user_study/process_data.py at master · hyperpro/user_study · GitHub

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importmath
importos

importnumpyasnp
importmatplotlib.pyplotasplt
# from scipy import optimize


deff_log(x,A,B,C):
returnA-B*np.log(x+C)

deff_exp(x,A,B,C,D):
returnA*np.exp(B*x) +C*np.exp(D*x)

defcheck_length(l, name):
flag=0
while(len(l) <14):
l.append(777)
flag=1
if(flag):
print("Warning: id- ", name, "has less than 14 samples.")
return

defprocess_user(f):
grade_str=f.readline().strip('\n').split(",")
video_order_str=f.readline().strip('\n').split(",")
video_time_str=f.readline().strip('\n').split(",")
decide_time_str=f.readline().strip('\n').split(",")
mturkID=f.readline().strip('\n')
device=f.readline().strip('\n')
age=f.readline().strip('\n')
network=f.readline().strip('\n')

check_length(grade_str, mturkID)
check_length(video_time_str, mturkID)
check_length(decide_time_str, mturkID)

grade=[]
video_order=[]
video_time= []
decide_time=[]
foriinrange(len(grade_str)):
grade.append(int(grade_str[i]))
video_order.append(int(video_order_str[i]))
video_time.append(int(video_time_str[i]))
decide_time.append(int(decide_time_str[i]))
result=[grade, video_order, video_time, decide_time, mturkID, device, age, network]
returnresult


fileroot='./results'
results=[]
list=os.listdir(fileroot)
printlen(list) ," files detected."
foriinrange(0, len(list)):
path=os.path.join(fileroot, list[i])
ifos.path.isfile(path):
f=open(path, 'r')
result=process_user(f)
results.append(result)

defgather_data():
gather_list=[]
foriinrange(len(results)):
gather_list.append(results[i][0])
data=np.array(gather_list)
print(np.shape(data))
returndata

defsave_order():
order_list= []
foriinrange(len(results)):
order_list.append(results[i][1])
order=np.array(order_list)
printnp.shape(order)
np.save("ms_order.npy", order)
returnorder

deflook_at_time(order):
time_list=[]
foriinrange(len(results)):
time_list.append(results[i][2])
time=np.array(time_list)
unordered_time_list=[]
foriinrange(len(results)):
temp=[]
forjinrange(13):
temp.append(time[i][order[i][j] -1])
unordered_time_list.append(temp)
unordered_time=np.array(unordered_time_list)
returnnp.mean(unordered_time, axis=0), np.mean(time, axis=0)


data=gather_data()
# data[:,[8,9]] = data[:,[9,8]]
# order = save_order()
np.save("./data/ms.npy", data)
# data_mean = np.mean(data, axis=0)

# # unordered, time = look_at_time(order)


# # print(data_mean)


# x_list =[0, 50, 100, 200, 300, 500, 750, 1000, 1250, 1500, 2000, 3000]
# x = np.array(x_list)
# x1 = x
# x2 = x
# A1, B1, C1 = optimize.curve_fit(f_log, x1, data_mean)[0]
# #A2, B2, C2, D2 =optimize.curve_fit(f_exp, x2, data_mean)[0]

# data_log = f_log(x1, A1, B1, C1)
# #data_exp = f_exp(x2, 1.699, -0.002706, 3.259, -0.0003815)


# plt.figure()
# plt.scatter(x[:], data_mean[:], 15,"red")
# plt.plot(x1, data_log, "blue")
# #plt.plot(x2, data_exp, "green")
# plt.show()