v0.84.2

v0.84.2 | 8 Nov 2024

Changes

• Adjust version number to avoid publishing conflict

Full Changelog: v0.84.1...v0.84.2

v0.84.1

v0.84.1 | 8 Nov 2024

Bugfixes

• Removed invalid imports

Tests

• Added test case for diive imports (tests.test_imports.TestImports.test_imports)
• 52/52 unittests ran successfully

What's Changed

• Hotifx imports by @holukas in #236

Full Changelog: v0.84.0...v0.84.1

v0.84.0

v0.84.0 | 7 Nov 2024

New features

• New class BinFitterCP for fitting function to binned data, includes confidence interval and prediction interval (
  diive.pkgs.fits.fitter.BinFitterCP)

Additions

• Added small function to detect duplicate entries in lists (diive.core.funcs.funcs.find_duplicates_in_list)
• Added new filetype (diive/configs/filetypes/ETH-MERCURY-CSV-20HZ.yml)
• Added new filetype (diive/configs/filetypes/GENERIC-CSV-HEADER-1ROW-TS-END-FULL-NS-20HZ.yml)

Bugfixes

• Not directly a bug fix, but when reading EddyPro fluxnet files with LoadEddyProOutputFiles (e.g., in the flux
  processing chain) duplicate columns are now automatically renamed by adding a numbered suffix. For example, if two
  variables are named CUSTOM_CH4_MEAN in the output file, they are automatically renamed to CUSTOM_CH4_MEAN_1 and
  CUSTOM_CH4_MEAN_2 (diive.core.dfun.frames.compare_len_header_vs_data)

Notebooks

• Added notebook example for BinFitterCP (notebooks/Fits/BinFitterCP.ipynb)
• Updated flux processing chain notebook to v8.6, import for loading EddyPro fluxnet output files was missing

Tests

• Added test case for BinFitterCP (tests.test_fits.TestFits.test_binfittercp)
• 51/51 unittests ran successfully

What's Changed

• Indev by @holukas in #235

Full Changelog: v0.83.2...v0.84.0

v0.83.2

v0.83.2 | 25 Oct 2024

From now on Python version 3.11.10 is used for developing Python (up to now, version 3.9 was used). All unittests
were successfully executed with this new Python version. In addition, all notebooks were re-run, all looked good.

JupyterLab is now included in the environment, which makes it
easier to quickly install diive (pip install diive) in an environment and directly use its notebooks, without the
need to install JupyterLab separately.

Environment

• diive will now be developed using Python version 3.11.10
• Added JupyterLab
• Added jupyter bokeh

Notebooks

• All notebooks were re-run and updated using Python version 3.11.10

Tests

• 50/50 unittests ran successfully with Python version 3.11.10

Changes

• Adjusted flags check in QCF flag report, the progressive flag must be the same as the previously calculated overall
  flag (diive.pkgs.qaqc.qcf.FlagQCF.report_qcf_evolution)

What's Changed

• Indev by @holukas in #234

Full Changelog: v0.83.1...v0.83.2

v0.83.1

v0.83.1 | 23 Oct 2024

Changes

• When detecting the frequency from the time delta of records, the inferred frequency is accepted if the most frequent
  timedelta was found for more than 50% of records (diive.core.times.times.timestamp_infer_freq_from_timedelta)
• Storage terms are now gap-filled using the rolling median in an expanding time window (
  FluxStorageCorrectionSinglePointEddyPro._gapfill_storage_term)

Notebooks

• Added notebook example for using the flux processing chain for CH4 flux from a subcanopy eddy covariance station (
  notebooks/Workbench/CH-DAS_2023_FluxProcessingChain/FluxProcessingChain_NEE_CH-DAS_2023.ipynb)

Bugfixes

• Fixed info for storage term correction report to account for cases when more storage terms than flux records are
  available (FluxStorageCorrectionSinglePointEddyPro.report)

Tests

• 50/50 unittests ran successfully

What's Changed

• Indev by @holukas in #233

Full Changelog: v0.83.0...v0.83.1

v0.83.0

v0.83.0 | 4 Oct 2024

MDS gap-filling

Finally it is possible to use the MDS (marginal distribution sampling) gap-filling method in diive. This method is
the current default and widely used gap-filling method for eddy covariance ecosystem fluxes. For a detailed description
of the method see Reichstein et al. (2005) and Pastorello et al. (2020; full references given below).

The implementation of MDS in diive (FluxMDS) follows the description in Reichstein et al. (2005) and should
therefore yield results similar to other implementations of this algorithm. FluxMDS can also easily output model
scores, such as r2 and error values.

At the moment it is not yet possible to use FluxMDS in the flux processing chain, but during the preparation of this
update the flux processing chain code was already refactored and prepared to include FluxMDS in one of the next
updates.

At the moment, FluxMDS is specifically tailored to gap-fill ecosystem fluxes, a more general implementation (e.g., to
gap-fill meteorological data) will follow.

New features

• Added new gap-filling class FluxMDS:
  • MDS stands for marginal distribution sampling. The method uses a time window to first identify meteorological
    conditions (short-wave incoming radiation, air temperature and VPD) similar to those when the missing data
    occurred. Gaps are then filled with the mean flux in the time window.
  • FluxMDS cannot be used in the flux processing chain, but will be implemented soon.
  • (diive.pkgs.gapfilling.mds.FluxMDS)

Changes

• Storage correction: By default, values missing in the storage term are now filled with a rolling mean in an
  expanding
  time window. Testing showed that the (single point) storage term is missing for between 2-3% of the data, which I
  think is reason enough to make filling these gaps the default option. Previously, it was optional to fill the gaps
  using random forest, however, results were not great since only the timestamp info was used as model features. Plots
  generated during Level-3.1 were also updated, now better showing the storage terms (gap-filled and non-gap-filled) and
  the flag indicating filled values (
  diive.pkgs.fluxprocessingchain.level31_storagecorrection.FluxStorageCorrectionSinglePointEddyPro)

Notebooks

• Added notebook example for FluxMDS (notebooks/GapFilling/FluxMDSGapFilling.ipynb)

Tests

• Added test case for FluxMDS (tests.test_gapfilling.TestGapFilling.test_fluxmds)
• 50/50 unittests ran successfully

Bugfixes

• Fixed bug: overall quality flag QCF was not created correctly for the different USTAR scenarios (
  diive.core.base.identify.identify_flagcols) (diive.pkgs.qaqc.qcf.FlagQCF)
• Fixed bug: calculation of QCF flag sums is now strictly done on flag columns. Before, sums were calculated across
  all columns in the flags dataframe, which resulted in erroneous overall flags after USTAR filtering (
  diive.pkgs.qaqc.qcf.FlagQCF._calculate_flagsums)

Environment

• Added polars

References

• Pastorello, G. et al. (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline
  for eddy covariance data. 27. https://doi.org/10.1038/s41597-020-0534-3
• Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N.,
  Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila,
  A., Loustau, D., Matteucci, G., … Valentini, R. (2005). On the separation of net ecosystem exchange into assimilation
  and ecosystem respiration: Review and improved algorithm. Global Change Biology, 11(9),
  1424–1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x

What's Changed

• Indev by @holukas in #229

Full Changelog: v0.82.1...v0.83.0

v0.82.1

v0.82.1 | 22 Sep 2024

Notebooks

• Added notebook showing an example for LongTermGapFillingRandomForestTS (
  notebooks/GapFilling/LongTermRandomForestGapFilling.ipynb)
• Added notebook example for MeasurementOffset (notebooks/Corrections/MeasurementOffset.ipynb)

Tests

• Added unittest for LongTermGapFillingRandomForestTS (
  tests.test_gapfilling.TestGapFilling.test_gapfilling_longterm_randomforest)
• Added unittest for WindDirOffset (tests.test_corrections.TestCorrections.test_winddiroffset)
• Added unittest for DaytimeNighttimeFlag (tests.test_createvar.TestCreateVar.test_daytime_nighttime_flag)
• Added unittest for calc_vpd_from_ta_rh (tests.test_createvar.TestCreateVar.test_calc_vpd)
• Added unittest for percentiles101 (tests.test_analyses.TestAnalyses.test_percentiles)
• Added unittest for GapFinder (tests.test_analyses.TestAnalyses.test_gapfinder)
• Added unittest for SortingBinsMethod (tests.test_analyses.TestAnalyses.test_sorting_bins_method)
• Added unittest for daily_correlation (tests.test_analyses.TestAnalyses.test_daily_correlation)
• Added unittest for QuantileXYAggZ (tests.test_analyses.TestCreateVar.test_quantilexyaggz)
• 49/49 unittests ran successfully

Bugfixes

• Fixed bug that caused results from long-term gap-filling to be inconsistent despite using a fixed random state. I
  found the following: when reducing features across years, the removal of duplicate features from a list of found
  features created a list where the order of elements changed each run. This in turn produced slightly different
  gap-filling results each time the long-term gap-filling was executed. Used Python version where this issue occurred
  was 3.9.19.
  • Here is a simplified example, where input_list is a list of elements with some duplicate elements:
  • Running output_list = list(set(input_list)) generates output_list where the elements would have a different
    output order each run. The elements were otherwise the same, only their order changed.
  • To keep the order of elements consistent it was necessary to output_list.sort().
  • (diive.pkgs.gapfilling.longterm.LongTermGapFillingBase.reduce_features_across_years)
• Corrected wind direction could be 360°, but will now be 0° (
  diive.pkgs.corrections.winddiroffset.WindDirOffset._correct_degrees)

What's Changed

• Indev by @holukas in #218

Full Changelog: v0.82.0...v0.82.1

v0.82.0

v0.82.0 | 19 Sep 2024

Long-term gap-filling

It is now possible to gap-fill multi-year datasets using the class LongTermGapFillingRandomForestTS. In this approach,
data from neighboring years are pooled together before training the random forest model for gap-filling a specific year.
This is especially useful for long-term, multi-year datasets where environmental conditions and drivers might change
over years and decades.

Why random forest? Because it performed well and to me it looks like the first choice for gap-filling ecosystem fluxes,
at least at the moment.

Long-term gap-filling using random forest is now also built into the flux processing chain (Level-4.1). This allows to
quickly gap-fill the different USTAR scenarios and to create some useful plots (I
hope). See the flux processing chain notebook for how this looks like.

In a future update it will be possible to either directly switch to XGBoost for gap-filling, or to use it (and other
machine-learning models) in combination with random forest in the flux processing chain.

Example

Here is an example for a dataset containing CO2 flux (NEE) measurements from 2005 to 2023:

• for gap-filling the year 2005, the model is trained on data from 2005, 2006 and 2007 (2005 has no previous year)
• for gap-filling the year 2006, the model is trained on data from 2005, 2006 and 2007 (same model as for 2005)
• for gap-filling the year 2007, the model is trained on data from 2006, 2007 and 2008
• ...
• for gap-filling the year 2012, the model is trained on data from 2011, 2012 and 2013
• for gap-filling the year 2013, the model is trained on data from 2012, 2013 and 2014
• for gap-filling the year 2014, the model is trained on data from 2013, 2014 and 2015
• ...
• for gap-filling the year 2021, the model is trained on data from 2020, 2021 and 2022
• for gap-filling the year 2022, the model is trained on data from 2021, 2022 and 2023 (same model as for 2023)
• for gap-filling the year 2023, the model is trained on data from 2021, 2022 and 2023 (2023 has no next year)

New features

• Added new method for long-term (multiple years) gap-filling using random forest to flux processing chain (
  diive.pkgs.fluxprocessingchain.fluxprocessingchain.FluxProcessingChain.level41_gapfilling_longterm)
• Added new class for long-term (multiple years) gap-filling using random forest (
  diive.pkgs.gapfilling.longterm.LongTermGapFillingRandomForestTS)
• Added class for plotting cumulative sums across all data, for multiple columns (
  diive.core.plotting.cumulative.Cumulative)
• Added class to detect a constant offset between two measurements (
  diive.pkgs.corrections.measurementoffset.MeasurementOffset)

Changes

• Creating lagged variants creates gaps which then leads to incomplete features in machine learning models. Now, gaps
  are filled using simple forward and backward filling, limited to the number of values defined in lag. For example,
  if variable TA is lagged by -2 value this creates two missing values for this variant at the start of the time series,
  which then are then gap-filled using the simple backwards fill with limit=2. (
  diive.core.dfun.frames.lagged_variants)

Notebooks

• Updated flux processing chain notebook to include long-term gap-filling using random forest (
  notebooks/FluxProcessingChain/FluxProcessingChain.ipynb)
• Added new notebook for plotting cumulative sums across all data, for multiple columns (
  notebooks/Plotting/Cumulative.ipynb)

Tests

• Unittest for flux processing chain now includes many more methods (
  tests.test_fluxprocessingchain.TestFluxProcessingChain.test_fluxprocessingchain)
• 39/39 unittests ran successfully

Bugfixes

• Fixed deprecation warning in (diive.core.ml.common.prediction_scores_regr)

What's Changed

• Indev by @holukas in #215

Full Changelog: v0.81.0...v0.82.0

v0.81.0

v0.81.0 | 11 Sep 2024

Expanding Flux Processing Capabilities

This update brings advancements for post-processing eddy covariance data in the context of the FluxProcessingChain.
The goal is to offer a complete chain for post-processing ecosystem flux data, specifically designed to work seamlessly
with the standardized _fluxnet output file from the
widely-used EddyPro software.

Now, diive offers the option for USTAR filtering based on known constant thresholds across the entire dataset (similar
to the CUT scenarios in FLUXNET data). While seasonal (DJF, MAM, JJA, SON) thresholds are calculated internally,
applying them on a seasonal basis or using variable thresholds per year (like FLUXNET's VUT scenarios) isn't yet
implemented.

With this update, the FluxProcessingChain class can handle various data processing steps:

• Level-2: Quality flag expansion
• Level-3.1: Storage correction
• Level-3.2: Outlier removal
• Level-3.3: (new) USTAR filtering (with constant thresholds for now)
• (upcoming) Level-4.1: long-term gap-filling using random forest and XGBoost
• For info about the different flux levels
  see Swiss FluxNet flux processing chain

New features

• Added class to apply multiple known constant USTAR (friction velocity) thresholds, creating flags that indicate time
  periods characterized by low turbulence for multiple USTAR scenarios. The constant thresholds must be known
  beforehand, e.g., from an earlier USTAR detection run, or from results from FLUXNET (
  diive.pkgs.flux.ustarthreshold.FlagMultipleConstantUstarThresholds)
• Added class to apply one single known constant USTAR thresholds (
  diive.pkgs.flux.ustarthreshold.FlagSingleConstantUstarThreshold)
• Added FlagMultipleConstantUstarThresholds to the flux processing chain (
  diive.pkgs.fluxprocessingchain.fluxprocessingchain.FluxProcessingChain.level33_constant_ustar)
• Added USTAR detection algorithm based on Papale et al., 2006 (diive.pkgs.flux.ustarthreshold.UstarDetectionMPT)
• Added function to analyze high-quality ecosystem fluxes that helps in understanding the range of highest-quality data(
  diive.pkgs.flux.hqflux.analyze_highest_quality_flux)

Additions

• LocalSD outlier detection can now use a constant SD:
  • Added parameter to use standard deviation across all data (constant) instead of the rolling SD to calculate the
    upper and lower limits that define outliers in the median rolling window (
    diive.pkgs.outlierdetection.localsd.LocalSD)
  • Added to step-wise outlier detection (
    diive.pkgs.outlierdetection.stepwiseoutlierdetection.StepwiseOutlierDetection.flag_outliers_localsd_test)
  • Added to meteoscreening from database (
    diive.pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.flag_outliers_localsd_test)
  • Added to flux processing chain (
    diive.pkgs.fluxprocessingchain.fluxprocessingchain.FluxProcessingChain.level32_flag_outliers_localsd_test)

Changes

• Replaced .plot_date() from the Matplotlib library with .plot() due to deprecation

Notebooks

• Added notebook for plotting cumulative sums per year (notebooks/Plotting/CumulativesPerYear.ipynb)
• Added notebook for removing outliers based on the z-score in rolling time window (
  notebooks/OutlierDetection/zScoreRolling.ipynb)

Bugfixes

• Fixed bug when saving a pandas Series to parquet (diive.core.io.files.save_parquet)
• Fixed bug when plotting doy_mean_cumulative: no longer crashes when years defined in parameter
  excl_years_from_reference are not in dataset (diive.core.times.times.doy_mean_cumulative)
• Fixed deprecation warning when plotting in bokeh (interactive plots)

Tests

• Added unittest for LocalSD using constant SD (
  tests.test_outlierdetection.TestOutlierDetection.test_localsd_with_constantsd)
• Added unittest for rolling z-score outlier removal (
  tests.test_outlierdetection.TestOutlierDetection.test_zscore_rolling)
• Improved check if figure and axis were created in (tests.test_plots.TestPlots.test_histogram)
• 39/39 unittests ran successfully

Environment

• Added new package scikit-optimize
• Added new package category_encoders

What's Changed

• Indev by @holukas in #205

Full Changelog: v0.80.0...v0.81.0

v0.80.0

v0.80.0 | 28 Aug 2024

Additions

• Added outlier tests to step-wise meteoscreening from database: Hampel, HampelDaytimeNighttime and TrimLow (
  diive.pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb)
• Added parameter to control whether or not to output the middle timestamp when loading parquet files with
  load_parquet(). By default, output_middle_timestamp=True. (diive.core.io.files.load_parquet)

Environment

• Re-created environment and created new lock file
• Currently using Python 3.9.19

Notebooks

• Added new notebook for creating a flag that indicates missing values (notebooks/OutlierDetection/MissingValues.ipynb)
• Updated notebook for meteoscreening from database (
  notebooks/MeteoScreening/StepwiseMeteoScreeningFromDatabase.ipynb)
• Updated notebook for loading and saving parquet files (notebooks/Formats/LoadSaveParquetFile.ipynb)

Tests

• Added unittest for flagging missing values (tests.test_outlierdetection.TestOutlierDetection.test_missing_values)
• 37/37 unittests ran successfully

Bugfixes

• Fixed links in README, needed absolute links to notebooks
• Fixed issue with return list in (diive.pkgs.analyses.histogram.Histogram.peakbins)

What's Changed

• Meteoscreening updates by @holukas in #184

Full Changelog: v0.79.1...v0.80.0