diff --git a/dap/algos/__init__.py b/dap/algos/__init__.py
index 1ca0eec..f30eec4 100644
--- a/dap/algos/__init__.py
+++ b/dap/algos/__init__.py
@@ -1,6 +1,7 @@
 
 from .addmask import calc_apply_additional_mask
 from .jfdata import JFData
+from .forcesend import calc_force_send
 from .mask import calc_mask_pixels
 from .peakfind import calc_peakfinder_analysis
 from .radprof import calc_radial_integration
diff --git a/dap/algos/forcesend.py b/dap/algos/forcesend.py
new file mode 100644
index 0000000..f336523
--- /dev/null
+++ b/dap/algos/forcesend.py
@@ -0,0 +1,40 @@
+import numpy as np
+
+
+def calc_force_send(results, data, pixel_mask_pf, image, n_aggregated_images, data_summed):
+    force_send_visualisation = False
+    if data.dtype != np.uint16:
+        apply_threshold = results.get("apply_threshold", False)
+        apply_aggregation = results.get("apply_aggregation", False)
+        if not apply_aggregation:
+            data_summed = None
+            n_aggregated_images = 1
+        if apply_threshold or apply_aggregation:
+            if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
+                threshold_min = float(results["threshold_min"])
+                threshold_max = float(results["threshold_max"])
+                data[data < threshold_min] = 0.0
+                if threshold_max > threshold_min:
+                    data[data > threshold_max] = 0.0
+            if apply_aggregation and "aggregation_max" in results:
+                if data_summed is not None:
+                    data += data_summed
+                    n_aggregated_images += 1
+                data_summed = data.copy()
+                data_summed[data == -np.nan] = -np.nan #TODO: this does nothing
+                results["aggregated_images"] = n_aggregated_images
+                results["worker"] = 1 #TODO: keep this for backwards compatibility?
+                if n_aggregated_images >= results["aggregation_max"]:
+                    force_send_visualisation = True
+                    data_summed = None
+                    n_aggregated_images = 1
+            if pixel_mask_pf is not None:
+                data[~pixel_mask_pf] = np.nan
+
+        else:
+            data = image
+
+    return data, force_send_visualisation, n_aggregated_images, data_summed
+
+
+
diff --git a/dap/worker.py b/dap/worker.py
index 1a402be..77cae96 100644
--- a/dap/worker.py
+++ b/dap/worker.py
@@ -3,7 +3,7 @@ from random import randint
 
 import numpy as np
 
-from algos import calc_apply_threshold, calc_mask_pixels, calc_peakfinder_analysis, calc_radial_integration, calc_roi, calc_spi_analysis, JFData
+from algos import calc_apply_threshold, calc_force_send, calc_mask_pixels, calc_peakfinder_analysis, calc_radial_integration, calc_roi, calc_spi_analysis, JFData
 from utils import BufferedJSON, read_bit
 from zmqsocks import ZMQSockets
 
@@ -118,37 +118,7 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
         calc_peakfinder_analysis(results, pfdata, pixel_mask_pf)
 
 # ???
-        force_send_visualisation = False
-        if data.dtype != np.uint16:
-            apply_threshold = results.get("apply_threshold", False)
-            apply_aggregation = results.get("apply_aggregation", False)
-            if not apply_aggregation:
-                data_summed = None
-                n_aggregated_images = 1
-            if apply_threshold or apply_aggregation:
-                if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
-                    threshold_min = float(results["threshold_min"])
-                    threshold_max = float(results["threshold_max"])
-                    data[data < threshold_min] = 0.0
-                    if threshold_max > threshold_min:
-                        data[data > threshold_max] = 0.0
-                if apply_aggregation and "aggregation_max" in results:
-                    if data_summed is not None:
-                        data += data_summed
-                        n_aggregated_images += 1
-                    data_summed = data.copy()
-                    data_summed[data == -np.nan] = -np.nan #TODO: this does nothing
-                    results["aggregated_images"] = n_aggregated_images
-                    results["worker"] = 1 #TODO: keep this for backwards compatibility?
-                    if n_aggregated_images >= results["aggregation_max"]:
-                        force_send_visualisation = True
-                        data_summed = None
-                        n_aggregated_images = 1
-                if pixel_mask_pf is not None:
-                    data[~pixel_mask_pf] = np.nan
-
-            else:
-                data = image
+        data, force_send_visualisation, n_aggregated_images, data_summed = calc_force_send(results, data, pixel_mask_pf, image, n_aggregated_images, data_summed)
 
         results["type"]  = str(data.dtype)
         results["shape"] = data.shape
@@ -157,6 +127,8 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
         zmq_socks.send_accumulator(results)
 
 
+        apply_aggregation = results.get("apply_aggregation", False)
+
         send_empty_cond1 = (apply_aggregation and "aggregation_max" in results and not force_send_visualisation)
         send_empty_cond2 = (not results["is_good_frame"] or not (results["is_hit_frame"] or randint(1, skip_frames_rate) == 1))