 'F') assert_equal(mintypecode('fD'), 'D') assert_equal(mintypecode('df'), 'd') assert_equal(mintypecode('dd'), 'd') #assert_equal(mintypecode('dF',savespace=1),'F') assert_equal(mintypecode('dF'), 'D') assert_equal(mintypecode('dD'), 'D') assert_equal(mintypecode('Ff'), 'F') #assert_equal(mintypecode('Fd',savespace=1),'F') assert_equal(mintypecode('Fd'), 'D') assert_equal(mintypecode('FF'), 'F') assert_equal(mintypecode('FD'), 'D') assert_equal(mintypecode('Df'), 'D') assert_equal(mintypecode('Dd'), 'D') assert_equal(mintypecode('DF'), 'D') assert_equal(mintypecode('DD'), 'D') def test_default_3(self): assert_equal(mintypecode('fdF'), 'D') #assert_equal(mintypecode('fdF',savespace=1),'F') assert_equal(mintypecode('fdD'), 'D') assert_equal(mintypecode('fFD'), 'D') assert_equal(mintypecode('dFD'), 'D') assert_equal(mintypecode('ifd'), 'd') assert_equal(mintypecode('ifF'), 'F') assert_equal(mintypecode('ifD'), 'D') assert_equal(mintypecode('idF'), 'D') #assert_equal(mintypecode('idF',savespace=1),'F') assert_equal(mintypecode('idD'), 'D') class TestIsscalar: def test_basic(self): assert_(np.isscalar(3)) assert_(not np.isscalar([3])) assert_(not np.isscalar((3,))) assert_(np.isscalar(3j)) assert_(np.isscalar(4.0)) class TestReal: def test_real(self): y = np.random.rand(10,) assert_array_equal(y, np.real(y)) y = np.array(1) out = np.real(y) assert_array_equal(y, out) assert_(isinstance(out, np.ndarray)) y = 1 out = np.real(y) assert_equal(y, out) assert_(not isinstance(out, np.ndarray)) def test_cmplx(self): y = np.random.rand(10,)+1jnp.random.rand(10,) assert_array_equal(y.real, np.real(y)) y = np.array(1 + 1j) out = np.real(y) assert_array_equal(y.real, out) assert_(isinstance(out, np.ndarray)) y = 1 + 1j out = np.real(y) assert_equal(1.0, out) assert_(not isinstance(out, np.ndarray)) class TestImag: def test_real(self): y = np.random.rand(10,) assert_array_equal(0, np.imag(y)) y = np.array(1) out = np.imag(y) assert_array_equal(0, out) assert_(isinstance(out, np.ndarray)) y = 1 out = np.imag(y) assert_equal(0, out) assert_(not isinstance(out, np.ndarray)) def test_cmplx(self): y = np.random.rand(10,)+1jnp.random.rand(10,) assert_array_equal(y.imag, np.imag(y)) y = np.array(1 + 1j) out = np.imag(y) assert_array_equal(y.imag, out) assert_(isinstance(out, np.ndarray)) y = 1 + 1j out = np.imag(y) assert_equal(1.0, out) assert_(not isinstance(out, np.ndarray)) class TestIscomplex: def test_fail(self): z = np.array([-1, 0, 1]) res = iscomplex(z) assert_(not np.any(res, axis=0)) def test_pass(self): z = np.array([-1j, 1, 0]) res = iscomplex(z) assert_array_equal(res, [1, 0, 0]) class TestIsreal: def test_pass(self): z = np.array([-1, 0, 1j]) res = isreal(z) assert_array_equal(res, [1, 1, 0]) def test_fail(self): z = np.array([-1j, 1, 0]) res = isreal(z) assert_array_equal(res, [0, 1, 1]) class TestIscomplexobj: def test_basic(self): z = np.array([-1, 0, 1]) assert_(not iscomplexobj(z)) z = np.array([-1j, 0, -1]) assert_(iscomplexobj(z)) def test_scalar(self): assert_(not iscomplexobj(1.0)) assert_(iscomplexobj(1+0j)) def test_list(self): assert_(iscomplexobj([3, 1+0j, True])) assert_(not iscomplexobj([3, 1, True])) def test_duck(self): class DummyComplexArray: @property def dtype(self): return np.dtype(complex) dummy = DummyComplexArray() assert_(iscomplexobj(dummy)) def test_pandas_duck(self): # This tests a custom np.dtype duck-typed class, such as used by pandas # (pandas.core.dtypes) class PdComplex(np.complex128): pass class PdDtype: name = 'category' names = None type = PdComplex kind = 'c' str = '<c16' base = np.dtype('complex128') class DummyPd: @property def dtype(self): return PdDtype dummy = DummyPd() assert_(iscomplexobj(dummy)) def test_custom_dtype_duck(self): class MyArray(list): @property def dtype(self): return complex a = MyArray([1+0j, 2+0j, 3+0j]) assert_(iscomplexobj(a)) class TestIsrealobj: def test_basic(self): z = np.array([-1, 0, 1]) assert_(isrealobj(z)) z = np.array([-1j, 0, -1]) assert_(not isrealobj(z)) class TestIsnan: def test_goodvalues(self): z = np.array((-1., 0., 1.)) res = np.isnan(z) == 0 assert_all(np.all(res, axis=0)) def test_posinf(self): with np.errstate(divide='ignore'): assert_all(np.isnan(np.array((1.,))/0.) == 0) def test_neginf(self): with np.errstate(divide='ignore'): assert_all(np.isnan(np.array((-1.,))/0.) == 0) def test_ind(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isnan(np.array((0.,))/0.) == 1) def test_integer(self): assert_all(np.isnan(1) == 0) def test_complex(self): assert_all(np.isnan(1+1j) == 0) def test_complex1(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isnan(np.array(0+0j)/0.) == 1) class TestIsfinite: # Fixme, wrong place, isfinite now ufunc def test_goodvalues(self): z = np.array((-1., 0., 1.)) res = np.isfinite(z) == 1 assert_all(np.all(res, axis=0)) def test_posinf(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isfinite(np.array((1.,))/0.) == 0) def test_neginf(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isfinite(np.array((-1.,))/0.) == 0) def test_ind(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isfinite(np.array((0.,))/0.) == 0) def test_integer(self): assert_all(np.isfinite(1) == 1) def test_complex(self): assert_all(np.isfinite(1+1j) == 1) def test_complex1(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isfinite(np.array(1+1j)/0.) == 0) class TestIsinf: # Fixme, wrong place, isinf now ufunc def test_goodvalues(self): z = np.array((-1., 0., 1.)) res = np.isinf(z) == 0 assert_all(np.all(res, axis=0)) def test_posinf(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isinf(np.array((1.,))/0.) == 1) def test_posinf_scalar(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isinf(np.array(1.,)/0.) == 1) def test_neginf(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isinf(np.array((-1.,))/0.) == 1) def test_neginf_scalar(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isinf(np.array(-1.)/0.) == 1) def test_ind(self): with np.errstate(divide='ignore', invalid='ignore'): assert_all(np.isinf(np.array((0.,))/0.) == 0) class TestIsposinf: def test_generic(self): with np.errstate(divide='ignore', invalid='ignore'): vals = isposinf(np.array((-1., 0, 1))/0.) assert_(vals[0] == 0) assert_(vals[1] == 0) assert_(vals[2] == 1) class TestIsneginf: def test_generic(self): with np.errstate(divide='ignore', invalid='ignore'): vals = isneginf(np.array((-1., 0, 1))/0.) assert_(vals[0] == 1) assert_(vals[1] == 0) assert_(vals[2] == 0) class TestNanToNum: def test_generic(self): with np.errstate(divide='ignore', invalid='ignore'): vals = nan_to_num(np.array((-1., 0, 1))/0.) assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0])) assert_(vals[1] == 0) assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2])) assert_equal(type(vals), np.ndarray) # perform the same tests but with nan, posinf and neginf keywords with np.errstate(divide='ignore', invalid='ignore'): vals = nan_to_num(np.array((-1., 0, 1))/0., nan=10, posinf=20, neginf=30) assert_equal(vals, [30, 10, 20]) assert_all(np.isfinite(vals[[0, 2]])) assert_equal(type(vals), np.ndarray) # perform the same test but in-place with np.errstate(divide='ignore', invalid='ignore'): vals = np.array((-1., 0, 1))/0. result = nan_to_num(vals, copy=False) assert_(result is vals) assert_all(vals[0] < -1e10) and assert_all(np.isfinite(vals[0])) assert_(vals[1] == 0) assert_all(vals[2] > 1e10) and assert_all(np.isfinite(vals[2])) assert_equal(type(vals), np.ndarray) # perform the same test but in-place with np.errstate(divide='ignore', invalid='ignore'): vals = np.array((-1., 0, 1))/0. result = nan_to_num(vals, copy=False, nan=10, posinf=20, neginf=30) assert_(result is vals) assert_equal(vals, [30, 10, 20]) assert_all(np.isfinite(vals[[0, 2]])) assert_equal(type(vals), np.ndarray) def test_array(self): vals = nan_to_num([1]) assert_array_equal(vals, np.array([1], int)) assert_equal(type(vals), np.ndarray) vals = nan_to_num([1], nan=10, posinf=20, neginf=30) assert_array_equal(vals, np.array([1], int)) assert_equal(type(vals), np.ndarray) def test_integer(self): vals = nan_to_num(1) assert_all(vals == 1) assert_equal(type(vals), np.int_) vals = nan_to_num(1, nan=10, posinf=20, neginf=30) assert_all(vals == 1) assert_equal(type(vals), np.int_) def test_float(self): vals = nan_to_num(1.0) assert_all(vals == 1.0) assert_equal(type(vals), np.float_) vals = nan_to_num(1.1, nan=10, posinf=20, neginf=30) assert_all(vals == 1.1) assert_equal(type(vals), np.float_) def test_complex_good(self): vals = nan_to_num(1+1j) assert_all(vals == 1+1j) assert_equal(type(vals), np.complex_) vals = nan_to_num(1+1j, nan=10, posinf=20, neginf=30) assert_all(vals == 1+1j) assert_equal(type(vals), np.complex_) def test_complex_bad(self): with np.errstate(divide='ignore', invalid='ignore'): v = 1 + 1j v += np.array(0+1.j)/0. vals = nan_to_num(v) # !! This is actually (unexpectedly) zero assert_all(np.isfinite(vals)) assert_equal(type(vals), np.complex_) def test_complex_bad2(self): with np.errstate(divide='ignore', invalid='ignore'): v = 1 + 1j v += np.array(-1+1.j)/0. vals = nan_to_num(v) assert_all(np.isfinite(vals)) assert_equal(type(vals), np.complex_) # Fixme #assert_all(vals.imag > 1e10) and assert_all(np.isfinite(vals)) # !! This is actually (unexpectedly) positive # !! inf. Comment out for now, and see if it # !! changes #assert_all(vals.real < -1e10) and assert_all(np.isfinite(vals)) def test_do_not_rewrite_previous_keyword(self): # This is done to test that when, for instance, nan=np.inf then these # values are not rewritten by posinf keyword to the posinf value. with np.errstate(divide='ignore', invalid='ignore'): vals = nan_to_num(np.array((-1., 0, 1))/0., nan=np.inf, posinf=999) assert_all(np.isfinite(vals[[0, 2]])) assert_all(vals[0] < -1e10) assert_equal(vals[[1, 2]], [np.inf, 999]) assert_equal(type(vals), np.ndarray) class TestRealIfClose: def test_basic(self): a = np.random.rand(10) b = real_if_close(a+1e-15j) assert_all(isrealobj(b)) assert_array_equal(a, b) b = real_if_close(a+1e-7j) assert_all(iscomplexobj(b)) b = real_if_close(a+1e-7j, tol=1e-6) assert_all(isrealobj(b)) class TestArrayConversion: def test_asfarray(self): a = asfarray(np.array([1, 2, 3])) assert_equal(a.__class__, np.ndarray) assert_(np.issubdtype(a.dtype, np.floating)) # previously this would infer dtypes from arrays, unlike every single # other numpy function assert_raises(TypeError, asfarray, np.array([1, 2, 3]), dtype=np.array(1.0))	14.76 KB	2026-01-20 12:58
📄 test_ufunclike.py import numpy as np import numpy.core as nx import numpy.lib.ufunclike as ufl from numpy.testing import ( assert_, assert_equal, assert_array_equal, assert_warns, assert_raises ) class TestUfunclike: def test_isposinf(self): a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0]) out = nx.zeros(a.shape, bool) tgt = nx.array([True, False, False, False, False, False]) res = ufl.isposinf(a) assert_equal(res, tgt) res = ufl.isposinf(a, out) assert_equal(res, tgt) assert_equal(out, tgt) a = a.astype(np.complex_) with assert_raises(TypeError): ufl.isposinf(a) def test_isneginf(self): a = nx.array([nx.inf, -nx.inf, nx.nan, 0.0, 3.0, -3.0]) out = nx.zeros(a.shape, bool) tgt = nx.array([False, True, False, False, False, False]) res = ufl.isneginf(a) assert_equal(res, tgt) res = ufl.isneginf(a, out) assert_equal(res, tgt) assert_equal(out, tgt) a = a.astype(np.complex_) with assert_raises(TypeError): ufl.isneginf(a) def test_fix(self): a = nx.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]]) out = nx.zeros(a.shape, float) tgt = nx.array([[1., 1., 1., 1.], [-1., -1., -1., -1.]]) res = ufl.fix(a) assert_equal(res, tgt) res = ufl.fix(a, out) assert_equal(res, tgt) assert_equal(out, tgt) assert_equal(ufl.fix(3.14), 3) def test_fix_with_subclass(self): class MyArray(nx.ndarray): def __new__(cls, data, metadata=None): res = nx.array(data, copy=True).view(cls) res.metadata = metadata return res def __array_wrap__(self, obj, context=None): if isinstance(obj, MyArray): obj.metadata = self.metadata return obj def __array_finalize__(self, obj): self.metadata = getattr(obj, 'metadata', None) return self a = nx.array([1.1, -1.1]) m = MyArray(a, metadata='foo') f = ufl.fix(m) assert_array_equal(f, nx.array([1, -1])) assert_(isinstance(f, MyArray)) assert_equal(f.metadata, 'foo') # check 0d arrays don't decay to scalars m0d = m[0,...] m0d.metadata = 'bar' f0d = ufl.fix(m0d) assert_(isinstance(f0d, MyArray)) assert_equal(f0d.metadata, 'bar') def test_scalar(self): x = np.inf actual = np.isposinf(x) expected = np.True_ assert_equal(actual, expected) assert_equal(type(actual), type(expected)) x = -3.4 actual = np.fix(x) expected = np.float64(-3.0) assert_equal(actual, expected) assert_equal(type(actual), type(expected)) out = np.array(0.0) actual = np.fix(x, out=out) assert_(actual is out)	2.91 KB	2026-01-20 12:58
📄 test_utils.py import inspect import sys import pytest import numpy as np from numpy.core import arange from numpy.testing import assert_, assert_equal, assert_raises_regex from numpy.lib import deprecate, deprecate_with_doc import numpy.lib.utils as utils from io import StringIO @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO") @pytest.mark.skipif( sys.version_info == (3, 10, 0, "candidate", 1), reason="Broken as of bpo-44524", ) def test_lookfor(): out = StringIO() utils.lookfor('eigenvalue', module='numpy', output=out, import_modules=False) out = out.getvalue() assert_('numpy.linalg.eig' in out) @deprecate def old_func(self, x): return x @deprecate(message="Rather use new_func2") def old_func2(self, x): return x def old_func3(self, x): return x new_func3 = deprecate(old_func3, old_name="old_func3", new_name="new_func3") def old_func4(self, x): """Summary. Further info. """ return x new_func4 = deprecate(old_func4) def old_func5(self, x): """Summary. Bizarre indentation. """ return x new_func5 = deprecate(old_func5, message="This function is\ndeprecated.") def old_func6(self, x): """ Also in PEP-257. """ return x new_func6 = deprecate(old_func6) @deprecate_with_doc(msg="Rather use new_func7") def old_func7(self,x): return x def test_deprecate_decorator(): assert_('deprecated' in old_func.__doc__) def test_deprecate_decorator_message(): assert_('Rather use new_func2' in old_func2.__doc__) def test_deprecate_fn(): assert_('old_func3' in new_func3.__doc__) assert_('new_func3' in new_func3.__doc__) def test_deprecate_with_doc_decorator_message(): assert_('Rather use new_func7' in old_func7.__doc__) @pytest.mark.skipif(sys.flags.optimize == 2, reason="-OO discards docstrings") @pytest.mark.parametrize('old_func, new_func', [ (old_func4, new_func4), (old_func5, new_func5), (old_func6, new_func6), ]) def test_deprecate_help_indentation(old_func, new_func): _compare_docs(old_func, new_func) # Ensure we don't mess up the indentation for knd, func in (('old', old_func), ('new', new_func)): for li, line in enumerate(func.__doc__.split('\n')): if li == 0: assert line.startswith(' ') or not line.startswith(' '), knd elif line: assert line.startswith(' '), knd def _compare_docs(old_func, new_func): old_doc = inspect.getdoc(old_func) new_doc = inspect.getdoc(new_func) index = new_doc.index('\n\n') + 2 assert_equal(new_doc[index:], old_doc) @pytest.mark.skipif(sys.flags.optimize == 2, reason="-OO discards docstrings") def test_deprecate_preserve_whitespace(): assert_('\n Bizarre' in new_func5.__doc__) def test_deprecate_module(): assert_(old_func.__module__ == __name__) def test_safe_eval_nameconstant(): # Test if safe_eval supports Python 3.4 _ast.NameConstant utils.safe_eval('None') class TestByteBounds: def test_byte_bounds(self): # pointer difference matches size * itemsize # due to contiguity a = arange(12).reshape(3, 4) low, high = utils.byte_bounds(a) assert_equal(high - low, a.size * a.itemsize) def test_unusual_order_positive_stride(self): a = arange(12).reshape(3, 4) b = a.T low, high = utils.byte_bounds(b) assert_equal(high - low, b.size * b.itemsize) def test_unusual_order_negative_stride(self): a = arange(12).reshape(3, 4) b = a.T[::-1] low, high = utils.byte_bounds(b) assert_equal(high - low, b.size * b.itemsize) def test_strided(self): a = arange(12) b = a[::2] low, high = utils.byte_bounds(b) # the largest pointer address is lost (even numbers only in the # stride), and compensate addresses for striding by 2 assert_equal(high - low, b.size * 2 * b.itemsize - b.itemsize) def test_assert_raises_regex_context_manager(): with assert_raises_regex(ValueError, 'no deprecation warning'): raise ValueError('no deprecation warning') def test_info_method_heading(): # info(class) should only print "Methods:" heading if methods exist class NoPublicMethods: pass class WithPublicMethods: def first_method(): pass def _has_method_heading(cls): out = StringIO() utils.info(cls, output=out) return 'Methods:' in out.getvalue() assert _has_method_heading(WithPublicMethods) assert not _has_method_heading(NoPublicMethods) def test_drop_metadata(): def _compare_dtypes(dt1, dt2): return np.can_cast(dt1, dt2, casting='no') # structured dtype dt = np.dtype([('l1', [('l2', np.dtype('S8', metadata={'msg': 'toto'}))])], metadata={'msg': 'titi'}) dt_m = utils.drop_metadata(dt) assert _compare_dtypes(dt, dt_m) is True assert dt_m.metadata is None assert dt_m['l1'].metadata is None assert dt_m['l1']['l2'].metadata is None # alignement dt = np.dtype([('x', '<f8'), ('y', '<i4')], align=True, metadata={'msg': 'toto'}) dt_m = utils.drop_metadata(dt) assert _compare_dtypes(dt, dt_m) is True assert dt_m.metadata is None # subdtype dt = np.dtype('8f', metadata={'msg': 'toto'}) dt_m = utils.drop_metadata(dt) assert _compare_dtypes(dt, dt_m) is True assert dt_m.metadata is None # scalar dt = np.dtype('uint32', metadata={'msg': 'toto'}) dt_m = utils.drop_metadata(dt) assert _compare_dtypes(dt, dt_m) is True assert dt_m.metadata is None @pytest.mark.parametrize("dtype", [np.dtype("i,i,i,i")[["f1", "f3"]], np.dtype("f8"), np.dtype("10i")]) def test_drop_metadata_identity_and_copy(dtype): # If there is no metadata, the identity is preserved: assert utils.drop_metadata(dtype) is dtype # If there is any, it is dropped (subforms are checked above) dtype = np.dtype(dtype, metadata={1: 2}) assert utils.drop_metadata(dtype).metadata is None	6.07 KB	2026-01-20 12:58

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