diff --git a/CHANGES.rst b/CHANGES.rst
index 6ad9a60a..72c722aa 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -10,6 +10,17 @@
 New Features
 ------------
 
+sbpy.activity
+^^^^^^^^^^^^^
+
+- Added ``VectorialModel.binned_production`` constructor for compatibility with
+  time-dependent production implemented in the original FORTRAN vectorial model
+  code by Festou. [#336]
+
+- Added ``VMResult``, ``VMFragmentSputterPolar``, ``VMParams``,
+  ``VMGridParams``, ``VMFragment``, and ``VMParent`` dataclasses to expose
+  details of ``VectorialModel`` results that may be of interest. [#336]
+
 sbpy.data
 ^^^^^^^^^
 
@@ -21,11 +32,10 @@ sbpy.data
 
 - Added ``DataClass.__contains__`` to enable `in` operator for ``DataClass``
   objects. [#357]
-
+  
 - Added ``DataClass.add_row``, ``DataClass.vstack``
   methods. [#367]
 
-
 sbpy.photometry
 ^^^^^^^^^^^^^^^
 
@@ -50,6 +60,15 @@ sbpy.data
   designations: they do not parse as cometary or asteroidal. [#334, #340]
 
 
+API Changes
+-----------
+
+sbpy.activity
+^^^^^^^^^^^^^
+
+- ``VectorialModel`` now no longer takes an ``angular_substeps`` parameter. [#336]
+
+
 0.3.0 (2022-04-28)
 ==================
 
diff --git a/docs/sbpy/activity/gas.rst b/docs/sbpy/activity/gas.rst
index 8d512e81..ca5e96a1 100644
--- a/docs/sbpy/activity/gas.rst
+++ b/docs/sbpy/activity/gas.rst
@@ -103,12 +103,6 @@ The gas coma models work with sbpy's apertures:
 Vectorial Model
 ^^^^^^^^^^^^^^^
 
-.. warning::
-
-  Literature tests with the Vectorial model are generally in agreement at the
-  20% level or better.  The cause for the differences with the Festou FORTRAN
-  code are not yet precisely known.  Help testing this feature is appreciated.
-
 The Vectorial model (`Festou 1981
 <https://ui.adsabs.harvard.edu/abs/1981A%26A....95...69F/abstract>`_) describes
 the spatial distribution of coma photolysis products.  Unlike the Haser model,
@@ -146,9 +140,9 @@ number of molecules in an aperture.  Parent and daughter data is provided via
   >>> Q = 1e28 / u.s        # water production rate
   >>> coma = gas.VectorialModel(Q, water, hydroxyl)
   >>> print(coma.column_density(10 * u.km))    # doctest: +FLOAT_CMP
-  2.951278139718558e+17 1 / m2
+  2.8976722840952486e+17 1 / m2
   >>> print(coma.total_number(1000 * u.km))    # doctest: +FLOAT_CMP
-  6.96687966256294e+29
+  6.995158827300034e+29
 
 Production Rate calculations
 ----------------------------
diff --git a/examples/activity/vectorial-model.ipynb b/examples/activity/vectorial-model.ipynb
index 6ade04cf..a5547ea7 100644
--- a/examples/activity/vectorial-model.ipynb
+++ b/examples/activity/vectorial-model.ipynb
@@ -12,7 +12,8 @@
     "import sbpy.activity as sba\n",
     "import matplotlib.pyplot as plt\n",
     "from sbpy.data import Phys\n",
-    "from astropy.visualization import quantity_support"
+    "from astropy.visualization import quantity_support\n",
+    "from sbpy.activity.gas.core import VMResult"
    ]
   },
   {
@@ -53,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "narrow-script",
    "metadata": {},
    "outputs": [
@@ -157,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "constant-software",
    "metadata": {},
    "outputs": [
@@ -205,15 +206,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "danish-coverage",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/selfreference/repos/my_sbpy_fork/sbpy/activity/gas/core.py:974: TestingNeeded: Literature tests with the Vectorial model are generally in agreement at the 20% level or better.  The cause for the differences with the Festou FORTRAN code are not yet precisely known.  Help testing this feature is appreciated.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computing: 100.0 %\r"
+      "Performing setup calculations...\n",
+      "Starting fragment density computations...\n",
+      "Interpolating radial fragment density...\n",
+      "Computing column densities...\n",
+      "Vectorial model calculations complete!\n"
      ]
     }
    ],
@@ -232,7 +245,47 @@
    "metadata": {},
    "source": [
     "# Examining the results\n",
-    "After the calculations are finished, the results are stored in the dictionary `vmodel` and contain information about the volume & column densities, the grids used to in the calculations, and some other things that might be useful."
+    "After the calculations are finished, the results are stored in a VMResult dataclass that holds the volume and column densities, the grids used for both, interpolations of both, and some other quantities to help gauge the quality of the calculation.  It is accessible as the class variable 'vmr':"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a21c4dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vmr = coma_sine.vmr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc74ca71",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## VMResult dataclass overview\n",
+    "### vmr.volume_density_grid, vmr.column_density_grid\n",
+    "Numpy arrays of the radial points around the nucleus that were used in the fragment density calculations\n",
+    "### vmr.volume_density, vmr.column_density\n",
+    "Numpy arrays of volume and column density at the respective grid points given above, with astropy units attached\n",
+    "### vmr.fragment_sputter, vmr.solid_angle_sputter\n",
+    "Two-dimensional information detailing how fragments are ejected from a single column of outflowing parents\n",
+    "### vmr.volume_density_interpolation, vmr.column_density_interpolation\n",
+    "Interpolations of the volume and column density dervide from the gridded values.  The volume density is given in units of 1/m^3, and column density in units of 1/m^2.\n",
+    "### vmr.collision_sphere_radius\n",
+    "Estimate of where the outflowing parents transition from collisional to non-collisional\n",
+    "### vmr.max_grid_radius\n",
+    "How far from the nucleus the grid reaches.  Beyond this value the interpolators above will give an answer, but typically not a useful one.\n",
+    "### vmr.coma_radius\n",
+    "Defines the radius beyond which the model considers there to be no more parents\n",
+    "### vmr.num_fragments_theory\n",
+    "Theoretical count of the total number of fragments we expect - most accurate when there is no time-dependent production\n",
+    "### vmr.num_fragments_grid\n",
+    "Count produced by integrating the volume density over the volume of the entire grid\n",
+    "### vmr.t_perm_flow\n",
+    "Time necessary to reach the permanent flow regime, where production of parents is exactly balanced by parent loss due to photodissociation"
    ]
   },
   {
@@ -253,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "id": "freelance-prime",
    "metadata": {
     "scrolled": true
@@ -267,64 +320,63 @@
       "\n",
       "Radius (km) vs Fragment density (1/cm3)\n",
       "---------------------------------------\n",
-      "    176 km :\t1.506e+05 1 / cm3\n",
-      "    210 km :\t1.271e+05 1 / cm3\n",
-      "    250 km :\t1.074e+05 1 / cm3\n",
-      "    297 km :\t9.092e+04 1 / cm3\n",
-      "    354 km :\t7.706e+04 1 / cm3\n",
-      "    421 km :\t6.539e+04 1 / cm3\n",
-      "    501 km :\t5.555e+04 1 / cm3\n",
-      "    597 km :\t4.725e+04 1 / cm3\n",
-      "    710 km :\t4.028e+04 1 / cm3\n",
-      "    845 km :\t3.443e+04 1 / cm3\n",
-      "   1006 km :\t2.949e+04 1 / cm3\n",
-      "   1197 km :\t2.531e+04 1 / cm3\n",
-      "   1424 km :\t2.179e+04 1 / cm3\n",
-      "   1695 km :\t1.881e+04 1 / cm3\n",
-      "   2017 km :\t1.628e+04 1 / cm3\n",
-      "   2401 km :\t1.413e+04 1 / cm3\n",
-      "   2857 km :\t1.229e+04 1 / cm3\n",
-      "   3400 km :\t1.071e+04 1 / cm3\n",
-      "   4047 km :\t9.350e+03 1 / cm3\n",
-      "   4816 km :\t8.168e+03 1 / cm3\n",
-      "   5731 km :\t7.133e+03 1 / cm3\n",
-      "   6821 km :\t6.215e+03 1 / cm3\n",
-      "   8118 km :\t5.384e+03 1 / cm3\n",
-      "   9661 km :\t4.608e+03 1 / cm3\n",
-      "  11497 km :\t3.863e+03 1 / cm3\n",
-      "  13682 km :\t3.137e+03 1 / cm3\n",
-      "  16283 km :\t2.437e+03 1 / cm3\n",
-      "  19379 km :\t1.793e+03 1 / cm3\n",
-      "  23062 km :\t1.247e+03 1 / cm3\n",
-      "  27446 km :\t8.475e+02 1 / cm3\n",
-      "  32663 km :\t6.049e+02 1 / cm3\n",
-      "  38872 km :\t4.702e+02 1 / cm3\n",
-      "  46262 km :\t3.600e+02 1 / cm3\n",
-      "  55055 km :\t2.477e+02 1 / cm3\n",
-      "  65521 km :\t1.713e+02 1 / cm3\n",
-      "  77976 km :\t1.195e+02 1 / cm3\n",
-      "  92798 km :\t7.363e+01 1 / cm3\n",
-      " 110438 km :\t4.693e+01 1 / cm3\n",
-      " 131431 km :\t2.933e+01 1 / cm3\n",
-      " 156414 km :\t1.726e+01 1 / cm3\n",
-      " 186147 km :\t9.656e+00 1 / cm3\n",
-      " 221532 km :\t5.481e+00 1 / cm3\n",
-      " 263643 km :\t2.977e+00 1 / cm3\n",
-      " 313758 km :\t1.519e+00 1 / cm3\n",
-      " 373401 km :\t7.649e-01 1 / cm3\n",
-      " 444380 km :\t3.623e-01 1 / cm3\n",
-      " 528852 km :\t1.660e-01 1 / cm3\n",
-      " 629381 km :\t6.543e-02 1 / cm3\n",
-      " 749020 km :\t2.549e-02 1 / cm3\n",
-      " 891401 km :\t9.045e-03 1 / cm3\n"
+      "    176 km :\t1.553e+05 1 / cm3\n",
+      "    210 km :\t1.310e+05 1 / cm3\n",
+      "    250 km :\t1.106e+05 1 / cm3\n",
+      "    297 km :\t9.353e+04 1 / cm3\n",
+      "    354 km :\t7.912e+04 1 / cm3\n",
+      "    421 km :\t6.701e+04 1 / cm3\n",
+      "    501 km :\t5.684e+04 1 / cm3\n",
+      "    597 km :\t4.829e+04 1 / cm3\n",
+      "    710 km :\t4.110e+04 1 / cm3\n",
+      "    845 km :\t3.505e+04 1 / cm3\n",
+      "   1006 km :\t2.996e+04 1 / cm3\n",
+      "   1197 km :\t2.567e+04 1 / cm3\n",
+      "   1424 km :\t2.206e+04 1 / cm3\n",
+      "   1695 km :\t1.901e+04 1 / cm3\n",
+      "   2017 km :\t1.643e+04 1 / cm3\n",
+      "   2401 km :\t1.424e+04 1 / cm3\n",
+      "   2857 km :\t1.237e+04 1 / cm3\n",
+      "   3400 km :\t1.077e+04 1 / cm3\n",
+      "   4047 km :\t9.394e+03 1 / cm3\n",
+      "   4816 km :\t8.201e+03 1 / cm3\n",
+      "   5731 km :\t7.155e+03 1 / cm3\n",
+      "   6821 km :\t6.232e+03 1 / cm3\n",
+      "   8118 km :\t5.395e+03 1 / cm3\n",
+      "   9661 km :\t4.616e+03 1 / cm3\n",
+      "  11497 km :\t3.868e+03 1 / cm3\n",
+      "  13682 km :\t3.140e+03 1 / cm3\n",
+      "  16283 km :\t2.440e+03 1 / cm3\n",
+      "  19379 km :\t1.795e+03 1 / cm3\n",
+      "  23062 km :\t1.248e+03 1 / cm3\n",
+      "  27446 km :\t8.478e+02 1 / cm3\n",
+      "  32663 km :\t6.053e+02 1 / cm3\n",
+      "  38872 km :\t4.707e+02 1 / cm3\n",
+      "  46262 km :\t3.602e+02 1 / cm3\n",
+      "  55055 km :\t2.475e+02 1 / cm3\n",
+      "  65521 km :\t1.711e+02 1 / cm3\n",
+      "  77976 km :\t1.198e+02 1 / cm3\n",
+      "  92798 km :\t7.374e+01 1 / cm3\n",
+      " 110438 km :\t4.676e+01 1 / cm3\n",
+      " 131431 km :\t2.942e+01 1 / cm3\n",
+      " 156414 km :\t1.725e+01 1 / cm3\n",
+      " 186147 km :\t9.686e+00 1 / cm3\n",
+      " 221532 km :\t5.478e+00 1 / cm3\n",
+      " 263643 km :\t2.962e+00 1 / cm3\n",
+      " 313758 km :\t1.525e+00 1 / cm3\n",
+      " 373401 km :\t7.704e-01 1 / cm3\n",
+      " 444380 km :\t3.687e-01 1 / cm3\n",
+      " 528852 km :\t1.647e-01 1 / cm3\n",
+      " 629381 km :\t6.962e-02 1 / cm3\n",
+      " 749020 km :\t2.709e-02 1 / cm3\n",
+      " 891401 km :\t9.271e-03 1 / cm3\n"
      ]
     }
    ],
    "source": [
     "print(\"\\n\\nRadius (km) vs Fragment density (1/cm3)\\n---------------------------------------\")\n",
-    "volume_densities = list(zip(coma_sine.vmodel['radial_grid'], coma_sine.vmodel['radial_density']))\n",
-    "for pair in volume_densities:\n",
-    "    print(f'{pair[0].to(u.km):7.0f} :\\t{pair[1].to(1/(u.cm**3)):5.3e}')"
+    "for r, n_r in zip(vmr.volume_density_grid, vmr.volume_density):\n",
+    "    print(f'{r.to(u.km):7.0f} :\\t{n_r.to(1/(u.cm**3)):5.3e}')"
    ]
   },
   {
@@ -332,8 +384,6 @@
    "id": "capable-barrel",
    "metadata": {},
    "source": [
-    "An interpolated function of this volume density is also available at `coma.vmodel['r_dens_interpolation']` that takes its argument in meters (no astropy units) and returns the volume density in 1/m^3 (also no units).\n",
-    "\n",
     "Note that the volume density is only tracked out to a certain radius, which can cause the column density at the edge of the coma to behave strangely if there is a significant amount of fragments near the edge of the model's grid."
    ]
   },
@@ -349,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "id": "static-bookmark",
    "metadata": {
     "scrolled": true
@@ -362,64 +412,63 @@
       "\n",
       "Radius (km) vs Column density (1/cm2)\n",
       "-------------------------------------\n",
-      "    176 km :\t4.196e+13 1 / cm2\n",
-      "    210 km :\t4.105e+13 1 / cm2\n",
-      "    250 km :\t4.015e+13 1 / cm2\n",
-      "    297 km :\t3.925e+13 1 / cm2\n",
-      "    354 km :\t3.834e+13 1 / cm2\n",
-      "    421 km :\t3.744e+13 1 / cm2\n",
-      "    501 km :\t3.653e+13 1 / cm2\n",
-      "    597 km :\t3.561e+13 1 / cm2\n",
-      "    710 km :\t3.470e+13 1 / cm2\n",
-      "    845 km :\t3.378e+13 1 / cm2\n",
-      "   1006 km :\t3.285e+13 1 / cm2\n",
-      "   1197 km :\t3.191e+13 1 / cm2\n",
-      "   1424 km :\t3.096e+13 1 / cm2\n",
-      "   1695 km :\t3.000e+13 1 / cm2\n",
-      "   2017 km :\t2.902e+13 1 / cm2\n",
-      "   2401 km :\t2.801e+13 1 / cm2\n",
-      "   2857 km :\t2.698e+13 1 / cm2\n",
-      "   3400 km :\t2.590e+13 1 / cm2\n",
-      "   4047 km :\t2.478e+13 1 / cm2\n",
-      "   4816 km :\t2.360e+13 1 / cm2\n",
-      "   5731 km :\t2.235e+13 1 / cm2\n",
-      "   6821 km :\t2.100e+13 1 / cm2\n",
-      "   8118 km :\t1.953e+13 1 / cm2\n",
-      "   9661 km :\t1.792e+13 1 / cm2\n",
-      "  11497 km :\t1.615e+13 1 / cm2\n",
-      "  13682 km :\t1.423e+13 1 / cm2\n",
-      "  16283 km :\t1.223e+13 1 / cm2\n",
-      "  19379 km :\t1.025e+13 1 / cm2\n",
-      "  23062 km :\t8.468e+12 1 / cm2\n",
-      "  27446 km :\t7.021e+12 1 / cm2\n",
-      "  32663 km :\t5.934e+12 1 / cm2\n",
-      "  38872 km :\t5.043e+12 1 / cm2\n",
-      "  46262 km :\t4.140e+12 1 / cm2\n",
+      "    176 km :\t4.243e+13 1 / cm2\n",
+      "    210 km :\t4.148e+13 1 / cm2\n",
+      "    250 km :\t4.055e+13 1 / cm2\n",
+      "    297 km :\t3.962e+13 1 / cm2\n",
+      "    354 km :\t3.868e+13 1 / cm2\n",
+      "    421 km :\t3.774e+13 1 / cm2\n",
+      "    501 km :\t3.681e+13 1 / cm2\n",
+      "    597 km :\t3.587e+13 1 / cm2\n",
+      "    710 km :\t3.492e+13 1 / cm2\n",
+      "    845 km :\t3.398e+13 1 / cm2\n",
+      "   1006 km :\t3.302e+13 1 / cm2\n",
+      "   1197 km :\t3.207e+13 1 / cm2\n",
+      "   1424 km :\t3.110e+13 1 / cm2\n",
+      "   1695 km :\t3.012e+13 1 / cm2\n",
+      "   2017 km :\t2.912e+13 1 / cm2\n",
+      "   2401 km :\t2.810e+13 1 / cm2\n",
+      "   2857 km :\t2.705e+13 1 / cm2\n",
+      "   3400 km :\t2.597e+13 1 / cm2\n",
+      "   4047 km :\t2.484e+13 1 / cm2\n",
+      "   4816 km :\t2.365e+13 1 / cm2\n",
+      "   5731 km :\t2.238e+13 1 / cm2\n",
+      "   6821 km :\t2.103e+13 1 / cm2\n",
+      "   8118 km :\t1.955e+13 1 / cm2\n",
+      "   9661 km :\t1.794e+13 1 / cm2\n",
+      "  11497 km :\t1.616e+13 1 / cm2\n",
+      "  13682 km :\t1.424e+13 1 / cm2\n",
+      "  16283 km :\t1.224e+13 1 / cm2\n",
+      "  19379 km :\t1.026e+13 1 / cm2\n",
+      "  23062 km :\t8.473e+12 1 / cm2\n",
+      "  27446 km :\t7.025e+12 1 / cm2\n",
+      "  32663 km :\t5.937e+12 1 / cm2\n",
+      "  38872 km :\t5.046e+12 1 / cm2\n",
+      "  46262 km :\t4.141e+12 1 / cm2\n",
       "  55055 km :\t3.256e+12 1 / cm2\n",
-      "  65521 km :\t2.549e+12 1 / cm2\n",
-      "  77976 km :\t1.935e+12 1 / cm2\n",
-      "  92798 km :\t1.393e+12 1 / cm2\n",
+      "  65521 km :\t2.550e+12 1 / cm2\n",
+      "  77976 km :\t1.938e+12 1 / cm2\n",
+      "  92798 km :\t1.394e+12 1 / cm2\n",
       " 110438 km :\t1.012e+12 1 / cm2\n",
-      " 131431 km :\t7.060e+11 1 / cm2\n",
-      " 156414 km :\t4.734e+11 1 / cm2\n",
-      " 186147 km :\t3.080e+11 1 / cm2\n",
-      " 221532 km :\t1.985e+11 1 / cm2\n",
-      " 263643 km :\t1.214e+11 1 / cm2\n",
-      " 313758 km :\t7.121e+10 1 / cm2\n",
-      " 373401 km :\t4.046e+10 1 / cm2\n",
-      " 444380 km :\t2.154e+10 1 / cm2\n",
-      " 528852 km :\t1.077e+10 1 / cm2\n",
-      " 629381 km :\t4.645e+09 1 / cm2\n",
-      " 749020 km :\t1.846e+09 1 / cm2\n",
-      " 891401 km :\t0.000e+00 1 / cm2\n"
+      " 131431 km :\t7.079e+11 1 / cm2\n",
+      " 156414 km :\t4.738e+11 1 / cm2\n",
+      " 186147 km :\t3.091e+11 1 / cm2\n",
+      " 221532 km :\t1.987e+11 1 / cm2\n",
+      " 263643 km :\t1.218e+11 1 / cm2\n",
+      " 313758 km :\t7.209e+10 1 / cm2\n",
+      " 373401 km :\t4.121e+10 1 / cm2\n",
+      " 444380 km :\t2.219e+10 1 / cm2\n",
+      " 528852 km :\t1.122e+10 1 / cm2\n",
+      " 629381 km :\t5.316e+09 1 / cm2\n",
+      " 749020 km :\t2.293e+09 1 / cm2\n",
+      " 891401 km :\t8.475e+08 1 / cm2\n"
      ]
     }
    ],
    "source": [
     "print(\"\\nRadius (km) vs Column density (1/cm2)\\n-------------------------------------\")\n",
-    "column_densities = list(zip(coma_sine.vmodel['column_density_grid'], coma_sine.vmodel['column_densities']))\n",
-    "for pair in column_densities:\n",
-    "    print(f'{pair[0].to(u.km):7.0f} :\\t{pair[1].to(1/(u.cm**2)):5.3e}')"
+    "for r, cd in zip(vmr.column_density_grid, vmr.column_density):\n",
+    "    print(f'{r.to(u.km):7.0f} :\\t{cd.to(1/(u.cm**2)):5.3e}')"
    ]
   },
   {
@@ -434,15 +483,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "id": "activated-graduation",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
+       "<Figure size 2000x1000 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -457,7 +506,7 @@
     "solarblackcol = np.array([0, 43, 54]) / 255.\n",
     "solarblack = (solarblackcol[0], solarblackcol[1], solarblackcol[2], 1)\n",
     "\n",
-    "def show_column_density_plots(coma, r_units, cd_units, frag_name):\n",
+    "def show_column_density_plots(vmr, r_units, cd_units, frag_name):\n",
     "    \"\"\" Show the radial density of the fragment species \"\"\"\n",
     "\n",
     "    x_min_logplot = 3\n",
@@ -467,14 +516,12 @@
     "    x_max_linear = (2000 * u.km).to(u.m)\n",
     "\n",
     "    lin_interp_x = np.linspace(x_min_linear.value, x_max_linear.value, num=200)\n",
-    "    lin_interp_y = coma.vmodel['column_density_interpolation'](lin_interp_x)/(u.m**2)\n",
+    "    lin_interp_y = vmr.column_density_interpolation(lin_interp_x)/(u.m**2)\n",
     "    lin_interp_x *= u.m\n",
-    "    lin_interp_x.to(r_units)\n",
     "\n",
     "    log_interp_x = np.logspace(x_min_logplot, x_max_logplot, num=200)\n",
-    "    log_interp_y = coma.vmodel['column_density_interpolation'](log_interp_x)/(u.m**2)\n",
+    "    log_interp_y = vmr.column_density_interpolation(log_interp_x)/(u.m**2)\n",
     "    log_interp_x *= u.m\n",
-    "    log_interp_x.to(r_units)\n",
     "\n",
     "    plt.style.use('Solarize_Light2')\n",
     "\n",
@@ -488,28 +535,26 @@
     "\n",
     "    ax1.set_xlim([x_min_linear, x_max_linear])\n",
     "    ax1.plot(lin_interp_x, lin_interp_y, color=\"red\",  linewidth=2.5, linestyle=\"-\", label=\"cubic spline\")\n",
-    "    ax1.plot(coma.vmodel['column_density_grid'], coma.vmodel['column_densities'], 'bo', label=\"model\")\n",
-    "    ax1.plot(coma.vmodel['column_density_grid'], coma.vmodel['column_densities'], 'g--', label=\"linear interpolation\")\n",
+    "    ax1.plot(vmr.column_density_grid, vmr.column_density, 'bo', label=\"model\")\n",
+    "    ax1.plot(vmr.column_density_grid, vmr.column_density, 'g--', label=\"linear interpolation\")\n",
     "\n",
     "    ax2.set_xscale('log')\n",
     "    ax2.set_yscale('log')\n",
     "    ax2.loglog(log_interp_x, log_interp_y, color=\"red\",  linewidth=2.5, linestyle=\"-\", label=\"cubic spline\")\n",
-    "    ax2.loglog(coma.vmodel['column_density_grid'], coma.vmodel['column_densities'], 'bo', label=\"model\")\n",
-    "    ax2.loglog(coma.vmodel['column_density_grid'], coma.vmodel['column_densities'], 'g--', label=\"linear interpolation\")\n",
+    "    ax2.loglog(vmr.column_density_grid, vmr.column_density, 'bo', label=\"model\")\n",
+    "    ax2.loglog(vmr.column_density_grid, vmr.column_density, 'g--', label=\"linear interpolation\")\n",
     "\n",
     "    ax1.set_ylim(bottom=0)\n",
     "\n",
-    "    ax2.set_xlim(right=coma.vmodel['max_grid_radius'])\n",
-    "\n",
     "    # Mark the beginning of the collision sphere\n",
-    "    ax1.axvline(x=coma.vmodel['collision_sphere_radius'], color=solarblue)\n",
-    "    ax2.axvline(x=coma.vmodel['collision_sphere_radius'], color=solarblue)\n",
+    "    ax1.axvline(x=vmr.collision_sphere_radius, color=solarblue)\n",
+    "    ax2.axvline(x=vmr.collision_sphere_radius, color=solarblue)\n",
     "\n",
     "    # Only plot as far as the maximum radius of our grid on log-log plot\n",
-    "    ax2.axvline(x=coma.vmodel['max_grid_radius'])\n",
+    "    ax2.set_xlim(right=vmr.max_grid_radius)\n",
     "\n",
     "    # Mark the collision sphere\n",
-    "    plt.text(coma.vmodel['collision_sphere_radius']*1.1, lin_interp_y[0]/10, 'Collision Sphere Edge', color=solarblue)\n",
+    "    plt.text(vmr.collision_sphere_radius*1.1, lin_interp_y[0]/10, 'Collision Sphere Edge', color=solarblue)\n",
     "\n",
     "    legend = plt.legend(loc='upper right', frameon=False)\n",
     "    for l_text in legend.get_texts():\n",
@@ -519,7 +564,7 @@
     "\n",
     "# for graphing with astropy units\n",
     "quantity_support()\n",
-    "show_column_density_plots(coma_sine, u.km, 1/u.cm**2, 'OH')"
+    "show_column_density_plots(vmr, u.km, 1/u.cm**2, 'OH')"
    ]
   },
   {
@@ -534,17 +579,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 23,
    "id": "nasty-reliance",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/1n/8mdnjzr95m3c_nwpkkxth8340000gn/T/ipykernel_34424/414349715.py:30: MatplotlibDeprecationWarning: The w_xaxis attribute was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use xaxis instead.\n",
+      "  ax.w_xaxis.set_pane_color(solargreen)\n",
+      "/var/folders/1n/8mdnjzr95m3c_nwpkkxth8340000gn/T/ipykernel_34424/414349715.py:31: MatplotlibDeprecationWarning: The w_yaxis attribute was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use yaxis instead.\n",
+      "  ax.w_yaxis.set_pane_color(solarblue)\n",
+      "/var/folders/1n/8mdnjzr95m3c_nwpkkxth8340000gn/T/ipykernel_34424/414349715.py:32: MatplotlibDeprecationWarning: The w_zaxis attribute was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use zaxis instead.\n",
+      "  ax.w_zaxis.set_pane_color(solarblack)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1440x1440 with 2 Axes>"
+       "<Figure size 1500x1500 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -552,13 +607,13 @@
     }
    ],
    "source": [
-    "def show_3d_column_density_plot(coma, x_min, x_max, y_min, y_max, grid_step_x, grid_step_y, r_units, cd_units, frag_name):\n",
+    "def show_3d_column_density_plot(vmr, x_min, x_max, y_min, y_max, grid_step_x, grid_step_y, r_units, cd_units, frag_name):\n",
     "    \"\"\" 3D plot of column density \"\"\"\n",
     "\n",
     "    x = np.linspace(x_min.to(u.m).value, x_max.to(u.m).value, grid_step_x)\n",
     "    y = np.linspace(y_min.to(u.m).value, y_max.to(u.m).value, grid_step_y)\n",
     "    xv, yv = np.meshgrid(x, y)\n",
-    "    z = coma.vmodel['column_density_interpolation'](np.sqrt(xv**2 + yv**2))\n",
+    "    z = vmr.column_density_interpolation(np.sqrt(xv**2 + yv**2))\n",
     "    # column_density_interpolation spits out m^-2\n",
     "    fz = (z/u.m**2).to(cd_units)\n",
     "\n",
@@ -570,9 +625,8 @@
     "    plt.style.use('dark_background')\n",
     "    plt.rcParams['grid.color'] = \"black\"\n",
     "\n",
-    "    fig = plt.figure(figsize=(20, 20))\n",
+    "    fig = plt.figure(figsize=(15, 15))\n",
     "    ax = plt.axes(projection='3d')\n",
-    "    # ax.grid(False)\n",
     "    surf = ax.plot_surface(xvu, yvu, fz, cmap='inferno', vmin=0, edgecolor='none')\n",
     "\n",
     "    plt.gca().set_zlim(bottom=0)\n",
@@ -591,7 +645,7 @@
     "    plt.show()\n",
     "\n",
     "\n",
-    "show_3d_column_density_plot(coma_sine, -100000*u.km, 100000*u.km, -100000*u.km, 100000*u.km, 1000, 1000, u.km, 1/u.cm**2, 'OH')"
+    "show_3d_column_density_plot(vmr, -100000*u.km, 100000*u.km, -100000*u.km, 100000*u.km, 50, 50, u.km, 1/u.cm**2, 'OH')"
    ]
   },
   {
@@ -599,13 +653,13 @@
    "id": "induced-telephone",
    "metadata": {},
    "source": [
-    "### Obtaining total counts of fragments within apertures\n",
-    "The model is compatible with any sbpy aperture for obtaining total counts of fragments:"
+    "### Obtaining total counts of fragments within apertures with total_number()\n",
+    "The model's coma object is compatible with any sbpy aperture for obtaining total counts of fragments:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "id": "challenging-trigger",
    "metadata": {},
    "outputs": [
@@ -614,20 +668,21 @@
      "output_type": "stream",
      "text": [
       "Number of fragments in\n",
-      "\tLarge rectangular aperture:  1.9456948716998955e+33\n",
-      "\tLarge circular aperture:  2.008412781839869e+33\n"
+      "\tLarge rectangular aperture:  1.9513579963113747e+33\n",
+      "\tLarge circular aperture:  2.023818343905977e+33\n"
      ]
     }
    ],
    "source": [
+    "max_r = vmr.max_grid_radius.to_value(u.m)\n",
     "print(\"Number of fragments in\")\n",
     "# Set rectangular aperture to be the size of the entire grid\n",
-    "ap1 = sba.RectangularAperture((coma_sine.vmodel['max_grid_radius'].value, \n",
-    "                               coma_sine.vmodel['max_grid_radius'].value) * u.m)\n",
+    "ap1 = sba.RectangularAperture((max_r, \n",
+    "                               max_r) * u.m )\n",
     "print(\"\\tLarge rectangular aperture: \", coma_sine.total_number(ap1))\n",
     "\n",
     "# One more time with a circular aperture\n",
-    "ap2 = sba.CircularAperture((coma_sine.vmodel['max_grid_radius'].value) * u.m)\n",
+    "ap2 = sba.CircularAperture((max_r) * u.m)\n",
     "print(\"\\tLarge circular aperture: \", coma_sine.total_number(ap2))"
    ]
   },
@@ -656,20 +711,26 @@
     "\n",
     "radial_points (int): Number of grid points to use for the radial density grids, both volume and column density\n",
     "\n",
-    "radial_substeps (int): Controls how much each grid point will slice up the density-contributing sections of the coma\n",
+    "radial_substeps (int): Controls how much each grid point will slice up the density-contributing sections of the outflowing column of parents\n",
     "\n",
-    "angular_points (int): Number of angular slices to take around the coma\n",
-    "\n",
-    "angular_substeps (int): Controls the angular slicing of the density-contributing sections of the coma\n",
+    "angular_points (int): Number of angular slices to take of the space around the nucleus\n",
     "\n",
     "parent_destruction_level (float): Percentage of parent molecules that will be destroyed before the grid gets cut off\n",
     "\n",
-    "fragment_destruction_level (float): Similar but for fragments\n",
+    "fragment_destruction_level (float): Similar, but for fragments\n",
     "\n",
     "max_fragment_lifetimes (float): If a fragment has to travel farther than this many lifetimes to contribute to the density at another point in the coma, we ignore it entirely\n",
     "\n",
     "print_progess (bool): This will periodically print out progress while calculating fragment density"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7aec478-423a-4468-8f3e-af7e1316ebb1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -688,7 +749,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/sbpy/activity/gas/core.py b/sbpy/activity/gas/core.py
index 64164b0e..68ef7544 100644
--- a/sbpy/activity/gas/core.py
+++ b/sbpy/activity/gas/core.py
@@ -4,16 +4,21 @@
 """
 
 __all__ = [
-    'photo_lengthscale',
-    'photo_timescale',
-    'fluorescence_band_strength',
-    'Haser',
-    'VectorialModel'
+    "photo_lengthscale",
+    "photo_timescale",
+    "fluorescence_band_strength",
+    "Haser",
+    "VectorialModel",
 ]
 
 from abc import ABC, abstractmethod
+
+# distutils is deprecated in python 3.10 and will be removed in 3.12 (PEP 632).
+# Migration from distutils.log -> logging
 from distutils.log import warn
 import warnings
+from dataclasses import dataclass
+from typing import Callable, Tuple
 
 import numpy as np
 import astropy.units as u
@@ -30,8 +35,13 @@
 from ... import data as sbd
 from ... import units as sbu
 from ...exceptions import RequiredPackageUnavailable, TestingNeeded
-from .. core import (Aperture, RectangularAperture, GaussianAperture,
-                     AnnularAperture, CircularAperture)
+from ..core import (
+    Aperture,
+    RectangularAperture,
+    GaussianAperture,
+    AnnularAperture,
+    CircularAperture,
+)
 
 
 def photo_lengthscale(species, source=None):
@@ -70,26 +80,26 @@ def photo_lengthscale(species, source=None):
     from .data import photo_lengthscale as data
 
     default_sources = {
-        'H2O': 'CS93',
-        'OH': 'CS93',
+        "H2O": "CS93",
+        "OH": "CS93",
     }
 
     if species not in data:
-        summary = ''
+        summary = ""
         for k, v in sorted(data.items()):
-            summary += '\n{} [{}]'.format(k, ', '.join(v.keys()))
+            summary += "\n{} [{}]".format(k, ", ".join(v.keys()))
 
-        raise ValueError(
-            'Invalid species {}.  Choose from:{}'
-            .format(species, summary))
+        raise ValueError("Invalid species {}.  Choose from:{}".format(species, summary))
 
     gas = data[species]
     source = default_sources[species] if source is None else source
 
     if source not in gas:
         raise ValueError(
-            'Source key {} not available for {}.  Choose from: {}'
-            .format(source, species, ', '.join(gas.keys())))
+            "Source key {} not available for {}.  Choose from: {}".format(
+                source, species, ", ".join(gas.keys())
+            )
+        )
 
     gamma, bibcode = gas[source]
     bib.register(photo_lengthscale, bibcode)
@@ -139,32 +149,32 @@ def photo_timescale(species, source=None):
     from .data import photo_timescale as data
 
     default_sources = {
-        'H2O': 'CS93',
-        'OH': 'CS93',
-        'HCN': 'C94',
-        'CH3OH': 'C94',
-        'H2CO': 'C94',
-        'CO2': 'CE83',
-        'CO': 'CE83',
-        'CN': 'H92'
+        "H2O": "CS93",
+        "OH": "CS93",
+        "HCN": "C94",
+        "CH3OH": "C94",
+        "H2CO": "C94",
+        "CO2": "CE83",
+        "CO": "CE83",
+        "CN": "H92",
     }
 
     if species not in data:
-        summary = ''
+        summary = ""
         for k, v in sorted(data.items()):
-            summary += '\n{} [{}]'.format(k, ', '.join(v.keys()))
+            summary += "\n{} [{}]".format(k, ", ".join(v.keys()))
 
-        raise ValueError(
-            "Invalid species {}.  Choose from:{}"
-            .format(species, summary))
+        raise ValueError("Invalid species {}.  Choose from:{}".format(species, summary))
 
     gas = data[species]
     source = default_sources[species] if source is None else source
 
     if source not in gas:
         raise ValueError(
-            'Source key {} not available for {}.  Choose from: {}'
-            .format(source, species, ', '.join(gas.keys())))
+            "Source key {} not available for {}.  Choose from: {}".format(
+                source, species, ", ".join(gas.keys())
+            )
+        )
 
     tau, bibcode = gas[source]
     bib.register(photo_timescale, bibcode)
@@ -213,28 +223,32 @@ def fluorescence_band_strength(species, eph=None, source=None):
     from .data import fluorescence_band_strength as data
 
     default_sources = {
-        'OH 0-0': 'SA88',
-        'OH 1-0': 'SA88',
-        'OH 1-1': 'SA88',
-        'OH 2-2': 'SA88',
-        'OH 0-1': 'SA88',
-        'OH 0-2': 'SA88',
-        'OH 2-0': 'SA88',
-        'OH 2-1': 'SA88',
+        "OH 0-0": "SA88",
+        "OH 1-0": "SA88",
+        "OH 1-1": "SA88",
+        "OH 2-2": "SA88",
+        "OH 0-1": "SA88",
+        "OH 0-2": "SA88",
+        "OH 2-0": "SA88",
+        "OH 2-1": "SA88",
     }
 
     if species not in data:
         raise ValueError(
-            'No data available for {}.  Choose one of: {}'
-            .format(species, ', '.join(data.keys())))
+            "No data available for {}.  Choose one of: {}".format(
+                species, ", ".join(data.keys())
+            )
+        )
 
     band = data[species]
     source = default_sources[species] if source is None else source
 
     if source not in band:
         raise ValueError(
-            'No source {} for {}.  Choose one of: {}'
-            .format(source, species, ', '.join(band.keys())))
+            "No source {} for {}.  Choose one of: {}".format(
+                source, species, ", ".join(band.keys())
+            )
+        )
 
     LN, note, bibcode = band[source]
     if bibcode is not None:
@@ -257,7 +271,7 @@ class GasComa(ABC):
 
     """
 
-    @u.quantity_input(Q=(u.s ** -1, u.mol / u.s), v=u.m / u.s)
+    @u.quantity_input(Q=(u.s**-1, u.mol / u.s), v=u.m / u.s)
     def __init__(self, Q, v):
         self.Q = Q
         self.v = v
@@ -280,10 +294,10 @@ def volume_density(self, r):
 
         """
 
-        return self._volume_density(r.to_value('m')) / u.m ** 3
+        return self._volume_density(r.to_value("m")) / u.m**3
 
     @sbd.dataclass_input(eph=sbd.Ephem)
-    @sbd.quantity_to_dataclass(eph=(sbd.Ephem, 'delta'))
+    @sbd.quantity_to_dataclass(eph=(sbd.Ephem, "delta"))
     def column_density(self, rho, eph=None):
         """Coma column density at a projected distance from nucleus.
 
@@ -311,11 +325,11 @@ def column_density(self, rho, eph=None):
         if eph is not None:
             equiv = sbu.projected_size(eph)
 
-        rho = rho.to_value('m', equiv)
-        return self._column_density(rho) / u.m ** 2
+        rho = rho.to_value("m", equiv)
+        return self._column_density(rho) / u.m**2
 
     @sbd.dataclass_input(eph=sbd.Ephem)
-    @sbd.quantity_to_dataclass(eph=(sbd.Ephem, 'delta'))
+    @sbd.quantity_to_dataclass(eph=(sbd.Ephem, "delta"))
     def total_number(self, aper, eph=None):
         """Total number of molecules in aperture.
 
@@ -428,18 +442,19 @@ def _integrate_volume_density(self, rho, epsabs=1.49e-8):
         """
 
         if not scipy:
-            raise RequiredPackageUnavailable('scipy')
+            raise RequiredPackageUnavailable("scipy")
 
         def f(s, rho2):
-            r = np.sqrt(rho2 + s ** 2)
+            r = np.sqrt(rho2 + s**2)
             return self._volume_density(r)
 
         # quad diverges integrating to infinity, but 1e6 × rho is good
         # enough
         limit = 30
         points = rho * np.logspace(-4, 4, limit // 2)
-        sigma, err = quad(f, 0, 1e6 * rho, args=(rho ** 2,),
-                          limit=limit, points=points, epsabs=epsabs)
+        sigma, err = quad(
+            f, 0, 1e6 * rho, args=(rho**2,), limit=limit, points=points, epsabs=epsabs
+        )
 
         # spherical symmetry
         sigma *= 2
@@ -473,13 +488,13 @@ def _integrate_column_density(self, aper, epsabs=1.49e-8):
         """
 
         if not scipy:
-            raise RequiredPackageUnavailable('scipy')
+            raise RequiredPackageUnavailable("scipy")
 
         if isinstance(aper, (CircularAperture, AnnularAperture)):
             if isinstance(aper, CircularAperture):
-                limits = (0, aper.radius.to_value('m'))
+                limits = (0, aper.radius.to_value("m"))
             else:
-                limits = aper.shape.to_value('m')
+                limits = aper.shape.to_value("m")
 
             # integrate in polar coordinates
             def f(rho):
@@ -494,7 +509,7 @@ def f(rho):
             N *= 2 * np.pi
             err *= 2 * np.pi
         elif isinstance(aper, RectangularAperture):
-            shape = aper.shape.to_value('m')
+            shape = aper.shape.to_value("m")
 
             def f(rho, th):
                 """Column density integration in polar coordinates.
@@ -538,10 +553,13 @@ def f(rho, sigma):
                 rho and sigma in m, column_density in m**-2
 
                 """
-                return (rho * np.exp(-rho ** 2 / sigma ** 2 / 2)
-                        * self._column_density(rho))
+                return (
+                    rho
+                    * np.exp(-(rho**2) / sigma**2 / 2)
+                    * self._column_density(rho)
+                )
 
-            sigma = aper.sigma.to_value('m')
+            sigma = aper.sigma.to_value("m")
             N, err = quad(f, 0, np.inf, args=(sigma,), epsabs=epsabs)
             N *= 2 * np.pi
             err *= 2 * np.pi
@@ -579,7 +597,7 @@ class Haser(GasComa):
 
     """
 
-    @bib.cite({'model': '1957BSRSL..43..740H'})
+    @bib.cite({"model": "1957BSRSL..43..740H"})
     @u.quantity_input(parent=u.m, daughter=u.m)
     def __init__(self, Q, v, parent, daughter=None):
         super().__init__(Q, v)
@@ -587,40 +605,46 @@ def __init__(self, Q, v, parent, daughter=None):
         self.daughter = daughter
 
     def _volume_density(self, r):
-        n = (self.Q / self.v).to_value('1/m') / r ** 2 / 4 / np.pi
-        parent = self.parent.to_value('m')
+        n = (self.Q / self.v).to_value("1/m") / r**2 / 4 / np.pi
+        parent = self.parent.to_value("m")
         if self.daughter is None or self.daughter == 0:
             # parent only
             n *= np.exp(-r / parent)
         else:
-            daughter = self.daughter.to_value('m')
-            n *= (daughter / (parent - daughter)
-                  * (np.exp(-r / parent) - np.exp(-r / daughter)))
+            daughter = self.daughter.to_value("m")
+            n *= (
+                daughter
+                / (parent - daughter)
+                * (np.exp(-r / parent) - np.exp(-r / daughter))
+            )
 
         return n
 
     def _iK0(self, x):
         """Integral of the modified Bessel function of 2nd kind, 0th order."""
         if not scipy:
-            raise RequiredPackageUnavailable('scipy')
+            raise RequiredPackageUnavailable("scipy")
         return special.iti0k0(x)[1]
 
     def _K1(self, x):
         """Modified Bessel function of 2nd kind, 1st order."""
         if not scipy:
-            raise RequiredPackageUnavailable('scipy')
+            raise RequiredPackageUnavailable("scipy")
         return special.k1(x)
 
-    @bib.cite({'model': '1978Icar...35..360N'})
+    @bib.cite({"model": "1978Icar...35..360N"})
     def _column_density(self, rho):
-        sigma = (self.Q / self.v).to_value('1/m') / rho / 2 / np.pi
-        parent = self.parent.to_value('m')
+        sigma = (self.Q / self.v).to_value("1/m") / rho / 2 / np.pi
+        parent = self.parent.to_value("m")
         if self.daughter is None or self.daughter == 0:
             sigma *= np.pi / 2 - self._iK0(rho / parent)
         else:
-            daughter = self.daughter.to_value('m')
-            sigma *= (daughter / (parent - daughter)
-                      * (self._iK0(rho / daughter) - self._iK0(rho / parent)))
+            daughter = self.daughter.to_value("m")
+            sigma *= (
+                daughter
+                / (parent - daughter)
+                * (self._iK0(rho / daughter) - self._iK0(rho / parent))
+            )
         return sigma
 
     def _total_number(self, aper):
@@ -633,7 +657,7 @@ def _total_number(self, aper):
             return N1 - N0
 
         # Solution for the circular aperture of radius rho:
-        bib.register(self.total_number, {'model': '1978Icar...35..360N'})
+        bib.register(self.total_number, {"model": "1978Icar...35..360N"})
 
         rho = aper.radius
         parent = self.parent.to(rho.unit)
@@ -643,97 +667,320 @@ def _total_number(self, aper):
             N *= 1 / x - self._K1(x) + np.pi / 2 - self._iK0(x)
         else:
             daughter = self.daughter.to(rho.unit)
-            y = (rho / daughter).to_value('')
-            N *= ((daughter / (parent - daughter)).to_value('')
-                  * (self._iK0(y) - self._iK0(x) + x ** -1 - y ** -1
-                     + self._K1(y) - self._K1(x)))
+            y = (rho / daughter).to_value("")
+            N *= (daughter / (parent - daughter)).to_value("") * (
+                self._iK0(y)
+                - self._iK0(x)
+                + x**-1
+                - y**-1
+                + self._K1(y)
+                - self._K1(x)
+            )
 
         return N
 
 
+@dataclass
+class VMParent:
+    """
+    Physical information about the parent necessary for the vectorial model
+
+    Parameters
+    ----------
+    v_outflow : float
+        See VectorialModel documentation.
+
+    tau_d : float
+        See VectorialModel documentation.
+
+    tau_T : float
+        See VectorialModel documentation.
+
+    sigma : float
+        See VectorialModel documentation.
+    """
+    v_outflow: float
+    tau_d: float
+    tau_T: float
+    sigma: float
+
+
+@dataclass
+class VMFragment:
+    """
+    Physical information about the fragment necessary for the vectorial model
+
+    Parameters
+    ----------
+    v_photo : float
+        See VectorialModel documentation.
+
+    tau_T : float
+        See VectorialModel documentation.
+    """
+    v_photo: float
+    tau_T: float
+
+
+@dataclass
+class VMGridParams:
+    """
+    Vectorial model gridding parameters to control how finely the space around
+    the comet should be gridded, and how detailed the outflow sampling should
+    be.
+
+    Parameters
+    ----------
+    radial_points : int
+        See VectorialModel documentation.
+
+    angular_points : int
+        See VectorialModel documentation.
+
+    radial_substeps : int
+        See VectorialModel documentation.
+    """
+    radial_points: int
+    angular_points: int
+    radial_substeps: int
+
+
+@dataclass
+class VMParams:
+    """
+    Vectorial model parameters unrelated to physical inputs, dealing with the
+    model's assumptions and detalis of the calculations.
+
+    Parameters
+    ----------
+    parent_destrution_level : float
+        See VectorialModel documentation.
+
+    fragment_destruction_level : float
+        See VectorialModel documentation.
+
+    max_fragment_lifetimes : float
+        See VectorialModel documentation.
+    """
+    parent_destruction_level: float
+    fragment_destruction_level: float
+    max_fragment_lifetimes: float
+
+
+@dataclass
+class VMFragmentSputterPolar:
+    """
+    Describes the distribution of fragment volume density
+    (``fragment_density[i][j]``) as function of (``rs[i]``, ``thetas[j]``) in a
+    spherical coordinate system, given a column of parent molecules flowing
+    outward along the azimuthal (z) axis.
+
+    Parameters
+    ----------
+    rs : `np.ndarray`
+        List of radii (`astropy.units.Quantity`).
+
+    thetas: `np.ndarray`
+        List of polar angles theta (radians) in a spherical coordinate system.
+
+    fragment_density: `np.ndarray`
+        List of fragment densities at the corresponding ``rs[i]`` and
+        ``thetas[j]``.
+    """
+    rs: np.ndarray
+    thetas: np.ndarray
+    fragment_density: np.ndarray
+
+
+@dataclass
+class VMResult:
+    """
+    Dataclass to hold a collection of vectorial model details and results in a
+    language- and model-independent way.
+
+    Parameters
+    ----------
+    volume_density_grid : `np.ndarray`
+        List of radii (`~astropy.units.Quantity`) that the model used for
+        volume density calculations.
+
+    volume_density : `np.ndarray`
+        List of volume densities (`~astropy.units.Quantity`) computed at the
+        corresponding radius: at radius = ``volume_density_grid[i]``, the volume
+        density is ``volume_density[i]``.
+
+    column_density_grid : `np.ndarray`
+        List of radii (`~astropy.units.Quantity`) that the model used for
+        column density calculations.
+
+    column_density : `np.ndarray`
+        List of column densities (`~astropy.units.Quantity`), computed at the
+        corresponding radius: at radius = ``column_density_grid[i]``, the column
+        density is ``column_density[i]``.
+
+    fragment_sputter : `VMFragmentSputterPolar`
+        Describes the distribution of fragment volume density as function of
+        (r, theta) in a spherical coordinate system, given a column of parent
+        molecules flowing outward along the azimuthal (z) axis.
+
+    solid_angle_sputter : `VMFragmentSputterPolar`
+        Similar to ``fragment_sputter``, but adjusted by a factor of
+        ``sin(theta)``.
+
+    volume_density_interpolation : `scipy.interpolate.PPoly`
+        Function that takes radius as a float in meters (no astropy units) and
+        returns the volume density in 1/m^3.  Not reliable beyond
+        ``max_grid_radius``, and questionable for radii less than
+        ``collision_sphere_radius``.
+
+    column_density_interpolation : `scipi.interpolate.PPoly`
+        Function that takes radius as a float in meters (no astropy units) and
+        returns the column density in 1/m^2.  Not relaible beyond
+        ``max_grid_radius``, and questionable for radii less than
+        ``collision_sphere_radius``.
+
+    collision_sphere_radius : `astropy.units.Quantity`
+        The radius of the collision sphere as described and calculated in
+        Festou (1981): the radius beyond which a molecule can expect to see one
+        collision over its lifetime.
+
+    max_grid_radius : `astropy.units.Quantity`
+        Cutoff radius for the model calculations.  Attempting to take results
+        from the model beyond this radius will be wrong or unreliable at best.
+
+    coma_radius : `astropy.units.Quantity`
+        Cutoff radius beyond which we take the parents to be entirely
+        dissociated.
+
+    num_fragments_theory : `float`
+        Total number of fragments that we expect based on a steady-state
+        calculation.  Unreliable when time dependence is strong.
+
+    num_fragments_grid : `float`
+        Total number of fragments based on the actual results of the model.
+        Agreement with ``num_fragments_theory`` is generally very good in the
+        steady-state case.
+
+    t_perm_flow : `float`
+        Time for the comet to reach a steady state/permanent flow regime, where
+        the number of fragments produced is equal to the number of fragments
+        lost to dissociation.  In a time-dependent production context, this
+        will also measure how long the effects of a single outburst can affect
+        the density.
+    """
+    volume_density_grid: np.ndarray = None
+    volume_density: np.ndarray = None
+    column_density_grid: np.ndarray = None
+    column_density: np.ndarray = None
+
+    fragment_sputter: VMFragmentSputterPolar = None
+    solid_angle_sputter: VMFragmentSputterPolar = None
+
+    volume_density_interpolation: scipy.interpolate.PPoly = None
+    column_density_interpolation: scipy.interpolate.PPoly = None
+
+    collision_sphere_radius: u.Quantity = None
+    max_grid_radius: u.Quantity = None
+    coma_radius: u.Quantity = None
+
+    num_fragments_theory: float = None
+    num_fragments_grid: float = None
+    t_perm_flow: u.Quantity = None
+
+
 class VectorialModel(GasComa):
-    """ Vectorial model for fragments in a coma produced
-         with a dissociative energy kick
+    """
+    Vectorial model for fragments in a coma produced with a dissociative energy
+    kick.
 
-        Parameters
-        ----------
-        base_q : `~astropy.units.Quantity`
-            Base production rate, per time
 
-        parent: `~sbpy.data.Phys`
-            Object with the following physical property fields:
-                * ``tau_T``: total lifetime of the parent molecule
-                * ``tau_d``: photodissociative lifetime of the parent molecule
-                * ``v_outflow``: outflow velocity of the parent molecule
-                * ``sigma``: cross-sectional area of the parent molecule
+    Parameters
+    ----------
+    base_q : `~astropy.units.Quantity`
+        Base production rate, per time
+
+    parent: `~sbpy.data.Phys`
+        Object with the following physical property fields:
+            * ``tau_T``: total lifetime of the parent molecule
+            * ``tau_d``: photodissociative lifetime of the parent molecule
+            * ``v_outflow``: outflow velocity of the parent molecule
+            * ``sigma``: cross-sectional area of the parent molecule
+
+    fragment: `~sbpy.data.Phys`
+        Object with the following physical property fields:
+            * ``tau_T``: total lifetime of the fragment molecule
+            * ``v_photo``: velocity of fragment resulting from
+                photodissociation of the parent
+
+    q_t: callable, optional
+        Calculates the parent production rate as a function of time: ``q_t(t)``.
+        The argument ``t`` is the look-back time as a float in units of seconds.
+        The return value is the production rate in units of inverse seconds.  If
+        provided, this value is added to ``base_q``.
+
+        If no time-dependence function is given, the model will run with steady
+        production at ``base_q`` stretching infinitely far into the past.
+
+    radial_points: int, optional
+        Number of radial grid points the model will use
+
+    radial_substeps: int, optional
+        Number of points along the outflow axis to integrate over
+
+    angular_points: int, optional
+        Number of angular grid points the model will use
+
+    parent_destruction_level: float, optional
+        Model will attempt to track parents until this percentage has
+        dissociated
+
+    fragment_destruction_level: float, optional
+        Model will attempt to track fragments until this percentage has
+        dissociated
+
+    max_fragment_lifetimes: float, optional
+        Fragments traveling through the coma will be ignored if they take longer
+        than this to arrive and contribute to the density at any considered
+        point.
+
+    print_progress: bool, optional
+        Print progress while calculating.
+
+
+    References
+    ----------
+    The density distribution of neutral compounds in cometary atmospheres. I -
+    Models and equations, Festou, M. C. 1981, Astronomy and Astrophysics, vol.
+    95, no. 1, Feb. 1981, p. 69-79.
 
-        fragment: `~sbpy.data.Phys`
-            Object with the following physical property fields:
-                * ``tau_T``: total lifetime of the fragment molecule
-                * ``v_photo``: velocity of fragment resulting from
-                    photodissociation of the parent
-
-        q_t: callable, optional
-            Calculates the parent production rate as a function of time:
-            ``q_t(t)``. The argument ``t`` is the look-back time as a float
-            in units of seconds.  The return value is the production rate in
-            units of inverse seconds.  If provided, this value is added to
-            ``base_q``.
-
-            If no time-dependence function is given, the model will run with
-            steady production at ``base_q`` stretching infinitely far into the
-            past.
-
-        radial_points: int, optional
-            Number of radial grid points the model will use
-
-        radial_substeps: int, optional
-            Number of points along the contributing axis to integrate over
-
-        angular_points: int, optional
-            Number of angular grid points the model will use
-
-        angular_substeps: int, optional
-            Number of angular steps per radial substep to integrate over
-
-        parent_destruction_level: float, optional
-            Model will attempt to track parents until
-            this percentage has dissociated
-
-        fragment_destruction_level: float, optional
-            Model will attempt to track fragments until
-            this percentage has dissociated
-
-        max_fragment_lifetimes: float, optional
-            Fragments traveling through the coma will be ignored if they take
-            longer than this to arrive and contribute to the density at any
-            considered point
-
-        print_progress: bool, optional
-            Print progress percentage while calculating
-
-        References:
-            The density distribution of neutral compounds in cometary
-            atmospheres. I - Models and equations,
-            Festou, M. C. 1981, Astronomy and Astrophysics, vol. 95, no. 1,
-            Feb. 1981, p. 69-79.
     """
-    @bib.cite({'model': '1981A&A....95...69F'})
-    @u.quantity_input(base_q=(u.s ** -1, u.mol / u.s))
-    def __init__(self, base_q, parent, fragment, q_t=None, radial_points=50,
-                 radial_substeps=12, angular_points=30, angular_substeps=7,
-                 parent_destruction_level=0.99,
-                 fragment_destruction_level=0.95,
-                 max_fragment_lifetimes=8.0,
-                 print_progress=False):
-
-        warnings.warn("Literature tests with the Vectorial model are generally"
-                      " in agreement at the 20% level or better.  The cause"
-                      " for the differences with the Festou FORTRAN code are"
-                      " not yet precisely known.  Help testing this feature is"
-                      " appreciated.", TestingNeeded)
-
-        super().__init__(base_q, parent['v_outflow'][0])
+
+    @bib.cite({"model": "1981A&A....95...69F"})
+    @u.quantity_input(base_q=(u.s**-1, u.mol / u.s))
+    def __init__(
+        self,
+        base_q,
+        parent,
+        fragment,
+        q_t=None,
+        radial_points=50,
+        radial_substeps=80,
+        angular_points=30,
+        parent_destruction_level=0.99,
+        fragment_destruction_level=0.95,
+        max_fragment_lifetimes=8.0,
+        print_progress=False,
+    ):
+        # warnings.warn(
+        #     "Literature tests with the Vectorial model are generally"
+        #     " in agreement at the 20% level or better.  The cause"
+        #     " for the differences with the Festou FORTRAN code are"
+        #     " not yet precisely known.  Help testing this feature is"
+        #     " appreciated.",
+        #     TestingNeeded,
+        # )
+
+        super().__init__(base_q, parent["v_outflow"][0])
 
         # Calculations are done internally in meters and seconds to match the
         # base GasComa class
@@ -749,160 +996,208 @@ def __init__(self, base_q, parent, fragment, q_t=None, radial_points=50,
 
         # Copy parent info, stripping astropy units and converting to meters
         # and seconds
-        self.parent = {
-            'tau_T': parent['tau_T'][0].to(u.s).value,
-            'tau_d': parent['tau_d'][0].to(u.s).value,
-            'v_outflow': parent['v_outflow'][0].to(u.m / u.s).value,
-            'sigma': parent['sigma'][0].to(u.m ** 2).value
-        }
+        self.parent = VMParent(
+            tau_T=parent["tau_T"][0].to(u.s).value,
+            tau_d=parent["tau_d"][0].to(u.s).value,
+            v_outflow=parent["v_outflow"][0].to(u.m / u.s).value,
+            sigma=parent["sigma"][0].to(u.m**2).value,
+        )
 
         # Same for the fragment info
-        self.fragment = {
-            'tau_T': fragment['tau_T'][0].to(u.s).value,
-            'v_photo': fragment['v_photo'][0].to(u.m / u.s).value
-        }
+        self.fragment = VMFragment(
+            tau_T=fragment["tau_T"][0].to(u.s).value,
+            v_photo=fragment["v_photo"][0].to(u.m / u.s).value,
+        )
 
         # Grid settings
-        self.radial_points = radial_points
-        self.radial_substeps = radial_substeps
-        self.angular_points = angular_points
-        self.angular_substeps = angular_substeps
-
-        # Helps define cutoff for radial grid at this percentage of parents
-        # lost to decay
-        self.parent_destruction_level = parent_destruction_level
-        # Default here is lower than parents because they are born farther from
-        # nucleus, tracking them too long will stretch the radial grid a bit
-        # too much
-        self.fragment_destruction_level = fragment_destruction_level
-
-        # If a fragment has to travel longer than this many lifetimes to
-        # contribute to the density at a point, ignore it
-        self.max_fragment_lifetimes = max_fragment_lifetimes
-
-        # Print progress during density calculations?
+        self.grid = VMGridParams(
+            radial_points=radial_points,
+            radial_substeps=radial_substeps,
+            angular_points=angular_points,
+        )
+
+        self.model_params = VMParams(
+            # Helps define cutoff for radial grid at this percentage of parents
+            # lost to decay
+            parent_destruction_level=parent_destruction_level,
+            # Default here is lower than parents because they are born farther
+            # from nucleus, tracking them too long will stretch the radial grid
+            # a bit too much
+            fragment_destruction_level=fragment_destruction_level,
+            # If a fragment has to travel longer than this many lifetimes to
+            # contribute to the density at a point, ignore it
+            max_fragment_lifetimes=max_fragment_lifetimes,
+        )
+
         self.print_progress = print_progress
 
-        """Initialize data structures to hold our calculations"""
-        self.vmodel = {}
+        self.vmr = VMResult()
 
         # Calculate up a few things
         self._setup_calculations()
 
         # Build the radial grid
-        self.vmodel['fast_radial_grid'] = self._make_radial_logspace_grid()
-        self.vmodel['radial_grid'] = self.vmodel['fast_radial_grid'] * (u.m)
+        self.fast_voldens_grid = self._make_radial_logspace_grid()
+        self.vmr.volume_density_grid = self.fast_voldens_grid * (u.m)
 
         # Angular grid
-        self.vmodel['d_alpha'] = self.vmodel[
-            'epsilon_max'] / self.angular_points
+        self.d_alpha = self.epsilon_max / self.grid.angular_points
         # Make array of angles adjusted up away from zero, to keep from
-        # calculating a radial line's contribution to itself
-        self.vmodel['angular_grid'] = np.linspace(
-            0, self.vmodel['epsilon_max'], num=self.angular_points,
-            endpoint=False
+        # encroaching on the outflow axis
+        self.angular_grid = np.linspace(
+            0, self.epsilon_max, num=self.grid.angular_points, endpoint=False
         )
-        # This maps addition over the whole array automatically
-        self.vmodel['angular_grid'] += self.vmodel['d_alpha'] / 2
+        self.angular_grid += self.d_alpha / 2
 
         # Makes a 2d array full of zero values
-        self.vmodel['density_grid'] = np.zeros((self.radial_points,
-                                                self.angular_points))
+        self.fragment_sputter = np.zeros(
+            (self.grid.radial_points, self.grid.angular_points)
+        )
 
         # Do the main computation
         self._compute_fragment_density()
 
-        # Turn our grid into a function of r
-        self._interpolate_radial_density()
+        # Turn our volume density into a function of r
+        self._interpolate_volume_density()
 
         # Get the column density at our grid points and interpolate
         self._compute_column_density()
 
         # Count up the number of fragments in the grid versus theoretical value
-        self.vmodel['num_fragments_theory'] = self.calc_num_fragments_theory()
-        self.vmodel['num_fragments_grid'] = self.calc_num_fragments_grid()
+        self.vmr.num_fragments_theory = self._calc_num_fragments_theory()
+        self.vmr.num_fragments_grid = self._calc_num_fragments_grid()
+
+        # Convert fragment sputters to group of one-dimensional arrays
+        # of the form r_i, theta_i, fragment_density_i
+        sputterlist = []
+        for (i, j), frag_dens in np.ndenumerate(self.fragment_sputter):
+            sputterlist.append(
+                [
+                    self.vmr.volume_density_grid[i].to(u.m).value,
+                    self.angular_grid[j],
+                    frag_dens,
+                ]
+            )
+        sputter = np.array(sputterlist)
+        # fill in the fragment sputter results
+        self.vmr.fragment_sputter = VMFragmentSputterPolar(
+            rs=sputter[:, 0] * u.m,
+            thetas=sputter[:, 1],
+            fragment_density=sputter[:, 2] / u.m**3,
+        )
+        normsputterlist = []
+        for (i, j), norm_dens in np.ndenumerate(self.solid_angle_sputter):
+            normsputterlist.append(
+                [
+                    self.vmr.volume_density_grid[i].to(u.m).value,
+                    self.angular_grid[j],
+                    norm_dens,
+                ]
+            )
+        normsputter = np.array(normsputterlist)
+        self.vmr.solid_angle_sputter = VMFragmentSputterPolar(
+            rs=normsputter[:, 0] * u.m,
+            thetas=normsputter[:, 1],
+            fragment_density=normsputter[:, 2] / u.m**3,
+        )
+
+        if print_progress:
+            print("Vectorial model calculations complete!")
 
     @classmethod
     def binned_production(cls, qs, fragment, parent, ts, **kwargs):
-        """ Alternate constructor for vectorial model
-
-            Parameters
-            ----------
-            qs : `~astropy.units.Quantity`
-                List of steady production rates, per time, with length equal to
-                that of ``ts``.
-
-            parent: `~sbpy.data.Phys`
-                Same as __init__
-
-            fragment: `~sbpy.data.Phys`
-                Same as __init__
-
-            ts : `~astropy.units.Quantity`
-                List of times corresponding to when the production qs begin,
-                with positive times indicating the past.
-
-            kwargs: variable, optional
-                Any additional parameters in kwargs are passed on to __init__,
-                which are documented above and may be passed in here.
-
-            Returns
-            -------
-            VectorialModel
-                Instance of the VectorialModel class
-
-            Examples
-            --------
-            This specifies that from 30 days ago to 7 days ago, the production
-            was 1.e27, changes to 3.e27 between 7 and 5 days ago, then falls to
-            2.e27 from 5 days ago until now
-            >>> q_example = [1.e27, 3.e27, 1.e27] * (1/u.s)
-            >>> t_example = [30, 7, 5] * u.day
-
-            Notes
-            -----
-            Preserves Festou's original fortran method of describing time
-            dependence in the model - time bins of steady production at
-            specified intervals
-
-            The base production of the model is taken from the first element in
-            the production array, which assumes the arrays are time-ordered
-            from oldest to most recent.  The base production extends backward
-            in time to infinity, so take care when using this method for time
-            dependence if that is not what is intended.
+        """Alternate constructor for vectorial model
+
+
+        Parameters
+        ----------
+        qs : `~astropy.units.Quantity`
+            List of steady production rates, per time, with length equal to that
+            of ``ts``.
+
+        parent: `~sbpy.data.Phys`
+            Same as __init__
+
+        fragment: `~sbpy.data.Phys`
+            Same as __init__
+
+        ts : `~astropy.units.Quantity`
+            List of times corresponding to when the production qs begin, with
+            positive times indicating the past.
+
+        kwargs: variable, optional
+            Any additional parameters in kwargs are passed on to __init__, which
+            are documented above and may be passed in here.
+
+
+        Returns
+        -------
+        VectorialModel
+            Instance of the VectorialModel class
+
+
+        Examples
+        --------
+        This specifies that from 30 days ago to 7 days ago, the production was
+        1.e27, changes to 3.e27 between 7 and 5 days ago, then falls to 2.e27
+        from 5 days ago until now: >>> q_example = [1.e27, 3.e27, 1.e27] *
+        (1/u.s) >>> t_example = [30, 7, 5] * u.day
+
+
+        Notes
+        -----
+        Preserves Festou's original fortran method of describing time dependence
+        in the model - time bins of steady production at specified intervals.
+
+        The base production of the model is taken from the first element in the
+        production array, which assumes the arrays are time-ordered from oldest
+        to most recent.  The base production extends backward in time to
+        infinity, so take care when using this method for time dependence if
+        that is not what is intended.
+
         """
-        return cls(base_q=qs[0],
-                   q_t=VectorialModel._make_binned_production(qs, ts),
-                   fragment=fragment, parent=parent, **kwargs)
-
-    def _make_binned_production(qs, ts):
-        """ Produces a time dependence function out of lists given to
-            binned_production constructor
-
-            Parameters
-            ----------
-            qs : `astropy.units.Quantity`
-                See binned_production for description
-
-            ts : `astropy.units.Quantity`
-                See binned_production for description
-
-            Returns
-            -------
-            q_t : function
-                See __init__ for description
-
-            Notes
-            -----
-            We create a model-compatible function for time dependence out of
-            the information specified in the arrays qs and ts
-            The resulting function gives a steady specified production within
-            the given time windows
+
+        return cls(
+            base_q=qs[0],
+            q_t=VectorialModel._make_binned_production(qs, ts),
+            fragment=fragment,
+            parent=parent,
+            **kwargs
+        )
+
+    def _make_binned_production(qs, ts) -> Callable[[np.float64], np.float64]:
+        """Produces a time dependence function out of lists given to
+        binned_production constructor.
+
+
+        Parameters
+        ----------
+        qs : `astropy.units.Quantity`
+            See binned_production for description
+
+        ts : `astropy.units.Quantity`
+            See binned_production for description
+
+
+        Returns
+        -------
+        q_t : function
+            See __init__ for description
+
+
+        Notes
+        -----
+        We create a model-compatible function for time dependence out of the
+        information specified in the arrays qs and ts The resulting function
+        gives a steady specified production within the given time windows.
+
         """
 
-        q_invsecs = qs.to(1 / (u.s)).value
-        t_at_p_secs = ts.to(u.s).value
         base_q = qs[0]
+        q_variations = qs - base_q
+
+        q_invsecs = q_variations.to_value(1 / (u.s))
+        t_at_p_secs = ts.to_value(u.s)
 
         # extend the arrays to simplify our comparisons in binned_q_t
         # last bin stops at observation time, t = 0
@@ -910,47 +1205,59 @@ def _make_binned_production(qs, ts):
         # junk value for production because this is in the future
         q_invsecs = np.append(q_invsecs, [0])
 
+        # this function represents the deviation from base_q at any time t, so
+        # we need to handle times outside of the range specified in 'ts'
         def q_t(t):
             # too long in the past?
-            if t > t_at_p_secs[0] or t < 0:
-                return base_q
+            if t > t_at_p_secs[0]:
+                return 0
             # in the future?
             if t < 0:
                 return 0
 
-            # loop over all elements except the last so we can always look at
-            # [index+1] for the comparison
+            # find which bin the given time falls in, and return the
+            # corresponding production for that interval
             for i in range(len(q_invsecs) - 1):
                 if t < t_at_p_secs[i] and t > t_at_p_secs[i + 1]:
                     return q_invsecs[i]
 
         return q_t
 
-    def _make_steady_production(self):
-        """ Produces a time dependence function that contributes no extra
-            parents at any time
+    def _make_steady_production(self) -> Callable[[np.float64], np.float64]:
+        """Produces a time dependence function that contributes no extra
+        parents at any time.
+
+
+        Returns
+        -------
+        q_t : function
+            See __init__ for description
+
+
+        Notes
+        -----
+        If no q_t is given, we use this as our time dependence as the
+        model needs a q_t to run
 
-            Notes
-            -----
-            If no q_t is given, we use this as our time dependence as the
-            model needs a q_t to run
         """
+
         # No additional production at any time
-        def q_t(t):
-            return 0
+        def q_t(_):
+            return 0.0
 
         return q_t
 
-    def _setup_calculations(self):
-        """ Miscellaneus calculations to inform the model later
+    def _setup_calculations(self) -> None:
+        """Miscellaneous calculations to inform the model later.
+
+        Notes
+        -----
+        Calculates the collision sphere radius, coma radius, time to
+        permanent flow regime, the maximum radius our grid could possibly
+        need to extend out to, and the maximum angle that a fragment's
+        trajectory can deviate from its parent's trajectory (which is
+        assumed to be radial).
 
-            Notes
-            -----
-            Calculates the collision sphere radius, coma radius, time to
-            permanent flow regime, the maximum radius our grid could possibly
-            need to extend out to, and the maximum angle that a fragment's
-            trajectory can deviate from its parent's trajectory (which is
-            assumed to be radial)
         """
 
         """
@@ -962,544 +1269,566 @@ def _setup_calculations(self):
             enough time to reach a steady state before letting production vary
             with time.
         """
+
+        if self.print_progress:
+            print("Performing setup calculations...")
+
         # This v_therm factor comes from molecular flux of ideal gas moving
         # through a surface, in our case the surface of the collision sphere
         # The gas is collisional inside this sphere and free beyond it, so we
         # can model this as effusion
-        v_therm = self.parent['v_outflow'] * 0.25
+        v_therm = self.parent.v_outflow * 0.25
         q = self.base_q
-        vp = self.parent['v_outflow']
-        vf = self.fragment['v_photo']
+        vp = self.parent.v_outflow
+        vf = self.fragment.v_photo
 
         # Eq. 5 of Festou 1981
-        self.vmodel['collision_sphere_radius'] = (
-            (self.parent['sigma'] * q * v_therm) / (vp * vp)
+        self.vmr.collision_sphere_radius = (
+            (self.parent.sigma * q * v_therm) / (vp * vp)
         ) * u.m
 
-        # Calculates the radius of the coma (parents only), given our input
-        # parameters
+        # Calculates the radius of the coma (defined as the space the parents
+        # occupy)
         # NOTE: Equation (16) of Festou 1981 where alpha is the percent
         # destruction of molecules
-        parent_beta_r = -np.log(1.0 - self.parent_destruction_level)
-        parent_r = parent_beta_r * vp * self.parent['tau_T']
-        self.vmodel['coma_radius'] = parent_r * u.m
+        parent_beta_r = -np.log(1.0 - self.model_params.parent_destruction_level)
+        parent_r = parent_beta_r * vp * self.parent.tau_T
+        self.vmr.coma_radius = parent_r * u.m
 
-        # Calculates the time needed to hit a steady, permanent production of
+        fragment_beta_r = -np.log(1.0 - self.model_params.fragment_destruction_level)
+        # Calculate the time needed to hit a steady, permanent production of
         # fragments
-        fragment_beta_r = -np.log(1.0 - self.fragment_destruction_level)
-
-        perm_flow_radius = (
-            self.vmodel['coma_radius'].value +
-            ((vp + vf) * fragment_beta_r * self.fragment['tau_T'])
+        perm_flow_radius = self.vmr.coma_radius.value + (
+            (vp + vf) * fragment_beta_r * self.fragment.tau_T
         )
 
-        t_secs = (
-            self.vmodel['coma_radius'].value / vp +
-            (perm_flow_radius - self.vmodel['coma_radius'].value)
-            / (vp + vf)
-        )
-        self.vmodel['t_perm_flow'] = (t_secs * u.s).to(u.day)
+        t_secs = self.vmr.coma_radius.value / vp + (
+            perm_flow_radius - self.vmr.coma_radius.value
+        ) / (vp + vf)
+        self.vmr.t_perm_flow = (t_secs * u.s).to(u.day)
 
         # This is the total radial size that parents & fragments occupy, beyond
         # which we assume zero density
-        self.vmodel['max_grid_radius'] = perm_flow_radius * u.m
+        self.vmr.max_grid_radius = perm_flow_radius * u.m
 
         # Two cases for angular range of ejection of fragment based on relative
         # velocities of parent and fragment species
-        if(vf < vp):
-            self.vmodel['epsilon_max'] = np.arcsin(vf / vp)
+        if vf < vp:
+            self.epsilon_max = np.arcsin(vf / vp)
         else:
-            self.vmodel['epsilon_max'] = np.pi
+            self.epsilon_max = np.pi
+
+    def production_at_time(self, t: np.float64) -> np.float64:
+        """Get production rate at time t.
+
 
-    def production_at_time(self, t):
-        """ Get production rate at time t
+        Parameters
+        ----------
+        t : numpy.float64
+            Time in seconds, with positive values representing the past
 
-            Parameters
-            ----------
-            t : float
-                Time in seconds, with positive values representing the past
 
-            Returns
-            -------
-            numpy.float64
-                Production rate, unitless, at the specified time
+        Returns
+        -------
+        numpy.float64
+            Production rate, unitless, at the specified time
 
         """
 
         return self.base_q + self.q_t(t)
 
-    def _make_radial_logspace_grid(self):
-        """ Create an appropriate radial grid based on the model parameters
-
-            Returns
-            -------
-            ndarray
-                Logarithmically spaced samples of the radial space around the
-                coma, out to a maximum distance
-
-            Notes
-            -----
-            Creates a grid (in meters) with numpy's logspace function that
-            covers the expected radial size, stretching from 2 times the
-            collision sphere radius (near the nucleus be dragons) out to the
-            calculated max.  If we get too close to the nucleus things go very
-            badly so don't do it, dear reader
+    def _make_radial_logspace_grid(self) -> np.ndarray:
+        """Create an appropriate radial grid based on the model parameters.
+
+
+        Returns
+        -------
+        ndarray
+            Logarithmically spaced samples of the radial space around the coma,
+            out to a maximum distance.
+
+
+        Notes
+        -----
+        Creates a grid (in meters) with numpy's logspace function that covers
+        the expected radial size, stretching from 2 times the collision sphere
+        radius (near the nucleus be dragons) out to the calculated max.  If we
+        get too close to the nucleus things go very badly so don't do it, dear
+        reader.
+
         """
-        start_power = np.log10(
-            self.vmodel['collision_sphere_radius'].value * 2
-        )
-        end_power = np.log10(self.vmodel['max_grid_radius'].value)
+
+        start_power = np.log10(self.vmr.collision_sphere_radius.value * 2)
+        end_power = np.log10(self.vmr.max_grid_radius.value)
         return np.logspace(
-            start_power, end_power,
-            num=self.radial_points, endpoint=True
+            start_power, end_power, num=self.grid.radial_points, endpoint=True
         )
 
-    def _compute_fragment_density(self):
-        """ Computes the density of fragments as a function of radius
-
-            Notes
-            -----
-            Computes the density at different radii and due to each ejection
-            angle, performing the radial integration of eq. (36), Festou 1981
-            with only one fragment velocity.  The resulting units will be in
-            1/(m^3) as we work in m, s, and m/s.
-
-            The density is first found on a radial grid, then interpolated to
-            find density as a function of arbitrary radius.  We use our results
-            from the grid to calculate the total number of fragments in the
-            coma for comparison to the theoretical number we expect, to provide
-            the user with a rough idea of how well the chosen radial and
-            angular grid sizes have captured the appropriate amount of
-            particles.  Note that some level of disagreement is expected
-            because the parent_destruction_level and fragment_destruction_level
-            parameters cut the grid off before all particles can dissociate,
-            and thus some escape the model and come up missing in the fragment
-            count based on the grid.
-        """
-        vp = self.parent['v_outflow']
-        vf = self.fragment['v_photo']
+    def _outflow_axis_sampling(
+        self, x: np.float64, y: np.float64, theta: np.float64
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """Construct a list of points along the outflow axis, sampled to be
+        more dense around the minimum distance to (x, y).
 
-        # Follow fragments until they have been totally destroyed
-        time_limit = self.max_fragment_lifetimes * self.fragment['tau_T']
-        r_coma = self.vmodel['coma_radius'].value
-        r_limit = r_coma
 
-        # temporary radial array for when we loop through 0 to epsilonMax
-        ejection_radii = np.zeros(self.radial_substeps)
+        Parameters
+        ----------
+        x : numpy.float64
+            x-coordinate of the space where we are calculating the fragment
+            density
 
-        p_tau_T = self.parent['tau_T']
-        f_tau_T = self.fragment['tau_T']
-        p_tau_d = self.parent['tau_d']
+        y : numpy.float64
+            y-coordinate of the space where we are calculating the fragment
+            density
 
-        # Compute this once ahead of time
-        # More factors to fill out integral similar to eq. (36) Festou 1981
-        integration_factor = (
-            (1 / (4 * np.pi * p_tau_d)) *
-            self.vmodel['d_alpha'] / (4.0 * np.pi)
+        theta: numpy.float64
+            polar spherical coordinate of (x, y) to avoid calculating it from
+            (x, y)
+
+
+        Returns
+        -------
+        tuple(numpy.ndarray, numpy.ndarray)
+            Tuple with list of ejection sites in the first index and their
+            extents in the second index
+
+
+        Notes
+        -----
+        Returns list of radial points along outflow axis to sample for fragment
+        ejection, with a list describing the size dr that each ejection point
+        occupies along the outflow axis.
+
+        """
+
+        outflow_axis_edge = self.vmr.coma_radius.value
+
+        # calc angle to edge of the grid, and use it if it's less than
+        # epsilon max.  without this, when epsilon_max approaches pi we
+        # get values of r outside the edge of the grid
+        grid_edge_angle = np.arctan2(x, (y - outflow_axis_edge))
+        if grid_edge_angle < self.epsilon_max:
+            max_subangle = grid_edge_angle
+        else:
+            max_subangle = self.epsilon_max
+
+        # Trace angles from our grid point at x, y and find where dissociations
+        # would have to occur along the outflow axis. This samples shorter
+        # trajectories more finely along the axis, which is necessary because
+        # the exponential decay along their transit means they contribute the
+        # most to the fragment density
+        ces = np.vectorize(lambda epsilon: y - x / np.tan(epsilon))
+
+        # array one element larger than radial_substeps because we will
+        # use the midpoints defined by this array
+        subangles = np.linspace(
+            theta, max_subangle, num=self.grid.radial_substeps + 1, endpoint=True
         )
 
-        # Calculate the density contributions over the volume of the comet
-        # atmosphere due to one ejection axis
-        for j in range(0, self.angular_points):
-            cur_angle = self.vmodel['angular_grid'][j]
-            # Loop through the radial points along this axis
-            for i in range(0, self.radial_points):
-
-                cur_r = self.vmodel['fast_radial_grid'][i]
-                x = cur_r * np.sin(cur_angle)
-                y = cur_r * np.cos(cur_angle)
-
-                # Decide how granular our epsilon should be
-                d_epsilon_steps = len(ejection_radii)
-                d_epsilon = (
-                            (self.vmodel['epsilon_max'] - cur_angle)
-                    / d_epsilon_steps
-                )
+        # Find where this set of angles intersects the outflow axis
+        ejection_grid = ces(subangles)
+        # Find dr for radial integration
+        drs = np.diff(ejection_grid)
+
+        # This puts the sampling of the ejection sites at the midpoints
+        # of our ejection grid
+        ejection_sites = (ejection_grid[1:] + ejection_grid[:-1]) / 2
 
-                # Maximum radius that contributes to point x,y when there is a
-                # a max ejection angle
-                if(self.vmodel['epsilon_max'] < np.pi):
-                    r_limit = y - (x / np.tan(self.vmodel['epsilon_max']))
-                # Set the last element to be r_coma or the above limit
-                ejection_radii[d_epsilon_steps - 1] = r_limit
-
-                # We already filled out the very last element in the array
-                # above, so it's d_epsilon_steps - 1
-                for dE in range(0, d_epsilon_steps - 1):
-                    ejection_radii[dE] = (
-                        y -
-                        x / np.tan((dE + 1) * d_epsilon + cur_angle)
-                    )
-
-                ejection_radii_start = 0
-                # Number of slices along the contributing axis for each step
-                num_slices = self.angular_substeps
-
-                # Loop over radial chunk that contributes to x,y
-                for ejection_radii_end in ejection_radii:
-
-                    # We are slicing up this axis into pieces
-                    dr = (
-                         (ejection_radii_end - ejection_radii_start) /
-                        num_slices
-                    )
-
-                    # Loop over tiny slices along this chunk
-                    for m in range(0, num_slices):
-
-                        # TODO: We could probably eliminate m by making a
-                        # linear space from ejection_radii_start to
-                        # ejection_radii_end
-
-                        # Current distance along contributing axis
-                        cur_r = (m + 0.5) * dr + ejection_radii_start
-                        # This is the distance from the NP axis point to the
-                        # current point on the ray, squared
-                        sep_dist = np.sqrt(x * x + (cur_r - y) * (cur_r - y))
-
-                        cos_eject = (y - cur_r) / sep_dist
-                        sin_eject = x / sep_dist
-
-                        # Calculate sqrt(vR^2 - u^2 sin^2 gamma)
-                        v_factor = np.sqrt(
-                            vf * vf - (vp * vp) * sin_eject ** 2)
-
-                        # The first (and largest) of the two solutions for the
-                        # velocity when it arrives
-                        v_one = vp * cos_eject + v_factor
-
-                        # Time taken to travel from the dissociation point at
-                        # v1, reject if the time is too large (and all
-                        # fragments have decayed)
-                        t_frag_one = sep_dist / v_one
-                        if t_frag_one > time_limit:
-                            continue
-
-                        # This is the total time between parent emission from
-                        # nucleus and fragment arriving at our point of
-                        # interest, which we then use to look up Q at that time
-                        # in the past
-                        t_total_one = (cur_r / vp) + t_frag_one
-
-                        # Division by parent velocity makes this production per
-                        # unit distance for radial integration q(r, epsilon)
-                        # given by eq. 32, Festou 1981
-                        prod_one = self.production_at_time(t_total_one) / vp
-                        q_r_eps_one = (
-                            (v_one * v_one * prod_one) /
-                            (vf * np.abs(v_one - vp * cos_eject))
-                        )
-
-                        # Parent extinction when traveling along to the
-                        # dissociation site
-                        p_extinction = np.e ** (-cur_r / (p_tau_T * vp))
-                        # Fragment extinction when traveling at speed v1
-                        f_extinction_one = np.e ** (-t_frag_one / f_tau_T)
-
-                        # First differential addition to the density
-                        # integrating along dr, similar to eq. (36) Festou
-                        # 1981, due to the first velocity
-                        n_r_one = (
-                            (p_extinction * f_extinction_one * q_r_eps_one) /
-                            (sep_dist ** 2 * v_one)
-                        )
-
-                        # Add this contribution to the density grid
-                        self.vmodel['density_grid'][i][j] = (
-                            self.vmodel['density_grid'][i][j] +
-                            n_r_one * dr
-                        )
-
-                        # Check if there is a second solution for the velocity
-                        if vf > vp:
-                            continue
-
-                        # Compute the contribution from the second solution for
-                        # v in the same way
-                        v_two = vp * cos_eject - v_factor
-                        t_frag_two = sep_dist / v_two
-                        if t_frag_two > time_limit:
-                            continue
-                        t_total_two = (cur_r / vp) + t_frag_two
-                        prod_two = self.production_at_time(t_total_two) / vp
-                        q_r_eps_two = (
-                            (v_two * v_two * prod_two) /
-                            (vf * np.abs(v_two - vp * cos_eject))
-                        )
-                        f_extinction_two = np.e ** (-t_frag_two / f_tau_T)
-                        n_r_two = (
-                            (p_extinction * f_extinction_two * q_r_eps_two) /
-                            (v_two * sep_dist ** 2)
-                        )
-                        self.vmodel['density_grid'][i][j] = (
-                            self.vmodel['density_grid'][i][j] +
-                            n_r_two * dr
-                        )
-
-                    # Next starting radial point is the current end point
-                    ejection_radii_start = ejection_radii_end
-
-            if(self.print_progress is True):
-                progress_percent = (j + 1) * 100 / self.angular_points
-                print(f'Computing: {progress_percent:3.1f} %', end='\r')
-
-        # Loops automatically over the 2d grid
-        self.vmodel['density_grid'] *= integration_factor
-        # phew
+        return ejection_sites, drs
+
+    def _fragment_sputter(self, r: np.float64, theta: np.float64) -> np.float64:
+        """Compute the fragment density at (r, theta) in a spherical
+        coordinate system where theta is the polar angle and the parents flow
+        radially outward along the z axis.
+
+
+        Parameters
+        ----------
+        r : numpy.float64
+            radial coordinate in meters where we are calculating the fragment
+            density.
+
+        theta : numpy.float64
+            polar spherical coordinate where we are calculating the fragment
+            density.
+
+
+        Returns
+        -------
+        np.float64
+            Fragment density in 1/m**3, no astropy units attached.
 
         """
-            Performs angular part of the integration to yield density in m^-3
-            as a function of radius.  Assumes spherical symmetry of parent
-            production.
-
-            Fills vmodel['radial_density'] and vmodel['fast_radial_density']
-            with and without units respectively
-            Fills vmodel['r_dens_interpolation'] with cubic spline
-            interpolation of the radial density, which takes radial coordinate
-            in m and outputs the density at that coord in m^-3, without units
-            attached
+
+        sputter = 0.0
+        vp = self.parent.v_outflow
+        vf = self.fragment.v_photo
+        p_tau_T = self.parent.tau_T
+        f_tau_T = self.fragment.tau_T
+
+        x = r * np.sin(theta)
+        y = r * np.cos(theta)
+
+        ejection_sites, drs = self._outflow_axis_sampling(x, y, theta)
+
+        # Loop over these ejection sites that contribute to x, y
+        for slice_r, dr in zip(ejection_sites, drs):
+            # Distance from dissociation site to our grid point
+            sep_dist = np.sqrt(x**2 + (slice_r - y) ** 2)
+
+            cos_eject = (y - slice_r) / sep_dist
+            sin_eject = x / sep_dist
+
+            # Parent extinction when traveling along to the
+            # dissociation site
+            p_extinction = np.e ** (-slice_r / (p_tau_T * vp))
+
+            # Calculate sqrt(vR^2 - u^2 sin^2 gamma)
+            v_factor = np.sqrt(vf * vf - (vp * vp) * sin_eject**2)
+
+            # The geometry of the problem can admit one or two solutions for
+            # the velocity of the fragment
+            v_one = vp * cos_eject + v_factor
+            if vf > vp:
+                velocities = [v_one]
+            else:
+                v_two = vp * cos_eject - v_factor
+                velocities = [v_one, v_two]
+
+            # TODO: this shouldn't be necessary if we only pick r, theta inside ejection
+            if v_one == 0.0:
+                continue
+
+            for v in velocities:
+                # Time taken to travel from the dissociation point at v, reject
+                # if the time is too large and fragments have decayed beyond
+                # self.fragment_destruction_level percent
+                t_frag = sep_dist / v
+                if t_frag > self.time_limit:
+                    continue
+
+                # total time between parent emission from nucleus and fragment
+                # arriving at our point of interest, which we then use to look
+                # up Q at that time in the past
+                t_total = (slice_r / vp) + t_frag
+
+                # Division by parent velocity makes this production per
+                # unit distance for radial integration q(r, epsilon)
+                # given by eq. 32, Festou 1981
+                q = self.production_at_time(t_total) / vp
+                q_r_eps = (v**2 * q) / (vf * np.abs(v - vp * cos_eject))
+
+                # Fragment extinction when traveling at speed v from
+                # dissociation site to x, y
+                f_extinction = np.e ** (-t_frag / f_tau_T)
+
+                # differential addition to the density integrating along dr,
+                # similar to eq. (36) Festou 1981
+                n_r = (p_extinction * f_extinction * q_r_eps) / (sep_dist**2 * v)
+
+                sputter += n_r * dr
+
+        return sputter
+
+    def _compute_fragment_density(self) -> None:
+        """Computes the density of fragments as a function of radius.
+
+        Notes
+        -----
+        Computes the density of fragments sputtered around the comet due to an
+        outflow of parents along the z-axis, performing the integration in eq.
+        (36), Festou 1981. The resulting radial fragment density will be in
+        units of 1/(m^3) as we work in m, s, and m/s.
+
+        We then interpolate the fragment density as a function of arbitrary
+        radius.  We use our results to calculate the total number of fragments
+        in the coma for comparison to the theoretical number we expect, to
+        provide the user with a rough idea of how well the chosen radial and
+        angular grid sizes have captured the appropriate amount of particles.
+        Note that some level of disagreement is expected because the
+        parent_destruction_level and fragment_destruction_level parameters cut
+        the grid off before all parents can dissociate, and thus some escape the
+        model and come up missing in the fragment count based on the grid.
+
         """
 
+        if self.print_progress:
+            print("Starting fragment density computations...")
+
+        # Follow fragments until they have been totally destroyed
+        self.time_limit = self.model_params.max_fragment_lifetimes * self.fragment.tau_T
+
+        # More factors to fill out integral similar to eq. (36) Festou 1981
+        integration_factor = (
+            (1 / (4 * np.pi * self.parent.tau_d)) * self.d_alpha / (4.0 * np.pi)
+        )
+
+        # vectorize and apply to each combination of (r, theta)
+        sputter_func = np.vectorize(self._fragment_sputter)
+        rs, thetas = np.meshgrid(
+            self.fast_voldens_grid, self.angular_grid, indexing="ij"
+        )
+        self.fragment_sputter = integration_factor * sputter_func(rs, thetas)
+
         # Make array to hold our data, no units
-        self.vmodel['fast_radial_density'] = np.zeros(self.radial_points)
-
-        # loop through grid array
-        for i in range(0, self.radial_points):
-            for j in range(0, self.angular_points):
-                # Current angle is theta
-                theta = self.vmodel['angular_grid'][j]
-                # Integration factors from angular part of integral, similar to
-                # eq. (36) Festou 1981
-                dens_contribution = (
-                    2.0 * np.pi * np.sin(theta) *
-                    self.vmodel['density_grid'][i][j]
-                )
-                self.vmodel['fast_radial_density'][i] += dens_contribution
+        self.fast_voldens = np.zeros(self.grid.radial_points)
+
+        # Integration factors from angular part of integral, similar to
+        # eq. (36) Festou 1981
+        # integrate over theta to produce radial fragment volume density
+        # axis = 1 sums over second column, theta
+        # Equivalent to summing over j for sin(theta[j]) *
+        # fragment_sputter[i][j] with numpy magic
+        self.solid_angle_sputter = np.sin(thetas) * self.fragment_sputter
+        self.fast_voldens = 2.0 * np.pi * np.sum(self.solid_angle_sputter, axis=1)
 
         # Tag with proper units
-        self.vmodel['radial_density'] = (
-            self.vmodel['fast_radial_density'] / (u.m ** 3)
-        )
+        self.vmr.volume_density = self.fast_voldens / (u.m**3)
 
-    def _interpolate_radial_density(self):
-        """ Interpolate the radial density.
+    def _interpolate_volume_density(self) -> None:
+        """Interpolate the volume density as a function of radial distance
+        from the nucleus.
 
         Takes our fragment density grid and constructs density as a function of
-        arbitrary radius
+        arbitrary radius.
+
         """
 
         if not scipy:
-            raise RequiredPackageUnavailable('scipy')
+            raise RequiredPackageUnavailable("scipy")
+
+        if self.print_progress:
+            print("Interpolating radial fragment density...")
+
         # Interpolate this radial density grid with a cubic spline for lookup
         # at non-grid radii, input in m, output in 1/m^3
-        self.vmodel['r_dens_interpolation'] = (
-            CubicSpline(self.vmodel['fast_radial_grid'],
-                        self.vmodel['fast_radial_density'],
-                        bc_type='natural')
+        self.vmr.volume_density_interpolation = CubicSpline(
+            self.fast_voldens_grid, self.fast_voldens, bc_type="natural"
         )
 
-    def _column_density_at_rho(self, rho):
-        """ Calculate the column density of fragments at a given impact parameter
-
-            Parameters
-            ----------
-            rho : float
-                Impact parameter of the column density integration, in meters
-
-            Returns
-            -------
-            float
-                Column density at the given impact parameter in m^-2, no
-                astropy units attached
-
-            Notes
-            -----
-            We return zero column density beyond the edge of our grid, so if
-            there is still significant column density near the edge of the grid
-            this can lead to strange graphing results and sharp cutoffs.
+    def _column_density_at_rho(self, rho: np.float64) -> np.float64:
+        """
+        Calculate the column density of fragments at a given impact parameter.
+
+
+        Parameters
+        ----------
+        rho : np.float64
+            Impact parameter of the column density integration, in meters
+
+
+        Returns
+        -------
+        np.float64
+            Column density at the given impact parameter in m^-2, no
+            astropy units attached
+
+
+        Notes
+        -----
+        We return zero column density beyond a certain distance past the
+        grid edge, which can lead to strange graphing results and sharp
+        cutoffs.
+
         """
 
-        r_max = self.vmodel['max_grid_radius'].value
-        if(rho > r_max):
+        reach_factor = 2.0
+
+        # _volume_density() approximates beyond the edge of the grid, so allow
+        # an arbirtary limit beyond which we just return zero column density
+        r_max = self.vmr.max_grid_radius.value * reach_factor
+
+        if rho > r_max:
             return 0
-        rhosq = rho ** 2
-        z_max = np.sqrt(r_max ** 2 - rhosq)
+
+        rhosq = rho**2
+        z_max = np.sqrt(r_max**2 - rhosq)
 
         def column_density_integrand(z):
-            return self.vmodel['r_dens_interpolation'](np.sqrt(z ** 2 + rhosq))
-
-        # Romberg is significantly slower for impact parameters near the
-        # nucleus, and becomes much faster at roughly 60 times the collision
-        # sphere radius, after a few tests
-        # The results of both were the same to within .1% or better, generally
-        # TODO: test this for a range of productions to see if this holds
-
-        if rho < (60 * self.vmodel['collision_sphere_radius'].value):
-            c_dens = (
-                quad(column_density_integrand,
-                     -z_max, z_max, limit=3000)
-            )[0]
-        else:
-            c_dens = (
-                2 * romberg(column_density_integrand,
-                            0, z_max, rtol=0.0001, divmax=20)
-            )
+            return self._volume_density(np.sqrt(z**2 + rhosq))
+
+        c_dens = 2 * romberg(column_density_integrand, 0, z_max, rtol=0.0001, divmax=50)
 
         # result is in 1/m^2
         return c_dens
 
-    def _compute_column_density(self):
-        """ Compute the column density on the grid and interpolate the results
+    def _compute_column_density(self) -> None:
+        """Compute the column density on the grid and interpolate the results.
 
-            Computes the fragment column density on a grid and interpolates for
-            fragment column density as a function of arbitrary radius.
+        Notes
+        -----
+        The interpolator returns column density in m^-2, no astropy units
+        attached.
 
-            Notes
-            -----
-            The interpolator returns column density in m^-2, no astropy units
-            attached
         """
+
         if not scipy:
-            raise RequiredPackageUnavailable('scipy')
+            raise RequiredPackageUnavailable("scipy")
+
+        if self.print_progress:
+            print("Computing column densities...")
 
         # make a grid for the column density values
         column_density_grid = self._make_radial_logspace_grid()
-        # vectorize so we can hand the whole grid to our column density
-        # computing function in one line
+        # vectorize so we can hand the whole grid to our function
         cd_vectorized = np.vectorize(self._column_density_at_rho)
         # array now holds corresponding column density values
         column_densities = cd_vectorized(column_density_grid)
 
-        self.vmodel['fast_column_density_grid'] = column_density_grid
-        self.vmodel['column_density_grid'] = column_density_grid * u.m
-        self.vmodel['column_densities'] = column_densities / (u.m ** 2)
+        self.fast_column_density_grid = column_density_grid
+        self.vmr.column_density_grid = column_density_grid * u.m
+        self.vmr.column_density = column_densities / (u.m**2)
         # Interpolation gives column density in m^-2
-        self.vmodel['column_density_interpolation'] = (
-            CubicSpline(
-                column_density_grid,
-                column_densities,
-                bc_type='natural'
-            )
+        self.vmr.column_density_interpolation = CubicSpline(
+            column_density_grid, column_densities, bc_type="natural"
         )
 
-    def calc_num_fragments_theory(self):
-        """ The total number of fragment species we expect in the coma
+    def _calc_num_fragments_theory(self) -> np.float64:
+        """The total number of fragment species we expect in the coma.
 
-            Returns
-            -------
-            float
-                Total number of fragment species we expect in the coma
-                theoretically
+        Returns
+        -------
+        np.float64
+            Total number of fragment species we expect in the coma theoretically
+
+        Notes
+        -----
+        Outbursts/time dependent production in general will make this result
+        poor due to the grid being sized to capture a certain fraction of
+        parents/fragments at the oldest (first) production rate.  The farther
+        you get from this base production, the farther the model will deviate
+        from capturing the requested percentage of particles.
 
-            Notes
-            -----
-            This needs to be rewritten to better handle time dependence; the
-            original implementation was designed for abrupt but small parent
-            production changes.
         """
 
-        # TODO: Re-implement this as differential equations with parent
-        # photodissociation as a source of fragments, and fragment
-        # photodissociation as a sink
-        vp = self.parent['v_outflow']
-        vf = self.fragment['v_photo']
-        p_tau_T = self.parent['tau_T']
-        f_tau_T = self.fragment['tau_T']
-        p_tau_d = self.parent['tau_d']
-        t_perm = self.vmodel['t_perm_flow'].to(u.s).value
+        vp = self.parent.v_outflow
+        vf = self.fragment.v_photo
+        p_tau_T = self.parent.tau_T
+        f_tau_T = self.fragment.tau_T
+        p_tau_d = self.parent.tau_d
+        t_perm = self.vmr.t_perm_flow.to(u.s).value
 
         alpha = f_tau_T * p_tau_T / p_tau_d
 
-        max_r = self.vmodel['max_grid_radius'].value
-        last_density_element = len(self.vmodel['fast_radial_density']) - 1
-        edge_adjust = (
-            (np.pi * max_r * max_r * (vf + vp) *
-             self.vmodel['fast_radial_density'][last_density_element])
-        )
+        max_r = self.vmr.max_grid_radius.value
+        edge_adjust = np.pi * max_r * max_r * (vf + vp) * self.fast_voldens[-1]
+
+        num_time_slices = 1000
 
-        num_time_slices = 10000
         # array starting at tperm (farthest back in time we expect something to
         # hang around), stepped down to zero (when the observation takes place)
-        time_slices = np.linspace(t_perm, 0, num_time_slices, endpoint=False)
+        time_slices = np.linspace(t_perm, 0, num_time_slices, endpoint=True)
 
+        # estimate based on discrete-time source and sink model
         theory_total = 0
-        for i, t in enumerate(time_slices[:-1], start=0):
-            if t > t_perm:
-                t1 = t_perm
-            else:
-                t1 = t
-            t1 /= p_tau_T
-
-            if i != (num_time_slices - 1):
-                t2 = time_slices[i + 1]
-            else:
-                t2 = 0
-            t2 /= p_tau_T
-            mult_factor = -np.e ** (-t1) + np.e ** (-t2)
+        for i, t in enumerate(time_slices[:-1]):
+            extinction_one = t / p_tau_T
+            extinction_two = time_slices[i + 1] / p_tau_T
+            mult_factor = -np.e ** (-extinction_one) + np.e ** (-extinction_two)
             theory_total += self.production_at_time(t) * mult_factor
 
-        theory_total *= alpha
-        theory_total -= edge_adjust
+        return theory_total * alpha - edge_adjust
+
+    def _calc_num_fragments_grid(self) -> np.float64:
+        """Total number of fragments in the coma.
+
+        Calculates the total number of fragments by integrating the density grid
+        over its volume
 
-        return theory_total
 
-    def calc_num_fragments_grid(self):
-        """ Total number of fragments in the coma.
+        Returns
+        -------
+        np.float64
+            Number of fragments in the coma based on our grid calculations
 
-            Calculates the total number of fragment species by integrating the
-            density grid over its volume
 
-            Returns
-            -------
-            float
-                Number of fragments in the coma based on our grid calculations
+        Notes
+        -----
+        Outbursts/time dependent production in general will make this result
+        poor due to the grid being sized to capture a certain fraction of
+        parents/fragments at the oldest (first) production rate.  The farther
+        you get from this base production, the farther the model will deviate
+        from capturing the requested percentage of particles.
 
-            Notes
-            -----
-            Outbursts/time dependent production in general will make this
-            result poor due to the grid being sized to capture a certain
-            fraction of parents/fragments at the oldest (first) production
-            rate.  The farther you get from this base production, the farther
-            the model will deviate from capturing the requested percentage of
-            particles.
         """
-        max_r = self.vmodel['max_grid_radius'].value
+
+        max_r = self.vmr.max_grid_radius.value
 
         def vol_integrand(r, r_func):
-            return (r_func(r) * r ** 2)
+            return r_func(r) * r**2
 
         r_int = romberg(
-            vol_integrand, 0, max_r,
-            args=(self.vmodel['r_dens_interpolation'], ),
-            rtol=0.0001, divmax=20)
+            vol_integrand,
+            0,
+            max_r,
+            args=(self.vmr.volume_density_interpolation,),
+            rtol=0.0001,
+            divmax=20,
+        )
         return 4 * np.pi * r_int
 
-    def _column_density(self, rho):
-        """ Gives fragment column density at arbitrary impact parameter
+    def _column_density(self, rho) -> np.float64:
+        """Gives fragment column density at arbitrary impact parameter.
 
-            Parameters
-            ----------
-            rho : float
-                Impact parameter, in meters, no astropy units attached
+        Parameters
+        ----------
+        rho : np.float64
+            Impact parameter, in meters, no astropy units attached.
+
+
+        Returns
+        -------
+        np.float64
+            Fragment column density at given impact parameter, in m^-2, no
+            astropy units.
 
-            Returns
-            -------
-            float
-                Fragment column density at given impact parameter, in m^-2
         """
-        return self.vmodel['column_density_interpolation'](rho)
 
-    def _volume_density(self, r):
-        """ Gives fragment volume density at arbitrary radius
+        if rho < self.fast_column_density_grid[0]:
+            return self.vmr.column_density[0].value
+        if rho > self.vmr.max_grid_radius.value:
+            return 0
+        return self.vmr.column_density_interpolation(rho)
+
+    def _volume_density(self, r) -> np.float64:
+        """Gives fragment volume density at arbitrary radius.
+
+
+        Parameters
+        ----------
+        r : np.float64
+            Distance from nucleus, in meters, no astropy units attached.
 
-            Parameters
-            ----------
-            r : float
-                Distance from nucles, in meters, no astropy units attached
 
-            Returns
-            -------
-            float
-                Fragment volume density at specified radius
+        Returns
+        -------
+        np.float64
+            Fragment volume density at specified radius, in m^-3, no astropy
+            units.
+
+
+        Notes
+        -----
+        When asked for a value at a radius smaller than the first grid point, we
+        have two choices: return a value based on the interpolation of the
+        volume density, or return the value of the closest grid point to the
+        nucleus. This function returns the closest grid point to the nucleus,
+        because the model does not say anything about what happens inside the
+        collision sphere - so we approximate by clamping the value as constant.
+        We can't trust the interpolation outside of the grid because the density
+        values can get very large or become negative.
+
+        Outside the radius of the grid, the volume density is approximated with
+        exponential decay based on the total lifetime of the fragment.
+
         """
-        return self.vmodel['r_dens_interpolation'](r)
+
+        if r < self.fast_voldens_grid[0]:
+            return self.fast_voldens[0]
+        if r > self.vmr.max_grid_radius.value:
+            diff = r - self.vmr.max_grid_radius.value
+            guess = self.fast_voldens[-1] * np.exp(
+                -diff / (self.fragment.tau_T * self.fragment.v_photo)
+            )
+            return guess
+        return self.vmr.volume_density_interpolation(r)
diff --git a/sbpy/activity/gas/tests/test_core.py b/sbpy/activity/gas/tests/test_core.py
index 72e3ff99..4caffa75 100644
--- a/sbpy/activity/gas/tests/test_core.py
+++ b/sbpy/activity/gas/tests/test_core.py
@@ -5,69 +5,74 @@
 import astropy.units as u
 import astropy.constants as const
 from .. import core
-from .. import (photo_lengthscale, photo_timescale, fluorescence_band_strength,
-                VectorialModel, Haser)
+from .. import (
+    photo_lengthscale,
+    photo_timescale,
+    fluorescence_band_strength,
+    VectorialModel,
+    Haser,
+)
 from .... import exceptions as sbe
 from ....data import Phys
 
 
 def test_photo_lengthscale():
-    gamma = photo_lengthscale('OH', 'CS93')
+    gamma = photo_lengthscale("OH", "CS93")
     assert gamma == 1.6e5 * u.km
 
 
 def test_photo_lengthscale_error():
     with pytest.raises(ValueError):
-        photo_lengthscale('asdf')
+        photo_lengthscale("asdf")
 
     with pytest.raises(ValueError):
-        photo_lengthscale('OH', source='asdf')
+        photo_lengthscale("OH", source="asdf")
 
 
 def test_photo_timescale():
-    tau = photo_timescale('CO2', 'CE83')
+    tau = photo_timescale("CO2", "CE83")
     assert tau == 5.0e5 * u.s
 
 
 def test_photo_timescale_error():
     with pytest.raises(ValueError):
-        photo_timescale('asdf')
+        photo_timescale("asdf")
 
     with pytest.raises(ValueError):
-        photo_timescale('OH', source='asdf')
-
-
-@pytest.mark.parametrize('band, test', (
-    ('OH 0-0', 1.54e-15 * u.erg / u.s),
-    ('OH 1-0', 1.79e-16 * u.erg / u.s),
-    ('OH 1-1', 2.83e-16 * u.erg / u.s),
-    ('OH 2-2', 1.46e-18 * u.erg / u.s),
-    ('OH 0-1', 0.00356 * 1.54e-15 * u.erg / u.s),
-    ('OH 0-2', 0.00021 * 1.54e-15 * u.erg / u.s),
-    ('OH 1-2', 0.00610 * 2.83e-16 * u.erg / u.s),
-    ('OH 2-0', 0.274 * 1.46e-18 * u.erg / u.s),
-    ('OH 2-1', 1.921 * 1.46e-18 * u.erg / u.s),
-))
+        photo_timescale("OH", source="asdf")
+
+
+@pytest.mark.parametrize(
+    "band, test",
+    (
+        ("OH 0-0", 1.54e-15 * u.erg / u.s),
+        ("OH 1-0", 1.79e-16 * u.erg / u.s),
+        ("OH 1-1", 2.83e-16 * u.erg / u.s),
+        ("OH 2-2", 1.46e-18 * u.erg / u.s),
+        ("OH 0-1", 0.00356 * 1.54e-15 * u.erg / u.s),
+        ("OH 0-2", 0.00021 * 1.54e-15 * u.erg / u.s),
+        ("OH 1-2", 0.00610 * 2.83e-16 * u.erg / u.s),
+        ("OH 2-0", 0.274 * 1.46e-18 * u.erg / u.s),
+        ("OH 2-1", 1.921 * 1.46e-18 * u.erg / u.s),
+    ),
+)
 def test_fluorescence_band_strength_OH_SA88(band, test):
     "Tests values for -1 km/s at 1 au"
-    eph = {
-        'rh': [1, 2] * u.au,
-        'rdot': [-1, -1] * u.km / u.s
-    }
-    LN = fluorescence_band_strength(band, eph, 'SA88').to(test.unit)
+    eph = {"rh": [1, 2] * u.au, "rdot": [-1, -1] * u.km / u.s}
+    LN = fluorescence_band_strength(band, eph, "SA88").to(test.unit)
     assert np.allclose(LN.value, test.value / np.r_[1, 2] ** 2)
 
 
 def test_fluorescence_band_strength_error():
     with pytest.raises(ValueError):
-        fluorescence_band_strength('asdf')
+        fluorescence_band_strength("asdf")
 
     with pytest.raises(ValueError):
-        fluorescence_band_strength('OH 0-0', source='asdf')
+        fluorescence_band_strength("OH 0-0", source="asdf")
 
 
 def test_gascoma_scipy_error(monkeypatch):
-    monkeypatch.setattr(core, 'scipy', None)
+    monkeypatch.setattr(core, "scipy", None)
     test = Haser(1 / u.s, 1 * u.km / u.s, 1e6 * u.km)
     with pytest.raises(sbe.RequiredPackageUnavailable):
         test._integrate_volume_density(1e5)
@@ -78,7 +83,6 @@ def test_gascoma_scipy_error(monkeypatch):
 
 
 class TestHaser:
-
     def test_volume_density(self):
         """Test a set of dummy values."""
         Q = 1e28 / u.s
@@ -87,11 +91,13 @@ def test_volume_density(self):
         daughter = 1e5 * u.km
         r = np.logspace(1, 7) * u.km
         n = Haser(Q, v, parent, daughter).volume_density(r)
-        rel = (daughter / (parent - daughter)
-               * (np.exp(-r / parent) - np.exp(-r / daughter)))
+        rel = (
+            daughter
+            / (parent - daughter)
+            * (np.exp(-r / parent) - np.exp(-r / daughter))
+        )
         # test radial profile
-        assert np.allclose((n / n[0]).value,
-                           (rel / rel[0] * (r[0] / r) ** 2).value)
+        assert np.allclose((n / n[0]).value, (rel / rel[0] * (r[0] / r) ** 2).value)
 
         # test parent-only coma near nucleus against that expected for
         # a long-lived species; will be close, but not exact
@@ -99,7 +105,8 @@ def test_volume_density(self):
         assert np.isclose(
             n.decompose().value,
             (Q / v / 4 / np.pi / (10 * u.km) ** 2).decompose().value,
-            rtol=0.001)
+            rtol=0.001,
+        )
 
     def test_column_density_small_aperture(self):
         """Test column density for aperture << lengthscale.
@@ -113,8 +120,7 @@ def test_column_density_small_aperture(self):
         parent = 1e4 * u.km
         N_avg = 2 * Haser(Q, v, parent).column_density(rho)
         ideal = Q / v / 2 / rho
-        assert np.isclose(N_avg.decompose().value, ideal.decompose().value,
-                          rtol=0.001)
+        assert np.isclose(N_avg.decompose().value, ideal.decompose().value, rtol=0.001)
 
     def test_column_density_small_angular_aperture(self):
         """Test column density for angular aperture << lengthscale.
@@ -130,11 +136,9 @@ def test_column_density_small_angular_aperture(self):
         eph = dict(delta=1 * u.au)
         parent = 1e4 * u.km
         N_avg = 2 * Haser(Q, v, parent).column_density(rho, eph)
-        rho_km = (rho * eph['delta'] * 725.24 * u.km / u.arcsec / u.au
-                  ).to('km')
+        rho_km = (rho * eph["delta"] * 725.24 * u.km / u.arcsec / u.au).to("km")
         ideal = Q / v / 2 / rho_km
-        assert np.isclose(N_avg.to_value('1/m2'),
-                          ideal.to_value('1/m2'), rtol=0.001)
+        assert np.isclose(N_avg.to_value("1/m2"), ideal.to_value("1/m2"), rtol=0.001)
 
     def test_column_density(self):
         """
@@ -147,7 +151,7 @@ def test_column_density(self):
         parent = 1000 * u.km
         coma = Haser(Q, v, parent)
         N_avg = coma.column_density(rho)
-        integral = coma._integrate_volume_density(rho.to('m').value)[0]
+        integral = coma._integrate_volume_density(rho.to("m").value)[0]
         assert np.isclose(N_avg.decompose().value, integral)
 
     def test_total_number_large_aperture(self):
@@ -176,7 +180,7 @@ def test_total_number_circular_aperture_angular(self):
         ap = core.CircularAperture(1 * u.arcsec).as_length(1 * u.au)
         coma = Haser(Q, v, parent, daughter)
         N = coma.total_number(ap)
-        assert np.isclose(N, 5.238964562688742e+26)
+        assert np.isclose(N, 5.238964562688742e26)
 
     def test_total_number_rho_AC75(self):
         """Reproduce A'Hearn and Cowan 1975
@@ -229,14 +233,14 @@ def test_total_number_rho_AC75(self):
         tab = [
             [1.773, 40272, 4.603e30, 0.000000, 0.000000, 26.16, 0.000, 0.00],
             [1.053, 43451, 3.229e31, 1.354e31, 9.527e30, 26.98, 26.52, 26.5],
-            [0.893, 39084, 4.097e31, 1.273e31, 2.875e31, 27.12, 26.54, 27.0]
+            [0.893, 39084, 4.097e31, 1.273e31, 2.875e31, 27.12, 26.54, 27.0],
         ]
 
         for rh, rho, NC2, NCN, NC3, QC2, QCN, QC3 in tab:
             if NC2 > 0:
                 parent = 1.0e4 * u.km
                 daughter = 6.61e4 * u.km
-                Q = 10 ** QC2 / u.s
+                Q = 10**QC2 / u.s
                 coma = Haser(Q, 1 * u.km / u.s, parent, daughter)
                 N = coma.total_number(rho * u.km)
                 assert np.isclose(NC2, N, rtol=0.01)
@@ -244,7 +248,7 @@ def test_total_number_rho_AC75(self):
             if NCN > 0:
                 parent = 1.3e4 * u.km
                 daughter = 1.48e5 * u.km
-                Q = 10 ** QCN / u.s
+                Q = 10**QCN / u.s
                 coma = Haser(Q, 1 * u.km / u.s, parent, daughter)
                 N = coma.total_number(rho * u.km)
                 assert np.isclose(NCN, N, rtol=0.01)
@@ -252,7 +256,7 @@ def test_total_number_rho_AC75(self):
             if NC3 > 0:
                 parent = 0 * u.km
                 daughter = 4.0e4 * u.km
-                Q = 10 ** QC3 / u.s
+                Q = 10**QC3 / u.s
                 coma = Haser(Q, 1 * u.km / u.s, parent, daughter)
                 N = coma.total_number(rho * u.km)
                 assert np.isclose(NC3, N, rtol=0.01)
@@ -303,10 +307,8 @@ def test_total_number_annulus(self):
         parent = 10 * u.km
         N = Haser(Q, v, parent).total_number(aper)
 
-        N1 = Haser(Q, v, parent).total_number(
-            core.CircularAperture(aper.dim[0]))
-        N2 = Haser(Q, v, parent).total_number(
-            core.CircularAperture(aper.dim[1]))
+        N1 = Haser(Q, v, parent).total_number(core.CircularAperture(aper.dim[0]))
+        N2 = Haser(Q, v, parent).total_number(core.CircularAperture(aper.dim[1]))
 
         assert np.allclose(N, N2 - N1)
 
@@ -340,7 +342,7 @@ def test_total_number_rectangular_ap(self):
         coma = Haser(Q, v, parent, daughter)
         N = coma.total_number(aper)
 
-        assert np.isclose(N, 3.449607967230623e+26)
+        assert np.isclose(N, 3.449607967230623e26)
 
     def test_total_number_gaussian_ap(self):
         """
@@ -371,10 +373,10 @@ def test_total_number_gaussian_ap(self):
         coma = Haser(Q, 1 * u.km / u.s, parent)
         N = coma.total_number(aper)
 
-        assert np.isclose(N, 5.146824269306973e+27, rtol=0.005)
+        assert np.isclose(N, 5.146824269306973e27, rtol=0.005)
 
     def test_missing_scipy(self, monkeypatch):
-        monkeypatch.setattr(core, 'scipy', None)
+        monkeypatch.setattr(core, "scipy", None)
         test = Haser(1 / u.s, 1 * u.km / u.s, 1e6 * u.km)
         with pytest.raises(sbe.RequiredPackageUnavailable):
             test._iK0(1)
@@ -383,139 +385,302 @@ def test_missing_scipy(self, monkeypatch):
 
 
 class TestVectorialModel:
+    def test_small_vphoto(self):
+        """
+        The other test using water as parent and hydroxyl as fragment have a
+        v_photo > v_outflow, but the model has a slightly different case for
+        v_photo < v_outflow
+        """
+
+        # Across python, fortran, and rust models, we get very very close to
+        # this number of fragments
+        num_fragments_grid = 1.2162140e33
+
+        base_q = 1.0e28 * 1 / u.s
+
+        # Parent molecule is H2O
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * u.s,
+                "tau_d": 101730 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
+        # Fragment molecule is OH, but v_photo is modified to be smaller than
+        # v_outflow
+        fragment = Phys.from_dict(
+            {"tau_T": photo_timescale("OH") * 0.93, "v_photo": 0.5 * u.km / u.s}
+        )
+
+        coma = VectorialModel(
+            base_q=base_q, parent=parent, fragment=fragment
+        )
+
+        assert np.isclose(
+            coma.vmr.num_fragments_grid, num_fragments_grid, rtol=0.02
+        )
+
+    def test_time_dependent_function(self):
+        """
+        Test handing off a time dependence to the model with zero additional
+        time-dependent production from q_t: results should match a model with
+        steady production specified by base_q
+        Also uses a model with print_progress=true to avoid the code coverage
+        tests being polluted with trivial branches about the model
+        conditionally printing
+        """
+        def q_t(t):
+            # for all times, return zero additional production
+            return t * 0
+
+        base_q = 1.0e28 * 1 / u.s
+
+        # Parent molecule is H2O
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * u.s,
+                "tau_d": 101730 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
+        # Fragment molecule is OH
+        fragment = Phys.from_dict(
+            {"tau_T": photo_timescale("OH") * 0.93, "v_photo": 1.05 * u.km / u.s}
+        )
+
+        coma_steady = VectorialModel(
+            base_q=base_q, parent=parent, fragment=fragment
+        )
+
+        coma_q_t = VectorialModel(
+            base_q=base_q, parent=parent, fragment=fragment, q_t=q_t,
+            print_progress=True
+        )
+
+        assert np.isclose(
+            coma_steady.vmr.num_fragments_grid, coma_q_t.vmr.num_fragments_grid, rtol=0.02
+        )
+
+    def test_binned_production_one_element_list(self):
+        """
+        Initialize a comet with the fortran-version style of specifying time
+        dependent production and make it essentially steady production, and
+        test against a comet with the same steady production but initialized
+        differently
+        This also tests the model dealing with times in the past farther back
+        than is specified in the variation list 'ts': it extends the oldest
+        production value (here, 1.0e28) back infinitely into the past
+        """
+        # production from 1 day ago until the present,
+        ts = [1] * u.day
+        # is a steady value of 1e28
+        qs = [1.0e28] / u.s
+
+        base_q = 1.0e28 * 1 / u.s
+
+        # Parent molecule is H2O
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * u.s,
+                "tau_d": 101730 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
+        # Fragment molecule is OH
+        fragment = Phys.from_dict(
+            {"tau_T": photo_timescale("OH") * 0.93, "v_photo": 1.05 * u.km / u.s}
+        )
+
+        coma_binned = VectorialModel.binned_production(
+            qs=qs, ts=ts, parent=parent, fragment=fragment
+        )
+
+        coma_steady = VectorialModel(
+            base_q=base_q, parent=parent, fragment=fragment
+        )
+
+        assert np.isclose(
+            coma_steady.vmr.num_fragments_grid, coma_binned.vmr.num_fragments_grid, rtol=0.02
+        )
+
+    def test_binned_production_multi_element_list(self):
+        """
+        Initialize a comet with the fortran-version style of specifying time
+        dependent production and make it steady production, then test against a
+        comet with the same steady production but initialized differently
+        """
+        # Specify that at multiple points in time,
+        ts = [60, 50, 40, 30, 20, 10] * u.day
+        # production is a steady value of 1e28
+        qs = [1.0e28, 1.0e28, 1.0e28, 1.0e28, 1.0e28, 1.0e28] / u.s
+
+        base_q = 1.0e28 * 1 / u.s
+
+        # Parent molecule is H2O
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * u.s,
+                "tau_d": 101730 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
+        # Fragment molecule is OH
+        fragment = Phys.from_dict(
+            {"tau_T": photo_timescale("OH") * 0.93, "v_photo": 1.05 * u.km / u.s}
+        )
+
+        coma_binned = VectorialModel.binned_production(
+            qs=qs, ts=ts, parent=parent, fragment=fragment
+        )
+
+        coma_steady = VectorialModel(
+            base_q=base_q, parent=parent, fragment=fragment
+        )
+
+        assert np.isclose(
+            coma_steady.vmr.num_fragments_grid, coma_binned.vmr.num_fragments_grid, rtol=0.02
+        )
 
     def test_grid_count(self):
         """
-            Compute theoretical number of fragments vs. integrated value from
-            grid. This is currently only a good estimate for steady production
-            due to our method for determining the theoretical count of
-            fragments
+        Compute theoretical number of fragments vs. integrated value from
+        grid. This is currently only a good estimate for steady production
+        due to our method for determining the theoretical count of
+        fragments
         """
 
-        base_q = 1.e28 * 1 / u.s
+        base_q = 1.0e28 * 1 / u.s
 
         # Parent molecule is H2O
-        parent = Phys.from_dict({
-            'tau_T': 86430 * u.s,
-            'tau_d': 101730 * u.s,
-            'v_outflow': 1 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2
-        })
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * u.s,
+                "tau_d": 101730 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
         # Fragment molecule is OH
-        fragment = Phys.from_dict({
-            'tau_T': photo_timescale('OH') * 0.93,
-            'v_photo': 1.05 * u.km / u.s
-        })
+        fragment = Phys.from_dict(
+            {"tau_T": photo_timescale("OH") * 0.93, "v_photo": 1.05 * u.km / u.s}
+        )
 
-        coma = VectorialModel(base_q=base_q,
-                              parent=parent,
-                              fragment=fragment)
+        coma = VectorialModel(base_q=base_q, parent=parent, fragment=fragment)
 
-        fragment_theory = coma.vmodel['num_fragments_theory']
-        fragment_grid = coma.vmodel['num_fragments_grid']
-        assert np.isclose(fragment_theory, fragment_grid, rtol=0.02)
+        assert np.isclose(
+            coma.vmr.num_fragments_theory, coma.vmr.num_fragments_grid, rtol=0.02
+        )
 
     def test_total_number_large_aperture(self):
         """
-            Compare theoretical number of fragments vs. integration of column
-            density over a large aperture
+        Compare theoretical number of fragments vs. integration of column
+        density over a large aperture
         """
 
         base_q = 1e28 * 1 / u.s
 
         # Parent molecule is H2O
-        parent = Phys.from_dict({
-            'tau_T': 86430 * u.s,
-            'tau_d': 101730 * u.s,
-            'v_outflow': 1 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2
-        })
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * u.s,
+                "tau_d": 101730 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
         # Fragment molecule is OH
-        fragment = Phys.from_dict({
-            'tau_T': photo_timescale('OH') * 0.93,
-            'v_photo': 1.05 * u.km / u.s
-        })
+        fragment = Phys.from_dict(
+            {"tau_T": photo_timescale("OH") * 0.93, "v_photo": 1.05 * u.km / u.s}
+        )
 
-        coma = VectorialModel(base_q=base_q,
-                              parent=parent,
-                              fragment=fragment)
+        coma = VectorialModel(base_q=base_q, parent=parent, fragment=fragment)
 
-        fragment_theory = coma.vmodel['num_fragments_theory']
-        ap = core.CircularAperture(coma.vmodel['max_grid_radius'])
-        assert np.isclose(fragment_theory, coma.total_number(ap), rtol=0.02)
+        ap = core.CircularAperture(coma.vmr.max_grid_radius)
+        assert np.isclose(
+            coma.vmr.num_fragments_theory, coma.total_number(ap), rtol=0.02
+        )
 
     def test_model_symmetry(self):
         """
-            The symmetry of the model allows the parent production to be
-            treated as an overall scaling factor, and does not affect the
-            features of the calculated densities.
-
-            If we assume an arbitrary fixed count inside an aperture, we can
-            use this to calculate what the production would need to be to
-            produce this count after running the model with a 'dummy' value for
-            the production.
-
-            If we then run another model at this calculated production and use
-            the same aperture, we should recover the number of counts assumed
-            in the paragraph above.  If we do not, we have broken our model
-            somehow and lost the symmetry during our calculations.
+        The symmetry of the model allows the parent production to be
+        treated as an overall scaling factor, and does not affect the
+        features of the calculated densities.
+
+        If we assume an arbitrary fixed count inside an aperture, we can
+        use this to calculate what the production would need to be to
+        produce this count after running the model with a 'dummy' value for
+        the production.
+
+        If we then run another model at this calculated production and use
+        the same aperture, we should recover the number of counts assumed
+        in the paragraph above.  If we do not, we have broken our model
+        somehow and lost the symmetry during our calculations.
         """
 
-        base_production = 1e26
+        base_production = 1e28
 
         # # 100,000 x 100,000 km aperture
-        ap = core.RectangularAperture((1.0e5, 1.0e5) * u.km)
+        ap = core.CircularAperture((1.0e6) * u.km)
         # assumed fragment count inside this aperture
-        assumed_count = 1e30
+        assumed_count = 1e32
 
         # Parent molecule is H2O
-        parent = Phys.from_dict({
-            'tau_T': 86430 * u.s,
-            'tau_d': 101730 * u.s,
-            'v_outflow': 1 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2
-        })
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * u.s,
+                "tau_d": 101730 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
         # Fragment molecule is OH
-        fragment = Phys.from_dict({
-            'tau_T': photo_timescale('OH') * 0.93,
-            'v_photo': 1.05 * u.km / u.s
-        })
-
-        coma = VectorialModel(base_q=base_production * (1 / u.s),
-                              parent=parent,
-                              fragment=fragment)
-        # mgr = coma.vmodel['max_grid_radius']
-        # ap = core.RectangularAperture((mgr, mgr))
+        fragment = Phys.from_dict(
+            {"tau_T": photo_timescale("OH") * 0.93, "v_photo": 1.05 * u.km / u.s}
+        )
+
+        coma = VectorialModel(
+            base_q=base_production * (1 / u.s), parent=parent, fragment=fragment
+        )
+
         model_count = coma.total_number(ap)
         calculated_q = (assumed_count / model_count) * base_production
-        print("Calculated: ", calculated_q)
 
-        # Parent molecule is H2O
-        parent_check = Phys.from_dict({
-            'tau_T': 86430 * u.s,
-            'tau_d': 101730 * u.s,
-            'v_outflow': 1 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2
-        })
-        # Fragment molecule is OH
-        fragment_check = Phys.from_dict({
-            'tau_T': photo_timescale('OH') * 0.93,
-            'v_photo': 1.05 * u.km / u.s
-        })
-        coma_check = VectorialModel(base_q=calculated_q * (1 / u.s),
-                                    parent=parent_check,
-                                    fragment=fragment_check)
+        # # Parent molecule is H2O
+        # parent_check = Phys.from_dict({
+        #     'tau_T': 86430 * u.s,
+        #     'tau_d': 101730 * u.s,
+        #     'v_outflow': 1 * u.km / u.s,
+        #     'sigma': 3e-16 * u.cm ** 2
+        # })
+        # # Fragment molecule is OH
+        # fragment_check = Phys.from_dict({
+        #     'tau_T': photo_timescale('OH') * 0.93,
+        #     'v_photo': 1.05 * u.km / u.s
+        # })
+        # coma_check = VectorialModel(base_q=calculated_q * (1 / u.s),
+        #                             parent=parent_check,
+        #                             fragment=fragment_check)
+        coma_check = VectorialModel(
+            base_q=calculated_q * (1 / u.s), parent=parent, fragment=fragment
+        )
         count_check = coma_check.total_number(ap)
-        print("Count/assumed: ", count_check / assumed_count)
+
         assert np.isclose(count_check, assumed_count, rtol=0.001)
 
-    @pytest.mark.parametrize("rh,delta,flux,g,Q", (
-        [1.2912, 0.7410, 337e-14, 2.33e-4, 1.451e28],
-        [1.2949, 0.7651, 280e-14, 2.60e-4, 1.228e28],
-        [1.3089, 0.8083, 480e-14, 3.36e-4, 1.967e28],
-        [1.3200, 0.8353, 522e-14, 3.73e-4, 2.025e28],
-        [1.3366, 0.8720, 560e-14, 4.03e-4, 2.035e28],
-    ))
+    @pytest.mark.parametrize(
+        "rh,delta,flux,g,Q",
+        (
+            [1.2912, 0.7410, 337e-14, 2.33e-4, 1.451e28],
+            [1.2949, 0.7651, 280e-14, 2.60e-4, 1.228e28],
+            [1.3089, 0.8083, 480e-14, 3.36e-4, 1.967e28],
+            [1.3200, 0.8353, 522e-14, 3.73e-4, 2.025e28],
+            [1.3366, 0.8720, 560e-14, 4.03e-4, 2.035e28],
+        ),
+    )
     def test_festou92(self, rh, delta, flux, g, Q):
         """Compare to Festou et al. 1992 production rates of comet 6P/d'Arrest.
 
@@ -534,7 +699,7 @@ def test_festou92(self, rh, delta, flux, g, Q):
         # add units
         rh = rh * u.au
         delta = delta * u.au
-        flux = flux * u.erg / u.s / u.cm ** 2
+        flux = flux * u.erg / u.s / u.cm**2
         g = g / u.s
         Q = Q / u.s
 
@@ -542,18 +707,18 @@ def test_festou92(self, rh, delta, flux, g, Q):
         L_N = g / (rh / u.au) ** 2 * const.h * const.c / (3086 * u.AA)
 
         # Parent molecule is H2O
-        parent = Phys.from_dict({
-            'tau_T': 65000 * (rh / u.au) ** 2 * u.s,
-            'tau_d': 72500 * (rh / u.au) ** 2 * u.s,
-            'v_outflow': 0.85 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2,
-
-        })
+        parent = Phys.from_dict(
+            {
+                "tau_T": 65000 * (rh / u.au) ** 2 * u.s,
+                "tau_d": 72500 * (rh / u.au) ** 2 * u.s,
+                "v_outflow": 0.85 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
         # Fragment molecule is OH
-        fragment = Phys.from_dict({
-            'tau_T': 160000 * (rh / u.au) ** 2 * u.s,
-            'v_photo': 1.05 * u.km / u.s
-        })
+        fragment = Phys.from_dict(
+            {"tau_T": 160000 * (rh / u.au) ** 2 * u.s, "v_photo": 1.05 * u.km / u.s}
+        )
 
         # https://pds.nasa.gov/ds-view/pds/viewInstrumentProfile.jsp?INSTRUMENT_ID=LWP&INSTRUMENT_HOST_ID=IUE
         # Large-Aperture Length(arcsec)   22.51+/-0.40
@@ -570,7 +735,7 @@ def test_festou92(self, rh, delta, flux, g, Q):
         coma = VectorialModel(base_q=Q0, parent=parent, fragment=fragment)
         N0 = coma.total_number(lwp, eph=delta)
 
-        Q_model = (Q0 * flux / (L_N * N0) * 4 * np.pi * delta ** 2).to(Q.unit)
+        Q_model = (Q0 * flux / (L_N * N0) * 4 * np.pi * delta**2).to(Q.unit)
 
         # absolute tolerance: Table 2 has 3 significant figures
         #
@@ -579,66 +744,25 @@ def test_festou92(self, rh, delta, flux, g, Q):
         atol = 1.01 * 10 ** (np.floor(np.log10(Q0.value)) - 2) * Q.unit
         assert u.allclose(Q, Q_model, atol=atol, rtol=0.14)
 
-    @pytest.mark.parametrize("rh,delta,N,Q", (
-        [1.8662, 0.9683, 0.2424e32, 1.048e29],
-        [0.8855, 0.9906, 3.819e32, 5.548e29],
-        [0.9787, 0.8337, 1.63e32, 3.69e29],
-        [1.0467, 0.7219, 0.8703e32, 2.813e29],
-        [1.9059, 1.4031, 1.07e32, 2.76e29],
-        [2.0715, 1.7930, 1.01e32, 1.88e29]
-    ))
-    def test_combi93(self, rh, delta, N, Q):
-        """Compare to results of Combi et al. 1993.
-
-        Combi et al. 1993 compared a Monte Carlo approach to the Vectorial model
-        for OH.  They find best agreement between the two models near 1 au,
-        likely due to the assumption that the water outflow speed is constant in
-        the VM runs, but the MC model only has 1 km/s speeds near 1 au.
-
-        """
-
-        # assign units
-        rh = rh * u.au
-        delta = delta * u.au
-        Q = Q / u.s  # Vectorial model run by Roettger
-        aper = core.RectangularAperture((10, 15) * u.arcsec)
-
-        # Parent molecule is H2O
-        parent = Phys.from_dict({
-            'tau_T': 8.2e4 * 0.88 * (rh / u.au) ** 2 * u.s,
-            'tau_d': 8.2e4 * (rh / u.au) ** 2 * u.s,
-            'v_outflow': 1 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2,
-        })
-
-        # Fragment molecule is OH
-        fragment = Phys.from_dict({
-            'tau_T': 2.0e5 * (rh / u.au) ** 2 * u.s,
-            'v_photo': 1.05 * u.km / u.s
-        })
-
-        Q0 = 2e29 / u.s
-        coma = VectorialModel(base_q=Q0, parent=parent, fragment=fragment)
-        N0 = coma.total_number(aper, eph=delta)
-
-        Q_model = (Q0 * N / N0).to(Q.unit)
-        assert u.allclose(Q, Q_model, rtol=0.13)
-
-    @pytest.mark.parametrize("rh,delta,N,Q", (
-        [1.8662, 0.9683, 0.2424e32, 1.048e29],
-        [0.8855, 0.9906, 3.819e32, 5.548e29],
-        [0.9787, 0.8337, 1.63e32, 3.69e29],
-        [1.0467, 0.7219, 0.8703e32, 2.813e29],
-        [1.9059, 1.4031, 1.07e32, 2.76e29],
-        [2.0715, 1.7930, 1.01e32, 1.88e29]
-    ))
+    @pytest.mark.parametrize(
+        "rh,delta,N,Q",
+        (
+            [1.8662, 0.9683, 0.2424e32, 1.048e29],
+            [0.8855, 0.9906, 3.819e32, 5.548e29],
+            [0.9787, 0.8337, 1.63e32, 3.69e29],
+            [1.0467, 0.7219, 0.8703e32, 2.813e29],
+            [1.9059, 1.4031, 1.07e32, 2.76e29],
+            [2.0715, 1.7930, 1.01e32, 1.88e29],
+        ),
+    )
     def test_combi93(self, rh, delta, N, Q):
         """Compare to results of Combi et al. 1993.
 
-        Combi et al. 1993 compared a Monte Carlo approach to the Vectorial model
-        for OH.  They find best agreement between the two models near 1 au,
-        likely due to the assumption that the water outflow speed is constant in
-        the VM runs, but the MC model only has 1 km/s speeds near 1 au.
+        Combi et al. 1993 compared a Monte Carlo approach to the Vectorial
+        model for OH.  They find best agreement between the two models near 1
+        au, likely due to the assumption that the water outflow speed is
+        constant in the VM runs, but the MC model only has 1 km/s speeds near 1
+        au.
 
         """
 
@@ -649,18 +773,19 @@ def test_combi93(self, rh, delta, N, Q):
         aper = core.RectangularAperture((10, 15) * u.arcsec)
 
         # Parent molecule is H2O
-        parent = Phys.from_dict({
-            'tau_T': 8.2e4 * 0.88 * (rh / u.au) ** 2 * u.s,
-            'tau_d': 8.2e4 * (rh / u.au) ** 2 * u.s,
-            'v_outflow': 1 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2,
-        })
+        parent = Phys.from_dict(
+            {
+                "tau_T": 8.2e4 * 0.88 * (rh / u.au) ** 2 * u.s,
+                "tau_d": 8.2e4 * (rh / u.au) ** 2 * u.s,
+                "v_outflow": 1 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
 
         # Fragment molecule is OH
-        fragment = Phys.from_dict({
-            'tau_T': 2.0e5 * (rh / u.au) ** 2 * u.s,
-            'v_photo': 1.05 * u.km / u.s
-        })
+        fragment = Phys.from_dict(
+            {"tau_T": 2.0e5 * (rh / u.au) ** 2 * u.s, "v_photo": 1.05 * u.km / u.s}
+        )
 
         Q0 = 2e29 / u.s
         coma = VectorialModel(base_q=Q0, parent=parent, fragment=fragment)
@@ -675,13 +800,13 @@ def test_vm_fortran(self):
         vm.f shared with MSK by Joel Parker.  Comments:
 
             WRITTEN BY M. C. FESTOU
-            (Minor modifications were made by MF on 8 Nov. 1988 on the Tempe version)
-            Some unused variables removed on 6 June 1995 in Baltimore when adapting
-            the code to run on a unix machine. A little bit of additional trimming
-            on 27 June 1995. New printing formats.
-            Few printing format changes made on 27 Sept. 96.
-            Formula on the collision radius added on 1 April 1997.
-            Modification in sub GAUSS (NP and coeff.) and in sub APP on 22-24/4/1997.
+            (Minor modifications were made by MF on 8 Nov. 1988 on the Tempe
+            version) Some unused variables removed on 6 June 1995 in Baltimore
+            when adapting the code to run on a unix machine. A little bit of
+            additional trimming on 27 June 1995. New printing formats.  Few
+            printing format changes made on 27 Sept. 96.  Formula on the
+            collision radius added on 1 April 1997.  Modification in sub GAUSS
+            (NP and coeff.) and in sub APP on 22-24/4/1997.
 
         Input fparam.dat:
 
@@ -723,31 +848,36 @@ def test_vm_fortran(self):
 
         """
 
-        eph = {'rh': 2.469 * u.au, 'delta': 1.514 * u.au}
+        eph = {"rh": 2.469 * u.au, "delta": 1.514 * u.au}
 
         # Parent molecule is H2O
-        parent = Phys.from_dict({
-            'tau_T': 86430 * (eph['rh'] / u.au) ** 2 * u.s,
-            'tau_d': 101730 * (eph['rh'] / u.au) ** 2 * u.s,
-            'v_outflow': 0.514 * u.km / u.s,
-            'sigma': 3e-16 * u.cm ** 2,
-
-        })
+        parent = Phys.from_dict(
+            {
+                "tau_T": 86430 * (eph["rh"] / u.au) ** 2 * u.s,
+                "tau_d": 101730 * (eph["rh"] / u.au) ** 2 * u.s,
+                "v_outflow": 0.514 * u.km / u.s,
+                "sigma": 3e-16 * u.cm**2,
+            }
+        )
         # Fragment molecule is OH
-        fragment = Phys.from_dict({
-            'tau_T': 129000 * (eph['rh'] / u.au) ** 2 * u.s,
-            'v_photo': 1.05 * u.km / u.s
-        })
+        fragment = Phys.from_dict(
+            {
+                "tau_T": 129000 * (eph["rh"] / u.au) ** 2 * u.s,
+                "v_photo": 1.05 * u.km / u.s,
+            }
+        )
 
         # test values are copy-pasted from wm.f output
-        collision_sphere_radius = 0.14E+07 * u.cm
+        collision_sphere_radius = 0.14e07 * u.cm
         collision_sphere_radius_atol = 0.011e7 * u.cm
-        N_fragments_theory = 0.668E+33
-        N_fragments_theory_atol = 0.0011e33
-        N_fragments = 0.657E+33
-        N_fragments_atol = 0.0011e33
-
-        fragment_volume_density = '''
+        N_fragments_theory = 0.668e33
+        # N_fragments_theory_atol = 0.0011e33
+        N_fragments_theory_rtol = 0.001
+        N_fragments = 0.657e33
+        # N_fragments_atol = 0.0011e33
+        N_fragments_rtol = 0.003
+
+        fragment_volume_density = """
 0.97E+03  0.43E+03    0.68E+04  0.59E+02    0.13E+05  0.31E+02    0.18E+05  0.21E+02
 0.24E+05  0.15E+02    0.31E+05  0.11E+02    0.43E+05  0.79E+01    0.54E+05  0.59E+01
 0.66E+05  0.46E+01    0.78E+05  0.38E+01    0.90E+05  0.31E+01    0.11E+06  0.24E+01
@@ -760,8 +890,8 @@ def test_vm_fortran(self):
 0.16E+07  0.44E-02    0.17E+07  0.31E-02    0.19E+07  0.23E-02    0.20E+07  0.17E-02
 0.22E+07  0.12E-02    0.24E+07  0.78E-03    0.27E+07  0.51E-03    0.29E+07  0.34E-03
 0.31E+07  0.23E-03    0.34E+07  0.15E-03    0.37E+07  0.90E-04    0.41E+07  0.53E-04
-0.44E+07  0.32E-04    0.48E+07  0.20E-04'''
-        fragment_column_density = '''
+0.44E+07  0.32E-04    0.48E+07  0.20E-04"""
+        fragment_column_density = """
 0.970E+03   4.77E+11    1.088E+03   4.69E+11    1.221E+03   4.61E+11    1.370E+03   4.53E+11
 1.537E+03   4.44E+11    1.724E+03   4.34E+11    1.935E+03   4.22E+11    2.171E+03   4.15E+11
 2.436E+03   4.00E+11    2.733E+03   3.85E+11    3.066E+03   3.74E+11    3.441E+03   3.67E+11
@@ -780,40 +910,45 @@ def test_vm_fortran(self):
 9.697E+05   4.25E+09    1.088E+06   3.19E+09    1.221E+06   2.34E+09    1.370E+06   1.68E+09
 1.537E+06   1.19E+09    1.724E+06   8.29E+08    1.935E+06   5.67E+08    2.171E+06   3.78E+08
 2.436E+06   2.45E+08    2.733E+06   1.54E+08    3.066E+06   9.34E+07    3.441E+06   5.39E+07
-'''
+"""
 
         # convert strings to arrays
-        x = np.fromstring(fragment_volume_density, sep=' ')
+        x = np.fromstring(fragment_volume_density, sep=" ")
         n0_rho = x[::2] * u.km
-        n0 = x[1::2] / u.cm ** 3
+        n0 = x[1::2] / u.cm**3
         # absolute tolerance: 2 significant figures
         n0_atol = 1.1 * 10 ** (np.floor(np.log10(n0.value)) - 1) * n0.unit
         n0_atol_revised = n0_atol * 7
 
-        x = np.fromstring(fragment_column_density, sep=' ')
+        x = np.fromstring(fragment_column_density, sep=" ")
         sigma0_rho = x[::2] * u.km
-        sigma0 = x[1::2] / u.cm ** 2
+        sigma0 = x[1::2] / u.cm**2
         # absolute tolerance: 3 significant figures
-        sigma0_atol = (1.1 * 10 ** (np.floor(np.log10(sigma0.value)) - 2)
-                       * sigma0.unit)
+        sigma0_atol = 1.1 * 10 ** (np.floor(np.log10(sigma0.value)) - 2) * sigma0.unit
         sigma0_atol_revised = sigma0_atol * 40
 
         # evaluate the model
         Q0 = 1e27 / u.s
         coma = VectorialModel(base_q=Q0, parent=parent, fragment=fragment)
-        n = coma.volume_density(n0_rho)
-        sigma = coma.column_density(sigma0_rho)
+        n = [coma.volume_density(r) for r in n0_rho]
+        sigma = [coma.column_density(r) for r in sigma0_rho]
 
         # compare results
-        assert u.isclose(coma.vmodel['collision_sphere_radius'],
-                         collision_sphere_radius,
-                         atol=collision_sphere_radius_atol)
+        assert u.isclose(
+            coma.vmr.collision_sphere_radius,
+            collision_sphere_radius,
+            atol=collision_sphere_radius_atol,
+        )
 
-        assert np.isclose(coma.calc_num_fragments_theory(),
-                          N_fragments_theory, atol=N_fragments_theory_atol)
+        assert np.isclose(
+            coma.vmr.num_fragments_theory,
+            N_fragments_theory,
+            rtol=N_fragments_theory_rtol,
+        )
 
-        assert np.isclose(coma.calc_num_fragments_grid(),
-                          N_fragments, atol=N_fragments_atol)
+        assert np.isclose(
+            coma.vmr.num_fragments_grid, N_fragments, rtol=N_fragments_rtol
+        )
 
         assert u.allclose(n, n0, atol=n0_atol_revised)