25194239086

Committed 26 Apr 2026 08:07PM UTC coverage: 61.697% (-28.8%) from 90.54%

Build # 25194239086

Build Type

push

github

Committed by

jameswilburlewis

Commit Message

Added test for loading Cluster CODIF differential energy flux

Coverage Stats

0 of 7 new or added lines in 1 file covered. (0.0%)

19460 existing lines in 418 files now uncovered.

30204 of 48955 relevant lines covered (61.7%)

1.44 hits per line

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100.0

/pyspedas/tplot_tools/tplot_math/tcrossp.py

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


def tcrossp(
        v1,
        v2,
        newname=None,
        return_data=False
):
    """
    Calculates the cross product of two tplot variables

    Parameters
    ----------
    v1: str
        First tplot variable
    v2: str
        Second tplot variable
    newname: str, optional
        Name of the output variable
        Default: v1_cross_v2
    return_data: bool
        Returns the data as an ndarray instead of creating a tplot variable
        Default: False

    Returns
    --------
    str
        Name of the tplot variable created

    Examples
    --------

        >>> # Compute the cross product of two time series
        >>> import pyspedas
        >>> x1 = [0, 4, 8]
        >>> x2 = [0, 4, 8]
        >>> time1 = [pyspedas.time_float("2020-01-01") + i for i in x1]
        >>> time2 = [pyspedas.time_float("2020-01-01") + i for i in x2]
        >>> pyspedas.store_data("a", data={"x": time1, "y": [[1, 2, 3],[2, 3, 4],[3, 4, 5]]})
        >>> pyspedas.store_data("c", data={"x": time2, "y": [[1, 4, 1],[2, 5, 2],[3, 5, 3]]})
        >>> n = pyspedas.tcrossp("a", "c", newname="a_cross_c")
        >>> print('new tplot variable:', n)
        >>> ac = pyspedas.get_data(n)
        >>> print(ac)

    """

    v1_data = None
    v2_data = None

    if not isinstance(v1, np.ndarray) and isinstance(v1, str):
        v1_data = get_data(v1)
        v1_name = v1

        if v1_data is not None:
            data1 = v1_data[1]
    else:
        v1_name = 'var1'
        data1 = v1

    if not isinstance(v2, np.ndarray) and isinstance(v2, str):
        v2_data = get_data(v2)
        v2_name = v2

        if v2_data is not None:
            data2 = v2_data[1]
    else:
        v2_name = 'var2'
        data2 = v2

    if newname is None:
        newname = v1_name + '_cross_' + v2_name

    cp = np.cross(data1, data2)

    if return_data:
        return cp
    else:
        out = cp
        if v2_data is None:
            if len(cp.shape) == 1:
                out = np.atleast_2d(cp)
            times = np.zeros(out.shape[0])
        else:
            times = v2_data[0]
        store_data(newname, data={'x': times, 'y': out})
        return newname

1	import numpy as np	3✔
2	from pyspedas.tplot_tools import get_data, store_data	3✔
3
4
5	def tcrossp(	3✔
6	v1,
7	v2,
8	newname=None,
9	return_data=False
10	):
11	"""
12	Calculates the cross product of two tplot variables
13
14	Parameters
15	----------
16	v1: str
17	First tplot variable
18	v2: str
19	Second tplot variable
20	newname: str, optional
21	Name of the output variable
22	Default: v1_cross_v2
23	return_data: bool
24	Returns the data as an ndarray instead of creating a tplot variable
25	Default: False
26
27	Returns
28	--------
29	str
30	Name of the tplot variable created
31
32	Examples
33	--------
34
35	>>> # Compute the cross product of two time series
36	>>> import pyspedas
37	>>> x1 = [0, 4, 8]
38	>>> x2 = [0, 4, 8]
39	>>> time1 = [pyspedas.time_float("2020-01-01") + i for i in x1]
40	>>> time2 = [pyspedas.time_float("2020-01-01") + i for i in x2]
41	>>> pyspedas.store_data("a", data={"x": time1, "y": [[1, 2, 3],[2, 3, 4],[3, 4, 5]]})
42	>>> pyspedas.store_data("c", data={"x": time2, "y": [[1, 4, 1],[2, 5, 2],[3, 5, 3]]})
43	>>> n = pyspedas.tcrossp("a", "c", newname="a_cross_c")
44	>>> print('new tplot variable:', n)
45	>>> ac = pyspedas.get_data(n)
46	>>> print(ac)
47
48	"""
49
50	v1_data = None	3✔
51	v2_data = None	3✔
52
53	if not isinstance(v1, np.ndarray) and isinstance(v1, str):	3✔
54	v1_data = get_data(v1)	3✔
55	v1_name = v1	3✔
56
57	if v1_data is not None:	3✔
58	data1 = v1_data[1]	3✔
59	else:
60	v1_name = 'var1'	3✔
61	data1 = v1	3✔
62
63	if not isinstance(v2, np.ndarray) and isinstance(v2, str):	3✔
64	v2_data = get_data(v2)	3✔
65	v2_name = v2	3✔
66
67	if v2_data is not None:	3✔
68	data2 = v2_data[1]	3✔
69	else:
70	v2_name = 'var2'	3✔
71	data2 = v2	3✔
72
73	if newname is None:	3✔
74	newname = v1_name + '_cross_' + v2_name	3✔
75
76	cp = np.cross(data1, data2)	3✔
77
78	if return_data:	3✔
79	return cp	3✔
80	else:
UNCOV 81	out = cp	1✔
UNCOV 82	if v2_data is None:	1✔
UNCOV 83	if len(cp.shape) == 1:	1✔
UNCOV 84	out = np.atleast_2d(cp)	1✔
UNCOV 85	times = np.zeros(out.shape[0])	1✔
86	else:
UNCOV 87	times = v2_data[0]	1✔
UNCOV 88	store_data(newname, data={'x': times, 'y': out})	1✔
UNCOV 89	return newname	1✔

spedas / pyspedas / 25194239086

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