get up to date with coverage and pep8

Author: phobson

.travis.yml

@@ -5,23 +5,23 @@ matrix:
     - python: 2.7
       env:
         - COVERAGE=false
-        - TESTERS="conda-forge pytest mock pytest-mpl coverage"
+        - EXTRATESTERS="mock"
         - ARGS="--verbose"
     - python: 3.4
       env:
         - COVERAGE=false
-        - TESTERS="conda-forge pytest pytest-mpl coverage"
+        - EXTRATESTERS=""
         - ARGS="--mpl --verbose"
     - python: 3.5
       env:
         - COVERAGE=false
-        - TESTERS="conda-forge pytest pytest-mpl coverage"
+        - EXTRATESTERS=""
         - ARGS="--mpl --verbose"
     - python: 3.5
       env:
         - COVERAGE=true
-        - TESTERS="conda-forge pytest pytest-mpl coverage"
-        - ARGS="--mpl --verbose"
+        - EXTRATESTERS=""
+        - ARGS="--mpl --verbose --pep8"
 
 before_install:
 
@@ -41,7 +41,7 @@ install:
 
   - conda create --yes -n test python=$TRAVIS_PYTHON_VERSION numpy matplotlib docopt requests pyyaml
   - source activate test
-  - conda install --yes --channel=${TESTERS}
+  - conda install --yes --channel=conda-forge pytest-mpl pytest-cov pytest-pep8 ${EXTRATESTERS}
   - if [ ${COVERAGE} = true ]; then conda install scipy --yes; fi
   - pip install codecov
   - pip install .

docs/readme.rst

@@ -1 +1 @@
-.. include:: ../README.rst
+.. include:: ../README.md

probscale/tests/__init__.py

@@ -4,7 +4,8 @@
 
 import probscale
 
+
 def test(*args):
     options = [resource_filename('probscale', 'tests')]
     options.extend(list(args))
-    return pytest.main(options)
+    return pytest.main(options)

probscale/tests/test_formatters.py

@@ -6,7 +6,10 @@
 from probscale import formatters
 
 
-@pytest.mark.parametrize("fmtr", [formatters.PctFormatter, formatters.ProbFormatter])
+@pytest.mark.parametrize("fmtr", [
+    formatters.PctFormatter,
+    formatters.ProbFormatter
+])
 def test_base_class_of_formatter(fmtr):
     assert issubclass(fmtr, formatters._FormatterMixin)
 

probscale/tests/test_probscale.py

@@ -58,9 +58,9 @@ def test_minimal_norm__approx_inv_erf(mn, mn_input):
 
 def test_minimal_norm_ppf(mn, mn_input):
     known_ppf = numpy.array([
-        -0.43715354,  0.6495236 , -1.49851307,  0.93847570,
+        -0.43715354,  0.64952360, -1.49851307,  0.93847570,
         -0.36648929, -1.34693863,  0.68713129, -0.40701088,
-         0.71275076,  0.40701088, -0.70952297,  1.34075503,
+        +0.71275076,  0.40701088, -0.70952297,  1.34075503,
     ])
     diff = mn.ppf(mn_input) - known_ppf
     assert numpy.all(numpy.abs(diff) < 0.001)

probscale/tests/test_transforms.py

@@ -8,7 +8,10 @@
 
 
 def test__mask_out_of_bounds():
-    known = numpy.array([numpy.nan, numpy.nan, 0.1, 0.5, 0.9, numpy.nan, numpy.nan])
+    known = numpy.array([
+        numpy.nan, numpy.nan, 0.1, 0.5,
+        0.9, numpy.nan, numpy.nan
+    ])
     x = [-0.1, 0, 0.1, 0.5, 0.9, 1.0, 1.1]
     result = transforms._mask_out_of_bounds(x)
     nptest.assert_array_equal(known, result)
@@ -22,7 +25,6 @@ def test__clip_out_of_bounds():
     assert numpy.all(diff < 0.0001)
 
 
-
 @pytest.fixture
 def prob_trans():
     cls = transforms.ProbTransform
@@ -80,13 +82,19 @@ def test_transform_inverted(trans, inver_cls):
     assert trans.out_of_bounds == t_inv.out_of_bounds
 
 
-@pytest.mark.parametrize('cls', [transforms.ProbTransform, transforms.QuantileTransform])
+@pytest.mark.parametrize('cls', [
+    transforms.ProbTransform,
+    transforms.QuantileTransform
+])
 def test_bad_out_of_bounds(cls):
     with pytest.raises(ValueError):
         cls(_minimal_norm, out_of_bounds='junk')
 
 
-@pytest.mark.parametrize('cls', [transforms.ProbTransform, transforms.QuantileTransform])
+@pytest.mark.parametrize('cls', [
+    transforms.ProbTransform,
+    transforms.QuantileTransform
+])
 @pytest.mark.parametrize(('method', 'func'), [
     ('clip', transforms._clip_out_of_bounds),
     ('mask', transforms._mask_out_of_bounds),

probscale/tests/test_validate.py

@@ -4,6 +4,7 @@
 
 from probscale import validate
 
+
 def test_axes_object_invalid():
     with pytest.raises(ValueError):
         validate.axes_object('junk')