adapt monte carlo experiment results pickle file paths

Author: juliusrueckin

experimental/notebooks/monte-carlo-experiments.ipynb

@@ -510,10 +510,10 @@
     "\n",
     "                print(f\"Estimation error measures: {estimation_error[m_candidate][l_candidate][rep_key]} \\n\")\n",
     "\n",
-    "    with open(f\"results/estimate_prob_random_walk_method.pkl\", \"wb\") as file:\n",
+    "    with open(f\"results/measurements/estimate_prob_random_walk_method.pkl\", \"wb\") as file:\n",
     "        pickle.dump(estimation_error, file, protocol=pickle.HIGHEST_PROTOCOL)\n",
     "else:\n",
-    "    with open(f\"results/estimate_prob_random_walk_method.pkl\", \"rb\") as file:\n",
+    "    with open(f\"results/measurements/estimate_prob_random_walk_method.pkl\", \"rb\") as file:\n",
     "        estimation_error = pickle.load(file)"
    ]
   },
@@ -719,10 +719,10 @@
     "\n",
     "                print(f\"Estimation error measures: {estimation_error[c_candidate][tau_candidate][rep_key]} \\n\")\n",
     "\n",
-    "    with open(f\"results/estimate_prob_pi_method.pkl\", \"wb\") as file:\n",
+    "    with open(f\"results/measurements/estimate_prob_pi_method.pkl\", \"wb\") as file:\n",
     "        pickle.dump(estimation_error, file, protocol=pickle.HIGHEST_PROTOCOL)\n",
     "else:\n",
-    "    with open(f\"results/estimate_prob_pi_method.pkl\", \"rb\") as file:\n",
+    "    with open(f\"results/measurements/estimate_prob_pi_method.pkl\", \"rb\") as file:\n",
     "        estimation_error = pickle.load(file)"
    ]
   },
@@ -866,9 +866,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(small_tau_c_run_times.keys(), small_tau_c_run_times.values(), color=\"blue\", label=\"tau = 1e-5\")\n",
-    "plt.plot(medium_tau_c_run_times.keys(), medium_tau_c_run_times.values(), color=\"green\", label=\"tau = 1e-3\")\n",
-    "plt.plot(large_tau_c_run_times.keys(), large_tau_c_run_times.values(), color=\"red\", label=\"tau = 1e-1\")\n",
+    "plt.plot(small_tau_c_run_times.keys(), small_tau_c_run_times.values(), color=\"blue\", label=f\"tau = {small_tau}\")\n",
+    "plt.plot(medium_tau_c_run_times.keys(), medium_tau_c_run_times.values(), color=\"green\", label=f\"tau = {round(medium_tau,3)}\")\n",
+    "plt.plot(large_tau_c_run_times.keys(), large_tau_c_run_times.values(), color=\"red\", label=f\"tau = {large_tau}\")\n",
     "\n",
     "\n",
     "plt.legend(loc=\"upper right\")\n",