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Adds radiography requirements 1. and 2.a #24
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This includes averaging out loaded data (for multiple reference images) and transmission calculation. The ROI code is WIP, however the transmission code causes us to run OOM, so it would be better to figure out a way of resolving that first
OwenArnold
reviewed
Jun 3, 2020
| "dark_field = radiography.fits_to_variable(dark_field_dir, average_data=True)\n", | ||
| "flat_field = radiography.fits_to_variable(flat_field_dir, average_data=True)\n", | ||
| "\n", | ||
| "# This blows through 32GB of memory as raw_data[\"sample\"] is already ~17 GB\n", |
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Can you implement below in place to avoid memory overhead of temporaries?
sample -= dark
flat -= dark
sample /= flat
or similar
OwenArnold
reviewed
Jun 3, 2020
| "\n", | ||
| "Before starting you must:\n", | ||
| "- Ensure that `scipp` and `mantid` are on your `PYTHONPATH`.\n", | ||
| "- Generate the `config.py` file using `make_config.py`. Refer to the `README.md` or `python make_config.py --help` for information.\n", |
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Download data (specify own-cloud location) as a step?
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This includes averaging out loaded data (for multiple reference images)
and transmission calculation.
The ROI code is WIP, however the transmission code causes us to run OOM,
so it would be better to figure out a way of resolving that first
One approach at the trade-off of space for speed is to calculate and apply the transmission correction projection by projection, rather than calculate it for the ~2000 projections in one