def main():
numOfAgent = 4
sheepId = 0
wolfId = 1
masterId = 2
distractorId = 3
manipulatedHyperVariables = co.OrderedDict()
conditionList = [1, 2, 3, 4]
#1：Chasing Present Master-Wolf Line; 2：Absent Master-Wolf Line
#3：Chasing Present Master-Distractor Line; 4：Absent Master-Distractor Line
manipulatedHyperVariables['ChaseCondition'] = conditionList
trajetoryIndexList = [-1]
manipulatedHyperVariables['TrajIndex'] = trajetoryIndexList
conditionValues = {
1: [wolfId, masterId],
2: [wolfId, masterId],
3: [distractorId, masterId],
4: [distractorId, masterId]
}
print('loading')
positionIndex = [0, 1]
FPS = 60
dataFileDir = '../PataData/withLine'
rawXRange = [-10, 10]
rawYRange = [-10, 10]
scaledXRange = [200, 600]
scaledYRange = [200, 600]
scaleTrajectory = ScaleTrajectory(positionIndex, rawXRange, rawYRange,
scaledXRange, scaledYRange)
oldFPS = 5
adjustFPS = AdjustDfFPStoTraj(oldFPS, FPS)
getTrajectory = lambda trajectoryDf: scaleTrajectory(
adjustFPS(trajectoryDf))
trajectoryDf = lambda condition, index: pd.read_pickle(
os.path.join(dataFileDir, '{}'.format(condition) + ' ({}).pickle'.
format(index)))
stimulus = {
condition: {
index: getTrajectory(trajectoryDf(condition, index))
for index in trajetoryIndexList
}
for condition in conditionList
}
savedataFileDir = '../PataData/withLineRescale'
[[
saveToPickle(
stimulus[condition][index],
os.path.join(
savedataFileDir, 'condition={}'.format(condition) +
'_Index=({}).pickle'.format(index)))
for index in trajetoryIndexList
] for condition in conditionList]
print('loding success')
def main():
numOfAgent = 4
manipulatedVariables = co.OrderedDict()
# manipulatedVariables['damping'] = [0.0] # [0.0, 1.0]
# manipulatedVariables['frictionloss'] = [0.0] # [0.0, 0.2, 0.4]
# manipulatedVariables['masterForce'] = [0.0]
manipulatedVariables['damping'] = [0.0, 0.5] # [0.0, 1.0]
manipulatedVariables['frictionloss'] = [1.0] # [0.0, 0.2, 0.4]
manipulatedVariables['masterForce'] = [0.0, 1.0] # [0.0, 2.0]
chaseTrailVariables = manipulatedVariables.copy()
catchTrailVariables = manipulatedVariables.copy()
chaseTrailVariables['hideId'] = [3, 4
] #0 wolf 1 sheep 2 master 3 4 distractor
catchTrailVariables['hideId'] = [1]
chaseTrailconditions = [
dict(list(specificValueParameter))
for specificValueParameter in it.product(
*[[(key, value) for value in values]
for key, values in chaseTrailVariables.items()])
]
# chaseTrailconditionsWithId =list (zip(range(len(conditions)),conditions ))
catchTrailconditions = [
dict(list(specificValueParameter))
for specificValueParameter in it.product(
*[[(key, value) for value in values]
for key, values in catchTrailVariables.items()])
]
# catchTrailconditionsWithId =list (zip(range(len(conditions)),conditions ))
# print('state',chaseTrailVariables,catchTrailVariables)
conditionsWithId = list(
zip(range(len(chaseTrailconditions) + len(catchTrailconditions)),
chaseTrailconditions + catchTrailconditions))
# print(conditionsWithId)
# conditions = [dict(list(specificValueParameter)) for specificValueParameter in it.product(*[[(key, value) for value in values] for key, values in manipulatedVariables.items()])]
# conditionsWithId =list (zip(range(len(conditions)),conditions ))
# print(conditionsWithId)
conditions = chaseTrailconditions + catchTrailconditions
conditions = [
condition.update({'conditionId': condtionId})
for condtionId, condition in zip(range(len(conditions)), conditions)
]
# print (conditions)
chaseTrailTrajetoryIndexList = range(2)
chaseTrailManipulatedVariablesForExp = co.OrderedDict()
chaseTrailManipulatedVariablesForExp['conditonId'] = range(
len(chaseTrailconditions))
chaseTrailManipulatedVariablesForExp[
'trajetoryIndex'] = chaseTrailTrajetoryIndexList
chaseTrailProductedValues = it.product(
*[[(key, value) for value in values]
for key, values in chaseTrailManipulatedVariablesForExp.items()])
catchTrailTrajetoryIndexList = range(1)
catchTrailManipulatedVariablesForExp = co.OrderedDict()
catchTrailManipulatedVariablesForExp['conditonId'] = range(
len(chaseTrailconditions),
len(chaseTrailconditions) + len(catchTrailconditions))
catchTrailManipulatedVariablesForExp[
'trajetoryIndex'] = catchTrailTrajetoryIndexList
catchTrailProductedValues = it.product(
*[[(key, value) for value in values]
for key, values in catchTrailManipulatedVariablesForExp.items()])
# print(productedValues)
exprimentVarableList = [
dict(list(specificValueParameter))
for specificValueParameter in chaseTrailProductedValues
] + [
dict(list(specificValueParameter))
for specificValueParameter in catchTrailProductedValues
]
[
exprimentVarable.update(
{'condition': conditionsWithId[exprimentVarable['conditonId']][1]})
for exprimentVarable in exprimentVarableList
]
# exprimentVarableList = [exprimentVarable.update({'condition': conditionsWithId[exprimentVarable['conditonId']][1]}) for exprimentVarable in exprimentVarableList ]
print(exprimentVarableList)
print(len(exprimentVarableList))
numOfBlock = 1
numOfTrialsPerBlock = 1
designValues = createDesignValues(
exprimentVarableList * numOfTrialsPerBlock, numOfBlock)
positionIndex = [0, 1]
FPS = 50
rawXRange = [-1, 1]
rawYRange = [-1, 1]
scaledXRange = [200, 600]
scaledYRange = [200, 600]
scaleTrajectoryInSpace = ScaleTrajectory(positionIndex, rawXRange,
rawYRange, scaledXRange,
scaledYRange)
oldFPS = 50
numFramesToInterpolate = int(FPS / oldFPS - 1)
interpolateState = InterpolateState(numFramesToInterpolate)
scaleTrajectoryInTime = ScaleTrajectoryInTime(interpolateState)
trajectoriesSaveDirectory = '../PataData/mujoco50Fps4Agent'
# trajectoriesSaveDirectory =os.path.join(dataFolder, 'trajectory', modelSaveName)
trajectorySaveExtension = '.pickle'
evaluateEpisode = 120000
evalNum = 20
fixedParameters = {
'distractorNoise': 3.0,
'evaluateEpisode': evaluateEpisode
}
generateTrajectoryLoadPath = GetSavePath(trajectoriesSaveDirectory,
trajectorySaveExtension,
fixedParameters)
trajectoryDf = lambda condition: pd.read_pickle(
generateTrajectoryLoadPath({
'hideId': condition['hideId'],
'damping': condition['damping'],
'frictionloss': condition['frictionloss'],
'masterForce': condition['masterForce'],
'evalNum': evalNum,
}))
# trajectoryDf = lambda condition: pd.read_pickle(generateTrajectoryLoadPath(condition))
getTrajectory = lambda trajectoryDf: scaleTrajectoryInTime(
scaleTrajectoryInSpace(trajectoryDf))
# getTrajectory = lambda trajectoryDf: scaleTrajectoryInSpace(trajectoryDf)
print('loading')
stimulus = {
conditionId: getTrajectory(trajectoryDf(condition))
for conditionId, condition in conditionsWithId
}
print(stimulus[0][0][0])
print(stimulus[10][0][0])
print('loding success')
# print(stimulus[1][1])
experimentValues = co.OrderedDict()
experimentValues["name"] = input("Please enter your name:").capitalize()
screenWidth = 800
screenHeight = 800
fullScreen = False
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
leaveEdgeSpace = 200
circleSize = 10
clickImageHeight = 80
lineWidth = 3
fontSize = 50
xBoundary = [leaveEdgeSpace, screenWidth - leaveEdgeSpace * 2]
yBoundary = [leaveEdgeSpace, screenHeight - leaveEdgeSpace * 2]
screenColor = THECOLORS['black']
lineColor = THECOLORS['white']
textColor = THECOLORS['white']
fixationPointColor = THECOLORS['white']
colorSpace = [(203, 164, 4, 255), (49, 153, 0, 255), (255, 90, 16, 255),
(251, 7, 255, 255), (9, 204, 172, 255), (3, 28, 255, 255)]
picturePath = os.path.join(
os.path.abspath(os.path.join(os.getcwd(), os.pardir)), 'pictures')
resultsPath = os.path.join(
os.path.abspath(os.path.join(os.getcwd(), os.pardir)), 'results')
introductionImage1 = pygame.image.load(
os.path.join(picturePath, 'mujocoIntro1.png'))
introductionImage2 = pygame.image.load(
os.path.join(picturePath, 'mujocoIntro2.png'))
finishImage = pygame.image.load(os.path.join(picturePath, 'over.jpg'))
introductionImage1 = pygame.transform.scale(introductionImage1,
(screenWidth, screenHeight))
introductionImage2 = pygame.transform.scale(introductionImage2,
(screenWidth, screenHeight))
finishImage = pygame.transform.scale(
finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
clickWolfImage = pygame.image.load(
os.path.join(picturePath, 'clickwolf.png'))
clickSheepImage = pygame.image.load(
os.path.join(picturePath, 'clicksheep.png'))
restImage = pygame.image.load(os.path.join(picturePath, 'rest.jpg'))
drawImage = DrawImage(screen)
drawText = DrawText(screen, fontSize, textColor)
drawBackGround = DrawBackGround(screen, screenColor, xBoundary, yBoundary,
lineColor, lineWidth)
drawFixationPoint = DrawFixationPoint(screen, drawBackGround,
fixationPointColor)
drawImageClick = DrawImageClick(screen, clickImageHeight, drawText)
drawState = DrawState(screen, circleSize, numOfAgent, positionIndex,
drawBackGround)
# drawStateWithRope = DrawStateWithRope(screen, circleSize, numOfAgent, positionIndex, ropeColor, drawBackground)
writerPath = os.path.join(resultsPath, experimentValues["name"]) + '.csv'
writer = WriteDataFrameToCSV(writerPath)
displayFrames = 500
keysForCheck = {'f': 0, 'j': 1}
checkHumanResponse = CheckHumanResponse(keysForCheck)
trial = ChaseTrialMujoco(conditionsWithId, displayFrames, drawState,
drawImage, stimulus, checkHumanResponse,
colorSpace, numOfAgent, drawFixationPoint,
drawText, drawImageClick, clickWolfImage,
clickSheepImage, FPS)
experiment = Experiment(trial, writer, experimentValues, drawImage,
restImage, drawBackGround)
restDuration = 20
drawImage(introductionImage1)
drawImage(introductionImage2)
experiment(designValues, restDuration)
# self.darwBackground()
drawImage(finishImage)
print("Result saved at {}".format(writerPath))
def main():
numOfAgent = 4
sheepId = 0
wolfId = 1
masterId = 2
distractorId = 3
manipulatedHyperVariables = co.OrderedDict()
conditionList = [1, 2, 3, 4]
#1：Chasing Present Master-Wolf Line; 2：Absent Master-Wolf Line
#3：Chasing Present Master-Distractor Line; 4：Absent Master-Distractor Line
manipulatedHyperVariables['ChaseCondition'] = conditionList
trajetoryIndexList = range(20)
manipulatedHyperVariables['TrajIndex'] = trajetoryIndexList
conditionValues = {
1: [wolfId, masterId],
2: [wolfId, masterId],
3: [distractorId, masterId],
4: [distractorId, masterId]
}
print('loading')
positionIndex = [0, 1]
FPS = 60
dataFileDir = '../PataData/withLineRescale'
rawXRange = [-10, 10]
rawYRange = [-10, 10]
scaledXRange = [200, 600]
scaledYRange = [200, 600]
scaleTrajectory = ScaleTrajectory(positionIndex, rawXRange, rawYRange,
scaledXRange, scaledYRange)
oldFPS = 5
adjustFPS = AdjustDfFPStoTraj(oldFPS, FPS)
# getTrajectory = lambda trajectoryDf: scaleTrajectory(adjustFPS(trajectoryDf))
# trajectoryDf = lambda condition,index: pd.read_pickle(os.path.join(dataFileDir, '{}'.format(condition)+' ({}).pickle'.format(index)))
# stimulus = {condition:[getTrajectory(trajectoryDf(condition,index)) for index in trajetoryIndexList] for condition in conditionList}
trajectoryDf = lambda condition, index: pd.read_pickle(
os.path.join(
dataFileDir, 'condition={}'.format(condition) +
'_Index=({}).pickle'.format(index)))
stimulus = {
condition: {
index: trajectoryDf(condition, index)
for index in trajetoryIndexList
}
for condition in conditionList
}
print('loding success')
experimentValues = co.OrderedDict()
experimentValues["name"] = input("Please enter your name:").capitalize()
# experimentValues["name"]='test'
screenWidth = 800
screenHeight = 800
fullScreen = True
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
leaveEdgeSpace = 200
circleSize = 10
clickImageHeight = 80
lineWidth = 3
fontSize = 50
xBoundary = [leaveEdgeSpace, screenWidth - leaveEdgeSpace * 2]
yBoundary = [leaveEdgeSpace, screenHeight - leaveEdgeSpace * 2]
screenColor = THECOLORS['black']
lineColor = THECOLORS['white']
textColor = THECOLORS['white']
fixationPointColor = THECOLORS['white']
colorSpace = [(203, 164, 4, 255), (49, 153, 0, 255), (255, 90, 16, 255),
(251, 7, 255, 255), (9, 204, 172, 255), (3, 28, 255, 255)]
picturePath = os.path.join(
os.path.abspath(os.path.join(os.getcwd(), os.pardir)), 'pictures')
resultsPath = os.path.join(
os.path.abspath(os.path.join(os.getcwd(), os.pardir)), 'results')
introductionImage1 = pygame.image.load(
os.path.join(picturePath, 'withLineIntroduction1.png'))
introductionImage2 = pygame.image.load(
os.path.join(picturePath, 'introduction2.png'))
finishImage = pygame.image.load(os.path.join(picturePath, 'over.jpg'))
introductionImage1 = pygame.transform.scale(introductionImage1,
(screenWidth, screenHeight))
introductionImage2 = pygame.transform.scale(introductionImage2,
(screenWidth, screenHeight))
finishImage = pygame.transform.scale(
finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
clickWolfImage = pygame.image.load(
os.path.join(picturePath, 'clickwolf.png'))
clickSheepImage = pygame.image.load(
os.path.join(picturePath, 'clicksheep.png'))
restImage = pygame.image.load(os.path.join(picturePath, 'rest.jpg'))
drawImage = DrawImage(screen)
drawText = DrawText(screen, fontSize, textColor)
drawBackGround = DrawBackGround(screen, screenColor, xBoundary, yBoundary,
lineColor, lineWidth)
drawFixationPoint = DrawFixationPoint(screen, drawBackGround,
fixationPointColor)
drawImageClick = DrawImageClick(screen, clickImageHeight, drawText)
ropeColor = THECOLORS['grey']
ropeWidth = 4
numRopePart = 9
ropePartIndex = list(range(numOfAgent, numOfAgent + numRopePart))
drawStateWithRope = DrawStateWithRope(screen, circleSize, numOfAgent,
positionIndex, ropePartIndex,
ropeColor, ropeWidth, drawBackGround)
writerPath = os.path.join(resultsPath, experimentValues["name"]) + '.csv'
writer = WriteDataFrameToCSV(writerPath)
displayFrames = FPS * 20
keysForCheck = {'f': 0, 'j': 1}
checkHumanResponse = CheckHumanResponse(keysForCheck)
#trial = ChaseTrial(conditionList,displayFrames, drawState, drawImage, stimulus, checkHumanResponse, colorSpace, numOfAgent, drawFixationPoint, drawText, drawImageClick, clickWolfImage, clickSheepImage, FPS)
trial = ChaseTrialWithRope(conditionValues, displayFrames,
drawStateWithRope, drawImage, stimulus,
checkHumanResponse, colorSpace, numOfAgent,
drawFixationPoint, drawText, drawImageClick,
clickWolfImage, clickSheepImage, FPS)
experiment = Experiment(trial, writer, experimentValues, drawImage,
restImage, drawBackGround)
numOfBlock = 1
numOfTrialsPerBlock = 1
exprimentVarableList = crateVariableProduct(manipulatedHyperVariables)
designValues = createDesignValues(
exprimentVarableList * numOfTrialsPerBlock, numOfBlock)
drawImage(introductionImage1)
drawImage(introductionImage2)
restDuration = 20
experiment(designValues, restDuration)
# self.darwBackground()
drawImage(finishImage)
print("Result saved at {}".format(writerPath))
def main():
numOfAgent = 4
manipulatedVariables = co.OrderedDict()
# manipulatedVariables['damping'] = [0.0] # [0.0, 1.0]
# manipulatedVariables['frictionloss'] = [0.0] # [0.0, 0.2, 0.4]
# manipulatedVariables['masterForce'] = [0.0]
manipulatedVariables['damping'] = [0.0, 0.5] # [0.0, 1.0]
manipulatedVariables['frictionloss'] = [1.0] # [0.0, 0.2, 0.4]
manipulatedVariables['masterForce'] = [0.0] # [0.0, 2.0]
manipulatedVariables['offset'] = [0.0, 1.0]
manipulatedVariables['hideId'] = [3, 4]
manipulatedVariables['fps'] = [40, 50]
manipulatedVariables['displayTime'] = [10, 15]
chaseTrailVariables = manipulatedVariables.copy()
# catchTrailVariables = manipulatedVariables.copy()
# chaseTrailVariables['hideId'] = [3,4] #0 wolf 1 sheep 2 master 3 4 distractor
# catchTrailVariables['hideId'] = [1]
chaseTrailconditions = [
dict(list(specificValueParameter))
for specificValueParameter in it.product(
*[[(key, value) for value in values]
for key, values in chaseTrailVariables.items()])
]
# catchTrailconditions = [dict(list(specificValueParameter)) for specificValueParameter in it.product(*[[(key, value) for value in values] for key, values in catchTrailVariables.items()])]
conditionsWithId = list(
zip(range(len(chaseTrailconditions)), chaseTrailconditions))
print(conditionsWithId)
conditions = chaseTrailconditions
conditions = [
condition.update({'conditionId': condtionId})
for condtionId, condition in zip(range(len(conditions)), conditions)
]
chaseTrailNum = 10
chaseTrailTrajetoryIndexList = range(chaseTrailNum)
chaseTrailManipulatedVariablesForExp = co.OrderedDict()
chaseTrailManipulatedVariablesForExp['conditonId'] = range(
len(chaseTrailconditions))
chaseTrailManipulatedVariablesForExp[
'trajetoryIndex'] = chaseTrailTrajetoryIndexList
chaseTrailProductedValues = it.product(
*[[(key, value) for value in values]
for key, values in chaseTrailManipulatedVariablesForExp.items()])
# catchTrailNum = 0
# catchTrailTrajetoryIndexList = range (catchTrailNum)
# catchTrailManipulatedVariablesForExp = co.OrderedDict()
# catchTrailManipulatedVariablesForExp['conditonId'] = range(len(chaseTrailconditions),len(chaseTrailconditions)+len(catchTrailconditions))
# catchTrailManipulatedVariablesForExp['trajetoryIndex'] = catchTrailTrajetoryIndexList
# catchTrailProductedValues = it.product(*[[(key, value) for value in values] for key, values in catchTrailManipulatedVariablesForExp.items()])
# print(productedValues)
exprimentVarableList = [
dict(list(specificValueParameter))
for specificValueParameter in chaseTrailProductedValues
]
[
exprimentVarable.update(
{'condition': conditionsWithId[exprimentVarable['conditonId']][1]})
for exprimentVarable in exprimentVarableList
]
# exprimentVarableList = [exprimentVarable.update({'condition': conditionsWithId[exprimentVarable['conditonId']][1]}) for exprimentVarable in exprimentVarableList ]
# print(exprimentVarableList)
# print(len(exprimentVarableList))
numOfBlock = 1
numOfTrialsPerBlock = 1
isShuffle = True
designValues = createDesignValues(
exprimentVarableList * numOfTrialsPerBlock, numOfBlock, isShuffle)
positionIndex = [0, 1]
standardFPS = 50
rawXRange = [200, 600]
rawYRange = [200, 600]
scaledXRange = [200, 600]
scaledYRange = [200, 600]
scaleTrajectoryInSpace = ScaleTrajectory(positionIndex, rawXRange,
rawYRange, scaledXRange,
scaledYRange)
oldFPS = 50
numFramesToInterpolate = int(standardFPS / oldFPS - 1)
interpolateState = InterpolateState(numFramesToInterpolate)
scaleTrajectoryInTime = ScaleTrajectoryInTime(interpolateState)
horizontalRotationTransformTrajectory = HorizontalRotationTransformTrajectory(
positionIndex, rawXRange, rawYRange)
rotationTransformTrajectory = RotationTransformTrajectory(
positionIndex, rawXRange, rawYRange)
def transFormTrajectory(trajList, randomId):
randomSeed = np.mod(randomId, 8)
rotationAngle = np.mod(randomSeed, 4) * np.pi / 2
rotationTraj = rotationTransformTrajectory(trajList, rotationAngle)
# if np.mod(randomSeed//4,2) ==1:
# finalTrajs = horizontalRotationTransformTrajectory(rotationTraj)
# else:
finalTrajs = rotationTraj
return finalTrajs
trajectoriesSaveDirectory = '../PataData/exp2Traj'
# trajectoriesSaveDirectory =os.path.join(dataFolder, 'trajectory', modelSaveName)
trajectorySaveExtension = '.pickle'
selctDict = {3: chaseTrailNum, 4: chaseTrailNum}
evaluateEpisode = 120000
evalNum = 20
fixedParameters = {
'distractorNoise': 3.0,
'evaluateEpisode': evaluateEpisode
}
generateTrajectoryLoadPath = GetSavePath(trajectoriesSaveDirectory,
trajectorySaveExtension,
fixedParameters)
trajectoryDf = lambda condition: loadFromPickle(
generateTrajectoryLoadPath({
'offset': condition['offset'],
'hideId': condition['hideId'],
'damping': condition['damping'],
'frictionloss': condition['frictionloss'],
'masterForce': condition['masterForce'],
'select': selctDict[condition['hideId']]
}))
getTrajectory = lambda trajectoryDf: scaleTrajectoryInTime(
scaleTrajectoryInSpace(trajectoryDf))
print('loading')
# stimulus = {conditionId:getTrajectory(trajectoryDf(condition)) for conditionId,condition in conditionsWithId}
#
transformedStimulus = {
conditionId:
transFormTrajectory(getTrajectory(trajectoryDf(condition)),
conditionId)
for conditionId, condition in conditionsWithId
}
print('loding success')
print(len(transformedStimulus[1]))
experimentValues = co.OrderedDict()
experimentValues["name"] = input("Please enter your name:").capitalize()
screenWidth = 800
screenHeight = 800
fullScreen = True
initializeScreen = InitializeScreen(screenWidth, screenHeight, fullScreen)
screen = initializeScreen()
leaveEdgeSpace = 180
circleSize = 10
clickImageHeight = 80
lineWidth = 3
fontSize = 50
xBoundary = [leaveEdgeSpace, screenWidth - leaveEdgeSpace * 2]
yBoundary = [leaveEdgeSpace, screenHeight - leaveEdgeSpace * 2]
screenColor = THECOLORS['black']
lineColor = THECOLORS['white']
textColor = THECOLORS['white']
fixationPointColor = THECOLORS['white']
colorSpace = [(203, 164, 4, 255), (49, 153, 0, 255), (255, 90, 16, 255),
(251, 7, 255, 255), (9, 204, 172, 255), (3, 28, 255, 255)]
picturePath = os.path.join(
os.path.abspath(os.path.join(os.getcwd(), os.pardir)), 'pictures')
resultsPath = os.path.join(
os.path.abspath(os.path.join(os.getcwd(), os.pardir)), 'results',
'exp2')
if not os.path.exists(resultsPath):
os.makedirs(resultsPath)
introductionImage1 = pygame.image.load(
os.path.join(picturePath, 'IdOnlyIntro1.png'))
introductionImage2 = pygame.image.load(
os.path.join(picturePath, 'IdOnlyIntro2.png'))
finishImage = pygame.image.load(os.path.join(picturePath, 'over.jpg'))
introductionImage1 = pygame.transform.scale(introductionImage1,
(screenWidth, screenHeight))
introductionImage2 = pygame.transform.scale(introductionImage2,
(screenWidth, screenHeight))
finishImage = pygame.transform.scale(
finishImage, (int(screenWidth * 2 / 3), int(screenHeight / 4)))
clickWolfImage = pygame.image.load(
os.path.join(picturePath, 'clickwolf.png'))
clickSheepImage = pygame.image.load(
os.path.join(picturePath, 'clicksheep.png'))
restImage = pygame.image.load(os.path.join(picturePath, 'rest.jpg'))
drawImage = DrawImage(screen)
drawText = DrawText(screen, fontSize, textColor)
drawBackGround = DrawBackGround(screen, screenColor, xBoundary, yBoundary,
lineColor, lineWidth)
drawFixationPoint = DrawFixationPoint(screen, drawBackGround,
fixationPointColor)
drawImageClick = DrawImageClick(screen, clickImageHeight, drawText)
drawState = DrawState(screen, circleSize, numOfAgent, positionIndex,
drawBackGround)
writerPath = os.path.join(resultsPath, experimentValues["name"]) + '.csv'
writer = WriteDataFrameToCSV(writerPath)
# displayFrames = 500
reactionWindowStart = 50
keysForCheck = {'space': 1}
checkHumanResponse = CheckHumanResponseWithSpace(keysForCheck)
trial = ChaseTrialMujocoFps(conditionsWithId, reactionWindowStart,
drawState, drawImage, transformedStimulus,
checkHumanResponse, colorSpace, numOfAgent,
drawFixationPoint, drawText, drawImageClick,
clickWolfImage, clickSheepImage)
experiment = Experiment(trial, writer, experimentValues, drawImage,
restImage, drawBackGround)
restDuration = 120
drawImage(introductionImage1)
drawImage(introductionImage2)
experiment(designValues, restDuration)
# self.darwBackground()
drawImage(finishImage)
print("Result saved at {}".format(writerPath))