"""
CTP404 Plotter
Creates visualization plots for 9-ROI contrast/sensitometry analysis results.
"""
from typing import Optional
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
from ..utils import compute_phantom_boundary
[docs]
class CTP404Plotter:
"""
Creates visualization plots for CTP404Analyzer results.
"""
def __init__(self, analyzer):
self.analyzer = analyzer
self.vmin = None
self.vmax = None
if not self.analyzer.results:
analyzer.analyze()
[docs]
def plot(
self,
figsize: Optional[tuple] = None,
vmin: Optional[float] = None,
vmax: Optional[float] = None,
**kwargs,
) -> plt.Figure:
if vmin is not None:
self.vmin = vmin
if vmax is not None:
self.vmax = vmax
results = self.analyzer.results.get("contrast", [])
n_rois = max(1, len(results))
if figsize is None:
figsize = (12, max(8, int(n_rois * 1.6)))
fig = plt.figure(figsize=figsize, constrained_layout=True)
gs = fig.add_gridspec(n_rois, 3, width_ratios=[3, 1, 1], wspace=0.3, hspace=0.6)
ax_img = fig.add_subplot(gs[:, 0])
img = self.analyzer.image
ax_img.imshow(img, cmap="gray", vmin=self.vmin, vmax=self.vmax)
ax_img.set_title("CTP404 Sensitometry - 9 Material ROIs")
boundary = getattr(self.analyzer, "boundary", None)
if boundary and "x" in boundary and "y" in boundary:
boundary_x = np.array(boundary["x"])
boundary_y = np.array(boundary["y"])
else:
pixel_spacing = getattr(self.analyzer, "pixel_spacing", None)
_, (boundary_x, boundary_y) = compute_phantom_boundary(
img, self.analyzer.center, pixel_spacing
)
if len(boundary_x) > 0:
ax_img.plot(
boundary_x,
boundary_y,
"r-",
linewidth=1.5,
alpha=0.5,
label="Phantom Boundary",
)
pixel_spacing = getattr(self.analyzer, "pixel_spacing", None)
if hasattr(self.analyzer, "material_distance") and pixel_spacing:
analysis_radius_px = self.analyzer.material_distance / pixel_spacing
t = np.linspace(0, 2 * np.pi, 100)
analysis_x = analysis_radius_px * np.cos(t) + self.analyzer.center[0]
analysis_y = analysis_radius_px * np.sin(t) + self.analyzer.center[1]
ax_img.plot(
analysis_x,
analysis_y,
"c--",
linewidth=1.0,
alpha=0.4,
label="Analysis Region",
)
if self.analyzer.center:
cx, cy = self.analyzer.center
ax_img.plot(cx, cy, "r+", markersize=15, markeredgewidth=2)
legend_handles = []
ny, nx = img.shape[:2]
for i, roi in enumerate(results):
material = roi.get("material", f"ROI{i+1}")
cx = roi.get("center_x", self.analyzer.center[0])
cy = roi.get("center_y", self.analyzer.center[1])
radius_mm = roi.get(
"roi_radius_mm", getattr(self.analyzer, "roi_radius", None)
)
radius_px = (
radius_mm / self.analyzer.pixel_spacing
if (radius_mm is not None and self.analyzer.pixel_spacing)
else self.analyzer.roi_radius / self.analyzer.pixel_spacing
)
color = plt.cm.tab10(i % 10)
circle = patches.Circle(
(cx, cy),
radius=radius_px,
edgecolor=color,
facecolor="none",
linewidth=2,
alpha=0.6,
)
ax_img.add_patch(circle)
label = f"ROI {roi.get('roi_number', i+1)} {material}: {roi.get('mean_hu', float('nan')):.1f} ± {roi.get('std_hu', float('nan')):.1f}"
legend_handles.append(
patches.Circle(
(0, 0),
radius=radius_px,
edgecolor=color,
facecolor="none",
alpha=0.6,
label=label,
)
)
ax_img.axis("off")
ax_img.set_xlabel("")
from matplotlib.lines import Line2D
mean_line = Line2D(
[0], [0], color="k", linestyle="--", linewidth=1, label="Mean"
)
median_line = Line2D(
[0], [0], color="k", linestyle=":", linewidth=1, label="Median"
)
for i, roi in enumerate(results):
ax_hist = fig.add_subplot(gs[i, 1])
cx = roi.get("center_x", self.analyzer.center[0])
cy = roi.get("center_y", self.analyzer.center[1])
radius_mm = roi.get(
"roi_radius_mm", getattr(self.analyzer, "roi_radius", None)
)
radius_px = (
radius_mm / self.analyzer.pixel_spacing
if (radius_mm is not None and self.analyzer.pixel_spacing)
else self.analyzer.roi_radius / self.analyzer.pixel_spacing
)
try:
mask = self.analyzer._create_circular_mask((cx, cy), radius_px)
data = img[mask]
except Exception:
data = np.array([])
if data.size > 0:
counts, bins = np.histogram(data.flatten(), bins=30)
bin_centers = (bins[:-1] + bins[1:]) / 2.0
ax_hist.bar(
bin_centers,
counts,
width=(bins[1] - bins[0]),
color=plt.cm.tab10(i % 10),
alpha=0.75,
edgecolor="k",
linewidth=0.3,
)
mean_val = float(roi.get("mean_hu", np.mean(data)))
std_val = float(roi.get("std_hu", np.std(data)))
median_val = float(np.median(data))
ypos = counts.max() * 0.9 if counts.size else 1.0
ax_hist.axvline(mean_val, color="k", linestyle="--", linewidth=1)
ax_hist.axvline(median_val, color="k", linestyle=":", linewidth=1)
ax_hist.errorbar(
[mean_val],
[ypos],
xerr=[std_val],
fmt="none",
ecolor="k",
capsize=3,
linewidth=1,
)
else:
ax_hist.text(0.5, 0.5, "no data", ha="center", va="center")
ax_hist.set_title(
f"ROI {roi.get('roi_number', i + 1)}: {roi.get('material', '')}",
fontsize=9,
)
ax_hist.grid(axis="y", linestyle="--", alpha=0.7)
ax_hist.tick_params(axis="both", which="major", labelsize=8)
ax_heat = fig.add_subplot(gs[i, 2])
if data.size > 0:
roi_img = np.zeros((int(radius_px * 2), int(radius_px * 2)))
y_indices, x_indices = np.ogrid[: roi_img.shape[0], : roi_img.shape[1]]
dist = np.sqrt(
(x_indices - radius_px) ** 2 + (y_indices - radius_px) ** 2
)
circle_mask = dist <= radius_px
flat = data.flatten()
count = circle_mask.sum()
if flat.size >= count:
roi_img[circle_mask] = flat[:count]
else:
temp = np.full(count, np.nan)
temp[: flat.size] = flat
roi_img[circle_mask] = temp
ax_heat.imshow(roi_img, cmap="gray", vmin=self.vmin, vmax=self.vmax)
else:
ax_heat.text(0.5, 0.5, "no data", ha="center", va="center")
ax_heat.set_title("Heatmap", fontsize=9)
ax_heat.axis("off")
if legend_handles:
ax_img.legend(
handles=legend_handles, loc="upper right", fontsize=8, framealpha=0.9
)
fig.legend(
handles=[mean_line, median_line],
loc="lower center",
ncol=2,
fontsize=10,
framealpha=0.9,
)
return fig