def _lorimer2012_msp_periods():
"""Picks a period at random from Dunc's
distribution as mentioned in IAU
(China 2012) proceedings
"""
# min max and n in distribution
logpmin = 0.
logpmax = 1.5
dist = [1., 3., 5., 16., 9., 5., 5., 3., 2.]
# calculate which bin to take value of
#
bin_num = dists.draw1d(dist)
# assume linear distn inside the bins
logp = logpmin + (logpmax-logpmin)*(bin_num+random.random())/len(dist)
return 10.**logp
def _lorimer2012_msp_periods():
"""Picks a period at random from Dunc's
distribution as mentioned in IAU
(China 2012) proceedings
"""
# min max and n in distribution
logpmin = 0.
logpmax = 1.5
dist = [1., 3., 5., 16., 9., 5., 5., 3., 2.]
# calculate which bin to take value of
#
bin_num = dists.draw1d(dist)
# assume linear distn inside the bins
logp = logpmin + (logpmax-logpmin)*(bin_num+random.random())/len(dist)
return 10.**logp
def generate(ngen,
surveyList=None,
pDistType='lnorm',
radialDistType='lfl06',
radialDistPars=7.5,
electronModel='ne2001',
pDistPars=[2.7, -0.34],
siDistPars=[-1.6, 0.35],
lumDistType='lnorm',
lumDistPars=[-1.1, 0.9],
zscaleType='exp',
zscale=0.33,
duty_percent=6.,
scindex=-3.86,
gpsArgs=[-1, -1],
doubleSpec=[-1, -1],
nostdout=False,
pattern='gaussian',
orbits=False,
dgf=None,
singlepulse=False,
giantpulse=False,
dither=False,
accelsearch=False,
jerksearch=False,
sig_factor=10.0,
bns = False,
orbparams = {'m': [1, 5], 'm1': [1.0, 2.4], 'm2': [0.2, 1e9], 'om': [0, 360.], 'inc': [0, 90], 'ec': [0., 1.], 'pod': [1e-3, 1e3]},
brDistType='log_unif'):
"""
Generate a population of pulsars.
Keyword args:
ngen -- the number of pulsars to generate (or detect)
surveyList -- a list of surveys you want to use to try and detect
the pulsars
pDistType -- the pulsar period distribution model to use (def=lnorm)
radialDistType -- radial distribution model
electronModel -- mode to use for Galactic electron distribution
pDistPars -- parameters to use for period distribution
siDistPars -- parameters to use for spectral index distribution
lumDistPars -- parameters to use for luminosity distribution
radialDistPars -- parameters for radial distribution
zscale -- if using exponential z height, set it here (in kpc)
scindex -- spectral index of the scattering model
gpsArgs -- add GPS-type spectrum sources
doubleSpec -- add double-spectrum type sources
nostdout -- (bool) switch off stdout
"""
pop = Population()
# check that the distribution types are supported....
if 'd_g' in (pDistType,lumDistType,zscaleType,radialDistType,brDistType) and dgf is None:
print("Provide the distribution generation file")
sys.exit()
elif dgf != None:
try:
f = open(dgf, 'rb')
except IOError:
print("Could not open file {0}.".format(dgf))
sys.exit()
dgf_pop_load = cPickle.load(f)
f.close()
if lumDistType not in ['lnorm', 'pow', 'log_unif', 'd_g', 'log_st']:
print("Unsupported luminosity distribution: {0}".format(lumDistType))
if brDistType not in ['log_unif', 'd_g']:
print("Unsupported burst rate distribution: {0}".format(brDistType))
if pDistType not in ['lnorm', 'norm', 'cc97', 'lorimer12','unif', 'd_g']:
print("Unsupported period distribution: {0}".format(pDistType))
if radialDistType not in ['lfl06', 'yk04', 'isotropic',
'slab', 'disk','unif' ,'gauss','d_g', 'gamma']:
print("Unsupported radial distribution: {0}".format(radialDistType))
if electronModel not in ['ne2001', 'lmt85','ymw16']:
print("Unsupported electron model: {0}".format(electronModel))
if pattern not in ['gaussian', 'airy']:
print("Unsupported gain pattern: {0}".format(pattern))
if duty_percent < 0.:
print("Unsupported value of duty cycle: {0}".format(duty_percent))
# need to use properties in this class so they're get/set-type props
pop.pDistType = pDistType
pop.radialDistType = radialDistType
pop.electronModel = electronModel
pop.lumDistType = lumDistType
pop.rsigma = radialDistPars
pop.pmean, pop.psigma = pDistPars
pop.simean, pop.sisigma = siDistPars
pop.gpsFrac, pop.gpsA = gpsArgs
pop.brokenFrac, pop.brokenSI = doubleSpec
#Set whether system is BNS:
pop.bns = bns
pop.orbparams = orbparams
if pop.lumDistType == 'lnorm':
pop.lummean, pop.lumsigma = \
lumDistPars[0], lumDistPars[1]
elif pop.lumDistType == 'log_unif':
pop.lumlow, pop.lumhigh = \
lumDistPars[0], lumDistPars[1]
elif pop.lumDistType == 'log_st':
try:
pop.lumlow, pop.lumhigh, pop.lumslope = \
lumDistPars[0], lumDistPars[1], lumDistPars[2]
except ValueError:
raise PopulateException('Not enough lum distn parameters')
elif pop.lumDistType == 'd_g':
pass
else:
try:
pop.lummin, pop.lummax, pop.lumpow = \
lumDistPars[0], lumDistPars[1], lumDistPars[2]
except ValueError:
raise PopulateException('Not enough lum distn parameters')
pop.zscaleType = zscaleType
pop.zscale = zscale
# store the dict of arguments inside the model. Could be useful.
try:
if sys.version_info[0] > 3:
argspec = inspect.getfullargspec(generate)
else:
argspec = inspect.getargspec(generate)
lcl = locals()
key_values = [(arg, lcl[arg]) for arg in argspec.args]
#key_values = [(arg, locals()['argspec'][arg]) for arg in argspec.args]
#pop.arguments = {key: value for (key, value) in key_values}
except SyntaxError:
pass
if not nostdout:
print("\tGenerating pulsars with parameters:")
param_string_list = []
for key, value in key_values:
s = ": ".join([key, str(value)])
param_string_list.append(s)
# join this list of strings, and print it
s = "\n\t\t".join(param_string_list)
print("\t\t{0}".format(s))
# set up progress bar for fun :)
prog = ProgressBar(min_value=0,
max_value=ngen,
width=65,
mode='dynamic')
# create survey objects here and put them in a list
if surveyList is not None:
surveys = [Survey(s, pattern) for s in surveyList]
# initialise these counters to zero
for surv in surveys:
surv.ndet = 0 # number detected
surv.nout = 0 # number outside survey region
surv.nsmear = 0 # number smeared out
surv.ntf = 0 # number too faint
surv.nbr = 0 #number didn't burst
# surv.gainpat=pattern
else:
# make an empty list here - makes some code just a little
# simpler - can still loop over an empty list (ie zero times)
surveys = []
while pop.ndet < ngen:
# Declare new pulsar object
p = Pulsar()
# period, alpha, rho, width distribution calls
# Start creating the pulsar!
if pop.pDistType == 'lnorm':
p.period = dists.drawlnorm(pop.pmean, pop.psigma)
elif pop.pDistType == 'unif':
p.period = dists.uniform(pop.pmean,pop.psigma)
elif pop.pDistType == 'norm':
p.period = random.gauss(pop.pmean, pop.psigma)
elif pop.pDistType == 'cc97':
p.period = _cc97()
elif pop.pDistType == 'gamma':
print("Gamma function not yet supported")
sys.exit()
elif pop.pDistType == 'd_g':
Pbin_num=dists.draw1d(dgf_pop_load['pHist'])
Pmin=dgf_pop_load['pBins'][0]
Pmax=dgf_pop_load['pBins'][-1]
p.period = Pmin + (Pmax-Pmin)*(Pbin_num+random.random())/len(dgf_pop_load['pHist'])
# p.period = dg.gen_nos(dgf_pop_load['pHist'],dgf_pop_load['pBins'])
elif pop.pDistType == 'lorimer12':
p.period = _lorimer2012_msp_periods()
if duty_percent > 0.:
# use a simple duty cycle for each pulsar
# with a log-normal scatter
width = (float(duty_percent)/100.) * p.period**0.9
width = math.log10(width)
width = dists.drawlnorm(width, 0.3)
# following are the numbers for the RRATs
# from the period pulse width relation
if singlepulse:
width = 1000
while width > 100:
A1=random.gauss(0.49986684,0.13035004)
A2=random.gauss(-0.77626305,0.4222085)
width = 10**(A1*math.log10(p.period)+ A2)
p.width_degree = width*360./p.period
else:
# use the model to caculate if beaming
p.alpha = _genAlpha()
p.rho, p.width_degree = _genRhoWidth(p)
if p.width_degree == 0.0 and p.rho == 0.0:
continue
# is pulsar beaming at us? If not, move on!
p.beaming = _beaming(p)
if not p.beaming:
continue
# Spectral index stuff here
# suppose it might be nice to be able to have GPS sources
# AND double spectra. But for now I assume only have one or
# none of these types.
if random.random() > pop.gpsFrac:
# This will evaluate true when gpsArgs[0] is -1
# might have to change in future
p.gpsFlag = 0
else:
p.gpsFlag = 1
p.gpsA = pop.gpsA
if random.random() > pop.brokenFrac:
p.brokenFlag = 0
else:
p.brokenFlag = 1
p.brokenSI = pop.brokenSI
p.spindex = random.gauss(pop.simean, pop.sisigma)
# get galactic position
# first, Galactic distribution models
if pop.radialDistType == 'isotropic':
# calculate gl and gb randomly
p.gb = math.degrees(math.asin(random.random()))
if random.random() < 0.5:
p.gb = 0.0 - p.gb
p.gl = random.random() * 360.0
# use gl and gb to compute galactic coordinates
# pretend the pulsar is at distance of 1kpc
# not sure why, ask Dunc!
#Adam comment ,1 Kpc makes no sense here...
p.galCoords = go.lb_to_xyz(p.gl, p.gb, 1.0)
elif pop.radialDistType == 'slab':
p.galCoords = go.slabdist()
p.gl, p.gb = go.xyz_to_lb(p.galCoords)
elif pop.radialDistType == 'disk':
p.galCoords = go.diskdist()
p.gl, p.gb = go.xyz_to_lb(p.galCoords)
else:
if pop.radialDistType == 'lfl06':
p.r0 = go.lfl06()
elif pop.radialDistType == 'gamma':
fit_alpha, fit_loc, fit_beta=1050.312227, -8.12315263841, 0.00756010693478
p.r0 = 8.5*stats.gamma.rvs(fit_alpha, loc=fit_loc, scale=fit_beta, size=1) + 8.5
elif pop.radialDistType == 'd_g':
Rbin_num=dists.draw1d(dgf_pop_load['RHist'])
Rmin=dgf_pop_load['RBins'][0]
Rmax=dgf_pop_load['RBins'][-1]
#p.r0 = dists.yet_another_try(dgf_pop_load['RHist'], dgf_pop_load['RBins'])
p.r0 = Rmin + (Rmax-Rmin)*(Rbin_num+random.random())/len(dgf_pop_load['RHist'])
elif pop.radialDistType == 'yk04':
p.r0 = go.ykr()
elif pop.radialDistType == 'unif':
p.r0 = random.uniform(0,12.3)
elif pop.radialDistType == 'gauss':
# guassian of mean 0
# and stdDev given by parameter (kpc)
p.r0 = random.gauss(0., pop.rsigma)
# then calc xyz,distance, l and b
if pop.zscaleType == 'exp':
zheight = go._double_sided_exp(zscale)
elif pop.zscaleType == 'unif':
zheight = random.uniform(-1.06489765758, 1.91849552866)
elif pop.zscaleType == 'd_g':
zbin_num=dists.draw1d(dgf_pop_load['ZHist'])
logzmin=dgf_pop_load['ZBins'][0]
logzmax=dgf_pop_load['ZBins'][-1]
logz = logzmin + (logzmax-logzmin)*(zbin_num+random.random())/len(dgf_pop_load['ZHist'])
zheight = logz
#zheight = dg.gen_nos(dgf_pop_load['ZHist'],dgf_pop_load['ZBins'])
else:
zheight = random.gauss(0., zscale)
gx, gy = go.calcXY(p.r0)
p.galCoords = gx, gy, zheight
p.gl, p.gb = go.xyz_to_lb(p.galCoords)
p.dtrue = go.calc_dtrue(p.galCoords)
if pop.lumDistType == 'lnorm':
p.lum_1400 = dists.drawlnorm(pop.lummean,
pop.lumsigma)
elif pop.lumDistType == 'pow':
p.lum_1400 = dists.powerlaw(pop.lummin,
pop.lummax,
pop.lumpow)
elif pop.lumDistType == 'log_st':
low_lim=pop.lumlow
upr_lim=pop.lumhigh
slope = pop.lumslope #-0.10227965
p.lum_1400= 10.0**(low_lim + dists.st_line(slope,(upr_lim-low_lim)))
elif pop.lumDistType == 'log_unif':
p.lum_1400 = 10.0**dists.uniform(pop.lumlow,pop.lumhigh)
elif pop.lumDistType == 'd_g':
lbin_num=dists.draw1d(dgf_pop_load['lHist'])
lmin=dgf_pop_load['lBins'][0]
lmax=dgf_pop_load['lBins'][-1]
logl = lmin + (lmax-lmin)*(lbin_num+random.random())/len(dgf_pop_load['lHist'])
p.lum_1400 = 10.0**logl
p.lum_inj_mu=p.lum_1400
# add in orbital parameters
if pop.bns:
assert isinstance(orbparams, dict), "Orbital parameter distribution limits should be a dictionary of form {'name of orb_param': [min, max]}"
#These represents the range in which NN was trained.
#DO NOT GO OUTSIDE THESE BOUNDS!!
default_orbparams = {'m': [1, 5], 'm1': [1.0, 2.4], 'm2': [0.2, 1e9], 'om': [0, 360.], 'inc': [0, 90], 'ec': [0., 1.], 'pod': [1e-3, 1e3]}
if len(pop.orbparams) == 0:
print("Warning: Supplied orbparams dict is empty; Setting ranges to default")
pop.orbparams = default_orbparams
else:
temp_opd = dict(default_orbparams)
temp_opd.update(orbparams)
pop.orbparams = temp_opd
#Draw a value for each of the orbital parameters from a uniform distribution
p.m = np.int(np.random.uniform(pop.orbparams['m'][0], pop.orbparams['m'][1], size = 1)) #Should typically fix this to one value!
p.m1 = np.random.uniform(pop.orbparams['m1'][0], pop.orbparams['m1'][1], size = 1)
p.m2 = np.random.uniform(pop.orbparams['m2'][0], pop.orbparams['m2'][1], size = 1)
p.om = np.random.uniform(pop.orbparams['om'][0], pop.orbparams['om'][1], size = 1)
p.inc = np.random.uniform(pop.orbparams['inc'][0], pop.orbparams['inc'][1], size = 1)
p.ec = np.random.uniform(pop.orbparams['ec'][0], pop.orbparams['ec'][1], size = 1)
p.pod = np.random.uniform(pop.orbparams['pod'][0], pop.orbparams['pod'][1], size = 1)
#dither the distance
if dither:
# find flux
flux = p.lum_inj_mu/(p.dtrue**2)
# dithered distance
p.dold = p.dtrue
p.dtrue += random.gauss(0.0,0.2*p.dtrue)
# new luminosity
p.lum_1400 = flux*p.dtrue**2
p.lum_inj_mu=p.lum_1400
# new R and z
p.galCoords = go.lb_to_xyz(p.gl, p.gb, p.dtrue)
p.r0=np.sqrt(p.galCoords[0]**2 + p.galCoords[1]**2)
#define a burst rate if single pulse option is on
if singlepulse:
if brDistType == 'd_g':
brbin_num=dists.draw1d(dgf_pop_load['brHist'])
brmin=dgf_pop_load['brBins'][0]
brmax=dgf_pop_load['brBins'][-1]
p.br = 10**(brmin + (brmax-brmin)*(brbin_num+random.random())/len(dgf_pop_load['brHist']))
else:
p.br=_burst()
p.lum_sig=sig_factor
p.det_nos=0
else:
p.br=None
p.det_nos=None
# then calc DM using fortran libs
if pop.electronModel == 'ne2001':
p.dm = go.ne2001_dist_to_dm(p.dtrue, p.gl, p.gb)
elif pop.electronModel == 'lmt85':
p.dm = go.lmt85_dist_to_dm(p.dtrue, p.gl, p.gb)
elif pop.electronModel == 'ymw16':
p.dm = go.ymw16_dist_to_dm(p.dtrue, p.gl, p.gb)
p.scindex = scindex
# then calc scatter time
p.t_scatter = go.scatter_bhat(p.dm, p.scindex)
# if no surveys, just generate ngen pulsars
if surveyList is None:
pop.population.append(p)
pop.ndet += 1
if not nostdout:
prog.increment_amount()
print(prog, '\r',)
sys.stdout.flush()
# if surveys are given, check if pulsar detected or not
# in ANY of the surveys
else:
# just a flag to increment if pulsar is detected
detect_int = 0
for surv in surveys:
# do SNR calculation
if singlepulse:
#pop_time = int(p.br*surv.tobs)
#if pop_time >= 1.0:
SNR = surv.SNRcalc(p, pop,rratssearch=True)
#else:
# SNR = -3
elif accelsearch:
SNR = surv.SNRcalc(p, pop, accelsearch=True)
elif jerksearch:
SNR = surv.SNRcalc(p, pop, jerksearch=True)
else:
SNR = surv.SNRcalc(p, pop, rratssearch=False)
if SNR > surv.SNRlimit:
# SNR is over threshold
# increment the flag
# and survey ndetected
detect_int += 1
surv.ndet += 1
continue
elif SNR == -1:
# pulse is smeared out
surv.nsmear += 1
continue
elif SNR == -2:
# pulsar is outside survey region
surv.nout += 1
continue
elif SNR == -3:
# rrat didn't burst
surv.nbr += 1
continue
else:
# pulsar is just too faint
surv.ntf += 1
continue
# add the pulsar to the population
pop.population.append(p)
# if detected, increment ndet (for whole population)
# and redraw the progress bar
if detect_int:
pop.ndet += 1
if not nostdout:
prog.increment_amount()
print(prog, '\r',)
sys.stdout.flush()
# print info to stdout
if not nostdout:
print("\n")
print(" Total pulsars = {0}".format(len(pop.population)))
print(" Total detected = {0}".format(pop.ndet))
# print " Number not beaming = {0}".format(surv.nnb)
for surv in surveys:
print("\n Results for survey '{0}'".format(surv.surveyName))
print(" Number detected = {0}".format(surv.ndet))
print(" Number too faint = {0}".format(surv.ntf))
print(" Number smeared = {0}".format(surv.nsmear))
print(" Number outside survey area = {0}".format(surv.nout))
if singlepulse:
print(" Number didn't burst = {0}".format(surv.nbr))
return pop