pip install command

README.md

@@ -2,23 +2,24 @@
 
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7009281.svg)](https://doi.org/10.5281/zenodo.7009281) Jupyter Lab:   [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/zolabar/trendPy/HEAD) Documentation: [![doc](https://img.shields.io/badge/Made%20with-Sphinx-1f425f.svg)](https://zolabar.github.io/trendPy/) WebApps: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/zolabar/trendPy/experimental?urlpath=voila%2Frender%2F/trendpy_webapp.ipynb) (binder) [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://zolabar-trendpy-trendpy2-app-kfqshb.streamlit.app/)
 
+# Usage
 
+```pip install trendpy2``` 
 
-
-
-Use the package **TrendPy** as shown in the ```example.ipynb``` to use 
+and use it as **TrendPy** as shown in the ```example.ipynb``` and approxmiate your time series with the following trends
 
 * linear $f(x)=a\cdot x+b$
 * polynomial $f(x)=a_n\cdot x^n+a_{n-1}\cdot x^{n-1}+...+a_0$
 * exponential $f(x)=a\cdot e^{b\cdot x}$
 * trigonometric $f(x)=a\cdot \cos(2\cdot \pi\cdot b\cdot x+c)$
 * "free" (for max. three parameters) (e.g.```a*arctan(b*x+c)```, ```a*exp(b*x+c)```, ```a*(x*b)+c```), the intial guess for a, b, c is 1.
 
-regressions in your Python scripts or jupyter notebooks or use the **WebApp** with the correspondig button above.
+in your Python scripts or jupyter notebooks and use the best of the numerical and symbolic worlds to make predictions and assess the quality of your fit!
+
+or use the one of the **WebApps** with the correspondig button above (voila app or streamlit app).
 
 For more, have a look at the [**sphinx-documentation**](https://zolabar.github.io/trendPy/)!
 
-The webApp is listed in the [Voila Gallery](https://voila-gallery.org/)!
 
 <img src="figures/screenshot3.PNG"  />