def setUp(self):
self.sink = df.Sink()
self.file_name1 = os.path.join(data_dir5, 'short.dat')
f1 = open(self.file_name1, 'wb')
try:
f1.write('one two three four five six')
finally:
f1.close()
def test_caps_phrase(self):
words_in_caps1 = ppln_demo.WordsInCaps(factory=self.factory)
sink = df.Sink()
words_in_caps1.next_filter = sink
words_in_caps1.send(dfb.DataPacket('hello world'))
self.assertEquals(sink.results, [])
## output1 = words_in_caps1.pump_data('hello world')
## self.assertEquals(output1, [])
## words_in_caps1.shut_down() # Flush joining data
## words_in_caps1.shut_down() # Flush joining data
words_in_caps1.first_filter.shut_down()
self.assertEquals(sink.results[-1].data, 'Hello~World')
words_in_caps2 = ppln_demo.WordsInCaps(factory=self.factory,
join_words_with='+')
sink2 = df.Sink()
words_in_caps2.next_filter = sink2
words_in_caps2.send(dfb.DataPacket('one two three four five'))
## words_in_caps2.shut_down()
words_in_caps2.first_filter.shut_down()
self.assertEquals(sink2.results[-1].data, 'One+Two+Three+Four+Five')
def test_extract_many_attributes_split_words(self):
data_in = """
TIME CHIP SERIAL IS: 5ABF3. OTHER VALUE IS: 312. SOME TRAILING TEXT
"""
extractor = ppln.ExtractManyAttributesSplitWords(\
factory=self.factory, split_on_str=". ", attr_delim='col')
extractor_filter = extractor.getf('attribute_extractor')
sink = df.Sink()
extractor_filter.next_filter = sink
extractor.send(dfb.DataPacket(data_in))
#TEST PACKET NOT FILTER
self.assertEqual(sink.results[0].time_chip_serial_is, '5ABF3')
self.assertEqual(sink.results[1].other_value_is, '312')
def pump_data(self, data_in=None, zero_inputs=True): # << TO-DO Needs work
##, close_at_end=True):
"""Put the data into the pipeline by wrapping it in a Datapacket.
Get the data out by temporarily sticking a Sink on the end,
returning all the packets received.
Remember to close the pipeline externally, if tidying up actions
need to be performed.
If zero_inputs is True, reinitialise the variables before pumping
the new data in. <<<<<<<zero_inputs<<<<<<<<< TO-DO <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
"""
pump_sink = df.Sink()
self.last_filter.next_filter = pump_sink
print '**10195** Connect (%s, pump_data) %s >>> %s' % (
self.name, self.last_filter.name, pump_sink.name)
try:
data_in + [] # TO-DO: Better way to check the data type
except TypeError: # Not a list
data_in = [data_in]
for data in data_in:
if data is None:
## packet = None
packet = dfb.DataPacket(None) # Or ''??
else:
packet = dfb.DataPacket(str(data))
if fut.debug > 1: #pragma: nocover
print '**3698**', 65 * '='
if fut.debug > 2: #pragma: nocover
show_data = '|'.join(fut.split_strings(str(data), 8))
print '**3705** data_in = "%s"' % (
fut.data_to_hex_string(show_data[:80]))
self.first_filter.send(packet)
##if close_at_end:
##self.first_filter.shut_down()
# All data has arrived, so disconnect sink
self.last_filter.next_filter = None
# Store the full results packets in self.results in case they are
# needed, returning the packet.data as a list.
self.results = pump_sink.results
# TO-DO: Decide about which data type here -- strings or lists
## return pump_sink.results[-1].data
if len(self.results) == 1:
return self.results[0].data
else:
return [pkt.data for pkt in self.results]
def test_extract_many_attributes(self):
data_in = """
person's name=Postman pat
age=500000
gender=mail
hair col0ur=balding
"""
extractor = ppln.ExtractManyAttributes(factory=self.factory,
attr_delim='equals')
extractor_filter = extractor.getf('attribute_extractor')
sink = df.Sink()
extractor_filter.next_filter = sink
extractor.send(dfb.DataPacket(data_in))
#TEST PACKET NOT FILTER
self.assertEqual(sink.results[0].persons_name, 'Postman pat')
self.assertEqual(sink.results[1].age, '500000')
self.assertEqual(sink.results[2].gender, 'mail')
self.assertEqual(sink.results[3].hair_col0ur, 'balding')
def setUp(self):
self.hidden_branch_filter = dfb.HiddenBranchRoute()
self.sink_main = df.Sink()
self.hidden_branch_filter.next_filter = self.sink_main
self.sink_branch = df.Sink()
self.hidden_branch_filter.branch_filter = self.sink_branch
def setUp(self):
self.factory = ff.DemoFilterFactory()
self.sink = df.Sink()
def setUp(self):
self.caps = dfd.Capitalise()
# Ready to check output
self.sink = df.Sink()
# Connect filters
self.caps.next_filter = self.sink
def setUp(self):
self.add_numbers = dfd.AddNumbers()
# Ready to check output
self.sink = df.Sink()
# Connect filters
self.add_numbers.next_filter = self.sink