fix linting issues raised from new pylint and codespell versions

pyproject.toml

@@ -156,7 +156,7 @@ values = [
 
 [tool.codespell]
 skip = 'xclim/data/*.json,docs/_build,docs/notebooks/xclim_training/*.ipynb,docs/references.bib,__pycache__,*.gz,*.nc,*.png,*.svg,*.whl'
-ignore-words-list = "absolue,astroid,bloc,bui,callendar,degreee,environnement,hanel,inferrable,lond,nam,nd,ressources,sie,vas"
+ignore-words-list = "absolue,astroid,bloc,bui,callendar,degreee,environnement,hanel,inferrable,lond,nam,nd,ressources,socio-economic,sie,vas"
 
 [tool.coverage.run]
 relative_files = true

tests/test_calendar.py

@@ -90,6 +90,8 @@ def test_time_bnds_irregular(typ):
  elif typ == "pd":
  start = pd.date_range("1990-01-01", periods=24, freq="MS")
  end = pd.date_range("1990-01-01 23:59:59.999999999", periods=24, freq="ME")
+ else:
+ raise ValueError("`typ` must be 'pd' or 'xr'")
 
  time = start + (end - start) / 2
 

tests/test_indices.py

@@ -2971,6 +2971,8 @@ def test_rain_season(pr_series, result_type, method_dry_start):
  elif result_type == "end_cond_fails":
  pr[{"time": 99 + 20 - 1}] = 5
  out_exp = [3, np.NaN, 363]
+ else:
+ raise ValueError(f"Unknown result_type: {result_type}")
 
  out = {}
  out["start"], out["end"], out["length"] = xci.rain_season(

xclim/indices/fire/_cffwis.py

@@ -597,6 +597,8 @@ def _fire_season(
  elif method in ["LA08", "GFWED"]:
  # In LA08, the check INCLUDES the current day,
  start_index = max(temp_condition_days, snow_condition_days)
+ else:
+ raise ValueError("`method` must be one of 'WF93', 'LA08' or 'GFWED'.")
 
  for it in range(start_index, tas.shape[-1]):
  if method == "WF93":
@@ -627,6 +629,8 @@ def _fire_season(
 
  # Shut down when mean snow OR mean temp are over/under threshold
  shut_down = (msnow >= snow_thresh) | (mtemp < temp_end_thresh)
+ else:
+ raise ValueError("`method` must be one of 'WF93', 'LA08' or 'GFWED'.")
 
  # Mask is on if the previous days was on OR is there is a start-up, AND if it's not a shut-down,
  # Aka is off if either the previous day was or it is a shut-down.
@@ -687,6 +691,10 @@ def _fire_weather_calc( # noqa: C901
 
  overwintering = params["overwintering"]
  dry_start = params["dry_start"]
+
+ ow_DC = dc0.copy() if dc0 is not None else None
+ ow_DMC = dmc0.copy() if dmc0 is not None else None
+
  if overwintering and "DC" in ind_prevs:
  # In overwintering, dc0 is understood as the previous season's last DC code.
  ow_DC = dc0.copy()

xclim/indices/fire/_ffdi.py

@@ -144,7 +144,7 @@ def _griffiths_drought_factor(p, smd, lim, df): # pragma: no cover
  # N = 0 defines a rainfall event since 9am today,
  # so doesn't apply here, where p is the rainfall
  # over previous 24 hours.
- x_ = N**1.3 / (N**1.3 + P - 2.0)
+ x_ = N**1.3 / (N**1.3 + P - 2.0) # pylint: disable=E0601
  x = min(x_, x)
 
  conseq = 0

xclim/sdba/adjustment.py

@@ -959,14 +959,18 @@ def _compute_transform_matrix(reference, historical):
  # This step needs vectorize with dask, but vectorize doesn't work with dask, argh.
  # Invert to get transformation matrix from hist to PC coords.
  Hinv = np.linalg.inv(H)
- # Fancy tricks to choose best orientation on each axes
+ # Fancy tricks to choose the best orientation on each axis.
  # (using eigenvectors, the output axes orientation is undefined)
  if best_orientation == "simple":
  orient = best_pc_orientation_simple(R, Hinv)
  elif best_orientation == "full":
  orient = best_pc_orientation_full(
  R, Hinv, reference.mean(axis=1), historical.mean(axis=1), historical
  )
+ else:
+ raise ValueError(
+ f"Unknown `best_orientation` method: {best_orientation}."
+ )
  # Get transformation matrix
  return (R * orient) @ Hinv
 

xclim/sdba/loess.py

@@ -68,21 +68,21 @@ def _loess_nb(
  Parameters
  ----------
  x : np.ndarray
- X-coordinates of the points.
+  X-coordinates of the points.
  y : np.ndarray
- Y-coordinates of the points.
+  Y-coordinates of the points.
  f : float
- Parameter controlling the shape of the weight curve. Behavior depends on the weighting function.
+  Parameter controlling the shape of the weight curve. Behavior depends on the weighting function.
  niter : int
- Number of robustness iterations to execute.
+  Number of robustness iterations to execute.
  weight_func : numba func
- Numba function giving the weights when passed abs(x - xi) / hi
+  Numba function giving the weights when passed abs(x - xi) / hi
  dx : float
- The spacing of the x coordinates. If above 0, this enables the optimization for equally spaced x coordinates.
- Must be 0 if spacing is unequal (default).
+  The spacing of the x coordinates. If above 0, this enables the optimization for equally spaced x coordinates.
+  Must be 0 if spacing is unequal (default).
  skipna : bool
- If True (default), remove NaN values before computing the loess. The output has the
- same missing values as the input.
+  If True (default), remove NaN values before computing the loess. The output has the
+  same missing values as the input.
 
  References
  ----------
@@ -150,7 +150,8 @@ def _loess_nb(
  else:
  h = (hw + 1) * dx
  wi = weight_func(diffs / h)
- w = di * wi
+ # Is it expected that `wi` always be assigned before first being called?
+ w = di * wi # pylint: disable=E0606
  else:
  # The weights computation is repeated niter times
  # The distance of points from the current centre point.

xclim/sdba/processing.py

@@ -7,6 +7,7 @@
 
 import types
 from collections.abc import Sequence
+from typing import cast
 
 import dask.array as dsk
 import numpy as np
@@ -584,14 +585,12 @@ def to_additive_space(
  float
  )
 
- from typing import cast
-
  with xr.set_options(keep_attrs=True), np.errstate(divide="ignore"):
  if trans == "log":
  out = cast(xr.DataArray, np.log(data - lower_bound_array))
- elif trans == "logit":
+ elif trans == "logit" and upper_bound is not None:
  data_prime = (data - lower_bound_array) / (
- upper_bound_array - lower_bound_array
+ upper_bound_array - lower_bound_array # pylint: disable=E0606
  )
  out = cast(xr.DataArray, np.log(data_prime / (1 - data_prime)))
  else:
@@ -684,7 +683,7 @@ def from_additive_space(
  lower_bound_array = np.array(data.attrs["sdba_transform_lower"]).astype(
  float
  )
- if trans == "logit":
+ if trans == "logit" and upper_bound is not None:
  upper_bound_array = np.array(data.attrs["sdba_transform_upper"]).astype(
  float
  )
@@ -714,10 +713,12 @@ def from_additive_space(
  with xr.set_options(keep_attrs=True):
  if trans == "log":
  out = np.exp(data) + lower_bound_array
- elif trans == "logit":
+ elif trans == "logit" and upper_bound is not None:
  out_prime = 1 / (1 + np.exp(-data))
  out = (
- out_prime * (upper_bound_array - lower_bound_array) + lower_bound_array
+ out_prime
+ * (upper_bound_array - lower_bound_array) # pylint: disable=E0606
+ + lower_bound_array
  )
  else:
  raise NotImplementedError("`trans` must be one of 'log' or 'logit'.")