Difference between revisions of "mrcImageNormalizing"
(→-m details) |
(→Execution example) |
||
| Line 174: | Line 174: | ||
</tr> | </tr> | ||
</table> | </table> | ||
| + | |||
| + | === Option -m === | ||
| + | ==== Case: m=5 ==== | ||
| + | ===== Case: A=1, B=0 ===== | ||
| + | <table> | ||
| + | <tr> | ||
| + | <td>[[File:Outdata-m5-mrcImageNormalizing.png]]</td> | ||
| + | <td><p align="left">Min<br> | ||
| + | Max<br> | ||
| + | Mean<br> | ||
| + | SD<br> | ||
| + | SE<br></p> | ||
| + | </td> | ||
| + | <td><p align="left">-2.5693 (10, 1, 0)<br> | ||
| + | 4.5421 (24, 39, 0)<br> | ||
| + | -3.70619e-12<br> | ||
| + | 1<br> | ||
| + | 0.0125<br></p> | ||
| + | </td> | ||
| + | </tr> | ||
| + | </table> | ||
| + | Data is Normalized as Mean=0, SD=1. | ||
| + | |||
| + | ===== Case: A=3, B=2 ===== | ||
| + | <table> | ||
| + | <tr> | ||
| + | <td>[[File:Outdata1-m5-mrcImageNormalizing.png]]</td> | ||
| + | <td><p align="left">Min<br> | ||
| + | Max<br> | ||
| + | Mean<br> | ||
| + | SD<br> | ||
| + | SE<br></p> | ||
| + | </td> | ||
| + | <td><p align="left">-5.70789 (10, 1, 0)<br> | ||
| + | 15.6263 (24, 39, 0)<br> | ||
| + | 2<br> | ||
| + | 3<br> | ||
| + | 0.0375<br></p> | ||
| + | </td> | ||
| + | </tr> | ||
| + | </table> | ||
| + | Data is Normalized as Mean=2, SD=3. | ||
Latest revision as of 04:55, 3 October 2014
mrcImageNormalizing is Eos's Command that performs normalizing the value of image.
Contents
List of option
Main option
| Option | Essential/Optional | Description | Default |
|---|---|---|---|
| -i | Essential | Input: mrcImage | NULL |
| -o | Essential | Output: mrcImage | NULL |
| -A | Optional | A | 1.0 |
| -B | Optional | B | 0.0 |
| -ContourMin | Optional | ContourMin | 0.0 |
| -ContourMax | Optional | ContourMax | 1.0 |
| -ContourSolvent | Optional | ContourSolvent | 0.0 |
| -c | Optional | ConfigurationFile | NULL |
| -m | Optional | Mode | 0 |
| -h | Optional | Help |
-m details
| Value | Description |
|---|---|
| 0 | Double Exponential: Solvent and Object Fitting histgram to double exponentials as Solvent and Object |
| 1 | Min-Max: Background and Object data = A*(data-Min)/(Max-Min) + B |
| 2 | Contour data = A*(data-ContourMin)/(ContourMax-ContourMin) + B |
| 3 | Contour and Solvent if data < ContourSolvent, data = ContourSolvent. After this, calculate the below. |
| 4 | No Estimation data = A*data + B |
| 5 | Assume the density as Gaussian data = A*Normalized(data) + B |
Execution example
Input file image
 |
Min Max |
-18651.7 (10, 1, 0) 52942.7 (24, 39, 0) |
Example of options only essential
 |
Min Max |
-0.00221962 (10, 1, 0) 0.00448009 (24, 39, 0) |
Option -A, -B
Case: A=1000, B=3
 |
Min Max |
0.780381 (10, 1, 0) 7.48009 (24, 39, 0) |
Option -m
Case: m=5
Case: A=1, B=0
 |
Min Max |
-2.5693 (10, 1, 0) 4.5421 (24, 39, 0) |
Data is Normalized as Mean=0, SD=1.
Case: A=3, B=2
 |
Min Max |
-5.70789 (10, 1, 0) 15.6263 (24, 39, 0) |
Data is Normalized as Mean=2, SD=3.
