def execute(self, arborapi=None):
print self.name + " executing"
# setup mongo connection and look through all input collections, copying
connection = Connection('localhost', 27017)
if len(self.databaseName) > 0:
db = connection[self.databaseName]
else:
db = connection['arbor']
outputcoll = db[self.outputInformation.collectionName]
# clear out the output to prepare for running an algorithm
outputcoll.drop()
# check that we have both inputs needed (tree and matrix specs). If so, then read the
# dataset spec records
# from the input collections and use the dataset references to invoke the algorithm, which
# requires just the references (since the algorithm opens the collections directly).
if len(self.inputs) == 2:
treeSpecCollection = db[self.inputs[0].collectionName]
matrixSpecCollection = db[self.inputs[1].collectionName]
treequery = dict()
treequery['key'] = 'project'
treeProjectName = treeSpecCollection.find(treequery)[0]['value']
treequery2 = dict()
treequery2['key'] = 'dataset'
treeDatasetName = treeSpecCollection.find(treequery2)[0]['value']
print "treeProject:", treeProjectName, " dataset:", treeDatasetName
matrixQuery = dict()
matrixQuery['key'] = 'dataset'
matrixDatasetName = matrixSpecCollection.find(
treequery2)[0]['value']
# all datasets have to be in the same project for the algorithms to look them up
# so all we need to read is the dataset name for the character matrix, use the
# rest of the information from the tree dataset
print "matrix Project:", treeProjectName, " dataset:", matrixDatasetName
# check that the two needed parameters (character and outputTree) are defined
# for this workstep instance, attempt processing only if they are defined. If everything
# is available, run the algorithm on the selected datasets. We use the pre-allocated version
# of the
if ('outputTree' in self.parameters):
print "dataIntegrator: found outputTree defined as: ", self.parameters[
'outputTree']
algorithms = ArborAlgorithmManager()
algorithms.setProjectManagerAPI(arborapi)
algorithms.initAlgorithmLibrary()
# only attempt to run the analysis if the algorithm subsystem is defined. This relies
# on the global algorithms definition
if algorithms != None:
print "dataIntegrator: running algorithm"
algorithm_result = algorithms.dataIntegrator(
self.databaseName, treeProjectName, treeDatasetName,
matrixDatasetName, None, self.parameters['outputTree'])
else:
print "dataIntegrator: couldn't connect with AlgorithmManager instance.. skipping"
else:
print "dataIntegrator: Please define an outputtree parameter before running dataIntegrator"
else:
print "dataIntegrator: Exactly two inputs (a treeSpec and matrixSpec) are required"
# a character matrix that is still a data.frame is returned. May need to use pandas to convert back to
# python.
print "dataIntegrator: character matrix output is not yet generated"
for i in range(len(algorithm_result)):
#print i
record = algorithm_result[i]
#print record
#outputcoll.insert(record)
# rest the filter's modified time and assign it to the output object
self.outputInformation.modifiedTime = self.modifiedTime = time.time()
print self.name, " completed fit continuous workstep "
connection.close()
def execute(self, arborapi=None):
print self.name+" executing"
# setup mongo connection and look through all input collections, copying
connection = Connection('localhost', 27017)
if len(self.databaseName)>0:
db = connection[self.databaseName]
else:
db = connection['arbor']
outputcoll = db[self.outputInformation.collectionName]
# clear out the output to prepare for running an algorithm
outputcoll.drop()
# check that we have both inputs needed (tree and matrix specs). If so, then read the
# dataset spec records
# from the input collections and use the dataset references to invoke the algorithm, which
# requires just the references (since the algorithm opens the collections directly).
if len(self.inputs) == 2:
treeSpecCollection = db[self.inputs[0].collectionName]
matrixSpecCollection = db[self.inputs[1].collectionName]
treequery = dict()
treequery['key'] = 'project'
treeProjectName = treeSpecCollection.find(treequery)[0]['value']
treequery2 = dict()
treequery2['key'] = 'dataset'
treeDatasetName = treeSpecCollection.find(treequery2)[0]['value']
print "treeProject:",treeProjectName," dataset:",treeDatasetName
matrixQuery = dict()
matrixQuery['key'] = 'dataset'
matrixDatasetName = matrixSpecCollection.find(treequery2)[0]['value']
# all datasets have to be in the same project for the algorithms to look them up
# so all we need to read is the dataset name for the character matrix, use the
# rest of the information from the tree dataset
print "matrix Project:",treeProjectName," dataset:",matrixDatasetName
# check that the two needed parameters (character and outputTree) are defined
# for this workstep instance, attempt processing only if they are defined. If everything
# is available, run the algorithm on the selected datasets. We use the pre-allocated version
# of the
if ('outputTree' in self.parameters):
print "dataIntegrator: found outputTree defined as: ",self.parameters['outputTree']
algorithms = ArborAlgorithmManager()
algorithms.setProjectManagerAPI(arborapi)
algorithms.initAlgorithmLibrary()
# only attempt to run the analysis if the algorithm subsystem is defined. This relies
# on the global algorithms definition
if algorithms != None:
print "dataIntegrator: running algorithm"
algorithm_result = algorithms.dataIntegrator(self.databaseName,treeProjectName,treeDatasetName, matrixDatasetName,
None,self.parameters['outputTree'])
else:
print "dataIntegrator: couldn't connect with AlgorithmManager instance.. skipping"
else:
print "dataIntegrator: Please define an outputtree parameter before running dataIntegrator"
else:
print "dataIntegrator: Exactly two inputs (a treeSpec and matrixSpec) are required"
# a character matrix that is still a data.frame is returned. May need to use pandas to convert back to
# python.
print "dataIntegrator: character matrix output is not yet generated"
for i in range(len(algorithm_result)):
#print i
record = algorithm_result[i]
#print record
#outputcoll.insert(record)
# rest the filter's modified time and assign it to the output object
self.outputInformation.modifiedTime = self.modifiedTime = time.time()
print self.name," completed fit continuous workstep "
connection.close()
def execute(self, arborapi=None):
print self.name + " executing"
# setup mongo connection and look through all input collections, copying
connection = Connection('localhost', 27017)
if len(self.databaseName) > 0:
db = connection[self.databaseName]
else:
db = connection['arbor']
outputcoll = db[self.outputInformation.collectionName]
# clear out the output to prepare for running an algorithm
outputcoll.drop()
# check that we have both inputs needed (tree and matrix specs). If so, then read the
# dataset spec records
# from the input collections and use the dataset references to invoke the algorithm, which
# requires just the references (since the algorithm opens the collections directly).
if len(self.inputs) == 2:
treeSpecCollection = db[self.inputs[0].collectionName]
matrixSpecCollection = db[self.inputs[1].collectionName]
treequery = dict()
treequery['key'] = 'project'
treeProjectName = treeSpecCollection.find(treequery)[0]['value']
treequery2 = dict()
treequery2['key'] = 'dataset'
treeDatasetName = treeSpecCollection.find(treequery2)[0]['value']
print "treeProject:", treeProjectName, " dataset:", treeDatasetName
matrixQuery = dict()
matrixQuery['key'] = 'dataset'
matrixDatasetName = matrixSpecCollection.find(
treequery2)[0]['value']
# all datasets have to be in the same project for the algorithms to look them up
# so all we need to read is the dataset name for the character matrix, use the
# rest of the information from the tree dataset
print "matrix Project:", treeProjectName, " dataset:", matrixDatasetName
# check that the two needed parameters (character and outputTree) are defined
# for this workstep instance, attempt processing only if they are defined. If everything
# is available, run the algorithm on the selected datasets. We use the pre-allocated version
# of the
if ('character' in self.parameters) and ('outputTree'
in self.parameters):
print "fitContinuous: found selected character defined as: ", self.parameters[
'character']
print "fitContinuous: found outputTree defined as: ", self.parameters[
'outputTree']
algorithms = ArborAlgorithmManager()
algorithms.setProjectManagerAPI(arborapi)
algorithms.initAlgorithmLibrary()
# look to see if the user defined model parameters on this workstep. If so, use
# them to override the default OU model
if ('model' in self.parameters):
modelToUse = self.parameters['model']
else:
modelToUse = '"OU"'
# only attempt to run the analysis if the algorithm subsystem is defined. This relies
# on the global algorithms definition
if algorithms != None:
print "fitContinous: running algorithm"
algorithm_result = algorithms.fitContinuous(
self.databaseName, treeProjectName, treeDatasetName,
matrixDatasetName,
self.parameters['selectedCharacter'],
self.parameters['outputTree'], modelToUse)
else:
print "fitContinous: couldn't connect with AlgorithmManager instance.. skipping"
else:
print "fitContinuous: Please define both a character parameter and an outputtree parameter before running fitContinuous"
else:
print "fitContinuous: Exactly two inputs (a treeSpec and matrixSpec) are required"
# write the output collection and indicate the output time changed. A tree is created and some
# output parameters are returned, put them in the standard key/value form of the table spec:
for result in algorithm_result:
record = dict()
record['key'] = result
record['value'] = algorithm_result[result]
outputcoll.insert(record)
# rest the filter's modified time and assign it to the output object
self.outputInformation.modifiedTime = self.modifiedTime = time.time()
print self.name, " completed fit continuous workstep "
connection.close()
def execute(self, arborapi=None):
print self.name+" executing"
# setup mongo connection and look through all input collections, copying
connection = Connection('localhost', 27017)
if len(self.databaseName)>0:
db = connection[self.databaseName]
else:
db = connection['arbor']
outputcoll = db[self.outputInformation.collectionName]
# clear out the output to prepare for running an algorithm
outputcoll.drop()
# check that we have both inputs needed (tree and matrix specs). If so, then read the
# dataset spec records
# from the input collections and use the dataset references to invoke the algorithm, which
# requires just the references (since the algorithm opens the collections directly).
if len(self.inputs) == 2:
treeSpecCollection = db[self.inputs[0].collectionName]
matrixSpecCollection = db[self.inputs[1].collectionName]
treequery = dict()
treequery['key'] = 'project'
treeProjectName = treeSpecCollection.find(treequery)[0]['value']
treequery2 = dict()
treequery2['key'] = 'dataset'
treeDatasetName = treeSpecCollection.find(treequery2)[0]['value']
print "treeProject:",treeProjectName," dataset:",treeDatasetName
matrixQuery = dict()
matrixQuery['key'] = 'dataset'
matrixDatasetName = matrixSpecCollection.find(treequery2)[0]['value']
# all datasets have to be in the same project for the algorithms to look them up
# so all we need to read is the dataset name for the character matrix, use the
# rest of the information from the tree dataset
print "matrix Project:",treeProjectName," dataset:",matrixDatasetName
# check that the two needed parameters (character and outputTree) are defined
# for this workstep instance, attempt processing only if they are defined. If everything
# is available, run the algorithm on the selected datasets. We use the pre-allocated version
# of the
if ('character' in self.parameters) and ('outputTree' in self.parameters):
print "fitContinuous: found selected character defined as: ",self.parameters['character']
print "fitContinuous: found outputTree defined as: ",self.parameters['outputTree']
algorithms = ArborAlgorithmManager()
algorithms.setProjectManagerAPI(arborapi)
algorithms.initAlgorithmLibrary()
# look to see if the user defined model parameters on this workstep. If so, use
# them to override the default OU model
if ('model' in self.parameters):
modelToUse = self.parameters['model']
else:
modelToUse = '"OU"'
# only attempt to run the analysis if the algorithm subsystem is defined. This relies
# on the global algorithms definition
if algorithms != None:
print "fitContinous: running algorithm"
algorithm_result = algorithms.fitContinuous(self.databaseName,treeProjectName,treeDatasetName, matrixDatasetName,
self.parameters['selectedCharacter'],self.parameters['outputTree'],modelToUse)
else:
print "fitContinous: couldn't connect with AlgorithmManager instance.. skipping"
else:
print "fitContinuous: Please define both a character parameter and an outputtree parameter before running fitContinuous"
else:
print "fitContinuous: Exactly two inputs (a treeSpec and matrixSpec) are required"
# write the output collection and indicate the output time changed. A tree is created and some
# output parameters are returned, put them in the standard key/value form of the table spec:
for result in algorithm_result:
record = dict()
record['key'] = result
record['value'] = algorithm_result[result]
outputcoll.insert(record)
# rest the filter's modified time and assign it to the output object
self.outputInformation.modifiedTime = self.modifiedTime = time.time()
print self.name," completed fit continuous workstep "
connection.close()