diff --git a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
index acd24e0..c507fa4 100644
--- a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
+++ b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
@@ -145,15 +145,15 @@ def anti_zeno_engine(
 
 model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch) = anti_zeno_engine(
     Δ=1.3,
-    ε=.5 ,
+    ε=.5,
     ω_c=0.2,
     ε_couple=0.7,
-    n=10,
+    n=3,
     detune=-0.19,
     ω_0=2,
     T_c=3,
     T_h=11,
-    δ=[.1] * 2,
+    δ=[1] * 2,
     γ=0.5 / 10,
     switch_cycles=1,
     therm_initial_state=False,
@@ -211,16 +211,16 @@ plt.plot(model.t, ut.smoothen(model.t, ρ_ee, frac=.06, it=0))
 
 with aux.get_data(model) as data:
   _, ax = plt.subplots()
-  # fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
-  # fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
+  fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
+  fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
   #fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
 
-  fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
+  #fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
   #fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
 
   #fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
   #fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
-  ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
+  #ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
   #ax.plot(model.t, np.gradient(model.total_energy(data).value))
 
 ts_begin = ((τ_c + τ_off) * np.arange(0, n) - Δ_switch)
diff --git a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
index b90b43a..2dd55d7 100644
--- a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
+++ b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
@@ -25,7 +25,7 @@ Init ray and silence stocproc.
 #+end_src
 
 #+RESULTS:
-: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-06-24_15-06-52_296364_396466/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-24_15-06-52_296364_396466/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-24_15-06-52_296364_396466', 'metrics_export_port': 61004, 'gcs_address': '141.30.17.225:47993', 'address': '141.30.17.225:47993', 'node_id': 'ed43d6aa4535a1f23b7d0d0dd45687a7221189a23e04487a355d297b'})
+: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-06-27_13-53-20_654030_8907/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-27_13-53-20_654030_8907/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-27_13-53-20_654030_8907', 'metrics_export_port': 62418, 'gcs_address': '141.30.17.225:62248', 'address': '141.30.17.225:62248', 'node_id': '6d4fbe591fb595ea55c2c527d1c4db5b6da8a62057f3c7403b87f1a8'})
 
 #+begin_src jupyter-python :results none
   from hops.util.logging_setup import logging_setup
@@ -165,15 +165,15 @@ Init ray and silence stocproc.
 #+begin_src jupyter-python
   model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch) = anti_zeno_engine(
       Δ=1.3,
-      ε=.5 ,
+      ε=.5,
       ω_c=0.2,
       ε_couple=0.7,
-      n=10,
+      n=3,
       detune=-0.19,
       ω_0=2,
       T_c=3,
       T_h=11,
-      δ=[.1] * 2,
+      δ=[1] * 2,
       γ=0.5 / 10,
       switch_cycles=1,
       therm_initial_state=False,
@@ -367,21 +367,21 @@ We need the time points where we sample the total energy.
 #+begin_src jupyter-python
   with aux.get_data(model) as data:
     _, ax = plt.subplots()
-    # fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
-    # fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
+    fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
+    fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
     #fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
 
-    fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
+    #fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
     #fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
 
     #fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
     #fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
-    ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
+    #ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
     #ax.plot(model.t, np.gradient(model.total_energy(data).value))
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/20bba0288df8bbbc25fb66e7f1ac29e453a9edf1.svg]]
+[[file:./.ob-jupyter/59c645c188bb0fbc31e34820aacd6233e0608688.svg]]