<jupyter_start><jupyter_text>Run Through<jupyter_code>model = mb()
model.add_k(poi)
model._poi.generate_3frame_tags()
model.add_enters([5], [0], [.08])
model.add_stops([1000, 1740],[0,0],[20,20])

model._poi.multiframe_epitopes[0]['T_Flag'] = [1250, 1260, 1270,1280,1290]
model._poi.multiframe_epitopes[0]['T_Hemagglutinin']= [100,110,120,130,140, ]
model.visualize_transcript()

t = np.linspace(0,1000,1001)
ssa_soln = model.run_ssa(t,n_traj=50)
plt.plot(ssa_soln[:,0,:].T, '#08ff08',alpha=.3)
plt.plot(ssa_soln[:,1,:].T, '#1cfff7',alpha=.3)
plt.xlabel('Time')
plt.ylabel('Intensity')
model = mb()
model.add_k(poi)
model._poi.generate_3frame_tags()
model.add_enters([5,1100], [0,0], [.08,.02])
model.add_stops([1000, 1740],[0,0],[20,20])

model._poi.multiframe_epitopes[0]['T_Flag'] = [1250, 1260, 1270,1280,1290]
model._poi.multiframe_epitopes[0]['T_Hemagglutinin']= [100,110,120,130,140, ]
model.visualize_transcript()

t = np.linspace(0,1000,1001)
ssa_soln = model.run_ssa(t,n_traj=50)


plt.plot(ssa_soln[:,0,:].T, '#08ff08',alpha=.3)
plt.plot(ssa_soln[:,1,:].T, '#1cfff7',alpha=.3)
plt.xlabel('Time')
plt.ylabel('Intensity')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2293103448275862, 'fraction_numerical': 0.13879310344827586, 'mean_word_length': 9.366071428571429, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '241423', 'license': 'MIT', 'n_tokens_mistral': 667, 'n_tokens_neox': 555, 'n_words': 53, 'path': 'rsnapsim/interactive_notebooks/.ipynb_checkpoints/Generic TASEP-checkpoint.ipynb', 'repo_name': 'MunskyGroup/rSNAPsim', 'size': '1160'}