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"])