def runSQL(item):
g = Get()
host = "host"
"""docstring for runSQL"""
platformName, resourceId = item
url = r"http://%s:7080/hqu/hqapi1/resource/get.hqu?id=%s&children=true&verbose=true" % (host, resourceId)
filename = platformName + ".resource.xml"
print " Now processing .. "
print filename
print url
print resourceId
file = open("data/" + filename, 'w')
data = g.get(url)
file.write(xml.dom.minidom.parseString(data).toprettyxml())
print filename + " done .. "
class GetAlertDefinitions(object):
"""docstring for GetAlertDefinitions"""
def __init__(self, host):
super(GetAlertDefinitions, self).__init__()
self.getTuple()
self.g = Get()
assert host
self.host = host
def getTuple(self):
"""docstring for getTuple"""
for line in sys.stdin:
line = line.strip()
platformName, resourceId = line.split()
yield (platformName, resourceId)
def runSQL(self):
"""docstring for runSQL"""
for platformName, resourceId in self.getTuple():
url = r"http://%s:7080/hqu/hqapi1/alertdefinition/listDefinitions.hqu?resourceId=%s&children=true&verbose=true" % (self.host, resourceId)
filename = platformName + ".xml"
print " Now processing .. "
print filename
print url
print resourceId
file = open("data/" + filename, 'w')
data = self.g.get(url)
file.write(xml.dom.minidom.parseString(data).toprettyxml())
print " filename done .. "
print "==========================="
def runSQL(item):
g = Get()
host = "host"
"""docstring for runSQL"""
platformName, resourceId = item
url = r"http://%s:7080/hqu/hqapi1/alertdefinition/listDefinitions.hqu?resourceId=%s&children=true&verbose=true" % (
host,
resourceId,
)
filename = platformName + ".xml"
print " Now processing .. "
print filename
print url
print resourceId
file = open("data/" + filename, "w")
data = g.get(url)
file.write(xml.dom.minidom.parseString(data).toprettyxml().encode("utf-8"))
print filename + " done .. "
def __init__(self, host): super(GetAlertDefinitions, self).__init__() self.getTuple() self.g = Get() assert host self.host = host
def RunLC(file='',
objectid='',
lctype='',
display=False,
readfile=False,
debug=False,
dofake=True,
dbmode='fits',
gapwindow=0.1,
maxgap=0.125,
fakefreq=.25,
mode='davenport',
iterations=10):
'''
Main wrapper to obtain and process a light curve
'''
# get the data
if debug is True:
print(str(datetime.datetime.now()) + ' GetLC started')
print(file, objectid)
if dbmode in ('txt', 'ktwo', 'everest', 'vdb', 'csv', 'kplr', 'k2sc',
'random', 'test'):
outfile, objectid, lc = Get(dbmode, file=file, objectid=objectid)
# UNUSED, UNTESTED, DELETE?
# elif dbmode = 'mysql':
# outfile, objectid, lc = GetLCdb(objectid, type='', readfile=False,
# savefile=False, exten = '.lc.gz',
# onecadence=False)
#-----------------------------------------------
if debug is True:
print('outfile = ' + outfile)
### Basic flattening
# flatten quarters with polymonial
flux_qtr = detrend.QtrFlat(lc.time.values, lc.flux_raw.values,
lc.qtr.iloc[0])
# then flatten between gaps
lc['flux'] = detrend.GapFlat(lc.time.values, flux_qtr, maxgap=maxgap)
#find continuous observing periods
_, dlr = detrend.FindGaps(lc.time.values, maxgap=maxgap)
if debug is True:
print("dl, dr: {}".format(dlr))
# uQtr = np.unique(qtr)
if debug is True:
print(str(datetime.datetime.now()) + ' MultiFind started')
istart, istop, lc['flux_model'] = MultiFind(lc,
dlr,
gapwindow=gapwindow,
debug=debug,
mode=mode)
df1 = pd.DataFrame({
'istart': istart,
'istop': istop,
'ed68': np.full_like(istart, -99),
'ed90': np.full_like(istart, -99)
})
allfakes = pd.DataFrame()
# run artificial flare test in this gap
if dofake is True:
dffake = pd.DataFrame()
for k in range(iterations):
fakeres = FakeFlares(df1,
lc,
dlr,
mode,
savefile=True,
gapwindow=gapwindow,
outfile='{}fake.json'.format(file),
display=display,
fakefreq=fakefreq,
debug=debug)
dffake = dffake.append(fakeres, ignore_index=True)
dffake.to_csv('{}_all_fakes.csv'.format(outfile))
df1['ed68'], df1['ed90'] = FakeCompleteness(dffake,
fakefreq,
iterations,
display=display,
file=objectid)
if display is True:
print(str(len(istart)) + ' flare candidates found.')
fig, ax = plt.subplots(figsize=(8, 4))
ax = help.Plot(lc, ax, istart=istart, istop=istop, onlybit=10.)
plt.show()
plt.savefig(file + '_lightcurve.png',
dpi=300,
bbox_inches='tight',
pad_inches=0.5)
plt.close()
# '''
# #-- IF YOU WANT TO PLAY WITH THE WAVELET STUFF MORE, WORK HERE
# test_model = detrend.WaveletSmooth(time, flux)
# test_cand = DetectCandidate(time, flux, error, test_model)
#
# print(len(cand))
# print(len(test_cand))
#
# if display is True:
# plt.figure()
# plt.plot(time, flux, 'k')
# plt.plot(time, test_model, 'green')
#
# plt.scatter(time[test_cand], flux[test_cand], color='orange', marker='p',s=60, alpha=0.8)
# plt.show()
# '''
# set this to silence bad fit warnings from polyfit
warnings.simplefilter('ignore', np.RankWarning)
metadata = {
'ObjectID': objectid,
'File': file,
'Date-Run': str(datetime.datetime.now()),
'Appaloosa-Version': __version__,
'N_epoch in LC': str(len(lc.time)),
'Total exp time of LC': str(np.sum(lc.exptime)),
}
if debug is True:
print(str(datetime.datetime.now()) + 'Getting output header')
header = FlareStats(lc, ReturnHeader=True)
header = header + ['ED68i', 'ED90i']
dfout = pd.DataFrame()
if debug is True:
print(str(datetime.datetime.now()) + 'Getting FlareStats')
# loop over EACH FLARE, compute stats
df2 = pd.DataFrame({'istart': istart, 'istop': istop})
stats_i = []
for i in range(0, len(istart)):
stats_i = FlareStats(lc, istart=istart[i], istop=istop[i])
stats_i = np.append(stats_i, [df1.ed68.iloc[i], df1.ed90.iloc[i]])
stats_i = [[item] for item in stats_i]
dfout = dfout.append(pd.DataFrame(dict(zip(header, stats_i))),
ignore_index=True)
if not dfout.empty:
dfout.to_csv(outfile + '_flare_stats.csv')
with open(outfile + '_flare_stats_meta.json', 'w') as f:
j = json.dumps(metadata)
f.write(j)
#Add the number of flares from this LC to the list
flist = 'flarelist.csv'.format(outfile)
header = [
'Object ID', ' Date of Run', 'Number of Flares', 'Filename',
'Total Exposure Time of LC in Days', 'BJD-2454833 days'
]
if glob.glob(flist) == []:
dfout = pd.DataFrame()
else:
dfout = pd.read_csv(flist)
line = [
objectid,
datetime.datetime.now(),
len(istart), file,
np.sum(lc.exptime), lc.time[0]
]
line = [[item] for item in line]
dfout = dfout.append(pd.DataFrame(dict(zip(header, line))),
ignore_index=True)
dfout.to_csv(flist)
return
