feets.datasets package

Submodules

feets.datasets.base module

Base code for IO dataset retrieval

class feets.datasets.base.Bunch(data=None, **kwargs)

Bases: collections.abc.Mapping

Container object for datasets Dictionary-like object that exposes its keys as attributes.

>>> b = Bunch(a=1, b=2)
>>> b['b']
2
>>> b.b
2
>>> b.a = 3
>>> b['a']
3
>>> b.c = 6
>>> b['c']
6

Methods

get((k[,d]) -> D[k] if k in D, …)
items(…)
keys(…)
values(…)

class feets.datasets.base.Data(id, ds_name, description, bands, metadata, data) → None

Bases: collections.abc.Mapping

This object encapsulates a full data with their metadata.

Attributes

id	(any object or None) the id of the lightcurve or None
ds_name	(str) The name of the dataset
description	(str) description about the dataset
bands	(tuple) the names of the attributes inside data
metadata	(dict-like) arbitrary data.
data	(dict-like) lightcurves collection in a dint-like object

Methods

get((k[,d]) -> D[k] if k in D, …)
items(…)
keys(…)
values(…)

class feets.datasets.base.LightCurve(time, magnitude, error=None, magnitude2=None, aligned_time=None, aligned_magnitude=None, aligned_magnitude2=None, aligned_error=None, aligned_error2=None) → None

Bases: feets.datasets.base.LightCurveBase, collections.abc.Mapping

Methods

get((k[,d]) -> D[k] if k in D, …)
items(…)
keys(…)
values(…)

class feets.datasets.base.LightCurveBase(time, magnitude, error=None, magnitude2=None, aligned_time=None, aligned_magnitude=None, aligned_magnitude2=None, aligned_error=None, aligned_error2=None) → None

Bases: object

feets.datasets.base.clear_data_home(data_home=None)

Delete all the content of the data home cache.

Parameters:

data_home : str | None

The path to feets data dir.

feets.datasets.base.fetch(url, dest, force=False)

Retrieve data from an url and store it into dest.

Parameters:

url: str

Link to the remote data

dest: str

Path where the file must be stored

force: bool (default=False)

Overwrite if the file exists

Returns:

cached: bool

True if the file already exists

dest: str

The same string of the parameter

feets.datasets.base.get_data_home(data_home=None)

Return the path of the feets data dir.

This folder is used by some large dataset loaders to avoid downloading the data several times.

By default the data dir is set to a folder named ‘feets_data’ in the user home folder.

Alternatively, it can be set by the ‘feets_DATA’ environment variable or programmatically by giving an explicit folder path. The ‘~’ symbol is expanded to the user home folder.

If the folder does not already exist, it is automatically created.

Parameters:

data_home : str | None

The path to feets data dir.

feets.datasets.macho module

IO code for read some macho lightcurves

The files are gathered from the original FATS project tutorial: https://github.com/isadoranun/tsfeat

feets.datasets.macho.available_MACHO_lc()

Retrieve a list with the available MACHO lightcurves

feets.datasets.macho.load_MACHO(macho_id)

lightcurve of 2 bands (R, B) from the MACHO survey.

Notes

The files are gathered from the original FATS project tutorial: https://github.com/isadoranun/tsfeat

feets.datasets.macho.load_MACHO_example()

lightcurve of 2 bands (R, B) from the MACHO survey. The Id of the source is 1.3444.614

Notes

The files are gathered from the original FATS project tutorial: https://github.com/isadoranun/tsfeat

feets.datasets.ogle3 module

Code for acces the OGLE-III On-line Catalog of Variable Stars.

The main goal of this catalog is to record all variable sources located in the OGLE-III fields in the Magellanic Clouds and Galactic bulge. The data currently available include:

• classical Cepheids in the Galactic Bulge, LMC and SMC,
• type II Cepheids in the Galactic Bulge, LMC and SMC,
• anomalous Cepheids in LMC and SMC,
• RR Lyrae stars in the Galactic Bulge, LMC and SMC,
• Long Period Variables in the Galactic Bulge, LMC and SMC,
• Double Period Variables in LMC,
• R CrB stars in LMC,
• Delta Sct stars in LMC.

The catalog data include basic parameters of the stars (coordinates, periods, mean magnitudes, amplitudes, parameters of the Fourier light curve decompositions), VI multi-epoch photometry collected since 2001, and for some stars supplemented with the OGLE-II photometry obtained between 1997 and 2000, finding charts and cross-identifications with previously published catalogs.

Note to the user: If you use or refer to the data obtained from this catalog in your scientific work, please cite the appropriate papers:

• Udalski, Szymanski, Soszynski and Poleski, 2008, Acta Astron., 58, 69 (OGLE-III photometry)
• Soszynski et al., 2008a, Acta Astron., 58, 163 (Classical Cepheids in the LMC)
• Soszynski et al., 2008b, Acta Astron., 58, 293 (Type II and Anomalous Cepheids in the LMC)
• Soszynski et al., 2009a, Acta Astron., 59, 1 (RR Lyrae Stars in the LMC)
• Soszynski et al., 2009b, Acta Astron., 59, 239 (Long Period Variables in the LMC)
• Soszynski et al., 2009c, Acta Astron., 59, 335 (R CrB Variables in the LMC)
• Poleski et al., 2010a, Acta Astron., 60, 1 (δ Scuti Variables in the LMC)
• Poleski et al., 2010b, Acta Astron., 60, 179 (Double Period Variables in the LMC)
• Soszynski et al., 2010a, Acta Astron., 60, 17 (Classical Cepheids in the SMC)
• Soszynski et al., 2010b, Acta Astron., 60, 91 (Type II Cepheids in the SMC)
• Soszynski et al., 2010c, Acta Astron., 60, 165 (RR Lyrae Stars in the SMC)
• Soszynski et al., 2011a, Acta Astron., 61, 1 (RR Lyrae Stars in the Galactic Bulge)
• Soszynski et al., 2011b, Acta Astron., 61, 217 (Long-Period Variables in the Small Magellanic Cloud)
• Soszynski et al., 2011c, Acta Astron., 61, 285; 2013b, Acta Astron., 63, 37; (Classical and Type II Cepheids in the Galactic Bulge)
• Soszynski et al., 2013a, Acta Astron., 63, 21 (Long-Period Variables in the Galactic Bulge)

More Info: http://ogledb.astrouw.edu.pl/~ogle/CVS/

feets.datasets.ogle3.fetch_OGLE3(ogle3_id, data_home=None, metadata=None, download_if_missing=True)

Retrieve a lighte curve from OGLE-3 database

Parameters:

ogle3_id : str

The id of the source (see: load_OGLE3_catalog() for available sources.

data_home : optional, default: None

Specify another download and cache folder for the datasets. By default all feets data is stored in ‘~/feets’ subfolders.

metadata : bool | None

If it’s True, the row of the dataframe from load_OGLE3_catalog() with the metadata of the source are added to the result.

download_if_missing : optional, True by default

If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

Returns:

A Data object.

Examples

>>> ds = fetch_OGLE3("OGLE-BLG-LPV-232377")
>>> ds
Data(id='OGLE-BLG-LPV-232377', ds_name='OGLE-III', bands=('I', 'V'))
>>> ds.bands
('I', 'V')
>>> ds.data.I
LightCurve(time[100], magnitude[100], error[100])
>>> ds.data.I.magnitude
array([ 13.816,  13.826,  13.818,  13.812,  13.8  ,  13.827,  13.797,
        13.82 ,  13.804,  13.783,  13.823,  13.8  ,  13.84 ,  13.817,
        13.802,  13.824,  13.822,  13.81 ,  13.844,  13.848,  13.813,
        13.836,  13.83 ,  13.83 ,  13.837,  13.811,  13.814,  13.82 ,
        13.826,  13.822,  13.821,  13.817,  13.813,  13.809,  13.817,
        13.836,  13.804,  13.801,  13.813,  13.823,  13.818,  13.831,
        13.833,  13.814,  13.814,  13.812,  13.822,  13.814,  13.818,
        13.817,  13.8  ,  13.804,  13.799,  13.809,  13.815,  13.846,
        13.796,  13.791,  13.804,  13.853,  13.839,  13.816,  13.825,
        13.81 ,  13.8  ,  13.807,  13.819,  13.829,  13.844,  13.84 ,
        13.842,  13.818,  13.801,  13.804,  13.814,  13.821,  13.821,
        13.822,  13.82 ,  13.803,  13.813,  13.826,  13.855,  13.865,
        13.854,  13.828,  13.809,  13.828,  13.833,  13.829,  13.816,
        13.82 ,  13.827,  13.834,  13.811,  13.817,  13.808,  13.834,
        13.814,  13.829])

feets.datasets.ogle3.load_OGLE3_catalog()

Return the full list of variables stars of OGLE-3 as a DataFrame

feets.datasets.synthetic module

Synthetic light curve generator.

feets.datasets.synthetic.create_normal(mu=0.0, sigma=1.0, mu_err=0.0, sigma_err=1.0, seed=None, **kwargs)

Generate a data with magnitudes that follows a Gaussian

distribution. Also their errors are gaussian.

Parameters:

mu : float (default=0)

Mean of the gaussian distribution of magnitudes

sigma : float (default=1)

Standar deviation of the gaussian distribution of magnitude errors

mu_err : float (default=0)

Mean of the gaussian distribution of magnitudes

sigma_err : float (default=1)

Standar deviation of the gaussian distribution of magnitude errorrs

seed : {None, int, array_like}, optional

Random seed used to initialize the pseudo-random number generator. Can be any integer between 0 and 2**32 - 1 inclusive, an array (or other sequence) of such integers, or None (the default). If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.

kwargs : optional

extra arguments for create_random.

Returns:

data

A Data object with a random lightcurves.

Examples

>>> ds = create_normal(0, 1, 0, .0008, seed=42)
>>> ds
Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))
>>> ds.data.B
LightCurve(time[10000], magnitude[10000], error[10000])
>>> ds.data.B.time
array([  0.00000000e+00,   1.00010001e-04,   2.00020002e-04, ...,
         9.99799980e-01,   9.99899990e-01,   1.00000000e+00])

feets.datasets.synthetic.create_periodic(mu_err=0.0, sigma_err=1.0, seed=None, **kwargs)

Generate a data with magnitudes with periodic variability

distribution; the error instead are gaussian.

Parameters:

mu_err : float (default=0)

Mean of the gaussian distribution of magnitudes

sigma_err : float (default=1)

Standar deviation of the gaussian distribution of magnitude errorrs

seed : {None, int, array_like}, optional

Random seed used to initialize the pseudo-random number generator. Can be any integer between 0 and 2**32 - 1 inclusive, an array (or other sequence) of such integers, or None (the default). If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.

kwargs : optional

extra arguments for create_random.

Returns:

data

A Data object with a random lightcurves.

Examples

>>> ds = synthetic.create_periodic(bands=["Ks"])
>>> ds
Data(id=None, ds_name='feets-synthetic', bands=('Ks',))
>>> ds.data.Ks.magnitude
array([ 0.95428053,  0.73022685,  0.03005121, ..., -0.26305297,
        2.57880082,  1.03376863])

feets.datasets.synthetic.create_random(magf, magf_params, errf, errf_params, timef=<function linspace>, timef_params=None, size=10000, id=None, ds_name='feets-synthetic', description='Lightcurve created with random numbers', bands=('B', 'V'), metadata=None)

Generate a data with any given random function.

Parameters:

magf : callable

Function to generate the magnitudes.

magf_params : dict-like

Parameters to feed the magf function.

errf : callable

Function to generate the magnitudes.

errf_params : dict-like

Parameters to feed the errf function.

timef : callable, (default=numpy.linspace)

Function to generate the times.

timef_params : dict-like or None, (default={“start”: 0., “stop”: 1.})

Parameters to feed the timef callable.

size : int (default=10000)

Number of obervation of the light curves

id : object (default=None)

Id of the created data.

ds_name : str (default=”feets-synthetic”)

Name of the dataset

description : str (default=”Lightcurve created with random numbers”)

Description of the data

bands : tuple of strings (default=(“B”, “V”))

The bands to be created

metadata : dict-like or None (default=None)

The metadata of the created data

Returns:

data

A Data object with a random lightcurves.

Examples

>>> from numpy import random
>>>  create_random(
...     magf=random.normal, magf_params={"loc": 0, "scale": 1},
...     errf=random.normal, errf_params={"loc": 0, "scale": 0.008})
Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))

feets.datasets.synthetic.create_uniform(low=0.0, high=1.0, mu_err=0.0, sigma_err=1.0, seed=None, **kwargs)

Generate a data with magnitudes that follows a uniform

distribution; the error instead are gaussian.

Parameters:

low : float, optional

Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.

high : float, optional

Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0.

mu_err : float (default=0)

Mean of the gaussian distribution of magnitudes

sigma_err : float (default=1)

Standar deviation of the gaussian distribution of magnitude errorrs

seed : {None, int, array_like}, optional

Random seed used to initialize the pseudo-random number generator. Can be any integer between 0 and 2**32 - 1 inclusive, an array (or other sequence) of such integers, or None (the default). If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise.

kwargs : optional

extra arguments for create_random.

Returns:

data

A Data object with a random lightcurves.

Examples

>>> ds = synthetic.create_uniform(1, 2, 0, .0008, 42)
>>> ds
Data(id=None, ds_name='feets-synthetic', bands=('B', 'V'))
>>> ds.data.B.magnitude
array([ 1.37454012,  1.95071431,  1.73199394, ...,  1.94670792,
        1.39748799,  1.2171404 ])

Module contents

The feets.datasets module includes utilities to load datasets, including methods to load and fetch some example light curves.