diff --git a/3rot_XYmixer.py b/3rot_XYmixer.py
index c5695232..40b4ca41 100644
--- a/3rot_XYmixer.py
+++ b/3rot_XYmixer.py
@@ -9,9 +9,10 @@
 import os
 
 num_rot = 3
-file_path = "RESULTS/3rot-XY-QAOA/12res-3rot.csv"
+num_res = 2
+file_path = f"RESULTS/{num_rot}rot-XY-QAOA/{num_res}res-{num_rot}rot.csv"
 # file_path = "RESULTS/hardware/7res-3rot-XY-hw.csv"
-file_path_depth = "RESULTS/Depths/3rot-XY-QAOA/12res-3rot.csv"
+file_path_depth = f"RESULTS/Depths/{num_rot}rot-XY-QAOA/{num_res}res-{num_rot}rot.csv"
 
 ########################### Configure the hamiltonian from the values calculated classically with pyrosetta ############################
 df1 = pd.read_csv("energy_files/one_body_terms.csv")
@@ -400,7 +401,7 @@ def calculate_bitstring_energy(bitstring, hamiltonian, backend=None):
 else:
     # File exists, append without writing the header
     df.to_csv(file_path, mode='a', index=False, header=False)
-
+exit()
 # %% ############################################# Hardware with QAOAAnastz ##################################################################
 from qiskit.circuit.library import QAOAAnsatz
 from qiskit_algorithms import SamplingVQE
diff --git a/Ising_Hamiltonian.py b/Ising_Hamiltonian.py
index b6e27313..c8ab0be2 100644
--- a/Ising_Hamiltonian.py
+++ b/Ising_Hamiltonian.py
@@ -21,7 +21,9 @@
 from pyrosetta import PyMOLMover
 
 # Initiate structure, scorefunction, change PDB files
-pose = pyrosetta.pose_from_pdb("input_files/4residue.pdb")
+num_res = 2
+num_rot = 3
+pose = pyrosetta.pose_from_pdb(f"input_files/{num_res}residue.pdb")
 
 
 residue_count = pose.total_residue()
@@ -86,7 +88,7 @@
 # pmm.apply(clone_pose)
 
 # to limit to n rotamers per residue, change based on how many rotamers desired
-num_rot = 3
+# num_rot = 2
 
 # Loop to find Hamiltonian values Jij - interaction of rotamers on NN residues
 for residue_number in range(1, residue_count):
diff --git a/Production/Ising_Hamiltonian_production.py b/Production/Ising_Hamiltonian_production.py
new file mode 100644
index 00000000..83418998
--- /dev/null
+++ b/Production/Ising_Hamiltonian_production.py
@@ -0,0 +1,169 @@
+### Take input pdb, score, repack and extract one and two body energies
+# Script for generating the test sturcture with its rotamers 
+#  lines 23 and 87 to vary
+import pyrosetta
+pyrosetta.init()
+
+from pyrosetta.teaching import *
+from pyrosetta import *
+
+import csv
+import sys
+import numpy as np
+import pandas as pd
+
+from matplotlib import pyplot as plt
+from pyrosetta.rosetta.core.pack.rotamer_set import RotamerSets
+from pyrosetta.rosetta.core.pack.task import TaskFactory
+from pyrosetta.rosetta.core.pack.rotamer_set import *
+from pyrosetta.rosetta.core.pack.interaction_graph import InteractionGraphFactory
+from pyrosetta.rosetta.core.pack.task import *
+from pyrosetta import PyMOLMover
+
+def create_energy_files(num_res, num_rot):
+
+    # Initiate structure, scorefunction, change PDB files
+    # num_res = 2
+    # num_rot = 3
+    pose = pyrosetta.pose_from_pdb(f"../input_files/{num_res}residue.pdb")
+
+
+    residue_count = pose.total_residue()
+    sfxn = get_score_function(True)
+    # print(pose.sequence())
+    # print(residue_count)
+
+
+    relax_protocol = pyrosetta.rosetta.protocols.relax.FastRelax()
+    relax_protocol.set_scorefxn(sfxn)
+    relax_protocol.apply(pose)
+
+    # Define task, interaction graph and rotamer sets (model_protein_csv.py)
+    task_pack = TaskFactory.create_packer_task(pose) 
+
+    rotsets = RotamerSets()
+    pose.update_residue_neighbors()
+    sfxn.setup_for_packing(pose, task_pack.designing_residues(), task_pack.designing_residues())
+    packer_neighbor_graph = pyrosetta.rosetta.core.pack.create_packer_graph(pose, sfxn, task_pack)
+    rotsets.set_task(task_pack)
+    rotsets.build_rotamers(pose, sfxn, packer_neighbor_graph)
+    rotsets.prepare_sets_for_packing(pose, sfxn) 
+    ig = InteractionGraphFactory.create_interaction_graph(task_pack, rotsets, pose, sfxn, packer_neighbor_graph)
+    # print("built", rotsets.nrotamers(), "rotamers at", rotsets.nmoltenres(), "positions.")
+    rotsets.compute_energies(pose, sfxn, packer_neighbor_graph, ig, 1)
+
+    # Output structure to be visualised in pymol
+    pose.dump_pdb("output_repacked.pdb")
+
+    # Define dimension for matrix
+    max_rotamers = 0
+    for residue_number in range(1, residue_count+1):
+        n_rots = rotsets.nrotamers_for_moltenres(residue_number)
+        # print(f"Residue {residue_number} has {n_rots} rotamers.")
+        if n_rots > max_rotamers:
+            max_rotamers = n_rots
+
+    # print("Maximum number of rotamers:", max_rotamers)
+
+
+    E = np.zeros((max_rotamers, max_rotamers))
+    Hamiltonian = np.zeros((max_rotamers, max_rotamers))
+
+    E1 = np.zeros((max_rotamers, max_rotamers))
+    Hamiltonian1 = np.zeros((max_rotamers, max_rotamers))
+
+    output_file = f"energy_files/{num_rot}rot_{num_res}_res_two_body_terms.csv"
+    output_file1 = f"energy_files/{num_rot}rot_{num_res}_res_one_body_terms.csv"
+    data_list = []
+    data_list1 = []
+    df = pd.DataFrame(columns=['res i', 'res j', 'rot A_i', 'rot B_j', 'E_ij'])
+    df1 = pd.DataFrame(columns=['res i', 'rot A_i', 'E_ii'])
+
+
+    # # Visualisation of structure after repacking with rotamers
+    # pmm = PyMOLMover()
+    # clone_pose = Pose()
+    # clone_pose.assign(pose)
+    # pmm.apply(clone_pose)
+    # pmm.send_hbonds(clone_pose)
+    # pmm.keep_history(True)
+    # pmm.apply(clone_pose)
+
+    # to limit to n rotamers per residue, change based on how many rotamers desired
+    # num_rot = 2
+
+    # Loop to find Hamiltonian values Jij - interaction of rotamers on NN residues
+    for residue_number in range(1, residue_count):
+        rotamer_set_i = rotsets.rotamer_set_for_residue(residue_number)
+        if rotamer_set_i == None: # skip if no rotamers for the residue
+            continue
+
+        residue_number2 = residue_number + 1
+        residue2 = pose.residue(residue_number2)
+        rotamer_set_j = rotsets.rotamer_set_for_residue(residue_number2)
+        if rotamer_set_j == None:
+            continue
+
+        molten_res_i = rotsets.resid_2_moltenres(residue_number)
+        molten_res_j = rotsets.resid_2_moltenres(residue_number2)
+
+        edge_exists = ig.find_edge(molten_res_i, molten_res_j)
+            
+        if not edge_exists:
+                continue
+        
+        for rot_i in range(1, rotamer_set_i.num_rotamers() + 1):
+            for rot_j in range(1, rotamer_set_j.num_rotamers() + 1):
+                E[rot_i-1, rot_j-1] = ig.get_two_body_energy_for_edge(molten_res_i, molten_res_j, rot_i, rot_j)
+                Hamiltonian[rot_i-1, rot_j-1] = E[rot_i-1, rot_j-1]
+
+        for rot_i in range(10, num_rot + 10):       #, rotamer_set_i.num_rotamers() + 1):
+            for rot_j in range(10, num_rot + 10):       #, rotamer_set_j.num_rotamers() + 1):
+                # print(f"Interaction energy between rotamers of residue {residue_number} rotamer {rot_i} and residue {residue_number2} rotamer {rot_j} :", Hamiltonian[rot_i-1, rot_j-1])
+                data = {'res i': residue_number, 'res j': residue_number2, 'rot A_i': rot_i, 'rot B_j': rot_j, 'E_ij': Hamiltonian[rot_i-1, rot_j-1]}
+                data_list.append(data)
+        
+
+    # Save the two-body energies to a csv file
+    df = pd.DataFrame(data_list)
+    df.to_csv(f'./energy_files/{num_rot}rot_{num_res}res_two_body_terms.csv', index=False)
+
+    # to choose the two rotamers with the largest energy in absolute value
+    # df.assign(abs_E=df['E_ij'].abs()).nlargest(2, 'abs_E').drop(columns=['abs_E']).to_csv('two_body_terms.csv', index=False)
+
+
+    # Loop to find Hamiltonian values Jii
+    for residue_number in range(1, residue_count + 1):
+        residue1 = pose.residue(residue_number)
+        rotamer_set_i = rotsets.rotamer_set_for_residue(residue_number)
+        if rotamer_set_i == None: 
+            continue
+
+        molten_res_i = rotsets.resid_2_moltenres(residue_number)
+
+        for rot_i in range(10, num_rot +10):        #, rotamer_set_i.num_rotamers() + 1):
+            E1[rot_i-1, rot_i-1] = ig.get_one_body_energy_for_node_state(molten_res_i, rot_i)
+            Hamiltonian1[rot_i-1, rot_i-1] = E1[rot_i-1, rot_i-1]
+            # print(f"Interaction score values of {residue1.name3()} rotamer {rot_i} with itself {Hamiltonian[rot_i-1,rot_i-1]}")
+            data1 = {'res i': residue_number, 'rot A_i': rot_i, 'E_ii': Hamiltonian1[rot_i-1, rot_i-1]}
+            data_list1.append(data1)
+        
+
+
+    # Save the one-body energies to a csv file
+    df1 = pd.DataFrame(data_list1)
+    df1.to_csv(f'./energy_files/{num_rot}rot_{num_res}res_one_body_terms.csv', index=False)
+    # to choose the two rotamers with the largest energy in absolute value
+    # df1.assign(abs_Ei=df1['E_ii'].abs()).nlargest(2, 'abs_Ei').drop(columns=['abs_Ei']).to_csv('one_body_terms.csv', index=False)
+
+    # run all above if called from command line
+
+    # get num_res and num_rot from command line
+
+if __name__ == "__main__":
+    # num_res = int(sys.argv[1])
+    # num_rot = int(sys.argv[2])
+    # main(num_res, num_rot)
+    pass
+
+
diff --git a/Production/RESULTS/CVaR/prob_distributions_alpha_1.pdf b/Production/RESULTS/CVaR/prob_distributions_alpha_1.pdf
new file mode 100644
index 00000000..93d24d5a
Binary files /dev/null and b/Production/RESULTS/CVaR/prob_distributions_alpha_1.pdf differ
diff --git a/Production/RESULTS/XY-QAOA/JSON/4res-3rot-1alpha-5000shots-1p.json b/Production/RESULTS/XY-QAOA/JSON/4res-3rot-1alpha-5000shots-1p.json
new file mode 100644
index 00000000..218bbc88
--- /dev/null
+++ b/Production/RESULTS/XY-QAOA/JSON/4res-3rot-1alpha-5000shots-1p.json
@@ -0,0 +1 @@
+{"params": [4, 3, 1, 5000, 1], "bitstrings": ["001010100010", "001001100010", "001010010010", "100010100010", "001001010010", "100001100010", "010010100010", "010001100010", "001100100010", "100010010010", "100001010010", "010010010010", "010001010010", "001100010010", "100100100010", "010100100010", "100100010010", "010100010010", "001010100001", "001001100001", "001010010001", "100010100001", "001001010001", "100001100001", "010010100001", "010001100001", "001100100001", "100010010001", "100001010001", "010010010001", "010001010001", "001100010001", "100100100001", "010100100001", "100100010001", "010100010001", "001010100100", "001001100100", "001010010100", "100010100100", "001001010100", "100001100100", "010010100100", "010001100100", "001100100100", "100010010100", "100001010100", "010010010100", "010001010100", "001100010100", "100100100100", "010100100100", "001010001010", "100100010100", "001001001010", "010100010100", "100010001010", "100001001010", "010010001010", "010001001010", "001100001010", "100100001010", "010100001010", "001010001001", "001001001001", "100010001001", "100001001001", "010010001001", "010001001001", "001100001001", "001010001100", "100100001001", "001001001100", "010100001001", "100010001100", "100001001100", "010010001100", "010001001100", "001100001100", "100100001100", "010100001100"], "probabilities": [0.00670270270270277, 0.008751351351351432, 0.0050486486486486975, 0.00576216216216222, 0.00549729729729735, 0.00983243243243253, 0.004205405405405443, 0.006994594594594662, 0.009054054054054137, 0.00440000000000004, 0.006394594594594655, 0.0026486486486486725, 0.004313513513513555, 0.004745945945945988, 0.010210810810810904, 0.0060108108108108665, 0.006237837837837895, 0.005129729729729777, 0.010270270270270368, 0.015118918918919061, 0.005129729729729779, 0.009221621621621715, 0.008027027027027104, 0.015670270270270428, 0.0073189189189189876, 0.009491891891891983, 0.013859459459459593, 0.005329729729729778, 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0.0002, "energy": 2.5178184639662504, "count": 1}], ["100011101100", {"probability": 0.0004, "energy": 2.5921642910689116, "count": 1}], ["100110101100", {"probability": 0.0002, "energy": 2.7509366404265165, "count": 1}], ["011100101100", {"probability": 0.0002, "energy": 2.865455402061343, "count": 1}], ["100110011100", {"probability": 0.0002, "energy": 2.9312072601169348, "count": 1}], ["001101011100", {"probability": 0.0002, "energy": 3.1252640020102262, "count": 1}], ["101010001011", {"probability": 0.0002, "energy": 3.325688375160098, "count": 1}], ["010100111100", {"probability": 0.0002, "energy": 3.3636744748800993, "count": 1}], ["001001011110", {"probability": 0.0002, "energy": 3.543914867565036, "count": 1}], ["110010001011", {"probability": 0.0002, "energy": 3.5551971327513456, "count": 1}], ["100000001101", {"probability": 0.0004, "energy": 3.614257885143161, "count": 1}], ["001010001101", {"probability": 0.0012, "energy": 4.515230311080813, "count": 1}], ["001001001101", {"probability": 0.0012, "energy": 4.5420767199248075, "count": 1}], ["100010001101", {"probability": 0.0008, "energy": 4.707251442596316, "count": 1}], ["100001001101", {"probability": 0.0016, "energy": 4.7341320645064116, "count": 1}], ["010010001101", {"probability": 0.001, "energy": 4.744739068672061, "count": 1}], ["010001001101", {"probability": 0.0004, "energy": 4.767470730468631, "count": 1}], ["001110101110", {"probability": 0.0002, "energy": 4.805253339931369, "count": 1}], ["001100001101", {"probability": 0.0018, "energy": 4.853995991870761, "count": 1}], ["110100111100", {"probability": 0.0002, "energy": 4.855486823245883, "count": 1}], ["100100001101", {"probability": 0.0018, "energy": 5.04605127684772, "count": 1}], ["010100001101", {"probability": 0.0014, "energy": 5.079389883205295, "count": 1}], ["011001001101", {"probability": 0.0002, "energy": 5.55073775164783, "count": 1}], ["001101001101", {"probability": 0.0002, "energy": 6.010930193588138, "count": 1}], ["011100001101", {"probability": 0.0002, "energy": 6.379146946594119, "count": 1}], ["110010001101", {"probability": 0.0002, "energy": 6.738852871581912, "count": 1}], ["001100011101", {"probability": 0.0002, "energy": 6.848258329555392, "count": 1}], ["010010001111", {"probability": 0.0002, "energy": 7.212829364463687, "count": 1}]]}
\ No newline at end of file
diff --git a/Production/noise_model.pkl b/Production/noise_model.pkl
new file mode 100644
index 00000000..9ae115ce
Binary files /dev/null and b/Production/noise_model.pkl differ
diff --git a/Production/noisy_simulations_array_job.py b/Production/noisy_simulations_array_job.py
new file mode 100644
index 00000000..4b3fad4c
--- /dev/null
+++ b/Production/noisy_simulations_array_job.py
@@ -0,0 +1,18 @@
+import noisy_simulations_production as NSP
+from supporting_functions import get_hyperparameters
+import sys
+
+num_rot_arr = [2, 3]
+num_res_arr = [2, 3, 4, 5, 6, 7, 8, 9]
+shots_arr = [10, 50, 100, 500, 1000, 5000]
+alpha_arr = [0.0001, 0.001, 0.01, 0.1, 1]
+p_arr = [1, 2]
+
+if __name__ == "__main__":
+    # get count arguments from command line, it is an integer
+    match = int(sys.argv[1])
+    num_rot, num_res, shots, alpha, p = get_hyperparameters(match, num_rot_arr, num_res_arr, shots_arr, alpha_arr, p_arr)
+    NSP.noisy_simulation(num_rot=num_rot, num_res=num_res, shots=shots, alpha=alpha, p=p)
+
+
+
diff --git a/Production/noisy_simulations_production.py b/Production/noisy_simulations_production.py
new file mode 100644
index 00000000..38d87804
--- /dev/null
+++ b/Production/noisy_simulations_production.py
@@ -0,0 +1,306 @@
+# %%
+# imports
+
+import numpy as np
+import pandas as pd
+import time
+from copy import deepcopy
+import os
+import pickle
+
+from supporting_functions import *
+
+def noisy_simulation(num_rot, num_res, alpha, shots, p):
+
+    # %%
+    # parameters
+
+    # num_rot = 3
+    # num_res = 4
+    num_qubits = num_rot * num_res
+    # alpha = 1
+    # shots = 5000
+    # p = 1
+
+    # %%
+    H = get_hamiltonian(num_rot, num_res)
+    q_hamiltonian = get_q_hamiltonian(num_qubits, H)
+    XY_mixer = get_XY_mixer(num_qubits, num_rot)
+
+    # %%
+    from qiskit_algorithms.minimum_eigensolvers import QAOA
+    from qiskit.quantum_info.operators import Pauli, SparsePauliOp
+    from qiskit_algorithms.optimizers import COBYLA, SPSA
+    from qiskit.primitives import Sampler
+    from qiskit import QuantumCircuit
+
+
+
+
+
+
+    # %%
+    initial_point = np.ones(2 * p)
+    initial_bitstring = generate_initial_bitstring(num_qubits, num_rot)
+    state_vector = np.zeros(2**num_qubits)
+    indexx = int(initial_bitstring, 2)
+    state_vector[indexx] = 1
+    qc = QuantumCircuit(num_qubits)
+    qc.initialize(state_vector, range(num_qubits))
+
+
+    # %%
+    from qiskit_aer import Aer
+    from qiskit_ibm_provider import IBMProvider
+    from qiskit_aer.noise import NoiseModel
+    from qiskit.primitives import BackendSampler
+    from qiskit.transpiler import PassManager
+
+    # %%
+    simulator = Aer.get_backend('qasm_simulator')
+    
+
+    if os.path.exists('noise_model.pkl'):
+        with open('noise_model.pkl', 'rb') as f:
+            noise_model = pickle.load(f)
+    else:
+        provider = IBMProvider()
+
+        device_backend = provider.get_backend('ibm_torino')
+        noise_model = NoiseModel.from_backend(device_backend)
+
+        with open('noise_model.pkl', 'wb') as f:
+            pickle.dump(noise_model, f)
+
+    # %%
+    options= {
+        "noise_model": noise_model,
+        "basis_gates": simulator.configuration().basis_gates,
+        "coupling_map": simulator.configuration().coupling_map,
+        "seed_simulator": 42,
+        "shots": shots,
+        "optimization_level": 3,
+        "resilience_level": 3
+    }
+
+    # %%
+    def callback(quasi_dists, parameters, energy):
+        intermediate_data.append(
+            quasi_dists
+        )
+
+    # %%
+    intermediate_data = []
+    noisy_sampler = BackendSampler(backend=simulator, options=options, bound_pass_manager=PassManager())
+    
+    # %%
+    start_time1 = time.time()
+    qaoa1 = QAOA(sampler=noisy_sampler, optimizer=COBYLA(), reps=p, initial_state=qc, mixer=XY_mixer, initial_point=initial_point,callback=callback, aggregation=alpha)
+    result1 = qaoa1.compute_minimum_eigenvalue(q_hamiltonian)
+    end_time1 = time.time()
+    elapsed_time1 = end_time1 - start_time1
+
+    # %%
+    # %% Post Selection
+
+
+
+    # %%
+    intermediate_data_dicts = []
+
+    for item in intermediate_data:
+        for dict_item in item:
+            intermediate_data_dicts.append(dict_item)
+
+    # %%
+
+    probability = []
+    total_arr = []
+    cumulative_probability_dict = {}
+    cumulative_total_dict = {}
+
+    for i, dict in enumerate(intermediate_data_dicts):
+        #print(f"\n\nIteration {i}")
+        #print(f"Dictionary: {dict}")
+        hits = 0.0
+        total = 0.0
+        for key in dict:
+            bitstring = int_to_bitstring(key, num_qubits)
+            #print(f"\nBitstring: {bitstring}")
+            hamming = check_hamming(bitstring, num_rot)
+        #  print(f"Hamming condition: {hamming}")
+            if check_hamming(bitstring, num_rot):
+                hits += dict[key]
+                total += dict[key]
+                #print(f"Bitstring: {bitstring} has a value of {dict[key]}")
+                if bitstring in cumulative_probability_dict:
+                    cumulative_probability_dict[bitstring] += dict[key]
+                else:
+                    cumulative_probability_dict[bitstring] = dict[key]
+            else:
+                total += dict[key]
+            if bitstring in cumulative_total_dict:
+                cumulative_total_dict[bitstring] += dict[key]
+            else:
+                cumulative_total_dict[bitstring] = dict[key]
+                #print(f"Bitstring: {bitstring} does not satisfy the Hamming condition.")
+                #pass
+        
+        probability.append(hits)
+        total_arr.append(total)
+
+    # %%
+    # sum the values of the cumulative_probability_dict and cumulative_total_dict
+
+    sum_total = sum(cumulative_total_dict.values())
+    sum_probability = sum(cumulative_probability_dict.values())
+
+    # print(f"Total probability: {sum_probability}, Total: {sum_total}")
+
+    norm = sum_total
+    fraction = sum_probability / sum_total
+
+
+
+
+
+
+
+
+
+    # %%
+    eigenstate_distribution = result1.eigenstate
+    best_measurement = result1.best_measurement
+    final_bitstrings = {state: probability for state, probability in eigenstate_distribution.items()}
+
+    all_bitstrings = {}
+    all_unrestricted_bitstrings = {}
+
+    for state, prob in final_bitstrings.items():
+        bitstring = int_to_bitstring(state, num_qubits)
+        energy = calculate_bitstring_energy(bitstring, q_hamiltonian)
+        if bitstring not in all_unrestricted_bitstrings:
+            all_unrestricted_bitstrings[bitstring] = {'probability': 0, 'energy': 0, 'count': 0}
+        all_unrestricted_bitstrings[bitstring]['probability'] += prob  # Aggregate probabilities
+        all_unrestricted_bitstrings[bitstring]['energy'] = (all_unrestricted_bitstrings[bitstring]['energy'] * all_unrestricted_bitstrings[bitstring]['count'] + energy) / (all_unrestricted_bitstrings[bitstring]['count'] + 1)
+        all_unrestricted_bitstrings[bitstring]['count'] += 1
+
+        if check_hamming(bitstring, num_rot):
+            if bitstring not in all_bitstrings:
+                all_bitstrings[bitstring] = {'probability': 0, 'energy': 0, 'count': 0}
+            all_bitstrings[bitstring]['probability'] += prob  # Aggregate probabilities
+            all_bitstrings[bitstring]['energy'] = (all_bitstrings[bitstring]['energy'] * all_bitstrings[bitstring]['count'] + energy) / (all_bitstrings[bitstring]['count'] + 1)
+            all_bitstrings[bitstring]['count'] += 1
+
+            ## here, count is not related to the number of counts of the optimiser,
+            ## it keeps track of number of times the bitstring has been seen in 
+            ## different iterations of the optimiser. This is used to calculate the
+            ## average energy of the bitstring across iterations. Ideally this should
+            ## be weighted by the probability of the bitstring in each iteration.
+            ## For the moment the energy is calculated by the statevector simulator,
+            ## so it should be fine. ##TODO : Adapt this for noisy simulations.
+
+
+    for data in intermediate_data_dicts:
+        
+        for int_bitstring in data:
+            probability = data[int_bitstring]
+            intermediate_bitstring = int_to_bitstring(int_bitstring, num_qubits)
+            energy = calculate_bitstring_energy(intermediate_bitstring, q_hamiltonian)
+            if bitstring not in all_unrestricted_bitstrings:
+                all_unrestricted_bitstrings[bitstring] = {'probability': 0, 'energy': 0, 'count': 0}
+            all_unrestricted_bitstrings[bitstring]['probability'] += probability  # Aggregate probabilities
+            all_unrestricted_bitstrings[bitstring]['energy'] = (all_unrestricted_bitstrings[bitstring]['energy'] * all_unrestricted_bitstrings[bitstring]['count'] + energy) / (all_unrestricted_bitstrings[bitstring]['count'] + 1)
+            all_unrestricted_bitstrings[bitstring]['count'] += 1
+
+            if check_hamming(intermediate_bitstring, num_rot):
+                if intermediate_bitstring not in all_bitstrings:
+                    all_bitstrings[intermediate_bitstring] = {'probability': 0, 'energy': 0, 'count': 0}
+                all_bitstrings[intermediate_bitstring]['probability'] += probability  # Aggregate probabilities
+                
+                all_bitstrings[intermediate_bitstring]['energy'] = (all_bitstrings[intermediate_bitstring]['energy'] * all_bitstrings[intermediate_bitstring]['count'] + energy) / (all_bitstrings[intermediate_bitstring]['count'] + 1)
+                all_bitstrings[intermediate_bitstring]['count'] += 1
+
+    # %%
+    sorted_bitstrings = sorted(all_bitstrings.items(), key=lambda x: x[1]['energy'])
+    sorted_unrestricted_bitstrings = sorted(all_unrestricted_bitstrings.items(), key=lambda x: x[1]['energy'])
+
+    # %%
+    # Store information
+    probabilities = []
+    sorted_bitstrings_arr = []
+
+
+    probabilities = [data['probability'] for bitstring, data in sorted_bitstrings]
+            
+
+    sorted_bitstrings_arr = [bitstring for bitstring, data in sorted_bitstrings]
+
+    probabilities = np.array(probabilities) / norm
+
+    # %%
+    result = {
+        'params' : (num_res, num_rot, alpha, shots, p),
+        'bitstrings': sorted_bitstrings_arr,
+        'probabilities': probabilities,
+        'fraction': fraction,
+        'norm': norm,
+        'energy': sorted_bitstrings[0][1]['energy'],
+        'elapsed_time': elapsed_time1,
+        'intermediate_data': intermediate_data,
+        'cumulative_probability_dict': cumulative_probability_dict,
+        'cumulative_total_dict': cumulative_total_dict,
+        'all_bitstrings': all_bitstrings,
+        'all_unrestricted_bitstrings': all_unrestricted_bitstrings,
+        'sorted_bitstrings': sorted_bitstrings,
+        'sorted_unrestricted_bitstrings': sorted_unrestricted_bitstrings
+    }
+
+    import json
+
+    # write json encoder
+
+
+        
+    # write json file
+
+    with open(f"RESULTS/XY-QAOA/JSON/{num_res}res-{num_rot}rot-{alpha}alpha-{shots}shots-{p}p.json", 'w') as f:
+        json.dump(result, f, cls=NumpyEncoder)
+
+    # with open(f"RESULTS/XY-QAOA/JSON/{num_res}res-{num_rot}rot-{alpha}alpha-{shots}shots-{p}p.json", 'w') as f:
+    #     json.dump(result, f)
+
+
+
+    # %%
+    # # Plotting the ratios
+    # fig, ax = plt.subplots()
+
+    # x3 = range(len(sorted_bitstrings_arr))
+
+    # # ax.plot(x, y_XY, label="XY mixer 2rot", marker='x')
+    # ax.plot(x3, probabilities, label=f"XY mixer 3 rot alpha : {alpha}", marker='1')
+
+    # ax.set_xlabel("Bitstrings")
+    # ax.set_ylabel("Probability")
+    # ax.set_title("Probability distributions of good bitstrings by Sorted Bitstrings")
+    # ax.legend()
+
+    # ax.set_xticks(x3)
+
+    # plt.tight_layout()
+    # # savefig with params in file name
+
+    # plt.savefig(f"./RESULTS/XY-QAOA/CVaR/prob_distributions_alpha_{alpha}.pdf")
+    # plt.show()
+
+    # %%
+
+
+    # %%
+
+
+    # %%
+
+
+
diff --git a/Production/supporting_functions.py b/Production/supporting_functions.py
new file mode 100644
index 00000000..40052e37
--- /dev/null
+++ b/Production/supporting_functions.py
@@ -0,0 +1,263 @@
+import numpy as np
+from copy import deepcopy
+import pandas as pd
+import ast
+import json
+
+from qiskit_algorithms.minimum_eigensolvers import QAOA
+from qiskit.quantum_info.operators import Pauli, SparsePauliOp
+from qiskit_algorithms.optimizers import COBYLA, SPSA
+from qiskit.primitives import Sampler
+from qiskit import QuantumCircuit
+from qiskit_aer import Aer
+from qiskit_aer.primitives import Estimator
+from qiskit import QuantumCircuit, transpile
+
+import Ising_Hamiltonian_production as IHP
+
+def get_hyperparameters(jobid, num_rot_arr, num_res_arr, shots_arr, alpha_arr, p_arr):
+    count = 1
+    for num_rot in num_rot_arr:
+        for num_res in num_res_arr:
+            for shots in shots_arr:
+                for alpha in alpha_arr:
+                    for p in p_arr:
+                        if count == jobid:
+                            return num_rot, num_res, shots, alpha, p
+                            
+                        count += 1
+
+def get_hamiltonian(num_rot, num_res):
+    IHP.create_energy_files(num_res, num_rot)
+    file_path = f"RESULTS/XY-QAOA/{num_res}res-{num_rot}rot.csv"
+
+    df1 = pd.read_csv(f"energy_files/{num_rot}rot_{num_res}res_one_body_terms.csv")
+    q = df1['E_ii'].values
+    num = len(q)
+    N = int(num/num_rot)
+    num_qubits = num
+
+    df = pd.read_csv(f"energy_files/{num_rot}rot_{num_res}res_two_body_terms.csv")
+    value = df['E_ij'].values
+    Q = np.zeros((num,num))
+    n = 0
+
+    if num_rot == 2:
+        for i in range(0, num-2):
+            if i%2 == 0:
+                Q[i][i+2] = deepcopy(value[n])
+                Q[i+2][i] = deepcopy(value[n])
+                Q[i][i+3] = deepcopy(value[n+1])
+                Q[i+3][i] = deepcopy(value[n+1])
+                n += 2
+            elif i%2 != 0:
+                Q[i][i+1] = deepcopy(value[n])
+                Q[i+1][i] = deepcopy(value[n])
+                Q[i][i+2] = deepcopy(value[n+1])
+                Q[i+2][i] = deepcopy(value[n+1])
+                n += 2      
+
+    
+    elif num_rot == 3:
+
+        for j in range(0, num-3, num_rot):
+            for i in range(j, j+num_rot):
+                Q[i][j+3] = deepcopy(value[n])
+                Q[j+3][i] = deepcopy(value[n])
+                Q[i][j+4] = deepcopy(value[n+1])
+                Q[j+4][i] = deepcopy(value[n+1])
+                Q[i][j+5] = deepcopy(value[n+2])
+                Q[j+5][i] = deepcopy(value[n+2])
+                n += num_rot
+
+    else:    
+        raise ValueError("Number of rotomers not supported.")
+    
+    H = np.zeros((num,num))
+
+    for i in range(num):
+        for j in range(num):
+            if i != j:
+                H[i][j] = np.multiply(0.25, Q[i][j])
+
+    for i in range(num):
+        H[i][i] = -(0.5 * q[i] + sum(0.25 * Q[i][j] for j in range(num) if j != i))
+
+    return H
+
+def X_op(i, num_qubits):
+    """Return an X Pauli operator on the specified qubit in a num-qubit system."""
+    op_list = ['I'] * num_qubits
+    op_list[i] = 'X'
+    return SparsePauliOp(Pauli(''.join(op_list)))
+
+def generate_pauli_zij(n, i, j):
+    if i<0 or i >= n or j<0 or j>=n:
+        raise ValueError(f"Indices out of bounds for n={n} qubits. ")
+        
+    pauli_str = ['I']*n
+
+    if i == j:
+        pauli_str[i] = 'Z'
+    else:
+        pauli_str[i] = 'Z'
+        pauli_str[j] = 'Z'
+
+    return Pauli(''.join(pauli_str))
+
+def get_XY_mixer(num_qubits, num_rot):
+    if num_rot == 2:
+        hamiltonian = SparsePauliOp(Pauli('I'*num_qubits), coeffs=[0])  
+        for i in range(0, num_qubits, 2):
+            if i + 1 < num_qubits:
+                xx_term = ['I'] * num_qubits
+                yy_term = ['I'] * num_qubits
+                xx_term[i] = 'X'
+                xx_term[i+1] = 'X'
+                yy_term[i] = 'Y'
+                yy_term[i+1] = 'Y'
+                xx_op = SparsePauliOp(Pauli(''.join(xx_term)), coeffs=[1/2])
+                yy_op = SparsePauliOp(Pauli(''.join(yy_term)), coeffs=[1/2])
+                hamiltonian += xx_op + yy_op
+        return -hamiltonian 
+    elif num_rot == 3:
+        hamiltonian = SparsePauliOp(Pauli('I' * num_qubits), coeffs=[0])
+        for i in range(0, num_qubits - 2, 3): 
+            x1x2 = ['I'] * num_qubits
+            y1y2 = ['I'] * num_qubits
+            x2x3 = ['I'] * num_qubits
+            y2y3 = ['I'] * num_qubits
+            x1x3 = ['I'] * num_qubits
+            y1y3 = ['I'] * num_qubits
+
+            x1x2[i] = 'X'
+            x1x2[i+1] = 'X'
+            y1y2[i] = 'Y'
+            y1y2[i+1] = 'Y'
+            x2x3[i+1] = 'X'
+            x2x3[i+2] = 'X'
+            y2y3[i+1] = 'Y'
+            y2y3[i+2] = 'Y'
+            x1x3[i] = 'X'
+            x1x3[i+2] = 'X'
+            y1y3[i] = 'Y'
+            y1y3[i+2] = 'Y' 
+
+            x1x2 = SparsePauliOp(Pauli(''.join(x1x2)), coeffs=[1/2])
+            y1y2 = SparsePauliOp(Pauli(''.join(y1y2)), coeffs=[1/2])
+            x2x3 = SparsePauliOp(Pauli(''.join(x2x3)), coeffs=[1/2])
+            y2y3 = SparsePauliOp(Pauli(''.join(y2y3)), coeffs=[1/2])
+            x1x3 = SparsePauliOp(Pauli(''.join(x1x3)), coeffs=[1/2])
+            y1y3 = SparsePauliOp(Pauli(''.join(y1y3)), coeffs=[1/2])
+
+            hamiltonian += x1x2 + y1y2 + x2x3 + y2y3 + x1x3 + y1y3
+        return hamiltonian
+    else:
+        raise ValueError("Number of rotomers not supported.")
+    
+def generate_pauli_zij(n, i, j):
+    if i<0 or i >= n or j<0 or j>=n:
+        raise ValueError(f"Indices out of bounds for n={n} qubits. ")
+        
+    pauli_str = ['I']*n
+
+    if i == j:
+        pauli_str[i] = 'Z'
+    else:
+        pauli_str[i] = 'Z'
+        pauli_str[j] = 'Z'
+
+    return Pauli(''.join(pauli_str))
+
+def get_q_hamiltonian(num_qubits, H):
+    q_hamiltonian = SparsePauliOp(Pauli('I'*num_qubits), coeffs=[0])
+
+    for i in range(num_qubits):
+        for j in range(i+1, num_qubits):
+            if H[i][j] != 0:
+                pauli = generate_pauli_zij(num_qubits, i, j)
+                op = SparsePauliOp(pauli, coeffs=[H[i][j]])
+                q_hamiltonian += op
+
+    for i in range(num_qubits):
+        pauli = generate_pauli_zij(num_qubits, i, i)
+        Z_i = SparsePauliOp(pauli, coeffs=[H[i][i]])
+        q_hamiltonian += Z_i
+
+    return q_hamiltonian
+    
+def generate_initial_bitstring(num_qubits, num_rot):
+    if num_rot == 2:
+        bitstring = [(i%2) for i in range(num_qubits)]
+        return ''.join(map(str, bitstring))
+    elif num_rot == 3:
+        pattern = '100'
+        bitstring = (pattern * (num_qubits // 3 + 1))[:num_qubits]
+        return bitstring
+    else:
+        raise ValueError("Number of rotomers not supported.")
+    
+def format_sparsepauliop(op):
+    terms = []
+    labels = [pauli.to_label() for pauli in op.paulis]
+    coeffs = op.coeffs
+    for label, coeff in zip(labels, coeffs):
+        terms.append(f"{coeff:.10f} * {label}")
+    return '\n'.join(terms)
+
+
+
+def int_to_bitstring(state, total_bits):
+    """Converts an integer state to a binary bitstring with padding of leading zeros."""
+    return format(state, '0{}b'.format(total_bits))
+
+def check_hamming(bitstring, substring_size):
+    """Check if each substring contains exactly one '1'."""
+    substrings = [bitstring[i:i+substring_size] for i in range(0, len(bitstring), substring_size)]
+    return all(sub.count('1') == 1 for sub in substrings)
+
+def calculate_bitstring_energy(bitstring, hamiltonian, backend=None):
+    """
+    Calculate the energy of a given bitstring for a specified Hamiltonian.
+
+    Args:
+        bitstring (str): The bitstring for which to calculate the energy.
+        hamiltonian (SparsePauliOp): The Hamiltonian operator of the system, defined as a SparsePauliOp.
+        backend (qiskit.providers.Backend): The quantum backend to execute circuits.
+
+    Returns:
+        float: The calculated energy of the bitstring.
+    """
+    # Prepare the quantum circuit for the bitstring
+    num_qubits = len(bitstring)
+    qc = QuantumCircuit(num_qubits)
+    for i, char in enumerate(bitstring):
+        if char == '1':
+            qc.x(i)  # Apply X gate if the bit in the bitstring is 1
+    
+    # Use Aer's statevector simulator if no backend provided
+    if backend is None:
+        backend = Aer.get_backend('aer_simulator_statevector')
+
+    qc = transpile(qc, backend)
+    estimator = Estimator()
+    resultt = estimator.run(observables=[hamiltonian], circuits=[qc], backend=backend).result()
+
+    return resultt.values[0].real
+
+def safe_literal_eval(value):
+    try:
+        return ast.literal_eval(value)
+    except (ValueError, SyntaxError) as e:
+        print(f"Error evaluating string: {value}, {e}")
+        return None
+    
+class NumpyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return super(NumpyEncoder, self).default(obj)
\ No newline at end of file
diff --git a/energy_files/one_body_terms.csv b/energy_files/one_body_terms.csv
index 0ec627b7..a0ae4fde 100644
--- a/energy_files/one_body_terms.csv
+++ b/energy_files/one_body_terms.csv
@@ -1,13 +1,7 @@
 res i,rot A_i,E_ii
-1,10,1.7497375011444092
-1,11,1.342016339302063
-1,12,2.2813830375671387
-2,10,1.5022609233856201
-2,11,0.6130506992340088
-2,12,0.3914695382118225
-3,10,1.2526496648788452
-3,11,0.5298484563827515
-3,12,1.2019305229187012
-4,10,1.6690243482589722
-4,11,1.999065637588501
-4,12,1.8981397151947021
+1,10,0.7228772044181824
+1,11,1.0608102083206177
+1,12,0.9661412835121155
+2,10,1.568386435508728
+2,11,1.3739349842071533
+2,12,1.7210133075714111
diff --git a/energy_files/two_body_terms.csv b/energy_files/two_body_terms.csv
index 91f819ff..2f2e7d20 100644
--- a/energy_files/two_body_terms.csv
+++ b/energy_files/two_body_terms.csv
@@ -1,28 +1,10 @@
 res i,res j,rot A_i,rot B_j,E_ij
-1,2,10,10,3.3755698204040527
-1,2,10,11,0.19628620147705078
-1,2,10,12,0.2281581163406372
-1,2,11,10,1.126073956489563
-1,2,11,11,-0.29206788539886475
-1,2,11,12,-0.2588125467300415
-1,2,12,10,3.3703629970550537
-1,2,12,11,0.8964073657989502
-1,2,12,12,0.9255384206771851
-2,3,10,10,0.7900418639183044
-2,3,10,11,0.4671710133552551
-2,3,10,12,0.6361902356147766
-2,3,11,10,0.44166499376296997
-2,3,11,11,0.11949361115694046
-2,3,11,12,0.2885180711746216
-2,3,12,10,0.4477546811103821
-2,3,12,11,0.12558335065841675
-2,3,12,12,0.29460781812667847
-3,4,10,10,-0.885757327079773
-3,4,10,11,-0.9904046058654785
-3,4,10,12,-0.9228173494338989
-3,4,11,10,0.13306832313537598
-3,4,11,11,0.03253579139709473
-3,4,11,12,0.09597408771514893
-3,4,12,10,-0.3692672848701477
-3,4,12,11,-0.4739146828651428
-3,4,12,12,-0.40632736682891846
+1,2,10,10,0.7207369208335876
+1,2,10,11,0.753841757774353
+1,2,10,12,0.8940832614898682
+1,2,11,10,0.09026861190795898
+1,2,11,11,0.1213676929473877
+1,2,11,12,0.26143527030944824
+1,2,12,10,0.5167786478996277
+1,2,12,11,0.5467525720596313
+1,2,12,12,0.6853535175323486
diff --git a/output_repacked.pdb b/output_repacked.pdb
index fd0f39b6..52294231 100644
--- a/output_repacked.pdb
+++ b/output_repacked.pdb
@@ -6,11 +6,11 @@ REMARK 220  EXPERIMENT TYPE                : THEORETICAL MODELLING
 REMARK 220  DATE OF DATA COLLECTION        : 31-MAY-24                          
 REMARK 220                                                                      
 REMARK 220 REMARK: MODEL GENERATED BY ROSETTA                                   
-REMARK 220  VERSION 2024.19+release.ec708a4c40                                  
+REMARK 220  VERSION 2024.18+release.117e2f6f54                                  
 ATOM      1  N   ALA A   1      -1.242  -5.643  -4.601  1.00  0.00           N  
 ATOM      2  CA  ALA A   1      -1.826  -6.840  -5.239  1.00  0.00           C  
 ATOM      3  C   ALA A   1      -0.961  -8.129  -5.060  1.00  0.00           C  
-ATOM      4  O   ALA A   1       0.236  -8.011  -5.086  1.00  0.00           O  
+ATOM      4  O   ALA A   1      -1.184  -8.828  -4.106  1.00  0.00           O  
 ATOM      5  CB  ALA A   1      -1.907  -6.473  -6.707  1.00  0.00           C  
 ATOM      6 1H   ALA A   1      -1.842  -4.858  -4.752  1.00  0.00           H  
 ATOM      7 2H   ALA A   1      -1.144  -5.803  -3.619  1.00  0.00           H  
@@ -19,59 +19,31 @@ ATOM      9  HA  ALA A   1      -2.813  -7.006  -4.808  1.00  0.00           H
 ATOM     10 1HB  ALA A   1      -2.332  -7.306  -7.267  1.00  0.00           H  
 ATOM     11 2HB  ALA A   1      -2.540  -5.594  -6.828  1.00  0.00           H  
 ATOM     12 3HB  ALA A   1      -0.909  -6.255  -7.083  1.00  0.00           H  
-ATOM     13  N   GLN A   2      -1.563  -9.256  -4.783  1.00  0.00           N  
-ATOM     14  CA  GLN A   2      -0.812 -10.564  -4.541  1.00  0.00           C  
-ATOM     15  C   GLN A   2      -0.369 -11.214  -5.864  1.00  0.00           C  
-ATOM     16  O   GLN A   2      -0.700 -12.300  -6.261  1.00  0.00           O  
-ATOM     17  CB  GLN A   2      -1.672 -11.561  -3.759  1.00  0.00           C  
-ATOM     18  CG  GLN A   2      -2.090 -11.075  -2.381  1.00  0.00           C  
-ATOM     19  CD  GLN A   2      -0.911 -10.926  -1.438  1.00  0.00           C  
-ATOM     20  OE1 GLN A   2      -0.253 -11.908  -1.085  1.00  0.00           O  
-ATOM     21  NE2 GLN A   2      -0.638  -9.694  -1.024  1.00  0.00           N  
-ATOM     22  H   GLN A   2      -2.572  -9.251  -4.729  1.00  0.00           H  
-ATOM     23  HA  GLN A   2       0.146 -10.332  -4.077  1.00  0.00           H  
-ATOM     24 1HB  GLN A   2      -2.576 -11.785  -4.325  1.00  0.00           H  
-ATOM     25 2HB  GLN A   2      -1.124 -12.495  -3.634  1.00  0.00           H  
-ATOM     26 1HG  GLN A   2      -2.573 -10.103  -2.481  1.00  0.00           H  
-ATOM     27 2HG  GLN A   2      -2.785 -11.795  -1.949  1.00  0.00           H  
-ATOM     28 1HE2 GLN A   2       0.129  -9.534  -0.400  1.00  0.00           H  
-ATOM     29 2HE2 GLN A   2      -1.197  -8.926  -1.335  1.00  0.00           H  
-ATOM     30  N   ALA A   3       0.560 -10.483  -6.493  1.00  0.00           N  
-ATOM     31  CA  ALA A   3       1.154 -10.750  -7.776  1.00  0.00           C  
-ATOM     32  C   ALA A   3       2.321 -11.721  -7.826  1.00  0.00           C  
-ATOM     33  O   ALA A   3       2.779 -11.948  -8.913  1.00  0.00           O  
-ATOM     34  CB  ALA A   3       1.492  -9.345  -8.253  1.00  0.00           C  
-ATOM     35  H   ALA A   3       0.850  -9.664  -5.977  1.00  0.00           H  
-ATOM     36  HA  ALA A   3       0.406 -11.241  -8.399  1.00  0.00           H  
-ATOM     37 1HB  ALA A   3       1.958  -9.397  -9.237  1.00  0.00           H  
-ATOM     38 2HB  ALA A   3       0.579  -8.753  -8.315  1.00  0.00           H  
-ATOM     39 3HB  ALA A   3       2.180  -8.879  -7.550  1.00  0.00           H  
-ATOM     40  N   VAL A   4       2.838 -12.229  -6.655  1.00  0.00           N  
-ATOM     41  CA  VAL A   4       3.991 -13.130  -6.416  1.00  0.00           C  
-ATOM     42  C   VAL A   4       3.617 -14.298  -5.390  1.00  0.00           C  
-ATOM     43  O   VAL A   4       4.203 -15.329  -5.505  1.00  0.00           O  
-ATOM     44  OXT VAL A   4       2.777 -14.115  -4.553  1.00  0.00           O  
-ATOM     45  CB  VAL A   4       5.178 -12.312  -5.873  1.00  0.00           C  
-ATOM     46  CG1 VAL A   4       6.369 -13.219  -5.601  1.00  0.00           C  
-ATOM     47  CG2 VAL A   4       5.549 -11.217  -6.861  1.00  0.00           C  
-ATOM     48  H   VAL A   4       2.301 -11.898  -5.867  1.00  0.00           H  
-ATOM     49  HA  VAL A   4       4.252 -13.615  -7.357  1.00  0.00           H  
-ATOM     50  HB  VAL A   4       4.893 -11.863  -4.921  1.00  0.00           H  
-ATOM     51 1HG1 VAL A   4       7.199 -12.625  -5.217  1.00  0.00           H  
-ATOM     52 2HG1 VAL A   4       6.092 -13.973  -4.864  1.00  0.00           H  
-ATOM     53 3HG1 VAL A   4       6.672 -13.709  -6.526  1.00  0.00           H  
-ATOM     54 1HG2 VAL A   4       6.388 -10.643  -6.471  1.00  0.00           H  
-ATOM     55 2HG2 VAL A   4       5.828 -11.667  -7.814  1.00  0.00           H  
-ATOM     56 3HG2 VAL A   4       4.694 -10.556  -7.009  1.00  0.00           H  
+ATOM     13  N   GLN A   2      -0.113  -8.452  -6.001  1.00  0.00           N  
+ATOM     14  CA  GLN A   2       0.795  -9.678  -5.920  1.00  0.00           C  
+ATOM     15  C   GLN A   2       1.943  -9.600  -6.941  1.00  0.00           C  
+ATOM     16  O   GLN A   2       2.192 -10.420  -7.785  1.00  0.00           O  
+ATOM     17  OXT GLN A   2       2.685  -8.656  -6.939  1.00  0.00           O  
+ATOM     18  CB  GLN A   2       0.005 -10.969  -6.154  1.00  0.00           C  
+ATOM     19  CG  GLN A   2       0.826 -12.238  -5.997  1.00  0.00           C  
+ATOM     20  CD  GLN A   2      -0.016 -13.492  -6.136  1.00  0.00           C  
+ATOM     21  OE1 GLN A   2      -1.030 -13.501  -6.840  1.00  0.00           O  
+ATOM     22  NE2 GLN A   2       0.399 -14.560  -5.464  1.00  0.00           N  
+ATOM     23  H   GLN A   2      -0.063  -7.857  -6.816  1.00  0.00           H  
+ATOM     24  HA  GLN A   2       1.336  -9.645  -4.974  1.00  0.00           H  
+ATOM     25 1HB  GLN A   2      -0.828 -11.018  -5.452  1.00  0.00           H  
+ATOM     26 2HB  GLN A   2      -0.414 -10.961  -7.160  1.00  0.00           H  
+ATOM     27 1HG  GLN A   2       1.597 -12.257  -6.767  1.00  0.00           H  
+ATOM     28 2HG  GLN A   2       1.285 -12.242  -5.009  1.00  0.00           H  
+ATOM     29 1HE2 GLN A   2      -0.116 -15.416  -5.517  1.00  0.00           H  
+ATOM     30 2HE2 GLN A   2       1.227 -14.509  -4.905  1.00  0.00           H  
 TER                                                                             
 # All scores below are weighted scores, not raw scores.
 #BEGIN_POSE_ENERGIES_TABLE output_repacked.pdb
 label fa_atr fa_rep fa_sol fa_intra_rep fa_intra_sol_xover4 lk_ball_wtd fa_elec pro_close hbond_sr_bb hbond_lr_bb hbond_bb_sc hbond_sc dslf_fa13 omega fa_dun p_aa_pp yhh_planarity ref rama_prepro total
 weights 1 0.55 1 0.005 1 1 1 1.25 1 1 1 1 1.25 0.4 0.7 0.6 0.625 1 0.45 NA
-pose -4.30706 0.47599 2.7962 0.03009 0.73732 -0.57284 -1.10688 0 -1.0569 0 0 0 0 -0.04133 1.69485 0.13551 0 3.8411 0.03356 2.65961
-ALA:NtermProteinFull_1 -1.04694 0.14092 0.59233 0.00182 0 -0.11319 -0.68058 0 0 0 0 0 0 0.01372 0 0 0 1.32468 0 0.23276
-GLN_2 -0.96365 0.02423 0.86392 0.00828 0.6054 -0.20093 0.46971 0 0 0 0 0 0 -0.01012 1.65643 0.09256 0 -1.45095 0.30184 1.39671
-ALA_3 -1.4141 0.21733 0.65757 0.00261 0 -0.11003 -0.68399 0 0 0 0 0 0 -0.04493 0 0.04295 0 1.32468 0.01678 0.00887
-VAL:CtermProteinFull_4 -0.88237 0.09352 0.68239 0.01737 0.13193 -0.14868 -0.21202 0 0 0 0 0 0 0 0.03841 0 0 2.64269 -0.28506 2.07817
+pose -0.72274 0.20393 0.47164 0.01714 0.50188 -0.08621 -0.89142 0 0 0 0 0 0 -0.01782 2.12142 0 0 -0.12627 0 1.47154
+ALA:NtermProteinFull_1 -0.36137 0.10196 0.23582 0.00176 0 -0.04311 -0.44571 0 0 0 0 0 0 -0.01782 0 0 0 1.32468 0 0.79622
+GLN:CtermProteinFull_2 -0.36137 0.10196 0.23582 0.01538 0.50188 -0.04311 -0.44571 0 0 0 0 0 0 0 2.12142 0 0 -1.45095 0 0.67533
 #END_POSE_ENERGIES_TABLE output_repacked.pdb
 
diff --git a/plot_probability.py b/plot_probability.py
index 6717899f..428103bd 100644
--- a/plot_probability.py
+++ b/plot_probability.py
@@ -11,7 +11,7 @@
 #     "no_penalty": "RESULTS/nopenalty-QAOA"
 # }
 
-file_XY_3rot = "RESULTS/3rot-XY-QAOA/4res-3rot.csv"
+file_XY_3rot = "RESULTS/3rot-XY-QAOA/2res-3rot.csv"
 
 # def custom_sort_key(filename):
 #     numbers = re.findall(r'\d+', filename)