def test_rule_inherit(self):
"""
Tests if TAF forecast periods selectively inherit features to calculate flight rules
"""
report = (
"CYKF 020738Z 0208/0220 34005KT P6SM FEW015 BKN070 "
"FM020900 VRB03KT P6SM FEW070 SCT120 "
"BECMG 0214/0216 12006KT "
"FM021800 14008KT P6SM BKN025 OVC090"
)
expected_rules = ('VFR', 'VFR', 'VFR', 'MVFR',)
tafobj = Taf(report[:4])
tafobj.update(report)
for i, line in enumerate(tafobj.data.forecast):
self.assertEqual(line.flight_rules, expected_rules[i])
def test_rule_inherit(self):
"""
Tests if TAF forecast periods selectively inherit features to calculate flight rules
"""
report = (
"CYKF 020738Z 0208/0220 34005KT P6SM FEW015 BKN070 "
"FM020900 VRB03KT P6SM FEW070 SCT120 "
"BECMG 0214/0216 12006KT "
"FM021800 14008KT P6SM BKN025 OVC090"
)
expected_rules = ('VFR', 'VFR', 'VFR', 'MVFR',)
tafobj = Taf(report[:4])
tafobj.update(report)
for i, line in enumerate(tafobj.data.forecast):
self.assertEqual(line.flight_rules, expected_rules[i])
def test_prob_tempo(self):
"""
Non-PROB types should take precident but still fill the probability value
"""
report = ("EGLL 192253Z 2000/2106 28006KT 9999 BKN035 "
"PROB30 TEMPO 2004/2009 BKN012 "
"PROB30 TEMPO 2105/2106 8000 BKN006")
tafobj = Taf('EGLL')
tafobj.update(report)
lines = tafobj.data.forecast
for line in lines:
self.assertIsInstance(line.start_time, structs.Timestamp)
self.assertIsInstance(line.end_time, structs.Timestamp)
for i in range(1, 3):
self.assertEqual(lines[i].type, 'TEMPO')
self.assertEqual(lines[i].probability.value, 30)
def test_prob_tempo(self):
"""
Non-PROB types should take precident but still fill the probability value
"""
report = ("EGLL 192253Z 2000/2106 28006KT 9999 BKN035 "
"PROB30 TEMPO 2004/2009 BKN012 "
"PROB30 TEMPO 2105/2106 8000 BKN006")
tafobj = Taf('EGLL')
tafobj.update(report)
lines = tafobj.data.forecast
for line in lines:
self.assertIsInstance(line.start_time, structs.Timestamp)
self.assertIsInstance(line.end_time, structs.Timestamp)
for i in range(1, 3):
self.assertEqual(lines[i].type, 'TEMPO')
self.assertEqual(lines[i].probability.value, 30)
def test_taf_ete(self):
"""
Performs an end-to-end test of all TAF JSON files
"""
nodate = lambda s: s[s.find('-')+2:]
for path in glob(os.path.dirname(os.path.realpath(__file__))+'/taf/*.json'):
ref = json.load(open(path))
station = Taf(path.split('/')[-1][:4])
self.assertIsNone(station.last_updated)
self.assertTrue(station.update(ref['data']['raw']))
self.assertIsInstance(station.last_updated, datetime)
# Clear timestamp due to parse_date limitations
nodt = deepcopy(station.data)
for key in ('time', 'start_time', 'end_time'):
setattr(nodt, key, None)
for i in range(len(nodt.forecast)):
for key in ('start_time', 'end_time'):
setattr(nodt.forecast[i], key, None)
self.assertEqual(asdict(nodt), ref['data'])
self.assertEqual(asdict(station.translations), ref['translations'])
self.assertEqual(station.summary, ref['summary'])
self.assertEqual(nodate(station.speech), nodate(ref['speech']))
self.assertEqual(asdict(station.station_info), ref['station_info'])
def test_taf_ete(self):
"""
Performs an end-to-end test of all TAF JSON files
"""
nodate = lambda s: s[s.find('-')+2:]
for path in glob(os.path.dirname(os.path.realpath(__file__))+'/taf/*.json'):
ref = json.load(open(path))
station = Taf(path.split('/')[-1][:4])
self.assertIsNone(station.last_updated)
self.assertTrue(station.update(ref['data']['raw']))
self.assertIsInstance(station.last_updated, datetime)
# Clear timestamp due to parse_date limitations
nodt = deepcopy(station.data)
for key in ('time', 'start_time', 'end_time'):
setattr(nodt, key, None)
for i in range(len(nodt.forecast)):
for key in ('start_time', 'end_time'):
setattr(nodt.forecast[i], key, None)
self.assertEqual(asdict(nodt), ref['data'])
self.assertEqual(asdict(station.translations), ref['translations'])
self.assertEqual(station.summary, ref['summary'])
self.assertEqual(nodate(station.speech), nodate(ref['speech']))
self.assertEqual(asdict(station.station_info), ref['station_info'])
def test_taf_ete(self):
"""
Performs an end-to-end test of all TAF JSON files
"""
nodate = lambda s: s[s.find("-") + 2 :]
for path in Path(__file__).parent.joinpath("taf").glob("*.json"):
ref = json.load(path.open())
station = Taf(path.stem)
self.assertIsNone(station.last_updated)
self.assertTrue(station.update(ref["data"]["raw"]))
self.assertIsInstance(station.last_updated, datetime)
# Clear timestamp due to parse_date limitations
nodt = deepcopy(station.data)
for key in ("time", "start_time", "end_time"):
setattr(nodt, key, None)
for i in range(len(nodt.forecast)):
for key in ("start_time", "end_time"):
setattr(nodt.forecast[i], key, None)
self.assertEqual(asdict(nodt), ref["data"])
self.assertEqual(asdict(station.translations), ref["translations"])
self.assertEqual(station.summary, ref["summary"])
self.assertEqual(nodate(station.speech), nodate(ref["speech"]))
self.assertEqual(asdict(station.station_info), ref["station_info"])
def test_wind_shear(self):
"""
Wind shear should be recognized as its own element in addition to wind
"""
report = ("TAF AMD CYOW 282059Z 2821/2918 09008KT WS015/20055KT P6SM BKN220 "
"BECMG 2821/2823 19015G25KT "
"FM290300 21013G23KT P6SM -SHRA BKN040 OVC100 "
"TEMPO 2903/2909 4SM BR OVC020 "
"FM290900 25012KT P6SM SCT030 "
"FM291300 32017G27KT P6SM OVC030 "
"TEMPO 2913/2918 P6SM -SHRA OVC020 RMK NXT FCST BY 290000Z")
taf = Taf('CYBC')
taf.update(report)
lines = taf.data.forecast
self.assertEqual(len(lines), 7)
self.assertEqual(lines[0].wind_shear, 'WS015/20055')
self.assertEqual(taf.translations.forecast[1].clouds, None)
def test_prob_line(self):
"""
Even though PROB__ is not in TAF_NEWLINE, it should still separate,
add a new line, and include the prob value in line.probability
"""
report = ("TAF AMD CYBC 271356Z 2714/2802 23015G25KT P6SM BKN090 "
"TEMPO 2714/2718 P6SM -SHRA BKN060 OVC090 "
"FM271800 22015G25KT P6SM OVC040 "
"TEMPO 2718/2724 6SM -SHRA "
"PROB30 2718/2724 VRB25G35KT 1SM +TSRA BR BKN020 OVC040CB "
"FM280000 23008KT P6SM BKN040 RMK FCST BASED ON AUTO OBS. NXT FCST BY 272000Z")
taf = Taf('CYBC')
taf.update(report)
lines = taf.data.forecast
self.assertEqual(len(lines), 6)
self.assertEqual(lines[3].probability, None)
self.assertEqual(lines[4].probability, core.make_number('30'))
self.assertTrue(lines[4].raw.startswith('PROB30'))
def test_prob_end(self):
"""
PROB and TEMPO lines are discrete times and should not affect FROM times
"""
report = ("MMMX 242331Z 2500/2606 24005KT P6SM VC RA SCT020CB SCT080 BKN200 TX25/2521Z TN14/2513Z "
"TEMPO 2500/2504 6SM TSRA BKN020CB "
"FM250600 04010KT P6SM SCT020 BKN080 "
"TEMPO 2512/2515 3SM HZ BKN015 "
"FM251600 22005KT 4SM HZ BKN020 "
"FM251800 22010KT 6SM HZ SCT020 BKN200 "
"PROB40 2522/2602 P6SM TSRA BKN020CB")
taf = Taf('CYBC')
taf.update(report)
lines = taf.data.forecast
self.assertEqual(lines[0].start_time.repr, '2500')
self.assertEqual(lines[0].end_time.repr, '2506')
self.assertEqual(lines[1].start_time.repr, '2500')
self.assertEqual(lines[1].end_time.repr, '2504')
self.assertEqual(lines[-2].start_time.repr, '2518')
self.assertEqual(lines[-2].end_time.repr, '2606')
self.assertEqual(lines[-1].start_time.repr, '2522')
self.assertEqual(lines[-1].end_time.repr, '2602')