diff --git a/tests/test_fft.py b/tests/test_fft.py
deleted file mode 100644
index 371c644..0000000
--- a/tests/test_fft.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import numpy as np
-
-import xdas as xd
-import xdas.fft as xfft
-
-
-class TestRFFT:
-    def test_with_non_dimensional(self):
-        da = xd.synthetics.wavelet_wavefronts()
-        da["latitude"] = ("distance", np.arange(da.sizes["distance"]))
-        xfft.rfft(da)
diff --git a/tests/test_signal.py b/tests/test_signal.py
index 51f658d..92b40c5 100644
--- a/tests/test_signal.py
+++ b/tests/test_signal.py
@@ -1,6 +1,3 @@
-import os
-import tempfile
-
 import numpy as np
 import scipy.signal as sp
 import xarray as xr
@@ -132,51 +129,3 @@ def test_sosfiltfilt(self):
         da = wavelet_wavefronts()
         sos = sp.iirfilter(2, 0.5, btype="low", output="sos")
         xp.sosfiltfilt(sos, da, "time", padtype=None)
-
-    def test_filter(self):
-        da = wavelet_wavefronts()
-        axis = da.get_axis_num("time")
-        fs = 1 / xdas.get_sampling_interval(da, "time")
-        sos = sp.butter(
-            4,
-            [5, 10],
-            "band",
-            output="sos",
-            fs=fs,
-        )
-        data = sp.sosfilt(sos, da.values, axis=axis)
-        expected = da.copy(data=data)
-        result = xp.filter(
-            da,
-            [5, 10],
-            btype="band",
-            corners=4,
-            zerophase=False,
-            dim="time",
-            parallel=False,
-        )
-        assert result.equals(expected)
-        data = sp.sosfiltfilt(sos, da.values, axis=axis)
-        expected = da.copy(data=data)
-        result = xp.filter(
-            da,
-            [5, 10],
-            btype="band",
-            corners=4,
-            zerophase=True,
-            dim="time",
-            parallel=False,
-        )
-        assert result.equals(expected)
-
-    def test_decimate_virtual_stack(self):
-        da = wavelet_wavefronts()
-        expected = xp.decimate(da, 5, dim="time")
-        chunks = xdas.split(da, 5, "time")
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            for i, chunk in enumerate(chunks):
-                chunk_path = os.path.join(tmpdirname, f"chunk_{i}.nc")
-                chunk.to_netcdf(chunk_path)
-            da_virtual = xdas.open_mfdataarray(os.path.join(tmpdirname, "chunk_*.nc"))
-            result = xp.decimate(da_virtual, 5, dim="time")
-        assert result.equals(expected)
diff --git a/xdas/core/routines.py b/xdas/core/routines.py
index 11f1a9e..a676e8d 100644
--- a/xdas/core/routines.py
+++ b/xdas/core/routines.py
@@ -967,7 +967,7 @@ def plot_availability(obj, dim="first", **kwargs):
     -------
     fig : plotly.graph_objects.Figure
         The timeline
-
+        
     Notes
     -----
     This function uses the `px.timeline` function from the `plotly.express` library.
diff --git a/xdas/fft.py b/xdas/fft.py
index fdf5055..3cd3312 100644
--- a/xdas/fft.py
+++ b/xdas/fft.py
@@ -56,7 +56,6 @@ def fft(da, n=None, dim={"last": "spectrum"}, norm=None, parallel=None):
     coords = {
         newdim if name == olddim else name: f if name == olddim else da.coords[name]
         for name in da.coords
-        if (da[name].dim != olddim or name == olddim)
     }
     dims = tuple(newdim if dim == olddim else dim for dim in da.dims)
     return DataArray(data, coords, dims, da.name, da.attrs)
@@ -112,7 +111,6 @@ def rfft(da, n=None, dim={"last": "frequency"}, norm=None, parallel=None):
     coords = {
         newdim if name == olddim else name: f if name == olddim else da.coords[name]
         for name in da.coords
-        if (da[name].dim != olddim or name == olddim)
     }
     dims = tuple(newdim if dim == olddim else dim for dim in da.dims)
     return DataArray(data, coords, dims, da.name, da.attrs)
diff --git a/xdas/signal.py b/xdas/signal.py
index 71078c1..f1cd199 100644
--- a/xdas/signal.py
+++ b/xdas/signal.py
@@ -102,9 +102,9 @@ def filter(da, freq, btype, corners=4, zerophase=False, dim="last", parallel=Non
     fs = 1.0 / get_sampling_interval(da, dim)
     sos = sp.iirfilter(corners, freq, btype=btype, ftype="butter", output="sos", fs=fs)
     if zerophase:
-        func = parallelize((None, across), across, parallel)(sp.sosfiltfilt)
-    else:
         func = parallelize((None, across), across, parallel)(sp.sosfilt)
+    else:
+        func = parallelize((None, across), across, parallel)(sp.sosfiltfilt)
     data = func(sos, da.values, axis=axis)
     return da.copy(data=data)
 
@@ -708,15 +708,10 @@ def decimate(da, q, n=None, ftype="iir", zero_phase=True, dim="last", parallel=N
 
     """
     axis = da.get_axis_num(dim)
-    dim = da.dims[axis]  # TODO: this fist last thing is a bad idea...
     across = int(axis == 0)
     func = parallelize(across, across, parallel)(sp.decimate)
     data = func(da.values, q, n, ftype, axis, zero_phase)
-    coords = da.coords.copy()
-    for name in coords:
-        if coords[name].dim == dim:
-            coords[name] = coords[name][::q]
-    return DataArray(data, coords, da.dims, da.name, da.attrs)
+    return da[{dim: slice(None, None, q)}].copy(data=data)
 
 
 @atomized