def nrg_data(self,
params: NRGParams,
x: np.ndarray,
which_data: str = 'dndt',
which_x: str = 'occupation',
real_data: Optional[np.ndarray] = None,
which_fit_data: str = 'i_sense') -> Data1D:
"""For NRG data only"""
nrg_generator = NrgUtil(inital_params=params)
if real_data is not None:
fit = nrg_generator.get_fit(x=x,
data=real_data,
which_data=which_fit_data)
params = NRGParams.from_lm_params(fit.params)
return nrg_generator.data_from_params(params,
x=x,
which_data=which_data,
which_x=which_x)
def get_nrg_integrated(dat: DatHDF) -> Data1D:
from temp import get_avg_i_sense_data, get_centered_x_at_half_occ, get_centers, get_2d_data
from dat_analysis.analysis_tools.nrg import NrgUtil, NRGParams
# i_data = get_avg_i_sense_data(dat, None, _center_func, overwrite=False, hot_or_cold='hot')
data2d = get_2d_data(dat, hot_or_cold='cold', csq_datnum=None)
if _center_func(dat):
centers = get_centers(dat, data2d, name=None, overwrite=False)
else:
centers = [0] * data2d.data.shape[0]
hot_data2d = get_2d_data(dat, hot_or_cold='hot', csq_datnum=None)
avg_data, avg_x = dat.NrgOcc.get_avg_data(x=data2d.x,
data=hot_data2d.data,
centers=centers,
return_x=True,
name='hot_cold_centers',
check_exists=False,
overwrite=False)
i_data = Data1D(avg_x, avg_data)
i_data.x = get_centered_x_at_half_occ(dat,
None,
fit_name='csq_forced_theta')
init_fit = dat.NrgOcc.get_fit(name='csq_forced_theta')
new_pars = U.edit_params(init_fit.params, ['theta', 'g'], [None, None],
vary=[True, False])
nrg = NrgUtil(None)
new_fit = nrg.get_fit(i_data.x,
i_data.data,
initial_params=new_pars,
which_data='i_sense')
expected_data = nrg.data_from_params(NRGParams.from_lm_params(
new_fit.params),
x=i_data.x,
which_data='dndt')
expected_data.data = np.cumsum(expected_data.data)
expected_data.data = expected_data.data / expected_data.data[-1] * np.log(
2)
expected_data.x = expected_data.x / 100 # Convert to real mV
return expected_data