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--- pipeline:utilities:sp_eval_isac [2020/07/24 23:20]
shaikh
+++ pipeline:utilities:sp_eval_isac [2020/10/21 10:17]
shaikh
@@ Line 15: / Line 15: @@
 ===== Typical usage =====
 [{{ :pipeline:utilities:regular-evil-isac.png?275|Regular ISAC (top) vs. Evil ISAC (bottom)}}]
-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 separate stacks for each class,
@@ Line 53: / Line 53: @@
 \\ __7. Apply a Gaussian band-pass filter to an image stack (e.g., class averages)__:[{{ :pipeline:utilities:plotbandpass.png?400|Shape of the band-pass filter}}]
-  sp_eval_isac.py input_class_avgs output_directory --particles=input_image_stack --align_isac_dir=isac_directory --write_centered --applyparams=centering_mode
+  sp_eval_isac.py input_class_avgs output_directory --apix=pixel_size --bandpass_radius=bandpass_radius_angstroms
-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:utilities:bandpass-example.png?400|(top) Beautified images and (bottom) band-pass filtered images}}]
+[{{pipeline:utilities:bandpass-example.png?400|(top) Beautified images and (bottom) band-pass filtered images, centered at 14 Angstroms}}]
 \\

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