fontsize = '14' N=100 # Data for plotting v=0.4 t_max = 5.0 gamma = (1-v**2)**(-0.5) # beta = np.arctan(gamma) t = np.linspace(0.0, t_max, N) s0=2 s1 = 0*t light = t ss = s0 + v*t # s = 1 + np.sin(2 * np.pi * t) fig, ax = texfig.subplots() ax.plot(s1, t, color='k', lw=2) ax.plot(ss, t, color='k', lw=2) # Luz 1 t_0 = 0.8 dt_0 = 0.8 tt_max = s0 + v*t_0 tt = np.linspace(0.0, tt_max, N) dist_0 = s0 + v*t_0 t_prime = np.linspace(t_0, tt_max+t_0, N) li_1 = dist_0 - tt ax.plot(li_1, t_prime, color='gold', zorder=0) # Legenda Luz 1
plt.yscale('log')
plt.xlabel(r'scale factor \(a\)')
plt.ylabel(r'tensor perturbations \(|h|\)')
handles, labels = plt.gca().get_legend_handles_labels()
slope_handle = plt.Line2D((0,1),(0,0), c='black', ls='dotted')
plt.legend(handles + [ slope_handle, bg_legend_handle ], labels + [ r"analytic slope $h(a) \propto a^{-\left(1+\frac{\alphaM}{2}\right)}$", "for " + r"$k=0.01$, $\cT=1$" ], loc='lower left')
texfig.savefig('plots/growing_aM')
# varying both alphaM and beta
plt.clf()
fig, axes = texfig.subplots(width=tex_width, nrows=2, ncols=2, sharex=True, sharey=True)
for (i, row) in enumerate([["0", "0.1"], ["0.4", "1"]]):
for (j, beta_str) in enumerate(row):
ax = axes[i][j]
plt.sca(ax)
plot_parametric_evolution('varying_aM0_beta_' + beta_str, ur'\alphaMnot', LCDM_pvalue=0)
ax.set_title(ur'$\beta=' + beta_str + '$')
ax.set_xscale('log')
ax.set_yscale('log')
if i==1:
ax.set_xlabel(r'scale factor \(a\)')