2023-05-18T09:06:49,719 Created temporary directory: /tmp/pip-build-tracker-d44mqqog 2023-05-18T09:06:49,722 Initialized build tracking at /tmp/pip-build-tracker-d44mqqog 2023-05-18T09:06:49,722 Created build tracker: /tmp/pip-build-tracker-d44mqqog 2023-05-18T09:06:49,722 Entered build tracker: /tmp/pip-build-tracker-d44mqqog 2023-05-18T09:06:49,724 Created temporary directory: /tmp/pip-wheel-af0nb4_5 2023-05-18T09:06:49,732 Created temporary directory: /tmp/pip-ephem-wheel-cache-ke9urdco 2023-05-18T09:06:49,791 Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple 2023-05-18T09:06:49,798 2 location(s) to search for versions of pyspk: 2023-05-18T09:06:49,798 * https://pypi.org/simple/pyspk/ 2023-05-18T09:06:49,798 * https://www.piwheels.org/simple/pyspk/ 2023-05-18T09:06:49,800 Fetching project page and analyzing links: https://pypi.org/simple/pyspk/ 2023-05-18T09:06:49,801 Getting page https://pypi.org/simple/pyspk/ 2023-05-18T09:06:49,805 Found index url 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page and analyzing links: https://www.piwheels.org/simple/pyspk/ 2023-05-18T09:06:50,047 Getting page https://www.piwheels.org/simple/pyspk/ 2023-05-18T09:06:50,049 Found index url https://www.piwheels.org/simple/ 2023-05-18T09:06:50,271 Fetched page https://www.piwheels.org/simple/pyspk/ as text/html 2023-05-18T09:06:50,275 Skipping link: No binaries permitted for pyspk: https://www.piwheels.org/simple/pyspk/pyspk-1.2-py3-none-any.whl#sha256=83010a88648bf4bbe2dffd0eb64c5a1892d598c03297cd19ce0d785f28662c28 (from https://www.piwheels.org/simple/pyspk/) 2023-05-18T09:06:50,275 Skipping link: No binaries permitted for pyspk: https://www.piwheels.org/simple/pyspk/pyspk-1.1-py3-none-any.whl#sha256=2c787ef97d85f7abf4348ac04aa1b05fc7f6dc20af7fb5e0dd1551349d6446ac (from https://www.piwheels.org/simple/pyspk/) 2023-05-18T09:06:50,276 Skipping link: No binaries permitted for pyspk: https://www.piwheels.org/simple/pyspk/pyspk-1.0-py3-none-any.whl#sha256=cfc057fd75e4214b94b5938b6e9840b03b3933ac7e44d0c84e2ce139ee2f42da (from https://www.piwheels.org/simple/pyspk/) 2023-05-18T09:06:50,277 Skipping link: not a file: https://www.piwheels.org/simple/pyspk/ 2023-05-18T09:06:50,277 Skipping link: not a file: https://pypi.org/simple/pyspk/ 2023-05-18T09:06:50,310 Given no hashes to check 1 links for project 'pyspk': discarding no candidates 2023-05-18T09:06:50,341 Collecting pyspk==1.3 2023-05-18T09:06:50,346 Created temporary directory: /tmp/pip-unpack-uw7_awdn 2023-05-18T09:06:50,520 Downloading pyspk-1.3.tar.gz (156 kB) 2023-05-18T09:06:50,754 Added pyspk==1.3 from https://files.pythonhosted.org/packages/5a/7f/3b4c6ab139cf3771fcb53c4cdd65e8c36cf86ba024e132aa3a38a0db5929/pyspk-1.3.tar.gz to build tracker '/tmp/pip-build-tracker-d44mqqog' 2023-05-18T09:06:50,759 Running setup.py (path:/tmp/pip-wheel-af0nb4_5/pyspk_735d64f32fc34fcd9d532e6e1b72f586/setup.py) egg_info for package pyspk 2023-05-18T09:06:50,760 Created temporary directory: /tmp/pip-pip-egg-info-2lctxy0y 2023-05-18T09:06:50,761 Preparing metadata (setup.py): started 2023-05-18T09:06:50,763 Running command python setup.py egg_info 2023-05-18T09:06:51,870 # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum 2023-05-18T09:06:51,871 _____ ____ ____ _ 2023-05-18T09:06:51,872 ____ __ __ / ___// __ \_/_/ /__| | 2023-05-18T09:06:51,872 / __ \/ / / /_____\__ \/ /_/ / // //_// / 2023-05-18T09:06:51,873 / /_/ / /_/ /_____/__/ / ____/ // ,< / / 2023-05-18T09:06:51,873 / .___/\__, / /____/_/ / //_/|_|/_/ 2023-05-18T09:06:51,873 /_/ /____/ |_| /_/ 2023-05-18T09:06:51,874 py-SP(k) is a python package aimed at predicting the suppression of the total matter power spectrum due to baryonic physics as a function of the baryon fraction of haloes and redshift. 2023-05-18T09:06:51,875 ## Requirements 2023-05-18T09:06:51,875 The module requires the following: 2023-05-18T09:06:51,876 - numpy 2023-05-18T09:06:51,876 - scipy 2023-05-18T09:06:51,877 ## Installation 2023-05-18T09:06:51,877 The easiest way to install py-SP(k) is using pip: 2023-05-18T09:06:51,878 ``` 2023-05-18T09:06:51,878 pip install pyspk [--user] 2023-05-18T09:06:51,879 ``` 2023-05-18T09:06:51,879 The --user flag may be required if you do not have root privileges. 2023-05-18T09:06:51,880 ## Usage 2023-05-18T09:06:51,881 py-SP(k) is not restrictive to a particular shape of the baryon fraction – halo mass relation. In order to provide flexibility to the user, we have implemented 3 different methods to provide py-SP(k) with the required $f_b$ - $M_\mathrm{halo}$ relation. In the following sections we describe these implementations. A jupyter notebook with more detailed examples can be found within this [repository](https://github.com/jemme07/pyspk/blob/main/examples/pySPk_Examples.ipynb). 2023-05-18T09:06:51,881 ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation 2023-05-18T09:06:51,882 py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form: 2023-05-18T09:06:51,883 $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$ 2023-05-18T09:06:51,883 where $M_{SO}$ could be either $M_{200c}$ or $M_{500c}$ in $\mathrm{M}_ \odot$, $a$ is the normalisation of the $f_b$ - $M_\mathrm{halo}$ relation at $M_\mathrm{pivot}$, and $b$ is the power-law slope. The power-law can be normalised at any pivot point in units of $\mathrm{M}_ {\odot}$. If a pivot point is not given, `spk.sup_model()` uses a default pivot point of $M_{\mathrm{pivot}} = 1 \mathrm{M}_ \odot$. $a$, $b$ and $M_\mathrm{pivot}$ can be specified at each redshift independently. 2023-05-18T09:06:51,884 Next, we show a simple example using power-law fit parameters: 2023-05-18T09:06:51,885 ``` 2023-05-18T09:06:51,885 import pyspk as spk 2023-05-18T09:06:51,886 z = 0.125 2023-05-18T09:06:51,886 fb_a = 0.4 2023-05-18T09:06:51,886 fb_pow = 0.3 2023-05-18T09:06:51,887 fb_pivot = 10 ** 13.5 2023-05-18T09:06:51,887 k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) 2023-05-18T09:06:51,888 ``` 2023-05-18T09:06:51,888 ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation. 2023-05-18T09:06:51,889 For the mass range that can be relatively well probed in current X-ray and Sunyaev-Zel'dovich effect observations (roughly $10^{13} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{15}$), the total baryon fraction of haloes can be roughly approximated by a power-law with constant slope (e.g. Mulroy et al. 2019; Akino et al. 2022). Akino et al. (2022) determined the of the baryon budget for X-ray-selected galaxy groups and clusters using weak-lensing mass measurements. They provide a parametric redshift-dependent power-law fit to the gas mass - halo mass and stellar mass - halo mass relations, finding very little redshift evolution. 2023-05-18T09:06:51,890 We implemented a modified version of the functional form presented in Akino et al. (2022), to fit the total $f_b$ - $M_\mathrm{halo}$ relation as follows: 2023-05-18T09:06:51,890 $$f_b/(\Omega_b/\Omega_m)= \left(\frac{0.1658}{\Omega_b/\Omega_m}\right) \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$ 2023-05-18T09:06:51,891 where $\alpha$ sets the power-law normalisation, $\beta$ sets power-law slope, $\gamma$ provides the redshift dependence and $E(z)$ is the usual dimensionless Hubble parameter. For simplicity, we use the cosmology implementation of `astropy` to specify the cosmological parameters in py-SP(k). 2023-05-18T09:06:51,892 Note that this power-law has a normalisation that is redshift dependent, while the the slope is constant in redshift. While this provides a less flexible approach compared with Methods 1 (simple power-law) and Method 3 (binned data), we find that this parametrisation provides a reasonable agreement with our simulations up to redshift $z=1$, which is the redshift range proved by Akino et al. (2022). For higher redshifts, we find that simulations require a mass-dependent slope, especially at the lower mass range required to predict the suppression of the total matter power spectrum at such redshifts. 2023-05-18T09:06:51,892 In the following example we use the redshift-dependent power-law fit parameters with a flat LambdaCDM cosmology. Note that any `astropy` cosmology could be used instead. 2023-05-18T09:06:51,893 ``` 2023-05-18T09:06:51,893 import pyspk.model as spk 2023-05-18T09:06:51,894 from astropy.cosmology import FlatLambdaCDM 2023-05-18T09:06:51,894 H0 = 70 2023-05-18T09:06:51,895 Omega_b = 0.0463 2023-05-18T09:06:51,895 Omega_m = 0.2793 2023-05-18T09:06:51,896 cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b) 2023-05-18T09:06:51,896 alpha = 4.189 2023-05-18T09:06:51,897 beta = 1.273 2023-05-18T09:06:51,897 gamma = 0.298 2023-05-18T09:06:51,897 z = 0.5 2023-05-18T09:06:51,898 k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo) 2023-05-18T09:06:51,898 ``` 2023-05-18T09:06:51,899 ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation. 2023-05-18T09:06:51,899 The final, and most flexible method is to provide py-SP(k) with the baryon fraction binned in bins of halo mass. This could be, for example, obtained from observational constraints, measured directly form simulations, or sampled from a predefined distribution or functional form. For an example using data obtained from the BAHAMAS simulations (McCarthy et al. 2017), please refer to the [examples](https://github.com/jemme07/pyspk/blob/main/examples/pySPk_Examples.ipynb) provided. 2023-05-18T09:06:51,900 ## Priors 2023-05-18T09:06:51,901 While py-SP(k) was calibrated using a wide range of sub-grid feedback parameters, some applications may require a more limited range of baryon fractions that encompass current observational constraints. For such applications, we used the gas mass - halo mass and stellar mass - halo mass constraints from the fits in Table 5 in Akino et al. (2022), and find the subset of simulations from our 400 models that agree to within $\pm 2$ or $3 \times \sigma$ of the inferred baryon budget at redshift $z=0.1$. We note that for our simulations, we include all stellar and gas particles within a spherical overdensity radius. Hence, in order to make reasonable comparisons with the fits in Akino et al. (2022), we included an additional 15\% contribution to the total stellar masses from the contribution of blue galaxies, and 30\% additional stellar mass to the brightest cluster galaxies (BCGs) to account for the diffuse intracluster light (ICL, see Akino et al. 2022). 2023-05-18T09:06:51,902 We utilised the simulations satisfying these restrictions to determine the redshift-dependent power-law parameters for the $f_b$ - $M_\mathrm{halo}$ relation up to redshift $z=1$ (Method 2), and then utilised these parameters to infer suitable priors. We limited the fitting range to $6 \times 10^{12} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{14}$. 2023-05-18T09:06:51,902 Priors inferred from simulations that fall within $\pm 2 \times \sigma$ of the inferred baryon budget: 2023-05-18T09:06:51,903 | Parameter | Description | Prior | 2023-05-18T09:06:51,903 | ----------- | ------------------ | --------------- | 2023-05-18T09:06:51,904 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.16, 0.07) | 2023-05-18T09:06:51,904 | $\beta$ | Slope | $\mathcal{N}$(1.20, 0.05) | 2023-05-18T09:06:51,904 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.39, 0.09) | 2023-05-18T09:06:51,905 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-05-18T09:06:51,906 Priors inferred from simulations that fall within $\pm 3 \times \sigma$ of the inferred baryon budget: 2023-05-18T09:06:51,906 | Parameter | Description | Prior | 2023-05-18T09:06:51,906 | ----------- | ------------------ | --------------- | 2023-05-18T09:06:51,907 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.18, 0.12) | 2023-05-18T09:06:51,907 | $\beta$ | Slope | $\mathcal{N}$(1.26, 0.08) | 2023-05-18T09:06:51,907 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.42, 0.10) | 2023-05-18T09:06:51,908 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-05-18T09:06:51,909 ## Acknowledging the code 2023-05-18T09:06:51,909 Please cite py-SP(k) using: 2023-05-18T09:06:51,910 ``` 2023-05-18T09:06:51,910 @ARTICLE{SPK_Salcido_2023, 2023-05-18T09:06:51,911 author = {Salcido, Jaime and McCarthy, Ian G and Kwan, Juliana and Upadhye, Amol and Font, Andreea S}, 2023-05-18T09:06:51,911 title = "{SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum}", 2023-05-18T09:06:51,911 journal = {Monthly Notices of the Royal Astronomical Society}, 2023-05-18T09:06:51,912 year = {2023}, 2023-05-18T09:06:51,912 month = {05}, 2023-05-18T09:06:51,912 issn = {0035-8711}, 2023-05-18T09:06:51,913 doi = {10.1093/mnras/stad1474}, 2023-05-18T09:06:51,913 url = {https://doi.org/10.1093/mnras/stad1474}, 2023-05-18T09:06:51,913 note = {stad1474}, 2023-05-18T09:06:51,914 eprint = {https://academic.oup.com/mnras/advance-article-pdf/doi/10.1093/mnras/stad1474/50356057/stad1474.pdf}, 2023-05-18T09:06:51,914 } 2023-05-18T09:06:51,914 ``` 2023-05-18T09:06:51,915 For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk* 2023-05-18T09:06:52,251 running egg_info 2023-05-18T09:06:52,254 creating /tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info 2023-05-18T09:06:52,312 writing /tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info/PKG-INFO 2023-05-18T09:06:52,317 writing dependency_links to /tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info/dependency_links.txt 2023-05-18T09:06:52,321 writing requirements to /tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info/requires.txt 2023-05-18T09:06:52,323 writing top-level names to /tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info/top_level.txt 2023-05-18T09:06:52,326 writing manifest file '/tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info/SOURCES.txt' 2023-05-18T09:06:52,508 reading manifest file '/tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info/SOURCES.txt' 2023-05-18T09:06:52,512 reading manifest template 'MANIFEST.in' 2023-05-18T09:06:52,526 adding license file 'LICENSE.md' 2023-05-18T09:06:52,532 writing manifest file '/tmp/pip-pip-egg-info-2lctxy0y/pyspk.egg-info/SOURCES.txt' 2023-05-18T09:06:52,650 Preparing metadata (setup.py): finished with status 'done' 2023-05-18T09:06:52,664 Source in /tmp/pip-wheel-af0nb4_5/pyspk_735d64f32fc34fcd9d532e6e1b72f586 has version 1.3, which satisfies requirement pyspk==1.3 from https://files.pythonhosted.org/packages/5a/7f/3b4c6ab139cf3771fcb53c4cdd65e8c36cf86ba024e132aa3a38a0db5929/pyspk-1.3.tar.gz 2023-05-18T09:06:52,665 Removed pyspk==1.3 from https://files.pythonhosted.org/packages/5a/7f/3b4c6ab139cf3771fcb53c4cdd65e8c36cf86ba024e132aa3a38a0db5929/pyspk-1.3.tar.gz from build tracker '/tmp/pip-build-tracker-d44mqqog' 2023-05-18T09:06:52,677 Created temporary directory: /tmp/pip-unpack-tq13eh5h 2023-05-18T09:06:52,678 Building wheels for collected packages: pyspk 2023-05-18T09:06:52,686 Created temporary directory: /tmp/pip-wheel-jrvhpnkr 2023-05-18T09:06:52,687 Building wheel for pyspk (setup.py): started 2023-05-18T09:06:52,689 Destination directory: /tmp/pip-wheel-jrvhpnkr 2023-05-18T09:06:52,690 Running command python setup.py bdist_wheel 2023-05-18T09:06:53,764 # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum 2023-05-18T09:06:53,765 _____ ____ ____ _ 2023-05-18T09:06:53,766 ____ __ __ / ___// __ \_/_/ /__| | 2023-05-18T09:06:53,766 / __ \/ / / /_____\__ \/ /_/ / // //_// / 2023-05-18T09:06:53,766 / /_/ / /_/ /_____/__/ / ____/ // ,< / / 2023-05-18T09:06:53,767 / .___/\__, / /____/_/ / //_/|_|/_/ 2023-05-18T09:06:53,767 /_/ /____/ |_| /_/ 2023-05-18T09:06:53,768 py-SP(k) is a python package aimed at predicting the suppression of the total matter power spectrum due to baryonic physics as a function of the baryon fraction of haloes and redshift. 2023-05-18T09:06:53,768 ## Requirements 2023-05-18T09:06:53,769 The module requires the following: 2023-05-18T09:06:53,770 - numpy 2023-05-18T09:06:53,770 - scipy 2023-05-18T09:06:53,771 ## Installation 2023-05-18T09:06:53,771 The easiest way to install py-SP(k) is using pip: 2023-05-18T09:06:53,772 ``` 2023-05-18T09:06:53,772 pip install pyspk [--user] 2023-05-18T09:06:53,772 ``` 2023-05-18T09:06:53,773 The --user flag may be required if you do not have root privileges. 2023-05-18T09:06:53,774 ## Usage 2023-05-18T09:06:53,774 py-SP(k) is not restrictive to a particular shape of the baryon fraction – halo mass relation. In order to provide flexibility to the user, we have implemented 3 different methods to provide py-SP(k) with the required $f_b$ - $M_\mathrm{halo}$ relation. In the following sections we describe these implementations. A jupyter notebook with more detailed examples can be found within this [repository](https://github.com/jemme07/pyspk/blob/main/examples/pySPk_Examples.ipynb). 2023-05-18T09:06:53,775 ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation 2023-05-18T09:06:53,776 py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form: 2023-05-18T09:06:53,776 $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$ 2023-05-18T09:06:53,777 where $M_{SO}$ could be either $M_{200c}$ or $M_{500c}$ in $\mathrm{M}_ \odot$, $a$ is the normalisation of the $f_b$ - $M_\mathrm{halo}$ relation at $M_\mathrm{pivot}$, and $b$ is the power-law slope. The power-law can be normalised at any pivot point in units of $\mathrm{M}_ {\odot}$. If a pivot point is not given, `spk.sup_model()` uses a default pivot point of $M_{\mathrm{pivot}} = 1 \mathrm{M}_ \odot$. $a$, $b$ and $M_\mathrm{pivot}$ can be specified at each redshift independently. 2023-05-18T09:06:53,778 Next, we show a simple example using power-law fit parameters: 2023-05-18T09:06:53,778 ``` 2023-05-18T09:06:53,779 import pyspk as spk 2023-05-18T09:06:53,779 z = 0.125 2023-05-18T09:06:53,780 fb_a = 0.4 2023-05-18T09:06:53,780 fb_pow = 0.3 2023-05-18T09:06:53,780 fb_pivot = 10 ** 13.5 2023-05-18T09:06:53,781 k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) 2023-05-18T09:06:53,781 ``` 2023-05-18T09:06:53,782 ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation. 2023-05-18T09:06:53,782 For the mass range that can be relatively well probed in current X-ray and Sunyaev-Zel'dovich effect observations (roughly $10^{13} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{15}$), the total baryon fraction of haloes can be roughly approximated by a power-law with constant slope (e.g. Mulroy et al. 2019; Akino et al. 2022). Akino et al. (2022) determined the of the baryon budget for X-ray-selected galaxy groups and clusters using weak-lensing mass measurements. They provide a parametric redshift-dependent power-law fit to the gas mass - halo mass and stellar mass - halo mass relations, finding very little redshift evolution. 2023-05-18T09:06:53,783 We implemented a modified version of the functional form presented in Akino et al. (2022), to fit the total $f_b$ - $M_\mathrm{halo}$ relation as follows: 2023-05-18T09:06:53,784 $$f_b/(\Omega_b/\Omega_m)= \left(\frac{0.1658}{\Omega_b/\Omega_m}\right) \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$ 2023-05-18T09:06:53,784 where $\alpha$ sets the power-law normalisation, $\beta$ sets power-law slope, $\gamma$ provides the redshift dependence and $E(z)$ is the usual dimensionless Hubble parameter. For simplicity, we use the cosmology implementation of `astropy` to specify the cosmological parameters in py-SP(k). 2023-05-18T09:06:53,785 Note that this power-law has a normalisation that is redshift dependent, while the the slope is constant in redshift. While this provides a less flexible approach compared with Methods 1 (simple power-law) and Method 3 (binned data), we find that this parametrisation provides a reasonable agreement with our simulations up to redshift $z=1$, which is the redshift range proved by Akino et al. (2022). For higher redshifts, we find that simulations require a mass-dependent slope, especially at the lower mass range required to predict the suppression of the total matter power spectrum at such redshifts. 2023-05-18T09:06:53,786 In the following example we use the redshift-dependent power-law fit parameters with a flat LambdaCDM cosmology. Note that any `astropy` cosmology could be used instead. 2023-05-18T09:06:53,786 ``` 2023-05-18T09:06:53,786 import pyspk.model as spk 2023-05-18T09:06:53,787 from astropy.cosmology import FlatLambdaCDM 2023-05-18T09:06:53,787 H0 = 70 2023-05-18T09:06:53,788 Omega_b = 0.0463 2023-05-18T09:06:53,788 Omega_m = 0.2793 2023-05-18T09:06:53,789 cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b) 2023-05-18T09:06:53,789 alpha = 4.189 2023-05-18T09:06:53,790 beta = 1.273 2023-05-18T09:06:53,790 gamma = 0.298 2023-05-18T09:06:53,790 z = 0.5 2023-05-18T09:06:53,791 k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo) 2023-05-18T09:06:53,791 ``` 2023-05-18T09:06:53,792 ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation. 2023-05-18T09:06:53,792 The final, and most flexible method is to provide py-SP(k) with the baryon fraction binned in bins of halo mass. This could be, for example, obtained from observational constraints, measured directly form simulations, or sampled from a predefined distribution or functional form. For an example using data obtained from the BAHAMAS simulations (McCarthy et al. 2017), please refer to the [examples](https://github.com/jemme07/pyspk/blob/main/examples/pySPk_Examples.ipynb) provided. 2023-05-18T09:06:53,793 ## Priors 2023-05-18T09:06:53,794 While py-SP(k) was calibrated using a wide range of sub-grid feedback parameters, some applications may require a more limited range of baryon fractions that encompass current observational constraints. For such applications, we used the gas mass - halo mass and stellar mass - halo mass constraints from the fits in Table 5 in Akino et al. (2022), and find the subset of simulations from our 400 models that agree to within $\pm 2$ or $3 \times \sigma$ of the inferred baryon budget at redshift $z=0.1$. We note that for our simulations, we include all stellar and gas particles within a spherical overdensity radius. Hence, in order to make reasonable comparisons with the fits in Akino et al. (2022), we included an additional 15\% contribution to the total stellar masses from the contribution of blue galaxies, and 30\% additional stellar mass to the brightest cluster galaxies (BCGs) to account for the diffuse intracluster light (ICL, see Akino et al. 2022). 2023-05-18T09:06:53,795 We utilised the simulations satisfying these restrictions to determine the redshift-dependent power-law parameters for the $f_b$ - $M_\mathrm{halo}$ relation up to redshift $z=1$ (Method 2), and then utilised these parameters to infer suitable priors. We limited the fitting range to $6 \times 10^{12} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{14}$. 2023-05-18T09:06:53,795 Priors inferred from simulations that fall within $\pm 2 \times \sigma$ of the inferred baryon budget: 2023-05-18T09:06:53,796 | Parameter | Description | Prior | 2023-05-18T09:06:53,796 | ----------- | ------------------ | --------------- | 2023-05-18T09:06:53,797 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.16, 0.07) | 2023-05-18T09:06:53,797 | $\beta$ | Slope | $\mathcal{N}$(1.20, 0.05) | 2023-05-18T09:06:53,797 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.39, 0.09) | 2023-05-18T09:06:53,798 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-05-18T09:06:53,798 Priors inferred from simulations that fall within $\pm 3 \times \sigma$ of the inferred baryon budget: 2023-05-18T09:06:53,799 | Parameter | Description | Prior | 2023-05-18T09:06:53,799 | ----------- | ------------------ | --------------- | 2023-05-18T09:06:53,800 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.18, 0.12) | 2023-05-18T09:06:53,800 | $\beta$ | Slope | $\mathcal{N}$(1.26, 0.08) | 2023-05-18T09:06:53,800 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.42, 0.10) | 2023-05-18T09:06:53,801 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-05-18T09:06:53,802 ## Acknowledging the code 2023-05-18T09:06:53,802 Please cite py-SP(k) using: 2023-05-18T09:06:53,803 ``` 2023-05-18T09:06:53,803 @ARTICLE{SPK_Salcido_2023, 2023-05-18T09:06:53,803 author = {Salcido, Jaime and McCarthy, Ian G and Kwan, Juliana and Upadhye, Amol and Font, Andreea S}, 2023-05-18T09:06:53,804 title = "{SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum}", 2023-05-18T09:06:53,804 journal = {Monthly Notices of the Royal Astronomical Society}, 2023-05-18T09:06:53,805 year = {2023}, 2023-05-18T09:06:53,805 month = {05}, 2023-05-18T09:06:53,805 issn = {0035-8711}, 2023-05-18T09:06:53,806 doi = {10.1093/mnras/stad1474}, 2023-05-18T09:06:53,806 url = {https://doi.org/10.1093/mnras/stad1474}, 2023-05-18T09:06:53,806 note = {stad1474}, 2023-05-18T09:06:53,806 eprint = {https://academic.oup.com/mnras/advance-article-pdf/doi/10.1093/mnras/stad1474/50356057/stad1474.pdf}, 2023-05-18T09:06:53,807 } 2023-05-18T09:06:53,807 ``` 2023-05-18T09:06:53,807 For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk* 2023-05-18T09:06:54,610 running bdist_wheel 2023-05-18T09:06:54,792 running build 2023-05-18T09:06:54,792 running build_py 2023-05-18T09:06:54,857 creating build 2023-05-18T09:06:54,857 creating build/lib 2023-05-18T09:06:54,859 creating build/lib/pyspk 2023-05-18T09:06:54,861 copying pyspk/__init__.py -> build/lib/pyspk 2023-05-18T09:06:54,865 copying pyspk/fit_vals.py -> build/lib/pyspk 2023-05-18T09:06:54,869 copying pyspk/model.py -> build/lib/pyspk 2023-05-18T09:06:54,874 running egg_info 2023-05-18T09:06:55,011 writing pyspk.egg-info/PKG-INFO 2023-05-18T09:06:55,017 writing dependency_links to pyspk.egg-info/dependency_links.txt 2023-05-18T09:06:55,021 writing requirements to pyspk.egg-info/requires.txt 2023-05-18T09:06:55,024 writing top-level names to pyspk.egg-info/top_level.txt 2023-05-18T09:06:55,096 reading manifest file 'pyspk.egg-info/SOURCES.txt' 2023-05-18T09:06:55,101 reading manifest template 'MANIFEST.in' 2023-05-18T09:06:55,118 adding license file 'LICENSE.md' 2023-05-18T09:06:55,125 writing manifest file 'pyspk.egg-info/SOURCES.txt' 2023-05-18T09:06:55,131 /usr/local/lib/python3.7/dist-packages/setuptools/command/build_py.py:201: _Warning: Package 'pyspk.__pycache__' is absent from the `packages` configuration. 2023-05-18T09:06:55,131 !! 2023-05-18T09:06:55,132 ******************************************************************************** 2023-05-18T09:06:55,132 ############################ 2023-05-18T09:06:55,132 # Package would be ignored # 2023-05-18T09:06:55,133 ############################ 2023-05-18T09:06:55,133 Python recognizes 'pyspk.__pycache__' as an importable package[^1], 2023-05-18T09:06:55,133 but it is absent from setuptools' `packages` configuration. 2023-05-18T09:06:55,134 This leads to an ambiguous overall configuration. If you want to distribute this 2023-05-18T09:06:55,134 package, please make sure that 'pyspk.__pycache__' is explicitly added 2023-05-18T09:06:55,134 to the `packages` configuration field. 2023-05-18T09:06:55,135 Alternatively, you can also rely on setuptools' discovery methods 2023-05-18T09:06:55,135 (for example by using `find_namespace_packages(...)`/`find_namespace:` 2023-05-18T09:06:55,135 instead of `find_packages(...)`/`find:`). 2023-05-18T09:06:55,136 You can read more about "package discovery" on setuptools documentation page: 2023-05-18T09:06:55,137 - https://setuptools.pypa.io/en/latest/userguide/package_discovery.html 2023-05-18T09:06:55,137 If you don't want 'pyspk.__pycache__' to be distributed and are 2023-05-18T09:06:55,137 already explicitly excluding 'pyspk.__pycache__' via 2023-05-18T09:06:55,138 `find_namespace_packages(...)/find_namespace` or `find_packages(...)/find`, 2023-05-18T09:06:55,138 you can try to use `exclude_package_data`, or `include-package-data=False` in 2023-05-18T09:06:55,138 combination with a more fine grained `package-data` configuration. 2023-05-18T09:06:55,139 You can read more about "package data files" on setuptools documentation page: 2023-05-18T09:06:55,139 - https://setuptools.pypa.io/en/latest/userguide/datafiles.html 2023-05-18T09:06:55,140 [^1]: For Python, any directory (with suitable naming) can be imported, 2023-05-18T09:06:55,141 even if it does not contain any `.py` files. 2023-05-18T09:06:55,141 On the other hand, currently there is no concept of package data 2023-05-18T09:06:55,142 directory, all directories are treated like packages. 2023-05-18T09:06:55,142 ******************************************************************************** 2023-05-18T09:06:55,142 !! 2023-05-18T09:06:55,143 check.warn(importable) 2023-05-18T09:06:55,145 copying pyspk/.DS_Store -> build/lib/pyspk 2023-05-18T09:06:55,150 copying pyspk/stat_errors_200.csv -> build/lib/pyspk 2023-05-18T09:06:55,168 copying pyspk/stat_errors_500.csv -> build/lib/pyspk 2023-05-18T09:06:55,185 creating build/lib/pyspk/__pycache__ 2023-05-18T09:06:55,187 copying pyspk/__pycache__/__init__.cpython-38.pyc -> build/lib/pyspk/__pycache__ 2023-05-18T09:06:55,192 copying pyspk/__pycache__/__init__.cpython-39.pyc -> build/lib/pyspk/__pycache__ 2023-05-18T09:06:55,196 copying pyspk/__pycache__/fit_vals.cpython-38.pyc -> build/lib/pyspk/__pycache__ 2023-05-18T09:06:55,201 copying pyspk/__pycache__/fit_vals.cpython-39.pyc -> build/lib/pyspk/__pycache__ 2023-05-18T09:06:55,206 copying pyspk/__pycache__/model.cpython-38.pyc -> build/lib/pyspk/__pycache__ 2023-05-18T09:06:55,212 copying pyspk/__pycache__/model.cpython-39.pyc -> build/lib/pyspk/__pycache__ 2023-05-18T09:06:55,287 /usr/local/lib/python3.7/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2023-05-18T09:06:55,288 !! 2023-05-18T09:06:55,288 ******************************************************************************** 2023-05-18T09:06:55,288 Please avoid running ``setup.py`` directly. 2023-05-18T09:06:55,289 Instead, use pypa/build, pypa/installer, pypa/build or 2023-05-18T09:06:55,289 other standards-based tools. 2023-05-18T09:06:55,290 See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2023-05-18T09:06:55,290 ******************************************************************************** 2023-05-18T09:06:55,291 !! 2023-05-18T09:06:55,291 self.initialize_options() 2023-05-18T09:06:55,353 installing to build/bdist.linux-armv7l/wheel 2023-05-18T09:06:55,353 running install 2023-05-18T09:06:55,412 running install_lib 2023-05-18T09:06:55,485 creating build/bdist.linux-armv7l 2023-05-18T09:06:55,486 creating build/bdist.linux-armv7l/wheel 2023-05-18T09:06:55,490 creating build/bdist.linux-armv7l/wheel/pyspk 2023-05-18T09:06:55,493 creating build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-05-18T09:06:55,495 copying build/lib/pyspk/__pycache__/model.cpython-38.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-05-18T09:06:55,501 copying build/lib/pyspk/__pycache__/__init__.cpython-38.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-05-18T09:06:55,505 copying build/lib/pyspk/__pycache__/fit_vals.cpython-39.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-05-18T09:06:55,509 copying build/lib/pyspk/__pycache__/fit_vals.cpython-38.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-05-18T09:06:55,513 copying build/lib/pyspk/__pycache__/model.cpython-39.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-05-18T09:06:55,519 copying build/lib/pyspk/__pycache__/__init__.cpython-39.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-05-18T09:06:55,524 copying build/lib/pyspk/__init__.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-05-18T09:06:55,529 copying build/lib/pyspk/fit_vals.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-05-18T09:06:55,534 copying build/lib/pyspk/model.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-05-18T09:06:55,540 copying build/lib/pyspk/stat_errors_200.csv -> build/bdist.linux-armv7l/wheel/pyspk 2023-05-18T09:06:55,558 copying build/lib/pyspk/stat_errors_500.csv -> build/bdist.linux-armv7l/wheel/pyspk 2023-05-18T09:06:55,573 copying build/lib/pyspk/.DS_Store -> build/bdist.linux-armv7l/wheel/pyspk 2023-05-18T09:06:55,577 running install_egg_info 2023-05-18T09:06:55,649 Copying pyspk.egg-info to build/bdist.linux-armv7l/wheel/pyspk-1.3-py3.7.egg-info 2023-05-18T09:06:55,671 running install_scripts 2023-05-18T09:06:55,704 adding license file "LICENSE.md" (matched pattern "LICEN[CS]E*") 2023-05-18T09:06:55,713 creating build/bdist.linux-armv7l/wheel/pyspk-1.3.dist-info/WHEEL 2023-05-18T09:06:55,719 creating '/tmp/pip-wheel-jrvhpnkr/pyspk-1.3-py3-none-any.whl' and adding 'build/bdist.linux-armv7l/wheel' to it 2023-05-18T09:06:55,725 adding 'pyspk/.DS_Store' 2023-05-18T09:06:55,729 adding 'pyspk/__init__.py' 2023-05-18T09:06:55,733 adding 'pyspk/fit_vals.py' 2023-05-18T09:06:55,739 adding 'pyspk/model.py' 2023-05-18T09:06:55,830 adding 'pyspk/stat_errors_200.csv' 2023-05-18T09:06:55,925 adding 'pyspk/stat_errors_500.csv' 2023-05-18T09:06:55,932 adding 'pyspk/__pycache__/__init__.cpython-38.pyc' 2023-05-18T09:06:55,935 adding 'pyspk/__pycache__/__init__.cpython-39.pyc' 2023-05-18T09:06:55,938 adding 'pyspk/__pycache__/fit_vals.cpython-38.pyc' 2023-05-18T09:06:55,941 adding 'pyspk/__pycache__/fit_vals.cpython-39.pyc' 2023-05-18T09:06:55,946 adding 'pyspk/__pycache__/model.cpython-38.pyc' 2023-05-18T09:06:55,952 adding 'pyspk/__pycache__/model.cpython-39.pyc' 2023-05-18T09:06:55,958 adding 'pyspk-1.3.dist-info/LICENSE.md' 2023-05-18T09:06:55,961 adding 'pyspk-1.3.dist-info/METADATA' 2023-05-18T09:06:55,964 adding 'pyspk-1.3.dist-info/WHEEL' 2023-05-18T09:06:55,965 adding 'pyspk-1.3.dist-info/top_level.txt' 2023-05-18T09:06:55,967 adding 'pyspk-1.3.dist-info/RECORD' 2023-05-18T09:06:55,975 removing build/bdist.linux-armv7l/wheel 2023-05-18T09:06:56,133 Building wheel for pyspk (setup.py): finished with status 'done' 2023-05-18T09:06:56,145 Created wheel for pyspk: filename=pyspk-1.3-py3-none-any.whl size=162389 sha256=9a5918d4a9524a9e7ae6cbb9fa78f638bf79250975d3a6df402bb9451aeea281 2023-05-18T09:06:56,147 Stored in directory: /tmp/pip-ephem-wheel-cache-ke9urdco/wheels/c7/ad/05/3f1fd0add07284d1e7fa4774917928cdaa7c8ed8377e26c5f0 2023-05-18T09:06:56,175 Successfully built pyspk 2023-05-18T09:06:56,200 Removed build tracker: '/tmp/pip-build-tracker-d44mqqog'