Overlay Model Over Plot

This example uses Matplotlib to composite the pixel data returned by opynsim.graphics.Texture2D.pixels_rgba32() over a line plot, so that raw data plots an be annotated with helpful visualizations.

import opynsim as opyn
import opynsim.examples
import opynsim.graphics
import numpy as np
import matplotlib.pyplot as plt  # from `matplotlib` package
from matplotlib.offsetbox import OffsetImage, AnnotationBbox

# Create/import a `Model` + `ModelState`.
model = opyn.examples.pendulum_model()
model_state = model.initial_state()
model.realize(model_state, opyn.STAGE_REPORT)  # usually required for rendering

# Define data/image sample points
x_min = -0.5*np.pi
x_max =  0.5*np.pi
data_xs = np.linspace(x_min, x_max, 100)

# Use OPynSim to simulate and produce output values
simulated_y_positions = []
simulated_y_accelerations = []
for x in data_xs:
    model.set_coordinate_value(model_state, "/jointset/pin/pin_coord_0", x)
    model.realize(model_state, opyn.STAGE_REPORT)
    simulated_y_positions.append(model.get_output_value(model_state, "/bodyset/head[position]")[1])
    simulated_y_accelerations.append(model.get_output_value(model_state, "/bodyset/head[linear_acceleration]")[1])

# Use OPynSim to render the model + state at some (sparser) positions in the plot
num_images = 8
image_xs = np.linspace(x_min, x_max, num_images)
images = []
for x in image_xs:
    model.set_coordinate_value(model_state, "/jointset/pin/pin_coord_0", x)
    model.realize(model_state, opyn.STAGE_REPORT)
    images.append(opyn.graphics.render_model_in_state(model, model_state))

# Create plot
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(data_xs, np.sin(data_xs + 0.5*np.pi), label='sin(x+0.5pi)')
ax.plot(data_xs, simulated_y_positions,  "o", label='y (simulated)')
ax.plot(data_xs, np.cos(data_xs + 0.5*np.pi), label='cos(x+0.5pi)')
ax.plot(data_xs, simulated_y_accelerations, label='y_accel (simulated)')
ax.set_title("Model Render Overlay at Peak")
plt.legend()

# Draw images as annotations over the plot
for i in range(num_images):
    # Create a Matplotlib `OffsetImage` using the rendered pixel data
    offset_image = OffsetImage(images[i].pixels_rgba32(), zoom=0.2, alpha=1.0)

    # Wrap the `OffsetImage` into an `AnnotationBox` to anchor the image somewhere
    # in the plot data (here, on the first peak of sin(x)).
    ab = AnnotationBbox(
        offset_image,              # The rendered scene, wrapped in `OffsetImage`
        (image_xs[i], 1),
        #xybox=(-25, 50),           # Offset the image from the peak
        xycoords='data',           # Use plot coordinates for the anchor
        boxcoords="offset points",
        frameon=False,
        pad=0.0
    )
    ax.add_artist(ab)  # Add image

# Show the figure in a window.
#
# This might require installing `python3-tk` on Linux.
plt.show()

# Alternatively, Save plot to PNG file
# plt.savefig("plot.png", dpi=300)