ogle = synthetic_data.CadenceFromSurvey(
database_location='../db/ogle_cadences.db')
cadence_dict = {'hip': hip, 'ogle': ogle}
period = 25
number_points = 4000
## make a curve
number_tfe = 60
power = 10
tfe = np.ndarray(number_tfe * 3).reshape((number_tfe, 3))
tfe[:, 0] = np.linspace(.01, .99, number_tfe)
tfe[:, 1] = np.abs(tfe[:, 0]) - pow(tfe[:, 0], power)
print tfe
## examine noisification
for i in range(1):
tfe2 = cadence_noisify_smoothed(
tfe, ['ogle', 'first', number_points, period, cadence_dict])
print tfe2
visualize.plot_curve(tfe2, period=period)
if 0:
randoms = np.random.normal(size=(10, 3))
randoms_out = cadence_noisify(randoms, [5, 'first', 6])
print randoms_out
print randoms
if 0:
noisification_dict = get_noisification_dict()
print noisification_dict
(times[:, np.newaxis], fluxes[:, np.newaxis], errors[:, np.newaxis]))
return tfe
## light curve now in aRRLyraeFund.curve_this
aRRLyraeFund = synthetic_data.RRLyraeFund()
aRRLyraeFund.generateCurve()
## put cadence in aCadence
filename = "../data/OGLEIII/classical-cepheid/OGLE-LMC-CEP-2233.dat"
aCadence = synthetic_data.CadenceFromTFE()
aCadence.generate_cadence(fname=filename)
## check that this worked
tfe = ComputeTfe(aRRLyraeFund, aCadence)
visualize.plot_curve(tfe, period=aRRLyraeFund.period_this)
## parameters for entering tfes into database
source_class = "rr lyrae"
survey = "full"
points_per_curve = len(aCadence.error_this)
period = aRRLyraeFund.period_this
curve_info = [
points_per_curve, source_class, 0, 0, 0, 0, None, survey, 0, period
]
curve_info_names = [
"number_points", "classification", "c1", "e1", "c2", "e2", "raw_xml",
"survey", "xml_filename", "true_period"
]
## generate light curve using full cadence
# make a nice view of the features table sql_cmd = """CREATE VIEW IF NOT EXISTS sources_short AS SELECT source_id,original_source_id,classification,noisification,noise_args,true_period FROM sources""" cursor.execute(sql_cmd) # set up the survey and # create two curves and visualize them aSurvey = synthetic_data.surveySetup() aCadence = synthetic_data.CadenceFromVOSource() aSurvey.aCadence = aCadence aSurvey.generateCurve() tfe = np.column_stack((aSurvey.times[:,np.newaxis], aSurvey.fluxes[:,np.newaxis], aSurvey.errors[:,np.newaxis])) visualize.plot_curve(tfe,period= (2*aSurvey.period_this)) # generate training data, add to db survey='normal' for i in range(1000): aSurvey.generateCurve() tfe = np.column_stack((aSurvey.times[:,np.newaxis],aSurvey.fluxes[:,np.newaxis],aSurvey.errors[:,np.newaxis])) points_per_curve = len(aSurvey.times) source_class = aSurvey.class_name period = aSurvey.period_this curve_info = [points_per_curve,source_class,0,0,0,0,None,survey,0,period] curve_info_names = ["number_points","classification","c1","e1","c2","e2","raw_xml","survey","xml_filename","true_period"] print source_class
connection.commit() ########### VISUALIZE SMOOTHED CURVES ########### ## does this work out okay? ## run several times and use evince to view both at once which = np.random.randint(low=0,high=len(db_info)) reload(visualize) tfe = create_database.get_measurements(db_info[which][0],cursor) visualize.plot_curve(tfe,1/db_info[which][4],classification=db_info[which][2],survey=db_info[which][3],show_plot=False,save_figure=True,save_figure_name='original_plot.pdf') tfe = create_database.get_measurements(db_info[which][0],cursor,table='measurements_smoothed') visualize.plot_curve(tfe,1/db_info[which][4],classification=db_info[which][2],survey=db_info[which][3],show_plot=False,save_figure=True,save_figure_name='smoothed_plot.pdf') ###### ###### ADD ENTRIES IN SOURCES FOR NOISIFIED VERSIONS ###### reload(create_database) sql_cmd = """DELETE from sources WHERE source_id != original_source_id""" cursor.execute(sql_cmd)
## light curve now in aRRLyraeFund.curve_this aRRLyraeFund = synthetic_data.RRLyraeFund() aRRLyraeFund.generateCurve() ## put cadence in aCadence filename = "../data/OGLEIII/classical-cepheid/OGLE-LMC-CEP-2233.dat" aCadence = synthetic_data.CadenceFromTFE() aCadence.generate_cadence(fname=filename) ## check that this worked tfe = ComputeTfe(aRRLyraeFund,aCadence) visualize.plot_curve(tfe,period=aRRLyraeFund.period_this) ## parameters for entering tfes into database source_class = "rr lyrae" survey = "full" points_per_curve = len(aCadence.error_this) period = aRRLyraeFund.period_this curve_info = [points_per_curve,source_class,0,0,0,0,None,survey,0,period] curve_info_names = ["number_points","classification","c1","e1","c2","e2","raw_xml","survey","xml_filename","true_period"]
cursor.execute(sql_cmd)
# make a nice view of the features table
sql_cmd = """CREATE VIEW IF NOT EXISTS sources_short AS SELECT source_id,original_source_id,classification,noisification,noise_args,true_period FROM sources"""
cursor.execute(sql_cmd)
# set up the survey and
# create two curves and visualize them
aSurvey = synthetic_data.surveySetup()
aCadence = synthetic_data.CadenceFromVOSource()
aSurvey.aCadence = aCadence
aSurvey.generateCurve()
tfe = np.column_stack(
(aSurvey.times[:, np.newaxis], aSurvey.fluxes[:, np.newaxis],
aSurvey.errors[:, np.newaxis]))
visualize.plot_curve(tfe, period=(2 * aSurvey.period_this))
# generate training data, add to db
survey = 'normal'
for i in range(1000):
aSurvey.generateCurve()
tfe = np.column_stack(
(aSurvey.times[:, np.newaxis], aSurvey.fluxes[:, np.newaxis],
aSurvey.errors[:, np.newaxis]))
points_per_curve = len(aSurvey.times)
source_class = aSurvey.class_name
period = aSurvey.period_this
curve_info = [
points_per_curve, source_class, 0, 0, 0, 0, None, survey, 0, period
]
curve_info_names = [
## get the cadences
hip = synthetic_data.CadenceFromSurvey(database_location='../db/hipparcos_cadences.db')
ogle = synthetic_data.CadenceFromSurvey(database_location='../db/ogle_cadences.db')
cadence_dict = {'hip':hip,'ogle':ogle}
period = 25
number_points = 4000
## make a curve
number_tfe = 60
power = 10
tfe = np.ndarray(number_tfe*3).reshape((number_tfe,3))
tfe[:,0] = np.linspace(.01,.99,number_tfe)
tfe[:,1] = np.abs(tfe[:,0]) - pow(tfe[:,0],power)
print tfe
## examine noisification
for i in range(1):
tfe2 = cadence_noisify_smoothed(tfe,['ogle','first',number_points,period,cadence_dict])
print tfe2
visualize.plot_curve(tfe2,period=period)
if 0:
randoms = np.random.normal(size=(10,3))
randoms_out = cadence_noisify(randoms,[5,'first',6])
print randoms_out
print randoms
if 0:
noisification_dict = get_noisification_dict()
print noisification_dict
tfe,
table='measurements_smoothed')
connection.commit()
########### VISUALIZE SMOOTHED CURVES ###########
## does this work out okay?
## run several times and use evince to view both at once
which = np.random.randint(low=0, high=len(db_info))
reload(visualize)
tfe = create_database.get_measurements(db_info[which][0], cursor)
visualize.plot_curve(tfe,
1 / db_info[which][4],
classification=db_info[which][2],
survey=db_info[which][3],
show_plot=False,
save_figure=True,
save_figure_name='original_plot.pdf')
tfe = create_database.get_measurements(db_info[which][0],
cursor,
table='measurements_smoothed')
visualize.plot_curve(tfe,
1 / db_info[which][4],
classification=db_info[which][2],
survey=db_info[which][3],
show_plot=False,
save_figure=True,
save_figure_name='smoothed_plot.pdf')
print aCadence.unique_source_ids.size
aCadence.generate_cadence()
print aCadence.cadence_this
print aCadence.error_this
if 0:
aSurvey = surveySetup(aCadence=CadenceFromSurvey())
aSurvey.generateCurve()
print "class is: " + aSurvey.class_name
tfe = np.column_stack(
(aSurvey.times[:, np.newaxis], aSurvey.fluxes[:, np.newaxis],
aSurvey.errors[:, np.newaxis]))
print "the period is:"
print aSurvey.period_this
visualize.plot_curve(tfe,
freq=(1 / (2 * aSurvey.period_this)),
classification=aSurvey.class_name)
if 0:
aRRLyraeFund = RRLyraeFund()
aRRLyraeFund.generateCurve()
print "RR Lyrae Fundamental Mode Period:"
print aRRLyraeFund.period_this
aJittered = jittered()
cadence = aJittered()
fluxes = aRRLyraeFund.curve_this(cadence)
tfe = np.column_stack((cadence[:, np.newaxis], fluxes[:, np.newaxis],
np.empty(fluxes.size)[:np.newaxis]))
visualize.plot_curve(tfe, freq=(1 / (2 * aRRLyraeFund.period_this)))
if 0:
print aCadence.unique_source_ids
print aCadence.unique_source_ids.size
aCadence.generate_cadence()
print aCadence.cadence_this
print aCadence.error_this
if 0:
aSurvey = surveySetup(aCadence = CadenceFromSurvey())
aSurvey.generateCurve()
print "class is: " + aSurvey.class_name
tfe = np.column_stack((aSurvey.times[:,np.newaxis],aSurvey.fluxes[:,np.newaxis],aSurvey.errors[:,np.newaxis]))
print "the period is:"
print aSurvey.period_this
visualize.plot_curve(tfe,
freq=(1 /
(2*aSurvey.period_this)),
classification=
aSurvey.class_name)
if 0:
aRRLyraeFund = RRLyraeFund()
aRRLyraeFund.generateCurve()
print "RR Lyrae Fundamental Mode Period:"
print aRRLyraeFund.period_this
aJittered = jittered()
cadence = aJittered()
fluxes = aRRLyraeFund.curve_this(cadence)
tfe = np.column_stack((cadence[:,np.newaxis],fluxes[:,np.newaxis], np.empty(fluxes.size)[:np.newaxis]))
visualize.plot_curve(tfe,freq= (1 / (2*aRRLyraeFund.period_this)))
# make a nice view of the features table sql_cmd = """CREATE VIEW IF NOT EXISTS features_short AS SELECT source_id,freq1_harmonics_freq_0,std,max,weighted_average FROM features""" cursor.execute(sql_cmd) # make a nice view of the features table sql_cmd = """CREATE VIEW IF NOT EXISTS sources_short AS SELECT source_id,original_source_id,classification,noisification,noise_args,true_period FROM sources""" cursor.execute(sql_cmd) # set up the survey and # create two curves and visualize them aSurvey = synthetic_data.surveySetup() aSurvey.generateCurve() tfe = np.column_stack((aSurvey.times[:,np.newaxis], aSurvey.fluxes[:,np.newaxis], aSurvey.errors[:,np.newaxis])) visualize.plot_curve(tfe,period=aSurvey.period_this) # generate training data, add to db survey='train' for i in range(500): aSurvey.generateCurve() tfe = np.column_stack((aSurvey.times[:,np.newaxis],aSurvey.fluxes[:,np.newaxis],aSurvey.errors[:,np.newaxis])) points_per_curve = len(aSurvey.times) source_class = aSurvey.class_name period = aSurvey.period_this curve_info = [points_per_curve,source_class,0,0,0,0,None,survey,0,period] curve_info_names = ["number_points","classification","c1","e1","c2","e2","raw_xml","survey","xml_filename","true_period"]
aBetaLyrae = synthetic_data.Eclipsing(dip_ratio=scipy.stats.uniform(loc=.5,
scale=.5),
fraction_flat=scipy.stats.uniform(
loc=0, scale=.5))
class_names = [
'Classical Cepheid', 'Mira', 'RR Lyrae Fundamental', 'Beta Persei',
'Beta Lyrae'
]
classes = [aClassicalCepheid, aMira, aRRLyraeFund, aBetaPersei, aBetaLyrae]
priors = np.array([.2, .2, .2, .2, .2])
aSurvey = synthetic_data.Survey(class_names, classes, priors, aCadence)
aSurvey.generateCurve()
tfe = np.column_stack(
(aSurvey.times[:, np.newaxis], aSurvey.fluxes[:, np.newaxis],
aSurvey.errors[:, np.newaxis]))
visualize.plot_curve(tfe, period=(2 * aSurvey.period_this))
# generate training data, add to db
survey = 'synthetic_train'
for i in range(500):
aSurvey.generateCurve()
tfe = np.column_stack(
(aSurvey.times[:, np.newaxis], aSurvey.fluxes[:, np.newaxis],
aSurvey.errors[:, np.newaxis]))
points_per_curve = len(aSurvey.times)
source_class = aSurvey.class_name
period = aSurvey.period_this
curve_info = [
points_per_curve, source_class, 0, 0, 0, 0, None, survey, 0, period
]
curve_info_names = [
aBetaPersei = synthetic_data.Eclipsing( dip_ratio=scipy.stats.uniform(loc=.2,scale=.8), fraction_flat=scipy.stats.uniform(loc=.2,scale=.6)) aBetaLyrae = synthetic_data.Eclipsing( dip_ratio=scipy.stats.uniform(loc=.5,scale=.5), fraction_flat=scipy.stats.uniform(loc=0,scale=.5)) class_names = ['Classical Cepheid','Mira','RR Lyrae Fundamental', 'Beta Persei','Beta Lyrae'] classes = [aClassicalCepheid,aMira,aRRLyraeFund,aBetaPersei,aBetaLyrae] priors = np.array([.2,.2,.2,.2,.2]) aSurvey = synthetic_data.Survey(class_names,classes,priors,aCadence) aSurvey.generateCurve() tfe = np.column_stack((aSurvey.times[:,np.newaxis], aSurvey.fluxes[:,np.newaxis], aSurvey.errors[:,np.newaxis])) visualize.plot_curve(tfe,period= (2*aSurvey.period_this)) # generate training data, add to db survey='synthetic_train' for i in range(500): aSurvey.generateCurve() tfe = np.column_stack((aSurvey.times[:,np.newaxis],aSurvey.fluxes[:,np.newaxis],aSurvey.errors[:,np.newaxis])) points_per_curve = len(aSurvey.times) source_class = aSurvey.class_name period = aSurvey.period_this curve_info = [points_per_curve,source_class,0,0,0,0,None,survey,0,period] curve_info_names = ["number_points","classification","c1","e1","c2","e2","raw_xml","survey","xml_filename","true_period"] print source_class