FIX: Call resample from scipy.signal namespace

Author: effigies

nitime/algorithms/tests/test_coherence.py

@@ -8,7 +8,7 @@
 
 import numpy as np
 import numpy.testing as npt
-from scipy.signal import signaltools
+from scipy import signal
 import pytest
 
 import matplotlib
@@ -239,7 +239,7 @@ def test_coherence_linear_dependence():
               "Fs": 2 * np.pi}
 
     f, c = tsa.coherence(np.vstack([x, y]), csd_method=method)
-    c_t = np.abs(signaltools.resample(c_t, c.shape[-1]))
+    c_t = np.abs(signal.resample(c_t, c.shape[-1]))
 
     npt.assert_array_almost_equal(c[0, 1], c_t, 2)
 

nitime/tests/test_algorithms.py

@@ -3,8 +3,7 @@
 import numpy as np
 import numpy.testing as npt
 
-from scipy.signal import signaltools
-from scipy import fftpack
+from scipy import fftpack, signal
 
 import nitime
 from nitime import algorithms as tsa
@@ -24,16 +23,16 @@ def test_scipy_resample():
          for f in freq_list]
     tst = np.array(a).sum(axis=0)
     # interpolate to 128 Hz sampling
-    t_up = signaltools.resample(tst, 128)
+    t_up = signal.resample(tst, 128)
     np.testing.assert_array_almost_equal(t_up[::2], tst)
     # downsample to 32 Hz
-    t_dn = signaltools.resample(tst, 32)
+    t_dn = signal.resample(tst, 32)
     np.testing.assert_array_almost_equal(t_dn, tst[::2])
 
     # downsample to 48 Hz, and compute the sampling analytically for comparison
     dn_samp_ana = np.array([np.sin(2 * np.pi * f * np.linspace(0, 1, 48, endpoint=False))
                             for f in freq_list]).sum(axis=0)
-    t_dn2 = signaltools.resample(tst, 48)
+    t_dn2 = signal.resample(tst, 48)
     npt.assert_array_almost_equal(t_dn2, dn_samp_ana)