fix docs

docs/src/background/index.rst

@@ -1,13 +1,13 @@
 Background
 ##########
 
-Some background on the physics of spectral line observations, the Bayesian reconstruction algorithm, and their numerical implementation.
-This is all based `this paper <https://ui.adsabs.harvard.edu/abs/2024arXiv240218525D/abstract>`_.
-Please cite `the paper <https://ui.adsabs.harvard.edu/abs/2024arXiv240218525D/abstract>`_ if you use any of this information.
+Here we provide some background on the physics of spectral line observations, the Bayesian reconstruction algorithm, and their numerical implementation in pomme.
+All of this is based on our paper `De Ceuster et. al (2024) <https://ui.adsabs.harvard.edu/abs/2024arXiv240218525D/abstract>`_.
+Please cite the paper if you use any of this information.
 
 .. toctree::
    :maxdepth: 4
    :caption: Contents:
 
-   forward_model
-   inverse_model
+   forward_problem
+   inverse_problem

docs/src/examples/spherical/synthetic_observations.ipynb

@@ -149,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "undefined.undefined.undefined"
   }
  },
  "nbformat": 4,

docs/src/examples/spherically_symmetric.ipynb

@@ -300,20 +300,34 @@
     "smodel.get_boundary_condition = get_boundary_condition"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the velocity/frequency range of interest around the lines."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "# Frequency data\n",
     "vdiff = 500   # velocity increment size [m/s]\n",
     "nfreq =  50   # number of frequencies\n",
     "\n",
     "velocities  = nfreq * vdiff * torch.linspace(-1, +1, nfreq, dtype=torch.float64)\n",
-    "frequencies = [(1.0 + velocities / constants.c.si.value) * line.frequency for line in lines]\n",
-    "\n"
+    "frequencies = [(1.0 + velocities / constants.c.si.value) * line.frequency for line in lines]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obss = smodel.image(lines, frequencies, r_max=r_out)"
    ]
   },
   {
@@ -352,6 +366,11 @@
     "    return loss"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 12,