All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.13.6] - 2020-07-19


  • RasterMath get_image_as_array() now supports mask.


  • Update groups management for sklearn>=0.25

  • Fixed bug with raster/vector datasets (mtb.datasets.load_historical_data())

  • Fixed bug with get_image_as_array() from RasterMath (completely rewrite this part)

[0.13.5] - 2020-06-24


  • Requirements is not directly written inside due to bugs.

[0.13.4] - 2020-06-24


  • Fix bug in using requirements.txt instead of ./requirements.txt

[0.13.3] - 2020-06-23


  • Adding psutil to depency

[0.13.2] - 2020-06-18


  • get_image_as_array function for RasterMath


  • train_test_split supports now groups=None

[0.13.1] - 2020-06-11


  • Support list for cross-validation in order to give an unready unfolded cv.

New features provided by @marclang for the charts module :

  • Allows to display both F1 and accuracy or mean metrics

  • Allows to display accuracy after have been displaying mean (and vice versa)

  • Allows to display float matrix


  • Fix path separator to access tutorial dataset

[0.13.0] - 2020-04-21


  • Final version for JOSS ( and paper.bib updated thanks to @kbarnhart)

[0.12.1-rc.1] - 2020-04-18


  • RasterMath use available memory to speed up process and manage now several cores (n_jobs)

  • train_test_split in cross_validation module


  • Enhance mask management for RasterMath

  • Move FlushCache to optimize RasterMath

  • RasterMath get_random_block returns only block which are not totally unmasked

  • charts.PlotConfusionMatrix has a default argument (zero_is_min=True)

[0.12.1-beta.2] - 2020-02-10


  • Fix bug when in RasterMath when input is only one band

  • Fix bug in RasterMath with mask and list


  • n_jobs for RasterMath (thanks to Helene @HTDBD and Arthur @ArthurDfs, two great students)

  • function write_block and generally a most intuitive way to use RasterMath (with the help of @HTDBD and @ArthurDfs)

[0.12.1-beta.1] - 2020-01-16


  • new branch spatial added


  • Added this line


  • SequentialFeatureSelection parameters order Changed. scoring is now before standardize.

  • Update doc for load_historical_data()


  • Fix bug in get_block() and get_random_block() which returned the same block each time due to new method.

  • Fix bug with nodata in RasterMath when output is of float type

[0.12] - 2019-12-13


  • RasterMath made a lot of improvements using block reading and writing. For example, the default block size is now 256x256 (you can keep the default block size by choosing block_size=False), and Museo ToolBox automatic detect if the geotiff will be tiled or not (it depends on the block size).

  • Some folders have Changed name :

    • raster_tools and vector_tools to processing

    • learn_tools to ai

  • some functions have Changed name :

    • getSamplesFromROI to extract_values

    • historicalMap to load_historical_data

    • getDistanceMatrix to get_distance_matrix

  • classes now always begin with a capital case :

    • learnAndPredict to SuperLearner

    • rasterMath to RasterMath

    • sequentialFeatureSelection to SequentialFeatureSelection


  • bug #7 : getSamplesFromROI (nowd extract_ROI) now extracts ROI values using by default memory. If it fails, it will create a temporary raster on disk then delete it when finished.


  • Remove command lines (cli)

[0.12rc5] - 2019-11-11


  • getSamplesFromROI return list of available fields if wrong field given.

  • rasterMath convert np.nan value to nodata value (if numpy >= 1.17)

[0.12rc4] - 2019-11-01


  • Minor fix when using learnAndPredict with an outside customized function

  • Better management fo cross-validation in learnAndPredict

  • Fix minor bug using False or None value with cv in learnAndPredict


  • Add an option to use SFS without writing each best model on the disk.

[0.12rc3] - 2019-10-29


  • Move some functions from vector_tools to raster_tools, functions are anyway still available from vector_tools


  • learnAndPredict manages int value for cross-validation by using RandomStratifiedKFold

  • Enhance blocksize management for rasterMath

  • Move command line code in _cli folder

[0.12rc2] - 2019-10-14


  • Improvements of rasterMath

    • customBlockSize defines now the same block size for window reading and for the output

    • add seed parameter (to set a random generator) in getRandomBlock()

    • add getRasterParameters() and customRasterParameters() function.

[0.12rc1] - 2019-10-12


  • update rasterMath to generate by default a 256*256 raster block size.

  • update rasterMath to prevent bug if user has osgeo/gdal version is lower than 2.1.

  • prevent bug when in rasterMath if processor has only 1 core.


  • minor fixes