Fixes some types and updates tests.

syncsweptsine.py

@@ -649,7 +649,7 @@ def _calculate_inverted_sweep_spectrum(self):
         sweepperiod = self._sweepperiod
         startfreq = self._startfreq
         freq = _np.linspace(0, samplerate/2, int(_np.round(self.fftlen/2+1)))
-        spectrum = _np.zeros_like(freq, dtype=_np.complex)
+        spectrum = _np.zeros_like(freq, dtype=_np.complex_)
         # eq. 43 definition of the inverse spectrum in frequency domain
         spectrum[1:] = (
             2*_np.sqrt(freq[1:]/sweepperiod) 
@@ -783,7 +783,7 @@ def hir_index(self, order, length, delay=0):
             Delay of system under test the hhir was derived from.
 
         """
-        return _np.arange(length)+delay+self.hir_sample_position(order)
+        return _np.arange(length, dtype=int)+int(delay)+self.hir_sample_position(order)
 
     def max_hir_length(self, order):
         """Returns the maximum length of mpulse responses for given orders.
@@ -856,7 +856,7 @@ def from_sweeps(cls, syncsweep, measuredsweep, fftlen=None, regularize=1e-6):
             Length of the calculated ffts. fftlen will be guessed from measuredsweep length if fftlen is None.
 
         """
-        fftlen = fftlen or _np.int(2**_np.ceil(1+_np.log2(len(measuredsweep))))
+        fftlen = fftlen or int(2**_np.ceil(1+_np.log2(len(measuredsweep))))
         rspec = _np.fft.rfft(measuredsweep, fftlen)
         rinvspec = InvertedSyncSweepSpectrum.from_sweep(syncsweep, fftlen=fftlen).spectrum
         freq = _np.fft.rfftfreq(fftlen, 1/syncsweep.samplerate)

test_syncsweptsine.py

@@ -181,11 +181,11 @@ def test_higher_harmonic_impulse_response():
     sweep = SyncSweep(10, 10000, 5, 20000)
     hhir = HigherHarmonicImpulseResponse.from_sweeps(sweep, sweep)
     assume(np.all(np.array(hhir) == hhir.hhir))
-    hir = hhir.harmonic_impulse_response(order=1, length=1024, delay=0, window=True)
+    hir = hhir.harmonic_impulse_response(order=1, length=1024, delay=-512, window=True)
     assume(type(hir) == np.ndarray)
     assume(len(hir) == 1024)
     assume(np.argmax(hir)==512)
-    hir = hhir.harmonic_impulse_response(order=1, length=1024, delay=0, window=np.linspace(0, 1, 1024))
+    hir = hhir.harmonic_impulse_response(order=1, length=1024, delay=-512, window=np.linspace(0, 1, 1024))
     assume(type(hir) == np.ndarray)
     assume(len(hir) == 1024)
     assume(np.argmax(hir)==512)
@@ -238,18 +238,19 @@ def nonlinear_system(sig):
     outsweep = nonlinear_system(np.array(sweep))
     hhir = HigherHarmonicImpulseResponse.from_sweeps(sweep, outsweep, regularize=False)
     hm = HammersteinModel.from_higher_harmonic_impulse_response(
-        hhir, 2048, orders=(1, 2, 3), delay=0)
+        hhir, 2048, orders=(1, 2, 3), delay=-1024)  # -delay since irs are acausal in this case.
     assume(hm.orders==(1, 2, 3))
     x = np.array([1]+127*[0])
     hm.filter(x)
     assume(hm.__repr__().startswith('HammersteinModel('))
     expectedcoeffs = [1.0, 0.25, 0.125]
     for kernel, expc in zip(hm.kernels, expectedcoeffs):
+        print(abs(np.median(abs(kernel.frf[100:200]))), expc)
         assume(abs(np.median(abs(kernel.frf[100:200]))-expc) < 0.01)
 
     with pytest.raises(ValueError):
         hm = HammersteinModel.from_higher_harmonic_impulse_response(
-            hhir, 96e4, orders=(1, 2, 3), delay=0)
+            hhir, hhir.max_hir_length(1)+1, orders=(1, 2, 3), delay=-1024)
     lm = LinearModel.from_hammerstein_model(hm)
 
 
@@ -266,7 +267,7 @@ def nonlinear_system(sig):
     y = lm.filter(x)
     with pytest.raises(ValueError):
         hm = LinearModel.from_higher_harmonic_impulse_response(
-            hhir, 96e4, delay=0)
+            hhir, hhir.max_hir_length(1)+1, delay=0)
 
 
 if __name__ == "__main__":