def getDuplicateAnalyses(self):
"""Return the duplicate analyses assigned to the current worksheet
:return: List of DuplicateAnalysis
:rtype: List of IDuplicateAnalysis objects"""
ans = self.getAnalyses()
duplicates = [an for an in ans if IDuplicateAnalysis.providedBy(an)]
return duplicates
def folderitem(self, obj, item, index):
item = super(QCAnalysesView, self).folderitem(obj, item, index)
obj = self.get_object(obj)
# Fill Worksheet cell
worksheet = obj.getWorksheet()
if not worksheet:
return item
# Fill the Worksheet cell
ws_id = api.get_id(worksheet)
ws_url = api.get_url(worksheet)
item["replace"]["Worksheet"] = get_link(ws_url, value=ws_id)
if IDuplicateAnalysis.providedBy(obj):
an_type = "d"
img_name = "duplicate.png"
parent = obj.getRequest()
else:
an_type = obj.getReferenceType()
img_name = an_type == "c" and "control.png" or "blank.png"
parent = obj.aq_parent
# Render the image
an_type = QCANALYSIS_TYPES.getValue(an_type)
item['before']['Service'] = get_image(img_name, title=an_type)
# Fill the Parent cell
parent_url = api.get_url(parent)
parent_id = api.get_id(parent)
item["replace"]["Parent"] = get_link(parent_url, value=parent_id)
return item
def is_result_range_compliant(analysis):
"""Returns whether the result range from the analysis matches with the
result range for the service counterpart defined in the Sample
"""
if not IRequestAnalysis.providedBy(analysis):
return True
if IDuplicateAnalysis.providedBy(analysis):
# Does not make sense to apply compliance to a duplicate, cause its
# valid range depends on the result of the original analysis
return True
rr = analysis.getResultsRange()
service_uid = rr.get("uid", None)
if not api.is_uid(service_uid):
return True
# Compare with Sample
sample = analysis.getRequest()
# If no Specification is set, assume is compliant
specification = sample.getRawSpecification()
if not specification:
return True
# Compare with the Specification that was initially set to the Sample
sample_rr = sample.getResultsRange(search_by=service_uid)
if not sample_rr:
# This service is not defined in Sample's ResultsRange, we
# assume this *does not* break the compliance
return True
return rr == sample_rr
def remove_duplicates(self, ws):
"""When this analysis is unassigned from a worksheet, this function
is responsible for deleting DuplicateAnalysis objects from the ws.
"""
for analysis in ws.objectValues():
if IDuplicateAnalysis.providedBy(analysis) \
and analysis.getAnalysis().UID() == self.UID():
ws.removeAnalysis(analysis)
def getSample(self):
# ReferenceSample cannot provide a 'getSample'
if IReferenceAnalysis.providedBy(self):
return None
if IDuplicateAnalysis.providedBy(self) \
or self.portal_type == 'RejectAnalysis':
return self.getAnalysis().aq_parent.getSample()
return self.aq_parent.getSample()
def remove_duplicates(self, ws):
"""When this analysis is unassigned from a worksheet, this function
is responsible for deleting DuplicateAnalysis objects from the ws.
"""
for analysis in ws.objectValues():
if IDuplicateAnalysis.providedBy(analysis) \
and analysis.getAnalysis().UID() == self.UID():
ws.removeAnalysis(analysis)
def getSample(self):
# ReferenceSample cannot provide a 'getSample'
if IReferenceAnalysis.providedBy(self):
return None
if IDuplicateAnalysis.providedBy(self) \
or self.portal_type == 'RejectAnalysis':
return self.getAnalysis().aq_parent.getSample()
return self.aq_parent.getSample()
def after_verify(obj):
"""Event fired after receive (Process) transition is triggered
"""
logger.info("*** Custom after_verify transition ***")
if IAnalysis.providedBy(obj) or IDuplicateAnalysis.providedBy(obj):
analysis_events.after_verify(obj)
if IAnalysisRequest.providedBy(obj):
_promote_transition(obj, "verify")
def after_submit(obj):
"""Event fired after submit transition is triggered
"""
logger.info("*** Custom after_submit transition ***")
if IAnalysis.providedBy(obj) or IDuplicateAnalysis.providedBy(obj):
analysis_events.after_submit(obj)
if IAnalysisRequest.providedBy(obj):
_promote_transition(obj, "submit")
def reindex_request(analysis, idxs=None):
"""Reindex the Analysis Request the analysis belongs to, as well as the
ancestors recursively
"""
if not IRequestAnalysis.providedBy(analysis) or \
IDuplicateAnalysis.providedBy(analysis):
# Analysis not directly bound to an Analysis Request. Do nothing
return
n_idxs = ['assigned_state', 'getDueDate']
n_idxs = idxs and list(set(idxs + n_idxs)) or n_idxs
request = analysis.getRequest()
ancestors = [request] + request.getAncestors(all_ancestors=True)
for ancestor in ancestors:
push_reindex_to_actions_pool(ancestor, n_idxs)
def get_slot_header_data(self, obj):
"""Prepare the data for the slot header template
"""
item_obj = None
item_title = ""
item_url = ""
item_img = ""
item_img_url = ""
item_img_text = ""
additional_item_icons = []
parent_obj = None
parent_title = ""
parent_url = ""
parent_img = ""
parent_img_text = ""
additional_parent_icons = []
sample_type_obj = None
sample_type_title = ""
sample_type_url = ""
sample_type_img = ""
sample_type_img_text = ""
if IDuplicateAnalysis.providedBy(obj):
# item
request = obj.getRequest()
item_obj = request
item_title = api.get_id(request)
item_url = api.get_url(request)
item_img = "duplicate.png"
item_img_url = api.get_url(request)
item_img_text = t(_("Duplicate"))
# additional item icons
additional_item_icons.append(self.render_remarks_tag(request))
# parent
client = request.getClient()
parent_obj = client
parent_title = api.get_title(client)
parent_url = api.get_url(client)
parent_img = "client.png"
parent_img_text = t(_("Client"))
# sample type
sample_type = request.getSampleType()
sample_type_title = request.getSampleTypeTitle()
sample_type_url = api.get_url(sample_type)
sample_type_img = "sampletype.png"
sample_type_img_text = t(_("Sample Type"))
elif IReferenceAnalysis.providedBy(obj):
# item
sample = obj.getSample()
item_obj = sample
item_title = api.get_id(sample)
item_url = api.get_url(sample)
item_img_url = api.get_url(sample)
item_img = "control.png"
item_img_text = t(_("Control"))
if obj.getReferenceType() == "b":
item_img = "blank.png"
item_img_text = t(_("Blank"))
# parent
supplier = obj.getSupplier()
parent_obj = supplier
parent_title = api.get_title(supplier)
parent_url = api.get_url(supplier)
parent_img = "supplier.png"
parent_img_text = t(_("Supplier"))
elif IRoutineAnalysis.providedBy(obj):
# item
request = obj.getRequest()
item_obj = request
item_title = api.get_id(request)
item_url = api.get_url(request)
item_img = "sample.png"
item_img_url = api.get_url(request)
item_img_text = t(_("Sample"))
# additional item icons
additional_item_icons.append(self.render_remarks_tag(request))
# parent
client = obj.getClient()
parent_obj = client
parent_title = api.get_title(client)
parent_url = api.get_url(client)
parent_img = "client.png"
parent_img_text = t(_("Client"))
# sample type
client_ref = request.getClientReference()
client_sid = request.getClientSampleID()
tokens = filter(None, [client_ref, client_sid])
sample_type_title = ' / '.join(tokens)
sample_type = obj.getSampleType()
sample_type_img = "sampletype.png"
sample_type_img_text = t(_("Tank / Blend ID"))
return {
# item
"item_obj":
item_obj,
"item_title":
item_title,
"item_url":
item_url,
"item_img":
get_image(item_img, title=item_img_text),
"item_img_url":
item_img_url,
"additional_item_icons":
additional_item_icons,
# parent
"parent_obj":
parent_obj,
"parent_title":
parent_title,
"parent_url":
parent_url,
"parent_img":
get_image(parent_img, title=parent_img_text),
"additional_parent_icons":
additional_parent_icons,
# sample type
"sample_type_obj":
sample_type_obj,
"sample_type_title":
sample_type_title,
"sample_type_url":
sample_type_url,
"sample_type_img":
get_image(sample_type_img, title=sample_type_img_text),
}
def is_out_of_range(brain_or_object, result=_marker):
"""Checks if the result for the analysis passed in is out of range and/or
out of shoulders range.
min max
warn min max warn
·········|---------------|=====================|---------------|·········
----- out-of-range -----><----- in-range ------><----- out-of-range -----
<-- shoulder --><----- in-range ------><-- shoulder -->
:param brain_or_object: A single catalog brain or content object
:param result: Tentative result. If None, use the analysis result
:type brain_or_object: ATContentType/DexterityContentType/CatalogBrain
:returns: Tuple of two elements. The first value is `True` if the result is
out of range and `False` if it is in range. The second value is `True` if
the result is out of shoulder range and `False` if it is in shoulder range
:rtype: (bool, bool)
"""
analysis = api.get_object(brain_or_object)
if not IAnalysis.providedBy(analysis) and \
not IReferenceAnalysis.providedBy(analysis):
api.fail("{} is not supported. Needs to be IAnalysis or "
"IReferenceAnalysis".format(repr(analysis)))
if result is _marker:
result = api.safe_getattr(analysis, "getResult", None)
if result in [None, '']:
# Empty result
return False, False
if IDuplicateAnalysis.providedBy(analysis):
# Result range for duplicate analyses is calculated from the original
# result, applying a variation % in shoulders. If the analysis has
# result options enabled or string results enabled, system returns an
# empty result range for the duplicate: result must match %100 with the
# original result
original = analysis.getAnalysis()
original_result = original.getResult()
# Does original analysis have a valid result?
if original_result in [None, '']:
return False, False
# Does original result type matches with duplicate result type?
if api.is_floatable(result) != api.is_floatable(original_result):
return True, True
# Does analysis has result options enabled or non-floatable?
if analysis.getResultOptions() or not api.is_floatable(original_result):
# Let's always assume the result is 'out from shoulders', cause we
# consider the shoulders are precisely the duplicate variation %
out_of_range = original_result != result
return out_of_range, out_of_range
elif not api.is_floatable(result):
# A non-duplicate with non-floatable result. There is no chance to know
# if the result is out-of-range
return False, False
# Convert result to a float
result = api.to_float(result)
# Note that routine analyses, duplicates and reference analyses all them
# implement the function getResultRange:
# - For routine analyses, the function returns the valid range based on the
# specs assigned during the creation process.
# - For duplicates, the valid range is the result of the analysis the
# the duplicate was generated from +/- the duplicate variation.
# - For reference analyses, getResultRange returns the valid range as
# indicated in the Reference Sample from which the analysis was created.
result_range = api.safe_getattr(analysis, "getResultsRange", None)
if not result_range:
# No result range defined or the passed in object does not suit
return False, False
# Maybe there is a custom adapter
adapters = getAdapters((analysis,), IResultOutOfRange)
for name, adapter in adapters:
ret = adapter(result=result, specification=result_range)
if not ret or not ret.get('out_of_range', False):
continue
if not ret.get('acceptable', True):
# Out of range + out of shoulders
return True, True
# Out of range, but in shoulders
return True, False
result_range = ResultsRangeDict(result_range)
# The assignment of result as default fallback for min and max guarantees
# the result will be in range also if no min/max values are defined
specs_min = api.to_float(result_range.min, result)
specs_max = api.to_float(result_range.max, result)
in_range = False
min_operator = result_range.min_operator
if min_operator == "geq":
in_range = result >= specs_min
else:
in_range = result > specs_min
max_operator = result_range.max_operator
if in_range:
if max_operator == "leq":
in_range = result <= specs_max
else:
in_range = result < specs_max
# If in range, no need to check shoulders
if in_range:
return False, False
# Out of range, check shoulders. If no explicit warn_min or warn_max have
# been defined, no shoulders must be considered for this analysis. Thus, use
# specs' min and max as default fallback values
warn_min = api.to_float(result_range.warn_min, specs_min)
warn_max = api.to_float(result_range.warn_max, specs_max)
in_shoulder = warn_min <= result <= warn_max
return True, not in_shoulder
def assign_retracted_to_retests(portal):
logger.info("Reassigning retracted to retests ...")
# Note this is confusing, getRetested index tells us if the analysis is a
# retest, not the other way round! (the analysis has been retested)
catalog = api.get_tool(CATALOG_ANALYSIS_LISTING)
if "getRetested" not in catalog.indexes():
return
processed = list()
query = dict(getRetested="True")
brains = api.search(query, CATALOG_ANALYSIS_LISTING)
total = len(brains)
for num, brain in enumerate(brains):
retest = api.get_object(brain)
retest_uid = api.get_uid(retest)
if retest.getRetestOf():
# We've been resolved this inconsistency already
total -= 1
continue
# Look for the retest
if IDuplicateAnalysis.providedBy(retest):
worksheet = retest.getWorksheet()
if not worksheet:
total -= 1
continue
for dup in worksheet.get_duplicates_for(retest.getAnalysis()):
if api.get_uid(dup) != retest_uid \
and api.get_workflow_status_of(dup) == "retracted":
retest.setRetestOf(dup)
processed.append(retest)
break
elif IReferenceAnalysis.providedBy(retest):
worksheet = retest.getWorksheet()
if not worksheet:
total -= 1
continue
ref_type = retest.getReferenceType()
slot = worksheet.get_slot_position(retest.getSample(), ref_type)
for ref in worksheet.get_analyses_at(slot):
if api.get_uid(ref) != retest_uid \
and api.get_workflow_status_of(ref) == "retracted":
retest.setRetestOf(ref)
processed.append(retest)
break
else:
request = retest.getRequest()
keyword = retest.getKeyword()
analyses = request.getAnalyses(review_state="retracted",
getKeyword=keyword)
if not analyses:
total -= 1
continue
retest.setRetestOf(analyses[-1])
processed.append(retest)
if num % 100 == 0:
logger.info("Reassigning retracted analysis: {}/{}".format(
num, total))
del_metadata(portal,
catalog_id=CATALOG_ANALYSIS_LISTING,
column="getRetested")
add_metadata(portal,
catalog_id=CATALOG_ANALYSIS_LISTING,
column="getRetestOfUID")
del_index(portal,
catalog_id=CATALOG_ANALYSIS_LISTING,
index_name="getRetested")
add_index(portal,
catalog_id=CATALOG_ANALYSIS_LISTING,
index_name="isRetest",
index_attribute="isRetest",
index_metatype="BooleanIndex")
total = len(processed)
for num, analysis in enumerate(processed):
if num % 100 == 0:
logger.info("Reindexing retests: {}/{}".format(num, total))
analysis.reindexObject(idxs="isRetest")
