def __init__(self, max_init_len=1000, **kwargs):
SchwarzschildEccentric.__init__(self, **kwargs)
AmplitudeBase.__init__(self, **kwargs)
self.few_dir = dir_path + "/../../"
# check if user has the necessary data
# if not, the data will automatically download
self.data_file = fp = "SchwarzschildEccentricInput.hdf5"
check_for_file_download(fp, self.few_dir)
# get information about this specific model from the file
with h5py.File(self.few_dir + "few/files/" + self.data_file,
"r") as fp:
num_teuk_modes = fp.attrs["num_teuk_modes"]
transform_factor = fp.attrs["transform_factor"]
self.break_index = fp.attrs["break_index"]
# adjust c++ classes based on gpu usage
if self.use_gpu:
self.neural_layer = neural_layer_wrap
self.transform_output = transform_output_wrap
else:
self.neural_layer = neural_layer_wrap_cpu
self.transform_output = transform_output_wrap_cpu
self.num_teuk_modes = num_teuk_modes
self.transform_factor_inv = 1 / transform_factor
self.max_init_len = max_init_len
self._initialize_weights()
def __init__(self, **kwargs):
SchwarzschildEccentric.__init__(self, **kwargs)
AmplitudeBase.__init__(self, **kwargs)
# check if you have the necessary file
# it will download from Zenodo if the user does not have it.
few_dir = dir_path + "/../../"
fp = "Teuk_amps_a0.0_lmax_10_nmax_30_new.h5"
check_for_file_download(fp, few_dir)
self.amplitude_generator = pyAmplitudeGenerator(self.lmax, self.nmax, few_dir)
def __init__(self, *args, **kwargs):
GPUModuleBase.__init__(self, *args, **kwargs)
SchwarzschildEccentric.__init__(self, *args, **kwargs)
SummationBase.__init__(self, *args, **kwargs)
self.kwargs = kwargs
if self.use_gpu:
self.get_waveform = get_waveform_wrap
else:
self.get_waveform = get_waveform_wrap_cpu
def __init__(
self,
inspiral_module,
amplitude_module,
sum_module,
inspiral_kwargs={},
amplitude_kwargs={},
sum_kwargs={},
Ylm_kwargs={},
mode_selector_kwargs={},
use_gpu=False,
normalize_amps=True,
):
GPUModuleBase.__init__(self, use_gpu=use_gpu)
SchwarzschildEccentric.__init__(self, use_gpu=use_gpu)
amplitude_kwargs, sum_kwargs = self.adjust_gpu_usage(
use_gpu, [amplitude_kwargs, sum_kwargs])
# normalize amplitudes to flux at each step from trajectory
self.normalize_amps = normalize_amps
# kwargs that are passed to the inspiral call function
self.inspiral_kwargs = inspiral_kwargs
# function for generating the inpsiral
self.inspiral_generator = inspiral_module()
# function for generating the amplitude
self.amplitude_generator = amplitude_module(**amplitude_kwargs)
# summation generator
self.create_waveform = sum_module(**sum_kwargs)
# angular harmonics generation
self.ylm_gen = GetYlms(use_gpu=use_gpu, **Ylm_kwargs)
# selecting modes that contribute at threshold to the waveform
self.mode_selector = ModeSelector(self.m0mask,
**mode_selector_kwargs,
use_gpu=use_gpu)
def __init__(self, *args, **kwargs):
TrajectoryBase.__init__(self, *args, **kwargs)
SchwarzschildEccentric.__init__(self, *args, **kwargs)
few_dir = dir_path + "/../../"
fp = "AmplitudeVectorNorm.dat"
check_for_file_download(fp, few_dir)
fp = "FluxNewMinusPNScaled_fixed_y_order.dat"
check_for_file_download(fp, few_dir)
self.flux_generator = pyFluxGenerator(few_dir)
self.specific_kwarg_keys = [
"T",
"dt",
"err",
"DENSE_STEPPING",
"max_init_len",
"use_rk4",
]
def __init__(self, *args, use_gpu=False, **kwargs):
GPUModuleBase.__init__(self, *args, **kwargs)
SchwarzschildEccentric.__init__(self, *args, **kwargs)
SummationBase.__init__(self, *args, **kwargs)