import numpy as np

import healpy as hp

import hp2np

import sourceProb

import mags

import modelRead

import mapsAtTimeT

# sims, mjds, distances, models = allMaps.veni();

# allMaps.vidi(sims, mjds, distances, models)

# sims, mjds, distances, models = allMaps.veni(False);

# allMaps.vidi(sims, mjds, distances, models, False)

# ra, dec, map, maglim, maglimg, prob, probMap, hx,hy = allMaps.readem( 789064, 0)

# sim,mjd,distance,snr, hlv, cra, cdec, p0,p1,p2,p3,p4,p5,p6,p7,p8,p9 = np.genfromtxt("all-maxtimes-2015.txt", unpack=True);

#

#=================================================

#

# RUN the LIGO Simulations!!!

#

#=================================================

def select_single_sim (

simNumber, data_dir="/data/des30.a/data/annis/des-gw/ligo/sims/") :

sims, mjds, distances, models = veni()

ix = sims==simNumber

sims, mjds, distances = sims[ix], mjds[ix], distances[ix]

print("found ",sims[0])

simfile="bayestar-{:d}.fits.gz".format(sims[0])

ligoMapFile = data_dir+simfile

return sims, mjds, distances, ligoMapFile

#

#==== Big routine # 1: collect metadata

#

# Get the list of Ligo Maps

# Get the NS models

# Definitively a prep into memory routine

#

def veni( ) :

type = "BBH"

if type == "2015" :

dir = "/data/des30.a/data/annis/des-gw/ligo/"

simsFile = "2015_inj.txt"

sims, mjds, distances = np.genfromtxt(dir+simsFile, unpack=True, skiprows=40, usecols=(0,2,8))

sims = sims.astype("int")

elif type == "2016" :

dir = "/data/des30.a/data/annis/des-gw/ligo/"

simsFile = "2016_inj.txt"

sims, mjds, distances = np.genfromtxt(dir+simsFile, unpack=True, skiprows=40, usecols=(0,2,8))

sims = sims.astype("int")

elif type == "BBH" :

import glob

dir = "/data/des30.a/data/annis/des-gw/ligo/burst/2015/"

file_list = glob.glob(dir+"BBH_LIB/*LIB_C.fits.gz")

gps = np.genfromtxt(dir+"all_BF2Y-2015-BBH_index.txt", unpack=True, skip_header=1, usecols=0)

sims = np.genfromtxt(dir+"all_BF2Y-2015-BBH_index.txt", unpack=True, skip_header=1, usecols=1, dtype="str")

distances= np.ones(sims.size)*20.0

mjds = gps2mjd(gps)

else :

raise Exception("no sim package known of type {}".format(type))

models = modelRead.getModels()

return sims, mjds, distances, models

def gps2mjd (gps):

"""

The BBH sims are given a time in gps seconds.

This is seconds since 0:0:0 6-Jan-1980 and as of 2016 is 17 seconds ahead of UTC

We take this zeropoint time to be MJD= 44244.0.

This routine corrects for the 17 seconds, which means it becomes progressively

less accuate as the date moves away from 2016.

"""

days = (gps-17.0)/3600./24.

mjd = days + 44244.0

return mjd

#==== Big routine # 2: find the probabilities over 10 days

#

# For each sim build the mags.observed object

#

# sims, mjds, distances, models=allMaps.veni()

# allMaps.vidi(sims, mjds, distances, models)

# test: ix=np.nonzero((sims==10934)|(sims==1087))

# allMaps.vidi(sims[ix], mjds[ix], distances[ix], models)

#

def vidi(sims, mjds, distances, models, quick=False) :

import os.path

type = "BBH"

if type == "2015" :

data_dir = "/data/des30.a/data/annis/des-gw/ligo/sims/"

odata_dir = "/data/des30.a/data/annis/des-gw/ligo/sims-2015-out/"

#odata_dir = "/data/des30.a/data/annis/des-gw/ligo/nsims-2015-out/"

odata_dir = "/data/des30.a/data/annis/des-gw/nsims-2015-out/"

file = "bayestar-{:d}.fits.gz"

trigger_type = "NS"

elif type == "2016" :

data_dir = "/data/des30.a/data/annis/des-gw/ligo/sims-2016/"

odata_dir = "/data/des30.a/data/annis/des-gw/ligo/sims-2016-out/"

#odata_dir = "/data/des30.a/data/annis/des-gw/ligo/nsims-2016-out/"

odata_dir = "/data/des30.a/data/annis/des-gw/nsims-2016-out/"

file = "bayestar-{:d}.fits.gz"

trigger_type = "NS"

elif type == "BBH" :

data_dir = "/data/des30.a/data/annis/des-gw/ligo/burst/2015/"

odata_dir = "/data/des30.a/data/annis/des-gw/ligo/burst/out2015/"

file = "BBH_LIB/{:s}-LIB_C.fits.gz"

trigger_type = "BH"

else :

raise Exception("no sim package known of type {}".format(type))

outfile = odata_dir + "mainInjector-sim-mjd-dist-bslot-nslot-probcovered-econ_area-need_area-quality.txt"

fd = open(outfile,"w"); fd.close()

counter = 0

for sim, mjd, distance in zip(sims,mjds,distances) :

simfile = file.format(sim)

print("sim, distance: ", sim, distance)

simfile = data_dir + simfile

outdir = odata_dir + str(sim) + "/"

name = outdir + str(sim)+"-probabilityPlot.png"

if not quick and os.path.exists(name) : continue

if not os.path.exists(simfile):

print(".... skipping as no such file")

continue

best_slot, n_slots, first_slot, \

econ_prob, econ_area, area_need, quality = \

mainInjector(sim, simfile, mjd, distance, trigger_type, outdir,

recycler_mjd=mjd+(0.5/24.), quick=quick)

fd=open(outfile,"a")

fd.write("{} {} {} {} {} {} {} {} {}\n".format(sim, mjd, distance, \

best_slot, n_slots, econ_prob, econ_area, area_need,\

quality))

fd.close()

counter += 1

#if counter >= 21 : raise Exception("im done here")

# sim,mjd,distance,snr, p0,p1,p2,p3,p4,p5,p6,p7,p8,p9 = np.genfromtxt("all-maxtimes-2015.txt", unpack=True);

#==== Big routine # 4: collate information into a single file

#

# There is no such thing as vedi. It is veni, vidi, vici.

# This is concerned with the LIGO ancillary data

# which it is going to connect to....

# the total probability for each day.

# and thus to a file. I don't know how the day maps are made....

#

# make the big files, the all-maxtimes files

def vedi2(sims, mjds, distances, do2015=True, doV=False) :

import os.path

import shutil

savefile="all-maxtimes-{}.txt"

snrFile = "/data/des30.a/data/annis/des-gw/ligo/{}_coinc.txt"

checkFile = "/data/des30.a/data/annis/des-gw/ligo/check-{}.txt"

if do2015:

data_dir = "/data/des30.a/data/annis/des-gw/ligo/sims-2015-out/"

if doV :

data_dir = "/data/des30.a/data/annis/des-gw/ligo/sims-2015-out-v/"

savefile = savefile.format("2015")

snrFile = snrFile.format("2015")

snrSim, snrNet = np.genfromtxt(snrFile, unpack=True,skiprows=35, usecols=(0,3))

checkFile = checkFile.format("2015")

network = np.zeros(snrSim.size)

else :

data_dir = "/data/des30.a/data/annis/des-gw/ligo/sims-2016-out/"

if doV :

data_dir = "/data/des30.a/data/annis/des-gw/ligo/sims-2016-out-v/"

savefile = savefile.format("2016")

snrFile = snrFile.format("2016")

snrSim, snrNet = np.genfromtxt(snrFile, unpack=True,skiprows=36, usecols=(0,3))

network = np.genfromtxt(snrFile, unpack=True,skiprows=36, usecols=(2),dtype=("str"))

checkFile = checkFile.format("2016")

check_sim, check_ra, check_dec = np.genfromtxt(checkFile, unpack=True)

csim,rapidarea = np.genfromtxt("../c2016.txt",unpack=True)

totalProbs = dict()

for day in range(0,10) :

totalProbs[day] = []

snr = []

hlv = []

cra, cdec = [],[]

carea =[]

for sim, mjd, distance in zip(sims,mjds,distances) :

print("sim, distance: ", sim, distance)

new_maxtimeFile = maxtimeFilename(sim, data_dir)

maxtimes, maxprobs = np.genfromtxt(new_maxtimeFile, unpack=True)

snr_ix = np.nonzero(snrSim == sim)

if network[snr_ix] == "HL" :

hlv.append(0)

elif network[snr_ix] == "HLV" :

hlv.append(1)

elif network[snr_ix] == "HV" :

hlv.append(2)

elif network[snr_ix] == "LV" :

hlv.append(3)

else :

hlv.append(4)

snr.append( snrNet[snr_ix] )

check_ix = np.nonzero(check_sim == sim)

cra.append(check_ra[check_ix])

cdec.append(check_dec[check_ix])

check_ix = np.nonzero(csim == sim)

carea.append(rapidarea[check_ix])

for day in range(0,10) :

simfile = data_dir+str(sim)+"-"+str(day)+"-map.hp"

if not os.path.exists(simfile) :

totalProb = 0.0

else :

ligo = hp.read_map(simfile,verbose=False)

simfile = data_dir+str(sim)+"-"+str(day)+"-probMap.hp"

decam = hp.read_map(simfile,verbose=False)

ligo = ligo/ligo.sum()

totalProb = (decam*ligo).sum()

totalProbs[day].append(totalProb)

snr= np.array(snr)

hlv= np.array(hlv).astype(int)

cra = np.array(cra)

cdec = np.array(cdec)

carea = np.array(carea)

for day in range(0,10) :

totalProbs[day] = np.array(totalProbs[day])

data = np.array([sims, mjds, distances, snr, hlv, cra, cdec, carea, totalProbs[0], \

totalProbs[1], totalProbs[2], totalProbs[3], totalProbs[4], \

totalProbs[5], totalProbs[6], totalProbs[7], totalProbs[8], \

totalProbs[9]])

np.savetxt(savefile, data.T, "%d %.5f %.0f %.1f %d %.6f %.5f %.3f %.5e %.5e %.5e %.5e %.5e %.5e %.5e %.5e %.5e %.5e")

# Get the saved maps for each day.

def readem (simNumber, day) :

n=str(simNumber)+"-"+str(day)

ra=hp.read_map(n+"-ra.hp");

dec=hp.read_map(n+"-dec.hp");

ha=hp.read_map(n+"-ha.hp");

map=hp.read_map(n+"-map.hp");

maglim=hp.read_map(n+"-maglim.hp");

maglimg=hp.read_map(n+"-maglim-global.hp");

prob=hp.read_map(n+"-prob.hp");

probMap=hp.read_map(n+"-probMap.hp");

hx=hp.read_map(n+"-hx.hp");

hy=hp.read_map(n+"-hy.hp");

return ra, dec, map, maglim, maglimg, prob, probMap, hx,hy

# Get the saved maps for each day and hour.

def readem2 (simNumber, day, hourno) :

n=str(simNumber)+"-"+str(day)+"-"+str(hourno)

ra=hp.read_map(n+"-ra.hp");

dec=hp.read_map(n+"-dec.hp");

ha=hp.read_map(n+"-ha.hp");

map=hp.read_map(n+"-map.hp");

maglim=hp.read_map(n+"-maglim.hp");

prob=hp.read_map(n+"-prob.hp");

probMap=hp.read_map(n+"-probMap.hp");

hx=hp.read_map(n+"-hx.hp");

hy=hp.read_map(n+"-hy.hp");

return ra, dec, map, maglim, prob, probMap, hx,hy

# A clear cut save routine

def saven2 (maxtimes, maxprobs, simNumber, data_dir) :

name = maxtimeFilename ( simNumber, data_dir)

np.savetxt(name, np.array([maxtimes,maxprobs]).T, "%.5f %.5e")

print("\t writing ", name)

# A file finder routine

def maxtimeFilename ( simNumber, data_dir) :

nameStem = data_dir + str(simNumber)

name = nameStem + "-maxtimes.txt"

return name

# for ten days,

# find the maximum total probability for each day

# return

def maximumProbabilityPerDay (totalProbs,times) :

maxtimes = []

maxprobs = []

# for ten days

for day in range(0,10) :

# for times measured in days

ix = (times >= day) & (times < day+(1.))

max = totalProbs[ix].max()

ix = np.nonzero((times >= day) & (times < day+(1.)) & (totalProbs == max))

if totalProbs[ix].sum() == 0 :

ix = ix[0][0]

if totalProbs[ix].size > 1 :

ix = ix[0][0]

if type(ix) == np.int64 :

t = times[ix]; tp = totalProbs[ix]

else :

t = times[ix][0]; tp = totalProbs[ix][0]

maxtimes.append(t)

maxprobs.append(tp)

maxtimes =np.array(maxtimes)

maxprobs = np.array(maxprobs)

return maxtimes, maxprobs

sim,mjd,distance,p0,p1,p2,p3,p4,p5,p6,p7,p8,p9 = np.genfromtxt("all-maxtimes-2015.txt", unpack=True);

ix = p0 > .01; p=p0[ix]; n=p0.size-p.size; ix=p0>.33; n2=p0[ix].size; plt.clf();a=plt.hist(p,bins=100,color="k"); plt.text(0.4,23,"day 0: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2))

ix = p1 > .01; p=p1[ix]; n=p1.size-p.size;ix=p1>.33; n2=p1[ix].size; a=plt.hist(p,bins=100,color="r",histtype="step"); plt.text(0.4,21,"day 1: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2),color="r")

ix = p4 > .01; p=p4[ix]; n=p4.size-p.size;ix=p4>.33; n2=p4[ix].size; a=plt.hist(p,bins=100,color="g",histtype="step"); plt.text(0.4,19,"day 4: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2),color="g")

ix = p9 > .01; p=p9[ix]; n=p9.size-p.size;ix=p9>.33; n2=p9[ix].size; a=plt.hist(p,bins=100,color="b"); plt.text(0.4,17,"day 9: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2),color="b")

plt.ylim(0,28);plt.xlabel("probability of detection");plt.ylabel("N");plt.title("2015")

plt.savefig("hist-2015.pdf")

os.chdir("/data/des30.a/data/annis/des-gw/ligo/sims-2016-out")

sim,mjd,distance,p0,p1,p2,p3,p4,p5,p6,p7,p8,p9 = np.genfromtxt("all-maxtimes-2016.txt", unpack=True);

ix = p0 > .01; p=p0[ix]; n=p0.size-p.size; ix=p0>.33; n2=p0[ix].size; plt.clf();a=plt.hist(p,bins=100,color="k"); plt.text(0.4,23,"day 0: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2))

ix = p1 > .01; p=p1[ix]; n=p1.size-p.size;ix=p1>.33; n2=p1[ix].size; a=plt.hist(p,bins=100,color="r",histtype="step"); plt.text(0.4,21,"day 1: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2),color="r")

ix = p4 > .01; p=p4[ix]; n=p4.size-p.size;ix=p4>.33; n2=p4[ix].size; a=plt.hist(p,bins=100,color="g",histtype="step"); plt.text(0.4,19,"day 4: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2),color="g")

ix = p9 > .01; p=p9[ix]; n=p9.size-p.size;ix=p9>.33; n2=p9[ix].size; a=plt.hist(p,bins=100,color="b"); plt.text(0.4,17,"day 9: n with p < 0.01: {:3d} n with p > 0.33: {:3d}".format(n,n2),color="b")

plt.ylim(0,28);plt.xlabel("probability of detection");plt.ylabel("N");plt.title("2016")

plt.savefig("hist-2016.pdf")