def main():
# Number of trials.
n = 10
# Other parameters.
baseline = 90. # days
minutes_per_day = 24. * 60.
signal_to_noise = 10000.
nsamples = np.ceil(baseline * minutes_per_day)
segsize, mindur, maxdur, nbins = (2., 0.01, 0.5, 1000)
# To make it deterministic, seed the PRNG.
np.random.seed(4)
period = np.random.uniform(segsize, 30.) # days
duration = np.random.uniform(1. / 24., 5. / 24.) # days
depth = np.random.uniform(-0.01, -0.5)
phase = 0.5
# Create logarithmically spaced numbers of lightcurves.
num_lightcurves = np.logspace(0., 3., n).astype('int64')
# Allocate memory to store results.
cython_times = np.zeros((n, ), dtype='float64')
for i in xrange(n):
# Print a status update message.
print 'BENCHMARK: # lightcurves =', num_lightcurves[
i], '/', i + 1, 'of', n, '...'
for j in xrange(num_lightcurves[i]):
# Simulate the lightcurve.
time, flux, fluxerr, _, _, _ = simulate_box_lightcurve(
period, duration, depth, phase, signal_to_noise, nsamples,
baseline)
cython_start = clock()
out_cython = bls_pulse_cython(time,
flux,
fluxerr,
nbins,
segsize,
mindur,
maxdur,
detrend_order=0,
direction=0)
cython_end = clock()
cython_times[i] += cython_end - cython_start
np.savez('unittests/benchmark2.npz',
num_lightcurves=num_lightcurves,
cython_times=cython_times)
__generate_figure()
def main():
# Number of trials.
n = 10
# Other parameters.
baseline = 90. # days
minutes_per_day = 24. * 60.
signal_to_noise = 10000.
nsamples = np.ceil(baseline * minutes_per_day)
segsize, mindur, maxdur, nbins = (2., 0.01, 0.5, 1000)
# To make it deterministic, seed the PRNG.
np.random.seed(4)
period = np.random.uniform(segsize, 30.) # days
duration = np.random.uniform(1. / 24., 5. / 24.) # days
depth = np.random.uniform(-0.01, -0.5)
phase = 0.5
# Create logarithmically spaced numbers of lightcurves.
num_lightcurves = np.logspace(0., 3., n).astype('int64')
# Allocate memory to store results.
cython_times = np.zeros((n,), dtype='float64')
for i in xrange(n):
# Print a status update message.
print 'BENCHMARK: # lightcurves =', num_lightcurves[i], '/', i + 1, 'of', n, '...'
for j in xrange(num_lightcurves[i]):
# Simulate the lightcurve.
time, flux, fluxerr, _, _, _ = simulate_box_lightcurve(period,
duration, depth, phase, signal_to_noise, nsamples, baseline)
cython_start = clock()
out_cython = bls_pulse_cython(time, flux, fluxerr, nbins, segsize, mindur, maxdur,
detrend_order=0, direction=0)
cython_end = clock()
cython_times[i] += cython_end - cython_start
np.savez('unittests/benchmark2.npz', num_lightcurves=num_lightcurves, cython_times=cython_times)
__generate_figure()
def main(err_on_fail=True, allow_straddling=True, ofile=None, mode='python'):
if ofile is not None:
ofile.write('#\tMeas. mid.\tAct. mid.\tMeas. dpth.\tAct. dpth.\tMeas. '
'dur.\tAct. dur\n')
# Define absolute/relative tolerances.
midtime_atol = 0.1
duration_rtol = 0.1
depth_rtol = 0.1
# How many lightcurves to simulate.
n = 10
# Other parameters.
minutes_per_day = 24. * 60.
signal_to_noise, baseline = (1.e5, 90.)
nsamples = np.ceil(baseline * minutes_per_day)
segsize, mindur, maxdur, nbins = (2., 0.01, 0.5, 1000)
# To make it deterministic, seed the PRNG.
np.random.seed(4)
period_list = np.random.uniform(segsize, 30., size=n) # days
duration_list = np.random.uniform(1. / 24., 5. / 24., size=n) # days
depth_list = np.random.uniform(-0.01, -0.5, size=n)
phase_list = np.random.uniform(0., 1., size=n)
for i, p, du, dp, ph in zip(np.arange(n), period_list, duration_list,
depth_list, phase_list):
# Print a status update message.
print 'TEST_BLS_PULSE: Test case', i + 1, 'of', n, '...'
# Simulate the lightcurve.
time, flux, fluxerr, duration, depth, midtime = \
simulate_box_lightcurve(p, du, dp, ph, signal_to_noise, nsamples,
baseline)
if mode == 'python':
out = bls_pulse_python(time, flux, fluxerr, nbins, segsize, mindur,
maxdur, detrend_order=0, direction=0)
elif mode == 'vec':
out = bls_pulse_vec(time, flux, fluxerr, nbins, segsize, mindur,
maxdur, detrend_order=0, direction=0)
elif mode == 'cython':
dtime, dflux, dfluxerr, dsamples, segstart, segend = \
bin_and_detrend(time, flux, fluxerr, nbins, segsize,
detrend_order=0)
out = bls_pulse_cython(dtime, dflux, dfluxerr, dsamples, nbins,
segsize, mindur, maxdur, direction=0)
else:
raise ValueError('Invalid test mode: %s' % mode)
bls_du = out['duration'].ravel()
bls_dp = out['depth'].ravel()
bls_mid = out['midtime'].ravel()
for j in xrange(len(midtime)):
ndx = np.nanargmin(np.absolute(midtime[j] - bls_mid))
if ofile:
ofile.write('%d\t%f\t%f\t%f\t%f\t%f\t%f\n' % (j, bls_mid[ndx],
midtime[j], bls_dp[ndx], depth[j], bls_du[ndx],
duration[j]))
if is_straddling(midtime[j], duration[j], segsize, time):
print ' Transit %02d.....PASS (straddling)' % j
continue
try:
diff_midtime = np.absolute(midtime[j] - bls_mid[ndx])
diff_depth = np.absolute(depth[j] - bls_dp[ndx])
diff_duration = np.absolute(duration[j] - bls_du[ndx])
errstring = ' Transit %02d.....FAIL\n' % j
if diff_midtime > midtime_atol:
errstring += 'MIDTIME: Expected ' + str(midtime[j]) + \
', measured ' + str(bls_mid[ndx]) + ', diff. ' + \
str(diff_midtime) + ', allowed diff. ' + \
str(midtime_atol)
print errstring
raise RuntimeError
#elif abs(diff_depth / depth[j]) > depth_rtol:
# errstring += 'DEPTH: Expected ' + str(depth[j]) + \
# ', measured ' + str(bls_dp[ndx]) + ', rel. diff. ' + \
# str(diff_depth / depth[j] * 100) + \
# '%, allowed rel. diff. ' + str(depth_rtol * 100) + '%'
# print errstring
# raise RuntimeError
elif abs(diff_duration / duration[j]) > duration_rtol:
errstring += 'DURATION: Expected ' + str(duration[j]) + \
', measured ' + str(bls_du[ndx]) + ', rel. diff. ' + \
str(diff_duration / duration[j] * 100) + \
'%, allowed diff. ' + str(duration_rtol * 100) + '%'
print errstring
raise RuntimeError
else:
# All values within relative or absolute tolerances.
print ' Transit %02d.....PASS' % j
except RuntimeError:
if err_on_fail:
sys.exit(1)
def main():
# Number of baselines to try.
n = 10
# Other parameters.
minutes_per_day = 24. * 60.
signal_to_noise = 10000.
segsize, mindur, maxdur, nbins = (2., 0.01, 0.5, 1000)
# To make it deterministic, seed the PRNG.
np.random.seed(4)
duration = np.random.uniform(1. / 24., 5. / 24.) # days
depth = np.random.uniform(-0.01, -0.5)
phase = 0.5
# Create logarithmically spaced baselines.
baselines = np.logspace(1., 3., n)
# Allocate memory to store results.
python_times = np.empty_like(baselines)
vec_times = np.empty_like(baselines)
cython_times = np.empty_like(baselines)
for i in xrange(n):
# Print a status update message.
print 'BENCHMARK: Baseline =', baselines[i], '/', i + 1, 'of', n, '...'
# Simulate the lightcurve.
nsamples = np.ceil(baselines[i] * minutes_per_day)
period = np.random.uniform(segsize, baselines[i]) # days
time, flux, fluxerr, _, _, _ = simulate_box_lightcurve(
period, duration, depth, phase, signal_to_noise, nsamples,
baselines[i])
sys.stdout.write(' Running Python test...')
sys.stdout.flush()
python_start = clock()
out_python = bls_pulse_python(time,
flux,
fluxerr,
nbins,
segsize,
mindur,
maxdur,
detrend_order=0,
direction=0)
python_end = clock()
sys.stdout.write(' done.\n')
sys.stdout.flush()
sys.stdout.write(' Running vectorized test...')
sys.stdout.flush()
vec_start = clock()
out_vec = bls_pulse_vec(time,
flux,
fluxerr,
nbins,
segsize,
mindur,
maxdur,
detrend_order=0,
direction=0)
vec_end = clock()
sys.stdout.write(' done.\n')
sys.stdout.flush()
sys.stdout.write(' Running Cython test...')
sys.stdout.flush()
cython_start = clock()
out_cython = bls_pulse_cython(time,
flux,
fluxerr,
nbins,
segsize,
mindur,
maxdur,
detrend_order=0,
direction=0)
cython_end = clock()
sys.stdout.write(' done.\n')
sys.stdout.flush()
python_times[i] = python_end - python_start
vec_times[i] = vec_end - vec_start
cython_times[i] = cython_end - cython_start
np.savez('unittests/benchmark.npz',
baselines=baselines,
python_times=python_times,
vec_times=vec_times,
cython_times=cython_times)
__generate_figure()
def main():
# Number of baselines to try.
n = 10
# Other parameters.
minutes_per_day = 24. * 60.
signal_to_noise = 10000.
segsize, mindur, maxdur, nbins = (2., 0.01, 0.5, 1000)
# To make it deterministic, seed the PRNG.
np.random.seed(4)
duration = np.random.uniform(1. / 24., 5. / 24.) # days
depth = np.random.uniform(-0.01, -0.5)
phase = 0.5
# Create logarithmically spaced baselines.
baselines = np.logspace(1., 3., n)
# Allocate memory to store results.
python_times = np.empty_like(baselines)
vec_times = np.empty_like(baselines)
cython_times = np.empty_like(baselines)
for i in xrange(n):
# Print a status update message.
print 'BENCHMARK: Baseline =', baselines[i], '/', i + 1, 'of', n, '...'
# Simulate the lightcurve.
nsamples = np.ceil(baselines[i] * minutes_per_day)
period = np.random.uniform(segsize, baselines[i]) # days
time, flux, fluxerr, _, _, _ = simulate_box_lightcurve(period,
duration, depth, phase, signal_to_noise, nsamples, baselines[i])
sys.stdout.write(' Running Python test...')
sys.stdout.flush()
python_start = clock()
out_python = bls_pulse_python(time, flux, fluxerr, nbins, segsize, mindur, maxdur,
detrend_order=0, direction=0)
python_end = clock()
sys.stdout.write(' done.\n')
sys.stdout.flush()
sys.stdout.write(' Running vectorized test...')
sys.stdout.flush()
vec_start = clock()
out_vec = bls_pulse_vec(time, flux, fluxerr, nbins, segsize, mindur, maxdur,
detrend_order=0, direction=0)
vec_end = clock()
sys.stdout.write(' done.\n')
sys.stdout.flush()
sys.stdout.write(' Running Cython test...')
sys.stdout.flush()
cython_start = clock()
out_cython = bls_pulse_cython(time, flux, fluxerr, nbins, segsize, mindur, maxdur,
detrend_order=0, direction=0)
cython_end = clock()
sys.stdout.write(' done.\n')
sys.stdout.flush()
python_times[i] = python_end - python_start
vec_times[i] = vec_end - vec_start
cython_times[i] = cython_end - cython_start
np.savez('unittests/benchmark.npz', baselines=baselines, python_times=python_times,
vec_times=vec_times, cython_times=cython_times)
__generate_figure()
def main(err_on_fail=True, allow_straddling=True, ofile=None, mode='python'):
if ofile is not None:
ofile.write(
'#\tMeas. mid.\tAct. mid.\tMeas. dpth.\tAct. dpth.\tMeas. dur.\tAct. dur\n'
)
# Define absolute/relative tolerances.
midtime_atol = 0.1
duration_rtol = 0.1
depth_rtol = 0.1
# How many lightcurves to simulate.
n = 10
# Other parameters.
minutes_per_day = 24. * 60.
signal_to_noise, baseline = (10000., 90.)
nsamples = np.ceil(baseline * minutes_per_day)
segsize, mindur, maxdur, nbins = (2., 0.01, 0.5, 1000)
# To make it deterministic, seed the PRNG.
np.random.seed(4)
period_list = np.random.uniform(segsize, 30., size=n) # days
duration_list = np.random.uniform(1. / 24., 5. / 24., size=n) # days
depth_list = np.random.uniform(-0.01, -0.5, size=n)
phase_list = np.random.uniform(0., 1., size=n)
for i, p, du, dp, ph in zip(np.arange(n), period_list, duration_list,
depth_list, phase_list):
# Print a status update message.
print 'TEST_BLS_PULSE: Test case', i + 1, 'of', n, '...'
# Simulate the lightcurve.
time, flux, fluxerr, duration, depth, midtime = simulate_box_lightcurve(
p, du, dp, ph, signal_to_noise, nsamples, baseline)
if mode == 'python':
out = bls_pulse_python(time,
flux,
fluxerr,
nbins,
segsize,
mindur,
maxdur,
detrend_order=0,
direction=0)
elif mode == 'vec':
out = bls_pulse_vec(time,
flux,
fluxerr,
nbins,
segsize,
mindur,
maxdur,
detrend_order=0,
direction=0)
elif mode == 'cython':
out = bls_pulse_cython(time,
flux,
fluxerr,
nbins,
segsize,
mindur,
maxdur,
detrend_order=0,
direction=0)
else:
raise ValueError('Invalid test mode: %s' % mode)
bls_du = out['duration'].ravel()
bls_dp = out['depth'].ravel()
bls_mid = out['midtime'].ravel()
for j in xrange(len(midtime)):
ndx = np.nanargmin(np.absolute(midtime[j] - bls_mid))
if ofile:
ofile.write('%d\t%f\t%f\t%f\t%f\t%f\t%f\n' %
(j, bls_mid[ndx], midtime[j], bls_dp[ndx],
depth[j], bls_du[ndx], duration[j]))
if is_straddling(midtime[j], duration[j], segsize, time):
print ' Transit %02d.....PASS (straddling)' % j
continue
try:
diff_midtime = np.absolute(midtime[j] - bls_mid[ndx])
diff_depth = np.absolute(depth[j] - bls_dp[ndx])
diff_duration = np.absolute(duration[j] - bls_du[ndx])
errstring = ' Transit %02d.....FAIL\n' % j
if diff_midtime > midtime_atol:
errstring += 'MIDTIME: Expected ' + str(midtime[j]) + ', measured ' + \
str(bls_mid[ndx]) + ', diff. ' + str(diff_midtime) + \
', allowed diff. ' + str(midtime_atol)
print errstring
raise RuntimeError
elif diff_depth / depth[j] > depth_rtol:
errstring += 'DEPTH: Expected ' + str(depth[j]) + ', measured ' + \
str(bls_dp[ndx]) + ', rel. diff. ' + \
str(diff_depth / depth[j] * 100) + '%, allowed rel. diff. ' + \
str(depth_rtol * 100) + '%'
print errstring
raise RuntimeError
elif diff_duration / duration[j] > duration_rtol:
errstring += 'DURATION: Expected ' + str(duration[j]) + ', measured ' + \
str(bls_du[ndx]) + ', rel. diff. ' + \
str(diff_duration / duration[j] * 100) + '%, allowed diff. ' + \
str(duration_rtol * 100) + '%'
print errstring
raise RuntimeError
else:
# All values within relative or absolute tolerances.
print ' Transit %02d.....PASS' % j
except RuntimeError:
if err_on_fail:
sys.exit(1)
