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_visualization.py
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import os
from typing import Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm, gridspec
from ..utils import _calc_plot_dim, elements
from . import MoleculeGraph
CLASS_COLORS = "rkbgcmy"
def plot_graphs(
pred: Optional[list[MoleculeGraph]] = None,
ref: Optional[list[MoleculeGraph]] = None,
box_borders: np.ndarray = np.array(((0, 0, -1.4), (16, 16, 0.5))),
outdir: str = "./graphs/",
classes: list[list[int]] = None,
class_colors: list[str] = CLASS_COLORS,
start_ind: int = 0,
verbose: int = 1,
):
"""
Plot batch of graphs into files 0_graph.png, 1_graph.png, ... etc.
Arguments:
pred: Predicted molecule graphs.
ref: Reference molecule graphs.
box_borders: Real-space extent of the plotting region in Ångströms. The array should be of the form
``((x_start, y_start, z_start), (x_end, y_end, z_end))``.
outdir: Directory where files are saved.
classes: Classes for categorizing atoms based on their chemical elements. Each class is a list of elements either
as atomic numbers or as chemical symbols.
class_colors: Colors for each atom class.
start_ind: Starting index for file naming.
verbose: Whether to print output information.
"""
n_plot = (pred is not None) + (ref is not None)
if n_plot == 0:
raise ValueError("pred and ref cannot both be None.")
if (pred is not None) and (ref is not None) and (len(pred) != len(ref)):
raise ValueError(f"pred ({len(pred)}) and ref ({len(ref)}) have different number of samples.")
n_samples = len(pred) if pred is not None else len(ref)
if not os.path.exists(outdir):
os.makedirs(outdir)
if classes is None:
atom_classes = []
for mols in [pred, ref]:
if mols is not None:
for m in mols:
atom_classes += list(m.array(class_index=True))
n_classes = max(atom_classes) + 1
classes = [f"Class {i}" for i in range(n_classes)]
else:
n_classes = len(classes)
classes = [", ".join([elements[e - 1] for e in c]) for c in classes]
z_min = box_borders[0][2]
z_max = box_borders[1][2]
scatter_size = 160
def get_marker_size(z, max_size):
return max_size * (z - z_min) / (z_max - z_min)
def plot_xy(ax, mol):
if len(mol) > 0:
mol_pos = mol.array(xyz=True)
s = get_marker_size(mol_pos[:, 2], scatter_size)
if (s < 0).any():
raise ValueError("Encountered atom z position(s) below box borders.")
c = np.array([class_colors[atom.class_index] for atom in mol.atoms])
for b in mol.bonds:
pos = np.vstack([mol_pos[b[0]], mol_pos[b[1]]])
ax.plot(pos[:, 0], pos[:, 1], "k", linewidth=2, zorder=1)
sort_mask = mol_pos[:, 2].argsort()
ax.scatter(mol_pos[sort_mask, 0], mol_pos[sort_mask, 1], c=c[sort_mask], s=s[sort_mask], edgecolors="k", zorder=2)
ax.set_xlim(box_borders[0][0], box_borders[1][0])
ax.set_ylim(box_borders[0][1], box_borders[1][1])
ax.set_aspect("equal", "box")
def plot_xz(ax, mol):
if len(mol) > 0:
order = list(np.argsort(mol.array(xyz=True)[:, 1])[::-1])
mol = mol.permute(order)
mol_pos = mol.array(xyz=True)
s = get_marker_size(mol_pos[:, 2], scatter_size)
if (s < 0).any():
raise ValueError("Encountered atom z position(s) below box borders.")
c = np.array([class_colors[atom.class_index] for atom in mol.atoms])
for b in mol.bonds:
pos = np.vstack([mol_pos[b[0]], mol_pos[b[1]]])
ax.plot(pos[:, 0], pos[:, 2], "k", linewidth=2, zorder=1)
sort_mask = (-mol_pos[:, 1]).argsort()
ax.scatter(mol_pos[sort_mask, 0], mol_pos[sort_mask, 2], c=c[sort_mask], s=s[sort_mask], edgecolors="k", zorder=2)
ax.set_xlim(box_borders[0][0], box_borders[1][0])
ax.set_ylim(box_borders[0][2], box_borders[1][2])
ax.set_aspect("equal", "box")
ind = start_ind
for i in range(n_samples):
# Setup plot grid
x_size = 5 * n_plot
x_extra = 0.35 * max([len(c) for c in classes])
fig = plt.figure(figsize=(x_size + x_extra, 6.5))
fig_grid = gridspec.GridSpec(1, 2, width_ratios=(x_size, x_extra), wspace=1 / (x_size + x_extra))
grid_graphs = fig_grid[0, 0].subgridspec(2, n_plot, height_ratios=(5, 1.5), hspace=0.1, wspace=0.2)
# Prediction
if pred is not None:
ax_xy_pred = fig.add_subplot(grid_graphs[0, 0])
ax_xz_pred = fig.add_subplot(grid_graphs[1, 0])
plot_xy(ax_xy_pred, pred[i])
plot_xz(ax_xz_pred, pred[i])
ax_xy_pred.set_xlabel("x (Å)", fontsize=12)
ax_xy_pred.set_ylabel("y (Å)", fontsize=12)
ax_xz_pred.set_xlabel("x (Å)", fontsize=12)
ax_xz_pred.set_ylabel("z (Å)", fontsize=12)
ax_xy_pred.set_title("Prediction", fontsize=20)
i_plot = 1
else:
i_plot = 0
# Reference
if ref is not None:
ax_xy_ref = fig.add_subplot(grid_graphs[0, i_plot])
ax_xz_ref = fig.add_subplot(grid_graphs[1, i_plot])
plot_xy(ax_xy_ref, ref[i])
plot_xz(ax_xz_ref, ref[i])
ax_xy_ref.set_xlabel("x (Å)", fontsize=12)
ax_xy_ref.set_ylabel("y (Å)", fontsize=12)
ax_xz_ref.set_xlabel("x (Å)", fontsize=12)
ax_xz_ref.set_ylabel("z (Å)", fontsize=12)
ax_xy_ref.set_title("Reference", fontsize=20)
# Plot legend
ax_legend = fig.add_subplot(fig_grid[0, 1])
# Class colors
dy = 0.08
dx = 0.35 / x_extra
y_start = 0.5 + dy * (n_classes + 3) / 2
for i, c in enumerate(classes):
ax_legend.scatter(dx, y_start - dy * i, s=scatter_size, c=class_colors[i], edgecolors="k")
ax_legend.text(2 * dx, y_start - dy * i, c, fontsize=16, ha="left", va="center_baseline")
# Marker sizes
y_start2 = y_start - (n_classes + 1) * dy
marker_zs = np.array([z_max, (z_min + z_max + 0.2) / 2, z_min + 0.2])
ss = get_marker_size(marker_zs, scatter_size)
for i, (s, z) in enumerate(zip(ss, marker_zs)):
ax_legend.scatter(dx, y_start2 - dy * i, s=s, c="w", edgecolors="k")
ax_legend.text(2 * dx, y_start2 - dy * i, f"z = {z:.2f}Å", fontsize=16, ha="left", va="center_baseline")
ax_legend.set_xlim(0, 1)
ax_legend.set_ylim(0, 1)
ax_legend.axis("off")
plt.savefig(save_path := os.path.join(outdir, f"{ind}_graph.png"))
if verbose > 0:
print(f"Graph image saved to {save_path}")
plt.close()
ind += 1
def plot_distribution_grid(
pred_dist: np.ndarray,
ref_dist: Optional[np.ndarray] = None,
box_borders: np.ndarray = np.array(((2, 2, -1.5), (18, 18, 0))),
outdir: str = "./graphs/",
start_ind: int = 0,
verbose: int = 1,
):
"""
Plot batch of position distribution grids into files 0_pred_dist.png, 1_pred_dist.png, ..., and 0_pred_dist2D.png
1_pred_dist2D.png, ... etc.
The full grids are divided into separate images for each z-slice in the arrays. The 2D grids are averaged over the
z-dimension of the full grids.
Arguments:
pred_dist: Predicted position distribution grid.
ref_dist: Reference position distribution grid.
box_borders: Real-space extent of the distribution grid region in Ångströms. The array should be of the form
``((x_start, y_start, z_start), (x_end, y_end, z_end))``.
outdir: Directory where files are saved.
start_ind: Starting index for file naming.
verbose: Whether to print output information.
"""
if ref_dist is not None:
assert pred_dist.shape == ref_dist.shape, (pred_dist.shape, ref_dist.shape)
n_img = 2 if ref_dist is not None else 1
if not os.path.exists(outdir):
os.makedirs(outdir)
fontsize = 24
z_start = box_borders[0][2]
z_res = (box_borders[1][2] - box_borders[0][2]) / (pred_dist.shape[-1] - 1)
extent = [box_borders[0][0], box_borders[1][0], box_borders[0][1], box_borders[1][1]]
ind = start_ind
for i in range(len(pred_dist)):
p = pred_dist[i]
r = ref_dist[i] if ref_dist is not None else None
# Plot grid in 2D
p_mean = p.mean(axis=-1)
if r is not None:
r_mean = r.mean(axis=-1)
vmin = min(r_mean.min(), p_mean.min())
vmax = max(r_mean.max(), p_mean.max())
else:
vmin, vmax = p_mean.min(), p_mean.max()
fig, axes = plt.subplots(1, n_img, figsize=(2 + 5 * n_img, 6), squeeze=False)
axes = axes[0]
axes[0].imshow(p_mean.T, origin="lower", vmin=vmin, vmax=vmax, extent=extent)
axes[0].set_title("Prediction")
if r is not None:
axes[1].imshow(r_mean.T, origin="lower", vmin=vmin, vmax=vmax, extent=extent)
axes[1].set_title("Reference")
# Colorbar
plt.tight_layout(rect=[0, 0, 0.9, 1])
pos = axes[-1].get_position()
cax = fig.add_axes(rect=[0.9, pos.ymin, 0.03, pos.ymax - pos.ymin])
m = cm.ScalarMappable()
m.set_array([vmin, vmax])
plt.colorbar(m, cax=cax)
plt.savefig(save_path := os.path.join(outdir, f"{ind}_pred_dist2D.png"))
if verbose > 0:
print(f"Position distribution 2D prediction image saved to {save_path}")
plt.close()
# Plot each z-slice separately
if r is not None:
vmin = min(r.min(), p.min())
vmax = max(r.max(), p.max())
else:
vmin, vmax = p.min(), p.max()
nrows, ncols = _calc_plot_dim(p.shape[-1], f=0.5)
fig = plt.figure(figsize=(4 * ncols, 4.25 * nrows * n_img))
fig_grid = fig.add_gridspec(nrows, ncols, wspace=0.05, hspace=0.15, left=0.03, right=0.98, bottom=0.02, top=0.98)
for iz in range(p.shape[-1]):
ix = iz % ncols
iy = iz // ncols
axes = fig_grid[iy, ix].subgridspec(n_img, 1, hspace=0.03).subplots(squeeze=False)[:, 0]
axes[0].imshow(p[:, :, iz].T, origin="lower", vmin=vmin, vmax=vmax, extent=extent)
axes[0].axis("off")
axes[0].set_title(f"z = {z_start + (iz + 0.5) * z_res:.2f}Å", fontsize=fontsize)
if r is not None:
axes[1].imshow(r[:, :, iz].T, origin="lower", vmin=vmin, vmax=vmax, extent=extent)
axes[1].axis("off")
if ix == 0:
axes[0].text(
-0.1,
0.5,
"Prediction",
ha="center",
va="center",
transform=axes[0].transAxes,
rotation="vertical",
fontsize=fontsize,
)
if r is not None:
axes[1].text(
-0.1,
0.5,
"Reference",
ha="center",
va="center",
transform=axes[1].transAxes,
rotation="vertical",
fontsize=fontsize,
)
plt.savefig(save_path := os.path.join(outdir, f"{ind}_pred_dist.png"))
if verbose > 0:
print(f"Position distribution prediction image saved to {save_path}")
plt.close()
ind += 1