2023-06-23T10:08:09,362 Created temporary directory: /tmp/pip-build-tracker-q90a601g 2023-06-23T10:08:09,365 Initialized build tracking at /tmp/pip-build-tracker-q90a601g 2023-06-23T10:08:09,366 Created build tracker: /tmp/pip-build-tracker-q90a601g 2023-06-23T10:08:09,366 Entered build tracker: /tmp/pip-build-tracker-q90a601g 2023-06-23T10:08:09,367 Created temporary directory: /tmp/pip-wheel-3c997jiy 2023-06-23T10:08:09,376 Created temporary directory: /tmp/pip-ephem-wheel-cache-3ec54mto 2023-06-23T10:08:09,428 Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple 2023-06-23T10:08:09,436 2 location(s) to search for versions of pyspk: 2023-06-23T10:08:09,436 * https://pypi.org/simple/pyspk/ 2023-06-23T10:08:09,436 * https://www.piwheels.org/simple/pyspk/ 2023-06-23T10:08:09,437 Fetching project page and analyzing links: https://pypi.org/simple/pyspk/ 2023-06-23T10:08:09,438 Getting page https://pypi.org/simple/pyspk/ 2023-06-23T10:08:09,443 Found index url 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Running command python setup.py egg_info 2023-06-23T10:08:11,484 # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum 2023-06-23T10:08:11,485 _____ ____ ____ _ 2023-06-23T10:08:11,486 ____ __ __ / ___// __ \_/_/ /__| | 2023-06-23T10:08:11,486 / __ \/ / / /_____\__ \/ /_/ / // //_// / 2023-06-23T10:08:11,487 / /_/ / /_/ /_____/__/ / ____/ // ,< / / 2023-06-23T10:08:11,487 / .___/\__, / /____/_/ / //_/|_|/_/ 2023-06-23T10:08:11,487 /_/ /____/ |_| /_/ 2023-06-23T10:08:11,488 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. 2023-06-23T10:08:11,489 ## Requirements 2023-06-23T10:08:11,489 The module requires the following: 2023-06-23T10:08:11,490 - numpy 2023-06-23T10:08:11,490 - scipy 2023-06-23T10:08:11,491 ## Installation 2023-06-23T10:08:11,492 The easiest way to install py-SP(k) is using pip: 2023-06-23T10:08:11,492 ``` 2023-06-23T10:08:11,493 pip install pyspk [--user] 2023-06-23T10:08:11,493 ``` 2023-06-23T10:08:11,494 The --user flag may be required if you do not have root privileges. 2023-06-23T10:08:11,494 ## Usage 2023-06-23T10:08:11,495 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). 2023-06-23T10:08:11,496 ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation 2023-06-23T10:08:11,496 py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form: 2023-06-23T10:08:11,497 $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$ 2023-06-23T10:08:11,498 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. 2023-06-23T10:08:11,498 Next, we show a simple example using power-law fit parameters: 2023-06-23T10:08:11,499 ``` 2023-06-23T10:08:11,499 import pyspk as spk 2023-06-23T10:08:11,500 z = 0.125 2023-06-23T10:08:11,500 fb_a = 0.4 2023-06-23T10:08:11,501 fb_pow = 0.3 2023-06-23T10:08:11,501 fb_pivot = 10 ** 13.5 2023-06-23T10:08:11,501 k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) 2023-06-23T10:08:11,502 ``` 2023-06-23T10:08:11,502 ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-23T10:08:11,503 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. 2023-06-23T10:08:11,504 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: 2023-06-23T10:08:11,504 $$f_b/(\Omega_b/\Omega_m)= \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$ 2023-06-23T10:08:11,505 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). 2023-06-23T10:08:11,506 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. 2023-06-23T10:08:11,507 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. 2023-06-23T10:08:11,507 ``` 2023-06-23T10:08:11,508 import pyspk.model as spk 2023-06-23T10:08:11,508 from astropy.cosmology import FlatLambdaCDM 2023-06-23T10:08:11,509 H0 = 70 2023-06-23T10:08:11,509 Omega_b = 0.0463 2023-06-23T10:08:11,509 Omega_m = 0.2793 2023-06-23T10:08:11,510 cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b) 2023-06-23T10:08:11,510 alpha = 4.189 2023-06-23T10:08:11,511 beta = 1.273 2023-06-23T10:08:11,511 gamma = 0.298 2023-06-23T10:08:11,511 z = 0.5 2023-06-23T10:08:11,512 k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo) 2023-06-23T10:08:11,512 ``` 2023-06-23T10:08:11,513 ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-23T10:08:11,514 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. 2023-06-23T10:08:11,514 ## Priors 2023-06-23T10:08:11,515 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). 2023-06-23T10:08:11,516 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 $6 \times 10^{12} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{14}$. 2023-06-23T10:08:11,517 Priors inferred from simulations that fall within $\pm 2 \times \sigma$ of the inferred baryon budget: 2023-06-23T10:08:11,517 | Parameter | Description | Prior | 2023-06-23T10:08:11,518 | ----------- | ------------------ | --------------- | 2023-06-23T10:08:11,518 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.16, 0.07) | 2023-06-23T10:08:11,518 | $\beta$ | Slope | $\mathcal{N}$(1.20, 0.05) | 2023-06-23T10:08:11,519 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.39, 0.09) | 2023-06-23T10:08:11,519 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-23T10:08:11,520 Priors inferred from simulations that fall within $\pm 3 \times \sigma$ of the inferred baryon budget: 2023-06-23T10:08:11,520 | Parameter | Description | Prior | 2023-06-23T10:08:11,521 | ----------- | ------------------ | --------------- | 2023-06-23T10:08:11,521 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.18, 0.12) | 2023-06-23T10:08:11,521 | $\beta$ | Slope | $\mathcal{N}$(1.26, 0.08) | 2023-06-23T10:08:11,522 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.42, 0.10) | 2023-06-23T10:08:11,522 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-23T10:08:11,523 ## Acknowledging the code 2023-06-23T10:08:11,524 Please cite py-SP(k) using: 2023-06-23T10:08:11,524 ``` 2023-06-23T10:08:11,525 @ARTICLE{SPK_Salcido_2023, 2023-06-23T10:08:11,525 author = {Salcido, Jaime and McCarthy, Ian G and Kwan, Juliana and Upadhye, Amol and Font, Andreea S}, 2023-06-23T10:08:11,525 title = "{SP(k) – a hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum}", 2023-06-23T10:08:11,526 journal = {Monthly Notices of the Royal Astronomical Society}, 2023-06-23T10:08:11,526 volume = {523}, 2023-06-23T10:08:11,526 number = {2}, 2023-06-23T10:08:11,527 pages = {2247-2262}, 2023-06-23T10:08:11,527 year = {2023}, 2023-06-23T10:08:11,527 month = {05}, 2023-06-23T10:08:11,528 issn = {0035-8711}, 2023-06-23T10:08:11,528 doi = {10.1093/mnras/stad1474}, 2023-06-23T10:08:11,528 url = {https://doi.org/10.1093/mnras/stad1474}, 2023-06-23T10:08:11,529 eprint = {https://academic.oup.com/mnras/article-pdf/523/2/2247/50512773/stad1474.pdf}, 2023-06-23T10:08:11,529 } 2023-06-23T10:08:11,529 ``` 2023-06-23T10:08:11,530 For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk* 2023-06-23T10:08:11,902 running egg_info 2023-06-23T10:08:11,905 creating /tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info 2023-06-23T10:08:11,971 writing /tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info/PKG-INFO 2023-06-23T10:08:11,976 writing dependency_links to /tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info/dependency_links.txt 2023-06-23T10:08:11,980 writing requirements to /tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info/requires.txt 2023-06-23T10:08:11,982 writing top-level names to /tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info/top_level.txt 2023-06-23T10:08:11,985 writing manifest file '/tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info/SOURCES.txt' 2023-06-23T10:08:12,184 reading manifest file '/tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info/SOURCES.txt' 2023-06-23T10:08:12,187 reading manifest template 'MANIFEST.in' 2023-06-23T10:08:12,198 adding license file 'LICENSE.md' 2023-06-23T10:08:12,203 writing manifest file '/tmp/pip-pip-egg-info-vnr47b1g/pyspk.egg-info/SOURCES.txt' 2023-06-23T10:08:12,323 Preparing metadata (setup.py): finished with status 'done' 2023-06-23T10:08:12,336 Source in /tmp/pip-wheel-3c997jiy/pyspk_10619cd2a55645d9a9632ee7d6099d0c has version 1.7, which satisfies requirement pyspk==1.7 from https://files.pythonhosted.org/packages/9e/f4/ace24aafe88a5335667e28463cecbd44021cd19ab628a1a157843b9c1c08/pyspk-1.7.tar.gz 2023-06-23T10:08:12,338 Removed pyspk==1.7 from https://files.pythonhosted.org/packages/9e/f4/ace24aafe88a5335667e28463cecbd44021cd19ab628a1a157843b9c1c08/pyspk-1.7.tar.gz from build tracker '/tmp/pip-build-tracker-q90a601g' 2023-06-23T10:08:12,351 Created temporary directory: /tmp/pip-unpack-7_xh8zrh 2023-06-23T10:08:12,352 Building wheels for collected packages: pyspk 2023-06-23T10:08:12,360 Created temporary directory: /tmp/pip-wheel-fdoyy0ni 2023-06-23T10:08:12,361 Building wheel for pyspk (setup.py): started 2023-06-23T10:08:12,363 Destination directory: /tmp/pip-wheel-fdoyy0ni 2023-06-23T10:08:12,364 Running command python setup.py bdist_wheel 2023-06-23T10:08:13,436 # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum 2023-06-23T10:08:13,438 _____ ____ ____ _ 2023-06-23T10:08:13,438 ____ __ __ / ___// __ \_/_/ /__| | 2023-06-23T10:08:13,438 / __ \/ / / /_____\__ \/ /_/ / // //_// / 2023-06-23T10:08:13,439 / /_/ / /_/ /_____/__/ / ____/ // ,< / / 2023-06-23T10:08:13,439 / .___/\__, / /____/_/ / //_/|_|/_/ 2023-06-23T10:08:13,439 /_/ /____/ |_| /_/ 2023-06-23T10:08:13,440 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. 2023-06-23T10:08:13,441 ## Requirements 2023-06-23T10:08:13,442 The module requires the following: 2023-06-23T10:08:13,442 - numpy 2023-06-23T10:08:13,443 - scipy 2023-06-23T10:08:13,443 ## Installation 2023-06-23T10:08:13,444 The easiest way to install py-SP(k) is using pip: 2023-06-23T10:08:13,444 ``` 2023-06-23T10:08:13,445 pip install pyspk [--user] 2023-06-23T10:08:13,445 ``` 2023-06-23T10:08:13,446 The --user flag may be required if you do not have root privileges. 2023-06-23T10:08:13,446 ## Usage 2023-06-23T10:08:13,447 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). 2023-06-23T10:08:13,448 ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation 2023-06-23T10:08:13,448 py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form: 2023-06-23T10:08:13,449 $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$ 2023-06-23T10:08:13,450 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. 2023-06-23T10:08:13,451 Next, we show a simple example using power-law fit parameters: 2023-06-23T10:08:13,451 ``` 2023-06-23T10:08:13,452 import pyspk as spk 2023-06-23T10:08:13,452 z = 0.125 2023-06-23T10:08:13,452 fb_a = 0.4 2023-06-23T10:08:13,453 fb_pow = 0.3 2023-06-23T10:08:13,453 fb_pivot = 10 ** 13.5 2023-06-23T10:08:13,454 k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot) 2023-06-23T10:08:13,454 ``` 2023-06-23T10:08:13,455 ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-23T10:08:13,455 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. 2023-06-23T10:08:13,456 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: 2023-06-23T10:08:13,457 $$f_b/(\Omega_b/\Omega_m)= \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$ 2023-06-23T10:08:13,457 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). 2023-06-23T10:08:13,458 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. 2023-06-23T10:08:13,459 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. 2023-06-23T10:08:13,459 ``` 2023-06-23T10:08:13,460 import pyspk.model as spk 2023-06-23T10:08:13,460 from astropy.cosmology import FlatLambdaCDM 2023-06-23T10:08:13,461 H0 = 70 2023-06-23T10:08:13,461 Omega_b = 0.0463 2023-06-23T10:08:13,461 Omega_m = 0.2793 2023-06-23T10:08:13,462 cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b) 2023-06-23T10:08:13,462 alpha = 4.189 2023-06-23T10:08:13,463 beta = 1.273 2023-06-23T10:08:13,463 gamma = 0.298 2023-06-23T10:08:13,463 z = 0.5 2023-06-23T10:08:13,464 k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo) 2023-06-23T10:08:13,464 ``` 2023-06-23T10:08:13,465 ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation. 2023-06-23T10:08:13,465 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. 2023-06-23T10:08:13,466 ## Priors 2023-06-23T10:08:13,467 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). 2023-06-23T10:08:13,468 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 $6 \times 10^{12} \leq M_{500c} [\mathrm{M}_ \odot] \leq 10^{14}$. 2023-06-23T10:08:13,468 Priors inferred from simulations that fall within $\pm 2 \times \sigma$ of the inferred baryon budget: 2023-06-23T10:08:13,469 | Parameter | Description | Prior | 2023-06-23T10:08:13,469 | ----------- | ------------------ | --------------- | 2023-06-23T10:08:13,470 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.16, 0.07) | 2023-06-23T10:08:13,470 | $\beta$ | Slope | $\mathcal{N}$(1.20, 0.05) | 2023-06-23T10:08:13,470 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.39, 0.09) | 2023-06-23T10:08:13,471 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-23T10:08:13,472 Priors inferred from simulations that fall within $\pm 3 \times \sigma$ of the inferred baryon budget: 2023-06-23T10:08:13,472 | Parameter | Description | Prior | 2023-06-23T10:08:13,473 | ----------- | ------------------ | --------------- | 2023-06-23T10:08:13,473 | $\alpha$ | Normaliasation | $\mathcal{N}$(4.18, 0.12) | 2023-06-23T10:08:13,473 | $\beta$ | Slope | $\mathcal{N}$(1.26, 0.08) | 2023-06-23T10:08:13,474 | $\gamma$ | Redshift evolution | $\mathcal{N}$(0.42, 0.10) | 2023-06-23T10:08:13,474 where $\mathcal{N}(\mu,\sigma)$ is a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$. 2023-06-23T10:08:13,475 ## Acknowledging the code 2023-06-23T10:08:13,476 Please cite py-SP(k) using: 2023-06-23T10:08:13,477 ``` 2023-06-23T10:08:13,477 @ARTICLE{SPK_Salcido_2023, 2023-06-23T10:08:13,477 author = {Salcido, Jaime and McCarthy, Ian G and Kwan, Juliana and Upadhye, Amol and Font, Andreea S}, 2023-06-23T10:08:13,478 title = "{SP(k) – a hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum}", 2023-06-23T10:08:13,478 journal = {Monthly Notices of the Royal Astronomical Society}, 2023-06-23T10:08:13,478 volume = {523}, 2023-06-23T10:08:13,479 number = {2}, 2023-06-23T10:08:13,479 pages = {2247-2262}, 2023-06-23T10:08:13,479 year = {2023}, 2023-06-23T10:08:13,480 month = {05}, 2023-06-23T10:08:13,480 issn = {0035-8711}, 2023-06-23T10:08:13,480 doi = {10.1093/mnras/stad1474}, 2023-06-23T10:08:13,481 url = {https://doi.org/10.1093/mnras/stad1474}, 2023-06-23T10:08:13,481 eprint = {https://academic.oup.com/mnras/article-pdf/523/2/2247/50512773/stad1474.pdf}, 2023-06-23T10:08:13,481 } 2023-06-23T10:08:13,482 ``` 2023-06-23T10:08:13,482 For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk* 2023-06-23T10:08:13,934 running bdist_wheel 2023-06-23T10:08:14,652 running build 2023-06-23T10:08:14,653 running build_py 2023-06-23T10:08:14,729 creating build 2023-06-23T10:08:14,730 creating build/lib 2023-06-23T10:08:14,732 creating build/lib/pyspk 2023-06-23T10:08:14,734 copying pyspk/__init__.py -> build/lib/pyspk 2023-06-23T10:08:14,738 copying pyspk/fit_vals.py -> build/lib/pyspk 2023-06-23T10:08:14,742 copying pyspk/model.py -> build/lib/pyspk 2023-06-23T10:08:14,746 running egg_info 2023-06-23T10:08:14,896 writing pyspk.egg-info/PKG-INFO 2023-06-23T10:08:14,901 writing dependency_links to pyspk.egg-info/dependency_links.txt 2023-06-23T10:08:14,905 writing requirements to pyspk.egg-info/requires.txt 2023-06-23T10:08:14,908 writing top-level names to pyspk.egg-info/top_level.txt 2023-06-23T10:08:14,979 reading manifest file 'pyspk.egg-info/SOURCES.txt' 2023-06-23T10:08:14,984 reading manifest template 'MANIFEST.in' 2023-06-23T10:08:14,994 adding license file 'LICENSE.md' 2023-06-23T10:08:15,000 writing manifest file 'pyspk.egg-info/SOURCES.txt' 2023-06-23T10:08:15,005 /home/piwheels/.local/lib/python3.7/site-packages/setuptools/command/build_py.py:201: _Warning: Package 'pyspk.__pycache__' is absent from the `packages` configuration. 2023-06-23T10:08:15,005 !! 2023-06-23T10:08:15,006 ******************************************************************************** 2023-06-23T10:08:15,006 ############################ 2023-06-23T10:08:15,007 # Package would be ignored # 2023-06-23T10:08:15,007 ############################ 2023-06-23T10:08:15,007 Python recognizes 'pyspk.__pycache__' as an importable package[^1], 2023-06-23T10:08:15,008 but it is absent from setuptools' `packages` configuration. 2023-06-23T10:08:15,008 This leads to an ambiguous overall configuration. If you want to distribute this 2023-06-23T10:08:15,009 package, please make sure that 'pyspk.__pycache__' is explicitly added 2023-06-23T10:08:15,009 to the `packages` configuration field. 2023-06-23T10:08:15,010 Alternatively, you can also rely on setuptools' discovery methods 2023-06-23T10:08:15,010 (for example by using `find_namespace_packages(...)`/`find_namespace:` 2023-06-23T10:08:15,010 instead of `find_packages(...)`/`find:`). 2023-06-23T10:08:15,011 You can read more about "package discovery" on setuptools documentation page: 2023-06-23T10:08:15,012 - https://setuptools.pypa.io/en/latest/userguide/package_discovery.html 2023-06-23T10:08:15,015 If you don't want 'pyspk.__pycache__' to be distributed and are 2023-06-23T10:08:15,016 already explicitly excluding 'pyspk.__pycache__' via 2023-06-23T10:08:15,017 `find_namespace_packages(...)/find_namespace` or `find_packages(...)/find`, 2023-06-23T10:08:15,018 you can try to use `exclude_package_data`, or `include-package-data=False` in 2023-06-23T10:08:15,020 combination with a more fine grained `package-data` configuration. 2023-06-23T10:08:15,021 You can read more about "package data files" on setuptools documentation page: 2023-06-23T10:08:15,021 - https://setuptools.pypa.io/en/latest/userguide/datafiles.html 2023-06-23T10:08:15,022 [^1]: For Python, any directory (with suitable naming) can be imported, 2023-06-23T10:08:15,023 even if it does not contain any `.py` files. 2023-06-23T10:08:15,023 On the other hand, currently there is no concept of package data 2023-06-23T10:08:15,023 directory, all directories are treated like packages. 2023-06-23T10:08:15,024 ******************************************************************************** 2023-06-23T10:08:15,024 !! 2023-06-23T10:08:15,025 check.warn(importable) 2023-06-23T10:08:15,025 copying pyspk/stat_errors_200.csv -> build/lib/pyspk 2023-06-23T10:08:15,025 copying pyspk/stat_errors_500.csv -> build/lib/pyspk 2023-06-23T10:08:15,036 creating build/lib/pyspk/__pycache__ 2023-06-23T10:08:15,038 copying pyspk/__pycache__/__init__.cpython-38.pyc -> build/lib/pyspk/__pycache__ 2023-06-23T10:08:15,042 copying pyspk/__pycache__/fit_vals.cpython-38.pyc -> build/lib/pyspk/__pycache__ 2023-06-23T10:08:15,046 copying pyspk/__pycache__/model.cpython-38.pyc -> build/lib/pyspk/__pycache__ 2023-06-23T10:08:15,127 /home/piwheels/.local/lib/python3.7/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2023-06-23T10:08:15,127 !! 2023-06-23T10:08:15,128 ******************************************************************************** 2023-06-23T10:08:15,128 Please avoid running ``setup.py`` directly. 2023-06-23T10:08:15,129 Instead, use pypa/build, pypa/installer, pypa/build or 2023-06-23T10:08:15,129 other standards-based tools. 2023-06-23T10:08:15,130 See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2023-06-23T10:08:15,130 ******************************************************************************** 2023-06-23T10:08:15,131 !! 2023-06-23T10:08:15,131 self.initialize_options() 2023-06-23T10:08:15,195 installing to build/bdist.linux-armv7l/wheel 2023-06-23T10:08:15,195 running install 2023-06-23T10:08:15,255 running install_lib 2023-06-23T10:08:15,326 creating build/bdist.linux-armv7l 2023-06-23T10:08:15,327 creating build/bdist.linux-armv7l/wheel 2023-06-23T10:08:15,330 creating build/bdist.linux-armv7l/wheel/pyspk 2023-06-23T10:08:15,333 creating build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-06-23T10:08:15,335 copying build/lib/pyspk/__pycache__/model.cpython-38.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-06-23T10:08:15,341 copying build/lib/pyspk/__pycache__/__init__.cpython-38.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-06-23T10:08:15,345 copying build/lib/pyspk/__pycache__/fit_vals.cpython-38.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__ 2023-06-23T10:08:15,348 copying build/lib/pyspk/__init__.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-23T10:08:15,352 copying build/lib/pyspk/fit_vals.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-23T10:08:15,357 copying build/lib/pyspk/model.py -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-23T10:08:15,361 copying build/lib/pyspk/stat_errors_200.csv -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-23T10:08:15,374 copying build/lib/pyspk/stat_errors_500.csv -> build/bdist.linux-armv7l/wheel/pyspk 2023-06-23T10:08:15,388 running install_egg_info 2023-06-23T10:08:15,465 Copying pyspk.egg-info to build/bdist.linux-armv7l/wheel/pyspk-1.7-py3.7.egg-info 2023-06-23T10:08:15,486 running install_scripts 2023-06-23T10:08:15,518 creating build/bdist.linux-armv7l/wheel/pyspk-1.7.dist-info/WHEEL 2023-06-23T10:08:15,523 creating '/tmp/pip-wheel-fdoyy0ni/pyspk-1.7-py3-none-any.whl' and adding 'build/bdist.linux-armv7l/wheel' to it 2023-06-23T10:08:15,528 adding 'pyspk/__init__.py' 2023-06-23T10:08:15,532 adding 'pyspk/fit_vals.py' 2023-06-23T10:08:15,537 adding 'pyspk/model.py' 2023-06-23T10:08:15,629 adding 'pyspk/stat_errors_200.csv' 2023-06-23T10:08:15,724 adding 'pyspk/stat_errors_500.csv' 2023-06-23T10:08:15,731 adding 'pyspk/__pycache__/__init__.cpython-38.pyc' 2023-06-23T10:08:15,734 adding 'pyspk/__pycache__/fit_vals.cpython-38.pyc' 2023-06-23T10:08:15,739 adding 'pyspk/__pycache__/model.cpython-38.pyc' 2023-06-23T10:08:15,745 adding 'pyspk-1.7.dist-info/LICENSE.md' 2023-06-23T10:08:15,749 adding 'pyspk-1.7.dist-info/METADATA' 2023-06-23T10:08:15,751 adding 'pyspk-1.7.dist-info/WHEEL' 2023-06-23T10:08:15,753 adding 'pyspk-1.7.dist-info/top_level.txt' 2023-06-23T10:08:15,755 adding 'pyspk-1.7.dist-info/RECORD' 2023-06-23T10:08:15,763 removing build/bdist.linux-armv7l/wheel 2023-06-23T10:08:15,928 Building wheel for pyspk (setup.py): finished with status 'done' 2023-06-23T10:08:15,939 Created wheel for pyspk: filename=pyspk-1.7-py3-none-any.whl size=154011 sha256=241669df5740a5c288f362b105952aea67d094ba03f4ab69bd89715c2144455d 2023-06-23T10:08:15,942 Stored in directory: /tmp/pip-ephem-wheel-cache-3ec54mto/wheels/21/be/0d/bd648e64df6bca27d3ac976790fe45dc2abb0ffa50d4768018 2023-06-23T10:08:15,973 Successfully built pyspk 2023-06-23T10:08:15,992 Removed build tracker: '/tmp/pip-build-tracker-q90a601g'