def plot_vecs(t1, t2):
gp3.splot(
Data(magvecs[(vecstime > t1) * (vecstime < t1 + 64. / 86400.)],
with_="vectors lt 3"))
te = t1 + 64. / 86400.
while te < t2:
print te
gp3.splot(
Data(magvecs[(vecstime > t1) * (vecstime < te)],
with_="vectors lt 3"))
print sqrt(
(magvecs[(vecstime > te - 64. / 86440) * (vecstime < te)][:, 0] +
magvecs[(vecstime > te - 64. / 86440) *
(vecstime < te)][:, 3])**2 +
(magvecs[(vecstime > te - 64. / 86440) * (vecstime < te)][:, 1] +
magvecs[(vecstime > te - 64. / 86440) *
(vecstime < te)][:, 4])**2 +
(magvecs[(vecstime > te - 64. / 86440) * (vecstime < te)][:, 2] +
magvecs[(vecstime > te - 64. / 86440) *
(vecstime < te)][:, 5])**2), hedata.vel[
(hedata.time > te - 64. / 86440.) *
(hedata.time < te + 656. / 86400.)], sqrt(
swdata.velx[(swdata.time > te - 64. / 86440.) *
(swdata.time < te)]**2 +
(swdata.vely[(swdata.time > te - 64. / 86440.) *
(swdata.time < te)] + 30.)**2 +
swdata.velz[(swdata.time > te - 64. / 86440.) *
(swdata.time < te)]**2)
te += 64. / 86400.
raw_input()
def buildplotmarks(self, panel):
for mark in self.plotmarks:
tmpmark = []
if (mark.style == "vline"):
tmppos = mark.pos
tmppos[0][1] = self.panel[panel].yrange[0] * 1.01
tmppos[0][3] = (self.panel[panel].yrange[1] -
self.panel[panel].yrange[0]) * 0.99
tmpmark = Data(tmppos)
if (mark.style == "hline"):
tmppos = mark.pos
tmppos[0][0] = self.xrange[0] * 1.01
tmppos[0][2] = (self.xrange[1] - self.xrange[0]) * 0.99
tmpmark = Data(tmppos)
if (mark.style == "box"):
if mark.pos[0][2] == 1e-20:
ymin = self.panel[panel].yrange[0]
else:
ymin = mark.pos[0][2]
if mark.pos[0][3] == 1e20:
ymax = self.panel[panel].yrange[1]
else:
ymax = mark.pos[0][3]
tmppos = [[mark.pos[0][0], ymin, ymax],
[mark.pos[0][1], ymin, ymax]]
#tmppos=[[mark.pos[0][0],self.panel[panel].yrange[1],self.panel[panel].yrange[0]],[mark.pos[0][2],self.panel[panel].yrange[1],self.panel[panel].yrange[0]]]
tmpmark = Data(tmppos)
if (mark.style == "function"):
tmpmark = mark.function
if (tmpmark != []):
mark.addplot(tmpmark)
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
def plotstep(step, gp=gp):
cut1 = [[210, 0.1], [210, 1.], [210, 1e2], [210, 1.e3], [210, 1.e4],
[210, 1.e5], [210, 1.e6], [210, 1.e7], [210, 1.e8]]
cut2 = [[408, 0.1], [408, 1.], [408, 1e2], [408, 1.e3], [408, 1.e4],
[408, 1.e5], [408, 1.e6], [408, 1.e7], [408, 1.e8]]
gp("set style data lines")
gp("set log y")
gp("set title '%i'" % (step))
#gp.plot(Data(tof2mq(step),dcr[step]))
gp.plot(Data(dcr[step]), Data(cut1, with_="l lt 0"),
Data(cut2, with_="l lt 0"))
def plot_vdf(t1, t2):
meanvalf = mean(valf[(valf[:, 0] > t1) * (valf[:, 0] < t2) *
(valf[:, 1] > 0.)][:, 1])
meanvsw = mean(swdata.vel[(swdata.time > t1) * (swdata.time < t2) *
(swdata.vel > 0.)])
gpvdf.plot(
Data(d3hdata[(d3hdata[:, 0] > t1) * (d3hdata[:, 0] < t2)][:, 1],
d3hdata[(d3hdata[:, 0] > t1) * (d3hdata[:, 0] < t2)][:, 2],
with_="l lt 1 lw 3",
title="H^{1+}"),
Data(d3hedata[(d3hedata[:, 0] > t1) * (d3hedata[:, 0] < t2)][:, 1],
d3hedata[(d3hedata[:, 0] > t1) * (d3hedata[:, 0] < t2)][:, 2],
with_="l lt 3 lw 3",
title="He^{2+}"),
Data(d3odata[(d3odata[:, 0] > t1) * (d3odata[:, 0] < t2)][:, 1],
d3odata[(d3odata[:, 0] > t1) * (d3odata[:, 0] < t2)][:, 2],
with_="l lt 4 lw 3",
title="O^{6+}"),
Data([[meanvsw, 0.], [meanvsw, 1.]], with_="l lt -1 lw 2"),
Data([[meanvsw + meanvalf, 0.], [meanvsw + meanvalf, 1.]],
with_="l lt 0 lw 2"),
Data([[meanvsw + 2. * meanvalf, 0.], [meanvsw + 2. * meanvalf, 1.]],
with_="l lt 0 lw 2"),
Data([[meanvsw - meanvalf, 0.], [meanvsw - meanvalf, 1.]],
with_="l lt 0 lw 2"),
Data([[meanvsw - 2. * meanvalf, 0.], [meanvsw - 2. * meanvalf, 1.]],
with_="l lt 0 lw 2"))
def main():
filename = '../tmp/config.txt'
c = ConfigReader()
c.parse(filename)
cluster = c.readCluster()
maxPerf = getMaxPerformance(cluster.availableServers)
outline = readOutlineFile('../tmp/outline.txt')
trace = generateTrace(outline, maxPerf, 20)
if Gnuplot is not None:
data = Data(trace, with_='l')
g = Gnuplot()
g('set ytics 0, 100')
g('set xtics 0, 20')
g('set grid')
g.plot(data)
raw_input()
file = open('../tmp/trace_gen.txt','w')
for value in trace:
file.write('%f\n' % value)
file.close()
def plot_step(self, step):
if self.valf[step] > 0.:
self.gp("unset label")
self.gp("set label 'v_{sw}=%f' at -1.5,0.95" %
(self.swedata.vel[step]))
self.gp("set label 'C_{A}=%f' at -1.5,0.90" % (self.valf[step]))
self.gp("set label '{/Symbol= Q}=%f' at -1.5,0.85" %
(self.theta[step] / pi * 180.))
self.gp("set label '{/Symbol= dQ}=%f' at -1.5,0.8" %
(self.mag.sigTHETA[step] / pi * 180.))
self.gp("set title '%.4i DoY %f'" %
(self.year, self.hedata.time[step]))
if amax(self.hedata.countspec[step, :, 1]) > 0.:
self.gp.plot(
Data((self.hedata.countspec[step, :, 0] -
self.swedata.vel[step]) / self.valf[step],
self.hedata.countspec[step, :, 1] /
amax(self.hedata.countspec[step, :, 1]),
with_="lp lt 3 lw 3",
title="He^{2+}"))
if amax(self.odata.countspec[step, :, 1]) > 0.:
self.gp.replot(
Data((self.odata.countspec[step, :, 0] -
self.swedata.vel[step]) / self.valf[step],
self.odata.countspec[step, :, 1] /
amax(self.odata.countspec[step, :, 1]),
with_="lp lt 4 lw 3",
title="O^{6+}"))
il = where(self.hdata.time == self.hedata.time[step])[0]
if il.shape[0] > 0 and amax(self.hdata.tcrspec[il[0], :] +
self.hdata.dcrspec[il[0], :]) > 0.:
self.gp.replot(
Data((self.hdata.velspec - self.swedata.vel[step]) /
self.valf[step],
(self.hdata.tcrspec[il[0], :] +
self.hdata.dcrspec[il[0], :]) /
amax(self.hdata.tcrspec[il[0], :] +
self.hdata.dcrspec[il[0], :]),
with_="lp lt 1 lw 3",
title="H^{1+}"))
self.gp.hardcopy("vdistperp_%.4i_%.3i.%.6i.ps" %
(self.year, int(self.hedata.time[step]),
int((self.hedata.time[step] % 1) * 1e6)),
color=True)
self.gp.sync()
def plotstepmq(step, gp=gp):
#cut1=[[210,0.1],[210,1.],[210,1e2],[210,1.e3],[210,1.e4],[210,1.e5],[210,1.e6],[210,1.e7],[210,1.e8]]
#cut2=[[408,0.1],[408,1.],[408,1e2],[408,1.e3],[408,1.e4],[408,1.e5],[408,1.e6],[408,1.e7],[408,1.e8]]
gp("set style data lines")
gp("set log y")
gp("set title '%i'" % (step))
gp.plot(Data(tof2mq(step), dcr[step]))
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 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
def plot_hist(self,arg,xvals,xlabel=None,ylabel=None,save=False,title=None,reset=False):
print self.get_hist(arg,xvals)[0],self.get_hist(arg,xvals)[1]
if reset:
self.gphist.reset()
if ylabel:
self.gphist("set ylabel '%s'"%(ylabel))
if xlabel:
self.gphist("set xlabel '%s'"%(xlabel))
if not title:
title=arg
self.gphist.replot(Data(self.get_hist(arg,xvals)[0],self.get_hist(arg,xvals)[1],with_="histeps lw 3",title=title))
if ylabel or xlabel:
self.gphist("unset xlabel")
def add_2ddata(self, data, linewidth=2, color='red'):
"""
Here you can add additional 2 dimensional data to the color contour plot
INPUT: data (must be of dimension of angular_bins, values must be in range of radial_bins)
linewidth of the data
color of the data
"""
from numpy import append
datax, datay = self.__polar_to_cart__(
append(data, data[0]),
append(self.angular_bins, self.angular_bins[0]))
dataobj = Data(datax,
datay,
with_="lines lw %s lc rgb '%s'" % (linewidth, color))
self.data2d.append(dataobj)
def __create_gpdata__(self):
"""
This routine create a Gnuplot data object from the given data, angular bins and radial bins
"""
self.__det_cbtics__()
if self.log:
max_counts = log10(max(self.data[nonzero(self.data)]))
min_counts = log10(min(self.data[nonzero(self.data)]))
else:
max_counts = max(self.data)
min_counts = min(self.data[nonzero(self.data)])
self.gparray = []
for rindex, radius in enumerate(self.radial_bins):
for aindex, angle in enumerate(self.angular_bins):
counts = self.data[aindex, rindex]
if counts > 0:
if self.log: counts = log10(counts)
rgb_color = long(1020. * (counts - min_counts) /
(max_counts - min_counts))
r, g, b = self.__det_rgb__(rgb_color)
r_s = radius - (self.radial_bins[1] -
self.radial_bins[0]) / 2.
r_e = radius + (self.radial_bins[1] -
self.radial_bins[0]) / 2.
a_s = angle - (self.angular_bins[1] -
self.angular_bins[0]) / 2.
a_e = angle + (self.angular_bins[1] -
self.angular_bins[0]) / 2.
a_s = angle
a_e = angle + (self.angular_bins[1] - self.angular_bins[0])
bin_koord = []
x1, y1 = self.__polar_to_cart__(r_s, a_s)
x2, y2 = self.__polar_to_cart__(r_e, a_s)
x3, y3 = self.__polar_to_cart__(r_e, a_e)
x4, y4 = self.__polar_to_cart__(r_s, a_e)
bin_koord.append([x1, y1])
bin_koord.append([x2, y2])
bin_koord.append([x3, y3])
bin_koord.append([x4, y4])
bin_koord.append([x1, y1])
dataobj = Data(
bin_koord,
with_='filledcurves lc rgb "#%02X%02X%02X"' %
(r, g, b))
self.gparray.append(dataobj)
def select(self):
""" Start interactive polygonal area selection. Please make sure that
the lines do NOT cross. """
gp=self.gp
# fix ranges so that the plot does not move while drawing the polygon
xr=gp.getXrange()
yr=gp.getYrange()
gp.setXrange(xr)
gp.setYrange(yr)
poly=[]
while True:
p=gp.getxy()
if gp.eval("MOUSE_KEY")==3:
break
poly.append(p)
if len(poly)>1:
gp._remove_last_from_queue()
d=Data(poly)
d.set_option(with="lines")
gp.replot(d)
def __create_gpdata__(self):
"""
This routine create a Gnuplot data object from the given data, angular bins and radial bins
"""
"""
if self.log:
max_counts=log10(max(self.data[nonzero(self.data)]))
min_counts=log10(min(self.data[nonzero(self.data)]))
else:
max_counts=max(self.data)
min_counts=min(self.data[nonzero(self.data)])
"""
self.gparray = []
for data_i, data in enumerate(self.data):
data -= min(data)
if self.log: data = log10(data + 0.01)
if self.plotmode == 'T':
data_processed = data / max(data) + 0.5 + data_i * 0.
elif self.plotmode == 'S':
data_processed = data / max(data) + 0.5 + data_i * 1.
datax, datay = self.__polar_to_cart__(data_processed,
self.angular_bins)
dataobj = Data(datax, datay, with_="lines lw 2")
self.gparray.append(dataobj)
weights=corrarr2[:, 1])
histvswmag, vswbins = histogram(corrarr2[:, 0],
linspace(200, 1000, 41),
weights=corrarr2[:, 2])
histvswcorrfac, vswbins = histogram(corrarr2[:, 0],
linspace(200, 1000, 41),
weights=corrarr2[:, 3])
normvswcounts, x = histogram(corrarr2[:, 0], linspace(200, 1000, 41))
histvswcounts /= normvswcounts
histvswmag /= normvswcounts
histvswcorrfac /= normvswcounts
vswbins = vswbins[:-1]
gp6 = Gnuplot()
gp6.plot(
Data(vswbins[histvswcounts > 0.],
histvswcounts[histvswcounts > 0.],
with_="steps lt 1"),
Data(vswbins[histvswmag > 0.],
histvswmag[histvswmag > 0.],
with_="steps lt 3"),
Data(vswbins[histvswcorrfac > 0.],
histvswcorrfac[histvswcorrfac > 0.],
with_="steps lt 4"),
Data(vswbins[histvswcorrfac > 0.],
histvswcorrfac[histvswcorrfac > 0.] * histvswmag[histvswcorrfac > 0.],
with_="steps lt 5"))
gp5 = Gnuplot()
#gp5.plot(Data(vswbins[histvswcorrfac>0.],histvswcounts[histvswcorrfac>0.]/histvswcorrfac[histvswcorrfac>0.]*histvswmag[histvswcorrfac>0.],with_="steps lt 3"))
gp5.plot(
Data(vswbins[histvswcorrfac > 0.],
histvswcounts[histvswcorrfac > 0.] /
ion_list=["He2+","O6+","O7+","C4+","C5+","C6+","N7+","Fe11+"]
swepamv=[]
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()
elem=elem+1
k=s.split()
if (float(k[8])>0. and float(k[8])<14.):
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")
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
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
data4[i][1] = float(i)**3
data5[i][1] = float(i)**3
panels = 4
mp = multiplot(panels)
mp.addplotmark("vline")
mp.addplotmark("vline")
#mp.addplotmark("box")
#mp.addplotmark("box")
mp.plotmarks[0].setvlinepos(10.)
mp.plotmarks[1].setvlinepos(15.)
mp.plotmarks[1].setpanel([0, 2])
#mp.plotmarks[2].setpanel([-1])
#mp.plotmarks[2].setboxrange(0.,10.)
#mp.plotmarks[3].setpanel([-1])
#mp.plotmarks[3].setboxrange(15.,40.)
for panel in range(panels):
mp.panel[panel].setylabel("Test")
if (panel == 1):
mp.panel[panel].setyrange(.1, 1e14)
#mp.panel[panel].setgrid(1)
else:
mp.panel[panel].setyrange(0., 600.)
if (panel == 1 or panel == 2):
mp.panel[panel].setlogscale(1)
mp.panel[panel].adddata(Data(data1))
mp.panel[panel].adddata(Data(data2))
mp.plot()
def hist_data(self):
from math import acos, cos, sqrt, pi
from libacepy.ancil import histogram, sig_histogram
from Gnuplot import Data
from numpy import array
from scipy import zeros
self.sumspec = zeros([15, 2], float)
self.sumspecprotons = zeros([15, 2], float)
for i in range(15):
self.sumspec[i][0] = -0.9 + i * 0.2
self.sumspecprotons[i][0] = -0.9 + i * 0.2
dvarr = []
swicsdvarr = []
swicsvarr = []
vtharr = []
valfarr = []
barr = []
axarr = []
azarr = []
varr = []
for i in range(len(self.iondata.time)):
# calculate mag angle
if (self.syncmag.phi[i][1] >= 0.
and self.syncmag.theta[i][1] != 0.):
tmpmag = acos(
abs(
cos(self.syncmag.phi[i][1]) *
cos(self.syncmag.theta[i][1])))
else:
tmpmag = 0.
# calculate alfvenspeed
if (self.syncswepam.dens[i][1] > 0.):
valf = 21.8 * self.syncmag.magb[i][1] / sqrt(
self.syncswepam.dens[i][1])
else:
valf = 0.
if (self.iondata.vel[i] > 100. and self.iondata.vel[i] < 1000.
and self.syncswepam.vel[i][1] > 600. and valf > 0.
and tmpmag > 0. and tmpmag < 1.6
and self.syncmag.phi[i][1] * self.syncmag.dpp[i][1] <
pi / 180. * 11.5
and self.syncmag.theta[i][1] * self.syncmag.dtt[i][1] <
pi / 180. * 11.5 and self.iondata.crvth[i] > 10.):
tmpasp = self.calc_newasp(self.syncmag.phi[i][1],
self.syncmag.theta[i][1],
self.syncatt.phi[i],
self.syncatt.theta[i])
dvarr.append([
tmpmag,
(self.iondata.crvel[i] - self.syncswepam.vel[i][1]) / valf
])
vtharr.append([tmpmag, self.iondata.crvth[i]])
valfarr.append([tmpmag, valf])
barr.append([tmpmag, tmpmag])
axarr.append([tmpmag, tmpasp[0]])
azarr.append([tmpmag, tmpasp[1]])
varr.append([tmpmag, self.syncswepam.vel[i][1]])
swicsdvarr.append([
tmpmag,
(self.iondata.crvel[i] - self.swicsprotons.crvel[i]) / valf
])
swicsvarr.append([tmpmag, self.swicsprotons.crvel[i]])
if (self.iondata.vel[i] > 100. and self.iondata.vel[i] < 1000.
and self.syncswepam.vel[i][1] > 600. and valf > 0.
and tmpmag > 0. and tmpmag < 0.2
and self.syncmag.phi[i][1] * self.syncmag.dpp[i][1] <
pi / 180. * 11.5
and self.syncmag.theta[i][1] * self.syncmag.dtt[i][1] <
pi / 180. * 11.5 and self.iondata.crvth[i] > 10.):
for j in range(len(self.iondata.countspec[i])):
vtemp = (self.iondata.countspec[i][j][0] -
self.syncswepam.vel[i][1]) / valf
#vtemp=(self.iondata.countspec[i][j][0]-self.iondata.vel[i])/valf
vtempprotons = (self.swicsprotons.countspec[i][j][0] -
self.syncswepam.vel[i][1]) / valf
if (j == 0):
deltav = 2 * (
self.iondata.countspec[i][j][0] -
self.iondata.countspec[i][j + 1][0]) / valf
deltavp = 2 * (
self.swicsprotons.countspec[i][j][0] -
self.swicsprotons.countspec[i][j + 1][0]) / valf
elif (j == len(self.iondata.countspec[i]) - 1):
deltav = 2 * (self.iondata.countspec[i][j - 1][0] -
self.iondata.countspec[i][j][0]) / valf
deltavp = 2 * (
self.swicsprotons.countspec[i][j - 1][0] -
self.swicsprotons.countspec[i][j][0]) / valf
else:
deltav = (self.iondata.countspec[i][j - 1][0] -
self.iondata.countspec[i][j + 1][0]) / valf
deltavp = (
self.swicsprotons.countspec[i][j - 1][0] -
self.swicsprotons.countspec[i][j + 1][0]) / valf
rrange = vtemp + deltav / 2.
lrange = vtemp - deltav / 2.
rrangep = vtempprotons + deltavp / 2.
lrangep = vtempprotons - deltavp / 2.
if (lrange < self.sumspec[14][0]
and rrange > self.sumspec[0][0]):
for k in range(15):
if (rrange <= self.sumspec[k][0] + 0.1
and lrange >= self.sumspec[k][0] - 0.1):
self.sumspec[k][1] += self.iondata.countspec[
i][j][1]
elif (rrange >= self.sumspec[k][0] + 0.1
and lrange <= self.sumspec[k][0] + 0.1):
fac = (self.sumspec[k][0] + 0.1 -
lrange) / (rrange - lrange)
self.sumspec[k][
1] += self.iondata.countspec[i][j][1] * fac
elif (rrange >= self.sumspec[k][0] - 0.1
and lrange <= self.sumspec[k][0] - 0.1):
fac = (rrange -
(self.sumspec[k][0] - 0.1)) / (rrange -
lrange)
self.sumspec[k][
1] += self.iondata.countspec[i][j][1] * fac
if (lrangep < self.sumspec[14][0]
and rrangep > self.sumspec[0][0]):
for k in range(15):
if (rrangep <= self.sumspec[k][0] + 0.1
and lrangep >= self.sumspec[k][0] - 0.1):
self.sumspecprotons[k][
1] += self.swicsprotons.countspec[i][j][1]
elif (rrangep >= self.sumspec[k][0] + 0.1
and lrangep <= self.sumspec[k][0] + 0.1):
fac = (self.sumspec[k][0] + 0.1 -
lrangep) / (rrangep - lrangep)
self.sumspecprotons[k][
1] += self.swicsprotons.countspec[i][j][
1] * fac
elif (rrangep >= self.sumspec[k][0] - 0.1
and lrangep <= self.sumspec[k][0] - 0.1):
fac = (rrangep -
(self.sumspec[k][0] - 0.1)) / (rrangep -
lrangep)
self.sumspecprotons[k][
1] += self.swicsprotons.countspec[i][j][
1] * fac
nrsumspec = 0
for val in self.sumspec:
nrsumspec += val[1]
for val in self.sumspec:
val[1] = val[1] / nrsumspec
nrsumspecprotons = 0
for val in self.sumspecprotons:
nrsumspecprotons += val[1]
for val in self.sumspecprotons:
val[1] = val[1] / nrsumspecprotons
valfhist = histogram(valfarr, 0., self.histstep, self.nrhist, 3)
bhist = histogram(barr, 0., self.histstep, self.nrhist, 3)
axhist = histogram(axarr, 0., self.histstep, self.nrhist, 3)
azhist = histogram(azarr, 0., self.histstep, self.nrhist, 3)
if (self.swepamorswics == 0):
vhist = histogram(varr, 0., self.histstep, self.nrhist, 3)
dvhist = histogram(dvarr, 0., self.histstep, self.nrhist, 4)
else:
vhist = histogram(swicsvarr, 0., self.histstep, self.nrhist, 3)
dvhist = histogram(swicsdvarr, 0., self.histstep, self.nrhist, 4)
sigdvhist = sig_histogram(dvarr, dvhist, 4)
vthhist = histogram(vtharr, 0., self.histstep, self.nrhist, 4)
sigvthhist = sig_histogram(vtharr, vthhist, 4)
self.dverr = []
for i in range(len(dvhist)):
if (sigdvhist[i][2] > 0.):
self.dverr.append([
dvhist[i][0], dvhist[i][1],
sigdvhist[i][1] / sqrt(sigdvhist[i][2])
])
else:
self.dverr.append([dvhist[i][0], dvhist[i][1], 0.])
self.vtherr = []
for i in range(len(dvhist)):
if (sigvthhist[i][2] > 0.):
self.vtherr.append([
vthhist[i][0], vthhist[i][1],
sigvthhist[i][1] / sqrt(sigvthhist[i][2])
])
else:
self.vtherr.append([vthhist[i][0], vthhist[i][1], 0.])
self.pdvhist = (Data(self.dverr))
self.pdvhist.set_option_colonsep("title", "'Observations'")
#self.pdvhist.set_option_colonsep("title","'Beobachtung'")
self.pdvhist.set_option_colonsep("with", "yerrorbars lt 1 lw 3")
self.pdvhist.set_option_colonsep("using", "1:2:3")
self.pvthhist = (Data(self.vtherr))
self.pvthhist.set_option_colonsep("title", "'Observations'")
#self.pvthhist.set_option_colonsep("title","'Beobachtung'")
self.pvthhist.set_option_colonsep("with", "yerrorbars lt 1 lw 3")
self.pvthhist.set_option_colonsep("using", "1:2:3")
# build array of x values for fitting
xvals = []
for i in range(len(vhist)):
xvals.append([
vhist[i][1], valfhist[i][1], bhist[i][1], 0., axhist[i][1],
azhist[i][1], 0.1, self.iondata.countspec[0],
self.iondata.name, self.iondata.mass, self.iondata.charge,
self.effpath
])
self.xvals = xvals
# build array of y and sig values for fitting
yvals = []
sigvals = []
for i in range(len(vhist)):
yvals.append([self.dverr[i][1], self.vtherr[i][1]])
sigvals.append([self.dverr[i][2], self.vtherr[i][2]])
self.yvals = array(yvals)
self.sigvals = array(sigvals)
def main():
traceFilename = '../../tmp/wc98_1.txt'
configFilename = '../../tmp/dynGamma.txt'
cReader = ConfigReader()
cReader.parse(configFilename)
cluster = cReader.readCluster()
predictor = cReader.readPredictor()
lb = cReader.readLoadBalancer(cluster)
cluster.loadBalancer = lb
config = cluster._availableServers[:]
bestConfig = config[:]
predConfig = config[:]
reactiveConfig = config[:]
bestConfigurator = cReader.readConfigurator(cluster, DummyFilter())
bestConfigurator.setEfficientServerList(cluster._availableServers)
bestConfigurator.gamma = 1.0 #do not change!!
predConfigurator = cReader.readConfigurator(cluster, predictor)
predConfigurator.setEfficientServerList(cluster._availableServers)
predConfigurator._gamma = 1.0
reactiveConfigurator = cReader.readConfigurator(cluster, DummyFilter())
reactiveConfigurator.setEfficientServerList(cluster._availableServers)
#reactiveConfigurator._gamma = 0.55
step = predConfigurator.windowSize
#reads the trace file
data = file2data(1, traceFilename)[0]
scale(data, cluster.availableServers)
predCounter, reactiveCounter, bestCounter = 0, 0, 0 #configuration counter
pred_upCounter, pred_downCounter = 0, 0
best_upCounter, best_downCounter = 0, 0
reactive_upCounter, reactive_downCounter = 0, 0
bestPerf = getMaxPerformance(bestConfig)
reactivePerf = getMaxPerformance(bestConfig)
predPerf = getMaxPerformance(bestConfig)
avgs = []
reactivePerfs = []
predPerfs = []
bestPerfs = []
gammas = []
for i in range(len(data) / step):
avgData = 0.0
for j in range(step):
avgData += data[i * step + j]
avgData /= step
avgs.append(avgData)
reactivePerfs.append(reactivePerf)
predPerfs.append(predPerf)
optimumConfig = None
'''
Will use the efficient list to create the optimumConfig
'''
optimumConfig = [bestConfigurator._efficientServerList[0]]
k = 1
while getMaxPerformance(optimumConfig) < avgData:
optimumConfig.append(bestConfigurator._efficientServerList[k])
k += 1
'''
Calculates the appropriate configuration, 'knowing' that avgData is true
'''
if i > 0: #calculates false alarms
tmp = bestConfigurator.getConfiguration(avgData, bestConfig,
bestConfigurator._gamma)
if tmp != bestConfig:
bestCounter += 1
bestConfig = tmp
bestPerf = getMaxPerformance(bestConfig)
bestPerfs.append(bestPerf)
'''
Compares the chosen configuration with a least powerful one
'''
if avgData > bestPerf:
best_downCounter += 1
elif getMaxPerformance(bestConfig) > getMaxPerformance(
optimumConfig):
best_upCounter += 1
if avgData > reactivePerf:
reactive_downCounter += 1
elif getMaxPerformance(reactiveConfig) > getMaxPerformance(
optimumConfig):
reactive_upCounter += 1
if avgData > predPerf:
pred_downCounter += 1
elif getMaxPerformance(predConfig) > getMaxPerformance(
optimumConfig):
pred_upCounter += 1
elif len(bestConfig) < len(predConfig):
pred_upCounter += 1
prediction = predictor.apply(avgData)
#actuation = prediction
actuation = max(prediction, avgData)
tmp = predConfigurator.getConfiguration(actuation, predConfig,
predConfigurator._gamma)
if set(tmp) != set(predConfig):
predCounter += 1
predConfig = tmp
predPerf = getMaxPerformance(predConfig)
if reactiveConfigurator.dynGamma:
idealConfig = bestConfigurator.getConfiguration(
avgData, reactiveConfig, reactiveConfigurator.MAX_GAMMA)
idealPerf = round(getMaxPerformance(idealConfig), 5)
currPerf = round(getMaxPerformance(reactiveConfig), 5)
reactiveConfigurator._gamma = reactiveConfigurator.adjustGamma(
currPerf - idealPerf)
gammas.append(reactiveConfigurator._gamma)
tmp = reactiveConfigurator.getConfiguration(
avgData, reactiveConfig, reactiveConfigurator._gamma)
if set(tmp) != set(reactiveConfig):
reactiveCounter += 1
reactiveConfig = tmp
reactivePerf = getMaxPerformance(reactiveConfig)
print 'Best - False alarms: up = %d, down = %d' % (best_upCounter,
best_downCounter)
print 'Predictive - False alarms: up = %d, down = %d' % (pred_upCounter,
pred_downCounter)
print 'Reactive - False alarms: up = %d, down = %d' % (
reactive_upCounter, reactive_downCounter)
print 'Best Configs = %d, Predictive Configs = %d, Reactive Configs = %d' %\
(bestCounter, predCounter, reactiveCounter)
g = Gnuplot(debug=0)
g('reset')
#g('set size 0.65,0.65')
g('set encoding iso_8859_1')
g('set terminal postscript eps enhanced monochrome')
g('set output "carlos2.ps"')
g('set key top left')
g('set xlabel "Tempo (s)"')
g('set xrange [0:%d]' % (len(data) / step))
spacing = 15
tics = ','.join([
'"%d" %d' % (x * step * spacing, x * spacing)
for x in range(1,
len(data) / (step * spacing) + 1)
])
g('set xtics (%s)' % tics)
g('set ylabel "Requisições"'.encode('iso_8859_1'))
g('set grid')
#plot incoming requests avg
with_plot = 'steps'
#plot configurations
pi1 = Data(avgs, with_=with_plot, title='Carga')
pi2 = Data(reactivePerfs, with_=with_plot, title='Reativo')
pi_gamma = Data(gammas, with_=with_plot, title='Gamma')
#pi3 = Data(predPerfs, with_=with_plot,title='Preditivo')
#pi4 = Data(bestPerfs, with_=with_plot,title='Perfect')
#g.plot(pi1,pi2)
#g.plot(pi_gamma)
#raw_input()
print 'Done'
phi = 0
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
if ((sdata[ion][time][step][7] >=
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
def plot_period(t1, t2, off=1, power=5. / 3.):
gp("set xrange[%f:%f]" % (t1, t2))
gp2("set xrange[%f:%f]" % (t1, t2))
#gp.splot(d3hdata[(d3hdata[:,0]>t1)*(d3hdata[:,0]<t2)],d3hedata[(d3hedata[:,0]>t1)*(d3hedata[:,0]<t2)],d3odata[(d3odata[:,0]>t1)*(d3odata[:,0]<t2)],d3fedata[(d3fedata[:,0]>t1)*(d3fedata[:,0]<t2)],d3hedata[(d3hedata[:,0]>t1)*(d3hedata[:,0]<t2)*(d3hedata[:,2]==1.)],d3odata[(d3odata[:,0]>t1)*(d3odata[:,0]<t2)*(d3odata[:,2]==1.)],d3fedata[(d3fedata[:,0]>t1)*(d3fedata[:,0]<t2)*(d3fedata[:,2]==1.)],d3hdata[(d3hdata[:,0]>t1)*(d3hdata[:,0]<t2)*(d3hdata[:,2]==1.)],d3swdata[d3swdata[:,1]>0.])
#gp.splot(d3hdata[(d3hdata[:,0]>t1)*(d3hdata[:,0]<t2)],d3hedata[(d3hedata[:,0]>t1)*(d3hedata[:,0]<t2)],d3hedata[(d3hedata[:,0]>t1)*(d3hedata[:,0]<t2)],d3hev) #gp.splot(d3hev,d3ov,d3fev,d3swdata[d3swdata[:,1]>0.])
#gp.plot(Data(d3hv[:,0],d3hv[:,1],with_="lp lt -1"),Data(d3hv2[:,0],d3hv2[:,1],with_="lp lt 0"),Data(d3hev[:,0],d3hev[:,1],with_="lp lt 1"),Data(d3ov[:,0],d3ov[:,1],with_="lp lt 3"),Data(d3fev[:,0],d3fev[:,1],with_="lp lt 4"),Data(d3swdata[d3swdata[:,1]>0.][:,0],d3swdata[d3swdata[:,1]>0.][:,1],with_="lp lt 5"))#,Data(d3swdens[d3swdens[:,1]>0.][:,0],d3swdens[d3swdens[:,1]>0.][:,1],with_="lp lt 6",axes="x1y2"))
#gp2.plot(Data(magdat.time,theta,with_="l lt 1"),"pi/2. w l lt -1",Data(d3swdata[:,0],((d3swdata[:,1]-550.)/20.)-1.),Data(vswvalf[:,0],vswvalf[:,1],with_="l lt 4",axes="x1y2"),Data(d3swdata[:,0],d3swdata[:,1],with_="l lt 5",axes="x1y2"))
gp2.plot(Data(magdat.time, theta, with_="l lt 1"), "pi/2. w l lt -1",
Data(valf[:, 0], valf[:, 1], with_="l lt 3", axes="x1y2"))
#gp2.hardcopy("magdata.eps",eps=True,color=True)
gpfft("f(x)=%f*x**-(%f)" % (off, power))
psdy = fftPlot(magdat.magby[(magdat.time > t1) * (magdat.time < t2)])
psdz = fftPlot(magdat.magbz[(magdat.time > t1) * (magdat.time < t2)])
psdx = fftPlot(magdat.magbx[(magdat.time > t1) * (magdat.time < t2)])
meanvalf = mean(valf[(valf[:, 0] > t1) * (valf[:, 0] < t2) *
(valf[:, 1] > 0.)][:, 1])
meanvsw = mean(swdata.vel[(swdata.time > t1) * (swdata.time < t2) *
(swdata.vel > 0.)])
gp.splot(
d3hdata[(d3hdata[:, 0] > t1) * (d3hdata[:, 0] < t2)],
d3hedata[(d3hedata[:, 0] > t1) * (d3hedata[:, 0] < t2)],
d3odata[(d3odata[:, 0] > t1) * (d3odata[:, 0] < t2)],
d3fedata[(d3fedata[:, 0] > t1) * (d3fedata[:, 0] < t2)],
d3sidata[(d3sidata[:, 0] > t1) * (d3sidata[:, 0] < t2)],
d3cdata[(d3cdata[:, 0] > t1) * (d3cdata[:, 0] < t2)], d3hv, d3hev,
d3ov, d3fev, d3swdata[d3swdata[:, 1] > 0.],
Data([[(t1 + t2) / 2., meanvsw, 0.], [(t1 + t2) / 2., meanvsw, 1.]],
with_="l lt -1"),
Data([[(t1 + t2) / 2., meanvsw + meanvalf, 0.],
[(t1 + t2) / 2., meanvsw + meanvalf, 1.]],
with_="l lt 0"),
Data([[(t1 + t2) / 2., meanvsw + 2 * meanvalf, 0.],
[(t1 + t2) / 2., meanvsw + 2 * meanvalf, 1.]],
with_="l lt 0"),
Data([[(t1 + t2) / 2., meanvsw - 1 * meanvalf, 0.],
[(t1 + t2) / 2., meanvsw - 1 * meanvalf, 1.]],
with_="l lt 0"),
Data([[(t1 + t2) / 2., meanvsw - 2 * meanvalf, 0.],
[(t1 + t2) / 2., meanvsw - 2 * meanvalf, 1.]],
with_="l lt 0"))
#gp.hardcopy("tsspecs.eps",eps=True,color=True)
gfe = mean(
magdat.magb[(magdat.time > t1) *
(magdat.time < t2)]) * 28. * (1 + meanvsw / meanvalf)
gfp = mean(magdat.magb[
(magdat.time > t1) *
(magdat.time < t2)]) * 0.01525 * abs(1 - meanvsw / meanvalf)
gfhe = gfp * 0.5
gfo = gfp * 6. / 16.
gfhep = gfhe / 0.5
gfhem = gfhe / 1.5
gfop = gfo / 0.5
gfom = gfo / 1.5
gffe = gfp * 12. / 56.
gfsi = gfp * 9. / 28.
gfpu = gfp * 0.25
#gfp=0.5-(gfp-0.5)
#print "wp =",gfp,"whe =",gfhe,"wo =",gfo,"wsi =",gfsi,"wfe =",gffe,"wpu =",gfpu
print "whe =", gfhe, "whe =", gfhem, "whep =", gfhep
print "vsw =", meanvsw, "valf =", meanvalf
psdy = array(psdy)
#psdy[:,1]=(psdy[:,1]/(off*psdy[:,0]**-power))
psdz = array(psdz)
psdx = array(psdx)
#psdz[:,0]/=abs(1-meanvsw/meanvalf)
#psdy[:,0]/=abs(1-meanvsw/meanvalf)
#psdx[:,0]/=abs(1-meanvsw/meanvalf)
fmax = meanvalf / 2 / meanvsw
print "fmax = ", fmax
#psdz[:,1]=(psdz[:,1]/(off*psdz[:,0]**-power))
bw = 5
psdyb = zeros([len(psdy) / bw, 2])
for i in range(len(psdy) / bw):
psdyb[i, 0] = mean(psdy[i * bw:i * bw + bw, 0])
psdyb[i, 1] = mean(psdy[i * bw:i * bw + bw, 1])
psdzb = zeros([len(psdz) / bw, 2])
for i in range(len(psdz) / bw):
psdzb[i, 0] = mean(psdz[i * bw:i * bw + bw, 0])
psdzb[i, 1] = mean(psdz[i * bw:i * bw + bw, 1])
psdxb = zeros([len(psdx) / bw, 2])
for i in range(len(psdx) / bw):
psdxb[i, 0] = mean(psdx[i * bw:i * bw + bw, 0])
psdxb[i, 1] = mean(psdx[i * bw:i * bw + bw, 1])
#gpfft.plot(Data(psdxb,with_="l lt 5",title="'Bx'"),Data(psdyb,with_="l lt 1",title="'By'"),Data(psdzb,with_="l lt 3",title="'Bz'"),"f(x) with l lt 4 title 'x^-(5/3)'",Data([[gfp,1.],[gfp,1e4]],with_="l lt -1",title="'gfp'"),Data([[gfhe,1.],[gfhe,1e4]],with_="l lt 0",title="'gfhe'"),Data([[gfo,1.],[gfo,1e4]],with_="l lt 1",title="'gfo'"),Data([[gffe,1.],[gffe,1e4]],with_="l lt 3",title="'gffe'"))#,Data([[gfe,1.],[gfe,1e4]],with_="l lt 6",title="'gfe'"))
gpfft.plot(
Data(psdxb, with_="l lt 5", title="'Bx'"),
Data(psdyb, with_="l lt 1", title="'By'"),
Data(psdzb, with_="l lt 3", title="'Bz'"),
"f(x) with l lt 4 title 'x^-(5/3)'",
Data([[gfhe, 1.], [gfhe, 1e4]], with_="l lt -1", title="'gfhe'"),
Data([[gfhem, 1.], [gfhem, 1e4]], with_="l lt 0", title="'gfhem'"),
Data([[gfhep, 1.], [gfhep, 1e4]], with_="l lt 0", title="'gfhep'"),
Data([[gfop, 1.], [gfop, 1e4]], with_="l lt 3", title="'gfop'"),
Data([[gfom, 1.], [gfom, 1e4]], with_="l lt 3", title="'gfom'"),
Data([[gfo, 1.], [gfo, 1e4]], with_="l lt 1", title="'gfo'"),
Data([[gfp, 1.], [gfp, 1e4]], with_="l lt 4", title="'gfp'"))
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
sumhist[i][2] += spec[i][2]
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])
tmpsemdat = semtot[semtot[:, 1] == years[i]]
if (years[i] != 2008):
semdat[sum(doys[0:i]):sum(doys[0:i]) + doys[i], 1] = tmpsemdat[:, 15]
else:
semdat[sum(doys[0:i]):sum(doys[0:i]) + doys[i] - 2, 1] = tmpsemdat[:,
15]
semdat2 = zeros([len(years) * 365 + 1, 2])
semdat2[:, 0] = arange(len(years) * 365. + 1.)
for i in range(len(years)):
tmpsemdat = semtot[semtot[:, 1] == years[i]]
if (years[i] != 2008):
semdat2[sum(doys[0:i]):sum(doys[0:i]) + doys[i], 1] = tmpsemdat[:, 14]
else:
semdat2[sum(doys[0:i]):sum(doys[0:i]) + doys[i] - 2, 1] = tmpsemdat[:,
14]
semdat[:, 1] /= 1e8
semdat[:, 1] *= 1.5
gp2 = Gnuplot()
gp2("set style data histeps")
gp2("set yrange[0:2000]")
gp2("set xlabel 'Date'")
gp2("set ylabel 'He^{1+} 1.5<w<2.0 / scaled UV-flux'")
gp2("set xtics ('2006' 1,'' 31+1 1,'' 31+28+1 1,'' 31+28+31+1 1,'' 31+28+31+30+1 1,'' 31+28+31+30+31+1 1,'' 31+28+31+30+31+30+1 1,'' 31+28+31+30+31+30+31+1 1,'' 31+28+31+30+31+30+31+31+1 1,'' 31+28+31+30+31+30+31+31+30+1 1,'' 31+28+31+30+31+30+31+31+30+31+1 1,'' 31+28+31+30+31+30+31+31+30+31+30+1 1,'2007' 365+1,'' 365+31+1 1,'' 365+31+28+1 1,'' 365+31+28+31+1 1,'' 365+31+28+31+30+1 1,'' 365+31+28+31+30+31+1 1,'' 365+31+28+31+30+31+30+1 1,'' 365+31+28+31+30+31+30+31+1 1,'' 365+31+28+31+30+31+30+31+31+1 1,'' 365+31+28+31+30+31+30+31+31+30+1 1,'' 365+31+28+31+30+31+30+31+31+30+31+1 1,'' 365+31+28+31+30+31+30+31+31+30+31+30+1 1,'2008' 2*365+1,'' 2*365+31+1 1,'' 2*365+31+29+1 1,'' 2*365+31+29+31+1 1,'' 2*365+31+29+31+30+1 1,'' 2*365+31+29+31+30+31+1 1,'' 2*365+31+29+31+30+31+30+1 1,'' 2*365+31+29+31+30+31+30+31+1 1,'' 2*365+31+29+31+30+31+30+31+31+1 1,'' 2*365+31+29+31+30+31+30+31+31+30+1 1,'' 2*365+31+29+31+30+31+30+31+31+30+31+1 1,'' 2*365+31+29+31+30+31+30+31+31+30+31+30+1 1,'2009' 3*365+2,'' 3*365+31+2 1,'' 3*365+31+28+2 1,'' 3*365+31+28+31+2 1,'' 3*365+31+28+31+30+2 1,'' 3*365+31+28+31+30+31+2 1,'' 3*365+31+28+31+30+31+30+2 1,'' 3*365+31+28+31+30+31+30+31+2 1,'' 3*365+31+28+31+30+31+30+31+31+2 1,'' 3*365+31+28+31+30+31+30+31+31+30+2 1,'' 3*365+31+28+31+30+31+30+31+31+30+31+2 1,'' 3*365+31+28+31+30+31+30+31+31+30+31+30+3 1,'2010' 4*365+2,'' 4*365+31+2 1,'' 4*365+31+28+2 1,'' 4*365+31+28+31+2 1,'' 4*365+31+28+31+30+2 1,'' 4*365+31+28+31+30+31+2 1,'' 4*365+31+28+31+30+31+30+2 1,'' 4*365+31+28+31+30+31+30+31+2 1,'' 4*365+31+28+31+30+31+30+31+31+2 1,'' 4*365+31+28+31+30+31+30+31+31+30+2 1,'' 4*365+31+28+31+30+31+30+31+31+30+31+2 1,'' 4*365+31+28+31+30+31+30+31+31+30+31+30+3 1)"
)
gp2.plot(Data(hetsdata, title='He^{1+} [cts/day]', with_='histeps lt 1'),
Data(herunav, title='He@_{RM}^{1+} (+-13 Days)', with_='l lt 3 lw 3'),
Data(semdat, title="SEM UV", with_="l lt 4"))
gp2.hardcopy("He1+_soho.ps", color=True)
