def test_dataparsers(self):
"""
Unit tests for Data parsers objects
"""
# Check against our test data and make sure we are correctly parsing & fetching the right objects from the data
bus_data = parse_bus_data(self.bot.browser.fetch_json(self.bot.urls.BUS_URL % "53410"), '15')
self.assertEqual(bus_data[0], Bus("Regent Street", gmt_to_localtime("1831")))
tube_data = parse_tube_data(self.bot.browser.fetch_xml_tree(self.bot.urls.TUBE_URL % ("D", "ECT")), RailStation("Earl's Court"), "D")
self.assertEqual(tube_data["Eastbound"][0], TubeTrain("Edgware Road", "Eastbound", "2139", "D", "075"))
dlr_data = parse_dlr_data(self.bot.browser.fetch_xml_tree(self.bot.urls.DLR_URL % "pop"), RailStation("Poplar"))
self.assertEqual(dlr_data['P1'][0], Train("Beckton", "2107"))
def parse_bus_data(bus_data, route_number):
"""
Take a parsed JSON object bus_data from a single bus stop and a specified route_number
Returns a list of Bus objects for that bus stop and route
"""
arrivals = bus_data.get('arrivals', [])
# Handle TfL's JSON-encoded error message
if not arrivals and bus_data.get('stopBoardMessage', '') == "noPredictionsDueToSystemError":
raise WhensMyTransportException('tfl_server_down')
# Do the user a favour - check for both number and possible Night Bus version of the bus
relevant_arrivals = [a for a in arrivals if (a['routeName'] == route_number or a['routeName'] == 'N' + route_number)
and a.get('isRealTime', True)
and not a.get('isCancelled', False)]
# FIXME Temporary logging code
for a in relevant_arrivals[:3]:
logging.debug("Found bus %s going to %s at %s" % (a['routeName'], a['destination'], a['scheduledTime']))
relevant_buses = [Bus(a['destination'], gmt_to_localtime(a['scheduledTime'])) for a in relevant_arrivals[:3]]
return relevant_buses
def test_stringutils(self):
"""
Unit test for stringutils' methods
"""
# Check capwords
capitalised_strings = ("Bank", "Morden East", "King's Cross St. Pancras", "Kennington Oval via Charing X")
for test_string in capitalised_strings:
self.assertEqual(test_string, capwords(test_string))
self.assertEqual(test_string, capwords(test_string.lower()))
self.assertEqual(test_string, capwords(test_string.upper()))
self.assertNotEqual(test_string.lower(), capwords(test_string))
self.assertNotEqual(test_string.upper(), capwords(test_string))
# Check to see cleanup string is working
random_string = lambda a, b: "".join([chr(random.Random().randint(a, b)) for _i in range(0, 10)])
dirty_strings = [random_string(48, 122) for _i in range(0, 10)]
undesirables = ("a", "b+", "[0-9]", "^x")
for dirty_string in dirty_strings:
cleaned_string = cleanup_name_from_undesirables(dirty_string, undesirables)
for undesirable in undesirables:
self.assertIsNone(re.search(undesirable, cleaned_string, flags=re.I))
# Check string similarities - 100 for identical strings, 90 or more for one character change
# and nothing at all for a totally unidentical string
similarity_string = random_string(65, 122)
self.assertEqual(get_name_similarity(similarity_string, similarity_string), 100)
self.assertGreaterEqual(get_name_similarity(similarity_string, similarity_string[:-1]), 90)
self.assertEqual(get_name_similarity(similarity_string, random_string(48, 57)), 0)
# Check to see most similar string gets picked out of an list of similar-looking strings, and that
# with very dissimilar strings, there is no candidate at all
similarity_candidates = (similarity_string[:3], similarity_string[:5], similarity_string[:9], "z" * 10)
self.assertEqual(get_best_fuzzy_match(similarity_string, similarity_candidates), similarity_candidates[-2])
dissimilarity_candidates = [random_string(48, 57) for _i in range(0, 10)]
self.assertIsNone(get_best_fuzzy_match(similarity_string, dissimilarity_candidates))
if time.localtime().tm_isdst:
self.assertEqual(gmt_to_localtime("2359"), "0059")
self.assertEqual(gmt_to_localtime("23:59"), "0059")
self.assertEqual(gmt_to_localtime("Tue 00:01"), "0101")
else:
self.assertEqual(gmt_to_localtime("2359"), "2359")
self.assertEqual(gmt_to_localtime("23:59"), "2359")
self.assertEqual(gmt_to_localtime("Tue 00:01"), "0001")
