def notify(self, key):
import seaborn as sns
y_shifts, x_shifts = (MotionCorrection() & key).fetch1(
'y_shifts', 'x_shifts')
fps, is_slow_stack = (StackInfo.ROI() & key).fetch1('fps', 'is_slow')
num_slices, num_frames = y_shifts.shape
fps = fps * (num_slices if is_slow_stack else 1)
seconds = np.arange(num_frames) / fps
with sns.axes_style('white'):
fig, axes = plt.subplots(2,
1,
figsize=(13, 10),
sharex=True,
sharey=True)
axes[0].set_title('Shifts in y for all slices')
axes[0].set_ylabel('Pixels')
axes[0].plot(seconds, y_shifts.T)
axes[1].set_title('Shifts in x for all slices')
axes[1].set_ylabel('Pixels')
axes[1].set_xlabel('Seconds')
axes[1].plot(seconds, x_shifts.T)
fig.tight_layout()
img_filename = '/tmp/' + key_hash(key) + '.png'
fig.savefig(img_filename)
plt.close(fig)
sns.reset_orig()
msg = 'MotionCorrection for `{}` has been populated.'.format(key)
(notify.SlackUser() & (experiment.Session() & key)).notify(
msg, file=img_filename, file_title='motion shifts')
def notify(self, key, frames):
import imageio
msg = 'Eye for `{}` has been populated. You can add a tracking task now. '.format(key)
img_filename = '/tmp/' + key_hash(key) + '.gif'
frames = frames.transpose([2, 0, 1])
frames = [imresize(img, 0.25) for img in frames]
imageio.mimsave(img_filename, frames, duration=0.5)
(notify.SlackUser() & (experiment.Session() & key)).notify(msg, file=img_filename,
file_title='preview frames')
def notify(self, key, frames):
import imageio
video_filename = '/tmp/' + key_hash(key) + '.gif'
frames = [imresize(img, 0.25) for img in frames.transpose([2, 0, 1])]
imageio.mimsave(video_filename, frames, duration=0.5)
msg = 'eye frames for {animal_id}-{session}-{scan_idx}'.format(**key)
slack_user = notify.SlackUser() & (experiment.Session() & key)
slack_user.notify(file=video_filename, file_title=msg, channel='#pipeline_quality')
def notify(self, key, frames):
import imageio
video_filename = '/tmp/' + key_hash(key) + '.gif'
frames = [imresize(img, 0.25) for img in frames.transpose([2, 0, 1])]
imageio.mimsave(video_filename, frames, duration=0.5)
msg = 'posture frames for {animal_id}-{session}-{scan_idx}'.format(**key)
slack_user = notify.SlackUser() & (experiment.Session() & key)
slack_user.notify(file=video_filename, file_title=msg, channel='#pipeline_quality')
def notify(self, key):
import imageio
volume = (self & key).get_stack(channel=key['channel'])
volume = volume[::int(volume.shape[0] / 8)] # volume at 8 diff depths
video_filename = '/tmp/' + key_hash(key) + '.gif'
imageio.mimsave(video_filename, float2uint8(volume), duration=1)
msg = 'CorrectedStack for {} has been populated.'.format(key)
(notify.SlackUser() & (experiment.Session() & key)).notify(
msg, file=video_filename, file_title='stitched ROI')
def temporary_image(array, key):
import matplotlib
matplotlib.rcParams['backend'] = 'Agg'
import matplotlib.pyplot as plt
import seaborn as sns
with sns.axes_style('white'):
plt.matshow(array, cmap='gray')
plt.axis('off')
filename = '/tmp/' + key_hash(key) + '.png'
plt.savefig(filename)
sns.reset_orig()
return filename
def temporary_image(array, key):
import matplotlib
matplotlib.rcParams['backend'] = 'Agg'
import matplotlib.pyplot as plt
import seaborn as sns
with sns.axes_style('white'):
plt.matshow(array, cmap='gray')
plt.axis('off')
filename = '/tmp/' + key_hash(key) + '.png'
plt.savefig(filename)
sns.reset_orig()
return filename
def notify(self, key):
time, velocity = (self & key).fetch1('treadmill_time', 'treadmill_vel')
fig = plt.figure()
plt.plot(time, velocity)
plt.ylabel('Treadmill velocity (cm/sec)')
plt.xlabel('Seconds')
img_filename = '/tmp/' + key_hash(key) + '.png'
fig.savefig(img_filename)
plt.close(fig)
msg = 'treadmill velocity for {animal_id}-{session}-{scan_idx}'.format(
**key)
slack_user = notify.SlackUser() & (experiment.Session() & key)
slack_user.notify(file=img_filename, file_title=msg)
def notify(self, key):
import matplotlib.pyplot as plt
time, velocity = (self & key).fetch1('treadmill_time', 'treadmill_vel')
fig = plt.figure()
plt.plot(time, velocity)
plt.ylabel('Treadmill velocity (cm/sec)')
plt.xlabel('Seconds')
img_filename = '/tmp/' + key_hash(key) + '.png'
fig.savefig(img_filename)
plt.close(fig)
msg = 'treadmill velocity for {animal_id}-{session}-{scan_idx}'.format(**key)
slack_user = notify.SlackUser() & (experiment.Session() & key)
slack_user.notify(file=img_filename, file_title=msg)
def notify(self, key, summary_frames, mean_intensities, contrasts):
""" Sends slack notification for a single slice + channel combination. """
# Send summary frames
import imageio
video_filename = '/tmp/' + key_hash(key) + '.gif'
percentile_99th = np.percentile(summary_frames, 99.5)
summary_frames = np.clip(summary_frames, None, percentile_99th)
summary_frames = float2uint8(summary_frames).transpose([2, 0, 1])
imageio.mimsave(video_filename, summary_frames, duration=0.4)
msg = 'Quality for `{}` has been populated.'.format(key)
(notify.SlackUser() & (experiment.Session() & key)).notify(
msg, file=video_filename, file_title='summary frames')
# Send intensity and contrasts
figsize = (min(4, contrasts.shape[1] / 10 + 1),
contrasts.shape[0] / 30 + 1) # set heuristically
fig, axes = plt.subplots(1,
2,
figsize=figsize,
sharex=True,
sharey=True)
fig.tight_layout()
axes[0].set_title('Mean intensity', size='small')
axes[0].imshow(mean_intensities)
axes[0].set_ylabel('Slices')
axes[0].set_xlabel('Frames')
axes[1].set_title('Contrast (99 - 1 percentile)', size='small')
axes[1].imshow(contrasts)
axes[1].set_xlabel('Frames')
img_filename = '/tmp/' + key_hash(key) + '.png'
fig.savefig(img_filename, bbox_inches='tight')
plt.close(fig)
(notify.SlackUser() & (experiment.Session() & key)).notify(
file=img_filename, file_title='quality images')
def notify(self, key):
ts, temperatures = (self & key).fetch1('temp_time', 'temperatures')
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10, 5))
plt.plot(ts, temperatures)
plt.ylabel('Temperature (C)')
plt.xlabel('Seconds')
img_filename = '/tmp/' + key_hash(key) + '.png'
fig.savefig(img_filename)
plt.close(fig)
msg = 'temperature for {animal_id}-{session}-{scan_idx}'.format(**key)
slack_user = notify.SlackUser() & (experiment.Session() & key)
slack_user.notify(file=img_filename, file_title=msg)
def notify(self, key):
ts, temperatures = (self & key).fetch1('temp_time', 'temperatures')
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10, 5))
plt.plot(ts, temperatures)
plt.ylabel('Temperature (C)')
plt.xlabel('Seconds')
img_filename = '/tmp/' + key_hash(key) + '.png'
fig.savefig(img_filename)
plt.close(fig)
msg = 'temperature for {animal_id}-{session}-{scan_idx}'.format(**key)
slack_user = notify.SlackUser() & (experiment.Session() & key)
slack_user.notify(file=img_filename, file_title=msg)
def _make_tuples(self, key):
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# Get all field keys and timestamps
field_key, field_ts, field_nframes, field_fps = ((experiment.Scan() * meso.ScanInfo() *
meso.ScanInfo.Field().proj()).proj('nframes', 'fps', 'scan_ts', scan_session='session')
& key & 'field < 10').fetch('KEY', 'scan_ts', 'nframes', 'fps', order_by='scan_ts')
if len(field_key) == 0:
print('Warning: No fields selected for', key)
return
initial_time = str(field_ts[0])
field_ts = [(ts - field_ts[0]).seconds for ts in field_ts] # in hours
field_duration = field_nframes / field_fps
# Plot
fig = plt.figure(figsize=(20, 8))
for fk, ft, fd in zip(field_key, field_ts, field_duration):
zs = (RegistrationOverTime() & key & fk).fetch('reg_z', order_by='frame_id')
ts = ft + np.linspace(0, 1, len(zs) + 2)[1:-1] * fd
plt.plot(ts / 3600, zs)
plt.title('Registered zs for {animal_id}-{scan_session} starting {t}'.format(t=initial_time, **key))
plt.ylabel('Registered zs')
plt.xlabel('Hours')
# Plot formatting
plt.gca().invert_yaxis()
plt.gca().yaxis.set_major_locator(ticker.MultipleLocator(10))
plt.grid(linestyle='--', alpha=0.8)
# Notify
img_filename = '/tmp/' + key_hash(key) + '.png'
fig.savefig(img_filename, bbox_inches='tight')
plt.close(fig)
msg = 'registration over time for {animal_id}-{scan_session} into {animal_id}-{stack_session}-{stack_idx}'.format(**key)
slack_user = notify.SlackUser() & (experiment.Session() & key & {'session': key['stack_session']})
slack_user.notify(file=img_filename, file_title=msg, channel='#pipeline_quality')
self.insert1(key, ignore_extra_fields=True)