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https://github.com/RWTH-EBC/ebcpy


https://github.com/RWTH-EBC/ebcpy

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README 

        
![E.ON EBC RWTH Aachen University](./docs/EBC_Logo.png)



[![DOI](https://joss.theoj.org/papers/10.21105/joss.03861/status.svg)](https://doi.org/10.21105/joss.03861)

[![pylint](https://rwth-ebc.github.io/ebcpy/master/pylint/pylint.svg )](https://rwth-ebc.github.io/ebcpy/master/pylint/pylint.html)

[![documentation](https://rwth-ebc.github.io/ebcpy/master/docs/doc.svg)](https://rwth-ebc.github.io/ebcpy/master/docs/index.html)

[![coverage](https://rwth-ebc.github.io/ebcpy/master/coverage/badge.svg)](https://rwth-ebc.github.io/ebcpy/master/coverage)

[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

[![build](https://rwth-ebc.github.io/ebcpy/master/build/build.svg)](https://rwth-ebc.github.io/ebcpy/master/build/build.svg)



# ebcpy



This **PY**thon package provides generic functions and classes commonly

used for the analysis and optimization of **e**nergy systems, **b**uildings and indoor **c**limate (**EBC**).



Key features are:



* `TimeSeriesData`

* `SimulationAPI`'s

* Optimization wrapper

* Pre-/Postprocessing

* Modelica utilities



It was developed together with `AixCaliBuHA`, a framework for an automated calibration of dynamic building and HVAC models. During this development, we found several interfaces relevant to further research. We thus decoupled these interfaces into `ebcpy` and used the framework, for instance in the design optimization of heat pump systems ([link](https://www.sciencedirect.com/science/article/abs/pii/S0196890421010645?via%3Dihub)).



# Installation



To install, simply run

```

pip install ebcpy

```



In order to use all optional dependencies (e.g. `pymoo` optimization), install via:



```

pip install ebcpy[full]

```



If you encounter an error with the installation of `scikit-learn`, first install `scikit-learn` separatly and then install `ebcpy`:



```

pip install scikit-learn

pip install ebcpy

```



If this still does not work, we refer to the troubleshooting section of `scikit-learn`: https://scikit-learn.org/stable/install.html#troubleshooting. Also check [issue 23](https://github.com/RWTH-EBC/ebcpy/issues/23) for updates.



In order to help development, install it as an egg:



```

git clone https://github.com/RWTH-EBC/ebcpy

pip install -e ebcpy

```



# How to get started?



We recommend running our jupyter-notebook to be guided through a **helpful tutorial**.  

For this, run the following code:

```

# If jupyter is not already installed:

pip install jupyter

# Go into your ebcpy-folder (cd \path_to_\ebcpy) or change the path to tutorial.ipynb and run:

jupyter notebook tutorial\tutorial.ipynb

```



Or, clone this repo and look at the examples\README.md file.

Here you will find several examples to execute.



# How to cite ebcpy



Please use the following metadata to cite `ebcpy` in your research:



```

@article{Wuellhorst2022,

  doi = {10.21105/joss.03861},

  url = {https://doi.org/10.21105/joss.03861},

  year = {2022},

  publisher = {The Open Journal},

  volume = {7},

  number = {72},

  pages = {3861},

  author = {Fabian Wüllhorst and Thomas Storek and Philipp Mehrfeld and Dirk Müller},

  title = {AixCaliBuHA: Automated calibration of building and HVAC systems},

  journal = {Journal of Open Source Software}

}

```



# TimeSeriesData

Note that we use our own `TimeSeriesData` object which inherits from `pd.DataFrame`. The aim is to make tasks like loading different filetypes or applying multiple tags to one variable more convenient, while conserving the powerful tools of the DataFrame.

Just a quick intro here:



## Variables and tags

```

>>> from ebcpy.data_types import TimeSeriesData

>>> tsd = TimeSeriesData(r"path_to_a_supported_file")

>>> print(tsd)

Variables    T_heater              T_heater_1            

Tags             meas         sim        meas         sim

Time                                                     

0.0        313.165863  313.165863  293.173126  293.173126

1.0        312.090271  310.787750  293.233002  293.352448

2.0        312.090027  310.796753  293.385925  293.719055

3.0        312.109436  310.870331  293.589233  294.141754

```



As you can see, our first column level is always a variable, and the second one a tag.

This is especially handy when dealing with calibration or processing tasks, where you will have multiple

versions (tags) for one variable. The default tag is `raw` to indicate the unmodified data.

To access a variable, you have to call `.loc`. To access multiple variables that all hold one tag use `xs`:

```python

# All tags:

tsd.loc[:, "variable_name"]

# One specific tag:

tsd.loc[:, ("variable_name", "tag_name")]

# One tag, all variables:

tsd.xs("tag_name", axis=1, level=1)

```

## FloatIndex and DateTimeIndex

Measured data typically holds a datetime stamps (`DateTimeIndex`) while simulation result files hold absolute seconds (`FloatIndex`). 

You can easily convert back and forth using:

```python

# From Datetime to float

tsd.to_float_index()

# From float to datetime

tsd.to_datetime_index()

# To clean your data and create a common frequency:

tsd.clean_and_space_equally(desired_freq="1s")

```



# Documentation

Visit our official [Documentation](https://rwth-ebc.github.io/ebcpy/master/docs/index.html).



# Problems?

Please [raise an issue here](https://github.com/RWTH-EBC/ebcpy/issues/new).