Merge branch 'docs' into develop

examples/exploring_data/data_download.ipynb

@@ -37,10 +37,10 @@
    "source": [
     "## Installation\n",
     "### Environment\n",
-    "To use IBL data you will need a python environment with python > 3.8. To create a new environment from scratch you can install [anaconda](https://www.anaconda.com/products/distribution#download-section) and follow the instructions below to create a new python environment (more information can also be found [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html))\n",
+    "To use IBL data you will need a python environment with python > 3.10, although Python 3.12 is recommended. To create a new environment from scratch you can install [anaconda](https://www.anaconda.com/products/distribution#download-section) and follow the instructions below to create a new python environment (more information can also be found [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html))\n",
     "\n",
     "```\n",
-    "conda create --name ibl python=3.11\n",
+    "conda create --name ibl python=3.12\n",
     "```\n",
     "Make sure to always activate this environment before installing or working with the IBL data\n",
     "```\n",
@@ -138,9 +138,33 @@
    "outputs": [],
    "source": [
     "# Each session is represented by a unique experiment id (eID)\n",
-    "print(sessions[0])"
+    "print(sessions[0],)"
    ]
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Find recordings of a specific brain region\n",
+    "If we are interested in a given brain region, we can use the `search_insertions` method to find all recordings associated with that region. For example, to find all recordings associated with the **Rhomboid Nucleus (RH)** region of the thalamus."
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "# this is the query that yields the few recordings for the Rhomboid Nucleus (RH) region\n",
+    "insertions_rh = one.search_insertions(atlas_acronym='RH', datasets='spikes.times.npy', project='brainwide')\n",
+    "\n",
+    "# if we want to extend the search to all thalamic regions, we can do the following\n",
+    "insertions_th = one.search_insertions(atlas_acronym='TH', datasets='spikes.times.npy', project='brainwide')\n",
+    "\n",
+    "# the Allen brain regions parcellation is hierarchical, and searching for Thalamus will return all child Rhomboid Nucleus (RH) regions\n",
+    "assert set(insertions_rh).issubset(set(insertions_th))\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -402,9 +426,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

examples/loading_data/loading_spikesorting_data.ipynb

@@ -43,7 +43,8 @@
     "## Relevant Alf objects\n",
     "* channels\n",
     "* clusters\n",
-    "* spikes"
+    "* spikes\n",
+    "* waveforms"
    ]
   },
   {
@@ -74,9 +75,10 @@
    "outputs": [],
    "source": [
     "pid = 'da8dfec1-d265-44e8-84ce-6ae9c109b8bd' \n",
-    "sl = SpikeSortingLoader(pid=pid, one=one)\n",
-    "spikes, clusters, channels = sl.load_spike_sorting()\n",
-    "clusters = sl.merge_clusters(spikes, clusters, channels)"
+    "ssl = SpikeSortingLoader(pid=pid, one=one)\n",
+    "spikes, clusters, channels = ssl.load_spike_sorting()\n",
+    "clusters = ssl.merge_clusters(spikes, clusters, channels)\n",
+    "waveforms = ssl.load_spike_sorting_object('waveforms')  # loads in the template waveforms"
    ]
   },
   {