def append_to_skymaps(row, times, data_map, ref_map, local_map,
n_side, n_resamples):
local_zenith = row['lap_zenith']
local_azimuth = row['lap_azimuth']
local_time = row['start_time_mjd']
# Local skymap
pix = hp.ang2pix(n_side, local_zenith, local_azimuth)
local_skymap[pix] += 1
# Data skymap
ra, dec = astro.dir_to_equa(local_zenith, local_azimuth, local_time)
hp_theta, hp_phi = equatorial_to_healpy(ra, dec)
pix = hp.ang2pix(n_side, hp_theta, hp_phi)
data_skymap[pix] += 1
# Reference skymap
rand_times = np.random.choice(times, size=n_resamples)
ra, dec = astro.dir_to_equa(local_zenith, local_azimuth, rand_times)
hp_theta, hp_phi = equatorial_to_healpy(ra, dec)
ref_skymap = append_time_scrambled_events(
hp_theta, hp_phi, ref_skymap,
n_side=n_side, n_resamples=20)
return
def make_skymaps(df, times, n_resamples=20, n_side=64, verbose=False):
n_pix = hp.nside2npix(n_side)
data_skymap = np.zeros(n_pix, dtype=float)
ref_skymap = np.zeros(n_pix, dtype=float)
local_skymap = np.zeros(n_pix, dtype=float)
for idx, row in df.iterrows():
local_zenith = row['lap_zenith']
local_azimuth = row['lap_azimuth']
local_time = row['start_time_mjd']
# Local skymap
pix = hp.ang2pix(n_side, local_zenith, local_azimuth)
local_skymap[pix] += 1
# Data skymap
ra, dec = astro.dir_to_equa(local_zenith, local_azimuth, local_time)
hp_theta, hp_phi = equatorial_to_healpy(ra, dec)
pix = hp.ang2pix(n_side, hp_theta, hp_phi)
data_skymap[pix] += 1
# Reference skymap
rand_times = np.random.choice(times, size=n_resamples)
ra, dec = astro.dir_to_equa(local_zenith, local_azimuth, rand_times)
hp_theta, hp_phi = equatorial_to_healpy(ra, dec)
ref_skymap = add_to_skymap(hp_theta, hp_phi, ref_skymap,
n_side=n_side, weights=1/n_resamples)
return data_skymap, ref_skymap, local_skymap
def get_coords(): ra86I_sim,dec86I_sim=astro.dir_to_equa(zen86I_sim,azi86I_sim,mjd86I_sim) ra86I_data,dec86I_data=astro.dir_to_equa(zen86I_data,azi86I_data,mjd86I_data) #scramble RA! We can't know this until unblinding. (Should I fix these in place, or renew them each time?) ra86I_sim = np.random.random(len(ra86I_sim))*2*np.pi ra86I_data = np.random.random(len(ra86I_data))*2*np.pi return ra86I_sim, dec86I_sim , ra86I_data, dec86I_data
def getDecRA(d, verbose=True):
if verbose:
print('Calculating equatorial coordinates...')
nevents = len(d['zenith'])
t = dc.I3Time()
# t = astro.Time()
# local = astro.LocalCoord()
# ice = astro.IceCubeDetector()
ra, dec = np.zeros((2, nevents))
for i in range(nevents):
zen = d['zenith'][i]
azi = d['azimuth'][i]
mjd = d['mjd'][i]
# t.set_mod_julian_time_double(mjd)
# t.SetTime(mjd)
# local.SetLocalRad(zen, azi)
# eqApparent = ice.LocalToEquatorial(local, t)
# dec[i] = eqApparent.GetDecRad()
# ra[i] = eqApparent.GetRaRad()
ra, dec = astro.dir_to_equa(zen, azi, mjd)
dec = np.pi/2. - dec
while ra.min() < 0:
ra[ra < 0] += 2*np.pi
if verbose:
print('Finished')
return dec, ra
def gen_appendix(frame, outcsv, inputparticle="OnlineL2_SplineMPE",
jsonfile="AlertShortFollowupMsg", eventheader="I3EventHeader"):
mpe = frame[inputparticle]
header = frame[eventheader]
info = frame[jsonfile]
message = info.value
time_mjd = header.start_time.mod_julian_day_double
zen_rad = mpe.dir.zenith
azi_rad = mpe.dir.azimuth
ra_rad, dec_rad = astro.dir_to_equa(zen_rad, azi_rad, time_mjd)
temp = flatten(eval(message.replace("null","None")))
df = pd.DataFrame(temp, index=[temp["event_id"]])
df_out = pd.DataFrame(data={"evtid":df.event_id.values[0],
"mjd":time_mjd,
"rec_x":mpe.pos.x,
"rec_y":mpe.pos.y,
"rec_z":mpe.pos.z,
"rec_t0":mpe.time,
"zen_rad":zen_rad,
"azi_rad":azi_rad,
"ra_rad":ra_rad,
"dec_rad":dec_rad},
index=df.run_id)
df_out.to_csv(outcsv, header=False)
def make_skymaps(df, times, n_side=64, verbose=False):
if not isinstance(df, pd.DataFrame):
raise ValueError('df must be a pandas DataFrame')
if not isinstance(times, np.ndarray):
raise ValueError('times must be a numpy array')
if not df.shape[0] == times.shape[0]:
raise ValueError('df and times are not compatible')
n_pix = hp.nside2npix(n_side)
data_skymap = np.zeros(n_pix, dtype=float)
ref_skymap = np.zeros(n_pix, dtype=float)
local_skymap = np.zeros(n_pix, dtype=float)
for idx, row in df.iterrows():
local_zenith = row['lap_zenith']
local_azimuth = row['lap_azimuth']
local_time = row['start_time_mjd']
# Local skymap
pix = hp.ang2pix(n_side, local_zenith, local_azimuth)
local_skymap[pix] += 1
# Data skymap
ra, dec = astro.dir_to_equa(local_zenith, local_azimuth, local_time)
hp_theta, hp_phi = equatorial_to_healpy(ra, dec)
pix = hp.ang2pix(n_side, hp_theta, hp_phi)
data_skymap[pix] += 1
# Reference skymap
rand_times = times[idx]
n_resamples = len(rand_times)
ra, dec = astro.dir_to_equa(local_zenith, local_azimuth, rand_times)
hp_theta, hp_phi = equatorial_to_healpy(ra, dec)
ref_skymap = add_to_skymap(hp_theta,
hp_phi,
ref_skymap,
n_side=n_side,
weights=1 / n_resamples)
return data_skymap, ref_skymap, local_skymap
def oversample(tmp_weight, tmp_nu_type,
tmp_energy_true, tmp_energy_reco,
tmp_zenith_true, tmp_zenith_reco,
tmp_azimuth_true, tmp_azimuth_reco,
nOversampling):
stime = time.mktime(time.strptime("1/1/2022/00/00/00", '%m/%d/%Y/%H/%M/%S'))
etime = time.mktime(time.strptime("1/1/2023/00/00/00", '%m/%d/%Y/%H/%M/%S'))
oversampled_psi_true = np.zeros((nOversampling,len(tmp_weight)))
oversampled_psi_reco = np.zeros((nOversampling,len(tmp_weight)))
oversampled_RA_reco = np.zeros((nOversampling,len(tmp_weight)))
oversampled_Dec_reco = np.zeros((nOversampling,len(tmp_weight)))
for i in range(nOversampling):
eventTime = stime + random.random() * (etime - stime)
eventTime = apTime(eventTime,format='unix').mjd
RA_true, DEC_true = astro.dir_to_equa(tmp_zenith_true,tmp_azimuth_true,eventTime)
RA_reco, DEC_reco = astro.dir_to_equa(tmp_zenith_reco,tmp_azimuth_reco,eventTime)
oversampled_psi_true[i] = astro.angular_distance(RA_true,DEC_true,GC_Pos[0],GC_Pos[1])
oversampled_psi_reco[i] = astro.angular_distance(RA_reco,DEC_reco,GC_Pos[0],GC_Pos[1])
oversampled_RA_reco[i] = RA_reco
oversampled_Dec_reco[i] = DEC_reco
# append n versions
oversampled_weight = np.tile(tmp_weight/nOversampling,nOversampling)
oversampled_nu_type = np.tile(tmp_nu_type,nOversampling)
oversampled_energy_true = np.tile(tmp_energy_true,nOversampling)
oversampled_energy_reco = np.tile(tmp_energy_reco,nOversampling)
return oversampled_weight, oversampled_nu_type, oversampled_energy_true, oversampled_energy_reco, oversampled_psi_true.flatten(), oversampled_psi_reco.flatten(), oversampled_RA_reco.flatten() , oversampled_Dec_reco.flatten()
def extract_MC_truth(state_dict):
if "GCDQp_packet" not in state_dict:
raise RuntimeError("GCDQp_packet not found in state_dict")
frame_packet = state_dict["GCDQp_packet"]
p_frame = frame_packet[-1]
if p_frame.Stop != icetray.I3Frame.Stream(
'p') and p_frame.Stop != icetray.I3Frame.Physics:
raise RuntimeError("last frame of GCDQp is neither Physics not 'p'")
q_frame = frame_packet[-2]
if q_frame.Stop != icetray.I3Frame.DAQ:
raise RuntimeError("second to last frame of GCDQp is not type Q")
if "I3MCTree_preMuonProp" not in q_frame:
return state_dict
mc_tree = q_frame["I3MCTree_preMuonProp"]
# find the muon
muon = None
for particle in mc_tree:
if particle.type not in [
dataclasses.I3Particle.ParticleType.MuPlus,
dataclasses.I3Particle.ParticleType.MuMinus
]:
continue
if muon is not None:
print("More than one muon in MCTree")
if particle.energy < muon.energy: continue
muon = particle
if muon is None:
# must be NC
return state_dict
# get event time
mjd = get_event_mjd(state_dict)
# convert to RA and dec
ra, dec = astro.dir_to_equa(muon.dir.zenith, muon.dir.azimuth, mjd)
ra = float(ra)
dec = float(dec)
dec = dec
state_dict['MCradec'] = (ra, dec)
return state_dict
parser = argparse.ArgumentParser(
description='Calculate opening angle between monopod and pegleg')
parser.add_argument('--infile',
type=str,
required=True,
help='Numpy file to read in')
args = parser.parse_args()
from numpy.lib.recfunctions import append_fields
neutrinos = np.load(args.infile)
mzens = neutrinos['monopod_zen'].astype(float)
mazis = neutrinos['monopod_azi'].astype(float)
times = neutrinos['time'].astype(float)
monopod_ra, monopod_dec = astro.dir_to_equa(mzens, mazis, times)
monopod_pegleg_dpsi = hp.rotator.angdist(
[np.rad2deg(neutrinos['ra']),
np.rad2deg(neutrinos['dec'])],
[np.rad2deg(monopod_ra), np.rad2deg(monopod_dec)],
lonlat=True)
newsavefile = append_fields(neutrinos, 'monopod_ra', monopod_ra, usemask=False)
newsavefile = append_fields(newsavefile,
'monopod_dec',
monopod_dec,
usemask=False)
newsavefile = append_fields(newsavefile,
'monopod_pegleg_dpsi',
monopod_pegleg_dpsi,
required=True,
help='Numpy file to read in')
args = parser.parse_args()
from numpy.lib.recfunctions import append_fields
neutrinos = np.load(args.infile)
true_dpsi = hp.rotator.angdist(
[np.rad2deg(neutrinos['ra']),
np.rad2deg(neutrinos['dec'])],
[np.rad2deg(neutrinos['trueRa']),
np.rad2deg(neutrinos['trueDec'])],
lonlat=True)
monopod_ra, monopod_dec = astro.dir_to_equa(
neutrinos['monopod_zen'].astype(float),
neutrinos['monopod_azi'].astype(float), neutrinos['time'].astype(float))
monopod_pegleg_dpsi = hp.rotator.angdist(
[np.rad2deg(neutrinos['ra']),
np.rad2deg(neutrinos['dec'])],
[np.rad2deg(monopod_ra), np.rad2deg(monopod_dec)],
lonlat=True)
newsavefile = append_fields(neutrinos, 'monopod_ra', monopod_ra, usemask=False)
newsavefile = append_fields(newsavefile,
'monopod_dec',
monopod_dec,
usemask=False)
newsavefile = append_fields(newsavefile,
'monopod_pegleg_dpsi',
from load_model import *
parser = argparse.ArgumentParser(
description='Calculate opening angle between monopod and pegleg')
parser.add_argument('--infiles',
type=str,
required=True,
help='Numpy file to read in')
args = parser.parse_args()
infiles = glob(args.infiles)
for infile in infiles:
events = np.load(infile)
monopod_ra, monopod_dec = astro.dir_to_equa(
events['monopod_zen'].astype(float),
events['monopod_azi'].astype(float), events['time'].astype(float))
monopod_pegleg_dpsi = hp.rotator.angdist(
[np.rad2deg(events['ra']),
np.rad2deg(events['dec'])],
[np.rad2deg(monopod_ra),
np.rad2deg(monopod_dec)],
lonlat=True)
#Add some fields to the numpy recarray
appended_events = append_fields(events,
'monopod_ra',
monopod_ra,
usemask=False)
appended_events = append_fields(appended_events,
