0.5.3

version 0.5.3

(logic) debugged variance error calculations for Area-to-Point Poisson Kriging

0.5.2

Updated dependencies (GeoPandas without version constrains, numpy < 2, and pylibtiff >= 0.6.1)

v 0.5.1

2024-02-19

version 0.5.1 (pre production development)

• (enhancement) interpolate_raster() function takes allow_approx_solutions parameter, and it protects from LinAlgError that might occur if interpolation points are duplicated (due to the floating point number representation).
• (refactoring) calc_point_to_point_distance function refactored to point_distance, changed input parameters' schema,
• (refactoring) new selection method for unequally spaced bins: select_values_between_lags
• (debug) np.float type casting has been changed to float

v 0.5.0.post1-updated-docs

Release 0.5.0.post1 with updated docs

v0.5.0.post1 - Mykolaiv

version 0.5

• (feature) to_tiff() function which writes kriging output from the interpolate_raster() function to tiff and tfw files,
• (debug) safe theoretical variogram models,
• (enhancement) model_types parameter can be string only (in the future the name of this parameter will be changed),
• (dependencies) fixed dependencies (hdbscan and scikit-learn),
• (enhancement) updated tutorials, we slightly changed their structure,
• (dependencies) End of support for Python 3.7,
• (invalid) Warning when user tries to use .plot() method of the ExperimentalVariogram class,
• (invalid) Default direction and tolerance are None instead of floats,
• (invalid) Removed unnecessary warning from the .autofit() method.

v0.4.2 - Kharkiv

Welcome to the new release of Pyinterpolate!

This release brings multiple new functionalities:

• Cluster detection with DBSCAN,
• Cluster aggregation,
• Gridding algorithm,
• Grid aggregation,
• Theoretical Variogram calculates Spatial Dependence Index,
• Indicator Kriging,
• Blocks and PointSupport read all files supported by GeoPandas.

The package is stable now, and future changes will focus on the speed and the new spatial interpolation algorithms.

Kyiv v2 (0.3.7)

Update of the Kyiv release!

0.3.7

• (enhancement) added logging to Poisson Kriging ATP process,
• (test) added functional test for smooth_blocks function,
• (debug) too broad exception in download_air_quality_poland is narrowed to KeyError,
• (enhancement) log points that cannot be assigned to any area in PointSupport class,
• (enhancement) transform_ps_to_dict() function takes custom parameters for lon, lat, value and index,
• (test) check_limits() function tests,
• (test) plotting function of the VariogramCloud() class is tested and slightly changed to return True if everything has worked fine,
• (tutorials) new tutorial about ExperimentalVariogram and VariogramCloud classes,
• (test) new tests for calculate_average_semivariance() function from block module,
• (enhancement) function inblock_semivariance has been optimized,
• (docs) updated __init__.py of variogram.theoretical module,
• (enhancement) scatter plot represented as a swarm plot in VariogramCloud,
• (enhancement) added directional kriging for ATA and ATP Poisson Kriging,
• (debug) warning for directional kriging functions,
• (enhancement) initialization of KrigingObject dataclass,
• (ci/cd) added new workflow tests for MacOS and Ubuntu,
• (enhancement) added logging to Simple Kriging process.

0.3.6

• (enhancement) Directional Centroid-based Poisson Kriging,
• (debug) Added origin (unknown point) to calculate directional Kriging and directional Centroid-based Poisson Kriging,
• (docs) Directional Ordinary Kriging tutorial,
• (engancement) logging of area to area PK function,
• (enhancement) tests package moved outside the main package,
• (feature) ordinary kriging from covariance terms,
• (feature) area-to-area PK from covariance terms,
• (debug) area-to-area PK debugged,
• (feature) area-to-point PK from covariance terms,
• (debug) area-to-point PK debugged,
• (feature) centroid-based PK from covariance terms,
• (debug) centroid-based PK debugged.
• updated and debugged directional variograms,
• updated and debugged Ordinary Kriging and Simple Kriging,
• faster Directional Variogram calculations.

Kyiv

The major release of pyinterpolate!

It brings many changes:

• First, from now on, we will name releases after the most prominent cities of Ukraine. The first, and the biggest, is Kyiv, the Ukrainian capital
• Calculations are faster, much faster than in release 0.2.5,
• API is enhanced, there are new classes and functions for semivariogram modeling and analysis, and for the complex data structures that are storing blocks and point support for Poisson Kriging and areal deconvolution,
• API is cleaner,
• paths are shorter,
• and the package works with any Python version from 3.7 up!

Generally speaking, it is the best surprise for the end of Summer.

If you are a newbie, I recommend going to tutorials to check what you can do. If you are a seasoned user, I recommend digging into the API (and tutorials) - some functions and classes are renamed, some are removed, and there are new additions.

Huygens Crater v2

The new release with the major fix of bug related to the prediction error variance calculation. It was wrong in the previous releases of the package (but it didn't affect predictions, just error variance terms).

Changes:

• neighbors selection (lags counting) has been changed,
• TheoreticalSemivariogram searches for optimal sill in a grid search algorithm,
• corrected error in Krige class; now calculation of error variance is correct,
• updated paper and package docs.

Predicted values have lower variance of predictions, and they are better because optimal sill is derived from the grid search. (In the previous releases, the sill was fixed and equal to data variance).

Huygens Crater

The new release with the major fix of bug related to the prediction error variance calculation. It was wrong in the previous releases of the package (but it didn't affect predictions, just error variance terms).

Changes:

• neighbors selection (lags counting) has been changed,
• TheoreticalSemivariogram searches for optimal sill in a grid search algorithm,
• corrected error in Krige class; now calculation of error variance is correct.

Now predicted values have a lower variance of predictions and they are better due to the fact, that optimal sill is derived from the grid search. (In the previous releases sill was fixed and equal to data variance).