def test_scalar_ops():
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), 3)
a.set(datetime.datetime(2015, 3, 4), 2)
ts_half = a.multiply(0.5)
ts_bool = a.to_bool(invert=False)
ts_threshold = a.threshold(value=1.1)
# test before domain, should give default value
assert ts_half[datetime.datetime(2015, 2, 24)] is None
assert ts_bool[datetime.datetime(2015, 2, 24)] == None
assert ts_threshold[datetime.datetime(2015, 2, 24)] == None
# test values throughout series
assert ts_half[datetime.datetime(2015, 3, 1, 6)] == 0.5
assert ts_bool[datetime.datetime(2015, 3, 1, 6)] == True
assert ts_threshold[datetime.datetime(2015, 3, 1, 6)] == False
assert ts_half[datetime.datetime(2015, 3, 2, 6)] == 0
assert ts_bool[datetime.datetime(2015, 3, 2, 6)] == False
assert ts_threshold[datetime.datetime(2015, 3, 2, 6)] == False
assert ts_half[datetime.datetime(2015, 3, 3, 6)] == 1.5
assert ts_bool[datetime.datetime(2015, 3, 3, 6)] == True
assert ts_threshold[datetime.datetime(2015, 3, 3, 6)] == True
# test after domain, should give last value
assert ts_half[datetime.datetime(2015, 3, 4, 18)] == 1
assert ts_bool[datetime.datetime(2015, 3, 4, 18)] == True
assert ts_threshold[datetime.datetime(2015, 3, 4, 18)] == True
def test_scalar_ops():
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), 3)
a.set(datetime.datetime(2015, 3, 4), 2)
ts_half = a.scale_by(0.5)
ts_bool = a.to_bool(invert=False)
ts_threshold = a.threshold(value=1.1)
# test before domain, should give default value
assert ts_half[datetime.datetime(2015, 2, 24)] == 0
assert ts_bool[datetime.datetime(2015, 2, 24)] == False
assert ts_threshold[datetime.datetime(2015, 2, 24)] == False
# test values throughout series
assert ts_half[datetime.datetime(2015, 3, 1, 6)] == 0.5
assert ts_bool[datetime.datetime(2015, 3, 1, 6)] == True
assert ts_threshold[datetime.datetime(2015, 3, 1, 6)] == False
assert ts_half[datetime.datetime(2015, 3, 2, 6)] == 0
assert ts_bool[datetime.datetime(2015, 3, 2, 6)] == False
assert ts_threshold[datetime.datetime(2015, 3, 2, 6)] == False
assert ts_half[datetime.datetime(2015, 3, 3, 6)] == 1.5
assert ts_bool[datetime.datetime(2015, 3, 3, 6)] == True
assert ts_threshold[datetime.datetime(2015, 3, 3, 6)] == True
# test after domain, should give last value
assert ts_half[datetime.datetime(2015, 3, 4, 18)] == True
assert ts_bool[datetime.datetime(2015, 3, 4, 18)] == True
assert ts_threshold[datetime.datetime(2015, 3, 4, 18)] == True
def test_iterperiods():
# timeseries with a default value and no points returns one period
# with the default value from -infinity to infinity, or
ts = TimeSeries(default=0)
assert list(ts.iterperiods()) == [(-inf, inf, 0)]
# timeseries with no default value and no points raises error
ts = TimeSeries()
with nose.tools.assert_raises(KeyError):
list(ts.iterperiods())
ts = TimeSeries()
ts.set(datetime.datetime(2015, 3, 1), 1)
ts.set(datetime.datetime(2015, 3, 2), 0)
ts.set(datetime.datetime(2015, 3, 3), 1)
ts.set(datetime.datetime(2015, 3, 4), 2)
answer = [
(datetime.datetime(2015, 3, 1), datetime.datetime(2015, 3, 2), 1),
(datetime.datetime(2015, 3, 2), datetime.datetime(2015, 3, 3), 0),
(datetime.datetime(2015, 3, 3), datetime.datetime(2015, 3, 4), 1)]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4)
):
result.append((t0, t1, v0))
assert answer == result
answer = [
(datetime.datetime(2015, 3, 1), datetime.datetime(2015, 3, 2), 1),
(datetime.datetime(2015, 3, 3), datetime.datetime(2015, 3, 4), 1),
]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4),
value=1,
):
result.append((t0, t1, v0))
assert answer == result
def filter(t0, t1, value):
return True if not value else False
answer = [
(datetime.datetime(2015, 3, 2), datetime.datetime(2015, 3, 3), 0),
]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4),
value=filter,
):
result.append((t0, t1, v0))
assert answer == result
def test_iterintervals():
ts = TimeSeries()
ts.set(datetime.datetime(2015, 3, 1), 1)
ts.set(datetime.datetime(2015, 3, 2), 0)
ts.set(datetime.datetime(2015, 3, 3), 1)
ts.set(datetime.datetime(2015, 3, 4), 2)
answer = [(1, 0), (0, 1), (1, 2)]
result = []
for (t0, v0), (t1, v1) in ts.iterintervals():
result.append((v0, v1))
assert answer == result
def test_iterperiods():
# timeseries with no points raises a KeyError
ts = TimeSeries()
with nose.tools.assert_raises(KeyError):
next(ts.iterperiods())
ts.set(datetime.datetime(2015, 3, 1), 1)
ts.set(datetime.datetime(2015, 3, 2), 0)
ts.set(datetime.datetime(2015, 3, 3), 1)
ts.set(datetime.datetime(2015, 3, 4), 2)
answer = [(datetime.datetime(2015, 3, 1), datetime.datetime(2015, 3,
2), 1),
(datetime.datetime(2015, 3, 2), datetime.datetime(2015, 3,
3), 0),
(datetime.datetime(2015, 3, 3), datetime.datetime(2015, 3,
4), 1)]
result = []
for (t0, t1, v0) in ts.iterperiods(start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4)):
result.append((t0, t1, v0))
assert answer == result
answer = [
(datetime.datetime(2015, 3, 1), datetime.datetime(2015, 3, 2), 1),
(datetime.datetime(2015, 3, 3), datetime.datetime(2015, 3, 4), 1),
]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4),
value=1,
):
result.append((t0, t1, v0))
assert answer == result
def filter(t0, t1, value):
return True if not value else False
answer = [
(datetime.datetime(2015, 3, 2), datetime.datetime(2015, 3, 3), 0),
]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4),
value=filter,
):
result.append((t0, t1, v0))
assert answer == result
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_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)
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_iterperiods():
ts = TimeSeries()
ts.set(datetime.datetime(2015, 3, 1), 1)
ts.set(datetime.datetime(2015, 3, 2), 0)
ts.set(datetime.datetime(2015, 3, 3), 1)
ts.set(datetime.datetime(2015, 3, 4), 2)
answer = [
(datetime.datetime(2015, 3, 1), datetime.datetime(2015, 3, 2), 1),
(datetime.datetime(2015, 3, 2), datetime.datetime(2015, 3, 3), 0),
(datetime.datetime(2015, 3, 3), datetime.datetime(2015, 3, 4), 1)]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4)
):
result.append((t0, t1, v0))
assert answer == result
answer = [
(datetime.datetime(2015, 3, 1), datetime.datetime(2015, 3, 2), 1),
(datetime.datetime(2015, 3, 3), datetime.datetime(2015, 3, 4), 1),
]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4),
value=1,
):
result.append((t0, t1, v0))
assert answer == result
def filter(t0, t1, value):
return True if not value else False
answer = [
(datetime.datetime(2015, 3, 2), datetime.datetime(2015, 3, 3), 0),
]
result = []
for (t0, t1, v0) in ts.iterperiods(
start=datetime.datetime(2015, 3, 1),
end=datetime.datetime(2015, 3, 4),
value=filter,
):
result.append((t0, t1, v0))
assert answer == result
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_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():
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 example_sum():
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, 5), 0)
a.set(datetime.datetime(2015, 3, 6), 0)
b = TimeSeries()
b.set(datetime.datetime(2015, 3, 1), 0)
b.set(datetime.datetime(2015, 3, 2, 12), 1)
b.set(datetime.datetime(2015, 3, 3, 13, 13), 0)
b.set(datetime.datetime(2015, 3, 4), 1)
b.set(datetime.datetime(2015, 3, 5), 0)
b.set(datetime.datetime(2015, 3, 5, 12), 1)
b.set(datetime.datetime(2015, 3, 5, 19), 0)
c = TimeSeries()
c.set(datetime.datetime(2015, 3, 1, 17), 0)
c.set(datetime.datetime(2015, 3, 1, 21), 1)
c.set(datetime.datetime(2015, 3, 2, 13, 13), 0)
c.set(datetime.datetime(2015, 3, 4, 18), 1)
c.set(datetime.datetime(2015, 3, 5, 4), 0)
# output the three time series
for i, ts in enumerate([a, b, c]):
for (t0, v0), (t1, v1) in ts.iterintervals(1):
print t0.isoformat(), i
print t1.isoformat(), i
print ''
for (t0, v0), (t1, v1) in ts.iterintervals(0):
print t0.isoformat(), i
print t1.isoformat(), i
print ''
# output the sum
# for dt, i in sum([a, b, c]):
# print dt.isoformat(), i
# print ''
for dt, i in TimeSeries.from_many_sum([a, b, c]):
print dt.isoformat(), i
def test_sum():
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)
b = TimeSeries()
b.set(datetime.datetime(2015, 3, 1), 0)
b.set(datetime.datetime(2015, 3, 1, 12), 1)
b.set(datetime.datetime(2015, 3, 2), 0)
b.set(datetime.datetime(2015, 3, 2, 12), 1)
b.set(datetime.datetime(2015, 3, 3), 0)
c = TimeSeries()
c.set(datetime.datetime(2015, 3, 1), 0)
c.set(datetime.datetime(2015, 3, 1, 18), 1)
c.set(datetime.datetime(2015, 3, 5), 0)
ts_sum = TimeSeries.merge([a, b, c], operation=ignorant(sum))
# test before domain, should give default value
assert ts_sum[datetime.datetime(2015, 2, 24)] == 0
# test values throughout sum
assert ts_sum[datetime.datetime(2015, 3, 1)] == 1
assert ts_sum[datetime.datetime(2015, 3, 1, 6)] == 1
assert ts_sum[datetime.datetime(2015, 3, 1, 12)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 13)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 17)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 18)] == 3
assert ts_sum[datetime.datetime(2015, 3, 1, 19)] == 3
assert ts_sum[datetime.datetime(2015, 3, 3)] == 2
assert ts_sum[datetime.datetime(2015, 3, 4)] == 1
assert ts_sum[datetime.datetime(2015, 3, 4, 18)] == 1
assert ts_sum[datetime.datetime(2015, 3, 5)] == 0
# test after domain, should give last value
assert ts_sum[datetime.datetime(2015, 3, 6)] == 0
assert 0 + a + b == a + b
def test_sum():
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)
b = TimeSeries()
b.set(datetime.datetime(2015, 3, 1), 0)
b.set(datetime.datetime(2015, 3, 1, 12), 1)
b.set(datetime.datetime(2015, 3, 2), 0)
b.set(datetime.datetime(2015, 3, 2, 12), 1)
b.set(datetime.datetime(2015, 3, 3), 0)
c = TimeSeries()
c.set(datetime.datetime(2015, 3, 1), 0)
c.set(datetime.datetime(2015, 3, 1, 18), 1)
c.set(datetime.datetime(2015, 3, 5), 0)
ts_sum = TimeSeries.from_many_sum([a, b, c])
# test before domain, should give default value
assert ts_sum[datetime.datetime(2015, 2, 24)] == 0
# test values throughout sum
assert ts_sum[datetime.datetime(2015, 3, 1)] == 1
assert ts_sum[datetime.datetime(2015, 3, 1, 6)] == 1
assert ts_sum[datetime.datetime(2015, 3, 1, 12)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 13)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 17)] == 2
assert ts_sum[datetime.datetime(2015, 3, 1, 18)] == 3
assert ts_sum[datetime.datetime(2015, 3, 1, 19)] == 3
assert ts_sum[datetime.datetime(2015, 3, 3)] == 2
assert ts_sum[datetime.datetime(2015, 3, 4)] == 1
assert ts_sum[datetime.datetime(2015, 3, 4, 18)] == 1
assert ts_sum[datetime.datetime(2015, 3, 5)] == 0
# test after domain, should give last value
assert ts_sum[datetime.datetime(2015, 3, 6)] == 0
baseline = 0
for ts in [a, b, c]:
for i, j in ts:
print i.isoformat(), j + baseline
print ''
baseline += 1.2
ts_sum = TimeSeries.from_many_sum([a, b, c])
for i, j in ts_sum:
print i.isoformat(), j + baseline
def example_sum():
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, 5), 0)
a.set(datetime.datetime(2015, 3, 6), 0)
b = TimeSeries()
b.set(datetime.datetime(2015, 3, 1), 0)
b.set(datetime.datetime(2015, 3, 2, 12), 1)
b.set(datetime.datetime(2015, 3, 3, 13, 13), 0)
b.set(datetime.datetime(2015, 3, 4), 1)
b.set(datetime.datetime(2015, 3, 5), 0)
b.set(datetime.datetime(2015, 3, 5, 12), 1)
b.set(datetime.datetime(2015, 3, 5, 19), 0)
c = TimeSeries()
c.set(datetime.datetime(2015, 3, 1, 17), 0)
c.set(datetime.datetime(2015, 3, 1, 21), 1)
c.set(datetime.datetime(2015, 3, 2, 13, 13), 0)
c.set(datetime.datetime(2015, 3, 4, 18), 1)
c.set(datetime.datetime(2015, 3, 5, 4), 0)
# output the three time series
for i, ts in enumerate([a, b, c]):
for (t0, v0), (t1, v1) in ts.iterintervals(1):
print(t0.isoformat(), i)
print(t1.isoformat(), i)
print('')
for (t0, v0), (t1, v1) in ts.iterintervals(0):
print(t0.isoformat(), i)
print(t1.isoformat(), i)
print('')
# output the sum
# for dt, i in sum([a, b, c]):
# print dt.isoformat(), i
# print ''
for dt, i in TimeSeries.merge([a, b, c], operation=sum):
print(dt.isoformat(), i)
