basic.py

"""@namespace IMP.pmi.restraints.basic
Some miscellaneous simple restraints.
"""

from __future__ import print_function
import IMP
import IMP.core
import IMP.algebra
import IMP.atom
import IMP.container
import IMP.pmi.tools

class ExternalBarrier(object):

    def __init__(
        self,
        representation,
        radius,
        hierarchies=None,
            resolution=None):
        self.m = representation.prot.get_model()
        self.rs = IMP.RestraintSet(self.m, 'barrier')

        self.radius = radius
        self.label = "None"

        c3 = IMP.algebra.Vector3D(0, 0, 0)
        ub3 = IMP.core.HarmonicUpperBound(radius, 10.0)
        ss3 = IMP.core.DistanceToSingletonScore(ub3, c3)
        lsc = IMP.container.ListSingletonContainer(self.m)
        # IMP.atom.get_by_type
        particles = IMP.pmi.tools.select(
            representation,
            resolution=resolution,
            hierarchies=hierarchies)
        lsc.add(particles)
        r3 = IMP.container.SingletonsRestraint(ss3, lsc)
        self.rs.add_restraint(r3)

    def set_label(self, label):
        self.label = label

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self.rs)

    def get_restraint(self):
        return self.rs

    def get_output(self):
        self.m.update()
        output = {}
        score = self.rs.unprotected_evaluate(None)
        output["_TotalScore"] = str(score)
        output["ExternalBarrier_" + self.label] = str(score)
        return output


class DistanceRestraint(object):

    def __init__(
        self,
        representation,
        tuple_selection1,
        tuple_selection2,
        distancemin,
        distancemax,
        resolution=1.0,
            kappa=1.0):
        self.m = representation.prot.get_model()
        self.rs = IMP.RestraintSet(self.m, 'distance')
        self.weight=1.0
        self.label="None"
        #ts = IMP.core.Harmonic(distance,kappa)

        ts1 = IMP.core.HarmonicUpperBound(distancemax, kappa)
        ts2 = IMP.core.HarmonicLowerBound(distancemin, kappa)

        # IMP.atom.get_by_type
        particles1 = IMP.pmi.tools.select(
            representation,
            resolution=resolution,
            name=tuple_selection1[2],
            residue=tuple_selection1[0])
        particles2 = IMP.pmi.tools.select(
            representation,
            resolution=resolution,
            name=tuple_selection2[2],
            residue=tuple_selection2[0])

        for p in particles1:
            print(p.get_name())

        for p in particles2:
            print(p.get_name())

        if len(particles1) > 1 or len(particles2) > 1:
            raise ValueError("more than one particle selected")

        self.rs.add_restraint(
            IMP.core.DistanceRestraint(self.m, ts1,
                                       particles1[0],
                                       particles2[0]))
        self.rs.add_restraint(
            IMP.core.DistanceRestraint(self.m, ts2,
                                       particles1[0],
                                       particles2[0]))

    def set_weight(self,weight):
        self.weight = weight
        self.rs.set_weight(weight)

    def set_label(self, label):
        self.label = label

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self.rs)

    def get_restraint(self):
        return self.rs

    def get_output(self):
        self.m.update()
        output = {}
        score = self.weight * self.rs.unprotected_evaluate(None)
        output["_TotalScore"] = str(score)
        output["DistanceRestraint_" + self.label] = str(score)
        return output

    def evaluate(self):
        return self.weight * self.rs.unprotected_evaluate(None)




class BiStableDistanceRestraint(IMP.Restraint):
    '''
    a python restraint with bistable potential
    Authors: G. Bouvier, R. Pellarin. Pasteur Institute.
    '''
    import numpy as np
    import math

    def __init__(self,m,p1,p2,dist1,dist2,sigma1,sigma2,weight1,weight2):
        '''
        input twp particles, the two equilibrium distances, their amplitudes, and their weights (populations)
        '''
        IMP.Restraint.__init__(self, m, "BiStableDistanceRestraint %1%")
        self.dist1=dist1
        self.dist2=dist2

        self.sigma1=sigma1
        self.sigma2=sigma2

        self.weight1=weight1
        self.weight2=weight2

        if self.weight1+self.weight2 != 1:
            raise ValueError("The sum of the weights must be one")

        self.d1=IMP.core.XYZ(p1)
        self.d2=IMP.core.XYZ(p2)
        self.particle_list=[p1,p2]

    def gaussian(self,x, mu, sig, w):
        return w*self.np.exp(-self.np.power(x - mu, 2.) / (2 * self.np.power(sig, 2.)))

    def unprotected_evaluate(self,da):
        dist=IMP.core.get_distance(self.d1,self.d2)
        prob=self.gaussian(dist,self.dist1,self.sigma1,self.weight1)+\
             self.gaussian(dist,self.dist2,self.sigma2,self.weight2)
        return -self.math.log(prob)

    def do_get_inputs(self):
        return self.particle_list