import sys
import os
import time
from datetime import datetime
from collections import namedtuple, defaultdict
import logging
from Queue import Empty
import math
from random import randrange, uniform, choice, random

from pygene.gene import FloatGeneMax, FloatGeneExchange
from pygene.organism import MendelOrganism
from pygene.population import Population
import numpy as np

from runphoton import BBox, Resolution
from utils.materials import loadMaterials, materialsAsXml, Brdf
from utils.xml import FileToTree, prettyPrint, StringToTree, subelement, TreeToFile
from utils.path import path
from utils import daemonthread

from utils import accelerated

import simulate as simulator

log = logging.getLogger('lws')

DEVICE_ADAPTION = [-90, -85, -80, -75,-70, -65, -60, -55, -50, -45, -40,-30, -20, 0]
DEVICE_ADAPTION = np.arange(-95, -20, 3.0)
#~ DEVICE_ADAPTION = [-90, -80, -70, -60, -50, -40, -30, 0]

DA_GENE_NAMES = ['da_%s' % -i for i in DEVICE_ADAPTION]
# set via server.py - holds lws instance
app = None

class PowerGene(FloatGeneExchange):
    """
    Gene which represents the numbers used in our organism
    """
    # genes get randomly generated within this range
    # initialized in startOptimizeRun()
    randMin = 0
    randMax = 0
    
    # probability of mutation
    mutProb = 0.2
    
    # degree of mutation
    mutAmt = 0.2
    
    def mutate(self):
        """
        Mutate this gene's value by a random amount
        within the range, which is determined by
        multiplying self.mutAmt by the distance of the
        gene's current value from either endpoint of legal values

        perform mutation IN-PLACE, ie don't return mutated copy
        """
        if random() < 0.5:
            # mutate downwards:
            fact = 1.0 - uniform(0, self.mutAmt)
            newval = self.value * fact
            self.value = max(self.randMin, newval)
        else:
            # mutate upwards:
            fact = 1.0 + uniform(0, self.mutAmt)
            newval = self.value * fact
            self.value = min(self.randMax, newval)
            
            
    def randomValue(self):
        """
        Generates a plausible random value
        for this gene.
        """
        values = []
        v = self.randMin
        while v < self.randMax:
            values.append(v)
            v = v * 1.3
            
        return choice(values)

class NormalizerGene(FloatGeneExchange):
    """
    Gene which represents the numbers used in our organism
    """
    # genes get randomly generated within this range
    randMin = 89.0
    randMax = 91.0
    
    # probability of mutation
    mutProb = 0.2
    
    # degree of mutation
    mutAmt = 0.2

class DeviceAdaptionGene(FloatGeneExchange):
    # genes get randomly generated within this range
    randMin = -8.0
    randMax = 8.0
    mutProb = 0.1
    mutAmt = 0.1

class MaterialGene(FloatGeneExchange):
    """
    Gene which represents the numbers used in our organism
    """
    # genes get randomly generated within this range
    randMin = 0.0
    randMax = 1.0
    
    # probability of mutation
    mutProb = 0.2
    
    # degree of mutation
    mutAmt = 0.1
    

        
def optimizeDeviceAdaption(station2apid_locid2measurement, apid_locid2estimated, max_generations):
    
    stations = station2apid_locid2measurement.keys()
    shape = station2apid_locid2measurement.values()[0].shape
    
    class DeviceAdaptionConverger(MendelOrganism):
        """
        Implements the organism which tries
        to converge a function
        """
        genome = {'%s_%s' % (station, n): DeviceAdaptionGene for n in DA_GENE_NAMES for station in stations}
        
        
        def genes2values(self):
            da_values = {}
            for station in station2apid_locid2measurement:
                da_values[station] = [(DEVICE_ADAPTION[i], self['%s_%s' % (station, n)]) for i, n in enumerate(DA_GENE_NAMES)]
            return da_values
            
        def fitness(self):
            if self.fitness_cache is not None:
                return self.fitness_cache
            
            da_values = self.genes2values()
            self.used_values = da_values
            
            avg_delta = accelerated.compareForDeviceAdaption(da_values, estimated_3d, station2apid_locid2measurement)
            
            #~ deltas = []
            #~ for i in range(shape[0]):
                #~ for j in range(shape[1]):
                    #~ res = a[i, j, 0] - self.apid_locid2measurement[i, j]
                    #~ if np.isfinite(res):
                        #~ deltas.append(abs(res))
            
            #~ avg_delta = sum(deltas) / len(deltas)
            fitness = avg_delta
            
            self.fitness_cache = fitness
            
            return fitness
        
     
        #~ print time.time() - t
        def __repr__(self):
            s = []
            for station in ['iconia']:
                davalues = [(DEVICE_ADAPTION[i], self['%s_%s' % (station, n)]) for i, n in enumerate(DA_GENE_NAMES)]
                s.append(station + ': ' + ', '.join('%s: %.1f' % e for e in davalues))
                    
            return "<DeviceAdaptionConverger> avg-delta=%.2f\n%s" % (self.fitness_cache, '\n'.join(s))
    ## ======================
    
    # reshape to 3 dim array
    estimated_3d = apid_locid2estimated.reshape(shape[0], shape[1], 1).astype(np.float32)
    
    childcull = 50
    childcount = 100
    
    pop = Population(species=DeviceAdaptionConverger, init=300) 
    
    t = time.time()
    #~ print DeviceAdaptionConverger().fitness()
    
    gen_idx = 0
    while gen_idx < max_generations:
        # execute a generation
        pop.gen(childcull, childcount)
        
        log.info('generation of genes: %s [population: %s] [fitness: %.2f]' % (gen_idx, len(pop.organisms), pop.best().fitness_cache))
        #~ if gen_idx-1 % 25 == 0:
            #~ log.info('%r' % pop.best())
        
        gen_idx += 1
    
    best = pop.best()
    return best.used_values, best.fitness()
    
    
    
class RadioPropConverger(MendelOrganism):
    """
    Implements the organism which tries
    to converge a function
    """
    genome = dict()
    
    ORGANISM_COUNT = 0
    
    def __init__(self, *args, **kwargs):
        RadioPropConverger.ORGANISM_COUNT += 1
        self.organismcount = RadioPropConverger.ORGANISM_COUNT # our id
        
        MendelOrganism.__init__(self, *args, **kwargs)
    
    
    def prepare_fitness(self):
        if not app.optimizer.running:
            return
            
        if not getattr(self, 'prepared', False):
            self.runs = set()
            
            materials = {}
            for matname in app.optimizer.adjustable_materials:
                materials[matname] =  Brdf(reflect=self['%s_reflect' % matname], alpha=self['%s_alpha' % matname])
            
            queued_ids = [] # only for logging
            
            for apid in app.optimizer.activeAPs:
                apcfg = app.config['aps'][apid]
                    
                power = self['%s_power' % apcfg['powerid']]
                ap = simulator.AccessPoint(id=apid, x=apcfg['x'], y=apcfg['y'], z=apcfg['z'], power=power)
                
                density = app.optimizer.density
                bbox = app.optimizer.bbox
                resolution = app.optimizer.resolution
                scenename = app.optimizer.scene
                numphotons = app.optimizer.numphotons
                
                
                disabledObjects = app.optimizer.disabledObjects 
                
                run = simulator.Run(scenename, app.activeScene['objfile'], materials, ap, bbox, resolution, 
                            density, numphotons, code='inspect_locids', locidinfo=app.known_locations, 
                            disabledObjects=disabledObjects)
                
                # if we use self.organismcount as priority, the organisms are evaluated pretty much sequentially
                # and that is good for parallelizablity
                priority = self.organismcount
                app.simulator.queue(run, priority=priority)
                self.runs.add(run)
                
                queued_ids.append(run.id)
                
            log.info('organism %s, queued %s runs: %s' % (self.organismcount, len(queued_ids), ','.join(queued_ids)))
            self.prepared = True
    
    def fitness_complete(self):
        remaining = len(self.runs - app.simulator.finished_jobs)
        return remaining == 0
    
    def fitness(self):
        if self.fitness_cache is not None:
            return self.fitness_cache
            
        self.prepare_fitness()
        
        while True:
            if not app.optimizer.running:
                #~ log.info('optimizer down, skipping fitness calculation')
                return 10**9 # return something really unfit
            
            if self.fitness_complete():
                break
            else:
                waiting, running, finished = app.simulator.queryRuns(simulator.Run.known)
                remaining = len(self.runs - finished)
                log.info('organism %s fitness calc: %s remaining [waiting: %s, running %s]' % (self.organismcount, remaining, len(waiting), len(running)))
                time.sleep(3.0)
        
        #~ da_values = {}
        #~ for station in app.optimizer.stations: 
            #~ da_values[station] = [(DEVICE_ADAPTION[i], self['%s_%s' % (station, n)]) for i, n in enumerate(DA_GENE_NAMES)]
        #~ rssieq = self['rssi_eq']
        
        max_locid = max(app.known_locations.keys())
        apid_locid2estimated = np.zeros(shape=(len(app.optimizer.activeAPs), max_locid+1)) + np.inf
        for run in self.runs:
            apidx = app.optimizer.activeAPs.index(run.ap.id)
            for locid in app.known_locations.keys():
                estimated = run.locid2rssi[str(locid)]
                #~ print estimated
                apid_locid2estimated[apidx, locid] = estimated
        
        OPTIMIZE_DAV = False
        if OPTIMIZE_DAV:
            da_values, avg_delta_opt = optimizeDeviceAdaption(app.optimizer.station2measurements, apid_locid2estimated, 20)
        else:
            da_values = {}
            for station in app.optimizer.station2measurements:
                da_values[station] = []
                avg_delta_opt = None
                
        deltas = []
        values = defaultdict(list) # locid, measurements, estimated, delta for each ap
        for run in self.runs:
            apidx = app.optimizer.activeAPs.index(run.ap.id)
            
            for station in da_values:
                apid_locid2measurement = app.optimizer.station2measurements[station]
                
                for locid in app.known_locations.keys():
                    measured = apid_locid2measurement[apidx, locid]
                        
                    # read simulated rss value for locid
                    # that has been transfered from worker node via worker_code_inspect_locids.py
                    estimated_raw = run.locid2rssi[str(locid)]
                    
                    if estimated_raw == 0:
                        continue
                        
                    estimated = math.log(estimated_raw, 10) * 10
                    
                    normalized = accelerated.normalize(estimated_raw, da_values[station], isInDB=False)
                    
                    delta = abs(measured - normalized)
                    
                    if np.isnan(delta) or not np.isfinite(delta) or delta > 100:
                        continue
                    
                    if not normalized > -100:
                        continue
                        
                    deltas.append(delta)
                    
                    values[run.ap.id].append((station, locid, measured, estimated, normalized, delta))
            
        # all runs have the same material params
        # use the params of the last run
        materials = run.materials
            
        #~ res = run(x)
        avg_delta = np.sum(deltas) / float(len(deltas))
        
        if avg_delta_opt is not None:
            assert round(avg_delta_opt, 4) == round(avg_delta, 4), '%s != %s' % (avg_delta_opt, avg_delta)
        
        powers = [(g[:-6], self[g]) for g in self.genome.keys() if g.endswith('_power')]
        
        app.optimizer.newSimulationResult(self.organismcount, materials, powers, da_values, avg_delta, values)
        
        fitness = avg_delta
        
        self.fitness_cache = fitness
        return fitness
        
    def __repr__(self):
        return "organism %s avg-delta=%.2f" % (self.organismcount, self.fitness_cache if self.fitness_cache is not None else -1.0)


#~ Iteration = namedtuple('Iteration', 'iter avg_delta paramxml path')
#~ def loadData(REP_DIR):
    #~ dirs = os.listdir(REP_DIR)
    #~ def parse(name):
        #~ try:
            #~ return datetime.strptime(name, '%Y-%m-%dT%H_%M_%S')
        #~ except Exception:
            #~ return None
    
    #~ optruns = []
    #~ for name in dirs:
        #~ if parse(name) is None:
            #~ continue
        
        #~ subdirs = [os.path.join(REP_DIR, name, p) for p in os.listdir(os.path.join(REP_DIR, name))]
        #~ if len(subdirs) == 1:
            #~ session = os.path.split(subdirs[0])[-1]
            
            #~ bestdir = os.path.join(subdirs[0], 'best')
            #~ best_iterations = []
            #~ avg_delta = None
            #~ iter = 0
            #~ if os.path.exists(bestdir):
            
                #~ for n in os.listdir(bestdir):
                    #~ iter = int(n.split('_')[0])
                    #~ avg_delta = float(n.split('_')[1])
                    
                    #~ paramxml = open(os.path.join(bestdir, n, 'params.xml')).read()
                    
                    #~ best_iterations.append(Iteration(iter, avg_delta, paramxml, os.path.join(bestdir, n)))
            
            #~ lessThanOneDayOld = os.stat(subdirs[0]).st_mtime > time.time() - 3600 * 24 * 7
            #~ interesting = (avg_delta is not None and avg_delta < 4.0) or (lessThanOneDayOld and iter > 1)
            
            #~ if interesting:
                #~ optruns.append((name, parse(name), session, best_iterations))
    #~ # return sorted by ts ascending
    #~ return list(sorted(optruns, key=lambda x: x[1]))
    

class Optimizer(object):
    def __init__(self):
        self.runner = None
        self.running = False
        self.startts = None
        self.timelimit = 0
        self.genlimit = 0
        
        self.sessiondir = None # directory for the current optsession with count: optsession_12345
        self.optsession = None # current optsession
        self.station = None # list of stations in string form
        self.stations = None # list of stations that should be included in optimize run
        
        self.gen_idx = 0 # current generation index
        self.min_avg_delta = 10**9 # best avg delta of current optsession (== best fitness)
        
        # optsession init params
        self.power = self.density = self.numphotons = self.resolution = self.bbox = self.scene = None
        
        self.tmpdir = app.config['tmp_optruns']
        if not self.tmpdir.exists():
            self.tmpdir.mkdir()
        
        # loaded measurements for each active station
        self.station2measurements = {}
        self.activeAPs = []
        
        daemonthread(target=self._checkTimelimit, name="optimizer_timelimit_check")
    
    def _checkTimelimit(self):
        while True:
            try:
                if self.running and self.genlimit > 0 and self.gen_idx > self.genlimit:
                    log.warn('GENERATION LIMIT reached, stopping!')
                    self.running = False
                    
                elif (self.running and self.timelimit > 0 and 
                    time.time() - self.startts > self.timelimit):
                    log.warn('TIMELIMIT reached, stopping!')
                    self.running = False
                        
            except Exception:
                log.exception('error during _checkTimelimit')
            
            try:
                time.sleep(1)
            except Exception:
                pass # ignore error during shutdown cleanup
                
            #~ if self.startts is not None:
                #~ log.info('TIMELIMIT: %s seconds remaining' % (self.timelimit - (time.time() - self.startts)))
                

    def loadPopulation(self, fname):
        tree = FileToTree(fname)
        #~ print 'load', fname
        print prettyPrint(tree)
        #~ tree.xpath()
    
    def newSimulationResult(self, orgid, materials, powers, da_values, avg_delta, deltavalues):
        minsfile = self.sessiondir.joinpath('mins.txt')
        minsfile.touch()
        
        allfile = self.sessiondir.joinpath('population_%s.txt' % self.gen_idx)
        allfile.touch()
        dt = datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
        
        def storeToFile(f):
            s = '%s gen:%s org:%s avg-delta:%.2f ' % (dt, self.gen_idx, orgid, avg_delta)
            for matname, brdf in sorted(materials.items()):
                s += '%s:%.3f,%.3f ' % (matname, brdf.reflect, brdf.alpha)
            
            for powid, power in powers:
                s += '%s:%.3e ' % (powid, power)
            
            #~ s += 'n/rssieq:%.3f ' % rssi_eq
            
            for station, dav in sorted(da_values.items()):
                for da_rssi, v in dav:
                    s += 'da/%s_%s:%.1f ' % (station, da_rssi, v)
                    
            open(f, 'a').write(s + '\n')
        
        storeToFile(allfile)
        
        if avg_delta < self.min_avg_delta:
            log.info('new minimum reached: %.2f' % avg_delta)
            storeToFile(minsfile)
            self.min_avg_delta = avg_delta
            
            minsdir = self.sessiondir.joinpath('mins')
            if not minsdir.exists():
                minsdir.mkdir()
            deltafile = minsdir.joinpath('deltas_%s_%s.txt' % (self.gen_idx, orgid))
            with deltafile.open('w') as f:
                for apid, values in sorted(deltavalues.items()):
                    f.write('ap: %s\n' % apid)
                    f.write('locid measured simulated normalized delta\n')
                    for station, locid, measured, estimated, normalized, delta in values:
                        f.write('%s %s %.1f %.1f %.1f %.1f\n' % (station, locid, measured, estimated, normalized, delta))
                        
            
        
    def startOptimizeRun(self, optsession, station, power, density, numphotons, 
                            resolution, bbox, scene, startpop, childcount, 
                            childcull, mutprob, mutamt, resume=None, neworgs=0, 
                            timelimit=0, genlimit=0):
        
        if self.running:
            log.error('an optimizer run is still running')
            pass
            
        else:
            
            self.station = station
            self.stations = station.split(',')
            
            self.timelimit = int(timelimit)
            self.genlimit = int(genlimit)
            self.power = power
            self.density = density
            self.numphotons = numphotons
            self.resolution = resolution
            self.bbox = bbox
            self.scene = scene
            self.startpop = startpop
            self.childcount = childcount
            self.childcull = childcull
            
            # setup PowerGene
            PowerGene.randMin = power * 0.05 # 1/10 of initial power = lower bound
            PowerGene.randMax = power * 50.0 # 10 times initial power = upper bound
            
            PowerGene.mutProb = mutprob
            PowerGene.mutAmt = mutamt
            
            MaterialGene.mutProb = mutprob
            MaterialGene.mutAmt = mutamt
            
            sesscount = max([0] + [int(d.name.split('_')[1]) for d in self.tmpdir.dirs() if d.name.split('_')[0] == optsession])
            
            self.optsession = '%s_%s' % (optsession, sesscount+1)
            
            self.sessiondir = self.tmpdir.joinpath(self.optsession)
            self.sessiondir.mkdir()
            
            
            ## HACKISH for eval
            self.adjustable_materials = ('MatConcrete', 'MatLightWalls')
            self.adjustable_materials = ('MatConcrete',)
            self.adjustable_materials = app.activeScene['materials'].scalars
            
            # [so.name for so in self.scene_objects if not ('light' in so.name.lower() or 'concrete' in so.name.lower())]
            
            self.disabledObjects = ['Iron_Doors_ID1', 'Fascade_OG2_M6.002', 'Cupboards_Plane.002', 'Fascade_EG_M6.001', 
            'Glass_Doors_Plane', 'Fascade_OG1_M6', 'Railing_Plane.Railing', 'Glass_Windows_OG1_GW01', 'Normal_Doors_ND1', 
            'Glass_Windows_OG2_GW01.002', 'Glass_Windows_EG1_GW01.001', 'Stairs_Plane.Stairs', 'Tables_Plane.004', 
            'Hardware_Plane.001']
            self.disabledObjects = []
            # see also simulator remapMaterials 
            
            initfile = self.sessiondir.joinpath('init.txt')
            
            s = 'session: %s\n' % optsession
            s += 'timelimit: %s\n' % timelimit
            s += 'genlimit: %s\n' % genlimit
            s += 'station: %s\n' % station
            s += 'power: %.1e\n' % power
            s += 'density: %.3f\n' % density
            s += 'numphotons: %s\n' % numphotons
            s += 'resolution: %s,%s,%s\n' % resolution
            s += 'bbox: %s,%s,%s,%s,%s,%s\n' % bbox
            s += 'scene: %s\n' % scene
            s += 'startpop: %s\n' % startpop
            s += 'childcount: %s\n' % childcount
            s += 'childcull: %s\n' % childcull
            s += 'mutprob: %s\n' % mutprob
            s += 'mutamt: %s\n' % mutamt
            
            initfile.write_text(s)
            
            apids = [apid for apid in app.config['aps'] if app.config['aps'][apid]['optimize']]
            self.activeAPs[:] = apids
            
            
            self._setupGenes()
            
            if resume:
                d = self.tmpdir.joinpath(resume)
                if not d.exists():
                    raise RuntimeError('no session %s available' % resume)
                popfiles = d.files('population_*.xml')
                if len(popfiles) == 0:
                    raise RuntimeError('no population for session %s available' % resume)
                popfiles.sort(key=lambda f: f.mtime)
                
                organisms = self.xml2population(FileToTree(popfiles[-1]))
                for i in range(neworgs):
                    organisms.append(RadioPropConverger())
            else:
                organisms = None
            
            self.min_avg_delta = 10**9
            self.running = True
            self.startts = time.time()
            self.runner = daemonthread(target=self._optimizeRun, name="optimizeRun", args=(organisms, ))
    
    def stopOptimizeRun(self):
        if self.running:
            self.running = False
            app.simulator.finishAll()
           
    def xml2population(self, tree):
        self._setupGenes()
        
        organisms = []
        for orgEl in tree.xpath('//organism'):
            
            genes = {}
            for geneEl in orgEl.xpath('genepair'):
                # get class from current module
                cls = globals()[geneEl.attrib['class']]
                
                g1 = float(geneEl.attrib['g1'])
                g2 = float(geneEl.attrib['g2'])
                genes[geneEl.attrib['name']] = (cls(g1), cls(g2))
            
            organism = RadioPropConverger(**genes)
            
            organisms.append(organism)
        
        return organisms
        
    def population2xml(self, pop, **kwds):
        tree = StringToTree('<population/>')
        for k, v in kwds.items():
            tree.getroot().attrib[k] = str(v)
        
        unique_genes = set()
        for org in pop.organisms:
            orgEl = subelement(tree.getroot(), 'organism', attribs={'fitness': org.fitness(),  'count': org.organismcount})
            for name, cls in org.genome.items():
                pairEl = subelement(orgEl, "genepair", attribs={'name': name, 'class': cls.__name__,})
                
                pair = org.genes[name]
                pairEl.attrib['g1'] = str(pair[0].value)
                pairEl.attrib['g2'] = str(pair[1].value)
                
                unique_genes.add(cls)
            
        for cls in unique_genes:
            attribs = {'class': cls.__name__, 'mutamt': cls.mutAmt, 
                        'mutprob': cls.mutProb, 'min': cls.randMin, 'max': cls.randMax}
            subelement(tree.getroot(), 'genespec', attribs=attribs)
            
        return tree
    
    def _setupGenes(self):
        aps = app.config['aps']
        powids = {aps[apid]['powerid'] for apid in self.activeAPs}
        RadioPropConverger.genome.clear()
        
        RadioPropConverger.genome.update(dict(
                        [('%s_reflect' % matname, MaterialGene) for matname in self.adjustable_materials] +
                        [('%s_alpha' % matname, MaterialGene) for matname in self.adjustable_materials] +
                        [('%s_power' % powid, PowerGene) for powid in powids]
                ))
            
    def _loadMeasurements(self):
        self.station2measurements.clear()
        
        max_locid = max(app.known_locations.keys())
        for station in self.stations:
            
            # initialize with infinity
            a = self.station2measurements[station] = np.zeros(shape=(len(self.activeAPs), max_locid+1)) + np.inf
            
            locid2measurecount = defaultdict(int)
            remainingActive = []
            for i, apid in enumerate(self.activeAPs):
                locid2measurement = app.getMeasurements(station, apid)
                
                if locid2measurement is not None:
                    #~ locid2measurement_overlay = app.getMeasurements(station, apid, fromMeasurementStore=False)
                    
                    for locid, (x, y, z, measured, all_rssis) in locid2measurement.items():
                        a[i, locid] = float(measured)
                        
                        locid2measurecount[locid] += 1
                        
                        # overwrite with values from tracked paths
                        #~ if locid in locid2measurement_overlay:
                            #~ x, y, z, measured, all_rssis = locid2measurement_overlay[locid]
                            #~ a[i, locid] = float(measured)
                            
                    remainingActive.append(apid)
            
            ### reduce to set of location with the 
            N = 50
            sorted_locs = sorted(locid2measurecount.items(), key=lambda e: e[1])
            for l, c in sorted_locs:
                print l, c
            
            topNlocids = set([locid for locid, count in sorted_locs][-N:])
            for i, apid in enumerate(self.activeAPs):
                locid2measurement = app.getMeasurements(station, apid)
                
                if locid2measurement is not None:
                    for locid, (x, y, z, measured, all_rssis) in locid2measurement.items():
                        if locid not in topNlocids: # clear measurement by setting to inf
                            a[i, locid] = np.inf
                            
            log.info('%s of %s remaining active aps due to available measurements' % (len(remainingActive), i))
            self.activeAPs[:] = remainingActive
                    
        
    def _optimizeRun(self, startorganisms):
        try:
            self._loadMeasurements()
            
            if startorganisms is not None:
                pop = Population(*startorganisms, init=self.startpop)
            else:
                pop = Population(species=RadioPropConverger, init=self.startpop) 
            
            for org in pop.organisms:
                org.prepare_fitness()
                
            self.gen_idx = 0
            while self.running:
                log.info('generation of genes: %s [population: %s]' % (self.gen_idx, len(pop.organisms)))
                
                # execute a generation
                pop.gen(self.childcull, self.childcount)
                
                f = self.tmpdir.joinpath(self.sessiondir, 'population_%s.xml' % self.gen_idx)
                tree = self.population2xml(pop, init=self.startpop, childCount=self.childcount, childCull=self.childcull)
                TreeToFile(tree, f)
                
                self.gen_idx += 1
        except Exception:
            log.exception('error during optrun')
        finally:
            self.running = False
    
    def getDeviceAdaptionFromOptrun(self, optsession, device):
        f = self.tmpdir.joinpath(optsession, 'mins.txt')
        davalues = defaultdict(list)
        
        if not f.exists():
            log.error('got no mins for optrun %s - empty davalues' % optsession)
            return davalues
        
        # take last minimum
        mindata = f.text().strip().split('\n')[-1].split()
        for s in mindata[1:]:
            name, value = s.split(':')
            if name.startswith('da/'):
                device_darssi = name[3:].split('_')
                if len(device_darssi) == 1:
                    # backward compat
                    davalues[device].append((float(device_darssi[0]), float(value)))
                else:
                    dev, darssi = device_darssi
                    davalues[dev].append((float(darssi), float(value)))
        
        return davalues