Merge branch 'JulianBMunoz:main' into main

.github/workflows/python-package-conda.yml

@@ -0,0 +1,34 @@
+name: Python Package using Conda
+
+on: [push]
+
+jobs:
+  build-linux:
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 5
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+    - name: Add conda to system path
+      run: |
+        # $CONDA is an environment variable pointing to the root of the miniconda directory
+        echo $CONDA/bin >> $GITHUB_PATH
+    - name: Install dependencies
+      run: |
+        conda env update --file environment.yml --name base
+    - name: Lint with flake8
+      run: |
+        conda install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        conda install pytest
+        pytest

CITATION.md

@@ -0,0 +1,31 @@
+If you find this code useful, please cite:
+
+[An Effective Model for the Cosmic-Dawn 21-cm Signal](https://arxiv.org/abs/2302.08506).
+Muñoz 2023
+
+@article{Munoz:2023kkg,
+    author = "Mu\~noz, Julian B.",
+    title = "{An Effective Model for the Cosmic-Dawn 21-cm Signal}",
+    eprint = "2302.08506",
+    archivePrefix = "arXiv",
+    primaryClass = "astro-ph.CO",
+    doi = "10.1093/mnras/stad1512",
+    month = "2",
+    year = "2023"
+}
+
+If additionally you use the UVLF functionality consider citing:
+
+[Breaking degeneracies in the first galaxies with clustering](https://arxiv.org/abs/2306.09403).
+Muñoz, Mirocha, Furlanetto, and Sabti 2023
+
+
+@article{Munoz:2023cup,
+    author = "Mu\~noz, Julian B. and Mirocha, Jordan and Furlanetto, Steven and Sabti, Nashwan",
+    title = "{Breaking degeneracies in the first galaxies with clustering}",
+    eprint = "2306.09403",
+    archivePrefix = "arXiv",
+    primaryClass = "astro-ph.CO",
+    month = "6",
+    year = "2023"
+}

README.md

@@ -2,11 +2,11 @@
 <img src="docs/zeusLogo.png" width=20% height=20%>
 </p>
 
-# Zeus21: Lightning-fast simulations of 21-cm at cosmic dawn
+# Zeus21: Lightning-fast simulations of cosmic dawn
 
-Zeus21 encodes the effective model for the 21-cm power spectrum and global signal from [Muñoz 2023](https://arxiv.org/abs/2302.08506). The goal is to capture all the nonlocal and nonlinear physics of cosmic dawn in a light and fully Pythonic code. Zeus21 takes advantage of the approximate log-normality of the star-formation rate density (SFRD) during cosmic dawn to compute the 21-cm power spectrum analytically. It agrees with more expensive semi-numerical simulations to roughly 10% precision, but has comparably negligible computational cost (~ s) and memory requirements.
+Zeus21 encodes the effective model for the 21-cm power spectrum, global signal from [Muñoz 2023a](https://arxiv.org/abs/2302.08506). The goal is to capture all the nonlocal and nonlinear physics of cosmic dawn in a light and fully Pythonic code. Zeus21 takes advantage of the approximate log-normality of the star-formation rate density (SFRD) during cosmic dawn to compute the 21-cm power spectrum analytically. It agrees with more expensive semi-numerical simulations to roughly 10% precision, but has comparably negligible computational cost (~ s) and memory requirements. Now Zeus21 can also predict galaxy UV luminosity functions (UVLFs) and their linear clustering (galaxy bias) at any z, see [Muñoz et al. 2023b](https://arxiv.org/abs/2306.09403) for the implementation and application to JWST.
 
-Zeus21 (Zippy Early-Universe Solver for 21-cm) pairs well with data from [HERA](https://reionization.org/), but can be used for any 21-cm inference or prediction. Current capabilities include finding the 21-cm power spectrum (at a broad range of k and z), the global signal, IGM temperatures (Tk, Ts, Tcolor), neutral fraction xHI, Lyman-alpha fluxes, and the evolution of the SFRD; all across cosmic dawn z=5-35. Zeus21 can use three different astrophysical models, one of which emulates 21cmFAST, and can vary the cosmology through CLASS.
+Zeus21 (Zippy Early-Universe Solver for 21-cm) pairs well with data from [HERA](https://reionization.org/), but can be used for any 21-cm inference or prediction. Current capabilities include finding the 21-cm power spectrum (at a broad range of k and z), the global signal, IGM temperatures (Tk, Ts, Tcolor), neutral fraction xHI, Lyman-alpha fluxes, and the evolution of the SFRD; all across cosmic dawn z=5-35. Zeus21 can use three different astrophysical models, one of which emulates 21cmFAST, and can vary the cosmology through CLASS. 
 
 If you want to get started I recommend checking the Jupyter tutorial in `docs/`. Full documentation in [ReadTheDocs](https://zeus21.readthedocs.io/en/latest/), more coming soon. Here is an example power spectrum (at k=0.3/Mpc) and global signal as a function of redshift, for two cases of X-ray luminosity. You can run it yourself with the tutorial included!
 
@@ -41,5 +41,11 @@ python setup.py install --user
 ## Citation
 
 If you find this code useful please cite:
+
 [An Effective Model for the Cosmic-Dawn 21-cm Signal](https://arxiv.org/abs/2302.08506)
+
 and include a link to [this Github](https://github.com/JulianBMunoz/Zeus21).
+If you use the UVLF module please consider citing:
+
+[Breaking degeneracies in the first galaxies with clustering](https://arxiv.org/abs/2306.09403)
+

zeus21/constants.py

@@ -108,3 +108,9 @@
 #RSD related
 MU_AVG = 0.6 #recovers (1+mu^2)^2 = 1.87, and very close for (1+mu^2) [for cross terms]
 MU_LoS = 1.0 #only fully LoS modes
+
+
+#UVLF related
+_MAGMAX = 10 #max abs magnitude to avoid infs
+FLAG_RENORMALIZE_LUV = False #whether to renormalize the lognormal LUV with sigmaUV to recover <LUV> or otherwise <MUV>. Recommend False.
+NZ_TOINT = 3 #how many zs around <z> with z_rms we use to predict. Only in HMF since the rest do not vary much.

zeus21/sfrd.py

@@ -444,6 +444,13 @@ def Matom(z):
     "Returns Matom as a function of z"
     return 3.3e7 * pow((1.+z)/(21.),-3./2)
 
+#fstar = Mstardot/Mhdot, parametrizes as you wish
+def fstarofz(Astro_Parameters, Cosmo_Parameters, z, Mhlist):
+    epsstar_ofz = Astro_Parameters.epsstar * 10**(Astro_Parameters.dlog10epsstardz * (z-Astro_Parameters._zpivot) )
+    return 2.0 * Cosmo_Parameters.OmegaB/Cosmo_Parameters.OmegaM * epsstar_ofz\
+        /(pow(Mhlist/Astro_Parameters.Mc,- Astro_Parameters.alphastar) + pow(Mhlist/Astro_Parameters.Mc,- Astro_Parameters.betastar) )
+
+
 #Only PopII for now (TODO, add PopIII)
 def SFR(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, z):
     "SFR in Msun/yr at redshift z. Evaluated at the halo masses Mh [Msun] of the HMF_interpolator, given Astro_Parameters"
@@ -457,7 +464,7 @@ def SFR(Astro_Parameters, Cosmo_Parameters, HMF_interpolator, z):
             fduty = np.heaviside(Mh - Astro_Parameters.Mturn_fixed, 0.5)
 
     if(Astro_Parameters.astromodel == 0): #GALLUMI-like
-        fstarM = 2.0 * Cosmo_Parameters.OmegaB/Cosmo_Parameters.OmegaM * Astro_Parameters.epsstar/(pow(Mh/Astro_Parameters.Mc,- Astro_Parameters.alphastar) + pow(Mh/Astro_Parameters.Mc,- Astro_Parameters.betastar) )
+        fstarM = fstarofz(Astro_Parameters, Cosmo_Parameters, z, Mh)
 
         if(Astro_Parameters.accretion_model == 0): #exponential accretion
             dMhdz = Mh * constants.ALPHA_accretion_exponential