watex.utils.baseutils.array2hdf5(filename, /, arr=None, dataname='data', task='store', as_frame=Ellipsis, columns=None)[source]#

Load or write array to hdf5

Parameters:

  • arr (Arraylike ( m_samples, n_features)) – Data to load or write

  • filename (str,) – Hdf5 disk file name whether to write or to load

  • task (str, {"store", "load", default='store'}) – Action to perform. user can use [‘write’|’store’] interchnageably. Both does the same task.

  • as_frame (bool, default=False) – Concert loaded array to data frame. Columns can be supplied to construct the datafame.

  • columns (List, Optional) – Columns used to construct the dataframe. When its given, it must be consistent with the shape of the arr along axis 1

Returns:

None| data

Return type:

ArrayLike or pd.DataFrame

Examples

>>> import numpy as np
>>> from watex.utils.baseutils import array2hdf5
>>> data = np.random.randn (100, 27 )
>>> array2hdf5 ('test.h5', data   )
>>> load_data = array2hdf5 ( 'test.h5', data, task ='load')
>>> load_data.shape
Out[177]: (100, 27)
watex.utils.baseutils.get_remote_data(rfile, /, savepath=None, raise_exception=True)[source]#

Try to retrieve data from remote.

Parameters:

  • rfile (str or PathLike-object) – Full path to the remote file. It can be the path to the repository root toward the file name. For instance, to retrieve the file 'AGSO.csv' which is located in watex/etc/ directory then the full path should be 'watex/etc/AGSO.csv'

  • savepath (str, optional) – Full path to place where to downloaded files should be located. If None data is saved to the current directory.

  • raise_exception (bool, default=True) – raise exception if connection failed.

Returns:

status –

False for failure and True otherwise i.e. successfully

downloaded.

Return type:

bool,

watex.utils.baseutils.lowertify(*values, strip=True, return_origin=Ellipsis)[source]#

Strip and convert value to lowercase.

Parameters:

value – str , value to convert

Returns:

value in lowercase and original value.

Example:
>>> from watex.utils.baseutils import lowertify
>>> lowertify ( 'KIND')
Out[19]: ('kind',)
>>> lowertify ( "KIND", return_origin =True )
Out[20]: (('kind', 'KIND'),)
>>> lowertify ( "args1", 120 , 'ArG3')
Out[21]: ('args1', '120', 'arg3')
>>> lowertify ( "args1", 120 , 'ArG3', return_origin =True )
Out[22]: (('args1', 'args1'), ('120', 120), ('arg3', 'ArG3'))
>>> (kind, kind0) , ( task, task0 ) = lowertify(
    "KIND", "task ", return_origin =True )
>>> kind, kind0, task, task0
Out[23]: ('kind', 'KIND', 'task', 'task ')
watex.utils.baseutils.request_data(url, /, task='get', data=None, as_json=Ellipsis, as_text=Ellipsis, stream=Ellipsis, raise_status=Ellipsis, save2file=Ellipsis, filename=None, **kws)[source]#

Fetch remotely data

Request data remotely https://docs.python-requests.org/en/latest/user/quickstart/#raw-response-content

r = requests.get(’https://api.github.com/user’, auth=(‘user’, ‘pass’)) r.status_code 200 r.headers[‘content-type’] ‘application/json; charset=utf8’ r.encoding ‘utf-8’ r.text ‘{“type”:”User”…’ r.json() {‘private_gists’: 419, ‘total_private_repos’: 77, …}

watex.utils.baseutils.save_or_load(fname, /, arr=None, task='save', format='.txt', compressed=Ellipsis, comments='#', delimiter=None, **kws)[source]#

Save or load Numpy array.

Parameters:

  • fname (file, str, or pathlib.Path) – File or filename to which the data is saved. - >.npy , .npz: If file is a file-object, then the filename is unchanged. If file is a string or Path, a .npy extension will be appended to the filename if it does not already have one. - >.txt: If the filename ends in .gz, the file is automatically saved in compressed gzip format. loadtxt understands gzipped files transparently.

  • arr (1D or 2D array_like) – Data to be saved to a text, npy or npz file.

  • task (str {"load", "save"}) – Action to perform. “Save” for storing file into the format “.txt”, “npy”, “.npz”. “load” for loading the data from storing files.

  • format (str {".txt", ".npy", ".npz"}) – The kind of format to save and load. Note that when loading the compressed data saved into npz format, it does not return systematically the array rather than np.lib.npyio.NpzFile files. Use either files attributes to get the list of registered files or f attribute dot the data name to get the loaded data set.

  • compressed (bool, default=False) – Compressed the file especially when file format is set to .npz.

  • comments (str or sequence of str or None, default='#') –

    The characters or list of characters used to indicate the start

    of a comment. None implies no comments. For backwards compatibility, byte strings will be decoded as ‘latin1’. This is useful when fname is in txt format.

    delimiter: str, optional

    The character used to separate the values. For backwards compatibility, byte strings will be decoded as ‘latin1’. The default is whitespace.

  • kws (np.save ,np.savetext, np.load , np.loadtxt) – Additional keywords arguments for saving and loading data.

Returns:

None| data

Return type:

ArrayLike

Examples

>>> import numpy as np
>>> from watex.utils.baseutils import save_or_load
>>> data = np.random.randn (2, 7)
>>> # save to txt
>>> save_or_load ( "test.txt" , data)
>>> save_or_load ( "test",  data, format='.npy')
>>> save_or_load ( "test",  data, format='.npz')
>>> save_or_load ( "test_compressed",  data, format='.npz', compressed=True )
>>> # load files
>>> save_or_load ( "test.txt", task ='load')
Out[36]:
array([[ 0.69265852,  0.67829574,  2.09023489, -2.34162127,  0.48689125,
        -0.04790965,  1.36510779],
       [-1.38349568,  0.63050939,  0.81771051,  0.55093818, -0.43066737,
        -0.59276321, -0.80709192]])
>>> save_or_load ( "test.npy", task ='load')
Out[39]: array([-2.34162127,  0.55093818])
>>> save_or_load ( "test.npz", task ='load')
<numpy.lib.npyio.NpzFile at 0x1b0821870a0>
>>> npzo = save_or_load ( "test.npz", task ='load')
>>> npzo.files
Out[44]: ['arr_0']
>>> npzo.f.arr_0
Out[45]:
array([[ 0.69265852,  0.67829574,  2.09023489, -2.34162127,  0.48689125,
        -0.04790965,  1.36510779],
       [-1.38349568,  0.63050939,  0.81771051,  0.55093818, -0.43066737,
        -0.59276321, -0.80709192]])
>>> save_or_load ( "test_compressed.npz", task ='load')
...