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https://github.com/RWTH-EBC/ebcpy
https://github.com/RWTH-EBC/ebcpy
hacktoberfest
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/RWTH-EBC/ebcpy
- Owner: RWTH-EBC
- License: bsd-3-clause
- Created: 2021-08-12T10:21:55.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-09-19T12:23:34.000Z (5 months ago)
- Last Synced: 2024-10-29T20:38:00.440Z (4 months ago)
- Topics: hacktoberfest
- Language: Python
- Homepage:
- Size: 39.6 MB
- Stars: 11
- Watchers: 15
- Forks: 2
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - ebcpy - Provides generic functions and classes commonly used for the analysis and optimization of energy systems, buildings and indoor climate. (Consumption / Buildings and Heating)
README
[](https://doi.org/10.21105/joss.03861)
[](https://rwth-ebc.github.io/ebcpy/master/pylint/pylint.html)
[](https://rwth-ebc.github.io/ebcpy/master/docs/index.html)
[](https://rwth-ebc.github.io/ebcpy/master/coverage)
[](https://opensource.org/licenses/BSD-3-Clause)
[](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).