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eyes:logics:rct [2017/06/12 18:08] dhaselb created |
eyes:logics:rct [2017/06/12 18:42] (current) jschlie1 |
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| Calculates Random conical Tilt volumes from a set of tilted raw images by the aid of a stack of the classified untilted counter parts. This is an experimental method to Determine the Euler angles | Calculates Random conical Tilt volumes from a set of tilted raw images by the aid of a stack of the classified untilted counter parts. This is an experimental method to Determine the Euler angles | ||
| ===== Usage ===== | ===== Usage ===== | ||
| - | Here, a general/generic description of HOW the logic is USED should be given. Try to be as general as possible, but also mention prerequisites, restrictions, advantages, requirements which are specific of this logic. Basically everything the user needs to know to successfully use this logic. | + | To run this logic two stacks of the same size, on of the tilted and one of the untilted images have to be prepared. It is necessary that the images in both stacks are in the same order. The tilted images should be at least normalised. The untilted images need to refined on a 2D level to give good class averages. in general Class averages should contain about 100 particles. |
| - | ===== Example ==== | + | ===== Parameter and I/Os ===== |
| - | Here, a very specific example should be given/described. In the future, this can be supported by screenshots etc.. For the moment, give an example easy enough for the user to understand, but specific enough to elaborate why a given parameter is a good set for this very situation. | + | |
| - | + | ||
| - | ===== Modes/Processes ===== | + | |
| - | ==== thisIsTheNameOfMode1 ==== | + | |
| - | 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. | + | |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Parameters ^ Description ^ | ^ Parameters ^ Description ^ | ||
| - | | Some changeable parameter | Description of this parameter | | + | | Class Number | from which class shout the RCT be calculated? If -1 is given the RCT is calculated for every class. | |
| - | | -> and its sub-parameter | more description | | + | | tilted inplane Rotation | inplane rotation of the tilt axis, **none** not recommended will yield in unuseable results, **simple** the same inplane rotation is assumed for every image, **complex** in plane rotation is given as IO, assuming that the in plane rotation is not the same for every micrograph. | |
| - | | Next main parameter | and more more more | | + | | -> tilted angle rotation | in plane rotation of tilt axis in degree. Can be determined by the picker | |
| - | | -> and its sub-parameter | ... descriptions | | + | | mode | Mode of reconstruction **exact** back projection, **fourier** Fourier reconstruction, **sirt** Sirt algorithm. For details see [[eyes:logics:reconstruction|]] | |
| + | | Normalize 3D | Should the resulting 3D Volumes be normalized? | | ||
| + | | Reproject 3D | Shall there be Backprojections of the input images? | | ||
| + | |Symmetry | Is there a point group symmetry known for the molecule? | | ||
| + | | experimental tilt angle | angle of goniometer rotation. Can be also determined in the picker | | ||
| + | |tilted with angle| FIXME| | ||
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Input ^ Description ^ | ^ Input ^ Description ^ | ||
| - | | FirstInput | Input Description 1 | | + | | tilted | normalized and maybe filtered otherwise untreated stack of tilted images | |
| - | | SecondInput | Input Description 2 | | + | | untilted | output of a [[eyes:logics:classification]] logic of the untilted images | |
| - | | //ThirdInput// | Input Description 3: Optional Input in Italic | | + | | //tilt angles// | stack of images containing the tilt angles in the header, as written by the picker | |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Output ^ Description ^ | ^ Output ^ Description ^ | ||
| - | | FirstOutput | Output Description | | + | | 3ds | Stack of reconstructed 3D volumes one stack for each 2D class | |
| - | |< 100% 30% >| | ||
| - | ^ 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? | | ||
| - | |||
| - | ==== thisIsTheNameOfMode2 ==== | ||
| - | 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. | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ 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 | | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ Input ^ Description ^ | ||
| - | | FirstInput | Input Description 1 | | ||
| - | | SecondImput | Input Description 2 | | ||
| - | | ThridImput | Input Description 1 | | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ Output ^ Description ^ | ||
| - | | FirstOutput | Output Description | | ||
| - | |||
| - | |< 100% 30% >| | ||
| - | ^ 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? | | ||
| ===== Concept ===== | ===== Concept ===== | ||
| - | 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. | + | Random conical tilt as described in [[http://www.sciencedirect.com/science/article/pii/S0969212610001577?via%3Dihub]] |