def simulationRun(t, running):
loop = 0
delta_t = 100.
r_offset = [0., 0., 0.]
v_offset = [0., 0., 0.]
while running:
for objCount in range(0, maxObjCount):
r_true, v_true, h_true = p[objCount].propagate(t)
# print "r,v,h: ",t, r_true, v_true, h_true
r_obs, v_obs, h_obs = p[objCount].observe(r_true, v_true, r_offset,
v_offset)
# Evaluation process
# - when new data is received compute element set
# - attempt to correlate element set with previously received data
# - if correlates, then evaluate statistics of data for this object
# - determine if any other object represents a collision hazard with this object
obs.model(t, r_obs, v_obs, h_obs)
t = t + delta_t
loop = loop + 1
if loop > 4:
running = False
return t
def __init__(self,fieldid,cadence,X1,Color,telescope):
X1_Color=[(X1,Color)]
zrange=[0.01]
zrange+=[val for val in np.arange(0.1,1.5,0.1)]
DayMax_step=0.5
filename='Observations/Obs_'+str(fieldid)+'_'+str(cadence)+'.txt'
obs_tot=Observations(fieldid=fieldid, filename=filename)
season=0
obs=obs_tot.seasons[season]
r=[]
mjds=np.arange(min(obs['mjd']),max(obs['mjd']),DayMax_step)
for z in zrange:
for mjd in mjds:
for (x1,c) in X1_Color:
r.append((z,x1,c,mjd))
params=np.rec.array(r, dtype=[('z', 'f8'),('X1', 'f8'), ('Color', 'f8'),('DayMax','f8')])
name='Light_Curves/Simul_'+str(fieldid)+'_Cadence_'+str(cadence)+'_X1_'+str(X1_Color[0][0])+'_Color_'+str(X1_Color[0][1])+'.pkl'
#simulate
self.Simul_LCs(params,obs,telescope,fname=name)
def Median_log_summary(fi, bands, j, out_q=None):
r = []
fieldid = (fi.split('/')[-1]).split('_')[1]
fieldid = int(fieldid.split('.')[0])
obs = Observations(fieldid,
filename=fi,
season_length=120.,
names=['observationStartMJD', 'fieldRA', 'fieldDec'])
#print(fi,len(obs.seasons))
for i in range(len(obs.seasons)):
season = obs.seasons[i]
duration_tot = np.max(season['observationStartMJD']) - np.min(
season['observationStartMJD'])
#print(i,season)
for band in bands:
idx = season['filter'] == band
sel = season[idx]
if len(sel) >= 1:
m5 = np.median(sel['fiveSigmaDepth'])
airmass = np.median(sel['airmass'])
sky = np.median(sel['skyBrightness'])
seeing = np.median(sel['finSeeing'])
moon = np.median(sel['moonPhase'])
moondist = np.median(sel['moonDistance'])
moonRA = np.median(sel['moonRA'])
moonDec = np.median(sel['moonDec'])
moonAlt = np.median(sel['moonAlt'])
moonAz = np.median(sel['moonAz'])
cloud = np.median(sel['cloud'])
cadence = np.median(sel['observationStartMJD'][1:] -
sel['observationStartMJD'][:-1])
ra_sn = np.median(sel['RA_SN'])
dec_sn = np.median(sel['Dec_SN'])
nvisits = np.median(sel['Nvisits'])
duration_band = np.max(sel['observationStartMJD']) - np.min(
sel['observationStartMJD'])
r.append(
(fieldid, ra_sn, dec_sn, band, cadence, m5, airmass, sky,
seeing, moon, nvisits, moondist, moonRA, moonDec, moonAlt,
moonAz, cloud, duration_band, duration_tot))
res = None
if len(r) > 0:
res = np.rec.fromrecords(r,
names=[
'fieldid', 'RA_SN', 'Dec_SN', 'band',
'cadence', 'm5', 'airmass',
'skyBrightness', 'finSeeing', 'moonPhase',
'Nvisits', 'moonDistance', 'moonRA',
'moonDec', 'moonAlt', 'moonAz', 'cloud',
'seas_length_band', 'seas_length_all'
])
if out_q is not None:
out_q.put({j: res})
else:
return res
def Process(fieldname,
fieldids,
simu_name,
thedir,
ifirst,
ilast,
inum,
season,
coadd=True):
myobs = {}
mylist = {}
season_loop = [season]
if season == -1:
season_loop = range(10)
print('season loop', season_loop)
for seasonl in season_loop:
mylist[seasonl] = []
for i, fieldid in enumerate(fieldids[ifirst:ilast]):
name = 'Observations_' + fieldname + '_' + str(fieldid) + '.txt'
obs = Observations(fieldid=fieldid,
filename=thedir + '/' + name,
season_length=50.)
#print('Nseasons',len(obs.seasons))
myobs[fieldid] = {}
for seasonl in season_loop:
#myobs[fieldid][season]=obs.seasons[season]
if seasonl in range(len(obs.seasons)):
mylist[seasonl].append((fieldid, obs.seasons[seasonl]))
#print(len(fieldids),ifirst,ilast,len(myobs))
bands = 'ugrizy'
tot_tab = None
tab_all = None
coadd = coadd
suffend = ''
if coadd:
suffend = '_all'
ifile = 0
"""
mylist=[]
for key,valb in myobs.items():
print(key,valb.keys())
if season in valb.keys():
mylist.append((key,valb[season]))
"""
#print('hello',len(mylist))
#inum=1
name = 'Cadence' + extent + '_' + fieldname + suffend
#tot_tab=Multi(mylist,tot_tab,coadd,season,name)
for seasonl in season_loop:
print('processing', seasonl)
Multi(mylist[seasonl], coadd, seasonl, name)
"""
def Get_duration(fieldname, fieldid, season, OpSim_Logs_dir):
filename = OpSim_Logs_dir + '/Observations_' + fieldname + '_' + str(
fieldid) + '.txt'
myobs = Observations(fieldid=fieldid, filename=filename)
myseason = myobs.seasons[season]
iddx = myseason['band'] != 'LSSTPG::u'
mysel = myseason[iddx]
min_season = np.min(mysel['mjd'])
max_season = np.max(mysel['mjd'])
return min_season, max_season
def Median_log_summary_old(files, bands, j, out_q=None):
r = []
#print('files',files)
#for fi in files[:1000]:
for fi in files:
print('hello', fi)
fieldid = (fi.split('/')[-1]).split('_')[1]
fieldid = int(fieldid.split('.')[0])
obs = Observations(
fieldid,
filename=fi,
season_length=365.,
names=['observationStartMJD', 'fieldRA', 'fieldDec'])
#print(fi,len(obs.seasons))
for i in range(len(obs.seasons)):
season = obs.seasons[i]
#print(i,season)
for band in bands:
idx = season['filter'] == band
sel = season[idx]
if len(sel) >= 2:
m5 = np.median(sel['fiveSigmaDepth'])
airmass = np.median(sel['airmass'])
sky = np.median(sel['skyBrightness'])
seeing = np.median(sel['finSeeing'])
moon = np.median(sel['moonPhase'])
cadence = np.median(sel['observationStartMJD'][1:] -
sel['observationStartMJD'][:-1])
ra_sn = np.median(sel['RA_SN'])
dec_sn = np.median(sel['Dec_SN'])
r.append((fieldid, ra_sn, dec_sn, band, cadence, m5,
airmass, sky, seeing, moon))
res = np.rec.fromrecords(r,
names=[
'fieldid', 'RA_SN', 'Dec_SN', 'band',
'cadence', 'm5', 'airmass', 'skyBrightness',
'finSeeing', 'moonPhase'
])
if out_q is not None:
out_q.put({j: res})
else:
return res
def Load_file(fi, j, out_q=None):
#print('here',fi)
fieldid = (fi.split('/')[-1]).split('_')[1]
fieldid = int(fieldid.split('.')[0])
obs = Observations(fieldid,
filename=fi,
season_length=365.,
names=['observationStartMJD', 'fieldRA', 'fieldDec'])
#print(len(obs.seasons))
res = None
if len(obs.seasons) > 0:
res = obs.seasons[0]
if out_q is not None:
out_q.put({j: res})
else:
return res
def generate_graph(self, postal_code, country_code, start, end,
selected_metrics, graph_type, grid):
observations = Observations.Observations(postal_code, country_code,
start, end)
figure(num=None, figsize=(18, 6), dpi=80, facecolor='w', edgecolor='k')
vs_titles = ''
for metric in selected_metrics:
title = stringcase.titlecase(metric)
metric_values = observations.get_values_by_key(metric)
label = title + ' ' + metric_values['unit_code']
if graph_type == 'bar':
plt.bar(metric_values['timestamps'],
metric_values['values'],
label=label)
elif graph_type == 'barh':
plt.barh(metric_values['timestamps'],
metric_values['values'],
label=label)
elif graph_type == 'scatter':
plt.scatter(metric_values['timestamps'],
metric_values['values'],
label=label)
else:
plt.plot(metric_values['timestamps'],
metric_values['values'],
label=label)
if vs_titles == '':
vs_titles += title
else:
vs_titles += ' vs. ' + title
plt.xlabel("Date")
plt.ylabel("Value")
plt.title(
f"{vs_titles}\n{postal_code}, {country_code}\nFrom {start} to {end}"
)
plt.legend()
plt.grid(grid)
plt.show()
self.update_status('Graph Successfully Created!', 'green')
min_rf_phase = opts.min_rf_phase
max_rf_phase = opts.max_rf_phase
T0min = opts.T0min
T0max = opts.T0max
dirout = opts.dirout
multiproc = opts.multiproc
X1C = opts.X1C
T0step = opts.T0step
OpSim_Logs_dir = os.getenv('OPSIM_LOGS')
filename = OpSim_Logs_dir + '/' + opts.dirmeas + '/Observations_' + opts.fieldname + '_' + str(
fieldid) + '.txt'
#filename='OpSimLogs/WFD/Observations_WFD_'+str(fieldid)+'.txt'
myobs = Observations(fieldid=fieldid, filename=filename)
dirout += '_' + str(T0step).replace('.', '_')
if X1C != '':
dirout += X1C
outdir = '/sps/lsst/data/dev/pgris/' + dirout + '/' + opts.dirmeas + '/' + str(
fieldid) + '/Season_' + str(num_season)
if not os.path.exists(outdir):
os.makedirs(outdir)
print(len(myobs.seasons))
tab_X1_c = None
if X1 == -999. or Color == -999.:
pkl_file = open('Map_X1_C' + X1C + '.pkl', 'rb')
tab_X1_c = pkl.load(pkl_file)
import numpy as np
from Observations import *
import pylab as plt
import heapq
import pickle as pkl
thedir = '/sps/lsst/users/gris/Files_from_OpSim/OpSimLogs'
fieldname = 'WFD'
fieldid = 586
name = 'Observations_' + fieldname + '_' + str(fieldid) + '.txt'
myobs = Observations(fieldid=fieldid,
filename=thedir + '/' + fieldname + '/' + name)
"""
dir_pkl='/sps/lsst/data/dev/pgris/sims_operations/SN_LC/Obs_minion_1016'
name_pkl='Observations_'+fieldname+'_'+str(fieldid)+'.pkl'
pkl_file = open(dir_pkl+'/'+name_pkl,'rb')
thedict=pkl.load(pkl_file)
obser_orig=thedict['dataSlice']
"""
#print len(myobs.seasons),obser_orig.dtype
for seas in range(10):
obs_tot = myobs.seasons[seas]
for band in 'g':
idx = obs_tot['band'] == 'LSSTPG::' + band
obs = obs_tot[idx]
obs = obs_tot
diff = obs['mjd'][1:] - obs['mjd'][:-1]
#thedir='/sps/lsst/users/gris/Files_from_OpSim/OpSimLogs'
thedir = '/pbs/throng/lsst/users/gris/SN_Simulation/Analyze_OpSim/OpSimLogs'
thedir += simuname
files = glob.glob(thedir + '/WFD/Year_2/N_healpix_64_coadd/*.txt')
#print(files)
r = []
num = -1
for fi in files:
spl = fi.split('/')[-1]
fieldid = spl.split('_')[-1].split('.')[0]
myobs = Observations(fieldid=int(fieldid), filename=fi, season_length=365.)
if len(myobs.seasons) >= 1:
season = myobs.seasons[0]
if len(season) >= 100:
#print fieldid,len(season)
num += 1
rcad = []
for band in 'ugrizy':
idx = season['band'] == 'LSSTPG::' + band
sel = season[idx]
diff = sel['mjd'][1:] - sel['mjd'][:-1]
if len(sel) >= 2:
#print fieldid,band,len(season),np.median(diff)
#r.append((fieldid,band,np.median(diff)))
rcad.append(np.median(diff))
else:
import Projectile as proj
import Observations as obs
import numpy as np
maxObjCount = 4
t = 0.0
obs = obs.Observations()
p = [None] * maxObjCount
def createObject(objCount):
# initialize element set with this TLE
# 1 05398U 71067E 17237.19596243 .00000191 00000-0 60767-4 0 9996
# 2 05398 87.6282 147.4622 0065864 59.7371 301.0323 14.33603389412679
# Semi-major axis
a = 7157.e3
# eccentricity
e = 0.0065864
# inclination
if (objCount == 1):
i = proj.deg2radian(1.0)
else:
i = proj.deg2radian(87.6282)
# argument of perigee
omega_AP = proj.deg2radian(59.7371)
# longitude of the acsending node
if (objCount < 2):
ra=[]
corresp=dict(zip(bands+'a',[i for i in range(len(bands)+1)]))
corresp_inverted=dict(zip([i for i in range(len(bands)+1)],bands+'a'))
field_radec=[]
thedir='OpSimLogs'+suffix+'/'+fieldname
files=glob.glob(thedir+'/*_'+fieldname+'_*.txt')
print 'alors',files,thedir+'/*_'+fieldname+'_*.txt'
myobs={}
for fi in files:
splita=fi.split('/')[-1]
splitb=splita.split('_')
rav=float(splitb[-2])
decv=float(splitb[-1].split('.txt')[0])
fieldid=str(rav)+'_'+str(decv)
myobs[fieldid]=Observations(fieldid=fieldid, filename=fi)
all_diff={}
for key, vals in myobs.items():
print key,len(vals.seasons)
all_diff[key]={}
print 'nombre de saison',key,len(vals.seasons)
for season in range(10):
myseason=vals.seasons[season]
all_diff[key][season]={}
full_season=myseason.copy()
full_season.sort(order='mjd')
idx = full_season['band'] != 'LSSTPG::u'
oob = obs['mjd'][1:] - obs['mjd'][:-1]
return np.median(oob)
def sel(tab, band):
idx = tab['band'] == 'LSSTPG::' + band
return tab[idx]
filea = '../../Ana_Cadence/OpSimLogs_test/DD/Observations_DD_290.txt'
fileb = 'OpSimLogs/DD/Observations_DD_290.txt'
obsa = Observations(fieldid=290, filename=filea)
obsb = Observations(fieldid=290, filename=fileb)
bands = 'g'
print len(obsa.seasons), len(obsb.seasons)
for season in range(len(obsa.seasons)):
seasa = obsa.seasons[season]
seasb = obsb.seasons[season]
#print seasa
for band in bands:
sela = sel(seasa, band)
selb = sel(seasb, band)
print Cadence(sela), Cadence(selb), len(sela), len(selb)
for i in range(len(sela)):