p_par.append(p_par_i)
r2_par.append(r_par_i ** 2)
pcurv_par.append(par_quad[par_quad['study'] == study][0][i + 2])
sample_comp_i = [sample_comp[y][i + 5] for y in xrange(len(sample_comp))]
mean_comp = [sample_comp['mean'][q] for q in xrange(len(sample_comp)) if sample_comp_i[q] != 0]
sample_comp_i = [sample_comp_i[q] for q in xrange(len(sample_comp_i)) if sample_comp_i[q] != 0]
b_comp_i, inter_comp_i, r_comp_i, p_comp_i, std_err_comp_i = stats.linregress(np.log(mean_comp), np.log(sample_comp_i))
b_comp.append(b_comp_i)
p_comp.append(p_comp_i)
r2_comp.append(r_comp_i ** 2)
pcurv_comp.append(comp_quad[comp_quad['study'] == study][0][i + 2])
fig = plt.figure(figsize = (7, 7))
ax_p = plt.subplot(221)
tl.plot_dens_par_comp(p_obs, p_par, p_comp, ax = ax_p, legend = True, loc = 1, vline = 0.05, xlim = [0, 0.2])
ax_p.annotate('(A)', xy = (0.05, 0.92), xycoords = 'axes fraction', fontsize = 10)
plt.xlabel('p-value for b', fontsize = 8)
plt.ylabel('Density', fontsize = 8)
ax_curv = plt.subplot(222)
tl.plot_dens_par_comp(pcurv_obs, pcurv_par, pcurv_comp, ax = ax_curv, vline = 0.05, xlim = [0, 1])
ax_curv.annotate('(B)', xy = (0.05, 0.92), xycoords = 'axes fraction', fontsize = 10)
plt.xlabel('p-value for quadratic term', fontsize = 8)
plt.ylabel('Density', fontsize = 8)
ax_r2 = plt.subplot(223)
tl.plot_dens_par_comp(r2_obs, r2_par, r2_comp, ax = ax_r2, xlim = [0, 1])
ax_r2.annotate('(C)', xy = (0.05, 0.92), xycoords = 'axes fraction', fontsize = 10)
plt.xlabel(r'$R^2$', fontsize = 8)
plt.ylabel('Density', fontsize = 8)
