Mercurial > repos > bgruening > cp_image_math

annotate test-data/identify_primary_objects_adv_global_rb.cppipe @ 2:c09dcda1143f draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 35da2dcd86747c9bff138e100dbe08c6106f3780"
author bgruening
date Sat, 06 Feb 2021 10:03:42 +0000
parents 6d3851ac885f
children 6100a22bf4f2
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6d3851ac885f "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit c3917e27eb1c1deeb381aa0dc8161c07699562fb"
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1 CellProfiler Pipeline: http://www.cellprofiler.org
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2 Version:3
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3 DateRevision:319
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4 GitHash:
6d3851ac885f "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit c3917e27eb1c1deeb381aa0dc8161c07699562fb"
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5 ModuleCount:5
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6 HasImagePlaneDetails:False
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8 Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
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9 :
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10 Filter images?:Images only
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11 Select the rule criteria:and (extension does isimage) (directory doesnot startwith ".")
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12
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13 Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
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14 Extract metadata?:Yes
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15 Metadata data type:Text
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16 Metadata types:{}
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17 Extraction method count:1
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18 Metadata extraction method:Extract from file/folder names
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19 Metadata source:File name
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20 Regular expression to extract from file name:(?P<field1>.*)_(?P<field2>[a-zA-Z0-9]+)_(?P<field3>[a-zA-Z0-9]+)_(?P<field4>[a-zA-Z0-9]+)
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21 Regular expression to extract from folder name:(?P<folderField1>.*)
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22 Extract metadata from:All images
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23 Select the filtering criteria:and (file does contain "")
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24 Metadata file location:
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25 Match file and image metadata:[]
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26 Use case insensitive matching?:No
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27
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28 NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
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29 Assign a name to:Images matching rules
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30 Select the image type:Grayscale image
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31 Name to assign these images:DNA
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32 Match metadata:[]
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33 Image set matching method:Order
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34 Set intensity range from:Image metadata
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35 Assignments count:1
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36 Single images count:0
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37 Maximum intensity:255.0
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38 Process as 3D?:No
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39 Relative pixel spacing in X:1.0
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40 Relative pixel spacing in Y:1.0
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41 Relative pixel spacing in Z:1.0
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42 Select the rule criteria:and (file does startwith "im")
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43 Name to assign these images:DNA
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44 Name to assign these objects:Cell
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45 Select the image type:Grayscale image
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46 Set intensity range from:Image metadata
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47 Select the image type:Grayscale image
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48 Maximum intensity:255.0
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49
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50 Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
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51 Do you want to group your images?:Yes
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52 grouping metadata count:1
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53 Metadata category:field1
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54
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55 IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:13|show_window:True|notes:\x5B\'Identify the nuclei from the DNA channel.\', \'PARAMS\x3A\', \'- Typical diameter of objects (Min,Max)\', \'- Method to distinguish clumped objects\x3A Shape/None. With Shape, the distance between the 2 centers can be changed.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False]
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56 Select the input image:DNA
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57 Name the primary objects to be identified:Nuclei
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58 Typical diameter of objects, in pixel units (Min,Max):10,40
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59 Discard objects outside the diameter range?:Yes
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60 Discard objects touching the border of the image?:Yes
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61 Method to distinguish clumped objects:Shape
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62 Method to draw dividing lines between clumped objects:Shape
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63 Size of smoothing filter:1
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64 Suppress local maxima that are closer than this minimum allowed distance:7
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65 Speed up by using lower-resolution image to find local maxima?:Yes
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66 Fill holes in identified objects?:After both thresholding and declumping
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67 Automatically calculate size of smoothing filter for declumping?:No
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68 Automatically calculate minimum allowed distance between local maxima?:No
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69 Handling of objects if excessive number of objects identified:Continue
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70 Maximum number of objects:500
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71 Use advanced settings?:Yes
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72 Threshold settings version:10
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73 Threshold strategy:Global
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74 Thresholding method:RobustBackground
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75 Threshold smoothing scale:1.4000
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76 Threshold correction factor:1.0
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77 Lower and upper bounds on threshold:0.0,1.0
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78 Manual threshold:0
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79 Select the measurement to threshold with:None
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80 Two-class or three-class thresholding?:Two classes
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81 Assign pixels in the middle intensity class to the foreground or the background?:Foreground
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82 Size of adaptive window:50
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83 Lower outlier fraction:0.06
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84 Upper outlier fraction:0.07
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85 Averaging method:Median
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86 Variance method:Median absolute deviation
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87 # of deviations:3.00
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88 Thresholding method:RobustBackground