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add datetime features #89

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add datetime features #89

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abc3485
add datetime features
peterdudfield Dec 6, 2024
a3d8046
only make in site dataset
peterdudfield Dec 6, 2024
bd75790
Merge commit 'aae2af3818f2f1371097be834554981e2c2f032d' into datetime…
peterdudfield Dec 20, 2024
f327ab9
PR comments
peterdudfield Dec 20, 2024
e8cc5e2
remove from proces and combine, as it wont be used there
peterdudfield Dec 20, 2024
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1 change: 1 addition & 0 deletions ocf_data_sampler/numpy_batch/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
"""Conversion from Xarray to NumpyBatch"""

from .datetime_features import make_datetime_numpy_batch
from .gsp import convert_gsp_to_numpy_batch, GSPBatchKey
from .nwp import convert_nwp_to_numpy_batch, NWPBatchKey
from .satellite import convert_satellite_to_numpy_batch, SatelliteBatchKey
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42 changes: 42 additions & 0 deletions ocf_data_sampler/numpy_batch/datetime_features.py
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"""Datapipes to trigonometric date and time to NumpyBatch"""

import numpy as np
import pandas as pd
from numpy.typing import NDArray


def _get_date_time_in_pi(
dt: pd.DatetimeIndex,
) -> tuple[NDArray[np.float64], NDArray[np.float64]]:
"""
Change the datetimes, into time and date scaled in radians
"""

day_of_year = dt.dayofyear
minute_of_day = dt.minute + dt.hour * 60

# converting into positions on sin-cos circle
time_in_pi = (2 * np.pi) * (minute_of_day / (24 * 60))
date_in_pi = (2 * np.pi) * (day_of_year / 365)

return date_in_pi, time_in_pi


def make_datetime_numpy_batch(datetimes: pd.DatetimeIndex, key_prefix: str = "wind") -> dict:
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Potentially update main function to include input validation - also to simplify the dict construction via literal. Feel this improves maintenance ease a little overall. As example:

def make_datetime_numpy_batch(datetimes: pd.DatetimeIndex, key_prefix: str = "wind") -> dict:
    """
    Make a dict of datetime features using sin/cos transformations
    """
    if datetimes.empty:
        raise ValueError("Input datetimes is empty")

    # Obtain scaled radians for date and time
    date_in_pi, time_in_pi = _get_date_time_in_pi(datetimes)

    # Create dict
    return {
        f"{key_prefix}_date_sin": np.sin(date_in_pi),
        f"{key_prefix}_date_cos": np.cos(date_in_pi),
        f"{key_prefix}_time_sin": np.sin(time_in_pi),
        f"{key_prefix}_time_cos": np.cos(time_in_pi),
    }

""" Make dictionary of datetime features"""
time_numpy_batch = {}

date_in_pi, time_in_pi = _get_date_time_in_pi(datetimes)

# Store
date_sin_batch_key = key_prefix + "_date_sin"
date_cos_batch_key = key_prefix + "_date_cos"
time_sin_batch_key = key_prefix + "_time_sin"
time_cos_batch_key = key_prefix + "_time_cos"

time_numpy_batch[date_sin_batch_key] = np.sin(date_in_pi)
time_numpy_batch[date_cos_batch_key] = np.cos(date_in_pi)
time_numpy_batch[time_sin_batch_key] = np.sin(time_in_pi)
time_numpy_batch[time_cos_batch_key] = np.cos(time_in_pi)

return time_numpy_batch
1 change: 1 addition & 0 deletions ocf_data_sampler/torch_datasets/process_and_combine.py
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Expand Up @@ -9,6 +9,7 @@
convert_nwp_to_numpy_batch,
convert_satellite_to_numpy_batch,
convert_gsp_to_numpy_batch,
make_datetime_numpy_batch,
make_sun_position_numpy_batch,
)
from ocf_data_sampler.numpy_batch.gsp import GSPBatchKey
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26 changes: 26 additions & 0 deletions tests/numpy_batch/test_datetime_features.py
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import numpy as np
import pandas as pd

from ocf_data_sampler.numpy_batch.datetime_features import make_datetime_numpy_batch

from ocf_data_sampler.numpy_batch import GSPBatchKey


def test_calculate_azimuth_and_elevation():

# Pick the day of the summer solstice
datetimes = pd.to_datetime(["2024-06-20 12:00", "2024-06-20 12:30", "2024-06-20 13:00"])

# Calculate sun angles
datetime_features = make_datetime_numpy_batch(datetimes)

assert len(datetime_features) == 4

assert len(datetime_features["wind_date_sin"]) == len(datetimes)
assert (datetime_features["wind_date_cos"] != datetime_features["wind_date_sin"]).all()

# assert all values are between -1 and 1
assert all(np.abs(datetime_features["wind_date_sin"]) <= 1)
assert all(np.abs(datetime_features["wind_date_cos"]) <= 1)
assert all(np.abs(datetime_features["wind_time_sin"]) <= 1)
assert all(np.abs(datetime_features["wind_time_cos"]) <= 1)
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Testing wise - perhaps update to additionaly verify if a custom prefix is sufficiently applied to all dict keys that the function generates and a further basic check if an empty input corresponds to error raising as implemented in main. As example:

def test_make_datetime_numpy_batch_custom_key_prefix():
    # Test function correctly applies custom prefix to dict keys
    datetimes = pd.to_datetime(["2024-06-20 12:00", "2024-06-20 12:30", "2024-06-20 13:00"])
    key_prefix = "solar"

    datetime_features = make_datetime_numpy_batch(datetimes, key_prefix=key_prefix)

    # Assert dict contains expected quantity of keys and verify starting with custom prefix
    assert len(datetime_features) == 4
    assert all(key.startswith(key_prefix) for key in datetime_features.keys())


def test_make_datetime_numpy_batch_empty_input():
    # Verification that function raises error for empty input
    datetimes = pd.DatetimeIndex([])
    
    with pytest.raises(ValueError, match="Input datetimes is empty"):
        make_datetime_numpy_batch(datetimes)

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