{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d758db2-4423-4a02-80f5-2684c8282410",
   "metadata": {},
   "source": [
    "# Multi plot annotations\n",
    "\n",
    ":::{note}\n",
    "What you see in this notebook will depend on whether you've run this notebook before and written annotations to the `annotations.db` database! For reproducibility, the rest of the notebook will assume the `annotations.db` has been deleted (if it exists).\n",
    ":::\n",
    "\n",
    "## Setup\n",
    "The first thing we need to do is get and plot the data we want to annotate. In this example we will make two different plots: one curve and one image plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "519af352-eba9-41d2-a164-c4b6ae4945d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import holoviews as hv\n",
    "import numpy as np\n",
    "from holonote.annotate import Annotator\n",
    "\n",
    "hv.extension(\"bokeh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e91ab795-0b03-4b9a-a31c-fb20dbf8035d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xs = np.arange(\"2005-02\", \"2005-04\", dtype=\"datetime64[D]\")\n",
    "ys = np.sin(np.linspace(0, 5 * np.pi, len(xs)))\n",
    "\n",
    "curve = hv.Curve((xs, ys), kdims=[\"TIME\"])\n",
    "curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8075ba5-8635-4e96-87e9-31d4e17be678",
   "metadata": {},
   "outputs": [],
   "source": [
    "xvals = np.linspace(-4, 0, 202)\n",
    "yvals = np.linspace(4, 0, 202)\n",
    "xs, ys = np.meshgrid(xvals, yvals)\n",
    "alpha, beta = 1, 0\n",
    "ab_data = np.sin(((ys / alpha) ** alpha + beta) * xs)\n",
    "\n",
    "image = hv.Image(ab_data, kdims=[\"A\", \"B\"]).opts(cmap=\"greens\")\n",
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f4a95b-2f7d-4f7b-83ef-b8f59e38fbee",
   "metadata": {},
   "source": [
    "We will now create the annotator with the specification consisting of the key dimensions (`kdims`) in both of the two plots: `TIME`, `A`, and `B`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d02d0a45-f044-40eb-ad99-1105826937ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "spec = {\"TIME\": np.datetime64, \"A\": np.float64, \"B\": np.float64}\n",
    "annotator = Annotator(spec, fields=[\"description\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b2b3fd-9c98-4735-a732-cbe2abfd9424",
   "metadata": {},
   "source": [
    "Then we can do the same as show in the basic example, and overlaying the annotator on top of both plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddba311b-489b-4f04-ac2d-a9ad3db25cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotator * curve + annotator * image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8472fe4-7824-4992-a50d-9d35bb6187e8",
   "metadata": {},
   "source": [
    "## Basic operations on annotations\n",
    "\n",
    "Using the select tool, you can define a region of interest on both of the plots and run the following cell. For more information check out the [basic example](./Basics.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2f94aec-581f-4b1b-8e3c-033f9c679cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotator.add_annotation(description='My first annotation!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1b1f18-af5a-4e18-ab3b-73502a9e3bf9",
   "metadata": {},
   "source": [
    "You can set the range of interest programmatically as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1c09972-3e85-4b7e-90cc-3f3edde71c15",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotator.set_regions(\n",
    "    TIME=(np.datetime64(\"2005-02-13\"), np.datetime64(\"2005-02-16\")),\n",
    "    A=(-0.25, 0.25),\n",
    "    B=(-0.1, 0.1),\n",
    ")\n",
    "annotator.add_annotation(description='A programmatically defined annotation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bd47182-e15e-4334-96f5-3c2ace10590b",
   "metadata": {},
   "source": [
    "To persist these annotations, we call the `.commit()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7688ef7-d815-4bee-a31a-ae2e5343f3cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotator.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1e18ec-4f80-4de0-b796-e069921e8d04",
   "metadata": {},
   "source": [
    "#### Simple selection of annotations\n",
    "\n",
    "Click on a range region in the plot above and run the following cell to see its UUID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b02700b-f2bf-4a9b-a93b-227b72bf6c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotator.selected_index"
   ]
  }
 ],
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