Merge branch 'master' into release-2.1

Python/Makefile

@@ -1,9 +1,12 @@
 # SWIG
 GENERATED  = primme_wrap.cxx primme_wrap.h Primme.py
 
-all: $(GENERATED) primmew.h primmew.cxx Primme.py setup.py
+all: $(GENERATED) primmew.h Primme.py setup.py
 	./setup.py build_ext -i
 
+install: $(GENERATED) primmew.h Primme.py setup.py
+	./setup.py install
+
 # Test target: run the tests
 test: tests.py examples.py
 	python tests.py
@@ -16,4 +19,4 @@ clean:
 veryclean: clean
 	$(RM) $(GENERATED)
 
-.PHONY : all clean veryclean test
+.PHONY : all clean veryclean test install

Python/README.rst

@@ -0,0 +1,95 @@
+
+PRIMME: PReconditioned Iterative MultiMethod Eigensolver
+========================================================
+
+`Primme` is a Python interface to PRIMME_, a C library for computing a few
+eigenvalues and their corresponding eigenvectors of a real symmetric or complex
+Hermitian matrix. It can also compute singular values and vectors of a square
+or rectangular matrix. It can find largest, smallest, or interior
+singular/eigenvalues and can use preconditioning to accelerate convergence. It
+is especially optimized for large, difficult problems, and can be a useful tool
+for both non-experts and experts.
+
+Install
+-------
+
+You can install the latest version with `pip`::
+
+    pip install numpy   # if numpy is not installed yet
+    pip install scipy   # if scipy is not installed yet
+    pip install primme
+
+Optionally for building the development version do::
+
+    git clone https://github.com/primme/primme
+    cd primme
+    make python_install
+
+Usage
+-----
+
+In the following examples it is computed few eigenvalues and eigenvectors from a real symmetric matrix::
+
+    >>> import Primme, scipy.sparse
+    >>> A = scipy.sparse.spdiags(range(100), [0], 100, 100) # sparse diag. matrix
+    >>> evals, evecs = Primme.eigsh(A, 3, tol=1e-6, which='LA')
+    >>> evals # the three largest eigenvalues of A
+    array([ 99.,  98.,  97.])
+
+    >>> new_evals, new_evecs = Primme.eigsh(A, 3, tol=1e-6, which='LA', ortho=evecs)
+    >>> new_evals # the next three largest eigenvalues
+    array([ 96.,  95.,  94.])
+
+In the following examples it is computed few singular values and vectors::
+
+    >>> import Primme, scipy.sparse
+    >>> A = scipy.sparse.spdiags(range(1, 11), [0], 100, 10) # sparse diag. rect. matrix
+    >>> svecs_left, svals, svecs_right = Primme.svds(A, 3, tol=1e-6, which='SM')
+    >>> svals # the three smallest singular values of A
+    array([ 1.,  2.,  3.])
+
+    >>> A = scipy.sparse.rand(10000, 100, random_state=10)
+    >>> prec = scipy.sparse.spdiags(np.reciprocal(A.multiply(A).sum(axis=0)),
+    ...           [0], 100, 100) # square diag. preconditioner
+    >>> svecs_left, svals, svecs_right = Primme.svds(A, 3, which=6.0, tol=1e-6,
+    ...           precAHA=prec)
+    >>> ["%.5f" % x for x in svals.flat] # the three closest singular values of A to 0.5
+    ['5.99871', '5.99057', '6.01065']
+
+Check further examples_ and the documentation of eigsh_ and svds_.
+
+Citing this code 
+----------------
+
+Please cite (bibtex_):
+
+* A. Stathopoulos and J. R. McCombs *PRIMME: PReconditioned Iterative
+  MultiMethod Eigensolver: Methods and software description*, ACM
+  Transaction on Mathematical Software Vol. 37, No. 2, (2010),
+  21:1-21:30.
+
+* L. Wu, E. Romero and A. Stathopoulos, *PRIMME_SVDS: A High-Performance
+  Preconditioned SVD Solver for Accurate Large-Scale Computations*,
+  arXiv:1607.01404
+
+License Information
+-------------------
+
+PRIMME and this interface is licensed under the 3-clause license BSD.
+
+Contact Information 
+-------------------
+
+For reporting bugs or questions about functionality contact `Andreas Stathopoulos`_ by
+email, `andreas` at `cs.wm.edu`. See further information in
+the webpage http://www.cs.wm.edu/~andreas/software.
+
+.. _PRIMME: https://github.com/primme/primme
+.. _`Andreas Stathopoulos`: http://www.cs.wm.edu/~andreas/software
+.. _`github`: https://github.com/primme/primme
+.. _`doc`: http://www.cs.wm.edu/~andreas/software/doc/readme.html
+.. _PETSc : http://www.mcs.anl.gov/petsc/
+.. _`bibtex`: https://raw.githubusercontent.com/primme/primme/master/doc/primme.bib
+.. _eigsh: http://www.cs.wm.edu/~andreas/software/doc/pyeigsh.html
+.. _svds: http://www.cs.wm.edu/~andreas/software/doc/pysvds.html
+.. _examples: https://github.com/primme/primme/blob/master/Python/examples.py