Metadata-Version: 2.1
Name: bbf
Version: 0.3.1
Summary: Fast computation of broadband fluxes and magnitudes
Keywords: astronomy,astrophysics
Author-Email: Nicolas Regnault <nicolas.regnault@lpnhe.in2p3.fr>
License: MIT License
        
        Copyright (c) 2024 Nicolas Regnault
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        SOFTWARE.
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Project-URL: Homepage, https://gitlab.in2p3.fr/lemaitre/bbf
Project-URL: Repository, https://gitlab.in2p3.fr/lemaitre/bbf
Project-URL: Changelog, https://gitlab.in2p3.fr/lemaitre/bbf/-/blob/main/CHANGELOG.md
Requires-Python: >=3.9
Requires-Dist: astropy
Requires-Dist: getCalspec
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: platformdirs
Requires-Dist: scipy>=1.10
Requires-Dist: scikit-sparse
Requires-Dist: sncosmo
Requires-Dist: pytest>=6.0; extra == "test"
Provides-Extra: test
Description-Content-Type: text/markdown

# Broadband fluxes (`bbf`)

[![PyPI - Version](https://img.shields.io/pypi/v/bbf.svg)](https://pypi.org/project/bbf)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bbf.svg)](https://pypi.org/project/bbf)

-----

**Table of contents**
- [Installation](#installation)
- [Getting started](#getting started)
- [License](#license)

A module to evaluate the broadband fluxes and magnitudes of spectrophotometric
standards.


# Installation

## virtual environments

We recommend using `conda` which comes with a compiled version of `suitesparse`.
`venv` is also a suitable option if suitesparse is already installed on your
machine, or if you are ready to compile it yourself.

As a reminder:

```bash
conda create -n MY_ENV
conda activate MY_ENV
```

or:

```bash
python -m venv MY_ENV
source MY_ENV/bin/activate
```

## Prerequisites

conda packages for `bbf` are in preparation (but not ready yet). Better install them directly in conda.

```bash
conda install ipython numpy scipy matplotlib scikit-sparse pandas h5py pyarrow libgomp
```

Moreover, `bbf` relies for the moment on a [modified version of
`sncosmo`](https://github.com/nregnault/sncosmo) for passbands and magsys
definition. You need to install it before installing `bbf`:

```bash
pip install git+https://github.com/nregnault/sncosmo
```


## Installing bbf

```bash
pip install bbf
```

If you prefer installing from sources:

```bash
git clone clone git@gitlab.in2p3.fr:lemaitre/bbf.git
cd bbf
pip install .
```

If your you are a developper and want to work on the `bbf` package:

```bash
pip install nanobind ninja scikit-build-core[pyproject]
pip install --no-build-isolation -Ceditable.rebuild=true -ve .
```


## Installing the Lemaitre bandpasses

If you plan to use the latest version of the megacam6, ztf and hsc passbands,
install the `lemaitre.bandpasses` package:

```bash
pip install lemaitre-bandpasses
```

or

```bash
git clone https://gitlab.in2p3.fr/lemaitre/lemaitre/bandpasses
cd bandpasses
git lfs pull
pip install .
```

# Getting started

The goal of `bbf` is to efficiently compute broadband fluxes and magnitudes,
i.e. quantities of the form:

$$f_{xys} = \int S(\lambda) \lambda T_{xys}(\lambda) d\lambda$$

where $\lambda$ is the SED of an object, $T_{xyz}(\lambda)$ is the bandpass of
the instrument used to observe it. $T$ may depend on the focal plane position of
the object and, if the focal plane is a mosaic of sensors, on the specific
sensor $s$ where the observation is made. In practice, $x,y$ are coordinates, in
pixels, in the sensor frame, and $s$ is a unique sensor index (or amplifier
index).

Computing magnitudes requires an additional ingredient: the flux of a reference
spectrum $S_{ref}(\lambda)$, usually the AB spectrum, integrated in the same
passband (same sensor, same position).

$$m = -2.5 \log_{10} \left(\frac{\int S(\lambda) \lambda T_{xyz}(\lambda) d\lambda}{\int S_{ref}(\lambda) \lambda T_{xyz}(\lambda) d\lambda}\right)$$

To compute these integrales, `bbf` uses the technique implemented in `nacl`,
which consists in projecting the bandpasses and SED on spline bases:

$$S(\lambda) = \sum_i \theta_i {\cal B}_i(\lambda)$$

and

$$T(\lambda) = \sum_j t_j {\cal B}_j(\lambda)$$

If we precompute the products $G_{ij} = \int \lambda {\cal B}_i(\lambda) {\cal B}_j(\lambda) d\lambda$
the integrals above can be expressed as a simple contraction:

$$f = \theta_i G_{ij} t_j$$

where $G$ is very sparse, since the B-Splines ${\cal B}_i$ have a compact
support. If the bandpass $T$ is spatially variable, the $t_j$ coefficients are
themselves developped on a spatial spline basis.

$$t_j = \sum_{kj} \tau_{kj} {\cal K}(x,y)$$

The contraction above is then of the form: ...

## FilterSets and StellarLibs

`bbf` implements two main kind of objects: `FilterLib`, which holds a set of
band passes, projected on spline bases (${\cal K_j(x,y)}$ and ${\cal
B}_i_(\lambda)$), and `StellarLib` which manages a set of spectra, also
projected on a spline basis (not necessily the splines used for the filters).


## Loading a filter lib

Building a complete version of a `FilterLib` requires some care. The standard
`FilterLib` used in the Lemaître analysis is build and maintained within the
package `lemaitre.bandpasses`. To access it:

``` python
from lemaitre import bandpasses

flib = bandpasses.get_filterlib()
```
The first time this function is called, the `FilterLib`` is built and cached. The subsequent calls
access the cached version, and never take more than a few milliseconds.

## Loading Stellar Libraries

As of today, `bbf` implements two kinds of StellarLibs: pickles and Calspec. An
interface to gaiaXP is in development.

To load the pickles library:

``` python

import bbf.stellarlib.pickles
pickles = bbf.stellarlib.pickles.fetch()
```

To load the most recent version of Calspec:

``` python
import bbf.stellarlib.calspec
calspec = bbf.stellarlib.calspec.fetch()
```


## Computing Broadband fluxes

With a `FilterSet` and a `StellarLib` in hand, one can compute broadband fluxes and broadband mags.

### Broadband fluxes

``` python
import bbf.stellarlib.pickles
from lemaitre import bandpasses

flib = bandpasses.get_filterlib()
pickles = bbf.stellarlib.pickles.fetch()

# number of measurements
nmeas = 100_000

# which stars ?
star = np.random.choice(np.arange(0, len(pickles)), size=nmeas)

# in which band ?
band = np.random.choice(['ztf::g', 'ztf::r', 'ztf::I'], size=nmeas)

# observation positions
x = np.random.uniform(0., 3072., size=nmeas)
y = np.random.uniform(0., 3080., size=nmeas)
sensor_id = np.random.choice(np.arange(1, 65), size=nmeas)

fluxes = flib.flux(pickles, star, band, x=x, y=y, sensor_id=sensor_id)
```


### Broadband magnitudes

To convert broadband fluxes into broadband magnitudes, we need to compute the reference fluxes,
in the same effective measurement band passes. This is done using an auxiliary object called `MagSys`:

``` python

from bbf.magsys import SpecMagSys
import bbf.stellarlib.pickles
from lemaitre import bandpasses

flib = bandpasses.get_filterlib()
pickles = bbf.stellarlib.pickles.fetch()

# number of measurements
nmeas = 100_000

# which stars ?
star = np.random.choice(np.arange(0, len(pickles)), size=nmeas)

# in which band ?
band = np.random.choice(['ztf::g', 'ztf::r', 'ztf::I'], size=nmeas)

# observation positions
x = np.random.uniform(0., 3072., size=nmeas)
y = np.random.uniform(0., 3080., size=nmeas)
sensor_id = np.random.choice(np.arange(1, 65), size=nmeas)

ms = SpecMagSys('AB')
mags = ms.mag(pickles, star, band, x=x, y=y, sensor_id=sensor_id)
```


# License

`bbf` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
