#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Module with some useful empirical models (plasmapause, magnetopause, Lmax)
Authors: Steve Morley, Josef Koller
Institution: Los Alamos National Laboratory
Contact: smorley@lanl.gov
Copyright 2010 Los Alamos National Security, LLC.
"""
from __future__ import division
import datetime
import warnings
from functools import partial
import numpy as np
import scipy.integrate as integ
if not hasattr(integ, "simpson"): # scipy<1.7.0
integ.simpson = integ.simps
from spacepy import help
import spacepy.datamodel as dm
import spacepy.toolbox as tb
import spacepy.omni as om
import spacepy.time as spt
__contact__ = 'Steve Morley, smorley@lanl.gov'
[docs]
def getLmax(ticks, model='JKemp', dbase='QDhourly'):
"""
calculate a simple empirical model for Lmax - last closed drift-shell
Uses the parametrized Lmax from:
Koller and Morley (2010)
'Magnetopause shadowing effects for radiation belt models during high-speed solar wind streams'
American Geophysical Union, Fall Meeting 2010, abstract #SM13A-1787
Parameters
==========
ticks : spacepy.time.Ticktock
Ticktock object of desired times
model : string, optional
'JKemp' (default - empirical model of J. Koller)
Returns
=======
out : np.ndarray
Lmax - L* of last closed drift shell
Examples
========
>>> from spacepy.empiricals import getLmax
>>> import spacepy.time as st
>>> import datetime
>>> ticks = st.tickrange(datetime.datetime(2000, 1, 1), datetime.datetime(2000, 1, 3), deltadays=1)
array([ 7.4928412, 8.3585632, 8.6463423])
See Also
========
spacepy.LANLstar.LANLmax
"""
omni = om.get_omni(ticks, dbase=dbase)
Dst = omni['Dst']
Lmax = np.zeros(len(Dst))
if model == 'JKemp':
for i, iDst in enumerate(Dst):
Lmax[i] = 6.07e-5*iDst*iDst + 0.0436*iDst + 9.37
else:
raise ValueError('Invalid model selection')
return Lmax
[docs]
def getPlasmaPause(ticks, model='M2002', LT='all', omnivals=None):
"""
Plasmapause location model(s)
CA1992 -- Carpenter, D. L., and R. R. Anderson, An ISEE/whistler
model of equatorial electron density in the magnetosphere,
J. Geophys. Res., 97, 1097, 1992.
M2002 -- Moldwin, M. B., L. Downward, H. K. Rassoul, R. Amin,
and R. R. Anderson, A new model of the location of the plasmapause:
CRRES results, J. Geophys. Res., 107(A11), 1339,
doi:10.1029/2001JA009211, 2002.
RT1970 -- Rycroft, M. J., and J. O. Thomas, The magnetospheric
plasmapause and the electron density trough at the alouette i
orbit, Planetary and Space Science, 18(1), 65-80, 1970
Parameters
==========
ticks : spacepy.time.Ticktock
TickTock object of desired times
Lpp_model : string, optional
'CA1992' or 'M2002' (default)
CA1992 returns the Carpenter and Anderson model,
M2002 returns the Moldwin et al. model
LT : int, float
requested local time sector, 'all' is valid option
omnivals : spacepy.datamodel.SpaceData, dict
dict-like containing UTC (datetimes) and Kp keys
Warns
=====
RuntimeWarning
If the CA1992 model is called with LT as it is not implemented
Returns
=======
out : float
Plasmapause radius in Earth radii
Examples
========
>>> import spacepy.time as spt
>>> import spacepy.empiricals as emp
>>> ticks = spt.tickrange('2002-01-01T12:00:00','2002-01-04T00:00:00',.25)
>>> emp.getPlasmaPause(ticks)
array([ 6.42140002, 6.42140002, 6.42140002, 6.42140002, 6.42140002,
6.42140002, 6.42140002, 6.26859998, 5.772 , 5.6574 ,
5.6574 ])
"""
def calcLpp(Kpmax, A, B, power=1):
currLpp = A - B*Kpmax**power
return currLpp
model_list = ['CA1992', 'M2002', 'RT1970']
if model == 'CA1992':
if LT != 'all':
warnings.warn('No LT dependence currently supported for CA1992 model',
RuntimeWarning, stacklevel=2)
if model not in model_list:
raise ValueError("Please specify a valid model:\n{0}".format(' or '.join(model_list)))
if LT=='all':
parA = {'CA1992': 5.6, 'M2002': 5.39, 'RT1970': 5.64}
parB = {'CA1992': 0.46, 'M2002': 0.382, 'RT1970': 0.78}
priorvals = {'CA1992': datetime.timedelta(hours=24),
'M2002': datetime.timedelta(hours=12),
'RT1970': datetime.timedelta(0)}
A, B = parA[model], parB[model]
prior = priorvals[model]
else:
try:
float(LT)
except (ValueError, TypeError):
raise ValueError("Please specify a valid LT:\n'all' or a numeric type")
parA = {'CA1992': [5.6]*24,
'M2002': [5.7]*3+[6.05]*6+[5.2]*6+[4.45]*6+[5.7]*3,
'RT1970': [5.64]*24}
parB = {'CA1992': [0.46]*24,
'M2002': [0.42]*3+[0.573]*6+[0.425]*6+[0.167]*6+[0.42]*3,
'RT1970': [0.78]*24}
priorvals = {'CA1992': [datetime.timedelta(hours=24)]*24,
'M2002': [datetime.timedelta(hours=12)]*24,
'RT1970': [datetime.timedelta(0)]*24}
try:
LThr = long(LT)
except NameError:
LThr = int(LT)
prior = priorvals[model][LThr]
A, B = parA[model][LThr], parB[model][LThr]
st, en = ticks.UTC[0]-prior, ticks.UTC[-1]
if omnivals is None:
omdat = om.get_omni(spt.tickrange(st, en, 1.0/24.0), dbase='QDhourly')
else:
#now test for sanity of input
try:
assert isinstance(omnivals, dict)
except:
raise TypeError('Not a valid input type for omnivals, expected spacepy.datamodel.SpaceData')
try:
assert 'UTC' in omnivals
assert 'Kp' in omnivals
except:
raise KeyError('Required data not found in input dict-like (omnivals)')
omdat = omnivals
einds, oinds = tb.tOverlap([st, en], omdat['UTC'])
utc = np.array(omdat['UTC'])[oinds]
Kp = np.array(omdat['Kp'])[oinds]
Lpp = np.zeros(len(ticks))
if model == 'RT1970':
power = 0.5
else:
power = 1
for i, t1 in enumerate(ticks.UTC):
t0 = t1-prior
iprevday, dum = tb.tOverlap(utc, [t0, t1])
if iprevday:
Kpmax = max(Kp[iprevday])
Lpp[i] = calcLpp(Kpmax, A, B, power=power)
else:
Lpp[i] = np.nan
return Lpp
[docs]
def getMagnetopause(ticks, LTs=None, dbase='QDhourly'):
'''Calculates the Shue et al. (1997) position in equatorial plane
Shue et al. (1997), A new functional form to study the solar wind
control of the magnetopause size and shape, J. Geophys. Res., 102(A5),
9497–9511, doi:10.1029/97JA00196.
Parameters
==========
ticks : spacepy.time.Ticktock
TickTock object of desired times (will be interpolated from hourly OMNI data)
OR dictionary of form {'P': [], 'Bz': []}
Where P is SW ram pressure [nPa] and Bz is IMF Bz (GSM) [nT]
LTs : array-like
Array-like of local times for evaluating the magnetopause model. Default is
6 LT to 18 LT in steps of 20 minutes.
Returns
=======
out : array
NxMx2 array of magnetopause positions [Re]
N is number of timesteps, M is number of local times. The 2 positions
are the X_GSE and Y_GSE positions of the magnetopause
Examples
========
>>> import spacepy.time as spt
>>> import spacepy.empiricals as emp
>>> ticks = spt.Ticktock(['2002-01-01T12:00:00','2002-01-04T00:00:00'])
>>> localtimes = [13,12,11]
>>> emp.getMagnetopause(ticks, LTs=localtimes)
array([[[ 10.27674331, -2.75364507],
[ 10.52909163, 0. ],
[ 10.27674331, 2.75364507]],
[[ 10.91791834, -2.9254474 ],
[ 11.18712131, 0. ],
[ 10.91791834, 2.9254474 ]]])
>>> emp.getMPstandoff(ticks) #should give same result as getMagnetopause for 12LT
array([ 10.52909163, 11.18712131])
To plot the magnetopause:
>>> import numpy as np
>>> import spacepy.plot as splot
>>> import matplotlib.pyplot as plt
>>> localtimes = np.arange(5, 19.1, 0.5)
>>> mp_pos = emp.getMagnetopause(ticks, localtimes)
>>> plt.plot(mp_pos[0,:,0], mp_pos[0,:,1])
>>> ax1 = plt.gca()
>>> ax1.set_xlim([-5,20])
>>> ax1.set_xlabel('X$_{GSE}$ [R$_E$]')
>>> ax1.set_ylabel('Y$_{GSE}$ [R$_E$]')
>>> splot.dual_half_circle(ax=ax1)
>>> ax1.axes.set_aspect('equal')
'''
alpha = []
r0 = getMPstandoff(ticks, dbase=dbase, alpha=alpha)
alpha = np.asarray(alpha)
if LTs is None:
LTs = np.arange(6, 18.1, 1/3.0)
r = np.zeros([len(ticks),len(LTs)])
out = np.zeros([len(ticks),len(LTs), 2])
angs = (12.0-np.asanyarray(LTs))*15.0
costheta = np.cos(np.deg2rad(angs))
sintheta = np.sin(np.deg2rad(angs))
for idx, ct in enumerate(costheta):
r[:,idx] = r0 * (2.0/(1+ct))**alpha
out[:,:,0] = r*costheta #Xgse
out[:,:,1] = r*sintheta #Ygse
return out
[docs]
def getMPstandoff(ticks, dbase='QDhourly', alpha=[]):
"""Calculates the Shue et al. (1997) subsolar magnetopause radius
Shue et al. (1997), A new functional form to study the solar wind
control of the magnetopause size and shape, J. Geophys. Res., 102(A5),
9497–9511, doi:10.1029/97JA00196.
Parameters
==========
ticks : spacepy.time.Ticktock
TickTock object of desired times (will be interpolated from hourly OMNI data)
OR dictionary of form {'P': [], 'Bz': []}
Where P is SW ram pressure [nPa] and Bz is IMF Bz (GSM) [nT]
alpha : list
Used as an optional return value to obtain the flaring angles. To use, assign
an empty list and pass to this function through the keyword argument. The list
will be modified in place, adding the flaring angles for each time step.
Returns
=======
out : float
Magnetopause (sub-solar point) standoff distance [Re]
Examples
========
>>> import spacepy.time as spt
>>> import spacepy.empiricals as emp
>>> ticks = spt.tickrange('2002-01-01T12:00:00','2002-01-04T00:00:00',.25)
>>> emp.getMPstandoff(ticks)
array([ 10.57319537, 10.91327764, 10.75086873, 10.77577207,
9.78180261, 11.0374474 , 11.4065 , 11.27555451,
11.47988573, 11.8202582 , 11.23834814])
>>> data = {'P': [2,4], 'Bz': [-2.4, -2.4]}
>>> emp.getMPstandoff(data)
array([ 9.96096838, 8.96790412])
"""
if type(ticks) == spt.Ticktock:
omni = om.get_omni(ticks, dbase=dbase)
P, Bz = omni['Pdyn'], omni['BzIMF']
elif isinstance(ticks, dict):
P, Bz = ticks['P'], ticks['Bz']
try:
len(P)
except TypeError:
P = [P]
try:
len(Bz)
except TypeError:
Bz = [Bz]
else:
raise(TypeError('Invalid Input type'))
try:
# Initialize r0 and make P and Bz numpy arrays
r0 = np.zeros((len(P)), dtype=float)
Bz = np.array(Bz)
P = np.array(P)
# Find where Bz >= 0 and where it is < 0
iBzPos = np.where(Bz >= 0)
iBzNeg = np.where(Bz < 0)
# Calculate r0
r0[iBzPos] = (11.4 + 0.013*Bz[iBzPos])*P[iBzPos]**(-1/6.6)
r0[iBzNeg] = (11.4 + 0.140*Bz[iBzNeg])*P[iBzNeg]**(-1/6.6)
flarang = (0.58 - 0.01*Bz)*(1.0 + 0.01*P)
alpha.extend(flarang)
return r0
except TypeError:
raise TypeError("Please check for valid input types")
[docs]
def getDststar(ticks, model='OBrien', dbase='QDhourly'):
"""Calculate the pressure-corrected Dst index, Dst*
We need to add in the references to the models here!
Parameters
==========
ticks : spacepy.time.Ticktock
TickTock object of desired times (will be interpolated from hourly OMNI data)
OR dictionary including 'Pdyn' and 'Dst' keys where data are lists or arrays
and Dst is in [nT], and Pdyn is in [nPa]
Returns
=======
out : float
Dst* - the pressure corrected Dst index from OMNI [nT]
Examples
========
Coefficients are applied to the standard formulation e.g. Burton et al., 1975
of Dst* = Dst - b*sqrt(Pdyn) + c
The default is the O'Brien and McPherron model (2002).
Other options are Burton et al. (1975) and Borovsky and Denton (2010)
>>> import spacepy.time as spt
>>> import spacepy.omni as om
>>> import spacepy.empiricals as emp
>>> ticks = spt.tickrange('2000-10-16T00:00:00', '2000-10-31T12:00:00', 1/24.)
>>> dststar = emp.getDststar(ticks)
>>> dststar[0]
-21.317220132108943
User-determined coefficients can also be supplied as a two-element list
or tuple of the form (b,c), e.g.
>>> dststar = emp.getDststar(ticks, model=(2,11)) #b is extreme driving from O'Brien
We have chosen the OBrien model as the default here as this was rigorously
determined from a very long data set and is pertinent to most conditions.
It is, however, the most conservative correction. Additionally, Siscoe,
McPherron and Jordanova (2005) argue that the pressure contribution to Dst diminishes
during magnetic storms.
To show the relative differences, run the following example:
>>> import matplotlib.pyplot as plt
>>> params = [('Burton','k-'), ('OBrien','r-'), ('Borovsky','b-')]
>>> for model, col in params:
dststar = getDststar(ticks, model=model)
plt.plot(ticks.UTC, dststar, col)
"""
model_params = {'Burton': (15.8, 20),
'OBrien': (7.26, 11),
'Borovsky': (20.7,27.7)}
if isinstance(model, str):
try:
b, c = model_params[model]
except KeyError:
raise ValueError('Invalid pressure correction model selected')
else:
try:
b, c = model[0], model[1]
except (KeyError, IndexError):
raise ValueError('Invalid coefficients set: must be of form (b,c)')
if isinstance(ticks, spt.Ticktock):
omni = om.get_omni(ticks, dbase=dbase)
P, Dst = omni['Pdyn'], omni['Dst']
elif isinstance(ticks, dict):
P, Dst = ticks['Pdyn'], ticks['Dst']
if isinstance(P, list):
P, Dst = np.array(P), np.array(Dst)
#get Dst*
Dststar = Dst - b*P**0.5 + c
return Dststar
[docs]
def getExpectedSWTemp(velo, model='XB15', units='K'):
'''Return the expected solar wind temperature based on the bulk velocity
The formulations used by this function are those given by,
L87 -- Lopez, R.E., J. Geophys. Res., 92, 11189-11194, 1987
BS06 -- Borovsky, J.E. and J.T. Steinberg, Geophysical Monograph Series 167, 59-76, 2006
XB15 -- Xu, F. and J.E. Borovsky, J. Geophys. Res., 120, 70-100, 2015
Parameters
==========
velo : array-like
Array like of solar wind bulk velocity values [km/s]
model : str [optional]
Name of model to use. Valid choices are L87, BS06 and XB15. Default is XB15
units : str [optional]
Units for output temperature, options are eV or K. Default is Kelvin [K]
Returns
=======
Texp : array-like
The expected solar wind temperature given the bulk velocity [K] or [eV]
'''
v = np.asanyarray(velo)
def bs06(v):
'''Borovsky and Steinberg 2006, Geophysical Monograph - median values'''
Texp = np.empty(len(v))
Texp.fill(np.nan)
Texp[v<372] = 1.28e-8*v[v<372]**3.324
Texp[v>=372] = 0.0572*v[v>=372] - 16.79
return Texp
def xb15(v):
'''Xu and Borovsky 2015, JGR'''
Texp = (v/258.0)**3.113
return Texp
def l87(v):
'''Lopez 1987, JGR - Tables 1&2 [T(V)] from IMP-8'''
Texp = np.empty(len(v))
Texp.fill(np.nan)
Texp[v<500] = (0.031*v[v<500] - 5.1)**2
Texp[v>=500] = (0.02*v[v>=500] + 0.5)**2
return Texp*1e3/1.16045221e4 #return in eV
formulae = {'BS06': bs06, 'XB15': xb15, 'L87': l87}
try:
mod = model.upper()
Texp = formulae[mod](v)
except KeyError:
raise KeyError('Invalid model specified for SW temperature')
if units.lower()=='k':
return Texp*1.16045221e4
elif units.lower()=='ev':
return Texp
else:
raise ValueError('Invalid units specified for SW temperature, must be "K" or "eV"')
[docs]
def vampolaPA(omniflux, **kwargs):
'''Pitch angle model of sin^n form
Parameters
==========
omniflux : arraylike or float
omnidirectional number flux data
order : integer or float (optional)
order of sin^n functional form for distribution (default=2)
alphas : arraylike (optional)
pitch angles at which to evaluate the differential number flux
(default is 5 to 90 degrees in 36 steps)
Returns
=======
dnflux : array
differential number flux corresponding to pitch angles alphas
alphas : array
pitch angles at which the differential number flux was evaluated
Examples
========
Omnidirectional number flux of [3000, 6000]
>>> from spacepy.empiricals import vampolaPA
>>> vampolaPA(3000, alpha=[45, 90])
(array([ 179.04931098, 358.09862196]), [45, 90])
>>> data, pas = vampolaPA([3000, 6000], alpha=[45, 90])
>>> pas
[45, 90]
>>> data
array([[ 179.04931098, 358.09862196],
[ 358.09862196, 716.19724391]])
Notes
=====
Directional number flux integrated over pitch angle from 0 to 90 degrees
is a factor of 4*pi lower than omnidirectional number flux.
'''
defaults = {'order': 2,
'alpha': tb.linspace(5,90,18)}
if hasattr(omniflux, '__iter__'):
omniflux = np.asanyarray(omniflux)
else:
omniflux = np.asanyarray([omniflux])
#substitute defaults
for key in defaults:
if key not in kwargs:
kwargs[key] = defaults[key]
if hasattr(kwargs['order'], '__iter__'):
try:
assert len(kwargs['order'])==len(omniflux)
except AssertionError:
raise ValueError('order must be either single-valued or the same length as omniflux')
else:
kwargs['order'] = np.asanyarray([kwargs['order']]*len(omniflux))
normfac = np.empty(len(kwargs['order']), dtype=float)
def sinfunc(x, order=kwargs['order']): #define distribution function
dum = np.sin(x)
return dum**order
for idx, tmporder in enumerate(kwargs['order']):
sinfunc_o = partial(sinfunc, order=tmporder+1)
normfac[idx] = omniflux[idx]/(2*np.pi*integ.quad(sinfunc_o, 0, np.pi)[0])
#now make the differential number flux
dnflux = np.zeros((len(kwargs['alpha']), len(omniflux)))
for i, a_val in enumerate(np.deg2rad(kwargs['alpha'])):
dnflux[i] = normfac * sinfunc(a_val)
return dnflux.squeeze(), kwargs['alpha']
[docs]
def getVampolaOrder(L):
'''Empirical lookup of power for sin^n pitch angle model from Vampola (1996)
Vampola, A.L. Outer zone energetic electron environment update,
Final Report of ESA/ESTEC/WMA/P.O. 151351, ESA-ESTEC, Noordwijk,
The Netherlands, 1996.
Parameters
==========
L : arraylike or float
Returns
=======
order : array
coefficient for sin^n model corresponding to McIlwain L (computed for OP77?)
Examples
========
Apply Vampola pitch angle model at L=[4, 6.6]
>>> from spacepy.empiricals import vampolaPA, getVampolaOrder
>>> order = getVampolaOrder([4,6.6])
>>> order
array([ 3.095 , 1.6402])
>>> vampolaPA([3000, 3000], alpha=[45, 90], order=order)
(array([[ 140.08798878, 192.33572182],
[ 409.49143136, 339.57417256]]), [45, 90])
'''
lmc = np.arange(3,8.00001,0.25)
vamp_n = [5.38, 5.078, 4.669, 3.916, 3.095, 2.494, 2.151, 1.998, 1.899,
1.942, 1.974, 1.939, 1.970, 2.136, 1.775, 1.438, 1.254, 1.194,
1.046, 0.989, 0.852]
if not hasattr(L, '__iter__'): L = [L]
L = np.asanyarray(L)
#if outside valid range, use end value
L[L<=3] = 3
L[L>=8] = 8
#interpolate to get order for the given L
order = np.interp(L, lmc, vamp_n)
return order
[docs]
def omniFromDirectionalFlux(fluxarr, alphas, norm=True):
'''
Calculate omnidirectional flux [(s cm^2 kev)^-1] from directional flux [(s sr cm^2 keV)^-1] array
J = 2.pi integ(j sin(a) da)
If kwarg 'norm' is True (default), the omni flux is normalized by 4.pi to make it per steradian,
in line with the PRBEM guidelines
Parameters
==========
fluxarr : arraylike
Array of directional flux values
alphas : arraylike
Array of pitch angles corresponding to fluxarr
Returns
=======
omniflux : float
Omnidirectional flux value
Examples
========
Roundtrip from omni flux, to directional flux (Vampola model),
integrate to get back to omni flux.
>>> from spacepy.empiricals import vampolaPA, omniFromDirectionalFlux
>>> dir_flux, pa = vampolaPA(3000, alpha=range(0,181,2), order=4)
>>> dir_flux[:10], pa[:10]
(array([ 0.00000000e+00, 6.64032473e-04, 1.05986545e-02,
5.34380898e-02, 1.67932162e-01, 4.06999226e-01,
8.36427502e-01, 1.53325140e+00, 2.58383611e+00,
4.08170975e+00]), [0, 2, 4, 6, 8, 10, 12, 14, 16, 18])
>>> omniFromDirectionalFlux(dir_flux, pa, norm=False)
3000.0000008112293
Calculate "spin-averaged" flux, giving answer per steradian
>>> omniFromDirectionalFlux(dir_flux, pa, norm=True)
238.73241470239859
'''
try:
flen = len(fluxarr)
except TypeError:
fluxarr = np.array([fluxarr])
flen = 1
finally:
if flen==1:
#assume isotropy
return fluxarr
if norm:
fac = 1
denomina = integ.quad(np.sin, 0, np.pi)[0]
else:
fac = 2*np.pi
denomina = 1
alphrad = np.deg2rad(alphas)
numera = integ.simpson(y=fluxarr*np.sin(alphrad), x=alphrad)
omniflux = fac*numera/denomina
return omniflux
[docs]
def getSolarRotation(ticks, rtype='carrington', fp=False, reverse=False):
'''Calculates solar rotation number (Carrington or Bartels) for a given date/time
Parameters
==========
ticks : spacepy.time.Ticktock or datetime.datetime
Returns
=======
rnumber : integer or array
Carrington (or Bartels) rotation number
'''
def total_seconds(dobj):
return dobj.days*24*3600 + dobj.seconds + dobj.microseconds/1e6
if rtype.lower() == 'carrington':
start_date = datetime.datetime(1853,11,9,21,38,44,160000)
#length = datetime.timedelta(days=27, minutes=396, seconds=25, microseconds=919999)
length = datetime.timedelta(days=27.2753)
elif rtype.lower() == 'bartels':
start_date = datetime.datetime(1832,2,8)
start_JD = spt.Ticktock(start_date).JD
length = datetime.timedelta(days=27)
else:
raise ValueError('Solar rotation type {0} not recognized: Must be either "carrington" or "Bartels"'.format(rtype))
if not reverse:
try:
nels = len(ticks)
except TypeError:
try:
rotation = total_seconds(ticks-start_date)/total_seconds(length)
rotation += 1
if not fp:
rotation = int(rotation)
return rotation
except:
raise RuntimeError('Unidentified problem with input time {0} in getSolarRotation'.format(ticks))
if isinstance(ticks, spt.Ticktock):
rotation = [total_seconds(tt-start_date)/total_seconds(length) for tt in ticks.UTC]
rotation = np.array(rotation) + 1
else:
try:
rotation = [total_seconds(tt-start_date)/total_seconds(length) for tt in ticks]
rotation = np.array(rotation) + 1
except:
raise RuntimeError('Unidentified problem with input time {0} in getSolarRotation'.format(ticks))
if not fp:
rotation = rotation.astype(int)
return rotation
else:
#for now just assume single input, non-iterable
elapsed = length*(ticks-1)
date = elapsed + start_date
return date
[docs]
def getSolarProtonSpectra(norm=3.20e7, gamma=-0.96, E0=15.0, Emin=.1, Emax=600, nsteps=100):
'''Returns a SpaceData with energy and fluence spectra of solar particle events
The formulation follows that of:
Ellison and Ramaty ApJ 298: 400-408, 1985
dJ/dE = K^{-\\gamma}exp(-E/E0)
and the defualt values are the 10/16/2003 SEP event of:
Mewaldt, R. A., et al. (2005), J. Geophys. Res., 110, A09S18, doi:10.1029/2005JA011038.
Other Parameters
================
norm : float
Normilization factor for the intensity of the SEP event
gamma : float
Power law index
E0 : float
Expoential scaling factor
Emin : float
Minimum energy for fit
Emax : float
Maximum energy for fit
nsteps : int
The number of log spaced energy steps to return
Returns
=======
data : dm.SpaceData
SpaceData with the energy and fluence values
'''
E = tb.logspace(Emin, Emax, nsteps)
fluence = norm*E**(gamma)*np.exp(-E/E0)
ans = dm.SpaceData()
ans['Energy'] = dm.dmarray(E)
ans['Energy'].attrs = {'UNITS':'MeV',
'DESCRIPTION':'Particle energy per nucleon'}
ans['Fluence'] = dm.dmarray(fluence)
ans['Fluence'].attrs = {'UNITS' : 'cm^{-2} sr^{-1} (MeV/nuc)^{-1}',
'DESCRIPTION':'Fluence spectra fir to the model'}
return ans
ShueMP = getMPstandoff
get_plasma_pause = getPlasmaPause
get_Lmax = getLmax
