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# Time and Dates (`astropy.time`)

## Introduction

The `astropy.time` package provides functionality for manipulating times and dates. Specific emphasis is placed on supporting time scales (e.g., UTC, TAI, UT1, TDB) and time representations (e.g., JD, MJD, ISO 8601) that are used in astronomy and required to calculate, for example, sidereal times and barycentric corrections. The `astropy.time` package is based on fast and memory efficient |PyERFA| wrappers around the |ERFA| time and calendar routines.

All time manipulations and arithmetic operations are done internally using two 64-bit floats to represent time. Floating point algorithms from [1] are used so that the |Time| object maintains sub-nanosecond precision over times spanning the age of the universe.

[1]

Shewchuk, 1997, Discrete & Computational Geometry 18(3):305-363

## Getting Started

The usual way to use `astropy.time` is to create a |Time| object by supplying one or more input time values as well as the time format and time scale of those values. The input time(s) can either be a single scalar like `"2010-01-01 00:00:00"` or a list or a `numpy` array of values as shown below. In general, any output values have the same shape (scalar or array) as the input.

### Examples

To create a |Time| object:

    >>> import numpy as np
    >>> from astropy.time import Time
    >>> times = ['1999-01-01T00:00:00.123456789', '2010-01-01T00:00:00']
    >>> t = Time(times, format='isot', scale='utc')
    >>> t
    <Time object: scale='utc' format='isot' value=['1999-01-01T00:00:00.123' '2010-01-01T00:00:00.000']>
    >>> t[1]
    <Time object: scale='utc' format='isot' value=2010-01-01T00:00:00.000>

The `format` argument specifies how to interpret the input values (e.g., ISO, JD, or Unix time). By default, the same format will be used to represent the time for output. One can change this format later as needed, but because this is just for representation, that does not affect the internal representation (which is always by two 64-bit values, the `jd1` and `jd2` attributes), nor any computations with the object.

The `scale` argument specifies the time scale for the values (e.g., UTC, TT, or UT1). The `scale` argument is optional and defaults to UTC except for Time from Epoch Formats. It is possible to change it (e.g., from UTC to TDB), which will cause the internal values to be adjusted accordingly.

We could have written the above as:

    >>> t = Time(times, format='isot')

When the format of the input can be unambiguously determined, the `format` argument is not required, so we can then simplify even further:

    >>> t = Time(times)

Now we can get the representation of these times in the JD and MJD formats by requesting the corresponding |Time| attributes:

    >>> t.jd
    array([2451179.50000143, 2455197.5       ])
    >>> t.mjd
    array([51179.00000143, 55197.        ])

The full power of output representation is available via the `to_value` method which also allows controlling the subformat. For instance, using `numpy.longdouble` as the output type for higher precision:

    >>> t.to_value('mjd', 'long')
    array([51179.00000143, 55197.        ], dtype=float128)

The default representation can be changed by setting the `format` attribute:

    >>> t.format = 'fits'
    >>> t
    <Time object: scale='utc' format='fits' value=['1999-01-01T00:00:00.123'
                                                   '2010-01-01T00:00:00.000']>
    >>> t.format = 'isot'

We can also convert to a different time scale, for instance from UTC to TT. This uses the same attribute mechanism as above but now returns a new |Time| object:

    >>> t2 = t.tt
    >>> t2
    <Time object: scale='tt' format='isot' value=['1999-01-01T00:01:04.307' '2010-01-01T00:01:06.184']>
    >>> t2.jd
    array([2451179.5007443 , 2455197.50076602])

Note that both the ISO (ISOT) and JD representations of `t2` are different than for `t` because they are expressed relative to the TT time scale. Of course, from the numbers or strings you would not be able to tell this was the case:

    >>> print(t2.fits)
    ['1999-01-01T00:01:04.307' '2010-01-01T00:01:06.184']

You can set the time values in place using the usual `numpy` array setting item syntax:

    >>> t2 = t.tt.copy()  # Copy required if transformed Time will be modified
    >>> t2[1] = '2014-12-25'
    >>> print(t2)
    ['1999-01-01T00:01:04.307' '2014-12-25T00:00:00.000']

The |Time| object also has support for missing values, which is particularly useful for Table Operations such as joining and stacking:

    >>> t2[0] = np.ma.masked  # Declare that first time is missing or invalid
    >>> print(t2)
    [                      ——— '2014-12-25T00:00:00.000']

Finally, some further examples of what is possible. For details, see the API documentation below.

    >>> dt = t[1] - t[0]
    >>> dt
    <TimeDelta object: scale='tai' format='jd' value=4018.00002172>

Here, note the conversion of the timescale to TAI. Time differences can only have scales in which one day is always equal to 86400 seconds.

    >>> import numpy as np
    >>> t[0] + dt * np.linspace(0., 1., 12)
    <Time object: scale='utc' format='isot' value=['1999-01-01T00:00:00.123' '2000-01-01T06:32:43.930'
     '2000-12-31T13:05:27.737' '2001-12-31T19:38:11.544'
     '2003-01-01T02:10:55.351' '2004-01-01T08:43:39.158'
     '2004-12-31T15:16:22.965' '2005-12-31T21:49:06.772'
     '2007-01-01T04:21:49.579' '2008-01-01T10:54:33.386'
     '2008-12-31T17:27:17.193' '2010-01-01T00:00:00.000']>

    >>> t.sidereal_time('apparent', 'greenwich')
    <Longitude [6.68050179, 6.70281947] hourangle>

You can also use time-based `Quantity` for time arithmetic:

    >>> import astropy.units as u
    >>> Time("2020-01-01") + 5 * u.day
    <Time object: scale='utc' format='iso' value=2020-01-06 00:00:00.000>

As of v5.1, |Time| objects can also be passed directly to `numpy.linspace` to create even-sampled time arrays, including support for non-scalar `start` and/or `stop` points - given compatible shapes.

    >>> stop = ['1999-01-05T00:00:00.123456789', '2010-05-01T00:00:00']
    >>> tstp = Time(stop, format='isot', scale='utc')
    >>> np.linspace(t, tstp, 4, endpoint=False)
    <Time object: scale='utc' format='isot' value=[['1999-01-01T00:00:00.123' '2010-01-01T00:00:00.000']
     ['1999-01-02T00:00:00.123' '2010-01-31T00:00:00.000']
     ['1999-01-03T00:00:00.123' '2010-03-02T00:00:00.000']
     ['1999-01-04T00:00:00.123' '2010-04-01T00:00:00.000']]>

## Using `astropy.time`

### Time Object Basics

In `astropy.time` a “time” is a single instant of time which is independent of the way the time is represented (the “format”) and the time “scale” which specifies the offset and scaling relation of the unit of time. There is no distinction made between a “date” and a “time” since both concepts (as loosely defined in common usage) are just different representations of a moment in time.

#### Time Format

The time format specifies how an instant of time is represented. The currently available formats are can be found in the `Time.FORMATS` dict and are listed in the table below. Each of these formats is implemented as a class that derives from the base `TimeFormat` class. This class structure can be adapted and extended by users for specialized time formats not supplied in `astropy.time`.

Format | Class | Example Argument
--- | --- | ---
byear | `TimeBesselianEpoch` | 1950.0
byear_str | `TimeBesselianEpochString` | ‘B1950.0’
cxcsec | `TimeCxcSec` | 63072064.184
datetime | `TimeDatetime` | datetime(2000, 1, 2, 12, 0, 0)
datetime64 | `TimeDatetime64` | np.datetime64(‘2000-01-01T01:01:01’)
decimalyear | `TimeDecimalYear` | 2000.45
fits | `TimeFITS` | ‘2000-01-01T00:00:00.000’
galexsec | `TimeGalexSec` | 758738047.995
gps | `TimeGPS` | 630720013.0
iso | `TimeISO` | ‘2000-01-01 00:00:00.000’
isot | `TimeISOT` | ‘2000-01-01T00:00:00.000’
jd | `TimeJD` | 2451544.5
jyear | `TimeJulianEpoch` | 2000.0
jyear_str | `TimeJulianEpochString` | ‘J2000.0’
mjd | `TimeMJD` | 51544.0
plot_date | `TimePlotDate` | 730120.0003703703
unix | `TimeUnix` | 946684800.0
unix_tai | `TimeUnixTai` | 946684800.0
yday | `TimeYearDayTime` | 2000:001:00:00:00.000
ymdhms | `TimeYMDHMS` | {‘year’: 2010, ‘month’: 3, ‘day’: 1}

Note

The `TimeFITS` format implements most of the FITS standard [2], including support for the `LOCAL` timescale. Note, though, that FITS supports some deprecated names for timescales; these are translated to the formal names upon initialization. Furthermore, any specific realization information, such as `UT(NIST)` is stored only as long as the time scale is not changed.

[2]

Rots et al. 2015, A&A 574:A36

##### Changing Format

The default representation can be changed by setting the `format` attribute:

    >>> t = Time('2000-01-02')
    >>> t.format = 'jd'
    >>> t
    <Time object: scale='utc' format='jd' value=2451545.5>

Be aware that when changing format, the current output subformat (see section below) may not exist in the new format. In this case, the subformat will not be preserved:

    >>> t = Time('2000-01-02', format='fits', out_subfmt='longdate')
    >>> t.value
    '+02000-01-02'
    >>> t.format = 'iso'
    >>> t.out_subfmt
    u'*'
    >>> t.format = 'fits'
    >>> t.value
    '2000-01-02T00:00:00.000'

##### Subformat

Many of the available time format classes support the concept of a subformat. This allows for variations on the basic theme of a format in both the input parsing/validation and the output.

The table below illustrates available subformats for the string formats

  `iso`, `fits`, and `yday` formats:

Format | Subformat | Input / Output
--- | --- | ---
`iso` | date_hms | 2001-01-02 03:04:05.678
`iso` | date_hm | 2001-01-02 03:04
`iso` | date | 2001-01-02
`fits` | date_hms | 2001-01-02T03:04:05.678
`fits` | longdate_hms | +02001-01-02T03:04:05.678
`fits` | longdate | +02001-01-02
`yday` | date_hms | 2001:032:03:04:05.678
`yday` | date_hm | 2001:032:03:04
`yday` | date | 2001:032

Numerical formats such as `mjd`, `jyear`, or `cxcsec` all support the subformats: `'float'`, `'long'`, `'decimal'`, `'str'`, and `'bytes'`. Here, `'long'` uses `numpy.longdouble` for somewhat enhanced precision (with the enhancement depending on platform), and `'decimal'` instances of `decimal.Decimal` for full precision. For the `'str'` and `'bytes'` subformats, the number of digits is also chosen such that time values are represented accurately.

When used on input, these formats allow creating a time using a single input value that accurately captures the value to the full available precision in |Time|. Conversely, the single value on output using |Time| `to_value` or |TimeDelta| `to_value` can have higher precision than the standard 64-bit float:

    >>> tm = Time('51544.000000000000001', format='mjd')  # String input
    >>> tm.mjd  # float64 output loses last digit but Decimal gets it
    np.float64(51544.0)
    >>> tm.to_value('mjd', subfmt='decimal')
    Decimal('51544.00000000000000099920072216264')
    >>> tm.to_value('mjd', subfmt='str')
    '51544.000000000000001'

The complete list of subformat options for the |Time| formats that have them is:

Format | Subformats
--- | ---
`byear` | float, long, decimal, str, bytes
`cxcsec` | float, long, decimal, str, bytes
`datetime64` | date_hms, date_hm, date
`decimalyear` | float, long, decimal, str, bytes
`fits` | date_hms, date, longdate_hms, longdate
`gps` | float, long, decimal, str, bytes
`iso` | date_hms, date_hm, date
`isot` | date_hms, date_hm, date
`jd` | float, long, decimal, str, bytes
`jyear` | float, long, decimal, str, bytes
`mjd` | float, long, decimal, str, bytes
`plot_date` | float, long, decimal, str, bytes
`unix` | float, long, decimal, str, bytes
`unix_tai` | float, long, decimal, str, bytes
`yday` | date_hms, date_hm, date

The complete list of subformat options for the |TimeDelta| formats that have them is:

Format | Subformats
--- | ---
`jd` | float, long, decimal, str, bytes
`sec` | float, long, decimal, str, bytes
`quantity_str` | multi, yr, d, hr, min, s

##### Time from Epoch Formats

The formats `cxcsec`, `gps`, `unix`, and `unix_tai` are special in that they provide a floating point representation of the elapsed time in seconds (with the caveat that `unix` does not include leap seconds) since a particular reference date. These formats have a intrinsic time scale which is used to compute the elapsed seconds since the reference date.

Format | Scale | Reference date
--- | --- | ---
`cxcsec` | TT | `1998-01-01 00:00:00`
`unix` | UTC | `1970-01-01 00:00:00`
`unix_tai` | TAI | `1970-01-01 00:00:00`
`gps` | TAI | `1980-01-06 00:00:19`

Unlike the other formats which default to UTC, if no `scale` is provided when initializing a |Time| object then the above intrinsic scale is used. This is done for computational efficiency.

#### Time Scale

The time scale (or time standard) is “a specification for measuring time: either the rate at which time passes; or points in time; or both” [3], [4].

    >>> Time.SCALES
    ('tai', 'tcb', 'tcg', 'tdb', 'tt', 'ut1', 'utc', 'local')

Scale | Description
--- | ---
tai | International Atomic Time (TAI)
tcb | Barycentric Coordinate Time (TCB)
tcg | Geocentric Coordinate Time (TCG)
tdb | Barycentric Dynamical Time (TDB)
tt | Terrestrial Time (TT)
ut1 | Universal Time (UT1)
utc | Coordinated Universal Time (UTC)
local | Local Time Scale (LOCAL)

[3]

Wikipedia time standard article

[4]

|SOFA| Time Scale and Calendar Tools (PDF)

Note

The `local` time scale is meant for free-running clocks or simulation times (i.e., to represent a time without a properly defined scale). This means it cannot be converted to any other time scale, and arithmetic is possible only with |Time| instances with scale `local` and with |TimeDelta| instances with scale `local` or `None`.

The system of transformation between supported time scales (i.e., all but `local`) is shown in the figure below. Further details are provided in the Convert time scale section.

#### Scalar or Array

A |Time| object can hold either a single time value or an array of time values. The distinction is made entirely by the form of the input time(s). If a |Time| object holds a single value then any format outputs will be a single scalar value, and likewise for arrays.

##### Example

Like other arrays and lists, |Time| objects holding arrays are subscriptable, returning scalar or array objects as appropriate:

    >>> from astropy.time import Time
    >>> t = Time(100.0, format='mjd')
    >>> t.jd
    np.float64(2400100.5)
    >>> t = Time([100.0, 200.0, 300.], format='mjd')
    >>> t.jd
    array([2400100.5, 2400200.5, 2400300.5])
    >>> t[:2]
    <Time object: scale='utc' format='mjd' value=[100. 200.]>
    >>> t[2]
    <Time object: scale='utc' format='mjd' value=300.0>
    >>> t = Time(np.arange(50000., 50003.)[:, np.newaxis],
    ...          np.arange(0., 1., 0.5), format='mjd')
    >>> t
    <Time object: scale='utc' format='mjd' value=[[50000.  50000.5]
     [50001.  50001.5]
     [50002.  50002.5]]>
    >>> t[0]
    <Time object: scale='utc' format='mjd' value=[50000.  50000.5]>

#### NumPy Method Analogs and Applicable NumPy Functions

For |Time| instances holding arrays, many of the same methods and attributes that work on `ndarray` instances can be used. For example, you can reshape |Time| instances and take specific parts using `reshape()`, `ravel()`, `flatten()`, `T`, `transpose()`, `swapaxes()`, `diagonal()`, `squeeze()`, or `take()`. Corresponding functions, as well as others that affect the shape, such as `atleast_1d` and `rollaxis`, work as expected. (The relevant functions have to be explicitly enabled in `astropy` source code; let us know if a `numpy` function is not supported that you think should work.)

##### Examples

To reshape |Time| instances:

    >>> t.reshape(2, 3)
    <Time object: scale='utc' format='mjd' value=[[50000.  50000.5 50001. ]
     [50001.5 50002.  50002.5]]>
    >>> t.T
    <Time object: scale='utc' format='mjd' value=[[50000.  50001.  50002. ]
     [50000.5 50001.5 50002.5]]>
    >>> np.roll(t, 1, axis=0)
    <Time object: scale='utc' format='mjd' value=[[50002.  50002.5]
     [50000.  50000.5]
     [50001.  50001.5]]>

Note that similarly to the `ndarray` methods, all but `flatten()` try to use new views of the data, with the data copied only if that is impossible (as discussed, for example, in the documentation for `numpy` `reshape()`).

Some arithmetic methods are supported as well: `min()`, `max()`, `ptp()`, `sort()`, `argmin()`, `argmax()`, and `argsort()`.

To apply arithmetic methods to |Time| instances:

    >>> t.max()
    <Time object: scale='utc' format='mjd' value=50002.5>
    >>> t.min()
    <Time object: scale='utc' format='mjd' value=50000.0>

#### Inferring Input Format

The |Time| class initializer will not accept ambiguous inputs, but it will make automatic inferences in cases where the inputs are unambiguous. This can apply when the times are supplied as objects, inputs for `ymdhms`, or strings. In the latter case it is not required to specify the format because the available string formats have no overlap. However, if the format is known in advance the string parsing will be faster if the format is provided.

##### Example

To infer input format:

    >>> from datetime import datetime, timezone
    >>> t = Time(datetime(2010, 1, 2, 1, 2, 3))
    >>> t.format
    'datetime'
    >>> t = Time('2010-01-02 01:02:03')
    >>> t.format
    'iso'

#### Internal Representation

The |Time| object maintains an internal representation of time as a pair of double precision numbers expressing Julian days. The sum of the two numbers is the Julian Date for that time relative to the given time scale. Users requiring no better than microsecond precision over human time scales (~100 years) can safely ignore the internal representation details and skip this section.

This representation is driven by the underlying |ERFA| C-library implementation. The ERFA routines take care throughout to maintain overall precision of the double pair. Users are free to choose the way in which total JD is provided, though internally one part contains integer days and the other the fraction of the day, as this ensures optimal accuracy for all conversions. The internal JD pair is available via the `jd1` and `jd2` attributes:

    >>> t = Time('2010-01-01 00:00:00', scale='utc')
    >>> t.jd1, t.jd2
    (2455198.0, -0.5)
    >>> t2 = t.tai
    >>> t2.jd1, t2.jd2
    (2455198., -0.49960648148148146)

### Creating a Time Object

The allowed |Time| arguments to create a time object are listed below:

val numpy ndarray, list, str, or number

  Data to initialize table.

val2 numpy ndarray, list, str, or number; optional

  Data to initialize table.

format str, optional

  Format of input value(s).

scale str, optional

  Time scale of input value(s).

precision int between 0 and 9 inclusive

  Decimal precision when outputting seconds as floating point.

in_subfmt str

  Unix glob to select subformats for parsing input times.

out_subfmt str

  Unix glob to select subformat for output times.

location|EarthLocation| or tuple, optional

  If a tuple, three |Quantity| items with length units for geocentric coordinates, or a longitude, latitude, and optional height for geodetic coordinates. Can be a single location, or one for each input time.

#### val

The `val` argument specifies the input time or times and can be a single string or number, or it can be a Python list or `numpy` array of strings or numbers. To initialize a |Time| object based on a specified time, it must be present.

In most situations, you also need to specify the time scale via the `scale` argument. The |Time| class will never guess the time scale, so a concise example would be:

    >>> t1 = Time(50100.0, scale='tt', format='mjd')
    >>> t2 = Time('2010-01-01 00:00:00', scale='utc')

It is possible to create a new |Time| object from one or more existing time objects. In this case, the format and scale will be inferred from the first object unless explicitly specified.

    >>> Time([t1, t2])
    <Time object: scale='tt' format='mjd' value=[50100. 55197.00076602]>

#### val2

The `val2` argument is available for those situations where high precision is required. Recall that the internal representation of time within `astropy.time` is two double-precision numbers that when summed give the Julian date. If provided, the `val2` argument is used in combination with `val` to set the second of the internal time values. The exact interpretation of `val2` is determined by the input format class. All string-valued formats ignore `val2` and all numeric inputs effectively add the two values in a way that maintains the highest precision. For example:

    >>> t = Time(100.0, 0.000001, format='mjd', scale='tt')
    >>> t.jd, t.jd1, t.jd2
    (np.float64(2400100.500001), 2400101.0, -0.499999)

#### format

The `format` argument sets the time time format, and as mentioned it is required unless the format can be unambiguously determined from the input times.

#### scale

The `scale` argument sets the time scale and is required except for time formats such as `plot_date` (`TimePlotDate`) and `unix` (`TimeUnix`). These formats represent the duration in SI seconds since a fixed instant in time is independent of time scale. See the Time from Epoch Formats for more details.

#### precision

The `precision` setting affects string formats when outputting a value that includes seconds. It must be an integer between 0 and 9. There is no effect when inputting time values from strings. The default precision is 3. Note that the limit of 9 digits is driven by the way that |ERFA| handles fractional seconds. In practice this should should not be an issue.

    >>> t = Time('B1950.0', precision=3)
    >>> t.byear_str
    'B1950.000'
    >>> t.precision = 0
    >>> t.byear_str
    'B1950'

#### in_subfmt

The `in_subfmt` argument provides a mechanism to select one or more subformat values from the available subformats for input. Multiple allowed subformats can be selected using Unix-style wildcard characters, in particular `*` and `?`, as documented in the Python fnmatch module.

The default value for `in_subfmt` is `*` which matches any available subformat. This allows for convenient input of values with unknown or heterogeneous subformat:

    >>> Time(['2000:001', '2000:002:03:04', '2001:003:04:05:06.789'])
    <Time object: scale='utc' format='yday'
     value=['2000:001:00:00:00.000' '2000:002:03:04:00.000' '2001:003:04:05:06.789']>

You can explicitly specify `in_subfmt` in order to strictly require a certain subformat:

    >>> t = Time('2000:002:03:04', in_subfmt='date_hm')
    >>> t = Time('2000:002', in_subfmt='date_hm')
    Traceback (most recent call last):
      ...
    ValueError: Input values did not match any of the formats where the
    format keyword is optional ['astropy_time', 'datetime',
    'byear_str', 'iso', 'isot', 'jyear_str', 'yday']

#### out_subfmt

The `out_subfmt` argument is similar to `in_subfmt` except that it applies to output formatting. In the case of multiple matching subformats, the first matching subformat is used.

    >>> Time('2000-01-01 02:03:04', out_subfmt='date').iso
    '2000-01-01'
    >>> Time('2000-01-01 02:03:04', out_subfmt='date_hms').iso
    '2000-01-01 02:03:04.000'
    >>> Time('2000-01-01 02:03:04', out_subfmt='date*').iso
    '2000-01-01 02:03:04.000'
    >>> Time('50814.123456789012345', format='mjd', out_subfmt='str').mjd
    '50814.123456789012345'

See also the subformat section.

#### location

This optional parameter specifies the observer location, using an |EarthLocation| object or a tuple containing any form that can initialize one: either a tuple with geocentric coordinates (X, Y, Z), or a tuple with geodetic coordinates (longitude, latitude, height; with height defaulting to zero). They are used for time scales that are sensitive to observer location (currently, only TDB, which relies on the |PyERFA| routine `erfa.dtdb` to determine the time offset between TDB and TT), as well as for sidereal time if no explicit longitude is given.

    >>> t = Time('2001-03-22 00:01:44.732327132980', scale='utc',
    ...          location=('120d', '40d'))
    >>> t.sidereal_time('apparent', 'greenwich')
    <Longitude 12. hourangle>
    >>> t.sidereal_time('apparent')
    <Longitude 20. hourangle>

Note

In future versions, we hope to add the possibility to add observatory objects and/or names.

#### Getting the Current Time

The current time can be determined as a |Time| object using the `now` class method:

    >>> nt = Time.now()
    >>> ut = Time(datetime.now(tz=timezone.utc), scale='utc')

The two should be very close to each other.

#### Fast C-based Date String Parser

Time formats that are based on a date string representation of time, including `TimeISO`, `TimeISOT`, and `TimeYearDayTime`, make use of a fast C-based date parser that improves speed by a factor of 20 or more for large arrays of times.

The C parser is stricter than the Python-based parser (which relies on `strptime`). In particular fields like the month or day of year must always have a fixed number of ASCII digits. As an example the Python parser will accept `2000-1-2T3:04:5.23` while the C parser requires `2000-01-02T03:04:05.23`

Use of the C parser is enabled by default except when the input subformat `in_subfmt` argument is different from the default value of `'*'`. If the fast C parser fails to parse the date values then the |Time| initializer will automatically fall through to the Python parser.

In rare cases where you need to explicitly control which parser gets used there is a configuration item `time.conf.use_fast_parser` that can be set. The default is `'True'`, which means to try the fast parser and fall through to Python parser if needed. Note that the configuration value is a string, not a bool object.

For example to disable the C parser use:

    >>> from astropy.time import conf
    >>> date = '2000-1-2T3:04:5.23'
    >>> t = Time(date, format='isot')  # Succeeds by default
    >>> with conf.set_temp('use_fast_parser', 'False'):
    ...     t = Time(date, format='isot')
    ...     print(t)
    2000-01-02T03:04:05.230

To force the user of the C parser (for example in testing) use:

    >>> with conf.set_temp('use_fast_parser', 'force'):
    ...     try:
    ...          t = Time(date, format='isot')
    ...     except ValueError as err:
    ...          print(err)
    Input values did not match the format class isot:
    ValueError: fast C time string parser failed: non-digit found where digit (0-9) required

### Using Time Objects

The operations available with |Time| objects include:

- Get and set time value(s) for an array-valued |Time| object.

- Set missing (masked) values.

- Get the representation of the time value(s) in a particular time format.

- Get a new time object for the same time value(s) but referenced to a different time scale.

- Calculate sidereal time and Earth rotation angle corresponding to the time value(s).

- Do time arithmetic involving |Time|, |TimeDelta| and/or |Quantity| objects with units of time.

#### Get and Set Values

For an existing |Time| object which is array-valued, you can use the usual `numpy` array item syntax to get either a single item or a subset of items. The returned value is a |Time| object with all the same attributes.

##### Examples

To get an item or a subset of items:

    >>> t = Time(['2001:020', '2001:040', '2001:060', '2001:080'],
    ...          out_subfmt='date')
    >>> print(t[1])
    2001:040
    >>> print(t[1:])
    ['2001:040' '2001:060' '2001:080']
    >>> print(t[[2, 0]])
    ['2001:060' '2001:020']

You can also set values in place for an array-valued |Time| object:

    >>> t = Time(['2001:020', '2001:040', '2001:060', '2001:080'],
    ...          out_subfmt='date')
    >>> t[1] = '2010:001'
    >>> print(t)
    ['2001:020' '2010:001' '2001:060' '2001:080']
    >>> t[[2, 0]] = '1990:123'
    >>> print(t)
    ['1990:123' '2010:001' '1990:123' '2001:080']

The new value (on the right hand side) when setting can be one of three possibilities:

- Scalar string value or array of string values where each value is in a valid time format that can be automatically parsed and used to create a |Time| object.

- Value or array of values where each value has the same `format` as the |Time| object being set. For instance, a float or `numpy` array of floats for an object with `format='unix'`.

- |Time| object with identical `location` (but `scale` and `format` need not be the same). The right side value will be transformed so the time `scale` matches.

Whenever any item is set, then the internal cache (see Caching) is cleared along with the `delta_tdb_tt` and/or `delta_ut1_utc` transformation offsets, if they have been set.

If it is required that the |Time| object be immutable, then set the `writeable` attribute to `False`. In this case, attempting to set a value will raise a `ValueError: Time object is read-only`. See the section on Caching for an example.

#### Missing Values

The |Time| and |TimeDelta| objects support functionality for marking values as missing or invalid. This is also known as masking, and is especially useful for Table Operations such as joining and stacking.

##### Example

You can set one or more items as missing when creating the object in one of two ways. First with a numpy masked array:

    >>> dates = np.ma.array(['2001:020', '...', '2001:060'], mask=[False, True, False])
    >>> print(Time(dates, out_subfmt="date"))
    ['2001:020'        ——— '2001:060']

Second with the `astropy.utils.masked.Masked` class:

    >>> from astropy.utils.masked import Masked
    >>> dates = Masked(['2001:020', '', '2001:060'], mask=[False, True, False])
    >>> t = Time(dates, out_subfmt="date")
    >>> print(t)
    ['2001:020'        ——— '2001:060']

You can also use the special `numpy.ma.masked` to set a value as missing in an existing |Time| object:

    >>> t = Time(["2001:020", "2001:040", "2001:060", "2001:080"], out_subfmt="date")
    >>> t[2] = np.ma.masked
    >>> print(t)
    ['2001:020' '2001:040'        ——— '2001:080']

If you want to get unmasked data, you can get those either by removing the mask using the `unmasked` attribute, or by filling any masked data with a chosen value:

    >>> print(t.unmasked)
    ['2001:020' '2001:040' '2001:060' '2001:080']
    >>> t_filled = t.filled('1999:365')
    >>> print(t_filled)
    ['2001:020' '2001:040' '1999:365' '2001:080']

You can also unset the mask on individual elements by assigning another special value, `numpy.ma.nomask`:

    >>> t[2] = np.ma.nomask
    >>> print(t)
    ['2001:020' '2001:040' '2001:060' '2001:080']

A subtle difference between the two approaches is that when you unset the mask by setting with `numpy.ma.nomask`, a mask is still present internally, and hence any output will have a mask as well. In contrast, using `unmasked` or `filled()` removes all masking, and hence any output is not masked. The `masked` property can be used to check whether or not a mask is in use internally:

    >>> t.masked
    True
    >>> t.value
    MaskedNDArray(['2001:020', '2001:040', '2001:060', '2001:080'],
                dtype='<U8')
    >>> t_filled.masked
    False
    >>> t_filled.value
    array(['2001:020', '2001:040', '1999:365', '2001:080'], dtype='<U8')

Note

When setting the mask, actual time data are kept. However, when initializing with a masked array, any masked time input data are overwritten internally, with a time equivalent to `2000-01-01 12:00:00` (in the same scale and format as the other values). This is to ensure no errors or warnings are raised by invalid data hidden by the mask. Hence, for initialization with masked data, there is no way to recover the original masked values:

    >>> dates = Masked(['2001:020', '2001:040', '2001:060'],
    ...                mask=[False, True, False])
    >>> tm = Time(dates, out_subfmt="date")
    >>> tm[2] = np.ma.masked
    >>> print(tm)
    ['2001:020'        ———        ———]
    >>> print(tm.unmasked)
    ['2001:020' '2000:001' '2001:060']

Once one or more values in the object are masked, any operations will propagate those values as masked, and access to format attributes such as `unix` or `value` will return a |Masked| object:

    >>> t[1:3] = np.ma.masked
    >>> t.isot
    MaskedNDArray(['2001-01-20',          ———,          ———, '2001-03-21'],
                  dtype='<U10')

You can view the `mask`, but note that it is read-only. Setting and clearing the mask is always done by setting the item to `masked` and `nomask`, respectively.

    >>> t.mask
    array([False, True,  True, False])

##### Choice of Masked Array Type

|Time| internally uses astropy’s |Masked| class to represent the mask. It is possible to initialize with data using numpy’s `MaskedArray` class, but by default all output will use |Masked|. For backward compatibility, it is possible to set `masked_array_type` to “numpy” to ensure that output uses `MaskedArray` where possible (for all but |Quantity|).

##### Custom Format Classes and Masked Values

For advanced users who have written a custom time format via a `TimeFormat` subclass, it may be necessary to modify your class if you wish to support masked values, and especially if you earlier supported having missing values by setting the `jd2` attribute to `numpy.nan`. For applications that do not need masked or missing values, no changes are required.

Prior to astropy 6.0, missing values in a `TimeFormat` subclass object were marked by setting the corresponding entries of the `jd2` attribute to be `numpy.nan` (but this was never done directly by the user). Since astropy 6.0, instead |Masked| arrays are used, and these are written to propagate properly through (almost) all numpy and |ERFA| functions.

In general, very few modifications should be needed to support |Masked| arrays. Generally, on input, no changes are needed since the format will be given unmasked values (with any masked input values replaced with the default value to ensure that only valid values are passed in). Some care may need to be taken, though, that the mask is propagated properly in calculating output values from `jd1` to `jd2` in the `value` property.

#### Get Representation

Instants of time can be represented in different ways, for instance as an ISO-format date string (`'1999-07-23 04:31:00'`) or seconds since 1998.0 (`49091460.0`) or Modified Julian Date (`51382.187451574`).

The representation of a |Time| object in a particular format is available by getting the object attribute corresponding to the format name. The list of available format names is in the time format section.

    >>> t = Time('2010-01-01 00:00:00', format='iso', scale='utc')
    >>> t.jd        # JD representation of time in current scale (UTC)
    np.float64(2455197.5)
    >>> t.iso       # ISO representation of time in current scale (UTC)
    '2010-01-01 00:00:00.000'
    >>> t.unix      # seconds since 1970.0 (UTC)
    np.float64(1262304000.0)
    >>> t.datetime  # Representation as datetime.datetime object
    datetime.datetime(2010, 1, 1, 0, 0)

##### Example

To get the representation of a |Time| object:

    >>> import matplotlib.pyplot as plt
    >>> jyear = np.linspace(2000, 2001, 20)
    >>> t = Time(jyear, format='jyear')
    >>> fig, ax = plt.subplots()
    >>> ax.scatter(t.datetime, jyear)
    >>> fig.autofmt_xdate()  # orient date labels at a slant
    >>> fig.show()

#### Convert Time Scale

A new |Time| object for the same time value(s) but referenced to a new time scale can be created getting the object attribute corresponding to the time scale name. The list of available time scale names is in the time scale section and in the figure below illustrating the network of time scale transformations.

##### Examples

To create a |Time| object with a new time scale:

    >>> t = Time('2010-01-01 00:00:00', format='iso', scale='utc')
    >>> t.tt        # TT scale
    <Time object: scale='tt' format='iso' value=2010-01-01 00:01:06.184>
    >>> t.tai
    <Time object: scale='tai' format='iso' value=2010-01-01 00:00:34.000>

In this process the `format` and other object attributes like `lon`, `lat`, and `precision` are also propagated to the new object.

As noted in the Time Object Basics section, a |Time| object can only be changed by explicitly setting some of its elements. The process of changing the time scale therefore begins by making a copy of the original object and then converting the internal time values in the copy to the new time scale. The new |Time| object is returned by the attribute access.

#### Caching

The computations for transforming to different time scales or formats can be time-consuming for large arrays. In order to avoid repeated computations, each |Time| or |TimeDelta| instance caches such transformations internally:

    >>> t = Time(np.arange(1e6), format='unix', scale='utc')

    >>> time x = t.tt
    CPU times: user 263 ms, sys: 4.02 ms, total: 267 ms
    Wall time: 267 ms

    >>> time x = t.tt
    CPU times: user 28 µs, sys: 9 µs, total: 37 µs
    Wall time: 32.9 µs

Actions such as changing the output precision or subformat will clear the cache. In order to explicitly clear the internal cache do:

    >>> del t.cache

    >>> time x = t.tt
    CPU times: user 263 ms, sys: 4.02 ms, total: 267 ms
    Wall time: 267 ms

In order to ensure consistency between the transformed (and cached) version and the original, the transformed object is set to be not writeable. For example:

    >>> x = t.tt
    >>> x[1] = '2000:001'
    Traceback (most recent call last):
      ...
    ValueError: Time object is read-only. Make a copy() or set "writeable" attribute to True.

If you require modifying the object then make a copy first, for example, `x = t.tt.copy()`.

##### Transformation Offsets

Time scale transformations that cross one of the orange circles in the image above require an additional offset time value that is model or observation dependent. See |SOFA| Time Scale and Calendar Tools for further details.

The two attributes `delta_ut1_utc` and `delta_tdb_tt` provide a way to set these offset times explicitly. These represent the time scale offsets UT1 - UTC and TDB - TT, respectively. As an example:

    >>> t = Time('2010-01-01 00:00:00', format='iso', scale='utc')
    >>> t.delta_ut1_utc = 0.334  # Explicitly set one part of the transformation
    >>> t.ut1.iso    # ISO representation of time in UT1 scale
    '2010-01-01 00:00:00.334'

For the UT1 to UTC offset, you have to interpolate the observed values provided by the International Earth Rotation and Reference Systems (IERS) Service. `astropy` will automatically download and use values from the IERS which cover times spanning from 1973-Jan-01 through one year into the future. In addition, the `astropy` package is bundled with a data table of values provided in Bulletin B, which cover the period from 1962 to shortly before an `astropy` release.

When the `delta_ut1_utc` attribute is not set explicitly, IERS values will be used (initiating a download of a few Mb file the first time). For details about how IERS values are used in `astropy` time and coordinates, and to understand how to control automatic downloads, see IERS data access (astropy.utils.iers). The example below illustrates converting to the `UT1` scale along with the auto-download feature:

    >>> t = Time('2016:001')
    >>> t.ut1
    Downloading https://maia.usno.navy.mil/ser7/finals2000A.all
    |==================================================================| 3.0M/3.0M (100.00%)         6s
    <Time object: scale='ut1' format='yday' value=2016:001:00:00:00.082>

Note

The `IERS_Auto` class contains machinery to ensure that the IERS table is kept up to date by auto-downloading the latest version as needed. This means that the IERS table is assured of having the state-of-the-art definitive and predictive values for Earth rotation. As a user it is your responsibility to understand the accuracy of IERS predictions if your science depends on that. If you request `UT1-UTC` for times beyond the range of IERS table data then the nearest available values will be provided.

In the case of the TDB to TT offset, most users need only provide the `location` when creating the |Time| object. If the `delta_tdb_tt` attribute is not explicitly set, then the |PyERFA| routine `erfa.dtdb` will be used to compute the TDB to TT offset. If `location` is not specified, the center of the Earth is assumed.

###### Example

The following code replicates an example in the |SOFA| Time Scale and Calendar Tools document. It does the transform from UTC to all supported time scales (TAI, TCB, TCG, TDB, TT, UT1, UTC). This requires an observer location (here, latitude and longitude).

    >>> import astropy.units as u
    >>> t = Time('2006-01-15 21:24:37.5', format='iso', scale='utc',
    ...          location=(-155.933222*u.deg, 19.48125*u.deg))
    >>> t.utc.iso
    '2006-01-15 21:24:37.500'
    >>> t.ut1.iso
    '2006-01-15 21:24:37.834'
    >>> t.tai.iso
    '2006-01-15 21:25:10.500'
    >>> t.tt.iso
    '2006-01-15 21:25:42.684'
    >>> t.tcg.iso
    '2006-01-15 21:25:43.323'
    >>> t.tdb.iso
    '2006-01-15 21:25:42.684'
    >>> t.tcb.iso
    '2006-01-15 21:25:56.894'

#### Hashing

A user can generate a unique hash key for scalar (0-dimensional) |Time| or |TimeDelta| objects. The key is based on a tuple of `jd1`, `jd2`, `scale`, and `location` (if present, `None` otherwise).

Note that two |Time| objects with a different `scale` can compare equally but still have different hash keys. This a practical consideration driven in by performance, but in most cases represents a desirable behavior.

#### Printing Time Arrays

If your `times` array contains a lot of elements, the `value` argument will display all the elements of the |Time| object `t` when it is called or printed. To control the number of elements to be displayed, set the `threshold` argument with `np.printoptions` as follows:

    >>> many_times = np.arange(1000)
    >>> t = Time(many_times, format='cxcsec')
    >>> with np.printoptions(threshold=10):
    ...     print(repr(t))
    ...     print(t.iso)
    <Time object: scale='tt' format='cxcsec' value=[  0.   1.   2. ... 997. 998. 999.]>
    ['1998-01-01 00:00:00.000' '1998-01-01 00:00:01.000'
     '1998-01-01 00:00:02.000' ... '1998-01-01 00:16:37.000'
     '1998-01-01 00:16:38.000' '1998-01-01 00:16:39.000']

### Sidereal Time and Earth Rotation Angle

Apparent or mean sidereal time can be calculated using `sidereal_time()`. The method returns a |Longitude| with units of hour angle, which by default is for the longitude corresponding to the location with which the |Time| object is initialized. Like the scale transformations, |ERFA| C-library routines are used under the hood, which support calculations following different IAU resolutions.

Similarly, one can calculate the Earth rotation angle with `earth_rotation_angle()`. Unlike sidereal time, which is referred to the equinox and is a complicated function of both UT1 and Terrestrial Time, the Earth rotation angle is referred to the Celestial Intermediate Origin (CIO) and is a linear function of UT1 alone.

For the recent IAU precession models, as well as for the Earth rotation angle, the result includes the TIO locator (s’), which positions the Terrestrial Intermediate Origin on the equator of the Celestial Intermediate Pole (CIP) and is rigorously corrected for polar motion.

#### Example

To calculate sidereal time:

    >>> t = Time('2006-01-15 21:24:37.5', scale='utc', location=('120d', '45d'))
    >>> t.sidereal_time('mean')
    <Longitude 13.08952187 hourangle>
    >>> t.sidereal_time('apparent')
    <Longitude 13.08950368 hourangle>
    >>> t.earth_rotation_angle()
    <Longitude 13.08436206 hourangle>
    >>> t.sidereal_time('apparent', 'greenwich')
    <Longitude 5.08950368 hourangle>
    >>> t.sidereal_time('apparent', '-90d')
    <Longitude 23.08950368 hourangle>
    >>> t.sidereal_time('apparent', '-90d', 'IAU1994')
    <Longitude 23.08950365 hourangle>

### Time Deltas

Time arithmetic is supported using the |TimeDelta| class. The following operations are available:

- Create a |TimeDelta| explicitly by instantiating a class object.

- Create a |TimeDelta| by subtracting two |Time| objects.

- Add a |TimeDelta| to a |Time| object to get a new |Time|.

- Subtract a |TimeDelta| from a |Time| object to get a new |Time|.

- Add two |TimeDelta| objects to get a new |TimeDelta|.

- Negate a |TimeDelta| or take its absolute value.

- Multiply or divide a |TimeDelta| by a constant or array.

- Convert |TimeDelta| objects to and from time-like |Quantity|’s.

The |TimeDelta| class is derived from the |Time| class and shares many of its properties. One difference is that the time scale has to be one for which one day is exactly 86400 seconds. Hence, the scale cannot be UTC.

|Quantity| objects with time units can also be used in place of |TimeDelta|.

The available time formats are:

Format | Class
--- | ---
sec | `TimeDeltaSec`
jd | `TimeDeltaJD`
datetime | `TimeDeltaDatetime`
quantity_str | `TimeDeltaQuantityString`

#### Examples

Use of the |TimeDelta| object is illustrated in the few examples below:

    >>> t1 = Time('2010-01-01 00:00:00')
    >>> t2 = Time('2010-02-01 00:00:00')
    >>> dt = t2 - t1  # Difference between two Times
    >>> dt
    <TimeDelta object: scale='tai' format='jd' value=31.0>
    >>> dt.sec
    np.float64(2678400.0)

    >>> from astropy.time import TimeDelta
    >>> dt2 = TimeDelta(50.0, format='sec')
    >>> t3 = t2 + dt2  # Add a TimeDelta to a Time
    >>> t3.iso
    '2010-02-01 00:00:50.000'

    >>> t2 - dt2  # Subtract a TimeDelta from a Time
    <Time object: scale='utc' format='iso' value=2010-01-31 23:59:10.000>

    >>> dt + dt2
    <TimeDelta object: scale='tai' format='jd' value=31.0005787037>

    >>> import numpy as np
    >>> t1 + dt * np.linspace(0, 1, 5)
    <Time object: scale='utc' format='iso' value=['2010-01-01 00:00:00.000'
    '2010-01-08 18:00:00.000' '2010-01-16 12:00:00.000' '2010-01-24 06:00:00.000'
    '2010-02-01 00:00:00.000']>

    >>> import astropy.units as u
    >>> t1 + 1 * u.hour
    <Time object: scale='utc' format='iso' value=2010-01-01 01:00:00.000>

A human-readable multi-scale format for string representation of a time delta is available via the `quantity_str` format. See the `TimeDeltaQuantityString` class docstring for more details:

    >>> TimeDelta(40.1 * u.hr).quantity_str
    '1d 16hr 6min'
    >>> t4 = TimeDelta("-1yr 2d 23hr 10min 5.6s")
    >>> print(t4)
    -368d 5hr 10min 5.6s
    >>> t4.to_value(subfmt="d")
    '-368.215d'

The now deprecated default assumes days for numeric inputs:

    >>> t1 + 5.0
    <Time object: scale='utc' format='iso' value=2010-01-06 00:00:00.000>
    TimeDeltaMissingUnitWarning: Numerical value without unit or explicit format passed to TimeDelta, assuming days

The |TimeDelta| has a `to_value` method which supports controlling the type of the output representation by providing either a format name and optional subformat or a valid `astropy` unit:

    >>> dt.to_value(u.hr)
    np.float64(744.0)
    >>> dt.to_value('jd', 'str')
    '31.0'

#### Time Scales for Time Deltas

We have shown in the above that the difference between two UTC times is a |TimeDelta| with a scale of TAI. This is because a UTC time difference cannot be uniquely defined unless the user knows the two times that were differenced (because of leap seconds, a day does not always have 86400 seconds). For all other time scales, the |TimeDelta| inherits the scale of the first |Time| object.

##### Examples

To get the time scale for a |TimeDelta| object:

    >>> t1 = Time('2010-01-01 00:00:00', scale='tcg')
    >>> t2 = Time('2011-01-01 00:00:00', scale='tcg')
    >>> dt = t2 - t1
    >>> dt
    <TimeDelta object: scale='tcg' format='jd' value=365.0>

When |TimeDelta| objects are added or subtracted from |Time| objects, scales are converted appropriately, with the final scale being that of the |Time| object:

    >>> t2 + dt
    <Time object: scale='tcg' format='iso' value=2012-01-01 00:00:00.000>
    >>> t2.tai
    <Time object: scale='tai' format='iso' value=2010-12-31 23:59:27.068>
    >>> t2.tai + dt
    <Time object: scale='tai' format='iso' value=2011-12-31 23:59:27.046>

|TimeDelta| objects can be converted only to objects with compatible scales (i.e., scales for which it is not necessary to know the times that were differenced):

    >>> dt.tt
    <TimeDelta object: scale='tt' format='jd' value=364.999999746>
    >>> dt.tdb
    Traceback (most recent call last):
      ...
    ScaleValueError: Cannot convert TimeDelta with scale 'tcg' to scale 'tdb'

|TimeDelta| objects can also have an undefined scale, in which case it is assumed that their scale matches that of the other |Time| or |TimeDelta| object (or is TAI in case of a UTC time):

    >>> t2.tai + TimeDelta(365., format='jd', scale=None)
    <Time object: scale='tai' format='iso' value=2011-12-31 23:59:27.068>

Note

Since internally |Time| uses floating point numbers, round-off errors can cause two times to be not strictly equal even if mathematically they should be. For times in UTC in particular, this can lead to surprising behavior, because when you add a |TimeDelta|, which cannot have a scale of UTC, the UTC time is first converted to TAI, then the addition is done, and finally the time is converted back to UTC. Hence, rounding errors can be incurred, which means that even expected equalities may not hold:

    >>> t = Time(2450000., 1e-6, format='jd')
    >>> t + TimeDelta(0, format='jd') == t
    False

### Barycentric and Heliocentric Light Travel Time Corrections

The arrival times of photons at an observatory are not particularly useful for accurate timing work, such as eclipse/transit timing of binaries or exoplanets. This is because the changing location of the observatory causes photons to arrive early or late. The solution is to calculate the time the photon would have arrived at a standard location; either the Solar System barycenter or the heliocenter.

#### Example

Suppose you observed the dwarf nova IP Peg from Greenwich and have a list of times in MJD form, in the UTC timescale. You then create appropriate |Time| and |SkyCoord| objects and calculate light travel times to the barycenter as follows:

    >>> from astropy import time, coordinates as coord, units as u
    >>> ip_peg = coord.SkyCoord("23:23:08.55", "+18:24:59.3",
    ...                         unit=(u.hourangle, u.deg), frame='icrs')
    >>> greenwich = coord.EarthLocation.of_site('greenwich')
    >>> times = time.Time([56325.95833333, 56325.978254], format='mjd',
    ...                   scale='utc', location=greenwich)
    >>> ltt_bary = times.light_travel_time(ip_peg)
    >>> ltt_bary
    <TimeDelta object: scale='tdb' format='jd' value=[-0.0037715  -0.00377286]>

If you desire the light travel time to the heliocenter instead, then use:

    >>> ltt_helio = times.light_travel_time(ip_peg, 'heliocentric')
    >>> ltt_helio
    <TimeDelta object: scale='tdb' format='jd' value=[-0.00376576 -0.00376712]>

The method returns an |TimeDelta| object, which can be added to your times to give the arrival time of the photons at the barycenter or heliocenter. Here, you should be careful with the timescales used; for more detailed information about timescales, see Time Scale.

The heliocenter is not a fixed point, and therefore the gravity continually changes at the heliocenter. Thus, the use of a relativistic timescale like TDB is not particularly appropriate, and, historically, times corrected to the heliocenter are given in the UTC timescale:

    >>> times_heliocentre = times.utc + ltt_helio

Corrections to the barycenter are more precise than the heliocenter, because the barycenter is a fixed point where gravity is constant. For maximum accuracy you want to have your barycentric corrected times in a timescale that has always ticked at a uniform rate, and ideally one whose tick rate is related to the rate that a clock would tick at the barycenter. For this reason, barycentric corrected times normally use the TDB timescale:

    >>> time_barycentre = times.tdb + ltt_bary

By default, the light travel time is calculated using the position and velocity of Earth and the Sun from |ERFA| routines, but you can also get more precise calculations using the JPL ephemerides (which are derived from dynamical models). An example using the JPL ephemerides is:

    >>> ltt_bary_jpl = times.light_travel_time(ip_peg, ephemeris='jpl')
    >>> ltt_bary_jpl
    <TimeDelta object: scale='tdb' format='jd' value=[-0.0037715  -0.00377286]>
    >>> (ltt_bary_jpl - ltt_bary).to(u.ms)
    <Quantity [-0.00132325, -0.00132861] ms>

The difference between the built-in ephemerides and the JPL ephemerides is normally of the order of 1/100th of a millisecond, so the built-in ephemerides should be suitable for most purposes. For more details about what ephemerides are available, including the requirements for using JPL ephemerides, see Solar System Ephemerides.

### Interaction with time-like Quantities

Where possible, |Quantity| objects with units of time are treated as |TimeDelta| objects with undefined scale (though necessarily with lower precision). They can also be used as input in constructing |Time| and |TimeDelta| objects, and |TimeDelta| objects can be converted to |Quantity| objects of arbitrary units of time.

#### Examples

To use |Quantity| objects with units of time:

    >>> import astropy.units as u
    >>> Time(10.*u.yr, format='gps')   # time-valued quantities can be used for
    ...                                # for formats requiring a time offset
    <Time object: scale='tai' format='gps' value=315576000.0>
    >>> Time(10.*u.yr, 1.*u.s, format='gps')
    <Time object: scale='tai' format='gps' value=315576001.0>
    >>> Time(2000.*u.yr, format='jyear')
    <Time object: scale='tt' format='jyear' value=2000.0>
    >>> Time(2000.*u.yr, format='byear')
    ...                                # but not for Besselian year, which implies
    ...                                # a different time scale
    ...
    Traceback (most recent call last):
      ...
    ValueError: Input values did not match the format class byear:
    ValueError: cannot use Quantities for 'byear' format, as the unit of year is defined as 365.25 days, while the length of year is variable in this format. Use float instead.

    >>> TimeDelta(10.*u.yr)            # With a quantity, no format is required
    <TimeDelta object: scale='None' format='jd' value=3652.5>

    >>> dt = TimeDelta([10., 20., 30.], format='jd')
    >>> dt.to(u.hr)                    # can convert TimeDelta to a quantity
    <Quantity [240., 480., 720.] h>
    >>> dt > 400. * u.hr               # and compare to quantities with units of time
    array([False,  True,  True]...)
    >>> dt + 1.*u.hr                   # can also add/subtract such quantities
    <TimeDelta object: scale='None' format='jd' value=[10.04166667 20.04166667 30.04166667]>
    >>> Time(50000., format='mjd', scale='utc') + 1.*u.hr
    <Time object: scale='utc' format='mjd' value=50000.0416667>
    >>> dt * 10.*u.km/u.s              # for multiplication and division with a
    ...                                # Quantity, TimeDelta is converted
    <Quantity [100., 200., 300.] d km / s>
    >>> dt * 10.*u.Unit(1)             # unless the Quantity is dimensionless
    <TimeDelta object: scale='None' format='jd' value=[100. 200. 300.]>

### Writing a Custom Format

Some applications may need a custom |Time| format, and this capability is available by making a new subclass of the `TimeFormat` class. When such a subclass is defined in your code, the format class and corresponding name is automatically registered in the set of available time formats.

#### Examples

The key elements of a new format class are illustrated by examining the code for the `jd` format (which is one of the most minimal):

    class TimeJD(TimeFormat):
        """
        Julian Date time format.
        """
        name = 'jd'  # Unique format name

        def set_jds(self, val1, val2):
            """
            Set the internal jd1 and jd2 values from the input val1, val2.
            The input values are expected to conform to this format, as
            validated by self._check_val_type(val1, val2) during __init__.
            """
            self._check_scale(self._scale)  # Validate scale.
            self.jd1, self.jd2 = day_frac(val1, val2)

        @property
        def value(self):
            """
            Return format ``value`` property from internal jd1, jd2
            """
            return self.jd1 + self.jd2

As mentioned above, the `_check_val_type(self, val1, val2)` method may need to be overridden to validate the inputs as conforming to the format specification. By default this checks for valid float, float array, or |Quantity| inputs. In contrast, the `iso` format class ensures the inputs meet the ISO format specification for strings.

One special case that is relatively common and more convenient to implement is a format that makes a small change to the date format. For instance, you could insert `T` in the `yday` format with the following `TimeYearDayTimeCustom` class. Notice how the `subfmts` definition is modified slightly from the standard `TimeISO` class from which it inherits:

    >>> from astropy.time import TimeISO
    >>> class TimeYearDayTimeCustom(TimeISO):
    ...    """
    ...    Year, day-of-year and time as "<YYYY>-<DOY>T<HH>:<MM>:<SS.sss...>".
    ...    The day-of-year (DOY) goes from 001 to 365 (366 in leap years).
    ...    For example, 2000-001T00:00:00.000 is midnight on January 1, 2000.
    ...    The allowed subformats are:
    ...    - 'date_hms': date + hours, mins, secs (and optional fractional secs)
    ...    - 'date_hm': date + hours, mins
    ...    - 'date': date
    ...    """
    ...    name = 'yday_custom'  # Unique format name
    ...    subfmts = (('date_hms',
    ...                '%Y-%jT%H:%M:%S',
    ...                '{year:d}-{yday:03d}T{hour:02d}:{min:02d}:{sec:02d}'),
    ...               ('date_hm',
    ...                '%Y-%jT%H:%M',
    ...                '{year:d}-{yday:03d}T{hour:02d}:{min:02d}'),
    ...               ('date',
    ...                '%Y-%j',
    ...                '{year:d}-{yday:03d}'))

    >>> t = Time('2000-01-01')
    >>> t.yday_custom
    '2000-001T00:00:00.000'
    >>> t2 = Time('2016-001T00:00:00')
    >>> t2.iso
    '2016-01-01 00:00:00.000'

Another special case that is relatively common is a format that represents the time since a particular epoch. The classic example is Unix time which is the number of seconds since 1970-01-01 00:00:00 UTC, not counting leap seconds. What if we wanted that value but do want to count leap seconds. This would be done by using the TAI scale instead of the UTC scale. In this case we inherit from the `TimeFromEpoch` class and define a few class attributes:

    >>> from astropy.time.formats import erfa, TimeFromEpoch
    >>> class TimeUnixLeap(TimeFromEpoch):
    ...    """
    ...    Seconds from 1970-01-01 00:00:00 TAI.  Similar to Unix time
    ...    but this includes leap seconds.
    ...    """
    ...    name = 'unix_leap'
    ...    unit = 1.0 / erfa.DAYSEC  # in days (1 day == 86400 seconds)
    ...    epoch_val = '1970-01-01 00:00:00'
    ...    epoch_val2 = None
    ...    epoch_scale = 'tai'  # Scale for epoch_val class attribute
    ...    epoch_format = 'iso'  # Format for epoch_val class attribute

    >>> t = Time('2000-01-01')
    >>> t.unix_leap
    np.float64(946684832.0)
    >>> t.unix_leap - t.unix
    np.float64(32.0)

Going beyond this will probably require looking at the `astropy` code for more guidance, but if you get stuck, the `astropy` developers are more than happy to help. If you write a format class that is widely useful we might want to include it in the core!

### Timezones

When a `Time` object is constructed from a timezone-aware `datetime`, no timezone information is saved in the `Time` object. However, `Time` objects can be converted to timezone-aware datetime objects.

#### Example

To convert a |Time| object to a timezone-aware datetime object:

    >>> from datetime import datetime
    >>> from astropy.time import Time, TimezoneInfo
    >>> import astropy.units as u
    >>> utc_plus_one_hour = TimezoneInfo(utc_offset=1*u.hour)
    >>> dt_aware = datetime(2000, 1, 1, 0, 0, 0, tzinfo=utc_plus_one_hour)
    >>> t = Time(dt_aware)  # Loses timezone info, converts to UTC
    >>> print(t)            # will return UTC
    1999-12-31 23:00:00
    >>> print(t.to_datetime(timezone=utc_plus_one_hour)) # to timezone-aware datetime
    2000-01-01 00:00:00+01:00

Timezone database packages, like pytz for example, may be more convenient to use to create `tzinfo` objects used to specify timezones rather than the `TimezoneInfo` object.

#### Example

Using the dateutil package, you can parse times in a wide variety of supported formats to generate a `datetime.datetime` object which can then be used to initialize a |Time| object:

    >>> from dateutil.parser import parse
    >>> dtime = parse('2020-10-29T08:20:46.950+1100')
    >>> Time(dtime)
    <Time object: scale='utc' format='datetime' value=2020-10-28 21:20:46.950000>

### Custom String Formats with `strftime` and `strptime`

The `Time` object supports output string representation using the format specification language defined in the Python standard library for `time.strftime`. This can be done using the `strftime` method.

#### Examples

To get output string representation using the `strftime` method:

    >>> from astropy.time import Time
    >>> t = Time('2018-01-01T10:12:58')
    >>> t.strftime('%H:%M:%S %d %b %Y')
    '10:12:58 01 Jan 2018'

Conversely, to create a `Time` object from a custom date string that can be parsed with Python standard library `time.strptime` (using the same format language linked above), use the `strptime` class method:

    >>> from astropy.time import Time
    >>> t = Time.strptime('23:59:60 30 June 2015', '%H:%M:%S %d %B %Y')
    >>> t
    <Time object: scale='utc' format='isot' value=2015-06-30T23:59:60.000>

## Performance Tips

Here we provide some tips and tricks for how to optimize performance of code using `astropy.time`.

### Broadcasting

Like most of Astropy’s classes, |Time| can be array-valued and fully supports NumPy’s broadcasting rules. The best performance will generally be had when making full use of that. For instance, when one wants to calculate the light travel time for a large number of sources, rather than loop over them, it is substantially faster to put all the sources inside a single |SkyCoord| instance and pass that to `light_travel_time()`. For a detailed example, see Determining and plotting the altitude/azimuth of a celestial object.

## Reference/API

- Reference/API
  - astropy.time Package

## Acknowledgments and Licenses

This package makes use of the |PyERFA| wrappers of the |ERFA| ANSI C library. The copyright of the |ERFA| software belongs to the NumFOCUS Foundation. The library is made available under the terms of the “BSD-three clauses” license.

The |ERFA| library is derived, with permission, from the International Astronomical Union’s “Standards of Fundamental Astronomy” (|SOFA|) library, available from https://www.iausofa.org.