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Wu, Mingxuan + and Wang, Hailong + and Turner, Adrian K. + and Singh, Balwinder + and Richter, Jadwiga H. + and Qin, Yi + and Petersen, Mark R. + and Mametjanov, Azamat + and Ma, Po-Lun + and Larson, Vincent E. + and Krishna, Jayesh + and Keen, Noel D. + and Jeffery, Nicole + and Hunke, Elizabeth C. + and Hannah, Walter M. + and Guba, Oksana + and Griffin, Brian M. + and Feng, Yan + and Engwirda, Darren + and Di Vittorio, Alan V. + and Dang, Cheng + and Conlon, LeAnn M. + and Chen, Chih-Chieh-Jack + and Brunke, Michael A. + and Bisht, Gautam + and Benedict, James J. + and Asay-Davis, Xylar S. + and Zhang, Yuying + and Zhang, Meng + and Zeng, Xubin + and Xie, Shaocheng + and Wolfram, Phillip J. + and Vo, Tom + and Veneziani, Milena + and Tesfa, Teklu K. + and Sreepathi, Sarat + and Salinger, Andrew G. + and Reeves Eyre, J. E. Jack + and Prather, Michael J. + and Mahajan, Salil + and Li, Qing + and Jones, Philip W. + and Jacob, Robert L. + and Huebler, Gunther W. + and Huang, Xianglei + and Hillman, Benjamin R. + and Harrop, Bryce E. + and Foucar, James G. + and Fang, Yilin + and Comeau, Darin S. + and Caldwell, Peter M. + and Bartoletti, Tony + and Balaguru, Karthik + and Taylor, Mark A. + and McCoy, Renata B. + and Leung, L. Ruby + and Bader, David C.}, + title = {The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation}, + journal = {Journal of Advances in Modeling Earth Systems}, + volume = {14}, + number = {12}, + pages = {e2022MS003156}, + keywords = {DOE E3SM, climate modeling}, + doi = {10.1029/2022MS003156}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003156}, + eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003156}, + note = {e2022MS003156 2022MS003156}, + year = {2022} +} + +@article{Hassell:2017, + title = {A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)}, + author = {Hassell, D. + and Gregory, J. + and Blower, J. + and Lawrence, B. N. + and Taylor, K. E.}, + doi = {10.5194/gmd-10-4619-2017}, + journal = {Geoscientific Model Development}, + number = {12}, + pages = {4619-4646}, + url = {https://gmd.copernicus.org/articles/10/4619/2017/}, + volume = {10}, + year = {2017} +} + +@article{Hoyer:2017, + title = {xarray: {N}-{D} labeled {Arrays} and {Datasets} in {Python}}, + author = {Hoyer, Stephan and Hamman, Joseph J.}, + doi = {10.5334/jors.148}, + issn = {2049-9647}, + journal = {Journal of Open Research Software}, + language = {en}, + month = apr, + pages = {10}, + shorttitle = {xarray}, + url = {http://openresearchsoftware.metajnl.com/articles/10.5334/jors.148/}, + urldate = {2019-07-02}, + volume = {5}, + year = {2017} +} + +@article{Lee:2023, + author = {Lee, J. + and Gleckler, P. J. + and Ahn, M.-S. + and Ordonez, A. + and Ullrich, P. A. + and Sperber, K. R. + and Taylor, K. E. + and Planton, Y. Y. + and Guilyardi, E. + and Durack, P. + and Bonfils, C. + and Zelinka, M. D. + and Chao, L.-W. + and Dong, B. + and Doutriaux, C. + and Zhang, C. + and Vo, T. + and Boutte, J. + and Wehner, M. F. + and Pendergrass, A. G. + and Kim, D. + and Xue, Z. + and Wittenberg, A. T. + and Krasting, J.}, + title = {Objective Evaluation of Earth System Models: PCMDI Metrics Package (PMP) version 3}, + journal = {EGUsphere}, + volume = {2023}, + year = {2023}, + pages = {1-48}, + url = {https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2720/}, + doi = {10.5194/egusphere-2023-2720} +} + +@article{Po-Chedley:2022, + author = {Stephen Po-Chedley + and John T. Fasullo + and Nicholas Siler + and Zachary M. Labe + and Elizabeth A. Barnes + and Céline J. W. Bonfils + and Benjamin D. Santer}, + title = {Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming}, + journal = {Proceedings of the National Academy of Sciences}, + volume = {119}, + number = {47}, + pages = {e2209431119}, + year = {2022}, + doi = {10.1073/pnas.2209431119}, + url = {https://www.pnas.org/doi/abs/10.1073/pnas.2209431119}, + EPRINT = {https://www.pnas.org/doi/pdf/10.1073/pnas.2209431119}, +} + +@article{Williams:2009, + author = {Williams, Dean N. + and Doutriaux, Charles M. + and Drach, Robert S. + and Mccoy, Renata B.}, + booktitle = {2009 IEEE International Conference on Data Mining Workshops}, + title = {The Flexible Climate Data Analysis Tools (CDAT) for Multi-model Climate Simulation Data}, + year = {2009}, + volume = {}, + number = {}, + pages = {254-261}, + doi = {10.1109/ICDMW.2009.64} +} + +@article{Williams:2014, + title = {Visualization and Analysis Tools for Ultrascale Climate Data}, + author = {Williams, D. N.}, + doi = {10.1002/2014EO420002}, + journal = {Advanced Earth and Space Sciences}, + number = {42}, + pages = {377-378}, + url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014EO420002}, + volume = {95}, + year = {2014} +} + +@article{Zhang:2022, + author = {Zhang, C. + and Golaz, J.-C. + and Forsyth, R. + and Vo, T. + and Xie, S. + and Shaheen, Z. + and Potter, G. L. + and Asay-Davis, X. S. + and Zender, C. S. + and Lin, W. + and Chen, C.-C. + and Terai, C. R. + and Mahajan, S. + and Zhou, T. + and Balaguru, K. + and Tang, Q. + and Tao, C. + and Zhang, Y. + and Emmenegger, T. + and Burrows, S. + and Ullrich, P. A.}, + title = {The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation}, + journal = {Geoscientific Model Development}, + volume = {15}, + year = {2022}, + number = {24}, + pages = {9031-9056}, + url = {https://gmd.copernicus.org/articles/15/9031/2022/}, + doi = {10.5194/gmd-15-9031-2022} +} + +@manual{dask:2016, + title = {Dask: Library for dynamic task scheduling}, + author = {Dask-Development-Team}, + year = {2016}, + url = {https://dask.org}, +} + +@software{cdat, + author = {Charles Doutriaux + and Denis Nadeau + and Scott Wittenburg + and Dan Lipsa + and Lina Muryanto + and Aashish Chaudhary + and Dean N. Williams}, + title = {CDAT/cdat: CDAT 8.1}, + month = mar, + year = 2019, + publisher = {Zenodo}, + version = {v8.1}, + doi = {10.5281/zenodo.2586088}, + url = {https://doi.org/10.5281/zenodo.2586088} +} + +@software{cf-xarray, + author = {Cherian, Deepak + and Almansi, Mattia + and Bourgault, Pascal + and Thyng, Kristen + and Thielen, Jonathan + and Magin, Justus + and Aoun, Abel + and Buntemeyer, Lars + and Caneill, Romain + and Davis, Luke + and Fernandes, Filipe + and Hauser, Matthias + and Heerdegen, Aidan + and Kent, Julia + and Mankoff, Ken + and Müller, Sebastian + and Schupfner, Martin + and Vo, Tom + and Haëck, Clément}, + title = {cf\_xarray}, + month = oct, + year = 2023, + publisher = {Zenodo}, + version = {v0.8.5}, + doi = {10.5281/zenodo.10038784}, + url = {https://doi.org/10.5281/zenodo.10038784} +} + +@software{e3sm-diags, + author = {Jill Chengzhu Zhang + and Zeshawn Shaheen + and Tom Vo + and Ryan Forsyth + and Golaz + and Xylar Asay-Davis + and Naser Mahfouz + and Andrew M. Bradley + and Charles Doutriaux}, + title = {E3SM-Project/e3sm\_diags: v2.9.0}, + month = sep, + year = 2023, + publisher = {Zenodo}, + version = {v2.9.0}, + doi = {10.5281/zenodo.8339034}, + url = {https://doi.org/10.5281/zenodo.8339034} +} + +@software{e3sm-unified, + author = {Asay-Davis, Xylar}, + title = {E3SM-Unified: A metapackage for a unified anaconda environment for analyzing results from the Energy Exascale Earth System Model (E3SM)}, + month = dec, + year = 2023, + publisher = {GitHub}, + version = {v1.9.1}, + url = {https://github.com/E3SM-Project/e3sm-unified} +} + +@software{pcmdi-metrics, + author = {Lee, Jiwoo + and Gleckler, Peter + and Ordonez, Ana + and Ahn, Min-Seop + and Ullrich, Paul + and Vo, Tom + and Boutte, Jason + and Doutriaux, Charles + and Durack, Paul + and Shaheen, Zeshawn + and Muryanto, Lina + and Painter, Jeffrey + and Krasting, John}, + title = {PCMDI/pcmdi\_metrics: PMP Version 3.1.2}, + month = nov, + year = 2023, + publisher = {Zenodo}, + version = {v3.1.2}, + doi = {10.5281/zenodo.10236521}, + url = {https://doi.org/10.5281/zenodo.10236521} +} + +@software{xesmf, + author = {Jiawei Zhuang + and Raphael Dussin + and David Huard + and Pascal Bourgault + and Anderson Banihirwe + and Stephane Raynaud + and Brewster Malevich + and Martin Schupfner + and Filipe + and Sam Levang + and Charles Gauthier + and André Jüling + and Mattia Almansi + and Richardscottoz + and Rondeaug + and Stephan Rasp + and Trevor James Smith + and Jemma Stachelek + and Matthew Plough + and Pierre + and Ray Bell + and Romain Caneill + and Xianxiang Li}, + title = {pangeo-data/xESMF: v0.8.2}, + month = sep, + year = 2023, + publisher = {Zenodo}, + version = {v0.8.2}, + doi = {10.5281/zenodo.8356796}, + url = {https://doi.org/10.5281/zenodo.8356796} +} + +@software{xgcm, + author = {Abernathey, Ryan P. + and Busecke, Julius J. M. + and Smith, Timothy Andrew + and Deauna, Josephine Dianne + and Banihirwe, Anderson + and Nicholas, Thomas + and Fernandes, Filipe + and James, Bourbeau + and Dussin, Raphael + and Cherian, Deepak A. + and Caneill, Romain + and Sinha, Anirban + and Uieda, Leonardo + and Rath, Willi + and Balwada, Dhruv + and Constantinou, Navid C. + and Ponte, Aurélien + and Zhou, Yuxin + and Uchida, Takaya + and Thielen, Jon}, + title = {xgcm}, + month = nov, + year = 2022, + publisher = {Zenodo}, + version = {v0.8.1}, + doi = {10.5281/zenodo.7348619}, + url = {https://doi.org/10.5281/zenodo.7348619} +} diff --git a/docs/paper/paper.md b/docs/paper/paper.md new file mode 100644 index 00000000..d9e05588 --- /dev/null +++ b/docs/paper/paper.md @@ -0,0 +1,108 @@ +--- +title: "xCDAT: A Python Package for Simple and Robust Analysis of Climate Data" +tags: + - Python + - python + - xarray + - climate science + - climate research + - climate data + - climate data analysis +authors: + - name: Tom Vo + orcid: 0000-0002-2461-0191 + affiliation: 1 + - name: Stephen Po-Chedley + orcid: 0000-0002-0390-238X + affiliation: 1 + - name: Jason Boutte + orcid: 0009-0009-3996-3772 + affiliation: 1 + - name: Jiwoo Lee + orcid: 0000-0002-0016-7199 + affiliation: 1 + - name: Chengzhu Zhang + orcid: 0000-0002-9632-0716 + affiliation: 1 +affiliations: + - name: Lawrence Livermore National Lab, Livermore, USA + index: 1 +date: 24 July 2024 +bibliography: paper.bib +--- + +# Summary + +xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray [@Hoyer:2017] for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT's key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools ([CDAT](https://cdat.llnl.gov/)) library [@Williams:2009] [@Williams:2014] [@cdat] and leverage powerful packages in the [Xarray](https://docs.xarray.dev/en/stable/) ecosystem including [xESMF](https://github.com/pangeo-data/xESMF) [@xesmf], [xgcm](https://xgcm.readthedocs.io/en/latest/) [@xgcm], and [CF xarray](https://cf-xarray.readthedocs.io/en/latest/) [@cf-xarray]. To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions [@Hassell:2017]. + +# Statement of Need + +Analysis of climate data frequently requires a number of core operations, including reading and writing of netCDF files, horizontal and vertical regridding, and spatial and temporal averaging. While many individual software packages address these needs in a variety of computational languages, CDAT stands out because it provides these essential operations via open-source, interoperable Python packages. Since CDAT’s inception, the volume of climate data has grown substantially as a result of both a larger pool of data products and increasing spatiotemporal resolution of model and observational data. Larger data stores are important for advancing geoscientific understanding, but also require increasingly performant software and hardware. These factors have sparked a need for new analysis software that offers core geospatial analysis functionalities capable of efficiently handling large datasets while using modern technologies and standardized software engineering principles. + +xCDAT addresses this need by combining the power of Xarray with meticulously developed geospatial analysis features inspired by CDAT. Xarray is the foundation of xCDAT because of its widespread adoption, technological maturity, and ability to handle large datasets with parallel computing via Dask. Xarray is also interoperable with the scientific Python ecosystem (e.g., [NumPy](https://numpy.org/), [pandas](https://pandas.pydata.org/), [Matplotlib](https://matplotlib.org/)), which greatly benefits users who need to use additional analysis tools. Since Xarray is designed as a general-purpose library, xCDAT fills in domain specific gaps by providing features to serve the climate science community _(refer to [Key Features](#key-features))_. + +Performance is one fundamental driver in how xCDAT is designed, especially with large datasets. xCDAT conveniently inherits Xarray's support for parallel computing with Dask [@dask:2016]. Parallel computing with Dask enables users to take advantage of compute resources through multithreading or multiprocessing. To use Dask's default multithreading scheduler, users only need to open and chunk datasets in Xarray before calling xCDAT APIs. xCDAT's seamless support for parallel computing enables users to run large-scale computations with minimal effort. If users require more resources, they can also configure and use a local Dask cluster to meet resource-intensive computational needs. Figure 1 shows xCDAT's significant performance advantage over CDAT for global spatial averaging on datasets of varying sizes. + +![A performance benchmark for global spatial averaging computations using CDAT (serial only) and xCDAT (serial and parallel with Dask distributed scheduler). xCDAT outperforms CDAT by a wide margin for the 7 GB and 12 GB datasets. Runtimes could not be captured for CDAT with datasets >= 22 GB and xCDAT serial for the 105 GB dataset due to memory allocation errors. The performance benchmark setup and scripts are available in the [`xcdat-validation` repo](https://github.com/xCDAT/xcdat-validation/tree/main/validation/v0.6.0/xcdat-cdat-perf-metrics). _Disclaimer: Performance will vary depending on hardware, dataset shapes/sizes, and how Dask and chunking schemes are configured. There are also some cases where selecting a regional averaging domain (e.g., Niño 3.4) can lead to CDAT outperforming xCDAT._ \label{fig:figure1}](figures/figure1.png){ height=40% } + +xCDAT's intentional design emphasizes software sustainability and reproducible science. It aims to make analysis code reusable, readable, and less-error prone by abstracting common Xarray boilerplate logic into simple and configurable APIs. xCDAT extends Xarray by using [accessor classes](https://docs.xarray.dev/en/stable/internals/extending-xarray.html) that operate directly on Xarray Dataset objects. xCDAT is rigorously tested using real-world datasets and maintains 100% unit test coverage (at the time this paper was written). To demonstrate the value in xCDAT's API design, Figure 2 compares code to calculate annual averages for global climatological anomalies using Xarray against xCDAT. xCDAT requires fewer lines of code and supports further user options (e.g., regional or seasonal averages, not shown). Figure 2 shows the plots for the results produced by xCDAT. + +![A comparison of the code to calculate annual averages for global climatological anomalies in A) Xarray and B) xCDAT. xCDAT abstracts most of the Xarray boilerplate logic for calculating weights and grouping data by specific time frequencies, leading to code that is more readable, maintainable, and flexible. The results from both sets of code are within machine precision. \label{fig:figure2}](figures/figure2.png){ height=100% } + +![A) Monthly surface skin temperature anomalies for September 1850. B) Monthly (gray) and annual (black) global mean surface skin temperature anomaly values. Temperature data is from an E3SMv2 climate model [@Golaz:2022] simulation over the historical period (1850 – 2014). \label{fig:figure3}](figures/figure3.png){ height=45% } + +xCDAT's mission is to provide a maintainable and extensible package that serves the needs of the climate community in the long-term. xCDAT is a community-driven project and the development team encourages all who are interested to get involved through the [GitHub repository](https://github.com/xCDAT/xcdat). + +# Key Features + +## Extension of `xarray.open_dataset()` and `xarray.open_mfdataset()` with post-processing options + +xCDAT extends `xarray.open_dataset()` and `xarray.open_mfdataset()` with additional post-processing operations to support climate data analysis. These APIs can generate missing coordinate bounds for the X, Y, T, and/or Z axes and lazily decode time coordinates represented by `cftime` ([more info](https://github.com/xCDAT/xcdat/pull/489#issuecomment-1579275827)). Other functionality includes re-centering time coordinates between time bounds and converting the longitudinal axis orientation between [0, 360) and [-180, 180). + +## Robust interpretation of CF metadata + +xCDAT uses [CF xarray](https://cf-xarray.readthedocs.io/en/latest/) to interpret CF metadata present in datasets, enabling xCDAT to operate generally across model and observational datasets that are CF-compliant. This feature enables xCDAT to generate missing coordinate bounds, recognize the coordinates and coordinate bounds to use for computational operations, and lazily decode time coordinates based on the CF calendar attribute. + +## Temporal averaging + +xCDAT's temporal averaging API utilizes Xarray and Pandas. It includes features for calculating time series averages (single-snapshot), grouped time series averages (e.g., seasonal or annual averages), climatologies, and departures. Averages can be weighted (default) or unweighted. There are optional configurations for seasonal grouping including how to group the month of December (DJF or JFD) and defining custom seasons to group by. + +## Geospatial weighted averaging + +xCDAT’s geospatial weighted averaging supports rectilinear grids with an option to compute averages over a regional domain (e.g., tropical region, Niño 3.4 region). + +## Horizontal structured regridding + +xCDAT makes use of [xESMF](https://pangeo-xesmf.readthedocs.io/en/latest/) for horizontal regridding capabilities. It simplifies and extends the xESMF horizontal regridding API by generating missing bounds and ensuring bounds and metadata are preserved in the output dataset. xCDAT also offers a Python implementation of [regrid2](https://cdms.readthedocs.io/en/latest/regrid2.html) for handling cartesian latitude by longitude grids. + +## Vertical structured regridding + +xCDAT makes use of [xgcm](https://xgcm.readthedocs.io/en/latest/) for vertical regridding capabilities. It simplifies and extends the xgcm vertical regridding API by automatically attempting to determine the grid point position relative to the bounds, transposing the output data to match the dimensional order of the input data, and ensuring bounds and metadata are preserved in the output dataset. + +# Documentation & Case Studies + +The xCDAT [documentation](https://xcdat.readthedocs.io/en/stable/index.html) includes the [public API list](https://xcdat.readthedocs.io/en/stable/api.html) and a Jupyter Notebook [Gallery](https://xcdat.readthedocs.io/en/stable/gallery.html) that demonstrates real-world applications of xCDAT: + +- [A Gentle Introduction to xCDAT (Xarray Climate Data Analysis Tools)](https://xcdat.readthedocs.io/en/stable/examples/introduction-to-xcdat.html) +- [General Dataset Utilities](https://xcdat.readthedocs.io/en/stable/examples/general-utilities.html) +- [Calculate Geospatial Weighted Averages from Monthly Time Series](https://xcdat.readthedocs.io/en/stable/examples/spatial-average.html) +- [Calculate Time Averages from Time Series Data](https://xcdat.readthedocs.io/en/stable/examples/temporal-average.html) +- [Calculating Climatology and Departures from Time Series Data](https://xcdat.readthedocs.io/en/stable/examples/climatology-and-departures.html) +- [Horizontal Regridding](https://xcdat.readthedocs.io/en/stable/examples/regridding-horizontal.html) +- [Vertical Regridding](https://xcdat.readthedocs.io/en/stable/examples/regridding-vertical.html) + +# Distribution + +xCDAT is available for Linux, MacOS, and Windows via the conda-forge channel on [Anaconda](https://anaconda.org/conda-forge/xcdat). The [GitHub Repository](https://github.com/xCDAT/xcdat) is where we host all development activity. xCDAT is released under the Apache 2-0 license. + +# Projects using xCDAT + +xCDAT is actively being integrated as a core component of the [Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package](https://pcmdi.github.io/pcmdi_metrics/) [@pcmdi-metrics] [@Lee:2023] and the [Energy Exascale Earth System Model (E3SM) Diagnostics Package](https://e3sm-project.github.io/e3sm_diags/_build/html/main/index.html) [@Zhang:2022] [@e3sm-diags]. xCDAT is also included in the [E3SM Unified Anaconda Environment](https://e3sm.org/resources/tools/other-tools/e3sm-unified-environment/) [@e3sm-unified] deployed on numerous U.S Department of Energy supercomputers to run E3SM software tools. Members of the development team are also active users of xCDAT and apply the software to advance their own climate research [@Po-Chedley:2022]. + +# Acknowledgements + +xCDAT is jointly developed by scientists and developers at Lawrence Livermore National Laboratory ([LLNL](https://www.llnl.gov/)) from the Energy Exascale Earth System Model ([E3SM](https://e3sm.org/)) Project and Program for Climate Model Diagnosis and Intercomparison ([PCMDI](https://pcmdi.llnl.gov/)). The work is performed for the E3SM project, which is sponsored by Earth System Model Development ([ESMD](https://climatemodeling.science.energy.gov/program/earth-system-model-development)) program, and the Simplifying ESM Analysis Through Standards ([SEATS](https://www.seatstandards.org/)) project, which is sponsored by the Regional and Global Model Analysis ([RGMA](https://climatemodeling.science.energy.gov/program/regional-global-model-analysis)) program. ESMD and RGMA are programs for the Earth and Environmental Systems Sciences Division ([EESSD](https://science.osti.gov/ber/Research/eessd)) in the Office of Biological and Environmental Research ([BER](https://science.osti.gov/ber)) within the [Department of Energy](https://www.energy.gov/)'s [Office of Science](https://science.osti.gov/). This work is performed under the auspices of the U.S. Department of Energy by LLNL under Contract No. DE-AC52-07NA27344. + +Thank you to all of the xCDAT contributors and users including Rob Jacob, Ana Ordonez, Mark Zelinka, Christopher Terai, Min-Seop Ahn, Celine Bonfils, Jean-Yves Peterschmitt, Olivier Marti, Andrew Manaster, and Andrew Friedman. We also give a special thanks to Karl Taylor, Peter Gleckler, Paul Durack, and Chris Golaz who all have provided valuable knowledge and guidance throughout the course of this project. + +# References From 1d57e25124d621b76ce402d90b6f21bad605f134 Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Mon, 24 Jun 2024 12:59:39 -0700 Subject: [PATCH 2/9] Bump to v0.7.1 (#667) --- .github/workflows/build_workflow.yml | 3 ++- HISTORY.rst | 37 +++++++++++++++++++++++++++- conda-env/ci.yml | 2 +- conda-env/dev.yml | 2 +- docs/paper/paper.md | 2 +- setup.py | 2 +- tbump.toml | 2 +- xcdat/__init__.py | 2 +- 8 files changed, 44 insertions(+), 8 deletions(-) diff --git a/.github/workflows/build_workflow.yml b/.github/workflows/build_workflow.yml index 3dcbf886..e8943794 100644 --- a/.github/workflows/build_workflow.yml +++ b/.github/workflows/build_workflow.yml @@ -95,7 +95,8 @@ jobs: run: | mamba env update -n xcdat_ci -f conda-env/ci.yml # Make sure the Python version in the env matches the current matrix version. - mamba install -c conda-forge python=${{ matrix.python-version }} + # Make sure numpy is not > 2.0. + mamba install -c conda-forge python=${{ matrix.python-version }} "numpy>=1.23.0,<2.0" - name: Install xcdat # Source: https://github.com/conda/conda-build/issues/4251#issuecomment-1053460542 diff --git a/HISTORY.rst b/HISTORY.rst index 9afad5bf..e48160b7 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,41 @@ History ======= +v0.7.1 (24 June 2024) +---------------------- + +This patch release fixes a bug in the Regrid2 API where a static order of dimensions are +incorrectly expected. It updates ``add_missing_bounds()`` to convert ``np.timedelta64`` +values to ``pandas.Timedelta`` objects to support Xarray's datetime component +accessor. + +This release also includes numerous updates to the documentation, including adding +a general guide to parallel computing with Dask notebook. It also ensures all existing +notebooks and documentation are up to date with the latest and relevant information. + +Bug Fixes +~~~~~~~~~ + +- Fixes regrid2 mapping output to input ordering by `Jason Boutte`_ + in https://github.com/xCDAT/xcdat/pull/653 +- Update ``add_missing_bounds()`` to convert ``np.timedelta64`` to ``pd.Timedelta`` + to support Xarray's datetime component accessor `_Jiwoo Lee` in https://github.com/xCDAT/xcdat/pull/660 + +Documentation +~~~~~~~~~~~~~ + +- Add JOSS paper by `Tom Vo`_ in https://github.com/xCDAT/xcdat/pull/567 +- Add Parallel Computing with Dask Jupyter Notebook by `Tom Vo`_ in https://github.com/xCDAT/xcdat/pull/489 +- Update regridding notebook for v0.7.0 by `Jill Chengzhu Zhang`_ in https://github.com/xCDAT/xcdat/pull/646 +- Update FAQs, HPC guide, and Gentle Introduction by `Tom Vo`_ in https://github.com/xCDAT/xcdat/pull/650 +- Simplify the contributing guide by `Tom Vo`_ in https://github.com/xCDAT/xcdat/pull/593 +- Update notebook env setup instructions with kernel by `Tom Vo`_ in https://github.com/xCDAT/xcdat/pull/652 +- Add instructions for setting `ESMFMKFILE` and update links to xESMF docs by `Tom Vo`_ in https://github.com/xCDAT/xcdat/pull/643 +- Temporal average notebooks maintanance by `Jiwoo Lee`_ in https://github.com/xCDAT/xcdat/pull/633 +- Review of spatial averaging and general dataset utilities by `Stephen Po-Chedley`_ in https://github.com/xCDAT/xcdat/pull/644 + +**Full Changelog**: https://github.com/xCDAT/xcdat/compare/v0.7.0...v0.7.1 + v0.7.0 (10 April 2024) ---------------------- @@ -54,7 +89,7 @@ Deprecations - Remove CDML/XML support from ``open_dataset()`` and ``open_mfdataset()`` since CDAT is EOL since Dec/2023 -**Full Changelog**: https://github.com/xCDAT/xcdat/compare/v0.6.1...0.7.0 +**Full Changelog**: https://github.com/xCDAT/xcdat/compare/v0.6.1...v0.7.0 v0.6.1 (29 November 2023) ------------------------- diff --git a/conda-env/ci.yml b/conda-env/ci.yml index 00b61efc..38cca780 100644 --- a/conda-env/ci.yml +++ b/conda-env/ci.yml @@ -11,7 +11,7 @@ dependencies: - cftime - dask - netcdf4 - - numpy >=1.23.0 + - numpy >=1.23.0,<2.0 - pandas - python-dateutil - xarray >=2022.02.0 diff --git a/conda-env/dev.yml b/conda-env/dev.yml index 7a3537fd..0951d196 100644 --- a/conda-env/dev.yml +++ b/conda-env/dev.yml @@ -11,7 +11,7 @@ dependencies: - cftime - dask - netcdf4 - - numpy >=1.23.0 + - numpy >=1.23.0,<2.0 - pandas - python-dateutil - xarray >=2022.02.0 diff --git a/docs/paper/paper.md b/docs/paper/paper.md index d9e05588..9680610a 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -27,7 +27,7 @@ authors: affiliations: - name: Lawrence Livermore National Lab, Livermore, USA index: 1 -date: 24 July 2024 +date: 24 June 2024 bibliography: paper.bib --- diff --git a/setup.py b/setup.py index fb239f6d..0e2bd481 100755 --- a/setup.py +++ b/setup.py @@ -35,6 +35,6 @@ test_suite="tests", tests_require=test_requires, url="https://github.com/xCDAT/xcdat", - version="0.7.0", + version="0.7.1", zip_safe=False, ) diff --git a/tbump.toml b/tbump.toml index 459175be..e7750dfa 100644 --- a/tbump.toml +++ b/tbump.toml @@ -2,7 +2,7 @@ github_url = "https://github.com/xCDAT/xcdat" [version] -current = "0.7.0" +current = "0.7.1" # Example of a semver regexp. # Make sure this matches current_version before diff --git a/xcdat/__init__.py b/xcdat/__init__.py index 9680627b..739c1fce 100644 --- a/xcdat/__init__.py +++ b/xcdat/__init__.py @@ -20,4 +20,4 @@ from xcdat.temporal import TemporalAccessor # noqa: F401 from xcdat.utils import compare_datasets # noqa: F401 -__version__ = "0.7.0" +__version__ = "0.7.1" From e1b5678bd44f9b140babf626deef1459a62ea543 Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Mon, 1 Jul 2024 11:26:02 -0700 Subject: [PATCH 3/9] Add JOSS badge to README (#674) --- README.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index c99095ce..e1f57f65 100644 --- a/README.rst +++ b/README.rst @@ -17,7 +17,7 @@ +====================+======================================================+ | Distribution | |conda-forge| |platforms| |conda-downloads| | +--------------------+------------------------------------------------------+ - | Citation | |zenodo-doi| | + | Citation | |zenodo-doi| |joss| | +--------------------+------------------------------------------------------+ | DevOps | |CI/CD Build Workflow| |codecov| |docs| | +--------------------+------------------------------------------------------+ @@ -36,6 +36,8 @@ :target: https://anaconda.org/conda-forge/xcdat .. |zenodo-doi| image:: https://zenodo.org/badge/354103918.svg :target: https://zenodo.org/badge/latestdoi/354103918 +.. |joss| image:: https://joss.theoj.org/papers/10.21105/joss.06426/status.svg + :target: https://doi.org/10.21105/joss.06426 .. |CI/CD Build Workflow| image:: https://github.com/xCDAT/xcdat/actions/workflows/build_workflow.yml/badge.svg :target: https://github.com/xCDAT/xcdat/actions/workflows/build_workflow.yml .. |docs| image:: https://readthedocs.org/projects/xcdat/badge/?version=latest From 7751452c0f1e68c7adb51098ac4cef774769158a Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Wed, 3 Jul 2024 10:51:00 -0700 Subject: [PATCH 4/9] Add SciPy 2024 talk material (#658) --- .gitignore | 5 + docs/_static/anaconda-logo.png | Bin 0 -> 73066 bytes docs/_static/conda-forge-logo.png | Bin 0 -> 13617 bytes docs/_static/matplotlib-logo.svg | 552 ++ docs/_static/pangeo-logo.png | Bin 0 -> 5234 bytes docs/_static/thumbnails/scipy-logo.png | Bin 0 -> 16165 bytes docs/_static/xarray-logo.png | Bin 106653 -> 88862 bytes docs/_static/xgcm-logo.png | Bin 0 -> 37659 bytes docs/demos.rst | 4 +- docs/demos.yml | 10 +- .../introduction-to-xcdat.ipynb | 4 +- .../24-07-11-scipy-2024/images/dask-guide.png | Bin 0 -> 78218 bytes .../images/joss-perf-metrics.png | Bin 0 -> 26551 bytes .../images/livermore-logo.png | Bin 0 -> 24092 bytes .../24-07-11-scipy-2024/images/scipy-logo.png | Bin 0 -> 16165 bytes docs/demos/24-07-11-scipy-2024/images/tom.jpg | Bin 0 -> 950116 bytes .../24-07-11-scipy-2024/scipy-2024.ipynb | 6300 +++++++++++++++++ .../tas_day_wget_script_2024-6-25_11-31-0.sh | 1059 +++ 18 files changed, 7929 insertions(+), 5 deletions(-) create mode 100644 docs/_static/anaconda-logo.png create mode 100644 docs/_static/conda-forge-logo.png create mode 100644 docs/_static/matplotlib-logo.svg create mode 100644 docs/_static/pangeo-logo.png create mode 100644 docs/_static/thumbnails/scipy-logo.png create mode 100644 docs/_static/xgcm-logo.png create mode 100644 docs/demos/24-07-11-scipy-2024/images/dask-guide.png create mode 100644 docs/demos/24-07-11-scipy-2024/images/joss-perf-metrics.png create mode 100644 docs/demos/24-07-11-scipy-2024/images/livermore-logo.png create mode 100644 docs/demos/24-07-11-scipy-2024/images/scipy-logo.png create mode 100644 docs/demos/24-07-11-scipy-2024/images/tom.jpg create mode 100644 docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb create mode 100644 docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr/tas_day_wget_script_2024-6-25_11-31-0.sh diff --git a/.gitignore b/.gitignore index 8b6882f0..2dff6984 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,8 @@ __pycache__/ *.py[cod] *$py.class +.DS_Store + # 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AMS 2023 Abstract demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb - ESGF2 - A Gentle Introduction to xCDAT (video introduction) + ESGF2 - A Gentle Introduction to xCDAT (Video Introduction) + SciPy 2024 (Abstract) + SciPy 2024 (Presentation Notebook) diff --git a/docs/demos.yml b/docs/demos.yml index 86086517..5323e1de 100644 --- a/docs/demos.yml +++ b/docs/demos.yml @@ -6,6 +6,14 @@ path: demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb thumbnail: _static/thumbnails/llnl-logo.png -- title: ESGF2 - A Gentle Introduction to xCDAT (video introduction) +- title: ESGF2 - A Gentle Introduction to xCDAT (Video Introduction, 03/23/24) path: https://youtu.be/sJpQ9vKDKa8?feature=shared thumbnail: _static/thumbnails/esgf2-logo.png + +- title: SciPy 2024 (Abstract, 07/11/24) + path: https://cfp.scipy.org/2024/talk/VRACYW/ + thumbnail: _static/thumbnails/scipy-logo.png + +- title: SciPy 2024 (Presentation Notebook, 07/11/24) + path: demos/24-07-11-scipy-2024/scipy-2024.ipynb + thumbnail: _static/thumbnails/scipy-logo.png diff --git a/docs/demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb b/docs/demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb index a25a1939..cc601b62 100644 --- a/docs/demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb +++ b/docs/demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": { "cell_style": "center", @@ -1546,7 +1545,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -4867,7 +4865,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.9" }, "rise": { "scroll": true, diff --git a/docs/demos/24-07-11-scipy-2024/images/dask-guide.png b/docs/demos/24-07-11-scipy-2024/images/dask-guide.png new file mode 100644 index 0000000000000000000000000000000000000000..e8a539a9f0fe0c01eb20434c9a468170cc46ac9c GIT binary patch literal 78218 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zU_P8wkXc(sF|?NH?N%d(C5vj{bI7czVUj0jhpGNmENo>TH$9JP=O@x`ZEpV2Zy{tq z!|7lD)&3J^JCB*tm(5uI_Z3o0i6w6?0aq=pKpUpy`c{Z44oy%p544h_wO+IHB%LIf zWkU>)QC|9!quQ)+Mk7pctI!H1o)ulvkTJ$So|Unr_)_~`U$tKo1-hS=M+Lw7)!RL_ zww8%!WSdSp){d2Cd6s4_$ET%Lx`JKVenxr&R_&djh^hHo`@YpVo6Ly$ndYdhiNTA4 zy}MS$wY--rx=w?jtt&k~MYt>n0=EaA06;PHsloxqLH49(XvhNy4Ooikvh88?#b1ID zgL9p~=hCju_VA`R=iAn)T&lDRTc@C`WH9YJzE)q#o%Yz^;PMY~R9f*>PWIi7wVfsR zI3wlarb(5Mv~BZt&riav!!^rpLIEcPe~n*-q$+Zt1@x*{H*OpO!1n20KkR_tj?gH74#Oe#esJblV#G@Z2F{?#J5>0N(rvcViIZVPDtI7)SoWGd&YN(zS~T)oGLwyj zZCv%jkViGw!L4|rIK{Qh(cDDJRFb%V&3<3fv8~~~bh=rFwU(4L10M+fN+;;MKVz6Ts1o@&`)FyMe%6ftPO;l4Y7He(yum zsjh@w0Sd@+nHc1Ll@8WoBsd2jhvip(Rlt-6Kai~m!h{E?9jZ;z89;~w#Z$yv{OMbf z*zx&QmNs(R;^8^|9`#4;v51CHpJ-Gh>>sP92wx4IPOG%hB zfrdW#s~M8qNbwy1013~hwOT+Q3P=Or+N&&TODtRy*PgWLQ4&q7?0&TPl{~`i2nX7y zk_44Ppu;XIyIZRO$F(vjJjkDKLOb#KSO3=j6?{i}TUDAup-VSFFbB|9G%>WeEn0Df zh+zj6IgwCqCly)dl2e7?RbtGts-AFbsj*)&WQ!etO7-gxw8~P?PEhh=8R!77OSSOD zwVkMv7T^KOt5owgJ4!$6kQ(P^y)D5!0nudHnB{Sm&n{*kWzr_WaC0&lRC@ZtD)z=D4Yr zNM`bzw>dQ$#~h3w?``ZoYI3ob3$*m?IH%2FYTti5O+gl$EGwAs2W<2Lqq}pyu+N<>9o}F=0U>b>|s5Tmq06JE_nJPi3!888=1F6TbBEHdqoh0)h;xpUpTTzUz z#{-_&s}Y9Vv3icYcBy$(K2{WCpZ@?|RtjP`&Q-C2{xw-Ydv+qm-)h}^Xkb-IUw5ac z=~d*0*!Ld)0MfIRM*EcDim*3Dg^!`)w8OUVE?(SY=}fvumNKf`d)5(2;haeRxW#KU zC6ucGqZtFeR%9v^r_2XZ-^#6L&0a7LMLab5;D%5;RY`7)D$J{m*aJN=USHzh7wS4a z$kS~TeZyn7O!`-4;ayK#eJjt_;r-@tfM8@-SvH|*YhxtS4Z9aP^a8x!!D_Q=S2yn& zW1bJ!o<9op6}X95O%NC&tXy2@E4M1ZU{a}x<4`!r\n", + " \"xCDAT\n", + "\n", + "\n", + "
    \n", + " \"SciPy\n", + "
    \n", + "\n", + "# SciPy 2024 (07/11/2024) - xCDAT: A Python Package for Simple and Robust Analysis of Climate Data\n", + "\n", + "
    \n", + "

    \n", + " Core Developers: Tom Vo, Stephen Po-Chedley, Jason Boutte, Jill Zhang, Jiwoo Lee\n", + "

    \n", + "

    With thanks to Peter Gleckler, Paul Durack, Karl Taylor, and Chris Golaz

    \n", + "
    \n", + "\n", + "---\n", + "\n", + "

    This work is performed under the auspices of the U. S. DOE by Lawrence Livermore National Laboratory under contract No. DE-AC52-07NA27344.

    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + }, + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Notebook Setup\n", + "\n", + "Create an Anaconda environment for this notebook using the command below:\n", + "\n", + "```bash\n", + "conda create -n xcdat_scipy_2024 -c conda-forge xcdat=0.7.0 nco matplotlib ipython ipykernel cartopy nc-time-axis gsw-xarray jupyter jupyter_contrib_nbextensions rise wget\n", + "\n", + "conda activate xcdat_scipy_2024\n", + "```\n", + "\n", + "Then run:\n", + "\n", + "```bash\n", + "jupyter contrib nbextension install --user\n", + "\n", + "jupyter nbextension enable splitcell/splitcell\n", + "```\n", + "\n", + "To open Jupyter Notebook GUI:\n", + "\n", + "```bash\n", + "jupyter notebook\n", + "```\n", + "\n", + "To print notebook as PDF:\n", + "\n", + "```bash\n", + "jupyter nbconvert docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb --to html\n", + "# Then \"Print HTML to PDF\" in your browser\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Before we start...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "### Who am I?\n", + "\n", + "- My name is Tom Vo\n", + "- **Climate Research Software Engineer** at **Lawrence Livermore National Laboratory (LLNL)**\n", + "- Work on the **Energy Exascale Earth System Model (E3SM)** and **Simplifying ESM Analysis Through Standards (SEATS)** projects\n", + "\n", + "
    \n", + " \"Tom\n", + " \"LLNL\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "### What do I do?\n", + "\n", + "- Lead developer of xCDAT (roadmap planning, end-to-end development)\n", + "- Contributor and DevOps engineer for various E3SM tools\n", + "\n", + "
    \n", + " \"xCDAT\n", + " \"E3SM\n", + " \"SEATS\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + }, + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## An Overview of this Talk\n", + "\n", + "1. The driving force behind xCDAT\n", + "2. Basic intro to Xarray\n", + "3. History, scope, and mission of xCDAT\n", + "4. Design philosophy and key features of xCDAT\n", + "5. Technical demo: end-to-end analysis workflow\n", + "6. Parallelism with xCDAT and Dask\n", + "7. xCDAT’s community and how to get involved\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Driving Force Behind xCDAT\n", + "\n", + "- CDAT (Community Data Analysis Tools) library is the predecessor to xCDAT.\n", + "- CDAT has provided open-source climate data analysis and visualization packages for over 20 years.\n", + "- Since CDAT’s inception, the volume of climate data has grown substantially as a result of:\n", + " - Larger pool of data products\n", + " - Increasing spatiotemporal resolution of model and observational data.\n", + "\n", + "
    \n", + "\"CDAT\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "### CDAT has been end-of-life as of December 2023\n", + "\n", + "- This presents an issue for the many users and packages that depend on CDAT\n", + "- All of these factors sparked a driving need for new analysis software\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### What should this new analysis software (aka xCDAT) offer?\n", + "\n", + "- Offer similar core capabilities as CDAT\n", + " - e.g., geospatial averaging, temporal averaging, regridding\n", + "- Use modern technologies in the library’s stack\n", + " - Capable of handling large datasets (e.g., parallelism, lazy operations)\n", + "- Be maintainable, extensible, and easy-to-use\n", + " - Python Enhancement Proposals (PEPs)\n", + "- Automated DevOps processes (unit testing, code coverage)\n", + " - Actively maintained documentation\n", + "- An open-source community that can sustain the project\n", + " - Serve the needs of the climate community in the long-term\n", + " - Community engagement efforts (e.g., Pangeo, ESGF)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "
    \n", + "\"Xarray\n", + "

    \"N-D labeled arrays and datasets in Python\"

    \n", + "
    \n", + "\n", + "**Xarray is the core technology of xCDAT**\n", + "\n", + "**Why Xarray?**\n", + "\n", + "- Mature, stable, widely adopted\n", + "- Introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays\n", + "- Intuitive, more concise, and less error-prone user experience\n", + "\n", + "**Key features include:**\n", + "\n", + "- File I/O, indexing and selecting, interpolating, grouping, aggregating, parallelism (Dask), plotting (matplotlib wrapper)\n", + "- Supports various file formats netCDF, Iris, OPeNDAP, Zarr, and more\n", + "- Interoperability with scientific Python ecosystem such as NumPy, Dask, Pandas, and Matplotlib\n", + "\n", + "
    \n", + " \"NumFocus\n", + " \"NumPy\n", + " \"NumPy\n", + " \"Pandas\n", + "\n", + "
    \n", + "\n", + "
    \n", + "\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "
    \n", + "\"xCDAT\n", + "

    Xarray Climate Data Analysis Tools for Structured Grid Analysis

    \n", + "
    \n", + "\n", + "- Collaboration between:\n", + "\n", + "
    \n", + " \"E3SM\n", + " \"PCMDI\n", + " \"SEATS\n", + "
    \n", + "\n", + "- xCDAT is an extension of Xarray for climate data analysis on structured grids, a modern successor to the Community Data Analysis Tools (CDAT) library\n", + "- Scope is focused on routine climate research analysis operations such as loading, wrangling, averaging, and regridding data\n", + "- Aims to provide features and utilities for simple and robust analysis of climate data\n", + "- Leverages other powerful Xarray-based packages such as xESMF, xgcm, and cf-xarray\n", + "\n", + "
    \n", + " \"Xarray\n", + " \"ESMF\n", + " \"xgcm\n", + " \"CF\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "
    \n", + "

    xCDAT features and utilities for simple, robust, and less error-prone analysis code

    \n", + "
    \n", + "\n", + "- Extension of `xr.open_dataset()` and `xr.open_mfdataset()` with post-processing options\n", + "- Robust interpretation of Climate and Forecast (CF) Compliant metadata using `cf-xarray`\n", + "- Generate missing bounds, center time coords, convert lon axis orientation\n", + "- Geospatial weighted averaging\n", + "- Temporal averaging, climatologies, departures\n", + "- Horizontal structured regridding (extension of xESMF and Python port of regrid2)\n", + "- Vertical structured regridding (extension of xGCM)\n", + "\n", + "
    \n", + " \"Spatial\n", + " \"Temporal\n", + " \"Vertical\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "
    \n", + "

    The Software Design Philosophy of xCDAT

    \n", + "
    \n", + "\n", + "- Intentionality designed to **encourage software sustainability** and **reproducible science**\n", + "- **Well-documented and configurable features** allow scientists to rapidly develop robust, reusable, less-error prone, more maintainable code\n", + "- xCDAT aims to contribute to **Pangeo's** effort to foster an ecosystem of mutually compatible geoscience Python packages\n", + "\n", + "
    \n", + " \"Pangeo\n", + " \"PCMDI\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### xCDAT simplifies Xarray code for specific analysis operations\n", + "\n", + "#### A comparison of code to calculate global-mean, monthly anomalies.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "HTTP download link: https://esgf-data04.diasjp.net/thredds/fileServer/esg_dataroot/CMIP6/CMIP/E3SM-Project/E3SM-2-0/historical/r1i1p1f1/Amon/tas/gr/v20220830/tas_Amon_E3SM-2-0_historical_r1i1p1f1_gr_200001-201412.nc\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "\n", + "dpath = Path(\n", + " \"docs/demos/24-07-11-scipy-2024/tas_Amon_E3SM-2-0_historical_r1i1p1f1_gr_200001-201412.nc\"\n", + ")\n", + "if not dpath.is_file():\n", + " dpath = \"http://esgf-data04.diasjp.net/thredds/dodsC/esg_dataroot/CMIP6/CMIP/E3SM-Project/E3SM-2-0/historical/r1i1p1f1/Amon/tas/gr/v20220830/tas_Amon_E3SM-2-0_historical_r1i1p1f1_gr_200001-201412.nc\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_style": "center", + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Code Comparison (Xarray vs. xCDAT)\n", + "\n", + "xCDAT uses less code, is more flexible, and is easier to read/write.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "# 1. Open the dataset.\n", + "ds = xr.open_dataset(dpath)\n", + "\n", + "# 2. Calculate monthly departures.\n", + "tas_mon = ds.tas.groupby(\"time.month\")\n", + "tas_mon_clim = tas_mon.mean(dim=\"time\")\n", + "tas_anom = tas_mon - tas_mon_clim\n", + "\n", + "# 3. Compute global average.\n", + "coslat = np.cos(np.deg2rad(ds.lat))\n", + "tas_anom_wgt = tas_anom.weighted(coslat)\n", + "tas_anom_global = tas_anom_wgt.mean(dim=\"lat\").mean(dim=\"lon\")\n", + "\n", + "# 4. Calculate annual averages.\n", + "# ncar.github.io/esds/posts/2021/yearly-averages-xarray/\n", + "mon_len = tas_anom_global.time.dt.days_in_month\n", + "mon_len_by_year = mon_len.groupby(\"time.year\")\n", + "wgts = mon_len_by_year / mon_len_by_year.sum()\n", + "\n", + "temp_sum = tas_anom_global * wgts\n", + "temp_sum = temp_sum.resample(time=\"YS\").sum(dim=\"time\")\n", + "denom_sum = (wgts).resample(time=\"YS\").sum(dim=\"time\")\n", + "\n", + "tas_anom_global_ann = temp_sum / denom_sum" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "import xcdat as xc\n", + "\n", + "# 1. Open the dataset.\n", + "ds = xc.open_dataset(dpath)\n", + "\n", + "# 2. Calculate monthly departures.\n", + "ds_anom = ds.temporal.departures(\"tas\", freq=\"month\")\n", + "\n", + "# 3. Compute global average.\n", + "ds_anom_glb = ds_anom.spatial.average(\"tas\")\n", + "\n", + "# 4. Calculate annual averages\n", + "ds_anom_glb_ann = ds_anom_glb.temporal.group_average(\"tas\", freq=\"year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Plot the outputs\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2)\n", + "fig.set_size_inches(12, 6)\n", + "fig.suptitle(\"Global Mean Monthly Anomalies\", fontsize=16)\n", + "\n", + "# Add the subplots\n", + "tas_anom_global_ann.plot(ax=ax1)\n", + "ds_anom_glb_ann.tas.plot(ax=ax2)\n", + "\n", + "# Configure subplots\n", + "ax1.set_ylabel(\"Near-Surface Temperature Anomalies [K]\")\n", + "ax2.set_ylabel(\"Near-Surface Temperature Anomalies [K]\")\n", + "ax1.title.set_text(\"Xarray\")\n", + "ax2.title.set_text(\"xCDAT\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Getting Started with xCDAT\n", + "\n", + "- xCDAT is available for installation through Anaconda on the `conda-forge` channel\n", + " - Install command: `conda install -c conda-forge xcdat`\n", + "- Check out xCDAT’s Read the Docs, which we strive to keep up-to-date\n", + " - https://xcdat.readthedocs.io/en/stable/\n", + "\n", + "
    \n", + " \"Anaconda\n", + " \"Conda\n", + "
    \n", + "\n", + "
    \n", + " \"Read\n", + " \"xCDAT\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_style": "center", + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### How to use xCDAT\n", + "\n", + "- xCDAT extends Xarray Dataset objects via \"accessor\" classes.\n", + "\n", + "
    \n", + " \"accessor\n", + "
    In the example above, custom spatial functionality is exposed by chaining the `spatial` accessor attribute to the Dataset object. This chaining enables access to the underlying `spatial.average()` method.
    \n", + "
    \n", + "\n", + "#### Accessors classes include:\n", + "\n", + "- `spatial`\n", + " - `.average()`, `.get_weights()`\n", + "- `temporal`\n", + " - `.average()`, `.group_average()`, `.climatology()`, `.depatures()`\n", + "- `bounds`\n", + " - `.get_bounds()`, `.add_bounds()`, `.add_missing_bounds()`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### xCDAT also provides general utilities as Python functions\n", + "\n", + "- `open_dataset()`, `open_mfdataset()`\n", + "- `center_times()`, `decode_time()`\n", + "- `swap_lon_axis()`\n", + "- `create_axis()`\n", + "- `create_grid()`\n", + "- `get_dim_coords()`\n", + "- `get_dim_keys()`\n", + "\n", + "Visit the API Reference page for a complete list: https://xcdat.readthedocs.io/en/latest/api.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## End-to-End Analysis and Visualization of CMIP Data using xCDAT\n", + "\n", + "### Overview\n", + "\n", + "This exercise will walkthrough using `xcdat` to perform computation and analysis\n", + "on E3SM v2 CMIP6 data.\n", + "\n", + "### Sections\n", + "\n", + "1. Setup Code\n", + "2. I/O\n", + "3. Horizontal Regridding\n", + "4. Vertical Regridding\n", + "5. Spatial Averaging\n", + "6. Temporal Computations\n", + "7. General Dataset Utilities\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 1. Setup Code\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This style import is necessary to properly render Xarray's HTML output with\n", + "# the Jupyer RISE extension.\n", + "# GitHub Issue: https://github.com/damianavila/RISE/issues/594\n", + "# Source: https://github.com/smartass101/xarray-pydata-prague-2020/blob/main/rise.css\n", + "\n", + "from IPython.core.display import HTML\n", + "\n", + "style = \"\"\"\n", + "\n", + "\"\"\"\n", + "\n", + "\n", + "HTML(style)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from xarray.coding.calendar_ops import _datetime_to_decimal_year as dt2decimal\n", + "import xcdat as xc\n", + "import cartopy.crs as ccrs\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 2. I/O\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "We will be analyzing E3SM v2 CMIP6 monthly near-sea surface air temperature (`tas`) data from 2000 to 2014.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "- Use `xc.open_dataset()` to a single netCDF dataset as an `xr.Dataset` object.\n", + "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xcdat.open_dataset.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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    +       "dask.array<open_dataset-tas, shape=(180, 180, 360), dtype=float32, chunksize=(180, 180, 360), chunktype=numpy.ndarray>\n",
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    +       "Attributes:\n",
    +       "    standard_name:  air_temperature\n",
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    +       "    comment:        near-surface (usually, 2 meter) air temperature\n",
    +       "    units:          K\n",
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    +       "    cell_measures:  area: areacella\n",
    +       "    history:        2022-08-31T00:41:19Z altered by CMOR: Treated scalar dime...
    " + ], + "text/plain": [ + " Size: 47MB\n", + "dask.array\n", + "Coordinates:\n", + " * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n", + " * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n", + " height float64 8B ...\n", + " * time (time) object 1kB 2000-01-16 12:00:00 ... 2014-12-16 12:00:00\n", + "Attributes:\n", + " standard_name: air_temperature\n", + " long_name: Near-Surface Air Temperature\n", + " comment: near-surface (usually, 2 meter) air temperature\n", + " units: K\n", + " cell_methods: area: time: mean\n", + " cell_measures: area: areacella\n", + " history: 2022-08-31T00:41:19Z altered by CMOR: Treated scalar dime..." + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xc.open_dataset(dpath, chunks={\"time\": \"auto\"})\n", + "\n", + "ds.tas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 3. Horizonal Regridding\n", + "\n", + "We often want to regrid a dataset to a new grid to facilitate data analysis or comparisons with other datasets.\n", + "\n", + "The current dataset is on a standard 1 x 1o, so we'll start be remapping it to a 4 x 4o grid.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "#### First, we specify the target grid\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "# create target axes\n", + "nlat = xc.create_axis(\n", + " \"lat\", np.arange(-88, 90, 4), attrs={\"units\": \"degrees_north\", \"axis\": \"Y\"}\n", + ")\n", + "nlon = xc.create_axis(\n", + " \"lon\", np.arange(2, 360, 4), attrs={\"units\": \"degrees_east\", \"axis\": \"X\"}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Then we create the target grid using the target axes and bounds.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "- API Documentation: https://xcdat.readthedocs.io/en/latest/generated/xcdat.create_grid.html#xcdat.create_grid\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ngrid = xc.create_grid(x=nlon, y=nlat)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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... 68 72 76 80 84 88\n", + "Attributes:\n", + " standard_name: air_temperature\n", + " long_name: Near-Surface Air Temperature\n", + " comment: near-surface (usually, 2 meter) air temperature\n", + " units: K\n", + " cell_methods: area: time: mean\n", + " cell_measures: area: areacella\n", + " history: 2022-08-31T00:41:19Z altered by CMOR: Treated scalar dime...\n", + " regrid_method: bilinear" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_xesmf = ds.regridder.horizontal(\"tas\", ngrid, tool=\"xesmf\", method=\"bilinear\")\n", + "\n", + "ds_xesmf_res = ds_xesmf.compute()\n", + "ds_xesmf_res.tas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Compare the results (for the first timestep)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'xESMF 4$^{\\\\circ}$ x 4$^{\\\\circ}$')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "map_proj = ccrs.Robinson()\n", + "\n", + "# plot original data (first time step)\n", + "plt.figure(figsize=(12, 5))\n", + "plt.subplot(1, 2, 1, projection=map_proj)\n", + "p = ds.tas[0].plot(\n", + " transform=ccrs.PlateCarree(), # the data's projection\n", + " subplot_kws={\"projection\": map_proj},\n", + " cbar_kwargs={\"orientation\": \"horizontal\"},\n", + " cmap=plt.cm.RdBu_r,\n", + ")\n", + "ax = plt.gca()\n", + "ax.coastlines()\n", + "plt.title(\"Original\")\n", + "\n", + "# plot the remapped data (first time step)\n", + "plt.subplot(1, 2, 2, projection=map_proj)\n", + "p = ds_xesmf.tas[0].plot(\n", + " transform=ccrs.PlateCarree(), # the data's projection\n", + " subplot_kws={\"projection\": map_proj},\n", + " cbar_kwargs={\"orientation\": \"horizontal\"},\n", + " cmap=plt.cm.RdBu_r,\n", + ")\n", + "ax = plt.gca()\n", + "ax.coastlines()\n", + "plt.title(\"xESMF 4$^{\\circ}$ x 4$^{\\circ}$\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 4. Vertical Regridding\n", + "\n", + "xCDAT can also regrid in the vertical dimension.\n", + "\n", + "Here we'll grab some cloud fraction data (`cl`) and regrid it from model hybrid coordinate to pressure levels.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "Documentation: https://xcdat.readthedocs.io/en/stable/examples/regridding-vertical.html#4:-Remap-cloud-fraction-from-model-hybrid-coordinate-to-pressure-levels\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "data_url = \"http://esgf-data04.diasjp.net/thredds/dodsC/esg_dataroot/CMIP6/CMIP/E3SM-Project/E3SM-2-0/historical/r1i1p1f1/Amon/cl/gr/v20220830/cl_Amon_E3SM-2-0_historical_r1i1p1f1_gr_200001-201412.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    <xarray.DataArray 'cl' (time: 180, lev: 72, lat: 180, lon: 360)> Size: 3GB\n",
    +       "dask.array<open_dataset-cl, shape=(180, 72, 180, 360), dtype=float32, chunksize=(7, 72, 180, 360), chunktype=numpy.ndarray>\n",
    +       "Coordinates:\n",
    +       "  * lev      (lev) float64 576B 0.9985 0.9938 0.9862 ... 0.0001828 0.0001238\n",
    +       "  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
    +       "  * lon      (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
    +       "  * time     (time) object 1kB 2000-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
    +       "Attributes:\n",
    +       "    standard_name:  cloud_area_fraction_in_atmosphere_layer\n",
    +       "    long_name:      Percentage Cloud Cover\n",
    +       "    comment:        Percentage cloud cover, including both large-scale and co...\n",
    +       "    units:          %\n",
    +       "    cell_methods:   area: time: mean\n",
    +       "    cell_measures:  area: areacella\n",
    +       "    history:        2022-08-30T15:06:03Z altered by CMOR: Inverted axis: lev.\n",
    +       "    _ChunkSizes:    [  1  36  90 180]
    " + ], + "text/plain": [ + " Size: 3GB\n", + "dask.array\n", + "Coordinates:\n", + " * lev (lev) float64 576B 0.9985 0.9938 0.9862 ... 0.0001828 0.0001238\n", + " * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n", + " * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n", + " * time (time) object 1kB 2000-01-16 12:00:00 ... 2014-12-16 12:00:00\n", + "Attributes:\n", + " standard_name: cloud_area_fraction_in_atmosphere_layer\n", + " long_name: Percentage Cloud Cover\n", + " comment: Percentage cloud cover, including both large-scale and co...\n", + " units: %\n", + " cell_methods: area: time: mean\n", + " cell_measures: area: areacella\n", + " history: 2022-08-30T15:06:03Z altered by CMOR: Inverted axis: lev.\n", + " _ChunkSizes: [ 1 36 90 180]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_3d = xc.open_dataset(data_url, chunks={\"time\": \"auto\"})\n", + "\n", + "ds_3d.cl" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Regrid the `cl` variable using `regridder.vertical()` with:\n", + "\n", + "- `new_pressure_grid` as the output grid\n", + "- `\"linear\"` method\n", + "- `pressure_data` as the target data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/xcdat_scipy_2024/lib/python3.11/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "def hybrid_coordinate(p0, a, b, ps, **kwargs):\n", + " return a * p0 + b * ps\n", + "\n", + "\n", + "pressure_data = hybrid_coordinate(**ds_3d.data_vars)\n", + "new_pressure_grid = xc.create_grid(z=xc.create_axis(\"lev\", np.linspace(100000, 1, 13)))\n", + "\n", + "ds_vr = ds_3d.regridder.vertical(\n", + " \"cl\", new_pressure_grid, method=\"linear\", target_data=pressure_data\n", + ")\n", + "ds_vr_res = ds_vr.compute()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Finally, we plot the result:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "image/png": 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yOgnQ+9ihpQN6fKOJtxwgIiIi8gJ7moiIiIKc307P9XBsNBEREQU5vR9Oz+n9k0pA4+k5IiIiIi+wp4mIiCjI8fScd9jTdAGeeeYZDBw4ECEhIUhLS8Mnn3zS1SkRERFdMPfpOV+Hno6NpnZ69dVXkZ2djUWLFuHAgQP4yU9+ghtuuAEnT57s6tSIiIioA7HR1E7Lli3DrFmzcM8992Do0KFYvnw5kpOT8eyzz3Z1akRERBfEfXrO16GnY6OpHex2O4qKipCRkaEpz8jIwI4dO1pdxmazoaqqSjMQEREFEglyg8CXoec3mdhoapcffvgBTqcT8fHxmvL4+HiUlZW1ukxeXh6sVqs6JCcnd0aqREREXmNPk3fYaLoALR+eKIRo84GKjz76KCorK9WhpKSkM1IkIiIiP+MtB9ohNjYWer3eo1epvLzco/fJzWw2w2w2d0Z6REREF4Q3t/QOe5rawWQyIS0tDYWFhZrywsJCjB49uouyIiIi8o3caPL19FxXb0XHY09TOz300EPIzMzEyJEjkZ6ejueffx4nT57Efffd19WpERERUQdio6mdfvGLX+D06dP4/e9/j9LSUqSmpuL9999H//79uzo1IiKiC8LTc95ho+kCZGVlISsrq6vTICIi8gs+RsU7vKaJiIiIyAvsaSIiIgpyOj+cnguGXhg2moiIiIIcT895JxgahkREREQ+Y08TERFRkOOv57zDRhMREVGQY6PJO2w0ERERBTle0+QdNpq6SMw1WZD0pq5Og7opSadvdawp08tjvUE+znQGozJuOu50Rvm1wWTRzNO7p41Nse569QaDMi1/Qer1npdGulwCgPwwawAQyrTT4WiKcdjlcaNdiXG2Om7+2tXKPI9Yp3aeez1N9bo8lmtZ34VwtZJDW9vkj/VRzyec9q5OgVpgo4mIiCjI6eGH03PCL6kENDaaiIiIgpzOD6fndD4u3x3wlgNEREREXmBPExERUZDzy6/nen5HExtNREREwc4vv57j6TkiIiIiAtjTREREFPR4es47bDQREREFOZ6e8w5PzxERERF5gT1NREREQU4nST7fZykY7tPERhMREVGQk/SS+mikC66DjSYiIiLq6XR6CTofG03B0NPEa5qIiIiIvMCeJiIiomCn10HS+diPIvX8J/ayp4mIiCjISTpJvq7Jl6Edp/dycnIgSZJmSEhIUOcLIZCTk4OkpCRYLBaMHTsWhw8f7ohNbxc2moiIiKjTXXLJJSgtLVWHgwcPqvOWLl2KZcuWYeXKldi7dy8SEhIwfvx4VFdXd2HGPD1HREQU9HR6CTofb+mtQ/uWNxgMmt4lNyEEli9fjkWLFmHKlCkAgPz8fMTHx2P9+vWYM2eOT3n6gj1NREREQU7S6fwyAEBVVZVmsNlsra7zv//9L5KSkjBw4ED88pe/xJdffgkAOHHiBMrKypCRkaHGms1mjBkzBjt27Oj4nXEObDQRERGR3yQnJ8NqtapDXl6eR8yoUaPw0ksvYfPmzVi1ahXKysowevRonD59GmVlZQCA+Ph4zTLx8fHqvK7C03NERERBzp+n50pKShAZGamWm81mj9gbbrhBfT18+HCkp6fjRz/6EfLz83HVVVcB8LxZphCiy2+gyZ4mIiKiIOfzL+eUAQAiIyM1Q2uNppbCwsIwfPhw/Pe//1Wvc2rZq1ReXu7R+9TZ2GgiIiKiLmWz2XD06FEkJiZi4MCBSEhIQGFhoTrfbrdj27ZtGD16dBdmydNzREREQU/uKfKtH0WCy+vYBQsWYPLkyejXrx/Ky8vxxz/+EVVVVZgxYwYkSUJ2djZyc3ORkpKClJQU5ObmIjQ0FNOnT/cpR1+x0URERBTkOvuWA9988w2mTZuGH374Ab1798ZVV12FXbt2oX///gCAhQsXor6+HllZWaioqMCoUaNQUFCAiIgIn3L0FRtNREREQU6S2ndH71brcHm//IYNG86bT05ODnJycnzKyd94TRMRERGRF9jTREREFOR0eh10Pl7TpBM9vx+GjSYiIqIg1/yWARdch+jaeyh1BjaaqFuRdPo2p92v3WO90QQA0Bnksd4UosbqzRYAgMFkUeYp05ZwAIDRbFJjDSa5PpNZ/ri4L5bUGzz/q3JfEyBcQlPudHj/qxJ3vfpm//W5czAY5bIQixEA0CtUHltDm/KNi5DviZLQS97e6BA5JlzJ39ys3hBlXeEmeZ5Fqd+kbKOl2TaGGeWyEJ28bTp7LQBAamxQxvVqrGSXX0suh1zgkB+jIByNTTEGo7Jxcr6ukAjNWJibLvisdcnbf6bBCQD4tkqu78ipGgDA56VND/H8pqIOAHC6Us6rVoltqG1UxnY1ttEmv3ba5HydSt4uh1zusDdtk3A6NfOEy4nmmh+ZwqV9v92xLZdpzuWOcWpjJH1TzS3nnau+9sR4E9ueeoh6KjaaiIiIghx7mrzDRhMREVGQ4zVN3un5W0hERETkB+xpIiIiCnZ+OD0Hnp4jIiKink4nSdD5eHNLndTzG008PUdERETkBfY0ERERBTlJr/P9gb2unt8Pw0YTERFRkPPLA3vb8ey57oqNJiIioiDnl/s0BUGjqef3pRERERH5AXuaiIiIghyvafIOG01ERERBTqeHH65p8lMyAaznNwuJiIiI/IA9TUREREFO0kmQfLy5pa/LdwdsNBEREQU5nc4PD+x19vyTVz1/C4mIiIj8gD1NREREQc4v92ny9YG/3QAbTUREREHOL7cc8HH57qDnbyERERGRH7CniYiIKMhJOh0knY89TT4u3x0E9Bbm5eXhiiuuQEREBOLi4nDLLbfg2LFjmhghBHJycpCUlASLxYKxY8fi8OHDmhibzYYHHngAsbGxCAsLw0033YRvvvlGE1NRUYHMzExYrVZYrVZkZmbi7NmzmpiTJ09i8uTJCAsLQ2xsLObNmwe73d4h205ERNRZdHqdX4aeLqC3cNu2bZg7dy527dqFwsJCOBwOZGRkoLa2Vo1ZunQpli1bhpUrV2Lv3r1ISEjA+PHjUV1drcZkZ2dj48aN2LBhA7Zv346amhpMmjQJTqdTjZk+fTqKi4uxadMmbNq0CcXFxcjMzFTnO51OTJw4EbW1tdi+fTs2bNiAN954A/Pnz++cnUFERNRRlGuafBkQBI2mgD49t2nTJs306tWrERcXh6KiIvz0pz+FEALLly/HokWLMGXKFABAfn4+4uPjsX79esyZMweVlZV48cUX8fLLL2PcuHEAgLVr1yI5ORkffPABJkyYgKNHj2LTpk3YtWsXRo0aBQBYtWoV0tPTcezYMQwePBgFBQU4cuQISkpKkJSUBAB4+umnMXPmTCxZsgSRkZGduGeIiIios3WrZmFlZSUAIDo6GgBw4sQJlJWVISMjQ40xm80YM2YMduzYAQAoKipCY2OjJiYpKQmpqalqzM6dO2G1WtUGEwBcddVVsFqtmpjU1FS1wQQAEyZMgM1mQ1FRUZs522w2VFVVaQYiIqJAIul872niNU0BRAiBhx56CNdccw1SU1MBAGVlZQCA+Ph4TWx8fLw6r6ysDCaTCVFRUeeMiYuL81hnXFycJqbleqKiomAymdSY1uTl5anXSVmtViQnJ7dns4mIiDqc+0JwX4eertts4f3334/PPvsMr7zyisc8SdLeUEsI4VHWUsuY1uIvJKalRx99FJWVlepQUlJyzryIiIgoMHWLRtMDDzyAd955B1u2bEHfvn3V8oSEBADw6OkpLy9Xe4USEhJgt9tRUVFxzpjvv//eY72nTp3SxLRcT0VFBRobGz16oJozm82IjIzUDERERIFEPsWm93HoFk0KnwT0heBCCDzwwAPYuHEjtm7dioEDB2rmDxw4EAkJCSgsLMSIESMAAHa7Hdu2bcMTTzwBAEhLS4PRaERhYSGmTp0KACgtLcWhQ4ewdOlSAEB6ejoqKyuxZ88eXHnllQCA3bt3o7KyEqNHj1ZjlixZgtLSUiQmJgIACgoKYDabkZaW1uH7QmcwAQAknR4AYDBb5HFImBpjsIQDAEyhVgCAXonRm5RYo16NNZkNSr06ZVqeZ7IYAQBmS9Oh0SvCDACICZfHViUmIkSOMRmaPij6Fk+5bj6vpXq7/OtFp0toyi0mvUdd7nqMSvevUbldf2izbQo3yflEKMu7p00GOVaH5r2G7nyVsVLgXmXzrXBn53QpYyGX2J3y2OZwqbF1jfI2NShl7ul6ZeyeBoAqm0OzH1rujzp7U6y7zL1uN5OyAeZW3oPKukbNstVKfcZmjzpo2m5JmZbLDXr3/m6KjVCOGasyDjOFAAAsBvn4soTFqLFhvZTlIe8HySnnIrkcaoyQlJyVsdMg11ev7Luahqb9WlrTAAD47+k6AMCRUvnawL0nzgAAKr6vUWNrzsqxDbVybGOtfC2kw16PloTLqaQgHzM6Zeyedn+WgKbPkftzZTDJnwP356r550uvvB86ZYe6p1uPlTRlocrnKlQ5ji2mps+i+tlQ3q+Wx0PLz1LzsqZxs+NVOSbsyj53j9XYZse2ENoyl1M77XQ2xbpalDkd2rx0Os/eeanFV0VbPfuaaWWVLiVf0Wz73a+bcpDHDuUz6Gx2PDjqa5S87Up9TqUO7Vizbqe2TGeUv6P1yne1vE3a4+lc9bUk6fRwNdaj/OC688b6A+8I7p2AbjTNnTsX69evx9tvv42IiAi1p8dqtcJisUCSJGRnZyM3NxcpKSlISUlBbm4uQkNDMX36dDV21qxZmD9/PmJiYhAdHY0FCxZg+PDh6q/phg4diuuvvx6zZ8/Gc889BwC49957MWnSJAwePBgAkJGRgWHDhiEzMxNPPvkkzpw5gwULFmD27NnsPSIiIgoCAd1oevbZZwEAY8eO1ZSvXr0aM2fOBAAsXLgQ9fX1yMrKQkVFBUaNGoWCggJERESo8X/+859hMBgwdepU1NfX47rrrsOaNWug1zf9t7du3TrMmzdP/ZXdTTfdhJUrV6rz9Xo93nvvPWRlZeHqq6+GxWLB9OnT8dRTT3XQ1hMREXUOnU4HnY8Xcvu6fHcQ0I2mll2xrZEkCTk5OcjJyWkzJiQkBCtWrMCKFSvajImOjsbatWvPua5+/frh3XffPW9ORERE3QlPz3knoBtNRERE1PHYaPJOz99CIiIiIj9gTxMREVGQkyTfb04ptfwJZA/ERhMREVGQ4+k57/T8LSQiIiLyA/Y0ERERBTn2NHmHjSYiIqIgp9ProPOx0ePr8t1Bz99CIiIiIj9gTxMREVGQk3SS77+ea+WZgj0NG01ERERBjtc0eafnbyERERGRH7CniYiIKMixp8k7bDQREREFOd4R3DtsNBEREQU5Sa+HTq/3uY6eruc3C4mIiIj8gD1NREREQY7XNHmHjSYiIqIgx0aTd3r+FhIRERH5AXuaiIiIgpyk88Ov53xcvjvo+VtIRERE5+Q+PefrcKHy8vIgSRKys7PVMiEEcnJykJSUBIvFgrFjx+Lw4cN+2NoLx0YTERERdZm9e/fi+eefx6WXXqopX7p0KZYtW4aVK1di7969SEhIwPjx41FdXd1FmbLRREREFPQkneR7T9MFPLC3pqYGt99+O1atWoWoqCi1XAiB5cuXY9GiRZgyZQpSU1ORn5+Puro6rF+/3p+b3i5sNBEREQU59zVNvg4AUFVVpRlsNlub6507dy4mTpyIcePGacpPnDiBsrIyZGRkqGVmsxljxozBjh07OmYneIGNJiIiIvKb5ORkWK1WdcjLy2s1bsOGDdi/f3+r88vKygAA8fHxmvL4+Hh1Xlfgr+eIiIiCnKTTQ9L5+BgVZfmSkhJERkaq5Waz2SO2pKQEDz74IAoKChASEtJ2nZL2lJ8QwqOsM7HR1EX+8dzvEBoeAQCwOVwAgN1fVQAAvjxVo8bV1DUCACwh8lvVK9QIALCGmuRpi1GN7R0pH5iRZu3b2ugSHus3G+RORqPSnRrrrldZT7jJ0CxWPkCtZvkDEWmSx3qn3OUqOZp1vbocyljeJsk97SaachF6zw8SAEjC5VEm9HI+whgqjw3ysqLZh0dS6lbX6XIq041KHU37yqUsX98or8uppGVXXjib5elU9p8L8rjWLi9TaZPr/UF5jwCgqkF+XVEvjyuVeXV2p6YueV0uTZldOQ7q7XL+7uNCM6/BoWyasqxD2eZmfcY65boC9/UFeuUXLep0s+sOTAZtZ7O+RUxESNM+syjvu/uYC1eOFaty7EQ0O2bClVhdiy83V7P9Wqfs+xplez8vlS/urFT23emapuPqbGWDvEyVXFZzVp6uVy4ItVefUWPttZXyuhx2AIBQjkWhHA/Nub/k3acVdAb5c6UzmjTzmy/fNLbIMZI81jf75ZBOL2+3WdlXlnC5Pqsy7qV8fgEg3Kz9bEeHaz/bocamHNyv3fvVqLxPRmV9zT/q7uNK3+I6k+aTrXw1tFqvXCZvX4hRGSvb697s1upyf47cb7v7cNA3Oy5MyjpC3PtM/W7y/MPoPn4cysqUQ0j93NqcTZ+ZBuWz4f6MVyifzTPK8VXf6GwW61LyUrZV2Sj3d2mE2fPYdr8X7k1x7+/KhqbvvDplHe7vF/c2xYc3vf/RFrnueKO8nK72tDxurENVdQ2i3/bYDR1Dp5cHX+sAEBkZqWk0taaoqAjl5eVIS0tTy5xOJz7++GOsXLkSx44dAyD3OCUmJqox5eXlHr1PnYmn54iIiIKdTuefwUvXXXcdDh48iOLiYnUYOXIkbr/9dhQXF+Oiiy5CQkICCgsL1WXsdju2bduG0aNHd8Qe8Ap7moiIiKhTRUREIDU1VVMWFhaGmJgYtTw7Oxu5ublISUlBSkoKcnNzERoaiunTp3dFygDYaCIiIgp6kl4PSe/jNU0+Lt/SwoULUV9fj6ysLFRUVGDUqFEoKChARESEX9fTHmw0ERERBTs/XtN0obZu3aqZliQJOTk5yMnJ8alef+I1TUREREReYE8TERFRsNPp/NDT1PP7YdhoIiIiCnLN7+jtSx09Xc/fQiIiIiI/YE8TERFRsJP8cCG45N9fzwUiNpqIiIiCXQD8eq474Ok5IiIiIi+wp4mIiCjI8UJw77DRREREFOx4es4rbDQREREFO96nySs9fwuJiIiI/IA9TUREREEuEB/YG4jYaCIiIgp2Op3vp9d4eo6IiIiIAPY0EREREX895xU2moiIiIKcpNND8rHR4+vy3QFPzxERERF5gT1NREREwU7yw4XgUs/vh2GjiYiIKMjx9Jx3en6zkIiIiMgP2NNEREQU7PgYFa+w0URERBTseHNLr7DRREREFOT4GBXvsNHURSZ8+x6ssfEAAL01BgBw2+jBAABn5MVqXJ1DAAAcLnksSRIAwKSXx+ZmDXupsV6zDmG0AAAanPKyVXaXOq9Gea1Xlo80aQ92ZTUAmi58MxvkV3qnTY5xKOPGhqbllDI47fK0aFqnXO5Em5RYSVlWOBqb6lX+gxGmMGUsb5swmJsW17U4nN3/9bjLm/2yw67sk7pGeZ21Dnl8uk5e5/c1djX2dJ38uqJenld6Vt7eM7VyeXlV0/ZXV8vb36DUY693AAAcdnm7XUI0pafsZJ1B+9+ZUN5r97g5p9OlmedSxjpd0xsmKa/d45br0Ruaxbrn6VrEKAeG0dx0XJjM8n4MDVHGyjFjMcnT4eam/R+hxOib5QUAdfam97/GJu+bmgZ5X51V9ll9g1zeaGuKbVDeA5uyP937wdVoV/ZDU6z7YlT3WLhcmulzcTnsmrFmnrIunV3+nBnsFqU8XM7XbFFjbQ1GAE3vf6Oyre7j4qzFpsZGhJnk7W4M0azPYpTztTbbr+7XRuXzb1SOcfd08/1tVF7rJfc8ubz5O+I+NszKMRHi/l5RjgOds9lnUPl+kex12mllX6mffQCiUXmtzBPuz72rxfcBAFdDrRxTr4yVaXW9IWFN2xQWCQCwKN+ZCImQx8ofa6E3NdWrzBMR8rjWpexn5TvVKTw/X+59ZVT2lXt/GprtV/d3rs5WrdlGySW/x5KuaZ8J5TtHnVdVLi9y9H9qjPN0GQCg5tQZAIC9Wt6/zgY7qm2exyF1LTaaiIiIgh3vCO4VNpqIiIiCHRtNXun5V20RERER+QF7moiIiIKcpNOp1476UkdP1622MC8vD5IkITs7Wy0TQiAnJwdJSUmwWCwYO3YsDh8+rFnOZrPhgQceQGxsLMLCwnDTTTfhm2++0cRUVFQgMzMTVqsVVqsVmZmZOHv2rCbm5MmTmDx5MsLCwhAbG4t58+bBbueFekRE1M1J+qZTdBc6SDw9FzD27t2L559/HpdeeqmmfOnSpVi2bBlWrlyJvXv3IiEhAePHj0d1dbUak52djY0bN2LDhg3Yvn07ampqMGnSJDib/ZJr+vTpKC4uxqZNm7Bp0yYUFxcjMzNTne90OjFx4kTU1tZi+/bt2LBhA9544w3Mnz+/4zeeiIiIuly3aDTV1NTg9ttvx6pVqxAVFaWWCyGwfPlyLFq0CFOmTEFqairy8/NRV1eH9evXAwAqKyvx4osv4umnn8a4ceMwYsQIrF27FgcPHsQHH3wAADh69Cg2bdqEF154Aenp6UhPT8eqVavw7rvv4tixYwCAgoICHDlyBGvXrsWIESMwbtw4PP3001i1ahWqqqo6f6cQERH5iyTJt2XxaZDOv55urls0mubOnYuJEydi3LhxmvITJ06grKwMGRkZapnZbMaYMWOwY8cOAEBRUREaGxs1MUlJSUhNTVVjdu7cCavVilGjRqkxV111FaxWqyYmNTUVSUlJasyECRNgs9lQVFTUZu42mw1VVVWagYiIKKD43GDSae6F11MF/IXgGzZswP79+7F3716PeWVl8k3B4uPjNeXx8fH4+uuv1RiTyaTpoXLHuJcvKytDXFycR/1xcXGamJbriYqKgslkUmNak5eXh8cff/x8m0lEREQBLqCbhSUlJXjwwQexdu1ahISEtBkntegSFEJ4lLXUMqa1+AuJaenRRx9FZWWlOpSUlJwzLyIios4mJJ1fhp4uoLewqKgI5eXlSEtLg8FggMFgwLZt2/DXv/4VBoNB7flp2dNTXl6uzktISIDdbkdFRcU5Y77//nuP9Z86dUoT03I9FRUVaGxs9OiBas5sNiMyMlIzEBERBRSenvNKQG/hddddh4MHD6K4uFgdRo4cidtvvx3FxcW46KKLkJCQgMLCQnUZu92Obdu2YfTo0QCAtLQ0GI1GTUxpaSkOHTqkxqSnp6OyshJ79uxRY3bv3o3KykpNzKFDh1BaWqrGFBQUwGw2Iy0trUP3AxERUYeSJP8MPVxAX9MUERGB1NRUTVlYWBhiYmLU8uzsbOTm5iIlJQUpKSnIzc1FaGgopk+fDgCwWq2YNWsW5s+fj5iYGERHR2PBggUYPny4emH50KFDcf3112P27Nl47rnnAAD33nsvJk2ahMGD5YfoZmRkYNiwYcjMzMSTTz6JM2fOYMGCBZg9ezZ7j4iIiIJAQDeavLFw4ULU19cjKysLFRUVGDVqFAoKChChPNkaAP785z/DYDBg6tSpqK+vx3XXXYc1a9ZAr2+6Ede6deswb9489Vd2N910E1auXKnO1+v1eO+995CVlYWrr74aFosF06dPx1NPPdV5G0tERNQRdDp58LWOHq7bNZq2bt2qmZYkCTk5OcjJyWlzmZCQEKxYsQIrVqxoMyY6Ohpr164957r79euHd999tz3pEhERBTx/XMjNC8GJiIiICEA37GkiIiIiP/PHr9+CoKeJjSYiIqJgx0aTV3r+FhIRERH5AXuaiIiIgh17mrzCRhMREVGQE5Lkh1/P9fybW/b8ZiERERGRH7CniYiIKNjx9JxX2GgiIiIKdv54dlwQnJ5jo4mIiCjYsafJKz1/C4mIiIj8gD1NREREQY7PnvMOG01ERETBTtIBOp6eOx82mrqIsNvgPPUtAMB1thwAYDIaAQA6e40aF2GOAABIzkZlQZc8djmUckdTpS6ndiU6PQDAoJff5jBjaNP6zRZ5rDfJBZJcr8sY4pGrJIR23U6nJgfNet0x7kmlfmG0eNQrlLzUD5pOnnb/tyK1qEteRt5HTr0ZAGB3NsU0OOU8HcrY7pLHtTY55sxpmxr7Q101AKC81g4AKKtsAACUKuPyqgY1trJGjrE3yNtrq29UxvK0XZkGALtNLnPa6+WxTR67HHIdotm+kpT3xz3WGUzabW35fjbjXsZNZzR5zHPXp9PJF2fq9PJ+1Rt0zWKVecoFnO5pg0mOcTqbviJc7v3bKOdVb5bnWUzKtL3pWKysl5c3GbR5OpX3BADsDqemzO5QjkHlPXU2e2+dyjyhxDaN5Trc+7f5a6e9QROjzm9lvwrlmJb0yr5r8d5oYpXl3WOnsj5dXVOse7l693ugjN31m0LD1NiQUHneqV7yZ688Tp5XrRxvNdFNn51GZbtDjXI9xhbvn1HfdCFuiPI+m5V1uufpm12sa9ALpUz5zCnL6BrlfSfZmr6LdDb5M6OrrwQAuCpPy9uvfJZctVVqrPraIX823PvKvV+avyeuRuUz06B8Rpzaz70+pOnYNoSHyzmEKd+LIWHKdKSmHAAMoVa5fos8DgvvLe+XEHk/O0XTseg+BvXK/jQqu0hy2JRxs+9ZZd/oGuXPttQgbz9qK+S6Kk6poUL9rlSOFeU7v+Lzr9SYujJ5P9aUyvus7od6ZTUO1Db/fqeA0PObhURERHRu7gvBfR289Oyzz+LSSy9FZGQkIiMjkZ6ejn//+9/qfCEEcnJykJSUBIvFgrFjx+Lw4cMdseXtwkYTERFRsOvkRlPfvn3xpz/9Cfv27cO+ffvws5/9DDfffLPaMFq6dCmWLVuGlStXYu/evUhISMD48eNRXV3dUXvAK2w0ERERUaeaPHkybrzxRgwaNAiDBg3CkiVLEB4ejl27dkEIgeXLl2PRokWYMmUKUlNTkZ+fj7q6Oqxfv75L82ajiYiIKNj5saepqqpKM9hstnOu2ul0YsOGDaitrUV6ejpOnDiBsrIyZGRkqDFmsxljxozBjh07OnQ3nA8bTUREREHO/cBe3wb5Cvrk5GRYrVZ1yMvLa3WdBw8eRHh4OMxmM+677z5s3LgRw4YNQ1lZGQAgPj5eEx8fH6/O6yoX9Ou5uro6hIaGnj+QiIiIgkpJSQkiIyPVabPZ3Grc4MGDUVxcjLNnz+KNN97AjBkzsG3bNnW+1OKxLEIIj7LOdkE9TXFxccjMzMTmzZvhcnn+LJyIiIi6ET+ennP/Is49tNVoMplMuPjiizFy5Ejk5eXhxz/+Mf7yl78gISEBADx6lcrLyz16nzrbBTWaXnrpJTQ0NODWW29FUlISHnzwQezdu9ffuREREVFncD+w19fBB0II2Gw2DBw4EAkJCSgsLFTn2e12bNu2DaNHj/Z1S31yQafnpkyZgilTpqC6uhqvv/46XnnlFYwePRoDBw7EHXfcgccee8zfeRIREVFH6eQH9v7mN7/BDTfcgOTkZFRXV2PDhg3YunUrNm3aBEmSkJ2djdzcXKSkpCAlJQW5ubkIDQ3F9OnTfcvRRz7toYiICNx1110oKCjAf/7zH4SFheHxxx/3V25ERETUA33//ffIzMzE4MGDcd1112H37t3YtGkTxo8fDwBYuHAhsrOzkZWVhZEjR+Lbb79FQUEBIiIizlNzx/LpMSoNDQ145513sH79emzatAlxcXFYsGCBv3IjIiKiTtDZD+x98cUXzzlfkiTk5OQgJyenXTl89tlnXsdeeuml7aobuMBGU0FBAdatW4e33noLer0eP//5z7F582aMGTPmQqojIiKirtTJp+c6ymWXXQZJkiCaPVuwOfc8SZLgdLb9fM+2XFCj6ZZbbsHEiRORn5+PiRMnwqg8aJaIiIioq5w4caJD67+gRlNZWZnmHgxERETUfck3t/Tx129dfA8lAOjfv7/6Oi8vD/Hx8bj77rs1Mf/4xz9w6tQpPPzww+2u/4L60iIjI/HFF1/gt7/9LaZNm4by8nIAwKZNmwLiKcRERETkPSH8MwSS5557DkOGDPEov+SSS/D3v//9guq8oEbTtm3bMHz4cOzevRtvvvkmampqAMgXYC1evPiCEiEiIiLyl7KyMiQmJnqU9+7dG6WlpRdU5wU1mh555BH88Y9/RGFhIUwmk1p+7bXXYufOnReUCBEREXUNlxB+GQJJcnIyPv30U4/yTz/9FElJSRdU5wVd03Tw4EGsX7/eo7x37944ffr0BSVCREREXUMog691BJJ77rkH2dnZaGxsxM9+9jMAwIcffoiFCxdi/vz5F1TnBTWaevXqhdLSUgwcOFBTfuDAAfTp0+eCEiEiIiLyl4ULF+LMmTPIysqC3W4HAISEhODhhx/Go48+ekF1XlCjafr06Xj44Yfx2muvQZIkuFwufPrpp1iwYAHuvPPOC0qEiIiIuoZLyIOvdQQSSZLwxBNP4He/+x2OHj0Ki8WClJSUNh8g7I0LajQtWbIEM2fORJ8+fSCEwLBhw+BwOHD77bfjt7/97QUnQ0RERJ1PCNHmDSHbU0cgCg8PxxVXXOGXui6o0WQ0GrFu3Tr84Q9/wP79++FyuTBixAikpKT4JSkiIiLqPD2xp6kjeN1oeuihh845f9euXerrZcuWXXhGRERERAHI60bTgQMHvIqTAuCOoERERNQ+QdBR5DOvG01btmzpyDyIiIioi/D0nHe6/pHERERERN3ABV0ITkRERD1HT/71nD+x0dRFzEPSYA5RHkGj0wMAhDUBAOAM763G1UtyjPtasRAh36BLV/ODXC7qPOoWBnkZYQoDALgsVrlefdO9KeodLgBAbaM8rlPGZ+prAQANynwA0CvrDjfJh0uIQR4b9OFygBSuxuoMrV/T5r69vg5N810Oucz9OXNfDneuz527+9cpbAAAu6MpuK7RqRnX2B3KWJ4+XWdXY09VycuXVjbI82rk6epaOaahtlGNbVCWs9XL9TmU+hptcrnTVq/GOhrk5zA6Hdp5wuXUjFujU943Sa8cD862Y90xknLs6A2mNmNb1q9rFute3l1fy/LmsXqD3DGt18tjg9Gdg6SZL8/TKcsr41audXQfE0J5U53KMedyytPu/QwAtgb5/bBVV8njmjMAgMbaSnlZe4Ma697Hrhb72r0/z/UeqLkp4+Zd8S6Hu36XZp2STqeMm/ahuq+N8tj9/uhNFnnbmu1XEWKUy5Tj9qxyTH6pzD9Ta1NjS8/K83qFystEh8v1GJUcQgyeJw+Myvtj0HvGxCr1CItRs912Jb+Q0Bg11qR8j7jMEfK2KdN6u3yM6221aqyrUn4yhKu6Qq7f0agZS83eA0n5zpGU/FzK59Y97R7LM52aeqC8By6d9vgFmn+e5G3T1co56ZRpg9RUr+SQ97HkVL4jhLIn9Mp3qa7pT6U7puXY1ahsY7PvA6Hk5x7bK6vlcXXT93ZtuVx29mv5WD5bKn+HnLE7US/Of6z6iwtN778vdfR0PD1HRERE5AX2NBEREQU5Ic7dy+9tHT0dG01ERERBjr+e8w5PzxERERF5gT1NREREQY6/nvMOG01ERERBjr+e8w4bTUREREFOwA8Xgvslk8DGa5qIiIiIvMCeJiIioiDnEkK94awvdfR0bDQREREFOQHfT6/1/CYTT88REREReYU9TUREREGON7f0DhtNREREwc4Pj1EJhvNzPD1HRERE5AX2NBEREQU5FwRcPnYV+bp8dxDwPU3ffvst7rjjDsTExCA0NBSXXXYZioqK1PlCCOTk5CApKQkWiwVjx47F4cOHNXXYbDY88MADiI2NRVhYGG666SZ88803mpiKigpkZmbCarXCarUiMzMTZ8+e1cScPHkSkydPRlhYGGJjYzFv3jzY7fYO23YiIqLOIIR/hp4uoBtNFRUVuPrqq2E0GvHvf/8bR44cwdNPP41evXqpMUuXLsWyZcuwcuVK7N27FwkJCRg/fjyqq6vVmOzsbGzcuBEbNmzA9u3bUVNTg0mTJsHpdKox06dPR3FxMTZt2oRNmzahuLgYmZmZ6nyn04mJEyeitrYW27dvx4YNG/DGG29g/vz5nbIviIiIqGsF9Om5J554AsnJyVi9erVaNmDAAPW1EALLly/HokWLMGXKFABAfn4+4uPjsX79esyZMweVlZV48cUX8fLLL2PcuHEAgLVr1yI5ORkffPABJkyYgKNHj2LTpk3YtWsXRo0aBQBYtWoV0tPTcezYMQwePBgFBQU4cuQISkpKkJSUBAB4+umnMXPmTCxZsgSRkZGdtFeIiIj8i7+e805A9zS98847GDlyJG677TbExcVhxIgRWLVqlTr/xIkTKCsrQ0ZGhlpmNpsxZswY7NixAwBQVFSExsZGTUxSUhJSU1PVmJ07d8JqtaoNJgC46qqrYLVaNTGpqalqgwkAJkyYAJvNpjld2JLNZkNVVZVmICIiCiQ8PeedgG40ffnll3j22WeRkpKCzZs347777sO8efPw0ksvAQDKysoAAPHx8Zrl4uPj1XllZWUwmUyIioo6Z0xcXJzH+uPi4jQxLdcTFRUFk8mkxrQmLy9PvU7KarUiOTm5PbuAiIiow7kvBPd16OkCutHkcrlw+eWXIzc3FyNGjMCcOXMwe/ZsPPvss5o4SZI000IIj7KWWsa0Fn8hMS09+uijqKysVIeSkpJz5kVERESBKaAbTYmJiRg2bJimbOjQoTh58iQAICEhAQA8enrKy8vVXqGEhATY7XZUVFScM+b777/3WP+pU6c0MS3XU1FRgcbGRo8eqObMZjMiIyM1AxERUSDh6TnvBHSj6eqrr8axY8c0ZcePH0f//v0BAAMHDkRCQgIKCwvV+Xa7Hdu2bcPo0aMBAGlpaTAajZqY0tJSHDp0SI1JT09HZWUl9uzZo8bs3r0blZWVmphDhw6htLRUjSkoKIDZbEZaWpqft5yIiKjzuITwy9DTBfSv5371q19h9OjRyM3NxdSpU7Fnzx48//zzeP755wHIp8uys7ORm5uLlJQUpKSkIDc3F6GhoZg+fToAwGq1YtasWZg/fz5iYmIQHR2NBQsWYPjw4eqv6YYOHYrrr78es2fPxnPPPQcAuPfeezFp0iQMHjwYAJCRkYFhw4YhMzMTTz75JM6cOYMFCxZg9uzZ7D0iIiIKAgHdaLriiiuwceNGPProo/j973+PgQMHYvny5bj99tvVmIULF6K+vh5ZWVmoqKjAqFGjUFBQgIiICDXmz3/+MwwGA6ZOnYr6+npcd911WLNmDfR6vRqzbt06zJs3T/2V3U033YSVK1eq8/V6Pd577z1kZWXh6quvhsViwfTp0/HUU091wp4gIiLqOE6XPPhaR08X0I0mAJg0aRImTZrU5nxJkpCTk4OcnJw2Y0JCQrBixQqsWLGizZjo6GisXbv2nLn069cP77777nlzJiIi6k78cXotGE7PBfQ1TURERESBIuB7mnoqV1gMXFExAABhMMllIVZ5LDWdNqy3yY96qbQ5ADTd9sBkkO8rZbE0tXuNOu2tDxoccqu/rk7uM7U7m56TZ1BW4f7H4Ex9o7KMHFtjb3rETGWDQ1OvNUQ+bKJCjJppADArL3WQlHzlaZPyQt8sR70k5+4uMigv9K3cwcEp3PkKzbQ7XwAIMeg0Y/e63LEmQ9O+Uue55OXrle112OXpRlvTNjc2yPPsyj6y18qP6HE01Cjza5rytNXLebrkZVyORs10cy6lTKdzv9+1AABJp/eIbW35tmLPp7VlJF3r/z+1HqvXjvV6j1j3a71ybLvpWkw313IbXY6m49WpvHbvX/c8p71Bnm62rFAej9TWPmurXDNPeftdrWyTe3t1LfaD3uS5r9zbbwgJBwCYI3oBAMJ7WdSYkFD5c2SJkGOTYkIBAL0jQgAAMeFN+yxaeW01y8uEK+t0fwaNbbyPANCoHOv6ZrdICTUq26AUuZdu7TPo0svr1IXIlz64lHXpdAaljqZ1u19JBnkZ4f4cKO+XaGz2zM5QuT6dkh+U99a9jDYJ5f1xvy/KtLte9/HQvEwKkX85LZlClPqVXOqaHrXlrJZjhJKDTsnJENdHnrY0Xe4h3Nvrcmi3qcUYABwVpwAA9eVy/TXf/gAAKNt/Uo05/fkZAMAXNfJ2f9cg51fZ6IIdnXe+yyUEnOxpOi82moiIiIKc/BgVXxtNfkomgPH0HBEREZEX2NNEREQU5PjrOe+w0URERBTk+Os577DRREREFOScfrgQ3NfluwNe00RERETkBfY0ERERBTkXfP/1WxBc0sRGExERUbBzugScPraafF2+O+DpOSIiIiIvsNFEREQU5ITy6zlfBtGOC8Hz8vJwxRVXICIiAnFxcbjllltw7Ngxj5xycnKQlJQEi8WCsWPH4vDhw/7e9HZho4mIiCjIOYV/Bm9t27YNc+fOxa5du1BYWAiHw4GMjAzU1taqMUuXLsWyZcuwcuVK7N27FwkJCRg/fjyqq6vPUXPH4jVNRERE1Kk2bdqkmV69ejXi4uJQVFSEn/70pxBCYPny5Vi0aBGmTJkCAMjPz0d8fDzWr1+POXPmdEXa7GkiIiIKdr6emmt+c8yqqirNYLPZzrv+yspKAEB0dDQA4MSJEygrK0NGRoYaYzabMWbMGOzYsaMD9oB32GgiIiIKcu5fz/k6AEBycjKsVqs65OXlnXPdQgg89NBDuOaaa5CamgoAKCsrAwDEx8drYuPj49V5XYGn54iIiMhvSkpKEBkZqU6bzeZzxt9///347LPPsH37do95kiRppoUQHmWdiY0mIiKiIOfPZ89FRkZqGk3n8sADD+Cdd97Bxx9/jL59+6rlCQkJAOQep8TERLW8vLzco/epM/H0HBERUZDr7F/PCSFw//33480338RHH32EgQMHauYPHDgQCQkJKCwsVMvsdju2bduG0aNH+2uz2409TUREREHOnz1N3pg7dy7Wr1+Pt99+GxEREep1SlarFRaLBZIkITs7G7m5uUhJSUFKSgpyc3MRGhqK6dOn+5SnL9hoIiIiok717LPPAgDGjh2rKV+9ejVmzpwJAFi4cCHq6+uRlZWFiooKjBo1CgUFBYiIiOjkbJuw0URERBTkXC4Bl4/PjmvP8t7cPVySJOTk5CAnJ8eHrPyLjSYiIqIg52rnNUlt1dHT8UJwIiIiIi+wp4mIiCjIdfaF4N0VG01ERERBzikEnD42enxdvjvg6TkiIiIiL7CniYiIKMh19q/nuis2moiIiIKcE77/es7pl0wCG0/PEREREXmBPU1dxJCYAqPyQMNfrNkDAFg/Rr6NvDBZ1LhYUxgAIDpUjnUaQgAAFQ1ym775dXcGnfzkZ4ujFgBgrT8NAJCcdgBAY8xFamytU46tb3TJMZIRAHC6rhEAYNQ1tadDjXoAQLhJHtsc8jIRZnm6d2jTYRRllpfT1VfI44ZquX6bnBPcYwBwyHlBJ9cjGu3QcLmaXiv5SGZl35jl/eKyWJvCw6IAAE5jKACgyi7vo2qbXM/p+hA1tjRCfp3QSx6f/KFOLq+sBwB8d6Zpm2rNNgCAQdn+Or2ci02vR1tcLbbF5fCMaes/FuGS83a5PP9vE05tmaTkoNO1nUtb9bdGalGPpHO1EiMvrzOYNDk01zIfd73N1+0uc4/d9al1NJuW7PXnzFPX7Fhpa/taLiOX6Vqd1zI3oNm+VvLSu7e/lfz1ynFqDJGPL0u4/JT3sEh5bI1u+own9pJfJ1q1x2S0Ra4vymJUY2ND5de9QuTj02KQ8w81ymN9s4e/65XvA4dyysSpjD3fUUCvPDXevbxReWFsVp/kkD8HUmO9Mlam7fJnB3Vn1VhnRbm8rmqlTHlPhPI+CZv2/QQAyWjyKAMAyRTiWeh+j1u8b8Le0BTi/n6prZLrCQnTLOuqq27Kt1L+rhROOT+DOyZM/t7VhzZ9z7jX6VK+g0RYjFy/VX6IrCk2SQ3Vn5G/0/XWEnlRo/y+OWqbtt8UJm932DdynoOr5Lwd9Q7UuZxY/b3n5ncE/nrOO2w0ERERBTn+es47bDQREREFOZdLqL2RvtTR0/GaJiIiIiIvsKeJiIgoyDn90NPk6/LdARtNREREQY6NJu/w9BwRERGRF9jTREREFOScLt97ipyt3c+ih2GjiYiIKMjx9Jx3eHqOiIiIyAvsaSIiIgpy7GnyDhtNREREQY43t/QOT88REREReYE9TUREREHOKfxweo7PniMiIqKejtc0eYeNJiIioiDHRpN3eE0TERERkRfY00RERBTkHC4BvY89RY4g6Glio4mIiCjI8fScd3h6joiIiMgL7GkiIiIKcry5pXfYaCIiIgpyTiF8vs9SMNyniafniIiIiLzAniYiIqIgxwvBvcNGExERUZBjo8k7PD1HRERE5AX2NBEREQU59jR5J6B7mhwOB377299i4MCBsFgsuOiii/D73/8eLpdLjRFCICcnB0lJSbBYLBg7diwOHz6sqcdms+GBBx5AbGwswsLCcNNNN+Gbb77RxFRUVCAzMxNWqxVWqxWZmZk4e/asJubkyZOYPHkywsLCEBsbi3nz5sFut3fY9hMREXUGp3DB6fJxEK7zr6ibC+iepieeeAJ///vfkZ+fj0suuQT79u3DXXfdBavVigcffBAAsHTpUixbtgxr1qzBoEGD8Mc//hHjx4/HsWPHEBERAQDIzs7Gv/71L2zYsAExMTGYP38+Jk2ahKKiIuj1egDA9OnT8c0332DTpk0AgHvvvReZmZn417/+BQBwOp2YOHEievfuje3bt+P06dOYMWMGhBBYsWKFT9v56swrNdNf/lCtvq6odwIAwoW7fSs30mps8sGpb9bsjTTL2xIXGgYAMFrNAACpvlKOrT2txoaF9wYA1DbK0yFKRQnhpjbztBjkGLNekpdRpg21P6gx+rPK67qzAABH2UkAgPN0GQDAZW9QY112h6Z+SclBZ5QPS11YZNO8EHmbdBG95PVExcnlRrMao7PVKC/0yjYZ5RwMcr69QjwPd6OyzgiTPK9PtAUAUBnfqMacrpH3+alqOfezdfK82tooAICtPlGNtdXL8xyN8vvjcihj5ae47mnA854mLqc8z2G3AQCctnp1nnA5Wx3rDPL7pTdbmvaDUmYwydtvMOo163E2y8HR6FTykrfR1SiPnfZ6JRfPHNz0ynokZX+719s8n5bz9IamA1Yo2y/p5PdHkiSPmJY5u/eRe9+5c9Ibmt5b9/bqWtQjWvkv2B3rzqGpXDm2TU37zhwi70+zRV6XQanf/d+1vlkd7tfueS1jLM3qtTvkbThdq/0HzL1MY7N/El0tf9KtHNMW5RgPbXaI6xrrNKFCUvaH8rkAAMlhUyrW/rGT6uVlJWdjs1j5+JeUY0LY5BhRXytX0fyzXSt/hzkryuVtqJZj60/L30XOBs9/Nk0RoQAAc5T8vW3s1UtenymkaZvc3wk6eVsk9zGnTDffDvV4dSjbYGjabgDQG5uOV3UdyudAZ42RY3r3lYuj+jZtW4hVrl85XiXlPZEa5f3iCotpqld5bUz8EQAgaqi8ryJGfNVUX6X8veyqPiuvq06pp9GBqnobkH0UnYH3afJOQDeadu7ciZtvvhkTJ04EAAwYMACvvPIK9u3bB0DuZVq+fDkWLVqEKVOmAADy8/MRHx+P9evXY86cOaisrMSLL76Il19+GePGjQMArF27FsnJyfjggw8wYcIEHD16FJs2bcKuXbswatQoAMCqVauQnp6OY8eOYfDgwSgoKMCRI0dQUlKCpKQkAMDTTz+NmTNnYsmSJYiMjGyZPhEREfUgAX167pprrsGHH36I48ePAwD+85//YPv27bjxxhsBACdOnEBZWRkyMjLUZcxmM8aMGYMdO3YAAIqKitDY2KiJSUpKQmpqqhqzc+dOWK1WtcEEAFdddRWsVqsmJjU1VW0wAcCECRNgs9lQVFTU5jbYbDZUVVVpBiIiokDivqbJ16GnC+iepocffhiVlZUYMmQI9Ho9nE4nlixZgmnTpgEAysrkUz7x8fGa5eLj4/H111+rMSaTCVFRUR4x7uXLysoQFxfnsf64uDhNTMv1REVFwWQyqTGtycvLw+OPP96ezSYiIupUDhcg+djocfT8S5oCu6fp1Vdfxdq1a7F+/Xrs378f+fn5eOqpp5Cfn6+Jc18L4SaE8ChrqWVMa/EXEtPSo48+isrKSnUoKSk5Z15EREQUmAK6p+nXv/41HnnkEfzyl78EAAwfPhxff/018vLyMGPGDCQkJACQe4ESE5suxi0vL1d7hRISEmC321FRUaHpbSovL8fo0aPVmO+//95j/adOndLUs3v3bs38iooKNDY2evRANWc2m2E2m9ucT0RE1NWcLgEdbzlwXgHd01RXVwedTpuiXq9XbzkwcOBAJCQkoLCwUJ1vt9uxbds2tUGUlpYGo9GoiSktLcWhQ4fUmPT0dFRWVmLPnj1qzO7du1FZWamJOXToEEpLS9WYgoICmM1mpKWl+XnLiYiIOg+vafJOQPc0TZ48GUuWLEG/fv1wySWX4MCBA1i2bBnuvvtuAPLpsuzsbOTm5iIlJQUpKSnIzc1FaGgopk+fDgCwWq2YNWsW5s+fj5iYGERHR2PBggUYPny4+mu6oUOH4vrrr8fs2bPx3HPPAZBvOTBp0iQMHjwYAJCRkYFhw4YhMzMTTz75JM6cOYMFCxZg9uzZ/OUcERFREAjoRtOKFSvwu9/9DllZWSgvL0dSUhLmzJmDxx57TI1ZuHAh6uvrkZWVhYqKCowaNQoFBQXqPZoA4M9//jMMBgOmTp2K+vp6XHfddVizZo16jyYAWLduHebNm6f+yu6mm27CypUr1fl6vR7vvfcesrKycPXVV8NisWD69Ol46qmnOmFPEBERdRyenvNOQDeaIiIisHz5cixfvrzNGEmSkJOTg5ycnDZjQkJCsGLFinPehDI6Ohpr1649Zz79+vXDu+++e760iYiIuhXe3NI7AX1NExEREVGgCOieJiIiIup4Tpfw+T5NPD1HREREPZ4QotXnM7a3jp6OjSYiIqIg53IJn69J4jVNRERERASAPU1ERERBTwjh8+k1np4jIiKiHk+4/HBNE0/PEREREfnfxx9/jMmTJyMpKQmSJOGtt97SzBdCICcnB0lJSbBYLBg7diwOHz7cNckq2GgiIiIKcu4LwX0d2qO2thY//vGPNU/faG7p0qVYtmwZVq5cib179yIhIQHjx49HdXW1Pzb5gvD0HBERUZATLnnwtY72uOGGG3DDDTe0XpcQWL58ORYtWoQpU6YAAPLz8xEfH4/169djzpw5viV7gdjTRERERH5TVVWlGWw2W7vrOHHiBMrKytTnwQKA2WzGmDFjsGPHDn+m2y5sNBEREQU596/nfB0AIDk5GVarVR3y8vLanU9ZWRkAID4+XlMeHx+vzusKPD1HREQU5Px5c8uSkhJERkaq5Waz+YLrlCRJMy2E8CjrTGw0ERERkd9ERkZqGk0XIiEhAYDc45SYmKiWl5eXe/Q+dSaeniMiIgpy7vs0+Tr4y8CBA5GQkIDCwkK1zG63Y9u2bRg9erTf1tNe7GkiIiIKdv5o9LRz+ZqaGvzvf/9Tp0+cOIHi4mJER0ejX79+yM7ORm5uLlJSUpCSkoLc3FyEhoZi+vTpvuXpAzaaiIiIgpxLCEg+PgbF1c7l9+3bh2uvvVadfuihhwAAM2bMwJo1a7Bw4ULU19cjKysLFRUVGDVqFAoKChAREeFTnr5go4mIiIg63dixY8/5vDpJkpCTk4OcnJzOS+o82GgiIiIKckL44dlzfGAvdYWLYpu6Hkcu3gwAmH3jEABAVUMjAMCpHNx9rBY1NiUmFAAQHaIHAJgc9QAAYbECAM44mq77Lzsj32wsxCCXmfXyTzgjTPKyNkfTrV1jQuXDxHjmawCArkG+hb2wy/Xb/rNdja38rhwA0HC6CgDgaLDLsU6nx3Yaw+TcQ2IilbGcpy5MntbHJKix+qg4+UVEDADAZZb3kSs0So2phxEAUGOXc6+yO+TyRnm6rrEpB/dr99iok7c/zChvP0Kb8tQr81qO3SSd589f9Qa53kabPHbYlXGzLyWHsm7h0o5djXbNdHM6g0keG02aaYPJ2LRuvU6TV8vp5vkalPdbJ7X+k+DWuttdyrHhzt/pcHjk67TVa5ZprK1stX45H71m3LQeu/raXbezWdn56JT6XK7W9/O5tJVTa/W2LAeavU8t3i93fQZT0/7WKe+P+73Qq9Py2Ghu+po2KMenTnkPQ0Ll971XuFx/36imA/eiuDAAQHy4vK7YUJMy3ZSnxWBWxvK6QgxyvZYI+TNosFWpsVKDHKNTP8t1AJrtT0dj085Q3if3Z9l9dNrOyt8djcpns3lZ1Qn53jumSPf3gvx9ED3sIrTJvW5lPwt7gzpLFyLvC8ki7wd94o/kGIOSjdT0fahTbmUtubeh5rS8Gd/K19tIp79TYw3Ke6izxmjrccrLuuprPdIUDfK+crRy/Eqhyr5W6tM3Nqrb1lhb7xHfUfjAXu/w13NEREREXmBPExERUZBzuQDJ55tb+imZAMZGExERUZBr/hgUX+ro6Xh6joiIiMgL7GkiIiIKcsIlD77W0dOx0URERBTkXC7hh2uaev7pOTaaiIiIghxvOeAdXtNERERE5AX2NBEREQU59jR5h40mIiKiINcVD+ztjnh6joiIiMgL7GkiIiIKcjw95x02moiIiIKcEH5oNPH0HBEREREB7GkiIiIKesIlfL45JU/PERERUY/HB/Z6h6fniIiIiLzAniYiIqIgx1/PeYeNJiIioiDncgmAD+w9LzaaiIiIgpxwOSFcTp/r6Ol4TRMRERGRF9jTREREFOTY0+QdNpqIiIiCnHC5/NBocvkpm8DF03NEREREXmBPExERUZATTieE08eeJh+X7w7YaCIiIgpyQvjhmibR8xtNPD1HRERE5AX2NAW4fY9POOf8lLkb1dc6nQQAqDt7FgAQkxwHALgsNQEAMP/aH6mxkWY9AKDGLl+41+CQx2fqPf9T+Px0PQDgizMhAIBjZY0AgC9POQAAZWVXqLGnS6sBAI5GuZ6GijIAgK36jFz/psfOuT3eeudQKQAgwmxXyxqdDQCA0hqbkl8tAKC6Qc6z+MvTamzl6ToAQH21XZOv++ZsLkdTvW7u/8LcXdDuGKe9Xo2x11YqZXIuDmWeq9GuqaM17fkvLxh+pdKT6QymC5p3vvdd0uk9yvRGuT5jaCQAIMTaW51nVl6HhpvlsVUem8zynwZTSNOfiPBQo6bemjr5f+66KgsAwNbQFNvYEKZsixzj/m5yf74Mxqb/18195HrDIs2aGPdzzEIrmtYb55S/g8KV/C6Kk9dTb5f3S3hUUw79rHJeg2PlGKOSg5sLnjdidCqLh0VcDACwDpDzlKSmZd3V2J3a5S3Ktuob69QyySV/90g2+btIp8xzhVjVmAZLDACgvFaOrXV/dzpcqK2u8sixo/DXc95ho4mIiCjIsdHkHZ6eIyIiIvICe5qIiIiCHHuavMNGExERUZDjzS29w0YTERFRkHO5nICPjSZXEPQ08ZomIiIiIi+wp4mIiCjI8Zom77DRREREFOTYaPIOT88REREReaFLG00ff/wxJk+ejKSkJEiShLfeekszXwiBnJwcJCUlwWKxYOzYsTh8+LAmxmaz4YEHHkBsbCzCwsJw00034ZtvvtHEVFRUIDMzE1arFVarFZmZmTir3DXb7eTJk5g8eTLCwsIQGxuLefPmwW7X3hX64MGDGDNmDCwWC/r06YPf//736l1riYiIui3lgb2+DAiCB/Z2aaOptrYWP/7xj7Fy5cpW5y9duhTLli3DypUrsXfvXiQkJGD8+PGorq5WY7Kzs7Fx40Zs2LAB27dvR01NDSZNmgRnszdv+vTpKC4uxqZNm7Bp0yYUFxcjMzNTne90OjFx4kTU1tZi+/bt2LBhA9544w3Mnz9fjamqqsL48eORlJSEvXv3YsWKFXjqqaewbNmyDtgzREREncf9wF6fhiB4YG+XXtN0ww034IYbbmh1nhACy5cvx6JFizBlyhQAQH5+PuLj47F+/XrMmTMHlZWVePHFF/Hyyy9j3LhxAIC1a9ciOTkZH3zwASZMmICjR49i06ZN2LVrF0aNGgUAWLVqFdLT03Hs2DEMHjwYBQUFOHLkCEpKSpCUlAQAePrppzFz5kwsWbIEkZGRWLduHRoaGrBmzRqYzWakpqbi+PHjWLZsGR566CHNs4mIiIio5wnYa5pOnDiBsrIyZGRkqGVmsxljxozBjh07AABFRUVobGzUxCQlJSE1NVWN2blzJ6xWq9pgAoCrrroKVqtVE5Oamqo2mABgwoQJsNlsKCoqUmPGjBkDs9msifnuu+/w1VdftbkdNpsNVVVVmoGIiCiQuG9u6dvQ829uGbCNprKyMgBAfHy8pjw+Pl6dV1ZWBpPJhKioqHPGxMXFedQfFxeniWm5nqioKJhMpnPGuKfdMa3Jy8tTr6WyWq1ITk4+94YTERF1Mt8bTL7/+q47CNhGk1vL015CiPOeCmsZ01q8P2LcF4GfK59HH30UlZWV6lBSUnLO3ImIiCgwBWyjKSEhAYBnL055ebnaw5OQkAC73Y6Kiopzxnz//fce9Z86dUoT03I9FRUVaGxsPGdMeXk5AM/esObMZjMiIyM1AxERUSCRT8/5PvR0AdtoGjhwIBISElBYWKiW2e12bNu2DaNHjwYApKWlwWg0amJKS0tx6NAhNSY9PR2VlZXYs2ePGrN7925UVlZqYg4dOoTS0lI1pqCgAGazGWlpaWrMxx9/rLkNQUFBAZKSkjBgwAD/7wAiIqJOwtNz3unSRlNNTQ2Ki4tRXFwMQL74u7i4GCdPnoQkScjOzkZubi42btyIQ4cOYebMmQgNDcX06dMBAFarFbNmzcL8+fPx4Ycf4sCBA7jjjjswfPhw9dd0Q4cOxfXXX4/Zs2dj165d2LVrF2bPno1JkyZh8ODBAICMjAwMGzYMmZmZOHDgAD788EMsWLAAs2fPVnuGpk+fDrPZjJkzZ+LQoUPYuHEjcnNz+cs5IiLq9tho8k6X3nJg3759uPbaa9Xphx56CAAwY8YMrFmzBgsXLkR9fT2ysrJQUVGBUaNGoaCgABEREeoyf/7zn2EwGDB16lTU19fjuuuuw5o1a6DX69WYdevWYd68eeqv7G666SbNvaH0ej3ee+89ZGVl4eqrr4bFYsH06dPx1FNPqTFWqxWFhYWYO3cuRo4ciaioKDz00ENqzkRERNSzdWmjaezYsee8o7YkScjJyUFOTk6bMSEhIVixYgVWrFjRZkx0dDTWrl17zlz69euHd99995wxw4cPx8cff3zOGCIiou7G5XJC4rPnzosP7CUiIgpywukCJB8bTU5eCE5EREREYE8TERFR0BPCCfh6ei4Inj3HniYiIqIg11W/nnvmmWcwcOBAhISEIC0tDZ988kkHbJ3/sNFEREREne7VV19FdnY2Fi1ahAMHDuAnP/kJbrjhBpw8ebKrU2sTG01ERERBrit6mpYtW4ZZs2bhnnvuwdChQ7F8+XIkJyfj2Wef7aCt9B0bTUREREGusxtNdrsdRUVF6v0T3TIyMrBjxw5/b57f8ELwTua+L1VVVZVf6nPZ65rq1klKWT0AwGmT59nragAANdVN63QZ5fZyrV3+iahLyau1X4zW2hwAgPpauT6bUl9jfS0AwNFQ65GPq9GpjOVchKMBgP+2u66mGgCga2w6hB1K8vU1NiVPJd8Gh0eeTpuyj5TH4qj5uuT94HI0PS7Hzf1cJeFUYp3uZRuaYpTtFA6bduxsVOpo+0ulPV84wXA/lJ5MnOMhAm3fuc6LC21deo8iIck1uo9N92cSaPq8Om1yvU73Z0XInytdsz8RjZJRU6+joVFZxqbU4VDnuesTTvl7Rv1uUj5fkqvp/3WHJK/DYXJoYtzflY1oWq/dIMfYnfIyDbXyZ7JB+R4zOJu2v04vx9aY5VyMOu1Od7Wyp93ff+7vR8mmjJs99cFdTaNTu3yjQY7VNzZ9J7vveyTZ5e8eSdn3rsam7a9vlLevpk7Ot1b5LrI5XKhVvufOdT9Dv3E2nvPY87YOwPN73mw2w2w2a8p++OEHOJ1Oj2e3xsfHezznNZCw0dTJqqvlD0FycnKHr+s7ZXxIGb/S4Ws8P6s1r6tTICLqVqqrq2G1WjukbpPJJD+Q/sg//VJfeHi4x9+3xYsXt3mT6paPIRNCBPSjydho6mRJSUkoKSlBRESEXw+MqqoqJCcno6SkRH1eXiBjvh2L+XYs5tuxmK9MCIHq6mokJSX5rc6WQkJCcOLECc3D6H3RWqOnZS8TAMTGxkKv13v0KpWXl3v0PgUSNpo6mU6nQ9++fTus/sjIyG7xJePGfDsW8+1YzLdjMV90WA9TcyEhIQgJCenw9TRnMpmQlpaGwsJC3HrrrWp5YWEhbr755k7NpT3YaCIiIqJO99BDDyEzMxMjR45Eeno6nn/+eZw8eRL33XdfV6fWJjaaiIiIqNP94he/wOnTp/H73/8epaWlSE1Nxfvvv4/+/ft3dWptYqOphzCbzVi8eHGr544DEfPtWMy3YzHfjsV8g0dWVhaysrK6Og2vSaJTfstIRERE1L3x5pZEREREXmCjiYiIiMgLbDQREREReYGNJiIiIiIvsNHUzR0/fhw333wzYmNjERkZiauvvhpbtmzRxJw8eRKTJ09GWFgYYmNjMW/ePL/d/fVCvPfeexg1ahQsFgtiY2MxZcoUzfxAy9fNZrPhsssugyRJKC4u1swLlJy/+uorzJo1CwMHDoTFYsGPfvQjLF682COXQMkXAJ555hkMHDgQISEhSEtLwyeffNIlebSUl5eHK664AhEREYiLi8Mtt9yCY8eOaWKEEMjJyUFSUhIsFgvGjh2Lw4cPd1HGWnl5eZAkCdnZ2WpZoOX77bff4o477kBMTAxCQ0Nx2WWXoaioKCDzdTgc+O1vf6t+ti666CL8/ve/h8vV9MDOQMqXOoigbu3iiy8WN954o/jPf/4jjh8/LrKyskRoaKgoLS0VQgjhcDhEamqquPbaa8X+/ftFYWGhSEpKEvfff3+X5Pv666+LqKgo8eyzz4pjx46Jzz//XLz22mvq/EDLt7l58+aJG264QQAQBw4cUMsDKed///vfYubMmWLz5s3iiy++EG+//baIi4sT8+fPD8h8N2zYIIxGo1i1apU4cuSIePDBB0VYWJj4+uuvOz2XliZMmCBWr14tDh06JIqLi8XEiRNFv379RE1NjRrzpz/9SURERIg33nhDHDx4UPziF78QiYmJoqqqqgszF2LPnj1iwIAB4tJLLxUPPvigWh5I+Z45c0b0799fzJw5U+zevVucOHFCfPDBB+J///tfQOb7xz/+UcTExIh3331XnDhxQrz22msiPDxcLF++PCDzpY7BRlM3durUKQFAfPzxx2pZVVWVACA++OADIYQQ77//vtDpdOLbb79VY1555RVhNptFZWVlp+bb2Ngo+vTpI1544YU2YwIp35Z5DRkyRBw+fNij0RSoObstXbpUDBw4UJ0OpHyvvPJKcd9992nKhgwZIh555JFOzcMb5eXlAoDYtm2bEEIIl8slEhISxJ/+9Cc1pqGhQVitVvH3v/+9q9IU1dXVIiUlRRQWFooxY8aojaZAy/fhhx8W11xzTZvzAy3fiRMnirvvvltTNmXKFHHHHXcIIQIvX+oYPD3XjcXExGDo0KF46aWXUFtbC4fDgeeeew7x8fFIS0sDAOzcuROpqamaBz5OmDABNptN0w3eGfbv349vv/0WOp0OI0aMQGJiIm644QZN93Ug5ev2/fffY/bs2Xj55ZcRGhrqMT8Qc26usrIS0dHR6nSg5Gu321FUVISMjAxNeUZGBnbs2NFpeXirsrISANR9eeLECZSVlWnyN5vNGDNmTJfmP3fuXEycOBHjxo3TlAdavu+88w5GjhyJ2267DXFxcRgxYgRWrVoVsPlec801+PDDD3H8+HEAwH/+8x9s374dN954Y0DmSx2DdwTvxiRJUh9uGBERAZ1Oh/j4eGzatAm9evUCAJSVlXk8MToqKgomk8nj6dId7csvvwQA5OTkYNmyZRgwYACefvppjBkzBsePH0d0dHRA5QvI1yjMnDkT9913H0aOHImvvvrKIybQcm7uiy++wIoVK/D000+rZYGS7w8//ACn0+mRS3x8fJfvt5aEEHjooYdwzTXXIDU1FQDUHFvL/+uvv+70HAFgw4YN2L9/P/bu3esxL9Dy/fLLL/Hss8/ioYcewm9+8xvs2bMH8+bNg9lsxp133hlw+T788MOorKzEkCFDoNfr4XQ6sWTJEkybNg1A4O1f6hjsaQpAOTk5kCTpnMO+ffsghEBWVhbi4uLwySefYM+ePbj55psxadIklJaWqvVJkuSxDiFEq+Udma/7gslFixbh//7v/5CWlobVq1dDkiS89tprnZZve3JesWIFqqqq8Oijj56zvkDZx8199913uP7663Hbbbfhnnvu6dR826PlOrsqj3O5//778dlnn+GVV17xmBco+ZeUlODBBx/E2rVrz/nE+kDJ1+Vy4fLLL0dubi5GjBiBOXPmYPbs2Xj22Wc1cYGS76uvvoq1a9di/fr12L9/P/Lz8/HUU08hPz9fExco+VLHYE9TALr//vvxy1/+8pwxAwYMwEcffYR3330XFRUViIyMBCD/EqmwsBD5+fl45JFHkJCQgN27d2uWraioQGNjo8d/RB2db3V1NQBg2LBharnZbMZFF12EkydPAkCn5NuenP/4xz9i165dHs+UGjlyJG6//Xbk5+cH1D52++6773DttdeqTw5vrrP28fnExsZCr9d79CqVl5d3ah7n88ADD+Cdd97Bxx9/jL59+6rlCQkJAOQehsTERLW8q/IvKipCeXm5emoeAJxOJz7++GOsXLlS/eVfoOSbmJio+S4AgKFDh+KNN94AEHj799e//jUeeeQR9XM4fPhwfP3118jLy8OMGTMCLl/qIF1zKRX5wzvvvCN0Op2orq7WlA8aNEgsWbJECNF00e93332nzt+wYUOXXPRbWVkpzGaz5kJwu90u4uLixHPPPRdw+QohxNdffy0OHjyoDps3bxYAxOuvvy5KSkoCMudvvvlGpKSkiF/+8pfC4XB4zA+kfK+88krx//7f/9OUDR06NCAuBHe5XGLu3LkiKSlJHD9+vNX5CQkJ4oknnlDLbDZbl134W1VVpTlWDx48KEaOHCnuuOMOcfDgwYDLd9q0aR4XgmdnZ4v09HQhRODt3+joaPHMM89oynJzc0VKSooQIvDypY7BRlM3durUKRETEyOmTJkiiouLxbFjx8SCBQuE0WgUxcXFQoimn5dfd911Yv/+/eKDDz4Qffv27bKf8D/44IOiT58+YvPmzeLzzz8Xs2bNEnFxceLMmTMBmW9LJ06caPOWA4GQ87fffisuvvhi8bOf/Ux88803orS0VB0CMV/3LQdefPFFceTIEZGdnS3CwsLEV1991em5tPT//t//E1arVWzdulWzH+vq6tSYP/3pT8JqtYo333xTHDx4UEybNi2gfmLe/NdzQgRWvnv27BEGg0EsWbJE/Pe//xXr1q0ToaGhYu3atQGZ74wZM0SfPn3UWw68+eabIjY2VixcuDAg86WOwUZTN7d3716RkZEhoqOjRUREhLjqqqvE+++/r4n5+uuvxcSJE4XFYhHR0dHi/vvvFw0NDV2Sr91uF/PnzxdxcXEiIiJCjBs3Thw6dChg822ptUaTEIGT8+rVqwWAVodAzFcIIf72t7+J/v37C5PJJC6//HL1J/1dra39uHr1ajXG5XKJxYsXi4SEBGE2m8VPf/pTcfDgwa5LuoWWjaZAy/df//qXSE1NFWazWQwZMkQ8//zzmvmBlG9VVZV48MEHRb9+/URISIi46KKLxKJFi4TNZgvIfKljSEII0QVnBYmIiIi6Ff56joiIiMgLbDQREREReYGNJiIiIiIvsNFERERE5AU2moiIiIi8wEYTERERkRfYaCIiIiLyAhtNRAFiwIABWL58eYfUPXbsWGRnZ/tUh/vBwL169TpnXE5ODi677DKf1kWevvrqK0iShOLi4jZjtm7dqr5Pt9xyS6flRhQs2GgiaqeZM2f69AdpzZo1rTY89u7di3vvvVedliQJb7311gWvpyOsXr0ax48f7+o0erwLPcZGjx6N0tJSTJ061f9JEREMXZ0AEcl69+7d1SmcV69evRAXF9fVaUAIAafTCYOhZ32FOZ1OSJJ0wcubTCYkJCTAYrHAZrP5MTMiAtjTROR3y5Ytw/DhwxEWFobk5GRkZWWhpqYGgHz65K677kJlZaV6GiUnJweA9vTcgAEDAAC33norJElSp1vrgcjOzsbYsWPV6draWtx5550IDw9HYmIinn76aY8c7XY7Fi5ciD59+iAsLAyjRo3C1q1bL2h7//SnPyE+Ph4RERGYNWsWGhoaPGJWr16NoUOHIiQkBEOGDMEzzzyjmb9jxw5cdtllCAkJwciRI/HWW29pTkW5Tztt3rwZI0eOhNlsxieffAIhBJYuXYqLLroIFosFP/7xj/H6669r6j5y5AhuvPFGhIeHIz4+HpmZmfjhhx/U+a+//jqGDx8Oi8WCmJgYjBs3DrW1tefdbvd78dRTTyExMRExMTGYO3cuGhsb1ZiKigrceeediIqKQmhoKG644Qb897//Vee7ex3fffddDBs2DGazGXfddRfy8/Px9ttvq8dI8/fmyy+/xLXXXovQ0FD8+Mc/xs6dO8+bKxH5BxtNRH6m0+nw17/+FYcOHUJ+fj4++ugjLFy4EIB8+mT58uWIjIxEaWkpSktLsWDBAo869u7dC0BubJSWlqrT3vj1r3+NLVu2YOPGjSgoKMDWrVtRVFSkibnrrrvw6aefYsOGDfjss89w22234frrr9f8QffGP//5TyxevBhLlizBvn37kJiY6NEgWrVqFRYtWoQlS5bg6NGjyM3Nxe9+9zvk5+cDAKqrqzF58mQMHz4c+/fvxx/+8Ac8/PDDra5v4cKFyMvLw9GjR3HppZfit7/9LVavXo1nn30Whw8fxq9+9Svccccd2LZtGwCgtLQUY8aMwWWXXYZ9+/Zh06ZN+P7779XTV6WlpZg2bRruvvtuHD16FFu3bsWUKVPg7SM5t2zZgi+++AJbtmxBfn4+1qxZgzVr1qjzZ86ciX379uGdd97Bzp07IYTAjTfeqGlY1dXVIS8vDy+88AIOHz6Mv/71r5g6dSquv/569RgZPXq0Gr9o0SIsWLAAxcXFGDRoEKZNmwaHw+FVvkTko658WjBRdzRjxgxx8803ex3/z3/+U8TExKjTq1evFlar1SOuf//+4s9//rM6DUBs3LjxvOt+8MEHxZgxY4QQQlRXVwuTySQ2bNigzj99+rSwWCzq0+7/97//CUmSxLfffqup57rrrhOPPvpom9vRWj7p6enivvvu05SNGjVK/PjHP1ank5OTxfr16zUxf/jDH0R6eroQQohnn31WxMTEiPr6enX+qlWrBABx4MABIYQQW7ZsEQDEW2+9pcbU1NSIkJAQsWPHDk3ds2bNEtOmTRNCCPG73/1OZGRkaOaXlJQIAOLYsWOiqKhIABBfffVVm9vdlhkzZoj+/fsLh8Ohlt12223iF7/4hRBCiOPHjwsA4tNPP1Xn//DDD8JisYh//vOfQgj5WAAgiouLPepu+T6fOHFCABAvvPCCWnb48GEBQBw9evS8yxOR73rWBQFEAWDLli3Izc3FkSNHUFVVBYfDgYaGBtTW1iIsLKxD1/3FF1/AbrcjPT1dLYuOjsbgwYPV6f3790MIgUGDBmmWtdlsiImJadf6jh49ivvuu09Tlp6eji1btgAATp06hZKSEsyaNQuzZ89WYxwOB6xWKwDg2LFjuPTSSxESEqLOv/LKK1td38iRI9XXR44cQUNDA8aPH6+JsdvtGDFiBACgqKgIW7ZsQXh4uEddX3zxBTIyMnDddddh+PDhmDBhAjIyMvDzn/8cUVFRXm3/JZdcAr1er04nJibi4MGDAOR9YzAYMGrUKHV+TEwMBg8ejKNHj6plJpMJl156qVfrA6CJTUxMBACUl5djyJAhXtdBRBeGjSYiP/r6669x44034r777sMf/vAHREdHY/v27Zg1a5bmlMyF0ul0HqeOmtfbcl5rXC4X9Ho9ioqKNH/wAbTauPCFy+UCIJ+ia954AKCuWwjhcfFzW9vRvNHprvu9995Dnz59NHFms1mNmTx5Mp544gmPuhITE6HX61FYWIgdO3agoKAAK1aswKJFi7B7924MHDjwvNtnNBo105IkqXm1tQ0tt9disbTr4u/m63Qv514nEXUsXtNE5Ef79u2Dw+HA008/jauuugqDBg3Cd999p4kxmUxwOp3nrctoNHrE9e7dG6WlpZqy5vftufjii2E0GrFr1y61rKKiQnObgBEjRsDpdKK8vBwXX3yxZkhISGjP5mLo0KGadQHQTMfHx6NPnz748ssvPdblbpQMGTIEn332mebXXvv27Tvvut0XTp88edKj7uTkZADA5ZdfjsOHD2PAgAEeMe4GmCRJuPrqq/H444/jwIEDMJlM2LhxY7v2Q1v5ORwO7N69Wy07ffo0jh8/jqFDh55zWW+PESLqXGw0EV2AyspKFBcXa4aTJ0/iRz/6ERwOB1asWIEvv/wSL7/8Mv7+979rlh0wYABqamrw4Ycf4ocffkBdXV2r6xgwYAA+/PBDlJWVoaKiAgDws5/9DPv27cNLL72E//73v1i8eDEOHTqkLhMeHo5Zs2bh17/+NT788EMcOnQIM2fOhE7X9FEfNGgQbr/9dtx555148803ceLECezduxdPPPEE3n///XbthwcffBD/+Mc/8I9//APHjx/H4sWLcfjwYU1MTk4O8vLy8Je//AXHjx/HwYMHsXr1aixbtgwAMH36dLhcLtx77704evQoNm/ejKeeegoAztkDExERgQULFuBXv/oV8vPz8cUXX+DAgQP429/+pl5kPnfuXJw5cwbTpk3Dnj178OWXX6KgoAB33303nE4ndu/ejdzcXOzbtw8nT57Em2++iVOnTp23UeONlJQU3HzzzZg9eza2b9+O//znP7jjjjvQp08f3HzzzedcdsCAAfjss89w7Ngx/PDDD37ppSQiP+jC66mIuqUZM2YIAB7DjBkzhBBCLFu2TCQmJgqLxSImTJggXnrpJQFAVFRUqHXcd999IiYmRgAQixcvFkJ4Xgj+zjvviIsvvlgYDAbRv39/tfyxxx4T8fHxwmq1il/96lfi/vvvVy8EF0K+GPyOO+4QoaGhIj4+XixdulSMGTNGvRBcCCHsdrt47LHHxIABA4TRaBQJCQni1ltvFZ999lmb241WLgQXQoglS5aI2NhYER4eLmbMmCEWLlyouRBcCCHWrVsnLrvsMmEymURUVJT46U9/Kt588011/qeffiouvfRSYTKZRFpamli/fr0AID7//HMhRNOF4M33oRBCuFwu8Ze//EUMHjxYGI1G0bt3bzFhwgSxbds2Neb48ePi1ltvFb169RIWi0UMGTJEZGdnC5fLJY4cOSImTJggevfuLcxmsxg0aJBYsWJFm/ugufNdlC+EEGfOnBGZmZnCarWqx8Px48fV+W39KKC8vFyMHz9ehIeHCwBiy5Yt6oXg7ovjhRCioqJCnX++3IjId5IQXv62loiCmiRJ2LhxY6c8nmPdunXq/awsFkuHr6+nmTlzJs6ePRtwd5Qn6u54eo6IvDZt2jT07dvX7/W+9NJL2L59O06cOIG33noLDz/8MKZOncoGUzt98sknCA8Px7p167o6FaIeib+eIyKvuG982fIXd/5QVlaGxx57DGVlZUhMTMRtt92GJUuW+H097XGuXxL++9//xk9+8pNOzMY7I0eOVH8Y4O9fQhIRwNNzRESt+N///tfmvD59+rAXjCgIsdFERERE5AVe00RERETkBTaaiIiIiLzARhMRERGRF9hoIiIiIvICG01EREREXmCjiYiIiMgLbDQREREReYGNJiIiIiIv/H+FJYvBQdLoxAAAAABJRU5ErkJggg==", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_vr_zonal = ds_vr.isel(time=0).spatial.average(\"cl\", axis=[\"X\"]).squeeze()\n", + "\n", + "ds_vr_zonal.cl.plot(cmap=plt.cm.RdBu_r)\n", + "plt.gca().invert_yaxis()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 5. Spatial Averaging\n", + "\n", + "- Area-weighted spatial averaging is a common technique to reduce dimensionality in geospatial datasets.\n", + "- xCDAT can perform this calculation over full domains or regions of interest.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "#### Calculate the spatial average of `tas` and store the results in a Python variable.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.spatial.average.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 19kB\n",
    +       "Dimensions:    (time: 180, bnds: 2, lat: 180, lon: 360)\n",
    +       "Coordinates:\n",
    +       "  * lat        (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
    +       "  * lon        (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
    +       "    height     float64 8B ...\n",
    +       "  * time       (time) object 1kB 2000-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
    +       "Dimensions without coordinates: bnds\n",
    +       "Data variables:\n",
    +       "    time_bnds  (time, bnds) object 3kB dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
    +       "    lat_bnds   (lat, bnds) float64 3kB dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
    +       "    lon_bnds   (lon, bnds) float64 6kB dask.array<chunksize=(360, 2), meta=np.ndarray>\n",
    +       "    tas        (time) float64 1kB dask.array<chunksize=(180,), meta=np.ndarray>\n",
    +       "Attributes: (12/48)\n",
    +       "    Conventions:            CF-1.7 CMIP-6.2\n",
    +       "    activity_id:            CMIP\n",
    +       "    branch_method:          standard\n",
    +       "    branch_time_in_child:   0.0\n",
    +       "    branch_time_in_parent:  36500.0\n",
    +       "    creation_date:          2022-08-31T00:41:19Z\n",
    +       "    ...                     ...\n",
    +       "    variant_label:          r1i1p1f1\n",
    +       "    license:                CMIP6 model data produced by E3SM-Project is lice...\n",
    +       "    cmor_version:           3.6.1\n",
    +       "    tracking_id:            hdl:21.14100/5e95fb4d-19ba-4037-8000-818fe3955601\n",
    +       "    version:                v20220830\n",
    +       "    references:             Golaz, J.-C., L. P. Van Roekel, X. Zheng and co-a...
    " + ], + "text/plain": [ + " Size: 19kB\n", + "Dimensions: (time: 180, bnds: 2, lat: 180, lon: 360)\n", + "Coordinates:\n", + " * lat (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n", + " * lon (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n", + " height float64 8B ...\n", + " * time (time) object 1kB 2000-01-16 12:00:00 ... 2014-12-16 12:00:00\n", + "Dimensions without coordinates: bnds\n", + "Data variables:\n", + " time_bnds (time, bnds) object 3kB dask.array\n", + " lat_bnds (lat, bnds) float64 3kB dask.array\n", + " lon_bnds (lon, bnds) float64 6kB dask.array\n", + " tas (time) float64 1kB dask.array\n", + "Attributes: (12/48)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: CMIP\n", + " branch_method: standard\n", + " branch_time_in_child: 0.0\n", + " branch_time_in_parent: 36500.0\n", + " creation_date: 2022-08-31T00:41:19Z\n", + " ... ...\n", + " variant_label: r1i1p1f1\n", + " license: CMIP6 model data produced by E3SM-Project is lice...\n", + " cmor_version: 3.6.1\n", + " tracking_id: hdl:21.14100/5e95fb4d-19ba-4037-8000-818fe3955601\n", + " version: v20220830\n", + " references: Golaz, J.-C., L. P. Van Roekel, X. Zheng and co-a..." + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_global = ds.spatial.average(\"tas\")\n", + "\n", + "ds_global" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Now let's plot the results (first 120 timesteps)\n", + "\n", + "Note that the spatial averager returns a dataset object so we still need to specify `tas` to plot the dataarray.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Near Surface Air Temperature [K]')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_global.tas.isel(time=slice(0, 120)).plot()\n", + "plt.title(\"Global Average Surface Temperature\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Near Surface Air Temperature [K]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Calculate the near-surface air temperature (`tas`) in the Niño 3.4 region.\n", + "\n", + "Users can also specify their own bounds for a region.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ds_nino34 = ds_xesmf.spatial.average(\n", + " \"tas\", lat_bounds=(-5, 5), lon_bounds=(190, 240), keep_weights=True\n", + ").compute()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "In this case, we specified `keep_weights=True`.\n", + "\n", + "- The weights provide full spatial weighting for grid cells entirely within the Niño 3.4 region.\n", + "- Partial weights for grid cells partially in the region.\n", + "\n", + "Other notes:\n", + "\n", + "- We use the 4 x 4 degree grid in this example to show the partial weights and to speed up plotting.\n", + "- You can also supply your own weights, but you can't automatically subset with `lat_bounds` and `lon_bounds` if you supply your own weights\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Plot the Niño 3.4 region time series\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Nino 3.4 Weights')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dtime = dt2decimal(ds_nino34.time) # decimal time\n", + "\n", + "plt.figure(figsize=(10, 2))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(dtime, ds_nino34[\"tas\"].values)\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Near-Surface Air Temperature [K]\")\n", + "plt.title(\"Niño 3.4 time series\")\n", + "\n", + "# show the weights\n", + "map_proj = ccrs.PlateCarree(central_longitude=180)\n", + "ax = plt.subplot(1, 2, 2, projection=map_proj)\n", + "plt.pcolor(\n", + " ds_nino34.lon,\n", + " ds_nino34.lat,\n", + " ds_nino34.lat_lon_wts.T,\n", + " transform=ccrs.PlateCarree(),\n", + " cmap=plt.cm.GnBu,\n", + ")\n", + "ax.set_extent([120, 300, -30, 30], crs=ccrs.PlateCarree())\n", + "ax.coastlines()\n", + "plt.colorbar(orientation=\"horizontal\")\n", + "plt.title(\"Nino 3.4 Weights\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 6. Temporal Computations with xCDAT\n", + "\n", + "In the examples below, we will performing temporal computations on the `xarray.Dataset` object using xCDAT.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### 6.1 Seasonal cycle mean\n", + "\n", + "In the global mean time series above, there are large seasonal swings in global near-surface air temperature. Here we compute the seasonal mean climatology.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "#### Calculate the seasonal mean climatology for the `tas` variable.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.climatology.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ds_clim = ds.temporal.climatology(\"tas\", freq=\"season\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Now we plot the season means\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "map_proj = ccrs.Robinson()\n", + "titles = [\"DJF\", \"MAM\", \"JJA\", \"SON\"]\n", + "plt.figure(figsize=(12, 10))\n", + "for i in range(4):\n", + " plt.subplot(2, 2, i + 1, projection=map_proj)\n", + " p = ds_clim.tas[i].plot(\n", + " transform=ccrs.PlateCarree(),\n", + " subplot_kws={\"projection\": map_proj},\n", + " cbar_kwargs={\"orientation\": \"horizontal\"},\n", + " cmap=plt.cm.RdBu_r,\n", + " vmin=220,\n", + " vmax=310,\n", + " )\n", + " ax = plt.gca()\n", + " ax.coastlines()\n", + " plt.title(titles[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### 6.2 Departures\n", + "\n", + "It can also be useful to show the departures from the climatological average.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "#### Calculate the monthly mean departures for the `tas` variable.\n", + "\n", + "In this case, `xcdat` will operate on the global mean time series we calculated above.\n", + "\n", + "Note that you can set the climatological reference period (e.g., with `reference_period=(\"2000-01-01\", \"2009-12-31\")` for historical era departures).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.departures.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/miniconda3/envs/xcdat_scipy_2024/lib/python3.11/site-packages/xarray/core/indexing.py:1617: PerformanceWarning: Slicing with an out-of-order index is generating 15 times more chunks\n", + " return self.array[key]\n" + ] + } + ], + "source": [ + "ds_global_anomaly = ds_global.temporal.departures(\n", + " \"tas\", freq=\"month\", reference_period=(\"2000-01-01\", \"2009-12-31\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Now let's plot the departures from the climatological average.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Global Mean Near-Surface Air Temperature Anomaly [K]')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(dtime, ds_global_anomaly.tas.values)\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Global Mean Near-Surface Air Temperature Anomaly [K]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### 6.3 Group averages\n", + "\n", + "`xcdat` also allows you to calculate group averages (e.g., annual or seasonal mean from monthly data or monthly mean from daily data).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "#### Calculate the annual mean from anomaly time series.\n", + "\n", + "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.group_averages.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ds_global_anomaly_annual = ds_global_anomaly.temporal.group_average(\"tas\", freq=\"year\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Now let's plot the results.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Global Mean Near-Surface Air Temperature [K]')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot data\n", + "dtime_annual = dt2decimal(ds_global_anomaly_annual.time) + 0.5\n", + "plt.plot(dtime, ds_global_anomaly.tas.values, label=\"Monthly departure\", color=\"gray\")\n", + "plt.plot(\n", + " dtime_annual,\n", + " ds_global_anomaly_annual.tas.values,\n", + " color=\"k\",\n", + " linestyle=\"\",\n", + " marker=\"_\",\n", + " label=\"Annual Mean\",\n", + ")\n", + "plt.legend(frameon=False)\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Global Mean Near-Surface Air Temperature [K]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### 7. General Dataset Utilities\n", + "\n", + "xCDAT includes various utilities for data manipulation, including\n", + "reorientation of the longitude axis, centering of time coordinates using time bounds, and adding and getting bounds.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### 7.1. Reorient the longitude axis\n", + "\n", + "- Longitude can be represented from 0 to 360 E or as 180 W to 180 E.\n", + "- xcdat allows you to convert between these axes systems using `xc.swap_lon_axis`.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xcdat.swap_lon_axis.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original lon axis: (0.5, 359.5)\n" + ] + } + ], + "source": [ + "print(f\"Original lon axis: ({ds.lon[0].item()}, {ds.lon[-1].item()})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Swapped lon axis: (-179.5, 179.5)\n" + ] + } + ], + "source": [ + "ds_swap = xc.swap_lon_axis(ds, to=(-180, 180))\n", + "\n", + "print(f\"Swapped lon axis: ({ds_swap.lon[0].item()}, {ds_swap.lon[-1].item()})\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### 7.2. Add missing bounds\n", + "\n", + "**Bounds are critical to many `xcdat` operations.**\n", + "\n", + "- For example, they are used in determining the weights in spatial or temporal averages and in regridding operations.\n", + "- `add_missing_bounds()` will attempt to produce bounds if they do not exist in the original dataset.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "- API documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.bounds.add_missing_bounds.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\"No bounds data variables were found for the 'T' axis. Make sure the dataset has bound data vars and their names match the 'bounds' attributes found on their related time coordinate variables. Alternatively, you can add bounds with `ds.bounds.add_missing_bounds()` or `ds.bounds.add_bounds()`.\"\n" + ] + } + ], + "source": [ + "# We are dropping the existing bounds to demonstrate adding bounds.\n", + "ds_no_bnds = ds.drop_vars(\"time_bnds\")\n", + "\n", + "try:\n", + " ds_no_bnds.bounds.get_bounds(\"T\")\n", + "except KeyError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    \n", + " \"Dask\n", + "
    \n", + "\n", + "> \"Nearly all existing xarray methods have been extended to work automatically with Dask arrays for parallelism\"\n", + "\n", + "- Parallelized xarray methods include **indexing, computation, concatenating and grouped operations**\n", + "- **xCDAT inherits Xarray's support for Dask parallelism** by operating on Xarray objects.\n", + "- Dask arrays are loaded into memory only when absolutely required (e.g., generating weights for averaging)\n", + "\n", + "— https://docs.xarray.dev/en/stable/user-guide/dask.html#using-dask-with-xarray\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Example of Parallelizing xCDAT with Dask\n", + "\n", + "1. Open ~11 GB dataset using `xc.open_mfdataset()`\n", + "2. Chunked on time dimension by 10 (parallel) and by 1 (serial)\n", + "3. Compare run times of global spatial averaging\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "_This exercise requires data to be downloaded locally using this command:\n", + "`bash docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr/tas_day_wget_script_2024-6-25_11-31-0.sh -s`_\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import xcdat as xc\n", + "\n", + "FILENAMES = [\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_18500101-18591231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_18600101-18691231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_18700101-18791231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_18800101-18891231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19000101-19091231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19100101-19191231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19300101-19391231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19400101-19491231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19600101-19691231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19700101-19791231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19800101-19891231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_19900101-19991231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_20000101-20091231.nc\",\n", + " \"tas_day_E3SM-2-0_historical_r1i1p1f1_gr_20100101-20141231.nc\",\n", + "]\n", + "\n", + "LOCAL_DIR = \"docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr\"\n", + "LOCAL_FILEPATHS = [os.path.join(LOCAL_DIR, file) for file in FILENAMES]\n", + "\n", + "ds_parallel = xc.open_mfdataset(LOCAL_FILEPATHS, chunks={\"time\": 10})\n", + "ds_serial = xc.open_mfdataset(LOCAL_FILEPATHS, chunks={\"time\": 1})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Now let's compare the performance of xCDAT `spatial.average()`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 6s, sys: 9.39 s, total: 1min 16s\n", + "Wall time: 1min 59s\n" + ] + } + ], + "source": [ + "%%time\n", + "ds_parallel_avg = ds_parallel.spatial.average(\"tas\")\n", + "ds_parallel_avg = ds_parallel.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 31s, sys: 14.1 s, total: 1min 45s\n", + "Wall time: 2min 37s\n" + ] + } + ], + "source": [ + "%%time\n", + "ds_serial_avg = ds_serial.spatial.average(\"tas\")\n", + "ds_serial_avg = ds_serial.compute()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "#### xCDAT's spatial averager is faster when chunking on the time dimension by 10 and executing in parallel.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### xCDAT also outperforms the older CDAT library by much large margins in some cases.\n", + "\n", + "For example, for global spatial averaging across various file sizes:\n", + "\n", + "
    \n", + " \"JOSS\n", + "

    * CDAT only runs functions serially and crashes with 22 GB+ datasets.

    \n", + "
    \n", + "\n", + "Source: https://joss.theoj.org/papers/10.21105/joss.06426\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Further Dask Guidance\n", + "\n", + "Visit these pages for more guidance (e.g., when to parallelize):\n", + "\n", + "- Parallel computing with Dask (xCDAT, image shown below): https://xcdat.readthedocs.io/en/latest/examples/parallel-computing-with-dask.html\n", + "- Parallel computing with Dask (Xarray): https://docs.xarray.dev/en/stable/user-guide/dask.html\n", + "- Xarray with Dask Arrays: https://examples.dask.org/xarray.html\n", + "\n", + "
    \n", + " \"Dask\n", + "
    \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "
    \n", + " \"xCDAT\n", + "

    Recap of Key Points

    \n", + "
    \n", + "\n", + "- xCDAT is an **extension of Xarray for climate data analysis on structured grids**, a modern successor to the Community Data Analysis Tools (CDAT) library\n", + "- **Focused on routine climate research analysis operations** including loading, wrangling, averaging, such as temporal averaging, spatial averaging, and regridding\n", + "- Designed to encourages **software sustainability and reproducible science**\n", + "- **Parallelizable** through Xarray’s support for Dask, which enables efficient processing of large datasets\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "
    \n", + "

    \"GitHub\n", + " \"GitHub\n", + " Get involved and join the xCDAT community!

    \n", + "
    \n", + "\n", + "- **Code contributions** are welcome and appreciated\n", + " - GitHub Repository: https://github.com/xCDAT/xcdat\n", + " - Contributing Guide: https://xcdat.readthedocs.io/en/latest/contributing.html\n", + "- **Submit and/or address tickets** for feature suggestions, bugs, and documentation updates\n", + " - GitHub Issues: https://github.com/xCDAT/xcdat/issues\n", + "- **Participate in forum discussions** on version releases, architecture, feature suggestions, etc.\n", + " - GitHub Discussions: https://github.com/xCDAT/xcdat/discussions\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + }, + "rise": { + "scroll": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr/tas_day_wget_script_2024-6-25_11-31-0.sh b/docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr/tas_day_wget_script_2024-6-25_11-31-0.sh new file mode 100644 index 00000000..d3b2560c --- /dev/null +++ b/docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr/tas_day_wget_script_2024-6-25_11-31-0.sh @@ -0,0 +1,1059 @@ +#!/bin/bash +############################################################################## +# ESG Federation download script +# +# Template version: 1.2 +# Generated by esgf-node.llnl.gov - 2024/06/25 11:31:00 +# Search URL: https://esgf-node.llnl.gov/esg-search/wget?dataset_id=CMIP6.CMIP.E3SM-Project.E3SM-2-0.historical.r1i1p1f1.day.tas.gr.v20220923|esgf-data2.llnl.gov +# +############################################################################### +# first be sure it's bash... anything out of bash or sh will break +# and the test will assure we are not using sh instead of bash +if [ $BASH ] && [ `basename $BASH` != bash ]; then + echo "######## This is a bash script! ##############" + echo "Change the execution bit 'chmod u+x $0' or start with 'bash $0' instead of sh." + echo "Trying to recover automatically..." + sleep 1 + /bin/bash $0 $@ + exit $? +fi + +version=1.3.2 +CACHE_FILE=.$(basename $0).status +openId= +search_url='https://esgf-node.llnl.gov/esg-search/wget?dataset_id=CMIP6.CMIP.E3SM-Project.E3SM-2-0.historical.r1i1p1f1.day.tas.gr.v20220923|esgf-data2.llnl.gov' + +#These are the embedded files to be downloaded +download_files="$(cat < 10#${ver2[i]})) + then + return 1 + fi + if ((10#${ver1[i]} < 10#${ver2[i]})) + then + return 2 + fi + done + return 0 +} + +check_commands() { + #check wget + local MIN_WGET_VERSION=1.10 + vercomp $(wget -V | sed -n 's/^.* \([1-9]\.[0-9.]*\) .*$/\1/p') $MIN_WGET_VERSION + case $? in + 2) #lower + wget -V + echo + echo "** ERROR: wget version is too old. Use version $MIN_WGET_VERSION or greater. **" >&2 + exit 1 + esac +} + +usage() { + echo "Usage: $(basename $0) [flags] [openid] [username]" + echo "Flags is one of:" + sed -n '/^while getopts/,/^done/ s/^\([^)]*\)[^#]*#\(.*$\)/\1 \2/p' $0 + echo + echo "This command stores the states of the downloads in .$0.status" + echo "For more information check the website: http://esgf.org/wiki/ESGF_wget" +} + +#defaults +debug=0 +clean_work=1 + +#parse flags +while getopts ':c:pfF:o:w:isuUndvqhHI:T' OPT; do + case $OPT in + H) skip_security=1 && use_http_sec=1;; # : Authenticate with OpenID (username,) and password, without the need for a certificate. + T) force_TLSv1=1;; # : Forces wget to use TLSv1. + c) ESG_CREDENTIALS="$OPTARG";; # : use this certificate for authentication. + f) force=1;; # : force certificate retrieval (defaults to only once per day); for certificate-less authentication (see -H option), this flag will force login and refresh cookies. + F) input_file="$OPTARG";; # : read input from file instead of the embedded one (use - to read from stdin) + o) openId="$OPTARG";; #: Provide OpenID instead of interactively asking for it. + I) username_supplied="$OPTARG";; # : Explicitly set user ID. By default, the user ID is extracted from the last component of the OpenID URL. Use this flag to override this behaviour. + w) output="$OPTARG";; # : Write embedded files into a file and exit + i) insecure=1;; # : set insecure mode, i.e. don't check server certificate + s) skip_security=1 && use_cookies_for_http_basic_auth_start=1;; # : completely skip security. It will only work if the accessed data is not secured at all. -- works only if the accessed data is unsecured or a certificate exists or cookies are saved (latter applies to -H option only). + u) update=1;; # : Issue the search again and see if something has changed. + U) update_files=1;; # : Update files from server overwriting local ones (detect with -u) + n) dry_run=1;; # : Don't download any files, just report. + p) clean_work=0;; # : preserve data that failed checksum + d) verbose=1;debug=1;; # : display debug information + v) verbose=1;; # : be more verbose + q) quiet=1;; # : be less verbose + h) usage && exit 0;; # : displays this help + \?) echo "Unknown option '$OPTARG'" >&2 && usage && exit 1;; + \:) echo "Missing parameter for flag '$OPTARG'" >&2 && usage && exit 1;; + esac +done +shift $(($OPTIND - 1)) + +#setup input as desired by the user +if [[ "$input_file" ]]; then + if [[ "$input_file" == '-' ]]; then + download_files="$(cat)" #read from STDIN + exec 0$output + exit +fi + + +#assure we have everything we need +check_commands + +if ((update)); then + echo "Checking the server for changes..." + new_wget="$(wget "$search_url" -qO -)" + compare_cmd="grep -vE '^(# Generated by|# Search URL|search_url=)'" + if diff -q <(eval $compare_cmd<<<"$new_wget") <(eval $compare_cmd $0) >/dev/null; then + echo "No changes detected." + else + echo "Wget was changed. Dowloading. (old renamed to $0.old.#N)" + counter=0 + while [[ -f $0.old.$counter ]]; do ((counter++)); done + mv $0 $0.old.$counter + echo "$new_wget" > $0 + fi + exit 0 +fi + + +############################################################################## +check_java() { + if ! type java >& /dev/null; then + echo "Java could not be found." >&2 + return 1 + fi + if java -version 2>&1|grep openjdk >/dev/null; then + openjdk=1; + else + openjdk=0; + fi + jversion=($(jversion=$(java -version 2>&1 | awk '/version/ {gsub("\"","");print $3}'); echo ${jversion//./ })) + mVer=${jversion[1]} + if [ $openjdk -eq 1 ]; then + mVer=${jversion[0]} + if ((mVer<5)); then + echo "Openjdk detected. Version 9+ is required for retrieving the certificate." >&2 + echo "Current version seems older: $(java -version | head -n1) " >&2 + return 1 + fi + else + + if ((mVer<5)); then + echo "Java version 1.5+ is required for retrieving the certificate." >&2 + echo "Current version seems older: $(java -version | head -n1) " >&2 + return 1 + fi + fi +} + +check_myproxy_logon() { + if ! type myproxy-logon >& /dev/null; then + echo "myproxy-logon could not be found." >&2 + return 1 + fi + echo "myproxy-logon found" >&2 +} + +proxy_to_java() { + local proxy_user proxy_pass proxy_server proxy_port + eval $(sed 's#^\(https\?://\)\?\(\([^:@]*\)\(:\([^@]*\)\)\?@\)\?\([^:/]*\)\(:\([0-9]*\)\)\?.*#proxy_user=\3;proxy_pass=\5;proxy_server=\6;proxy_port=\8#'<<<$http_proxy) + local JAVA_PROXY= + [[ "$proxy_server" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttp.proxyHost=$proxy_server" + [[ "$proxy_port" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttp.proxyPort=$proxy_port" + eval $(sed 's#^\(https\?://\)\?\(\([^:@]*\)\(:\([^@]*\)\)\?@\)\?\([^:/]*\)\(:\([0-9]*\)\)\?.*#proxy_user=\3;proxy_pass=\5;proxy_server=\6;proxy_port=\8#'<<<$https_proxy) + [[ "$proxy_server" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttps.proxyHost=$proxy_server" + [[ "$proxy_port" ]] && JAVA_PROXY=$JAVA_PROXY" -Dhttps.proxyPort=$proxy_port" + + echo "$JAVA_PROXY" +} + +# get certificates from github +get_certificates() { + # don't if this was already done today + [[ -z $force && "$(find $ESG_CERT_DIR -type d -mtime -1 2>/dev/null)" ]] && return 0 + echo -n "Retrieving Federation Certificates..." >&2 + + if ! wget -O $ESG_HOME/esg-truststore.ts --no-check-certificate https://github.com/ESGF/esgf-dist/raw/master/installer/certs/esg-truststore.ts; then + echo "Could not fetch esg-truststore"; + return 1 + fi + + if ! wget --no-check-certificate https://raw.githubusercontent.com/ESGF/esgf-dist/master/installer/certs/esg_trusted_certificates.tar -O - -q | tar x -C $ESG_HOME; then + #certificates tarred into esg_trusted_certificates. (if it breaks, let the user know why + wget --no-check-certificate https://raw.githubusercontent.com/ESGF/esgf-dist/master/installer/certs/esg_trusted_certificates.tar + echo "Could't update certs!" >&2 + return 1 + else + #if here everythng went fine. Replace old cert with this ones + [[ -d $ESG_CERT_DIR ]] && rm -r $ESG_CERT_DIR || mkdir -p $(dirname $ESG_CERT_DIR) + mv $ESG_HOME/esg_trusted_certificates $ESG_CERT_DIR + touch $ESG_CERT_DIR + echo "done!" >&2 + fi + +} + +# Retrieve ESG credentials +unset pass +get_credentials() { + if check_java + then + use_java=1 + else + use_java=0 + echo "No suitable java for obtaining certificate - checking for myproxy-logon instead" >&2 + check_myproxy_logon || exit 1 + fi + #get all certificates + get_certificates + + if [[ -z "$(find $MYPROXY_GETCERT -type f -mtime -1 2>/dev/null)" ]]; then + echo -n "(Downloading $MYPROXY_GETCERT... " + mkdir -p $(dirname $MYPROXY_GETCERT) + if wget -q --no-check-certificate https://raw.githubusercontent.com/ESGF/esgf-dist/master/installer/certs/getcert.jar -O $MYPROXY_GETCERT;then + echo 'done)' + touch $MYPROXY_GETCERT + else + echo 'failed)' + fi + fi + + #if the user already defined one, use it + if [[ -z $openId ]]; then + #try to parse the last valid value if any + [[ -f "$MYPROXY_STATUS" ]] && openId=$(awk -F= '/^OpenID/ {gsub("\\\\", ""); print $2}' $MYPROXY_STATUS) + if [[ -z $openId ]]; then + #no OpenID, we need to ask the user + echo -n "Please give your OpenID (Example: https://myserver/example/username) ? " + else + #Allow the user to change it if desired + echo -n "Please give your OpenID (hit ENTER to accept default: $openId)? " + fi + read -e + [[ "$REPLY" ]] && openId="$REPLY" + else + ((verbose)) && echo "Using user defined OpenID $openId (to change use -o )" + fi + + if grep -q ceda.ac.uk <<<$openId; then + username=${openId##*/} + echo -n "Please give your username if different [$username]: " + read -e + [[ "$REPLY" ]] && username="$REPLY" + fi + + + + if [ $use_java -eq 1 ] + then + local args= + #get password + [[ ! "$pass" ]] && read -sp "MyProxy Password? " pass + [[ "$openId" ]] && args=$args" --oid $openId" + [[ "$pass" ]] && args=$args" -P $pass" + [[ "$username" ]] && args=$args" -l $username" + + echo -n $'\nRetrieving Credentials...' >&2 + if ! java $(proxy_to_java) -jar $MYPROXY_GETCERT $args --ca-directory $ESG_CERT_DIR --output $ESG_CREDENTIALS ; then + echo "Certificate could not be retrieved" + exit 1 + fi + echo "done!" >&2 + else + args=`openid_to_myproxy_args $openId $username` || exit 1 + if ! myproxy-logon $args -b -o $ESG_CREDENTIALS + then + echo "Certificate could not be retrieved" + exit 1 + fi + cp $HOME/.globus/certificates/* $ESG_CERT_DIR/ + fi +} + +openid_to_myproxy_args() { + python - </dev/null; then + #check openssl and certificate + if ! openssl x509 -checkend $CERT_EXPIRATION_WARNING -noout -in $ESG_CERT 2>/dev/null; then + echo "The certificate expires in less than $((CERT_EXPIRATION_WARNING / 60 / 60)) hour(s). Renewing..." + get_credentials + else + #ok, certificate is fine + return 0 + fi + fi +} + +# +# Detect ESG credentials +# +find_credentials() { + + #is X509_USER_PROXY or $HOME/.esg/credential.pem + if [[ -f "$ESG_CREDENTIALS" ]]; then + # file found, proceed. + ESG_CERT="$ESG_CREDENTIALS" + ESG_KEY="$ESG_CREDENTIALS" + elif [[ -f "$X509_USER_CERT" && -f "$X509_USER_KEY" ]]; then + # second try, use these certificates. + ESG_CERT="$X509_USER_CERT" + ESG_KEY="$X509_USER_KEY" + else + # If credentials are not present, just point to where they should go + echo "No ESG Credentials found in $ESG_CREDENTIALS" >&2 + ESG_CERT="$ESG_CREDENTIALS" + ESG_KEY="$ESG_CREDENTIALS" + #they will be retrieved later one + fi + + + #chek openssl and certificate + if (which openssl &>/dev/null); then + if ( openssl version | grep 'OpenSSL 1\.0' ); then + echo '** WARNING: ESGF Host certificate checking might not be compatible with OpenSSL 1.0+' + fi + check_cert || { (($?==1)); exit 1; } + fi + + if [[ $CHECK_SERVER_CERT == "Yes" ]]; then + [[ -d "$ESG_CERT_DIR" ]] || { echo "CA certs not found. Aborting."; exit 1; } + PKI_WGET_OPTS="--ca-directory=$ESG_CERT_DIR" + fi + + #some wget version complain if there's no file present + [[ -f $COOKIE_JAR ]] || touch $COOKIE_JAR + + PKI_WGET_OPTS="$PKI_WGET_OPTS --certificate=$ESG_CERT --private-key=$ESG_KEY --save-cookies=$COOKIE_JAR --load-cookies=$COOKIE_JAR --ca-certificate=$ESG_CERT" + +} + +check_chksum() { + local file="$1" + local chk_type=$2 + local chk_value=$3 + local local_chksum=Unknown + + case $chk_type in + md5) local_chksum=$(md5sum_ $file | cut -f1 -d" ");; + sha256) local_chksum=$(sha256sum_ $file|awk '{print $1}'|cut -d ' ' -f1);; + *) echo "Can't verify checksum." && return 0;; + esac + + #verify + ((debug)) && echo "local:$local_chksum vs remote:$chk_value" >&2 + echo $local_chksum +} + +#Our own md5sum function call that takes into account machines that don't have md5sum but do have md5 (i.e. mac os x) +md5sum_() { + hash -r + if type md5sum >& /dev/null; then + echo $(md5sum $@) + else + echo $(md5 $@ | sed -n 's/MD5[ ]*\(.*\)[^=]*=[ ]*\(.*$\)/\2 \1/p') + fi +} + +#Our own sha256sum function call that takes into account machines that don't have sha256sum but do have sha2 (i.e. mac os x) +sha256sum_() { + hash -r + if type sha256sum >& /dev/null; then + echo $(sha256sum $@) + elif type shasum >& /dev/null; then + echo $(shasum -a 256 $@) + else + echo $(sha2 -q -256 $@) + fi +} + +get_mod_time_() { + if ((MACOSX)); then + #on a mac modtime is stat -f %m + echo "$(stat -f %m $@)" + else + #on linux (cygwin) modtime is stat -c %Y + echo "$(stat -c %Y $@)" + fi + return 0; +} + +remove_from_cache() { + local entry="$1" + local tmp_file="$(grep -ve "^$entry" "$CACHE_FILE")" + echo "$tmp_file" > "$CACHE_FILE" + unset cached +} + +#Download data from node using cookies and not certificates. +download_http_sec() +{ + #The data to be downloaded. + data=" $url" + filename="$file" + + #Wget args. + if ((insecure)) + then + wget_args=" --no-check-certificate --cookies=on --keep-session-cookies --save-cookies $COOKIES_FOLDER/wcookies.txt " + else + wget_args=" --ca-directory=$WGET_TRUSTED_CERTIFICATES --cookies=on --keep-session-cookies --save-cookies $COOKIES_FOLDER/wcookies.txt " + fi + + if ((use_cookies_for_http_basic_auth_start)) || ((use_cookies_for_http_basic_auth)) + then + wget_args=" $wget_args"" --load-cookies $COOKIES_FOLDER/wcookies.txt" + fi + + if((force_TLSv1)) + then + wget_args=" $wget_args"" --secure-protocol=TLSv1 " + fi + + + if [[ ! -z "$ESGF_WGET_OPTS" ]] + then + wget_args="$wget_args $ESGF_WGET_OPTS" + fi + + + #use cookies for the next downloads + use_cookies_for_http_basic_auth=1; + + #Debug message. + if ((debug)) + then + echo -e "\nExecuting:\n" + echo -e "wget $wget_args $data\n" + fi + + + #Try to download the data. + command="wget $wget_args -O $filename $data" + http_resp=$(eval $command 2>&1) + cmd_exit_status="$?" + + if ((debug)) + then + echo -e "\nHTTP response:\n $http_resp\n" + fi + + #Extract orp service from url ? + #Evaluate response. + #redirects=$(echo "$http_resp" | egrep -c ' 302 ') + #(( "$redirects" == 1 )) && + if echo "$http_resp" | grep -q "/esg-orp/" + then + urls=$(echo "$http_resp" | egrep -o 'https://[^ ]+' | cut -d'/' -f 3) + orp_service=$(echo "$urls" | tr '\n' ' ' | cut -d' ' -f 2) + + + #Use cookies for transaction with orp. + wget_args=" $wget_args"" --load-cookies $COOKIES_FOLDER/wcookies.txt" + + #Download data using either http basic auth or http login form. + if [[ "$openid_c" == */openid/ || "$openid_c" == */openid ]] + then + download_http_sec_open_id + else + download_http_sec_decide_service + fi + else + if echo "$http_resp" | grep -q "401 Unauthorized" \ + || echo "$http_resp" | grep -q "403: Forbidden" \ + || echo "$http_resp" | grep -q "Connection timed out." \ + || echo "$http_resp" | grep -q "no-check-certificate" \ + || (( $cmd_exit_status != 0 )) + then + echo "ERROR : http request to OpenID Relying Party service failed." + failed=1 + fi + fi +} + + +#Function that decides which implementaion of idp to use. +download_http_sec_decide_service() +{ + #find claimed id + + pos=$(echo "$openid_c" | egrep -o '/' | wc -l) + username_c=$(echo "$openid_c" | cut -d'/' -f "$(($pos + 1))") + esgf_uri=$(echo "$openid_c" | egrep -o '/esgf-idp/openid/') + + host=$(echo "$openid_c" | cut -d'/' -f 3) + #test ceda first. + + if [[ -z "$esgf_uri" ]] + then + openid_c_tmp="https://""$host""/openid/" + else + openid_c_tmp="https://""$host""/esgf-idp/openid/" + fi + + command="wget "$openid_c_tmp" --no-check-certificate ${force_TLSv1:+--secure-protocol=TLSv1} -O-" + + if [[ ! -z "$ESGF_WGET_OPTS" ]] + then + command="$command $ESGF_WGET_OPTS" + fi + + #Debug message. + if ((debug)) + then + echo -e "\nExecuting:\n" + echo -e "$command\n" + fi + + + #Execution of command. + http_resp=$(eval $command 2>&1) + cmd_exit_status="$?" + + + if ((debug)) + then + echo -e "\nHTTP response:\n $http_resp\n" + fi + + + if echo "$http_resp" | grep -q "[application/xrds+xml]" \ + && echo "$http_resp" | grep -q "200 OK" \ + && (( cmd_exit_status == 0 )) + then + openid_c=$openid_c_tmp + download_http_sec_open_id + else + if [[ -z "$esgf_uri" ]] + then + echo "ERROR : HTTP request to OpenID Relying Party service failed." + failed=1 + else + download_http_sec_cl_id + fi + fi +} + + +download_http_sec_retry() +{ + echo -e "\nRetrying....\n" + #Retry in case that last redirect did not work, this happens with older version of wget. + command="wget $wget_args $data" + + #Debug message. + if ((debug)) + then + echo -e "Executing:\n" + echo -e "$command\n" + fi + + http_resp=$(eval $command 2>&1) + cmd_exit_status="$?" + + if ((debug)) + then + echo -e "\nHTTP response:\n $http_resp\n" + fi + + if echo "$http_resp" | grep -q "401 Unauthorized" \ + || echo "$http_resp" | grep -q "403: Forbidden" \ + || echo "$http_resp" | grep -q "Connection timed out." \ + || echo "$http_resp" | grep -q "no-check-certificate" \ + || (( $cmd_exit_status != 0 )) + then + echo -e "\nERROR : Retry failed.\n" + #rm "$filename" + failed=1 + fi #if retry failed. +} + +#Function for downloading data using the claimed id. +download_http_sec_cl_id() +{ + #Http request for sending openid to the orp service. + command="wget --post-data \"openid_identifier=$openid_c&rememberOpenid=on\" $wget_args -O- https://$orp_service/esg-orp/j_spring_openid_security_check.htm " + + #Debug message. + if ((debug)) + then + echo -e "Executing:\n" + echo -e "wget $command\n" + fi + + + #Execution of command. + http_resp=$(eval $command 2>&1) + cmd_exit_status="$?" + + + if ((debug)) + then + echo -e "\nHTTP response:\n $http_resp\n" + fi + + + #Extract orp service from openid ? + #Evaluate response.If redirected to idp service send the credentials. + #redirects=$(echo "$http_resp" | egrep -c ' 302 ') + #(( redirects == 2 )) && + if echo "$http_resp" | grep -q "login.htm" && (( cmd_exit_status == 0 )) + then + + urls=$(echo "$http_resp" | egrep -o 'https://[^ ]+' | cut -d'/' -f 3) + idp_service=$(echo "$urls" | tr '\n' ' ' | cut -d' ' -f 2) + + command="wget --post-data password=\"$password_c\" $wget_args ${quiet:+-q} ${quiet:--v} -O $filename https://$idp_service/esgf-idp/idp/login.htm" + + + #Debug message. + if ((debug)) + then + echo -e "Executing:\n" + echo -e "wget $command\n" + fi + + #Execution of command. + http_resp=$(eval $command 2>&1) + cmd_exit_status="$?" + + if ((debug)) + then + echo -e "\nHTTP response:\n $http_resp\n" + fi + + #Evaluate response. + #redirects=$(echo "$http_resp" | egrep -c ' 302 ') + #(( "$redirects" != 5 )) \ + if echo "$http_resp" | grep -q "text/html" \ + || echo "$http_resp" | grep -q "403: Forbidden" \ + || (( cmd_exit_status != 0 )) + then + rm "$filename" + download_http_sec_retry + fi + + else + echo "ERROR : HTTP request to OpenID Provider service failed." + failed=1 + fi #if redirected to idp. +} + + + +download_http_sec_open_id() +{ + #Http request for sending openid to the orp web service. + command="wget --post-data \"openid_identifier=$openid_c&rememberOpenid=on\" --header=\"esgf-idea-agent-type:basic_auth\" --http-user=\"$username_c\" --http-password=\"$password_c\" $wget_args ${quiet:+-q} ${quiet:--v} -O $filename https://$orp_service/esg-orp/j_spring_openid_security_check.htm " + + + #Debug message. + if ((debug)) + then + echo -e "Executing:\n" + echo -e "$command\n" + fi + + #Execution of command. + http_resp=$(eval $command 2>&1) + cmd_exit_status="$?" + + + if ((debug)) + then + echo -e "\nHTTP response:\n $http_resp\n" + fi + + #Evaluate response. + #redirects=$(echo "$http_resp" | egrep -c ' 302 ') + #(( "$redirects" != 7 )) || + if echo "$http_resp" | grep -q "text/html" || (( $cmd_exit_status != 0 )) + then + rm "$filename" + download_http_sec_retry + fi #if error during http basic authentication. + +} + + +download() { + wget="wget ${insecure:+--no-check-certificate} ${quiet:+-q} ${quiet:--v} -c ${force_TLSv1:+--secure-protocol=TLSv1} $PKI_WGET_OPTS" + + while read line + do + # read csv here document into proper variables + eval $(awk -F "' '" '{$0=substr($0,2,length($0)-2); $3=tolower($3); print "file=\""$1"\";url=\""$2"\";chksum_type=\""$3"\";chksum=\""$4"\""}' <(echo $line) ) + + #Process the file + echo -n "$file ..." + + #get the cached entry if any. + cached="$(grep -e "^$file" "$CACHE_FILE")" + + #if we have the cache entry but no file, clean it. + if [[ ! -f $file && "$cached" ]]; then + #the file was removed, clean the cache + remove_from_cache "$file" + unset cached + fi + + #check it wasn't modified + if [[ -n "$cached" && "$(get_mod_time_ $file)" == $(echo "$cached" | cut -d ' ' -f2) ]]; then + if [[ "$chksum" == "$(echo "$cached" | cut -d ' ' -f3)" ]]; then + echo "Already downloaded and verified" + continue + elif ((update_files)); then + #user want's to overwrite newer files + rm $file + remove_from_cache "$file" + unset cached + else + #file on server is different from what we have. + echo "WARNING: The remote file was changed (probably a new version is available). Use -U to Update/overwrite" + continue + fi + fi + unset chksum_err_value chksum_err_count + + while : ; do + # (if we had the file size, we could check before trying to complete) + echo "Downloading" + [[ ! -d "$(dirname "$file")" ]] && mkdir -p "$(dirname "$file")" + if ((dry_run)); then + #all important info was already displayed, if in dry_run mode just abort + #No status will be stored + break + else + if ((use_http_sec)) + then + download_http_sec + if ((failed)) + then + break + fi + else + $wget -O "$file" $url || { failed=1; break; } + fi + fi + + #check if file is there + if [[ -f $file ]]; then + ((debug)) && echo file found + if [[ ! "$chksum" ]]; then + echo "Checksum not provided, can't verify file integrity" + break + fi + result_chksum=$(check_chksum "$file" $chksum_type $chksum) + if [[ "$result_chksum" != "$chksum" ]]; then + echo " $chksum_type failed!" + if ((clean_work)); then + if !((chksum_err_count)); then + chksum_err_value=$result_chksum + chksum_err_count=2 + elif ((checksum_err_count--)); then + if [[ "$result_chksum" != "$chksum_err_value" ]]; then + #this is a real transmission problem + chksum_err_value=$result_chksum + chksum_err_count=2 + fi + else + #ok if here we keep getting the same "different" checksum + echo "The file returns always a different checksum!" + echo "Contact the data owner to verify what is happening." + echo + sleep 1 + break + fi + + rm $file + #try again + echo -n " re-trying..." + continue + else + echo " don't use -p or remove manually." + fi + else + echo " $chksum_type ok. done!" + echo "$file" $(get_mod_time_ "$file") $chksum >> $CACHE_FILE + fi + fi + #done! + break + done + + if ((failed)); then + echo "download failed" + # most common failure is certificate expiration, so check this + #if we have the pasword we can retrigger download + ((!skip_security)) && [[ "$pass" ]] && check_cert + unset failed + fi + +done <<<"$download_files" + +} + +dedup_cache_() { + local file=${1:-${CACHE_FILE}} + ((debug)) && echo "dedup'ing cache ${file} ..." + local tmp=$(LC_ALL='C' sort -r -k1,2 $file | awk '!($1 in a) {a[$1];print $0}' | sort -k2,2) + ((DEBUG)) && echo "$tmp" + echo "$tmp" > $file + ((debug)) && echo "(cache dedup'ed)" +} + +http_basic_auth_func_info_message() +{ + echo "********************************************************************************" + echo "* *" + echo "* Note that new functionality to allow authentication without the need for *" + echo "* certificates is available with this version of the wget script. To enable, *" + echo "* use the \"-H\" option and enter your OpenID and password when prompted: *" + echo "* *" + echo "* $ "$(basename "$0")" -H [options...] *" + echo "* *" + echo "* For a full description of the available options use the help option: *" + echo "* *" + echo "* $ "$(basename "$0")" -h *" + echo "* *" + echo "********************************************************************************" +} + +# +# MAIN +# + +if ((!use_http_sec)) +then + http_basic_auth_func_info_message +fi + +echo "Running $(basename $0) version: $version" +((verbose)) && echo "we use other tools in here, don't try to user their proposed 'options' directly" +echo "Use $(basename $0) -h for help."$'\n' + +((debug)) && cat< 1)) || (("$#" == 1)) ) + then + openid_c=$1 + else + read -p "Enter your openid : " openid_c + fi + + + #Read username. + if [[ ! -z "$username_supplied" ]] + then + username_c="$username_supplied" + elif (("$#" == 2)) + then + username_c=$2 + elif [[ "$openid_c" == */openid/ || "$openid_c" == */openid ]] + then + read -p "Enter username : " username_c + fi + + #Read password. + read -s -p "Enter password : " password_c + echo -e "\n" + + fi #use cookies + +fi #use_http_sec + + +#do we have old results? Create the file if not +[ ! -f $CACHE_FILE ] && echo "#filename mtime checksum" > $CACHE_FILE && chmod 666 $CACHE_FILE + +#clean the force parameter if here (at htis point we already have the certificate) +unset force + +download + +dedup_cache_ + +mkdir -p docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr +mv tas*.nc docs/demos/24-07-11-scipy-2024/tas_day_E3SM-2-0_historical_r1i1p1f1_gr + +echo "done" From 05518acfe97b2c9f03db01b478a5cab2fc716db1 Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Wed, 10 Jul 2024 17:03:18 -0700 Subject: [PATCH 5/9] Add SciPy edits (#678) --- .../images/doc-qr-code.png | Bin 0 -> 13905 bytes .../images/pres-qr-code.png | Bin 0 -> 15561 bytes docs/demos/24-07-11-scipy-2024/images/tom.jpg | Bin 950116 -> 3024172 bytes .../24-07-11-scipy-2024/scipy-2024.ipynb | 221 +++++++++++------- 4 files changed, 141 insertions(+), 80 deletions(-) create mode 100644 docs/demos/24-07-11-scipy-2024/images/doc-qr-code.png create mode 100644 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zwVkMv7T^KOt5owgJ4!$6kQ(P^y)D5!0nudHnB{Sm&n{*kWzr_WaC0&lRC@ZtD)z=D4Yr zNM`bzw>dQ$#~h3w?``ZoYI3ob3$*m?IH%2FYTti5O+gl$EGwAs2W<2Lqq}pyu+N<>9o}F=0U>b>|s5Tmq06JE_nJPi3!888=1F6TbBEHdqoh0)h;xpUpTTzUz z#{-_&s}Y9Vv3icYcBy$(K2{WCpZ@?|RtjP`&Q-C2{xw-Ydv+qm-)h}^Xkb-IUw5ac z=~d*0*!Ld)0MfIRM*EcDim*3Dg^!`)w8OUVE?(SY=}fvumNKf`d)5(2;haeRxW#KU zC6ucGqZtFeR%9v^r_2XZ-^#6L&0a7LMLab5;D%5;RY`7)D$J{m*aJN=USHzh7wS4a z$kS~TeZyn7O!`-4;ayK#eJjt_;r-@tfM8@-SvH|*YhxtS4Z9aP^a8x!!D_Q=S2yn& zW1bJ!o<9op6}X95O%NC&tXy2@E4M1ZU{a}x<4`!r\n", "\n", "\n", - "# SciPy 2024 (07/11/2024) - xCDAT: A Python Package for Simple and Robust Analysis of Climate Data\n", + "# SciPy 2024 - xCDAT (Xarray Climate Data Analysis Tools)\n", "\n", - "
      \n", - "

      \n", - " Core Developers: Tom Vo, Stephen Po-Chedley, Jason Boutte, Jill Zhang, Jiwoo Lee\n", - "

      \n", - "

      With thanks to Peter Gleckler, Paul Durack, Karl Taylor, and Chris Golaz

      \n", + "
      \n", + "

      \"A Python Package for Simple and Robust Analysis of Climate Data\"

      \n", + "
      \n", + "\n", + "
      \n", + "

      Presented by Tom Vo

      \n", "
      \n", "\n", "---\n", @@ -73,6 +74,23 @@ "```\n" ] }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### This presentation is available on xCDAT's Read The Docs Page\n", + "\n", + "
      \n", + " \"Presentation\n", + "
      \n", + "\n", + "#### https://xcdat.readthedocs.io/en/latest/demos/24-07-11-scipy-2024/scipy-2024.html\n" + ] + }, { "cell_type": "markdown", "metadata": { @@ -87,20 +105,21 @@ { "cell_type": "markdown", "metadata": { - "cell_style": "split", + "cell_style": "center", "slideshow": { "slide_type": "-" } }, "source": [ - "### Who am I?\n", + "### A little about me\n", "\n", - "- My name is Tom Vo\n", - "- **Climate Research Software Engineer** at **Lawrence Livermore National Laboratory (LLNL)**\n", - "- Work on the **Energy Exascale Earth System Model (E3SM)** and **Simplifying ESM Analysis Through Standards (SEATS)** projects\n", + "- Software Engineer at **Lawrence Livermore National Laboratory (LLNL)**\n", + "- **Energy Exascale Earth System Model (E3SM)** and **Simplifying ESM Analysis Through Standards (SEATS)** projects\n", + "- Lead developer of xCDAT\n", + "- Contributor and DevOps engineer for various E3SM tools\n", "\n", "
      \n", - " \"Tom\n", + " \"Tom\n", " \"LLNL\n", "
      \n" ] @@ -108,22 +127,19 @@ { "cell_type": "markdown", "metadata": { - "cell_style": "split", "slideshow": { - "slide_type": "-" + "slide_type": "slide" } }, "source": [ - "### What do I do?\n", + "#### A big shoutout to the xCDAT core development team including\n", "\n", - "- Lead developer of xCDAT (roadmap planning, end-to-end development)\n", - "- Contributor and DevOps engineer for various E3SM tools\n", + "- Stephen Po-Chedley\n", + "- Jason Boutte\n", + "- Jill Zhang\n", + "- Jiwoo Lee\n", "\n", - "
      \n", - " \"xCDAT\n", - " \"E3SM\n", - " \"SEATS\n", - "
      \n" + "With thanks to Peter Gleckler, Paul Durack, Karl Taylor, and Chris Golaz.\n" ] }, { @@ -140,12 +156,11 @@ "## An Overview of this Talk\n", "\n", "1. The driving force behind xCDAT\n", - "2. Basic intro to Xarray\n", - "3. History, scope, and mission of xCDAT\n", - "4. Design philosophy and key features of xCDAT\n", - "5. Technical demo: end-to-end analysis workflow\n", - "6. Parallelism with xCDAT and Dask\n", - "7. xCDAT’s community and how to get involved\n" + "2. Scope and mission of xCDAT\n", + "3. Design philosophy and key features of xCDAT\n", + "4. Technical demo: end-to-end analysis workflow\n", + "5. Parallelism with xCDAT and Dask\n", + "6. xCDAT’s community and how to get involved\n" ] }, { @@ -173,13 +188,13 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "subslide" } }, "source": [ - "### CDAT has been end-of-life as of December 2023\n", + "### The present-day challenge: **CDAT is end-of-life** as of December 2023\n", "\n", - "- This presents an issue for the many users and packages that depend on CDAT\n", + "- A big issue for users and packages that depend on CDAT\n", "- All of these factors sparked a driving need for new analysis software\n" ] }, @@ -193,15 +208,15 @@ "source": [ "### What should this new analysis software (aka xCDAT) offer?\n", "\n", - "- Offer similar core capabilities as CDAT\n", + "- **Offer similar core capabilities** as CDAT\n", " - e.g., geospatial averaging, temporal averaging, regridding\n", - "- Use modern technologies in the library’s stack\n", + "- **Use modern technologies** in the library’s stack\n", " - Capable of handling large datasets (e.g., parallelism, lazy operations)\n", - "- Be maintainable, extensible, and easy-to-use\n", + "- **Maintainable, extensible, and easy-to-use**\n", " - Python Enhancement Proposals (PEPs)\n", - "- Automated DevOps processes (unit testing, code coverage)\n", - " - Actively maintained documentation\n", - "- An open-source community that can sustain the project\n", + " - Software sustainability\n", + " - Reproducible science\n", + "- **Foster open-source community**\n", " - Serve the needs of the climate community in the long-term\n", " - Community engagement efforts (e.g., Pangeo, ESGF)\n" ] @@ -219,9 +234,7 @@ "

      \"N-D labeled arrays and datasets in Python\"

      \n", "
      \n", "\n", - "**Xarray is the core technology of xCDAT**\n", - "\n", - "**Why Xarray?**\n", + "**Why is Xarray the core technology of xCDAT?**\n", "\n", "- Mature, stable, widely adopted\n", "- Introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays\n", @@ -267,9 +280,21 @@ " \"SEATS\n", "\n", "\n", - "- xCDAT is an extension of Xarray for climate data analysis on structured grids, a modern successor to the Community Data Analysis Tools (CDAT) library\n", - "- Scope is focused on routine climate research analysis operations such as loading, wrangling, averaging, and regridding data\n", - "- Aims to provide features and utilities for simple and robust analysis of climate data\n", + "- xCDAT is an extension of Xarray for climate data analysis on structured grids\n", + "- A modern successor to the Community Data Analysis Tools (CDAT) library\n", + "- The core development team of software engineers and climate scientists are users of the software\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- **Scope** is focused on routine climate research analysis operations such as loading, wrangling, averaging, and regridding data\n", + "- **Goal** of providing features and utilities for simple and robust analysis of climate data\n", "- Leverages other powerful Xarray-based packages such as xESMF, xgcm, and cf-xarray\n", "\n", "
      \n", @@ -298,7 +323,7 @@ "- Geospatial weighted averaging\n", "- Temporal averaging, climatologies, departures\n", "- Horizontal structured regridding (extension of xESMF and Python port of regrid2)\n", - "- Vertical structured regridding (extension of xGCM)\n", + "- Vertical structured regridding (extension of xgcm)\n", "\n", "
      \n", " \"Spatial\n", @@ -319,9 +344,9 @@ "

      The Software Design Philosophy of xCDAT

      \n", "
      \n", "\n", - "- Intentionality designed to **encourage software sustainability** and **reproducible science**\n", + "- **Encourage software sustainability** and **reproducible science**\n", "- **Well-documented and configurable features** allow scientists to rapidly develop robust, reusable, less-error prone, more maintainable code\n", - "- xCDAT aims to contribute to **Pangeo's** effort to foster an ecosystem of mutually compatible geoscience Python packages\n", + "- **Contribute to Pangeo's** effort of fostering an ecosystem of mutually compatible geoscience Python packages\n", "\n", "
      \n", " \"Pangeo\n", @@ -4981,14 +5006,19 @@ "source": [ "#### 6.2 Departures\n", "\n", - "It can also be useful to show the departures from the climatological average.\n" + "It can also be useful to show the departures (\"anomalies\") from the climatological average.\n", + "\n", + "In climatology, anomalies refer to the difference between the value during a given time\n", + "interval and the long-term average value for that time interval.\n", + "\n", + "- _For example, the difference between the January average surface air temperature and the average surface temperature over the last 30 Januaries._\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "source": [ @@ -4996,18 +5026,8 @@ "\n", "In this case, `xcdat` will operate on the global mean time series we calculated above.\n", "\n", - "Note that you can set the climatological reference period (e.g., with `reference_period=(\"2000-01-01\", \"2009-12-31\")` for historical era departures).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, - "source": [ - "API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.departures.html\n" + "Note that you can set the climatological reference period for historical era departures.\n", + "We use `reference_period=(\"2000-01-01\", \"2009-12-31\")` below.\n" ] }, { @@ -5034,6 +5054,17 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "source": [ + "API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xarray.Dataset.temporal.departures.html\n" + ] + }, { "cell_type": "markdown", "metadata": { @@ -5091,7 +5122,9 @@ "source": [ "#### 6.3 Group averages\n", "\n", - "`xcdat` also allows you to calculate group averages (e.g., annual or seasonal mean from monthly data or monthly mean from daily data).\n" + "`xcdat` also allows you to calculate group averages.\n", + "\n", + "- _For example, annual or seasonal mean from monthly data or monthly mean from daily data_\n" ] }, { @@ -5188,8 +5221,14 @@ "source": [ "### 7. General Dataset Utilities\n", "\n", - "xCDAT includes various utilities for data manipulation, including\n", - "reorientation of the longitude axis, centering of time coordinates using time bounds, and adding and getting bounds.\n" + "xCDAT includes various utilities for data manipulation including:\n", + "\n", + "- Reorient the longitude axis\n", + "- Add missing bounds\n", + "- Centering of time coordinates using time bounds\\*\n", + "- Add and get bounds\\*\n", + "\n", + "

      * not shown

      \n" ] }, { @@ -5295,7 +5334,7 @@ "execution_count": 30, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -5320,7 +5359,11 @@ { "cell_type": "code", "execution_count": 31, - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, "outputs": [ { "data": { @@ -6201,7 +6244,7 @@ "For example, for global spatial averaging across various file sizes:\n", "\n", "
      \n", - " \"JOSS\n", + " \"JOSS\n", "

      * CDAT only runs functions serially and crashes with 22 GB+ datasets.

      \n", "
      \n", "\n", @@ -6220,13 +6263,10 @@ "\n", "Visit these pages for more guidance (e.g., when to parallelize):\n", "\n", - "- Parallel computing with Dask (xCDAT, image shown below): https://xcdat.readthedocs.io/en/latest/examples/parallel-computing-with-dask.html\n", + "- Parallel computing with Dask (xCDAT): https://xcdat.readthedocs.io/en/latest/examples/parallel-computing-with-dask.html\n", "- Parallel computing with Dask (Xarray): https://docs.xarray.dev/en/stable/user-guide/dask.html\n", "- Xarray with Dask Arrays: https://examples.dask.org/xarray.html\n", - "\n", - "
      \n", - " \"Dask\n", - "
      \n" + "\n" ] }, { @@ -6237,15 +6277,12 @@ } }, "source": [ - "
      \n", - " \"xCDAT\n", - "

      Recap of Key Points

      \n", - "
      \n", + "### What are some things in the works with xCDAT?\n", "\n", - "- xCDAT is an **extension of Xarray for climate data analysis on structured grids**, a modern successor to the Community Data Analysis Tools (CDAT) library\n", - "- **Focused on routine climate research analysis operations** including loading, wrangling, averaging, such as temporal averaging, spatial averaging, and regridding\n", - "- Designed to encourages **software sustainability and reproducible science**\n", - "- **Parallelizable** through Xarray’s support for Dask, which enables efficient processing of large datasets\n" + "- **Collaborate with UXarray** for interoperation of two tools to support end-to-end and more streamlined operation on unstructured (i.e. E3SM native output) datasets.\n", + "- **Continue assisting DOE funded projects** including PCMDI Metrics Package, E3SM Diags\n", + "- **Explore other DOE funded projects** to integrate xCDAT for analysis capabilities (e.g., ARM Diags and ILAMB)\n", + "- **Explore xarray-datatree** package for analyzing ensembles with xCDAT capabilities\n" ] }, { @@ -6262,13 +6299,37 @@ " Get involved and join the xCDAT community!\n", "
      \n", "\n", + "
      \n", + " \"QR\n", + "
      \n", + "\n", "- **Code contributions** are welcome and appreciated\n", " - GitHub Repository: https://github.com/xCDAT/xcdat\n", " - Contributing Guide: https://xcdat.readthedocs.io/en/latest/contributing.html\n", "- **Submit and/or address tickets** for feature suggestions, bugs, and documentation updates\n", " - GitHub Issues: https://github.com/xCDAT/xcdat/issues\n", "- **Participate in forum discussions** on version releases, architecture, feature suggestions, etc.\n", - " - GitHub Discussions: https://github.com/xCDAT/xcdat/discussions\n" + "\n", + "- GitHub Discussions: https://github.com/xCDAT/xcdat/discussions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "
      \n", + " \"xCDAT\n", + "

      Recap of Key Points

      \n", + "
      \n", + "\n", + "- xCDAT is an **extension of Xarray for climate data analysis on structured grids**, a modern successor to the Community Data Analysis Tools (CDAT) library\n", + "- **Focused on routine climate research analysis operations** including loading, wrangling, averaging, such as temporal averaging, spatial averaging, and regridding\n", + "- Designed to encourages **software sustainability and reproducible science**\n", + "- **Parallelizable** through Xarray’s support for Dask, which enables efficient processing of large datasets\n" ] } ], From e2ada5e8cd8aa2030a32541772fae51e3f3d61a9 Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Thu, 11 Jul 2024 11:56:21 -0700 Subject: [PATCH 6/9] Update SciPy talk notebook (#679) --- .../24-07-11-scipy-2024/scipy-2024.ipynb | 209 ++++++++++-------- 1 file changed, 117 insertions(+), 92 deletions(-) diff --git a/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb b/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb index 8c180b4a..e6b7090b 100644 --- a/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb +++ b/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb @@ -9,11 +9,11 @@ }, "source": [ "
      \n", - " \"xCDAT\n", + " \"xCDAT\n", "
      \n", "\n", "
      \n", - " \"SciPy\n", + " \"SciPy\n", "
      \n", "\n", "# SciPy 2024 - xCDAT (Xarray Climate Data Analysis Tools)\n", @@ -85,7 +85,7 @@ "### This presentation is available on xCDAT's Read The Docs Page\n", "\n", "
      \n", - " \"Presentation\n", + " \"Presentation\n", "
      \n", "\n", "#### https://xcdat.readthedocs.io/en/latest/demos/24-07-11-scipy-2024/scipy-2024.html\n" @@ -113,8 +113,8 @@ "source": [ "### A little about me\n", "\n", - "- Software Engineer at **Lawrence Livermore National Laboratory (LLNL)**\n", - "- **Energy Exascale Earth System Model (E3SM)** and **Simplifying ESM Analysis Through Standards (SEATS)** projects\n", + "- Software Engineer at Lawrence Livermore National Laboratory (**LLNL**)\n", + "- Energy Exascale Earth System Model (**E3SM**) and Simplifying ESM Analysis Through Standards (**SEATS**)\n", "- Lead developer of xCDAT\n", "- Contributor and DevOps engineer for various E3SM tools\n", "\n", @@ -155,12 +155,14 @@ "source": [ "## An Overview of this Talk\n", "\n", - "1. The driving force behind xCDAT\n", - "2. Scope and mission of xCDAT\n", - "3. Design philosophy and key features of xCDAT\n", - "4. Technical demo: end-to-end analysis workflow\n", - "5. Parallelism with xCDAT and Dask\n", - "6. xCDAT’s community and how to get involved\n" + "__Objective: Learn about the grounds-up development of an open-source Python package targeted at a specific scientific domain__\n", + "\n", + "* Driving force behind xCDAT\n", + "* Scope and mission of xCDAT\n", + "* Design philosophy and key features\n", + "* Technical demo of an end-to-end analysis workflow\n", + "* Parallelism with Dask\n", + "* How to get involved\n" ] }, { @@ -173,29 +175,59 @@ "source": [ "## The Driving Force Behind xCDAT\n", "\n", - "- CDAT (Community Data Analysis Tools) library is the predecessor to xCDAT.\n", - "- CDAT has provided open-source climate data analysis and visualization packages for over 20 years.\n", - "- Since CDAT’s inception, the volume of climate data has grown substantially as a result of:\n", - " - Larger pool of data products\n", - " - Increasing spatiotemporal resolution of model and observational data.\n", + "- Analysis of climate data frequently requires a number of core operations. For example: \n", + " - Reading and writing netCDF files\n", + " - Regridding\n", + " - Spatial and temporal averaging \n", + "- Highly performant operations to handle the growing volume of climate data \n", + " - Larger pool of data products \n", + " - Increasing spatiotemporal resolution of model and observational data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### CDAT, the predecessor to xCDAT\n", "\n", "
      \n", - "\"CDAT\n", - "
      \n" + "\"CDAT\n", + "
      \n", + "\n", + "- CDAT (Community Data Analysis Tools) library provided open-source climate data analysis and visualization packages for over 20 years\n", + "\n", + "\n", + "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "fragment" } }, "source": [ "### The present-day challenge: **CDAT is end-of-life** as of December 2023\n", "\n", "- A big issue for users and packages that depend on CDAT\n", - "- All of these factors sparked a driving need for new analysis software\n" + "- A driving need for new analysis software\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + " xCDAT addresses this need by **combining the power of Xarray** with **geospatial analysis features inspired by CDAT**." ] }, { @@ -206,17 +238,17 @@ } }, "source": [ - "### What should this new analysis software (aka xCDAT) offer?\n", + "### What are the general goals of xCDAT?\n", "\n", - "- **Offer similar core capabilities** as CDAT\n", + "- **Offer similar core capabilities to CDAT**\n", " - e.g., geospatial averaging, temporal averaging, regridding\n", "- **Use modern technologies** in the library’s stack\n", " - Capable of handling large datasets (e.g., parallelism, lazy operations)\n", "- **Maintainable, extensible, and easy-to-use**\n", " - Python Enhancement Proposals (PEPs)\n", - " - Software sustainability\n", " - Reproducible science\n", "- **Foster open-source community**\n", + " - Software sustainability\n", " - Serve the needs of the climate community in the long-term\n", " - Community engagement efforts (e.g., Pangeo, ESGF)\n" ] @@ -231,16 +263,32 @@ "source": [ "
      \n", "\"Xarray\n", - "

      \"N-D labeled arrays and datasets in Python\"

      \n", "
      \n", + "

      \"N-D labeled arrays and datasets in Python\"

      \n", + "\n", "\n", "**Why is Xarray the core technology of xCDAT?**\n", "\n", - "- Mature, stable, widely adopted\n", + "- Mature widely adopted \n", + "- Fiscal funding from NumFocus\n", "- Introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays\n", "- Intuitive, more concise, and less error-prone user experience\n", "\n", - "**Key features include:**\n", + "
      \n", + " \"NumFocus\n", + "
      \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Key features of Xarray\n", "\n", "- File I/O, indexing and selecting, interpolating, grouping, aggregating, parallelism (Dask), plotting (matplotlib wrapper)\n", "- Supports various file formats netCDF, Iris, OPeNDAP, Zarr, and more\n", @@ -269,7 +317,7 @@ "source": [ "
      \n", "\"xCDAT\n", - "

      Xarray Climate Data Analysis Tools for Structured Grid Analysis

      \n", + "

      Xarray Climate Data Analysis Tools

      \n", "
      \n", "\n", "- Collaboration between:\n", @@ -282,7 +330,7 @@ "\n", "- xCDAT is an extension of Xarray for climate data analysis on structured grids\n", "- A modern successor to the Community Data Analysis Tools (CDAT) library\n", - "- The core development team of software engineers and climate scientists are users of the software\n" + "- Team composed of software engineers and climate scientists who are also users of the software\n" ] }, { @@ -293,8 +341,10 @@ } }, "source": [ - "- **Scope** is focused on routine climate research analysis operations such as loading, wrangling, averaging, and regridding data\n", - "- **Goal** of providing features and utilities for simple and robust analysis of climate data\n", + "#### Scope of xCDAT\n", + "\n", + "- Focused on routine climate research analysis operations such as loading, wrangling, averaging, and regridding data\n", + "- Provide features and utilities for simple and robust analysis of climate data\n", "- Leverages other powerful Xarray-based packages such as xESMF, xgcm, and cf-xarray\n", "\n", "
      \n", @@ -314,7 +364,7 @@ }, "source": [ "
      \n", - "

      xCDAT features and utilities for simple, robust, and less error-prone analysis code

      \n", + "

      xCDAT features for simple, robust, and less error-prone analysis code

      \n", "
      \n", "\n", "- Extension of `xr.open_dataset()` and `xr.open_mfdataset()` with post-processing options\n", @@ -322,8 +372,8 @@ "- Generate missing bounds, center time coords, convert lon axis orientation\n", "- Geospatial weighted averaging\n", "- Temporal averaging, climatologies, departures\n", - "- Horizontal structured regridding (extension of xESMF and Python port of regrid2)\n", - "- Vertical structured regridding (extension of xgcm)\n", + "- Horizontal regridding (extension of `xesmf` and Python port of `regrid2`)\n", + "- Vertical regridding (extension of `xgcm`)\n", "\n", "
      \n", " \"Spatial\n", @@ -571,12 +621,10 @@ "\n", "#### Accessors classes include:\n", "\n", - "- `spatial`\n", - " - `.average()`, `.get_weights()`\n", - "- `temporal`\n", - " - `.average()`, `.group_average()`, `.climatology()`, `.depatures()`\n", - "- `bounds`\n", - " - `.get_bounds()`, `.add_bounds()`, `.add_missing_bounds()`\n" + "- `spatial` -- `.average()`, `.get_weights()`\n", + "- `temporal` -- `.average()`, `.group_average()`, `.climatology()`, `.depatures()`\n", + "- `regridding` -- `horizontal()`, `vertical()`\n", + "- `bounds` -- `.get_bounds()`, `.add_bounds()`, `.add_missing_bounds()`\n" ] }, { @@ -612,8 +660,7 @@ "\n", "### Overview\n", "\n", - "This exercise will walkthrough using `xcdat` to perform computation and analysis\n", - "on E3SM v2 CMIP6 data.\n", + "Use `xcdat` to perform computation and analysis on CMIP6 data from the E3SM v2 model.\n", "\n", "### Sections\n", "\n", @@ -622,8 +669,7 @@ "3. Horizontal Regridding\n", "4. Vertical Regridding\n", "5. Spatial Averaging\n", - "6. Temporal Computations\n", - "7. General Dataset Utilities\n" + "6. Temporal Computations" ] }, { @@ -731,23 +777,19 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "notes" } }, "source": [ - "We will be analyzing E3SM v2 CMIP6 monthly near-sea surface air temperature (`tas`) data from 2000 to 2014.\n" + "- Use `xc.open_dataset()` to a single netCDF dataset as an `xr.Dataset` object.\n", + "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xcdat.open_dataset.html\n" ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "notes" - } - }, + "metadata": {}, "source": [ - "- Use `xc.open_dataset()` to a single netCDF dataset as an `xr.Dataset` object.\n", - "- API Documentation: https://xcdat.readthedocs.io/en/stable/generated/xcdat.open_dataset.html\n" + "Analyzing monthly `tas` (near-sea surface air temperature) data from 2000 to 2014." ] }, { @@ -4781,7 +4823,10 @@ "source": [ "#### Calculate the near-surface air temperature (`tas`) in the Niño 3.4 region.\n", "\n", - "Users can also specify their own bounds for a region.\n" + "Users can also specify their own bounds for a region. In this case, we specified `keep_weights=True`.\n", + "\n", + "- The weights provide full spatial weighting for grid cells entirely within the Niño 3.4 region.\n", + "- Partial weights for grid cells partially in the region." ] }, { @@ -4799,25 +4844,6 @@ ").compute()" ] }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "In this case, we specified `keep_weights=True`.\n", - "\n", - "- The weights provide full spatial weighting for grid cells entirely within the Niño 3.4 region.\n", - "- Partial weights for grid cells partially in the region.\n", - "\n", - "Other notes:\n", - "\n", - "- We use the 4 x 4 degree grid in this example to show the partial weights and to speed up plotting.\n", - "- You can also supply your own weights, but you can't automatically subset with `lat_bounds` and `lon_bounds` if you supply your own weights\n" - ] - }, { "cell_type": "markdown", "metadata": { @@ -5018,7 +5044,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "subslide" } }, "source": [ @@ -5051,7 +5077,7 @@ "source": [ "ds_global_anomaly = ds_global.temporal.departures(\n", " \"tas\", freq=\"month\", reference_period=(\"2000-01-01\", \"2009-12-31\")\n", - ")" + ") " ] }, { @@ -5116,7 +5142,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "source": [ @@ -5131,7 +5157,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "source": [ @@ -5145,7 +5171,7 @@ "execution_count": 25, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [], @@ -5157,7 +5183,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "source": [ @@ -5169,7 +5195,7 @@ "execution_count": 26, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -5215,7 +5241,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "source": [ @@ -5235,7 +5261,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "source": [ @@ -5262,7 +5288,7 @@ "metadata": { "cell_style": "split", "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -5284,7 +5310,7 @@ "metadata": { "cell_style": "split", "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -5306,7 +5332,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "source": [ @@ -5322,7 +5348,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "notes" + "slide_type": "skip" } }, "source": [ @@ -5334,7 +5360,7 @@ "execution_count": 30, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [ @@ -5361,7 +5387,7 @@ "execution_count": 31, "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "skip" } }, "outputs": [ @@ -6265,8 +6291,7 @@ "\n", "- Parallel computing with Dask (xCDAT): https://xcdat.readthedocs.io/en/latest/examples/parallel-computing-with-dask.html\n", "- Parallel computing with Dask (Xarray): https://docs.xarray.dev/en/stable/user-guide/dask.html\n", - "- Xarray with Dask Arrays: https://examples.dask.org/xarray.html\n", - "\n" + "- Xarray with Dask Arrays: https://examples.dask.org/xarray.html\n" ] }, { @@ -6323,11 +6348,11 @@ "source": [ "
      \n", " \"xCDAT\n", - "

      Recap of Key Points

      \n", + "

      Key takeaways

      \n", "
      \n", "\n", - "- xCDAT is an **extension of Xarray for climate data analysis on structured grids**, a modern successor to the Community Data Analysis Tools (CDAT) library\n", - "- **Focused on routine climate research analysis operations** including loading, wrangling, averaging, such as temporal averaging, spatial averaging, and regridding\n", + "- xCDAT is an **extension of Xarray for climate data analysis on structured grids**\n", + "- Focused on routine **climate research analysis operations** \n", "- Designed to encourages **software sustainability and reproducible science**\n", "- **Parallelizable** through Xarray’s support for Dask, which enables efficient processing of large datasets\n" ] From 994e783f1309a3db0e53d53d4b048a8bd9d94d31 Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Thu, 11 Jul 2024 13:26:41 -0700 Subject: [PATCH 7/9] Update SciPy talk (#680) --- .../24-07-11-scipy-2024/scipy-2024.ipynb | 62 ++++++++----------- 1 file changed, 25 insertions(+), 37 deletions(-) diff --git a/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb b/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb index e6b7090b..6aa560c2 100644 --- a/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb +++ b/docs/demos/24-07-11-scipy-2024/scipy-2024.ipynb @@ -320,7 +320,7 @@ "

      Xarray Climate Data Analysis Tools

      \n", "
      \n", "\n", - "- Collaboration between:\n", + "- Team of climate scientists and software engineers from:\n", "\n", "
      \n", " \"E3SM\n", @@ -328,31 +328,15 @@ " \"SEATS\n", "
      \n", "\n", - "- xCDAT is an extension of Xarray for climate data analysis on structured grids\n", - "- A modern successor to the Community Data Analysis Tools (CDAT) library\n", - "- Team composed of software engineers and climate scientists who are also users of the software\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Scope of xCDAT\n", - "\n", - "- Focused on routine climate research analysis operations such as loading, wrangling, averaging, and regridding data\n", - "- Provide features and utilities for simple and robust analysis of climate data\n", + "- Focused on routine climate research analysis operations\n", "- Leverages other powerful Xarray-based packages such as xESMF, xgcm, and cf-xarray\n", "\n", "
      \n", - " \"Xarray\n", " \"ESMF\n", " \"xgcm\n", " \"CF\n", - "
      \n" + "
      \n", + "\n" ] }, { @@ -1547,7 +1531,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -2088,7 +2072,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -2865,7 +2849,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -2877,7 +2861,7 @@ "execution_count": 12, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "-" } }, "outputs": [ @@ -3761,7 +3745,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -3808,7 +3792,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -4767,7 +4751,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -4821,12 +4805,17 @@ } }, "source": [ - "#### Calculate the near-surface air temperature (`tas`) in the Niño 3.4 region.\n", - "\n", + "#### Calculate the near-surface air temperature (`tas`) in the Niño 3.4 region." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "Users can also specify their own bounds for a region. In this case, we specified `keep_weights=True`.\n", "\n", - "- The weights provide full spatial weighting for grid cells entirely within the Niño 3.4 region.\n", - "- Partial weights for grid cells partially in the region." + "- Full weight for grid cells entirely in the region\n", + "- Partial weights for grid cells partially in the region" ] }, { @@ -4848,7 +4837,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -4976,7 +4965,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ @@ -5034,10 +5023,9 @@ "\n", "It can also be useful to show the departures (\"anomalies\") from the climatological average.\n", "\n", - "In climatology, anomalies refer to the difference between the value during a given time\n", - "interval and the long-term average value for that time interval.\n", + "In climatology, **anomalies** refer to the **difference between the value during a given time interval** and the **long-term average value for that time interval**.\n", "\n", - "- _For example, the difference between the January average surface air temperature and the average surface temperature over the last 30 Januaries._\n" + "- _For example, the difference between a January average surface air temperature and the average surface temperature over the last 30 Januaries._\n" ] }, { @@ -5095,7 +5083,7 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "subslide" + "slide_type": "-" } }, "source": [ From cdae8263b0ce279ffebc36cad98a95e3ee20a5e5 Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Tue, 30 Jul 2024 10:47:59 -0700 Subject: [PATCH 8/9] Add links to JOSS and DOE EESM content (#682) --- docs/_static/thumbnails/doe-logo.jpg | Bin 0 -> 52606 bytes docs/_static/thumbnails/joss-logo.jpg | Bin 0 -> 371731 bytes docs/demos.rst | 5 ++++- docs/demos.yml | 16 ++++++++++++++-- 4 files changed, 18 insertions(+), 3 deletions(-) create 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z{_hI+|J<|x|KZ;ME=B*=-@mgS|E~Z3*5ALi{{F82{*UeK|DE;ruV?>V|NU)$|I&rO gIU&-}ao`&)ni(uKd{=a13<1qgTIzyJUM literal 0 HcmV?d00001 diff --git a/docs/demos.rst b/docs/demos.rst index 8cb80dd3..7c09ad35 100644 --- a/docs/demos.rst +++ b/docs/demos.rst @@ -12,5 +12,8 @@ This page includes relevant xCDAT presentations, demos, and papers. AMS 2023 Abstract demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb ESGF2 - A Gentle Introduction to xCDAT (Video Introduction) - SciPy 2024 (Abstract) + Journal of Open Source Software Paper + DOE EESM Research Highlight + SciPy 2024 (Abstract) SciPy 2024 (Presentation Notebook) + DOE EESM PI Meeting Presentation diff --git a/docs/demos.yml b/docs/demos.yml index 5323e1de..e7722cef 100644 --- a/docs/demos.yml +++ b/docs/demos.yml @@ -2,14 +2,22 @@ path: https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/412648 thumbnail: _static/thumbnails/ams-logo.jpg -- title: LLNL Climate and Weather Seminar Series (01/25/2023) - A Gentle Introduction to xCDAT +- title: LLNL Climate and Weather Seminar Series (01/25/23) - A Gentle Introduction to xCDAT path: demos/1-25-23-cwss-seminar/introduction-to-xcdat.ipynb thumbnail: _static/thumbnails/llnl-logo.png -- title: ESGF2 - A Gentle Introduction to xCDAT (Video Introduction, 03/23/24) +- title: ESGF2 - A Gentle Introduction to xCDAT (Video Introduction, 03/23/23) path: https://youtu.be/sJpQ9vKDKa8?feature=shared thumbnail: _static/thumbnails/esgf2-logo.png +- title: Journal of Open Source Software Paper (06/29/24) + path: https://joss.theoj.org/papers/10.21105/joss.06426 + thumbnail: _static/thumbnails/joss-logo.jpg + +- title: DOE EESM Research Highlight (06/29/24) + path: https://climatemodeling.science.energy.gov/research-highlights/xcdat-python-package-simple-and-robust-analysis-climate-data + thumbnail: _static/thumbnails/doe-logo.jpg + - title: SciPy 2024 (Abstract, 07/11/24) path: https://cfp.scipy.org/2024/talk/VRACYW/ thumbnail: _static/thumbnails/scipy-logo.png @@ -17,3 +25,7 @@ - title: SciPy 2024 (Presentation Notebook, 07/11/24) path: demos/24-07-11-scipy-2024/scipy-2024.ipynb thumbnail: _static/thumbnails/scipy-logo.png + +- title: DOE EESM PI Meeting 2024 Presentation (08/08/24) + path: https://climatemodeling.science.energy.gov/presentations/xcdat-xarray-climate-data-analysis-tools-python-package-simple-and-robust-analysis + thumbnail: _static/thumbnails/doe-logo.jpg From 584fccee91f559089e4bb24c9cc45983b998bf4a Mon Sep 17 00:00:00 2001 From: Tom Vo Date: Wed, 7 Aug 2024 15:38:58 -0400 Subject: [PATCH 9/9] Add project logos to README and project overview page on docs (#686) --- README.rst | 8 ++++++++ docs/getting-started-guide/overview.rst | 8 ++++++++ 2 files changed, 16 insertions(+) diff --git a/README.rst b/README.rst index e1f57f65..e89a5c18 100644 --- a/README.rst +++ b/README.rst @@ -252,6 +252,14 @@ Environmental Research (`BER`_) within the `Department of Energy`_'s `Office of .. _Department of Energy: https://www.energy.gov/ .. _Office of Science: https://science.osti.gov/ +.. raw:: html + +

      + E3SM logo + PCMDI logo + SEATS logo +

      + Contributors ------------ diff --git a/docs/getting-started-guide/overview.rst b/docs/getting-started-guide/overview.rst index 77cf6492..fb3504dd 100644 --- a/docs/getting-started-guide/overview.rst +++ b/docs/getting-started-guide/overview.rst @@ -200,6 +200,14 @@ Environmental Research (`BER`_) within the `Department of Energy`_'s `Office of .. _Department of Energy: https://www.energy.gov/ .. _Office of Science: https://science.osti.gov/ +.. raw:: html + +

      + E3SM logo + PCMDI logo + SEATS logo +

      + Contributors ------------
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