Probe geometry doc

examples/loading_data/loading_raw_audio_data.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5683982d",
+   "metadata": {},
+   "source": [
+    "# Loading Raw Audio Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b2485da",
+   "metadata": {
+    "nbsphinx": "hidden"
+   },
+   "outputs": [],
+   "source": [
+    "# Turn off logging, this is a hidden cell on docs page\n",
+    "import logging\n",
+    "logger = logging.getLogger('ibllib')\n",
+    "logger.setLevel(logging.CRITICAL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16345774",
+   "metadata": {},
+   "source": [
+    "The audio file is saved from the microphone. It is useful to look at it to plot a spectrogram and confirm the sounds played during the task are indeed audible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d62c890",
+   "metadata": {},
+   "source": [
+    "## Relevant datasets\n",
+    "* _iblrig_micData.raw.flac\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc23fdf7",
+   "metadata": {},
+   "source": [
+    "## Loading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9103084d",
+   "metadata": {},
+   "source": [
+    "### Loading raw audio file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b807296",
+   "metadata": {
+    "ibl_execute": false
+   },
+   "outputs": [],
+   "source": [
+    "from one.api import ONE\n",
+    "import soundfile as sf\n",
+    "\n",
+    "one = ONE()\n",
+    "eid = '4ecb5d24-f5cc-402c-be28-9d0f7cb14b3a'\n",
+    "\n",
+    "# -- Get raw data\n",
+    "filename = one.load_dataset(eid, '_iblrig_micData.raw.flac', download_only=True)\n",
+    "with open(filename, 'rb') as f:\n",
+    "    wav, fs = sf.read(f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "203d23c1",
+   "metadata": {},
+   "source": [
+    "## Plot the spectrogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "811e3533",
+   "metadata": {
+    "ibl_execute": false
+   },
+   "outputs": [],
+   "source": [
+    "from ibllib.io.extractors.training_audio import welchogram\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# -- Compute spectrogram over first 2 minutes\n",
+    "t_idx = 120 * fs\n",
+    "tscale, fscale, W, detect = welchogram(fs, wav[:t_idx])\n",
+    "\n",
+    "# -- Put data into single variable\n",
+    "TF = {}\n",
+    "\n",
+    "TF['power'] = W.astype(np.single)\n",
+    "TF['frequencies'] = fscale[None, :].astype(np.single)\n",
+    "TF['onset_times'] = detect\n",
+    "TF['times_mic'] = tscale[:, None].astype(np.single)\n",
+    "\n",
+    "# # -- Plot spectrogram\n",
+    "tlims = TF['times_mic'][[0, -1]].flatten()\n",
+    "flims = TF['frequencies'][0, [0, -1]].flatten()\n",
+    "fig = plt.figure(figsize=[16, 7])\n",
+    "ax = plt.axes()\n",
+    "im = ax.imshow(20 * np.log10(TF['power'].T), aspect='auto', cmap=plt.get_cmap('magma'),\n",
+    "               extent=np.concatenate((tlims, flims)),\n",
+    "               origin='lower')\n",
+    "ax.set_xlabel(r'Time (s)')\n",
+    "ax.set_ylabel(r'Frequency (Hz)')\n",
+    "plt.colorbar(im)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bef6702e",
+   "metadata": {},
+   "source": [
+    "## More details\n",
+    "* [Description of audio datasets](https://docs.google.com/document/d/1OqIqqakPakHXRAwceYLwFY9gOrm8_P62XIfCTnHwstg/edit#heading=h.n61f0vdcplxp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e9dd4b9",
+   "metadata": {},
+   "source": [
+    "## Useful modules\n",
+    "* [ibllib.io.extractors.training_audio](https://int-brain-lab.github.io/iblenv/_autosummary/ibllib.io.extractors.training_audio.html#module-ibllib.io.extractors.training_audio)"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Edit Metadata",
+  "kernelspec": {
+   "display_name": "Python [conda env:iblenv] *",
+   "language": "python",
+   "name": "conda-env-iblenv-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/loading_data/loading_raw_ephys_data.ipynb

@@ -152,7 +152,7 @@
     "\n",
     "# Use spikeglx reader to read in the whole raw data\n",
     "sr = spikeglx.Reader(bin_file)\n",
-    "sr.shape\n"
+    "print(sr.shape)"
    ]
   },
   {
@@ -326,6 +326,62 @@
     "destriped = destripe(raw, fs=sr.fs)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Get the probe geometry"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Using the `eid` and `probe` information"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from brainbox.io.one import load_channel_locations\n",
+    "channels = load_channel_locations(eid, probe)\n",
+    "print(channels[probe].keys())\n",
+    "# Use the axial and lateral coordinates ; Print example first 4 channels\n",
+    "print(channels[probe][\"axial_um\"][0:4])\n",
+    "print(channels[probe][\"lateral_um\"][0:4])"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Using the reader and the `.cbin` file"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# You would have loaded the bin file as per the loading example above\n",
+    "# sr = spikeglx.Reader(bin_file)\n",
+    "sr.geometry"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "markdown",
    "id": "9851b10d",