reverting to changes from the main branch. This is to remove code not…

… related to tests passing

scripts/deterministic_agent.py

@@ -1,49 +1,41 @@
 from relaqs.save_results import SaveResults
 import numpy as np
 from relaqs.api.utils import (run,
-    run_noisy_one_qubit_experiment,
     sample_noise_parameters,
     get_best_episode_information,
     return_env_from_alg
 )
-from relaqs.api.gates import H, X
-from relaqs.plot_data import plot_results, plot_data
+from relaqs.api.gates import H
 
 
-n_training_iterations = 300
+n_training_iterations = 250
 figure_title ="Inferencing on multiple noisy environments with different detuning noise"
 noise_file = "april/ibmq_belem_month_is_4.json"
 noise_file_2 = "april/ibmq_quito_month_is_4.json"
 path_to_detuning = "qubit_detuning_data.json"
 
 # --------------------------> Training of model <-----------------------------------------------------
-alg, list_of_results = run_noisy_one_qubit_experiment(X(), 
+alg = run(H(), 
     n_training_iterations, 
     noise_file=noise_file
     )
 
 # ----------------------- Creating the deterministic agent using actions from the best episode -------------------------------
 env = return_env_from_alg(alg)
-t1_list,t2_list,_  = sample_noise_parameters(noise_file_2, detuning_noise_file=path_to_detuning)
-detuning_list = np.random.normal(1e8, 1e8, 9).tolist()
+t1_list,t2_list,  = sample_noise_parameters(noise_file_2, detuning_noise_file=path_to_detuning)
+detuning_list = np.random.normal(1e8, 1e4, 9).tolist()
 # t2_list = np.random.normal(1e-9, 1e-5, 135)
 env.relaxation_rates_list = [np.reciprocal(t1_list).tolist(), np.reciprocal(t2_list).tolist()]
 env.delta = detuning_list 
 
-sr = SaveResults(env, alg, results=list_of_results)
+sr = SaveResults(env, alg)
 save_dir = sr.save_results()
 print("Results saved to:", save_dir)
-plot_data(save_dir, episode_length=alg._episode_history[0].episode_length, figure_title=figure_title)
-plot_results(save_dir, figure_title=figure_title)
 
-# best_episode_information = get_best_episode_information(save_dir + "env_data.csv")
-best_episode_information = get_best_episode_information(save_dir + "env_data.pkl")
+best_episode_information = get_best_episode_information(save_dir + "env_data.csv")
+
+actions = [np.asarray(eval(best_episode_information.iloc[0,2])), np.asarray(eval(best_episode_information.iloc[1,2]))]
 
-print("best_episode_information actions first row: ", best_episode_information["Actions"].iloc[0])
-print("best_episode_information actions second row: ", best_episode_information["Actions"].iloc[1])
-# actions = [np.asarray(eval(best_episode_information.iloc[0,2])), np.asarray(eval(best_episode_information.iloc[1,2]))]
-actions = [best_episode_information["Actions"].iloc[0], best_episode_information["Actions"].iloc[1]]
-print("actions: ", actions)
 num_episodes = 0
 episode_reward = 0.0
 
@@ -60,6 +52,4 @@
         print(f"Episode done: Total reward = {episode_reward}")
         obs, info = env.reset()
         num_episodes += 1
-        episode_reward = 0.0
-
-
+        episode_reward = 0.0

scripts/inferencing.py

@@ -1,59 +1,35 @@
 from relaqs.save_results import SaveResults
-from relaqs.plot_data import plot_data, plot_results
+from relaqs.plot_data import plot_data
 import numpy as np
-from relaqs.api.utils import do_inferencing, get_best_episode_information
-from relaqs.api.gates import X, H
-from relaqs.api.utils import (
-    run_noisy_one_qubit_experiment,
-    sample_noise_parameters,
-    return_env_from_alg
-)
-
-best_fidelities_found = []
-for _ in range(6):
-    n_training_iterations = 250
-    n_episodes_for_inferencing= 400
-    figure_title ="Inferencing on multiple noisy environments with different detuning noise for X gate"
-    noise_file = "april/ibmq_belem_month_is_4.json"
-    noise_file_2 = "april/ibmq_quito_month_is_4.json"
-    path_to_detuning = "qubit_detuning_data.json"
-
-    # -----------------------> Training model <------------------------
-    alg, list_of_results = run_noisy_one_qubit_experiment(X(), 
-        n_training_iterations, 
+from relaqs.api.utils import do_inferencing, run
+from relaqs.api.gates import H
+
+noise_file = "april/ibmq_belem_month_is_4.json"
+inferencing_noise_file = "april/ibmq_manila_month_is_4.json"
+n_episodes_for_inferencing = 10
+save = True
+plot = True
+figure_title = "Inferencing with model"
+n_training_iterations = 1
+
+# -----------------------> Training model <------------------------
+alg = run(gate=H(), 
+        n_training_iterations=n_training_iterations, 
         noise_file=noise_file
-        )
-
-    # ----------------------- Creating new environment with new detuning -------------------------------
-    env = return_env_from_alg(alg)
-    t1_list,t2_list,_  = sample_noise_parameters(noise_file_2, detuning_noise_file=path_to_detuning)
-    detuning_list = np.random.normal(1e8, 1e12, 9).tolist()
-    # t2_list = np.random.normal(1e-9, 1e-5, 135)x
-    env.relaxation_rates_list = [np.reciprocal(t1_list).tolist(), np.reciprocal(t2_list).tolist()]
-    env.delta = detuning_list 
-
-    # -----------------------> Inferencing <---------------------------
-    inferencing_env, inferencing_alg = do_inferencing(alg, n_episodes_for_inferencing,quantum_noise_file_path=noise_file_2)
-
-    # -------------------> Save Inferencing Results <---------------------------------------
-    sr = SaveResults(inferencing_env, inferencing_alg)
-    save_dir = sr.save_results()
-    print("Results saved to:", save_dir)
-    # best_episode_information = get_best_episode_information(save_dir + "env_data.csv")
-    best_episode_information = get_best_episode_information(save_dir + "env_data.pkl")
-
-    print("Fidelities from best epsiode: ", [best_episode_information.iloc[0,0], best_episode_information.iloc[1,0]])
-    best_fidelities_found.append((best_episode_information.iloc[0,0],best_episode_information.iloc[1,0] ))
-    best_fidelity_tuple = str((best_episode_information.iloc[0,0],best_episode_information.iloc[1,0]))
-    best_fidelity_file = "best_fidelities.txt"
-    with open(save_dir + best_fidelity_file, 'w') as file:
-        file.write(best_fidelity_tuple)
+    )
 
+# -----------------------> Inferencing <---------------------------
+env, alg = do_inferencing(alg, n_episodes_for_inferencing,quantum_noise_file_path=inferencing_noise_file)
 
-    # ---------------------> Plot Data <-------------------------------------------
-    plot_data(save_dir, episode_length=inferencing_alg._episode_history[0].episode_length, figure_title=figure_title)
-
+# -------------------> Save Inferencing Results <---------------------------------------
+sr = SaveResults(env, alg)
+save_dir = sr.save_results()
+print("Results saved to:", save_dir)
 
-print(best_fidelities_found)
+# ---------------------> Plot Data <-------------------------------------------
+assert save is True, "If plot=True, then save must also be set to True"
 
+plot_data(save_dir, episode_length=alg._episode_history[0].episode_length, figure_title=figure_title)
+print("Plots Created")
+# --------------------------------------------------------------