def updateVideoFile(self, vfilename):
# close the if there was another file opened before.
if self.fid is not None:
self.fid.close()
self.mainImage.cleanCanvas()
self.fid = None
self.image_group = None
self.imgstore_name = ''
self.fovsplitter = None
self.well_name = ''
self.well_names = []
self.tiles = None
self.ui.wells_comboBox.clear()
self.wells_df = None
self.vfilename = vfilename
self.imgstore_name = Path(vfilename).parent.name
self.ui.label_vid.setText(self.imgstore_name)
# self.videos_dir = self.vfilename.rpartition(os.sep)[0] + os.sep
# try:
# with tables.File(vfilename, 'r') as fid:
# if '/fov_wells' not in fid:
# QMessageBox.critical(self, '',
# "The FOV was not split",
# QMessageBox.Ok)
# return
# except (IOError, tables.exceptions.HDF5ExtError):
# self.fid = None
# self.image_group = None
# QMessageBox.critical(
# self, '',
# "The selected file is not a valid .hdf5. Please select a valid file",
# QMessageBox.Ok)
# return
fovsplitter = FOVMultiWellsSplitter(self.vfilename)
with tables.File(vfilename) as fid:
img_stack = fid.get_node('/full_data').read().copy()
tiles_list = fovsplitter.tile_FOV(img_stack)
self.wells_df = fovsplitter.wells.copy().set_index('well_name')
self.tiles = {k: v for (k, v) in tiles_list}
self.well_names = self.wells_df.index.to_list()
self.ui.wells_comboBox.clear()
for wi, wn in enumerate(self.well_names):
self.ui.wells_comboBox.addItem(wn)
self.updateImGroup(0)
def plot_well_trajectory(featuresfilepath,
maskedvideopath,
well_name,
downsample=10,
filter_trajectories=False,
ax=None,
verbose=True,
**kwargs):
""" Plot centroid coordinates for worms in a given well """
# plot first frame of video for sample well
FOVsplitter = FOVMultiWellsSplitter(maskedvideopath)
fov_wells = FOVsplitter.wells
well_fov = fov_wells[fov_wells['well_name'] == well_name]
if well_fov.iloc[0]['is_good_well'] != 1:
print("WARNING: Bad well data for:\n%s\t%s" %
(featuresfilepath, well_name))
return
if not ax:
fig, ax = plt.subplots(**kwargs)
img_list = FOVsplitter.tile_FOV(FOVsplitter.img)
well_img = [i[1] for i in img_list if i[0] == well_name][0]
ax.imshow(well_img, cmap='gray')
df = read_timeseries(featuresfilepath,
names=[
'worm_index', 'timestamp', 'well_name',
'coord_x_body', 'coord_y_body'
],
only_wells=[well_name])
microns_per_pixel = read_microns_per_pixel(featuresfilepath)
df['x'] = df['coord_x_body'] / microns_per_pixel
df['y'] = df['coord_y_body'] / microns_per_pixel
# subtract x,y offset to set bottom left coords of well as plot origin for trajectory plot
df['x'] = df['x'] - well_fov.iloc[0]['x_min']
df['y'] = df['y'] - well_fov.iloc[0]['y_min']
# Optional - filter trajectories using movement/time threshold parameters (globals)
if filter_trajectories:
df, _ = filter_worm_trajectories(
df,
threshold_move=THRESHOLD_DISTANCE_PIXELS,
threshold_time=THRESHOLD_DURATION_FRAMES,
fps=read_fps(featuresfilepath),
microns_per_pixel=microns_per_pixel,
timestamp_col='timestamp',
worm_id_col='worm_index',
x_coord_col='x',
y_coord_col='y',
verbose=verbose)
# Plot trajectory
if downsample is not None:
# Downsample frames for plotting
downsample = 1 if downsample < 1 else downsample
ax.scatter(x=df['x'][::downsample],
y=df['y'][::downsample],
c=df['timestamp'][::downsample],
cmap='plasma',
s=7)
else:
ax.scatter(x=df['x'], y=df['y'], c=df['timestamp'], cmap='plasma', s=7)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.autoscale(enable=True, axis='x', tight=True) # re-scaling axes
ax.autoscale(enable=True, axis='y', tight=True)
return