Source code for spacepy.empiricals

#!/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

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]) """ 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) 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))).squeeze() for i, a_val in enumerate(np.deg2rad(kwargs['alpha'])): dnflux[i] = normfac * sinfunc(a_val) return dnflux, 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.simps(fluxarr*np.sin(alphrad), 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