run pre-commit fixes

docs/conf.py

@@ -15,7 +15,6 @@
 
 import utide
 
-
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.

tests/test_HNL.py

@@ -2,14 +2,11 @@
 
 import numpy as np
 import pytest
-
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 
 import utide
-
 from utide.utilities import Bunch, convert_unicode_arrays, loadbunch
 
-
 thisdir = os.path.dirname(__file__)
 datapath = os.path.join(thisdir, "data")
 HNL_path = os.path.join(datapath, "HNL2010.asc")

tests/test_harmonics.py

@@ -14,7 +14,6 @@
 from utide.harmonics import FUV
 from utide.utilities import loadbunch
 
-
 fname = os.path.join(_base_dir, "FUV0.npz")
 
 

tests/test_order_constit.py

@@ -4,7 +4,6 @@
 from utide import reconstruct, solve
 from utide._ut_constants import constit_index_dict, ut_constants
 
-
 ts = 735604
 duration = 35
 
@@ -42,7 +41,6 @@
 
 @pytest.mark.parametrize("conf_int", ["none", "linear", "MC"])
 def test_order(conf_int):
-
  orders = [None, "PE", "frequency", opts0["constit"]]
  if conf_int != "none":
  orders.append("SNR")

tests/test_solve.py

@@ -13,7 +13,6 @@
 from utide._ut_constants import ut_constants
 from utide.utilities import Bunch
 
-
 ts = 735604
 duration = 35
 
@@ -196,7 +195,7 @@ def test_MC():
  ):
  print(f"{name:>5} {AA:10.4g} {AA_ci:10.4g} {gg:10.4g} {gg_ci:10.4g}")
 
- for (name, Lsmaj, Lsmaj_ci, Lsmin, Lsmin_ci, theta, theta_ci, gg, gg_ci) in zip(
+ for name, Lsmaj, Lsmaj_ci, Lsmin, Lsmin_ci, theta, theta_ci, gg, gg_ci in zip(
  speed_coef.name,
  speed_coef.Lsmaj,
  speed_coef.Lsmaj_ci,

tests/test_uv.py

@@ -4,7 +4,6 @@
 """
 import numpy as np
 import pytest
-
 from pandas import date_range
 
 from utide import reconstruct, solve

utide/__init__.py

@@ -9,7 +9,6 @@
  ut_constants,
 )
 
-
 try:
  __version__ = pkg_resources.get_distribution("utide").version
 except Exception:

utide/_reconstruct.py

@@ -91,7 +91,6 @@ def reconstruct(
 
 
 def _reconstruct(t, goodmask, coef, verbose, constit, min_SNR, min_PE):
-
  twodim = coef["aux"]["opt"]["twodim"]
 
  # Determine constituents to include.

utide/_solve.py

@@ -12,7 +12,6 @@
 from .robustfit import robustfit
 from .utilities import Bunch
 
-
 default_opts = {
  "constit": "auto",
  "order_constit": None,
@@ -225,7 +224,6 @@ def solve(t, u, v=None, lat=None, **opts):
 
 
 def _solv1(tin, uin, vin, lat, **opts):
-
  # The following returns a possibly modified copy of tin (ndarray).
  # t, u, v are fully edited ndarrays (unless v is None).
  packed = _slvinit(tin, uin, vin, lat, **opts)
@@ -262,7 +260,6 @@ def _solv1(tin, uin, vin, lat, **opts):
  B = np.hstack((E, E.conj()))
 
  if opt.infer is not None:
-
  Etilp = np.empty((nt, coef.nR), dtype=complex)
  Etilm = np.empty((nt, coef.nR), dtype=complex)
 
@@ -465,7 +462,6 @@ def _reorder(coef, opt):
 
 
 def _slvinit(tin, uin, vin, lat, **opts):
-
  if lat is None:
  raise ValueError("Latitude must be supplied")
 

utide/_time_conversion.py

@@ -4,11 +4,10 @@
 
 import numpy as np
 
-
 # to be added to get 1 on 1st January of year 1 from unix epoch '1970-01-01'
 _DAY_TO_GREGORIAN_EPOCH = 719163
 
-# milisecond in a day
+# millisecond in a day
 _MS_PER_DAY = 1000 * 86400
 
 
@@ -25,17 +24,17 @@ def _date2num(date, epoch="1970-01-01 00:00:00.000"):
 
  try:
  date = date.astype("datetime64[ms]")
- except ValueError:
+ except ValueError as err:
  raise ValueError(
  f"Cannot convert date argument ({date}) to scalar or array of numpy datetime64 dtype.",
- )
+ ) from err
 
  try:
  epoch = np.datetime64(epoch, "ms")
- except ValueError:
+ except ValueError as err:
  raise ValueError(
  f"Cannot convert epoch argument ({epoch}) to numpy datetime64 dtype.",
- )
+ ) from err
 
  # datenum calculation
  datenum = (date - epoch).astype(float) / _MS_PER_DAY
@@ -49,7 +48,7 @@ def _python_gregorian_datenum(date):
  Python gregorian time is 1 on 1st day of 1st year. Essentially, it means,
  the epoch for python gregorian time is 0000-12-31. With _date2num() defined
  above, this amounts to 719163 days from the unix-epoch 1970-01-01 00:00:00.
- To avoid repetative calculation, this is defined as _DAY_TO_GREGORIAN_EPOCH.
+ To avoid repetitive calculation, this is defined as _DAY_TO_GREGORIAN_EPOCH.
  """
  return _date2num(date) + _DAY_TO_GREGORIAN_EPOCH
 
@@ -76,8 +75,10 @@ def _normalize_time(t, epoch=None):
  else:
  try:
  ofs = _python_gregorian_datenum(epoch)
- except ValueError:
- raise ValueError("Cannot parse epoch as string or date or datetime")
+ except ValueError as err:
+ raise ValueError(
+ "Cannot parse epoch as string or date or datetime",
+ ) from err
  else:
  return t + ofs
  else:

utide/_ut_constants.py

@@ -4,7 +4,6 @@
 
 from .utilities import Bunch, convert_unicode_arrays, loadbunch
 
-
 _base_dir = os.path.join(os.path.dirname(__file__), "data")
 _ut_constants_fname = os.path.join(_base_dir, "ut_constants.npz")
 

utide/astronomy.py

@@ -1,6 +1,5 @@
 import numpy as np
 
-
 # (comments based on t_tide)
 # Coefficients of the formulas in the Explan. Suppl.
 _sc = np.array([270.434164, 13.1763965268, -0.0000850, 0.000000039])

utide/confidence.py

@@ -138,7 +138,8 @@ def nearestSPD(A):
  # Normally no more than one adjustment will be needed.
  if k > 100:
  warnings.warn(
- "adjustment in nearestSPD did not converge; " "returning diagonal",
+ "adjustment in nearestSPD did not converge; returning diagonal",
+ stacklevel=2,
  )
  return np.diag(np.diag(A))
  return Ahat

utide/constituent_selection.py

@@ -68,7 +68,7 @@ def ut_cnstitsel(tref, minres, incnstit, infer):
  _r = infer.reference_names
  allrefs = list(OrderedDict(zip(_r, [1] * len(_r))).keys())
  nR = len(allrefs)
- for k, name in enumerate(allrefs):
+ for _k, name in enumerate(allrefs):
  refstruct = Bunch(name=name)
  refstruct.lind = constit_index_dict[name]
  refstruct.frq = freqs[refstruct.lind]

utide/data/make_FUV_data.py

@@ -8,11 +8,9 @@
 """
 
 import numpy as np
-
 from oct2py import Oct2PyError, octave
 from scipy.io.matlab import savemat
 
-
 octave.convert_to_float = False
 
 
@@ -61,7 +59,16 @@
  except Oct2PyError:
  print("failed")
 
- save_args = dict(t=t, t0=t0, lat=lat, lind=lind, flags=flags, Fo=Fo, Uo=Uo, Vo=Vo)
+ save_args = {
+ "t": t,
+ "t0": t0,
+ "lat": lat,
+ "lind": lind,
+ "flags": flags,
+ "Fo": Fo,
+ "Uo": Uo,
+ "Vo": Vo,
+ }
 
  np.savez("FUV%d.npz" % ilind, **save_args)
 

utide/data/mat2npz.py

@@ -4,7 +4,6 @@
 
 from utide.utilities import loadbunch
 
-
 datadir = os.path.join(os.path.dirname(__file__))
 
 fname = "ut_constants"

utide/harmonics.py

@@ -8,7 +8,6 @@
 from ._ut_constants import ut_constants
 from .astronomy import ut_astron
 
-
 sat = ut_constants.sat
 const = ut_constants.const
 shallow = ut_constants.shallow

utide/periodogram.py

@@ -13,12 +13,10 @@
 """
 
 import numpy as np
-
 from scipy import signal
 
 from utide.utilities import Bunch
 
-
 # __all__ = ['freq_bands', 'band_psd']
 
 # Frequency bands in cycles per hour for
@@ -115,7 +113,7 @@ def _lomb_freqs(t, fbands=None, ofac=1, max_per_band=500):
  nfreq = len(freq)
 
  freqs = []
- for k, (f0, f1) in enumerate(fbands):
+ for _k, (f0, f1) in enumerate(fbands):
  i0, i1 = np.searchsorted(freq, [f0, f1])
  i1 = min(nfreq, i1 + 1)
  inband = slice(i0, i1)
@@ -354,7 +352,6 @@ def band_psd(t, e, cfrq, equi=True, frqosamp=1):
 
  # If e is complex, handle imaginary part.
  if e.dtype.kind == "c":
-
  if equi: # If even sampling, Welch.
  Pvv1s = _psd(e.imag, hn, fs)
  Puv1s = _psd(e, hn, fs) # complex cross-periodogram

utide/robustfit.py

@@ -61,17 +61,17 @@ def welsch(r):
  return w
 
 
-wfuncdict = dict(
- andrews=andrews,
- bisquare=bisquare,
- cauchy=cauchy,
- fair=fair,
- huber=huber,
- logistic=logistic,
- ols=ols,
- talwar=talwar,
- welsch=welsch,
-)
+wfuncdict = {
+ "andrews": andrews,
+ "bisquare": bisquare,
+ "cauchy": cauchy,
+ "fair": fair,
+ "huber": huber,
+ "logistic": logistic,
+ "ols": ols,
+ "talwar": talwar,
+ "welsch": welsch,
+}
 
 tune_defaults = {
  "andrews": 1.339,
@@ -208,7 +208,7 @@ def robustfit(
  rsumsq = rsumsq[0]
  if i == 0:
  rms_resid = np.sqrt(rsumsq / n)
- out.update(dict(ols_b=b, ols_rms_resid=rms_resid))
+ out.update({"ols_b": b, "ols_rms_resid": rms_resid})
 
  # Weighted mean of squared weighted residuals:
  rmeansq = rsumsq / w.sum()
@@ -247,14 +247,14 @@ def robustfit(
  rms_resid = np.sqrt(np.mean(np.abs(resid) ** 2))
 
  out.update(
- dict(
- iterations=iterations,
- b=b,
- s=sing,
- w=w,
- rank=rank,
- rms_resid=rms_resid,
- ),
+ {
+ "iterations": iterations,
+ "b": b,
+ "s": sing,
+ "w": w,
+ "rank": rank,
+ "rms_resid": rms_resid,
+ },
  )
 
  return out
@@ -263,7 +263,6 @@ def robustfit(
 # Some simple test cases; this probably will be removed.
 
 if __name__ == "__main__":
-
  np.random.seed(1)
  n = 10000
  x = np.arange(n)