changed repository and package name

Author: alexander-mead

README.md

@@ -26,7 +26,7 @@ While the quoted accuracy of `HMcode-2020` relative to simulations is RMS ~2.5%,
 - Here the *cold* matter power spectrum is taken directly from `CAMB` whereas in `CAMB-HMcode` the *cold* spectrum is calculated approximately from the total matter power spectrum using approximations for the scale-dependent growth rate from [Eisenstein & Hu (1999)](https://arxiv.org/abs/astro-ph/9710252).
 - If I resort to the old approximation for the *cold* matter power spectrum (and therefore the cold $\sigma(R)$) then the level of agreement between the codes for nu-k-w(a)-CDM improves to: Mean error: 0.15%; Std error: 0.11%; Worst error; 1.15%.
 
-Using the actual cold matter spectrum is definitely an improvement from a theoretical perspective (and for speed), so I decided to keep that at the cost of the disagreement between this code at `CAMB-HMcode` for models with very high neutrino mass. If better agreement between this code and `CAMB-HMcode` is important to you then the old approximate cold spectrum can be used by changing the `sigma_cold_approx` flag at the top of `pyhmcode.py`. Note that while ignoring the actual energy-density scaling of massive neutrinos might seem to be a (small) problem, keep in mind the comments below regarding the linear growth factor.
+Using the actual cold matter spectrum is definitely an improvement from a theoretical perspective (and for speed), so I decided to keep that at the cost of the disagreement between this code at `CAMB-HMcode` for models with very high neutrino mass. If better agreement between this code and `CAMB-HMcode` is important to you then the old approximate cold spectrum can be used by changing the `sigma_cold_approx` flag at the top of `hmcode.py`. Note that while ignoring the actual energy-density scaling of massive neutrinos might seem to be a (small) problem, keep in mind the comments below regarding the linear growth factor.
 
 I think any residual differences between codes must therefore stem from:
 - The BAO de-wiggling process (different `k` grids)

comparisons/CAMB.py

@@ -3,9 +3,9 @@
 import numpy as np
 
 # Project imports
-import pyhmcode.camb_stuff as camb_stuff
-import pyhmcode.utility as util
-from pyhmcode import hmcode as HMcode
+import hmcode
+import hmcode.camb_stuff as camb_stuff
+import hmcode.utility as util
 
 # Vary these parameters
 vary_Omega_k = True
@@ -70,7 +70,7 @@
         Pk_CAMB[iz, :] = Pk_nonlin_interp(z, k)
 
     # Get the new pyHMcode spectrum
-    Pk_HMcode = HMcode(k, zs, results, verbose=verbose)
+    Pk_HMcode = hmcode.power(k, zs, results, verbose=verbose)
 
     # Calculate maximum deviation between pyHMcode and the version in CAMB
     max_error = np.max(np.abs(-1.+Pk_HMcode/Pk_CAMB))

hmcode/__init__.py

@@ -0,0 +1 @@
+from .hmcode import power

pyhmcode/camb_stuff.py hmcode/camb_stuff.pypyhmcode/camb_stuff.py renamed to hmcode/camb_stuff.py

@@ -15,14 +15,17 @@
 Pk_lin_extrap_kmax = 1e10 # NOTE: This interplays with the sigmaV integration in a disconcerting way
 sigma_cold_approx = False # Should the Eisenstein & Hu (1999) approximation be used for the cold transfer function?
 
-def hmcode(k:np.array, zs:np.array, CAMB_results:camb.CAMBdata, 
-           Mmin=1e0, Mmax=1e18, nM=256, verbose=False) -> np.ndarray:
+def power(k:np.array, zs:np.array, CAMB_results:camb.CAMBdata, 
+          Mmin=1e0, Mmax=1e18, nM=256, verbose=False) -> np.ndarray:
     '''
     Calculates the HMcode matter-matter power spectrum
     Args:
         k: Array of comoving wavenumbers [h/Mpc]
         zs: Array of redshifts (ordered from high to low)
         CAMB_results: CAMBdata structure
+        Mmin: Minimum mass for the halo-model calculation [Msun/h]
+        Mmax: Maximum mass for the halo-model calculation [Msun/h]
+        nM: Number of mass bins for the halo-model calculation
     Returns:
         Array of matter power spectra: Pk[z, k]
     '''

pyhmcode/constants.py hmcode/constants.pypyhmcode/constants.py renamed to hmcode/constants.py

@@ -1,7 +1,6 @@
 # Standard imports
 import numpy as np
 
-
 def derivative_from_samples(x:float, xs:np.array, fs:np.array) -> float:
     '''
     Calculates the derivative of the function f(x) which is sampled as fs at values xs

pyhmcode/cosmology.py hmcode/cosmology.pypyhmcode/cosmology.py renamed to hmcode/cosmology.py

@@ -27,7 +27,7 @@
     "import pyhalomodel as halo\n",
     "\n",
     "# Project imports\n",
-    "from pyhmcode import hmcode"
+    "import hmcode"
    ]
   },
   {
@@ -139,7 +139,7 @@
    "outputs": [],
    "source": [
     "# Get the new HMcode spectrum\n",
-    "Pk_HMcode = hmcode(k, zs, results, verbose=True)"
+    "Pk_HMcode = hmcode.power(k, zs, results, verbose=True)"
    ]
   },
   {
@@ -243,7 +243,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pyhmcode-b1kLUJ5J-py3.10",
+   "display_name": "hmcode-S0Z6NeuA-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -262,7 +262,7 @@
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "b96bbc5816cd274b1c58d775ba8f893f60624f82ce43b36018f126a8bc6f1f92"
+    "hash": "e82eed08d8d58f92fc7a23e50eeb23ca4d9777479ef0aea969fd18c18280722f"
    }
   }
  },