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2022-11-18T12:05:27,579   # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum
2022-11-18T12:05:27,580                             _____ ____   ____   _
2022-11-18T12:05:27,580           ____  __  __     / ___// __ \_/_/ /__| |
2022-11-18T12:05:27,581          / __ \/ / / /_____\__ \/ /_/ / // //_// /
2022-11-18T12:05:27,581         / /_/ / /_/ /_____/__/ / ____/ // ,<  / /
2022-11-18T12:05:27,581        / .___/\__, /     /____/_/   / //_/|_|/_/
2022-11-18T12:05:27,582       /_/    /____/                 |_|    /_/

2022-11-18T12:05:27,582   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.

2022-11-18T12:05:27,583   ## Requirements

2022-11-18T12:05:27,584   The module requires the following:

2022-11-18T12:05:27,584   - numpy
2022-11-18T12:05:27,585   - scipy

2022-11-18T12:05:27,585   ## Installation

2022-11-18T12:05:27,586   The easiest way to install py-SP(k) is using pip:

2022-11-18T12:05:27,587   ```
2022-11-18T12:05:27,587   pip install pyspk [--user]
2022-11-18T12:05:27,587   ```

2022-11-18T12:05:27,588   The --user flag may be required if you do not have root privileges.

2022-11-18T12:05:27,589   ## Usage

2022-11-18T12:05:27,589   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).

2022-11-18T12:05:27,590   ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation

2022-11-18T12:05:27,591   py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form:

2022-11-18T12:05:27,591   $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$

2022-11-18T12:05:27,592   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.

2022-11-18T12:05:27,593   Next, we show a simple example using power-law fit parameters:

2022-11-18T12:05:27,593   ```
2022-11-18T12:05:27,593   import pyspk as spk

2022-11-18T12:05:27,594   z = 0.125
2022-11-18T12:05:27,595   fb_a = 0.4
2022-11-18T12:05:27,595   fb_pow = 0.3
2022-11-18T12:05:27,595   fb_pivot = 10 ** 13.5

2022-11-18T12:05:27,596   k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot)
2022-11-18T12:05:27,596   ```

2022-11-18T12:05:27,597   ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation.

2022-11-18T12:05:27,597   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.

2022-11-18T12:05:27,598   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:

2022-11-18T12:05:27,599   $$f_b/(\Omega_b/\Omega_m)= \left(\frac{0.1658}{\Omega_b/\Omega_m}\right) \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$

2022-11-18T12:05:27,600   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).

2022-11-18T12:05:27,600   Note that this power-law has a normalisation that is redshift dependent, while the the slop 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 agrees well 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.

2022-11-18T12:05:27,601   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.

2022-11-18T12:05:27,602   ```
2022-11-18T12:05:27,602   import pyspk.model as spk
2022-11-18T12:05:27,602   from astropy.cosmology import FlatLambdaCDM

2022-11-18T12:05:27,603   H0 = 70
2022-11-18T12:05:27,603   Omega_b = 0.0463
2022-11-18T12:05:27,603   Omega_m = 0.2793

2022-11-18T12:05:27,604   cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b)

2022-11-18T12:05:27,605   alpha = 4.189
2022-11-18T12:05:27,605   beta = 1.273
2022-11-18T12:05:27,605   gamma = 0.298
2022-11-18T12:05:27,606   z = 0.5

2022-11-18T12:05:27,606   k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo)
2022-11-18T12:05:27,606   ```

2022-11-18T12:05:27,607   ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation.

2022-11-18T12:05:27,608   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](https://academic.oup.com/mnras/article/465/3/2936/2417021)), please refer to the [examples](https://github.com/jemme07/pyspk/blob/main/examples/pySPk_Examples.ipynb) provided.


2022-11-18T12:05:27,609   ## Priors

2022-11-18T12:05:27,609   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 Table 5 in Akino et al. 2022, and find the subset of simulations from our 400 models that agree with the inferred baryon budget at redshift $z=0.1$. We note that we constrained our simulations to within a normalisation of $\pm 3 \times \sigma$ at $M_{500c} = 10^{14} \mathrm{M}_ \odot$.

2022-11-18T12:05:27,610   Using the simulations that fall within these constraints, we can impose observational priors for the redshift-dependent power-law fitting parameters for the $f_b$ - $M_\mathrm{halo}$ relation in Method 3 as follows:

2022-11-18T12:05:27,611   | Parameter   | Description        | Prior           |
2022-11-18T12:05:27,611   | ----------- | ------------------ | --------------- |
2022-11-18T12:05:27,611   | $\alpha$    | Normaliasation     | G(4.189, 0.066) |
2022-11-18T12:05:27,611   | $\beta$     | Slope              | G(1.273, 0.044) |
2022-11-18T12:05:27,612   | $\gamma$    | Redshift evolution | G(0.298, 0.063) |

2022-11-18T12:05:27,612   where G(x, y) is a Gaussian distribution with center x and width y.

2022-11-18T12:05:27,613   A less conservative approach could be to use a flat priors over the entire range of parameters fitted to simulations that fall within Akino et al. 2022 these constraints:

2022-11-18T12:05:27,614   | Parameter   | Description        | Prior           |
2022-11-18T12:05:27,614   | ----------- | ------------------ | --------------- |
2022-11-18T12:05:27,614   | $\alpha$    | Normaliasation     | U(4.060, 4.306) |
2022-11-18T12:05:27,615   | $\beta$     | Slope              | U(1.199, 1.347) |
2022-11-18T12:05:27,615   | $\gamma$    | Redshift evolution | U(0.159, 0.414) |

2022-11-18T12:05:27,616   where U(x, y) is a uniform distribution over [x, y].

2022-11-18T12:05:27,616   Finally, the full range of fitted parameters spanned by our simulations, regardless of whether or not they agree with observational constraints is:

2022-11-18T12:05:27,617   | Parameter   | Description        | Prior           |
2022-11-18T12:05:27,617   | ----------- | ------------------ | --------------- |
2022-11-18T12:05:27,617   | $\alpha$    | Normaliasation     | U(3.060, 4.508) |
2022-11-18T12:05:27,618   | $\beta$     | Slope              | U(0.989, 1.620) |
2022-11-18T12:05:27,618   | $\gamma$    | Redshift evolution | U(0.046, 0.631) |

2022-11-18T12:05:27,619   where U(x, y) is a uniform distribution over [x, y].

2022-11-18T12:05:27,619   ## Acknowledging the code

2022-11-18T12:05:27,620   Please cite py-SP(k) using:

2022-11-18T12:05:27,620   ```
2022-11-18T12:05:27,621   @ARTICLE{spk,
2022-11-18T12:05:27,621          author = {},
2022-11-18T12:05:27,621           title = "{}",
2022-11-18T12:05:27,622         journal = {\mnras},
2022-11-18T12:05:27,622        keywords = {},
2022-11-18T12:05:27,622            year = ,
2022-11-18T12:05:27,623           month = ,
2022-11-18T12:05:27,623          volume = {},
2022-11-18T12:05:27,623          number = {},
2022-11-18T12:05:27,623           pages = {},
2022-11-18T12:05:27,624             doi = {},
2022-11-18T12:05:27,624   archivePrefix = {arXiv},
2022-11-18T12:05:27,624          eprint = {},
2022-11-18T12:05:27,625    primaryClass = {astro-ph.CO},
2022-11-18T12:05:27,625          adsurl = {},
2022-11-18T12:05:27,625         adsnote = {}
2022-11-18T12:05:27,626   }
2022-11-18T12:05:27,626   ```
2022-11-18T12:05:27,626   For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk*



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2022-11-18T12:05:30,109   # py-SP(k) - A hydrodynamical simulation-based model for the impact of baryon physics on the non-linear matter power spectrum
2022-11-18T12:05:30,111                             _____ ____   ____   _
2022-11-18T12:05:30,111           ____  __  __     / ___// __ \_/_/ /__| |
2022-11-18T12:05:30,111          / __ \/ / / /_____\__ \/ /_/ / // //_// /
2022-11-18T12:05:30,112         / /_/ / /_/ /_____/__/ / ____/ // ,<  / /
2022-11-18T12:05:30,112        / .___/\__, /     /____/_/   / //_/|_|/_/
2022-11-18T12:05:30,112       /_/    /____/                 |_|    /_/

2022-11-18T12:05:30,113   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.

2022-11-18T12:05:30,114   ## Requirements

2022-11-18T12:05:30,114   The module requires the following:

2022-11-18T12:05:30,115   - numpy
2022-11-18T12:05:30,115   - scipy

2022-11-18T12:05:30,116   ## Installation

2022-11-18T12:05:30,116   The easiest way to install py-SP(k) is using pip:

2022-11-18T12:05:30,117   ```
2022-11-18T12:05:30,117   pip install pyspk [--user]
2022-11-18T12:05:30,117   ```

2022-11-18T12:05:30,118   The --user flag may be required if you do not have root privileges.

2022-11-18T12:05:30,119   ## Usage

2022-11-18T12:05:30,120   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).

2022-11-18T12:05:30,120   ### Method 1: Using a power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation

2022-11-18T12:05:30,121   py-SP(k) can be provided with power-law fitted parameters to the $f_b$ - $M_\mathrm{halo}$ relation using the functional form:

2022-11-18T12:05:30,121   $$f_b/(\Omega_b/\Omega_m)=a\left(\frac{M_{SO}}{M_{\mathrm{pivot}}}\right)^{b},$$

2022-11-18T12:05:30,122   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.

2022-11-18T12:05:30,123   Next, we show a simple example using power-law fit parameters:

2022-11-18T12:05:30,123   ```
2022-11-18T12:05:30,124   import pyspk as spk

2022-11-18T12:05:30,124   z = 0.125
2022-11-18T12:05:30,125   fb_a = 0.4
2022-11-18T12:05:30,125   fb_pow = 0.3
2022-11-18T12:05:30,125   fb_pivot = 10 ** 13.5

2022-11-18T12:05:30,126   k, sup = spk.sup_model(SO=200, z=z, fb_a=fb_a, fb_pow=fb_pow, fb_pivot=fb_pivot)
2022-11-18T12:05:30,126   ```

2022-11-18T12:05:30,127   ### Method 2: Redshift-dependent power-law fit to the $f_b$ - $M_\mathrm{halo}$ relation.

2022-11-18T12:05:30,127   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.

2022-11-18T12:05:30,128   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:

2022-11-18T12:05:30,129   $$f_b/(\Omega_b/\Omega_m)= \left(\frac{0.1658}{\Omega_b/\Omega_m}\right) \left(\frac{e^\alpha}{100}\right) \left(\frac{M_{500c}}{10^{14} \mathrm{M}_ \odot}\right)^{\beta - 1} \left(\frac{E(z)}{E(0.3)}\right)^{\gamma},$$

2022-11-18T12:05:30,130   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).

2022-11-18T12:05:30,131   Note that this power-law has a normalisation that is redshift dependent, while the the slop 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 agrees well 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.

2022-11-18T12:05:30,131   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.

2022-11-18T12:05:30,132   ```
2022-11-18T12:05:30,132   import pyspk.model as spk
2022-11-18T12:05:30,133   from astropy.cosmology import FlatLambdaCDM

2022-11-18T12:05:30,134   H0 = 70
2022-11-18T12:05:30,134   Omega_b = 0.0463
2022-11-18T12:05:30,134   Omega_m = 0.2793

2022-11-18T12:05:30,135   cosmo = FlatLambdaCDM(H0=H0, Om0=Omega_m, Ob0=Omega_b)

2022-11-18T12:05:30,135   alpha = 4.189
2022-11-18T12:05:30,136   beta = 1.273
2022-11-18T12:05:30,136   gamma = 0.298
2022-11-18T12:05:30,136   z = 0.5

2022-11-18T12:05:30,137   k, sup = spk.sup_model(SO=500, z=z, alpha=alpha, beta=beta, gamma=gamma, cosmo=cosmo)
2022-11-18T12:05:30,137   ```

2022-11-18T12:05:30,138   ### Method 3: Binned data for the $f_b$ - $M_\mathrm{halo}$ relation.

2022-11-18T12:05:30,139   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](https://academic.oup.com/mnras/article/465/3/2936/2417021)), please refer to the [examples](https://github.com/jemme07/pyspk/blob/main/examples/pySPk_Examples.ipynb) provided.


2022-11-18T12:05:30,140   ## Priors

2022-11-18T12:05:30,140   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 Table 5 in Akino et al. 2022, and find the subset of simulations from our 400 models that agree with the inferred baryon budget at redshift $z=0.1$. We note that we constrained our simulations to within a normalisation of $\pm 3 \times \sigma$ at $M_{500c} = 10^{14} \mathrm{M}_ \odot$.

2022-11-18T12:05:30,141   Using the simulations that fall within these constraints, we can impose observational priors for the redshift-dependent power-law fitting parameters for the $f_b$ - $M_\mathrm{halo}$ relation in Method 3 as follows:

2022-11-18T12:05:30,141   | Parameter   | Description        | Prior           |
2022-11-18T12:05:30,142   | ----------- | ------------------ | --------------- |
2022-11-18T12:05:30,142   | $\alpha$    | Normaliasation     | G(4.189, 0.066) |
2022-11-18T12:05:30,142   | $\beta$     | Slope              | G(1.273, 0.044) |
2022-11-18T12:05:30,142   | $\gamma$    | Redshift evolution | G(0.298, 0.063) |

2022-11-18T12:05:30,143   where G(x, y) is a Gaussian distribution with center x and width y.

2022-11-18T12:05:30,144   A less conservative approach could be to use a flat priors over the entire range of parameters fitted to simulations that fall within Akino et al. 2022 these constraints:

2022-11-18T12:05:30,144   | Parameter   | Description        | Prior           |
2022-11-18T12:05:30,145   | ----------- | ------------------ | --------------- |
2022-11-18T12:05:30,145   | $\alpha$    | Normaliasation     | U(4.060, 4.306) |
2022-11-18T12:05:30,145   | $\beta$     | Slope              | U(1.199, 1.347) |
2022-11-18T12:05:30,146   | $\gamma$    | Redshift evolution | U(0.159, 0.414) |

2022-11-18T12:05:30,146   where U(x, y) is a uniform distribution over [x, y].

2022-11-18T12:05:30,147   Finally, the full range of fitted parameters spanned by our simulations, regardless of whether or not they agree with observational constraints is:

2022-11-18T12:05:30,148   | Parameter   | Description        | Prior           |
2022-11-18T12:05:30,148   | ----------- | ------------------ | --------------- |
2022-11-18T12:05:30,148   | $\alpha$    | Normaliasation     | U(3.060, 4.508) |
2022-11-18T12:05:30,149   | $\beta$     | Slope              | U(0.989, 1.620) |
2022-11-18T12:05:30,149   | $\gamma$    | Redshift evolution | U(0.046, 0.631) |

2022-11-18T12:05:30,150   where U(x, y) is a uniform distribution over [x, y].

2022-11-18T12:05:30,150   ## Acknowledging the code

2022-11-18T12:05:30,151   Please cite py-SP(k) using:

2022-11-18T12:05:30,151   ```
2022-11-18T12:05:30,152   @ARTICLE{spk,
2022-11-18T12:05:30,152          author = {},
2022-11-18T12:05:30,152           title = "{}",
2022-11-18T12:05:30,153         journal = {\mnras},
2022-11-18T12:05:30,153        keywords = {},
2022-11-18T12:05:30,153            year = ,
2022-11-18T12:05:30,154           month = ,
2022-11-18T12:05:30,154          volume = {},
2022-11-18T12:05:30,154          number = {},
2022-11-18T12:05:30,154           pages = {},
2022-11-18T12:05:30,155             doi = {},
2022-11-18T12:05:30,155   archivePrefix = {arXiv},
2022-11-18T12:05:30,155          eprint = {},
2022-11-18T12:05:30,156    primaryClass = {astro-ph.CO},
2022-11-18T12:05:30,156          adsurl = {},
2022-11-18T12:05:30,156         adsnote = {}
2022-11-18T12:05:30,157   }
2022-11-18T12:05:30,157   ```
2022-11-18T12:05:30,157   For any questions and enquires please contact me via email at *j.salcidonegrete@ljmu.ac.uk*



2022-11-18T12:05:30,544   running bdist_wheel
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2022-11-18T12:05:31,349   creating build/lib/pyspk
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2022-11-18T12:05:31,582   reading manifest file 'pyspk.egg-info/SOURCES.txt'
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2022-11-18T12:05:31,604   writing manifest file 'pyspk.egg-info/SOURCES.txt'
2022-11-18T12:05:31,609   /usr/local/lib/python3.7/dist-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'pyspk.__pycache__' as data is deprecated, please list it in `packages`.
2022-11-18T12:05:31,610       !!


2022-11-18T12:05:31,611       ############################
2022-11-18T12:05:31,611       # Package would be ignored #
2022-11-18T12:05:31,611       ############################
2022-11-18T12:05:31,612       Python recognizes 'pyspk.__pycache__' as an importable package,
2022-11-18T12:05:31,612       but it is not listed in the `packages` configuration of setuptools.

2022-11-18T12:05:31,613       'pyspk.__pycache__' has been automatically added to the distribution only
2022-11-18T12:05:31,613       because it may contain data files, but this behavior is likely to change
2022-11-18T12:05:31,613       in future versions of setuptools (and therefore is considered deprecated).

2022-11-18T12:05:31,614       Please make sure that 'pyspk.__pycache__' is included as a package by using
2022-11-18T12:05:31,614       the `packages` configuration field or the proper discovery methods
2022-11-18T12:05:31,614       (for example by using `find_namespace_packages(...)`/`find_namespace:`
2022-11-18T12:05:31,615       instead of `find_packages(...)`/`find:`).

2022-11-18T12:05:31,615       You can read more about "package discovery" and "data files" on setuptools
2022-11-18T12:05:31,616       documentation page.


2022-11-18T12:05:31,617   !!

2022-11-18T12:05:31,617     check.warn(importable)
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2022-11-18T12:05:31,647   creating build/lib/pyspk/__pycache__
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2022-11-18T12:05:31,729   /usr/local/lib/python3.7/dist-packages/setuptools/command/install.py:37: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
2022-11-18T12:05:31,729     setuptools.SetuptoolsDeprecationWarning,
2022-11-18T12:05:31,792   installing to build/bdist.linux-armv7l/wheel
2022-11-18T12:05:31,793   running install
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2022-11-18T12:05:31,920   creating build/bdist.linux-armv7l
2022-11-18T12:05:31,920   creating build/bdist.linux-armv7l/wheel
2022-11-18T12:05:31,924   creating build/bdist.linux-armv7l/wheel/pyspk
2022-11-18T12:05:31,925   copying build/lib/pyspk/__init__.py -> build/bdist.linux-armv7l/wheel/pyspk
2022-11-18T12:05:31,929   copying build/lib/pyspk/stat_errors_500.csv -> build/bdist.linux-armv7l/wheel/pyspk
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2022-11-18T12:05:31,959   copying build/lib/pyspk/fit_vals.py -> build/bdist.linux-armv7l/wheel/pyspk
2022-11-18T12:05:31,962   copying build/lib/pyspk/model.py -> build/bdist.linux-armv7l/wheel/pyspk
2022-11-18T12:05:31,967   copying build/lib/pyspk/.DS_Store -> build/bdist.linux-armv7l/wheel/pyspk
2022-11-18T12:05:31,972   creating build/bdist.linux-armv7l/wheel/pyspk/__pycache__
2022-11-18T12:05:31,974   copying build/lib/pyspk/__pycache__/model.cpython-39.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__
2022-11-18T12:05:31,979   copying build/lib/pyspk/__pycache__/fit_vals.cpython-39.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__
2022-11-18T12:05:31,982   copying build/lib/pyspk/__pycache__/__init__.cpython-39.pyc -> build/bdist.linux-armv7l/wheel/pyspk/__pycache__
2022-11-18T12:05:31,985   running install_egg_info
2022-11-18T12:05:32,059   Copying pyspk.egg-info to build/bdist.linux-armv7l/wheel/pyspk-1.1-py3.7.egg-info
2022-11-18T12:05:32,079   running install_scripts
2022-11-18T12:05:32,272   adding license file "LICENSE.md" (matched pattern "LICEN[CS]E*")
2022-11-18T12:05:32,281   creating build/bdist.linux-armv7l/wheel/pyspk-1.1.dist-info/WHEEL
2022-11-18T12:05:32,286   creating '/tmp/pip-wheel-29csf9p_/pyspk-1.1-py3-none-any.whl' and adding 'build/bdist.linux-armv7l/wheel' to it
2022-11-18T12:05:32,291   adding 'pyspk/.DS_Store'
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2022-11-18T12:05:32,525   removing build/bdist.linux-armv7l/wheel
2022-11-18T12:05:32,782   Building wheel for pyspk (setup.py): finished with status 'done'
2022-11-18T12:05:32,794   Created wheel for pyspk: filename=pyspk-1.1-py3-none-any.whl size=153928 sha256=2c787ef97d85f7abf4348ac04aa1b05fc7f6dc20af7fb5e0dd1551349d6446ac
2022-11-18T12:05:32,796   Stored in directory: /tmp/pip-ephem-wheel-cache-tykj38xg/wheels/3d/ae/92/16f5831f6f6be1bdb3b8be502a2db7539e97e450757198e224
2022-11-18T12:05:32,824 Successfully built pyspk
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