def test_orbital_symmetry_flags():
# TODO: add 4d2s -- this will give non-trival combinations of this flag
""" Check valid combinations of orbital symmetry flags"""
EOBNRv2_sur = gws.EvaluateSurrogate(path_to_surrogate)
# These three combinations should pass
EOBNRv2_sur = gws.EvaluateSurrogate(path_to_surrogate, use_orbital_plane_symmetry=True)
modes, t, hp, hc = EOBNRv2_sur(q=1.14,ell=[2],m=[2],mode_sum=False,fake_neg_modes=True)
EOBNRv2_sur = gws.EvaluateSurrogate(path_to_surrogate, use_orbital_plane_symmetry=True)
modes, t, hp, hc = EOBNRv2_sur(q=1.14,ell=[2],m=[2],mode_sum=False,fake_neg_modes=False)
EOBNRv2_sur = gws.EvaluateSurrogate(path_to_surrogate, use_orbital_plane_symmetry=False)
modes, t, hp, hc = EOBNRv2_sur(q=1.14,ell=[2],m=[2],mode_sum=False,fake_neg_modes=False)
# This should fail with a ValueError. So seeing this error is a passed test
try:
EOBNRv2_sur = gws.EvaluateSurrogate(path_to_surrogate, use_orbital_plane_symmetry=False)
modes, t, hp, hc = EOBNRv2_sur(q=1.14,ell=[2],m=[2],mode_sum=False,fake_neg_modes=True)
except ValueError:
pass
import gwsurrogate as gws
from gwsurrogate import gwtools
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
# ell_m specific modes to pull from data: we are pulling (2,2) modes
spec = gws.EvaluateSurrogate('SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5',
ell_m=[(2, 2)])
timeLength = 28501
massRatioFirstTerm = [1, 2, 3, 4, 5, 6, 7]
massRatioSecondTerm = [.2, .4, .6, .8]
combined_mass_ratios = []
# generate mass ratios (1.2 - 9.8)
for i in range(len(massRatioFirstTerm)):
for j in range(len(massRatioSecondTerm)):
massRatio = massRatioFirstTerm[i] + massRatioSecondTerm[j]
combined_mass_ratios.append(massRatio)
# parse time, cross/plus polarization data, saves to text file in same directory
for i in range(len(combined_mass_ratios)):
formatted_data = []
modes, times, hp, hc = spec(q=combined_mass_ratios[i],
ell=[2],
m=[2],
mode_sum=False,
fake_neg_modes=False)
sur = gwsnew.FastTensorSplineSurrogate()
sur.load(ts_filename)
# These are useful to have in gws surrogates. Not sure if mandatory.
v = [np.load('vandermond/mode%s.npy'%(i)) for i in range(12)]
lm_modes = []
for ell in range(2, 4):
for m in range(-ell, ell+1):
lm_modes.append((ell, m))
print 'Saving gws surrogate...'
save_gws_surrogate(out_filename, lm_modes, sur.ei, v, sur.cre, sur.cim)
print 'Loading gws surrogate...'
sur_new = gws.EvaluateSurrogate(out_filename, use_orbital_plane_symmetry=False)
print 'Testing...'
x = np.array([1.2, 0.3, 0.4, 0.5, -0.2])
h_ts = sur(x)
lm_modes, t, hre, him = sur_new(x, mode_sum=False, fake_neg_modes=False)
h_gws = (hre + 1.j*him).T
print 'Evaluation was successfull! Checking errors...'
max_err = 0.
for mode_gws, (ell, m) in zip(h_gws, lm_modes):
mode_ts = h_ts[ell, m]
err = np.max(abs(mode_ts - mode_gws))
max_err = max(err, max_err)
phi = polarisation angle (often called \psi) -- two waveforms are output, one with phi, one with phi = phi + pi/2
z_rot = a proxy for the phase -- therefore leave fixed
'''
q = 1
M = 60.
dist = 410.
theta = 140. # theta = iota (i.e., inclination angle)
phi = 0. # polarisation angle
z_rot = 0. # hardcode the phase, Phi = 0.
path_to_surrogate = '/home/ethan/LIGO/'
surrogate_file = path_to_surrogate + 'SpEC_q1_10_NoSpin_nu5thDegPoly_exclude_2_0.h5'
spec = gws.EvaluateSurrogate(surrogate_file, ell_m=[(2, 2)])
## Calculate the plus and cross components for the phi = 0 case
time = np.linspace(-0.4, 0.03, 10**5)
_, hp_zero, hc_zero = spec(q=q,
M=M,
dist=dist,
theta=theta,
phi=phi,
z_rot=z_rot,
mode_sum=True,
fake_neg_modes=True,
samples=time,
samples_units='mks')
def test_model_regression(generate_regression_data=False):
""" If generate_regression_data = True, this script will generate
an hdf5 file to diff against. No regression will be done.
If generate_regression_data = False, this script will compare
model evaluations to the hdf5 file produced when True. In a typical use case,
this regression file will be downloaded. """
if generate_regression_data:
h5_file = "model_regression_data.h5"
print(
"Generating regression data file... Make sure this step is done BEFORE making any code changes!\n"
)
print(os.path.exists(h5_file))
if os.path.exists(h5_file):
raise RuntimeError("Refusing to overwrite a regression file!")
else:
h5_file = "test/comparison_data.h5" # assumes pytest runs from project-level folder
try: # try importing data. If it doesn't exist, download it
fp_regression = h5py.File("test/model_regression_data.h5", 'r')
except IOError:
print("Downloading regression data...")
os.system(
'wget --directory-prefix=test https://www.dropbox.com/s/vxqsr7fjoffxm5w/model_regression_data.h5'
)
fp_regression = h5py.File("test/model_regression_data.h5", 'r')
# remove models if you don't have them
dont_test = [
"EMRISur1dq1e4", # model data not currently available in public
"NRSur4d2s_TDROM_grid12", # 10 GB file
"NRSur4d2s_FDROM_grid12", # 10 GB file
#"SpEC_q1_10_NoSpin_linear_alt",
#"SpEC_q1_10_NoSpin_linear",
"EOBNRv2", #TODO: this is two surrogates in one. Break up?
#"SpEC_q1_10_NoSpin",
#"EOBNRv2_tutorial",
#"NRHybSur3dq8",
#"NRHybSur3dq8Tidal",
#"NRSur7dq4"
]
# Common directory where all surrogates are assumed to be located
surrogate_path = gws.catalog.download_path()
# repeatability can be useful for regression tests
np.random.seed(0)
# for each model, associate its surrogate data file
models = [model for model in gws.catalog._surrogate_world]
print(models)
models_to_test = {}
for model in models:
surrogate_data = surrogate_path + os.path.basename(
gws.catalog._surrogate_world[model][0])
if os.path.isfile(surrogate_data): # surrogate data file exists
models_to_test[model] = surrogate_data
else: # file missing
msg = "WARNING: Surrogate missing!!!\n"
msg += "Surrogate data assumed to be in the path %s.\n" % surrogate_data
msg += "If the data is somewhere else, change the path or move the file.\n\n"
msg += "To download this surrogate, from ipython do\n\n >>> gws.catalog.pull(%s)\n" % model
print(msg)
time.sleep(1)
# also test the tutorial surrogate
models_to_test["EOBNRv2_tutorial"] = gws.__path__[
0] + "/../tutorial/TutorialSurrogate/EOB_q1_2_NoSpin_Mode22/"
# remove models from testing...
for i in dont_test:
try:
models_to_test.pop(i)
print("model %s removed from testing" % i)
except KeyError:
print("model %s cannot be removed" % i)
fp = h5py.File(h5_file, "w")
# for each model, select three random points to evaluate at
param_samples_tested = []
for model, datafile in models_to_test.items():
print("Generating regression data for model = %s" % model)
print(datafile)
if model in surrogate_old_interface:
sur = gws.EvaluateSurrogate(datafile)
p_mins = sur.param_space.min_vals()
p_maxs = sur.param_space.max_vals()
elif model in surrogate_loader_interface:
# sur = gws.LoadSurrogate(datafile) # tidal and aligned models use the same h5 file (so wrong one loaded)
sur = gws.LoadSurrogate(model)
try:
p_mins = sur._sur_dimless.param_space.min_vals()
p_maxs = sur._sur_dimless.param_space.max_vals()
except AttributeError: # NRSur7dq4 does not have object sur._sur_dimless.param_space
p_mins = None
p_maxs = None
else:
sur = surrogate.FastTensorSplineSurrogate()
sur.load(datafile)
p_mins = sur.param_space.min_vals()
p_maxs = sur.param_space.max_vals()
print("parameter minimum values", p_mins)
print("parameter maximum values", p_maxs)
param_samples = []
if generate_regression_data: # pick new points to compute regression data at
for i in range(3): # sample parameter space 3 times
if model in list(model_sampler.keys()):
custom_sampler = model_sampler[model]
x, tidOpts, pecOpts = custom_sampler(
i) # [q, chiA, chiB], tidOpts
else: # default sampler for spin-aligned BBH models
x = [] # [q, chiAz, chiBz]
for j in range(len(p_mins)):
xj_min = p_mins[j]
xj_max = p_maxs[j]
tmp = float(np.random.uniform(xj_min, xj_max, size=1))
x.append(tmp)
tidOpts = None
pecOpts = None
param_samples.append([x, tidOpts, pecOpts])
else: # get point at which to compute comparison waveform data
for i in range(3):
if model in list(
model_sampler.keys()): # use sample points if provided
custom_sampler = model_sampler[model]
x, tidOpts, pecOpts = custom_sampler(
i) # [q, chiA, chiB], tidOpts
else: # pull regression points from regression data file; none-tidal models only
print(model + "/parameter%i/parameter" % i)
x = []
x.append(
list(fp_regression[model + "/parameter%i/parameter" %
i][:])) # [q, chiAz, chiBz]
x = list(fp_regression[model + "/parameter%i/parameter" %
i][:]) # [q, chiAz, chiBz]
tidOpts = None
pecOpts = None
param_samples.append([x, tidOpts, pecOpts])
param_samples_tested.append(param_samples)
model_grp = fp.create_group(model)
for i, ps in enumerate(param_samples):
x = ps[0]
tidOpts = ps[1]
pecOpts = ps[2]
if model in surrogate_old_interface:
ps_float = x[0] # TODO: generalize interface
modes, t, hp, hc = sur(q=ps_float,
mode_sum=False,
fake_neg_modes=True)
else:
if model in surrogate_loader_interface:
q = x[0]
if type(x[1]) is np.float64 or type(x[1]) is float: # chiz
chiA = np.array([0, 0, x[1]])
chiB = np.array([0, 0, x[2]])
elif len(x[1]) == 3: # spin vector
chiA = np.array(x[1])
chiB = np.array(x[2])
else:
raise ValueError
try:
# Regression samples outside of the training interval.
# Warnings are raised (as they should) but could
# appear bad to a new user
if model in ["NRSur7dq4"]:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
t, h, dyanmics = sur(q,
chiA,
chiB,
f_low=0.0,
tidal_opts=tidOpts,
precessing_opts=pecOpts)
else:
t, h, dyanmics = sur(q,
chiA,
chiB,
f_low=0.0,
tidal_opts=tidOpts,
precessing_opts=pecOpts)
except ValueError: # some models do not allow for f_low=0.0 and require a time step
# step size, Units of M
# initial frequency, Units of cycles/M
t, h, dyanmics = sur(q,
chiA,
chiB,
dt=0.25,
f_low=3.e-3,
tidal_opts=tidOpts,
precessing_opts=pecOpts)
else:
h = sur(x)
try:
h_np = [h[mode] for mode in sur.mode_list]
except AttributeError: # for new interface
h_np = [h[mode] for mode in sur._sur_dimless.mode_list]
h_np = np.vstack(h_np)
hp = np.real(h_np)
hc = np.imag(h_np)
samplei = model_grp.create_group("parameter" + str(i))
if model in list(model_sampler.keys()):
# model samplers return a list of lists, which we flatten into
# a list of numbers for storing in an h5 dataset
x = flatten_params(x)
samplei.create_dataset("parameter", data=x)
samplei.create_dataset("hp", data=hp, dtype='float32')
samplei.create_dataset("hc", data=hc, dtype='float32')
fp.close()
if not generate_regression_data:
fp = h5py.File(h5_file, "r") # reopen comparison data
for model in models_to_test.keys():
print("testing model %s ..." % model)
for i in range(3): # 3 parameter samples
hp_regression = fp_regression[model + "/parameter%i/hp" % i][:]
hc_regression = fp_regression[model + "/parameter%i/hp" % i][:]
hp_comparison = fp[model + "/parameter%i/hp" % i][:]
hc_comparison = fp[model + "/parameter%i/hp" % i][:]
if model == "NRHybSur3dq8":
local_rtol = rtol_gsl
else:
local_rtol = rtol_gsl
np.testing.assert_allclose(hp_regression,
hp_comparison,
rtol=local_rtol,
atol=atol)
np.testing.assert_allclose(hc_regression,
hc_comparison,
rtol=local_rtol,
atol=atol)
# fails due to round-off error differences of different machines
#fp_regression.close()
#process = subprocess.Popen(["h5diff", "test/model_regression_data.h5",h5_file],
# stdin=subprocess.PIPE,
# stdout=subprocess.PIPE,
# stderr=subprocess.PIPE)
#returncode = process.wait()
#stdout, stderr = process.communicate()
#if returncode == 0:
# assert(True)
#else:
# print(stdout)
# print(stderr)
# assert(False)
print("models tested... ")