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dataSettings.py
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import numpy as np
import copy
import torch
# number of x points in profile data
nx=33
# timestep in dataset, in seconds
DT=0.02
# No normalization for qpsi! Instead, code normalizes/denormalizes w/ inverse
# i.e. by transforming to iota = 1/q (mean & std for q would be ignored)
'''normalizations={
'zipfit_etempfit_rho': {'mean': 0, 'std': 2},
'zipfit_edensfit_rho': {'mean': 0, 'std': 2},
'neped_joe': {'mean': 0, 'std': 1},
'zipfit_trotfit_rho': {'mean': 0, 'std': 1e2},
'zipfit_itempfit_rho': {'mean': 0, 'std': 2},
'zipfit_zdensfit_rho': {'mean': 0, 'std': 2},
'pres_EFIT01': {'mean': 0, 'std': 1e4},
'pinj': {'mean': 0, 'std': 2e3},
'tinj': {'mean': 0, 'std': 2},
'ipsiptargt': {'mean': 0, 'std': 1},
'ip': {'mean': 0, 'std': 1e6},
'bt': {'mean': 0, 'std': 1},
'dstdenp': {'mean': 0, 'std': 2},
'gasA': {'mean': 0, 'std': 1},
'gasB': {'mean': 0, 'std': 1},
'gasC': {'mean': 0, 'std': 1},
'gasD': {'mean': 0, 'std': 1},
'li_EFIT01': {'mean': 0, 'std': 1},
'tribot_EFIT01': {'mean': 0, 'std': 1},
'tritop_EFIT01': {'mean': 0, 'std': 1},
'aminor_EFIT01': {'mean': 0, 'std': 1},
'rmaxis_EFIT01': {'mean': 0, 'std': 1},
'dssdenest': {'mean': 0, 'std': 2},
'qmin_EFIT01': {'mean': 0, 'std': 1},
'qmin_EFITRT2': {'mean': 0, 'std': 1},
'kappa_EFIT01': {'mean': 0, 'std': 1},
'volume_EFIT01': {'mean': 0, 'std': 10},
'betan_EFIT01': {'mean': 0, 'std': 1},
'betan_EFITRT2': {'mean': 0, 'std': 1},
'epedHeight': {'mean': 0, 'std': 5e-3},
'eped_te_prediction': {'mean': 0, 'std': 1},
'epedHeightForNe1': {'mean': 0, 'std': 5e-3},
'epedHeightForNe3': {'mean': 0, 'std': 5e-3},
'epedHeightForNe5': {'mean': 0, 'std': 5e-3},
'epedHeightForNe7': {'mean': 0, 'std': 5e-3},
'D_tot': {'mean': 0, 'std': 1e2},
'H_tot': {'mean': 0, 'std': 1e2},
'Ar_tot': {'mean': 0, 'std': 1e2},
'Ne_tot': {'mean': 0, 'std': 1e2},
'He_tot': {'mean': 0, 'std': 1e2},
'N_tot': {'mean': 0, 'std': 1e2},
'ech_pwr_total': {'mean': 0, 'std': 1e6},
'P_AUXILIARY': {'mean': 0, 'std': 2e3}, # custom signals,
'zeff_rho': {'mean': 0, 'std': 2}, # defined in customDatasetMakers ad hoc
'p' : {'mean': 0, 'std': 100},
'qinv': {'mean': 0, 'std': 1},
'1/q': {'mean': 0, 'std': 1},
'j': {'mean': 0, 'std': 1},
'ne': {'mean': 0, 'std': 10},
'Te': {'mean': 0, 'std': 1},
'Vtor': {'mean': 0, 'std': 100},
'Ti': {'mean': 0, 'std': 1},
'Vpol': {'mean': 0, 'std': 100},
'bmspinj': {'mean': 0, 'std': 2},
'bmstinj': {'mean': 0, 'std': 2},
'PCBCOIL': {'mean': 0, 'std': 100000}
}'''
normalizations={
'zipfit_etempfit_rho': {'mean': 0, 'std': 2},
'zipfit_edensfit_rho': {'mean': 0, 'std': 2},
'neped_joe': {'mean': 0, 'std': 1},
'zipfit_trotfit_rho': {'mean': 0, 'std': 1e2},
'zipfit_itempfit_rho': {'mean': 0, 'std': 2},
'zipfit_zdensfit_rho': {'mean': 0, 'std': 2},
'pres_EFIT01': {'mean': 0, 'std': 1e4},
'pinj': {'mean': 0, 'std': 2e3},
'tinj': {'mean': 0, 'std': 2},
'ipsiptargt': {'mean': 0, 'std': 1},
'ip': {'mean': 989467, 'std': 389572},
'bt': {'mean': 0, 'std': 1},
'dstdenp': {'mean': 2.9, 'std': 2.015},
'gasA': {'mean': 0, 'std': 1e2},
'gasA_voltage': {'mean': 0.2318070580561956, 'std': 1.6204600868125758},
'gasB': {'mean': 0, 'std': 1},
'gasC': {'mean': 0, 'std': 1},
'gasD': {'mean': 0, 'std': 1},
'li_EFIT01': {'mean': 0, 'std': 1},
'li_EFITRT2': {'mean': 0, 'std': 1},
'tribot_EFIT01': {'mean': 0, 'std': 1},
'tritop_EFIT01': {'mean': 0, 'std': 1},
'aminor_EFIT01': {'mean': 0, 'std': 1},
'rmaxis_EFIT01': {'mean': 0, 'std': 1},
'dssdenest': {'mean': 0, 'std': 2},
'qmin_EFIT01': {'mean': 0, 'std': 1},
'qmin_EFITRT2': {'mean': 0, 'std': 1},
'kappa_EFIT01': {'mean': 0, 'std': 1},
'volume_EFIT01': {'mean': 0, 'std': 10},
'betan_EFIT01': {'mean': 0, 'std': 1},
'betan_EFITRT2': {'mean': 0, 'std': 1},
'epedHeight': {'mean': 0, 'std': 5e-3},
'eped_te_prediction': {'mean': 0, 'std': 1},
'epedHeightForNe1': {'mean': 0, 'std': 5e-3},
'epedHeightForNe3': {'mean': 0, 'std': 5e-3},
'epedHeightForNe5': {'mean': 0, 'std': 5e-3},
'epedHeightForNe7': {'mean': 0, 'std': 5e-3},
'D_tot': {'mean': 0, 'std': 1e2},
'H_tot': {'mean': 0, 'std': 1e2},
'Ar_tot': {'mean': 0, 'std': 1e2},
'Ne_tot': {'mean': 0, 'std': 1e2},
'He_tot': {'mean': 0, 'std': 1e2},
'N_tot': {'mean': 0, 'std': 1e2},
'ech_pwr_total': {'mean': 0, 'std': 1e6},
'P_AUXILIARY': {'mean': 0, 'std': 2e3}, # custom signals,
'zeff_rho': {'mean': 0, 'std': 2}, # defined in customDatasetMakers ad hoc
'p' : {'mean': 0, 'std': 100},
'qinv': {'mean': 0, 'std': 1},
'1/q': {'mean': 0, 'std': 1},
'j': {'mean': 0, 'std': 1},
'ne': {'mean': 0, 'std': 10},
'Te': {'mean': 0, 'std': 1},
'Vtor': {'mean': 0, 'std': 100},
'Ti': {'mean': 0, 'std': 1},
'Vpol': {'mean': 0, 'std': 100},
'bmspinj': {'mean': 4.072876, 'std': 3.145593},
'bmstinj': {'mean': 3.38, 'std': 2.70},
'PCBCOIL': {'mean': 0, 'std': 58800}
}
pcs_normalizations={
'zipfit_etempfit_rho': {'mean': 0, 'std': 2},
'zipfit_edensfit_rho': {'mean': 0, 'std': 2},
'neped_joe': {'mean': 0, 'std': 1},
'zipfit_trotfit_rho': {'mean': 0, 'std': 1e2},
'zipfit_itempfit_rho': {'mean': 0, 'std': 2},
'zipfit_zdensfit_rho': {'mean': 0, 'std': 2},
'pres_EFIT01': {'mean': 0, 'std': 1e4},
'pinj': {'mean': 0, 'std': 2e3},
'tinj': {'mean': 0, 'std': 2},
'ipsiptargt': {'mean': 0, 'std': 1},
'ip': {'mean': 989467, 'std': 389572},
'bt': {'mean': 0, 'std': 1},
'dstdenp': {'mean': 2.9, 'std': 2.015},
'gasA': {'mean': 0, 'std': 1e2},
'gasB': {'mean': 0, 'std': 1},
'gasC': {'mean': 0, 'std': 1},
'gasD': {'mean': 0, 'std': 1},
'gasA_voltage': {'mean': 0.2318070580561956, 'std': 1.6204600868125758},
'li_EFIT01': {'mean': 0, 'std': 1},
'li_EFITRT2': {'mean': 0, 'std': 1},
'tribot_EFIT01': {'mean': 0, 'std': 1},
'tritop_EFIT01': {'mean': 0, 'std': 1},
'aminor_EFIT01': {'mean': 0, 'std': 1},
'rmaxis_EFIT01': {'mean': 0, 'std': 1},
'dssdenest': {'mean': 0, 'std': 2},
'qmin_EFIT01': {'mean': 0, 'std': 1},
'qmin_EFITRT2': {'mean': 0, 'std': 1},
'kappa_EFIT01': {'mean': 0, 'std': 1},
'volume_EFIT01': {'mean': 0, 'std': 10},
'betan_EFIT01': {'mean': 0, 'std': 1},
'betan_EFITRT2': {'mean': 0, 'std': 1},
'epedHeight': {'mean': 0, 'std': 5e-3},
'eped_te_prediction': {'mean': 0, 'std': 1},
'epedHeightForNe1': {'mean': 0, 'std': 5e-3},
'epedHeightForNe3': {'mean': 0, 'std': 5e-3},
'epedHeightForNe5': {'mean': 0, 'std': 5e-3},
'epedHeightForNe7': {'mean': 0, 'std': 5e-3},
'D_tot': {'mean': 0, 'std': 1e2},
'H_tot': {'mean': 0, 'std': 1e2},
'Ar_tot': {'mean': 0, 'std': 1e2},
'Ne_tot': {'mean': 0, 'std': 1e2},
'He_tot': {'mean': 0, 'std': 1e2},
'N_tot': {'mean': 0, 'std': 1e2},
'ech_pwr_total': {'mean': 0, 'std': 1e6},
'P_AUXILIARY': {'mean': 0, 'std': 2e3}, # custom signals,
'zeff_rho': {'mean': 0, 'std': 2}, # defined in customDatasetMakers ad hoc
'p' : {'mean': 0, 'std': 100},
'qinv': {'mean': 0, 'std': 1},
'1/q': {'mean': 0, 'std': 1},
'j': {'mean': 0, 'std': 1},
'ne': {'mean': 0, 'std': 10},
'Te': {'mean': 0, 'std': 1},
'Vtor': {'mean': 0, 'std': 100},
'Ti': {'mean': 0, 'std': 1},
'Vpol': {'mean': 0, 'std': 100},
'bmspinj': {'mean': 4.072876, 'std': 3.145593},
'bmstinj': {'mean': 3.38, 'std': 2.70},
'PCBCOIL': {'mean': 0, 'std': 58800}
}
clipped_signals={}
# add ASTRA stuff, for all possible ASTRA runs
sig_normalizations={
'CD': {'mean': 0, 'std': 1},
'CC': {'mean': 0, 'std': 50},
'CUBS': {'mean': 0, 'std': 1},
'HE': {'mean': 0, 'std': 1},
'XI': {'mean': 0, 'std': 1},
'PITOT': {'mean': 0, 'std': 2},
'PIBM': {'mean': 0, 'std': 2},
'PETOT': {'mean': 0, 'std': 2},
'PEBM': {'mean': 0, 'std': 2},
'TE': {'mean': 0, 'std': 1},
'TI': {'mean': 0, 'std': 1},
'NI': {'mean': 0, 'std': 2},
'ANGF': {'mean': 0, 'std': 1e2},
'UPAR': {'mean': 0, 'std': 1e2},
'NE': {'mean': 0, 'std': 1}
}
sig_bounds={
'HE': {'min': 0, 'max': 20},
'XI': {'min': 0, 'max': 20}
}
'''pcs_normalizations={
'zipfit_etempfit_rho': {'mean': 1.587, 'std': 1.560}, # pcs is in eV but dataSettings is in keV, so divide pcs norms by 1000
'zipfit_edensfit_rho': {'mean': 3.977, 'std': 2.764},
'zipfit_trotfit_rho': {'mean': 42.93 * 1.0e3, 'std': 58.47 * 1.0e3}, # pcs is in Hz, what is dataSettings in?
'zipfit_itempfit_rho': {'mean': 1.23526171875, 'std': 1.3855859375},
'pinj': {'mean': 4072.8761393229165, 'std': 3145.5935872395835}, # PCS is in W, here it's kW
'tinj': {'mean': 3.38, 'std': 2.70},
'ip': {'mean': 989467.8541666666, 'std': 389572.2916666666},
'bt': {'mean': 0, 'std': 1}, # No normalizing seen on PCS. This may be wrong
'gasA': {'mean': 0.2318070580561956, 'std': 1.6204600868125758},
'gasB': {'mean': 0, 'std': 1}, # not present in pcs
'gasC': {'mean': 0, 'std': 1}, # not present in pcs
'gasD': {'mean': 0, 'std': 1}, # not present in pcs
'li_EFIT01': {'mean': 0, 'std': 1}, # not present in pcs
'tribot_EFIT01': {'mean': 0.41, 'std': 0.31},
'tritop_EFIT01': {'mean': 0.60, 'std': 0.31},
'aminor_EFIT01': {'mean': 0.586, 'std': 0.023},
'rmaxis_EFIT01': {'mean': 0, 'std': 1}, # not present in pcs
'qmin_EFIT01': {'mean': 0, 'std': 1},
'kappa_EFIT01': {'mean': 1.82, 'std': 0.078},
'volume_EFIT01': {'mean': 0, 'std': 10}, # not present in pcs
'betan_EFIT01': {'mean': 1.6395551, 'std': 0.793226},
'D_tot': {'mean': 0, 'std': 1e2}, # not present in pcs
'H_tot': {'mean': 0, 'std': 1e2}, # not present in pcs
'Ar_tot': {'mean': 0, 'std': 1e2}, # not present in pcs
'Ne_tot': {'mean': 0, 'std': 1e2}, # not present in pcs
'He_tot': {'mean': 0, 'std': 1e2}, # not present in pcs
'N_tot': {'mean': 0, 'std': 1e2}, # not present in pcs
'ech_pwr_total': {'mean': 0, 'std': 1e6}, # pcs is in MW but here it's W
}
'''
for astrasim in ['astrainterpretive','astrapredictEPEDNNTGLFNNfullyZIPFIT',
'astrainterpretZIPFIT', 'astrapredictTGLFNNZIPFIT']:
for sig in sig_normalizations:
normalizations[f'{sig}_{astrasim}']=sig_normalizations[sig]
for sig in sig_bounds:
clipped_signals[f'{sig}_{astrasim}']=sig_bounds[sig]
# if use_gyroBohm:
# normalizations['zipfit_edensfit_rho'] = {'mean': 0, 'std': 5e-6}
# #normalizations['zipfit_etempfit_rho'] = {'mean': 0, 'std': 1}
# normalizations['zipfit_itempfit_rho'] = {'mean': 0, 'std': 1}
# ohmic power in Watts, to add to Pinj to get power for taue calculation
ohmicPower=5e5
# min and max taue in seconds
taueMin=0.010
taueMax=1.000
IMPURITY_FRACTION=0.04
# or from
#Zeff=2 flat profile
#Zimp=6
#IMPURITY_FRACTION=(Zeff-1)/(Zimp*(Zimp-Zeff)) #=1/24~4%
IMPURITY_Z=6
KEV_PER_1019_TO_J=1.602e3
def get_rotation_sigs(sigs):
rotation_signals=[]
for sig in sigs:
if sig=='zipfit_trotfit_rho' or sig.startswith('UPAR_'):
rotation_signals.append(sig)
return rotation_signals
def get_density_sigs(sigs):
density_signals=[]
for sig in sigs:
if sig=='zipfit_edensfit_rho' or sig.startswith('NE_'):
density_signals.append(sig)
return density_signals
# excluded_sigs for e.g. shotnum and times from preprocessed data
# assumes dictionary of signals, each of form [...,num_rho] / [...]
# e.g. (rho) / scalar; (time, rho) / (time); or (nsamples, time, rho) / (nsamples, time)
def get_normalized_dic(denormed_dic, excluded_sigs=['shotnum','times'], use_fancy_normalization=False, pcs_normalize=False):
for sig in denormed_dic:
denormed_dic[sig]=np.array(denormed_dic[sig])
normed_dic={}
excluded_sigs=[sig for sig in denormed_dic.keys() if sig in excluded_sigs]
for sig in excluded_sigs:
normed_dic[sig]=denormed_dic[sig]
considered_sigs=[sig for sig in denormed_dic.keys() if sig not in excluded_sigs]
if use_fancy_normalization:
gyrobohm_rotation_signals=get_rotation_sigs(denormed_dic.keys())
gyrobohm_density_signals=get_density_sigs(denormed_dic.keys())
for sig in gyrobohm_rotation_signals+gyrobohm_density_signals+['pinj']:
if sig in denormed_dic:
considered_sigs.remove(sig)
if use_fancy_normalization:
density_sig='zipfit_edensfit_rho'
volume_sig='volume_EFIT01'
r_sig='rmaxis_EFIT01'
a_sig='aminor_EFIT01'
ip_sig='ip'
# use volume average power
if 'pinj' in denormed_dic:
normed_dic['pinj']=(denormed_dic['pinj']/normalizations['pinj']['std']) / (denormed_dic[volume_sig]/normalizations[volume_sig]['std'])
for sig in gyrobohm_density_signals:
# ip can be negative, density is always positive so only need the abs in the normalization and not denormalization
greenwald_density=(np.abs(denormed_dic[ip_sig])/normalizations[ip_sig]['std']) / (denormed_dic[a_sig]/normalizations[a_sig]['std'])**2
normed_dic[sig]=(denormed_dic[sig]/normalizations[sig]['std'])/ greenwald_density[...,None]
for sig in gyrobohm_rotation_signals:
num_rho=denormed_dic[sig].shape[-1]
rho=np.linspace(0,1,num_rho)
# convert upar to momentum~upar*M*R^2~upar*ne*Vol*R^2
# since ne~sum(Z_i n_i) by quasineutrality and Z_i~mass (though complicated for partially ionized Tungsten)
# and to avoid division by 0 take integral of ne*Vol, which is like sum(rho * ne)*V since volume of elemnts scales like rho
mass=np.mean(rho* (denormed_dic[density_sig]/normalizations[density_sig]['std']), axis=-1)* (denormed_dic[volume_sig]/normalizations[volume_sig]['std'])
moment_of_inertia=mass[...,None]* (denormed_dic[r_sig][...,None]/normalizations[r_sig]['std'])**2
normed_dic[sig]=denormed_dic[sig]/normalizations[sig]['std']* moment_of_inertia
for sig in considered_sigs:
if 'qpsi' in sig:
normed_dic[sig] = 1. / denormed_dic[sig]
else:
if pcs_normalize:
normed_dic[sig] = (denormed_dic[sig] - pcs_normalizations[sig]['mean']) / pcs_normalizations[sig]['std']
else:
normed_dic[sig] = (denormed_dic[sig] - normalizations[sig]['mean']) / normalizations[sig]['std']
return normed_dic
def get_denormalized_dic(normed_dic, excluded_sigs=['shotnum','times'], use_fancy_normalization=False, pcs_normalize=False):
for sig in normed_dic:
normed_dic[sig]=np.array(normed_dic[sig])
denormed_dic={}
excluded_sigs=[sig for sig in normed_dic.keys() if sig in excluded_sigs]
for sig in excluded_sigs:
denormed_dic[sig]=normed_dic[sig]
considered_sigs=[sig for sig in normed_dic.keys() if sig not in excluded_sigs]
if use_fancy_normalization:
gyrobohm_rotation_signals=get_rotation_sigs(normed_dic.keys())
gyrobohm_density_signals=get_density_sigs(normed_dic.keys())
for sig in gyrobohm_rotation_signals+gyrobohm_density_signals+['pinj']:
if sig in normed_dic:
considered_sigs.remove(sig)
for sig in considered_sigs:
if 'qpsi' in sig:
denormed_dic[sig] = 1. / normed_dic[sig]
else:
if pcs_normalize:
denormed_dic[sig] = (normed_dic[sig] * pcs_normalizations[sig]['std']) + pcs_normalizations[sig]['mean']
else:
denormed_dic[sig] = (normed_dic[sig] * normalizations[sig]['std']) + normalizations[sig]['mean']
if use_fancy_normalization:
density_sig='zipfit_edensfit_rho'
volume_sig='volume_EFIT01'
r_sig='rmaxis_EFIT01'
a_sig='aminor_EFIT01'
ip_sig='ip'
if 'pinj' in normed_dic:
denormed_dic['pinj']=normed_dic['pinj']*(denormed_dic[volume_sig]/normalizations[volume_sig]['std'])* normalizations['pinj']['std']
for sig in gyrobohm_density_signals:
greenwald_density=(denormed_dic[ip_sig]/normalizations[ip_sig]['std']) / (denormed_dic[a_sig]/normalizations[a_sig]['std'])**2
denormed_dic[sig]=(normed_dic[sig]*greenwald_density[...,None])* normalizations[sig]['std']
for sig in gyrobohm_rotation_signals:
num_rho=normed_dic[sig].shape[-1]
rho=np.linspace(0,1,num_rho)
mass=np.mean(rho* (denormed_dic[density_sig]/normalizations[density_sig]['std']) ,axis=-1)* (denormed_dic[volume_sig]/normalizations[volume_sig]['std'])
moment_of_inertia=mass[...,None]*denormed_dic[r_sig][...,None]**2/normalizations[r_sig]['std']**2
denormed_dic[sig]=normalizations[sig]['std']*normed_dic[sig] / moment_of_inertia
return denormed_dic