for ns, this_simname in enumerate(sim_list):
Pearson_rand = []
Pearson_sim = []
alpha_rand = []
alpha_sim = []
if ns == 0:
lab = sim_list[ns]
else:
lab = None
Pearson_rand += (spheres[this_simname].pearson_randos)
Pearson_sim += (atlist[this_simname].pearson_t[0])
alpha_rand += (spheres[this_simname].alpha_randos)
alpha_sim += (atlist[this_simname].alpha_t[0])
plt.clf()
fig, ax, ax_alpha, ax_r = means_etc.three_way_bean()
ax_r.hist(Pearson_sim,
histtype='step',
color='r',
linestyle='-',
density=True,
orientation='horizontal')
ax_r.hist(Pearson_rand,
histtype='step',
color='k',
linestyle='-',
density=True,
label=lab,
orientation='horizontal')
ax.scatter(alpha_sim,
Pearson_sim,
'''
three way plots to see how alpha 1 vs alpha 2 relate...
'''
from starter2 import *
import means_etc
figa,axa,axtop,axright = means_etc.three_way_bean()
axa.scatter( NumberOfOverlap, Fraction,c='k') #I don't think we need this 'b'
axa.scatter( NumberOfOverlap,Max ,c='r')
axtop.hist( NumberOfOverlap, bins=bins_n, histtype='step',color='k')
axright.hist( Fraction, bins=bins_f, histtype='step',color='k',orientation='horizontal')
axright.hist( Max, bins=bins_f, histtype='step',color='r',orientation='horizontal')
#QUESTION: what was the purpose of plotting MAX here?
if 1:
axbonk(axa,xlabel=r'$N_{\rm{overlap}}$', ylabel='Overlap Fraction')
axa.set_xlim([-0.1,nmax+0.1])
axbonk(axtop,xlabel='',ylabel=r'$N$')
axbonk(axright,xlabel=r'$N$',ylabel='')
axright.set_ylim( axa.get_ylim())
axtop.set_xlim( axa.get_xlim())
axtop.set_xticks([])
axright.set_yticks([])
return np.log10(arr)
def log_or_notv(arr):
return np.log10(arr)/2
dlim = [dbins.min(), dbins.max()]
vlim = [vbins.min(), vbins.max()]
import colors
if 1:
if 1:
c1 = colors.color['u401']
c2 = colors.color['u402']
c3 = colors.color['u403']
l1 = 'sim1'
l2 = 'sim2'
l3 = 'sim3'
ax, ax_den_hist,ax_vel_hist=means_etc.three_way_bean()
ok = slice(None)
ok1 = m1.npart > 1
ok2 = m2.npart > 1
ok3 = m3.npart > 1
ax.scatter(log_or_not(m1.dmeans[ok1]),log_or_notv(m1.variance[ok1]),c=c1,label=l1, s=0.1)
ax.scatter(log_or_not(m2.dmeans[ok2]),log_or_notv(m2.variance[ok2]),c=c2,label=l2, s=0.1)
ax.scatter(log_or_not(m3.dmeans[ok3]),log_or_notv(m3.variance[ok3]),c=c3,label=l3, s=0.1)
r1 = ax_den_hist.hist(log_or_not(m1.dmeans[ok1]), histtype='step',color=c1,label=l1,bins=dbins)
r2 = ax_den_hist.hist(log_or_not(m2.dmeans[ok2]), histtype='step',color=c2,label=l2,bins=dbins)
r3 = ax_den_hist.hist(log_or_not(m3.dmeans[ok3]), histtype='step',color=c3,label=l3,bins=dbins)
v1 = ax_vel_hist.hist(log_or_notv(m1.variance[ok1]), histtype='step', orientation='horizontal',color=c1,bins=vbins)
v2 = ax_vel_hist.hist(log_or_notv(m2.variance[ok2]), histtype='step', orientation='horizontal',color=c2,bins=vbins)
v3 = ax_vel_hist.hist(log_or_notv(m3.variance[ok3]), histtype='step', orientation='horizontal',color=c3,bins=vbins)
if 1: # FOR ONE FRAME PER TIME; SINGLE PANEL
xlims = 0.4,1.8
ylims = 0.0,5.0
for i in range(len(axplts)):
if 1:
if nt == 0:
color = 'r'
if nt == 1:
color = 'b'
if nt == 2:
color = 'g'
the_bins = np.linspace(-10,10)
if ncore == 0:
figs[i],axplts[i],axtop[i],axright[i] = met.three_way_bean()
axplts[i].scatter(this_alphaS[i],this_alphaP[i],c=color)
axtop[i].hist(this_alphaS[i], bins=the_bins, histtype='step',color='k')
axright[i].hist(this_alphaP[i], bins=the_bins, histtype='step',color='k',orientation='horizontal')
outname_frame='AlphaSP_%s_%d'%(simnames[nt],i)
#axplts[i].scatter(this_alphaS[i],this_alphaP[i],c=color,marker='*')
magfield_density_tool.labelled(axplts[i],xscale=None,yscale=None,xlabel='Sum',ylabel='Product',\
title=None, xlim=None, ylim=None) #xlims,ylim=ylims)
if 0:
axplts[i].scatter(this_rho[i],this_field[i],c='g',marker='*')
axplts[i].scatter(this_rho[i],the_zz[i],c='g',alpha=0.2)
magfield_density_tool.labelled(axplts[i],xscale=None,yscale=None,xlabel=r'$\rho$',ylabel=r'$log(B/ \rho)$',\
title=None, xlim=None,ylim=None)
outname_frame='Scatter_LogBRhovsRho_%s_%d'%(simnames[nt],i)