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# -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
The astropy.time package provides functionality for manipulating times and
dates. Specific emphasis is placed on supporting time scales (e.g. UTC, TAI,
UT1) and time representations (e.g. JD, MJD, ISO 8601) that are used in
astronomy.
"""
import copy
import operator
from datetime import datetime, date, timedelta
from time import strftime, strptime
import numpy as np
from .. import units as u, constants as const
from .. import _erfa as erfa
from ..units import UnitConversionError
from ..utils import ShapedLikeNDArray
from ..utils.compat.misc import override__dir__
from ..utils.data_info import MixinInfo, data_info_factory
from .utils import day_frac
from .formats import (TIME_FORMATS, TIME_DELTA_FORMATS,
TimeJD, TimeUnique, TimeAstropyTime, TimeDatetime)
# Import TimeFromEpoch to avoid breaking code that followed the old example of
# making a custom timescale in the documentation.
from .formats import TimeFromEpoch # pylint: disable=W0611
from ..extern import _strptime
__all__ = ['Time', 'TimeDelta', 'TIME_SCALES', 'STANDARD_TIME_SCALES', 'TIME_DELTA_SCALES',
'ScaleValueError', 'OperandTypeError', 'TimeInfo']
STANDARD_TIME_SCALES = ('tai', 'tcb', 'tcg', 'tdb', 'tt', 'ut1', 'utc')
LOCAL_SCALES = ('local',)
TIME_TYPES = dict((scale, scales) for scales in (STANDARD_TIME_SCALES, LOCAL_SCALES) for scale in scales)
TIME_SCALES = STANDARD_TIME_SCALES + LOCAL_SCALES
MULTI_HOPS = {('tai', 'tcb'): ('tt', 'tdb'),
('tai', 'tcg'): ('tt',),
('tai', 'ut1'): ('utc',),
('tai', 'tdb'): ('tt',),
('tcb', 'tcg'): ('tdb', 'tt'),
('tcb', 'tt'): ('tdb',),
('tcb', 'ut1'): ('tdb', 'tt', 'tai', 'utc'),
('tcb', 'utc'): ('tdb', 'tt', 'tai'),
('tcg', 'tdb'): ('tt',),
('tcg', 'ut1'): ('tt', 'tai', 'utc'),
('tcg', 'utc'): ('tt', 'tai'),
('tdb', 'ut1'): ('tt', 'tai', 'utc'),
('tdb', 'utc'): ('tt', 'tai'),
('tt', 'ut1'): ('tai', 'utc'),
('tt', 'utc'): ('tai',),
}
GEOCENTRIC_SCALES = ('tai', 'tt', 'tcg')
BARYCENTRIC_SCALES = ('tcb', 'tdb')
ROTATIONAL_SCALES = ('ut1',)
TIME_DELTA_TYPES = dict((scale, scales)
for scales in (GEOCENTRIC_SCALES, BARYCENTRIC_SCALES,
ROTATIONAL_SCALES, LOCAL_SCALES) for scale in scales)
TIME_DELTA_SCALES = GEOCENTRIC_SCALES + BARYCENTRIC_SCALES + ROTATIONAL_SCALES + LOCAL_SCALES
# For time scale changes, we need L_G and L_B, which are stored in erfam.h as
# /* L_G = 1 - d(TT)/d(TCG) */
# define ERFA_ELG (6.969290134e-10)
# /* L_B = 1 - d(TDB)/d(TCB), and TDB (s) at TAI 1977/1/1.0 */
# define ERFA_ELB (1.550519768e-8)
# These are exposed in erfa as erfa.ELG and erfa.ELB.
# Implied: d(TT)/d(TCG) = 1-L_G
# and d(TCG)/d(TT) = 1/(1-L_G) = 1 + (1-(1-L_G))/(1-L_G) = 1 + L_G/(1-L_G)
# scale offsets as second = first + first * scale_offset[(first,second)]
SCALE_OFFSETS = {('tt', 'tai'): None,
('tai', 'tt'): None,
('tcg', 'tt'): -erfa.ELG,
('tt', 'tcg'): erfa.ELG / (1. - erfa.ELG),
('tcg', 'tai'): -erfa.ELG,
('tai', 'tcg'): erfa.ELG / (1. - erfa.ELG),
('tcb', 'tdb'): -erfa.ELB,
('tdb', 'tcb'): erfa.ELB / (1. - erfa.ELB)}
# triple-level dictionary, yay!
SIDEREAL_TIME_MODELS = {
'mean': {
'IAU2006': {'function': erfa.gmst06, 'scales': ('ut1', 'tt')},
'IAU2000': {'function': erfa.gmst00, 'scales': ('ut1', 'tt')},
'IAU1982': {'function': erfa.gmst82, 'scales': ('ut1',)}},
'apparent': {
'IAU2006A': {'function': erfa.gst06a, 'scales': ('ut1', 'tt')},
'IAU2000A': {'function': erfa.gst00a, 'scales': ('ut1', 'tt')},
'IAU2000B': {'function': erfa.gst00b, 'scales': ('ut1',)},
'IAU1994': {'function': erfa.gst94, 'scales': ('ut1',)}}}
class TimeInfo(MixinInfo):
"""
Container for meta information like name, description, format. This is
required when the object is used as a mixin column within a table, but can
be used as a general way to store meta information.
"""
attrs_from_parent = set(['unit']) # unit is read-only and None
attr_names = MixinInfo.attr_names | {'serialize_method'}
_supports_indexing = True
# The usual tuple of attributes needed for serialization is replaced
# by a property, since Time can be serialized different ways.
_represent_as_dict_extra_attrs = ('format', 'scale', 'precision',
'in_subfmt', 'out_subfmt', 'location',
'_delta_ut1_utc', '_delta_tdb_tt')
# When serializing, write out the `value` attribute using the column name.
_represent_as_dict_primary_data = 'value'
mask_val = np.ma.masked
@property
def _represent_as_dict_attrs(self):
method = self.serialize_method[self._serialize_context]
if method == 'formatted_value':
out = ('value',)
elif method == 'jd1_jd2':
out = ('jd1', 'jd2')
else:
raise ValueError("serialize method must be 'formatted_value' or 'jd1_jd2'")
return out + self._represent_as_dict_extra_attrs
def __init__(self, bound=False):
super().__init__(bound)
# If bound to a data object instance then create the dict of attributes
# which stores the info attribute values.
if bound:
# Specify how to serialize this object depending on context.
# If ``True`` for a context, then use formatted ``value`` attribute
# (e.g. the ISO time string). If ``False`` then use float jd1 and jd2.
self.serialize_method = {'fits': 'jd1_jd2',
'ecsv': 'formatted_value',
'hdf5': 'jd1_jd2',
'yaml': 'jd1_jd2',
None: 'jd1_jd2'}
@property
def unit(self):
return None
info_summary_stats = staticmethod(
data_info_factory(names=MixinInfo._stats,
funcs=[getattr(np, stat) for stat in MixinInfo._stats]))
# When Time has mean, std, min, max methods:
# funcs = [lambda x: getattr(x, stat)() for stat_name in MixinInfo._stats])
def _construct_from_dict_base(self, map):
if 'jd1' in map and 'jd2' in map:
format = map.pop('format')
map['format'] = 'jd'
map['val'] = map.pop('jd1')
map['val2'] = map.pop('jd2')
else:
format = map['format']
map['val'] = map.pop('value')
out = self._parent_cls(**map)
out.format = format
return out
def _construct_from_dict(self, map):
delta_ut1_utc = map.pop('_delta_ut1_utc', None)
delta_tdb_tt = map.pop('_delta_tdb_tt', None)
out = self._construct_from_dict_base(map)
if delta_ut1_utc is not None:
out._delta_ut1_utc = delta_ut1_utc
if delta_tdb_tt is not None:
out._delta_tdb_tt = delta_tdb_tt
return out
def new_like(self, cols, length, metadata_conflicts='warn', name=None):
"""
Return a new Time instance which is consistent with the input Time objects
``cols`` and has ``length`` rows.
This is intended for creating an empty Time instance whose elements can
be set in-place for table operations like join or vstack. It checks
that the input locations and attributes are consistent. This is used
when a Time object is used as a mixin column in an astropy Table.
Parameters
----------
cols : list
List of input columns (Time objects)
length : int
Length of the output column object
metadata_conflicts : str ('warn'|'error'|'silent')
How to handle metadata conflicts
name : str
Output column name
Returns
-------
col : Time (or subclass)
Empty instance of this class consistent with ``cols``
"""
# Get merged info attributes like shape, dtype, format, description, etc.
attrs = self.merge_cols_attributes(cols, metadata_conflicts, name,
('meta', 'description'))
attrs.pop('dtype') # Not relevant for Time
col0 = cols[0]
# Check that location is consistent for all Time objects
for col in cols[1:]:
# This is the method used by __setitem__ to ensure that the right side
# has a consistent location (and coerce data if necessary, but that does
# not happen in this case since `col` is already a Time object). If this
# passes then any subsequent table operations via setitem will work.
try:
col0._make_value_equivalent(slice(None), col)
except ValueError:
raise ValueError('input columns have inconsistent locations')
# Make a new Time object with the desired shape and attributes
shape = (length,) + attrs.pop('shape')
jd2000 = 2451544.5 # Arbitrary JD value J2000.0 that will work with ERFA
jd1 = np.full(shape, jd2000, dtype='f8')
jd2 = np.zeros(shape, dtype='f8')
tm_attrs = {attr: getattr(col0, attr)
for attr in ('scale', 'location',
'precision', 'in_subfmt', 'out_subfmt')}
out = self._parent_cls(jd1, jd2, format='jd', **tm_attrs)
out.format = col0.format
# Set remaining info attributes
for attr, value in attrs.items():
setattr(out.info, attr, value)
return out
class TimeDeltaInfo(TimeInfo):
_represent_as_dict_extra_attrs = ('format', 'scale')
def _construct_from_dict(self, map):
return self._construct_from_dict_base(map)
def new_like(self, cols, length, metadata_conflicts='warn', name=None):
"""
Return a new TimeDelta instance which is consistent with the input Time objects
``cols`` and has ``length`` rows.
This is intended for creating an empty Time instance whose elements can
be set in-place for table operations like join or vstack. It checks
that the input locations and attributes are consistent. This is used
when a Time object is used as a mixin column in an astropy Table.
Parameters
----------
cols : list
List of input columns (Time objects)
length : int
Length of the output column object
metadata_conflicts : str ('warn'|'error'|'silent')
How to handle metadata conflicts
name : str
Output column name
Returns
-------
col : Time (or subclass)
Empty instance of this class consistent with ``cols``
"""
# Get merged info attributes like shape, dtype, format, description, etc.
attrs = self.merge_cols_attributes(cols, metadata_conflicts, name,
('meta', 'description'))
attrs.pop('dtype') # Not relevant for Time
col0 = cols[0]
# Make a new Time object with the desired shape and attributes
shape = (length,) + attrs.pop('shape')
jd1 = np.zeros(shape, dtype='f8')
jd2 = np.zeros(shape, dtype='f8')
out = self._parent_cls(jd1, jd2, format='jd', scale=col0.scale)
out.format = col0.format
# Set remaining info attributes
for attr, value in attrs.items():
setattr(out.info, attr, value)
return out
class Time(ShapedLikeNDArray):
"""
Represent and manipulate times and dates for astronomy.
A `Time` object is initialized with one or more times in the ``val``
argument. The input times in ``val`` must conform to the specified
``format`` and must correspond to the specified time ``scale``. The
optional ``val2`` time input should be supplied only for numeric input
formats (e.g. JD) where very high precision (better than 64-bit precision)
is required.
The allowed values for ``format`` can be listed with::
>>> list(Time.FORMATS)
['jd', 'mjd', 'decimalyear', 'unix', 'cxcsec', 'gps', 'plot_date',
'datetime', 'iso', 'isot', 'yday', 'datetime64', 'fits', 'byear',
'jyear', 'byear_str', 'jyear_str']
Parameters
----------
val : sequence, ndarray, number, str, bytes, or `~astropy.time.Time` object
Value(s) to initialize the time or times. Bytes are decoded as ascii.
val2 : sequence, ndarray, or number; optional
Value(s) to initialize the time or times. Only used for numerical
input, to help preserve precision.
format : str, optional
Format of input value(s)
scale : str, optional
Time scale of input value(s), must be one of the following:
('tai', 'tcb', 'tcg', 'tdb', 'tt', 'ut1', 'utc')
precision : int, optional
Digits of precision in string representation of time
in_subfmt : str, optional
Subformat for inputting string times
out_subfmt : str, optional
Subformat for outputting string times
location : `~astropy.coordinates.EarthLocation` or tuple, optional
If given as an tuple, it should be able to initialize an
an EarthLocation instance, i.e., either contain 3 items with units of
length for geocentric coordinates, or contain a longitude, latitude,
and an optional height for geodetic coordinates.
Can be a single location, or one for each input time.
copy : bool, optional
Make a copy of the input values
"""
SCALES = TIME_SCALES
"""List of time scales"""
FORMATS = TIME_FORMATS
"""Dict of time formats"""
# Make sure that reverse arithmetic (e.g., TimeDelta.__rmul__)
# gets called over the __mul__ of Numpy arrays.
__array_priority__ = 20000
# Declare that Time can be used as a Table column by defining the
# attribute where column attributes will be stored.
_astropy_column_attrs = None
def __new__(cls, val, val2=None, format=None, scale=None,
precision=None, in_subfmt=None, out_subfmt=None,
location=None, copy=False):
if isinstance(val, cls):
self = val.replicate(format=format, copy=copy)
else:
self = super().__new__(cls)
return self
def __getnewargs__(self):
return (self._time,)
def __init__(self, val, val2=None, format=None, scale=None,
precision=None, in_subfmt=None, out_subfmt=None,
location=None, copy=False):
if location is not None:
from ..coordinates import EarthLocation
if isinstance(location, EarthLocation):
self.location = location
else:
self.location = EarthLocation(*location)
if self.location.size == 1:
self.location = self.location.squeeze()
else:
self.location = None
if isinstance(val, self.__class__):
# Update _time formatting parameters if explicitly specified
if precision is not None:
self._time.precision = precision
if in_subfmt is not None:
self._time.in_subfmt = in_subfmt
if out_subfmt is not None:
self._time.out_subfmt = out_subfmt
self.SCALES = TIME_TYPES[self.scale]
if scale is not None:
self._set_scale(scale)
else:
self._init_from_vals(val, val2, format, scale, copy,
precision, in_subfmt, out_subfmt)
self.SCALES = TIME_TYPES[self.scale]
if self.location is not None and (self.location.size > 1 and
self.location.shape != self.shape):
try:
# check the location can be broadcast to self's shape.
self.location = np.broadcast_to(self.location, self.shape,
subok=True)
except Exception:
raise ValueError('The location with shape {0} cannot be '
'broadcast against time with shape {1}. '
'Typically, either give a single location or '
'one for each time.'
.format(self.location.shape, self.shape))
def _init_from_vals(self, val, val2, format, scale, copy,
precision=None, in_subfmt=None, out_subfmt=None):
"""
Set the internal _format, scale, and _time attrs from user
inputs. This handles coercion into the correct shapes and
some basic input validation.
"""
if precision is None:
precision = 3
if in_subfmt is None:
in_subfmt = '*'
if out_subfmt is None:
out_subfmt = '*'
# Coerce val into an array
val = _make_array(val, copy)
# If val2 is not None, ensure consistency
if val2 is not None:
val2 = _make_array(val2, copy)
try:
np.broadcast(val, val2)
except ValueError:
raise ValueError('Input val and val2 have inconsistent shape; '
'they cannot be broadcast together.')
if scale is not None:
if not (isinstance(scale, str) and
scale.lower() in self.SCALES):
raise ScaleValueError("Scale {0!r} is not in the allowed scales "
"{1}".format(scale,
sorted(self.SCALES)))
# If either of the input val, val2 are masked arrays then
# find the masked elements and fill them.
mask, val, val2 = _check_for_masked_and_fill(val, val2)
# Parse / convert input values into internal jd1, jd2 based on format
self._time = self._get_time_fmt(val, val2, format, scale,
precision, in_subfmt, out_subfmt)
self._format = self._time.name
# If any inputs were masked then masked jd2 accordingly. From above
# routine ``mask`` must be either Python bool False or an bool ndarray
# with shape broadcastable to jd2.
if mask is not False:
mask = np.broadcast_to(mask, self._time.jd2.shape)
self._time.jd2[mask] = np.nan
def _get_time_fmt(self, val, val2, format, scale,
precision, in_subfmt, out_subfmt):
"""
Given the supplied val, val2, format and scale try to instantiate
the corresponding TimeFormat class to convert the input values into
the internal jd1 and jd2.
If format is `None` and the input is a string-type or object array then
guess available formats and stop when one matches.
"""
if format is None and val.dtype.kind in ('S', 'U', 'O', 'M'):
formats = [(name, cls) for name, cls in self.FORMATS.items()
if issubclass(cls, TimeUnique)]
err_msg = ('any of the formats where the format keyword is '
'optional {0}'.format([name for name, cls in formats]))
# AstropyTime is a pseudo-format that isn't in the TIME_FORMATS registry,
# but try to guess it at the end.
formats.append(('astropy_time', TimeAstropyTime))
elif not (isinstance(format, str) and
format.lower() in self.FORMATS):
if format is None:
raise ValueError("No time format was given, and the input is "
"not unique")
else:
raise ValueError("Format {0!r} is not one of the allowed "
"formats {1}".format(format,
sorted(self.FORMATS)))
else:
formats = [(format, self.FORMATS[format])]
err_msg = 'the format class {0}'.format(format)
for format, FormatClass in formats:
try:
return FormatClass(val, val2, scale, precision, in_subfmt, out_subfmt)
except UnitConversionError:
raise
except (ValueError, TypeError):
pass
else:
raise ValueError('Input values did not match {0}'.format(err_msg))
@classmethod
def now(cls):
"""
Creates a new object corresponding to the instant in time this
method is called.
.. note::
"Now" is determined using the `~datetime.datetime.utcnow`
function, so its accuracy and precision is determined by that
function. Generally that means it is set by the accuracy of
your system clock.
Returns
-------
nowtime
A new `Time` object (or a subclass of `Time` if this is called from
such a subclass) at the current time.
"""
# call `utcnow` immediately to be sure it's ASAP
dtnow = datetime.utcnow()
return cls(val=dtnow, format='datetime', scale='utc')
info = TimeInfo()
@classmethod
def strptime(cls, time_string, format_string, **kwargs):
"""
Parse a string to a Time according to a format specification.
See `time.strptime` documentation for format specification.
>>> Time.strptime('2012-Jun-30 23:59:60', '%Y-%b-%d %H:%M:%S')
<Time object: scale='utc' format='isot' value=2012-06-30T23:59:60.000>
Parameters
----------
time_string : string, sequence, ndarray
Objects containing time data of type string
format_string : string
String specifying format of time_string.
kwargs : dict
Any keyword arguments for ``Time``. If the ``format`` keyword
argument is present, this will be used as the Time format.
Returns
-------
time_obj : `~astropy.time.Time`
A new `~astropy.time.Time` object corresponding to the input
``time_string``.
"""
time_array = np.asarray(time_string)
if time_array.dtype.kind not in ('U', 'S'):
err = "Expected type is string, a bytes-like object or a sequence"\
" of these. Got dtype '{}'".format(time_array.dtype.kind)
raise TypeError(err)
to_string = (str if time_array.dtype.kind == 'U' else
lambda x: str(x.item(), encoding='ascii'))
iterator = np.nditer([time_array, None],
op_dtypes=[time_array.dtype, 'U30'])
for time, formatted in iterator:
tt, fraction = _strptime._strptime(to_string(time), format_string)
time_tuple = tt[:6] + (fraction,)
formatted[...] = '{:04}-{:02}-{:02}T{:02}:{:02}:{:02}.{:06}'\
.format(*time_tuple)
format = kwargs.pop('format', None)
out = cls(*iterator.operands[1:], format='isot', **kwargs)
if format is not None:
out.format = format
return out
@property
def writeable(self):
return self._time.jd1.flags.writeable & self._time.jd2.flags.writeable
@writeable.setter
def writeable(self, value):
self._time.jd1.flags.writeable = value
self._time.jd2.flags.writeable = value
@property
def format(self):
"""
Get or set time format.
The format defines the way times are represented when accessed via the
``.value`` attribute. By default it is the same as the format used for
initializing the `Time` instance, but it can be set to any other value
that could be used for initialization. These can be listed with::
>>> list(Time.FORMATS)
['jd', 'mjd', 'decimalyear', 'unix', 'cxcsec', 'gps', 'plot_date',
'datetime', 'iso', 'isot', 'yday', 'datetime64', 'fits', 'byear',
'jyear', 'byear_str', 'jyear_str']
"""
return self._format
@format.setter
def format(self, format):
"""Set time format"""
if format not in self.FORMATS:
raise ValueError('format must be one of {0}'
.format(list(self.FORMATS)))
format_cls = self.FORMATS[format]
# If current output subformat is not in the new format then replace
# with default '*'
if hasattr(format_cls, 'subfmts'):
subfmt_names = [subfmt[0] for subfmt in format_cls.subfmts]
if self.out_subfmt not in subfmt_names:
self.out_subfmt = '*'
self._time = format_cls(self._time.jd1, self._time.jd2,
self._time._scale, self.precision,
in_subfmt=self.in_subfmt,
out_subfmt=self.out_subfmt,
from_jd=True)
self._format = format
def __repr__(self):
return ("<{0} object: scale='{1}' format='{2}' value={3}>"
.format(self.__class__.__name__, self.scale, self.format,
getattr(self, self.format)))
def __str__(self):
return str(getattr(self, self.format))
def strftime(self, format_spec):
"""
Convert Time to a string or a numpy.array of strings according to a
format specification.
See `time.strftime` documentation for format specification.
Parameters
----------
format_spec : string
Format definition of return string.
Returns
-------
formatted : string, numpy.array
String or numpy.array of strings formatted according to the given
format string.
"""
formatted_strings = []
for sk in self.replicate('iso')._time.str_kwargs():
date_tuple = date(sk['year'], sk['mon'], sk['day']).timetuple()
datetime_tuple = (sk['year'], sk['mon'], sk['day'],
sk['hour'], sk['min'], sk['sec'],
date_tuple[6], date_tuple[7], -1)
fmtd_str = format_spec
if '%f' in fmtd_str:
fmtd_str = fmtd_str.replace('%f', '{frac:0{precision}}'.format(frac=sk['fracsec'], precision=self.precision))
fmtd_str = strftime(fmtd_str, datetime_tuple)
formatted_strings.append(fmtd_str)
if self.isscalar:
return formatted_strings[0]
else:
return np.array(formatted_strings).reshape(self.shape)
@property
def scale(self):
"""Time scale"""
return self._time.scale
def _set_scale(self, scale):
"""
This is the key routine that actually does time scale conversions.
This is not public and not connected to the read-only scale property.
"""
if scale == self.scale:
return
if scale not in self.SCALES:
raise ValueError("Scale {0!r} is not in the allowed scales {1}"
.format(scale, sorted(self.SCALES)))
# Determine the chain of scale transformations to get from the current
# scale to the new scale. MULTI_HOPS contains a dict of all
# transformations (xforms) that require intermediate xforms.
# The MULTI_HOPS dict is keyed by (sys1, sys2) in alphabetical order.
xform = (self.scale, scale)
xform_sort = tuple(sorted(xform))
multi = MULTI_HOPS.get(xform_sort, ())
xforms = xform_sort[:1] + multi + xform_sort[-1:]
# If we made the reverse xform then reverse it now.
if xform_sort != xform:
xforms = tuple(reversed(xforms))
# Transform the jd1,2 pairs through the chain of scale xforms.
jd1, jd2 = self._time.jd1, self._time.jd2_filled
for sys1, sys2 in zip(xforms[:-1], xforms[1:]):
# Some xforms require an additional delta_ argument that is
# provided through Time methods. These values may be supplied by
# the user or computed based on available approximations. The
# get_delta_ methods are available for only one combination of
# sys1, sys2 though the property applies for both xform directions.
args = [jd1, jd2]
for sys12 in ((sys1, sys2), (sys2, sys1)):
dt_method = '_get_delta_{0}_{1}'.format(*sys12)
try:
get_dt = getattr(self, dt_method)
except AttributeError:
pass
else:
args.append(get_dt(jd1, jd2))
break
conv_func = getattr(erfa, sys1 + sys2)
jd1, jd2 = conv_func(*args)
if self.masked:
jd2[self.mask] = np.nan
self._time = self.FORMATS[self.format](jd1, jd2, scale, self.precision,
self.in_subfmt, self.out_subfmt,
from_jd=True)
@property
def precision(self):
"""
Decimal precision when outputting seconds as floating point (int
value between 0 and 9 inclusive).
"""
return self._time.precision
@precision.setter
def precision(self, val):
del self.cache
if not isinstance(val, int) or val < 0 or val > 9:
raise ValueError('precision attribute must be an int between '
'0 and 9')
self._time.precision = val
@property
def in_subfmt(self):
"""
Unix wildcard pattern to select subformats for parsing string input
times.
"""
return self._time.in_subfmt
@in_subfmt.setter
def in_subfmt(self, val):
del self.cache
if not isinstance(val, str):
raise ValueError('in_subfmt attribute must be a string')
self._time.in_subfmt = val
@property
def out_subfmt(self):
"""
Unix wildcard pattern to select subformats for outputting times.
"""
return self._time.out_subfmt
@out_subfmt.setter
def out_subfmt(self, val):
del self.cache
if not isinstance(val, str):
raise ValueError('out_subfmt attribute must be a string')
self._time.out_subfmt = val
@property
def shape(self):
"""The shape of the time instances.
Like `~numpy.ndarray.shape`, can be set to a new shape by assigning a
tuple. Note that if different instances share some but not all
underlying data, setting the shape of one instance can make the other
instance unusable. Hence, it is strongly recommended to get new,
reshaped instances with the ``reshape`` method.
Raises
------
AttributeError
If the shape of the ``jd1``, ``jd2``, ``location``,
``delta_ut1_utc``, or ``delta_tdb_tt`` attributes cannot be changed
without the arrays being copied. For these cases, use the
`Time.reshape` method (which copies any arrays that cannot be
reshaped in-place).
"""
return self._time.jd1.shape
@shape.setter
def shape(self, shape):
del self.cache
# We have to keep track of arrays that were already reshaped,
# since we may have to return those to their original shape if a later
# shape-setting fails.
reshaped = []
oldshape = self.shape
# In-place reshape of data/attributes. Need to access _time.jd1/2 not
# self.jd1/2 because the latter are not guaranteed to be the actual
# data, and in fact should not be directly changeable from the public
# API.
for obj, attr in ((self._time, 'jd1'),
(self._time, 'jd2'),
(self, '_delta_ut1_utc'),
(self, '_delta_tdb_tt'),
(self, 'location')):
val = getattr(obj, attr, None)
if val is not None and val.size > 1:
try:
val.shape = shape
except AttributeError:
for val2 in reshaped:
val2.shape = oldshape
raise
else:
reshaped.append(val)
def _shaped_like_input(self, value):
out = value
if value.dtype.kind == 'M':
return value[()]
if not self._time.jd1.shape and not np.ma.is_masked(value):
out = value.item()
return out
@property
def jd1(self):
"""
First of the two doubles that internally store time value(s) in JD.
"""
jd1 = self._time.mask_if_needed(self._time.jd1)
return self._shaped_like_input(jd1)
@property
def jd2(self):
"""
Second of the two doubles that internally store time value(s) in JD.
"""
jd2 = self._time.mask_if_needed(self._time.jd2)
return self._shaped_like_input(jd2)
@property
def value(self):
"""Time value(s) in current format"""
# The underlying way to get the time values for the current format is:
# self._shaped_like_input(self._time.to_value(parent=self))
# This is done in __getattr__. By calling getattr(self, self.format)
# the ``value`` attribute is cached.
return getattr(self, self.format)
@property
def masked(self):
return self._time.masked
@property
def mask(self):
return self._time.mask
def insert(self, obj, values, axis=0):
"""
Insert values before the given indices in the column and return
a new `~astropy.time.Time` or `~astropy.time.TimeDelta` object.
The values to be inserted must conform to the rules for in-place setting
of ``Time`` objects (see ``Get and set values`` in the ``Time``
documentation).
The API signature matches the ``np.insert`` API, but is more limited.
The specification of insert index ``obj`` must be a single integer,
and the ``axis`` must be ``0`` for simple row insertion before the
index.
Parameters
----------
obj : int
Integer index before which ``values`` is inserted.
values : array_like
Value(s) to insert. If the type of ``values`` is different
from that of quantity, ``values`` is converted to the matching type.
axis : int, optional
Axis along which to insert ``values``. Default is 0, which is the
only allowed value and will insert a row.
Returns
-------
out : `~astropy.time.Time` subclass
New time object with inserted value(s)
"""
# Validate inputs: obj arg is integer, axis=0, self is not a scalar, and
# input index is in bounds.
try:
idx0 = operator.index(obj)
except TypeError:
raise TypeError('obj arg must be an integer')
if axis != 0:
raise ValueError('axis must be 0')
if not self.shape:
raise TypeError('cannot insert into scalar {} object'
.format(self.__class__.__name__))
if abs(idx0) > len(self):
raise IndexError('index {} is out of bounds for axis 0 with size {}'
.format(idx0, len(self)))
# Turn negative index into positive
if idx0 < 0:
idx0 = len(self) + idx0
# For non-Time object, use numpy to help figure out the length. (Note annoying
# case of a string input that has a length which is not the length we want).
if not isinstance(values, Time):
values = np.asarray(values)
n_values = len(values) if values.shape else 1
# Finally make the new object with the correct length and set values for the
# three sections, before insert, the insert, and after the insert.
out = self.__class__.info.new_like([self], len(self) + n_values, name=self.info.name)
out._time.jd1[:idx0] = self._time.jd1[:idx0]
out._time.jd2[:idx0] = self._time.jd2[:idx0]
# This uses the Time setting machinery to coerce and validate as necessary.
out[idx0:idx0 + n_values] = values
out._time.jd1[idx0 + n_values:] = self._time.jd1[idx0:]
out._time.jd2[idx0 + n_values:] = self._time.jd2[idx0:]
return out
def _make_value_equivalent(self, item, value):
"""Coerce setitem value into an equivalent Time object"""
# If there is a vector location then broadcast to the Time shape
# and then select with ``item``
if self.location is not None and self.location.shape:
self_location = np.broadcast_to(self.location, self.shape, subok=True)[item]
else:
self_location = self.location
if isinstance(value, Time):
# Make sure locations are compatible. Location can be either None or
# a Location object.
if self_location is None and value.location is None:
match = True
elif ((self_location is None and value.location is not None) or
(self_location is not None and value.location is None)):
match = False
else:
match = np.all(self_location == value.location)
if not match:
raise ValueError('cannot set to Time with different location: '
'expected location={} and '
'got location={}'
.format(self_location, value.location))
else:
try:
value = self.__class__(value, scale=self.scale, location=self_location)
except Exception:
try:
value = self.__class__(value, scale=self.scale, format=self.format,
location=self_location)
except Exception as err:
raise ValueError('cannot convert value to a compatible Time object: {}'
.format(err))
return value
def __setitem__(self, item, value):
if not self.writeable:
if self.shape:
raise ValueError('{} object is read-only. Make a '
'copy() or set "writeable" attribute to True.'
.format(self.__class__.__name__))
else:
raise ValueError('scalar {} object is read-only.'
.format(self.__class__.__name__))
# Any use of setitem results in immediate cache invalidation
del self.cache
# Setting invalidates transform deltas
for attr in ('_delta_tdb_tt', '_delta_ut1_utc'):
if hasattr(self, attr):
delattr(self, attr)
if value is np.ma.masked or value is np.nan:
self._time.jd2[item] = np.nan
return
value = self._make_value_equivalent(item, value)
# Finally directly set the jd1/2 values. Locations are known to match.
if self.scale is not None:
value = getattr(value, self.scale)
self._time.jd1[item] = value._time.jd1
self._time.jd2[item] = value._time.jd2
def light_travel_time(self, skycoord, kind='barycentric', location=None, ephemeris=None):
"""Light travel time correction to the barycentre or heliocentre.
The frame transformations used to calculate the location of the solar
system barycentre and the heliocentre rely on the erfa routine epv00,
which is consistent with the JPL DE405 ephemeris to an accuracy of
11.2 km, corresponding to a light travel time of 4 microseconds.
The routine assumes the source(s) are at large distance, i.e., neglects
finite-distance effects.
Parameters
----------
skycoord : `~astropy.coordinates.SkyCoord`
The sky location to calculate the correction for.
kind : str, optional
``'barycentric'`` (default) or ``'heliocentric'``
location : `~astropy.coordinates.EarthLocation`, optional
The location of the observatory to calculate the correction for.
If no location is given, the ``location`` attribute of the Time
object is used
ephemeris : str, optional
Solar system ephemeris to use (e.g., 'builtin', 'jpl'). By default,
use the one set with ``astropy.coordinates.solar_system_ephemeris.set``.
For more information, see `~astropy.coordinates.solar_system_ephemeris`.
Returns
-------
time_offset : `~astropy.time.TimeDelta`
The time offset between the barycentre or Heliocentre and Earth,
in TDB seconds. Should be added to the original time to get the
time in the Solar system barycentre or the Heliocentre.
Also, the time conversion to BJD will then include the relativistic correction as well.
"""
if kind.lower() not in ('barycentric', 'heliocentric'):
raise ValueError("'kind' parameter must be one of 'heliocentric' "
"or 'barycentric'")
if location is None:
if self.location is None:
raise ValueError('An EarthLocation needs to be set or passed '
'in to calculate bary- or heliocentric '
'corrections')
location = self.location
from ..coordinates import (UnitSphericalRepresentation, CartesianRepresentation,
HCRS, ICRS, GCRS, solar_system_ephemeris)
# ensure sky location is ICRS compatible
if not skycoord.is_transformable_to(ICRS()):
raise ValueError("Given skycoord is not transformable to the ICRS")
# get location of observatory in ITRS coordinates at this Time
try:
itrs = location.get_itrs(obstime=self)
except Exception:
raise ValueError("Supplied location does not have a valid `get_itrs` method")
with solar_system_ephemeris.set(ephemeris):
if kind.lower() == 'heliocentric':
# convert to heliocentric coordinates, aligned with ICRS
cpos = itrs.transform_to(HCRS(obstime=self)).cartesian.xyz
else:
# first we need to convert to GCRS coordinates with the correct
# obstime, since ICRS coordinates have no frame time
gcrs_coo = itrs.transform_to(GCRS(obstime=self))
# convert to barycentric (BCRS) coordinates, aligned with ICRS
cpos = gcrs_coo.transform_to(ICRS()).cartesian.xyz
# get unit ICRS vector to star
spos = (skycoord.icrs.represent_as(UnitSphericalRepresentation).
represent_as(CartesianRepresentation).xyz)
# Move X,Y,Z to last dimension, to enable possible broadcasting below.
cpos = np.rollaxis(cpos, 0, cpos.ndim)
spos = np.rollaxis(spos, 0, spos.ndim)
# calculate light travel time correction
tcor_val = (spos * cpos).sum(axis=-1) / const.c
return TimeDelta(tcor_val, scale='tdb')
def sidereal_time(self, kind, longitude=None, model=None):
"""Calculate sidereal time.
Parameters
---------------
kind : str
``'mean'`` or ``'apparent'``, i.e., accounting for precession
only, or also for nutation.
longitude : `~astropy.units.Quantity`, `str`, or `None`; optional
The longitude on the Earth at which to compute the sidereal time.
Can be given as a `~astropy.units.Quantity` with angular units
(or an `~astropy.coordinates.Angle` or
`~astropy.coordinates.Longitude`), or as a name of an
observatory (currently, only ``'greenwich'`` is supported,
equivalent to 0 deg). If `None` (default), the ``lon`` attribute of
the Time object is used.
model : str or `None`; optional
Precession (and nutation) model to use. The available ones are:
- {0}: {1}
- {2}: {3}
If `None` (default), the last (most recent) one from the appropriate
list above is used.
Returns
-------
sidereal time : `~astropy.coordinates.Longitude`
Sidereal time as a quantity with units of hourangle
""" # docstring is formatted below
from ..coordinates import Longitude
if kind.lower() not in SIDEREAL_TIME_MODELS.keys():
raise ValueError('The kind of sidereal time has to be {0}'.format(
' or '.join(sorted(SIDEREAL_TIME_MODELS.keys()))))
available_models = SIDEREAL_TIME_MODELS[kind.lower()]
if model is None:
model = sorted(available_models.keys())[-1]
else:
if model.upper() not in available_models:
raise ValueError(
'Model {0} not implemented for {1} sidereal time; '
'available models are {2}'
.format(model, kind, sorted(available_models.keys())))
if longitude is None:
if self.location is None:
raise ValueError('No longitude is given but the location for '
'the Time object is not set.')
longitude = self.location.lon
elif longitude == 'greenwich':
longitude = Longitude(0., u.degree,
wrap_angle=180.*u.degree)
else:
# sanity check on input
longitude = Longitude(longitude, u.degree,
wrap_angle=180.*u.degree)
gst = self._erfa_sidereal_time(available_models[model.upper()])
return Longitude(gst + longitude, u.hourangle)
if isinstance(sidereal_time.__doc__, str):
sidereal_time.__doc__ = sidereal_time.__doc__.format(
'apparent', sorted(SIDEREAL_TIME_MODELS['apparent'].keys()),
'mean', sorted(SIDEREAL_TIME_MODELS['mean'].keys()))
def _erfa_sidereal_time(self, model):
"""Calculate a sidereal time using a IAU precession/nutation model."""
from ..coordinates import Longitude
erfa_function = model['function']
erfa_parameters = [getattr(getattr(self, scale)._time, jd_part)
for scale in model['scales']
for jd_part in ('jd1', 'jd2_filled')]
sidereal_time = erfa_function(*erfa_parameters)
if self.masked:
sidereal_time[self.mask] = np.nan
return Longitude(sidereal_time, u.radian).to(u.hourangle)
def copy(self, format=None):
"""
Return a fully independent copy the Time object, optionally changing
the format.
If ``format`` is supplied then the time format of the returned Time
object will be set accordingly, otherwise it will be unchanged from the
original.
In this method a full copy of the internal time arrays will be made.
The internal time arrays are normally not changeable by the user so in
most cases the ``replicate()`` method should be used.
Parameters
----------
format : str, optional
Time format of the copy.
Returns
-------
tm : Time object
Copy of this object
"""
return self._apply('copy', format=format)
def replicate(self, format=None, copy=False):
"""
Return a replica of the Time object, optionally changing the format.
If ``format`` is supplied then the time format of the returned Time
object will be set accordingly, otherwise it will be unchanged from the
original.
If ``copy`` is set to `True` then a full copy of the internal time arrays
will be made. By default the replica will use a reference to the
original arrays when possible to save memory. The internal time arrays
are normally not changeable by the user so in most cases it should not
be necessary to set ``copy`` to `True`.
The convenience method copy() is available in which ``copy`` is `True`
by default.
Parameters
----------
format : str, optional
Time format of the replica.
copy : bool, optional
Return a true copy instead of using references where possible.
Returns
-------
tm : Time object
Replica of this object
"""
return self._apply('copy' if copy else 'replicate', format=format)
def _apply(self, method, *args, format=None, **kwargs):
"""Create a new time object, possibly applying a method to the arrays.
Parameters
----------
method : str or callable
If string, can be 'replicate' or the name of a relevant
`~numpy.ndarray` method. In the former case, a new time instance
with unchanged internal data is created, while in the latter the
method is applied to the internal ``jd1`` and ``jd2`` arrays, as
well as to possible ``location``, ``_delta_ut1_utc``, and
``_delta_tdb_tt`` arrays.
If a callable, it is directly applied to the above arrays.
Examples: 'copy', '__getitem__', 'reshape', `~numpy.broadcast_to`.
args : tuple
Any positional arguments for ``method``.
kwargs : dict
Any keyword arguments for ``method``. If the ``format`` keyword
argument is present, this will be used as the Time format of the
replica.
Examples
--------
Some ways this is used internally::
copy : ``_apply('copy')``
replicate : ``_apply('replicate')``
reshape : ``_apply('reshape', new_shape)``
index or slice : ``_apply('__getitem__', item)``
broadcast : ``_apply(np.broadcast, shape=new_shape)``
"""
new_format = self.format if format is None else format
if callable(method):
apply_method = lambda array: method(array, *args, **kwargs)
else:
if method == 'replicate':
apply_method = None
else:
apply_method = operator.methodcaller(method, *args, **kwargs)
jd1, jd2 = self._time.jd1, self._time.jd2
if apply_method:
jd1 = apply_method(jd1)
jd2 = apply_method(jd2)
# Get a new instance of our class and set its attributes directly.
tm = super().__new__(self.__class__)
tm._time = TimeJD(jd1, jd2, self.scale, self.precision,
self.in_subfmt, self.out_subfmt, from_jd=True)
# Optional ndarray attributes.
for attr in ('_delta_ut1_utc', '_delta_tdb_tt', 'location',
'precision', 'in_subfmt', 'out_subfmt'):
try:
val = getattr(self, attr)
except AttributeError:
continue
if apply_method:
# Apply the method to any value arrays (though skip if there is
# only a single element and the method would return a view,
# since in that case nothing would change).
if getattr(val, 'size', 1) > 1:
val = apply_method(val)
elif method == 'copy' or method == 'flatten':
# flatten should copy also for a single element array, but
# we cannot use it directly for array scalars, since it
# always returns a one-dimensional array. So, just copy.
val = copy.copy(val)
setattr(tm, attr, val)
# Copy other 'info' attr only if it has actually been defined.
# See PR #3898 for further explanation and justification, along
# with Quantity.__array_finalize__
if 'info' in self.__dict__:
tm.info = self.info
# Make the new internal _time object corresponding to the format
# in the copy. If the format is unchanged this process is lightweight
# and does not create any new arrays.
if new_format not in tm.FORMATS:
raise ValueError('format must be one of {0}'
.format(list(tm.FORMATS)))
NewFormat = tm.FORMATS[new_format]
tm._time = NewFormat(tm._time.jd1, tm._time.jd2,
tm._time._scale, tm.precision,
tm.in_subfmt, tm.out_subfmt,
from_jd=True)
tm._format = new_format
tm.SCALES = self.SCALES
return tm
def __copy__(self):
"""
Overrides the default behavior of the `copy.copy` function in
the python stdlib to behave like `Time.copy`. Does *not* make a
copy of the JD arrays - only copies by reference.
"""
return self.replicate()
def __deepcopy__(self, memo):
"""
Overrides the default behavior of the `copy.deepcopy` function
in the python stdlib to behave like `Time.copy`. Does make a
copy of the JD arrays.
"""
return self.copy()
def _advanced_index(self, indices, axis=None, keepdims=False):
"""Turn argmin, argmax output into an advanced index.
Argmin, argmax output contains indices along a given axis in an array
shaped like the other dimensions. To use this to get values at the
correct location, a list is constructed in which the other axes are
indexed sequentially. For ``keepdims`` is ``True``, the net result is
the same as constructing an index grid with ``np.ogrid`` and then
replacing the ``axis`` item with ``indices`` with its shaped expanded
at ``axis``. For ``keepdims`` is ``False``, the result is the same but
with the ``axis`` dimension removed from all list entries.
For ``axis`` is ``None``, this calls :func:`~numpy.unravel_index`.
Parameters
----------
indices : array
Output of argmin or argmax.
axis : int or None
axis along which argmin or argmax was used.
keepdims : bool
Whether to construct indices that keep or remove the axis along
which argmin or argmax was used. Default: ``False``.
Returns
-------
advanced_index : list of arrays
Suitable for use as an advanced index.
"""
if axis is None:
return np.unravel_index(indices, self.shape)
ndim = self.ndim
if axis < 0:
axis = axis + ndim
if keepdims and indices.ndim < self.ndim:
indices = np.expand_dims(indices, axis)
return [(indices if i == axis else np.arange(s).reshape(
(1,)*(i if keepdims or i < axis else i-1) + (s,) +
(1,)*(ndim-i-(1 if keepdims or i > axis else 2))))
for i, s in enumerate(self.shape)]
def argmin(self, axis=None, out=None):
"""Return indices of the minimum values along the given axis.
This is similar to :meth:`~numpy.ndarray.argmin`, but adapted to ensure
that the full precision given by the two doubles ``jd1`` and ``jd2``
is used. See :func:`~numpy.argmin` for detailed documentation.
"""
# first get the minimum at normal precision.
jd = self.jd1 + self.jd2
approx = np.min(jd, axis, keepdims=True)
# Approx is very close to the true minimum, and by subtracting it at
# full precision, all numbers near 0 can be represented correctly,
# so we can be sure we get the true minimum.
# The below is effectively what would be done for
# dt = (self - self.__class__(approx, format='jd')).jd
# which translates to:
# approx_jd1, approx_jd2 = day_frac(approx, 0.)
# dt = (self.jd1 - approx_jd1) + (self.jd2 - approx_jd2)
dt = (self.jd1 - approx) + self.jd2
return dt.argmin(axis, out)
def argmax(self, axis=None, out=None):
"""Return indices of the maximum values along the given axis.
This is similar to :meth:`~numpy.ndarray.argmax`, but adapted to ensure
that the full precision given by the two doubles ``jd1`` and ``jd2``
is used. See :func:`~numpy.argmax` for detailed documentation.
"""
# For procedure, see comment on argmin.
jd = self.jd1 + self.jd2
approx = np.max(jd, axis, keepdims=True)
dt = (self.jd1 - approx) + self.jd2
return dt.argmax(axis, out)
def argsort(self, axis=-1):
"""Returns the indices that would sort the time array.
This is similar to :meth:`~numpy.ndarray.argsort`, but adapted to ensure
that the full precision given by the two doubles ``jd1`` and ``jd2``
is used, and that corresponding attributes are copied. Internally,
it uses :func:`~numpy.lexsort`, and hence no sort method can be chosen.
"""
jd_approx = self.jd
jd_remainder = (self - self.__class__(jd_approx, format='jd')).jd
if axis is None:
return np.lexsort((jd_remainder.ravel(), jd_approx.ravel()))
else:
return np.lexsort(keys=(jd_remainder, jd_approx), axis=axis)
def min(self, axis=None, out=None, keepdims=False):
"""Minimum along a given axis.
This is similar to :meth:`~numpy.ndarray.min`, but adapted to ensure
that the full precision given by the two doubles ``jd1`` and ``jd2``
is used, and that corresponding attributes are copied.
Note that the ``out`` argument is present only for compatibility with
``np.min``; since `Time` instances are immutable, it is not possible
to have an actual ``out`` to store the result in.
"""
if out is not None:
raise ValueError("Since `Time` instances are immutable, ``out`` "
"cannot be set to anything but ``None``.")
return self[self._advanced_index(self.argmin(axis), axis, keepdims)]
def max(self, axis=None, out=None, keepdims=False):
"""Maximum along a given axis.
This is similar to :meth:`~numpy.ndarray.max`, but adapted to ensure
that the full precision given by the two doubles ``jd1`` and ``jd2``
is used, and that corresponding attributes are copied.
Note that the ``out`` argument is present only for compatibility with
``np.max``; since `Time` instances are immutable, it is not possible
to have an actual ``out`` to store the result in.
"""
if out is not None:
raise ValueError("Since `Time` instances are immutable, ``out`` "
"cannot be set to anything but ``None``.")
return self[self._advanced_index(self.argmax(axis), axis, keepdims)]
def ptp(self, axis=None, out=None, keepdims=False):
"""Peak to peak (maximum - minimum) along a given axis.
This is similar to :meth:`~numpy.ndarray.ptp`, but adapted to ensure
that the full precision given by the two doubles ``jd1`` and ``jd2``
is used.
Note that the ``out`` argument is present only for compatibility with
`~numpy.ptp`; since `Time` instances are immutable, it is not possible
to have an actual ``out`` to store the result in.
"""
if out is not None:
raise ValueError("Since `Time` instances are immutable, ``out`` "
"cannot be set to anything but ``None``.")
return (self.max(axis, keepdims=keepdims) -
self.min(axis, keepdims=keepdims))
def sort(self, axis=-1):
"""Return a copy sorted along the specified axis.
This is similar to :meth:`~numpy.ndarray.sort`, but internally uses
indexing with :func:`~numpy.lexsort` to ensure that the full precision
given by the two doubles ``jd1`` and ``jd2`` is kept, and that
corresponding attributes are properly sorted and copied as well.
Parameters
----------
axis : int or None
Axis to be sorted. If ``None``, the flattened array is sorted.
By default, sort over the last axis.
"""
return self[self._advanced_index(self.argsort(axis), axis,
keepdims=True)]
@property
def cache(self):
"""
Return the cache associated with this instance.
"""
return self._time.cache
@cache.deleter
def cache(self):
del self._time.cache
def __getattr__(self, attr):
"""
Get dynamic attributes to output format or do timescale conversion.
"""
if attr in self.SCALES and self.scale is not None:
cache = self.cache['scale']
if attr not in cache:
if attr == self.scale:
tm = self
else:
tm = self.replicate()
tm._set_scale(attr)
if tm.shape:
# Prevent future modification of cached array-like object
tm.writeable = False
cache[attr] = tm
return cache[attr]
elif attr in self.FORMATS:
cache = self.cache['format']
if attr not in cache:
if attr == self.format:
tm = self
else:
tm = self.replicate(format=attr)
value = tm._shaped_like_input(tm._time.to_value(parent=tm))
cache[attr] = value
return cache[attr]
elif attr in TIME_SCALES: # allowed ones done above (self.SCALES)
if self.scale is None:
raise ScaleValueError("Cannot convert TimeDelta with "
"undefined scale to any defined scale.")
else:
raise ScaleValueError("Cannot convert {0} with scale "
"'{1}' to scale '{2}'"
.format(self.__class__.__name__,
self.scale, attr))
else:
# Should raise AttributeError
return self.__getattribute__(attr)
@override__dir__
def __dir__(self):
result = set(self.SCALES)
result.update(self.FORMATS)
return result
def _match_shape(self, val):
"""
Ensure that `val` is matched to length of self. If val has length 1
then broadcast, otherwise cast to double and make sure shape matches.
"""
val = _make_array(val, copy=True) # be conservative and copy
if val.size > 1 and val.shape != self.shape:
try:
# check the value can be broadcast to the shape of self.
val = np.broadcast_to(val, self.shape, subok=True)
except Exception:
raise ValueError('Attribute shape must match or be '
'broadcastable to that of Time object. '
'Typically, give either a single value or '
'one for each time.')
return val
def get_delta_ut1_utc(self, iers_table=None, return_status=False):
"""Find UT1 - UTC differences by interpolating in IERS Table.
Parameters
----------
iers_table : ``astropy.utils.iers.IERS`` table, optional
Table containing UT1-UTC differences from IERS Bulletins A
and/or B. If `None`, use default version (see
``astropy.utils.iers``)
return_status : bool
Whether to return status values. If `False` (default), iers
raises `IndexError` if any time is out of the range
covered by the IERS table.
Returns
-------
ut1_utc : float or float array
UT1-UTC, interpolated in IERS Table
status : int or int array
Status values (if ``return_status=`True```)::
``astropy.utils.iers.FROM_IERS_B``
``astropy.utils.iers.FROM_IERS_A``
``astropy.utils.iers.FROM_IERS_A_PREDICTION``
``astropy.utils.iers.TIME_BEFORE_IERS_RANGE``
``astropy.utils.iers.TIME_BEYOND_IERS_RANGE``
Notes
-----
In normal usage, UT1-UTC differences are calculated automatically
on the first instance ut1 is needed.
Examples
--------
To check in code whether any times are before the IERS table range::
>>> from astropy.utils.iers import TIME_BEFORE_IERS_RANGE
>>> t = Time(['1961-01-01', '2000-01-01'], scale='utc')
>>> delta, status = t.get_delta_ut1_utc(return_status=True)
>>> status == TIME_BEFORE_IERS_RANGE
array([ True, False]...)
"""
if iers_table is None:
from ..utils.iers import IERS
iers_table = IERS.open()
return iers_table.ut1_utc(self.utc, return_status=return_status)
# Property for ERFA DUT arg = UT1 - UTC
def _get_delta_ut1_utc(self, jd1=None, jd2=None):
"""
Get ERFA DUT arg = UT1 - UTC. This getter takes optional jd1 and
jd2 args because it gets called that way when converting time scales.
If delta_ut1_utc is not yet set, this will interpolate them from the
the IERS table.
"""
# Sec. 4.3.1: the arg DUT is the quantity delta_UT1 = UT1 - UTC in
# seconds. It is obtained from tables published by the IERS.
if not hasattr(self, '_delta_ut1_utc'):
from ..utils.iers import IERS_Auto
iers_table = IERS_Auto.open()
# jd1, jd2 are normally set (see above), except if delta_ut1_utc
# is access directly; ensure we behave as expected for that case
if jd1 is None:
self_utc = self.utc
jd1, jd2 = self_utc._time.jd1, self_utc._time.jd2_filled
scale = 'utc'
else:
scale = self.scale
# interpolate UT1-UTC in IERS table
delta = iers_table.ut1_utc(jd1, jd2)
# if we interpolated using UT1 jds, we may be off by one
# second near leap seconds (and very slightly off elsewhere)
if scale == 'ut1':
# calculate UTC using the offset we got; the ERFA routine
# is tolerant of leap seconds, so will do this right
jd1_utc, jd2_utc = erfa.ut1utc(jd1, jd2, delta.to_value(u.s))
# calculate a better estimate using the nearly correct UTC
delta = iers_table.ut1_utc(jd1_utc, jd2_utc)
self._set_delta_ut1_utc(delta)
return self._delta_ut1_utc
def _set_delta_ut1_utc(self, val):
del self.cache
if hasattr(val, 'to'): # Matches Quantity but also TimeDelta.
val = val.to(u.second).value
val = self._match_shape(val)
self._delta_ut1_utc = val
# Note can't use @property because _get_delta_tdb_tt is explicitly
# called with the optional jd1 and jd2 args.
delta_ut1_utc = property(_get_delta_ut1_utc, _set_delta_ut1_utc)
"""UT1 - UTC time scale offset"""
# Property for ERFA DTR arg = TDB - TT
def _get_delta_tdb_tt(self, jd1=None, jd2=None):
if not hasattr(self, '_delta_tdb_tt'):
# If jd1 and jd2 are not provided (which is the case for property
# attribute access) then require that the time scale is TT or TDB.
# Otherwise the computations here are not correct.
if jd1 is None or jd2 is None:
if self.scale not in ('tt', 'tdb'):
raise ValueError('Accessing the delta_tdb_tt attribute '
'is only possible for TT or TDB time '
'scales')
else:
jd1 = self._time.jd1
jd2 = self._time.jd2_filled
# First go from the current input time (which is either
# TDB or TT) to an approximate UT1. Since TT and TDB are
# pretty close (few msec?), assume TT. Similarly, since the
# UT1 terms are very small, use UTC instead of UT1.
njd1, njd2 = erfa.tttai(jd1, jd2)
njd1, njd2 = erfa.taiutc(njd1, njd2)
# subtract 0.5, so UT is fraction of the day from midnight
ut = day_frac(njd1 - 0.5, njd2)[1]
if self.location is None:
from ..coordinates import EarthLocation
location = EarthLocation.from_geodetic(0., 0., 0.)
else:
location = self.location
# Geodetic params needed for d_tdb_tt()
lon = location.lon
rxy = np.hypot(location.x, location.y)
z = location.z
self._delta_tdb_tt = erfa.dtdb(
jd1, jd2, ut, lon.to_value(u.radian),
rxy.to_value(u.km), z.to_value(u.km))
return self._delta_tdb_tt
def _set_delta_tdb_tt(self, val):
del self.cache
if hasattr(val, 'to'): # Matches Quantity but also TimeDelta.
val = val.to(u.second).value
val = self._match_shape(val)
self._delta_tdb_tt = val
# Note can't use @property because _get_delta_tdb_tt is explicitly
# called with the optional jd1 and jd2 args.
delta_tdb_tt = property(_get_delta_tdb_tt, _set_delta_tdb_tt)
"""TDB - TT time scale offset"""
def __sub__(self, other):
if not isinstance(other, Time):
try:
other = TimeDelta(other)
except Exception:
return NotImplemented
# Tdelta - something is dealt with in TimeDelta, so we have
# T - Tdelta = T
# T - T = Tdelta
other_is_delta = isinstance(other, TimeDelta)
# we need a constant scale to calculate, which is guaranteed for
# TimeDelta, but not for Time (which can be UTC)
if other_is_delta: # T - Tdelta
out = self.replicate()
if self.scale in other.SCALES:
if other.scale not in (out.scale, None):
other = getattr(other, out.scale)
else:
if other.scale is None:
out._set_scale('tai')
else:
if self.scale not in TIME_TYPES[other.scale]:
raise TypeError("Cannot subtract Time and TimeDelta instances "
"with scales '{0}' and '{1}'"
.format(self.scale, other.scale))
out._set_scale(other.scale)
# remove attributes that are invalidated by changing time
for attr in ('_delta_ut1_utc', '_delta_tdb_tt'):
if hasattr(out, attr):
delattr(out, attr)
else: # T - T
# the scales should be compatible (e.g., cannot convert TDB to LOCAL)
if other.scale not in self.SCALES:
raise TypeError("Cannot subtract Time instances "
"with scales '{0}' and '{1}'"
.format(self.scale, other.scale))
self_time = (self._time if self.scale in TIME_DELTA_SCALES
else self.tai._time)
# set up TimeDelta, subtraction to be done shortly
out = TimeDelta(self_time.jd1, self_time.jd2, format='jd',
scale=self_time.scale)
if other.scale != out.scale:
other = getattr(other, out.scale)
jd1 = out._time.jd1 - other._time.jd1
jd2 = out._time.jd2 - other._time.jd2
out._time.jd1, out._time.jd2 = day_frac(jd1, jd2)
if other_is_delta:
# Go back to left-side scale if needed
out._set_scale(self.scale)
return out
def __add__(self, other):
if not isinstance(other, Time):
try:
other = TimeDelta(other)
except Exception:
return NotImplemented
# Tdelta + something is dealt with in TimeDelta, so we have
# T + Tdelta = T
# T + T = error
if not isinstance(other, TimeDelta):
raise OperandTypeError(self, other, '+')
# ideally, we calculate in the scale of the Time item, since that is
# what we want the output in, but this may not be possible, since
# TimeDelta cannot be converted arbitrarily
out = self.replicate()
if self.scale in other.SCALES:
if other.scale not in (out.scale, None):
other = getattr(other, out.scale)
else:
if other.scale is None:
out._set_scale('tai')
else:
if self.scale not in TIME_TYPES[other.scale]:
raise TypeError("Cannot add Time and TimeDelta instances "
"with scales '{0}' and '{1}'"
.format(self.scale, other.scale))
out._set_scale(other.scale)
# remove attributes that are invalidated by changing time
for attr in ('_delta_ut1_utc', '_delta_tdb_tt'):
if hasattr(out, attr):
delattr(out, attr)
jd1 = out._time.jd1 + other._time.jd1
jd2 = out._time.jd2 + other._time.jd2
out._time.jd1, out._time.jd2 = day_frac(jd1, jd2)
# Go back to left-side scale if needed
out._set_scale(self.scale)
return out
def __radd__(self, other):
return self.__add__(other)
def __rsub__(self, other):
out = self.__sub__(other)
return -out
def _time_comparison(self, other, op):
"""If other is of same class as self, compare difference in self.scale.
Otherwise, return NotImplemented
"""
if other.__class__ is not self.__class__:
try:
other = self.__class__(other, scale=self.scale)
except Exception:
# Let other have a go.
return NotImplemented
if(self.scale is not None and self.scale not in other.SCALES or
other.scale is not None and other.scale not in self.SCALES):
# Other will also not be able to do it, so raise a TypeError
# immediately, allowing us to explain why it doesn't work.
raise TypeError("Cannot compare {0} instances with scales "
"'{1}' and '{2}'".format(self.__class__.__name__,
self.scale, other.scale))
if self.scale is not None and other.scale is not None:
other = getattr(other, self.scale)
return op((self.jd1 - other.jd1) + (self.jd2 - other.jd2), 0.)
def __lt__(self, other):
return self._time_comparison(other, operator.lt)
def __le__(self, other):
return self._time_comparison(other, operator.le)
def __eq__(self, other):
"""
If other is an incompatible object for comparison, return `False`.
Otherwise, return `True` if the time difference between self and
other is zero.
"""
return self._time_comparison(other, operator.eq)
def __ne__(self, other):
"""
If other is an incompatible object for comparison, return `True`.
Otherwise, return `False` if the time difference between self and
other is zero.
"""
return self._time_comparison(other, operator.ne)
def __gt__(self, other):
return self._time_comparison(other, operator.gt)
def __ge__(self, other):
return self._time_comparison(other, operator.ge)
def to_datetime(self, timezone=None):
tm = self.replicate(format='datetime')
return tm._shaped_like_input(tm._time.to_value(timezone))
to_datetime.__doc__ = TimeDatetime.to_value.__doc__
class TimeDelta(Time):
"""
Represent the time difference between two times.
A TimeDelta object is initialized with one or more times in the ``val``
argument. The input times in ``val`` must conform to the specified
``format``. The optional ``val2`` time input should be supplied only for
numeric input formats (e.g. JD) where very high precision (better than
64-bit precision) is required.
The allowed values for ``format`` can be listed with::
>>> list(TimeDelta.FORMATS)
['sec', 'jd', 'datetime']
Note that for time differences, the scale can be among three groups:
geocentric ('tai', 'tt', 'tcg'), barycentric ('tcb', 'tdb'), and rotational
('ut1'). Within each of these, the scales for time differences are the
same. Conversion between geocentric and barycentric is possible, as there
is only a scale factor change, but one cannot convert to or from 'ut1', as
this requires knowledge of the actual times, not just their difference. For
a similar reason, 'utc' is not a valid scale for a time difference: a UTC
day is not always 86400 seconds.
Parameters
----------
val : sequence, ndarray, number, `~astropy.units.Quantity` or `~astropy.time.TimeDelta` object
Value(s) to initialize the time difference(s). Any quantities will
be converted appropriately (with care taken to avoid rounding
errors for regular time units).
val2 : sequence, ndarray, number, or `~astropy.units.Quantity`; optional
Additional values, as needed to preserve precision.
format : str, optional
Format of input value(s)
scale : str, optional
Time scale of input value(s), must be one of the following values:
('tdb', 'tt', 'ut1', 'tcg', 'tcb', 'tai'). If not given (or
``None``), the scale is arbitrary; when added or subtracted from a
``Time`` instance, it will be used without conversion.
copy : bool, optional
Make a copy of the input values
"""
SCALES = TIME_DELTA_SCALES
"""List of time delta scales."""
FORMATS = TIME_DELTA_FORMATS
"""Dict of time delta formats."""
info = TimeDeltaInfo()
def __init__(self, val, val2=None, format=None, scale=None, copy=False):
if isinstance(val, TimeDelta):
if scale is not None:
self._set_scale(scale)
else:
if format is None:
format = 'datetime' if isinstance(val, timedelta) else 'jd'
self._init_from_vals(val, val2, format, scale, copy)
if scale is not None:
self.SCALES = TIME_DELTA_TYPES[scale]
def replicate(self, *args, **kwargs):
out = super().replicate(*args, **kwargs)
out.SCALES = self.SCALES
return out
def to_datetime(self):
"""
Convert to ``datetime.timedelta`` object.
"""
tm = self.replicate(format='datetime')
return tm._shaped_like_input(tm._time.value)
def _set_scale(self, scale):
"""
This is the key routine that actually does time scale conversions.
This is not public and not connected to the read-only scale property.
"""
if scale == self.scale:
return
if scale not in self.SCALES:
raise ValueError("Scale {0!r} is not in the allowed scales {1}"
.format(scale, sorted(self.SCALES)))
# For TimeDelta, there can only be a change in scale factor,
# which is written as time2 - time1 = scale_offset * time1
scale_offset = SCALE_OFFSETS[(self.scale, scale)]
if scale_offset is None:
self._time.scale = scale
else:
jd1, jd2 = self._time.jd1, self._time.jd2
offset1, offset2 = day_frac(jd1, jd2, factor=scale_offset)
self._time = self.FORMATS[self.format](
jd1 + offset1, jd2 + offset2, scale,
self.precision, self.in_subfmt,
self.out_subfmt, from_jd=True)
def __add__(self, other):
# only deal with TimeDelta + TimeDelta
if isinstance(other, Time):
if not isinstance(other, TimeDelta):
return other.__add__(self)
else:
try:
other = TimeDelta(other)
except Exception:
return NotImplemented
# the scales should be compatible (e.g., cannot convert TDB to TAI)
if(self.scale is not None and self.scale not in other.SCALES or
other.scale is not None and other.scale not in self.SCALES):
raise TypeError("Cannot add TimeDelta instances with scales "
"'{0}' and '{1}'".format(self.scale, other.scale))
# adjust the scale of other if the scale of self is set (or no scales)
if self.scale is not None or other.scale is None:
out = self.replicate()
if other.scale is not None:
other = getattr(other, self.scale)
else:
out = other.replicate()
jd1 = self._time.jd1 + other._time.jd1
jd2 = self._time.jd2 + other._time.jd2
out._time.jd1, out._time.jd2 = day_frac(jd1, jd2)
return out
def __sub__(self, other):
# only deal with TimeDelta - TimeDelta
if isinstance(other, Time):
if not isinstance(other, TimeDelta):
raise OperandTypeError(self, other, '-')
else:
try:
other = TimeDelta(other)
except Exception:
return NotImplemented
# the scales should be compatible (e.g., cannot convert TDB to TAI)
if(self.scale is not None and self.scale not in other.SCALES or
other.scale is not None and other.scale not in self.SCALES):
raise TypeError("Cannot subtract TimeDelta instances with scales "
"'{0}' and '{1}'".format(self.scale, other.scale))
# adjust the scale of other if the scale of self is set (or no scales)
if self.scale is not None or other.scale is None:
out = self.replicate()
if other.scale is not None:
other = getattr(other, self.scale)
else:
out = other.replicate()
jd1 = self._time.jd1 - other._time.jd1
jd2 = self._time.jd2 - other._time.jd2
out._time.jd1, out._time.jd2 = day_frac(jd1, jd2)
return out
def __neg__(self):
"""Negation of a `TimeDelta` object."""
new = self.copy()
new._time.jd1 = -self._time.jd1
new._time.jd2 = -self._time.jd2
return new
def __abs__(self):
"""Absolute value of a `TimeDelta` object."""
jd1, jd2 = self._time.jd1, self._time.jd2
negative = jd1 + jd2 < 0
new = self.copy()
new._time.jd1 = np.where(negative, -jd1, jd1)
new._time.jd2 = np.where(negative, -jd2, jd2)
return new
def __mul__(self, other):
"""Multiplication of `TimeDelta` objects by numbers/arrays."""
# check needed since otherwise the self.jd1 * other multiplication
# would enter here again (via __rmul__)
if isinstance(other, Time):
raise OperandTypeError(self, other, '*')
try: # convert to straight float if dimensionless quantity
other = other.to(1)
except Exception:
pass
try:
jd1, jd2 = day_frac(self.jd1, self.jd2, factor=other)
out = TimeDelta(jd1, jd2, format='jd', scale=self.scale)
except Exception as err: # try downgrading self to a quantity
try:
return self.to(u.day) * other
except Exception:
raise err
if self.format != 'jd':
out = out.replicate(format=self.format)
return out
def __rmul__(self, other):
"""Multiplication of numbers/arrays with `TimeDelta` objects."""
return self.__mul__(other)
def __div__(self, other):
"""Division of `TimeDelta` objects by numbers/arrays."""
return self.__truediv__(other)
def __rdiv__(self, other):
"""Division by `TimeDelta` objects of numbers/arrays."""
return self.__rtruediv__(other)
def __truediv__(self, other):
"""Division of `TimeDelta` objects by numbers/arrays."""
# cannot do __mul__(1./other) as that looses precision
try:
other = other.to(1)
except Exception:
pass
try: # convert to straight float if dimensionless quantity
jd1, jd2 = day_frac(self.jd1, self.jd2, divisor=other)
out = TimeDelta(jd1, jd2, format='jd', scale=self.scale)
except Exception as err: # try downgrading self to a quantity
try:
return self.to(u.day) / other
except Exception:
raise err
if self.format != 'jd':
out = out.replicate(format=self.format)
return out
def __rtruediv__(self, other):
"""Division by `TimeDelta` objects of numbers/arrays."""
return other / self.to(u.day)
def to(self, *args, **kwargs):
return u.Quantity(self._time.jd1 + self._time.jd2,
u.day).to(*args, **kwargs)
def _make_value_equivalent(self, item, value):
"""Coerce setitem value into an equivalent TimeDelta object"""
if not isinstance(value, TimeDelta):
try:
value = self.__class__(value, scale=self.scale, format=self.format)
except Exception as err:
raise ValueError('cannot convert value to a compatible TimeDelta '
'object: {}'.format(err))
return value
class ScaleValueError(Exception):
pass
def _make_array(val, copy=False):
"""
Take ``val`` and convert/reshape to an array. If ``copy`` is `True`
then copy input values.
Returns
-------
val : ndarray
Array version of ``val``.
"""
val = np.array(val, copy=copy, subok=True)
# Allow only float64, string or object arrays as input
# (object is for datetime, maybe add more specific test later?)
# This also ensures the right byteorder for float64 (closes #2942).
if not (val.dtype == np.float64 or val.dtype.kind in 'OSUMa'):
val = np.asanyarray(val, dtype=np.float64)
return val
def _check_for_masked_and_fill(val, val2):
"""
If ``val`` or ``val2`` are masked arrays then fill them and cast
to ndarray.
Returns a mask corresponding to the logical-or of masked elements
in ``val`` and ``val2``. If neither is masked then the return ``mask``
is ``None``.
If either ``val`` or ``val2`` are masked then they are replaced
with filled versions of themselves.
Parameters
----------
val : ndarray or MaskedArray
Input val
val2 : ndarray or MaskedArray
Input val2
Returns
-------
mask, val, val2: ndarray or None
Mask: (None or bool ndarray), val, val2: ndarray
"""
def get_as_filled_ndarray(mask, val):
"""
Fill the given MaskedArray ``val`` from the first non-masked
element in the array. This ensures that upstream Time initialization
will succeed.
Note that nothing happens if there are no masked elements.
"""
fill_value = None
if np.any(val.mask):
# Final mask is the logical-or of inputs
mask = mask | val.mask
# First unmasked element. If all elements are masked then
# use fill_value=None from above which will use val.fill_value.
# As long as the user has set this appropriately then all will
# be fine.
val_unmasked = val.compressed() # 1-d ndarray of unmasked values
if len(val_unmasked) > 0:
fill_value = val_unmasked[0]
# Fill the input ``val``. If fill_value is None then this just returns
# an ndarray view of val (no copy).
val = val.filled(fill_value)
return mask, val
mask = False
if isinstance(val, np.ma.MaskedArray):
mask, val = get_as_filled_ndarray(mask, val)
if isinstance(val2, np.ma.MaskedArray):
mask, val2 = get_as_filled_ndarray(mask, val2)
return mask, val, val2
class OperandTypeError(TypeError):
def __init__(self, left, right, op=None):
op_string = '' if op is None else ' for {0}'.format(op)
super().__init__(
"Unsupported operand type(s){0}: "
"'{1}' and '{2}'".format(op_string,
left.__class__.__name__,
right.__class__.__name__))