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msmWE.py
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from __future__ import division, print_function; __metaclass__ = type
import numpy as np
import os
import sys
import subprocess
import h5py
from scipy.sparse import coo_matrix
#sys.path.append(os.environ["WEST_ROOT"]+'/src')
#sys.path.append(os.environ["WEST_ROOT"]+'/lib/west_tools')
#sys.path.append(os.environ["WEST_ROOT"]+'/src/west')
#sys.path.append(os.environ["WEST_ROOT"]+'/lib/wwmgr')
#sys.path.append(os.environ["WEST_ROOT"]+'/src/west')
#import west
#from west import WESTSystem
#from westpa.binning import RectilinearBinMapper
#import warnings
import matplotlib
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import mdtraj as md
import pyemma.coordinates as coor
import pyemma.coordinates.clustering as clustering
import pyemma
class modelWE():
"""Implementation of haMSM model building, particularly for steady-state estimation (but there are lots of extras), from WE sampling with basis (source) and target (sink) states with recycling. Set up for typical west.h5 file structure, with coordinates to be stored in west.h5 /iterations/auxdata/coord and basis and target definitions from progress coordinates. Check out run_msmWE.slurm and run_msmWE_flux.py in scripts folder for an implementation example. See Copperman and Zuckerman, Accelerated estimation of long-timescale kinetics by combining weighted ensemble simulation with Markov model microstates using non-Markovian theory, arXiv (2020)."""
def initialize(self,fileSpecifier,refPDBfile,initPDBfile,modelName):
self.modelName=modelName
pCommand='ls '+fileSpecifier
p = subprocess.Popen(pCommand, stdout=subprocess.PIPE, shell=True)
(output, err) = p.communicate()
output=output.decode()
fileList=output.split('\n')
fileList=fileList[0:-1]
self.fileList=fileList
nF=len(fileList)
self.nF=nF
self.pcoord_ndim=1
self.pcoord_len=2
tau=10.0e-12
self.tau=tau
self.set_topology(refPDBfile)
self.set_basis(initPDBfile)
self.WEtargetp1=1.25 #target def on WE p1
self.WEbasisp1_min=12.0 #WE bin where basis structure is mapped
self.WEbasisp1_max=12.5
self.dimReduceMethod='pca'
self.vamp_lag=10
self.vamp_dim=10
self.nB=48 #number of bins for optimized WE a la Aristoff
self.nW=40 #number of walkers for optimized WE a la Aristoff
self.min_walkers=1 #minimum number of walkers per bin
self.binMethod='adaptive' #adaptive for dynamic k-means bin edges, uniform for equal spacing on kh
self.allocationMethod='adaptive' #adaptive for dynamic allocation, uniform for equal allocation
try:
self.get_iter_data(1)
self.get_iter_coordinates0()
self.coordsExist=True
except:
sys.stdout.write('problem getting coordinates \n')
self.coordsExist=False
def is_WE_basis(self,pcoords):
isBasis=np.logical_and(pcoords[:,0]>self.WEbasisp1_min,pcoords[:,0]<self.WEbasisp1_max)
return isBasis
def is_WE_target(self,pcoords):
isTarget=pcoords[:,0]<self.WEtargetp1
return isTarget
def get_iter_data(self,n_iter):
self.n_iter=n_iter
westList=np.array([])
segindList=np.array([])
weightList=np.array([])
pcoord0List=np.empty((0,self.pcoord_ndim))
pcoord1List=np.empty((0,self.pcoord_ndim))
nSeg=0
for iF in range(self.nF):
fileName=self.fileList[iF]
try:
dataIn=h5py.File(fileName,'r')
dsetName = "/iterations/iter_%08d/seg_index" % int(n_iter)
e = dsetName in dataIn
if e:
dset=dataIn[dsetName]
newSet=dset[:]
nS=np.shape(newSet)
nS=nS[0]
dsetNameP = "/iterations/iter_%08d/pcoord" % int(n_iter)
dsetP=dataIn[dsetNameP]
pcoord=dsetP[:]
for iS in range(nS):
#if np.sum(pcoord[iS,self.pcoord_len-1,:])==0.0: #intentionally using this to write in dummy pcoords, this is a good thing to have for post-analysis though!
# raise ValueError('Sum pcoord is 0, probably middle of WE iteration, not using iteration') f
westList=np.append(westList,iF)
segindList=np.append(segindList,iS)
weightList=np.append(weightList,newSet[iS][0])
pcoord0List=np.append(pcoord0List,np.expand_dims(pcoord[iS,0,:],0),axis=0)
pcoord1List=np.append(pcoord1List,np.expand_dims(pcoord[iS,self.pcoord_len-1,:],0),axis=0)
nSeg=nSeg+1
dataIn.close()
except:
sys.stdout.write('error in '+fileName+str(sys.exc_info()[0])+'\n')
self.westList=westList.astype(int)
self.segindList=segindList.astype(int)
self.weightList=weightList
self.nSeg=nSeg
self.pcoord0List=pcoord0List
self.pcoord1List=pcoord1List
def get_iterations(self):
numFiles=np.array([])
numSegments=np.array([])
iterationList=np.array([])
nSeg=1
n_iter=1
while nSeg>0:
nSeg=0
for iF in range(self.nF):
fileName=self.fileList[iF]
try:
dataIn=h5py.File(fileName,'r')
dsetName = "/iterations/iter_%08d/seg_index" % int(n_iter)
e = dsetName in dataIn
if e:
dset=dataIn[dsetName]
newSet=dset[:]
nS=np.shape(newSet)
nSeg=nS[0]+nSeg
dataIn.close()
except:
sys.stdout.write('no segments in '+fileName+str(sys.exc_info()[0])+'\n')
if nSeg>0:
numSegments=np.append(numSegments,nSeg)
sys.stdout.write('Iteration '+str(n_iter)+' has '+str(nSeg)+' segments...\n')
n_iter=n_iter+1
self.numSegments=numSegments
self.maxIter=numSegments.size
def get_iterations_iters(self,first_iter,last_iter):
numFiles=np.array([])
numSegments=np.array([])
iterationList=np.array([])
nSeg=1
for n_iter in range(first_iter,last_iter+1):
nSeg=0
for iF in range(self.nF):
fileName=self.fileList[iF]
try:
dataIn=h5py.File(fileName,'r')
dsetName = "/iterations/iter_%08d/seg_index" % int(n_iter)
e = dsetName in dataIn
if e:
dset=dataIn[dsetName]
newSet=dset[:]
nS=np.shape(newSet)
nSeg=nS[0]+nSeg
dataIn.close()
except:
sys.stdout.write('no segments in '+fileName+str(sys.exc_info()[0])+'\n')
if nSeg>0:
numSegments=np.append(numSegments,nSeg)
sys.stdout.write('Iteration '+str(n_iter)+' has '+str(nSeg)+' segments...\n')
self.numSegments=numSegments
self.maxIter=last_iter
def set_topology(self,PDBfile):
if PDBfile[-3:]=='dat':
self.reference_coord=np.loadtxt(PDBfile)
self.nAtoms=1
elif PDBfile[-3:]=='pdb':
struct=md.load(PDBfile)
self.reference_structure=struct
self.reference_coord=np.squeeze(struct._xyz)
self.nAtoms=struct.topology.n_atoms
def set_basis(self,PDBfile):
if PDBfile[-3:]=='dat':
self.basis_coords=np.loadtxt(PDBfile)
elif PDBfile[-3:]=='pdb':
struct=md.load(PDBfile)
self.basis_structure=struct
self.basis_coords=np.squeeze(struct._xyz)
def get_transition_data(self,n_lag): #get segment history data at lag time n_lag from current iter
if n_lag>self.n_iter:
sys.stdout.write('too much lag for iter... n_lag reduced to: '+str(self.n_iter)+'\n')
n_lag=self.n_iter
if n_lag>=self.n_hist:
sys.stdout.write('too much lag for stored history... recalculating...\n')
self.get_seg_histories(n_lag)
self.n_lag=n_lag
segindList_lagged=self.seg_histories[:,n_lag]
warpList=self.seg_histories[:,0:n_lag]#check for warps
warpList=np.sum(warpList<0,1)
weightList_lagged=self.weight_histories[:,n_lag]
weightList=self.weightList
coordPairList=np.zeros((self.nSeg,self.nAtoms,3,2))
prewarpedStructures=np.zeros((self.nSeg,self.nAtoms,3))
nWarped=0
for iS in range(self.nSeg):
try:
if iS==0:
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coords_current = dset[:]
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter-n_lag)
dset = dataIn[dsetName]
coords_lagged = dset[:]
elif self.westList[iS] != self.westList[iS-1]:
dataIn.close()
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coords_current = dset[:]
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter-n_lag)
dset = dataIn[dsetName]
coords_lagged = dset[:]
coordPairList[iS,:,:,1]=coords_current[self.segindList[iS],1,:,:]
if warpList[iS]==0:
coordPairList[iS,:,:,0]=coords_lagged[segindList_lagged[iS],0,:,:]
elif warpList[iS]>0:
prewarpedStructures[nWarped,:,:]=coords_lagged[segindList_lagged[iS],0,:,:]
coordPairList[iS,:,:,0]=self.basis_coords
nWarped=nWarped+1
except:
weightList_lagged[iS]=0.0
weightList[iS]=0.0 #set transitions without structures to zero weight
indWarped=np.squeeze(np.where(warpList>0))
indWarpedArray=np.empty(nWarped)
indWarpedArray[0:nWarped]=indWarped
indWarped=indWarpedArray.astype(int)
transitionWeights=weightList.copy()
departureWeights=weightList_lagged.copy()
for iW in range(nWarped):
coordPair=np.zeros((1,self.nAtoms,3,2))
coordPair[0,:,:,0]=prewarpedStructures[iW,:,:]
coordPair[0,:,:,1]=self.reference_coord
coordPairList=np.append(coordPairList,coordPair,axis=0)
iterWarped=np.squeeze(np.where(self.seg_histories[indWarped[iW],:]<0))
try:
nW=np.shape(iterWarped)
iterWarped=iterWarped[0]
sys.stdout.write(' segment '+str(indWarped[iW])+' warped '+str(nW)+' times\n')
except:
sys.stdout.write(' segment '+str(indWarped[iW])+' warped 1 time\n')
transitionWeights=np.append(transitionWeights,self.weight_histories[indWarped[iW],iterWarped])
departureWeights=np.append(departureWeights,self.weight_histories[indWarped[iW],n_lag])
self.coordPairList=coordPairList
self.transitionWeights=transitionWeights
self.departureWeights=departureWeights
def get_transition_data_lag0(self): #get segment history data at lag time n_lag from current iter
weightList=self.weightList
coordPairList=np.zeros((self.nSeg,self.nAtoms,3,2))
for iS in range(self.nSeg):
try:
if iS==0:
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coords = dset[:]
elif self.westList[iS] != self.westList[iS-1]:
dataIn.close()
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coords = dset[:]
coordPairList[iS,:,:,1]=coords[self.segindList[iS],self.pcoord_len-1,:,:]
coordPairList[iS,:,:,0]=coords[self.segindList[iS],0,:,:]
except:
self.errorWeight=self.errorWeight+weightList[iS]
weightList[iS]=0.0 #set transitions without structures to zero weight
self.errorCount=self.errorCount+1
transitionWeights=weightList.copy()
departureWeights=weightList.copy()
self.coordPairList=coordPairList
self.transitionWeights=transitionWeights
self.departureWeights=departureWeights
def get_warps_from_parent(self,first_iter,last_iter): #get all warps and weights over set of iterations
warpedWeights=[]
for iS in range(first_iter+1,last_iter+1):
self.get_iter_data(iS+1)
self.get_seg_histories(2)
parentList=self.seg_histories[:,1]
warpList=np.where(parentList<0)
warpedWeights.append(self.weightList[warpList])
return warpedWeights
def get_warps_from_pcoord(self,first_iter,last_iter): #get all warps and weights over set of iterations
warpedWeights=[]
for iS in range(first_iter,last_iter+1):
self.get_iter_data(iS)
pcoord=self.pcoord1List[:,0]
warpList=np.where(pcoord<self.WEtargetp1)
warpedWeights.append(self.weightList[warpList])
meanJ=np.mean(self.weightList[warpList])/self.tau/np.sum(self.weightList)
sys.stdout.write('Jdirect: '+str(meanJ)+' iter: '+str(iS)+'\n')
return warpedWeights
def get_direct_target_flux(self,first_iter,last_iter,window):
nIterations=last_iter-first_iter
Jdirect=np.zeros(nIterations-1)
f=h5py.File(self.modelName+'.h5','a')
dsetName = '/s'+str(first_iter)+'_e'+str(last_iter)+'_w'+str(window)+'/Jdirect'
e = dsetName in f
if not e:
warpedWeights=self.get_warps_from_pcoord(first_iter,last_iter)
self.warpedWeights=warpedWeights
JdirectTimes=np.zeros(nIterations-1)
for iS in range(nIterations-1):
end=iS+1
start=iS-window
if start<0:
start=0
nI=end-start
warpedWeightsI=np.array([])
for i in range(start,end):
warpedWeightsI=np.append(warpedWeightsI,warpedWeights[i])
nWarped=warpedWeightsI.size
particles=(nWarped*warpedWeightsI)/nI
Jdirect[iS]=np.mean(particles)
JdirectTimes[iS]=(first_iter+iS)*self.tau
Jdirect=Jdirect/self.tau
dsetP = f.create_dataset(dsetName, np.shape(Jdirect))
dsetP[:] = Jdirect
dsetName = '/s'+str(first_iter)+'_e'+str(last_iter)+'_w'+str(window)+'/JdirectTimes'
dsetP = f.create_dataset(dsetName, np.shape(JdirectTimes))
dsetP[:] = JdirectTimes
elif e:
dsetP = f[dsetName]
Jdirect=dsetP[:]
dsetName = '/s'+str(first_iter)+'_e'+str(last_iter)+'_w'+str(window)+'/JdirectTimes'
dsetP = f[dsetName]
JdirectTimes=dsetP[:]
f.close()
self.Jdirect=Jdirect/self.nF #correct for number of trees
self.JdirectTimes=JdirectTimes
def get_seg_histories(self,n_hist):
if n_hist>self.n_iter:
sys.stdout.write('we have too much history... n_hist reduced to: '+str(self.n_iter)+'\n')
n_hist=self.n_iter
self.n_hist=n_hist
seg_histories=np.zeros((self.nSeg,self.n_hist+1))
weight_histories=np.zeros((self.nSeg,self.n_hist))
for iS in range(self.nSeg):
if iS%100==0:
sys.stdout.write(' getting history for iteration '+str(self.n_iter)+' segment '+str(iS)+'...\n')
if iS==0:
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
elif self.westList[iS] != self.westList[iS-1]:
dataIn.close()
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
seg_histories[iS,0]=self.segindList[iS]
warped=0
for iH in range(1,self.n_hist+1):
indCurrentSeg=seg_histories[iS,iH-1]
if indCurrentSeg<0 and warped==0:
sys.stdout.write('Segment '+str(iS)+' warped last iter: History must end NOW!\n')
warped=1
elif indCurrentSeg>=0 and warped==0:
dsetName = "/iterations/iter_%08d/seg_index" % int(self.n_iter-iH+1)
dset=dataIn[dsetName]
seg_histories[iS,iH]=dset[indCurrentSeg][1]
weight_histories[iS,iH-1]=dset[indCurrentSeg][0]
if seg_histories[iS,iH]<0:
sys.stdout.write(' segment '+str(iS)+' warped '+str(iH)+' iters ago\n')
self.seg_histories=seg_histories[:,:-1].astype(int)
self.weight_histories=weight_histories
#weight histories and segment histories go in reverse order, so final current iter is first of 0 index
def collect_iter_coordinates(self): #grab coordinates from WE traj_segs folder
nS=self.nSeg
westFile=self.fileList[self.westList[0]]
dataIn=h5py.File(westFile,'a')
coords=np.zeros((0,2,self.nAtoms,3))
for iS in range(self.nSeg):
westFile=self.fileList[self.westList[iS]]
WEfolder=westFile.replace('west.h5', '')
trajpath=WEfolder+"traj_segs/%06d/%06d" % (self.n_iter,iS)
coord0=np.squeeze(md.load(trajpath+'/parent.rst7',top=self.reference_structure.topology)._xyz)
coord1=np.squeeze(md.load(trajpath+'/seg.rst7',top=self.reference_structure.topology)._xyz)
coordT=np.array([coord0,coord1])
coordT=coordT[np.newaxis,:,:,:]
coords=np.append(coords,coordT,axis=0)
try:
if iS > 0:
if self.westList[iS] != self.westList[iS-1] and iS<nS-1:
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
try:
dset = dataIn.create_dataset(dsetName, np.shape(coords[:-1,:,:,:]))
dset[:] = coords[:-1,:,:,:]
except:
del dataIn[dsetName]
dset = dataIn.create_dataset(dsetName, np.shape(coords[:-1,:,:,:]))
dset[:] = coords[:-1,:,:,:]
sys.stdout.write('coords exist for iteration '+str(n_iter)+' overwritten\n')
dataIn.close()
coords=np.zeros((0,2,self.nAtoms,3))
coords=np.append(coords,coordT,axis=0)
dataIn=h5py.File(westFile,'a')
elif iS==nS-1:
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
try:
dset = dataIn.create_dataset(dsetName, np.shape(coords))
dset[:] = coords
except:
del dataIn[dsetName]
dset = dataIn.create_dataset(dsetName, np.shape(coords))
dset[:] = coords
sys.stdout.write('coords exist for iteration '+str(self.n_iter)+' overwritten\n')
dataIn.close()
except:
sys.stdout.write('error collecting coordinates from '+WEfolder+' , iter '+str(self.n_iter)+' segment '+str(self.segindList[iS])+'\n')
def get_iter_coordinates(self): #get iteration final coordinates
coordList=np.zeros((self.nSeg,self.nAtoms,3))
for iS in range(self.nSeg):
try:
if iS==0:
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coord = dset[:]
elif self.westList[iS] != self.westList[iS-1]:
dataIn.close()
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coord = dset[:]
coordList[iS,:,:]=coord[self.segindList[iS],1,:,:]
except:
sys.stdout.write('error getting coordinates from '+self.fileList[self.westList[iS]]+' , iter '+str(self.n_iter)+' segment '+str(self.segindList[iS])+'\n')
coordList[iS,:,:]=np.zeros((self.nAtoms,3))
self.coordsExist=False
self.coordList=coordList
def get_iter_coordinates0(self): #get iteration initial coordinates
coordList=np.zeros((self.nSeg,self.nAtoms,3))
for iS in range(self.nSeg):
if iS==0:
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coord = dset[:]
elif self.westList[iS] != self.westList[iS-1]:
dataIn.close()
westFile=self.fileList[self.westList[iS]]
dataIn=h5py.File(westFile,'r')
dsetName="/iterations/iter_%08d/auxdata/coord" % int(self.n_iter)
dset = dataIn[dsetName]
coord = dset[:]
coordList[iS,:,:]=coord[self.segindList[iS],0,:,:]
self.coordList=coordList
def get_coordinates(self,first_iter,last_iter):
self.first_iter=first_iter
self.last_iter=last_iter
iters=range(self.first_iter,self.last_iter+1)
coordSet=np.zeros((0,self.nAtoms,3))
for iter in iters:
if iter%50==0:
sys.stdout.write(' gathering structures from iteration '+str(iter)+'...\n')
self.get_iter_data(iter)
self.get_iter_coordinates()
if self.coordsExist:
coordSet=np.append(coordSet,self.coordList,axis=0)
self.coordSet=coordSet
def get_coordSet(self,last_iter,n_coords):
last_iter_cluster=last_iter
i=last_iter_cluster
numCoords=0
coordSet=np.zeros((0,self.nAtoms,3))
pcoordSet=np.zeros((0,self.pcoord_ndim))
while numCoords<n_coords:
self.get_iter_data(i)
self.get_iter_coordinates()
indGood=np.squeeze(np.where(np.sum(np.sum(self.coordList,2),1)!=0))
coordSet=np.append(coordSet,self.coordList[indGood,:,:],axis=0)
pcoordSet=np.append(pcoordSet,self.pcoord1List[indGood,:],axis=0)
numCoords=np.shape(coordSet)[0]
i=i-1
self.coordSet=coordSet
self.pcoordSet=pcoordSet
first_iter_cluster=i
self.first_iter=first_iter_cluster
self.last_iter=last_iter_cluster
self.n_coords=np.shape(self.coordSet)[0]
def get_traj_coordinates(self,from_iter,traj_length):
if traj_length>from_iter:
traj_length=from_iter-1
sys.stdout.write('trajectory length too long: set to '+str(traj_length)+'\n')
self.get_iter_data(from_iter)
self.get_seg_histories(traj_length)
traj_iters=np.zeros((traj_length,self.nSeg,self.nAtoms,3))
ic=traj_length-1
iH=0
nS=self.nSeg
westList=self.westList.copy()
for i in range(from_iter,from_iter-traj_length,-1):
sys.stdout.write(' gathering structures from iteration '+str(i)+'...\n')
self.get_iter_data(i)
self.get_iter_coordinates()
seg_history=self.seg_histories[:,iH]
seg_history_index=np.zeros(nS).astype(int)
last_index=np.zeros(nS).astype(int) #list of final iterations for warped particles
for iS in range(nS):
if seg_history[iS]>0:
seg_history_index[iS]=np.where(np.logical_and(self.segindList==seg_history[iS],self.westList==westList[iS]))[0][0] #segment indexes are local to the westfile, not global for the analysis set
last_index[iS]=0
elif seg_history[iS]<0:
last_index[iS]=i-(from_iter-traj_length)-1
traj_iters[ic,:,:,:]=self.coordList[seg_history_index,:,:]
ic=ic-1
iH=iH+1
self.get_iter_data(from_iter)
traj_iters=np.swapaxes(traj_iters,0,1)
self.trajSet=[None]*self.nSeg
for iS in range(self.nSeg):
self.trajSet[iS]=traj_iters[iS,last_index[iS]:,:,:]
def dimReduce(self):
#here we can put method to reduce dimensionalitya
nC=np.shape(self.coordSet)
nC=nC[0]
if self.dimReduceMethod=='pca':
data=self.processCoordinates(self.coordSet)
self.coordinates=coor.pca(data, dim=-1, var_cutoff=0.95, mean=None, stride=1, skip=0)
self.ndim=self.coordinates.dimension()
if self.dimReduceMethod=='vamp':
ntraj=len(self.trajSet)
data=[None]*ntraj
for itraj in range(ntraj):
data[itraj]=self.processCoordinates(self.trajSet[itraj])
self.coordinates=coor.vamp(data, lag=self.vamp_lag, dim=self.vamp_dim, scaling=None, right=False, stride=1, skip=0)
self.ndim=self.coordinates.dimension()
if self.dimReduceMethod=='none':
data=self.coordSet.reshape(nC,3*self.nAtoms)
self.coordinates=self.Coordinates()
self.ndim=3*self.nAtoms
#self.coordinates.transform=self.processCoordinates
class Coordinates(object):
# The class "constructor" - It's actually an initializer
def __init__(self):
self.explanation='coordinate object'
def transform(self,coords):
return coords
def processCoordinates(self,coords):
if self.dimReduceMethod=='none':
nC=np.shape(coords)
nC=nC[0]
ndim=3*self.nAtoms
data=coords.reshape(nC,3*self.nAtoms)
return data
if self.dimReduceMethod=='pca' or self.dimReduceMethod=='vamp':
xt=md.Trajectory(xyz=coords,topology=None)
indCA=self.reference_structure.topology.select('name CA')
pair1,pair2=np.meshgrid(indCA,indCA,indexing='xy')
indUT=np.where(np.triu(pair1,k=1)>0)
pairs=np.transpose(np.array([pair1[indUT],pair2[indUT]])).astype(int)
dist=md.compute_distances(xt, pairs, periodic=True, opt=True)
return dist
def reduceCoordinates(self,coords):
if self.dimReduceMethod=='none':
nC=np.shape(coords)
nC=nC[0]
ndim=3*self.nAtoms
data=coords.reshape(nC,3*self.nAtoms)
return data
if self.dimReduceMethod=='pca' or self.dimReduceMethod=='vamp':
coords=self.processCoordinates(coords)
coords=self.coordinates.transform(coords)
return coords
def cluster_coordinates(self,n_clusters):
self.n_clusters=n_clusters
nC=np.shape(self.coordSet)
nC=nC[0]
if self.dimReduceMethod=='none':
if self.nAtoms>1:
self.clusters=coor.cluster_kmeans([self.coordSet.reshape(nC,3*self.nAtoms)],k=n_clusters,metric='minRMSD') #,max_iter=100)
elif self.nAtoms==1:
self.clusters=coor.cluster_kmeans([self.coordSet.reshape(nC,3*self.nAtoms)],k=n_clusters,metric='euclidean')
if self.dimReduceMethod=='pca' or self.dimReduceMethod=='vamp':
self.clusters=coor.cluster_kmeans(self.coordinates.get_output(),k=n_clusters,metric='euclidean') #,max_iter=100)
self.clusterFile=self.modelName+'_clusters_s'+str(self.first_iter)+'_e'+str(self.last_iter)+'_nC'+str(self.n_clusters)+'.h5'
self.clusters.save(self.clusterFile, save_streaming_chain=True)
def load_clusters(self,clusterFile):
self.clusters=pyemma.load(clusterFile)
self.n_clusters=np.shape(self.clusters.clustercenters)[0]
def get_iter_fluxMatrix(self,n_iter):
sys.stdout.write('iteration '+str(n_iter)+': data \n')
self.get_iter_data(n_iter)
if self.n_lag>0:
sys.stdout.write('segment histories \n')
self.get_seg_histories(self.n_lag+1)
sys.stdout.write(' transition data...\n')
self.get_transition_data(self.n_lag)
elif self.n_lag==0:
self.get_transition_data_lag0()
nT=np.shape(self.transitionWeights)[0]
indTargetCluster=self.n_clusters+1
indBasisCluster=self.n_clusters
cluster0=self.clusters.assign(self.reduceCoordinates(self.coordPairList[:,:,:,0]))
cluster1=self.clusters.assign(self.reduceCoordinates(self.coordPairList[:,:,:,1]))
indTarget1=np.where(self.is_WE_target(self.pcoord1List))
if indTarget1[0].size>0:
sys.stdout.write('Target1: '+str(indTarget1[0].size)+'\n')
indBasis0=np.where(self.is_WE_basis(self.pcoord0List)) #needs to fit definition of target in WE
if indBasis0[0].size>0:
sys.stdout.write('Basis0: '+str(indBasis0[0].size)+'\n')
indBasis1=np.where(self.is_WE_basis(self.pcoord1List))
if indBasis1[0].size>0:
sys.stdout.write('Basis1: '+str(indBasis1[0].size)+'\n')
cluster1[indTarget1]=indTargetCluster
cluster0[indBasis0]=indBasisCluster
cluster1[indBasis1]=indBasisCluster
fluxMatrix=coo_matrix((self.transitionWeights,(cluster0,cluster1)),shape=(self.n_clusters+2,self.n_clusters+2)).todense()
return fluxMatrix
def get_pcoord1D_fluxMatrix(self,n_lag,first_iter,last_iter,binbounds):
self.n_lag=n_lag
nBins=binbounds.size-1
fluxMatrix=np.zeros((nBins,nBins))
nI=0
f=h5py.File(self.modelName+'.h5','a')
dsetName = '/s'+str(first_iter)+'_e'+str(last_iter)+'_lag'+str(n_lag)+'_b'+str(nBins)+'/pcoord1D_fluxMatrix'
e = dsetName in f
if not e:
dsetP = f.create_dataset(dsetName, np.shape(fluxMatrix))
for iS in range(first_iter+1,last_iter+1):
if n_lag>0:
fluxMatrixI=self.get_iter_pcoord1D_fluxMatrix(iS,binbounds)
elif n_lag==0:
fluxMatrixI=self.get_iter_pcoord1D_fluxMatrix_lag0(iS,binbounds)
fluxMatrixI=fluxMatrixI/np.sum(self.weightList) #correct for multiple trees
fluxMatrix=fluxMatrix+fluxMatrixI
nI=nI+1
dsetP[:] = fluxMatrix/nI
dsetP.attrs['iter']=iS
fluxMatrix=fluxMatrix/nI
elif e:
dsetP = f[dsetName]
fluxMatrix=dsetP[:]
try:
nIter=dsetP.attrs['iter']
except:
nIter=first_iter+1
fluxMatrix=np.zeros((nBins,nBins))
nI=1
if nIter<last_iter:
for iS in range(nIter,last_iter+1):
if n_lag>0:
fluxMatrixI=self.get_iter_pcoord1D_fluxMatrix(iS,binbounds)
if n_lag==0:
fluxMatrixI=self.get_iter_pcoord1D_fluxMatrix_lag0(iS,binbounds)
fluxMatrix=fluxMatrix+fluxMatrixI
nI=nI+1
dsetP[:] = fluxMatrix/nI
dsetP.attrs['iter']=iS
fluxMatrix=fluxMatrix/nI
f.close()
self.pcoord1D_fluxMatrix=fluxMatrix
def get_iter_pcoord1D_fluxMatrix_lag0(self,n_iter,binbounds):
sys.stdout.write('iteration '+str(n_iter)+': data \n')
self.get_iter_data(n_iter)
nT=np.shape(self.weightList)[0]
nBins=binbounds.size-1
fluxMatrix=np.zeros((nBins,nBins))
pcoord0=self.pcoord0List[:,0]
pcoord1=self.pcoord1List[:,0]
bins0=np.digitize(pcoord0,binbounds)
bins1=np.digitize(pcoord1,binbounds)
for iP in range(nT):
dT0=bins0[iP]-1
dT1=bins1[iP]-1
if np.abs(dT0-dT1)>12:
self.weightList[iP]=0.0
fluxMatrix[dT0,dT1]=fluxMatrix[dT0,dT1]+self.weightList[iP]
return fluxMatrix
def get_fluxMatrix(self,n_lag,first_iter,last_iter):
self.n_lag=n_lag
self.errorWeight=0.0
self.errorCount=0
fluxMatrix=np.zeros((self.n_clusters+2,self.n_clusters+2))
nI=0
fileName=self.modelName+'_s'+str(first_iter)+'_e'+str(last_iter)+'_lag'+str(n_lag)+'_clust'+str(self.n_clusters)
f=h5py.File(fileName+'.h5','a')
dsetName = 'fluxMatrix'
e = dsetName in f
if not e:
dsetP = f.create_dataset(dsetName, np.shape(fluxMatrix))
dsetP[:] = fluxMatrix
f.close()
for iS in range(first_iter+1,last_iter+1):
fluxMatrixI=self.get_iter_fluxMatrix(iS)
fluxMatrix=fluxMatrix+fluxMatrixI
nI=nI+1
f=h5py.File(fileName+'.h5','a')
dsetP = f[dsetName]
dsetP[:] = fluxMatrix/nI
dsetP.attrs['iter']=iS
f.close()
sys.stdout.write('getting fluxMatrix iter: '+str(iS)+'\n')
fluxMatrix=fluxMatrix/nI
elif e:
dsetP = f[dsetName]
fluxMatrix=dsetP[:]
nIter=dsetP.attrs['iter']
f.close()
nI=1
if nIter<last_iter:
for iS in range(nIter,last_iter+1):
fluxMatrixI=self.get_iter_fluxMatrix(iS)
fluxMatrix=fluxMatrix+fluxMatrixI
nI=nI+1
f=h5py.File(fileName+'.h5','a')
dsetP = f[dsetName]
dsetP[:] = fluxMatrix/nI
dsetP.attrs['iter']=iS
f.close()
sys.stdout.write('getting fluxMatrix iter: '+str(iS)+'\n')
fluxMatrix=fluxMatrix/nI
f.close()
self.fluxMatrixRaw=fluxMatrix
def organize_fluxMatrix(self): #processes raw fluxMatrix
nT=np.shape(self.coordSet)
nT=nT[0]
dtraj=self.clusters.assign(self.reduceCoordinates(self.coordSet))
indTargetCluster=self.n_clusters+1 #Target at -1
indBasisCluster=self.n_clusters #basis at -2
indBasisOrig=indBasisCluster
#targetRMSD=self.get_reference_rmsd(self.coordSet[:,:,:])
#basisRMSD=self.get_basis_rmsd(self.coordSet[:,:,:])
#indTarget=np.where(targetRMSD<self.target_rmsd)
#indBasis=np.where(basisRMSD<self.basis_rmsd)
#dtraj[indTarget]=indTargetCluster
#dtraj[indBasis]=indBasisCluster
indData=np.ones(self.n_clusters+2)
targetRMSD_centers=np.zeros(self.n_clusters+2)
#targetRMSD_centers[indTargetCluster]=self.target_rmsd
targetRMSD_centers[indTargetCluster]=self.WEtargetp1
#targetRMSD_centers[indBasisCluster]=self.get_reference_rmsd(self.basis_coords)
targetRMSD_centers[indBasisCluster]=self.WEbasisp1_min
nTraps=1000
fluxMatrixTraps=self.fluxMatrixRaw.copy()
while nTraps>0:
nTraps=0
for iC in range(self.n_clusters):
ind=np.where(dtraj==iC)
if np.shape(ind)[1]==0:
indData[iC]=0
elif np.shape(ind)[1]>0:
#targetRMSD_centers[iC]=np.mean(self.get_reference_rmsd(self.coordSet[ind[0],:,:]))
targetRMSD_centers[iC]=np.mean(self.pcoordSet[ind[0],0])
statesum=np.sum(fluxMatrixTraps[:,iC])+np.sum(fluxMatrixTraps[iC,:])
if statesum==0.0:
indData[iC]=0
if statesum>0:
indNotSelf=np.setdiff1d(range(self.n_clusters),iC)
fromSum=np.sum(fluxMatrixTraps[iC,indNotSelf])
toSum=np.sum(fluxMatrixTraps[indNotSelf,iC])
if fromSum==0.0 or toSum==0.0:
nTraps=nTraps+1
indData[iC]=0
fluxMatrixTraps[:,iC]=0.0
fluxMatrixTraps[iC,:]=0.0
indData[indBasisCluster]=1
indData[indTargetCluster]=1
indData=np.squeeze(np.where(indData>0))
fluxMatrix=self.fluxMatrixRaw[indData,:]
fluxMatrix=fluxMatrix[:,indData]
targetRMSD_centers=targetRMSD_centers[indData]
indp1=np.argsort(targetRMSD_centers)
self.targetRMSD_centers=targetRMSD_centers[indp1]
fluxMatrix=fluxMatrix[indp1,:]
fluxMatrix=fluxMatrix[:,indp1]
self.fluxMatrix=fluxMatrix/np.sum(fluxMatrix) #average weight transitioning or staying put should be 1
originalClusters=indData[indp1]
self.indBasis=np.where(originalClusters==indBasisCluster)[0]
self.indTargets=np.where(originalClusters==indTargetCluster)[0]
self.originalClusters=originalClusters
self.binCenters=targetRMSD_centers[indp1]
self.nBins=np.shape(self.binCenters)[0]
def get_model_clusters(self): #define new clusters from organized flux matrix corresponding to model
clustercenters=np.zeros((self.n_clusters+2,self.ndim))
clustercenters[0:self.n_clusters,:]=self.clusters.clustercenters
if self.dimReduceMethod=='none':
coords=np.array([self.basis_coords,self.reference_coord])
clustercenters[self.n_clusters:,:]=self.reduceCoordinates(coords) #add in basis and target
self.model_clusters=clustering.AssignCenters(self.reduceCoordinates(clustercenters[self.originalClusters,:]), metric='euclidean', stride=1, n_jobs=None, skip=0)
if self.dimReduceMethod=='pca' or self.dimReduceMethod=='vamp':
coords=np.array([self.reduceCoordinates(self.basis_coords),self.reduceCoordinates(self.reference_coord)])
clustercenters[self.n_clusters:,:]=np.squeeze(coords) #add in basis and target
self.model_clusters=clustering.AssignCenters(clustercenters[self.originalClusters,:], metric='euclidean', stride=1, n_jobs=None, skip=0)
def get_Tmatrix(self):
Mt=self.fluxMatrix.copy()
nR=np.shape(Mt)
sM=np.sum(Mt,1)
for iR in range(nR[0]):
if sM[iR]>0:
Mt[iR,:]=Mt[iR,:]/sM[iR]
if sM[iR]==0.0:
Mt[iR,iR]=1.0
#make steady state matrix #first sink
sinkBins=self.indTargets #np.where(avBinPnoColor==0.0)
nsB=np.shape(sinkBins)
nsB=nsB[0]
sinkRates=np.zeros((1,self.nBins))
sinkRates[0,self.indBasis]=1./self.indBasis.size
Mss=Mt.copy()
Mss[sinkBins,:]=np.tile(sinkRates,(nsB,1))
self.Tmatrix=Mss
def get_eqTmatrix(self):
Mt=self.fluxMatrix.copy()
n=np.shape(Mt)[0]
indSpace=np.arange(n).astype(int)
indSpace=np.setdiff1d(indSpace,np.append(self.indTargets,self.indBasis))
Mt=Mt[indSpace,:]
Mt=Mt[:,indSpace]
nR=np.shape(Mt)
sM=np.sum(Mt,1)
for iR in range(nR[0]):
if sM[iR]>0:
Mt[iR,:]=Mt[iR,:]/sM[iR]
if sM[iR]==0.0:
Mt[iR,iR]=1.0
self.Tmatrix=Mt
def get_steady_state_algebraic(self):
n=np.shape(self.Tmatrix)[0]
w,v=np.linalg.eig(np.transpose(self.Tmatrix))
pSS=np.real(v[:, np.argmax(np.real(w))])
pSS=pSS/np.sum(pSS)
self.pSS=pSS
def get_steady_state_matrixpowers(self,conv):
max_iters=10000
Mt=self.Tmatrix.copy()
dconv=1.e100
N=1
pSS=np.mean(Mt,0)
pSSp=np.ones_like(pSS)
while dconv>conv and N<max_iters:
Mt=np.matmul(self.Tmatrix,Mt)
N=N+1
if N%10 == 0:
pSS=np.mean(Mt,0)
pSS=pSS/np.sum(pSS)
dconv=np.sum(np.abs(pSS-pSSp))
pSSp=pSS.copy()
sys.stdout.write('N='+str(N)+' dconv: '+str(dconv)+'\n')
self.pSS=pSS.copy()
def get_steady_state_target_flux(self):
Mss=self.Tmatrix
pSS=np.squeeze(np.array(self.pSS))
self.lagtime=self.tau*(self.n_lag+1)
nTargets=self.indTargets.size
indNotTargets=np.setdiff1d(range(self.nBins),self.indTargets)
Jt=0.0
for j in range(nTargets):
jj=self.indTargets[j]
Jt=Jt+np.sum(np.multiply(pSS[indNotTargets],np.squeeze(np.array(Mss[indNotTargets,jj*np.ones_like(indNotTargets)]))))
self.JtargetSS=Jt/self.lagtime
def evolve_probability(self,nEvolve,nStore): #iterate nEvolve times, storing every nStore iterations, initial condition at basis
nIterations=np.ceil(nEvolve/nStore).astype(int)+1
self.nEvolve=nEvolve
self.nStore=nStore
Mss=self.Tmatrix
nBins=self.nBins
binCenters=self.binCenters
probBasis=np.zeros((1,self.nBins))
probBasis[0,self.indBasis]=1.0
pSS=probBasis.copy()
pSSPrev=np.ones_like(pSS)
iT=1
probTransient=np.zeros((nIterations,nBins))#while dConv>.00001:
probTransient[0,:]=probBasis
for i in range(nEvolve):
pSS=np.matmul(pSS,Mss)
dConv=np.sum(np.abs(pSS-pSSPrev))
pSSPrev=pSS.copy()
if i%nStore==0:
sys.stdout.write("SS conv: "+str(dConv)+" iter: "+str(i))
try:
plt.plot(binCenters,np.squeeze(pSS),'-',color=plt.cm.Greys(float(i)/float(nEvolve)))
plt.yscale('log')
except:
try:
plt.plot(binCenters,pSS.A1,'-',color=plt.cm.Greys(float(i)/float(nEvolve)))
except:
pass
probTransient[iT,:]=np.squeeze(pSS)
iT=iT+1
probTransient=probTransient[0:iT,:]
self.probTransient=probTransient
pSS=np.squeeze(np.array(pSS))
self.pSS=pSS/np.sum(pSS)
try:
plt.pause(4)
plt.close()
except:
pass
def evolve_probability2(self,nEvolve,nStore): #iterate nEvolve times, storing every nStore iterations, initial condition spread for everything at RMSD higher than basis
nIterations=np.ceil(nEvolve/nStore).astype(int)+1
self.nEvolve=nEvolve
self.nStore=nStore
Mss=self.Tmatrix
nBins=self.nBins
binCenters=self.binCenters
probBasis=np.zeros((1,self.nBins))
probBasis[0,self.indBasis[0]:]=1.0 #assign initial probability to everything at RMSD higher than the basis, for case when nothing observed leaving exact basis
probBasis=probBasis/np.sum(probBasis)
pSS=probBasis.copy()
pSSPrev=np.ones_like(pSS)
iT=1
probTransient=np.zeros((nIterations,nBins))#while dConv>.00001:
probTransient[0,:]=probBasis
for i in range(nEvolve):
pSS=np.matmul(pSS,Mss)
dConv=np.sum(np.abs(pSS-pSSPrev))
pSSPrev=pSS.copy()
if i%nStore==0:
sys.stdout.write("SS conv: "+str(dConv)+" iter: "+str(i))
try:
plt.plot(binCenters,np.squeeze(pSS),'-',color=plt.cm.Greys(float(i)/float(nEvolve)))
plt.yscale('log')
except:
try:
plt.plot(binCenters,pSS.A1,'-',color=plt.cm.Greys(float(i)/float(nEvolve)))
except:
pass
#plt.ylim([1e-100,1])
# plt.title(str(iT)+' of '+str(nIterations))
# plt.pause(.1)
probTransient[iT,:]=np.squeeze(pSS)
iT=iT+1
probTransient=probTransient[0:iT,:]
self.probTransient=probTransient
pSS=np.squeeze(np.array(pSS))
self.pSS=pSS/np.sum(pSS)
try:
plt.pause(4)
plt.close()
except:
pass
def evolve_probability_from_initial(self,p0,nEvolve,nStore): #iterate nEvolve times, storing every nStore iterations, initial condition provided
nIterations=np.ceil(nEvolve/nStore).astype(int)+1
self.nEvolve=nEvolve
self.nStore=nStore
Mss=self.Tmatrix
nBins=self.nBins
binCenters=self.binCenters
probBasis=np.zeros((1,self.nBins))
if np.shape(probBasis)[1]==np.shape(p0)[0]:
probBasis[0,:]=p0
else:
probBasis=p0
pSS=probBasis.copy()
pSSPrev=np.ones_like(pSS)
iT=1
probTransient=np.zeros((nIterations,nBins))#while dConv>.00001:
probTransient[0,:]=probBasis
for i in range(nEvolve):
pSS=np.matmul(pSS,Mss)