/
visualize_graphs.py
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/
visualize_graphs.py
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#----------------------------------------------------------------------
# Script to visualize galaxy catalogues as graphs
# Author: Pablo Villanueva Domingo
# Last update: 4/22
#----------------------------------------------------------------------
import time, datetime
from Source.plotting import *
from Source.load_data import *
from torch_geometric.utils import degree
from torch_cluster import radius_graph
fontsize = 8
# Visualization routine for plotting graphs
def visualize_graph(data, ind, sizes=0.1, projection="3d", edge_index=None):
fig = plt.figure(figsize=(4, 4))
if projection=="3d":
ax = fig.add_subplot(projection ="3d")
pos = data.x[:,:3]
elif projection=="2d":
ax = fig.add_subplot()
pos = data.x[:,:2]
pos *= boxsize/1.e3 # show in Mpc
# Draw lines for each edge
if edge_index is not None:
for (src, dst) in edge_index.t().tolist():
src = pos[src].tolist()
dst = pos[dst].tolist()
if projection=="3d":
ax.plot([src[0], dst[0]], [src[1], dst[1]], zs=[src[2], dst[2]], linewidth=0.1, color='black')
elif projection=="2d":
ax.plot([src[0], dst[0]], [src[1], dst[1]], linewidth=0.1, color='black')
# Plot nodes
if projection=="3d":
ax.scatter(pos[:, 0], pos[:, 1], pos[:, 2], s=sizes, zorder=1000, alpha=0.5)
elif projection=="2d":
ax.scatter(pos[:, 0], pos[:, 1], s=sizes, zorder=1000, alpha=0.5)
ax.xaxis.set_tick_params(labelsize=fontsize)
ax.yaxis.set_tick_params(labelsize=fontsize)
ax.zaxis.set_tick_params(labelsize=fontsize)
fig.savefig("Plots/visualize_graph_"+str(ind), bbox_inches='tight', dpi=300)
plt.close(fig)
# Plot the degree distribution of the graph (see e.g. http://networksciencebook.com/)
def plot_degree_distribution(degrees):
listbins = np.linspace(0,80,num=12)
deg_dist = []
for array in degrees:
hist, bins = np.histogram(array, bins=listbins)
deg_dist.append(hist)
dist_mean = np.mean(deg_dist,axis=0)
dist_std = np.std(deg_dist,axis=0)
fig_deg, ax_deg = plt.subplots(figsize=(6, 4))
ax_deg.set_yscale("log")
#ax_deg.set_xscale("log")
ax_deg.plot(bins[:-1], dist_mean, color=colorsuite(simsuite))
ax_deg.fill_between(bins[:-1], dist_mean+dist_std, dist_mean-dist_std, color=colorsuite(simsuite), alpha=0.3)
ax_deg.set_xlim([bins[0],bins[-2]])
ax_deg.set_xlabel(r"$k$")
ax_deg.set_ylabel(r"$p_k$")
fig_deg.savefig("Plots/degree_distribution.pdf", bbox_inches='tight', dpi=300)
# Main routine to display graphs from several simulations
def display_graphs(simsuite, n_sims, r_link, simset="LH", showgraph=True, get_degree=False):
if get_degree:
degrees = []
# Load data and create graph
for simnumber in range(n_sims):
simpath = simpathroot + simsuite + "/"+simset+"_"
catalogue = simpath + str(simnumber)+"/fof_subhalo_tab_033.hdf5"
# Read the catalogue
f = h5py.File(catalogue, 'r')
pos = f['/Subhalo/SubhaloPos'][:]/boxsize
Nstar = f['/Subhalo/SubhaloLenType'][:,4] #number of stars
Mstar = f['/Subhalo/SubhaloMassType'][:,4] #Msun/h
indexes = np.where(Nstar>Nstar_th)[0]
pos = pos[indexes]
Mstar = Mstar[indexes]
tab = np.column_stack((pos, Mstar))
#edge_index, edge_attr = get_edges(pos, r_link, use_loops=False)
edge_index = radius_graph(torch.tensor(pos,dtype=torch.float32), r=r_link, loop=False)
data = Data(x=tab, edge_index=torch.tensor(edge_index, dtype=torch.long))
if showgraph:
#visualize_graph(data, simnumber, "2d", edge_index)
visualize_graph(data, simnumber, projection="3d", edge_index=data.edge_index)
if get_degree:
degrees.append( degree(edge_index[0], data.num_nodes).numpy() )
if get_degree:
plot_degree_distribution(degrees)
#--- MAIN ---#
if __name__=="__main__":
time_ini = time.time()
for path in ["Plots"]:
if not os.path.exists(path):
os.mkdir(path)
# Linking radius
r_link = 0.05
# Simulation suite, choose between "IllustrisTNG" and "SIMBA"
simsuite = "IllustrisTNG"
# Number of simulations considered, maximum 27 for CV and 1000 for LH
n_sims = 20
display_graphs(simsuite, n_sims, r_link)
print("Finished. Time elapsed:",datetime.timedelta(seconds=time.time()-time_ini))