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357 lines (312 loc) · 13.6 KB
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import ConfigParser, os
import logging
import random
import numpy as np
from numpy import array as nparray
from functools import partial
from bitstring import *
from math import exp
import sys
from sys import stdout
from subprocess import call
from utils import *
from utils.bitstrutils import *
from time import clock
import arn
from arn import bindparams, generatechromo, buildpromlist, \
buildproducts, getbindings, _getweights, _getSignalArray
log = logging.getLogger(__name__)
def displayARNresults(proteins, ccs,
samplerate=1.0, temp = 0,extralabels=None,**kwargs):
log.warning('Plotting simulation results for ' +
str(len(proteins)) + ' genes/proteins')
#plt.figure(kwargs['figure'])
arn.plt.clf()
fig, ax = arn.plt.subplots()
xx = nparray(range(ccs.shape[1]))
if extralabels:
for i in range(len(proteins)):
ax.plot(xx, ccs[i],label="%s%i"%(extralabels[i],proteins[i][0],))
ax.legend()
#handles, labels = arn.plt.get_legend_handles_labels()
for line,label in zip(ax.lines, extralabels):
if label[0] == 'R':
line.set_marker('*')
else:
for i in range(len(proteins)):
arn.plt.plot(xx, ccs[i])
arn.plt.savefig('ccoutput_' + str(temp) + '.png')
#plt.show()
#call(["open",'ccoutput_' + str(temp) + '.png'])
'''
def bindparams(config,fun):
#Binds the ARN configuration file parameters to a function.
return partial(fun,
bindingsize = config.getint('default','bindingsize'),
proteinsize = config.getint('default','proteinsize'),
genesize = config.getint('default','genesize'),
promoter = config.get('default','promoter'),
excite_offset = config.getint('default','excite_offset'),
match_threshold = config.getint('default','match_threshold'),
beta = config.getfloat('default','beta'),
delta = config.getfloat('default','delta'),
samplerate = config.getfloat('default','samplerate'),
simtime = config.getint('default','simtime'),
simstep = config.getint('default','simstep'),
silentmode = config.getboolean('default','silentmode'),
initdm = config.getint('default','initdm'),
mutratedm = config.getfloat('default','mutratedm'))
def generatechromo(initdm, mutratedm, genesize,
promoter, excite_offset, **bindargs):
#
# Default function to generate an ARN chromosome.
# To be used with bindparams.
#
valid = False
while not 48 > valid >= 4:
genome = BitStream(float=random.random(),length=32);
for i in range(0,initdm):
genome = dm_event(genome, mutratedm)
promlist = buildpromlist(genome, excite_offset, genesize, promoter)
valid = len(promlist)
return genome
def buildpromlist(genome, excite_offset, genesize, promoter,**kwargs):
gene_index = genome.findall(BitStream(bin=promoter))
promsize = len(promoter)
promlist = filter( lambda index:
int(excite_offset) <= index < (genome.length-
(int(genesize)+
promsize )),
gene_index)
#NOTE: non-overlapping genes
proms = reduce(lambda indxlst, indx:
indxlst + [indx] if(indx-indxlst[-1] >= 32 + genesize + 64) else indxlst,
promlist,
[0])
return proms[1:]
def buildproducts(genome, promlist, excite_offset, promoter,
genesize, bindingsize, proteinsize, **kwargs):
log.debug("Building ARN with " + str(len(promlist)) + " genes")
#each protein is
#[protein_index(=prom_index), e-bind, h-bind,
# bind-signature, function-signature ]
proteins = list()
for pidx in promlist:
proteins.append(_getprotein(pidx,
genome[pidx-excite_offset:pidx+genesize+len(promoter)],
bindingsize,
genesize,
proteinsize))
return proteins
#organized in columns for the target equation
def getbindings(bindtype, proteins, match_threshold,**kwargs):
return nparray([[XORmatching(p[3],otherps[1+bindtype],match_threshold)
for otherps in proteins]
for p in proteins],dtype=float);'''
def iterate(arnet,samplerate, simtime, silentmode, simstep,delta,**kwargs):
#s = clock()
time = 1
nump = arnet.numtf
numsamples = 1
numrec = arnet.numrec
numeff = arnet.numeff
#print kwargs['inputs']
while time <= simtime:
for i in range(numrec):
arnet.ccs[nump+i] = kwargs['inputs'][i]
_update(arnet.proteins,arnet.ccs,arnet.eweights,arnet.iweights,
delta, numtf = nump)
#normalize ccs, ignoring effectors
#print arnet.ccs[:nump]
totparcels = arnet.ccs[:nump+numrec].tolist()
#receptors ccs is not modified here
arnet.ccs[:nump] /= sum(totparcels)
#normalize outputs
#FIXME: should this be done?
for i in range(numeff):
if arnet.ccs[-1-i] > 1.0:
arnet.ccs[-1-i] = 1.0
elif arnet.ccs[-1-i] < .0:
arnet.ccs[-1-i] = .0
if numeff > 1:
arnet.ccs[nump+numrec:] /= sum(arnet.ccs[nump+numrec:])
if time % int(simtime*samplerate) == 0:
log.debug('TIME: '+ str(time))
arnet.cchistory = np.column_stack((arnet.cchistory,
arnet.ccs[:nump]))
arnet.receptorhist = np.column_stack((arnet.receptorhist,
arnet.ccs[nump:nump+numrec]))
arnet.effectorhist = np.column_stack((arnet.effectorhist,
arnet.ccs[nump+numrec:]))
time+=simstep
#print 'Elapsed time: %f sec.' % (clock()-s,)
return arnet
def _update(proteins, ccs, exciteweights, inhibitweights,delta,**kwargs):
#print ccs.shape
deltas = (_getSignalArray(ccs[:len(exciteweights)],exciteweights) -
_getSignalArray(ccs[:len(inhibitweights)],inhibitweights))
#print deltas.shape
deltas *= delta
#NOTE: previous output concentration shall not be accounted for
numreg = exciteweights.shape[0]
deltas[:numreg] *= ccs[:numreg]
#NOTE: not touching receptors ccs
ccs[:kwargs['numtf']] += deltas[:kwargs['numtf']]
ccs[numreg:] += deltas[numreg:]
#ccs/=total
'''
def _getSignalArray(ccs, weightstable):
return 1.0/len(ccs) * np.dot(ccs,weightstable)
def _getprotein(idx, code, bind_size, gene_size, protein_size):
signature = BitStream(bin=
applymajority(code[bind_size*3:bind_size*3+gene_size],
protein_size))
#EXTENDED version - Weak linkage (needs double size gene/proteins)
#p = [code[:self.bind_size],
# code[self.bind_size:self.bind_size*2],
# signature[0:self.bind_size],
# signature[self.bind_size:self.protein_size]]
#ORIGINAL version
p = [idx,
code[:bind_size],
code[bind_size:bind_size*2],
signature,
signature]
log.debug(p)
return p
def _getweights(bindings, bindingsize, beta, **kwargs):
weights = bindings - bindingsize
weights *= beta
return np.exp(weights)
'''
class ARNetwork(arn.ARNetwork):
def __init__(self, gcode, config, **kwargs):
self.code = gcode
self.simtime = config.getint('default','simtime')
promfun = bindparams(config, buildpromlist)
productsfun = bindparams(config, buildproducts)
self.promlist = promfun(gcode)
self.proteins = productsfun( gcode, self.promlist)
self.effectors=[]
self.effectorproms = promfun(gcode, promoter='00000000')
if self.effectorproms:
#print 'EFFECTORS:', self.effectorproms
self.effectors = productsfun(gcode,self.effectorproms)
self.receptors=[]
self.receptorproms = promfun(gcode, promoter='11111111')
if self.receptorproms:
#print 'RECEPTORS:', self.receptorproms
self.receptors = productsfun(gcode,self.receptorproms)
pbindfun = bindparams(config, getbindings)
weightsfun = bindparams(config, _getweights)
prob = kwargs['problem']
self.numtf = len(self.proteins)
self.numeff = min(len(self.effectors),prob.nout)
self.effectors = self.effectors[:self.numeff]
self.numrec = min(len(self.receptors),prob.ninp)
self.receptors = self.receptors[:self.numrec]
self.ccs = []
if self.promlist:
self.ccs = nparray([1.0/(self.numtf+self.numeff+self.numrec)]*
(self.numtf+self.numeff+self.numrec))
self._initializehistory()
self._initializebindings(pbindfun)
self._initializeweights(weightsfun)
for i in range(len(self.proteins)):
self.proteins[i].append(self.ccs[i])
self.simfun = bindparams(config,iterate)
self.delta = config.getfloat('default','delta')
def _initializebindings(self, pbindfun):
self.ebindings = pbindfun(0, self.proteins + self.receptors +
self.effectors)
self.ibindings = pbindfun(1, self.proteins + self.receptors +
self.effectors)
#effectors and dummy receptors do not regulate
if self.effectors or self.receptors:
self.ebindings = self.ebindings[:self.numtf+self.numrec,:]
#print 'ebinds: ', self.ebindings.shape
self.ibindings = self.ibindings[:self.numtf+self.numrec,:]
#print 'ibinds: ', self.ibindings.shape
def _initializeweights(self, weightsfun):
self.eweights = weightsfun(self.ebindings)
#print 'ebinds: ', self.eweights.shape
self.iweights = weightsfun(self.ibindings)
#print 'ebinds: ', self.iweights.shape
def _initializehistory(self):
self.cchistory=nparray(self.ccs[:self.numtf])
self.receptorhist=nparray(self.ccs[self.numtf:self.numtf+self.numrec])
self.effectorhist=nparray(self.ccs[self.numtf+self.numrec:])
def reset(self, cc_state = None):
if len(cc_state)==0:
self.ccs = nparray([1.0/(self.numtf+self.numeff+self.numrec)]*
(self.numtf+self.numeff+self.numrec))
else:
self.ccs = copy.deepcopy(cc_state)
self._initializehistory()
def __str__(self):
return str(self.proteins)
def simulate(self, *inputs):
if not inputs:
inputs = [.0]*self.numrec
#print self.numtf
#print self.numrec
#print str(len(self.ccs) - (self.numtf+self.numrec))
if self.simtime > 0:
self.simfun(self,inputs = inputs)
for i in range(self.numtf):
self.proteins[i][-1] = self.ccs[i]
#for i in range(len(self.effectors)):
# self.effectors[i][-1] = self.ccs[i+self.numtf+self.numrec]
def stepsimulate(self, proteins, ccs):
_updatenonorm(proteins, ccs, self.eweights, self.iweights, self.delta)
return ccs
def nstepsim(self, n = 1000, *inputs):
self.simfun(self, simtime = n, numeffectors = self.numeff,
inputs = inputs)
for i in range(len(self.proteins)):
self.proteins[i][-1] = self.ccs[i]
################################################################################
### Test ########
################################################################################
if __name__ == '__main__':
arnconfigfile = '../configfiles/arnsimlong.cfg'
class Problem:
pass
p = Problem()
p.ninp=3
p.nout = 1
log.setLevel(logging.DEBUG)
cfg = ConfigParser.ConfigParser()
cfg.readfp(open(arnconfigfile))
proteins=[]
nump = 0
prob_inp=[.0,.0,.0]
try:
f = open(sys.argv[1], 'r')
genome = BitStream(bin=f.readline())
arnet = ARNetwork(genome, cfg, problem = p)
except:
while nump < 4 or nump > 32 or numeff == 0 or not arnet.receptors:
genome = BitStream(float=random.random(), length=32)
for i in range(cfg.getint('default','initdm')):
genome = dm_event(genome,
.02)
arnet = ARNetwork(genome, cfg, problem = p)
nump = len(arnet.promlist)
numeff = len(arnet.effectors)
arnet.simulate()
if not cfg.getint('default', 'silentmode'):
displayARNresults(arnet.proteins, arnet.cchistory,
cfg.getfloat( 'default','samplerate'), temp=0)
extralabels = ['R']*arnet.numrec + ['E']*arnet.numeff
struct_prots = arnet.receptors + arnet.effectors
hist = np.vstack((arnet.receptorhist,arnet.effectorhist))
displayARNresults(struct_prots, hist,
cfg.getfloat( 'default','samplerate'),
temp = 1, extralabels = extralabels)
for p in arnet.proteins: print p
print 'effectors: ', arnet.effectors
f = open('genome.save','w')
f.write(genome.bin)
f.close
#print genome.bin