update

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test__datasource.py

@@ -0,0 +1,350 @@
+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__()

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test__iotools.py

@@ -0,0 +1,353 @@
+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 > 2**32
+        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])

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test__version.py

@@ -0,0 +1,64 @@
+"""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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_array_utils.py

@@ -0,0 +1,33 @@
+import numpy as np
+
+from numpy.lib import array_utils
+from numpy.testing import assert_equal
+
+
+class TestByteBounds:
+    def test_byte_bounds(self):
+        # pointer difference matches size * itemsize
+        # due to contiguity
+        a = np.arange(12).reshape(3, 4)
+        low, high = array_utils.byte_bounds(a)
+        assert_equal(high - low, a.size * a.itemsize)
+
+    def test_unusual_order_positive_stride(self):
+        a = np.arange(12).reshape(3, 4)
+        b = a.T
+        low, high = array_utils.byte_bounds(b)
+        assert_equal(high - low, b.size * b.itemsize)
+
+    def test_unusual_order_negative_stride(self):
+        a = np.arange(12).reshape(3, 4)
+        b = a.T[::-1]
+        low, high = array_utils.byte_bounds(b)
+        assert_equal(high - low, b.size * b.itemsize)
+
+    def test_strided(self):
+        a = np.arange(12)
+        b = a[::2]
+        low, high = array_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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_arrayterator.py

@@ -0,0 +1,46 @@
+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(2*els)
+    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))

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_histograms.py

@@ -0,0 +1,837 @@
+import numpy as np
+
+from numpy 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([-2**exponent + 4, 2**exponent - 4], dtype=dtype)
+        hist, e = histogram(arr, bins=2)
+        assert_equal(e, [-2**exponent + 4, 0, 2**exponent - 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, [0])
+        assert_equal(x_loc.dtype, float_large)
+
+    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, [0])
+
+        assert_equal(x_loc.dtype, float_large)
+
+    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
+    @pytest.mark.skip(reason="Bad memory reports lead to OOM in ci testing")
+    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)))
+
+    def test_gh_23110(self):
+        hist, e = np.histogram(np.array([-0.9e-308], dtype='>f8'),
+                               bins=2,
+                               range=(-1e-308, -2e-313))
+        expected_hist = np.array([1, 0])
+        assert_array_equal(hist, expected_hist)
+
+
+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).astype(float)
+            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)
+
+    @pytest.mark.parametrize("bins", ['auto', 'fd', 'doane', 'scott',
+                                      'stone', 'rice', 'sturges'])
+    def test_integer(self, bins):
+        """
+        Test that bin width for integer data is at least 1.
+        """
+        with suppress_warnings() as sup:
+            if bins == 'stone':
+                sup.filter(RuntimeWarning)
+            assert_equal(
+                np.histogram_bin_edges(np.tile(np.arange(9), 1000), bins),
+                np.arange(9))
+
+    def test_integer_non_auto(self):
+        """
+        Test that the bin-width>=1 requirement *only* applies to auto binning.
+        """
+        assert_equal(
+            np.histogram_bin_edges(np.tile(np.arange(9), 1000), 16),
+            np.arange(17) / 2)
+        assert_equal(
+            np.histogram_bin_edges(np.tile(np.arange(9), 1000), [.1, .2]),
+            [.1, .2])
+
+    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 / (8*8))
+
+    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])

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_index_tricks.py

@@ -0,0 +1,553 @@
+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_impl import (
+    mgrid, ogrid, ndenumerate, fill_diagonal, diag_indices, diag_indices_from,
+    index_exp, ndindex, c_, 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((2*3 + 1)*6 + 4, (4, 3, 6)), [2, 1, 4])
+        assert_equal(
+            np.ravel_multi_index([2, 1, 4], (4, 3, 6)), (2*3 + 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, (2**32-1, 2**31+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 = 8*coords[0]+coords[1]
+            assert_equal(np.ravel_multi_index(coords, shape), uncoords)
+            assert_equal(coords, np.unravel_index(uncoords, shape))
+            uncoords = coords[0]+5*coords[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*(8*coords[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]+8*coords[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]+19*0.1, 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], -2*np.ones(10, 'd'))
+        assert_array_almost_equal(c[0][-1, :], np.ones(10, 'd'), 11)
+        assert_array_almost_equal(c[1][:, -1], 2*np.ones(10, 'd'), 11)
+        assert_array_almost_equal(d[0, 1, :] - d[0, 0, :],
+                                  0.1*np.ones(20, 'd'), 11)
+        assert_array_almost_equal(d[1, :, 1] - d[1, :, 0],
+                                  0.2*np.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_array_almost_equal(grid64, grid32)
+        # At some point this was float64, but NEP 50 changed it:
+        assert grid32.dtype == np.float32
+        assert grid64.dtype == np.float64
+
+        # 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 = 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, [])

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_mixins.py

@@ -0,0 +1,216 @@
+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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_packbits.py

@@ -0,0 +1,376 @@
+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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_polynomial.py

@@ -0,0 +1,303 @@
+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 `np.bool` and `np.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) + 1j*np.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))

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_regression.py

@@ -0,0 +1,228 @@
+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,
+    )
+import pytest
+
+
+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(q*v + 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_large_fancy_indexing(self):
+        # Large enough to fail on 64-bit.
+        nbits = np.dtype(np.intp).itemsize * 8
+        thesize = int((2**nbits)**(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_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()

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_shape_base.py

@@ -0,0 +1,808 @@
+import numpy as np
+import functools
+import sys
+import pytest
+
+from numpy 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.exceptions import AxisError
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_raises, assert_warns
+    )
+
+
+IS_64BIT = sys.maxsize > 2**32
+
+
+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(AxisError, take_along_axis, a, ai, axis=10)
+        # invalid indices
+        assert_raises(ValueError, take_along_axis, a, ai, axis=None)
+
+    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)
+
+    def test_invalid(self):
+        """ Test invalid inputs """
+        a_base = np.array([[10, 30, 20], [60, 40, 50]])
+        indices = np.array([[0], [1]])
+        values = np.array([[2], [1]])
+
+        # sanity check
+        a = a_base.copy()
+        put_along_axis(a, indices, values, axis=0)
+        assert np.all(a == [[2, 2, 2], [1, 1, 1]])
+
+        # invalid indices
+        a = a_base.copy()
+        with assert_raises(ValueError) as exc:
+            put_along_axis(a, indices, values, axis=None)
+        assert "single dimension" in str(exc.exception)
+
+
+
+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(6*3).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(6*5*3).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(6*3).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(AxisError, expand_dims, a, -6)
+        assert_raises(AxisError, expand_dims, a, 5)
+
+        a = np.empty((3, 3, 3))
+        assert_raises(AxisError, expand_dims, a, (0, -6))
+        assert_raises(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.asmatrix]
+    )
+    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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_stride_tricks.py

@@ -0,0 +1,647 @@
+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_impl 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 = 10*i + 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 = 10*i + 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 = 10*i + 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 = 10*i + 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 = 10*i + 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
+    test_cases = [((False,), broadcast_arrays(original,)),
+                  ((True, False), broadcast_arrays(0, original))]
+    for is_broadcast, results in test_cases:
+        for array_is_broadcast, result in zip(is_broadcast, results):
+            # This will change to False in a future version
+            if array_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])
+
+    test_cases = [((False, ), broadcast_arrays(original,)),
+                  ((True, False), broadcast_arrays(0, original))]
+    for is_broadcast, results in test_cases:
+        for array_is_broadcast, result in zip(is_broadcast, results):
+            # This will change to False in a future version
+            if array_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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_twodim_base.py

@@ -0,0 +1,541 @@
+"""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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_type_check.py

@@ -0,0 +1,465 @@
+import numpy as np
+from numpy import (
+    common_type, mintypecode, isreal, iscomplex, isposinf, isneginf,
+    nan_to_num, isrealobj, iscomplexobj, real_if_close
+    )
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_raises
+    )
+
+
+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.complex64)
+        acd = np.array([[1+5j, 2+6j], [3+7j, 4+8j]], dtype=np.complex128)
+        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.complex64)
+        assert_(common_type(acd) == np.complex128)
+
+
+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,)+1j*np.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,)+1j*np.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.float64)
+        vals = nan_to_num(1.1, nan=10, posinf=20, neginf=30)
+        assert_all(vals == 1.1)
+        assert_equal(type(vals), np.float64)
+
+    def test_complex_good(self):
+        vals = nan_to_num(1+1j)
+        assert_all(vals == 1+1j)
+        assert_equal(type(vals), np.complex128)
+        vals = nan_to_num(1+1j, nan=10, posinf=20, neginf=30)
+        assert_all(vals == 1+1j)
+        assert_equal(type(vals), np.complex128)
+
+    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.complex128)
+
+    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.complex128)
+        # 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))

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_ufunclike.py

@@ -0,0 +1,100 @@
+import numpy as np
+
+from numpy import fix, isposinf, isneginf
+from numpy.testing import (
+    assert_, assert_equal, assert_array_equal, assert_raises
+)
+
+
+class TestUfunclike:
+
+    def test_isposinf(self):
+        a = np.array([np.inf, -np.inf, np.nan, 0.0, 3.0, -3.0])
+        out = np.zeros(a.shape, bool)
+        tgt = np.array([True, False, False, False, False, False])
+
+        res = isposinf(a)
+        assert_equal(res, tgt)
+        res = isposinf(a, out)
+        assert_equal(res, tgt)
+        assert_equal(out, tgt)
+
+        a = a.astype(np.complex128)
+        with assert_raises(TypeError):
+            isposinf(a)
+
+    def test_isneginf(self):
+        a = np.array([np.inf, -np.inf, np.nan, 0.0, 3.0, -3.0])
+        out = np.zeros(a.shape, bool)
+        tgt = np.array([False, True, False, False, False, False])
+
+        res = isneginf(a)
+        assert_equal(res, tgt)
+        res = isneginf(a, out)
+        assert_equal(res, tgt)
+        assert_equal(out, tgt)
+
+        a = a.astype(np.complex128)
+        with assert_raises(TypeError):
+            isneginf(a)
+
+    def test_fix(self):
+        a = np.array([[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]])
+        out = np.zeros(a.shape, float)
+        tgt = np.array([[1., 1., 1., 1.], [-1., -1., -1., -1.]])
+
+        res = fix(a)
+        assert_equal(res, tgt)
+        res = fix(a, out)
+        assert_equal(res, tgt)
+        assert_equal(out, tgt)
+        assert_equal(fix(3.14), 3)
+
+    def test_fix_with_subclass(self):
+        class MyArray(np.ndarray):
+            def __new__(cls, data, metadata=None):
+                res = np.array(data, copy=True).view(cls)
+                res.metadata = metadata
+                return res
+
+            def __array_wrap__(self, obj, context=None, return_scalar=False):
+                if not isinstance(obj, MyArray):
+                    obj = obj.view(MyArray)
+                if obj.metadata is None:
+                    obj.metadata = self.metadata
+                return obj
+
+            def __array_finalize__(self, obj):
+                self.metadata = getattr(obj, 'metadata', None)
+                return self
+
+        a = np.array([1.1, -1.1])
+        m = MyArray(a, metadata='foo')
+        f = fix(m)
+        assert_array_equal(f, np.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 = 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)

.CondaPkg/env/Lib/site-packages/numpy/lib/tests/test_utils.py

@@ -0,0 +1,80 @@
+import pytest
+
+import numpy as np
+from numpy.testing import assert_raises_regex
+import numpy.lib._utils_impl as _utils_impl
+
+from io import StringIO
+
+
+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()
+        np.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_impl.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
+
+    # alignment
+    dt = np.dtype([('x', '<f8'), ('y', '<i4')],
+                  align=True,
+                  metadata={'msg': 'toto'})
+    dt_m = _utils_impl.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_impl.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_impl.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_impl.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_impl.drop_metadata(dtype).metadata is None