def complex_op_chain(gc):
# All road junctions in the South West that were heavily used by buses in year 2000.
junctions = gc.execute_operations(operations=[
g.GetAdjacentIds(input=[g.EntitySeed(vertex='South West')],
view=g.View(edges=[
g.ElementDefinition(
group='RegionContainsLocation', group_by=[])
])),
g.GetAdjacentIds(view=g.View(edges=[
g.ElementDefinition(group='LocationContainsRoad', group_by=[])
])),
g.ToSet(),
g.GetAdjacentIds(view=g.View(
edges=[g.ElementDefinition(group='RoadHasJunction', group_by=[])
])),
g.GetElements(view=g.View(entities=[
g.ElementDefinition(
group='JunctionUse',
group_by=[],
transient_properties=[
g.Property('busCount', 'java.lang.Long')
],
pre_aggregation_filter_functions=[
g.PredicateContext(selection=['startDate'],
predicate=g.InDateRange(
start='2000/01/01',
end='2001/01/01'))
],
post_aggregation_filter_functions=[
g.PredicateContext(
selection=['countByVehicleType'],
predicate=g.PredicateMap(predicate=g.IsMoreThan(
value={'java.lang.Long': 1000}, or_equal_to=False),
key='BUS'))
],
transform_functions=[
g.FunctionContext(selection=['countByVehicleType'],
function=g.FreqMapExtractor(key='BUS'),
projection=['busCount'])
])
]),
include_incoming_out_going=g.InOutType.OUT),
g.ToCsv(element_generator=g.CsvGenerator(fields={
'VERTEX': 'Junction',
'busCount': 'Bus Count'
},
quoted=False),
include_header=True)
])
print(
'All road junctions in the South West that were heavily used by buses in year 2000.'
)
print(junctions)
print()
def get_sub_graph(gc):
# Export and Get to/from an in memory set
entity_seeds = gc.execute_operations([
g.GetAdjacentIds(input=[g.EntitySeed('South West')],
include_incoming_out_going=g.InOutType.OUT),
g.ExportToSet(),
g.GetAdjacentIds(include_incoming_out_going=g.InOutType.OUT),
g.ExportToSet(),
g.DiscardOutput(),
g.GetSetExport()
])
print('Export and Get to/from an in memory set')
print(entity_seeds)
print()
def get_adj_seeds(gc):
# Adjacent Elements - chain 2 adjacent entities together
adj_seeds = gc.execute_operations([
g.GetAdjacentIds(
input=[g.EntitySeed(vertex='M5')],
view=g.View(
edges=[g.ElementDefinition('RoadHasJunction', group_by=[])]),
include_incoming_out_going=g.InOutType.OUT),
g.GetAdjacentIds(
view=g.View(edges=[g.ElementDefinition('RoadUse', group_by=[])]),
include_incoming_out_going=g.InOutType.OUT)
])
print('Adjacent entities - 2 hop')
print(adj_seeds)
print()
def get_job_details(gc):
# Get all job details
job_details_initial = gc.execute_operations([
g.GetAdjacentIds(input=[g.EntitySeed('1')], ),
g.ExportToGafferResultCache(),
g.DiscardOutput(),
g.GetJobDetails()
])
job_id = job_details_initial['jobId']
job_details = gc.execute_operation(g.GetJobDetails(job_id=job_id), )
print('Get job details')
print(job_details)
print()
def export_to_gaffer_result_cache(gc):
# Export to Gaffer Result Cache and Get from Gaffer Result Cache
job_details = gc.execute_operations([
g.GetAdjacentIds(input=[g.EntitySeed('South West')],
include_incoming_out_going=g.InOutType.OUT),
g.ExportToGafferResultCache(),
g.DiscardOutput(),
g.GetJobDetails()
])
print('Export to Gaffer Result Cache. Job Details:')
print(job_details)
print()
job_id = job_details['jobId']
entity_seeds = gc.execute_operation(
g.GetGafferResultCacheExport(job_id=job_id), )
print('Get Gaffer Result Cache Export.')
print(entity_seeds)
print()
def complex_op_chain(gc):
# All road junctions in the South West that were heavily used by buses in year 2000.
junctions = gc.execute_operations(
operations=[
g.GetAdjacentIds(
input=[g.EntitySeed(vertex='South West')],
view=g.View(
edges=[
g.ElementDefinition(
group='RegionContainsLocation',
group_by=[]
)
]
)
),
g.GetAdjacentIds(
view=g.View(
edges=[
g.ElementDefinition(
group='LocationContainsRoad',
group_by=[]
)
]
)
),
g.ToSet(),
g.GetAdjacentIds(
view=g.View(
edges=[
g.ElementDefinition(
group='RoadHasJunction',
group_by=[]
)
]
)
),
g.GetElements(
view=g.View(
entities=[
g.ElementDefinition(
group='JunctionUse',
group_by=[],
transient_properties=[
g.Property('busCount', 'java.lang.Long')
],
pre_aggregation_filter_functions=[
g.PredicateContext(
selection=['startDate'],
predicate=g.IsMoreThan(
value={'java.util.Date': 946684800000},
or_equal_to=True
)
),
g.PredicateContext(
selection=['startDate'],
predicate=g.IsLessThan(
value={'java.util.Date': 978307200000},
or_equal_to=False
)
)
],
post_aggregation_filter_functions=[
g.PredicateContext(
selection=['startDate'],
predicate=g.IsMoreThan(
value={'java.util.Date': 946684800000},
or_equal_to=True
)
),
g.PredicateContext(
selection=['countByVehicleType'],
predicate=g.PredicateMap(
predicate=g.IsMoreThan(
value={'java.lang.Long': 1000},
or_equal_to=False
),
key='BUS'
)
)
],
transform_functions=[
g.FunctionContext(
selection=['countByVehicleType'],
function=g.Function(
class_name='uk.gov.gchq.gaffer.types.function.FreqMapExtractor',
fields={'key': 'BUS'}
),
projection=['busCount']
)
]
)
]
),
include_incoming_out_going=g.InOutType.OUT
),
g.ToCsv(
element_generator={
'class': 'uk.gov.gchq.gaffer.data.generator.CsvGenerator',
'fields': {
'VERTEX': 'Junction',
'busCount': 'Bus Count'
},
'quoted': False,
'header': 'Junction,Bus Count'
}
)
]
)
print(
'All road junctions in the South West that were heavily used by buses in year 2000.')
print(junctions)
print()
