# Licensed under a 3-clause BSD style license - see LICENSE.rst
from copy import deepcopy
import numpy as np
from astropy import units as u
from astropy.table import QTable, Table, groups
from astropy.time import Time, TimeDelta
from astropy.timeseries.core import BaseTimeSeries, autocheck_required_columns
from astropy.units import Quantity
__all__ = ["BinnedTimeSeries"]
[docs]@autocheck_required_columns
class BinnedTimeSeries(BaseTimeSeries):
"""
A class to represent binned time series data in tabular form.
`~astropy.timeseries.BinnedTimeSeries` provides a class for
representing time series as a collection of values of different
quantities measured in time bins (for time series with values
sampled at specific times, see the `~astropy.timeseries.TimeSeries`
class). `~astropy.timeseries.BinnedTimeSeries` is a sub-class of
`~astropy.table.QTable` and thus provides all the standard table
maniplation methods available to tables, but it also provides
additional conveniences for dealing with time series, such as a
flexible initializer for setting up the times, and attributes to
access the start/center/end time of bins.
See also: https://docs.astropy.org/en/stable/timeseries/
Parameters
----------
data : numpy ndarray, dict, list, table-like object, optional
Data to initialize time series. This does not need to contain the
times, which can be provided separately, but if it does contain the
times they should be in columns called ``'time_bin_start'`` and
``'time_bin_size'`` to be automatically recognized.
time_bin_start : `~astropy.time.Time` or iterable
The times of the start 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. If this is given, then
the remaining time-related arguments should not be used. This can also
be a scalar value if ``time_bin_size`` is provided.
time_bin_end : `~astropy.time.Time` or iterable
The times of the end of each bin - this can be either given directly
as a `~astropy.time.Time` array or as any value or iterable that
initializes the `~astropy.time.Time` class. If this is given, then the
remaining time-related arguments should not be used. This can only be
given if ``time_bin_start`` is an array of values. If ``time_bin_end``
is a scalar, time bins are assumed to be contiguous, such that 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, the
time bins do not need to be contiguous. If this argument is provided,
``time_bin_size`` should not be provided.
time_bin_size : `~astropy.time.TimeDelta` or `~astropy.units.Quantity`
The size of the time bins, either as 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.
n_bins : int
The number of time bins for the series. This is only used if both
``time_bin_start`` and ``time_bin_size`` are provided and are scalar
values.
**kwargs : dict, optional
Additional keyword arguments are passed to `~astropy.table.QTable`.
"""
_required_columns = ["time_bin_start", "time_bin_size"]
def __init__(
self,
data=None,
*,
time_bin_start=None,
time_bin_end=None,
time_bin_size=None,
n_bins=None,
**kwargs,
):
super().__init__(data=data, **kwargs)
# For some operations, an empty time series needs to be created, then
# columns added one by one. We should check that when columns are added
# manually, time is added first and is of the right type.
if (
data is None
and time_bin_start is None
and time_bin_end is None
and time_bin_size is None
and n_bins is None
):
self._required_columns_relax = True
return
# First if time_bin_start and time_bin_end have been given in the table data, we
# should extract them and treat them as if they had been passed as
# keyword arguments.
if "time_bin_start" in self.colnames:
if time_bin_start is None:
time_bin_start = self.columns["time_bin_start"]
else:
raise TypeError(
"'time_bin_start' has been given both in the table "
"and as a keyword argument"
)
if "time_bin_size" in self.colnames:
if time_bin_size is None:
time_bin_size = self.columns["time_bin_size"]
else:
raise TypeError(
"'time_bin_size' has been given both in the table "
"and as a keyword argument"
)
if time_bin_start is None:
raise TypeError("'time_bin_start' has not been specified")
if time_bin_end is None and time_bin_size is None:
raise TypeError(
"Either 'time_bin_size' or 'time_bin_end' should be specified"
)
if 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)
if time_bin_size is not None and not isinstance(
time_bin_size, (Quantity, TimeDelta)
):
raise TypeError("'time_bin_size' should be a Quantity or a TimeDelta")
if isinstance(time_bin_size, TimeDelta):
time_bin_size = time_bin_size.sec * u.s
if n_bins is not None and time_bin_size is not None:
if not (time_bin_start.isscalar and time_bin_size.isscalar):
raise TypeError(
"'n_bins' cannot be specified if 'time_bin_start' or "
"'time_bin_size' are not scalar'"
)
if time_bin_start.isscalar:
# We interpret this as meaning that this is the start of the
# first bin and that the bins are contiguous. In this case,
# we require time_bin_size to be specified.
if time_bin_size is None:
raise TypeError(
"'time_bin_start' is scalar, so 'time_bin_size' is required"
)
if time_bin_size.isscalar:
if data is not None:
if n_bins is not None:
if n_bins != len(self):
raise TypeError(
"'n_bins' has been given and it is not the "
"same length as the input data."
)
else:
n_bins = len(self)
time_bin_size = np.repeat(time_bin_size, n_bins)
time_delta = np.cumsum(time_bin_size)
time_bin_end = time_bin_start + time_delta
# Now shift the array so that the first entry is 0
time_delta = np.roll(time_delta, 1)
time_delta[0] = 0.0 * u.s
# Make time_bin_start into an array
time_bin_start = time_bin_start + time_delta
else:
if len(self.colnames) > 0 and len(time_bin_start) != len(self):
raise ValueError(
f"Length of 'time_bin_start' ({len(time_bin_start)}) should match "
f"table length ({len(self)})"
)
if time_bin_end is not None:
if time_bin_end.isscalar:
times = time_bin_start.copy()
times[:-1] = times[1:]
times[-1] = time_bin_end
time_bin_end = times
time_bin_size = (time_bin_end - time_bin_start).sec * u.s
if time_bin_size.isscalar:
time_bin_size = np.repeat(time_bin_size, len(self))
with self._delay_required_column_checks():
if "time_bin_start" in self.colnames:
self.remove_column("time_bin_start")
if "time_bin_size" in self.colnames:
self.remove_column("time_bin_size")
self.add_column(time_bin_start, index=0, name="time_bin_start")
self.add_index("time_bin_start")
self.add_column(time_bin_size, index=1, name="time_bin_size")
@property
def time_bin_start(self):
"""
The start times of all the time bins.
"""
return self["time_bin_start"]
@property
def time_bin_center(self):
"""
The center times of all the time bins.
"""
return self["time_bin_start"] + self["time_bin_size"] * 0.5
@property
def time_bin_end(self):
"""
The end times of all the time bins.
"""
return self["time_bin_start"] + self["time_bin_size"]
@property
def time_bin_size(self):
"""
The sizes of all the time bins.
"""
return self["time_bin_size"]
def __getitem__(self, item):
if self._is_list_or_tuple_of_str(item):
if "time_bin_start" not in item or "time_bin_size" not in item:
out = QTable(
[self[x] for x in item],
meta=deepcopy(self.meta),
copy_indices=self._copy_indices,
)
out._groups = groups.TableGroups(
out, indices=self.groups._indices, keys=self.groups._keys
)
return out
return super().__getitem__(item)
[docs] @classmethod
def read(
self,
filename,
time_bin_start_column=None,
time_bin_end_column=None,
time_bin_size_column=None,
time_bin_size_unit=None,
time_format=None,
time_scale=None,
format=None,
*args,
**kwargs,
):
"""
Read and parse a file and returns a `astropy.timeseries.BinnedTimeSeries`.
This method uses the unified I/O infrastructure in Astropy which makes
it easy to define readers/writers for various classes
(https://docs.astropy.org/en/stable/io/unified.html). By default, this
method will try and use readers defined specifically for the
`astropy.timeseries.BinnedTimeSeries` class - however, it is also
possible to use the ``format`` keyword to specify formats defined for
the `astropy.table.Table` class - in this case, you will need to also
provide the column names for column containing the start times for the
bins, as well as other column names (see the Parameters section below
for details)::
>>> from astropy.timeseries.binned import BinnedTimeSeries
>>> ts = BinnedTimeSeries.read('binned.dat', format='ascii.ecsv',
... time_bin_start_column='date_start',
... time_bin_end_column='date_end') # doctest: +SKIP
Parameters
----------
filename : str
File to parse.
format : str
File format specifier.
time_bin_start_column : str
The name of the column with the start time for each bin.
time_bin_end_column : str, optional
The name of the column with the end time for each bin. Either this
option or ``time_bin_size_column`` should be specified.
time_bin_size_column : str, optional
The name of the column with the size for each bin. Either this
option or ``time_bin_end_column`` should be specified.
time_bin_size_unit : `astropy.units.Unit`, optional
If ``time_bin_size_column`` is specified but does not have a unit
set in the table, you can specify the unit manually.
time_format : str, optional
The time format for the start and end columns.
time_scale : str, optional
The time scale for the start and end columns.
*args : tuple, optional
Positional arguments passed through to the data reader.
**kwargs : dict, optional
Keyword arguments passed through to the data reader.
Returns
-------
out : `astropy.timeseries.binned.BinnedTimeSeries`
BinnedTimeSeries corresponding to the file.
"""
try:
# First we try the readers defined for the BinnedTimeSeries class
return super().read(filename, format=format, *args, **kwargs)
except TypeError:
# Otherwise we fall back to the default Table readers
if time_bin_start_column is None:
raise ValueError(
"``time_bin_start_column`` should be provided since the default"
" Table readers are being used."
)
if time_bin_end_column is None and time_bin_size_column is None:
raise ValueError(
"Either `time_bin_end_column` or `time_bin_size_column` should be"
" provided."
)
elif time_bin_end_column is not None and time_bin_size_column is not None:
raise ValueError(
"Cannot specify both `time_bin_end_column` and"
" `time_bin_size_column`."
)
table = Table.read(filename, format=format, *args, **kwargs)
if time_bin_start_column in table.colnames:
time_bin_start = Time(
table.columns[time_bin_start_column],
scale=time_scale,
format=time_format,
)
table.remove_column(time_bin_start_column)
else:
raise ValueError(
f"Bin start time column '{time_bin_start_column}' not found in the"
" input data."
)
if time_bin_end_column is not None:
if time_bin_end_column in table.colnames:
time_bin_end = Time(
table.columns[time_bin_end_column],
scale=time_scale,
format=time_format,
)
table.remove_column(time_bin_end_column)
else:
raise ValueError(
f"Bin end time column '{time_bin_end_column}' not found in the"
" input data."
)
time_bin_size = None
elif time_bin_size_column is not None:
if time_bin_size_column in table.colnames:
time_bin_size = table.columns[time_bin_size_column]
table.remove_column(time_bin_size_column)
else:
raise ValueError(
f"Bin size column '{time_bin_size_column}' not found in the"
" input data."
)
if time_bin_size.unit is None:
if time_bin_size_unit is None or not isinstance(
time_bin_size_unit, u.UnitBase
):
raise ValueError(
"The bin size unit should be specified as an astropy Unit"
" using ``time_bin_size_unit``."
)
time_bin_size = time_bin_size * time_bin_size_unit
else:
time_bin_size = u.Quantity(time_bin_size)
time_bin_end = None
if time_bin_start.isscalar and time_bin_size.isscalar:
return BinnedTimeSeries(
data=table,
time_bin_start=time_bin_start,
time_bin_end=time_bin_end,
time_bin_size=time_bin_size,
n_bins=len(table),
)
else:
return BinnedTimeSeries(
data=table,
time_bin_start=time_bin_start,
time_bin_end=time_bin_end,
time_bin_size=time_bin_size,
)