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/")
plots.plot_po_model_scores(pof, False, outputdir + "/apo_total_score.png", 500)
plots.plot_po_model_scores(pof2, False, outputdir + "/apo2_total_score.png",
500)
plots.plot_residue_protection_factors([pof, pof2],
num_best_models=500,
sort_sectors=True)
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))
# The output here is a list of clusters, as POF objects, containing the models from that cluster.
# We use these for downstream analysis/plotting
pofs = conv.cluster_at_threshold_and_return_pofs(sampling_precision)
# This command plots the protection factor distribution curves. It will do it for up to 5? POF files. After that, you
# run out of colors and it gets too busy anyways.
# - first input is a list of POF objects.
# - num_best_models is self-explanatory for POF. Set to "all" for all of them (should do that if you've already clustered)
# - resrange is the range to plot
# - true_vals are a list of numerical (log) protection values per residue. They will show up as horizontal green lines. Default is None.
# - sort_sectors sorts residues in the sectors by increasing Pf value by model.
# - outputdir is self explanatory.
plots.plot_residue_protection_factors(pofs,
num_best_models=num_best_models,
resrange=resrange,
true_vals=res_pfs,
sort_sectors=True,
outputdir="./")
# Plot the incorporation curves.
#
# Use imagemagick montage to put them all together
# Name
exit()
plots.plot_incorporation_curve_fits(pofs[0],
num_best_models,
write_plots=True,
output_directory=outputdir)
import plots
import cProfile
##########################################
### File/Directory Setup
outputdir = "./testing_heurtemp" # output directory for the simulation results.
outputdir2 = "./testing_heurtemp2" # output directory for the simulation results.
sequence = "GMAEDMAADEVTAPPRKVLIISAGASHSVALLSGDIVCSWGRGEDGQLGHGDAEDRPSPTQLSALDGHQIVSVTCGADHTVAYSQSGMEVYSWGWGDFGRLGHGNSSDLFTPLPIKALHGIRIKQIACGDSHCLAVTMEGEVQSWGRNQNGQLGLGDTEDSLVPQKIQAFEGIRIKMVAAGAEHTAAVTEDGDLYGWGWGRYGNLGLGDRTDRLVPERVTSTGGEKMSMVACGWRHTISVSYSGALYTYGWSKYGQLGHGDLEDHLIPHKLEALSNSFISQISGGWRHTMALTSDGKLYGWGWNKFGQVGVGNNLDQCSPVQVRFPDDQKVVQVSCGWRHTLAVTERNNVFAWGRGTNGQLGIGESVDRNFPKIIEALSVDGASGQHIESSNIDPSSGKSWVSPAERYAVVPDETGLTDGSSKGNGGDISVPQTDVKRVRI" # FASTA sequence
resrange = (100, 200
) # Residue range is a tuple in pdb numbering (starts at 1).
num_best_models = 1000
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
### 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-Apo.dat",
state)
pof2 = analysis.ParseOutputFile(outputdir2 + "/models_scores_sigmas-Apo.dat",
state)
plots.plot_residue_protection_factors([pof, pof2],
num_best_models=num_best_models)
#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)
#for i in range(2,10):
# pof2.cluster_models_kmeans(nmodels=1000, nclust=i)
'''
exit()
for pep in dataset.get_peptides():
for tp in pep.get_timepoints():
#try:
i = tp.get_replicates()[0]
rep_score = -1*math.log(state.scoring_function.replicate_score(tp.get_model_deuteration()/pep.num_observable_amides*100, tp.get_replicates()[0].deut, tp.get_sigma()))