def load_sample_set(workspace_url, ws_name, reads_refs, conditions,
library_type, target_name):
"""
Upload a set of files as a sample set.
library_type = "SingleEnd" or "PairedEnd"
"""
sample_set = {
"Library_type": library_type,
"domain": "Prokaryotes",
"num_samples": len(reads_refs),
"platform": None,
"publication_id": None,
"sample_ids": reads_refs,
"sampleset_desc": None,
"sampleset_id": target_name,
"condition": conditions,
"source": None
}
ws_client = Workspace(workspace_url)
ss_obj = ws_client.save_objects({
"workspace":
ws_name,
"objects": [{
"type": "KBaseRNASeq.RNASeqSampleSet",
"data": sample_set,
"name": target_name,
"provenance": [{
"input_ws_objects": reads_refs
}]
}]
})
ss_ref = "{}/{}/{}".format(ss_obj[0][6], ss_obj[0][0], ss_obj[0][4])
return ss_ref
def _put_cached_index(self, assembly_info, index_files_basename,
output_dir, ws_for_cache):
if not ws_for_cache:
print(
'WARNING: bowtie2 index cannot be cached because "ws_for_cache" field not set'
)
return False
try:
dfu = DataFileUtil(self.callback_url)
result = dfu.file_to_shock({
'file_path': output_dir,
'make_handle': 1,
'pack': 'targz'
})
bowtie2_index = {
'handle': result['handle'],
'size': result['size'],
'assembly_ref': assembly_info['ref'],
'index_files_basename': index_files_basename
}
ws = Workspace(self.ws_url)
save_params = {
'objects': [{
'hidden': 1,
'provenance': self.provenance,
'name': os.path.basename(output_dir),
'data': bowtie2_index,
'type': 'KBaseRNASeq.Bowtie2IndexV2'
}]
}
if ws_for_cache.strip().isdigit():
save_params['id'] = int(ws_for_cache)
else:
save_params['workspace'] = ws_for_cache.strip()
save_result = ws.save_objects(save_params)
print('Bowtie2IndexV2 cached to: ')
pprint(save_result[0])
return True
except Exception:
# if we fail in saving the cached object, don't worry
print(
'WARNING: exception encountered when trying to cache the index files:'
)
print(traceback.format_exc())
print(
'END WARNING: exception encountered when trying to cache the index files'
)
return False
class NarrativeManager:
KB_CELL = 'kb-cell'
KB_TYPE = 'type'
KB_APP_CELL = 'kb_app'
KB_FUNCTION_CELL = 'function_input'
KB_OUTPUT_CELL = 'function_output'
KB_ERROR_CELL = 'kb_error'
KB_CODE_CELL = 'kb_code'
KB_STATE = 'widget_state'
DEBUG = False
DATA_PALETTES_TYPES = DataPaletteTypes(False)
def __init__(self, config, ctx, set_api_client, data_palette_client):
self.narrativeMethodStoreURL = config['narrative-method-store']
self.set_api_cache = set_api_client # DynamicServiceCache type
self.data_palette_client = data_palette_client # DynamicServiceCache type
self.token = ctx["token"]
self.user_id = ctx["user_id"]
self.ws = Workspace(config['workspace-url'], token=self.token)
self.intro_md_file = config['intro-markdown-file']
# We switch DPs on only for internal Continuous Integration environment for now:
if config['kbase-endpoint'].startswith("https://ci.kbase.us/") or \
'USE_DP' in os.environ:
self.DATA_PALETTES_TYPES = DataPaletteTypes(True)
def list_objects_with_sets(self,
ws_id=None,
ws_name=None,
workspaces=None,
types=None,
include_metadata=0,
include_data_palettes=0):
if not workspaces:
if not ws_id and not ws_name:
raise ValueError(
"One and only one of 'ws_id', 'ws_name', 'workspaces' " +
"parameters should be set")
workspaces = [self._get_workspace_name_or_id(ws_id, ws_name)]
return self._list_objects_with_sets(workspaces, types,
include_metadata,
include_data_palettes)
def _list_objects_with_sets(self, workspaces, types, include_metadata,
include_data_palettes):
type_map = None
if types is not None:
type_map = {key: True for key in types}
processed_refs = {}
data = []
if self.DEBUG:
print("NarrativeManager._list_objects_with_sets: processing sets")
t1 = time.time()
set_ret = self.set_api_cache.call_method(
"list_sets", [{
'workspaces': workspaces,
'include_set_item_info': 1,
'include_metadata': include_metadata
}], self.token)
sets = set_ret['sets']
for set_info in sets:
# Process
target_set_items = []
for set_item in set_info['items']:
target_set_items.append(set_item['info'])
if self._check_info_type(set_info['info'], type_map):
data_item = {
'object_info': set_info['info'],
'set_items': {
'set_items_info': target_set_items
}
}
data.append(data_item)
processed_refs[set_info['ref']] = data_item
if self.DEBUG:
print(" (time=" + str(time.time() - t1) + ")")
if self.DEBUG:
print("NarrativeManager._list_objects_with_sets: loading ws_info")
t2 = time.time()
ws_info_list = []
# for ws in workspaces:
if len(workspaces) == 1:
ws = workspaces[0]
ws_id = None
ws_name = None
if str(ws).isdigit():
ws_id = int(ws)
else:
ws_name = str(ws)
ws_info_list.append(
self.ws.get_workspace_info({
"id": ws_id,
"workspace": ws_name
}))
else:
ws_map = {key: True for key in workspaces}
for ws_info in self.ws.list_workspace_info({'perm': 'r'}):
if ws_info[1] in ws_map or str(ws_info[0]) in ws_map:
ws_info_list.append(ws_info)
if self.DEBUG:
print(" (time=" + str(time.time() - t2) + ")")
if self.DEBUG:
print(
"NarrativeManager._list_objects_with_sets: loading workspace objects"
)
t3 = time.time()
for info in WorkspaceListObjectsIterator(
self.ws,
ws_info_list=ws_info_list,
list_objects_params={'includeMetadata': include_metadata}):
item_ref = str(info[6]) + '/' + str(info[0]) + '/' + str(info[4])
if item_ref not in processed_refs and self._check_info_type(
info, type_map):
data_item = {'object_info': info}
data.append(data_item)
processed_refs[item_ref] = data_item
if self.DEBUG:
print(" (time=" + str(time.time() - t3) + ")")
return_data = {"data": data}
if include_data_palettes == 1:
if self.DEBUG:
print(
"NarrativeManager._list_objects_with_sets: processing DataPalettes"
)
t5 = time.time()
dp_ret = self.data_palette_client.call_method(
"list_data", [{
'workspaces': workspaces,
'include_metadata': include_metadata
}], self.token)
for item in dp_ret['data']:
ref = item['ref']
if self._check_info_type(item['info'], type_map):
data_item = None
if ref in processed_refs:
data_item = processed_refs[ref]
else:
data_item = {'object_info': item['info']}
processed_refs[ref] = data_item
data.append(data_item)
dp_info = {}
if 'dp_ref' in item:
dp_info['ref'] = item['dp_ref']
if 'dp_refs' in item:
dp_info['refs'] = item['dp_refs']
data_item['dp_info'] = dp_info
return_data["data_palette_refs"] = dp_ret['data_palette_refs']
if self.DEBUG:
print(" (time=" + str(time.time() - t5) + ")")
return return_data
def _check_info_type(self, info, type_map):
if type_map is None:
return True
obj_type = info[2].split('-')[0]
return type_map.get(obj_type, False)
def copy_narrative(self, newName, workspaceRef, workspaceId):
time_ms = int(round(time.time() * 1000))
newWsName = self.user_id + ':narrative_' + str(time_ms)
# add the 'narrative' field to newWsMeta later.
newWsMeta = {"narrative_nice_name": newName, "searchtags": "narrative"}
# start with getting the existing narrative object.
currentNarrative = self.ws.get_objects([{'ref': workspaceRef}])[0]
if not workspaceId:
workspaceId = currentNarrative['info'][6]
# Let's prepare exceptions for clone the workspace.
# 1) currentNarrative object:
excluded_list = [{'objid': currentNarrative['info'][0]}]
# 2) let's exclude objects of types under DataPalette handling:
## DP CODE
# data_palette_type = "DataPalette.DataPalette"
# excluded_types = [data_palette_type]
# excluded_types.extend(self.DATA_PALETTES_TYPES.keys())
# add_to_palette_list = []
# dp_detected = False
## END DP CODE
# for obj_type in excluded_types:
# list_objects_params = {'type': obj_type}
## DP CODE
# if obj_type == data_palette_type:
# list_objects_params['showHidden'] = 1
## END DP CODE
# for info in WorkspaceListObjectsIterator(self.ws,
# ws_id=workspaceId,
# list_objects_params=list_objects_params):
## DP CODE
# if obj_type == data_palette_type:
# dp_detected = True
# else:
# add_to_palette_list.append({
# 'ref': str(info[6]) + '/' + str(info[0]) + '/' + str(info[4])
# })
## END DP CODE
# excluded_list.append({'objid': info[0]})
# clone the workspace EXCEPT for currentNarrative object
newWsId = self.ws.clone_workspace({
'wsi': {
'id': workspaceId
},
'workspace': newWsName,
'meta': newWsMeta,
'exclude': excluded_list
})[0]
try:
## DP CODE
# if dp_detected:
# self.data_palette_client.call_method(
# "copy_palette",
# [{'from_workspace': str(workspaceId), 'to_workspace': str(newWsId)}],
# self.token
# )
# if len(add_to_palette_list) > 0:
# # There are objects in source workspace that have type under DataPalette handling
# # but these objects are physically stored in source workspace rather that saved
# # in DataPalette object. So they weren't copied by "dps.copy_palette".
# self.data_palette_client.call_method(
# "add_to_palette",
# [{'workspace': str(newWsId), 'new_refs': add_to_palette_list}],
# self.token
# )
## END DP CODE
# update the ref inside the narrative object and the new workspace metadata.
newNarMetadata = currentNarrative['info'][10]
newNarMetadata['name'] = newName
newNarMetadata['ws_name'] = newWsName
newNarMetadata['job_info'] = json.dumps({
'queue_time': 0,
'running': 0,
'completed': 0,
'run_time': 0,
'error': 0
})
is_temporary = newNarMetadata.get('is_temporary', 'false')
if 'is_temporary' not in newNarMetadata:
if newNarMetadata['name'] == 'Untitled' or newNarMetadata[
'name'] is None:
is_temporary = 'true'
newNarMetadata['is_temporary'] = is_temporary
currentNarrative['data']['metadata']['name'] = newName
currentNarrative['data']['metadata']['ws_name'] = newWsName
currentNarrative['data']['metadata']['job_ids'] = {
'apps': [],
'methods': [],
'job_usage': {
'queue_time': 0,
'run_time': 0
}
}
# save the shiny new Narrative so it's at version 1
newNarInfo = self.ws.save_objects({
'id':
newWsId,
'objects': [{
'type': currentNarrative['info'][2],
'data': currentNarrative['data'],
'provenance': currentNarrative['provenance'],
'name': currentNarrative['info'][1],
'meta': newNarMetadata
}]
})
# now, just update the workspace metadata to point
# to the new narrative object
if 'worksheets' in currentNarrative['data']: # handle legacy.
num_cells = len(
currentNarrative['data']['worksheets'][0]['cells'])
else:
num_cells = len(currentNarrative['data']['cells'])
newNarId = newNarInfo[0][0]
self.ws.alter_workspace_metadata({
'wsi': {
'id': newWsId
},
'new': {
'narrative': str(newNarId),
'is_temporary': is_temporary,
'cell_count': str(num_cells)
}
})
return {'newWsId': newWsId, 'newNarId': newNarId}
except Exception:
# let's delete copy of workspace so it's out of the way - it's broken
self.ws.delete_workspace({'id': newWsId})
raise
def create_new_narrative(self, app, method, appparam, appData, markdown,
copydata, importData, includeIntroCell, title):
if app and method:
raise ValueError(
"Must provide no more than one of the app or method params")
if not importData and copydata:
importData = copydata.split(';')
if not appData and appparam:
appData = []
for tmp_item in appparam.split(';'):
tmp_tuple = tmp_item.split(',')
step_pos = None
if tmp_tuple[0]:
try:
step_pos = int(tmp_tuple[0])
except ValueError:
pass
appData.append([step_pos, tmp_tuple[1], tmp_tuple[2]])
cells = None
if app:
cells = [{"app": app}]
elif method:
cells = [{"method": method}]
elif markdown:
cells = [{"markdown": markdown}]
narr_info = self._create_temp_narrative(cells, appData, importData,
includeIntroCell, title)
if title is not None:
# update workspace info so it's not temporary
pass
return narr_info
def _get_intro_markdown(self):
"""
Creates and returns a cell with the introductory text included.
"""
# Load introductory markdown text
with open(self.intro_md_file) as intro_file:
intro_md = intro_file.read()
return intro_md
def _create_temp_narrative(self, cells, parameters, importData,
includeIntroCell, title):
# Migration to python of JavaScript class from https://github.com/kbase/kbase-ui/blob/4d31151d13de0278765a69b2b09f3bcf0e832409/src/client/modules/plugins/narrativemanager/modules/narrativeManager.js#L414
narr_id = int(round(time.time() * 1000))
workspaceName = self.user_id + ':narrative_' + str(narr_id)
narrativeName = "Narrative." + str(narr_id)
ws = self.ws
ws_info = ws.create_workspace({
'workspace': workspaceName,
'description': ''
})
[narrativeObject, metadataExternal
] = self._fetchNarrativeObjects(workspaceName, cells, parameters,
includeIntroCell, title)
is_temporary = 'true'
if title is not None and title != 'Untitled':
is_temporary = 'false'
metadataExternal['is_temporary'] = is_temporary
objectInfo = ws.save_objects({
'workspace':
workspaceName,
'objects': [{
'type':
'KBaseNarrative.Narrative',
'data':
narrativeObject,
'name':
narrativeName,
'meta':
metadataExternal,
'provenance': [{
'script':
'NarrativeManager.py',
'description':
'Created new ' + 'Workspace/Narrative bundle.'
}],
'hidden':
0
}]
})[0]
objectInfo = ServiceUtils.object_info_to_object(objectInfo)
ws_info = self._completeNewNarrative(ws_info[0], objectInfo['id'],
importData, is_temporary, title,
len(narrativeObject['cells']))
return {
'workspaceInfo': ServiceUtils.workspace_info_to_object(ws_info),
'narrativeInfo': objectInfo
}
def _fetchNarrativeObjects(self, workspaceName, cells, parameters,
includeIntroCell, title):
if not cells:
cells = []
if not title:
title = 'Untitled'
# fetchSpecs
appSpecIds = []
methodSpecIds = []
specMapping = {'apps': {}, 'methods': {}}
for cell in cells:
if 'app' in cell:
appSpecIds.append(cell['app'])
elif 'method' in cell:
methodSpecIds.append(cell['method'])
nms = NarrativeMethodStore(self.narrativeMethodStoreURL,
token=self.token)
if len(appSpecIds) > 0:
appSpecs = nms.get_app_spec({'ids': appSpecIds})
for spec in appSpecs:
spec_id = spec['info']['id']
specMapping['apps'][spec_id] = spec
if len(methodSpecIds) > 0:
methodSpecs = nms.get_method_spec({'ids': methodSpecIds})
for spec in methodSpecs:
spec_id = spec['info']['id']
specMapping['methods'][spec_id] = spec
# end of fetchSpecs
metadata = {
'job_ids': {
'methods': [],
'apps': [],
'job_usage': {
'queue_time': 0,
'run_time': 0
}
},
'format': 'ipynb',
'creator': self.user_id,
'ws_name': workspaceName,
'name': title,
'type': 'KBaseNarrative.Narrative',
'description': '',
'data_dependencies': []
}
cellData = self._gatherCellData(cells, specMapping, parameters,
includeIntroCell)
narrativeObject = {
'nbformat_minor': 0,
'cells': cellData,
'metadata': metadata,
'nbformat': 4
}
metadataExternal = {}
for key in metadata:
value = metadata[key]
if isinstance(value, str):
metadataExternal[key] = value
else:
metadataExternal[key] = json.dumps(value)
return [narrativeObject, metadataExternal]
def _gatherCellData(self, cells, specMapping, parameters,
includeIntroCell):
cell_data = []
if includeIntroCell == 1:
cell_data.append({
'cell_type': 'markdown',
'source': self._get_intro_markdown(),
'metadata': {}
})
for cell_pos, cell in enumerate(cells):
if 'app' in cell:
cell_data.append(
self._buildAppCell(len(cell_data),
specMapping['apps'][cell['app']],
parameters))
elif 'method' in cell:
cell_data.append(
self._buildMethodCell(
len(cell_data), specMapping['methods'][cell['method']],
parameters))
elif 'markdown' in cell:
cell_data.append({
'cell_type': 'markdown',
'source': cell['markdown'],
'metadata': {}
})
else:
raise ValueError("cannot add cell #" + str(cell_pos) +
", unrecognized cell content")
return cell_data
def _buildAppCell(self, pos, spec, params):
cellId = 'kb-cell-' + str(pos) + '-' + str(uuid.uuid4())
cell = {
"cell_type":
"markdown",
"source":
"<div id='" + cellId + "'></div>" + "\n<script>" + "$('#" +
cellId + "').kbaseNarrativeAppCell({'appSpec' : '" +
self._safeJSONStringify(spec) + "', 'cellId' : '" + cellId +
"'});" + "</script>",
"metadata": {}
}
cellInfo = {}
widgetState = []
cellInfo[self.KB_TYPE] = self.KB_APP_CELL
cellInfo['app'] = spec
if params:
steps = {}
for param in params:
stepid = 'step_' + str(param[0])
if stepid not in steps:
steps[stepid] = {}
steps[stepid]['inputState'] = {}
steps[stepid]['inputState'][param[1]] = param[2]
state = {'state': {'step': steps}}
widgetState.append(state)
cellInfo[self.KB_STATE] = widgetState
cell['metadata'][self.KB_CELL] = cellInfo
return cell
def _buildMethodCell(self, pos, spec, params):
cellId = "kb-cell-" + str(pos) + "-" + str(uuid.uuid4())
cell = {
"cell_type":
"markdown",
"source":
"<div id='" + cellId + "'></div>" + "\n<script>" + "$('#" +
cellId + "').kbaseNarrativeMethodCell({'method' : '" +
self._safeJSONStringify(spec) + "'});" + "</script>",
"metadata": {}
}
cellInfo = {"method": spec, "widget": spec["widgets"]["input"]}
cellInfo[self.KB_TYPE] = self.KB_FUNCTION_CELL
widgetState = []
if params:
wparams = {}
for param in params:
wparams[param[1]] = param[2]
widgetState.append({"state": wparams})
cellInfo[self.KB_STATE] = widgetState
cell["metadata"][self.KB_CELL] = cellInfo
return cell
def _completeNewNarrative(self, workspaceId, objectId, importData,
is_temporary, title, num_cells):
"""
'Completes' the new narrative by updating workspace metadata with the required fields and
copying in data from the importData list of references.
"""
new_meta = {
'narrative': str(objectId),
'is_temporary': is_temporary,
'searchtags': 'narrative',
'cell_count': str(num_cells)
}
if is_temporary == 'false' and title is not None:
new_meta['narrative_nice_name'] = title
self.ws.alter_workspace_metadata({
'wsi': {
'id': workspaceId
},
'new': new_meta
})
# copy_to_narrative:
if importData:
objectsToCopy = [{'ref': x} for x in importData]
infoList = self.ws.get_object_info_new({
'objects': objectsToCopy,
'includeMetadata': 0
})
for item in infoList:
objectInfo = ServiceUtils.object_info_to_object(item)
self.copy_object(objectInfo['ref'], workspaceId, None, None,
objectInfo)
return self.ws.get_workspace_info({'id': workspaceId})
def _safeJSONStringify(self, obj):
return json.dumps(self._safeJSONStringifyPrepare(obj))
def _safeJSONStringifyPrepare(self, obj):
if isinstance(obj, str):
return obj.replace("'", "'").replace('"', """)
elif isinstance(obj, list):
for pos in range(len(obj)):
obj[pos] = self._safeJSONStringifyPrepare(obj[pos])
elif isinstance(obj, dict):
obj_keys = list(obj.keys())
for key in obj_keys:
obj[key] = self._safeJSONStringifyPrepare(obj[key])
else:
pass # it's boolean/int/float/None
return obj
def _get_workspace_name_or_id(self, ws_id, ws_name):
ret = ws_name
if not ret:
ret = str(ws_id)
return ret
def copy_object(self, ref, target_ws_id, target_ws_name, target_name,
src_info):
"""
Copies an object from one workspace to another.
"""
if not target_ws_id and not target_ws_name:
raise ValueError("Neither target workspace id nor name is defined")
if not src_info:
src_info_tuple = self.ws.get_object_info_new({
'objects': [{
'ref': ref
}],
'includeMetadata':
0
})[0]
src_info = ServiceUtils.object_info_to_object(src_info_tuple)
if not target_name:
target_name = src_info['name']
obj_info_tuple = self.ws.copy_object({
'from': {
'ref': ref
},
'to': {
'wsid': target_ws_id,
'workspace': target_ws_name,
'name': target_name
}
})
obj_info = ServiceUtils.object_info_to_object(obj_info_tuple)
return {'info': obj_info}
def list_available_types(self, workspaces):
data = self.list_objects_with_sets(workspaces=workspaces)['data']
type_stat = {}
for item in data:
info = item['object_info']
obj_type = info[2].split('-')[0]
if obj_type in type_stat:
type_stat[obj_type] += 1
else:
type_stat[obj_type] = 1
return {'type_stat': type_stat}
