def test_find_missing_taf_times(self):
"""
Tests that missing forecast times can be interpretted by
"""
good_lines = [{
'type': 'FROM',
'start_time': '3021',
'end_time': '3023'
}, {
'type': 'FROM',
'start_time': '3023',
'end_time': '0105'
}, {
'type': 'FROM',
'start_time': '0105',
'end_time': '0108'
}, {
'type': 'FROM',
'start_time': '0108',
'end_time': '0114'
}]
for line in good_lines:
for key in ('start_time', 'end_time'):
line[key] = _core.make_timestamp(line[key])
bad_lines = deepcopy(good_lines)
bad_lines[0]['start_time'] = None
bad_lines[1]['start_time'] = None
bad_lines[2]['end_time'] = None
bad_lines[3]['end_time'] = None
start, end = good_lines[0]['start_time'], good_lines[-1]['end_time']
self.assertEqual(_core.find_missing_taf_times(bad_lines, start, end),
good_lines)
def parse(station: str, report: str) -> (TafData, Units):
"""
Returns TafData and Units dataclasses with parsed data and their associated units
"""
if not report:
return None, None
valid_station(station)
while len(report) > 3 and report[:4] in ("TAF ", "AMD ", "COR "):
report = report[4:]
retwx = {
"end_time": None,
"raw": report,
"remarks": None,
"start_time": None
}
report = _core.sanitize_report_string(report)
_, station, time = _core.get_station_and_time(report[:20].split())
retwx["station"] = station
retwx["time"] = _core.make_timestamp(time)
report = report.replace(station, "")
if time:
report = report.replace(time, "").strip()
if uses_na_format(station):
use_na = True
units = Units(**NA_UNITS)
else:
use_na = False
units = Units(**IN_UNITS)
# Find and remove remarks
report, retwx["remarks"] = _core.get_taf_remarks(report)
# Split and parse each line
lines = _core.split_taf(report)
parsed_lines = parse_lines(lines, units, use_na)
# Perform additional info extract and corrections
if parsed_lines:
parsed_lines[-1]["other"], retwx["max_temp"], retwx[
"min_temp"] = _core.get_temp_min_and_max(parsed_lines[-1]["other"])
if not (retwx["max_temp"] or retwx["min_temp"]):
parsed_lines[0]["other"], retwx["max_temp"], retwx[
"min_temp"] = _core.get_temp_min_and_max(
parsed_lines[0]["other"])
# Set start and end times based on the first line
start, end = parsed_lines[0]["start_time"], parsed_lines[0]["end_time"]
parsed_lines[0]["end_time"] = None
retwx["start_time"], retwx["end_time"] = start, end
parsed_lines = _core.find_missing_taf_times(parsed_lines, start, end)
parsed_lines = _core.get_taf_flight_rules(parsed_lines)
# Extract Oceania-specific data
if retwx["station"][0] == "A":
parsed_lines[-1]["other"], retwx["alts"], retwx[
"temps"] = _core.get_oceania_temp_and_alt(
parsed_lines[-1]["other"])
# Convert to dataclass
retwx["forecast"] = [TafLineData(**line) for line in parsed_lines]
return TafData(**retwx), units
def test_find_missing_taf_times(self):
"""
Tests that missing forecast times can be interpretted by
"""
good_lines = [
{
"type": "FROM",
"start_time": "3021",
"end_time": "3023"
},
{
"type": "FROM",
"start_time": "3023",
"end_time": "0105"
},
{
"type": "FROM",
"start_time": "0105",
"end_time": "0108"
},
{
"type": "FROM",
"start_time": "0108",
"end_time": "0114"
},
]
for line in good_lines:
for key in ("start_time", "end_time"):
line[key] = _core.make_timestamp(line[key])
bad_lines = deepcopy(good_lines)
bad_lines[0]["start_time"] = None
bad_lines[1]["start_time"] = None
bad_lines[2]["end_time"] = None
bad_lines[3]["end_time"] = None
start, end = good_lines[0]["start_time"], good_lines[-1]["end_time"]
self.assertEqual(_core.find_missing_taf_times(bad_lines, start, end),
good_lines)