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revererting back changes to growth.py
This commit is contained in:
Francois Lanusse
parent
2d21985279
commit
77ea494e4f
1 changed files with 46 additions and 61 deletions
103
jaxpm/growth.py
103
jaxpm/growth.py
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@ -1,5 +1,3 @@
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import os
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import jax.numpy as np
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from jax.numpy import interp
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from jax_cosmo.background import *
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@ -121,7 +119,7 @@ def growth_factor(cosmo, a):
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if cosmo._flags["gamma_growth"]:
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return _growth_factor_gamma(cosmo, a)
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else:
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return _growth_factor_ODE(cosmo, a)[0]
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return _growth_factor_ODE(cosmo, a)
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def growth_factor_second(cosmo, a):
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@ -227,7 +225,7 @@ def growth_rate_second(cosmo, a):
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return _growth_rate_second_ODE(cosmo, a)
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def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=256, eps=1e-4):
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def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=128, eps=1e-4):
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"""Compute linear growth factor D(a) at a given scale factor,
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normalised such that D(a=1) = 1.
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@ -245,11 +243,7 @@ def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=256, eps=1e-4):
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Growth factor computed at requested scale factor
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"""
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# Check if growth has already been computed
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CACHING_ACTIVATED = os.environ.get("JC_CACHE", "1") == "1"
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if CACHING_ACTIVATED and "background.growth_factor" in cosmo._workspace.keys(
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):
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cache = cosmo._workspace["background.growth_factor"]
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else:
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if not "background.growth_factor" in cosmo._workspace.keys():
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# Compute tabulated array
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atab = np.logspace(log10_amin, 0.0, steps)
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@ -296,10 +290,10 @@ def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=256, eps=1e-4):
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"f2": f2tab,
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"h2": h2tab,
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}
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if CACHING_ACTIVATED:
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cosmo._workspace["background.growth_factor"] = cache
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return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0), cache
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else:
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cache = cosmo._workspace["background.growth_factor"]
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return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0)
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def _growth_rate_ODE(cosmo, a):
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@ -320,8 +314,9 @@ def _growth_rate_ODE(cosmo, a):
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Growth rate computed at requested scale factor
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"""
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# Check if growth has already been computed, if not, compute it
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cache = _growth_factor_ODE(cosmo, np.atleast_1d(1.0))[1]
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if not "background.growth_factor" in cosmo._workspace.keys():
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_growth_factor_ODE(cosmo, np.atleast_1d(1.0))
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cache = cosmo._workspace["background.growth_factor"]
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return interp(a, cache["a"], cache["f"])
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@ -343,12 +338,36 @@ def _growth_factor_second_ODE(cosmo, a):
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Second order growth factor computed at requested scale factor
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"""
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# Check if growth has already been computed, if not, compute it
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#if not "background.growth_factor" in cosmo._workspace.keys():
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# _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
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cache = _growth_factor_ODE(cosmo, a)[1]
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if not "background.growth_factor" in cosmo._workspace.keys():
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_growth_factor_ODE(cosmo, np.atleast_1d(1.0))
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cache = cosmo._workspace["background.growth_factor"]
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return interp(a, cache["a"], cache["g2"])
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def _growth_rate_ODE(cosmo, a):
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"""Compute growth rate dD/dlna at a given scale factor by solving the linear
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growth ODE.
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Parameters
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----------
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cosmo: `Cosmology`
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Cosmology object
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a: array_like
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Scale factor
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Returns
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-------
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f: ndarray, or float if input scalar
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Second order growth rate computed at requested scale factor
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"""
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# Check if growth has already been computed, if not, compute it
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if not "background.growth_factor" in cosmo._workspace.keys():
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_growth_factor_ODE(cosmo, np.atleast_1d(1.0))
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cache = cosmo._workspace["background.growth_factor"]
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return interp(a, cache["a"], cache["f"])
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def _growth_rate_second_ODE(cosmo, a):
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"""Compute second order growth rate dD2/dlna at a given scale factor by solving the linear
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growth ODE.
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@ -367,9 +386,9 @@ def _growth_rate_second_ODE(cosmo, a):
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Second order growth rate computed at requested scale factor
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"""
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# Check if growth has already been computed, if not, compute it
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#if not "background.growth_factor" in cosmo._workspace.keys():
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# _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
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cache = _growth_factor_ODE(cosmo, a)[1]
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if not "background.growth_factor" in cosmo._workspace.keys():
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_growth_factor_ODE(cosmo, np.atleast_1d(1.0))
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cache = cosmo._workspace["background.growth_factor"]
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return interp(a, cache["a"], cache["f2"])
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@ -392,11 +411,7 @@ def _growth_factor_gamma(cosmo, a, log10_amin=-3, steps=128):
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"""
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# Check if growth has already been computed, if not, compute it
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CACHING_ACTIVATED = os.environ.get("JC_CACHE", "1") == "1"
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if CACHING_ACTIVATED and "background.growth_factor" in cosmo._workspace.keys(
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):
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cache = cosmo._workspace["background.growth_factor"]
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else:
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if not "background.growth_factor" in cosmo._workspace.keys():
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# Compute tabulated array
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atab = np.logspace(log10_amin, 0.0, steps)
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@ -407,8 +422,9 @@ def _growth_factor_gamma(cosmo, a, log10_amin=-3, steps=128):
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gtab = np.exp(odeint(integrand, np.log(atab[0]), np.log(atab)))
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gtab = gtab / gtab[-1] # Normalize to a=1.
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cache = {"a": atab, "g": gtab}
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if CACHING_ACTIVATED:
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cosmo._workspace["background.growth_factor"] = cache
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else:
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cache = cosmo._workspace["background.growth_factor"]
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return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0)
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@ -505,35 +521,6 @@ def Gf2(cosmo, a):
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return D2f * np.power(a, 3) * np.power(Esqr(cosmo, a), 0.5)
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def gp(cosmo, a):
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r""" Derivative of D1 against a
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Parameters
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----------
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cosmo: dict
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Cosmology dictionary.
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a : array_like
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Scale factor.
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Returns
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-------
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Scalar float Tensor : the derivative of D1 against a.
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Notes
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-----
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The expression for :math:`gp(a)` is:
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.. math::
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gp(a)=\frac{dD1}{da}= D'_{1norm}/a
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"""
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f1 = growth_rate(cosmo, a)
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g1 = growth_factor(cosmo, a)
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D1f = f1 * g1 / a
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return D1f
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def dGfa(cosmo, a):
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r""" Derivative of Gf against a
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@ -562,8 +549,7 @@ def dGfa(cosmo, a):
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f1 = growth_rate(cosmo, a)
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g1 = growth_factor(cosmo, a)
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D1f = f1 * g1 / a
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#cache = cosmo._workspace['background.growth_factor']
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cache = _growth_factor_ODE(cosmo, a)[1]
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cache = cosmo._workspace['background.growth_factor']
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f1p = cache['h'] / cache['a'] * cache['g']
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f1p = interp(np.log(a), np.log(cache['a']), f1p)
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Ea = E(cosmo, a)
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@ -598,8 +584,7 @@ def dGf2a(cosmo, a):
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f2 = growth_rate_second(cosmo, a)
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g2 = growth_factor_second(cosmo, a)
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D2f = f2 * g2 / a
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#cache = cosmo._workspace['background.growth_factor']
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cache = _growth_factor_ODE(cosmo, a)[1]
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cache = cosmo._workspace['background.growth_factor']
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f2p = cache['h2'] / cache['a'] * cache['g2']
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f2p = interp(np.log(a), np.log(cache['a']), f2p)
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E_a = E(cosmo, a)
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