tssm 0.0.4
Package to create out of a single load profile a profile for a whole district using the diversity factor
License: BSD 3-Clause License
Requires Python: >=3.8
Keywords: load,simultaneity factor,scaling,load profile,time series

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# Time Series Scaling Module  (TSSM)
<img src="./docs/sources/_static/Logo.svg" width="100" align="left">

**TSSM** is a python package for the up-scaling of time series or load such as electricity, heating, etc. 


**Warning**
```{warning} 
This package is under heavy development!
```

## Getting started

### Install TSSM

Install tssm directly from PyPi as follows: 

```console
pip install tssm
```

Further installation instructions can be found in the [**documentation**](http://tssm.rtfd.io/) under 'Getting started'.


### Usage

example usages can be found in the [**examples'**](https://git.fh-aachen.de/tb5152e/tssm/-/blob/main/examples) folder.


#### Basic workflow

A small example how tssm can be used is described as follows:

```python
# import module and Daily period variable
from tssm import TimeSeriesScalingModule as tssm, DAILY

# initialize class with a number of buildings of 202 with a simultaneity factor of 0.786
scaling = tssm(number_of_buildings=202, simultaneity_factor=0.786)
# read profile from data.csv file and use the Electricity and Date column
scaling.data.read_profile_from_csv_with_date(path="./data.csv", column_of_load="Electricity", column_of_date="Date")
# calculate linear scaled values with a daily simultaneity factor and average value
daily_scaled_values = scaling.calculate_using_average_values(period=DAILY)
```

#### Examples
A [**first example**](https://git.fh-aachen.de/tb5152e/tssm/-/blob/main/examples/example_linear.py) shows the linear approach. It scales the time series between the scaled time series and an average.

A [**second example**](https://git.fh-aachen.de/tb5152e/tssm/-/blob/main/examples/example_scaling.py) shows the scaling approach. It scales the time series between the scaled time series and a scaling time 
series.

A [**third example**](https://git.fh-aachen.de/tb5152e/tssm/-/blob/main/examples/example_normal_distribution.py) shows the normal distribution approach. It scales the time series by applying a normal 
distribution to every time step.

A [**fourth example**](https://git.fh-aachen.de/tb5152e/tssm/-/blob/main/examples/example_of_different_method_2_import_profiles.py) shows the different ways to import the data.

A [**fifth example**](https://git.fh-aachen.de/tb5152e/tssm/-/blob/main/examples/example_speed_comparison.py) shows the speed of the different approaches.

### License

The module is licensed under BSD 3-Clause License.

Further, License information can be found [**here**](https://git.fh-aachen.de/tb5152e/tssm/-/blob/main/LICENSE).

### Reference

### Acknowledgements


## Content

The documentation of the tssm code can be found [**here**](http://tssm.rtfd.io/).