.. image:: https://github.com/ome/omero-metadata/workflows/OMERO/badge.svg
:target: https://github.com/ome/omero-metadata/actions
.. image:: https://badge.fury.io/py/omero-metadata.svg
:target: https://badge.fury.io/py/omero-metadata
OMERO metadata plugin
=====================
Plugin for use in the OMERO CLI. Provides tools for bulk
management of annotations on objects in OMERO.
Requirements
============
* OMERO 5.6.0 or newer
* Python 3.6 or newer
Installing from PyPI
====================
This section assumes that an OMERO.py is already installed.
Install the command-line tool using `pip <https://pip.pypa.io/en/stable/>`_:
::
$ pip install -U omero-metadata
Note the original version of this code is still available as deprecated code in
version 5.4.x of OMERO.py. When using the CLI metadata plugin, the
`OMERO_DEV_PLUGINS` environment variable should not be set to prevent
conflicts when importing the Python module.
Usage
=====
The plugin is called from the command-line using the ``omero metadata`` command::
$ omero metadata <subcommand>
Help for each command can be shown using the ``-h`` flag.
Objects can be specified as arguments in the format ``Class:ID``, such
as ``Project:123``.
Bulk-annotations are HDF-based tables with the NSBULKANNOTATION
namespace, sometimes referred to as OMERO.tables.
Available subcommands are:
- ``allanns``: Provide a list of all annotations linked to the given object
- ``bulkanns``: Provide a list of the NSBULKANNOTATION tables linked to the given object
- ``mapanns``: Provide a list of all MapAnnotations linked to the given object
- ``measures``: Provide a list of the NSMEASUREMENT tables linked to the given object
- ``original``: Print the original metadata in ini format
- ``pixelsize``: Set physical pixel size
- ``populate``: Add metadata (bulk-annotations) to an object (see below)
- ``rois``: Manage ROIs
- ``summary``: Provide a general summary of available metadata
- ``testtables``: Tests whether tables can be created and initialized
populate
--------
This command creates an ``OMERO.table`` (bulk annotation) from a ``CSV`` file and links
the table as a ``File Annotation`` to a parent container such as Screen, Plate, Project,
Dataset or Image. It also attempts to convert Image, Well or ROI names from the ``CSV`` into
object IDs in the ``OMERO.table``.
The ``CSV`` file must be provided as local file with ``--file path/to/file.csv``.
OMERO.tables have defined column types to specify the data-type such as ``double`` or ``long`` and special object-types of each column for storing OMERO object IDs such as ``ImageColumn`` or ``WellColumn``.
The default behaviour of the script is to automatically detect the column types from an input ``CSV``. This behaviour works as follows:
* Columns named with a supported object-type (e.g. ``plate``, ``well``, ``image``, ``dataset``, or ``roi``), with ``<object> id`` or ``<object> name`` will generate the corresponding column type in the OMERO.table. See table below for full list of supported column names.
============ ================= ==================== ====================================================================
Column Name Column type Detected Header Type Notes
============ ================= ==================== ====================================================================
Image ``ImageColumn`` ``image`` Accepts image IDs. Appends new 'Image Name' column with image names.
Image Name ``StringColumn`` ``s`` Accepts image names. Appends new 'Image' column with image IDs.
Image ID ``ImageColumn`` ``image`` Accepts image IDs. Appends new 'Image Name' column with image names.
Dataset ``DatasetColumn`` ``dataset`` Accepts dataset IDs.
Dataset Name ``StringColumn`` ``s`` Accepts dataset names.
Dataset ID ``DatasetColumn`` ``dataset`` Accepts dataset IDs.
Plate ``PlateColumn`` ``plate`` Accepts plate names. Adds new 'Plate' column with plate IDs.
Plate Name ``PlateColumn`` ``plate`` Accepts plate names. Adds new 'Plate' column with plate IDs.
Plate ID ``LongColumn`` ``l`` Accepts plate IDs.
Well ``WellColumn`` ``well`` Accepts well names. Adds new 'Well' column with well IDs.
Well Name ``WellColumn`` ``well`` Accepts well names. Adds new 'Well' column with well IDs.
Well ID ``LongColumn`` ``l`` Accepts well IDs.
ROI ``RoiColumn`` ``roi`` Accepts ROI IDs. Appends new 'ROI Name' column with ROI names.
ROI Name ``StringColumn`` ``s`` Accepts ROI names. Appends new 'ROI' column with ROI IDs.
ROI ID ``RoiColumn`` ``roi`` Accepts ROI IDs. Appends new 'ROI Name' column with ROI names.
============ ================= ==================== ====================================================================
Note: Column names are case insensitive. Space, no space, and underscore are all accepted as separators for column names (i.e. ``<object> name``/``<object> id```, ``<object>name``/``<object>id``, ``<object>_name``/``<object>_id`` are all accepted)
NB: Column names should not contain spaces if you want to be able to query by these columns.
* All other column types will be detected based on the column's data using the pandas library. See table below.
=============== ================= ====================
Column Name Column type Detected Header Type
=============== ================= ====================
Example String ``StringColumn`` ``s``
Example Long ``LongColumn`` ``l``
Example Float ``DoubleColumn`` ``d``
Example boolean ``BoolColumn`` ``b``
=============== ================= ====================
In the case of missing values, the column will be detected as ``StringColumn`` by default. If ``--allow-nan`` is passed to the
``omero metadata populate`` commands, missing values in floating-point columns will be detected as ``DoubleColumn`` and the
missing values will be stored as NaN.
However, it is possible to manually define the header types, ignoring the automatic header detection, if a ``CSV`` with a ``# header`` row is passed. The ``# header`` row should be the first row of the CSV and defines columns according to the following list (see examples below):
- ``d``: ``DoubleColumn``, for floating point numbers
- ``l``: ``LongColumn``, for integer numbers
- ``s``: ``StringColumn``, for text
- ``b``: ``BoolColumn``, for true/false
- ``plate``, ``well``, ``image``, ``dataset``, ``roi`` to specify objects
Automatic header detection can also be ignored if using the ``--manual_headers`` flag. If the ``# header`` is not present and this flag is used, column types will default to ``String`` (unless the column names correspond to OMERO objects such as ``image`` or ``plate``).
Examples
^^^^^^^^^
The examples below will use the default automatic column types detection behaviour. It is possible to achieve the same results (or a different desired result) by manually adding a custom ``# header`` row at the top of the CSV.
**Project / Dataset**
^^^^^^^^^^^^^^^^^^^^^^
To add a table to a Project, the ``CSV`` file needs to specify ``Dataset Name`` or ``Dataset ID``
and ``Image Name`` or ``Image ID``::
$ omero metadata populate Project:1 --file path/to/project.csv
Using ``Image Name`` and ``Dataset Name``:
project.csv::
Image Name,Dataset Name,ROI_Area,Channel_Index,Channel_Name
img-01.png,dataset01,0.0469,1,DAPI
img-02.png,dataset01,0.142,2,GFP
img-03.png,dataset01,0.093,3,TRITC
img-04.png,dataset01,0.429,4,Cy5
The previous example will create an OMERO.table linked to the Project as follows with
a new ``Image`` column with IDs:
========== ============ ======== ============= ============ =====
Image Name Dataset Name ROI_Area Channel_Index Channel_Name Image
========== ============ ======== ============= ============ =====
img-01.png dataset01 0.0469 1 DAPI 36638
img-02.png dataset01 0.142 2 GFP 36639
img-03.png dataset01 0.093 3 TRITC 36640
img-04.png dataset01 0.429 4 Cy5 36641
========== ============ ======== ============= ============ =====
Note: equivalent to adding ``# header s,s,d,l,s`` row to the top of the ``project.csv`` for manual definition.
Using ``Image ID`` and ``Dataset ID``:
project.csv::
image id,Dataset ID,ROI_Area,Channel_Index,Channel_Name
36638,101,0.0469,1,DAPI
36639,101,0.142,2,GFP
36640,101,0.093,3,TRITC
36641,101,0.429,4,Cy5
The previous example will create an OMERO.table linked to the Project as follows with
a new ``Image Name`` column with Names:
===== ======= ======== ============= ============ ==========
Image Dataset ROI_Area Channel_Index Channel_Name Image Name
===== ======= ======== ============= ============ ==========
36638 101 0.0469 1 DAPI img-01.png
36639 101 0.142 2 GFP img-02.png
36640 101 0.093 3 TRITC img-03.png
36641 101 0.429 4 Cy5 img-04.png
===== ======= ======== ============= ============ ==========
Note: equivalent to adding ``# header image,dataset,d,l,s`` row to the top of the ``project.csv`` for manual definition.
For both examples above, alternatively, if the target is a Dataset instead of a Project, the ``Dataset`` or ``Dataset Name`` column is not needed.
**Screen / Plate**
^^^^^^^^^^^^^^^^^^^
To add a table to a Screen, the ``CSV`` file needs to specify ``Plate`` name and ``Well``.
If a ``# header`` is specified, column types must be ``well`` and ``plate``::
$ omero metadata populate Screen:1 --file path/to/screen.csv
screen.csv::
Well,Plate,Drug,Concentration,Cell_Count,Percent_Mitotic
A1,plate01,DMSO,10.1,10,25.4
A2,plate01,DMSO,0.1,1000,2.54
A3,plate01,DMSO,5.5,550,4
B1,plate01,DrugX,12.3,50,44.43
This will create an OMERO.table linked to the Screen, with the
``Well Name`` and ``Plate Name`` columns added and the ``Well`` and
``Plate`` columns used for IDs:
===== ====== ====== ============== =========== ================ =========== ===========
Well Plate Drug Concentration Cell_Count Percent_Mitotic Well Name Plate Name
===== ====== ====== ============== =========== ================ =========== ===========
9154 3855 DMSO 10.1 10 25.4 a1 plate01
9155 3855 DMSO 0.1 1000 2.54 a2 plate01
9156 3855 DMSO 5.5 550 4.0 a3 plate01
9157 3855 DrugX 12.3 50 44.43 b1 plate01
===== ====== ====== ============== =========== ================ =========== ===========
If the target is a Plate instead of a Screen, the ``Plate`` column is not needed.
Note: equivalent to adding ``# header well,plate,s,d,l,d`` row to the top of the ``screen.csv`` for manual definition.
**ROIs**
^^^^^^^^^
If the target is an Image or a Dataset, a ``CSV`` with ROI-level or Shape-level data can be used to create an
``OMERO.table`` (bulk annotation) as a ``File Annotation`` linked to the target object.
If there is an ``roi`` column (header type ``roi``) containing ROI IDs, an ``Roi Name``
column will be appended automatically (see example below). If a column of Shape IDs named ``shape``
of type ``l`` is included, the Shape IDs will be validated (and set to -1 if invalid).
Also if an ``image`` column of Image IDs is included, an ``Image Name`` column will be added.
NB: Columns of type ``shape`` aren't yet supported on the OMERO.server::
$ omero metadata populate Image:1 --file path/to/image.csv
image.csv::
Roi,shape,object,probability,area
501,1066,1,0.8,250
502,1067,2,0.9,500
503,1068,3,0.2,25
503,1069,4,0.8,400
503,1070,5,0.5,200
This will create an OMERO.table linked to the Image like this:
=== ===== ====== =========== ==== ========
Roi shape object probability area Roi Name
=== ===== ====== =========== ==== ========
501 1066 1 0.8 250 Sample1
502 1067 2 0.9 500 Sample2
503 1068 3 0.2 25 Sample3
503 1069 4 0.8 400 Sample3
503 1070 5 0.5 200 Sample3
=== ===== ====== =========== ==== ========
Note: equivalent to adding ``# header roi,l,l,d,l`` row to the top of the ``image.csv`` for manual definition.
Alternatively, if the target is an Image, the ROI input column can be
``Roi Name`` (with type ``s``), and an ``roi`` type column will be appended containing ROI IDs.
In this case, it is required that ROIs on the Image in OMERO have the ``Name`` attribute set.
Note that the ROI-level data from an ``OMERO.table`` is not visible
in the OMERO.web UI right-hand panel under the ``Tables`` tab,
but the table can be visualized by clicking the "eye" on the bulk annotation attachment on the Image.
Developer install
=================
This plugin can be installed from the source code with::
$ cd omero-metadata
$ pip install .
License
-------
This project, similar to many Open Microscopy Environment (OME) projects, is
licensed under the terms of the GNU General Public License (GPL) v2 or later.
Copyright
---------
2018-2022, The Open Microscopy Environment and Glencoe Software, Inc
Raw data
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"download_url": "https://files.pythonhosted.org/packages/f1/7e/5edfe8e117279c295fe57200ee7c7063031a847c0c1960b48205fc7a037f/omero-metadata-0.12.0.tar.gz",
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"description": ".. image:: https://github.com/ome/omero-metadata/workflows/OMERO/badge.svg\n :target: https://github.com/ome/omero-metadata/actions\n\n.. image:: https://badge.fury.io/py/omero-metadata.svg\n :target: https://badge.fury.io/py/omero-metadata\n\nOMERO metadata plugin\n=====================\n\nPlugin for use in the OMERO CLI. Provides tools for bulk\nmanagement of annotations on objects in OMERO.\n\nRequirements\n============\n\n* OMERO 5.6.0 or newer\n* Python 3.6 or newer\n\n\nInstalling from PyPI\n====================\n\nThis section assumes that an OMERO.py is already installed.\n\nInstall the command-line tool using `pip <https://pip.pypa.io/en/stable/>`_:\n\n::\n\n $ pip install -U omero-metadata\n\nNote the original version of this code is still available as deprecated code in\nversion 5.4.x of OMERO.py. When using the CLI metadata plugin, the\n`OMERO_DEV_PLUGINS` environment variable should not be set to prevent\nconflicts when importing the Python module.\n\nUsage\n=====\n\nThe plugin is called from the command-line using the ``omero metadata`` command::\n\n $ omero metadata <subcommand>\n\nHelp for each command can be shown using the ``-h`` flag.\nObjects can be specified as arguments in the format ``Class:ID``, such\nas ``Project:123``.\n\nBulk-annotations are HDF-based tables with the NSBULKANNOTATION\nnamespace, sometimes referred to as OMERO.tables.\n\nAvailable subcommands are:\n\n- ``allanns``: Provide a list of all annotations linked to the given object\n- ``bulkanns``: Provide a list of the NSBULKANNOTATION tables linked to the given object\n- ``mapanns``: Provide a list of all MapAnnotations linked to the given object\n- ``measures``: Provide a list of the NSMEASUREMENT tables linked to the given object\n- ``original``: Print the original metadata in ini format\n- ``pixelsize``: Set physical pixel size\n- ``populate``: Add metadata (bulk-annotations) to an object (see below)\n- ``rois``: Manage ROIs\n- ``summary``: Provide a general summary of available metadata\n- ``testtables``: Tests whether tables can be created and initialized\n\npopulate\n--------\n\nThis command creates an ``OMERO.table`` (bulk annotation) from a ``CSV`` file and links \nthe table as a ``File Annotation`` to a parent container such as Screen, Plate, Project,\nDataset or Image. It also attempts to convert Image, Well or ROI names from the ``CSV`` into\nobject IDs in the ``OMERO.table``.\n\nThe ``CSV`` file must be provided as local file with ``--file path/to/file.csv``.\n\nOMERO.tables have defined column types to specify the data-type such as ``double`` or ``long`` and special object-types of each column for storing OMERO object IDs such as ``ImageColumn`` or ``WellColumn``.\n\nThe default behaviour of the script is to automatically detect the column types from an input ``CSV``. This behaviour works as follows:\n\n* Columns named with a supported object-type (e.g. ``plate``, ``well``, ``image``, ``dataset``, or ``roi``), with ``<object> id`` or ``<object> name`` will generate the corresponding column type in the OMERO.table. See table below for full list of supported column names.\n\n============ ================= ==================== ====================================================================\nColumn Name Column type Detected Header Type Notes\n============ ================= ==================== ====================================================================\nImage ``ImageColumn`` ``image`` Accepts image IDs. Appends new 'Image Name' column with image names.\nImage Name ``StringColumn`` ``s`` Accepts image names. Appends new 'Image' column with image IDs.\nImage ID ``ImageColumn`` ``image`` Accepts image IDs. Appends new 'Image Name' column with image names.\nDataset ``DatasetColumn`` ``dataset`` Accepts dataset IDs.\nDataset Name ``StringColumn`` ``s`` Accepts dataset names.\nDataset ID ``DatasetColumn`` ``dataset`` Accepts dataset IDs.\nPlate ``PlateColumn`` ``plate`` Accepts plate names. Adds new 'Plate' column with plate IDs.\nPlate Name ``PlateColumn`` ``plate`` Accepts plate names. Adds new 'Plate' column with plate IDs.\nPlate ID ``LongColumn`` ``l`` Accepts plate IDs.\nWell ``WellColumn`` ``well`` Accepts well names. Adds new 'Well' column with well IDs.\nWell Name ``WellColumn`` ``well`` Accepts well names. Adds new 'Well' column with well IDs.\nWell ID ``LongColumn`` ``l`` Accepts well IDs.\nROI ``RoiColumn`` ``roi`` Accepts ROI IDs. Appends new 'ROI Name' column with ROI names.\nROI Name ``StringColumn`` ``s`` Accepts ROI names. Appends new 'ROI' column with ROI IDs.\nROI ID ``RoiColumn`` ``roi`` Accepts ROI IDs. Appends new 'ROI Name' column with ROI names.\n============ ================= ==================== ====================================================================\n \nNote: Column names are case insensitive. Space, no space, and underscore are all accepted as separators for column names (i.e. ``<object> name``/``<object> id```, ``<object>name``/``<object>id``, ``<object>_name``/``<object>_id`` are all accepted)\n\nNB: Column names should not contain spaces if you want to be able to query by these columns.\n\n* All other column types will be detected based on the column's data using the pandas library. See table below.\n\n=============== ================= ====================\nColumn Name Column type Detected Header Type\n=============== ================= ====================\nExample String ``StringColumn`` ``s`` \nExample Long ``LongColumn`` ``l`` \nExample Float ``DoubleColumn`` ``d`` \nExample boolean ``BoolColumn`` ``b`` \n=============== ================= ====================\n\nIn the case of missing values, the column will be detected as ``StringColumn`` by default. If ``--allow-nan`` is passed to the\n``omero metadata populate`` commands, missing values in floating-point columns will be detected as ``DoubleColumn`` and the\nmissing values will be stored as NaN.\n\nHowever, it is possible to manually define the header types, ignoring the automatic header detection, if a ``CSV`` with a ``# header`` row is passed. The ``# header`` row should be the first row of the CSV and defines columns according to the following list (see examples below):\n\n- ``d``: ``DoubleColumn``, for floating point numbers\n- ``l``: ``LongColumn``, for integer numbers\n- ``s``: ``StringColumn``, for text\n- ``b``: ``BoolColumn``, for true/false\n- ``plate``, ``well``, ``image``, ``dataset``, ``roi`` to specify objects\n\nAutomatic header detection can also be ignored if using the ``--manual_headers`` flag. If the ``# header`` is not present and this flag is used, column types will default to ``String`` (unless the column names correspond to OMERO objects such as ``image`` or ``plate``).\n\n\nExamples\n^^^^^^^^^\n\nThe examples below will use the default automatic column types detection behaviour. It is possible to achieve the same results (or a different desired result) by manually adding a custom ``# header`` row at the top of the CSV.\n\n**Project / Dataset**\n^^^^^^^^^^^^^^^^^^^^^^\n\nTo add a table to a Project, the ``CSV`` file needs to specify ``Dataset Name`` or ``Dataset ID``\nand ``Image Name`` or ``Image ID``::\n\n $ omero metadata populate Project:1 --file path/to/project.csv\n \nUsing ``Image Name`` and ``Dataset Name``:\n\nproject.csv::\n\n Image Name,Dataset Name,ROI_Area,Channel_Index,Channel_Name\n img-01.png,dataset01,0.0469,1,DAPI\n img-02.png,dataset01,0.142,2,GFP\n img-03.png,dataset01,0.093,3,TRITC\n img-04.png,dataset01,0.429,4,Cy5\n \n\nThe previous example will create an OMERO.table linked to the Project as follows with\na new ``Image`` column with IDs:\n\n========== ============ ======== ============= ============ =====\nImage Name Dataset Name ROI_Area Channel_Index Channel_Name Image\n========== ============ ======== ============= ============ =====\nimg-01.png dataset01 0.0469 1 DAPI 36638\nimg-02.png dataset01 0.142 2 GFP 36639\nimg-03.png dataset01 0.093 3 TRITC 36640\nimg-04.png dataset01 0.429 4 Cy5 36641\n========== ============ ======== ============= ============ =====\n\nNote: equivalent to adding ``# header s,s,d,l,s`` row to the top of the ``project.csv`` for manual definition.\n\nUsing ``Image ID`` and ``Dataset ID``:\n\nproject.csv::\n\n image id,Dataset ID,ROI_Area,Channel_Index,Channel_Name\n 36638,101,0.0469,1,DAPI\n 36639,101,0.142,2,GFP\n 36640,101,0.093,3,TRITC\n 36641,101,0.429,4,Cy5\n\n\nThe previous example will create an OMERO.table linked to the Project as follows with\na new ``Image Name`` column with Names:\n\n===== ======= ======== ============= ============ ==========\nImage Dataset ROI_Area Channel_Index Channel_Name Image Name\n===== ======= ======== ============= ============ ==========\n36638 101 0.0469 1 DAPI img-01.png \n36639 101 0.142 2 GFP img-02.png \n36640 101 0.093 3 TRITC img-03.png \n36641 101 0.429 4 Cy5 img-04.png\n===== ======= ======== ============= ============ ==========\n\nNote: equivalent to adding ``# header image,dataset,d,l,s`` row to the top of the ``project.csv`` for manual definition.\n\nFor both examples above, alternatively, if the target is a Dataset instead of a Project, the ``Dataset`` or ``Dataset Name`` column is not needed.\n\n**Screen / Plate**\n^^^^^^^^^^^^^^^^^^^\n\nTo add a table to a Screen, the ``CSV`` file needs to specify ``Plate`` name and ``Well``.\nIf a ``# header`` is specified, column types must be ``well`` and ``plate``::\n\n $ omero metadata populate Screen:1 --file path/to/screen.csv\n\nscreen.csv::\n\n Well,Plate,Drug,Concentration,Cell_Count,Percent_Mitotic\n A1,plate01,DMSO,10.1,10,25.4\n A2,plate01,DMSO,0.1,1000,2.54\n A3,plate01,DMSO,5.5,550,4\n B1,plate01,DrugX,12.3,50,44.43\n\n\nThis will create an OMERO.table linked to the Screen, with the\n``Well Name`` and ``Plate Name`` columns added and the ``Well`` and\n``Plate`` columns used for IDs:\n\n===== ====== ====== ============== =========== ================ =========== ===========\nWell Plate Drug Concentration Cell_Count Percent_Mitotic Well Name Plate Name\n===== ====== ====== ============== =========== ================ =========== ===========\n9154 3855 DMSO 10.1 10 25.4 a1 plate01\n9155 3855 DMSO 0.1 1000 2.54 a2 plate01\n9156 3855 DMSO 5.5 550 4.0 a3 plate01\n9157 3855 DrugX 12.3 50 44.43 b1 plate01\n===== ====== ====== ============== =========== ================ =========== ===========\n\nIf the target is a Plate instead of a Screen, the ``Plate`` column is not needed.\n\nNote: equivalent to adding ``# header well,plate,s,d,l,d`` row to the top of the ``screen.csv`` for manual definition.\n\n**ROIs**\n^^^^^^^^^\n\nIf the target is an Image or a Dataset, a ``CSV`` with ROI-level or Shape-level data can be used to create an\n``OMERO.table`` (bulk annotation) as a ``File Annotation`` linked to the target object.\nIf there is an ``roi`` column (header type ``roi``) containing ROI IDs, an ``Roi Name``\ncolumn will be appended automatically (see example below). If a column of Shape IDs named ``shape``\nof type ``l`` is included, the Shape IDs will be validated (and set to -1 if invalid).\nAlso if an ``image`` column of Image IDs is included, an ``Image Name`` column will be added.\nNB: Columns of type ``shape`` aren't yet supported on the OMERO.server::\n\n $ omero metadata populate Image:1 --file path/to/image.csv\n\nimage.csv::\n\n Roi,shape,object,probability,area\n 501,1066,1,0.8,250\n 502,1067,2,0.9,500\n 503,1068,3,0.2,25\n 503,1069,4,0.8,400\n 503,1070,5,0.5,200\n \n\nThis will create an OMERO.table linked to the Image like this:\n\n=== ===== ====== =========== ==== ========\nRoi shape object probability area Roi Name\n=== ===== ====== =========== ==== ========\n501 1066 1 0.8 250 Sample1\n502 1067 2 0.9 500 Sample2\n503 1068 3 0.2 25 Sample3\n503 1069 4 0.8 400 Sample3\n503 1070 5 0.5 200 Sample3\n=== ===== ====== =========== ==== ========\n\nNote: equivalent to adding ``# header roi,l,l,d,l`` row to the top of the ``image.csv`` for manual definition.\n\nAlternatively, if the target is an Image, the ROI input column can be\n``Roi Name`` (with type ``s``), and an ``roi`` type column will be appended containing ROI IDs.\nIn this case, it is required that ROIs on the Image in OMERO have the ``Name`` attribute set.\n\nNote that the ROI-level data from an ``OMERO.table`` is not visible\nin the OMERO.web UI right-hand panel under the ``Tables`` tab,\nbut the table can be visualized by clicking the \"eye\" on the bulk annotation attachment on the Image.\n\nDeveloper install\n=================\n\nThis plugin can be installed from the source code with::\n\n $ cd omero-metadata\n $ pip install .\n\n\nLicense\n-------\n\nThis project, similar to many Open Microscopy Environment (OME) projects, is\nlicensed under the terms of the GNU General Public License (GPL) v2 or later.\n\nCopyright\n---------\n\n2018-2022, The Open Microscopy Environment and Glencoe Software, Inc\n\n\n",
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