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.utils.exceptions import AstropyUserWarning

from astropy.timeseries.sampled import TimeSeries
from astropy.timeseries.binned import BinnedTimeSeries

__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))
        result = []
        for i in range(len(indices) - 1):
            if indices[i+1] <= indices[i]+1:
        return np.array(result)

[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 keep = ((ts_sorted.time >= bin_start[0]) & (ts_sorted.time <= bin_end[-1])) # Find out indices to be removed because of uncontiguous bins for ind in range(n_bins-1): delete_indices = np.where(np.logical_and(ts_sorted.time > bin_end[ind], ts_sorted.time < bin_start[ind+1])) keep[delete_indices] = False subset = ts_sorted[keep] # Figure out which bin each row falls in by sorting with respect # to the bin end times indices = np.searchsorted(bin_end, ts_sorted.time[keep]) # For time == bin_start[i+1] == bin_end[i], let bin_start takes precedence if len(indices) and np.all(bin_start[1:] >= bin_end[:-1]): indices_start = np.searchsorted(subset.time, bin_start[bin_start <= 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 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 =, dtype=values.dtype) data.mask = 1 data[unique_indices] = reduceat(values, groups, aggregate_func) data.mask[unique_indices] = 0 binned[colname] = data return binned