def update(i):
Frame = dlc.get_frame(Vid,i)
im.set_data(Frame)
# frame counter
frame_counter.set_text("frame: %i - %i/%i" % (i, ix.index(i),len(ix)))
t_abs = Sync.convert(i,'dlc','arduino') / 1000
m = Sync.pairs[('dlc','arduino')][0]
t_rel = (ix.index(i) * m) / 1000
time_counter.set_text("time: %.2f - %.2f/%.2f" % (t_abs, t_rel,len(ix)*m/1000))
# body parts
for j, bp in enumerate(bodyparts):
data = DlcDfSlice[bp].loc[i]
if data['likelihood'] > p:
bp_markers[j].set_data(data['x'], data['y'])
else:
bp_markers[j].set_data(sp.nan, sp.nan)
# trace
for j, bp in enumerate(bodyparts):
i0 = i - n_segments*trace_len
data = DlcDfSlice[bp].loc[i0:i]
data.loc[data['likelihood'] < p] = sp.nan
data = data[['x','y']].values[::-1,:]
segments = bp_traces[j].get_segments()
for k in range(n_segments):
try:
segments[-k] = data[k*trace_len-5:(k+1)*trace_len+5,:]
except:
pass
bp_traces[j].set_segments(segments)
for j, event in enumerate(display_events):
t = Sync.convert(i, 'dlc', 'arduino')
if sp.any(sp.logical_and(t > event_times[j], t < event_times[j] + event_display_dur)):
event_texts[j].set_color(event_colors[event])
else:
event_texts[j].set_color(inactive_color)
return (im, frame_counter, time_counter) + tuple(bp_markers) + tuple(bp_traces) + tuple(event_texts)
def make_annotated_video(Vid, t_on, t_off, LogDf, DlcDf, fps=20, save=None):
LogDfSlice = bhv.time_slice(LogDf, t_on, t_off)
DlcDfSlice = bhv.time_slice(DlcDf, t_on, t_off)
# what events to display
display_events = list(LogDfSlice.name.unique())
# display_events = ['GO_CUE_SHORT_EVENT', 'GO_CUE_LONG_EVENT', 'CHOICE_CORRECT_EVENT', 'CHOICE_INCORRECT_EVENT', 'REWARD_LEFT_EVENT','REWARD_RIGHT_EVENT', 'REACH_LEFT_ON', 'REACH_LEFT_OFF', 'REACH_RIGHT_ON', 'REACH_RIGHT_OFF']
if sp.nan in display_events:
display_events.remove(np.nan)
frame_on = DlcDfSlice.index[0]
frame_off = DlcDfSlice.index[-1]
ix = list(range(frame_on, frame_off))
# plotting
fig, ax = plt.subplots()
ax.axis('off')
if save is not None:
import matplotlib as mpl
# from matplotlib.animation import FFMpegWriter as AniWriter
# Writer = AniWriter(fps=20, bitrate=7500, codec="h264", extra_args=['-pix_fmt','yuv420p'])
# Writer = AniWriter(fps=20, bitrate=10000, codec="h264")
from matplotlib.animation import FFMpegFileWriter as AniWriter
Writer = AniWriter(fps=fps, codec="h264", bitrate=-1)
mpl.rcParams['animation.ffmpeg_path'] = "/usr/bin/ffmpeg"
# image
ax.set_aspect('equal')
frame = dlc.get_frame(Vid, ix[0])
im = ax.imshow(frame, cmap='gray')
# body parts
bp_left = [bp for bp in bodyparts if bp.endswith('L')]
bp_right = [bp for bp in bodyparts if bp.endswith('R')]
c_l = sns.color_palette('viridis', n_colors=len(bp_left))
c_r = sns.color_palette('magma', n_colors=len(bp_right))
bp_cols = dict(zip(bp_left+bp_right,c_l+c_r))
bp_markers = []
for i, bp in enumerate(bodyparts):
marker, = ax.plot([],[], 'o', color=bp_cols[bp], markersize=10)
bp_markers.append(marker)
# traces
from matplotlib.collections import LineCollection
n_segments = 10
trace_len = 3
lws = sp.linspace(0,5,n_segments)
bp_traces = []
for i, bp in enumerate(bodyparts):
segments = []
for j in range(n_segments):
segment = sp.zeros((trace_len,2))
segments.append(segment)
lc = LineCollection(sp.array(segments),linewidths=lws,color=bp_cols[bp], alpha=0.75)
bp_traces.append(lc)
ax.add_artist(lc)
p = 0.0
# frame text
inactive_color = 'white'
frame_counter = ax.text(5, frame.shape[0]-25, '', color=inactive_color)
time_counter = ax.text(5, frame.shape[0]-5, '', color=inactive_color)
# event text annotations
# color setup
c = sns.color_palette('husl', n_colors=len(display_events))
event_colors = dict(zip(display_events,c))
event_display_dur = 50 # ms
event_texts = []
event_times = []
for i, event in enumerate(display_events):
# times
try:
times = LogDfSlice.groupby('name').get_group(event)['t'].values
except KeyError:
times = [np.nan]
event_times.append(times)
# plot
# bg_text = ax.text(10, i*20 + 20, event, color='black', fontweight='heavy', fontsize=6)
text = ax.text(10, i*20 + 20, event, color=inactive_color, fontweight='heavy', fontsize=6)
event_texts.append(text)
fig.tight_layout()
# the animation function
def update(i):
Frame = dlc.get_frame(Vid,i)
im.set_data(Frame)
# frame counter
frame_counter.set_text("frame: %i - %i/%i" % (i, ix.index(i),len(ix)))
t_abs = Sync.convert(i,'dlc','arduino') / 1000
m = Sync.pairs[('dlc','arduino')][0]
t_rel = (ix.index(i) * m) / 1000
time_counter.set_text("time: %.2f - %.2f/%.2f" % (t_abs, t_rel,len(ix)*m/1000))
# body parts
for j, bp in enumerate(bodyparts):
data = DlcDfSlice[bp].loc[i]
if data['likelihood'] > p:
bp_markers[j].set_data(data['x'], data['y'])
else:
bp_markers[j].set_data(sp.nan, sp.nan)
# trace
for j, bp in enumerate(bodyparts):
i0 = i - n_segments*trace_len
data = DlcDfSlice[bp].loc[i0:i]
data.loc[data['likelihood'] < p] = sp.nan
data = data[['x','y']].values[::-1,:]
segments = bp_traces[j].get_segments()
for k in range(n_segments):
try:
segments[-k] = data[k*trace_len-5:(k+1)*trace_len+5,:]
except:
pass
bp_traces[j].set_segments(segments)
for j, event in enumerate(display_events):
t = Sync.convert(i, 'dlc', 'arduino')
if sp.any(sp.logical_and(t > event_times[j], t < event_times[j] + event_display_dur)):
event_texts[j].set_color(event_colors[event])
else:
event_texts[j].set_color(inactive_color)
return (im, frame_counter, time_counter) + tuple(bp_markers) + tuple(bp_traces) + tuple(event_texts)
ani = FuncAnimation(fig, update, frames=ix, blit=True, interval=1)
if save is not None:
utils.printer("saving video to %s" % save, 'msg')
ani.save(save, writer=Writer)
plt.close(fig)
fig.tight_layout()
# %%
"""
.########..##........#######..########.########.####.##....##..######..
.##.....##.##.......##.....##....##.......##.....##..###...##.##....##.
.##.....##.##.......##.....##....##.......##.....##..####..##.##.......
.########..##.......##.....##....##.......##.....##..##.##.##.##...####
.##........##.......##.....##....##.......##.....##..##..####.##....##.
.##........##.......##.....##....##.......##.....##..##...###.##....##.
.##........########..#######.....##.......##....####.##....##..######..
"""
# %% plot a single frame with DLC markers and Skeleton
fig, axes = plt.subplots()
i = 8000 # frame index
Frame = dlc_utils.get_frame(Vid, i)
axes = dlc_utils.plot_frame(Frame, axes=axes)
axes = dlc_utils.plot_bodyparts(bodyparts, DlcDf, i, axes=axes)
# %% plot a heatmap of movement for both paws on a 2D background
fig, axes = plt.subplots()
i = 4000 # frame index
Frame = dlc_utils.get_frame(Vid, i)
axes = dlc_utils.plot_frame(Frame, axes=axes)
axes = dlc_utils.plot_trajectories(DlcDf, paws, axes=axes,lw=0.025)
axes.axis('off')
axes.set_title('Whole session heatmap of paw placement')
plt.savefig(plot_dir / ('heatmap_both_paws.png'), dpi=600)
# trial selection # SDf = bhv.groupby_dict(SessionDf, dict(outcome='correct', correct_side='left')) # TrialDf = TrialDfs[SDf.index[0]] TrialDf = TrialDfs[0] Df = bhv.event_slice(TrialDf, 'TRIAL_ENTRY_EVENT', 'ITI_STATE') t_on = Df.iloc[0]['t'] t_off = Df.iloc[-1]['t'] # %% static image with trajectory between t_on and t_off bp_cols = make_bodypart_colors(bodyparts) fig, axes = plt.subplots() frame_ix = Sync.convert(t_on, 'arduino', 'dlc') frame = dlc.get_frame(Vid, frame_ix) dlc.plot_frame(frame, axes=axes) dlc.plot_bodyparts(bodyparts, DlcDf, frame_ix, colors=bp_cols, axes=axes) # trajectory DlcDfSlice = bhv.time_slice(DlcDf, t_on, t_off) dlc.plot_trajectories(DlcDfSlice, bodyparts, axes=axes, colors=bp_cols, lw=1, p=0.99) # %% plot all of the selected trial type # SDf = bhv.groupby_dict(SessionDf, dict(outcome='correct', correct_side='right', paw_resting=False)) # trial selection SDf = bhv.groupby_dict(SessionDf, dict(has_choice=True, correct_side='left', outcome='correct')) # plot some random frame
fig, axes = plt.subplots(figsize=(4,3))
for i in np.arange(0,n_clusters+1):
axes.scatter(i, np.sum(code == i))
axes.set_ylabel('No. neurons')
axes.set_xlabel('No. of clusters')
axes.set_title('How many neurons belong to each cluster')
fig.tight_layout()
# plots image of brain
neural_vid_path = neural_data_path / "reconstructed.avi"
neural_vid = dlc_utils.read_video(str(neural_vid_path))
fig, axes = plt.subplots(figsize=(5,5))
frame_ix = 10
frame = dlc_utils.get_frame(neural_vid, frame_ix)
dlc_utils.plot_frame(frame, axes=axes)
# for each cluster plot pixel color coded by cluster
for i in np.arange(0,n_clusters+1):
cluster_idxs = code == i
axes.scatter( neuron_coords[cluster_idxs,0],neuron_coords[cluster_idxs,1], s = 1,
alpha = 0.75, label = i)
axes.legend(frameon=False, bbox_to_anchor=(1,1), title = 'Cluster No.')
fig.tight_layout()
# %% Defining groups based on brain areas
# %% Neurons significantly modulated by events