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()