# Hyperspectral Python: HypPy
## 5 Conclusions  
In this paper, we described the design, implementation, and application of the Python package called Hyperspectral Python, HypPy. The idea of HypPy is to be able to process hyperspectral images using free and open-source software. HypPy was developed using the Python language and relies heavily on the array-processing capabilities of packages like NumPy and SciPy. HypPy can be used for early data analysis and prototyping of processing chains. HypPy was designed with simplicity and practical imaging spectrometry in mind. Simultaneously, it incorporates several innovative and potent concepts.  
HypPy is a package in development and for development, and has proven its merits. Its simplicity and flexible top-level menu structure makes HypPy attractive for use in education and short courses. New methods for research can quickly be programmed and tested. Examples of such research projects are the ESA Mars planetary mapping pilot project and the research project for determining the keystone and smile of hyperspectral line cameras.  
In projects, it has been used to build entire processing chains from hyperspectral data to information. In short, HypPy can be used as a platform for generating and testing new ideas for the processing of hyperspectral data. The core of reading and writing hyperspectral images, the wavelength mapper, hyperspectral gradient filters, spatio-spectral filters, band math and spectral math tools with their own expression language, decision tree tools, the use of zonal statistics, and dedicated image and spectral library viewers are good examples of that.  
We expect HypPy to also be of great added value to other organizations and individuals in research, education, and projects using hyperspectral data.  
Conceptualization, W.B. and F.v.R.; methodology, W.B.; software, W.B.; validation, W.B., F.v.R., H.v.d.W. and C.H.; resources, A.D.; writing--original draft preparation, W.B.; writing--review and editing, H.v.d.W.; visualization, W.B.; supervision, H.v.d.W. and F.v.d.M.; project administration, W.B.; funding acquisition, F.v.d.M. and F.v.R. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the financial support received from the European Space Agency (the Mars planetary mapping project) and [PERSON] of Deep Atlas. The funding provided by these organizations played a crucial role in the successful execution of our research project.  
HypPy can be downloaded from Github and Zenodo, see [42]; Name: HypPy (Hyperspectral Python); License: GPU General Public License Version 3; Contact: [EMAIL_ADDRESS]; Hardware requirements: regular windows, mac, or linux laptop or desktop; Program language: Python version 3; Minimum software requirement: numpy, scipy, pillow, matplotlib, psyptools; Program size: 12.2 MB.  
We are grateful to the University of Twente for furnishing the essential resources and facilities that greatly supported the seamless advancement of our research. Furthermore, we are grateful to all students who directly or indirectly contributed to this work by using HypPy during their studies. Their collaboration and encouragement were instrumental in the shaping of HypPy.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} API & A Programming Interface \\ BBL & Bad Band List \\ BIL & Band Interleaved by Line \\ BSQ & Band Sequential \\ CLI & Command-Line Interface \\ GUI & Graphical User-Interface \\ SWIR & Shortwave Infrared \\ VNIR & Visible and Near-Infrared \\ \end{tabular}