我们从Python开源项目中，提取了以下24个代码示例，用于说明如何使用nose.tools.ok_()。
def test_strip_column_names(): d = {'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']), 'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd']), 'PD L1 (val)': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd']), 'PD L1 (>1)': pd.Series([0., 1., 1., 1.], index=['a', 'b', 'c', 'd']), } df = pd.DataFrame(d) # should not error & should rename columns df2 = df.rename(columns=strip_column_names(df.columns)) ok_((df2.columns != df.columns).any()) # should not rename columns -- should raise a warning with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') df3 = df.rename(columns=strip_column_names( df.columns, keep_paren_contents=False)) ok_(len(w) > 0, 'warning not raised when keep_paren_contents results in dups') ok_((df3.columns == df.columns).all())
def test_pvs_effective_permittivity_real(): testpack = setup_func_pc(0.3e-3) em = setup_func_mm(testpack) # Allow 5% error ok_(abs(em._effective_permittivity.real - 1.52441173e+00) < tolerance_pc * em._effective_permittivity.real) # eq_(em._effective_permittivity.real, 1.52441173e+00)
def test_ks_pc_is_0p3_mm(): testpack = setup_func_pc(0.3e-3) em = setup_func_em(testpack) # Allow 5% error memls_ks = 4.13718676e+00 # eq_(em.ks, memls_ks) ok_(abs(em.ks - memls_ks) < tolerance_pc * em.ks)
def test_ks_pc_is_0p25_mm(): testpack = setup_func_pc(0.25e-3) em = setup_func_em(testpack) # Allow 5% error memls_ks = 2.58158887e+00 # eq_(em.ks, memls_ks) ok_(abs(em.ks - memls_ks) < tolerance_pc * em.ks)
def test_ks_pc_is_0p2_mm(): testpack = setup_func_pc(0.2e-3) em = setup_func_em(testpack) # Allow 5% error memls_ks = 1.41304849e+00 # eq_(em.ks, memls_ks) ok_(abs(em.ks - memls_ks) < tolerance_pc * em.ks)
def test_ks_pc_is_0p15_mm(): testpack = setup_func_pc(0.15e-3) em = setup_func_em(testpack) # Allow 5% error memls_ks = 6.30218291e-01 # eq_(em.ks, memls_ks) ok_(abs(em.ks - memls_ks) < tolerance_pc * em.ks)
def test_ks_pc_is_0p2_mm(): testpack = setup_func_pc(0.05e-3) em = setup_func_em(testpack) # Allow 5% error memls_ks = 2.49851702e-02 # eq_(em.ks, memls_ks) ok_(abs(em.ks - memls_ks) < tolerance_pc * em.ks)
def test_ks_pc_is_0p1_mm(): testpack = setup_func_pc(0.1e-3) em = setup_func_mm(testpack) # Allow 5% error memls_ks = 1.94727497e-01 # eq_(em.ks, memls_ks) ok_(abs(em.ks - memls_ks) < tolerance_pc * em.ks)
def test_memlsks_pc_is_0p05_mm(): testpack = setup_func_pc(0.05e-3) em = setup_func_mm(testpack) # Allow 5% error memls_ks = 2.49851702e-02 # eq_(em.ks, memls_ks) ok_(abs(em.ks - memls_ks) / em.ks < tolerance_pc)
def test_memls_ka(): testpack = setup_func_pc(0.05e-3) # Corr fn is irrelevant em = setup_func_mm(testpack) # Allow 5% error memls_ka = 3.00937657e-01 # eq_(em.ka, memls_ka) ok_(abs(em.ka - memls_ka) / em.ka < tolerance_pc)
def test_iba_vs_rayleigh_passive_m0(): em_iba, em_ray = setup_func_rayleigh() mu = setup_mu(1. / 64) ok_((abs(em_iba.ft_even_phase(0, mu, npol=2) / em_iba.ks - em_ray.ft_even_phase(0, mu, npol=2) / em_ray.ks) < tolerance_pc).all())
def test_iba_vs_rayleigh_active_m1(): em_iba, em_ray = setup_func_rayleigh() mu = setup_mu(1. / 64, bypass_exception=True) # Clear cache em_iba.cached_mu = None ok_((abs(em_iba.ft_even_phase(1, mu, npol=3) / em_iba.ks - em_ray.ft_even_phase(1, mu, npol=3) / em_ray.ks) < tolerance_pc).all())
def test_iba_vs_rayleigh_active_m2(): em_iba, em_ray = setup_func_rayleigh() mu = setup_mu(1. / 64, bypass_exception=True) ok_((abs(em_iba.ft_even_phase(2, mu, npol=3) / em_iba.ks - em_ray.ft_even_phase(2, mu, npol=3) / em_ray.ks) < tolerance_pc).all())
def test_iba_raise_exception_mu_is_1(): shs_pack = setup_func_shs() em = setup_func_active(testpack=shs_pack) bad_mu = np.array([0.2, 1]) em.ft_even_phase(2, bad_mu, npol=3) # def test_equivalence_ft_phase_and_phase(): # em = setup_func_em() # em.set_max_mode(4) # mu = setup_mu() # phi = np.arange(0., 2. * np.pi, 2. * np.pi / mu.size) # phi_diff = phi - phi[:, np.newaxis] # p = em.phase(mu, phi) # pft = em.ft_phase(0, mu) # # Construct phi_diff matrix to recombine ft_phase # npol = 2 # n = len(phi_diff) # pd = np.empty((npol * n, npol * n)) # pd[0::npol, 0::npol] = phi_diff # pd[0::npol, 1::npol] = phi_diff # pd[1::npol, 0::npol] = phi_diff # pd[1::npol, 1::npol] = phi_diff # # Sum over decomposition modes # for m in range(1, 3): # pft += em.ft_phase(m, mu).real * np.cos(m * pd) + em.ft_phase(m, mu).imag * np.sin(m * pd) # Imaginary component should be zero # phase_diff = p - pft # ok_(phase_diff.all() < TOLERANCE)
def tests(): for i in range(len(trees)): _ = lambda: ok_( newick_parser.parse_string( trees[i] ) == results[i] ) _.description = "check tree parsing " + str(i) yield _,
def test_index(self): """The front page is working properly""" response = self.app.get('/') msg = 'TurboGears 2 is rapid web application development toolkit '\ 'designed to make your life easier.' # You can look for specific strings: ok_(msg in response) # You can also access a BeautifulSoup'ed response in your tests # (First run $ easy_install BeautifulSoup # and then uncomment the next two lines) # links = response.html.findAll('a') # print(links) # ok_(links, "Mummy, there are no links here!")
def test_environ(self): """Displaying the wsgi environ works""" response = self.app.get('/environ.html') ok_('The keys in the environment are:' in response)
def test_data_json(self): """The data display demo works with JSON""" resp = self.app.get('/data.json?a=1&b=2') ok_( dict(page='data', params={'a': '1', 'b': '2'}) == resp.json, resp.json )
def test_secc_with_manager(self): """The manager can access the secure controller""" # Note how authentication is forged: environ = {'REMOTE_USER': 'manager'} resp = self.app.get('/secc', extra_environ=environ, status=200) ok_('Secure Controller here' in resp.text, resp.text)
def test_as_dataframe_generic(): df_hello, cohort = prep_test_cohort() # test that column names haven't changed df = cohort.as_dataframe(join_with="hello") # column names should match those in df_hello res = compare_column_names(expected = df_hello.columns, observed = df.columns) ok_(res, 'columns names failed to match expected')
def test_as_dataframe_good_rename(): df_hello, cohort = prep_alt_test_cohort() # test behavior with rename_cols=True. should not raise a warning df = cohort.as_dataframe(rename_cols=True, join_with='hello') res = compare_column_names(expected = strip_column_names(df_hello.columns), observed = df.columns) ok_(res, 'column names failed to match expected')
def test_as_dataframe_bad_rename(): df_hello, cohort = prep_test_cohort() # test behavior with rename_cols=True. should raise a warning with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") df = cohort.as_dataframe(rename_cols=True, join_with='hello') # skip test since warnings (for some reason) don't propagate #ok_(len(w) > 0, 'fail to generate dups warning when using rename_cols=True') res = compare_column_names(expected = df_hello.columns, observed = df.columns) ok_(res, 'columns names failed to match expected')
def test_as_dataframe_drop_parens(): df_hello, cohort = prep_test_cohort() # test behavior with keep_paren_contents=False with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") df = cohort.as_dataframe(rename_cols=True, keep_paren_contents=False, join_with='hello') # skip test for warning since warning doesn't propagate (not sure why) #ok_(len(w) > 0, 'no warning when duplicates resulting from rename_cols') res = compare_column_names(expected = df_hello.columns, observed = df.columns) ok_(res, 'columns names failed to match expected')
def test_mixed_emmodel(): # prepare inputs l = 2 nl = l//2 # // Forces integer division thickness = np.array([0.1, 0.1]*nl) thickness[-1] = 100 # last one is semi-infinit radius = np.array([2e-4]*l) temperature = np.array([250.0, 250.0]*nl) density = [200, 400]*nl stickiness = [0.1, 0.1]*nl emmodel = ["dmrt_qcacp_shortrange", "iba"]*nl # create the snowpack snowpack = make_snowpack(thickness, "sticky_hard_spheres", density=density, temperature=temperature, radius=radius, stickiness=stickiness) # create the EM Model m = make_model(emmodel, "dort") # create the sensor radiometer = sensor_list.amsre('37V') # run the model res = m.run(radiometer, snowpack) print(res.TbV(), res.TbH()) #ok_((res.TbV() - 203.84730126016882) < 1e-4) #ok_((res.TbH() - 189.53130277932084) < 1e-4) #ok_((res.TbV() - 203.8473395866384) < 1e-4) #ok_((res.TbH() - 189.53346053779396) < 1e-4) ok_((res.TbV() - 204.62367102418355) < 1e-4) ok_((res.TbH() - 190.38540104288276) < 1e-4)
