Merge pull request #122 from rjlopez2:main

updtae brnach from main

Author: rjlopez2

docs/_static/butter_filter.gif

@@ -4,31 +4,122 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{currentmodule} optimap\n",
-    "```"
+    "# Filters \n",
+    "\n",
+    "in this tutorial:\n",
+    "- [ ] invert and normalize signal\n",
+    "- [ ] using temporal filter(s)\n",
+    "- [ ] using spatial filter(s)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{tip}\n",
-    "Download this tutorial as a {download}`Jupyter notebook <converted/filter.ipynb>`, or as a {download}`python script <converted/filter.py>` with code cells. We highly recommend using [Visual Studio Code](https://code.visualstudio.com/) to execute this tutorial. Alternatively, you could run the Python script in a terminal with ``python filter.py`` from the folder where the file is located.\n",
-    "```"
+    "Before we proceed to use the different filters to process and imporve the signal quality of the traces, let's first try to pree-process this example image by inverting and normalizing the signal."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Filters \n",
+    "## Invert and normalize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![plot viewer](/_static/invert_normalize_1.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that there are three different normalization options:\n",
     "\n",
-    "{{under_construction_warn}}\n",
+    " 1. Local Max: perform pixel wise normalization.\n",
     "\n",
-    "todo list:\n",
-    "- using temporal filter(s)\n",
-    "- using spatial filter(s)\n"
+    " 2. Slide window: same as local max but max is defined by a defined slide window.\n",
+    "\n",
+    " 1. Global: uses the averaged maximum value for normalization.\n",
+    "\n",
+    "In the previous example, we use the default normalization method (Local max)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's playback the whole sequence to visualize the AP propagation after normalization of the signal. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![plot viewer](/_static/playback_normalized_signal.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spatial filters\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The foldable filter tab will display a number of filter that we can use. The filter operations are typically costly for large images, however, if you have a GPU available you can benefit of optimized accelerated version of these filters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![plot viewer](/_static/gaussian_filter.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Temporal filters\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![plot viewer](/_static/butter_filter.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To better visualize the effect of the filter, specially the temporal filter, let's compare the profiles of the different resulting images after applying the spatial and temporal filters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![plot viewer](/_static/compare_filter_profiles.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {