numpy.fromfile — NumPy v1.11 Manual (original) (raw)
numpy.fromfile(file, dtype=float, count=-1, sep='')¶
Construct an array from data in a text or binary file.
A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the_tofile_ method can be read using this function.
| Parameters: | file : file or str Open file object or filename. dtype : data-type Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file. count : int Number of items to read. -1 means all items (i.e., the complete file). sep : str Separator between items if file is a text file. Empty (“”) separator means the file should be treated as binary. Spaces (” ”) in the separator match zero or more whitespace characters. A separator consisting only of spaces must match at least one whitespace. |
|---|
Notes
Do not rely on the combination of tofile and fromfile for data storage, as the binary files generated are are not platform independent. In particular, no byte-order or data-type information is saved. Data can be stored in the platform independent .npy format using save and load instead.
Examples
Construct an ndarray:
dt = np.dtype([('time', [('min', int), ('sec', int)]), ... ('temp', float)]) x = np.zeros((1,), dtype=dt) x['time']['min'] = 10; x['temp'] = 98.25 x array([((10, 0), 98.25)], dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
Save the raw data to disk:
import os fname = os.tmpnam() x.tofile(fname)
Read the raw data from disk:
np.fromfile(fname, dtype=dt) array([((10, 0), 98.25)], dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
The recommended way to store and load data:
np.save(fname, x) np.load(fname + '.npy') array([((10, 0), 98.25)], dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])