2023-06-09T17:18:26,124 Created temporary directory: /tmp/pip-build-tracker-e62pd9nv 2023-06-09T17:18:26,129 Initialized build tracking at /tmp/pip-build-tracker-e62pd9nv 2023-06-09T17:18:26,129 Created build tracker: /tmp/pip-build-tracker-e62pd9nv 2023-06-09T17:18:26,129 Entered build tracker: /tmp/pip-build-tracker-e62pd9nv 2023-06-09T17:18:26,131 Created temporary directory: /tmp/pip-wheel-x1mekwu9 2023-06-09T17:18:26,139 Created temporary directory: /tmp/pip-ephem-wheel-cache-dc94hm_a 2023-06-09T17:18:26,203 Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple 2023-06-09T17:18:26,211 2 location(s) to search for versions of pyspk: 2023-06-09T17:18:26,211 * https://pypi.org/simple/pyspk/ 2023-06-09T17:18:26,211 * https://www.piwheels.org/simple/pyspk/ 2023-06-09T17:18:26,212 Fetching project page and analyzing links: https://pypi.org/simple/pyspk/ 2023-06-09T17:18:26,213 Getting page https://pypi.org/simple/pyspk/ 2023-06-09T17:18:26,218 Found index url 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Getting page https://www.piwheels.org/simple/pyspk/ 2023-06-09T17:18:26,429 Found index url https://www.piwheels.org/simple/ 2023-06-09T17:18:26,653 Fetched page https://www.piwheels.org/simple/pyspk/ as text/html 2023-06-09T17:18:26,659 Skipping link: No binaries permitted for pyspk: https://www.piwheels.org/simple/pyspk/pyspk-1.4-py3-none-any.whl#sha256=dea174bb5bfae84e6b0cc9e4565f70dc0182a66c20f3065689e23598401574a3 (from https://www.piwheels.org/simple/pyspk/) 2023-06-09T17:18:26,660 Skipping link: No binaries permitted for pyspk: https://www.piwheels.org/simple/pyspk/pyspk-1.3-py3-none-any.whl#sha256=9a5918d4a9524a9e7ae6cbb9fa78f638bf79250975d3a6df402bb9451aeea281 (from https://www.piwheels.org/simple/pyspk/) 2023-06-09T17:18:26,660 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/) 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pyspk==1.5 from https://files.pythonhosted.org/packages/de/42/7fc705db4a4cc3f762a58788ed3e9e2bafb87eb5ffa06ac12fda19ef0c90/pyspk-1.5.tar.gz to build tracker '/tmp/pip-build-tracker-e62pd9nv' 2023-06-09T17:18:27,114 Running setup.py (path:/tmp/pip-wheel-x1mekwu9/pyspk_0027dbbdc0854dcb87227fd9e4d705b1/setup.py) egg_info for package pyspk 2023-06-09T17:18:27,116 Created temporary directory: /tmp/pip-pip-egg-info-c4ga7s1l 2023-06-09T17:18:27,116 Preparing metadata (setup.py): started 2023-06-09T17:18:27,118 Running command python setup.py egg_info 2023-06-09T17:18:28,231 # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum 2023-06-09T17:18:28,233 _____ ____ ____ _ 2023-06-09T17:18:28,233 ____ __ __ / ___// __ \_/_/ /__| | 2023-06-09T17:18:28,234 / __ \/ / / /_____\__ \/ /_/ / // //_// / 2023-06-09T17:18:28,234 / /_/ / /_/ /_____/__/ / ____/ // ,< / / 2023-06-09T17:18:28,235 / .___/\__, / /____/_/ / //_/|_|/_/ 2023-06-09T17:18:28,235 /_/ /____/ |_| /_/ 2023-06-09T17:18:28,236 py-SP(k) [(Salcido et al. 2023)](https://academic.oup.com/mnras/article/523/2/2247/7165765) 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. 2023-06-09T17:18:28,236 ## Requirements 2023-06-09T17:18:28,237 The module requires the following: 2023-06-09T17:18:28,238 - numpy 2023-06-09T17:18:28,238 - scipy 2023-06-09T17:18:28,239 ## Installation 2023-06-09T17:18:28,239 The easiest way to install py-SP(k) is using pip: 2023-06-09T17:18:28,240 ``` 2023-06-09T17:18:28,240 pip install pyspk [--user] 2023-06-09T17:18:28,241 ``` 2023-06-09T17:18:28,241 The --user flag may be required if you do not have root privileges. 2023-06-09T17:18:28,242 ## Usage 2023-06-09T17:18:28,243 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). 2023-06-09T17:18:28,243 ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation 2023-06-09T17:18:28,244 py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form: 2023-06-09T17:18:28,245 $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$ 2023-06-09T17:18:28,245 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. 2023-06-09T17:18:28,246 Next, we show a simple example using power-law fit parameters: 2023-06-09T17:18:28,247 ``` 2023-06-09T17:18:28,247 import pyspk as spk 2023-06-09T17:18:28,248 z = 0.125 2023-06-09T17:18:28,248 fb_a = 0.4 2023-06-09T17:18:28,248 fb_pow = 0.3 2023-06-09T17:18:28,249 fb_pivot = 10 ** 13.5 2023-06-09T17:18:28,249 k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) 2023-06-09T17:18:28,250 ``` 2023-06-09T17:18:28,250 ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-09T17:18:28,251 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. 2023-06-09T17:18:28,252 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: 2023-06-09T17:18:28,252 $$f_b/(\Omega_b/\Omega_m)= \left(\frac{0.1658}{\Omega_b/\Omega_m}\right) \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$ 2023-06-09T17:18:28,253 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). 2023-06-09T17:18:28,254 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. 2023-06-09T17:18:28,254 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. 2023-06-09T17:18:28,255 ``` 2023-06-09T17:18:28,255 import pyspk.model as spk 2023-06-09T17:18:28,256 from astropy.cosmology import FlatLambdaCDM 2023-06-09T17:18:28,256 H0 = 70 2023-06-09T17:18:28,257 Omega_b = 0.0463 2023-06-09T17:18:28,257 Omega_m = 0.2793 2023-06-09T17:18:28,258 cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b) 2023-06-09T17:18:28,258 alpha = 4.189 2023-06-09T17:18:28,258 beta = 1.273 2023-06-09T17:18:28,259 gamma = 0.298 2023-06-09T17:18:28,259 z = 0.5 2023-06-09T17:18:28,260 k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo) 2023-06-09T17:18:28,260 ``` 2023-06-09T17:18:28,261 ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-09T17:18:28,261 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. 2023-06-09T17:18:28,262 ## Priors 2023-06-09T17:18:28,263 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). 2023-06-09T17:18:28,264 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 $6 \times 10^{12} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{14}$. 2023-06-09T17:18:28,264 Priors inferred from simulations that fall within $\pm 2 \times \sigma$ of the inferred baryon budget: 2023-06-09T17:18:28,265 | Parameter | Description | Prior | 2023-06-09T17:18:28,265 | ----------- | ------------------ | --------------- | 2023-06-09T17:18:28,266 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.16, 0.07) | 2023-06-09T17:18:28,266 | $\beta$ | Slope | $\mathcal{N}$(1.20, 0.05) | 2023-06-09T17:18:28,266 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.39, 0.09) | 2023-06-09T17:18:28,267 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-09T17:18:28,268 Priors inferred from simulations that fall within $\pm 3 \times \sigma$ of the inferred baryon budget: 2023-06-09T17:18:28,268 | Parameter | Description | Prior | 2023-06-09T17:18:28,269 | ----------- | ------------------ | --------------- | 2023-06-09T17:18:28,269 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.18, 0.12) | 2023-06-09T17:18:28,269 | $\beta$ | Slope | $\mathcal{N}$(1.26, 0.08) | 2023-06-09T17:18:28,270 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.42, 0.10) | 2023-06-09T17:18:28,270 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-09T17:18:28,271 ## Acknowledging the code 2023-06-09T17:18:28,271 Please cite py-SP(k) using: 2023-06-09T17:18:28,272 ``` 2023-06-09T17:18:28,272 @ARTICLE{SPK_Salcido_2023, 2023-06-09T17:18:28,273 author = {Salcido, Jaime and McCarthy, Ian G and Kwan, Juliana and Upadhye, Amol and Font, Andreea S}, 2023-06-09T17:18:28,273 title = "{SP(k) – a hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum}", 2023-06-09T17:18:28,273 journal = {Monthly Notices of the Royal Astronomical Society}, 2023-06-09T17:18:28,274 volume = {523}, 2023-06-09T17:18:28,274 number = {2}, 2023-06-09T17:18:28,274 pages = {2247-2262}, 2023-06-09T17:18:28,275 year = {2023}, 2023-06-09T17:18:28,275 month = {05}, 2023-06-09T17:18:28,275 issn = {0035-8711}, 2023-06-09T17:18:28,276 doi = {10.1093/mnras/stad1474}, 2023-06-09T17:18:28,276 url = {https://doi.org/10.1093/mnras/stad1474}, 2023-06-09T17:18:28,276 eprint = {https://academic.oup.com/mnras/article-pdf/523/2/2247/50512773/stad1474.pdf}, 2023-06-09T17:18:28,277 } 2023-06-09T17:18:28,277 ``` 2023-06-09T17:18:28,277 For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk* 2023-06-09T17:18:28,631 running egg_info 2023-06-09T17:18:28,634 creating /tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info 2023-06-09T17:18:28,708 writing /tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info/PKG-INFO 2023-06-09T17:18:28,713 writing dependency_links to /tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info/dependency_links.txt 2023-06-09T17:18:28,717 writing requirements to /tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info/requires.txt 2023-06-09T17:18:28,719 writing top-level names to /tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info/top_level.txt 2023-06-09T17:18:28,722 writing manifest file '/tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info/SOURCES.txt' 2023-06-09T17:18:28,946 reading manifest file '/tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info/SOURCES.txt' 2023-06-09T17:18:28,958 reading manifest template 'MANIFEST.in' 2023-06-09T17:18:28,965 adding license file 'LICENSE.md' 2023-06-09T17:18:28,970 writing manifest file '/tmp/pip-pip-egg-info-c4ga7s1l/pyspk.egg-info/SOURCES.txt' 2023-06-09T17:18:29,090 Preparing metadata (setup.py): finished with status 'done' 2023-06-09T17:18:29,105 Source in /tmp/pip-wheel-x1mekwu9/pyspk_0027dbbdc0854dcb87227fd9e4d705b1 has version 1.5, which satisfies requirement pyspk==1.5 from https://files.pythonhosted.org/packages/de/42/7fc705db4a4cc3f762a58788ed3e9e2bafb87eb5ffa06ac12fda19ef0c90/pyspk-1.5.tar.gz 2023-06-09T17:18:29,107 Removed pyspk==1.5 from https://files.pythonhosted.org/packages/de/42/7fc705db4a4cc3f762a58788ed3e9e2bafb87eb5ffa06ac12fda19ef0c90/pyspk-1.5.tar.gz from build tracker '/tmp/pip-build-tracker-e62pd9nv' 2023-06-09T17:18:29,120 Created temporary directory: /tmp/pip-unpack-m82upzyt 2023-06-09T17:18:29,121 Building wheels for collected packages: pyspk 2023-06-09T17:18:29,130 Created temporary directory: /tmp/pip-wheel-auj0csq_ 2023-06-09T17:18:29,130 Building wheel for pyspk (setup.py): started 2023-06-09T17:18:29,133 Destination directory: /tmp/pip-wheel-auj0csq_ 2023-06-09T17:18:29,133 Running command python setup.py bdist_wheel 2023-06-09T17:18:30,209 # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum 2023-06-09T17:18:30,211 _____ ____ ____ _ 2023-06-09T17:18:30,211 ____ __ __ / ___// __ \_/_/ /__| | 2023-06-09T17:18:30,212 / __ \/ / / /_____\__ \/ /_/ / // //_// / 2023-06-09T17:18:30,212 / /_/ / /_/ /_____/__/ / ____/ // ,< / / 2023-06-09T17:18:30,212 / .___/\__, / /____/_/ / //_/|_|/_/ 2023-06-09T17:18:30,213 /_/ /____/ |_| /_/ 2023-06-09T17:18:30,213 py-SP(k) [(Salcido et al. 2023)](https://academic.oup.com/mnras/article/523/2/2247/7165765) 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. 2023-06-09T17:18:30,214 ## Requirements 2023-06-09T17:18:30,215 The module requires the following: 2023-06-09T17:18:30,215 - numpy 2023-06-09T17:18:30,216 - scipy 2023-06-09T17:18:30,216 ## Installation 2023-06-09T17:18:30,217 The easiest way to install py-SP(k) is using pip: 2023-06-09T17:18:30,218 ``` 2023-06-09T17:18:30,218 pip install pyspk [--user] 2023-06-09T17:18:30,218 ``` 2023-06-09T17:18:30,219 The --user flag may be required if you do not have root privileges. 2023-06-09T17:18:30,220 ## Usage 2023-06-09T17:18:30,220 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). 2023-06-09T17:18:30,221 ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation 2023-06-09T17:18:30,222 py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form: 2023-06-09T17:18:30,222 $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$ 2023-06-09T17:18:30,223 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. 2023-06-09T17:18:30,224 Next, we show a simple example using power-law fit parameters: 2023-06-09T17:18:30,224 ``` 2023-06-09T17:18:30,225 import pyspk as spk 2023-06-09T17:18:30,225 z = 0.125 2023-06-09T17:18:30,226 fb_a = 0.4 2023-06-09T17:18:30,226 fb_pow = 0.3 2023-06-09T17:18:30,227 fb_pivot = 10 ** 13.5 2023-06-09T17:18:30,227 k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) 2023-06-09T17:18:30,228 ``` 2023-06-09T17:18:30,228 ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-09T17:18:30,229 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. 2023-06-09T17:18:30,230 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: 2023-06-09T17:18:30,230 $$f_b/(\Omega_b/\Omega_m)= \left(\frac{0.1658}{\Omega_b/\Omega_m}\right) \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$ 2023-06-09T17:18:30,231 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). 2023-06-09T17:18:30,232 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. 2023-06-09T17:18:30,233 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. 2023-06-09T17:18:30,233 ``` 2023-06-09T17:18:30,234 import pyspk.model as spk 2023-06-09T17:18:30,234 from astropy.cosmology import FlatLambdaCDM 2023-06-09T17:18:30,234 H0 = 70 2023-06-09T17:18:30,235 Omega_b = 0.0463 2023-06-09T17:18:30,235 Omega_m = 0.2793 2023-06-09T17:18:30,236 cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b) 2023-06-09T17:18:30,236 alpha = 4.189 2023-06-09T17:18:30,237 beta = 1.273 2023-06-09T17:18:30,237 gamma = 0.298 2023-06-09T17:18:30,238 z = 0.5 2023-06-09T17:18:30,238 k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo) 2023-06-09T17:18:30,239 ``` 2023-06-09T17:18:30,239 ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-09T17:18:30,240 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. 2023-06-09T17:18:30,241 ## Priors 2023-06-09T17:18:30,241 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). 2023-06-09T17:18:30,242 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 $6 \times 10^{12} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{14}$. 2023-06-09T17:18:30,243 Priors inferred from simulations that fall within $\pm 2 \times \sigma$ of the inferred baryon budget: 2023-06-09T17:18:30,244 | Parameter | Description | Prior | 2023-06-09T17:18:30,244 | ----------- | ------------------ | --------------- | 2023-06-09T17:18:30,245 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.16, 0.07) | 2023-06-09T17:18:30,245 | $\beta$ | Slope | $\mathcal{N}$(1.20, 0.05) | 2023-06-09T17:18:30,245 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.39, 0.09) | 2023-06-09T17:18:30,246 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-09T17:18:30,247 Priors inferred from simulations that fall within $\pm 3 \times \sigma$ of the inferred baryon budget: 2023-06-09T17:18:30,247 | Parameter | Description | Prior | 2023-06-09T17:18:30,248 | ----------- | ------------------ | --------------- | 2023-06-09T17:18:30,248 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.18, 0.12) | 2023-06-09T17:18:30,248 | $\beta$ | Slope | $\mathcal{N}$(1.26, 0.08) | 2023-06-09T17:18:30,249 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.42, 0.10) | 2023-06-09T17:18:30,249 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-09T17:18:30,250 ## Acknowledging the code 2023-06-09T17:18:30,250 Please cite py-SP(k) using: 2023-06-09T17:18:30,251 ``` 2023-06-09T17:18:30,251 @ARTICLE{SPK_Salcido_2023, 2023-06-09T17:18:30,252 author = {Salcido, Jaime and McCarthy, Ian G and Kwan, Juliana and Upadhye, Amol and Font, Andreea S}, 2023-06-09T17:18:30,252 title = "{SP(k) – a hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum}", 2023-06-09T17:18:30,252 journal = {Monthly Notices of the Royal Astronomical Society}, 2023-06-09T17:18:30,253 volume = {523}, 2023-06-09T17:18:30,253 number = {2}, 2023-06-09T17:18:30,253 pages = {2247-2262}, 2023-06-09T17:18:30,254 year = {2023}, 2023-06-09T17:18:30,254 month = {05}, 2023-06-09T17:18:30,254 issn = {0035-8711}, 2023-06-09T17:18:30,255 doi = {10.1093/mnras/stad1474}, 2023-06-09T17:18:30,255 url = {https://doi.org/10.1093/mnras/stad1474}, 2023-06-09T17:18:30,255 eprint = {https://academic.oup.com/mnras/article-pdf/523/2/2247/50512773/stad1474.pdf}, 2023-06-09T17:18:30,256 } 2023-06-09T17:18:30,256 ``` 2023-06-09T17:18:30,257 For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk* 2023-06-09T17:18:30,690 running bdist_wheel 2023-06-09T17:18:31,396 running build 2023-06-09T17:18:31,396 running build_py 2023-06-09T17:18:31,472 creating build 2023-06-09T17:18:31,473 creating build/lib 2023-06-09T17:18:31,475 creating build/lib/pyspk 2023-06-09T17:18:31,477 copying pyspk/model.py -> build/lib/pyspk 2023-06-09T17:18:31,482 copying pyspk/__init__.py -> build/lib/pyspk 2023-06-09T17:18:31,486 copying pyspk/fit_vals.py -> build/lib/pyspk 2023-06-09T17:18:31,489 running egg_info 2023-06-09T17:18:31,639 writing pyspk.egg-info/PKG-INFO 2023-06-09T17:18:31,643 writing dependency_links to pyspk.egg-info/dependency_links.txt 2023-06-09T17:18:31,648 writing requirements to pyspk.egg-info/requires.txt 2023-06-09T17:18:31,651 writing top-level names to pyspk.egg-info/top_level.txt 2023-06-09T17:18:31,719 reading manifest file 'pyspk.egg-info/SOURCES.txt' 2023-06-09T17:18:31,723 reading manifest template 'MANIFEST.in' 2023-06-09T17:18:31,730 adding license file 'LICENSE.md' 2023-06-09T17:18:31,736 writing manifest file 'pyspk.egg-info/SOURCES.txt' 2023-06-09T17:18:31,740 copying pyspk/stat_errors_200.csv -> build/lib/pyspk 2023-06-09T17:18:31,754 copying pyspk/stat_errors_500.csv -> build/lib/pyspk 2023-06-09T17:18:31,839 /usr/local/lib/python3.7/dist-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2023-06-09T17:18:31,839 !! 2023-06-09T17:18:31,840 ******************************************************************************** 2023-06-09T17:18:31,840 Please avoid running ``setup.py`` directly. 2023-06-09T17:18:31,841 Instead, use pypa/build, pypa/installer, pypa/build or 2023-06-09T17:18:31,841 other standards-based tools. 2023-06-09T17:18:31,842 See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2023-06-09T17:18:31,842 ******************************************************************************** 2023-06-09T17:18:31,843 !! 2023-06-09T17:18:31,843 self.initialize_options() 2023-06-09T17:18:31,907 installing to build/bdist.linux-armv7l/wheel 2023-06-09T17:18:31,908 running install 2023-06-09T17:18:31,970 running install_lib 2023-06-09T17:18:32,037 creating build/bdist.linux-armv7l 2023-06-09T17:18:32,038 creating build/bdist.linux-armv7l/wheel 2023-06-09T17:18:32,042 creating build/bdist.linux-armv7l/wheel/pyspk 2023-06-09T17:18:32,045 copying build/lib/pyspk/stat_errors_200.csv -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-09T17:18:32,059 copying build/lib/pyspk/model.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-09T17:18:32,063 copying build/lib/pyspk/__init__.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-09T17:18:32,067 copying build/lib/pyspk/stat_errors_500.csv -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-09T17:18:32,081 copying build/lib/pyspk/fit_vals.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-09T17:18:32,084 running install_egg_info 2023-06-09T17:18:32,158 Copying pyspk.egg-info to build/bdist.linux-armv7l/wheel/pyspk-1.5-py3.7.egg-info 2023-06-09T17:18:32,177 running install_scripts 2023-06-09T17:18:32,207 creating build/bdist.linux-armv7l/wheel/pyspk-1.5.dist-info/WHEEL 2023-06-09T17:18:32,212 creating '/tmp/pip-wheel-auj0csq_/pyspk-1.5-py3-none-any.whl' and adding 'build/bdist.linux-armv7l/wheel' to it 2023-06-09T17:18:32,216 adding 'pyspk/__init__.py' 2023-06-09T17:18:32,220 adding 'pyspk/fit_vals.py' 2023-06-09T17:18:32,225 adding 'pyspk/model.py' 2023-06-09T17:18:32,317 adding 'pyspk/stat_errors_200.csv' 2023-06-09T17:18:32,410 adding 'pyspk/stat_errors_500.csv' 2023-06-09T17:18:32,419 adding 'pyspk-1.5.dist-info/LICENSE.md' 2023-06-09T17:18:32,423 adding 'pyspk-1.5.dist-info/METADATA' 2023-06-09T17:18:32,425 adding 'pyspk-1.5.dist-info/WHEEL' 2023-06-09T17:18:32,427 adding 'pyspk-1.5.dist-info/top_level.txt' 2023-06-09T17:18:32,429 adding 'pyspk-1.5.dist-info/RECORD' 2023-06-09T17:18:32,436 removing build/bdist.linux-armv7l/wheel 2023-06-09T17:18:32,595 Building wheel for pyspk (setup.py): finished with status 'done' 2023-06-09T17:18:32,606 Created wheel for pyspk: filename=pyspk-1.5-py3-none-any.whl size=146356 sha256=93305fb0b22ddc50cd6b6f015ba94f8a7a76d3d68323707388b4d03718c46854 2023-06-09T17:18:32,608 Stored in directory: /tmp/pip-ephem-wheel-cache-dc94hm_a/wheels/d4/ae/cf/9d4e2fc0a2fd83d414d0d4c265df7e37d3998f08b4ee130048 2023-06-09T17:18:32,637 Successfully built pyspk 2023-06-09T17:18:32,656 Removed build tracker: '/tmp/pip-build-tracker-e62pd9nv'