def test_mean_operand_not_iterable(self): """ Tests return of a TypeError if passing non-datetime.datetime objects """ x1 = 1 with self.assertRaises(TypeError): dts.mean(x1)
def test_mean_invalid_operant_object(self): """ Tests return of a TypeError if passing non-datetime.datetime objects """ x1 = ['not a datetime.datetime'] with self.assertRaises(TypeError): dts.mean(x1)
def test_mean_empty(self): """ Tests return of IndexError when passing a zero-length list """ n1 = list() with self.assertRaises(IndexError): dts.mean(n1)
def test_mean_tz_1(self): """ Tests that mean of a single-length list of 1 datetime with timezone should return itself """ noon_zulu = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.utc) t1 = [noon_zulu] self.assertEquals(dts.mean(t1), noon_zulu)
def test_mean_naive_1(self): """ Tests that mean of a single-length list of a datetime should return itself """ naive_1 = dt.datetime(2015, 9, 10, 12, 0, 0) n1 = [naive_1] self.assertEquals(dts.mean(n1), naive_1)
def test_mean_same_tz_2_diff_vals(self): """ Tests that mean of 1200 UTC and 1300 UTC is 1230 UTC. """ z_1200 = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.utc) z_1300 = dt.datetime(2014, 1, 1, 13, 0, 0, tzinfo=pytz.utc) t2 = [z_1300, z_1200] self.assertEquals(dts.mean(t2), dt.datetime(2014, 1, 1, 12, 30, 0, tzinfo=pytz.utc))
def test_mean_tz_2_same(self): """ Tests short-circuit that mean list of two duplicates should just return the first element. This is faster than averageing two identical items. """ noon_zulu = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.utc) noon_zulu_ditto = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.utc) t2 = [noon_zulu, noon_zulu_ditto] self.assertEquals(dts.mean(t2), noon_zulu)
def test_mean_naive_2_diff(self): """ Tests that the mean of 2015-09-10 12:30:00.00 and 2015-09-10 12:00:00.00 (both naive) is 2015-09-10 12:15:00.00. Also tests length==2 short-circuit. """ naive_1 = dt.datetime(2015, 9, 10, 12, 30, 0) naive_2 = dt.datetime(2015, 9, 10, 12, 0, 0) n2 = [naive_1, naive_2] self.assertEquals(dts.mean(n2), dt.datetime(2015, 9, 10, 12, 15, 0))
def test_mean_same_tz_3_vals(self): """ Tests that mean of 1200 UTC, 1300 UTC, and 1315 UTC is 1245 UTC. """ z_1200 = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.utc) z_1300 = dt.datetime(2014, 1, 1, 13, 0, 0, tzinfo=pytz.utc) z_1315 = dt.datetime(2014, 1, 1, 13, 15, 0, tzinfo=pytz.utc) t3 = [z_1300, z_1315, z_1200] self.assertEquals(dts.mean(t3), dt.datetime(2014, 1, 1, 12, 45, 0, tzinfo=pytz.utc))
def test_mean_naive_2_same(self): """ Tests that mean short-circuit that list of two duplicates should just return returnthe first element. This is faster than averageing two identical items. """ naive_1 = dt.datetime(2015, 9, 10, 12, 0, 0) naive_2 = dt.datetime(2015, 9, 10, 12, 0, 0) n2 = [naive_1, naive_2] self.assertEquals(dts.mean(n2), naive_1)
def test_mean_naive_3(self): """ Tests that the mean of 2015-09-10 12:00:00.00, 2015-09-30 12:00:00.00, and 2015-09-22 12:00.00.000 (all naive) is 2015-09-21 04:00:00.00 """ naive_1 = dt.datetime(2015, 9, 10, 12, 0, 0) naive_2 = dt.datetime(2015, 9, 30, 12, 0, 0) naive_3 = dt.datetime(2015, 9, 22, 12, 0, 0) n3 = [naive_1, naive_2, naive_3] self.assertEquals(dts.mean(n3), dt.datetime(2015, 9, 21, 4, 0, 0))
def test_median_naive_4_diff(self): """ Tests that the median of 2015-09-10 12:30:00.00 and 2015-09-10 12:00:00.00 (both naive) is 2015-09-10 12:15:00.00 (arithmetic mean of middle two. Also tests length==2 condition as this is a sub-set of this test. """ naive_1 = dt.datetime(2015, 9, 10, 12, 30, 0) naive_2 = dt.datetime(2015, 9, 10, 12, 0, 0) naive_3 = dt.datetime(2014, 9, 10, 12, 30, 0) naive_4 = dt.datetime(2016, 9, 10, 12, 30, 0) n4 = [naive_1, naive_2, naive_3, naive_4] self.assertEquals(dts.median(n4), dts.mean([naive_1, naive_2]))
def test_median_diff_tz_4_vals(self):
"""
Tests median for four values across different timezones.
Note: This sorts than computes the arithmetic mean of the middle two values.
As mentioned above, pytz does not always case to full timezone offsets
"""
chicago = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('America/Chicago'))
nyc = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('America/New_York'))
london = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('Europe/London'))
singapore = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('Asia/Singapore'))
t4 = [chicago, nyc, london, singapore]
self.assertEquals(dts.median(t4), dts.mean([nyc, london]))
def test_mean_diff_tz_2_vals(self):
"""
Tests mean of two values (short-circuit) across different timezones.
Note: PYTZ does not cast timezones exactly to hours.
Try yourself (see the README)
mean of (2014-01-01 12:00:00-04:56, 2014-01-01 12:00:00-00:01)
is actually 2014-01-01 14:28:30+00:00
"""
nyc = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('America/New_York'))
london = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('Europe/London'))
t2 = [nyc, london]
self.assertEquals(dts.mean(t2), dt.datetime(2014, 1, 1, 14, 28, 30, tzinfo=pytz.utc))
def test_mean_diff_tz_4_vals(self):
"""
Tests mean for four values (not short-circuit) across multiple timezones.
Note: PYTZ does not cast timezones exactly to hours.
Try yourself (see the README)
"""
chicago = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('America/Chicago'))
nyc = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('America/New_York'))
london = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('Europe/London'))
singapore = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('Asia/Singapore'))
t4 = [chicago, nyc, london, singapore]
self.assertEquals(dts.mean(t4), dt.datetime(2014, 1, 1, 12, 58, 15, tzinfo=pytz.utc))
def test_mean_diff_tz_3_vals(self):
"""
Tests mean of three values across different timezones.
Note: PYTZ does not cast timezones exactly to hours.
Try yourself (see the README)
mean of (2014-01-01 12:00:00-04:56, 2014-01-01 12:00:00-00:01, 2014-01-01 12:00:00+06:55)
is actually 2014-01-01 11:20:40+00:00
"""
nyc = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('America/New_York'))
london = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('Europe/London'))
singapore = dt.datetime(2014, 1, 1, 12, 0, 0, tzinfo=pytz.timezone('Asia/Singapore'))
t3 = [nyc, london, singapore]
self.assertEquals(dts.mean(t3), dt.datetime(2014, 1, 1, 11, 20, 40, tzinfo=pytz.utc))
intertemp_C = '{0:0.3F}'.format(internal)
intertemp_F = '{0:0.3F}'.format(c_to_f(internal))
if i < 3:
alldata[i, 0] = thermotemp_C
alldata[i, 1] = thermotemp_F
alldata[i, 2] = intertemp_C
alldata[i, 3] = intertemp_F
times.append(now)
i += 1
else:
#print alldata,'\t'
i = 0
average = mean(times[:])
a = '{0}\t{1}\t{2}\t{3}\t{4}\n'.format(average,
np.nanmean(alldata[:, 0]),
np.nanmean(alldata[:, 1]),
np.nanmean(alldata[:, 2]),
np.nanmean(alldata[:, 3]))
#print a
times = []
alldata = np.zeros((3, 4))
alldata[:] = np.NAN
infile.write(a)
time.sleep(0.05)
i = 0
alldata = np.zeros(
