diff --git a/africanus/averaging/bda_mapping.py b/africanus/averaging/bda_mapping.py
index e5504c4f..92db5f97 100644
--- a/africanus/averaging/bda_mapping.py
+++ b/africanus/averaging/bda_mapping.py
@@ -5,7 +5,7 @@
 import numpy as np
 import numba
 from numba.experimental import jitclass
-import numba.types
+from numba import types
 
 from africanus.constants import c as lightspeed
 from africanus.util.numba import (
@@ -64,7 +64,7 @@ def max_chan_width(ref_freq, fractional_bandwidth):
                              "nchan", "flag"])
 
 
-class Binner(object):
+class Binner:
     def __init__(self, row_start, row_end,
                  max_lm, decorrelation, time_bin_secs,
                  max_chan_freq):
@@ -305,7 +305,7 @@ def bda_mapper_impl(time, interval, ant1, ant2, uvw,
                min_nchan=1):
     return NotImplementedError
 
-@overload(bda_mapper_impl, jit_options=JIT_OPTIONS)
+@overload(bda_mapper_impl, jit_options={"nogil": True})
 def nb_bda_mapper(time, interval, ant1, ant2, uvw,
                chan_width, chan_freq,
                max_uvw_dist,
@@ -316,7 +316,7 @@ def nb_bda_mapper(time, interval, ant1, ant2, uvw,
                min_nchan=1):
     have_time_bin_secs = not is_numba_type_none(time_bin_secs)
 
-    Omitted = numba.types.misc.Omitted
+    Omitted = types.misc.Omitted
 
     decorr_type = (numba.typeof(decorrelation.value)
                    if isinstance(decorrelation, Omitted)
@@ -347,7 +347,7 @@ def nb_bda_mapper(time, interval, ant1, ant2, uvw,
         ('max_chan_freq', chan_freq.dtype),
         ('max_uvw_dist', max_uvw_dist)]
 
-    JitBinner = st(spec)(Binner)
+    JitBinner = jitclass(spec)(Binner)
 
     def impl(time, interval, ant1, ant2, uvw,
              chan_width, chan_freq,
diff --git a/africanus/averaging/tests/test_bda_mapping.py b/africanus/averaging/tests/test_bda_mapping.py
index b0ed2e65..58b1b8a5 100644
--- a/africanus/averaging/tests/test_bda_mapping.py
+++ b/africanus/averaging/tests/test_bda_mapping.py
@@ -5,7 +5,7 @@
 import pytest
 
 from africanus.averaging.bda_mapping import bda_mapper, Binner
-
+from africanus.util.numba import njit
 
 @pytest.fixture(scope="session", params=[4096])
 def nchan(request):
@@ -172,43 +172,43 @@ def synthesized_uvw(ants, time, phase_dir, auto_correlations):
     return ant1, ant2, uvw
 
 
-# @pytest.mark.parametrize("decorrelation", [0.95])
-# @pytest.mark.parametrize("min_nchan", [1])
-# def test_bda_mapper(time, synthesized_uvw, interval,
-#                     chan_freq, chan_width,
-#                     decorrelation, min_nchan):
-#     time = np.unique(time)
-#     ant1, ant2, uvw = synthesized_uvw
-
-#     nbl = ant1.shape[0]
-#     ntime = time.shape[0]
-
-#     time = np.repeat(time, nbl)
-#     interval = np.repeat(interval, nbl)
-#     ant1 = np.tile(ant1, ntime)
-#     ant2 = np.tile(ant2, ntime)
-#     flag_row = np.zeros(time.shape[0], dtype=np.int8)
-
-#     max_uvw_dist = np.sqrt(np.sum(uvw**2, axis=1)).max()
-
-#     row_meta = bda_mapper(time, interval, ant1, ant2, uvw,  # noqa :F841
-#                           chan_width, chan_freq,
-#                           max_uvw_dist,
-#                           flag_row=flag_row,
-#                           max_fov=3.0,
-#                           decorrelation=decorrelation,
-#                           min_nchan=min_nchan)
-
-#     offsets = np.unique(row_meta.map[np.arange(time.shape[0]), 0])
-#     assert_array_equal(offsets, row_meta.offsets[:-1])
-#     assert row_meta.map.max() + 1 == row_meta.offsets[-1]
-
-#     # NUM_CHAN divides number of channels exactly
-#     num_chan = np.diff(row_meta.offsets)
-#     _, remainder = np.divmod(chan_width.shape[0], num_chan)
-#     assert np.all(remainder == 0)
-#     decorr_cw = chan_width.sum() / num_chan
-#     assert_array_equal(decorr_cw, row_meta.decorr_chan_width)
+@pytest.mark.parametrize("decorrelation", [0.95])
+@pytest.mark.parametrize("min_nchan", [1])
+def test_bda_mapper(time, synthesized_uvw, interval,
+                    chan_freq, chan_width,
+                    decorrelation, min_nchan):
+    time = np.unique(time)
+    ant1, ant2, uvw = synthesized_uvw
+
+    nbl = ant1.shape[0]
+    ntime = time.shape[0]
+
+    time = np.repeat(time, nbl)
+    interval = np.repeat(interval, nbl)
+    ant1 = np.tile(ant1, ntime)
+    ant2 = np.tile(ant2, ntime)
+    flag_row = np.zeros(time.shape[0], dtype=np.int8)
+
+    max_uvw_dist = np.sqrt(np.sum(uvw**2, axis=1)).max()
+
+    row_meta = bda_mapper(time, interval, ant1, ant2, uvw,  # noqa :F841
+                          chan_width, chan_freq,
+                          max_uvw_dist,
+                          flag_row=flag_row,
+                          max_fov=3.0,
+                          decorrelation=decorrelation,
+                          min_nchan=min_nchan)
+
+    offsets = np.unique(row_meta.map[np.arange(time.shape[0]), 0])
+    assert_array_equal(offsets, row_meta.offsets[:-1])
+    assert row_meta.map.max() + 1 == row_meta.offsets[-1]
+
+    # NUM_CHAN divides number of channels exactly
+    num_chan = np.diff(row_meta.offsets)
+    _, remainder = np.divmod(chan_width.shape[0], num_chan)
+    assert np.all(remainder == 0)
+    decorr_cw = chan_width.sum() / num_chan
+    assert_array_equal(decorr_cw, row_meta.decorr_chan_width)
 
 
 def test_bda_binner(time, interval, synthesized_uvw,