How to use the lifelines.utils function in lifelines

def test_format_p_values():
    assert utils.format_p_value(2)(0.004) == "<0.005"
    assert utils.format_p_value(3)(0.004) == "0.004"

    assert utils.format_p_value(3)(0.000) == "<0.0005"
    assert utils.format_p_value(3)(0.005) == "0.005"
    assert utils.format_p_value(3)(0.2111) == "0.211"
    assert utils.format_p_value(3)(0.2119) == "0.212"

def test_redundant_cv_columns_are_dropped(self, seed_df):
        seed_df = seed_df[seed_df["id"] == 1]
        cv = pd.DataFrame.from_records(
            [
                {"id": 1, "t": 0, "var3": 0, "var4": 1},
                {"id": 1, "t": 1, "var3": 0, "var4": 1},  # redundant, as nothing changed during the interval
                {"id": 1, "t": 3, "var3": 0, "var4": 1},  # redundant, as nothing changed during the interval
                {"id": 1, "t": 6, "var3": 1, "var4": 1},
                {"id": 1, "t": 9, "var3": 1, "var4": 1},  # redundant, as nothing changed during the interval
            ]
        )

        df = seed_df.pipe(utils.add_covariate_to_timeline, cv, "id", "t", "E")
        assert df.shape[0] == 2

integer weights per observation
        initial_point: (d,) numpy array, optional
            initialize the starting point of the iterative
            algorithm. Default is the zero vector.

        Returns
        -------
          self
            self with new properties like ``cumulative_hazard_``, ``survival_function_``

        """
        self.upper_bound = np.atleast_1d(utils.pass_for_numeric_dtypes_or_raise_array(upper_bound))
        self.lower_bound = np.atleast_1d(utils.pass_for_numeric_dtypes_or_raise_array(lower_bound))

        utils.check_nans_or_infs(self.lower_bound)
        utils.check_positivity(self.upper_bound)

        if (self.upper_bound < self.lower_bound).any():
            raise ValueError("All upper_bound times must be greater than or equal to lower_bound times.")

        if event_observed is None:
            event_observed = self.upper_bound == self.lower_bound

        if ((self.lower_bound == self.upper_bound) != event_observed).any():
            raise ValueError(
                "For all rows, lower_bound == upper_bound if and only if event observed = 1 (uncensored). Likewise, lower_bound < upper_bound if and only if event observed = 0 (censored)"
            )

        return self._fit(
            (np.clip(self.lower_bound, 1e-20, 1e25), np.clip(self.upper_bound, 1e-20, 1e25)),
            event_observed=event_observed,
            timeline=timeline,

def _create_Xs_dict(self, df):
        return utils.DataframeSliceDict(df, self.regressors)

if self.weights_col:
            if (weights.astype(int) != weights).any() and not self.robust:
                warnings.warn(
                    dedent(
                        """It appears your weights are not integers, possibly propensity or sampling scores then?
                                        It's important to know that the naive variance estimates of the coefficients are biased. Instead a) set `robust=True` in the call to `fit`, or b) use Monte Carlo to
                                        estimate the variances. See paper "Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis"""
                    ),
                    utils.StatisticalWarning,
                )
            if (weights <= 0).any():
                raise ValueError("values in weight column %s must be positive." % self.weights_col)

        if self.entry_col:
            utils.check_entry_times(T, entries)