added notebook + cleanup

README.md

@@ -12,19 +12,30 @@ HexagDLy provides convolution and pooling methods for hexagonally sampled data w
 
 ## Getting Started
 
-HexagDLy requires a working installation of [PyTorch](https://github.com/pytorch/pytorch). Please visit the PyTorch website http://pytorch.org/ or github page https://github.com/pytorch/pytorch and follow the installation instructions.
-If you have downloaded HexagDLy, just add the directory `hexagdly/src` to your system's `$PYTHONPATH`, e.g. by adding the following line to your `.bashrc` or `.bash_profile` (or wherever your paths can be set)
+There are different ways to get HexagDLy up and running on your system as shown below. Basic examples for the application of HexagDLy are given as [notebooks](notebooks). Additionally unit tests are provided in [tests](tests).
+
+### Pip Installation
+
+The suggested way to install HexagDLy is to set up a clean virtual python environment (e.g. with conda, see [https://www.anaconda.com/](https://www.anaconda.com/)) and use the provided pip installer. To install basic functionalities only use:
 
 ```
-export PYTHONPATH='/path/to/hexagdly/src':$PYTHONPATH
+pip install hexagdly
 ```
 
-Basic examples for the application of HexagDLy are given as [notebooks](notebooks). Additionally unit tests are provided in [tests](tests). To run the notebooks and tests, additional packages are required as listed in [hexagdly_conda_prereqs.txt](hexagdly_conda_prereqs.txt).
+To get all necessary dependencies to run the provided [unit tests](tests) and [notebooks](notebooks), add the ```dev``` option:
 
-To set up a new conda environment (see [https://www.anaconda.com/](https://www.anaconda.com/)) on a Linux system, in which all provided code should be working, execute
+```
+pip install hexagdly[dev]
+```
+
+
+### Manual Installation
+
+HexagDLy requires a working installation of [PyTorch](https://github.com/pytorch/pytorch). Please visit the PyTorch website http://pytorch.org/ or github page https://github.com/pytorch/pytorch and follow the installation instructions.
+If you have downloaded HexagDLy, just add the directory `hexagdly/src` to your system's `$PYTHONPATH`, e.g. by adding the following line to your `.bashrc` or `.bash_profile` (or wherever your paths can be set)
 
 ```
-conda create --name hexagdly --file /path/to/hexagdly/hexagdly_conda_prereqs.txt
+export PYTHONPATH='/path/to/hexagdly/src':$PYTHONPATH
 ```
 
 ## How to use HexagDLy

notebooks/addressing_utils.py

@@ -2,7 +2,6 @@
 import matplotlib.colors as mcolors
 from matplotlib.patches import RegularPolygon
 from matplotlib.collections import PatchCollection
-import seaborn as sns
 
 class Detector:
     def __init__(self):
@@ -68,7 +67,6 @@ def plot(ax,x,y,grid=False,hexagons=False,lines=False):
         collection.set_array(np.array(colors))
         ax.add_collection(collection)
     ax.scatter(x, y, color='k')
-    cmap = sns.cubehelix_palette(len(x), rot=-1, gamma=1, dark=.1, light=.7, hue=1, as_cmap=True)
     c = mcolors.ColorConverter().to_rgb
     rvb = make_colormap([c('red'), c('violet'), 0.33, c('violet'), c('blue'), 0.66, c('blue')])
     ax.scatter(x, y, c=x, cmap=rvb)