Support SciPy 1.0.0+ too (#37)

* Relax SciPy version constraint

Allow users to install with SciPy 1.0.0+.

* Use SciPy 1.0.0 on CIs

Start testing with SciPy 1.0.0 on CIs.

* Skip some tests that fail due to a SciPy 1.0.0 bug

Appears that SciPy 1.0.0+ has a bug that causes it to incorrectly
compute some Fourier filters when the data has double precision. So add
a check to skip these cases. We still test the filters for other
precision. So this doesn't block us from testing them as well.

* Switch back to SciPy 0.19.1 on CIs

As coverage drops when testing with SciPy 1.0.0+ due to some tests that
need to be skipped due to a SciPy bug, switch back to testing with SciPy
0.19.1. After all there appear to be no other significant differences
between the two versions of SciPy.

setup.py

@@ -31,7 +31,7 @@ def run_tests(self):
 requirements = [
     "dask >=0.13.0",
     "numpy >=1.11.3",
-    "scipy >=0.18.1,<1.0.0",
+    "scipy >=0.18.1",
     "pims >=0.3.3",
 ]
 

tests/test_dask_image/test_ndfourier/test_core.py

@@ -4,10 +4,12 @@
 from __future__ import absolute_import
 
 import numbers
+from distutils.version import LooseVersion
 
 import pytest
 
 import numpy as np
+import scipy as sp
 import scipy.ndimage.fourier as sp_ndf
 
 import dask.array as da
@@ -94,6 +96,17 @@ def test_fourier_filter_identity(funcname, s):
     ]
 )
 def test_fourier_filter_type(funcname, upcast_type, dtype):
+    if (LooseVersion(sp.__version__) >= "1.0.0" and (
+            dtype is np.int64 or
+            dtype is np.float64
+        ) and (
+            funcname is "fourier_gaussian" or
+            funcname is "fourier_uniform"
+        )):
+        pytest.skip(
+            "SciPy 1.0.0+ doesn't handle double precision values correctly."
+        )
+
     dtype = np.dtype(dtype).type
 
     s = 1