def createInputStoreAutoProc(dataCollectionId, integrationId, isAnomalous=False,
programId=None, status="SUCCESS", timeStart=None, timeEnd=None,
processingCommandLine=None, processingPrograms=None):
autoProcContainer = AutoProcContainer()
integrationContainer = AutoProcIntegrationContainer()
scalingContainer = AutoProcScalingContainer()
integration = AutoProcIntegration()
if integrationId is not None:
integration.autoProcIntegrationId = integrationId
integration.anomalous = isAnomalous
integrationContainer.AutoProcIntegration = integration
image = Image()
image.dataCollectionId = dataCollectionId
integrationContainer.Image = image
scalingContainer.AutoProcIntegrationContainer = integrationContainer
programContainer = AutoProcProgramContainer()
programContainer.AutoProcProgram = EDHandlerXSDataISPyBv1_4.createAutoProcProgram(
programId=programId, status=status, timeStart=timeStart, timeEnd=timeEnd,
processingCommandLine=processingCommandLine, processingPrograms=processingPrograms)
autoProcContainer.AutoProcProgramContainer = programContainer
autoProcContainer.AutoProcScalingContainer = scalingContainer
inputStoreAutoProc = XSDataInputStoreAutoProc()
inputStoreAutoProc.AutoProcContainer = autoProcContainer
return inputStoreAutoProc
def createXSDataInputStoreAutoProc(self, xsDataResultXDSAPP, processDirectory, template,
strPathXscaleLp, isAnom, proposal, timeStart, timeEnd, dataCollectionId,
integrationId=None, programId=None):
# Parse log file
dictLog = self.parseLogFile(xsDataResultXDSAPP.logFile.path.value)
dictXscale = self.parseXscaleLp(strPathXscaleLp)
# Create path to pyarch
self.pyarchDirectory = EDHandlerESRFPyarchv1_0.createPyarchFilePath(self.resultsDirectory)
self.pyarchDirectory = self.pyarchDirectory.replace('PROCESSED_DATA', 'RAW_DATA')
if self.pyarchDirectory is not None and not os.path.exists(self.pyarchDirectory):
os.makedirs(self.pyarchDirectory, 0o755)
# Determine pyarch prefix
listPrefix = template.split("_")
self.pyarchPrefix = "xa_{0}_run{1}".format(listPrefix[-3], listPrefix[-2])
xsDataInputStoreAutoProc = XSDataInputStoreAutoProc()
autoProcContainer = AutoProcContainer()
# AutoProc
autoProc = AutoProc()
autoProc.spaceGroup = dictLog["spaceGroup"]
autoProc.refinedCell_a = dictLog["cellA"]
autoProc.refinedCell_b = dictLog["cellB"]
autoProc.refinedCell_c = dictLog["cellC"]
autoProc.refinedCell_alpha = dictLog["cellAlpha"]
autoProc.refinedCell_beta = dictLog["cellBeta"]
autoProc.refinedCell_gamma = dictLog["cellGamma"]
autoProcContainer.AutoProc = autoProc
# AutoProcIntegrationContainer
autoProcIntegrationContainer = AutoProcIntegrationContainer()
autoProcIntegration = AutoProcIntegration()
autoProcIntegration.autoProcIntegrationId = integrationId
autoProcIntegration.cell_a = dictLog["cellA"]
autoProcIntegration.cell_b = dictLog["cellB"]
autoProcIntegration.cell_c = dictLog["cellC"]
autoProcIntegration.cell_alpha = dictLog["cellAlpha"]
autoProcIntegration.cell_beta = dictLog["cellBeta"]
autoProcIntegration.cell_gamma = dictLog["cellGamma"]
autoProcIntegration.anomalous = isAnom
image = Image()
image.dataCollectionId = dataCollectionId
autoProcIntegrationContainer.AutoProcIntegration = autoProcIntegration
autoProcIntegrationContainer.Image = image
# Scaling container
if xsDataResultXDSAPP.correctLP is not None:
isa = self.parseCorrectLp(xsDataResultXDSAPP.correctLP.path.value)
else:
isa = None
autoProcScalingContainer = AutoProcScalingContainer()
autoProcScaling = AutoProcScaling()
autoProcScaling.recordTimeStamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
autoProcScalingContainer.AutoProcScaling = autoProcScaling
for scalingStatisticsType in dictXscale:
autoProcScalingStatistics = AutoProcScalingStatistics()
autoProcScalingStatistics.scalingStatisticsType = scalingStatisticsType
autoProcScalingStatistics.anomalous = isAnom
for scalingStatisticsAttribute in dictXscale[scalingStatisticsType]:
setattr(autoProcScalingStatistics, scalingStatisticsAttribute, dictXscale[scalingStatisticsType][scalingStatisticsAttribute])
if scalingStatisticsType == "overall" and isa is not None:
autoProcScalingStatistics.isa = isa
autoProcScalingContainer.addAutoProcScalingStatistics(autoProcScalingStatistics)
autoProcScalingContainer.AutoProcIntegrationContainer = autoProcIntegrationContainer
autoProcContainer.AutoProcScalingContainer = autoProcScalingContainer
# Program
autoProcProgramContainer = AutoProcProgramContainer()
autoProcProgram = EDHandlerXSDataISPyBv1_4.createAutoProcProgram(
programId=programId, status="SUCCESS", timeStart=timeStart, timeEnd=timeEnd,
processingCommandLine=self.processingCommandLine, processingPrograms=self.processingPrograms)
autoProcProgramContainer.AutoProcProgram = autoProcProgram
# Attached files
pyarchPath = EDHandlerESRFPyarchv1_0.createPyarchFilePath(processDirectory)
pyarchResultPath = os.path.join(pyarchPath, "results")
if not os.path.exists(pyarchResultPath):
os.makedirs(pyarchResultPath, 0o755)
# XDSAPP log and result files
if xsDataResultXDSAPP.logFile is not None:
self.addAttachment(autoProcProgramContainer, xsDataResultXDSAPP.logFile.path.value,
"xdsapp", "log", isAnom, attachmentType="Log")
if xsDataResultXDSAPP.pointlessLog is not None:
self.addAttachment(autoProcProgramContainer, xsDataResultXDSAPP.pointlessLog.path.value,
"pointless", "log", isAnom, attachmentType="Log")
if xsDataResultXDSAPP.phenixXtriageLog is not None:
self.addAttachment(autoProcProgramContainer, xsDataResultXDSAPP.phenixXtriageLog.path.value,
"xtriage", "log", isAnom, attachmentType="Log")
if xsDataResultXDSAPP.correctLP is not None:
self.addAttachment(autoProcProgramContainer, xsDataResultXDSAPP.correctLP.path.value,
"CORRECT", "LP", isAnom, attachmentType="Log")
if xsDataResultXDSAPP.XDS_ASCII_HKL is not None:
self.addAttachment(autoProcProgramContainer, xsDataResultXDSAPP.XDS_ASCII_HKL.path.value,
"XDS_ASCII", "HKL", isAnom, attachmentType="Result", doGzip=True)
if xsDataResultXDSAPP.XDS_INP is not None:
self.addAttachment(autoProcProgramContainer, xsDataResultXDSAPP.XDS_INP.path.value,
"XDS", "INP", isAnom, attachmentType="Result", doGzip=False, noMergedString=True)
for mtz_F in xsDataResultXDSAPP.mtz_F:
basenameMtz_F = os.path.splitext(os.path.basename(mtz_F.path.value))[0]
self.addAttachment(autoProcProgramContainer, mtz_F.path.value,
basenameMtz_F, "mtz", isAnom, attachmentType="Result")
for mtz_F_plus_F_minus in xsDataResultXDSAPP.mtz_F_plus_F_minus:
basenameMtz_F_plus_F_minus = os.path.splitext(os.path.basename(mtz_F_plus_F_minus.path.value))[0]
self.addAttachment(autoProcProgramContainer, mtz_F_plus_F_minus.path.value,
basenameMtz_F_plus_F_minus, "mtz", isAnom, attachmentType="Result")
# for mtz_I in xsDataResultXDSAPP.mtz_I:
# basenameMtz_I = os.path.splitext(os.path.basename(mtz_I.path.value))[0]
# self.addAttachment(autoProcProgramContainer, mtz_I.path.value,
# basenameMtz_I, "mtz", isAnom, attachmentType="Result")
# for hkl in xsDataResultXDSAPP.hkl:
# basenameHkl = os.path.splitext(os.path.basename(hkl.path.value))[0]
# self.addAttachment(autoProcProgramContainer, hkl.path.value,
# basenameHkl, "hkl", isAnom, attachmentType="Result", doGzip=True)
# for cv in xsDataResultXDSAPP.cv:
# basenameCv = os.path.splitext(os.path.basename(cv.path.value))[0]
# self.addAttachment(autoProcProgramContainer, cv.path.value,
# basenameCv, "cv", isAnom, attachmentType="Result", doGzip=True)
if os.path.exists(strPathXscaleLp):
self.addAttachment(autoProcProgramContainer, strPathXscaleLp,
"XSCALE", "LP", isAnom, isMerged=True, attachmentType="Result")
autoProcContainer.AutoProcProgramContainer = autoProcProgramContainer
xsDataInputStoreAutoProc.AutoProcContainer = autoProcContainer
return xsDataInputStoreAutoProc
def createXSDataInputStoreAutoProc(self,
xsDataResultXDSAPP,
processDirectory,
template,
strPathXscaleLp,
isAnom,
proposal,
timeStart,
timeEnd,
dataCollectionId,
integrationId=None,
programId=None):
# Parse log file
dictLog = self.parseLogFile(xsDataResultXDSAPP.logFile.path.value)
dictXscale = self.parseXscaleLp(strPathXscaleLp)
# Create path to pyarch
self.pyarchDirectory = EDHandlerESRFPyarchv1_0.createPyarchFilePath(
self.resultsDirectory)
self.pyarchDirectory = self.pyarchDirectory.replace(
'PROCESSED_DATA', 'RAW_DATA')
if self.pyarchDirectory is not None and not os.path.exists(
self.pyarchDirectory):
os.makedirs(self.pyarchDirectory, 0o755)
# Determine pyarch prefix
listPrefix = template.split("_")
self.pyarchPrefix = "xa_{0}_run{1}".format(listPrefix[-3],
listPrefix[-2])
xsDataInputStoreAutoProc = XSDataInputStoreAutoProc()
autoProcContainer = AutoProcContainer()
# AutoProc
autoProc = AutoProc()
autoProc.spaceGroup = dictLog["spaceGroup"]
autoProc.refinedCell_a = dictLog["cellA"]
autoProc.refinedCell_b = dictLog["cellB"]
autoProc.refinedCell_c = dictLog["cellC"]
autoProc.refinedCell_alpha = dictLog["cellAlpha"]
autoProc.refinedCell_beta = dictLog["cellBeta"]
autoProc.refinedCell_gamma = dictLog["cellGamma"]
autoProcContainer.AutoProc = autoProc
# AutoProcIntegrationContainer
autoProcIntegrationContainer = AutoProcIntegrationContainer()
autoProcIntegration = AutoProcIntegration()
autoProcIntegration.autoProcIntegrationId = integrationId
autoProcIntegration.cell_a = dictLog["cellA"]
autoProcIntegration.cell_b = dictLog["cellB"]
autoProcIntegration.cell_c = dictLog["cellC"]
autoProcIntegration.cell_alpha = dictLog["cellAlpha"]
autoProcIntegration.cell_beta = dictLog["cellBeta"]
autoProcIntegration.cell_gamma = dictLog["cellGamma"]
autoProcIntegration.anomalous = isAnom
image = Image()
image.dataCollectionId = dataCollectionId
autoProcIntegrationContainer.AutoProcIntegration = autoProcIntegration
autoProcIntegrationContainer.Image = image
# Scaling container
if xsDataResultXDSAPP.correctLP is not None:
isa = self.parseCorrectLp(xsDataResultXDSAPP.correctLP.path.value)
else:
isa = None
autoProcScalingContainer = AutoProcScalingContainer()
autoProcScaling = AutoProcScaling()
autoProcScaling.recordTimeStamp = time.strftime(
"%Y-%m-%d %H:%M:%S", time.localtime())
autoProcScalingContainer.AutoProcScaling = autoProcScaling
for scalingStatisticsType in dictXscale:
autoProcScalingStatistics = AutoProcScalingStatistics()
autoProcScalingStatistics.scalingStatisticsType = scalingStatisticsType
autoProcScalingStatistics.anomalous = isAnom
for scalingStatisticsAttribute in dictXscale[
scalingStatisticsType]:
setattr(
autoProcScalingStatistics, scalingStatisticsAttribute,
dictXscale[scalingStatisticsType]
[scalingStatisticsAttribute])
if scalingStatisticsType == "overall" and isa is not None:
autoProcScalingStatistics.isa = isa
autoProcScalingContainer.addAutoProcScalingStatistics(
autoProcScalingStatistics)
autoProcScalingContainer.AutoProcIntegrationContainer = autoProcIntegrationContainer
autoProcContainer.AutoProcScalingContainer = autoProcScalingContainer
# Program
autoProcProgramContainer = AutoProcProgramContainer()
autoProcProgram = EDHandlerXSDataISPyBv1_4.createAutoProcProgram(
programId=programId,
status="SUCCESS",
timeStart=timeStart,
timeEnd=timeEnd,
processingCommandLine=self.processingCommandLine,
processingPrograms=self.processingPrograms)
autoProcProgramContainer.AutoProcProgram = autoProcProgram
# Attached files
pyarchPath = EDHandlerESRFPyarchv1_0.createPyarchFilePath(
processDirectory)
pyarchResultPath = os.path.join(pyarchPath, "results")
if not os.path.exists(pyarchResultPath):
os.makedirs(pyarchResultPath, 0o755)
# XDSAPP log and result files
if xsDataResultXDSAPP.logFile is not None:
self.addAttachment(autoProcProgramContainer,
xsDataResultXDSAPP.logFile.path.value,
"xdsapp",
"log",
isAnom,
attachmentType="Log")
if xsDataResultXDSAPP.pointlessLog is not None:
self.addAttachment(autoProcProgramContainer,
xsDataResultXDSAPP.pointlessLog.path.value,
"pointless",
"log",
isAnom,
attachmentType="Log")
if xsDataResultXDSAPP.phenixXtriageLog is not None:
self.addAttachment(autoProcProgramContainer,
xsDataResultXDSAPP.phenixXtriageLog.path.value,
"xtriage",
"log",
isAnom,
attachmentType="Log")
if xsDataResultXDSAPP.correctLP is not None:
self.addAttachment(autoProcProgramContainer,
xsDataResultXDSAPP.correctLP.path.value,
"CORRECT",
"LP",
isAnom,
attachmentType="Log")
if xsDataResultXDSAPP.XDS_ASCII_HKL is not None:
self.addAttachment(autoProcProgramContainer,
xsDataResultXDSAPP.XDS_ASCII_HKL.path.value,
"XDS_ASCII",
"HKL",
isAnom,
attachmentType="Result",
doGzip=True)
if xsDataResultXDSAPP.XDS_INP is not None:
self.addAttachment(autoProcProgramContainer,
xsDataResultXDSAPP.XDS_INP.path.value,
"XDS",
"INP",
isAnom,
attachmentType="Result",
doGzip=False,
noMergedString=True)
for mtz_F in xsDataResultXDSAPP.mtz_F:
basenameMtz_F = os.path.splitext(os.path.basename(
mtz_F.path.value))[0]
self.addAttachment(autoProcProgramContainer,
mtz_F.path.value,
basenameMtz_F,
"mtz",
isAnom,
attachmentType="Result")
for mtz_F_plus_F_minus in xsDataResultXDSAPP.mtz_F_plus_F_minus:
basenameMtz_F_plus_F_minus = os.path.splitext(
os.path.basename(mtz_F_plus_F_minus.path.value))[0]
self.addAttachment(autoProcProgramContainer,
mtz_F_plus_F_minus.path.value,
basenameMtz_F_plus_F_minus,
"mtz",
isAnom,
attachmentType="Result")
# for mtz_I in xsDataResultXDSAPP.mtz_I:
# basenameMtz_I = os.path.splitext(os.path.basename(mtz_I.path.value))[0]
# self.addAttachment(autoProcProgramContainer, mtz_I.path.value,
# basenameMtz_I, "mtz", isAnom, attachmentType="Result")
# for hkl in xsDataResultXDSAPP.hkl:
# basenameHkl = os.path.splitext(os.path.basename(hkl.path.value))[0]
# self.addAttachment(autoProcProgramContainer, hkl.path.value,
# basenameHkl, "hkl", isAnom, attachmentType="Result", doGzip=True)
# for cv in xsDataResultXDSAPP.cv:
# basenameCv = os.path.splitext(os.path.basename(cv.path.value))[0]
# self.addAttachment(autoProcProgramContainer, cv.path.value,
# basenameCv, "cv", isAnom, attachmentType="Result", doGzip=True)
if os.path.exists(strPathXscaleLp):
self.addAttachment(autoProcProgramContainer,
strPathXscaleLp,
"XSCALE",
"LP",
isAnom,
isMerged=True,
attachmentType="Result")
autoProcContainer.AutoProcProgramContainer = autoProcProgramContainer
xsDataInputStoreAutoProc.AutoProcContainer = autoProcContainer
return xsDataInputStoreAutoProc
def postProcess(self, _edObject = None):
EDPluginControl.postProcess(self)
self.DEBUG("EDPluginControlAutoproc.postProcess")
#Now that we have executed the whole thing we need to create
#the suitable ISPyB plugin input and serialize it to the file
#we've been given as input
output = AutoProcContainer()
# AutoProc attr
autoproc = AutoProc()
xdsout = self.xds_first.dataOutput
if xdsout.sg_number is not None: # and it should not
autoproc.spaceGroup = SPACE_GROUP_NAMES[xdsout.sg_number.value]
autoproc.refinedCell_a = xdsout.cell_a.value
autoproc.refinedCell_b = xdsout.cell_b.value
autoproc.refinedCell_c = xdsout.cell_c.value
autoproc.refinedCell_alpha = xdsout.cell_alpha.value
autoproc.refinedCell_beta = xdsout.cell_beta.value
autoproc.refinedCell_gamma = xdsout.cell_gamma.value
output.AutoProc = autoproc
# scaling container and all the things that go in
scaling_container_noanom = AutoProcScalingContainer()
scaling = AutoProcScaling()
scaling.recordTimeStamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
scaling_container_noanom.AutoProcScaling = scaling
# NOANOM PATH
xscale_stats_noanom = self.xscale_generate.dataOutput.stats_noanom_merged
inner_stats_noanom = xscale_stats_noanom.completeness_entries[0]
outer_stats_noanom = xscale_stats_noanom.completeness_entries[-1]
# use the previous shell's res as low res, if available
prev_res = self.low_resolution_limit
try:
prev_res = xscale_stats_noanom.completeness_entries[-2].outer_res.value
except IndexError:
pass
total_stats_noanom = xscale_stats_noanom.total_completeness
stats = _create_scaling_stats(inner_stats_noanom, 'innerShell',
self.low_resolution_limit, False)
overall_low = stats.resolutionLimitLow
scaling_container_noanom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(outer_stats_noanom, 'outerShell',
prev_res, False)
overall_high = stats.resolutionLimitHigh
scaling_container_noanom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(total_stats_noanom, 'overall',
self.low_resolution_limit, False)
stats.resolutionLimitLow = overall_low
stats.resolutionLimitHigh = overall_high
scaling_container_noanom.AutoProcScalingStatistics.append(stats)
integration_container_noanom = AutoProcIntegrationContainer()
image = Image()
image.dataCollectionId = self.dataInput.data_collection_id.value
integration_container_noanom.Image = image
integration_noanom = AutoProcIntegration()
if self.integration_id_noanom is not None:
integration_noanom.autoProcIntegrationId = self.integration_id_noanom
crystal_stats = self.parse_xds_noanom.dataOutput
integration_noanom.cell_a = crystal_stats.cell_a.value
integration_noanom.cell_b = crystal_stats.cell_b.value
integration_noanom.cell_c = crystal_stats.cell_c.value
integration_noanom.cell_alpha = crystal_stats.cell_alpha.value
integration_noanom.cell_beta = crystal_stats.cell_beta.value
integration_noanom.cell_gamma = crystal_stats.cell_gamma.value
integration_noanom.anomalous = 0
# done with the integration
integration_container_noanom.AutoProcIntegration = integration_noanom
scaling_container_noanom.AutoProcIntegrationContainer = integration_container_noanom
# ANOM PATH
scaling_container_anom = AutoProcScalingContainer()
scaling = AutoProcScaling()
scaling.recordTimeStamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
scaling_container_anom.AutoProcScaling = scaling
xscale_stats_anom = self.xscale_generate.dataOutput.stats_anom_merged
inner_stats_anom = xscale_stats_anom.completeness_entries[0]
outer_stats_anom = xscale_stats_anom.completeness_entries[-1]
# use the previous shell's res as low res if available
prev_res = self.low_resolution_limit
try:
prev_res = xscale_stats_anom.completeness_entries[-2].outer_res.value
except IndexError:
pass
total_stats_anom = xscale_stats_anom.total_completeness
stats = _create_scaling_stats(inner_stats_anom, 'innerShell',
self.low_resolution_limit, True)
overall_low = stats.resolutionLimitLow
scaling_container_anom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(outer_stats_anom, 'outerShell',
prev_res, True)
overall_high = stats.resolutionLimitHigh
scaling_container_anom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(total_stats_anom, 'overall',
self.low_resolution_limit, True)
stats.resolutionLimitLow = overall_low
stats.resolutionLimitHigh = overall_high
scaling_container_anom.AutoProcScalingStatistics.append(stats)
integration_container_anom = AutoProcIntegrationContainer()
image = Image()
image.dataCollectionId = self.dataInput.data_collection_id.value
integration_container_anom.Image = image
integration_anom = AutoProcIntegration()
crystal_stats = self.parse_xds_anom.dataOutput
if self.integration_id_anom is not None:
integration_anom.autoProcIntegrationId = self.integration_id_anom
integration_anom.cell_a = crystal_stats.cell_a.value
integration_anom.cell_b = crystal_stats.cell_b.value
integration_anom.cell_c = crystal_stats.cell_c.value
integration_anom.cell_alpha = crystal_stats.cell_alpha.value
integration_anom.cell_beta = crystal_stats.cell_beta.value
integration_anom.cell_gamma = crystal_stats.cell_gamma.value
integration_anom.anomalous = 1
# done with the integration
integration_container_anom.AutoProcIntegration = integration_anom
scaling_container_anom.AutoProcIntegrationContainer = integration_container_anom
# ------ NO ANOM / ANOM end
program_container = AutoProcProgramContainer()
program_container.AutoProcProgram = AutoProcProgram()
program_container.AutoProcProgram.processingCommandLine = ' '.join(sys.argv)
program_container.AutoProcProgram.processingPrograms = 'edna-fastproc'
# now for the generated files. There's some magic to do with
# their paths to determine where to put them on pyarch
pyarch_path = None
# Note: the path is in the form /data/whatever
# remove the edna-autoproc-import suffix
original_files_dir = self.file_conversion.dataInput.output_directory.value
#files_dir, _ = os.path.split(original_files_dir)
files_dir = original_files_dir
# the whole transformation is fragile!
if files_dir.startswith('/data/visitor'):
# We might get empty elements at the head/tail of the list
tokens = [elem for elem in files_dir.split(os.path.sep)
if len(elem) > 0]
pyarch_path = os.path.join('/data/pyarch',
tokens[3], tokens[2],
*tokens[4:])
else:
# We might get empty elements at the head/tail of the list
tokens = [elem for elem in files_dir.split(os.path.sep)
if len(elem) > 0]
if tokens[2] == 'inhouse':
pyarch_path = os.path.join('/data/pyarch', tokens[1],
*tokens[3:])
if pyarch_path is not None:
pyarch_path = pyarch_path.replace('PROCESSED_DATA', 'RAW_DATA')
try:
os.makedirs(pyarch_path)
except OSError:
# dir already exists, may happen when testing
EDVerbose.screen('Target directory on pyarch ({0}) already exists, ignoring'.format(pyarch_path))
file_list = []
# we can now copy the files to this dir
for f in os.listdir(original_files_dir):
current = os.path.join(original_files_dir, f)
if not os.path.isfile(current):
continue
if not os.path.splitext(current)[1].lower() in ISPYB_UPLOAD_EXTENSIONS:
continue
new_path = os.path.join(pyarch_path, f)
file_list.append(new_path)
shutil.copyfile(current,
new_path)
# now add those to the ispyb upload
for path in file_list:
dirname, filename = os.path.split(path)
attach = AutoProcProgramAttachment()
attach.fileType = "Result"
attach.fileName = filename
attach.filePath = dirname
program_container.AutoProcProgramAttachment.append(attach)
program_container.AutoProcProgram.processingStatus = True
output.AutoProcProgramContainer = program_container
# first with anom
output.AutoProcScalingContainer = scaling_container_anom
ispyb_input = XSDataInputStoreAutoProc()
ispyb_input.AutoProcContainer = output
with open(self.dataInput.output_file.path.value, 'w') as f:
f.write(ispyb_input.marshal())
# store results in ispyb
self.store_autoproc_anom.dataInput = ispyb_input
t0=time.time()
self.store_autoproc_anom.executeSynchronous()
self.stats['ispyb_upload'] = time.time() - t0
with open(self.log_file_path, 'w') as f:
json.dump(self.stats, f)
if self.store_autoproc_anom.isFailure():
self.ERROR('could not send results to ispyb')
else:
# store the autoproc ID as a filename in the
# fastproc_integration_ids directory
os.mknod(os.path.join(self.autoproc_ids_dir, str(self.integration_id_anom)), 0755)
# then noanom stats
output.AutoProcScalingContainer = scaling_container_noanom
ispyb_input = XSDataInputStoreAutoProc()
ispyb_input.AutoProcContainer = output
with open(self.dataInput.output_file.path.value, 'w') as f:
f.write(ispyb_input.marshal())
# store results in ispyb
self.store_autoproc_noanom.dataInput = ispyb_input
t0=time.time()
self.store_autoproc_noanom.executeSynchronous()
self.stats['ispyb_upload'] = time.time() - t0
with open(self.log_file_path, 'w') as f:
json.dump(self.stats, f)
if self.store_autoproc_noanom.isFailure():
self.ERROR('could not send results to ispyb')
else:
# store the autoproc id
os.mknod(os.path.join(self.autoproc_ids_dir, str(self.integration_id_noanom)), 0755)
def postProcess(self, _edObject=None):
EDPluginControl.postProcess(self)
self.DEBUG("EDPluginControlAutoproc.postProcess")
#Now that we have executed the whole thing we need to create
#the suitable ISPyB plugin input and serialize it to the file
#we've been given as input
output = AutoProcContainer()
# AutoProc attr
autoproc = AutoProc()
xdsout = self.xds_first.dataOutput
if xdsout.sg_number is not None: # and it should not
autoproc.spaceGroup = SPACE_GROUP_NAMES[xdsout.sg_number.value]
autoproc.refinedCell_a = xdsout.cell_a.value
autoproc.refinedCell_b = xdsout.cell_b.value
autoproc.refinedCell_c = xdsout.cell_c.value
autoproc.refinedCell_alpha = xdsout.cell_alpha.value
autoproc.refinedCell_beta = xdsout.cell_beta.value
autoproc.refinedCell_gamma = xdsout.cell_gamma.value
output.AutoProc = autoproc
# scaling container and all the things that go in
scaling_container_noanom = AutoProcScalingContainer()
scaling = AutoProcScaling()
scaling.recordTimeStamp = time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime())
scaling_container_noanom.AutoProcScaling = scaling
# NOANOM PATH
xscale_stats_noanom = self.xscale_generate.dataOutput.stats_noanom_merged
inner_stats_noanom = xscale_stats_noanom.completeness_entries[0]
outer_stats_noanom = xscale_stats_noanom.completeness_entries[-1]
# use the previous shell's res as low res, if available
prev_res = self.low_resolution_limit
try:
prev_res = xscale_stats_noanom.completeness_entries[
-2].outer_res.value
except IndexError:
pass
total_stats_noanom = xscale_stats_noanom.total_completeness
stats = _create_scaling_stats(inner_stats_noanom, 'innerShell',
self.low_resolution_limit, False)
overall_low = stats.resolutionLimitLow
scaling_container_noanom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(outer_stats_noanom, 'outerShell',
prev_res, False)
overall_high = stats.resolutionLimitHigh
scaling_container_noanom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(total_stats_noanom, 'overall',
self.low_resolution_limit, False)
stats.resolutionLimitLow = overall_low
stats.resolutionLimitHigh = overall_high
scaling_container_noanom.AutoProcScalingStatistics.append(stats)
integration_container_noanom = AutoProcIntegrationContainer()
image = Image()
image.dataCollectionId = self.dataInput.data_collection_id.value
integration_container_noanom.Image = image
integration_noanom = AutoProcIntegration()
if self.integration_id_noanom is not None:
integration_noanom.autoProcIntegrationId = self.integration_id_noanom
crystal_stats = self.parse_xds_noanom.dataOutput
integration_noanom.cell_a = crystal_stats.cell_a.value
integration_noanom.cell_b = crystal_stats.cell_b.value
integration_noanom.cell_c = crystal_stats.cell_c.value
integration_noanom.cell_alpha = crystal_stats.cell_alpha.value
integration_noanom.cell_beta = crystal_stats.cell_beta.value
integration_noanom.cell_gamma = crystal_stats.cell_gamma.value
integration_noanom.anomalous = 0
# done with the integration
integration_container_noanom.AutoProcIntegration = integration_noanom
scaling_container_noanom.AutoProcIntegrationContainer = integration_container_noanom
# ANOM PATH
scaling_container_anom = AutoProcScalingContainer()
scaling = AutoProcScaling()
scaling.recordTimeStamp = time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime())
scaling_container_anom.AutoProcScaling = scaling
xscale_stats_anom = self.xscale_generate.dataOutput.stats_anom_merged
inner_stats_anom = xscale_stats_anom.completeness_entries[0]
outer_stats_anom = xscale_stats_anom.completeness_entries[-1]
# use the previous shell's res as low res if available
prev_res = self.low_resolution_limit
try:
prev_res = xscale_stats_anom.completeness_entries[
-2].outer_res.value
except IndexError:
pass
total_stats_anom = xscale_stats_anom.total_completeness
stats = _create_scaling_stats(inner_stats_anom, 'innerShell',
self.low_resolution_limit, True)
overall_low = stats.resolutionLimitLow
scaling_container_anom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(outer_stats_anom, 'outerShell', prev_res,
True)
overall_high = stats.resolutionLimitHigh
scaling_container_anom.AutoProcScalingStatistics.append(stats)
stats = _create_scaling_stats(total_stats_anom, 'overall',
self.low_resolution_limit, True)
stats.resolutionLimitLow = overall_low
stats.resolutionLimitHigh = overall_high
scaling_container_anom.AutoProcScalingStatistics.append(stats)
integration_container_anom = AutoProcIntegrationContainer()
image = Image()
image.dataCollectionId = self.dataInput.data_collection_id.value
integration_container_anom.Image = image
integration_anom = AutoProcIntegration()
crystal_stats = self.parse_xds_anom.dataOutput
if self.integration_id_anom is not None:
integration_anom.autoProcIntegrationId = self.integration_id_anom
integration_anom.cell_a = crystal_stats.cell_a.value
integration_anom.cell_b = crystal_stats.cell_b.value
integration_anom.cell_c = crystal_stats.cell_c.value
integration_anom.cell_alpha = crystal_stats.cell_alpha.value
integration_anom.cell_beta = crystal_stats.cell_beta.value
integration_anom.cell_gamma = crystal_stats.cell_gamma.value
integration_anom.anomalous = 1
# done with the integration
integration_container_anom.AutoProcIntegration = integration_anom
scaling_container_anom.AutoProcIntegrationContainer = integration_container_anom
# ------ NO ANOM / ANOM end
program_container = AutoProcProgramContainer()
program_container.AutoProcProgram = AutoProcProgram()
program_container.AutoProcProgram.processingCommandLine = ' '.join(
sys.argv)
program_container.AutoProcProgram.processingPrograms = 'edna-fastproc'
# now for the generated files. There's some magic to do with
# their paths to determine where to put them on pyarch
pyarch_path = None
# Note: the path is in the form /data/whatever
# remove the edna-autoproc-import suffix
original_files_dir = self.file_conversion.dataInput.output_directory.value
#files_dir, _ = os.path.split(original_files_dir)
files_dir = original_files_dir
# the whole transformation is fragile!
if files_dir.startswith('/data/visitor'):
# We might get empty elements at the head/tail of the list
tokens = [
elem for elem in files_dir.split(os.path.sep) if len(elem) > 0
]
pyarch_path = os.path.join('/data/pyarch', tokens[3], tokens[2],
*tokens[4:])
else:
# We might get empty elements at the head/tail of the list
tokens = [
elem for elem in files_dir.split(os.path.sep) if len(elem) > 0
]
if tokens[2] == 'inhouse':
pyarch_path = os.path.join('/data/pyarch', tokens[1],
*tokens[3:])
if pyarch_path is not None:
pyarch_path = pyarch_path.replace('PROCESSED_DATA', 'RAW_DATA')
try:
os.makedirs(pyarch_path)
except OSError:
# dir already exists, may happen when testing
EDVerbose.screen(
'Target directory on pyarch ({0}) already exists, ignoring'
.format(pyarch_path))
file_list = []
# we can now copy the files to this dir
for f in os.listdir(original_files_dir):
current = os.path.join(original_files_dir, f)
if not os.path.isfile(current):
continue
if not os.path.splitext(
current)[1].lower() in ISPYB_UPLOAD_EXTENSIONS:
continue
new_path = os.path.join(pyarch_path, f)
file_list.append(new_path)
shutil.copyfile(current, new_path)
# now add those to the ispyb upload
for path in file_list:
dirname, filename = os.path.split(path)
attach = AutoProcProgramAttachment()
attach.fileType = "Result"
attach.fileName = filename
attach.filePath = dirname
program_container.AutoProcProgramAttachment.append(attach)
program_container.AutoProcProgram.processingStatus = True
output.AutoProcProgramContainer = program_container
# first with anom
output.AutoProcScalingContainer = scaling_container_anom
ispyb_input = XSDataInputStoreAutoProc()
ispyb_input.AutoProcContainer = output
with open(self.dataInput.output_file.path.value, 'w') as f:
f.write(ispyb_input.marshal())
# store results in ispyb
self.store_autoproc_anom.dataInput = ispyb_input
t0 = time.time()
self.store_autoproc_anom.executeSynchronous()
self.stats['ispyb_upload'] = time.time() - t0
with open(self.log_file_path, 'w') as f:
json.dump(self.stats, f)
if self.store_autoproc_anom.isFailure():
self.ERROR('could not send results to ispyb')
else:
# store the autoproc ID as a filename in the
# fastproc_integration_ids directory
os.mknod(
os.path.join(self.autoproc_ids_dir,
str(self.integration_id_anom)), 0755)
# then noanom stats
output.AutoProcScalingContainer = scaling_container_noanom
ispyb_input = XSDataInputStoreAutoProc()
ispyb_input.AutoProcContainer = output
with open(self.dataInput.output_file.path.value, 'w') as f:
f.write(ispyb_input.marshal())
# store results in ispyb
self.store_autoproc_noanom.dataInput = ispyb_input
t0 = time.time()
self.store_autoproc_noanom.executeSynchronous()
self.stats['ispyb_upload'] = time.time() - t0
with open(self.log_file_path, 'w') as f:
json.dump(self.stats, f)
if self.store_autoproc_noanom.isFailure():
self.ERROR('could not send results to ispyb')
else:
# store the autoproc id
os.mknod(
os.path.join(self.autoproc_ids_dir,
str(self.integration_id_noanom)), 0755)