def mp1_mp2_cpuntag(i, j, k, l): # untag_CP(B>X(s+d) + gamma)
print(i, j, k, l)
m1_axis = np.array([i for i in np.arange(10, 570, 20)])
m2_axis = np.array([i for i in np.arange(10, 1070, 20)])
m2 = m2_axis[0]
m1 = m1_axis[0]
emptytag = []
for m2 in m2_axis:
for m1 in m1_axis:
tag= bsg.untag_cp(mb,mw,m1,m2,\
[exe.Y2(i,j,k,l)],[ - exe.X2(i,j,k,l) * np.conjugate(exe.Y2(i,j,k,l) )],\
[exe.Y3(i,j,k,l)],[ - exe.X3(i,j,k,l) * np.conjugate(exe.Y3(i,j,k,l) )])
# print(acppd)
emptytag.append(tag)
# print(emptytag)
resulttag = plt.contourf(m1_axis, m2_axis, \
np.resize(np.array(emptytag),len(np.array(emptytag))).\
reshape(len(m2_axis),len(m1_axis)), \
# colors = ['black','royalblue','purple','darkgreen','brown','red','gray','orange'],\
levels = np.array([0.4,0.6,0.8,1.2,1.4,1.6])
)
plt.colorbar(resulttag)
plt.xlabel('$M_{H^{\pm}_{1}}$')
plt.ylabel('$M_{H^{\pm}_{2}}$')
plt.title('$A_{CP} (B \\to X_{s + d} \gamma )$ for 3HDM')
# plt.grid(axis='y', linestyle='-', color='0.75') # show y-axis grid line
# plt.grid(axis='x', linestyle='-', color='0.75') # show x-axis grid line
# plt.axis([50,200, 50.0, 1000.0])
plt.axis([0, 500, 0.0, 500.0])
plt.show()
plt.close()
return
def mp1_A_cpsdiffer(i, j, k, l):
m1_axis = np.array([i for i in np.arange(1, 551, 25)])
# m2_axis = np.array([ i for i in np.arange(10,1050,50)] )
cps_li = []
for j in exe.tbe:
print('j', j)
for m1 in m1_axis:
acpp= bsg.newa_cp(mb,mw,m1,300,\
[exe.Y2(i,j,k,l)],[- exe.X2(i,j,k,l) * np.conjugate(exe.Y2(i,j,k,l) )],\
[exe.Y3(i,j,k,l)],[- exe.X3(i,j,k,l) * np.conjugate(exe.Y3(i,j,k,l) )])
cps_li.append(acpp)
resultcps = plt.contourf(m1_axis, exe.tbe, \
np.resize(np.array(cps_li),len(np.array(cps_li))).\
reshape(len(exe.tbe),len(m1_axis)), \
# colors = ['black','royalblue','purple','darkgreen','brown','red','gray','orange'],\
# levels = np.array([0,0.5,1,1.5,2])
)
plt.colorbar(resultcps)
plt.xlabel('$M_{H^{\pm}_{1}}$')
plt.ylabel(exe.readlist[int(exe.read1)])
plt.title('$\\Delta_{X_s\gamma}$ for 3HDM')
plt.show()
plt.close()
return
def mp1_mp2_cps(i, j, k, l):
m1_axis = np.array([i for i in np.arange(10, 550, 50)])
m2_axis = np.array([i for i in np.arange(10, 1050, 50)])
m2 = m2_axis[0]
m1 = m1_axis[0]
empty = []
for m2 in m2_axis:
for m1 in m1_axis:
acpp= bsg.newa_cp(mb,mw,m1,m2,\
[exe.Y2(i,j,k,l)],[- exe.X2(i,j,k,l) * np.conjugate(exe.Y2(i,j,k,l) )],\
[exe.Y3(i,j,k,l)],[- exe.X3(i,j,k,l) * np.conjugate(exe.Y3(i,j,k,l) )])
empty.append(acpp)
resultcps = plt.contourf(m1_axis, m2_axis, \
np.resize(np.array(empty),len(np.array(empty))).\
reshape(len(m2_axis),len(m1_axis)), \
# colors = ['black','royalblue','purple','darkgreen','brown','red','gray','orange'],\
# levels = np.array([-0.08,0.2])
)
plt.colorbar(resultcps)
plt.xlabel('$M_{H^{\pm}_{1}}$')
plt.ylabel('$M_{H^{\pm}_{2}}$')
plt.title('$A_{CP} (b \\to s \gamma)$ for 3HDM')
# plt.grid(axis='y', linestyle='-', color='0.75') # show y-axis grid line
# plt.grid(axis='x', linestyle='-', color='0.75') # show x-axis grid line
# plt.axis([50,200, 50.0, 1000.0])
plt.axis([0, 500, 0.0, 1000.0])
plt.show()
plt.close()
return
def mp1_mp2_cpsdiffer(i, j, k, l): # Delta_CP(B>Xs + gamma)
print(i, j, k, l)
m1_axis = np.array([i for i in np.arange(10, 550, 50)])
m2_axis = np.array([i for i in np.arange(10, 1050, 50)])
m2 = m2_axis[0]
m1 = m1_axis[0]
emptycpsd = []
for m2 in m2_axis:
for m1 in m1_axis:
acppd= bsg.newdifferacps(mb,mw,m1,m2,\
[exe.Y2(i,j,k,l)],[ - exe.X2(i,j,k,l) * np.conjugate(exe.Y2(i,j,k,l) )],\
[exe.Y3(i,j,k,l)],[ - exe.X3(i,j,k,l) * np.conjugate(exe.Y3(i,j,k,l) )])
# print(acppd)
emptycpsd.append(acppd)
resultcpsd = plt.contourf(m1_axis, m2_axis, \
np.resize(np.array(emptycpsd),len(np.array(emptycpsd))).\
reshape(len(m2_axis),len(m1_axis)), \
# colors = ['black','royalblue','purple','darkgreen','brown','red','gray','orange'],\
# levels = np.array([0.0,0.5,1,1.5,2])
)
plt.colorbar(resultcpsd)
plt.xlabel('$M_{H^{\pm}_{1}}$')
plt.ylabel('$M_{H^{\pm}_{2}}$')
plt.title('$\\Delta_{X_s\gamma}$ for 3HDM')
# plt.grid(axis='y', linestyle='-', color='0.75') # show y-axis grid line
# plt.grid(axis='x', linestyle='-', color='0.75') # show x-axis grid line
# plt.axis([50,200, 50.0, 1000.0])
plt.axis([0, 500, 0.0, 500.0])
plt.show()
plt.close()
return
def plot_under_deltascan(i, j, k, l):
m1_axis = np.array([i for i in np.arange(50, 550, 50)])
m2_axis = np.array([i for i in np.arange(50, 1050, 50)])
m2 = m2_axis[0]
m1 = m1_axis[0]
# print('i,j,k,l',i,j,k,l)
# xx, yy = np.meshgrid(m1_axis, m2_axis)
print('i,j,k,l', i, j, k, l)
empty = []
for m2 in m2_axis:
for m1 in m1_axis:
threehdm = bsg.BR_B_Xs_gamma(mb,mw,m1,m2,\
[exe.Y2(i,j,k,l)],[- exe.X2(i,j,k,l) * np.conjugate(exe.Y2(i,j,k,l) )],\
[exe.Y3(i,j,k,l)],[- exe.X3(i,j,k,l) * np.conjugate(exe.Y3(i,j,k,l) )])
empty.append(threehdm)
result = plt.contourf(m1_axis, m2_axis, \
np.resize(np.array(empty) / (1e-4),len(np.array(empty) / (1e-4))).\
reshape(len(m2_axis),len(m1_axis)), \
colors = ['black','royalblue','purple','darkgreen','brown','red','gray','orange'],\
levels = np.array([2.99,3.55])
)
plt.colorbar(result)
plt.xlabel('$M_{H^{\pm}_{1}}$')
plt.ylabel('$M_{H^{\pm}_{2}}$')
plt.title('BR($\\bar{B} \\to X_{s} \gamma$) $\\times 10^{4}$')
plt.grid(axis='y', linestyle='-', color='0.75') # show y-axis grid line
plt.grid(axis='x', linestyle='-', color='0.75') # show x-axis grid line
# plt.axis([50,200, 50.0, 1000.0])
plt.axis([0, 500, 0.0, 1000.0])
plt.show()
plt.close()