18362798857

Committed 09 Oct 2025 01:33AM UTC coverage: 89.477% (-0.01%) from 89.491%

Build # 18362798857

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push

github

Committed by

jameswilburlewis

Commit Message

Added test for FEEPS get_err switch

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96.55

/pyspedas/projects/mms/feeps_tools/feeps.py

from pyspedas.projects.mms.mms_load_data import mms_load_data
from pyspedas.projects.mms.feeps_tools.mms_feeps_correct_energies import mms_feeps_correct_energies
from pyspedas.projects.mms.feeps_tools.mms_feeps_flat_field_corrections import mms_feeps_flat_field_corrections
from pyspedas.projects.mms.feeps_tools.mms_feeps_active_eyes import mms_feeps_active_eyes
from pyspedas.projects.mms.feeps_tools.mms_feeps_split_integral_ch import mms_feeps_split_integral_ch
from pyspedas.projects.mms.feeps_tools.mms_feeps_remove_bad_data import mms_feeps_remove_bad_data
from pyspedas.projects.mms.feeps_tools.mms_feeps_remove_sun import mms_feeps_remove_sun
from pyspedas.projects.mms.feeps_tools.mms_feeps_omni import mms_feeps_omni
from pyspedas.projects.mms.feeps_tools.mms_feeps_spin_avg import mms_feeps_spin_avg
from pyspedas.projects.mms.mms_config import CONFIG
from pyspedas.tplot_tools import time_clip as tclip

def recvary_log_filter(log):
    if 'record-varying' in log.msg:
        return False
    else:
        return True

def mms_load_feeps(trange=['2015-10-16', '2015-10-17'],
                   probe='1',
                   data_rate='srvy',
                   level='l2',
                   datatype='electron',
                   varformat=None,
                   varnames=[],
                   get_support_data=True,
                   suffix='',
                   time_clip=False,
                   no_update=False,
                   available=False,
                   notplot=False,
                   no_flatfield_corrections=False,
                   data_units=['count_rate', 'intensity'],
                   latest_version=False,
                   major_version=False,
                   min_version=None,
                   cdf_version=None,
                   spdf=False,
                   always_prompt=False,
                   filter_recvary_warnings=True,
                   quality_flag=3,
                   get_err=False,
                   ):
    """
    Load data from the MMS Fly's Eye Energetic Particle Sensor (FEEPS)
    
    Parameters
    ----------
        trange : list of str
            time range of interest [start time, end time] with the format
            'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day 
            ['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
            Default: ['2015-10-16','2015-10-17']

        probe : str or list of str
            list of probes, valid values for MMS probes are ['1','2','3','4'].
            Default: '1'

        data_rate : str or list of str
            instrument data rates for FEEPS include ['brst', 'srvy'].
            Default: 'srvy'

        level : str
            indicates level of data processing. the default if no level is specified is 'l2'
            Default: 'l2'

        datatype : str or list of str
            Valid datatypes for FEEPS are::

                       L2, L1b: ['electron', 'ion']
                       L1a: ['electron-bottom', 'electron-top', 'ion-bottom', 'ion-top']

            Default: 'electron'

        data_units : str or list of str
            Unit types to be loaded, options are 'count_rate', 'intensity'
            Default: ['count_rate', 'intensity']

        no_flatfield_corrections: bool
            If True, no flatfield corrections are performed.
            Default: False

        get_support_data: bool
            Data with an attribute "VAR_TYPE" with a value of "support_data"
            will be loaded into tplot.
            Default: True

        time_clip: bool
            Data will be clipped to the exact trange specified by the trange keyword.
            Default: False
            
        varformat: str
            The file variable formats to load into tplot.  Wildcard character
            "*" is accepted.
            Default: None (all variables are loaded)

        varnames: list of str
            List of variable names to load. If list is empty or not specified,
            all data variables are loaded.
            Default: []

        suffix: str
            The tplot variable names will be given this suffix.  By default, 
            no suffix is added.
            Default: None

        notplot: bool
            If True, then data are returned in a hash table instead of 
            being stored in tplot variables (useful for debugging, and
            access to multidimensional data products)
            Default: False

        available: bool
            If True, simply return the available data files (without downloading)
            for the requested parameters
            Default: False

        no_update: bool
            Set this flag to preserve the original data. if not set and newer 
            data is found the existing data will be overwritten
            Default: False

        cdf_version: str
            Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
            Default: None

        min_version: str
            Specify a minimum CDF version # to load
            Default: None

        latest_version: bool
            Only grab the latest CDF version in the requested time interval

        major_version: bool
            Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

        always_prompt: bool
            Set this keyword to always prompt for the user's username and password;
            useful if you accidentally save an incorrect password, or if your SDC password has changed

        spdf: bool
            If True, download the data from the SPDF instead of the SDC

        filter_recvary_warnings: bool
            If True, capture warnings from cdf_to_tplot and filter out the ones complaining about non-record-varying support data with timestamps

        quality_flag: int
            Value can range from 2-4. Determines which eyes to use to calculate omnidirectional flux at a given time
            based on how many sectors were masked for sunlight. For BRST or RAW::

                Green (Quality Indicator = 0): No contaminated or masked spin sectors (best data quality)
                Red (Quality Indicator = 3): Contaminated spin sector is associated with BURST and RAW data
                    (this data is not recommended for scientific use)
                Grey (Quality Indicator = 4): FEEPS calibration data (this data is generated by the instrument itself
                    and is not recommended for scientific use)

            For data in SURVEY mode, there are 5 possible quality indicators::

                Green (Quality Indicator = 0): No contaminated or masked spin sectors (best data quality)
                Yellow (Quality Indicator = 1): Among the spin sectors used to create a SURVEY aggregate
                     spin sector, onboard masking is successfully applied to a minimum of 1 and a maximum of 7
                     spin sectors and there is no contamination in any of the individual spin sectors. Note that
                     since this indicator applies only when onboard masking is applied, some sectors are not
                     observed.
                Orange (Quality Indicator = 2): There is contamination of between 0 and 50% of the notmasked sectors. Warning: this data contains contamination.
                Red (Quality Indicator = 3): Same as orange except that there is contamination of 50% or
                     more of the not-masked sectors. IN ADDITION, this indicator applies to the case where
                     onboard masking is applied to all 8 spin sectors in the aggregate sector.
                Grey (Quality Indicator = 4): FEEPS calibration data (this data is generated by the instrument
                     itself and is not recommended for scientific use)

        get_err: bool
            Gets the error in the omnidirectional l2 intensity.  Default: False. User MUST run mms_load_feeps for the
            l1b counts data FIRST to use this function. (Note: the FEEPS l1b data needed for this switch is restricted
            to MMS team members at present.)


    Returns
    --------
        list of str
            List of tplot variables created.

    Example
    -------

    >>> import pyspedas
    >>> from pyspedas import tplot
    >>> feeps_data = pyspedas.projects.mms.mms_load_feeps(trange=['2015-10-16', '2015-10-17'], probe='1', datatype='electron')
    >>> tplot(['mms1_epd_feeps_srvy_l2_electron_intensity_omni_spin', 'mms1_epd_feeps_srvy_l2_electron_intensity_omni'])



    """

    # For log filtering
    from pyspedas import logger
    # as of 3 July 2023, there's a mixture of v7.x.x and v6.x.x files at the SDC
    # these files aren't compatible, so we need to only load the latest major version
    # to avoid crashes (unless otherwise specified)
    if not latest_version and not major_version and min_version is None and cdf_version is None:
        major_version = True

    if filter_recvary_warnings:
        logger.addFilter(recvary_log_filter)

    tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='feeps',
            datatype=datatype, varformat=varformat, varnames=varnames, get_support_data=get_support_data, suffix=suffix,
            no_update=no_update, available=available, latest_version=latest_version,
            major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt)

    if filter_recvary_warnings:
        logger.removeFilter(recvary_log_filter)

    if tvars == [] or available or notplot or CONFIG['download_only'] or tvars is None:
        return tvars

    probes = probe if isinstance(probe, list) else [probe]
    data_rates = data_rate if isinstance(data_rate, list) else [data_rate]
    levels = level if isinstance(level, list) else [level]
    datatypes = datatype if isinstance(datatype, list) else [datatype]
    data_units = data_units if isinstance(data_units, list) else [data_units]

    probes = [str(p) for p in probes]

    mms_feeps_correct_energies(probes, data_rate, level=level, suffix=suffix)

    if not no_flatfield_corrections:
        mms_feeps_flat_field_corrections(probes=probes, data_rate=data_rate, suffix=suffix)

    for probe in probes:
        for lvl in levels:
            for drate in data_rates:
                for datatype in datatypes:
                    mms_feeps_remove_bad_data(trange=trange, probe=probe, data_rate=drate, datatype =datatype, level=lvl, suffix=suffix)

                    for data_unit in data_units:
                        eyes = mms_feeps_active_eyes(trange, probe, drate, datatype, lvl)

                        split_vars = mms_feeps_split_integral_ch(data_unit, datatype, probe, suffix=suffix, data_rate=drate, level=lvl, sensor_eyes=eyes)

                        sun_removed_vars = mms_feeps_remove_sun(eyes, trange, probe=probe, datatype=datatype, data_units=data_unit, data_rate=drate, level=lvl, suffix=suffix)

                        omni_vars = mms_feeps_omni(eyes, probe=probe, datatype=datatype, data_units=data_unit, data_rate=drate, level=lvl, suffix=suffix, quality_flag=quality_flag, get_err=get_err)

                        if split_vars is not None:
                            tvars = tvars + split_vars

                        if sun_removed_vars is not None:
                            tvars = tvars + sun_removed_vars

                        if omni_vars is not None:
                            tvars = tvars + omni_vars

                        spin_avg_vars = mms_feeps_spin_avg(probe=probe, data_units=data_unit, datatype=datatype, data_rate=drate, level=lvl, suffix=suffix)

                        if spin_avg_vars is not None:
                            tvars.append(spin_avg_vars)

    if time_clip:
        for new_var in tvars:
            tclip(new_var, trange[0], trange[1], suffix='')

    return tvars

1	from pyspedas.projects.mms.mms_load_data import mms_load_data	1✔
2	from pyspedas.projects.mms.feeps_tools.mms_feeps_correct_energies import mms_feeps_correct_energies	1✔
3	from pyspedas.projects.mms.feeps_tools.mms_feeps_flat_field_corrections import mms_feeps_flat_field_corrections	1✔
4	from pyspedas.projects.mms.feeps_tools.mms_feeps_active_eyes import mms_feeps_active_eyes	1✔
5	from pyspedas.projects.mms.feeps_tools.mms_feeps_split_integral_ch import mms_feeps_split_integral_ch	1✔
6	from pyspedas.projects.mms.feeps_tools.mms_feeps_remove_bad_data import mms_feeps_remove_bad_data	1✔
7	from pyspedas.projects.mms.feeps_tools.mms_feeps_remove_sun import mms_feeps_remove_sun	1✔
8	from pyspedas.projects.mms.feeps_tools.mms_feeps_omni import mms_feeps_omni	1✔
9	from pyspedas.projects.mms.feeps_tools.mms_feeps_spin_avg import mms_feeps_spin_avg	1✔
10	from pyspedas.projects.mms.mms_config import CONFIG	1✔
11	from pyspedas.tplot_tools import time_clip as tclip	1✔
12
13	def recvary_log_filter(log):	1✔
14	if 'record-varying' in log.msg:	1✔
15	return False	1✔
16	else:
17	return True	1✔
18
19	def mms_load_feeps(trange=['2015-10-16', '2015-10-17'],	1✔
20	probe='1',
21	data_rate='srvy',
22	level='l2',
23	datatype='electron',
24	varformat=None,
25	varnames=[],
26	get_support_data=True,
27	suffix='',
28	time_clip=False,
29	no_update=False,
30	available=False,
31	notplot=False,
32	no_flatfield_corrections=False,
33	data_units=['count_rate', 'intensity'],
34	latest_version=False,
35	major_version=False,
36	min_version=None,
37	cdf_version=None,
38	spdf=False,
39	always_prompt=False,
40	filter_recvary_warnings=True,
41	quality_flag=3,
42	get_err=False,
43	):
44	"""
45	Load data from the MMS Fly's Eye Energetic Particle Sensor (FEEPS)
46
47	Parameters
48	----------
49	trange : list of str
50	time range of interest [start time, end time] with the format
51	'YYYY-MM-DD','YYYY-MM-DD'] or to specify more or less than a day
52	['YYYY-MM-DD/hh:mm:ss','YYYY-MM-DD/hh:mm:ss']
53	Default: ['2015-10-16','2015-10-17']
54
55	probe : str or list of str
56	list of probes, valid values for MMS probes are ['1','2','3','4'].
57	Default: '1'
58
59	data_rate : str or list of str
60	instrument data rates for FEEPS include ['brst', 'srvy'].
61	Default: 'srvy'
62
63	level : str
64	indicates level of data processing. the default if no level is specified is 'l2'
65	Default: 'l2'
66
67	datatype : str or list of str
68	Valid datatypes for FEEPS are::
69
70	L2, L1b: ['electron', 'ion']
71	L1a: ['electron-bottom', 'electron-top', 'ion-bottom', 'ion-top']
72
73	Default: 'electron'
74
75	data_units : str or list of str
76	Unit types to be loaded, options are 'count_rate', 'intensity'
77	Default: ['count_rate', 'intensity']
78
79	no_flatfield_corrections: bool
80	If True, no flatfield corrections are performed.
81	Default: False
82
83	get_support_data: bool
84	Data with an attribute "VAR_TYPE" with a value of "support_data"
85	will be loaded into tplot.
86	Default: True
87
88	time_clip: bool
89	Data will be clipped to the exact trange specified by the trange keyword.
90	Default: False
91
92	varformat: str
93	The file variable formats to load into tplot. Wildcard character
94	"*" is accepted.
95	Default: None (all variables are loaded)
96
97	varnames: list of str
98	List of variable names to load. If list is empty or not specified,
99	all data variables are loaded.
100	Default: []
101
102	suffix: str
103	The tplot variable names will be given this suffix. By default,
104	no suffix is added.
105	Default: None
106
107	notplot: bool
108	If True, then data are returned in a hash table instead of
109	being stored in tplot variables (useful for debugging, and
110	access to multidimensional data products)
111	Default: False
112
113	available: bool
114	If True, simply return the available data files (without downloading)
115	for the requested parameters
116	Default: False
117
118	no_update: bool
119	Set this flag to preserve the original data. if not set and newer
120	data is found the existing data will be overwritten
121	Default: False
122
123	cdf_version: str
124	Specify a specific CDF version # to load (e.g., cdf_version='4.3.0')
125	Default: None
126
127	min_version: str
128	Specify a minimum CDF version # to load
129	Default: None
130
131	latest_version: bool
132	Only grab the latest CDF version in the requested time interval
133
134	major_version: bool
135	Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval
136
137	always_prompt: bool
138	Set this keyword to always prompt for the user's username and password;
139	useful if you accidentally save an incorrect password, or if your SDC password has changed
140
141	spdf: bool
142	If True, download the data from the SPDF instead of the SDC
143
144	filter_recvary_warnings: bool
145	If True, capture warnings from cdf_to_tplot and filter out the ones complaining about non-record-varying support data with timestamps
146
147	quality_flag: int
148	Value can range from 2-4. Determines which eyes to use to calculate omnidirectional flux at a given time
149	based on how many sectors were masked for sunlight. For BRST or RAW::
150
151	Green (Quality Indicator = 0): No contaminated or masked spin sectors (best data quality)
152	Red (Quality Indicator = 3): Contaminated spin sector is associated with BURST and RAW data
153	(this data is not recommended for scientific use)
154	Grey (Quality Indicator = 4): FEEPS calibration data (this data is generated by the instrument itself
155	and is not recommended for scientific use)
156
157	For data in SURVEY mode, there are 5 possible quality indicators::
158
159	Green (Quality Indicator = 0): No contaminated or masked spin sectors (best data quality)
160	Yellow (Quality Indicator = 1): Among the spin sectors used to create a SURVEY aggregate
161	spin sector, onboard masking is successfully applied to a minimum of 1 and a maximum of 7
162	spin sectors and there is no contamination in any of the individual spin sectors. Note that
163	since this indicator applies only when onboard masking is applied, some sectors are not
164	observed.
165	Orange (Quality Indicator = 2): There is contamination of between 0 and 50% of the notmasked sectors. Warning: this data contains contamination.
166	Red (Quality Indicator = 3): Same as orange except that there is contamination of 50% or
167	more of the not-masked sectors. IN ADDITION, this indicator applies to the case where
168	onboard masking is applied to all 8 spin sectors in the aggregate sector.
169	Grey (Quality Indicator = 4): FEEPS calibration data (this data is generated by the instrument
170	itself and is not recommended for scientific use)
171
172	get_err: bool
173	Gets the error in the omnidirectional l2 intensity. Default: False. User MUST run mms_load_feeps for the
174	l1b counts data FIRST to use this function. (Note: the FEEPS l1b data needed for this switch is restricted
175	to MMS team members at present.)
176
177
178	Returns
179	--------
180	list of str
181	List of tplot variables created.
182
183	Example
184	-------
185
186	>>> import pyspedas
187	>>> from pyspedas import tplot
188	>>> feeps_data = pyspedas.projects.mms.mms_load_feeps(trange=['2015-10-16', '2015-10-17'], probe='1', datatype='electron')
189	>>> tplot(['mms1_epd_feeps_srvy_l2_electron_intensity_omni_spin', 'mms1_epd_feeps_srvy_l2_electron_intensity_omni'])
190
191
192
193	"""
194
195	# For log filtering
196	from pyspedas import logger	1✔
197	# as of 3 July 2023, there's a mixture of v7.x.x and v6.x.x files at the SDC
198	# these files aren't compatible, so we need to only load the latest major version
199	# to avoid crashes (unless otherwise specified)
200	if not latest_version and not major_version and min_version is None and cdf_version is None:	1✔
201	major_version = True	1✔
202
203	if filter_recvary_warnings:	1✔
204	logger.addFilter(recvary_log_filter)	1✔
205
206	tvars = mms_load_data(trange=trange, notplot=notplot, probe=probe, data_rate=data_rate, level=level, instrument='feeps',	1✔
207	datatype=datatype, varformat=varformat, varnames=varnames, get_support_data=get_support_data, suffix=suffix,
208	no_update=no_update, available=available, latest_version=latest_version,
209	major_version=major_version, min_version=min_version, cdf_version=cdf_version, spdf=spdf, always_prompt=always_prompt)
210
211	if filter_recvary_warnings:	1✔
212	logger.removeFilter(recvary_log_filter)	1✔
213
214	if tvars == [] or available or notplot or CONFIG['download_only'] or tvars is None:	1✔
UNCOV 215	return tvars	×
216
217	probes = probe if isinstance(probe, list) else [probe]	1✔
218	data_rates = data_rate if isinstance(data_rate, list) else [data_rate]	1✔
219	levels = level if isinstance(level, list) else [level]	1✔
220	datatypes = datatype if isinstance(datatype, list) else [datatype]	1✔
221	data_units = data_units if isinstance(data_units, list) else [data_units]	1✔
222
223	probes = [str(p) for p in probes]	1✔
224
225	mms_feeps_correct_energies(probes, data_rate, level=level, suffix=suffix)	1✔
226
227	if not no_flatfield_corrections:	1✔
228	mms_feeps_flat_field_corrections(probes=probes, data_rate=data_rate, suffix=suffix)	1✔
229
230	for probe in probes:	1✔
231	for lvl in levels:	1✔
232	for drate in data_rates:	1✔
233	for datatype in datatypes:	1✔
234	mms_feeps_remove_bad_data(trange=trange, probe=probe, data_rate=drate, datatype =datatype, level=lvl, suffix=suffix)	1✔
235
236	for data_unit in data_units:	1✔
237	eyes = mms_feeps_active_eyes(trange, probe, drate, datatype, lvl)	1✔
238
239	split_vars = mms_feeps_split_integral_ch(data_unit, datatype, probe, suffix=suffix, data_rate=drate, level=lvl, sensor_eyes=eyes)	1✔
240
241	sun_removed_vars = mms_feeps_remove_sun(eyes, trange, probe=probe, datatype=datatype, data_units=data_unit, data_rate=drate, level=lvl, suffix=suffix)	1✔
242
243	omni_vars = mms_feeps_omni(eyes, probe=probe, datatype=datatype, data_units=data_unit, data_rate=drate, level=lvl, suffix=suffix, quality_flag=quality_flag, get_err=get_err)	1✔
244
245	if split_vars is not None:	1✔
246	tvars = tvars + split_vars	1✔
247
248	if sun_removed_vars is not None:	1✔
249	tvars = tvars + sun_removed_vars	1✔
250
251	if omni_vars is not None:	1✔
UNCOV 252	tvars = tvars + omni_vars	×
253
254	spin_avg_vars = mms_feeps_spin_avg(probe=probe, data_units=data_unit, datatype=datatype, data_rate=drate, level=lvl, suffix=suffix)	1✔
255
256	if spin_avg_vars is not None:	1✔
257	tvars.append(spin_avg_vars)	1✔
258
259	if time_clip:	1✔
260	for new_var in tvars:	1✔
261	tclip(new_var, trange[0], trange[1], suffix='')	1✔
262
263	return tvars	1✔

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