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pipeline:utilities:sp_eval_isac [2020/07/24 23:18] shaikh |
pipeline:utilities:sp_eval_isac [2020/10/21 10:17] shaikh |
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===== Typical usage ===== | ===== Typical usage ===== | ||
[{{ : | [{{ : | ||
- | The purpose of sp_separate_class.py is to: | + | The purpose of sp_eval_isac.py is to: |
: write particle-membership lists for each class | : write particle-membership lists for each class | ||
: write separate stacks for each class, | : write separate stacks for each class, | ||
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\\ __7. Apply a Gaussian band-pass filter to an image stack (e.g., class averages)__: | \\ __7. Apply a Gaussian band-pass filter to an image stack (e.g., class averages)__: | ||
- | sp_eval_isac.py input_class_avgs output_directory --particles=input_image_stack | + | sp_eval_isac.py input_class_avgs output_directory --apix=pixel_size |
- | The default beautifier settings (CTF-correction using a Wiener filter and power-spectrum adjustment) will amplify the low-resolution data, and may make the averages more difficult to interpret. A band-pass filter will dampen the lowest-resolution data, and also the high-resolution noise. | + | The default beautifier settings (CTF-correction using a Wiener filter and power-spectrum adjustment) will amplify the low-resolution data, and may make the averages more difficult to interpret. A band-pass filter will dampen the lowest-resolution data, and also the high-resolution noise. This filtration may be helpful for recognizing smaller complexes. |
- | [{{pipeline: | + | [{{pipeline: |
\\ | \\ |