diff options
Diffstat (limited to 'numpy/lib/io.py')
| -rw-r--r-- | numpy/lib/io.py | 43 |
1 files changed, 29 insertions, 14 deletions
diff --git a/numpy/lib/io.py b/numpy/lib/io.py index deb86fb61..3d41abeef 100644 --- a/numpy/lib/io.py +++ b/numpy/lib/io.py @@ -79,7 +79,7 @@ class NpzFile(object): else: raise KeyError, "%s is not a file in the archive" % key -def load(file, memmap=False): +def load(file, mmap_mode=None): """ Load a pickled, ``.npy``, or ``.npz`` binary file. @@ -87,10 +87,15 @@ def load(file, memmap=False): ---------- file : file-like object or string The file to read. It must support ``seek()`` and ``read()`` methods. - memmap : bool - If True, then memory-map the ``.npy`` file (or unzip the ``.npz`` file - into a temporary directory and memory-map each component). This has - no effect for a pickled file. + mmap_mode: {None, 'r+', 'r', 'w+', 'c'}, optional + If not None, then memory-map the file, using the given mode + (see `numpy.memmap`). The mode has no effect for pickled or + zipped files. + A memory-mapped array is stored on disk, and not directly loaded + into memory. However, it can be accessed and sliced like any + ndarray. Memory mapping is especially useful for accessing + small fragments of large files without reading the entire file + into memory. Returns ------- @@ -104,18 +109,28 @@ def load(file, memmap=False): Notes ----- - - If file contains pickle data, then whatever is stored in the + - If the file contains pickle data, then whatever is stored in the pickle is returned. - If the file is a ``.npy`` file, then an array is returned. - If the file is a ``.npz`` file, then a dictionary-like object is - returned, containing {filename: array} key-value pairs, one for - every file in the archive. + returned, containing ``{filename: array}`` key-value pairs, one for + each file in the archive. Examples -------- - >>> np.save('/tmp/123', np.array([1, 2, 3]) + Store data to disk, and load it again: + + >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]])) >>> np.load('/tmp/123.npy') - array([1, 2, 3]) + array([[1, 2, 3], + [4, 5, 6]]) + + Mem-map the stored array, and then access the second row + directly from disk: + + >>> X = np.load('/tmp/123.npy', mmap_mode='r') + >>> X[1, :] + memmap([4, 5, 6]) """ if isinstance(file, basestring): @@ -123,9 +138,6 @@ def load(file, memmap=False): else: fid = file - if memmap: - raise NotImplementedError - # Code to distinguish from NumPy binary files and pickles. _ZIP_PREFIX = 'PK\x03\x04' N = len(format.MAGIC_PREFIX) @@ -134,7 +146,10 @@ def load(file, memmap=False): if magic.startswith(_ZIP_PREFIX): # zip-file (assume .npz) return NpzFile(fid) elif magic == format.MAGIC_PREFIX: # .npy file - return format.read_array(fid) + if mmap_mode: + return format.open_memmap(file, mode=mmap_mode) + else: + return format.read_array(fid) else: # Try a pickle try: return _cload(fid) |