def prepare_data(arena, path, smoothstr, smooth):
dat = json.load(open(path))
fly_data = dat['data']
bpath = dat.get('_base',os.path.abspath(os.path.dirname(path)))
pooled_on = {k:[] for k in "axbhwq"}
pooled_off = {k:[] for k in "axbhwq"}
pooled_lon = {k:[] for k in "axbhwq"}
for exp in fly_data:
geom, dfs = madplot.load_bagfile(
madplot.get_path(path,dat,exp["bag"]),
arena,
smooth=smooth)
l_df = dfs["targeted"]
t_df = dfs["tracked"]
h_df = dfs["ttm"]
#find when the laser was on
l_on = l_df[l_df['laser_power'] > 0]
#time of first laser on
l_on0 = l_df.index[0] + datetime.timedelta(seconds=30)
#t_off = t_df.head(3000)
#t_on = t_df.tail(3000)
#fig = plt.figure()
#ax = fig.gca()
#t_df.plot(ax=ax)
#fig.savefig("%s_%s.png" % (exp["bag"],exp["type"]), bbox_inches='tight')
#the laser was off at the start and on at the end
#tracking data when the laser was on
t_on = t_df[l_on0:]
#tracking data when the laser was off
t_off = t_df[:l_on0]
pooled_on[exp["type"]].append(t_on)
pooled_off[exp["type"]].append(t_off)
pooled_lon[exp["type"]].append(l_on)
cPickle.dump(pooled_on, open(os.path.join(bpath,'pooled_on_%s_%s.pkl' % (arena.unit,smoothstr)),'wb'), -1)
cPickle.dump(pooled_off, open(os.path.join(bpath,'pooled_off_%s_%s.pkl' % (arena.unit,smoothstr)),'wb'), -1)
cPickle.dump(pooled_lon, open(os.path.join(bpath,'pooled_lon_%s_%s.pkl' % (arena.unit,smoothstr)),'wb'), -1)
return pooled_on, pooled_off, pooled_lon
def prepare_data(path):
if os.path.isdir(path):
path = path + "/"
dat = {"coupled": []}
for trialn, b in enumerate(
sorted(glob.glob(os.path.join(path, "*.bag")))):
dat["coupled"].append({
"bag": os.path.basename(b),
"label": "trial %d" % trialn
})
with open(os.path.join(path, "example.json"), "w") as f:
json.dump(dat, f)
fname = "example"
else:
dat = json.load(open(path))
fname = os.path.splitext(os.path.basename(path))[0]
jobs = {}
pool = multiprocessing.Pool()
for k in dat:
if k.startswith("_"):
continue
for trialn, trials in enumerate(dat[k]):
bags = trials["bag"]
if not isinstance(bags, list):
bags = [bags]
else:
if not "label" in trials:
print "WARNING: Trial missing label"
for bname in bags:
bpath = madplot.get_path(path, dat, bname)
jobs[bname] = pool.apply_async(madplot.load_bagfile,
(bpath, arena),
{'smooth': smooth_trajectories})
pool.close()
pool.join()
for k in dat:
if k.startswith("_"):
continue
for trialn, trials in enumerate(dat[k]):
bags = trials["bag"]
if not isinstance(bags, list):
bags = [bags]
data = []
for bname in bags:
print "merge", bname, "to trial", trialn
bdat = jobs[bname].get()
data.append(bdat)
# dt = bdat[1]['t_dt'].values
# print "\tdt",dt.mean(),"+/-",dt.std()
# ax = plt.figure(bname).add_subplot(1,1,1)
# ax.plot(dt)
# ax = plt.figure(bname+"hist").add_subplot(1,1,1)
# ax.hist(dt,bins=20)
# plt.show(block=False)
trials["data"] = madplot.merge_bagfiles(
data, dat.get('_geom_must_intersect', True))
with open(madplot.get_path(path, dat, "%s_%s.pkl" % (fname, unit_ext)),
'wb') as f:
cPickle.dump(dat, f, -1)
return dat
def load_data(path):
dat = json.load(open(path))
fname = os.path.splitext(os.path.basename(path))[0]
with open(madplot.get_path(path, dat, "%s_%s.pkl" % (fname, unit_ext)),
'rb') as f:
return cPickle.load(f)
def prepare_data(arena, path, smoothstr, smooth):
dat = json.load(open(path))
fly_data = dat['data']
bpath = dat.get('_base', os.path.abspath(os.path.dirname(path)))
pooled_on = {k: [] for k in "axbhwq"}
pooled_off = {k: [] for k in "axbhwq"}
pooled_lon = {k: [] for k in "axbhwq"}
for exp in fly_data:
geom, dfs = madplot.load_bagfile(madplot.get_path(
path, dat, exp["bag"]),
arena,
smooth=smooth)
l_df = dfs["targeted"]
t_df = dfs["tracked"]
h_df = dfs["ttm"]
#find when the laser was on
l_on = l_df[l_df['laser_power'] > 0]
#time of first laser on
l_on0 = l_df.index[0] + datetime.timedelta(seconds=30)
#t_off = t_df.head(3000)
#t_on = t_df.tail(3000)
#fig = plt.figure()
#ax = fig.gca()
#t_df.plot(ax=ax)
#fig.savefig("%s_%s.png" % (exp["bag"],exp["type"]), bbox_inches='tight')
#the laser was off at the start and on at the end
#tracking data when the laser was on
t_on = t_df[l_on0:]
#tracking data when the laser was off
t_off = t_df[:l_on0]
pooled_on[exp["type"]].append(t_on)
pooled_off[exp["type"]].append(t_off)
pooled_lon[exp["type"]].append(l_on)
cPickle.dump(
pooled_on,
open(
os.path.join(bpath,
'pooled_on_%s_%s.pkl' % (arena.unit, smoothstr)),
'wb'), -1)
cPickle.dump(
pooled_off,
open(
os.path.join(bpath,
'pooled_off_%s_%s.pkl' % (arena.unit, smoothstr)),
'wb'), -1)
cPickle.dump(
pooled_lon,
open(
os.path.join(bpath,
'pooled_lon_%s_%s.pkl' % (arena.unit, smoothstr)),
'wb'), -1)
return pooled_on, pooled_off, pooled_lon
def load_data(path):
dat = json.load(open(path))
fname = os.path.splitext(os.path.basename(path))[0]
with open(madplot.get_path(path, dat, "%s_%s.pkl" % (fname,unit_ext)), 'rb') as f:
return cPickle.load(f)
def prepare_data(path):
if os.path.isdir(path):
path = path + "/"
dat = {"coupled":[]}
for trialn,b in enumerate(sorted(glob.glob(os.path.join(path,"*.bag")))):
dat["coupled"].append({"bag":os.path.basename(b),
"label":"trial %d" % trialn}
)
with open(os.path.join(path,"example.json"), "w") as f:
json.dump(dat, f)
fname = "example"
else:
dat = json.load(open(path))
fname = os.path.splitext(os.path.basename(path))[0]
jobs = {}
pool = multiprocessing.Pool()
for k in dat:
if k.startswith("_"):
continue
for trialn,trials in enumerate(dat[k]):
bags = trials["bag"]
if not isinstance(bags,list):
bags = [bags]
else:
if not "label" in trials:
print "WARNING: Trial missing label"
for bname in bags:
bpath = madplot.get_path(path, dat, bname)
jobs[bname] = pool.apply_async(
madplot.load_bagfile,
(bpath, arena),
{'smooth':smooth_trajectories})
pool.close()
pool.join()
for k in dat:
if k.startswith("_"):
continue
for trialn,trials in enumerate(dat[k]):
bags = trials["bag"]
if not isinstance(bags,list):
bags = [bags]
data = []
for bname in bags:
print "merge", bname, "to trial", trialn
bdat = jobs[bname].get()
data.append( bdat )
# dt = bdat[1]['t_dt'].values
# print "\tdt",dt.mean(),"+/-",dt.std()
# ax = plt.figure(bname).add_subplot(1,1,1)
# ax.plot(dt)
# ax = plt.figure(bname+"hist").add_subplot(1,1,1)
# ax.hist(dt,bins=20)
# plt.show(block=False)
trials["data"] = madplot.merge_bagfiles(
data,
dat.get('_geom_must_intersect', True))
with open(madplot.get_path(path, dat, "%s_%s.pkl" % (fname,unit_ext)), 'wb') as f:
cPickle.dump(dat, f, -1)
return dat
