def plot_trajectory(ax,arena,ldf,tdf,hdf,geom,debug):
madplot.plot_tracked_trajectory(ax, tdf, arena,
debug_plot=debug,
color='k',
)
ax.add_patch(arena.get_patch(color='k', alpha=0.1))
patch = arena.get_intersect_patch(geom, fill=True, color='r', closed=True, alpha=0.2)
if patch is not None:
ax.add_patch(patch)
def plot_trajectory(ax, arena, ldf, tdf, hdf, geom, debug):
madplot.plot_tracked_trajectory(
ax,
tdf,
arena,
debug_plot=debug,
color='k',
)
ax.add_patch(arena.get_patch(color='k', alpha=0.1))
patch = arena.get_intersect_patch(geom,
fill=True,
color='r',
closed=True,
alpha=0.2)
if patch is not None:
ax.add_patch(patch)
BAG = '/mnt/strawscience/data/FlyMAD/velocity_test/2013-11-21-18-00-00.bag'
oid = 52251
smooth = True
smoothstr = {True:'smooth',False:'nosmooth'}[smooth]
arena = madplot.Arena('mm')
geom, dfs = madplot.load_bagfile(BAG, arena, smooth=smooth)
l_df = dfs["targeted"]
t_df = dfs["tracked"]
h_df = dfs["ttm"]
fig = plt.figure('traj %s' % smoothstr)
fig.suptitle('trajectory %s' % smoothstr)
ax = fig.add_subplot(1,1,1)
madplot.plot_tracked_trajectory(ax, t_df, arena, debug_plot=False)
ax.add_patch(arena.get_patch(fill=False))
ax.set_xlabel('position (%s)' % arena.unit)
fig.savefig('dorothea_pos_%s.png' % smoothstr)
df = t_df[t_df['tobj_id'] == oid].resample('100L')
#print df.iloc[39].index[0] - df.iloc[0]
times = [(ix - df.index[0]).total_seconds() for ix in df.index]
#print times[39]
######yyyyyyyyyy
fig = plt.figure('y %s' % smoothstr,figsize=(10,8))
fig.suptitle('y %s' % smoothstr)
def get_df(smooth):
smoothstr = {True:'smooth',False:'nosmooth'}[smooth]
geom, dfs = madplot.load_bagfile(BAG, arena, smooth=smooth)
t_df = dfs["tracked"]
fig = plt.figure('traj %s' % smoothstr)
fig.suptitle('trajectory %s' % smoothstr)
ax = fig.add_subplot(1,1,1)
madplot.plot_tracked_trajectory(ax, t_df, arena, debug_plot=False)
ax.add_patch(arena.get_patch(fill=False))
ax.set_xlabel('position (%s)' % arena.unit)
fig.savefig('testsmooth_mwb_pos_%s.png' % smoothstr)
df = t_df[t_df['tobj_id'] == oid]
#print df.iloc[39].index[0] - df.iloc[0]
times = [(ix - df.index[0]).total_seconds() for ix in df.index]
#print times[39]
######yyyyyyyyyy
fig = plt.figure('y %s' % smoothstr,figsize=(10,8))
fig.suptitle('y %s' % smoothstr)
ax = fig.add_subplot(2,2,1)
ax.set_title('position')
ax.plot(times, df['y_px'].values)
ax.set_ylabel('y (px/s)')
ax = fig.add_subplot(2,2,2)
ax.set_title('converted position')
ax.plot(times, df['y'].values)
ax.set_ylabel('y (%s)' % arena.unit)
ax = fig.add_subplot(2,2,3)
ax.set_title('velocity')
ax.plot(times, df['vy_px'].values)
ax.set_ylabel('y-velocity (px/s)')
ax = fig.add_subplot(2,2,4)
ax.set_title('converted velocity')
ax.plot(times, df['vy'].values)
ax.set_ylabel('y-velocity (%s/s)' % arena.unit)
fig.savefig('testsmooth_mwb_y_%s.png' % smoothstr)
######xxxxxxxxxxx
fig = plt.figure('x %s' % smoothstr,figsize=(10,8))
fig.suptitle('x %s' % smoothstr)
ax = fig.add_subplot(2,2,1)
ax.set_title('position')
ax.plot(times, df['x_px'].values)
ax.set_ylabel('x (px/s)')
ax = fig.add_subplot(2,2,2)
ax.set_title('converted position')
ax.plot(times, df['x'].values)
ax.set_ylabel('x (%s)' % arena.unit)
ax = fig.add_subplot(2,2,3)
ax.set_title('velocity')
ax.plot(times, df['vx_px'].values)
ax.set_ylabel('x-velocity (px/s)')
ax = fig.add_subplot(2,2,4)
ax.set_title('converted velocity')
ax.plot(times, df['vx'].values)
ax.set_ylabel('x-velocity (%s/s)' % arena.unit)
fig.savefig('testsmooth_mwb_x_%s.png' % smoothstr)
#######
df = t_df[t_df['tobj_id'] == oid].resample('500L')
times = [(ix - df.index[0]).total_seconds() for ix in df.index]
fig = plt.figure('speed %s' % smoothstr,figsize=(8,4))
fig.suptitle('speed %s' % smoothstr)
ax = fig.add_subplot(1,2,1)
ax.plot(times, df['v_px'].values)
ax.set_ylabel('speed (px/s)')
ax = fig.add_subplot(1,2,2)
ax.plot(times, df['v'].values)
ax.set_ylabel('speed (%s/s)' % arena.unit)
fig.savefig('testsmooth_mwb_speed_%s.png' % smoothstr)
return t_df[t_df['tobj_id'] == oid]
def plot_data(path, dat, debug_plot):
if os.path.isdir(path):
plotdir = path
conf = dat
else:
if path.endswith('.json'):
conf = json.load(open(path))
else:
conf = dat
plotdir = os.path.dirname(path)
plot_exps = conf.get('_plot', ['coupled'])
exps = [e for e in plot_exps if e in dat]
exps_colors = [plt.cm.gnuplot(i) for i in np.linspace(0, 1.0, len(exps))]
if '_ntrials' in conf:
ntrials = conf['_ntrials']
else:
#check all trials have the same number
trial_lens = set(map(len, (dat[e] for e in exps)))
if len(trial_lens) != 1:
raise Exception("experiments contain different numbers of trials")
ntrials = trial_lens.pop()
pct_in_area_total = {k: [] for k in exps}
pct_in_area_per_time = {k: [] for k in exps}
pct_in_area_per_time_lbls = {k: [] for k in exps}
latency_to_first_contact = {k: [] for k in exps}
velocity_outside_area = {k: [] for k in exps}
velocity_inside_area = {k: [] for k in exps}
path_length = {k: [] for k in exps}
pct_in_area_per_time_bins = range(0, 300, 30)
for exp in exps:
ordered_trials = dat[exp][0:ntrials]
gs = _get_plot_gs(ordered_trials)
fig = plt.figure("%s Trajectories" % exp.title(), figsize=(16, 8))
gsh = _get_plot_gs(ordered_trials)
figh = plt.figure("%s Trajectories (Hist)" % exp.title(),
figsize=(16, 8))
for i, trial in enumerate(ordered_trials):
label = trial['label']
ldf, tdf, hdf, geom = trial['data']
dbg_plot_title = " %s %s" % (exp, label)
print exp.title(), label
ax = fig.add_subplot(gs[i])
madplot.plot_tracked_trajectory(ax,
tdf,
arena,
debug_plot=debug_plot,
title=dbg_plot_title)
patch = arena.get_intersect_patch(geom,
fill=True,
color='r',
closed=True,
alpha=0.4,
zorder=9)
if patch is not None:
ax.add_patch(patch)
ax.add_patch(arena.get_patch(fill=False, color='k', zorder=10))
ok = tdf.dropna(axis=0, how='any', subset=['x', 'y'])
axh = figh.add_subplot(gs[i])
axh.set_aspect('equal')
xlim, ylim = arena.get_limits()
axh.set_xlim(*xlim)
axh.set_ylim(*ylim)
#manually scale the lognorm
lognorm = matplotlib.colors.LogNorm(vmin=1e-7, vmax=1e-3)
#(counts, xedges, yedges, Image)
_, _, _, _im = axh.hist2d(ok['x'],
ok['y'],
bins=30,
range=(xlim, ylim),
normed=True,
norm=lognorm)
l_f = matplotlib.ticker.LogFormatter(10, labelOnlyBase=False)
figh.colorbar(_im, ax=axh, format=l_f)
for _ax in (ax, axh):
_ax.set_title(label)
_ax.xaxis.set_visible(False)
_ax.yaxis.set_visible(False)
pct_in_area_per_time_lbls[exp].append(label)
pct_in_area_per_time[exp].append(
madplot.calculate_time_in_area(tdf, 300,
pct_in_area_per_time_bins))
pct_in_area_total[exp].append(
madplot.calculate_pct_in_area_per_objid_only_vals(tdf))
tts, vel_out, vel_in, path_l = madplot.calculate_latency_and_velocity_to_stay(
tdf,
holdtime=20,
tout_reset_time=2,
arena=arena,
geom=geom,
debug_plot=debug_plot,
title=dbg_plot_title)
latency_to_first_contact[exp].append(tts)
velocity_outside_area[exp].append(vel_out)
velocity_inside_area[exp].append(vel_in)
path_length[exp].append(path_l)
fig.savefig(os.path.join(plotdir, '%s_trajectories.png' % exp))
#need more colors
fig = plt.figure("%s Time in Area" % exp.title())
ax = fig.add_subplot(1, 1, 1)
colormap = plt.cm.gnuplot
ax.set_color_cycle(madplot.colors_hsv_circle(len(ordered_trials)))
for lbl, pct in zip(pct_in_area_per_time_lbls[exp],
pct_in_area_per_time[exp]):
mean = pct.mean(axis=0)
std = pct.mean(axis=0)
ax.plot(pct_in_area_per_time_bins,
mean,
linestyle='solid',
label=lbl)
ax.legend()
plt.savefig(os.path.join(plotdir, '%s_time.png' % exp))
ind = np.arange(ntrials) # the x locations for the groups
width = 1.0 / len(exps) # no gaps between
width -= (0.2 * width) # 20% gap
ticklabels = [str(i) for i in range(ntrials)]
#plot time in area percent
_plot_bar_and_line(pct_in_area_total, exps, 'Time in Area', 'Trial',
'Percentage of time spent in area', ind, width, ntrials,
ticklabels, exps_colors, 'timeinarea', plotdir)
#plot latency to first 20s in area
_plot_bar_and_line(latency_to_first_contact, exps,
'Latency to first 20s contact', 'Trial',
'Latency to first 20s contact', ind, width, ntrials,
ticklabels, exps_colors, 'latency', plotdir)
#velocity
_plot_bar_and_line(velocity_outside_area, exps, 'Velocity Outside Area',
'Trial', 'Velocity Outside Area', ind, width, ntrials,
ticklabels, exps_colors, 'velocity_out', plotdir)
_plot_bar_and_line(velocity_inside_area, exps, 'Velocity Inside Area',
'Trial', 'Velocity Inside Area', ind, width, ntrials,
ticklabels, exps_colors, 'velocity_in', plotdir)
#path length
_plot_bar_and_line(path_length, exps, 'Path Length', 'Trial',
'Path Length', ind, width, ntrials, ticklabels,
exps_colors, 'path_length', plotdir)
def prepare_data(path, arena, smoothstr, smooth, medfilt, gts):
pooldf = DataFrame()
for csvfile in sorted(glob.glob(path + "/*.csv")):
cache_args = os.path.basename(csvfile), arena, smoothstr
cache_fname = csvfile+'.madplot-cache'
results = madplot.load_bagfile_cache(cache_args, cache_fname)
if results is None:
results = flymad_analysis.load_and_smooth_csv(csvfile, arena, smooth)
if results is not None:
#update the cache
madplot.save_bagfile_cache(results, cache_args, cache_fname)
else:
print "skipping", csvfile
continue
df,dt,experimentID,date,time,genotype,laser,repID = results
#we plot head v thorax v nolaser (so for the same of plotting, consider
#these the genotypes
genotype = genotype + '-' + laser
if genotype not in gts:
print "\tskipping genotype", genotype
continue
if 0:
fig = plt.figure()
fig.suptitle(os.path.basename(csvfile))
ax = fig.add_subplot(1,1,1)
df['experiment'] = 1
df['tobj_id'] = 1
madplot.plot_tracked_trajectory(ax, df, arena,
debug_plot=False,
color='k',
)
ax.add_patch(arena.get_patch(color='k', alpha=0.1))
duration = (df.index[-1] - df.index[0]).total_seconds()
if duration < EXPERIMENT_DURATION:
print "\tmissing data", csvfilefn
continue
print "\t%ss experiment" % duration
#MAXIMUM SPEED = 300:
df['v'][df['v'] >= 300] = np.nan
df['v'] = df['v'].fillna(method='ffill')
try:
df = flymad_analysis.align_t_by_laser_on(
df, min_experiment_duration=EXPERIMENT_DURATION,
align_first_only=True,
exact_num_ranges=1)
except flymad_analysis.AlignError, err:
print "\talign error %s (%s)" % (csvfile, err)
continue
#median filter
if medfilt:
df['v'] = scipy.signal.medfilt(df['v'].values, medfilt)
df['obj_id'] = flymad_analysis.create_object_id(date,time)
df['Genotype'] = genotype
df['lasergroup'] = laser
pooldf = pd.concat([pooldf, df])
def prepare_data(path, arena, smoothstr, smooth, medfilt, gts):
pooldf = DataFrame()
for csvfile in sorted(glob.glob(path + "/*.csv")):
cache_args = os.path.basename(csvfile), arena, smoothstr
cache_fname = csvfile + '.madplot-cache'
results = madplot.load_bagfile_cache(cache_args, cache_fname)
if results is None:
results = flymad_analysis.load_and_smooth_csv(
csvfile, arena, smooth)
if results is not None:
#update the cache
madplot.save_bagfile_cache(results, cache_args, cache_fname)
else:
print "skipping", csvfile
continue
df, dt, experimentID, date, time, genotype, laser, repID = results
#we plot head v thorax v nolaser (so for the same of plotting, consider
#these the genotypes
genotype = genotype + '-' + laser
if genotype not in gts:
print "\tskipping genotype", genotype
continue
if 0:
fig = plt.figure()
fig.suptitle(os.path.basename(csvfile))
ax = fig.add_subplot(1, 1, 1)
df['experiment'] = 1
df['tobj_id'] = 1
madplot.plot_tracked_trajectory(
ax,
df,
arena,
debug_plot=False,
color='k',
)
ax.add_patch(arena.get_patch(color='k', alpha=0.1))
duration = (df.index[-1] - df.index[0]).total_seconds()
if duration < EXPERIMENT_DURATION:
print "\tmissing data", csvfilefn
continue
print "\t%ss experiment" % duration
#MAXIMUM SPEED = 300:
df['v'][df['v'] >= 300] = np.nan
df['v'] = df['v'].fillna(method='ffill')
try:
df = flymad_analysis.align_t_by_laser_on(
df,
min_experiment_duration=EXPERIMENT_DURATION,
align_first_only=True,
exact_num_ranges=1)
except flymad_analysis.AlignError, err:
print "\talign error %s (%s)" % (csvfile, err)
continue
#median filter
if medfilt:
df['v'] = scipy.signal.medfilt(df['v'].values, medfilt)
df['obj_id'] = flymad_analysis.create_object_id(date, time)
df['Genotype'] = genotype
df['lasergroup'] = laser
pooldf = pd.concat([pooldf, df])
def plot_data(path, dat, debug_plot):
if os.path.isdir(path):
plotdir = path
conf = dat
else:
if path.endswith('.json'):
conf = json.load(open(path))
else:
conf = dat
plotdir = os.path.dirname(path)
plot_exps = conf.get('_plot', ['coupled'])
exps = [e for e in plot_exps if e in dat]
exps_colors = [plt.cm.gnuplot(i) for i in np.linspace(0, 1.0, len(exps))]
if '_ntrials' in conf:
ntrials = conf['_ntrials']
else:
#check all trials have the same number
trial_lens = set(map(len, (dat[e] for e in exps)))
if len(trial_lens) != 1:
raise Exception("experiments contain different numbers of trials")
ntrials = trial_lens.pop()
pct_in_area_total = {k:[] for k in exps}
pct_in_area_per_time = {k:[] for k in exps}
pct_in_area_per_time_lbls = {k:[] for k in exps}
latency_to_first_contact = {k:[] for k in exps}
velocity_outside_area = {k:[] for k in exps}
velocity_inside_area = {k:[] for k in exps}
path_length = {k:[] for k in exps}
pct_in_area_per_time_bins = range(0,300,30)
for exp in exps:
ordered_trials = dat[exp][0:ntrials]
gs = _get_plot_gs(ordered_trials)
fig = plt.figure("%s Trajectories" % exp.title(), figsize=(16,8))
gsh = _get_plot_gs(ordered_trials)
figh = plt.figure("%s Trajectories (Hist)" % exp.title(), figsize=(16,8))
for i,trial in enumerate(ordered_trials):
label = trial['label']
ldf, tdf, hdf, geom = trial['data']
dbg_plot_title = " %s %s" % (exp,label)
print exp.title(), label
ax = fig.add_subplot(gs[i])
madplot.plot_tracked_trajectory(ax, tdf, arena, debug_plot=debug_plot, title=dbg_plot_title)
patch = arena.get_intersect_patch(geom, fill=True, color='r', closed=True, alpha=0.4, zorder=9)
if patch is not None:
ax.add_patch(patch)
ax.add_patch(arena.get_patch(fill=False, color='k', zorder=10))
ok = tdf.dropna(axis=0,how='any',subset=['x','y'])
axh = figh.add_subplot(gs[i])
axh.set_aspect('equal')
xlim,ylim = arena.get_limits()
axh.set_xlim(*xlim)
axh.set_ylim(*ylim)
#manually scale the lognorm
lognorm = matplotlib.colors.LogNorm(vmin=1e-7, vmax=1e-3)
#(counts, xedges, yedges, Image)
_,_,_,_im = axh.hist2d(ok['x'],ok['y'],bins=30,range=(xlim,ylim), normed=True, norm=lognorm)
l_f = matplotlib.ticker.LogFormatter(10, labelOnlyBase=False)
figh.colorbar(_im,ax=axh,format=l_f)
for _ax in (ax,axh):
_ax.set_title(label)
_ax.xaxis.set_visible(False)
_ax.yaxis.set_visible(False)
pct_in_area_per_time_lbls[exp].append( label )
pct_in_area_per_time[exp].append(
madplot.calculate_time_in_area(tdf, 300,
pct_in_area_per_time_bins))
pct_in_area_total[exp].append(
madplot.calculate_pct_in_area_per_objid_only_vals(tdf))
tts, vel_out, vel_in, path_l = madplot.calculate_latency_and_velocity_to_stay(
tdf, holdtime=20,
tout_reset_time=2, arena=arena, geom=geom,
debug_plot=debug_plot, title=dbg_plot_title)
latency_to_first_contact[exp].append(tts)
velocity_outside_area[exp].append(vel_out)
velocity_inside_area[exp].append(vel_in)
path_length[exp].append(path_l)
fig.savefig(os.path.join(plotdir,'%s_trajectories.png' % exp))
#need more colors
fig = plt.figure("%s Time in Area" % exp.title())
ax = fig.add_subplot(1,1,1)
colormap = plt.cm.gnuplot
ax.set_color_cycle(madplot.colors_hsv_circle(len(ordered_trials)))
for lbl,pct in zip(pct_in_area_per_time_lbls[exp], pct_in_area_per_time[exp]):
mean = pct.mean(axis=0)
std = pct.mean(axis=0)
ax.plot(pct_in_area_per_time_bins, mean, linestyle='solid', label=lbl)
ax.legend()
plt.savefig(os.path.join(plotdir,'%s_time.png' % exp))
ind = np.arange(ntrials) # the x locations for the groups
width = 1.0 / len(exps) # no gaps between
width -= (0.2 * width) # 20% gap
ticklabels = [str(i) for i in range(ntrials)]
#plot time in area percent
_plot_bar_and_line(pct_in_area_total, exps,
'Time in Area', 'Trial', 'Percentage of time spent in area',
ind, width, ntrials,
ticklabels, exps_colors,
'timeinarea', plotdir)
#plot latency to first 20s in area
_plot_bar_and_line(latency_to_first_contact, exps,
'Latency to first 20s contact', 'Trial', 'Latency to first 20s contact',
ind, width, ntrials,
ticklabels, exps_colors,
'latency', plotdir)
#velocity
_plot_bar_and_line(velocity_outside_area, exps,
'Velocity Outside Area', 'Trial', 'Velocity Outside Area',
ind, width, ntrials,
ticklabels, exps_colors,
'velocity_out', plotdir)
_plot_bar_and_line(velocity_inside_area, exps,
'Velocity Inside Area', 'Trial', 'Velocity Inside Area',
ind, width, ntrials,
ticklabels, exps_colors,
'velocity_in', plotdir)
#path length
_plot_bar_and_line(path_length, exps,
'Path Length', 'Trial', 'Path Length',
ind, width, ntrials,
ticklabels, exps_colors,
'path_length', plotdir)
BAG = '/mnt/strawscience/data/FlyMAD/velocity_test/2013-11-21-18-00-00.bag'
oid = 52251
smooth = True
smoothstr = {True: 'smooth', False: 'nosmooth'}[smooth]
arena = madplot.Arena('mm')
geom, dfs = madplot.load_bagfile(BAG, arena, smooth=smooth)
l_df = dfs["targeted"]
t_df = dfs["tracked"]
h_df = dfs["ttm"]
fig = plt.figure('traj %s' % smoothstr)
fig.suptitle('trajectory %s' % smoothstr)
ax = fig.add_subplot(1, 1, 1)
madplot.plot_tracked_trajectory(ax, t_df, arena, debug_plot=False)
ax.add_patch(arena.get_patch(fill=False))
ax.set_xlabel('position (%s)' % arena.unit)
fig.savefig('dorothea_pos_%s.png' % smoothstr)
df = t_df[t_df['tobj_id'] == oid].resample('100L')
#print df.iloc[39].index[0] - df.iloc[0]
times = [(ix - df.index[0]).total_seconds() for ix in df.index]
#print times[39]
######yyyyyyyyyy
fig = plt.figure('y %s' % smoothstr, figsize=(10, 8))
fig.suptitle('y %s' % smoothstr)
