def test_snd_discrete_histogram_random_time(self):
interpolate = False
removeTokens = False
ts = randint(0, 5000)
histo = {ts: 1}
when = "snd"
self.run_primitive(histo, removeTokens, interpolate, when)
ts_l = self.extract_ts(self.proto_server.history['rcv'])
iats = gu.map_to_each_n_elements_in_list(ts_l, gu.get_iats)
mean_iat = mathutil.mean(iats)
self.assertAlmostEqual(ts / const.SCALE, mean_iat, 2)
def test_snd_interpolate_histogram_random_time(self):
interpolate = False
removeTokens = False
ts = randint(0, 5000)
histo = {ts: 1}
when = "snd"
self.run_primitive(histo, removeTokens, interpolate, when)
ts_l = self.extract_ts(self.proto_server.history['rcv'])
iats = gu.map_to_each_n_elements_in_list(ts_l, gu.get_iats)
for iat in iats:
self.assertGreater(iat, 0)
self.assertGreater(ts, iat)
def test_snd_removetoks_histogram_random_time(self):
interpolate = False
removeTokens = True
ts = randint(0, 5000)
n_tokens = 50
histo = {ts: n_tokens}
when = "snd"
self.run_primitive(histo, removeTokens, interpolate, when)
ts_l = self.extract_ts(self.proto_server.history['rcv'])
iats = gu.map_to_each_n_elements_in_list(ts_l, gu.get_iats)
tokens = self.pt_client._burstHistoProbdist[when].hist[ts]
self.assertEqual(tokens, n_tokens - N_SAMPLES)
self.assertEqual(N_SAMPLES, len(iats))
def test_rcv_discrete_histogram_random_time(self):
interpolate = False
removeTokens = False
ts = randint(0, 5000)
histo = {ts: 1}
when = "rcv"
self.run_primitive(histo, removeTokens, interpolate, when, endpoint=self.pt_server)
padd_ts = self.extract_ts(self.proto_server.history['rcv'], isPadding)
data_ts = self.extract_ts(self.proto_server.history['snd'], isData)
ts_l = gu.combine_lists_alternate(data_ts, padd_ts)
iats = gu.map_to_each_n_elements_in_list(ts_l, gu.get_iats)
mean_iat = mathutil.mean(iats)
self.assertAlmostEqual(ts / const.SCALE, mean_iat, 2)
