def test_DHDX_different_files(self):
output_file1 = input_dir + "/cytc_output/prod1/models_scores_sigmas-CytC_pH_6.5.dat"
output_file2 = input_dir + "/cytc_output/prod1/models_scores_sigmas-CytC_pH_7.4.dat"
pof1 = analysis.ParseOutputFile(output_file1)
pof2 = analysis.ParseOutputFile(output_file2)
dhdx = analysis.DeltaHDX(pof1, pof2)
diff, Z, mean1, mean2, sd1, sd2 = dhdx.calculate_dhdx()
dhdx.write_dhdx_file()
def test_DHDX_same_file(self):
output_file = input_dir + "/cytc_output/prod1/models_scores_sigmas-CytC_pH_6.5.dat"
pof1 = analysis.ParseOutputFile(output_file)
pof2 = analysis.ParseOutputFile(output_file)
dhdx = analysis.DeltaHDX(pof1, pof2)
diff, Z, mean1, mean2, sd1, sd2 = dhdx.calculate_dhdx()
for res in pof1.observed_residues:
self.assertEqual(diff[res - 1], 0)
self.assertEqual(Z[res - 1], 0)
def test_get_sequence(self):
output_file = input_dir + "/cytc_output/prod1/models_scores_sigmas-CytC_pH_6.5.dat"
pof = analysis.ParseOutputFile(output_file)
seq = pof.get_sequence()
cytc_seq = "MGDVEKGKKIFVQKCAQCHTVEKGGKHKTGPNLHGLFGRKTGQAPGFTYTDANKNKGITWKEETLMEYLENPKKYIPGTKMIFAGIKKKTEREDLIAYLKKATNE"
self.assertEqual(seq, cytc_seq)
def test_create_pof(self):
output_file = input_dir + "/cytc_output/prod1/models_scores_sigmas-CytC_pH_6.5.dat"
pof = analysis.ParseOutputFile(output_file)
self.assertEqual(pof.output_file, output_file)
self.assertEqual(len(pof.get_datasets()), 1)
self.assertEqual(pof.molecule_name, "CytC")
self.assertEqual(pof.grid_size, 50)
pof.clear_models()
self.assertEqual(len(pof.models), 0)
import sys
sys.path.append("../../pyext/src")
import plots
import analysis
outputdir = "./test_simulated_data_50k/"
pof = analysis.ParseOutputFile(outputdir + "/models_scores_sigmas-Apo.dat",
"Apo")
pof2 = analysis.ParseOutputFile(outputdir + "/models_scores_sigmas-Apo2.dat",
"Apo2")
pof.generate_datasets()
pof2.generate_datasets()
#pof.calculate_random_sample_convergence()
#pof2.calculate_random_sample_convergence()
conv = analysis.Convergence(pof, pof2, 500)
print(conv.total_score_pvalue_and_cohensd())
ranges = [0.01, 0.1, 0.2, 0.3, 0.4]
#print(conv.get_clusters(ranges))
#exit()
#print(conv.residue_pvalue_and_cohensd())
plots.plot_incorporation_curve_fits(pof, 500,
outputdir + "/incorporation_plots/")
plots.plot_incorporation_curve_fits(pof2, 500,
outputdir + "/incorporation_plots2/")
def test_get_models_datasets(self):
output_file = input_dir + "/cytc_output/prod1/models_scores_sigmas-CytC_pH_6.5.dat"
pof = analysis.ParseOutputFile(output_file)
self.assertEqual(len(pof.get_all_models()), 5100)
state.set_output_model(output_model)
output_model1 = model.ResidueGridModel(state1, grid_size=num_exp_bins)
state1.set_output_model(output_model1)
output_model2 = model.ResidueGridModel(state2, grid_size=num_exp_bins)
state2.set_output_model(output_model2)
sampler = sampling.MCSampler(sys,
pct_moves=20) #, sigma_sample_level="timepoint")
sys.output.initialize_output_model_file(state, output_model.pf_grids)
sys.output.initialize_output_model_file(state1, output_model1.pf_grids)
sys.output.initialize_output_model_file(state2, output_model2.pf_grids)
sampler.run(nsteps, 2.0, write=True)
pof = analysis.ParseOutputFile(outputdir + "/models_scores_sigmas-Apo.dat",
state)
pof2 = analysis.ParseOutputFile(outputdir + "/models_scores_sigmas-Apo2.dat",
state1)
pof3 = analysis.ParseOutputFile(outputdir + "/models_scores_sigmas-Apo3.dat",
state2)
#pof.calculate_random_sample_convergence()
#pof2.calculate_random_sample_convergence()
conv = analysis.Convergence(pof, pof2)
print(conv.total_score_pvalue_and_cohensd())
#print(conv.residue_pvalue_and_cohensd())
plots.plot_residue_protection_factors([pof, pof2, pof3],
num_best_models=200,
sequence = "GMAEDMAADEVTAPPRKVLIISAGASHSVALLSGDIVCSWGRGEDGQLGHGDAEDRPSPTQLSALDGHQIVSVTCGADHTVAYSQSGMEVYSWGWGDFGRLGHGNSSDLFTPLPIKALHGIRIKQIACGDSHCLAVTMEGEVQSWGRNQNGQLGLGDTEDSLVPQKIQAFEGIRIKMVAAGAEHTAAVTEDGDLYGWGWGRYGNLGLGDRTDRLVPERVTSTGGEKMSMVACGWRHTISVSYSGALYTYGWSKYGQLGHGDLEDHLIPHKLEALSNSFISQISGGWRHTMALTSDGKLYGWGWNKFGQVGVGNNLDQCSPVQVRFPDDQKVVQVSCGWRHTLAVTERNNVFAWGRGTNGQLGIGESVDRNFPKIIEALSVDGASGQHIESSNIDPSSGKSWVSPAERYAVVPDETGLTDGSSKGNGGDISVPQTDVKRVRI" # FASTA sequence
resrange = (100, 200
) # Residue range is a tuple in pdb numbering (starts at 1).
num_best_models = 200
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
### Analysis.
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Initialize System
sys = system.System(output_dir=None)
mol = sys.add_macromolecule(sequence, "ERa")
state = mol.get_apo_state()
#mol.add_state("088074")
pof = analysis.ParseOutputFile(outputdir + "/models_scores_sigmas-ERa_Apo.dat",
state)
pof2 = analysis.ParseOutputFile(
outputdir2 + "/models_scores_sigmas-ERa_Apo.dat", state)
pof3 = analysis.ParseOutputFile(
outputdir3 + "/models_scores_sigmas-ERa_Apo.dat", state)
pof4 = analysis.ParseOutputFile(
outputdir4 + "/models_scores_sigmas-ERa_Apo.dat", state)
pof4 = analysis.ParseOutputFile(
outputdir5 + "/models_scores_sigmas-ERa_Apo.dat", state)
plots.plot_residue_protection_factors([pof, pof2, pof3, pof4],
num_best_models=num_best_models,
resrange=(240, 260))
states.append(s)
output_models.append(om)
sys.output.initialize_output_model_file(state, om.pf_grids)
#sampler = sampling.EnumerationSampler(sys)
#sampler.run(write=True)
#pof = analysis.ParseOutputFile(outputdir + "/models_scores_sigmas-Apo.dat", state)
sys.output.change_output_directory(output_dir_sample)
#sys.output.initialize_output_model_file(state, output_model.pf_grids)
sampler = sampling.MCSampler(sys)
sampler.run(10000, 2.0, write=True)
pof = analysis.ParseOutputFile(
output_dir_sample + "/models_scores_sigmas-Apo.dat", states[0])
pof1 = analysis.ParseOutputFile(
output_dir_sample + "/models_scores_sigmas-Apo1.dat", states[1])
pof2 = analysis.ParseOutputFile(
output_dir_sample + "/models_scores_sigmas-Apo2.dat", states[2])
pof3 = analysis.ParseOutputFile(
output_dir_sample + "/models_scores_sigmas-Apo3.dat", states[3])
plots.plot_residue_protection_factors([pof, pof1, pof2, pof3],
num_best_models=1000,
sort_sectors=True,
show=True)
#plots.plot_po_model_scores(pof)
#plots.plot_po_model_scores(pof2)
