Diffstar without a gas consumption timescale

alt_ms_kernels.py

"""
"""

from diffmah.defaults import MAH_K
from diffmah.individual_halo_assembly import _calc_halo_history_scalar
from jax import jit as jjit
from jax import nn
from jax import numpy as jnp

INDX_K = 9.0  # Main sequence efficiency transition speed
INDX_HI = -1.0


@jjit
def _sigmoid(x, x0, k, ymin, ymax):
    height_diff = ymax - ymin
    return ymin + height_diff * nn.sigmoid(k * (x - x0))


@jjit
def _sfr_eff_plaw(lgm, lgmcrit, lgy_at_mcrit, indx_lo):
    slope = _sigmoid(lgm, lgmcrit, INDX_K, indx_lo, INDX_HI)
    eff = lgy_at_mcrit + slope * (lgm - lgmcrit)
    return 10**eff


@jjit
def _sfr_nolag_scalar_kern(t, mah_params, ms_params, lgt0, fb):
    logmp, logtc, early, late = mah_params
    all_mah_params = lgt0, logmp, logtc, MAH_K, early, late
    lgt = jnp.log10(t)
    res = _calc_halo_history_scalar(lgt, *all_mah_params)
    dmhdt, log_mah = res

    sfr_eff = _sfr_eff_plaw(log_mah, *ms_params)

    dmgdt = fb * dmhdt
    sfr = dmgdt * sfr_eff

    return sfr

diffstar_nolag_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0f61e6c-52f0-4190-8add-d04f4ee4674d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from alt_ms_kernels import _sfr_nolag_scalar_kern\n",
    "from diffmah.defaults import DEFAULT_MAH_PARAMS\n",
    "from diffstar.defaults import T_TABLE_MIN, TODAY\n",
    "from diffstar.defaults import FB\n",
    "\n",
    "ntimes = 100\n",
    "lgt0 = np.log10(TODAY)\n",
    "\n",
    "tarr = np.linspace(T_TABLE_MIN, TODAY, ntimes)\n",
    "\n",
    "lgmcrit=12.0\n",
    "lgy_at_mcrit=-1.0\n",
    "indx_lo=1.0\n",
    "ms_params_nolag = lgmcrit, lgy_at_mcrit, indx_lo\n",
    "\n",
    "_sfr_nolag_scalar_kern_vmap = jjit(vmap(_sfr_nolag_scalar_kern, in_axes=(0, None, None, None, None)))\n",
    "sfh_nolag = _sfr_nolag_scalar_kern_vmap(tarr, DEFAULT_MAH_PARAMS, ms_params_nolag, lgt0, FB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fc52df5-f3b8-48b3-a784-22e18f5a737c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "yscale = ax.set_yscale('log')\n",
    "ylim = ax.set_ylim(1e-2, 5)\n",
    "\n",
    "__=ax.plot(tarr, sfh_nolag, color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de113b10-9269-4046-a6da-58ac0c8daee2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1337f17-a39c-4591-8638-991e9c16af15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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