[Gotthard2][Dark] Calculate noise bpixs in multiple iterations

Description

@mramilli mentioned that reiterating on calculating NOISE_OUT_OF_THRESHOLD badpixel type for gotthard2 showed a difference in the number of badpixels identified.

In the below figure are his test results. Each plot is the difference in bpix numbers for each gain for one of the production constants.

Below are my test results showing that sometimes you can get a different number of badpixels with more iterations than 2.

This MR exposes an iteration parameter to control it if needed (default 5).

Now GH2 darks will iterate to calculate the more accurate number of badpixels. each iteration costs =< 1ms, so no obvious performance drawback on dark processing.

How Has This Been Tested?

Relevant Documents (optional)

Types of changes

Checklist:

Reviewers

@mramilli @schmidtp @kluyvert

notebooks/Gotthard2/Characterize_Darks_Gotthard2_NBC.ipynb

 acquisition_rate = -1.  # Detector acquisition rate (1.1/4.5), set to -1 to use value in raw file.
 single_photon = -1  # Detector single photon mode (High/Low CDS), set to -1 to use value in raw file.
 
+# Parameters used for calculating calibration constants
+bpix_noise_nitrs = 2  # number of maximum iterations to actively try to reach correct number of badpixels of NOISE_OUT_OF_THRESHOLD type. Leave -1 to keep iterating until finding maximum number of badpixels.
 # Parameters used during selecting raw data trains.
 min_trains = 1  # Minimum number of trains that should be available to process dark constants. Default 1.
 max_trains = 1000  # Maximum number of trains to use for processing dark constants. Set to 0 to use all available trains.
         ~np.isfinite(offset_map[mod])
     ] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
     badpixels_map[mod][
-        eval_bpidx(noise_map[mod])
-    ] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
-
-    badpixels_map[mod][
         ~np.isfinite(noise_map[mod])
     ] |= BadPixels.OFFSET_NOISE_EVAL_ERROR.value
-    if not local_output:
+
+    nmap = noise_map[mod].copy()
+    last_nbpix = 0
+    # Calculate NOISE_OUT_OF_THRESHOLD bpix in multiple iterations.
+    while bpix_noise_nitrs > 0:
+        badpixels_map[mod][
+            eval_bpidx(nmap)
+        ] |= BadPixels.NOISE_OUT_OF_THRESHOLD.value
+        nmap[badpixels_map[mod] > 0] = np.nan
+        recent_nbpix = np.count_nonzero(badpixels_map[mod])
+
+        if last_nbpix == recent_nbpix:
+            bpix_noise_nitrs = 0
+        else:
+            bpix_noise_nitrs = bpix_noise_nitrs - 1
+        last_nbpix = recent_nbpix
+
+    if local_output:
         for cell in [0, 1]:
             fig, ax = plt.subplots(figsize=(10, 5))
             for g_idx in [0, 1, 2]: