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kstar_simulator_v0.py
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kstar_simulator_v0.py
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#!/usr/bin/env python
import os, sys, time
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from PyQt5.QtCore import pyqtSignal,Qt
from PyQt5.QtWidgets import QApplication,\
QPushButton,\
QWidget,\
QHBoxLayout,\
QVBoxLayout,\
QGridLayout,\
QLabel,\
QLineEdit,\
QTabWidget,\
QTabBar,\
QGroupBox,\
QDialog,\
QTableWidget,\
QTableWidgetItem,\
QInputDialog,\
QMessageBox,\
QComboBox,\
QShortcut,\
QFileDialog,\
QCheckBox,\
QRadioButton,\
QHeaderView,\
QSlider,\
QSpinBox,\
QDoubleSpinBox
from keras import models,layers
from scipy import interpolate
# Setting
base_path = os.path.abspath(os.path.dirname(sys.argv[0]))
background_path = base_path + '/images/insideKSTAR.jpg'
lstm_model_path = base_path + '/weights/lstm/'
nn_model_path = base_path + '/weights/nn'
bpw_model_path = base_path + '/weights/bpw'
k2rz_model_path = base_path + '/weights/k2rz'
MAX_MODELS = 5
MAX_SHAPE_MODELS = 1
decimals = np.log10(200)
DPI = 1
PLOT_LENGTH = 40
YEAR_IN = 2021
EC_FREQ = 105.e9
STEADY_MODEL = False
# Matplotlib rcParams setting
matplotlib.rcParams['axes.linewidth']=1.*(100/DPI)
matplotlib.rcParams['axes.labelsize']=10*(100/DPI)
matplotlib.rcParams['axes.titlesize']=10*(100/DPI)
matplotlib.rcParams['xtick.labelsize']=10*(100/DPI)
matplotlib.rcParams['ytick.labelsize']=10*(100/DPI)
matplotlib.rcParams['xtick.major.size']=3.5*(100/DPI)
matplotlib.rcParams['xtick.major.width']=0.8*(100/DPI)
matplotlib.rcParams['xtick.minor.size']=2*(100/DPI)
matplotlib.rcParams['xtick.minor.width']=0.6*(100/DPI)
matplotlib.rcParams['ytick.major.size']=3.5*(100/DPI)
matplotlib.rcParams['ytick.major.width']=0.8*(100/DPI)
matplotlib.rcParams['ytick.minor.size']=2*(100/DPI)
matplotlib.rcParams['ytick.minor.width']=0.6*(100/DPI)
# Wall in KSTAR
Rwalls = np.array([1.265, 1.608, 1.683, 1.631, 1.578, 1.593, 1.626, 2.006,
2.233, 2.235, 2.263, 2.298, 2.316, 2.316, 2.298, 2.263,
2.235, 2.233, 2.006, 1.626, 1.593, 1.578, 1.631, 1.683,
1.608, 1.265, 1.265
])
Zwalls = np.array([1.085, 1.429, 1.431, 1.326, 1.32, 1.153, 1.09, 0.773,
0.444, 0.369, 0.31, 0.189, 0.062, -0.062, -0.189, -0.31,
-0.369, -0.444, -0.773, -1.09, -1.153, -1.32, -1.326, -1.431,
-1.429, -1.085, 1.085
])
# Inputs
input_params = ['Ip [MA]','Bt [T]','GW.frac. [-]',\
'Pnb1a [MW]','Pnb1b [MW]','Pnb1c [MW]',\
'Pec2 [MW]','Pec3 [MW]','Zec2 [cm]','Zec3 [cm]',\
'In.Mid. [m]','Out.Mid. [m]','Elon. [-]','Up.Tri. [-]','Lo.Tri [-]']
input_mins = [0.3,1.5,0.2, 0.0, 0.0, 0.0, 0.0,0.0,-10,-10, 1.265,2.18,1.6,0.1,0.5 ]
input_maxs = [0.8,2.7,0.6, 1.75,1.75,1.5, 0.8,0.8, 10, 10, 1.36, 2.29,2.0,0.5,0.9 ]
input_init = [0.5,1.8,0.4, 1.5, 0.0, 0.0, 0.0,0.0,0.0,0.0, 1.34, 2.22,1.7,0.3,0.75]
# Outputs
output_params0 = ['betan','q95','q0','li']
output_params1 = ['betap','wmhd']
output_params2 = ['betan','betap','h89','h98','q95','q0','li','wmhd']
def i2f(i,decimals=decimals):
return float(i/10**decimals)
def f2i(f,decimals=decimals):
return int(f*10**decimals)
class KSTARWidget(QDialog):
def __init__(self, parent=None):
super(KSTARWidget, self).__init__(parent)
self.originalPalette = QApplication.palette()
# Initial condition
self.first = True
self.time = np.linspace(-0.1*(PLOT_LENGTH-1),0,PLOT_LENGTH)
self.outputs = {}
self.outputs['betan'] = [1.4035932701005382]
self.outputs['betap'] = [1.0824991083280546]
self.outputs['h89'] = [1.9199754370035778]
self.outputs['h98'] = [1.2875278961044707]
self.outputs['q95'] = [4.445880212274074]
self.outputs['q0'] = [1.3098279277874445]
self.outputs['li'] = [1.1197781355250758]
self.outputs['wmhd'] = [186764.7911504754]
self.x = np.zeros([10,21])
# Load models
self.k2rz = k2rz(n_models=MAX_SHAPE_MODELS)
if STEADY_MODEL:
self.kstar_nn = kstar_nn(n_models=MAX_MODELS)
else:
self.kstar_nn = kstar_nn(n_models=1)
self.kstar_lstm = kstar_lstm(n_models=MAX_MODELS)
self.bpw_nn = bpw_nn(n_models=MAX_MODELS)
# Top layout
top_layout = QHBoxLayout()
n_model_label = QLabel('# of models:')
self.n_model_box = QSpinBox()
self.n_model_box.setMinimum(1)
self.n_model_box.setMaximum(MAX_MODELS)
self.n_model_box.setValue(1)
self.reset_model_number()
self.n_model_box.valueChanged.connect(self.reset_model_number)
self.rt_run_push_button = QPushButton('Run')
self.rt_run_push_button.setCheckable(True)
self.rt_run_push_button.setChecked(True)
self.rt_run_push_button.clicked.connect(self.re_create_output_box)
self.shuffle_model_push_button = QPushButton('Shuffle models')
self.shuffle_model_push_button.clicked.connect(self.shuffle_models)
self.plot_heat_load_checkbox = QCheckBox('Plot heat load')
self.plot_heat_load_checkbox.setChecked(True)
self.plot_heat_load_checkbox.stateChanged.connect(self.re_create_output_box)
self.over_plot_checkbox = QCheckBox('Overlap device')
self.over_plot_checkbox.setChecked(True)
self.over_plot_checkbox.stateChanged.connect(self.re_create_output_box)
top_layout.addWidget(n_model_label)
top_layout.addWidget(self.n_model_box)
top_layout.addWidget(self.rt_run_push_button)
top_layout.addWidget(self.shuffle_model_push_button)
top_layout.addWidget(self.plot_heat_load_checkbox)
top_layout.addWidget(self.over_plot_checkbox)
# Middle layout
self.create_input_box()
self.create_output_box()
# Bottom layout
self.run_1_s_button = QPushButton('▶▶ 1s ▶▶')
self.run_1_s_button.clicked.connect(self.relax_run_1s)
self.run_2_s_button = QPushButton('▶▶ 2s ▶▶')
self.run_2_s_button.clicked.connect(self.relax_run_2s)
self.dump_button = QPushButton('Dump outputs')
self.dump_button.clicked.connect(self.dump_output)
# Main layout
self.main_layout = QGridLayout()
self.main_layout.addLayout(top_layout,0,0,1,2)
self.main_layout.addWidget(self.input_box,1,0)
self.main_layout.addWidget(self.output_box,1,1,1,2)
self.main_layout.addWidget(self.run_1_s_button,2,0)
self.main_layout.addWidget(self.run_2_s_button,2,1)
self.main_layout.addWidget(self.dump_button,2,2)
self.setLayout(self.main_layout)
self.setWindowTitle("KSTAR-NN simulator v0")
self.tmp = 0
def reset_model_number(self):
if STEADY_MODEL:
self.kstar_nn.nmodels = self.n_model_box.value()
else:
self.kstar_lstm.nmodels = self.n_model_box.value()
self.bpw_nn.nmodels = self.n_model_box.value()
def create_input_box(self):
self.input_box = QGroupBox('Input parameters')
layout = QGridLayout()
self.input_slider_dict = {}
self.input_value_label_dict = {}
for input_param in input_params:
idx = input_params.index(input_param)
input_label = QLabel(input_param)
self.input_slider_dict[input_param] = QSlider(Qt.Horizontal, self.input_box)
self.input_slider_dict[input_param].setMinimum(f2i(input_mins[idx]))
self.input_slider_dict[input_param].setMaximum(f2i(input_maxs[idx]))
self.input_slider_dict[input_param].setValue(f2i(input_init[idx]))
self.input_slider_dict[input_param].valueChanged.connect(self.update_inputs)
self.input_value_label_dict[input_param] = QLabel(f'{self.input_slider_dict[input_param].value()/10**decimals:.3f}')
self.input_value_label_dict[input_param].setMinimumWidth(40)
layout.addWidget(input_label,idx,0)
layout.addWidget(self.input_slider_dict[input_param],idx,1)
layout.addWidget(self.input_value_label_dict[input_param],idx,2)
#for widget in inputLabel,self.inputSliderDict[input_param],self.inputValueLabelDict[input_param]:
# widget.setMaximumWidth(30)
self.run_slider = QSlider(Qt.Horizontal, self.input_box)
self.run_slider.setMinimum(0)
self.run_slider.setMaximum(100)
self.run_slider.setValue(0)
self.run_slider.valueChanged.connect(self.update_inputs)
self.run_label = QLabel('0.1s ▶')
layout.addWidget(QLabel('Run only'),len(input_params),0)
layout.addWidget(self.run_slider,len(input_params),1)
layout.addWidget(self.run_label,len(input_params),2)
self.input_box.setLayout(layout)
self.input_box.setMaximumWidth(300)
def update_inputs(self):
for input_param in input_params:
self.input_value_label_dict[input_param].setText(f'{self.input_slider_dict[input_param].value()/10**decimals:.3f}')
if self.rt_run_push_button.isChecked() and time.time()-self.tmp>0.05:
self.re_create_output_box()
self.tmp = time.time()
def create_output_box(self):
self.output_box = QGroupBox('Output')
self.fig = plt.figure(figsize=(6*(100/DPI),4*(100/DPI)),dpi=DPI)
self.plot_plasma()
self.canvas = FigureCanvas(self.fig)
self.layout = QGridLayout()
self.layout.addWidget(self.canvas)
self.output_box.setLayout(self.layout)
def re_create_output_box(self):
self.output_box = QGroupBox(' ')
plt.clf()
self.plot_plasma()
self.canvas = FigureCanvas(self.fig)
self.layout = QGridLayout()
self.layout.addWidget(self.canvas)
self.output_box.setLayout(self.layout)
self.main_layout.replaceWidget(self.main_layout.itemAtPosition(1,1).widget(),self.output_box)
def plot_plasma(self):
# Predict plasma
self.predict_boundary()
if self.first or STEADY_MODEL:
self.predict0d(steady=True)
else:
self.predict0d(steady=False)
ts = self.time[-len(self.outputs['betan']):]
# Plot 2D view
plt.subplot(1,2,1)
plt.title('2D poloidal view')
if self.over_plot_checkbox.isChecked():
self.plot_background()
plt.fill_between(self.rbdry,self.zbdry,color='b',alpha=0.2,linewidth=0.0)
plt.plot(Rwalls,Zwalls,'k',linewidth=1.5*(100/DPI),label='Wall')
plt.plot(self.rbdry,self.zbdry,'b',linewidth=2*(100/DPI),label='LCFS')
#self.plotXpoints()
if self.plot_heat_load_checkbox.isChecked():
self.plot_heat_loads()
self.plot_heating()
plt.xlabel('R [m]')
plt.ylabel('Z [m]')
if self.over_plot_checkbox.isChecked():
self.plot_x_points()
plt.xlim([0.8,2.5])
plt.ylim([-1.55,1.55])
else:
plt.axis('scaled')
plt.grid(linewidth=0.5*(100/DPI))
plt.legend(loc='center',fontsize=7.5*(100/DPI),markerscale=0.7,frameon=False)
#plt.tight_layout(rect=(0.15,0.05,1.0,0.95))
# Plot 0D evolution
plt.subplot(4,2,2)
plt.title('0D evolution')
plt.plot(ts,self.outputs['betan'],'k',linewidth=2*(100/DPI),label='βN')
plt.plot(ts,self.outputs['betap'],'b',linewidth=2*(100/DPI),label='βp')
plt.grid(linewidth=0.5*(100/DPI))
plt.legend(loc='upper left',fontsize=7.5*(100/DPI),frameon=False)
plt.xlim([-0.1*PLOT_LENGTH-0.2,0.2])
plt.ylim([0.5,3.0])
plt.xticks(color='w')
plt.subplot(4,2,4)
plt.plot(ts,1.e-5*np.array(self.outputs['wmhd']),'k',linewidth=2*(100/DPI),label='10*Wmhd [MW]')
plt.plot(ts,self.outputs['h89'],'b',linewidth=2*(100/DPI),label='H89')
#plt.plot(ts,self.outputs['h98'],'b',linewidth=2*(100/dpi),label='H98')
plt.grid(linewidth=0.5*(100/DPI))
plt.legend(loc='upper left',fontsize=7.5*(100/DPI),frameon=False)
plt.xlim([-0.1*PLOT_LENGTH-0.2,0.2])
plt.ylim([1.5,4.5])
plt.xticks(color='w')
plt.subplot(4,2,6)
plt.plot(ts,self.outputs['q95'],'k',linewidth=2*(100/DPI),label='q95')
plt.plot(ts,self.outputs['q0'],'b',linewidth=2*(100/DPI),label='q0')
plt.grid(linewidth=0.5*(100/DPI))
plt.legend(loc='upper left',fontsize=7.5*(100/DPI),frameon=False)
plt.xlim([-0.1*PLOT_LENGTH-0.2,0.2])
plt.ylim([1.0,None])
plt.xticks(color='w')
plt.subplot(4,2,8)
plt.plot(ts,self.outputs['li'],'k',linewidth=2*(100/DPI),label='li')
plt.plot(ts,2*np.array(self.outputs['betan'])*self.outputs['h89']/np.array(self.outputs['q95'])**2,'b',linewidth=2*(100/DPI),label='2*G')
plt.grid(linewidth=0.5*(100/DPI))
plt.legend(loc='upper left',fontsize=7.5*(100/DPI),frameon=False)
plt.xlim([-0.1*PLOT_LENGTH-0.2,0.2])
plt.ylim([0.4,1.2])
plt.xlabel('Relative time [s]')
plt.subplots_adjust(hspace=0.1)
self.first = False
def predict_boundary(self):
ip = self.input_slider_dict[input_params[0]].value()/10**decimals
bt = self.input_slider_dict[input_params[1]].value()/10**decimals
bp = self.outputs['betap'][-1]
rin = self.input_slider_dict[input_params[10]].value()/10**decimals
rout = self.input_slider_dict[input_params[11]].value()/10**decimals
k = self.input_slider_dict[input_params[12]].value()/10**decimals
du = self.input_slider_dict[input_params[13]].value()/10**decimals
dl = self.input_slider_dict[input_params[14]].value()/10**decimals
self.k2rz.set_inputs(ip,bt,bp,rin,rout,k,du,dl)
self.rbdry,self.zbdry = self.k2rz.predict(post=True)
self.rx1 = self.rbdry[np.argmin(self.zbdry)]
self.zx1 = np.min(self.zbdry)
self.rx2 = self.rx1
self.zx2 = -self.zx1
def plot_x_points(self,mode=0):
if mode==0:
self.rx1 = self.rbdry[np.argmin(self.zbdry)]
self.zx1 = np.min(self.zbdry)
self.rx2 = self.rx1
self.zx2 = -self.zx1
plt.scatter([self.rx1,self.rx2],[self.zx1,self.zx2],marker='x',color='w',s=100*(100/DPI)**2,linewidths=2*(100/DPI),label='X-points')
def plot_heat_loads(self,n=10,both_side=True):
kinds = ['linear','quadratic'] #,'cubic']
wall_path = Path(np.array([Rwalls,Zwalls]).T)
idx1 = list(self.zbdry).index(self.zx1)
for kind in kinds:
f = interpolate.interp1d(self.rbdry[idx1-5:idx1],self.zbdry[idx1-5:idx1],kind=kind,fill_value='extrapolate')
rsol1 = np.linspace(self.rbdry[idx1],np.min(Rwalls)+1.e-4,n)
zsol1 = np.array([f(r) for r in rsol1])
is_inside1 = wall_path.contains_points(np.array([rsol1,zsol1]).T)
f = interpolate.interp1d(self.zbdry[idx1+5:idx1:-1],self.rbdry[idx1+5:idx1:-1],kind=kind,fill_value='extrapolate')
zsol2 = np.linspace(self.zbdry[idx1],np.min(Zwalls)+1.e-4,n)
rsol2 = np.array([f(z) for z in zsol2])
is_inside2 = wall_path.contains_points(np.array([rsol2,zsol2]).T)
if not np.all(zsol1[is_inside1]>self.zbdry[idx1+1]):
plt.plot(rsol1[is_inside1],zsol1[is_inside1],'r',linewidth=1.5*(100/DPI))
plt.plot(rsol2[is_inside2],zsol2[is_inside2],'r',linewidth=1.5*(100/DPI))
if both_side:
plt.plot(self.rbdry[idx1-4:idx1+4],-self.zbdry[idx1-4:idx1+4],'b',linewidth=2*(100/DPI),alpha=0.1)
plt.plot(rsol1[is_inside1],-zsol1[is_inside1],'r',linewidth=1.5*(100/DPI),alpha=0.2)
plt.plot(rsol2[is_inside2],-zsol2[is_inside2],'r',linewidth=1.5*(100/DPI),alpha=0.2)
for kind in kinds:
f = interpolate.interp1d(self.rbdry[idx1-5:idx1+1],self.zbdry[idx1-5:idx1+1],kind=kind,fill_value='extrapolate')
rsol1 = np.linspace(self.rbdry[idx1],np.min(Rwalls)+1.e-4,n)
zsol1 = np.array([f(r) for r in rsol1])
is_inside1 = wall_path.contains_points(np.array([rsol1,zsol1]).T)
f = interpolate.interp1d(self.zbdry[idx1+5:idx1-1:-1],self.rbdry[idx1+5:idx1-1:-1],kind=kind,fill_value='extrapolate')
zsol2 = np.linspace(self.zbdry[idx1],np.min(Zwalls)+1.e-4,n)
rsol2 = np.array([f(z) for z in zsol2])
is_inside2 = wall_path.contains_points(np.array([rsol2,zsol2]).T)
if not np.all(zsol1[is_inside1]>self.zbdry[idx1+1]):
plt.plot(rsol1[is_inside1],zsol1[is_inside1],'r',linewidth=1.5*(100/DPI))
plt.plot(rsol2[is_inside2],zsol2[is_inside2],'r',linewidth=1.5*(100/DPI))
if both_side:
plt.plot(rsol1[is_inside1],-zsol1[is_inside1],'r',linewidth=1.5*(100/DPI),alpha=0.2)
plt.plot(rsol2[is_inside2],-zsol2[is_inside2],'r',linewidth=1.5*(100/DPI),alpha=0.2)
plt.plot([self.rx1],[self.zx1],'r',linewidth=1*(100/DPI),label='Heat load')
def plot_background(self):
img = plt.imread(background_path)
plt.imshow(img,extent=[-1.6,2.45,-1.5,1.35])
def plot_heating(self):
pnb1a = self.input_slider_dict['Pnb1a [MW]'].value()/10**decimals
pnb1b = self.input_slider_dict['Pnb1b [MW]'].value()/10**decimals
pnb1c = self.input_slider_dict['Pnb1c [MW]'].value()/10**decimals
pec2 = self.input_slider_dict['Pec2 [MW]'].value()/10**decimals
pec3 = self.input_slider_dict['Pec3 [MW]'].value()/10**decimals
zec2 = self.input_slider_dict['Zec2 [cm]'].value()/10**decimals
zec3 = self.input_slider_dict['Zec3 [cm]'].value()/10**decimals
bt = self.input_slider_dict['Bt [T]'].value()/10**decimals
#rt1,rt2,rt3 = 1.48,1.73,1.23
rt1,rt2,rt3 = 1.486,1.720,1.245
#w,h = 0.114,0.42
w,h = 0.13,0.45
plt.fill_between([rt1-w/2,rt1+w/2],[-h/2,-h/2],[h/2,h/2],color='g',alpha=0.9 if pnb1a>0.5 else 0.3)
plt.fill_between([rt2-w/2,rt2+w/2],[-h/2,-h/2],[h/2,h/2],color='g',alpha=0.9 if pnb1b>0.5 else 0.3)
plt.fill_between([rt3-w/2,rt3+w/2],[-h/2,-h/2],[h/2,h/2],color='g',alpha=0.9 if pnb1c>0.5 else 0.3\
,label='NBI')
for ns in [1,2,3]:
rs = 1.60219e-19*1.8*bt/(2.*np.pi*9.10938e-31*EC_FREQ)*ns
if min(Rwalls)<rs<max(Rwalls):
break
dz = 0.05
rpos,zpos = 2.449,0.35
zres = zpos + (zec2/100-zpos)*(rs-rpos)/(1.8-rpos)
plt.fill_between([rs,rpos],[zres-dz,zpos],[zres+dz,zpos],color='orange',alpha=0.9 if pec2>0.2 else 0.3)
rpos,zpos = 2.451,-0.35
zres = zpos + (zec3/100-zpos)*(rs-rpos)/(1.8-rpos)
plt.fill_between([rs,rpos],[zres-dz,zpos],[zres+dz,zpos],color='orange',alpha=0.9 if pec3>0.2 else 0.3,\
label='ECH')
def predict0d(self,steady=True):
# Predict output_params0 (betan, q95, q0, li)
if steady:
x = np.zeros(17)
idx_convert = [0,1,3,4,5,6,7,8,9,10,11,12,13,14,10,2]
for i in range(len(x)-1):
x[i] = self.input_slider_dict[input_params[idx_convert[i]]].value()/10**decimals
x[9],x[10] = 0.5*(x[9]+x[10]),0.5*(x[10]-x[9])
x[14] = 1 if x[14]>1.265+1.e-4 else 0
x[-1] = YEAR_IN
y = self.kstar_nn.predict(x)
for i in range(len(output_params0)):
if len(self.outputs[output_params0[i]]) >= PLOT_LENGTH:
del self.outputs[output_params0[i]][0]
elif len(self.outputs[output_params0[i]]) == 1:
self.outputs[output_params0[i]][0] = y[i]
self.outputs[output_params0[i]].append(y[i])
self.x[:,:len(output_params0)] = y
self.x[:,len(output_params0):] = x
else:
self.x[:-1,len(output_params0):] = self.x[1:,len(output_params0):]
idx_convert = [0,1,3,4,5,6,7,8,9,10,11,12,13,14,10,2]
for i in range(len(self.x[0])-1-4):
self.x[-1,i+4] = self.input_slider_dict[input_params[idx_convert[i]]].value()/10**decimals
self.x[-1,13],self.x[-1,14] = 0.5*(self.x[-1,13]+self.x[-1,14]),0.5*(self.x[-1,14]-self.x[-1,13])
self.x[-1,18] = 1 if self.x[-1,18]>1.265+1.e-4 else 0
y = self.kstar_lstm.predict(self.x)
self.x[:-1,:len(output_params0)] = self.x[1:,:len(output_params0)]
self.x[-1,:len(output_params0)] = y
for i in range(len(output_params0)):
if len(self.outputs[output_params0[i]]) >= PLOT_LENGTH:
del self.outputs[output_params0[i]][0]
elif len(self.outputs[output_params0[i]]) == 1:
self.outputs[output_params0[i]][0] = y[i]
self.outputs[output_params0[i]].append(y[i])
# Predict output_params1 (betap, wmhd)
x = np.zeros(8)
idx_convert = [0,0,1,10,11,12,13,14]
x[0] = self.outputs['betan'][-1]
for i in range(1,len(x)):
x[i] = self.input_slider_dict[input_params[idx_convert[i]]].value()/10**decimals
x[3],x[4] = 0.5*(x[3]+x[4]),0.5*(x[4]-x[3])
y = self.bpw_nn.predict(x)
for i in range(len(output_params1)):
if len(self.outputs[output_params1[i]]) >= PLOT_LENGTH:
del self.outputs[output_params1[i]][0]
elif len(self.outputs[output_params1[i]]) == 1:
self.outputs[output_params1[i]][0] = y[i]
self.outputs[output_params1[i]].append(y[i])
# Estimate H factors (h89, h98)
ip = self.input_slider_dict['Ip [MA]'].value()/10**decimals
bt = self.input_slider_dict['Bt [T]'].value()/10**decimals
fgw = self.input_slider_dict['GW.frac. [-]'].value()/10**decimals
ptot = max(self.input_slider_dict['Pnb1a [MW]'].value()/10**decimals \
+ self.input_slider_dict['Pnb1b [MW]'].value()/10**decimals \
+ self.input_slider_dict['Pnb1c [MW]'].value()/10**decimals \
+ self.input_slider_dict['Pec2 [MW]'].value()/10**decimals \
+ self.input_slider_dict['Pec3 [MW]'].value()/10**decimals \
, 1.e-1) # Not to diverge
rin = self.input_slider_dict['In.Mid. [m]'].value()/10**decimals
rout = self.input_slider_dict['Out.Mid. [m]'].value()/10**decimals
k = self.input_slider_dict['Elon. [-]'].value()/10**decimals
rgeo,amin = 0.5*(rin+rout),0.5*(rout-rin)
ne = fgw*10*(ip/(np.pi*amin**2))
m = 2.0 # Mass number
tau89 = 0.038*ip**0.85*bt**0.2*ne**0.1*ptot**-0.5*rgeo**1.5*k**0.5*(amin/rgeo)**0.3*m**0.5
tau98 = 0.0562*ip**0.93*bt**0.15*ne**0.41*ptot**-0.69*rgeo**1.97*k**0.78*(amin/rgeo)**0.58*m**0.19
h89 = 1.e-6*self.outputs['wmhd'][-1]/ptot/tau89
h98 = 1.e-6*self.outputs['wmhd'][-1]/ptot/tau98
if len(self.outputs['h89']) >= PLOT_LENGTH:
del self.outputs['h89'][0], self.outputs['h98'][0]
elif len(self.outputs['h89']) == 1:
self.outputs['h89'][0], self.outputs['h98'][0] = h89, h98
self.outputs['h89'].append(h89)
self.outputs['h98'].append(h98)
def shuffle_models(self):
np.random.shuffle(self.k2rz.models)
if STEADY_MODEL:
np.random.shuffle(self.kstar_nn.models)
else:
np.random.shuffle(self.kstar_lstm.models)
np.random.shuffle(self.bpw_nn.models)
print('Models shuffled!')
def relax_run_1s(self):
for i in range(10-1):
if self.first or STEADY_MODEL:
self.predict0d(steady=True)
else:
self.predict0d(steady=False)
self.re_create_output_box()
self.tmp = time.time()
def relax_run_2s(self):
for i in range(20-1):
if self.first or STEADY_MODEL:
self.predict0d(steady=True)
else:
self.predict0d(steady=False)
self.re_create_output_box()
self.tmp = time.time()
def dump_output(self):
print('')
print(f"Time [s]: {self.time[-len(self.outputs['betan']):]}")
for output in output_params2:
print(f'{output}: {self.outputs[output]}')
def r2_k(y_true, y_pred):
#SS_res = K.sum(K.square(y_true - y_pred))
#SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
#return ( 1 - SS_res/(SS_tot + epsilon) )
return 1.0
class k2rz():
def __init__(self,model_path=k2rz_model_path,n_models=10,ntheta=64,closed_surface=True,xpt_correction=True):
self.nmodels = n_models
self.ntheta = ntheta
self.closed_surface = closed_surface
self.xpt_correction = xpt_correction
self.models = []
for i in range(self.nmodels):
self.models.append(models.load_model(model_path+f'/best_model{i}',custom_objects={'r2_k':r2_k}))
def set_inputs(self,ip,bt,betap,rin,rout,k,du,dl):
self.x = np.array([ip,bt,betap,rin,rout,k,du,dl])
def predict(self,post=True):
#print('predicting...')
self.y = np.zeros(2*self.ntheta)
for i in range(self.nmodels):
self.y += self.models[i].predict(np.array([self.x]))[0]/self.nmodels
rbdry,zbdry = self.y[:self.ntheta],self.y[self.ntheta:]
if post:
if self.xpt_correction:
rgeo = 0.5*(max(rbdry)+min(rbdry))
amin = 0.5*(max(rbdry)-min(rbdry))
if self.x[6]<=self.x[7]:
rx = rgeo-amin*self.x[7]
zx = max(zbdry) - 2.*self.x[5]*amin
rx2 = rgeo-amin*self.x[6]
rbdry[np.argmin(zbdry)] = rx
zbdry[np.argmin(zbdry)] = zx
rbdry[np.argmax(zbdry)] = rx2
if self.x[6]>=self.x[7]:
rx = rgeo-amin*self.x[6]
zx = min(zbdry) + 2.*self.x[5]*amin
rx2 = rgeo-amin*self.x[7]
rbdry[np.argmax(zbdry)] = rx
zbdry[np.argmax(zbdry)] = zx
rbdry[np.argmin(zbdry)] = rx2
if self.closed_surface:
rbdry = np.append(rbdry,rbdry[0])
zbdry = np.append(zbdry,zbdry[0])
return rbdry,zbdry
class kstar_lstm():
def __init__(self,model_path=lstm_model_path,n_models=10):
self.nmodels = n_models
self.ymean = [1.30934765, 5.20082444, 1.47538417, 1.14439883]
self.ystd = [0.74135689, 1.44731883, 0.56747578, 0.23018484]
self.models = []
for i in range(self.nmodels):
self.models.append(models.Sequential())
self.models[i].add(layers.BatchNormalization(input_shape=(10,21)))
self.models[i].add(layers.LSTM(200,return_sequences=True))
self.models[i].add(layers.BatchNormalization())
self.models[i].add(layers.LSTM(200,return_sequences=False))
self.models[i].add(layers.BatchNormalization())
self.models[i].add(layers.Dense(200,activation='sigmoid'))
self.models[i].add(layers.BatchNormalization())
self.models[i].add(layers.Dense(4,activation='linear'))
self.models[i].load_weights(model_path+f'/best_model{i}')
def set_inputs(self,x):
if len(np.shape(x)) == 3:
self.x = np.array(x)
else:
self.x = np.array([x])
def predict(self,x=None):
if type(x) == type(np.zeros(1)):
if len(np.shape(x)) == 3:
self.x = np.array(x)
else:
self.x = np.array([x])
self.y = np.zeros_like(self.ymean)
for i in range(self.nmodels):
self.y += (self.models[i].predict(self.x)[0]*self.ystd+self.ymean)/self.nmodels
return self.y
class kstar_nn():
def __init__(self,model_path=nn_model_path,n_models=10):
self.nmodels = n_models
self.ymean = [1.22379703, 5.2361062, 1.64438005, 1.12040048]
self.ystd = [0.72255576, 1.5622809, 0.96563557, 0.23868018]
self.models = []
for i in range(self.nmodels):
self.models.append(models.load_model(model_path+f'/best_model{i}',custom_objects={'r2_k':r2_k}))
def set_inputs(self,x):
self.x = np.array([x])
def predict(self,x=None):
if type(x) == type(np.zeros(1)):
if len(np.shape(x)) == 2:
self.x = x
else:
self.x = np.array([x])
self.y = np.zeros(len(output_params0))
for i in range(self.nmodels):
self.y += (self.models[i].predict(self.x)[0]*self.ystd+self.ymean)/self.nmodels
return self.y
class bpw_nn():
def __init__(self,model_path=bpw_model_path,n_models=10):
self.nmodels = n_models
self.ymean = np.array([1.02158800e+00, 1.87408512e+05])
self.ystd = np.array([6.43390272e-01, 1.22543529e+05])
self.models = []
for i in range(self.nmodels):
self.models.append(models.load_model(model_path+f'/best_model{i}',custom_objects={'r2_k':r2_k}))
def set_inputs(self,x):
self.x = np.array([x])
def predict(self,x=None):
if type(x) == type(np.zeros(1)):
if len(np.shape(x)) == 2:
self.x = x
else:
self.x = np.array([x])
self.y = np.zeros(len(output_params1))
for i in range(self.nmodels):
self.y += (self.models[i].predict(self.x)[0]*self.ystd+self.ymean)/self.nmodels
return self.y
if __name__ == '__main__':
app = QApplication([])
window = KSTARWidget()
window.show()
app.exec()