Merge 8145043 into 79e7b60

gstools/covmodel/base.py

@@ -9,7 +9,7 @@
 .. autosummary::
    CovModel
 """
-# pylint: disable=C0103, R0201, E1101
+# pylint: disable=C0103, R0201, E1101, C0302, W0613
 
 import copy
 import numpy as np
@@ -212,14 +212,12 @@ def __init_subclass__(cls):
             cls.__doc__ = "User defined GSTools Covariance-Model."
         cls.__doc__ += CovModel.__doc__[45:]
         # overridden functions get standard doc if no new doc was created
-        ignore = ["__", "variogram", "covariance", "cor"]
-        for attr in cls.__dict__:
-            if any(
-                [attr.startswith(ign) for ign in ignore]
-            ) or attr not in dir(CovModel):
+        ign = ["__", "variogram", "covariance", "cor"]
+        for att in cls.__dict__:
+            if any(att.startswith(i) for i in ign) or att not in dir(CovModel):
                 continue
-            attr_doc = getattr(CovModel, attr).__doc__
-            attr_cls = cls.__dict__[attr]
+            attr_doc = getattr(CovModel, att).__doc__
+            attr_cls = cls.__dict__[att]
             if attr_cls.__doc__ is None:
                 attr_cls.__doc__ = attr_doc
 

gstools/covmodel/fit.py

@@ -9,8 +9,8 @@
 .. autosummary::
    fit_variogram
 """
+# pylint: disable=C0103, W0632
 
-# pylint: disable=C0103
 import numpy as np
 from scipy.optimize import curve_fit
 from gstools.covmodel.tools import check_arg_in_bounds, default_arg_from_bounds
@@ -341,7 +341,7 @@ def _check_vario(model, x_data, y_data):
         raise ValueError(
             "CovModel.fit_variogram: lat-lon models don't support anisotropy."
         )
-    elif model.latlon:
+    if model.latlon:
         # convert to yadrenko model
         x_data = 2 * np.sin(x_data / 2)
     return x_data, y_data, is_dir_vario

gstools/covmodel/models.py

@@ -26,7 +26,7 @@
 import numpy as np
 from scipy import special as sps
 from gstools.covmodel.tools import AttributeWarning
-from gstools.covmodel import CovModel
+from gstools.covmodel.base import CovModel
 
 __all__ = [
     "Gaussian",

gstools/covmodel/plot.py

@@ -23,11 +23,11 @@
    plot_spectral_density
    plot_spectral_rad_pdf
 """
-# pylint: disable=C0103, C0415
-import numpy as np
-
-import gstools
+# pylint: disable=C0103, C0415, E1130
 
+import numpy as np
+from gstools.tools.geometric import generate_grid
+from gstools.tools.misc import get_fig_ax
 
 __all__ = [
     "plot_variogram",
@@ -51,69 +51,58 @@
 # plotting routines #######################################################
 
 
-def _get_fig_ax(fig, ax, ax_name="rectilinear"):  # pragma: no cover
-    from matplotlib import pyplot as plt
+def _plot_spatial(dim, pos, field, fig, ax, latlon, **kwargs):
+    from gstools.field.plot import plot_1d, plot_nd
 
-    if fig is None and ax is None:
-        fig = plt.figure()
-        ax = fig.add_subplot(111, projection=ax_name)
-    elif ax is None:
-        ax = fig.add_subplot(111, projection=ax_name)
-    elif fig is None:
-        fig = ax.get_figure()
-        assert ax.name == ax_name
-    else:
-        assert ax.name == ax_name
-        assert ax.get_figure() == fig
-    return fig, ax
+    if dim == 1:
+        return plot_1d(pos, field, fig, ax, **kwargs)
+    return plot_nd(pos, field, "structured", fig, ax, latlon, **kwargs)
 
 
 def plot_vario_spatial(
-    model, x_min=0.0, x_max=None, fig=None, ax=None
+    model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot spatial variogram of a given CovModel."""
-    field = gstools.field.base.Field(model, "scalar")
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(-x_max, x_max) + x_min
-    iso_pos, shape = field.pre_pos([x_s] * model.dim, "structured")
-    field.field = model.vario_spatial(model.anisometrize(iso_pos)).reshape(
-        shape
-    )
-    return field.plot(fig=fig, ax=ax)
+    pos = [x_s] * model.dim
+    shp = tuple(len(p) for p in pos)
+    fld = model.vario_spatial(generate_grid(pos)).reshape(shp)
+    return _plot_spatial(model.dim, pos, fld, fig, ax, model.latlon, **kwargs)
 
 
 def plot_cov_spatial(
-    model, x_min=0.0, x_max=None, fig=None, ax=None
+    model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot spatial covariance of a given CovModel."""
-    field = gstools.field.base.Field(model, "scalar")
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(-x_max, x_max) + x_min
-    iso_pos, shape = field.pre_pos([x_s] * model.dim, "structured")
-    field.field = model.cov_spatial(model.anisometrize(iso_pos)).reshape(shape)
-    return field.plot(fig=fig, ax=ax)
+    pos = [x_s] * model.dim
+    shp = tuple(len(p) for p in pos)
+    fld = model.cov_spatial(generate_grid(pos)).reshape(shp)
+    return _plot_spatial(model.dim, pos, fld, fig, ax, model.latlon, **kwargs)
 
 
 def plot_cor_spatial(
-    model, x_min=0.0, x_max=None, fig=None, ax=None
+    model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot spatial correlation of a given CovModel."""
-    field = gstools.field.base.Field(model, "scalar")
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(-x_max, x_max) + x_min
-    iso_pos, shape = field.pre_pos([x_s] * model.dim, "structured")
-    field.field = model.cor_spatial(model.anisometrize(iso_pos)).reshape(shape)
-    return field.plot(fig=fig, ax=ax)
+    pos = [x_s] * model.dim
+    shp = tuple(len(p) for p in pos)
+    fld = model.cor_spatial(generate_grid(pos)).reshape(shp)
+    return _plot_spatial(model.dim, pos, fld, fig, ax, model.latlon, **kwargs)
 
 
 def plot_variogram(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot variogram of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -128,7 +117,7 @@ def plot_covariance(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot covariance of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -143,7 +132,7 @@ def plot_correlation(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot correlation function of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -158,7 +147,7 @@ def plot_vario_yadrenko(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot Yadrenko variogram of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = min(3 * model.len_rescaled, np.pi)
     x_s = np.linspace(x_min, x_max)
@@ -173,7 +162,7 @@ def plot_cov_yadrenko(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot Yadrenko covariance of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = min(3 * model.len_rescaled, np.pi)
     x_s = np.linspace(x_min, x_max)
@@ -188,7 +177,7 @@ def plot_cor_yadrenko(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot Yadrenko correlation function of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = min(3 * model.len_rescaled, np.pi)
     x_s = np.linspace(x_min, x_max)
@@ -203,7 +192,7 @@ def plot_vario_axis(
     model, axis=0, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot variogram of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -218,7 +207,7 @@ def plot_cov_axis(
     model, axis=0, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot variogram of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -233,7 +222,7 @@ def plot_cor_axis(
     model, axis=0, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot variogram of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 * model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -248,7 +237,7 @@ 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."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 / model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -263,7 +252,7 @@ def plot_spectral_density(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot spectral density of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 / model.len_scale
     x_s = np.linspace(x_min, x_max)
@@ -278,7 +267,7 @@ def plot_spectral_rad_pdf(
     model, x_min=0.0, x_max=None, fig=None, ax=None, **kwargs
 ):  # pragma: no cover
     """Plot radial spectral pdf of a given CovModel."""
-    fig, ax = _get_fig_ax(fig, ax)
+    fig, ax = get_fig_ax(fig, ax)
     if x_max is None:
         x_max = 3 / model.len_scale
     x_s = np.linspace(x_min, x_max)

gstools/covmodel/tpl_models.py

@@ -16,7 +16,7 @@
 
 import warnings
 import numpy as np
-from gstools.covmodel import CovModel
+from gstools.covmodel.base import CovModel
 from gstools.covmodel.tools import AttributeWarning
 from gstools.tools.special import (
     tplstable_cor,

gstools/field/base.py

@@ -9,7 +9,7 @@
 .. autosummary::
    Field
 """
-# pylint: disable=C0103
+# pylint: disable=C0103, W0107, C0415
 from functools import partial
 import numpy as np
 from gstools.covmodel.base import CovModel
@@ -83,9 +83,8 @@ def __init__(
         self.normalizer = normalizer
         self.trend = trend
 
-    def __call__(*args, **kwargs):
+    def __call__(self, *args, **kwargs):
         """Generate the field."""
-        pass
 
     def structured(self, *args, **kwargs):
         """Generate a field on a structured mesh.
@@ -297,9 +296,8 @@ def plot(
                 "Specify 'scalar' or 'vector' before plotting."
             )
 
-        elif self.value_type == "scalar":
+        if self.value_type == "scalar":
             r = plot_field(self, field, fig, ax, **kwargs)
-
         elif self.value_type == "vector":
             if self.model.dim == 2:
                 r = plot_vec_field(self, field, fig, ax, **kwargs)

gstools/field/cond_srf.py

@@ -9,7 +9,7 @@
 .. autosummary::
    CondSRF
 """
-# pylint: disable=C0103
+# pylint: disable=C0103, W0231, W0221, E1102
 
 import numpy as np
 from gstools.field.generator import RandMeth

gstools/field/generator.py

@@ -10,7 +10,7 @@
    RandMeth
    IncomprRandMeth
 """
-# pylint: disable=C0103
+# pylint: disable=C0103, W0222
 
 import warnings
 from copy import deepcopy as dcp