diff --git a/.gitignore b/.gitignore
index 68bc17f..3c63430 100644
--- a/.gitignore
+++ b/.gitignore
@@ -158,3 +158,7 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+fpl-scipy2023-data.zip
+fpl-scipy2023-data/
+.idea/
\ No newline at end of file
diff --git a/README.md b/README.md
index 5172e8d..67b25a7 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,51 @@
-# fastplotlib-scipy2023
-fastplotlib demos at scipiy 2023
+# fastplotlib SciPy 2023
+
+Materials for the [`fastplotlib`](https://github.com/kushalkolar/fastplotlib/) talk at SciPy 2023. This repo includes installation instructions and the demo notebooks covered in our talk. 
+
+For more info on `fastplotlib` see the repo: https://github.com/kushalkolar/fastplotlib/
+
+The demos cover the following topics:
+1. `Images` - plotting simple images, feature changes, image updates, `ImageWidget`
+2. `Lines` - 2D line plots, fancy indexing of features, `LineCollection`, `LineStack`, `LinearSelector`, `LinearRegionSelector`, `Heatmap`
+4. Interactivity - high-level interactivity system to link graphics and their features together, addding event handlers for further interaction
+5. `GridPlots` - grid of `Subplots`
+6. `Scatters` - 2D and 3D scatter plots
+
+# Installation instructions
+
+See the `fastplotlib` repo for [installation](https://github.com/kushalkolar/fastplotlib#installation). 
+
+In order to run the notebooks you will also need to have `imageio` and `zarr` installed. These are not dependencies of `fastplotlib`, but are being used in these demos.
+
+### Install using pip
+```
+# other packages specifically used for this demo
+# jupyterlab v3 because of sidecar, and pin pylingalg because of pygfx
+pip install "jupyterlab<4" imageio "imageio[pyav]" zarr sidecar pylinalg==0.4.0 scikit-image glfw
+
+# optional, you'll need C compilers
+pip install simplejpeg
+
+# fastplotlib with notebook dependencies
+pip install "fastplotlib[notebook]"
+```
+
+# General `fastplotlib` API
+## 1. Graphics - objects that are drawn
+- `Image`, `Line`, `Scatter`, `Heatmap`
+- Collections - `LineCollection`, `LineStack` (ex: neural timeseries data)
+- Interactions
+## 2. Layouts
+- `Plot` - a single plot area
+- `GridPlot` - a grid of `Subplots`
+## 3. Widgets - high level widgets to make repetitive UIs easier
+- `ImageWidget`- n-dimensional widget for Image data
+- Sliders, support window functions, `GridPlot`, etc.
+
+# Docs
+For a more in-depth look at our API, please visit our [docs](https://fastplotlib.readthedocs.io/en/).
+
+# Contributions
+`fastplotlib` is a relatively new library, and we are always looking for feedback or help! Please see the [contributing guide](https://github.com/kushalkolar/fastplotlib/blob/master/CONTRIBUTING.md). 
+
+You can also look at our [Roadmap for 2023](https://github.com/kushalkolar/fastplotlib/issues/55) and [Issues](https://github.com/kushalkolar/fastplotlib/issues) for more ideas on how to contribute.
diff --git a/download_data.py b/download_data.py
new file mode 100644
index 0000000..197d69b
--- /dev/null
+++ b/download_data.py
@@ -0,0 +1,39 @@
+import os
+import requests
+from tqdm import tqdm
+from pathlib import Path
+from zipfile import ZipFile
+
+# make data dir if not exists
+data_dir = Path(os.path.dirname(os.path.abspath(__file__)), "fpl-scipy2023-data")
+os.makedirs(data_dir, exist_ok=True)
+
+data_file = Path(
+    os.path.dirname(os.path.abspath(__file__)), "fpl-scipy2023-data.zip"
+)
+
+
+def download_data():
+
+    # if data already exists, return
+    if os.path.exists(data_dir) and len(list(data_dir.iterdir())) != 0:
+        print("data already exists")
+        return
+
+    # else download
+    print(f"Downloading data")
+    url = f"https://zenodo.org/record/8140484/files/fpl-scipy2023-data.zip"
+
+    # basically from https://stackoverflow.com/questions/37573483/progress-bar-while-download-file-over-http-with-requests/37573701
+    response = requests.get(url, stream=True)
+    total_size_in_bytes = int(response.headers.get("content-length", 0))
+    block_size = 1024  # 1 Kibibyte
+    progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
+
+    with open(data_file, "wb") as file:
+        for data in response.iter_content(block_size):
+            progress_bar.update(len(data))
+            file.write(data)
+    progress_bar.close()
+
+    ZipFile(data_file).extractall(data_dir.parent)
diff --git a/fastplotlib scipy 2023.pdf b/fastplotlib scipy 2023.pdf
new file mode 100644
index 0000000..92ed279
Binary files /dev/null and b/fastplotlib scipy 2023.pdf differ
diff --git a/gridplot.ipynb b/gridplot.ipynb
deleted file mode 100644
index b6e89aa..0000000
--- a/gridplot.ipynb
+++ /dev/null
@@ -1,333 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5968b66a-8e78-488e-a2a0-1b2ac72cbb28",
-   "metadata": {},
-   "source": [
-    "# GridPlot\n",
-    "\n",
-    "Allows for proper subplotting. Create graphics and add them to a `GridPlot`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "66eaaf8e-3324-4e77-9cc4-f52682c7caea",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import fastplotlib as fpl\n",
-    "import numpy as np\n",
-    "from sidecar import Sidecar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "e481359b-6b49-40c5-86dd-b1c177314cd6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "01f8aa22c8c94c8a81eba580d256bb4b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RFBOutputContext()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:34: UserWarning: converting float64 array to float32\n",
-      "  warn(f\"converting {array.dtype} array to float32\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "# GridPlot of shape 2 x 3 with all controllers synced\n",
-    "grid_plot = fpl.GridPlot(shape=(2, 3), controllers=\"sync\")\n",
-    "\n",
-    "# Make a random image graphic for each subplot\n",
-    "for subplot in grid_plot:\n",
-    "    # create image data\n",
-    "    data = np.random.rand(512, 512)\n",
-    "    # add an image to the subplot\n",
-    "    subplot.add_image(data, name=\"rand-img\")\n",
-    "\n",
-    "# Define a function to update the image graphics with new data\n",
-    "# add_animations will pass the gridplot to the animation function\n",
-    "def update_data(gp):\n",
-    "    for sp in gp:\n",
-    "        new_data = np.random.rand(512, 512)\n",
-    "        # index the image graphic by name and set the data\n",
-    "        sp[\"rand-img\"].data = new_data\n",
-    "        \n",
-    "# add the animation function\n",
-    "grid_plot.add_animations(update_data)\n",
-    "\n",
-    "sc = Sidecar(title=\"gridplot\")\n",
-    "\n",
-    "# show the gridplot \n",
-    "with sc:\n",
-    "    display(grid_plot.show())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30596ad1-ffb7-4beb-a723-7cb5a2f45eac",
-   "metadata": {},
-   "source": [
-    "## Accessing subplots within `GridPlot`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ed86a89d-0cb9-44e6-97ac-56499c9a0a2e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "unnamed: Subplot @ 0x7fac550680d0\n",
-       "  parent: fastplotlib.GridPlot @ 0x7fac550a0d10\n",
-       "\n",
-       "  Graphics:\n",
-       "\t'rand-img': ImageGraphic @ 0x7fac3c169310"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# positional indexing\n",
-    "# row 0 and col 0\n",
-    "grid_plot[0, 0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd073684-8f3a-4283-b9c1-aad271c82752",
-   "metadata": {},
-   "source": [
-    "### You can get the graphics within a subplot, just like with simple `Plot`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "bf671e47-70b7-428b-bfca-55123e99344d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<weakproxy at 0x7fac0472ca90 to ImageGraphic at 0x7fac04725810>,)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid_plot[0, 1].graphics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25a0ef00-05f3-484a-9b38-91d72104e736",
-   "metadata": {},
-   "source": [
-    "### and change their properties"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2b9f4996-9321-488c-aa7d-99129a8a0dd0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "grid_plot[0, 1].graphics[0].cmap.vmax = 0.5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5337a9cf-4823-43f3-9fa5-9641f4026d50",
-   "metadata": {},
-   "source": [
-    "### more indexing with `GridPlot`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "fb186973-1ce2-4361-bb97-0f50342f9861",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# you can give subplots human-readable string names\n",
-    "grid_plot[0, 2].name = \"top-right-plot\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "403d2d0a-7cbf-4e43-a75a-dfff98d40cf2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "top-right-plot: Subplot @ 0x7fac3c092e50\n",
-       "  parent: fastplotlib.GridPlot @ 0x7fac550a0d10\n",
-       "\n",
-       "  Graphics:\n",
-       "\t'rand-img': ImageGraphic @ 0x7fac04727310"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid_plot[\"top-right-plot\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4f296891-a8b4-47d1-8d17-7554c36b00ed",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 2)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# view its position\n",
-    "grid_plot[\"top-right-plot\"].position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "85f06940-3eec-49e9-97a0-00054ad27d8a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# these are really the same\n",
-    "grid_plot[\"top-right-plot\"] is grid_plot[0, 2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33ced14b-f958-474e-85a9-5f395a8de82b",
-   "metadata": {},
-   "source": [
-    "Indexing with subplot name and graphic name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "75ecdd19-bd9a-4d93-bb18-a6216e3fde1b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "grid_plot[\"top-right-plot\"][\"rand-img\"].cmap.vmin = 0.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "4f5f7fab-d7f2-4b6b-8f2d-c0745f9193cb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "sc.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "204385ee-b164-4e74-8b03-e2f0013f9e47",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/image.ipynb b/image.ipynb
index 0d2dd71..9f5bb5b 100644
--- a/image.ipynb
+++ b/image.ipynb
@@ -13,7 +13,10 @@
   {
    "cell_type": "markdown",
    "id": "807a45e5-5473-477b-9cb3-41a03a8135c7",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "## `fastplotlib` API\n",
     "\n",
@@ -33,6 +36,21 @@
     "    "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "03169ecd-8916-4ddd-a023-066f7be7d648",
+   "metadata": {},
+   "source": [
+    "# Running remotely on a workstation on the East Coast!!\n",
+    "\n",
+    "Using Jupyter remote frame buffer: https://github.com/vispy/jupyter_rfb\n",
+    "\n",
+    "Rendering all being done on remote GPU. We are only displaying a rasterized image in the Jupyter canvas.\n",
+    "\n",
+    "### GPU: Nvidia RTX 3090\n",
+    "### CPU: AMD Ryzen 16 core (5950X)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b95759bc-b567-4327-b825-68fe871c3473",
@@ -54,81 +72,71 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "775411ec-15da-4231-a67f-9300265c6ac6",
+   "id": "9aeb2e39-cc13-4662-ada0-cfed7ee2b26c",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: imageio in /home/clewis7/venvs/mescore/lib/python3.11/site-packages (2.28.1)\n",
-      "Requirement already satisfied: numpy in /home/clewis7/venvs/mescore/lib/python3.11/site-packages (from imageio) (1.23.5)\n",
-      "Requirement already satisfied: pillow>=8.3.2 in /home/clewis7/venvs/mescore/lib/python3.11/site-packages (from imageio) (9.5.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install imageio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6dc8a89-f6c3-4e22-84c5-7e615b452a1e",
-   "metadata": {},
+   "outputs": [],
    "source": [
-    "**An example video used later required the use of tiffile to load it into memory, but tiffile is not required to use fastplotlib**"
+    "import imageio.v3 as iio\n",
+    "\n",
+    "# likewise there is an image which we will load with tifffile\n",
+    "import tifffile"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "c74ee3e0-572f-4619-b875-d313de81d7ef",
+   "execution_count": 2,
+   "id": "fb57c3d3-f20d-4d88-9e7a-04b9309bc637",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: tiffile in /home/clewis7/venvs/mescore/lib/python3.11/site-packages (2018.10.18)\n",
-      "Requirement already satisfied: tifffile in /home/clewis7/venvs/mescore/lib/python3.11/site-packages (from tiffile) (2023.4.12)\n",
-      "Requirement already satisfied: numpy in /home/clewis7/venvs/mescore/lib/python3.11/site-packages (from tifffile->tiffile) (1.23.5)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!pip install tiffile"
+    "import fastplotlib as fpl\n",
+    "from ipywidgets import VBox, HBox, IntSlider\n",
+    "import numpy as np\n",
+    "from sidecar import Sidecar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f02c2fe-e77d-447d-9483-da66574a3950",
+   "metadata": {},
+   "source": [
+    "**Additionally, the data we are using throughout these demos can be downloaded from Zenodo.**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "9aeb2e39-cc13-4662-ada0-cfed7ee2b26c",
+   "execution_count": 3,
+   "id": "99f0c624-85a6-4a42-92b0-30b0b04075b4",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "import imageio.v3 as iio\n",
-    "import tifffile"
+    "from download_data import download_data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "fb57c3d3-f20d-4d88-9e7a-04b9309bc637",
+   "execution_count": 4,
+   "id": "630e4762-de01-47fe-be9d-547182a1fbf9",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data already exists\n"
+     ]
+    }
+   ],
    "source": [
-    "import fastplotlib as fpl\n",
-    "from ipywidgets import VBox, HBox, IntSlider\n",
-    "import numpy as np\n",
-    "from sidecar import Sidecar"
+    "download_data()"
    ]
   },
   {
@@ -141,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "237823b7-e2c0-4e2f-9ee8-e3fc2b4453c4",
    "metadata": {
     "tags": []
@@ -150,7 +158,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "224fd3b3c63449e3956c393bfa930772",
+       "model_id": "06aa06e6af134d61bb3a40df62a601c2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -164,7 +172,7 @@
    ],
    "source": [
     "# create a `Plot` instance\n",
-    "plot = fpl.Plot()\n",
+    "plot = fpl.Plot(size=(600, 500))\n",
     "\n",
     "# get a grayscale image\n",
     "data = iio.imread(\"imageio:camera.png\")\n",
@@ -172,8 +180,8 @@
     "# plot the image data\n",
     "image_graphic = plot.add_image(data=data, name=\"sample-image\")\n",
     "\n",
-    "# display plot in side car\n",
-    "sc = Sidecar(title=\"sample image\")\n",
+    "# display plot in sidecar\n",
+    "sc = Sidecar(title=\"sample image\", layout={'width': '800px'})\n",
     "\n",
     "with sc:\n",
     "    # show the plot\n",
@@ -206,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "de816c88-1c4a-4071-8a5e-c46c93671ef5",
    "metadata": {
     "tags": []
@@ -219,434 +227,105 @@
   {
    "cell_type": "markdown",
    "id": "7303677c-9f7a-44dd-8660-eced0cdca565",
-   "metadata": {},
-   "source": [
-    "### Slicing data\n",
-    "\n",
-    "**Most features, such as `data`, support slicing!**\n",
-    "\n",
-    "Our image data is of shape [n_rows, n_columns]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "83b2db1b-2783-4e89-bcf3-66bb6e09e18a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "image_graphic.data[::15, :] = 1\n",
-    "image_graphic.data[:, ::15] = 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98e41780-eb84-46d4-8c06-ec59b3238572",
-   "metadata": {},
-   "source": [
-    "**Fancy indexing**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3e298c1c-7551-4401-ade0-b9af7d2bbe23",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "image_graphic.data[data > 175] = 255"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "958b4e14-d820-4acb-af75-3485c844de17",
-   "metadata": {},
-   "source": [
-    "**Adjust vmin vmax**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ea100318-023c-4963-b859-8c2a8fcce301",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "image_graphic.cmap.vmin = 50\n",
-    "image_graphic.cmap.vmax = 150"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba4be6e6-454b-465f-9eac-3cde424963aa",
-   "metadata": {},
-   "source": [
-    "**Set the entire data array again**\n",
+    "### Slicing data\n",
     "\n",
-    "Note: The shape of the new data array must match the current data shown in the `Graphic`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f6f6a977-f96b-43f4-8235-0f4fce1f6f57",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(512, 512, 3)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_data = iio.imread(\"imageio:astronaut.png\")\n",
-    "new_data.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38b76440-9b10-42dd-8ca5-2530d2a1da6e",
-   "metadata": {},
-   "source": [
-    "This is an RGB image, convert to grayscale to maintain the shape of (512, 512)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "7a3dab00-6a92-4ba7-a19a-a4a3c23f3421",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(512, 512)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gray = new_data.dot([0.3, 0.6, 0.1])\n",
-    "gray.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ef731a15-59bb-4bbf-9955-c6069d1c9a7e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:34: UserWarning: converting float64 array to float32\n",
-      "  warn(f\"converting {array.dtype} array to float32\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "image_graphic.data = gray"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea76f601-f655-43a2-a4ab-2730643fc6a2",
-   "metadata": {},
-   "source": [
-    "**reset vmin vmax**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a1320f9f-fff5-4b9b-9a49-0695042ab8ab",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "image_graphic.cmap.reset_vmin_vmax()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67b92ffd-40cc-43fe-9df9-0e0d94763d8e",
-   "metadata": {},
-   "source": [
-    "### Indexing plots\n",
+    "**Most features, such as `data`, support slicing!**\n",
     "\n",
-    "**Plots are indexable and give you their graphics by name**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "e6ba689c-ff4a-44ef-9663-f2c8755072c4",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "unnamed: Plot @ 0x7f0c4b8be810\n",
-       "  parent: None\n",
-       "  Graphics:\n",
-       "\t'sample-image': ImageGraphic @ 0x7f0c044ffed0"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "5b18f4e3-e13b-46d5-af1f-285c5a7fdc12",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<weakproxy at 0x7f0c04508540 to ImageGraphic at 0x7f0c044ffed0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot[\"sample-image\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "490f0040-0462-4fac-b91e-7537e2d0b2c2",
-   "metadata": {},
-   "source": [
-    "**You can also use numerical indexing on `plot.graphics`**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "2b5c1321-1fd4-44bc-9433-7439ad3e22cf",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<weakproxy at 0x7f0c04508540 to ImageGraphic at 0x7f0c044ffed0>,)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot.graphics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "930ccff4-b678-4e21-a8b1-7f77a232d811",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<weakproxy at 0x7f0c04508540 to ImageGraphic at 0x7f0c044ffed0>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot.graphics[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57cfdeae-b6ae-409b-abe7-4b76155f1f39",
-   "metadata": {},
-   "source": [
-    "The `Graphic` instance is also returned when you call `plot.add_<graphic_type>`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "b12bf75e-4e93-4930-9146-e96324fdf3f6",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<weakproxy at 0x7f0c04508540 to ImageGraphic at 0x7f0c044ffed0>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "image_graphic "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "b56420be-7121-4275-9444-803308400a63",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "image_graphic == plot[\"sample-image\"]"
+    "Our image data is of shape [n_rows, n_columns]"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "4c369d3d-7219-4231-80cd-ce82b6a67a20",
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "83b2db1b-2783-4e89-bcf3-66bb6e09e18a",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# close plot and sidecar\n",
-    "plot.close()\n",
-    "sc.close()"
+    "image_graphic.data[::15, :] = 1\n",
+    "image_graphic.data[:, ::15] = 1"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "070e92b9-ac90-426d-b3d5-bd74a9a8a0be",
+   "id": "98e41780-eb84-46d4-8c06-ec59b3238572",
    "metadata": {},
    "source": [
-    "**Note: if you have a good enough gpu, you do not need to close the plots. We are doing this here for gpu conservation.**"
+    "**Fancy indexing**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3e298c1c-7551-4401-ade0-b9af7d2bbe23",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "image_graphic.data[data > 175] = 255"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f7ed56a9-c613-4a4a-9147-7dc135a00ed2",
+   "id": "958b4e14-d820-4acb-af75-3485c844de17",
    "metadata": {},
    "source": [
-    "### RGB images are also supported\n",
-    "\n",
-    "`cmap` arguments are ignored for rgb images, but vmin vmax still works"
+    "**Adjust vmin vmax**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "7f545e43-d4de-4eab-ba8d-6d98c651ffd5",
+   "execution_count": 9,
+   "id": "ea100318-023c-4963-b859-8c2a8fcce301",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3c3a7147853a4bdc947e17744c8ab3cc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RFBOutputContext()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plot_rgb = fpl.Plot()\n",
-    "\n",
-    "plot_rgb.add_image(new_data, name=\"rgb-image\")\n",
-    "\n",
-    "sc = Sidecar(title=\"rgb image\")\n",
-    "\n",
-    "with sc:\n",
-    "    display(plot_rgb.show())"
+    "image_graphic.cmap.vmin = 50\n",
+    "image_graphic.cmap.vmax = 150"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "de797c6c-fafd-4db4-9c6b-ef48b71cd9b5",
+   "execution_count": 10,
+   "id": "a1320f9f-fff5-4b9b-9a49-0695042ab8ab",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "plot_rgb[\"rgb-image\"].cmap.vmin = 100"
+    "image_graphic.cmap.reset_vmin_vmax()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "d5de925b-1dfc-4d53-9f7b-eb85b8bb7791",
+   "execution_count": 11,
+   "id": "4c369d3d-7219-4231-80cd-ce82b6a67a20",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "plot_rgb.close()\n",
+    "# close plot and sidecar\n",
+    "plot.close()\n",
     "sc.close()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "070e92b9-ac90-426d-b3d5-bd74a9a8a0be",
+   "metadata": {},
+   "source": [
+    "**Note: if you have a good enough gpu, you do not need to close the plots. We are doing this here for gpu conservation.**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1cb03f42-1029-4b16-a16b-35447d9e2955",
@@ -661,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "id": "aadd757f-6379-4f52-a709-46aa57c56216",
    "metadata": {
     "tags": []
@@ -670,7 +349,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "423592513ddd4ac7afc01e60c217614f",
+       "model_id": "140a3876e95c46c784ca047e1aeb8fab",
        "version_major": 2,
        "version_minor": 0
       },
@@ -680,11 +359,19 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:34: UserWarning: converting float64 array to float32\n",
+      "  warn(f\"converting {array.dtype} array to float32\")\n"
+     ]
     }
    ],
    "source": [
     "# create another `Plot` instance\n",
-    "plot_v = fpl.Plot()\n",
+    "plot_v = fpl.Plot(size=(600, 500))\n",
     "\n",
     "plot.canvas.max_buffered_frames = 1\n",
     "\n",
@@ -711,168 +398,17 @@
     "    display(plot_v.show())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b313eda1-6e6c-466f-9fd5-8b70c1d3c110",
-   "metadata": {},
-   "source": [
-    "### We can share controllers across plots\n",
-    "\n",
-    "This example creates a new plot, but it synchronizes the pan-zoom controller"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "86e70b1e-4328-4035-b992-70dff16d2a69",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "887a2d48c313467197aac3074ca57f25",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RFBOutputContext()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "340c5d8ac0fb4d86b72dc7e822bd7e1a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(JupyterWgpuCanvas(), HBox(children=(Button(icon='expand-arrows-alt', layout=Layout(width='auto'…"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_sync = fpl.Plot(controller=plot_v.controller)\n",
-    "\n",
-    "data = np.random.rand(512, 512)\n",
-    "\n",
-    "image_graphic_instance = plot_sync.add_image(data=data, cmap=\"viridis\")\n",
-    "\n",
-    "# you will need to define a new animation function for this graphic\n",
-    "def update_data_2():\n",
-    "    new_data = np.random.rand(512, 512)\n",
-    "    # alternatively, you can use the stored reference to the graphic as well instead of indexing the Plot\n",
-    "    image_graphic_instance.data = new_data\n",
-    "\n",
-    "plot_sync.add_animations(update_data_2)\n",
-    "\n",
-    "plot_sync.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f226c9c2-8d0e-41ab-9ab9-1ae31fd91de5",
-   "metadata": {},
-   "source": [
-    "#### Keeping a reference to the Graphic instance, as shown above `image_graphic_instance`, is useful if you're creating something where you need flexibility in the naming of the graphics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "bd073e07-5721-4957-8a16-0767b9d64d25",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "sc.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d11fabb7-7c76-4e94-893d-80ed9ee3be3d",
-   "metadata": {},
-   "source": [
-    "### You can also use `ipywidgets.VBox` and `HBox` to stack plots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "ef9743b3-5f81-4b79-9502-fa5fca08e56d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2296fe63359b499398f2384f3bd54664",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(VBox(children=(JupyterWgpuCanvas(frame_feedback={'index': 59, 'timestamp': 1688730919.6128232, …"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "VBox([plot_v.show(), plot_sync.show()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "11839d95-8ff7-444c-ae13-6b072c3112c5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ffc38bd05bc549dd843d30576e15e1e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(VBox(children=(JupyterWgpuCanvas(frame_feedback={'index': 76, 'timestamp': 1688730921.1709845, …"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "HBox([plot_v.show(), plot_sync.show()])"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "e8b3468a-0c92-42b0-8158-6c3c4cb83fa1",
+   "execution_count": 13,
+   "id": "99d234c6-220a-4093-a6a0-6036d2e40000",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# close plots\n",
     "plot_v.close()\n",
-    "plot_sync.close()"
+    "sc.close()"
    ]
   },
   {
@@ -895,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "id": "5139ad7c-2d72-402b-9239-27dcf5bd0c89",
    "metadata": {
     "tags": []
@@ -908,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "id": "207632a8-9f7d-4931-bebc-b43a49e63b9e",
    "metadata": {
     "tags": []
@@ -917,7 +453,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3fdc5bdd21194da0808e4742f7895609",
+       "model_id": "2b0b5a73f52744eaa29a75685d10ed7c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -931,7 +467,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "540ce3502bc5435f9a067fa2a3dd7a43",
+       "model_id": "0edf24e9f6764619877928989b16e4ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -939,7 +475,7 @@
        "VBox(children=(VBox(children=(JupyterWgpuCanvas(), HBox(children=(Button(icon='expand-arrows-alt', layout=Layo…"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,14 +512,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "id": "7f32b582-b01d-41ab-bcfb-88ddcf1fd3be",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "plot_movie.close()"
+    "plot_movie.close()\n",
+    "slider.close()"
    ]
   },
   {
@@ -991,23 +528,29 @@
    "id": "f672dcd8-f55b-4476-b72a-d7a292fee32e",
    "metadata": {},
    "source": [
-    "## Single image sequence `ImageWidget` using zebrafish data"
+    "## `ImageWidget` using zebrafish whole-brain data\n",
+    "\n",
+    "## Supports numpy-like array objects and large array formats including: memmaps, zarr, hdf5, etc. via lazy loading\n",
+    "\n",
+    "#### Visualization is limited by file-formats and file system access performance: it will work with files of arbitrary size!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 17,
    "id": "d64091d5-4071-49b0-b852-348657e9abbd",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# load in zebrafish data\n",
-    "movie = tifffile.memmap('/home/clewis7/Downloads/zfish_proj/images/CNMF: H2BGc6s_FB_3Rep_161121_2-_--_-f0887281-5b83-4a22-8fd2-6480bafd8865.tiff')"
+    "movie = tifffile.memmap('./fpl-scipy2023-data/neural_data/zfish_vol.tiff')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 18,
    "id": "0377bd4e-ba0a-48e9-a647-a8ee7d770afa",
    "metadata": {
     "tags": []
@@ -1019,7 +562,7 @@
        "(1309, 30, 512, 512)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1028,10 +571,26 @@
     "movie.shape"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "95063299-e5e2-4298-99d4-05fe827258f6",
+   "metadata": {},
+   "source": [
+    "This is `[t, z, x, y]`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1992f2b-abb5-42c8-990b-78d3a1698811",
+   "metadata": {},
+   "source": [
+    "## View each plane in a separate subplot"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "7a76119e-2035-48ba-9a88-a8a6f64a3365",
+   "execution_count": 19,
+   "id": "770f8242-e8ff-40fc-86c3-9d44abca85d6",
    "metadata": {
     "tags": []
    },
@@ -1039,7 +598,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eac6cbc632384e3192274d301edd04b3",
+       "model_id": "92df5a7b047e44e18374c9f85ec2ac14",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1052,28 +611,34 @@
     }
    ],
    "source": [
+    "# get a list of txy arrays for each plane\n",
+    "planes = [movie[:, i] for i in range(movie.shape[1])]\n",
+    "\n",
     "iw = fpl.ImageWidget(\n",
-    "    data=movie,\n",
-    "    vmin_vmax_sliders=True,\n",
-    "    dims_order=\"tzxy\",\n",
-    "    slider_dims=[\"t\", \"z\"],\n",
+    "    data=planes,\n",
+    "    vmin_vmax_sliders=False,\n",
+    "    grid_plot_kwargs={\"size\": (600, 500), \"controllers\": \"sync\"},\n",
     "    cmap=\"gnuplot2\"\n",
-    ")"
+    ")\n",
+    "\n",
+    "\n",
+    "sc = Sidecar(title=\"zfish planes\")\n",
+    "\n",
+    "with sc:\n",
+    "    display(iw.show())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "id": "6cac96f4-a8e9-481a-8014-2d2570c04db3",
+   "execution_count": 20,
+   "id": "4329ac46-7820-4a4b-8b03-dbbc1d7b545e",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "sc = Sidecar(title=\"zebra fish\")\n",
-    "\n",
-    "with sc:\n",
-    "    display(iw.show())"
+    "for subplot in iw.gridplot:\n",
+    "    subplot[\"image_widget_managed\"].cmap.reset_vmin_vmax()"
    ]
   },
   {
@@ -1088,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 21,
    "id": "d29c857c-78f8-4bc5-a00f-83d9985eaeb7",
    "metadata": {
     "tags": []
@@ -1096,52 +661,27 @@
    "outputs": [],
    "source": [
     "# must be in the form of {dim: (func, window_size)}\n",
-    "iw.window_funcs = {\"t\": (np.mean, 13)}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "31542e8e-9549-4878-bbea-25136fd1b3ad",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# change the winow size\n",
-    "iw.window_funcs[\"t\"].window_size = 23"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "2433e117-8b99-4ca4-89fb-b6283c007620",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# change the function\n",
-    "iw.window_funcs[\"t\"].func = np.max"
+    "iw.window_funcs = {\"t\": (np.mean, 3)}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "id": "58695549-089f-4af3-abab-289ad47c0681",
+   "execution_count": 22,
+   "id": "48951d83-c273-451d-84ce-be14cd7fc1c3",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# or set it again\n",
-    "iw.window_funcs = {\"t\": (np.min, 11)}"
+    "for subplot in iw.gridplot:\n",
+    "    subplot[\"image_widget_managed\"].cmap.vmin = -0.12\n",
+    "    subplot[\"image_widget_managed\"].cmap.vmax = 2.3"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "id": "86261ca9-1cd8-43ee-ad72-7a05b3489510",
+   "execution_count": 23,
+   "id": "e49e7b96-c1a7-4c1f-956a-1bab482b0ce0",
    "metadata": {
     "tags": []
    },
@@ -1154,15 +694,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "03837ed8-05b1-4518-85b3-8fdf7263b52e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "26b9725e-93c0-45cf-b7af-8d3efe58dabf",
+   "id": "ee57b4be-7849-43c1-bc80-bfd9dc684acb",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/interactivity.ipynb b/interactivity.ipynb
index 4028929..93576f8 100644
--- a/interactivity.ipynb
+++ b/interactivity.ipynb
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "6193b269-fbac-46de-9404-e9fc4097c068",
    "metadata": {
     "tags": []
@@ -43,82 +43,13 @@
    "outputs": [],
    "source": [
     "# reconstructed movie of CaImAn Sue demo movie after running motion correction and CNMF algorithms\n",
-    "movie = np.load('/home/clewis7/Desktop/scipy_demos/rcm.npy')\n",
+    "movie = np.load('./fpl-scipy2023-data/neural_data/rcm.npy')\n",
     "\n",
     "# temporal traces of identified neurons\n",
-    "temporal = np.load('/home/clewis7/Desktop/scipy_demos/temporal.npy')\n",
+    "temporal = np.load('./fpl-scipy2023-data/neural_data/temporal.npy')\n",
     "\n",
     "# contours of identified neurons\n",
-    "contours = np.load('/home/clewis7/Desktop/scipy_demos/contours.npy', allow_pickle=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6017327f-b197-4d1a-a6d0-36d9712957b1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3000, 170, 170)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "movie.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f0df4095-d3cb-42b0-8330-a54681ed4b7f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(155, 3000)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temporal.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0d236133-7a66-42a3-b493-c999368e9fac",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(155,)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "contours.shape"
+    "contours = np.load(\"./fpl-scipy2023-data/neural_data/contours.npy\", allow_pickle=True)"
    ]
   },
   {
@@ -131,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "4315315a-32cc-4bae-939b-d2bfd300aefa",
    "metadata": {
     "tags": []
@@ -172,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 4,
    "id": "93e26775-3d24-4eb4-8831-fae5cb5fd71c",
    "metadata": {
     "tags": []
@@ -181,7 +112,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e46e5e356d3b4023a03315b2d7b66458",
+       "model_id": "7a304a953bff41fd8716352cb1b733d9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -192,10 +123,18 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:34: UserWarning: converting float64 array to float32\n",
+      "  warn(f\"converting {array.dtype} array to float32\")\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "467e387e8eba41ccb072b6d4be597909",
+       "model_id": "fd443f94b80340bc94b30561abb3ce0b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -209,19 +148,52 @@
    ],
    "source": [
     "# for the image data and contours\n",
-    "cnmf_iw = fpl.ImageWidget(movie, vmin_vmax_sliders=True, cmap=\"gnuplot2\")\n",
-    "\n",
-    "# add contours to the plot within the widget\n",
-    "contours_graphic = cnmf_iw.gridplot[0,0].add_line_collection(contours, colors=\"cyan\", name=\"contours\")\n",
+    "cnmf_iw = fpl.ImageWidget(\n",
+    "    movie, \n",
+    "    vmin_vmax_sliders=True, \n",
+    "    grid_plot_kwargs={\"size\": (600, 400)},\n",
+    "    cmap=\"gnuplot2\",\n",
+    ")\n",
     "\n",
     "# temporal plot\n",
     "plot_temporal = fpl.Plot(size=(600,100))\n",
+    "plot_temporal.add_line(temporal[0], colors=\"magenta\")\n",
     "\n",
     "# stack the plots and show them in sidecar\n",
     "sc = Sidecar(title=\"interactivity\")\n",
     "\n",
     "with sc:\n",
-    "    display(VBox([cnmf_iw.show(), plot_temporal.show()]))"
+    "    display(VBox([cnmf_iw.show(), plot_temporal.show(maintain_aspect=False)]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f99342b0-27c0-4b1a-9091-0ec3cde9632f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# add contours\n",
+    "contours_graphic = cnmf_iw.gridplot[0,0].add_line_collection(\n",
+    "    contours, \n",
+    "    colors=\"red\", \n",
+    "    thickness=2, \n",
+    "    name=\"contours\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "99a3d096-3b72-4c20-98c8-a5fb27ab8fe3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cnmf_iw.vmin_vmax_sliders[0].value = (-5, 15)"
    ]
   },
   {
@@ -234,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "id": "0a4968fc-1926-4eb7-85a5-2bedf1581b7f",
    "metadata": {
     "tags": []
@@ -248,7 +220,7 @@
     "    \"click\",\n",
     "    target=contours_graphic,\n",
     "    feature=\"colors\", \n",
-    "    new_data=\"w\", \n",
+    "    new_data=\"cyan\", \n",
     "    callback=euclidean\n",
     ")\n",
     "\n",
@@ -257,6 +229,7 @@
     "    ix = ev.pick_info[\"selected_index\"]\n",
     "    cnmf_iw.sliders[\"t\"].value = ix\n",
     "\n",
+    "\n",
     "# callback function to display correct temporal trace\n",
     "def generate_temporal(ev):\n",
     "    # clear the plot \n",
@@ -278,12 +251,12 @@
     "contours_graphic[:].colors.add_event_handler(generate_temporal)\n",
     "\n",
     "# thickness of contour\n",
-    "contours_graphic.link(\"colors\", target=contours_graphic, feature=\"thickness\", new_data=3)"
+    "contours_graphic.link(\"colors\", target=contours_graphic, feature=\"thickness\", new_data=5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 8,
    "id": "f7b78a88-7aa5-4de7-a796-4a99514631c8",
    "metadata": {
     "tags": []
@@ -298,7 +271,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8f2a0763-5868-4b67-9c3e-3fa7a7531d8a",
+   "id": "8baf21c1-036e-4882-8a1c-4c5ab2e5f605",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/lines.ipynb b/lines.ipynb
index 50dbe4b..ca8e926 100644
--- a/lines.ipynb
+++ b/lines.ipynb
@@ -20,9 +20,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "177e0cc3-870c-4570-bf59-af3528ba94bf",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import fastplotlib as fpl\n",
@@ -44,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "9bf0017a-e319-4f4c-8bf8-91cff3755099",
    "metadata": {
     "tags": []
@@ -77,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "970a5baf-a27d-4030-9659-247fccb4eff6",
    "metadata": {
     "tags": []
@@ -86,7 +88,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d5c72fb7208b4cd7bc2b00e754e65a70",
+       "model_id": "2d4371cb3aad4a3591c998144d28127f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -108,7 +110,7 @@
    ],
    "source": [
     "# Create a plot instance\n",
-    "plot_l = fpl.Plot()\n",
+    "plot_l = fpl.Plot(size=(600, 300))\n",
     "\n",
     "# plot sine wave, use a single color\n",
     "sine_graphic = plot_l.add_line(data=sine, thickness=5, colors=\"magenta\")\n",
@@ -128,75 +130,35 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f2fb6e9-a4c8-473f-969f-9e0e1fe26184",
-   "metadata": {},
-   "source": [
-    "### \"stretching\" the camera, useful for large timeseries data\n",
-    "\n",
-    "Set `maintain_aspect = False` on a camera, and then use the right mouse button and move the mouse to stretch and squeeze the view!\n",
-    "\n",
-    "You can also click the `1:1` button to toggle this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ccda4b46-147e-41d5-a9b2-4e36c307ca16",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "plot_l.camera.maintain_aspect = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "543bca00-ac5b-4ce1-89aa-2b4a9a4071c8",
+   "id": "2200da08-205e-4ae0-a3e7-9ec4049606c9",
    "metadata": {},
    "source": [
-    "### reset the plot area"
+    "### Graphic features support slicing! :D"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "3d330edd-8455-4897-b1a5-9d1a344f108b",
+   "id": "633aa156-9443-41c4-beeb-b0db4e9a1099",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "plot_l.auto_scale(maintain_aspect=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2200da08-205e-4ae0-a3e7-9ec4049606c9",
-   "metadata": {},
-   "source": [
-    "### Graphic features support slicing! :D"
+    "# indexing of colors\n",
+    "cosine_graphic.colors[:40] = \"magenta\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "94af10a4-5ed5-419a-b3d9-c409f7de5a23",
+   "id": "df876d25-42e1-4a19-ba00-25cbaf87b706",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# indexing of colors\n",
-    "cosine_graphic.colors[:15] = \"magenta\"\n",
-    "cosine_graphic.colors[90:] = \"red\"\n",
-    "cosine_graphic.colors[60] = \"w\"\n",
-    "\n",
-    "# indexing to assign colormaps to entire lines or segments\n",
-    "sinc_graphic.cmap[10:50] = \"gray\"\n",
-    "sine_graphic.cmap = \"seismic\"\n",
-    "\n",
-    "# more complex indexing, set the blue value directly from an array\n",
+    "# more complex indexing, set the red value directly from an array\n",
     "cosine_graphic.colors[65:90, 0] = np.linspace(0, 1, 90-65)"
    ]
   },
@@ -236,9 +198,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "FeatureEvent @ 0x7f7db3de2ad0\n",
+      "FeatureEvent @ 0x7efe4bfa4e50\n",
       "type: colors\n",
-      "pick_info: {'index': range(15, 50, 3), 'collection-index': None, 'world_object': <weakproxy at 0x7f7dbdb62f20 to Line at 0x7f7db3d84d50>, 'new_data': array([[0., 1., 1., 1.],\n",
+      "pick_info: {'index': range(15, 50, 3), 'collection-index': None, 'world_object': <weakproxy at 0x7efe51c507c0 to Line at 0x7efe4bf27ed0>, 'new_data': array([[0., 1., 1., 1.],\n",
       "       [0., 1., 1., 1.],\n",
       "       [0., 1., 1., 1.],\n",
       "       [0., 1., 1., 1.],\n",
@@ -256,136 +218,13 @@
    ],
    "source": [
     "# more complex indexing of colors\n",
-    "# from point 15 - 30, set every 3rd point as \"cyan\"\n",
+    "# from point 15 - 50, set every 3rd point as \"cyan\"\n",
     "cosine_graphic.colors[15:50:3] = \"cyan\""
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d67fcec7-ca2c-4403-9b79-23fefeb8c0d1",
-   "metadata": {},
-   "source": [
-    "### Graphic data is itself also indexable"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "fa7ecf35-bd91-4209-8d3f-d554977b5ff1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:31: UserWarning: converting int64 array to int32\n",
-      "  warn(f\"converting {array.dtype} array to int32\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "cosine_graphic.data[10:50:5, :2] = sine[10:50:5]\n",
-    "cosine_graphic.data[90:, 1] = 7\n",
-    "cosine_graphic.data[0] = np.array([[-10, 0, 0]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfbc8c70-1173-4143-8638-78716b9a86c7",
-   "metadata": {},
-   "source": [
-    "### Toggle the presence of a graphic within the scene"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8617b111-9d09-4bee-a875-79771c2e38d3",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "sinc_graphic.present = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d2486714-9cba-4ab9-bf17-9c0c52587c1c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "sinc_graphic.present = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c398cdad-acae-4fb8-a254-44debaaacdef",
-   "metadata": {},
-   "source": [
-    "### You can create callbacks to this too, for example to re-scale the plot w.r.t. graphics that are present in the scene\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "d03b1b6e-c7d3-40d3-97ab-a855c821e277",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "sinc_graphic.present.add_event_handler(plot_l.auto_scale)\n",
-    "\n",
-    "sinc_graphic.present = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "36a4e681-6a21-4dc4-a3b5-9f454095bce7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "sinc_graphic.present = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98cf3630-9eff-4d77-a798-ad53bd6e52aa",
-   "metadata": {},
-   "source": [
-    "### You can set the z-positions of graphics to have them appear under other graphics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "29db2967-b260-4bb9-a503-d9406294e31e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "img = np.random.rand(20, 100)\n",
-    "\n",
-    "plot_l.add_image(img, name=\"image\", cmap=\"gray\")\n",
-    "\n",
-    "# z axix position -1 so it is below all the lines\n",
-    "plot_l[\"image\"].position_z = -1\n",
-    "plot_l[\"image\"].position_x = -50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
    "id": "44d55357-4bd9-47ba-9f39-001c12e83eb5",
    "metadata": {
     "tags": []
@@ -401,12 +240,12 @@
    "id": "d9db0e4b-c747-4e0f-b2e8-1bee48d03f02",
    "metadata": {},
    "source": [
-    "### Can also have fancy indexing of colormaps"
+    "### colormaps"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "2c6eda00-3883-48d2-af14-31c5a4b6bcd1",
    "metadata": {
     "tags": []
@@ -415,7 +254,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "04cfb9f0dcf2450ea46ddda837d50587",
+       "model_id": "0c3f05008a0249869be6eea987829958",
        "version_major": 2,
        "version_minor": 0
       },
@@ -428,7 +267,7 @@
     }
    ],
    "source": [
-    "plot = fpl.Plot()\n",
+    "plot = fpl.Plot(size=(600, 300))\n",
     "\n",
     "plot.add_line(sine, thickness=10, name=\"sine\")\n",
     "\n",
@@ -440,68 +279,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "id": "e3d190b5-a2c7-4ec4-918d-b934270bf1c4",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "# set cmap \n",
     "plot[\"sine\"].cmap = \"jet\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "ff9eff43-16fb-477e-b201-da728f18d7e1",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "# set cmap values equal sine wave y-values \n",
     "plot[\"sine\"].cmap.values = sine[:, 1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "id": "74a592de-76be-4726-ae29-4a0b93ae9456",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "# set cmap values equal to cosine wave y-values\n",
     "plot[\"sine\"].cmap.values = cosine[:, 1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "id": "10be65e2-5dce-49d1-8bb7-c12bec835a2b",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "# change the cmap, cmap values remain the same\n",
     "plot[\"sine\"].cmap = \"viridis\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "66de4d43-11e6-4662-be23-57b8e400ab75",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "**Qualitative data**"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "id": "6865697d-159e-47c1-a372-9fc350e02d31",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "# qualitatively set the cmap values\n",
     "cmap_values = [0] * 25 + [1] * 5 + [2] * 50 + [3] * 20\n",
     "plot[\"sine\"].cmap.values = cmap_values"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b2924933-e217-4f57-8617-52032d807489",
+   "metadata": {},
+   "source": [
+    "**Qualitative cmap**"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
+   "id": "53c12b6b-485e-4623-a111-46127206223b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# now change the cmap to new cmap with those same cmap values\n",
+    "plot[\"sine\"].cmap = \"tab10\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "id": "bf9fbc9a-a887-45c9-921b-54d037aa1b57",
    "metadata": {
     "tags": []
@@ -525,12 +400,12 @@
    "id": "817355c4-0150-41de-a1d9-4c1f3680a7bc",
    "metadata": {},
    "source": [
-    "Generate some data"
+    "**Generate some data, 4 x 4 circles**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "id": "5d92ecd2-2177-4cd9-8643-c8691ee1e988",
    "metadata": {
     "tags": []
@@ -561,12 +436,14 @@
    "id": "fbed2426-5d27-4f8b-91bd-5a0fe514a0ed",
    "metadata": {},
    "source": [
-    "### set cmap values as LUT"
+    "### Qualitative cmap\n",
+    "\n",
+    "Useful for clustering or classification"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "id": "c9f83313-60ef-4b58-baaf-bcbf3564eb31",
    "metadata": {
     "tags": []
@@ -575,7 +452,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f42e9011e33a44dab509830d294e0495",
+       "model_id": "2ec123f30def4c7f9fa7a196fde3f490",
        "version_major": 2,
        "version_minor": 0
       },
@@ -588,17 +465,22 @@
     }
    ],
    "source": [
-    "plot = fpl.Plot()\n",
+    "plot = fpl.Plot(size=(600, 600))\n",
     "\n",
-    "# highest values, lowest values, mid-high values, mid values\n",
-    "cmap_values = cmap_values = [\n",
+    "# things like class labels, cluster labels, etc.\n",
+    "cmap_values = [\n",
     "    0, 1, 1, 2,\n",
     "    0, 0, 1, 1,\n",
     "    2, 2, 3, 3,\n",
     "    1, 1, 1, 5\n",
     "]\n",
     "\n",
-    "plot.add_line_collection(circles, cmap=\"tab10\", cmap_values=cmap_values, thickness=5)\n",
+    "plot.add_line_collection(\n",
+    "    circles, \n",
+    "    cmap=\"tab10\", \n",
+    "    cmap_values=cmap_values, \n",
+    "    thickness=15\n",
+    ")\n",
     "\n",
     "sc = Sidecar(title=\"lines collection\")\n",
     "\n",
@@ -608,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "id": "981bbd25-ca42-492d-a722-185ffc877462",
    "metadata": {
     "tags": []
@@ -621,16 +503,32 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c55c8340-7e85-46cb-aedc-8112138c1b75",
+   "id": "54374981-9a79-4887-a297-8a5012ebb4b7",
    "metadata": {},
    "source": [
-    "### can also set colors"
+    "## neuro data example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6901f4b7-d1f8-4747-b8b0-de078ae4c825",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# load in movie \n",
+    "movie = np.load('./fpl-scipy2023-data/neural_data/rcm.npy')\n",
+    "\n",
+    "# load in identified nuerons\n",
+    "contours = np.load('./fpl-scipy2023-data/neural_data/contours.npy', allow_pickle=True)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "ca7e277f-8630-4691-96e9-44da495ca57e",
+   "id": "162a1602-2201-45be-bcc2-6fd7550cf0e4",
    "metadata": {
     "tags": []
    },
@@ -638,7 +536,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ccbde8ee3c504de9aced0c3021124121",
+       "model_id": "03ad937b478444248aac85fa2ae4c62d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -651,126 +549,282 @@
     }
    ],
    "source": [
-    "plot = fpl.Plot()\n",
+    "# for the image data and contours\n",
+    "cnmf_iw = fpl.ImageWidget(\n",
+    "    movie, \n",
+    "    vmin_vmax_sliders=True, \n",
+    "    grid_plot_kwargs={\"size\": (600, 500)},\n",
+    "    cmap=\"gray\"\n",
+    ")\n",
     "\n",
-    "colors = [\"green\"] * 4 + [\"red\"] * 4 + [\"yellow\"] * 4 + [\"w\"] * 4\n",
-    "\n",
-    "plot.add_line_collection(circles, colors=colors, thickness=5)\n",
-    "\n",
-    "sc = Sidecar(title=\"lines collection\")\n",
+    "# stack the plots and show them in sidecar\n",
+    "sc = Sidecar(title=\"good/bad comps\")\n",
     "\n",
     "with sc:\n",
-    "    display(plot.show())"
+    "    display(cnmf_iw.show())"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "9792dea6-a5cd-4b11-abd3-0cf4c2b8661b",
+   "id": "2e34929f-55dd-43d5-8736-8253ad11d96e",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "plot.close()\n",
-    "sc.close()"
+    "cnmf_iw.vmin_vmax_sliders[0].value = (-5, 15)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a1a34cfd-3746-489e-aeff-7373d538ab8b",
+   "id": "3189906a-d7e7-40b5-8bb5-a438ca7ecb0a",
    "metadata": {},
    "source": [
-    "## Line Stack"
+    "### Colors based on classifier output"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "e4ea3e6c-39f7-4796-b784-9ca175318356",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6a9005e6-2c6b-4561-8890-0a576273e609",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
-    "Generate some data"
+    "# indices of accepted_ixs components\n",
+    "accepted_ixs = np.load('./fpl-scipy2023-data/neural_data/good_ixs.npy')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "d7d5e7ca-f32d-4d3b-8ca3-3cfa72852614",
+   "execution_count": 25,
+   "id": "63c95616-80d2-42aa-948b-2c5ea48c349d",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1,\n",
+       "       0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n",
+       "       1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n",
+       "       1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
+       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
+       "       0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
+       "       1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1,\n",
+       "       1])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "xs = np.linspace(0, 100, 1000)\n",
-    "# sine wave\n",
-    "ys = np.sin(xs) * 20\n",
+    "classifier = np.zeros(len(contours), dtype=int)\n",
+    "classifier[accepted_ixs] = 1\n",
     "\n",
-    "# make 25 lines\n",
-    "data = np.vstack([ys] * 25)"
+    "classifier"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "a70bf880-5497-476d-ba43-4c1687b49abc",
+   "id": "83cb0f1b-7ade-4237-afb3-ad4516f893cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# add contours to the plot within the widget\n",
+    "contours_graphic = cnmf_iw.gridplot[0,0].add_line_collection(\n",
+    "    contours, \n",
+    "    cmap=\"Set1\",\n",
+    "    cmap_values=classifier,\n",
+    "    thickness=3, \n",
+    "    name=\"contours\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ee60d93-8be8-4cf3-8b4c-75e521379d4e",
+   "metadata": {},
+   "source": [
+    "## Red: class 0 - rejected\n",
+    "\n",
+    "## Blue: class 1 - accepted"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a254ed4-370c-4b5a-8c55-8b9603af7816",
+   "metadata": {},
+   "source": [
+    "### cmaps for quantitative metrics "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90e7e79a-2879-454a-9293-e8abda55808e",
+   "metadata": {},
+   "source": [
+    "#### yet another measure, signal-to-noise measure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f7490d2f-f667-445f-aeb8-d8d83de77372",
    "metadata": {
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe4fb92bdeab4eb3bc8744efea40b9b1",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "RFBOutputContext()"
+       "array([ 1.30693739,  0.98832133,  0.61094959,  0.60680478,  0.78905172,\n",
+       "        0.75528032,  0.07143519,  0.20501458,  0.82659848,  0.72253894,\n",
+       "        0.50721547,  0.06808205,  0.09170267,  0.16848598,  1.21936687,\n",
+       "        0.60382365,  0.46201108,  0.62335868,  0.31698763,  0.6426985 ,\n",
+       "        0.60684258,  0.5431985 ,  0.59940984,  0.64724762,  0.29722249,\n",
+       "        0.29662902,  0.61015591,  0.31969486,  1.19550207,  0.5710063 ,\n",
+       "        0.46383772,  1.02528406,  0.72630303,  0.40776261,  0.19654701,\n",
+       "        0.52004469,  0.09369996, -0.14752256,  1.05310796,  0.37688607,\n",
+       "        0.41622687,  1.17317543,  0.73611689,  0.42182707,  0.3150909 ,\n",
+       "        0.21921744,  0.3789719 , -0.06830937,  0.35099327, -0.08058595,\n",
+       "        0.71089304,  0.44862497,  0.34431778,  0.03256675,  0.28882971,\n",
+       "        0.61887717,  0.76572043,  0.51032459,  0.63065994,  0.95790937,\n",
+       "        0.63282506,  0.65043055,  0.44677766,  0.31964022,  0.41866778,\n",
+       "        0.18621914,  0.90540626,  0.7797958 ,  0.3603792 ,  0.51412954,\n",
+       "        0.32145975,  1.16006419,  0.05515471,  0.46310168,  0.66458612,\n",
+       "        0.58251201,  0.33845468,  0.46522393,  0.01540801, -0.14980613,\n",
+       "        0.14016509,  0.39933608,  0.16530968,  0.15592701,  0.20202153,\n",
+       "       -0.41106995,  0.1119999 ,  0.42393534,  1.22942341,  0.13633389,\n",
+       "        0.22779662,  0.24890452,  0.212523  ,  0.49994003,  0.19132364,\n",
+       "        0.72399517,  0.47901872,  0.01965678,  0.18721191,  0.54839699,\n",
+       "        0.10548312,  0.36355986, -0.30731671,  0.57231072,  0.28601665,\n",
+       "       -0.3082514 ,  0.23907098,  1.03830112,  0.14206076,  0.55663581,\n",
+       "       -0.21548934,  0.38826583,  0.31809138,  0.14230872,  0.87761188,\n",
+       "        0.83902665,  0.58369984,  0.419747  ,  0.47968704,  0.31675268,\n",
+       "        0.84700962,  0.21892391,  0.56579811, -0.28278322,  0.61361115,\n",
+       "        1.40618154,  1.23015299,  0.64253497,  1.120344  ,  1.0578779 ,\n",
+       "        1.09035117,  0.52459236,  0.89245759,  0.574191  ,  0.52985482,\n",
+       "        0.80768994,  0.56405192,  0.35819001,  0.69566203,  0.56113173,\n",
+       "        0.97317729,  1.10191489,  0.88968622,  0.66573856,  0.79040525,\n",
+       "        0.58058598,  0.71475437,  0.56642353,  0.74560174,  0.72704435,\n",
+       "        0.53325174,  0.82447437,  0.59551337,  0.01806211,  1.07601128])"
       ]
      },
+     "execution_count": 27,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plot = fpl.Plot()\n",
-    "\n",
-    "# line stack takes all the same arguments as line collection and behaves similarly\n",
-    "plot.add_line_stack(data, cmap=\"jet\")\n",
-    "\n",
-    "sc = Sidecar(title=\"line stack\")\n",
+    "# load in SNR comps\n",
+    "snr_comps = np.load('./fpl-scipy2023-data/neural_data/SNR_comps.npy')\n",
+    "np.log10(snr_comps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d5b21e2e-c9af-4459-86ac-2b61996ee55b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "contours_graphic.cmap = \"spring\"\n",
+    "contours_graphic.cmap_values = np.log10(snr_comps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb48230f-258f-48b1-b131-90f03ce8ca75",
+   "metadata": {},
+   "source": [
+    "## low signal to noise: purple\n",
     "\n",
-    "with sc:\n",
-    "    display(plot.show(maintain_aspect=False))"
+    "## high signal to noise: yellow"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "7a3b8014-a89e-4138-98ac-ed3cbf1f0d51",
+   "execution_count": 30,
+   "id": "32ed59ef-d956-4e83-b12f-8c496e1579e4",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "plot.close()\n",
+    "cnmf_iw.gridplot.close()\n",
     "sc.close()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "be5725e1-5f43-4c81-a35a-d86b658ed709",
+   "id": "a1a34cfd-3746-489e-aeff-7373d538ab8b",
+   "metadata": {},
+   "source": [
+    "## Line Stack - useful for time series data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4ea3e6c-39f7-4796-b784-9ca175318356",
    "metadata": {},
    "source": [
-    "## Linear Selector\n",
+    "### 3 Generate some sine data, 3 million points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "d7d5e7ca-f32d-4d3b-8ca3-3cfa72852614",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xs = np.linspace(0, 2_000, 3_000)\n",
+    "# sine wave\n",
+    "ys = np.sin(xs) * 20\n",
     "\n",
-    "Creates a horizontal or vertical line slider. Can be synced to an `ipywidget IntSlider`. Like other `Graphic` types, it can be linked to events. "
+    "data = np.vstack([ys] * 1_000)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "f5ca2643-f1c2-4cf4-ab05-daac52261830",
+   "id": "ed90fe0e-38ee-4ffd-97f7-3b62ef4dc6db",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 3000)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "a70bf880-5497-476d-ba43-4c1687b49abc",
    "metadata": {
     "tags": []
    },
@@ -778,7 +832,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b1b4be802384e489eaf4c7a37ef0a86",
+       "model_id": "af520ea5065b442c9e193784e656eb62",
        "version_major": 2,
        "version_minor": 0
       },
@@ -791,64 +845,85 @@
     }
    ],
    "source": [
-    "plot = fpl.Plot()\n",
-    "\n",
-    "# data to plot\n",
-    "xs = np.linspace(0, 100, 1000)\n",
-    "sine = np.sin(xs) * 20\n",
+    "plot = fpl.Plot(size=(600, 500), canvas=\"jupyter\")\n",
     "\n",
-    "# make sine along x axis\n",
-    "sine_graphic = plot.add_line(np.column_stack([xs, sine]).astype(np.float32))\n",
+    "# line stack takes all the same arguments as line collection\n",
+    "# and behaves similarly\n",
+    "plot.add_line_stack(data, cmap=\"jet\", separation=15, name=\"many-lines\")\n",
     "\n",
-    "# make some selectors\n",
-    "selector = sine_graphic.add_linear_selector()\n",
-    "selector2 = sine_graphic.add_linear_selector(20)\n",
-    "selector3 = sine_graphic.add_linear_selector(40)\n",
-    "\n",
-    "# add ability to synchronize selectors\n",
-    "ss = Synchronizer(selector, selector2, selector3)\n",
-    "\n",
-    "plot.auto_scale()\n",
-    "\n",
-    "# fastplotlib LineSelector can make an ipywidget slider and return it :D \n",
-    "ipywidget_slider = selector.make_ipywidget_slider()\n",
-    "\n",
-    "sc = Sidecar(title=\"linear selectors\")\n",
+    "sc = Sidecar(title=\"line stack\")\n",
     "\n",
     "with sc:\n",
-    "    display(VBox([plot.show(), ipywidget_slider]))"
+    "    display(plot.show(maintain_aspect=False))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "338d362b-521b-4abf-9148-cc0d8d2a55db",
+   "id": "ff71b41c-3dae-43ad-906b-db6e34a13b6b",
    "metadata": {},
    "source": [
-    "### can add an event handler to a selector"
+    "## Performant changes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "id": "16ae2820-4808-4890-9ca1-3507fb0e0799",
+   "execution_count": 53,
+   "id": "ba65f98c-42db-4c7c-80a6-daf3bad54d78",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "def set_color_at_index(ev):\n",
-    "    # changes the color at the index where the slider is\n",
-    "    ix = ev.pick_info[\"selected_index\"]\n",
-    "    g = ev.pick_info[\"graphic\"].parent\n",
-    "    g.colors[ix] = \"magenta\"\n",
+    "plot[\"many-lines\"].cmap = \"viridis\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e39322e-909a-41c1-8b07-ef12e0f67213",
+   "metadata": {},
+   "source": [
+    "**more numpy-like indexing :D**\n",
     "\n",
-    "selector.selection.add_event_handler(set_color_at_index)"
+    "Set the cmap of the first 500 individual lines to `plasma`. \n",
+    "This sets the cmap _along the line_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "858477e5-4b7a-4e63-b711-72f01b3a4941",
+   "execution_count": 54,
+   "id": "963daef5-3701-49bf-832a-74c85efcdaed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plot[\"many-lines\"][:500].cmap = \"plasma\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "394a267f-ff08-43df-844b-0a311e009dfd",
+   "metadata": {},
+   "source": [
+    "Every 5th datapoint to \"white\" or \"black\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "f05b4023-f949-4043-b9ef-82d9728512e4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plot[\"many-lines\"][:500].colors[::5] = \"w\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7a3b8014-a89e-4138-98ac-ed3cbf1f0d51",
    "metadata": {
     "tags": []
    },
@@ -871,7 +946,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 38,
+   "id": "68403bc0-262c-42b9-986c-8cad25152f39",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def interpolate(subdata: np.ndarray, axis: int):\n",
+    "    \"\"\"1D interpolation to display within the preallocated data array\"\"\"\n",
+    "    x = np.arange(0, zoomed_prealloc)\n",
+    "    xp = np.linspace(0, zoomed_prealloc, subdata.shape[0])\n",
+    "    \n",
+    "    # interpolate to preallocated size\n",
+    "    return np.interp(x, xp, fp=subdata[:, axis])  # use the y-values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "id": "87670f5c-ec83-4d71-87ec-1d13bb05dc47",
    "metadata": {
     "tags": []
@@ -880,7 +973,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "09099d4b37aa45bcbf8ff4208b914cdd",
+       "model_id": "77b6cedc1b2c45eb8b2f70e80a825af5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -890,43 +983,51 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:34: UserWarning: converting float64 array to float32\n",
-      "  warn(f\"converting {array.dtype} array to float32\")\n"
-     ]
     }
    ],
    "source": [
     "# data to plot\n",
-    "xs = np.linspace(0, 250, 500)\n",
+    "xs = np.linspace(0, 100, 1_000)\n",
     "sine = np.sin(xs) * 20\n",
     "cosine = np.cos(xs) * 20\n",
     "\n",
-    "plot = fpl.Plot()\n",
+    "plot = fpl.GridPlot((5, 1), size=(600, 600))\n",
+    "\n",
+    "# preallocated size for zoomed data\n",
+    "zoomed_prealloc = 1_000\n",
+    "zoomed_init = np.column_stack([np.arange(zoomed_prealloc), np.random.rand(zoomed_prealloc)])\n",
     "\n",
     "# sines and cosines\n",
-    "sines = [sine] * 5\n",
-    "cosines = [cosine] * 5\n",
+    "sines = [sine] * 2\n",
+    "cosines = [cosine] * 2\n",
     "\n",
     "# make line stack\n",
-    "line_stack = plot.add_line_stack(sines + cosines, separation=50)\n",
+    "line_stack = plot[0, 0].add_line_stack(sines + cosines, separation=50)\n",
     "\n",
     "# make selector\n",
-    "stack_selector = line_stack.add_linear_region_selector()\n",
+    "selector = line_stack.add_linear_region_selector()\n",
+    "\n",
+    "# populate subplots with preallocated graphics\n",
+    "for i, subplot in enumerate(plot):\n",
+    "    if i == 0:\n",
+    "        # skip the first one\n",
+    "        continue\n",
+    "    # make line graphics for displaying zoomed data\n",
+    "    subplot.add_line(zoomed_init, name=\"zoomed\")\n",
     "\n",
-    "def update_color(ev):\n",
-    "    selected_indices = stack_selector.get_selected_indices()\n",
+    "\n",
+    "def update_zoomed_subplots(ev):\n",
+    "    \"\"\"update the zoomed subplots\"\"\"\n",
+    "    zoomed_data = selector.get_selected_data()\n",
     "    \n",
-    "    for i in range(len(selected_indices)):\n",
-    "        line_stack.graphics[i].colors = \"w\"\n",
-    "        line_stack.graphics[i].colors[selected_indices[i]] = \"magenta\"\n",
+    "    for i in range(len(zoomed_data)):\n",
+    "        data = interpolate(zoomed_data[i], axis=1)\n",
+    "        plot[i + 1, 0][\"zoomed\"].data = data\n",
+    "        plot[i + 1, 0].auto_scale()\n",
     "\n",
-    "stack_selector.selection.add_event_handler(update_color)\n",
     "\n",
+    "selector.selection.add_event_handler(update_zoomed_subplots)\n",
+    "plot.show()\n",
     "sc = Sidecar(title=\"linear region selector\")\n",
     "\n",
     "with sc: \n",
@@ -935,7 +1036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 40,
    "id": "89b9ed5b-8fdb-4fa7-95f7-f955cec9ed74",
    "metadata": {
     "tags": []
@@ -956,14 +1057,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 4,
+   "id": "d201138f-a44f-4f87-b456-33b0ad7f41ae",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "temporal = np.load('./fpl-scipy2023-data/neural_data/temporal.npy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "id": "a24b06d3-f9db-406a-a63d-0159007a09c6",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b55410322ef945c78344b240a007c105",
+       "model_id": "5a8c28a77f5c407d8aea02964e8a6988",
        "version_major": 2,
        "version_minor": 0
       },
@@ -975,65 +1090,91 @@
      "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/clewis7/repos/fastplotlib/fastplotlib/graphics/_features/_base.py:34: UserWarning: converting float64 array to float32\n",
-      "  warn(f\"converting {array.dtype} array to float32\")\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "547a8fc6813e4931a7441670b792a3b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "RFBOutputContext()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "# create plot\n",
-    "plot = fpl.GridPlot(shape=(1,2), names=[[\"heatmap\", \"traces\"]])\n",
+    "plot = fpl.GridPlot(shape=(1,2), names=[[\"heatmap\", \"timeseries\"]])\n",
+    "\n",
+    "def normalize(array):\n",
+    "    out = np.zeros(array.shape, dtype=array.dtype)\n",
+    "    for i in range(array.shape[0]):\n",
+    "        a = array[i]\n",
+    "        out[i] = ((a - np.min(a)) / (np.max(a - np.min(a))))\n",
+    "        \n",
+    "    return out\n",
     "\n",
+    "temporal_norm = normalize(temporal)\n",
+    "        \n",
     "# add temporal traces as heatmap\n",
-    "heatmap = plot[0,0].add_heatmap(temporal)\n",
+    "heatmap = plot[0,0].add_heatmap(temporal_norm)\n",
     "\n",
     "# add temporal traces as line stack\n",
     "temporal_stack = plot[0,1].add_line_stack(temporal, colors=\"magenta\")\n",
     "\n",
-    "# add linear selector to heatmap\n",
-    "selector = heatmap.add_linear_region_selector()\n",
+    "for subplot in plot:\n",
+    "    subplot.camera.maintain_aspect = False\n",
+    "\n",
+    "# heatmap support the same selectors as lines!\n",
+    "heatmap_ls = heatmap.add_linear_selector()\n",
+    "temporal_stack_ls = temporal_stack.add_linear_selector()\n",
+    "temporal_stack_ls.position_z = 10\n",
+    "\n",
+    "# the corresponding imaging data and some callbacks to update frames\n",
+    "cnmf_iw = fpl.ImageWidget(movie, vmin_vmax_sliders=True, cmap=\"gray\")\n",
+    "\n",
+    "# update the linear selectors with current frame index\n",
+    "def update_linear_selector(change):\n",
+    "    ix = change[\"new\"]\n",
+    "    \n",
+    "    heatmap_ls.selection = ix\n",
+    "    temporal_stack_ls.selection = ix\n",
+    "    \n",
+    "def update_ipywidget(ev):\n",
+    "    ix = ev.pick_info[\"selected_index\"]\n",
+    "    # for line collection sends as list\n",
+    "    if not isinstance(ix, int):\n",
+    "        ix = ix[0]\n",
+    "    cnmf_iw.sliders[\"t\"].value = ix\n",
+    "    \n",
+    "cnmf_iw.sliders[\"t\"].observe(update_linear_selector, \"value\")\n",
+    "heatmap_ls.selection.add_event_handler(update_ipywidget)\n",
+    "temporal_stack_ls.selection.add_event_handler(update_ipywidget)\n",
     "\n",
+    "    \n",
     "sc = Sidecar(title=\"heatmap interactive\")\n",
     "\n",
     "with sc:\n",
-    "    display(plot.show())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50c5c9bf-7881-4f19-bcdc-ab30cc96e7c7",
-   "metadata": {},
-   "source": [
-    "### plot interaction using event handlers"
+    "    display(VBox([cnmf_iw.show(), plot.show()]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "273a2d45-0423-470d-9192-8b14b3d08d3a",
+   "execution_count": 12,
+   "id": "c509263b-7120-4e0c-a2cd-f68b6525994c",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# define event handler\n",
-    "def color_change(ev):\n",
-    "    selected_indices = ev.pick_info[\"selected_indices\"]\n",
-    "    \n",
-    "    for i in range(len(temporal_stack.graphics)):\n",
-    "        temporal_stack.graphics[i].colors = \"magenta\"\n",
-    "        temporal_stack.graphics[i].colors[selected_indices] = \"white\"\n",
-    "\n",
-    "# add event handler\n",
-    "selector.selection.add_event_handler(color_change)"
+    "cnmf_iw.vmin_vmax_sliders[0].value = (-5, 15)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 7,
    "id": "4447805e-c477-48b4-8dc1-8740c2a70232",
    "metadata": {
     "tags": []
@@ -1043,6 +1184,14 @@
     "plot.close()\n",
     "sc.close()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0095e961-b72b-4e41-8502-696f03b2a6e4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/scatterplot.ipynb b/scatterplot.ipynb
deleted file mode 100644
index d6ea93a..0000000
--- a/scatterplot.ipynb
+++ /dev/null
@@ -1,247 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cb36b8f4-62d9-45a1-9c0b-dff34c08472b",
-   "metadata": {},
-   "source": [
-    "## Scatterplot\n",
-    "\n",
-    "`GridPlot` layout of scatter plots with a mix of 2d an 3d cameras"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c1f92c66-b30d-4c45-8033-addc1c3231eb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import fastplotlib as fpl\n",
-    "import numpy as np\n",
-    "from sidecar import Sidecar"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13604307-8fb9-4aae-beca-195b57a5cddb",
-   "metadata": {},
-   "source": [
-    "Generate data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "743c1d94-e733-4e03-954f-d55125bf781b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "n_points = 10_000\n",
-    "\n",
-    "dims = (n_points, 3)\n",
-    "\n",
-    "offset = 15\n",
-    "\n",
-    "normal = np.random.normal(size=dims, scale=5)\n",
-    "cloud = np.vstack(\n",
-    "    [\n",
-    "        normal - offset,\n",
-    "        normal,\n",
-    "        normal + offset,\n",
-    "    ]\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2df0522-4b3e-4d93-a461-e86e6187f698",
-   "metadata": {},
-   "source": [
-    "Create `GridPlot` instance"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c3437acf-74f0-4820-86f1-fec0a79f8a6d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ffb8e467c6046a482d0ccc504609e6c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "RFBOutputContext()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "colors = [\"yellow\"] * n_points + [\"cyan\"] * n_points + [\"magenta\"] * n_points\n",
-    "\n",
-    "# grid with 2 rows and 2 columns\n",
-    "shape = (2, 2)\n",
-    "\n",
-    "# define the camera\n",
-    "# a mix of 2d and 3d\n",
-    "cameras = [\n",
-    "    ['2d', '3d'], \n",
-    "    ['3d', '2d']\n",
-    "]\n",
-    "\n",
-    "# pan-zoom controllers for each view\n",
-    "# views are synced if they have the \n",
-    "# same controller ID\n",
-    "# you can only sync controllers that use the same camera type\n",
-    "# i.e. you cannot sync between 2d and 3d subplots\n",
-    "controllers = [\n",
-    "    [0, 1],\n",
-    "    [1, 0]\n",
-    "]\n",
-    "\n",
-    "# create the grid plot\n",
-    "grid_plot = fpl.GridPlot(\n",
-    "    shape=shape,\n",
-    "    cameras=cameras,\n",
-    "    controllers=controllers\n",
-    ")\n",
-    "\n",
-    "# add scatter graphics to subplots\n",
-    "for subplot in grid_plot:\n",
-    "    subplot.add_scatter(data=cloud, colors=colors, alpha=0.7, sizes=5)\n",
-    "    \n",
-    "    subplot.set_axes_visibility(True)\n",
-    "    subplot.set_grid_visibility(True)\n",
-    "\n",
-    "\n",
-    "# view with sidecar \n",
-    "sc = Sidecar(title=\"scatter plot\")\n",
-    "\n",
-    "with sc:\n",
-    "    display(grid_plot.show())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e44e2cf-6c72-46eb-9902-717a61b5c20e",
-   "metadata": {},
-   "source": [
-    "### Feature changes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "794a8d9a-0ee9-4e94-b60a-0bdf5feb1818",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "grid_plot[0, 1].graphics[0].colors[n_points:int(n_points * 1.5)] = \"r\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ca6c0825-3350-45a9-b620-5809d46f1af9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "grid_plot[0, 1].graphics[0].colors[:n_points:10] = \"blue\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "18540716-08fe-49fb-8400-69d7860ac0be",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "grid_plot[0, 0].graphics[0].colors[n_points:] = \"green\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "db46264d-96bf-4870-a5ca-94d14134002f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "grid_plot[0, 1].graphics[0].colors[n_points:, -1] = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "034fede4-9e63-482f-b77d-ea1fe2d09577",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "grid_plot[0, 1].graphics[0].data[:n_points] = grid_plot[0, 1].graphics[0].data[n_points * 2:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "4b9e10c3-46f4-4b5f-abf8-b869d1385e5b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "gridplot.close()\n",
-    "sc.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "763d8832-3a44-49e9-bd09-000aecc0e62f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}