synimatic 1.0.0

Simple and effective matplotlib animation wrapper

Synimatic

Synimatic simplifies animation creation by wrapping matplotlib's FuncAnimation. It removes repetitive setup, detects the execution environment (Jupyter or terminal), features progress indicators, and offers straightforward methods to display or save animations.

---

Features

• Easy Setup: Define animation logic clearly through setup and update functions.
• Environment Detection: Automatically renders inline in notebooks or in separate windows in terminals.
• Progress Display: Shows progress bars during rendering and saving processes.
• Simple API: .show() and .save() methods for effortless display or export.

---

Installation

pip3 install synimatic

---

Core Concepts

Animations require two key functions:

1. setup(): Executes once to establish the look of a static plot elements like figure, axes, and artists to animate. Returns (fig, ax, *artists).

2. update(frame, context): Called each frame to update plot elements.

   • frame: Current frame index or value.
   • context: Tuple of objects returned by setup.

---

Usage Example

Graph Update :

import numpy as np
import matplotlib.pyplot as plt
from synimatic import Synimator

def setup():
    fig, ax = plt.subplots()
    ax.set_xlim(0, 4 * np.pi)
    ax.set_ylim(-2.2, 2.2)
    line, = ax.plot([], [], lw=2)
    return fig, ax, line

def update(frame, context):
    fig, ax, line = context
    x = np.linspace(0, 4 * np.pi, 500)
    y1 = np.sin(x + frame / 10)
    y2 = np.sin(1.2 * x - frame / 15)
    line.set_data(x, y1 + y2)

animator = Synimator(setup)
animator.animate(update, frames=200, fps=30)
animator.show()

---

API Reference

• Synimator(setup): Creates the animator instance.

  • setup: Function returning (fig, ax, *artists).

• animator.animate(update, frames, **kwargs): Builds the animation.

  • update: Function with signature update(frame, context).
  • frames: Frame count or iterable of frame values.
  • fps: Frames per second, default 16.
  • blit: Enable blitting for performance, default True.
  • embed: Jupyter embedding format ('jshtml' or 'video'), default 'jshtml'.
  • embed_limit: Maximum file size for embedding animations in notebooks (MB).
  • save_only: Skip display pre-rendering to optimize save performance in notebooks, default False.

• animator.show(): Displays the animation according to the environment.

• animator.save(filename, **kwargs): Saves animation to file.

  • filename: Output filename (e.g., animation.mp4).
  • **kwargs: Additional arguments for matplotlib's save method (e.g., dpi).

---

Additional Examples

Conway's Game of Life animation

import numpy as np
import matplotlib.pyplot as plt
from synimatic import Synimator

def setup():
    fig, ax = plt.subplots()
    grid = np.random.choice([0, 1], (30, 30), p=[0.8, 0.2])
    img = ax.imshow(grid, cmap='gray', vmin=0, vmax=1)
    ax.axis('off')
    update.grid = grid
    return fig, ax, img

def update(frame, context):
    fig, ax, img = context
    G = update.grid
    neighbors = sum(np.roll(np.roll(G, i, 0), j, 1)
                    for i in (-1, 0, 1) for j in (-1, 0, 1)
                    if (i != 0 or j != 0))
    G = ((neighbors == 3) | ((G == 1) & (neighbors == 2))).astype(int)
    update.grid = G
    img.set_data(G)

animator = Synimator(setup)
animator.animate(update, frames=100, fps=20)
animator.show()

# To save the animation:
# animator.save("game_of_life.mp4")

Double Pendulum

import numpy as np
import matplotlib.pyplot as plt
from synimatic import Synimator

def setup():
    fig, ax = plt.subplots()
    ax.set_xlim(-2, 2)
    ax.set_ylim(-2, 2)
    ax.set_aspect('equal')
    arm1, = ax.plot([], [], lw=2, color='red')
    arm2, = ax.plot([], [], lw=2, color='blue')
    trail, = ax.plot([], [], lw=1, color='green', alpha=0.5)
    update.trail_x, update.trail_y = [], []
    return fig, ax, arm1, arm2, trail

def update(frame, context):
    fig, ax, arm1, arm2, trail = context
    theta1 = np.pi/2 * np.cos(frame)
    theta2 = np.pi/2 * np.sin(2 * frame)
    x1, y1 = np.sin(theta1), -np.cos(theta1)
    x2, y2 = x1 + np.sin(theta2), y1 - np.cos(theta2)
    arm1.set_data([0, x1], [0, y1])
    arm2.set_data([x1, x2], [y1, y2])
    update.trail_x.append(x2)
    update.trail_y.append(y2)
    trail.set_data(update.trail_x[-100:], update.trail_y[-100:])

frames = np.linspace(0, 4 * np.pi, 500)
animator = Synimator(setup)
animator.animate(update, frames, fps=60)
animator.show()

---

Contributing

Contributions welcome. Report issues or submit pull requests at the GitHub repository.

---

License

Distributed under the MIT License.

Author

Jonathan Ayalew — GitHub