From df42021f98ce93a2283c18702b2c782ead583280 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 22 Feb 2026 12:17:28 +0200 Subject: [PATCH 1/2] Updated equations in documentation --- CHANGELOG.rst | 2 +- .../build/doctrees/api/core/pipelines.doctree | Bin 181865 -> 184982 bytes .../doctrees/api/distance/distance.doctree | Bin 51907 -> 57683 bytes .../api/kriging/block_kriging.doctree | Bin 72911 -> 77492 bytes .../api/semivariogram/experimental.doctree | Bin 485836 -> 486380 bytes docs/build/doctrees/environment.pickle | Bin 642234 -> 654918 bytes .../core/pipelines/block_filter.html | 48 +++- .../pyinterpolate/distance/block.html | 27 +- .../block/area_to_area_poisson_kriging.html | 14 +- .../block/area_to_point_poisson_kriging.html | 64 +++-- .../block/centroid_based_poisson_kriging.html | 15 +- .../pyinterpolate/kriging/point/ordinary.html | 242 +++++++++++++++++- .../experimental_semivariogram.html | 11 +- docs/build/html/api/core/pipelines.html | 8 +- docs/build/html/api/distance/distance.html | 27 ++ .../build/html/api/kriging/block_kriging.html | 12 +- .../html/api/semivariogram/experimental.html | 3 +- docs/build/html/objects.inv | Bin 5957 -> 5981 bytes docs/build/html/searchindex.js | 2 +- src/pyinterpolate/distance/block.py | 21 ++ .../experimental_semivariogram.py | 5 +- 21 files changed, 433 insertions(+), 68 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 352d6e3b..47ce3e38 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -8,7 +8,7 @@ Changes - from version >= 1.x * [experimental] LSA-method Ordinary Kriging tests and experiments * [enhancement] Users can set negative predictions to zero in Area-to-Area, Area-to-Point, and Centroid-based Poisson Kriging - +* [docs] Added block-to-block distance equation, and corrected semivariance equation in functions docstrings 2025-12-26 ---------- diff --git a/docs/build/doctrees/api/core/pipelines.doctree 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z55?j=^*b-_soyO4Cl{`Hb21{0)QwDd(k|Dq)U{kt!6NqFX>g(>e(E?St9 zH~wKs#rVuWpuXTwb%yW!gX8ezAC^p{pZUYW27|VjxHOEvWU)f?mtC@?fw$?Br6YLz zE?F|b`|gq@It1%O*Il$Ex}>9SN!QT7-2h!QW?5(oc*B>4qJCYpEHtS(>z=c6HUWmo zbmC2+9qGYkq2Unw*0NC4#eXadMJ3$B9@-t1=`4F_7VqS%rQCH`?NiG`J9Bxck#ghF zs86In#+u{kS$n7n-u&4f+6~`KS{|BV#M8MD&+<9HJk*GvU)q<4riNg|#^s?2NI0=P zG})EX%?TrzV(Pad^x$qoH@O-8Ez_9FW#E;ZbC7Qmo!HyhF19Fte%`GM@+0Wy)~RuFo)w=s$??{91m zNoHiRl&i9gs0E*g5Kl=Axvk*C-z;NClMG)9Bkzb_MpXGDdKtTMOJbHGH;*37GDe5x zdW4J zzprr+uSiH}Zu?Lg(c2hJ`}8y7WMe` - pyinterpolate.core.pipelines.block_filter — pyinterpolate 1.1.0 documentation + pyinterpolate.core.pipelines.block_filter — pyinterpolate 1.2.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.1.0 documentation

+

pyinterpolate 1.2.0 documentation

 @@ -459,6 +459,7 @@

Source code for pyinterpolate.core.pipelines.block_filter

data_crs=None, raise_when_negative_prediction=True, raise_when_negative_error=False, + negative_prediction_to_zero=False, verbose=True) -> gpd.GeoDataFrame: """ Function filters block data using Poisson Kriging. By filtering we @@ -492,6 +493,9 @@

Source code for pyinterpolate.core.pipelines.block_filter

raise_when_negative_error : bool, default=True Raise error when prediction error is negative. + negative_prediction_to_zero : bool, default=False + When predicted value is below zero then set it to zero. + verbose : bool, default=True Show progress bar @@ -580,7 +584,8 @@

Source code for pyinterpolate.core.pipelines.block_filter

number_of_neighbors=number_of_neighbors, data_crs=data_crs, raise_when_negative_prediction=raise_when_negative_prediction, - raise_when_negative_error=raise_when_negative_error + raise_when_negative_error=raise_when_negative_error, + negative_prediction_to_zero=negative_prediction_to_zero ) return parsed
@@ -594,6 +599,7 @@

Source code for pyinterpolate.core.pipelines.block_filter

data_crs=None, raise_when_negative_prediction=True, raise_when_negative_error=True, + negative_prediction_to_zero=False, verbose=True) -> gpd.GeoDataFrame: """ Function smooths aggregated block values, and transform those into @@ -620,6 +626,9 @@

Source code for pyinterpolate.core.pipelines.block_filter

raise_when_negative_error : bool, default=True Raise error when prediction error is negative. + negative_prediction_to_zero : bool, default=False + When predicted value is below zero then set it to zero. + verbose : bool, default=True Show progress bar @@ -705,7 +714,8 @@

Source code for pyinterpolate.core.pipelines.block_filter

number_of_neighbors=number_of_neighbors, data_crs=data_crs, raise_when_negative_prediction=raise_when_negative_prediction, - raise_when_negative_error=raise_when_negative_error + raise_when_negative_error=raise_when_negative_error, + negative_prediction_to_zero=negative_prediction_to_zero ) return parsed
@@ -853,7 +863,8 @@

Source code for pyinterpolate.core.pipelines.block_filter

number_of_neighbors, data_crs=None, raise_when_negative_prediction=True, - raise_when_negative_error=True) -> gpd.GeoDataFrame: + raise_when_negative_error=True, + negative_prediction_to_zero: bool = False) -> gpd.GeoDataFrame: """ Function regularizes whole dataset and creates new values and error maps based on the kriging type. Function does not predict unknown @@ -874,6 +885,9 @@

Source code for pyinterpolate.core.pipelines.block_filter

raise_when_negative_error : bool, default=True Raise error when prediction error is negative. + negative_prediction_to_zero : bool, default=False + When predicted value is below zero then set it to zero. + Returns ------- regularized : gpd.GeoDataFrame @@ -890,7 +904,8 @@

Source code for pyinterpolate.core.pipelines.block_filter

uid=block_id, n_neighbours=number_of_neighbors, pred_raise=raise_when_negative_prediction, - err_raise=raise_when_negative_error + err_raise=raise_when_negative_error, + negative_prediction_to_zero=negative_prediction_to_zero ) interpolation_results.extend( @@ -932,7 +947,12 @@

Source code for pyinterpolate.core.pipelines.block_filter

return k_type - def _interpolate(self, uid, n_neighbours, pred_raise, err_raise) -> Dict: + def _interpolate(self, + uid, + n_neighbours, + pred_raise, + err_raise, + negative_prediction_to_zero) -> Dict: """ Function interpolates block values using one of Poisson Kriging types. @@ -950,6 +970,9 @@

Source code for pyinterpolate.core.pipelines.block_filter

err_raise : bool Raise error when prediction error is negative. + negative_prediction_to_zero : bool + When predicted value is below zero then set it to zero. + Returns ------- : dict @@ -969,7 +992,8 @@

Source code for pyinterpolate.core.pipelines.block_filter

unknown_block_index=uid, number_of_neighbors=n_neighbours, raise_when_negative_error=err_raise, - raise_when_negative_prediction=pred_raise) + raise_when_negative_prediction=pred_raise, + negative_prediction_to_zero=negative_prediction_to_zero) elif self.kriging_type == 'atp': model_output = area_to_point_pk(semivariogram_model=self.semivariogram_model, @@ -977,14 +1001,16 @@

Source code for pyinterpolate.core.pipelines.block_filter

unknown_block_index=uid, number_of_neighbors=n_neighbours, raise_when_negative_prediction=pred_raise, - raise_when_negative_error=err_raise) + raise_when_negative_error=err_raise, + negative_prediction_to_zero=negative_prediction_to_zero) elif self.kriging_type == 'cb': model_output = centroid_poisson_kriging(semivariogram_model=self.semivariogram_model, point_support=self.point_support, unknown_block_index=uid, number_of_neighbors=n_neighbours, raise_when_negative_prediction=pred_raise, - raise_when_negative_error=err_raise) + raise_when_negative_error=err_raise, + negative_prediction_to_zero=negative_prediction_to_zero) else: self._raise_wrong_kriging_type_error() diff --git a/docs/build/html/_modules/pyinterpolate/distance/block.html b/docs/build/html/_modules/pyinterpolate/distance/block.html index 84979812..9a91b201 100644 --- a/docs/build/html/_modules/pyinterpolate/distance/block.html +++ b/docs/build/html/_modules/pyinterpolate/distance/block.html @@ -7,7 +7,7 @@ - pyinterpolate.distance.block — pyinterpolate 1.1.0 documentation + pyinterpolate.distance.block — pyinterpolate 1.2.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.1.0 documentation

+

pyinterpolate 1.2.0 documentation

@@ -639,6 +639,27 @@

Source code for pyinterpolate.distance.block

    AttributeError
         Blocks are provided as DataFrame but column names were not given.
 
+    Notes
+    -----
+    The weighted distance between blocks is derived from the equation 3 given
+    in publication [1] from References.
+
+    .. math:: d(v_{a}, v_{b})=\frac{1}{\sum_{s=1}^{P_{a}} \sum_{s'=1}^{P_{b}} n(u_{s}) n(u_{s'})} * \sum_{s=1}^{P_{a}} \sum_{s'=1}^{P_{b}} n(u_{s})n(u_{s'})||u_{s}-u_{s'}||
+
+    where:
+      * :math:`P_{a}` and :math:`P_{b}`: number of points :math:`u_{s}`
+        and :math:`u_{s'}` used to discretize the two units :math:`v_{a}`
+        and :math:`v_{b}`
+      * :math:`n(u_{s})` and :math:`n(u_{s'})` - population size in
+        the cells :math:`u_{s}` and :math:`u_{s'}`
+
+    References
+    ----------
+    .. [1] Goovaerts, P. Kriging and Semivariogram Deconvolution in the
+           Presence of Irregular Geographical Units.
+           Math Geosci 40, 101–128 (2008).
+           https://doi.org/10.1007/s11004-007-9129-1
+
     Examples
     --------
     >>> import os
diff --git a/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_area_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_area_poisson_kriging.html
index 442b9b80..f829aee5 100644
--- a/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_area_poisson_kriging.html
+++ b/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_area_poisson_kriging.html
@@ -7,7 +7,7 @@
   
     
     
-    pyinterpolate.kriging.block.area_to_area_poisson_kriging — pyinterpolate 1.1.0 documentation
+    pyinterpolate.kriging.block.area_to_area_poisson_kriging — pyinterpolate 1.2.0 documentation
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -111,7 +111,7 @@
   
   
   
-    
pyinterpolate 1.1.0 documentation

+    
pyinterpolate 1.2.0 documentation
@@ -450,7 +450,8 @@

Source code for pyinterpolate.kriging.block.area_to_area_poisson_kriging

number_of_neighbors: int, neighbors_range: float = None, raise_when_negative_prediction=True, - raise_when_negative_error=True) -> dict: + raise_when_negative_error=True, + negative_prediction_to_zero=False) -> dict: """ Function predicts areal value in an unknown location based on the area-to-area Poisson Kriging @@ -480,6 +481,9 @@

Source code for pyinterpolate.kriging.block.area_to_area_poisson_kriging

raise_when_negative_error : bool, default=True Raise error when prediction error is negative. + negative_prediction_to_zero : bool, default=False + While prediction is negative, then set it to 0. + Returns ------- results : Dict @@ -639,6 +643,8 @@

Source code for pyinterpolate.kriging.block.area_to_area_poisson_kriging

f'not be lower than 0. Check your sampling ' f'grid, samples, number of neighbors or ' f'semivariogram model type.') + if negative_prediction_to_zero: + zhat = 0 sigmasq = np.matmul(w.T, covars) diff --git a/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_point_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_point_poisson_kriging.html index 65344675..fa9bbe19 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_point_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/block/area_to_point_poisson_kriging.html @@ -7,7 +7,7 @@ - pyinterpolate.kriging.block.area_to_point_poisson_kriging — pyinterpolate 1.1.0 documentation + pyinterpolate.kriging.block.area_to_point_poisson_kriging — pyinterpolate 1.2.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.1.0 documentation

+

pyinterpolate 1.2.0 documentation

@@ -437,8 +437,10 @@

Source code for pyinterpolate.kriging.block.area_to_point_poisson_krigingfrom pyinterpolate.kriging.block.weights import pk_weights_array from pyinterpolate.semivariogram.deconvolution.block_to_block_semivariance import \ weighted_avg_point_support_semivariances -from pyinterpolate.semivariogram.theoretical.classes.theoretical_variogram import TheoreticalVariogram -from pyinterpolate.transform.select_poisson_kriging_data import select_poisson_kriging_data +from pyinterpolate.semivariogram.theoretical.classes.theoretical_variogram import \ + TheoreticalVariogram +from pyinterpolate.transform.select_poisson_kriging_data import \ + select_poisson_kriging_data from pyinterpolate.transform.statistical import sem_to_cov from pyinterpolate.transform.transform import add_ones @@ -452,7 +454,8 @@

Source code for pyinterpolate.kriging.block.area_to_point_poisson_krigingneighbors_range: float = None, raise_when_negative_prediction=True, raise_when_negative_error=True, - err_to_nan=True): + err_to_nan=True, + negative_prediction_to_zero=False): """ Function predicts point-support value in the unknown location based on the area-to-point Poisson Kriging @@ -486,6 +489,9 @@

Source code for pyinterpolate.kriging.block.area_to_point_poisson_kriging When point interpolation returns ``ValueError`` then set prediction or variance error to ``NaN``. + negative_prediction_to_zero : bool, default=False + While prediction is negative, then set it to 0. + Returns ------- results : Dict[numpy array] @@ -687,16 +693,21 @@

Source code for pyinterpolate.kriging.block.area_to_point_poisson_krigingif zhat < 0: if raise_when_negative_prediction: - if err_to_nan: - predicted_points.append( - [upoint[0], upoint[1], np.nan, np.nan] - ) - continue - else: - raise ValueError(f'Predicted value is {zhat} and it ' - f'should not be lower than 0. Check your ' - f'sampling grid, samples, number of ' - f'neighbors or semivariogram model type.') + raise ValueError(f'Predicted value is {zhat} and it ' + f'should not be lower than 0. Check your ' + f'sampling grid, samples, number of ' + f'neighbors or semivariogram model type.') + if err_to_nan: + predicted_points.append( + [upoint[0], upoint[1], np.nan, np.nan] + ) + continue + + if negative_prediction_to_zero: + predicted_points.append( + [upoint[0], upoint[1], 0, np.nan] + ) + continue point_pop = upoint[2] zhat = (zhat * point_pop) / tot_unknown_value @@ -708,17 +719,16 @@

Source code for pyinterpolate.kriging.block.area_to_point_poisson_krigingif sigmasq < 0: if raise_when_negative_error: - if err_to_nan: - predicted_points.append( - [upoint[0], upoint[1], zhat, np.nan] - ) - continue - else: - raise ValueError(f'Predicted error value is {sigmasq} and ' - f'it should not be lower than 0. ' - f'Check your sampling grid, samples, ' - f'number of neighbors or semivariogram ' - f'model type.') + raise ValueError(f'Predicted error value is {sigmasq} and ' + f'it should not be lower than 0. ' + f'Check your sampling grid, samples, ' + f'number of neighbors or semivariogram ' + f'model type.') + if err_to_nan: + predicted_points.append( + [upoint[0], upoint[1], zhat, np.nan] + ) + continue else: sigma = 0 else: diff --git a/docs/build/html/_modules/pyinterpolate/kriging/block/centroid_based_poisson_kriging.html b/docs/build/html/_modules/pyinterpolate/kriging/block/centroid_based_poisson_kriging.html index d31125dd..e0cb2fc3 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/block/centroid_based_poisson_kriging.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/block/centroid_based_poisson_kriging.html @@ -7,7 +7,7 @@ - pyinterpolate.kriging.block.centroid_based_poisson_kriging — pyinterpolate 1.1.0 documentation + pyinterpolate.kriging.block.centroid_based_poisson_kriging — pyinterpolate 1.2.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.1.0 documentation

+

pyinterpolate 1.2.0 documentation

@@ -451,7 +451,8 @@

Source code for pyinterpolate.kriging.block.centroid_based_poisson_krigingis_weighted_by_point_support=True, raise_when_negative_prediction=True, raise_when_negative_error=True, - allow_lsa=False) -> Dict: + allow_lsa=False, + negative_prediction_to_zero=False) -> Dict: """ Function performs centroid-based Poisson Kriging of blocks (areal) data. @@ -490,6 +491,9 @@

Source code for pyinterpolate.kriging.block.centroid_based_poisson_kriging when you have clusters in your dataset, that can lead to singular or near-singular matrix creation. + negative_prediction_to_zero : bool, default=False + While prediction is negative, then set it to 0. + Returns ------- results : Dict @@ -633,6 +637,9 @@

Source code for pyinterpolate.kriging.block.centroid_based_poisson_krigingf'grid, samples, number of neighbors or ' f'semivariogram model type.') + if negative_prediction_to_zero: + zhat = 0 + sigmasq = np.matmul(output_weights.T, covars) if sigmasq < 0: diff --git a/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html b/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html index 49bbc1db..4a9f873c 100644 --- a/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html +++ b/docs/build/html/_modules/pyinterpolate/kriging/point/ordinary.html @@ -7,7 +7,7 @@ - pyinterpolate.kriging.point.ordinary — pyinterpolate 1.1.0 documentation + pyinterpolate.kriging.point.ordinary — pyinterpolate 1.2.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.1.0 documentation

+

pyinterpolate 1.2.0 documentation

@@ -446,7 +446,8 @@

Source code for pyinterpolate.kriging.point.ordinary

from pyinterpolate.kriging.utils.errors import singular_matrix_error # Pyinterpolate from pyinterpolate.kriging.utils.point_kriging_solve import (get_predictions, - solve_weights) + solve_weights, + __experimental_solve_weights_lsa_only) from pyinterpolate.transform.statistical import sem_to_cov from pyinterpolate.semivariogram.theoretical.theoretical import TheoreticalVariogram @@ -828,6 +829,239 @@

Source code for pyinterpolate.kriging.point.ordinary

return [zhat, sigma, unknown_location[0], unknown_location[1]] except np.linalg.LinAlgError as _: singular_matrix_error() + + +def experimental_ordinary_kriging_lsa( + theoretical_model: TheoreticalVariogram, + unknown_locations: Union[ + np.ndarray, Point, List, Tuple, GeoSeries, GeometryArray, ArrayLike], + known_locations: ArrayLike = None, + known_values: ArrayLike = None, + known_geometries: ArrayLike = None, + neighbors_range=None, + no_neighbors=4, + max_tick=5., + use_all_neighbors_in_range=False, + progress_bar: bool = True +) -> np.ndarray: + """ + Function predicts value at unknown location using Least Squares + Approximation. + + Parameters + ---------- + theoretical_model : TheoreticalVariogram + Fitted theoretical variogram model. + + unknown_locations : Union[ArrayLike, Point] + Points where you want to estimate value ``(x, y) <-> (lon, lat)``. + + known_locations : numpy array + Known locations: ``[x, y, value]``. + + known_values : ArrayLike, optional + Observation in the i-th geometry (from ``known_geometries``). Optional + parameter, if not given then ``known_locations`` must be provided. + + known_geometries : ArrayLike, optional + Array or similar structure with geometries. It must have the same + length as ``known_values``. Optional parameter, if not given then + ``known_locations`` must be provided. Point type geometry. + + neighbors_range : float, default=None + The maximum distance where we search for neighbors. If ``None`` is + given then the range is selected from the Theoretical + Model's ``rang`` attribute. + + no_neighbors : int, default = 4 + The number of **n-closest neighbors** used for interpolation. + + max_tick : float, default=5. + If searching for neighbors in a specific direction how big should be + a tolerance for increasing the search angle (how many degrees more). + + use_all_neighbors_in_range : bool, default = False + ``True``: if the real number of neighbors within + the ``neighbors_range`` is greater than the ``number_of_neighbors`` + then take all of them anyway. + + progress_bar : bool, default=True + Show a progress bar during the interpolation process. + + Returns + ------- + : numpy array + ``[predicted value, variance error, longitude (x), latitude (y)]`` + + Raises + ------ + RunetimeError + Singular Matrix in the Kriging system. + + Examples + -------- + >>> import geopandas as gpd + >>> import numpy as np + >>> import pandas as pd + >>> + >>> from pyinterpolate import (build_experimental_variogram, + ... build_theoretical_variogram, ordinary_kriging) + >>> + >>> dem = gpd.read_file('dem.gpkg') + >>> unknown_locations = gpd.read_file('unknown_locations.gpkg') + >>> step_size = 500 + >>> max_range = 10000 + >>> exp_variogram = build_experimental_variogram( + ... values=dem['dem'], + ... geometries=dem['geometry'], + ... step_size=step_size, + ... max_range=max_range + ... ) + >>> theo_variogram = build_theoretical_variogram(exp_variogram) + >>> interp = ordinary_kriging( + ... theoretical_model=theo_variogram, + ... unknown_locations=unknown_locations['geometry'], + ... known_values=dem['dem'], + ... known_geometries=dem['geometry'] + ... ) + >>> print(interp[0]) + [7.91222896e+01 9.72740449e+01 2.38012302e+05 5.51466805e+05] + """ + # Check if known locations are in the right format + if not isinstance(known_locations, VariogramPoints): + known_locations = VariogramPoints(points=known_locations, + geometries=known_geometries, + values=known_values) + known_locations = known_locations.points + + unknown_locations = InterpolationPoints(unknown_locations).points + + interpolated_results = [] + + _disable_progress_bar = not progress_bar + + for upoints in tqdm(unknown_locations, disable=_disable_progress_bar): + res = __experimental_ok_calc_lsa( + theoretical_model=theoretical_model, + known_locations=known_locations, + unknown_location=upoints, + neighbors_range=neighbors_range, + no_neighbors=no_neighbors, + max_tick=max_tick, + use_all_neighbors_in_range=use_all_neighbors_in_range + ) + + interpolated_results.append( + res + ) + + return np.array(interpolated_results) + + +def __experimental_ok_calc_lsa( + theoretical_model: TheoreticalVariogram, + unknown_location: ArrayLike, + known_locations: ArrayLike = None, + known_values: ArrayLike = None, + known_geometries: ArrayLike = None, + neighbors_range=None, + no_neighbors=4, + max_tick=5., + use_all_neighbors_in_range=False +): + """ + Function predicts value at unknown location with Ordinary Kriging + technique. + + Parameters + ---------- + theoretical_model : TheoreticalVariogram + Fitted theoretical variogram model. + + unknown_location : Union[ArrayLike, Point] + Points where you want to estimate value ``(x, y) <-> (lon, lat)``. + + known_locations : numpy array + Known locations: ``[x, y, value]``. + + known_values : ArrayLike, optional + Observation in the i-th geometry (from ``known_geometries``). Optional + parameter, if not given then ``known_locations`` must be provided. + + known_geometries : ArrayLike, optional + Array or similar structure with geometries. It must have the same + length as ``known_values``. Optional parameter, if not given then + ``known_locations`` must be provided. Point type geometry. + + neighbors_range : float, default=None + The maximum distance where we search for neighbors. If ``None`` is + given then the range is selected from the Theoretical + Model's ``rang`` attribute. + + no_neighbors : int, default = 4 + The number of **n-closest neighbors** used for interpolation. + + max_tick : float, default=5. + If searching for neighbors in a specific direction how big should be + a tolerance for increasing the search angle (how many degrees more). + + use_all_neighbors_in_range : bool, default = False + ``True``: if the real number of neighbors within + the ``neighbors_range`` is greater than the ``number_of_neighbors`` + then take all of them anyway. + + Returns + ------- + : numpy array + ``[predicted value, variance error, longitude (x), latitude (y)]`` + + Raises + ------ + RunetimeError + Singular Matrix in the Kriging system. + """ + # Check if known locations are in the right format + # Validate points + if not isinstance(known_locations, VariogramPoints): + known_locations = VariogramPoints(points=known_locations, + geometries=known_geometries, + values=known_values) + known_locations = known_locations.points + + # Check if unknown location is Point + if isinstance(unknown_location, Point): + unknown_location = ( + unknown_location.x, + unknown_location.y + ) + unknown_location = np.array(unknown_location) + + k, predicted, dataset = get_predictions(theoretical_model, + known_locations, + unknown_location, + neighbors_range, + no_neighbors, + use_all_neighbors_in_range, + max_tick) + + k_ones = np.ones(1)[0] + k = np.r_[k, k_ones] + + p_ones = np.ones((predicted.shape[0], 1)) + predicted_with_ones_col = np.c_[predicted, p_ones] + p_ones_row = np.ones((1, predicted_with_ones_col.shape[1])) + p_ones_row[0][-1] = 0. + weights = np.r_[predicted_with_ones_col, p_ones_row] + + output_weights = __experimental_solve_weights_lsa_only(weights, k) + zhat = dataset[:, -2].dot(output_weights[:-1]) + + sigma = np.matmul(output_weights.T, k) + + if sigma < 0: + return [zhat, np.nan, unknown_location[0], unknown_location[1]] + + return [zhat, sigma, unknown_location[0], unknown_location[1]]
diff --git a/docs/build/html/_modules/pyinterpolate/semivariogram/experimental/experimental_semivariogram.html b/docs/build/html/_modules/pyinterpolate/semivariogram/experimental/experimental_semivariogram.html index c3435445..c9112828 100644 --- a/docs/build/html/_modules/pyinterpolate/semivariogram/experimental/experimental_semivariogram.html +++ b/docs/build/html/_modules/pyinterpolate/semivariogram/experimental/experimental_semivariogram.html @@ -7,7 +7,7 @@ - pyinterpolate.semivariogram.experimental.experimental_semivariogram — pyinterpolate 1.1.0 documentation + pyinterpolate.semivariogram.experimental.experimental_semivariogram — pyinterpolate 1.2.0 documentation @@ -38,7 +38,7 @@ - + @@ -111,7 +111,7 @@ -

pyinterpolate 1.1.0 documentation

+

pyinterpolate 1.2.0 documentation

@@ -453,7 +453,7 @@

Source code for pyinterpolate.semivariogram.experimental.experimental_semiva custom_bins: Union[ArrayLike, Any] = None, custom_weights: ArrayLike = None, ) -> np.ndarray: - """ + r""" Calculates experimental semivariance. Parameters @@ -521,11 +521,12 @@

Source code for pyinterpolate.semivariogram.experimental.experimental_semiva We calculate the empirical semivariance as: - .. math:: g(h) = 0.5 * n(h)^(-1) * (SUM|i=1, n(h)|: [z(x_i + h) - z(x_i)]^2) + .. math:: g(h) = 0.5 * \frac{1}{n(h)} * \sum_{i=1}^{n(h)}{[z(x_i + h) - z(x_i)]^2} where: - :math:`h`: lag, + - :math:`n(h)`: number of point pairs within the lag :math:`h`, - :math:`g(h)`: empirical semivariance for lag :math:`h`, - :math:`n(h)`: number of point pairs within a specific lag, - :math:`z(x_i)`: point a (value of observation at point a), diff --git a/docs/build/html/api/core/pipelines.html b/docs/build/html/api/core/pipelines.html index a36e34fb..70e70786 100644 --- a/docs/build/html/api/core/pipelines.html +++ b/docs/build/html/api/core/pipelines.html @@ -455,7 +455,7 @@

Pipelines#

-pyinterpolate.filter_blocks(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, number_of_neighbors, kriging_type='ata', data_crs=None, raise_when_negative_prediction=True, raise_when_negative_error=False, verbose=True) → GeoDataFrame[source]
+pyinterpolate.filter_blocks(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, number_of_neighbors, kriging_type='ata', data_crs=None, raise_when_negative_prediction=True, raise_when_negative_error=False, negative_prediction_to_zero=False, verbose=True) → GeoDataFrame[source]

Function filters block data using Poisson Kriging. By filtering we understand computing aggregated values again using point support data for ratios regularization.

@@ -484,6 +484,8 @@

Poisson Kriging pipelinesbool, default=True

Raise error when prediction error is negative.

+
negative_prediction_to_zerobool, default=False

When predicted value is below zero then set it to zero.

+
verbosebool, default=True

Show progress bar

@@ -567,7 +569,7 @@

Poisson Kriging pipelines
-pyinterpolate.smooth_blocks(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, number_of_neighbors, data_crs=None, raise_when_negative_prediction=True, raise_when_negative_error=True, verbose=True) → GeoDataFrame[source]
+pyinterpolate.smooth_blocks(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, number_of_neighbors, data_crs=None, raise_when_negative_prediction=True, raise_when_negative_error=True, negative_prediction_to_zero=False, verbose=True) → GeoDataFrame[source]

Function smooths aggregated block values, and transform those into point support.

@@ -586,6 +588,8 @@

Poisson Kriging pipelinesbool, default=True

Raise error when prediction error is negative.

+
negative_prediction_to_zerobool, default=False

When predicted value is below zero then set it to zero.

+
verbosebool, default=True

Show progress bar

diff --git a/docs/build/html/api/distance/distance.html b/docs/build/html/api/distance/distance.html index 3890e0aa..32c0b2bf 100644 --- a/docs/build/html/api/distance/distance.html +++ b/docs/build/html/api/distance/distance.html @@ -45,6 +45,8 @@ + + @@ -538,6 +540,31 @@

Block#<

+

Notes

+

The weighted distance between blocks is derived from the equation 3 given +in publication [1] from References.

+
+\[d(v_{a}, v_{b})=\frac{1}{\sum_{s=1}^{P_{a}} \sum_{s'=1}^{P_{b}} n(u_{s}) n(u_{s'})} * \sum_{s=1}^{P_{a}} \sum_{s'=1}^{P_{b}} n(u_{s})n(u_{s'})||u_{s}-u_{s'}||\]
+
+
where:
    +

  • \(P_{a}\) and \(P_{b}\): number of points \(u_{s}\) +and \(u_{s'}\) used to discretize the two units \(v_{a}\) +and \(v_{b}\)

    • +

  • \(n(u_{s})\) and \(n(u_{s'})\) - population size in +the cells \(u_{s}\) and \(u_{s'}\)

    • +
+
+
+

References

+
+
+[1] +

Goovaerts, P. Kriging and Semivariogram Deconvolution in the +Presence of Irregular Geographical Units. +Math Geosci 40, 101–128 (2008). +https://doi.org/10.1007/s11004-007-9129-1

+
+

Examples

>>> import os
 >>> import geopandas as gpd
diff --git a/docs/build/html/api/kriging/block_kriging.html b/docs/build/html/api/kriging/block_kriging.html
index 58d64fb2..d2aeed52 100644
--- a/docs/build/html/api/kriging/block_kriging.html
+++ b/docs/build/html/api/kriging/block_kriging.html
@@ -455,7 +455,7 @@ 
Block and Poisson Kriging#

 

 

-pyinterpolate.centroid_poisson_kriging(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, unknown_block_index: str | Hashable, number_of_neighbors: int, neighbors_range: float = None, is_weighted_by_point_support=True, raise_when_negative_prediction=True, raise_when_negative_error=True, allow_lsa=False) → Dict[source]

+pyinterpolate.centroid_poisson_kriging(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, unknown_block_index: str | Hashable, number_of_neighbors: int, neighbors_range: float = None, is_weighted_by_point_support=True, raise_when_negative_prediction=True, raise_when_negative_error=True, allow_lsa=False, negative_prediction_to_zero=False) → Dict[source]
 
Function performs centroid-based Poisson Kriging of blocks (areal) data.

 

 
Parameters:

@@ -484,6 +484,8 @@ 
Centroid-based Poisson Krigingbool, default=False
While prediction is negative, then set it to 0.

+

 

 

 
Returns:

@@ -571,7 +573,7 @@ 
Centroid-based Poisson Kriging#

 

 

-pyinterpolate.area_to_area_pk(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, unknown_block_index: str | Hashable, number_of_neighbors: int, neighbors_range: float = None, raise_when_negative_prediction=True, raise_when_negative_error=True) → dict[source]

+pyinterpolate.area_to_area_pk(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, unknown_block_index: str | Hashable, number_of_neighbors: int, neighbors_range: float = None, raise_when_negative_prediction=True, raise_when_negative_error=True, negative_prediction_to_zero=False) → dict[source]
 
Function predicts areal value in an unknown location based on
 the area-to-area Poisson Kriging

 

@@ -593,6 +595,8 @@ 
Area-to-area Poisson Krigingbool, default=True
Raise error when prediction error is negative.

 

+
negative_prediction_to_zerobool, default=False
While prediction is negative, then set it to 0.

+

 

 

 
Returns:

@@ -680,7 +684,7 @@ 
Area-to-area Poisson Kriging#

 

 

-pyinterpolate.area_to_point_pk(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, unknown_block_index: str | Hashable, number_of_neighbors: int, neighbors_range: float = None, raise_when_negative_prediction=True, raise_when_negative_error=True, err_to_nan=True)[source]

+pyinterpolate.area_to_point_pk(semivariogram_model: TheoreticalVariogram, point_support: PointSupport, unknown_block_index: str | Hashable, number_of_neighbors: int, neighbors_range: float = None, raise_when_negative_prediction=True, raise_when_negative_error=True, err_to_nan=True, negative_prediction_to_zero=False)[source]
 
Function predicts point-support value in the unknown location based on
 the area-to-point Poisson Kriging

 

@@ -705,6 +709,8 @@ 
Area-to-point Poisson Krigingbool, default=True
When point interpolation returns ValueError then set prediction
 or variance error to NaN.

 

+
negative_prediction_to_zerobool, default=False
While prediction is negative, then set it to 0.

+

 

 

 
Returns:

diff --git a/docs/build/html/api/semivariogram/experimental.html b/docs/build/html/api/semivariogram/experimental.html
index 363c8383..a145d88d 100644
--- a/docs/build/html/api/semivariogram/experimental.html
+++ b/docs/build/html/api/semivariogram/experimental.html
@@ -775,10 +775,11 @@ 
Experimental Variogram
 
We calculate the empirical semivariance as:

 

-\[g(h) = 0.5 * n(h)^(-1) * (SUM|i=1, n(h)|: [z(x_i + h) - z(x_i)]^2)\]

+\[g(h) = 0.5 * \frac{1}{n(h)} * \sum_{i=1}^{n(h)}{[z(x_i + h) - z(x_i)]^2}\]

where:

  • \(h\): lag,

    • +

  • \(n(h)\): number of point pairs within the lag \(h\),

  • \(g(h)\): empirical semivariance for lag \(h\),

  • \(n(h)\): number of point pairs within a specific lag,

  • \(z(x_i)\): point a (value of observation at point a),

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Create Variogram Point Cloud": [[35, "1.-Create-Variogram-Point-Cloud"]], "1. Directional process": [[34, "1.-Directional-process"]], "1. Introduction - IDW as bechmarking tool": [[37, "1.-Introduction---IDW-as-bechmarking-tool"]], "1. Prepare data": [[38, "1.-Prepare-data"], [40, "1.-Prepare-data"], [41, "1.-Prepare-data"], [42, "1.-Prepare-data"], [43, "1.-Prepare-data"], [44, "1.-Prepare-data"]], "1. Set semivariogram model (fit)": [[36, "1.-Set-semivariogram-model-(fit)"]], "2. Analyze Variogram Point Cloud": [[35, "2.-Analyze-Variogram-Point-Cloud"]], "2. Analyze data distribution and remove potential outliers": [[38, "2.-Analyze-data-distribution-and-remove-potential-outliers"]], "2. Create Ordinary and Simple Kriging models": [[36, "2.-Create-Ordinary-and-Simple-Kriging-models"]], "2. Create directional and isotropic semivariograms": [[34, "2.-Create-directional-and-isotropic-semivariograms"]], "2. Create directional semivariograms": [[39, "2.-Create-directional-semivariograms"]], "2. Detect and remove outliers": [[40, "2.-Detect-and-remove-outliers"]], "2. Load regularized semivariogram model": [[42, "2.-Load-regularized-semivariogram-model"], [43, "2.-Load-regularized-semivariogram-model"], [44, "2.-Load-regularized-semivariogram-model"]], "2. Perform IDW and validate outputs": [[37, "2.-Perform-IDW-and-validate-outputs"]], "2. Set semivariogram parameters": [[41, "2.-Set-semivariogram-parameters"]], "2. Why do we use Spatial Dependency Index?": [[33, "2.-Why-do-we-use-Spatial-Dependency-Index?"]], "3. Compare semivariograms": [[34, "3.-Compare-semivariograms"]], "3. Create Variogram Clouds": [[38, "3.-Create-Variogram-Clouds"]], "3. Detect and remove outliers": [[35, "3.-Detect-and-remove-outliers"]], "3. Example: Spatial Dependence over the same study extent but for different elements": [[33, "3.-Example:-Spatial-Dependence-over-the-same-study-extent-but-for-different-elements"]], "3. Fit semivariogram model": [[40, "3.-Fit-semivariogram-model"]], "3. Interpolate with directional Kriging": [[39, "3.-Interpolate-with-directional-Kriging"]], "3. Perform Kriging and validate outputs": [[37, "3.-Perform-Kriging-and-validate-outputs"]], "3. Predict values at unknown locations and evaluate output": [[36, "3.-Predict-values-at-unknown-locations-and-evaluate-output"]], "3. Prepare data for Poisson Kriging": [[42, "3.-Prepare-data-for-Poisson-Kriging"], [43, "3.-Prepare-data-for-Poisson-Kriging"]], "3. Regularize semivariogram": [[41, "3.-Regularize-semivariogram"]], "3. Smooth blocks": [[44, "3.-Smooth-blocks"]], "4. API": [[33, "4.-API"]], "4. Compare models": [[39, "4.-Compare-models"]], "4. 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"workshop": 28, "x": 1}}) \ No newline at end of file +Search.setIndex({"alltitles": {"1. Create Variogram Point Cloud": [[35, "1.-Create-Variogram-Point-Cloud"]], "1. Directional process": [[34, "1.-Directional-process"]], "1. Introduction - IDW as bechmarking tool": [[37, "1.-Introduction---IDW-as-bechmarking-tool"]], "1. Prepare data": [[38, "1.-Prepare-data"], [40, "1.-Prepare-data"], [41, "1.-Prepare-data"], [42, "1.-Prepare-data"], [43, "1.-Prepare-data"], [44, "1.-Prepare-data"]], "1. Set semivariogram model (fit)": [[36, "1.-Set-semivariogram-model-(fit)"]], "2. Analyze Variogram Point Cloud": [[35, "2.-Analyze-Variogram-Point-Cloud"]], "2. Analyze data distribution and remove potential outliers": [[38, "2.-Analyze-data-distribution-and-remove-potential-outliers"]], "2. Create Ordinary and Simple Kriging models": [[36, "2.-Create-Ordinary-and-Simple-Kriging-models"]], "2. Create directional and isotropic semivariograms": [[34, "2.-Create-directional-and-isotropic-semivariograms"]], "2. Create directional semivariograms": [[39, "2.-Create-directional-semivariograms"]], "2. Detect and remove outliers": [[40, "2.-Detect-and-remove-outliers"]], "2. Load regularized semivariogram model": [[42, "2.-Load-regularized-semivariogram-model"], [43, "2.-Load-regularized-semivariogram-model"], [44, "2.-Load-regularized-semivariogram-model"]], "2. Perform IDW and validate outputs": [[37, "2.-Perform-IDW-and-validate-outputs"]], "2. Set semivariogram parameters": [[41, "2.-Set-semivariogram-parameters"]], "2. Why do we use Spatial Dependency Index?": [[33, "2.-Why-do-we-use-Spatial-Dependency-Index?"]], "3. Compare semivariograms": [[34, "3.-Compare-semivariograms"]], "3. Create Variogram Clouds": [[38, "3.-Create-Variogram-Clouds"]], "3. Detect and remove outliers": [[35, "3.-Detect-and-remove-outliers"]], "3. 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Experimental variogram from the point cloud": [[35, "4.-Experimental-variogram-from-the-point-cloud"]], "4. Export results": [[44, "4.-Export-results"]], "4. Filtering areas": [[42, "4.-Filtering-areas"], [43, "4.-Filtering-areas"]], "4. Prepare canvas": [[40, "4.-Prepare-canvas"]], "4. Remove outliers from the point cloud": [[38, "4.-Remove-outliers-from-the-point-cloud"]], "4. Visualize process": [[41, "4.-Visualize-process"]], "5. Evaluate": [[42, "5.-Evaluate"], [43, "5.-Evaluate"]], "5. Export semivariogram": [[41, "5.-Export-semivariogram"]], "5. Interpolate": [[40, "5.-Interpolate"]], "5. Is variogram point cloud a scatter plot?": [[35, "5.-Is-variogram-point-cloud-a-scatter-plot?"]], "5. 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"unknown": 36, "us": [17, 33], "valid": [5, 37], "valu": 36, "variogram": [9, 10, 31, 32, 34, 35, 38], "version": [1, 22, 24], "violin": 35, "visual": [13, 41], "we": 33, "weight": 6, "west": 34, "what": 33, "why": 33, "work": 27, "workshop": 28, "x": 1}}) \ No newline at end of file diff --git a/src/pyinterpolate/distance/block.py b/src/pyinterpolate/distance/block.py index a73f154e..0bda267d 100644 --- a/src/pyinterpolate/distance/block.py +++ b/src/pyinterpolate/distance/block.py @@ -213,6 +213,27 @@ def calc_block_to_block_distance( AttributeError Blocks are provided as DataFrame but column names were not given. + Notes + ----- + The weighted distance between blocks is derived from the equation 3 given + in publication [1] from References. + + .. math:: d(v_{a}, v_{b})=\frac{1}{\sum_{s=1}^{P_{a}} \sum_{s'=1}^{P_{b}} n(u_{s}) n(u_{s'})} * \sum_{s=1}^{P_{a}} \sum_{s'=1}^{P_{b}} n(u_{s})n(u_{s'})||u_{s}-u_{s'}|| + + where: + * :math:`P_{a}` and :math:`P_{b}`: number of points :math:`u_{s}` + and :math:`u_{s'}` used to discretize the two units :math:`v_{a}` + and :math:`v_{b}` + * :math:`n(u_{s})` and :math:`n(u_{s'})` - population size in + the cells :math:`u_{s}` and :math:`u_{s'}` + + References + ---------- + .. [1] Goovaerts, P. Kriging and Semivariogram Deconvolution in the + Presence of Irregular Geographical Units. + Math Geosci 40, 101–128 (2008). + https://doi.org/10.1007/s11004-007-9129-1 + Examples -------- >>> import os diff --git a/src/pyinterpolate/semivariogram/experimental/experimental_semivariogram.py b/src/pyinterpolate/semivariogram/experimental/experimental_semivariogram.py index d039e664..9dab8008 100644 --- a/src/pyinterpolate/semivariogram/experimental/experimental_semivariogram.py +++ b/src/pyinterpolate/semivariogram/experimental/experimental_semivariogram.py @@ -27,7 +27,7 @@ def calculate_semivariance(ds: Union[ArrayLike, VariogramPoints] = None, custom_bins: Union[ArrayLike, Any] = None, custom_weights: ArrayLike = None, ) -> np.ndarray: - """ + r""" Calculates experimental semivariance. Parameters @@ -95,11 +95,12 @@ def calculate_semivariance(ds: Union[ArrayLike, VariogramPoints] = None, We calculate the empirical semivariance as: - .. math:: g(h) = 0.5 * n(h)^(-1) * (SUM|i=1, n(h)|: [z(x_i + h) - z(x_i)]^2) + .. math:: g(h) = 0.5 * \frac{1}{n(h)} * \sum_{i=1}^{n(h)}{[z(x_i + h) - z(x_i)]^2} where: - :math:`h`: lag, + - :math:`n(h)`: number of point pairs within the lag :math:`h`, - :math:`g(h)`: empirical semivariance for lag :math:`h`, - :math:`n(h)`: number of point pairs within a specific lag, - :math:`z(x_i)`: point a (value of observation at point a), From 5192e66db44df9195b4aabb51849e2f1436298f3 Mon Sep 17 00:00:00 2001 From: Simon <31246246+SimonMolinsky@users.noreply.github.com> Date: Sun, 22 Feb 2026 12:18:11 +0200 Subject: [PATCH 2/2] Update block.py --- src/pyinterpolate/distance/block.py | 135 ---------------------------- 1 file changed, 135 deletions(-) diff --git a/src/pyinterpolate/distance/block.py b/src/pyinterpolate/distance/block.py index 0bda267d..55c6c5e5 100644 --- a/src/pyinterpolate/distance/block.py +++ b/src/pyinterpolate/distance/block.py @@ -62,87 +62,6 @@ def _calc_b2b_dist_from_dataframe( ) -# def _calc_b2b_dist_from_dataframe( -# ps_blocks: Union[pd.DataFrame, gpd.GeoDataFrame], -# lon_col_name: Union[str, Hashable], -# lat_col_name: Union[str, Hashable], -# val_col_name: Union[str, Hashable], -# block_id_col_name: Union[str, Hashable], -# verbose=False -# ) -> pd.DataFrame: -# r""" -# Function calculates distances between the blocks' point supports. -# -# Parameters -# ---------- -# ps_blocks : Union[pd.DataFrame, gpd.GeoDataFrame] -# DataFrame with point supports and block indexes. -# -# lon_col_name : Union[str, Hashable] -# Longitude or x coordinate. -# -# lat_col_name : Union[str, Hashable] -# Latitude or y coordinate. -# -# val_col_name : Union[str, Hashable] -# The point support values column. -# -# block_id_col_name : Union[str, Hashable] -# Column with block names / indexes. -# -# verbose : bool, default = False -# Show progress bar. -# -# Returns -# ------- -# block_distances : DataFrame -# Indexes and columns are block indexes, cells are distances. -# -# """ -# calculated_pairs = set() -# unique_blocks = list(ps_blocks[block_id_col_name].unique()) -# -# col_set = [lon_col_name, lat_col_name, val_col_name] -# -# results = [] -# -# for block_i in tqdm(unique_blocks, disable=not verbose): -# for block_j in unique_blocks: -# # Check if it was estimated -# if not (block_i, block_j) in calculated_pairs: -# if block_i == block_j: -# results.append([block_i, block_j, 0]) -# else: -# i_value = ps_blocks[ -# ps_blocks[block_id_col_name] == block_i -# ] -# j_value = ps_blocks[ -# ps_blocks[block_id_col_name] == block_j -# ] -# value = _calculate_block_to_block_distance( -# i_value[col_set].to_numpy(), -# j_value[col_set].to_numpy() -# ) -# results.append([block_i, block_j, value]) -# results.append([block_j, block_i, value]) -# calculated_pairs.add((block_i, block_j)) -# calculated_pairs.add((block_j, block_i)) -# -# # Create output dataframe -# df = pd.DataFrame(data=results, columns=['block_i', 'block_j', 'z']) -# df = df.pivot_table( -# values='z', -# index='block_i', -# columns='block_j' -# ) -# -# # sort -# df = df.reindex(columns=unique_blocks) -# df = df.reindex(index=unique_blocks) -# -# return df - - # noinspection PyUnresolvedReferences def _calc_b2b_dist_from_ps(ps_blocks: 'PointSupport') -> Dict: r""" @@ -312,60 +231,6 @@ def calc_block_to_block_distance( return block_distances -# def _calculate_block_to_block_distance(ps_block_1: np.ndarray, -# ps_block_2: np.ndarray) -> float: -# r""" -# Function calculates distance between two blocks' point supports. -# -# Parameters -# ---------- -# ps_block_1 : numpy array -# Point support of the first block. -# -# ps_block_2 : numpy array -# Point support of the second block. -# -# Returns -# ------- -# weighted_distances : float -# Weighted point-support distance between blocks. -# -# Notes -# ----- -# The weighted distance between blocks is derived from the equation given -# in publication [1] from References. This distance is weighted by -# -# References -# ---------- -# .. [1] Goovaerts, P. Kriging and Semivariogram Deconvolution in the -# Presence of Irregular Geographical Units. -# Math Geosci 40, 101–128 (2008). -# https://doi.org/10.1007/s11004-007-9129-1 -# -# TODO -# ---- -# * Add reference equation to the special part of the documentation. -# """ -# -# a_shape = ps_block_1.shape[0] -# b_shape = ps_block_2.shape[0] -# ax = ps_block_1[:, 0].reshape(1, a_shape) -# bx = ps_block_2[:, 0].reshape(b_shape, 1) -# dx = ax - bx -# ay = ps_block_1[:, 1].reshape(1, a_shape) -# by = ps_block_2[:, 1].reshape(b_shape, 1) -# dy = ay - by -# aval = ps_block_1[:, -1].reshape(1, a_shape) -# bval = ps_block_2[:, -1].reshape(b_shape, 1) -# w = aval * bval -# -# dist = np.sqrt(dx ** 2 + dy ** 2, dtype=float, casting='unsafe') -# -# wdist = dist * w -# distances_sum = np.sum(wdist) / np.sum(w) -# return distances_sum - - def select_neighbors_in_range(data: pd.DataFrame, current_lag: float, previous_lag: float):