diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml
index 0c68f183..edd8614f 100644
--- a/.github/workflows/testing.yml
+++ b/.github/workflows/testing.yml
@@ -18,6 +18,13 @@ jobs:
         python-version: ["3.8", "3.9", "3.10"]
         numpy-version: ["1.21"]
         pyqt-version: ["5.12"]
+        include:
+          - host-os: "ubuntu-latest"
+            conda-mamba: "mamba"
+          - host-os: "macos-latest"
+            conda-mamba: "mamba"
+          - host-os: "windows-latest"
+            conda-mamba: "conda"
       fail-fast: false
 
     runs-on: ${{ matrix.host-os }}
@@ -76,7 +83,9 @@ jobs:
       run: |
         set -vxeo pipefail
 
-        conda install -y \
+        conda install mamba -n base -c conda-forge
+
+        ${{ matrix.conda-mamba }} install -y \
           python=${{ matrix.python-version }} \
           numpy=${{ matrix.numpy-version }} \
           pyqt=${{ matrix.pyqt-version }} \
@@ -94,7 +103,7 @@ jobs:
         set -vxeo pipefail
 
         # There are issues in building wheels for pystackreg>0.2.2 on OSX, so install pystackreg from CF
-        conda install -y pystackreg -c conda-forge
+        ${{ matrix.conda-mamba }} install -y pystackreg -c conda-forge
 
         conda list
         pip list
@@ -103,7 +112,7 @@ jobs:
       run: |
         set -vxeuo pipefail
 
-        conda install -y pystackreg -c conda-forge  # Some issue with pystackreg from PyPI
+        ${{ matrix.conda-mamba }} install -y pystackreg -c conda-forge  # Some issue with pystackreg from PyPI
 
         # pip install --upgrade pip setuptools
         pip install codecov
diff --git a/pyxrf/core/map_processing.py b/pyxrf/core/map_processing.py
index de56bb65..4c2bb3a1 100644
--- a/pyxrf/core/map_processing.py
+++ b/pyxrf/core/map_processing.py
@@ -468,6 +468,12 @@ def _prepare_xrf_mask(data, mask=None, selection=None):
     return mask
 
 
+def _masked_sum(data, mask):
+    mask = np.broadcast_to(np.expand_dims(mask, axis=2), data.shape)
+    sm = np.sum(np.sum(data * mask, axis=0), axis=0)
+    return np.array([[sm]])
+
+
 def compute_total_spectrum(
     data, *, selection=None, mask=None, chunk_pixels=5000, n_chunks_min=4, progress_bar=None, client=None
 ):
@@ -519,12 +525,6 @@ def compute_total_spectrum(
     if mask is None:
         result_fut = da.sum(da.sum(data, axis=0), axis=0).persist(scheduler=client)
     else:
-
-        def _masked_sum(data, mask):
-            mask = np.broadcast_to(np.expand_dims(mask, axis=2), data.shape)
-            sm = np.sum(np.sum(data * mask, axis=0), axis=0)
-            return np.array([[sm]])
-
         result_fut = da.blockwise(_masked_sum, "ijk", data, "ijk", mask, "ij", dtype="float").persist(
             scheduler=client
         )
@@ -550,6 +550,22 @@ def _masked_sum(data, mask):
     return result
 
 
+def _process_block(data):
+    data = data[0]  # Data is passed as a list of ndarrays
+    _spectrum = np.sum(np.sum(data, axis=0), axis=0)
+    _count_total = np.sum(data, axis=2)
+    return np.array([[{"spectrum": _spectrum, "count_total": _count_total}]])
+
+
+def _process_block_with_mask(data, mask):
+    data = data[0]  # Data is passed as a list of ndarrays
+    mask = np.broadcast_to(np.expand_dims(mask, axis=2), data.shape)
+    masked_data = data * mask
+    _spectrum = np.sum(np.sum(masked_data, axis=0), axis=0)
+    _count_total = np.sum(masked_data, axis=2)
+    return np.array([[{"spectrum": _spectrum, "count_total": _count_total}]])
+
+
 def compute_total_spectrum_and_count(
     data, *, selection=None, mask=None, chunk_pixels=5000, n_chunks_min=4, progress_bar=None, client=None
 ):
@@ -602,25 +618,9 @@ def compute_total_spectrum_and_count(
     logger.info(f"Dask distributed client: {n_workers} workers")
 
     if mask is None:
-
-        def _process_block(data):
-            data = data[0]  # Data is passed as a list of ndarrays
-            _spectrum = np.sum(np.sum(data, axis=0), axis=0)
-            _count_total = np.sum(data, axis=2)
-            return np.array([[{"spectrum": _spectrum, "count_total": _count_total}]])
-
         result_fut = da.blockwise(_process_block, "ij", data, "ijk", dtype=float).persist(scheduler=client)
     else:
-
-        def _process_block(data, mask):
-            data = data[0]  # Data is passed as a list of ndarrays
-            mask = np.broadcast_to(np.expand_dims(mask, axis=2), data.shape)
-            masked_data = data * mask
-            _spectrum = np.sum(np.sum(masked_data, axis=0), axis=0)
-            _count_total = np.sum(masked_data, axis=2)
-            return np.array([[{"spectrum": _spectrum, "count_total": _count_total}]])
-
-        result_fut = da.blockwise(_process_block, "ij", data, "ijk", mask, "ij", dtype=float).persist(
+        result_fut = da.blockwise(_process_block_with_mask, "ij", data, "ijk", mask, "ij", dtype=float).persist(
             scheduler=client
         )
 
diff --git a/pyxrf/db_config/tes_db_config.py b/pyxrf/db_config/tes_db_config.py
index ab73c5a8..a7804c38 100644
--- a/pyxrf/db_config/tes_db_config.py
+++ b/pyxrf/db_config/tes_db_config.py
@@ -23,7 +23,10 @@
 
 
 class BulkXSPRESS(HandlerBase):
-    HANDLER_NAME = "XPS3_FLY"
+    specs = {
+        "XPS3_FLY",  # Old incorrect name
+        "XSP3_FLY",
+    }
 
     def __init__(self, resource_fn):
         self._handle = h5py.File(resource_fn, "r")
@@ -32,4 +35,5 @@ def __call__(self):
         return self._handle["entry/instrument/detector/data"][:]
 
 
-db.reg.register_handler(BulkXSPRESS.HANDLER_NAME, BulkXSPRESS, overwrite=True)
+for spec in BulkXSPRESS.specs:
+    db.reg.register_handler(spec, BulkXSPRESS, overwrite=True)