refactor: readability improvements in docstrings and comments

Signed-off-by: Ryan Russell <[email protected]>

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]_.
 
@@ -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)

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)

src/gstools/random/tools.py

@@ -57,7 +57,7 @@ def dist_gen(pdf_in=None, cdf_in=None, ppf_in=None, **kwargs):
     Parameters
     ----------
     pdf_in : :any:`callable` or :any:`None`, optional
-        Proprobability distribution function of the given distribution, that
+        Probability distribution function of the given distribution, that
         takes a single argument
         Default: ``None``
     cdf_in : :any:`callable` or :any:`None`, optional

src/gstools/tools/special.py

@@ -178,7 +178,7 @@ def tplstable_cor(r, len_scale, hurst, alpha):
 
 def tpl_exp_spec_dens(k, dim, len_scale, hurst, len_low=0.0):
     r"""
-    Spectal density of the TPLExponential covariance model.
+    Spectral density of the TPLExponential covariance model.
 
     Parameters
     ----------
@@ -197,7 +197,7 @@ def tpl_exp_spec_dens(k, dim, len_scale, hurst, len_low=0.0):
     Returns
     -------
     :class:`float`
-        spectal density of the TPLExponential model
+        spectral density of the TPLExponential model
     """
     if np.isclose(len_low, 0.0):
         k = np.asarray(k, dtype=np.double)
@@ -217,7 +217,7 @@ def tpl_exp_spec_dens(k, dim, len_scale, hurst, len_low=0.0):
 
 def tpl_gau_spec_dens(k, dim, len_scale, hurst, len_low=0.0):
     r"""
-    Spectal density of the TPLGaussian covariance model.
+    Spectral density of the TPLGaussian covariance model.
 
     Parameters
     ----------
@@ -236,7 +236,7 @@ def tpl_gau_spec_dens(k, dim, len_scale, hurst, len_low=0.0):
     Returns
     -------
     :class:`float`
-        spectal density of the TPLExponential model
+        spectral density of the TPLExponential model
     """
     if np.isclose(len_low, 0.0):
         k = np.asarray(k, dtype=np.double)

src/gstools/transform/array.py

@@ -56,7 +56,7 @@ def array_discrete(
         the thresholds, where the value classes are separated
         possible values are:
         * "arithmetic": the mean of the 2 neighbouring values
-        * "equal": devide the field into equal parts
+        * "equal": divide the field into equal parts
         * an array of explicitly given thresholds
         Default: "arithmetic"
     mean : :class:`float`or :any:`None`

src/gstools/transform/field.py

@@ -307,7 +307,7 @@ def discrete(
         the thresholds, where the value classes are separated
         possible values are:
         * "arithmetic": the mean of the 2 neighbouring values
-        * "equal": devide the field into equal parts
+        * "equal": divide the field into equal parts
         * an array of explicitly given thresholds
         Default: "arithmetic"
     field : :class:`str`, optional

src/gstools/variogram/variogram.py

@@ -117,14 +117,14 @@ def vario_estimate(
     being the bins.
     The Cressie estimator is more robust to outliers [Webster2007]_.
 
-    By provding `direction` vector[s] or angles, a directional variogram
+    By providing `direction` vector[s] or angles, a directional variogram
     can be calculated. If multiple directions are given, a set of variograms
     will be returned.
-    Directional bining is controled by a given angle tolerance (`angles_tol`)
+    Directional bining is controlled by a given angle tolerance (`angles_tol`)
     and an optional `bandwidth`, that truncates the width of the search band
     around the given direction[s].
 
-    To reduce the calcuation time, `sampling_size` could be passed to sample
+    To reduce the calculation time, `sampling_size` could be passed to sample
     down the number of field points.
 
     Parameters
@@ -167,13 +167,13 @@ def vario_estimate(
         Default: False
     direction : :class:`list` of :class:`numpy.ndarray`, optional
         directions to evaluate a directional variogram.
-        Anglular tolerance is given by `angles_tol`.
+        Angular tolerance is given by `angles_tol`.
         bandwidth to cut off how wide the search for point pairs should be
         is given by `bandwidth`.
         You can provide multiple directions at once to get one variogram
         for each direction.
         For a single direction you can also use the `angles` parameter,
-        to provide the direction by its spherical coordianates.
+        to provide the direction by its spherical coordinates.
         Default: :any:`None`
     angles : :class:`numpy.ndarray`, optional
         the angles of the main axis to calculate the variogram for in radians