def _plot(p1_infiles,p2_infiles2,bottom_label,left_label,tau_b=1000):
fig = plt.figure(figsize=(12,9))
ax_host = SubplotHost(fig, 1,1,1)
fig.add_subplot(ax_host)
for p1_file, p2_file in zip(p1_infiles,p2_infiles):
p1, p2 = get_ave_ste(p1_file, p2_file, tau_b=1000)
ax_host.errorbar(p1[0],p2[0], xerr=p1[1], yerr=p2[1],label=p1_file[:4])
ax_host.text(p1[0]*1.02,p2[0]*1.02,p1_file[:7])
ax_host.axis["bottom"].set_label(bottom_label)
ax_host.axis["left"].set_label(left_label)
ax_host.grid()
# if wanna legend, uncomment the following line
# plt.legend()
plt.show()
ax_kms = SubplotHost(fig, 1,1,1, aspect=1.)
# angular proper motion("/yr) to linear velocity(km/s) at distance=2.3kpc
pm_to_kms = 1./206265.*2300*3.085e18/3.15e7/1.e5
aux_trans = mtransforms.Affine2D().scale(pm_to_kms, 1.)
ax_pm = ax_kms.twin(aux_trans)
ax_pm.set_viewlim_mode("transform")
fig.add_subplot(ax_kms)
for n, ds, dse, w, we in obs:
time = ((2007+(10. + 4/30.)/12)-1988.5)
v = ds / time * pm_to_kms
ve = dse / time * pm_to_kms
ax_kms.errorbar([v], [w], xerr=[ve], yerr=[we], color="k")
ax_kms.axis["bottom"].set_label("Linear velocity at 2.3 kpc [km/s]")
ax_kms.axis["left"].set_label("FWHM [km/s]")
ax_pm.axis["top"].set_label("Proper Motion [$^{''}$/yr]")
ax_pm.axis["top"].label.set_visible(True)
ax_pm.axis["right"].major_ticklabels.set_visible(False)
ax_kms.set_xlim(950, 3700)
ax_kms.set_ylim(950, 3100)
# xlim and ylim of ax_pms will be automatically adjusted.
plt.draw()
plt.show()
ax_kms = SubplotHost(fig, 1, 1, 1, aspect=1.)
# angular proper motion("/yr) to linear velocity(km/s) at distance=2.3kpc
pm_to_kms = 1./206265.*2300*3.085e18/3.15e7/1.e5
aux_trans = mtransforms.Affine2D().scale(pm_to_kms, 1.)
ax_pm = ax_kms.twin(aux_trans)
ax_pm.set_viewlim_mode("transform")
fig.add_subplot(ax_kms)
for n, ds, dse, w, we in obs:
time = ((2007 + (10. + 4/30.)/12) - 1988.5)
v = ds / time * pm_to_kms
ve = dse / time * pm_to_kms
ax_kms.errorbar([v], [w], xerr=[ve], yerr=[we], color="k")
ax_kms.axis["bottom"].set_label("Linear velocity at 2.3 kpc [km/s]")
ax_kms.axis["left"].set_label("FWHM [km/s]")
ax_pm.axis["top"].set_label(r"Proper Motion [$''$/yr]")
ax_pm.axis["top"].label.set_visible(True)
ax_pm.axis["right"].major_ticklabels.set_visible(False)
ax_kms.set_xlim(950, 3700)
ax_kms.set_ylim(950, 3100)
# xlim and ylim of ax_pms will be automatically adjusted.
plt.show()
## Add overall OR first
summ_lines = summ_handle.readlines()
summ_list = []
for line in summ_lines:
entry = line.rstrip('\n').split(' ')
summ_list.append(entry[2])
#pdb.set_trace()
ov_a_hat = summ_list[0]
ov_se = summ_list[1]
# ov_odds = math.exp(float(ov_a_hat))
##
ov_l95 = float(ov_a_hat) - 1.96 * float(ov_se)
ov_new_se = float(ov_a_hat) - ov_l95
ax_kms.errorbar(float(ov_a_hat), 1, xerr=ov_new_se, color="r", fmt='o')
##
for key, a, y, new_se in plot_list:
ax_kms.errorbar([float(a)], y, xerr=[new_se], color="k", fmt='o')
ax_kms.axis["bottom"].set_label("Log of Odds Ratio")
ax_kms.axis["left"].set_label("SNPs")
title('Estimated Causal Effect of ' + Trait + ' on ' + Disease)
rank_list = [1]
rs_list = ['Overall']
count = 2
for snp in indiv_dict.keys():
rs_list.append(snp)
rank_list.append(count)
count += 1
## Add overall OR first
summ_lines = summ_handle.readlines()
summ_list =[]
for line in summ_lines:
entry = line.rstrip('\n').split(' ')
summ_list.append(entry[2])
#pdb.set_trace()
ov_a_hat = summ_list[0]
ov_se = summ_list[1]
# ov_odds = math.exp(float(ov_a_hat))
##
ov_l95 = float(ov_a_hat) - 1.96*float(ov_se)
ov_new_se = float(ov_a_hat) - ov_l95
ax_kms.errorbar(float(ov_a_hat), 1, xerr=ov_new_se, color = "r", fmt = 'o')
##
for key, a, y, new_se in plot_list:
ax_kms.errorbar([float(a)], y, xerr=[new_se], color="k", fmt='o')
ax_kms.axis["bottom"].set_label("Log of Odds Ratio")
ax_kms.axis["left"].set_label("SNPs")
title('Estimated Causal Effect of ' + Trait + ' on ' + Disease)
rank_list = [1]
rs_list = ['Overall']
count = 2
for snp in indiv_dict.keys():
rs_list.append(snp)
rank_list.append(count)
#---------------------
#Plotting the analytical models and the data
#-------------------
fig = pl.figure()
host = SubplotHost(fig, 1,1,1)
host.set_xlabel('$z$',fontsize=21)
host.set_ylabel('$\mu$',fontsize=21)
fig.add_subplot(host)
p1 = host.plot(z_ana,mu_ana,'r-',lw=1.5,label="$\Omega_m = 0.3$")
p2 = host.errorbar(z,mu,yerr=0.1,fmt='o',color='k',lw=1.5,label="SN data")
leg = pl.legend(loc=4,fontsize=18)
#host.set_ylim(0,48)
#pl.xticks(visible=False)
#pl.yticks(visible=False)
#host.yaxis.get_label().set_color(p1.get_color())
#leg.texts[0].set_color(p1.get_color())
#host.yaxis.get_label().set_color(p2.get_color())
#leg.texts[1].set_color(p2.get_color())
#host.yaxis.get_label().set_color(p3.get_color())
#leg.texts[2].set_color(p3.get_color())
#host.yaxis.get_label().set_color(p4.get_color())
#leg.texts[3].set_color(p4.get_color())
pl.draw()
#Plotting the analytical models and the data
#-------------------
fig = pl.figure()
host = SubplotHost(fig, 1,1,1)
host.set_xlabel('$z$',fontsize=21)
host.set_ylabel('$\mu$',fontsize=21)
fig.add_subplot(host)
p1 = host.plot(z,mu[0,:],'r-',lw=1.5,label="$\Omega_m = 0.2$")
p2 = host.plot(z,mu[1,:],'b--',lw=1.5,label="$\Omega_m = 0.3$")
p3 = host.plot(z,mu[2,:],'k-.',lw=1.5,label="$\Omega_m = 0.4$")
p4 = host.plot(z,mu[3,:],'m:',lw=1.5,label="$\Omega_m = 0.5$")
p5 = host.errorbar(z_data,mu_data,yerr=sigma_data,fmt='o',color='k',lw=1.5,label="SN data")
leg = pl.legend(loc=4,fontsize=18)
#host.set_ylim(0,48)
#pl.xticks(visible=False)
#pl.yticks(visible=False)
#host.yaxis.get_label().set_color(p1.get_color())
#leg.texts[0].set_color(p1.get_color())
#host.yaxis.get_label().set_color(p2.get_color())
#leg.texts[1].set_color(p2.get_color())
#host.yaxis.get_label().set_color(p3.get_color())
#leg.texts[2].set_color(p3.get_color())
#host.yaxis.get_label().set_color(p4.get_color())
#leg.texts[3].set_color(p4.get_color())
pl.draw()
