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A General Timeseries Class for Astropy -------------------------------------

author: Stuart Mumford

date-created: 2016 March 24

date-last-revised: 2023 Oct 20

type: Standard Track

status: Accepted

Abstract

The goal of a timeseries object in astropy is to provide a core set of functionality for representing data where time is the physical event list. This includes both continuous values binned in time or event list type data.

The motivation for creation of this class is to support representation and analysis of astrophysical timeseries data, including support for the Units, Time and Coordinates packages in Astropy. This object should also be compatible with other packages in the wider scientific Python ecosystem, and try to minimise overlap in functionality with these packages.

This document proposes an implementation based on Table, with extra constraints and specific functionality dedicated to timeseries type data.

Detailed description

Many different areas of astrophysics have to deal with 1D timeseries data, i.e. either sampling a continuous variable at fixed times or counting some events binned into time windows. These types of applications require some basic functionality:

  1. Extending timeseries with extra rows
  2. Concatenating multiple timeseries objects (e.g. adding new columns)
  3. Sorting
  4. Slicing / selecting time ranges (indexing)
  5. Re-binning and re-sampling timeseries
  6. Interpolating to different time stamps.
  7. Masking and support for missing values (NaN)
  8. Support for subtraction and addition (e.g. background).
  9. Plotting and visualisation.
  10. Have high performance into ~millions of rows.

While this functionality is found in non-domain specific packages such as pandas, a timeseries object in Astropy would also provide some functionality which is more related to Astronomy:

  1. Converting between time systems
  2. Astropy unit support
  3. Support variable width time bins.

It is proposed that this functionality be implemented via a subclass of the QTable class, with a few requirements specific to the timeseries class:

  1. A 'Time' index column exists which enforces unique indexes.
  2. The table is always sorted in terms of increasing time.

Binned Data vs Sampled Data

There are two different types of data that a timeseries class or classes would have to support, sampled data and binned data. Sampled data is taken at a precise time stamp whereas binned data is a number of counts contained within a some time window. These two different types of timeseries data require different handling of methods such as re-binning or re-sampling, as well as for binned data the width of the bins must be stored.

To distinguish between binned and sampled timseries we propose having two ways of constructing a timeseries, BinnedTimeSeries and SampledTimeSeries. This distinction in the name of the classes will allow a easier to understand API. Some examples of constructing these objects is given below.

Initialize a SampledTimeSeries with a time and a data column:

ts = SampledTimeSeries(time=['2016-03-22T12:30:31', '2016-03-22T12:30:32', '2016-03-22T12:30:40'],
                       data=[1, 4, 3])

Initialize a SampledTimeSeries with a time and a fixed delta:

ts = SampledTimeSeries(time='2016-03-22T12:30:31', time_delta=3 * u.s, n_samples=10)

Initialize a SampledTimeSeries with a time and an array of deltas (also no data):

ts = SampledTimeSeries(time="2011-01-01T00:00:00", time_delta=np.random.random((10,))*u.s)

Initialize a BinnedTimeSeries with even contiguous bins by specifying the bin width:

ts = BinnedTimeSeries(start_time='2016-03-22T12:30:31', bin_size=3 * u.s, data=[1, 4, 3])

Initialize a BinnedTimeSeries with uneven non-contiguous bins with lists of start times, bin sizes and data:

ts = BinnedTimeSeries(start_time=['2016-03-22T12:30:31', '2016-03-22T12:30:38', '2016-03-22T12:34:40'], 
                      bin_sizes=[5, 100, 2]*u.s,
                      data=[1, 4, 3])

Initialize a BinnedTimeSeries with uneven contiguous bins by giving an end time:

ts = BinnedTimeSeries(start_time=['2016-03-22T12:30:31', '2016-03-22T12:30:32', '2016-03-22T12:30:40'], 
                      end_time='2016-03-22T12:30:55',
                      data=[1, 4, 3])

Initialize a BinnedTimeSeries with uneven non-contiguous bins by specifying the start and end times for the bins:

ts = BinnedTimeSeries(start_time=['2016-03-22T12:30:31', '2016-03-22T12:30:33', '2016-03-22T12:30:40'], 
                      end_time=['2016-03-22T12:30:32', '2016-03-22T12:30:35', '2016-03-22T12:30:41'],
                      data=[1, 4, 3])

Once a binned timeseries was constructed it would present an API that had a 'time' and a 'timedelta' column, which represents the start time of the bin and the width of the bin. The timedelta column would not have to be stored in memory for all of the examples above and could be optimised to allow computation on-the-fly based on if the bins are contiguous or not.

Branches and pull requests

N/A

Implementation

Backward compatibility

This would be new functionality.

Alternatives

Forego the functionality provided by Time and Units and recommend everyone use pandas.

Decision rationale

This APE has long been implemented in astropy/astropy#8540 and somehow the Coco just never merged it.