Remove unneeded Jackknife and BCA functions (#134)

physical_validation/util/trajectory.py

@@ -349,40 +349,3 @@ def bootstrap(traj, n_samples):
             yield traj[resample_idx]
         else:
             yield traj[:, resample_idx]
-
-
-def jackknife(traj):
-    traj = np.array(traj)
-    if traj.ndim == 1:
-        n_traj = traj.size
-        for idx in range(n_traj):
-            yield np.concatenate((traj[:idx], traj[idx + 1 :]))
-    elif traj.ndim == 2:
-        n_traj = traj.shape[1]
-        for idx in range(n_traj):
-            yield np.concatenate((traj[:, :idx], traj[:, idx + 1 :]))
-    else:
-        raise NotImplementedError(
-            "trajectory.jackknife() is not implemented for "
-            "trajectories with more than 2 dimensions."
-        )
-
-
-def bca(param, param_boot, param_jack, alpha):
-    param_boot = np.array(param_boot)
-    param_jack = np.array(param_jack)
-    n_boot = param_boot.size
-
-    z0 = stats.norm.ppf(np.sum(param_boot < param) / n_boot)
-    zlow = stats.norm.ppf(alpha / 2)
-    zhigh = stats.norm.ppf(1 - alpha / 2)
-
-    avg_p_jack = np.mean(param_jack)
-    a = np.sum((avg_p_jack - param_jack) ** 3) / (
-        6 * (np.sum((avg_p_jack - param_jack) ** 2) ** (3 / 2))
-    )
-
-    a1 = stats.norm.cdf(z0 + (z0 + zlow) / (1 - a * (z0 + zlow)))
-    a2 = stats.norm.cdf(z0 + (z0 + zhigh) / (1 - a * (z0 + zhigh)))
-
-    return np.percentile(param_boot, a1 * 100), np.percentile(param_boot, a2 * 100)