Elegantly set thresholds for empty channels

[adamjtaylor]

Auto-minerva story.py fails on blank channels as follows (where channel 15 is all zero)
[note there is also a Warning for channel 1]

This is likely to occur for techniques such as MIBI where there is very low contrast, particularly for normal tissues with no/few cells exhibiting a particular marker and for included control channels.

Could we add a try/except into the mixture fitting that perhaps just sets [0,1] threshold when fitting fails

nxf-scratch-dir ip-172-22-12-59.ec2.internal:/tmp/nxf.MWYxHayB6P
opening image: Point3301_31717.ome.tif
reading metadata
WARNING: Could not read OME metadata. Story will use generic channel names and
    the scale bar will be omitted.
analyzing channel 1/45
/venv/lib/python3.9/site-packages/sklearn/mixture/_base.py:143: ConvergenceWarning: Number of distinct clusters (2) found smaller than n_clusters (3). Possibly due to duplicate points in X.
  cluster.KMeans(
analyzing channel 2/45
analyzing channel 3/45
analyzing channel 4/45
analyzing channel 5/45
analyzing channel 6/45
analyzing channel 7/45
analyzing channel 8/45
analyzing channel 9/45
analyzing channel 10/45
analyzing channel 11/45
analyzing channel 12/45
analyzing channel 13/45
analyzing channel 14/45
analyzing channel 15/45
Traceback (most recent call last):
  File "/auto-minerva/story.py", line 139, in
    main()
  File "/auto-minerva/story.py", line 120, in main
    vmin, vmax = auto_threshold(img)
  File "/auto-minerva/story.py", line 22, in auto_threshold
    gmm.fit(img_log.reshape((-1,1)))
  File "/venv/lib/python3.9/site-packages/sklearn/mixture/_base.py", line 198, in fit
    self.fit_predict(X, y)
  File "/venv/lib/python3.9/site-packages/sklearn/mixture/_base.py", line 228, in fit_predict
    X = self._validate_data(X, dtype=[np.float64, np.float32], ensure_min_samples=2)
  File "/venv/lib/python3.9/site-packages/sklearn/base.py", line 561, in _validate_data
    X = check_array(X, **check_params)
  File "/venv/lib/python3.9/site-packages/sklearn/utils/validation.py", line 797, in check_array
    raise ValueError(
ValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 2 is required.