| --- |
| 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. |
