def extract_results(X_test, Z_test, generative):
global NUM_NUCLEOTIDES, FINAL_RESULTS_CSV, TYPE_NUCLEOTIDES
headers = ("Sequence", "Degrees", "Score DB", "Score Generated")
with open(FINAL_RESULTS_CSV, "w", newline="", encoding="utf-8") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(headers)
gen_imgs = generative.predict(X_test)
for i in range(1, len(X_test)):
pose_new = Pose()
sequence = ""
muestra = X_test[i]
for no in range(0, NUM_NUCLEOTIDES):
if muestra[0, no] == 1:
sequence = sequence + "A[ADE]"
elif muestra[1, no] == 1:
sequence = sequence + "G[GUA]"
elif muestra[2, no] == 1:
sequence = sequence + "C[CYT]"
elif muestra[3, no] == 1:
sequence = sequence + "T[THY]"
pose_new = pose_from_sequence(sequence)
data = []
gen_img = gen_imgs[i]
for k in range(0, NUM_NUCLEOTIDES):
data.append(gen_img[0, k, 0])
pose_new.set_gamma(k + 1, gen_img[0, k, 0])
data.append(gen_img[1, k, 0])
pose_new.set_epsilon(k + 1, gen_img[1, k, 0])
data.append(gen_img[2, k, 0])
pose_new.set_delta(k + 1, gen_img[2, k, 0])
data.append(gen_img[3, k, 0])
pose_new.set_chi(k + 1, gen_img[3, k, 0])
data.append(gen_img[4, k, 0])
pose_new.set_zeta(k + 1, gen_img[4, k, 0])
score = scorefxn(pose_new)
table = [sequence, data, Z_test[i], score]
with open(FINAL_RESULTS_CSV, "a", newline="",
encoding="utf-8") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(table)
def save_created_figures(epoch, my_generative, X_test):
#noise = noise_nucleotides(1)
idx = np.random.randint(0, X_test.shape[0], 1)
X_test = X_test[idx]
gen_img = my_generative.predict(X_test)
pose_new = Pose()
sequence = ""
global NUM_NUCLEOTIDES
for no in range(0, NUM_NUCLEOTIDES):
if X_test[0, 0, no] == 1:
sequence = sequence + "A[ADE]"
elif X_test[0, 1, no] == 1:
sequence = sequence + "G[GUA]"
elif X_test[0, 2, no] == 1:
sequence = sequence + "C[CYT]"
elif X_test[0, 3, no] == 1:
sequence = sequence + "T[THY]"
pose_new = pose_from_sequence(sequence)
data = []
for k in range(0, NUM_NUCLEOTIDES):
data.append(gen_img[0, 0, k, 0])
pose_new.set_gamma(k + 1, gen_img[0, 0, k, 0])
data.append(gen_img[0, 1, k, 0])
pose_new.set_epsilon(k + 1, gen_img[0, 1, k, 0])
data.append(gen_img[0, 2, k, 0])
pose_new.set_delta(k + 1, gen_img[0, 2, k, 0])
data.append(gen_img[0, 3, k, 0])
pose_new.set_chi(k + 1, gen_img[0, 3, k, 0])
data.append(gen_img[0, 4, k, 0])
pose_new.set_zeta(k + 1, gen_img[0, 4, k, 0])
score = scorefxn(pose_new)
save_to_csv(sequence, data, score)
create_pdb_and_save(pose_new, score, epoch)
