Python numpy.array_equal函数代码示例

    def test_square_matrices_1(self):
        op4 = OP4()
        # matrices = op4.read_op4(os.path.join(op4Path, fname))
        form1 = 1
        form2 = 2
        form3 = 2
        from numpy import matrix, ones, reshape, arange

        A1 = matrix(ones((3, 3), dtype="float64"))
        A2 = reshape(arange(9, dtype="float64"), (3, 3))
        A3 = matrix(ones((1, 1), dtype="float32"))
        matrices = {"A1": (form1, A1), "A2": (form2, A2), "A3": (form3, A3)}

        for (is_binary, fname) in [(False, "small_ascii.op4"), (True, "small_binary.op4")]:
            op4_filename = os.path.join(op4Path, fname)
            op4.write_op4(op4_filename, matrices, name_order=None, precision="default", is_binary=False)
            matrices2 = op4.read_op4(op4_filename, precision="default")
            (form1b, A1b) = matrices2["A1"]
            (form2b, A2b) = matrices2["A2"]
            self.assertEqual(form1, form1b)
            self.assertEqual(form2, form2b)

            (form1b, A1b) = matrices2["A1"]
            (form2b, A2b) = matrices2["A2"]
            (form3b, A3b) = matrices2["A3"]
            self.assertEqual(form1, form1b)
            self.assertEqual(form2, form2b)
            self.assertEqual(form3, form3b)

            self.assertTrue(array_equal(A1, A1b))
            self.assertTrue(array_equal(A2, A2b))
            self.assertTrue(array_equal(A3, A3b))
            del A1b, A2b, A3b
            del form1b, form2b, form3b

 def testNegativeDelta(self):
   self.assertTrue(
       np.array_equal(self._Range(5, -1, -1), np.array([5, 4, 3, 2, 1, 0])))
   self.assertTrue(
       np.allclose(self._Range(2.5, 0, -0.5), np.array([2.5, 2, 1.5, 1, 0.5])))
   self.assertTrue(
       np.array_equal(self._Range(-5, -10, -3), np.array([-5, -8])))

    def test_train(self):
        # y = x + 0
        x1 = np.array([2, 4])
        y1 = np.array([2, 4])
        m1 = LinearRegression(1)
        m1.train(x1, y1, n_iter=1, lr=0.1)

        # expected W and b after 1 iteration with lr 0.1
        exp_W1 = np.array([1.0])
        exp_b1 = 0.3
        self.assertTrue(np.array_equal(m1.W, exp_W1))
        self.assertAlmostEqual(m1.b[0], exp_b1)

        # y = x1 + x2 + 0
        x2 = np.array([[2, 2],
                       [4, 4]])
        y2 = np.array([4, 8])
        m2 = LinearRegression(2)
        m2.train(x2, y2, n_iter=1, lr=0.1)

        # expected W and b after 1 iteration with lr 0.1
        exp_W2 = np.array([2.0, 2.0])
        exp_b2 = 0.6
        self.assertTrue(np.array_equal(m2.W, exp_W2))
        self.assertAlmostEqual(m2.b[0], exp_b2)

def _testFloodFill(SegmentationHelper):
    filledMask = SegmentationHelper._floodFill(
        testImage, (1, 1), 5, connectivity=8)
    assert numpy.array_equal(
        filledMask,
        numpy.array([
            [255, 255, 0, 0, 0, 0],
            [255, 255, 255, 255, 0, 0],
            [0, 255, 0, 255, 255, 255],
            [0, 255, 255, 255, 0, 0],
            [0, 0, 255, 0, 0, 0],
            [0, 0, 0, 255, 0, 0]
        ], dtype=numpy.uint8))
    assert numpy.array_equal(testImage, originalTestImage)

    # Now, with connectivity=4
    filledMask = SegmentationHelper._floodFill(
        testImage, (1, 1), 5, connectivity=4)
    assert numpy.array_equal(
        filledMask,
        numpy.array([
            [255, 255, 0, 0, 0, 0],
            [255, 255, 255, 255, 0, 0],
            [0, 255, 0, 255, 255, 255],
            [0, 255, 255, 255, 0, 0],
            [0, 0, 255, 0, 0, 0],
            [0, 0, 0, 0, 0, 0]
        ], dtype=numpy.uint8))
    assert numpy.array_equal(testImage, originalTestImage)

def test_Moster13SmHm_behavior():
	"""
	"""
	default_model = Moster13SmHm()
	mstar1 = default_model.mean_stellar_mass(prim_haloprop = 1.e12)
	ratio1 = mstar1/3.4275e10
	np.testing.assert_array_almost_equal(ratio1, 1.0, decimal=3)

	default_model.param_dict['n10'] *= 1.1
	mstar2 = default_model.mean_stellar_mass(prim_haloprop = 1.e12)
	assert mstar2 > mstar1

	default_model.param_dict['n11'] *= 1.1
	mstar3 = default_model.mean_stellar_mass(prim_haloprop = 1.e12)
	assert mstar3 == mstar2

	mstar4_z1 = default_model.mean_stellar_mass(prim_haloprop = 1.e12, redshift=1)
	default_model.param_dict['n11'] *= 1.1
	mstar5_z1 = default_model.mean_stellar_mass(prim_haloprop = 1.e12, redshift=1)
	assert mstar5_z1 != mstar4_z1

	mstar_realization1 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=43)
	mstar_realization2 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=43)
	mstar_realization3 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=44)
	assert np.array_equal(mstar_realization1, mstar_realization2)
	assert not np.array_equal(mstar_realization1, mstar_realization3)

	measured_scatter1 = np.std(np.log10(mstar_realization1))
	model_scatter = default_model.param_dict['scatter_model_param1']
	np.testing.assert_allclose(measured_scatter1, model_scatter, rtol=1e-3)

	default_model.param_dict['scatter_model_param1'] = 0.3
	mstar_realization4 = default_model.mc_stellar_mass(prim_haloprop = np.ones(1e4)*1e12, seed=43)
	measured_scatter4 = np.std(np.log10(mstar_realization4))
	np.testing.assert_allclose(measured_scatter4, 0.3, rtol=1e-3)

    def test_A(self):
        cid0 = CORD2R()
        Lx = 2.
        Ly = 0.
        Lz = 3.
        Fy = 1.
        origin = array([-Lx, 0., -Lz])
        z_axis = origin + array([0., 0., 1.])
        xz_plane = origin + array([1., 0., 1.])
        rid = 0
        data = [1, rid] + list(origin) + list(z_axis) + list(xz_plane)

        Fxyz = [0., -Fy, 0.]
        Mxyz = [0., 0., 0.]
        cid_new = CORD2R(data=data)
        model = None

        Fxyz_local, Mxyz_local = TransformLoadWRT(Fxyz, Mxyz, cid0, cid_new,
                                                  model, is_cid_int=False)

        r = array([Lx, Ly, Lz])
        F = array([0., -Fy, 0.])
        M = cross(r, F)
        self.assertTrue(array_equal(Fxyz_local, F)), "expected=%s actual=%s" % (F, Fxyz_local)
        self.assertTrue(array_equal(Mxyz_local, cross(r, F))), "expected=%s actual=%s" % (M, Mxyz_local)

def test_Period():
    p = Period(numbers[0:2], units[0])
    assert sa_asserts.sa_access(p)
    assert i_asserts.interval(p, start=numbers[0], stop=numbers[1])
    assert np.array_equal(p.info['start'], p.start)
    assert np.array_equal(p.info['stop'], p.stop)
    assert np.array_equal(p.info['length'], p.length)

    def test_find_optimal_paras_rf(self):
        # first, load the file
        f = h5py.File('tuning_ref_results/NIS_results.hdf5', 'r+')
        # get the filter
        grp = f['ICA/tuning']
        wica = grp['Wica'][:]  # should be
        # get the reference results
        ica_optx = grp.attrs['ica_optx']
        ica_opty = grp.attrs['ica_opty']
        ica_optfreq = grp.attrs['ica_optfreq']
        ica_optor = grp.attrs['ica_optor']
        ica_optphase = grp.attrs['ica_optphase']

        # now shuffle this Wica. 1024x256
        wica = transpose_c_and_f(wica.T)

        # get result
        result = tuning.find_optimal_paras_rf(w=wica, legacy=True)

        # print result
        self.assertTrue(np.allclose(result['optx'], ica_optx))
        self.assertTrue(np.allclose(result['opty'], ica_opty))
        self.assertTrue(np.array_equal(result['optfreq'], ica_optfreq))
        self.assertTrue(np.array_equal(result['optor'], ica_optor))
        self.assertTrue(np.array_equal(result['optphase'], ica_optphase))
        f.close()

def _read_volume_info(fobj):
    """An implementation of nibabel.freesurfer.io._read_volume_info, since old
    versions of nibabel (<=2.1.0) don't have it.
    """
    volume_info = dict()
    head = np.fromfile(fobj, '>i4', 1)
    if not np.array_equal(head, [20]):  # Read two bytes more
        head = np.concatenate([head, np.fromfile(fobj, '>i4', 2)])
        if not np.array_equal(head, [2, 0, 20]):
            warnings.warn("Unknown extension code.")
            return volume_info

    volume_info['head'] = head
    for key in ['valid', 'filename', 'volume', 'voxelsize', 'xras', 'yras',
                'zras', 'cras']:
        pair = fobj.readline().decode('utf-8').split('=')
        if pair[0].strip() != key or len(pair) != 2:
            raise IOError('Error parsing volume info.')
        if key in ('valid', 'filename'):
            volume_info[key] = pair[1].strip()
        elif key == 'volume':
            volume_info[key] = np.array(pair[1].split()).astype(int)
        else:
            volume_info[key] = np.array(pair[1].split()).astype(float)
    # Ignore the rest
    return volume_info

 def test_setPairMask(self):
     '''check different setPairMask arguments.
     '''
     bdc = self.bdc
     dall = bdc(self.nickel)
     bdc.maskAllPairs(False)
     self.assertEqual(0, len(bdc(self.nickel)))
     for i in range(4):
         bdc.setPairMask(0, i, True)
     dst0a = bdc(self.nickel)
     bdc.setPairMask(range(4), 0, True, others=False)
     dst0b = bdc(self.nickel)
     self.assertTrue(numpy.array_equal(dst0a, dst0b))
     bdc.maskAllPairs(False)
     bdc.setPairMask(0, -7, True)
     dst0c = bdc(self.nickel)
     self.assertTrue(numpy.array_equal(dst0a, dst0c))
     bdc.maskAllPairs(False)
     bdc.setPairMask(0, 'all', True)
     dst0d = bdc(self.nickel)
     self.assertTrue(numpy.array_equal(dst0a, dst0d))
     bdc.setPairMask('all', 'all', False)
     self.assertEqual(0, len(bdc(self.nickel)))
     bdc.setPairMask('all', range(4), True)
     dall2 = bdc(self.nickel)
     self.assertTrue(numpy.array_equal(dall, dall2))
     self.assertRaises(ValueError, bdc.setPairMask, 'fooo', 2, True)
     self.assertRaises(ValueError, bdc.setPairMask, 'aLL', 2, True)
     return

    def test_tz_localize_dti(self):
        from pandas.tseries.offsets import Hour

        dti = DatetimeIndex(start='1/1/2005', end='1/1/2005 0:00:30.256',
                            freq='L')
        dti2 = dti.tz_localize('US/Eastern')

        dti_utc = DatetimeIndex(start='1/1/2005 05:00',
                                end='1/1/2005 5:00:30.256', freq='L',
                                tz='utc')

        self.assert_(np.array_equal(dti2.values, dti_utc.values))

        dti3 = dti2.tz_convert('US/Pacific')
        self.assert_(np.array_equal(dti3.values, dti_utc.values))

        dti = DatetimeIndex(start='11/6/2011 1:59',
                            end='11/6/2011 2:00', freq='L')
        self.assertRaises(pytz.AmbiguousTimeError, dti.tz_localize,
                          'US/Eastern')

        dti = DatetimeIndex(start='3/13/2011 1:59', end='3/13/2011 2:00',
                            freq='L')
        self.assertRaises(pytz.AmbiguousTimeError, dti.tz_localize,
                          'US/Eastern')

def test_tabulate():
    scalar_no_units = lambda wlen: math.sqrt(wlen)
    scalar_with_units = lambda wlen: math.sqrt(wlen.value)
    array_no_units = lambda wlen: 1. + np.sqrt(wlen)
    array_with_units = lambda wlen: np.sqrt(wlen.value)
    add_units = lambda fval: (lambda wlen: fval(wlen) * u.erg)
    wlen = np.arange(1, 3) * u.Angstrom
    for v in True, False:
        # Test each mode without any function return units.
        f1 = tabulate_function_of_wavelength(scalar_no_units, wlen, v)
        assert f1[1] == None
        f2 = tabulate_function_of_wavelength(scalar_with_units, wlen, v)
        assert f2[1] == None
        f3 = tabulate_function_of_wavelength(array_no_units, wlen, v)
        assert f3[1] == None
        f4 = tabulate_function_of_wavelength(scalar_with_units, wlen, v)
        assert f4[1] == None
        # Now test with return units.
        g1 = tabulate_function_of_wavelength(
            add_units(scalar_no_units), wlen, v)
        assert np.array_equal(f1[0], g1[0]) and g1[1] == u.erg
        g2 = tabulate_function_of_wavelength(
            add_units(scalar_with_units), wlen, v)
        assert np.array_equal(f2[0], g2[0]) and g2[1] == u.erg
        g3 = tabulate_function_of_wavelength(
            add_units(array_no_units), wlen, v)
        assert np.array_equal(f3[0], g3[0]) and g3[1] == u.erg
        g4 = tabulate_function_of_wavelength(
            add_units(scalar_with_units), wlen, v)
        assert np.array_equal(f4[0], g4[0]) and g4[1] == u.erg

def test_StatesMatrixMerger():
    val_mat1_text = list(tools.ngram(test_text_df["text"].values[0], [1]))
    val_mat2_text = list(tools.ngram(test_text_df["text"].values[1], [1]))
    val_mat1 = ValuesMatrix(val_mat1_text, force2d="as_col")
    val_mat2 = ValuesMatrix(val_mat2_text, force2d="as_col")

    idx_data_mat1 = val_mat1.build_index_data_matrix()
    idx_data_mat2 = val_mat2.build_index_data_matrix()
    idx_data_mat1_old_ref_data = idx_data_mat1.states_matrix._ref_data
    idx_data_mat2_old_ref_data = idx_data_mat2.states_matrix._ref_data

    old_idx_data_mat1 = idx_data_mat1.index_matrix.copy()
    old_idx_data_mat2 = idx_data_mat2.index_matrix.copy()

    assert len(idx_data_mat1_old_ref_data) > 0
    assert len(idx_data_mat2_old_ref_data) > 0

    states_mats_merger = StatesMatrixMerger(idx_data_mat1.states_matrix,
                                            idx_data_mat2.states_matrix)

    assert len(states_mats_merger._unique_states_matrix_ids) > 1

    states_mats_merger.update()

    assert len(idx_data_mat1_old_ref_data) == 0
    assert len(idx_data_mat2_old_ref_data) == 0
    assert id(idx_data_mat1.states_matrix) == id(idx_data_mat2.states_matrix)
    assert len(idx_data_mat2.states_matrix._ref_data) > 1

    assert np.array_equal(idx_data_mat1.index_matrix, old_idx_data_mat1)
    assert not np.array_equal(idx_data_mat2.index_matrix, old_idx_data_mat2)

    assert np.array_equal(idx_data_mat1._data, val_mat1)
    assert np.array_equal(idx_data_mat2._data, val_mat2)

def test_two_triangles_without_edges():
    grid = two_triangles_with_depths()
    grid.edges = None

    grid.save_as_netcdf("2_triangles_without_edges.nc")

    # read it back in and check it out
    ug = UGrid.from_ncfile("2_triangles_without_edges.nc", load_data=True)

    assert ug.nodes.shape == (4, 2)
    assert ug.nodes.shape == grid.nodes.shape

    # not ideal to pull specific values out, but how else to test?
    assert np.array_equal(ug.nodes[0, :], (0.1, 0.1))
    assert np.array_equal(ug.nodes[-1, :], (3.1, 2.1))
    assert np.array_equal(ug.nodes, grid.nodes)

    assert ug.faces.shape == grid.faces.shape

    assert ug.edges is None

    depths = find_depths(ug)
    assert depths.data.shape == (4,)
    assert depths.data[0] == 1
    assert depths.attributes["units"] == "unknown"

    def test_n_largest_area_contours_images__with_invert(self):
        # given
        image = cv2.imread("./images/SnipNLargestAreaContours/"
                           "test_n_largest_area_contours_image__with_invert__input_image.png",
                           cv2.IMREAD_GRAYSCALE)
        n = 2
        invert_flag = True
        snip_n_largest_area_contours = SnipNLargestAreaContours(image, n, invert_flag)

        expected_largest_contour_image_1 = cv2.imread(
            "./images/SnipNLargestAreaContours/test_n_largest_area_contours_images__with_invert__snipped_image_1.png",
            flags=cv2.IMREAD_GRAYSCALE)
        expected_largest_contour_image_2 = cv2.imread(
            "./images/SnipNLargestAreaContours/test_n_largest_area_contours_images__with_invert__snipped_image_2.png",
            flags=cv2.IMREAD_GRAYSCALE)

        expected_n_largest_area_contours_images = [expected_largest_contour_image_1, expected_largest_contour_image_2]

        # when
        actual_n_largest_area_contours_images = snip_n_largest_area_contours.n_largest_area_contours_images

        # that
        self.assertEqual(np.array_equal(actual_n_largest_area_contours_images[0],
                                        expected_n_largest_area_contours_images[0]), True)
        self.assertEqual(np.array_equal(actual_n_largest_area_contours_images[1],
                                        expected_n_largest_area_contours_images[1]), True)