import xdas.signal as xs.

docs/user-guide/atoms.md

@@ -23,12 +23,12 @@ There are three "flavours" declaring the atoms that can be used to compose a seq
 ```{code-cell} 
 import numpy as np
 import xdas
-import xdas.signal as xp
+import xdas.signal as xs
 from xdas.atoms import Partial, Sequential, IIRFilter
 
 sequence = Sequential(
     [
-      xp.taper(..., dim="time"),
+      xs.taper(..., dim="time"),
       Partial(np.square),
       IIRFilter(order=4, cutoff=1.5, btype="highpass", dim="time"),
     ]

docs/user-guide/convert-displacement.md

@@ -28,11 +28,11 @@ strain_rate.plot(yincrease=False, vmin=-0.5, vmax=0.5);
 Then convert strain rate to deformation and then to displacement.
 
 ```{code-cell} 
-import xdas.signal as xp
+import xdas.signal as xs
 
-strain = xp.integrate(strain_rate, dim="time")
-deformation = xp.integrate(strain, dim="distance")
-displacement = xp.sliding_mean_removal(deformation, wlen=2000.0, dim="distance")
+strain = xs.integrate(strain_rate, dim="time")
+deformation = xs.integrate(strain, dim="distance")
+displacement = xs.sliding_mean_removal(deformation, wlen=2000.0, dim="distance")
 displacement.plot(yincrease=False, vmin=-0.5, vmax=0.5);
 ```
 

tests/test_signal.py

@@ -171,14 +171,14 @@ def test_filter(self):
 
     def test_decimate_virtual_stack(self):
         da = wavelet_wavefronts()
-        expected = xp.decimate(da, 5, dim="time")
+        expected = xs.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")
+            result = xs.decimate(da_virtual, 5, dim="time")
         assert result.equals(expected)
 
 

tests/test_xarray.py

@@ -1,14 +1,14 @@
 import numpy as np
 
-import xdas.core.methods as xp
+import xdas.core.methods as xm
 from xdas.core.dataarray import DataArray
 from xdas.synthetics import wavelet_wavefronts
 
 
 class TestXarray:
     def test_returns_dataarray(self):
         da = wavelet_wavefronts()
-        for name, func in xp.HANDLED_METHODS.items():
+        for name, func in xm.HANDLED_METHODS.items():
             if callable(func):
                 if name in [
                     "percentile",
@@ -31,7 +31,7 @@ def test_returns_dataarray(self):
 
     def test_mean(self):
         da = wavelet_wavefronts()
-        result = xp.mean(da, "time")
+        result = xm.mean(da, "time")
         result_method = da.mean("time")
         expected = np.mean(da, 0)
         assert result.equals(expected)

xdas/atoms/core.py

@@ -194,15 +194,15 @@ class Sequential(Atom, list):
     Examples
     --------
     >>> from xdas.atoms import Partial, Sequential
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> import numpy as np
 
     Basic usage:
 
     >>> seq = Sequential(
     ...     [
-    ...         Partial(xp.taper, dim="time"),
-    ...         Partial(xp.lfilter, [1.0], [0.5], ..., dim="time", zi=...),
+    ...         Partial(xs.taper, dim="time"),
+    ...         Partial(xs.lfilter, [1.0], [0.5], ..., dim="time", zi=...),
     ...         Partial(np.square),
     ...     ],
     ...     name="Low frequency energy",
@@ -217,7 +217,7 @@ class Sequential(Atom, list):
 
     >>> seq = Sequential(
     ...     [
-    ...         Partial(xp.decimate, 16, dim="distance"),
+    ...         Partial(xs.decimate, 16, dim="distance"),
     ...         seq,
     ...     ]
     ... )
@@ -330,12 +330,12 @@ class Partial(Atom):
     --------
     >>> import numpy as np
     >>> import scipy.signal as sp
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.atoms import Partial
 
     Examples of a stateless atom:
 
-    >>> Partial(xp.decimate, 2, dim="time")
+    >>> Partial(xs.decimate, 2, dim="time")
     decimate(..., 2, dim=time)
 
     >>> Partial(np.square)
@@ -344,7 +344,7 @@ class Partial(Atom):
     Examples of a stateful atom with input data as second argument:
 
     >>> sos = sp.iirfilter(4, 0.1, btype="lowpass", output="sos")
-    >>> Partial(xp.sosfilt, sos, ..., dim="time", zi=...)
+    >>> Partial(xs.sosfilt, sos, ..., dim="time", zi=...)
     sosfilt(<ndarray>, ..., dim=time)  [stateful]
 
     """

xdas/signal.py

@@ -142,11 +142,11 @@ def hilbert(da, N=None, dim="last", parallel=None):
     --------
     In this example we use the Hilbert transform to determine the analytic signal.
 
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
-    >>> xp.hilbert(da, dim="time")
+    >>> xs.hilbert(da, dim="time")
     <xdas.DataArray (time: 300, distance: 401)>
     [[ 0.0497+0.1632j -0.0635+0.0125j ...  0.1352-0.3107j -0.2832-0.0126j]
      [-0.1096-0.0335j  0.124 +0.0257j ... -0.0444+0.2409j  0.1378-0.2702j]
@@ -203,11 +203,11 @@ def resample(da, num, dim="last", window=None, domain="time", parallel=None):
     A synthetic dataarray is resample from 300 to 100 samples along the time dimension.
     The 'hamming' window is used.
 
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
-    >>> xp.resample(da, 100, dim='time', window='hamming', domain='time')
+    >>> xs.resample(da, 100, dim='time', window='hamming', domain='time')
     <xdas.DataArray (time: 100, distance: 401)>
     [[ 0.039988  0.04855  -0.08251  ...  0.02539  -0.055219 -0.006693]
      [-0.032913 -0.016732  0.033743 ...  0.028534 -0.037685  0.032918]
@@ -294,11 +294,11 @@ def resample_poly(
     with an original shape of 300 in time. The choosed window is a 'hamming' window.
     The dataarray is synthetic data.
 
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
-    >>> xp.resample_poly(da, 2, 5, dim='time')
+    >>> xs.resample_poly(da, 2, 5, dim='time')
     <xdas.DataArray (time: 120, distance: 401)>
     [[-0.006378  0.012767 -0.002068 ... -0.033461  0.002603 -0.027478]
      [ 0.008851 -0.037799  0.009595 ...  0.053291 -0.0396    0.026909]
@@ -377,12 +377,12 @@ def lfilter(b, a, da, dim="last", zi=None, parallel=None):
     Examples
     --------
     >>> import scipy.signal as sp
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
     >>> b, a = sp.iirfilter(4, 0.5, btype="low")
-    >>> xp.lfilter(b, a, da, dim='time')
+    >>> xs.lfilter(b, a, da, dim='time')
     <xdas.DataArray (time: 300, distance: 401)>
     [[ 0.004668 -0.005968  0.007386 ... -0.0138    0.01271  -0.026618]
      [ 0.008372 -0.01222   0.022552 ... -0.041387  0.046667 -0.093521]
@@ -485,12 +485,12 @@ def filtfilt(
     Examples
     --------
     >>> import scipy.signal as sp
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
     >>> b, a = sp.iirfilter(4, 0.5, btype="low")
-    >>> xp.lfilter(b, a, da, dim='time')
+    >>> xs.lfilter(b, a, da, dim='time')
     <xdas.DataArray (time: 300, distance: 401)>
     [[ 0.004668 -0.005968  0.007386 ... -0.0138    0.01271  -0.026618]
      [ 0.008372 -0.01222   0.022552 ... -0.041387  0.046667 -0.093521]
@@ -554,12 +554,12 @@ def sosfilt(sos, da, dim="last", zi=None, parallel=None):
     Examples
     --------
     >>> import scipy.signal as sp
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
     >>> sos = sp.iirfilter(4, 0.5, btype="low", output="sos")
-    >>> xp.sosfilt(sos, da, dim='time')
+    >>> xs.sosfilt(sos, da, dim='time')
     <xdas.DataArray (time: 300, distance: 401)>
     [[ 0.004668 -0.005968  0.007386 ... -0.0138    0.01271  -0.026618]
      [ 0.008372 -0.01222   0.022552 ... -0.041387  0.046667 -0.093521]
@@ -642,12 +642,12 @@ def sosfiltfilt(sos, da, dim="last", padtype="odd", padlen=None, parallel=None):
     Examples
     --------
     >>> import scipy.signal as sp
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
     >>> sos = sp.iirfilter(4, 0.5, btype="low", output="sos")
-    >>> xp.sosfiltfilt(sos, da, dim='time')
+    >>> xs.sosfiltfilt(sos, da, dim='time')
     <xdas.DataArray (time: 300, distance: 401)>
     [[ 0.04968  -0.063651  0.078731 ... -0.146869  0.135149 -0.283111]
      [-0.01724   0.018588 -0.037267 ...  0.025092 -0.107095  0.127912]
@@ -904,11 +904,11 @@ def medfilt(da, kernel_dim):  # TODO: parallelize
     A median filter is applied to some synthetic dataarray with a median window size
     of 7 along the time dimension and 5 along the space dimension.
 
-    >>> import xdas.signal as xp
+    >>> import xdas.signal as xs
     >>> from xdas.synthetics import wavelet_wavefronts
 
     >>> da = wavelet_wavefronts()
-    >>> xp.medfilt(da, {"time": 7, "distance": 5})
+    >>> xs.medfilt(da, {"time": 7, "distance": 5})
     <xdas.DataArray (time: 300, distance: 401)>
     [[ 0.        0.        0.       ...  0.        0.        0.      ]
      [ 0.        0.        0.       ...  0.        0.        0.      ]