def makespectrums(basedir,configfile,remakealldata):
""" This will make all of the spectra for a set of data and save it in a
folder in basedir called Spectrums. It is assumed that the data in the Origparams
is time tagged in the title with a string seperated by a white space and the
rest of the file name. For example ~/DATA/Basedir/0 origdata.h5.
Inputs:
basedir: A string for the directory that will hold all of the data for the simulation.
configfile: The configuration file for the simulation.
"""
dirio=('Origparams','Spectrums')
inputdir = os.path.join(basedir,dirio[0])
outputdir = os.path.join(basedir,dirio[1])
# determine the list of h5 files in Origparams directory
dirlist = glob.glob(os.path.join(inputdir,'*.h5'))
# Make the lists of numbers and file names for the dictionary
(listorder,timevector,filenumbering,timebeg,time_s) = IonoContainer.gettimes(dirlist)
slist = [dirlist[ikey] for ikey in listorder]
(sensdict,simparams) = readconfigfile(configfile)
# Delete data
outfiles = glob.glob(os.path.join(outputdir,'*.h5'))
for ifile in outfiles:
os.remove(ifile)
for inum, curfile in zip(timebeg,slist):
outfile = os.path.join(outputdir,str(inum)+' spectrum.h5')
print('Processing file {0} starting at {1}\n'.format(os.path.split(curfile)[1]
,datetime.now()))
curiono = IonoContainer.readh5(curfile)
curiono.makespectruminstanceopen(specfuncs.ISRSspecmake,sensdict,
simparams['numpoints']).saveh5(outfile)
print('Finished file {0} starting at {1}\n'.format(os.path.split(curfile)[1],datetime.now()))
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):
"""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 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):
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 main():
curpath = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
testpath = os.path.join(os.path.split(curpath)[0],'Testdata','MatrixTest')
origparamsdir = os.path.join(testpath,'Origparams')
if not os.path.exists(testpath):
os.mkdir(testpath)
print "Making a path for matrix test at "+testpath
if not os.path.exists(origparamsdir):
os.mkdir(origparamsdir)
print "Making a path for testdata at "+origparamsdir
# clear everything out
folderlist = ['Origparams','Spectrums','Radardata','ACF','Fitted']
for ifl in folderlist:
flist = glob.glob(os.path.join(testpath,ifl,'*.h5'))
for ifile in flist:
os.remove(ifile)
# Make Config file
configname = os.path.join(testpath,'config.ini')
if ~os.path.isfile(configname):
makedefaultfile(configname)
# make the coordinates
xvec = sp.zeros((1))
yvec = sp.zeros((1))
zvec = sp.arange(50.0,900.0,2.0)
# Mesh grid is set up in this way to allow for use in MATLAB with a simple reshape command
xx,zz,yy = sp.meshgrid(xvec,zvec,yvec)
coords = sp.zeros((xx.size,3))
coords[:,0] = xx.flatten()
coords[:,1] = yy.flatten()
coords[:,2] = zz.flatten()
Icont1 = MakeTestIonoclass(testv=True,testtemp=True,coords=coords)
Icont1.saveh5(os.path.join(origparamsdir,'0 testiono.h5'))
Icont1.saveh5(os.path.join(testpath,'startdata.h5'))
funcnamelist=['spectrums']
runsim.main(funcnamelist,testpath,configname,True)
Ispec = IonoContainer.readh5(os.path.join(testpath,'Spectrums','0 testiono spectrum.h5'))
Spec = Ispec.Param_List.real
acflen = 14
# get matrix
outmat = makematPA(Ispec.Sphere_Coords,Ispec.Time_Vector,configname)
(outn,inn) = outmat.shape
acfs = sp.ifft(scfft.ifftshift(Spec,axes=-1),axis=-1)[:,:,:acflen]
acfout = sp.zeros((outn,acflen)).astype(acfs.dtype)
acfs = sp.reshape(acfs,(inn,acflen),order='F')
for i in range(acflen):
acfout[:,i] = sp.dot(outmat,acfs[:,i])
def applymat(basedir,configfile,optinputs):
""" This function apply the matrix version of the space time ambiugty function
to the ACFs and save the outcome in h5 files within the directory ACFMat.
Inputs:
basedir: A string for the directory that will hold all of the data for the simulation.
configfile: The configuration file for the simulation.
"""
dirio = ('Spectrums','Mat','ACFMat')
inputdir = os.path.join(basedir,dirio[0])
outputdir2 = os.path.join(basedir,dirio[2])
dirlist = glob.glob(os.path.join(inputdir,'*.h5'))
(listorder,timevector,filenumbering,timebeg,time_s) = IonoContainer.gettimes(dirlist)
Ionolist = [dirlist[ikey] for ikey in listorder]
RSTO = RadarSpaceTimeOperator(Ionolist,configfile,timevector,mattype='matrix')
Ionoout = RSTO.mult_iono(Ionolist)
outfile=os.path.join(outputdir2,'00lags.h5')
Ionoout.saveh5(outfile)
def startvalfunc(Ne_init, loc, time, exinputs):
""" """
Ionoin = IonoContainer.readh5(exinputs[0])
numel = sp.prod(Ionoin.Param_List.shape[-2:]) + 1
xarray = sp.zeros((loc.shape[0], len(time), numel))
for ilocn, iloc in enumerate(loc):
(datast, vel) = Ionoin.getclosest(iloc, time)[:2]
datast[:, -1, 0] = Ne_init[ilocn, :]
ionoden = datast[:, :-1, 0]
ionodensum = sp.repeat(sp.sum(ionoden, axis=-1)[:, sp.newaxis], ionoden.shape[-1], axis=-1)
ionoden = sp.repeat(Ne_init[ilocn, :, sp.newaxis], ionoden.shape[-1], axis=-1) * ionoden / ionodensum
datast[:, :-1, 0] = ionoden
xarray[ilocn, :, :-1] = sp.reshape(datast, (len(time), numel - 1))
locmag = sp.sqrt(sp.sum(iloc * iloc))
ilocmat = sp.repeat(iloc[sp.newaxis, :], len(time), axis=0)
xarray[ilocn, :, -1] = sp.sum(vel * ilocmat) / locmag
return xarray
def makeradardata(basedir,configfile,remakealldata):
""" This function will make the radar data and create the acf estimates.
Inputs:
basedir: A string for the directory that will hold all of the data for the simulation.
configfile: The configuration file for the simulation.
remakealldata: A bool that determines if the radar data is remade. If false
only the acfs will be estimated using the radar that is already made."""
dirio = ('Spectrums','Radardata','ACF')
inputdir = os.path.join(basedir,dirio[0])
outputdir = os.path.join(basedir,dirio[1])
outputdir2 = os.path.join(basedir,dirio[2])
# determine the list of h5 files in Origparams directory
dirlist = glob.glob(os.path.join(inputdir,'*.h5'))
# Make the lists of numbers and file names for the dictionary
if len(dirlist)>0:
(listorder,timevector,filenumbering,timebeg,time_s) = IonoContainer.gettimes(dirlist)
Ionodict = {timebeg[itn]:dirlist[it] for itn, it in enumerate(listorder)}
else:
Ionodict = {0:os.path.join(inputdir,'00.h5')}
# Find all of the raw data files
radardatalist = glob.glob(os.path.join(outputdir,'*RawData.h5'))
if radardatalist and (not remakealldata):
# XXX need to work on time stuff
outlist2 = radardatalist
else:
outlist2 = None
# create the radar data file class
rdata = RadarDataFile(Ionodict,configfile,outputdir,outfilelist=outlist2)
# From the ACFs and uncertainties
(ionoout,ionosig) = rdata.processdataiono()
# save the acfs and uncertianties in ionocontainer h5 files.
ionoout.saveh5(os.path.join(outputdir2,'00lags.h5'))
ionosig.saveh5(os.path.join(outputdir2,'00sigs.h5'))
return ()
def makespectrums(basedir, configfile, remakealldata):
dirio = ("Origparams", "Spectrums")
inputdir = os.path.join(basedir, dirio[0])
outputdir = os.path.join(basedir, dirio[1])
dirlist = glob.glob(os.path.join(inputdir, "*.h5"))
numlist = [os.path.splitext(os.path.split(x)[-1])[0] for x in dirlist]
numdict = {numlist[i]: dirlist[i] for i in range(len(dirlist))}
slist = sorted(numlist, key=ke)
(sensdict, simparams) = readconfigfile(configfile)
for inum in slist:
outfile = os.path.join(outputdir, inum + " spectrum.h5")
curfile = numdict[inum]
print("Processing file {} starting at {}\n".format(os.path.split(curfile)[1], datetime.now()))
curiono = IonoContainer.readh5(curfile)
# if curiono.Time_Vector[0,0]==1e-6:
# curiono.Time_Vector[0,0] = 0.0
# curiono.coordreduce(coordlims)
# curiono.saveh5(os.path.join(inputdir,inum+' red.h5'))
curiono.makespectruminstanceopen(specfuncs.ISRSspecmake, sensdict, simparams["numpoints"]).saveh5(outfile)
print("Finished file {} starting at {}\n".format(os.path.split(curfile)[1], datetime.now()))
def plotacfs(coords,
times,
configfile,
maindir,
cartcoordsys=True,
indisp=True,
acfdisp=True,
fitdisp=True,
filetemplate='acf',
suptitle='ACF Comparison'):
""" This will create a set of images that compare the input ISR acf to the
output ISR acfs from the simulator.
Inputs
coords - An Nx3 numpy array that holds the coordinates of the desired points.
times - A numpy list of times in seconds.
configfile - The name of the configuration file used.
cartcoordsys - (default True)A bool, if true then the coordinates are given in cartisian if
false then it is assumed that the coords are given in sphereical coordinates.
specsfilename - (default None) The name of the file holding the input spectrum.
acfname - (default None) The name of the file holding the estimated ACFs.
filetemplate (default 'spec') This is the beginning string used to save the images.
"""
# indisp = specsfilename is not None
# acfdisp = acfname is not None
sns.set_style("whitegrid")
sns.set_context("notebook")
acfname = os.path.join(maindir, 'ACF', '00lags.h5')
ffit = os.path.join(maindir, 'Fitted', 'fitteddata.h5')
specsfiledir = os.path.join(maindir, 'Spectrums')
(sensdict, simparams) = readconfigfile(configfile)
simdtype = simparams['dtype']
npts = simparams['numpoints'] * 3.0
amb_dict = simparams['amb_dict']
if sp.ndim(coords) == 1:
coords = coords[sp.newaxis, :]
Nt = len(times)
Nloc = coords.shape[0]
sns.set_style("whitegrid")
sns.set_context("notebook")
pulse = simparams['Pulse']
ts = sensdict['t_s']
tau1 = sp.arange(pulse.shape[-1]) * ts
if indisp:
dirlist = os.listdir(specsfiledir)
timelist = sp.array([int(float(i.split()[0])) for i in dirlist])
for itn, itime in enumerate(times):
filear = sp.argwhere(timelist >= itime)
if len(filear) == 0:
filenum = len(timelist) - 1
else:
filenum = filear[0][0]
specsfilename = os.path.join(specsfiledir, dirlist[filenum])
Ionoin = IonoContainer.readh5(specsfilename)
if itn == 0:
specin = sp.zeros(
(Nloc, Nt, Ionoin.Param_List.shape[-1])).astype(
Ionoin.Param_List.dtype)
omeg = Ionoin.Param_Names
npts = Ionoin.Param_List.shape[-1]
for icn, ic in enumerate(coords):
if cartcoordsys:
tempin = Ionoin.getclosest(ic, times)[0]
else:
tempin = Ionoin.getclosestsphere(ic, times)[0]
# if sp.ndim(tempin)==1:
# tempin = tempin[sp.newaxis,:]
specin[icn, itn] = tempin[0, :] / npts
if acfdisp:
Ionoacf = IonoContainer.readh5(acfname)
ACFin = sp.zeros(
(Nloc, Nt,
Ionoacf.Param_List.shape[-1])).astype(Ionoacf.Param_List.dtype)
omeg = sp.arange(-sp.ceil((npts + 1) / 2), sp.floor(
(npts + 1) / 2)) / ts / npts
for icn, ic in enumerate(coords):
if cartcoordsys:
tempin = Ionoacf.getclosest(ic, times)[0]
else:
tempin = Ionoacf.getclosestsphere(ic, times)[0]
if sp.ndim(tempin) == 1:
tempin = tempin[sp.newaxis, :]
ACFin[icn] = tempin
if fitdisp:
Ionofit = IonoContainer.readh5(ffit)
(omegfit, outspecsfit) = ISRspecmakeout(Ionofit.Param_List,
sensdict['fc'], sensdict['fs'],
simparams['species'], npts)
Ionofit.Param_List = outspecsfit
Ionofit.Param_Names = omegfit
specfit = sp.zeros((Nloc, Nt, npts))
for icn, ic in enumerate(coords):
if cartcoordsys:
tempin = Ionofit.getclosest(ic, times)[0]
else:
tempin = Ionofit.getclosestsphere(ic, times)[0]
if sp.ndim(tempin) == 1:
tempin = tempin[sp.newaxis, :]
specfit[icn] = tempin / npts / npts
nfig = int(sp.ceil(Nt * Nloc / 6.0))
imcount = 0
for i_fig in range(nfig):
lines = [None] * 6
labels = [None] * 6
(figmplf, axmat) = plt.subplots(2, 3, figsize=(24, 18), facecolor='w')
axvec = axmat.flatten()
for iax, ax in enumerate(axvec):
if imcount >= Nt * Nloc:
break
iloc = int(sp.floor(imcount / Nt))
itime = int(imcount - (iloc * Nt))
maxvec = []
minvec = []
if indisp:
# apply ambiguity funciton to spectrum
curin = specin[iloc, itime]
(tau, acf) = spect2acf(omeg, curin)
acf1 = scfft.ifftshift(acf)[:len(pulse)]
rcs = acf1[0].real
guess_acf = sp.dot(amb_dict['WttMatrix'], acf)
guess_acf = guess_acf * rcs / guess_acf[0].real
# fit to spectrums
maxvec.append(guess_acf.real.max())
maxvec.append(guess_acf.imag.max())
minvec.append(acf1.real.min())
minvec.append(acf1.imag.min())
lines[0] = ax.plot(tau1 * 1e6,
guess_acf.real,
label='Input',
linewidth=5)[0]
labels[0] = 'Input ACF With Ambiguity Applied Real'
lines[1] = ax.plot(tau1 * 1e6,
guess_acf.imag,
label='Input',
linewidth=5)[0]
labels[1] = 'Input ACF With Ambiguity Applied Imag'
if fitdisp:
curinfit = specfit[iloc, itime]
(taufit, acffit) = spect2acf(omegfit, curinfit)
rcsfit = curinfit.sum()
guess_acffit = sp.dot(amb_dict['WttMatrix'], acffit)
guess_acffit = guess_acffit * rcsfit / guess_acffit[0].real
lines[2] = ax.plot(tau1 * 1e6,
guess_acffit.real,
label='Input',
linewidth=5)[0]
labels[2] = 'Fitted ACF real'
lines[3] = ax.plot(tau1 * 1e6,
guess_acffit.imag,
label='Input',
linewidth=5)[0]
labels[3] = 'Fitted ACF Imag'
if acfdisp:
lines[4] = ax.plot(tau1 * 1e6,
ACFin[iloc, itime].real,
label='Output',
linewidth=5)[0]
labels[4] = 'Estimated ACF Real'
lines[5] = ax.plot(tau1 * 1e6,
ACFin[iloc, itime].imag,
label='Output',
linewidth=5)[0]
labels[5] = 'Estimated ACF Imag'
maxvec.append(ACFin[iloc, itime].real.max())
maxvec.append(ACFin[iloc, itime].imag.max())
minvec.append(ACFin[iloc, itime].real.min())
minvec.append(ACFin[iloc, itime].imag.min())
ax.set_xlabel('t in us')
ax.set_ylabel('Amp')
ax.set_title('Location {0}, Time {1}'.format(
coords[iloc], times[itime]))
ax.set_ylim(min(minvec), max(maxvec) * 1)
ax.set_xlim([tau1.min() * 1e6, tau1.max() * 1e6])
imcount = imcount + 1
figmplf.suptitle(suptitle, fontsize=20)
if None in labels:
labels.remove(None)
lines.remove(None)
plt.figlegend(lines,
labels,
loc='lower center',
ncol=5,
labelspacing=0.)
fname = filetemplate + '_{0:0>3}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
Titemp[auglist] = Titemp[auglist]*ratiodiff
Tetemp[auglist] = Tetemp[auglist]*ratiodiff
curprofile[auglist] = val
#densitys
params[:,it,0,0] = curprofile
params[:,it,1,0] = curprofile
#tempreturs
params[:,it,0,1] = Tetemp
params[:,it,1,1] = Titemp
#veclocity
params[:,it,0,2] = Vi_all
params[:,it,1,2] = Vi_all
#charge
params[:,it,0,3] = 1.0
params[:,it,1,3] = 1.0
#mass
params[:,it,0,4] = 1.0
params[:,it,1,4] = 16.0
#collision frequency
params[:,it,0,5] = 0.0
params[:,it,1,5] = 0.0
Icont1 = IonoContainer(coordlist=coords,paramlist=params,times = timevec,coordvecs=coordVecs)
# Icont1.savemat(os.path.join(testpath,'pathwtempdoppler.mat'))
sensdict = sensconst.getConst('risr')
Icont2 = Icont1.makespectruminstance(sensdict,128)
# Icont2.savemat(os.path.join(testpath,'pa'))
def plotbeamparameters(times,configfile,maindir,params=['Ne'],indisp=True,fitdisp = True,filetemplate='params',suptitle = 'Parameter Comparison',werrors=False):
""" """
sns.set_style("whitegrid")
sns.set_context("notebook")
# rc('text', usetex=True)
ffit = os.path.join(maindir,'Fitted','fitteddata.h5')
inputfiledir = os.path.join(maindir,'Origparams')
(sensdict,simparams) = readconfigfile(configfile)
paramslower = [ip.lower() for ip in params]
Nt = len(times)
Np = len(params)
if fitdisp:
Ionofit = IonoContainer.readh5(ffit)
dataloc = Ionofit.Sphere_Coords
pnames = Ionofit.Param_Names
pnameslower = sp.array([ip.lower() for ip in pnames.flatten()])
p2fit = [sp.argwhere(ip==pnameslower)[0][0] if ip in pnameslower else None for ip in paramslower]
time2fit = [None]*Nt
for itn,itime in enumerate(times):
filear = sp.argwhere(Ionofit.Time_Vector>=itime)
if len(filear)==0:
filenum = len(Ionofit.Time_Vector)-1
else:
filenum = filear[0][0]
time2fit[itn] = filenum
angles = dataloc[:,1:]
rng = sp.unique(dataloc[:,0])
b = np.ascontiguousarray(angles).view(np.dtype((np.void, angles.dtype.itemsize * angles.shape[1])))
_, idx, invidx = np.unique(b, return_index=True,return_inverse=True)
beamlist = angles[idx]
Nb = beamlist.shape[0]
if indisp:
dirlist = glob.glob(os.path.join(inputfiledir,'*.h5'))
filesonly= [os.path.splitext(os.path.split(ifile)[-1])[0] for ifile in dirlist]
timelist = sp.array([int(i.split()[0]) for i in filesonly])
time2file = [None]*Nt
for itn,itime in enumerate(times):
filear = sp.argwhere(timelist>=itime)
if len(filear)==0:
filenum = len(timelist)-1
else:
filenum = filear[0][0]
time2file[itn] = filenum
nfig = int(sp.ceil(Nt*Nb*Np/9.0))
imcount = 0
curfilenum = -1
for i_fig in range(nfig):
lines = [None]*2
labels = [None]*2
(figmplf, axmat) = plt.subplots(3, 3,figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
for iax,ax in enumerate(axvec):
if imcount>=Nt*Nb*Np:
break
itime = int(sp.floor(imcount/Nb/Np))
iparam = int(imcount/Nb-Np*itime)
ibeam = int(imcount-(itime*Np*Nb+iparam*Nb))
curbeam = beamlist[ibeam]
altlist = sp.sin(curbeam[1]*sp.pi/180.)*rng
if fitdisp:
indxkep = np.argwhere(invidx==ibeam)[:,0]
curfit = Ionofit.Param_List[indxkep,time2fit[itime],p2fit[iparam]]
rng_fit= dataloc[indxkep,0]
alt_fit = rng_fit*sp.sin(curbeam[1]*sp.pi/180.)
errorexist = 'n'+paramslower[iparam] in pnameslower
if errorexist and werrors:
eparam = sp.argwhere( 'n'+paramslower[iparam]==pnameslower)[0][0]
curerror = Ionofit.Param_List[indxkep,time2fit[itime],eparam]
lines[1]=ax.errorbar(curfit, alt_fit, xerr=curerror,fmt='-.',c='g')[0]
else:
lines[1]= ax.plot(curfit,alt_fit,marker='.',c='g')[0]
labels[1] = 'Fitted Parameters'
if indisp:
filenum = time2file[itime]
if curfilenum!=filenum:
curfilenum=filenum
datafilename = dirlist[filenum]
Ionoin = IonoContainer.readh5(datafilename)
if 'ti' in paramslower:
Ionoin = maketi(Ionoin)
pnames = Ionoin.Param_Names
pnameslowerin = sp.array([ip.lower() for ip in pnames.flatten()])
curparm = paramslower[iparam]
if curparm == 'nepow':
curparm = 'ne'
prmloc = sp.argwhere(curparm==pnameslowerin)
if prmloc.size !=0:
curprm = prmloc[0][0]
curcoord = sp.zeros(3)
curcoord[1:] = curbeam
curdata = sp.zeros(len(rng))
for irngn, irng in enumerate(rng):
curcoord[0] = irng
tempin = Ionoin.getclosestsphere(curcoord,times)[0]
Ntloc = tempin.shape[0]
tempin = sp.reshape(tempin,(Ntloc,len(pnameslowerin)))
curdata[irngn] = tempin[0,curprm]
lines[0]= ax.plot(curdata,altlist,marker='o',c='b')[0]
labels[0] = 'Input Parameters'
if curparm!='ne':
ax.set(xlim=[0.25*sp.amin(curdata),2.5*sp.amax(curdata)])
if curparm=='ne':
ax.set_xscale('log')
ax.set_xlabel(params[iparam])
ax.set_ylabel('Alt km')
ax.set_title('{0} vs Altitude, Time: {1}s Az: {2}$^o$ El: {3}$^o$'.format(params[iparam],times[itime],*curbeam))
imcount=imcount+1
figmplf.suptitle(suptitle, fontsize=20)
if None in labels:
labels.remove(None)
lines.remove(None)
plt.figlegend( lines, labels, loc = 'lower center', ncol=5, labelspacing=0. )
fname= filetemplate+'_{0:0>3}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
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)
def plotacfs(coords,times,configfile,maindir,cartcoordsys = True, indisp=True,acfdisp= True,
fitdisp=True, filetemplate='acf',suptitle = 'ACF Comparison'):
""" This will create a set of images that compare the input ISR acf to the
output ISR acfs from the simulator.
Inputs
coords - An Nx3 numpy array that holds the coordinates of the desired points.
times - A numpy list of times in seconds.
configfile - The name of the configuration file used.
cartcoordsys - (default True)A bool, if true then the coordinates are given in cartisian if
false then it is assumed that the coords are given in sphereical coordinates.
specsfilename - (default None) The name of the file holding the input spectrum.
acfname - (default None) The name of the file holding the estimated ACFs.
filetemplate (default 'spec') This is the beginning string used to save the images."""
# indisp = specsfilename is not None
# acfdisp = acfname is not None
acfname = os.path.join(maindir,'ACF','00lags.h5')
ffit = os.path.join(maindir,'Fitted','fitteddata.h5')
specsfiledir = os.path.join(maindir,'Spectrums')
(sensdict,simparams) = readconfigfile(configfile)
simdtype = simparams['dtype']
npts = simparams['numpoints']*3.0
amb_dict = simparams['amb_dict']
if sp.ndim(coords)==1:
coords = coords[sp.newaxis,:]
Nt = len(times)
Nloc = coords.shape[0]
sns.set_style("whitegrid")
sns.set_context("notebook")
pulse = simparams['Pulse']
ts = sensdict['t_s']
tau1 = sp.arange(pulse.shape[-1])*ts
if indisp:
dirlist = os.listdir(specsfiledir)
timelist = sp.array([int(i.split()[0]) for i in dirlist])
for itn,itime in enumerate(times):
filear = sp.argwhere(timelist>=itime)
if len(filear)==0:
filenum = len(timelist)-1
else:
filenum = filear[0][0]
specsfilename = os.path.join(specsfiledir,dirlist[filenum])
Ionoin = IonoContainer.readh5(specsfilename)
if itn==0:
specin = sp.zeros((Nloc,Nt,Ionoin.Param_List.shape[-1])).astype(Ionoin.Param_List.dtype)
omeg = Ionoin.Param_Names
npts = Ionoin.Param_List.shape[-1]
for icn, ic in enumerate(coords):
if cartcoordsys:
tempin = Ionoin.getclosest(ic,times)[0]
else:
tempin = Ionoin.getclosestsphere(ic,times)[0]
# if sp.ndim(tempin)==1:
# tempin = tempin[sp.newaxis,:]
specin[icn,itn] = tempin[0,:]/npts
if acfdisp:
Ionoacf = IonoContainer.readh5(acfname)
ACFin = sp.zeros((Nloc,Nt,Ionoacf.Param_List.shape[-1])).astype(Ionoacf.Param_List.dtype)
omeg = sp.arange(-sp.ceil((npts+1)/2),sp.floor((npts+1)/2))/ts/npts
for icn, ic in enumerate(coords):
if cartcoordsys:
tempin = Ionoacf.getclosest(ic,times)[0]
else:
tempin = Ionoacf.getclosestsphere(ic,times)[0]
if sp.ndim(tempin)==1:
tempin = tempin[sp.newaxis,:]
ACFin[icn] = tempin
if fitdisp:
Ionofit = IonoContainer.readh5(ffit)
(omegfit,outspecsfit) =ISRspecmakeout(Ionofit.Param_List,sensdict['fc'],sensdict['fs'],simparams['species'],npts)
Ionofit.Param_List= outspecsfit
Ionofit.Param_Names = omegfit
specfit = sp.zeros((Nloc,Nt,npts))
for icn, ic in enumerate(coords):
if cartcoordsys:
tempin = Ionofit.getclosest(ic,times)[0]
else:
tempin = Ionofit.getclosestsphere(ic,times)[0]
if sp.ndim(tempin)==1:
tempin = tempin[sp.newaxis,:]
specfit[icn] = tempin/npts/npts
nfig = int(sp.ceil(Nt*Nloc/6.0))
imcount = 0
for i_fig in range(nfig):
lines = [None]*6
labels = [None]*6
(figmplf, axmat) = plt.subplots(2, 3,figsize=(24, 18), facecolor='w')
axvec = axmat.flatten()
for iax,ax in enumerate(axvec):
if imcount>=Nt*Nloc:
break
iloc = int(sp.floor(imcount/Nt))
itime = int(imcount-(iloc*Nt))
maxvec = []
minvec = []
if indisp:
# apply ambiguity funciton to spectrum
curin = specin[iloc,itime]
(tau,acf) = spect2acf(omeg,curin)
acf1 = scfft.ifftshift(acf)[:len(pulse)]
rcs=acf1[0].real
guess_acf = sp.dot(amb_dict['WttMatrix'],acf)
guess_acf = guess_acf*rcs/guess_acf[0].real
# fit to spectrums
maxvec.append(guess_acf.real.max())
maxvec.append(guess_acf.imag.max())
minvec.append(acf1.real.min())
minvec.append(acf1.imag.min())
lines[0]= ax.plot(tau1*1e6,guess_acf.real,label='Input',linewidth=5)[0]
labels[0] = 'Input ACF With Ambiguity Applied Real'
lines[1]= ax.plot(tau1*1e6,guess_acf.imag,label='Input',linewidth=5)[0]
labels[1] = 'Input ACF With Ambiguity Applied Imag'
if fitdisp:
curinfit = specfit[iloc,itime]
(taufit,acffit) = spect2acf(omegfit,curinfit)
rcsfit=acffit[0].real
guess_acffit = sp.dot(amb_dict['WttMatrix'],acffit)
guess_acffit = guess_acffit*rcsfit/guess_acffit[0].real
lines[2]= ax.plot(tau1*1e6,guess_acffit.real,label='Input',linewidth=5)[0]
labels[2] = 'Fitted ACF real'
lines[3]= ax.plot(tau1*1e6,guess_acffit.imag,label='Input',linewidth=5)[0]
labels[3] = 'Fitted ACF Imag'
if acfdisp:
lines[4]=ax.plot(tau1*1e6,ACFin[iloc,itime].real,label='Output',linewidth=5)[0]
labels[4] = 'Estimated ACF Real'
lines[5]=ax.plot(tau1*1e6,ACFin[iloc,itime].imag,label='Output',linewidth=5)[0]
labels[5] = 'Estimated ACF Imag'
maxvec.append(ACFin[iloc,itime].real.max())
maxvec.append(ACFin[iloc,itime].imag.max())
minvec.append(ACFin[iloc,itime].real.min())
minvec.append(ACFin[iloc,itime].imag.min())
ax.set_xlabel('t in us')
ax.set_ylabel('Amp')
ax.set_title('Location {0}, Time {1}'.format(coords[iloc],times[itime]))
ax.set_ylim(min(minvec),max(maxvec)*1)
ax.set_xlim([tau1.min()*1e6,tau1.max()*1e6])
imcount=imcount+1
figmplf.suptitle(suptitle, fontsize=20)
if None in labels:
labels.remove(None)
lines.remove(None)
plt.figlegend( lines, labels, loc = 'lower center', ncol=5, labelspacing=0. )
fname= filetemplate+'_{0:0>3}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
def makehistdata(params, maindir):
""" This will make the histogram data for the statistics.
Inputs
params - A list of parameters that will have statistics created
maindir - The directory that the simulation data is held.
Outputs
datadict - A dictionary with the data values in numpy arrays. The keys are param names.
errordict - A dictionary with the data values in numpy arrays. The keys are param names.
errdictrel - A dictionary with the error values in numpy arrays, normalized by the correct value. The keys are param names.
"""
ffit = os.path.join(maindir, 'Fitted', 'fitteddata.h5')
inputfiledir = os.path.join(maindir, 'Origparams')
paramslower = [ip.lower() for ip in params]
eparamslower = ['n' + ip.lower() for ip in params]
# set up data dictionary
errordict = {ip: [] for ip in params}
errordictrel = {ip: [] for ip in params}
#Read in fitted data
Ionofit = IonoContainer.readh5(ffit)
times = Ionofit.Time_Vector
dataloc = Ionofit.Sphere_Coords
pnames = Ionofit.Param_Names
pnameslower = sp.array([ip.lower() for ip in pnames.flatten()])
p2fit = [
sp.argwhere(ip == pnameslower)[0][0] if ip in pnameslower else None
for ip in paramslower
]
datadict = {
ip: Ionofit.Param_List[:, :, p2fit[ipn]].flatten()
for ipn, ip in enumerate(params)
}
ep2fit = [
sp.argwhere(ip == pnameslower)[0][0] if ip in pnameslower else None
for ip in eparamslower
]
edatadict = {
ip: Ionofit.Param_List[:, :, ep2fit[ipn]].flatten()
for ipn, ip in enumerate(params)
}
# Determine which input files are to be used.
dirlist = glob.glob(os.path.join(inputfiledir, '*.h5'))
sortlist, outime, filelisting, timebeg, timelist_s = IonoContainer.gettimes(
dirlist)
time2files = []
for itn, itime in enumerate(times):
log1 = (outime[:, 0] >= itime[0]) & (outime[:, 0] < itime[1])
log2 = (outime[:, 1] > itime[0]) & (outime[:, 1] <= itime[1])
log3 = (outime[:, 0] <= itime[0]) & (outime[:, 1] > itime[1])
tempindx = sp.where(log1 | log2 | log3)[0]
time2files.append(filelisting[tempindx])
curfilenum = -1
for iparam, pname in enumerate(params):
curparm = paramslower[iparam]
# Use Ne from input to compare the ne derived from the power.
if curparm == 'nepow':
curparm = 'ne'
datalist = []
for itn, itime in enumerate(times):
for iplot, filenum in enumerate(time2files[itn]):
filenum = int(filenum)
if curfilenum != filenum:
curfilenum = filenum
datafilename = dirlist[filenum]
Ionoin = IonoContainer.readh5(datafilename)
if ('ti' in paramslower) or ('vi' in paramslower):
Ionoin = maketi(Ionoin)
pnames = Ionoin.Param_Names
pnameslowerin = sp.array(
[ip.lower() for ip in pnames.flatten()])
prmloc = sp.argwhere(curparm == pnameslowerin)
if prmloc.size != 0:
curprm = prmloc[0][0]
# build up parameter vector bs the range values by finding the closest point in space in the input
curdata = sp.zeros(len(dataloc))
for irngn, curcoord in enumerate(dataloc):
tempin = Ionoin.getclosestsphere(curcoord, [itime])[0]
Ntloc = tempin.shape[0]
tempin = sp.reshape(tempin, (Ntloc, len(pnameslowerin)))
curdata[irngn] = tempin[0, curprm]
datalist.append(curdata)
errordict[pname] = datadict[pname] - sp.hstack(datalist)
errordictrel[pname] = 100. * errordict[pname] / sp.absolute(
sp.hstack(datalist))
return datadict, errordict, errordictrel, edatadict
def plotbeamparametersv2(times,
configfile,
maindir,
fitdir='Fitted',
params=['Ne'],
filetemplate='params',
suptitle='Parameter Comparison',
werrors=False,
nelog=True):
""" This function will plot the desired parameters for each beam along range.
The values of the input and measured parameters will be plotted
Inputs
Times - A list of times that will be plotted.
configfile - The INI file with the simulation parameters that will be useds.
maindir - The directory the images will be saved in.
params - List of Parameter names that will be ploted. These need to match
in the ionocontainer names.
filetemplate - The first part of a the file names.
suptitle - The supertitle for the plots.
werrors - A bools that determines if the errors will be plotted.
"""
sns.set_style("whitegrid")
sns.set_context("notebook")
# rc('text', usetex=True)
ffit = os.path.join(maindir, fitdir, 'fitteddata.h5')
inputfiledir = os.path.join(maindir, 'Origparams')
(sensdict, simparams) = readconfigfile(configfile)
paramslower = [ip.lower() for ip in params]
Nt = len(times)
Np = len(params)
#Read in fitted data
Ionofit = IonoContainer.readh5(ffit)
dataloc = Ionofit.Sphere_Coords
pnames = Ionofit.Param_Names
pnameslower = sp.array([ip.lower() for ip in pnames.flatten()])
p2fit = [
sp.argwhere(ip == pnameslower)[0][0] if ip in pnameslower else None
for ip in paramslower
]
time2fit = [None] * Nt
for itn, itime in enumerate(times):
filear = sp.argwhere(Ionofit.Time_Vector >= itime)
if len(filear) == 0:
filenum = len(Ionofit.Time_Vector) - 1
else:
filenum = filear[0][0]
time2fit[itn] = filenum
times_int = [Ionofit.Time_Vector[i] for i in time2fit]
# determine the beams
angles = dataloc[:, 1:]
rng = sp.unique(dataloc[:, 0])
b = np.ascontiguousarray(angles).view(
np.dtype((np.void, angles.dtype.itemsize * angles.shape[1])))
_, idx, invidx = np.unique(b, return_index=True, return_inverse=True)
beamlist = angles[idx]
Nb = beamlist.shape[0]
# Determine which imput files are to be used.
dirlist = glob.glob(os.path.join(inputfiledir, '*.h5'))
filesonly = [
os.path.splitext(os.path.split(ifile)[-1])[0] for ifile in dirlist
]
sortlist, outime, outfilelist, timebeg, timelist_s = IonoContainer.gettimes(
dirlist)
timelist = sp.array([int(i.split()[0]) for i in filesonly])
time2file = [None] * Nt
time2intime = [None] * Nt
# go through times find files and then times in files
for itn, itime in enumerate(times):
filear = sp.argwhere(timelist >= itime)
if len(filear) == 0:
filenum = [len(timelist) - 1]
else:
filenum = filear[0]
flist1 = []
timeinflist = []
for ifile in filenum:
filetimes = timelist_s[ifile]
log1 = (filetimes[:, 0] >= times_int[itn][0]) & (filetimes[:, 0] <
times_int[itn][1])
log2 = (filetimes[:, 1] > times_int[itn][0]) & (filetimes[:, 1] <=
times_int[itn][1])
log3 = (filetimes[:, 0] <= times_int[itn][0]) & (filetimes[:, 1] >
times_int[itn][1])
log4 = (filetimes[:, 0] > times_int[itn][0]) & (filetimes[:, 1] <
times_int[itn][1])
curtimes1 = sp.where(log1 | log2 | log3 | log4)[0].tolist()
flist1 = flist1 + [ifile] * len(curtimes1)
timeinflist = timeinflist + curtimes1
time2intime[itn] = timeinflist
time2file[itn] = flist1
nfig = int(sp.ceil(Nt * Nb * Np / 9.0))
imcount = 0
curfilenum = -1
# Loop for the figures
for i_fig in range(nfig):
lines = [None] * 2
labels = [None] * 2
(figmplf, axmat) = plt.subplots(3, 3, figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
# loop that goes through each axis loops through each parameter, beam
# then time.
for iax, ax in enumerate(axvec):
if imcount >= Nt * Nb * Np:
break
itime = int(sp.floor(imcount / Nb / Np))
iparam = int(imcount / Nb - Np * itime)
ibeam = int(imcount - (itime * Np * Nb + iparam * Nb))
curbeam = beamlist[ibeam]
altlist = sp.sin(curbeam[1] * sp.pi / 180.) * rng
curparm = paramslower[iparam]
# Use Ne from input to compare the ne derived from the power.
if curparm == 'nepow':
curparm_in = 'ne'
else:
curparm_in = curparm
curcoord = sp.zeros(3)
curcoord[1:] = curbeam
for iplot, filenum in enumerate(time2file[itime]):
if curfilenum != filenum:
curfilenum = filenum
datafilename = dirlist[filenum]
Ionoin = IonoContainer.readh5(datafilename)
if ('ti' in paramslower) or ('vi' in paramslower):
Ionoin = maketi(Ionoin)
pnames = Ionoin.Param_Names
pnameslowerin = sp.array(
[ip.lower() for ip in pnames.flatten()])
prmloc = sp.argwhere(curparm_in == pnameslowerin)
if prmloc.size != 0:
curprm = prmloc[0][0]
# build up parameter vector bs the range values by finding the closest point in space in the input
curdata = sp.zeros(len(rng))
for irngn, irng in enumerate(rng):
curcoord[0] = irng
tempin = Ionoin.getclosestsphere(curcoord)[0][
time2intime[itime]]
Ntloc = tempin.shape[0]
tempin = sp.reshape(tempin, (Ntloc, len(pnameslowerin)))
curdata[irngn] = tempin[0, curprm]
#actual plotting of the input data
lines[0] = ax.plot(curdata,
altlist,
marker='o',
c='b',
linewidth=2)[0]
labels[0] = 'Input Parameters'
# Plot fitted data for the axis
indxkep = np.argwhere(invidx == ibeam)[:, 0]
curfit = Ionofit.Param_List[indxkep, time2fit[itime],
p2fit[iparam]]
rng_fit = dataloc[indxkep, 0]
alt_fit = rng_fit * sp.sin(curbeam[1] * sp.pi / 180.)
errorexist = 'n' + paramslower[iparam] in pnameslower
if errorexist and werrors:
eparam = sp.argwhere('n' +
paramslower[iparam] == pnameslower)[0][0]
curerror = Ionofit.Param_List[indxkep, time2fit[itime], eparam]
lines[1] = ax.errorbar(curfit,
alt_fit,
xerr=curerror,
fmt='-.',
c='g',
linewidth=2)[0]
else:
lines[1] = ax.plot(curfit,
alt_fit,
marker='o',
c='g',
linewidth=2)[0]
labels[1] = 'Fitted Parameters'
# get and plot the input data
numplots = len(time2file[itime])
# set the limit for the parameter
if curparm_in != 'ne':
ax.set(xlim=[0.75 * sp.amin(curfit), 1.25 * sp.amax(curfit)])
if curparm == 'vi':
ax.set(xlim=[
-1.25 * sp.amax(sp.absolute(curfit)), 1.25 *
sp.amax(sp.absolute(curfit))
])
if (curparm_in == 'ne') and nelog:
ax.set_xscale('log')
ax.set_xlabel(params[iparam])
ax.set_ylabel('Alt km')
ax.set_title(
'{0} vs Altitude, Time: {1}s Az: {2}$^o$ El: {3}$^o$'.format(
params[iparam], times[itime], *curbeam))
imcount = imcount + 1
# save figure
figmplf.suptitle(suptitle, fontsize=20)
if None in labels:
labels.remove(None)
lines.remove(None)
plt.figlegend(lines,
labels,
loc='lower center',
ncol=5,
labelspacing=0.)
fname = filetemplate + '_{0:0>3}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
def beamvstime(configfile,
maindir,
params=['Ne'],
filetemplate='AltvTime',
suptitle='Alt vs Time'):
""" This will create a altitude time image for the data for ionocontainer files
that are in sphereical coordinates.
Inputs
Times - A list of times that will be plotted.
configfile - The INI file with the simulation parameters that will be useds.
maindir - The directory the images will be saved in.
params - List of Parameter names that will be ploted. These need to match
in the ionocontainer names.
filetemplate - The first part of a the file names.
suptitle - The supertitle for the plots.
"""
sns.set_style("whitegrid")
sns.set_context("notebook")
# rc('text', usetex=True)
(sensdict, simparams) = readconfigfile(configfile)
paramslower = [ip.lower() for ip in params]
Np = len(params)
inputfile = os.path.join(maindir, 'Fitted', 'fitteddata.h5')
Ionofit = IonoContainer.readh5(inputfile)
times = Ionofit.Time_Vector
Nt = len(times)
dataloc = Ionofit.Sphere_Coords
pnames = Ionofit.Param_Names
pnameslower = sp.array([ip.lower() for ip in pnames.flatten()])
p2fit = [
sp.argwhere(ip == pnameslower)[0][0] if ip in pnameslower else None
for ip in paramslower
]
angles = dataloc[:, 1:]
b = np.ascontiguousarray(angles).view(
np.dtype((np.void, angles.dtype.itemsize * angles.shape[1])))
_, idx, invidx = np.unique(b, return_index=True, return_inverse=True)
beamlist = angles[idx]
Nb = beamlist.shape[0]
newfig = True
imcount = 0
ifig = -1
for iparam in range(Np):
for ibeam in range(Nb):
if newfig:
(figmplf, axmat) = plt.subplots(3,
3,
figsize=(20, 15),
facecolor='w',
sharex=True,
sharey=True)
axvec = axmat.flatten()
newfig = False
ix = 0
ifig += 1
ax = axvec[ix]
curbeam = beamlist[ibeam]
curparm = paramslower[iparam]
if curparm == 'nepow':
curparm = 'ne'
indxkep = np.argwhere(invidx == ibeam)[:, 0]
rng_fit = dataloc[indxkep, 0]
rngargs = np.argsort(rng_fit)
rng_fit = rng_fit[rngargs]
alt_fit = rng_fit * sp.sin(curbeam[1] * sp.pi / 180.)
curfit = Ionofit.Param_List[indxkep, :, p2fit[iparam]]
curfit = curfit[rngargs]
Tmat, Amat = np.meshgrid(times[:, 0], alt_fit)
image = ax.pcolor(Tmat, Amat, curfit, cmap='jet')
if curparm == 'ne':
image.set_norm(colors.LogNorm(vmin=1e9, vmax=5e12))
cbarstr = params[iparam] + ' m-3'
else:
image.set_norm(colors.PowerNorm(gamma=1., vmin=500, vmax=3e3))
cbarstr = params[iparam] + ' K'
if ix > 5:
ax.set_xlabel("Time in s")
if sp.mod(ix, 3) == 0:
ax.set_ylabel('Alt km')
ax.set_title('{0} vs Altitude, Az: {1}$^o$ El: {2}$^o$'.format(
params[iparam], *curbeam))
imcount = imcount + 1
ix += 1
if ix == 9 or ibeam + 1 == Nb:
cbar_ax = figmplf.add_axes([.91, .3, .06, .4])
cbar = plt.colorbar(image, cax=cbar_ax)
cbar.set_label(cbarstr)
figmplf.suptitle(suptitle, fontsize=20)
figmplf.tight_layout(rect=[0, .05, .9, .95])
fname = filetemplate + '_{0:0>3}.png'.format(ifig)
plt.savefig(fname)
plt.close(figmplf)
newfig = True
