Allow env variable control of caching in growth

jaxpm/growth.py

@@ -1,3 +1,5 @@
+import os
+
 import jax.numpy as np
 from jax.numpy import interp
 from jax_cosmo.background import *
@@ -243,56 +245,61 @@ def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=256, eps=1e-4):
         Growth factor computed at requested scale factor
     """
     # Check if growth has already been computed
-    #if not "background.growth_factor" in cosmo._workspace.keys():
-    # Compute tabulated array
-    atab = np.logspace(log10_amin, 0.0, steps)
+    CACHING_ACTIVATED = os.environ.get("JC_CACHE", "1") == "1"
+    if CACHING_ACTIVATED and "background.growth_factor" in cosmo._workspace.keys(
+    ):
+        cache = cosmo._workspace["background.growth_factor"]
+    else:
+        # Compute tabulated array
+        atab = np.logspace(log10_amin, 0.0, steps)
 
-    def D_derivs(y, x):
-        q = (2.0 - 0.5 *
-                (Omega_m_a(cosmo, x) +
-                (1.0 + 3.0 * w(cosmo, x)) * Omega_de_a(cosmo, x))) / x
-        r = 1.5 * Omega_m_a(cosmo, x) / x / x
+        def D_derivs(y, x):
+            q = (2.0 - 0.5 *
+                 (Omega_m_a(cosmo, x) +
+                  (1.0 + 3.0 * w(cosmo, x)) * Omega_de_a(cosmo, x))) / x
+            r = 1.5 * Omega_m_a(cosmo, x) / x / x
 
-        g1, g2 = y[0]
-        f1, f2 = y[1]
-        dy1da = [f1, -q * f1 + r * g1]
-        dy2da = [f2, -q * f2 + r * g2 - r * g1**2]
-        return np.array([[dy1da[0], dy2da[0]], [dy1da[1], dy2da[1]]])
+            g1, g2 = y[0]
+            f1, f2 = y[1]
+            dy1da = [f1, -q * f1 + r * g1]
+            dy2da = [f2, -q * f2 + r * g2 - r * g1**2]
+            return np.array([[dy1da[0], dy2da[0]], [dy1da[1], dy2da[1]]])
 
-    y0 = np.array([[atab[0], -3.0 / 7 * atab[0]**2],
-                    [1.0, -6.0 / 7 * atab[0]]])
-    y = odeint(D_derivs, y0, atab)
+        y0 = np.array([[atab[0], -3.0 / 7 * atab[0]**2],
+                       [1.0, -6.0 / 7 * atab[0]]])
+        y = odeint(D_derivs, y0, atab)
 
-    # compute second order derivatives growth
-    dyda2 = D_derivs(np.transpose(y, (1, 2, 0)), atab)
-    dyda2 = np.transpose(dyda2, (2, 0, 1))
+        # compute second order derivatives growth
+        dyda2 = D_derivs(np.transpose(y, (1, 2, 0)), atab)
+        dyda2 = np.transpose(dyda2, (2, 0, 1))
 
-    # Normalize results
-    y1 = y[:, 0, 0]
-    gtab = y1 / y1[-1]
-    y2 = y[:, 0, 1]
-    g2tab = y2 / y2[-1]
-    # To transform from dD/da to dlnD/dlna: dlnD/dlna = a / D dD/da
-    ftab = y[:, 1, 0] / y1[-1] * atab / gtab
-    f2tab = y[:, 1, 1] / y2[-1] * atab / g2tab
-    # Similarly for second order derivatives
-    # Note: these factors are not accessible as parent functions yet
-    # since it is unclear what to refer to them with.
-    htab = dyda2[:, 1, 0] / y1[-1] * atab / gtab
-    h2tab = dyda2[:, 1, 1] / y2[-1] * atab / g2tab
+        # Normalize results
+        y1 = y[:, 0, 0]
+        gtab = y1 / y1[-1]
+        y2 = y[:, 0, 1]
+        g2tab = y2 / y2[-1]
+        # To transform from dD/da to dlnD/dlna: dlnD/dlna = a / D dD/da
+        ftab = y[:, 1, 0] / y1[-1] * atab / gtab
+        f2tab = y[:, 1, 1] / y2[-1] * atab / g2tab
+        # Similarly for second order derivatives
+        # Note: these factors are not accessible as parent functions yet
+        # since it is unclear what to refer to them with.
+        htab = dyda2[:, 1, 0] / y1[-1] * atab / gtab
+        h2tab = dyda2[:, 1, 1] / y2[-1] * atab / g2tab
 
+        cache = {
+            "a": atab,
+            "g": gtab,
+            "f": ftab,
+            "h": htab,
+            "g2": g2tab,
+            "f2": f2tab,
+            "h2": h2tab,
+        }
+        if CACHING_ACTIVATED:
+            cosmo._workspace["background.growth_factor"] = cache
 
-    cache = {
-        "a": atab,
-        "g": gtab,
-        "f": ftab,
-        "h": htab,
-        "g2": g2tab,
-        "f2": f2tab,
-        "h2": h2tab,
-    }
-
-    return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0) , cache
+    return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0), cache
 
 
 def _growth_rate_ODE(cosmo, a):
@@ -313,10 +320,11 @@ def _growth_rate_ODE(cosmo, a):
         Growth rate computed at requested scale factor
     """
     # Check if growth has already been computed, if not, compute it
-        
+
     cache = _growth_factor_ODE(cosmo, np.atleast_1d(1.0))[1]
     return interp(a, cache["a"], cache["f"])
 
+
 def _growth_factor_second_ODE(cosmo, a):
     """Compute second order growth factor D2(a) at a given scale factor,
     normalised such that D(a=1) = 1.
@@ -384,7 +392,11 @@ def _growth_factor_gamma(cosmo, a, log10_amin=-3, steps=128):
 
     """
     # Check if growth has already been computed, if not, compute it
-    if not "background.growth_factor" in cosmo._workspace.keys():
+    CACHING_ACTIVATED = os.environ.get("JC_CACHE", "1") == "1"
+    if CACHING_ACTIVATED and "background.growth_factor" in cosmo._workspace.keys(
+    ):
+        cache = cosmo._workspace["background.growth_factor"]
+    else:
         # Compute tabulated array
         atab = np.logspace(log10_amin, 0.0, steps)
 
@@ -395,9 +407,8 @@ def _growth_factor_gamma(cosmo, a, log10_amin=-3, steps=128):
         gtab = np.exp(odeint(integrand, np.log(atab[0]), np.log(atab)))
         gtab = gtab / gtab[-1]  # Normalize to a=1.
         cache = {"a": atab, "g": gtab}
-        cosmo._workspace["background.growth_factor"] = cache
-    else:
-        cache = cosmo._workspace["background.growth_factor"]
+        if CACHING_ACTIVATED:
+            cosmo._workspace["background.growth_factor"] = cache
     return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0)
 
 
@@ -522,6 +533,7 @@ def gp(cosmo, a):
     D1f = f1 * g1 / a
     return D1f
 
+
 def dGfa(cosmo, a):
     r""" Derivative of Gf against a
 
@@ -592,4 +604,4 @@ def dGf2a(cosmo, a):
     f2p = interp(np.log(a), np.log(cache['a']), f2p)
     E_a = E(cosmo, a)
     return (f2p * a**3 * E_a + D2f * a**3 * dEa(cosmo, a) +
-            3 * a**2 * E_a * D2f)
+            3 * a**2 * E_a * D2f)