23369877357

Committed 21 Mar 2026 02:13AM UTC coverage: 90.512% (+0.02%) from 90.497%

Build # 23369877357

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push

github

Committed by

web-flow

Commit Message

Merge pull request #1342 from spedas/t_1341

Adds support to load USGS Variometer data, adds test cases for loading variometer data.

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13 of 48 new or added lines in 3 files covered. (27.08%)

10491 existing lines in 260 files now uncovered.

44303 of 48947 relevant lines covered (90.51%)

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100.0

/pyspedas/tplot_tools/tplot_math/crop.py

# Copyright 2018 Regents of the University of Colorado. All Rights Reserved.
# Released under the MIT license.
# This software was developed at the University of Colorado's Laboratory for Atmospheric and Space Physics.
# Verify current version before use at: https://github.com/MAVENSDC/Pytplot

import pyspedas
import copy

def crop(tvar1,tvar2, replace=True):
    """
    Crops both tplot variable so that their times are the same.  This is done automatically by other processing routines if times do not match up.

    Parameters
    -----------
        tvar1 : str
            Name of the first tplot variable
        tvar2 : str
            Name of the second tplot variable
        replace : bool, optional
            If true, the data in the original tplot variables are replaced.  Otherwise, new variables are created.

    Returns
    -------
        None


    Examples
    --------

        >>> pyspedas.store_data('a', data={'x':[0,4,8,12,16], 'y':[1,2,3,4,5]})
        >>> pyspedas.store_data('b', data={'x':[2,5,8,11,14,17,20], 'y':[[1,1,1,1,1,1],[2,2,5,4,1,1],[100,100,3,50,1,1],[4,4,8,58,1,1],[5,5,9,21,1,1],[6,6,2,2,1,1],[7,7,1,6,1,1]]})
        >>> pyspedas.crop('a','b')


    """

    # grab time and data arrays
    tv1 = pyspedas.tplot_tools.data_quants[tvar1].copy()
    tv2 = pyspedas.tplot_tools.data_quants[tvar2].copy()
    # find first and last time indices
    t0_1 = tv1.coords['time'][0]
    t0_2 = tv2.coords['time'][0]
    tx_1 = tv1.coords['time'][-1]
    tx_2 = tv2.coords['time'][-1]
    # find cut locations
    cut1 = max([t0_1, t0_2])
    cut2 = min([tx_1, tx_2])
    # trim data
    tv1 = tv1.sel(time=slice(cut1, cut2))
    tv1.attrs = copy.deepcopy(pyspedas.tplot_tools.data_quants[tvar1].attrs)
    tv2 = tv2.sel(time=slice(cut1, cut2))
    tv2.attrs = copy.deepcopy(pyspedas.tplot_tools.data_quants[tvar2].attrs)
    # Replace the variables if specified
    if replace:
        pyspedas.tplot_tools.data_quants[tvar1] = tv1
        pyspedas.tplot_tools.data_quants[tvar1].name = tvar1
        pyspedas.tplot_tools.data_quants[tvar2] = tv2
        pyspedas.tplot_tools.data_quants[tvar2].name = tvar2
        return
    else:
       pyspedas.tplot_tools.data_quants[tvar1 + '_cropped'] = copy.deepcopy(tv1)
       pyspedas.tplot_tools.data_quants[tvar1 + '_cropped'].attrs = copy.deepcopy(tv1.attrs)
       pyspedas.tplot_tools.data_quants[tvar1 + '_cropped'].name = tvar1+ '_cropped'
       pyspedas.tplot_tools.data_quants[tvar2 + '_cropped'] = copy.deepcopy(tv2)
       pyspedas.tplot_tools.data_quants[tvar2 + '_cropped'].attrs = copy.deepcopy(tv2.attrs)
       pyspedas.tplot_tools.data_quants[tvar2 + '_cropped'].name = tvar2 + '_cropped'
    return tvar2 + '_cropped'

1	# Copyright 2018 Regents of the University of Colorado. All Rights Reserved.
2	# Released under the MIT license.
3	# This software was developed at the University of Colorado's Laboratory for Atmospheric and Space Physics.
4	# Verify current version before use at: https://github.com/MAVENSDC/Pytplot
5
6	import pyspedas	2✔
7	import copy	2✔
8
9	def crop(tvar1,tvar2, replace=True):	2✔
10	"""
11	Crops both tplot variable so that their times are the same. This is done automatically by other processing routines if times do not match up.
12
13	Parameters
14	-----------
15	tvar1 : str
16	Name of the first tplot variable
17	tvar2 : str
18	Name of the second tplot variable
19	replace : bool, optional
20	If true, the data in the original tplot variables are replaced. Otherwise, new variables are created.
21
22	Returns
23	-------
24	None
25
26
27	Examples
28	--------
29
30	>>> pyspedas.store_data('a', data={'x':[0,4,8,12,16], 'y':[1,2,3,4,5]})
31	>>> pyspedas.store_data('b', data={'x':[2,5,8,11,14,17,20], 'y':[[1,1,1,1,1,1],[2,2,5,4,1,1],[100,100,3,50,1,1],[4,4,8,58,1,1],[5,5,9,21,1,1],[6,6,2,2,1,1],[7,7,1,6,1,1]]})
32	>>> pyspedas.crop('a','b')
33
34
35	"""
36
37	# grab time and data arrays
UNCOV 38	tv1 = pyspedas.tplot_tools.data_quants[tvar1].copy()	1✔
UNCOV 39	tv2 = pyspedas.tplot_tools.data_quants[tvar2].copy()	1✔
40	# find first and last time indices
UNCOV 41	t0_1 = tv1.coords['time'][0]	1✔
UNCOV 42	t0_2 = tv2.coords['time'][0]	1✔
UNCOV 43	tx_1 = tv1.coords['time'][-1]	1✔
UNCOV 44	tx_2 = tv2.coords['time'][-1]	1✔
45	# find cut locations
UNCOV 46	cut1 = max([t0_1, t0_2])	1✔
UNCOV 47	cut2 = min([tx_1, tx_2])	1✔
48	# trim data
UNCOV 49	tv1 = tv1.sel(time=slice(cut1, cut2))	1✔
UNCOV 50	tv1.attrs = copy.deepcopy(pyspedas.tplot_tools.data_quants[tvar1].attrs)	1✔
UNCOV 51	tv2 = tv2.sel(time=slice(cut1, cut2))	1✔
UNCOV 52	tv2.attrs = copy.deepcopy(pyspedas.tplot_tools.data_quants[tvar2].attrs)	1✔
53	# Replace the variables if specified
UNCOV 54	if replace:	1✔
UNCOV 55	pyspedas.tplot_tools.data_quants[tvar1] = tv1	1✔
UNCOV 56	pyspedas.tplot_tools.data_quants[tvar1].name = tvar1	1✔
UNCOV 57	pyspedas.tplot_tools.data_quants[tvar2] = tv2	1✔
UNCOV 58	pyspedas.tplot_tools.data_quants[tvar2].name = tvar2	1✔
UNCOV 59	return	1✔
60	else:
UNCOV 61	pyspedas.tplot_tools.data_quants[tvar1 + '_cropped'] = copy.deepcopy(tv1)	1✔
UNCOV 62	pyspedas.tplot_tools.data_quants[tvar1 + '_cropped'].attrs = copy.deepcopy(tv1.attrs)	1✔
UNCOV 63	pyspedas.tplot_tools.data_quants[tvar1 + '_cropped'].name = tvar1+ '_cropped'	1✔
UNCOV 64	pyspedas.tplot_tools.data_quants[tvar2 + '_cropped'] = copy.deepcopy(tv2)	1✔
UNCOV 65	pyspedas.tplot_tools.data_quants[tvar2 + '_cropped'].attrs = copy.deepcopy(tv2.attrs)	1✔
UNCOV 66	pyspedas.tplot_tools.data_quants[tvar2 + '_cropped'].name = tvar2 + '_cropped'	1✔
UNCOV 67	return tvar2 + '_cropped'	1✔

spedas / pyspedas / 23369877357

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