def fig_fff2(Rpc, ds_sim, k, t):
test_read_m16_ds()
plt.plot(Rpc[:], ds_sim[k, t, :], '-*')
nn = ds_sim[k, t, :].size #求样本大小
x_mean = np.mean(ds_sim[k, t, :]) #求算术平均值
x_std = np.std(ds_sim[k, t, :]) #求标准偏差
x_se = x_std / np.sqrt(nn) #求标准误差
dof = nn - 1 #自由度计算
alpha = 1.0 - 0.95
conf_region = st.ppf(1-alpha/2.,dof)*x_std*\
np.sqrt(1.+1./nn)#设置置信区间
plt.errorbar(Rpc[:], ds_sim[k, t, :], yerr=x_std, fmt='-', linewidth=0.5)
plt.xlabel(r'$R-Mpc/h$')
plt.ylabel(r'$\Delta\Sigma-hM_\odot/{pc^2}$')
return ()
def c_fit_datar(x0r):
# (mr,d_exr) = x0r
rp,mr,mb,d_exr,d_exb,b,a_r,a_b = va_da(p=True)
d_exr = x0r
#lmr = len(mr)#质量数组的长度
ldr = len(d_exr)#指数数组的长度
#dssimr = np.zeros((lmr,ldr,b),dtype=np.float)
dssimr = np.zeros((ldr,b),dtype=np.float)
#for k in range(0,lmr):
for t in range(0,ldr):
#m_ = mr[k]
m = mr
xr = d_exr[t]
#cRp = rp
#dssimr[t,:] = c_deltaSigmar
#c_Rp,c_Sigmar,c_deltaSigmar = calcu_sigma(cRp,m,xr)
#修正
cRp = rp
z_r = 0.230
#z_r = 0.105
#计算模型在对应的投射距离上的预测信号取值
#c_表示该变量做继续比较量
#修正
c_Rp,c_Sigmar,c_deltaSigmar = calcu_sigmaz(cRp,m,xr,z_r)
dssimr[t,:] = c_deltaSigmar
size_ds1 = np.shape(dssimr)
print(size_ds1)
delta_r = np.zeros(ldr,dtype=np.float)
rsa,dssa,ds_errsa = test_read_m16_ds()
# for k in range(0,lmr):
for t in range(0,ldr):
d_r = 0
for n in range(0,b):
d_r = d_r+((dssimr[t,n]-dssa[0,n])/ds_errsa[0,n])**2
delta_r[t] = d_r
# print(delta_r)
#下面用bestr和bestb记录最后比较结果
'''
#下面求取最小值及索引号
aa = delta_r.tolist()
xa = aa.index(min(delta_r))
deltar = min(delta_r)
delta_r = deltar
bestfr = d_exr[xa]
dexr = bestfr
best_mr = mr*10**dexr
print(delta_r)
print('mr=',best_mr)
print('dexr=',bestfr)
'''
return delta_r
def c_fit_datab(x0b):
# (mb,d_exb) = x0b
rp,mr,mb,d_exr,d_exb,b,a_r,a_b = va_da(p=True)
d_exb = x0b
# lmb = len(mb)#质量数组的长度
ldb = len(d_exb)
dssimb = np.zeros((ldb,b),dtype=np.float)
# for k in range(0,lmb):
for t in range(0,ldb):
#m_ = mb[k]
m = mb
xb = d_exb[t]
#cRp = rp
#计算模型在对应的投射距离上的预测信号取值
#c_Rp,c_Sigmab,c_deltaSigmab = calcu_sigma(cRp,m,xb)
#dssimb[t,:] = c_deltaSigmab
#修正
cRp = rp
z_b = 0.246
#z_b = 0.124
#c_表示该变量做继续比较量
#修正
c_Rp,c_Sigmab,c_deltaSigmab = calcu_sigmaz(cRp,m,xb,z_b)
dssimb[t,:] = c_deltaSigmab
size_ds2 = np.shape(dssimb)
print(size_ds2)
delta_b = np.zeros(ldb,dtype=np.float)
rsa,dssa,ds_errsa = test_read_m16_ds()
# for k in range(0,lmb):
for t in range(0,ldb):
d_b = 0
for n in range(0,b):
d_b = d_b+((dssimb[t,n]-dssa[1,n])/ds_errsa[1,n])**2
delta_b[t] = d_b
# print(delta_b)
#下面用bestr和bestb记录最后比较结果
#下面求取最小值及索引号
'''
bb = delta_b.tolist()
xb = bb.index(min(delta_b))
deltab = min(delta_b)
delta_b = deltab
bestfb = d_exb[xb]
dexb = bestfb
best_mb = mr*10**dexb
print(delta_b)
print('mb=',best_mb)
print('dexb=',bestfb)
'''
return delta_b
def fig_f(inm, Rp, r, rs, r_200, rho_R, sigma, deltasigma):
#def fig_f(ff):
#inm,Rs,r,rs,r_200,rho_R,sigma,deltasigma=semula_tion(m_,x)
lm = len(inm)
plt.figure()
plt.subplot(121)
# delta1 = np.zeros(lm,dtype=np.float)
# delta2 = np.zeros(lm,dtype=np.float)
for k in range(0, lm):
plt.loglog(r[k, :], rho_R[k])
'''
delta1[k] = r_200[k]
delta2[k] = rs[k]
plt.axvline(delta1[k],ls='--',linewidth=0.5,color='red')
plt.axvline(delta2[k],ls='--',linewidth=0.5,color='blue')
'''
plt.xlabel(r'$r-Mpc/h$')
plt.ylabel(r'$\rho(R)-M_\odot/h$')
plt.grid()
print('mh_inte=', inm)
plt.subplot(122)
#下面引入具体的deltasigma数据的观测值,和所给质量比较
test_read_m16_ds()
delta1 = np.zeros(lm, dtype=np.float)
delta2 = np.zeros(lm, dtype=np.float)
for k in range(0, lm):
plt.loglog(Rp[k, :], deltasigma[k, :])
delta1[k] = r_200[k]
delta2[k] = rs[k]
plt.axvline(delta1[k], ls='--', linewidth=0.5, color='red')
plt.axvline(delta2[k], ls='--', linewidth=0.5, color='blue')
plt.xlabel(r'$R-Mpc/h$')
plt.ylabel(r'$\Delta\Sigma-hM_\odot/{pc^2}$')
plt.tight_layout()
plt.show()
out = [inm, rho_R, deltasigma, sigma]
return (out)
def c_fit_datar(u):
#(mr,mb,d_exr,d_exb) = arr
# (mr,d_exr) = x0r
rp, mr, mb, d_exr, d_exb, b, a_r, a_b = va_da(p=True)
lmr = len(mr) #质量数组的长度
ldr = len(d_exr) #指数数组的长度
dssimr = np.zeros((lmr, ldr, b), dtype=np.float)
for k in range(0, lmr):
for t in range(0, ldr):
m_ = mr[k]
xr = d_exr[t]
#c_表示该变量做继续比较量
#cRp = rp
#c_Rp,c_Sigmar,c_deltaSigmar = calcu_sigma(cRp,m_,xr)
#dssimr[t,:] = c_deltaSigmar
#修正
cRp = rp
z_r = 0.105
#计算模型在对应的投射距离上的预测信号取值
c_Rp, c_Sigmar, c_deltaSigmar = calcu_sigmaz(cRp, m_, xr, z_r)
dssimr[k, t, :] = c_deltaSigmar
#修正
size_ds1 = np.shape(dssimr)
print(size_ds1)
fitr = np.zeros((lmr, 2), dtype=np.int)
delta_rr = np.zeros((lmr, ldr), dtype=np.float)
rsa, dssa, ds_errsa = test_read_m16_ds()
for k in range(0, lmr):
for t in range(0, ldr):
d_r = 0
for n in range(0, size_ds1[2]):
d_r = d_r + (
(dssimr[k, t, n] - dssa[0, n]) / ds_errsa[0, n])**2
delta_rr[k, t] = d_r
print('size_x^2_r=', np.shape(delta_rr))
#print(delta_rr)
#下面这段求每个质量级对应的方差最小值
for k in range(0, lmr):
aa = delta_rr[k, :].tolist()
#先把np的定义数组转为列表，再寻找索引号，tolist()表示内置函数调用
xa = aa.index(min(delta_rr[k, :]))
fitr[k, 0] = k
fitr[k, 1] = xa
# print(fitr)
#比较提取出的五个最小值的最小值为最后结果
deltar = np.zeros(lmr, dtype=np.float)
xrr = np.zeros((lmr, 2), dtype=np.float)
for k in range(0, lmr):
deltar[k] = delta_rr[fitr[k, 0], fitr[k, 1]]
xrr[k] = [mr[fitr[k, 0]], d_exr[fitr[k, 1]]]
#下面用bestr和bestb记录最后比较结果
bestfr = np.zeros(2, dtype=np.float)
#下面求取最小值及索引号
aa = deltar.tolist()
xa = aa.index(min(deltar))
delta_r = min(deltar)
bestfr = fitr[xa, :]
bestr = mr[bestfr[0]]
d_ex_r = d_exr[bestfr[1]]
dexr1 = d_ex_r
##对比文献修正指数如下
h = 0.673
dexr = d_ex_r + np.log10(bestr * h)
best_mr = bestr
#作图对比最佳情况
plt.figure()
# fig_ff(rp,dssimr,lmr,fitr)
l1 = bestfr[0]
l2 = bestfr[1]
fig_fff2(rp, dssimr, l1, l2)
plt.title('Red')
plt.show()
print(bestfr)
print('mr=', best_mr)
print('dexr=', dexr1)
print('co_dex=', dexr)
print('corr_mr=', 10**dexr)
print('x^2=', delta_r)
# return rp,best_mr,dexr,a,delta_r,a,b,yy
return delta_r, lmr, dssimr, fitr, delta_rr, xrr, dexr1, ldr
def c_fit_datab(v):
#(mr,mb,d_exr,d_exb) = arr
# (mb,d_exb) = x0b
rp, mr, mb, d_exr, d_exb, b, a_r, a_b = va_da(p=True)
lmb = len(mb) #质量数组的长度
ldb = len(d_exb)
dssimb = np.zeros((lmb, ldb, b), dtype=np.float)
for k in range(0, lmb):
for t in range(0, ldb):
m_ = mb[k]
xb = d_exb[t]
#c_表示该变量做继续比较量
#cRp = rp
#c_Rp,c_Sigmab,c_deltaSigmab = calcu_sigma(cRp,m_,xb)
#dssimb[t,:] = c_deltaSigmab
#修正
cRp = rp
z_b = 0.124
#计算模型在对应的投射距离上的预测信号取值
c_Rp, c_Sigmab, c_deltaSigmab = calcu_sigmaz(cRp, m_, xb, z_b)
dssimb[k, t, :] = c_deltaSigmab
size_ds2 = np.shape(dssimb)
print(size_ds2)
fitb = np.zeros((lmb, 2), dtype=np.int)
delta_bb = np.zeros((lmb, ldb), dtype=np.float)
rsa, dssa, ds_errsa = test_read_m16_ds()
for k in range(0, lmb):
for t in range(0, ldb):
d_b = 0
for n in range(0, size_ds2[2]):
d_b = d_b + (
(dssimb[k, t, n] - dssa[1, n]) / ds_errsa[1, n])**2
delta_bb[k, t] = d_b
print('size_x^2_b=', np.shape(delta_bb))
#print(delta_bb)
#下面这段求每个质量级对应的方差最小值
for k in range(0, lmb):
bb = delta_bb[k, :].tolist()
#先把np的定义数组转为列表，再寻找索引号，tolist()表示内置函数调用
xb = bb.index(min(delta_bb[k, :]))
fitb[k, 0] = k
fitb[k, 1] = xb
# print(fitr)
# print(fitb)
#比较提取出的五个最小值的最小值为最后结果
xbb = np.zeros((lmb, 2), dtype=np.float)
deltab = np.zeros(lmb, dtype=np.float)
for k in range(0, lmb):
deltab[k] = delta_bb[fitb[k, 0], fitb[k, 1]]
xbb[k] = [mb[fitb[k, 0]], d_exb[fitb[k, 1]]]
#下面用bestr和bestb记录最后比较结果
bestfb = np.zeros(2, dtype=np.float)
#下面求取最小值及索引号
bb = deltab.tolist()
xb = bb.index(min(deltab))
delta_b = min(deltab)
bestfb = fitb[xb, :]
bestb = mb[bestfb[0]]
d_ex_b = d_exb[bestfb[1]]
dexb1 = d_ex_b
##对比文献，修正指数如下
dexb = d_ex_b + np.log10(bestb)
best_mb = bestb
#作图对比最佳情况
plt.figure()
# fig_ff(rp,dssimr,lmr,fitr)
l1 = bestfb[0]
l2 = bestfb[1]
fig_fff2(rp, dssimb, l1, l2)
plt.title('blue')
plt.show()
print(bestfb)
print('co_dex=', dexb)
print('dex=', dexb1)
print('mb=', best_mb)
print('corr_mb=', 10**dexb)
print('x^2=', delta_b)
# return rp,best_mb,,dexb,a,b,yy,delta_b
return delta_b, lmb, dssimb, fitb, delta_bb, xbb, dexb1, ldb
def fit_datab(y):
h = 0.673
#先找出观测值对应的rp
rsa, dssa, ds_errsa = test_read_m16_ds()
# print('ds=',dssa)
# print('ds_err=',ds_errsa)
#print('rp=',rsa)
a = np.shape(dssa)
print('size a=', a)
##注意到观测数值选取的是物理坐标，需要靠里尺度银子修正，修正如下
z_b = 0.246
#z_b = 0.124
a_b = 1 / (1 + z_b)
##此时对于预测的R也要做修正
rp = np.array(
[rsa[0, k] for k in range(0, len(rsa[0, :])) if rsa[0, k] * h <= 2])
# rp = [rsa[0,k] for k in range(0,len(rsa[0,:])) if rsa[0,k]*h<=2]
# rp = rsa[0,:]
#该句直接对数组筛选，计算2Mpc以内（保证NFW模型适用的情况下）信号
print(rp)
b = len(rp)
m, m_dex, lmw, lml = dolad_data(m=True, hz=True)
#下面做两组数据的方差比较,注意不能对观测数据进行插值
#比较方法，找出观测的sigma数值对应的Rp,再根据模型计算此时的模型数值Sigma（该步以完成）
ds_simb = np.zeros((lmw, lml, b), dtype=np.float)
rb = np.zeros((lmw, lml), dtype=np.float)
for k in range(0, lmw):
for t in range(0, lml):
m_ = m[k, t]
x = m_dex[k]
#Rpc = rp
#Rpc,Sigma,deltaSigma = calcu_sigma(Rpc,m_,x,z_b)
#ds_simb[k,t,:] = deltaSigma
#对比文献，把物理距离转为共动距离
Rpc = rp
#计算模型在对应的投射距离上的预测信号取值
Rpc, Sigma, deltaSigma, rs = calcu_sigmaz(Rpc, m_, x, z_b)
#预测信号也做修正
ds_simb[k, t, :] = deltaSigma
rb[k, t] = rs
yy = np.shape(ds_simb)
#print('rb=',rb)#输出查看ds_sim的维度，即模型预测下的透镜信号
#比较观测的sigma和预测的Sigma,比较结果用fitr和fitb记录比较结果
fitb = np.zeros((lmw, 2), dtype=np.int)
delta_b = np.zeros((lmw, lml), dtype=np.float)
for k in range(0, lmw):
for t in range(0, lml):
d_b = 0
for n in range(0, yy[2]):
d_b = d_b + (
(ds_simb[k, t, n] - dssa[1, n]) / ds_errsa[1, n])**2
delta_b[k, t] = d_b
#print(delta_b)
#下面这段求每个质量级对应的方差最小值
for k in range(0, lmw):
#先把np的定义数组转为列表，再寻找索引号，tolist()表示内置函数调用
bb = delta_b[k, :].tolist()
xb = bb.index(min(delta_b[k, :]))
fitb[k, 0] = k
fitb[k, 1] = xb
print(fitb)
#下面做图比较几个最佳预测值与观测的对比情况
Rpc = rp
fig_ff(Rpc, ds_simb, lmw, fitb)
plt.title('B-galaxy')
plt.show()
#比较提取出的五个最小值的最小值为最后结果
deltab = np.zeros(lmw, dtype=np.float)
for k in range(0, lmw):
deltab[k] = delta_b[fitb[k, 0], fitb[k, 1]]
#下面用bestr和bestb记录最后比较结果
bestfb = np.zeros(2, dtype=np.float)
bb = deltab.tolist()
xb = bb.index(min(deltab))
bestfb = fitb[xb, :]
bestb = m[bestfb[0], bestfb[1]]
best_mb = bestb
dexb1 = m_dex[bestfb[0]]
#下面是与文献对比做的修正
dexb = m_dex[bestfb[0]] + np.log10(bestb * h)
#作图对比最佳情况
plt.figure()
k = bestfb[0]
t = bestfb[1]
fig_fff2(rp, ds_simb, k, t)
plt.title('Blue')
#plt.savefig('fit_b.png',dpi=600)
plt.show()
print('x^2=', min(deltab))
print('mb=', best_mb) #此句质量计算正常
print('positionb=', bestfb)
print('dexb=', dexb1)
print('co_dexb=', dexb)
print('corr_mb=', 10**dexb)
return rp, best_mb, dexb1, a, b, yy, dexb, a_b, delta_b
def fit_datar(y):
h = 0.673
#先找出观测值对应的rp
rsa, dssa, ds_errsa = test_read_m16_ds()
# print('ds=',dssa)
# print('ds_err=',ds_errsa)
#print('rp=',rsa)
a = np.shape(dssa)
print('size a=', a)
##注意到观测数值选取的是物理坐标，需要靠里尺度银子修正，修正如下
z_r = 0.230
#z_r = 0.105
a_r = 1 / (1 + z_r)
##此时对于预测的R也要做修正
rp = np.array(
[rsa[0, k] for k in range(0, len(rsa[0, :])) if rsa[0, k] * h <= 2])
#rp = np.array([rsa[0,k] for k in range(0,len(rsa[0,:])) if rsa[0,k]*h<=5])
#rp = rsa[0,:]
#该句直接对数组筛选，计算2Mpc以内（保证NFW模型适用的情况下）信号
print(rp)
b = len(rp)
m, m_dex, lmw, lml = dolad_data(m=True, hz=True)
#下面做两组数据的方差比较,注意不能对观测数据进行插值
#比较方法，找出观测的sigma数值对应的Rp,再根据模型计算此时的模型数值Sigma（该步以完成）
ds_simr = np.zeros((lmw, lml, b), dtype=np.float)
rr = np.zeros((lmw, lml), dtype=np.float)
for k in range(0, lmw):
for t in range(0, lml):
m_ = m[k, t]
x = m_dex[k]
#Rpc = rp
#Rpc,Sigma,deltaSigma = calcu_sigma(Rpc,m_,x)
#ds_simr[k,t,:] = deltaSigma
#对比文献，加入尺度因子修正如下,把物理的距离转为共动的距离
#计算模型在对应的投射距离上的预测信号取值
Rpc = rp
Rpc, Sigma, deltaSigma, rs = calcu_sigmaz(Rpc, m_, x, z_r)
#对预测信号做相应的变化，把共动的转为物理的
ds_simr[k, t, :] = deltaSigma
rr[k, t] = rs
yy = np.shape(ds_simr)
#print('rr=',rr)#输出查看ds_sim的维度，即模型预测下的透镜信号
#比较观测的sigma和预测的Sigma,比较结果用fitr和fitb记录比较结果
fitr = np.zeros((lmw, 2), dtype=np.int)
delta_r = np.zeros((lmw, lml), dtype=np.float)
for k in range(0, lmw):
for t in range(0, lml):
d_r = 0
for n in range(0, yy[2]):
d_r = d_r + (
(ds_simr[k, t, n] - dssa[0, n]) / ds_errsa[0, n])**2
delta_r[k, t] = d_r
#print(delta_r)
#下面这段求每个质量级对应的方差最小值
for k in range(0, lmw):
#先把np的定义数组转为列表，再寻找索引号，tolist()表示内置函数调用
aa = delta_r[k, :].tolist()
xa = aa.index(min(delta_r[k, :]))
fitr[k, 0] = k
fitr[k, 1] = xa
print(fitr)
#下面做图比较几个最佳预测值与观测的对比情况
Rpc = rp
fig_ff(Rpc, ds_simr, lmw, fitr)
plt.title('R-galaxy')
plt.show()
#比较提取出的五个最小值的最小值为最后结果
deltar = np.zeros(lmw, dtype=np.float)
for k in range(0, lmw):
deltar[k] = delta_r[fitr[k, 0], fitr[k, 1]]
#下面用bestr和bestb记录最后比较结果
bestfr = np.zeros(2, dtype=np.float)
aa = deltar.tolist()
xa = aa.index(min(deltar))
bestfr = fitr[xa, :]
bestr = m[bestfr[0], bestfr[1]]
best_mr = bestr
dexr1 = m_dex[bestfr[0]]
#下面两句是为与文献对比做的修正
dexr = m_dex[bestfr[0]] + np.log10(bestr * h)
#作图对比最佳情况
plt.figure()
k = bestfr[0]
t = bestfr[1]
fig_fff2(rp, ds_simr, k, t)
plt.title('Red')
#plt.savefig('fit_r.png',dpi=600)
plt.show()
print('x^2=', min(deltar))
print('mr=', best_mr) #此句质量计算正常
print('positionr=', bestfr)
print('dexr=', dexr1)
print('co_dexr=', dexr)
print('corr_mr=', 10**dexr)
return rp, best_mr, dexr1, a, b, yy, dexr, a_r, delta_r
def fit_datab(y):
#def fit_datab(mass_bin):
h = 0.7
#先找出观测值对应的rp
rsa,dssa,ds_errsa = test_read_m16_ds()
#rsa,dssa,ds_errsa = test_read_m16_ds(mass_bin=mass_bin)
# print('ds=',dssa)
# print('ds_err=',ds_errsa)
#print('rp=',rsa)
a = np.shape(dssa)
print('size a=',a)
##注意到观测数值选取的是物理坐标，需要靠里尺度银子修正，修正如下
z_b = 0.1
#z_b = 0.124
a_b = 1/(1+z_b)
##此时对于预测的R也要做修正
rp = np.array([rsa[0,k] for k in range(0,len(rsa[0,:])) if rsa[0,k]*h<=5])
# rp = [rsa[0,k] for k in range(0,len(rsa[0,:])) if rsa[0,k]*h<=2]
# rp = rsa[0,:]
#该句直接对数组筛选，计算2Mpc以内（保证NFW模型适用的情况下）信号
print(rp)
b = len(rp)
mr,mb,N = input_mass_bin(v=True)
#m,N = input_mass_bin(v=True)
#下面做两组数据的方差比较,注意不能对观测数据进行插值
#比较方法，找出观测的sigma数值对应的Rp,再根据模型计算此时的模型数值Sigma（该步以完成）
ds_simb = np.zeros((N,b),dtype=np.float)
rb = np.zeros(N,dtype=np.float)
for t in range(0,N):
m_ = 1
x = mb[t]
#x = m[t]
#Rpc = rp
#Rpc,Sigma,deltaSigma = calcu_sigma(Rpc,m_,x)
#ds_simr[k,t,:] = deltaSigma
#对比文献，加入尺度因子修正如下,把物理的距离转为共动的距离
#计算模型在对应的投射距离上的预测信号取值
Rpc = rp
Rpc,Sigma,deltaSigma,rs = calcu_sigmaz(Rpc,m_,x,z_b)
#对预测信号做相应的变化，把共动的转为物理的
ds_simb[t,:] = deltaSigma
rb[t] = rs
fig_fff1(rp,ds_simb,t)
plt.title('Blue')
#plt.legend(m[:],bbox_to_anchor=(1,1))
plt.legend(mb[:])
#plt.savefig('Fit-blue.png',dpi=600)
#plt.savefig('compare_2_bl.png',dpi=600)
plt.show()
yy = np.shape(ds_simb)
#print('rr=',rr)#输出查看ds_sim的维度，即模型预测下的透镜信号
#比较观测的sigma和预测的Sigma,比较结果用fitr和fitb记录比较结果
delta_b = np.zeros(N,dtype=np.float)
for t in range(0,N):
d_b = 0
for n in range(0,yy[1]):
d_b = d_b+((ds_simb[t,n]-dssa[1,n])/ds_errsa[1,n])**2
delta_b[t] = d_b
#print(delta_r)
#下面这段求每个质量级对应的方差最小值
deltab = delta_b.tolist()
xb = deltab.index(min(deltab))
bestfmb = mb[xb]
#bestfmb = m[xb]
best_mb = bestfmb+np.log10(h)
#作图对比最佳情况
'''
plt.figure()
k = xb
fig_fff1(rp,ds_simb,k)
plt.title('Blue')
#plt.savefig('fit_r.png',dpi=600)
plt.show()
'''
print('x^2=',min(deltab))
print('co_mb=',best_mb)
print('mb=',bestfmb)
print('positionr=',xb)
return rp,best_mb,delta_b,bestfmb
