"""
This is the **shenfun** package

What is **shenfun**?
================================

Shenfun is a toolbox for automating the spectral Galerkin method.  The user
interface to shenfun is very similar to FEniCS (fenicsproject.org), but works
only for tensor product grids and the spectral Galerkin method. The code is
parallelized with MPI through the
[*mpi4py-fft*](https://bitbucket.org/mpi4py/mpi4py-fft) package.

"""
#pylint: disable=wildcard-import,no-name-in-module

__version__ = '1.2.0'
__author__ = 'Mikael Mortensen'

import numpy as np
from . import chebyshev
from . import legendre
from . import fourier
from . import matrixbase
from .fourier import energy_fourier
from .io import *
from .matrixbase import *
from .forms import *
from .tensorproductspace import *
from .utilities import *
from .utilities.lagrangian_particles import *
from .utilities.integrators import *
from .optimization import Cheb, la, Matvec, convolve, evaluate

@@ -89,43 +89,113 @@
 ==%0A%0A
+%60%60
 Shenfun
+%60%60
  is a 
-toolbox for automating
+high performance computing platform for solving partial%0Adifferential equations (PDEs) by
  the
@@ -220,17 +220,16 @@
  method.
- 
  The use
@@ -273,17 +273,26 @@
 r to
- 
+%0A%60
 FEniCS 
-(
+%3Chttps://
 feni
@@ -308,194 +308,816 @@
 .org
-)
+%3E%60_
 , but 
-works%0Aonly for tensor product grids and the spectral Galerkin method. The code is%0Aparallelized with MPI through the%0A%5B*mpi4py-fft*%5D(https://bitbucket.org/mpi4py/mpi4py-fft) package
+applications are limited to%0Amultidimensional tensor product grids. The code is parallelized with MPI%0Athrough the %60mpi4py-fft %3Chttps://bitbucket.org/mpi4py/mpi4py-fft%3E%60_%0Apackage.%0A%0A%60%60Shenfun%60%60 enables fast development of efficient and accurate PDE solvers%0A(spectral order and accuracy), in the comfortable high-level Python language.%0AThe spectral accuracy is ensured from using high-order *global* orthogonal%0Abasis functions (Fourier, Legendre and Chebyshev), as opposed to finite element%0Acodes like %60FEniCS %3Chttps://fenicsproject.org%3E%60_ that are using low-order%0A*local* basis functions. Efficiency is ensured through vectorization%0A(%60Numpy %3Chttps://www.numpy.org/%3E%60_), parallelization%0A(%60mpi4py %3Chttps://bitbucket.org/mpi4py/mpi4py%3E%60_) and by moving critical%0Aroutines to %60Cython %3Chttps://cython.org/%3E%60_
 .%0A%0A%22
@@ -1194,9 +1194,9 @@
 1.2.
-0
+1
 '%0A__

bump version number for joss release