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ai_control_v1.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 scipy import interpolate
from common.model_structure import *
from common.wall import *
from common.setting import *
# 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/'
rl_1s_model_path = base_path + '/weights/rl/nbi_control/interval_1s/best_model0.zip'
rl_2s_model_path = base_path + '/weights/rl/nbi_control/interval_2s/best_model0.zip'
rl_k2x_model_path = base_path + '/weights/rl/k2x/TD3_test_model_1.zip'
max_models = 10
init_models = 4
max_shape_models = 4
decimals = np.log10(1000)
dpi = 1
plot_length = 165
t_delay = 0.002
steady_model = False
bavg = 0.0
# Fixed setting
year_in = 2021
ec_freq = 105.e9
# Matplotlib rcParams setting
rcParamsSetting(dpi)
# RL setting
interval = 20
low_state = [0.35, 1.65, 0.15, 0.5, 1.265, 2.18, 0.0, 0.0, 0.0, 1.1, 3.8, 0.84, 1.1, 3.8, 0.84]
high_state = [0.75, 1.9, 0.5, 0.85, 1.36, 2.29, 1.75, 1.75, 1.1, 2.1, 6.2, 1.06, 2.1, 6.2, 1.06]
low_action = [0.35, 1.65, 0.15, 0.5, 1.265, 2.18, 0.0, 0.0, 0.0]
high_action = [0.75, 1.9, 0.5, 0.85, 1.36, 2.29, 1.75, 1.75, 1.1]
low_state_k2x = [0.4, 0.5, 1.265, 2.18, 1.6, 0.1, 0.55]
high_state_k2x = [0.8, 3.0, 1.34, 2.29, 1.9, 0.5, 0.95]
low_action_k2x = [1.35, 0.7, -0.05]
high_action_k2x = [1.60, 1.05, 0.0]
# 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 = [0.5,1.8,0.3, 1.5, 1.5, 0.5, 0.0,0.0,0.0,0.0, 1.32, 2.22,1.7,0.3,0.75]
# Outputs
output_params0 = ['βn','q95','q0','li']
output_params1 = ['βp','wmhd']
output_params2 = ['βn','βp','h89','h98','q95','q0','li','wmhd']
dummy_params = ['Ip [MA]', 'Elon. [-]', 'Up.Tri. [-]', 'Lo.Tri. [-]', 'In.Mid. [m]', 'Out.Mid. [m]', 'Pnb1a [MW]','Pnb1b [MW]','Pnb1c [MW]']
# Targets
target_params = ['βp','q95','li']
target_mins = [ 1.1, 3.8,0.84]
target_maxs = [ 2.1, 6.2,1.06]
target_init = [ 1.6, 5.0,0.95]
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.dummy = {}, {}
for p in output_params2:
self.outputs[p] = [0.]
for p in dummy_params:
self.dummy[p] = [0.]
self.x = np.zeros([10,21])
self.targets = {}
for i,target_param in enumerate(target_params):
self.targets[target_param] = [target_init[i],target_init[i]]
# Load models
self.k2rz = k2rz(model_path=k2rz_model_path, n_models=max_shape_models)
if steady_model:
self.kstar_nn = kstar_nn(model_path=nn_model_path, n_models=max_models)
else:
self.kstar_nn = kstar_nn(model_path=nn_model_path, n_models=1)
self.kstar_lstm = kstar_lstm(model_path=lstm_model_path, n_models=max_models)
self.bpw_nn = bpw_nn(model_path=bpw_model_path, n_models=max_models)
# Load agents
self.designer_1s = SB2_model(
model_path = rl_1s_model_path,
low_state = low_state,
high_state = high_state,
low_action = low_action,
high_action = high_action,
activation='relu',
last_actv='tanh',
norm=True,
bavg=bavg
)
self.designer_2s = SB2_model(
model_path = rl_2s_model_path,
low_state = low_state,
high_state = high_state,
low_action = low_action,
high_action = high_action,
activation='relu',
last_actv='tanh',
norm=True,
bavg=bavg
)
self.rl_k2x = SB2_model(
model_path = rl_k2x_model_path,
low_state = low_state_k2x,
high_state = high_state_k2x,
low_action = low_action_k2x,
high_action = high_action_k2x,
activation='relu',
last_actv='tanh',
norm=True,
bavg=bavg
)
# Top layout
topLayout = QHBoxLayout()
nModelLabel = QLabel('# of models:')
self.nModelBox = QSpinBox()
self.nModelBox.setMinimum(1)
self.nModelBox.setMaximum(max_models)
self.nModelBox.setValue(init_models)
self.resetModelNumber()
self.nModelBox.valueChanged.connect(self.resetModelNumber)
intervalLabel = QLabel(' Control/Relax interval [0.1s]:')
self.intervalBox = QSpinBox()
self.intervalBox.setMinimum(2)
self.intervalBox.setMaximum(interval)
self.intervalBox.setValue(interval)
self.intervalBox.valueChanged.connect(self.resetInterval)
self.resetInterval()
self.rtRunPushButton = QPushButton('Run')
self.rtRunPushButton.setCheckable(True)
self.rtRunPushButton.setChecked(True)
self.rtRunPushButton.clicked.connect(self.reCreateOutputBox)
self.shuffleModelPushButton = QPushButton('Shuffle models')
self.shuffleModelPushButton.clicked.connect(self.shuffleModels)
self.plotHeatingCheckBox = QCheckBox('Plot NBI/EC')
self.plotHeatingCheckBox.setChecked(True)
self.plotHeatingCheckBox.stateChanged.connect(self.rePlotOutputBox)
self.plotHeatLoadCheckBox = QCheckBox('Plot heat load')
self.plotHeatLoadCheckBox.setChecked(True)
self.plotHeatLoadCheckBox.stateChanged.connect(self.rePlotOutputBox)
self.overplotCheckBox = QCheckBox('Overlap device')
self.overplotCheckBox.setChecked(True)
self.overplotCheckBox.stateChanged.connect(self.rePlotOutputBox)
self.showInputCheckBox = QCheckBox('Show input panel')
self.showInputCheckBox.setChecked(False)
topLayout.addWidget(nModelLabel)
topLayout.addWidget(self.nModelBox)
topLayout.addWidget(intervalLabel)
topLayout.addWidget(self.intervalBox)
#topLayout.addWidget(self.rtRunPushButton)
topLayout.addWidget(self.shuffleModelPushButton)
topLayout.addWidget(self.plotHeatingCheckBox)
topLayout.addWidget(self.plotHeatLoadCheckBox)
topLayout.addWidget(self.overplotCheckBox)
#topLayout.addWidget(self.showInputCheckBox)
# Middle layout
self.createInputBox()
self.createOutputBox()
self.createAutonomousBox()
# Bottom layout
self.run1sButton = QPushButton('▶▶ 1s ▶▶')
self.run1sButton.setFixedWidth(320)
self.run1sButton.clicked.connect(self.relaxRun1s)
self.run2sButton = QPushButton('▶▶ 2s ▶▶')
self.run2sButton.setFixedWidth(320)
self.run2sButton.clicked.connect(self.relaxRun2s)
self.dumpButton = QPushButton('Dump outputs')
self.dumpButton.setFixedWidth(800)
self.dumpButton.clicked.connect(self.dumpOutput)
self.autoButton = QPushButton('AI control')
self.autoButton.setFixedWidth(120)
self.autoButton.clicked.connect(self.autoControl)
# Main layout
self.mainLayout = QGridLayout()
self.mainLayout.addLayout(topLayout,0,0,1,2)
if self.showInputCheckBox.isChecked():
self.mainLayout.addWidget(self.inputBox,1,0)
self.mainLayout.addWidget(self.outputBox,1,0+self.showInputCheckBox.isChecked(),1,2)
self.mainLayout.addWidget(self.autonomousBox,1,2+self.showInputCheckBox.isChecked())
#self.mainLayout.addWidget(self.run1sButton,2,0)
#self.mainLayout.addWidget(self.run2sButton,2,1)
self.mainLayout.addWidget(self.dumpButton,2,0)
self.mainLayout.addWidget(self.autoButton,2,2)
self.setLayout(self.mainLayout)
self.setWindowTitle("AI-controlled KSTAR tokamak v1")
self.tmp = 0
def resetModelNumber(self):
if steady_model:
self.kstar_nn.nmodels = self.nModelBox.value()
else:
self.kstar_lstm.nmodels = self.nModelBox.value()
self.bpw_nn.nmodels = self.nModelBox.value()
#self.k2rz.nmodels = self.nModelBox.value()
def resetInterval(self):
self.interval = self.intervalBox.value()
self.designer = self.designer_1s if self.interval < 15 else self.designer_2s
def createInputBox(self):
self.inputBox = QGroupBox('Input parameters')
self.inputLayout = QGridLayout()
self.inputSliderDict = {}
self.inputValueLabelDict = {}
for input_param in input_params:
idx = input_params.index(input_param)
inputLabel = QLabel(input_param)
self.inputSliderDict[input_param] = QSlider(Qt.Horizontal) #, self.inputBox)
self.inputSliderDict[input_param].setMinimum(f2i(input_mins[idx]))
self.inputSliderDict[input_param].setMaximum(f2i(input_maxs[idx]))
self.inputSliderDict[input_param].setValue(f2i(input_init[idx]))
self.inputSliderDict[input_param].valueChanged.connect(self.updateInputs)
self.inputValueLabelDict[input_param] = QLabel(f'{self.inputSliderDict[input_param].value()/10**decimals:.3f}')
self.inputValueLabelDict[input_param].setMinimumWidth(40)
self.inputLayout.addWidget(inputLabel,idx,0)
self.inputLayout.addWidget(self.inputSliderDict[input_param],idx,1)
self.inputLayout.addWidget(self.inputValueLabelDict[input_param],idx,2)
#for widget in inputLabel,self.inputSliderDict[input_param],self.inputValueLabelDict[input_param]:
# widget.setMaximumWidth(30)
self.runSlider = QSlider(Qt.Horizontal) #, self.inputBox)
self.runSlider.setMinimum(0)
self.runSlider.setMaximum(100)
self.runSlider.setValue(0)
self.runSlider.valueChanged.connect(self.updateInputs)
self.runLabel = QLabel('0.1s ▶')
self.inputLayout.addWidget(QLabel('Run only'),len(input_params),0)
self.inputLayout.addWidget(self.runSlider,len(input_params),1)
self.inputLayout.addWidget(self.runLabel,len(input_params),2)
self.inputBox.setLayout(self.inputLayout)
self.inputBox.setMaximumWidth(320)
def updateInputs(self):
for input_param in input_params:
self.inputValueLabelDict[input_param].setText(f'{self.inputSliderDict[input_param].value()/10**decimals:.3f}')
if self.rtRunPushButton.isChecked() and time.time()-self.tmp>t_delay:
self.reCreateOutputBox()
self.tmp = time.time()
def updateInputs_without_plot(self):
if self.rtRunPushButton.isChecked() and time.time()-self.tmp>t_delay:
self.predict0d(steady=steady_model)
def createOutputBox(self):
self.outputBox = QGroupBox('AI control output')
self.fig = plt.figure(figsize=(6*(100/dpi),4*(100/dpi)),dpi=dpi)
self.plotPlasma()
self.canvas = FigureCanvas(self.fig)
self.layout = QGridLayout()
self.layout.addWidget(self.canvas)
self.outputBox.setLayout(self.layout)
def reCreateOutputBox(self,predict=True):
self.outputBox = QGroupBox(' ')
plt.clf()
self.plotPlasma(predict=predict)
self.canvas = FigureCanvas(self.fig)
self.layout = QGridLayout()
self.layout.addWidget(self.canvas)
self.outputBox.setLayout(self.layout)
#self.mainLayout.replaceWidget(self.mainLayout.itemAtPosition(1,1).widget(),self.outputBox)
self.mainLayout.addWidget(self.outputBox,1,0+self.showInputCheckBox.isChecked(),1,2)
def rePlotOutputBox(self):
self.reCreateOutputBox(predict=False)
def createAutonomousBox(self):
self.autonomousBox = QGroupBox('Target setting')
layout = QGridLayout()
self.targetSliderDict = {}
self.targetValueLabelDict = {}
for target_param in target_params:
idx = target_params.index(target_param)
targetLabel = QLabel(target_param)
targetLabel.setAlignment(Qt.AlignCenter)
targetLabel.setMaximumWidth(40)
self.targetSliderDict[target_param] = QSlider(Qt.Vertical, self.autonomousBox)
self.targetSliderDict[target_param].setMinimum(f2i(target_mins[idx]))
self.targetSliderDict[target_param].setMaximum(f2i(target_maxs[idx]))
self.targetSliderDict[target_param].setValue(f2i(target_init[idx]))
self.targetSliderDict[target_param].valueChanged.connect(self.updateTargets)
self.targetValueLabelDict[target_param] = QLabel(f'{self.targetSliderDict[target_param].value()/10**decimals:.3f}')
self.targetValueLabelDict[target_param].setMinimumWidth(40)
layout.addWidget(targetLabel,idx,0)
layout.addWidget(self.targetSliderDict[target_param],idx,1)
layout.addWidget(self.targetValueLabelDict[target_param],idx,2)
self.autonomousBox.setLayout(layout)
self.autonomousBox.setMaximumWidth(120)
def updateTargets(self):
for target_param in target_params:
self.targetValueLabelDict[target_param].setText(f'{self.targetSliderDict[target_param].value()/10**decimals:.3f}')
def autoControl(self):
idx_convert = [0, 12, 13, 14, 10, 11, 3, 4, 5]
observation = np.zeros_like(low_state)
observation[:9] = [i2f(self.inputSliderDict[input_params[i]].value()) for i in idx_convert]
observation[9:12] = [self.outputs[output_params2[i]][-1] for i in [1, 4, 6]]
observation[12:] = [i2f(self.targetSliderDict[target_params[i]].value()) for i in [0, 1, 2]]
new_action = self.designer.predict(observation)
current_action = [i2f(self.inputSliderDict[input_params[i]].value()) for i in idx_convert]
daction = (new_action - current_action) / self.interval
for i in range(self.interval): # Control phase
current_action += daction
current_action[6:] = new_action[6:] # Step function for NBIs
for i, idx in enumerate(idx_convert):
self.inputSliderDict[input_params[idx]].setValue(f2i(current_action[i]))
time.sleep(t_delay)
for i in range(self.interval): # Relaxation phase
#self.reCreateOutputBox()
#time.sleep(t_delay)
if i < self.interval - 1:
self.predict0d(steady=False)
else:
self.reCreateOutputBox()
def plotPlasma(self,predict=True):
# Predict plasma
if predict:
self.predictBoundary()
if self.first or steady_model:
self.predict0d(steady=True)
else:
self.predict0d(steady=False)
ts = self.time[-len(self.outputs['βn']):]
# Plot 2D view
plt.subplot(1,3,1)
plt.title('AI-designed plasma shape')
if self.overplotCheckBox.isChecked():
self.plotBackground()
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')
if self.plotHeatingCheckBox.isChecked():
self.plotHeating()
if self.plotHeatLoadCheckBox.isChecked():
self.plotHeatLoads()
plt.xlabel('R [m]')
plt.ylabel('Z [m]')
if self.overplotCheckBox.isChecked():
self.plotXpoints()
plt.xlim([1.1,2.4])
plt.ylim([-1.6,1.6])
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)
# Plot operation trajectory
plt.subplot(3,3,2)
pnb = np.sum([self.dummy['Pnb1a [MW]'], self.dummy['Pnb1b [MW]'], self.dummy['Pnb1c [MW]']], axis=0)
plt.title('AI operation trajectory')
plt.plot(ts,self.dummy['Ip [MA]'],'k',linewidth=2*(100/dpi),label='Ip [MA]')
plt.step(ts,0.1*pnb,'grey',linewidth=2*(100/dpi),label='0.1*Pnb [MW]',where='mid')
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([0.1, 0.7])
plt.xticks(color='w')
plt.subplot(3,3,5)
plt.plot(ts,np.array(self.dummy['Elon. [-]']) - 1,'k',linewidth=2*(100/dpi),label='Elon.-1')
plt.plot(ts,self.dummy['Up.Tri. [-]'],'lightgrey',linewidth=2*(100/dpi),label='Up.Tri.')
plt.plot(ts,self.dummy['Lo.Tri. [-]'],'grey',linewidth=2*(100/dpi),label='Lo.Tri.')
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([0.15, 1])
plt.xticks(color='w')
plt.subplot(3,3,8)
plt.plot(ts,np.array(self.dummy['In.Mid. [m]']) - 1.265,'k',linewidth=2*(100/dpi),label='Ingap [m]')
plt.plot(ts,2.316 - np.array(self.dummy['Out.Mid. [m]']),'grey',linewidth=2*(100/dpi),label='Outgap [m]')
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, 0.14])
plt.xlabel('Relative time [s]')
# Plot 0D evolution
alpha = 0.5
period = 2 * 0.1 * self.interval
is_relax = ts % period > 0.5 * period
plt.subplot(3,3,3)
plt.title('Response and target')
plt.plot(ts,self.outputs['βp'],'k',linewidth=2*(100/dpi),label='βp')
#plt.plot(ts,self.targets['βp'],'k',linestyle='--',linewidth=2*(100/dpi),label='Target')
for i in range(int((max(ts) - min(ts)) / period) + 1):
label = 'Target' if i == 0 else None
plt.plot(ts[::-1][2 * i * self.interval + 1 : (2 * i + 1) * self.interval], self.targets['βp'][::-1][2 * i * self.interval + 1: (2 * i + 1) * self.interval], 'b', alpha=alpha, linewidth=4*(100/dpi), label=label)
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])
gap = 0.1*(target_maxs[0] - target_mins[0])
plt.ylim([target_mins[0] - gap, target_maxs[0] + gap])
plt.xticks(color='w')
plt.subplot(3,3,6)
plt.plot(ts,self.outputs['q95'],'k',linewidth=2*(100/dpi),label='q95')
#plt.plot(ts,self.targets['q95'],'k',linestyle='--',linewidth=2*(100/dpi),label='Target')
for i in range(int((max(ts) - min(ts)) / period) + 1):
label = 'Target' if i == 0 else None
plt.plot(ts[::-1][2 * i * self.interval + 1: (2 * i + 1) * self.interval], self.targets['q95'][::-1][2 * i * self.interval + 1: (2 * i + 1) * self.interval], 'b', alpha=alpha, linewidth=4*(100/dpi), label=label)
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])
gap = 0.1*(target_maxs[1] - target_mins[1])
plt.ylim([target_mins[1] - gap, target_maxs[1] + gap])
plt.xticks(color='w')
plt.subplot(3,3,9)
plt.plot(ts,self.outputs['li'],'k',linewidth=2*(100/dpi),label='li')
#plt.plot(ts,self.targets['li'],'k',linestyle='--',linewidth=2*(100/dpi),label='Target')
for i in range(int((max(ts) - min(ts)) / period) + 1):
label = 'Target' if i == 0 else None
plt.plot(ts[::-1][2 * i * self.interval + 1: (2 * i + 1) * self.interval], self.targets['li'][::-1][2 * i * self.interval + 1: (2 * i + 1) * self.interval], 'b', alpha=alpha, linewidth=4*(100/dpi), label=label)
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])
gap = 0.1*(target_maxs[2] - target_mins[2])
plt.ylim([target_mins[2] - gap, target_maxs[2] + gap])
plt.xlabel('Relative time [s]')
plt.tight_layout(h_pad=0., rect=(0.05,0.05,0.95,0.95))
self.first = False
def predictBoundary(self):
ip = self.inputSliderDict[input_params[0]].value()/10**decimals
bt = self.inputSliderDict[input_params[1]].value()/10**decimals
bp = self.outputs['βp'][-1]
rin = self.inputSliderDict[input_params[10]].value()/10**decimals
rout = self.inputSliderDict[input_params[11]].value()/10**decimals
k = self.inputSliderDict[input_params[12]].value()/10**decimals
du = self.inputSliderDict[input_params[13]].value()/10**decimals
dl = self.inputSliderDict[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 plotXpoints(self,mode=0,zorder=100):
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',zorder=zorder)
def plotHeatLoads(self,n=10,both_side=True):
kinds = ['linear','quadratic'] #,'cubic']
wallPath = 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 = wallPath.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 = wallPath.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 = wallPath.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 = wallPath.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 plotBackground(self):
img = plt.imread(background_path)
plt.imshow(img,extent=[-1.6,2.45,-1.5,1.35])
def plotHeating(self):
pnb1a = self.inputSliderDict['Pnb1a [MW]'].value()/10**decimals
pnb1b = self.inputSliderDict['Pnb1b [MW]'].value()/10**decimals
pnb1c = self.inputSliderDict['Pnb1c [MW]'].value()/10**decimals
pec2 = self.inputSliderDict['Pec2 [MW]'].value()/10**decimals
pec3 = self.inputSliderDict['Pec3 [MW]'].value()/10**decimals
zec2 = self.inputSliderDict['Zec2 [cm]'].value()/10**decimals
zec3 = self.inputSliderDict['Zec3 [cm]'].value()/10**decimals
bt = self.inputSliderDict['Bt [T]'].value()/10**decimals
rt1,rt2,rt3 = 1.486,1.720,1.245
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 (βn, 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.inputSliderDict[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.inputSliderDict[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])
# Update output targets (βp, q95, li)
if not self.first:
for i,target_param in enumerate(target_params):
if len(self.targets[target_param]) >= plot_length:
del self.targets[target_param][0]
elif len(self.targets[target_param]) == 1:
self.targets[target_param][0] = i2f(self.targetSliderDict[target_param].value())
self.targets[target_param].append(i2f(self.targetSliderDict[target_param].value()))
# Predict output_params1 (βp, wmhd)
x = np.zeros(8)
idx_convert = [0,0,1,10,11,12,13,14]
x[0] = self.outputs['βn'][-1]
for i in range(1,len(x)):
x[i] = self.inputSliderDict[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])
# Store dummy parameters (Ip)
for p in dummy_params:
if len(self.dummy[p]) >= plot_length:
del self.dummy[p][0]
elif len(self.dummy[p]) == 1:
self.dummy[p][0] = i2f(self.inputSliderDict[p].value())
self.dummy[p].append(i2f(self.inputSliderDict[p].value()))
# Estimate H factors (h89, h98)
ip = self.inputSliderDict['Ip [MA]'].value()/10**decimals
bt = self.inputSliderDict['Bt [T]'].value()/10**decimals
fgw = self.inputSliderDict['GW.frac. [-]'].value()/10**decimals
ptot = max(self.inputSliderDict['Pnb1a [MW]'].value()/10**decimals \
+ self.inputSliderDict['Pnb1b [MW]'].value()/10**decimals \
+ self.inputSliderDict['Pnb1c [MW]'].value()/10**decimals \
+ self.inputSliderDict['Pec2 [MW]'].value()/10**decimals \
+ self.inputSliderDict['Pec3 [MW]'].value()/10**decimals \
, 1.e-1) # Not to diverge
rin = self.inputSliderDict['In.Mid. [m]'].value()/10**decimals
rout = self.inputSliderDict['Out.Mid. [m]'].value()/10**decimals
k = self.inputSliderDict['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 shuffleModels(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 relaxRun1s(self):
for i in range(10 - 1):
self.predict0d(steady = self.first or steady_model)
self.reCreateOutputBox()
self.tmp = time.time()
def relaxRun2s(self):
for i in range(20 - 1):
self.predict0d(steady = self.first or steady_model)
self.reCreateOutputBox()
self.tmp = time.time()
def dumpOutput(self):
print('\nTrajectories:')
print(f"Time [s]: {self.time[-len(self.outputs['βn']):]}")
for dummy in dummy_params:
print(f'{dummy}: {self.dummy[dummy]}')
for output in output_params2:
print(f'{output}: {self.outputs[output]}')
print('\nCurrent operation control by AI:')
for input_param in input_params:
print(f'{input_param}: {i2f(self.inputSliderDict[input_param].value())}')
def autoRun05(self):
for i in range(1):
self.autoAction()
#time.sleep(t_delay)
#if self.first or steady_model:
# self.predict0d(steady=True)
#else:
# self.predict0d(steady=False)
#self.reCreateOutputBox()
self.tmp = time.time()
if __name__ == '__main__':
app = QApplication([])
window = KSTARWidget()
window.show()
app.exec()