Update docs!

Readme.md

 ## Generating DL images based on KM3NeT neutrino simulation data 
 
-OrcaSong is a project that produces 2D/3D/4D histograms ('images') for deep neural networks based on raw MC h5 files.
-Currently, only ORCA detector simulations are supported, but ARCA geometries can be easily implemented as well.
+The WIP documentation for OrcaSong can be found at http://ml.pages.km3net.de/OrcaSong!
 
-The main code for generating the images is located in orcanet/h5_data_to_h5_input.py. <br>
-If the simulated .h5 files are not calibrated yet, you need to specify the directory of a .detx file in 'h5_data_to_h5_input.py'.
+OrcaSong is a part of the Deep Learning efforts of the neutrino telescope KM3NeT.
+Find more information about KM3NeT on http://www.km3net.org.
 
-Currently, a bin size of 11x13x18x60 (x/y/z/t) is used for the final ORCA detector layout. 
+In this context, OrcaSong is a project that produces KM3NeT event images based on the raw detector data.
+This means that OrcaSong takes a datafile with (neutrino-) events and based on this data, it produces 2D/3D/4D 'images' (histograms).
+Currently, only simulations with a hdf5 data format are supported as an input.
+These event 'images' are required for some Deep Learning machine learning algorithms, e.g. Convolutional Neural Networks.

docs/conf.py

@@ -57,8 +57,10 @@ templates_path = ['_templates']
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 #
-# source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+#source_parsers = {
+#   '.md': 'recommonmark.parser.CommonMarkParser',}
+#source_suffix = ['.rst', '.md']
+source_suffix = ['.rst']
 
 # The master toctree document.
 master_doc = 'index'
@@ -86,6 +88,10 @@ pygments_style = None
 #
 html_theme = 'sphinx_rtd_theme'
 
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+html_logo = "_static/orcasong_wide_transparent_white.png"
+
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.

docs/index.rst

@@ -2,9 +2,25 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to OrcaSong's documentation!
+Welcome to the documentation of OrcaSong!
 ===================================
 
+OrcaSong is a part of the Deep Learning efforts of the neutrino telescope KM3NeT.
+Find more information about KM3NeT on http://www.km3net.org.
+
+In this context, OrcaSong is a project that produces KM3NeT event images based on the raw detector data.
+This means that OrcaSong takes a datafile with (neutrino-) events and based on this data, it produces 2D/3D/4D 'images' (histograms).
+Currently, only simulations with a hdf5 data format are supported as an input.
+These event 'images' are required for some Deep Learning machine learning algorithms, e.g. Convolutional Neural Networks.
+
+As of now, only ORCA detector simulations are supported, but ARCA geometries can be easily implemented as well.
+
+The main code for generating the images is located in orcanet/data_to_images.py.
+If the simulated hdf5 files are not calibrated yet, you need to specify the directory of a .detx file in 'data_to_images.py'.
+
+This documentation is currently WIP, and as of now, it only offers an (extensive) API documentation.
+Please feel free to contact me or just open an issue on Gitlab / Github if you have any suggestions.
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents: