--- license: mit language: - en pipeline_tag: unconditional-image-generation --- # galaxy_gen `galaxy_gen` is a library to generate galaxy data/distributions. The models used are present in this page. ## Installation You can install the package using pip: ```sh pip install galaxy_gen ``` ## Usage Here is an example of how to use the galaxy_gen library: ```python # example_usage.py import torch import matplotlib.pyplot as plt import galaxy_gen from galaxy_gen.sampler import load_model, generate_samples import os # Path to your saved model checkpoint. model_path = os.path.join(os.path.dirname(galaxy_gen.__file__), 'models/sample_model') device = 'cpu' # or 'cuda' if you have a GPU # Load the model. model = load_sample_model(model_path, device=device) # Generate random samples. samples = generate_samples(model) # (Optional) Visualize the samples. samples = samples.cpu().numpy() fig, axes = plt.subplots(4, 4, figsize=(8, 8)) for i, ax in enumerate(axes.flatten()): ax.imshow(samples[i][0], cmap='gray') ax.axis('off') plt.show() ``` Another expample to use the pre-trained model ```python # example_usage.py import torch import matplotlib.pyplot as plt from galaxy_gen.sampler import load_model, generate_metallicity_samples, generate_formationtime_samples # Path to your saved model checkpoint. model_path = 'models/formationtime_model.pth' device = 'cpu' # or 'cuda' if you have a GPU # Load the model. model = load_model("formation_time",model_path, device=device) # Generate random samples. samples = generate_formationtime_samples(model) # (Optional) Visualize the samples. samples = samples.cpu().numpy() fig, axes = plt.subplots(4, 4, figsize=(8, 8)) for i, ax in enumerate(axes.flatten()): ax.imshow(samples[i][0]) ax.axis('off') plt.show() ``` ## License This project is licensed under the MIT License - see the LICENSE file for details.