def ISRspecmakeout(paramvals,fc,fs,species,npts):
if paramvals.ndim==2:
paramvals=paramvals[sp.newaxis]
(N_x,N_t) = paramvals.shape[:2]
Nsp = len(species)
Vi = paramvals[:,:,2*Nsp]
Parammat = paramvals[:,:,:2*Nsp].reshape((N_x,N_t,Nsp,2))
outspecs=sp.zeros((N_x,N_t,npts))
specobj = ISRSpectrum(centerFrequency =fc,nspec = npts,sampfreq=fs)
outspecsorig = sp.zeros_like(outspecs)
outrcs = sp.zeros((N_x,N_t))
for i_x in sp.arange(N_x):
for i_t in sp.arange(N_t):
cur_params = Parammat[i_x,i_t]
cur_vel = Vi[i_x,i_t]
(omeg,cur_spec,rcs) = specobj.getspecsep(cur_params,species,cur_vel,rcsflag=True)
specsum = sp.absolute(cur_spec).sum()
cur_spec_weighted = len(cur_spec)**2*cur_spec*rcs/specsum
outspecsorig[i_x,i_t] = cur_spec
outrcs[i_x,i_t] = rcs
outspecs[i_x,i_t] = cur_spec_weighted
return (omeg,outspecs)
def ISRSspecmake(ionocont,sensdict,npts,ifile=0.,nfiles=1.,print_line=True):
""" This function will take an ionocontainer instance of plasma parameters and create
ISR spectra for each object.
Inputs
ionocont - An instance of the ionocontainer class with plasma parameters. Its param list
must an array of [Nl,Nt,Ni,2].
sensdict - A dictionary with sensort information.
npts - The number of points for the spectra.
Outputs
omeg - The frequency vector in Hz.
outspects - The spectra which have been weighted using the RCS. The
weighting is npts^2 *rcs.
"""
Vi = ionocont.getDoppler()
specobj = ISRSpectrum(centerFrequency=sensdict['fc'], nspec=npts,
sampfreq=sensdict['fs'])
if ionocont.Time_Vector is None:
N_x = ionocont.Param_List.shape[0]
N_t = 1
outspecs = np.zeros((N_x, 1, npts))
full_grid = False
else:
(N_x, N_t) = ionocont.Param_List.shape[:2]
outspecs = np.zeros((N_x, N_t, npts))
full_grid = True
(N_x, N_t) = outspecs.shape[:2]
outspecsorig = np.zeros_like(outspecs)
outrcs = np.zeros((N_x, N_t))
#pdb.set_trace()
for i_x in np.arange(N_x):
for i_t in np.arange(N_t):
if print_line:
curnum = ifile/nfiles + float(i_x)/N_x/nfiles+float(i_t)/N_t/N_x/nfiles
outstr = 'Time:{0:d} of {1:d} Location:{2:d} of {3:d}, now making spectrum.'.format(i_t, N_t, i_x ,N_x)
update_progress(curnum, outstr)
if full_grid:
cur_params = ionocont.Param_List[i_x, i_t]
cur_vel = Vi[i_x, i_t]
else:
cur_params = ionocont.Param_List[i_x]
(omeg, cur_spec, rcs) = specobj.getspecsep(cur_params, ionocont.Species,
cur_vel, rcsflag=True)
specsum = np.absolute(cur_spec).sum()
cur_spec_weighted = len(cur_spec)*cur_spec*rcs/specsum
outspecsorig[i_x, i_t] = cur_spec
outrcs[i_x, i_t] = rcs
outspecs[i_x, i_t] = cur_spec_weighted
return (omeg, outspecs)
def plotparams(datapath,indch):
# read in the files
geofile = os.path.join(datapath,'Fitted','fitteddataGEOD.h5')
acffile = os.path.join(datapath,'ACF','00lags.h5')
geod = GeoData.read_h5(geofile)
acfIono = IonoContainer.readh5(acffile)
picklefile = os.path.join(datapath,'config.ini')
(sensdict,simparams) = readconfigfile(picklefile)
#determine the ind
dataloc = geod.dataloc
rngloc = 210
ind = sp.argmin(sp.absolute(dataloc[:,0]-rngloc))
#
Ne = geod.data['Ne'][ind]
Ti = geod.data['Ti'][ind]
Te = geod.data['Te'][ind]
#make a spectrum
npts = 128
datablock = sp.array([[Ne[indch],Ti[indch]],[Ne[indch],Te[indch]]])
specobj = ISRSpectrum(centerFrequency =sensdict['fc'],nspec = npts,sampfreq=sensdict['fs'])
(omeg,specexample,rcs) = specobj.getspecsep(datablock,simparams['species'],rcsflag=True)
specexample = rcs*npts*specexample/sp.absolute(specexample).sum()
acf = acfIono.Param_List[ind,indch]
origspec = scfft.fftshift(scfft.fft(acf,n=npts)).real
(figmplf, [[ax1,ax2],[ax3,ax4]]) = plt.subplots(2, 2,figsize=(16, 12), facecolor='w')
ax1.plot(sp.arange(len(Ne)),Ne)
ax1.set_title('$N_e$')
ax2.plot(sp.arange(len(Ti)),Ti)
ax2.set_title('$T_i$')
ax3.plot(sp.arange(len(Te)),Te)
ax3.set_title('$T_e$');
spec1 = ax4.plot(omeg*1e-3,origspec,label='Measured')
spec2 = ax4.plot(omeg*1e-3,specexample,label='Fitted')
ax4.set_title('Measured and Fitted Spectrums')
ax4.set_xlabel('Frequency KHz')
ax4.legend(loc = 1)
# Nevals = sp.linspace(5e10,5e11,10)
# Tivals = sp.linspace(1e3,2e5,10)
# Tevals = sp.linspace(1e3,2e5,10)
# xlist = [[1],Tivals,Nevals,Tevals,[0]]
# outsurf = makefitsurf(xlist,acf,sensdict,simparams)
return(figmplf)
def ISRSspecmake(ionocont,sensdict,npts):
""" This function will take an ionocontainer instance of plasma parameters and create
ISR spectra for each object.
Inputs
ionocont - An instance of the ionocontainer class with plasma parameters. Its param list
must an array of [Nl,Nt,Ni,2].
sensdict - A dictionary with sensort information.
npts - The number of points for the spectra.
Outputs
omeg - The frequency vector in Hz.
outspects - The spectra which have been weighted using the RCS. The
weighting is npts^2 *rcs.
"""
Vi = ionocont.getDoppler()
specobj = ISRSpectrum(centerFrequency =sensdict['fc'],nspec = npts,sampfreq=sensdict['fs'])
if ionocont.Time_Vector is None:
N_x = ionocont.Param_List.shape[0]
N_t = 1
outspecs = sp.zeros((N_x,1,npts))
full_grid = False
else:
(N_x,N_t) = ionocont.Param_List.shape[:2]
outspecs = sp.zeros((N_x,N_t,npts))
full_grid = True
(N_x,N_t) = outspecs.shape[:2]
outspecsorig = sp.zeros_like(outspecs)
outrcs = sp.zeros((N_x,N_t))
#pdb.set_trace()
for i_x in sp.arange(N_x):
for i_t in sp.arange(N_t):
print('\t Time:{0:d} of {1:d} Location:{2:d} of {3:d}, now making spectrum.'.format(i_t,N_t,i_x,N_x))
if full_grid:
cur_params = ionocont.Param_List[i_x,i_t]
cur_vel = Vi[i_x,i_t]
else:
cur_params = ionocont.Param_List[i_x]
(omeg,cur_spec,rcs) = specobj.getspecsep(cur_params,ionocont.Species,cur_vel,rcsflag=True)
specsum = sp.absolute(cur_spec).sum()
cur_spec_weighted = len(cur_spec)**2*cur_spec*rcs/specsum
outspecsorig[i_x,i_t] = cur_spec
outrcs[i_x,i_t] = rcs
outspecs[i_x,i_t] = cur_spec_weighted
return (omeg,outspecs)
def ISRspecmakeout(paramvals, fc, fs, species, npts):
""" This will make a spectra for a set a param values. This is mainly used
in the plotting functions to get spectra for given parameters.
Input
paramvals - A N_x x N_t x 2Nsp+1 numpy array that holds the parameter
values. Nx is number of spatial locations, N_t is number of
times and Nsp is number of ion and electron species.
fc - The carrier frequency of the ISR.
fs - The sampling frequency of the ISR.
species - A list of species.
npts - The number of points for each spectrum
Output
omeg - Frequency vector in Hz.
outspecs - the spectra to be output."""
if paramvals.ndim == 2:
paramvals = paramvals[np.newaxis]
(N_x, N_t) = paramvals.shape[:2]
Nsp = len(species)
Vi = paramvals[:, :, 2 * Nsp]
Parammat = paramvals[:, :, :2 * Nsp].reshape((N_x, N_t, Nsp, 2))
outspecs = np.zeros((N_x, N_t, npts))
specobj = ISRSpectrum(centerFrequency=fc, nspec=npts, sampfreq=fs)
outspecsorig = np.zeros_like(outspecs)
outrcs = np.zeros((N_x, N_t))
for i_x in np.arange(N_x):
for i_t in np.arange(N_t):
cur_params = Parammat[i_x, i_t]
cur_vel = Vi[i_x, i_t]
(omeg, cur_spec, rcs) = specobj.getspecsep(cur_params,
species,
cur_vel,
rcsflag=True)
specsum = np.absolute(cur_spec).sum()
cur_spec_weighted = 0.5 * np.pi * len(
cur_spec)**2 * cur_spec * rcs / specsum
outspecsorig[i_x, i_t] = cur_spec
outrcs[i_x, i_t] = rcs
outspecs[i_x, i_t] = cur_spec_weighted
return (omeg, outspecs)
def ISRSspecmake(ionocont,sensdict,npts):
"""This function will take an ionocontainer instance of plasma parameters and create
ISR spectra for each object.
Inputs
ionocont - An instance of the ionocontainer class with plasma parameters. Its param list
must an array of [Nl,Nt,Ni,2]."""
Vi = ionocont.getDoppler()
specobj = ISRSpectrum(centerFrequency =sensdict['fc'],nspec = npts,sampfreq=sensdict['fs'])
if ionocont.Time_Vector is None:
N_x = ionocont.Param_List.shape[0]
N_t = 1
outspecs = sp.zeros((N_x,1,npts))
full_grid = False
else:
(N_x,N_t) = ionocont.Param_List.shape[:2]
outspecs = sp.zeros((N_x,N_t,npts))
full_grid = True
(N_x,N_t) = outspecs.shape[:2]
outspecsorig = sp.zeros_like(outspecs)
outrcs = sp.zeros((N_x,N_t))
#pdb.set_trace()
for i_x in sp.arange(N_x):
for i_t in sp.arange(N_t):
print('\t Time:{0:d} of {1:d} Location:{2:d} of {3:d}, now making spectrum.'.format(i_t,N_t,i_x,N_x))
if full_grid:
cur_params = ionocont.Param_List[i_x,i_t]
cur_vel = Vi[i_x,i_t]
else:
cur_params = ionocont.Param_List[i_x]
(omeg,cur_spec,rcs) = specobj.getspecsep(cur_params,ionocont.Species,cur_vel,rcsflag=True)
specsum = sp.absolute(cur_spec).sum()
cur_spec_weighted = len(cur_spec)**2*cur_spec*rcs/specsum
outspecsorig[i_x,i_t] = cur_spec
outrcs[i_x,i_t] = rcs
outspecs[i_x,i_t] = cur_spec_weighted
return (omeg,outspecs,npts)
def ISRspecmakeout(paramvals,fc,fs,species,npts):
""" This will make a spectra for a set a param values. This is mainly used
in the plotting functions to get spectra for given parameters.
Input
paramvals - A N_x x N_t x 2Nsp+1 numpy array that holds the parameter
values. Nx is number of spatial locations, N_t is number of
times and Nsp is number of ion and electron species.
fc - The carrier frequency of the ISR.
fs - The sampling frequency of the ISR.
species - A list of species.
npts - The number of points for each spectrum
Output
omeg - Frequency vector in Hz.
outspecs - the spectra to be output."""
if paramvals.ndim == 2:
paramvals = paramvals[np.newaxis]
(N_x, N_t) = paramvals.shape[:2]
Nsp = len(species)
Vi = paramvals[:, :, 2*Nsp]
Parammat = paramvals[:, :, :2*Nsp].reshape((N_x, N_t, Nsp, 2))
outspecs = np.zeros((N_x, N_t, npts))
specobj = ISRSpectrum(centerFrequency=fc, nspec=npts, sampfreq=fs)
outspecsorig = np.zeros_like(outspecs)
outrcs = np.zeros((N_x, N_t))
for i_x in np.arange(N_x):
for i_t in np.arange(N_t):
cur_params = Parammat[i_x, i_t]
cur_vel = Vi[i_x, i_t]
(omeg, cur_spec, rcs) = specobj.getspecsep(cur_params, species, cur_vel, rcsflag=True)
specsum = np.absolute(cur_spec).sum()
cur_spec_weighted = 0.5*np.pi*len(cur_spec)**2*cur_spec*rcs/specsum
outspecsorig[i_x, i_t] = cur_spec
outrcs[i_x, i_t] = rcs
outspecs[i_x, i_t] = cur_spec_weighted
return (omeg, outspecs)
def makeallspectrumsv2(self,sensdict,npts):
"""This will create a numpy array of all of the spectrums for the data in
the instance of the class.
inputs:
sensdict - The structured dictionary of for the sensor.
npts - The number of points the spectrum is to be evaluated at.
Output:
omeg - A npts length numpy array. The frequency points that the ISR spectrum
is evaluated over in Hz
outspecs - A NlocxNtxnpts numpy array. The power spectrums for the plasma
in the entire instance of the class. The spectrum will be the correct power
level for the plasma parameters
npts - The actual number of points that the spectrum is evaluated over."""
specobj = ISRSpectrum(centerFrequency =sensdict['fc'],nspec = npts,sampfreq=sensdict['fs'])
paramshape = self.Param_List.shape
if self.Time_Vector is None:
outspecs = np.zeros((paramshape[0],1,npts))
full_grid = False
else:
outspecs = np.zeros((paramshape[0],paramshape[1],npts))
full_grid = True
(N_x,N_t) = outspecs.shape[:2]
#pdb.set_trace()
for i_x in np.arange(N_x):
for i_t in np.arange(N_t):
if full_grid:
cur_params = self.Param_List[i_x,i_t]
else:
cur_params = self.Param_List[i_x]
(omeg,cur_spec,rcs) = specobj.getspec(cur_params,rcsflag=True)
cur_spec_weighted = len(cur_spec)**2*cur_spec*rcs/cur_spec.sum()
outspecs[i_x,i_t] = cur_spec_weighted
return (omeg,outspecs,npts)
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)
"""
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
#
from ISRSpectrum.ISRSpectrum import ISRSpectrum
if __name__ == '__main__':
sns.set_style("whitegrid")
sns.set_context("notebook")
f = np.linspace(20e3, 2.5e6, 2**10)
ISpec = ISRSpectrum(nspec=2**16,
bMag=3.5e-5,
sampfreq=15e6,
alphamax=80,
f=f,
dFlag=True)
species = ['O+', 'e-']
databloc = np.array([[1.66e10, 863.], [1.66e10, 863.]])
#%% No B-Field
f, [iline, eline] = ISpec.getspecsep(databloc,
species,
alphadeg=90,
seplines=True)
plt.figure()
spec1 = iline + eline
maxy = spec1.max()
"""
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("notebook")
#
from ISRSpectrum.ISRSpectrum import ISRSpectrum
if __name__ == '__main__':
ISS2 = ISRSpectrum(centerFrequency=440.2 * 1e6,
bMag=0.4e-4,
nspec=129,
sampfreq=50e3,
dFlag=True)
ti_list = np.linspace(1000, 5000, 5)
te = 2e3
Ne = 1e11
Ni = 1e11
species = ['O+', 'e-']
databloc = np.array([[1e11, 1100.], [1e11, 2500.]])
fig, ax = plt.subplots()
for ti in ti_list:
datablock = np.array([[Ni, ti], [Ne, te]])
species = ['O+', 'e-']
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)
sns.set_context("notebook")
#
from ISRSpectrum.ISRSpectrum import ISRSpectrum
from isrutilities.physConstants import v_C_0
if __name__ == '__main__':
databloc = np.array([[1e11, 1e3], [1e11, 2.5e3]])
species = ['O+', 'e-']
newcentfreq = 449e6 + np.linspace(-200, 550, 250) * 1e6
k_all = 2 * 2 * np.pi * newcentfreq / v_C_0
k_lims = np.array([k_all.min(), k_all.max()])
freq_lims = np.array([newcentfreq.min(), newcentfreq.max()])
oution = []
for i, if_0 in enumerate(newcentfreq):
ISpec_ion = ISRSpectrum(centerFrequency=if_0,
nspec=256,
sampfreq=50e3,
dFlag=False)
fion, ionline = ISpec_ion.getspecsep(databloc, species)
oution.append(ionline)
oution = np.array(oution)
F, K_b = np.meshgrid(fion, k_all)
fig, ax = plt.subplots(1, 1, sharey=True, figsize=(4, 4), facecolor='w')
l1 = ax.pcolor(F * 1e-3, K_b, oution / oution.max(), cmap='viridis')
cb1 = plt.colorbar(l1, ax=ax)
ax.set_xlim([-25, 25])
ax.set_ylim(k_lims)
ax.set_xlabel('Frequency (kHz)', fontsize=14)
def ISRSfitfunction(x, y_acf, sensdict, simparams, Niratios, y_err=None):
"""
This is the fit fucntion that is used with scipy.optimize.leastsquares. It will
take a set parameter values construct a spectrum/acf based on those values, apply
the ambiguity function and take the difference between the two. Since the ACFs are
complex the arrays split up and the size doubled as it is output.
Inputs
x - A Np array of parameter values used
y_acf - This is the esitmated ACF/spectrum represented as a complex numpy array
sensdict - This is a dictionary that holds many of the sensor parameters.
simparams - This is a dictionary that holds info on the simulation parameters.
y_err - default None - A numpy array of size Nd that holds the standard deviations of the data.
Output
y_diff - A Nd or 2Nd array if input data is complex that is the difference
between the data and the fitted model"""
npts = simparams['numpoints']
specs = simparams['species']
amb_dict = simparams['amb_dict']
numtype = simparams['dtype']
if 'FitType' in simparams.keys():
fitspec = simparams['FitType']
else:
fitspec = 'Spectrum'
nspecs = len(specs)
(Ti, Ne, Te, v_i) = x
datablock = np.zeros((nspecs, 2), dtype=x.dtype)
datablock[:-1, 0] = Ne * Niratios
datablock[:-1, 1] = Ti
datablock[-1, 0] = Ne
datablock[-1, 1] = Te
# determine if you've gone beyond the bounds
# penalty for being less then zero
grt0 = np.exp(-datablock)
pentsum = np.zeros(grt0.size + 1)
pentsum[:-1] = grt0.flatten()
specobj = ISRSpectrum(centerFrequency=sensdict['fc'],
nspec=npts,
sampfreq=sensdict['fs'])
(omeg, cur_spec, rcs) = specobj.getspecsep(datablock,
specs,
v_i,
rcsflag=True)
cur_spec.astype(numtype)
# Create spectrum guess
(tau, acf) = spect2acf(omeg, cur_spec)
if amb_dict['WttMatrix'].shape[-1] != acf.shape[0]:
pdb.set_trace()
guess_acf = np.dot(amb_dict['WttMatrix'], acf)
# apply ambiguity function
guess_acf = guess_acf * rcs / guess_acf[0].real
if fitspec.lower() == 'spectrum':
# fit to spectrums
spec_interm = scfft.fft(guess_acf, n=len(cur_spec))
spec_final = spec_interm.real
y_interm = scfft.fft(y_acf, n=len(spec_final))
y = y_interm.real
yout = (y - spec_final)
elif fitspec.lower() == 'acf':
yout = y_acf - guess_acf
if y_err is not None:
yout = yout * 1. / y_err
# Cannot make the output a complex array! To avoid this problem simply double
# the size of the array and place the real and imaginary parts in alternating spots.
if np.iscomplexobj(yout):
youttmp = yout.copy()
yout = np.zeros(2 * len(youttmp)).astype(youttmp.real.dtype)
yout[::2] = youttmp.real
yout[1::2] = youttmp.imag
penadd = np.sqrt(np.power(np.absolute(yout), 2).sum()) * pentsum.sum()
return yout + penadd
"""
import numpy as np
from matplotlib.pyplot import subplots, show
import seaborn as sns
sns.set_style("white")
sns.set_context("talk", font_scale=1.25)
#
from ISRSpectrum.ISRSpectrum import ISRSpectrum
if __name__ == '__main__':
databloc = np.array([[1e11, 1e3], [1e11, 2.5e3]])
f = np.linspace(3.03e6, 3.034e6, 256)
f_n = -f[::-1]
# f = np.logspace(1,np.log10(8e3),2**10)
ISpec = ISRSpectrum(centerFrequency=449e6, f=f, dFlag=True)
ISpec_n = ISRSpectrum(centerFrequency=449e6, f=f_n, dFlag=True)
ISpec_ion = ISRSpectrum(centerFrequency=449e6,
nspec=256,
sampfreq=50e3,
dFlag=True)
species = ['O+', 'e-']
# databloc = np.array([[1.66e10,863.],[1.66e10,863.]])
flims = np.array([3.03e6, 3.034e6])
#%% With B-Field
eline = ISpec.getspecsep(databloc, species)[1]
eline_neg = ISpec_n.getspecsep(databloc, species)[1]
fion, ionline = ISpec_ion.getspecsep(databloc, species)
fall = np.concatenate((f_n, fion, f), 0)
def ISRSfitfunction(x, y_acf, sensdict, simparams, Niratios, y_err=None ):
"""
This is the fit fucntion that is used with scipy.optimize.leastsquares. It will
take a set parameter values construct a spectrum/acf based on those values, apply
the ambiguity function and take the difference between the two. Since the ACFs are
complex the arrays split up and the size doubled as it is output.
Inputs
x - A Np array of parameter values used
y_acf - This is the esitmated ACF/spectrum represented as a complex numpy array
sensdict - This is a dictionary that holds many of the sensor parameters.
simparams - This is a dictionary that holds info on the simulation parameters.
y_err - default None - A numpy array of size Nd that holds the standard deviations of the data.
fitmethod - default 0 - A number representing the input parameters
Output
y_diff - A Nd or 2Nd array if input data is complex that is the difference
between the data and the fitted model"""
npts = simparams['numpoints']
specs = simparams['species']
amb_dict = simparams['amb_dict']
numtype = simparams['dtype']
if 'FitType' in simparams.keys():
fitspec = simparams['FitType']
else:
fitspec = 'Spectrum'
nspecs = len(specs)
if not 'fitmode' in simparams.keys():
(Ti, Ne, Te, v_i) = x
elif simparams['fitmode'] == 0:
(Ti, Ne, Te, v_i) = x
elif simparams['fitmode'] == 1:
(Ti, Ne, TeoTi, v_i) = x
Te = TeoTi*Ti
elif simparams['fitmode'] == 2:
(Ti, acfnorm, TeoTi, v_i) = x
Te = TeoTi*Ti
Ne = acfnorm*(1+TeoTi)
datablock = np.zeros((nspecs, 2), dtype=x.dtype)
datablock[:-1, 0] = Ne*Niratios
datablock[:-1, 1] = Ti
datablock[-1, 0] = Ne
datablock[-1, 1] = Te
# determine if you've gone beyond the bounds
# penalty for being less then zero
grt0 = np.exp(-datablock)
pentsum = np.zeros(grt0.size+1)
pentsum[:-1] = grt0.flatten()
specobj = ISRSpectrum(centerFrequency=sensdict['fc'], nspec=npts, sampfreq=sensdict['fs'])
(omeg, cur_spec, rcs) = specobj.getspecsep(datablock, specs, v_i, rcsflag=True)
cur_spec.astype(numtype)
# Create spectrum guess
(_, acf) = spect2acf(omeg, cur_spec)
if amb_dict['WttMatrix'].shape[-1] != acf.shape[0]:
pdb.set_trace()
guess_acf = np.dot(amb_dict['WttMatrix'], acf)
# apply ambiguity function
guess_acf = guess_acf*rcs/guess_acf[0].real
if fitspec.lower() == 'spectrum':
# fit to spectrums
spec_interm = scfft.fft(guess_acf, n=len(cur_spec))
spec_final = spec_interm.real
y_interm = scfft.fft(y_acf, n=len(spec_final))
y_spec = y_interm.real
yout = y_spec-spec_final
elif fitspec.lower() == 'acf':
yout = y_acf-guess_acf
if y_err is not None:
yout = yout*1./y_err
# Cannot make the output a complex array! To avoid this problem simply double
# the size of the array and place the real and imaginary parts in alternating spots.
if np.iscomplexobj(yout):
youttmp = yout.copy()
yout = np.zeros(2*len(youttmp)).astype(youttmp.real.dtype)
yout[::2] = youttmp.real
yout[1::2] = youttmp.imag
penadd = np.sqrt(np.power(np.absolute(yout), 2).sum())*pentsum.sum()
return yout+penadd
import numpy as np
import os,inspect
from ISRSpectrum.ISRSpectrum import ISRSpectrum
import matplotlib.pylab as plt
import seaborn as sns
if __name__== '__main__':
sns.set_style("whitegrid")
sns.set_context("notebook")
curpath = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
imagepath = os.path.join(os.path.split(curpath)[0],'Doc','Figs')
f = np.logspace(2,8,2**10)
ISpec = ISRSpectrum(nspec=2**16,sampfreq=15e6,alphamax=60,f=f,dFlag=True)
species=['O+','e-']
databloc = np.array([[1e11,1100.],[1e11,2500.]])
#%% No B-Field
f,[iline,eline] = ISpec.getspecsep(databloc,species,alphadeg=90,seplines=True)
plt.figure()
spec1=iline+eline
maxy=spec1.max()
l1=plt.plot(f,spec1,'-',label='Sum',lw=3)[0]
l2=plt.plot(f,iline,':',label='Ion Line',lw=3)[0]
l3=plt.plot(f,eline,'--',label='Elec. Line',lw=3)[0]
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)
import matplotlib.pylab as plt
import seaborn as sns
sns.set_style("white")
sns.set_context("notebook")
#
from ISRSpectrum.ISRSpectrum import ISRSpectrum
if __name__ == '__main__':
mpl.rcParams['text.usetex'] = True
mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'
] #for \text command
databloc = np.array([[1e11, 1e3], [1e11, 2.5e3]])
nspec = 256
spfreq = 50e3
ISpec_ion = ISRSpectrum(centerFrequency=449e6,
nspec=nspec,
sampfreq=spfreq,
dFlag=True)
species = ['O+', 'e-']
# databloc = np.array([[1.66e10,863.],[1.66e10,863.]])
ylim = [0, 1.4]
ylim2 = [-.5, 1.4]
flims = np.array([3.03e6, 3.034e6])
#%% With B-Field
fion, ionline = ISpec_ion.getspecsep(databloc, species)
acf = scfft.ifft(scfft.ifftshift(ionline)).real
tau = scfft.ifftshift(
np.arange(-np.ceil((float(nspec) - 1) / 2),
np.floor((float(nspec) - 1) / 2) + 1)) / spfreq
