def plot(settings):
# load interaction data
interact = interactionData(settings['interactionFileLoc'])
interactScore, interactDate = segmentInteractionIntoDays(interact)
# load PA data
PA = PAdata(settings['PAfileLoc'])
PAscore, PAdate = segmentPAIntoDays(PA)
pltName = 'user PA and avatar influence vs time'
barWidth = 0.2
print 'making plot "' + pltName + '"'
pylab.figure(pltName)
p1 = pylab.plt.bar(date2num(interactDate) + barWidth / 2.0,
interactScore,
color='g',
width=barWidth,
linewidth=0)
p2 = pylab.plt.bar(date2num(PAdate) - barWidth / 2.0,
PAscore,
color='b',
width=barWidth,
linewidth=0)
pylab.plt.legend((p1, p2), ('avatar influence', 'subject PA'))
pylab.plt.ylabel('PA score & interaction score')
pylab.plt.xlabel('time')
pylab.plt.draw()
def plot():
# load in all the data
participants = list()
PA = list()
for pNum in range(HIGHEST_P_NUMBER + 1): #cycle through all participants
settings = setup(dataset='USF', data_loc=DATA_LOC, subject_n=pNum)
try:
pid = str(pNum)
fName = settings.getFileName('fitbit')
pa = PAdata(fName, method='fitbit', timeScale='minute')
# TODO the rest...
except IOError:
print 'p ' + pid + ' PA file "' + fName + '" not found'
continue
# if no problems:
participants.append(pid)
PA.append(pa)
print 'p' + pid + ' data loaded.'
#scale all the data
#TODO
print '\n =================================================='
print '\t total participants: ' + str(len(participants))
print ' =================================================='
# shift to time since start...
# for pnum in range(0,len(PA)):
# start_t = min(PA[pnum].time)
# for i in range (0, len(PA[pnum])):
# PA[pnum].time[i] -= start_t
# # shift all to same daily cycle
# for pnum in range(0,len(PA)):
# timeOfDay = PA[pnum].time[0] #assuming earliest time is first...
# ToD_start = (timeOfDay.hour*60 + timeOfDay.minute) * 60
# for i in range (0, len(PA[pnum])):
# PA[pnum].time[i] += ToD_start
# make the plots
figName = "PA_sparkScatterplot"
pylab.figure(figName)
num = 0
for pa in PA:
pylab.subplot(len(PA), 1, num)
pylab.scatter(pa.time, pa.steps, marker='x', color='b')
num += 1
def plot(settings):
# load interaction data
data = PAdata(settings['PAfileLoc'])
scores, dates = segmentPAIntoDays(data)
pltName = 'physical activity vs day'
print 'making plot "' + pltName + '"'
pylab.figure(pltName)
pylab.plt.bar(dates, scores)
pylab.plt.ylabel('day')
pylab.plt.xlabel('PA score')
pylab.plt.draw()
def plot(settings):
PA = PAdata(settings['PAfileLoc'])
print 'making plots...'
pylab.figure('dailyMinutes')
#p = pylab.figure()
# lineGraph = p.add_subplot(211)
# lineGraph.plot(x,y)
#barGraph = p.add_subplot(111)
pl1 = pylab.plt.bar(PA.time, PA.sedentary, linewidth=0, color='b')
base = PA.sedentary
pl2 = pylab.plt.bar(PA.time,
PA.light,
linewidth=0,
bottom=base,
color='indigo')
base = [base[i] + PA.light[i] for i in range(len(base))]
pl3 = pylab.plt.bar(PA.time, PA.mod, linewidth=0, bottom=base, color='r')
base = [base[i] + PA.mod[i] for i in range(len(base))]
pl4 = pylab.plt.bar(PA.time,
PA.mod_vig,
linewidth=0,
bottom=base,
color='orange')
base = [base[i] + PA.mod_vig[i] for i in range(len(base))]
pl5 = pylab.plt.bar(PA.time, PA.vig, linewidth=0, bottom=base, color='y')
base = [base[i] + PA.vig[i] for i in range(len(base))]
pl6 = pylab.plt.bar(PA.time,
PA.nonWear,
linewidth=0,
bottom=base,
color='dimGrey')
base = [base[i] + PA.nonWear[i] for i in range(len(base))]
pl7 = pylab.plt.bar(PA.time,
PA.unknown,
linewidth=0,
bottom=base,
color='lightGrey')
pylab.plt.ylabel('amount [min]')
pylab.plt.legend( (pl1[0],pl2[0],pl3[0],pl4[0],pl5[0],pl6[0],pl7[0]),\
('sedentary','light','mod','mod_vig','vig','nonWear','unknown') )
pylab.plt.draw()
def test_dataLooksGood(self):
PA = PAdata(self.settings['PAfileLoc'])
self.assertTrue(PA.count == 5)
self.assertTrue(PA.rowCount == 6)
self.assertTrue(PA.sedentary == [161.0, 0.0, 0.0, 324.0, 43.0])
self.assertTrue(PA.light == [64.0, 0.0, 25.0, 191.0, 17.0])
self.assertTrue(PA.mod == [43.0, 0.0, 9.0, 40.0, 4.0])
self.assertTrue(PA.mod_vig == [0.0, 0.0, 0.0, 0.0, 0.0])
self.assertTrue(PA.vig == [0.0, 0.0, 0.0, 0.0, 0.0])
self.assertTrue(PA.nonWear == [27.0, 0.0, 233.0, 884.0, 227.0])
self.assertTrue(PA.unknown == [1145.4, 1440.0, 1173.0, 0.6, 1148.4])
import datetime
self.assertTrue(PA.time == [
datetime.datetime(2013, 6, 16, 0, 0),
datetime.datetime(2013, 6, 15, 0, 0),
datetime.datetime(2013, 6, 14, 0, 0),
datetime.datetime(2013, 6, 13, 0, 0),
datetime.datetime(2013, 6, 12, 0, 0)
])
def plot(dataset='test',
dataLoc="./data/",
paMethod=DEFAULT_METHOD,
bypass_data_check=False):
# change plot font
# font = {'family' : 'monospace',
# 'weight' : 'normal',
# 'size' : 16}
# pylab.plt.rc('font', **font)
pltName = 'participants\' avg PA for sedentary & active avatar days'
print 'making plot "' + pltName + '"'
pylab.figure(pltName)
cmap = pylab.cm.get_cmap(name='terrain')
activePAs = list()
sedentPAs = list()
zeroPAs = list()
dataCounter = 0 # a count of how many datapoints are used so far (for colormapping and debug)
for pNum in range(HIGHEST_P_NUMBER + 1): #cycle through all participants
print '===p:' + str(pNum) + '==='
if pNum == 1 or pNum == 3:
print 'skipping p1 & p3 b/c data is incomplete.\n'
continue
elif pNum == 13:
print 'skip p13 b/c data needs some massaging.\n'
continue
elif pNum == 14:
print 'skip p14 b/c data is incomplete.\n'
continue
else:
activePAtotal = sedentPAtotal = zeroPAtotal = 0
activePAcount = sedentPAcount = zeroPAcount = 0
try:
settings = setup(dataset=dataset,
data_loc=dataLoc,
subject_n=pNum)
# load interaction data
interact = interactionData(settings.getFileName('viewLog'))
interactScore, interactDate = segmentInteractionIntoDays(
interact)
# load PA data
PA = PAdata(PAfile=settings.getFileName(paMethod),
method=paMethod,
timeScale='daily')
if paMethod == 'mMonitor':
PAscore, PAdate = segmentPAIntoDays(
PA, PAscoreFunction=getPAscore_postiveOnly)
elif paMethod == 'fitbit':
PAscore = PA.steps
PAdate = PA.time
else:
raise ValueError('unknown PA method "' + str(paMethod) +
'"')
if not bypass_data_check:
trim_data(interactDate, PAdate, interactScore, PAscore)
interactions = list()
for iScore in interactScore:
if iScore > 0:
interactions.append(1) #'active')
elif iScore < 0:
interactions.append(-1) #'sedentary')
else: # iScore = 0
interactions.append(0) #'0')
for day in range(len(PAscore)):
#print 'day ='+str(day)
#print 'interact='+str(interactions[day])
#print 'PAscore ='+str(PAscore[day])
if interactions[day] > 0:
activePAtotal += PAscore[day]
activePAcount += 1
elif interactions[day] < 0:
sedentPAtotal += PAscore[day]
sedentPAcount += 1
else: # interactions[day] == 0
zeroPAtotal += PAscore[day]
zeroPAcount += 1
activePAs.append(activePAtotal / activePAcount)
sedentPAs.append(sedentPAtotal / sedentPAcount)
try:
zeroPAs.append(zeroPAtotal / zeroPAcount)
except ZeroDivisionError:
zeroPAs.append(0)
# no exceptions means this data was good, increment counter
dataCounter += 1
print str(pNum) + ' loaded.\n'
except InputError as e:
print e.message
print 'participant ' + str(pNum) + ' not valid.\n'
except IOError as e:
print e.message
print 'participant ' + str(pNum) + ' not found.\n'
except Warning as w:
print w.message
print 'some issues loading participant ' + str(
pNum) + '; continuing anyway.\n'
if paMethod == 'mMonitor':
pylab.plt.ylabel('physical activity score')
elif paMethod == 'fitbit':
pylab.plt.ylabel('average step counts')
pylab.plt.xlabel(
'<-sedentary active->\navatar behavior'
)
pylab.plt.gca().axes.get_xaxis().set_ticks([])
pylab.plt.draw()
print activePAs
print sedentPAs
print zeroPAs
# TODO: colormapping should use dataCounter and actual number of data available (instead of pNum and HIGHEST_P_NUM, respectively)
base = 0
for PA in activePAs:
pylab.plt.bar(
1, PA, bottom=base, linewidth=1,
width=1.7) #, color=cmap(float(pNum)/float(HIGHEST_P_NUMBER)) )
base += PA
base = 0
for PA in sedentPAs:
pylab.plt.bar(
-1, PA, bottom=base, linewidth=1,
width=1.7) #, color=cmap(float(pNum)/float(HIGHEST_P_NUMBER)) )
base += PA
base = 0
for PA in zeroPAs:
pylab.plt.bar(
0, PA, bottom=base, linewidth=1,
width=1.7) #, color=cmap(float(pNum)/float(HIGHEST_P_NUMBER)) )
base += PA
paired_sample = stats.ttest_rel(sedentPAs, activePAs)
print "================================================"
print str(dataCounter) + " subjects analyzed using " + paMethod + " data."
print "The t-statistic is %.3f and the p-value is %.3f." % paired_sample
print "================================================"
print 'done.'
def plot(settings):
# load interaction data
interact = interactionData(settings['interactionFileLoc'])
interactScore, interactDate = segmentInteractionIntoDays(interact)
# load PA data
PA = PAdata(settings['PAfileLoc'])
PAscore, PAdate = segmentPAIntoDays(PA)
# data check
print 'data is ' + str(len(PAscore)) + 'x' + str(len(interactScore))
while (interactDate[0].date() != PAdate[0].date()) or (
interactDate[-1].date() !=
PAdate[-1].date()) or (len(interactScore) != len(PAscore)):
print 'day mismatch: '
#print 'data is ' + str(len(PAscore)) + 'x' + str(len(interactScore))
print '\t NAME \tSTART \t\t\tEND \t\t\tLEN'
print '\t interact\t' + str(interactDate[0]) + '\t' + str(
interactDate[-1]) + '\t' + str(len(interactScore))
print '\t PA \t' + str(PAdate[0]) + '\t' + str(
PAdate[-1]) + '\t' + str(len(PAscore)) + '\n'
if (PAdate[0].date() <
interactDate[0].date()): #if pa starts before interact
print 'pa data removed from start'
PAdate.pop(0)
PAscore.pop(0)
elif PAdate[0].date() > interactDate[0].date(
): #if pa starts after interact
print 'interact data removed from start'
interactDate.pop(0)
interactScore.pop(0)
elif PAdate[-1].date() < interactDate[-1].date(
): #if pa ends before interact
print 'pa data removed from end'
interactDate.pop()
interactScore.pop()
elif PAdate[-1].date() > interactDate[-1].date(
): #if pa ends after interact
print 'interact data removed from end'
PAscore.pop()
PAdate.pop()
else: # uneven values must be from missing days in middle of one dataset
longer = list()
shorter = list()
ldate = list()
sdate = list()
shortName = ''
if len(PAscore) > len(interactScore):
shortName = 'interaction'
longName = 'PA'
longer = PAscore
shorter = interactScore
ldate = PAdate
sdate = interactDate
else:
shortName = 'PA'
longName = 'interaction'
longer = interactScore
shorter = PAscore
ldate = interactDate
sdate = PAdate
for i in range(len(longer)):
#if i >= len(shorter): #check for end of shortlist reached
# print '\nERR: unknown data mismatch!!!\n'
# print 'data dump:'
# print '=== PA ==='
# print 'DATES='+str(PAdate)
# print 'VALUES='+str(PAscore)
# print '===interact==='
# print 'DATES='+str(interactDate)
# print 'VALUES='+str(interactScore)
# return
if i >= len(shorter) or sdate[i].date() != ldate[i].date(
): #remove extra dates not in shortlist
print 'value removed from ' + longName + ' at ' + str(
ldate[i])
longer.pop(i)
ldate.pop(i)
#shorter.insert(i,0)
#sdate.insert(i,ldate[i])
#print 'zero value inserted into '+shortName+' at '+str(sdate[i])
break
if len(PAscore) <= 0 or len(interactScore) <= 0:
print '\n ERR: data has no overlap!\n'
return
print '\t NAME \tSTART \t\t\tEND \t\t\tLEN'
print '\t interact\t' + str(interactDate[0]) + '\t' + str(
interactDate[-1]) + '\t' + str(len(interactScore))
print '\t PA \t' + str(PAdate[0]) + '\t' + str(
PAdate[-1]) + '\t' + str(len(PAscore)) + '\n'
#print '\n'+str(interactDate)
#print '\n'+str(PAdate)+'\n'
pltName = 'PA vs interaction'
print 'making plot "' + pltName + '"'
pylab.figure(pltName)
#p = pylab.figure()
# lineGraph = p.add_subplot(211)
# lineGraph.plot(x,y)
#barGraph = p.add_subplot(111)
pylab.plt.scatter(PAscore, interactScore, color='b')
pylab.plt.ylabel('physical activity score')
pylab.plt.xlabel('proteus PA influence')
pylab.plt.draw()
def plot():
# load in all the data
PA_participants = list()
PA = list()
for pNum in range(HIGHEST_P_NUMBER + 1): #cycle through all participants
settings = setup(dataset='USF', data_loc=DATA_LOC, subject_n=pNum)
try:
pid = pNum
fName = settings.getFileName('fitbit')
pa = PAdata(fName, method='fitbit', timeScale='minute')
except IOError:
print 'p ' + str(pid) + ' PA file "' + fName + '" not found'
continue
# if no problems:
PA_participants.append(pid)
PA.append(pa)
# print 'p' + pid + ' data loaded.'
interact_participants = list()
interactions = list()
for pNum in range(HIGHEST_P_NUMBER + 1): #cycle through all participants
settings = setup(dataset='USF', data_loc=DATA_LOC, subject_n=pNum)
try:
pid = pNum
fName = settings.getFileName('viewLog')
interact = (interactionData(fName))
except IOError:
print 'p ' + str(pid) + ' interactions file not found'
continue
# if no problems:
interact_participants.append(pid)
interactions.append(interact)
# print 'p' + pid + ' data loaded.'
#scale all the data
PAscaler = 1.0 / 50.0
for pnum in range(0, len(PA)):
for i in range(0, len(PA[pnum])):
PA[pnum].steps[i] *= PAscaler
# shift to time since start...
# for pnum in range(0,len(PA)):
# start_t = min(PA[pnum].timestamp)
# for i in range (0, len(PA[pnum])):
# PA[pnum].timestamp[i] -= start_t
# for pnum in range(0,len(interactions)):
# start_t = min(interactions[pnum].t)
# for i in range (0, len(interactions[pnum])):
# interactions[pnum].t[i] -= start_t
# # shift all to same daily cycle
# for pnum in range(0,len(PA)):
# timeOfDay = PA[pnum].time[0] #assuming earliest time is first...
# for i in range (0, len(PA[pnum])):
# PA[pnum].time[i] += timeOfDay
# timeOfDay = interactions[pnum].t[0] #assuming earliest time is first...
# ToD_start = (timeOfDay.hour*60 + timeOfDay.minute) * 60
# for i in range (0, len(interactions[pnum])):
# interactions[pnum].t[i] += ToD_start
# class Bunch:
# ''' bunches! makes a dictionary look like a class! bam! '''
# __init__ = lambda self, **kw: setattr(self, '__dict__', kw)
# make the plots
figName = "scatterplot_masterdash"
pylab.figure(figName)
pylab.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
num = 0
max_p = max(max(PA_participants), max(interact_participants)) + 1
pCount = max(len(PA_participants), len(interact_participants)) + 1
actualCount = 1
for i in range(max_p):
pylab.subplot(pCount, 1, actualCount)
try:
pa = PA[PA_participants.index(num)]
except ValueError as m:
print 'p' + m.message + ' of PA'
pa = None # Bunch(time=0,steps=0)
try:
interact = interactions[interact_participants.index(num)]
except ValueError as m:
print 'p' + m.message + ' of interactions'
interact = None # Bunch(t=0,v=0)
num += 1
isData = False
try:
pylab.scatter(pa.timestamp, pa.steps, marker='x', color='g')
isData = True
except AttributeError as e:
pass
try:
pylab.scatter(interact.viewTimes, [0] * len(interact.viewTimes),
marker='+',
color=interact.viewMarkerColor)
isData = True
except AttributeError as e:
pass
if isData:
actualCount += 1
def plot(settings):
# load interaction data
interact = interactionData(settings['interactionFileLoc'])
interactScore, interactDate = segmentInteractionIntoDays(interact)
# load PA data
PA = PAdata(settings['PAfileLoc'])
PAscore, PAdate = segmentPAIntoDays(PA,
PAscoreFunction=getPAscore_postiveOnly)
# data check
print 'data is ' + str(len(PAscore)) + 'x' + str(len(interactScore))
while (interactDate[0].date() != PAdate[0].date()) or (
interactDate[-1].date() !=
PAdate[-1].date()) or (len(interactScore) != len(PAscore)):
print 'day mismatch: '
#print 'data is ' + str(len(PAscore)) + 'x' + str(len(interactScore))
print '\t NAME \tSTART \t\t\tEND \t\t\tLEN'
print '\t interact\t' + str(interactDate[0]) + '\t' + str(
interactDate[-1]) + '\t' + str(len(interactScore))
print '\t PA \t' + str(PAdate[0]) + '\t' + str(
PAdate[-1]) + '\t' + str(len(PAscore)) + '\n'
if (PAdate[0].date() <
interactDate[0].date()): #if pa starts before interact
print 'pa data removed from start'
PAdate.pop(0)
PAscore.pop(0)
elif PAdate[0].date() > interactDate[0].date(
): #if pa starts after interact
print 'interact data removed from start'
interactDate.pop(0)
interactScore.pop(0)
elif PAdate[-1].date() < interactDate[-1].date(
): #if pa ends before interact
print 'pa data removed from end'
interactDate.pop()
interactScore.pop()
elif PAdate[-1].date() > interactDate[-1].date(
): #if pa ends after interact
print 'interact data removed from end'
PAscore.pop()
PAdate.pop()
else: # uneven values must be from missing days in middle of one dataset
longer = list()
shorter = list()
ldate = list()
sdate = list()
shortName = ''
if len(PAscore) > len(interactScore):
shortName = 'interaction'
longName = 'PA'
longer = PAscore
shorter = interactScore
ldate = PAdate
sdate = interactDate
else:
shortName = 'PA'
longName = 'interaction'
longer = interactScore
shorter = PAscore
ldate = interactDate
sdate = PAdate
for i in range(len(longer)):
#if i >= len(shorter): #check for end of shortlist reached
# print '\nERR: unknown data mismatch!!!\n'
# print 'data dump:'
# print '=== PA ==='
# print 'DATES='+str(PAdate)
# print 'VALUES='+str(PAscore)
# print '===interact==='
# print 'DATES='+str(interactDate)
# print 'VALUES='+str(interactScore)
# return
if i >= len(shorter) or sdate[i].date() != ldate[i].date(
): #remove extra dates not in shortlist
print 'value removed from ' + longName + ' at ' + str(
ldate[i])
longer.pop(i)
ldate.pop(i)
#shorter.insert(i,0)
#sdate.insert(i,ldate[i])
#print 'zero value inserted into '+shortName+' at '+str(sdate[i])
break
if len(PAscore) <= 0 or len(interactScore) <= 0:
print '\n ERR: data has no overlap!\n'
return
print '\t NAME \tSTART \t\t\tEND \t\t\tLEN'
print '\t interact\t' + str(interactDate[0]) + '\t' + str(
interactDate[-1]) + '\t' + str(len(interactScore))
print '\t PA \t' + str(PAdate[0]) + '\t' + str(
PAdate[-1]) + '\t' + str(len(PAscore)) + '\n'
# modify interaction score to just separate (+) and (-) values
interactions = list()
for iScore in interactScore:
if iScore > 0:
interactions.append(1) #'active')
elif iScore < 0:
interactions.append(-1) #'sedentary')
else: # iScore = 0
interactions.append(0) #'0')
pltName = 'user PA for sedentary & active avatar days'
print 'making plot "' + pltName + '"'
pylab.figure(pltName)
activeBase = 0
sedentBase = 0
zeroBase = 0
for day in range(len(PAscore)):
#print 'day ='+str(day)
#print 'interact='+str(interactions[day])
#print 'PAscore ='+str(PAscore[day])
if interactions[day] > 0:
pylab.plt.bar(interactions[day],
PAscore[day],
bottom=activeBase,
linewidth=1)
activeBase += PAscore[day]
elif interactions[day] < 0:
pylab.plt.bar(interactions[day],
PAscore[day],
bottom=sedentBase,
linewidth=1)
sedentBase += PAscore[day]
else: # interactions[day] == 0
pylab.plt.bar(interactions[day],
PAscore[day],
bottom=zeroBase,
linewidth=1)
zeroBase += PAscore[day]
pylab.plt.ylabel('physical activity score')
pylab.plt.xlabel(
'<-sedentary zero active->\navatar behavior'
)
pylab.plt.draw()