blaze2d 0.1.0

BLAZE - Band-structure LOBPCG Accelerated Zone Eigensolver for 2D Photonic Crystals

BLAZE - Band-structure LOBPCG Accelerated Zone Eigensolver

High-performance 2D photonic crystal band structure solver with Python bindings.

Installation

pip install blaze2d

Quick Start

from blaze import BulkDriver

# Load configuration from TOML file
driver = BulkDriver("config.toml")
print(f"Will run {driver.job_count} jobs")

# Stream results as they complete (for live plotting)
for result in driver.run_streaming():
    print(f"Job {result['job_index']}: {result['num_bands']} bands")
    # result['bands'] is a 2D array [k_index][band_index]
    # result['distances'] is the k-path distance for plotting

# Or collect all results at once
results, stats = driver.run_collect()
print(f"Completed {len(results)} jobs in {stats['total_time_secs']:.2f}s")

Configuration

BLAZE uses TOML configuration files. Example for a parameter sweep:

[bulk]
output_dir = "results"
output_mode = "full"

[bulk.ranges]
radius = { min = 0.2, max = 0.4, step = 0.05 }
eps_inside = { values = [9.0, 12.0, 13.0] }
polarization = ["te", "tm"]

[simulation]
resolution = 32
num_bands = 8

[lattice]
type = "square"

[[atoms]]
position = [0.5, 0.5]
radius = 0.3
epsilon = 1.0

API Reference

BulkDriver

driver = BulkDriver(config_path: str, threads: int = 0)

• config_path: Path to TOML configuration file
• threads: Thread count (-1 = adaptive, 0 = auto, >0 = fixed)

Properties

• job_count: Number of jobs to execute
• config_path: Path to configuration file

Methods

Method	Description
run_streaming()	Iterator yielding results as they complete
run_streaming_filtered(k_indices, band_indices)	Filtered streaming
run_collect(k_indices, band_indices)	Collect all results into list
run_batched(buffer_size_mb)	Run with buffered disk I/O
run()	Synchronous execution
dry_run()	Count jobs without executing

Result Dictionary

Each result is a dictionary with:

Key	Type	Description
job_index	int	Job identifier
params	dict	Simulation parameters
k_path	list	K-points as (kx, ky) tuples
distances	list	Cumulative k-path distance
bands	list	2D array [k_index][band_index]
num_k_points	int	Number of k-points
num_bands	int	Number of bands

Plotting Example

import matplotlib.pyplot as plt
from blaze import BulkDriver

driver = BulkDriver("sweep.toml")

# Live plotting with streaming
plt.ion()
fig, ax = plt.subplots()

for result in driver.run_streaming():
    ax.clear()
    for band_idx in range(result['num_bands']):
        band = [result['bands'][k][band_idx] for k in range(result['num_k_points'])]
        ax.plot(result['distances'], band)
    ax.set_xlabel('k-path')
    ax.set_ylabel('ω (normalized)')
    ax.set_title(f"Job {result['job_index']}")
    plt.pause(0.01)

plt.ioff()
plt.show()

Filtered Streaming

For large simulations, filter to only the k-points and bands you need:

# Stream only Gamma (0), X (10), M (15) and first 4 bands
for result in driver.run_streaming_filtered(
    k_indices=[0, 10, 15],
    band_indices=[0, 1, 2, 3]
):
    assert result['num_k_points'] == 3
    assert result['num_bands'] == 4

License

MIT