Pyinterpolate is the Python library for geostatistics and spatial statistics. The package provides access to spatial statistics tools used in various studies. This package helps you interpolate spatial data with the Kriging technique.
version 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.
version 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.
version 0.3.5
- (debug) Updated directional variogram algorithm: now angle moves counterclockwise (instead of clockwise).
- (feature) Directional Ordinary Kriging,
- (feature) Directional Simple Kriging,
- (feature) Angle calculations (angle to origin, angle between vectors),
- (enhancement) direction parameter is None default, to avoid hard-to-track bugs,
- (debug) debugged interpolate_raster() function,
- (enhancement) kriging data selection - a small refactoring,
- (docs) Updated distance module docs,
- (enhancement) point kriging functions refactoring and update, better management of a singular matrices and duplicated points.
version 0.3.4.post1
- (setup) added pyogrio to dependencies due to the new fiona version (1.8.22) and gdal errors.
version 0.3.4
- (debug) control of data dtypes after transformations and preparation of PointSupport and Blocks,
- (debug) updated data selection methods for Poisson Kriging to avoid mixing column of numerical and non-numerical values in a single numpy array, (it makes algorithm faster),
- (update) updated tutorials,
- (feature) check area and point support indexes with smooth_area_to_point_pk(),
- (docs) updated docstrings for calculate_covariance() and calculate_semivariance() functions.
version 0.3.3
- Semivariogram Deconvolution takes possible model types as a parameter,
- Semivariogram Deconvolution uses basic set of variogram models (spherical, linear, power, exponential),
- New class: DirectionalVariogram calculates experimental variograms in four directions along with isotropic variogram,
- Corrected directions (angles were described wrong 0 degrees is W-E, -90 deg is N-S direction),
- Directional variogram calculations are faster due to the change of selection method (only non-weighted case in this release),
- Numpy's sqrt() method casts ints into floats (see Issue 306),
- Users can pass a nugget for autofit() method of TheoreticalVariogram() class, the same for Deconvolution() process.
version 0.3.2
- new test dataset with regular blocks,
- more tests for Deconvolution, area_to_point_pk(), area_to_area_pk(), and centroid_based_pk(),
- if there are no values for a given lag in experimental variogram RunetimeError() is raised,
- average_block_to_block_semivariances() appends 0 to the lags with 0 points,
- calculate_block_to_block_semivariance() - valid calculation of number of point pairs.
version 0.3.1
- experimental variogram, covariogram, and variogram cloud function and classes check if there are NaN's in the input data and raise ValueError,
- the length of major and minor axes of a directional variogram ellipsis are calculated differently from the tolerance parameter, (now we have a less of chaos),
- tutorial for directional variograms (Basic),
- updated download_air_quality_poland() function, now it can store downloaded data,
- updated documentation.
version 0.3.0
- module io_ops renamed to io,
- the refactored function read_point_data (old) into read_txt, new functions to read csv and blocks data,
- the new objects to store block data and its point-support: Blocks and PointSupport,
- Kriging is now supported by functions, not by classes, to speed up some calculations. In the future, classes will be introduced again,
- user has much more control over the variograms development. ExperimentalVariogram class calculates variance, covariance, and semivariance, has own plotting function. TheoreticalVariogram has more models to fit, and gives more control to search for the best fit - the algorithm searches over ranges and sills. Nugget is still fixed,
- module pipelines has the function for the block data smoothing (area-to-point Poisson Kriging), the class for block data filtering (area-to-area Poisson Kriging), the kriging comparison class, and method to download sample air pollution data,
- there are many small changes and API transformations... The package is faster and more stable,
- it works with Python 3.7, 3.8, 3.9, and 3.10,
- Ordinary and Simple Kriging of large datasets may be performed in parallel,
- the package has a few warnings and raises custom errors,
- setup.py is removed, now package installs from setup.cfg,
- data structures are more complex, but they allow user to be more flexible with an input.
version 0.2.5
- 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.
version 0.2.4
- self.points_values chenged to self.points_array in TheoreticalSemivariogram class,
- NaN values are tested and checked in calc_semivariance_from_pt_cloud() function,
- new semivariogram models included in the package: cubic, circular, power,
- corrected calculation of the closest neighbors for kriging interpolation,
- changed prepare_kriging_data() function,
- the new optional parameter check_coordinates (bool) of calc_point_to_point_distance() function to control the coordinates uniqueness tests. This test is very resource-consuming and should be avoided in a normal work and it should be performed before data injection into the modeling pipeline.
- the new dev/profiling/ directory to test and profile parts of a code.
version 0.2.3.post1
- the outliers removal function: you can choose side for outlier detection and remove. Default is top, available are: both, top, down,
- the outliers removal function: changed algorithm,
- new tutorial about outliers and their influence on the final model.
version 0.2.3
- more parameters to store (and access) in TheoreticalSemivariogram class,
- error weighting against the linear regression model (ax + b),
- global mean for Simple Kriging as a required parameter,
- tqdm progress bar to RegularizedSemivariogram.transform() and interpolate_raster() functions,
- refactored Semivariogram Regularization: ranges are controlled by algorithm, not an user,
- added pull request template,
- added issues templates,
- bug in spherical semivariogram model,
- experimental variogram as points (not a solid line),
- inverse distance weighting function: algorithm, tests, documentation and new tutorial,
- changed output names of regularized data (ArealKriging.regularize_data) from estimated value to reg.est and from estimated prediction error to reg.err,
- error related to the id column as a string removed,
- TheoreticalSemivariogram params attribute changed to nugget, sill and range attributes.
version 0.2.2.post2
- directional semivariograms methods, docs and tests added,
- check if points are within elliptical area around point of interest method, docs and tests added,
- broken dependency in README.md corrected.
version 0.2.2.post1
- variogram point cloud methods, tutorials, docs and tests added,
- updated tutorials and baseline datasets to show examples with spatial correlation,
- updated README.md: contribution, example, sample image,
- data is tested against duplicates (points with the same coordinates),
- removed bug in interpolate_raster() method.