Merge pull request #500 from DataverseLabs/499-inconsistency-of-api-with-the-ecosystem

499 inconsistency of api with the ecosystem

Author: SimonMolinsky

CHANGELOG.rst

@@ -1,6 +1,45 @@
 Changes - from version >= 1.x
 =============================
 
+2025-11-0
+----------
+
+**version 1.2.0**
+
+* [enhancement] `Blocks` has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [enhancement] `calculate_semivariance()` function has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [enhancement] `interpolate_points()` function has the new parameters `known_values` and `known_geometries`, that might be provided instead of the `known_locations` parameter
+* [enhancement] `validate_kriging()` function has the new parameters `known_values` and `known_geometries`, that might be provided instead of the `known_locations` parameter
+* [enhancement] `validate_kriging()` has new parameter: `progress_bar` that controls `tqdm` progress bar
+* [enhancement] `inverse_distance_weighting()` function has the new parameters `known_values` and `known_geometries`, that might be provided instead of the `known_locations` parameter
+* [enhancement] `IndicatorKriging` class has the new parameters `known_values` and `known_geometries`, that might be provided instead of the `known_locations` parameter
+* [enhancement] `ordinary_kriging()` function has the new parameters `known_values` and `known_geometries`, that might be provided instead of the `known_locations` parameter
+* [enhancement] `simple_kriging()` function has the new parameters `known_values` and `known_geometries`, that might be provided instead of the `known_locations` parameter
+* [enhancement] `UniversalKriging` class has the new parameters `known_values` and `known_geometries`, that might be provided instead of the `known_locations` parameter
+* [api change] `inblock_semivariance()` function is now private: `_inblock_semivariance()`
+* [enhancement] `DirectionalVariogram()` class has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [tests] Added basic tests for `DirectionalVariogram()` class
+* [enhancement] `ExperimentalVariogram()` class has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [tests] Added basic tests for `ExperimentalVariogram()` class
+* [enhancement] `build_experimental_variogram()` function has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [tests] Added basic tests for `build_experimental_variogram()` function
+* [enhancement] `VariogramCloud()` class has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [api change] `directional_weighted_semivariance()` function is now private: `_directional_weighted_semivariance()`
+* [api change] `omnidirectional_variogram()` function is now private: `_omnidirectional_variogram()`
+* [api change] `omnidirectional_semivariogram_cloud()` function is now private: `_omnidirectional_semivariogram_cloud()`
+* [enhancement] `calculate_covariance()` function has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [api change] `directional_covariance()` function is now private: `_directional_covariance()`
+* [api change] `omnidirectional_covariance()` function is now private: `_omnidirectional_covariance()`
+* [api change] `directional_semivariance()` function is now private: `_directional_semivariance()`
+* [api change] `directional_semivariance_cloud()` function is now private: `_directional_semivariance_cloud()`
+* [api change] `omnidirectional_semivariance()` function is now private: `_omnidirectional_semivariance()`
+* [enhancement] `point_cloud_semivariance()` function has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [enhancement] `code_indicator()` function has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [enhancement] `ExperimentalIndicatorVariogram` and `IndicatorVariogramData` classes have the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [enhancement] `PointSupport` has the new parameters `values` and `geometries`, that might be provided instead of `ds` parameter
+* [docs] updated tutorials - new parameters are used in kriging operations, and when experimental variogram is computed
+* # todo [docs] docstring examples - all user-facing API endpoints has examples in docstrings, using new input types (values | geometries)
+
 2025-10-11
 ----------
 

README.md

@@ -1,6 +1,6 @@
 ![JOSS](https://joss.theoj.org/papers/3f87f562264c4e5174d9e6ed6d8812aa/status.svg) [![License](https://img.shields.io/badge/License-BSD_2--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause) ![Documentation Status](https://readthedocs.org/projects/pyinterpolate/badge/?version=latest) [![CodeFactor](https://www.codefactor.io/repository/github/dataverselabs/pyinterpolate/badge)](https://www.codefactor.io/repository/github/dataverselabs/pyinterpolate)
 
-[![Run Unit Test via Pytest](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/python-install-and-test-on-linux-always.yml/badge.svg)](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/python-install-and-test-on-linux-always.yml) [![CodeQL](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/github-code-scanning/codeql) ![Tests Coverage](https://github.com/DataverseLabs/pyinterpolate/blob/main/coverage.svg) 
+[![Run Unit Test via Pytest](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/python-install-and-test-on-linux-always.yml/badge.svg)](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/python-install-and-test-on-linux-always.yml) [![CodeQL](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/DataverseLabs/pyinterpolate/actions/workflows/github-code-scanning/codeql) ![Tests Coverage](https://raw.githubusercontent.com/DataverseLabs/pyinterpolate/6a18f86ab3927e48009107e7eda7d6c833a4a610/coverage.svg) 
 
 
 
@@ -10,7 +10,7 @@
 
 **version 1.1.0**
 
-![Logo](pyinterpolate-banner.png)
+![Logo](https://raw.githubusercontent.com/DataverseLabs/pyinterpolate/refs/heads/main/pyinterpolate-banner.png)
 
 ## Important notice
 
@@ -113,7 +113,7 @@ print(prediction)  # [predicted, variance error, lon, lat]
 
 With Pyinterpolate you can analyze and transform aggregated data. Here is the example of spatial disaggregation of areal data into point support using Poisson Kriging:
 
-![Example use case](fig1_example.png)
+![Example use case](https://raw.githubusercontent.com/DataverseLabs/pyinterpolate/refs/heads/main/fig1_example.png)
 
 ## Status
 

dev/profile/semivariogram/experimental/profile_from_ellipse_weighted.py

@@ -1,6 +1,6 @@
 import numpy as np
 from pyinterpolate.semivariogram.experimental.functions.directional import \
-    directional_weighted_semivariance
+    _directional_weighted_semivariance
 
 
 def profile_select_in_ellipse():
@@ -9,7 +9,7 @@ def profile_select_in_ellipse():
     step_size = 0.05
     max_range = 0.6
 
-    _ = directional_weighted_semivariance(
+    _ = _directional_weighted_semivariance(
         points=points,
         lags=np.linspace(step_size, max_range, 10),
         custom_weights=weights,