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stats.py
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stats.py
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import math
import os, os.path
import scipy.stats as st
import rectToSph
#Ben Lister
#compute average position (in sphereical coordinates) of every joint in a given frame
#returns a dictionary of polar data and associated means and statistics
def getStats(directory):
a_means = {}
b_means = {}
a_stdevs = {}
b_stdevs = {}
a_data = {}
b_data = {}
numActions = 0
minTheta = math.pi
maxTheta = 0
minAlpha = 2*math.pi
maxAlpha = 0
for i in range(0, len(os.listdir(directory))):
ssD = directory + "\\" + os.listdir(directory)[i]
if (os.listdir(directory)[i] != ".DS_Store"):
for j in range(0, len((os.listdir(ssD)))):
sD = ssD + "\\" + os.listdir(ssD)[j]
if (os.listdir(ssD)[j] != ".DS_Store"):
data = rectToSph.sphereical(sD+"\\skeleton_pos.txt")
a_data[numActions] = data['a data']
b_data[numActions] = data['b data']
a_action_means = [0]*30
b_action_means = [0]*30
a_action_devs = [0]*30
b_action_devs = [0]*30
for k in range(0, 30):
for m in range(0, len(data['a data'])):
a_action_means[k] = a_action_means[k] + data['a data'][m][k]
b_action_means[k] = b_action_means[k] + data['b data'][m][k]
#prints min and max alpha and theta
if (k%2==0):
if (data['a data'][m][k] < minAlpha):
minAlpha = data['a data'][m][k]
if (data['b data'][m][k] < minAlpha):
minAlpha = data['b data'][m][k]
if (data['a data'][m][k] > maxAlpha):
maxAlpha = data['a data'][m][k]
if (data['b data'][m][k] > maxAlpha):
maxAlpah = data['a data'][m][k]
if (k%2==1):
if (data['a data'][m][k] < minTheta):
minTheta = data['a data'][m][k]
if (data['b data'][m][k] < minTheta):
minTheta = data['b data'][m][k]
if (data['a data'][m][k] > maxTheta):
maxTheta = data['a data'][m][k]
if (data['b data'][m][k] > maxTheta):
maxTheta = data['b data'][m][k]
a_action_means[k] = a_action_means[k]/len(data['a data'])
b_action_means[k] = b_action_means[k]/len(data['b data'])
for n in range(0, len(data['a data'])):
a_action_devs[k] = a_action_devs[k] + math.pow(data['a data'][n][k] - a_action_means[k], 2)
b_action_devs[k] = b_action_devs[k] + math.pow(data['b data'][n][k] - b_action_means[k], 2)
a_action_devs[k] = math.sqrt((1/len(data['a data']))*a_action_devs[k])
b_action_devs[k] = math.sqrt((1/len(data['a data']))*b_action_devs[k])
a_means[numActions] = a_action_means
b_means[numActions] = b_action_means
a_stdevs[numActions] = a_action_devs
b_stdevs[numActions] = b_action_devs
numActions = numActions+1
print(minAlpha, maxAlpha)
print(minTheta, maxTheta)
data = {'a data': a_data, 'b data': b_data}
stats = {'a means': a_means, 'b means': b_means, 'a devs': a_stdevs, 'b devs': b_stdevs}
return {'data': data, 'stats': stats}
#returned data structure is of the form ['<a or b> postures'][<action>][<frame>][<joint>]['<angle or probs>'][<1x2 1D array or 3x24 2D array>]
def getHisto(data, stats):
histoA = {}
histoB = {}
#defining bin boundaries
alphaBins = []
for x in range (0, 25):
alphaBins.append(x*math.pi/12 - math.pi)
thetaBins = alphaBins[12:16]
#for every action
for z in range(0, len(data['a data'])):
actionA = {}
actionB = {}
#for every frame
for p in range(0, len(data['a data'][z])):
frameA = [0]*15
frameB = [0]*15
#for every joint
k = 0
while (k < 29):
jointA = {'angle':[], 'prob':[]}
jointB = {'angle':[], 'prob':[]}
alphaA = data['a data'][z][p][k]
thetaA = data['a data'][z][p][k+1]
alphaB = data['b data'][z][p][k]
thetaB = data['b data'][z][p][k+1]
jointA['angle'] = [alphaA, thetaA]
jointB['angle'] = [alphaB, thetaB]
probsA = [[0 for x in range(24)] for y in range(3)]
probsB = [[0 for x in range(24)] for y in range(3)]
alphaProbA = [0]*24
alphaProbB = [0]*24
thetaProbA = [0]*3
thetaProbB = [0]*3
for i in range(0, 24):
alphaProbA[i] = st.norm.cdf((alphaA-alphaBins[i+1])/stats['a devs'][z][k]) - st.norm.cdf((alphaA-alphaBins[i])/stats['a devs'][z][k])
alphaProbB[i] = st.norm.cdf((alphaB-alphaBins[i+1])/stats['b devs'][z][k]) - st.norm.cdf((alphaB-alphaBins[i])/stats['b devs'][z][k])
for j in range(0, 3):
thetaProbA[j] = st.norm.cdf((thetaA-thetaBins[j+1])/stats['a devs'][z][k+1]) - st.norm.cdf((thetaA-thetaBins[j])/stats['a devs'][z][k+1])
thetaProbB[j] = st.norm.cdf((thetaB-thetaBins[j+1])/stats['b devs'][z][k+1]) - st.norm.cdf((thetaB-thetaBins[j])/stats['b devs'][z][k+1])
for thetaIndex in range(0, 3):
for alphaIndex in range(0, 24):
probsA[thetaIndex][alphaIndex] = math.fabs(alphaProbA[alphaIndex]*thetaProbA[thetaIndex])
probsB[thetaIndex][alphaIndex] = math.fabs(alphaProbB[alphaIndex]*thetaProbB[thetaIndex])
jointA['prob'] = probsA
jointB['prob'] = probsB
frameA[int(k/2)] = jointA
frameB[int(k/2)] = jointB
k = k + 2
actionA[p] = frameA
actionB[p] = frameB
histoA[z] = actionA
histoB[z] = actionB
postures = {'a postures': histoA, 'b postures': histoB}
return postures