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%)

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8.62

/pyspedas/tplot_tools/tplot_math/tpwrspc.py

import logging
import numpy as np
from pyspedas.tplot_tools import tnames, get_data, store_data, options
from pyspedas.tplot_tools import pwrspc


def tpwrspc(varname, newname=None, overwrite=False, noline=False, nohanning=False, bin=3, notperhz=False, trange=None, axis=0):
    """
    This function is a wrapper for pwrspc.
    It applies pwrspc to a tplot variable and stores the result in a new tplot variable.

    Parameters
    ----------
        varname: str
            Name of the tplot variable.

        newname: str, optional
            New name for the output tplot variable. If not set, default output variable will be
            Default: None

        overwrite: bool, optional
            If True, overwrite the existing tplot variable.
            If True, then the newname keyword has no effect.
            Default: False

        noline: bool, optional
            If True, straight line is not subtracted from the data.
            Default: False

        nohanning: bool, optional
            If True, no Hanning window is applied to the data.
            Default: False
        
        bin: int, optional
            Bin size for binning the data. 
            Default: 3

        notperhz: bool, optional
            If True, the output units are the square of the input units.
            Default: False

        trange: list, optional
            Time range for the data extraction.
            Default: None

        axis: int, optional
            If the input variable is multi-dimensional, this specifies the axis to operate along.
            Default: 0, first axis.

    Returns
    --------
    str
            Name of the new tplot variable created by this function.
            The output variable contains a single data point, frequency as v, and power as y.
    
    Examples
    --------
        >>> import pyspedas
        >>> import numpy as np
        >>> pyspedas.store_data('a', data={'x': range(100), 'y': np.random.random(100)})
        >>> pyspedas.tpwrspc('a')
        >>> pyspedas.get_data('a_pwrspc')
    """

    # Check if the variable exists
    if tnames(varname) == []:
        logging.info('This tplot variable does not exist.')
        return ''

    # Check for conflicting arguments
    if overwrite:
        logging.info('Variable will be overwritten.')
        newname = varname
    else:
        if newname:
            if tnames(newname) != []:
                logging.info('This new tplot variable already exists. Please use overwrite=True to overwrite it.')
                return ''
        else:
            newname = varname + '_pwrspc'

    # Get data from tplot variable
    d = get_data(varname)
    t = np.array(d[0], dtype='float64')
    data = np.array(d[1], dtype='float64')

    # If the input variable is multi-dimensional, operate along the specified axis
    if data.ndim == 1:
        y = data
    elif data.ndim == 2:
        if axis < 1:
            axis = 0
        elif axis > data.shape[1]:
            axis = data.shape[1] - 1
        logging.info(f'Operating along axis {axis}')
        y = data[:, axis]
    else:
        logging.info('Cannot handle data with more than two dimensions.')
        return ''

    # Remove NaN points
    tav = np.mean(t)  # We are going to store f,p at the time average point
    goodpoints = ~np.isnan(t) & ~np.isnan(y)
    if np.any(goodpoints):
        t = t[goodpoints]
        y = y[goodpoints]
        logging.info('NaN points have been removed.')
    else:
        logging.info(f'No valid data points in {varname}')
        return ''

    # Restrict to a time range if specified
    if trange and len(trange) == 2:
        ok = (trange[0] <= t < trange[1])
        if not np.any(ok):
            logging.info('No data in time range for:', varname)
            logging.info('No Power spectrum for:', varname)
            return ''
        else:
            t = t[ok]
            y = y[ok]

    # Call the power spectrum function (previously defined)
    t = t-t[0]
    f, p = pwrspc(t, y, noline=noline, nohanning=nohanning, bin=bin, notperhz=notperhz)

    if f is not None and p is not None and len(f) > 0 and len(p) > 0 and len(f) == len(p):
        # Store the result in a new tplot variable
        # A single data point will be created, with frequency as v, and power as y
        pp = np.array([p,])
        ff = np.array([f,])
        tt = np.array([tav,], dtype='float64')
        store_data(newname, data={'x': tt, 'y':  pp, 'v':  ff})
        options(newname, 'spec', 1)
        options(newname, 'ylog', 1)
    else:
        print('No Power spectrum for:', varname)
        return ''

    return newname

1	import logging	3✔
2	import numpy as np	3✔
3	from pyspedas.tplot_tools import tnames, get_data, store_data, options	3✔
4	from pyspedas.tplot_tools import pwrspc	3✔
5
6
7	def tpwrspc(varname, newname=None, overwrite=False, noline=False, nohanning=False, bin=3, notperhz=False, trange=None, axis=0):	3✔
8	"""
9	This function is a wrapper for pwrspc.
10	It applies pwrspc to a tplot variable and stores the result in a new tplot variable.
11
12	Parameters
13	----------
14	varname: str
15	Name of the tplot variable.
16
17	newname: str, optional
18	New name for the output tplot variable. If not set, default output variable will be
19	Default: None
20
21	overwrite: bool, optional
22	If True, overwrite the existing tplot variable.
23	If True, then the newname keyword has no effect.
24	Default: False
25
26	noline: bool, optional
27	If True, straight line is not subtracted from the data.
28	Default: False
29
30	nohanning: bool, optional
31	If True, no Hanning window is applied to the data.
32	Default: False
33
34	bin: int, optional
35	Bin size for binning the data.
36	Default: 3
37
38	notperhz: bool, optional
39	If True, the output units are the square of the input units.
40	Default: False
41
42	trange: list, optional
43	Time range for the data extraction.
44	Default: None
45
46	axis: int, optional
47	If the input variable is multi-dimensional, this specifies the axis to operate along.
48	Default: 0, first axis.
49
50	Returns
51	--------
52	str
53	Name of the new tplot variable created by this function.
54	The output variable contains a single data point, frequency as v, and power as y.
55
56	Examples
57	--------
58	>>> import pyspedas
59	>>> import numpy as np
60	>>> pyspedas.store_data('a', data={'x': range(100), 'y': np.random.random(100)})
61	>>> pyspedas.tpwrspc('a')
62	>>> pyspedas.get_data('a_pwrspc')
63	"""
64
65	# Check if the variable exists
UNCOV 66	if tnames(varname) == []:	×
67	logging.info('This tplot variable does not exist.')	×
68	return ''	×
69
70	# Check for conflicting arguments
UNCOV 71	if overwrite:	×
72	logging.info('Variable will be overwritten.')	×
73	newname = varname	×
74	else:
UNCOV 75	if newname:	×
76	if tnames(newname) != []:	×
77	logging.info('This new tplot variable already exists. Please use overwrite=True to overwrite it.')	×
78	return ''	×
79	else:
UNCOV 80	newname = varname + '_pwrspc'	×
81
82	# Get data from tplot variable
UNCOV 83	d = get_data(varname)	×
UNCOV 84	t = np.array(d[0], dtype='float64')	×
UNCOV 85	data = np.array(d[1], dtype='float64')	×
86
87	# If the input variable is multi-dimensional, operate along the specified axis
UNCOV 88	if data.ndim == 1:	×
UNCOV 89	y = data	×
90	elif data.ndim == 2:	×
91	if axis < 1:	×
92	axis = 0	×
93	elif axis > data.shape[1]:	×
94	axis = data.shape[1] - 1	×
95	logging.info(f'Operating along axis {axis}')	×
96	y = data[:, axis]	×
97	else:
98	logging.info('Cannot handle data with more than two dimensions.')	×
99	return ''	×
100
101	# Remove NaN points
UNCOV 102	tav = np.mean(t) # We are going to store f,p at the time average point	×
UNCOV 103	goodpoints = ~np.isnan(t) & ~np.isnan(y)	×
UNCOV 104	if np.any(goodpoints):	×
UNCOV 105	t = t[goodpoints]	×
UNCOV 106	y = y[goodpoints]	×
UNCOV 107	logging.info('NaN points have been removed.')	×
108	else:
109	logging.info(f'No valid data points in {varname}')	×
110	return ''	×
111
112	# Restrict to a time range if specified
UNCOV 113	if trange and len(trange) == 2:	×
114	ok = (trange[0] <= t < trange[1])	×
115	if not np.any(ok):	×
116	logging.info('No data in time range for:', varname)	×
117	logging.info('No Power spectrum for:', varname)	×
118	return ''	×
119	else:
120	t = t[ok]	×
121	y = y[ok]	×
122
123	# Call the power spectrum function (previously defined)
UNCOV 124	t = t-t[0]	×
UNCOV 125	f, p = pwrspc(t, y, noline=noline, nohanning=nohanning, bin=bin, notperhz=notperhz)	×
126
UNCOV 127	if f is not None and p is not None and len(f) > 0 and len(p) > 0 and len(f) == len(p):	×
128	# Store the result in a new tplot variable
129	# A single data point will be created, with frequency as v, and power as y
UNCOV 130	pp = np.array([p,])	×
UNCOV 131	ff = np.array([f,])	×
UNCOV 132	tt = np.array([tav,], dtype='float64')	×
UNCOV 133	store_data(newname, data={'x': tt, 'y': pp, 'v': ff})	×
UNCOV 134	options(newname, 'spec', 1)	×
UNCOV 135	options(newname, 'ylog', 1)	×
136	else:
137	print('No Power spectrum for:', varname)	×
138	return ''	×
139
UNCOV 140	return newname	×

spedas / pyspedas / 25194239086

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