Minor updates after MNRAS accepted the paper

.github/workflows/nirdust_ci.yml

@@ -14,12 +14,12 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@master
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version}}
       - name: Install pandoc
@@ -29,7 +29,7 @@ jobs:
       - name: Test with tox
         run: tox -r
       - name: Test Coverage
-        if: ${{ matrix.python-version == '3.8' }}
+        if: ${{ matrix.python-version == '3.11' }}
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}        
         run: |

README.md

@@ -11,6 +11,7 @@
 [![Coverage Status](https://coveralls.io/repos/github/Gaiana/nirdust/badge.svg?branch=main)](https://coveralls.io/github/Gaiana/nirdust?branch=main)
 [![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/release/python-370/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![arXiv](https://img.shields.io/badge/arXiv-2401.01888-b31b1b.svg)](https://arxiv.org/abs/2401.01888)
 
 
 NIRDust is a python package that uses K-band (2.2 micrometers) spectra to 
@@ -60,7 +61,33 @@ Or, for the develovepment instalation clone this repository and then inside the
 $ pip install -e .
 ```
 
-Authors: Gaia Gaspar, José Alacoria, Juan B. Cabral & Martin Chalela
+## Citation
+
+If you use *NIRDust* in a scientific publication, we would appreciate citations to the following paper:
+
+> Gaspar, Gaia and Chalela, Martín and Cabral, Juan and Alacoria, José and Mast, Damián and Díaz, Rubén J (2024). 
+> NIRDust: probing hot dust emission around type 2 AGN using K-band spectra. 10.1093/mnras/stae008
+
+### Bibtex
+
+```bibtex
+@article{10.1093/mnras/stae008,
+    author = {Gaspar, Gaia and Chalela, Martín and Cabral, Juan and Alacoria, José and Mast, Damián and Díaz, Rubén J},
+    title = "{nirdust: probing hot dust emission around type 2 AGN using K-band spectra}",
+    journal = {Monthly Notices of the Royal Astronomical Society},
+    volume = {528},
+    number = {2},
+    pages = {2952-2963},
+    year = {2024},
+    month = {01},
+    issn = {0035-8711},
+    doi = {10.1093/mnras/stae008},
+    url = {https://doi.org/10.1093/mnras/stae008},
+    eprint = {https://academic.oup.com/mnras/article-pdf/528/2/2952/56541590/stae008.pdf},
+}
+```
+
+Full-text: [https://arxiv.org/abs/1912.09585](https://arxiv.org/abs/2401.01888)
 
 
 

docs/source/api.rst

@@ -1,4 +1,4 @@
-NIRDust API Reference
+API Reference
 =====================
 
 .. automodule:: nirdust

docs/source/conf.py

@@ -79,7 +79,7 @@
 
 
 html_theme_options = {
-    "logotext1": "Nirdust",  # white,  semi-bold
+    "logotext1": "NIRDust",  # white,  semi-bold
     "logotext2": "",  # orange, light
     "logotext3": ":docs",  # white,  light
     "astropy_project_menubar": False,

docs/source/index.rst

@@ -74,11 +74,11 @@ User Documentation
 ------------------
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
 
    installation
+   tutorials/index
    api
-   tutorials/NGC5128.ipynb
    licence
 
 

docs/source/tutorials/NGC5128.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Example of Nirdust application\n",
+    "## Example of NIRDust application\n",
     "The example imputs are two Flamingos-2 (Gemini South) $K_{long}$ band longslit spectra of NGC 5128 (Centaurus A) a Seyfert 2 galaxy.\n",
     "\n",
     "The spectra correspond to:\n",

docs/source/tutorials/fit_blackbody.ipynb

@@ -0,0 +1,26 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fitting a BlackBody model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "work in progress..."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/source/tutorials/index.rst

@@ -0,0 +1,15 @@
+Tutorials
+=========
+
+The following page contains a collection of tutorials and practical examples
+on how to use NIRDust. From reading your spectra from a file, to modelling the
+hot dust emission and visualizing the results.
+
+
+.. toctree::
+   :maxdepth: 2
+
+   reading_spectra.ipynb
+   preprocessing.ipynb
+   fit_blackbody.ipynb
+   NGC5128.ipynb

docs/source/tutorials/preprocessing.ipynb

@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocessing utilities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "work in progress..."
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "dfefad9444caa34b651035342248c936eda24540a2756afa4fe280ef294fff37"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit ('nirdust': virtualenv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/source/tutorials/reading_spectra.ipynb

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reading Spectra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NIRDust provides easy and quick functionalities to read your spectra. If you have your data in FITS format or just a common text ASCII file, you can use one of the following functions. If you don't have your data in any of these formats, by the end of this tutorial you can see how to load your data to create a NirdustSpectrum object directly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading from a FITS file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say you have your spectra in a FITS file. This format, in most cases, comes with a header and a science extension. The science extension stores the flux intesity of the spectrum and the respective pixel axis. The header has the information to transform the pixel axis into a spectral axis. The manual input of the redshift of the galaxy, which isn't included in most headers, is necesary to get the spectral axis in rest frame. The following code shows the easiest way to read a spectrum:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NirdustSpectrum(z=0.00183, spectral_length=1751, spectral_range=[18889.58-25106.71] Angstrom)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import nirdust as nd\n",
+    "\n",
+    "nuclear_spectrum = nd.read_fits(\"nuclear_spectrum.fits\", z=0.00183)\n",
+    "\n",
+    "# Show some information\n",
+    "nuclear_spectrum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If your FITS file has multiple extensions, the automatic detection of the science extension may fail. In such cases, you can manually indicate the correct extension by passing the parameter ``extension=1`` to the function (if 1 is the correct number)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading from an ASCII file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function ``read_table`` can be used to read directly from a text file and supports multiple formats. In this case the file should contain these two columns: wavelength [A] and flux [arbitrary units]. If you have multiple columns you should indicate which columns correspond to the spectrum data. For a list of supported formats see [this link](https://docs.astropy.org/en/stable/io/ascii/index.html#supported-formats)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spectrum = nd.read_table(\"file_name.csv\", wavelength_column=2, flux_column=4, format=\"csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "dfefad9444caa34b651035342248c936eda24540a2756afa4fe280ef294fff37"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit ('nirdust': virtualenv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

nirdust/bbody.py

@@ -117,9 +117,7 @@ def negative_gaussian_log_likelihood(
     diff = target_spectrum.flux.value - prediction
 
     loglike = np.sum(
-        -0.5 * np.log(2.0 * np.pi)
-        - np.log(noise)
-        - diff**2 / (2.0 * noise**2)
+        -0.5 * np.log(2.0 * np.pi) - np.log(noise) - diff**2 / (2.0 * noise**2)
     )
     return -loglike
 

nirdust/preprocessing.py

@@ -140,10 +140,10 @@ def match_spectral_axes(
         else:
             second_sp = _rescale(second_sp, reference_sp=first_sp)
 
-    else:
-        # they have the same dispersion, is that equivalent
-        # to equal spectral_axis?
-        pass
+    # else:
+    #     # they have the same dispersion, is that equivalent
+    #     # to equal spectral_axis?
+    #     pass
 
     if clean:
         first_sp, second_sp = _clean_and_match(first_sp, second_sp)
@@ -190,13 +190,11 @@ def line_spectrum(
 
     Return
     ------
-    out: flux axis, list, list
-        Returns in the first element a flux axis of the same lenght as the
-        original spectrum containing the fitted lines. In the second position,
-        returns the intervals where those lines were finded determined by
-        3-sigma values around the center of the line. In the third position
-        returns an array with the quality of the fitting for each line (not yet
-        implemented).
+    out: NirdustSpectrum, Quantity
+        Returns in the first element a NirdustSpectrum of the same lenght as
+        the original spectrum containing the fitted lines. In the 2nd position,
+        returns the intervals where those lines were found determined by
+        3-sigma values around the center of the line.
     """
     # values in correct units
     window = u.Quantity(window, u.AA)

tests/test_preprocessing.py

@@ -118,13 +118,14 @@ def test_line_spectrum(NGC4945_continuum_rest_frame):
         z=0,
     ).compute_noise(23000, 24000)
 
-    lines = preprocessing.line_spectrum(snth_line_spectrum, 5, window=80)[
-        0
-    ].flux
-
-    all_zeros = any(lines)
+    line_nd_spectrum, line_intervals = preprocessing.line_spectrum(
+        snth_line_spectrum,
+        noise_factor=5,
+        window=80,
+    )
 
-    assert all_zeros is True
+    assert isinstance(line_nd_spectrum, core.NirdustSpectrum)
+    assert isinstance(line_intervals, u.Quantity)
 
 
 def test_spectral_dispersion(NGC4945_continuum_rest_frame):

tox.ini

@@ -1,11 +1,14 @@
 [tox]
-envlist = py38, py39, py310, style, docstyle, check-manifest, make-docs, coverage
+envlist = py{38,39,310,311,312}, style, docstyle, check-manifest, make-docs, coverage
 
 [gh-actions]
 python =
-    3.8: py38, style, coverage, docstyle, check-manifest, make-docs
+    3.8: py38
     3.9: py39
     3.10: py310
+    3.11: py311, style, coverage, docstyle, check-manifest, make-docs
+    3.12: py312
+
 
 [testenv]
 usedevelop = True