def __init__(self,features,mlib=None):#mlib does not need to be supplied anymore
self.features=features
#self.preprocess_features()
self.featurelist=[]
self.featurenames=[]
self.mlib=mlib
self.atomclasslib='atmcls-mf.lib'
self.fclist=re.findall('[a-z]{1,4}[0-9\#\-\.]{1,20}',self.features) #feature code list
self.ffclist=copy.deepcopy(self.fclist)
self.frlist=[] # feature range list
self.fdlist=[] # feature bin number list
self.fslist=[] # feature start post list
for i in range(len(self.fclist)):
sfc=self.fclist[i]
if sfc.count('#')==2:
rer=re.search('([a-z]{1,5})([0-9]{1,8})\#([0-9\-\.]{1,5})\#([0-9\-\.]{1,5})',sfc)
self.fclist[i]=rer.group(1)
self.fdlist.append(int(rer.group(2)))
self.frlist.append(float(rer.group(3)))
self.fslist.append(float(rer.group(4)))
if sfc.count('#')==1:
rer=re.search('([a-z]{1,5})([0-9]{1,8})\#([0-9\.]{1,5})',sfc)
self.fclist[i]=rer.group(1)
self.fdlist.append(int(rer.group(2)))
self.frlist.append(float(rer.group(3)))
self.fslist.append(0)
elif sfc.count('#')==0:
self.frlist.append(0)
rer=re.search('([a-z]{1,5})([0-9]{1,8})',sfc)
self.fdlist.append(int(rer.group(2)))
self.fslist.append(0)
if not mlib:
env = runenv.env
#log.minimal()
self.mlib=mdt.Library(env)
from modeller import *
import os
import mdt
import mdt.features
env = Environ()
mlib = mdt.Library(env)
xray = mdt.features.XRayResolution(mlib,
bins=[(0.51, 2.001, 'High res(2.0A)')])
restyp = mdt.features.ResidueType(mlib)
phi = mdt.features.PhiDihedral(mlib, bins=mdt.uniform_bins(72, -180, 5.0))
m = mdt.Table(mlib, file='mdt.mdt')
# remove the bins corresponding to undefined values for each of the 3 variables:
m = m.reshape(features=(xray, restyp, phi),
offset=(0, 0, 0),
shape=(-1, -2, -1))
# Let's get rid of the resolution variable from the output MDT table:
m = m.integrate(features=(restyp, phi))
# Process the raw histograms to get appropriate pdf 1D splines for restraints:
# Start by smoothing with a uniform prior (equal weight when 10 points per bin),
# producing a normalized distribution that sums to 1 (not a pdf when dx != 1):
m = m.smooth(dimensions=1, weight=10)
# Normalize it to get the true pdf (Integral p(x) dx = 1):
# (the scaling actually does not matter, because I am eventually taking the
# log and subtracting the smallest element of the final pdf, so this command
def get_mdt_library(self, **vars):
"""Read in MDT library and bin definitions"""
env = self.get_environ()
return mdt.Library(env, **vars)
