Supported Instruments

DMSP_IVM

Supports the Defense Meteorological Satelitte Program (DMSP) Ion Velocity Meter (IVM) Madrigal data.

Supports the Defense Meteorological Satellite Program (DMSP) IVM instruments.

The Ion Velocity Meter (IVM) is comprised of the Retarding Potential Analyzer (RPA) and Drift Meter (DM). The RPA measures the energy of plasma along the direction of satellite motion. By fitting these measurements to a theoretical description of plasma the number density, plasma composition, plasma temperature, and plasma motion may be determined. The DM directly measures the arrival angle of plasma. Using the reported motion of the satellite the angle is converted into ion motion along two orthogonal directions, perpendicular to the satellite track. The IVM is part of the Special Sensor for Ions, Electrons, and Scintillations (SSIES) instrument suite on DMSP.

Downloads data from the National Science Foundation Madrigal Database. The routine is configured to utilize data files with instrument performance flags generated at the Center for Space Sciences at the University of Texas at Dallas.

Properties

platform: ‘dmsp’
name: ‘ivm’
tag: ‘utd’, ‘’
inst_id: [‘f11’, ‘f12’, ‘f13’, ‘f14’, ‘f15’, ‘f16’, ‘f17’, ‘f18’]

Example

import pysat
dmsp = pysat.Instrument('dmsp', 'ivm', 'utd', 'f15', clean_level='clean')
dmsp.download(dt.datetime(2017, 12, 30), dt.datetime(2017, 12, 31),
              user='Firstname+Lastname', password='email@address.com')
dmsp.load(2017, 363)

Note

Please provide name and email when downloading data with this routine.

pysatMadrigal.instruments.dmsp_ivm.clean(self)[source]: Clean DMSP IVM data to the specified level.

pysatMadrigal.instruments.dmsp_ivm.download(date_array, tag='', inst_id='', data_path=None, user=None, password=None, file_type='hdf5')[source]

Download data from Madrigal.

Parameters:

date_arrayarray-like: list of datetimes to download data for. The sequence of dates need not be contiguous.
tagstr: Tag identifier used for particular dataset. This input is provided by pysat. (default=’’)
inst_idstr: Satellite ID string identifier used for particular dataset. This input is provided by pysat. (default=’’)
data_pathstr: Path to directory to download data to. (default=None)
userstr: User string input used for download. Provided by user and passed via pysat. If an account is required for downloads this routine here must error if user not supplied. (default=None)
passwordstr: Password for data download. (default=None)
file_typestr: File format for Madrigal data. (default=’hdf5’)

pysatMadrigal.instruments.dmsp_ivm.init(self)[source]: Initialize the Instrument object with values specific to DMSP IVM.

DMSP_SSJ

Supports the Defense Meteorological Satellite Program (DMSP) Special Sensor J (SSJ) Madrigal data.

Support the DMSP Special Sensor-J (SSJ) instrument and derived products.

The Defense Meteorological Satellite Program (DMSP) SSJ measures precipitating particles using spectrometery. The Auroral Boundary Index (ABI) is automatically computed from this data set and marks the midnight equatorward boundary in each hemisphere.

Further questions can be addressed to:: Gordon Wilson Air Force Research Lab RVBXP 3550 Aberdeen Avenue SE, Bldg 570 Kirtland Air Force Base, NM 87117-5776 Phone: 505-853-2027 e-mail: gordon.wilson@kirtland.af.mil
or: Ernie Holeman (ernestholeman7408@comcast.net)

Please send a copy of all publications that use the Auroral Boundary Index (ABI) to Dr. Gordon Wilson at the above address.

Properties

platform: ‘dmsp’
name: ‘ssj’
tag: ‘abi’
inst_id: ‘f11’

Example

import pysat
dmsp = pysat.Instrument('dmsp', 'ssj', 'abi', clean_level='clean')
dmsp.download(dt.datetime(2017, 12, 30), dt.datetime(2017, 12, 31),
              user='Firstname+Lastname', password='email@address.com')
dmsp.load(2017, 363)

Note

Please provide name and email when downloading data with this routine.

pysatMadrigal.instruments.dmsp_ssj.clean(self)[source]

Clean DMSP IVM data cleaned to the specified level.

Warning

UserWarning: If the ‘dirty’ level is invoked (same as no cleaning)

pysatMadrigal.instruments.dmsp_ssj.download(date_array, tag='', inst_id='', data_path=None, user=None, password=None, file_type='hdf5')[source]

Download DMSP SSJ4 data from Madrigal.

Parameters:

date_arrayarray-like: list of datetimes to download data for. The sequence of dates need not be contiguous.
tagstr: Tag identifier used for particular dataset. This input is provided by pysat. (default=’’)
inst_idstr: Satellite ID string identifier used for particular dataset. This input is provided by pysat. (default=’’)
data_pathstr: Path to directory to download data to. (default=None)
userstr: User string input used for download. Provided by user and passed via pysat. If an account is required for downloads this routine here must error if user not supplied. (default=None)
passwordstr: Password for data download. (default=None)
file_typestr: File format for Madrigal data. (default=’hdf5’)

pysatMadrigal.instruments.dmsp_ssj.init(self)[source]: Initialize the Instrument object with values specific to DMSP IVM.

pysatMadrigal.instruments.dmsp_ssj.load(fnames, tag='', inst_id='')[source]

Load DMSP SSJ4 data from Madrigal after accounting for date tags.

Parameters:

fnamesarray-like: Iterable of filename strings, full path, to data files to be loaded. This input is nominally provided by pysat itself.
tagstr: Tag name used to identify particular data set to be loaded. This input is nominally provided by pysat itself and is not used here. (default=’’)
inst_idstr: Instrument ID used to identify particular data set to be loaded. This input is nominally provided by pysat itself, and is not used here. (default=’’)

Returns:

datapds.DataFrame or xr.Dataset: A pandas DataFrame or xarray Dataset holding the data from the file
metapysat.Meta: Metadata from the file, as well as default values from pysat

Raises:

ValueError: If data columns expected to create the time index are missing or if coordinates are not supplied for all data columns.

GNSS_TEC

The Global Navigation Satellite System (GNSS) Total Electron Content (TEC) provides a measure of column plasma density over the globle. The Madrigal TEC is provided by MIT Haystack.

Supports the MIT Haystack GNSS TEC data products.

The Global Navigation Satellite System (GNSS) is used in conjunction with a world-wide receiver network to produce total electron content (TEC) data products, including vertical and line-of-sight (or slant) TEC.

Downloads data from the MIT Haystack Madrigal Database.

Properties

platform: ‘gnss’
name: ‘tec’
tag: ‘vtec’, ‘site’, ‘los’
inst_id: ‘’ (not used)

Examples

import datetime as dt
import pysat
import pysatMadrigal as pymad

# Get and load all vertical TEC for 19 Nov 2017
vtec = pysat.Instrument(inst_module=pymad.instruments.gnss_tec, tag='vtec')
vtec.download(dt.datetime(2017, 11, 19), dt.datetime(2017, 11, 20),
              user='Firstname+Lastname', password='email@address.com')
vtec.load(date=dt.datetime(2017, 11, 19))

# Get and load the GLONASS slant TEC from the zzon site on 1 Jan 2023
stec = pysat.Instrument(inst_module=pymad.instruments.gnss_tec, tag='los')
stec.download(start=dt.datetime(2023, 1, 1), user='Firstname+Lastname',
              password='email@address.com')
stec.load(2023, 1, los_method='site', los_value='zzon',
          gnss_network='glonass')

Note

Please provide name and email when downloading data with this routine.

The line-of-sight data is too large to load an entire file at once. Data may be loaded by individual receiver site for any number of days (recommended to load one day) or a given time. To discover the available sites and times (exact times are required for selection), you may use the pysatMadrigal.instruments.methods.gnss.get_los_times and pysatMadrigal.instruments.methods.gnss.get_los_receiver_sites functions.

pysatMadrigal.instruments.gnss_tec.clean(self)[source]: Clean GNSS TEC data to a specific level.

pysatMadrigal.instruments.gnss_tec.download(date_array, tag='', inst_id='', data_path=None, user=None, password=None, url='http://cedar.openmadrigal.org', file_type='netCDF4')[source]

Download data from Madrigal.

Parameters:

date_arrayarray-like: list of datetimes to download data for. The sequence of dates need not be contiguous.
tagstr: Tag identifier used for particular dataset. This input is provided by pysat. (default=’’)
inst_idstr: Instrument ID string identifier used for particular dataset. This input is provided by pysat. (default=’’)
data_pathstr: Path to directory to download data to. (default=None)
userstr: User string input used for download. Provided by user and passed via pysat. (default=None)
passwordstr: Password for data download. (default=None)
urlstr: URL for Madrigal site (default=’http://cedar.openmadrigal.org’)
file_typestr: File format for Madrigal data. (default=’netCDF4’)

pysatMadrigal.instruments.gnss_tec.init(self)[source]: Initialize the Instrument object with values specific to GNSS TEC.

pysatMadrigal.instruments.gnss_tec.list_files(tag, inst_id, data_path=None, format_str=None, file_cadence=datetime.timedelta(days=1), delimiter=None, file_type=None)[source]

Return a Pandas Series of every file for chosen Instrument data.

Parameters:

tagstr: Denotes type of file to load. Accepts strings corresponding to the appropriate Madrigal Instrument tags.
inst_idstr: Specifies the instrument ID to load. Accepts strings corresponding to the appropriate Madrigal Instrument inst_ids.
data_pathstr or NoneType: Path to data directory. If None is specified, the value previously set in Instrument.files.data_path is used. (default=None)
format_strstr or NoneType: User specified file format. If None is specified, the default formats associated with the supplied tags are used. (default=None)
file_cadencedt.timedelta or pds.DateOffset: pysat assumes a daily file cadence, but some instrument data file contain longer periods of time. This parameter allows the specification of regular file cadences greater than or equal to a day (e.g., weekly, monthly, or yearly). (default=dt.timedelta(days=1))
two_digit_year_breakint or NoneType: If filenames only store two digits for the year, then ‘1900’ will be added for years >= two_digit_year_break and ‘2000’ will be added for years < two_digit_year_break. If None, then four-digit years are assumed. (default=None)
delimiterstr or NoneType: Delimiter string upon which files will be split (e.g., ‘.’). If None, filenames will be parsed presuming a fixed width format. (default=None)
file_typestr or NoneType: File format for Madrigal data. Load routines currently accepts ‘hdf5’, ‘simple’, and ‘netCDF4’, but any of the Madrigal options may be used here. If None, will look for all known file types. (default=None)

Returns:

outpds.Series: A pandas Series containing the verified available files

pysatMadrigal.instruments.gnss_tec.list_remote_files(tag, inst_id, start=datetime.datetime(1998, 10, 15, 0, 0), stop=datetime.datetime(2024, 3, 15, 20, 57, 52, 267966), user=None, password=None)[source]

Create a Pandas Series of every file for chosen remote data.

Parameters:

tagstr: Denotes type of file to load. This input is nominally provided by pysat itself.
inst_idstr: Specifies the satellite or instrument ID. This input is nominally provided by pysat itself.
startdt.datetime or NoneType: Starting time for file list. If None, replaced with default. (default=10-15-1998)
stopdt.datetime or NoneType: Ending time for the file list. If None, replaced with default. (default=time of run)
userstr or NoneType: Username to be passed along to resource with relevant data. (default=None)
passwordstr or NoneType: User password to be passed along to resource with relevant data. (default=None)

Returns:

filespds.Series: A series of filenames, see pysat.utils.files.process_parsed_filenames for more information.

JRO_ISR

The incoherent scatter radar (ISR) at the Jicamarca Radio Observatory regularly measures the velocity, density, and other ionospheric characteristics near the magnetic equator over Peru.

Supports the Incoherent Scatter Radar at the Jicamarca Radio Observatory.

Properties

platform: ‘jro’
name: ‘isr’
tag: ‘drifts’, ‘drifts_ave’, ‘oblique_stan’, ‘oblique_rand’, ‘oblique_long’

Examples

import pysat
jro = pysat.Instrument('jro', 'isr', 'drifts', clean_level='clean')
jro.download(pysat.datetime(2017, 12, 30), pysat.datetime(2017, 12, 31),
             user='Firstname+Lastname', password='email@address.com')
jro.load(2017, 363)

Note

The Incoherent Scatter Radar (ISR) at the Jicamarca Radio Observatory (JRO) observes ion drifts, line-of-sight neutral winds, electron density and temperature, ion temperature, and ion composition through three overarching experiments.

Downloads data from the JRO Madrigal Database.

Please provide name (user) and email (password) when downloading data with this routine.

pysatMadrigal.instruments.jro_isr.clean(self)[source]: Clean the JRO ISR data cleaned to the specified level.

pysatMadrigal.instruments.jro_isr.download(date_array, tag='', inst_id='', data_path=None, user=None, password=None, file_type='hdf5')[source]

Download data from Madrigal.

Parameters:

date_arrayarray-like: list of datetimes to download data for. The sequence of dates need not be contiguous.
tagstr: Tag identifier used for particular dataset. This input is provided by pysat. (default=’’)
inst_idstr: Satellite ID string identifier used for particular dataset. This input is provided by pysat. (default=’’)
data_pathstr: Path to directory to download data to. (default=None)
userstr: User string input used for download. Provided by user and passed via pysat. If an account is required for dowloads this routine here must error if user not supplied. (default=None)
passwordstr: Password for data download. (default=None)
file_typestr: File format for Madrigal data. (default=’hdf5’)

Notes

The user’s names should be provided in field user. Ruby Payne-Scott should be entered as “Ruby Payne-Scott”

The password field should be the user’s email address. These parameters are passed to Madrigal when downloading.

The affiliation field is set to pysat to enable tracking of pysat downloads.

pysatMadrigal.instruments.jro_isr.init(self)[source]: Initialize the Instrument object with values specific to JRO ISR.

pysatMadrigal.instruments.jro_isr.load(fnames, tag='', inst_id='')[source]

Load the JRO ISR data.

Parameters:

fnameslist: List of filenames
tagstr: tag name used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)
inst_idstr: Instrument ID used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)

Returns:

dataxarray.Dataset: Object containing satellite data
metapysat.Meta: Object containing metadata such as column names and units

pysatMadrigal.instruments.jro_isr.preprocess(self)[source]: Preprocess data to default loaded data to a single day.

Madrigal_Dst

An instrument for the Madrigal Dst index data. This data set spans the years of 1957 through a period close to today, with all data saved in a single file. Because of this, you only need to download the data once and any desired time period may be loaded (unless you require a time between your last download and now).

Supports access to Dst data archived at Madrigal.

Properties

platform: ‘madrigal’
name: ‘dst’
tag: None supported
inst_id: None supported

Note

Please provide name (user) and email (password) when downloading data with this routine.

Warnings

The entire data set (1 Jan 1597 through a period close to the recent day) is provided in a single file on Madrigal.

Examples

import datetime as dt
import pysat
import pysatMadrigal as py_mad

# Download Dst data from Madrigal
dst = pysat.Instrument(inst_module=py_mad.instruments.madrigal_dst)
dst.download(start=py_mad.instruments.madrigal_dst.madrigal_start,
             user='Firstname+Lastname', password='email@address.com')
dst.load(date=dt.datetime(1981, 1, 1))

pysatMadrigal.instruments.madrigal_dst.clean(self)[source]: Raise warning that cleaning is not needed for the OMNI2 data.

pysatMadrigal.instruments.madrigal_dst.init(self)[source]

Initialize the Instrument object in support of Madrigal access.

Parameters:

kindatstr: Madrigal instrument experiment code(s). (default=’’)

pysatMadrigal.instruments.madrigal_dst.load(fnames, tag='', inst_id='')[source]

Load the Madrigal Dst data.

Parameters:

fnameslist: List of filenames
tagstr: tag name used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)
inst_idstr: Instrument ID used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)

Returns:

datapds.DataFrame: Object containing IMF data
metapysat.Meta: Object containing metadata such as column names and units

Raises:

ValueError: Unexpected time variable names

Madrigal_Geoind

An instrument for the Madrigal geomagnetic index data. This data set spans the years of 1950 through a period close to today, with all data saved in a single file. Because of this, you only need to download the data once and any desired time period may be loaded (unless you require a time between your last download and now).

Supports access to F10.7, Kp, and Ap data archived at Madrigal.

Properties

platform: ‘madrigal’
name: ‘geoind’
tag: None supported
inst_id: None supported

Note

Please provide name (user) and email (password) when downloading data with this routine.

Warnings

The entire data set (1 Jan 1950 through 31 Dec 1987) is provided in a single file on Madrigal.

Examples

import datetime as dt
import pysat
import pysatMadrigal as py_mad

# Download geophysical indices from Madrigal
gind = pysat.Instrument(inst_module=py_mad.instruments.madrigal_geoind)
gind.download(start=py_mad.instruments.madrigal_geoind.madrigal_start,
              user='Firstname+Lastname', password='email@address.com')
gind.load(date=dt.datetime(1981, 1, 1))

pysatMadrigal.instruments.madrigal_geoind.clean(self)[source]: Raise warning that cleaning is not needed for the OMNI2 data.

pysatMadrigal.instruments.madrigal_geoind.init(self)[source]: Initialize the Instrument object in support of Madrigal access.

pysatMadrigal.instruments.madrigal_geoind.load(fnames, tag='', inst_id='')[source]

Load the Madrigal geoindex data.

Parameters:

fnameslist: List of filenames
tagstr: tag name used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)
inst_idstr: Instrument ID used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)

Returns:

datapds.DataFrame: Object containing geoindex data
metapysat.Meta: Object containing metadata such as column names and units

Raises:

ValueError: Unexpected time variable names

Madrigal_Pandas

A general instrument for Madrigal time-series data. This pysat.Instrument uses Madrigal instrument codes and kindats to support the use of any of the Madrigal time-series data sets. There are some further constraints in that the data set’s Madrigal naming convention must be parsable by pysat. Currently three Madrigal instrument codes are supported by this pysat.Instrument. When possible, using a specific instrument module is recommended, since that instrument module will have additional support (e.g., cleaning methods, experiment acknowledgements, and references).

Supports generalized access to Madrigal Data.

Properties

platform: ‘madrigal’
name: ‘pandas’
tag: Madrigal instrument code as a string
inst_id: Madrigal kindat as a string

Note

To use this routine, you need to know both the Madrigal Instrument code as well as the data tag numbers that Madrigal uses to uniquely identify data sets. You also need to know that the data is a simple time series (e.g., satellite in situ observations).

Multiple kindat values are supported, as long as they are separated by commas.

Although you can use this instrument module for any time-series data set, we highly recommend using the instrument-specific module if it exists.

Please provide name (user) and email (password) when downloading data with this routine.

Warnings

All data downloaded under this general support is placed in the same directory, pysat_data_dir/madrigal/pandas/. For technical reasons, the file search algorithm for pysat’s Madrigal support is set to permissive defaults. Thus, all instrument files downloaded via this interface will be picked up by the madrigal pandas pysat Instrument object unless the file_format keyword is used at instantiation.

Files can be safely downloaded without knowing the file_format keyword, or equivalently, how Madrigal names the files. See Examples for more.

Examples

import datetime as dt
import pysat
import pysatMadrigal as py_mad

# Download DMSP data from Madrigal
dmsp_abi = pysat.Instrument(inst_module=py_mad.instruments.madrigal_pandas,
                            tag='180', kindat='17110')
dmsp_abi.download(dt.datetime(2015, 1, 1), dt.datetime(2015, 1, 2),
                  user='Firstname+Lastname', password='email@address.com')
dmsp_abi.load(date=dt.datetime(2015, 1, 1))

pysatMadrigal.instruments.madrigal_pandas.clean(self)[source]: Raise warning that cleaning is not possible for general data.

pysatMadrigal.instruments.madrigal_pandas.download(date_array, tag, inst_id, data_path, user=None, password=None, file_type='hdf5', kindat='')[source]

Download data from Madrigal.

Parameters:

date_arrayarray-like: List of datetimes to download data for. The sequence of dates need not be contiguous.
tagstr: Madrigal Instrument code cast as a string.
inst_idstr: Instrument ID used for particular dataset.
data_pathstr: Path to directory to download data to.
userstr or NoneType: User name, raises an error if user not supplied. (default=None)
passwordstr or NoneType: User email, raises an error if not supplied. (default=None)
file_typestr: File format for Madrigal data. (default=’hdf5’)
kindatstr: Madrigal KINDAT code, specifies an experiment for the specified instrument. May be a single value, blank (all), or a comma-delimited list. (defaut=’’)

Raises:

ValueError: If user or password are not supplied

pysatMadrigal.instruments.madrigal_pandas.init(self, kindat='')[source]

Initialize the Instrument object in support of Madrigal access.

Parameters:

kindatstr: Madrigal instrument experiment code(s). (default=’’)

pysatMadrigal.instruments.madrigal_pandas.list_files(tag, inst_id, data_path, kindat='', format_str=None, file_cadence=datetime.timedelta(days=1), delimiter=None, file_type=None)[source]

Create a Pandas Series of every file for chosen Instrument data.

Parameters:

tagstr: Denotes type of file to load. Accepts strings corresponding to the appropriate Madrigal Instrument tags.
inst_idstr: Specifies the instrument ID to load. Accepts strings corresponding to the appropriate Madrigal Instrument inst_ids.
data_pathstr: Path to data directory.
kindatstr: Madrigal KINDAT code, specifies an experiment for the specified instrument. May be a single value, blank (all), or a comma-delimited list. (defaut=’’)
format_strstr or NoneType: User specified file format. If None is specified, the default formats associated with the supplied tags are used. (default=None)
supported_tagsdict or NoneType: Keys are inst_id, each containing a dict keyed by tag where the values file format template strings. (default=None)
file_cadencedt.timedelta or pds.DateOffset: pysat assumes a daily file cadence, but some instrument data file contain longer periods of time. This parameter allows the specification of regular file cadences greater than or equal to a day (e.g., weekly, monthly, or yearly). (default=dt.timedelta(days=1))
delimiterstr or NoneType: Delimiter string upon which files will be split (e.g., ‘.’). If None, filenames will be parsed presuming a fixed width format. (default=None)
file_typestr or NoneType: File format for Madrigal data. Load routines currently accepts ‘hdf5’, ‘simple’, and ‘netCDF4’, but any of the Madrigal options may be used here. If None, will look for all known file types. (default=None)

Returns:

outpds.Series: A pandas Series containing the verified available files

pysatMadrigal.instruments.madrigal_pandas.list_remote_files(tag, inst_id, kindat='', user=None, password=None, url='http://cedar.openmadrigal.org', start=datetime.datetime(1900, 1, 1, 0, 0), stop=datetime.datetime(2024, 3, 15, 20, 57, 52, 274729))[source]

List files available from Madrigal.

Parameters:

tagstr: Denotes type of file to load. Accepts strings corresponding to the appropriate Madrigal Instrument tags.
inst_idstr: Specifies the instrument ID to load. Accepts strings corresponding to the appropriate Madrigal Instrument inst_ids.
kindatstr: Madrigal KINDAT code, specifies an experiment for the specified instrument. May be a single value, blank (all), or a comma-delimited list. (defaut=’’)
data_pathstr or NoneType: Path to directory to download data to. (default=None)
userstr or NoneType: User string input used for download. Provided by user and passed via pysat. If an account is required for dowloads this routine here must error if user not supplied. (default=None)
passwordstr or NoneType: Password for data download. (default=None)
urlstr: URL for Madrigal site (default=’http://cedar.openmadrigal.org’)
startdt.datetime: Starting time for file list (default=dt.datetime(1900, 1, 1))
stopdt.datetime: Ending time for the file list (default=dt.datetime.utcnow())

Returns:

remote_filespds.Series: A series of filenames, see pysat.utils.files.process_parsed_filenames for more information.

Raises:

ValueError: For missing kwarg input
KeyError: For dictionary input missing requested tag/inst_id

NGDC_AE

An instrument for the Geophysical indices from NGDC, which include AE, AL, AU, and AO. The name is AE due to the Madrigal naming conventions. The data set spans the years of 1978 through 1987, will all data saved in a single file. Because of this, you only need to download the data once and any desired time period may be loaded.

Supports access to taped data of AE from the World Data Center A (Boulder).

Properties

platform: ‘ngdc’
name: ‘ae’
tag: None supported
inst_id: None supported

Note

Please provide name (user) and email (password) when downloading data with this routine.

Warnings

The entire data set (1 Jan 1978 through 31 Dec 1987) is provided in a single file on Madrigal.

Examples

import datetime as dt
import pysat
import pysatMadrigal as py_mad

# Download AE data from Madrigal
aei = pysat.Instrument(inst_module=py_mad.instruments.ngdc_ae)
aei.download(start=py_mad.instruments.ngdc_ae.madrigal_start,
             user='Firstname+Lastname', password='email@address.com')
aei.load(date=dt.datetime(1981, 1, 1))

pysatMadrigal.instruments.ngdc_ae.clean(self)[source]: Raise warning that cleaning is not possible for general data.

pysatMadrigal.instruments.ngdc_ae.init(self)[source]: Initialize the Instrument object in support of Madrigal access.

pysatMadrigal.instruments.ngdc_ae.load(fnames, tag='', inst_id='')[source]

Load the NGDC AE data.

Parameters:

fnameslist: List of filenames
tagstr: tag name used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)
inst_idstr: Instrument ID used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)

Returns:

datapds.DataFrame: Object containing satellite data
metapysat.Meta: Object containing metadata such as column names and units

Raises:

ValueError: Unexpected time variable names

OMNI2_IMF

An instrument for the interplanetary magnetic field (IMF) data from OMNI 2. The data starts in 1963 and the entire data set is contained in a single file. The file is occasionally updated, and so obtaining the most recent data means that all historic data must also be downloaded (or re-downloaded). OMNI data may also be obtained more directly using pysatNASA.

Supports access to OMNI 2 IMF data archived at Madrigal.

Properties

platform: ‘omni2’
name: ‘imf’
tag: None supported
inst_id: None supported

Note

Please provide name (user) and email (password) when downloading data with this routine.

Glenn Campbell and Bill Rideout completely rebuilt the Madrigal interplanetary magnetic field data using data from: ftp://nssdcftp.gsfc.nasa.gov/spacecraft_data/omni/. The old file had originally come from Cedar and had gaps even in places where there was data available. This new Madrigal file is based on the Omni 2 data set, described at http://nssdc.gsfc.nasa.gov/omniweb/. (4 May 2004, brideout@haystack.mit.edu)

The OMNI data may be directly downloaded using pysatNASA and is now described at: https://omniweb.gsfc.nasa.gov/html/ow_data.html

Warnings

The entire data set (27 Nov 1963 through 3 Jun 2019) is provided in a single file on Madrigal. The download method will break this file up by year for easier access.

Examples

import datetime as dt
import pysat
import pysatMadrigal as py_mad

# Download IMF data from Madrigal
imf = pysat.Instrument(inst_module=py_mad.instruments.omni2_imf)
imf.download(start=py_mad.instruments.omni2_imf.madrigal_start,
             user='Firstname+Lastname', password='email@address.com')
imf.load(date=dt.datetime(1981, 1, 1))

pysatMadrigal.instruments.omni2_imf.clean(self)[source]: Raise warning that cleaning is not needed for the OMNI2 data.

pysatMadrigal.instruments.omni2_imf.init(self)[source]: Initialize the Instrument object in support of Madrigal access.

pysatMadrigal.instruments.omni2_imf.load(fnames, tag='', inst_id='')[source]

Load the OMNI2 IMF data.

Parameters:

fnameslist: List of filenames
tagstr: tag name used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)
inst_idstr: Instrument ID used to identify particular data set to be loaded. This input is nominally provided by pysat itself. (default=’’)

Returns:

datapds.DataFrame: Object containing IMF data
metapysat.Meta: Object containing metadata such as column names and units

Raises:

ValueError: Unexpected time variable names