Version 0.1.0

Initial PyPI release.

Author: hbldh

.travis.yml

@@ -15,6 +15,11 @@ matrix:
     - python: "3.5-dev"
     - python: "nightly"
     - python: pypy
+addons:
+  apt:
+    packages:
+    - libatlas-base-dev
+    - liblapack-dev
 branches:
   only:
     - master

HISTORY.rst

@@ -0,0 +1,3 @@
+v0.1.0 (2016-02-09)
+===================
+- Initial release

README.rst

@@ -26,7 +26,7 @@ approximating the shape of the contour::
     from pyefd import elliptic_fourier_descriptors
     coeffs = elliptic_fourier_descriptors(contour, order=10)
    
-The coefficients returned are the `a_n`, `b_n`, `c_n` and `d_n` of
+The coefficients returned are the ``a_n``, ``b_n``, ``c_n`` and ``d_n`` of
 the following Fourier series representation of the shape.
 
 The coefficients returned are by default normalized so that they are 
@@ -46,8 +46,8 @@ To use these as features, one can write a small wrapper function::
         coeffs = elliptic_fourier_descriptors(contour, order=10, normalize=True)
         return coeffs.flatten()[3:]
 
-If the coefficients are normalized, then `coeffs[0, 0] = 1.0`, 
-`coeffs[0, 1] = 0.0` and `coeffs[0, 2] = 0.0`, so they can be disregarded when using
+If the coefficients are normalized, then ``coeffs[0, 0] = 1.0``,
+``coeffs[0, 1] = 0.0`` and ``coeffs[0, 2] = 0.0``, so they can be disregarded when using
 the elliptic Fourier descriptors as features.
 
 See \[1\] for more technical details.

setup.py

@@ -30,7 +30,7 @@
     sys.exit()
 
 
-version = '0.1.0.dev1'
+version = '0.1.0'
 requires = ["numpy>=1.7.0"]
 
 
@@ -52,7 +52,7 @@ def read(f):
         'Operating System :: OS Independent',
         'Intended Audience :: Science/Research',
         'Intended Audience :: Developers',
-        'License :: MIT',
+        'License :: OSI Approved :: MIT License',
         'Topic :: Software Development',
         'Topic :: Scientific/Engineering',
         'Programming Language :: Python :: 2',