API documentation

`arvi.timeseries.RV`

A class holding RV observations

Parameters:

Name	Type	Description	Default
`star`	`str`	Name of the star	required
`instrument`	`(str, list)`	Name of the instrument or list of instruments	`None`
`verbose`	`bool`	Print logging messages	`True`
`do_maxerror`	`float`	Mask points based on a maximum RV uncertainty	`None`
`do_secular_acceleration`	`bool`	Apply secular acceleration correction. This only applies to certain instruments.	`True`
`do_sigma_clip`	`bool`	Apply sigma clipping on the RVs	`False`
`do_adjust_means`	`bool`	Subtract individual weighted mean RV from each instrument	`True`
`only_latest_pipeline`	`bool`	Select only the latest pipeline from each instrument	`True`
`load_extra_data`	`bool`		`False`
`check_drs_qc`	`bool`	Mask points based on DRS quality control flags	`True`
`user`	`str`	User name for DACE queries (should be a section in `~/.dacerc` file)	`None`

Examples:

>>> s = RV('Proxima')
>>> s = RV('HD10180', instrument='HARPS')

Attributes

`N` `property`

Total number of observations

`NN` `property`

Total number of observations per instrument

`N_nights` `property`

Number of individual nights

`download_directory` `property` `writable`

Directory where to download data

`msvrad` `property`

Masked array of radial velocity uncertainties

`mtime` `property`

Masked array of times

`mvrad` `property`

Masked array of radial velocities

`planets` `property`

Query the NASA Exoplanet Archive for any known planets

`rms` `property`

Weighted rms of the (masked) radial velocities

`sigma` `property`

Average radial velocity uncertainty

Functions

`add_to_quantity(quantity, values)`

Add a value or array of values to the given quantity of all instruments

`add_to_vrad(values)`

Add a value or array of values to the RVs of all instruments

`adjust_means(just_rv=False, exclude_rv=False, instrument=None, **kwargs)`

Subtract individual weighted mean RV from each instrument or from specific instruments

`bin()`

Nightly bin the observations.

Warning

This creates and returns a new object and does not modify self.

`change_instrument_name(old_name, new_name, strict=False)`

Change the name of an instrument

Parameters:

Name	Type	Description	Default
`old_name`	`str`	The old name of the instrument	required
`new_name`	`str`	The new name of the instrument, or postfix if `strict` is False	required
`strict`	`bool`	Whether to match (each) `instrument` exactly	`False`

`checksum(write_to=None)`

Calculate a hash based on the data

`choose_n_points(n, seed=None, instrument=None)`

Randomly choose n observations and mask out the remaining ones

Parameters:

Name	Type	Description	Default
`n`	`int`	Number of observations to keep.	required
`seed`	`int`	Random seed for reproducibility.	`None`
`instrument`	`str or list`	For which instrument to choose points (default is all).	`None`

`clip_maxerror(maxerror)`

Mask out points with RV error larger than a given value

Parameters:

Name	Type	Description	Default
`maxerror`	`float`	Maximum error to keep.	required

`detrend(degree=1)`

Detrend the RVs of all instruments

`download_ccf(instrument=None, index=None, limit=None, directory=None, clobber=False, symlink=False, load=True, **kwargs)`

Download CCFs from DACE

Parameters:

Name	Type	Description	Default
`instrument`	`str`	Specific instrument for which to download data	`None`
`index`	`int`	Specific index of point for which to download data (0-based)	`None`
`limit`	`int`	Maximum number of files to download.	`None`
`directory`	`str`	Directory where to store data.	`None`
`clobber`	`bool`	Whether to overwrite existing files.	`False`

`download_s1d(instrument=None, index=None, limit=None, directory=None, clobber=False, apply_mask=True, symlink=False, **kwargs)`

Download S1Ds from DACE

Parameters:

Name	Type	Description	Default
`instrument`	`str`	Specific instrument for which to download data	`None`
`index`	`int`	Specific index of point for which to download data (0-based)	`None`
`limit`	`int`	Maximum number of files to download.	`None`
`directory`	`str`	Directory where to store data.	`None`
`clobber`	`bool`	Whether to overwrite existing files.	`False`
`apply_mask`	`bool`	Apply mask to the observations before downloading.	`True`

`download_s2d(instrument=None, index=None, limit=None, directory=None, clobber=False, symlink=False, **kwargs)`

Download S2Ds from DACE

Parameters:

Name	Type	Description	Default
`instrument`	`str`	Specific instrument for which to download data	`None`
`index`	`int`	Specific index of point for which to download data (0-based)	`None`
`limit`	`int`	Maximum number of files to download.	`None`
`directory`	`str`	Directory where to store data.	`None`
`clobber`	`bool`	Whether to overwrite existing files.	`False`

`from_ccf(files, star=None, instrument=None, **kwargs)` `classmethod`

Create an RV object from a CCF file or a list of CCF files

Note

This function relies on the iCCF package

Parameters:

Name	Type	Description	Default
`files`	`str or list`	CCF file or list of CCF files	required
`star`	`str`	Star name. If not provided, it will be inferred from the header of the CCF file	`None`
`instrument`	`str`	Instrument name. If not provided, it will be inferred from the header of the CCF file	`None`

`from_rdb(files, star=None, instrument=None, instrument_suffix=None, units='ms', header_skip=2, **kwargs)` `classmethod`

Create an RV object from an rdb file or a list of rdb files

Parameters:

Name	Type	Description	Default
`files`	`(str, list)`	File name, file object, or list of file names	required
`star`	`str`	Name of the star. If None, try to infer it from file name	`None`
`instrument`	`(str, list)`	Name of the instrument(s). If None, try to infer it from file name	`None`
`units`	`str`	Units of the radial velocities. Defaults to 'ms'.	`'ms'`
`header_skip`	`int`	Number of lines to skip in the header. Defaults to 2.	`2`

Examples:

>>> s = RV.from_rdb('star_HARPS.rdb')

`nth_day_mean(n=1.0, masked=True)`

Calculate the n-th day rolling mean of the radial velocities

`put_at_systemic_velocity()`

For instruments in which mean(RV) < ptp(RV), "move" RVs to the systemic velocity from simbad. This is useful if some instruments are centered at zero while others are not, and instead of calling .adjust_means(), but it only works when the systemic velocity is smaller than ptp(RV).

`remove_after_bjd(bjd)`

Remove observations after a given BJD

`remove_before_bjd(bjd)`

Remove observations before a given BJD

`remove_between_bjds(bjd1, bjd2)`

Remove observations between two BJDs

`remove_condition(condition)`

Remove all observations that satisfy a condition

Parameters:

Name	Type	Description	Default
`condition`	`ndarray`	Boolean array of the same length as the observations	required

`remove_instrument(instrument, strict=False)`

Remove all observations from one instrument

Parameters:

Name	Type	Description	Default
`instrument`	`str or list`	The instrument(s) for which to remove observations.	required
`strict`	`bool`	Whether to match (each) `instrument` exactly	`False`

Note

A common name can be used to remove observations for several subsets of a given instrument. For example

s.remove_instrument('HARPS')

will remove observations from HARPS03 and HARPS15, if they exist. But

s.remove_instrument('HARPS03')

will only remove observations from the specific subset.

`remove_non_public()`

Remove non-public observations

`remove_point(index)`

Remove individual observations at a given index (or indices).

Note

Like Python, the index is 0-based.

Parameters:

Name	Type	Description	Default
`index`	`(int, list, ndarray)`	Single index, list, or array of indices to remove.	required

`remove_prog_id(prog_id)`

Remove observations from a given program ID

`remove_public()`

Remove public observations

`remove_single_observations()`

Remove instruments for which there is a single observation

`replace_quantity(quantity, values)`

Replace the given quantity of all instruments by a value or array of values

`replace_svrad(values)`

Replace the RV uncertainties of all instruments with a value or array of values

`replace_vrad(values)`

Replace the RVs of all instruments with a value or array of values

`restore_point(index)`

Restore previously deleted individual observations at a given index (or indices).

Note

Like Python, the index is 0-based

Parameters:

Name	Type	Description	Default
`index`	`(int, list, ndarray)`	Single index, list, or array of indices to restore	required

`save(directory=None, instrument=None, full=False, postfix=None, save_masked=False, save_nans=True)`

Save the observations in .rdb files.

Parameters:

Name	Type	Description	Default
`directory`	`str`	Directory where to save the .rdb files.	`None`
`instrument`	`str`	Instrument for which to save observations.	`None`
`full`	`bool`	Save just RVs and errors (False) or more indicators (True).	`False`
`postfix`	`str`	Postfix to add to the filenames ([star][instrument][postfix].rdb).	`None`

`secular_acceleration(epoch=None, just_compute=False, force_simbad=False)`

Remove secular acceleration from RVs. This uses the proper motions from Gaia (in self.gaia) if available, otherwise from Simbad (in self.simbad), unless force_simbad=True.

Parameters:

Name	Type	Description	Default
`epoch`	`float`	The reference epoch (DACE uses 55500, 31/10/2010)	`None`
`just_compute`	`bool`	Just compute the secular acceleration and return, without changing the RVs	`False`
`force_simbad`	`bool`	Use Simbad proper motions even if Gaia is available	`False`

`sigmaclip(sigma=5, instrument=None, strict=True)`

Sigma-clip RVs (per instrument!), by MAD away from the median.

Parameters:

Name	Type	Description	Default
`sigma`	`float`	Number of MADs to clip	`5`
`instrument`	`(str, list)`	Instrument(s) to sigma-clip	`None`

`sort_instruments(by_first_observation=True, by_last_observation=False)`

Sort instruments by first or last observation date.

Parameters:

Name	Type	Description	Default
`by_first_observation`	`bool`	Sort by first observation date	`True`
`by_last_observation`	`bool`	Sort by last observation date	`False`

`subtract_mean()`

Subtract (a single) non-weighted mean RV from all instruments

Sub-packages

The following are mostly internal packages and functions with which a user may not need to interact directly.

arvi.simbad API

`arvi.simbad(star, _debug=False)`

A very simple wrapper around a TAP query to simbad for a given target. This class simply runs a few TAP queries and stores the result as attributes.

Attributes:

Name	Type	Description
`ra`	`float`	right ascension
`dec`	`float`	declination
`coords`	`SkyCoord`	coordinates as a SkyCoord object
`main_id`	`str`	main identifier
`gaia_id`	`int`	Gaia DR3 identifier
`plx`	`float`	parallax
`rvz_radvel`	`float`	radial velocity
`sp_type`	`str`	spectral type
`B`	`float`	B magnitude
`V`	`float`	V magnitude
`ids`	`list`	list of identifiers

Parameters:

Name	Type	Description	Default
`star`	`str`	The name of the star to query simbad	required

arvi.stats API

`arvi.stats`

`rms(a, ignore_nans=False)`

Root mean square of array a

Parameters:

Name	Type	Description	Default
`a`	`array`	Array containing data	required

`sigmaclip_median(a, low=4.0, high=4.0)`

Same as scipy.stats.sigmaclip but using the median and median absolute deviation instead of the mean and standard deviation.

Parameters:

Name	Type	Description	Default
`a`	`array`	Array containing data	required
`low`	`float`	Number of MAD to use for the lower clipping limit	`4.0`
`high`	`float`	Number of MAD to use for the upper clipping limit	`4.0`

Returns: SigmaclipResult: Object with the following attributes: - clipped: Masked array of data - lower: Lower clipping limit - upper: Upper clipping limit

`wmean(a, e)`

Weighted mean of array a, with uncertainties given by e. The weighted mean is calculated using 1/e**2 as weights.

Parameters:

Name	Type	Description	Default
`a`	`array`	Array containing data	required
`e`	`array`	Uncertainties on `a`	required

`wrms(a, e, ignore_nans=False)`

Weighted root mean square of array a, with uncertanty given by e. The weighted rms is calculated using the weighted mean, where the weights are equal to 1/e**2.

Parameters:

Name	Type	Description	Default
`a`	`array`	Array containing data	required
`e`	`array`	Uncertainties on `a`	required

API documentation

arvi.timeseries.RV

Attributes

N property

NN property

N_nights property

download_directory property writable

msvrad property

mtime property

mvrad property

planets property

rms property

sigma property

Functions

add_to_quantity(quantity, values)

add_to_vrad(values)

adjust_means(just_rv=False, exclude_rv=False, instrument=None, **kwargs)

bin()

change_instrument_name(old_name, new_name, strict=False)

checksum(write_to=None)

choose_n_points(n, seed=None, instrument=None)

clip_maxerror(maxerror)

detrend(degree=1)

download_ccf(instrument=None, index=None, limit=None, directory=None, clobber=False, symlink=False, load=True, **kwargs)

download_s1d(instrument=None, index=None, limit=None, directory=None, clobber=False, apply_mask=True, symlink=False, **kwargs)

download_s2d(instrument=None, index=None, limit=None, directory=None, clobber=False, symlink=False, **kwargs)

from_ccf(files, star=None, instrument=None, **kwargs) classmethod

from_rdb(files, star=None, instrument=None, instrument_suffix=None, units='ms', header_skip=2, **kwargs) classmethod

nth_day_mean(n=1.0, masked=True)

put_at_systemic_velocity()

remove_after_bjd(bjd)

remove_before_bjd(bjd)

remove_between_bjds(bjd1, bjd2)

remove_condition(condition)

remove_instrument(instrument, strict=False)

remove_non_public()

remove_point(index)

remove_prog_id(prog_id)

remove_public()

remove_single_observations()

replace_quantity(quantity, values)

replace_svrad(values)

replace_vrad(values)

restore_point(index)

save(directory=None, instrument=None, full=False, postfix=None, save_masked=False, save_nans=True)

secular_acceleration(epoch=None, just_compute=False, force_simbad=False)

sigmaclip(sigma=5, instrument=None, strict=True)

sort_instruments(by_first_observation=True, by_last_observation=False)

subtract_mean()

Sub-packages

arvi.simbad(star, _debug=False)

arvi.stats

rms(a, ignore_nans=False)

sigmaclip_median(a, low=4.0, high=4.0)

wmean(a, e)

wrms(a, e, ignore_nans=False)

`arvi.timeseries.RV`

`N` `property`

`NN` `property`

`N_nights` `property`

`download_directory` `property` `writable`

`msvrad` `property`

`mtime` `property`

`mvrad` `property`

`planets` `property`

`rms` `property`

`sigma` `property`

`add_to_quantity(quantity, values)`

`add_to_vrad(values)`

`adjust_means(just_rv=False, exclude_rv=False, instrument=None, **kwargs)`

`bin()`

`change_instrument_name(old_name, new_name, strict=False)`

`checksum(write_to=None)`

`choose_n_points(n, seed=None, instrument=None)`

`clip_maxerror(maxerror)`

`detrend(degree=1)`

`download_ccf(instrument=None, index=None, limit=None, directory=None, clobber=False, symlink=False, load=True, **kwargs)`

`download_s1d(instrument=None, index=None, limit=None, directory=None, clobber=False, apply_mask=True, symlink=False, **kwargs)`

`download_s2d(instrument=None, index=None, limit=None, directory=None, clobber=False, symlink=False, **kwargs)`

`from_ccf(files, star=None, instrument=None, **kwargs)` `classmethod`

`from_rdb(files, star=None, instrument=None, instrument_suffix=None, units='ms', header_skip=2, **kwargs)` `classmethod`

`nth_day_mean(n=1.0, masked=True)`

`put_at_systemic_velocity()`

`remove_after_bjd(bjd)`

`remove_before_bjd(bjd)`

`remove_between_bjds(bjd1, bjd2)`

`remove_condition(condition)`

`remove_instrument(instrument, strict=False)`

`remove_non_public()`

`remove_point(index)`

`remove_prog_id(prog_id)`

`remove_public()`

`remove_single_observations()`

`replace_quantity(quantity, values)`

`replace_svrad(values)`

`replace_vrad(values)`

`restore_point(index)`

`save(directory=None, instrument=None, full=False, postfix=None, save_masked=False, save_nans=True)`

`secular_acceleration(epoch=None, just_compute=False, force_simbad=False)`

`sigmaclip(sigma=5, instrument=None, strict=True)`

`sort_instruments(by_first_observation=True, by_last_observation=False)`

`subtract_mean()`

`arvi.simbad(star, _debug=False)`

`arvi.stats`

`rms(a, ignore_nans=False)`

`sigmaclip_median(a, low=4.0, high=4.0)`

`wmean(a, e)`

`wrms(a, e, ignore_nans=False)`