diff --git a/.gitignore b/.gitignore index 46a2c6e..06b2ce1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ # Custom +.idea/ dist/ _archive/ _raw_photoshop/ diff --git a/.idea/DYCO_Dynamic_Lag_Compensation.iml b/.idea/DYCO_Dynamic_Lag_Compensation.iml index 662eebd..14770dc 100644 --- a/.idea/DYCO_Dynamic_Lag_Compensation.iml +++ b/.idea/DYCO_Dynamic_Lag_Compensation.iml @@ -12,7 +12,7 @@ - + diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml index c2836d8..b8bca9a 100644 --- a/.idea/inspectionProfiles/Project_Default.xml +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -99,5 +99,10 @@ + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml index 095ec02..8569286 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,7 +1,10 @@ + + - + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 9d7919e..3d100e6 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -1,7 +1,19 @@ + + - + + + + + + + + + + + + + { + "associatedIndex": 8 +} - - - - - - - - - - + + + + - - + + + - + + + + + - - + + + + - - + + + + + + @@ -371,6 +410,9 @@ + + + @@ -389,45 +431,47 @@ - - - - - - - - - - - - - - - - - - - - - + - - - - - + + + + + - - - - - + + + + + + + + + + + + + - - + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..ebecf2c --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,18 @@ +# CHANGELOG + +## v1.2.0 | 6 Mar 2024 + +- Refactored code to work with newest package versions +- Several small bugfixes +- Now using `poetry` for dependency management +- Now using Python `3.9.18` +- All dependencies were updated to their newest possible versions +- Added example for using the class `DynamicLagCompensation` to run `dyco` directly from + code (`example.example_kwargs.example`) + +## v1.1.2 | 16 Jun 2021 + +### Release version for publication in JOSS + +- JOSS: https://joss.theoj.org/ +- DYCO open review: openjournals/joss-reviews#2575 diff --git a/LICENSE b/LICENSE index be393bb..4136bd0 100644 --- a/LICENSE +++ b/LICENSE @@ -632,7 +632,7 @@ state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -645,14 +645,14 @@ the "copyright" line and a pointer to where the full notice is found. GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: - DYCO Dynamic Lag Compensation Copyright (C) 2020 holukas + DYCO Dynamic Lag Compensation Copyright (C) 2020-2024 Lukas Hörtnagl This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. @@ -664,7 +664,7 @@ might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see -. +. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you diff --git a/README.md b/README.md index 7dbf05c..f654fd5 100644 --- a/README.md +++ b/README.md @@ -4,32 +4,54 @@ [![DOI](https://zenodo.org/badge/311300577.svg)](https://zenodo.org/badge/latestdoi/311300577) - - # DYCO - Dynamic Lag Compensation -The lag detection between the turbulent departures of measured wind and the scalar of interest is a central step in the calculation of eddy covariance (EC) ecosystem fluxes. In case the covariance maximization fails to detect a clear peak in the covariance function between the wind and the scalar, current flux calculation software can apply a constant default (nominal) time lag to the respective scalar. However, both the detection of clear covariance peaks in a pre-defined time window and the definition of a reliable default time lag is challenging for compounds which are often characterized by low SNR, such as N2O. In addition, the application of one static default time lag may produce inaccurate results in case systematic time shifts are present in the raw data. +The lag detection between the turbulent departures of measured wind and the scalar of interest is a central step in the +calculation of eddy covariance (EC) ecosystem fluxes. In case the covariance maximization fails to detect a clear peak +in the covariance function between the wind and the scalar, current flux calculation software can apply a constant +default (nominal) time lag to the respective scalar. However, both the detection of clear covariance peaks in a +pre-defined time window and the definition of a reliable default time lag is challenging for compounds which are often +characterized by low SNR, such as N2O. In addition, the application of one static default time lag may +produce inaccurate results in case systematic time shifts are present in the raw data. - `DYCO` is meant to assist current flux processing software in the calculation of fluxes for compounds with low SNR. In the context of current flux processing schemes, the unique features offered as part of the `DYCO` package include: +`DYCO` is meant to assist current flux processing software in the calculation of fluxes for compounds with low SNR. In +the context of current flux processing schemes, the unique features offered as part of the `DYCO` package include: -- (i) the dynamic application of progressively smaller time windows during lag search for a *reference* compound (e.g. CO2), +- (i) the dynamic application of progressively smaller time windows during lag search for a *reference* compound (e.g. + CO2), - (ii) the calculation of default time lags on a daily scale for the *reference* compound, - (iii) the application of daily default *reference* time lags to one or more *target* compounds (e.g. N2O) - (iv) the dynamic normalization of time lags across raw data files, -- (v) the automatic correction of systematic time shifts in *target* raw data time series, e.g. due to failed synchronization of instrument clocks, and -- (vi) the application of instantaneous *reference* time lags, calculated from lag-normalized files, to one or more *target* compounds. - -As `DYCO` aims to complement current flux processing schemes, final lag-removed files are produced that can be directly used in current flux calculation software. - +- (v) the automatic correction of systematic time shifts in *target* raw data time series, e.g. due to failed + synchronization of instrument clocks, and +- (vi) the application of instantaneous *reference* time lags, calculated from lag-normalized files, to one or more + *target* compounds. +As `DYCO` aims to complement current flux processing schemes, final lag-removed files are produced that can be directly +used in current flux calculation software. ## Scientific background -In ecosystem research, the EC method is widely used to quantify the biosphere-atmosphere exchange of greenhouse gases (GHGs) and energy (Aubinet et al., 2012; Baldocchi et al., 1988). The raw ecosystem flux (i.e. net exchange) is calculated by the covariance between the turbulent vertical wind component measured by a sonic anemometer and the entity of interest, e.g. CO2, measured by a gas analyzer. Due to the application of two different instruments, wind and gas are not recorded at exactly the same time, resulting in a time lag between the two time series. For the calculation of ecosystem fluxes this time delay has to be quantified and corrected for, otherwise fluxes are systematically biased. Time lags for each averaging interval can be estimated by finding the maximum absolute covariance between the two turbulent time series at different time steps in a pre-defined time window of physically possible time-lags (e.g., McMillen, 1988; Moncrieff et al., 1997). Lag detection works well when processing fluxes for compounds with high signal-to-noise ratio (SNR), which is typically the case for e.g. CO2. In contrast, for compounds with low SNR (e.g., N2O, CH4) the cross-covariance function with the turbulent wind component yields noisier results and calculated fluxes are biased towards larger absolute flux values (Langford et al., 2015), which in turn renders the accurate calculation of yearly ecosystem GHG budgets more difficult and results may be inaccurate. - -One suggestion to adequately calculate fluxes for compounds with low SNR is to first calculate the time lag for a *reference* compound with high SNR (e.g. CO2) and then apply the same time lag to the *target* compound of interest (e.g. N2O), with both compounds being recorded by the same analyzer (Nemitz et al., 2018). `DYCO` follows up on this suggestion by facilitating the dynamic lag-detection between the turbulent wind data and a *reference* compound and the subsequent application of found *reference* time lags to one or more *target* compounds. - - +In ecosystem research, the EC method is widely used to quantify the biosphere-atmosphere exchange of greenhouse gases ( +GHGs) and energy (Aubinet et al., 2012; Baldocchi et al., 1988). The raw ecosystem flux (i.e. net exchange) is +calculated by the covariance between the turbulent vertical wind component measured by a sonic anemometer and the entity +of interest, e.g. CO2, measured by a gas analyzer. Due to the application of two different instruments, wind +and gas are not recorded at exactly the same time, resulting in a time lag between the two time series. For the +calculation of ecosystem fluxes this time delay has to be quantified and corrected for, otherwise fluxes are +systematically biased. Time lags for each averaging interval can be estimated by finding the maximum absolute covariance +between the two turbulent time series at different time steps in a pre-defined time window of physically possible +time-lags (e.g., McMillen, 1988; Moncrieff et al., 1997). Lag detection works well when processing fluxes for compounds +with high signal-to-noise ratio (SNR), which is typically the case for e.g. CO2. In contrast, for compounds +with low SNR (e.g., N2O, CH4) the cross-covariance function with the turbulent wind component +yields noisier results and calculated fluxes are biased towards larger absolute flux values (Langford et al., 2015), +which in turn renders the accurate calculation of yearly ecosystem GHG budgets more difficult and results may be +inaccurate. + +One suggestion to adequately calculate fluxes for compounds with low SNR is to first calculate the time lag for a +*reference* compound with high SNR (e.g. CO2) and then apply the same time lag to the *target* compound of +interest (e.g. N2O), with both compounds being recorded by the same analyzer (Nemitz et al., 2018). `DYCO` +follows up on this suggestion by facilitating the dynamic lag-detection between the turbulent wind data and a +*reference* compound and the subsequent application of found *reference* time lags to one or more *target* compounds. ## Processing chain @@ -39,15 +61,32 @@ One suggestion to adequately calculate fluxes for compounds with low SNR is to f `DYCO` uses eddy covariance raw data files as input and produces lag-compensated raw data files as output. -The full `DYCO` processing chain comprises four phases and several iterations during which *reference* lags are refined iteratively in progressively smaller search windows (Figure 1). Generally, the *reference* lag search is facilitated by prior normalization of default (nominal) time lags across files. This is achieved by compensating *reference* and *target* time series data for daily default *reference* lags, calculated from high-quality *reference* lags available around the respective date (Figure 2). Due to this normalization, *reference* lags fall into a specific, pre-defined and therefore known time range, which in turn allows the application of increasingly narrower time windows during lag search. This approach has the advantage that *reference* lags can be calculated from a *reference* signal that shows clear peaks in the cross-covariance analysis with the wind data and thus yields unambiguous time lags due to its high SNR. In case the lag search failed to detect a clear time delay for the *reference* variable (e.g. during the night), the respective daily default *reference* lag is used instead. *Reference* lags can then be used to compensate *target* variables with low SNR for detected *reference* time delays. - -A description of the different Phases along with output examples can be found in the Wiki: [Processing Chain](https://github.com/holukas/dyco/wiki/Processing-Chain). +The full `DYCO` processing chain comprises four phases and several iterations during which *reference* lags are refined +iteratively in progressively smaller search windows (Figure 1). Generally, the *reference* lag search is facilitated by +prior normalization of default (nominal) time lags across files. This is achieved by compensating *reference* and +*target* time series data for daily default *reference* lags, calculated from high-quality *reference* lags available +around the respective date (Figure 2). Due to this normalization, *reference* lags fall into a specific, pre-defined and +therefore known time range, which in turn allows the application of increasingly narrower time windows during lag +search. This approach has the advantage that *reference* lags can be calculated from a *reference* signal that shows +clear peaks in the cross-covariance analysis with the wind data and thus yields unambiguous time lags due to its high +SNR. In case the lag search failed to detect a clear time delay for the *reference* variable (e.g. during the night), +the respective daily default *reference* lag is used instead. *Reference* lags can then be used to compensate *target* +variables with low SNR for detected *reference* time delays. + +A description of the different Phases along with output examples can be found in the +Wiki: [Processing Chain](https://github.com/holukas/dyco/wiki/Processing-Chain). ![Normalization example](images/fig_PHASE-1_ITERATION-3_TIMESERIES-PLOT_segment_lag_times_iteration.png) -**Figure 2**. *Example showing the normalization of default reference time lags across files. Shown are found instantaneous time lags (red) between turbulent wind data and turbulent CO2 mixing ratios, calculated daily reference default lags (yellow bars), normalization correction (blue arrows) and the daily default reference lag after normalization (green bar). Negative lag values mean that the CO2 signal was lagged behind the wind data, e.g. -400 means that the instantaneous CO2 records arrived 400 records (corresponds to 20s in this example) later at the analyzer than the wind data. Daily default reference lags were calculated as the 3-day median time lag from a selection of high-quality time lags, i.e. when cross-covariance analyses yielded a clear covariance peak. The normalization correction is applied dynamically to shift the CO2 data so that the default time lag is found close to zero across files. Note the systematic shift in time lags starting after 27 Oct 2016.* - - +**Figure 2**. *Example showing the normalization of default reference time lags across files. Shown are found +instantaneous time lags (red) between turbulent wind data and turbulent CO2 mixing ratios, calculated daily +reference default lags (yellow bars), normalization correction (blue arrows) and the daily default reference lag after +normalization (green bar). Negative lag values mean that the CO2 signal was lagged behind the wind data, e.g. +-400 means that the instantaneous CO2 records arrived 400 records (corresponds to 20s in this example) later +at the analyzer than the wind data. Daily default reference lags were calculated as the 3-day median time lag from a +selection of high-quality time lags, i.e. when cross-covariance analyses yielded a clear covariance peak. The +normalization correction is applied dynamically to shift the CO2 data so that the default time lag is found +close to zero across files. Note the systematic shift in time lags starting after 27 Oct 2016.* ## Installation @@ -55,72 +94,110 @@ A description of the different Phases along with output examples can be found in `pip install dyco` - - ## Usage `DYCO` is run from the command line interface (CLI). `usage: dyco.py [-h] [-i INDIR] [-o OUTDIR] [-fnd FILENAMEDATEFORMAT]` - `[-fnp FILENAMEPATTERN] [-flim LIMITNUMFILES] [-fgr FILEGENRES]` - `[-fdur FILEDURATION] [-dtf DATATIMESTAMPFORMAT]` - `[-dres DATANOMINALTIMERES] [-lss LSSEGMENTDURATION]` - `[-lsw LSWINSIZE] [-lsi LSNUMITER] [-lsf {0,1}]` - `[-lsp LSPERCTHRES] [-lt TARGETLAG] [-del {0,1}]` - `var_reference var_lagged var_target [var_target ...]` +`[-fnp FILENAMEPATTERN] [-flim LIMITNUMFILES] [-fgr FILEGENRES]` +`[-fdur FILEDURATION] [-dtf DATATIMESTAMPFORMAT]` +`[-dres DATANOMINALTIMERES] [-lss LSSEGMENTDURATION]` +`[-lsw LSWINSIZE] [-lsi LSNUMITER] [-lsf {0,1}]` +`[-lsp LSPERCTHRES] [-lt TARGETLAG] [-del {0,1}]` +`var_reference var_lagged var_target [var_target ...]` - Example usage with full arguments can be found here: [Example](https://github.com/holukas/dyco/wiki/Example) -- For an overview of arguments see here: [Usage](https://github.com/holukas/dyco/wiki/Usage) -- `DYCO` creates a range of output folders which are described here: [Results Output Folders](https://github.com/holukas/dyco/wiki/Results-Output-Folders) - - +- For an overview of arguments see here: [Usage](https://github.com/holukas/dyco/wiki/Usage) +- `DYCO` creates a range of output folders which are described + here: [Results Output Folders](https://github.com/holukas/dyco/wiki/Results-Output-Folders) ## Documentation Please refer to the [Wiki](https://github.com/holukas/dyco/wiki) for documentation and additional examples. - - ## Real-world examples -The [ICOS](https://www.icos-cp.eu/) Class 1 site [Davos](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/) (CH-Dav), a subalpine forest ecosystem station in the east of Switzerland, provides one of the longest continuous time series (24 years and running) of ecosystem fluxes globally. Since 2016, measurements of the strong GHG N2O are recorded by a closed-path gas analyzer that also records CO2. To calculate fluxes using the EC method, wind data from the sonic anemometer is combined with instantaneous gas measurements from the gas analyzer. However, the air sampled by the gas analyzer needs a certain amount of time to travel from the tube inlet to the measurement cell in the analyzer and is thus lagged behind the wind signal. The lag between the two signals needs to be compensated for by detecting and then removing the time lag at which the cross-covariance between the turbulent wind and the turbulent gas signal reaches the maximum absolute value. This works generally well when using CO2 (high SNR) but is challenging for N2O (low SNR). Using covariance maximization to search for the lag between wind and N2O mostly fails to accurately detect time lags between the two variables (noisy cross-correlation function), resulting in relatively noisy fluxes. However, since N2O has similar adsorption / desorption characteristics as CO2 it is valid to assume that both compounds need approx. the same time to travel through the tube to the analyzer, i.e. the time lag for both compounds in relation to the wind is similar. Therefore, `DYCO` can be applied (i) to calculate time lags across files for CO2 (*reference* compound), and then (ii) to remove found CO2 time delays from the N2O signal (*target* compound). The lag-compensated files produced by `DYCO` can then be used to calculate N2O fluxes. Since `DYCO` normalizes time lags across files and compensated the N2O signal for instantaneous CO2 lags, the *true* lag between wind and N2O can be found close to zero, which in turn facilitates the application of a small time window for the final lag search during flux calculations. - -Another application example are managed grasslands where the biosphere-atmosphere exchange of N2O is often characterized by sporadic high-emission events (e.g., Hörtnagl et al., 2018; Merbold et al., 2014). While high N2O quantities can be emitted during and after management events such as fertilizer application and ploughing, fluxes in between those events typically remain low and often below the limit-of-detection of the applied analyzer. In this case, calculating N2O fluxes works well during the high-emission periods (high SNR) but is challenging during the rest of the year (low SNR). Here, `DYCO` can be used to first calculate time lags for a *reference* gas measured in the same analyzer (e.g. CO2, CO, CH4) and then remove *reference* time lags from the N2O data. - - +The [ICOS](https://www.icos-cp.eu/) Class 1 +site [Davos](https://www.swissfluxnet.ethz.ch/index.php/sites/ch-dav-davos/site-info-ch-dav/) (CH-Dav), a subalpine +forest ecosystem station in the east of Switzerland, provides one of the longest continuous time series (24 years and +running) of ecosystem fluxes globally. Since 2016, measurements of the strong GHG N2O are recorded by a +closed-path gas analyzer that also records CO2. To calculate fluxes using the EC method, wind data from the +sonic anemometer is combined with instantaneous gas measurements from the gas analyzer. However, the air sampled by the +gas analyzer needs a certain amount of time to travel from the tube inlet to the measurement cell in the analyzer and is +thus lagged behind the wind signal. The lag between the two signals needs to be compensated for by detecting and then +removing the time lag at which the cross-covariance between the turbulent wind and the turbulent gas signal reaches the +maximum absolute value. This works generally well when using CO2 (high SNR) but is challenging for N +2O (low SNR). Using covariance maximization to search for the lag between wind and N2O mostly fails to +accurately detect time lags between the two variables (noisy cross-correlation function), resulting in relatively noisy +fluxes. However, since N2O has similar adsorption / desorption characteristics as CO2 it is valid +to assume that both compounds need approx. the same time to travel through the tube to the analyzer, i.e. the time lag +for both compounds in relation to the wind is similar. Therefore, `DYCO` can be applied (i) to calculate time lags +across files for CO2 (*reference* compound), and then (ii) to remove found CO2 time delays from +the N2O signal (*target* compound). The lag-compensated files produced by `DYCO` can then be used to +calculate N2O fluxes. Since `DYCO` normalizes time lags across files and compensated the N2O +signal for instantaneous CO2 lags, the *true* lag between wind and N2O can be found close to zero, +which in turn facilitates the application of a small time window for the final lag search during flux calculations. + +Another application example are managed grasslands where the biosphere-atmosphere exchange of N2O is often +characterized by sporadic high-emission events (e.g., Hörtnagl et al., 2018; Merbold et al., 2014). While high N +2O quantities can be emitted during and after management events such as fertilizer application and ploughing, +fluxes in between those events typically remain low and often below the limit-of-detection of the applied analyzer. In +this case, calculating N2O fluxes works well during the high-emission periods (high SNR) but is challenging +during the rest of the year (low SNR). Here, `DYCO` can be used to first calculate time lags for a *reference* gas +measured in the same analyzer (e.g. CO2, CO, CH4) and then remove *reference* time lags from the +N2O data. ## Contributing -All contributions in the form of code, bug reports, comments or general feedback are always welcome and greatly appreciated! Credit will always be given. - -- **Pull requests**: If you added new functionality or made the `DYCO` code run faster (always welcome), please create a fork in GitHub, make the contribution public in your repo and then issue a [pull request](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork). Please include tests in your pull requests. -- **Issues**: If you experience any issue, please use the [issue tracker](https://github.com/holukas/dyco/issues) to submit it as an issue ticket with the label 'bug'. Please also include a minimal code example that produces the issue. -- **Feature request**: If there is a feature that you would like to see in a later version, please use the [issue tracker](https://github.com/holukas/dyco/issues) to submit it as an issue ticket with the label 'feature request'. -- **Contact details**: For direct questions or enquiries the maintainer of this repository can be contacted directly by writing an email with the title "DYCO" to: holukas@ethz.ch - - +All contributions in the form of code, bug reports, comments or general feedback are always welcome and greatly +appreciated! Credit will always be given. + +- **Pull requests**: If you added new functionality or made the `DYCO` code run faster (always welcome), please create a + fork in GitHub, make the contribution public in your repo and then issue + a [pull request](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork). + Please include tests in your pull requests. +- **Issues**: If you experience any issue, please use the [issue tracker](https://github.com/holukas/dyco/issues) to + submit it as an issue ticket with the label 'bug'. Please also include a minimal code example that produces the issue. +- **Feature request**: If there is a feature that you would like to see in a later version, please use + the [issue tracker](https://github.com/holukas/dyco/issues) to submit it as an issue ticket with the label 'feature + request'. +- **Contact details**: For direct questions or enquiries the maintainer of this repository can be contacted directly by + writing an email with the title "DYCO" to: holukas@ethz.ch ## Acknowledgements -This work was supported by the Swiss National Science Foundation SNF (ICOS CH, grant nos. 20FI21_148992, 20FI20_173691) and the EU project Readiness of ICOS for Necessities of integrated Global Observations RINGO (grant no. 730944). - - +This work was supported by the Swiss National Science Foundation SNF (ICOS CH, grant nos. 20FI21_148992, 20FI20_173691) +and the EU project Readiness of ICOS for Necessities of integrated Global Observations RINGO (grant no. 730944). ## References -Aubinet, M., Vesala, T., Papale, D. (Eds.), 2012. Eddy Covariance: A Practical Guide to Measurement and Data Analysis. Springer Netherlands, Dordrecht. https://doi.org/10.1007/978-94-007-2351-1 +Aubinet, M., Vesala, T., Papale, D. (Eds.), 2012. Eddy Covariance: A Practical Guide to Measurement and Data Analysis. +Springer Netherlands, Dordrecht. https://doi.org/10.1007/978-94-007-2351-1 -Baldocchi, D.D., Hincks, B.B., Meyers, T.P., 1988. Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods. Ecology 69, 1331–1340. https://doi.org/10.2307/1941631 +Baldocchi, D.D., Hincks, B.B., Meyers, T.P., 1988. Measuring Biosphere-Atmosphere Exchanges of Biologically Related +Gases with Micrometeorological Methods. Ecology 69, 1331–1340. https://doi.org/10.2307/1941631 -Hörtnagl, L., Barthel, M., Buchmann, N., Eugster, W., Butterbach-Bahl, K., Díaz-Pinés, E., Zeeman, M., Klumpp, K., Kiese, R., Bahn, M., Hammerle, A., Lu, H., Ladreiter-Knauss, T., Burri, S., Merbold, L., 2018. Greenhouse gas fluxes over managed grasslands in Central Europe. Glob. Change Biol. 24, 1843–1872. https://doi.org/10.1111/gcb.14079 +Hörtnagl, L., Barthel, M., Buchmann, N., Eugster, W., Butterbach-Bahl, K., Díaz-Pinés, E., Zeeman, M., Klumpp, K., +Kiese, R., Bahn, M., Hammerle, A., Lu, H., Ladreiter-Knauss, T., Burri, S., Merbold, L., 2018. Greenhouse gas fluxes +over managed grasslands in Central Europe. Glob. Change Biol. 24, 1843–1872. https://doi.org/10.1111/gcb.14079 -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 Meas. Tech. 8, 4197–4213. https://doi.org/10.5194/amt-8-4197-2015 +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 Meas. Tech. 8, +4197–4213. https://doi.org/10.5194/amt-8-4197-2015 -McMillen, R.T., 1988. An eddy correlation technique with extended applicability to non-simple terrain. Bound.-Layer Meteorol. 43, 231–245. https://doi.org/10.1007/BF00128405 +McMillen, R.T., 1988. An eddy correlation technique with extended applicability to non-simple terrain. Bound.-Layer +Meteorol. 43, 231–245. https://doi.org/10.1007/BF00128405 -Merbold, L., Eugster, W., Stieger, J., Zahniser, M., Nelson, D., Buchmann, N., 2014. Greenhouse gas budget (CO2 , CH4 and N2O) of intensively managed grassland following restoration. Glob. Change Biol. 20, 1913–1928. https://doi.org/10.1111/gcb.12518 +Merbold, L., Eugster, W., Stieger, J., Zahniser, M., Nelson, D., Buchmann, N., 2014. Greenhouse gas budget (CO +2 , CH4 and N2O) of intensively managed grassland following restoration. Glob. Change Biol. +20, 1913–1928. https://doi.org/10.1111/gcb.12518 -Moncrieff, J.B., Massheder, J.M., de Bruin, H., Elbers, J., Friborg, T., Heusinkveld, B., Kabat, P., Scott, S., Soegaard, H., Verhoef, A., 1997. A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide. J. Hydrol. 188–189, 589–611. https://doi.org/10.1016/S0022-1694(96)03194-0 +Moncrieff, J.B., Massheder, J.M., de Bruin, H., Elbers, J., Friborg, T., Heusinkveld, B., Kabat, P., Scott, S., +Soegaard, H., Verhoef, A., 1997. A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon +dioxide. J. Hydrol. 188–189, 589–611. https://doi.org/10.1016/S0022-1694(96)03194-0 -Nemitz, E., Mammarella, I., Ibrom, A., Aurela, M., Burba, G.G., Dengel, S., Gielen, B., Grelle, A., Heinesch, B., Herbst, M., Hörtnagl, L., Klemedtsson, L., Lindroth, A., Lohila, A., McDermitt, D.K., Meier, P., Merbold, L., Nelson, D., Nicolini, G., Nilsson, M.B., Peltola, O., Rinne, J., Zahniser, M., 2018. Standardisation of eddy-covariance flux measurements of methane and nitrous oxide. Int. Agrophysics 32, 517–549. https://doi.org/10.1515/intag-2017-0042 +Nemitz, E., Mammarella, I., Ibrom, A., Aurela, M., Burba, G.G., Dengel, S., Gielen, B., Grelle, A., Heinesch, B., +Herbst, M., Hörtnagl, L., Klemedtsson, L., Lindroth, A., Lohila, A., McDermitt, D.K., Meier, P., Merbold, L., Nelson, +D., Nicolini, G., Nilsson, M.B., Peltola, O., Rinne, J., Zahniser, M., 2018. Standardisation of eddy-covariance flux +measurements of methane and nitrous oxide. Int. Agrophysics 32, 517–549. https://doi.org/10.1515/intag-2017-0042 diff --git a/dyco/analyze.py b/dyco/analyze.py index 2eb3751..8b374fd 100644 --- a/dyco/analyze.py +++ b/dyco/analyze.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -99,18 +99,18 @@ def plot_final_instantaneous_lagtimes(outdir, phase, df): # Accepted reference lags ax.plot_date(pd.to_datetime(df.index), df['INSTANTANEOUS_LAG'], - alpha=1, marker='o', ms=6, color='black', lw=0, ls='-', + alpha=1, fmt='o', ms=6, color='black', lw=0, ls='-', label=f'final reference time lag (absolute limit {abs_limit})', markeredgecolor='None', zorder=100) # Found lags in Phase 3 ax.plot_date(pd.to_datetime(df.index), df['PEAK-COVABSMAX_SHIFT'], - alpha=1, marker='o', ms=12, color='#FFC107', lw=0, ls='-', markeredgecolor='None', zorder=99, + alpha=1, fmt='o', ms=12, color='#FFC107', lw=0, ls='-', markeredgecolor='None', zorder=99, label=f'found Phase 3 time lag (search between {lagsearch_start} and {lagsearch_end})') # Marks lags that were outside limit and therefore set to the default lag set_to_default_lags = df.loc[df['SET_TO_DEFAULT'] == True, ['INSTANTANEOUS_LAG']] ax.plot_date(pd.to_datetime(set_to_default_lags.index), set_to_default_lags['INSTANTANEOUS_LAG'], - alpha=1, marker='o', ms=12, color='#8BC34A', lw=0, ls='-', markeredgecolor='None', zorder=99, + alpha=1, fmt='o', ms=12, color='#8BC34A', lw=0, ls='-', markeredgecolor='None', zorder=99, label=f'found Phase 3 time lag was set to default') plot.default_format(ax=ax, label_color='black', fontsize=12, @@ -337,6 +337,8 @@ def make_lut_agg(self, target_lag: int, segment_lagtimes_df: pd.DataFrame): subset = peaks_hq_S[filter_around_this_day] num_vals = len(subset) + # lut_df.loc[this_date, 'median'] = 9 + if num_vals >= 5: # print(f"{this_date} {num_vals} {subset.median()}") lut_df.loc[this_date, 'median'] = subset.median() @@ -360,7 +362,9 @@ def make_lut_agg(self, target_lag: int, segment_lagtimes_df: pd.DataFrame): col='correction') self.logger.warning(f"No correction could be generated from data for dates: {missing_df.index.to_list()}") self.logger.warning(f"Filling missing corrections for dates: {missing_df.index.to_list()}") - lut_df['correction'].fillna(method='ffill', inplace=True, limit=1) + lut_df['correction'] = lut_df['correction'].ffill(limit=1) + lut_df['correction'] = lut_df['correction'].bfill(limit=1) + # lut_df['correction'] = lut_df['correction'].fillna(method='ffill', inplace=False, limit=1) # deprecated try: lut_df['recommended_default_winsize'] = \ @@ -407,7 +411,7 @@ def get_hq_peaks(self, df): """ df.set_index('start', inplace=True) - df.index = pd.to_datetime(df.index) + df.index = pd.to_datetime(df.index, format="mixed") peaks_hq_S = df.loc[df['PEAK-COVABSMAX_SHIFT'] == df['PEAK-AUTO_SHIFT'], 'PEAK-COVABSMAX_SHIFT'] peaks_hq_S.index = peaks_hq_S.index.to_pydatetime() # Convert to DatetimeIndex diff --git a/dyco/cli.py b/dyco/cli.py index f4bb17e..f219097 100644 --- a/dyco/cli.py +++ b/dyco/cli.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -43,7 +43,7 @@ def validate_args(args): def get_args(): """Get args from CLI input""" - parser = argparse.ArgumentParser(description="BICO - Conversion of ETH binary files to ASCII", + parser = argparse.ArgumentParser(description="dyco - dynamic lag compensation", formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Positional args @@ -62,7 +62,7 @@ def get_args(): # Optional args parser.add_argument('-i', '--indir', type=Path, - help="Path to the source folder that contains the data files, e.g. 'C:/bico/input'") + help="Path to the source folder that contains the data files, e.g. 'C:/dyco/input'") parser.add_argument('-o', '--outdir', type=Path, help="Path to output folder, e.g. C:/bico/output") parser.add_argument('-fnd', '--filenamedateformat', type=str, default='%Y%m%d%H%M%S', diff --git a/dyco/correction.py b/dyco/correction.py index 81b685c..8677588 100644 --- a/dyco/correction.py +++ b/dyco/correction.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -86,6 +86,7 @@ def run(self): if not self.phase == 3: this_date = file_info_row['start'].date() + this_date = pd.to_datetime(this_date) shift_correction = self.lut_df.loc[this_date][self.lut_col] else: this_date = file_info_row['start'] @@ -184,7 +185,7 @@ def read_lut_time_lags(self): """Read csv file that contains the look-up table for lag correction""" filepath = self.outdirs[ f'{self.phase}-6_{self.phase_files}_normalization_lookup_table'] / f'LUT_default_agg_time_lags.csv' - parse = lambda x: dt.datetime.strptime(x, '%Y-%m-%d') + # parse = lambda x: dt.datetime.strptime(x, '%Y-%m-%d') # now deprecated df = pd.read_csv(filepath, skiprows=None, header=0, @@ -192,10 +193,11 @@ def read_lut_time_lags(self): # na_values=-9999, encoding='utf-8', delimiter=',', - mangle_dupe_cols=True, + # mangle_dupe_cols=True, # keep_date_col=False, parse_dates=True, - date_parser=parse, + # date_parser=parse, # now deprecated + date_format='%Y-%m-%d', index_col=0, dtype=None, engine='c') diff --git a/dyco/dyco.py b/dyco/dyco.py index 2beabab..d7a9c8e 100644 --- a/dyco/dyco.py +++ b/dyco/dyco.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ import os @@ -109,7 +109,7 @@ def _update_args(self, args, prev_phase, prev_phase_files, prev_outdir_files, pr args['lgs_winsize'] = 100 # Small window for instantaneous search in Phase 3 args['lgs_num_iter'] = 1 - args['indir'] = prev_outdir_files / f"{prev_phase}-7_{prev_phase_files}_normalized" + args['indir'] = Path(prev_outdir_files) / f"{prev_phase}-7_{prev_phase_files}_normalized" args['var_lagged'] = f"{args['var_lagged']}_DYCO" # Use normalized signal filename, file_extension = os.path.splitext(args['fnm_pattern']) args['fnm_pattern'] = f"{filename}_DYCO{file_extension}" # Search normalized files @@ -165,13 +165,13 @@ def __init__(self, fnm_date_format: str = '%Y%m%d%H%M%S', fnm_pattern: str = '*.csv', files_how_many: None or int = None, - file_generation_res: str = '30T', - file_duration: str = '30T', + file_generation_res: str = '30min', + file_duration: str = '30min', dat_recs_timestamp_format: None or str = None, dat_recs_nominal_timeres: float = 0.05, - lgs_segment_dur: str = '30T', + lgs_segment_dur: str = '30min', lgs_winsize: int = 1000, - lgs_num_iter: int = 3, + lgs_num_iter: int = 1, lgs_hist_remove_fringe_bins: bool = True, lgs_hist_perc_thres: float = 0.9, target_lag: int = 0, @@ -226,16 +226,16 @@ def __init__(self, Frequency at which new files were generated. This does not relate to the data records but to the file creation time. Examples: - * '30T' means a new file was generated every 30 minutes. - * '1H' means a new file was generated every hour. - * '6H' means a new file was generated every six hours. + * '30min' means a new file was generated every 30 minutes. + * '1h' means a new file was generated every hour. + * '6h' means a new file was generated every six hours. For pandas DateOffset options see: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases file_duration: str (pandas DateOffset) Duration of one data file. Example: - * '30T': data file contains data from 30 minutes. + * '30min': data file contains data from 30 minutes. For pandas DateOffset options see: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases @@ -246,10 +246,10 @@ def __init__(self, *file_duration*, then the file data is split into segments and the lag time is calculated for each segment separately. Examples: - * '10T': calculates lag times for 10-minute segments. + * '10m': calculates lag times for 10-minute segments. * With the settings - file_duration = '30T' and - lgs_segments_dur = '10T' + file_duration = '30min' and + lgs_segments_dur = '10m' the 30-minute data file is split into three 10-minute segments and the lag time is determined in each of the segments, yielding three lag times. @@ -435,10 +435,8 @@ def setup(self): return logfile_path, files_overview_df def calculate_lags(self): - """ - Calculate covariances and detect covariance peaks to determine lags - for each file segment - """ + """Calculate covariances and detect covariance peaks to determine lags + for each file segment.""" for iteration in range(1, 1 + self.lgs_num_iter): loop_iter = loop.Loop(dyco_instance=self, iteration=iteration) diff --git a/dyco/files.py b/dyco/files.py index 795df11..5ec7305 100644 --- a/dyco/files.py +++ b/dyco/files.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -47,7 +47,7 @@ def read_segment_lagtimes_file(filepath): # na_values=-9999, encoding='utf-8', delimiter=',', - mangle_dupe_cols=True, + # mangle_dupe_cols=True, # keep_date_col=False, parse_dates=False, # date_parser=parse, @@ -96,12 +96,12 @@ def read_raw_data(filepath, data_timestamp_format): num_missing_header_cols=num_missing_header_cols) if data_timestamp_format: - parse = lambda x: dt.datetime.strptime(x, data_timestamp_format) - date_parser = parse + # parse = lambda x: dt.datetime.strptime(x, data_timestamp_format) # now deprecated + # date_parser = parse # now deprecated parse_dates = True index_col = 0 else: - date_parser = None + # date_parser = None # now deprecated parse_dates = False index_col = None @@ -112,10 +112,11 @@ def read_raw_data(filepath, data_timestamp_format): na_values=-9999, encoding='utf-8', delimiter=',', - mangle_dupe_cols=True, - keep_date_col=False, + # mangle_dupe_cols=True, # now deprecated + # keep_date_col=False, # now deprecated parse_dates=parse_dates, - date_parser=date_parser, + # date_parser=date_parser, # now deprecated + date_format=data_timestamp_format, index_col=index_col, dtype=None, engine='c', diff --git a/dyco/lag.py b/dyco/lag.py index 669b834..2afdb80 100644 --- a/dyco/lag.py +++ b/dyco/lag.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -148,7 +148,8 @@ def setup_lagsearch_df(lgs_winsize: list, shift_stepsize: int, segment_name): df['shift'] = range(int(lgs_winsize[0]), int(lgs_winsize[1]) + shift_stepsize, shift_stepsize) - df['index'] = np.nan + df['index'] = pd.NaT + # df['index'] = np.nan df['segment_name'] = segment_name df['cov'] = np.nan df['cov_abs'] = np.nan @@ -159,10 +160,9 @@ def setup_lagsearch_df(lgs_winsize: list, shift_stepsize: int, segment_name): @staticmethod def find_auto_peak(cov_df: pd.DataFrame): - """ - Automatically find peaks in covariance time series + """Automatically find peaks in covariance time series. - The found peak is flagged TRUE in cov_df. + The found peak is flagged TRUE in *cov_df*. Peaks are searched automatically using scipy's .find_peaks method. The peak_score is calculated for each peak, the top-scoring peaks @@ -256,7 +256,9 @@ def find_max_cov_peak(segment_df, cov_df, var_lagged, var_reference): shift = int(row['shift']) try: if shift < 0: - index_shifted = str(_segment_df['index'][-shift]) # Note the negative sign + index_shifted = _segment_df['index'].iloc[-shift] # Note the negative sign + # index_shifted = str(_segment_df['index'].iloc[-shift]) # Note the negative sign + # index_shifted = str(_segment_df['index'][-shift]) # Note the negative sign else: index_shifted = pd.NaT scalar_data_shifted = _segment_df[var_lagged].shift(shift) diff --git a/dyco/loop.py b/dyco/loop.py index 5b7a8df..b430dbe 100644 --- a/dyco/loop.py +++ b/dyco/loop.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -211,9 +211,20 @@ def plot_segment_lagtimes_ts(segment_lagtimes_df: pd.DataFrame, alpha = .5 iteration_grouped = _df.groupby('iteration') for idx, group_df in iteration_grouped: - ax.plot_date(pd.to_datetime(group_df['start']), group_df['PEAK-COVABSMAX_SHIFT'], - alpha=alpha, marker='o', ms=6, color=colors[int(idx - 1)], lw=0, ls='-', - label=f'found lag times in iteration {int(idx)}', markeredgecolor='None') + try: + # ax.plot_date(pd.to_datetime(group_df['start']), group_df['PEAK-COVABSMAX_SHIFT'], + # alpha=alpha, fmt='o', ms=6, color=colors[int(idx - 1)], lw=0, ls='-', + # label=f'found lag times in iteration {int(idx)}', markeredgecolor='None') + + # Below, "format='mixed'" is used because although all dates have the same format + # (in this case '%Y-%m-%d %H:%M:%S.%f') pandas seems to interpret the timestamp + # '2016-10-24 13:00:00.000000' as '2016-10-24 13:00:00' and therefore raises a ValueError. + ax.plot_date(pd.to_datetime(group_df['start'], format='mixed'), group_df['PEAK-COVABSMAX_SHIFT'], + alpha=alpha, fmt='o', ms=6, color=colors[int(idx - 1)], lw=0, ls='-', + label=f'found lag times in iteration {int(idx)}', markeredgecolor='None') + + except ValueError as e: + print(e) plot.default_format(ax=ax, label_color='black', fontsize=12, txt_xlabel='segment date', txt_ylabel='lag', txt_ylabel_units='[records]') @@ -431,8 +442,13 @@ def store_search_results(self, stor_df: pd.DataFrame, stor_cols: dict, stor_idx: """ # Init columns - for key, col in stor_cols.items(): - stor_df.loc[stor_idx, col] = np.nan + # for key, col in stor_cols.items(): + # stor_df.loc[stor_idx, col] = np.nan + + # # todo remove? + # stor_df['PEAK-COVABSMAX_SHIFT'] = np.nan + # stor_df['PEAK-COVABSMAX_COV'] = np.nan + # stor_df['PEAK-COVABSMAX_TIMESTAMP'] = pd.NaT # Get indices of peaks and instantaneous default lag flag_idx = lag.LagSearch.get_peak_idx(df=flag_df, flag_col=flag_col) @@ -694,7 +710,10 @@ def cov_collection(indir, outdir, logfile_path): for idx, filename in enumerate(filelist): filepath = os.path.join(str(indir), filename) segment_cov_df = files.read_segment_lagtimes_file(filepath=filepath) - cov_collection_df = cov_collection_df.append(segment_cov_df) # Collect for median and quantiles calc + + # todo check if correct: + cov_collection_df = pd.concat([cov_collection_df, segment_cov_df], axis=0, ignore_index=True) # Collect for median and quantiles calc + # cov_collection_df = cov_collection_df.append(segment_cov_df) # Collect for median and quantiles calc # Plot each segment covariance file args = dict(alpha=0.05, c='black', lw=0.5, marker='None', zorder=98) @@ -714,7 +733,11 @@ def q75(x): linestyle = ['-', '--', '--'] colors = ['#f44336', '#2196F3', '#3F51B5'] iter_df = cov_collection_df[cov_collection_df['segment_name'].str.contains('iter')] - _df = iter_df.groupby('shift').agg(f) + + _df = iter_df.copy() + _df = _df.select_dtypes(include=['number']) # Keep numeric columns + # _df = _df._get_numeric_data() + _df = _df.groupby('shift').agg(f) args = dict(alpha=1, lw=1, marker='None', zorder=98) for label_idx, label_name in enumerate(labels): ax1.plot(_df.index, _df['cov'][label_name], diff --git a/dyco/plot.py b/dyco/plot.py index 24e1c1e..3e8fa65 100644 --- a/dyco/plot.py +++ b/dyco/plot.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -97,19 +97,19 @@ def run(self): def _add_lines_cov(self, ax, df, last_iter_phase_1, last_iter_phase_2): props = {'y': df[f'PHASE-1_ITER-{last_iter_phase_1}_PEAK-COVABSMAX_COV'], 'label': f'Phase 1', - 'zorder': 100, 'alpha': 1, 'marker': 's', 'ms': 6, 'lw': 1, 'ls': 'dotted', + 'zorder': 100, 'alpha': 1, 'fmt': 's', 'ms': 6, 'lw': 1, 'ls': 'dotted', 'markeredgecolor': '#80DEEA', 'color': '#80DEEA', 'markerfacecolor': 'white'} ax.plot_date(df.index, **props) props = {'y': df[f'PHASE-2_ITER-{last_iter_phase_1}_PEAK-COVABSMAX_COV'], 'label': f'Phase 2', - 'zorder': 100, 'alpha': 1, 'marker': 's', 'ms': 6, 'lw': 1, 'ls': '--', + 'zorder': 100, 'alpha': 1, 'fmt': 's', 'ms': 6, 'lw': 1, 'ls': '--', 'markeredgecolor': '#F48FB1', 'color': '#F48FB1', 'markerfacecolor': 'white'} ax.plot_date(df.index, **props) props = {'y': df['PHASE-3_INSTANTANEOUS_LAG_COV'], 'label': 'Phase 3', - 'zorder': 100, 'alpha': 1, 'marker': 'o', 'ms': 6, 'lw': 2, 'ls': '-', + 'zorder': 100, 'alpha': 1, 'fmt': 'o', 'ms': 6, 'lw': 2, 'ls': '-', 'markeredgecolor': '#8BC34A', 'color': '#8BC34A', 'markerfacecolor': '#8BC34A'} ax.plot_date(df.index, **props) @@ -260,7 +260,7 @@ def _get_lut_last_iteration(self, outdirs, phase, phase_files): return lut_df def _make_outdir(self): - outdir = self.instance_phase_1.outdir / 'SUMMARY' + outdir = Path(self.instance_phase_1.outdir) / 'SUMMARY' if not Path.is_dir(outdir): print(f"Creating folder {outdir} ...") os.makedirs(outdir) diff --git a/dyco/setup_dyco.py b/dyco/setup_dyco.py index 10e5f7c..ba450ee 100644 --- a/dyco/setup_dyco.py +++ b/dyco/setup_dyco.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ @@ -132,7 +132,7 @@ def setup_output_dirs(self): # Create Path to required directories, store keys and full paths in dict for nd in required_dirs: - outdirs[nd] = self.root_dir / nd + outdirs[nd] = Path(self.root_dir) / nd # Delete dirs if needed for key, path in outdirs.items(): @@ -191,7 +191,10 @@ def run(self): self.files_overview_df = self.add_expected() self.files_overview_df = self.add_unexpected() self.files_overview_df = self.calc_expected_values() - self.files_overview_df.loc[:, 'file_available'].fillna(0, inplace=True) + _temp = self.files_overview_df.loc[:, 'file_available'].copy() + _temp = _temp.fillna(0) + self.files_overview_df.loc[:, 'file_available'] = _temp + # self.files_overview_df.loc[:, 'file_available'].fillna(0, inplace=True) self.files_overview_df = self.limit_num_files() self.logger.info(f"Found {int(self.files_overview_df['file_available'].sum())} available files") diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 24793bb..0000000 --- a/environment.yml +++ /dev/null @@ -1,116 +0,0 @@ -name: DYCO1 -channels: - - plotly - - conda-forge - - anaconda - - defaults -dependencies: - - astroid=2.4.2=py36_0 - - atomicwrites=1.4.0=py_0 - - attrs=19.3.0=py_0 - - blas=1.0=mkl - - brotlipy=0.7.0=py36h779f372_1000 - - ca-certificates=2020.6.24=0 - - certifi=2020.6.20=py36_0 - - cffi=1.14.0=py36ha419a9e_0 - - colorama=0.4.3=py_0 - - cryptography=2.8=py36hb32ad35_1 - - cycler=0.10.0=py36h009560c_0 - - entrypoints=0.3=py36h9f0ad1d_1001 - - freetype=2.9.1=ha9979f8_1 - - icc_rt=2019.0.0=h0cc432a_1 - - icu=58.2=ha66f8fd_1 - - idna=2.9=py_1 - - importlib-metadata=1.6.0=py36h9f0ad1d_0 - - importlib_metadata=1.6.0=0 - - intel-openmp=2020.0=166 - - isort=4.3.21=py36_0 - - jinja2=2.11.2=pyh9f0ad1d_0 - - jpeg=9b=hb83a4c4_2 - - jsonschema=3.2.0=py36h9f0ad1d_1 - - kiwisolver=1.1.0=py36ha925a31_0 - - lazy-object-proxy=1.4.3=py36he774522_0 - - libpng=1.6.37=h2a8f88b_0 - - markupsafe=1.1.1=py36h68a101e_1 - - 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six=1.14.0=py36_0 - - sqlite=3.31.1=he774522_0 - - toml=0.10.1=py_0 - - toolz=0.10.0=py_0 - - tornado=6.0.4=py36he774522_1 - - typed-ast=1.4.1=py36he774522_0 - - urllib3=1.25.9=py_0 - - vc=14.1=h0510ff6_4 - - vega_datasets=0.8.0=py_0 - - vs2015_runtime=14.16.27012=hf0eaf9b_1 - - wcwidth=0.2.5=py_0 - - wheel=0.34.2=py36_0 - - win_inet_pton=1.1.0=py36_0 - - wincertstore=0.2=py36h7fe50ca_0 - - wrapt=1.11.2=py36he774522_0 - - zipp=3.1.0=py_0 - - zlib=1.2.11=h62dcd97_4 - - pip: - - bleach==3.1.5 - - brotli==1.0.7 - - chardet==3.0.4 - - click==7.1.2 - - dash==1.12.0 - - dash-core-components==1.10.0 - - dash-html-components==1.0.3 - - dash-renderer==1.4.1 - - dash-table==4.7.0 - - docutils==0.16 - - dyco==1.0.2 - - flask==1.1.2 - - flask-compress==1.5.0 - - future==0.18.2 - - itsdangerous==1.1.0 - - keyring==21.2.1 - - numpy==1.18.1 - - pkginfo==1.5.0.1 - - pygments==2.6.1 - - pywin32-ctypes==0.2.0 - - readme-renderer==26.0 - - requests==2.24.0 - - requests-toolbelt==0.9.1 - - rfc3986==1.4.0 - - tqdm==4.47.0 - - twine==3.2.0 - - webencodings==0.5.1 - - werkzeug==1.0.1 -prefix: C:\Users\holukas\Anaconda3\envs\DYCO1 diff --git a/example/example.py b/example/example_cli.py similarity index 100% rename from example/example.py rename to example/example_cli.py diff --git a/example/example_kwargs.py b/example/example_kwargs.py new file mode 100644 index 0000000..9f7cc0e --- /dev/null +++ b/example/example_kwargs.py @@ -0,0 +1,40 @@ +""" +Example settings + +DYCO can be run using keyword arguments. + +For an overview of arguments see here: +https://github.com/holukas/dyco/wiki/Usage +""" + +from dyco.dyco import DynamicLagCompensation + + +def example(): + """Main function that is called with the given args when the script + is executed from the command line.""" + DynamicLagCompensation(var_reference='w_ms-1_rot_turb', + var_lagged='co2_ppb_qcl_turb', + var_target=['co2_ppb_qcl_turb', 'n2o_ppb_qcl', 'ch4_ppb_qcl'], + indir=r'F:\TMP\das\input_data', + outdir=r'F:\TMP\das', + fnm_date_format='%Y%m%d%H%M%S', + fnm_pattern='*.csv', + files_how_many=0, + file_generation_res='30min', + file_duration='30min', + dat_recs_timestamp_format="%Y-%m-%d %H:%M:%S.%f", + dat_recs_nominal_timeres=0.05, + lgs_segment_dur='30min', + lgs_winsize=1000, + # lgs_winsize=400, + # lgs_num_iter=1, + lgs_num_iter=3, + lgs_hist_remove_fringe_bins=True, + lgs_hist_perc_thres=0.9, + target_lag=0, + del_previous_results=True) + + +if __name__ == '__main__': + example() diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..ef3ab4c --- /dev/null +++ b/poetry.lock @@ -0,0 +1,661 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "contourpy" +version = "1.2.0" +description = "Python library for calculating contours of 2D quadrilateral grids" +optional = false +python-versions = ">=3.9" +files = [ + {file = "contourpy-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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0000000..d6bcb43 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,17 @@ +[tool.poetry] +name = "dyco" +version = "1.2.0" +description = "Dynamic lag compensation" +authors = ["Lukas Hörtnagl "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.9" +pandas = "^2.2.1" +matplotlib = "^3.8.3" +scipy = "^1.12.0" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 22715b6..0000000 --- a/requirements.txt +++ /dev/null @@ -1,82 +0,0 @@ -astroid==2.4.2 -atomicwrites==1.4.0 -attrs==19.3.0 -bleach==3.1.5 -Brotli==1.0.7 -brotlipy==0.7.0 -certifi==2020.6.20 -cffi==1.14.0 -chardet==3.0.4 -click==7.1.2 -colorama==0.4.3 -cryptography==2.8 -cycler==0.10.0 -dash==1.12.0 -dash-core-components==1.10.0 -dash-html-components==1.0.3 -dash-renderer==1.4.1 -dash-table==4.7.0 -docutils==0.16 -dyco==1.0.2 -entrypoints==0.3 -Flask==1.1.2 -Flask-Compress==1.5.0 -future==0.18.2 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-wrapt==1.11.2 -zipp==3.1.0 diff --git a/setup.py b/setup.py index 68fd4ce..c5c163d 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,6 @@ """ DYCO Dynamic Lag Compensation - Copyright (C) 2020 holukas + Copyright (C) 2020-2024 Lukas Hörtnagl This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by @@ -13,7 +13,7 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License - along with this program. If not, see . + along with this program. If not, see . """ import setuptools