weights /= sum(weights)
new_signal.append(
np.array(
np.sum([
old_signal[ind] * weights[j]
for j, ind in enumerate(inds_in_range)
],
axis=0)))
else:
new_signal.append(np.mean(old_signal[inds_in_range], axis=0))
return np.array(new_signal)
######## FETCH SCALARS #############
for sig_name in scalar_sig_names:
signal = PtDataSignal(sig_name)
pipeline.fetch('{}_full'.format(sig_name), signal)
######## FETCH SCALARS #############
for sig_name in nb_sig_names:
signal = MdsSignal(sig_name, 'NB', location='remote://atlas.gat.com')
pipeline.fetch('{}_full'.format(sig_name), signal)
######## FETCH EFIT PROFILES #############
for sig_name in efit_profile_sig_names:
signal = MdsSignal('RESULTS.GEQDSK.{}'.format(sig_name),
efit_type,
location='remote://atlas.gat.com',
dims=['psi', 'times'])
pipeline.fetch('{}_full'.format(sig_name), signal)
if len(old_signal.shape)==1:
new_signal.append(np.nan)
else:
new_signal.append(np.full(old_signal.shape[1:],np.nan))
else:
if exponential_falloff:
weights=np.array([np.exp(- np.abs(standard_times[i]-old_timebase[ind]) / falloff_rate) for ind in inds_in_range])
weights/=sum(weights)
new_signal.append( np.array( np.sum( [old_signal[ind]*weights[j] for j,ind in enumerate(inds_in_range)], axis=0) ) )
else:
new_signal.append(np.mean(old_signal[inds_in_range],axis=0))
return np.array(new_signal)
######## FETCH SCALARS #############
for sig_name in cfg['data']['scalar_sig_names']:
signal=PtDataSignal(sig_name)
pipeline.fetch('{}_full'.format(sig_name),signal)
######## FETCH STABILITY #############
for sig_name in cfg['data']['stability_sig_names']:
signal=MdsSignal('.MIRNOV.{}'.format(sig_name),
'MHD',
location='remote://atlas.gat.com')
pipeline.fetch('{}_full'.format(sig_name),signal)
######## FETCH SCALARS #############
for sig_name in cfg['data']['nb_sig_names']:
signal=MdsSignal(sig_name,
'NB',
location='remote://atlas.gat.com')
pipeline.fetch('{}_full'.format(sig_name),signal)
from toksearch import PtDataSignal, MdsSignal, Pipeline
import matplotlib.pyplot as plt
import matplotlib
font = {'family': 'normal', 'weight': 'bold', 'size': 22}
matplotlib.rc('font', **font)
#plt.rcParams.update({'text.usetex': True})
import numpy as np
shot = 187076
req_sig_name = 'etsrpinj'
true_sig_name = 'pinj'
controlled_sig_name = 'etste1'
offline_controlled_sig_name = 'etempfit'
req_sig = PtDataSignal(req_sig_name).fetch(shot)
true_sig = MdsSignal(true_sig_name, 'NB',
location='remote://atlas.gat.com').fetch(shot)
controlled_sig = PtDataSignal(controlled_sig_name).fetch(shot)
#offline_controlled_sig = MdsSignal(r'\ZIPFIT01::TOP.PROFILES.{}'.format(offline_controlled_sig_name),'ZIPFIT01',location='remote://atlas.gat.com',dims=['rhon','times']).fetch(shot)
which_proposal = PtDataSignal('etswchprop').fetch(shot)
req_scaling = 1e-6
true_scaling = 1e-3
controlled_scaling = 1e-3
offline_controlled_scaling = 1
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True)
axes[0, 0].plot(controlled_sig['times'],
controlled_sig['data'] * controlled_scaling,
label='core Te (keV)')
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 22}
matplotlib.rc('font', **font)
#plt.rcParams.update({'text.usetex': True})
import numpy as np
shot=187076
req_sig_name='etsrpinj'
true_sig_name='pinj'
controlled_sig_name='etstein1'
offline_controlled_sig_name='etempfit'
te_std_dev=1.560
te_mean=1.587
req_sig=PtDataSignal(req_sig_name).fetch(shot)
true_sig=MdsSignal(true_sig_name,
'NB',
location='remote://atlas.gat.com').fetch(shot)
controlled_sig=PtDataSignal(controlled_sig_name).fetch(shot)
#offline_controlled_sig = MdsSignal(r'\ZIPFIT01::TOP.PROFILES.{}'.format(offline_controlled_sig_name),'ZIPFIT01',location='remote://atlas.gat.com',dims=['rhon','times']).fetch(shot)
which_proposal=PtDataSignal('etswchprop').fetch(shot)
prop_colors=['r','r','y','g','g']
props=[]
for i in range(5):
props.append(PtDataSignal(f'etsteprop{i}').fetch(shot))
req_scaling=1e-6
true_scaling=1e-3
controlled_scaling=1e-3
offline_controlled_scaling=1
ricardo_fit_psi_correction = 1. #0.792
max_ind = {'temp': 50, 'dens': 50, 'itemp': 16}
ricardo_input_max_ind = max_ind[which_sig]
ricardo_fit_max_ind = 121
rho_or_psin = 'psin'
zipfit_norm = {'temp': 1e3, 'dens': 1, 'itemp': 1}
ricardo_norm = {'temp': 1e3, 'dens': 1, 'itemp': 1}
y = []
x = []
for i in range(1, ricardo_input_max_ind):
sig_name = 'fts{}{}'.format(ricardo_input_signame[which_sig], i)
print(sig_name)
result = PtDataSignal(sig_name).fetch(shot)
y.append(result['data'])
if i == 1:
times = result['times']
if which_sig in ['dens', 'temp']:
sig_name = 'fts{}{}'.format(rho_or_psin, i)
else:
sig_name = 'fts{}{}'.format('cpsin', i)
result = PtDataSignal(sig_name).fetch(shot)
x.append(result['data'])
time_ind = np.searchsorted(times, mytime)
x = np.array(x).T
y = np.array(y).T