def __init__(self, x, y):
self.xFile = []
self.yFile = '{}'.format(y[0])
self.length = 0
self.volume = 0
self.peak = []
self.filterSignal = []
i = 0
while i < len(x):
self.xFile.append('{}'.format(x[i]))
i += 1
self.title = '{}'.format(namespace.title)
self.output = '{}'.format(namespace.output)
self.dataoutput = '{}'.format(namespace.dataoutput)
self.dataStressFile = '{}'.format(namespace.dataStressFile)
self.dataDir = '{}'.format(namespace.dataDir)
self.distanceDump = '{}'.format(namespace.distanceDump)
self.distanceStat = '{}'.format(namespace.distanceStat)
self.xlabel = '{}'.format(namespace.xlabel)
self.ylabel = '{}'.format(namespace.ylabel)
self.l1 = '{}'.format(namespace.l1)
self.l2 = '{}'.format(namespace.l2)
self.l3 = '{}'.format(namespace.l3)
self.time = np.array(getColumn(self.yFile, 0)).astype(float) * 0.001
self.sxx = np.array(getColumn(self.yFile, 1)).astype(float)
self.syy = np.array(getColumn(self.yFile, 2)).astype(float)
self.szz = np.array(getColumn(self.yFile, 3)).astype(float)
if namespace.toforce:
self.sxx *= np.pi / 100
self.syy *= np.pi / 100
self.szz *= np.pi / 100
self.computeDist()
self.sxx /= self.volume
self.syy /= self.volume
self.szz /= self.volume
if namespace.sumfilter:
self.filterSignal = noiseFilter(self.syy, 5, 75)
if namespace.plotpeaks:
self.peakDetect(self.filterSignal, 12000, 40, 150, 5950)
if namespace.writedata:
self.writeToFile()
if namespace.stdout and namespace.plotpeaks:
self.stdOut()
if namespace.dataStressFile:
self.writeStressFile()
self.computeDistAnalizeAtoms()
def plotChainStress():
""" atomStress() ploting graphix chain stress """
pFile = "%s/stress/null.stress.profile" % tempT
pxx = np.array(getColumn(pFile, 1)).astype(float)
pyy = np.array(getColumn(pFile, 2)).astype(float)
pzz = np.array(getColumn(pFile, 3)).astype(float)
xFile0 = "%s/stress/atom2.coordinate" % tempT
xFile1 = "%s/stress/atom3.coordinate" % tempT
xFile2 = "%s/stress/atom4.coordinate" % tempT
xFile3 = "%s/stress/atom5.coordinate" % tempT
xFile4 = "%s/stress/atom6.coordinate" % tempT
xFile5 = "%s/stress/atom7.coordinate" % tempT
xFile6 = "%s/stress/atom8.coordinate" % tempT
xFile7 = "%s/stress/atom9.coordinate" % tempT
xFile8 = "%s/stress/atom10.coordinate" % tempT
xFile9 = "%s/stress/atom11.coordinate" % tempT
l = (computeDist(xFile0, xFile1) +
computeDist(xFile1, xFile2) +
computeDist(xFile2, xFile3) +
computeDist(xFile3, xFile4) +
computeDist(xFile4, xFile5) +
computeDist(xFile5, xFile6) +
computeDist(xFile6, xFile7) +
computeDist(xFile7, xFile8) +
computeDist(xFile8, xFile9))
v = (l + 1.34) * np.pi
if namespace.stresstime:
time = np.array(getColumn(pFile, 0)).astype(float) * 0.001
p1 = pxx / v
p2 = pyy / v
p3 = pzz / v
if namespace.toforce:
p1 = np.pi * p1 / 100
p2 = np.pi * p2 / 100
p3 = np.pi * p3 / 100
plt.plot(time, p1, 'b', label=r'$\sigma_x$')
plt.plot(time, p2, 'r', label=r'$\sigma_y$')
plt.plot(time, p3, 'g', label=r'$\sigma_z$')
if namespace.sumfilter:
sumfsignal = noiseFilterPlot(time, p2)
if namespace.plotpeaks:
peak = plotPeaks(time, sumfsignal, 800, 20, 200, 5900)
def computeDist(xfile0, xfile1):
xx0 = np.array(getColumn(xfile0, 1)).astype(float)
xx1 = np.array(getColumn(xfile1, 1)).astype(float)
xy0 = np.array(getColumn(xfile0, 2)).astype(float)
xy1 = np.array(getColumn(xfile1, 2)).astype(float)
xz0 = np.array(getColumn(xfile0, 3)).astype(float)
xz1 = np.array(getColumn(xfile1, 3)).astype(float)
dist = np.sqrt((xx0 - xx1) ** 2 + (xy0 - xy1) ** 2 + (xz0 - xz1) ** 2)
return dist
'b',
'r',
'g',
'c',
'm'
]
f_txt = ['data/4,2.dat',
'data/77.dat',
'data/300.dat',
'data/600.dat',
'data/1000.dat',
'data/1500.dat'
]
force = [np.array(getColumn(f_txt[0], 1)).astype(float),
np.array(getColumn(f_txt[1], 1)).astype(float),
np.array(getColumn(f_txt[2], 1)).astype(float),
np.array(getColumn(f_txt[3], 1)).astype(float),
np.array(getColumn(f_txt[4], 1)).astype(float),
np.array(getColumn(f_txt[5], 1)).astype(float),
]
time = [np.array(getColumn(f_txt[0], 2)).astype(float),
np.array(getColumn(f_txt[1], 2)).astype(float),
np.array(getColumn(f_txt[2], 2)).astype(float),
np.array(getColumn(f_txt[3], 2)).astype(float),
np.array(getColumn(f_txt[4], 2)).astype(float),
np.array(getColumn(f_txt[5], 2)).astype(float),
]
l1 = "{}".format(namespace.l1)
l2 = "{}".format(namespace.l2)
l3 = "{}".format(namespace.l3)
yfile0 = "{}".format(namespace.yfile[0])
yfile1 = "{}".format(namespace.yfile[1])
yfile2 = "{}".format(namespace.yfile[2])
yfile3 = "{}".format(namespace.yfile[3])
yfile4 = "{}".format(namespace.yfile[4])
yfile5 = "{}".format(namespace.yfile[5])
yfile6 = "{}".format(namespace.yfile[6])
yfile7 = "{}".format(namespace.yfile[7])
yfile8 = "{}".format(namespace.yfile[8])
yfile9 = "{}".format(namespace.yfile[9])
yx0 = np.array(getColumn(yfile0, 1)).astype(float)
yx1 = np.array(getColumn(yfile1, 1)).astype(float)
yx2 = np.array(getColumn(yfile2, 1)).astype(float)
yx3 = np.array(getColumn(yfile3, 1)).astype(float)
yx4 = np.array(getColumn(yfile4, 1)).astype(float)
yx5 = np.array(getColumn(yfile5, 1)).astype(float)
yx6 = np.array(getColumn(yfile6, 1)).astype(float)
yx7 = np.array(getColumn(yfile7, 1)).astype(float)
yx8 = np.array(getColumn(yfile8, 1)).astype(float)
yx9 = np.array(getColumn(yfile9, 1)).astype(float)
yy0 = np.array(getColumn(yfile0, 2)).astype(float)
yy1 = np.array(getColumn(yfile1, 2)).astype(float)
yy2 = np.array(getColumn(yfile2, 2)).astype(float)
yy3 = np.array(getColumn(yfile3, 2)).astype(float)
yy4 = np.array(getColumn(yfile4, 2)).astype(float)
def read(self, distance_stat_file, file_type):
"""
Read from statistic file data
:param distance_stat_file: standart "chain.dat" or "bound.dat" statistic file
:param file_type: "chain" or "bound" file
:return: self
"""
if file_type == 'chain':
self.critLen = np.array(getColumn(distance_stat_file, 0)).astype(float)
self.initLen = np.array(getColumn(distance_stat_file, 1)).astype(float)
self.deltaLen = np.array(getColumn(distance_stat_file, 2)).astype(float)
self.normalStrain = np.array(getColumn(distance_stat_file, 3)).astype(float)
self.trueStrain = np.array(getColumn(distance_stat_file, 4)).astype(float)
self.tau = np.array(getColumn(distance_stat_file, 5)).astype(float)
self.stress = np.array(getColumn(distance_stat_file, 6)).astype(float)
elif file_type == 'bound':
number = np.array(getColumn(distance_stat_file, 0)).astype(float)
self.number = int(number[0])
self.critLen = np.array(getColumn(distance_stat_file, 1)).astype(float)
self.initLen = np.array(getColumn(distance_stat_file, 2)).astype(float)
self.deltaLen = np.array(getColumn(distance_stat_file, 3)).astype(float)
self.normalStrain = np.array(getColumn(distance_stat_file, 4)).astype(float)
self.trueStrain = np.array(getColumn(distance_stat_file, 5)).astype(float)
self.tau = np.array(getColumn(distance_stat_file, 6)).astype(float)
self.stress = np.array(getColumn(distance_stat_file, 7)).astype(float)
return self
# 'data/1000.dat',
# 'data/1500.dat'
# ]
# for data 15.12.2015
dataDir='data/15_12_15'
f_txt = ['%s/stress_4.2.dat'%(dataDir),
'%s/stress_300.dat'%(dataDir),
'%s/stress_600.dat'%(dataDir),
'%s/stress_1000.dat'%(dataDir),
]
force = [np.array(getColumn(f_txt[0], 1)).astype(float),
np.array(getColumn(f_txt[1], 1)).astype(float),
np.array(getColumn(f_txt[2], 1)).astype(float),
np.array(getColumn(f_txt[3], 1)).astype(float),
# np.array(getColumn(f_txt[4], 1)).astype(float),
# np.array(getColumn(f_txt[5], 1)).astype(float),
]
time = [np.array(getColumn(f_txt[0], 2)).astype(float),
np.array(getColumn(f_txt[1], 2)).astype(float),
np.array(getColumn(f_txt[2], 2)).astype(float),
np.array(getColumn(f_txt[3], 2)).astype(float),
#np.array(getColumn(f_txt[4], 2)).astype(float),
#np.array(getColumn(f_txt[5], 2)).astype(float),
]
namespace = parser.parse_args(sys.argv[1:])
title = '{}'.format(namespace.title)
output = '{}'.format(namespace.output)
dataoutput = '{}'.format(namespace.dataoutput)
xlabel = '{}'.format(namespace.xlabel)
ylabel = '{}'.format(namespace.ylabel)
l1 = '{}'.format(namespace.l1)
l2 = '{}'.format(namespace.l2)
l3 = '{}'.format(namespace.l3)
pfile = '{}'.format(namespace.yfile[0])
pxx = np.array(getColumn(pfile, 1)).astype(float)
pyy = np.array(getColumn(pfile, 2)).astype(float)
pzz = np.array(getColumn(pfile, 3)).astype(float)
c1 = [50, 20, 25, 10]
c2 = [400, 100, 200, 70]
if namespace.group:
data = plotChainStress()
if namespace.atom:
data = plotAtomStress()
if namespace.writedata:
odata = np.matrix(data)
header = '%s is data output\n%s file\ntimeMax tmaximum timeMin minimum\n (100*ps) (GPa or nN) (100*ps) (GPa or nN)' % (
l5 = '{}'.format(namespace.l5)
l6 = '{}'.format(namespace.l6)
l7 = '{}'.format(namespace.l7)
l8 = '{}'.format(namespace.l8)
l9 = '{}'.format(namespace.l9)
l10 = '{}'.format(namespace.l10)
plt.figure()
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
if number == 1:
x = getColumn(filename,0)
y = getColumn(filename,1)
p1 = plt.plot(x, y, 'r', label=l1)
if number == 2:
x = getColumn(filename,0)
y1 = getColumn(filename,1)
y2 = getColumn(filename,2)
p1 = plt.plot(x, y1, 'b', label=l1)
p2 = plt.plot(x, y2, 'r', label=l2)
if number == 3:
x = getColumn(filename,0)
y1 = getColumn(filename,1)
y2 = getColumn(filename,2)
if __name__ == '__main__':
parser = createParser()
namespace = parser.parse_args(sys.argv[1:])
number = namespace.number
filename = '{}'.format(namespace.file)
title = '{}'.format(namespace.title)
xlabel = '{}'.format(namespace.xlabel)
ylabel = '{}'.format(namespace.ylabel)
output = '{}'.format(namespace.output)
plt.figure()
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
x = getColumn(filename,0)
y = getColumn(filename,1)
p1 = plt.plot(x, y, 'r')
if namespace.sumfilter:
noiseFilterPlot(x,y)
if namespace.fft:
fourierFilter(x,y)
if namespace.plotlegend:
plt.legend()
plt.savefig(output, dpi=150)
title = '{}'.format(namespace.title)
output = '{}'.format(namespace.output)
xlabel = '{}'.format(namespace.xlabel)
ylabel1 = '{}'.format(namespace.ylabel1)
ylabel2 = '{}'.format(namespace.ylabel2)
ylabel3 = '{}'.format(namespace.ylabel3)
l1 = '{}'.format(namespace.l1)
l2 = '{}'.format(namespace.l2)
l3 = '{}'.format(namespace.l3)
if number == 1:
x = getColumn(filename,0)
y1 = getColumn(filename,1)
plt.figure()
p1 =plt.subplot(1, 1, 1)
subplots_adjust(hspace=0.1)
p1.plot(x, y1, 'b', label=l1)
plt.xlabel(xlabel)
plt.ylabel(ylabel1)
plt.title(title)
plt.grid(True)
plt.legend()
if number == 2:
x = getColumn(filename,0)
import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt from column import getColumn import scipy.stats as stats from scipy.optimize import curve_fit def weib(x,n,a): return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a) num_bins = 20 num_weib = 200 file = "null.stress.profile" x = np.array(getColumn(file, 2)).astype(float) plt.figure(figsize=(10,5)) ax1=plt.subplot(121) n, bins, patches = ax1.hist(x, num_bins, facecolor='green', normed=True, alpha=0.5) idBinds = max(enumerate(n),key=lambda j: j[1])[0] mu = bins[idBinds+1] sigma = 23. scale = n.max()/weib(bins, mu, sigma).max() linsp = np.linspace(bins[0], bins[num_bins], num_weib)
def plotChainStress():
""" atomStress() ploting graphix chain stress """
xfile0 = '{}'.format(namespace.xfile[0])
xfile1 = '{}'.format(namespace.xfile[1])
xfile2 = '{}'.format(namespace.xfile[2])
xfile3 = '{}'.format(namespace.xfile[3])
xfile4 = '{}'.format(namespace.xfile[4])
xfile5 = '{}'.format(namespace.xfile[5])
xfile6 = '{}'.format(namespace.xfile[6])
xfile7 = '{}'.format(namespace.xfile[7])
xfile8 = '{}'.format(namespace.xfile[8])
xfile9 = '{}'.format(namespace.xfile[9])
l = (computeDist(xfile0, xfile1) + computeDist(xfile1, xfile2) + computeDist(xfile2, xfile3) + computeDist(xfile3,
xfile4) + computeDist(
xfile4, xfile5) + computeDist(xfile5, xfile6) + computeDist(xfile6, xfile7) + computeDist(xfile7,
xfile8) + computeDist(
xfile8, xfile9))
epsilon = (l - l[0]) / l[0]
v = (l + 1.34) * np.pi
if namespace.stressdia:
time = np.array(getColumn(pfile, 0)).astype(float) * 0.001
p1 = pxx / v
p2 = pyy / v
p3 = pzz / v
if namespace.toforce:
p1 = np.pi * p1 / 100
p2 = np.pi * p2 / 100
p3 = np.pi * p3 / 100
plt.figure(title)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
from operator import itemgetter
list1 = epsilon
list2 = p2
x, y = [list(x) for x in zip(*sorted(zip(list1, list2), key=itemgetter(0)))]
# plt.plot(l, p1, 'b', label=l1)
plt.plot(x, y, 'rx', label=l2)
# plt.plot(l, p3, 'g', label=l3)
if namespace.sumfilter:
sumfsignal = noiseFilterPlot(time, p2)
if namespace.plotpeaks:
peak = plotPeaks(time, sumfsignal, 800, 20, 200, 5900)
if namespace.windowfit:
plotPolinom2Window(l, p1, c1[0], c1[1], c1[2], c1[3], 'k--')
plotPolinom2Window(l, p2, c2[0], c2[1], c2[2], c2[3], 'k-')
if namespace.stresstime:
time = np.array(getColumn(pfile, 0)).astype(float) * 0.001
p1 = pxx / v
p2 = pyy / v
p3 = pzz / v
if namespace.toforce:
p1 = np.pi * p1 / 100
p2 = np.pi * p2 / 100
p3 = np.pi * p3 / 100
plt.figure(title)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
plt.plot(time, p1, 'b', label=l1)
plt.plot(time, p2, 'r', label=l2)
plt.plot(time, p3, 'g', label=l3)
if namespace.sumfilter:
sumfsignal = noiseFilterPlot(time, p2)
if namespace.plotpeaks:
peak = plotPeaks(time, sumfsignal, 800, 20, 200, 5900)
if namespace.windowfit:
plotPolinom2Window(time, p1, c1[0], c1[1], c1[2], c1[3], 'k--')
plotPolinom2Window(time, p2, c2[0], c2[1], c2[2], c2[3], 'k-')
if namespace.plotpeaks:
return peak
def plotAtomStress():
""" TODO atomStress() ploting graphix single atom stress
"""
if namespace.true:
xfile0 = '{}'.format(namespace.xfile[0])
xfile1 = '{}'.format(namespace.xfile[1])
xfile2 = '{}'.format(namespace.xfile[2])
l1 = computeDist(xfile0, xfile1)
l2 = computeDist(xfile1, xfile2)
v = (0.5 * l1 + 0.5 * l2) * np.pi
time = np.array(getColumn(pfile, 0)).astype(float) * 0.001
p1 = pxx / v
p2 = pyy / v
p3 = pzz / v
if namespace.toforce:
p1 = np.pi * p1 / 100
p2 = np.pi * p2 / 100
p3 = np.pi * p3 / 100
plt.figure(title)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
plt.plot(time, p1, 'b')
plt.plot(time, p2, 'r')
plt.plot(time, p3, 'g')
if namespace.sumfilter:
sumfsignal = noiseFilterPlot(time, p2)
if namespace.plotpeaks:
peak = plotPeaks(time, sumfsignal, 800, 20, 200, 5900)
if namespace.windowfit:
plotPolinom2Window(time, p1, c1[0], c1[1], c1[2], c1[3], 'k--')
plotPolinom2Window(time, p2, c2[0], c2[1], c2[2], c2[3], 'k-')
if namespace.conditional:
v = 1.34 * np.pi
time = np.array(getColumn(pfile, 0)).astype(float) * 0.001
p1 = pxx / v
p2 = pyy / v
p3 = pzz / v
if namespace.toforce:
p1 = np.pi * p1 / 100
p2 = np.pi * p2 / 100
p3 = np.pi * p3 / 100
plt.figure(title)
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.grid(True)
plt.plot(time, p1, 'b')
plt.plot(time, p2, 'r')
plt.plot(time, p3, 'g')
if namespace.sumfilter:
sumfsignal = noiseFilterPlot(time, p2)
if namespace.plotpeaks:
peak = plotPeaks(time, sumfsignal, 800, 20, 200, 5900)
if namespace.windowfit:
plotPolinom2Window(time, p1, c1[0], c1[1], c1[2], c1[3], 'k--')
plotPolinom2Window(time, p2, c2[0], c2[1], c2[2], c2[3], 'k-')
if namespace.plotpeaks:
return peak
parser = createParser()
namespace = parser.parse_args(sys.argv[1:])
title = '{}'.format(namespace.title)
output = '{}'.format(namespace.output)
xlabel = '{}'.format(namespace.xlabel)
ylabel = '{}'.format(namespace.ylabel)
label = '{}'.format(namespace.label)
if namespace.dist:
stepf = '{}'.format(namespace.file[0])
step = np.array(getColumn(stepf,0)).astype(float)
xfile0 = '{}'.format(namespace.file[0])
xfile1 = '{}'.format(namespace.file[1])
lenght = computeDist(xfile0,xfile1)
plt.figure(title)
if namespace.apr:
p = np.polyfit(step,lenght,1)
if namespace.dif:
vel=p[0]/0.001*0.1
plt.plot(step, lenght, 'g', label=vel)
else:
plt.plot(step, lenght, 'g')
# number plot namespace
num = argparse.Namespace()
num.fit = 200
num.dism = 1.0
num.dism_plot = '1'
num.xmax = 2400**num.dism
num.ymax = 12
# read file
data = argparse.Namespace()
#data.file = "dat/hist.dat"
data.file = "dat/dispersion"
#data.s_05 = np.array(getColumn(data.file, 0)).astype(float)
#data.s_50 = np.array(getColumn(data.file, 1)).astype(float)
data.s_95 = np.array(getColumn(data.file, 2)).astype(float)
data.temp = np.array(getColumn(data.file, 0)).astype(float)**num.dism
data.linespace = np.linspace(0, num.xmax, num.fit)
#data.fit_s_05 = np.polyval(np.polyfit(data.temp,data.s_05,3), data.linespace)
data.fit_s_95 = np.polyval(np.polyfit(data.temp,data.s_95, 3), data.linespace)
#data.err_upper = data.s_95 - data.s_50
#data.err_lower = data.s_50 - data.s_05
#data.asymmetric_error = [data.err_lower, data.err_upper]
# building graphix
plt.figure()
if num.dism != 1:
plt.xlabel(r'Temperatute, $T^{%s}$ ($K^{%s}$)'%(num.dism_plot,num.dism_plot), fontsize=16)
elif num.dism == 1:
plt.xlabel(r'Temperatute, $T$ ($K$)', fontsize=17)
plt.tick_params(axis='both', which='major', labelsize=15)
#exponweib.pdf = lambda x, k, l, a: a*k/l #exponweib.cdf = lambda x, k, l, a: #exponweib.fit = lambda x, y: curve_fit(exponweib.cdf, x, y, p0 = [10., 250.]) # number plot namespace num = argparse.Namespace() num.bins = 20 num.histbins = 50 num.weib = 200 num.alpha = 0.8 # read file data = argparse.Namespace() data.numfile = '1000' data.file = "dat/stress_%s.dat" % (data.numfile) data.x = np.array(getColumn(data.file, 2)).astype(float) data.t = np.array(getColumn(data.file, 1)).astype(float) # create histogram hist, bin_edges = np.histogram(data.x, num.histbins, density=True) # Calculate histogram data.x_hist = bin_edges[1:] # Take the upper edges of the bins data.y_hist = hist.cumsum()/hist.cumsum().max() # Normalise the cumulative sum t_hist, t_bin_edges = np.histogram(data.t, num.histbins, density=True) # Calculate histogram data.t_x_hist = t_bin_edges[1:] # Take the upper edges of the bins data.t_y_hist = t_hist.cumsum()/t_hist.cumsum().max() # Normalise the cumulative sum # distribution namespace coeff = argparse.Namespace() coeff.popt, coeff.pcov = weib.fit(data.x_hist, data.y_hist) coeff.shape = coeff.popt[0] # Scipy's shape parameter corresponds to log(geo. std. dev.)
if __name__ == '__main__':
parser = createParser()
namespace = parser.parse_args(sys.argv[1:])
file = '{}'.format(namespace.file)
title = '{}'.format(namespace.title)
output = '{}'.format(namespace.output)
xlabel = '{}'.format(namespace.xlabel)
ylabel1 = '{}'.format(namespace.ylabel1)
ylabel2 = '{}'.format(namespace.ylabel2)
ylabel3 = '{}'.format(namespace.ylabel3)
#l1 = '{}'.format(namespace.l1)
#l2 = '{}'.format(namespace.l2)
#l3 = '{}'.format(namespace.l3)
xx = np.array(getColumn(file,0)).astype(float)
yx = np.array(getColumn(file,1)).astype(float)
yy = np.array(getColumn(file,2)).astype(float)
yz = np.array(getColumn(file,3)).astype(float)
if namespace.abs:
y = np.sqrt(yx**2+yy**2+yx**2)
plt.figure()
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel1)
plt.vlines(xx, [0], y*1.604, 'b')
plt.grid(True)
#leg = plt.legend()
