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.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 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, n_bins=None, aggregate_func=None): """ Downsample a time series by binning values into bins with a fixed size, 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` The time interval for the binned time series. time_bin_start : `~astropy.time.Time`, optional The start time for the binned time series. Defaults to the first time in the sampled time series. n_bins : int, optional The number of bins to use. Defaults to the number needed to fit all the original points. 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 not isinstance(time_bin_size, u.Quantity): raise TypeError("time_bin_size should be a astropy.unit quantity") bin_size_sec = time_bin_size.to_value(u.s) # Use the table sorted by time sorted = time_series.iloc[:] # Determine start time if needed if time_bin_start is None: time_bin_start = sorted.time[0] # Find the relative time since the start time, in seconds relative_time_sec = (sorted.time - time_bin_start).sec # Determine the number of bins if needed if n_bins is None: n_bins = int(np.ceil(relative_time_sec[-1] / bin_size_sec)) if aggregate_func is None: aggregate_func = np.nanmean # Determine the bins relative_bins_sec = np.cumsum(np.hstack([0, np.repeat(bin_size_sec, n_bins)])) bins = time_bin_start + relative_bins_sec * u.s # Find the subset of the table that is inside the bins keep = ((relative_time_sec >= relative_bins_sec[0]) & (relative_time_sec < relative_bins_sec[-1])) subset = sorted[keep] # Figure out which bin each row falls in - the -1 is because items # falling in the first bins will have index 1 but we want that to be 0 indices = np.searchsorted(relative_bins_sec, relative_time_sec[keep]) - 1 # Add back the first time. indices[relative_time_sec[keep] == relative_bins_sec[0]] = 0 # Create new binned time series binned = BinnedTimeSeries(time_bin_start=bins[:-1], time_bin_end=bins[-1]) # Determine rows where values are defined groups = np.hstack([0, np.nonzero(np.diff(indices))[0] + 1]) # 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