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@@ -53,12 +53,12 @@ I coded this `Python` version of `HMcode-2020` ([Mead et al. 2021](https://arxiv
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You might also be interested in [`pyhalomodel`](https://pypi.org/project/pyhalomodel/), upon which this code depends, which implements a vanilla halo-model calculation for any desired large-scale-structure tracer. Alternatively, and very confusingly, you might be interested in this [`pyhmcode`](https://pypi.org/project/pyhmcode/), which provides a wrapper around the original `Fortran` `HMcode` implementation.
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I compared it against the `CAMB-HMcode` version for 100 random sets of cosmological parameters ($k < 10 h\mathrm{Mpc}^{-1}$; $z < 3$). The level of agreement between the two codes is as follows:
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- LCDM: Mean error: 0.10%; Std error: 0.03%; Worst error; 0.21%
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- k-LCDM: Mean error: 0.11%; Std error: 0.03%; Worst error; 0.23%
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- w-CDM: Mean error: 0.10%; Std error: 0.03%; Worst error; 0.20%
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- w(a)-CDM: Mean error: 0.13%; Std error: 0.06%; Worst error; 0.48%
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- nu-LCDM: Mean error: 0.47%; Std error: 0.44%; Worst error; 2.01% (larger errors strongly correlated with neutrino mass)
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- nu-k-w(a)-CDM: Mean error: 0.42%; Std error: 0.43%; Worst error; 2.02% (larger errors strongly correlated with neutrino mass)
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- LCDM: Mean error: 0.10%; Standard deviation of error: 0.03%; Worst-case error; 0.21%
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- k-LCDM: Mean error: 0.11%; Standard deviation of error: 0.03%; Worst-case error; 0.23%
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- w-CDM: Mean error: 0.10%; Standard deviation of error: 0.03%; Worst-case error; 0.20%
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- w(a)-CDM: Mean error: 0.13%; Standard deviation of error: 0.06%; Worst-case error; 0.48%
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- nu-LCDM: Mean error: 0.47%; Standard deviation of error: 0.44%; Worst-case error; 2.01% (larger errors strongly correlated with neutrino mass)
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- nu-k-w(a)-CDM: Mean error: 0.42%; Standard deviation of error: 0.43%; Worst-case error; 2.02% (larger errors strongly correlated with neutrino mass)
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These comparisons can be reproduced using the `comparisons/CAMB.py` script.
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