Use the latest version of numpy and scipy

.travis.yml

@@ -21,7 +21,7 @@ install:
   - conda update -q conda
   # Useful for debugging any issues with conda
   - conda info -a
-  - conda create -q -n test-environment -c pytorch -c hcc -c conda-forge python=$TRAVIS_PYTHON_VERSION numpy=1.12 scipy=0.19 cython pytorch=0.4 torchvision libsndfile=1.0.28 libsamplerate=0.1.8 libflac=1.3.1 libogg=1.3.2
+  - conda create -q -n test-environment -c pytorch -c hcc -c conda-forge python=$TRAVIS_PYTHON_VERSION numpy=1.15.3 scipy=1.1.0 cython pytorch=0.4 torchvision libsndfile=1.0.28 libsamplerate=0.1.8 libflac=1.3.1 libogg=1.3.2
   - sudo ldconfig /opt/conda/lib
   - source activate test-environment
   - python setup.py install

Dockerfile

@@ -51,7 +51,7 @@ RUN echo 'export PATH=/opt/conda/bin:$PATH' > /etc/profile.d/conda.sh \
 
 ENV PATH /opt/conda/bin:$PATH
 
-RUN conda install -y -c pytorch numpy=1.12 scipy=0.19 pytorch=0.4
+RUN conda install -y -c pytorch numpy=1.15.3 scipy=1.1.0 pytorch=0.4
 
 RUN pip install zounds
 

requirements.txt

@@ -7,6 +7,6 @@ tornado==4.5.3
 pysoundfile
 matplotlib==1.5.0
 intervaltree
-numpy==1.12.1
-scipy==0.19
+numpy==1.15.3
+scipy==1.1.0
 torch==0.4.0

setup.py

@@ -87,8 +87,8 @@
         'argparse',
         'intervaltree',
         'ujson',
-        'numpy==1.12.1',
-        'scipy==0.19',
+        'numpy==1.15.3',
+        'scipy==1.1.0',
         'torch==0.4.0'
     ],
     package_data={

zounds/core/axis.py

@@ -299,24 +299,6 @@ def _new_dims(self, index, new_arr):
         for dim in self.dimensions[dims_pos:]:
             yield dim
 
-    def _wrap(self, other):
-        return self.__class__(other, self.dimensions)
-
-    def __gt__(self, other):
-        return self._wrap(np.asarray(self).__gt__(other))
-
-    def __ge__(self, other):
-        return self._wrap(np.asarray(self).__ge__(other))
-
-    def __lt__(self, other):
-        return self._wrap(np.asarray(self).__lt__(other))
-
-    def __le__(self, other):
-        return self._wrap(np.asarray(self).__le__(other))
-
-    def __eq__(self, other):
-        return self._wrap(np.asarray(self).__eq__(other))
-
     def _tuplify(self, a):
         if isinstance(a, list):
             t = set(map(lambda x: x.__class__, a))

zounds/spectral/sliding_window.py

@@ -65,23 +65,26 @@ def _wdata(self, size):
             self._cache[size] = window
             return window
 
-    def __numpy_ufunc__(self, *args, **kwargs):
-        # KLUDGE: This seems really odd, but the mere presence of this
-        # numpy-specific magic method seems to serve as a hint to to call
-        # this instances __rmul__ implementation, instead of doing element-wise
-        # multiplication, as per:
-        # http://docs.scipy.org/doc/numpy/reference/arrays.classes.html#numpy.class.__numpy_ufunc__
-        raise NotImplementedError()
-
-    def __mul__(self, other):
-        size = other.shape[-1]
-        wdata = self._wdata(size)
-        if wdata is None:
-            return other
-        return wdata.astype(other.dtype) * other
-
-    def __rmul__(self, other):
-        return self.__mul__(other)
+    def __array_ufunc__(self, ufunc, method, *args, **kwargs):
+
+        if args[0] is self:
+            second_arg = args[1]
+            size = second_arg.shape[-1]
+            dtype = second_arg.dtype
+            wdata = self._wdata(size)
+            if wdata is None:
+                return second_arg
+            first_arg = wdata.astype(dtype)
+        else:
+            first_arg = args[0]
+            size = first_arg.shape[-1]
+            dtype = first_arg.dtype
+            wdata = self._wdata(size)
+            if wdata is None:
+                return first_arg
+            second_arg = wdata.astype(dtype)
+
+        return getattr(ufunc, method)(first_arg, second_arg, **kwargs)
 
 
 class IdentityWindowingFunc(WindowingFunc):

zounds/spectral/test_weighting.py

@@ -104,3 +104,9 @@ def test_can_apply_weighting_to_filter_bank(self):
         weighted = bank * AWeighting()
         self.assertSequenceEqual(bank.dimensions, weighted.dimensions)
 
+    def test_multiplication_by_weighting_is_commutative(self):
+        sr = SR11025()
+        band = FrequencyBand(20, sr.nyquist)
+        scale = MelScale(band, 100)
+        bank = fir_filter_bank(scale, 256, sr, np.hanning(25))
+        np.testing.assert_allclose(bank * AWeighting(), AWeighting() * bank)

zounds/spectral/weighting.py

@@ -35,18 +35,17 @@ def _get_factors(self, arr):
 
         raise ValueError('arr must have a frequency dimension')
 
-    def __mul__(self, other):
-        factors = self._get_factors(other)
-        return factors * other
-
-    def __rmul__(self, other):
-        return self.__mul__(other)
-
-    def __div__(self, other):
-        return other / self._get_factors(other)
-
-    def __rdiv__(self, other):
-        return self.__div__(other)
+    def __array_ufunc__(self, ufunc, method, *args, **kwargs):
+        if ufunc == np.multiply or ufunc == np.divide:
+            if args[0] is self:
+                first_arg = self._get_factors(args[1])
+                second_arg = args[1]
+            else:
+                first_arg = args[0]
+                second_arg = self._get_factors(args[0])
+            return getattr(ufunc, method)(first_arg, second_arg, **kwargs)
+        else:
+            return NotImplemented
 
 
 class AWeighting(FrequencyWeighting):