All notable changes to this project will be documented in this file.
[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)
[0.13.4] - 2020-06-24¶
Fix bug in setup.py using requirements.txt instead of ./requirements.txt
[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 (paper.md 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.