import numpy as np
import pandas as pd
from ..power.overconsumption.find import from_power_threshold
[docs]
def example_power():
"""Return an example power series, threshold and overshoot overconsumption.
The power is sampled with 30min period such that any error on the bounds by
+/- 1 step is pretty obvious in terms of energy.
The power is piecewise-constant, equal to 1W everywhere except :
- during 1h from 10:00 to 11:00 when it is 11W
- during 1h30min from 14:00 to 15:30 when it is 21W.
"""
times = pd.date_range(
start=pd.Timestamp("2023-03-26", tz="Europe/Paris"), # DST
end=pd.Timestamp("2023-03-27", tz="Europe/Paris"),
inclusive="left",
freq="30min",
)
pow = pd.Series(np.ones_like(times), index=times)
s1 = pd.Timestamp("2023-03-26 10:00", tz="Europe/Paris")
e1 = pd.Timestamp("2023-03-26 10:30", tz="Europe/Paris")
s2 = pd.Timestamp("2023-03-26 14:00", tz="Europe/Paris")
e2 = pd.Timestamp("2023-03-26 15:00", tz="Europe/Paris")
threshold = pow.copy()
pow.loc[s1:e1] = 11 # inclusive loc
pow.loc[s2:e2] = 21 # inclusive loc
intervals = pd.DataFrame.from_dict(
{
"start": [s1, s2],
"end": [e1, e2],
"duration": [3600, 5400],
"energy": [3600 * 10, 5400 * 20],
},
)
intervals["end"] += pd.Timedelta("30min") # doom pandas inclusive loc
return pow, threshold, intervals
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def test_from_power_threshold_constant():
"""Check the function with constant threshold as float and series"""
power, threshold, intervals = example_power()
# constant as series
found_intervals = from_power_threshold(
power,
overshoot_tshd=threshold,
)
pd.testing.assert_frame_equal(found_intervals, intervals, check_dtype=False)
# constant as float
found_intervals = from_power_threshold(
power,
overshoot_tshd=1.0,
)
pd.testing.assert_frame_equal(found_intervals, intervals, check_dtype=False)
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def test_from_power_threshold_over_power():
"""Check the function with threshold over max power"""
power, _, _ = example_power()
assert from_power_threshold(power, 100.0).empty
assert from_power_threshold(power, power.max()).empty
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def test_from_power_threshold_under_power():
"""Check the function with threshold under min power.
Double limit-case :
- DST : the day is shorter than 24h
- Whole day : the end of the overconsumption is the last instant in the
series, as the moment after is not known. (should be the end of the timestep).
"""
power, _, intervals = example_power()
found_intervals = from_power_threshold(power, 0.0)
intervals_expect = pd.DataFrame.from_dict(
{
"start": [power.index[0]],
"end": [power.index[-1]],
"duration": 1800 * 45,
"energy": 1800 * 45 + intervals["energy"].sum(),
},
)
pd.testing.assert_frame_equal(found_intervals, intervals_expect, check_dtype=False)
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def test_from_power_threshold_series():
"""Check that the function works correctly with a variable threshold"""
power, threshold, intervals = example_power()
threshold.loc[: pd.Timestamp("2023-03-26 12:00", tz="Europe/Paris"),] = 11
found_intervals = from_power_threshold(
power,
overshoot_tshd=threshold,
)
pd.testing.assert_frame_equal(
found_intervals, intervals[1:].reset_index(drop=True), check_dtype=False,
)
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def test_from_power_threshold_custom_ref():
"""Check that the custom energy reference is correctly accounted for"""
power, threshold, intervals = example_power()
ref = threshold.copy()
threshold.loc[:] = 11
found_intervals = from_power_threshold(
power,
overshoot_tshd=threshold,
reference_energy_tshd=ref,
)
pd.testing.assert_frame_equal(
found_intervals, intervals[1:].reset_index(drop=True), check_dtype=False,
)
