def test_mask():
start = datetime.datetime(2015, 3, 1)
# v. simple
a = TimeSeries()
a.set(start, 1)
a.set(datetime.datetime(2015, 4, 2), 0)
a.set(datetime.datetime(2015, 4, 3), 1)
a.set(datetime.datetime(2015, 4, 4), 0)
end = datetime.datetime(2015, 4, 5)
mask = TimeSeries(default=False)
mask[datetime.datetime(2015, 4, 1)] = True
mask[datetime.datetime(2015, 4, 3)] = False
# not normalized
distribution = a.distribution(
start=start,
end=end,
normalized=False,
mask=mask,
)
assert distribution[0] == 24 * 60 * 60 # one day
assert distribution[1] == 24 * 60 * 60
# normalized
distribution = a.distribution(
start=start,
end=end,
mask=mask,
)
assert distribution[0] == 0.5
assert distribution[1] == 0.5
def test_mask():
start_time = datetime.datetime(2015, 3, 1)
# v. simple
a = TimeSeries()
a.set(start_time, 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 4), 0)
end_time = datetime.datetime(2015, 3, 5)
mask = TimeSeries()
mask.set(datetime.datetime(2015, 3, 1), 1)
mask.set(datetime.datetime(2015, 3, 3), 0)
# not normalized
distribution = a.distribution(
start_time=start_time, end_time=end_time, normalized=False, mask=mask,
)
assert distribution[0] == 24 * 60 * 60 # one day
assert distribution[1] == 24 * 60 * 60
# normalized
distribution = a.distribution(
start_time=start_time, end_time=end_time, mask=mask,
)
assert distribution[0] == 0.5
assert distribution[1] == 0.5
def test_distribution():
start = datetime.datetime(2015, 3, 1)
# v. simple
a = TimeSeries()
a.set(start, 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 4), 0)
end = datetime.datetime(2015, 3, 5)
# not normalized
distribution = a.distribution(
start=start,
end=end,
normalized=False,
)
assert distribution[0] == 24 * 60 * 60 * 2 # two days
assert distribution[1] == 24 * 60 * 60 * 2
# normalized
distribution = a.distribution(start=start, end=end)
assert distribution[0] == 0.5
assert distribution[1] == 0.5
def test_mask():
start = datetime.datetime(2015, 3, 1)
# v. simple
a = TimeSeries()
a.set(start, 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 4), 0)
end = datetime.datetime(2015, 3, 5)
mask = Domain(datetime.datetime(2015, 3, 1), datetime.datetime(2015, 3, 3))
# not normalized
distribution = a.distribution(
start=start,
end=end,
normalized=False,
mask=mask,
)
assert distribution[0] == 24 * 60 * 60 # one day
assert distribution[1] == 24 * 60 * 60
# normalized
distribution = a.distribution(
start=start,
end=end,
mask=mask,
)
assert distribution[0] == 0.5
assert distribution[1] == 0.5
def test_distribution_empty():
ts = TimeSeries()
mask = TimeSeries(default=0)
mask[0] = 1
mask[2] = 0
# distribution with default args and no default value on empty
# TimeSeries doesn't know what to do
nose.tools.assert_raises(ValueError, ts.distribution)
# distribution with start and end, but no default value on empty
# TimeSeries doesn't know what to do
nose.tools.assert_raises(ValueError, ts.distribution, 0, 10)
# no matter what is passed in to distribution, if the default
# value is not set on an empty TimeSeries this should be an error
nose.tools.assert_raises(ValueError, ts.distribution, mask=mask)
ts = TimeSeries(default=0)
# no mask or start/end on empty TimeSeries, don't know what to do
nose.tools.assert_raises(ValueError, ts.distribution)
# start and end or mask given, is fine
distribution = ts.distribution(0, 10)
assert distribution[0] == 1.0
distribution = ts.distribution(mask=mask)
assert distribution[0] == 1.0
def test_none_handling():
ts = TimeSeries()
ts[1] = (0, 1)
ts[2] = (None, 0)
ts[3] = (2, 0)
# print(ts.distribution(normalized=False))
assert (ts.distribution()[(0, 1)] == 0.5)
assert (ts.distribution()[(None, 0)] == 0.5)
def test_distribution():
ts = TimeSeries([[1, 1], [2, 0], [6, 1], [8, 0]],
domain=Domain([1, 2], [3, 5], [6, 8]))
distribution = ts.distribution()
assert distribution[0] == 2. / 5
assert distribution[1] == 3. / 5
distribution = ts.distribution(0, 9)
assert distribution[0] == 2. / 5
assert distribution[1] == 3. / 5
def test_default_values():
# v. simple
a = TimeSeries()
a.set(datetime.datetime(2015, 3, 1), 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 4), 0)
start = datetime.datetime(2015, 3, 1)
end = datetime.datetime(2015, 3, 4)
default = a.distribution()
distribution = a.distribution(start=start, end=end)
assert default == distribution
assert distribution[0] == 1.0 / 3
assert distribution[1] == 2.0 / 3
def test_integer_times():
# v. simple
a = TimeSeries()
a[0] = 1
a[1] = 0
a[3] = 1
a[4] = 0
distribution = a.distribution(start=0, end=6)
assert distribution[0] == 2.0 / 3
assert distribution[1] == 1.0 / 3
def test_distribution():
start_time = datetime.datetime(2015, 3, 1)
# v. simple
a = TimeSeries()
a.set(start_time, 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 4), 0)
end_time = datetime.datetime(2015, 3, 5)
# not normalized
distribution = a.distribution(
start_time=start_time, end_time=end_time, normalized=False,
)
assert distribution[0] == 24 * 60 * 60 * 2 # two days
assert distribution[1] == 24 * 60 * 60 * 2
# normalized
distribution = a.distribution(start_time=start_time, end_time=end_time)
assert distribution[0] == 0.5
assert distribution[1] == 0.5
def test_default_values():
# v. simple
a = TimeSeries()
a.set(datetime.datetime(2015, 3, 1), 1)
a.set(datetime.datetime(2015, 3, 2), 0)
a.set(datetime.datetime(2015, 3, 3), 1)
a.set(datetime.datetime(2015, 3, 4), 0)
# not normalized
distribution = a.distribution()
total = 24 * 60 * 60 * 3
assert distribution[0] == 24 * 60 * 60 * 1 / float(total)
assert distribution[1] == 24 * 60 * 60 * 2 / float(total)
