code/code/arn.py at master · rmlopes/code · GitHub

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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:]

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