Merge branch 'gstatsim-interface' into resolve-gstatsim-conflict

docs/reference/interfaces.rst

@@ -0,0 +1,20 @@
+=======================
+Scikit-GStat interfaces
+=======================
+
+Scikit-GStat can interface with a number of other packages.
+This sections describes the interface functions. Check the
+tutorials folder for examples.
+
+GSTools
+=======
+
+.. automodule:: skgstat.interfaces.gstools
+    :members: stable_scale, skgstat_to_gstools, skgstat_to_krige
+
+
+GStatSim
+========
+
+.. automodule:: skgstat.interfaces.gstatsim_mod
+    :members: Grid, prediction_grid, simulation_params, run_simulation, simulate

docs/reference/reference.rst

@@ -13,6 +13,7 @@ Code Reference
     estimator
     models
     kriging
+    interfaces
     data
     metric_space
     util

requirements.txt

@@ -6,3 +6,4 @@ numba
 scikit-learn
 imageio
 tqdm
+typing_extensions

requirements.unittest.3.10.txt

@@ -4,3 +4,4 @@ pytest-depends
 pykrige
 gstools>=1.3
 plotly
+gstatsim

requirements.unittest.3.8.txt

@@ -4,3 +4,4 @@ pytest-depends
 pykrige
 gstools>=1.3
 plotly
+gstatsim

requirements.unittest.3.9.txt

@@ -4,3 +4,4 @@ pytest-depends
 pykrige
 gstools>=1.3
 plotly
+gstatsim

skgstat/Variogram.py

@@ -3,8 +3,9 @@
 """
 import copy
 import inspect
+from typing_extensions import Literal
 import warnings
-from typing import Iterable, Callable, Union, Tuple
+from typing import Iterable, Callable, List, Optional, Union, Tuple
 
 import numpy as np
 from pandas import DataFrame
@@ -18,6 +19,7 @@
 from skgstat.util import shannon_entropy
 from .MetricSpace import MetricSpace, ProbabalisticMetricSpace
 from skgstat.interfaces.gstools import skgstat_to_gstools, skgstat_to_krige
+from skgstat.interfaces import gstatsim_mod 
 
 
 class Variogram(object):
@@ -2794,6 +2796,59 @@ def to_DataFrame(self, n=100, force=False):
             self._model_name: data}
         ).copy()
 
+    def gstatsim_prediction_grid(self, resolution: Optional[int] = None, rows: Optional[int] = None, cols: Optional[int] = None, as_numpy: bool = False) -> Union[gstatsim_mod.Grid, np.ndarray]:
+        """
+        Generate a structured gried of coordinates from this Variogram instance.
+        The grid has the shape (N, 2), where N is the number of grid points.
+        It can be created by specifiying the resolution or the number of rows and cols.
+        If rows and cols are used, the grid will have the same resolution in both directions,
+        which means, that the final grid will have a different number of rows, cols
+        than specified.
+
+        Parameters
+        ----------
+        resolution : int, optional
+            The resolution of the grid, by default None
+        rows : int, optional
+            The number of rows, by default None
+        cols : int, optional
+            The number of cols, by default None
+        as_numpy : bool, optional
+            If True, the grid will be returned as a numpy.ndarray, by default False
+
+        Raises
+        ------
+        ValueError
+            If the Variogram instance is not 2D
+
+        Returns
+        -------
+        Union[gstatsim_mod.Grid, np.ndarray]
+            The grid as a gstatsim_mod.Grid instance or a numpy.ndarray
+        """
+        # this does only work in 2D
+        if self.dim != 2:
+            raise ValueError('This function only works in 2D')
+
+        # generate the grid
+        grid = gstatsim_mod.prediction_grid(self, resolution, rows, cols, as_numpy=as_numpy)
+        return grid
+
+    def simulation(
+        self, 
+        grid: Optional[Union[gstatsim_mod.Grid, np.ndarray, Union[int, float], Tuple[int, int]]] = None, 
+        num_points: int = 20, 
+        radius: Optional[Union[int, float]] = None,
+        method: Union[Literal['simple'], Literal['ordinary']] = 'simple',
+        quiet: bool = True,
+        n_jobs: int = 1,
+        size: int  = 1,
+        **kwargs,
+    ) -> List[np.ndarray]:
+        """"""
+        fields = gstatsim_mod.simulate(self, grid, num_points, radius, method, quiet, n_jobs, size, **kwargs)
+        return fields
+
     def to_gstools(self, **kwargs):
         """
         Instantiate a corresponding GSTools CovModel.