import matplotlib.pyplot as plt
import matplotlib
matplotlib.rc('font', family='Arial')
from sklearn.metrics import roc_auc_score, roc_curve
DTCRS = DeepTCR_SS('reg_flu', device=2)
alpha = 'CAGAGSQGNLIF'
beta = 'CASSSRSSYEQYF'
contacts_alpha = [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
contacts_beta = [0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0]
input_alpha = np.array([alpha, alpha])
input_beta = np.array([beta, beta])
fig_rsl, ax_rsl = DTCRS.Residue_Sensitivity_Logo(input_alpha,
input_beta,
background_color='black',
Load_Prev_Data=False)
df_alpha = pd.DataFrame()
df_alpha['seq'] = list(alpha)
df_alpha['mag'] = DTCRS.mag_alpha
df_alpha['label'] = contacts_alpha
df_beta = pd.DataFrame()
df_beta['seq'] = list(beta)
df_beta['mag'] = DTCRS.mag_beta
df_beta['label'] = contacts_beta
df = pd.concat([df_alpha, df_beta])
roc_auc_score(df['label'], df['mag'])
plt.tight_layout()
fig.savefig(os.path.join(dir_write, l + '.png'), dpi=1200)
plt.close()
#Get Residue Sensitivity Logo for select epitopes
DTCR.Representative_Sequences(top_seq=100, make_seq_logos=False)
test_peptide = 'TSTLQEQIGW'
rep_seq = DTCR.Rep_Seq[test_peptide]['beta'][0:10]
models = np.random.choice(range(100), 5, replace=False)
models = ['model_' + str(x) for x in models]
models = None
DTCR.Residue_Sensitivity_Logo(beta_sequences=np.array(rep_seq),
models=models,
class_sel=test_peptide,
Load_Prev_Data=False,
background_color='black',
edgewidth=0.0,
figsize=(3, 4),
min_size=0.25,
norm_to_seq=True)
plt.savefig(test_peptide + '.png', dpi=1200)
test_peptide = 'TSTLTEQVAW'
rep_seq = DTCR.Rep_Seq[test_peptide]['beta'][0:10]
DTCR.Residue_Sensitivity_Logo(beta_sequences=np.array(rep_seq),
models=models,
class_sel=test_peptide,
Load_Prev_Data=False,
background_color='black',
edgewidth=0.0,
figsize=(3, 4),
