def dih_plotter3(dirname,savename,numplot):
inlist = dih_tablereader(dirname)
plotlist = inlist[0:numplot]
colors = iter(cm.rainbow(np.linspace(0,1,len(plotlist)))) #creates color table
for memberlist in plotlist:
x = memberlist[0] #x coordinate data
y = memberlist[1] #y coordinate data
ysmooth = dih_boxcar(y)
xshort = x[0:len(ysmooth)]
peaklist =signal.find_peaks_cwt(ysmooth, np.arange(1,30))#continuous wavelet transformation
plt.plot(xshort,ysmooth,color = next(colors))
for num in peaklist:
plt.plot(xshort[num],ysmooth[num],'gD')#places markers on peaks
peak = max(ysmooth)
peaklist2 = [i for i, j in enumerate(ysmooth) if j == peak]
for num in peaklist2:
plt.plot(xshort[num],ysmooth[num],'rD')
#finish up plot characteristics
plt.title('Super Most Awesome Graph!')
plt.ylabel('Flux')
plt.xlabel('Time')
pylab.ylim([-5,5])
pylab.xlim([0,6.3])
plt.savefig(savename)#saves postscript file
return plotlist
def dih_minlistmaker(dirname):
indata = d.dih_tablereader(dirname)
timelist = []
endlist = []
for memberlist in indata:
y = memberlist[1]
cave = min(y)
cavelist = [i for i, j in enumerate(y) if j == cave]
timelist.append(cavelist[0])
for member in timelist:
for item in member:
endlist.append(item)
return endlist
def dih_errmaker(dirname): indata = d.dih_tablereader(dirname) endlist =[] for member in indata: y = member[1] diff = abs(max(y)-min(y)) if diff > 2.0: err = 6.3/11 elif diff < 1.0: err = 6.3/5 else: err = 6.3/9 endlist.append(err) return endlist
def dih_maxlistmaker(dirname): indata = d.dih_tablereader(dirname) timelist =[] endlist =[] for memberlist in indata: y = memberlist[1] peak = max(y) peaklist = [i for i, j in enumerate(y) if j==peak] timelist.append(peaklist[0]) for member in timelist: for item in member: endlist.append(item) return endlist
def dih_basichist(dirname,histname):
indata = d.dih_tablereader(dirname)
peaklist = []
for memberlist in indata:
y = memberlist[1]
peak = max(y)
peaklist.append(peak)
P.figure()
n, bins, patches = P.hist(peaklist,12, histtype = "stepfilled")
(mu,sigma) = norm.fit(peaklist)
plotline = mlab.normpdf(bins,mu,sigma)
plt.plot(bins, plotline)
plt.xlabel('Max value of Random Data')
plt.ylabel('Fraction of Counts')
plt.title('Practice with Histograms')
plt.savefig(histname)
return peaklist