def errorbars_vs_beta2(final64,use_rms_beta=True,name='test.png'):
'''all mdot quantities are in units of mdot/mdotB'''
sns.set_style('ticks') #,{"axes.facecolor": ".97"})
sns.set_palette('colorblind')
c_med='k' #median
c_mean= sns.color_palette()[0] #'b'
c_bhline= sns.color_palette()[1] #'g'
c_aaron=sns.color_palette()[2] #error on median box 'r'
c_mark= sns.color_palette()[3] #'pink'
#kwargs
laba=dict(fontweight='bold',fontsize=dFS)
kwargs_axtext=dict(fontweight='bold',fontsize=FS,va='top')
# orig final measure, REPLACED BY bootstrap_PDF.txt
#med,med_low,med_hi,std_low,std_hi,yax_val,rms_beta,med_beta,name= put_errbars_in_arrays_linear(final64)
# MEDIAN, bootstrap PDF
bPDF= get_measurment('boot_PDF')
bPDF= add_to_measurement(bPDF)
# MEAN, bootstrap 2
b2= get_measurment('boot_2')
b2= add_to_measurement(b2)
# get rms_beta
rms_beta= bPDF['rms_beta']
#marks results
rmdot0= 0.0166
# Values when submitted to MNRAS
#mark_norm_mdot0=dict(median_sim=0.35,median_pred_256=0.36,err_median_pred_256=0.1,\
# mean_pred_256=1.26,err_mean_pred_256=0.1,\
# mean_sim=0.9,err_mean_sim=0.1) # 0.1 is guess
# Values for revision, after fitting marks table 1
mark_norm_mdot0=dict(median_sim=0.35,median_pred_256=0.36,err_median_pred_256=0.106,\
mean_pred_256=1.26,err_mean_pred_256=0.100,\
mean_sim=0.9,err_mean_sim=0.1) # 0.1 is guess
mark_norm_mdotB={}
for key in mark_norm_mdot0.keys():
mark_norm_mdotB[key]= mark_norm_mdot0[key]* 4/np.exp(1.5)/5.**3
#special figure for different sized subplots
fig = plt.figure(figsize=(10,6))
gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
fig.subplots_adjust(wspace=0.05)
ax=[0,0]
ax[0] = plt.subplot(gs[0])
ax[1] = plt.subplot(gs[1])
#B != 0 points go on left side
#ax[0].errorbar(rms_beta[:-2],med[:-2]/mdotB(),yerr=[med_low[:-2]/mdotB(),med_hi[:-2]/mdotB()], fmt='o',ms=6,mew=2.,mfc='none',mec=c_med,c=c_med,label=r'This Study (Median)')
# My Magnetic Points
# median
#ax[0].errorbar(rms_beta[:-2],med[:-2]/mdotB(),yerr=[med_low[:-2]/mdotB(),med_hi[:-2]/mdotB()], fmt='o',ms=6,mew=2.,mfc='none',mec=c_mark,c=c_mark,label='Krumholz et al. (2006)')
ax[0].errorbar(bPDF['rms_beta'][:-2],bPDF['med'][:-2],yerr=[bPDF['med_low'][:-2],bPDF['med_hi'][:-2]], fmt='o',ms=6,mew=2.,mfc='none',mec=c_mark,c=c_mark,label='Krumholz et al. (2006)')
ax[0].errorbar(bPDF['rms_beta'][:-2],bPDF['med'][:-2],yerr=[bPDF['med_low'][:-2],bPDF['med_hi'][:-2]], fmt='o',ms=6,mew=2.,mfc='none',mec=c_med,c=c_med,label='Median')
#ax[0].errorbar(b2['rms_beta'][:-2],b2['med'][:-2],yerr=[b2['med_low'][:-2],b2['med_hi'][:-2]], fmt='o',ms=6,mew=2.,mfc='none',mec='b',c='b',label='boot 2 median')
# mean
ax[0].errorbar(b2['rms_beta'][:-2],b2['mean'][:-2],yerr=[b2['mean_low'][:-2],b2['mean_hi'][:-2]], fmt='o',ms=6,mew=2.,mfc='none',mec=c_mean,c=c_mean,label='Mean')
#just for legend, plot some junk with right colors for mine and marks
#ax[0].plot([5,6],med[-2:],c=c_med,visible=False,label=r'This Study (Median $\mathbf{Log_{\rm{10}} \,\, \dot{M} }$)')
#ax[0].plot([5,6],med[-2:],c=c_mark,visible=False,label='Krumholz et al. (2006)')
#aarons
cont_beta=np.logspace(-3,1,num=50)
ax[0].plot(cont_beta,lee_parallel(cont_beta,b_norm=True),c=c_aaron,ls='--',lw=2,label='Lee et al. (2014)')
#print useful number for paper
#print 'ratio of mdot to (mdot_perp+mdot_par)/2 is: betarms,mdot,mdotpar,mdot/mdotpar'
#for myb,mymdot,mdotpar in zip(rms_beta[:-2],med[:-2],np.log10(lee_parallel(rms_beta[:-2]))): print myb,mymdot,mdotpar,10**(mdotpar)/10**(mymdot)
#mdot BH
ax[0].plot(ax[0].get_xlim(),[mdotBH()/mdotB()]*2,c=c_bhline,ls='--',lw=2)
ax[0].text(1e-2,mdotBH()/mdotB(),r'$\mathbf{ \dot{M}_{BH} }$',verticalalignment='bottom',**text) #,transform=ax[0].transAxes)
#ax[0].text(1e-2,np.log10(mdotBH()),r'$\mathbf{ \dot{M}_{BH} }$',color=c_bhline,**kwargs_ax[0].ext)
#B = 0 points go on right side
ax[1].plot(range(10),visible=False)
#Marks prediction
ax[1].errorbar(4,mark_norm_mdotB['median_pred_256'],yerr=mark_norm_mdotB['err_median_pred_256'],fmt='o',ms=6,mew=2.,mfc='none',mec=c_mark,c=c_mark,label='Krumholz et al. (2006)')
#ax[1].errorbar(4,mark_norm_mdotB['mean_pred_256'],yerr=mark_norm_mdotB['err_mean_pred_256'],fmt='o',ms=6,mew=2.,mfc='none',mec=c_mark,c=c_mark,label='Krumholz et al. (2006)')
ax[1].errorbar(4,mark_norm_mdotB['mean_sim'],yerr=mark_norm_mdotB['err_mean_sim'],fmt='o',ms=6,mew=2.,mfc='none',mec=c_mark,c=c_mark,label='Krumholz et al. (2006)')
#my hydro pts
#ax[1].errorbar([5,6],med[-2:]/mdotB(),yerr=[med_low[-2:]/mdotB(),med_hi[-2:]/mdotB()], fmt='o',ms=6,mew=2.,mfc='none',mec=c_med,c=c_med)
ax[1].errorbar([5,6],bPDF['med'][-2:],yerr=[bPDF['med_low'][-2:],bPDF['med_hi'][-2:]], fmt='o',ms=6,mew=2.,mfc='none',mec=c_med,c=c_med)
#ax[1].errorbar([9,10],b2['med'][-2:],yerr=[b2['med_low'][-2:],b2['med_hi'][-2:]], fmt='o',ms=6,mew=2.,mfc='none',mec='r',c='r')
ax[1].errorbar([5,6],b2['mean'][-2:],yerr=[b2['mean_low'][-2:],b2['mean_hi'][-2:]], fmt='o',ms=6,mew=2.,mfc='none',mec=c_mean,c=c_mean)
#mdotBH and same for hydro limit of lee14
ax[1].plot(ax[1].get_xlim(),[mdotBH()/mdotB()]*2,c=c_bhline,ls='--',lw=2)
ax[1].plot([0.5,8.5],[mdotBH()/mdotB()]*2,c=c_aaron,ls='--',lw=2)
#finish labeling
for i in range(2):
ax[i].set_ylim(6e-4,1e-2)
ax[0].set_xscale('log')
ax[0].set_xlim(8e-3,1e1)
#ax[1].set_xlim(2,1e1)
ylab= ax[0].set_ylabel(r'$\mathbf{\dot{M}/\dot{M}_{\rm{B}} }$',**laba)
xlab= fig.text(0.5, 0., r'$\mathbf{ \beta_{turb} }$', ha='center', **laba) #ax[0].set_xlabel(r'$\mathbf{ \beta_{rms} }$',**laba)
ax[0].legend(loc=4,fontsize=FS,frameon=True,fancybox=True)
#reorganize legend
handles, labels = ax[0].get_legend_handles_labels()
handles= [handles[-1],handles[-2],handles[1],handles[0]]
labels= [labels[-1],labels[-2],labels[1],labels[0]]
ax[0].legend(handles, labels,loc=4,frameon=True,fancybox=True,fontsize=FS)
#remove top and right axis lines
#sns.despine()
#remove spines and ticks between ax and ax2
#ax[0].spines['right'].set_visible(False)
#ax[1].spines['left'].set_visible(False)
for i in range(2): ax[i].tick_params(axis='both', which='major', labelsize=FS)
ax[1].set_yticks([])
#add slashes indicating break in axis
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax[0].transAxes, color='k', clip_on=False)
ax[0].plot((1-d,1+d), (-d,+d), **kwargs)
ax[0].plot((1-d,1+d),(1-d,1+d), **kwargs)
kwargs.update(transform=ax[1].transAxes) # switch to the bottom axes
ax[1].plot((-d*3,+d*3), (1-d,1+d), **kwargs)
ax[1].plot((-d*3,+d*3), (-d,+d), **kwargs)
#put infinity on 2nd axis
ax[1].set_xticks([])
ax[1].set_xlabel(r'$\mathbf{ \infty }$',**laba)
#plt.tick_params(axis='both', which='major', labelsize='large')
# show only some of yaxis ticks
#ax[0].yaxis.tick_left()
ax[1].yaxis.tick_right()
# y log scale
for i in range(2): ax[i].set_yscale('log')
#SAVE IS BROKEN, save manually
plt.tight_layout()
#plt.show()
#save
name='testing.png'
plt.savefig(name, bbox_extra_artists=[xlab,ylab], bbox_inches='tight',dpi=150)
print 'wrote %s' % name
def errorbars_vs_beta2(final64, use_rms_beta=True):
"""all mdot quantities are in units of mdot/mdotB"""
sns.set_style("ticks") # ,{"axes.facecolor": ".97"})
sns.set_palette("colorblind")
c_med = "k" # median
c_bhline = sns.color_palette()[0] #'b'
c_aaron = sns.color_palette()[2] # error on median box 'r'
c_mark = sns.color_palette()[1] #'g'
# kwargs
laba = dict(fontweight="bold", fontsize="xx-large")
kwargs_axtext = dict(fontweight="bold", fontsize="x-large", va="top")
# final measure
med, med_low, med_hi, std_low, std_hi, yax_val, rms_beta, med_beta, name = put_errbars_in_arrays_linear(final64)
# marks results
rmdot0 = 0.0166
mark_norm_mdot0 = dict(median_sim=0.35, median_pred_256=0.36, err_median_pred_256=0.1)
mark_norm_mdotB = {}
for key in mark_norm_mdot0.keys():
mark_norm_mdotB[key] = mark_norm_mdot0[key] * 4 / np.exp(1.5) / 5.0 ** 3
# special figure for different sized subplots
fig = plt.figure()
gs = gridspec.GridSpec(1, 2, width_ratios=[3, 1])
fig.subplots_adjust(wspace=0.05)
ax = [0, 0]
ax[0] = plt.subplot(gs[0])
ax[1] = plt.subplot(gs[1])
# B != 0 points go on left side
ax[0].errorbar(
rms_beta[:-2],
med[:-2] / mdotB(),
yerr=[med_low[:-2] / mdotB(), med_hi[:-2] / mdotB()],
fmt="o",
ms=6,
mew=2.0,
mfc="none",
mec=c_mark,
c=c_mark,
label="Krumholz et al. (2006)",
)
ax[0].errorbar(
rms_beta[:-2],
med[:-2] / mdotB(),
yerr=[med_low[:-2] / mdotB(), med_hi[:-2] / mdotB()],
fmt="o",
ms=6,
mew=2.0,
mfc="none",
mec=c_med,
c=c_med,
label=r"This Study (Median)",
)
# just for legend, plot some junk with right colors for mine and marks
# ax[0].plot([5,6],med[-2:],c=c_med,visible=False,label=r'This Study (Median $\mathbf{Log_{\rm{10}} \,\, \dot{M} }$)')
# ax[0].plot([5,6],med[-2:],c=c_mark,visible=False,label='Krumholz et al. (2006)')
# aarons
cont_beta = np.logspace(-3, 1, num=50)
ax[0].plot(cont_beta, lee_parallel(cont_beta, b_norm=True), c=c_aaron, ls="--", lw=2, label="Lee et al. (2014)")
# print useful number for paper
print "ratio of mdot to (mdot_perp+mdot_par)/2 is: betarms,mdot,mdotpar,mdot/mdotpar"
for myb, mymdot, mdotpar in zip(rms_beta[:-2], med[:-2], np.log10(lee_parallel(rms_beta[:-2]))):
print myb, mymdot, mdotpar, 10 ** (mdotpar) / 10 ** (mymdot)
# mdot BH
ax[0].plot(
ax[0].get_xlim(), [mdotBH() / mdotB()] * 2, c=c_bhline, ls="--", lw=2, label=r"$\mathbf{ \dot{M}_{BH} }$"
)
# ax[0].text(1e-2,np.log10(mdotBH()),r'$\mathbf{ \dot{M}_{BH} }$',color=c_bhline,**kwargs_ax[0].ext)
# B = 0 points go on right side
ax[1].plot(range(10), visible=False)
# Marks prediction
ax[1].errorbar(
4,
mark_norm_mdotB["median_pred_256"],
yerr=mark_norm_mdotB["err_median_pred_256"],
fmt="o",
ms=6,
mew=2.0,
mfc="none",
mec=c_mark,
c=c_mark,
label="Krumholz et al. (2006)",
)
# my hydro pts
ax[1].errorbar(
[5, 6],
med[-2:] / mdotB(),
yerr=[med_low[-2:] / mdotB(), med_hi[-2:] / mdotB()],
fmt="o",
ms=6,
mew=2.0,
mfc="none",
mec=c_med,
c=c_med,
)
# mdotBH and same for hydro limit of lee14
ax[1].plot(ax[1].get_xlim(), [mdotBH() / mdotB()] * 2, c=c_bhline, ls="--", lw=2)
ax[1].plot([0.5, 8.5], [mdotBH() / mdotB()] * 2, c=c_aaron, ls="--", lw=2)
# finish labeling
for i in range(2):
ax[i].set_ylim(6e-4, 1e-2)
ax[0].set_xscale("log")
ax[0].set_xlim(8e-3, 1e1)
# ax[1].set_xlim(2,1e1)
ylab = ax[0].set_ylabel(r"$\mathbf{\dot{M}/\dot{M}_{\rm{B}} }$", **laba)
xlab = fig.text(
0.5, 0.0, r"$\mathbf{ \beta_{rms} }$", ha="center", **laba
) # ax[0].set_xlabel(r'$\mathbf{ \beta_{rms} }$',**laba)
ax[0].legend(loc=4, fontsize="medium", frameon=True, fancybox=True)
# reorganize legend
handles, labels = ax[0].get_legend_handles_labels()
handles = [handles[-1], handles[-2], handles[0], handles[1]]
labels = [labels[-1], labels[-2], labels[0], labels[1]]
ax[0].legend(handles, labels, loc=4, fontsize="medium", frameon=True, fancybox=True)
# remove top and right axis lines
# sns.despine()
# remove spines and ticks between ax and ax2
# ax[0].spines['right'].set_visible(False)
# ax[1].spines['left'].set_visible(False)
for i in range(2):
ax[i].tick_params(axis="both", which="major", labelsize="large")
ax[1].set_yticks([])
# add slashes indicating break in axis
d = 0.015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
kwargs = dict(transform=ax[0].transAxes, color="k", clip_on=False)
ax[0].plot((1 - d, 1 + d), (-d, +d), **kwargs)
ax[0].plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)
kwargs.update(transform=ax[1].transAxes) # switch to the bottom axes
ax[1].plot((-d * 3, +d * 3), (1 - d, 1 + d), **kwargs)
ax[1].plot((-d * 3, +d * 3), (-d, +d), **kwargs)
# put infinity on 2nd axis
ax[1].set_xticks([])
ax[1].set_xlabel(r"$\mathbf{ \infty }$", fontsize="large")
# plt.tick_params(axis='both', which='major', labelsize='large')
# show only some of yaxis ticks
# ax[0].yaxis.tick_left()
ax[1].yaxis.tick_right()
# y log scale
for i in range(2):
ax[i].set_yscale("log")
# save
plt.savefig(
os.path.join(args.outdir, "errorbars_v_brms2.png"), bbox_extra_artists=[xlab, ylab], bbox_inches="tight"
)
