Changelog

v0.84.2 | 8 Nov 2024

Changes

Adjust version number to avoid publishing conflict

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

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

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)

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

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

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)

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)

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

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)

v0.79.1 | 26 Aug 2024

Additions

Added new function to apply quality flags to certain time periods only (diive.pkgs.qaqc.flags.restrict_application)
Added to option to restrict the application of the angle-of-attack flag to certain time periods ( diive.pkgs.fluxprocessingchain.level2_qualityflags.FluxQualityFlagsEddyPro.angle_of_attack_test)

Changes

Test options in FluxProcessingChain are now always passed as dict. This has the advantage that in addition to run the test by setting the dict key apply to True, various other test settings can be passed, for example the new parameter application dates for the angle-of-attack flag. ( diive.pkgs.fluxprocessingchain.fluxprocessingchain.FluxProcessingChain)

Tests

Added unittest for Flux Processing Chain up to Level-2 ( tests.test_fluxprocessingchain.TestFluxProcessingChain.test_fluxprocessingchain_level2)
36/36 unittests ran successfully

v0.79.0 | 22 Aug 2024

This version introduces a histogram plot that has the option to display z-score as vertical lines superimposed on the distribution, which helps in assessing z-score settings used by some outlier removal functions.

Histogram plot of half-hourly air temperature measurements at the ICOS Class 1 ecosystem station Davos between 2013 and 2022, displayed in 20 equally-spaced bins. The dashed vertical lines show the z-score and the corresponding value calculated based on the time series. The bin with most counts is highlighted orange.

New features

Added new class HistogramPlotfor plotting histograms, based on the Matplotlib implementation (diive.core.plotting.histogram.HistogramPlot)
Added function to calculate the value for a specific z-score, e.g., based on a time series it calculates the value where z-score = 3 etc. (diive.core.funcs.funcs.val_from_zscore)

Additions

Added histogram plots to FlagBase, histograms are now shown for all outlier methods ( diive.core.base.flagbase.FlagBase.defaultplot)
Added daytime/nighttime histogram plots to (diive.pkgs.outlierdetection.hampel.HampelDaytimeNighttime)
Added daytime/nighttime histogram plots to (diive.pkgs.outlierdetection.zscore.zScoreDaytimeNighttime)
Added daytime/nighttime histogram plots to (diive.pkgs.outlierdetection.lof.LocalOutlierFactorDaytimeNighttime)
Added daytime/nighttime histogram plots to ( diive.pkgs.outlierdetection.absolutelimits.AbsoluteLimitsDaytimeNighttime)
Added option to calculate the z-score with sign instead of absolute (diive.core.funcs.funcs.zscore)

Changes

Improved daytime/nighttime outlier plot used by various outlier removal classes ( diive.core.base.flagbase.FlagBase.plot_outlier_daytime_nighttime)

Notebooks

Added notebook for plotting histograms (notebooks/Plotting/Histogram.ipynb)
Added notebook for manual removal of data points (notebooks/OutlierDetection/ManualRemoval.ipynb)
Added notebook for outlier detection using local outlier factor, separately during daytime and nighttime ( notebooks/OutlierDetection/LocalOutlierFactorDaytimeNighttime.ipynb)
Updated notebook (notebooks/OutlierDetection/HampelDaytimeNighttime.ipynb)
Updated notebook (notebooks/OutlierDetection/AbsoluteLimitsDaytimeNighttime.ipynb)
Updated notebook (notebooks/OutlierDetection/zScoreDaytimeNighttime.ipynb)
Updated notebook (notebooks/OutlierDetection/LocalOutlierFactorAllData.ipynb)

Tests

Added unittest for plotting histograms (tests.test_plots.TestPlots.test_histogram)
Added unittest for calculating histograms (without plotting) (tests.test_analyses.TestCreateVar.test_histogram)

v0.78.1.1 | 19 Aug 2024

Additions

Added CITATIONS file

v0.78.1 | 19 Aug 2024

Changes

Added option to set different n_sigma for daytime and nightime data in HampelDaytimeNighttime (diive.pkgs.outlierdetection.hampel.HampelDaytimeNighttime)
Updated flag_outliers_hampel_dtnt_test in step-wise outlier detection
Updated level32_flag_outliers_hampel_dtnt_test in flux processing chain

Notebooks

Updated notebook HampelDaytimeNighttime
Updated notebook FluxProcessingChain

Tests

Updated unittest test_hampel_filter_daytime_nighttime
35/35 unittests ran successfully

v0.78.0 | 18 Aug 2024

New features

Added new class for outlier removal, based on the rolling z-score. It can also be used in step-wise outlier detection and during meteoscreening from the database. (diive.pkgs.outlierdetection.zscore.zScoreRolling, diive.pkgs.outlierdetection.stepwiseoutlierdetection.StepwiseOutlierDetection, diive.pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb).
Added Hampel filter for outlier removal (diive.pkgs.outlierdetection.hampel.Hampel)
Added Hampel filter (separate daytime, nighttime) for outlier removal (diive.pkgs.outlierdetection.hampel.HampelDaytimeNighttime)
Added function to plot daytime and nighttime outliers during outlier tests (diive.core.plotting.outlier_dtnt.outlier_daytime_nighttime)

Changes

Flux processing chain:
- Several changes to the flux processing chain to make sure it can also work with data files not directly output by EddyPro. The class FluxProcessingChain can now handle files that have a different format than the two EddyPro output files EDDYPRO-FLUXNET-CSV-30MIN and EDDYPRO-FULL-OUTPUT-CSV-30MIN. See following notes.
- Removed option to process EddyPro _full_output_ files, since it as an older format and its variables do not follow FLUXNET conventions.
- Removed keyword filetype in class FluxProcessingChain. It is now assumed that the variable names follow the FLUXNET convention. Variables used in FLUXNET are listed here ( diive.pkgs.fluxprocessingchain.fluxprocessingchain.FluxProcessingChain)
- When detecting the base variable from which a flux variable was calculated, the variables defined for filetype EDDYPRO-FLUXNET-CSV-30MIN are now assumed by default. (diive.pkgs.flux.common.detect_basevar)
- Renamed function that detects the base variable that was used to calculate the respective flux (diive.pkgs.flux.common.detect_fluxbasevar)
- Renamed gas in functions related to completeness tests to fluxbasevar to better reflect that the completeness test does not necessarily require a gas (e.g. T_SONIC is used to calculate the completeness for sensible heat flux) (flag_fluxbasevar_completeness_eddypro_test)
Removing the radiation offset now uses 0.001 (W m-2) instead of 50 as the threshold value to flag nighttime values for the correction (diive.pkgs.corrections.offsetcorrection.remove_radiation_zero_offset)
The database tag for meteo data screened with diive is now meteoscreening_diive (diive.pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.resample)
During noise generation, function now uses the absolute values of the min/max of a series to calculate minimum noise and maximum noise (diive.pkgs.createvar.noise.add_impulse_noise)

Notebooks

Added new notebook for outlier detection using class zScore (notebooks/OutlierDetection/zScore.ipynb)
Added new notebook for outlier detection using class zScoreDaytimeNighttime (notebooks/OutlierDetection/zScoreDaytimeNighttime.ipynb)
Added new notebook for outlier removal using trimming (notebooks/OutlierDetection/TrimLow.ipynb)
Updated notebook (notebooks/MeteoScreening/StepwiseMeteoScreeningFromDatabase_v7.0.ipynb)
When uploading screened meteo data to the database using the notebook StepwiseMeteoScreeningFromDatabase, variables with the same name, measurement and data version as the screened variable(s) are now deleted from the database before the new data are uploaded. Implemented in the Python package dbc-influxdb to avoid duplicates in the database. Such duplicates can occur when one of the tags of an otherwise identical variable changed, e.g., when one of the tags of the originally uploaded data was wrong and needed correction. The database InfluxDB stores a new time series alongside the previous time series when one of the tags is different in an otherwise identical time series.

Tests

Added test case for Hampel filter (tests.test_outlierdetection.TestOutlierDetection.test_hampel_filter)
Added test case for HampelDaytimeNighttime filter (tests.test_outlierdetection.TestOutlierDetection.test_hampel_filter_daytime_nighttime)
Added test case for zScore (tests.test_outlierdetection.TestOutlierDetection.test_zscore)
Added test case for TrimLow (tests.test_outlierdetection.TestOutlierDetection.test_trim_low_nt)
Added test case for zScoreDaytimeNighttime (tests.test_outlierdetection.TestOutlierDetection.test_zscore_daytime_nighttime)
33/33 unittests ran successfully

Environment

Added package sktime, a unified framework for machine learning with time series.

v0.77.0 | 11 Jun 2024

Additions

Plotting cumulatives with CumulativeYear now also shows the cumulative for the reference, i.e. for the mean over the reference years (diive.core.plotting.cumulative.CumulativeYear)
Plotting DielCycle now accepts ylim parameter (diive.core.plotting.dielcycle.DielCycle)
Added long-term dataset for local testing purposes (internal only) (diive.configs.exampledata.load_exampledata_parquet_long)
Added several classes in preparation for long-term gap-filling for a future update

Changes

Several updates and changes to the base class for regressor decision trees (diive.core.ml.common.MlRegressorGapFillingBase):
- The data are now split into training set and test set at the very start of regressor setup. This test set is used to evaluate models on unseen data. The default split is 80% training and 20% test data.
- Plotting (scores, importances etc.) is now generally separated from the method where they are calculated.
- the same random_state is now used for all processing steps
- refactored code
- beautified console output
When correcting for relative humidity values above 100%, the maximum of the corrected time series is now set to 100, after the (daily) offset was removed (diive.pkgs.corrections.offsetcorrection.remove_relativehumidity_offset)
During feature reduction in machine learning regressors, features with permutation importance < 0 are now always removed (diive.core.ml.common.MlRegressorGapFillingBase._remove_rejected_features)
Changed default parameters for quick random forest gap-filling (diive.pkgs.gapfilling.randomforest_ts.QuickFillRFTS)
I tried to improve the console output (clarity) for several functions and methods

Environment

Added package dtreeviz to visualize decision trees

Notebooks

Updated notebook (notebooks/GapFilling/RandomForestGapFilling.ipynb)
Updated notebook (notebooks/GapFilling/LinearInterpolation.ipynb)
Updated notebook (notebooks/GapFilling/XGBoostGapFillingExtensive.ipynb)
Updated notebook (notebooks/GapFilling/XGBoostGapFillingMinimal.ipynb)
Updated notebook (notebooks/GapFilling/RandomForestParamOptimization.ipynb)
Updated notebook (notebooks/GapFilling/QuickRandomForestGapFilling.ipynb)

Tests

Updated and fixed test case (tests.test_outlierdetection.TestOutlierDetection.test_zscore_increments)
Updated and fixed test case (tests.test_gapfilling.TestGapFilling.test_gapfilling_randomforest)

v0.76.2 | 23 May 2024

Additions

Added function to calculate absolute double differences of a time series, which is the sum of absolute differences between a data record and its preceding and next record. Used in class zScoreIncrements for finding (isolated) outliers that are distant from neighboring records. (diive.core.dfun.stats.double_diff_absolute)
Added small function to calculate z-score stats of a time series (diive.core.dfun.stats.sstats_zscore)
Added small function to calculate stats for absolute double differences of a time series (diive.core.dfun.stats.sstats_doublediff_abs)

Changes

Changed the algorithm for outlier detection when using zScoreIncrements. Data points are now flagged as outliers if the z-scores of three absolute differences (previous record, next record and the sum of both) all exceed a specified threshold. (diive.pkgs.outlierdetection.incremental.zScoreIncrements)

Notebooks

Added new notebook for outlier detection using class LocalOutlierFactorAllData (notebooks/OutlierDetection/LocalOutlierFactorAllData.ipynb)

Tests

Added new test case for LocalOutlierFactorAllData (tests.test_outlierdetection.TestOutlierDetection.test_lof_alldata)

v0.76.1 | 17 May 2024

Additions

It is now possible to set a fixed random seed when creating impulse noise (diive.pkgs.createvar.noise.add_impulse_noise)

Changes

In class zScoreIncrements, outliers are now detected by calculating the sum of the absolute differences between a data point and its respective preceding and next data point. Before, only the non-absolute difference of the preceding data point was considered. The sum of absolute differences is then used to calculate the z-score and in further consequence to flag outliers. (diive.pkgs.outlierdetection.incremental.zScoreIncrements)

Notebooks

Added new notebook for outlier detection using class zScoreIncrements (notebooks/OutlierDetection/zScoreIncremental.ipynb)
Added new notebook for outlier detection using class LocalSD (notebooks/OutlierDetection/LocalSD.ipynb)

Tests

Added new test case for zScoreIncrements (tests.test_outlierdetection.TestOutlierDetection.test_zscore_increments)
Added new test case for LocalSD (tests.test_outlierdetection.TestOutlierDetection.test_localsd)

v0.76.0 | 14 May 2024

Diel cycle plot

The new class DielCycle allows to plot diel cycles per month or across all data for time series data. At the moment, it plots the (monthly) diel cycles as means (+/- standard deviation). It makes use of the time info contained in the datetime timestamp index of the data. All aggregates are calculated by grouping data by time and (optional) separately for each month. The diel cycles have the same time resolution as the time component of the timestamp index, e.g. hourly.

New features

Added new class DielCycle for plotting diel cycles per month (diive.core.plotting.dielcycle.DielCycle)
Added new function diel_cycle for calculating diel cycles per month. This function is also used by the plotting class DielCycle (diive.core.times.resampling.diel_cycle)

Additions

Added color scheme that contains 12 colors, one for each month. Not perfect, but better than before. (diive.core.plotting.styles.LightTheme.colors_12_months)

Notebooks

Added new notebook for plotting diel cycles (per month) (notebooks/Plotting/DielCycle.ipynb)
Added new notebook for calculating diel cycles (per month) (notebooks/Resampling/ResamplingDielCycle.ipynb)

Tests

Added test case for new function diel_cycle (tests.test_resampling.TestResampling.test_diel_cycle)

v0.75.0 | 26 Apr 2024

XGBoost gap-filling

XGBoost can now be used to fill gaps in time series data. In diive, XGBoost is implemented in class XGBoostTS, which adds additional options for easily including e.g. lagged variants of feature variables, timestamp info (DOY, month, ...) and a continuous record number. It also allows direct feature reduction by including a purely random feature (consisting of completely random numbers) and calculating the 'permutation importance'. All features where the permutation importance is lower than for the random feature can then be removed from the dataset, i.e., the list of features, before building the final model.

XGBoostTS and RandomForestTS both use the same base class MlRegressorGapFillingBase. This base class will also facilitate the implementation of other gap-filling algorithms in the future.

Another fun (for me) addition is the new class TimeSince. It allows to calculate the time since the last occurrence of specific conditions. One example where this class can be useful is the calculation of 'time since last precipitation', expressed as number of records, which can be helpful in identifying dry conditions. More examples: 'time since freezing conditions' based on air temperature; 'time since management' based on management info, e.g. fertilization events. Please see the notebook for some illustrative examples.

Please note that diive is still under developement and bugs can be expected.

New features

Added gap-filling class XGBoostTS for time series data, using XGBoost (diive.pkgs.gapfilling.xgboost_ts.XGBoostTS)
Added new class TimeSince: counts number of records (inceremental number / counter) since the last time a time series was inside a specified range, useful for e.g. counting the time since last precipitation, since last freezing temperature, etc. (diive.pkgs.createvar.timesince.TimeSince)

Additions

Added base class for machine learning regressors, which is basically the code shared between the different methods. At the moment used by RandomForestTS and XGBoostTS. (diive.core.ml.common.MlRegressorGapFillingBase)
Added option to change line color directly in TimeSeries plots (diive.core.plotting.timeseries.TimeSeries.plot)

Notebooks

Added new notebook for gap-filling using XGBoostTS with mininmal settings (notebooks/GapFilling/XGBoostGapFillingMinimal.ipynb)
Added new notebook for gap-filling using XGBoostTS with more extensive settings (notebooks/GapFilling/XGBoostGapFillingExtensive.ipynb)
Added new notebook for creating TimeSince variables (notebooks/CalculateVariable/TimeSince.ipynb)

Tests

Added test case for XGBoost gap-filling (tests.test_gapfilling.TestGapFilling.test_gapfilling_xgboost)
Updated test case for random forest gap-filling (tests.test_gapfilling.TestGapFilling.test_gapfilling_randomforest)
Harmonized test case for XGBoostTS with test case of RandomForestTS
Added test case for TimeSince variable creation (tests.test_createvar.TestCreateVar.test_timesince)

v0.74.1 | 23 Apr 2024

This update adds the first notebooks (and tests) for outlier detection methods. Only two tests are included so far and both tests are relatively simple, but both notebooks already show in principle how outlier removal is handled. An important aspect is that diive single outlier methods do not remove outliers by default, but instead a flag is created that shows where the outliers are located. The flag can then be used to remove the data points.
This update also includes the addition of a small function that creates artificial spikes in time series data and is therefore very useful for testing outlier detection methods.
More outlier removal notebooks will be added in the future, including a notebook that shows how to combine results from multiple outlier tests into one single overall outlier flag.

New features

Added: new function to add impulse noise to time series (diive.pkgs.createvar.noise.impulse)

Notebooks

Added: new notebook for outlier detection: absolute limits, separately for daytime and nighttime data (notebooks/OutlierDetection/AbsoluteLimitsDaytimeNighttime.ipynb)
Added: new notebook for outlier detection: absolute limits (notebooks/OutlierDetection/AbsoluteLimits.ipynb)

Tests

Added: test case for outlier detection: absolute limits, separately for daytime and nighttime data (tests.test_outlierdetection.TestOutlierDetection.test_absolute_limits)
Added: test case for outlier detection: absolute limits (tests.test_outlierdetection.TestOutlierDetection.test_absolute_limits)

v0.74.0 | 21 Apr 2024

Additions

Added: new function to remove rows that do not have timestamp info (NaT) (diive.core.times.times.remove_rows_nat and diive.core.times.times.TimestampSanitizer)
Added: new settings VARNAMES_ROW and VARUNITS_ROW in filetypes YAML files, allows better and more specific configuration when reading data files (diive/configs/filetypes)
Added: many (small) example data files for various filetypes, e.g. ETH-RECORD-TOA5-CSVGZ-20HZ
Added: new optional check in TimestampSanitizer that compares the detected time resolution of a time series with the nominal (expected) time resolution. Runs automatically when reading files with ReadFileType, in which case the FREQUENCY from the filetype configs is used as the nominal time resolution. (diive.core.times.times.TimestampSanitizer, diive.core.io.filereader.ReadFileType)
Added: application of TimestampSanitizer after inserting a timestamp and setting it as index with function insert_timestamp, this makes sure the freq/freqstr info is available for the new timestamp index (diive.core.times.times.insert_timestamp)

Notebooks

General: Ran all notebook examples to make sure they work with this version of diive
Added: new notebook for reading EddyPro fluxnet output file with DataFileReader parameters (notebooks/ReadFiles/Read_single_EddyPro_fluxnet_output_file_with_DataFileReader.ipynb)
Added: new notebook for reading EddyPro fluxnet output file with ReadFileType and pre-defined filetype EDDYPRO-FLUXNET-CSV-30MIN ( notebooks/ReadFiles/Read_single_EddyPro_fluxnet_output_file_with_ReadFileType.ipynb)
Added: new notebook for reading multiple EddyPro fluxnet output files with MultiDataFileReader and pre-defined filetype EDDYPRO-FLUXNET-CSV-30MIN ( notebooks/ReadFiles/Read_multiple_EddyPro_fluxnet_output_files_with_MultiDataFileReader.ipynb)

Changes

Renamed: function get_len_header to parse_header(diive.core.dfun.frames.parse_header)
Renamed: exampledata files (diive/configs/exampledata)
Renamed: filetypes YAML files to always include the file extension in the file name (diive/configs/filetypes)
Reduced: file size for most example data files

Tests

Added: various test cases for loading filetypes (tests/test_loaddata.py)
Added: test case for loading and merging multiple files (tests.test_loaddata.TestLoadFiletypes.test_load_exampledata_multiple_EDDYPRO_FLUXNET_CSV_30MIN)
Added: test case for reading EddyPro fluxnet output file with DataFileReader parameters ( tests.test_loaddata.TestLoadFiletypes.test_load_exampledata_EDDYPRO_FLUXNET_CSV_30MIN_datafilereader_parameters)
Added: test case for resampling series to 30MIN time resolution (tests.test_time.TestTime.test_resampling_to_30MIN)
Added: test case for inserting timestamp with a different convention (middle, start, end) (tests.test_time.TestTime.test_insert_timestamp)
Added: test case for inserting timestamp as index (tests.test_time.TestTime.test_insert_timestamp_as_index)

Bugfixes

Fixed: bug in class DetectFrequency when inferred frequency is None (diive.core.times.times.DetectFrequency)
Fixed: bug in class DetectFrequency where pd.Timedelta() would crash if the input frequency does not have a number. Timedelta does not accept e.g. the frequency string min for minutely time resolution, even though e.g. pd.infer_freq() outputs min for data in 1-minute time resolution. TimeDelta requires a number, in this case 1min. Results from infer_freq() are now checked if they contain a number and if not, 1 is added at the beginning of the frequency string. (diive.core.times.times.DetectFrequency)
Fixed: bug in notebook WindDirectionOffset, related to frequency detection during heatmap plotting
Fixed: bug in TimestampSanitizer where the script would crash if the timestamp contained an element that could not be converted to datetime, e.g., when there is a string mixed in with the regular timestamps. Data rows with invalid timestamps are now parsed as NaT by using errors='coerce' in pd.to_datetime(data.index, errors='coerce'). (diive.core.times.times.convert_timestamp_to_datetime and diive.core.times.times.TimestampSanitizer)
Fixed: bug when plotting heatmap (diive.core.plotting.heatmap_datetime.HeatmapDateTime)

v0.73.0 | 17 Apr 2024

New features

Added new function trim_frame that allows to trim the start and end of a dataframe based on available records of a variable (diive.core.dfun.frames.trim_frame)
Added new option to export borderless heatmaps (diive.core.plotting.heatmap_base.HeatmapBase.export_borderless_heatmap)

Additions

Added more info in comments of class WindRotation2D (diive.pkgs.echires.windrotation.WindRotation2D)
Added example data for EddyPro full_output files (diive.configs.exampledata.load_exampledata_eddypro_full_output_CSV_30MIN)
Added code in an attempt to harmonize frequency detection from data: in class DetectFrequency the detected frequency strings are now converted from Timedelta (pandas) to offset (pandas) to .freqstr. This will yield the frequency string as seen by (the current version of) pandas. The idea is to harmonize between different representations e.g. T or min for minutes ( see here). ( diive.core.times.times.DetectFrequency)

Changes

Updated class DataFileReader to comply with new pandas kwargs when using .read_csv() (diive.core.io.filereader.DataFileReader._parse_file)
Environment: updated pandas to v2.2.2 and pyarrow to v15.0.2
Updated date offsets in config filetypes to be compliant with pandas version 2.2+ ( see here and here), e.g., 30T was changed to 30min. This seems to work without raising a warning, however, if frequency is inferred from available data, the resulting frequency string shows e.g. 30T, i.e. still showing T for minutes instead of min. (diive/configs/filetypes)
Changed variable names in WindRotation2D to be in line with the variable names given in the paper by Wilczak et al. (2001) https://doi.org/10.1023/A:1018966204465

Removals

Removed function timedelta_to_string because this can be done with pandas to_offset().freqstr
Removed function generate_freq_str (unused)

Tests

Added test case for reading EddyPro full_output files (tests.test_loaddata.TestLoadFiletypes.test_load_exampledata_eddypro_full_output_CSV_30MIN)
Updated test for frequency detection (tests.test_timestamps.TestTime.test_detect_freq)

v0.72.1 | 26 Mar 2024

pyproject.toml now uses the inequality syntax >= instead of caret syntax ^ because the version capping is restrictive and prevents compatibility in conda installations. See #74
Added badges in README.md
Smaller diive logo in README.md

v0.72.0 | 25 Mar 2024

New feature

Added new heatmap plotting class HeatmapYearMonth that allows to plot a variable in year/month classes(diive.core.plotting.heatmap_datetime.HeatmapYearMonth)

Changes

Refactored code for class HeatmapDateTime (diive.core.plotting.heatmap_datetime.HeatmapDateTime)
Added new base class HeatmapBase for heatmap plots. Currently used by HeatmapYearMonth and HeatmapDateTime (diive.core.plotting.heatmap_base.HeatmapBase)

Notebooks

Added new notebook for HeatmapDateTime (notebooks/Plotting/HeatmapDateTime.ipynb)
Added new notebook for HeatmapYearMonth (notebooks/Plotting/HeatmapYearMonth.ipynb)

Bugfixes

Fixed bug in HeatmapDateTime where the last record of each day was not shown

v0.71.6 | 23 Mar 2024

Notebooks

Added new notebook for Percentiles (notebooks/Analyses/Percentiles.ipynb)
Added new notebook for LinearInterpolation (notebooks/GapFilling/LinearInterpolation.ipynb)
Added new notebook for calculating z-aggregates in quantiles (classes) of x and y (notebooks/Analyses/CalculateZaggregatesInQuantileClassesOfXY.ipynb)
Updated notebook for DaytimeNighttimeFlag (notebooks/CalculateVariable/DaytimeNighttimeFlag.ipynb)

v0.71.5 | 22 Mar 2024

Changes

Updated notebook for SortingBinsMethod (diive.pkgs.analyses.decoupling.SortingBinsMethod)

Plot showing vapor pressure deficit (y) in 10 classes of short-wave incoming radiation (x), separate for 5 classes of air temperature (z). All values shown are medians of the respective variable. The shaded errorbars refer to the interquartile range for the respective class. Plot was generated using the class SortingBinsMethod.

v0.71.4 | 20 Mar 2024

Changes

Refactored class LongtermAnomaliesYear (diive.core.plotting.bar.LongtermAnomaliesYear)

Notebooks

Added new notebook for LongtermAnomaliesYear (notebooks/Plotting/LongTermAnomalies.ipynb)

v0.71.3 | 19 Mar 2024

Changes

Refactored class SortingBinsMethod: Allows to investigate binned aggregates of a variable z in binned classes of x and y. All bins now show medians and interquartile ranges. (diive.pkgs.analyses.decoupling.SortingBinsMethod)

Notebooks

Added new notebook for SortingBinsMethod

Bugfixes

Added absolute links to example notebooks in README.md

Other

From now on, diive is officially published on pypi

v0.71.2 | 18 Mar 2024

Notebooks

Added new notebook for daily_correlation function (notebooks/Analyses/DailyCorrelation.ipynb)
Added new notebook for Histogram class (notebooks/Analyses/Histogram.ipynb)

Bugfixes & changes

Daily correlations are now returned with daily (1d) timestamp index (diive.pkgs.analyses.correlation.daily_correlation)
Updated README
Environment: Added ruff to dev dependencies for linting

v0.71.1 | 15 Mar 2024

Bugfixes & changes

Fixed: Replaced all references to old filetypes using the underscore to their respective new filetype names, e.g. all occurrences of EDDYPRO_FLUXNET_30MIN were replaced with the new name EDDYPRO-FLUXNET-CSV-30MIN.
Environment: Python 3.11 is now allowed in pyproject.toml: python = ">=3.9,<3.12"
Environment: Removed fitter library from dependencies, was not used.
Docs: Testing documentation generation using Sphinx, although it looks very rough at the moment.

v0.71.0 | 14 Mar 2024

High-resolution update

This update focuses on the implementation of several classes that work with high-resolution (20 Hz) data.

The main motivation behind these implementations is the upcoming new version of another script, dyco, which will make direct use of these new classes. dyco allows to detect and remove time lags from time series data and can also handle drifting lags, i.e., lags that are not constant over time. This is especially useful for eddy covariance data, where the detection of accurate time lags is of high importance for the calculation of ecosystem fluxes.

Plot showing the covariance between the turbulent departures of vertical wind and CO2 measurements. Maximum (absolute) covariance was found at record -26, which means that the CO2 signal has to be shifted by 26 records in relation to the wind data to obtain the maximum covariance between the two variables. Since the covariance was calculated on 20 Hz data, this corresponds to a time lag of 1.3 seconds between CO2 and wind (20 Hz = measurement every 0.05 seconds, 26 * 0.05 = 1.3), or, to put it another way, the CO2 signal arrived 1.3 seconds later at the sensor than the wind signal. Maximum covariance was calculated using the MaxCovariance class.

New features

Added new class MaxCovariance to find the maximum covariance between two variables (diive.pkgs.echires.lag.MaxCovariance)
Added new class FileDetector to detect expected and unexpected files from a list of files (diive.core.io.filesdetector.FileDetector)
Added new class FileSplitter to split file into multiple smaller parts and export them as multiple CSV files. (diive.core.io.filesplitter.FileSplitter)
Added new class FileSplitterMulti to split multiple files into multiple smaller parts and save them as CSV or compressed CSV files. (diive.core.io.filesplitter.FileSplitterMulti)
Added new function create_timestamp that calculates the timestamp for each record in a dataframe, based on number of records in the file and the file duration. (diive.core.times.times.create_timestamp)

Additions

Added new filetype ETH-SONICREAD-BICO-CSVGZ-20HZ, these files contain data that were originally logged by the sonicread script which is in use in the ETH Grassland Sciences group since the early 2000s to record eddy covariance data within the Swiss FluxNet. Data were then converted to a regular format using the Python script bico, which also compressed the resulting CSV files to gz files (gzipped).
Added new filetype GENERIC-CSV-HEADER-1ROW-TS-MIDDLE-FULL-NS-20HZ, which corresponds to a CSV file with one header row with variable names, a timestamp that describes the middle of the averaging period, whereby the timestamp also includes nanoseconds. Time resolution of the file is 20 Hz.

Changes

Renamed class TurbFlux to WindRotation2D and updated code a bit, e.g., now it is possible to get rotated values for all three wind components (u', v', w') in addition to the rotated scalar c'. (diive.pkgs.echires.windrotation.WindRotation2D)
Renamed filetypes: all filetypes now use the dash instead of an underscore
Renamed filetype to ETH-RECORD-DAT-20HZ: this filetype originates from the new eddy covariance real-time logging script rECord (currently not open source)
Missing values are now defined for all files as: NA_VALUES: [ -9999, -6999, -999, "nan", "NaN", "NAN", "NA", "inf", "-inf", "-" ]

v0.70.1 | 1 Mar 2024

Updated (and cleaned) notebook StepwiseMeteoScreeningFromDatabase.ipynb

v0.70.0 | 28 Feb 2024

New features

In StepwiseOutlierDetection, it is now possible to re-run an outlier detection method. The re-run(s) would produce flag(s) with the same name(s) as for the first (original) run. Therefore, an integer is added to the flag name. For example, if the test z-score daytime/nighttime is run the first time, it produces the flag with the name FLAG_TA_T1_2_1_OUTLIER_ZSCOREDTNT_TEST. When the test is run again (e.g. with different settings) then the name of the flag of this second run is FLAG_TA_T1_2_1_OUTLIER_ZSCOREDTNT_2_TEST, etc ... The script now checks whether a flag of the same name was already created, in which case an integer is added to the flag name. These re-runs are now available in addition to the repeat=True keyword. (diive.pkgs.outlierdetection.stepwiseoutlierdetection.StepwiseOutlierDetection.addflag) Example:
- METHOD with SETTINGS is applied with repeat=True and therefore repeated until no more outliers were found with these settings. The name of the flag produced is TEST_METHOD_FLAG.
- Next, METHOD is applied again with repeat=True, but this time with different SETTINGS. Like before, the test is repeated until no more outliers were found with the new settings. The name of the flag produced is TEST_METHOD_2_FLAG.
- METHOD can be re-run any number of times, each time producing a new flag: TEST_METHOD_3_FLAG, TEST_METHOD_4_FLAG, ...
Added new function to format timestamps to FLUXNET ISO format (YYYYMMDDhhmm) (diive.core.times.times.format_timestamp_to_fluxnet_format)

Bugfixes

Refactored and fixed class to reformat data for FLUXNET upload (diive.pkgs.formats.fluxnet.FormatEddyProFluxnetFileForUpload)
Fixed None error when reading data files (diive.core.io.filereader.DataFileReader._parse_file)

Notebooks

Updated notebook FormatEddyProFluxnetFileForUpload.ipynb

v0.69.0 | 23 Feb 2024

New features

Added new functions to extract info from a binary that was stored as integer. These functions convert a subrange of bits from an integer or an integer series to floats with an optional gain applied. See docstring of the respective functions for more info. (diive.pkgs.binary.extract.get_encoded_value_from_int) (diive.pkgs.binary.extract.get_encoded_value_series)
Added new filetype RECORD_DAT_20HZ (diive/configs/filetypes/RECORD_DAT_20HZ.yml) for eddy covariance high-resolution (20Hz) raw data files recorded by the ETH rECord logging script.

v0.68.1 | 5 Feb 2024

Fixed bugs in FluxProcessingChain, flag creation for missing values did not work because of the missing repeat keyword (diive.pkgs.fluxprocessingchain.fluxprocessingchain.FluxProcessingChain)

v0.68.0 | 30 Jan 2024

Updates to stepwise outlier detection

Harmonized the way outlier flags are calculated. Outlier flags are all based on the same base class diive.core.base.flagbase.FlagBase like before, but the base class now includes more code that is shared by the different outlier detection methods. For example, FlagBase includes a method that enables repeated execution of a single outlier detection method multiple times until all outliers are removed. Results from all iterations are then combined into one single flag.

The class StepwiseMeteoScreeningDb that makes direct use of the stepwise outlier detection was adjusted accordingly.

Notebooks

Updated notebook StepwiseMeteoScreeningFromDatabase.ipynb

Removed features

Removed outlier test based on seasonal-trend decomposition and z-score calculations (OutlierSTLRZ). The test worked in principle, but at the moment it is unclear how to set reliable parameters. In addition the test is slow when used with multiple years of high-resolution data. De-activated for the moment.

v0.67.1 | 10 Jan 2024

Updated: many docstrings.

v0.67.0 | 9 Jan 2024

Updates to flux processing chain

The flux processing chain was updated in an attempt to make processing more streamlined and easier to follow. One of the biggest changes is the implementation of the repeat keyword for outlier tests. With this keyword set to True, the respective test is repeated until no more outliers can be found. How the flux processing chain can be used is shown in the updated FluxProcessingChainnotebook (notebooks/FluxProcessingChain/FluxProcessingChain.ipynb).

New features

Added new class QuickFluxProcessingChain, which allows to quickly execute a simplified version of the flux processing chain. This quick version runs with a lot of default values and thus not a lot of user input is needed, only some basic settings. (diive.pkgs.fluxprocessingchain.fluxprocessingchain.QuickFluxProcessingChain)
Added new repeater function for outlier detection: repeater is wrapper that allows to execute an outlier detection method multiple times, where each iteration gets its own outlier flag. As an example: the simple z-score test is run a first time and then repeated until no more outliers are found. Each iteration outputs a flag. This is now used in the StepwiseOutlierDetection and thus the flux processing chain Level-3.2 (outlier detection) and the meteoscreening in StepwiseMeteoScreeningDb (not yet checked in this update). To repeat an outlier method use the repeat keyword arg (see the FluxProcessingChain notebook for examples).(diive.pkgs.outlierdetection.repeater.repeater)
Added new function filter_strings_by_elements: Returns a list of strings from list1 that contain all of the elements in list2.(core.funcs.funcs.filter_strings_by_elements)
Added new function flag_steadiness_horizontal_wind_eddypro_test: Create flag for steadiness of horizontal wind u from the sonic anemometer. Makes direct use of the EddyPro output files and converts the flag to a standardized 0/1 flag.(pkgs.qaqc.eddyproflags.flag_steadiness_horizontal_wind_eddypro_test)

Changes

Added automatic calculation of daytime and nighttime flags whenever the flux processing chain is started flags (diive.pkgs.fluxprocessingchain.fluxprocessingchain.FluxProcessingChain._add_swinpot_dt_nt_flag)

Removed features

Removed class ThymeBoostOutlier for outlier detection. At the moment it was not possible to get it to work properly.

Changes

It appears that the kwarg fmt is used slightly differently for plot_date and plot in matplotlib. It seems it is always defined for plot_date, while it is optional for plot. Now using fmt kwarg to avoid the warning: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string "o" (-> marker='o'). The keyword argument will take precedence. Therefore using 'fmt="X"' instead of 'marker="X"'. See also answer here

Environment

Removed thymeboost

v0.66.0 | 2 Nov 2023

New features

Added new class ScatterXY: a simple scatter plot that supports bins (core.plotting.scatter.ScatterXY)

Notebooks

Added notebook notebooks/Plotting/ScatterXY.ipynb

v0.64.0 | 31 Oct 2023

New features

Added new class DaytimeNighttimeFlag to calculate daytime flag (1=daytime, 0=nighttime), nighttime flag (1=nighttime, 0=daytime) and potential radiation from latitude and longitude (diive.pkgs.createvar.daynightflag.DaytimeNighttimeFlag)

Additions

Added support for N2O and CH4 fluxes during the calculation of the QCF quality flag in class FlagQCF
Added first code for USTAR threshold detection for NEE

Notebooks

Added new notebook notebooks/CalculateVariable/Daytime_and_nighttime_flag.ipynb

v0.63.1 | 25 Oct 2023

Changes

diive repository is now hosted on GitHub.

Additions

Added first code for XGBoost gap-filling, not production-ready yet
Added check if enough columns for lagging features in class RandomForestTS
Added more details in report for class FluxStorageCorrectionSinglePointEddyPro

Bugfixes

Fixed check in RandomForestTS for bug in QuickFillRFTS: number of available columns was checked too early
Fixed QuickFillRFTS implementation in OutlierSTLRZ
Fixed QuickFillRFTS implementation in ThymeBoostOutlier

Environment

Added new package xgboost
Updated all packages

v0.63.0 | 5 Oct 2023

New features

Implemented feature reduction (permutation importance) as separate method in RandomForestTS
Added new function to set values within specified time ranges to a constant value(pkgs.corrections.setto_value.setto_value)
- The function is now also implemented as method in StepwiseMeteoScreeningDb (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.correction_setto_value)

Notebooks

Updated notebook notebooks/GapFilling/RandomForestGapFilling.ipynb
Updated notebook notebooks/GapFilling/QuickRandomForestGapFilling.ipynb
Updated notebook notebooks/MeteoScreening/StepwiseMeteoScreeningFromDatabase.ipynb

Environment

Added new package SHAP
Added new package eli5

Tests

Updated testcase for gap-filling with random forest (test_gapfilling.TestGapFilling.test_gapfilling_randomforest)

v0.62.0 | 1 Oct 2023

New features

Re-implemented gap-filling of long-term time series spanning multiple years, where the model to gap-fill a specific year is built from data from the respective year and its two closest neighboring years. (pkgs.gapfilling.randomforest_ts.LongTermRandomForestTS)

Bugfixes

Fixed bug in StepwiseMeteoScreeningDb where position of return during setup was incorrect

v0.61.0 | 28 Sep 2023

New features

Added function to calculate the daily correlation between two time series (pkgs.analyses.correlation.daily_correlation)
Added function to calculate potential radiation (pkgs.createvar.potentialradiation.potrad)

Bugfixes

Fixed bug in StepwiseMeteoScreeningDb where the subclass StepwiseOutlierDetection did not use the already sanitized timestamp from the parent class, but sanitized the timestamp a second time, leading to potentially erroneous and irregular timestamps.

Changes

RandomForestTS now has the following functions included as methods:
- steplagged_variants: includes lagged variants of features
- include_timestamp_as_cols: includes timestamp info as data columns
- add_continuous_record_number: adds continuous record number as new column
- sanitize: validates and prepares timestamps for further processing
RandomForestTS now outputs an additional predictions column where predictions from the full model and predictions from the fallback model are collected
Renamed function steplagged_variants to lagged_variants (core.dfun.frames.lagged_variants)
Updated function lagged_variants: now accepts a list of lag times. This makes it possible to lag variables in both directions, i.e., the observed value can be paired with values before and after the actual time. For example, the variable TA is the observed value at the current timestamp, TA-1 is the value from the preceding record, and TA+1 is the value from the next record. Using values from the next record can be useful when modeling observations using data from a neighboring measurement location that has similar records but lagged in time due to distance.
Updated README

Tests

Updated testcase for gap-filling with random forest (test_gapfilling.TestGapFilling.test_gapfilling_randomforest)

Notebooks

Updated notebooks/MeteoScreening/StepwiseMeteoScreeningFromDatabase.ipynb

Additions

Added more args for better control of TimestampSanitizer (core.times.times.TimestampSanitizer)
Refined various docstrings

v0.60.0 | 17 Sep 2023

New features

Added new class for optimizing random forest parameters (pkgs.gapfilling.randomforest_ts.OptimizeParamsRFTS)
Added new plots for prediction error and residuals (core.ml.common.plot_prediction_residuals_error_regr)
Added function that adds a continuous record number as new column in a dataframe. This could be useful to include as feature in gap-filling models for long-term datasets spanning multiple years. (core.dfun.frames.add_continuous_record_number)

Changes

When reading CSV files with pandas .read_csv(), the arg mangle_dupe_cols=True was removed because it is deprecated since pandas 2.0 ...
... therefore the check for duplicate column names in class ColumnNamesSanitizer has been refactored. In case of duplicate columns names, an integer suffix is added to the column name. For example: VAR is renamed to VAR.1 if it already exists in the dataframe. In case VAR.1 also already exists, it is renamed to VAR.2, and so on. The integer suffix is increased until the variable name is unique. (core.io.filereader.ColumnNamesSanitizer)
Similarly, when reading CSV files with pandas .read_csv(), the arg date_parser was removed because it is deprecated since pandas 2.0. When reading a CSV, the arg date_format is now used instead. The input format remains unchanged, it is still a string giving the datetime format, such as "%Y%m%d%H%M".
The random feature variable is now generated using the same random state as the model. (pkgs.gapfilling.randomforest_ts.RandomForestTS)
Similarly, train_test_split is now also using the same random state as the model. (pkgs.gapfilling.randomforest_ts.RandomForestTS)

Notebooks

Added new notebook notebooks/GapFilling/RandomForestParamOptimization.ipynb

Tests

Added testcase for loading dataframe from parquet file (test_loaddata.TestLoadFiletypes.test_exampledata_parquet)
Added testcase for gap-filling with random forest (test_gapfilling.TestGapFilling.test_gapfilling_randomforest)

Environment

Updated poetry to latest version 1.6.1
Updated all packages to their latest versions
Added new package yellowbrick

v0.59.0 | 14 Sep 2023

MeteoScreening from database - update

The class StepwiseMeteoScreeningDb, which is used for quality-screening of meteo data stored in the ETH Grassland Sciences database, has been refactored. It is now using the previously introduced class StepwiseOutlierDetection for outlier tests. (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb)

Removed

The following classes are no longer used and were removed from step-wise outlier detection:

Removed z-score IQR test, too unreliable (pkgs.outlierdetection.zscore.zScoreIQR)
Similarly, removed seasonal trend decomposition that used z-score IQR test, too unreliable (pkgs.outlierdetection.seasonaltrend.OutlierSTLRIQRZ)

Notebooks

Updated notebook notebooks/MeteoScreening/StepwiseMeteoScreeningFromDatabase.ipynb

v0.58.1 | 13 Sep 2023

Notebooks

Added new notebook notebooks/GapFilling/RandomForestGapFilling.ipynb
Added new notebook notebooks/GapFilling/QuickRandomForestGapFilling.ipynb
Added new notebook notebooks/Workbench/Remove_unneeded_cols.ipynb

v0.58.0 | 7 Sep 2023

Random forest update

The class RandomForestTS has been refactored. In essence, it still uses the same RandomForestRegressor as before, but now outputs feature importances additionally as computed by permutation. More details about permutation importance can be found in scikit's official documentation here: Permutation feature importance.

When the model is trained using .trainmodel(), a random variable is included as additional feature. Permutation importances of all features - including the random variable - are then analyzed. Variables that yield a lower importance score than the random variables are removed from the dataset and are not used to build the model. Typically, the permutation importance for the random variable is very close to zero or even negative.

The built-in importance calculation in the RandomForestRegressor uses the Gini importance, an impurity-based feature importance that favors high cardinality features over low cardinality features. This is not ideal in case of time series data that is combined with categorical data. Permutation importance is therefore a better indicator whether a variable included in the model is an important predictor or not.

The class now splits input data into training and testing datasets (holdout set). By default, the training set comprises 75% of the input data, the testing set 25%. After the model was trained, it is tested on the testing set. This should give a better indication of how well the model works on unseen data.

Once .trainmodel() is finished, the model is stored internally and can be used to gap-fill the target variable by calling .fillgaps().

In addition, the class now offers improved output with additional text output and plots that give more info about model training, testing and application during gap-filling.

RandomForestTS has also been streamlined. The option to include timestamp info as features (e.g., a column describing the season of the respective record) during model building is now its own function (.include_timestamp_as_cols()) and was removed from the class.

New features

New class QuickFillRFTS that uses RandomForestTS in the background to quickly fill time series data (pkgs.gapfilling.randomforest_ts.QuickFillRFTS)
New function to include timestamp info as features, e.g. YEAR and DOY (core.times.times.include_timestamp_as_cols)
New function to calculate various model scores, e.g. mean absolute error, R2 and more (core.ml.common.prediction_scores_regr)
New function to insert the meteorological season (Northern hemisphere) as variable (core.times.times.insert_season). For each record in the time series, the seasonal info between spring (March, April, May) and winter (December, January, February) is added as integer number (0=spring, summer=1, autumn=2, winter=3).

Additions

Added new example dataset, comprising ecosystem fluxes between 1997 and 2022 from the ICOS Class 1 Ecosystem station CH-Dav. This dataset will be used for testing code on long-term time series. The dataset is stored in the parquet file format, which allows fast loading and saving of datafiles in combination with good compression. The simplest way to load the dataset is to use:

from diive.configs.exampledata import load_exampledata_parquet

df = load_exampledata_parquet()

Changes

Updated README with installation details

Notebooks

Updated notebook notebooks/CalculateVariable/Calculate_VPD_from_TA_and_RH.ipynb

v0.57.1 | 23 Aug 2023

Changes

Updates to class FormatEddyProFluxnetFileForUpload, for quickly formatting the EddyPro fluxnet output file to comply with FLUXNET requirements for uploading data.

Additions

Formatting EddyPro fluxnet files for upload to FLUXNET: FormatEddyProFluxnetFileForUpload
- Added new method to rename variables from the EddyPro fluxnet file to comply with FLUXNET variable codes. ._rename_to_variable_codes()
- Added new method to remove errneous time periods from dataset .remove_erroneous_data()
- Added new method to remove fluxes from time periods of insufficient signal strength / AGC .remove_low_signal_data()

Bugfixes

Fixed bug: when data points are removed manually using class ManualRemoval and the data to be removed is a single datetime (e.g., 2005-07-05 23:15:00) then the removal now also works if the provided datetime is not found in the time series. Previously, the class raised the error that the provided datetime is not part of the index. (pkgs.outlierdetection.manualremoval.ManualRemoval)

Notebooks

Updated notebook notebooks/Formats\FormatEddyProFluxnetFileForUpload.ipynb to version 3

v0.57.0 | 22 Aug 2023

Changes

Relaxed conditions a bit when inferring time resolution of time series (core.times.times.timestamp_infer_freq_progressively, core.times.times.timestamp_infer_freq_from_timedelta)

Additions

When reading parquet files, the TimestampSanitizer is applied by default to detect e.g. the time resolution of the time series. Parquet files do not store info on time resolution like it is stored in pandas dataframes (e.g. 30T for 30MIN time resolution), even if the dataframe containing that info was saved to a parquet file.

Bugfixes

Fixed bug where interactive time series plot did not show in Jupyter notebooks (core.plotting.timeseries.TimeSeries)
Fixed bug where certain parts of the flux processing chain could not be used for the sensible heat flux H. The issue was that H is calculated from sonic temperature (T_SONIC in EddyPro _fluxnet_ output files), which was not considered in function pkgs.flux.common.detect_flux_basevar.
Fixed bug: interactive plotting in notebooks using bokeh did not work. The reason was that the bokeh plot tools (controls) ZoomInTool() and ZoomOutTool() do not seem to work anymore. Both tools are now deactivated.

Notebooks

Added new notebook for simple (interactive) time series plotting notebooks/Plotting/TimeSeries.ipynb
Updated notebook notebooks/FluxProcessingChain/FluxProcessingChain.ipynb to version 3

v0.55.0 | 18 Aug 2023

This update focuses on the flux processing chain, in particular the creation of the extended quality flags, the flux storage correction and the creation of the overall quality flag QCF.

New Features

Added new class StepwiseOutlierDetection that can be used for general outlier detection in time series data. It is based on the StepwiseMeteoScreeningDb class introduced in v0.50.0, but aims to be more generally applicable to all sorts of time series data stored in files (pkgs.outlierdetection.stepwiseoutlierdetection.StepwiseOutlierDetection)
Added new outlier detection class that identifies outliers based on seasonal-trend decomposition and z-score calculations (pkgs.outlierdetection.seasonaltrend.OutlierSTLRZ)
Added new outlier detection class that flags values based on absolute limits that can be defined separately for daytime and nighttime (pkgs.outlierdetection.absolutelimits.AbsoluteLimitsDaytimeNighttime)
Added small functions to directly save (core.io.files.save_as_parquet) and load (core.io.files.load_parquet) parquet files. Parquet files offer fast loading and saving in combination with good compression. For more information about the Parquet format see here

Additions

Angle-of-attack: The angle-of-attack test can now be used during QC flag creation (pkgs.fluxprocessingchain.level2_qualityflags.FluxQualityFlagsLevel2.angle_of_attack_test)
Various smaller additions

Changes

Renamed class FluxQualityFlagsLevel2 to FluxQualityFlagsLevel2EddyPro because it is directly based on the EddyPro output (pkgs.fluxprocessingchain.level2_qualityflags.FluxQualityFlagsLevel2EddyPro)
Renamed class FluxStorageCorrectionSinglePoint to FluxStorageCorrectionSinglePointEddyPro ( pkgs.fluxprocessingchain.level31_storagecorrection.FluxStorageCorrectionSinglePointEddyPro)
Refactored creation of flux quality flags (pkgs.fluxprocessingchain.level2_qualityflags.FluxQualityFlagsLevel2EddyPro)
Missing storage correction terms are now gap-filled using random forest before the storage terms are added to the flux. For some records, the calculated flux was available but the storage term was missing, resulting in a missing storage-corrected flux (example: 97% of fluxes had storage term available, but for 3% it was missing). The gap-filling makes sure that each flux values has a corresponding storage term and thus more values are available for further processing. The gap-filling is done solely based on timestamp information, such as DOY and hour. (pkgs.fluxprocessingchain.level31_storagecorrection.FluxStorageCorrectionSinglePoint)
The outlier detection using z-scores for daytime and nighttime data uses latitude/longitude settings to calculate daytime/nighttime via pkgs.createvar.daynightflag.nighttime_flag_from_latlon. Before z-score calculation, the time resolution of the time series is now checked and assigned automatically. (pkgs.outlierdetection.zscore.zScoreDaytimeNighttime)
Removed pkgs.fluxprocessingchain.level32_outlierremoval.FluxOutlierRemovalLevel32 since flux outlier removal is now done in the generally applicable class StepwiseOutlierDetection (see new features)
Various smaller changes and refactorings

Environment

Updated poetry to newest version v1.5.1. The lock files have a new format since v1.3.0.
Created new lock file for poetry.
Added new package pyarrow.
Added new package pymannkendall (see GitHub) to analyze time series data for trends. Functions of this package are not yet implemented in diive.

Notebooks

Added new notebook for loading and saving parquet files in notebooks/Formats/LoadSaveParquetFile.ipynb
Flux processing chain: Added new notebook for flux post-processing in notebooks/FluxProcessingChain/FluxProcessingChain.ipynb.

v0.54.0 | 16 Jul 2023

New Features

Identify critical heat days for ecosytem flux NEE (net ecosystem exchange, based on air temperature and VPD (pkgs.flux.criticalheatdays.FluxCriticalHeatDaysP95)
Calculate z-aggregates in classes of x and y (pkgs.analyses.quantilexyaggz.QuantileXYAggZ)
Plot heatmap from pivoted dataframe, using x,y,z values (core.plotting.heatmap_xyz.HeatmapPivotXYZ)
Calculate stats for time series and store results in dataframe (core.dfun.stats.sstats)
New helper function to load and merge files of a specific filetype (core.io.files.loadfiles)

Additions

Added more parameters when formatting EddyPro fluxnet file for FLUXNET (pkgs.formats.fluxnet.FormatEddyProFluxnetFileForUpload)

Changes

Removed left-over code
Multiple smaller refactorings

Notebooks

Added new notebook for calculating VPD in notebooks/CalculateVariable/Calculate_VPD_from_TA_and_RH.ipynb
Added new notebook for calculating time series stats notebooks/Stats/TimeSeriesStats.ipynb
Added new notebook for formatting EddyPro output for upload to FLUXNET notebooks/Formats/FormatEddyProFluxnetFileForUpload.ipynb

v0.53.3 | 23 Apr 2023

Notebooks

Added new notebooks for reading data files (ICOS BM files)
Added additional output to other notebooks
Added new notebook section Workbench for practical use cases

Additions

New filetype configs/filetypes/ICOS_H1R_CSVZIP_1MIN.yml

v0.53.2 | 23 Apr 2023

Changes

Added more output for detecting frequency from timeseries index (core.times.times.DetectFrequency)
- The associated functions have been updated accordingly: core.times.times.timestamp_infer_freq_from_fullset, core.times.times.timestamp_infer_freq_progressively, core.times.times.timestamp_infer_freq_from_timedelta
- Added new notebook (notebooks/TimeStamps/Detect_time_resolution.ipynb )
- Added new unittest (tests/test_timestamps.py)

v0.53.1 | 18 Apr 2023

Changes

GapFinder now gives by default sorted output, i.e. the output dataframe shows start and end date for the largest gaps first (pkgs.analyses.gapfinder.GapFinder)

Notebooks

Added new notebook for finding gaps in time series in notebooks/Analyses/GapFinder.ipynb
Added new notebook for time functions in notebooks/TimeFunctions/times.ipynb

Other

New repository branch indev is used as developement branch from now on
Branch main will contain code from the most recent release

v0.53.0 | 17 Apr 2023

This update focuses on wind direction time series and adds the first example notebooks to diive. From now on, new example notebooks will be added regularly.

New features

Wind direction offset correction: Compare yearly wind direction histograms to reference, detect offset in comparison to reference and correct wind directions for offset per year (pkgs.corrections.winddiroffset.WindDirOffset)
Wind direction aggregation: Calculate mean etc. of wind direction in degrees (core.funcs.funcs.winddirection_agg_kanda)

Notebooks

Added new notebook for wind direction offset correction in notebooks/Corrections/WindDirectionOffset.ipynb
Added new notebok for reading ICOS BM files in notebooks/ReadFiles/Read_data_from_ICOS_BM_files.ipynb

Changes

Histogram analysis now accepts pandas Series as input (pkgs.analyses.histogram.Histogram)

Additions

Added unittests for reading (some) filetypes

v0.52.7 | 16 Mar 2023

Additions

The DataFileReader can now directly read zipped files (core.io.filereader.DataFileReader)
Interactive time series plot: (core.plotting.timeseries.TimeSeries.plot_interactive)
- added x- and y-axis to the plots
- new parameters width and height allow to control the size of the plot
- more controls such as undo/redo and zoom in/zoom out buttons were added
The filetypes defined in diive/configs/filetypes now accept the setting COMPRESSION: "zip". In essence, this allows to read zipped files directly.
New filetype ICOS_H2R_CSVZIP_10S

Changes

Compression in filetypes is now given as COMPRESSION: "None" for no compression, and COMPRESSION: "zip" for zipped CSV files.

v0.52.6 | 12 Mar 2023

Additions

LocalSD in StepwiseMeteoScreeningDb now accepts the parameter winsize to define the size of the rolling window (default None, in which case the window size is calculated automatically as 1/20 of the number of records). (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.flag_outliers_localsd_test)

Bugfix

Fixed bug: outlier test LocalSD did not consider user input n_sd (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.flag_outliers_localsd_test)

v0.52.4 and v0.52.5 | 10 Mar 2023

Bugfix

Fixed bug: during resampling, the info for the tag data_version was incorrectly stored in tag freq. (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.resample)

v0.52.3 | 10 Mar 2023

Additions

Added plotting library bokeh to dependencies

Changes

When combining data of different time resolutions, the data are now combined using .combine_first() instead of .concat() to avoid duplicates during merging. This should work reliably because data of the highest resolution are available first, and then lower resolution upsampled (backfilled) data are added, filling gaps in the high resolution data. Because gaps are filled, overlaps between the two resolutions are avoided. With .concat(), gaps were not filled, but timestamps were simply added as new records, and thus duplicates in the timestamp occurred. (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb._harmonize_timeresolution)
Updated dependencies to newest possible versions

v0.52.2 | 9 Mar 2023

Changes

Removed the packages jupyterlab and jupyter-bokeh from dependencies, because the latter caused issues when trying to install diive in a conda environment on a shared machine. Both dependencies are still listed in the pyproject.toml file as dev dependencies. It makes sense to keep both packages separate from diive because they are specifically for jupyter notebooks and not strictly related to diive functionality.

v0.52.1 | 7 Mar 2023

Additions

In StepwiseMeteoScreeningDb the current cleaned timeseries can now be plotted with showplot_current_cleaned.
Timeseries can now be plotted using the bokeh library. This plot are interactive and can be directly used in jupyter notebooks. (core.plotting.timeseries.TimeSeries)
Added new plotting package jupyter_bokeh for interactive plotting in Jupyter lab.
Added new plotting package seaborn.

Bugfixes

StepwiseMeteoScreeningDb now works on a copy of the input data to avoid unintended data overwrite of input.

v0.52.0 | 6 Mar 2023

New Features

Data formats: Added new package diive/pkgs/formats that assists in converting data outputs to formats required e.g. for data sharing with FLUXNET.
- Convert the EddyPro _fluxnet_ output file to the FLUXNET data format for data upload (data sharing). (pkgs.formats.fluxnet.ConvertEddyProFluxnetFileForUpload)
Insert timestamp column: Insert timestamp column that shows the START, END or MIDDLE time of the averaging interval (core.times.times.insert_timestamp)
Manual removal of data points: Flag manually defined data points as outliers. (pkgs.outlierdetection.manualremoval.ManualRemoval)

Additions

Added additional outlier detection algorithms to StepwiseMeteoScreeningDb (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb):

Added local outlier factor test, across all data (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.flag_outliers_lof_test)
Added local outlier factor test, separately for daytime and nighttime (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.flag_outliers_lof_dtnt_test)

v0.51.0 | 3 Mar 2023

Random uncertainty

Implemented random flux uncertainty calculation, based on Holliner and Richardson (2005) and Pastorello et al. (2020). Calculations also include a first estimate of the error propagation when summing up flux values to annual sums. See end of CHANGELOG for links to references. (pkgs.flux.uncertainty.RandomUncertaintyPAS20)

Additions

Added example data in diive/configs/exampledata, including functions to load the data.

Changes

In core.io.filereader, the following classes now also accept output_middle_timestamp (boolean with default True) as parameter: MultiDataFileReader, ReadFileType,DataFileReader. This allows to keep the original timestamp of the data.
Some minor plotting adjustments

v0.50.0 | 12 Feb 2023

StepwiseMeteoScreeningDb

Stepwise quality-screening of meteorological data, directly from the database

In this update, the stepwise meteoscreening directly from the database introduced in the previous update was further refined and extended, with additional outlier tests and corrections implemented. The stepwise meteoscreening allows to perform step-by-step quality tests on meteorological. A preview plot after running a test is shown and the user can decide if results are satisfactory or if the same test with different parameters should be re-run. Once results are satisfactory, the respective test flag is added to the data. After running the desired tests, an overall flag QCF is calculated from all individual tests.

In addition to the creation of quality flags, the stepwise screening allows to correct data for common issues. For example, short-wave radiation sensors often measure negative values during the night. These negative values are useful because they give info about the accuracy and precision of the sensor. In this case, values during the night should be zero. Instead of cutting off negative values, diive detects the nighttime offset for each day and then calculates a correction slope between individual days. This way, the daytime values are also corrected.

After quality-screening and corrections, data are resampled to 30MIN time resolution.

At the moment, the stepwise meteoscreening works for data downloaded from the InfluxDB database. The screening respects the database format (including tags) and prepares the screened, corrected and resampled data for direct database upload.

Due to its modular approach, the stepwise screening can be easily adjusted to work with any type of data files. This adjustment will be done in one of the next updates.

Changes

Renamed class MetScrDbMeasurementVars to StepwiseMeteoScreeningDb (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb)

Additions

Stepwise MeteoScreening: Added access to multiple methods for easy stepwise execution:
- Added local SD outlier test (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.flag_outliers_localsd_test)
- Added absolute limits outlier test (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.flag_outliers_abslim_test)
- Added correction to remove radiation zero offset (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.correction_remove_radiation_zero_offset)
- Added correction to remove relative humidity offset (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.correction_remove_relativehumidity_offset)
- Added correction to set values above a threshold to threshold (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.correction_setto_max_threshold)
- Added correction to set values below a threshold to threshold (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.correction_setto_min_threshold)
- Added comparison plot before/after QC and corrections (pkgs.qaqc.meteoscreening.StepwiseMeteoScreeningDb.showplot_resampled)

v0.49.0 | 10 Feb 2023

New Features

Stepwise MeteoScreening: (pkgs.qaqc.meteoscreening.MetScrDbMeasurementVars)
- Helper class to screen time series of meteo variables directly from the database. The class is optimized to work in Jupyter notebooks. Various outlier detection methods can be called on-demand. Outlier results are displayed and the user can accept the results and proceed, or repeat the step with adjusted method parameters. An unlimited amount of tests can be chained together. At the end of the screening, an overall flag is calculated from ALL single flags. The overall flag is then used to filter the time series.
- Variables: The class allows the simultaneous quality-screening of multiple variables from one single measurement, e.g., multiple air temperature variables.
- Resampling:Filtered time series are resampled to 30MIN time resolution.
- Database tags: Is optimized to work with the InfluxDB format of the ETH Grassland Sciences Group. The class can handle database tags and updates tags after data screening and resampling.
- Handling different time resolutions: One challenging aspect of the screening were the different time resolutions of the raw data. In some cases, the time resolution changed from e.g. 10MIN for older data to 1MIN for newer date. In cases of different time resolution, the lower resolution is upsampled to the higher resolution, the emerging gaps are back-filled with available data. Back-filling is used because the timestamp in the database always is TIMESTAMP_END, i.e., it gives the end of the averaging interval. The advantage of upsampling is that all outlier detection routines can be applied to the whole dataset. Since data are resampled to 30MIN after screening and since the TIMESTAMP_END is respected, the upsampling itself has no impact on resulting aggregates.

Changes

Generating the plot NEP penalty vs hours above threshold now requires a minimum of 2 bootstrap runs to calculate prediction intervals (pkgs.flux.nep_penalty.NEPpenalty.plot_critical_hours)

Bugfixes

Fixed bug in BinFitter, the parameter to set the number of predictions is now correctly named n_predictions. Similar n_bins_x.
Fixed typos in functions insert_aggregated_in_hires, SortingBinsMethod, FindOptimumRange and pkgs.analyses.optimumrange.FindOptimumRange._values_in_optimum_range and others.
Other typos

v0.48.0 | 1 Feb 2023

New Features

USTAR threshold: (pkgs.flux.ustarthreshold.UstarThresholdConstantScenarios)
- Calculates how many records of e.g. a flux variable are still available after the application of different USTAR thresholds. In essence, it gives an overview of the sensitivity of the variable to different thresholds.
Outlier detection, LOF across all data: (pkgs.outlierdetection.lof.LocalOutlierFactorAllData)
- Calculation of the local outlier factor across all data, i.e., no differentiation between daytime and nighttime data.
Outlier detection, increments: (pkgs.outlierdetection.incremental.zScoreIncremental)
- Based on the absolute change of on record in comparison to the previous record. These differences are stored as timeseries, the z-score is calculated and outliers are removed based on the observed differences. Works well with data that do not have a diel cycle, e.g. soil water content.

v0.47.0 | 28 Jan 2023

New Features

Outlier detection: LOF, local outlier factor**: (pkgs.outlierdetection.lof.LocalOutlierFactorDaytimeNighttime)
- Identify outliers based on the local outlier factor, done separately for daytime and nighttime data
Multiple z-score outlier detections:
- Simple outlier detection based on the z-score of observations, calculated from mean and std from the complete timeseries. (pkgs.outlierdetection.zscore.zScore)
- z-score outlier detection separately for daytime and nighttime data (pkgs.outlierdetection.zscore.zScoreDaytimeNighttime)
- Identify outliers based on the z-score of the interquartile range data (pkgs.outlierdetection.zscore.zScoreIQR)
Outlier detection: (pkgs.fluxprocessingchain.level32_outlierremoval.OutlierRemovalLevel32):
- Class that allows to apply multiple methods for outlier detection during as part of the flux processing chain

Changes

Flux Processing Chain:
- Worked on making the chain more accessible to users. The purpose of the modules in pkgs/fluxprocessingchain is to expose functionality to the user, i.e., they make functionality needed in the chain accessible to the user. This should be as easy as possible and this update further simplified this access. At the moment there are three modules in pkgs/fluxprocessingchain/: level2_qualityflags.py, level31_storagecorrection.py and level32_outlierremoval.py. An example for the chain is given in fluxprocessingchain.py.
QCF flag: (pkgs.qaqc.qcf.FlagQCF)
- Refactored code: the creation of overall quality flags QCF is now done using the same code for flux and meteo data. The general logic of the QCF calculation is that results from multiple quality checks that are stored as flags in the data are combined into one single quality flag.
Outlier Removal using STL:
- Module was renamed to pkgs.outlierdetection.seasonaltrend.OutlierSTLRIQRZ. It is not the most convenient name, I know, but it stands for Seasonal Trend decomposition using LOESS, based on Residual analysis of the InterQuartile Range using Z-scores
Search files can now search in subfolders of multiple base folders (core.io.filereader.search_files)

v0.46.0 | 23 Jan 2023

New Features

Outlier Removal using STL: (pkgs.outlierdetection.seasonaltrend.OutlierSTLIQR)
- Implemented first code to remove outliers using seasonal-srend decomposition using LOESS. This method divides a time series into seasonal, trend and residual components. diive uses the residuals to detect outliers based on z-score calculations.
Overall quality flag for meteo data: (pkgs.qaqc.qcf.MeteoQCF)
- Combines the results from multiple flags into one single flag
- Very similar to the calculation of the flux QCF flag

Changes

MeteoScreening: (diive/pkgs/qaqc/meteoscreening.py)
- Refactored most of the code relating to the quality-screening of meteo data
- Implemented the calculation of the overall quality flag QCF
- Two overview figures are now created at the end on the screening
- Flags for tests used during screening are now created using a base class (core.base.flagbase.FlagBase)
Flux Processing Chain: All modules relating to the Swiss FluxNet flux processing chain are now collected in the dedicated package fluxprocessingchain. Relevant modules were moved to this package, some renamed:
- pkgs.fluxprocessingchain.level2_qualityflags.QualityFlagsLevel2
- pkgs.fluxprocessingchain.level31_storagecorrection.StorageCorrectionSinglePoint
- pkgs.fluxprocessingchain.qcf.QCF
Reading YAML files: (core.io.filereader.ConfigFileReader)
- Only filetype configuration files are validated, i.e. checked if they follow the expected file structure. However, there can be other YAML files, such as the file pipes_meteo.yaml that defines the QA/QC steps for each meteo variable. For the moment, only the filetype files are validated and the validation is skipped for the pipes file.
Refactored calculation of nighttime flag from sun altitude: code is now vectorized and runs - unsurprisingly - much faster (pkgs.createvar.nighttime_latlon.nighttime_flag_from_latlon)
Some smaller changes relating to text output to the console

v0.45.0 | 13 Jan 2023

New Features

Flux storage correction: (pkgs.flux.storage.StorageCorrectionSinglePoint)
- Calculate storage-corrected fluxes
- Creates Level-3.1 in the flux processing chain
Overall quality flag: (pkgs.qaqc.qcf.QCF)
- Calculate overall quality flag from multiple individual flags

Changes

Flux quality-control: (pkgs.qaqc.fluxes.QualityFlagsLevel2)
- Flags now have the string _L2_ in their name to identify them as flags created during Level-2 calculations in the Swiss FluxNet flux processing chain.
- All flags can now be returned to the main data
Renamed pkgs.qaqc.fluxes.FluxQualityControlFlag to pkgs.qaqc.fluxes.QualityFlagsLevel2

v0.44.1 | 11 Jan 2023

Changes

Flux quality-control: (pkgs.qaqc.fluxes.FluxQualityControlFlag)
- Added heatmap plots for before/after QC comparison
- Improved code for calculation of overall flag QCF
- Improved console output

v0.44.0 | 9 Jan 2023

New Features

Flux quality-control: (pkgs.qaqc.fluxes.FluxQualityControlFlag)
- First implementation of quality control of ecosystem fluxes. Generates one overall flag (QCF=quality control flag) from multiple quality test results in EddyPro's fluxnet output file. The resulting QCF is Level-2 in the Swiss FluxNet processing chain, described here. QCF is mostly based on the ICOS methodology, described by Sabbatini et al. (2018).
Histogram: (pkgs.analyses.histogram.Histogram)
- Calculates histogram from time series, identifies peak distribution
Percentiles: (pkgs.analyses.quantiles.percentiles)
- Calculates percentiles (0-100) for a time series
Scatter: Implemented first version of core.plotting.scatter.Scatter, which will be used for scatter plots in the future

Changes

Critical days: (pkgs.flux.criticaldays.CriticalDays)
- Renamed Variables, now using Dcrit (instead of CRD) and nDcrit (instead of nCRD)
NEP Penalty: (pkgs.flux.nep_penalty.NEPpenalty)
- Code was refactored to work with NEP (net ecosystem productivity) instead of NEE (net ecosystem exchange)
- CO2 penalty was renamed to the more descriptive NEP penalty
Sanitize column names: implemented in core.io.filereader.ColumnNamesSanitizer Column names are now checked for duplicates. Found duplicates are renamed by adding a suffix to the column name. Example: co2_mean and co2_mean are renamed to co2_mean.1 and co2_mean.2. This check is now implemented during the reading of the data file in core.io.filereader.DataFileReader.
Configuration files: When reading filetype configuration files in core.io.filereader.ConfigFileReader, the resulting dictionary that contains all configurations is now validated. The validation makes sure the parameters for .read_csv() are in the proper format.
Updated all dependencies to their newest (possible) version

Additions

Added support for filetype EDDYPRO_FLUXNET_30MIN (configs/filetypes/EDDYPRO_FLUXNET_30MIN.yml)

v0.43.0 | 8 Dec 2022

New Features

Frequency groups detection: Data in long-term datasets are often characterized by changing time resolutions at which data were recorded. core.times.times.detect_freq_groups detects changing time resolutions in datasets and adds a group identifier in a new column that gives info about the detected time resolution in seconds, e.g., 600 for 10MIN data records. This info allows to address and process the different time resolutions separately during later processing, which is needed e.g. during data quality-screening and resampling.
Outlier removal using z-score: First version of pkgs.outlierdetection.zscore.zscoreiqr Removes outliers based on the z-score of interquartile range data. Data are divided into 8 groups based on quantiles. The z-score is calculated for each data point in the respective group and based on the mean and SD of the respective group. The z-score threshold to identify outlier data is calculated as the max of z-scores found in IQR data multiplied by factor. z-scores above the threshold are marked as outliers.
Outlier removal using local standard deviation: First version of pkgs.outlierdetection.local3sd.localsd Calculates mean and SD in a rolling window and marks data points outside a specified range.

Additions

MeteoScreening: Added the new parameter resampling_aggregation in the meteoscreening setting diive/pkgs/qaqc/pipes_meteo.yaml. For example, TA needs mean, PRECIP needs sum.

Changes

MeteoScreening: pkgs.qaqc.meteoscreening.MeteoScreeningFromDatabaseSingleVar Refactored the merging of quality-controlled 30MIN data when more than one raw data time resolution is involved.
Resampling: core.times.resampling.resample_series_to_30MIN The minimum required values for resampling is 1. However, this is only relevant for lower resolution data e.g. 10MIN and 30MIN, because for higher resolutions the calculated value for minimum required values yields values > 1 anyway. In addition, if data are already in 30MIN resolution, they are still going through the resampling processing although it would not be necessary, because the processing includes other steps relevant to all data resolutions, such as the change of the timestamp from TIMESTAMP_MIDDLE to TIMESTAMP_END.

Bugs

Removed display bug when showing data after high-res meteoscreening in heatmap. Plot showed original instead of meteoscreened data

v0.42.0 | 27 Nov 2022

New Features

Decoupling: Added first version of decoupling code (pkgs.analyses.decoupling.SortingBinsMethod). This allows the investigation of binned aggregates of a variable z in binned classes of x and y. For example: show mean GPP (y) in 5 classes of VPD (x), separate for 10 classes of air temperature (z).

Time series plot: core.plotting.timeseries.TimeSeries plots a simple time series. This will be the default method to plot time series.

Changes

Critical days: Several changes in pkgs.flux.criticaldays.CriticalDays:
- By default, daily aggregates are now calculated from 00:00 to 00:00 (before it was 7:00 to 07:00).
- Added parameters for specifying the labels for the x- and y-axis in output figure
- Added parameter for setting dpi of output figure
- Some smaller adjustments
- pkgs.flux.co2penalty.CO2Penalty.plot_critical_hours: 95% predicion bands are now smoothed (rolling mean)
CO2 penalty: (since v0.44.0 renamed to NEP penalty)
- Some code refactoring in pkgs.flux.co2penalty.CO2Penalty, e.g. relating to plot appearances

v0.41.0 | 5 Oct 2022

BinFitterBTS

pkgs.fits.binfitter.BinFitterBTS fits a quadratic or linear equation to data.
This is a refactored version of the previous BinFitter to allow more options.
Implemented pkgs.fits.binfitter.PlotBinFitterBTS for plotting BinFitterBTS results
PlotBinFitterBTS now allows plotting of confidence intervals for the upper and lower prediction bands
The updated BinFitterBTS is now implemented in pkgs.flux.criticaldays.CriticalDays

Example of updated `BinFitterBTS` as used in `CriticalDays`

It is now possible to show confidence intervals for the upper and lower prediction bands.

Other

core.plotting.heatmap_datetime.HeatmapDateTime now accepts figsize
When reading a file using core.io.filereader.ReadFileType, the index column is now parsed to a temporarily named column. After reading the file data, the temporary column name is renamed to the correct name. This was implemented to avoid duplicate issues regarding the index column when parsing the file, because a data column with the same name as the index column might be in the dataset.

Bugfixes

Fixed bug in pkgs.gapfilling.randomforest_ts.RandomForestTS: fallback option for gap-filling was never used and some gaps would remain in the time series.

v0.40.0 | 23 Sep 2022

CO2 Penalty

New analysis: pkgs.flux.co2penalty.CO2Penalty calculates the CO2 penalty as the difference between the observed co2 flux and the potential co2 flux modelled from less extreme environmental conditions.

VPD Calculation

New calculation: pkgs.createvar.vpd.calc_vpd_from_ta_rh calculates vapor pressure deficit (VPD) from air temperature and relative humidity

Fixes

Fixed: core.plotting.cumulative.CumulativeYear now shows zero line if needed
Fixed: core.plotting.cumulative.CumulativeYear now shows proper axis labels

v0.39.0 | 4 Sep 2022

Critical Days

New analysis: pkgs.flux.criticaldays.CriticalDays detects days in y that are above a detected x threshold. At the moment, this is implemented to work with half-hourly flux data as input and was tested with VPD (x) and NEE (y). In the example below critical days are defined as the VPD daily max value where the daily sum of NEE (in g CO2 m-2 d-1) becomes positive (i.e., emission of CO2 from the ecosystem to the atmosphere).

v0.38.0 | 3 Sep 2022

Optimum Range Detection

New analysis: pkgs.analyses.optimumrange.FindOptimumRange finds the optimum for a variable in binned other variable. This is useful for e.g. detecting the VPD range where CO2 uptake was highest (=most negative).

v0.37.0 | 2 Sep 2022

Cumulative and Anomaly Plots

New plot: core.plotting.cumulative.CumulativeYear plots cumulative sums per year
New plot: core.plotting.bar.LongtermAnomaliesYear plots yearly anomalies in relation to a reference period
Refactored various code bits for plotting

v0.36.0 | 27 Aug 2022

Random Forest Update

Refactored code for pkgs/gapfilling/randomforest_ts.py
- Implemented lagged variants of variables
- Implemented long-term gap-filling, where the model to gap-fill a specific year is built from the respective year and its neighboring years
- Implemented feature reduction using sklearn's RFECV
- Implemented TimeSeriesSplit used as the cross-validation splitting strategy during feature reduction
Implemented TimestampSanitizer also when reading from file with core.io.filereader.DataFileReader
Removed old code in .core.dfun.files and moved files logistics to .core.io.files instead
Implemented saving and loading Python pickles in .core.io.files

v0.35.0 | 19 Aug 2022

Meteoscreening PA, RH

Added function pkgs.corrections.offsetcorrection.remove_relativehumidity_offset to correct humidity measurements for values > 100%

Other

Added first code for outlier detection via seasonal trends in pkgs/outlierdetection/seasonaltrend.py
Prepared pkgs/analyses/optimumrange.py for future updates

v0.34.0 | 29 Jul 2022

MeteoScreening Radiation

MeteoScreening

Implemented corrections and quality screening for radiation data in pkgs.qaqc.meteoscreening

Corrections

Additions to pkgs.corrections:

Added function .offsetcorrection.remove_radiation_zero_offset to correct radiation data for nighttime offsets
Added function .setto_threshold.setto_threshold to set values above or below a specfied threshold value to the threshold.

Plotting

Added function core.plotting.plotfuncs.quickplot for quickly plotting pandas Series and DataFrame data

Resampling

Implemented TimeSanitizer in core.times.resampling.resample_series_to_30MIN

Other

Added decorator class core.utils.prints.ConsoleOutputDecorator, a wrapper to execute functions with additional info that is output to the console.

v0.33.0 | 26 Jul 2022

MeteoScreening Preparations

Added new class core.times.times.TimestampSanitizer
- Class that handles timestamp checks and fixes, such as the creation of a continuous timestamp without date gaps.
Added pkgs.createvar.nighttime_latlon.nighttime_flag_from_latlon
- Function for the calculation of a nighttime flag (1=nighttime) from latitude and longitude coordinates of a specific location.
Added core.plotting.heatmap_datetime.HeatmapDateTime
- Class to generate a heatmap plot from timeseries data.

v0.32.0 | 22 Jul 2022

MeteoScreening Air Temperature

MeteoScreening uses a general settings file pipes_meteo.yaml that contains info how specific measurements should be screened. Such measurements group similar variables together, e.g. different air temperatures are measurement TA.
Additions to module pkgs.qaqc.meteoscreening:

Added class ScreenVar
- Performs quality screening of air temperature TA.
- As first check, I implemented outlier detection via the newly added package ThymeBoost, along with checks for absolute limits.
- Screening applies the checks defined in the file pipes_meteo.yaml for the respective measurement, e.g. TA for air temperature.
- The screening outputs a separate dataframe that contains QCF flags for each check.
- The checks do not change the original time series. Instead, only the flags are generated.
- Screening routines for more variables will be added over the next updates.
Added class MeteoScreeningFromDatabaseSingleVar
- Performs quality screening and resampling to 30MIN of variables downloaded from the database.
- It uses the detailed data when downloading data from the database using dbc-influxdb.
- The detailed data contains the measurement of the variable, along with multiple tags that describe the data. The tags are needed for storage in the database.
- After quality screening of the original high-resolution data, flagged values are removed and then data are resampled.
- It also handles the issue that data downloaded for a specific variable can have different time resolution over the years, although I still need to test this.
- After screening and resampling, data are in a format that can be directly uploaded to the database using dbc-influxdb.
Added class MeteoScreeningFromDatabaseMultipleVars
- Wrapper where multiple variables can be screened in one run.
- This should also work in combination of different measurements. For example, screening radiation and temperature data in one run.

Outlier Detection

Additions to pkgs.outlierdetection:

Added module thymeboost
Added module absolute_limits

v0.31.0 | 4 Apr 2022

Carbon cost

GENERAL

This version introduces the code for calculating carbon cost and critical heat days.

NEW PACKAGES

Added new package for flux-specific calculations: diive.pkgs.flux

NEW MODULES

Added new module for calculating carbon cost: diive.pkgs.flux.carboncost
Added new module for calculating critical heat days: diive.pkgs.flux.criticalheatdays

CHANGES & ADDITIONS

None

BUGFIXES

None

v0.30.0 | 15 Feb 2022

Starting diive library

GENERAL

The diive library contains packages and modules that aim to facilitate working with time series data, in particular ecosystem data.

Previous versions of diive included a GUI. The GUI component will from now on be developed separately as diive-gui, which makes use of the diive library.

Previous versions of diive (up to v0.22.0) can be found in the separate repo diive-legacy.

This initial version of the diive library contains several first versions of packages that will be extended with the next versions.

Notable introduction in this version is the package echires for working with high-resolution eddy covariance data. This package contains the module fluxdetectionlimit, which allows the calculation of the flux detection limit following Langford et al. (2015).

NEW PACKAGES

Added common: Common functionality, e.g. reading data files
Added pkgs > analyses: General analyses
Added pkgs > corrections: Calculate corrections for existing variables
Added pkgs > createflag: Create flag variables, e.g. for quality checks
Added pkgs > createvar: Calculate new variables, e.g. potential radiation
Added pkgs > echires: Calculations for eddy covariance high-resolution data, e.g. 20Hz data
Added pkgs > gapfilling: Gap-filling routines
Added pkgs > outlierdetection: Outlier detection
Added pkgs > qaqc: Quality screening for timeseries variables

NEW MODULES

Added optimumrange in pkgs > analyses
Added gapfinder in pkgs > analyses
Added offsetcorrection in pkgs > corrections
Added setto_threshold in pkgs > corrections
Added outsiderange in pkgs > createflag
Added potentialradiation in pkgs > createvar
Added fluxdetectionlimit in pkgs > echires
Added interpolate in pkgs > gapfilling
Added hampel in pkgs > outlierdetection
Added meteoscreening in pkgs > qaqc

CHANGES & ADDITIONS

None

BUGFIXES

None

REFERENCES

Hollinger, D. Y., & Richardson, A. D. (2005). Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiology, 25(7), 873–885. https://doi.org/10.1093/treephys/25.7.873
Langford, B., Acton, W., Ammann, C., Valach, A., & Nemitz, E. (2015). Eddy-covariance data with low signal-to-noise ratio: Time-lag determination, uncertainties and limit of detection. Atmospheric Measurement Techniques, 8(10), 4197–4213. https://doi.org/10.5194/amt-8-4197-2015
Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., & Yakir, D. (2006). Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: Algorithms and uncertainty estimation. Biogeosciences, 3(4), 571–583. https://doi.org/10.5194/bg-3-571-2006
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

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v0.84.2 | 8 Nov 2024

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v0.84.1 | 8 Nov 2024

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v0.84.0 | 7 Nov 2024

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v0.83.2 | 25 Oct 2024

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v0.83.1 | 23 Oct 2024

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v0.83.0 | 4 Oct 2024

MDS gap-filling

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v0.82.1 | 22 Sep 2024

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v0.82.0 | 19 Sep 2024

Long-term gap-filling

Example

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v0.81.0 | 11 Sep 2024

Expanding Flux Processing Capabilities

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v0.80.0 | 28 Aug 2024

Additions

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v0.79.1 | 26 Aug 2024

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v0.79.0 | 22 Aug 2024

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v0.78.1.1 | 19 Aug 2024

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v0.78.1 | 19 Aug 2024

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