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allevitanandClaude Sonnet 4.6
dd744a00a4
- Switch panel plots to use subfigures with constrained_layout for better layout management - Add plot_loss_history method to CDIModel base class - Fix matplotlib compatibility for older versions (interactive backend detection) - Make CUDA usage conditional in examples - Add panel_plot_mode and plot_level params to FancyPtycho constructor - Default plot_level filtering to 1 instead of 0 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
"""
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Runs a very simple reconstruction using the SimplePtycho model, which was
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designed to be an easy introduction to show how the models are made and used.
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For a more realistic example of how to use cdtools for real-world data,
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look at fancy_ptycho.py and gold_ball_ptycho.py, both of which use the
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more powerful FancyPtycho model and include more information on how to
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correct for common sources of error.
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"""
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import cdtools
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import torch as t
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from matplotlib import pyplot as plt
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# We load an example dataset from a .cxi file
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filename = 'example_data/lab_ptycho_data.cxi'
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dataset = cdtools.datasets.Ptycho2DDataset.from_cxi(filename)
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# We create a ptychography model from the dataset
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model = cdtools.models.SimplePtycho.from_dataset(dataset)
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# We move the model to the GPU, if possible
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if t.cuda.is_available():
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model.to(device='cuda')
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dataset.get_as(device='cuda')
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model.inspect(dataset)
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print('hi')
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# We run the reconstruction
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for loss in model.Adam_optimize(30, dataset, batch_size=10):
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# We print a quick report of the optimization status
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print(model.report())
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# And liveplot the updates to the model as they happen
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if model.epoch % 10 == 0:
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model.inspect(dataset)
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# We study the results
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model.inspect(dataset, replot_all=True)
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model.compare(dataset)
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plt.show()
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