def profile_system():
import pycuda.driver
# pycuda.driver.initialize_profiler()
# initialize sampler
myDriver = orbitize.driver.Driver(input_file,
'OFTI',
1,
1.22,
56.95,
mass_err=0.08,
plx_err=0.26)
s = myDriver.sampler
# change eccentricity prior
myDriver.system.sys_priors[1] = priors.LinearPrior(-2.18, 2.01)
# test num_samples=1
s.run_sampler(0, num_samples=1)
# test to make sure outputs are reasonable
pycuda.driver.start_profiler()
start = time.time()
orbitize.cext = True
orbitize.cuda_ext = True
orbits = s.run_sampler(30000, num_samples=10000, num_cores=1)
end = time.time()
print()
print("CUDA Runtime: " + str(end - start) + " s")
print()
print(orbits[0])
start = time.time()
orbitize.cext = True
orbitize.cuda_ext = False
orbits = s.run_sampler(30000)
end = time.time()
pycuda.driver.stop_profiler()
print()
print("MULTIPROCESSING Runtime: " + str(end - start) + " s")
print()
print(orbits[0])
start = time.time()
orbitize.cext = True
orbitize.cuda_ext = False
orbits = s.run_sampler(30000, num_cores=1)
end = time.time()
pycuda.driver.stop_profiler()
pycuda.autoinit.context.detach()
print()
print("single threaded Runtime: " + str(end - start) + " s")
print()
print(orbits[0])
def test_run_sampler():
# initialize sampler
myDriver = orbitize.driver.Driver(input_file, 'OFTI',
1, 1.22, 56.95,mass_err=0.08, plx_err=0.26)
s = myDriver.sampler
# change eccentricity prior
myDriver.system.sys_priors[1] = priors.LinearPrior(-2.18, 2.01)
# test num_samples=1
s.run_sampler(0,num_samples=1)
# test to make sure outputs are reasonable
start=time.time()
orbits = s.run_sampler(1000,num_cores=4)
end=time.time()
print()
print("Runtime: "+str(end-start) +" s")
print()
print(orbits[0])
# test that lnlikes being saved are correct
returned_lnlike_test = s.results.lnlike[0]
computed_lnlike_test = s._logl(orbits[0])
assert returned_lnlike_test == pytest.approx(computed_lnlike_test, abs=0.01)
print()
idx = s.system.param_idx
sma = np.median([x[idx['sma1']] for x in orbits])
ecc = np.median([x[idx['ecc1']] for x in orbits])
inc = np.median([x[idx['inc1']] for x in orbits])
# expected values from Blunt et al. (2017)
sma_exp = 48.
ecc_exp = 0.19
inc_exp = np.radians(140)
# test to make sure OFTI values are within 20% of expectations
assert sma == pytest.approx(sma_exp, abs=0.2*sma_exp)
assert ecc == pytest.approx(ecc_exp, abs=0.2*ecc_exp)
assert inc == pytest.approx(inc_exp, abs=0.2*inc_exp)
# test with only one core
orbits = s.run_sampler(100,num_cores=1)
# test with only one epoch
myDriver = orbitize.driver.Driver(input_file_1epoch, 'OFTI',
1, 1.22, 56.95,mass_err=0.08, plx_err=0.26)
s = myDriver.sampler
s.run_sampler(1)
print()
def test_run_sampler():
# initialize sampler
myDriver = orbitize.driver.Driver(input_file,
'OFTI',
1,
1.22,
56.95,
mass_err=0.08,
plx_err=0.26)
s = myDriver.sampler
# change eccentricity prior
myDriver.system.sys_priors[1] = priors.LinearPrior(-2.18, 2.01)
# test num_samples=1
s.run_sampler(0, num_samples=1)
# test to make sure outputs are reasonable
orbits = s.run_sampler(1000)
print()
idx = s.system.param_idx
sma = np.median([x[idx['sma1']] for x in orbits])
ecc = np.median([x[idx['ecc1']] for x in orbits])
inc = np.median([x[idx['inc1']] for x in orbits])
# expected values from Blunt et al. (2017)
sma_exp = 48.
ecc_exp = 0.19
inc_exp = np.radians(140)
# test to make sure OFTI values are within 20% of expectations
assert sma == pytest.approx(sma_exp, abs=0.2 * sma_exp)
assert ecc == pytest.approx(ecc_exp, abs=0.2 * ecc_exp)
assert inc == pytest.approx(inc_exp, abs=0.2 * inc_exp)
# test with only one epoch
myDriver = orbitize.driver.Driver(input_file_1epoch,
'OFTI',
1,
1.22,
56.95,
mass_err=0.08,
plx_err=0.26)
s = myDriver.sampler
s.run_sampler(1)
print()
def test_OFTI_covariances():
"""
Test OFTI fits by turning sep/pa measurements to RA/Dec measurements with covariances
Needs to be run after test_run_sampler()!!
"""
# only run if these variables are set.
if sma_seppa == 0 or seppa_lnprob_compare is None:
print(
"Skipping OFTI covariances test because reference data not initalized. Please make sure test_run_sampler is run first."
)
return
# read in seppa data table and turn into raddec data table
data_table = orbitize.read_input.read_file(input_file)
data_ra, data_dec = system.seppa2radec(data_table['quant1'],
data_table['quant2'])
data_raerr, data_decerr, data_radeccorr = [], [], []
for row in data_table:
raerr, decerr, radec_corr = system.transform_errors(row['quant1'],
row['quant2'],
row['quant1_err'],
row['quant2_err'],
0,
system.seppa2radec,
nsamps=10000000)
data_raerr.append(raerr)
data_decerr.append(decerr)
data_radeccorr.append(radec_corr)
data_table['quant1'] = data_ra
data_table['quant2'] = data_dec
data_table['quant1_err'] = np.array(data_raerr)
data_table['quant2_err'] = np.array(data_decerr)
data_table['quant12_corr'] = np.array(data_radeccorr)
data_table['quant_type'] = np.array(['radec' for _ in data_table])
# initialize system
my_sys = system.System(1,
data_table,
1.22,
56.95,
mass_err=0.08,
plx_err=0.26)
# initialize sampler
s = sampler.OFTI(my_sys)
# change eccentricity prior
my_sys.sys_priors[1] = priors.LinearPrior(-2.18, 2.01)
# test num_samples=1
s.run_sampler(0, num_samples=1)
# test to make sure outputs are reasonable
orbits = s.run_sampler(1000, num_cores=4)
# test that lnlikes being saved are correct
returned_lnlike_test = s.results.lnlike[0]
computed_lnlike_test = s._logl(orbits[0])
assert returned_lnlike_test == pytest.approx(computed_lnlike_test,
abs=0.01)
# test that the lnlike is very similar to the values computed in seppa space
ref_params, ref_lnlike = seppa_lnprob_compare
computed_lnlike_ref = s._logl(ref_params)
assert ref_lnlike == pytest.approx(
computed_lnlike_ref, abs=0.05) # 5% differencesin lnprob is allowable.
idx = s.system.param_idx
sma = np.median([x[idx['sma1']] for x in orbits])
ecc = np.median([x[idx['ecc1']] for x in orbits])
inc = np.median([x[idx['inc1']] for x in orbits])
# test against seppa fits to see they are similar
assert sma_seppa == pytest.approx(sma, abs=0.2 * sma_seppa)