| import os |
| import time |
|
|
| import pandas as pd |
|
|
| from get_predictions import predict, load_model, parse_args |
| import matplotlib.pyplot as plt |
|
|
|
|
| if __name__=="__main__": |
| args = parse_args() |
| |
| outer_save_dir = "/Users/judewells/Documents/dataScienceProgramming/data_for_domdet/human200_ss17_model_w_post_proc" |
| structure_dir = "/Users/judewells/Documents/dataScienceProgramming/data_for_domdet/UP000005640_9606_HUMAN_v4" |
| |
| os.makedirs(outer_save_dir, exist_ok=True) |
| model = load_model(model_dir=args.model_dir, remove_disordered_domain_threshold=args.remove_disordered_domain_threshold, |
| min_ss_components=args.min_ss_components, min_domain_length=args.min_domain_length) |
| with open("experiments/sampled_structures.txt", 'r') as f: |
| lines = f.readlines() |
| res_list = [] |
| time_list = [] |
| af_id_list = [] |
| for i, af_id in enumerate(lines): |
| try: |
| af_id, n_res = af_id.split(",") |
| af_id = af_id.strip() |
| print(af_id, n_res) |
| pdb_path = os.path.join(structure_dir, f"{af_id}.pdb") |
| start = time.time() |
| prediction_results = predict(model, pdb_path, renumber_pdbs=False) |
| end = time.time() |
| res_list.append(int(n_res)) |
| time_list.append(end-start) |
| af_id_list.append(af_id) |
| except: |
| pass |
| plt.scatter(res_list, time_list) |
| plt.xlabel("Number of residues") |
| plt.ylabel("Time (s)") |
| plt.savefig("chainsaw_macbook_speed_test.png") |
| plt.show() |
| df = pd.DataFrame({"af_id": af_id_list, "n_res": res_list, "time": time_list}) |
| df.to_csv("chainsaw_macbook_speed_test.csv", index=False) |
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|
