def mass_flux_plot(*args,**kwargs):
fltm = idls.read(args[0])
injm = idls.read(args[1])
f1 = plt.figure()
ax1 = f1.add_subplot(211)
plt.plot(injm.nt_sc,injm.nmf_rscale,'r')
plt.plot(injm.nt_sc,injm.nmf_zscale,'b')
plt.plot(injm.nt_sc,injm.nmf_z0scale,'k')
plt.plot(injm.nt_sc,(injm.nmf_rscale+injm.nmf_zscale),'g')
plt.axis([0.0,160.0,0.0,3.5e-5])
plt.minorticks_on()
locs,labels = plt.yticks()
plt.yticks(locs, map(lambda x: "%.1f" % x, locs*1e5))
plt.text(0.0, 1.03, r'$10^{-5}$', transform = plt.gca().transAxes)
plt.xlabel(r'Time [yr]',labelpad=6)
plt.ylabel(r'$\dot{\rm M}_{\rm out} [ \rm{M}_{\odot} \rm{yr}^{-1} ]$',labelpad=15)
ax2 = f1.add_subplot(212)
plt.plot(fltm.nt_sc,fltm.nmf_rscale,'r')
plt.plot(fltm.nt_sc,fltm.nmf_zscale,'b')
plt.plot(fltm.nt_sc,fltm.nmf_z0scale,'k')
plt.plot(fltm.nt_sc,(fltm.nmf_rscale+fltm.nmf_zscale),'g')
plt.axis([0.0,160.0,0.0,4.0e-5])
plt.minorticks_on()
locs,labels = plt.yticks()
plt.yticks(locs, map(lambda x: "%.1f" % x, locs*1e5))
plt.text(0.0, 1.03, r'$10^{-5}$', transform = plt.gca().transAxes)
plt.xlabel(r'Time [yr]',labelpad=6)
plt.ylabel(r'$\dot{\rm M}_{\rm out} [ \rm {M}_{\odot} \rm{yr}^{-1} ]$',labelpad=15)
def minj_mflux(*args,**kwargs):
Qu = jana.quantities()
Minj_List =[]
Minj_MHD_List=[]
for i in range(len(args[1])):
Minj_List.append(Qu.Mflux(args[1][i],Mstar=args[0][i],scale=True)['Mfr']+Qu.Mflux(args[1][i],Mstar=args[0][i],scale=True)['Mfz'])
MHD_30minj = Qu.Mflux(args[2],Mstar=30.0,scale=True)['Mfr']+Qu.Mflux(args[2],Mstar=30.0,scale=True)['Mfz']
for i in args[0]:
Minj_MHD_List.append(MHD_30minj*np.sqrt(i/30.0))
f1 = plt.figure(num=1)
ax1 = f1.add_subplot(211)
plt.axis([10.0,70.0,2.0e-5,7.99e-5])
plt.plot(args[0],Minj_MHD_List,'k*')
plt.plot(args[0],Minj_List,'ko')
plt.minorticks_on()
locs,labels = plt.yticks()
plt.yticks(locs, map(lambda x: "%.1f" % x, locs*1e5))
plt.text(0.0, 1.03, r'$10^{-5}$', transform = plt.gca().transAxes)
plt.xlabel(r'Stellar Mass : $M_{*} [M_{\odot}]$')
plt.ylabel(r' $\dot{M}_{\rm vert} + \dot{M}_{\rm rad}\, [M_{\odot}\,\rm yr^{-1}]$')
ax2 = f1.add_subplot(212)
plt.axis([10.0,70.0,0.0,50.0])
plt.plot(args[0],100*((np.array(Minj_List)-np.array(Minj_MHD_List))/np.array(Minj_MHD_List)),'k^')
plt.minorticks_on()
plt.xlabel(r'Stellar Mass : $M_{*} [M_{\odot}]$')
plt.ylabel(r'$\%$ Change in Total Mass Outflow Rates')
def do_plot_extras(self, extra):
""" Plot other observed quantities as a function of time.
Parameters
----------
extra: string
One of the quantities available in system.extras
"""
# import pyqtgraph as pg
colors = 'bgrcmykw' # lets hope for less than 9 data-sets
t = self.time
# handle inexistent field
if extra not in self.extras._fields:
from shell_colors import red
msg = red('ERROR: ') + 'The name "%s" is not available in extras.\n' % extra
clogger.fatal(msg)
return
i = self.extras._fields.index(extra) # index corresponding to this quantity
plt.figure()
# p = pg.plot()
plt.plot(t, self.extras[i], 'o', label=extra)
plt.xlabel('Time [days]')
plt.ylabel(extra + ' []')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05))
plt.minorticks_on()
plt.tight_layout()
plt.show()
def fourcumplot(x1,x2,x3,x4,xmin,xmax,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',x4leg='$x_3$',xlabel='',ylabel='$N(x>x\')$',fig=1,sharey=False,fontsize=12,bins1=50,bins2=50,bins3=50,bins4=50):
"""
Script that plots the cumulative histograms of four variables x1, x2, x3 and x4
sharing the same X-axis. For each bin, Y is the fraction of the sample
with values above X.
Arguments:
- x1,x2,x3,x4: arrays with data to be plotted
- xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range
for both histograms.
- x1leg, x2leg, x3leg, x4leg: legends for each histogram
- xlabel: self-explanatory.
- sharey: sharing the Y-axis among the histograms?
- bins1,bins2,...: number of bins in each histogram
- fig: which plot window should I use?
Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_.
v1 Jun. 2012: inherited from fourhists.
"""
pylab.rcParams.update({'font.size': fontsize})
fig=pylab.figure(fig)
pylab.clf()
a=fig.add_subplot(4,1,1)
if sharey==True:
b=fig.add_subplot(4,1,2, sharex=a, sharey=a)
c=fig.add_subplot(4,1,3, sharex=a, sharey=a)
d=fig.add_subplot(4,1,4, sharex=a, sharey=a)
else:
b=fig.add_subplot(4,1,2, sharex=a)
c=fig.add_subplot(4,1,3, sharex=a)
d=fig.add_subplot(4,1,4, sharex=a)
a.hist(x1,bins1,label=x1leg,color='b',cumulative=-True,normed=True,histtype='stepfilled')
a.legend(loc='best',frameon=False)
a.set_xlim(xmin,xmax)
b.hist(x2,bins2,label=x2leg,color='r',cumulative=-True,normed=True,histtype='stepfilled')
b.legend(loc='best',frameon=False)
c.hist(x3,bins3,label=x3leg,color='y',cumulative=-True,normed=True,histtype='stepfilled')
c.legend(loc='best',frameon=False)
d.hist(x4,bins4,label=x4leg,color='g',cumulative=-True,normed=True,histtype='stepfilled')
d.legend(loc='best',frameon=False)
pylab.setp(a.get_xticklabels(), visible=False)
pylab.setp(b.get_xticklabels(), visible=False)
pylab.setp(c.get_xticklabels(), visible=False)
d.set_xlabel(xlabel)
c.set_ylabel(ylabel)
pylab.minorticks_on()
pylab.subplots_adjust(hspace=0.15)
pylab.draw()
pylab.show()
def deg_coll_plots(dirlist=None, **kwargs):
if kwargs.get("perzeta",False)==True:
zetalist = []
for dirnames in dirlist:
zetalist.append(zetaper(w_dir=dirnames,nrad=kwargs.get('nrad',638)))
#f1 = plt.figure()
plt.xlabel(r'Distance Along Field Line : s [AU]',labelpad=6)
plt.ylabel(r'$\Delta \zeta [\%]$',labelpad=10)
plt.plot(zetalist[0][0],zetalist[0][1],'k-.')
plt.plot(zetalist[1][0],zetalist[1][1],'k--')
plt.plot(zetalist[2][0],zetalist[2][1],'k')
plt.axis([0.0,150.0,0.0,40.0])
plt.minorticks_on()
else:
magalf = np.linspace(19.67,19.67,100)
magfast = np.linspace(12.53,12.53,100)
Msarr = [20.0,25.0,30.0,45.0,50.0,60.0]
alfMs = [20.44,23.27,26.05, 26.84, 28.86,31.45]
fastMs = [15.57,19.94,23.40,24.35,26.03,29.25]
asymMs=[]
Alparr = [0.55,0.60,0.65]
alfAlp =[31.35,23.73,20.93]
fastAlp=[29.25,21.48,17.28]
asymAlp =[]
Denarr =[3.0e-14,5.0e-14,1.0e-13,5.0e-13]
alfDen =[30.30,26.05,22.91,20.42]
fastDen =[27.79,23.40,19.47,15.03]
asymDen=[]
Betarr=[1.0,3.0,5.0]
alfBet =[21.98,25.63,26.05]
fastBet=[16.45,20.68,23.40]
asymBet=[]
#nice_plots()
#f1 = plt.figure()
#ax1 = f1.add_subplot(111)
plt.ylabel(r"Opening Angle : $\phi^\circ$",labelpad=10)
plt.minorticks_on()
if kwargs.get('cplot',False)==True:
dum1 = np.linspace(1.0e-14,1.0e-12,100)
ms1 = plt.semilogx(Denarr,alfDen,'r*')
ms2 = plt.semilogx(Denarr,fastDen,'bo')
plt.xlabel(r"Base Density : $\rho_0$ [g/cm$^{3}$]")
plt.semilogx(dum1,magalf,'r--',dum1,magfast,'b--')
plt.axis([1.0e-14,1.0e-12,10.0,35.0])
# plt.legend([ms1,ms2],[r'Alfven point',r'Fast point'],loc='lower left')
else:
dum1 = np.linspace(10.0,70.0,100)
ms1 = plt.plot(Msarr,alfMs,'k*')
ms2 = plt.plot(Msarr,fastMs,'ko')
plt.xlabel(r"Stellar Mass : $M_{*}$ [$M_{\odot}$]",labelpad=6)
plt.plot(dum1,magalf,'k--',dum1,magfast,'k')
plt.axis([10.0,70.0,10.0,35.0])
def threehists(x1,x2,x3,xmin,xmax,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',xlabel='',fig=1,sharey=False,fontsize=12):
"""
Script that plots three histograms of quantities x1, x2 and x3
sharing the same X-axis.
Arguments:
- x1,x2,x3: arrays with data to be plotted
- xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range for both histograms.
- x1leg, x2leg, x3leg: legends for each histogram
- xlabel: self-explanatory.
- sharey: sharing the Y-axis among the histograms?
- fig: which plot window should I use?
Example:
x1=Lbol(AD), x2=Lbol(JD), x3=Lbol(EHF10)
>>> threehists(x1,x2,x3,38,44,'AD','JD','EHF10','$\log L_{\\rm bol}$ (erg s$^{-1}$)',sharey=True)
Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_.
"""
pylab.rcParams.update({'font.size': fontsize})
fig=pylab.figure(fig)
pylab.clf()
a=fig.add_subplot(3,1,1)
if sharey==True:
b=fig.add_subplot(3,1,2, sharex=a, sharey=a)
c=fig.add_subplot(3,1,3, sharex=a, sharey=a)
else:
b=fig.add_subplot(3,1,2, sharex=a)
c=fig.add_subplot(3,1,3, sharex=a)
a.hist(x1,label=x1leg,color='b',histtype='stepfilled')
a.legend(loc='best',frameon=False)
a.set_xlim(xmin,xmax)
b.hist(x2,label=x2leg,color='r',histtype='stepfilled')
b.legend(loc='best',frameon=False)
c.hist(x3,label=x3leg,color='y',histtype='stepfilled')
c.legend(loc='best',frameon=False)
pylab.setp(a.get_xticklabels(), visible=False)
pylab.setp(b.get_xticklabels(), visible=False)
c.set_xlabel(xlabel)
b.set_ylabel('Number')
pylab.minorticks_on()
pylab.subplots_adjust(hspace=0.15)
pylab.draw()
pylab.show()
def twohists(x1,x2,xmin,xmax,range=None,x1leg='$x_1$',x2leg='$x_2$',xlabel='',fig=1,sharey=False,fontsize=12,bins1=10,bins2=10):
"""
Script that plots two histograms of quantities x1 and x2
sharing the same X-axis.
:param x1,x2: arrays with data to be plotted
:param xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range
for both histograms.
:param x1leg, x2leg: legends for each histogram
:param xlabel: self-explanatory.
:param bins1,bins2: number of bins in each histogram
:param fig: which plot window should I use?
:param range: in the form (xmin,xmax), same as range argument for hist and applied to both
histograms.
Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_.
"""
pylab.rcParams.update({'font.size': fontsize})
fig=pylab.figure(fig)
pylab.clf()
a=fig.add_subplot(2,1,1)
if sharey==True:
b=fig.add_subplot(2,1,2, sharex=a, sharey=a)
else:
b=fig.add_subplot(2,1,2, sharex=a)
a.hist(x1,bins1,label=x1leg,color='b',histtype='stepfilled',range=range)
a.legend(loc='best',frameon=False)
a.set_xlim(xmin,xmax)
b.hist(x2,bins2,label=x2leg,color='r',histtype='stepfilled',range=range)
b.legend(loc='best',frameon=False)
pylab.setp(a.get_xticklabels(), visible=False)
b.set_xlabel(xlabel)
b.set_ylabel('Number',verticalalignment='bottom')
pylab.minorticks_on()
pylab.subplots_adjust(hspace=0.15)
pylab.draw()
pylab.show()
def plotter(filename, ptype):
data = np.loadtxt(dir+filename)
# frequencies
omegaR = data[:,3]
omegaz = data[:,5]
# angles
thetaR = data[:,6]
thetaz = data[:,8]
thetaR_t = (np.pi+(thetaR-np.median(data[:,6]))) % (2.*np.pi)
thetaz_t = (np.pi+(thetaz-np.median(data[:,8]))) % (2.*np.pi)
colors = cm.rainbow(np.linspace(0, 1, len(omegaR)))
if ptype == "freqs":
plt.scatter(omegaR*fact,omegaz*fact, color=colors)
# progenitor location
plt.plot(value[3]*fact,value[5]*fact,"ko", ms=12)
plt.xlabel(r"$\Omega_R \mathrm{(Gyr^{-1})}$",fontsize=20)
plt.ylabel(r"$\Omega_z \mathrm{(Gyr^{-1})}$",fontsize=20)
plt.grid(which='minor')
plt.minorticks_on()
elif ptype == "angles":
# moved stream particles
plt.scatter(thetaR_t,thetaz_t, color=colors)
# progenitor location
plt.plot(val6,val8,"ko", ms=12)
plt.grid(which='minor')
plt.minorticks_on()
plt.xlabel(r"$\theta_R}$",fontsize=20)
plt.ylabel(r"$\theta_z}$",fontsize=20)
def log_datafit(x, y, deg):
z = np.polyfit(np.log10(x), np.log10(y), deg)
p = np.poly1d(z)
A = np.zeros(np.shape(p)[0])
for i in range(np.shape(p)[0]):
A[::-1][i] = p[i]
yvals = 0.
for j in range(np.shape(p)[0]):
yvals += (((np.log10(x))**j)*A[::-1][j])
plt.ion()
plt.loglog(x, y, 'bo', label='Data')
plt.loglog(x, 10**(yvals), 'g--', lw=2, label='Best Fit')
plt.legend(loc='best')
plt.grid(which='minor')
plt.minorticks_on()
print "Ax+B"
print "A = ", A[0]
print "B =", A[1]
def plot_spectrum(self, ax, data, stat=None, label_axes=True):
""" Compute and plot spectral statistic.
If stat is set to None, will use currentIndex of stat_select combo box
"""
if len(data.shape) == 1:
data = np.reshape(data, (1, len(data)))
x_pow = np.abs(data)
if stat == None:
if self.scale_select.currentIndex() == 0:
ax.plot(x_pow, label='linear')
elif self.scale_select.currentIndex() == 1:
ax.plot(10*np.log10(x_pow), label='decibels')
elif self.scale_select.currentIndex() == 2:
ax.plot(np.var(x_pow, axis=0), label='variance')
elif self.scale_select.currentIndex() == 3:
ax.plot(skew(x_pow, axis=0), label='skew')
elif self.scale_select.currentIndex() == 4:
ax.plot(kurtosis(x_pow, axis=0), label='kurtosis')
else:
if self.scale_select.currentIndex() == 1:
x_pow = 10*np.log10(x_pow)
if stat == 'median' or stat == 'med':
ax.plot(np.median(x_pow, axis=0), label='median')
if stat == 'min':
ax.plot(np.min(x_pow, axis=0), label='min')
if stat == 'max':
ax.plot(np.max(x_pow, axis=0), label='max')
plt.minorticks_on()
if label_axes:
self.updateFreqAxis(ax, n_ticks=10)
plt.xlabel("Frequency")
plt.ylabel("Amplitude")
plt.legend()
def threehistsx(x1,x2,x3,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',fig=1,fontsize=12,bins1=10,bins2=10,bins3=10):
"""
Script that pretty-plots three histograms of quantities x1, x2 and x3.
Arguments:
:param x1,x2,x3: arrays with data to be plotted
:param x1leg, x2leg, x3leg: legends for each histogram
:param fig: which plot window should I use?
Example:
x1=Lbol(AD), x2=Lbol(JD), x3=Lbol(EHF10)
>>> threehists(x1,x2,x3,38,44,'AD','JD','EHF10','$\log L_{\\rm bol}$ (erg s$^{-1}$)')
Inspired by http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label.
"""
pylab.rcParams.update({'font.size': fontsize})
pylab.figure(fig)
pylab.clf()
pylab.subplot(3,1,1)
pylab.hist(x1,label=x1leg,color='b',bins=bins1)
pylab.legend(loc='best',frameon=False)
pylab.subplot(3,1,2)
pylab.hist(x2,label=x2leg,color='r',bins=bins2)
pylab.legend(loc='best',frameon=False)
pylab.subplot(3,1,3)
pylab.hist(x3,label=x3leg,color='y',bins=bins3)
pylab.legend(loc='best',frameon=False)
pylab.minorticks_on()
pylab.subplots_adjust(hspace=0.15)
pylab.draw()
pylab.show()
N_KOS = np.linspace(0, 60, 121)
N_motor_arr = []
for i in I_ARR:
N_motor_arr.append(N_KOS * i)
N_motor = N_KOS * I_TOTAL
N_m_1 = N_KOS * I_ARR[1]
import matplotlib
import matplotlib.pylab as plt
#from matplotlib import rc
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5, forward=True)
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
for item, num in enumerate(N_motor_arr):
#print (num)
plt.plot(N_KOS, N_motor_arr[item], label='$i = %i*3.2$' % I_RED[item])
# for i in range(len(N_motor_arr)):
# plt.plot(N_KOS, N_motor_arr[i], label='$i = %i*3.2$' % I_RED[i])
plt.ylim(ymax=7000, ymin=0)
plt.minorticks_on()
plt.grid(b=True, which='major', color='k', linestyle='-')
plt.grid(b=True, which='minor', color='r', linestyle='--')
plt.title(r'\textbf{Brzina vrtnje kosarice za Intecno reduktore}')
plt.ylabel(r'\textbf{Brzina vrtnje motora} [o/min]')
plt.xlabel(r'\textbf{Brzina vrtnje kosarice} [o/min]')
plt.legend()
plt.show()
def fourhists(x1,x2,x3,x4,xmin,xmax,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',x4leg='$x_3$',xlabel='',fig=1,sharey=False,fontsize=12,bins1=10,bins2=10,bins3=10,bins4=10,line1=None,line2=None,line3=None,line4=None,line1b=None,line2b=None,line3b=None,line4b=None,loc='best'):
"""
Script that plots four histograms of quantities x1, x2, x3 and x4
sharing the same X-axis.
Arguments:
- x1,x2,x3,x4: arrays with data to be plotted
- xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range
or both histograms.
- x1leg, x2leg, x3leg, x4leg: legends for each histogram
- xlabel: self-explanatory.
- sharey: sharing the Y-axis among the histograms?
- bins1,bins2,...: number of bins in each histogram
- fig: which plot window should I use?
- line?: draws vertical solid lines at the positions indicated in each panel
- line?b: draws vertical dashed lines at the positions indicated in each panel
.. figure:: ../figures/fourhists.png
:scale: 100 %
:alt: Four histograms in the same figure
Four histograms in the same figure.
Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_.
"""
pylab.rcParams.update({'font.size': fontsize})
fig=pylab.figure(fig)
pylab.clf()
a=fig.add_subplot(4,1,1)
if sharey==True:
b=fig.add_subplot(4,1,2, sharex=a, sharey=a)
c=fig.add_subplot(4,1,3, sharex=a, sharey=a)
d=fig.add_subplot(4,1,4, sharex=a, sharey=a)
else:
b=fig.add_subplot(4,1,2, sharex=a)
c=fig.add_subplot(4,1,3, sharex=a)
d=fig.add_subplot(4,1,4, sharex=a)
def vline(hist,value,linestyle='k'):
"""Draw vertical line"""
yax=hist.set_ylim()
hist.plot([value,value],[yax[0],yax[1]],linestyle,linewidth=2)
a.hist(x1,bins1,label=x1leg,color='b',histtype='stepfilled')
a.legend(loc=loc,frameon=False)
a.set_xlim(xmin,xmax)
if line1!=None: vline(a,line1)
if line1b!=None: vline(a,line1b,'k--')
b.hist(x2,bins2,label=x2leg,color='r',histtype='stepfilled')
b.legend(loc=loc,frameon=False)
if line2!=None: vline(b,line2)
if line2b!=None: vline(b,line2b,'k--')
c.hist(x3,bins3,label=x3leg,color='y',histtype='stepfilled')
c.legend(loc=loc,frameon=False)
if line3!=None: vline(c,line3)
if line3b!=None: vline(c,line3b,'k--')
d.hist(x4,bins4,label=x4leg,color='g',histtype='stepfilled')
d.legend(loc=loc,frameon=False)
if line4!=None: vline(d,line4)
if line4b!=None: vline(d,line4b,'k--')
pylab.setp(a.get_xticklabels(), visible=False)
pylab.setp(b.get_xticklabels(), visible=False)
pylab.setp(c.get_xticklabels(), visible=False)
d.set_xlabel(xlabel)
c.set_ylabel('Number')
pylab.minorticks_on()
pylab.subplots_adjust(hspace=0.15)
pylab.draw()
pylab.show()
#plt.subplot(3,1,3)
y, binEdges = np.histogram(l3, bins=nbins, density=False)
bincenters = 0.5 * (binEdges[1:] + binEdges[:-1])
plt.plot(bincenters, y, '-', lw=1.5, label=r"$\lambda_3$")
#strLabel = r"$\lambda_3$"
#errorfill(bincenters, y, np.sqrt(y), color='r', alpha_fill=0.3)
plt.yscale('log')
#plt.xscale('symlog')
plt.ylabel("PDF")
plt.xlabel(r"$\lambda$")
plt.legend(loc="upper left")
plt.xlim(-xlimits, xlimits)
plt.minorticks_on()
#plt.ylim(1e-5,)
#plt.show()
plt.savefig("plots/Evalcompare.pdf", bbox_inches="tight")
#
# ------------------- Non-Void distribution ------------------------------------
#file_nstr = './npy/Half/numFieldHalf_032_'+ str(refFactor)+'.npy'
file_nstr = './npy/numField_051_' + str(refFactor) + '.npy'
nstream = np.load(file_nstr).astype(np.float64)
#l3 = np.sum(l, axis = 3)
l1 = l[:, :, :, 0]
k0 = 0
#Gráficos:
for a in a_s:
#-X-
plb.subplot(121)
exponente = -((np.sqrt(2) * x / a)**2)
A = 2 / (a * (np.sqrt(2 * np.pi)))
PSI_modcdr = A * np.exp(exponente)
plb.xlabel("$x$", fontweight='bold', size=20)
plb.ylabel("$|\psi(x,0)|^{2}$", fontweight='bold', size=20)
plb.plot(x, PSI_modcdr, lw=3)
#Para la cuadrícula
plb.grid(b=True, which='major', color='k', linestyle='-')
plb.minorticks_on()
plb.grid(b=True, which='minor', color='k', linestyle=':', alpha=1)
#Para la leyenda
plb.legend([r"$a=%.2f$" % (i) for i in a_s], loc="upper right")
#-K-
plb.subplot(122)
exponente = ((-(a**2) / 2)) * ((k - k0)**2)
A = a / (np.sqrt(2 * np.pi))
PHI_modcdr = A * (np.exp(exponente))
plb.xlabel("$k$", fontweight='bold', size=20)
plb.ylabel("$|\phi(k)|^{2}$", fontweight='bold', size=20)
plb.plot(k, PHI_modcdr, lw=3)
#Para la cuadrícula
plb.grid(b=True, which='major', color='k', linestyle='-')
plb.minorticks_on()