diff --git a/README.md b/README.md index b68008b..55a5413 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,10 @@ # `cfr`: a Python package for Climate Field Reconstruction > [!NOTE] -> Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024 +> If you use `cfr` in any way for your publications, please cite: +> +> + Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024 +> + Zhu, F., Emile-Geay, J., Anchukaitis, K.J., McKay, N.P., Stevenson, S., Meng, Z., 2023. A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models. Sci Data 10, 624. https://doi.org/10.1038/s41597-023-02489-1 `cfr` aims to provide a universal framework for climate field reconstruction (CFR). It provides a toolkit for diff --git a/docs/_images/notebooks_psm-ocn-TEX86_1_0.png b/docs/_images/notebooks_psm-ocn-TEX86_1_0.png deleted file mode 100644 index aa29b55..0000000 Binary files a/docs/_images/notebooks_psm-ocn-TEX86_1_0.png and /dev/null differ diff --git a/docs/_sources/index.rst b/docs/_sources/index.rst index 9c7a1a2..f9e64f0 100644 --- a/docs/_sources/index.rst +++ b/docs/_sources/index.rst @@ -3,7 +3,11 @@ *********************************************************** .. note:: - Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024 + If you use `cfr` in any way for your publications, please cite: + + + Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024 + + Zhu, F., Emile-Geay, J., Anchukaitis, K.J., McKay, N.P., Stevenson, S., Meng, Z., 2023. A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models. Sci Data 10, 624. https://doi.org/10.1038/s41597-023-02489-1 + `cfr` aims to provide a universal framework for climate field reconstruction (CFR). It provides a toolkit for diff --git a/docs/_sources/notebooks/psm-ocn-TEX86.ipynb b/docs/_sources/notebooks/psm-ocn-TEX86.ipynb deleted file mode 100644 index f928744..0000000 --- a/docs/_sources/notebooks/psm-ocn-TEX86.ipynb +++ /dev/null @@ -1,112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024.1.26\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import cfr\n", - "print(cfr.__version__)\n", - "import numpy as np\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import bayspar as bsr\n", - "\n", - "sst = np.arange(0, 41)\n", - "prediction = bsr.predict_tex(sst, lon=34.0733, lat=31.6517, temptype='sst')\n", - "\n", - "ax = bsr.predictplot(prediction, x=sst, xlabel='SST (°C)', ylabel=r'TEX$_{86}$')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.2692508 0.28437885 0.29893764 0.3170121 0.3309044 0.34721148\n", - " 0.36371272 0.37706489 0.39648115 0.41018133 0.42505943 0.44242118\n", - " 0.45852373 0.47390029 0.48921162 0.50320318 0.51911414 0.53492034\n", - " 0.55187766 0.56663146 0.58309529 0.59724898 0.61576552 0.62866408\n", - " 0.64550433 0.66072121 0.6767111 0.69433802 0.70737955 0.72440517\n", - " 0.73982124 0.755043 0.77102635 0.78659133 0.8032723 0.81710502\n", - " 0.83408906 0.84912732 0.86433709 0.88054535 0.89587475]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/glade/derecho/scratch/fengzhu/tmp/ipykernel_62881/2164839693.py:1: DeprecationWarning: the `interpolation=` argument to percentile was renamed to `method=`, which has additional options.\n", - "Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22)\n", - " print(prediction.percentile()[:, 1])\n" - ] - } - ], - "source": [ - "print(prediction.percentile()[:, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:cfr-env]", - "language": "python", - "name": "conda-env-cfr-env-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/cg-overview.html b/docs/cg-overview.html index 375783a..a99d646 100644 --- a/docs/cg-overview.html +++ b/docs/cg-overview.html @@ -525,7 +525,7 @@

Bug reports and enhancement requests - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/cg-updating-docs.html b/docs/cg-updating-docs.html index 19560fa..ed5bf09 100644 --- a/docs/cg-updating-docs.html +++ b/docs/cg-updating-docs.html @@ -541,7 +541,7 @@

Pushing your changes - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/cg-working-with-codebase.html b/docs/cg-working-with-codebase.html index 95b4c62..85b3972 100644 --- a/docs/cg-working-with-codebase.html +++ b/docs/cg-working-with-codebase.html @@ -717,7 +717,7 @@

Tips for a successful pull request - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/genindex.html b/docs/genindex.html index e2ca035..c10ec8e 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -836,7 +836,7 @@

W

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/index.html b/docs/index.html index e716a2d..f1b2031 100644 --- a/docs/index.html +++ b/docs/index.html @@ -420,7 +420,11 @@

cfr: a Python package for Climate Field Reconstruction

cfr: a Python package for Climate Field Reconstruction#

Note

-

Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024

+

If you use cfr in any way for your publications, please cite:

+
    +

  • Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024

    • +

  • Zhu, F., Emile-Geay, J., Anchukaitis, K.J., McKay, N.P., Stevenson, S., Meng, Z., 2023. A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models. Sci Data 10, 624. https://doi.org/10.1038/s41597-023-02489-1

    • +

cfr aims to provide a universal framework for climate field reconstruction (CFR). It provides a toolkit for

@@ -567,7 +571,7 @@

cfr: a Python package for Climate Field Reconstruction - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/climate-io.html b/docs/notebooks/climate-io.html index ae77f25..a60820f 100644 --- a/docs/notebooks/climate-io.html +++ b/docs/notebooks/climate-io.html @@ -3848,7 +3848,7 @@

Remote loading iCESM simulations - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/climate-ops.html b/docs/notebooks/climate-ops.html index a94e148..013a0ca 100644 --- a/docs/notebooks/climate-ops.html +++ b/docs/notebooks/climate-ops.html @@ -7675,7 +7675,7 @@

Validate a field against another - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/graphem-cli.html b/docs/notebooks/graphem-cli.html index e92aab6..4b7bd2f 100644 --- a/docs/notebooks/graphem-cli.html +++ b/docs/notebooks/graphem-cli.html @@ -1169,7 +1169,7 @@

Validation - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/graphem-primer.html b/docs/notebooks/graphem-primer.html index c43f986..7fe30a4 100644 --- a/docs/notebooks/graphem-primer.html +++ b/docs/notebooks/graphem-primer.html @@ -2437,7 +2437,7 @@

Timeseries comparison - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/graphem-real-pages2k.html b/docs/notebooks/graphem-real-pages2k.html index 579c1f9..20b08a3 100644 --- a/docs/notebooks/graphem-real-pages2k.html +++ b/docs/notebooks/graphem-real-pages2k.html @@ -1364,7 +1364,7 @@

Comparison to the LMR/PDA based reconstruction - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/lmr-assim-no-calib.html b/docs/notebooks/lmr-assim-no-calib.html index b12c795..cc2db2a 100644 --- a/docs/notebooks/lmr-assim-no-calib.html +++ b/docs/notebooks/lmr-assim-no-calib.html @@ -1420,7 +1420,7 @@

Validation - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/lmr-cli.html b/docs/notebooks/lmr-cli.html index ed70551..eb348b1 100644 --- a/docs/notebooks/lmr-cli.html +++ b/docs/notebooks/lmr-cli.html @@ -1051,7 +1051,7 @@

Leveraging the CLI - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/lmr-real-pages2k.html b/docs/notebooks/lmr-real-pages2k.html index 47d101e..f9b044f 100644 --- a/docs/notebooks/lmr-real-pages2k.html +++ b/docs/notebooks/lmr-real-pages2k.html @@ -1820,7 +1820,7 @@

Timeseries validation - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/lmr-recon-pr.html b/docs/notebooks/lmr-recon-pr.html index 7f51079..58cda18 100644 --- a/docs/notebooks/lmr-recon-pr.html +++ b/docs/notebooks/lmr-recon-pr.html @@ -1115,7 +1115,7 @@

Field validation - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/pp2k-dashboards.html b/docs/notebooks/pp2k-dashboards.html index 08f07b1..65b1df7 100644 --- a/docs/notebooks/pp2k-dashboards.html +++ b/docs/notebooks/pp2k-dashboards.html @@ -906,7 +906,7 @@

Dashboard for a - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/pp2k-pdb-filter.html b/docs/notebooks/pp2k-pdb-filter.html index 5ec814a..e3de10e 100644 --- a/docs/notebooks/pp2k-pdb-filter.html +++ b/docs/notebooks/pp2k-pdb-filter.html @@ -776,7 +776,7 @@

By a circle (center + distance) - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/pp2k-pdb-load-viz.html b/docs/notebooks/pp2k-pdb-load-viz.html index 29f7bc3..1dbf37b 100644 --- a/docs/notebooks/pp2k-pdb-load-viz.html +++ b/docs/notebooks/pp2k-pdb-load-viz.html @@ -1580,7 +1580,7 @@

Check the proxy IDs on an interactive map - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/pp2k-ppe-graphem.html b/docs/notebooks/pp2k-ppe-graphem.html index 06b1056..da9887e 100644 --- a/docs/notebooks/pp2k-ppe-graphem.html +++ b/docs/notebooks/pp2k-ppe-graphem.html @@ -1625,7 +1625,7 @@

Validate the reconstructed fields - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/pp2k-ppe-pda.html b/docs/notebooks/pp2k-ppe-pda.html index d0dc46e..f6af124 100644 --- a/docs/notebooks/pp2k-ppe-pda.html +++ b/docs/notebooks/pp2k-ppe-pda.html @@ -1316,7 +1316,7 @@

Validate the reconstructed fields - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/proxy-analysis.html b/docs/notebooks/proxy-analysis.html index 1835ec4..7b76fd8 100644 --- a/docs/notebooks/proxy-analysis.html +++ b/docs/notebooks/proxy-analysis.html @@ -619,7 +619,7 @@

Annualize a Pro

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/proxy-composites.html b/docs/notebooks/proxy-composites.html index b5e8e8c..bd84628 100644 --- a/docs/notebooks/proxy-composites.html +++ b/docs/notebooks/proxy-composites.html @@ -1039,7 +1039,7 @@

Make a composite of multiple archives - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/proxy-dups.html b/docs/notebooks/proxy-dups.html index f052c84..8b9accb 100644 --- a/docs/notebooks/proxy-dups.html +++ b/docs/notebooks/proxy-dups.html @@ -690,7 +690,7 @@

Find the duplicates - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/proxy-io.html b/docs/notebooks/proxy-io.html index 4e0afde..3354b12 100644 --- a/docs/notebooks/proxy-io.html +++ b/docs/notebooks/proxy-io.html @@ -1359,7 +1359,7 @@

a netCDF file => - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/proxy-ops.html b/docs/notebooks/proxy-ops.html index ec6d691..948ecc5 100644 --- a/docs/notebooks/proxy-ops.html +++ b/docs/notebooks/proxy-ops.html @@ -1053,7 +1053,7 @@

Filter a ProxyD

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/psm-bilinear.html b/docs/notebooks/psm-bilinear.html index 9161677..2d4ceb2 100644 --- a/docs/notebooks/psm-bilinear.html +++ b/docs/notebooks/psm-bilinear.html @@ -1184,7 +1184,7 @@

Create a PSM object - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/psm-coral-SrCa.html b/docs/notebooks/psm-coral-SrCa.html index eb7aa53..ab5fc03 100644 --- a/docs/notebooks/psm-coral-SrCa.html +++ b/docs/notebooks/psm-coral-SrCa.html @@ -652,7 +652,7 @@

Create a PSM object - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/psm-coral-d18O.html b/docs/notebooks/psm-coral-d18O.html index 986b3e3..5d83598 100644 --- a/docs/notebooks/psm-coral-d18O.html +++ b/docs/notebooks/psm-coral-d18O.html @@ -654,7 +654,7 @@

Create a PSM object - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/psm-ice-d18O.html b/docs/notebooks/psm-ice-d18O.html index 391869a..b766eb5 100644 --- a/docs/notebooks/psm-ice-d18O.html +++ b/docs/notebooks/psm-ice-d18O.html @@ -1114,7 +1114,7 @@

Create a PSM object - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/psm-lake-VarveThickness.html b/docs/notebooks/psm-lake-VarveThickness.html index f89af58..f8d400e 100644 --- a/docs/notebooks/psm-lake-VarveThickness.html +++ b/docs/notebooks/psm-lake-VarveThickness.html @@ -648,7 +648,7 @@

Create a PSM object - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/psm-linear.html b/docs/notebooks/psm-linear.html index 68f654c..939f515 100644 --- a/docs/notebooks/psm-linear.html +++ b/docs/notebooks/psm-linear.html @@ -1786,7 +1786,7 @@

Create a PSM object - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/notebooks/psm-ocn-TEX86.html b/docs/notebooks/psm-ocn-TEX86.html deleted file mode 100644 index 4b2387d..0000000 --- a/docs/notebooks/psm-ocn-TEX86.html +++ /dev/null @@ -1,592 +0,0 @@ - - - - - - - - - - - <no title> — cfr documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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%load_ext autoreload
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-import cfr
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-[0.2692508  0.28437885 0.29893764 0.3170121  0.3309044  0.34721148
- 0.36371272 0.37706489 0.39648115 0.41018133 0.42505943 0.44242118
- 0.45852373 0.47390029 0.48921162 0.50320318 0.51911414 0.53492034
- 0.55187766 0.56663146 0.58309529 0.59724898 0.61576552 0.62866408
- 0.64550433 0.66072121 0.6767111  0.69433802 0.70737955 0.72440517
- 0.73982124 0.755043   0.77102635 0.78659133 0.8032723  0.81710502
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-/glade/derecho/scratch/fengzhu/tmp/ipykernel_62881/2164839693.py:1: DeprecationWarning: the `interpolation=` argument to percentile was renamed to `method=`, which has additional options.
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- - \ No newline at end of file diff --git a/docs/notebooks/psm-ocn-TEX86.ipynb b/docs/notebooks/psm-ocn-TEX86.ipynb deleted file mode 100644 index f928744..0000000 --- a/docs/notebooks/psm-ocn-TEX86.ipynb +++ /dev/null @@ -1,112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024.1.26\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import cfr\n", - "print(cfr.__version__)\n", - "import numpy as np\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import bayspar as bsr\n", - "\n", - "sst = np.arange(0, 41)\n", - "prediction = bsr.predict_tex(sst, lon=34.0733, lat=31.6517, temptype='sst')\n", - "\n", - "ax = bsr.predictplot(prediction, x=sst, xlabel='SST (°C)', ylabel=r'TEX$_{86}$')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.2692508 0.28437885 0.29893764 0.3170121 0.3309044 0.34721148\n", - " 0.36371272 0.37706489 0.39648115 0.41018133 0.42505943 0.44242118\n", - " 0.45852373 0.47390029 0.48921162 0.50320318 0.51911414 0.53492034\n", - " 0.55187766 0.56663146 0.58309529 0.59724898 0.61576552 0.62866408\n", - " 0.64550433 0.66072121 0.6767111 0.69433802 0.70737955 0.72440517\n", - " 0.73982124 0.755043 0.77102635 0.78659133 0.8032723 0.81710502\n", - " 0.83408906 0.84912732 0.86433709 0.88054535 0.89587475]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/glade/derecho/scratch/fengzhu/tmp/ipykernel_62881/2164839693.py:1: DeprecationWarning: the `interpolation=` argument to percentile was renamed to `method=`, which has additional options.\n", - "Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22)\n", - " print(prediction.percentile()[:, 1])\n" - ] - } - ], - "source": [ - "print(prediction.percentile()[:, 1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:cfr-env]", - "language": "python", - "name": "conda-env-cfr-env-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/notebooks/psm-tree-TRW.html b/docs/notebooks/psm-tree-TRW.html index f0f2937..7207e6b 100644 --- a/docs/notebooks/psm-tree-TRW.html +++ b/docs/notebooks/psm-tree-TRW.html @@ -1623,7 +1623,7 @@

Create a PSM object - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/objects.inv b/docs/objects.inv index 30e3ca6..ce0b863 100644 Binary files a/docs/objects.inv and b/docs/objects.inv differ diff --git a/docs/search.html b/docs/search.html index 2e32694..ea99ae7 100644 --- a/docs/search.html +++ b/docs/search.html @@ -403,7 +403,7 @@

Search

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

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[13, 29], "trw": [13, 29], "mxd": 13, "coral": [13, 24, 25], "d18o": [13, 25, 26], "srca": 13, "ic": [13, 19, 26], "lake": [13, 27], "varve_thick": 13, "pseudopaegs2k": [14, 15], "By": 14, "type": [14, 17, 19], "id": [14, 15], "latitud": 14, "rang": 14, "longitud": 14, "squar": 14, "circl": 14, "distanc": 14, "visual": 15, "access": [15, 18], "specif": [15, 23, 24, 25, 26, 27, 28, 29], "check": 15, "an": [15, 19, 22], "interact": 15, "ppe": [16, 17], "paleoclim": 17, "ta": 17, "pr": 17, "setup": 17, "differ": 17, "mont": 17, "carlo": 17, "iter": 17, "analysi": 18, "network": [18, 22], "proxyrecord": [18, 21, 22, 23, 24, 25, 26, 27, 28, 29], "proxydatabas": [18, 19, 20, 21, 22], "composit": 19, "archiv": 19, "With": 19, "The": 19, "core": [19, 26], "multipl": 19, "handl": 20, "duplic": 20, "find": 20, "panda": 21, "datafram": 21, "index": 22, "add": 22, "sever": 22, "togeth": 22, "form": 22, "list": 22, "substract": 22, "from": 22, "bivari": 23, "linear": [23, 28], "regress": [23, 28], "sr": 24, "ca": 24, "varv": 27, "thick": 27, "univari": 28, "v": 29, "lite": 29, "api": 30, "refer": 30, "enst": 30, "instal": 33, "conda": 33, "download": 33, "script": 33, "miniconda3": 33, "maco": 33, "intel": 33, "appl": 33, "silicon": 33, "linux": 33, "3": 33}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx": 60}, "alltitles": {"Overview": [[0, "overview"]], "Bug reports and enhancement requests": [[0, "bug-reports-and-enhancement-requests"]], "Updating the Documentation": [[1, "updating-the-documentation"]], "About the cfr documentation": [[1, "about-the-cfr-documentation"]], "Updating docstrings": [[1, "updating-docstrings"]], "Updating tutorial notebooks": [[1, "updating-tutorial-notebooks"]], "Building the cfr documentation": [[1, "building-the-cfr-documentation"]], "Previewing the cfr documentation": [[1, "previewing-the-cfr-documentation"]], "Pushing your changes": [[1, "pushing-your-changes"], [2, "pushing-your-changes"]], "Working with the Codebase": [[2, "working-with-the-codebase"]], "Version control, Git, and GitHub": [[2, "version-control-git-and-github"]], "Forking": [[2, "forking"]], "Creating a development environment": [[2, "creating-a-development-environment"]], "Creating a branch": [[2, "creating-a-branch"]], "Contributing new or updating existing functionalities": [[2, "contributing-new-or-updating-existing-functionalities"]], "Stylistic considerations": [[2, "stylistic-considerations"]], "Conventions": [[2, "conventions"]], "Committing your code": [[2, "committing-your-code"]], "Filing a Pull Request": [[2, "filing-a-pull-request"]], "Updating your pull request": [[2, "updating-your-pull-request"]], "Delete your merged branch (optional)": [[2, "delete-your-merged-branch-optional"]], "Tips for a successful pull request": [[2, "tips-for-a-successful-pull-request"]], "cfr: a Python package for Climate Field Reconstruction": [[3, "cfr-a-python-package-for-climate-field-reconstruction"]], "Input/Output": [[4, "Input/Output"], [21, "Input/Output"]], "a netCDF file => cfr.ClimateField": [[4, "a-netCDF-file-=>-cfr.ClimateField"]], "cfr.ClimateField => a netCDF file": [[4, "cfr.ClimateField-=>-a-netCDF-file"]], "xarray.DataArray => cfr.ClimateField": [[4, "xarray.DataArray-=>-cfr.ClimateField"]], "cfr.ClimateField => xarray.DataArray": [[4, "cfr.ClimateField-=>-xarray.DataArray"]], "numpy.ndarray => cfr.ClimateField": [[4, "numpy.ndarray-=>-cfr.ClimateField"]], "cfr.ClimateField => numpy.ndarray": [[4, "cfr.ClimateField-=>-numpy.ndarray"]], "Remote loading gridded climate data": [[4, "Remote-loading-gridded-climate-data"]], "Remote loading iCESM simulations": [[4, "Remote-loading-iCESM-simulations"]], "Basic Operations": [[5, "Basic-Operations"], [22, "Basic-Operations"]], "Load the test netCDF file as a ClimateField": [[5, "Load-the-test-netCDF-file-as-a-ClimateField"]], "Time slicing": [[5, "Time-slicing"]], "Rename the variable": [[5, "Rename-the-variable"]], "Plot a field map at a time point": [[5, "Plot-a-field-map-at-a-time-point"]], "Get the anomaly field": [[5, "Get-the-anomaly-field"]], "Center the field": [[5, "Center-the-field"]], "Annualize/Seasonalize a ClimateField": [[5, "Annualize/Seasonalize-a-ClimateField"]], "Regrid a ClimateField": [[5, "Regrid-a-ClimateField"]], "Crop a cfr.ClimateField": [[5, "Crop-a-cfr.ClimateField"]], "Calculate the weighted mean of a sptial area": [[5, "Calculate-the-weighted-mean-of-a-sptial-area"]], "Validate a field against another": [[5, "Validate-a-field-against-another"]], "Running GraphEM with the Command Line Interface (CLI)": [[6, "Running-GraphEM-with-the-Command-Line-Interface-(CLI)"]], "Generating the configs.yml file": [[6, "Generating-the-configs.yml-file"], [10, "Generating-the-configs.yml-file"]], "Testing running the reconstruction job based on the generated configs.yml file": [[6, "Testing-running-the-reconstruction-job-based-on-the-generated-configs.yml-file"], [10, "Testing-running-the-reconstruction-job-based-on-the-generated-configs.yml-file"]], "Leveraging the CLI": [[6, "Leveraging-the-CLI"], [10, "Leveraging-the-CLI"]], "Validation": [[6, "Validation"], [9, "Validation"]], "A primer on GraphEM": [[7, "A-primer-on-GraphEM"]], "Data Preparation": [[7, "Data-Preparation"]], "Graph estimation": [[7, "Graph-estimation"]], "1a) Cross-validation": [[7, "1a)-Cross-validation"]], "1b Reconstruction": [[7, "1b-Reconstruction"]], "running GraphEM": [[7, "running-GraphEM"]], "1.c Validation": [[7, "1.c-Validation"]], "Spatially-averaged Statistics": [[7, "Spatially-averaged-Statistics"], [7, "id1"]], "Map of CE": [[7, "Map-of-CE"], [7, "id2"]], "Timeseries comparison": [[7, "Timeseries-comparison"], [7, "id3"]], "2. Graphical Lasso": [[7, "2.-Graphical-Lasso"]], "2a. Greedy Search": [[7, "2a.-Greedy-Search"]], "2b. Cross-Validation": [[7, "2b.-Cross-Validation"]], "2c. Reconstruction": [[7, "2c.-Reconstruction"]], "Reconstructing the tropical Pacific SST with PAGES2k and GraphEM": [[8, "Reconstructing-the-tropical-Pacific-SST-with-PAGES2k-and-GraphEM"]], "GraphEM steps": [[8, "GraphEM-steps"], [16, "GraphEM-steps"]], "Create a reconstruction job object cfr.ReconJob and load the pseudoPAGES2k database": [[8, "Create-a-reconstruction-job-object-cfr.ReconJob-and-load-the-pseudoPAGES2k-database"], [16, "Create-a-reconstruction-job-object-cfr.ReconJob-and-load-the-pseudoPAGES2k-database"], [17, "Create-a-reconstruction-job-object-cfr.ReconJob-and-load-the-pseudoPAGES2k-database"]], "Annualize each proxy record": [[8, "Annualize-each-proxy-record"], [16, "Annualize-each-proxy-record"]], "Load the instrumental observations": [[8, "Load-the-instrumental-observations"], [11, "Load-the-instrumental-observations"], [12, "Load-the-instrumental-observations"], [16, "Load-the-instrumental-observations"], [17, "Load-the-instrumental-observations"]], "Annualize the observation fields": [[8, "Annualize-the-observation-fields"], [16, "Annualize-the-observation-fields"]], "Regrid the observation fields": [[8, "Regrid-the-observation-fields"], [16, "Regrid-the-observation-fields"]], "Crop the observations fields to make the problem size smaller": [[8, "Crop-the-observations-fields-to-make-the-problem-size-smaller"], [16, "Crop-the-observations-fields-to-make-the-problem-size-smaller"]], "(Optional) Save the job object for later reload": [[8, "(Optional)-Save-the-job-object-for-later-reload"], [16, "(Optional)-Save-the-job-object-for-later-reload"], [17, "(Optional)-Save-the-job-object-for-later-reload"]], "Prepare the GraphEM solver": [[8, "Prepare-the-GraphEM-solver"], [16, "Prepare-the-GraphEM-solver"]], "Run the GraphEM solver": [[8, "Run-the-GraphEM-solver"], [16, "Run-the-GraphEM-solver"]], "Validation steps": [[8, "Validation-steps"], [16, "Validation-steps"], [17, "Validation-steps"]], "Create the reconstruction result object cfr.ReconRes.": [[8, "Create-the-reconstruction-result-object-cfr.ReconRes."], [16, "Create-the-reconstruction-result-object-cfr.ReconRes."], [17, "Create-the-reconstruction-result-object-cfr.ReconRes."]], "Load the reconstructed variables": [[8, "Load-the-reconstructed-variables"], [16, "Load-the-reconstructed-variables"], [17, "Load-the-reconstructed-variables"]], "Validate the reconstructed NINO3.4": [[8, "Validate-the-reconstructed-NINO3.4"], [16, "Validate-the-reconstructed-NINO3.4"], [17, "Validate-the-reconstructed-NINO3.4"]], "Validate the reconstructed fields": [[8, "Validate-the-reconstructed-fields"], [16, "Validate-the-reconstructed-fields"], [17, "Validate-the-reconstructed-fields"]], "Comparison to the LMR/PDA based reconstruction": [[8, "Comparison-to-the-LMR/PDA-based-reconstruction"]], "Assimilating records whose PSMs cannot be calibrated": [[9, "Assimilating-records-whose-PSMs-cannot-be-calibrated"]], "Create a reconstruction job object": [[9, "Create-a-reconstruction-job-object"]], "Load a proxy database": [[9, "Load-a-proxy-database"]], "Filter the proxy database": [[9, "Filter-the-proxy-database"]], "Annualize/seasonalize the proxy database": [[9, "Annualize/seasonalize-the-proxy-database"]], "Load climate model priors": [[9, "Load-climate-model-priors"]], "Forward modeling without calling the PSMs": [[9, "Forward-modeling-without-calling-the-PSMs"]], "Annualize/seasonalize the climate model prior": [[9, "Annualize/seasonalize-the-climate-model-prior"]], "Regrid the prior field": [[9, "Regrid-the-prior-field"]], "Crop the prior field": [[9, "Crop-the-prior-field"]], "Data assimilation": [[9, "Data-assimilation"]], "Running DA with the Command Line Interface (CLI)": [[10, "Running-DA-with-the-Command-Line-Interface-(CLI)"]], "Reconstructing the tropical Pacific SST with PAGES2k": [[11, "Reconstructing-the-tropical-Pacific-SST-with-PAGES2k"]], "Load the PAGES2k database": [[11, "Load-the-PAGES2k-database"], [12, "Load-the-PAGES2k-database"]], "Filter the database": [[11, "Filter-the-database"]], "Annualize the database": [[11, "Annualize-the-database"]], "Load the gridded climate data": [[11, "Load-the-gridded-climate-data"], [12, "Load-the-gridded-climate-data"]], "Load the model prior": [[11, "Load-the-model-prior"], [12, "Load-the-model-prior"]], "Proxy system modeling": [[11, "Proxy-system-modeling"], [12, "Proxy-system-modeling"]], "Calibrating the proxy system models": [[11, "Calibrating-the-proxy-system-models"], [12, "Calibrating-the-proxy-system-models"]], "Forwarding the proxy system models": [[11, "Forwarding-the-proxy-system-models"], [12, "Forwarding-the-proxy-system-models"]], "Annualizing, regridding, and cropping the prior field": [[11, "Annualizing,-regridding,-and-cropping-the-prior-field"], [12, "Annualizing,-regridding,-and-cropping-the-prior-field"]], "Run the DA solver": [[11, "Run-the-DA-solver"], [12, "Run-the-DA-solver"]], "Validate the reconstruction": [[11, "Validate-the-reconstruction"], [12, "Validate-the-reconstruction"]], "Load the validation target": [[11, "Load-the-validation-target"], [12, "Load-the-validation-target"]], "Field validation": [[11, "Field-validation"], [12, "Field-validation"]], "Timeseries validation": [[11, "Timeseries-validation"]], "Reconstructing precipitation": [[12, "Reconstructing-precipitation"]], "Validation Dashboards": [[13, "Validation-Dashboards"]], "Load the real and pseudo PAEGS2k datasets": [[13, "Load-the-real-and-pseudo-PAEGS2k-datasets"]], "Dashboard for a tree.TRW record": [[13, "Dashboard-for-a-tree.TRW-record"]], "Dashboard for a tree.MXD record": [[13, "Dashboard-for-a-tree.MXD-record"]], "Dashboard for a coral.d18O record": [[13, "Dashboard-for-a-coral.d18O-record"]], "Dashboard for a coral.SrCa record": [[13, "Dashboard-for-a-coral.SrCa-record"]], "Dashboard for a ice.d18O record": [[13, "Dashboard-for-a-ice.d18O-record"]], "Dashboard for a lake.varve_thickness record": [[13, "Dashboard-for-a-lake.varve_thickness-record"]], "Database Filtering": [[14, "Database-Filtering"]], "Load the pseudoPAEGS2k dataset": [[14, "Load-the-pseudoPAEGS2k-dataset"]], "Filter the pseudoPAGES2k dataset": [[14, "Filter-the-pseudoPAGES2k-dataset"]], "By proxy types": [[14, "By-proxy-types"]], "By proxy IDs": [[14, "By-proxy-IDs"]], "By a latitude range": [[14, "By-a-latitude-range"]], "By a longitude range": [[14, "By-a-longitude-range"]], "By a square (latitude + longitude ranges)": [[14, "By-a-square-(latitude-+-longitude-ranges)"]], "By a circle (center + distance)": [[14, "By-a-circle-(center-+-distance)"]], "Loading and Visualization": [[15, "Loading-and-Visualization"]], "Load the pseudoPAGES2k dataset with xarray": [[15, "Load-the-pseudoPAGES2k-dataset-with-xarray"]], "Load the pseudoPAEGS2k dataset with cfr": [[15, "Load-the-pseudoPAEGS2k-dataset-with-cfr"]], "Visualize the pseudoPAGES2k dataset": [[15, "Visualize-the-pseudoPAGES2k-dataset"]], "Access and visualize a specific record": [[15, "Access-and-visualize-a-specific-record"]], "Check the proxy IDs on an interactive map": [[15, "Check-the-proxy-IDs-on-an-interactive-map"]], "PPE: A GraphEM-based Reconstruction": [[16, "PPE:-A-GraphEM-based-Reconstruction"]], "Center each proxy record": [[16, "Center-each-proxy-record"]], "PPE: A PDA-based Reconstruction": [[17, "PPE:-A-PDA-based-Reconstruction"]], "Paleoclimate data assimilation (PDA) steps": [[17, "Paleoclimate-data-assimilation-(PDA)-steps"]], "Load the model prior (iCESM simulated tas & pr fields)": [[17, "Load-the-model-prior-(iCESM-simulated-tas-&-pr-fields)"]], "Setup the proxy system models (PSMs) for different proxy types": [[17, "Setup-the-proxy-system-models-(PSMs)-for-different-proxy-types"]], "Calibrate the PSMs": [[17, "Calibrate-the-PSMs"]], "Run the PSMs": [[17, "Run-the-PSMs"]], "Annualize the model prior fields": [[17, "Annualize-the-model-prior-fields"]], "Regrid the model prior fields": [[17, "Regrid-the-model-prior-fields"]], "Run the DA solver with Monte-Carlo iterations": [[17, "Run-the-DA-solver-with-Monte-Carlo-iterations"]], "Analysis": [[18, "Analysis"]], "Load the PAGES2k network and access a ProxyRecord": [[18, "Load-the-PAGES2k-network-and-access-a-ProxyRecord"]], "Annualize a ProxyRecord": [[18, "Annualize-a-ProxyRecord"]], "Annualize a ProxyDatabase": [[18, "Annualize-a-ProxyDatabase"]], "Composites": [[19, "Composites"]], "Load a ProxyDatabase": [[19, "Load-a-ProxyDatabase"]], "Create composites for each archive type": [[19, "Create-composites-for-each-archive-type"]], "Without calibration to an instrumental target": [[19, "Without-calibration-to-an-instrumental-target"]], "With calibration to an instrumental target": [[19, "With-calibration-to-an-instrumental-target"]], "The composite of ice core records": [[19, "The-composite-of-ice-core-records"]], "Make a composite of multiple archives": [[19, "Make-a-composite-of-multiple-archives"]], "Handling duplicate proxies in a ProxyDatabase": [[20, "Handling-duplicate-proxies-in-a-ProxyDatabase"]], "Load a database with duplicated proxies": [[20, "Load-a-database-with-duplicated-proxies"]], "Find the duplicates": [[20, "Find-the-duplicates"]], "Remote loading databases": [[21, "Remote-loading-databases"]], "Remote loading PAGES2k": [[21, "Remote-loading-PAGES2k"]], "Remote loading pseudoPAGES2k": [[21, "Remote-loading-pseudoPAGES2k"]], "cfr.ProxyDatabase => pandas.DataFrame": [[21, "cfr.ProxyDatabase-=>-pandas.DataFrame"]], "pandas.DataFrame => cfr.ProxyDatabase": [[21, "pandas.DataFrame-=>-cfr.ProxyDatabase"]], "cfr.ProxyDatabase => a netCDF file": [[21, "cfr.ProxyDatabase-=>-a-netCDF-file"]], "a netCDF file => cfr.ProxyDatabase": [[21, "a-netCDF-file-=>-cfr.ProxyDatabase"]], "cfr.ProxyRecord => a netCDF file": [[21, "cfr.ProxyRecord-=>-a-netCDF-file"]], "a netCDF file => cfr.ProxyRecord": [[21, "a-netCDF-file-=>-cfr.ProxyRecord"]], "Load the PAGES2k network": [[22, "Load-the-PAGES2k-network"]], "Slice a ProxyDatabase by index": [[22, "Slice-a-ProxyDatabase-by-index"]], "Slice a ProxyRecord": [[22, "Slice-a-ProxyRecord"]], "Add several ProxyRecords together to form a ProxyDatabase": [[22, "Add-several-ProxyRecords-together-to-form-a-ProxyDatabase"]], "Add a list of ProxyRecords into an existing ProxyDatabase": [[22, "Add-a-list-of-ProxyRecords-into-an-existing-ProxyDatabase"]], "Merge several ProxyDatabases": [[22, "Merge-several-ProxyDatabases"]], "Substract a list of ProxyRecords from a ProxyDatabase": [[22, "Substract-a-list-of-ProxyRecords-from-a-ProxyDatabase"]], "Substract a ProxyDatabase from another": [[22, "Substract-a-ProxyDatabase-from-another"]], "Filter a ProxyDatabase": [[22, "Filter-a-ProxyDatabase"]], "Bivariate linear regression": [[23, "Bivariate-linear-regression"]], "Data preparation": [[23, "Data-preparation"], [24, "Data-preparation"], [25, "Data-preparation"], [26, "Data-preparation"], [27, "Data-preparation"], [28, "Data-preparation"], [29, "Data-preparation"]], "Proxy": [[23, "Proxy"], [24, "Proxy"], [25, "Proxy"], [26, "Proxy"], [27, "Proxy"], [28, "Proxy"], [29, "Proxy"], [30, "proxy"], [36, "proxy"]], "Model": [[23, "Model"], [24, "Model"], [25, "Model"], [26, "Model"], [27, "Model"], [28, "Model"], [29, "Model"]], "Instrumental observations": [[23, "Instrumental-observations"], [28, "Instrumental-observations"]], "Get climate data for a specific ProxyRecord": [[23, "Get-climate-data-for-a-specific-ProxyRecord"], [24, "Get-climate-data-for-a-specific-ProxyRecord"], [25, "Get-climate-data-for-a-specific-ProxyRecord"], [26, "Get-climate-data-for-a-specific-ProxyRecord"], [27, "Get-climate-data-for-a-specific-ProxyRecord"], [28, "Get-climate-data-for-a-specific-ProxyRecord"], [29, "Get-climate-data-for-a-specific-ProxyRecord"]], "Create a PSM object": [[23, "Create-a-PSM-object"], [24, "Create-a-PSM-object"], [25, "Create-a-PSM-object"], [26, "Create-a-PSM-object"], [27, "Create-a-PSM-object"], [28, "Create-a-PSM-object"], [29, "Create-a-PSM-object"]], "PSM for coral Sr/Ca": [[24, "PSM-for-coral-Sr/Ca"]], "PSM for coral d18O": [[25, "PSM-for-coral-d18O"]], "PSM for ice core d18O": [[26, "PSM-for-ice-core-d18O"]], "PSM for lake varve thickness": [[27, "PSM-for-lake-varve-thickness"]], "Univariate linear regression": [[28, "Univariate-linear-regression"]], "PSM for tree TRW (VS-Lite)": [[29, "PSM-for-tree-TRW-(VS-Lite)"]], "Instrumental observation": [[29, "Instrumental-observation"]], "API Reference": [[30, "api-reference"]], "Climate": [[30, "climate"], [31, "climate"]], "PSM": [[30, "psm"], [37, "psm"]], "DA": [[30, "da"]], "ReconJob": [[30, "reconjob"]], "ReconRes": [[30, "reconres"]], "EnsTS": [[30, "ensts"]], "GraphEM": [[32, "graphem"]], "Installation": [[33, "installation"]], "Install the Conda environment": [[33, "install-the-conda-environment"]], "Step 1: Download the installation script for miniconda3": [[33, "step-1-download-the-installation-script-for-miniconda3"]], "macOS (Intel)": [[33, "macos-intel"]], "macOS (Apple Silicon)": [[33, "macos-apple-silicon"]], "Linux": [[33, "linux"]], "Step 2: Install Miniconda3": [[33, "step-2-install-miniconda3"]], "Step 3: Test your Installation": [[33, "step-3-test-your-installation"]], "Install cfr": [[33, "install-cfr"]], "LMR": [[34, "lmr"]], "pseudoPAGES2k": [[35, "pseudopages2k"]]}, "indexentries": {"bilinear (class in cfr.psm)": [[30, "cfr.psm.Bilinear"]], "climatefield (class in cfr.climate)": [[30, "cfr.climate.ClimateField"]], "coral_srca (class in cfr.psm)": [[30, "cfr.psm.Coral_SrCa"]], "coral_d18o (class in cfr.psm)": [[30, "cfr.psm.Coral_d18O"]], "enkf (class in cfr.da)": [[30, "cfr.da.EnKF"]], "ensts (class in cfr.ts)": [[30, "cfr.ts.EnsTS"]], "ice_d18o (class in cfr.psm)": [[30, "cfr.psm.Ice_d18O"]], "lake_varvethickness (class in cfr.psm)": [[30, "cfr.psm.Lake_VarveThickness"]], "linear (class in cfr.psm)": [[30, "cfr.psm.Linear"]], "proxydatabase (class in cfr.proxy)": [[30, "cfr.proxy.ProxyDatabase"]], "proxyrecord (class in cfr.proxy)": [[30, 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"nens (cfr.ts.ensts attribute)": [[30, "cfr.ts.EnsTS.nEns"]], "nrec_tags() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.nrec_tags"]], "nt (cfr.ts.ensts attribute)": [[30, "cfr.ts.EnsTS.nt"]], "plot() (cfr.climate.climatefield method)": [[30, "cfr.climate.ClimateField.plot"]], "plot() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.plot"]], "plot() (cfr.proxy.proxyrecord method)": [[30, "cfr.proxy.ProxyRecord.plot"]], "plot() (cfr.ts.ensts method)": [[30, "cfr.ts.EnsTS.plot"]], "plot_compare() (cfr.proxy.proxyrecord method)": [[30, "cfr.proxy.ProxyRecord.plot_compare"]], "plot_composite() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.plot_composite"]], "plot_dups() (cfr.proxy.proxyrecord method)": [[30, "cfr.proxy.ProxyRecord.plot_dups"]], "plot_eof() (cfr.climate.climatefield method)": [[30, "cfr.climate.ClimateField.plot_eof"]], "plot_qs() (cfr.ts.ensts method)": [[30, "cfr.ts.EnsTS.plot_qs"]], "plot_valid() (cfr.reconres.reconres method)": [[30, "cfr.reconres.ReconRes.plot_valid"]], "plotly() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.plotly"]], "plotly() (cfr.proxy.proxyrecord method)": [[30, "cfr.proxy.ProxyRecord.plotly"]], "plotly_concise() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.plotly_concise"]], "plotly_count() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.plotly_count"]], "plotly_grid() (cfr.climate.climatefield method)": [[30, "cfr.climate.ClimateField.plotly_grid"]], "prep_da_cfg() (cfr.reconjob.reconjob method)": [[30, "cfr.reconjob.ReconJob.prep_da_cfg"]], "prep_graphem() (cfr.reconjob.reconjob method)": [[30, "cfr.reconjob.ReconJob.prep_graphem"]], "refresh() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.refresh"]], "regrid() (cfr.climate.climatefield method)": [[30, "cfr.climate.ClimateField.regrid"]], "regrid_clim() (cfr.reconjob.reconjob method)": [[30, 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"cfr.climate.ClimateField.to_nc"]], "to_nc() (cfr.proxy.proxydatabase method)": [[30, "cfr.proxy.ProxyDatabase.to_nc"]], "to_nc() (cfr.proxy.proxyrecord method)": [[30, "cfr.proxy.ProxyRecord.to_nc"]], "valid() (cfr.reconres.reconres method)": [[30, "cfr.reconres.ReconRes.valid"]], "wrap_lon() (cfr.climate.climatefield method)": [[30, "cfr.climate.ClimateField.wrap_lon"]], "write_cfg() (cfr.reconjob.reconjob method)": [[30, "cfr.reconjob.ReconJob.write_cfg"]]}}) \ No newline at end of file diff --git a/docs/ug-api.html b/docs/ug-api.html index 1cb65a6..61d2a4a 100644 --- a/docs/ug-api.html +++ b/docs/ug-api.html @@ -2736,7 +2736,7 @@

EnsTS#<

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/ug-climate.html b/docs/ug-climate.html index 785487c..8c4a5b4 100644 --- a/docs/ug-climate.html +++ b/docs/ug-climate.html @@ -481,7 +481,7 @@

Climate#<

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/ug-graphem.html b/docs/ug-graphem.html index 3c81802..c78990d 100644 --- a/docs/ug-graphem.html +++ b/docs/ug-graphem.html @@ -479,7 +479,7 @@

GraphEM#<

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/ug-installation.html b/docs/ug-installation.html index aed969f..ffaebde 100644 --- a/docs/ug-installation.html +++ b/docs/ug-installation.html @@ -599,7 +599,7 @@

Install cfr - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/ug-lmr.html b/docs/ug-lmr.html index 07e7d44..e7f5198 100644 --- a/docs/ug-lmr.html +++ b/docs/ug-lmr.html @@ -482,7 +482,7 @@

LMR#

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/ug-pp2k.html b/docs/ug-pp2k.html index 8ae6f83..a4d527b 100644 --- a/docs/ug-pp2k.html +++ b/docs/ug-pp2k.html @@ -482,7 +482,7 @@

pseudoPAGES2k - © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/ug-proxy.html b/docs/ug-proxy.html index edafa42..cbd441c 100644 --- a/docs/ug-proxy.html +++ b/docs/ug-proxy.html @@ -483,7 +483,7 @@

Proxy#<

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docs/ug-psm.html b/docs/ug-psm.html index db5dffd..7003e85 100644 --- a/docs/ug-psm.html +++ b/docs/ug-psm.html @@ -483,7 +483,7 @@

PSM#

- © Copyright 2023, Feng Zhu, Julien Emile-Geay. + © Copyright 2024, Feng Zhu, Julien Emile-Geay.

diff --git a/docsrc/_build/doctrees/index.doctree b/docsrc/_build/doctrees/index.doctree index 8802114..1251412 100644 Binary files a/docsrc/_build/doctrees/index.doctree and b/docsrc/_build/doctrees/index.doctree differ diff --git a/docsrc/conf.py b/docsrc/conf.py index e6437c5..1cd7372 100644 --- a/docsrc/conf.py +++ b/docsrc/conf.py @@ -18,7 +18,7 @@ project = 'cfr' # author = 'Feng Zhu, Julien Emile-Geay' # copyright = f'2023, {author}' -copyright = f'2023, Feng Zhu, Julien Emile-Geay' +copyright = f'2024, Feng Zhu, Julien Emile-Geay' # -- General configuration --------------------------------------------------- diff --git a/docsrc/index.rst b/docsrc/index.rst index 9c7a1a2..f9e64f0 100644 --- a/docsrc/index.rst +++ b/docsrc/index.rst @@ -3,7 +3,11 @@ *********************************************************** .. note:: - Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024 + If you use `cfr` in any way for your publications, please cite: + + + Zhu, F., Emile-Geay, J., Hakim, G. J., Guillot, D., Khider, D., Tardif, R., & Perkins, W. A. (2024). cfr (v2024.1.26): a Python package for climate field reconstruction. Geoscientific Model Development, 17(8), 3409–3431. https://doi.org/10.5194/gmd-17-3409-2024 + + Zhu, F., Emile-Geay, J., Anchukaitis, K.J., McKay, N.P., Stevenson, S., Meng, Z., 2023. A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models. Sci Data 10, 624. https://doi.org/10.1038/s41597-023-02489-1 + `cfr` aims to provide a universal framework for climate field reconstruction (CFR). It provides a toolkit for