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CMethods must not be instantiated to call the methods - removed this from all examples
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‎Makefile

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@@ -46,7 +46,7 @@ pre-commit:
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## Clean the workspace
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##
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clean:
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rm -rf .pytest_cache build/ dist/ python_cmethods.egg-info docs/_build
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rm -rf .pytest_cache build/ dist/ python_cmethods.egg-info docs/_build examples/.ipynb_checkpoints
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rm -f .coverage cmethods/_version.py
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find tests -name "__pycache__" | xargs rm -rf

‎README.md

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2020

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This Python module contains a collection of different scale- and distribution-based bias adjustment techniques for climatic research (see `/examples/examples.ipynb` for help).
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The documentation can be found here: [https://python-kraken-sdk.readthedocs.io/en/stable/](https://python-kraken-sdk.readthedocs.io/en/stable/)
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The documentation is available at: [https://python-kraken-sdk.readthedocs.io/en/stable/](https://python-kraken-sdk.readthedocs.io/en/stable/)
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> ⚠️ For the application of bias corrections on _lage data sets_ it is recomanded to use the command-line tool [BiasAdjustCXX](https://github.com/btschwertfeger/BiasAdjustCXX) since bias corrections are complex statistical transformation which are very slow in Python compared to the C++ implementation.
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@@ -96,8 +96,7 @@ python3 -m pip install python-cmethods
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```python
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import xarray as xr
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from cmethods.CMethods import CMethods
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cm = CMethods()
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from cmethods.CMethods import CMethods as cm
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obsh = xr.open_dataset('input_data/observations.nc')
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simh = xr.open_dataset('input_data/control.nc')

‎cmethods/CMethods.py

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@@ -48,8 +48,7 @@ def __init__(self, method: str, available_methods: list):
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class CMethods:
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"""
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The CMethods class serves a collection of bias correction procedures to adjust
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time-series of climate data. It accept no parameters but must be instantiated
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to apply the bias correction techniques.
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time-series of climate data.
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The following bias correction techniques are available:
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Scaling-based techniques:
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:linenos:
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:caption: Example: Get available methods
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>>> from cmethods.CMethods import CMethods
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>>> CMethods().get_available_methods()
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>>> from cmethods.CMethods import CMethods as cm
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>>> cm.get_available_methods()
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[
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"linear_scaling", "variance_scaling", "delta_method",
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"quantile_mapping", "quantile_delta_mapping"
@@ -169,7 +169,7 @@ def adjust_3d(
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:caption: Example application - 3-dimensinoal bias correction
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>>> import xarray as xr
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>>> from cmethods.CMethods import CMethods
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>>> from cmethods.CMethods import CMethods as cm
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>>> # Note: The data sets must contain the dimensions "time", "lat", and "lon"
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>>> # for the respective variable.
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>>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc")
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>>> variable = "tas" # temperatures
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>>> cm = CMethods()
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'''
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In the following the Quantile Delta Mapping techniques is applied on
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3-dimensional time-series data sets containing the variable "tas". The
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:caption: Example: Linear Scaling
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>>> import xarray as xr
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>>> from cmethods.CMethods import CMethods
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>>> from cmethods.CMethods import CMethods as cm
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>>> # Note: The data sets must contain the dimension "time"
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>>> # for the respective variable.
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>>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc")
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>>> variable = "tas" # temperatures
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>>> cm = CMethods()
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>>> ls_adjusted = cm.linear_scaling(
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... obs=obs[variable],
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... simh=simh[variable],
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:caption: Example: Variance Scaling
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>>> import xarray as xr
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>>> from cmethods.CMethods import CMethods
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>>> from cmethods.CMethods import CMethods as cm
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>>> # Note: The data sets must contain the dimension "time"
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>>> # for the respective variable.
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>>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc")
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>>> variable = "tas" # temperatures
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>>> cm = CMethods()
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>>> vs_adjusted = cm.variance_scaling(
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... obs=obs[variable],
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... simh=simh[variable],
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:caption: Example: Delta Method
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>>> import xarray as xr
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>>> from cmethods.CMethods import CMethods
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>>> from cmethods.CMethods import CMethods as cm
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>>> # Note: The data sets must contain the dimension "time"
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>>> # for the respective variable.
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>>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc")
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>>> variable = "tas" # temperatures
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>>> cm = CMethods()
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>>> dm_adjusted = cm.delta_method(
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... obs=obs[variable],
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... simh=simh[variable],
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:caption: Example: Quantile Mapping
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>>> import xarray as xr
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>>> from cmethods.CMethods import CMethods
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>>> from cmethods.CMethods import CMethods as cm
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>>> # Note: The data sets must contain the dimension "time"
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>>> # for the respective variable.
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>>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc")
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>>> variable = "tas" # temperatures
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>>> cm = CMethods()
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>>> qm_adjusted = cm.quantile_mapping(
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... obs=obs[variable],
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... simh=simh[variable],
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:caption: Example: Quantile Delta Mapping
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>>> import xarray as xr
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>>> from cmethods.CMethods import CMethods
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>>> from cmethods.CMethods import CMethods as cm
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>>> # Note: The data sets must contain the dimension "time"
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>>> # for the respective variable.
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>>> simp = xr.open_dataset("path/to/the_dataset_to_adjust-scenario_period.nc")
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>>> variable = "tas" # temperatures
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>>> cm = CMethods()
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>>> qdm_adjusted = cm.quantile_delta_mapping(
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... obs=obs[variable],
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... simh=simh[variable],
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:linenos:
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:caption: Compute the probability density function :math:`P(x)`
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>>> from cmethods.CMethods import CMethods
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>>> cm = CMethods()
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>>> from cmethods.CMethods import CMethods as cm
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>>> x = [1, 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 10]
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>>> xbins = [0, 3, 6, 10]
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>>> print(cm.get_pdf(x=x, xbins=xbins))
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:linenos:
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:caption: Compute the cmmulative distribution function :math:`F(x)`
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>>> from cmethods.CMethods import CMethods
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>>> cm = CMethods()
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>>> from cmethods.CMethods import CMethods as cm
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>>> x = [1, 2, 3, 4, 5, 5, 5, 6, 7, 8, 9, 10]
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>>> xbins = [0, 3, 6, 10]
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>>> print(cm.get_cdf(x=x, xbins=xbins))

‎docs/src/getting_started.rst

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@@ -23,8 +23,7 @@ method specific documentation.
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:caption: Apply the Linear Scaling bias correction technique on 1-dimensional data
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import xarray as xr
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from cmethods.CMethods import CMethods
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cm = CMethods()
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from cmethods.CMethods import CMethods as cm
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obsh = xr.open_dataset('input_data/observations.nc')
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simh = xr.open_dataset('input_data/control.nc')
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:caption: Apply the Quantile Delta Mapping bias correction technique on 1-dimensional data
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import xarray as xr
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from cmethods.CMethods import CMethods
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cm = CMethods()
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from cmethods.CMethods import CMethods as cm
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obsh = xr.open_dataset('input_data/observations.nc')
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simh = xr.open_dataset('input_data/control.nc')
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n_quaniles = 1000,
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kind = '+'
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)
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```

‎examples/examples.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"from cmethods.CMethods import CMethods\n",
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"cm = CMethods()"
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"from cmethods.CMethods import CMethods as cm\n"
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]
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},
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{

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