# simulation.py
import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.title("Sine Wave")
plt.savefig("plot.png")

optixlog add-logging simulation.py

import optixlog
import os
import numpy as np
import matplotlib.pyplot as plt

# Initialize OptixLog
with optixlog.run(
    run_name="experiment",
    api_key=os.getenv("OPTIX_API_KEY"),
    project=os.getenv("OPTIX_PROJECT", "MyProject"),
    config={},
    create_project_if_not_exists=True
) as client:
    x = np.linspace(0, 10, 100)
    y = np.sin(x)
    
    plt.plot(x, y)
    plt.title("Sine Wave")
    
    # Log the plot
    fig = plt.gcf()
    client.log_matplotlib("plot", fig)
    
    plt.savefig("plot.png")

optixlog add-logging simulation.py --smart

import optixlog
import os
import numpy as np
import matplotlib.pyplot as plt

if __name__ == "__main__":
    # Initialize OptixLog with context manager
    with optixlog.run(
        run_name="experiment",
        api_key=os.getenv("OPTIX_API_KEY"),
        project=os.getenv("OPTIX_PROJECT", "MyProject"),
        config={},
        create_project_if_not_exists=True
    ) as client:
        # Your experiment code here
        x = np.linspace(0, 10, 100)
        y = np.sin(x)
        
        # Use client.log_matplotlib() instead of manual PIL conversion
        fig, ax = plt.subplots()
        ax.plot(x, y)
        ax.set_title("Sine Wave")
        client.log_matplotlib("plot", fig)
        
        plt.savefig("plot.png")

# training.py
import numpy as np

losses = []
accuracies = []

for epoch in range(100):
    loss = train_step()
    accuracy = validate()
    
    losses.append(loss)
    accuracies.append(accuracy)
    
    print(f"Epoch {epoch}: loss={loss:.4f}, accuracy={accuracy:.4f}")

optixlog add-logging training.py --smart

import optixlog
import os
import numpy as np

if __name__ == "__main__":
    with optixlog.run(
        run_name="experiment",
        api_key=os.getenv("OPTIX_API_KEY"),
        project=os.getenv("OPTIX_PROJECT", "MyProject"),
        config={},
        create_project_if_not_exists=True
    ) as client:
        losses = []
        accuracies = []
        
        for epoch in range(100):
            loss = train_step()
            accuracy = validate()
            
            losses.append(loss)
            accuracies.append(accuracy)
            
            # Log metrics
            client.log(step=epoch, loss=loss, accuracy=accuracy)
            
            print(f"Epoch {epoch}: loss={loss:.4f}, accuracy={accuracy:.4f}")
        
        # Log final plot
        client.log_plot("loss_curve", range(100), losses, 
                         title="Training Loss", ylabel="Loss")
        client.log_plot("accuracy_curve", range(100), accuracies,
                         title="Accuracy", ylabel="Accuracy")

# Cell 1
import numpy as np
import matplotlib.pyplot as plt

# Cell 2
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Cell 3
plt.plot(x, y)
plt.title("Sine Wave")

optixlog add-logging notebook.ipynb

# sweep.py
wavelengths = [1.3, 1.4, 1.5, 1.6]

for wavelength in wavelengths:
    transmission = simulate(wavelength)
    print(f"λ={wavelength}: T={transmission:.4f}")

optixlog add-logging sweep.py --smart

import optixlog
import os

wavelengths = [1.3, 1.4, 1.5, 1.6]

for wavelength in wavelengths:
    with optixlog.run(
        run_name=f"sweep_{wavelength}",
        api_key=os.getenv("OPTIX_API_KEY"),
        project=os.getenv("OPTIX_PROJECT", "MyProject"),
        config={"wavelength": wavelength},
        create_project_if_not_exists=True
    ) as client:
        transmission = simulate(wavelength)
        
        # Log result
        client.log(step=0, transmission=transmission, wavelength=wavelength)
        
        print(f"λ={wavelength}: T={transmission:.4f}")

# meep_sim.py
import meep as mp

sim = mp.Simulation(
    cell_size=mp.Vector3(10, 10, 0),
    resolution=30,
    sources=[...]
)

for step in range(100):
    sim.run(until=1)
    field = sim.get_array(...)
    # Save field data

optixlog add-logging meep_sim.py --smart

import optixlog
import os
import meep as mp

if __name__ == "__main__":
    with optixlog.run(
        run_name="experiment",
        api_key=os.getenv("OPTIX_API_KEY"),
        project=os.getenv("OPTIX_PROJECT", "MyProject"),
        config={"resolution": 30, "cell_size": [10, 10, 0]},
        create_project_if_not_exists=True
    ) as client:
        sim = mp.Simulation(
            cell_size=mp.Vector3(10, 10, 0),
            resolution=30,
            sources=[...]
        )
        
        for step in range(100):
            sim.run(until=1)
            field = sim.get_array(...)
            
            # Log field snapshot every 10 steps
            if step % 10 == 0:
                client.log_array_as_image(f"field_{step}", field, 
                                            cmap='RdBu', 
                                            title=f"Field at step {step}")

# Instrument all Python files in directory
for file in *.py; do
    echo "Instrumenting $file..."
    optixlog add-logging "$file" --smart
done

# Run all
for file in *.py; do
    echo "Running $file..."
    python "$file"
done

# View all runs
optixlog runs --limit 20