apply black 24

examples/00_misc/00_tpl_stable.py

@@ -40,6 +40,7 @@
    \sigma^2_{\ell_{\mathrm{up}}} &=
    C\cdot\frac{\ell_{\mathrm{up}}^{2H}}{2H}
 """
+
 import numpy as np
 
 import gstools as gs

examples/00_misc/01_export.py

@@ -7,6 +7,7 @@
 
 These can be viewed for example with `Paraview <https://www.paraview.org/>`__.
 """
+
 # sphinx_gallery_thumbnail_path = 'pics/paraview.png'
 import gstools as gs
 

examples/00_misc/02_check_rand_meth_sampling.py

@@ -2,6 +2,7 @@
 Check Random Sampling
 ---------------------
 """
+
 import numpy as np
 from matplotlib import pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D

examples/00_misc/04_herten.py

@@ -30,6 +30,7 @@
 functions, since the only produce the ``herten_transmissivity.gz``
 and ``grid_dim_origin_spacing.txt``, which are already present.
 """
+
 import os
 
 import matplotlib.pyplot as plt

examples/00_misc/05_standalone_field.py

@@ -7,6 +7,7 @@
 In the following example we will produce 10000 random points in 4D with
 random values and plot them.
 """
+
 import numpy as np
 
 import gstools as gs

examples/01_random_field/03_unstr_srf_export.py

@@ -6,6 +6,7 @@
 Normally, such a grid would be read in, but we can simply generate one and
 then create a random field at those coordinates.
 """
+
 import numpy as np
 
 import gstools as gs

examples/01_random_field/04_srf_merge.py

@@ -6,6 +6,7 @@
 to merge two unstructured rectangular fields.
 
 """
+
 # sphinx_gallery_thumbnail_number = 2
 import numpy as np
 

examples/01_random_field/06_pyvista_support.py

@@ -12,6 +12,7 @@
 The :any:`Field.mesh` method enables easy field creation on PyVista meshes
 used by the :any:`SRF` or :any:`Krige` class.
 """
+
 # sphinx_gallery_thumbnail_path = 'pics/GS_pyvista_cut.png'
 import pyvista as pv
 

examples/02_cov_model/01_basic_methods.py

@@ -36,6 +36,7 @@
 correlation function as demonstrated in the introductory example.
 If one of the above functions is given, the others will be determined:
 """
+
 import gstools as gs
 
 model = gs.Exponential(dim=3, var=2.0, len_scale=10, nugget=0.5)

examples/02_cov_model/02_aniso_rotation.py

@@ -6,6 +6,7 @@
 represents the isotropic case for the model.
 Nevertheless, you can provide anisotropy ratios by:
 """
+
 import gstools as gs
 
 model = gs.Gaussian(dim=3, var=2.0, len_scale=10, anis=0.5)

examples/02_cov_model/03_spectral_methods.py

@@ -23,6 +23,7 @@
 
 You can access these methods by:
 """
+
 import gstools as gs
 
 model = gs.Gaussian(dim=3, var=2.0, len_scale=10)

examples/02_cov_model/04_different_scales.py

@@ -16,6 +16,7 @@
 
 You can access it by:
 """
+
 import gstools as gs
 
 model = gs.Stable(dim=3, var=2.0, len_scale=10)

examples/02_cov_model/05_additional_para.py

@@ -10,6 +10,7 @@
 
 This leads to the so called **stable** covariance model and we can define it by
 """
+
 import numpy as np
 
 import gstools as gs

examples/02_cov_model/06_fitting_para_ranges.py

@@ -6,6 +6,7 @@
 variogram data. In the following we will use the self defined stable model
 from a previous example.
 """
+
 import numpy as np
 
 import gstools as gs

examples/03_variogram/00_fit_variogram.py

@@ -2,6 +2,7 @@
 Fit Variogram
 -------------
 """
+
 import numpy as np
 
 import gstools as gs

examples/03_variogram/01_find_best_model.py

@@ -2,6 +2,7 @@
 Finding the best fitting variogram model
 ----------------------------------------
 """
+
 import numpy as np
 from matplotlib import pyplot as plt
 

examples/03_variogram/02_multi_vario.py

@@ -5,6 +5,7 @@
 In this example, we demonstrate how to estimate a variogram from multiple
 fields on the same point-set that should have the same statistical properties.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/03_variogram/03_directional_2d.py

@@ -7,6 +7,7 @@
 
 Afterwards we will fit a model to this estimated variogram and show the result.
 """
+
 import numpy as np
 from matplotlib import pyplot as plt
 

examples/03_variogram/04_directional_3d.py

@@ -7,6 +7,7 @@
 
 Afterwards we will fit a model to this estimated variogram and show the result.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 from mpl_toolkits.mplot3d import Axes3D

examples/03_variogram/05_auto_fit_variogram.py

@@ -2,6 +2,7 @@
 Fit Variogram with automatic binning
 ------------------------------------
 """
+
 import numpy as np
 
 import gstools as gs

examples/03_variogram/06_auto_bin_latlon.py

@@ -8,6 +8,7 @@
 
 We use a data set from 20 meteo-stations choosen randomly.
 """
+
 import numpy as np
 
 import gstools as gs

examples/04_vector_field/00_2d_vector_field.py

@@ -5,6 +5,7 @@
 As a first example we are going to generate a 2d vector field with a Gaussian
 covariance model on a structured grid:
 """
+
 import numpy as np
 
 import gstools as gs

examples/04_vector_field/01_3d_vector_field.py

@@ -6,6 +6,7 @@
 Gaussian covariance model. The mesh on which we generate the field will be
 externally defined and it will be generated by PyVista.
 """
+
 # sphinx_gallery_thumbnail_path = 'pics/GS_3d_vector_field.png'
 import pyvista as pv
 

examples/05_kriging/00_simple_kriging.py

@@ -27,6 +27,7 @@
 The mean of the field has to be given beforehand.
 
 """
+
 import numpy as np
 
 from gstools import Gaussian, krige

examples/05_kriging/01_ordinary_kriging.py

@@ -27,6 +27,7 @@
 Here we use ordinary kriging in 1D (for plotting reasons) with 5 given observations/conditions.
 The estimated mean can be accessed by ``krig.mean``.
 """
+
 import numpy as np
 
 from gstools import Gaussian, krige

examples/05_kriging/02_pykrige_interface.py

@@ -11,6 +11,7 @@
 To demonstrate the general workflow, we compare ordinary kriging of PyKrige
 with the corresponding GSTools routine in 2D:
 """
+
 import numpy as np
 from matplotlib import pyplot as plt
 from pykrige.ok import OrdinaryKriging

examples/05_kriging/03_compare_kriging.py

@@ -2,6 +2,7 @@
 Compare Kriging
 ---------------
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/05_kriging/04_extdrift_kriging.py

@@ -2,6 +2,7 @@
 External Drift Kriging
 ----------------------
 """
+
 import numpy as np
 
 from gstools import SRF, Gaussian, krige

examples/05_kriging/05_universal_kriging.py

@@ -13,6 +13,7 @@
 To access only the estimated mean/drift, we provide a switch `only_mean`
 in the call routine.
 """
+
 import numpy as np
 
 from gstools import SRF, Gaussian, krige

examples/05_kriging/06_detrended_kriging.py

@@ -2,6 +2,7 @@
 Detrended Kriging
 -----------------
 """
+
 import numpy as np
 
 from gstools import SRF, Gaussian, krige

examples/05_kriging/07_detrended_ordinary_kriging.py

@@ -2,6 +2,7 @@
 Detrended Ordinary Kriging
 --------------------------
 """
+
 import numpy as np
 
 from gstools import SRF, Gaussian, krige

examples/05_kriging/09_pseudo_inverse.py

@@ -17,6 +17,7 @@
 In the following we have two different values at the same location.
 The resulting kriging field will hold the average at this point.
 """
+
 import numpy as np
 
 from gstools import Gaussian, krige

examples/06_conditioned_fields/00_condition_ensemble.py

@@ -6,6 +6,7 @@
 with 5 given observations/conditions,
 to generate an ensemble of conditioned random fields.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/06_conditioned_fields/01_2D_condition_ensemble.py

@@ -4,6 +4,7 @@
 
 Let's create an ensemble of conditioned random fields in 2D.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/07_transformations/00_log_normal.py

@@ -6,6 +6,7 @@
 
 See :any:`transform.normal_to_lognormal`
 """
+
 import gstools as gs
 
 # structured field with a size of 100x100 and a grid-size of 1x1

examples/07_transformations/01_binary.py

@@ -8,6 +8,7 @@
 
 See :any:`transform.binary`
 """
+
 import gstools as gs
 
 # structured field with a size of 100x100 and a grid-size of 1x1

examples/07_transformations/02_discrete.py

@@ -9,6 +9,7 @@
 
 See :any:`transform.discrete`
 """
+
 import numpy as np
 
 import gstools as gs

examples/07_transformations/03_zinn_harvey.py

@@ -9,6 +9,7 @@
 
 See :any:`transform.zinnharvey`
 """
+
 import gstools as gs
 
 # structured field with a size of 100x100 and a grid-size of 1x1

examples/07_transformations/04_bimodal.py

@@ -11,6 +11,7 @@
 
 See: :any:`transform.normal_to_arcsin` and :any:`transform.normal_to_uquad`
 """
+
 import gstools as gs
 
 # structured field with a size of 100x100 and a grid-size of 1x1

examples/07_transformations/05_combinations.py

@@ -15,6 +15,7 @@
 
 If you don't specify `field` and `store` everything happens inplace.
 """
+
 # sphinx_gallery_thumbnail_number = 1
 import gstools as gs
 

examples/08_geo_coordinates/00_field_generation.py

@@ -16,6 +16,7 @@
 To generate the field, we simply pass ``(lat, lon)`` as the position tuple
 to the :any:`SRF` class.
 """
+
 import numpy as np
 
 import gstools as gs

examples/08_geo_coordinates/01_dwd_krige.py

@@ -15,6 +15,7 @@
 In order to keep the number of dependecies low, the calls of both functions
 shown beneath are commented out.
 """
+
 # sphinx_gallery_thumbnail_number = 2
 import matplotlib.pyplot as plt
 import numpy as np

examples/09_spatio_temporal/03_geographic_coordinates.py

@@ -18,6 +18,7 @@
 
 We will set a spatial length-scale of `1000` and a time length-scale of `100` days.
 """
+
 import numpy as np
 
 import gstools as gs

examples/10_normalizer/00_lognormal_kriging.py

@@ -14,6 +14,7 @@
 
 In this example we will use ordinary kriging.
 """
+
 import numpy as np
 
 import gstools as gs

examples/10_normalizer/01_auto_fit.py

@@ -18,6 +18,7 @@
 We will generate the "original" field on a 60x60 mesh, from which we will take
 samples in order to pretend a situation of data-scarcity.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/10_normalizer/02_compare.py

@@ -6,6 +6,7 @@
 
 But first, we define a convenience routine and make some imports as always.
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 

setup.py

@@ -1,4 +1,5 @@
 """GSTools: A geostatistical toolbox."""
+
 import os
 
 import numpy as np

src/gstools/__init__.py

@@ -129,6 +129,7 @@
    DEGREE_SCALE
    RADIAN_SCALE
 """
+
 # Hooray!
 from gstools import (
     config,

src/gstools/config.py

@@ -4,6 +4,7 @@
 .. currentmodule:: gstools.config
 
 """
+
 NUM_THREADS = None
 
 # pylint: disable=W0611

src/gstools/covmodel/base.py

@@ -8,6 +8,7 @@
 .. autosummary::
    CovModel
 """
+
 # pylint: disable=C0103, R0201, E1101, C0302, W0613
 import copy