def test_durationAttenuation():
attenuation = .9
timespan_days = 90
period = 5
size = timespan_days*50
sigma = 1
depth = 20
rSun = 6.96e8
mSun = 2e30
x = np.linspace(0, timespan_days, size)
y = sigma * np.random.randn(size)
#Compute a plausible duration for transit
durObj = fbls.AstrophysicalDurationsSearch(.333, 5, rSun, mSun, attenuation)
duration_days = np.mean(durObj(period))
duration_cadences = int(duration_days/float(timespan_days) * size)
#Add in some transits
phase = 25
for i in np.arange(0, size, size*period/float(timespan_days)):
y[i+phase:i+phase+duration_cadences] -= depth
periodList = [5]
blsArray = fbls.computefBls(x, y,sigma, periodList, 10,
durObj)
n0 = duration_cadences * timespan_days/period
expectedMax = (depth*n0) / (sigma*np.sqrt(n0))
assert( blsArray[0,2] > attenuation*expectedMax)
def test_strengthVScatter():
"""False alarm prob should be independent of the amount of scatter,
all else being equal"""
period = [200]
size = 1000
sigma = 1
overres = 10
width = [6]
x = np.arange(size)
noise = np.random.randn(size)
mp.clf()
sigmaList = [1,2,4,8]
signal = np.zeros_like(sigmaList)
for i,sigma in enumerate(sigmaList):
y = sigma * noise
blsArray = fbls.computefBls(x, y,sigma, [period], overres,
width)
signal[i] = np.min(blsArray[:,1])
signal /= np.mean(signal) - 1
assert np.all( np.fabs(signal) < .01)
def test_strengthVDuration():
"""Test that measured signal strength is strongest at the
true duration of the transit
"""
period = 200
size = 1000
sigma = 1
overres = 10
injectedWidth = 10
for kk in range(30):
x = np.arange(size)
y = sigma * np.random.randn(size)
#Add a small signal
y[400:400+injectedWidth] -= 20
trialWidths = np.arange(2, 100, 4)
signal = np.zeros_like(trialWidths)
for i, tw in enumerate(trialWidths):
durFunc = lambda x: [tw]
blsArray = fbls.computefBls(x, y,sigma, [period], overres,
durFunc)
signal[i] = np.min(blsArray[:,1])
wh = np.argmin( np.fabs(trialWidths - injectedWidth) )
assert(signal[wh] < .95*np.min(signal))
def test_strengthVTimespan():
"""Test that signal strength is independent of timespan"""
size = 10000
sigma = 1
overres = 10
width = 10
#BLS strength should be constant with timespan, but FAP should
#decrease for a signal of a given strength when timespan increases
#because the number of draws increases
x = np.arange(size)
y = sigma * np.random.randn(size)
#Add a small signal
y[10:10+width] -= 2
for iter in range(100):
nList = [30, 100, 300, 1000, 3000, 10000]
signal = np.zeros_like(nList)
for i,n in enumerate(nList):
durFunc = lambda x: [width]
blsArray = fbls.computefBls(x[:n], y[:n], sigma, [n], overres,
durFunc)
signal[i] = np.max(blsArray[:,2])
assert np.all( np.fabs(signal - np.mean(signal) ) < .001)
def test_periodAttenuation():
attenuation = .9
timespan_days = 90
period = 5
size = timespan_days*50
sigma = 1
depth = 20
rSun = 6.96e8
mSun = 2e30
x = np.linspace(0, timespan_days, size)
y = sigma * np.random.randn(size)
durObj = fbls.AstrophysicalDurationsSearch(.99, 1.01, rSun, mSun, attenuation)
duration_days = durObj(period)[0]
duration_cadences = int(duration_days/float(timespan_days) * size)
# duration_cadences = 4
#Add in some transits
phase = 25
for i in np.arange(0, size, size*period/float(timespan_days)):
y[i+phase:i+phase+duration_cadences] -= depth
periodList = fbls.computePeriodListForAstrophysicalDurations(4,6, \
timespan_days, rSun, mSun, attenuation)
blsArray = fbls.computefBls(x, y,sigma, periodList, 10,
[duration_days])
n0 = duration_cadences * timespan_days/period
expectedMax = (depth*n0) / (sigma*np.sqrt(n0))
assert( np.max(blsArray[:,2]) > attenuation*expectedMax)
def test_strengthVPhaseOverres():
"""Test that measured strength at a given period is not
sensitive to the choice of phase over resolution
"""
transitWidth = lambda x: [10] #Function that always returns 10
period = 200
size = 1000
sigma = 1
x = np.arange(size)
y = sigma * np.random.randn(size)
#If the BLS is under resolved we get an underestimated signal strength.
#So we start at a phase over resolution of 16 for the purpose of this
#test. Your actual needs may tolerate a lower overresolution.
overresList = 2** np.arange(4,10)
signal = np.zeros_like(overresList)
for i in range(len(overresList)):
blsArray = fbls.computefBls(x, y,sigma, [period], overresList[i],
transitWidth)
signal[i] = np.min(blsArray[:,1])
signal -= np.mean(signal)
assert np.max( np.fabs(signal) < 1e-3)
def test_strengthVTimespan():
"""Test that signal strength is independent of timespan"""
size = 10000
sigma = 1
overres = 10
width = 10
#BLS strength should be constant with timespan, but FAP should
#decrease for a signal of a given strength when timespan increases
#because the number of draws increases
x = np.arange(size)
y = sigma * np.random.randn(size)
#Add a small signal
y[10:10 + width] -= 2
for iter in range(100):
nList = [30, 100, 300, 1000, 3000, 10000]
signal = np.zeros_like(nList)
for i, n in enumerate(nList):
durFunc = lambda x: [width]
blsArray = fbls.computefBls(x[:n], y[:n], sigma, [n], overres,
durFunc)
signal[i] = np.max(blsArray[:, 2])
assert np.all(np.fabs(signal - np.mean(signal)) < .001)
def test_durationAttenuation():
attenuation = .9
timespan_days = 90
period = 5
size = timespan_days * 50
sigma = 1
depth = 20
rSun = 6.96e8
mSun = 2e30
x = np.linspace(0, timespan_days, size)
y = sigma * np.random.randn(size)
#Compute a plausible duration for transit
durObj = fbls.AstrophysicalDurationsSearch(.333, 5, rSun, mSun,
attenuation)
duration_days = np.mean(durObj(period))
duration_cadences = int(duration_days / float(timespan_days) * size)
#Add in some transits
phase = 25
for i in np.arange(0, size, size * period / float(timespan_days)):
y[i + phase:i + phase + duration_cadences] -= depth
periodList = [5]
blsArray = fbls.computefBls(x, y, sigma, periodList, 10, durObj)
n0 = duration_cadences * timespan_days / period
expectedMax = (depth * n0) / (sigma * np.sqrt(n0))
assert (blsArray[0, 2] > attenuation * expectedMax)
def test_strengthVDuration():
"""Test that measured signal strength is strongest at the
true duration of the transit
"""
period = 200
size = 1000
sigma = 1
overres = 10
injectedWidth = 10
for kk in range(30):
x = np.arange(size)
y = sigma * np.random.randn(size)
#Add a small signal
y[400:400 + injectedWidth] -= 20
trialWidths = np.arange(2, 100, 4)
signal = np.zeros_like(trialWidths)
for i, tw in enumerate(trialWidths):
durFunc = lambda x: [tw]
blsArray = fbls.computefBls(x, y, sigma, [period], overres,
durFunc)
signal[i] = np.min(blsArray[:, 1])
wh = np.argmin(np.fabs(trialWidths - injectedWidth))
assert (signal[wh] < .95 * np.min(signal))
def test_strengthVPhaseOverres():
"""Test that measured strength at a given period is not
sensitive to the choice of phase over resolution
"""
transitWidth = lambda x: [10] #Function that always returns 10
period = 200
size = 1000
sigma = 1
x = np.arange(size)
y = sigma * np.random.randn(size)
#If the BLS is under resolved we get an underestimated signal strength.
#So we start at a phase over resolution of 16 for the purpose of this
#test. Your actual needs may tolerate a lower overresolution.
overresList = 2**np.arange(4, 10)
signal = np.zeros_like(overresList)
for i in range(len(overresList)):
blsArray = fbls.computefBls(x, y, sigma, [period], overresList[i],
transitWidth)
signal[i] = np.min(blsArray[:, 1])
signal -= np.mean(signal)
assert np.max(np.fabs(signal) < 1e-3)
def test_periodAttenuation():
attenuation = .9
timespan_days = 90
period = 5
size = timespan_days * 50
sigma = 1
depth = 20
rSun = 6.96e8
mSun = 2e30
x = np.linspace(0, timespan_days, size)
y = sigma * np.random.randn(size)
durObj = fbls.AstrophysicalDurationsSearch(.99, 1.01, rSun, mSun,
attenuation)
duration_days = durObj(period)[0]
duration_cadences = int(duration_days / float(timespan_days) * size)
# duration_cadences = 4
#Add in some transits
phase = 25
for i in np.arange(0, size, size * period / float(timespan_days)):
y[i + phase:i + phase + duration_cadences] -= depth
periodList = fbls.computePeriodListForAstrophysicalDurations(4,6, \
timespan_days, rSun, mSun, attenuation)
blsArray = fbls.computefBls(x, y, sigma, periodList, 10, [duration_days])
n0 = duration_cadences * timespan_days / period
expectedMax = (depth * n0) / (sigma * np.sqrt(n0))
assert (np.max(blsArray[:, 2]) > attenuation * expectedMax)
def computeBlsOfNoise(size, sigma, trialTransitWidth, periodList, phaseOverres):
#Compute lnFAP
x = np.arange(size)
y = sigma * np.random.randn(size)
lnFapArray = fbls.computefBls(x, y, sigma, periodList,
[trialTransitWidth], phaseOverres)
idx= lnFapArray > 1e3
lnFapArray[idx] = 0
#Compute BLS spectrum
nPeriod = len(periodList)
blsArray = np.zeros( nPeriod )
for j in range(nPeriod):
blsArray[j] = np.min( lnFapArray[j,:,:])
return blsArray
def computeBlsOfNoise(size, sigma, trialTransitWidth, periodList,
phaseOverres):
#Compute lnFAP
x = np.arange(size)
y = sigma * np.random.randn(size)
lnFapArray = fbls.computefBls(x, y, sigma, periodList, [trialTransitWidth],
phaseOverres)
idx = lnFapArray > 1e3
lnFapArray[idx] = 0
#Compute BLS spectrum
nPeriod = len(periodList)
blsArray = np.zeros(nPeriod)
for j in range(nPeriod):
blsArray[j] = np.min(lnFapArray[j, :, :])
return blsArray
def test_strengthVScatter():
"""False alarm prob should be independent of the amount of scatter,
all else being equal"""
period = [200]
size = 1000
sigma = 1
overres = 10
width = [6]
x = np.arange(size)
noise = np.random.randn(size)
mp.clf()
sigmaList = [1, 2, 4, 8]
signal = np.zeros_like(sigmaList)
for i, sigma in enumerate(sigmaList):
y = sigma * noise
blsArray = fbls.computefBls(x, y, sigma, [period], overres, width)
signal[i] = np.min(blsArray[:, 1])
signal /= np.mean(signal) - 1
assert np.all(np.fabs(signal) < .01)