def makedata(testpath):
""" This will make the input data for the test case. The data will have the
default set of parameters Ne=Ne=1e11 and Te=Ti=2000.
Inputs
testpath - Directory that will hold the data.
"""
finalpath = os.path.join(testpath, 'Origparams')
if not os.path.isdir(finalpath):
os.mkdir(finalpath)
data = SIMVALUES
z = sp.linspace(50., 1e3, 50)
nz = len(z)
params = sp.tile(data[sp.newaxis, sp.newaxis, :, :], (nz, 1, 1, 1))
coords = sp.column_stack((sp.ones(nz), sp.ones(nz), z))
species = ['O+', 'e-']
times = sp.array([[0, 1e3]])
vel = sp.zeros((nz, 1, 3))
Icont1 = IonoContainer(coordlist=coords,
paramlist=params,
times=times,
sensor_loc=sp.zeros(3),
ver=0,
coordvecs=['x', 'y', 'z'],
paramnames=None,
species=species,
velocity=vel)
finalfile = os.path.join(finalpath, '0 stats.h5')
Icont1.saveh5(finalfile)
# set start temp to 1000 K.
Icont1.Param_List[:, :, :, 1] = 1e3
Icont1.saveh5(os.path.join(testpath, 'startfile.h5'))
def makeinputh5(Iono,basedir):
"""This will make a h5 file for the IonoContainer that can be used as starting
points for the fitter. The ionocontainer taken will be average over the x and y dimensions
of space to make an average value of the parameters for each altitude.
Inputs
Iono - An instance of the Ionocontainer class that will be averaged over so it can
be used for fitter starting points.
basdir - A string that holds the directory that the file will be saved to.
"""
# Get the parameters from the original data
Param_List = Iono.Param_List
dataloc = Iono.Cart_Coords
times = Iono.Time_Vector
velocity = Iono.Velocity
zlist,idx = sp.unique(dataloc[:,2],return_inverse=True)
siz = list(Param_List.shape[1:])
vsiz = list(velocity.shape[1:])
datalocsave = sp.column_stack((sp.zeros_like(zlist),sp.zeros_like(zlist),zlist))
outdata = sp.zeros([len(zlist)]+siz)
outvel = sp.zeros([len(zlist)]+vsiz)
# Do the averaging across space
for izn,iz in enumerate(zlist):
arr = sp.argwhere(idx==izn)
outdata[izn]=sp.mean(Param_List[arr],axis=0)
outvel[izn]=sp.mean(velocity[arr],axis=0)
Ionoout = IonoContainer(datalocsave,outdata,times,Iono.Sensor_loc,ver=0,
paramnames=Iono.Param_Names, species=Iono.Species,velocity=outvel)
Ionoout.saveh5(os.path.join(basedir,'startdata.h5'))
def makeinputh5(Iono, basedir):
Param_List = Iono.Param_List
dataloc = Iono.Cart_Coords
times = Iono.Time_Vector
velocity = Iono.Velocity
zlist, idx = sp.unique(dataloc[:, 2], return_inverse=True)
siz = list(Param_List.shape[1:])
vsiz = list(velocity.shape[1:])
datalocsave = sp.column_stack(
(sp.zeros_like(zlist), sp.zeros_like(zlist), zlist))
outdata = sp.zeros([len(zlist)] + siz)
outvel = sp.zeros([len(zlist)] + vsiz)
for izn, iz in enumerate(zlist):
arr = sp.argwhere(idx == izn)
outdata[izn] = sp.mean(Param_List[arr], axis=0)
outvel[izn] = sp.mean(velocity[arr], axis=0)
Ionoout = IonoContainer(datalocsave,
outdata,
times,
Iono.Sensor_loc,
ver=0,
paramnames=Iono.Param_Names,
species=Iono.Species,
velocity=outvel)
Ionoout.saveh5(os.path.join(basedir, 'startdata.h5'))
def fitdata(basedir, configfile, optintputs):
dirio = ("ACF", "Fitted")
inputdir = os.path.join(basedir, dirio[0])
outputdir = os.path.join(basedir, dirio[1])
dirlist = glob.glob(os.path.join(inputdir, "*lags.h5"))
dirlistsig = glob.glob(os.path.join(inputdir, "*sigs.h5"))
Ionoin = IonoContainer.readh5(dirlist[0])
Ionoinsig = IonoContainer.readh5(dirlistsig[0])
fitterone = Fitterionoconainer(Ionoin, Ionoinsig, configfile)
(fitteddata, fittederror) = fitterone.fitdata(
ISRSfitfunction, startvalfunc, exinputs=[fitterone.simparams["startfile"]]
)
if fitterone.simparams["Pulsetype"].lower() == "barker":
paramlist = fitteddata
species = fitterone.simparams["species"]
paranamsf = ["Ne"]
else:
(Nloc, Ntimes, nparams) = fitteddata.shape
fittederronly = fittederror[:, :, range(nparams), range(nparams)]
paramnames = []
species = fitterone.simparams["species"]
Nions = len(species) - 1
Nis = fitteddata[:, :, 0 : Nions * 2 : 2]
Tis = fitteddata[:, :, 1 : Nions * 2 : 2]
Nisum = sp.nansum(Nis, axis=2)[:, :, sp.newaxis]
Tisum = sp.nansum(Nis * Tis, axis=2)[:, :, sp.newaxis]
Ti = Tisum / Nisum
nNis = fittederronly[:, :, 0 : Nions * 2 : 2]
nTis = fittederronly[:, :, 1 : Nions * 2 : 2]
nNisum = sp.sqrt(sp.nansum(Nis * nNis ** 2, axis=2))[:, :, sp.newaxis] / Nisum
nTisum = sp.sqrt(sp.nansum(Nis * nTis ** 2, axis=2))[:, :, sp.newaxis]
nTi = nTisum / Nisum
paramlist = sp.concatenate((fitteddata, Nisum, Ti, fittederronly, nNisum, nTi), axis=2)
for isp in species[:-1]:
paramnames.append("Ni_" + isp)
paramnames.append("Ti_" + isp)
paramnames = paramnames + ["Ne", "Te", "Vi", "Nepow", "Ni", "Ti"]
paramnamese = ["n" + ip for ip in paramnames]
paranamsf = sp.array(paramnames + paramnamese)
Ionoout = IonoContainer(
Ionoin.Sphere_Coords,
paramlist,
Ionoin.Time_Vector,
ver=1,
coordvecs=Ionoin.Coord_Vecs,
paramnames=paranamsf,
species=species,
)
outfile = os.path.join(outputdir, "fitteddata.h5")
Ionoout.saveh5(outfile)
def makeinputh5(Iono,basedir):
Param_List = Iono.Param_List
dataloc = Iono.Cart_Coords
times = Iono.Time_Vector
velocity = Iono.Velocity
zlist,idx = sp.unique(dataloc[:,2],return_inverse=True)
siz = list(Param_List.shape[1:])
vsiz = list(velocity.shape[1:])
datalocsave = sp.column_stack((sp.zeros_like(zlist),sp.zeros_like(zlist),zlist))
outdata = sp.zeros([len(zlist)]+siz)
outvel = sp.zeros([len(zlist)]+vsiz)
for izn,iz in enumerate(zlist):
arr = sp.argwhere(idx==izn)
outdata[izn]=sp.mean(Param_List[arr],axis=0)
outvel[izn]=sp.mean(velocity[arr],axis=0)
Ionoout = IonoContainer(datalocsave,outdata,times,Iono.Sensor_loc,ver=0,
paramnames=Iono.Param_Names, species=Iono.Species,velocity=outvel)
Ionoout.saveh5(os.path.join(basedir,'startdata.h5'))
def fitdata(basedir,configfile,optinputs):
""" This function will run the fitter on the estimated ACFs saved in h5 files.
Inputs:
basedir: A string for the directory that will hold all of the data for the simulation.
configfile: The configuration file for the simulation.
optinputs:A string that helps determine the what type of acfs will be fitted.
"""
# determine the input folders which can be ACFs from the full simulation
dirdict = {'fitting':('ACF','Fitted'),'fittingmat':('ACFMat','FittedMat'),
'fittinginv':('ACFInv','FittedInv'),'fittingmatinv':('ACFMatInv','FittedMatInv')}
dirio = dirdict[optinputs[0]]
inputdir = os.path.join(basedir,dirio[0])
outputdir = os.path.join(basedir,dirio[1])
fitlist=optinputs[1]
if len(optinputs)>2:
exstr=optinputs[2]
else:
exstr=''
dirlist = glob.glob(os.path.join(inputdir,'*lags{0}.h5'.format(exstr)))
dirlistsig = glob.glob(os.path.join(inputdir,'*sigs{0}.h5'.format(exstr)))
Ionoin=IonoContainer.readh5(dirlist[0])
if len(dirlistsig)==0:
Ionoinsig=None
else:
Ionoinsig=IonoContainer.readh5(dirlistsig[0])
fitterone = Fitterionoconainer(Ionoin,Ionoinsig,configfile)
(fitteddata,fittederror,funcevals) = fitterone.fitdata(ISRSfitfunction,fitterone.simparams['startfile'],fittimes=fitlist)
if fitterone.simparams['Pulsetype'].lower() == 'barker':
paramlist=fitteddata
species = fitterone.simparams['species']
paranamsf=['Ne']
else:
(Nloc,Ntimes,nparams)=fitteddata.shape
fittederronly = sp.sqrt(fittederror)
paramnames = []
species = fitterone.simparams['species']
# Seperate Ti and put it in as an element of the ionocontainer.
Ti = fitteddata[:,:,1]
nTi = fittederronly[:,:,1]
paramlist = sp.concatenate((fitteddata,Ti[:,:,sp.newaxis],fittederronly,nTi[:,:,sp.newaxis],funcevals[:,:,sp.newaxis]),axis=2)
for isp in species[:-1]:
paramnames.append('Ni_'+isp)
paramnames.append('Ti_'+isp)
paramnames = paramnames+['Ne','Te','Vi','Nepow','Ti']
paramnamese = ['n'+ip for ip in paramnames]
paranamsf = sp.array(paramnames+paramnamese+['FuncEvals'])
if fitlist is None:
timevec = Ionoin.Time_Vector
else:
if len(fitlist)==0:
timevec = Ionoin.Time_Vector
else:
timevec = Ionoin.Time_Vector[fitlist]
# This requires
if set(Ionoin.Coord_Vecs)=={'x','y','z'}:
newver=0
Ionoout=IonoContainer(Ionoin.Cart_Coords,paramlist,timevec,ver =newver,coordvecs = Ionoin.Coord_Vecs, paramnames=paranamsf,species=species)
elif set(Ionoin.Coord_Vecs)=={'r','theta','phi'}:
newver=1
Ionoout=IonoContainer(Ionoin.Sphere_Coords,paramlist,timevec,ver =newver,coordvecs = Ionoin.Coord_Vecs, paramnames=paranamsf,species=species)
outfile = os.path.join(outputdir,'fitteddata{0}.h5'.format(exstr))
Ionoout.saveh5(outfile)
