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353 lines (306 loc) · 12.7 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 *
try:
import matplotlib.pyplot as plt
from matplotlib import collections, legend
except ImportError:
logging.warning('matplotlib not found.')
logging.warning('use of silentmode = 0 will result in an error...')
log = logging.getLogger(__name__)
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'),
overlapgenes = config.getboolean('default','overlapgenes'))
def neutralshare(arnet):
try:
proms = arnet.promlist + arnet.effectorproms
except:
proms = arnet.promlist
neutrals = proms[0] - 88
for i in range(0, len(proms),2):
if proms[i] == proms[-1]:
break
if proms[i] + 168 < proms[i+1] - 88:
neutrals += (proms[i+1] - 88) - (proms[i] + 168)
neutrals += len(arnet.code) - (proms[-1]+168)
return float(neutrals) / len(arnet.code)
def generatechromo(initdm, mutratedm, genesize, promoter,
excite_offset, overlapgenes,**bindargs):
'''
Default function to generate an ARN chromosome.
To be used with bindparams.
'''
log.debug('Creating DM agent.')
valid = False
while True:
genome = bitarray(getrndstr(32))
for i in range(0,initdm):
genome = dm_event(genome, mutratedm)
if genome.search(bitarray(promoter)):
break
return genome
def generatechromo_rnd( genomesize, mutratedm, genesize, promoter,
excite_offset, overlapgenes, **bindargs):
'''
Default function to generate an ARN chromosome.
To be used with bindparams.
'''
log.debug('Creating random agent.')
valid = False
genome = bitarray(getrndstr(genomesize))
while not genome.search(bitarray(promoter)):
genome = bitarray(getrndstr(genomesize))
return genome
def displayARNresults(proteins, ccs, step=1):
log.warning('Plotting simulation results for ' +
str(len(proteins)) + ' genes/proteins')
plt.clf()
xx=[i*step for i in range(len(ccs[0]))]
for i in range(len(proteins)):
plt.plot(xx, ccs[i],label="%i"%(proteins[i][0],))
plt.legend()
plt.savefig('ccoutput.png')
call(["open", "ccoutput.png"])
return "ARN simulation displayed"
def buildpromlist(genome, excite_offset, genesize, promoter,
overlapgenes, **kwargs):
gene_index = genome.search(bitarray(promoter))
promsize = len(promoter)
promlist = filter( lambda index:
int(excite_offset) <= index < (genome.length()-(int(genesize)+promsize )),
gene_index)
genegap = 32 + genesize + 64
if overlapgenes:
#promotor size only
genegap = 32
proms = reduce(lambda indxlst, indx:
indxlst + [indx] if indx-indxlst[-1] >= genegap else indxlst,
promlist[1:],
promlist[:1])
return proms
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,
bitarray(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):
time = 1
while time <= simtime:
_update(arnet.proteins,arnet.ccs,arnet.eweights,
arnet.iweights,delta)
if(not(silentmode) and
(time % (simtime*samplerate) == 0)):
log.debug('TIME: '+ str(time))
for p in proteins:
arnet.updatehistory()
time+=simstep
if not silentmode:
displayARNresults(proteins, cchistory,simstep)
return arnet.ccs
def _update(proteins, ccs, exciteweights, inhibitweights,delta):
deltas = (_getSignalArray(ccs,exciteweights) -
_getSignalArray(ccs,inhibitweights))
deltas *= delta
deltas *= ccs
total = sum(ccs)+sum(deltas)
ccs+=deltas
ccs/=total
def _updatenonorm(proteins, ccs, exciteweights, inhibitweights,delta):
deltas = (_getSignalArray(ccs,exciteweights) -
_getSignalArray(ccs,inhibitweights))
deltas *= delta
deltas *= ccs
ccs+=deltas
def _getSignalArray(ccs, weightstable):
return 1.0/len(ccs) * np.dot(ccs,weightstable)
def _getprotein(idx, code, bind_size, gene_size, protein_size):
signature = bitarray(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:
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.excite_offset = config.getint('default','excite_offset')
pbindfun = bindparams(config, getbindings)
weightsfun = bindparams(config, _getweights)
nump = len(self.proteins)
self.ccs = []
if self.promlist:
self.ccs=nparray([1.0/nump]*nump)
for i in range(len(self.proteins)):
self.proteins[i].append(self.ccs[i])
self._initializehistory()
self._initializebindings(pbindfun)
self._initializeweights(weightsfun)
self.simfun = bindparams(config,iterate)
self.delta = config.getfloat('default','delta')
self.numtf = len(self.proteins)
def _initializebindings(self, pbindfun):
self.ebindings = pbindfun(0, self.proteins)
self.ibindings = pbindfun(1, self.proteins)
def _initializeweights(self, weightsfun):
self.eweights = weightsfun(self.ebindings)
self.iweights = weightsfun(self.ibindings)
def _initializehistory(self):
self.cchistory=nparray(self.ccs)
def updatehistory(self):
self.cchistory = np.column_stack((self.cchistory,
self.ccs))
def __str__(self):
return str(self.proteins)
def simulate(self):
if self.simtime > 0:
self.simfun(self)
for i in range(len(self.proteins)):
self.proteins[i][-1] = self.ccs[i]
def stepsimulate(self, proteins, ccs):
_updatenonorm(proteins, ccs, self.eweights, self.iweights, self.delta)
return ccs
def nstepsim(self, n = 1000):
self.simfun(self.proteins, self.ccs,
self.eweights, self.iweights,simtime=n)
for i in range(len(self.proteins)):
self.proteins[i][-1] = self.ccs[i]
def getneutralshare(self):
return neutralshare(self)
def snapshot(self):
s = 'digraph best {\nordering = out;\n'
shape = 'hexagon'
labelidx = 0
for tf in self.promlist:
s += '%i [label="%s"];\n' % (tf, labelidx )
for e,h,i in zip(self.ebindings[:,labelidx],
self.ibindings[:,labelidx],
range(len(self.ebindings))):
if e > 0:
s += '%i -> %i [dir=back];\n' % \
(tf, self.promlist[i])
if h > 0:
s += '%i -> %i [dir=back,style=dotted];\n' % \
(tf, self.promlist[i])
labelidx += 1
s += '}'
return s
###########################################################################
### Test ###
###########################################################################
if __name__ == '__main__':
arnconfigfile = '../configfiles/arnsim.cfg'
log.setLevel(logging.DEBUG)
cfg = ConfigParser.ConfigParser()
cfg.readfp(open(arnconfigfile))
proteins=[]
nump = 0
try:
f = open(sys.argv[1], 'r')
genome = BitStream(bin=f.readline())
arnet = ARNetwork(genome, cfg)
except:
while nump < 4 or nump > 12:
genome = BitStream(float=random.random(), length=32)
for i in range(cfg.getint('default','initdm')):
genome = dm_event(genome,
.02)
arnet = ARNetwork(genome, cfg)
nump = len(arnet.promlist)
for p in arnet.proteins: print p
f = open('genome.save','w')
f.write(genome.bin)
f.close
#print genome.bin
arnet.simulate()
###########################################################################
### Other Helpers ###
###########################################################################
#deprecated
def generatechromoepi(init_dm, dm_mutrate,**bindargs):
valid = False
#initdm = random.gauss(float(init_dm),1.0)
while not 30 > valid >= 4:
genome = BitStream(float=random.random(),length=32);
for i in range(0,int(init_dm)):
genome = dm_event(genome, dm_mutrate)
promlist = buildpromlist(genome, bindargs['excite_offset'],
bindargs['genesize'], bindargs['promoter'])
valid = len(promlist)
proteins = buildproducts(genome, promlist,
bindargs['excite_offset'],
len(bindargs['promoter']),
bindargs['genesize'],
bindargs['bindingsize'],
bindargs['proteinsize'])
cromatines = [1.0/float(valid)]*valid
return (genome,dict(zip(promlist,proteins)),
dict(zip(promlist,cromatines)),10000,0)
#deprecated
def buildpromlistEPI(genome, excite_offset, genesize, promoter):
gene_index = genome.findall(promoter)
promsize = len(promoter)
promlist = filter( lambda index:
excite_offset <= index < (genome.length-(genesize+promsize )),
gene_index)
proms = reduce(lambda indxlst, indx:
indxlst + [indx] if(indx-indxlst[-1] >= genesize+3*32) else indxlst,
promlist,
[0])
return proms[1:]