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 loadfile(self):
"""Imports parameters from old files"""
fn = tkFileDialog.askopenfilename(title="Load File",
filetypes=[('INI', '.ini'),
('PICKLE', '.pickle')])
try:
sensdict, simparams = readconfigfile(fn)
rdrnames = {
'PFISR': 'PFISR',
'pfisr': 'PFISR',
'risr': 'RISR-N',
'RISR-N': 'RISR-N',
'RISR': 'RISR-N'
}
currdr = rdrnames[sensdict['Name']]
fitnfound = True
for i in simparams:
try:
if i == 'RangeLims':
self.paramdic[i][0].delete(0, END)
self.paramdic[i][1].delete(0, END)
self.paramdic[i][0].insert(0, str(simparams[i][0]))
self.paramdic[i][1].insert(0, str(simparams[i][1]))
elif i == 'species':
self.paramdic[i].delete(0, END)
string = ''
if isinstance(simparams[i], list):
for a in simparams[i]:
string += a
string += " "
else:
string = simparams[i]
self.paramdic[i].insert(0, string)
elif i == 'Pulselength' or i == 't_s':
self.paramdic[i].delete(0, END)
num = float(simparams[i]) * 10**6
self.paramdic[i].insert(0, str(num))
elif i == 'FitType':
self.fittype = simparams[i]
fitnfound = False
else:
self.paramdic[i].delete(0, END)
self.paramdic[i].insert(0, str(simparams[i]))
except:
if simparams[i] == sp.complex128:
self.paramdic[i].set('complex128')
elif simparams[i] == sp.complex64:
self.paramdic[i].set('complex64')
elif i in self.paramdic:
self.paramdic[i].set(simparams[i])
if fitnfound:
self.fittype = 'Spectrum'
self.pickbeams.var.set(currdr)
self.pickbeams.Changefile()
self.pickbeams.addbeamlist(simparams['angles'])
except:
print "Failed to import file."
def loadfile(self):
"""Imports parameters from old files"""
fn = tkFileDialog.askopenfilename(title="Load File",filetypes=[('INI','.ini'),('PICKLE','.pickle')])
try:
sensdict,simparams = readconfigfile(fn)
rdrnames = {'PFISR':'PFISR','pfisr':'PFISR','risr':'RISR-N','RISR-N':'RISR-N','RISR':'RISR-N'}
currdr = rdrnames[sensdict['Name']]
fitnfound = True
for i in simparams:
try:
if i=='RangeLims':
self.paramdic[i][0].delete(0,END)
self.paramdic[i][1].delete(0,END)
self.paramdic[i][0].insert(0,str(simparams[i][0]))
self.paramdic[i][1].insert(0,str(simparams[i][1]))
elif i=='species':
self.paramdic[i].delete(0,END)
string=''
if isinstance(simparams[i],list):
for a in simparams[i]:
string+=a
string+=" "
else:
string = simparams[i]
self.paramdic[i].insert(0,string)
elif i=='Pulselength' or i=='t_s':
self.paramdic[i].delete(0,END)
num = float(simparams[i])*10**6
self.paramdic[i].insert(0,str(num))
elif i== 'FitType':
self.fittype = simparams[i]
fitnfound=False
else:
self.paramdic[i].delete(0,END)
self.paramdic[i].insert(0,str(simparams[i]))
except:
if simparams[i]==sp.complex128:
self.paramdic[i].set('complex128')
elif simparams[i]==sp.complex64:
self.paramdic[i].set('complex64')
elif i in self.paramdic:
self.paramdic[i].set(simparams[i])
if fitnfound:
self.fittype = 'Spectrum'
self.pickbeams.var.set(currdr)
self.pickbeams.Changefile()
self.pickbeams.addbeamlist(simparams['angles'])
except:
print "Failed to import file."
def main(plist=None,
functlist=['spectrums', 'radardata', 'fitting', 'analysis', 'stats']):
""" This function will call other functions to create the input data, config
file and run the radar data sim. The path for the simulation will be
created in the Testdata directory in the RadarDataSim module. The new
folder will be called BasicTest. The simulation is a long pulse simulation
will the desired number of pulses from the user.
Inputs
npulse - Number of pulses for the integration period, default==100.
functlist - The list of functions for the RadarDataSim to do.
"""
if plist is None:
plist = sp.array([50, 100, 200, 500, 1000, 2000, 5000])
if isinstance(plist, list):
plist = sp.array(plist)
curloc = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
testpath = os.path.join(os.path.split(curloc)[0], 'Testdata', 'StatsTest')
if not os.path.isdir(testpath):
os.mkdir(testpath)
functlist_default = ['spectrums', 'radardata', 'fitting']
check_list = sp.array([i in functlist for i in functlist_default])
check_run = sp.any(check_list)
functlist_red = sp.array(functlist_default)[check_list].tolist()
allfolds = []
# rsystools = []
for ip in plist:
foldname = 'Pulses_{:04d}'.format(ip)
curfold = os.path.join(testpath, foldname)
allfolds.append(curfold)
if not os.path.isdir(curfold):
os.mkdir(curfold)
configfilesetup(curfold, ip)
makedata(curfold)
config = os.path.join(curfold, 'stats.ini')
(sensdict, simparams) = readconfigfile(config)
# rtemp = RadarSys(sensdict,simparams['Rangegatesfinal'],ip)
# rsystools.append(rtemp.rms(sp.array([1e12]),sp.array([2.5e3]),sp.array([2.5e3])))
if check_run:
runsim(functlist_red, curfold, os.path.join(curfold, 'stats.ini'),
True)
if 'analysis' in functlist:
analysisdump(curfold, config)
if 'stats' in functlist:
makehist(curfold, ip)
def configfilesetup(testpath, npulses):
""" This will create the configureation file given the number of pulses for
the test. This will make it so that there will be 12 integration periods
for a given number of pulses.
Input
testpath - The location of the data.
npulses - The number of pulses.
"""
curloc = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
defcon = os.path.join(curloc, 'statsbase.ini')
(sensdict, simparams) = readconfigfile(defcon)
tint = simparams['IPP'] * npulses
ratio1 = tint / simparams['Tint']
simparams['Tint'] = ratio1 * simparams['Tint']
simparams['Fitinter'] = ratio1 * simparams['Fitinter']
simparams['TimeLim'] = ratio1 * simparams['TimeLim']
simparams['startfile'] = 'startfile.h5'
makeconfigfile(os.path.join(testpath, 'stats.ini'), simparams['Beamlist'],
sensdict['Name'], simparams)
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 analysisdump(maindir,configfile,suptitle=None):
""" """
plotdir = os.path.join(maindir,'AnalysisPlots')
if not os.path.isdir(plotdir):
os.mkdir(plotdir)
#plot spectrums
filetemplate1 = os.path.join(maindir,'AnalysisPlots','Spec')
filetemplate3 = os.path.join(maindir,'AnalysisPlots','ACF')
(sensdict,simparams) = readconfigfile(configfile)
angles = simparams['angles']
ang_data = sp.array([[iout[0],iout[1]] for iout in angles])
zenang = ang_data[sp.argmax(ang_data[:,1])]
rnggates = simparams['Rangegatesfinal']
rngchoices = sp.linspace(sp.amin(rnggates),sp.amax(rnggates),4)
angtile = sp.tile(zenang,(len(rngchoices),1))
coords = sp.column_stack((sp.transpose(rngchoices),angtile))
times = simparams['Timevec']
filetemplate2= os.path.join(maindir,'AnalysisPlots','Params')
if simparams['Pulsetype'].lower()=='barker':
if suptitle is None:
plotbeamparameters(times,configfile,maindir,params=['Ne'],filetemplate=filetemplate2,werrors=True)
else:
plotbeamparameters(times,configfile,maindir,params=['Ne'],filetemplate=filetemplate2,suptitle=suptitle,werrors=True)
else:
if suptitle is None:
plotspecs(coords,times,configfile,maindir,cartcoordsys = False, filetemplate=filetemplate1)
plotacfs(coords,times,configfile,maindir,cartcoordsys = False, filetemplate=filetemplate3)
plotbeamparameters(times,configfile,maindir,params=['Ne','Nepow','Te','Ti'],filetemplate=filetemplate2,werrors=True)
else:
plotspecs(coords,times,configfile,maindir,cartcoordsys = False, filetemplate=filetemplate1,suptitle=suptitle)
plotacfs(coords,times,configfile,maindir,cartcoordsys = False, filetemplate=filetemplate3,suptitle=suptitle)
plotbeamparameters(times,configfile,maindir,params=['Ne','Nepow','Te','Ti'],filetemplate=filetemplate2,suptitle=suptitle,werrors=True)
def fitsurfaceplot(paramdict,
plotvals,
configfile,
y_acf,
yerr=None,
filetemplate='fitsurfs',
suptitle='Fit Surfaces'):
""" This will create a fit surface plot.
Inputs
paramdict - A dictionary with the followign key value pairs.
Ne - Array of possible electron density values.
Te - Array of possible electron tempreture values.
Ti - Array of possible ion tempreture values.
frac - Array of possible fraction shares of the ion make up.
plotvals - A dictionary with key value pars.
setparam - A string that describes he parameter thats set.
xparam - The parameter that's varied along the x axis of the image.
yparam - The parameter that's varied along the y axis of the image.
indx - The index from the paramdict for the set variable.
configfile - The file thats used for the simulation.
y_acf - the complex ACF used to create the errors.
yerr - The standard deviation of the acf measurement.
filetemplate - The template on how the file will be named.
suptitle - The super title for the plots.
"""
sns.set_style("whitegrid")
sns.set_context("notebook")
(sensdict, simparams) = readconfigfile(configfile)
specs = simparams['species']
nspecs = len(specs)
# make param lists
paramlist = [[]] * (2 * nspecs + 1)
paramlist[2 * (nspecs - 1)] = paramdict['Ne']
paramlist[2 * (nspecs - 1) + 1] = paramdict['Te']
if 'frac' in paramdict.keys():
frac = paramdict['frac']
else:
frac = [[1. / (nspecs - 1)]] * (nspecs - 1)
for ispec in range(nspecs - 1):
paramlist[2 * ispec] = frac[ispec]
paramlist[2 * ispec + 1] = paramdict['Ti'][ispec]
if 'Vi' in paramdict.keys():
paramlist[-1] = paramdict['Vi']
else:
paramlist[-1] = [0.]
pvals = {
'Ne': 2 * (nspecs - 1),
'Te': 2 * (nspecs - 1) + 1,
'Ti': 1,
'frac': 0
}
fitsurfs = makefitsurf(paramlist, y_acf, sensdict, simparams, yerr)
quad = (3, 3)
i_fig = 0
for iplt, idict in enumerate(plotvals):
iaxn = sp.mod(iplt, sp.prod(quad))
if iaxn == 0:
(figmplf, axmat) = plt.subplots(quad[0],
quad[1],
figsize=(20, 15),
facecolor='w')
axvec = axmat.flatten()
setstr = idict['setparam']
xstr = idict['xparam']
ystr = idict['yparam']
mloc = pvals[setstr]
xdim = pvals[xstr]
ydim = pvals[ystr]
setval = paramlist[setstr][idict['indx']]
transarr = sp.arange(2 * nspecs + 1).tolist()
transarr.remove(mloc)
transarr.remove(xdim)
transarr.remove(ydim)
transarr = [mloc, ydim, xdim] + transarr
fitupdate = sp.transpose(fitsurfs, transarr)
while fitupdate.ndim > 3:
fitupdate = sp.nanmean(fitupdate, dim=-1)
Z1 = fitupdate[idict['indx']]
iax = axvec[iaxn]
xvec = paramdict[xstr]
yvec = paramdict[ystr]
[Xmat, Ymat] = sp.meshgrid(xvec, yvec)
iax.pcolor(Xmat,
Ymat,
Z1,
norm=colors.LogNorm(vmin=Z1.min(), vmax=Z1.max()))
iax.xlabel = xstr
iax.ylabel = ystr
iax.title('{0} at {0}'.format(setstr, setval))
if iaxn == sp.prod(quad) - 1:
figmplf.suptitle(suptitle, fontsize=20)
fname = filetemplate + '_{0:0>4}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
i_fig += 1
def main():
sns.set_style("whitegrid")
sns.set_context("notebook")
inifile= "/Users/Bodangles/Documents/Python/RadarDataSim/Testdata/PFISRExample.pickle"
(sensdict,simparams) = readconfigfile(inifile)
simdtype = simparams['dtype']
sumrule = simparams['SUMRULE']
npts = simparams['numpoints']
amb_dict = simparams['amb_dict']
# for spectrum
ISS2 = ISRSpectrum(centerFrequency = 440.2*1e6, bMag = 0.4e-4, nspec=npts, sampfreq=sensdict['fs'],dFlag=True)
ti = 2e3
te = 2e3
Ne = 1e11
Ni = 1e11
datablock90 = sp.array([[Ni,ti],[Ne,te]])
species = simparams['species']
(omega,specorig,rcs) = ISS2.getspecsep(datablock90, species,rcsflag = True)
cur_filt = sp.sqrt(scfft.ifftshift(specorig*npts*npts*rcs/specorig.sum()))
#for data
Nrep = 10000
pulse = sp.ones(14)
lp_pnts = len(pulse)
N_samps = 100
minrg = -sumrule[0].min()
maxrg = N_samps+lp_pnts-sumrule[1].max()
Nrng2 = maxrg-minrg;
out_data = sp.zeros((Nrep,N_samps+lp_pnts),dtype=simdtype)
samp_num = sp.arange(lp_pnts)
for isamp in range(N_samps):
cur_pnts = samp_num+isamp
cur_pulse_data = MakePulseDataRep(pulse,cur_filt,rep=Nrep)
out_data[:,cur_pnts] = cur_pulse_data+out_data[:,cur_pnts]
lagsData = CenteredLagProduct(out_data,numtype=simdtype,pulse =pulse)
lagsData=lagsData/Nrep # divide out the number of pulses summed
Nlags = lagsData.shape[-1]
lagsDatasum = sp.zeros((Nrng2,Nlags),dtype=lagsData.dtype)
for irngnew,irng in enumerate(sp.arange(minrg,maxrg)):
for ilag in range(Nlags):
lagsDatasum[irngnew,ilag] = lagsData[irng+sumrule[0,ilag]:irng+sumrule[1,ilag]+1,ilag].mean(axis=0)
lagsDatasum=lagsDatasum/lp_pnts # divide out the pulse length
(tau,acf) = spect2acf(omega,specorig)
# apply ambiguity function
tauint = amb_dict['Delay']
acfinterp = sp.zeros(len(tauint),dtype=simdtype)
acfinterp.real =spinterp.interp1d(tau,acf.real,bounds_error=0)(tauint)
acfinterp.imag =spinterp.interp1d(tau,acf.imag,bounds_error=0)(tauint)
# Apply the lag ambiguity function to the data
guess_acf = sp.zeros(amb_dict['Wlag'].shape[0],dtype=sp.complex128)
for i in range(amb_dict['Wlag'].shape[0]):
guess_acf[i] = sp.sum(acfinterp*amb_dict['Wlag'][i])
# pdb.set_trace()
guess_acf = guess_acf*rcs/guess_acf[0].real
# fit to spectrums
spec_interm = scfft.fftshift(scfft.fft(guess_acf,n=npts))
spec_final = spec_interm.real
allspecs = scfft.fftshift(scfft.fft(lagsDatasum,n=len(spec_final),axis=-1),axes=-1)
# allspecs = scfft.fftshift(scfft.fft(lagsDatasum,n=npts,axis=-1),axes=-1)
fig = plt.figure()
plt.plot(omega,spec_final.real,label='In',linewidth=5)
plt.hold(True)
plt.plot(omega,allspecs[40].real,label='Out',linewidth=5)
plt.axis((omega.min(),omega.max(),0.0,2e11))
plt.show(False)
def fitcheck(repall=[100]):
x_0 = sp.array([[[1.00000000e+11, 2.00000000e+03],
[1.00000000e+11, 2.00000000e+03]],
[[5.00000000e+11, 2.00000000e+03],
[5.00000000e+11, 2.00000000e+03]],
[[1.00000000e+11, 3.00000000e+03],
[1.00000000e+11, 2.00000000e+03]],
[[1.00000000e+11, 2.00000000e+03],
[1.00000000e+11, 3.00000000e+03]]])
sns.set_style("whitegrid")
sns.set_context("notebook")
x_0_red = x_0[0].flatten()
x_0_red[-2] = x_0_red[-1]
x_0_red[-1] = 0.
configfile = 'statsbase.ini'
(sensdict, simparams) = readconfigfile(configfile)
ambdict = simparams['amb_dict']
pulse = simparams['Pulse']
l_p = len(pulse)
Nlags = l_p
lagv = sp.arange(l_p)
ntypes = x_0.shape[0]
nspec = 128
nrg = 64
des_pnt = 16
ISpec = ISRSpectrum(nspec=nspec, sampfreq=50e3)
species = ['O+', 'e-']
spvtime = sp.zeros((ntypes, nspec))
lablist = ['Normal', 'E-Ne', 'E-Ti', 'E-Te']
v_i = 0
fitfunc = ISRSfitfunction
sumrule = simparams['SUMRULE']
Nrg1 = nrg + 1 - l_p
minrg = -sumrule[0].min()
maxrg = Nrg1 - sumrule[1].max()
Nrg2 = maxrg - minrg
for i in range(ntypes):
f, curspec, rcs = ISpec.getspecsep(x_0[i], species, v_i, rcsflag=True)
specsum = sp.absolute(curspec).sum()
spvtime[i] = rcs * curspec * nspec**2 / specsum
acforig = scfft.ifft(scfft.ifftshift(spvtime, axes=1), axis=1) / nspec
acfamb = sp.dot(ambdict['WttMatrix'],
scfft.fftshift(acforig, axes=1).transpose()).transpose()
specamb = scfft.fftshift(scfft.fft(acfamb, n=nspec, axis=1), axes=1)
fig, axmat = plt.subplots(2, 2)
axvec = axmat.flatten()
figs, axmats = plt.subplots(2, 2)
axvecs = axmats.flatten()
for i in range(ntypes):
ax = axvec[i]
ax.plot(lagv, acfamb[i].real, label='Input')
ax.set_title(lablist[i])
axs = axvecs[i]
axs.plot(f * 1e-3, specamb[i].real, label='Input', linewidth=4)
axs.set_title(lablist[i])
for irep, rep1 in enumerate(repall):
rawdata = sp.zeros((ntypes, rep1, nrg), dtype=sp.complex128)
acfest = sp.zeros((ntypes, Nrg1, l_p), dtype=rawdata.dtype)
acfestsr = sp.zeros((ntypes, Nrg2, l_p), dtype=rawdata.dtype)
specest = sp.zeros((ntypes, nspec), dtype=rawdata.dtype)
for i in range(ntypes):
for j in range(nrg - (l_p - 1)):
rawdata[i, :, j:j + l_p] = MakePulseDataRepLPC(
pulse, spvtime[i], 20, rep1) + rawdata[i, :, j:j + l_p]
acfest[i] = CenteredLagProduct(rawdata[i], pulse=pulse) / (rep1)
for irngnew, irng in enumerate(sp.arange(minrg, maxrg)):
for ilag in range(Nlags):
acfestsr[i][irngnew,
ilag] = acfest[i][irng +
sumrule[0, ilag]:irng +
sumrule[1, ilag] + 1,
ilag].mean(axis=0)
ax = axvec[i]
ax.plot(lagv,
acfestsr[i, des_pnt].real / l_p,
label='Np = {0}'.format(rep1))
if irep == len(repall) - 1:
ax.legend()
specest[i] = scfft.fftshift(
scfft.fft(acfestsr[i, des_pnt], n=nspec))
axs = axvecs[i]
axs.plot(f * 1e-3,
specest[i].real / l_p,
label='Np = {0}'.format(rep1),
linewidth=4)
if irep == len(repall) - 1:
axs.legend()
print('Parameters fitted after {0} pulses'.format(rep1))
print('Ni Ti Te Vi')
for i in range(ntypes):
d_func = (acfestsr[i, des_pnt] / l_p, sensdict, simparams)
(x, cov_x, infodict, mesg,
ier) = scipy.optimize.leastsq(func=fitfunc,
x0=x_0_red,
args=d_func,
full_output=True)
print(x)
print(' ')
fig.suptitle('ACF with Sum Rule')
fig.savefig('pulsetestacf.png', dpi=400)
plt.close(fig)
figs.suptitle('Spectrum Full Array')
figs.savefig('pulsetestspec.png', dpi=400)
plt.close(figs)
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 analysisdump(maindir, configfile, suptitle=None):
""" This function will perform all of the plotting functions in this module
given the main directory that all of the files live.
Inputs
maindir - The directory for the simulation.
configfile - The name of the configuration file used.
suptitle - The supertitle used on the files.
"""
plotdir = os.path.join(maindir, 'AnalysisPlots')
if not os.path.isdir(plotdir):
os.mkdir(plotdir)
#plot spectrums
filetemplate1 = os.path.join(maindir, 'AnalysisPlots', 'Spec')
filetemplate3 = os.path.join(maindir, 'AnalysisPlots', 'ACF')
filetemplate4 = os.path.join(maindir, 'AnalysisPlots', 'AltvTime')
(sensdict, simparams) = readconfigfile(configfile)
angles = simparams['angles']
ang_data = sp.array([[iout[0], iout[1]] for iout in angles])
if not sensdict['Name'].lower() in ['risr', 'pfisr']:
ang_data_temp = ang_data.copy()
beamlistlist = sp.array(simparams['outangles']).astype(int)
ang_data = sp.array(
[ang_data_temp[i].mean(axis=0) for i in beamlistlist])
zenang = ang_data[sp.argmax(ang_data[:, 1])]
rnggates = simparams['Rangegatesfinal']
rngchoices = sp.linspace(sp.amin(rnggates), sp.amax(rnggates), 4)
angtile = sp.tile(zenang, (len(rngchoices), 1))
coords = sp.column_stack((sp.transpose(rngchoices), angtile))
times = simparams['Timevec']
filetemplate2 = os.path.join(maindir, 'AnalysisPlots', 'Params')
if simparams['Pulsetype'].lower() == 'barker':
params = ['Ne']
if suptitle is None:
plotbeamparametersv2(times,
configfile,
maindir,
params=params,
filetemplate=filetemplate2,
werrors=True)
else:
plotbeamparametersv2(times,
configfile,
maindir,
params=params,
filetemplate=filetemplate2,
suptitle=suptitle,
werrors=True)
else:
params = ['Ne', 'Nepow', 'Te', 'Ti', 'Vi']
if suptitle is None:
plotspecs(coords,
times,
configfile,
maindir,
cartcoordsys=False,
filetemplate=filetemplate1)
plotacfs(coords,
times,
configfile,
maindir,
cartcoordsys=False,
filetemplate=filetemplate3)
plotbeamparametersv2(times,
configfile,
maindir,
params=params,
filetemplate=filetemplate2,
werrors=True)
beamvstime(configfile,
maindir,
params=params,
filetemplate=filetemplate4)
else:
plotspecs(coords,
times,
configfile,
maindir,
cartcoordsys=False,
filetemplate=filetemplate1,
suptitle=suptitle)
plotacfs(coords,
times,
configfile,
maindir,
cartcoordsys=False,
filetemplate=filetemplate3,
suptitle=suptitle)
plotbeamparametersv2(times,
configfile,
maindir,
params=params,
filetemplate=filetemplate2,
suptitle=suptitle,
werrors=True)
beamvstime(configfile,
maindir,
params=params,
filetemplate=filetemplate4,
suptitle=suptitle)
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)
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
def fitsurfaceplot(paramdict,plotvals,configfile,y_acf,yerr=None,filetemplate='fitsurfs',suptitle = 'Fit Surfaces'):
(sensdict,simparams) = readconfigfile(configfile)
specs = simparams['species']
nspecs = len(specs)
# make param lists
paramlist = [[]]*(2*nspecs+1)
paramlist[2*(nspecs-1)] =paramdict['Ne']
paramlist[2*(nspecs-1)+1] =paramdict['Te']
if 'frac' in paramdict.keys():
frac = paramdict['frac']
else:
frac = [[1./(nspecs-1)]]*(nspecs-1)
for ispec in range(nspecs-1):
paramlist[2*ispec] =frac[ispec]
paramlist[2*ispec+1] = paramdict['Ti'][ispec]
if 'Vi' in paramdict.keys():
paramlist[-1] = paramdict['Vi']
else:
paramlist[-1] =[0.]
pvals = {'Ne':2*(nspecs-1),'Te':2*(nspecs-1)+1,'Ti':1,'frac':0}
fitsurfs= makefitsurf(paramlist,y_acf,sensdict,simparams,yerr)
quad = (3,3)
i_fig=0
for iplt, idict in enumerate(plotvals):
iaxn = sp.mod(iplt,sp.prod(quad))
if iaxn==0:
(figmplf, axmat) = plt.subplots(quad[0],quad[1],figsize=(20, 15), facecolor='w')
axvec = axmat.flatten()
setstr = idict['setparam']
xstr = idict['xparam']
ystr = idict['yparam']
mloc = pvals[setstr]
xdim = pvals[xstr]
ydim = pvals[ystr]
setval = paramlist[setstr][idict['indx']]
transarr = sp.arange(2*nspecs+1).tolist()
transarr.remove(mloc)
transarr.remove(xdim)
transarr.remove(ydim)
transarr = [mloc,ydim,xdim] +transarr
fitupdate = sp.transpose(fitsurfs,transarr)
while fitupdate.ndim>3:
fitupdate = sp.nanmean(fitupdate,dim=-1)
Z1 = fitupdate[idict['indx']]
iax = axvec[iaxn]
xvec = paramdict[xstr]
yvec = paramdict[ystr]
[Xmat,Ymat]= sp.meshgrid(xvec,yvec)
iax.pcolor(Xmat,Ymat,Z1,norm=LogNorm(vmin=Z1.min(), vmax=Z1.max()))
iax.xlabel=xstr
iax.ylabel=ystr
iax.title('{0} at {0}'.format(setstr,setval))
if iaxn ==sp.prod(quad)-1:
figmplf.suptitle(suptitle, fontsize=20)
fname= filetemplate+'_{0:0>4}.png'.format(i_fig)
plt.savefig(fname)
plt.close(figmplf)
i_fig+=1
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)