fix some code lints, use pycodestyle, and skip E226

.travis.yml

@@ -36,5 +36,6 @@ script:
       fi
 
     - if [[ $TEST_TARGET == 'coding_standards' ]]; then
-        find . -type f -name "*.py" ! -name 'conf.py' | xargs flake8 --ignore=E741 --max-line-length=100 ;
+        pycodestyle --ignore=E741,E226 --max-line-length=100 tests ;
+        pycodestyle --ignore=E741,E226 --max-line-length=100 utide ;
       fi

requirements-dev.txt

@@ -1,5 +1,5 @@
-flake8
+matplotlib
+pandas
+pycodestyle
 pytest
 pytest-cov
-pandas
-matplotlib

tests/test_HNL.py

@@ -1,12 +1,13 @@
 import os
 
-import pytest
-
 import numpy as np
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 
+import pytest
+
 import utide
-from utide.utilities import Bunch, loadbunch, convert_unicode_arrays
+from utide.utilities import Bunch, convert_unicode_arrays, loadbunch
+
 
 thisdir = os.path.dirname(__file__)
 datapath = os.path.join(thisdir, 'data')
@@ -16,8 +17,13 @@
 
 epoch = '1700-01-01'
 
-solve_kw = dict(epoch=epoch, lat=21, constit='auto', method='ols',
-                conf_int='linear')
+solve_kw = {
+    'epoch': epoch,
+    'lat': 21,
+    'constit': 'auto',
+    'method': 'ols',
+    'conf_int': 'linear',
+}
 
 coef_all = utide.solve(t, h, **solve_kw)
 

tests/test_astron.py

@@ -13,6 +13,7 @@
 from __future__ import (absolute_import, division, print_function)
 
 import numpy as np
+
 from utide.astronomy import ut_astron
 
 

tests/test_harmonics.py

@@ -12,9 +12,9 @@
 
 from numpy.testing import assert_array_almost_equal
 
+from utide._ut_constants import _base_dir
 from utide.harmonics import FUV
 from utide.utilities import loadbunch
-from utide._ut_constants import _base_dir
 
 fname = os.path.join(_base_dir, 'FUV0.npz')
 
@@ -27,7 +27,7 @@ def test_FUV():
 
     for i, flag in enumerate(x.flags):
         F, U, V = FUV(x.t, x.t0, x.lind-1, x.lat, flag)
-        print('i:', i, "ngflgs:", flag)
+        print('i: {} ngflags: {}'.format(i, flag))
 
         # We use broadcasting instead of replication, so
         # we need to test only against the first row of

tests/test_normalize.py

@@ -3,6 +3,7 @@
 import datetime
 
 import numpy as np
+
 from utide._time_conversion import _normalize_time
 
 

tests/test_periodogram.py

@@ -5,17 +5,18 @@
 from __future__ import (absolute_import, division, print_function)
 
 import numpy as np
+
 import utide.periodogram as pgram
 
 
-def random_ts(ndays, dt_hours, complex=True):
+def random_ts(ndays, dt_hours, is_complex=True):
     """Returns t (time in days) and x (random series)."""
     np.random.seed(1)
     npts = int(ndays * 24 / dt_hours)
     if npts % 2:
         npts -= 1
     t = np.arange(npts, dtype=float) * dt_hours / 24
-    if complex:
+    if is_complex:
         x = np.random.randn(npts) + 1j * np.random.randn(npts)
     else:
         x = np.random.randn(npts)
@@ -39,7 +40,7 @@ def test_fft_ls_consistency():
 
 
 def test_uv_consistency():
-    t, x = random_ts(20, 0.5, complex=False)
+    t, x = random_ts(20, 0.5, is_complex=False)
     xx = x + 1j * x
     y_fft = pgram.band_psd(t, xx, [1/12.42], equi=True)
     y_ls = pgram.band_psd(t, xx, [1/12.42], equi=False)

tests/test_solve.py

@@ -11,9 +11,9 @@
 
 import numpy as np
 
-from utide._ut_constants import ut_constants
-from utide import solve
 from utide import reconstruct
+from utide import solve
+from utide._ut_constants import ut_constants
 from utide.utilities import Bunch
 
 
@@ -38,14 +38,15 @@ def test_roundtrip():
     arg = 2 * np.pi * (time - tref) * freq_cpd[jj] - np.deg2rad(phase)
     time_series = amp * np.cos(arg)
 
-    opts = dict(constit='auto',
-                phase='raw',
-                nodal=False,
-                trend=False,
-                method='ols',
-                conf_int='linear',
-                Rayleigh_min=0.95,
-                )
+    opts = {
+        'constit': 'auto',
+        'phase': 'raw',
+        'nodal': False,
+        'trend': False,
+        'method': 'ols',
+        'conf_int': 'linear',
+        'Rayleigh_min': 0.95,
+    }
 
     speed_coef = solve(time, time_series, time_series, lat=lat, **opts)
     elev_coef = solve(time, time_series, lat=lat, **opts)
@@ -100,14 +101,15 @@ def test_masked_input():
     arg = 2 * np.pi * (time - tref) * freq_cpd[jj] - np.deg2rad(phase)
     time_series = amp * np.cos(arg)
 
-    opts = dict(constit='auto',
-                phase='raw',
-                nodal=False,
-                trend=False,
-                method='ols',
-                conf_int='linear',
-                Rayleigh_min=0.95,
-                )
+    opts = {
+        'constit': 'auto',
+        'phase': 'raw',
+        'nodal': False,
+        'trend': False,
+        'method': 'ols',
+        'conf_int': 'linear',
+        'Rayleigh_min': 0.95,
+    }
 
     t = np.ma.array(time)
     t[[10, 15, 20, 21]] = np.ma.masked
@@ -170,14 +172,15 @@ def test_robust():
     time_series[:5] = 10
     time_series[-5:] = -10
 
-    opts = dict(constit='auto',
-                phase='raw',
-                nodal=False,
-                trend=False,
-                method='robust',
-                conf_int='linear',
-                Rayleigh_min=0.95,
-                )
+    opts = {
+        'constit': 'auto',
+        'phase': 'raw',
+        'nodal': False,
+        'trend': False,
+        'method': 'robust',
+        'conf_int': 'linear',
+        'Rayleigh_min': 0.95,
+    }
 
     speed_coef = solve(time, time_series, time_series, lat=lat, **opts)
     elev_coef = solve(time, time_series, lat=lat, **opts)
@@ -210,17 +213,18 @@ def test_MC():
     np.random.seed(1)
     time_series += 0.01 * np.random.randn(len(time_series))
 
-    opts = dict(constit='auto',
-                phase='raw',
-                nodal=False,
-                trend=False,
-                method='ols',
-                conf_int='MC',
-                white=False,
-                Rayleigh_min=0.95,
-                )
-
-    speed_coef = solve(time, time_series,  time_series, lat=lat, **opts)
+    opts = {
+        'constit': 'auto',
+        'phase': 'raw',
+        'nodal': False,
+        'trend': False,
+        'method': 'ols',
+        'conf_int': 'MC',
+        'white': False,
+        'Rayleigh_min': 0.95,
+    }
+
+    speed_coef = solve(time, time_series, time_series, lat=lat, **opts)
     elev_coef = solve(time, time_series, lat=lat, **opts)
 
     for name, AA, AA_ci, gg, gg_ci in zip(elev_coef.name,

tests/test_uv.py

@@ -2,15 +2,16 @@
 Full example test.
 
 """
-import pytest
+
+from matplotlib.dates import date2num
 
 import numpy as np
+
 from pandas import date_range
-from matplotlib.dates import date2num
 
-from utide import solve
-from utide import reconstruct
+import pytest
 
+from utide import reconstruct, solve
 from utide.utilities import Bunch
 
 

utide/confidence.py

@@ -12,8 +12,8 @@
 
 import numpy as np
 
-from utide.periodogram import band_psd
 from utide.ellipse_params import ut_cs2cep
+from utide.periodogram import band_psd
 from utide.utilities import complex_interp
 
 
@@ -346,7 +346,7 @@ def _confidence(coef, cnstit, opt, t, e, tin, elor, xraw, xmod, W, m, B,
                         coef.theta_ci[ind] = 1.96 * sig2.imag
                     ind += 1
         else:
-            raise NotImplementedError("Monte Carlo inference not implemented")
+            raise NotImplementedError('Monte Carlo inference not implemented')
 
     return coef
 

utide/utilities.py

@@ -302,8 +302,7 @@ def convert_unicode_arrays(b):
             if val.dtype.kind == 'O':
                 newval = np.empty(shape=val.shape, dtype=val.dtype)
                 for k, x in enumerate(val):
-                    if (isinstance(x, np.ndarray) and x.dtype.kind == 'U'
-                            and x.size == 1):
+                    if (isinstance(x, np.ndarray) and x.dtype.kind == 'U' and x.size == 1):  # noqa
                         newval[k] = x.item()
                     else:
                         newval[k] = x