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 mult_iono(self, ionoin_list):
"""
This will apply the forward model to the contents of an ionocontainer object. It is assuming that
this is an ionocontainer holding the spectra.
"""
ntout = self.Time_Out.shape[0]
nlout = self.Cart_Coords_Out.shape[0]
blist_in, blist_out = self.blocklocs
amb_dict = self.simparams['amb_dict']
ambmat = amb_dict['WttMatrix']
overlaps = self.overlaps
t_s = self.sensdict['t_s']
if isinstance(ionoin_list, list) or isinstance(ionoin_list, str):
Iono_in = makeionocombined(ionoin_list)
else:
Iono_in = ionoin_list
ionocart = Iono_in.Cart_Coords
if self.simparams['numpoints'] == Iono_in.Param_List.shape[-1]:
tau, acf = spect2acf(Iono_in.Param_Names, Iono_in.Param_List)
np = ambmat.shape[0]
else:
acf = Iono_in.Param_List
np = acf.shape[-1]
np_in = acf.shape[-1]
tau_out = t_s * sp.arange(np)
outdata = sp.zeros((nlout, ntout, np), dtype=acf.dtype)
assert sp.allclose(
ionocart,
self.Cart_Coords_In), "Spatial Coordinates need to be the same"
for it_out in range(ntout):
overlists = overlaps[it_out]
irows = blist_out[it_out]
curintimes = [i[0] for i in overlists]
curintratio = [i[1] for i in overlists]
if self.mattype.lower() == 'sim':
curintimes = [curintimes[0]]
curintratio = [1.]
cur_outmat = self.RSTMat[irows[0]:irows[1], :]
icols = blist_in[it_out]
cur_mat = cur_outmat[:, icols[0]:icols[1]]
for i_it, it_in in enumerate(curintimes):
tempdata = sp.zeros((np_in, nlout), dtype=acf.dtype)
for iparam in range(np_in):
tempdata[iparam] = cur_mat.dot(acf[:, it_in, iparam])
if self.simparams['numpoints'] == Iono_in.Param_List.shape[-1]:
tempdata = sp.dot(ambmat, tempdata)
outdata[:, it_out] = sp.transpose(
tempdata) * curintratio[i_it] + outdata[:, it_out]
outiono = IonoContainer(self.Sphere_Coords_Out,
outdata,
times=self.Time_Out,
sensor_loc=Iono_in.Sensor_loc,
ver=1,
coordvecs=['r', 'theta', 'phi'],
paramnames=tau_out)
return outiono
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 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)
