pyphot package

pyphot package#

Subpackages#

Submodules#

pyphot.config module#

pyphot.helpers module#

STmag_from_flux(v)[source]#

Convert to ST magnitude from erg/s/cm2/AA (Flambda)

\[ \begin{align}\begin{aligned}mag = -2.5 \log_{10}(F) - 21.10\\M0 = 21.10 F0 = 3.6307805477010028 10^{-9} erg/s/cm2/AA\end{aligned}\end{align} \]

Parameters:: v (np.ndarray[float, ndim=N], or float) – array of fluxes
Returns:: mag – array of magnitudes
Return type:: np.ndarray[float, ndim=N], or float

STmag_to_flux(v)[source]#

Convert an ST magnitude to erg/s/cm2/AA (Flambda)

\[ \begin{align}\begin{aligned}mag = -2.5 \log_{10}(F) - 21.10\\M0 = 21.10 F0 = 3.6307805477010028 10^{-9} erg/s/cm2/AA\end{aligned}\end{align} \]

Parameters:: v (np.ndarray[float, ndim=N] or float) – array of magnitudes
Returns:: flux – array of fluxes
Return type:: np.ndarray[float, ndim=N], or float

deprecated(message)[source]#: Deprecated warning decorator

extractPhotometry(lamb, spec, flist, absFlux=True, progress=True)[source]#

Extract seds from a one single spectrum

Parameters:

lamb (ndarray[float,ndim=1]) – wavelength of spec
spec (ndarray[float, ndim=1]) – spectrum
flist (list[filter]) – list of filter objects
absflux (bool) – return SEDs in absolute fluxes if set
progress (bool) – show progression if set

Returns:

cls (ndarray[float, ndim=1]) – filters central wavelength
seds (ndarray[float, ndim=1]) – integrated sed

extractSEDs(lamb, specs, flist, absFlux=True, progress=True)[source]#

Extract seds from a grid

Parameters:

g0 (ModelGrid instance) – initial spectral grid
flist (sequence(filter)) – list of filter object instances
absflux (bool) – return SEDs in absolute fluxes if set
progress (bool) – show progression if set

Returns:

cls (ndarray[float, ndim=1]) – filters central wavelength
seds (ndarray[float, ndim=1]) – integrated sed
grid (Table) – SED grid properties table from g0 (g0.grid)

fluxErrTomag(flux, fluxerr)[source]#

Return the magnitudes and associated errors from fluxes and flux error values

Parameters:

flux (np.ndarray[float, ndim=1]) – array of fluxes
fluxerr (np.ndarray[float, ndim=1]) – array of flux errors

Returns:

mag (np.ndarray[float, ndim=1]) – array of magnitudes
err (np.ndarray[float, ndim=1]) – array of magnitude errors

fluxToMag(flux)[source]#

Return the magnitudes from flux values

Parameters:: flux (np.ndarray[float, ndim=N]) – array of fluxes
Returns:: mag – array of magnitudes
Return type:: np.ndarray[float, ndim=N]

magErrToFlux(mag, err)[source]#

Return the flux and associated errors from magnitude and mag error values

Parameters:

mag (np.ndarray[float, ndim=1]) – array of magnitudes
err (np.ndarray[float, ndim=1]) – array of magnitude errors

Returns:

flux (np.ndarray[float, ndim=1]) – array of fluxes
fluxerr (np.ndarray[float, ndim=1]) – array of flux errors

magToFlux(mag)[source]#

Return the flux from magnitude values

Parameters:: mag (np.ndarray[float, ndim=N]) – array of magnitudes
Returns:: flux – array of fluxes
Return type:: np.ndarray[float, ndim=N]

progress_enumerate(it, *args, **kwargs)[source]#

Enumerate over a sequence with progression if requested

Parameter#

show_progress: bool: set to show progress

pyphot.licks module#

Lick indices calculations

This package provides function to compute spectral indices

A collection of many common indices is available in licks.dat

The Lick system of spectral line indices is one of the most commonly used methods of determining ages and metallicities of unresolved (integrated light) stellar populations.

The calibration of the Lick/ IDS system is complicated because the original Lick spectra were not flux calibrated, so there are usually systematic effects due to differences in continuum shape. Proper calibration involves observing many of the original Lick/IDS standard stars and deriving offsets to the standard system.

References

Worthey G., Faber S. M., Gonzalez J. J., Burstein D., 1994, ApJS, 94, 687 Worthey G., Ottaviani D. L., 1997, ApJS, 111, 377 Puzia et al. 2002 Zhang, Li & Han 2005, http://arxiv.org/abs/astro-ph/0508634v1

Notes

In Vazdekis et al. (2010), we propose a new Line Index System, hereafter LIS, with three new spectral resolutions at which to measure the Lick indices. Note that this new system should not be restricted to the Lick set of indices in a flux calibrated system. In fact, LIS can be used for any index in the literature (e.g., for the Rose (1984) indices), including newly defined indices (e.g., Cervantes & Vazdekis 2009).

The LIS system is defined for 3 different spectral resolutions which are best suited for the following astrophysical cases:

LIS-5.0AA: globular clusters LIS-8.4AA: low and intermediate-mass galaxies LIS-14.0AA: massive galaxies Conversions to transform the data from the Lick/IDS system to LIS can be found

discussion of indices and information Johansson, Thomas & Maraston 2010 http://wwwmpa.mpa-garching.mpg.de/~jonasj/milesff/milesff.pdf

class LickIndex[source]#

Bases: object

Define a Lick Index similarily to a Filter object

__init__(name, lick, unit='AA')[source]#

Constructor

Parameters:

name (str) – name of the index
lick (dict) – expecting ‘blue’, ‘red’, ‘band’, and ‘unit’ definitions blue and red are used to continuum normalize the spectra band covers the index itself. unit gives the index measurement units, either magnitudes (mag) or equivalent width (ew)
unit (str) – wavelength unit of the intervals

property band#: Unitwise band definition

property blue#: Unitwise band definition

classmethod continuum_normalized_region_around_line(wi, fi, blue, red, band=None, degree=1)[source]#

cut out and normalize flux around a line

Parameters:

wi (ndarray (nw, )) – array of wavelengths in AA
fi (ndarray (N, nw)) – array of flux values for different spectra in the series
blue (tuple(2)) – selection for blue continuum estimate
red (tuple(2)) – selection for red continuum estimate
band (tuple(2), optional) – select region in this band only. default is band = (min(blue), max(red))
degree (int) – degree of the polynomial fit to the continuum

Returns:

wnew (ndarray (nw1, )) – wavelength of the selection in AA
f (ndarray (N, len(wnew))) – normalized flux in the selection region

Example

# indice of CaII
# wavelength are always supposed in AA
w, f = region_around_line(
    wavelength, flux, [3925, 3930],[3938, 3945]]
    )

get(wave, flux, **kwargs)[source]#

compute spectral index after continuum subtraction

Parameters:

w (ndarray (nw, )) – array of wavelengths in AA
flux (ndarray (N, nw)) – array of flux values for different spectra in the series
degree (int (default 1)) – degree of the polynomial fit to the continuum
nocheck (bool) – set to silently pass on spectral domain mismatch. otherwise raises an error when index is not covered

Returns:

ew – equivalent width or magnitude array

Return type:

ndarray (N,)

Raises:

ValueError – when the spectral coverage wave does not cover the index:
range –

property index_unit#

info()[source]#: display information about the current Index

property red#: Unitwise band definition

to_dict()[source]#: return a dictionary of the current index

class LickLibrary[source]#

Bases: object

Collection of Lick indices

__init__(fname=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/licks.dat'), comment='#')[source]#

property content#

property description#: any comment in the input file

find(name, case_sensitive=True)[source]#

get_library_content()[source]#

reduce_resolution(wi, fi, fwhm0=0.55, sigma_floor=0.2)[source]#

Adapt the resolution of the spectra to match the lick definitions

Lick definitions have different resolution elements as function of wavelength. These definition are hard-coded in this function

Parameters:

wi (ndarray (n, )) – wavelength definition
fi (ndarray (nspec, n) or (n, )) – spectra to convert
fwhm0 (float) – initial broadening in the spectra fi
sigma_floor (float) – minimal dispersion to consider

Returns:

flux_red – reduced spectra

Return type:

ndarray (nspec, n) or (n, )

pyphot.pbar module#

Simple progressbar#

This package implement a unique progress bar class that can be used to decorate an iterator, a function or even standalone.

The format of the meter is flexible and can display along with the progress meter, the running time, an eta, and the rate of the iterations.

An example is::: description [———-] k/n 10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]

class Pbar[source]#

Bases: object

make a progress string in a shape of:

[----------] k/n  10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]

time#

if set, add the runtime information

Type:: bool, optional (default: True)

eta#

if set, add an estimated time to completion

Type:: bool, optional (default: True)

rate#

if set, add the rate information

Type:: bool, optional (default: True)

length#

number of characters showing the progress meter itself if None, the meter will adapt to the buffer width

TODO: make it variable with the buffer length

Type:: int, optional (default: None)

keep#

If not set, deletes its traces from screen after completion

Type:: bool, optional (default: True)

file#

the buffer to write into

Type:: buffer

mininterval#

minimum time in seconds between two updates of the meter

Type:: float (default: 0.5)

miniters#

minimum iteration number between two updates of the meter

Type:: int, optional (default: 1)

units#

unit of the iteration

Type:: str, optional (default: ‘iters’)

__init__(maxval=None, desc=None, time=True, eta=True, rate=True, length=None, file=None, keep=True, mininterval=0.5, miniters=1, units='iters', **kwargs)[source]#

build_str_meter(n, total, elapsed)[source]#

make a progress string in a shape of:

[----------] k/n  10% [time: 00:00:00, eta: 00:00:00, 2.7 iters/sec]

Parameters:

n (int) – number of finished iterations
total (int) – total number of iterations, or None
elapsed (int) – number of seconds passed since start

Returns:

txt – string representing the meter

Return type:

str

decorator(func)[source]#: Provide a function decorator allowing for counting calls and rates

static format_interval(t)[source]#

make a human readable time interval decomposed into days, hours, minutes and seconds

Parameters:: t (int) – interval in seconds
Returns:: txt – string representing the interval (format: <days>d <hrs>:<min>:<sec>)
Return type:: str

handle_resize(signum, frame)[source]#

iterover(iterable, total=None)[source]#

Get an iterable object, and return an iterator which acts exactly like the iterable, but prints a progress meter and updates it every time a value is requested.

Parameters:

iterable (generator or iterable object) – object to iter over.
total (int, optional) – the number of iterations is assumed to be the length of the iterator. But sometimes the iterable has no associated length or its length is not the actual number of future iterations. In this case, total can be set to define the number of iterations.

Returns:

gen – pass the values from the initial iterator

Return type:

generator

print_status(s)[source]#

print a status s on the last file line and clean the rest of the line

Parameters:: s (str) – message to write

update(n, desc=None, total=None)[source]#

Kept for backward compatibility and the decorator feature

Parameters:

n (int) – force iteration number n
desc (str) – update description string
total (int) – update the total number of iterations

pyphot.phot module#

Photometric package#

Defines a Filter class and associated functions to extract photometry.

This also include functions to keep libraries up to date

Note

integrations are done using trapezoid() Why not Simpsons? Simpsons principle is to take sequence of 3 points to make a quadratic interpolation. Which in the end, when filters have sharp edges, the error due to this “interpolation” are extremely large in comparison to the uncertainties induced by trapeze integration.

class Ascii_Library[source]#

Bases: Library

Interface one or multiple directory or many files as a filter library

>>> lib = Ascii_Library(['ground', 'hst', 'myfilter.csv'])

__init__(source)[source]#: Construct the library

add_filters(filter_object, fmt='%.6f', **kwargs)[source]#

Add a filter to the library permanently

Parameters:: filter_object (Filter object) – filter to add

get_library_content()[source]#: get the content of the library

load_all_filters(interp=True, lamb=None)[source]#: load all filters from the library

load_filters(names, interp=True, lamb=None, filterLib=None)[source]#

load a limited set of filters

Parameters:

names (list[str]) – normalized names according to filtersLib
interp (bool) – reinterpolate the filters over given lambda points
lamb (ndarray[float, ndim=1]) – desired wavelength definition of the filter
filterLib (path) – path to the filter library hd5 file

Returns:

filters – list of filter objects

Return type:

list[filter]

class Constants[source]#

Bases: object

A namespace for constants

c = <Quantity(2.99792458e+18, 'angstrom / second')>#

h = <Quantity(6.62607554e-27, 'erg * second')>#

class Filter[source]#

Bases: object

Class filter

Define a filter by its name, wavelength and transmission The type of detector (energy or photon counter) can be specified for adapting calculations. (default: photon)

name#

name of the filter

Type:: str

norm#

normalization factor of the filter

Type:: float

transmit#

transmission curve of the filter

Type:: ndarray

dtype#

detector type, either “photon” or “energy” counter

Type:: str

unit#

wavelength units

Type:: str

property AB_zero_Jy#: AB flux zero point in Jansky (Jy)

property AB_zero_flux#: AB flux zero point in erg/s/cm2/AA

property AB_zero_mag#

AB magnitude zero point

ABmag = -2.5 * log10(f_nu) - 48.60: = -2.5 * log10(f_lamb) - 2.5 * log10(lpivot ** 2 / c) - 48.60 = -2.5 * log10(f_lamb) - zpts

property ST_zero_Jy#: ST flux zero point in Jansky (Jy)

property ST_zero_flux#: ST flux zero point in erg/s/cm2/AA

property ST_zero_mag#: ST magnitude zero point STmag = -2.5 * log10(f_lamb) -21.1

property Vega_zero_Jy#: Vega flux zero point in Jansky (Jy)

property Vega_zero_flux#: Vega flux zero point in erg/s/cm2/AA

property Vega_zero_mag#: vega magnitude zero point vegamag = -2.5 * log10(f_lamb) + 2.5 * log10(f_vega) vegamag = -2.5 * log10(f_lamb) - zpts

property Vega_zero_photons#: Vega number of photons per wavelength unit

Note

see self.get_Nphotons

__init__(wavelength, transmit, name='', dtype='photon', unit=None)[source]#: Constructor

applyTo(slamb, sflux)[source]#: For compatibility but bad name

apply_transmission(slamb, sflux)[source]#

Apply filter transmission to a spectrum (with reinterpolation of the filter)

Parameters:

slamb (ndarray) – spectrum wavelength definition domain
sflux (ndarray) – associated flux

Returns:

flux – new spectrum values accounting for the filter

Return type:

float

property cl#: Unitwise wavelength definition

classmethod from_ascii(fname, dtype='csv', **kwargs)[source]#: Load filter from ascii file

property fwhm#

the difference between the two wavelengths for which filter transmission is half maximum

..note::: This calculation is not exact but rounded to the nearest passband data points

getFlux(slamb, sflux, axis=-1)[source]#

Integrate the flux within the filter and return the integrated energy If you consider applying the filter to many spectra, you might want to consider extractSEDs.

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux

Returns:

flux – Energy of the spectrum within the filter

Return type:

float

get_Nphotons(slamb, sflux, axis=-1)[source]#

getNphot the number of photons through the filter (Ntot / width in the documentation)

getflux() * leff / hc

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux in erg/s/cm2/AA

Returns:

N – Number of photons of the spectrum within the filter

Return type:

float

get_flux(slamb, sflux, axis=-1)[source]#

getFlux Integrate the flux within the filter and return the integrated energy If you consider applying the filter to many spectra, you might want to consider extractSEDs.

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux

Returns:

flux – Energy of the spectrum within the filter

Return type:

float

info(show_zeropoints=True)[source]#: display information about the current filter

property leff#: Unitwise Effective wavelength leff = int (lamb * T * Vega dlamb) / int(T * Vega dlamb)

property lmax#: Calculated as the last value with a transmission at least 1% of maximum transmission

property lmin#: Calculate das the first value with a transmission at least 1% of maximum transmission

property lphot#

Photon distribution based effective wavelength. Defined as

lphot = int(lamb ** 2 * T * Vega dlamb) / int(lamb * T * Vega dlamb)

which we calculate as

lphot = get_flux(lamb * vega) / get_flux(vega)

property lpivot#: Unitwise wavelength definition

classmethod make_integration_filter(lmin, lmax, name='', dtype='photon', unit=None)[source]#: Generate an heavyside filter between lmin and lmax

reinterp(lamb)[source]#: reinterpolate filter onto a different wavelength definition

set_dtype(dtype)[source]#: Set the detector type (photon or energy)

set_wavelength_unit(unit)[source]#: Set the wavelength units

to_Table(**kwargs)[source]#

Export filter to a SimpleTable object

Parameters:

fname (str) – filename
parameters (Uses SimpleTable)

to_dict()[source]#: Return a dictionary of the filter

property wavelength#: Unitwise wavelength definition

property width#

Effective width Equivalent to the horizontal size of a rectangle with height equal to maximum transmission and with the same area that the one covered by the filter transmission curve.

W = int(T dlamb) / max(T)

write_to(fname, **kwargs)[source]#

Export filter to a file

Parameters:

fname (str) – filename
parameters (Uses SimpleTable.write)

class HDF_Library[source]#

Bases: Library

Storage based on HDF

__init__(source=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/new_filters.hd5'), mode='r')[source]#: Construct the library

add_filter(f, **kwargs)[source]#

Add a filter to the library permanently

Parameters:: f (Filter object) – filter to add

get_library_content()[source]#: get the content of the library

load_all_filters(interp=True, lamb=None)[source]#

load all filters from the library

Parameters:

interp (bool) – reinterpolate the filters over given lambda points
lamb (ndarray[float, ndim=1]) – desired wavelength definition of the filter

Returns:

filters – list of filter objects

Return type:

list[filter]

load_filters(names, interp=True, lamb=None, filterLib=None)[source]#

load a limited set of filters

Parameters:

names (list[str]) – normalized names according to filtersLib
interp (bool) – reinterpolate the filters over given lambda points
lamb (ndarray[float, ndim=1]) – desired wavelength definition of the filter
filterLib (path) – path to the filter library hd5 file

Returns:

filters – list of filter objects

Return type:

list[filter]

class Library[source]#

Bases: object

Common grounds for filter libraries

__init__(source=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/new_filters.hd5'), *args, **kwargs)[source]#: Construct the library

add_filter(f)[source]#: add a filter to the library

property content#: Get the content list

find(name, case_sensitive=True)[source]#

classmethod from_ascii(filename, **kwargs)[source]#

classmethod from_hd5(filename, **kwargs)[source]#

get_library_content()[source]#: get the content of the library

load_all_filters(interp=True, lamb=None)[source]#: load all filters from the library

to_csv(directory='./', progress=True, **kwargs)[source]#

Export each filter into a csv file with its own name

Parameters:

directory (str) – directory to write into
progress (bool) – show progress if set

to_hdf(fname='filters.hd5', progress=True, **kwargs)[source]#

Export each filter into a csv file with its own name

Parameters:

directory (str) – directory to write into
progress (bool) – show progress if set

class UncertainFilter[source]#

Bases: Filter

What could be a filter with uncertainties

mean#

mean passband transmission

Type:: Filter

samples#

samples from the uncertain passband transmission model

Type:: sequence(Filter)

name#

name of the passband

Type:: string

dtype#

detector type, either “photon” or “energy” counter

Type:: str

unit#

wavelength units

Type:: str

property AB_zero_Jy#: AB flux zero point in Jansky (Jy)

property AB_zero_flux#: AB flux zero point in erg/s/cm2/AA

property AB_zero_mag#

AB magnitude zero point

ABmag = -2.5 * log10(f_nu) - 48.60: = -2.5 * log10(f_lamb) - 2.5 * log10(lpivot ** 2 / c) - 48.60 = -2.5 * log10(f_lamb) - zpts

property ST_zero_Jy#: ST flux zero point in Jansky (Jy)

property ST_zero_flux#: ST flux zero point in erg/s/cm2/AA

property ST_zero_mag#: ST magnitude zero point STmag = -2.5 * log10(f_lamb) -21.1

property Vega_zero_Jy#: Vega flux zero point in Jansky (Jy)

property Vega_zero_flux#: Vega flux zero point in erg/s/cm2/AA

property Vega_zero_mag#: Vega magnitude zero point Vegamag = -2.5 * log10(f_lamb) + 2.5 * log10(f_vega) Vegamag = -2.5 * log10(f_lamb) - zpts

property Vega_zero_photons#: Vega number of photons per wavelength unit

Note

see self.get_Nphotons

__init__(wavelength, mean_transmit, samples, name='', dtype='photon', unit=None)[source]#: Constructor

apply_transmission(slamb, sflux)[source]#

Apply filter transmission to a spectrum (with reinterpolation of the filter)

Parameters:

slamb (ndarray) – spectrum wavelength definition domain
sflux (ndarray) – associated flux

Returns:

flux – new spectrum values accounting for the filter

Return type:

float

property cl#: Unitwise wavelength definition

classmethod from_ascii(fname, dtype='csv', **kwargs)[source]#: Load filter from ascii file

classmethod from_gp_model(model, xprime=None, n_samples=10, **kwargs)[source]#

Generate a filter object from a sklearn GP model

Parameters:

model (sklearn.gaussian_process.GaussianProcessRegressor) – model of the passband
xprime (ndarray) – wavelength to express the model in addition to the training points
n_samples (int) – number of samples to generate from the model.
kwawrgs (dict) – UncertainFilter keywords

property fwhm#

the difference between the two wavelengths for which filter transmission is half maximum

..note:

This calculation is not exact but rounded to the nearest passband
data points

getFlux(slamb, sflux, axis=-1)[source]#

Integrate the flux within the filter and return the integrated energy If you consider applying the filter to many spectra, you might want to consider extractSEDs.

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux

Returns:

flux – Energy of the spectrum within the filter

Return type:

float

get_Nphotons(slamb, sflux, axis=-1)[source]#

getNphot the number of photons through the filter (Ntot / width in the documentation)

getflux() * leff / hc

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux in erg/s/cm2/AA

Returns:

N – Number of photons of the spectrum within the filter

Return type:

float

info(show_zeropoints=True)[source]#: display information about the current filter

property leff#: Unitwise Effective wavelength leff = int (lamb * T * Vega dlamb) / int(T * Vega dlamb)

property lmax#: Calculated as the last value with a transmission at least 1% of maximum transmission

property lmin#: Calculate das the first value with a transmission at least 1% of maximum transmission

property lphot#

Photon distribution based effective wavelength. Defined as

lphot = int(lamb ** 2 * T * Vega dlamb) / int(lamb * T * Vega dlamb)

which we calculate as

lphot = get_flux(lamb * vega) / get_flux(vega)

property lpivot#: Unitwise wavelength definition

reinterp(lamb)[source]#: reinterpolate filter onto a different wavelength definition

set_dtype(dtype)[source]#: Set the detector type (photon or energy)

set_wavelength_unit(unit)[source]#: Set the wavelength units

to_Table(**kwargs)[source]#

Export filter to a SimpleTable object

Parameters:

fname (str) – filename
parameters (Uses SimpleTable)

property transmit#: Transmission curves

property wavelength#: Unitwise wavelength definition

property wavelength_unit#: Unit wavelength definition

property width#

Effective width Equivalent to the horizontal size of a rectangle with height equal to maximum transmission and with the same area that the one covered by the filter transmission curve.

W = int(T dlamb) / max(T)

get_library(fname=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/new_filters.hd5'), **kwargs)[source]#: Finds the appropriate class to load the library

class set_method_default_units[source]#

Bases: object

Decorator for classmethods that makes sure that the inputs of slamb, sflux are in given units

expects the decorated method to be defined as: >> def methodname(self, lamb, flux)

__init__(wavelength_unit, flux_unit, output_unit=None)[source]#

classmethod force_units(value, unit)[source]#

pyphot.sandbox module#

Photometric package using Astropy Units#

Defines a Filter class and associated functions to extract photometry.

This also include functions to keep libraries up to date

Note

integrations are done using trapezoid() Why not Simpsons? Simpsons principle is to take sequence of 3 points to make a quadratic interpolation. Which in the end, when filters have sharp edges, the error due to this “interpolation” are extremely large in comparison to the uncertainties induced by trapeze integration.

class Constants[source]#

Bases: object

A namespace for constants

c = <Quantity(2.99792458e+18, 'angstrom / second')>#

h = <Quantity(6.62607554e-27, 'erg * second')>#

class UncertainFilter[source]#

Bases: UnitFilter

What could be a filter with uncertainties

wavelength#

wavelength sequence defining the filter transmission curve

Type:: ndarray

mean#

mean passband transmission

Type:: Filter

samples#

samples from the uncertain passband transmission model

Type:: sequence(Filter)

name#

name of the passband

Type:: string

dtype#

detector type, either “photon” or “energy” counter

Type:: str

unit#

wavelength units

Type:: str

vega#

Vega flavor to use for calculations, default is ‘default’ (see pyphot.vega.Vega for details)

Type:: str

property AB_zero_Jy#: AB flux zero point in Jansky (Jy)

property AB_zero_flux#: AB flux zero point in erg/s/cm2/AA

property AB_zero_mag#

AB magnitude zero point

ABmag = -2.5 * log10(f_nu) - 48.60: = -2.5 * log10(f_lamb) - 2.5 * log10(lpivot ** 2 / c) - 48.60 = -2.5 * log10(f_lamb) - zpts

property ST_zero_Jy#: ST flux zero point in Jansky (Jy)

property ST_zero_flux#: ST flux zero point in erg/s/cm2/AA

property ST_zero_mag#: ST magnitude zero point STmag = -2.5 * log10(f_lamb) -21.1

property Vega_zero_Jy#: Vega flux zero point in Jansky (Jy)

property Vega_zero_flux#: Vega flux zero point in erg/s/cm2/AA

property Vega_zero_mag#: Vega magnitude zero point Vegamag = -2.5 * log10(f_lamb) + 2.5 * log10(f_vega) Vegamag = -2.5 * log10(f_lamb) - zpts

property Vega_zero_photons#: Vega number of photons per wavelength unit

Note

see self.get_Nphotons

__init__(wavelength, mean_transmit, samples, name='', dtype='photon', unit=None, vega='stis_003')[source]#: Constructor

apply_transmission(slamb, sflux)[source]#

Apply filter transmission to a spectrum (with reinterpolation of the filter)

Parameters:

slamb (ndarray) – spectrum wavelength definition domain
sflux (ndarray) – associated flux

Returns:

flux – new spectrum values accounting for the filter

Return type:

float

property cl#: Unitwise wavelength definition

classmethod from_ascii(fname, dtype='csv', **kwargs)[source]#: Load filter from ascii file

classmethod from_gp_model(model, xprime=None, n_samples=10, **kwargs)[source]#

Generate a filter object from a sklearn GP model

Parameters:

model (sklearn.gaussian_process.GaussianProcessRegressor) – model of the passband
xprime (ndarray) – wavelength to express the model in addition to the training points
n_samples (int) – number of samples to generate from the model.
kwargs (dict) – UncertainFilter keywords

property fwhm#

the difference between the two wavelengths for which filter transmission is half maximum

..note::: This calculation is not exact but rounded to the nearest passband data points

getFlux(slamb, sflux, axis=-1)[source]#

Integrate the flux within the filter and return the integrated energy If you consider applying the filter to many spectra, you might want to consider extractSEDs.

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux

Returns:

flux – Energy of the spectrum within the filter

Return type:

float

get_Nphotons(slamb, sflux, axis=-1)[source]#

getNphot the number of photons through the filter (Ntot / width in the documentation)

getflux() * leff / hc

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux in erg/s/cm2/AA

Returns:

N – Number of photons of the spectrum within the filter

Return type:

float

info(show_zeropoints=True)[source]#: display information about the current filter

property leff#: Unitwise Effective wavelength leff = int (lamb * T * Vega dlamb) / int(T * Vega dlamb)

property lmax#: Calculated as the last value with a transmission at least 1% of maximum transmission

property lmin#: Calculate das the first value with a transmission at least 1% of maximum transmission

property lphot#

Photon distribution based effective wavelength. Defined as

lphot = int(lamb ** 2 * T * Vega dlamb) / int(lamb * T * Vega dlamb)

which we calculate as

lphot = get_flux(lamb * vega) / get_flux(vega)

property lpivot#: Unitwise wavelength definition

reinterp(lamb)[source]#: reinterpolate filter onto a different wavelength definition

set_dtype(dtype)[source]#: Set the detector type (photon or energy)

set_wavelength_unit(unit)[source]#: Set the wavelength units

to_Table(**kwargs)[source]#

Export filter to a SimpleTable object

Parameters:

fname (str) – filename
parameters (Uses SimpleTable)

property transmit#: Transmission curves

property wavelength#: Unitwise wavelength definition

property wavelength_unit#: Unit wavelength definition

property width#

Effective width Equivalent to the horizontal size of a rectangle with height equal to maximum transmission and with the same area that the one covered by the filter transmission curve.

W = int(T dlamb) / max(T)

class UnitAscii_Library[source]#

Bases: UnitLibrary

Interface one or multiple directory or many files as a filter library

>>> lib = Ascii_Library(['ground', 'hst', 'myfilter.csv'])

__init__(source)[source]#: Construct the library

add_filters(filter_object, fmt='%.6f', **kwargs)[source]#

Add a filter to the library permanently

Parameters:: filter_object (Filter object) – filter to add

get_library_content()[source]#: get the content of the library

load_all_filters(interp=True, lamb=None)[source]#: load all filters from the library

load_filters(names, interp=True, lamb=None, filterLib=None)[source]#

load a limited set of filters

Parameters:

names (list[str]) – normalized names according to filtersLib
interp (bool) – reinterpolate the filters over given lambda points
lamb (ndarray[float, ndim=1]) – desired wavelength definition of the filter
filterLib (path) – path to the filter library hd5 file

Returns:

filters – list of filter objects

Return type:

list[filter]

class UnitFilter[source]#

Bases: object

Evolution of Filter that makes sure the input spectra and output fluxes have units to avoid mis-interpretation.

Note the usual (non SI) units of flux definitions:: flam = erg/s/cm**2/AA fnu = erg/s/cm**2/Hz photflam = photon/s/cm**2/AA photnu = photon/s/cm**2/Hz

Define a filter by its name, wavelength and transmission The type of detector (energy or photon counter) can be specified for adapting calculations. (default: photon)

name#

name of the filter

Type:: str

cl#

central wavelength of the filter

Type:: float

norm#

normalization factor of the filter

Type:: float

lpivot#

pivot wavelength of the filter

Type:: float

wavelength#

wavelength sequence defining the filter transmission curve

Type:: ndarray

transmit#

transmission curve of the filter

Type:: ndarray

dtype#

detector type, either “photon” or “energy” counter

Type:: str

unit#

wavelength units

Type:: str

vega#

Vega flavor to use for calculations, default is ‘default’ (see pyphot.vega.Vega for details)

Type:: str

property AB_zero_Jy#: AB flux zero point in Jansky (Jy)

property AB_zero_flux#: AB flux zero point in erg/s/cm2/AA

property AB_zero_mag#

AB magnitude zero point

ABmag = -2.5 * log10(f_nu) - 48.60: = -2.5 * log10(f_lamb) - 2.5 * log10(lpivot ** 2 / c) - 48.60 = -2.5 * log10(f_lamb) - zpts

property ST_zero_Jy#: ST flux zero point in Jansky (Jy)

property ST_zero_flux#: ST flux zero point in erg/s/cm2/AA

property ST_zero_mag#: ST magnitude zero point STmag = -2.5 * log10(f_lamb) -21.1

property Vega_zero_Jy#: Vega flux zero point in Jansky (Jy)

property Vega_zero_flux#: Vega flux zero point in erg/s/cm2/AA

property Vega_zero_mag#: vega magnitude zero point vegamag = -2.5 * log10(f_lamb) + 2.5 * log10(f_vega) vegamag = -2.5 * log10(f_lamb) - zpts

property Vega_zero_photons#: Vega number of photons per wavelength unit

Note

see self.get_Nphotons

__init__(wavelength, transmit, name='', dtype='photon', unit=None, vega='stis_003')[source]#: Constructor

applyTo(slamb, sflux)[source]#: For compatibility but bad name

apply_transmission(slamb, sflux)[source]#

Apply filter transmission to a spectrum (with reinterpolation of the filter)

Parameters:

slamb (ndarray) – spectrum wavelength definition domain
sflux (ndarray) – associated flux

Returns:

flux – new spectrum values accounting for the filter

Return type:

float

property cl#: Unitwise wavelength definition

classmethod from_ascii(fname, dtype='csv', **kwargs)[source]#: Load filter from ascii file

property fwhm#

the difference between the two wavelengths for which filter transmission is half maximum

..note::: This calculation is not exact but rounded to the nearest passband data points

getFlux(slamb, sflux, axis=-1)[source]#

Integrate the flux within the filter and return the integrated energy If you consider applying the filter to many spectra, you might want to consider extractSEDs.

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux

Returns:

flux – Energy of the spectrum within the filter

Return type:

float

get_Nphotons(slamb, sflux, axis=-1)[source]#

getNphot the number of photons through the filter (Ntot / width in the documentation)

getflux() * leff / hc

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux in erg/s/cm2/AA

Returns:

N – Number of photons of the spectrum within the filter

Return type:

float

get_flux(slamb, sflux, axis=-1)[source]#

getFlux Integrate the flux within the filter and return the integrated energy If you consider applying the filter to many spectra, you might want to consider extractSEDs.

Parameters:

slamb (ndarray(dtype=float, ndim=1)) – spectrum wavelength definition domain
sflux (ndarray(dtype=float, ndim=1)) – associated flux

Returns:

flux – Energy of the spectrum within the filter

Return type:

float

info(show_zeropoints=True)[source]#: display information about the current filter

property leff#: Unitwise Effective wavelength leff = int (lamb * T * Vega dlamb) / int(T * Vega dlamb)

property lmax#: Calculated as the last value with a transmission at least 1% of maximum transmission

property lmin#: Calculate das the first value with a transmission at least 1% of maximum transmission

property lphot#

Photon distribution based effective wavelength. Defined as

lphot = int(lamb ** 2 * T * Vega dlamb) / int(lamb * T * Vega dlamb)

which we calculate as

lphot = get_flux(lamb * vega) / get_flux(vega)

property lpivot#: Unitwise wavelength definition

classmethod make_integration_filter(lmin, lmax, name='', dtype='photon', unit=None)[source]#: Generate an heavyside filter between lmin and lmax

reinterp(lamb)[source]#: reinterpolate filter onto a different wavelength definition

set_dtype(dtype)[source]#: Set the detector type (photon or energy)

set_vega_flavor(vega)[source]#: Set the Vega flavor to use for calculations.

set_wavelength_unit(unit)[source]#: Set the wavelength units

to_Table(**kwargs)[source]#

Export filter to a SimpleTable object

Parameters:

fname (str) – filename
parameters (Uses SimpleTable)

to_dict()[source]#: Return a dictionary of the filter

property wavelength#: Unitwise wavelength definition

property width#

Effective width Equivalent to the horizontal size of a rectangle with height equal to maximum transmission and with the same area that the one covered by the filter transmission curve.

W = int(T dlamb) / max(T)

write_to(fname, **kwargs)[source]#

Export filter to a file

Parameters:

fname (str) – filename
parameters (Uses SimpleTable.write)

class UnitHDF_Library[source]#

Bases: UnitLibrary

Storage based on HDF

__init__(source=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/new_filters.hd5'), mode='r')[source]#: Construct the library

add_filter(f, **kwargs)[source]#

Add a filter to the library permanently

Parameters:: f (Filter object) – filter to add

get_library_content()[source]#: get the content of the library

load_all_filters(interp=True, lamb=None)[source]#

load all filters from the library

Parameters:

interp (bool) – reinterpolate the filters over given lambda points
lamb (ndarray[float, ndim=1]) – desired wavelength definition of the filter

Returns:

filters – list of filter objects

Return type:

list[filter]

load_filters(names, interp=True, lamb=None, filterLib=None)[source]#

load a limited set of filters

Parameters:

names (list[str]) – normalized names according to filtersLib
interp (bool) – reinterpolate the filters over given lambda points
lamb (ndarray[float, ndim=1]) – desired wavelength definition of the filter
filterLib (path) – path to the filter library hd5 file

Returns:

filters – list of filter objects

Return type:

list[filter]

class UnitLibrary[source]#

Bases: object

Common grounds for filter libraries

__init__(source=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/new_filters.hd5'), *args, **kwargs)[source]#: Construct the library

add_filter(f)[source]#: add a filter to the library

property content#: Get the content list

find(name, case_sensitive=True)[source]#

classmethod from_ascii(filename, **kwargs)[source]#

classmethod from_hd5(filename, **kwargs)[source]#

get_library_content()[source]#: get the content of the library

load_all_filters(interp=True, lamb=None)[source]#: load all filters from the library

to_csv(directory='./', progress=True, **kwargs)[source]#

Export each filter into a csv file with its own name

Parameters:

directory (str) – directory to write into
progress (bool) – show progress if set

to_hdf(fname='filters.hd5', progress=True, **kwargs)[source]#

Export each filter into a csv file with its own name

Parameters:

directory (str) – directory to write into
progress (bool) – show progress if set

class UnitLickIndex[source]#

Bases: LickIndex

Define a Lick Index similarily to a Filter object

get(wave, flux, **kwargs)[source]#

compute spectral index after continuum subtraction

Parameters:

w (ndarray (nw, )) – array of wavelengths in AA
flux (ndarray (N, nw)) – array of flux values for different spectra in the series
degree (int (default 1)) – degree of the polynomial fit to the continuum
nocheck (bool) – set to silently pass on spectral domain mismatch. otherwise raises an error when index is not covered

Returns:

ew – equivalent width or magnitude array

Return type:

ndarray (N,)

Raises:

ValueError – when the spectral coverage wave does not cover the index:
range –

class UnitLickLibrary[source]#

Bases: LickLibrary

Collection of Lick indices

__init__(fname=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/licks.dat'), comment='#')[source]#

property content#

property description#: any comment in the input file

find(name, case_sensitive=True)[source]#

get_library_content()[source]#

get_library(fname=PosixPath('/home/runner/work/pyphot/pyphot/pyphot/libs/new_filters.hd5'), **kwargs)[source]#: Finds the appropriate class to load the library

hasUnit(val)[source]#: Check is an object has units

reduce_resolution(wi, fi, fwhm0=<Quantity(0.55, 'angstrom')>, sigma_floor=<Quantity(0.2, 'angstrom')>)[source]#

Adapt the resolution of the spectra to match the lick definitions

Lick definitions have different resolution elements as function of wavelength. These definition are hard-coded in this function

Parameters:

wi (ndarray (n, )) – wavelength definition
fi (ndarray (nspec, n) or (n, )) – spectra to convert
fwhm0 (float) – initial broadening in the spectra fi
sigma_floor (float) – minimal dispersion to consider

Returns:

flux_red – reduced spectra

Return type:

ndarray (nspec, n) or (n, )

class set_method_default_units[source]#

Bases: object

Decorator for classmethods that makes sure that the inputs of slamb, sflux are in given units

expects the decorated method to be defined as: >> def methodname(self, lamb, flux)

__init__(wavelength_unit, flux_unit, output_unit=None)[source]#

classmethod force_units(value, unit)[source]#

pyphot.simpletable module#

This file implements a Table class: that is designed to be the basis of any format

Requirements#

FIT format:
- astropy:
  provides a replacement to pyfits pyfits can still be used instead but astropy is now the default
HDF5 format:
- pytables

RuntimeError will be raised when writing to a format associated with missing package.

class AstroHelpers[source]#

Bases: object

Helpers related to astronomy data

static conesearch(ra0, dec0, ra, dec, r, outtype=0)[source]#

Perform a cone search on a table

Parameters:

ra0 (ndarray[ndim=1, dtype=float]) – column name to use as RA source in degrees
dec0 (ndarray[ndim=1, dtype=float]) – column name to use as DEC source in degrees
ra (float) – ra to look for (in degree)
dec (float) – ra to look for (in degree)
r (float) – distance in degrees
outtype (int) –

type of outputs
0 – minimal, indices of matching coordinates 1 – indices and distances of matching coordinates 2 – full, boolean filter and distances

Returns:

t –

if outtype is 0:: only return indices from ra0, dec0
elif outtype is 1:: return indices from ra0, dec0 and distances
elif outtype is 2:: return conditional vector and distance to all ra0, dec0

Return type:

tuple

static deg2dms(val, delim=':')[source]#

Convert degrees into hex coordinates

Parameters:

deg (float) – angle in degrees
delimiter (str) – character delimiting the fields

Returns:

str – string to convert

Return type:

string or sequence

static deg2hms(val, delim=':')[source]#

Convert degrees into hex coordinates

Parameters:

deg (float) – angle in degrees
delimiter (str) – character delimiting the fields

Returns:

str – string to convert

Return type:

string or sequence

static dms2deg(_str, delim=':')[source]#

Convert hex coordinates into degrees

Parameters:

str (string or sequence) – string to convert
delimiter (str) – character delimiting the fields

Returns:

deg – angle in degrees

Return type:

float

static euler(ai_in, bi_in, select, b1950=False, dtype='f8')[source]#

Transform between Galactic, celestial, and ecliptic coordinates. Celestial coordinates (RA, Dec) should be given in equinox J2000 unless the b1950 is True.

select	From	To	select	From	To
1	RA-Dec (2000)	Galactic	4	Ecliptic	RA-Dec
2	Galactic	RA-DEC	5	Ecliptic	Galactic
3	RA-Dec	Ecliptic	6	Galactic	Ecliptic

Parameters:

long_in (float, or sequence) – Input Longitude in DEGREES, scalar or vector.
lat_in (float, or sequence) – Latitude in DEGREES
select (int) – Integer from 1 to 6 specifying type of coordinate transformation.
b1950 (bool) – set equinox set to 1950

Returns:

long_out (float, seq) – Output Longitude in DEGREES
lat_out (float, seq) – Output Latitude in DEGREES

Note

Written W. Landsman, February 1987 Adapted from Fortran by Daryl Yentis NRL Converted to IDL V5.0 W. Landsman September 1997 Made J2000 the default, added /FK4 keyword W. Landsman December 1998 Add option to specify SELECT as a keyword W. Landsman March 2003 Converted from IDL to numerical Python: Erin Sheldon, NYU, 2008-07-02

static hms2deg(_str, delim=':')[source]#

Convert hex coordinates into degrees

Parameters:

str (string or sequence) – string to convert
delimiter (str) – character delimiting the fields

Returns:

deg – angle in degrees

Return type:

float

static sphdist(ra1, dec1, ra2, dec2)[source]#

measures the spherical distance between 2 points

Parameters:

ra1 (float or sequence) – first right ascensions in degrees
dec1 (float or sequence) – first declination in degrees
ra2 (float or sequence) – second right ascensions in degrees
dec2 (float or sequence) – first declination in degrees

Returns:

Outputs – returns a distance in degrees

Return type:

float or sequence

class AstroTable[source]#

Bases: SimpleTable

Derived from the Table, this class add implementations of common astro tools especially conesearch

__init__(*args, **kwargs)[source]#

coneSearch(ra, dec, r, outtype=0)[source]#

Perform a cone search on a table

Parameters:

ra0 (ndarray[ndim=1, dtype=float]) – column name to use as RA source in degrees
dec0 (ndarray[ndim=1, dtype=float]) – column name to use as DEC source in degrees
ra (float) – ra to look for (in degree)
dec (float) – ra to look for (in degree)
r (float) – distance in degrees
outtype (int) –

type of outputs
0 – minimal, indices of matching coordinates 1 – indices and distances of matching coordinates 2 – full, boolean filter and distances

Returns:

t –

if outtype is 0:: only return indices from ra0, dec0
elif outtype is 1:: return indices from ra0, dec0 and distances
elif outtype is 2:: return conditional vector and distance to all ra0, dec0

Return type:

tuple

get_DEC(degree=True)[source]#: Returns RA, converted from hexa/sexa into degrees

get_RA(degree=True)[source]#: Returns RA, converted from hexa/sexa into degrees

info()[source]#: prints information on the table

selectWhere(fields, condition=None, condvars=None, cone=None, zone=None, **kwargs)[source]#

Read table data fulfilling the given condition. Only the rows fulfilling the condition are included in the result. conesearch is also possible through the keyword cone formatted as (ra, dec, r) zonesearch is also possible through the keyword zone formatted as (ramin, ramax, decmin, decmax)

Combination of multiple selections is also available.

set_DEC(val)[source]#: Set the column that defines DEC coordinates

set_RA(val)[source]#: Set the column that defines RA coordinates

where(condition=None, condvars=None, cone=None, zone=None, **kwargs)[source]#

Read table data fulfilling the given condition. Only the rows fulfilling the condition are included in the result.

Parameters:

condition (str) – expression to evaluate on the table includes mathematical operations and attribute names
condvars (dictionary, optional) – A dictionary that replaces the local operands in current frame.

Returns:

out (ndarray/ tuple of ndarrays)
result equivalent to np.where()

zoneSearch(ramin, ramax, decmin, decmax, outtype=0)[source]#

Perform a zone search on a table, i.e., a rectangular selection

Parameters:

ramin (float) – minimal value of RA
ramax (float) – maximal value of RA
decmin (float) – minimal value of DEC
decmax (float) – maximal value of DEC
outtype (int) –

type of outputs
0 or 1 – minimal, indices of matching coordinates 2 – full, boolean filter and distances

Returns:

r – indices or conditional sequence of matching values

Return type:

sequence

class SimpleTable[source]#

Bases: object

Table class that is designed to be the basis of any format wrapping around numpy recarrays

fname#

if str, the file to read from. This may be limited to the format currently handled automatically. If the format is not correctly handled, you can try by providing an object.__

if object with a structure like dict, ndarray, or recarray-like: the data will be encapsulated into a Table

Type:: str or object

caseless#

if set, column names will be caseless during operations

Type:: bool

aliases#

set of column aliases (can be defined later set_alias())

Type:: dict

units#

set of column units (can be defined later set_unit())

Type:: dict

desc#

set of column description or comments (can be defined later set_comment())

Type:: dict

header#

key, value pair corresponding to the attributes of the table

Type:: dict

__init__(fname, *args, **kwargs)[source]#

addCol(name, data, dtype=None, unit=None, description=None)#

Add one or multiple columns to the table

Parameters:

name (str or sequence(str)) – The name(s) of the column(s) to add
data (ndarray, or sequence of ndarray) – The column data, or sequence of columns
dtype (dtype) – numpy dtype for the data to add
unit (str) – The unit of the values in the column
description (str) – A description of the content of the column

addLine(iterable)#

Append one row in this table.

pyphot.sun module#

Handle the Sun Spectrum

class Sun[source]#

Bases: object

Class that handles the Sun’s spectrum and references.

Observed solar spectrum comes from: ftp://ftp.stsci.edu/cdbs/current_calspec/sun_reference_stis_001.fits

and theoretical spectrum comes from: ftp://ftp.stsci.edu/cdbs/grid/k93models/standards/sun_kurucz93.fits

The theoretical spectrum is scaled to match the observed spectrum from 1.5 - 2.5 microns, and then it is used where the observed spectrum ends. The theoretical model of the Sun from Kurucz’93 atlas using the following parameters when the Sun is at 1 au.

log_Z T_eff log_g V_{Johnson} +0.0 5777 +4.44 -26.75

source#

filename of the sun library

Type:: str

data#

data table

Type:: SimpleTable

units#

detected units from file header

Type:: tuple

distance#

distance to the observed Sun (default, 1 au)

Type:: float

flavor#

either ‘observed’ using the stis reference, or ‘theoretical’ for the Kurucz model.

Type:: str, (default theoretical)

__init__(source=None, distance=<Quantity(1, 'astronomical_unit')>, flavor='theoretical')[source]#: Constructor

property flux#

property wavelength#

pyphot.svo module#

Link to the SVO filter profile service

http://svo2.cab.inta-csic.es/theory/fps/

If your research benefits from the use of the SVO Filter Profile Service, include the following acknowledgement in your publication:

> This research has made use of the SVO Filter Profile Service > (http://svo2.cab.inta-csic.es/theory/fps/) supported from the Spanish MINECO > through grant AYA2017-84089.

and please include the following references in your publication:

The SVO Filter Profile Service. Rodrigo, C., Solano, E., Bayo, A., 2012; https://ui.adsabs.harvard.edu/abs/2012ivoa.rept.1015R/abstract
The SVO Filter Profile Service. Rodrigo, C., Solano, E., 2020; https://ui.adsabs.harvard.edu/abs/2020sea..confE.182R/abstract

Example

>>> lst = "2MASS/2MASS.J 2MASS/2MASS.H 2MASS/2MASS.Ks HST/ACS_WFC.F475W HST/ACS_WFC.F814W".split()
    objects = [get_pyphot_filter(k) for k in lst]

get_pyphot_astropy_filter(identifier)[source]#

Query the SVO filter profile service and return the filter object

Parameters:: identifier (str) – SVO identifier of the filter profile e.g., 2MASS/2MASS.Ks HST/ACS_WFC.F475W The identifier is the first column on the webpage of the facilities.
Returns:: filter – Filter object
Return type:: pyphot.astropy.UnitFilter

get_pyphot_filter(identifier)[source]#

Query the SVO filter profile service and return the filter object

Parameters:: identifier (str) – SVO identifier of the filter profile e.g., 2MASS/2MASS.Ks HST/ACS_WFC.F475W The identifier is the first column on the webpage of the facilities.
Returns:: filter – Filter object
Return type:: pyphot.astropy.UnitFilter

pyphot.vega module#

Handle vega spec/mags/fluxes manipulations

Works with both ascii and hd5 files for back-compatibility

Vega.wavelength and Vega.flux have now units!

class Vega[source]#

Bases: object

Class that handles vega spectrum and references. This class know where to find the Vega synthetic spectrum in order to compute fluxes and magnitudes in given filters

Default Vega spectrum is Bohlin 2007, alpha_Lyr_mod_003.fits from the HST CDBS database, which is a synthetic spectrum of Vega

source#

filename of the vega library

Type:: str

data#

data table

Type:: SimpleTable

units#

detected units from file header

Type:: tuple

flavor#

[“mod_002”, “mod_003”, “mod_004”] or [“stis_011”]

Type:: str, (default theoretical)

An instance can be used as a context manager as

>>> filters = ['HST_WFC3_F275W', 'HST_WFC3_F336W', 'HST_WFC3_F475W', 'HST_WFC3_F814W', 'HST_WFC3_F110W', 'HST_WFC3_F160W']

with Vega(flavor=’stis_011’) as v:: vega_f, vega_mag, flamb = v.getSed(filters)

print(vega_f, vega_mag, flamb)

__init__(source=None, flavor='stis_003')[source]#: Constructor

property flux#

getFlux(filters)[source]#: Return vega abs. fluxes in filters

getMag(filters)[source]#: Return vega abs. magnitudes in filters

property wavelength#

from_Vegamag_to_Flux(lamb, vega_mag)[source]#: function decorator that transforms vega magnitudes to fluxes (without vega reference)

from_Vegamag_to_Flux_SN_errors(lamb, vega_mag)[source]#: function decorator that transforms vega magnitudes to fluxes (without vega reference)

pyphot package

Contents

pyphot package#

Subpackages#

Submodules#

pyphot.config module#

pyphot.helpers module#

Parameter#

pyphot.licks module#

pyphot.pbar module#

Simple progressbar#

pyphot.phot module#

Photometric package#

pyphot.sandbox module#

Photometric package using Astropy Units#

pyphot.simpletable module#

Requirements#

pyphot.sun module#

pyphot.svo module#

pyphot.vega module#

pyphot.version module#

Module contents#