def test_set_attribute_type_pass():
name = "att1"
type = AttributeType.TEXT
new_type = AttributeType.NUMERIC
attribute = Attribute(name, type)
attribute.type = new_type
assert attribute.type == new_type.value
def test_set_name_pass():
name = "att1"
new_name = "att-modified"
type = AttributeType.TEXT
attribute = Attribute(name, type)
attribute.name = new_name
assert attribute.name == new_name
def test_set_attributes_pass():
tag = "set1"
type = SetType.INPUT
attributes = [Attribute("att1", AttributeType.TEXT)]
new_attributes = [Attribute("att1", AttributeType.NUMERIC)]
expected_result = [x.get_specification() for x in new_attributes]
set = Set(tag, type, attributes)
set.attributes = new_attributes
assert set.attributes == expected_result
def test_get_dependency_pass():
tag = "set1"
type = SetType.INPUT
dependency = "dependency"
attributes = [Attribute("att1", AttributeType.TEXT)]
set = Set(tag, type, attributes, dependency=dependency)
assert set.dependency == dependency
def test_set_output_pass():
attributes = [Attribute("att1", AttributeType.TEXT)]
sets = [Set("set1", SetType.OUTPUT, attributes)]
output = [Set("set4", SetType.OUTPUT, attributes)]
expected_result = [x.get_specification() for x in output]
transformation = Transformation("tf1", sets=sets)
transformation.output = output
assert transformation.output == expected_result
def test_set_type_pass():
tag = "set1"
type = SetType.INPUT
new_type = SetType.OUTPUT
attributes = [Attribute("att1", AttributeType.TEXT)]
set = Set(tag, type, attributes)
set.type = new_type
assert set.type == new_type.value
def test_get_specification_pass():
tag = "tf1"
sets = [
Set("set1", SetType.INPUT, [Attribute("att1", AttributeType.TEXT)])
]
expected_result = {
"sets": [x.get_specification() for x in sets],
"tag": tag
}
transformation = Transformation(tag, sets)
assert transformation.get_specification() == expected_result
def test_set_sets_pass():
attributes = [Attribute("att1", AttributeType.TEXT)]
sets = [Set("set1", SetType.INPUT, attributes)]
new_sets = [
Set("set1", SetType.INPUT, attributes),
Set("set2", SetType.OUTPUT, attributes)
]
expected_result = [x.get_specification() for x in new_sets]
transformation = Transformation("tf1", sets=sets)
transformation.sets = new_sets
assert transformation.sets == expected_result
def test_get_specification_pass():
tag = "set1"
type = SetType.INPUT
attributes = [Attribute("att1", AttributeType.TEXT)]
expected_result = {
"attributes": [attributes[0].get_specification()],
"tag": tag,
"type": type.value
}
set = Set(tag, type, attributes)
assert set.get_specification() == expected_result
def test_get_name_pass():
name = "att1"
type = AttributeType.TEXT
attribute = Attribute(name, type)
assert attribute.name == name
def test_get_specification_pass():
name = "att1"
type = AttributeType.TEXT
expected_specification = {"name": name, "type": type.value}
attribute = Attribute(name, type)
assert attribute.get_specification() == expected_specification
def test_get_attribute_type_pass():
name = "att1"
type = AttributeType.TEXT
attribute = Attribute(name, type)
assert attribute.type == type.value
def test_get_input_pass():
attributes = [Attribute("att1", AttributeType.TEXT)]
sets = [Set("set1", SetType.INPUT, attributes)]
expected_result = [sets[0].get_specification()]
transformation = Transformation("tf1", sets=sets)
assert transformation.input == expected_result
#PROVENIÊNCIA
############################
dataflow_tag = "prov-df-{}".format(aggreg_unit)
df = Dataflow(dataflow_tag)
logger.info('Inicializando o processador Spark')
processador = ProcessadorSparkClass(logger, spark, df, dataflow_tag)
##PROVENIÊNCIA PROSPECTIVA
#Transformação para extrair o primeiro stats: ExtrairStats1
tf1 = Transformation('load_data') ## Usando o nome da task spark
tf1_input = Set("i{}1".format('load_data'), SetType.INPUT,
[
Attribute("datafiles", AttributeType.TEXT),
Attribute("tables", AttributeType.TEXT),
Attribute("currenttime", AttributeType.TEXT),
Attribute("aggregationunit", AttributeType.TEXT),
Attribute("csvseparator", AttributeType.TEXT)
])
tf1_output = Set("o{}1".format('load_data'), SetType.OUTPUT,
[
Attribute("currenttime", AttributeType.TEXT),
Attribute("elapsedtime", AttributeType.NUMERIC)
])
tf1.set_sets([tf1_input, tf1_output])
df.add_transformation(tf1)
tf2 = Transformation('initial_data_stats') ## Usando o nome da task spark
#dirin_arg_pas = sys.argv[0:]
###print "O nome do diretorio de entrada do ficheiro e: " + dirin_do_ficheiro
###print "E os argumentos passados sao: " + str(dirin_arg_pas)
############################
#PROVENIÊNCIA
############################
dataflow_tag = "mafft-df"
df = Dataflow(dataflow_tag)
##PROVENIÊNCIA PROSPECTIVA
#Transformação para extrair nome dos arquivos: ExtrairNome
tf1 = Transformation("ExtrairNome")
tf1_input = Set("iExtrairNome", SetType.INPUT,
[Attribute("DIRIN_FILE", AttributeType.FILE)])
tf1_output = Set("oExtrairNome", SetType.OUTPUT,
[Attribute("FASTA_FILE", AttributeType.FILE),
Attribute("MAFFT_FILE", AttributeType.FILE)])
tf1.set_sets([tf1_input, tf1_output])
df.add_transformation(tf1)
#Transformação para ler o arquivo e contar o numero de sequencias: ContarSequencias
tf2 = Transformation("ContarSequencias")
tf2_input = Set("iContarSequencias", SetType.INPUT,
[Attribute("FASTA_FILE", AttributeType.FILE)])#leitor file-fasta/att text-file
tf2_output = Set("oContarSequencias", SetType.OUTPUT,
[Attribute("NUMERO_SEQUENCIAS", AttributeType.NUMERIC)])
tf2.set_sets([tf2_input, tf2_output])
df.add_transformation(tf2)
def test_get_type_pass():
tag = "set1"
type = SetType.INPUT
attributes = [Attribute("att1", AttributeType.TEXT)]
set = Set(tag, type, attributes)
assert set.type == type.value
