MotionTracking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0fd3bd40-529d-4a82-b356-cc1a3a92bb57",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aicspylibczi import CziFile\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import matplotlib.animation as animation\n",
    "import IPython\n",
    "from IPython.display import HTML\n",
    "\n",
    "from multiprocessing import Pool\n",
    "import cv2\n",
    "import os\n",
    "import ffmpeg\n",
    "import time\n",
    "import pandas as pd\n",
    "from cellpose import io, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e2fbd5c-5686-4281-83c4-6861c8f9e868",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.measure import label, regionprops_table # version at least 0.22\n",
    "from skimage.filters import threshold_otsu # version at least 0.22\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74c4e7a3-0832-4424-987c-6e797c0001e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dicty_factin_pip3-03_MIP.czi with dims [{'X': (0, 474), 'Y': (0, 2048), 'C': (0, 2), 'T': (0, 90)}]\n"
     ]
    }
   ],
   "source": [
    "video = get_file('mip', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "40fdb75e-c207-4701-9422-6912295c4b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "frames, shp = video.read_image(C=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41a711e7-c787-4d2f-8dfb-1f72d1322ad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.25 s, sys: 441 ms, total: 2.69 s\n",
      "Wall time: 2.69 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(90, 2048, 474)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "frames = scale_img(frames.squeeze())\n",
    "frames.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5fbf160f-3644-40c8-92ce-3bbd19efbe4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1a014edc-f957-46d6-bd7b-71ecd3d72f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_otsu_thresholding(frame):\n",
    "    return (frame >= threshold_otsu(frame))\n",
    "\n",
    "def frame_connected_components(thresholded_frame):\n",
    "    return label(thresholded_frame, background=0, connectivity=2)\n",
    "\n",
    "def binarize_video_fast(frames):\n",
    "\n",
    "    # computes a unique otsu threshhold for all videos\n",
    "    with Pool(processes=os.cpu_count()) as pool:\n",
    "        # Map apply_otsu_threshold to each frame\n",
    "        video_threshed = pool.map(frame_otsu_thresholding, frames)\n",
    "        video_labeled = pool.map(frame_connected_components, video_threshed)\n",
    "    \n",
    "    return video_labeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53a66083-c73e-47b4-a101-a8b39858f43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 804 ms, sys: 1.32 s, total: 2.13 s\n",
      "Wall time: 2.24 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "masks = binarize_video_fast(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d1c75b74-f220-4e8f-b77b-3aec56e39aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.96 s, sys: 276 ms, total: 2.23 s\n",
      "Wall time: 2.23 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "masks = binarize_video(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "531fd636-5ed6-4e08-a8f4-89325f0ff720",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bounding_boxes(mask, min_area=300):    \n",
    "    # deducing centroids, max side length, \n",
    "    all_bboxes_coords = list()\n",
    "    max_num_cells=0\n",
    "    num_cells_frames = list()\n",
    "    # region props can calculate a LOT of useful properties\n",
    "    # might want to look into better cell filtering\n",
    "    props = regionprops_table(label_image=mask,\n",
    "                              properties=['bbox','area']\n",
    "    )\n",
    "    # each row corresponds to cell id, columns corresponds to properties\n",
    "    # box returned as min_row, min_col, max_row, max_col\n",
    "    df = pd.DataFrame(props)\n",
    "    df = df[df['area'] >= min_area]\n",
    "    cell_areas = df['area'].values\n",
    "    df= df.drop(columns = ['area'])\n",
    "    bboxes, num_cells = list(df.itertuples(index=False, name=None)), df.shape[0]\n",
    "    return bboxes, num_cells, cell_areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4df6660-676d-4cbd-a689-ffe2ded4186c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def box_tracking_video(frames, masks=-1, thickness=2):\n",
    "    if(type(masks) == int):\n",
    "        # if not provided, calculated\n",
    "        masks = binarize_video(frames)\n",
    "        print(\"Computed binary masks\")\n",
    "    video = []\n",
    "    num_cells_per_frame = []\n",
    "    cell_areas = []\n",
    "    for frame_idx in range(len(frames)):\n",
    "        boxes, num_cells, areas = bounding_boxes(masks[frame_idx])\n",
    "        num_cells_per_frame.append(num_cells)\n",
    "        cell_areas.append(areas)\n",
    "\n",
    "        img_with_boxes = draw_boxes(frames[frame_idx], boxes, thickness, val=1)\n",
    "        video.append(img_with_boxes)\n",
    "        \n",
    "    return(video, num_cells_per_frame, cell_areas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "94d45cc2-4808-45a6-aef8-855f1296b937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 8\n",
    "i=5\n",
    "list(range(max(0, s-i), s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e0f9bf38-539b-45f8-b7ed-32a41c71439a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def track_cells(cell_id, frames, masks=-1, padding=0, history_length=1, verbose=False):\n",
    "    N = frames.shape[0]\n",
    "    if(type(masks) == int):\n",
    "        # if not provided, calculated\n",
    "        masks = binarize_video(frames)\n",
    "        print(\"Computed binary masks\")\n",
    "\n",
    "    data = {\"patches\" : [], 'boxes' : [], \"masks\" : []}\n",
    "    areas_frames = []\n",
    "    num_cells_frames = []\n",
    "    \n",
    "    def get_corresponding_cell_box(curr_boxes, curr_num, curr_areas, frame_idx, num_past_to_consider, verbose=False):\n",
    "        ## TO DO : Ensure that index of boxes stays consistent even as boxes appear and dissapear\n",
    "        if(frame_idx == 0):\n",
    "            return(cell_id)\n",
    "\n",
    "        assert len(data['boxes']) >= frame_idx, f\"Missing frame history at {frame_idx}\"\n",
    "        \n",
    "        scores = np.zeros(len(boxes))\n",
    "        for i in range(max(0, len(data['boxes']) - num_past_to_consider), len(data['boxes'])):\n",
    "            prev_box, prev_num, prev_area = data['boxes'][i], num_cells_frames[i], areas_frames[i]\n",
    "            for j in range(len(scores)):\n",
    "                delta_area = abs(prev_area - curr_areas[j])\n",
    "                delta_x = abs(prev_box[0] - curr_boxes[j][0])\n",
    "                delta_y = abs(prev_box[1] - curr_boxes[j][1])\n",
    "                if(verbose):\n",
    "                    print(f\"Score {j} comprised of {delta_area}, {delta_x}, {delta_y}\")\n",
    "                scores[j] += 50*(delta_x + delta_y) + delta_area / 30\n",
    "        index = np.argmin(scores)\n",
    "        if(index != cell_id and verbose):\n",
    "            print(f\"Choosing {index} instead of cell id {cell_id} at frame {frame_idx}\\nAreas: {curr_areas}\\n boxes: {curr_boxes}\")\n",
    "            print(f\"Previous box and area: {prev_box}\\n {prev_area}\")\n",
    "            print(\"\\n\\n\\n\")\n",
    "        return(index)\n",
    "    \n",
    "    h_max, w_max = 0, 0\n",
    "    skip_frames = []\n",
    "    for frame_idx in range(N):\n",
    "        boxes, num_cells, areas = bounding_boxes(masks[frame_idx]) # all on frame\n",
    "        index_of_cell = get_corresponding_cell_box(boxes, num_cells, areas, frame_idx, history_length, verbose) # right now assume same index throughout entire video\n",
    "        \n",
    "        bbox = boxes[index_of_cell] # cell id is meant to track a SPECIFIC cell, ensure maintain integrity of cell over time\n",
    "        min_row, min_col, max_row, max_col = bbox\n",
    "        #TODO might expand boxes to homogenize sizes; \n",
    "        min_row -= padding\n",
    "        min_col -= padding # adjust boxes based on padding\n",
    "        max_row += padding\n",
    "        max_col += padding\n",
    "\n",
    "        patch = frames[frame_idx, min_row:max_row, min_col:max_col]\n",
    "        mask_patch = masks[frame_idx, min_row:max_row, min_col:max_col]\n",
    "\n",
    "        data['boxes'].append((min_row, min_col, max_row, max_col))\n",
    "        data['patches'].append(patch)\n",
    "        data['masks'].append(mask_patch)\n",
    "\n",
    "        num_cells_frames.append(num_cells)\n",
    "        areas_frames.append(areas[index_of_cell])\n",
    "\n",
    "    return(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "38ebb60d-de51-4d91-a519-5cf1f9053d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(img, box, thickness, val):\n",
    "    min_row, min_col, max_row, max_col = box\n",
    "    img = cv2.rectangle(img, (min_col, min_row), (max_col, max_row), val, thickness)\n",
    "    return(img)\n",
    "\n",
    "def draw_boxes(frame, boxes, thickness=2, val=1, cell_id=-1):\n",
    "    img = frame.copy()\n",
    "    \n",
    "    if(cell_id==-1):\n",
    "        for bbox in boxes:\n",
    "            img = draw(img, bbox, thickness, val)\n",
    "    else:\n",
    "        img = draw(img, boxes[cell], thickness, val) \n",
    "        \n",
    "    return(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df24b22a-334c-44e2-8092-3aab4ff0d4f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 620 ms, sys: 3.2 ms, total: 623 ms\n",
      "Wall time: 622 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data = track_cells(4, frames, masks, padding=0, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4d37a8ed-a7ae-4f9f-a038-d07af9597341",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_cell_tracker(frames, data, thickness=2, val=1):\n",
    "    video_frames = []\n",
    "    for frame_idx in range(len(frames)):\n",
    "        box = data['boxes'][frame_idx]\n",
    "        img = draw(frames[frame_idx].copy(), box, thickness, val)\n",
    "        video_frames.append(img)\n",
    "    return(video_frames)\n",
    "\n",
    "video_cell_4 = np.array(visualize_cell_tracker(frames, data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4fc3c631-b00c-441a-8248-b0f7e5dbcfa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "video, cell_nums, areas = box_tracking_video(frames, masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e7c809ea-f376-46bf-aad5-c2ce920ba839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4e833a8a90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(video[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5d000b30-0e1f-45d7-8315-39006cb291ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4e83292370>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(video[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c288fa3a-1270-4edc-90fe-01b868702471",
   "metadata": {},
   "outputs": [],
   "source": [
    "def animate_frames(frames):\n",
    "    fig = plt.figure()\n",
    "    im = plt.imshow(frames[0])\n",
    "    \n",
    "    plt.close() # this is required to not display the generated image\n",
    "    \n",
    "    def init():\n",
    "        im.set_data(frames[0])\n",
    "    \n",
    "    def animate(i):\n",
    "        im.set_data(frames[i])\n",
    "        return im\n",
    "    \n",
    "    anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(frames),\n",
    "                                   interval=200)\n",
    "    \n",
    "    HTML(anim.to_html5_video())\n",
    "    plt.show()\n",
    "    return(anim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5e805076-1cb2-4f01-bf54-cd15c7729260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1713734387.3142734"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c277693-e10a-4a14-86e0-24cdfa81dec4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b7294c96-30cb-4ff5-b6e6-ba27b788ddcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Track time is 630.3272247314453ms\n",
      "Video creation time is 1164.2751693725586ms\n",
      "Animation time is 11282.115697860718ms\n",
      "CPU times: user 10.6 s, sys: 489 ms, total: 11.1 s\n",
      "Wall time: 11.3 s\n"
     ]
    },
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "start = time.time()\n",
    "data = track_cells(6, frames, masks, padding=0, verbose=False)\n",
    "print(f\"Track time is {1000*(time.time() - start)}ms\")\n",
    "video_cell = np.array(visualize_cell_tracker(frames, data))\n",
    "print(f\"Video creation time is {1000*(time.time() - start)}ms\")\n",
    "anim = animate_frames(video_cell)\n",
    "print(f\"Animation time is {1000*(time.time() - start)}ms\")\n",
    "HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "87890085-42b8-4767-a801-2d6f451941b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['boxes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ce5f765f-df84-4b4b-8663-511839a08a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "#anim = animate_frames(data['patches'])\n",
    "#HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6432c417-a117-43e4-9837-7f35c87d1ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "bboxes, num_cells, areas = bounding_boxes(masks[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a08f78f5-d302-436a-97ff-d2af05ec7e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(422, 31, 533, 87),\n",
       "  (535, 55, 630, 166),\n",
       "  (900, 228, 1042, 332),\n",
       "  (1025, 76, 1059, 97),\n",
       "  (1363, 240, 1447, 330),\n",
       "  (1690, 63, 1784, 148),\n",
       "  (1883, 44, 1983, 115)],\n",
       " 7,\n",
       " array([ 3662.,  6367., 10794.,   345.,  3789.,  4684.,  4332.]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bboxes, num_cells, areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "445f81b1-0cc4-44fc-bd7a-b8690351157b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d68e0b73-3c75-4648-be57-8418c80872ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_traces(frames, masks, hist=2, get_check_vids=True):\n",
    "    bboxes, num_cells, areas = bounding_boxes(masks[0])\n",
    "    vid_data = []\n",
    "    videos_for_checking = []\n",
    "    for i in range(num_cells):\n",
    "        print(\"Extracting cell \", i)\n",
    "        data = track_cells(i, frames, masks, padding=0, history_length=hist, verbose=False)\n",
    "        vid_data.append(data)\n",
    "        if(get_check_vids):\n",
    "            videos_for_checking.append(np.array(visualize_cell_tracker(frames, data)))\n",
    "\n",
    "    return(vid_data, videos_for_checking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "cc593a65-9516-4320-aab3-b1e18552a096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting cell  0\n",
      "Extracting cell  1\n",
      "Extracting cell  2\n",
      "Extracting cell  3\n",
      "Extracting cell  4\n",
      "Extracting cell  5\n",
      "Extracting cell  6\n"
     ]
    }
   ],
   "source": [
    "data, videos = extract_traces(frames[:15], masks[:15], hist=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ce4e1d6-dbfc-4e77-9d95-5c57730e0798",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f76b45b7-0b44-438b-9e09-9d73fc450e0f",
   "metadata": {},
   "outputs": [
    {
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       "LjI5LjEwMA==\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anim = animate_frames(videos[1])\n",
    "HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "037109ae-bd84-48e7-8b0a-31e5b8550f7d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'bbox' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[35], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbbox\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'bbox' is not defined"
     ]
    }
   ],
   "source": [
    "def process_video(czi, sequence_length=10):\n",
    "\n",
    "    \n",
    "    \n",
    "    num_frames = len(frames)\n",
    "    assert num_frames == len(masks), \"frames and masks do not correspond length\"\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dbd1a5f-6fa2-4a0b-bd8e-06a00f4c000d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "885f37b6-99c1-4356-886c-af229f908a9b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sammykernel",
   "language": "python",
   "name": "sammykernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}