diff --git a/dap/algos/streakfind.py b/dap/algos/streakfind.py
index 39ef5fa..0e95b59 100644
--- a/dap/algos/streakfind.py
+++ b/dap/algos/streakfind.py
@@ -6,28 +6,19 @@ Requires Convergent beam streak finder package installed:
 https://github.com/simply-nicky/streak_finder/
 (note g++ 11 required for building, numpy 2+ required)
 """
+
 import h5py
 import numpy as np
+
 from streak_finder.label import Structure2D
 from streak_finder.streak_finder import detect_peaks, detect_streaks, filter_peaks
 from streak_finder._src.src.median import median
 
+
 DEFAULT_MIN_HIT_STREAKS = 5
 DEFAULT_NUM_THREADS = 16
 
 
-def _handle_negative_values(data, mask, handler: str):
-    if not handler or np.all(data>=0):
-        return
-    if handler == "shift":
-        # Shift to min=0
-        data -= np.min(data)
-    elif handler == "mask":
-        mask[data<0] = np.nan
-    elif handler == "zero":
-        data[data<0] = 0
-
-
 def calc_streakfinder_analysis(results, data, pixel_mask):
     do_snr = results.get("do_snr", False)
     do_streakfinder_analysis = results.get("do_streakfinder_analysis", False)
@@ -54,6 +45,19 @@ def calc_streakfinder_analysis(results, data, pixel_mask):
 
     return data
 
+
+def _handle_negative_values(data, mask, handler: str):
+    if not handler or np.all(data>=0):
+        return
+    if handler == "shift":
+        # Shift to min=0
+        data -= np.min(data)
+    elif handler == "mask":
+        mask[data<0] = np.nan
+    elif handler == "zero":
+        data[data<0] = 0
+
+
 def _calc_snr(results, data, pixel_mask):
     params_required = [
         "cbd_whitefield_data_file",
@@ -97,6 +101,7 @@ def _calc_snr(results, data, pixel_mask):
     )
     return snr
 
+
 def _scale_whitefield(data, mask, whitefield, std, num_threads):
     mask = mask & (std > 0.0)
     y = np.divide(data, std, out=np.zeros_like(data), where=mask)[mask]
@@ -106,19 +111,6 @@ def _scale_whitefield(data, mask, whitefield, std, num_threads):
              median(w * w, axis=0, num_threads=num_threads)
     whitefield *= scales
 
-def _get_concentric_only_mask(x_center, y_center, crop_roi, streak_lines, threshold=0.33):
-    if x_center is not None and y_center is not None:
-        if crop_roi is not None:
-            x_center -= crop_roi[0]
-            y_center -= crop_roi[2]
-        centers = np.mean(streak_lines.reshape(-1, 2, 2), axis=1)
-        norm = np.stack([streak_lines[:, 3] - streak_lines[:, 1],
-                         streak_lines[:, 0] - streak_lines[:, 2]], axis=-1)
-        r = centers - np.asarray([x_center, y_center])
-        prod = np.sum(norm * r, axis=-1)[..., None]
-        proj = r - prod * norm / np.sum(norm ** 2, axis=-1)[..., None]
-        streaks_mask = np.sqrt(np.sum(proj ** 2, axis=-1)) / np.sqrt(np.sum(r ** 2, axis=-1)) < threshold
-        return streaks_mask
 
 def _calc_streakfinder_analysis(results, snr, mask):
     do_streakfinder_analysis = results.get("do_streakfinder_analysis", False)
@@ -225,3 +217,21 @@ def _calc_streakfinder_analysis(results, snr, mask):
     results["streaks"] = list_result
     results["streak_lengths"] = streak_lengths
     results["bragg_counts"] = bragg_counts
+
+
+def _get_concentric_only_mask(x_center, y_center, crop_roi, streak_lines, threshold=0.33):
+    if x_center is not None and y_center is not None:
+        if crop_roi is not None:
+            x_center -= crop_roi[0]
+            y_center -= crop_roi[2]
+        centers = np.mean(streak_lines.reshape(-1, 2, 2), axis=1)
+        norm = np.stack([streak_lines[:, 3] - streak_lines[:, 1],
+                         streak_lines[:, 0] - streak_lines[:, 2]], axis=-1)
+        r = centers - np.asarray([x_center, y_center])
+        prod = np.sum(norm * r, axis=-1)[..., None]
+        proj = r - prod * norm / np.sum(norm ** 2, axis=-1)[..., None]
+        streaks_mask = np.sqrt(np.sum(proj ** 2, axis=-1)) / np.sqrt(np.sum(r ** 2, axis=-1)) < threshold
+        return streaks_mask
+
+
+