diff --git a/README.md b/README.md
index b7ecc9c..ba3c2f0 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,41 @@
 # hybrid_template_library
 Library of templates to create hybrid data for spike sorting benchmarks
 
+[click here for access to the library](https://spikeinterface.github.io/hybrid_template_library/)
 
-How to run a python server
 
+## Testing locally
+
+How to run a python server for testing zarr access
+
+
+The following code makes use of the local python server to serve the zarr files. This is useful for testing the zarr files in the browser.  You can run this code in the folder where your zarr files are to start the server and make the zarr files accessible in the browser.
+
+```python
+from http.server import HTTPServer, SimpleHTTPRequestHandler
+import sys
+
+class CORSHTTPRequestHandler(SimpleHTTPRequestHandler):
+    def end_headers(self):
+        self.send_header('Access-Control-Allow-Origin', '*')
+        super().end_headers()
+
+    def log_message(self, format, *args):
+        sys.stderr.write("%s - - [%s] %s\n" %
+                         (self.client_address[0],
+                          self.log_date_time_string(),
+                          format % args))
+
+httpd = HTTPServer(('localhost', 8000), CORSHTTPRequestHandler)
+httpd.serve_forever()
+
+```
+
+Execute a script with this code in bash directly or save it to a file and run it with python. 
 
 ```bash
-python -m http.server
+python -c "from http.server import HTTPServer, SimpleHTTPRequestHandler; import sys; class CORSHTTPRequestHandler(SimpleHTTPRequestHandler): def end_headers(self): self.send_header('Access-Control-Allow-Origin', '*'); super().end_headers(); def log_message(self, format, *args): sys.stderr.write('%s - - [%s] %s\n' % (self.client_address[0], self.log_date_time_string(), format % args)); httpd = HTTPServer(('localhost', 8000), CORSHTTPRequestHandler); httpd.serve_forever()"
 ```
 
+
+
diff --git a/notebooks/how_to_calculate_templates_from_dandisets.ipynb b/notebooks/how_to_calculate_templates_from_dandisets.ipynb
new file mode 100644
index 0000000..7a11b36
--- /dev/null
+++ b/notebooks/how_to_calculate_templates_from_dandisets.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to estimate templates from Dandisets\n",
+    "The purpose of this draft notebook is to showcase how templates can be calculated by means of the `SortingAnalyzer` object.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Error 403 while sending HEAD request to https://api.dandiarchive.org/api/assets/a0a735eb-fcc6-4ec4-af3b-e590d0706d45/download/: \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/heberto/Downloads/ONE/openalyx.internationalbrainlab.org/histology/ATLAS/Needles/Allen/average_template_25.nrrd\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dandi.dandiapi import DandiAPIClient\n",
+    "from spikeinterface.extractors import NwbRecordingExtractor, IblSortingExtractor\n",
+    "\n",
+    "client = DandiAPIClient.for_dandi_instance(\"dandi\")\n",
+    "\n",
+    "# We specifiy a dataset by is dandiset_id and its asset path\n",
+    "dandiset_id = \"000409\"\n",
+    "dandiset = client.get_dandiset(dandiset_id)\n",
+    "\n",
+    "asset_path = \"sub-KS042/sub-KS042_ses-8c552ddc-813e-4035-81cc-3971b57efe65_behavior+ecephys+image.nwb\"\n",
+    "recording_asset = dandiset.get_asset_by_path(path=asset_path)\n",
+    "url = recording_asset.get_content_url(follow_redirects=True, strip_query=True)\n",
+    "file_path = url\n",
+    "\n",
+    "\n",
+    "# Note that this ElectricalSeries corresponds to the data from probe 00\n",
+    "electrical_series_path = \"acquisition/ElectricalSeriesAp00\"\n",
+    "recording = NwbRecordingExtractor(file_path=file_path, stream_mode=\"remfile\", electrical_series_path=electrical_series_path)  \n",
+    "session_id = recording._file[\"general\"][\"session_id\"][()].decode()\n",
+    "eid = session_id.split(\"-chunking\")[0] # eid : experiment id\n",
+    "\n",
+    "\n",
+    "# We use the sorting extractor from the IBL spike sorting pipeline that matches with eid\n",
+    "from one.api import ONE\n",
+    "ONE.setup(base_url='https://openalyx.internationalbrainlab.org', silent=True)\n",
+    "one_instance = ONE(password='international')\n",
+    "\n",
+    "\n",
+    "# Then we match the available probes with the probe number in the electrical series path\n",
+    "pids, probes = one_instance.eid2pid(eid)\n",
+    "probe_number = electrical_series_path.split(\"Ap\")[-1]\n",
+    "\n",
+    "sorting_pid = None \n",
+    "for pid, probe in zip(pids, probes):\n",
+    "    probe_number_in_pid = probe[-2:]\n",
+    "    if probe_number_in_pid == probe_number:\n",
+    "        sorting_pid = pid\n",
+    "        break\n",
+    "    \n",
+    "    \n",
+    "sorting = IblSortingExtractor(pid=sorting_pid, one=one_instance, good_clusters_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have our sorting and recording objects. We perform some preprocessing on our recording and slice ouf objects so we only estimate templates from the last minutes of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spikeinterface.preprocessing import astype, phase_shift, common_reference, highpass_filter\n",
+    "\n",
+    "pre_processed_recording = common_reference(\n",
+    "    highpass_filter(phase_shift(astype(recording=recording, dtype=\"float32\")), freq_min=1.0)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# take first and last minute\n",
+    "sampling_frequency_recording =pre_processed_recording.sampling_frequency\n",
+    "sorting_sampling_frequency = sorting.sampling_frequency\n",
+    "num_samples = pre_processed_recording.get_num_samples()\n",
+    "\n",
+    "# Take the last 10 minutes of the recording\n",
+    "minutes = 10\n",
+    "seconds = minutes * 60\n",
+    "samples_before_end = int(seconds * sampling_frequency_recording)\n",
+    "\n",
+    "start_frame_recording = num_samples - samples_before_end\n",
+    "end_frame_recording = num_samples\n",
+    "\n",
+    "recording_end = pre_processed_recording.frame_slice(\n",
+    "    start_frame=start_frame_recording,\n",
+    "    end_frame=end_frame_recording\n",
+    ")\n",
+    "\n",
+    "# num_samples = sorting.get_num_frames()\n",
+    "samples_before_end = int(seconds * sorting_sampling_frequency)\n",
+    "start_frame_sorting = num_samples - samples_before_end\n",
+    "end_frame_sorting = num_samples\n",
+    "\n",
+    "sorting_end = sorting.frame_slice(\n",
+    "    start_frame=start_frame_sorting,\n",
+    "    end_frame=end_frame_sorting\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now use the `SortingAnalyzer` object to estimate templates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/heberto/development/spikeinterface/src/spikeinterface/core/baserecordingsnippets.py:244: UserWarning: There is no Probe attached to this recording. Creating a dummy one with contact positions\n",
+      "  warn(\"There is no Probe attached to this recording. Creating a dummy one with contact positions\")\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e86346cb6784f83a53e813f19c71b37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "estimate_templates_with_accumulator:   0%|          | 0/600 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from spikeinterface.core import create_sorting_analyzer\n",
+    "\n",
+    "analyzer = create_sorting_analyzer(sorting_end, recording_end, sparse=False, folder=f\"analyzer_{eid}\")\n",
+    "\n",
+    "\n",
+    "random_spike_parameters = {\n",
+    "    \"method\": \"all\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "template_extension_parameters = {\n",
+    "    \"ms_before\": 3.0,\n",
+    "    \"ms_after\": 5.0,\n",
+    "    \"operators\": [\"average\", \"std\"],\n",
+    "}\n",
+    "\n",
+    "extensions = {\n",
+    "    \"random_spikes\": random_spike_parameters,\n",
+    "    \"templates\": template_extension_parameters,\n",
+    "}\n",
+    "\n",
+    "analyzer.compute_several_extensions(\n",
+    "    extensions=extensions,\n",
+    "    n_jobs=-1,\n",
+    "    progress_bar=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates_extension = analyzer.get_extension(\"templates\")\n",
+    "template_object = templates_extension.get_data(outputs=\"Templates\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's it. We now have our data in a templates object (note the outputs keyword on `get_data`). As a visual test that the pipeline works we show how the best chanenl (defined as the one with the maximum peak to peak amplitude) and plot some unit's templates for that channel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "\n",
+    "def find_channels_with_max_peak_to_peak_vectorized(templates_array):\n",
+    "    \"\"\"\n",
+    "    Find the channel indices with the maximum peak-to-peak value in each waveform template\n",
+    "    using a vectorized operation for improved performance.\n",
+    "\n",
+    "    Parameters:\n",
+    "    templates_array (numpy.ndarray): The waveform templates_array, typically a 3D array (units x time x channels).\n",
+    "\n",
+    "    Returns:\n",
+    "    numpy.ndarray: An array of indices of the channel with the maximum peak-to-peak value for each unit.\n",
+    "    \"\"\"\n",
+    "    # Compute the peak-to-peak values along the time axis (axis=1) for each channel of each unit\n",
+    "    peak_to_peak_values = np.ptp(templates_array, axis=1)\n",
+    "\n",
+    "    # Find the indices of the channel with the maximum peak-to-peak value for each unit\n",
+    "    best_channels = np.argmax(peak_to_peak_values, axis=1)\n",
+    "\n",
+    "    return best_channels\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7b476fe790f0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x2000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Adjust global font size\n",
+    "plt.rcParams.update({\"font.size\": 18})\n",
+    "\n",
+    "unit_ids = template_object.unit_ids\n",
+    "best_channels = find_channels_with_max_peak_to_peak_vectorized(template_object.templates_array)\n",
+    "\n",
+    "\n",
+    "num_columns = 3\n",
+    "num_rows = 3\n",
+    "\n",
+    "fig, axs = plt.subplots(num_rows, num_columns, figsize=(15, 20), sharex=True, sharey=True)\n",
+    "\n",
+    "center = template_object.nbefore\n",
+    "\n",
+    "for unit_index, unit_id in enumerate(unit_ids[: num_columns * num_rows]):\n",
+    "    row, col = divmod(unit_index, num_columns)\n",
+    "    ax = axs[row, col]\n",
+    "    best_channel = best_channels[unit_index]\n",
+    "\n",
+    "    ax.plot(template_object.templates_array[unit_index, :, best_channel], linewidth=3, label=\"best channel\", color=\"black\")\n",
+    "\n",
+    "    ax.axvline(center, linestyle=\"--\", color=\"red\", linewidth=1)\n",
+    "    ax.axhline(0, linestyle=\"--\", color=\"gray\", linewidth=1)\n",
+    "    ax.set_title(f\"Unit {unit_id}\")\n",
+    "\n",
+    "    # Hide all spines and ticks\n",
+    "    ax.tick_params(axis=\"both\", which=\"both\", length=0)\n",
+    "\n",
+    "    # Hide all spines\n",
+    "    for spine in ax.spines.values():\n",
+    "        spine.set_visible(False)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Create the legend with specified colors\n",
+    "handles, labels = axs[0, 0].get_legend_handles_labels()\n",
+    "fig.legend(handles, labels, loc=\"upper center\", bbox_to_anchor=(0.5, 1.02), ncol=3, frameon=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuroconv_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}