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@peterdudfield
peterdudfield committed Aug 2, 2024
2 parents 14dc676 + d0f4771 commit 6276ba3
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15 changes: 2 additions & 13 deletions README.md
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# OCF Template Repository
Template Repository for OCF Projects

## Usage

Do the following to customize the repo to the project:

- Replace `src` with name of the library/project
- Update `setup.py` with the proper info
- Change `commit` to `True` in `.bumpversion.cfg` if you want the minor version
to increment on every commit.
- Add PyPi access token to release to PyPi
- Update name of folder in the test workflow
# OCF Data Sampler
A repo for sampling from weather data for renewable energy prediction
2 changes: 1 addition & 1 deletion setup.py
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name="ocf_data_sampler",
version="0.0.1",
license="MIT",
description="Super cool OCF Repo",
description="Sample from weather data for renewable energy prediction",
author="James Fulton, Peter Dudfield, and the Open Climate Fix team",
author_email="[email protected]",
company="Open Climate Fix Ltd",
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194 changes: 194 additions & 0 deletions tests/select/test_find_contiguous_t0_time_periods.py
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from datetime import timedelta
import pandas as pd

from ocf_data_sampler.select.find_contiguous_t0_time_periods import (
find_contiguous_t0_time_periods, find_contiguous_t0_periods_nwp,
intersection_of_multiple_dataframes_of_periods,
)



def test_find_contiguous_t0_time_periods():

# Create 5-minutely data timestamps
freq = timedelta(minutes=5)
history_duration = timedelta(minutes=60)
forecast_duration = timedelta(minutes=15)

datetimes = (
pd.date_range("2023-01-01 12:00", "2023-01-01 17:00", freq=freq)
.delete([5, 6, 30])
)

periods = find_contiguous_t0_time_periods(
datetimes=datetimes,
history_duration=history_duration,
forecast_duration=forecast_duration,
sample_period_duration=freq,
)

expected_results = pd.DataFrame(
{
"start_dt": pd.to_datetime(
[
"2023-01-01 13:35",
"2023-01-01 15:35",
]
),
"end_dt": pd.to_datetime(
[
"2023-01-01 14:10",
"2023-01-01 16:45",
]
),
},
)

assert periods.equals(expected_results)


def test_find_contiguous_t0_time_periods_nwp():

# These are the expected results of the test
expected_results = [
pd.DataFrame(
{
"start_dt": pd.to_datetime(["2023-01-01 03:00", "2023-01-02 03:00"]),
"end_dt": pd.to_datetime(["2023-01-01 21:00", "2023-01-03 06:00"]),
},
),
pd.DataFrame(
{
"start_dt": pd.to_datetime(
[
"2023-01-01 05:00",
"2023-01-02 05:00",
"2023-01-02 14:00",
]
),
"end_dt": pd.to_datetime(
[
"2023-01-01 21:00",
"2023-01-02 12:00",
"2023-01-03 06:00",
]
),
},
),
pd.DataFrame(
{
"start_dt": pd.to_datetime(
[
"2023-01-01 05:00",
"2023-01-01 11:00",
"2023-01-02 05:00",
"2023-01-02 14:00",
]
),
"end_dt": pd.to_datetime(
[
"2023-01-01 09:00",
"2023-01-01 18:00",
"2023-01-02 09:00",
"2023-01-03 03:00",
]
),
},
),
pd.DataFrame(
{
"start_dt": pd.to_datetime(
[
"2023-01-01 05:00",
"2023-01-01 11:00",
"2023-01-01 14:00",
"2023-01-02 05:00",
"2023-01-02 14:00",
"2023-01-02 17:00",
"2023-01-02 20:00",
"2023-01-02 23:00",
]
),
"end_dt": pd.to_datetime(
[
"2023-01-01 06:00",
"2023-01-01 12:00",
"2023-01-01 15:00",
"2023-01-02 06:00",
"2023-01-02 15:00",
"2023-01-02 18:00",
"2023-01-02 21:00",
"2023-01-03 00:00",
]
),
},
),
]

# Create 3-hourly init times with a few time stamps missing
freq = timedelta(minutes=180)

datetimes = (
pd.date_range("2023-01-01 03:00", "2023-01-02 21:00", freq=freq)
.delete([1, 4, 5, 6, 7, 9, 10])
)
steps = [timedelta(hours=i) for i in range(24)]

# Choose some history durations and max stalenesses
history_durations_hr = [0, 2, 2, 2]
max_stalenesses_hr = [9, 9, 6, 3]

for i in range(len(expected_results)):
history_duration = timedelta(hours=history_durations_hr[i])
max_staleness = timedelta(hours=max_stalenesses_hr[i])

time_periods = find_contiguous_t0_periods_nwp(
datetimes=datetimes,
history_duration=history_duration,
max_staleness=max_staleness,
max_dropout = timedelta(0),
)

# Check if results are as expected
assert time_periods.equals(expected_results[i])


def test_intersection_of_multiple_dataframes_of_periods():
periods_1 = pd.DataFrame(
{
"start_dt": pd.to_datetime(["2023-01-01 05:00", "2023-01-01 14:10"]),
"end_dt": pd.to_datetime(["2023-01-01 13:35", "2023-01-01 18:00"]),
},
)

periods_2 = pd.DataFrame(
{
"start_dt": pd.to_datetime(["2023-01-01 12:00"]),
"end_dt": pd.to_datetime(["2023-01-02 00:00"]),
},
)

periods_3 = pd.DataFrame(
{
"start_dt": pd.to_datetime(["2023-01-01 00:00", "2023-01-01 13:00"]),
"end_dt": pd.to_datetime(["2023-01-01 12:30", "2023-01-01 23:00"]),
},
)

expected_result = pd.DataFrame(
{
"start_dt": pd.to_datetime(
["2023-01-01 12:00", "2023-01-01 13:00", "2023-01-01 14:10"]
),
"end_dt": pd.to_datetime([
"2023-01-01 12:30", "2023-01-01 13:35", "2023-01-01 18:00"]
),
},
)

overlaping_periods = intersection_of_multiple_dataframes_of_periods(
[periods_1, periods_2, periods_3]
)

# Check if results are as expected
assert overlaping_periods.equals(expected_result)

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