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- For the multiplicative techniques a maximum scaling factor of 10 is defined.
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This can be changed by passing the optional parameter `max_scaling_factor`.
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- Except for detrended quantile mapping, all implemented techniques can be
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applied to single and multdimensional data sets by executing the
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applied to single and multi-dimensional data sets by executing the
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`cmethods.adjust` function.
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## Examples (see repository on [GitHub](https://github.com/btschwertfeger/python-cmethods))
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Notebook with different methods and plots: `/examples/examples.ipynb`
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There is also an example script (`/examples/biasadjust.py`) that can be used to
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apply the available bias correction methods on 1- and 3-dimensional data sets
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(see `/examples/input_data/*.nc`).
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Help:
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```bash
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╰─ python3 biasadjust.py --help
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```
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(1.) Example - Quantile Mapping bias correction on the provided example data:
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```bash
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╰─ python3 biasadjust.py \
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--ref input_data/observations.nc \
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--contr input_data/control.nc \
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--scen input_data/scenario.nc \
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--kind "+" \
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--variable "tas" \
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--quantiles 10 \
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--method quantile_mapping
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```
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(2.) Example - Linear Scaling bias correction on the provided example data:
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```bash
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╰─ python3 biasadjust.py \
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--ref input_data/observations.nc \
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--contr input_data/control.nc \
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--scen input_data/scenario.nc \
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--kind "+" \
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--variable "tas" \
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--group "time.month" \
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--method linear_scaling
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```
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Notes:
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- Data sets must have the same spatial resolutions.
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- This script is far away from perfect - so please see it, as a starting point.
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(:
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- A Jupyter notebook applying all those methods is provided here:
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`/examples/examples.ipynb`
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- The example data is located at: `/examples/input_data/*.nc`
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<a name="notes"></a>
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