pip install optixlog

from optixlog import Optixlog

# Create client
client = Optixlog(api_key="your_api_key_here")

# Get or create a project
project = client.project(name="MyProject")

# Create a run
run = project.run(name="experiment_1", config={"lr": 0.001})

# Log metrics
for step in range(100):
    run.log(step=step, loss=0.5, accuracy=0.9)

# Log matplotlib plots
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [1, 4, 9])
run.log_matplotlib("my_plot", fig)

# Client → Project → Run
client = Optixlog(api_key=api_key)
project = client.project(name="my_project")
run = project.run(name="experiment_1")

run = project.run(name="experiment")
with run:
    run.log(step=0, loss=0.5)
# Automatic status reporting

# Log matplotlib figures directly
run.log_matplotlib("plot", fig)

# Create and log plots in one call
run.log_plot("spectrum", x, y, title="Spectrum")

# Convert arrays to heatmaps
run.log_array_as_image("field", field_data, cmap='hot')

# Create histograms, scatter plots, and more
run.log_histogram("distribution", data, bins=50)
run.log_scatter("correlation", x, y)

mpirun -n 4 python simulation.py

# Only master process logs - no code changes needed
client = Optixlog(api_key=api_key)
print(f"Master: {client.is_master}, Rank: {client.rank}, Size: {client.size}")

run = client.project(name="sim").run(name="parallel_run")
run.log(step=step, power=power)  # Only master logs

# Creates "NewProject" if it doesn't exist
project = client.project(name="NewProject", create_if_not_exists=True)

import optixlog

runs = optixlog.list_runs(api_url, api_key, project="MyProject")
metrics = optixlog.get_metrics(api_url, api_key, run_id)
artifacts = optixlog.get_artifacts(api_url, api_key, run_id)
comparison = optixlog.compare_runs(api_url, api_key, [run_id1, run_id2])

from optixlog import Optixlog

client = Optixlog(api_key="your_key")
run = client.project(name="training").run(
    name="experiment_v1",
    config={"lr": 0.001, "optimizer": "adam"}
)

for epoch in range(100):
    train_loss = train_epoch()
    val_loss = validate()
    run.log(step=epoch, train_loss=train_loss, val_loss=val_loss)

import meep as mp
from optixlog import Optixlog

client = Optixlog(api_key="your_key")
run = client.project(name="simulations").run(
    name="waveguide_sim",
    config={"wavelength": 1.55, "resolution": 30}
)

sim = mp.Simulation(...)
for step in range(100):
    sim.run(until=1)
    power = calculate_power(sim)
    run.log(step=step, power=power)

    if step % 10 == 0:
        field = sim.get_array(...)
        run.log_array_as_image(f"field_{step}", field, cmap='hot')

client = Optixlog(api_key="your_key")
project = client.project(name="sweeps")

for wavelength in [1.3, 1.4, 1.5, 1.6]:
    run = project.run(
        name=f"sweep_{wavelength}",
        config={"wavelength": wavelength}
    )

    result = simulate(wavelength)
    run.log(step=0, transmission=result)

client = Optixlog(api_key="your_key")
run = client.project(name="hardware").run(name="spectrum_test")

for freq in range(1000, 2000, 10):
    instrument.write(f"FREQ {freq}MHz")
    power = float(instrument.query("POW?"))
    run.log(step=freq, power_dBm=power)

from optixlog import Optixlog

# 1. Create client
client = Optixlog(api_key="your_key", api_url="https://optixlog.com")

# 2. Get or create a project
project = client.project(name="my_project", create_if_not_exists=True)

# 3. Create a run
run = project.run(name="experiment_1", config={"lr": 0.001})

# 4. Log data
run.log(step=0, loss=0.5)
run.log_matplotlib("plot", fig)
run.log_file("data", "results.csv", "text/csv")

# 5. Query data (module-level functions)
runs = optixlog.list_runs(api_url, api_key, project="my_project")
metrics = optixlog.get_metrics(api_url, api_key, run_id)

export OPTIX_API_URL="http://localhost:3000"