diff --git a/.flake8 b/.flake8 index e8aa4485ab56..7c966c4aca93 100644 --- a/.flake8 +++ b/.flake8 @@ -13,7 +13,7 @@ ignore = N801, N802, N803, N806, N812, # pydocstyle D100, D101, D102, D103, D104, D105, D106, D107, - D200, D202, D203, D204, D205, D207, D209, D212, D213, + D200, D202, D203, D204, D205, D207, D212, D213, D301 D400, D401, D402, D403, D413, diff --git a/lib/matplotlib/backends/backend_pdf.py b/lib/matplotlib/backends/backend_pdf.py index f4e254a0e9cb..f0a6620fba06 100644 --- a/lib/matplotlib/backends/backend_pdf.py +++ b/lib/matplotlib/backends/backend_pdf.py @@ -95,9 +95,12 @@ def fill(strings, linelen=75): - """Make one string from sequence of strings, with whitespace - in between. The whitespace is chosen to form lines of at most - linelen characters, if possible.""" + """ + Make one string from sequence of strings, with whitespace in between. + + The whitespace is chosen to form lines of at most *linelen* characters, + if possible. + """ currpos = 0 lasti = 0 result = [] @@ -295,8 +298,7 @@ def pdfRepr(self): class Verbatim: - """Store verbatim PDF command content for later inclusion in the - stream.""" + """Store verbatim PDF command content for later inclusion in the stream.""" def __init__(self, x): self._x = x @@ -322,9 +324,16 @@ def pdfRepr(self): def _paint_path(fill, stroke): - """Return the PDF operator to paint a path in the following way: - fill: fill the path with the fill color - stroke: stroke the outline of the path with the line color""" + """ + Return the PDF operator to paint a path. + + Parameters + ---------- + fill: bool + Fill the path with the fill color. + stroke: bool + Stroke the outline of the path with the line color. + """ if stroke: if fill: return Op.fill_stroke @@ -339,7 +348,8 @@ def _paint_path(fill, stroke): class Stream: - """PDF stream object. + """ + PDF stream object. This has no pdfRepr method. Instead, call begin(), then output the contents of the stream by calling write(), and finally call end(). diff --git a/lib/matplotlib/dviread.py b/lib/matplotlib/dviread.py index ba973c23a77e..37a50194dbb3 100644 --- a/lib/matplotlib/dviread.py +++ b/lib/matplotlib/dviread.py @@ -69,45 +69,55 @@ # argument bytes in this delta. def _arg_raw(dvi, delta): - """Return *delta* without reading anything more from the dvi file""" + """Return *delta* without reading anything more from the dvi file.""" return delta def _arg(bytes, signed, dvi, _): - """Read *bytes* bytes, returning the bytes interpreted as a - signed integer if *signed* is true, unsigned otherwise.""" + """ + Read *bytes* bytes, returning the bytes interpreted as a signed integer + if *signed* is true, unsigned otherwise. + """ return dvi._arg(bytes, signed) def _arg_slen(dvi, delta): - """Signed, length *delta* + """ + Signed, length *delta* - Read *delta* bytes, returning None if *delta* is zero, and - the bytes interpreted as a signed integer otherwise.""" + Read *delta* bytes, returning None if *delta* is zero, and the bytes + interpreted as a signed integer otherwise. + """ if delta == 0: return None return dvi._arg(delta, True) def _arg_slen1(dvi, delta): - """Signed, length *delta*+1 + """ + Signed, length *delta*+1 - Read *delta*+1 bytes, returning the bytes interpreted as signed.""" + Read *delta*+1 bytes, returning the bytes interpreted as signed. + """ return dvi._arg(delta+1, True) def _arg_ulen1(dvi, delta): - """Unsigned length *delta*+1 + """ + Unsigned length *delta*+1 - Read *delta*+1 bytes, returning the bytes interpreted as unsigned.""" + Read *delta*+1 bytes, returning the bytes interpreted as unsigned. + """ return dvi._arg(delta+1, False) def _arg_olen1(dvi, delta): - """Optionally signed, length *delta*+1 + """ + Optionally signed, length *delta*+1 Read *delta*+1 bytes, returning the bytes interpreted as - unsigned integer for 0<=*delta*<3 and signed if *delta*==3.""" + unsigned integer for 0<=*delta*<3 and signed if *delta*==3. + """ return dvi._arg(delta + 1, delta == 3) @@ -122,7 +132,8 @@ def _arg_olen1(dvi, delta): def _dispatch(table, min, max=None, state=None, args=('raw',)): - """Decorator for dispatch by opcode. Sets the values in *table* + """ + Decorator for dispatch by opcode. Sets the values in *table* from *min* to *max* to this method, adds a check that the Dvi state matches *state* if not None, reads arguments from the file according to *args*. diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py index b0d6e7f0bda1..c5497ab4d406 100644 --- a/lib/matplotlib/tests/test_colors.py +++ b/lib/matplotlib/tests/test_colors.py @@ -592,8 +592,10 @@ def test_light_source_topo_surface(): def test_light_source_shading_default(): - """Array comparison test for the default "hsv" blend mode. Ensure the - default result doesn't change without warning.""" + """ + Array comparison test for the default "hsv" blend mode. Ensure the + default result doesn't change without warning. + """ y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j] z = 10 * np.cos(x**2 + y**2) @@ -647,8 +649,10 @@ def test_light_source_shading_default(): # additional elements being masked when calculating the gradient thus # the output is different with earlier numpy versions. def test_light_source_masked_shading(): - """Array comparison test for a surface with a masked portion. Ensures that - we don't wind up with "fringes" of odd colors around masked regions.""" + """ + Array comparison test for a surface with a masked portion. Ensures that + we don't wind up with "fringes" of odd colors around masked regions. + """ y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j] z = 10 * np.cos(x**2 + y**2) @@ -701,8 +705,10 @@ def test_light_source_masked_shading(): def test_light_source_hillshading(): - """Compare the current hillshading method against one that should be - mathematically equivalent. Illuminates a cone from a range of angles.""" + """ + Compare the current hillshading method against one that should be + mathematically equivalent. Illuminates a cone from a range of angles. + """ def alternative_hillshade(azimuth, elev, z): illum = _sph2cart(*_azimuth2math(azimuth, elev)) @@ -730,20 +736,25 @@ def alternative_hillshade(azimuth, elev, z): def test_light_source_planar_hillshading(): - """Ensure that the illumination intensity is correct for planar - surfaces.""" + """ + Ensure that the illumination intensity is correct for planar surfaces. + """ def plane(azimuth, elevation, x, y): - """Create a plane whose normal vector is at the given azimuth and - elevation.""" + """ + Create a plane whose normal vector is at the given azimuth and + elevation. + """ theta, phi = _azimuth2math(azimuth, elevation) a, b, c = _sph2cart(theta, phi) z = -(a*x + b*y) / c return z def angled_plane(azimuth, elevation, angle, x, y): - """Create a plane whose normal vector is at an angle from the given - azimuth and elevation.""" + """ + Create a plane whose normal vector is at an angle from the given + azimuth and elevation. + """ elevation = elevation + angle if elevation > 90: azimuth = (azimuth + 180) % 360 @@ -775,8 +786,10 @@ def _sph2cart(theta, phi): def _azimuth2math(azimuth, elevation): - """Converts from clockwise-from-north and up-from-horizontal to - mathematical conventions.""" + """ + Convert from clockwise-from-north and up-from-horizontal to mathematical + conventions. + """ theta = np.radians((90 - azimuth) % 360) phi = np.radians(90 - elevation) return theta, phi @@ -795,8 +808,10 @@ def test_pandas_iterable(pd): @pytest.mark.parametrize('name', sorted(cm.cmap_d)) def test_colormap_reversing(name): - """Check the generated _lut data of a colormap and corresponding - reversed colormap if they are almost the same.""" + """ + Check the generated _lut data of a colormap and corresponding reversed + colormap if they are almost the same. + """ cmap = plt.get_cmap(name) cmap_r = cmap.reversed() if not cmap_r._isinit: diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py index 6d2f204793e2..80389a93c220 100644 --- a/lib/matplotlib/tests/test_mlab.py +++ b/lib/matplotlib/tests/test_mlab.py @@ -1561,44 +1561,38 @@ def test_single_dataset_element(self): mlab.GaussianKDE([42]) def test_silverman_multidim_dataset(self): - """Use a multi-dimensional array as the dataset and test silverman's - output""" + """Test silverman's for a multi-dimensional array.""" x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) with pytest.raises(np.linalg.LinAlgError): mlab.GaussianKDE(x1, "silverman") def test_silverman_singledim_dataset(self): - """Use a single dimension list as the dataset and test silverman's - output.""" + """Test silverman's output for a single dimension list.""" x1 = np.array([-7, -5, 1, 4, 5]) mygauss = mlab.GaussianKDE(x1, "silverman") y_expected = 0.76770389927475502 assert_almost_equal(mygauss.covariance_factor(), y_expected, 7) def test_scott_multidim_dataset(self): - """Use a multi-dimensional array as the dataset and test scott's output - """ + """Test scott's output for a multi-dimensional array.""" x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) with pytest.raises(np.linalg.LinAlgError): mlab.GaussianKDE(x1, "scott") def test_scott_singledim_dataset(self): - """Use a single-dimensional array as the dataset and test scott's - output""" + """Test scott's output a single-dimensional array.""" x1 = np.array([-7, -5, 1, 4, 5]) mygauss = mlab.GaussianKDE(x1, "scott") y_expected = 0.72477966367769553 assert_almost_equal(mygauss.covariance_factor(), y_expected, 7) def test_scalar_empty_dataset(self): - """Use an empty array as the dataset and test the scalar's cov factor - """ + """Test the scalar's cov factor for an empty array.""" with pytest.raises(ValueError): mlab.GaussianKDE([], bw_method=5) def test_scalar_covariance_dataset(self): - """Use a dataset and test a scalar's cov factor - """ + """Test a scalar's cov factor.""" np.random.seed(8765678) n_basesample = 50 multidim_data = [np.random.randn(n_basesample) for i in range(5)] @@ -1607,8 +1601,7 @@ def test_scalar_covariance_dataset(self): assert kde.covariance_factor() == 0.5 def test_callable_covariance_dataset(self): - """Use a multi-dimensional array as the dataset and test the callable's - cov factor""" + """Test the callable's cov factor for a multi-dimensional array.""" np.random.seed(8765678) n_basesample = 50 multidim_data = [np.random.randn(n_basesample) for i in range(5)] @@ -1619,8 +1612,7 @@ def callable_fun(x): assert kde.covariance_factor() == 0.55 def test_callable_singledim_dataset(self): - """Use a single-dimensional array as the dataset and test the - callable's cov factor""" + """Test the callable's cov factor for a single-dimensional array.""" np.random.seed(8765678) n_basesample = 50 multidim_data = np.random.randn(n_basesample) pFad - Phonifier reborn

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