Update the README.md file

is given. Most operating systems implement strong file caching so

using a file as a data backend won't usually affect performance.

The `shape` and `dtype` arguments are the same as the numpy function

`numpy.zeros()` and the returned array is indeed initialized to zero.

The `shape` and `dtype` arguments are identical to those of the numpy

function `numpy.zeros()`, and the returned array is indeed initialized

to zeros.

The content of the array lives in shared memory and/or in a file and

won't be lost when the numpy array is deleted, nor when the python

interpreter exits. To delete a shared array reclaim system resources

use the `SharedArray.delete()` function.

interpreter exits. To delete a shared array and reclaim system

resources use the `SharedArray.delete()` function.

### SharedArray.attach(name)

This function returns a list of previously created arrays stored as

POSIX SHM objects, along with their name, data type and dimensions.

At the moment this function only works on Linux because it accesses

files exposed under `/dev/shm`.  There doesn't seem to be a portable

method of doing that.

This function only works on Linux because it directly accesses files

exposed under `/dev/shm`. There doesn't seem to be a portable method

of achieving this.

## Constants

### SharedArray.MS_ASYNC

Flag for the `msync()` method of the base object of the returned numpy

array (see below). Specifies that an update be scheduled, but the call

returns immediately.

### SharedArray.MS_SYNC

Flag for the `msync()` method of the base object of the returned numpy

array (see below). Requests an update and waits for it to complete.

### SharedArray.MS_INVALIDATE

### SharedArray.msync(array, flags)

Flag for the `msync()` method of the base object of the returned numpy

array (see below). Asks to invalidate other mappings of the same file

(so that they can be updated with the fresh values just written).

This function is a wrapper around `msync(2)` and is only useful when

## Base object

SharedArray registers its own python object as the

[base](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.base.html)

object of the returned numpy array. This base object exposes the

following methods and attributes:

### msync(array, flags)

This method is a wrapper around `msync(2)` and is only useful when

using file-backed arrays (i.e. not POSIX shared memory). msync(2)

flushes the mapped memory region back to the filesystem. The `flags`

are exported as constants in the module definition (see below) and are

are exported as constants in the module definition (see above) and are

a 1:1 map of the `msync(2)` flags, please refer to the manual page of

`msync(2)` for details.

### SharedArray.mlock(array)

### mlock(array)

This function is a wrapper around `mlock(2)`: lock the memory map into

This method is a wrapper around `mlock(2)`: lock the memory map into

RAM, preventing that memory from being paged to the swap area.

### SharedArray.munlock(array)

### munlock(array)

This function is a wrapper around `munlock(2)`: unlock the memory map,

This method is a wrapper around `munlock(2)`: unlock the memory map,

allowing that memory to be paged to the swap area.

## Constants

### SharedArray.MS_ASYNC

Flag for `SharedArray.msync()`. Specifies that an update be scheduled,

but the call returns immediately.

### SharedArray.MS_SYNC

Flag for `SharedArray.msync()`. Requests an update and waits for it to

complete.

### SharedArray.MS_INVALIDATE

### name

Flag for `SharedArray.msync()`. Asks to invalidate other mappings of

the same file (so that they can be updated with the fresh values just

written).

This constant string is the name of the array as passed to

`SharedArray.create()` or `SharedArray.attach()`. It may be passed to

`SharedArray.delete()`.

## Requirements

SharedArray uses the posix shm interface (`shm_open` and `shm_unlink`)

and so should work on most POSIX operating systems (Linux, BSD,

etc.).

etc.). It has been reported to work on macOS, and it is unlikely to

work on Windows.

## Installation

	"ArrayDesc",

	"Description of an attachable numpy shared array",

	fields,

	3,

	sizeof (fields) / sizeof (*fields) - 1,

};

/*