ArrayServer

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Serves Numpy Arrays Persistently in Memory Mapped Files

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ArrayServer
===========

**Serves Numpy Arrays Persistently from Memory Mapped Files**

Ever wanted to store large numpy arrays persistently, but using something faster than disk? Well here you go.  The server process holds a handle to a memory mapped file containing a binary representation of a numpy array.  When the client requests the array, the server returns the filename and the client can access the memory mapped array without having to read from disk.

Obligatory
----------

This is *not* production quality code.  It is a one-hour hack.  I do think it's not too far off from being useful for a lot of people.  Pull-requests welcome!

Starting the server
-------------------

```bash
$ python -m arrayserver
```

Storing a large array
---------------------

```python
>>> from arrayserver import ArrayClient
>>> import numpy as np
>>> c = ArrayClient()
>>> myarray = np.random.random(500000)
>>> myarray.sum()
249876.10611505382
>>> c['myarray'] = myarray
>>> quit() # the array is still in memory in the server process
```

Accessing the array later (look ma, no disk!)
---------------------------------------------

```python
>>> from arrayserver import ArrayClient

>>> c = ArrayClient()
>>> myarray = c['myarray']
>>> myarray.shape
(500000,)
>>> myarray.sum()
memmap(249876.10611505382)
```

Deleting the array
------------------

```python
>>> from arrayserver import ArrayClient

>>> c = ArrayClient()
>>> del c['myarray']
```

TODO
----

* create a package
* oh god how do I test this?
* benchmarks
* maybe persist mmapped files if server crashes?
* implement ``keys()`` method
* see how this plays with Pandas Series/DataFrame

ArrayServer

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