def plot_forces_on_init(session_folder, save=None):
LogDf = bhv.get_LogDf_from_path(session_folder / "arduino_log.txt")
### LoadCell Data
LoadCellDf = bhv.parse_bonsai_LoadCellData(session_folder / 'bonsai_LoadCellData.csv')
# Syncer
from Utils import sync
lc_sync_event = sync.parse_harp_sync(session_folder / 'bonsai_harp_sync.csv', trig_len=100, ttol=5)
arduino_sync_event = sync.get_arduino_sync(session_folder / 'arduino_log.txt')
Sync = sync.Syncer()
Sync.data['arduino'] = arduino_sync_event['t'].values
Sync.data['loadcell'] = lc_sync_event['t'].values
Sync.sync('arduino','loadcell')
LogDf['t_orig'] = LogDf['t']
LogDf['t'] = Sync.convert(LogDf['t'].values, 'arduino', 'loadcell')
# preprocessing
samples = 10000 # 10s buffer: harp samples at 1khz, arduino at 100hz, LC controller has 1000 samples in buffer
LoadCellDf['x'] = LoadCellDf['x'] - LoadCellDf['x'].rolling(samples).mean()
LoadCellDf['y'] = LoadCellDf['y'] - LoadCellDf['y'].rolling(samples).mean()
# plot forces
times = LogDf.groupby('name').get_group('TRIAL_ENTRY_EVENT')['t'].values
pre, post = -1000, 1000
fig, axes = plt.subplots(nrows=2,sharex=True,sharey=False)
x_avgs = []
y_avgs = []
for i,t in enumerate(tqdm(times)):
Df = bhv.time_slice(LoadCellDf, t+pre, t+post, reset_index=False)
# these colors need to be thorougly checked
axes[0].plot(Df['t'].values - t, Df['x'])
axes[1].plot(Df['t'].values - t, Df['y'])
x_avgs.append(Df['x'].values)
y_avgs.append(Df['y'].values)
x_avgs = np.average(np.array(x_avgs),axis=0)
y_avgs = np.average(np.array(y_avgs),axis=0)
tvec = np.linspace(pre,post,x_avgs.shape[0])
axes[0].plot(tvec, x_avgs, color='k',lw=2)
axes[1].plot(tvec, y_avgs, color='k',lw=2)
kws = dict(linestyle=':',lw=1, alpha=0.8, color='k')
for ax in axes:
ax.axhline(-500, **kws)
ax.axvline(0, **kws)
# deco
Session = utils.Session(session_folder)
Animal = utils.Animal(session_folder.parent)
title = ' - '.join([Animal.display(), Session.date, 'day: %s'% Session.day])
for ax in axes:
ax.set_ylim(-2500,2500)
ax.set_ylabel('Force [au]')
axes[1].set_xlabel('time (ms)')
sns.despine(fig)
fig.suptitle(title)
fig.tight_layout()
fig.subplots_adjust(top=0.9)
if save is not None:
os.makedirs(session_folder / 'plots', exist_ok=True)
plt.savefig(save, dpi=600)
plt.close(fig)
LogDf = bhv.get_LogDf_from_path(log_path)
# %% Syncer
lc_sync_event = sync.parse_harp_sync(folder / 'bonsai_harp_sync.csv',trig_len=100, ttol=4)
arduino_sync_event = sync.get_arduino_sync(folder / 'arduino_log.txt')
Sync = Syncer()
Sync.data['arduino'] = arduino_sync_event['t'].values
Sync.data['loadcell'] = lc_sync_event['t'].values
Sync.sync('arduino','loadcell')
# DlcDf['t'] = Sync.convert(DlcDf.index.values, 'dlc', 'arduino')
# %% Loadcell
csv_path = log_path.parent / "bonsai_LoadCellData.csv"
LoadCellDf = bhv.parse_bonsai_LoadCellData(csv_path)
# %%
LogDf['t_original'] = LogDf['t']
LogDf['t'] = Sync.convert(LogDf['t'].values,'arduino','loadcell')
# %% median removal for loadcelldata
# %% median correction
samples = 10000 # 10s buffer: harp samples at 1khz, arduino at 100hz, LC controller has 1000 samples in buffer
LoadCellDf['x'] = LoadCellDf['x'] - LoadCellDf['x'].rolling(samples).median()
LoadCellDf['y'] = LoadCellDf['y'] - LoadCellDf['y'].rolling(samples).median()
# %%
Spans = bhv.get_spans_from_names(LogDf, "REWARD_RIGHT_VALVE_ON", "REWARD_RIGHT_VALVE_OFF")
N = 2000
Data = sp.zeros((N,27,2))
####### ####### # # ###### ### # # ##### """ fd_path = utils.get_folder_dialog(initial_dir="/media/storage/shared-paton/georg/mesoscope_testings/behavior") # Video data video_path = fd_path / "bonsai_video.avi" Vid = dlc_utils.read_video(str(video_path)) # Arduino data log_path = fd_path / 'arduino_log.txt' LogDf = bhv.get_LogDf_from_path(log_path) # LoadCell data LoadCellDf = bhv.parse_bonsai_LoadCellData(fd_path / 'bonsai_LoadCellData.csv') # Moving average mean subtraction samples = 1000 # ms LoadCellDf['x'] = LoadCellDf['x'] - LoadCellDf['x'].rolling(samples).mean() LoadCellDf['y'] = LoadCellDf['y'] - LoadCellDf['y'].rolling(samples).mean() ## Synching from Utils import sync # Parse sync events/triggers cam_sync_event = sync.parse_cam_sync(fd_path / 'bonsai_frame_stamps.csv') lc_sync_event = sync.parse_harp_sync(fd_path / 'bonsai_harp_sync.csv') arduino_sync_event = sync.get_arduino_sync(fd_path / 'arduino_log.txt') # Get the values out of them
def plot_init_hist(session_folder, save=None):
LogDf = bhv.get_LogDf_from_path(session_folder / "arduino_log.txt")
# Sync first
loadcell_sync_event = sync.parse_harp_sync(session_folder /
'bonsai_harp_sync.csv',
trig_len=100,
ttol=5)
arduino_sync_event = sync.get_arduino_sync(session_folder /
'arduino_log.txt')
Sync = sync.Syncer()
Sync.data['arduino'] = arduino_sync_event['t'].values
Sync.data['loadcell'] = loadcell_sync_event['t'].values
success = Sync.sync('arduino', 'loadcell')
# abort if sync fails
if not success:
utils.printer(
"trying to plot_init_hist, but failed to sync in file %s, - aborting"
% session_folder)
return None
LogDf['t_orig'] = LogDf['t']
LogDf['t'] = Sync.convert(LogDf['t'].values, 'arduino', 'loadcell')
LoadCellDf = bhv.parse_bonsai_LoadCellData(session_folder /
'bonsai_LoadCellData.csv')
# preprocessing
samples = 10000 # 10s buffer: harp samples at 1khz, arduino at 100hz, LC controller has 1000 samples in buffer
LoadCellDf['x'] = LoadCellDf['x'] - LoadCellDf['x'].rolling(samples).mean()
LoadCellDf['y'] = LoadCellDf['y'] - LoadCellDf['y'].rolling(samples).mean()
# smoothing forces
F = LoadCellDf[['x', 'y']].values
w = np.ones(100)
F[:, 0] = np.convolve(F[:, 0], w, mode='same')
F[:, 1] = np.convolve(F[:, 1], w, mode='same')
# detect pushes
th = 500
L = F < -th
events = np.where(np.diff(np.logical_and(L[:, 0], L[:, 1])) == 1)[0]
times = [LoadCellDf.iloc[int(i)]['t'] for i in events]
# histogram of pushes pre vs pushes post trial available
trial_times = bhv.get_events_from_name(LogDf,
'TRIAL_AVAILABLE_EVENT')['t'].values
post = []
pre = []
for t in trial_times:
dt = times - t
try:
post.append(np.min(dt[dt > 0]))
except ValueError:
# thrown when no more pushes after last init
pass
try:
pre.append(np.min(-1 * dt[dt < 0]))
except ValueError:
# thrown when no pushes before first init
pass
fig, axes = plt.subplots()
bins = np.linspace(0, 5000, 25)
axes.hist(pre, bins=bins, alpha=0.5, label='pre')
axes.hist(post, bins=bins, alpha=0.5, label='post')
axes.set_xlabel('time (ms)')
axes.set_ylabel('count')
axes.legend()
Session = utils.Session(session_folder)
Animal = utils.Animal(session_folder.parent)
title = ' - '.join(
[Animal.display(), Session.date,
'day: %s' % Session.day])
sns.despine(fig)
fig.suptitle(title)
fig.tight_layout()
fig.subplots_adjust(top=0.85)
if save is not None:
os.makedirs(save.parent, exist_ok=True)
plt.savefig(save, dpi=600)
plt.close(fig)
## DeepLabCut data #h5_path = utils.get_file_dialog() #DlcDf = read_dlc_h5(h5_path) #bodyparts = sp.unique([j[0] for j in DlcDf.columns[1:]]) # all body parts # Video data #video_path = fd_path / "bonsai_video.avi" #Vid = dlc_utils.read_video(str(video_path)) # Arduino data log_path = fd_path / 'arduino_log.txt' LogDf = bhv.get_LogDf_from_path(log_path) # LoadCell data LoadCellDf = bhv.parse_bonsai_LoadCellData(fd_path / 'bonsai_LoadCellData.csv') ## Synching from Utils import sync # Parse sync events/triggers cam_sync_event = sync.parse_cam_sync(fd_path / 'bonsai_frame_stamps.csv') lc_sync_event = sync.parse_harp_sync(fd_path / 'bonsai_harp_sync.csv') arduino_sync_event = sync.get_arduino_sync(fd_path / 'arduino_log.txt') # Get the values out of them Sync = sync.Syncer() Sync.data['arduino'] = arduino_sync_event['t'].values Sync.data['loadcell'] = lc_sync_event['t'].values #Sync.data['dlc'] = cam_sync_event.index.values # the frames are the DLC