Adjust cf. Will's comments

index.rst

@@ -30,8 +30,8 @@ and how to use those extensions points e.g. OMERO.iviewer, OMERO.figure, OMERO.p
 
 :doc:`external_tools` introduces how to analyze data using third party tools e.g. Fiji, CellProfiler, ilastik.
 
-Prepare a training
-------------------
+Prepare an OMERO server for training
+------------------------------------
 
 :doc:`training` presents a step-by-step example of a setup needed to get a training server similar to the one used by the OME Team.
 

training.rst

@@ -1,5 +1,5 @@
-Prepare a training on OMERO
-===========================
+Prepare an OMERO server for training
+====================================
 
 This chapter describes a setup of a training OMERO.server at 
 your institution. Further, it gives an overview of the other training
@@ -27,43 +27,43 @@ Download of scripts and OMERO.cli environment setup
 Setup of groups and users
 -------------------------
 
-Prepare groups and users as listed in the `provided template sheet <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/create_groups_users_setup>`_. Assuming you have the `template sheet <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/create_groups_users_setup>`_ in the same directory as the script `create_groups_users.sh <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/create_groups_users.sh>`_, you can run::
+Prepare groups and users as listed in the `provided template sheet <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/create_groups_users_setup>`_. Assuming you have the `template sheet <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/create_groups_users_setup>`_ in the same directory as the script `create_groups_users.sh <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/create_groups_users.sh>`_, you can run::
 
     $ cd training-scripts/maintenance/scripts
     $ HOST=$YOUR_SERVER_ADDRESS PASSWORD=$PASSWORD_FOR_ROOT bash create_groups_users.sh
 
 which will create 50 users in your database.
 The users are members of four main OMERO.groups, which cover
 the group permissions allowed in OMERO. The ``Read-annotate`` group ``Lab 1`` is the main group used in the trainings,
-which contains most of the data in a typical training. The setup in the `template sheet <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/create_groups_users_setup>`_ sets this ``Lab 1`` group as a default group in OMERO for all users.
+which contains most of the data in a typical training. The setup in the `template sheet <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/create_groups_users_setup>`_ sets this ``Lab 1`` group as a default group in OMERO for all users.
 
-Rename users to have first and last names of real people (the list of famous scientist names is used) running `the renaming script <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/rename_users.py>`_::
+Rename users to have first and last names of real people (the list of famous scientist names is used) running `the renaming script <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/rename_users.py>`_::
 
     $ python rename_users.py trainer-1 $PASSWORD --server $YOUR_SERVER_ADDRESS
 
 Import images
 -------------
 
 We like to use `in-place import <https://omero-guides.readthedocs.io/en/latest/upload/docs/import-cli.html#in-place-import-using-the-cli>`_ because it avoids duplicating the data on disk, but if you are only importing a small amount of data you may find non-in-place import (described below) is more convenient.
-In-place import is achieved by using `import bash script <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/in_place_import_as.sh>`_. Use the `publicly available data provided <https://downloads.openmicroscopy.org/images/>`_ or use your own data or `data downloaded from IDR <https://idr.openmicroscopy.org/about/download.html>`_. Shell into the machine where you have installed your OMERO.server (necessary for in-place import) and, assuming that you have for example your data in a folder ``my-folder`` which is visible from that machine::
+In-place import is achieved by using `import bash script <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/in_place_import_as.sh>`_. Use the `publicly available data provided <https://downloads.openmicroscopy.org/images/>`_ or use your own data or `data downloaded from IDR <https://idr.openmicroscopy.org/about/download.html>`_. Shell into the machine where you have installed your OMERO.server (necessary for in-place import) and, assuming that you have for example your data in a folder ``my-folder`` which is visible from that machine, run::
 
     $ HOST=localhost SUDOER=trainer-1 PASSWORD=$PASSWORD_FOR_trainer-1 IMPORTTYPE=normal NUMBER=15 FOLDER=/path/to/my-folder bash in_place_import_as.sh
 
-will in-place import the images from ``my-folder`` into a new Dataset named ``/path/to/my-folder`` for user-1 through user-15.
+This will in-place import the images from ``my-folder`` into a new Dataset named ``/path/to/my-folder`` for user-1 through user-15.
 
 To achieve a better reproducibility of your imports, you can use a `bulk file <https://omero-guides.readthedocs.io/en/latest/upload/docs/import-cli.html#bulk-import-using-the-cli>`_ pointing to a list of paths of the image files. Assuming you have for example the bulk file `idr0021-experimentA-bulk.yml <https://github.com/IDR/idr0021-lawo-pericentriolarmaterial/blob/master/experimentA/idr0021-experimentA-bulk.yml>`_ located on the filesystem you are working on, together with the corresponding `idr0021-experimentA-filePaths.tsv <https://github.com/IDR/idr0021-lawo-pericentriolarmaterial/blob/master/experimentA/idr0021-experimentA-filePaths.tsv>`_ file and the images as specified in the `idr0021-experimentA-filePaths.tsv <https://github.com/IDR/idr0021-lawo-pericentriolarmaterial/blob/master/experimentA/idr0021-experimentA-filePaths.tsv>`_ which you have edited accordingly, you can run::
 
     $ HOST=localhost SUDOER=trainer-1 PASSWORD=$PASSWORD_FOR_trainer-1 OMEUSER=trainer NUMBER=2 BULKFILE=/path/to/idr0021-experimentA-bulk.yml bash in_place_import_as.sh
 
 to import the images for trainer-1 and trainer-2.
 
-The `script <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/in_place_import_as.sh>`_ assumes by default `in-place imports <https://omero-guides.readthedocs.io/en/latest/upload/docs/import-cli.html#in-place-import-using-the-cli>`_. You can adjust it for "classsical", non-in-place import by deleting the ``--transfer=ln_s`` from the script lines or, if using the `bulk file <https://omero-guides.readthedocs.io/en/latest/upload/docs/import-cli.html#bulk-import-using-the-cli>`_ workflow, comment out the ``transfer`` line from the bulk file.
+The `script <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/in_place_import_as.sh>`_ assumes by default `in-place imports <https://omero-guides.readthedocs.io/en/latest/upload/docs/import-cli.html#in-place-import-using-the-cli>`_. You can adjust it for "classsical", non-in-place import by deleting the ``--transfer=ln_s`` from the script lines or, if using the `bulk file <https://omero-guides.readthedocs.io/en/latest/upload/docs/import-cli.html#bulk-import-using-the-cli>`_ workflow, comment out the ``transfer`` line from the bulk file.
 
-Image data can also be populated by a `Python script <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/idr_copy_plate.py>`_ which copies images as pixeldata (i.e. not the original images) from `IDR <http://idr.openmicroscopy.org/>`_ by default. You can adjust the `appropriate line <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/idr_copy_plate.py#L80>`_ to copy from other OMERO.servers. Note that the script is only creating `images of single T and Z <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/idr_copy_plate.py#L36>`_, and thus reducing the original images dimensions in case these are multi-z or T images::
+Image data can also be populated by a `Python script <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/idr_copy_plate.py>`_ which copies images as pixeldata (i.e. not the original images) from `IDR <http://idr.openmicroscopy.org/>`_ by default. You can adjust the three lines beginning with  `idr_client = omero.client(host="idr.openmicroscopy.org", port=4064) <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/idr_copy_plate.py#L80>`_ to copy from other OMERO.servers. Note that the script is only creating `images of single Z and T <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/idr_copy_plate.py#L36>`_, and thus reducing the original images dimensions in case these are multi-Z or T images.::
 
     $ python idr_copy_plate.py trainer-1 --server $YOUR_SERVER_ADDRESS $PASSWORD $PLATE_ID
 
-will copy the Plate with $PLATE_ID from IDR as trainer-1 into your server, but note that this ``idr_copy_plate.py`` script only works on Plates which have a single Field (Image) per Well.
+This will copy the Plate with ``$PLATE_ID`` from IDR as trainer-1 into your server, but note that this ``idr_copy_plate.py`` script only works on Plates which have a single Field (Image) per Well.
 
 
 Import metadata
@@ -73,23 +73,23 @@ Import metadata from a CSV via `OMERO.web <https://omero-guides.readthedocs.io/e
 
 Annotate images using training-scripts:
 
-- `Copy Key-Value pairs <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/idr_get_map_annotations.py>`_ from IDR or other OMERO server. The command below will copy MapAnnotations from IDR to your server between images inside Projects with IDs 1 (in IDR) and 2 (in your server). The Project in your server is owned and the action is executed by `trainer-1`.::
+- `Copy Key-Value pairs <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/idr_get_map_annotations.py>`_ from IDR or other OMERO server. This script will copy MapAnnotations from Images in IDR to Images with the same names in your server (Datasets names must also match). The command below copies from `Project:1` in IDR to `Project:2` in your server. The Project in your server is owned and the action is executed by `trainer-1`.::
 
-    $ python idr_get_map_annotation.py trainer-1 $PASSWORD Project:1 Project:2 --server $YOUR_SERVER_ADDRESS
+    $ python idr_get_map_annotation.py trainer-1 $PASSWORD_FOR_trainer-1 Project:1 Project:2 --server $YOUR_SERVER_ADDRESS
 
-- `Add new Key-Value pairs <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/key_value_pairs.py>`_. The command below will add Key-Value pairs defined inside the script randomly to the images inside Datasets with name ``big-dataset`` for all 50 users in your server. The $PASSWORD for all the users must be the same.::
+- `Add new Key-Value pairs <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/key_value_pairs.py>`_. The command below will add Key-Value pairs defined inside the script randomly to the images inside Datasets with name ``big-dataset`` for all 50 users in your server. The ``$PASSWORD`` for all the users must be the same.::
 
     $ python key_value_pairs.py $PASSWORD big-dataset --server $YOUR_SERVER_ADDRESS
 
-- `Calibrate images <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/calibrate_images.py>`_. The command below will calibrate all the images within Dataset named ``western-blots`` to `0.33 micrometers per pixel <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/calibrate_images.py#L72>`_ for all 50 users in your server. The $PASSWORD for all the users must be the same.::
+- `Calibrate images <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/calibrate_images.py>`_. The command below will calibrate all the images within Dataset named ``western-blots`` to `0.33 micrometers per pixel <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/calibrate_images.py#L72>`_ for all 50 users in your server. The ``$PASSWORD`` for all the users must be the same.::
 
     $ python calibrate_images.py $PASSWORD western-blots --server $YOUR_SERVER_ADDRESS
 
-- `Add timestamps <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/set_timestamps.py>`_. The command below will set timestamps on the timelapse images within Dataset named `timestamps` with `delta T of 300 seconds <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/set_timestamps.py#L71>`_ for all 50 users in your server. The $PASSWORD for all the users must be the same.::
+- `Add timestamps <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/set_timestamps.py>`_. The command below will set timestamps on the timelapse images within Dataset named `timestamps` with `delta T of 300 seconds <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/set_timestamps.py#L71>`_ for all 50 users in your server. The ``$PASSWORD`` for all the users must be the same.::
 
     $ python set_timestamps.py $PASSWORD timestamp --server $YOUR_SERVER_ADDRESS
 
-- `Propagate tags and ratings to all users <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/copy_tags_ratings.py>`_. Supposing that the ``trainer-1`` has a Dataset ``to-tag`` with Tags and Ratings on the images in the Dataset. Further, each user, such as ``user-1``, ``user-2`` has the same-named Dataset with equivalent images in it, but with no Tags and Ratings (a typical situation after a fresh import of images). The command below will link the Tags of ``trainer-1`` which are linked to the images in the ``to-tag`` Dataset to the corresponding images in the ``to-tag`` Datasets of the users. The links between the Tags and the Images will belong to each user. Also, the Ratings which are on the Images of the ``to-tag`` Dataset of ``trainer-1`` will be re-created for the corresponding Images of the users and will belong to those users.::
+- `Propagate tags and ratings to all users <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/copy_tags_ratings.py>`_. Supposing that the ``trainer-1`` has a Dataset ``to-tag`` with Tags and Ratings on the images in the Dataset. Further, each user, such as ``user-1``, ``user-2`` has the same-named Dataset with equivalent images in it, but with no Tags and Ratings (a typical situation after a fresh import of images). The command below will link the Tags of ``trainer-1`` which are linked to the images in the ``to-tag`` Dataset to the corresponding images in the ``to-tag`` Datasets of the users. The links between the Tags and the Images will belong to each user. Also, the Ratings which are on the Images of the ``to-tag`` Dataset of ``trainer-1`` will be re-created for the corresponding Images of the users and will belong to those users.::
 
     $ python copy_tags_ratings.py to-tag $PASSWORD --server $YOUR_SERVER_ADDRESS
 
@@ -108,10 +108,10 @@ Cleanup scripts
 
 It might be of great advantage to be able to clean up in batches, but still selectively, metadata added to the images on your training server.
 
-- `Delete ROIs <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/delete_ROIs.py>`_ on all Images inside Datasets of specified name for all users on the server who have such Datasets. In the example below, the Dataset's name is ``with-rois``. The $PASSWORD is the password of the user deleting the ROIs. The deleting user is ``trainer-1`` by default.::
+- `Delete ROIs <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/delete_ROIs.py>`_ on all Images inside Datasets of specified name for all users on the server who have such Datasets. In the example below, the Dataset's name is ``with-rois``. The ``$PASSWORD`` is the password of the user deleting the ROIs. The deleting user is ``trainer-1`` by default.::
 
     $ python delete_ROIs.py --datasetname with-rois --server $YOUR_SERVER_ADDRESS $PASSWORD
 
-- `Delete Annotations <https://github.com/ome/training-scripts/blob/master/maintenance/scripts/delete_annotations.py>`_ on all Images inside Datasets of specified name for all users on the server who have such Datasets. In the example below, the Dataset's name is ``western-blots`` and the deleted annotation type is ``FileAnnotation``. All other annotation types such as ``TagAnnotation`` etc. will be preserved. The $PASSWORD is the password of the user deleting the ROIs. The deleting user is ``trainer-1`` by default.::
+- `Delete Annotations <https://github.com/ome/training-scripts/blob/v0.7.3/maintenance/scripts/delete_annotations.py>`_ on all Images inside Datasets of specified name for all users on the server who have such Datasets. In the example below, the Dataset's name is ``western-blots`` and the deleted annotation type is ``FileAnnotation``. All other annotation types such as ``TagAnnotation`` etc. will be preserved. The ``$PASSWORD`` is the password of the user deleting the ROIs. The deleting user is ``trainer-1`` by default.::
 
     $ python delete_annotations.py --anntype file  --namespace none --server $YOUR_SERVER_ADDRESS $PASSWORD western-blots