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fixed example in README.md
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README.md

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@@ -32,7 +32,6 @@ simh = xr.open_dataset('input_data/contr.nc')
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simp = xr.open_dataset('input_data/scen.nc')
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ls_result = cm.linear_scaling(
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method = 'quantile_delta_mapping',
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obs = obsh['tas'][:,0,0],
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simh = simh['tas'][:,0,0],
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simp = simp['tas'][:,0,0],
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## Notes:
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- Computation in Python takes some time, so this is only for demonstration. When adjusting large datasets, its best to the C++ implementation mentioned above.
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## Equation references
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## References
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- Schwertfeger, Benjamin Thomas (2022) The influence of bias corrections on variability, distribution, and correlation of temperatures in comparison to observed and modeled climate data in Europe (https://epic.awi.de/id/eprint/56689/)
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- Linear Scaling and Variance Scaling based on: Teutschbein, Claudia and Seibert, Jan (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods (https://doi.org/10.1016/j.jhydrol.2012.05.052)
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- Delta Method based on: Beyer, R. and Krapp, M. and Manica, A.: An empirical evaluation of bias correction methods for palaeoclimate simulations (https://doi.org/10.5194/cp-16-1493-2020)
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- Quantile and Detrended Quantile Mapping based on: Alex J. Cannon and Stephen R. Sobie and Trevor Q. Murdock Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (https://doi.org/10.1175/JCLI-D-14-00754.1)

examples/examples.ipynb

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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [conda env:testenv]",
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"display_name": "awipyenv [conda env:awipyenv]",
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"language": "python",
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"name": "conda-env-testenv-py"
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"name": "conda-env-awipyenv-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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"version": "3.9.7"
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},
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"vscode": {
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"interpreter": {

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