Update README

README.md

@@ -11,75 +11,50 @@ in Python. It is able to do TT-interpolation, solve linear systems, eigenproblem
 Several computational routines are done in Fortran (which can be used separatedly), and are wrapped with the f2py tool.
 Installation
 ============
+##Prerequisites
+
+**It is recommended** that you use [Anaconda Python distribution](https://store.continuum.io/cshop/anaconda/) 
+  which has MKL library built-in. Anaconda Python is used for the development  of ttpy.
 
-##From a binstar repository
-If you use 64 bit linux, you can install the module from the binstar repository:
+## Pip install 
+Install dependencies (numpy and cython)
+```
+   conda install numpy cython
 ```
-sudo apt-get install libgfortran3
-conda install -c https://conda.binstar.org/bihaqo ttpy
+Install ttpy
 ```
-##Downloading the code
-This installation works with git submodules, so you should be sure, that you got them all right.
-The best way is to work with this repository is to use git with a version >= 1.6.5.
-Then, to clone the repository, you can simply run
+   pip install ttpy
 ```
-git clone --recursive git://github.com/oseledets/ttpy.git
 
+##Installing from source code
+
+To install the development version, you need to install from the source.
+First, clone the repository with all submodules:
 ```
-To update to the latest version, run
+   git clone --recursive git://github.com/oseledets/ttpy.git
 ```
-git pull
-git submodule update --init --recursive *
 ```
-This command will update the submodules as well.
-##Prerequisites
-
-**It is highly recommended** that you use either
-
-- [Anaconda Python distribution](https://store.continuum.io/cshop/anaconda/) 
-  which has MKL in built in for the [academics](https://store.continuum.io/cshop/academicanaconda)
-  Anaconda Python is the version for which the development of ttpy is done
-
-- [Enthought Python distribution](https://www.enthought.com/products/epd/) -- should work as well with 
-  the non-free version, but not tested
-
-
-
-##Installing the package
-The installation of the package is done via **setup.py** scripts.
-
-The installation script is a very newbie one, but it seems to work.
+   python setup.py install
 ```
-python setup.py install
+To update to the latest version (with all submodules) run
+```
+git pull
+git submodule update --init --recursive *
 ```
-
-## BLAS
-
-Almost all of the packages depend on the BLAS/LAPACK libraries, but the code 
-is **not explicitly linked** against them, so if you do not have the BLAS/LAPACK
-in the global namespace of your Python interpreter, you will encounter "undefined symbols"
-error during the import of the **tt** package. If you have Anaconda or EPD with MKL installed, it should
-work ``as it is''. The  [initialization file of **ttpy**](/tt/__init__.py) tries to dynamically load runtime MKL or lapack libraries 
-(should work on Linux if those libraries are in LD_LIBRARY_PATH).
 
 What those packages do
 ======================
 
 They have the following functionality
 
-- **tt** : The main package, with tt.vector and tt.matrix classes, basic arithmetic,
+- `tt` : The main package, with tt.vector and tt.matrix classes, basic arithmetic,
        norms, scalar products, rounding full -> tt and tt -> full conversion routines, and many others
-
-- **tt.amen** : AMEN solver for linear systems (Python wrapper for Fortran code written by S. V. Dolgov and D. V. Savostyanov) 
+- `tt.amen` : AMEN solver for linear systems (Python wrapper for Fortran code written by S. V. Dolgov and D. V. Savostyanov) 
                 it can be also used for fast matrix-by-vector products. 
-
-- **tt.eigb** : Block eigenvalue solver in the TT-format 
-            (subroutine **tt.eigb.eigb**) 
-
-- **tt.ksl** :  Solution of the linear dynamic problems in the TT-format, using the projector-splitting 
+- `tt.eigb` : Block eigenvalue solver in the TT-format 
+- `tt.ksl` :  Solution of the linear dynamic problems in the TT-format, using the projector-splitting 
                 KSL scheme. A Python wrapper for a Fortran code (I. V. Oseledets)
-
-- **tt.cross** : Has a working implementation of the black-box cross method. For now, please use the rect_cross function.
+- `tt.cross` : Has a working implementation of the black-box cross method. 
 
 
 Documentation and examples

conda-recipe/meta.yaml

@@ -3,10 +3,13 @@ package:
   version: "1.0"
 
 source:
-  fn: ttpy-1.0.tar
-  url: https://dl.dropboxusercontent.com/u/49234889/ttpy-1.0.tar
-  md5: ff081595d853c4c563dba96aa9180f1c
-#  patches:
+    #      fn: ttpy-1.0.tar
+    #url: https://dl.dropboxusercontent.com/u/49234889/ttpy-1.0.tar
+    #md5: ff081595d853c4c563dba96aa9180f1c
+source:
+    git_url: git://github.com/oseledets/ttpy
+    git_branch: master
+   #  patches:
    # List any patch files here
    # - fix.patch
 
@@ -33,13 +36,13 @@ requirements:
     - setuptools
     - numpy
     - cython
-    - accelerate
+      #    - accelerate
 
   run:
     - python
     - numpy
     - cython
-    - accelerate
+      #    - accelerate
 
 test:
   # Python imports

setup.py

@@ -66,6 +66,7 @@ def setup_package():
         download_url='https://github.com/oseledets/ttpy/tarball/v' + VERSION,
         author='Ivan Oseledets',
         maintainer='Ivan Oseledets',
+        author_email='[email protected]',
         platforms=PLATFORMS,
         classifiers=[line for line in CLASSIFIERS.split('\n') if line],
         configuration=configuration,