def fig_0f1(Rpc, ds_sim, k, mass_bin):
test_read_m16_ds_1(mass_bin=mass_bin)
plt.plot(Rpc[:], ds_sim[k, :], '--')
nn = ds_sim[k, :].size #求样本大小
x_mean = np.mean(ds_sim[k, :]) #求算术平均值
x_std = np.std(ds_sim[k, :]) #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, :], yerr=x_std, fmt='-', linewidth=0.5)
plt.xlabel(r'$R-Mpc/h$')
plt.ylabel(r'$\Delta\Sigma-hM_\odot/{pc^2}$')
return x_mean, x_se, conf_region
def fig_mass(pp):
m_bin = ['10.0_10.4', '10.4_10.7', '10.7_11.0', '11.0_15.0']
mbin = [10.2, 10.55, 10.9, 13.0]
#作图取点
m, N, z = input_mass_bin(v=True)
z_use = np.zeros((len(m_bin), 2), dtype=np.float)
##计算误差棒并存储的数组
err_bar_r = np.zeros((len(m_bin), 2), dtype=np.float)
err_bar_b = np.zeros((len(m_bin), 2), dtype=np.float)
for k in range(0, len(m_bin)):
if k == len(m_bin) - 1:
z_use[k, :] = z[:, -1]
else:
z_use[k, :] = z[:, k]
print(z_use)
#print(m_bin)
#该句导入拟合质量
Pr_mh = np.zeros((len(m_bin), 2), dtype=np.float)
b_m_r = np.zeros(len(m_bin), dtype=np.float)
b_m_b = np.zeros(len(m_bin), dtype=np.float)
#Pr_mh的第一行存red的质量，第二行存blue的质量,x_std也一样
for k in range(0, len(m_bin)):
rp, best_mr, delta_r, bestfmr = fit_datar_1(m_bin[k], z_use[k, 0], k)
rp, best_mb, delta_b, bestfmb = fit_datab_1(m_bin[k], z_use[k, 1], k)
Pr_mh[k, 0] = best_mr
Pr_mh[k, 1] = best_mb
b_m_r[k] = bestfmr
b_m_b[k] = bestfmb
##下面把x^2转化为相应的概率分布，并让mh的划分区间服从这个分布
mr = m[k]
mb = m[k]
rsa, dssa, ds_errsa = test_read_m16_ds_1(mass_bin=m_bin[k])
pr = np.ones(len(m[k]), dtype=np.float)
pb = np.ones(len(m[k]), dtype=np.float)
ds_errsar = ds_errsa[0, 0:len(rp)]
ds_errsab = ds_errsa[1, 0:len(rp)]
for q in range(0, len(mr)):
ss = 1
for t in range(0, len(rp)):
ss = ss * (1 / (np.sqrt(np.pi * 2) * ds_errsar[t])) * np.exp(
(-1 / 2) * delta_r[q])
pr[q] = ss
for q in range(0, len(mb)):
qq = 1
for t in range(0, len(rp)):
qq = qq * (1 / (np.sqrt(np.pi * 2) * ds_errsab[t])) * np.exp(
(-1 / 2) * delta_b[q])
pb[q] = qq
##第一次归一化，把概率密度函数归一到0~1之间
pr = (pr - np.min(pr)) / (np.max(pr) - np.min(pr))
pb = (pb - np.min(pb)) / (np.max(pb) - np.min(pb))
'''
plt.figure()
plt.plot(mr,pr,'r')
plt.title('Probability distribution')
plt.xlabel(r'$log(M_h/M_\odot)$')
plt.ylabel(r'$dP(M_h|M_\ast)/dM_h$')
plt.show()
plt.plot(mb,pb,'b')
plt.title('Probability distribution')
plt.xlabel(r'$log(M_h/M_\odot)$')
plt.ylabel(r'$dP(M_h|M_\ast)/dM_h$')
plt.show()
'''
#print('r=',delta_r)
#print('b=',delta_b)
##第二次归一化，目的是让所有概率求和为1
fr = np.zeros(len(mr), dtype=np.float)
fb = np.zeros(len(mb), dtype=np.float)
ss = 0
qq = 0
Fr = np.zeros(len(mr), dtype=np.float)
Fb = np.zeros(len(mb), dtype=np.float)
for q in range(0, len(mr)):
ss = ss + pr[q] * (mr[1] - mr[0])
fr[q] = ss
for q in range(0, len(mb)):
qq = qq + pb[q] * (mb[1] - mb[0])
fb[q] = qq
Ar = np.max(fr)
Ab = np.max(fb)
ss = 0
qq = 0
for q in range(0, len(mr)):
ss = ss + pr[q] * (mr[1] - mr[0]) / Ar
Fr[q] = ss
for q in range(0, len(mb)):
qq = qq + pb[q] * (mb[1] - mb[0]) / Ab
Fb[q] = qq
#plt.figure()
#plt.plot(mr,Fr,'r')
#plt.show()
#plt.plot(mb,Fb,'b')
#plt.show()
vr = np.interp(0.16, Fr, mr)
ur = np.interp(0.84, Fr, mr)
err_bar_r[k, :] = [vr - bestfmr, ur - bestfmr]
vb = np.interp(0.16, Fb, mb)
ub = np.interp(0.84, Fb, mb)
err_bar_b[k, :] = [vb - bestfmb, ub - bestfmb]
##对应for循环前面的plt.figure
#print('phy_mr=',Pr_mh[:,0])
#print('phy_mb=',Pr_mh[:,1])
#print('errbar_r=',err_bar_r)
#print('errbar_b=',err_bar_b)
#####注意errorbar作图命令
plt.figure()
color_list = ['r', 'b', 'g']
line1, caps1, bars1 = plt.errorbar(mbin,
Pr_mh[:, 0],
yerr=[err_bar_r[:, 0], err_bar_r[:, 1]],
fmt="ks-",
linewidth=1,
elinewidth=0.5,
ecolor='k',
capsize=1,
capthick=0.5,
label='red')
line3, caps3, bars3 = plt.errorbar(mbin,
Pr_mh[:, 1],
yerr=[err_bar_b[:, 0], err_bar_b[:, 1]],
fmt="k^-",
linewidth=1,
elinewidth=0.5,
ecolor='k',
capsize=1,
capthick=0.5,
label='blue')
plt.fill_between(mbin,
Pr_mh[:, 0] + err_bar_r[:, 0],
Pr_mh[:, 0] + err_bar_r[:, 1],
facecolor=color_list[0],
alpha=.20)
plt.fill_between(mbin,
Pr_mh[:, 1] + err_bar_b[:, 0],
Pr_mh[:, 1] + err_bar_b[:, 1],
facecolor=color_list[1],
alpha=.20)
plt.xlabel(r'$log[M_\ast/M_\odot]$')
plt.ylabel(r'$log{\langle M_{200} \rangle/[M_\odot h^{-1}]}$')
plt.legend(loc=2)
plt.title(r'$Standard Deviation-1\sigma_{p}$')
plt.axis([10, 14, 11, 14])
#plt.savefig('Standard_deviation.png',dpi=600)
plt.show()
return Pr_mh
def fit_datab_1(mass_bin, z_int, T):
h = 0.673
#先找出观测值对应的rp
#rsa,dssa,ds_errsa = test_read_m16_ds_1()
rsa, dssa, ds_errsa = test_read_m16_ds_1(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
#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)
m, N, z = input_mass_bin(v=True)
mb = m[T]
#print('mr=',mb)
lm_bin = len(mb)
#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((lm_bin, b), dtype=np.float)
#rb = np.zeros(lm_bin,dtype=np.float)
#该句对应下面作图部分
for t in range(0, lm_bin):
m_ = 1
x = mb[t]
z_b = z_int
#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
##下面部分作图对比
#rb[t] = rs
#fig_0f1(rp,ds_simb,t,mass_bin)
#plt.title('Blue')
#plt.legend(m[:],bbox_to_anchor=(1,1))
#plt.legend(m[:])
#plt.savefig('Fit-blue2.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(lm_bin, dtype=np.float)
for t in range(0, lm_bin):
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]
best_mb = bestfmb + np.log10(h)
#作图对比最佳情况
'''
plt.figure()
k = xb
fig_0f1(rp,ds_simb,k,mass_bin)
plt.title('Blue')
#plt.savefig('fit_r.png',dpi=600)
plt.show()
'''
#print('x^2=',min(deltab))
#print('mb=',best_mb)
#print('positionr=',xb)
return rp, best_mb, delta_b, bestfmb
def fig_mass(pp):
m_bin = ['10.0_10.4', '10.4_10.7', '10.7_11.0', '11.0_15.0']
mbin = [10.2, 10.55, 10.9, 13.0]
#质量区间
x_m = np.logspace(1, 1.18, 4)
##画图质量区间
m, N = input_mass_bin(v=True)
#print(m)
#该句导入拟合质量
Pr_mh = np.zeros((len(m_bin), 2), dtype=np.float)
#保存以Msolar/h为单位的最佳质量
b_m_r = np.zeros(len(m_bin), dtype=np.float)
b_m_b = np.zeros(len(m_bin), dtype=np.float)
#分别保存以Msolar为单位的最佳拟合质量
x_std = np.zeros((len(m_bin), 2), dtype=np.float)
#Pr_mh的第一行存red的质量，第二行存blue的质量,x_std也一样
##计算误差棒并存储的数组
err_bar_r = np.zeros((len(m_bin), 2), dtype=np.float)
err_bar_b = np.zeros((len(m_bin), 2), dtype=np.float)
##为了X^2检测，需要记录并提取每一个m*区间的概率分布（Ps:不是概率密度分布）
Dp_r = np.array([np.zeros(len(m[1]),dtype=np.float),np.zeros(len(m[2]),dtype=np.float),\
np.zeros(len(m[2]),dtype=np.float),np.zeros(len(m[3]),dtype=np.float)])
Dp_b = np.array([np.zeros(len(m[1]),dtype=np.float),np.zeros(len(m[2]),dtype=np.float),\
np.zeros(len(m[2]),dtype=np.float),np.zeros(len(m[3]),dtype=np.float)])
##记录最小的x^2的值，用于x^2检测
c_xs_r = np.zeros(len(m_bin), dtype=np.float)
c_xs_b = np.zeros(len(m_bin), dtype=np.float)
##尝试直接从chi-square出发，求出一倍sigma的误差棒函数
D_chi_r = np.array([np.zeros(len(m[1]),dtype=np.float),np.zeros(len(m[2]),dtype=np.float),\
np.zeros(len(m[2]),dtype=np.float),np.zeros(len(m[3]),dtype=np.float)])
D_chi_b = np.array([np.zeros(len(m[1]),dtype=np.float),np.zeros(len(m[2]),dtype=np.float),\
np.zeros(len(m[2]),dtype=np.float),np.zeros(len(m[3]),dtype=np.float)])
for k in range(0, len(m_bin)):
rp, best_mr, delta_r, bestfmr = fit_datar_1(m_bin[k], k)
rp, best_mb, delta_b, bestfmb = fit_datab_1(m_bin[k], k)
Pr_mh[k, 0] = best_mr
Pr_mh[k, 1] = best_mb
b_m_r[k] = bestfmr
b_m_b[k] = bestfmb
c_xs_r[k] = np.min(delta_r)
c_xs_b[k] = np.min(delta_b)
mr = m[k]
mb = m[k]
##不考虑插值时直接带入mr,mb计算，求出误差棒
##考虑插值时，先对最佳拟合质量附近考虑插值,后续代码相应符号(mr_,mb_,or chir,chib)表示以插值部分量做计算
mr_ = np.linspace(b_m_r[k] - 0.2, b_m_r[k] + 0.2, len(m[k]))
mb_ = np.linspace(b_m_b[k] - 0.2, b_m_b[k] + 0.2, len(m[k]))
chir = np.interp(mr_, mr, delta_r)
chib = np.interp(mb_, mb, delta_b)
##chir,chib表示即将带入计算概率分布的X^2.
##下面几句直接从x^2求解1倍sigma对应的mh,为此需要先做如下处理，1-sigma的取值见后面
dchir = delta_r - np.min(delta_r)
dchib = delta_b - np.min(delta_b)
D_chi_r[k] = dchir
D_chi_b[k] = dchib
##下面把x^2转化为相应的概率分布，并让mh的划分区间服从这个分布
rsa, dssa, ds_errsa = test_read_m16_ds_1(mass_bin=m_bin[k])
pr = np.ones(len(m[k]), dtype=np.float)
pb = np.ones(len(m[k]), dtype=np.float)
pr_ = np.ones(len(m[k]), dtype=np.float)
pb_ = np.ones(len(m[k]), dtype=np.float)
ds_errsar = ds_errsa[0, 0:len(rp)]
ds_errsab = ds_errsa[1, 0:len(rp)]
##假设x^2的数据分布服从高斯分布
for q in range(0, len(mr)):
##未考虑插值的处理
ss = 1
for t in range(0, len(rp)):
ss = ss * (1 / (np.sqrt(np.pi * 2) * ds_errsar[t])) * np.exp(
(-1 / 2) * delta_r[q])
pr[q] = ss
for q in range(0, len(mr_)):
##考虑插值的计算处理
ss = 1
for t in range(0, len(rp)):
ss = ss * (1 / (np.sqrt(np.pi * 2) * ds_errsar[t])) * np.exp(
(-1 / 2) * chir[q])
pr_[q] = ss
for q in range(0, len(mb)):
##未考虑插值的处理
qq = 1
for t in range(0, len(rp)):
qq = qq * (1 / (np.sqrt(np.pi * 2) * ds_errsab[t])) * np.exp(
(-1 / 2) * delta_b[q])
pb[q] = qq
for q in range(0, len(mb_)):
##考虑插值的计算处理
qq = 1
for t in range(0, len(rp)):
qq = qq * (1 / (np.sqrt(np.pi * 2) * ds_errsab[t])) * np.exp(
(-1 / 2) * chib[q])
pb_[q] = qq
##第一次归一化，把概率密度函数归一到0~1之间
pr = (pr - np.min(pr)) / (np.max(pr) - np.min(pr))
pb = (pb - np.min(pb)) / (np.max(pb) - np.min(pb))
pr_ = (pr_ - np.min(pr_)) / (np.max(pr_) - np.min(pr_))
pb_ = (pb_ - np.min(pb_)) / (np.max(pb_) - np.min(pb_))
'''
plt.figure()
plt.plot(mr,pr,'r')
plt.title('Probability distribution')
plt.xlabel(r'$log(M_h/M_\odot)$')
plt.ylabel(r'$dP(M_h|M_\ast)/dM_h$')
plt.show()
plt.plot(mb,pb,'b')
plt.title('Probability distribution')
plt.xlabel(r'$log(M_h/M_\odot)$')
plt.ylabel(r'$dP(M_h|M_\ast)/dM_h$')
plt.show()
plt.figure()
plt.plot(mr_,pr_,'r')
plt.title('Probability distribution')
plt.xlabel(r'$log(M_h/M_\odot)$')
plt.ylabel(r'$dP(M_h|M_\ast)/dM_h$')
plt.show()
plt.plot(mb_,pb_,'b')
plt.title('Probability distribution')
plt.xlabel(r'$log(M_h/M_\odot)$')
plt.ylabel(r'$dP(M_h|M_\ast)/dM_h$')
plt.show()
'''
#print('r=',delta_r)
#print('b=',delta_b)
##第二次归一化，目的是让所有概率求和为1
##未考虑插值的处理
fr = np.zeros(len(mr), dtype=np.float)
fb = np.zeros(len(mb), dtype=np.float)
##考虑插值的处理
fr_ = np.zeros(len(mr_), dtype=np.float)
fb_ = np.zeros(len(mb_), dtype=np.float)
ss = 0
qq = 0
##未考虑插值的处理
Fr = np.zeros(len(mr), dtype=np.float)
Fb = np.zeros(len(mb), dtype=np.float)
##考虑插值的处理
Fr_ = np.zeros(len(mr_), dtype=np.float)
Fb_ = np.zeros(len(mb_), dtype=np.float)
##在不考虑插值时调用该部分
for q in range(0, len(mr)):
ss = ss + pr[q] * (mr[1] - mr[0])
fr[q] = ss
##考虑插值的处理
for q in range(0, len(mr_)):
ss = ss + pr[q] * (mr_[1] - mr_[0])
fr_[q] = ss
##在不考虑插值时调用该部分
for q in range(0, len(mb)):
qq = qq + pb[q] * (mb[1] - mb[0])
fb[q] = qq
##考虑插值的处理
for q in range(0, len(mb_)):
qq = qq + pb[q] * (mb_[1] - mb_[0])
fb_[q] = qq
Ar = np.max(fr)
Ab = np.max(fb)
Ar_ = np.max(fr)
Ab_ = np.max(fb)
ss = 0
qq = 0
##在不考虑插值时调用该部分
for q in range(0, len(mr)):
ss = ss + pr[q] * (mr[1] - mr[0]) / Ar
Fr[q] = ss
##考虑插值时调用该部分
for q in range(0, len(mr_)):
ss = ss + pr[q] * (mr_[1] - mr_[0]) / Ar
Fr_[q] = ss
##在不考虑插值时调用该部分
for q in range(0, len(mb)):
qq = qq + pb[q] * (mb[1] - mb[0]) / Ab
Fb[q] = qq
##考虑插值时调用该部分
for q in range(0, len(mb_)):
qq = qq + pb[q] * (mb_[1] - mb_[0]) / Ab
Fb_[q] = qq
#plt.figure()
#plt.plot(mr,Fr,'r')
#plt.show()
#plt.plot(mb,Fb,'b')
#plt.show()
#plt.figure()
#plt.plot(mr_,Fr_,'r')
#plt.show()
#plt.plot(mb_,Fb_,'b')
#plt.show()
vr = np.interp(0.16, Fr, mr)
ur = np.interp(0.84, Fr, mr)
err_bar_r[k, :] = [vr - bestfmr, ur - bestfmr]
vb = np.interp(0.16, Fb, mb)
ub = np.interp(0.84, Fb, mb)
err_bar_b[k, :] = [vb - bestfmb, ub - bestfmb]
##记录每一次概率分布
Dp_r[k] = pr
Dp_b[k] = pb
#print('phy_mr=',Pr_mh[:,0])
#print('phy_mb=',Pr_mh[:,1])
print('err_mr=', err_bar_r)
print('err_mb=', err_bar_b)
#scatter函数参数设置
color_list = ['r', 'b', 'g']
bar_list = ['s', '^', 'v']
##直接从概率分布得到误差棒
plt.figure()
line1, caps1, bars1 = plt.errorbar(mbin,
Pr_mh[:, 0],
yerr=[err_bar_r[:, 0], err_bar_r[:, 1]],
fmt="rs-",
linewidth=1,
elinewidth=0.5,
ecolor='k',
capsize=1,
capthick=0.5,
label='red')
line3, caps3, bars3 = plt.errorbar(mbin,
Pr_mh[:, 1],
yerr=[err_bar_b[:, 0], err_bar_b[:, 1]],
fmt="b^-",
linewidth=1,
elinewidth=0.5,
ecolor='k',
capsize=1,
capthick=0.5,
label='blue')
plt.fill_between(mbin,
Pr_mh[:, 0] + err_bar_r[:, 0],
Pr_mh[:, 0] + err_bar_r[:, 1],
facecolor=color_list[0],
alpha=.20)
plt.fill_between(mbin,
Pr_mh[:, 1] + err_bar_b[:, 0],
Pr_mh[:, 1] + err_bar_b[:, 1],
facecolor=color_list[1],
alpha=.20)
plt.xlabel(r'$log[M_\ast/M_\odot]$')
plt.ylabel(r'$log{\langle M_{200} \rangle/[M_\odot h^{-1}]}$')
plt.legend(loc=2)
plt.title(r'$Standard Deviation-1\sigma_{p}$')
plt.axis([10, 13.5, 11, 13.5])
#plt.savefig('Standard_deviation-1.png',dpi=600)
plt.show()
##比较X^2差值的变化
###(该部分同时说明怎么样循环生成排列子图)
plt.figure()
for k in range(0, 2 * len(m_bin)):
plt.subplot(241 + k)
if k <= 3:
plt.plot(m[k], D_chi_r[k], 'r')
plt.title(r'$\Delta\chi^2_{red}$')
plt.axis([b_m_r[k] + 1, b_m_r[k] - 1, 0, 1])
else:
plt.plot(m[k - 4], D_chi_b[k - 4], 'b')
plt.title(r'$\Delta\chi^2_{blue}$')
plt.axis([b_m_b[k - 4] + 1, b_m_b[k - 4] - 1, 0, 1])
plt.grid()
plt.tight_layout()
#plt.subplots_adjust(wspace=0,hspace=0)
#plt.savefig('delta_x^2',dpi=600)
plt.show()
##观察概率分布函数，在大质量端取样性应该越来越好
plt.figure()
for k in range(0, 2 * len(m_bin)):
plt.subplot(241 + k)
if k <= 3:
plt.plot(m[k - 4], Dp_r[k - 4], 'r')
plt.title(r'$probability_{red}$')
else:
plt.plot(m[k - 4], Dp_b[k - 4], 'b')
plt.title(r'$Probability_{blue}$')
plt.grid()
plt.tight_layout()
#plt.savefig('probability',dpi=600)
plt.show()
##做出由x^2直接得到的1-sigma的误差棒图像
######注意插值函数的要求：在差值区间要求目标函数为单调递增函数
Dchir = np.zeros((len(m_bin), 2), dtype=np.float)
Dchib = np.zeros((len(m_bin), 2), dtype=np.float)
for k in range(0, len(m_bin)):
mr_ = m[k]
mb_ = m[k]
##把相应的x^2的差值分两个区间
Dchir1_ = D_chi_r[k]
ix = mr_ < b_m_r[k]
Dchir_1 = Dchir1_[ix]
mr1 = mr_[ix]
Dchir_2 = Dchir1_[~ix]
mr2 = mr_[~ix]
##下面求1-sigma的点
m_r1 = np.interp(-1.0, -Dchir_1, mr1)
m_r2 = np.interp(1.0, Dchir_2, mr2)
##把相应的x^2的差值分两个区间
Dchib1_ = D_chi_b[k]
ix = mb_ < b_m_b[k]
Dchib_1 = Dchib1_[ix]
mb1 = mb_[ix]
Dchib_2 = Dchib1_[~ix]
mb2 = mb_[~ix]
##下面求1-sigma的点
m_b1 = np.interp(-1.0, -Dchib_1, mb1)
m_b2 = np.interp(1.0, Dchib_2, mb2)
##插值后比较
Dchir[k, :] = np.array([m_r1 - b_m_r[k], m_r2 - b_m_r[k]])
Dchib[k, :] = np.array([m_b1 - b_m_b[k], m_b2 - b_m_b[k]])
print(Dchir)
print(Dchib)
plt.figure()
line1, caps1, bars1 = plt.errorbar(mbin,
Pr_mh[:, 0],
yerr=[err_bar_r[:, 0], err_bar_r[:, 1]],
fmt="rs-",
linewidth=1,
elinewidth=0.5,
ecolor='k',
capsize=1,
capthick=0.5,
label='red')
line3, caps3, bars3 = plt.errorbar(mbin,
Pr_mh[:, 1],
yerr=[err_bar_b[:, 0], err_bar_b[:, 1]],
fmt="b^-",
linewidth=1,
elinewidth=0.5,
ecolor='k',
capsize=1,
capthick=0.5,
label='blue')
plt.fill_between(mbin,
Pr_mh[:, 0] + err_bar_r[:, 0],
Pr_mh[:, 0] + err_bar_r[:, 1],
facecolor=color_list[0],
alpha=.20)
plt.fill_between(mbin,
Pr_mh[:, 1] + err_bar_b[:, 0],
Pr_mh[:, 1] + err_bar_b[:, 1],
facecolor=color_list[1],
alpha=.20)
plt.xlabel(r'$log[M_\ast/M_\odot]$')
plt.ylabel(r'$log{\langle M_{200} \rangle/[M_\odot h^{-1}]}$')
plt.legend(loc=4)
plt.title(r'$Standard Deviation-1\sigma_{\chi^2}$')
plt.axis([10, 13.5, 11, 14])
#plt.savefig('Standard_deviation.png',dpi=600)
plt.show()
##下面部分做x^2检测
print('min_x^2_r=', c_xs_r)
print('min_x^2_b=', c_xs_b)
return Pr_mh
def fit_datar_1(mass_bin, T):
h = 0.7
#先找出观测值对应的rp
#rsa,dssa,ds_errsa = test_read_m16_ds_1()
rsa, dssa, ds_errsa = test_read_m16_ds_1(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_r = 0.1
#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 <= 5])
#rp = [rsa[0,k] for k in range(0,len(rsa[0,:])) if rsa[0,k]*h<=2]
#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
b = len(rp)
m, N = input_mass_bin(v=True)
mr = m[T]
#print('mr=',mr)
lm_bin = len(mr)
#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((lm_bin, b), dtype=np.float)
#rr = np.zeros(lm_bin,dtype=np.float)
for t in range(0, lm_bin):
m_ = 1
x = mr[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_r)
#对预测信号做相应的变化，把共动的转为物理的
ds_simr[t, :] = deltaSigma
#下面部分作图对比
#rr[t] = rs
#fig_0f1(rp,ds_simr,t,mass_bin)
#plt.title('Red')
#plt.legend(m[:],bbox_to_anchor=(1,1))
#plt.legend(m[:])
#plt.savefig('Fit-red2.png',dpi=600)
#plt.savefig('compare_1_rd.png',dpi=600)
#plt.show()
yy = np.shape(ds_simr)
#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_r = np.zeros(lm_bin, dtype=np.float)
for t in range(0, lm_bin):
d_r = 0
for n in range(0, yy[1]):
d_r = d_r + ((ds_simr[t, n] - dssa[0, n]) / ds_errsa[0, n])**2
delta_r[t] = d_r
#print(delta_r)
#下面这段求每个质量级对应的方差最小值
deltar = delta_r.tolist()
xr = deltar.index(min(deltar))
bestfmr = mr[xr]
best_mr = bestfmr + np.log10(h)
#作图对比最佳情况
'''
plt.figure()
k = xr
fig_0f1(rp,ds_simr,k,mass_bin)
plt.title('Red')
#plt.savefig('fit_r.png',dpi=600)
plt.show()
'''
#print('x^2=',min(deltar))
#print('mr=',best_mr)
#print('positionr=',xr)
return rp, best_mr, delta_r, bestfmr
