refactor(src): readability improvements

src/gstools/covmodel/base.py

@@ -89,7 +89,7 @@ class CovModel:
         Default: :any:`None`
     rescale : :class:`float` or :any:`None`, optional
         Optional rescaling factor to divide the length scale with.
-        This could be used for unit convertion or rescaling the length scale
+        This could be used for unit conversion or rescaling the length scale
         to coincide with e.g. the integral scale.
         Will be set by each model individually.
         Default: :any:`None`
@@ -569,14 +569,14 @@ def fit_variogram(
         **para_select,
     ):
         """
-        Fiting the variogram-model to an empirical variogram.
+        Fitting the variogram-model to an empirical variogram.
 
         Parameters
         ----------
         x_data : :class:`numpy.ndarray`
             The bin-centers of the empirical variogram.
         y_data : :class:`numpy.ndarray`
-            The messured variogram
+            The measured variogram
             If multiple are given, they are interpreted as the directional
             variograms along the main axis of the associated rotated
             coordinate system.
@@ -601,7 +601,7 @@ def fit_variogram(
 
             If variance is deselected, it needs to be less than the sill,
             otherwise a ValueError comes up. Same for nugget.
-            If sill=False, it will be deslected from estimation
+            If sill=False, it will be deselected from estimation
             and set to the current sill of the model.
             Then, the procedure above is applied.
             Default: None
@@ -680,7 +680,7 @@ def fit_variogram(
         Returns
         -------
         fit_para : :class:`dict`
-            Dictonary with the fitted parameter values
+            Dictionary with the fitted parameter values
         pcov : :class:`numpy.ndarray`
             The estimated covariance of `popt` from
             :any:`scipy.optimize.curve_fit`.
@@ -734,7 +734,7 @@ def set_arg_bounds(self, check_args=True, **kwargs):
         ----------
         check_args : bool, optional
             Whether to check if the arguments are in their valid bounds.
-            In case not, a propper default value will be determined.
+            In case not, a proper default value will be determined.
             Default: True
         **kwargs
             Parameter name as keyword ("var", "len_scale", "nugget", <opt_arg>)
@@ -772,7 +772,7 @@ def var_bounds(self, bounds):
 
     @property
     def len_scale_bounds(self):
-        """:class:`list`: Bounds for the lenght scale.
+        """:class:`list`: Bounds for the length scale.
 
         Notes
         -----

src/gstools/covmodel/fit.py

@@ -47,7 +47,7 @@ def fit_variogram(
     x_data : :class:`numpy.ndarray`
         The bin-centers of the empirical variogram.
     y_data : :class:`numpy.ndarray`
-        The messured variogram
+        The measured variogram
         If multiple are given, they are interpreted as the directional
         variograms along the main axis of the associated rotated
         coordinate system.
@@ -72,7 +72,7 @@ def fit_variogram(
 
         If variance is deselected, it needs to be less than the sill,
         otherwise a ValueError comes up. Same for nugget.
-        If sill=False, it will be deslected from estimation
+        If sill=False, it will be deselected from estimation
         and set to the current sill of the model.
         Then, the procedure above is applied.
         Default: None
@@ -149,7 +149,7 @@ def fit_variogram(
     Returns
     -------
     fit_para : :class:`dict`
-        Dictonary with the fitted parameter values
+        Dictionary with the fitted parameter values
     pcov : :class:`numpy.ndarray`
         The estimated covariance of `popt` from
         :any:`scipy.optimize.curve_fit`.

src/gstools/covmodel/models.py

@@ -357,7 +357,7 @@ def cor(self, h):
         ) * sps.kv(self.nu, np.sqrt(self.nu) * h_gz)
         # if nu >> 1 we get errors for the farfield, there 0 is approached
         res[np.logical_not(np.isfinite(res))] = 0.0
-        # covariance is positiv
+        # covariance is positive
         res = np.maximum(res, 0.0)
         return res
 

src/gstools/covmodel/plot.py

@@ -236,7 +236,7 @@ def plot_cor_axis(
 def plot_spectrum(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
-    """Plot specturm of a given CovModel."""
+    """Plot spectrum of a given CovModel."""
     fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 / model.len_scale

src/gstools/covmodel/tools.py

@@ -166,7 +166,7 @@ def set_opt_args(model, opt_arg):
                 "Or you made a Typo... hehe.",
                 AttributeWarning,
             )
-    # add the default vaules if not specified
+    # add the default values if not specified
     for def_arg in default:
         if def_arg not in opt_arg:
             opt_arg[def_arg] = default[def_arg]
@@ -400,7 +400,7 @@ def set_arg_bounds(model, check_args=True, **kwargs):
         The covariance model in use.
     check_args : bool, optional
         Whether to check if the arguments are in their valid bounds.
-        In case not, a propper default value will be determined.
+        In case not, a proper default value will be determined.
         Default: True
     **kwargs
         Parameter name as keyword ("var", "len_scale", "nugget", <opt_arg>)
@@ -438,7 +438,7 @@ def set_arg_bounds(model, check_args=True, **kwargs):
                 setattr(model, arg, [def_arg] * (model.dim - 1))
             else:
                 setattr(model, arg, def_arg)
-    # set var last like allways
+    # set var last like always
     if var_bnds:
         model.var_bounds = var_bnds
         if check_args and check_arg_in_bounds(model, "var") > 0:

src/gstools/field/base.py

@@ -228,7 +228,7 @@ def mesh(
             of the given mesh.
             Default: "centroids"
         direction : :class:`str` or :class:`list`, optional
-            Here you can state which direction should be choosen for
+            Here you can state which direction should be chosen for
             lower dimension. For example, if you got a 2D mesh in xz direction,
             you have to pass "xz". By default, all directions are used.
             One can also pass a list of indices.

src/gstools/field/cond_srf.py

@@ -78,7 +78,7 @@ def __call__(
             the position tuple, containing main direction and transversal
             directions
         seed : :class:`int`, optional
-            seed for RNG for reseting. Default: keep seed from generator
+            seed for RNG for resetting. Default: keep seed from generator
         mesh_type : :class:`str`
             'structured' / 'unstructured'
         post_process : :class:`bool`, optional

src/gstools/field/generator.py

@@ -97,10 +97,10 @@ def __init__(
     ):
         if kwargs:
             warnings.warn("gstools.RandMeth: **kwargs are ignored")
-        # initialize atributes
+        # initialize attributes
         self._mode_no = int(mode_no)
         self._verbose = bool(verbose)
-        # initialize private atributes
+        # initialize private attributes
         self._model = None
         self._seed = None
         self._rng = None
@@ -237,7 +237,7 @@ def reset_seed(self, seed=np.nan):
         self._z_2 = self._rng.random.normal(size=self._mode_no)
         # sample uniform on a sphere
         sphere_coord = self._rng.sample_sphere(self.model.dim, self._mode_no)
-        # sample radii acording to radial spectral density of the model
+        # sample radii according to radial spectral density of the model
         if self.sampling == "inversion" or (
             self.sampling == "auto" and self.model.has_ppf
         ):

src/gstools/field/srf.py

@@ -119,7 +119,7 @@ def __call__(
             the position tuple, containing main direction and transversal
             directions
         seed : :class:`int`, optional
-            seed for RNG for reseting. Default: keep seed from generator
+            seed for RNG for resetting. Default: keep seed from generator
         point_volumes : :class:`float` or :class:`numpy.ndarray`
             If your evaluation points for the field are coming from a mesh,
             they are probably representing a certain element volume.

src/gstools/field/tools.py

@@ -109,7 +109,7 @@ def generate_on_mesh(
         of the given mesh.
         Default: "centroids"
     direction : :class:`str` or :class:`list`, optional
-        Here you can state which direction should be choosen for
+        Here you can state which direction should be chosen for
         lower dimension. For example, if you got a 2D mesh in xz direction,
         you have to pass "xz". By default, all directions are used.
         One can also pass a list of indices.
@@ -119,7 +119,7 @@ def generate_on_mesh(
         cell_data. If to few names are given, digits will be appended.
         Default: "field"
     **kwargs
-        Keyword arguments forwareded to `Field.__call__`.
+        Keyword arguments forwarded to `Field.__call__`.
 
     Notes
     -----

src/gstools/field/upscaling.py

@@ -71,7 +71,7 @@ def var_coarse_graining(model, point_volumes=0.0):
         warnings.warn(
             "var_coarse_graining: non-zero nugget will violate upscaling!"
         )
-    # interprete volume as a hypercube and calculate the edge length
+    # interpret volume as a hypercube and calculate the edge length
     edge = point_volumes ** (1.0 / model.dim)
     var_factor = (
         model.len_scale**2 / (model.len_scale**2 + edge**2 / 4)

src/gstools/krige/base.py

@@ -44,7 +44,7 @@ class Krige(Field):
     A Swiss Army knife for kriging.
 
     A Kriging class enabling the basic kriging routines:
-    Simple-, Ordinary-, Univseral-, External Drift-
+    Simple-, Ordinary-, Universal-, External Drift-
     and detrended/regression-Kriging as well as
     Kriging the Mean [Wackernagel2003]_.
 
@@ -72,7 +72,7 @@ class Krige(Field):
         Normalizer to be applied to the input data to gain normality.
         The default is None.
     trend : :any:`None` or :class:`float` or :any:`callable`, optional
-        A callable trend function. Should have the signiture: f(x, [y, z, ...])
+        A callable trend function. Should have the signature: f(x, [y, z, ...])
         This is used for detrended kriging, where the trended is subtracted
         from the conditions before kriging is applied.
         This can be used for regression kriging, where the trend function
@@ -110,10 +110,10 @@ class Krige(Field):
         you can pass a callable which takes a matrix and returns the inverse.
         Default: `"pinv"`
     fit_normalizer : :class:`bool`, optional
-        Wheater to fit the data-normalizer to the given conditioning data.
+        Whether to fit the data-normalizer to the given conditioning data.
         Default: False
     fit_variogram : :class:`bool`, optional
-        Wheater to fit the given variogram model to the data.
+        Whether to fit the given variogram model to the data.
         This is done by using isotropy settings of the given model,
         assuming the sill to be the data variance and with the
         standard bins provided by the :any:`standard_bins` routine.
@@ -335,7 +335,7 @@ def _get_krige_vecs(
             # set points to limit of the covariance to only get the mean
             res[: self.cond_no, :] = 0
         else:
-            # get correct covarinace functions (depending on exact values)
+            # get correct covariance functions (depending on exact values)
             cf = self.model.cov_nugget if self.exact else self.model.covariance
             res[: self.cond_no, :] = cf(
                 self._get_dists(self._krige_pos, pos, chunk_slice)
@@ -490,10 +490,10 @@ def set_condition(
             The "exact=True" variant only works with "cond_err='nugget'".
             Default: "nugget"
         fit_normalizer : :class:`bool`, optional
-            Wheater to fit the data-normalizer to the given conditioning data.
+            Whether to fit the data-normalizer to the given conditioning data.
             Default: False
         fit_variogram : :class:`bool`, optional
-            Wheater to fit the given variogram model to the data.
+            Whether to fit the given variogram model to the data.
             This is done by using isotropy settings of the given model,
             assuming the sill to be the data variance and with the
             standard bins provided by the :any:`standard_bins` routine.

src/gstools/krige/methods.py

@@ -40,7 +40,7 @@ class Simple(Krige):
         Normalizer to be applied to the input data to gain normality.
         The default is None.
     trend : :any:`None` or :class:`float` or :any:`callable`, optional
-        A callable trend function. Should have the signiture: f(x, [y, z, ...])
+        A callable trend function. Should have the signature: f(x, [y, z, ...])
         This is used for detrended kriging, where the trended is subtracted
         from the conditions before kriging is applied.
         This can be used for regression kriging, where the trend function
@@ -74,10 +74,10 @@ class Simple(Krige):
         you can pass a callable which takes a matrix and returns the inverse.
         Default: `"pinv"`
     fit_normalizer : :class:`bool`, optional
-        Wheater to fit the data-normalizer to the given conditioning data.
+        Whether to fit the data-normalizer to the given conditioning data.
         Default: False
     fit_variogram : :class:`bool`, optional
-        Wheater to fit the given variogram model to the data.
+        Whether to fit the given variogram model to the data.
         This is done by using isotropy settings of the given model,
         assuming the sill to be the data variance and with the
         standard bins provided by the :any:`standard_bins` routine.
@@ -134,7 +134,7 @@ class Ordinary(Krige):
         Normalizer to be applied to the input data to gain normality.
         The default is None.
     trend : :any:`None` or :class:`float` or :any:`callable`, optional
-        A callable trend function. Should have the signiture: f(x, [y, z, ...])
+        A callable trend function. Should have the signature: f(x, [y, z, ...])
         This is used for detrended kriging, where the trended is subtracted
         from the conditions before kriging is applied.
         This can be used for regression kriging, where the trend function
@@ -168,10 +168,10 @@ class Ordinary(Krige):
         you can pass a callable which takes a matrix and returns the inverse.
         Default: `"pinv"`
     fit_normalizer : :class:`bool`, optional
-        Wheater to fit the data-normalizer to the given conditioning data.
+        Whether to fit the data-normalizer to the given conditioning data.
         Default: False
     fit_variogram : :class:`bool`, optional
-        Wheater to fit the given variogram model to the data.
+        Whether to fit the given variogram model to the data.
         This is done by using isotropy settings of the given model,
         assuming the sill to be the data variance and with the
         standard bins provided by the :any:`standard_bins` routine.
@@ -238,7 +238,7 @@ class Universal(Krige):
         Normalizer to be applied to the input data to gain normality.
         The default is None.
     trend : :any:`None` or :class:`float` or :any:`callable`, optional
-        A callable trend function. Should have the signiture: f(x, [y, z, ...])
+        A callable trend function. Should have the signature: f(x, [y, z, ...])
         This is used for detrended kriging, where the trended is subtracted
         from the conditions before kriging is applied.
         This can be used for regression kriging, where the trend function
@@ -272,10 +272,10 @@ class Universal(Krige):
         you can pass a callable which takes a matrix and returns the inverse.
         Default: `"pinv"`
     fit_normalizer : :class:`bool`, optional
-        Wheater to fit the data-normalizer to the given conditioning data.
+        Whether to fit the data-normalizer to the given conditioning data.
         Default: False
     fit_variogram : :class:`bool`, optional
-        Wheater to fit the given variogram model to the data.
+        Whether to fit the given variogram model to the data.
         This is done by using isotropy settings of the given model,
         assuming the sill to be the data variance and with the
         standard bins provided by the :any:`standard_bins` routine.
@@ -339,7 +339,7 @@ class ExtDrift(Krige):
         Normalizer to be applied to the input data to gain normality.
         The default is None.
     trend : :any:`None` or :class:`float` or :any:`callable`, optional
-        A callable trend function. Should have the signiture: f(x, [y, z, ...])
+        A callable trend function. Should have the signature: f(x, [y, z, ...])
         This is used for detrended kriging, where the trended is subtracted
         from the conditions before kriging is applied.
         This can be used for regression kriging, where the trend function
@@ -373,10 +373,10 @@ class ExtDrift(Krige):
         you can pass a callable which takes a matrix and returns the inverse.
         Default: `"pinv"`
     fit_normalizer : :class:`bool`, optional
-        Wheater to fit the data-normalizer to the given conditioning data.
+        Whether to fit the data-normalizer to the given conditioning data.
         Default: False
     fit_variogram : :class:`bool`, optional
-        Wheater to fit the given variogram model to the data.
+        Whether to fit the given variogram model to the data.
         This is done by using isotropy settings of the given model,
         assuming the sill to be the data variance and with the
         standard bins provided by the :any:`standard_bins` routine.
@@ -439,7 +439,7 @@ class Detrended(Krige):
     cond_val : :class:`numpy.ndarray`
         the values of the conditions (nan values will be ignored)
     trend_function : :any:`callable`
-        The callable trend function. Should have the signiture: f(x, [y, z])
+        The callable trend function. Should have the signature: f(x, [y, z])
     exact : :class:`bool`, optional
         Whether the interpolator should reproduce the exact input values.
         If `False`, `cond_err` is interpreted as measurement error
@@ -467,7 +467,7 @@ class Detrended(Krige):
         you can pass a callable which takes a matrix and returns the inverse.
         Default: `"pinv"`
     fit_variogram : :class:`bool`, optional
-        Wheater to fit the given variogram model to the data.
+        Whether to fit the given variogram model to the data.
         This is done by using isotropy settings of the given model,
         assuming the sill to be the data variance and with the
         standard bins provided by the :any:`standard_bins` routine.

src/gstools/normalizer/base.py

@@ -198,7 +198,7 @@ def fit(self, data, skip=None, **kwargs):
         data : array_like
             Input data to fit the transformation to in order to gain normality.
         skip : :class:`list` of :class:`str` or :any:`None`, optional
-            Names of parameters to be skiped in fitting.
+            Names of parameters to be skipped in fitting.
             The default is None.
         **kwargs
             Keyword arguments passed to :any:`scipy.optimize.minimize_scalar`
@@ -207,7 +207,7 @@ def fit(self, data, skip=None, **kwargs):
         Returns
         -------
         :class:`dict`
-            Optimal paramters given by names.
+            Optimal parameters given by names.
         """
         skip = [] if skip is None else skip
         all_names = sorted(self.default_parameter)