def test_docs_data_samples(self):
df = wefacts.get_weather('5000 Forbes Ave, Pittsburgh, PA 15213',
20160112,
20160112,
dump_csv=True)
self.assertEqual(len(df), 24)
df = wefacts.get_weather('15213', 20160215, 20160314, dump_csv=True)
self.assertEqual(len(df), 24 * 29 - 1) # daylight saving
df = wefacts.get_weather('Moffett Field, CA',
20170110,
20170123,
dump_csv=True)
self.assertEqual(len(df), 24 * 14)
def test_Seattle_rainy_days(self):
df = wefacts.get_weather('SEATTLE', 20160101, 20161231)
records = wefacts._summarize_daily(df)
count_rainy = 0
for d, summary in records:
if summary['MSG'] == 'Rainy':
count_rainy += 1
self.assertGreater(count_rainy, 35)
def test_LA_sunny_days(self):
df = wefacts.get_weather('Beverly Hills', 20160101, 20161231)
records = wefacts._summarize_daily(df)
count_sunny = 0
for d, summary in records:
if summary['MSG'] == 'Sunny':
count_sunny += 1
self.assertGreater(count_sunny, 300)
def test_DalyCity(self):
df = wefacts.get_weather('Pittsburgh',
20160101,
20170201,
dump_csv=True)
records = wefacts._summarize_daily(df)
for d, summary in records:
print d, summary
def test_Boston_snow_days(self):
# todo
df = wefacts.get_weather('Boston', 20160101, 20161231)
records = wefacts._summarize_daily(df)
count_snow = 0
for d, summary in records:
if summary['MSG'] == 'Snow':
count_snow += 1
self.assertGreater(count_snow, 11)
def test_SFO_windy_days(self):
df = wefacts.get_weather('SAN FRANCISCO INTERNATIONAL AIRPORT',
20160101, 20161231)
records = wefacts._summarize_daily(df)
count_windy = 0
for d, summary in records:
if summary['MSG'] == 'Windy':
count_windy += 1
self.assertGreater(count_windy, 80)
def test_CrossYear_SFO(self):
df = wefacts.get_weather('SFO', 20151224, 20170106, False)
temperature = [t / 10.0 for t in df['OAT'].values]
count_nan = 0
for t in temperature:
if t != t:
count_nan += 1
else:
self.assertGreater(t, -10)
self.assertLess(t, 40)
self.assertEqual(len(temperature), (366 + 14) * 24)
self.assertLessEqual(count_nan, 10)
def test_CrossYear_SquirrelHill(self):
df = wefacts.get_weather('Squirrel Hill', 20161224, 20170106, False)
temperature = [t / 10.0 for t in df['OAT'].values]
self.assertEqual(len(temperature), 14 * 24)
count_nan = 0
for t in temperature:
if t != t:
count_nan += 1
else:
self.assertGreater(t, -30)
self.assertLess(t, 40)
self.assertLessEqual(count_nan, 1)
def test_yesterday_weather(self):
import urllib2
import json
import datetime
# geo-locate the connecting IP
f = urllib2.urlopen('http://freegeoip.net/json/')
json_string = f.read()
f.close()
location = json.loads(json_string)
lat, lng = location['latitude'], location['longitude']
date1 = int((datetime.datetime.now() -
datetime.timedelta(days=1)).strftime('%Y%m%d'))
df = wefacts.get_weather('%f, %f' % (lat, lng), date1, date1)
records = wefacts._summarize_daily(df)
self.assertGreaterEqual(len(records), 1)
def test_Boston_1997_AprilFoolStorm(self):
df = wefacts.get_weather('Boston', 19970331, 19970401, False)
precipitation = df['PPT'].values
self.assertGreater(sum(precipitation), 300)
def test_Pittsburgh(self):
df = wefacts.get_weather('Pittsburgh',
20130101,
20170301,
dump_csv=True)
self.assertGreater(len(df), 365 * 3)
def test_Pittsburgh_1950_BigSnow(self):
df = wefacts.get_weather('Pittsburgh', 19501124, 19501130, False)
precipitation = df['PPT'].values
self.assertGreater(sum(precipitation), 300)