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# aggregate_downsample

astropy.timeseries.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 `TimeSeries`

      The time series to downsample.

    time_bin_size `Quantity` or `TimeDelta` [:ref: ‘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 `Time` or python:iterable, optional

      The start time for the binned time series - this can be either given directly as a `Time` array or as any iterable that initializes the `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 `Time` or python:iterable, optional

      The times of the end of each bin - this can be either given directly as a `Time` array or as any iterable that initializes the `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 `python: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 `python:callable()`, optional

      The function to use for combining points in the same bin. Defaults to an internal implementation of nanmean.

  Returns:
    binned_time_series `BinnedTimeSeries`

      The downsampled time series.