demo.ipynb

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   "source": [
    "# IRVIZ in Jupyter\n",
    "\n",
    "Demonstration of the IRVIZ Dash app running within a Jupyter environment (notebook or lab)."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Reading Data\n",
    "\n",
    "The IRVIZ viewer requires that data is passed to it.\n",
    "\n",
    "Typically, the user will need to provide a way (function) to read their data.\n",
    "This function *should* return a dask array for better performance.\n",
    "\n",
    "In this case, with the provided `BP-area3a.h5` file, we can use `irviz.app.open_map_file` to read the data and return a dask array."
   ]
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   "source": [
    "from dask import array as da\n",
    "import h5py as h5\n",
    "import numpy as np\n",
    "import sklearn.decomposition\n",
    "\n",
    "from irviz.app import open_map_file, open_ir_file\n",
    "\n",
    "# Open the data\n",
    "data_file =  \"INSERT FILE PATH HERE\" \n",
    "# data_file =  \"./data/BP-area3a.h5\"\n",
    "data, bounds = open_map_file(data_file)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Creating the Viewer (App)\n",
    "\n",
    "The Viewer creates the Dash app if necessary, and create all of the visualization components to that app.\n",
    "\n",
    "We will need to create a Viewer by using the `irviz.viewer.notebook_viewer` function.\n",
    "This function has many optional arguments that can be passed to it to define which components get created, how they look, etc.\n",
    "\n",
    "### Documentation\n",
    "\n",
    "The documentation for the `notebook_viewer` and its arguments can be found by running:\n",
    "\n",
    "`help(notebook_viewer)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e664a911",
   "metadata": {},
   "outputs": [],
   "source": [
    "from irviz.viewer import notebook_viewer\n",
    "\n",
    "help(notebook_viewer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55062f41",
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   "source": [
    "### Optional Data and Arguments for the Viewer\n",
    "\n",
    "Below we will be preparing optional data and arguments for the viewer.\n",
    "\n",
    "#### Decomposition and Component Labeling\n",
    "\n",
    "In this example, we will use PCA."
   ]
  },
  {
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   "execution_count": null,
   "id": "aa728453",
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   "source": [
    "model = sklearn.decomposition.PCA(n_components=3)\n",
    "decomposition = model.fit_transform(data.transpose(1,2,0).reshape(-1, data.shape[0])).T.reshape(-1, *data.shape[1:])\n",
    "cluster_labels = np.argmax(decomposition, axis=0)\n",
    "cluster_label_names = ['Alpha', 'Bravo', 'Charlie']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a93254",
   "metadata": {},
   "source": [
    "#### Spectra Annotations\n",
    "\n",
    "You can add annotations to the spectra plot by providing an annotations dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22525890",
   "metadata": {},
   "outputs": [],
   "source": [
    "from irviz.viewer import notebook_viewer\n",
    "\n",
    "annotations = {\n",
    "    'X1': {\n",
    "        'range': [3200, 2800],\n",
    "        'color': 'orange'  # Defaults to gray if no color specified\n",
    "    },\n",
    "    'X2': {\n",
    "        'position': 1000\n",
    "    },\n",
    "    'X3': {\n",
    "        'range': (1500, 2000),\n",
    "        'position': 1900,\n",
    "        'color': \"#fa00a5\"\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d76bd6-4d07-4f11-bee8-892b191c13e8",
   "metadata": {},
   "source": [
    "### Calling notebook_viewer\n",
    "\n",
    "Here, we will be using the `notebook_viewer` function to create our Viewer with the optional arguments we have set up above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae7ddb38-b7bc-4270-8bdd-9919f90623ec",
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   "source": [
    "from irviz.viewer import notebook_viewer\n",
    "\n",
    "viewer = notebook_viewer(data,\n",
    "                         annotations=annotations,\n",
    "                         decomposition=decomposition,\n",
    "                         bounds=bounds,\n",
    "                         x_axis_title='X (μm)',\n",
    "                         y_axis_title='Y (μm)',\n",
    "                         spectra_axis_title='Wavenumber (cm⁻¹)',\n",
    "                         intensity_axis_title='Intensity',\n",
    "                         invert_spectra_axis=True,\n",
    "                         cluster_labels=cluster_labels,\n",
    "                         cluster_label_names=cluster_label_names,\n",
    "                         component_spectra=model.components_,\n",
    "                         height=1500)"
   ]
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  {
   "cell_type": "markdown",
   "id": "407701a4-675e-4163-8bd7-3696bdac199c",
   "metadata": {},
   "source": [
    "### Viewer Properties Examples\n",
    "\n"
   ]
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   "source": [
    "print(f'current spectrum in the spectra plot:\\n\\t{viewer.spectrum}',\n",
    "      f'current spectral value in the spectra plot (i.e. the value at the current x position):\\n\\t{viewer.spectral_value}',\n",
    "      f'current x index of the spectra plot:\\n\\t{viewer.spectral_index}', sep='\\n\\n')\n"
   ]
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  {
   "cell_type": "markdown",
   "id": "9d785bff-68ab-45ad-b320-73fd7362d957",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43322610-fb31-410b-a730-d34da8918472",
   "metadata": {
    "tags": []
   },
   "source": [
    "## (TESTING: a second viewer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf0d092b-129b-4e63-bd1d-c242d0f3ba23",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create 2nd notebook viewer (after waiting until first has loaded completely)\n",
    "import time\n",
    "time.sleep(2)\n",
    "viewer = notebook_viewer(data, decomposition=decomposition, bounds=bounds, height=1500)"
   ]
  },
  {
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   "id": "20217970-7f51-4365-acdf-d8d610ffaecb",
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