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Fix adapt_freq example in notebook #1742

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May 2, 2024
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Fix adapt_freq example in notebook #1742

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cd56ade
Fix adapt_freq example in notebook
aulemahal May 2, 2024
c47a664
woups double typo
aulemahal May 2, 2024
f6cb0e3
fix it in adv too
aulemahal May 2, 2024
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1 change: 1 addition & 0 deletions CHANGES.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,7 @@ Bug fixes
* Fixed bug QuantileDeltaMapping adjustment not working for seasonal grouping (:issue:`1704`, :pull:`1716`).
* The codebase has been adjusted to address several (~400) `mypy`-related errors attributable to inaccurate function call signatures and variable name shadowing. (:issue:`1719`, :pull:`1721`).
* ``xclim.core.formatting.generate_indicator_docstring`` has been modified to ensure that the `numpy`-docstrings of all Indicators are consistent in their formatting. (:pull:`1731`).
* Fixed documentation example for frequency adaptation with `sdba`. (:issue:`1740`, :pull:`1742`).

Internal changes
^^^^^^^^^^^^^^^^
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10 changes: 5 additions & 5 deletions docs/notebooks/sdba-advanced.ipynb
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Expand Up @@ -678,11 +678,11 @@
"from xclim import sdba\n",
"\n",
"group = sdba.Grouper(\"time.dayofyear\", window=31)\n",
"sim_ad, pth, dP0 = sdba.processing.adapt_freq(\n",
" pr_ref, pr_sim, thresh=\"0.05 mm d-1\", group=group\n",
"hist_ad, pth, dP0 = sdba.processing.adapt_freq(\n",
" pr_ref, pr_hist, thresh=\"0.05 mm d-1\", group=group\n",
")\n",
"QM_ad = sdba.EmpiricalQuantileMapping.train(\n",
" pr_ref, sim_ad, nquantiles=15, kind=\"*\", group=group\n",
" pr_ref, hist_ad, nquantiles=15, kind=\"*\", group=group\n",
")\n",
"scen_ad = QM_ad.adjust(pr_sim)\n",
"\n",
Expand All @@ -698,7 +698,7 @@
"source": [
"In the figure above, `scen` occasionally has small peaks where `sim` is 0, indicating that there are more \"dry days\" (days with almost no precipitation) in `hist` than in `ref`. The frequency-adaptation [Themeßl et al. (2010)](https://doi.org/10.1007/s10584-011-0224-4) performed in the step above only worked partially. \n",
"\n",
"The reason for this is the following. The first step above combines precipitations in 365 overlapping blocks of 31 days * Y years, one block for each day of the year. Each block is adapted, and the 16th day-of-year slice (at the center of the block) is assigned to the corresponding day-of-year in the adapted dataset `sim_ad`. As we proceed to the training, we re-form those 31 days * Y years blocks, but this step does not invert the last one: There can still be more zeroes in the simulation than in the reference. \n",
"The reason for this is the following. The first step above combines precipitations in 365 overlapping blocks of 31 days * Y years, one block for each day of the year. Each block is adapted, and the 16th day-of-year slice (at the center of the block) is assigned to the corresponding day-of-year in the adapted dataset `hist_ad`. As we proceed to the training, we re-form those 31 days * Y years blocks, but this step does not invert the last one: There can still be more zeroes in the simulation than in the reference. \n",
"\n",
"To alleviate this issue, another way of proceeding is to perform a frequency adaptation on the blocks, and then use the same blocks in the training step, as we show below.\n"
]
Expand All @@ -714,7 +714,7 @@
"\n",
"QM_ad = sdba.EmpiricalQuantileMapping.train(\n",
" pr_ref,\n",
" sim_ad,\n",
" pr_hist,\n",
" nquantiles=15,\n",
" kind=\"*\",\n",
" group=group,\n",
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10 changes: 5 additions & 5 deletions docs/notebooks/sdba.ipynb
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Expand Up @@ -269,7 +269,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"In the figure above, `scen` has small peaks where `sim` is 0. This problem originates from the fact that there are more \"dry days\" (days with almost no precipitation) in `hist` than in `ref`. The next example works around the problem using frequency-adaptation, as described in [Themeßl et al. (2010)](https://doi.org/10.1007/s10584-011-0224-4)."
"In the figure above, `scen` has small peaks where `sim` is 0. This problem originates from the fact that there are more \"dry days\" (days with almost no precipitation) in `hist` than in `ref`. The next example works around the problem using frequency-adaptation, as described in [Themeßl et al. (2012)](https://doi.org/10.1007/s10584-011-0224-4)."
]
},
{
Expand All @@ -279,11 +279,11 @@
"outputs": [],
"source": [
"# 2nd try with adapt_freq\n",
"sim_ad, pth, dP0 = sdba.processing.adapt_freq(\n",
" pr_ref, pr_sim, thresh=\"0.05 mm d-1\", group=\"time\"\n",
"hist_ad, pth, dP0 = sdba.processing.adapt_freq(\n",
" pr_ref, pr_hist, thresh=\"0.05 mm d-1\", group=\"time\"\n",
")\n",
"QM_ad = sdba.EmpiricalQuantileMapping.train(\n",
" pr_ref, sim_ad, nquantiles=15, kind=\"*\", group=\"time\"\n",
" pr_ref, hist_ad, nquantiles=15, kind=\"*\", group=\"time\"\n",
")\n",
"scen_ad = QM_ad.adjust(pr_sim)\n",
"\n",
Expand Down Expand Up @@ -686,7 +686,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
"version": "3.12.2"
},
"toc": {
"base_numbering": 1,
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