act.transform.interpolate#

act.transform.interpolate(data, target, dim, qc=None, qc_mask=None, t_range=None)[source]#

Bilinearly interpolate data onto target coordinate values along dim.

Parameters:

  • data (xarray.DataArray) – Input DataArray.

  • target (xarray.DataArray or numpy.ndarray) – Target coordinate values to interpolate onto.

  • dim (str) – Name of the dimension along which to apply the transform.

  • qc (xarray.DataArray, optional) – Optional integer QC DataArray with same shape as data.

  • qc_mask (int, str, list[str], or None) – Integer bitmask, QC assessment name, or None. An assessment name is matched against qc.attrs['flag_assessments']; None selects "Bad", which requires flag_masks metadata on qc (a ValueError is raised otherwise); without qc no QC bits are excluded.

  • t_range (float or numpy.timedelta64, optional) – Maximum distance from a target point to an input point for interpolation. Must be a timedelta when dim is a datetime coordinate and a number otherwise. Defaults to no limit.

Returns:

  • result (xarray.DataArray) – Interpolated DataArray on the target coordinate.

  • result_qc (xarray.DataArray) – Integer QC DataArray with same shape as result, with CF flag_masks/flag_meanings/flag_assessments attributes set.

See also

act.transform.bin_average

Weighted average over output bins.

act.transform.subsample

Nearest-neighbor selection onto the target coordinate.

act.transform.transform_dataset

Apply a transform to every variable in a Dataset.

Examples

Interpolate onto a finer time base. Interpolation reports an instant rather than summarizing an interval, so prefer it over bin_average when moving to a finer grid or onto offset timestamps.

import act

ds = act.io.arm.read_arm_netcdf(filename, cleanup_qc=True)
target = act.transform.make_coord('2023-03-01', '2023-03-02', '30s')

result, result_qc = act.transform.interpolate(ds['temp_mean'], target, dim='time')

Honor input QC and limit how far an output point may reach for an input sample. When the coordinate is a datetime, t_range must be a timedelta; by default there is no limit. Output points with no usable sample within range are flagged QC_OUTSIDE_RANGE.

import numpy as np
from act.transform.constants import QC_INTERPOLATE, QC_OUTSIDE_RANGE

result, result_qc = act.transform.interpolate(
    ds['temp_mean'],
    target,
    dim='time',
    qc=ds['qc_temp_mean'],
    qc_mask=4,
    t_range=np.timedelta64(5, 'm'),
)

# Points where a bad or missing neighbor had to be skipped over.
stretched = (result_qc.values & QC_INTERPOLATE).astype(bool)