File size: 1,911 Bytes
80a72c3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | 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()
# structure_dir = "/Users/judewells/Documents/dataScienceProgramming/data_for_domdet/human_200_c2m"
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"
# outer_save_dir = "/Users/judewells/Documents/dataScienceProgramming/data_for_domdet/human200_mse_ss_excl_f32_MSE_sym"
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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