PyOpenSci updates and slightly changed api

CHANGELOG.rst

@@ -1,6 +1,66 @@
 Changes - from version >= 1.x
 =============================
 
+2025-11-23
+----------
+
+**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
+* [docs] added missing badges (Python version and package version)
+* [docs] `CITATION.cff`, citation section in readme has been moved up, bibtex citation has been added
+* [docs] corrected broken links to `CONTRIBUTING.md` file in readme and docs
+* [setup] links to documentation have been added to `pyproject.toml`
+* [docs] docstring examples - all user-facing API endpoints have examples in docstrings, using new input types (values | geometries)
+* [docs] updated docstrings (examples) in: `PointSupportDistance`, `core.block_filter`, `pipelines.interpolate`, `pyinterpolate.distance.block.calc_block_to_block_distance`, `validate_kriging`, `metrics` module functions, `inverse_distance_weighting`, `centroid_poisson_kriging`, `area_to_area_pk`, `area_to_point_pk`, `area_to_point_pk`, `area_to_point_pk`, `ordinary_kriging`, `simple_kriging`, `IndicatorKriging`, `UniversalKriging`, `Deconvolution`,
+* [enhancement] `ExperimentalVariogram` has new property - `lag_semivariance_array` that returns numpy array with lags and semivariances
+* [enhancement] `TheoreticalVariogram` has new property - `lag_yhat_array` that returns numpy array with lags and predicted semivariances
+* [api change] `Deviation` class parameters have been renamed to clearly point what is passed as the input
+* [tests] `Deviation` class tests and debug (method selection)
+* [docs] docstring example for `Deviation`
+* [docs] docstring example for `AggregatedVariogram` and `regularize()`
+* [docs] docstring example for `ExperimentalVariogram` and `build_experimental_variogram()`, `calculate_semivariance()`, used parameters `values` and `geometries`
+* [docs] docstring example for `calculate_covariance()`, used parameters `values` and `geometries`
+* [docs] docstring example for `DirectionalVariogram`, used parameters `values` and `geometries`
+* [docs] docstring example for `VariogramCloud`, used parameters `values` and `geometries`
+* [docs] `ExperimentalIndicatorVariogram` and `TheoreticalIndicatorVariogram` - updated examples in docstrings
+* [docs] `TheoreticalVariogram`, and `build_theoretical_variogram()` examples
+* [docs] `calculate_spatial_dependence_index()` example
+* [bug/feature] `ExperimentalVariogram` class removes lags with NaN values (in covariance and semivariance)
+* [docs] Example in `interpolate_raster()` function
+
+
 2025-10-11
 ----------
 

CITATION.cff

@@ -0,0 +1,23 @@
+cff-version: 1.2.0
+message: If you use this software, please cite both the article from preferred-citation and the software itself.
+authors:
+  - family-names: Moliński
+    given-names: Szymon
+title: 'Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets'
+version: 1.0.0
+url: https://doi.org/10.21105/joss.02869
+doi: 10.21105/joss.02869
+date-released: '2022-11-09'
+preferred-citation:
+  authors:
+    - family-names: Moliński
+      given-names: Szymon
+  title: 'Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets'
+  doi: 10.21105/joss.02869
+  url: https://doi.org/10.21105/joss.02869
+  type: article-journal
+  pages: '2869'
+  year: '2022'
+  conference: {}
+  publisher:
+    name: The Open Journal

README.md

@@ -1,14 +1,13 @@
-![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://raw.githubusercontent.com/DataverseLabs/pyinterpolate/6a18f86ab3927e48009107e7eda7d6c833a4a610/coverage.svg) 
-
-
+![PyPI - Version](https://img.shields.io/pypi/v/pyinterpolate)  ![Conda Version](https://img.shields.io/conda/v/conda-forge/pyinterpolate)
+ ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyinterpolate)
 
+![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://raw.githubusercontent.com/DataverseLabs/pyinterpolate/6a18f86ab3927e48009107e7eda7d6c833a4a610/coverage.svg)
 
 # Pyinterpolate
 
-**version 1.1.0**
+**version 1.2.0**
 
 ![Logo](https://raw.githubusercontent.com/DataverseLabs/pyinterpolate/refs/heads/main/pyinterpolate-banner.png)
 
@@ -17,6 +16,27 @@
 The package was updated to version 1.0 in June 2025. There are breaking API changes, so please, refer to the [CHANGELOG](https://github.com/DataverseLabs/pyinterpolate/blob/main/CHANGELOG.rst) to know more about the changes.
 Right now, the package in version 1.0.0 is in the **beta** stage, which means that it is stable but be careful with the production use. There might be some minor bugs, and large swaths of code are not optimized yet. If you find any bugs, please report them in the [issue tracker](https://github.com/DataverseLabs/pyinterpolate/issues).
 
+## Citation
+
+Moliński, S., (2022). Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets. Journal of Open Source Software, 7(70), 2869, https://doi.org/10.21105/joss.02869
+
+### Bibtex
+
+```text
+@article{Moliński2022,
+  doi = {10.21105/joss.02869},
+  url = {https://doi.org/10.21105/joss.02869},
+  year = {2022},
+  publisher = {The Open Journal},
+  volume = {7},
+  number = {70},
+  pages = {2869},
+  author = {Moliński, Szymon},
+  title = {Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets},
+  journal = {Journal of Open Source Software} }
+
+```
+
 ## Introduction
 
 Pyinterpolate is the Python library for **spatial statistics**. The package provides access to spatial statistics tools (variogram analysis, Kriging, Poisson Kriging, Indicator Kriging, Inverse Distance Weighting).
@@ -158,7 +178,7 @@ A world of advice, you should **always** use Virtual Environment for the install
 
 ## Tests and contribution
 
-All tests are grouped in the `test` directory. If you would like to contribute, then you won't avoid testing, but it is described step-by-step here: [CONTRIBUTION.md](https://github.com/DataverseLabs/pyinterpolate/blob/main/CONTRIBUTION.md)
+All tests are grouped in the `test` directory. If you would like to contribute, then you won't avoid testing, but it is described step-by-step here: [CONTRIBUTING.md](https://github.com/DataverseLabs/pyinterpolate/blob/main/CONTRIBUTING.md)
 
 ## Use cases
 
@@ -181,11 +201,6 @@ Pyinterpolate was created thanks to many resources and all of them are pointed h
 
 Join our community in Discord: [Discord Server Pyinterpolate](https://discord.gg/3EMuRkj)
 
-## How to cite
-
-Moliński, S., (2022). Pyinterpolate: Spatial interpolation in Python for point measurements and aggregated datasets. Journal of Open Source Software, 7(70), 2869, https://doi.org/10.21105/joss.02869
-
-
 ## Requirements and dependencies (v 1.x)
 
 Core requirements and dependencies are:

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,