notebooks/jupyter/classification/labeling_interactive.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6860758-e614-4e0b-b9bb-3cc55af78e9c",
   "metadata": {},
   "source": [
    "# Intro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a37590-30ae-4d92-a889-0c173759fae9",
   "metadata": {},
   "source": [
    "Welcome to the interactive labeling notebook!\n",
    "\n",
    "The notebook proceeds as follows:\n",
    "\n",
    "1. **Import** libraries\n",
    "2. Define **paths** to data\n",
    "3. **Label** ROI images using a simple GUI\n",
    "4. **Visualize** results\n",
    "5. **Save** results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3bccb7",
   "metadata": {},
   "source": [
    "- If you need to restart a session, don't worry. Your results are continuously saved in the CSV file and you can just start from where you left off.\n",
    "- We recommend labeling one session at a time so that the results can be saved with the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a1f6ef-7075-4cc3-a046-dd170bb957b9",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "452c74a7-c5bb-4b8b-ad89-b52323b131b0",
   "metadata": {},
   "source": [
    "Widen the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bab4b303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container {width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# widen jupyter notebook window\n",
    "from IPython.display import display, HTML\n",
    "display(HTML(\"<style>.container {width:95% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fe2758-1ac9-4a38-986e-5fd0dc26746b",
   "metadata": {},
   "source": [
    "Import basic libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7049a2b0-a884-424a-b685-52205b885254",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e77df19-80a4-4fa9-b7df-e19647d0727a",
   "metadata": {},
   "source": [
    "Import `roicat`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a8e0dd0-f42b-48a9-a0ac-6a928e9e056f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load_ext autoreload\n",
    "# %autoreload 2\n",
    "import roicat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670046e1-e139-4a5b-a946-57ba7e59794c",
   "metadata": {},
   "source": [
    "# Find paths to data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "872ed40f-14d1-47d5-82cb-27eb991e49e0",
   "metadata": {},
   "source": [
    "##### Prepare list of paths to data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "469b96a5-db27-418e-8f40-f62a6760f6d6",
   "metadata": {},
   "source": [
    "In this example we are using suite2p output files, but other data types can be used (CaImAn, etc.) \\\n",
    "See the notebook on ingesting diverse data: https://github.com/RichieHakim/ROICaT/blob/main/notebooks/jupyter/other/demo_custom_data_importing.ipynb\n",
    "\n",
    "Make a list containing the paths to all the input files.\n",
    "\n",
    "In this example we are using suite2p, so the following are defined:\n",
    "1. `paths_allStat`: a list to all the stat.npy files\n",
    "2. `paths_allOps`: a list with ops.npy files that correspond 1-to-1 with the stat.npy files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "988251d8-83f2-4840-a509-0baa812c3688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting: Importing FOV images from ops files\n",
      "Completed: Set FOV_height and FOV_width successfully.\n",
      "Completed: Imported 1 FOV images.\n",
      "Completed: Set FOV_images for 1 sessions successfully.\n",
      "Importing spatial footprints from stat files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  1.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported 1 sessions of spatial footprints into sparse arrays.\n",
      "Completed: Set spatialFootprints for 1 sessions successfully.\n",
      "Completed: Created session_bool.\n",
      "Completed: Created centroids.\n",
      "Staring: Creating centered ROI images from spatial footprints...\n",
      "Completed: Created ROI images.\n"
     ]
    }
   ],
   "source": [
    "dir_data = r'/media/rich/bigSSD/downloads_tmp/tmp_data/mouse_0322R/statFiles/20230420/'\n",
    "\n",
    "\n",
    "path_stat = str(Path(dir_data) / 'stat.npy')\n",
    "path_ops = str(Path(dir_data) / 'ops.npy')\n",
    "\n",
    "data = roicat.data_importing.Data_suite2p(\n",
    "    paths_statFiles=[path_stat],\n",
    "    paths_opsFiles=[path_ops],\n",
    "    new_or_old_suite2p='new',\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "assert data.check_completeness(verbose=False)['classification_inference'], f\"Data object is missing attributes necessary for tracking.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9940b64f",
   "metadata": {},
   "source": [
    "# Label data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229488f2",
   "metadata": {},
   "source": [
    "How to use this GUI:\n",
    "- Each image will be shown one at a time. You can go to previous and next images by using the ***Left*** and ***Right*** arrow keys, and can close the GUI by pressing the ***Escape*** key.\n",
    "- Label the image by pressing any key on your keyboard. This key will be used as the label. After each key press, the following occurs:\n",
    "    1. The results will be saved as a CSV file at `path_csv` (will be a temporary file if not specified)\n",
    "    2. The results will also be stored in the python object as `ImageLabeler_obj.labels_`.\n",
    "    2. The GUI should progress to the next image.\n",
    "    3. Finish labeling by closing out of the GUI window or pressing the user defined '`end_key`' (***Escape*** by default).\n",
    "- The results are stored in a temporary CSV file with two columns: 'index' and 'label'. This file can be used to recover lost labeling session data. We will save more comprehensive results files at the end of the notebook.\n",
    "    - **'index'**: The index within the data object. 0-indexed.\n",
    "    - **'label'**: The key pressed for that image in the labeling GUI.\n",
    "- To start at a different image index than 0, change the `start_index` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88429888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image 0: g\n",
      "Image 1: b\n",
      "Image 2: g\n",
      "Image 3: g\n",
      "Image 4: g\n",
      "Image 5: g\n",
      "Image 6: g\n",
      "Image 7: b\n",
      "Image 8: g\n",
      "Image 9: g\n",
      "Image 10: g\n",
      "Image 11: g\n",
      "Image 12: g\n",
      "Image 13: b\n",
      "Image 14: b\n",
      "Image 15: g\n",
      "Image 16: b\n",
      "Image 17: g\n",
      "Image 17: b\n",
      "Image 18: b\n",
      "Image 19: b\n",
      "Image 20: g\n",
      "Image 21: b\n",
      "Image 22: g\n",
      "Image 23: b\n",
      "Image 24: b\n",
      "Image 25: g\n",
      "Image 26: g\n",
      "Image 27: g\n",
      "Image 28: g\n",
      "Image 29: b\n",
      "Number of labeled ROIs: 30\n",
      "path to csv file: /tmp/roicat_labels_20230623_192710.csv\n"
     ]
    }
   ],
   "source": [
    "images = data.ROI_images[0]\n",
    "\n",
    "## Run the ImageLabeler class\n",
    "with roicat.helpers.ImageLabeler(\n",
    "    image_array=images, \n",
    "    start_index=0,  ## Change the starting image index here.\n",
    "    path_csv=None,  ## If None, then a temporary path will be used.\n",
    "    resize_factor=10.0,  \n",
    "    verbose=True,\n",
    "    key_end='Escape',  ## Closes GUI\n",
    "    key_prev='Left',  ## Goes to previous image\n",
    "    key_next='Right',  ## Goes to next image\n",
    ") as labeler:  ## Running this object within a context manager allows it to crash gracefully\n",
    "    labeler.run()\n",
    "    \n",
    "path_csv = labeler.path_csv\n",
    "labels = labeler.get_labels(kind='dataframe')\n",
    "\n",
    "print(f\"Number of labeled ROIs: {len(labels['index'])}\")\n",
    "print(f\"path to csv file: {path_csv}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff959bb",
   "metadata": {},
   "source": [
    "# Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f148ff17",
   "metadata": {},
   "outputs": [
    {
     "data": {
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AYmNjZbFYZLFYdMcddzTa36FDB7388stuGexsXIWHh7tsDw8PdwmvnystLVV9fX2T5xw6dKjZz0pNTVVKSopzvaKiQjabrbWjAwAAH3deAVRYWCiHw6Ho6Gjt3btXYWFhzn3t27dX165dFRgY6PYhPc1qtTr/XyMAAHDxO68A6t69uySpoaHBI8P8XEREhCSppKREkZGRzu0lJSWKjY1t8pzQ0FAFBgaqpKTEZXtJSYnzegAAAOcVQD+Xn5+vjz76SKdOnWoURHPnzr3gwa6++mpFREQoKyvLGTwVFRX65JNPNH369CbPad++vQYOHKisrCyNGTNG0v/HWlZWlmbMmHHBMwEAgItDqwLo1Vdf1fTp0xUaGqqIiAhZLBbnPovFcs4BVFVVpSNHjjjXCwsLlZeXpy5duqhbt26aOXOmnn32WfXs2VNXX3215syZo6ioKGfcSNLw4cM1duxYZ+CkpKQoOTlZN910kwYNGqRly5apurpakydPbs2tAgCAi1CrAujZZ5/VX/7yF82ePfuCPjw3N1fDhg1zrp99ETk5OVnp6el64oknVF1drYceekhlZWW67bbblJmZqaCgIOc5BQUFKi0tda6PHz9ep0+f1ty5c2W32xUbG6vMzMxGL0YDAABzWRwOh+N8TwoODlZeXp6io6M9MZPXVVRUKCQkROXl5QoODnb79Xs8udnt1/S0o4vu9vYIAIAm8DPlv87n53er/iPEcePG6YMPPmjVcAAAAN7Wqq/Arr32Ws2ZM0d79uxR//79dckll7jsf/TRR90yHAAAgCe0KoBeeeUVdezYUdnZ2crOznbZZ7FYCCAAAODTWhVAhYWF7p4DAACgzbTqHSAAAAB/1qonQFOmTGlxf0u/eR0AAMDbWhVA3333nct6XV2dDh48qLKysiZ/SSoAAIAvaVUAvfvuu422NTQ0aPr06brmmmsueCgAAABPcts7QAEBAUpJSdHSpUvddUkAAACPcOtL0AUFBfrpp5/ceUkAAAC3a9VXYGd/Z9dZDodDJ0+e1ObNm5WcnOyWwQAAADylVQF04MABl/WAgACFhYVp8eLFv/gvxAAAALytVQH00UcfuXsOAACANtOqADrr9OnTOnz4sCSpV69eCgsLc8tQAAAAntSql6Crq6s1ZcoURUZG6vbbb9ftt9+uqKgoTZ06Vd9//727ZwQAAHCrVgVQSkqKsrOz9f7776usrExlZWXatGmTsrOz9ac//cndMwIAALhVq74C+9e//qV//vOfGjp0qHPbr3/9a3Xo0EH33XefVq5c6a75AAAA3K5VT4C+//57hYeHN9retWtXvgIDAAA+r1UBFB8fr3nz5unHH390bvvhhx/0zDPPKD4+3m3DAQAAeEKrvgJbtmyZRo4cqauuukoxMTGSpM8++0xWq1UffPCBWwcEAABwt1YFUP/+/ZWfn69169bp0KFDkqSkpCRNmDBBHTp0cOuAAAAA7taqAEpLS1N4eLimTZvmsn3NmjU6ffq0Zs+e7ZbhAAAAPKFV7wCtXr1avXv3brS9b9++WrVq1QUPBQAA4EmtCiC73a7IyMhG28PCwnTy5MkLHgoAAMCTWhVANptNH3/8caPtH3/8saKioi54KAAAAE9q1TtA06ZN08yZM1VXV6c77rhDkpSVlaUnnniC/wkaAAD4vFYF0KxZs3TmzBn98Y9/VG1trSQpKChIs2fPVmpqqlsHBAAAcLdWBZDFYtFzzz2nOXPm6Ouvv1aHDh3Us2dPWa1Wd88HAADgdq0KoLM6duyom2++2V2zAAAAtIlWvQQNAADgzwggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMbx+QDq0aOHLBZLo+WRRx5p8vj09PRGxwYFBbXx1AAAwJdd0P8E3RY+/fRT1dfXO9cPHjyoO++8U+PGjWv2nODgYB0+fNi5brFYPDojAADwLz4fQGFhYS7rixYt0jXXXKMhQ4Y0e47FYlFERISnRwMAAH7K578C+7na2lq9+eabmjJlSotPdaqqqtS9e3fZbDaNHj1aX375ZYvXrampUUVFhcsCAAAuXn4VQBkZGSorK9OkSZOaPaZXr15as2aNNm3apDfffFMNDQ269dZbdfz48WbPSUtLU0hIiHOx2WwemB4AAPgKvwqg1157TYmJiYqKimr2mPj4eE2cOFGxsbEaMmSI3nnnHYWFhWn16tXNnpOamqry8nLnUlxc7InxAQCAj/D5d4DOOnbsmLZt26Z33nnnvM675JJLdMMNN+jIkSPNHmO1WmW1Wi90RAAA4Cf85gnQ66+/rq5du+ruu+8+r/Pq6+v1xRdfKDIy0kOTAQAAf+MXAdTQ0KDXX39dycnJatfO9aHVxIkTlZqa6lxfsGCBPvjgA3377bfav3+/fv/73+vYsWN68MEH23psAADgo/ziK7Bt27apqKhIU6ZMabSvqKhIAQH/7bjvvvtO06ZNk91u1+WXX66BAwcqJydHffr0acuRAQCAD/OLALrrrrvkcDia3Ld9+3aX9aVLl2rp0qVtMBUAAPBXfvEVGAAAgDsRQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDg+HUDz58+XxWJxWXr37t3iOW+//bZ69+6toKAg9e/fX1u2bGmjaQEAgL/w6QCSpL59++rkyZPOZdeuXc0em5OTo6SkJE2dOlUHDhzQmDFjNGbMGB08eLANJwYAAL7O5wOoXbt2ioiIcC6hoaHNHvviiy9q5MiRmjVrlq6//notXLhQN954o5YvX96GEwMAAF/n8wGUn5+vqKgoRUdHa8KECSoqKmr22N27d2vEiBEu2xISErR79+4WP6OmpkYVFRUuCwAAuHj5dADFxcUpPT1dmZmZWrlypQoLCzV48GBVVlY2ebzdbld4eLjLtvDwcNnt9hY/Jy0tTSEhIc7FZrO57R4AAIDv8ekASkxM1Lhx4zRgwAAlJCRoy5YtKisr01tvveXWz0lNTVV5eblzKS4uduv1AQCAb2nn7QHOR+fOnXXdddfpyJEjTe6PiIhQSUmJy7aSkhJFRES0eF2r1Sqr1eq2OQEAgG/z6SdA/6uqqkoFBQWKjIxscn98fLyysrJctm3dulXx8fFtMR4AAPATPh1Ajz/+uLKzs3X06FHl5ORo7NixCgwMVFJSkiRp4sSJSk1NdR7/2GOPKTMzU4sXL9ahQ4c0f/585ebmasaMGd66BQAA4IN8+iuw48ePKykpSWfOnFFYWJhuu+027dmzR2FhYZKkoqIiBQT8t+FuvfVWrV+/Xk8//bSeeuop9ezZUxkZGerXr5+3bgEAAPggnw6gDRs2tLh/+/btjbaNGzdO48aN89BEAADgYuDTX4EBAAB4AgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAADAOAQQAAIxDAAEAAOMQQAAAwDgEEAAAMA4BBAAAjEMAAQAA4xBAAADAOAQQAAAwDgEEAACM49MBlJaWpptvvlmdOnVS165dNWbMGB0+fLjFc9LT02WxWFyWoKCgNpoYAAD4A58OoOzsbD3yyCPas2ePtm7dqrq6Ot11112qrq5u8bzg4GCdPHnSuRw7dqyNJgYAAP6gnbcHaElmZqbLenp6urp27ap9+/bp9ttvb/Y8i8WiiIgIT48HAAD8lE8/Afpf5eXlkqQuXbq0eFxVVZW6d+8um82m0aNH68svv2zx+JqaGlVUVLgsAADg4uU3AdTQ0KCZM2fqV7/6lfr169fscb169dKaNWu0adMmvfnmm2poaNCtt96q48ePN3tOWlqaQkJCnIvNZvPELQAAAB/hNwH0yCOP6ODBg9qwYUOLx8XHx2vixImKjY3VkCFD9M477ygsLEyrV69u9pzU1FSVl5c7l+LiYnePDwAAfIhPvwN01owZM/Tvf/9bO3bs0FVXXXVe515yySW64YYbdOTIkWaPsVqtslqtFzomAADwEz79BMjhcGjGjBl699139eGHH+rqq68+72vU19friy++UGRkpAcmBAAA/sinnwA98sgjWr9+vTZt2qROnTrJbrdLkkJCQtShQwdJ0sSJE3XllVcqLS1NkrRgwQLdcsstuvbaa1VWVqbnn39ex44d04MPPui1+wAAAL7FpwNo5cqVkqShQ4e6bH/99dc1adIkSVJRUZECAv77IOu7777TtGnTZLfbdfnll2vgwIHKyclRnz592mpsAADg43w6gBwOxy8es337dpf1pUuXaunSpR6aCAAAXAx8+h0gAAAATyCAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcQggAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxiGAAACAcfwigFasWKEePXooKChIcXFx2rt3b4vHv/322+rdu7eCgoLUv39/bdmypY0mBQAA/sDnA2jjxo1KSUnRvHnztH//fsXExCghIUGnTp1q8vicnBwlJSVp6tSpOnDggMaMGaMxY8bo4MGDbTw5AADwVT4fQEuWLNG0adM0efJk9enTR6tWrdKll16qNWvWNHn8iy++qJEjR2rWrFm6/vrrtXDhQt14441avnx5G08OAAB8VTtvD9CS2tpa7du3T6mpqc5tAQEBGjFihHbv3t3kObt371ZKSorLtoSEBGVkZDT7OTU1NaqpqXGul5eXS5IqKiouYPrmNdR875HrepKn/i4AABeGnymNr+twOH7xWJ8OoNLSUtXX1ys8PNxle3h4uA4dOtTkOXa7vcnj7XZ7s5+TlpamZ555ptF2m83WiqkvTiHLvD0BAOBi4emfKZWVlQoJCWnxGJ8OoLaSmprq8tSooaFB//nPf3TFFVfIYrG49bMqKipks9lUXFys4OBgt14bAABf58mfgw6HQ5WVlYqKivrFY306gEJDQxUYGKiSkhKX7SUlJYqIiGjynIiIiPM6XpKsVqusVqvLts6dO7du6HMUHBxMAAEAjOWpn4O/9OTnLJ9+Cbp9+/YaOHCgsrKynNsaGhqUlZWl+Pj4Js+Jj493OV6Stm7d2uzxAADAPD79BEiSUlJSlJycrJtuukmDBg3SsmXLVF1drcmTJ0uSJk6cqCuvvFJpaWmSpMcee0xDhgzR4sWLdffdd2vDhg3Kzc3VK6+84s3bAAAAPsTnA2j8+PE6ffq05s6dK7vdrtjYWGVmZjpfdC4qKlJAwH8fZN16661av369nn76aT311FPq2bOnMjIy1K9fP2/dggur1ap58+Y1+soNAAAT+MrPQYvjXP6tGAAAwEXEp98BAgAA8AQCCAAAGIcAAgAAxiGAAACAxw0dOlQzZ8709hhOBBAAADAOAQQAAIxDALWRyspKTZgwQZdddpkiIyO1dOlSn3scCACAJ/3000+aMWOGQkJCFBoaqjlz5pzTb273BAKojaSkpOjjjz/We++9p61bt2rnzp3av3+/t8cCAKDNrF27Vu3atdPevXv14osvasmSJfrb3/7mlVl8/n+CvhhUVlZq7dq1Wr9+vYYPHy5Jev3118/pt9UCAHCxsNlsWrp0qSwWi3r16qUvvvhCS5cu1bRp09p8Fp4AtYFvv/1WdXV1GjRokHNbSEiIevXq5cWpAABoW7fccossFotzPT4+Xvn5+aqvr2/zWQggAABgHAKoDURHR+uSSy7Rp59+6txWXl6ub775xotTAQDQtj755BOX9T179qhnz54KDAxs81l4B6gNdOrUScnJyZo1a5a6dOmirl27at68eQoICHB5FAgAwMWsqKhIKSkpevjhh7V//369/PLLWrx4sVdmIYDayJIlS/SHP/xBv/nNbxQcHKwnnnhCxcXFCgoK8vZoAAC0iYkTJ+qHH37QoEGDFBgYqMcee0wPPfSQV2axOLz1D/ANV11drSuvvFKLFy/W1KlTvT0OAABG4QlQGzlw4IAOHTqkQYMGqby8XAsWLJAkjR492suTAQBgHgKoDb3wwgs6fPiw2rdvr4EDB2rnzp0KDQ319lgAABiHr8AAAIBx+GfwAADAOAQQAAAwDgEEAACMQwABAADjEEAAAMA4BBAAvzN06FDNnDnznI7dvn27LBaLysrKLugze/TooWXLll3QNQD4DgIIAAAYhwACAADGIYAA+LW///3vuummm9SpUydFRETogQce0KlTpxod9/HHH2vAgAEKCgrSLbfcooMHD7rs37VrlwYPHqwOHTrIZrPp0UcfVXV1dVvdBoA2RgAB8Gt1dXVauHChPvvsM2VkZOjo0aOaNGlSo+NmzZqlxYsX69NPP1VYWJhGjRqluro6SVJBQYFGjhype++9V59//rk2btyoXbt2acaMGW18NwDaCr8LDIBfmzJlivPP0dHReumll3TzzTerqqpKHTt2dO6bN2+e7rzzTknS2rVrddVVV+ndd9/Vfffdp7S0NE2YMMH5YnXPnj310ksvaciQIVq5cqWCgoLa9J4AeB5PgAD4tX379mnUqFHq1q2bOnXqpCFDhkiSioqKXI6Lj493/rlLly7q1auXvv76a0nSZ599pvT0dHXs2NG5JCQkqKGhQYWFhW13MwDaDE+AAPit6upqJSQkKCEhQevWrVNYWJiKioqUkJCg2trac75OVVWVHn74YT366KON9nXr1s2dIwPwEQQQAL916NAhnTlzRosWLZLNZpMk5ebmNnnsnj17nDHz3Xff6ZtvvtH1118vSbrxxhv11Vdf6dprr22bwQF4HV+BAfBb3bp1U/v27fXyyy/r22+/1XvvvaeFCxc2eeyCBQuUlZWlgwcPatKkSQoNDdWYMWMkSbNnz1ZOTo5mzJihvLw85efna9OmTbwEDVzECCAAfissLEzp6el6++231adPHy1atEgvvPBCk8cuWrRIjz32mAYOHCi73a73339f7du3lyQNGDBA2dnZ+uabbzR48GDdcMMNmjt3rqKiotrydgC0IYvD4XB4ewgAAIC2xBMgAABgHAIIAAAYhwACAADGIYAAAIBxCCAAAGAcAggAABiHAAIAAMYhgAAAgHEIIAAAYBwCCAAAGIcAAgAAxvk/VAmGT32tGzQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(labels['label']);\n",
    "plt.xlabel('label');\n",
    "plt.ylabel('counts');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b8990d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roicat.visualization.display_labeled_ROIs(\n",
    "    images=images,\n",
    "#     labels=labels['label'].astype(np.int64),\n",
    "    labels=labels,\n",
    "    max_images_per_label=10,\n",
    "    figsize=(10, 3),\n",
    "    fontsize=25,\n",
    "    shuffle=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53667b6f-0bb0-4738-8878-754998be0a55",
   "metadata": {},
   "source": [
    "# Save results\n",
    "\n",
    "The results file can be opened using any of the following methods:\n",
    "1. `roicat.helpers.pickle_load(path)`\n",
    "2. `np.load(path)`\n",
    "3. \n",
    "``` \n",
    "import pickle\n",
    "with open(path_save, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "  ```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b05ce96",
   "metadata": {},
   "source": [
    "##### Make the results dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e83c4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_info = data.serializable_dict  ## pull out the data from the data object into a dictionary\n",
    "[data_info.pop(key) for key in ['ROI_images', 'spatialFootprints'] if key in data_info]  ## remove the large data items from the data object\n",
    "\n",
    "results = {\n",
    "    'labels': labels,\n",
    "    'images': images,\n",
    "    'data_info': data_info,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5680357c",
   "metadata": {},
   "source": [
    "##### Save the results dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "be8cdb20-eb78-4cb7-9e85-b67c68c358ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "path_save: /home/rich/Desktop/test.ROICaT.labeling.results.pkl\n"
     ]
    }
   ],
   "source": [
    "dir_save = r'/home/rich/Desktop/'\n",
    "filename_save = 'test'\n",
    "\n",
    "path_save = str(Path(dir_save).resolve() / (filename_save + '.ROICaT.labeling.results' + '.pkl'))\n",
    "print(f'path_save: {path_save}')\n",
    "\n",
    "roicat.helpers.pickle_save(results, path_save)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a77d9f-a95c-4533-9df7-07f105595630",
   "metadata": {},
   "source": [
    "# Thank you\n",
    "If you encountered any difficulties, please let us know at the issues page: https://github.com/RichieHakim/ROICaT/issues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc6f6de",
   "metadata": {},
   "source": [
    "---\n",
    "# Extras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d17a236",
   "metadata": {},
   "source": [
    "## Merge and convert GUI CSV files into a results file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde9a8a1",
   "metadata": {},
   "source": [
    "##### 1. Make labels dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c10c79e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "path_csv = r'/home/rich/Desktop/test.csv'  ## Path to the raw output .csv file from the GUI\n",
    "\n",
    "with open(path_csv, 'r') as f:\n",
    "    lines = np.array([line for line in csv.reader(f)], dtype=str)\n",
    "    labels = {}\n",
    "    labels['index'] = np.array(lines[1:, 0], dtype=np.int64)\n",
    "    labels['label'] = lines[1:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8831b6",
   "metadata": {},
   "source": [
    "##### 2. Make images array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e69f525",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting: Importing FOV images from ops files\n",
      "Completed: Set FOV_height and FOV_width successfully.\n",
      "Completed: Imported 1 FOV images.\n",
      "Completed: Set FOV_images for 1 sessions successfully.\n",
      "Importing spatial footprints from stat files.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  1.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported 1 sessions of spatial footprints into sparse arrays.\n",
      "Completed: Set spatialFootprints for 1 sessions successfully.\n",
      "Completed: Created session_bool.\n",
      "Completed: Created centroids.\n",
      "Staring: Creating centered ROI images from spatial footprints...\n",
      "Completed: Created ROI images.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dir_data = r'/media/rich/bigSSD/downloads_tmp/tmp_data/mouse_0322R/statFiles/20230420/'\n",
    "\n",
    "\n",
    "path_stat = str(Path(dir_data) / 'stat.npy')\n",
    "path_ops = str(Path(dir_data) / 'ops.npy')\n",
    "\n",
    "data = roicat.data_importing.Data_suite2p(\n",
    "    paths_statFiles=[path_stat],\n",
    "    paths_opsFiles=[path_ops],\n",
    "    new_or_old_suite2p='new',\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "assert data.check_completeness(verbose=False)['classification_inference'], f\"Data object is missing attributes necessary for tracking.\"\n",
    "\n",
    "images = data.ROI_images[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f24fc8",
   "metadata": {},
   "source": [
    "##### 3. Make the results dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5baef8e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_info = data.serializable_dict  ## pull out the data from the data object into a dictionary\n",
    "[data_info.pop(key) for key in ['ROI_images', 'spatialFootprints'] if key in data_info]  ## remove the large data items from the data object\n",
    "\n",
    "results = {\n",
    "    'labels': labels,\n",
    "    'images': images,\n",
    "    'data_info': data_info,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d17e16b",
   "metadata": {},
   "source": [
    "##### 4. Save the results dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c74f445",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_save = r'/home/rich/Desktop/'\n",
    "filename_save = 'test'\n",
    "\n",
    "path_save = str(Path(dir_save).resolve() / (filename_save + '.ROICaT.labeling.results' + '.pkl'))\n",
    "print(f'path_save: {path_save}')\n",
    "\n",
    "roicat.helpers.pickle_save(results, path_save)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e37361",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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