larray.Session.load

Session.load(fname, names=None, engine='auto', display=False, **kwargs)

Loads array objects from a file, or several .csv files.

WARNING: never load a file using the pickle engine (.pkl or .pickle) from an untrusted source, as it can lead to arbitrary code execution.

Parameters:

fname : str

This can be either the path to a single file, a path to a directory containing .csv files or a pattern representing several .csv files.

names : list of str, optional

List of arrays to load. If fname is None, list of paths to CSV files. Defaults to all valid objects present in the file/directory.

engine : {‘auto’, ‘pandas_csv’, ‘pandas_hdf’, ‘pandas_excel’, ‘xlwings_excel’, ‘pickle’}, optional

Load using engine. Defaults to ‘auto’ (use default engine for the format guessed from the file extension).

display : bool, optional

Whether or not to display which file is being worked on. Defaults to False.

Examples

In one module

>>> arr1, arr2, arr3 = ndtest((2, 2)), ndtest(4), ndtest((3, 2))   
>>> s = Session([('arr1', arr1), ('arr2', arr2), ('arr3', arr3)])  
>>> s.save('input.h5')                                             

In another module

>>> s = Session()                                 
>>> s.load('input.h5', ['arr1', 'arr2', 'arr3'])  
>>> arr1, arr2, arr3 = s['arr1', 'arr2', 'arr3']  
>>> # only if you know the order of arrays stored in session
>>> arr1, arr2, arr3 = s.values()                 

Using .csv files (assuming the same session as above)

>>> s.save('data')                                
>>> s = Session()                                 
>>> # load all .csv files starting with "output" in the data directory
>>> s.load('data')                                
>>> # or equivalently in this case
>>> s.load('data/arr*.csv')