style: Update to Black v20.8 formatting

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/psf/black
-    rev: stable
+    rev: 20.8b1
     hooks:
     - id: black
       language_version: python3

docs/development.rst

@@ -19,6 +19,15 @@ Then setup the Git pre-commit hook for `Black <https://github.com/psf/black>`__
 
     pre-commit install
 
+as the ``rev`` gets updated through time to track changes of different hooks,
+simply run
+
+.. code-block:: console
+
+    pre-commit autoupdate
+
+to have pre-commit install the new version.
+
 Testing
 -------
 

src/pyhf/cli/patchset.py

@@ -18,9 +18,7 @@ def cli():
 
 @cli.command()
 @click.argument('patchset', default='-')
-@click.option(
-    '--name', help='The name of the patch to extract.', default=None,
-)
+@click.option('--name', help='The name of the patch to extract.', default=None)
 @click.option(
     '--output-file',
     help='The location of the output json file. If not specified, prints to screen.',
@@ -64,9 +62,7 @@ def extract(patchset, name, output_file, with_metadata):
 @cli.command()
 @click.argument('background-only', default='-')
 @click.argument('patchset', default='-')
-@click.option(
-    '--name', help='The name of the patch to extract.', default=None,
-)
+@click.option('--name', help='The name of the patch to extract.', default=None)
 @click.option(
     '--output-file',
     help='The location of the output json file. If not specified, prints to screen.',

src/pyhf/cli/spec.py

@@ -346,7 +346,7 @@ def digest(workspace, algorithm, output_json):
     }
 
     if output_json:
-        output = json.dumps(digests, indent=4, sort_keys=True,)
+        output = json.dumps(digests, indent=4, sort_keys=True)
     else:
         output = '\n'.join(
             f"{hash_alg}:{digest}" for hash_alg, digest in digests.items()

src/pyhf/constraints.py

@@ -82,10 +82,10 @@ def _precompute(self):
         self.access_field = tensorlib.astensor(self._access_field, dtype='int')
 
     def has_pdf(self):
-        '''
+        """
         Returns:
             flag (`bool`): Whether the model has a Gaussian Constraint
-        '''
+        """
         return bool(self.param_viewer.index_selection)
 
     def make_pdf(self, pars):
@@ -213,10 +213,10 @@ def _precompute(self):
         self.batched_factors = tensorlib.astensor(self._batched_factors)
 
     def has_pdf(self):
-        '''
+        """
         Returns:
             flag (`bool`): Whether the model has a Gaussian Constraint
-        '''
+        """
         return bool(self.param_viewer.index_selection)
 
     def make_pdf(self, pars):

src/pyhf/modifiers/histosys.py

@@ -80,10 +80,10 @@ def _precompute(self):
             )
 
     def apply(self, pars):
-        '''
+        """
         Returns:
             modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
-        '''
+        """
         if not self.param_viewer.index_selection:
             return
 

src/pyhf/modifiers/lumi.py

@@ -57,10 +57,10 @@ def _precompute(self):
         self.lumi_default = tensorlib.ones(self.lumi_mask.shape)
 
     def apply(self, pars):
-        '''
+        """
         Returns:
             modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
-        '''
+        """
         if not self.param_viewer.index_selection:
             return
 

src/pyhf/modifiers/normfactor.py

@@ -58,10 +58,10 @@ def _precompute(self):
         self.normfactor_default = tensorlib.ones(self.normfactor_mask.shape)
 
     def apply(self, pars):
-        '''
+        """
         Returns:
             modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
-        '''
+        """
         if not self.param_viewer.index_selection:
             return
         tensorlib, _ = get_backend()

src/pyhf/modifiers/normsys.py

@@ -81,10 +81,10 @@ def _precompute(self):
             )
 
     def apply(self, pars):
-        '''
+        """
         Returns:
             modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
-        '''
+        """
         if not self.param_viewer.index_selection:
             return
 

src/pyhf/modifiers/shapefactor.py

@@ -131,10 +131,10 @@ def _precompute(self):
         self.sample_ones = tensorlib.ones(tensorlib.shape(self.shapefactor_mask)[1])
 
     def apply(self, pars):
-        '''
+        """
         Returns:
             modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
-        '''
+        """
         if not self.param_viewer.index_selection:
             return
 

src/pyhf/modifiers/shapesys.py

@@ -154,10 +154,10 @@ def finalize(self, pdfconfig):
             pdfconfig.param_set(pname).auxdata = default_backend.tolist(factors)
 
     def apply(self, pars):
-        '''
+        """
         Returns:
             modification tensor: Shape (n_modifiers, n_global_samples, n_alphas, n_global_bin)
-        '''
+        """
         tensorlib, _ = get_backend()
         if not self.param_viewer.index_selection:
             return

src/pyhf/optimize/mixins.py

@@ -29,7 +29,7 @@ def __init__(self, **kwargs):
             )
 
     def _internal_minimize(
-        self, func, x0, do_grad=False, bounds=None, fixed_vals=None, options={},
+        self, func, x0, do_grad=False, bounds=None, fixed_vals=None, options={}
     ):
 
         minimizer = self._get_minimizer(

src/pyhf/parameters/paramview.py

@@ -9,17 +9,15 @@ def _tensorviewer_from_parmap(par_map, batch_size):
     names, slices, _ = list(
         zip(
             *sorted(
-                [(k, v['slice'], v['slice'].start,) for k, v in par_map.items()],
+                [(k, v['slice'], v['slice'].start) for k, v in par_map.items()],
                 key=lambda x: x[2],
             )
         )
     )
     return _tensorviewer_from_slices(slices, names, batch_size)
 
 
-def extract_index_access(
-    baseviewer, subviewer, indices,
-):
+def extract_index_access(baseviewer, subviewer, indices):
     tensorlib, _ = get_backend()
 
     index_selection = []

src/pyhf/pdf.py

@@ -99,7 +99,7 @@ def _paramset_requirements_from_modelspec(spec, channel_nbins):
 
 def _nominal_and_modifiers_from_spec(config, spec):
     default_data_makers = {
-        'histosys': lambda: {'hi_data': [], 'lo_data': [], 'nom_data': [], 'mask': [],},
+        'histosys': lambda: {'hi_data': [], 'lo_data': [], 'nom_data': [], 'mask': []},
         'lumi': lambda: {'mask': []},
         'normsys': lambda: {'hi': [], 'lo': [], 'nom_data': [], 'mask': []},
         'normfactor': lambda: {'mask': []},

src/pyhf/tensor/common.py

@@ -74,7 +74,7 @@ def _tensorviewer_from_slices(target_slices, names, batch_size):
 
 
 def _tensorviewer_from_sizes(sizes, names, batch_size):
-    '''
+    """
     Creates a _Tensorviewer based on tensor sizes.
 
     the TV will be able to stitch together data with
@@ -83,7 +83,7 @@ def _tensorviewer_from_sizes(sizes, names, batch_size):
     tv.stitch([foo[slice1],foo[slice2],foo[slice3])
 
     and split them again accordingly.
-    '''
+    """
     target_slices = []
     start = 0
     for sz in sizes:

src/pyhf/tensor/tensorflow_backend.py

@@ -460,7 +460,7 @@ def normal_cdf(self, x, mu=0.0, sigma=1):
             TensorFlow Tensor: The CDF
         """
         normal = tfp.distributions.Normal(
-            self.astensor(mu, dtype='float'), self.astensor(sigma, dtype='float'),
+            self.astensor(mu, dtype='float'), self.astensor(sigma, dtype='float')
         )
         return normal.cdf(x)
 

src/pyhf/writexml.py

@@ -245,7 +245,7 @@ def writexml(spec, specdir, data_rootdir, resultprefix):
         Path(specdir).parent.joinpath('HistFactorySchema.dtd'),
     )
     combination = ET.Element(
-        "Combination", OutputFilePrefix=str(Path(specdir).joinpath(resultprefix)),
+        "Combination", OutputFilePrefix=str(Path(specdir).joinpath(resultprefix))
     )
 
     with uproot.recreate(

tests/conftest.py

@@ -144,11 +144,11 @@ def interpcode(request):
 
 @pytest.fixture(scope='function')
 def datadir(tmpdir, request):
-    '''
+    """
     Fixture responsible for searching a folder with the same name of test
     module and, if available, moving all contents to a temporary directory so
     tests can use them freely.
-    '''
+    """
     # this gets the module name (e.g. /path/to/pyhf/tests/test_schema.py)
     # and then gets the directory by removing the suffix (e.g. /path/to/pyhf/tests/test_schema)
     test_dir = pathlib.Path(request.module.__file__).with_suffix('')

tests/test_backend_consistency.py

@@ -115,7 +115,7 @@ def test_hypotest_qmu_tilde(
         pyhf.set_backend(backend)
 
         qmu_tilde = pyhf.infer.test_statistics.qmu_tilde(
-            1.0, data, pdf, pdf.config.suggested_init(), pdf.config.suggested_bounds(),
+            1.0, data, pdf, pdf.config.suggested_init(), pdf.config.suggested_bounds()
         )
         test_statistic.append(qmu_tilde)
 

tests/test_combined_modifiers.py

@@ -396,7 +396,7 @@ def test_shapesys(backend):
         par_map={
             'dummy1': {
                 'paramset': paramset(
-                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False,
+                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False
                 ),
                 'slice': slice(0, 1),
             },
@@ -424,7 +424,7 @@ def test_shapesys(backend):
             },
             'dummy2': {
                 'paramset': paramset(
-                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False,
+                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False
                 ),
                 'slice': slice(4, 5),
             },
@@ -495,13 +495,13 @@ def test_normfactor(backend):
         par_map={
             'mu1': {
                 'paramset': unconstrained(
-                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False,
+                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False
                 ),
                 'slice': slice(0, 1),
             },
             'mu2': {
                 'paramset': unconstrained(
-                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False,
+                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False
                 ),
                 'slice': slice(1, 2),
             },
@@ -575,7 +575,7 @@ def test_shapesys_zero(backend):
         par_map={
             'SigXsecOverSM': {
                 'paramset': paramset(
-                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False,
+                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False
                 ),
                 'slice': slice(0, 1),
             },
@@ -669,16 +669,13 @@ def test_shapefactor(backend):
         par_map={
             'shapefac1': {
                 'paramset': unconstrained(
-                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False,
+                    n_parameters=1, inits=[0], bounds=[[0, 10]], fixed=False
                 ),
                 'slice': slice(0, 1),
             },
             'shapefac2': {
                 'paramset': unconstrained(
-                    n_parameters=2,
-                    inits=[0, 0],
-                    bounds=[[0, 10], [0, 10]],
-                    fixed=False,
+                    n_parameters=2, inits=[0, 0], bounds=[[0, 10], [0, 10]], fixed=False
                 ),
                 'slice': slice(1, 3),
             },

tests/test_infer.py

@@ -120,11 +120,11 @@ def test_hypotest_return_expected_set(tmpdir, hypotest_args):
 
 
 def test_inferapi_pyhf_independence():
-    '''
+    """
     pyhf.infer should eventually be factored out so it should be
     infependent from pyhf internals. This is testing that
     a much simpler model still can run through pyhf.infer.hypotest
-    '''
+    """
     from pyhf import get_backend
 
     class _NonPyhfConfig(object):

tests/test_optim.py

@@ -27,9 +27,7 @@ def rosen(x):
 
 
 @pytest.mark.parametrize('do_stitch', [False, True], ids=['no_stitch', 'do_stitch'])
-@pytest.mark.parametrize(
-    'precision', ['32b', '64b'], ids=['32b', '64b'],
-)
+@pytest.mark.parametrize('precision', ['32b', '64b'], ids=['32b', '64b'])
 @pytest.mark.parametrize(
     'tensorlib',
     [
@@ -141,7 +139,7 @@ def test_optimizer_mixin_extra_kwargs(optimizer):
 @pytest.mark.parametrize(
     'backend,backend_new',
     itertools.permutations(
-        [('numpy', False), ('pytorch', True), ('tensorflow', True), ('jax', True),], 2
+        [('numpy', False), ('pytorch', True), ('tensorflow', True), ('jax', True)], 2
     ),
     ids=lambda pair: f'{pair[0]}',
 )

tests/test_patchset.py

@@ -25,7 +25,7 @@ def patch():
 
 @pytest.mark.parametrize(
     'patchset_file',
-    ['patchset_bad_empty_patches.json', 'patchset_bad_no_version.json',],
+    ['patchset_bad_empty_patches.json', 'patchset_bad_no_version.json'],
 )
 def test_patchset_invalid_spec(datadir, patchset_file):
     patchsetspec = json.load(open(datadir.join(patchset_file)))

tests/test_pdf.py

@@ -753,7 +753,7 @@ def test_sample_wrong_bins():
     [
         (
             None,
-            {'normsys': {'interpcode': 'code4'}, 'histosys': {'interpcode': 'code4p'},},
+            {'normsys': {'interpcode': 'code4'}, 'histosys': {'interpcode': 'code4p'}},
         )
     ],
 )

tests/test_schema.py

@@ -419,9 +419,7 @@ def test_jsonpatch_fail(patch):
         pyhf.utils.validate([patch], 'jsonpatch.json')
 
 
-@pytest.mark.parametrize(
-    'patchset_file', ['patchset_good.json'],
-)
+@pytest.mark.parametrize('patchset_file', ['patchset_good.json'])
 def test_patchset(datadir, patchset_file):
     patchset = json.load(open(datadir.join(patchset_file)))
     pyhf.utils.validate(patchset, 'patchset.json')