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# 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 enum
import operator
import os
import threading
from collections import defaultdict
from datetime import UTC, date, datetime
from itertools import pairwise
from time import strftime
from typing import TYPE_CHECKING, Union
from warnings import warn
from weakref import WeakValueDictionary
import erfa
import numpy as np
from astropy import constants as const
from astropy import units as u
from astropy.extern import _strptime
from astropy.units import UnitConversionError
from astropy.utils import lazyproperty
from astropy.utils.compat import NUMPY_LT_2_5
from astropy.utils.data_info import MixinInfo, data_info_factory
from astropy.utils.decorators import deprecated
from astropy.utils.exceptions import AstropyDeprecationWarning, AstropyWarning
from astropy.utils.masked import (
MaskableShapedLikeNDArray,
Masked,
combine_masks,
get_data_and_mask,
)
# Below, import TimeFromEpoch to avoid breaking code that followed the old
# example of making a custom timescale in the documentation.
from . import conf
from .formats import (
TIME_DELTA_FORMATS,
TIME_FORMATS,
TimeAstropyTime,
TimeDatetime,
TimeDeltaNumeric,
TimeFromEpoch, # noqa: F401
TimeJD,
TimeUnique,
)
from .time_helper.function_helpers import CUSTOM_FUNCTIONS, UNSUPPORTED_FUNCTIONS
from .utils import day_frac
if TYPE_CHECKING:
import astropy.coordinates
__all__ = [
"STANDARD_TIME_SCALES",
"TIME_DELTA_SCALES",
"TIME_SCALES",
"OperandTypeError",
"ScaleValueError",
"Time",
"TimeBase",
"TimeDelta",
"TimeDeltaMissingUnitWarning",
"TimeInfo",
"TimeInfoBase",
"update_leap_seconds",
]
STANDARD_TIME_SCALES = ("tai", "tcb", "tcg", "tdb", "tt", "ut1", "utc")
LOCAL_SCALES = ("local",)
TIME_TYPES = {
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 = {
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 / (1.0 - erfa.ELG),
("tcg", "tai"): -erfa.ELG,
("tai", "tcg"): erfa.ELG / (1.0 - erfa.ELG),
("tcb", "tdb"): -erfa.ELB,
("tdb", "tcb"): erfa.ELB / (1.0 - 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",), "include_tio": False},
},
"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",), "include_tio": False},
},
}
class _LeapSecondsCheck(enum.Enum):
NOT_STARTED = 0 # No thread has reached the check
RUNNING = 1 # A thread is running update_leap_seconds (_LEAP_SECONDS_LOCK is held)
DONE = 2 # update_leap_seconds has completed
_LEAP_SECONDS_CHECK = _LeapSecondsCheck.NOT_STARTED
_LEAP_SECONDS_LOCK = threading.RLock()
def _compress_array_dims(arr):
"""Compress array by allowing at most 2 * edgeitems + 1 in each dimension.
Parameters
----------
arr : array-like
Array to compress.
Returns
-------
out : array-like
Compressed array.
"""
idxs = []
edgeitems = np.get_printoptions()["edgeitems"]
# Build up a list of index arrays for each dimension, allowing no more than
# 2 * edgeitems + 1 elements in each dimension.
for dim in range(arr.ndim):
if arr.shape[dim] > 2 * edgeitems:
# The middle [edgeitems] value does not matter as it gets replaced
# by ... in the output.
idxs.append(
np.concatenate(
[np.arange(edgeitems), [edgeitems], np.arange(-edgeitems, 0)]
)
)
else:
idxs.append(np.arange(arr.shape[dim]))
# Use the magic np.ix_ function to effectively treat each index array as a
# slicing operator.
idxs_ix = np.ix_(*idxs)
out = arr[idxs_ix]
return out
class TimeInfoBase(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.
This base class is common between TimeInfo and TimeDeltaInfo.
"""
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",
"parquet": "jd1_jd2",
None: "jd1_jd2",
}
def get_sortable_arrays(self):
"""
Return a list of arrays which can be lexically sorted to represent
the order of the parent column.
Returns
-------
arrays : list of ndarray
"""
# Same approach as Time.argsort(): split the full jd1 + jd2 precision
# into an approximate value and a remainder, both of which are plain
# float arrays that can be lexically sorted. This avoids the far more
# expensive approach of creating a new Time object and subtracting.
parent = self._parent
jd1, jd2 = parent.jd1, parent.jd2
jd_approx = jd1 + jd2
jd_remainder = (jd1 - jd_approx) + jd2
return [jd_approx, jd_remainder]
@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(self, map):
if "jd1" in map and "jd2" in map:
# Initialize as JD but revert to desired format and out_subfmt (if needed)
format = map.pop("format")
out_subfmt = map.pop("out_subfmt", None)
map["format"] = "jd"
map["val"] = map.pop("jd1")
map["val2"] = map.pop("jd2")
out = self._parent_cls(**map)
out.format = format
if out_subfmt is not None:
out.out_subfmt = out_subfmt
else:
map["val"] = map.pop("value")
out = self._parent_cls(**map)
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")
}
out = self._parent_cls(jd1, jd2, format="jd", **tm_attrs)
out.format = col0.format
out.out_subfmt = col0.out_subfmt
out.in_subfmt = col0.in_subfmt
# Set remaining info attributes
for attr, value in attrs.items():
setattr(out.info, attr, value)
return out
class TimeInfo(TimeInfoBase):
"""
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.
"""
def _represent_as_dict(self, attrs=None):
"""Get the values for the parent ``attrs`` and return as a dict.
By default, uses '_represent_as_dict_attrs'.
"""
map = super()._represent_as_dict(attrs=attrs)
# TODO: refactor these special cases into the TimeFormat classes?
# The datetime64 format requires special handling for ECSV (see #12840).
# The `value` has numpy dtype datetime64 but this is not an allowed
# datatype for ECSV. Instead convert to a string representation.
if (
self._serialize_context == "ecsv"
and map["format"] == "datetime64"
and "value" in map
):
map["value"] = map["value"].astype("U")
# The datetime format is serialized as ISO with no loss of precision.
if map["format"] == "datetime" and "value" in map:
map["value"] = np.vectorize(lambda x: x.isoformat())(map["value"])
return map
def _construct_from_dict(self, map):
# See comment above. May need to convert string back to datetime64.
# Note that _serialize_context is not set here so we just look for the
# string value directly.
if (
map["format"] == "datetime64"
and "value" in map
and map["value"].dtype.kind == "U"
):
map["value"] = map["value"].astype("datetime64")
# Convert back to datetime objects for datetime format.
if map["format"] == "datetime" and "value" in map:
from datetime import datetime
map["value"] = np.vectorize(datetime.fromisoformat)(map["value"])
delta_ut1_utc = map.pop("_delta_ut1_utc", None)
delta_tdb_tt = map.pop("_delta_tdb_tt", None)
out = super()._construct_from_dict(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
class TimeDeltaInfo(TimeInfoBase):
"""
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.
"""
_represent_as_dict_extra_attrs = ("format", "scale")
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 TimeBase(MaskableShapedLikeNDArray):
"""Base time class from which Time and TimeDelta inherit."""
# 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 __getnewargs__(self):
return (self._time,)
def __getstate__(self):
# For pickling, we remove the cache from what's pickled
state = super().__getstate__().copy()
state.pop("_id_cache", None)
state.pop("cache", None)
return state
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 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(
f"Scale {scale!r} is not in the allowed scales "
f"{sorted(self.SCALES)}"
)
# If either of the input val, val2 are masked arrays then
# find the masked elements and fill them. ``get_data_and_mask`` gives a
# mask of `None` if and only if the input was not masked, so this tells
# us whether masking is in play at all, independently of whether any
# element is actually masked.
data1, mask1 = get_data_and_mask(val)
data2, mask2 = get_data_and_mask(val2)
masked_input = mask1 is not None or mask2 is not None
mask = combine_masks([mask1, mask2])
# Parse / convert input values into internal jd1, jd2 based on format
self._time = self._get_time_fmt(
data1, data2, format, scale, precision, in_subfmt, out_subfmt, mask
)
self._format = self._time.name
# Hack from #9969 to allow passing the location value that has been
# collected by the TimeAstropyTime format class up to the Time level.
# TODO: find a nicer way.
if hasattr(self._time, "_location"):
self._location = self._time._location
del self._time._location
# If any input was masked then mask both jd1 and jd2 accordingly, using a
# shared mask. Note that we deliberately do not check whether any element
# is actually masked: masked input always gives masked output, so that the
# "maskedness" of the input is preserved. From above, ``mask`` is either
# a bool scalar or a bool array broadcastable to the jd1/jd2 shape.
if masked_input:
# Ensure that if the class is already masked, we do not lose it.
self._time.jd1 = Masked(self._time.jd1, copy=False)
self._time.jd1.mask |= mask
# Ensure we share the mask (it may have been broadcast).
self._time.jd2 = Masked(
self._time.jd2, mask=self._time.jd1.mask, copy=False
)
def _get_time_fmt(
self, val, val2, format, scale, precision, in_subfmt, out_subfmt, mask
):
"""
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:
# If val and val2 broadcasted shape is (0,) (i.e. empty array input) then we
# cannot guess format from the input values. But a quantity is fine (as
# long as it has time units, but that will be checked later).
empty_array = val.size == 0 and (val2 is None or val2.size == 0)
if not (isinstance(self, TimeDelta) and isinstance(val, u.Quantity)) and (
empty_array or np.all(mask)
):
raise ValueError(
"cannot guess format from input values with zero-size array"
" or all elements masked"
)
formats = [
(name, cls)
for name, cls in self.FORMATS.items()
if issubclass(cls, TimeUnique)
]
# AstropyTime is a pseudo-format that isn't in the TIME_FORMATS registry,
# but try to guess it at the end.
if isinstance(self, Time):
formats.append(("astropy_time", TimeAstropyTime))
elif not isinstance(format, str):
raise TypeError("format must be a string")
elif format.lower() not in self.FORMATS:
raise ValueError(
f"Format {format!r} is not one of the allowed formats "
f"{sorted(self.FORMATS)}"
)
else:
formats = [(format, self.FORMATS[format])]
masked = np.any(mask)
oval, oval2 = val, val2
problems = {}
for name, cls in formats:
try:
if masked:
val, val2 = cls._fill_masked_values(oval, oval2, mask, in_subfmt)
return cls(val, val2, scale, precision, in_subfmt, out_subfmt)
except UnitConversionError:
raise
except (ValueError, TypeError) as err:
# If ``format`` specified then there is only one possibility, so raise
# immediately and include the upstream exception message to make it
# easier for user to see what is wrong.
if len(formats) == 1:
raise ValueError(
f"Input values did not match the format class {format}:"
+ os.linesep
+ f"{err.__class__.__name__}: {err}"
) from err
else:
problems[name] = err
message = (
"Input values did not match any of the formats where the format "
"keyword is optional:\n"
) + "\n".join(f"- '{name}': {err}" for name, err in problems.items())
raise ValueError(message)
@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', 'unix_tai', 'cxcsec', 'galexsec', 'gps',
'plot_date', 'stardate', 'datetime', 'ymdhms', '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(f"format must be one of {list(self.FORMATS)}")
format_cls = self.FORMATS[format]
# Get the new TimeFormat object to contain time in new format. Possibly
# coerce in/out_subfmt to '*' (default) if existing subfmt values are
# not valid in the new format.
self._time = format_cls(
self._time.jd1,
self._time.jd2,
self._time._scale,
self.precision,
in_subfmt=format_cls._get_allowed_subfmt(self.in_subfmt),
out_subfmt=format_cls._get_allowed_subfmt(self.out_subfmt),
from_jd=True,
)
self._format = format
def to_string(self):
"""Output a string representation of the Time or TimeDelta object.
Similar to ``str(self.value)`` (which uses numpy array formatting) but
array values are evaluated only for the items that actually are output.
For large arrays this can be a substantial performance improvement.
Returns
-------
out : str
String representation of the time values.
"""
npo = np.get_printoptions()
if self.size < npo["threshold"]:
out = str(self.value)
else:
# Compress time object by allowing at most 2 * npo["edgeitems"] + 1
# in each dimension. Then force numpy to use "summary mode" of
# showing only the edge items by setting the size threshold to 0.
# TODO: use np.core.arrayprint._leading_trailing if we have support for
# np.concatenate. See #8610.
tm = _compress_array_dims(self)
with np.printoptions(threshold=0):
out = str(tm.value)
return out
def __repr__(self):
return (
f"<{type(self).__name__} object: scale='{self.scale}' "
f"format='{self.format}' value={self.to_string()}>"
)
def __str__(self):
return self.to_string()
def __hash__(self):
try:
loc = getattr(self, "location", None)
if loc is not None:
loc = loc.x.to_value(u.m), loc.y.to_value(u.m), loc.z.to_value(u.m)
return hash((self.jd1, self.jd2, self.scale, loc))
except TypeError:
if self.ndim != 0:
reason = "(must be scalar)"
elif self.masked:
reason = "(value is masked)"
else:
raise
raise TypeError(f"unhashable type: '{self.__class__.__name__}' {reason}")
@property
def location(self) -> Union["astropy.coordinates.EarthLocation", None]:
return self._location
@location.setter
def location(self, value):
if hasattr(self, "_location"):
# since astropy 6.1.0
warn(
"Setting the location attribute post initialization will be "
"disallowed in a future version of Astropy. "
"Instead you should set the location when creating the Time object. "
"In the future, this will raise an AttributeError.",
category=FutureWarning,
stacklevel=2,
)
self._location = value
@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(
f"Scale {scale!r} is not in the allowed scales {sorted(self.SCALES)}"
)
if scale == "utc" or self.scale == "utc":
# If doing a transform involving UTC then check that the leap
# seconds table is up to date.
_check_leapsec()
# 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
for sys1, sys2 in pairwise(xforms):
# 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_{}_{}".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)
jd1, jd2 = day_frac(jd1, jd2)
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
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):
self._time.in_subfmt = val
del self.cache
@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):
# Setting the out_subfmt property here does validation of ``val``
self._time.out_subfmt = val
del self.cache
@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
------
ValueError
If the new shape has the wrong total number of elements.
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 None or val.size <= 1:
continue
try:
if NUMPY_LT_2_5:
val.shape = shape
else:
val._set_shape(shape)
except Exception:
for val2 in reshaped:
if NUMPY_LT_2_5:
val2.shape = oldshape
else:
val2._set_shape(oldshape)
raise
else:
reshaped.append(val)
def _shaped_like_input(self, value):
if self.masked:
# Ensure the mask is independent.
value = conf._masked_cls(value, mask=self.mask.copy())
# For new-style, we do not treat masked scalars differently from arrays.
if isinstance(value, Masked):
return value
if self._time.jd1.shape:
if isinstance(value, np.ndarray):
return value
else:
raise TypeError(
f"JD is an array ({self._time.jd1!r}) but value is not ({value!r})"
)
else:
# zero-dimensional array, is it safe to unbox? The tricky comparison
# of the mask is for the case that value is structured; otherwise, we
# could just use np.ma.is_masked(value).
if (
isinstance(value, np.ndarray)
and not value.shape
and (
(mask := getattr(value, "mask", np.False_)) == np.zeros_like(mask)
).all()
):
if value.dtype.kind == "M":
# existing test doesn't want datetime64 converted
return value[()]
elif value.dtype.fields:
# Unpack but keep field names; .item() doesn't
# Still don't get python types in the fields
return value[()]
else:
return value.item()
else:
return value
@property
def jd1(self):
"""
First of the two doubles that internally store time value(s) in JD.
"""
return self._shaped_like_input(self._time.jd1)