Add tolerance and avoid log(0) warnings

src/pst/models.py

@@ -45,8 +45,7 @@ def wrapper_stellar_mass_formed(*args):
         quenching_time = getattr(args[0], "quenching_time", 20.0 << u.Gyr)
         stellar_mass = stellar_mass_formed(*args)
         final_mass = stellar_mass_formed(args[0], quenching_time)
-        stellar_mass[args[1] > quenching_time] = final_mass
-        return stellar_mass
+        return np.where(args[1] < quenching_time, stellar_mass, final_mass)
     return wrapper_stellar_mass_formed
 
 
@@ -407,9 +406,9 @@ def __init__(self, **kwargs):
 
     @u.quantity_input
     def stellar_mass_formed(self, times: u.Quantity):
-        z = - np.log(times / self.t0) / self.scale
-        m = 0.5 * (1 - special.erf(z / SQRT_2))
-        m[times <= 0] = 0
+        z = - np.log(times[times > 0] / self.t0) / self.scale
+        m = np.zeros(times.shape)
+        m[times > 0] = 0.5 * (1 - special.erf(z / SQRT_2))
         return m * self.mass_norm
 
     @u.quantity_input

tests/test_models.py

@@ -62,7 +62,7 @@ def test_lognormal_zpowerlaw(self):
         self.assertEqual(mass[0], 0.0)
         self.assertTrue(np.isclose(mass[-1], 1.0 * u.Msun, rtol=1e-4))
         self.assertEqual(metals[0], 0.0)
-        #self.assertTrue(np.isclose(metals[-1], 0.02))
+        self.assertTrue(np.isclose(metals[-1], 0.02, rtol=1e-4))
 
     def test_tabular(self):
         low_res_time = np.linspace(0, 13.7, 10) * u.Gyr

tutorials/models/analytical.ipynb

@@ -228,16 +228,6 @@
     "ssp_weights = model.interpolate_ssp_masses(ssp, t_obs=today)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d91db156-54a7-4ef2-8a0f-9316bc8e2322",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.ism_metallicity(2*u.Gyr)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -260,16 +250,6 @@
     "ax.plot(today-cosmic_time, model.ism_metallicity(cosmic_time), 'k-', alpha=.5)\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1d035a95-dcae-4a49-bb37-2c172dfe6f06",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.nanmax(ssp_weights)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f1e6080b",