25391193746

Committed 05 May 2026 05:15PM UTC coverage: 76.027% (+19.6%) from 56.446%

Build # 25391193746

Build Type

push

github

Committed by

jameswilburlewis

Commit Message

Restore Cluster tests -- CSA seems to be back online

Coverage Stats

37244 of 48988 relevant lines covered (76.03%)

2.41 hits per line

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100.0

/pyspedas/tplot_tools/tplot_math/tnormalize.py

import numpy as np
from pyspedas.tplot_tools import get_data, store_data


def tnormalize(variable, newname=None, return_data=False):
    """
    Normalize all the vectors stored in a tplot variable

    Parameters
    ----------
        variable: str or np.ndarray
            tplot variable (or numpy array) containing the vectors to be normalized

        newname: str, optional
            name of the output variable; If not set, output name will be variable + '_normalize'
            Default: None

        return_data: bool, optional
            If true, return the normalized vectors instead of creating a tplot variable
            Default: False
    
    Returns
    -------
        str or ndarray of float
            name of the tplot variable created or normalized vectors if return_data is set
    
    Examples
    ---------
        >>> import pyspedas
        >>> pyspedas.store_data('a', data={'x':[1, 2, 3], 'y':[[1,2,3],[4,5,6],[7,8,9]]})
        >>> pyspedas.tnormalize('a')


    """
    metadata_in = {}
    if isinstance(variable, str):
        data_in = get_data(variable)
        metadata_in = get_data(variable, metadata=True)
        data = data_in[1]
        times = data_in[0]
    else:
        data = np.atleast_2d(variable)
        times = np.zeros(data.shape[0])

    n = np.sqrt(np.nansum(data**2, axis=1))

    # to do element-wise division, the magnitude needs to be repeated for each component
    norm_reshaped = np.reshape(n, [len(times), 1])
    norm_mag = np.repeat(norm_reshaped, len(data[0, :]), axis=1)

    data_norm = data/norm_mag

    if return_data:
        return data_norm
    else:
        if newname is None:
            newname = variable + '_normalized'
        store_data(newname, data={'x': times, 'y': data_norm}, attr_dict=metadata_in)
        return newname

1	import numpy as np	4✔
2	from pyspedas.tplot_tools import get_data, store_data	4✔
3
4
5	def tnormalize(variable, newname=None, return_data=False):	4✔
6	"""
7	Normalize all the vectors stored in a tplot variable
8
9	Parameters
10	----------
11	variable: str or np.ndarray
12	tplot variable (or numpy array) containing the vectors to be normalized
13
14	newname: str, optional
15	name of the output variable; If not set, output name will be variable + '_normalize'
16	Default: None
17
18	return_data: bool, optional
19	If true, return the normalized vectors instead of creating a tplot variable
20	Default: False
21
22	Returns
23	-------
24	str or ndarray of float
25	name of the tplot variable created or normalized vectors if return_data is set
26
27	Examples
28	---------
29	>>> import pyspedas
30	>>> pyspedas.store_data('a', data={'x':[1, 2, 3], 'y':[[1,2,3],[4,5,6],[7,8,9]]})
31	>>> pyspedas.tnormalize('a')
32
33
34	"""
35	metadata_in = {}	4✔
36	if isinstance(variable, str):	4✔
37	data_in = get_data(variable)	4✔
38	metadata_in = get_data(variable, metadata=True)	4✔
39	data = data_in[1]	4✔
40	times = data_in[0]	4✔
41	else:
42	data = np.atleast_2d(variable)	4✔
43	times = np.zeros(data.shape[0])	4✔
44
45	n = np.sqrt(np.nansum(data**2, axis=1))	4✔
46
47	# to do element-wise division, the magnitude needs to be repeated for each component
48	norm_reshaped = np.reshape(n, [len(times), 1])	4✔
49	norm_mag = np.repeat(norm_reshaped, len(data[0, :]), axis=1)	4✔
50
51	data_norm = data/norm_mag	4✔
52
53	if return_data:	4✔
54	return data_norm	4✔
55	else:
56	if newname is None:	4✔
57	newname = variable + '_normalized'	3✔
58	store_data(newname, data={'x': times, 'y': data_norm}, attr_dict=metadata_in)	4✔
59	return newname	4✔

spedas / pyspedas / 25391193746

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