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nrlmsise00.dataset.core.msise_4d() B
last analyzed 2024-09-30 15:28 UTC

↳ Parent: nrlmsise00.dataset.core

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How to fix Long Method Many Parameters

# -*- coding: utf-8 -*-
# Copyright (c) 2020 Stefan Bender
#
# This file is part of pynrlmsise00.
# pynrlmsise00 is free software: you can redistribute it or modify
# it under the terms of the GNU General Public License as published
# by the Free Software Foundation, version 2.
# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html.
"""Python 4-D `xarray.dataset` interface to the NRLMSISE-00 model

"""
from collections import OrderedDict

import numpy as np
import pandas as pd
import xarray as xr

from spaceweather import sw_daily

from ..core import msise_flat

__all__ = ["msise_4d"]

MSIS_OUTPUT = [
	# name, long name, units
	# number densities
	("He", "He number density", "cm^-3"),
	("O", "O number density", "cm^-3"),
	("N2", "N2 number density", "cm^-3"),
	("O2", "O2 number density", "cm^-3"),
	("Ar", "AR number density", "cm^-3"),
	("rho", "total mass density", "g cm^-3"),  # includes d[8] in gtd7d
	("H", "H number density", "cm^-3"),
	("N", "N number density", "cm^-3"),
	("AnomO", "Anomalous oxygen number density", "cm^-3"),
	# temperature
	("Texo", "Exospheric temperature", "K"),
	("Talt", "Temperature at alt", "K"),
]

SW_INDICES = [
	# name, long name, units
	("Ap", "Daily Ap index", "nT"),
	(
		"f107",
		"Observed solar f10.7 cm radio flux of the previous day",
		"sfu, 10^-22 W m^-2 Hz^-1"
	),
	(
		"f107a",
		"Observed 81-day running average of the solar f10.7 cm radio flux centred on day",
		"sfu, 10^-22 W m^-2 Hz^-1"
	),
]


# helper functions
def _check_nd(a, ndim=1):
	a = np.atleast_1d(a)
	if a.ndim > ndim:
		raise ValueError(
			"Only scalars and up to {ndim}-D arrays are currently supported, "
			"got {a.ndim} dimensional array.".format(ndim=ndim, a=a)
		)
	return a


def _check_gm(gm, dts, df=None):
	"""Check that GM indices have the correct shape

	Returns the GM index broadcasted to shape (`dts`,).
	"""
	def _dfloc(t, df=None):
		return df.loc[t.floor("D")]

	if gm is None and df is not None:
		# select arbitrary shapes from pandas dataframes
		return np.vectorize(_dfloc, excluded=["df"])(dts, df=df)
	gm = np.atleast_1d(gm)
	if gm.ndim > 1:
		raise ValueError(
			"GM coefficients must be either scalars or one-dimensional arrays, "
			"other shapes are currently not supported."
		)
	if gm.shape != dts.shape:
		gm = np.broadcast_to(gm, dts.shape)
	return gm


def _check_lst(lst, time, lon):
	"""Check that LST has the correct shape

	Returns the LST broadcasted to shape (`time`, `lon`).
	"""
	lst = np.atleast_1d(lst)
	if lst.ndim > 2:
		raise ValueError("Only up to 2-D arrays are supported for LST.")
	if lst.ndim == 2:
		if (
			lst.shape[0] in [1, lon.size]
			and lst.shape[1] in [1, time.size]
		):
			# got shape (lon, time), (1, time), (lon, 1) or (1, 1)
			lst = lst.T
		else:
			# require shape (time, lon), (1, lon), (time, 1), or (1, 1)
			assert (
				lst.shape[0] in [1, time.size]
				and lst.shape[1] in [1, lon.size]
			)
	elif lst.ndim == 1:
		if lst.size == lon.size:
			# longitude takes precedence
			lst = lst[None, :]
		else:
			assert lst.size in [1, time.size]
			lst = lst[:, None]
	lsts = np.broadcast_to(lst, (time.size, lon.size))
	return lsts


def msise_4d(
	time, alt, lat, lon,
	f107a=None, f107=None, ap=None,
	lst=None,
	ap_a=None, flags=None,
	method="gtd7",
):
	u"""4-D Xarray Interface to :func:`msise_flat()`.

	4-D MSIS model atmosphere as a :class:`xarray.Dataset` with dimensions
	(time, alt, lat, lon). Only scalars and 1-D arrays for `time`, `alt`,
	`lat`, and `lon` are supported.

	The geomagnetic and Solar flux indices can be acquired using the
	`spaceweather` package (when set to `None`, the default).
	They can also be supplied explicitly as scalars or 1-D arrays with
	the same shape as `time`.

	The local solar time (LST) will usually be calculated from `time` and `lon`,
	but can be set explicitly using a fixed scalar LST, a 1-D array matching
	the shape of either `time` or `lon`, or a 2-D array matching the shape
	of (`time`, `lon`).

	Parameters
	----------
	time: `datetime.datetime`, `pandas` datetime, str, or 1-d array_like (I,)
		Time as `datetime.datetime`s, a `pandas` datetime object, a date-time
		string supported by `pandas.to_datetime()`, or an array of those.
		Will be converted with `pandas.to_datetime()`.
	alt: float or 1-d array_like (J,)
		Altitudes in [km].
	lat: float or 1-d array_like (K,)
		Latitudes in [°N].
	lon: float or 1-d array_like (L,)
		Longitudes in [°E]
	f107a: float or 1-d array_like (I,), optional
		The 81-day running average Solar 10.7cm radio flux,
		centred at the day(s) of `time`.
		Set to `None` (default) to use the `spaceweather` package.
	f107: float or 1-d array_like (I,), optional
		The Solar 10.7cm radio flux at the previous day(s) of `time`.
		Set to `None` (default) to use the `spaceweather` package.
	ap: float or 1-d array_like (I,), optional
		The daily geomagnetic Ap index at the day(s) of `time`.
		Set to `None` (default) to use the `spaceweather` package.
	lst: float, 1-d (I,) or (L,) or 2-d array_like (I,L), optional
		The local solar time at `time` and `lon`, to override the
		calculated values.
		Default: `None` (calculate from `time` and `lon`)
	ap_a: list of int (7,), optional
		List of Ap indices, passed to `msise_flat()`,
		broadcasting is currently not supported.
	flags: list of int (23,), optional
		List of flags, passed to `msise_flat()`,
		broadcasting is currently not supported.
	method: str, optional, default "gtd7"
		Select MSISE-00 method, changes the output of "rho",
		the atmospheric mass density.

	Returns
	-------
	msise_4d: :class:`xarray.Dataset`
		The MSIS atmosphere with dimensions ("time", "alt", "lat", "lon")
		and shape (I, J, K, L) containing the data arrays:
		"He", "O", "N2", "O2", "Ar", "rho", "H", "N", "AnomO", "Texo", "Talt",
		as well as the local solar times "lst" (I,L), and the
		values used for "Ap" (I,), "f107" (I,), "f107a" (I,).

	Example
	-------
	Using "internal" setting of the geomagnetic and Solar indices
	and automatic conversion to 4-D looks like this
	("time" can be an array of :class:`datetime.datetime` as well):

	>>> from datetime import datetime
	>>> from nrlmsise00.dataset import msise_4d
	>>> alts = np.arange(200, 401, 100.)  # = [200, 300, 400] [km]
	>>> lats = np.arange(60, 71, 10.)  # = [60, 70] [°N]
	>>> lons = np.arange(-70., 71., 35.)  # = [-70, -35,  0, 35, 70] [°E]
	>>> # broadcasting is done internally
	>>> ds = msise_4d(datetime(2009, 6, 21, 8, 3, 20), alts, lats, lons)
	>>> ds  # doctest: +SKIP
	<xarray.Dataset>
	Dimensions:  (alt: 3, lat: 2, lon: 5, time: 1)
	Coordinates:
	  * time     (time) datetime64[ns] 2009-06-21T08:03:20
	  * alt      (alt) float64 200.0 300.0 400.0
	  * lat      (lat) float64 60.0 70.0
	  * lon      (lon) float64 -70.0 -35.0 0.0 35.0 70.0
	Data variables:
	    He       (time, alt, lat, lon) float64 8.597e+05 1.063e+06 ... 4.936e+05
	    O        (time, alt, lat, lon) float64 1.248e+09 1.46e+09 ... 2.635e+07
	    N2       (time, alt, lat, lon) float64 2.555e+09 2.654e+09 ... 1.667e+06
	    O2       (time, alt, lat, lon) float64 2.1e+08 2.062e+08 ... 3.471e+04
	    Ar       (time, alt, lat, lon) float64 3.16e+06 3.287e+06 ... 76.55 67.16
	    rho      (time, alt, lat, lon) float64 1.635e-13 1.736e-13 ... 7.984e-16
	    H        (time, alt, lat, lon) float64 3.144e+05 3.02e+05 ... 1.237e+05
	    N        (time, alt, lat, lon) float64 9.095e+06 1.069e+07 ... 6.765e+05
	    AnomO    (time, alt, lat, lon) float64 1.173e-08 1.173e-08 ... 1.101e+04
	    Texo     (time, alt, lat, lon) float64 805.2 823.7 807.1 ... 818.7 821.2
	    Talt     (time, alt, lat, lon) float64 757.9 758.7 766.4 ... 818.7 821.1
	    lst      (time, lon) float64 3.389 5.722 8.056 10.39 12.72
	    Ap       (time) int32 6
	    f107     (time) float64 66.7
	    f107a    (time) float64 69.0

	See also
	--------
	msise_flat
	"""

	time = _check_nd(time)
	alt = _check_nd(alt)
	lat = _check_nd(lat)
	lon = _check_nd(lon)

	sw = sw_daily()
	if sw.index.tz is None:
		sw = sw.tz_localize("utc")
	# convert arbitrary shapes
	dts = pd.to_datetime(time, utc=True)
	# convert to numpy array for further processing
	dtsv = dts.to_numpy()
	# previous day for f10.7
	dtps = dtsv - pd.to_timedelta("1d")

	ap = _check_gm(ap, dtsv, df=sw[["Apavg"]])
	f107 = _check_gm(f107, dtps, df=sw[["f107_obs"]])
	f107a = _check_gm(f107a, dtsv, df=sw[["f107_81ctr_obs"]])

	# expand dimensions to 4d
	ts = dtsv[:, None, None, None]
	alts = alt[None, :, None, None]
	lats = lat[None, None, :, None]
	lons = lon[None, None, None, :]

	aps = ap[:, None, None, None]
	f107s = f107[:, None, None, None]
	f107as = f107a[:, None, None, None]

	if lst is not None:
		lsts = _check_lst(lst, time, lon)
		# used for MSIS below
		lst = lsts[:, None, None, :]
	else:
		# calculated for the returned dataset
		lsts = np.array([
			t.hour + t.minute / 60. + t.second / 3600. + lon / 15.
			for t in dts
		])

	msis_data = msise_flat(
		ts, alts, lats, lons,
		f107as, f107s, aps,
		lst=lst, ap_a=ap_a, flags=flags, method=method,
	)
	ret = xr.Dataset(
		OrderedDict([(
			m[0], (
				["time", "alt", "lat", "lon"],
				d,
				{"long_name": m[1], "units": m[2]}
			))
			for m, d in zip(MSIS_OUTPUT, np.rollaxis(msis_data, -1))
		]),
		coords=OrderedDict([
			("time", dts.tz_localize(None)),
			("alt", ("alt", alt, {"long_name": "altitude", "units": "km"})),
			("lat", ("lat", lat, {"long_name": "latitude", "units": "degrees_north"})),
			("lon", ("lon", lon, {"long_name": "longitude", "units": "degrees_east"})),
		]),
	)
	ret["lst"] = (
		["time", "lon"], lsts, {"long_name": "Mean Local Solar Time", "units": "h"}
	)
	ret["Ap"] = (["time"], ap)
	ret["f107"] = (["time"], f107)
	ret["f107a"] = (["time"], f107a)
	for _sw in SW_INDICES:
		ret[_sw[0]].attrs.update({"long_name": _sw[1], "units": _sw[2]})
	return ret


1			# -- coding: utf-8 --
2			# Copyright (c) 2020 Stefan Bender
3			#
4			# This file is part of pynrlmsise00.
5			# pynrlmsise00 is free software: you can redistribute it or modify
6			# it under the terms of the GNU General Public License as published
7			# by the Free Software Foundation, version 2.
8			# See accompanying LICENSE file or http://www.gnu.org/licenses/gpl-2.0.html.
9			"""Python 4-D `xarray.dataset` interface to the NRLMSISE-00 model
10
11			"""
12			from collections import OrderedDict
13
14			import numpy as np
15			import pandas as pd
16			import xarray as xr
17
18			from spaceweather import sw_daily
19
20			from ..core import msise_flat
21
22			__all__ = ["msise_4d"]
23
24			MSIS_OUTPUT = [
25			# name, long name, units
26			# number densities
27			("He", "He number density", "cm^-3"),
28			("O", "O number density", "cm^-3"),
29			("N2", "N2 number density", "cm^-3"),
30			("O2", "O2 number density", "cm^-3"),
31			("Ar", "AR number density", "cm^-3"),
32			("rho", "total mass density", "g cm^-3"), # includes d[8] in gtd7d
33			("H", "H number density", "cm^-3"),
34			("N", "N number density", "cm^-3"),
35			("AnomO", "Anomalous oxygen number density", "cm^-3"),
36			# temperature
37			("Texo", "Exospheric temperature", "K"),
38			("Talt", "Temperature at alt", "K"),
39			]
40
41			SW_INDICES = [
42			# name, long name, units
43			("Ap", "Daily Ap index", "nT"),
44			(
45			"f107",
46			"Observed solar f10.7 cm radio flux of the previous day",
47			"sfu, 10^-22 W m^-2 Hz^-1"
48			),
49			(
50			"f107a",
51			"Observed 81-day running average of the solar f10.7 cm radio flux centred on day",
52			"sfu, 10^-22 W m^-2 Hz^-1"
53			),
54			]
55
56
57			# helper functions
58			def _check_nd(a, ndim=1):
59			a = np.atleast_1d(a)
60			if a.ndim > ndim:
61			raise ValueError(
62			"Only scalars and up to {ndim}-D arrays are currently supported, "
63			"got {a.ndim} dimensional array.".format(ndim=ndim, a=a)
64			)
65			return a
66
67
68			def _check_gm(gm, dts, df=None):
69			"""Check that GM indices have the correct shape
70
71			Returns the GM index broadcasted to shape (`dts`,).
72			"""
73			def _dfloc(t, df=None):
74			return df.loc[t.floor("D")]
75
76			if gm is None and df is not None:
77			# select arbitrary shapes from pandas dataframes
78			return np.vectorize(_dfloc, excluded=["df"])(dts, df=df)
79			gm = np.atleast_1d(gm)
80			if gm.ndim > 1:
81			raise ValueError(
82			"GM coefficients must be either scalars or one-dimensional arrays, "
83			"other shapes are currently not supported."
84			)
85			if gm.shape != dts.shape:
86			gm = np.broadcast_to(gm, dts.shape)
87			return gm
88
89
90			def _check_lst(lst, time, lon):
91			"""Check that LST has the correct shape
92
93			Returns the LST broadcasted to shape (`time`, `lon`).
94			"""
95			lst = np.atleast_1d(lst)
96			if lst.ndim > 2:
97			raise ValueError("Only up to 2-D arrays are supported for LST.")
98			if lst.ndim == 2:
99			if (
100			lst.shape[0] in [1, lon.size]
101			and lst.shape[1] in [1, time.size]
102			):
103			# got shape (lon, time), (1, time), (lon, 1) or (1, 1)
104			lst = lst.T
105			else:
106			# require shape (time, lon), (1, lon), (time, 1), or (1, 1)
107			assert (
108			lst.shape[0] in [1, time.size]
109			and lst.shape[1] in [1, lon.size]
110			)
111			elif lst.ndim == 1:
112			if lst.size == lon.size:
113			# longitude takes precedence
114			lst = lst[None, :]
115			else:
116			assert lst.size in [1, time.size]
117			lst = lst[:, None]
118			lsts = np.broadcast_to(lst, (time.size, lon.size))
119			return lsts
120
121
122			def msise_4d(
123			time, alt, lat, lon,
124			f107a=None, f107=None, ap=None,
125			lst=None,
126			ap_a=None, flags=None,
127			method="gtd7",
128			):
129			u"""4-D Xarray Interface to :func:`msise_flat()`.
130
131			4-D MSIS model atmosphere as a :class:`xarray.Dataset` with dimensions
132			(time, alt, lat, lon). Only scalars and 1-D arrays for `time`, `alt`,
133			`lat`, and `lon` are supported.
134
135			The geomagnetic and Solar flux indices can be acquired using the
136			`spaceweather` package (when set to `None`, the default).
137			They can also be supplied explicitly as scalars or 1-D arrays with
138			the same shape as `time`.
139
140			The local solar time (LST) will usually be calculated from `time` and `lon`,
141			but can be set explicitly using a fixed scalar LST, a 1-D array matching
142			the shape of either `time` or `lon`, or a 2-D array matching the shape
143			of (`time`, `lon`).
144
145			Parameters
146			----------
147			time: `datetime.datetime`, `pandas` datetime, str, or 1-d array_like (I,)
148			Time as `datetime.datetime`s, a `pandas` datetime object, a date-time
149			string supported by `pandas.to_datetime()`, or an array of those.
150			Will be converted with `pandas.to_datetime()`.
151			alt: float or 1-d array_like (J,)
152			Altitudes in [km].
153			lat: float or 1-d array_like (K,)
154			Latitudes in [°N].
155			lon: float or 1-d array_like (L,)
156			Longitudes in [°E]
157			f107a: float or 1-d array_like (I,), optional
158			The 81-day running average Solar 10.7cm radio flux,
159			centred at the day(s) of `time`.
160			Set to `None` (default) to use the `spaceweather` package.
161			f107: float or 1-d array_like (I,), optional
162			The Solar 10.7cm radio flux at the previous day(s) of `time`.
163			Set to `None` (default) to use the `spaceweather` package.
164			ap: float or 1-d array_like (I,), optional
165			The daily geomagnetic Ap index at the day(s) of `time`.
166			Set to `None` (default) to use the `spaceweather` package.
167			lst: float, 1-d (I,) or (L,) or 2-d array_like (I,L), optional
168			The local solar time at `time` and `lon`, to override the
169			calculated values.
170			Default: `None` (calculate from `time` and `lon`)
171			ap_a: list of int (7,), optional
172			List of Ap indices, passed to `msise_flat()`,
173			broadcasting is currently not supported.
174			flags: list of int (23,), optional
175			List of flags, passed to `msise_flat()`,
176			broadcasting is currently not supported.
177			method: str, optional, default "gtd7"
178			Select MSISE-00 method, changes the output of "rho",
179			the atmospheric mass density.
180
181			Returns
182			-------
183			msise_4d: :class:`xarray.Dataset`
184			The MSIS atmosphere with dimensions ("time", "alt", "lat", "lon")
185			and shape (I, J, K, L) containing the data arrays:
186			"He", "O", "N2", "O2", "Ar", "rho", "H", "N", "AnomO", "Texo", "Talt",
187			as well as the local solar times "lst" (I,L), and the
188			values used for "Ap" (I,), "f107" (I,), "f107a" (I,).
189
190			Example
191			-------
192			Using "internal" setting of the geomagnetic and Solar indices
193			and automatic conversion to 4-D looks like this
194			("time" can be an array of :class:`datetime.datetime` as well):
195
196			>>> from datetime import datetime
197			>>> from nrlmsise00.dataset import msise_4d
198			>>> alts = np.arange(200, 401, 100.) # = [200, 300, 400] [km]
199			>>> lats = np.arange(60, 71, 10.) # = [60, 70] [°N]
200			>>> lons = np.arange(-70., 71., 35.) # = [-70, -35, 0, 35, 70] [°E]
201			>>> # broadcasting is done internally
202			>>> ds = msise_4d(datetime(2009, 6, 21, 8, 3, 20), alts, lats, lons)
203			>>> ds # doctest: +SKIP
204			<xarray.Dataset>
205			Dimensions: (alt: 3, lat: 2, lon: 5, time: 1)
206			Coordinates:
207			* time (time) datetime64[ns] 2009-06-21T08:03:20
208			* alt (alt) float64 200.0 300.0 400.0
209			* lat (lat) float64 60.0 70.0
210			* lon (lon) float64 -70.0 -35.0 0.0 35.0 70.0
211			Data variables:
212			He (time, alt, lat, lon) float64 8.597e+05 1.063e+06 ... 4.936e+05
213			O (time, alt, lat, lon) float64 1.248e+09 1.46e+09 ... 2.635e+07
214			N2 (time, alt, lat, lon) float64 2.555e+09 2.654e+09 ... 1.667e+06
215			O2 (time, alt, lat, lon) float64 2.1e+08 2.062e+08 ... 3.471e+04
216			Ar (time, alt, lat, lon) float64 3.16e+06 3.287e+06 ... 76.55 67.16
217			rho (time, alt, lat, lon) float64 1.635e-13 1.736e-13 ... 7.984e-16
218			H (time, alt, lat, lon) float64 3.144e+05 3.02e+05 ... 1.237e+05
219			N (time, alt, lat, lon) float64 9.095e+06 1.069e+07 ... 6.765e+05
220			AnomO (time, alt, lat, lon) float64 1.173e-08 1.173e-08 ... 1.101e+04
221			Texo (time, alt, lat, lon) float64 805.2 823.7 807.1 ... 818.7 821.2
222			Talt (time, alt, lat, lon) float64 757.9 758.7 766.4 ... 818.7 821.1
223			lst (time, lon) float64 3.389 5.722 8.056 10.39 12.72
224			Ap (time) int32 6
225			f107 (time) float64 66.7
226			f107a (time) float64 69.0
227
228			See also
229			--------
230			msise_flat
231			"""
232
233			time = _check_nd(time)
234			alt = _check_nd(alt)
235			lat = _check_nd(lat)
236			lon = _check_nd(lon)
237
238			sw = sw_daily()
239			if sw.index.tz is None:
240			sw = sw.tz_localize("utc")
241			# convert arbitrary shapes
242			dts = pd.to_datetime(time, utc=True)
243			# convert to numpy array for further processing
244			dtsv = dts.to_numpy()
245			# previous day for f10.7
246			dtps = dtsv - pd.to_timedelta("1d")
247
248			ap = _check_gm(ap, dtsv, df=sw[["Apavg"]])
249			f107 = _check_gm(f107, dtps, df=sw[["f107_obs"]])
250			f107a = _check_gm(f107a, dtsv, df=sw[["f107_81ctr_obs"]])
251
252			# expand dimensions to 4d
253			ts = dtsv[:, None, None, None]
254			alts = alt[None, :, None, None]
255			lats = lat[None, None, :, None]
256			lons = lon[None, None, None, :]
257
258			aps = ap[:, None, None, None]
259			f107s = f107[:, None, None, None]
260			f107as = f107a[:, None, None, None]
261
262			if lst is not None:
263			lsts = _check_lst(lst, time, lon)
264			# used for MSIS below
265			lst = lsts[:, None, None, :]
266			else:
267			# calculated for the returned dataset
268			lsts = np.array([
269			t.hour + t.minute / 60. + t.second / 3600. + lon / 15.
270			for t in dts
271			])
272
273			msis_data = msise_flat(
274			ts, alts, lats, lons,
275			f107as, f107s, aps,
276			lst=lst, ap_a=ap_a, flags=flags, method=method,
277			)
278			ret = xr.Dataset(
279			OrderedDict([(
280			m[0], (
281			["time", "alt", "lat", "lon"],
282			d,
283			{"long_name": m[1], "units": m[2]}
284			))
285			for m, d in zip(MSIS_OUTPUT, np.rollaxis(msis_data, -1))
286			]),
287			coords=OrderedDict([
288			("time", dts.tz_localize(None)),
289			("alt", ("alt", alt, {"long_name": "altitude", "units": "km"})),
290			("lat", ("lat", lat, {"long_name": "latitude", "units": "degrees_north"})),
291			("lon", ("lon", lon, {"long_name": "longitude", "units": "degrees_east"})),
292			]),
293			)
294			ret["lst"] = (
295			["time", "lon"], lsts, {"long_name": "Mean Local Solar Time", "units": "h"}
296			)
297			ret["Ap"] = (["time"], ap)
298			ret["f107"] = (["time"], f107)
299			ret["f107a"] = (["time"], f107a)
300			for _sw in SW_INDICES:
301			ret[_sw[0]].attrs.update({"long_name": _sw[1], "units": _sw[2]})
302			return ret
303

st-bender / pynrlmsise00

nrlmsise00.dataset.core.msise_4d() B last analyzed 2024-09-30 15:28 UTC

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nrlmsise00.dataset.core.msise_4d() B
last analyzed 2024-09-30 15:28 UTC