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Use the ClassClean logic to separate good particle images from bad “junk” particles, a general prerequisite for obtaining a good 3d structure.
The ClassClean logic computes a quality score for particles in context of a group of particles (“class”). Therefore, this logic requires particles to be aligned and classified beforehand - either by assignment to a reference from alignment or a class from classification. ClassClean can be operated in two modes: In “clean” mode particles are evaluated within each class and dependent on chosen parameters a certain fraction of only the best particles is kept. In “refine” mode existing classes from a previous “clean” mode run may be supplemented with a new set of particles. These new particles are then i) assigned to the best matching class, ii) evaluated within this class and iii) either kept when they improve the quality measure of this class or discarded.
Use this mode to remove bad particles from a data set.
| Parameters | Description |
| Class truncation - enabled | Use this option to define how many of the best particles should be kept per class at maximum |
| → Class truncation threshold | Maximum number of best particles to be kept for each class |
| Class truncation - disabled | Use this option to define the fraction of best particles to be kept solely by the “Sigma value” |
| Next main parameter | and more more more |
| → and its sub-parameter | … descriptions |
| Input | Description |
| FirstInput | Input Description 1 |
| SecondInput | Input Description 2 |
| ThirdInput | Input Description 3: Optional Input in Italic |
| Output | Description |
| FirstOutput | Output Description |
| New/Changed Header Values | Description |
| headerValue1 | what does it say? how is it changed? |
| headerValue2 | what does it say? how is it changed? |
| headerValue3 | what does it say? how is it changed? |
| headerValue4 | what does it say? how is it changed? |
Here, a short introduction for the given mode should be placed. Again, state WHAT and WHY this mode us useful in not more than 2 sentences.
| Parameters | Description |
| Some changeable parameter | Description of this parameter |
| → and its sub-parameter | more description |
| Next main parameter | and more more more |
| → and its sub-parameter | … descriptions |
| Input | Description |
| FirstInput | Input Description 1 |
| SecondImput | Input Description 2 |
| ThridImput | Input Description 1 |
| Output | Description |
| FirstOutput | Output Description |
| New/Changed Header Values | Description |
| headerValue1 | what does it say? how is it changed? |
| headerValue2 | what does it say? how is it changed? |
| headerValue3 | what does it say? how is it changed? |
| headerValue4 | what does it say? how is it changed? |
In this paragraph, the “HOW a logic works under the hood” and WHY someone should use it can be elaborated with higher detail. Describes a scenario in an image processing workflow where this logic can be used to solve the resulting problem. Also, wikipages, publications or anything else describing the theory behind an algorithm should be linked here, if applicable.