{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0f87eb4d-b0fd-401d-ac6e-92042583292f",
   "metadata": {},
   "source": [
    "## SLAP2 Utils Example Notebook\n",
    "\n",
    "This notebook will highlight core features included in `SLAP2_Utils`. The example will demonstrate how to extract useful metadata, extract a trace signal, and use basic plotting. This will give users a better understanding of how to work with the library with their own data. For further information on any functions used in the notebook, please reference the [documentation](https://peter-hogg.github.io/SLAP2_Utils/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12af0fa7-b0e2-4f52-8372-367931141ce9",
   "metadata": {},
   "source": [
    "### Loading a Data File\n",
    "Loading data from a SLAP2 binary file requires importing the DataFile class from the library. The coorsponding meta data file should be in the same directory with the same file name. If either of these conditions are not met, then an error will be thrown during loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b219d69-ceaf-4adf-b7be-47e62b1830a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from slap2_utils.datafile import DataFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f255492c-0e05-4557-838e-33483bd3070a",
   "metadata": {},
   "outputs": [],
   "source": [
    "binaryFilePath = \"3DExampleScan.dat\"\n",
    "dataFile = DataFile(binaryFilePath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd7bb0d-dc45-40de-9ba1-4fe2b692fe04",
   "metadata": {},
   "source": [
    "Basic information about the acquisition is stored inside the file header. We can inspect this by accessing the header attribute in the datafile object. This attribute stores information inside a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec1f034b-00ac-45fc-bcf1-86be4d12cf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'firstCycleOffsetBytes': 136.0,\n",
       " 'lineHeaderSizeBytes': 40.0,\n",
       " 'laserPathIdx': 0.0,\n",
       " 'bytesPerCycle': 31270.0,\n",
       " 'linesPerCycle': 302.0,\n",
       " 'superPixelsPerCycle': 9595.0,\n",
       " 'dmdPixelsPerRow': 1280.0,\n",
       " 'dmdPixelsPerColumn': 800.0,\n",
       " 'numChannels': 1.0,\n",
       " 'channelMask': 1.0,\n",
       " 'numSlices': 28.0,\n",
       " 'channelsInterleave': 0.0,\n",
       " 'fpgaSystemClock_Hz': 200000000.0,\n",
       " 'referenceTimestamp_lower': 3670612649.0,\n",
       " 'referenceTimestamp_upper': 197.0,\n",
       " 'channels': [0],\n",
       " 'referenceTimestamp': 849779169961,\n",
       " 'file_version': 2,\n",
       " 'magic_start': 322379495,\n",
       " 'magic_end': 322379495}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dataFile.header)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2477fed9-06c7-4bdc-bf41-8c14bb9d01fc",
   "metadata": {},
   "source": [
    "If we wanted to access specific information stored in the header we can do so by using the corresponding key. For this simulated recording we can tell that simulated scan is a single channel volumetric recording with 28 slices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04a7e1ca-d152-43a4-a0a7-711dd8f9bf72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of imaging planes:  28.0 \n",
      "List of recorded channels : [0]\n"
     ]
    }
   ],
   "source": [
    "print('Number of imaging planes: ', dataFile.header['numSlices'], '\\nList of recorded channels :',dataFile.header['channels'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca1bdd2-5f7f-4a78-8035-184e92360c21",
   "metadata": {},
   "source": [
    "### Inspecting Acquisition Metadata\n",
    "Once loaded into Python, the acquisition metadata is stored in the `MetaData` subclass of the `DataFile` object. This object has more detailed information about the microscope settings during the acquisition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57189fbb-2bb3-4da6-87ca-166871afe2cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The laser AOM was set to:  1.9500000000000002\n",
      "The duration of the acquisition was 10.0 secs\n"
     ]
    }
   ],
   "source": [
    "# Here is an example of import data stored in the `MetaData` object\n",
    "print('The laser AOM was set to: ', dataFile.metaData.aomVoltage)\n",
    "print('The duration of the acquisition was', dataFile.metaData.acqDuration_s, 'secs')\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e9205b-06d1-40d2-bb1b-32ec40b30fc7",
   "metadata": {},
   "source": [
    "The `MetaData` object is the parent object of other objects, such as the `AcquisitionContainer`, which stores a list of all regions of interest (ROI). Information for each ROI is stored in `slapROI` objects. Now we'll look at how to pull out this information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d3c1d9a-c8dd-412e-8612-b1c7c967a58d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of ROIs in the acquistion is:  643\n"
     ]
    }
   ],
   "source": [
    "print('The number of ROIs in the acquistion is: ', len(dataFile.metaData.AcquisitionContainer.ROIs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e4fa84c-9a8d-4dc2-b4fc-c0f8d28dd3c5",
   "metadata": {},
   "source": [
    "Let's look at the information stored in a `slapROI` object.\n",
    "* `slapROI.imageMode`: SLAP2 has two types of ROIs, raster and integrated\n",
    "* `slapROI.roiType`: ROI shapes can be rectangular or arbitrary\n",
    "* `slapROI.shapeData`: Arbitrary ROI shapes are stored in a series of coordinates\n",
    "* `slapROI.z`: The remote focusing positiion is stored in the z attribute\n",
    "\n",
    "['slapROI' documentation](https://peter-hogg.github.io/SLAP2_Utils/modules/slapROI.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a3a2799f-ee60-42dc-9a76-56c99fc8578a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROI Imaging Mode:  Integrate \n",
      "\n",
      "ROI Type:  ArbitraryRoi \n",
      "\n",
      "Shape Data:  [[375. 376. 374. 375. 376. 377. 374. 375. 376. 377. 374. 375. 376. 375.]\n",
      " [716. 716. 717. 717. 717. 717. 718. 718. 718. 718. 719. 719. 719. 720.]] \n",
      "\n",
      "Remote focusing position (um):  229.0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"ROI Imaging Mode: \", dataFile.metaData.AcquisitionContainer.ROIs[0].imageMode, \"\\n\")\n",
    "print(\"ROI Type: \", dataFile.metaData.AcquisitionContainer.ROIs[0].roiType, \"\\n\")\n",
    "print(\"Shape Data: \", dataFile.metaData.AcquisitionContainer.ROIs[0].shapeData, \"\\n\")\n",
    "print(\"Remote focusing position (um): \", dataFile.metaData.AcquisitionContainer.ROIs[0].z, \"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64888b3f-47f8-4a20-bbb9-0f228fffad0b",
   "metadata": {},
   "source": [
    "'SLAP2_Utils' has inbuilt utils for interacting with ROI shape data which may be useful for users building analysis or visualization pipelines. These are stored in `slap2_utils.utils.roi_utils`. Let's look at these functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a84b0bed-2bf1-43d2-95af-3a45ed0dbf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "from slap2_utils.utils import roi_utils\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cbce9d9-fc08-423c-bbac-1a5e157b6793",
   "metadata": {},
   "source": [
    "`roi_utils.roiImg` will return a 2D array of the optical plane the ROI is in (`slapROI.z`). Pixels belonging to the ROI will be set to the value one, while the other pixels will be set to a value of 0.\n",
    "\n",
    "`roi_utils.roiBoolean' returns the same data but as a boolean, which is useful for generating activity traces\n",
    "\n",
    "`roi_utils.roiLabels' returns either a 2D or 3D label array, representing each ROI as an integer. An option argument is a path of a SLAP2 reference TIF file, which will match the ROI position in Z to image slices in the volumetric TIF file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8064dde6-4dee-459b-afc2-dc7b7601830f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n"
     ]
    }
   ],
   "source": [
    "# Plotting the pixels belonging to the ROI at index 10 in dataFile.metaData.AcquisitionContainer.ROIs\n",
    "roi_utils.roiImg(dataFile, 10)\n",
    "plt.imshow(roi_utils.roiImg(dataFile, 10))\n",
    "plt.show()\n",
    "\n",
    "# The same data in an boolean array\n",
    "print(np.unique(roi_utils.roiBoolean(dataFile, 10)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59437d75-dced-4444-a1f6-f64f2012984b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SeDMz6yfFnrn/HfANoCttjwa2RkT3K2htwLjUHge0AqT929L4/UiaI2mppKV72X1k1ZuZWY96DXdJXwQ2RkRJ350SEfMjYmpETK1jcCl/tJnZMa+YSyE/B1wl6UqgHhgBfA9okFSbzs7HA+1pfDswAWiTVAuMBDaXvHIzMzukXs/cI+L2iBgfEZOA64FnI+JG4DngmjRsFvBoai9M26T9z0ZUw9fwmJllx9Fc5/7fga9JaiG/pn5v6r8XGJ36vwbMPboSzcysr/r0DtWI+Dnw89ReDVzQw5iPgWtLUJuZmR0hv0PVzCyDHO5mZhnkcDczyyCHu5lZBjnczcwyyOFuZpZBDnczswxyuJuZZZDD3cwsgxzuZmYZ5HA3M8sgh7uZWQY53M3MMsjhbmaWQQ53M7MMcribmWWQw93MLIOKCndJ70p6XdKrkpamvlGSnpa0Kt03pn5JultSi6RlkqaUcwJmZnawvpy5/1ZEnBsRU9P2XGBRRDQDi/j1d6VeATSn2xxgXqmKNTOz4hzNssxMYEFqLwCuLui/P/JeABokjT2K5zEzsz4qNtwDeErSS5LmpL6miFiX2uuBptQeB7QWPLYt9e1H0hxJSyUt3cvuIyjdzMwOpbbIcb8REe2STgSelvRW4c6ICEnRlyeOiPnAfIARGtWnx5qZ2eEVdeYeEe3pfiPwU+ACYEP3cku635iGtwMTCh4+PvWZmVk/6TXcJQ2VNLy7DVwKLAcWArPSsFnAo6m9ELgpXTUzDdhWsHxjZmb9oJhlmSbgp5K6x/8wIn4maQnwsKTZwFrgujT+CeBKoAXYBdxc8qrNzOyweg33iFgNnNND/2ZgRg/9AdxSkurMzOyI+B2qZmYZ5HA3M8sgh7uZWQY53M3MMsjhbmaWQQ53M7MMcribmWWQw93MLIMc7mZmGeRwNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llkMPdzCyDHO5mZhlUVLhLapD0iKS3JL0pabqkUZKelrQq3TemsZJ0t6QWScskTSnvFMzM7EDFnrl/D/hZRJxO/iv33gTmAosiohlYlLYBrgCa020OMK+kFZuZWa96DXdJI4HfBO4FiIg9EbEVmAksSMMWAFen9kzg/sh7AWiQNLbEdZuZ2WEUc+Y+GXgf+AdJr0j6gaShQFNErEtj1gNNqT0OaC14fFvqMzOzflJMuNcCU4B5EXEesJNfL8EAEBEBRF+eWNIcSUslLd3L7r481MzMelFMuLcBbRGxOG0/Qj7sN3Qvt6T7jWl/OzCh4PHjU99+ImJ+REyNiKl1DD7S+s3MrAe9hntErAdaJZ2WumYAbwALgVmpbxbwaGovBG5KV81MA7YVLN+YmVk/qC1y3H8FHpA0CFgN3Ez+H4aHJc0G1gLXpbFPAFcCLcCuNNbMzPpRUeEeEa8CU3vYNaOHsQHccnRlmZnZ0fA7VM3MMsjhbmaWQQ53M7MMcribmWWQw93MLIMc7mZmGeRwNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llkMPdzCyDHO5mZhnkcDczyyCHu5lZBjnczcwyqNdwl3SapFcLbtsl3SZplKSnJa1K941pvCTdLalF0jJJU8o/DTMzK1TMF2SvjIhzI+Jc4Hzy34v6U2AusCgimoFFaRvgCqA53eYA88pQt5mZHUZfl2VmAO9ExFpgJrAg9S8Ark7tmcD9kfcC0CBpbCmKNTOz4vQ13K8HHkztpohYl9rrgabUHge0FjymLfWZmVk/KTrcJQ0CrgJ+fOC+iAgg+vLEkuZIWipp6V529+WhZmbWi76cuV8BvBwRG9L2hu7llnS/MfW3AxMKHjc+9e0nIuZHxNSImFrH4L5XbmZmh9SXcL+BXy/JACwEZqX2LODRgv6b0lUz04BtBcs3ZmbWD2qLGSRpKHAJ8F8Kuu8AHpY0G1gLXJf6nwCuBFrIX1lzc8mqNTOzohQV7hGxExh9QN9m8lfPHDg2gFtKUp2ZmR0Rv0PVzCyDHO5mZhnkcDczyyCHu5lZBjnczcwyyOFuZpZBDnczswxyuJuZZZDD3cwsgxzuZmYZ5HA3M8sgh7uZWQY53M3MMsjhbmaWQQ53M7MMcribmWWQw93MLIMc7mZmGeRwNzPLIIe7mVkGOdzNzDJIEVHpGpC0A1hZ6TpK7HhgU6WLKCHPZ2DL2nwge3Mqx3wmRsQJPe2oLfETHamVETG10kWUkqSlWZqT5zOwZW0+kL059fd8vCxjZpZBDnczswwaKOE+v9IFlEHW5uT5DGxZmw9kb079Op8B8YKqmZmV1kA5czczsxKqeLhLulzSSkktkuZWup5iSJog6TlJb0haIenW1D9K0tOSVqX7xtQvSXenOS6TNKWyM+iZpJykVyQ9lrYnS1qc6n5I0qDUPzhtt6T9kypaeA8kNUh6RNJbkt6UND0Dx+eP0u/bckkPSqqvpmMk6T5JGyUtL+jr8zGRNCuNXyVpViXmUlBLT3P6bvq9Wybpp5IaCvbdnua0UtJlBf2lz8GIqNgNyAHvACcDg4DXgDMrWVORdY8FpqT2cOBt4Ezgr4C5qX8ucGdqXwn8KyBgGrC40nM4xLy+BvwQeCxtPwxcn9r3AF9N7d8H7knt64GHKl17D3NZAPzn1B4ENFTz8QHGAWuA4wqOzZer6RgBvwlMAZYX9PXpmACjgNXpvjG1GwfYnC4FalP7zoI5nZkybjAwOWVfrlw5WOmDPR14smD7duD2Sv8SHsE8HgUuIf9GrLGpbyz56/cB/h64oWD8vnED5QaMBxYBFwOPpf+pNhX8ku47VsCTwPTUrk3jVOk5FMxlZApCHdBfzcdnHNCaQq02HaPLqu0YAZMOCMI+HRPgBuDvC/r3GzcQ5nTAvt8GHkjt/fKt+xiVKwcrvSzT/QvbrS31VY305+55wGKgKSLWpV3rgabUroZ5/h3wDaArbY8GtkZER9ourHnffNL+bWn8QDEZeB/4h7TM9ANJQ6ni4xMR7cBfA78C1pH/b/4S1XuMuvX1mAz4Y3WAr5D/CwT6eU6VDveqJmkY8E/AbRGxvXBf5P8JropLkSR9EdgYES9VupYSqSX/p/K8iDgP2En+T/59qun4AKS16Jnk/+H6BDAUuLyiRZVYtR2T3kj6JtABPFCJ5690uLcDEwq2x6e+AU9SHflgfyAifpK6N0gam/aPBTam/oE+z88BV0l6F/gR+aWZ7wENkro/oqKw5n3zSftHApv7s+BetAFtEbE4bT9CPuyr9fgAfB5YExHvR8Re4Cfkj1u1HqNufT0m1XCskPRl4IvAjekfLejnOVU63JcAzekV/0HkX/hZWOGaeiVJwL3AmxHxtwW7FgLdr97PIr8W391/U7oCYBqwreBP0YqLiNsjYnxETCJ/DJ6NiBuB54Br0rAD59M9z2vS+AFzxhUR64FWSaelrhnAG1Tp8Ul+BUyTNCT9/nXPqSqPUYG+HpMngUslNaa/Zi5NfQOGpMvJL3FeFRG7CnYtBK5PVzJNBpqBFylXDlbyhYj0u3Yl+atN3gG+Wel6iqz5N8j/+bgMeDXdriS/prkIWAU8A4xK4wV8P83xdWBqpedwmLldxK+vljk5/fK1AD8GBqf++rTdkvafXOm6e5jHucDSdIz+mfyVFVV9fIA/Bd4ClgP/SP6qi6o5RsCD5F8v2Ev+r6vZR3JMyK9jt6TbzQNwTi3k19C7s+GegvHfTHNaCVxR0F/yHPQ7VM3MMqjSyzJmZlYGDnczswxyuJuZZZDD3cwsgxzuZmYZ5HA3M8sgh7uZWQY53M3MMuj/AyE1qxi2F4D7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting all the ROIs on a particular plane of the acquisition\n",
    "roiLabelArray = roi_utils.roiLabels(dataFile)\n",
    "plt.imshow(roiLabelArray[15, :, :])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756ae7ab-68b5-47e2-ab73-023d5529b72c",
   "metadata": {},
   "source": [
    "### Plotting \n",
    "`SLAP2_Utils` has a few basic plotting features to better visualize the data in ROIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8ecf044-787c-4b5d-a669-23664beb47c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from slap2_utils.utils.plots import slap2Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92d4fb7c-61a5-483e-8167-ebf30a7d0cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can plot the contours of a given ROI over a reference image\n",
    "# This simulated data does not have a reference image, so we will use an array of zero\n",
    "slap2Plots.roiOverlay(dataFile, np.zeros_like(roiLabelArray), 15)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c4c11ea-37f9-4ced-b976-6e8f25a4e4ca",
   "metadata": {},
   "source": [
    "### Trace Extraction\n",
    "Trace extraction can be handled directly using the `Trace` object. However, `SLAP2_Utils` includes helper functions to streamline this process further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a6b9b21-8461-4e12-b7a9-4044ce9b1492",
   "metadata": {},
   "outputs": [],
   "source": [
    "from slap2_utils.utils import trace\n",
    "from slap2_utils.functions import tracefunctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f4c8c0a-80a4-4cfc-bbdb-0c6194b0a669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The trace smoothed with a weighted average\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The raw trace\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The raw trace cleaned to include only data corresponding the correct axial focus\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's extract a trace for ROI 15\n",
    "# we need to get determine what channel, there is only one in this simulated recording\n",
    "chIdx = 1\n",
    "# We need to find the ROI's Z position in the fastz of the DataFile\n",
    "roi15 = dataFile.metaData.AcquisitionContainer.ROIs[15]\n",
    "zIdx = dataFile.fastZs.index(roi15.z)\n",
    "\n",
    "\n",
    "\n",
    "# Can now initialize the Trace object\n",
    "Trace = trace.Trace(dataFile, zIdx, chIdx)\n",
    "\n",
    "# We need to set the pixel mask for Trace generation\n",
    "roi15Mask = roi_utils.roiBoolean(dataFile, 15)\n",
    "\n",
    "# Exclude any raster-mode pixels\n",
    "rasterPixels = None\n",
    "\n",
    "Trace.setPixelIdxs(rasterPixels, roi15Mask)\n",
    "\n",
    "#We can smooth the trace with temporal weights \n",
    "print('The trace smoothed with a weighted average')\n",
    "trace15, sumDataWeighted, sumExpected, sumExpectedWeighted  = Trace.process(150, 1000)\n",
    "plt.plot(trace15)\n",
    "plt.show()\n",
    "\n",
    "# Or we can set the weighted windows to 1 and return the raw trace\n",
    "print('The raw trace')\n",
    "rawtrace15, sumDataWeighted, sumExpected, sumExpectedWeighted  = Trace.process(1, 1)\n",
    "plt.plot(rawtrace15)\n",
    "plt.show()\n",
    "\n",
    "# Traces from volumetric acquisitions need to be further processed to remove times when the microscope was focused on a different plane\n",
    "print('The raw trace cleaned to include only data corresponding the correct axial focus')\n",
    "volumetricTrace15 = tracefunctions.cleanVolumeTrace(dataFile, zIdx, rawtrace15)\n",
    "plt.plot(volumetricTrace15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0066ef-5235-45b1-bfbf-8844e66f67b5",
   "metadata": {},
   "source": [
    "All of these steps can excuted using the `returnVolumeTrace` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60477a68-1e90-4b59-9a28-217782ea2159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returning the raw trace for the ROI with one line\n",
    "rawTrace15, volumeTrace15 = tracefunctions.returnVolumeTrace(dataFile, 15)\n",
    "plt.plot(volumeTrace15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad58540-e3f4-4796-9af0-a3c692a8b9c2",
   "metadata": {},
   "source": [
    "### Extracting Line Data\n",
    "\n",
    "The 'getLineData' can be used to return the recorded data for a specific index, cycle, and channel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ccec021-2b8b-42ee-a47f-da090c22d382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([], shape=(0, 1), dtype=int16)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFile.getLineData(1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e21bc44-c0ee-42dd-80bc-da4d35a630f9",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}