cli_runner = <click.testing.CliRunner object at 0x7f0fd8025030>
tmpdir = local('/home/runner/work/emodel-generalisation/emodel-generalisation/.tox/py310/tmp/test_adapt0')

    def test_adapt(cli_runner, tmpdir):
        """Test cli adapt."""
        # fmt: off
        response = cli_runner.invoke(
            tested.cli,
            [
                "-v", "adapt",
                "--input-node-path", str(DATA / "sonata_v6.h5"),
                "--output-node-path", str(tmpdir / "sonata_v6_adapted.h5"),
                "--morphology-path", str(DATA / "morphologies"),
                "--config-path", str(DATA / "config"),
                "--final-path", str(DATA / "final.json"),
                "--local-dir", str(tmpdir / 'local'),
                "--output-hoc-path", str(tmpdir / "hoc"),
                "--min-scale", 0.9,
                "--max-scale", 1.1,
            ],
        )
        # fmt: on
        assert response.exit_code == 0
    
        df = pd.read_csv(tmpdir / "local" / "adapt_df.csv")
        # df.drop(columns=["path"]).to_csv(DATA / "adapt_df.csv", index=None)
        expected_df = pd.read_csv(DATA / "adapt_df.csv")
        assert df.loc[0, "ais_scaler"] == expected_df.loc[0, "ais_scaler"]
        assert df.loc[0, "soma_scaler"] == expected_df.loc[0, "soma_scaler"]
        assert df.loc[0, "ais_model"] == expected_df.loc[0, "ais_model"]
        assert df.loc[0, "soma_model"] == expected_df.loc[0, "soma_model"]
    
        # retest evaluate with ais and soma scalers
        # fmt: off
        response = cli_runner.invoke(
            tested.cli,
            [
                "-v", "evaluate",
                "--input-path", str(tmpdir / "sonata_v6_adapted.h5"),
                "--output-path", str(tmpdir / "adapted_evaluation_df.csv"),
                "--morphology-path", str(DATA / "morphologies"),
                "--config-path", str(DATA / "config"),
                "--local-dir", str(tmpdir / 'local'),
                "--final-path", str(DATA / "final.json"),
            ],
        )
        # fmt: on
        assert response.exit_code == 0
    
        df = pd.read_csv(tmpdir / "adapted_evaluation_df.csv")
        # df.drop(columns=["path"]).to_csv(DATA / "adapted_evaluation_df.csv", index=None)
        expected_df = pd.read_csv(DATA / "adapted_evaluation_df.csv")
    
        for f, f_exp in zip(
            json.loads(df.loc[0, "features"]).values(),
            json.loads(expected_df.loc[0, "features"]).values(),
        ):
            npt.assert_allclose(f, f_exp, rtol=1e-1)
        for f, f_exp in zip(
            json.loads(df.loc[1, "features"]).values(),
            json.loads(expected_df.loc[1, "features"]).values(),
        ):
            npt.assert_allclose(f, f_exp, rtol=1e-1)
        for f, f_exp in zip(
            json.loads(df.loc[0, "scores"]).values(), json.loads(expected_df.loc[0, "scores"]).values()
        ):
            npt.assert_allclose(f, f_exp, rtol=1e-1)
        for f, f_exp in zip(
            json.loads(df.loc[1, "scores"]).values(), json.loads(expected_df.loc[1, "scores"]).values()
        ):
            npt.assert_allclose(f, f_exp, rtol=1e-1)
    
        # fmt: off
        response = cli_runner.invoke(
            tested.cli,
            [
                "-v", "compute_currents",
                "--input-path", str(tmpdir / "sonata_v6_adapted.h5"),
                "--output-path", str(tmpdir / "sonata_currents_adapted.h5"),
                "--morphology-path", str(DATA / "morphologies"),
                "--protocol-config-path", str(DATA / "protocol_config.yaml"),
                "--hoc-path", str(tmpdir / "hoc"),
            ],
        )
        # fmt: on
        assert response.exit_code == 0
    
        df = CellCollection().load_sonata(tmpdir / "sonata_currents_adapted.h5").as_dataframe()
>       npt.assert_allclose(
            df["@dynamics:resting_potential"].to_list(),
            [-72.841806, -71.32893],
            rtol=1e-5,
        )

tests/test_cli.py:196: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (<function assert_allclose.<locals>.compare at 0x7f0fd782a7a0>, array([0., 0.]), array([-72.841806, -71.32893 ]))
kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=0', 'verbose': True}

    @wraps(func)
    def inner(*args, **kwds):
        with self._recreate_cm():
>           return func(*args, **kwds)
E           AssertionError: 
E           Not equal to tolerance rtol=1e-05, atol=0
E           
E           Mismatched elements: 2 / 2 (100%)
E           Max absolute difference: 72.841806
E           Max relative difference: 1.
E            x: array([0., 0.])
E            y: array([-72.841806, -71.32893 ])

/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/contextlib.py:79: AssertionError