Add single-cell std dev

microutil/btrack/btrack.py

@@ -43,7 +43,7 @@ def gogogo_btrack(labels, config_file, radius, tracks_out):
  tracker.volume = ((0, labels.shape[-2]), (0, labels.shape[-1]), (-1e5, 1e5))
 
  # track them (in interactive mode)
- tracker.track_interactive(step_size=100)
+ tracker.track(step_size=100)
 
  # generate hypotheses and run the global optimizer
  tracker.optimize()

microutil/leica/leica.py

@@ -296,7 +296,8 @@ def ldm_to_time(inds):
  for i, s in inds.groupby('S'):
  new_vals = np.arange(s['T'].nunique())
  old_vals = sorted(s['T'].unique())
- mapper = mapper.append(pd.Series(data=new_vals, index=old_vals))
+ # mapper = mapper.append(pd.Series(data=new_vals, index=old_vals))
+ mapper = pd.concat([mapper, pd.Series(data=new_vals, index=old_vals)])
  # return mapper
  inds['T'] = mapper[inds['T'].values].values
  return inds

microutil/single_cell/single_cell.py

@@ -2,6 +2,7 @@
  "average",
  "center_of_mass",
  "area",
+ "standard_dev",
  "cell_op",
  "bootstrap",
  "regionprops",
@@ -185,6 +186,42 @@ def padded_mean(intensity, labels, Nmax=None):
  )
 
 
+def standard_dev(ds, intensity, label_name='labels', cell_dim_name="CellID", dims='STCZYX'):
+ """
+ Compute the standard deviation of the inntensity array over each labelled area.
+
+ Parameters
+ ----------
+ ds : xr.Dataset
+ Dataset containing labels
+ intensity : xr.DataArray
+ Dataarray containing values that will be averaged over each labelled region.
+ label_name : str
+ Name of Variable in ds that contains labelled regions.
+ cell_dim_name : str default "CellID"
+ Name of dimension in output array that indexes individual cells.
+ dims : str or list of str, default 'STCZYX`
+ Dimensions names that correspond to STCZYX
+ """
+
+ if isinstance(dims, str):
+ S, T, C, Z, Y, X = list(dims)
+ elif isinstance(dims, list):
+ S, T, C, Z, Y, X = dims
+
+ def padded_mean(intensity, labels, Nmax=None):
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore')
+ out = np.asarray(
+ ndi.standard_deviation(intensity, labels=labels, index=np.arange(1, Nmax))
+ )
+ return out
+
+ return cell_op(
+ ds, padded_mean, intensity, label_name=label_name, cell_dim_name=cell_dim_name, dims=dims
+ )
+
+
 def center_of_mass(ds, com_name='com', label_name='labels', cell_dim_name='CellID', dims='STCZYX'):
  """
  Compute the center of mass of each labeled cell in a dataset.
@@ -372,6 +409,12 @@ def regionprops_pandas(ds, properties=DEFAULT_PROPERTIES, label_name='labels', d
  Loop over the frames of ds and compute the regionprops for
  each labelled image in each frame.
  """
+ warnings.warn(
+ "Use the implementation in dask-regionprops for new code.",
+ DeprecationWarning,
+ stack_level=2,
+ )
+
  if isinstance(dims, str):
  S, T, C, Z, Y, X = list(dims)
  elif isinstance(dims, list):