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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
pybis.py
"""
import os
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
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import time
from datetime import datetime
import pandas as pd
from pandas import DataFrame, Series
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import threading
from threading import Thread
from queue import Queue
DROPBOX_PLUGIN = "jupyter-uploader-api"
search_for_type = {
"space": "as.dto.space.search.SpaceSearchCriteria",
"project": "as.dto.project.search.ProjectSearchCriteria",
"experiment": "as.dto.experiment.search.ExperimentSearchCriteria",
"code": "as.dto.common.search.CodeSearchCriteria",
"sample_type":"as.dto.sample.search.SampleTypeSearchCriteria",
"space": { "@type": "as.dto.space.fetchoptions.SpaceFetchOptions" },
"project": { "@type": "as.dto.project.fetchoptions.ProjectFetchOptions" },
"experiment": { "@type": "as.dto.experiment.fetchoptions.ExperimentFetchOptions" },
"sample": { "@type": "as.dto.sample.fetchoptions.SampleFetchOptions" },
"dataset": { "@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions" },
"propertyAssignments" : { "@type" : "as.dto.property.fetchoptions.PropertyAssignmentFetchOptions" },
"physicalData": { "@type": "as.dto.dataset.fetchoptions.PhysicalDataFetchOptions" },
"linkedData": { "@type": "as.dto.dataset.fetchoptions.LinkedDataFetchOptions" },
"properties": { "@type": "as.dto.property.fetchoptions.PropertyFetchOptions" },
"tags": { "@type": "as.dto.tag.fetchoptions.TagFetchOptions" },
"registrator": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"modifier": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"leader": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"attachments": { "@type": "as.dto.attachment.fetchoptions.AttachmentFetchOptions" },
"history": { "@type": "as.dto.history.fetchoptions.HistoryEntryFetchOptions" },
"dataStore": { "@type": "as.dto.datastore.fetchoptions.DataStoreFetchOptions" },
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def parse_jackson(input_json):
"""openBIS uses a library called «jackson» to automatically generate the JSON RPC output.
Objects that are found the first time are added an attribute «@id».
Any further findings only carry this reference id.
This function is used to dereference the output.
"""
interesting=['tags', 'registrator', 'modifier', 'type', 'parents',
'children', 'containers', 'properties', 'experiment', 'sample',
'project', 'space', 'propertyType'
]
found = {}
def build_cache(graph):
if isinstance(graph, list):
for item in graph:
build_cache(item)
elif isinstance(graph, dict) and len(graph) > 0:
for key, value in graph.items():
if key in interesting:
if isinstance(value, dict):
if '@id' in value:
found[value['@id']] = value
build_cache(value)
elif isinstance(value, list):
for item in value:
if isinstance(item, dict):
if '@id' in item:
found[item['@id']] = item
build_cache(item)
elif isinstance(value, dict):
build_cache(value)
elif isinstance(value, list):
build_cache(value)
def deref_graph(graph):
if isinstance(graph, list):
for item in graph:
deref_graph(item)
elif isinstance(graph, dict) and len(graph) > 0:
for key, value in graph.items():
if key in interesting:
if isinstance(value, dict):
deref_graph(value)
elif isinstance(value, int):
graph[key] = found[value]
elif isinstance(value, list):
for i, list_item in enumerate(value):
if isinstance(list_item, int):
value[i] = found[list_item]
elif isinstance(value, dict):
deref_graph(value)
elif isinstance(value, list):
deref_graph(value)
build_cache(input_json)
deref_graph(input_json)
def search_request_for_identifier(identifier, entity_type):
search_request = {}
# assume we got a sample identifier e.g. /TEST/TEST-SAMPLE
match = re.match('/', identifier)
if match:
search_request = {
"identifier": identifier.upper(),
"@type": "as.dto.{}.id.{}Identifier".format(entity_type.lower(), entity_type.capitalize())
}
else:
search_request = {
"permId": identifier,
"@type": "as.dto.{}.id.{}PermId".format(entity_type.lower(), entity_type.capitalize())
}
return search_request
def table_for_attributes(attributes):
table = '<table border="1" class="dataframe"><thead><tr style="text-align: right;"> <th>attribute</th> <th>value</th> </tr> </thead><tbody>'
for key, val in attributes.items():
table += '<tr><th>{}</th><td>{}</td></tr>'.format(key, val)
table += '</tbody></table>'
return table
def format_timestamp(ts):
return datetime.fromtimestamp(round(ts/1000)).strftime('%Y-%m-%d %H:%M:%S')
def extract_code(obj):
return obj['code']
def extract_deletion(obj):
del_objs = []
for deleted_object in obj['deletedObjects']:
del_objs.append({
"reason": obj['reason'],
"permId": deleted_object["id"]["permId"],
"type": deleted_object["id"]["@type"]
})
return del_objs
def extract_identifier(ident):
if not isinstance(ident, dict):
return str(ident)
return ident['identifier']
def extract_nested_identifier(ident):
if not isinstance(ident, dict):
return str(ident)
return ident['identifier']['identifier']
def extract_permid(permid):
if not isinstance(permid, dict):
return str(permid)
return permid['permId']
def extract_nested_permid(permid):
if not isinstance(permid, dict):
return str(permid)
return permid['permId']['permId']
def extract_property_assignments(pas):
pa_strings = []
for pa in pas:
if not isinstance(pa['propertyType'], dict):
pa_strings.append(pa['propertyType'])
else:
pa_strings.append(pa['propertyType']['label'])
return pa_strings
def extract_person(person):
if 'email' in person and person['email'] is not '':
return "%s %s <%s>" % (person['firstName'], person['lastName'], person['email'])
else:
return "%s %s" % (person['firstName'], person['lastName'])
def extract_properties(prop):
if isinstance(prop, dict):
newline = "; "
props = []
for key in prop:
props.append("%s: %s" % (key, prop[key]))
return newline.join(props)
def extract_tags(tags):
if isinstance(tags, dict):
tags = [tags]
new_tags = []
for tag in tags:
new_tags.append(tag["code"])
return new_tags
def extract_attachments(attachments):
att = []
for attachment in attachments:
att.append(attachment['fileName'])
return att
def crc32(fileName):
prev = 0
for eachLine in open(fileName,"rb"):
prev = zlib.crc32(eachLine, prev)
return prev
# return as hex
#return "%X"%(prev & 0xFFFFFFFF)
def _create_tagIds(tags=None):
if tags is None:
return None
tagIds = []
for tag in tags:
tagIds.append({ "code": tag, "@type": "as.dto.tag.id.TagCode" })
return tagIds
def _tagIds_for_tags(tags=None, action='Add'):
"""creates an action item to add or remove tags. Action is either 'Add', 'Remove' or 'Set'
"""
if tags is None:
return
if not isinstance(tags, list):
tags = [tags]
items = []
for tag in tags:
items.append({
"code": tag,
"@type": "as.dto.tag.id.TagCode"
})
tagIds = {
"actions": [
{
"items": items,
"@type": "as.dto.common.update.ListUpdateAction{}".format(action.capitalize())
}
],
"@type": "as.dto.common.update.IdListUpdateValue"
}
return tagIds
def _create_typeId(type):
return {
"permId": type.upper(),
"@type": "as.dto.entitytype.id.EntityTypePermId"
}
def _create_projectId(ident):
match = re.match('/', ident)
if match:
return {
"identifier": ident,
"@type": "as.dto.project.id.ProjectIdentifier"
}
else:
return {
"permId": ident,
"@type": "as.dto.project.id.ProjectPermId"
}
def _criteria_for_code(code):
return {
"fieldValue": {
"value": code,
"@type": "as.dto.common.search.StringEqualToValue"
},
"@type": "as.dto.common.search.CodeSearchCriteria"
}
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def _subcriteria_for_type(code, entity_type):
return {
"@type": "as.dto.{}.search.{}TypeSearchCriteria".format(entity_type.lower(), entity_type),
"criteria": [
{
"@type": "as.dto.common.search.CodeSearchCriteria",
"fieldValue": {
"value": code.upper(),
"@type": "as.dto.common.search.StringEqualToValue"
}
}
]
}
def _subcriteria_for_tags(tags):
if not isinstance(tags, list):
tags = [tags]
criterias = []
for tag in tags:
criterias.append({
"fieldName": "code",
"fieldType": "ATTRIBUTE",
"fieldValue": {
"value": tag,
"@type": "as.dto.common.search.StringEqualToValue"
},
"@type": "as.dto.common.search.CodeSearchCriteria"
})
return {
"@type": "as.dto.tag.search.TagSearchCriteria",
"operator": "AND",
"criteria": criterias
}
def _subcriteria_for_is_finished(is_finished):
return {
"@type": "as.dto.common.search.StringPropertySearchCriteria",
"fieldName": "FINISHED_FLAG",
"fieldType": "PROPERTY",
"fieldValue": {
"value": is_finished,
"@type": "as.dto.common.search.StringEqualToValue"
}
}
def _subcriteria_for_properties(prop, val):
return {
"@type": "as.dto.common.search.StringPropertySearchCriteria",
"fieldName": prop.upper(),
"fieldType": "PROPERTY",
"fieldValue": {
"value": val,
"@type": "as.dto.common.search.StringEqualToValue"
}
}
def _subcriteria_for_permid(permids, entity_type, parents_or_children='Parents'):
if not isinstance(permids, list):
permids = [permids]
criterias = []
for permid in permids:
criterias.append( {
"@type": "as.dto.common.search.PermIdSearchCriteria",
"fieldValue": {
"value": permid,
"@type": "as.dto.common.search.StringEqualToValue"
},
"fieldType": "ATTRIBUTE",
"fieldName": "code"
} )
criteria = {
"criteria": criterias,
"@type": "as.dto.sample.search.{}{}SearchCriteria".format(
entity_type, parents_or_children
),
"operator": "OR"
}
return criteria
def _subcriteria_for_code(code, object_type):
criteria = {
"criteria": [
{
"fieldName": "code",
"fieldType": "ATTRIBUTE",
"fieldValue": {
"value": code,
"@type": "as.dto.common.search.StringEqualToValue"
},
"@type": "as.dto.common.search.CodeSearchCriteria"
}
],
"@type": search_for_type[object_type],
"operator": "AND"
}
return criteria
class Openbis:
"""Interface for communicating with openBIS. A current version of openBIS is needed.
(minimum version 16.05).
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def __init__(self, url='https://localhost:8443', verify_certificates=True, token=None):
"""Initialize a new connection to an openBIS server.
"""
url_obj = urlparse(url)
if url_obj.netloc is None:
raise ValueError("please provide the url in this format: https://openbis.host.ch:8443")
self.url_obj = url_obj
self.url = url_obj.geturl()
self.port = url_obj.port
self.hostname = url_obj.hostname
self.as_v3 = '/openbis/openbis/rmi-application-server-v3.json'
self.as_v1 = '/openbis/openbis/rmi-general-information-v1.json'
self.reg_v1 = '/openbis/openbis/rmi-query-v1.json'
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self.verify_certificates = verify_certificates
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self.token = token
self.dataset_types = None
self.sample_types = None
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self.files_in_wsp = []
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self.token_path = None
# some default settings for working with samples etc.
self.default_space = None
self.default_project = None
self.default_experiment = None
self.default_sample_type = None
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# use an existing token, if available
if self.token is None:
@property
def spaces(self):
return self.get_spaces()
@property
def projects(self):
return self.get_projects()
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"""Read the token from the cache, and set the token ivar to it, if there, otherwise None.
If the token is not valid anymore, delete it.
"""
token_path = self.gen_token_path()
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if not os.path.exists(token_path):
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return None
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try:
with open(token_path) as f:
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token = f.read()
if not self.is_token_valid(token):
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os.remove(token_path)
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return None
else:
return token
except FileNotFoundError:
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return None
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def gen_token_path(self, parent_folder=None):
"""generates a path to the token file.
The token is usually saved in a file called
~/.pybis/hostname.token
"""
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if parent_folder is None:
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# save token under ~/.pybis folder
parent_folder = os.path.join(
os.path.expanduser("~"),
'.pybis'
)
path = os.path.join(parent_folder, self.hostname + '.token')
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return path
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def save_token(self, token=None, parent_folder=None):
""" saves the session token to the disk, usually here: ~/.pybis/hostname.token. When a new Openbis instance is created, it tries to read this saved token by default.
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if token is None:
token = self.token
token_path = None;
if parent_folder is None:
token_path = self.gen_token_path()
else:
token_path = self.gen_token_path(parent_folder)
# create the necessary directories, if they don't exist yet
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os.makedirs(os.path.dirname(token_path), exist_ok=True)
with open(token_path, 'w') as f:
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f.write(token)
self.token_path = token_path
def delete_token(self, token_path=None):
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if token_path is None:
token_path = self.token_path
os.remove(token_path)
def _post_request(self, resource, data):
""" internal method, used to handle all post requests and serializing / deserializing
data
"""
if "id" not in data:
data["id"] = "1"
if "jsonrpc" not in data:
data["jsonrpc"] = "2.0"
resp = requests.post(
self.url + resource,
json.dumps(data),
verify=self.verify_certificates
)
data = resp.json()
if 'error' in data:
raise ValueError('an error has occured: ' + data['error']['message'] )
elif 'result' in data:
return data['result']
else:
raise ValueError('request did not return either result nor error')
else:
raise ValueError('general error while performing post request')
""" Log out of openBIS. After logout, the session token is no longer valid.
if self.token is None:
return
logout_request = {
"method":"logout",
"params":[self.token],
}
resp = self._post_request(self.as_v3, logout_request)
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self.token = None
self.token_path = None
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def login(self, username=None, password=None, save_token=False):
"""Log into openBIS.
Expects a username and a password and updates the token (session-ID).
The token is then used for every request.
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Clients may want to store the credentials object in a credentials store after successful login.
Throw a ValueError with the error message if login failed.
"""
login_request = {
"method":"login",
"params":[username, password],
}
result = self._post_request(self.as_v3, login_request)
if result is None:
raise ValueError("login to openBIS failed")
else:
self.token = result
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if save_token:
self.save_token()
return self.token
def get_datastores(self):
""" Get a list of all available datastores. Usually there is only one, but in some cases
there might be more. If you upload a file, you need to specifiy the datastore you want
the file uploaded to.
"""
if len(self.datastores) == 0:
request = {
"method": "listDataStores",
"params": [ self.token ],
}
resp = self._post_request(self.as_v1, request)
if resp is not None:
self.datastores = DataFrame(resp)[['code','downloadUrl', 'hostUrl']]
return self.datastores
else:
raise ValueError("No datastore found!")
else:
return self.datastores
def get_spaces(self, code=None):
""" Get a list of all available spaces (DataFrame object). To create a sample or a
dataset, you need to specify in which space it should live.
"""
criteria = {}
options = {}
"params": [ self.token,
criteria,
options,
],
}
resp = self._post_request(self.as_v3, request)
if resp is not None:
spaces = DataFrame(resp['objects'])
spaces['registrationDate']= spaces['registrationDate'].map(format_timestamp)
spaces['modificationDate']= spaces['modificationDate'].map(format_timestamp)
sp = Things(
self,
'space',
spaces[['code', 'description', 'registrationDate', 'modificationDate']]
)
return sp
def get_space(self, spaceId):
""" Returns a Space object for a given identifier (spaceId).
"""
request = {
"method": "getSpaces",
"params": [
self.token,
[{
"@id": 0,
"permId": spaceId,
"@type": "as.dto.space.id.SpacePermId"
}],
{
"@id": 0,
"@type": "as.dto.space.fetchoptions.SpaceFetchOptions",
"registrator": None,
"samples": None,
"projects": None,
"sort": None
}
],
}
resp = self._post_request(self.as_v3, request)
if len(resp) == 0:
raise ValueError("No such space: %s" % spaceId)
return Space(self, resp[spaceId])
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def get_samples(self, code=None, space=None, project=None, experiment=None, type=None,
withParents=None, withChildren=None):
""" Get a list of all samples for a given space/project/experiment (or any combination)
"""
if space is None:
space = self.default_space
if project is None:
project = self.default_project
if experiment is None:
experiment = self.default_experiment
sub_criteria = []
if space:
sub_criteria.append(_subcriteria_for_code(space, 'space'))
exp_crit = _subcriteria_for_code(experiment, 'experiment')
proj_crit = _subcriteria_for_code(project, 'project')
exp_crit['criteria'] = []
exp_crit['criteria'].append(proj_crit)
sub_criteria.append(exp_crit)
sub_criteria.append(_subcriteria_for_code(experiment, 'experiment'))
if type:
sub_criteria.append(_subcriteria_for_code(type, 'sample_type'))
if code:
sub_criteria.append(_criteria_for_code(code))
if withParents:
sub_criteria.append(_subcriteria_for_permid(withParents, 'Sample', 'Parents'))
if withChildren:
sub_criteria.append(_subcriteria_for_permid(withChildren, 'Sample', 'Children'))
criteria = {
"criteria": sub_criteria,
"@type": "as.dto.sample.search.SampleSearchCriteria",
"operator": "AND"
}
"properties": { "@type": "as.dto.property.fetchoptions.PropertyFetchOptions" },
"tags": { "@type": "as.dto.tag.fetchoptions.TagFetchOptions" },
"registrator": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"modifier": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"experiment": { "@type": "as.dto.experiment.fetchoptions.ExperimentFetchOptions" },
"type": { "@type": "as.dto.sample.fetchoptions.SampleTypeFetchOptions" },
"@type": "as.dto.sample.fetchoptions.SampleFetchOptions",
request = {
"method": "searchSamples",
"params": [ self.token,
criteria,
options,
],
}
resp = self._post_request(self.as_v3, request)
if resp is not None:
objects = resp['objects']
parse_jackson(objects)
samples = DataFrame(objects)
if len(samples) is 0:
raise ValueError("No samples found!")
samples['registrationDate']= samples['registrationDate'].map(format_timestamp)
samples['modificationDate']= samples['modificationDate'].map(format_timestamp)
samples['registrator'] = samples['registrator'].map(extract_person)
samples['modifier'] = samples['modifier'].map(extract_person)
samples['identifier'] = samples['identifier'].map(extract_identifier)
samples['experiment'] = samples['experiment'].map(extract_nested_identifier)
samples['sample_type'] = samples['type'].map(extract_nested_permid)
ss = samples[['code', 'identifier', 'experiment', 'sample_type', 'registrator', 'registrationDate', 'modifier', 'modificationDate']]
return Things(self, 'sample', ss, 'identifier')
else:
raise ValueError("No samples found!")
def get_experiments(self, code=None, type=None, space=None, project=None, tags=None, is_finished=None, **properties):
""" Get a list of all experiment for a given space or project (or any combination)
"""
if space is None:
space = self.default_space
if project is None:
project = self.default_project
sub_criteria = []
if space:
sub_criteria.append(_subcriteria_for_code(space, 'space'))
sub_criteria.append(_subcriteria_for_code(project, 'project'))
sub_criteria.append(_criteria_for_code(code))
if type:
sub_criteria.append(_subcriteria_for_type(type, 'Experiment'))
if tags:
sub_criteria.append(_subcriteria_for_tags(tags))
if is_finished is not None:
sub_criteria.append(_subcriteria_for_is_finished(is_finished))
if properties is not None:
for prop in properties:
sub_criteria.append(_subcriteria_for_properties(prop, properties[prop]))
criteria = {
"criteria": sub_criteria,
"@type": "as.dto.experiment.search.ExperimentSearchCriteria",
"operator": "AND"
}
options = {
"properties": { "@type": "as.dto.property.fetchoptions.PropertyFetchOptions" },
"tags": { "@type": "as.dto.tag.fetchoptions.TagFetchOptions" },
"registrator": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"modifier": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"project": { "@type": "as.dto.project.fetchoptions.ProjectFetchOptions" },
"type": { "@type": "as.dto.experiment.fetchoptions.ExperimentTypeFetchOptions" },
"@type": "as.dto.experiment.fetchoptions.ExperimentFetchOptions"
}
request = {
"method": "searchExperiments",
"params": [ self.token,
criteria,
options,
],
}
resp = self._post_request(self.as_v3, request)
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if len(resp['objects']) == 0:
raise ValueError("No experiments found!")
objects = resp['objects']
parse_jackson(objects)
experiments = DataFrame(objects)
experiments['registrationDate']= experiments['registrationDate'].map(format_timestamp)
experiments['modificationDate']= experiments['modificationDate'].map(format_timestamp)
experiments['project']= experiments['project'].map(extract_code)
experiments['registrator'] = experiments['registrator'].map(extract_person)
experiments['modifier'] = experiments['modifier'].map(extract_person)
experiments['identifier'] = experiments['identifier'].map(extract_identifier)
experiments['type'] = experiments['type'].map(extract_code)
exps = experiments[['code', 'identifier', 'project', 'type', 'registrator',
'registrationDate', 'modifier', 'modificationDate']]
return Things(self, 'experiment', exps, 'identifier')
def get_datasets(self, code=None, type=None, withParents=None, withChildren=None):
sub_criteria = []
if code:
sub_criteria.append(_criteria_for_code(code))
if type:
sub_criteria.append(_subcriteria_for_type(type, 'DataSet'))
if withParents:
sub_criteria.append(_subcriteria_for_permid(withParents, 'DataSet', 'Parents'))
if withChildren:
sub_criteria.append(_subcriteria_for_permid(withChildren, 'DataSet', 'Children'))
criteria = {
"criteria": sub_criteria,
"@type": "as.dto.dataset.search.DataSetSearchCriteria",
"operator": "AND"
}
fetchopts = {
# "parents": { "@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions" },
# "children": { "@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions" },
"containers": { "@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions" },
"type": { "@type": "as.dto.dataset.fetchoptions.DataSetTypeFetchOptions" }
}
for option in ['tags', 'properties', 'sample']:
fetchopts[option] = fetch_option[option]
request = {
"method": "searchDataSets",
"params": [ self.token,
criteria,
fetchopts,
],
}
resp = self._post_request(self.as_v3, request)
if resp is not None:
objects = resp['objects']
parse_jackson(objects)
datasets = DataFrame(objects)
datasets['registrationDate']= datasets['registrationDate'].map(format_timestamp)
datasets['modificationDate']= datasets['modificationDate'].map(format_timestamp)
datasets['sample']= datasets['sample'].map(extract_nested_identifier)
datasets['type']= datasets['type'].map(extract_code)
ds = Things(
self,
'dataset',
datasets[['code', 'properties', 'type', 'sample', 'registrationDate', 'modificationDate']]
)
return ds
def get_experiment(self, expId):
""" Returns an experiment object for a given identifier (expId).
"""
fetchopts = {
"@type": "as.dto.experiment.fetchoptions.ExperimentFetchOptions"
}
search_request = search_request_for_identifier(expId, 'experiment')
for option in ['tags', 'properties', 'attachments', 'project']:
fetchopts[option] = fetch_option[option]
request = {
"method": "getExperiments",
"params": [
self.token,
[ search_request ],
fetchopts
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],
}
resp = self._post_request(self.as_v3, request)
if len(resp) == 0:
raise ValueError("No such experiment: %s" % expId)
return Experiment(self, resp[expId])
def new_experiment(self, project_ident, code, type, properties=None, attachments=None, tags=None):
tagIds = _create_tagIds(tags)
typeId = _create_typeId(type)
projectId = _create_projectId(project_ident)
if properties is None:
properties = {}
request = {
"method": "createExperiments",
"params": [
self.token,
[
{
"properties": properties,
"code": code,
"typeId" : typeId,
"projectId": projectId,
"tagIds": tagIds,
"attachments": attachments,
"@type": "as.dto.experiment.create.ExperimentCreation",
}
]
],
}
resp = self._post_request(self.as_v3, request)
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return self.get_experiment(resp[0]['permId'])
def update_experiment(self, experimentId, properties=None, tagIds=None, attachments=None):
params = {
"experimentId": {
"permId": experimentId,
"@type": "as.dto.experiment.id.ExperimentPermId"
},
"@type": "as.dto.experiment.update.ExperimentUpdate"
}
if properties is not None:
params["properties"]= properties
if tagIds is not None:
params["tagIds"] = tagIds
if attachments is not None:
params["attachments"] = attachments
request = {
"method": "updateExperiments",
"params": [
self.token,
[ params ]
]
}
self._post_request(self.as_v3, request)
def delete_entity(self, what, permid, reason):
"""Deletes Spaces, Projects, Experiments, Samples and DataSets
"""
entity_type = "as.dto.{}.id.{}PermId".format(what.lower(), what.capitalize())
request = {
"method": "delete" + what.capitalize() + 's',
"params": [
self.token,
[
{
"permId": permid,
"@type": entity_type
}
],
{
"reason": reason,
"@type": "as.dto.{}.delete.{}DeletionOptions".format(what.lower(), what.capitalize())
}
]
}
self._post_request(self.as_v3, request)
def get_deletions(self):
request = {
"method": "searchDeletions",
"params": [
self.token,
{},
{
"deletedObjects": {
"@type": "as.dto.deletion.fetchoptions.DeletedObjectFetchOptions"
}
}
]
}
resp = self._post_request(self.as_v3, request)
objects = resp['objects']
parse_jackson(objects)
new_objs = []
for value in objects:
del_objs = extract_deletion(value)
if len(del_objs) > 0:
new_objs.append(*del_objs)
return DataFrame(new_objs)
def get_projects(self, space=None):
""" Get a list of all available projects (DataFrame object).
"""
if space is None:
space = self.default_space
sub_criteria = []
if space:
sub_criteria.append(_subcriteria_for_code(space, 'space'))
criteria = {
"criteria": sub_criteria,
"@type": "as.dto.project.search.ProjectSearchCriteria",
"operator": "AND"
}
options = {
"registrator": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"modifier": { "@type": "as.dto.person.fetchoptions.PersonFetchOptions" },
"experiments": { "@type": "as.dto.experiment.fetchoptions.ExperimentFetchOptions", },
"space": { "@type": "as.dto.space.fetchoptions.SpaceFetchOptions" },
"@type": "as.dto.project.fetchoptions.ProjectFetchOptions"
}
request = {
"method": "searchProjects",
"params": [ self.token,
criteria,
options,
],
}
resp = self._post_request(self.as_v3, request)
if resp is not None:
objects = resp['objects']
parse_jackson(objects)
projects = DataFrame(objects)
if len(projects) is 0:
raise ValueError("No projects found!")
projects['registrationDate']= projects['registrationDate'].map(format_timestamp)
projects['modificationDate']= projects['modificationDate'].map(format_timestamp)
projects['registrator'] = projects['registrator'].map(extract_person)
projects['modifier'] = projects['modifier'].map(extract_person)
projects['permid'] = projects['permId'].map(extract_permid)
projects['identifier'] = projects['identifier'].map(extract_identifier)
projects['space'] = projects['space'].map(extract_code)
pros=projects[['code', 'space', 'registrator', 'registrationDate',
'modifier', 'modificationDate', 'permid', 'identifier']]
return Things(self, 'project', pros, 'identifier')
else:
raise ValueError("No projects found!")
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def _create_get_request(self, method_name, entity_type, permids, options):
if not isinstance(permids, list):
permids = [permids]
type = "as.dto.{}.id.{}".format(entity_type.lower(), entity_type.capitalize())
search_params = []
for permid in permids:
# decide if we got a permId or an identifier
match = re.match('/', permid)
if match:
search_params.append(
{ "identifier" : permid, "@type" : type + 'Identifier' }
)
else:
search_params.append(
{ "permId" : permid, "@type": type + 'PermId' }
)
fo = {}
for option in options:
fo[option] = fetch_option[option]
request = {
"method": method_name,
"params": [
self.token,
search_params,
fo
],
}
return request
def get_project(self, projectId):
request = self._create_get_request('getProjects', 'project', projectId, ['attachments'])
resp = self._post_request(self.as_v3, request)
return resp
def new_project(self, space_code, code, description, leaderId):
request = {
"method": "createProjects",
"params": [
self.token,
[
{
"code": code,
"spaceId": {
"permId": space_code,
"@type": "as.dto.space.id.SpacePermId"
},
"@type": "as.dto.project.create.ProjectCreation",
"description": description,
"leaderId": leaderId,
"attachments": None
}
]
],
}
resp = self._post_request(self.as_v3, request)
return resp
def get_sample_types(self):
""" Returns a list of all available sample types
return self._get_types_of("searchSampleTypes", "Sample", ["generatedCodePrefix"])
def get_experiment_types(self):
""" Returns a list of all available experiment types
"""
return self._get_types_of("searchExperimentTypes", "Experiment")
def get_material_types(self):
""" Returns a list of all available material types
"""
return self._get_types_of("searchMaterialTypes", "Material")
""" Returns a list (DataFrame object) of all currently available dataset types
"""
return self._get_types_of("searchDataSetTypes", "DataSet")
def get_vocabulary(self):
""" Returns a DataFrame of all vocabulary terms available
fetch_options = {
"vocabulary" : { "@type" : "as.dto.vocabulary.fetchoptions.VocabularyFetchOptions" },
"@type": "as.dto.vocabulary.fetchoptions.VocabularyTermFetchOptions"
}
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request = {
"method": "searchVocabularyTerms",
"params": [ self.token, {}, fetch_options ]
}
resp = self._post_request(self.as_v3, request)
objects = DataFrame(resp['objects'])
objects['registrationDate'] = objects['registrationDate'].map(format_timestamp)
objects['modificationDate'] = objects['modificationDate'].map(format_timestamp)
return objects[['code', 'description', 'registrationDate', 'modificationDate']]
def get_tags(self):
""" Returns a DataFrame of all
"""
request = {
"method": "searchTags",
"params": [ self.token, {}, {} ]
}
resp = self._post_request(self.as_v3, request)
objects = DataFrame(resp['objects'])
objects['registrationDate'] = objects['registrationDate'].map(format_timestamp)
return objects[['code', 'registrationDate']]
def _get_types_of(self, method_name, entity_type, additional_attributes=[]):
""" Returns a list of all available experiment types
"""
attributes = ['code', 'description', *additional_attributes]
fetch_options = {}
if entity_type is not None:
fetch_options = {
"propertyAssignments" : {
"@type" : "as.dto.property.fetchoptions.PropertyAssignmentFetchOptions"
},
"@type": "as.dto.{}.fetchoptions.{}TypeFetchOptions".format(entity_type.lower(), entity_type)
}
attributes.append('propertyAssignments')
"params": [ self.token, {}, fetch_options ],
}
resp = self._post_request(self.as_v3, request)
parse_jackson(resp)
if len(resp['objects']) >= 1:
types = DataFrame(resp['objects'])
if 'propertyAssignments' in fetch_options:
types['propertyAssignments'] = types['propertyAssignments'].map(extract_property_assignments)
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def is_session_active(self):
""" checks whether a session is still active. Returns true or false.
"""
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return self.is_token_valid(self.token)
def is_token_valid(self, token=None):
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"""Check if the connection to openBIS is valid.
This method is useful to check if a token is still valid or if it has timed out,
requiring the user to login again.
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:return: Return True if the token is valid, False if it is not valid.
"""
if token is None:
token = self.token
if token is None:
return False
request = {
"method": "isSessionActive",
"params": [ token ],
resp = self._post_request(self.as_v1, request)
"""fetch a dataset and some metadata attached to it:
- properties
- sample
- parents
- children
- containers
- dataStore
- physicalData
- linkedData
:return: a DataSet object
"""
criteria = [{
"permId": permid,
"@type": "as.dto.dataset.id.DataSetPermId"
}]
fetchopts = {
"parents": { "@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions" },
"children": { "@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions" },
"containers": { "@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions" },
"@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions",
}
for option in ['tags', 'properties', 'dataStore', 'physicalData', 'linkedData',
'experiment', 'sample']:
fetchopts[option] = fetch_option[option]
request = {
"params": [ self.token,
criteria,
fetchopts,
resp = self._post_request(self.as_v3, request)
if resp is not None:
for permid in resp:
return DataSet(self, resp[permid])
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def get_sample(self, sample_ident):
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"""Retrieve metadata for the sample.
Get metadata for the sample and any directly connected parents of the sample to allow access
to the same information visible in the ELN UI. The metadata will be on the file system.
:param sample_identifiers: A list of sample identifiers to retrieve.
"""
search_request = search_request_for_identifier(sample_ident, 'sample')
"type": {
"@type": "as.dto.sample.fetchoptions.SampleTypeFetchOptions"
},
"parents": {
"@type": "as.dto.sample.fetchoptions.SampleFetchOptions",
"properties": {
"@type": "as.dto.property.fetchoptions.PropertyFetchOptions"
},
"children": {
"@type": "as.dto.sample.fetchoptions.SampleFetchOptions",
"properties": {
"@type": "as.dto.property.fetchoptions.PropertyFetchOptions"
}
},
"dataSets": {
"@type": "as.dto.dataset.fetchoptions.DataSetFetchOptions",
"properties": {
"@type": "as.dto.property.fetchoptions.PropertyFetchOptions"
},
"type": {
"@type": "as.dto.dataset.fetchoptions.DataSetTypeFetchOptions"
},
},
"properties": fetch_option['properties'],
"registrator": fetch_option['registrator'],
"tags": fetch_option['tags'],
}
sample_request = {
"method": "getSamples",
"params": [
self.token,
[ search_request ],
resp = self._post_request(self.as_v3, sample_request)
parse_jackson(resp)
if resp is None or len(resp) == 0:
raise ValueError('no such sample found: '+sample_ident)
else:
for sample_ident in resp:
return Sample(self, resp[sample_ident])
def new_space(self, name, description=None):
""" Creates a new space in the openBIS instance. Returns a list of all spaces
"""
request = {
"method": "createSpaces",
"params": [
self.token,
[ {
"@id": 0,
"code": name,
"description": description,
"@type": "as.dto.space.create.SpaceCreation"
} ]
],
}
resp = self._post_request(self.as_v3, request)
return self.get_spaces(refresh=True)
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def new_analysis(self, name, description=None, sample=None, dss_code=None, result_files=None,
notebook_files=None, parents=[]):
""" An analysis contains the Jupyter notebook file(s) and some result files.
Technically this method involves uploading files to the session workspace
and activating the dropbox aka dataset ingestion service "jupyter-uploader-api"
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"""
if dss_code is None:
dss_code = self.get_datastores()['code'][0]
# if a sample identifier was given, use it as a string.
# if a sample object was given, take its identifier
# TODO: handle permId's
sample_identifier = None
if isinstance(sample, str):
sample_identifier = sample
else:
sample_identifier = sample.ident
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folder = time.strftime('%Y-%m-%d_%H-%M-%S')
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data_sets = []
if notebook_files is not None:
notebooks_folder = os.path.join(folder, 'notebook_files')
self.upload_files(
datastore_url = datastore_url,
files=notebook_files,
folder= notebooks_folder,
wait_until_finished=True
)
data_sets.append({
"dataSetType" : "JUPYTER_NOTEBOOk",
"sessionWorkspaceFolder": notebooks_folder,
"fileNames" : notebook_files,
"properties" : {}
})
if result_files is not None:
results_folder = os.path.join(folder, 'result_files')
self.upload_files(
datastore_url = datastore_url,
files=result_files,
folder=results_folder,
wait_until_finished=True
)
data_sets.append({
"dataSetType" : "JUPYTER_RESULT",
"sessionWorkspaceFolder" : results_folder,
"fileNames" : result_files,
"properties" : {}
})
request = {
"method": "createReportFromAggregationService",
"params": [
self.token,
dss_code,
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DROPBOX_PLUGIN,
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"identifier" : sample.identifier
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"containers" : [
{
"dataSetType" : "JUPYTER_CONTAINER",
"properties" : {
"NAME" : name,
"DESCRIPTION" : description
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}
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"dataSets" : data_sets,
"parents" : parents,
resp = self._post_request(self.reg_v1, request)
try:
if resp['rows'][0][0]['value'] == 'OK':
return resp['rows'][0][1]['value']
except:
return resp
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def new_sample(self, sample_name, space_name, sample_type, tags=[], **kwargs):
""" Creates a new sample of a given sample type. sample_name, sample_type and space are
mandatory arguments.
if isinstance(tags, str):
tags = [tags]
tag_ids = []
for tag in tags:
tag_dict = {
"code":tag,
"@type":"as.dto.tag.id.TagCode"
}
tag_ids.append(tag_dict)
sample_create_request = {
"method":"createSamples",
"params":[
self.token,
"properties":{},
"typeId":{
"permId": sample_type,
"@type":"as.dto.entitytype.id.EntityTypePermId"
},
"code": sample_name,
"spaceId":{
"permId": space_name,
"@type":"as.dto.space.id.SpacePermId"
},
"tagIds":tag_ids,
"@type":"as.dto.sample.create.SampleCreation",
"experimentId":None,
"containerId":None,
"componentIds":None,
"parentIds":None,
"childIds":None,
"attachments":None,
"creationId":None,
"autoGeneratedCode":None
],
}
resp = self._post_request(self.as_v3, sample_create_request)
if 'permId' in resp[0]:
return self.get_sample(resp[0]['permId'])
else:
raise ValueError("error while trying to fetch sample from server: " + str(resp))
def _get_dss_url(self, dss_code=None):
""" internal method to get the downloadURL of a datastore.
"""
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dss = self.get_datastores()
if dss_code is None:
return dss['downloadUrl'][0]
else:
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return dss[dss['code'] == dss_code]['downloadUrl'][0]
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def upload_files(self, datastore_url=None, files=None, folder=None, wait_until_finished=False):
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if datastore_url is None:
if files is None:
raise ValueError("Please provide a filename.")
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if folder is None:
# create a unique foldername
folder = time.strftime('%Y-%m-%d_%H-%M-%S')
if isinstance(files, str):
files = [files]
self.files = files
self.startByte = 0
self.endByte = 0
# define a queue to handle the upload threads
queue = DataSetUploadQueue()
real_files = []
for filename in files:
if os.path.isdir(filename):
real_files.extend([os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser(filename)) for f in fn])
else:
real_files.append(os.path.join(filename))
# compose the upload-URL and put URL and filename in the upload queue
for filename in real_files:
file_in_wsp = os.path.join(folder, filename)
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self.files_in_wsp.append(file_in_wsp)
upload_url = (
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datastore_url + '/session_workspace_file_upload'
+ '?filename=' + os.path.join(folder,filename)
+ '&id=1'
+ '&startByte=0&endByte=0'
+ '&sessionID=' + self.token
)
queue.put([upload_url, filename, self.verify_certificates])
# wait until all files have uploaded
if wait_until_finished:
queue.join()
# return files with full path in session workspace
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return self.files_in_wsp
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class DataSetUploadQueue:
def __init__(self, workers=20):
# maximum files to be uploaded at once
self.upload_queue = Queue()
# define number of threads and start them
for t in range(workers):
t = Thread(target=self.upload_file)
t.daemon = True
t.start()
def put(self, things):
""" expects a list [url, filename] which is put into the upload queue
"""
self.upload_queue.put(things)
def join(self):
""" needs to be called if you want to wait for all uploads to be finished
"""
self.upload_queue.join()
def upload_file(self):
while True:
# get the next item in the queue
upload_url, filename, verify_certificates = self.upload_queue.get()
filesize = os.path.getsize(filename)
# upload the file to our DSS session workspace
with open(filename, 'rb') as f:
resp = requests.post(upload_url, data=f, verify=verify_certificates)
resp.raise_for_status()
data = resp.json()
assert filesize == int(data['size'])
# Tell the queue that we are done
self.upload_queue.task_done()
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class DataSetDownloadQueue:
def __init__(self, workers=20):
# maximum files to be downloaded at once
self.download_queue = Queue()
# define number of threads
for t in range(workers):
t = Thread(target=self.download_file)
t.daemon = True
t.start()
def put(self, things):
""" expects a list [url, filename] which is put into the download queue
"""
self.download_queue.put(things)
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def join(self):
""" needs to be called if you want to wait for all downloads to be finished
"""
self.download_queue.join()
def download_file(self):
while True:
url, filename, file_size, verify_certificates = self.download_queue.get()
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# create the necessary directory structure if they don't exist yet
os.makedirs(os.path.dirname(filename), exist_ok=True)
# request the file in streaming mode
r = requests.get(url, stream=True, verify=verify_certificates)
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with open(filename, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
assert os.path.getsize(filename) == int(file_size)
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self.download_queue.task_done()
class DataSet():
""" DataSet are openBIS objects that contain the actual files.
"""
def __init__(self, openbis_obj, data):
self.data = data
self.permid = data["code"]
self.permId = data["code"]
if data['physicalData'] is None:
self.shareId = None
self.location = None
else:
self.shareId = data['physicalData']['shareId']
self.location = data['physicalData']['location']
def _repr_html_(self):
html = """
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>attribute</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr> <th>permId</th> <td>{}</td> </tr>
<tr> <th>properties</th> <td>{}</td> </tr>
<tr> <th>tags</th> <td>{}</td> </tr>
</tbody>
</table>
"""
return html.format(self.permid, self.data['properties'], self.data['tags'])
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def download(self, files=None, wait_until_finished=True, workers=10):
""" download the actual files and put them by default in the following folder:
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__current_dir__/hostname/dataset_permid/
If no files are specified, all files of a given dataset are downloaded.
Files are usually downloaded in parallel, using 10 workers by default. If you want to wait until
all the files are downloaded, set the wait_until_finished option to True.
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"""
if files == None:
files = self.file_list()
elif isinstance(files, str):
files = [files]
base_url = self.data['dataStore']['downloadUrl'] + '/datastore_server/' + self.permid + '/'
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queue = DataSetDownloadQueue(workers=workers)
# get file list and start download
for filename in files:
file_info = self.get_file_list(start_folder=filename)
file_size = file_info[0]['fileSize']
download_url = base_url + filename + '?sessionID=' + self.openbis.token
filename = os.path.join(self.openbis.hostname, self.permid, filename)
queue.put([download_url, filename, file_size, self.openbis.verify_certificates])
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# wait until all files have downloaded
if wait_until_finished:
queue.join()
print("Files downloaded to: %s" % os.path.join(self.openbis.hostname, self.permid))
def get_parents(self):
return self.openbis.get_datasets(withChildren=self.permid)
def get_children(self):
return self.openbis.get_datasets(withParents=self.permid)
def file_list(self):
files = []
for file in self.get_file_list(recursive=True):
if file['isDirectory']:
pass
else:
files.append(file['pathInDataSet'])
return files
def get_files(self, start_folder='/'):
""" Returns a DataFrame of all files in this dataset
"""
def createRelativePath(pathInDataSet):
if self.shareId is None:
return ''
else:
return os.path.join(self.shareId, self.location, pathInDataSet)
files = self.get_file_list(start_folder=start_folder)
df = DataFrame(files)
df['relativePath'] = df['pathInDataSet'].map(createRelativePath)
df['crc32Checksum'] = df['crc32Checksum'].fillna(0.0).astype(int)
return df[['isDirectory', 'pathInDataSet', 'fileSize', 'crc32Checksum']]
def get_file_list(self, recursive=True, start_folder="/"):
""" Lists all files of a given dataset. You can specifiy a start_folder other than "/".
By default, all directories and their containing files are listed recursively. You can
turn off this option by setting recursive=False.
"""
request = {
"method" : "listFilesForDataSet",
"params" : [
self.openbis.token,
self.permid,
recursive,
],
"id":"1"
}
self.data["dataStore"]["downloadUrl"] + '/datastore_server/rmi-dss-api-v1.json',
json.dumps(request),
verify=self.openbis.verify_certificates
)
data = resp.json()
if 'error' in data:
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raise ValueError('Error from openBIS: ' + data['error'] )
elif 'result' in data:
return data['result']
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raise ValueError('request to openBIS did not return either result nor error')
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raise ValueError('internal error while performing post request')
class Sample():
""" A Sample is one of the most commonly used objects in openBIS.
"""
def __init__(self, openbis_obj, data):
self.openbis = openbis_obj
self.data = data
self.permid = data['permId']['permId']
self.permId = data['permId']['permId']
self.identifier = data['identifier']['identifier']
self.ident = data['identifier']['identifier']
self.properties = data['properties']
self.tags = extract_tags(data['tags'])
self.openbis.delete_entity('sample', self.permId, reason)
def get_datasets(self):
parse_jackson(objects)
datasets = DataFrame(objects)
datasets['registrationDate'] = datasets['registrationDate'].map(format_timestamp)
datasets['properties'] = datasets['properties'].map(extract_properties)
datasets['type'] = datasets['type'].map(extract_code)
return datasets
#return datasets[['code','properties', 'type', 'registrationDate']]
def get_parents(self):
return self.openbis.get_samples(withChildren=self.permId)
def get_children(self):
return self.openbis.get_samples(withParents=self.permId)
class Space(dict):
""" managing openBIS spaces
"""
def __init__(self, openbis_obj, *args, **kwargs):
super(Space, self).__init__(*args, **kwargs)
self.__dict__ = self
self.openbis = openbis_obj
""" Lists all samples in a given space. A pandas DataFrame object is returned.
"""
return self.openbis.get_samples(space=self.code, *args, **kwargs)
@property
def projects(self):
return self.openbis.get_projects(space=self.code)
def new_project(self, **kwargs):
return self.openbis.new_project(space=self.code, **kwargs)
@property
def experiments(self):
return self.openbis.get_experiments(space=self.code)
class Things():
"""An object that contains a DataFrame object about an entity available in openBIS.
"""
def __init__(self, openbis_obj, what, df, identifier_name='code'):
self.openbis = openbis_obj
self.what = what
self.df = df
self.identifier_name = identifier_name
def _repr_html_(self):
return self.df._repr_html_()
def __getitem__(self, key):
if self.df is not None and len(self.df) > 0:
row = None
if isinstance(key, int):
# get thing by rowid
row = self.df.loc[[key]]
elif isinstance(key, list):
# treat it as a normal dataframe
return self.df[key]
else:
# get thing by code
row = self.df[self.df[self.identifier_name]==key.upper()]
if row is not None:
# invoke the openbis.get_what() method
return getattr(self.openbis, 'get_'+self.what)(row[self.identifier_name].values[0])
class Experiment():
""" managing openBIS experiments
"""
def __init__(self, openbis_obj, data):
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self.permid = data['permId']['permId']
self.permId = data['permId']['permId']
self.identifier = data['identifier']['identifier']
self.properties = data['properties']
self.tags = extract_tags(data['tags'])
self.attachments = extract_attachments(data['attachments'])
self.project = data['project']['code']
self.data = data
def __setitem__(self, key, value):
self.openbis.update_experiment(self.permid, key, value)
def set_properties(self, properties):
self.openbis.update_experiment(self.permid, properties=properties)
def set_tags(self, tags):
tagIds = _tagIds_for_tags(tags, 'Set')
self.openbis.update_experiment(self.permid, tagIds=tagIds)
def add_tags(self, tags):
tagIds = _tagIds_for_tags(tags, 'Add')
self.openbis.update_experiment(self.permid, tagIds=tagIds)
def del_tags(self, tags):
tagIds = _tagIds_for_tags(tags, 'Remove')
self.openbis.update_experiment(self.permid, tagIds=tagIds)
def delete(self, reason):
self.openbis.delete_entity('experiment', self.permid, reason)
def _repr_html_(self):
html = """
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>attribute</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr> <th>permId</th> <td>{}</td> </tr>
<tr> <th>identifier</th> <td>{}</td> </tr>
<tr> <th>project</th> <td>{}</td> </tr>
<tr> <th>properties</th> <td>{}</td> </tr>
<tr> <th>tags</th> <td>{}</td> </tr>
<tr> <th>attachments</th> <td>{}</td> </tr>
</tbody>
</table>
"""
return html.format(self.permid, self.identifier, self.project, self.properties, self.tags, self.attachments)
data["identifier"] = self.identifier
data["permid"] = self.permid
data["properties"] = self.properties
data["tags"] = self.tags