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)