def dump():
h5file = tables.openFile(DATA_FILE, mode='r')
data = h5file.root.ramppi11.rawdata[:]
sids = SESSIONS[-3:-1]
#sids = np.unique(data['session_id'])#[-2:]
sbends = [BENDS[1], BENDS[3]]
for sid in sids:
datapath = dirs[sid]
ls = os.listdir(datapath)
vids = [file for file in ls if file.lower().endswith('.mp4')]
print sid, vids
if len(vids) > 1:
vid = os.path.join(datapath, vids[1])
else:
vid = os.path.join(datapath, vids[0])
frame_to_vid = sync_smarteye_video.se_timestamp_fit(vid)
print frame_to_vid
dir1 = os.path.join(naksudir, str(sid))
if not os.path.exists(dir1):
os.mkdir(dir1)
for bend in sbends:
dir2 = os.path.join(dir1, 'bend'+str(BENDS.index(bend)+1))
if not os.path.exists(dir2):
os.mkdir(dir2)
bdata = get_segment_data(sid, bend, data)
laps = np.unique(bdata['lap'])
for lap in laps:
dir3 = os.path.join(dir2, 'lap'+str(lap))
if not os.path.exists(dir3):
os.mkdir(dir3)
ldata = bdata[ bdata['lap'] == lap ]
videots = np.polyval(frame_to_vid, ldata['se_frame_number'])
#plt.figure()
#plt.plot(ldata['se_frame_number'], videots, ',b')
#plt.show()
print lap, videots[0], videots[-1]
duration = videots[-1] - videots[0]
Popen("avconv -i " + vid + " -qscale 1 -ss " + str(videots[0]) + " -t " +
str(duration) + " " + os.path.join(dir3, "image%5d.jpg"),
stdout=PIPE, shell=True).communicate()[0]
h5file.close()
def plot_naksu_by_sid_and_bend():
data = get_merged_data()
ci = 3
pdf_out = PdfPages('/tmp/naksu_by_sid_and_bend_'+str(ci)+'.pdf')
for sid in SESSIONS:
sdata = data[ data['session_id'] == sid]
plt.figure()
plt.title(sid)
for lap in np.unique(sdata['lap']):
ldata = sdata[ sdata['lap'] == lap ]
segdata = get_segment_data(sid, CORNERING[ci], ldata)
plt.plot(segdata['dist'], segdata['naksu_x'], '-g')
pdf_out.savefig()
plt.close()
pdf_out.close()
