def hist_plot(ax, deg_dists, colors, graph_names, graph_ls):
# Loop through all desired graphs
for deg, col, lab, ls in zip(deg_dists, colors, graph_names, graph_ls):
hist, plt_bins = np.histogram(deg, lin_bins, normed=True)
# Correct x-coords to center them on histogram bins
plt_bins_mid = plt_bins + np.diff(plt_bins)[0] * 0.5
ax.plot(plt_bins_mid[:-1], hist, ls=ls, lw=3, color=col, label=lab)
# Set axis limits and ticks, and label subplots
ax.set_xlim([0, 150])
#ax.set_ylim([0, .15])
ax.locator_params(axis='x', nbins=5)
#a.locator_params(axis='y', nbins=5)
# Set title
ax.set_title('Degree Distributions')
# Set xlabels
ax.set_xlabel('Degree')
ax.set_ylabel('P(k)')
# Set all fontsizes and axis colors
set_all_text_fontsizes(ax, FONT_SIZE)
def hist_plot(ax, deg_dists, colors, gammas, alphas, bins):
for deg, col, g, alpha in zip(deg_dists, colors, gammas, alphas):
hist, plt_bins = np.histogram(deg, bins, normed=True)
ax.plot(plt_bins[:-1], hist, lw=2, color=col, label=str(g),
alpha=alpha)
# Set axis limits and ticks, and label subplots
ax.set_xlim([0, 150])
#ax.set_ylim([0, .15])
ax.locator_params(axis='x', nbins=5)
#a.locator_params(axis='y', nbins=5)
ax.legend(loc='best', fontsize=ft_size - 6)
# Set xlabels
ax.set_xlabel('Degree')
ax.set_ylabel('P(k)')
# Set all fontsizes and axis colors
set_all_text_fontsizes(ax, ft_size)
def hist_plot(ax, deg_dists, colors, graph_names, graph_ls):
# Loop through all desired graphs
for deg, col, lab, ls in zip(deg_dists, colors, graph_names, graph_ls):
hist, plt_bins = np.histogram(deg, lin_bins, normed=True)
# Correct x-coords to center them on histogram bins
plt_bins_mid = plt_bins + np.diff(plt_bins)[0] * 0.5
ax.plot(plt_bins_mid[:-1], hist, ls=ls, lw=3, color=col, label=lab)
# Set axis limits and ticks, and label subplots
ax.set_xlim([0, 150])
ax.locator_params(axis='x', nbins=5)
# Set title
ax.set_title('Degree Distributions')
# Set xlabels
ax.set_xlabel('Degree')
ax.set_ylabel('P(k)')
# Set all fontsizes and axis colors
set_all_text_fontsizes(ax, FONT_SIZE)
print('generating model...')
G_pa, _, _ = pa(N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed, L=np.inf)
# cast both models to undirected graphs
# plot preferential growth (L = np.inf) and pref attachment
fig, ax = plt.subplots(1, 1, facecolor=FACE_COLOR, figsize=FIG_SIZE,
tight_layout=True)
colors = (COLORS['pref-attachment'], COLORS['pref-growth'], COLORS['pgpa'], COLORS['brain'])
for G, color in zip((G_pa, G_pgpa_L_inf, G_pgpa, G_brain), colors):
deg = nx.degree(G.to_undirected()).values()
cc = nx.clustering(G.to_undirected()).values()
ax.scatter(deg, cc, c=color, lw=0, alpha=ALPHA)
ax.set_xlabel('Degree')
ax.set_ylabel('Clustering\ncoefficient')
ax.set_xlim(0, 150)
ax.set_xticks((0, 50, 100, 150))
ax.set_ylim(0, 1)
ax.set_yticks((0, 0.2, 0.4, 0.6, 0.8, 1))
ax.legend(('preferential attachment', 'preferential growth', 'PGPA', 'brain'), fontsize=FONT_SIZE)
change_settings.set_all_text_fontsizes(ax, FONT_SIZE)
plt.draw()
plt.show(block=True)
fig, axs = plt.subplots(1, 2, figsize=cf.FIGSIZE_DIRECTED_MODELS,
facecolor=FACECOLOR, tight_layout=True)
# Plot clustering vs. degree scatter plots
for ctr, ax in enumerate(axs.flatten()):
deg = nx.degree(Gs[ctr].to_undirected()).values()
cc = nx.clustering(Gs[ctr].to_undirected()).values()
ax.scatter(deg, cc, s=cf.MARKERSIZE, c=colors[ctr], lw=cf.LW)
# Set labels and limits
for ax in axs:
ax.set_xlabel('Degree')
ax.set_xlim(cf.XLIMITS)
ax.set_ylim(cf.YLIMITS)
ax.set_xticks([0,125,250])
axs[0].set_ylabel('Clustering\ncoefficient')
axs[0].set_title('Random\nattachment')
axs[1].set_title('Proximal\nattachment')
# Set all fontsizes and axis colors
for ax in axs.flatten():
set_all_text_fontsizes(ax, cf.FONTSIZE)
set_all_colors(ax, 'w')
plt.draw()
fig.savefig('/Users/rkp/Desktop/directed_clustering_vs_degree.png',
transparent=True)
ax0.set_xlabel('Indegree')
ax0.set_ylabel('Outdegree')
ax0_histTop.set_title('In- vs. Outdegree', fontsize=FONTSIZE + 2,
va='bottom')
ax0.set_xlim(*cf.IN_OUT_SCATTER_XLIM)
ax0.set_ylim(*cf.IN_OUT_SCATTER_YLIM)
ax0.set_aspect('auto')
ax0_histTop.set_ylabel('# nodes')
ax0_histRight.set_xlabel('# nodes')
##########################################################################
# Plot percent_indeg vs. degree (RIGHT SIDE)
ax1.scatter(deg, percent_indeg, s=cf.MARKERSIZE, lw=0, c=MARKERCOLOR)
ax1.set_xlabel('Total degree (in + out)')
ax1.set_ylabel('Proportion in-degree')
ax1.xaxis.set_major_locator(plt.MaxNLocator(4))
ax1.set_yticks(np.arange(0, 1.1, .2))
ax1.set_title('Proportion of Edges that are Incoming\nvs. Degree',
fontsize=cf.FONTSIZE + 2, va='bottom')
ax1.set_ylim([0., 1.05])
##########################################################################
# Set background color and text size for all spines/ticks
for temp_ax in [ax0, ax0_histRight, ax0_histTop, ax1]:
set_all_text_fontsizes(temp_ax, FONTSIZE)
set_all_colors(temp_ax, cf.LABELCOLOR)
#temp_ax.patch.set_facecolor(FACECOLOR) # Set color of plot area
temp_ax.tick_params(width=cf.TICKSIZE)
#fig.savefig('/Users/richpang/Desktop/brain_in_out.png', transparent=True)
plt.show()
axs[0].plot(bincs, cts_mean, lw=2, color=COLORS['sgpa'])
axs[0].fill_between(bincs, cts_mean - cts_std, cts_mean + cts_std,
color=COLORS['sgpa'], alpha=0.5, zorder=2)
axs[0].set_xlim(0, 150)
axs[0].set_ylim(0, 0.04)
axs[0].set_xticks(np.arange(0, 151, 30))
axs[0].set_yticks(np.linspace(0, 0.04, 5, endpoint=True))
axs[0].set_xlabel('Undirected Degree')
axs[0].set_ylabel('Probability')
brain_handle = axs[1].hist(degree_brain, bins=BINS, color=COLORS['brain'],
normed=True)
sgpa_handle = axs[1].plot(bincs, cts_mean, lw=2, color=COLORS['sgpa'])
axs[1].fill_between(bincs, cts_mean - cts_std, cts_mean + cts_std,
color=COLORS['sgpa'], alpha=0.5, zorder=2)
axs[1].set_xlim(0, 150)
axs[1].set_ylim(1e-4, 1e-1)
axs[1].set_xticks(np.arange(0, 151, 30))
axs[1].set_xlabel('Undirected Degree')
axs[1].set_ylabel('Log probability')
axs[1].set_yscale('log')
axs[1].legend([brain_handle[-1][0], sgpa_handle[0]], ['Connectome', 'SGPA'])
[change_settings.set_all_text_fontsizes(ax, FONT_SIZE) for ax in axs]
for ax, label in zip(axs, labels):
ax.text(0.05, 0.95, label, fontsize=20, fontweight='bold',
transform=ax.transAxes, ha='center', va='center')
fig.savefig('fig6fs2.png', dpi=300)
fig.savefig('fig6fs2.pdf', dpi=300)
axs[3].scatter(BA_degree, BA_clustering, color=BA_COLOR)
axs[3].plot(x,np.exp(reg_BA[1])*x**reg_BA[0],'k',linestyle='--',lw=3)
# Set axis limits and ticks, and label subplots
for ax_idx, ax in enumerate(axs.flatten()):
ax.set_xlim(0, DEG_MAX)
ax.set_ylim(0, 1)
ax.set_xticks(DEG_TICKS)
ax.set_yticks(CC_TICKS)
ax.text(.08, .87, labels[ax_idx], color='k', fontsize=FONT_SIZE,
fontweight='bold', transform=ax.transAxes)
ax.set_xlabel('Degree')
set_all_text_fontsizes(ax, FONT_SIZE)
set_all_colors(ax, 'k')
# Set titles
ax.set_title(graph_names[ax_idx], fontsize=FONT_SIZE)
# Hide x ticklabels in top row & y ticklabels in right columns
if ax_idx == 0:
ax.set_ylabel('Clustering\ncoefficient')
ax.text(0.5, 0.85, r'$\mathrm{R^{2}}$ = %.2f' % r_squared_vals[ax_idx],
color='k', fontsize=FONT_SIZE - 2, transform=ax.transAxes)
else:
ax.text(0.725, 0.85, '%0.2f' % r_squared_vals[ax_idx],
right_main_ax.set_xlabel('Total degree (in + out)')
right_main_ax.set_ylabel('Proportion in-degree')
right_main_ax.text(1.,
1.2,
labels[1],
fontsize=FONTSIZE + 2,
fontweight='bold',
transform=right_main_ax.transAxes,
ha='right')
right_main_ax.set_xlim([0., 150.])
right_main_ax.set_ylim([-0.025, 1.025])
right_main_ax.legend(loc=(-0.35, 1.12), prop={'size': 12})
for temp_ax in [
left_main_ax, right_main_ax, top_margin_ax, right_margin_ax
]:
set_all_text_fontsizes(temp_ax, FONTSIZE)
set_all_colors(temp_ax, cf.LABELCOLOR)
temp_ax.tick_params(width=1.)
fig.subplots_adjust(left=0.125, top=0.925, right=0.95, bottom=0.225)
fig.savefig('fig5sf5{}{}.pdf'.format(*labels), dpi=300)
fig.savefig('fig5sf5{}{}.png'.format(*labels), dpi=300)
plt.show(block=False)
r, p = stats.spearmanr(indeg_sg_total, outdeg_sg_total)
print('r = {}'.format(r))
print('p = {}'.format(p))
right_main_ax.set_xlabel('Total degree (in + out)')
right_main_ax.set_ylabel('Proportion in-degree')
right_main_ax.xaxis.set_major_locator(plt.MaxNLocator(4))
right_main_ax.set_yticks(np.arange(0, 1.1, .25))
right_main_ax.set_ylim([0., 1.05])
right_main_ax.set_xlim([0, 120])
right_main_ax.set_xticks(np.arange(0, 151, 50))
right_main_ax.text(1., 1.2, 'b', fontsize=FONT_SIZE + 2, fontweight='bold',
transform=right_main_ax.transAxes, ha='right')
top_margin_ax.set_ylabel('$P(K_\mathrm{in}=k)$')
right_margin_ax.set_xlabel('$P(K_\mathrm{out}=k)$')
for temp_ax in [left_main_ax, right_main_ax, top_margin_ax, right_margin_ax,
top_dummy_ax, right_dummy_ax]:
set_all_text_fontsizes(temp_ax, FONT_SIZE)
set_all_colors(temp_ax, cf.LABELCOLOR)
top_lin_ticks = top_margin_ax.get_yticklabels()
right_lin_ticks = right_margin_ax.get_xticklabels()
top_log_ticks = top_dummy_ax.get_yticklabels()
right_log_ticks = right_dummy_ax.get_xticklabels()
for tick in top_lin_ticks+right_lin_ticks:
tick.set_color('k')
tick.set_fontsize(FONT_SIZE-2)
for tick in top_log_ticks+right_log_ticks:
tick.set_color('blue')
tick.set_fontsize(FONT_SIZE-5)
ax.scatter(deg, clust, s=MARKER_SIZE, color=cols[ax_i])
ax.plot(x, np.exp(reg[ax_i][1]) * x ** reg[ax_i][0], 'k', linestyle='--',
lw=2)
# Setting other parameters
ax.set_xlim(0, DEG_MAX)
ax.set_ylim(0, 1)
ax.set_aspect(DEG_MAX / 1.)
ax.set_xticks(DEG_TICKS)
ax.set_yticks(CC_TICKS)
ax.set_title(graph_names[ax_i], fontsize=FONT_SIZE)
ax.text(.1, .87, labels[ax_i], fontsize=FONT_SIZE-1, fontweight='bold',
transform=ax.transAxes)
ax.set_xlabel('Degree')
set_all_text_fontsizes(ax, FONT_SIZE)
set_all_colors(ax, 'k')
# Hide x ticklabels in top row & y ticklabels in right columns
if ax_i == 0:
ax.set_ylabel('Clustering\ncoefficient')
ax.text(0.4, 0.85, r'R$^2$ = %.2f' % r_squared_vals[ax_i],
fontsize=FONT_SIZE - 2, transform=ax.transAxes)
else:
ax.text(0.725, 0.85, '%0.2f' % r_squared_vals[ax_i],
fontsize=FONT_SIZE - 2, transform=ax.transAxes)
ax.set_yticklabels('')
fig.set_tight_layout({'pad': 1.02, 'w_pad': 0.4})
fig.savefig('fig1_cdef.png', dpi=300)
counts_nodal_efficiency_mean[key].argmax()],
counts_nodal_efficiency_mean[key].max()))
ax.set_xlim(X_LIM_EFFICIENCY)
ax.set_ylim(Y_LIM_EFFICIENCY)
ax.set_xlabel('Nodal efficiency')
ax.set_ylabel('Number of nodes')
lines = [h_line[0] for h_line in lines_temp] + [hist_connectome[-1][0]]
axs[1].legend(lines, LABELS + ['Connectome'], fontsize=FONT_SIZE)
texts = ('a', 'b')
for ax, text in zip(axs, texts):
change_settings.set_all_colors(ax, AX_COLOR)
change_settings.set_all_text_fontsizes(ax, FONT_SIZE)
ax.text(0.05,
0.95,
text,
fontsize=20,
fontweight='bold',
transform=ax.transAxes,
ha='center',
va='center')
# add inset with power-law fit bar plot
ax_inset = fig.add_axes(INSET_COORDINATES)
gamma_means = [power_law_fits[key].mean() for key in PLOT_KEYS + ['brain']]
gamma_stds = [power_law_fits[key].std() for key in PLOT_KEYS + ['brain']]
gamma_median_r_squareds = [
np.median(fits_r_squared[key]) for key in PLOT_KEYS + ['brain']
right_main_ax.text(1.,
1.2,
'b',
fontsize=FONT_SIZE + 2,
fontweight='bold',
transform=right_main_ax.transAxes,
ha='right')
top_margin_ax.set_ylabel('$P(K_\mathrm{in}=k)$')
right_margin_ax.set_xlabel('$P(K_\mathrm{out}=k)$')
for temp_ax in [
left_main_ax, right_main_ax, top_margin_ax, right_margin_ax,
top_dummy_ax, right_dummy_ax
]:
set_all_text_fontsizes(temp_ax, FONT_SIZE)
set_all_colors(temp_ax, cf.LABELCOLOR)
top_lin_ticks = top_margin_ax.get_yticklabels()
right_lin_ticks = right_margin_ax.get_xticklabels()
top_log_ticks = top_dummy_ax.get_yticklabels()
right_log_ticks = right_dummy_ax.get_xticklabels()
for tick in top_lin_ticks + right_lin_ticks:
tick.set_color('k')
tick.set_fontsize(FONT_SIZE - 2)
for tick in top_log_ticks + right_log_ticks:
tick.set_color('blue')
tick.set_fontsize(FONT_SIZE - 5)
