It was from long-ago marked as "XXX move to common place".

For tests this makes sure that if one test fails, it won't make following
tests fail just because the next test will fail trying to lock test database.

For regular code (demo_zbigarray.py) this is also a good thing to do -
to always close the database irregardless of whether an exception was
raised before program reached end of main.

Pygolang becomes regular - not test only - dependency. Being regular
dependency is currently required only by demo_zbigarray.py, but it will
be also used in upcoming wcfs, so adding pygolang into wendelin.core
dependencies aligns with the plan.

dbclose now uses defer almost everywhere - there are still few places in
tests, where one test function is opening/closing test database multiple
times - those were not (yet ?) converted.

Instead of

	raises(Exception, 'code')

do

	with raises(Exception):
		code

This removes lots of warnings, similar to below example:

	bigfile/tests/test_basic.py::test_basic
	  /home/kirr/src/wendelin/wendelin.core/bigfile/tests/test_basic.py:79: PytestDeprecationWarning: raises(..., 'code(as_a_string)') is deprecated, use the context manager form or use `exec()` directly

	  See https://docs.pytest.org/en/latest/deprecations.html#raises-warns-exec
	    raises(ROAttributeError, "f.blksize = 1") # RO attribute

RAMArray is compatible to ZBigArray in API and semantic, but stores its
data in RAM only. It is useful in situations where ZBigArray compatible
data type is needed, but the amount of data is small and the data itself
is needed only temporarily - e.g. in a simulation.

Implementation is based on mmapping temporary files from /dev/shm/... and
passing them as file handles, similarly to how ZBigArray works, to BigArray.
We don't use just numpy.ndarray because of append - for ZBigArray append
works in O(1), but more importantly it does not copy data. This way
mmapings previously created for ZBigArray views, continue to correctly
alias array data. If we would be using ndarray directly, since
ndarray.resize copies data, that property would not be preserved.

Original patch by Klaus Wölfel <klaus@nexedi.com>
(nexedi/wendelin.core!8)

bigarray/tests: Factor out a way to spcify on which BigFile/BigFileH an array is tested into fixture parameter

Currently we have only one BigFile and its BigFileH handle. However in
the next patch, for RAMArray, we'll be adding handles for opened RAM
files, and it would be good to test whole BigArray functionality on
data served by those handles too.

Prepare for this and first factor out into testbig fixture the way to
open such handles.

Rationale
---------

Array reference could be useful in situations where one needs to pass arrays
between processes and instead of copying array data, leverage the fact that
top-level array, for example ZBigArray, is already persisted separately, and
only send small amount of information referencing data in question.

Implementation
--------------

BigArray is not regular NumPy array and so needs explicit support in
ArrayRef code to find root object and indices. This patch adds such
support via the following way:

- when BigArray.__getitem__ creates VMA, it remembers in the VMA
  the top-level BigArray object under which this VMA was created.

- when ArrayRef is finding root, it can detect such VMAs, because it will
  be pointed to by the most top regular ndarray's .base, and in turn gets
  top-level BigArray object from the VMA.

- further all indices computations are performed, similarly to complete regular
  ndarrays case, on ndarrays root and a. But in the end .lo and .hi are
  adjusted for the corresponding offset of where root is inside whole
  BigArray.

- there is no need to adjust .deref() at all.

For remembering information into a VMA and also to be able to get
(readonly) its mapping addresses _bigfile.c extension has to be extended
a bit. Since we are now storing arbitrary python object attached to
PyVMA - it can create cycles - and so PyVMA accordingly adjusted to
support cyclic garbage collector.

Please see the patch itself for more details and comments.

ArrayRef is a tool to find out for a NumPy array its top-level root
parent and remember instructions how to recreate original array from
the root. For example if

	root = arange(1E7)
	z = root[1000:2000]
	a = z[10:20]

`ArrayRef(a)` will find out that the root array for `a` is `root` and
that `a` occupies 1010:1020 bytes in it. The vice versa operation is
also possible, for example given

	aref = ArrayRef(a)

it is possible to restore original `a` from `aref`:

	a_ = aref.deref()
	assert array_equal(a_, a)

the restoration works without copying by creating appropriate view of
root.

ArrayRef should work reliably for arrays of arbitrary dimensions,
strides etc - even fancy arrays created via stride tricks such as arrays
whose elements overlap each other should be supported.

This patch adds ArrayRef with support for regular ndarrays only.

The next patch will add ArrayRef support for BigArray and description
for ArrayRef rationale.

Relicense to GPLv3+ with wide exception for all Free Software / Open Source projects + Business options.

Nexedi stack is licensed under Free Software licenses with various exceptions
that cover three business cases:

- Free Software
- Proprietary Software
- Rebranding

As long as one intends to develop Free Software based on Nexedi stack, no
license cost is involved. Developing proprietary software based on Nexedi stack
may require a proprietary exception license. Rebranding Nexedi stack is
prohibited unless rebranding license is acquired.

Through this licensing approach, Nexedi expects to encourage Free Software
development without restrictions and at the same time create a framework for
proprietary software to contribute to the long term sustainability of the
Nexedi stack.

Please see https://www.nexedi.com/licensing for details, rationale and options.

Remove debug print leftover from added test in e44bd761.

From time to time people keep trying to use wendelin.core with
dtype=object arrays and get segfaults without anything in logs or
whatever else.

Wendelin.core does not support it, because in case of dtype=object elements are
really pointers and data for each object is stored in separate place in RAM
with different per-object size.

As we are memory-mapping arrays this won't work. It also does not
essentially work for numpy.memmap for the same reason:

    (z4+numpy) kirr@mini:~/src/wendelin$ dd if=/dev/zero of=zero.dat bs=128 count=1
    1+0 records in
    1+0 records out
    128 bytes copied, 0.000209873 s, 610 kB/s
    (z4+numpy) kirr@mini:~/src/wendelin$ dd if=/dev/urandom of=random.dat bs=128 count=1
    1+0 records in
    1+0 records out
    128 bytes copied, 0.000225726 s, 567 kB/s
    (z4+numpy) kirr@mini:~/src/wendelin$ ipython
    ...

    In [1]: import numpy as np

    In [2]: np.memmap('zero.dat', dtype=np.object)
    Out[2]:
    memmap([None, None, None, None, None, None, None, None, None, None, None,
           None, None, None, None, None], dtype=object)

    In [3]: np.memmap('random.dat', dtype=np.object)
    Out[3]: Segmentation fault

So let's clarify this to users via explicitly raising exception when
BigArray with non-appropriate dtype is trying to be created with
descriptive explanation also logged.

/reviewed-on nexedi/wendelin.core!4

Commit ab9ca2df (bigarray: Add support for FORTRAN ordering) added
ability to define array order, but there I made a mistake of not caring
about how previously-saved to DB arrays would be read back.

The thing is BigArray gained new data member ._order which is
automatically saved to DB thanks to ZBigArray inheriting from
Persistent; on load-from-db path we just read object state from DB,
which for ZBigArray is dict, and restore object attributes from it.

But for previously-saved data, obviously, there is no 'order' entry and thus
this way restored objects are restored not in full to current code expectations
and it can boom e.g. this way:

    zarray.resize((new_one,old_shape[1]))
  Module wendelin.bigarray, line 190, in resize
    self._init0(new_shape, self.dtype, order=self._order)
  AttributeError: 'ZBigArray' object has no attribute '_order'

Solution to fix is: on restore-from-DB path, see if a data member is not
present on restored object, and if it has default value in BigArray set it to
that.

( code to get function defaults is from
  http://stackoverflow.com/questions/12627118/get-a-function-arguments-default-value )

/cc @Tyagov, @klaus

NotifyChannel was introduced in c7c01ce4 (bigfile/zodb: ZODB.Connection
can migrate between threads on close/open and we have to care) to test
thread interaction specific to ZODB.

We'll however need NotifyChannel to do more threading test of virtmem
core, and this way the proper place for NotifyChannel is test_thread.py
itself.

Move it.

Until now BigArrays could use only C-style ordering - where major index
is the first one.

Fortran ordering is the opposite - where major index is the last one -
and is used in Fortran world and sometimes by scientists in other areas.

As people keep on asking for Fortran-ordered BigArrays, let's add
support for it. The essential code change is to change 0'th index to
major index in __getitem__ and rest of the code.

For the reference: ndarray memory layout for different orders is
described here:

    http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html#internal-memory-layout-of-an-ndarray

When we serve indexing request, we first compute page range in backing
file, which contains the result based on major index range, then mmap
that file range and pick up result from there.

Page range math was however not correct: e.g. for positive strides, last
element's byte is (byte0_stop-1), NOT (byte0_stop - byte0_stride) which
for cases where byte0_stop is just a bit after page boundary, can make a
difference - page_max will be 1 page less what it should be and then
whole ndarray view creation breaks:

    ...
    Module wendelin.bigarray, line 381, in __getitem__
      view0 = ndarray(view0_shape, self._dtype, vma0, view0_offset, view0_stridev)
  ValueError: strides is incompatible with shape of requested array and size of buffer

( because vma0 was created less in size than what is needed to create view0_shape
  shaped array starting from view0_offset in vma0. )

Similar story for negative strides math - it was not correct neither.

Fix it.

/reported-by @Camata

We'll need this class in tests in the next patch.

bigfile/zodb/tests: Make sure _p_invalidate() in Zblk.loadblk() does not lead to reloading data updated

Thanks to ZODB being MVCC this does not happen, but we better test
explicitly.

e.g. on .shape

When there is a conflict (on any object, but on ZBlk in particular) ZODB
machinery calls its ._p_invalidate() twice:

  File ".../wendelin.core/bigfile/tests/test_filezodb.py", line 661, in test_bigfile_filezodb_vs_conflicts
    tm2.commit()    # this should raise ConflictError and stay at 11 state
  File ".../transaction/_manager.py", line 111, in commit
    return self.get().commit()
  File ".../transaction/_transaction.py", line 271, in commit
    self._commitResources()
  File ".../transaction/_transaction.py", line 414, in _commitResources
    self._cleanup(L)
  File ".../transaction/_transaction.py", line 426, in _cleanup
    rm.abort(self)
  File ".../ZODB/Connection.py", line 436, in abort
    self._abort()
  File ".../ZODB/Connection.py", line 479, in _abort
    self._cache.invalidate(oid)
  File ".../wendelin.core/bigfile/file_zodb.py", line 148, in _p_invalidate
    traceback.print_stack()

and

  File ".../wendelin.core/bigfile/tests/test_filezodb.py", line 661, in test_bigfile_filezodb_vs_conflicts
    tm2.commit()    # this should raise ConflictError and stay at 11 state
  File ".../transaction/_manager.py", line 111, in commit
    return self.get().commit()
  File ".../transaction/_transaction.py", line 271, in commit
    self._commitResources()
  File ".../transaction/_transaction.py", line 416, in _commitResources
    self._synchronizers.map(lambda s: s.afterCompletion(self))
  File ".../transaction/weakset.py", line 59, in map
    f(elt)
  File ".../transaction/_transaction.py", line 416, in <lambda>
    self._synchronizers.map(lambda s: s.afterCompletion(self))
  File ".../ZODB/Connection.py", line 831, in _storage_sync
    self._flush_invalidations()
  File ".../ZODB/Connection.py", line 539, in _flush_invalidations
    self._cache.invalidate(invalidated)
  File ".../wendelin.core/bigfile/file_zodb.py", line 148, in _p_invalidate
    traceback.print_stack()

i.e. first invalidation is done by commit cleanup:

    https://github.com/zopefoundation/transaction/blob/1.4.4/transaction/_transaction.py#L414
    https://github.com/zopefoundation/ZODB/blob/3.10/src/ZODB/Connection.py#L479

and then Connection.afterCompletion() flushes invalidation again:

    https://github.com/zopefoundation/transaction/blob/1.4.4/transaction/_transaction.py#L416
    https://github.com/zopefoundation/ZODB/blob/3.10/src/ZODB/Connection.py#L833
    https://github.com/zopefoundation/ZODB/blob/3.10/src/ZODB/Connection.py#L539

If there was no conflict - there will be no ConflictError raised and
thus no Transaction._cleanup() done in its ._commitResources() ->
invalidation called only once. But with ConflictError - it is twice.

Adjust ZBlk._p_invalidate() not to delve into real invalidation more
than once - else we will fail, as ZBlk._v_zfile becomes unbound after
invalidation done the first time.

All is currently handled correctly, but an observation is made that upon
such invalidation we through away ._v_fileh i.e. we through away whole
data cache just because an array was resized.

Continuing theme from the previous patch, here is propagation of
invalidation messages from ZODB to BigFileH memory.

The use-case here is that e.g. one fileh mapping was created in one
connection, another in another, and after doing changes in second
connection and committing there, the first fileh has to invalidate
appropriate already-loaded pages, so its next transaction won't work
with stale data.

To do it, we hook into ZBlk._p_invalidate() and propagate the
invalidation message to ZBigFile which then notifies all
opened-through-it ZBigFileH to invalidate a page.

ZBlk -> ZBigFile lookup is done without storing backpointer in ZODB -
instead, every time ZBigFile touches ZBlk object (and thus potentially
does GHOST -> Live transition to it), we (re-)bind it back to ZBigFile.
Since ZBigFile is the only class that works with ZBlk objects it is safe
to do so.

For ZBigFile to notify "all-opened-through-it" ZBigFileH, a weakset is
introduced to track them.

Otherwise the real page invalidation work is done by virtmem (see
previous patch).

Intro
-----

ZODB maintains pool of opened-to-DB connections. For each request Zope
opens 1 connection and, after request handling is done, returns the
connection back to ZODB pool (via Connection.close()). The same
connection will be opened again for handling some future next request at
some future time. This next open can happen in different-from-first
request worker thread.

TransactionManager  (as accessed by transaction.{get,commit,abort,...})
is thread-local, that is e.g. transaction.get() returns different
transaction for threads T1 and T2.

When _ZBigFileH hooks into txn_manager to get a chance to run its
.beforeCompletion() when transaction.commit() is run, it hooks into
_current_ _thread_ transaction manager.

Without unhooking on connection close, and circumstances where
connection migrates to different thread this can lead to
dissynchronization between ZBigFileH managing fileh pages and Connection
with ZODB objects. And even to data corruption, e.g.

    T1              T2

    open
    zarray[0] = 11
    commit
    close

                    open                # opens connection as closed in T1
    open
                    zarray[0] = 21
    commit
                    abort

    close           close

Here zarray[0]=21 _will_ be committed by T1 as part of T1 transaction -
because when T1 does commit .beforeCompletion() for zarray is invoked,
sees there is dirty data and propagate changes to zodb objects in
connection for T2, joins connection for T2 into txn for T1, and then txn
for t1 when doing two-phase-commit stores modified objects to DB ->
oops.

----------------------------------------

To prevent such dissynchronization _ZBigFileH needs to be a DataManager
which works in sync with the connection it was initially created under -
on connection close, unregister from transaction_manager, and on
connection open, register to transaction manager in current, possibly
different, thread context. Then there won't be incorrect
beforeCompletion() notification and corruption.

This issue, besides possible data corruption, was probably also exposing
itself via following ways we've seen in production (everywhere
connection was migrated from T1 to T2):

1. Exception ZODB.POSException.ConnectionStateError:
        ConnectionStateError('Cannot close a connection joined to a transaction',)
        in <bound method Cleanup.__del__ of <App.ZApplication.Cleanup instance at 0x7f10f4bab050>> ignored

     T1          T2

                 modify zarray
                 commit/abort    # does not join zarray to T2.txn,
                                 # because .beforeCompletion() is
                                 # registered in T1.txn_manager

     commit                      # T1 invokes .beforeCompletion()
     ...                         # beforeCompletion() joins ZBigFileH and zarray._p_jar (=T2.conn) to T1.txn
     ...                         # commit is going on in progress
     ...
     ...         close           # T2 thinks request handling is done and
     ...                         # and closes connection. But T2.conn is
     ...                         # still joined to T1.txn

2. Traceback (most recent call last):
     File ".../wendelin/bigfile/file_zodb.py", line 121, in storeblk
       def storeblk(self, blk, buf):   return self.zself.storeblk(blk, buf)
     File ".../wendelin/bigfile/file_zodb.py", line 220, in storeblk
       zblk._v_blkdata = bytes(buf)    # FIXME does memcpy
     File ".../ZODB/Connection.py", line 857, in setstate
       raise ConnectionStateError(msg)
   ZODB.POSException.ConnectionStateError: Shouldn't load state for 0x1f23a5 when the connection is closed

   Similar to "1", but close in T2 happens sooner, so that when T1 does
   the commit and tries to store object to database, Connection refuses to
   do the store:

     T1          T2

                 modify zarray
                 commit/abort

     commit
     ...         close
     ...
     ...
     . obj.store()
     ...
     ...

3. Traceback (most recent call last):
     File ".../wendelin/bigfile/file_zodb.py", line 121, in storeblk
       def storeblk(self, blk, buf):   return self.zself.storeblk(blk, buf)
     File ".../wendelin/bigfile/file_zodb.py", line 221, in storeblk
       zblk._p_changed = True          # if zblk was already in DB: _p_state -> CHANGED
     File ".../ZODB/Connection.py", line 979, in register
       self._register(obj)
     File ".../ZODB/Connection.py", line 989, in _register
       self.transaction_manager.get().join(self)
     File ".../transaction/_transaction.py", line 220, in join
       Status.ACTIVE, Status.DOOMED, self.status))
   ValueError: expected txn status 'Active' or 'Doomed', but it's 'Committing'

  ( storeblk() does zblk._p_changed -> Connection.register(zblk) ->
    txn.join() but txn is already committing

    IOW storeblk() was invoked with txn.state being already 'Committing' )

    T1          T2

                modify obj      # this way T2.conn joins T2.txn
                modify zarray

    commit                      # T1 invokes .beforeCompletion()
    ...                         # beforeCompletion() joins only _ZBigFileH to T1.txn
    ...                         # (because T2.conn is already marked as joined)
    ...
    ...         commit/abort    # T2 does commit/abort - this touches only T2.conn, not ZBigFileH
    ...                         # in particular T2.conn is now reset to be not joined
    ...
    . tpc_begin                 # actual active commit phase of T1 was somehow delayed a bit
    . tpc_commit                # when changes from RAM propagate to ZODB objects associated
    .  storeblk                 # connection (= T2.conn !) is notified again,
    .   zblk = ...              # wants to join txn for it thinks its transaction_manager,
                                # which when called from under T1 returns *T1* transaction manager for
                                # which T1.txn is already in state='Committing'

4. Empty transaction committed to NEO

   ( different from doing just transaction.commit() without changing
     any data - a connection was joined to txn, but set of modified
     object turned out to be empty )

   This is probably a race in Connection._register when both T1 and T2
   go to it at the same time:

   https://github.com/zopefoundation/ZODB/blob/3.10/src/ZODB/Connection.py#L988

   def _register(self, obj=None):
        if self._needs_to_join:
            self.transaction_manager.get().join(self)
            self._needs_to_join = False

    T1                          T2

                                modify zarray
    commit
    ...
    .beforeCompletion           modify obj
    . if T2.conn.needs_join      if T2.conn.needs_join      # race here
    .   T2.conn.join(T1.txn)       T2.conn.join(T2.txn)     # as a result T2.conn joins both T1.txn and T2.txn
    .
    commit finishes             # T2.conn registered-for-commit object list is now empty

                                commit
                                 tpc_begin
                                  storage.tpc_begin
                                 tpc_commit
                                  # no object stored, because for-commit-list is empty

/cc @jm, @klaus, @Tyagov, @vpelletier

( without dbclose, next test will not be able to open database - will
  timeout on open on waiting for FileStorage lock )

ca064f75 (bigarray: Support resizing in-place) added O(1) in-place
BigArray.resize() which makes possible for users to append data to BigArray in
O(δ) time.

But it is easy for people to make off-by-one mistakes when calculating
indices for append.

So provide a convenient BigArray.append() which simplifies the following

    A                               # ZBigArray e.g. of shape       (N, 3)
    values                          # ndarray to append of shape    (δ, 3)
    n, δ = len(A), len(values)      # length of A's major index  =N
    A.resize((n+δ, A.shape[1:]))    # add δ new entries ; now len(A) =N+δ
    A[-δ:] = values                 # set data for last new δ entries

into

    A.append(values)

/cc @klaus

We stopped using numpy.multiply in 73926487 (*: It is not safe to use
multiply.reduce() - it overflows).

We compare A_[10*PS-1] (which is A_[1]) to 0, but

    A_= ndarray ((10*PS,), uint8)

and that means the array memory is not initialized. So the comparison
works sometimes and sometimes it does not.

Initialize compared element explicitly.

NOTE: A (without _) element does not need to be initialized -
because not-initialized BigArray parts read as zeros.

Previously we were always testing with DBs backed up by FileStorage. Now
we provide a way to run the testsuite with user selected storage
backend:

    $ WENDELIN_CORE_TEST_DB="<fs>"   make test.py     # test with temporary db with FileStorage
    $ WENDELIN_CORE_TEST_DB="<zeo>"  make test.py     # ----------//---------- with ZEO
    $ WENDELIN_CORE_TEST_DB="<neo>"  make test.py     # ----------//---------- with NEO

    $ WENDELIN_CORE_TEST_DB=neo://db@master  make test.py     # test with externally provided DB

Default is still to run tests with FileStorage.

/cc @jm

Factor out those routines to open a ZODB database to common place.

The reason for doing so is that we'll soon teach dbopen to automatically
recognize several protocols, e.g. neo:// and zeo:// and this way,
clients who use dbopen() could automatically access storages besides
FileStorage.

BigArrays can be big - up to 2^64 bytes, and thus in general it is not
possible to represent whole BigArray as ndarray view, because address
space is usually smaller on 64bit architectures.

However users often try to pass BigArrays to numpy functions as-is, and
numpy finds a way to convert, or start converting, BigArray to ndarray -
via detecting it as a sequence, and extracting elements one-by-one.
Which is slooooow.

Because of the above, we provide users a well-defined service:
- if virtual address space is available - we succeed at creating ndarray
  view for whole BigArray, without delay and copying.
- if not - we report properly the error and give hint how BigArrays have
  to be processed in chunks.

Verifying that big BigArrays cannot be converted to ndarray also tests
for behaviour and issues fixed in last 5 patches.

/cc @Tyagov
/cc @klaus

e.g.

    In [1]: multiply.reduce((1<<30, 1<<30, 1<<30))
    Out[1]: 0

instead of

    In [2]: (1<<30) * (1<<30) * (1<<30)
    Out[2]: 1237940039285380274899124224

    In [3]: 1<<90
    Out[3]: 1237940039285380274899124224

also multiply.reduce returns int64, instead of python int:

    In [4]: type( multiply.reduce([1,2,3]) )
    Out[4]: numpy.int64

which also leads to overflow-related problems if we further compute with
this value and other integers and results exceeds int64 - it becomes
float:

    In [5]: idx0_stop = 18446744073709551615

    In [6]: stride0   = numpy.int64(1)

    In [7]: byte0_stop = idx0_stop * stride0

    In [8]: byte0_stop
    Out[8]: 1.8446744073709552e+19

and then it becomes a real problem for BigArray.__getitem__()

    wendelin.core/bigarray/__init__.py:326: RuntimeWarning: overflow encountered in long_scalars
      page0_min  = min(byte0_start, byte0_stop+byte0_stride) // pagesize # TODO -> fileh.pagesize

and then

    >           vma0 = self._fileh.mmap(page0_min, page0_max-page0_min+1)
    E           TypeError: integer argument expected, got float

~~~~

So just avoid multiple.reduce() and do our own mul() properly the same
way sum() is builtin into python, and we avoid overflow-related
problems.

It was hanging with NumPy-1.9 before 425dc5d1 (bigarray: Raise
IndexError for out-of-bound element access), because of the following
correct NumPy commit:

    https://github.com/numpy/numpy/commit/d36f8227

and in particular

    https://github.com/numpy/numpy/commit/d36f8227#diff-6d326badc0872de91e025cbfb0be1aafR522

That PySequence_Fast(obj)    (with obj being BigArray)

creates iterator on top of obj and before our previous IndexError fix in
425dc5d1, this was looping forever.

Test explicitly with both NumPy 1.8 and NumPy 1.9, that this construct
does not hang.

/cc @Tyagov

The way BigArray.__getitem__ works for element access is that for e.g.

    A[i]

it translates the request to

    A[i:i+1]

and remembers to lower the dimensionality at scalar index

    dim_adjust = (0,)

so, in full, A[i] is computed this way:

    A[i] -> A[i:i+1](0,)

( it is done this way to unify code for scalar / slice access in
  __getitem__ - see 0c826d5c "BigArray: An ndarray-like on top of
  BigFile memory mappings" )

The code for slice access also has a shortcut - if it sees that slice
results in empty array (e.g. for out-of-bound slice), we can avoid
spending time to create a file vma mapping only to create empty view on
top of it.

In 0c826d5c, that optimization, however forgot to apply the "lower the
dimensionality" step on top of resulting empty view, and that turned out
for not raising IndexError for out-of-bounds scalar access:

    A = BigArray((10,), uint8)
    In [1]: A[0]
    Out[1]: 0

    In [2]: A[1]
    Out[2]: 0

    In [3]: A[2]
    Out[3]: 0

    In [4]: A[9]
    Out[4]: 0

    In [5]: A[10]
    Out[5]: array([], dtype=uint8)

NOTE that A[10] returns empty array instead of raising IndexError.

So do not forget to apply the "reduce dimensionality" step for empty
views, and this way we get proper IndexError (because for empty view,
scalar access results in IndexError).

NOTE:

this bug was also preventing for e.g.

    list(A)

to work, because list(A) internally works this way:

    l = []
    i = iter(A)
    for _ in i:
        l.append(_)

but iterating would not stop after 10 elements - after array end, _ will
be always array([], dtype=uint8), and thus the loop never finished and
memory usage grow to infinity.

/cc @Tyagov

In NumPy speak advanced indexing is picking up arbitrarily requested
elemtnts, e.g.

    a = arange(10)
    a[[0,3,2]]  -> array([0, 3, 2])

The way this indexing schem works is - it creates a new array with
len = len(key), and picks up requested elements sequentially into new
area.

So it is very not the same as creating _view_ to original array data by
using basic indexing [1]

BigArray does not support advanced indexing, because its main job is to
organize an ndarray _view_ backed up by BigFile data and give that view
to clients, and then it is up to clients how to use that view with full
numpy api available with it.

So be explicit, and reject advanced indexing in __getitem__ right at the
beginning.

[1] http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html

In NumPy, ndarray has .resize() but actually it does a whole array
copy into newly allocated larger segment which makes e.g. appending O(n).

For BigArray, we don't have that internal constraint NumPy has - to
keep the array itself contiguously _stored_ (compare to contiguously
_presented_ in memory). So we can have O(1) resize for big arrays.

NOTE having O(1) resize, here is how O(δ) append can be done:

    A                               # ZBigArray e.g. of shape   (N, 3)
    n = len(A)                      # lengh of A's major index  =N
    A.resize((n+δ, A.shape[1:]))    # add δ new entries ; now len(A) =N+δ
    A[-δ:] = <new-data>             # set data for last new δ entries

/cc @klaus

test_bigarray_indexing_Nd() contains useful class to have a BigFile
connected to ndarray storage. Factor it out so that all tests could use
it.

BigFile_Data.storeblk() is newly introduced and is currently unused, but
will be convenient to have later.

This is like to BigArray, like ZBigFile is to BigFile (4174b84a
"bigfile: BigFile backend to store data in ZODB")

I.e. something like numpy.memmap for numpy.ndarray and OS files. The whole
bigarray cannot 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/Fortran code. Slice size is limited by mapping-size (=
address-space size) limit, i.e. to ~ max 127TB on Linux/amd64.

Changes to bigarray memory are changes to bigfile memory mapping and as such
can be discarded or saved back to bigfile using mapping (= BigFileH) dirty
discard/writeout interface.

For the same reason the whole amount of changes to memory is limited by amount
of physical RAM.