def _loadFuncs(self):
self.log('Start files loading...')
filesMap = self.filesMap
for key in filesMap:
filePath = filesMap[key]
self.log(f'Try to load {key} file: {filePath}')
func = None
if filePath.endswith('.pickle'):
grid = utils.loadPickle(filePath, 'rb')
if grid is None:
self.error(
f'Unable to load {key} function as pickle:\n{filePath}.'
)
func = num_methods.interpolation.SplineInterpolation(grid)
elif filePath.endswith('.json'):
jsn = utils.loadJson(filePath, 'r')
if jsn is None:
self.error(
f'Unable to load {key} function interpolation as json:\n{filePath}.'
)
func = num_methods.interpolation.SplineInterpolation(
None).load_from_dict(jsn)
else:
grid = utils.loadCSV(filePath)
if grid is None:
self.error(
f'Unable to load {key} function as csv:\n{filePath}.')
func = num_methods.interpolation.SplineInterpolation(grid)
self.funcDict[key] = func
self.log('Successful')
self.log('All files loaded')
def initStimuli():
"""called whenever a new run starts. all stimulus will run from the beginig.
"""
global repNo, stimulusListLoc, stimulusList
repNo = 0
stimulusListLoc = 0
stimulusList = loadCSV(STIMULUS_CONFIG)
initShape(stimulusList[0])
def __init__(self, canvas, app, output_device=None):
self.output_device = output_device
self.batchNo = 0
self.canvas = canvas
self.app = app
self.stimulusObjList = []
stim_id = 0
for st in loadCSV(STIMULUS_CONFIG):
stim_id += 1
self.stimulusObjList.append(Stimulus(st, canvas, app, stim_id))
self.print_stimulus_list()
def initApp():
global vsWidth, vsHeight, vsX, vsY, appConfig, stimulusList, f9CommunicationEnabled, bgColor, vsColor, stimulusColor
appConfig = loadCSV(APP_CONFIG_FILE)
vsX = getAppConfig("fishScreenStartX")
vsY = getAppConfig("fishScreenStartY")
vsWidth = getAppConfig("fishScreenWidth")
vsHeight = getAppConfig("fishScreenHeight")
bgColor = getAppConfig("backgroundColor", "str")
vsColor = getAppConfig("virtualScreenColor", "str")
stimulusColor = getAppConfig("stimulusColor", "str")
f9CommunicationEnabled = getAppConfig("f9CommunicationEnabled", "str")
if f9CommunicationEnabled.lower() == "on":
f9CommunicationEnabled = False
else:
f9CommunicationEnabled = False
logging.info("f9CommunicationEnabled=" + str(f9CommunicationEnabled))
printVirtualScreenData()
GAMMA = para.GAMMA # decay rate of past observations original 0.99
OBSERVATION = para.OBSERVATION # timesteps to observe before training
EXPLORE = para.EXPLORE # frames over which to anneal epsilon
FINAL_EPSILON = para.FINAL_EPSILON # final value of epsilon
INITIAL_EPSILON = para.INITIAL_EPSILON # starting value of epsilon
REPLAY_MEMORY = para.REPLAY_MEMORY # number of previous transitions to remember
BATCH = para.BATCH # size of minibatch
FRAME_PER_ACTION = para.FRAME_PER_ACTION
LEARNING_RATE = para.LEARNING_RATE
loss_file_path = para.loss_file_path
actions_file_path = para.actions_file_path
q_value_file_path = para.q_value_file_path
scores_file_path = para.scores_file_path
loss_df = utils.loadCSV(loss_file_path, 'loss')
scores_df = utils.loadCSV(scores_file_path, 'scores')
actions_df = utils.loadCSV(actions_file_path, 'actions')
q_values_df = utils.loadCSV(q_value_file_path, 'qvalues')
'''
main training module
Parameters:
* model => Keras Model to be trained
* game_state => Game State module with access to game environment and dino
* observe => flag to indicate wherther the model is to be trained(weight updates), else just play
'''
def trainNetwork(model, game_state, observe=False):
last_time = time.time()
# store the previous observations in replay memory
def __init__(self, screen):
self.debug = False
self.controlMode = "l" # l = location, s = size
# user by the x (cross) button to show virtual screen cross
self.xMode = False # false - no x is displayed in the middle of the screen
self.xVertical = 0
self.xHorizental = 0
self.xBoundry = 0
# used by the y (mid button) button to show mid screen marker
self.yMode = False # False - no midScreen marker is displayed
self.yVertical = 0
self.yHorizental = 0
#####
self.bgColor = 0
self.vsColor = 0
self.stimulusColor = 0
self.appConfig = loadCSV(constants.APP_CONFIG_FILE)
self.vsX = self.getAppConfig("fishScreenStartX")
self.vsY = self.getAppConfig("fishScreenStartY")
self.vsWidth = self.getAppConfig("fishScreenWidth")
self.vsHeight = self.getAppConfig("fishScreenHeight")
self.bgColor = self.getAppConfig("backgroundColor", "str")
self.vsColor = self.getAppConfig("virtualScreenColor", "str")
self.stimulusColor = self.getAppConfig("stimulusColor", "str")
self.f9CommunicationEnabled = self.getAppConfig(
"f9CommunicationEnabled", "str")
self.NiDaqPulseEnabled = self.getAppConfig("NiDaqPulseEnabled", "str")
self.DishRadiusSize = self.getAppConfig("DishRadiusSize")
self.VirtualScreenDegrees = self.getAppConfig("VirtualScreenDegrees")
self.VirtualScreenWidthActualSize = self.getAppConfig(
"VirtualScreenWidthActualSize")
self.VirtualScreenHeightActualSize = self.getAppConfig(
"VirtualScreenHeightActualSize")
self.projectorOnMonitor = self.getAppConfig("projectorOnMonitor")
self.deltaX = 0
self.deltaY = 0
self.positionDegreesToVSTable = []
self.calcConvertPositionToPixelsTable() # populate the above table
if self.NiDaqPulseEnabled.lower() == "on":
# try to add channel
try:
task = NiDaqPulse(device_name="Dev2/port{0}/line{1}".format(
self.port, self.line))
self.output_device = task
except nidaqmx.DaqError as e:
print(e)
task.stop()
else:
self.output_device = None
if self.f9CommunicationEnabled.lower() == "on":
self.f9CommunicationEnabled = False
else:
self.f9CommunicationEnabled = False
logging.info("f9CommunicationEnabled=" +
str(self.f9CommunicationEnabled))
self.printVirtualScreenData()
self.screen_width = int(screen.winfo_screenwidth() / 4)
self.screen_height = int(screen.winfo_screenheight() / 4)
screen.geometry(
self.calcGeometry(self.screen_width, self.screen_height))
self.canvas = Canvas(screen, background="black")
self.textVar = tkinter.StringVar()
self.label = ttk.Label(self.canvas, textvariable=self.textVar)
self.label.config(font=("Courier", 20))
self.label.grid(column=0, row=0)
self.canvas.pack(fill=BOTH, expand=1)
self.vs = self.canvas.create_rectangle(self.vsX,
self.vsY,
self.vsX + self.vsWidth,
self.vsY + self.vsHeight,
fill=self.vsColor)
screen.bind('<Up>', self.processEvent)
screen.bind('<Down>', self.processEvent)
screen.bind('<Left>', self.processEvent)
screen.bind('<Right>', self.processEvent)
screen.bind(constants.DEBUG, self.turnDebug)
screen.bind(constants.LOCATION, self.changeControlMode)
screen.bind(constants.SIZE, self.changeControlMode)
screen.bind(constants.UPDATE, self.updateConfig)
screen.bind(constants.EXIT, self.leaveProg)
screen.bind(constants.RUN, self.manageStimulus)
screen.bind(constants.PAUSE, self.manageStimulus)
screen.bind(constants.CROSS, self.showCrossAndBoundries)
screen.bind(constants.MID_SCREEN, self.showMidScreen)
screen.bind('?', self.printHelp)
self.printHelp("")
parser = argparse.ArgumentParser()
parser.add_argument('case', help='PINN ID')
parser.add_argument('plot', help='Plot or not(key in 1 to plot)')
args = parser.parse_args()
project_name = args.case
model_path = '/home/zhida/Documents/PINN/2d_inviscid_model/%s/'%project_name
for f in os.listdir(model_path):
if (f.endswith('.csv') and f[-5] != 's'):
pb = (f[f.index('=')+1:-4])
test_path = '/home/zhida/Documents/PINN/data/bp=%s.csv'%pb
predict_path = model_path + '%s_bp=%s.csv' %(project_name,pb)
P_back_test,x_test,y_test,P_test,rho_test,u_test,v_test,T_test,Et_test,E_test = utils.loadCSV(test_path)
P_back_pred,x_pred,y_pred,P_pred,rho_pred,u_pred,v_pred,T_pred,Et_pred,E_pred = utils.loadCSV(predict_path)
print(Et_pred.shape)
print(Et_test.shape)
#Error
error_P = np.linalg.norm(P_test-P_pred,2)/np.linalg.norm(P_test,2)
print("Test Error in P: "+str(error_P))
error_rho = np.linalg.norm(rho_test-rho_pred,2)/np.linalg.norm(rho_test,2)
print("Test Error in rho: "+str(error_rho))
error_u = np.linalg.norm(u_test-u_pred,2)/np.linalg.norm(u_test,2)
print("Test Error in u: "+str(error_u))
error_v = np.linalg.norm(v_test-v_pred,2)/np.linalg.norm(v_test,2)
print("Test Error in v: "+str(error_v))
error_Et = np.linalg.norm(Et_test- Et_pred,2)/np.linalg.norm(Et_test,2)