# Source code for astropy.timeseries.downsample

```
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import warnings
import numpy as np
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.timeseries.binned import BinnedTimeSeries
from astropy.timeseries.sampled import TimeSeries
from astropy.utils.exceptions import AstropyUserWarning
__all__ = ["aggregate_downsample"]
def reduceat(array, indices, function):
"""
Manual reduceat functionality for cases where Numpy functions don't have a reduceat.
It will check if the input function has a reduceat and call that if it does.
"""
if len(indices) == 0:
return np.array([])
elif hasattr(function, "reduceat"):
return np.array(function.reduceat(array, indices))
else:
result = []
for i in range(len(indices) - 1):
if indices[i + 1] <= indices[i] + 1:
result.append(function(array[indices[i]]))
else:
result.append(function(array[indices[i] : indices[i + 1]]))
result.append(function(array[indices[-1] :]))
return np.array(result)
def _to_relative_longdouble(time: Time, rel_base: Time) -> np.longdouble:
# Convert the time objects into plain ndarray
# so that they be used to make various operations faster, including
# - `np.searchsorted()`
# - time comparison.
#
# Relative time in seconds with np.longdouble type is used to:
# - a consistent format for search, irrespective of the format/scale of the inputs,
# - retain the best precision possible
return (time - rel_base).to_value(format="sec", subfmt="long")
[docs]def aggregate_downsample(
time_series,
*,
time_bin_size=None,
time_bin_start=None,
time_bin_end=None,
n_bins=None,
aggregate_func=None
):
"""
Downsample a time series by binning values into bins with a fixed size or
custom sizes, using a single function to combine the values in the bin.
Parameters
----------
time_series : :class:`~astropy.timeseries.TimeSeries`
The time series to downsample.
time_bin_size : `~astropy.units.Quantity` or `~astropy.time.TimeDelta` ['time'], optional
The time interval for the binned time series - this is either a scalar
value (in which case all time bins will be assumed to have the same
duration) or as an array of values (in which case each time bin can
have a different duration). If this argument is provided,
``time_bin_end`` should not be provided.
time_bin_start : `~astropy.time.Time` or iterable, optional
The start time for the binned time series - this can be either given
directly as a `~astropy.time.Time` array or as any iterable that
initializes the `~astropy.time.Time` class. This can also be a scalar
value if ``time_bin_size`` or ``time_bin_end`` is provided.
Defaults to the first time in the sampled time series.
time_bin_end : `~astropy.time.Time` or iterable, optional
The times of the end of each bin - this can be either given directly as
a `~astropy.time.Time` array or as any iterable that initializes the
`~astropy.time.Time` class. This can only be given if ``time_bin_start``
is provided or its default is used. If ``time_bin_end`` is scalar and
``time_bin_start`` is an array, time bins are assumed to be contiguous;
the end of each bin is the start of the next one, and ``time_bin_end`` gives
the end time for the last bin. If ``time_bin_end`` is an array and
``time_bin_start`` is scalar, bins will be contiguous. If both ``time_bin_end``
and ``time_bin_start`` are arrays, bins do not need to be contiguous.
If this argument is provided, ``time_bin_size`` should not be provided.
n_bins : int, optional
The number of bins to use. Defaults to the number needed to fit all
the original points. If both ``time_bin_start`` and ``time_bin_size``
are provided and are scalar values, this determines the total bins
within that interval. If ``time_bin_start`` is an iterable, this
parameter will be ignored.
aggregate_func : callable, optional
The function to use for combining points in the same bin. Defaults
to np.nanmean.
Returns
-------
binned_time_series : :class:`~astropy.timeseries.BinnedTimeSeries`
The downsampled time series.
"""
if not isinstance(time_series, TimeSeries):
raise TypeError("time_series should be a TimeSeries")
if time_bin_size is not None and not isinstance(
time_bin_size, (u.Quantity, TimeDelta)
):
raise TypeError("'time_bin_size' should be a Quantity or a TimeDelta")
if time_bin_start is not None and not isinstance(time_bin_start, (Time, TimeDelta)):
time_bin_start = Time(time_bin_start)
if time_bin_end is not None and not isinstance(time_bin_end, (Time, TimeDelta)):
time_bin_end = Time(time_bin_end)
# Use the table sorted by time
ts_sorted = time_series.iloc[:]
# If start time is not provided, it is assumed to be the start of the timeseries
if time_bin_start is None:
time_bin_start = ts_sorted.time[0]
# Total duration of the timeseries is needed for determining either
# `time_bin_size` or `nbins` in the case of scalar `time_bin_start`
if time_bin_start.isscalar:
time_duration = (ts_sorted.time[-1] - time_bin_start).sec
if time_bin_size is None and time_bin_end is None:
if time_bin_start.isscalar:
if n_bins is None:
raise TypeError(
"With single 'time_bin_start' either 'n_bins', "
"'time_bin_size' or time_bin_end' must be provided"
)
else:
# `nbins` defaults to the number needed to fit all points
time_bin_size = time_duration / n_bins * u.s
else:
time_bin_end = np.maximum(ts_sorted.time[-1], time_bin_start[-1])
if time_bin_start.isscalar:
if time_bin_size is not None:
if time_bin_size.isscalar:
# Determine the number of bins
if n_bins is None:
bin_size_sec = time_bin_size.to_value(u.s)
n_bins = int(np.ceil(time_duration / bin_size_sec))
elif time_bin_end is not None:
if not time_bin_end.isscalar:
# Convert start time to an array and populate using `time_bin_end`
scalar_start_time = time_bin_start
time_bin_start = time_bin_end.replicate(copy=True)
time_bin_start[0] = scalar_start_time
time_bin_start[1:] = time_bin_end[:-1]
# Check for overlapping bins, and warn if they are present
if time_bin_end is not None:
if (
not time_bin_end.isscalar
and not time_bin_start.isscalar
and np.any(time_bin_start[1:] < time_bin_end[:-1])
):
warnings.warn(
"Overlapping bins should be avoided since they "
"can lead to double-counting of data during binning.",
AstropyUserWarning,
)
binned = BinnedTimeSeries(
time_bin_size=time_bin_size,
time_bin_start=time_bin_start,
time_bin_end=time_bin_end,
n_bins=n_bins,
)
if aggregate_func is None:
aggregate_func = np.nanmean
# Start and end times of the binned timeseries
bin_start = binned.time_bin_start
bin_end = binned.time_bin_end
# Set `n_bins` to match the length of `time_bin_start` if
# `n_bins` is unspecified or if `time_bin_start` is an iterable
if n_bins is None or not time_bin_start.isscalar:
n_bins = len(bin_start)
# Find the subset of the table that is inside the union of all bins
# - output: `keep` a mask to create the subset
# - use relative time in seconds `np.longdouble`` in in creating `keep` to speed up
# (`Time` object comparison is rather slow)
# - tiny sacrifice on precision (< 0.01ns on 64 bit platform)
rel_base = ts_sorted.time[0]
rel_bin_start = _to_relative_longdouble(bin_start, rel_base)
rel_bin_end = _to_relative_longdouble(bin_end, rel_base)
rel_ts_sorted_time = _to_relative_longdouble(ts_sorted.time, rel_base)
keep = (rel_ts_sorted_time >= rel_bin_start[0]) & (
rel_ts_sorted_time <= rel_bin_end[-1]
)
# Find out indices to be removed because of noncontiguous bins
#
# Only need to check when adjacent bins have gaps, i.e.,
# bin_start[ind + 1] > bin_end[ind]
# - see: https://github.com/astropy/astropy/issues/13058#issuecomment-1090846697
# on thoughts on how to reduce the number of times to loop
noncontiguous_bins_indices = np.where(rel_bin_start[1:] > rel_bin_end[:-1])[0]
for ind in noncontiguous_bins_indices:
delete_indices = np.where(
np.logical_and(
rel_ts_sorted_time > rel_bin_end[ind],
rel_ts_sorted_time < rel_bin_start[ind + 1],
)
)
keep[delete_indices] = False
rel_subset_time = rel_ts_sorted_time[keep]
# Figure out which bin each row falls in by sorting with respect
# to the bin end times
indices = np.searchsorted(rel_bin_end, rel_subset_time)
# For time == bin_start[i+1] == bin_end[i], let bin_start takes precedence
if len(indices) and np.all(rel_bin_start[1:] >= rel_bin_end[:-1]):
indices_start = np.searchsorted(
rel_subset_time, rel_bin_start[rel_bin_start <= rel_ts_sorted_time[-1]]
)
indices[indices_start] = np.arange(len(indices_start))
# Determine rows where values are defined
if len(indices):
groups = np.hstack([0, np.nonzero(np.diff(indices))[0] + 1])
else:
groups = np.array([])
# Find unique indices to determine which rows in the final time series
# will not be empty.
unique_indices = np.unique(indices)
# Add back columns
subset = ts_sorted[keep]
for colname in subset.colnames:
if colname == "time":
continue
values = subset[colname]
# FIXME: figure out how to avoid the following, if possible
if not isinstance(values, (np.ndarray, u.Quantity)):
warnings.warn(
"Skipping column {0} since it has a mix-in type", AstropyUserWarning
)
continue
if isinstance(values, u.Quantity):
data = u.Quantity(np.repeat(np.nan, n_bins), unit=values.unit)
data[unique_indices] = u.Quantity(
reduceat(values.value, groups, aggregate_func), values.unit, copy=False
)
else:
data = np.ma.zeros(n_bins, dtype=values.dtype)
data.mask = 1
data[unique_indices] = reduceat(values, groups, aggregate_func)
data.mask[unique_indices] = 0
binned[colname] = data
return binned
```