add support for numpy 1.18.1

.travis.yml

@@ -56,15 +56,17 @@ install:
     # install dependencies yt
     if [[ $TRAVIS_BUILD_STAGE_NAME != "Lint" ]]; then
       if [[ $MINIMAL == 1 ]]; then
-        $PIP install numpy==1.10.4 cython==0.24
+        # The first numpy to support py3.6 is 1.12, but numpy 1.13 matches 
+        # unyt so we'll match it here. 
+        $PIP install numpy==1.13.3 cython==0.24
         $PIP install -r tests/test_minimal_requirements.txt
       else
         # Getting cartopy installed requires getting cython and numpy installed
         # first; this is potentially going to be fixed with the inclusion of
         # pyproject.toml in cartopy.
         # These versions are pinned, so we will need to update/remove them when
         # the hack is no longer necessary.
-        $PIP install numpy==1.16.2 cython==0.29.6
+        $PIP install numpy==1.18.1 cython==0.29.6
         $PIP install -r tests/test_requirements.txt
       fi
       $PIP install -e .
@@ -78,21 +80,12 @@ jobs:
       python: 3.6
       script: flake8 yt/
 
-    - stage: lint
-      python: 2.7
-      script: flake8 yt/
-
     - stage: tests
-      name: "Python: 2.7 Unit Tests"
-      python: 2.7
+      name: "Python: 3.6 Minimal Dependency Unit Tests"
+      python: 3.6
       env: MINIMAL=1
       script: coverage run $(which nosetests) -c nose_unit.cfg
 
-    - stage: tests
-      name: "Python: 2.7 Unit Tests"
-      python: 2.7
-      script: coverage run $(which nosetests) -c nose_unit.cfg
-
     - stage: tests
       name: "Python: 3.5 Unit Tests"
       python: 3.5
@@ -103,13 +96,6 @@ jobs:
       python: 3.6
       script: coverage run $(which nosetests) -c nose_unit.cfg
 
-    - stage: tests
-      name: "Python: 2.7 Minimal Dependency Answer Tests"
-      python: 2.7
-      env: MINIMAL=1
-      script: coverage run $(which nosetests) -c nose_answer.cfg
-      after_failure: python tests/report_failed_answers.py -f -m --xunit-file "answer_nosetests.xml"
-
     - stage: tests
       name: "Python: 3.6 Answer Tests"
       python: 3.6

yt/units/tests/test_ytarray.py

@@ -828,7 +828,10 @@ def unary_ufunc_comparison(ufunc, a):
 
 
 def binary_ufunc_comparison(ufunc, a, b):
-    out = a.copy()
+    if ufunc in [np.divmod]:
+        out = (a.copy(), a.copy())
+    else:
+        out = a.copy()
     if ufunc in yield_np_ufuncs([
             'add', 'subtract', 'remainder', 'fmod', 'mod', 'arctan2', 'hypot',
             'greater', 'greater_equal', 'less', 'less_equal', 'equal',
@@ -860,7 +863,10 @@ def binary_ufunc_comparison(ufunc, a, b):
         assert_true(not isinstance(ret, YTArray) and
                     isinstance(ret, np.ndarray))
     if isinstance(ret, tuple):
-        assert_array_equal(ret[0], out)
+        assert isinstance(out, tuple)
+        assert len(out) == len(ret)
+        for o, r in zip(out, ret):
+            assert_array_equal(o, r)
     else:
         assert_array_equal(ret, out)
     if (ufunc in (np.divide, np.true_divide, np.arctan2) and

yt/units/yt_array.py

@@ -308,6 +308,8 @@ def _unit_repr_check_same(my_units, other_units):
     sin, cos, tan,
 )
 
+multiple_output_operators = {modf: 2, frexp: 2, divmod_: 2}
+
 class YTArray(np.ndarray):
     """
     An ndarray subclass that attaches a symbolic unit object to the array data.
@@ -1370,9 +1372,25 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
             func = getattr(ufunc, method)
             if 'out' in kwargs:
                 out_orig = kwargs.pop('out')
-                out = np.asarray(out_orig[0])
+                if ufunc in multiple_output_operators:
+                    outs = []
+                    for arr in out_orig:
+                        outs.append(arr.view(np.ndarray))
+                    out = tuple(outs)
+                else:
+                    out_element = out_orig[0]
+                    if out_element.dtype.kind in ("u", "i"):
+                        new_dtype = "f" + str(out_element.dtype.itemsize)
+                        float_values = out_element.astype(new_dtype)
+                        out_element.dtype = new_dtype
+                        np.copyto(out_element, float_values)
+                    out = out_element.view(np.ndarray)
             else:
-                out = None
+                if ufunc in multiple_output_operators:
+                    num_outputs = multiple_output_operators[ufunc]
+                    out = (None,) * num_outputs
+                else:
+                    out = None
             if len(inputs) == 1:
                 _, inp, u = get_inp_u_unary(ufunc, inputs)
                 out_arr = func(np.asarray(inp), out=out, **kwargs)
@@ -1439,14 +1457,15 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                 else:
                     out_arr = ret_class(np.asarray(out_arr), unit)
             if out is not None:
-                out_orig[0].flat[:] = out.flat[:]
-                if isinstance(out_orig[0], YTArray):
-                    out_orig[0].units = unit
+                if ufunc not in multiple_output_operators:
+                    out_orig[0].flat[:] = out.flat[:]
+                    if isinstance(out_orig[0], YTArray):
+                        out_orig[0].units = unit
             return out_arr
 
         def copy(self, order='C'):
             return type(self)(np.copy(np.asarray(self)), self.units)
-    
+
     def __array_finalize__(self, obj):
         if obj is None and hasattr(self, 'units'):
             return

yt/visualization/color_maps.py

@@ -548,10 +548,10 @@ def make_colormap(ctuple_list, name=None, interpolate=True):
 
         # Interpolate the R, G, and B channels from one color to the next
         # Use np.round to make sure you're on a discrete index
-        interval = np.round(next_index)-np.round(rolling_index)
+        interval = int(np.round(next_index)-np.round(rolling_index))
         for j in np.arange(3):
             cmap[int(np.rint(rolling_index)):int(np.rint(next_index)), j] = \
-                np.linspace(color[j], next_color[j], interval)
+                np.linspace(color[j], next_color[j], num=interval)
 
         rolling_index = next_index