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Nicolas 2025-12-06 18:09:41 +01:00
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@ -9,11 +9,11 @@ Embedded systems comes with a variety of CPU architectures. The **copy and patch
The summarized main features are: The summarized main features are:
- Fast to write & easy to read - Fast to write & easy to read
- Memory and type safety, minimal set of runtime errors [^1] - Memory and type safety, minimal set of runtime errors
- deterministic execution - deterministic execution
- Auto grad for efficient realtime optimizations - Auto grad for efficient realtime optimizations
- Optimized machine code for the target architectures x68_64, Aarch64 and ARMv7 [^3] - Optimized machine code for the target architectures x68_64, Aarch64 and ARMv7
- Very portable to new architectures [^2] - Very portable to new architectures
- Small python package, minimal dependencies, no cross compile toolchain required - Small python package, minimal dependencies, no cross compile toolchain required
## Current state ## Current state
@ -24,21 +24,23 @@ Currently worked on:
- For targeting Crossover‑MCUs, support for Thumb instructions required by ARM*-M is on the way. - For targeting Crossover‑MCUs, support for Thumb instructions required by ARM*-M is on the way.
- Constant-regrouping for symbolic optimization of the computation graph. - Constant-regrouping for symbolic optimization of the computation graph.
## Getting started & example ## Install
To install copapy, you can use pip. Precompiled wheels are available for Linux (x86_64, Aarch64 and ARMv7), Windows (x86_64) and Mac OS (x86_64, Aarch64): To install copapy, you can use pip. Precompiled wheels are available for Linux (x86_64, Aarch64 and ARMv7), Windows (x86_64) and Mac OS (x86_64, Aarch64):
```bash ```bash
pip install copapy pip install copapy
``` ```
## Examples
### Basic example
A very simple example program using copapy can look like this: A very simple example program using copapy can look like this:
```python ```python
import copapy as cp import copapy as cp
# Define variables # Define variables
a = cp.variable(0.25) a = cp.value(0.25)
b = cp.variable(0.87) b = cp.value(0.87)
# Define computations # Define computations
c = a + b * 2.0 c = a + b * 2.0
@ -56,6 +58,7 @@ print("Result d:", tg.read_value(d))
print("Result e:", tg.read_value(e)) print("Result e:", tg.read_value(e))
``` ```
### Inverse kinematics
An other example using autograd in copapy. Here for for implementing An other example using autograd in copapy. Here for for implementing
gradient descent to solve a reverse kinematic problem for gradient descent to solve a reverse kinematic problem for
a two joint 2D arm: a two joint 2D arm:
@ -80,15 +83,14 @@ def forward_kinematics(theta1, theta2):
return joint, end_effector return joint, end_effector
# Start values # Start values
theta = cp.vector([cp.variable(0.0), cp.variable(0.0)]) theta = cp.vector([cp.value(0.0), cp.value(0.0)])
# Iterative inverse kinematic # Iterative inverse kinematic
for _ in range(48): for _ in range(48):
joint, effector = forward_kinematics(theta[0], theta[1]) joint, effector = forward_kinematics(theta[0], theta[1])
error = ((target - effector) ** 2).sum() error = ((target - effector) ** 2).sum()
grad_vec = cp.grad(error, theta) theta -= alpha * cp.grad(error, theta)
theta -= alpha * grad_vec
tg = cp.Target() tg = cp.Target()
tg.compile(error, theta, joint) tg.compile(error, theta, joint)