def plot_2D_comparison(batch_case,
network_tupla,
title,
savepath,
method="f0"):
best_index = batch_case[1].index(min(batch_case[1]))
weight_list = np.asarray([a for a in batch_case[0]])
best_weights = weight_list[best_index]
original_coords = network_tupla[1]
base_coords = nt.get_spectral_coordinates(nx.laplacian_matrix(
network_tupla[0]).todense(),
dim=2)
if method == "f0":
after_coords = nt.get_spectral_coordinates(
nx.laplacian_matrix(network_tupla[0]).todense(),
mod_matrix=nt.create_inverse_mod_matrix(best_weights),
dim=2)
elif method == "f1":
after_coords = nt.get_spectral_coordinates(
laplacian=nt.create_customized_laplacian(network_tupla[0],
best_weights),
dim=2)
elif method == "f2":
after_coords = nt.get_spectral_coordinates(
laplacian=nt.create_weighted_laplacian(network_tupla[0],
best_weights),
dim=2)
else:
assert False
base_coords = pd.DataFrame(rmsd.kabsch_rotate(base_coords.values,
original_coords.values),
columns=["x", "y", "z"])
after_coords = pd.DataFrame(rmsd.kabsch_rotate(after_coords.values,
original_coords.values),
columns=["x", "y", "z"])
plt.scatter(original_coords["x"],
original_coords["y"],
color='C0',
marker='.',
label="Originali",
s=50)
plt.scatter(base_coords["x"],
base_coords["y"],
color='C2',
marker='x',
label="SD di base",
s=10)
plt.scatter(after_coords["x"],
after_coords["y"],
s=10,
color='C1',
marker='*',
label="SD con modifica")
plt.title("")
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=3)
plt.xlabel("$X$")
plt.ylabel("$Y$")
plt.tight_layout()
plt.savefig(savepath, dpi=DPI)
plt.clf()
def fitness_single(masses, fitness_parameters):
# fitness_parameters[0] = protein_network
# fitness_parameters[1] = target_coordinates
network = nt.modify_edges_weitghts(fitness_parameters[0], masses)
guess_coordinates = nt.get_spectral_coordinates(
nx.laplacian_matrix(network).toarray(),
mod_matrix=np.diag([float(1 / a[1]) for a in network.degree]),
dim=3)
return rmsd.kabsch_rmsd(guess_coordinates.values,
fitness_parameters[1].values)
def fitness_all(masses, fitness_parameters):
# fitness_parameters[0] = protein_network_list
# fitness_parameters[1] = target_coordinates_list
# fitness_parameters[2] = dataset_list
# fitness_parameters[3] = edge_aa_list
aa_contact_map = create_AA_contact_map(masses)
fitness = 0.0
laplacian_list = refresh_network_weights(fitness_parameters[2],
fitness_parameters[0],
aa_contact_map,
fitness_parameters[3])
for i in range(len(laplacian_list)):
guess_coordinates = nt.get_spectral_coordinates(
laplacian_list[i],
mod_matrix=np.diag(1 / np.diag(laplacian_list[i])),
dim=3)
fitness += rmsd.kabsch_rmsd(guess_coordinates.values,
fitness_parameters[1][i].values)
return fitness
def plot_3D_comparison(batch_case,
network_tupla,
title,
savepath,
method="f0",
angle=30):
best_index = batch_case[1].index(min(batch_case[1]))
weight_list = np.asarray([a for a in batch_case[0]])
best_weights = weight_list[best_index]
original_coords = network_tupla[1]
base_coords = nt.get_spectral_coordinates(nx.laplacian_matrix(
network_tupla[0]).todense(),
dim=3)
if method == "f0":
after_coords = nt.get_spectral_coordinates(
nx.laplacian_matrix(network_tupla[0]).todense(),
mod_matrix=nt.create_inverse_mod_matrix(best_weights),
dim=3)
elif method == "f1":
after_coords = nt.get_spectral_coordinates(
laplacian=nt.create_customized_laplacian(network_tupla[0],
best_weights),
dim=3)
elif method == "f2":
after_coords = nt.get_spectral_coordinates(
laplacian=nt.create_weighted_laplacian(network_tupla[0],
best_weights),
dim=3)
else:
assert False
base_coords = pd.DataFrame(rmsd.kabsch_rotate(base_coords.values,
original_coords.values),
columns=["x", "y", "z"])
after_coords = pd.DataFrame(rmsd.kabsch_rotate(after_coords.values,
original_coords.values),
columns=["x", "y", "z"])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(original_coords["x"],
original_coords["y"],
original_coords["z"],
color='C0',
marker='.',
label="Originali")
ax.scatter(base_coords["x"],
base_coords["y"],
base_coords["z"],
color='C2',
marker='x',
label="SD base")
ax.scatter(after_coords["x"],
after_coords["y"],
after_coords["z"],
color='C1',
marker='*',
label="SD con modifica")
ax.set_title("")
ax.legend()
ax.set_xlabel("$X$")
ax.set_ylabel("$Y$")
ax.set_zlabel("$Z$")
ax.view_init(30, angle)
plt.tight_layout()
plt.savefig(savepath, dpi=DPI)
plt.clf()
def plot_protein_network(network,
distance_matrix,
threshold,
coords_original,
coords_modified=pd.DataFrame(),
spectral_basic=False,
title="",
savepath="",
showfig=True,
view_thet=30,
view_phi=30):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Make network to be plotted (low threshold)
plt_network = make_network_from_distance_matrix(distance_matrix, threshold)
# Plot original coords
ax.scatter(coords_original["x"],
coords_original["y"],
coords_original["z"],
label="Originale",
c="C0")
for edge in list(plt_network.edges):
ax.plot((coords_original.iloc[edge[0]]["x"],
coords_original.iloc[edge[1]]["x"]),
(coords_original.iloc[edge[0]]["y"],
coords_original.iloc[edge[1]]["y"]),
(coords_original.iloc[edge[0]]["z"],
coords_original.iloc[edge[1]]["z"]),
c="grey",
alpha=0.7)
# If any, plot given coords
if not coords_modified.empty:
print("given coords")
# APPLY THE RMSD (you never know...)
coords_modified = pd.DataFrame(rmsd.kabsch_rotate(
coords_modified.values, coords_original.values),
columns=["x", "y", "z"])
score_modified = rmsd.kabsch_rmsd(coords_modified.values,
coords_original.values)
ax.scatter(coords_modified["x"],
coords_modified["y"],
coords_modified["z"],
label="SD Perturbato, RMSD = {:.6f}".format(score_modified),
c="C1")
for edge in list(plt_network.edges):
ax.plot((coords_modified.iloc[edge[0]]["x"],
coords_modified.iloc[edge[1]]["x"]),
(coords_modified.iloc[edge[0]]["y"],
coords_modified.iloc[edge[1]]["y"]),
(coords_modified.iloc[edge[0]]["z"],
coords_modified.iloc[edge[1]]["z"]),
c="red",
alpha=0.4)
# Do you also want the spectral basic?
if spectral_basic:
print("spectral_basic")
coords_basic = nt.get_spectral_coordinates(
nx.laplacian_matrix(network).todense(),
mod_matrix=np.diag([1 / a[1] for a in list(network.degree)]),
dim=3)
coords_basic = pd.DataFrame(rmsd.kabsch_rotate(coords_basic.values,
coords_original.values),
columns=["x", "y", "z"])
score_basic = rmsd.kabsch_rmsd(coords_basic.values,
coords_original.values)
ax.scatter(coords_basic["x"],
coords_basic["y"],
coords_basic["z"],
label="SD Originale, RMSD = {:.6f}".format(score_basic),
c="C2")
for edge in list(plt_network.edges):
ax.plot((coords_basic.iloc[edge[0]]["x"],
coords_basic.iloc[edge[1]]["x"]),
(coords_basic.iloc[edge[0]]["y"],
coords_basic.iloc[edge[1]]["y"]),
(coords_basic.iloc[edge[0]]["z"],
coords_basic.iloc[edge[1]]["z"]),
c="green",
alpha=0.4)
ax.legend()
ax.set_xlabel("X $[\\AA]$")
ax.set_ylabel("Y $[\\AA]$")
ax.set_zlabel("Z $[\\AA]$")
if title != "":
ax.set_title(title)
if showfig:
ax.view_init(view_thet, view_phi)
plt.show()
if savepath != "":
ax.view_init(view_thet, view_phi)
plt.savefig(savepath, dpi=300)
plt.clf()
fig.close()