diff --git a/api-openbis-python3-pybis/src/python/README.md b/api-openbis-python3-pybis/src/python/README.md
index 20ba1f6a2f70b28e8dfc956bf85df9267cf4b84f..6fdfba3d6f8004ef4f248d7a1033e60d52e56448 100644
--- a/api-openbis-python3-pybis/src/python/README.md
+++ b/api-openbis-python3-pybis/src/python/README.md
@@ -11,7 +11,7 @@ pyBIS is a Python module for interacting with openBIS. pyBIS is designed to be m
## Installation
-```
+```commandline
pip install --upgrade pybis
```
@@ -19,7 +19,7 @@ That command will download install pyBIS and all its dependencies. If pyBIS is a
If you haven't done yet, install Jupyter and/or Jupyter Lab (the next Generation of Jupyter):
-```
+```commandline
pip install jupyter
pip install jupyterlab
```
@@ -190,7 +190,7 @@ https://osxfuse.github.io
**Unix Cent OS 7**
-```
+```commandline
$ sudo yum install epel-release
$ sudo yum --enablerepo=epel -y install fuse-sshfs
$ user="$(whoami)"
@@ -203,7 +203,7 @@ After the installation, an `sshfs` command should be available.
Because the mount/unmount procedure differs from platform to platform, pyBIS offers two simple methods:
-```
+```python
o.mount()
o.mount(username, password, hostname, mountpoint, volname)
o.is_mounted()
@@ -232,7 +232,7 @@ OpenBIS stores quite a lot of meta-data along with your dataSets. The collection
## browse masterdata
-```
+```python
sample_types = o.get_sample_types() # get a list of sample types
sample_types.df # DataFrame object
st = o.get_sample_types()[3] # get 4th element of that list
@@ -269,7 +269,7 @@ o.get_tags()
The first step in creating a new entity type is to create a so called **property type**:
-```
+```python
pt_text = o.new_property_type(
code = 'MY_NEW_PROPERTY_TYPE',
label = 'yet another property type',
@@ -340,7 +340,7 @@ The second step (after creating a property type, see above) is to create the **s
- `new_sample_type()` == `new_object_type()`
-```
+```python
sample_type = o.new_sample_type(
code = 'my_own_sample_type', # mandatory
generatedCodePrefix = 'S', # mandatory
@@ -358,7 +358,7 @@ sample_type.save()
When `autoGeneratedCode` attribute is set to `True`, then you don't need to provide a value for `code` when you create a new sample. You can get the next autoGeneratedCode like this:
-```
+```python
sample_type.get_next_sequence() # eg. 67
sample_type.get_next_code() # e.g. FLY77
```
@@ -369,11 +369,11 @@ From pyBIS 1.31.0 onwards, you can provide a `code` even for samples where its s
The third step, after saving the sample type, is to **assign or revoke properties** to the newly created sample type. This assignment procedure applies to all entity types (dataset type, experiment type).
-```
+```python
sample_type.assign_property(
- prop = 'diff_time', # mandatory
- section = '',
- ordinal = 5,
+ prop = 'diff_time', # Mandatory value
+ section = '', # Name of the section
+ ordinal = 1, # Position of property
mandatory = True,
initialValueForExistingEntities = 'initial value'
showInEditView = True,
@@ -383,11 +383,18 @@ sample_type.revoke_property('diff_time')
sample_type.get_property_assignments()
```
+***âš ï¸ Note: ordinal position***
+
+If a new property is assigned in a place of an existing property, the old property assignment ordinal value will be increased by 1
+
+
+
+
## create a dataset type
The second step (after creating a **property type**, see above) is to create the **dataset type**. The third step is to **assign or revoke the properties** to the newly created dataset type.
-```
+```python
dataset_type = o.new_dataset_type(
code = 'my_dataset_type', # mandatory
description = None,
@@ -410,7 +417,7 @@ The new name for **experiment** is **collection**. You can use both methods inte
- `new_experiment_type()` == `new_collection_type()`
-```
+```python
experiment_type = o.new_experiment_type(
code,
description = None,
@@ -426,7 +433,7 @@ experiment_type.get_property_assignments()
Materials and material types are deprecated in newer versions of openBIS.
-```
+```python
material_type = o.new_material_type(
code,
description=None,
@@ -443,7 +450,7 @@ material_type.get_property_assignments()
Plugins are Jython scripts that can accomplish more complex data-checks than ordinary types and vocabularies can achieve. They are assigned to entity types (dataset type, sample type etc). [Documentation and examples can be found here](https://wiki-bsse.ethz.ch/display/openBISDoc/Properties+Handled+By+Scripts)
-```
+```python
pl = o.new_plugin(
name ='my_new_entry_validation_plugin',
pluginType ='ENTITY_VALIDATION', # or 'DYNAMIC_PROPERTY' or 'MANAGED_PROPERTY',
@@ -463,7 +470,7 @@ Users can only login into the openBIS system when:
- the user is already added to the openBIS user list (see below)
- the user is assigned a role which allows a login, either directly assigned or indirectly assigned via a group membership
-```
+```python
o.get_groups()
group = o.new_group(code='group_name', description='...')
group = o.get_group('group_name')
@@ -506,7 +513,7 @@ Spaces are fundamental way in openBIS to divide access between groups. Within a
- sample / object
- dataset
-```
+```python
space = o.new_space(code='space_name', description='')
space.save()
o.get_spaces(
@@ -543,7 +550,7 @@ Projects live within spaces and usually contain experiments (aka collections):
- sample / object
- dataset
-```
+```python
project = o.new_project(
space = space,
code = 'project_name',
@@ -607,7 +614,7 @@ The new name for **experiment** is **collection**. You can use boths names inter
### create a new experiment
-```
+```python
exp = o.new_experiment
code='MY_NEW_EXPERIMENT',
type='DEFAULT_EXPERIMENT',
@@ -619,7 +626,7 @@ exp.save()
### search for experiments
-```
+```python
experiments = o.get_experiments(
project = 'YEASTS',
space = 'MY_SPACE',
@@ -645,7 +652,7 @@ However, if you want to include specific attributes in the results, you can do s
The `get_experiments()` method results include only `identifier`, `permId`, `type`, `registrator`, `registrationDate`, `modifier`, `modificationDate`
-```get attributes
+```python
experiments = o.get_experiments(
project = 'YEASTS',
space = 'MY_SPACE',
@@ -661,7 +668,7 @@ experiments = o.get_experiments(
### Experiment attributes
-```
+```python
exp.attrs.all() # returns all attributes as a dict
exp.attrs.tags = ['some', 'tags']
@@ -686,7 +693,7 @@ exp.save() # needed to save/update the changed attribute
**Getting properties**
-```
+```python
experiment.props == ds.p # you can use either .props or .p to access the properties
experiment.p # in Jupyter: show all properties in a nice table
experiment.p() # get all properties as a dict
@@ -731,7 +738,7 @@ The new name for **sample** is **object**. You can use boths names interchangeab
etc.
-```
+```python
sample = o.new_sample(
type = 'YEAST',
space = 'MY_SPACE',
@@ -784,7 +791,7 @@ Creating a single sample takes some time. If you need to create many samples, yo
**create many samples in one transaction**
-```
+```python
trans = o.new_transaction()
for i in range (0, 100):
sample = o.new_sample(...)
@@ -795,7 +802,7 @@ trans.commit()
**update many samples in one transaction**
-```
+```python
trans = o.new_transaction()
for sample in o.get_samples(count=100):
sample.prop.some_property = 'different value'
@@ -806,7 +813,7 @@ trans.commit()
**delete many samples in one transaction**
-```
+```python
trans = o.new_transaction()
for sample in o.get_samples(count=100):
sample.mark_to_be_deleted()
@@ -820,7 +827,7 @@ trans.commit()
### parents, children, components and container
-```
+```python
sample.get_parents()
sample.set_parents(['/MY_SPACE/PARENT_SAMPLE_NAME')
sample.add_parents('/MY_SPACE/PARENT_SAMPLE_NAME')
@@ -850,7 +857,7 @@ sample.del_components('/MY_SPACE/COMPONENT_NAME')
### sample tags
-```
+```python
sample.get_tags()
sample.set_tags('tag1')
sample.add_tags(['tag2','tag3'])
@@ -861,7 +868,7 @@ sample.del_tags('tag1')
**Getting properties**
-```
+```python
sample.attrs.all() # returns all attributes as a dict
sample.attribute_name # return the attribute value
@@ -876,7 +883,7 @@ sample.p['property'] # get the value of a property
**Setting properties**
-```
+```python
sample.experiment = 'first_exp' # assign sample to an experiment
sample.project = 'my_project' # assign sample to a project
@@ -896,7 +903,7 @@ sample.save() # needed to save/update the attributes and
The result of a search is always list, even when no items are found. The `.df` attribute returns
the Pandas dataFrame of the results.
-```
+```python
samples = o.get_samples(
space ='MY_SPACE',
type ='YEAST',
@@ -940,13 +947,13 @@ samples = o.get_samples(props="*") # retrieve all properties of all samples
***Note: Attributes download***
-The `get_samples()` method, by default, returns fewer details to make the download process faster.
+The `get_samples()` method, by default, returns fewer attributes to make the download process faster.
However, if you want to include specific attributes in the results, you can do so by using the `attrs` parameter.
The `get_samples()` method results include only `identifier`, `permId`, `type`, `registrator`, `registrationDate`, `modifier`, `modificationDate`
-```get attributes
-experiments = o.get_samples(
+```python
+samples = o.get_samples(
space = 'MY_SPACE',
type = 'YEAST',
attrs = ["parents", "children"]
@@ -958,9 +965,53 @@ experiments = o.get_samples(
```
-### freezing samples
+**âš ï¸ Clarification**
+
+- `get_samples()` method is always downloading object properties
+- Not downloaded properties (e.g `parents`, `children`) will not be removed upon `save()` unless explicitly done by the user.
+- `None` values of list attributes are ignored during saving process
+
+**Example:**
+```python
+# get sample with get_sample() method
+sample = o.get_sample('/DEFAULT/DEFAULT/EXP2')
+sample
+
+Out[1]:
+attribute value
+------------------- ------------------------------
+code EXP2
+permId 20230823205338303-49
+identifier /DEFAULT/DEFAULT/EXP2
+type EXPERIMENTAL_STEP
+project /DEFAULT/DEFAULT
+parents [] # empty list
+children ['/DEFAULT/DEFAULT/EXP3']
+components []
+```
+
+```python
+# get sample with get_samples() method
+samples = o.get_samples(identifier='/DEFAULT/DEFAULT/EXP2')
+samples[0]
+
+Out[1]:
+attribute value
+------------------- ------------------------------
+code EXP2
+permId 20230823205338303-49
+identifier /DEFAULT/DEFAULT/EXP2
+type EXPERIMENTAL_STEP
+project /DEFAULT/DEFAULT
+parents # None value
+children # None value
+components []
```
+
+### freezing samples
+
+```python
sample.freeze = True
sample.freezeForComponents = True
sample.freezeForChildren = True
@@ -989,7 +1040,7 @@ This example does the following
- print the list of all files in this dataset
- download the dataset
-```
+```python
datasets = sample.get_datasets(type='SCANS', start_with=0, count=10)
for dataset in datasets:
print(dataset.props())
@@ -1006,8 +1057,8 @@ However, if you want to include specific attributes in the results, you can do s
The `get_datasets()` method results include only `permId`, `type`, `experiment`, `sample`, `registrationDate`, `modificationDate`,
`location`, `status`, `presentInArchive`, `size`
-```get attributes
-experiments = o.get_datasets(
+```python
+datasets = o.get_datasets(
space = 'MY_SPACE',
attrs = ["parents", "children"]
)
@@ -1020,7 +1071,7 @@ experiments = o.get_datasets(
**More dataset functions:**
-```
+```python
ds = o.get_dataset('20160719143426517-259')
ds.get_parents()
ds.get_children()
@@ -1048,7 +1099,7 @@ ds.download_attachments(<path or cwd>) # Deprecated, as attachments are not com
### download dataSets
-```
+```python
o.download_prefix # used for download() and symlink() method.
# Is set to data/hostname by default, but can be changed.
ds.get_files(start_folder="/") # get file list as Pandas dataFrame
@@ -1070,7 +1121,7 @@ ds.is_physical() # TRUE if dataset is physically
Instead of downloading a dataSet, you can create a symbolic link to a dataSet in the openBIS dataStore. To do that, the openBIS dataStore needs to be mounted first (see mount method above). **Note:** Symbolic links and the mount() feature currently do not work with Windows.
-```
+```python
o.download_prefix # used for download() and symlink() method.
# Is set to data/hostname by default, but can be changed.
ds.symlink() # creates a symlink for this dataset: data/hostname/permId
@@ -1087,7 +1138,7 @@ ds.is_symlink()
**Getting properties**
-```
+```python
ds.attrs.all() # returns all attributes as a dict
ds.attribute_name # return the attribute value
@@ -1102,7 +1153,7 @@ ds.p['property'] # get the value of a property
**Setting properties**
-```
+```python
ds.experiment = 'first_exp' # assign dataset to an experiment
ds.sample = 'my_sample' # assign dataset to a sample
@@ -1120,7 +1171,7 @@ ds.set_props({ key: value }) # set the values of some properties
- The result of a search is always list, even when no items are found
- The `.df` attribute returns the Pandas dataFrame of the results
-```
+```python
datasets = o.get_datasets(
type ='MY_DATASET_TYPE',
**{ "SOME.WEIRD:PROP": "value"}, # property name contains a dot or a
@@ -1155,7 +1206,7 @@ df = datasets.df # returns a Pandas dataFrame object of the
In some cases, you might want to retrieve precisely certain datasets. This can be achieved by
methods chaining (but be aware, it might not be very performant):
-```
+```python
datasets = o.get_experiments(project='YEASTS')\
.get_samples(type='FLY')\
.get_datasets(
@@ -1167,7 +1218,7 @@ datasets = o.get_experiments(project='YEASTS')\
- another example:
-```
+```python
datasets = o.get_experiment('/MY_NEW_SPACE/MY_PROJECT/MY_EXPERIMENT4')\
.get_samples(type='UNKNOWN')\
.get_parents()\
@@ -1179,7 +1230,7 @@ datasets = o.get_experiment('/MY_NEW_SPACE/MY_PROJECT/MY_EXPERIMENT4')\
- once a dataSet has been frozen, it cannot be changed by anyone anymore
- so be careful!
-```
+```python
ds.freeze = True
ds.freezeForChildren = True
ds.freezeForParents = True
@@ -1190,7 +1241,7 @@ ds.save()
### create a new dataSet
-```
+```python
ds_new = o.new_dataset(
type = 'ANALYZED_DATA',
experiment = '/SPACE/PROJECT/EXP1',
@@ -1234,7 +1285,7 @@ ds_new.save()
| `../../myData/` | `myData/` |
| `some/experiment/results/` | `results/` |
-```
+```python
ds_new = o.new_dataset(
type = 'RAW_DATA',
sample = '/SPACE/SAMP1',
@@ -1247,7 +1298,7 @@ ds_new.save()
A DataSet of kind=CONTAINER contains other DataSets, but no files:
-```
+```python
ds_new = o.new_dataset(
type = 'ANALYZED_DATA',
experiment = '/SPACE/PROJECT/EXP1',
@@ -1260,7 +1311,7 @@ ds_new.save()
### get, set, add and remove parent datasets
-```
+```python
dataset.get_parents()
dataset.set_parents(['20170115220259155-412'])
dataset.add_parents(['20170115220259155-412'])
@@ -1269,7 +1320,7 @@ dataset.del_parents(['20170115220259155-412'])
#### get, set, add and remove child datasets
-```
+```python
dataset.get_children()
dataset.set_children(['20170115220259155-412'])
dataset.add_children(['20170115220259155-412'])
@@ -1282,7 +1333,7 @@ dataset.del_children(['20170115220259155-412'])
- As opposed to Samples, DataSets may belong (contained) to more than one DataSet-container
- caveat: containers are NOT compatible with ELN-LIMS
-```
+```python
dataset.get_containers()
dataset.set_containers(['20170115220259155-412'])
dataset.add_containers(['20170115220259155-412'])
@@ -1294,7 +1345,7 @@ dataset.del_containers(['20170115220259155-412'])
- you may also use the xxx_contained() functions, which are just aliases.
- caveat: components are NOT compatible with ELN-LIMS
-```
+```python
dataset.get_components()
dataset.set_components(['20170115220259155-412'])
dataset.add_components(['20170115220259155-412'])
@@ -1305,7 +1356,7 @@ dataset.del_components(['20170115220259155-412'])
create semantic annotation for sample type 'UNKNOWN':
-```
+```python
sa = o.new_semantic_annotation(
entityType = 'UNKNOWN',
@@ -1321,14 +1372,14 @@ sa.save()
Create semantic annotation for property type (predicate and descriptor values omitted for brevity)
-```
+```python
sa = o.new_semantic_annotation(propertyType = 'DESCRIPTION', ...)
sa.save()
```
**Create** semantic annotation for sample property assignment (predicate and descriptor values omitted for brevity)
-```
+```python
sa = o.new_semantic_annotation(
entityType = 'UNKNOWN',
propertyType = 'DESCRIPTION',
@@ -1339,26 +1390,26 @@ sa.save()
**Create** a semantic annotation directly from a sample type. Will also create sample property assignment annotations when propertyType is given:
-```
+```python
st = o.get_sample_type("ORDER")
st.new_semantic_annotation(...)
```
**Get all** semantic annotations
-```
+```python
o.get_semantic_annotations()
```
**Get** semantic annotation by perm id
-```
+```python
sa = o.get_semantic_annotation("20171015135637955-30")
```
**Update** semantic annotation
-```
+```python
sa.predicateOntologyId = 'new_po_id'
sa.descriptorOntologyId = 'new_do_id'
sa.save()
@@ -1366,13 +1417,13 @@ sa.save()
**Delete** semantic annotation
-```
+```python
sa.delete('reason')
```
## Tags
-```
+```python
new_tag = o.new_tag(
code = 'my_tag',
description = 'some descriptive text'
@@ -1410,7 +1461,7 @@ So for example, you want to add a property called **Animal** to a Sample and you
**create new Vocabulary with three VocabularyTerms**
-```
+```python
voc = o.new_vocabulary(
code = 'BBB',
description = 'description of vocabulary aaa',
@@ -1430,7 +1481,7 @@ voc.save() # update
**create additional VocabularyTerms**
-```
+```python
term = o.new_term(
code='TERM_CODE_XXX',
vocabularyCode='BBB',
@@ -1444,7 +1495,7 @@ term.save()
To change the ordinal of a term, it has to be moved either to the top with the `.move_to_top()` method or after another term using the `.move_after_term('TERM_BEFORE')` method.
-```
+```python
voc = o.get_vocabulary('STORAGE')
term = voc.get_terms()['RT']
term.label = "Room Temperature"