def getcircle(r):
tmpcirc = []
for y in range(int(-r), int(r)):
tmpcirc.append([circle(y, r), y])
ptmpcirc = Data(tmpcirc)
ptmpcirc.set_option_colonsep("with", "l lt -0")
return ptmpcirc
def getarc(x0, y0, ang0, ang1, r, lt):
tmparc = []
for ang in range(int(min(ang0, ang1)), int(max(ang0, ang1) + 1)):
radang = pi / 180. * float(ang)
tmparc.append([x0 + r * cos(radang), y0 + r * sin(radang)])
ptmparc = Data(tmparc)
ptmparc.set_option_colonsep("with", "l ls %i" % (lt))
radang = pi / 180. * (ang0 + ang1) / 2.
labelpos = [x0 + r * 2. / 3. * cos(radang), y0 + r * 2. / 3. * sin(radang)]
return ptmparc, labelpos
k=s.split()
name2="/data/ivar/berger/Regensburg/2002/"+str(i*10)+"/axlv2_sumspeed_0"+str(i*10)+"-0"+str((i+1)*10)+".dat"
f2=open(name2)
s2=f2.readline()
k2=s2.split()
summe+=float(k[2])/(5.*24.)
summe2+=float(k2[2])/(5.*24.)
hist.append([float(i*10),float(k[2])/(5.*24.),float(k2[2])/(5.*24.)])
hist2.append([float(i*10),float(k[2])/(5.*24.),float(k2[2])/(5.*24.)])
f.close()
f2.close()
#for i in range(len(hist)/2):
# hist[i*2][1]+=hist[i*2+1][1]
#for i in range(len(hist)/2):
# hist[i+len(hist)/2][1]+=0.
gphist=Data(hist)
gphist.set_option_colonsep("using", "1:2")
gphist.set_option_colonsep("title", "'2005'")
gphist2=Data(hist2)
gphist2.set_option_colonsep("using", "1:3")
gphist2.set_option_colonsep("lt","3")
gphist2.set_option_colonsep("title", "'2002'")
gp=Gnuplot()
gp.plot(gphist,gphist2)
print "summe2005 = ",summe
print "summe2002 = ",summe2
meancharge[elem].append([float(time),float(k[8])])
elemfile.close()
listin.close()
"""
generate gnuplot data
"""
#erzeugt plotdaten
plotdens=[]
for ion in range(len(ionnames)):
if (len(iondens[ion])>1):
tmpplotdens=Data(iondens[ion])
else:
tmpplotdens=Data([[0.,0.]])
tmpplotdens.set_option_colonsep("using", "1:2 ")
tmpplotdens.set_option_colonsep("title", "'%s density*10^7'" %(ionnames[ion]))
tmpplotdens.set_option_colonsep("with", "points")
plotdens.append(tmpplotdens)
plotmaxdens=[]
for ion in range(len(ionnames)):
if (len(ionmaxdens[ion])>1):
tmpplotmaxdens=Data(ionmaxdens[ion])
else:
tmpplotmaxdens=Data([[0.,0.]])
tmpplotmaxdens.set_option_colonsep("using", "1:2 ")
tmpplotmaxdens.set_option_colonsep("title", "'%s density*10^7'" %(ionnames[ion]))
tmpplotmaxdens.set_option_colonsep("with", "points")
plotmaxdens.append(tmpplotmaxdens)
plotvelocity=[]
for ion in range(len(ionnames)):
alphanfft = []
for i in range(N):
alphanfft.append(alphan[i][1])
psdnraw = fft(alphanfft)
psdnges = []
for i in range(len(psdnraw) / 2 + 1):
psdnges.append([
(float(i) / (float(N) * timeres)),
2. * abs(psdnraw[i].imag) / N + 2. * abs(psdnraw[i].real) / N
])
if (bin == 0):
psdngessum = psdnges
if (bin != 0):
for i in range(len(psdngessum)):
psdngessum[i][1] += psdnges[i][1]
plotpsdngessum = Data(psdngessum)
plotpsdngessum.set_option_colonsep("with", "lines")
gp = Gnuplot()
gp("set title '%f , gap = %i'" % (doy, gap))
gp("set logscale x")
gp("set logscale y")
gp("set yrange[1e-5:0.1]")
gp("set xrange[0.001:1.]")
gp.plot(plotpsdngessum)
gp.hardcopy("%f_%is_%i_%i.ps" % (doy, int(timeres), N, binning),
color=True,
fontsize=18)
del gp
else:
phi = pi - atan(mdata.magby[i][1] / mdata.magbx[i][1])
# Ende: Winkel ausrechnen
# Jedes binres-te Element neue bin anlegen und letzte normieren
if (i % binres == 0):
if (i > 0):
mbin[len(mbin) - 1][1] /= binres
mbin.append([mdata.time[i], phi])
else:
mbin[len(mbin) - 1][1] += phi
if (i % binres != 0):
mbin[len(mbin) - 1][1] /= (i % binres)
plotdata = Data(mbin)
plotdata.set_option_colonsep("title", "'{/Symbol F}_B'")
mp.panel[mpi].adddata(plotdata)
mp.panel[mpi].adddata(
Data([[timeframe[0], pi / 2 + parkerwinkel],
[timeframe[1], pi / 2 + parkerwinkel]]))
mp.panel[mpi].adddata(
Data([[timeframe[0], parkerwinkel], [timeframe[1], parkerwinkel]]))
mp.panel[mpi].adddata(
Data([[timeframe[0], pi + parkerwinkel], [timeframe[1],
pi + parkerwinkel]]))
mp.panel[mpi].setyrange(-2, 6)
mp.panel[mpi].setlogscale(0)
mp.panel[mpi].setylabel("B-Winkel")
mpi += 1
del plotdata
del mdata
1.5 * sdata[ion][time][maxflux][7])
and (sdata[ion][time][step][7] <=
1.69 * sdata[ion][time][maxflux][7])):
tmpsumme += sdata[ion][time][step][12]
if (sdata[ion][time][maxflux][12]):
tailbulk = tmpsumme / sdata[ion][time][maxflux][12]
else:
tailbulk = 0.
else:
tailbulk = 0.
tailbulkdisttmp.append([(279. + time * 5. * 0.00833), tailbulk])
tailbulkdist.append(tailbulkdisttmp)
gptailbulkdist = []
for ion in range(len(sdata)):
tmpgptailbulk = Data(tailbulkdist[ion])
tmpgptailbulk.set_option_colonsep("using", "1:2")
tmpgptailbulk.set_option_colonsep("title", "'%s'" % (ion_list[ion]))
gptailbulkdist.append(tmpgptailbulk)
gp = Gnuplot()
gp("set autoscale")
gp("set yrange[0.00001:1.]")
gp("set logscale y")
gp("set xlabel 'DoY'")
gp("set ylabel 'tail/bulk ratio' ")
for ion in range(len(sdata)):
if (first):
gp.plot(gptailbulkdist[ion])
first = 0
else:
dcounts.append(counts)
plotcounts = Data(counts)
allfiles.append(plotcounts)
f.close()
offs = 10
for step in range(dim):
tmpcounts = 0.
tmperrors = 0.
for i in range(1):
tmpcounts += dcounts[i + offs][step][1]
tmperrors += dcounts[i + offs][step][2]**2
sumcounts.append([dcounts[offs][step][0], tmpcounts, sqrt(tmperrors)])
plotsumcounts = Data(sumcounts)
plotsumcounts.set_option_colonsep("using", "1:2:3")
plotsumcounts.set_option_colonsep("with", "errorbars")
i = 18
j = 20
allfiles[i].set_option_colonsep("title", "'Data 18:00-19:00'")
allfiles[j].set_option_colonsep("title", "'Data 20:00-21:00'")
allfiles[i].set_option_colonsep("with", "errorbars")
allfiles[j].set_option_colonsep("with", "errorbars")
allfiles[i].set_option_colonsep("using", "1:2:3")
allfiles[j].set_option_colonsep("using", "1:2:3")
print "ionvel = ", ionvel
gp = Gnuplot()
gp("set log y")
#gp("set log x")
#gp("set xrange[250:400]")
interval = int(data.countspec[ts][vs][0] / datah2.vel[ts] /
intervalrange)
#print interval
if (interval < int(maxrange / intervalrange)):
datadenssum[i][interval] += data.countspec[ts][vs][1]
for interval in range(len(datadenssum[i])):
datadenssum[i][interval] /= len(data.countspec)
if (datadenssum[i][interval] > 1.e-24):
if (datadenssum[i][interval] < datamin[i]):
datamin[i] = datadenssum[i][interval]
if (datadenssum[i][interval] > datamax[i]):
datamax[i] = datadenssum[i][interval]
pdata = Data(zip(frange(0, maxrange, intervalrange), datadenssum[i]))
pdata.set_option_colonsep("title", "'" + str(swstart) + "'")
mp.panel[i].adddata(pdata)
del pdata
del datadenssum
#data = swicsprotondata(ionnames[i],jahr[jh][1],path)
#data.load()
#gp.plot(Data(data.countspec[0]))
for i in range(len(ionnames)):
mp.panel[i].setyrange(datamin[i] * 0.9, datamax[i] * 1.1)
mp.panel[i].setlogscale(1)
mp.panel[i].setylabel(ionnames[i] + " counts per hour")
mp.plot()
hist[p,w,:,0] = linspace(0,(bincount-1)*binsize,bincount) + binsize/2
# Ende: Geschwindigkeiten anfügen
mp = multiplot(plotcount+1,1)
mpi = 0
mp.setgv(1)
mp.setxlabel("Geschwindigkeitsbins")
mp.setxrange(200,bincount*binsize)
#Anfang: Panels plotten
for p in range(len(hist)):
for w in range(len(marks)):
plotdata = Data(hist[p,w,:,0:3])
plotdata.set_option_colonsep("title","'"+title[p]+" "+marktitle[w]+"'")
plotdata.set_option_colonsep("with","yerrorline")
mp.panel[mpi].adddata(plotdata)
mp.panel[mpi].setyrange(ranges[p][0],ranges[p][1])
mp.panel[mpi].setlogscale(log[p])
del plotdata
mpi += 1
#Ende: Panels plotten
#Anfang: Counts plotten
plotdata = Data(zip(hist[0,0,:,0],hist[0,0,:,3]))
mp.panel[mpi].adddata(plotdata)
plotdata = Data(zip(hist[0,1,:,0],hist[0,1,:,3]))
mp.panel[mpi].adddata(plotdata)
mp.panel[mpi].setyrange(1,900)
mp.panel[mpi].setlogscale(1)
onedaym = histogram(cleanmeanch, 85, 1, 20, 4)
onedaym2 = sig_histogram(cleanmeanch, onedaym, 4)
onedaym3 = []
for i in range(len(onedaym)):
onedaym3.append([onedaym[i][0], onedaym[i][1], onedaym2[i][1]])
#for i in range(16):
# nrsum=0
# onedaymean=0.
# for j in range(24):
# if (fedata.meanch[i*24+j][1]>0. and fedata.meanch[i*24+j][1]<20.):
# nrsum+=1
# onedaymean+=fedata.meanch[i*24+j][1]
# if (nrsum>0):
# onedaym.append([90.5+float(i),onedaymean/nrsum])
ponedaym = Data(onedaym3)
ponedaym.set_option_colonsep("using", "1:2:3")
ponedaym.set_option_colonsep("with", "yerrorbars lw 3 pt 2")
gp = Gnuplot()
gp("set grid xtics mxtics noytics back lt -1 lt 0")
gp("set yrange[0:20]")
gp("set xrange[85:105]")
gp("set mxtics 5")
gp("set mytics 5")
gp("set title 'ACE/SWICS Maximum Likelihood PHA Analyse'")
gp("set ylabel 'Qm(Fe)'")
gp("set xlabel 'Doy 2001'")
gp.plot(ponedaym)
gp.hardcopy("meanchfe.ps", color="true")
vax = vp[0][2] + vd[0][2]
vay = vp[0][3] + vd[0][3]
var = sqrt(vax**2 + vay**2)
radius[1] = var
vaang = asin(vay / var) * 180. / pi
#va=[[0,0,vd[0][2]+vp[0][2],vd[0][3]+vp[0][3]]]
va, valabel = getvec(0., 0., var, vaang)
vdsw, vdswlabel = getvec(radius[0], 50., radius[1] - radius[0], 0.)
vpsw, vpswlabel = getvec(0., 75., radius[0], 0.)
vasw, vaswlabel = getvec(0., 100., radius[1], 0.)
#vdrec,vdreclabel=getvec(radius[0],25.,radius[0]*cos((180-beta+alpha)*pi/180.)+sqrt(radius[0]**2*cos((180-beta+alpha)*pi/180.)**2+radius[1]**2-radius[0]**2),0.)
vdrec, vdreclabel = getvec(radius[0], 25., dv0, 0.)
vdrot, vdrotlabel = getvec(vp[0][2], vp[0][3], dv0, alpha)
pvp = Data(vp)
pvp.set_option_colonsep("with", "vectors size 7,25 filled ls 10")
pva = Data(va)
pva.set_option_colonsep("with", "vectors size 7,25 filled ls 11")
pvd = Data(vd)
pvd.set_option_colonsep("with", "vectors size 7,25 filled ls 12")
pvdsw = Data(vdsw)
pvdsw.set_option_colonsep("with", "vectors heads front size 5,90 ls 13")
pvdrec = Data(vdrec)
pvdrec.set_option_colonsep("with", "vectors heads front size 5,90ls 12")
pvpsw = Data(vpsw)
pvpsw.set_option_colonsep("with", "vectors heads front size 5,90 ls 10")
pvasw = Data(vasw)
pvasw.set_option_colonsep("with", "vectors heads front size 5,90 ls 11")
pvdrot = Data(vdrot)
pvdrot.set_option_colonsep("with", "vectors size 7,25 filled ls 17")
alphaarc, alphaarclabel = getarc(vp[0][2], vp[0][3], 180, 180 + int(alpha),
for i in range(dim):
tmpsum = 0.
for j in range(len(fftdata) / 2 + 1):
tmpsum += sin(
float(j) / float(N) * 2 * pi * float(i)) * (fftdata[j].real) + cos(
float(j) / float(N) * 2 * pi * float(i)) * (fftdata[j].imag)
# tmpsum=tmpsum*2.
for j in range(len(fftdata) / 2):
tmpsum += sin(
float(len(fftdata) - j - 1) / float(N) * 2 * pi *
float(i)) * (fftdata[len(fftdata) - j - 1].real) + cos(
float(len(fftdata) - j - 1) / float(N) * 2 * pi *
float(i)) * (fftdata[len(fftdata) - j - 1].imag)
tmpsum = tmpsum / dim
sumdata2.append([float(i) / 2., tmpsum])
plotsum = Data(sumdata)
plotsum.set_option_colonsep("with", "line")
plotsum2 = Data(sumdata2)
plotsum2.set_option_colonsep("with", "line")
gp = Gnuplot()
gp("set logscale y")
#gp("set logscale x")
gp.plot(plotdata, plotdata2)
gp2 = Gnuplot()
#gp2("set logscale y")
gp2("set samples 12800")
gp2("set xrange[80:128]")
gp2.plot("(sin(x)+sin(2*x))", plotsum, plotsum2)
N2=len(autoc)
for i in range(N2):
autocfft.append(autoc[i][1])
psdaraw=fft(autocfft)
psdages=[]
for i in range(len(psdaraw)/2+1):
psdages.append([(float(i)/(float(N2)*timeres)),2.*abs(psdaraw[i].imag)/N2+2.*abs(psdaraw[i].real)/N2])
if (bin==0):
psdagessum=psdages
if (bin!=0):
for i in range(len(psdagessum)):
psdagessum[i][1]+=psdages[i][1]
if (doautocorr):
plotpsdagessum=Data(psdagessum)
plotpsdagessum.set_option_colonsep("with", "lines")
plotautoc=Data(autoc)
plotautoc.set_option_colonsep("with", "lines")
if (dofft):
plotpsdngessum=Data(psdngessum)
plotpsdngessum.set_option_colonsep("with", "lines")
plotpsdmagbzges=Data(psdmagbzges)
plotpsdmagbzges.set_option_colonsep("with", "lines")
plotalphan=Data(alphan)
plotalphan.set_option_colonsep("with", "lines")
plotbeta=Data(beta)
plotbeta.set_option_colonsep("with", "lines")
plotpsdbetages=Data(psdbetages)
plotpsdbetages.set_option_colonsep("with", "lines")
if (dofft):
pspec[len(pspec) - 1].set_option_colonsep("with", "yerrorbars lw 3")
pspec[len(pspec) - 1].set_option_colonsep("using", "1:2:3")
i += 1
phist = []
i = 0
for spec in histarr:
phist.append(Data(spec))
phist[len(phist) - 1].set_option_colonsep("title",
"'%s'" % (fedata[i].name))
phist[len(phist) - 1].set_option_colonsep("with", "yerrorbars lw 3")
phist[len(phist) - 1].set_option_colonsep("using", "1:2:3")
i += 1
psumhist = Data(sumhist)
psumhist.set_option_colonsep("title", "'Fe'")
psumhist.set_option_colonsep("with", "yerrorbars lw 3")
psumhist.set_option_colonsep("using", "1:2:3")
gp = Gnuplot()
#gp("f(x)=3.e6*x**-5.7")
gp("set log yx")
gp("set yrange[0.1:1e5]")
gp("set xrange[.1:100.]")
gp("set format y '10^{%T}'")
gp("set xlabel 'keV/amu'")
gp("set ylabel 'N[#]'")
if (len(hedata.time) == 1):
gp("set title '2007 DoY %.3f'" % (hedata.time[0]))
else:
gp("set title '2007 DoY %.3f-%.3f'" %
tmpq=tmpq+(results[ion+42][interv][1]*(5.+float(ion)))
sumfl=sumfl+(results[ion+42][interv][1])
qtmp8.append([results[0][interv][0],(tmpq/sumfl)])
qges.append(qtmp1)
qges.append(qtmp2)
qges.append(qtmp3)
qges.append(qtmp4)
qges.append(qtmp5)
qges.append(qtmp6)
qges.append(qtmp7)
qges.append(qtmp8)
gpqges=[]
for ion in range(8):
tmpgpq=Data(qges[ion])
tmpgpq.set_option_colonsep("using", "1:2")
tmpgpq.set_option_colonsep("title", "'%s'" %(ion_list2[ion]))
gpqges.append(tmpgpq)
# qfe.append([results[0][interv][0],(tmpflux2/tmpflux)])
#gpqfe=Data(qfe)
#gpqfe.set_option_colonsep("using", "1:2")
#gpqfe.set_option_colonsep("title", "'QFe'")
tmpgpsumme=Data(summe)
tmpgpsumme.set_option_colonsep("using", "1:2")
tmpgpsumme.set_option_colonsep("title", "'O'")
histarr2.append(tmphist2)
meandvvsvparr[i][2] = tmpsig
meandvvsvparr[i][3] = (len(tmpval[j0:-j0 - 1]))
dvrmean = []
for ion in dvvsvparr:
dvrmean.append([])
for i in range(len(ion) - 20):
dvrmean[-1].append(mean(ion[i:i + 20]))
magrmean = []
for i in range(len(magarr) - 20):
magrmean.append(mean(magarr[i:i + 20]))
meanhevse = meandvvsvparr
pmeandvvsvparr = Data(meandvvsvparr)
pmeandvvsvparr.set_option_colonsep("using", "1:4:2")
"""
i=-1
for ion in meandvvsvparr:
i+=1
tmpsum=0.
deltasum=0.
nrtmpsum=0
j=-1
for val in ion:
j+=1
if (val[0]>0.01 and val[0]<0.4 and meandvvsvparr[-1][j][1]>0.):
delta=val[2]/meandvvsvparr[-1][j][1]+val[1]/meandvvsvparr[-1][j][1]**2*meandvvsvparr[-1][j][2]
tmpsum+=val[1]/meandvvsvparr[-1][j][1]
deltasum+=delta**2
nrtmpsum+=1.
daten[11, :, 1] += tmp[:]
tmp = array(get_densratio(odata, pdata, mark[m]))
daten[12, :, 1] += tmp[:]
del mdata
del pdata
del he2data
del o6data
del o7data
if len(mark) > 0:
daten[:, :, 1] /= len(mark)
for i in [1, 2, 10, 5, 3, 11, 12]:
plotdata = Data(daten[i])
plotdata.set_option_colonsep("title", "'" + title[i] + "'")
if (log[i]):
unten = 0.3 * min(daten[i, :24 * 8 - 3, 1])
oben = 3. * max(daten[i, :24 * 8 - 3, 1])
else:
unten = min(daten[i, :24 * 8 - 3,
1]) - 0.3 * abs(min(daten[i, :24 * 8 - 3, 1]))
oben = max(daten[i, :24 * 8 - 3,
1]) + 0.3 * abs(min(daten[i, :24 * 8 - 3, 1]))
if (title[i] == "v_p"):
unten = 250
mp.panel[mpi].setyrange(unten, oben)
mp.panel[mpi].adddata(plotdata)
mp.panel[mpi].setlogscale(log[i])
for i in range(len(points1)):
pointsmat[i][i] = 1.
xvalpoints[i] = points1[i][0]
yvalpoints[i] = points1[i][1]
gpdata = GridData(et_mat_plot, xval, yval, binary=0)
gpdata.set_option_colonsep("with", "lines")
gppoints = GridData(pointsmat, xvalpoints, yvalpoints, binary=0)
#gppoints=Data(points1,binary=0)
gppoints.set_option_colonsep("with", "p pt 2 lt 5 ps 2.5")
#gppoints.set_option_colonsep("at", "bs")
gpxerrorp = Data(xerrorp)
gpxerrorm = Data(xerrorm)
gpyerrorp = Data(yerrorp)
gpyerrorm = Data(yerrorm)
#gpvectors.set_option_colonsep("using", "1:2:3:4")
gpxerrorp.set_option_colonsep("with", "vectors head filled lt 5 lw 2")
gpxerrorm.set_option_colonsep("with", "vectors head filled lt 5 lw 2")
gpyerrorp.set_option_colonsep("with", "vectors head filled lt 5 lw 2")
gpyerrorm.set_option_colonsep("with", "vectors head filled lt 5 lw 2")
#yrange=[6,40]
#xrange=[100,223]
yrange = [2, 20]
xrange = [200, 512]
#yrange=[0,127]
#xrange=[80,330]
for ion in range(len(ion_names)):
labelx = ((points1[ion][0] - float(xrange[0])) /
float(xrange[1] - xrange[0])) - 0.025
labely = (1. - (points1[ion][1] - float(yrange[0])) /
(yrange[1] - yrange[0])) - 0.04
alpha = zeros([4, 4])
covar = zeros([4, 4])
ia = zeros([4], int)
ia[0] = ia[1] = 1
ia[2] = ia[3] = 1
x = range(-5, 6)
x = array(x)
y = zeros([11])
sig = zeros([11])
for i in range(len(x)):
y[i] = a * x[i]**3 + b * x[i]**2 + c
sig[i] = sqrt(abs(y[i]))
print x
print y
merit = 0.
merit = maxlifit(x, y, sig, p, ia, covar, alpha, merit, g)
data = []
for i in range(len(x)):
data.append([x[i], y[i], sig[i]])
pdata = Data(data)
pdata.set_option_colonsep("using", "1:2:3")
pdata.set_option_colonsep("with", "yerrorbars")
gp = Gnuplot()
gp("f(x)=%f*x**3+%f*x**2+%f*x+%f" % (p[0], p[1], p[2], p[3]))
gp("g(x)=%f*x**2+%f*x+%f" % (p0[0], p0[1], p0[2]))
gp.plot(pdata, "f(x)")
s = f.readline()
s = f.readline()
for s in f:
k = s.split()
for ion in range(len(ion_names)):
if (ion_names[ion] == k[2]):
ionpos[ion].append([(float(k[3]) + 0.5) / 2.,
(float(k[5]) + 0.5) / 2.])
f.close()
plotionpos = []
for ion in range(len(ionpos)):
tmpion = Data(ionpos[ion])
if (ion_names[ion] != "20Ne8+"):
tmpion.set_option_colonsep("title", "'%s'" % (ion_names[ion]))
else:
tmpion.set_option_colonsep("title", "'Ne8+'")
plotionpos.append(tmpion)
yrange = [0, 127]
xrange = [50, 300]
gp = Gnuplot()
gp("set mouse")
gp("set xlabel 'Time-of-Flight Channel'")
gp("set ylabel 'Energy Channel' ")
gp("set xrange[%i:%i]" % (xrange[0], xrange[1]))
gp("set yrange[%i:%i]" % (yrange[0], yrange[1]))
gp.plot(plotionpos[0])
for ion in range(len(ionpos) - 1):
pvthhisttheovs.set_option_colonsep("title","'Model'")
pvthhisttheovs.set_option_colonsep("with","steps lt 3 lw 2")
pvthhisttheovs.set_option_colonsep("using","1:2")
"""
a = mag.getvals(["time", "phi"])
b = mag.getvals(["time", "theta"])
c = mag.getvals(["time", "phi"])
for i in range(len(c)):
c[i, 1] = acos(cos(a[i, 1]) * cos(b[i, 1]))
#c[:,1]-=pi/2.
#c[:,1]=-c[:,1]
d = iondata.getvals(["time", "vel"])
d[:, 1] /= 100
pa = Data(a)
pa.set_option_colonsep("with", "l lt 1")
pb = Data(b)
pc = Data(c)
pc.set_option_colonsep("with", "l lt 3")
pd = Data(d)
pd.set_option_colonsep("with", "l lt 4")
denssw = iondata.getvals(["time", "dens"])
dens = swepam.getvals(["time", "dens"])
vel = iondata.getvals(["time", "vel"])
vels = swepam.getvals(["time", "vel"])
temp = iondata.getvals(["time", "temp"])
temps = swepam.getvals(["time", "temp"])
magb = mag.getvals(["time", "magb"])
prdbb = mag.getvals(["time", "prdbb"])
dbb = mag.getvals(["time", "dbb"])
class error:
"Class error : contains errordata\n\t name = array[steps][ions] ..."
def __init__(self,filename):
self.filename=filename
self.name=[]
self.ionname=""
self.step=0
self.counts=0.
self.frac=0.
self.error=0.
self.fiterg=[]
self.normerg=[]
self.histogram=[]
# self.gp=Gnuplot()
"""
method : reads in maximum-likelihood-fitresults to generated data. the name of the file is in self.filename
"""
def readerrordata(self):
dim=200
infile=open(self.filename)
tmpfitergion1=[]
tmpfitergion2=[]
tmpnormergion1=[]
tmpnormergion2=[]
for step in range(dim):
s=infile.readline()
k=s.split()
numions=int(k[5])
numions=2
s=infile.readline()
for ion in range(numions):
s=infile.readline()
k=s.split()
if (step==0):
self.name.append(k[0])
if (ion==0):
tmpfitergion1.append(float(k[10]))
tmpnormergion1.append(float(k[11]))
if (ion==1):
tmpfitergion2.append(float(k[10]))
tmpnormergion2.append(float(k[11]))
self.fiterg.append(tmpfitergion1)
self.fiterg.append(tmpfitergion2)
self.normerg.append(tmpnormergion1)
self.normerg.append(tmpnormergion2)
print "load : ",self.filename
"""
method : calculates error from already loaded maximum-likelihodd-fitresults (via readerrordata)
gethistogram first makes a histogram out of the m-l-fitresults. afterwards getfitpara determines the parameters for the final fitting to the histogram. in the end the fit to the histogram is performed.
"""
def calcerror(self):
gethistogram(self)
self.getfitpara()
self.geterror()
"""
method :
"""
def geterror(self):
gp=Gnuplot()
# gp("set yrange[0:1e20]")
gp("a=%f" %(self.counts))
print len(self.histogram[0])
gp("b=5.")
gp("c=200.")
gp("f(x)=c/(sqrt(2.*pi)*b)*exp(-((x-a)**2/(2.*b**2)))")
self.gphist=Data(self.histogram[0])
self.gphist.set_option_colonsep("with","boxes")
if (len(self.histogram[0])>3):
gp("fit f(x) '"+self.gphist.filename+"' using 1:2 via a,b")
gp("fit f(x) '"+self.gphist.filename+"' using 1:2 via b")
else:
gp("b=0.1")
# gp.plot(self.gphist,"f(x)")
# gp("pause 3")
self.error=gp.eval("b")/self.counts
print self.error
"""
method :
"""
def getfitpara(self):
positions=[]
for i in range(len(self.filename)):
if (self.filename[i:i+1]=="_"):
positions.append(i)
print positions
self.ionname=self.filename[positions[2]+1:positions[3]]
self.step=int(self.filename[positions[3]+5:positions[4]])
self.counts=float(self.filename[positions[4]+1:positions[5]])
self.frac=float(self.filename[positions[5]+1:positions[6]])
print self.ionname
print self.step
print self.counts
print self.frac
print self.name
class cascade:
def __init__(self, B, power, index, eff, ions, vsw, valf):
"B\t->\t float : magnetic field strength in nT\npower\t->\t float : scales the wavepower spectrum\nindex\t->\t float : spectral index of cascade\neff\t->\t float : efficiency for energy drain from cascade\nions\t->\t [nrions][ioname(str),ionmass(float,in amu),ioncharge(float,in e),iondens(float,in respect to protons)\nvsw\t->\t float : solar wind speed\nvalf\t->\t float : alfven speed"
self.B = B * 1e-9
self.power = power
self.index = index
self.eff = eff
self.vsw = vsw
self.valf = valf
self.name = []
self.dens = []
self.mass = []
self.mqarr = []
self.charge = []
self.sequence = []
for ion in ions:
self.name.append(ion[0])
self.mass.append(ion[1])
self.charge.append(ion[2])
self.dens.append(ion[3])
self.mqarr.append(ion[2] / ion[1])
self.sequence.append(0)
self.qm0 = 1.602176487e-19 / 1.66053886e-27
self.warr = []
self.dim = len(self.mqarr)
self.get_sequence()
self.build_warr()
self.calc_casc()
def set_index(self, a):
self.index = a
def set_B(self, a):
self.B = a * 1e-9
def set_efficiency(self, a):
self.eff = a
def build_warr(self):
"calculates the resonance frequencies"
for i in self.sequence:
self.warr.append(-(self.mqarr[i] * self.qm0 * self.B) /
(1. - self.vsw / self.valf))
def get_sequence(self):
mqarr = []
sequence = []
for val in self.mqarr:
mqarr.append(val)
for i in range(self.dim):
maxval = min(mqarr)
maxpos = 0
i = 0
while (mqarr[i] != maxval):
i += 1
maxpos = i
"""
for i in range(self.dim):
if (mqarr[i]==maxval):
maxpos=i
"""
sequence.append(maxpos)
newmqarr = []
for i in range(self.dim):
if (i != maxpos):
newmqarr.append(mqarr[i])
else:
newmqarr.append(100.)
mqarr = []
for val in newmqarr:
mqarr.append(val)
self.sequence = sequence
def calc_casc(self):
Parr = [[.01, self.power]]
dParr = [[.01, 0.]]
for i in range(self.dim):
Parr.append([self.warr[i], 0.])
dParr.append([self.warr[i], 0.])
Parr[0][1] = (self.power * Parr[0][0]**-self.index)
dParr[0][1] = (self.power * Parr[0][0]**-self.index)
#dParr[0][1]=0.
Parr[1][1] = (self.power * Parr[1][0]**-self.index) - (
self.power * Parr[1][0]**-self.index
) * self.dens[self.sequence[0]] * self.eff * self.charge[
self.sequence[0]]**2 / self.mass[self.sequence[0]]
dParr[1][1] = (self.power * Parr[1][0]**-self.index) * self.dens[
self.sequence[0]] * self.eff * self.charge[
self.sequence[0]]**2 / self.mass[self.sequence[0]]
for i in range(self.dim - 1):
P0 = (self.power * Parr[i + 2][0]**-self.index) - (
(self.power * Parr[i + 1][0]**-self.index) - Parr[i + 1][1])
dP = P0 * self.dens[self.sequence[i + 1]] * self.eff * self.charge[
self.sequence[i + 1]]**2 / self.mass[self.sequence[i + 1]]
print "P0,dP"
print P0, dP
if (dP < P0):
Parr[i + 2][1] = P0 - dP
else:
Parr[i + 2][1] = 1.
dP = P0
dParr[i + 2][1] = dP
if (i == 0):
dParr[i + 2][1] = dP
if (i == 1):
dParr[i + 2][1] = dP
if (i == 2):
dParr[i + 2][1] = dP
self.Parr = Parr
self.dParr = dParr
dParr2 = []
dParr3 = []
dVarr = []
E0 = 0.
ppos = 0
hepos = 0
for j in range(len(self.sequence)):
if (self.name[self.sequence[j]] == "H1+"):
ppos = j
if (self.name[self.sequence[j]] == "He2+"):
hepos = j
for i in range(self.dim):
pos = self.sequence[i]
print "pos = ", i, self.sequence[i], pos
print "p,hepos = ", ppos, hepos
dParr2.append([
dParr[i + 1][0], (dParr[i + 1][1] / self.dens[pos]) /
(dParr[ppos + 1][1] / self.dens[self.sequence[ppos]])
])
# dv/dvHe
#a=sqrt((dParr[i+1][1]*2./self.dens[i]**2)/self.mass[i])*(self.charge[i]**1.5/self.mass[i])/(sqrt((dParr[self.dim-3][1]*2./self.dens[self.dim-4]**2)/self.mass[self.dim-4])*(self.charge[self.dim-4]**1.5/self.mass[self.dim-4]))
a = sqrt(
(dParr[i + 1][1] * 2. / self.dens[pos]**1.) / self.mass[pos]
) * (self.charge[pos]**1.5 / self.mass[pos]**1.) / (sqrt(
(dParr[hepos + 1][1] * 2. / self.dens[self.sequence[hepos]]**
1.) / self.mass[self.sequence[hepos]]) * (
self.charge[self.sequence[hepos]]**1.5 /
self.mass[self.sequence[hepos]]**1.))
# b = E/Ep
b = (dParr[i + 1][1] / self.dens[pos]) / (
dParr[ppos + 1][1] / self.dens[self.sequence[ppos]])
print a, b
#dParr3.append([self.mass[i]/self.charge[i],(a)*(self.charge[i]**1.5/self.mass[i])])
dParr3.append([self.charge[pos] / self.mass[pos], b])
dVarr.append([self.charge[pos] / self.mass[pos], a])
pdParr = []
pdVarr = []
self.pdVarr = []
self.pdParr = []
for j in range(self.dim):
for k in range(self.dim):
if (self.name[self.sequence[k]] == self.name[j]):
pos = k
if (self.name[j] != "H1+"):
self.pdVarr.append(Data([dVarr[pos]]))
self.pdVarr[len(self.pdVarr) - 1].set_option_colonsep(
"with", "p pt %i lt 3" % (j + 1))
self.pdParr.append(Data([dParr3[pos]]))
if (self.name[j] == "H1+"):
self.pdParr[len(self.pdParr) - 1].set_option_colonsep(
"title", "'%s'" % ("H^{1+}"))
if (self.name[j] == "H1+"):
self.pdParr[len(self.pdVarr) - 1].set_option_colonsep(
"with", "p pt %i lt 1" % (j + 1))
else:
self.pdParr[len(self.pdVarr) - 1].set_option_colonsep(
"with", "p pt %i lt 3" % (j + 1))
self.dParr2 = dParr2
self.dParr3 = dParr3
self.dVarr = dVarr
def plot(self):
self.gp = Gnuplot()
#self.gp("set autoscale")
self.gp("set size square")
self.gp("set xrange[0.01:.1]")
self.gp("set yrange[100.:1000.]")
self.gp("set format xy '10^%T'")
self.gp("set xlabel '{/Symbol w} [Hz]'")
self.gp("set ylabel 'Wave Power [arb.]'")
self.gp("set logscale xy")
self.pParr = Data(self.Parr)
self.pParr.set_option_colonsep("with", "lp")
self.gp.plot(self.pParr)
#self.gp.replot(Data(self.dParr2))
#self.gp.replot(Data(self.dParr2))
self.gp("a=%f" % (self.power))
self.gp("k=%f" % (self.index))
self.gp("f(x)=a*x**-k")
self.gp("f2(x)=(a*.7)*x**-(k+.1)")
self.gp("f3(x)=(a*1e-5)*x**-(k+4.)")
#self.gp.replot("f(x) with l lt 3","f2(x) with l lt 4","f3(x) with l lt 5")
self.gp("set term postscript color 18 lw 2 eps")
self.gp.hardcopy("Pcascall.ps", color="true", fontsize=18)
self.gp2 = Gnuplot()
self.gp2("set logscale xy")
self.gp2("set title 'theocasc dE'")
#gp2("set yrange[1:1.1]")
first = 1
for j in range(self.dim):
if (first == 1):
self.gp2.plot(self.pdParr[j])
first = 0
else:
self.gp2.replot(self.pdParr[j])
self.gp3 = Gnuplot()
self.gp3("set logscale xy")
self.gp3("set title 'theocasc dV'")
first = 1
for j in range(self.dim):
if (first == 1):
self.gp3.plot(self.pdVarr[j])
first = 0
else:
self.gp3.replot(self.pdVarr[j])
def mrqcof(self, deriv, plot):
from numpy import array, zeros
from Gnuplot import Data
dim = len(self.yvals[0])
dyda = zeros([self.ma, dim])
tmpdv = []
tmpvth = []
self.chisq = 0.
for j in range(self.ma):
for k in range(self.ma):
self.covar[j][k] = 0.
self.da[j] = 0.
for i in range(self.ndata):
ymod = self.func(self.xvals[i], self.paratry, dyda, deriv, self.ia)
if (plot == 1):
tmpdv.append([i * 0.2, ymod[0]])
tmpvth.append([i * 0.2, ymod[1]])
sig2i = []
for j in range(dim):
if (self.sigvals[i][j] != 0.):
sig2i.append(1. / (self.sigvals[i][j]**2))
else:
sig2i.append(1.)
dy = []
for j in range(dim):
dy.append(self.yvals[i][j] - ymod[j])
if (deriv == 1):
j = 0
for l in range(self.ma):
if (self.ia[l]):
wt = []
for n in range(dim):
wt.append(dyda[l][n] * sig2i[n])
k = 0
for m in range(l + 1):
if (self.ia[m]):
for n in range(dim):
self.covar[j][k] += wt[n] * dyda[m][n]
k += 1
for n in range(dim):
self.da[j] += dy[n] * wt[n]
j += 1
for n in range(dim):
self.chisq += dy[n] * dy[n] * sig2i[n]
for j in range(1, self.mfit):
for k in range(j):
self.covar[k][j] = self.covar[j][k]
if (plot == 1):
tmpdv.append([1.6, 0.0])
tmpdvplot = Data(tmpdv)
# tmpdvplot.set_option_colonsep("title","'chisq=%.2f,bp=%.2f,pb=%.2f,vpara=%.2f,vperp=%.2f'"%(self.chisq,self.paratry[0],self.paratry[1],self.paratry[2],self.paratry[3]))
tmpdvplot.set_option_colonsep("title", "'Model'")
tmpdvplot.set_option_colonsep("with", "steps lt 3 lw 3")
tmpvth.append([1.6, 0.0])
tmpvthplot = Data(tmpvth)
tmpvthplot.set_option_colonsep("title", "'Model'")
tmpvthplot.set_option_colonsep("with", "steps lt 3 lw 3")
self.pthdv.append(tmpdvplot)
self.pthvth.append(tmpvthplot)
del dyda
return
k2=s2.split()
aspang.append([time,float(k2[2])])
s2=swicsdatain.readline()
k2=s2.split()
swicspspeed.append([time,float(k2[2])])
swicsdatain.close()
plotaspang=Data(aspang)
plotswepamdata=Data(swepamdata)
plotswepampdata=Data(swepampdata)
plotswicsdata=Data(swicsdata)
plotswepamspeed=Data(swepamspeed)
plotswicsspeed=Data(swicsspeed)
plotswicspspeed=Data(swicspspeed)
plotswepamdata.set_option_colonsep("title", "'SWEPAM'")
plotswepamspeed.set_option_colonsep("with", "lines")
plotswicsdata.set_option_colonsep("with", "points ls 3")
plotswicsdata.set_option_colonsep("title", "'SWICS'")
plotswicsspeed.set_option_colonsep("with", "lines")
plotswicspspeed.set_option_colonsep("with", "lines")
plotaspang.set_option_colonsep("with", "lines")
gp=Gnuplot()
gp("set xrange[0:60]")
gp("set format y '10^{%T}'")
gp("set xlabel 'DoY 2007'")
gp("set ylabel 'He^{2+} Density [1cm^{-3}]'")
#gp("set yrange[0.01:1.]")
gp("set log y")
gp.plot(plotswepamdata,plotswicsdata,plotaspang)
swepamt=[]
swepamd=[]
swepamr=[]
swepamin=open("Swepamd279-289y200516min.dat")
for s in swepamin:
k=s.split()
swepamv.append([float(k[0]),float(k[4])])
swepamt.append([float(k[0]),float(k[2])])
swepamd.append([float(k[0]),float(k[1])])
swepamr.append([float(k[0]),float(k[3])])
plotswepamv=Data(swepamv)
plotswepamt=Data(swepamt)
plotswepamd=Data(swepamd)
plotswepamr=Data(swepamr)
plotswepamv.set_option_colonsep("title", "'SWEPAM H1+'")
plotswepamt.set_option_colonsep("title", "'SWEPAM H1+'")
plotswepamd.set_option_colonsep("title", "'SWEPAM H1+'")
plotswepamr.set_option_colonsep("title", "'SWEPAM He2+/H1+'")
pathl=open("pathlist2.in")
data=[]
print "readinding data"
for s in pathl:
p=s.split()
liste=open(p[0]+"list.in")
s2=liste.readline()
for s2 in liste:
filen=s2.split()
tmpdata=readres(p[0]+"res_"+filen[0])
data.append(tmpdata)
for ion in range(numberions):
for step in range(dim):
for val in range((diffrange[ion]*2)):
if (fitval[ion][step]==hist[ion][val][0]):
hist[ion][val][1]+=1
for val in range((diffrangenorm[ion]*2)):
if (normval[ion][step]==histnorm[ion][val][0]):
histnorm[ion][val][1]+=1
gphist=[]
gphistnorm=[]
for ion in range(numberions):
gphisttmp=Data(hist[ion])
gphisttmp.set_option_colonsep("using", "1:2")
# gphisttmp.set_option_colonsep("title", "'fm0 %s rc:%f mw:%f sig:%f'" %(ion_names[ion],nomcounts[ion],mittelwert[ion],sigma[ion]))
gphisttmp.set_option_colonsep("title", "'%s (%i counts generated)'" %(ion_names[ion],nomcounts[ion]))
gphisttmp.set_option_colonsep("with","boxes")
gphist.append(gphisttmp)
for ion in range(numberions):
gphisttmp=Data(histnorm[ion])
gphisttmp.set_option_colonsep("using", "1:2")
# gphisttmp.set_option_colonsep("title", "'norm fm0 %s rc:%f mw:%f sig:%f'" %(ion_names[ion],nomcounts[ion],mwnorm[ion],signorm[ion]))
gphisttmp.set_option_colonsep("title", "'%s'" %(ion_names[ion]))
gphisttmp.set_option_colonsep("with","boxes")
gphistnorm.append(gphisttmp)
#load 2. erg-file
f=open("/home/ivar/berger/projects/ionpositions/test_analyse_data/erg_testdata30_1000_fm1.dat")
sumhist[i][3] = step / 2.
deg = pi / 180.
#corrarr=[1.,sin(.2*deg),sin(5.*deg),sin(90.*deg),0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.]
#for i in range(int(dim/step)):
# sumhist[i][1]=sumhist[i][1]*corrarr[i]
# Plotobjekte werden erzeugt
fitspec = []
for val in epaspec[fi]:
if (val[0] > fg):
fitspec.append(val)
pfitspec = Data(fitspec)
pfitspec.set_option_colonsep("title", "'%s'" % (fedata[i].name))
pfitspec.set_option_colonsep("with", "xyerrorbars")
pfitspec.set_option_colonsep("using", "1:2:5:3")
i = 0
while (epaspec[fi][i][0] > fg):
i += 1
i = i - 1
print "i = ", i
normval = []
xval = []
for j in range(i):
if (epaspec[fi][i - j] != 0.):
normval.append(epaspec[fi][i - j][1])
xval.append(epaspec[fi][i - j][0])
astring = "("
for i in range(len(dvzeros)):
if sum(vivsvparr[i]) > 0.:
dvzeros[i][1] /= (sum(vivsvparr[i]) + dvzeros[i][1])
xvals = []
yvals = []
for i in range(nrxbins):
#xvals.append(float(i*vxbinwidth))
xvals.append(10**(-3 + float(i * vxbinwidth)))
for i in range(nrybins):
yvals.append(float(i * vybinwidth) + vyoffset)
pvivsvparr = GridData(vivsvparr, xvals, yvals, binary=0)
pdvzeros = Data(dvzeros)
pdvzeros.set_option_colonsep("with", "l lt -1")
xrange = [0., 3.]
#xrange=[300.,800.]
yrange = [0., 2.]
a = [[xrange[0], yrange[0], 1.], [xrange[1], yrange[1], 1.]]
pa = Data(a)
pa.set_option_colonsep("with", "l lt -1")
gp = Gnuplot()
gp("set pm3d at bs map explicit corners2color c1")
#gp("set pm3d map")
gp("set size square")
gp("set xrange[%f:%f]" % (xrange[0], xrange[1]))
gp("set yrange[%f:%f]" % (yrange[0], yrange[1]))
#gp("set logscale x")
gp("set palette model RGB functions gray<1.e-20 ? 1 : gray<0.5 ? gray*2 : 1 , gray<1.e-20 ? 1 : gray<0.5 ? gray*2 : 2-gray*2, gray <1.e-20 ? 1 : gray<0.5 ? 1-sqrt(gray*2) : 0"
)