def plotFishPositionVsTime(jsonData, startState=1, endState=0, smooth=5, axis=0, fmt='b-',wid=1):
state = jsonData['stateinfo']
midLine = (jsonData['tankSize_mm'][axis])/2.0
st,_,nS,_ = aba.state_to_time(jsonData,startState)
_,et,_,nE = aba.state_to_time(jsonData,endState)
tracking = aba.getTracking(jsonData)
tracking = tracking[np.logical_and(tracking[:,0] > st, tracking[:,0] < et),:].copy()
frametime = tracking[:,0] - st
position = tracking[:,axis+1]
if 'OMRinfo' in jsonData.keys():
results = aba.getOMRinfo(jsonData, tankLength =midLine*2)
color = {-1:'red', 1:'blue'}
hatch = {-1:'\\', 1:'/'}
os = results['omrResults']['st']
oe = results['omrResults']['et']
od = results['omrResults']['dir']
for n in range(len(os)):
p1 = mpl.patches.Rectangle((os[n]-st,0),
width=oe[n]-os[n],
height=midLine*2,alpha=0.5,
color=[.5,1,.5],hatch=hatch[od[n]])
pyplot.gca().add_patch(p1)
#pyplot.text(os[n]-st+(oe[n]-os[n])/3, 45, '%0.2f'%results['omrResults']['maxdist'][n])
pyplot.plot(frametime, position, fmt, lw=1)
if smooth>0:
import scipy
pyplot.plot(frametime, scipy.convolve(position,np.ones(smooth)/smooth, mode='same'))
pyplot.ylim([0,midLine*2])
pyplot.xlim([0,et-st])
if axis==0:
pyplot.ylabel('')
else:
pyplot.ylabel('')
pyplot.xlabel('Time (s)')
def getmintime(d):
tracking = aba.getTracking(d)
frametime = tracking[:,0]
mintime = max(frametime)-min(frametime)
return mintime
whichndx = [1,2,3,4,5,6,7]
compare = [[d_pre[whichndx],d_veh[whichndx]],[d_veh[whichndx], d_sch[whichndx]]]
trange = [[[1000,1700],[0,360]], [[1000,1700],[0,360]]]
titles = ['Vehicle','Schreckstoff']
labels = [['Baseline','Vehicle_Start'],['Vehicle_End','Schreckstoff_Start']]
bins = np.linspace(0,300,50)
f = pyplot.figure(7)
f.set_facecolor('w')
mmperpixel=35.0/300
center=(150,150)
center_rad = 100
for compnum,comp in enumerate(compare):
timeInCenter = np.zeros([len(comp),len(comp[0])])
for condnum, cond in enumerate(comp):
for fishnum, fish in enumerate(cond):
tracking = aba.getTracking(fish)
tracking[:,1]-=tracking[:,1].min()
tracking[:,2]-=tracking[:,2].min()
trel = trange[compnum][condnum] + tracking[0,0]
tndx = np.logical_and(tracking[:,0]>=trel[0], tracking[:,0]<trel[1])
heatmap, xedges, yedges = np.histogram2d(tracking[tndx,1],tracking[tndx,2],bins=bins,normed=True)
xx,yy = np.meshgrid([x1+x2/2.0 for x1, x2 in itertools.izip(xedges[1:], xedges)],
[y1+y2/2.0 for y1, y2 in itertools.izip(yedges[1:], yedges)])
xxndx,yyndx = np.meshgrid(range(len(xedges)-1),range(len(yedges)-1))
centerndx = np.nonzero(np.sqrt(pow(xx-center[0],2)+pow(yy-center[1],2))<center_rad)
timeInCenter[condnum][fishnum] = heatmap[xxndx[centerndx],yyndx[centerndx]].sum() / heatmap.sum()
print timeInCenter
(t,p) = scipy.stats.ttest_rel(timeInCenter[0,:],timeInCenter[1,:])
pyplot.subplot(1,len(compare),1+compnum)
pyplot.plot(range(len(comp)),timeInCenter,'o-b')
pyplot.xlim([-0.5,len(comp)-0.5])
