readme: Initial draft

This is a general readme about what wendelin.core is and how to use
ZBigArray from user point of view.

README.rst

+==========================================
+ Wendelin.core - Out-of-core NumPy arrays
+==========================================
+
+Wendelin.core allows you to work with arrays bigger than RAM and local disk.
+Bigarrays are persisted to storage, and can be changed in transactional manner.
+
+In other words bigarrays are something like `numpy.memmap`_ for numpy.ndarray
+and OS files, but support transactions and files bigger than disk. The whole
+bigarray cannot generally be used as a drop-in replacement for numpy arrays,
+but bigarray *slices* are real ndarrays and can be used everywhere ndarray can
+be used, including in C/Cython/Fortran code. Slice size is limited by
+virtual address-space size, which is ~ max 127TB on Linux/amd64.
+
+The main class to work with is `ZBigArray` and is used like `ndarray` from
+`NumPy`_:
+
+1. create array::
+
+    from wendelin.bigarray.array_zodb import ZBigArray
+    import transaction
+
+    # root is connected to opened database
+    root['A'] = A = ZBigArray(shape=..., dtype=...)
+    transaction.commit()
+
+2. view array as a real ndarray::
+
+    a = A[:]        # view which covers all array, if it fits into address-space
+    b = A[10:100]
+
+   data for views will be loaded lazily on memory access.
+
+3. work with views, including using C/Cython/Fortran functions from NumPy
+   and other libraries to read/modify data::
+
+    a[2] = 1
+    a[10:20] = numpy.arange(10)
+    numpy.mean(a)
+
+   | the amount of modifications in one transaction should be less than available RAM.
+   | the amount of data read is limited only by virtual address-space size.
+
+4. data can be appended to array in O(δ) time::
+
+    values                  # ndarray to append of shape  (δ,)
+    A.append(values)
+
+   and array itself can be resized in O(1) time::
+
+    A.resize(newshape)
+
+5. changes to array data can be either discarded or saved back to DB::
+
+    transaction.abort()     # discard all made changes
+    transaction.commit()    # atomically save all changes
+
+
+
+When using NEO_ or ZEO_ as a database, bigarrays can be simultaneously used by
+several nodes in a cluster.
+
+
+Please see `demo/demo_zbigarray.py`__ for a complete example.
+
+
+.. _NumPy:          http://www.numpy.org/
+.. _numpy.memmap:   http://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html
+.. _NEO:            http://www.neoppod.org/
+.. _ZEO:            https://pypi.python.org/pypi/ZEO
+
+.. relative links are made so they show ok on project main page
+__ wendelin.core/blob/master/demo/demo_zbigarray.py