def p9figs(events=None, directory='./', idl=False, titles=True,
isinteractive=isinteractive):
if events == None:
events = p7Restore(directory, idl)
nummodels = np.array([len(event.params.model) for event in events],
dtype=int)
printout = po.init(events[0].params.printout, events)
for j, event in enumerate(events):
print("\n" + event.eventname, file=printout)
nummin = nummodels.min()
for i in range(nummin):
print("\nCurrent model = " + str(event.params.model[i]),
file=printout)
p9.figs (event, i, j*nummin+i, titles=titles)
po.close(printout)
if isinteractive == False:
plt.close('all')
else:
plt.show()
return
def p9figs(event=None,
filedir='.',
topdir=None,
idl=False,
isinteractive=True):
import p9figs as p9
#reload(p9)
#global numevents, nummodels, isinteractive
if event == None:
event = p7Restore(filedir, topdir, idl)
cwd = os.getcwd().split("/")
if cwd[-1] == event[0].modeldir:
os.chdir('..')
printout = printoutput.init(event[0].params.printout, event)
numevents = len(event)
nummodels = np.zeros(numevents, dtype=int)
for j in range(numevents):
nummodels[j] = len(event[j].params.model)
for j in range(numevents):
print("\n" + event[j].eventname, file=printout)
for i in range(nummodels.min()):
print("\nCurrent model = " + str(event[j].params.model[i]),
file=printout)
p9.figs(event[j], i, j * nummodels.min() + i)
printoutput.close(printout)
if isinteractive == False:
plt.close('all')
else:
plt.show()
return
def p7anal(events=None, directory='./', idl=False, islak=False,
isinteractive=isinteractive):
if events == None:
events = p6Restore(directory, idl)
nummodels = np.array([len(event.params.model) for event in events],
dtype=int)
printout = po.init(events[0].params.printout, events)
for j, event in enumerate(events):
print("\n" + event.eventname, file=printout)
minnum = nummodels.min()
for i in range(minnum):
print("\nCurrent model = " + str(event.params.model[i]),
file=printout)
p7.stdanal(event, event.fit[i], j*minnum+i,
printout, islak)
p7Save(event, directory)
po.close(printout)
if isinteractive == False:
plt.close('all')
else:
plt.show()
return
def p8tables(events=None, directory='./', idl=False, eclphase=0.5):
if events == None:
events = p7Restore(directory, idl)
nummodels = np.array([len(event.params.model) for event in events],
dtype=int)
printout = po.init(events[0].params.printout, events)
for event in events:
print("\n" + event.eventname, file=printout)
event.meanphase = eclphase
for i in range(nummodels.min()):
print("\nCurrent model = " + str(event.params.model[i]),
file=printout)
p8.tables(event, i, printout)
po.close(printout)
return
def p8tables(event=None, filedir='.', topdir=None, idl=False, eclphase=0.5):
import p8tables as p8
#reload(p8)
#global numevents, nummodels
if event == None:
event = p7Restore(filedir, topdir, idl)
cwd = os.getcwd().split("/")
if cwd[-1] == event[0].modeldir:
os.chdir('..')
printout = printoutput.init(event[0].params.printout, event)
for j in range(numevents):
print("\n" + event[j].eventname, file=printout)
event[j].meanphase = eclphase
for i in range(nummodels.min()):
print("\nCurrent model = " + str(event[j].params.model[i]),
file=printout)
p8.tables(event[j], i, printout)
printoutput.close(printout)
return
def p6model(event=None,
newdir=True,
filedir='..',
topdir=None,
clip=None,
idl=False,
isinteractive=True):
"""
"""
import p6model as p6
reload(p6)
#global numevents, nummodels, isinteractive
if event == None:
if idl == False:
event = poetRestore(filedir, topdir, clip)
else:
event = p5idlRestore(filedir)
if len(event) == 0:
print("Event object is empty.")
return
if newdir == True:
#CREATE NEW DIRECTORY FOR MODEL RUN
modeldir = datetime.datetime.strftime(datetime.datetime.today(),
"%Y-%m-%d_%H:%M")
os.mkdir(modeldir)
elif type(newdir) == str:
#CREATE NEW DIRECTORY FOR MODEL RUN WITH CUSTOM NAME
modeldir = datetime.datetime.strftime(datetime.datetime.today(),
"%Y-%m-%d_%H:%M") + "-" + newdir
os.mkdir(modeldir)
else:
try:
modeldir = event[0].modeldir
except:
print("Model directory has not been specified.")
return
#RELOAD MODEL PARAMETERS
numevents = len(event)
nummodels = np.zeros(numevents, dtype=int)
for j in range(numevents):
exec('import ' + event[j].eventname + '_params as op' + str(j))
exec("reload(op" + str(j) + ")")
event[j].params = readeventhdf.initParams()
exec("op" + str(j) + ".modelparams(event[" + str(j) + "].params)")
#exec("event[" + str(j) + "].params = op" + str(j) + ".params")
event[j].fit = []
event[j].modeldir = modeldir
if hasattr(
event[j].params,
'noisewavelet') and event[j].params.noisewavelet[0] == 'all':
event[j].params.noisewavelet = pywt.wavelist(kind='discrete')
nummodels[j] = len(pywt.wavelist(kind='discrete'))
event[j].params.model = [
event[j].params.model[0] for i in range(nummodels[j])
]
else:
nummodels[j] = len(event[j].params.model)
if j > 0 and nummodels[j] != nummodels[j - 1]:
print("WARNING: Number of models in each event does not match.")
#INITIALIZE OUTPUT TYPE: stdout, A FILE OBJECT OR A FILE
printout = printoutput.init(event[0].params.printout, event)
#Execute rundmc
for i in range(nummodels.min()):
p6.rundmc(event, i, printout, isinteractive=isinteractive)
if hasattr(event[0].params,
'savedata') and event[0].params.savedata == False:
pass
else:
for j in range(numevents):
p6Save(event[j])
#PRINT PARAMETERS USED FOR COMPARISON
print("\nBest-fit eclipse depths or transit radius ratios with errors:",
file=printout)
for j in range(numevents):
event[j].minbic = np.inf #Minimum BIC value of all fits for one event.
print(event[j].eventname, file=printout)
for i in range(len(event[j].fit)):
if hasattr(event[j].fit[i].i, 'depth'):
print(event[j].fit[i].bestp[event[j].fit[i].i.depth],
event[j].fit[i].medianp[event[j].fit[i].i.depth, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'depth2'):
print(event[j].fit[i].bestp[event[j].fit[i].i.depth2],
event[j].fit[i].medianp[event[j].fit[i].i.depth2, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'depth3'):
print(event[j].fit[i].bestp[event[j].fit[i].i.depth3],
event[j].fit[i].medianp[event[j].fit[i].i.depth3, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'trqrprs'):
print(event[j].fit[i].bestp[event[j].fit[i].i.trqrprs],
event[j].fit[i].medianp[event[j].fit[i].i.trqrprs, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'trq2rprs'):
print(event[j].fit[i].bestp[event[j].fit[i].i.trq2rprs],
event[j].fit[i].medianp[event[j].fit[i].i.trq2rprs, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'trrprs'):
print(event[j].fit[i].bestp[event[j].fit[i].i.trrprs],
event[j].fit[i].medianp[event[j].fit[i].i.trrprs, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'trrprs2'):
print(event[j].fit[i].bestp[event[j].fit[i].i.trrprs2],
event[j].fit[i].medianp[event[j].fit[i].i.trrprs2, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'rprs'):
print(event[j].fit[i].bestp[event[j].fit[i].i.rprs],
event[j].fit[i].medianp[event[j].fit[i].i.rprs, 1],
event[j].fit[i].saveext,
file=printout)
if hasattr(event[j].fit[i].i, 'rprs2'):
print(event[j].fit[i].bestp[event[j].fit[i].i.rprs2],
event[j].fit[i].medianp[event[j].fit[i].i.rprs2, 1],
event[j].fit[i].saveext,
file=printout)
event[j].minbic = np.min((event[j].minbic, event[j].fit[i].bic))
print(
"\n S/N SDNR \xce\x94BIC MODEL NUMIT BIN_SZ(y,x) MinNumPts Wavelet",
file=printout)
#Delta = '\xce\x94' in utf-8
for j in range(numevents):
print(event[j].eventname, file=printout)
minbic = event[j].minbic
for i in range(len(event[j].fit)):
try:
sdnr = event[j].fit[i].sdnr
bic = event[j].fit[i].bic - minbic
model = event[j].fit[i].saveext
numit = event[j].params.numit[1]
if len(event[j].params.ystep) == len(event[j].params.model):
ystep, xstep = event[j].params.ystep[i], event[
j].params.xstep[i]
else:
ystep, xstep = event[j].params.ystep[0], event[
j].params.xstep[0]
if len(event[j].params.minnumpts) == len(
event[j].params.model):
minnumpts = event[j].params.minnumpts[i]
else:
minnumpts = event[j].params.minnumpts[0]
if event[j].params.model[i].__contains__('rednoise'):
if len(event[j].params.noisewavelet) == len(
event[j].params.model):
wavelet = event[j].params.noisewavelet[i]
else:
wavelet = event[j].params.noisewavelet[0]
else:
wavelet = 'None'
snrkeys = 'depth', 'depth2', 'depth3', 'trrprs', 'trrprs2', 'trqrprs', 'trq2rprs', 'rprs', 'rprs2', 'fpfs', 'btrprs', 'berprs'
for snrkey in snrkeys:
if hasattr(event[j].fit[i].i, snrkey):
snr = event[j].fit[i].bestp [getattr(event[j].fit[i].i, snrkey)] / \
event[j].fit[i].medianp[getattr(event[j].fit[i].i, snrkey), 1]
print(
'%8.4f %9.7f %8.1f %11s %7.1e %6.3f,%5.3f %4.0f %12s'
% (snr, sdnr, bic, model, numit, ystep, xstep,
minnumpts, wavelet),
file=printout)
except:
print("Error calculating values. %13s" %
event[j].fit[i].saveext,
file=printout)
printoutput.close(printout)
if isinteractive == False:
plt.close('all')
else:
plt.show()
return
def p6model(events=None, directory='./', modeldir=None, clip=None,
idl=False, mode='full', numit=None, nodate=True, rundir=None,
justplot=False, isinteractive=isinteractive, control=None,
retmoddir=False, params_override=None):
# adjust modeldir
if modeldir is None or not nodate:
date = datetime.datetime.today().strftime("%Y-%m-%d_%H_%M")
# if modeldir is not None:
# modeldir = date + modeldir + '/'
# If a control is given, recursively
# call on all paths specified in control.
if control is not None:
if isinstance(control, dict):
p6pcf = rd.Pcf(control)
else:
p6pcf = rd.read_pcf(control, 'p6', simple=True,
d1lists=["centering", "photometry"],
d2lists=["path"])
paths = getp6filedirs(p6pcf, directory)
if modeldir is None:
modeldir = p6pcf.modeldir
if modeldir is None:
modeldir = date
elif not nodate:
modeldir = date + modeldir + '/'
if p6pcf.threads < 1:
print("ERROR: less than 1 threads specified in p6.pcf."
"\nExiting...")
return
procs = []
for path in paths:
while len(mp.active_children()) >= p6pcf.threads:
time.sleep(10)
p = mp.Process(target=p6model,
kwargs={"events":events, "directory":path,
"modeldir":modeldir, "clip":clip,
"idl":idl, "mode":mode, "rundir":rundir,
"numit":numit, "nodate":True,
"isinteractive":isinteractive,
"control":None,
"params_override":params_override})
p.start()
procs.append(p)
for proc in procs:
proc.join()
print("Making p6 plots.")
if retmoddir:
return modeldir, plots.p6plots(modeldir, paths, directory)
return plots.p6plots(modeldir, paths, directory)
if modeldir is None:
modeldir = date
# Create new directory for model run with custom name:
if os.path.isdir(directory + '/' + modeldir):
print("path :", directory + '/' + modeldir,
"already exists.\nSkipping this p6 run...")
return "skip"
try:
os.mkdir(directory + '/' + modeldir)
except:
print("WARNING: Specified model directory already exists.")
if events == None:
if idl:
events = p5idlRestore(directory)
else:
events = poetRestore(directory, clip, rundir, modeldir,
params_override=params_override)
numevents = len(events)
if numevents == 0:
print("Event object is empty.")
return
# Reload model parameters:
nummodels = np.zeros(numevents, dtype=int)
# attributes to be converted to 1d-lists
d1lists = ['modelfile', 'numit', 'preclip', 'postclip',
'priorvars', 'xstep', 'ystep', 'minnumpts',
'sssteps', 'nx', 'ny', 'sx', 'sy']
# attributes to be converted to 2d-lists
d2lists = ['model', 'ortholist', 'interclip', 'priorvals',
'ipclip']
# attributes to be converted to 2d-lists
nparrs = ['numit', 'priorvals']
for i, event in enumerate(events):
event.params = rd.read_pcf(event.paramsfile, 'params',
simple=True, d1lists=d1lists,
d2lists=d2lists, nparrs=nparrs)
if params_override:
for key in params_override:
setattr(event.params, key, params_override[key])
event.fit = []
event.modeldir = directory + '/' + modeldir
nummodels[i] = len(event.params.model)
if i > 0 and nummodels[i] != nummodels[i-1]:
print("WARNING: Number of models in each event does not"
+ "match.")
# Initialize output type: stdout, a file object or a file
printout = po.init(events[0].params.printout, events)
# Execute p6 setup
for i in range(nummodels.min()):
p6.setup(events, i, printout, mode=mode, numit=numit)
printout = po.init(events[0].params.printout, events)
dataout = open(directory + '/plotpoint.txt', 'a')
for i in range(nummodels.min()):
p6.finalrun(events, i, printout, numit=numit)
for event in events:
p6Save(event, directory + '/' + modeldir)
# Print parameters used for comparison:
print("\nBest-fit eclipse depths or transit radius ratios with"
+ " errors:", file=printout)
for event in events:
event.minbic = np.inf # Min BIC value of all fits for one
# event
print(event.eventname, file=printout)
print(event.eventname, file=dataout)
for fit in event.fit:
if hasattr(fit.i,'depth'):
print(fit.bestp[fit.i.depth],
fit.std [fit.i.depth],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.depth],
fit.std [fit.i.depth],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'depth2'):
print(fit.bestp[fit.i.depth2],
fit.std [fit.i.depth2],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.depth2],
fit.std [fit.i.depth2],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'depth3'):
print(fit.bestp[fit.i.depth3],
fit.std [fit.i.depth3],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.depth3],
fit.std [fit.i.depth3],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'trrprs'):
print(fit.bestp[fit.i.trrprs],
fit.std [fit.i.trrprs],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.trrprs],
fit.std [fit.i.trrprs],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'trrprs2'):
print(fit.bestp[fit.i.trrprs2],
fit.std [fit.i.trrprs2],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.trrprs2],
fit.std [fit.i.trrprs2],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'rprs'):
print(fit.bestp[fit.i.rprs],
fit.std [fit.i.rprs],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.rprs],
fit.std [fit.i.rprs],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'rprs2'):
print(fit.bestp[fit.i.rprs2],
fit.std [fit.i.rprs2],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.rprs2],
fit.std [fit.i.rprs2],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'trdepth'):
print(fit.bestp[fit.i.trdepth],
fit.std [fit.i.trdepth],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.trdepth],
fit.std [fit.i.trdepth],
fit.saveext, fit.bsigchi, file=dataout)
if hasattr(fit.i,'gdepth'):
print(fit.bestp[fit.i.gdepth],
fit.std [fit.i.gdepth],
fit.saveext, fit.bsigchi, file=printout)
print(fit.bestp[fit.i.gdepth],
fit.std [fit.i.gdepth],
fit.saveext, fit.bsigchi, file=dataout)
event.minbic = np.min((event.minbic,fit.bic))
# Print SDNR for joint fit if more than one event:
if len(events) > 1:
# Joint-fit normalized residuals:
jnres = np.array([])
for event in events:
# Force the normalized residuals to 1D shape:
nres = event.fit[0].normresiduals.flatten()
# Concatenate values:
jnres = np.concatenate((jnres,nres))
# Joint SDNR:
jsdnr = np.std(jnres)
print("\nJoint-fit SDNR: %9.7f"%jsdnr, file=printout)
print("\n S/N SDNR \xce\x94BIC MODEL NUMIT "
+ "BIN_SZ(y,x) MinNumPts", file=printout)
# Delta = '\xce\x94' in utf-8
for event in events:
print(event.eventname, file=printout)
print(event.eventname, file=dataout)
minbic = event.minbic
for i, fit in enumerate(event.fit):
try:
sdnr = fit.sdnr
bic = fit.bic - minbic
model = fit.saveext
numit = event.params.numit[1]
if len(event.params.ystep) == \
len(event.params.model):
ystep, xstep = event.params.ystep[i],\
event.params.xstep[i]
else:
ystep, xstep = event.params.ystep[0],\
event.params.xstep[0]
if len(event.params.minnumpts) == \
len(event.params.model):
minnumpts = event.params.minnumpts[i]
else:
minnumpts = event.params.minnumpts[0]
# extract string code
ec ='%8.4f %9.7f %8.1f %11s %7.1e %6.3f,%5.3f %4.0f'
print(sdnr, file=dataout)
if hasattr(fit.i,'depth'):
snr = fit.bestp[fit.i.depth] /\
fit.std [fit.i.depth]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'depth2'):
snr = fit.bestp[fit.i.depth2] /\
fit.std [fit.i.depth2]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'depth3'):
snr = fit.bestp[fit.i.depth3] /\
fit.std [fit.i.depth3]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'trrprs'):
snr = fit.bestp[fit.i.trrprs] /\
fit.std [fit.i.trrprs]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'trrprs2'):
snr = fit.bestp[fit.i.trrprs2] /\
fit.std [fit.i.trrprs2]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'rprs'):
snr = fit.bestp[fit.i.rprs] /\
fit.std [fit.i.rprs]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'rprs2'):
snr = fit.bestp[fit.i.rprs2] /\
fit.std [fit.i.rprs2]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'trdepth'):
snr = fit.bestp[fit.i.trdepth] /\
fit.std [fit.i.trdepth]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
if hasattr(fit.i,'gdepth'):
snr = fit.bestp[fit.i.gdepth] /\
fit.std [fit.i.gdepth]
print(ec%(snr, sdnr, bic, model, numit, ystep,
xstep, minnumpts), file=printout)
except:
print("Error calculating values. %13s" %
event.fit[i].saveext, file=printout)
po.close(printout)
if isinteractive == False:
plt.close('all')
else:
plt.show()
return event.modeldir
