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  • 313-allow-non-monotonic-window-lead-times-in-helpers-statistics-py-ahead_names-definition
  • 414-include-crps-analysis-and-other-ens-verif-methods-or-plots
  • IntelliO3-ts
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  • IntelliO3-ts-v1.0_initial-submit
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Created with Raphaël 2.2.025May22212019181764329Apr282721201918161514131211109876131Mar302926252322181716151211109854326Feb2524232219181716151211108432129Jan272120191818Dec171615111098732124Nov231817161312116543230Oct292622211587130Sep2824232221small changeadd vanilla rnndisable plotting for now to have running modulestore intermediate plot implementation, must be refactoreduse single precision for filter, reduce data size always as much as possibleextra loop for each year to prevent memory issuesnew version now applies along window axis instead of just using t0Merge branch 'develop' into felix_issue287_tech-wrf-datahandler-should-inherit-from-singlestationdatahandleradd some documentationadd maplots in __main__add index error to f_procadd another gc collectadd another gc collectinterpolate now takes care, that missing values are not dropped but indicated with nansmore loggingsmore loggingsadded many loggings to find HPC errorsolved lazy error, some station removals have to be fixed stilladd gc collect statementuse less memory intense version for clim_filter_vectorized to avoid memory issuesfilter plots different seasons, filter returns data with missing values now (not squeezed version)new data handler for mixed sampling and climate fir, fine tuning is required for new parameter apriori_diurnalnew try with convolvespeed comparisonmaybe found axis bugtry out dask approachremoved debug sliceadd exception if filter input plot wasn't successfuladded debug loggingadded a numpy filter version (should be again faster)add log statement to track progressimplemented vectorized version of fir filter for faster computationclimFIR data handler can create a sample plot if a path is provided.filter code has much more comments for understanding, time range of apriori is extended if requirednew fir filter data handler using climatological heuristicsMLAir can store parameters during preprocessing from train subset when using store_attributessmall fixMerge branch 'lukas_issue306_feat_data-handler-fir-filter' of ssh://gitlab.version.fz-juelich.de:10022/esde/machine-learning/mlair into lukas_issue306_feat_data-handler-fir-filterfir filter can be used with daily dataMerge branch 'lukas_issue307_bug_data-handler-fails-if-no-chem-variable-is-used' into 'lukas_issue306_feat_data-handler-fir-filter'
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