def test_Gskew(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Gskew'])
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= -0.2
and a.result(method='array') <= 0.2)
def test_Q31(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Q31'])
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= 1.30
and a.result(method='array') <= 1.38)
def test_Period_Psi(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['PeriodLS', 'Period_fit','Psi_CS','Psi_eta'], PeriodLS = mjd, Psi_CS= mjd) a=a.calculateFeature(fake_lc[0]) assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)
def test_Eta_e(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['Eta_e']) a=a.calculateFeature(white_noise) assert(a.result(method='array') >= 1.9 and a.result(method='array') <= 2.1)
def test_MedianBRP(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['MedianBRP'] , MaxSlope=mjd) a=a.calculateFeature(fake_lc[0]) assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)
def test_Period_Psi(periodic_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['PeriodLS', 'Period_fit','Psi_CS','Psi_eta']) a=a.calculateFeature(periodic_lc) # print a.result(method='array'), len(periodic_lc[0]) assert(a.result(method='array')[0] >= 19 and a.result(method='array')[0] <= 21)
def test_Beyond1Std(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['Beyond1Std']) a=a.calculateFeature(white_noise) assert(a.result(method='array') >= 0.30 and a.result(method='array') <= 0.40)
def test_MedianAbsDev(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['MedianAbsDev']) a=a.calculateFeature(white_noise) assert(a.result(method='array') >= 0.630 and a.result(method='array') <= 0.700)
def test_PairSlopeTrend(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['PairSlopeTrend']) a=a.calculateFeature(white_noise) assert(a.result(method='array') >= -0.25 and a.result(method='array') <= 0.25)
def test_Con(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['Con'] , Con=1) a=a.calculateFeature(white_noise) assert(a.result(method='array') >= 0.04 and a.result(method='array') <= 0.05)
def test_CAR(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['CAR_sigma', 'CAR_tau', 'CAR_tmean'] , CAR_sigma=[mjd, error]) a=a.calculateFeature(fake_lc[0]) assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)
def test_PercentDifferenceFluxPercentile(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['PercentDifferenceFluxPercentile']) a=a.calculateFeature(fake_lc[0]) assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)
def test_Eta_e(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['Eta_e'] ) a=a.calculateFeature(fake_lc[0]) assert(a.result(method='array') >= 0.043 and a.result(method='array') <= 0.046)
def test_Mean(white_noise):
a = FeatureSpace(featureList=['Mean'])
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= -0.1
and a.result(method='array') <= 0.1)
def test_Meanvariance(uniform_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['Meanvariance']) a=a.calculateFeature(uniform_lc) assert(a.result(method='array') >= 0.575 and a.result(method='array') <= 0.580)
def test_Con(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Con'], Con=1)
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= 0.04
and a.result(method='array') <= 0.05)
def test_Eta_color(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Eta_color'])
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= 1.9
and a.result(method='array') <= 2.1)
def test_Meanvariance(uniform_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Meanvariance'])
a = a.calculateFeature(uniform_lc)
assert (a.result(method='array') >= 0.575
and a.result(method='array') <= 0.580)
def test_MedianAbsDev(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['MedianAbsDev'])
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= 0.630
and a.result(method='array') <= 0.700)
def test_PairSlopeTrend(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['PairSlopeTrend'])
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= -0.25
and a.result(method='array') <= 0.25)
def test_Beyond1Std(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=['Beyond1Std'])
a = a.calculateFeature(white_noise)
assert (a.result(method='array') >= 0.30
and a.result(method='array') <= 0.40)
def test_Period_Psi(periodic_lc):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(
featureList=['PeriodLS', 'Period_fit', 'Psi_CS', 'Psi_eta'])
a = a.calculateFeature(periodic_lc)
# print a.result(method='array'), len(periodic_lc[0])
assert (a.result(method='array')[0] >= 19
and a.result(method='array')[0] <= 21)
def test_StructureFunction(random_walk):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=[
'StructureFunction_index_21', 'StructureFunction_index_31',
'StructureFunction_index_32'
])
a = a.calculateFeature(random_walk)
assert (a.result(method='array')[0] >= 1.520
and a.result(method='array')[0] <= 2.067)
assert (a.result(method='array')[1] >= 1.821
and a.result(method='array')[1] <= 3.162)
assert (a.result(method='array')[2] >= 1.243
and a.result(method='array')[2] <= 1.562)
def test_FluxPercentile(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['FluxPercentileRatioMid20','FluxPercentileRatioMid35','FluxPercentileRatioMid50','FluxPercentileRatioMid65','FluxPercentileRatioMid80'] ) a=a.calculateFeature(white_noise) assert(a.result(method='array')[0] >= 0.145 and a.result(method='array')[0] <= 0.160) assert(a.result(method='array')[1] >= 0.260 and a.result(method='array')[1] <= 0.290) assert(a.result(method='array')[2] >= 0.350 and a.result(method='array')[2] <= 0.450) assert(a.result(method='array')[3] >= 0.540 and a.result(method='array')[3] <= 0.580) assert(a.result(method='array')[4] >= 0.760 and a.result(method='array')[4] <= 0.800)
def test_Stetson(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['SlottedA_length','StetsonK', 'StetsonK_AC', 'StetsonJ', 'StetsonL']) a=a.calculateFeature(white_noise) assert(a.result(method='array')[1] >= 0.790 and a.result(method='array')[1] <= 0.85) assert(a.result(method='array')[2] >= 0.20 and a.result(method='array')[2] <= 0.45) assert(a.result(method='array')[3] >= -0.1 and a.result(method='array')[3] <= 0.1) assert(a.result(method='array')[4] >= -0.1 and a.result(method='array')[4] <= 0.1)
def calculate_features(lc_fn, feature_list):
lc = ReadLC_MACHO(lc_fn)
[data, mjd, error] = lc.ReadLC()
preprocessed_data = Preprocess_LC(data, mjd, error)
fs = FeatureSpace(featureList=feature_list,
Automean=[0,0],
#Beyond1Std=[np.array(error)],
CAR_sigma=[mjd, error],
Eta_e=mjd,
LinearTrend=mjd,
MaxSlope=mjd,
PeriodLS=mjd,
Psi_CS=mjd
)
values = fs.calculateFeature(data)
value_dict = values.result(method='dict')
A, PH, scaledPH = calculate_periodic_features(mjd, data)
for i in range(len(A)):
for j in range(len(A[i])):
value_dict['freq'+str(i+1)+'_harmonics_amplitude_'+str(j)] = A[i][j]
value_dict['freq'+str(i+1)+'_harmonics_rel_phase_'+str(j)] = scaledPH[i][j]
return value_dict
def main(argv):
path = argv + '/'
count = 0
check = False
for filename in os.listdir(path):
[mag, time, error] = R.ReadLC_Catalina(path+filename)
a = FeatureSpace(Data=['magnitude', 'time', 'error'], featureList=None)
lc = np.array([mag,time,error])
try:
a=a.calculateFeature(lc)
idx = filename.split('.')[0]
count = count + 1
if count == 1:
df = pd.DataFrame(np.asarray(a.result(method='array')).reshape((1,len(a.result(method='array')))), columns = a.result(method='features'), index =[idx])
else:
df2 = pd.DataFrame(np.asarray(a.result(method='array')).reshape((1,len(a.result(method='array')))), columns = a.result(method='features'), index =[idx])
df = pd.concat([df, df2])
check = True
except:
pass
if check:
file_name = path.split('/')[4] + '.csv'
#df.to_csv('/n/home10/inun/Extract_features/'+file_name)
df.to_csv('/n/regal/TSC/Catalina_features/'+file_name)
def test_Mean(white_noise): a = FeatureSpace(featureList=['Mean']) a=a.calculateFeature(white_noise) assert(a.result(method='array') >= -0.1 and a.result(method='array') <= 0.1)
[data, mjd, error] = lc_B.leerLC() [data2, mjd2, error2] = lc_R.leerLC() preproccesed_data = Preprocess_LC(data, mjd, error) [data, mjd, error] = preproccesed_data.Preprocess() preproccesed_data = Preprocess_LC(data2, mjd2, error2) [second_data, mjd2, error2] = preproccesed_data.Preprocess() if len(data) != len(second_data): [aligned_data, aligned_second_data, aligned_mjd] = Align_LC(mjd, mjd2, data, second_data, error, error2) else: aligned_data = data aligned_second_data = second_data aligned_mjd = mjd a = FeatureSpace(category='all',featureList=None, automean=[0,0], StetsonL=[aligned_second_data, aligned_data] , B_R=second_data, Beyond1Std=error, StetsonJ=[aligned_second_data, aligned_data], MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=[aligned_second_data, aligned_data, aligned_mjd], Eta_e=mjd, Q31B_R=[aligned_second_data, aligned_data], PeriodLS=mjd, Psi_CS =mjd, CAR_sigma=[mjd, error], SlottedA = mjd) try: a=a.calculateFeature(data) idx = [id[:-6]] contador = contador + 1 check = True if contador == 1: print "contador1" df = pd.DataFrame(a.result(method='array').reshape((1,len(a.result(method='array')))), columns = a.result(method='features'), index =[idx]) print "hice mi primer data frame" else: df2 = pd.DataFrame(a.result(method='array').reshape((1,len(a.result(method='array')))), columns = a.result(method='features'), index =[idx]) df = pd.concat([df, df2])
def test_Q31B_R(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['Q31B_R'], Q31B_R = [aligned_second_data, aligned_data]) a=a.calculateFeature(fake_lc[0])
def test_SlottedA(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['SlottedA'], SlottedA = [mjd, 1]) a=a.calculateFeature(fake_lc[0])
aligned_mjd] = Align_LC(mjd, mjd2, data,
second_data, error,
error2)
else:
aligned_data = data
aligned_second_data = second_data
aligned_mjd = mjd
a = FeatureSpace(
featureList=['Bmean'],
automean=[0, 0],
StetsonL=[aligned_second_data, aligned_data],
StetsonK_AC=mjd,
B_R=second_data,
Beyond1Std=error,
StetsonJ=[aligned_second_data, aligned_data],
MaxSlope=mjd,
LinearTrend=mjd,
Eta_B_R=[
aligned_second_data, aligned_data, aligned_mjd
],
Eta_e=mjd,
Q31B_R=[aligned_second_data, aligned_data],
PeriodLS=mjd,
CAR_sigma=[mjd, error],
SlottedA=mjd)
try:
a = a.calculateFeature(data)
guardar = np.vstack(
(guardar,
np.hstack(
(id[:-6], a.result(method='array')))))
except:
from Feature import FeatureSpace import numpy as np data = np.random.randint(0,10000, 100000000) a = FeatureSpace(category='all', automean=[0,0]) print a.featureList a=a.calculateFeature(data) print a.result(method='') print a.result(method='dict')
from Feature import FeatureSpace import numpy as np data = np.random.randint(0, 10000, 100000000) a = FeatureSpace(category='all', automean=[0, 0]) print a.featureList a = a.calculateFeature(data) print a.result(method='') print a.result(method='dict')
[data, mjd, error] = lc_B.leerLC()
[data2, mjd2, error2] = lc_R.leerLC()
preproccesed_data = Preprocess_LC(data, mjd, error)
[data, mjd, error] = preproccesed_data.Preprocess()
preproccesed_data = Preprocess_LC(data2, mjd2, error2)
[second_data, mjd2, error2] = preproccesed_data.Preprocess()
if len(data) != len(second_data):
[aligned_data, aligned_second_data, aligned_mjd] = Align_LC(mjd, mjd2, data, second_data, error, error2)
else:
aligned_data = data
aligned_second_data = second_data
aligned_mjd = mjd
a = FeatureSpace(featureList=['Bmean'], automean=[0,0], StetsonL=[aligned_second_data, aligned_data] , StetsonK_AC=mjd, B_R=second_data, Beyond1Std=error, StetsonJ=[aligned_second_data, aligned_data], MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=[aligned_second_data, aligned_data, aligned_mjd], Eta_e=mjd, Q31B_R=[aligned_second_data, aligned_data], PeriodLS=mjd, CAR_sigma=[mjd, error], SlottedA = mjd)
try:
a=a.calculateFeature(data)
guardar = np.vstack((guardar, np.hstack((id[:-6] , a.result(method='array') ))))
except:
pass
if count == 1:
folder = (member.name.split('lc')[0]).split('/')[0]
field = (member.name.split('lc')[0]).split('/')[1]
file_name = folder + '_' + field + '.csv'
nombres = np.hstack(("MACHO_Id" , a.result(method='features')))
guardar = np.vstack((nombres, guardar[1:]))
np.savetxt(file_name, guardar, delimiter="," ,fmt="%s")
guardar = np.zeros(shape=(1,2))
else:
#Opening the light curve
lc_B = LeerLC_MACHO('lc_58.6272.729.B.mjd')
lc_R = LeerLC_MACHO('lc_58.6272.729.R.mjd')
[data, mjd, error] = lc_B.leerLC()
[data2, mjd2, error2] = lc_R.leerLC()
preproccesed_data = Preprocess_LC(data, mjd, error)
[data, mjd, error] = preproccesed_data.Preprocess()
preproccesed_data = Preprocess_LC(data2, mjd2, error2)
[second_data, mjd2, error2] = preproccesed_data.Preprocess()
if len(data) != len(second_data):
[aligned_data, aligned_second_data] = Align_LC(mjd, mjd2, data, second_data, error, error2)
#Calculating the features
a = FeatureSpace(featureList=['StetsonL', 'StetsonJ', 'Q31B_R', 'Eta_B_R'], automean=[0,0], StetsonL=[aligned_second_data, aligned_data] , B_R=second_data, Beyond1Std=error, StetsonJ=[aligned_second_data, aligned_data], MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=[aligned_second_data, aligned_data], Eta_e=mjd, Q31B_R=[aligned_second_data, aligned_data], PeriodLS=mjd, CAR_sigma=[mjd, error], SlottedA = mjd)
a=a.calculateFeature(data)
print a.result(method='features')
# nombres = a.result(method='features')
# guardar = np.vstack((nombres,a.result(method='array')))
# # a=np.vstack((previous_data,a))
# np.savetxt('test_real.csv', guardar, delimiter="," ,fmt="%s")
#B_R = second_data, Eta_B_R = second_data, Eta_e = mjd, MaxSlope = mjd, PeriodLS = mjd, Q31B_R = second_data, StetsonJ = second_data, StetsonL = second_data)
second_data, error,
error2)
else:
aligned_data = data
aligned_second_data = second_data
aligned_mjd = mjd
a = FeatureSpace(
category='all',
featureList=None,
automean=[0, 0],
StetsonL=[aligned_second_data, aligned_data],
Color=second_data,
Beyond1Std=error,
StetsonJ=[aligned_second_data, aligned_data],
MaxSlope=mjd,
LinearTrend=mjd,
Eta_color=[
aligned_second_data, aligned_data, aligned_mjd
],
Eta_e=mjd,
Q31_color=[aligned_second_data, aligned_data],
PeriodLS=mjd,
Psi_CS=mjd,
CAR_sigma=[mjd, error],
SlottedA_length=mjd)
try:
a = a.calculateFeature(data)
idx = [id[:-6]]
contador = contador + 1
check = True
def test_SmallKurtosis(white_noise): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise() a = FeatureSpace(featureList=['SmallKurtosis']) a=a.calculateFeature(white_noise) assert(a.result(method='array') >= -0.2 and a.result(method='array') <= 0.2)
def test_FluxPercentile(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=[
'FluxPercentileRatioMid20', 'FluxPercentileRatioMid35',
'FluxPercentileRatioMid50', 'FluxPercentileRatioMid65',
'FluxPercentileRatioMid80'
])
a = a.calculateFeature(white_noise)
assert (a.result(method='array')[0] >= 0.145
and a.result(method='array')[0] <= 0.160)
assert (a.result(method='array')[1] >= 0.260
and a.result(method='array')[1] <= 0.290)
assert (a.result(method='array')[2] >= 0.350
and a.result(method='array')[2] <= 0.450)
assert (a.result(method='array')[3] >= 0.540
and a.result(method='array')[3] <= 0.580)
assert (a.result(method='array')[4] >= 0.760
and a.result(method='array')[4] <= 0.800)
lc_B = LeerLC_MACHO(path + j +'/'+ i[2:])
lc_R = LeerLC_MACHO(path + j +'/'+ i[2:-5] + 'R.mjd')
#Opening the light curve
[data, mjd, error] = lc_B.leerLC()
[data2, mjd2, error2] = lc_R.leerLC()
preproccesed_data = Preprocess_LC(data, mjd, error)
[data, mjd, error] = preproccesed_data.Preprocess()
preproccesed_data = Preprocess_LC(data2, mjd2, error2)
[second_data, mjd2, error2] = preproccesed_data.Preprocess()
a = FeatureSpace(category='all',featureList=None, automean=[0,0], StetsonL=second_data , B_R=second_data, Beyond1Std=error, StetsonJ=second_data, MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=second_data, Eta_e=mjd, Q31B_R=second_data, PeriodLS=mjd, CAR_sigma=[mjd, error], SlottedA = mjd)
a=a.calculateFeature(data)
if count == 1:
nombres = np.hstack(("MACHO_Id" , a.result(method='features') , "Class"))
guardar = np.vstack((nombres, np.hstack((i[5:-6] , a.result(method='array') , folder ))))
np.savetxt('test_real.csv', guardar, delimiter="," ,fmt="%s")
else:
my_data = np.genfromtxt('test_real.csv', delimiter=',', dtype=None)
guardar = np.vstack((nombres,my_data[1:], np.hstack((i[5:-6] , a.result(method='array') , folder ))))
np.savetxt('test_real.csv', guardar, delimiter="," ,fmt="%s")
folder = folder + 1
def main(argv):
check = False
if tarfile.is_tarfile(self.path):
df = []
contador = 0
tar = tarfile.open(self.path, 'r')
for member in tar.getmembers():
if member.name.endswith("B.mjd"):
id = member.name.split('lc_')[1]
for member2 in tar.getmembers():
if member2.name == (member.name[:-5] + 'R.mjd'):
check = True
f = tar.extractfile(member)
g = tar.extractfile(member2)
content1 = f.read().split('\n')
content2 = g.read().split('\n')
lc_B = ReadLC_MACHO(content1)
lc_R = ReadLC_MACHO(content2)
[data, mjd, error] = lc_B.ReadLC()
[data2, mjd2, error2] = lc_R.ReadLC()
preproccesed_data = Preprocess_LC(data, mjd, error)
[data, mjd, error] = preproccesed_data.Preprocess()
preproccesed_data = Preprocess_LC(data2, mjd2, error2)
[second_data, mjd2, error2] = preproccesed_data.Preprocess()
if len(data) != len(second_data):
[aligned_data, aligned_second_data, aligned_mjd] = Align_LC(mjd, mjd2, data, second_data, error, error2)
else:
aligned_data = data
aligned_second_data = second_data
aligned_mjd = mjd
a = FeatureSpace(featureList=['Bmean'], automean=[0,0], StetsonL=[aligned_second_data, aligned_data] , Color=second_data, Beyond1Std=error, StetsonJ=[aligned_second_data, aligned_data], MaxSlope=mjd, LinearTrend=mjd, Eta_color=[aligned_second_data, aligned_data, aligned_mjd], Eta_e=mjd, Q31_color=[aligned_second_data, aligned_data], PeriodLS=mjd, CAR_sigma=[mjd, error], SlottedA_length = mjd)
try:
a=a.calculateFeature(data)
idx = [id[:-6]]
contador = contador + 1
if contador == 1:
df = pd.DataFrame(a.result(method='array').reshape((1,len(a.result(method='array')))), columns = a.result(method='features'), index =[idx])
#df.to_csv('sabrina.csv')
else:
df2 = pd.DataFrame(a.result(method='array').reshape((1,len(a.result(method='array')))), columns = a.result(method='features'), index =[idx])
df = pd.concat([df, df2])
except:
pass
if check:
folder = (member.name.split('lc')[0]).split('/')[0]
field = (member.name.split('lc')[0]).split('/')[1]
file_name = folder + '_' + field + '.csv'
df.to_csv(file_name)
import numpy as np
#"test"
data = np.random.uniform(-5,-3, 1000)
second_data = np.random.uniform(-5,-3, 1000)
error= np.random.uniform(0.000001,1, 1000)
mjd= np.random.uniform(40000,50000, 1000)
# minper=1.
# maxper=100.
# subsample=1
# Npeaks=1
# clip=5.0
# clipiter=1
# whiten=0
# a = FeatureSpace(category='all',featureList=None, automean=[0,0], StetsonL=second_data , B_R=second_data, Beyond1Std=error, StetsonJ=second_data, MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=second_data, Eta_e=mjd, Q31B_R=second_data, PeriodLS=mjd)
# PeriodLS=[mjd,error,minper, maxper, subsample, Npeaks, clip, clipiter, whiten]
a = FeatureSpace(category='basic', automean=[0,0])
#print a.featureList
a=a.calculateFeature(data)
#print a.result(method='')
np.savetxt('test.txt',a.result(method='array'))
print a.result(method='dict')
def test_Std(fake_lc): # data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = fake_lc() a = FeatureSpace(featureList=['Std']) a=a.calculateFeature(fake_lc[0])
preproccesed_data = Preprocess_LC(data2, mjd2, error2)
[second_data, mjd2, error2] = preproccesed_data.Preprocess()
if len(data) != len(second_data):
[aligned_data, aligned_second_data,
aligned_mjd] = Align_LC(mjd, mjd2, data, second_data, error, error2)
#Calculating the features
a = FeatureSpace(featureList=['PeriodLS', 'Psi_CS', 'Psi_eta'],
automean=[0, 0],
StetsonL=[aligned_second_data, aligned_data],
B_R=second_data,
Beyond1Std=error,
StetsonJ=[aligned_second_data, aligned_data],
MaxSlope=mjd,
LinearTrend=mjd,
Eta_B_R=[aligned_second_data, aligned_data, aligned_mjd],
Eta_e=mjd,
Q31B_R=[aligned_second_data, aligned_data],
PeriodLS=mjd,
Psi_CS=mjd,
CAR_sigma=[mjd, error],
SlottedA=mjd)
a = a.calculateFeature(data)
print a.result(method='dict')
# nombres = a.result(method='features')
# guardar = np.vstack((nombres,a.result(method='array')))
# # a=np.vstack((previous_data,a))
# np.savetxt('test_real.csv', guardar, delimiter="," ,fmt="%s")
def test_Stetson(white_noise):
# data, mjd, error, second_data, aligned_data, aligned_second_data, aligned_mjd = white_noise()
a = FeatureSpace(featureList=[
'SlottedA_length', 'StetsonK', 'StetsonK_AC', 'StetsonJ', 'StetsonL'
])
a = a.calculateFeature(white_noise)
assert (a.result(method='array')[1] >= 0.790
and a.result(method='array')[1] <= 0.85)
assert (a.result(method='array')[2] >= 0.20
and a.result(method='array')[2] <= 0.45)
assert (a.result(method='array')[3] >= -0.1
and a.result(method='array')[3] <= 0.1)
assert (a.result(method='array')[4] >= -0.1
and a.result(method='array')[4] <= 0.1)
def main(argv):
check = False
if tarfile.is_tarfile(self.path):
df = []
contador = 0
tar = tarfile.open(self.path, 'r')
for member in tar.getmembers():
if member.name.endswith("B.mjd"):
id = member.name.split('lc_')[1]
for member2 in tar.getmembers():
if member2.name == (member.name[:-5] + 'R.mjd'):
check = True
f = tar.extractfile(member)
g = tar.extractfile(member2)
content1 = f.read().split('\n')
content2 = g.read().split('\n')
lc_B = ReadLC_MACHO(content1)
lc_R = ReadLC_MACHO(content2)
[data, mjd, error] = lc_B.ReadLC()
[data2, mjd2, error2] = lc_R.ReadLC()
preproccesed_data = Preprocess_LC(data, mjd, error)
[data, mjd, error] = preproccesed_data.Preprocess()
preproccesed_data = Preprocess_LC(data2, mjd2, error2)
[second_data, mjd2,
error2] = preproccesed_data.Preprocess()
if len(data) != len(second_data):
[aligned_data, aligned_second_data,
aligned_mjd] = Align_LC(mjd, mjd2, data, second_data,
error, error2)
else:
aligned_data = data
aligned_second_data = second_data
aligned_mjd = mjd
a = FeatureSpace(
featureList=['Bmean'],
automean=[0, 0],
StetsonL=[aligned_second_data, aligned_data],
Color=second_data,
Beyond1Std=error,
StetsonJ=[aligned_second_data, aligned_data],
MaxSlope=mjd,
LinearTrend=mjd,
Eta_color=[
aligned_second_data, aligned_data, aligned_mjd
],
Eta_e=mjd,
Q31_color=[aligned_second_data, aligned_data],
PeriodLS=mjd,
CAR_sigma=[mjd, error],
SlottedA_length=mjd)
try:
a = a.calculateFeature(data)
idx = [id[:-6]]
contador = contador + 1
if contador == 1:
df = pd.DataFrame(
a.result(method='array').reshape(
(1, len(a.result(method='array')))),
columns=a.result(method='features'),
index=[idx])
#df.to_csv('sabrina.csv')
else:
df2 = pd.DataFrame(
a.result(method='array').reshape(
(1, len(a.result(method='array')))),
columns=a.result(method='features'),
index=[idx])
df = pd.concat([df, df2])
except:
pass
if check:
folder = (member.name.split('lc')[0]).split('/')[0]
field = (member.name.split('lc')[0]).split('/')[1]
file_name = folder + '_' + field + '.csv'
df.to_csv(file_name)
[data, mjd, error] = lc_B.leerLC()
[data2, mjd2, error2] = lc_R.leerLC()
preproccesed_data = Preprocess_LC(data, mjd, error)
[data, mjd, error] = preproccesed_data.Preprocess()
preproccesed_data = Preprocess_LC(data2, mjd2, error2)
[second_data, mjd2, error2] = preproccesed_data.Preprocess()
if len(data) != len(second_data):
[aligned_data, aligned_second_data, aligned_mjd] = Align_LC(mjd, mjd2, data, second_data, error, error2)
# a = FeatureSpace(category='all',featureList=None, automean=[0,0], StetsonL=[aligned_second_data, aligned_data] , B_R=second_data, Beyond1Std=error, StetsonJ=[aligned_second_data, aligned_data], MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=[aligned_second_data, aligned_data], Eta_e=mjd, Q31B_R=[aligned_second_data, aligned_data], PeriodLS=mjd, CAR_sigma=[mjd, error], SlottedA = mjd)
a = FeatureSpace(featureList=['PeriodLS', 'Psi_CS','Psi_eta','Rcs'], Automean=[0,0], StetsonL=[aligned_second_data, aligned_data] , B_R=second_data, Beyond1Std=error, StetsonJ=[aligned_second_data, aligned_data], MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=[aligned_second_data, aligned_data, aligned_mjd], Eta_e=mjd, Q31B_R=[aligned_second_data, aligned_data], CAR_sigma=[mjd, error], SlottedA = mjd, PeriodLS=mjd, Psi_CS = mjd)
try:
a=a.calculateFeature(data)
guardar = np.vstack((guardar, np.hstack((i[3:-6] , a.result(method='array') , folder ))))
except:
pass
folder = folder + 1
if count == 1:
nombres = np.hstack(("MACHO_Id" , a.result(method='features') , "Class"))
guardar = np.vstack((nombres, guardar[1:]))
np.savetxt('test_real_Psi.csv', guardar, delimiter="," ,fmt="%s")
guardar = np.zeros(shape=(1,3))
from Feature import FeatureSpace
import numpy as np
#"test"
data = np.random.uniform(-5, -3, 1000)
second_data = np.random.uniform(-5, -3, 1000)
error = np.random.uniform(0.000001, 1, 1000)
mjd = np.random.uniform(40000, 50000, 1000)
# minper=1.
# maxper=100.
# subsample=1
# Npeaks=1
# clip=5.0
# clipiter=1
# whiten=0
# a = FeatureSpace(category='all',featureList=None, automean=[0,0], StetsonL=second_data , B_R=second_data, Beyond1Std=error, StetsonJ=second_data, MaxSlope=mjd, LinearTrend=mjd, Eta_B_R=second_data, Eta_e=mjd, Q31B_R=second_data, PeriodLS=mjd)
# PeriodLS=[mjd,error,minper, maxper, subsample, Npeaks, clip, clipiter, whiten]
a = FeatureSpace(category='basic', automean=[0, 0])
#print a.featureList
a = a.calculateFeature(data)
#print a.result(method='')
np.savetxt('test.txt', a.result(method='array'))
print a.result(method='dict')
