README and setup.py update

.gitignore

@@ -99,3 +99,6 @@ ENV/
 
 # mypy
 .mypy_cache/
+
+# PyCharm
+.idea

README.rst

@@ -1,18 +1,78 @@
 Scikit-Gstat
 ============
 
-description
+This module offers at the current state a scipy-styled `Variogram` class for performing geostatistical analysis.
+This class is can be used to derive variograms. Key benefits are a number of semivariance estimators and theoretical
+variogram functions. The module is planned to be hold in the manner of scikit modules and be based upon `numpy` and
+`scipy` whenever possible. There is also a distance matrix extension available, with a function for calculating
+n.dimensional distance matrices for the variogram.
+The estimators include:
+
+- matheron
+- cressie
+- dowd
+- genton (still buggy)
+- entropy (not tested)
+
+The models include:
+
+- sperical
+- exponential
+- gaussian
+- cubic
+- stable
+- matérn
+
+with all of them in a nugget and no-nugget variation. All the estimator functions are written `numba` compatible,
+therefore you can just download it and include the `@jit` decorator. This can speed up the calculation for bigger
+data sets up to 100x. Nevertheless, this is not included in this sckit-gstat version as these functions might be
+re-implemented using Cython. This is still under evaluation.
+
+At the current stage, the package does not inlcude any kriging. This is planned for a future release.
 
 
 Installation
 ~~~~~~~~~~~~
 
 You can either install scikit-gstat using pip or you download the latest version from github.
 
+PyPI:
+
 .. code-block:: bash
 
   pip install scikit-gstat
 
+GIT:
+
+.. code-block:: bash
+
+  git clone https://github.com/mmaelicke/scikit-gstat.git
+  cd scikit-gstat
+  pip install -r requirements.txt
+  pip install -e .
+
 Usage
 ~~~~~
 
+The `Variogram` class needs at least a list of coordiantes and values. All other attributes are set by default.
+You can easily set up an example by generating some random data:
+
+.. code-block:: python
+
+  import numpy as np
+  import skgstat as skg
+
+  coordinates = np.random.gamma(0.7, 2, (30,2))
+  values = np.random.gamma(2, 2, 30)
+
+  V = skg.Variogram(coordinates=coordinates, values=values)
+  print(V)
+
+.. code-block:: bash
+
+  spherical Variogram
+  -------------------
+  Estimator:    matheron
+  Range:        1.64
+  Sill:         5.35
+  Nugget:       0.00

VERSION

@@ -1 +1 @@
-0.1
+0.1.1

setup.py

@@ -23,6 +23,7 @@ def classifiers():
 
 setup(name='scikit-gstat',
       license=license(),
+      version=version(),
       author='Mirko Maelicke',
       author_email='mirko.maelicke@kit.edu',
       description='Geostatistical expansion in the scipy style',