diff --git a/src/seismic_hazard_forecasting.py b/src/seismic_hazard_forecasting.py
index b74cf34..5e2b674 100644
--- a/src/seismic_hazard_forecasting.py
+++ b/src/seismic_hazard_forecasting.py
@@ -152,19 +152,19 @@ def apply_beast(act_rate):
     prob : A list of probabilities and change points extracted from BEAST results.
     """
 
-    mirror_len = int(np.ceil(0.20 * len(act_rate)))
-    left_mirror = act_rate[:mirror_len][::-1]
-    right_mirror = act_rate[-mirror_len:][::-1]
-    act_rate_mirrored = np.concatenate([left_mirror, act_rate, right_mirror])
+    #mirror_len = int(np.ceil(0.20 * len(act_rate)))
+    #left_mirror = act_rate[:mirror_len][::-1]
+    #right_mirror = act_rate[-mirror_len:][::-1]
+    #act_rate_mirrored = np.concatenate([left_mirror, act_rate, right_mirror])
 
     mcmc_th = int(np.clip(np.ceil(len(act_rate) / 100), 2, 15))
-    beast_result = rb.beast(act_rate_mirrored, period=0,
+    beast_result = rb.beast(act_rate, period=0,
                             tcp_minmax=[0, tcp_max],
                             torder_minmax=[torder_min, torder_max],
                             tseg_minlength=2, mcmc_chains=10,
                             mcmc_thin=mcmc_th, mcmc_seed=10)
 
-    # User-driven ncp selection
+# User-driven ncp selection
     if ncp_choice == 'median':
         ncp = beast_result.trend.ncp_median
         if np.isnan(ncp) or ncp == 0:
@@ -187,17 +187,20 @@ def apply_beast(act_rate):
             return beast_result, np.array([])
 
     ncp = int(ncp)
+    # Filter NaNs first — BEAST fills unused cp slots with nan
+    valid_cps = beast_result.trend.cp[~np.isnan(beast_result.trend.cp)]
     cps = beast_result.trend.cp[:ncp]
 
     # Discard mirrored zone changepoints and correct indices
-    valid_mask = (cps > mirror_len) & (cps <= mirror_len + len(act_rate))
-    cps = cps[valid_mask] - mirror_len
+    #valid_mask = (cps > mirror_len) & (cps <= mirror_len + len(act_rate))
+    #cps = cps[valid_mask] - mirror_len
 
-    # Discard changepoints too close to the end (artifacts of end-mirroring). 
-    # bins_after_cp sets the minimum number of bins required after a changepoint.
-    bins_after_cp = 1
-    if len(cps) > 0:
-        cps = cps[cps <= len(act_rate) - bins_after_cp]
+    # Discard changepoints too close to the start or end (artifacts of mirroring).
+    # bins_after_cp / bins_before_cp set the minimum buffer bins required at each end.
+    #bins_before_cp = 2
+    #bins_after_cp = 2
+    #if len(cps) > 0:
+    #    cps = cps[(cps >= bins_before_cp) & (cps <= len(act_rate) - bins_after_cp)]
 
     return beast_result, np.sort(cps)