def handle_model(self):
"""Creates a model is model is not set. Loads a model from a string. Or assign a model to self.model.out_folder
Calling this function results in self.model to be and htmd.model.Model class
"""
from htmd.model import Model
from htmd.molecule.molecule import Molecule
if not self.model:
from IDP_htmd.IDP_analysis import analyze_folder
print("Creating new analysis")
self.write_parameters()
self.model = analyze_folder(self.input_folder, self.out_folder, self.skip, self.metrics, self.cluster,
self.tica, self.ticadim, self.ticalag, self.modellag, self.modelunits, self.macronum, self.bulk_split,
self.fes, self.rg_analysis, self.save_model, self.data_fstep)
if isinstance(self.model, str):
try:
print("Loading model")
model = Model()
model.load(self.model)
self.model = model
except:
print("Could not load the model")
return
if isinstance(self.model, Model):
print("Model loaded")
self.mol = Molecule(self.model.data.simlist[0].molfile)
mt = ModelAnalysis("/workspace8/excitome/adaptiveRun/O75376_MOR_58/",
"/home/pablo/testModel/")
mt.metrics = [
MetricDistance(
sel1="noh and protein",
sel2="noh and protein",
metric="contacts",
threshold=5,
groupsel1="residue",
groupsel2="residue")
]
model = Model()
model.load("/home/pablo/testModel/model.dat")
mt.model = model
mt.handle_model()
mt.sasa_variation()
# mt.model = "/home/pablo/testModel/model.dat"
# mt.plot_dihedral = "2_dihedral"
# mt.macronum = 4
# mt.plot_contacts = [
# ('all_contacts', 'noh and protein', 5),
# ('backbone', 'noh and backbone', 5),
# ('sidechain', 'noh and sidechain', 4),
# ]
# mt.write_parameters()
# mt.generate_html_summary()
# mt.perfom_analysis()
if __name__ == "__main__":
from htmd.model import getStateStatistic
from htmd.projections.metric import MetricData
from htmd.projections.metricdistance import MetricDistance
from htmd.model import Model
from htmd.molecule.molecule import Molecule
import numpy as np
data = MetricData()
data.load(
"/workspace8/p27_sj403/10-11-2018_p27_short_sj403/analysis/17_11_2018/testing.dat"
)
model = Model()
model.load(
"/workspace8/p27_sj403/10-11-2018_p27_short_sj403/analysis/17_11_2018/model.dat"
)
mol = Molecule(model.data.simlist[0].molfile)
mean_dat = getStateStatistic(model, data, range(model.macronum))
met = MetricDistance(sel1="noh and protein or resname MOL",
sel2="noh and protein or resname MOL",
groupsel1="residue",
groupsel2="residue",
metric="distances",
pbc=False)
mapping = met.getMapping(mol)
contact_plot(mean_dat,
mol,
rows=2,
cols=2,
model=model,
