diff --git a/bw_timex/timex_lca.py b/bw_timex/bw_timex.py
similarity index 100%
rename from bw_timex/timex_lca.py
rename to bw_timex/bw_timex.py
diff --git a/tests/test_dynamic_characterization.py b/tests/test_dynamic_characterization.py
deleted file mode 100644
index 4c461ae..0000000
--- a/tests/test_dynamic_characterization.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import bw2data as bd
-import numpy as np
-import pandas as pd
-import pytest
-
-from bw_timex import TimexLCA
-
-
-def test_custom_dynamic_characterization(point_emission_in_ten_years_db):
-    demand = {("test", "A"): 1}
-    gwp = ("GWP", "example")
-    tlca = TimexLCA(demand, gwp)
-    tlca.build_timeline()
-    tlca.lci()
-
-    characterization_function_dict_HFC23 = {
-        bd.get_node(code="HFC23").id: characterize_HFC23,
-    }
-    tlca.dynamic_lcia(
-        metric="radiative_forcing",
-        time_horizon=100,
-        characterization_function_dict=characterization_function_dict_HFC23,
-    )
-
-    sum_of_forcing = tlca.dynamic_score.sum()
-    AGWP100_CO2 = 0.0895e-12
-    GWP100_HFC23 = 14600
-    AGWP100_HFC23 = 1310e-12
-
-    assert sum_of_forcing == pytest.approx(
-        AGWP100_HFC23, abs=2e-11
-    )  # allow for some rounding errors
-    assert sum_of_forcing / AGWP100_CO2 == pytest.approx(
-        GWP100_HFC23, abs=5e-15
-    )  # allow for some rounding errors
-
-    tlca.dynamic_lcia(
-        metric="GWP",
-        time_horizon=100,
-        characterization_function_dict=characterization_function_dict_HFC23,
-    )
-    assert tlca.dynamic_score == pytest.approx(
-        AGWP100_HFC23, abs=5e-15
-    )  # allow for some rounding errors
-
-
-def characterize_HFC23(
-    series,
-    period: int = 100,
-    cumulative=False,
-) -> pd.DataFrame:
-    """
-    This is not the same function as in the dynamic_characterization module. This does not include indirect effects to be more comparable to standard GWP values.
-    """
-
-    # functional variables and units (from publications listed in docstring)
-    radiative_efficiency_ppb = 0.191  # W/m2/ppb; 2019 background cch4 concentration; IPCC AR6 Table 7.15. This number includes indirect effects.
-
-    # for conversion from ppb to kg-CH4
-    M_HFC23 = 0.07002e3  # g/mol
-    M_air = 28.97  # g/mol, dry air
-    m_atmosphere = 5.135e18  # kg [Trenberth and Smith, 2005]
-
-    radiative_efficiency_kg = (
-        radiative_efficiency_ppb * M_air / M_HFC23 * 1e9 / m_atmosphere
-    )  # W/m2/kg-CH4
-
-    tau = 228  # Lifetime (years)
-
-    date_beginning: np.datetime64 = series["date"].to_numpy()
-    date_characterized: np.ndarray = date_beginning + np.arange(
-        start=0, stop=period, dtype="timedelta64[Y]"
-    ).astype("timedelta64[s]")
-
-    decay_multipliers: list = np.array(
-        [
-            radiative_efficiency_kg * tau * (1 - np.exp(-year / tau))
-            for year in range(period)
-        ]
-    )
-
-    forcing = pd.Series(data=series.amount * decay_multipliers, dtype="float64")
-
-    if not cumulative:
-        forcing = forcing.diff(periods=1).fillna(0)
-
-    return pd.DataFrame(
-        {
-            "date": pd.Series(data=date_characterized, dtype="datetime64[s]"),
-            "amount": forcing,
-            "flow": series.flow,
-            "activity": series.activity,
-        }
-    )