python-lesson.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b2607b31-4d84-486a-961c-533dde36c990",
   "metadata": {
    "tags": []
   },
   "source": [
    "# SWC python lesson\n",
    "\n",
    "## Basics\n",
    "\n",
    "Python can be used as a plain calculator. But it becomes more powerful when we start using variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e427ae27-94fc-4c70-a5db-7f91adfea829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 + 3\n",
    "17 + 3 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21fccaf8-61eb-4c26-8e1d-a8c269befc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg = 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2dee2026-67a6-4b61-b078-3dc17674fb32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40ca3c8f-8e7d-419f-9cb1-4b117353f1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_kg=50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "57199986-666a-4b48-9281-11e0432190ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af6c57df-37bb-4bb0-96ae-47d28002fc0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_kg * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "54997d1f-964b-4cd3-acf8-a276ded5808e",
   "metadata": {},
   "outputs": [],
   "source": [
    "double_weight_kg = weight_kg * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55fadc03-0236-4ded-8bc5-6e7a8cfb01a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double_weight_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1bec1e69-a0a2-459b-83e6-a67fb595b0e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1ca6cb4-09a0-424c-b07b-d35e5f79b33b",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-13-83fc17a42fb9>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[13], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    1x = 7\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "1x = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a9fca5cd-f5d5-46b2-8aee-7453726e433d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a comment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a08a4d66-3423-41e3-b2f4-9158555ba036",
   "metadata": {},
   "source": [
    "### Types of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c8d2381-fb0a-4806-9382-73d9b2c231cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4af00348-f11c-4113-bc71-6ec2859d425c",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"bla bla bla\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9819d88-a0b9-4d16-9514-3368088b9c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bla bla bla'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44fc4799-9f58-428c-9190-5093fdcd9c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b1301095-73d2-4e09-beac-ef5f8c2d1b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "336961fd-00db-4968-9cad-85bc45b7bd8e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'true' 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[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrue\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'true' is not defined"
     ]
    }
   ],
   "source": [
    "true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fe22d394-8d3f-4fce-82be-125d6cd1a283",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "cannot assign to True (<ipython-input-21-6fa9faf6b782>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[21], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    True = False\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to True\n"
     ]
    }
   ],
   "source": [
    "True = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9bada7b6-3679-4ad5-85f5-d04f00250ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "variable_a = 5\n",
    "variable_b = variable_a\n",
    "variable_a = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "25f0b940-9440-4193-b046-4f6ce52bd88b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c338b8b8-94ce-4248-a466-8ec2c2fb12af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bc37e2d1-54c8-460b-ac5e-3cb0d8db5704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello there'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Hello\" + \" \" + \"there\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "07e975c5-3c11-46ff-990a-ebdd66a1b11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello3'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Hello\" + str(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "907df47b-b554-455d-8e22-122eb472147e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HelloHelloHello'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Hello\" * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f20c67af-f843-4430-83ed-ca92e84eeceb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello there\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Hello there\")\n",
    "1 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7a4cf0c5-e774-411b-a1f4-78b9e5bf6731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "efa83dd5-8066-411d-bc28-1d4ea4487826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(7.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d1d57277-dec0-47cc-80ee-381f2bd284cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"bla\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "285d7208-fdd5-4296-9e1c-88f264795ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('bla')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3aa33db-9295-4f0d-86fc-4eb997aa28db",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c15499a9-4f12-4f56-9dee-947fb237e5df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4299836a-252b-45da-8ff2-f8f3ba8396dc",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'skimage2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[42], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mskimage2\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skimage2'"
     ]
    }
   ],
   "source": [
    "import skimage2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "06f00a5f-c88c-441d-bc4f-72b2d6768d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 3., 0., 0.],\n",
       "       [0., 1., 2., ..., 1., 0., 1.],\n",
       "       [0., 1., 1., ..., 2., 1., 1.],\n",
       "       ...,\n",
       "       [0., 1., 1., ..., 1., 1., 1.],\n",
       "       [0., 0., 0., ..., 0., 2., 0.],\n",
       "       [0., 0., 1., ..., 1., 1., 0.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.loadtxt(\"data/inflammation-01.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b670e12d-1bdb-485c-94c5-ce64ba8414c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = numpy.loadtxt(\"data/inflammation-01.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "65e55ccc-6a02-4f3e-bb03-592a8e1d36af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 3., 0., 0.],\n",
       "       [0., 1., 2., ..., 1., 0., 1.],\n",
       "       [0., 1., 1., ..., 2., 1., 1.],\n",
       "       ...,\n",
       "       [0., 1., 1., ..., 1., 1., 1.],\n",
       "       [0., 0., 0., ..., 0., 2., 0.],\n",
       "       [0., 0., 1., ..., 1., 1., 0.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "afe43405-b7ee-4ae5-bfb3-06e5bbaec0e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "17210221-3f80-4b68-993b-988780f80e84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9368bf52-c5d0-49d7-bcd8-5e1ec58f55c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[3,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fea48f3b-39e9-400c-9d45-371770a680d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c6a5f97b-ff5d-45ea-b9d6-1b3d3d123d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7bb55a-4d4d-4976-a8f7-b0b680ba6e01",
   "metadata": {},
   "source": [
    "Warning! Indexing in python starts at 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2204cd70-74b9-4d18-a6af-60be15e2fb55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  2.,  0.,  4.,  2.,  2.,  1.,  6.,  7., 10.,  7.,  9.,\n",
       "       13.,  8.,  8., 15., 10., 10.,  7., 17.,  4.,  4.,  7.,  6., 15.,\n",
       "        6.,  4.,  9., 11.,  3.,  5.,  6.,  3.,  3.,  4.,  2.,  3.,  2.,\n",
       "        1.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7501e303-37a1-48cb-9582-aac07dd07795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7., 2., 2., 1., 5., 1., 5., 3., 5., 3., 5., 7., 4., 6., 1., 3., 5.,\n",
       "       3., 2., 2., 1., 6., 7., 2., 3., 7., 3., 5., 3., 3., 6., 3., 6., 7.,\n",
       "       5., 4., 1., 1., 1., 5., 4., 1., 1., 6., 6., 4., 6., 2., 2., 7., 3.,\n",
       "       4., 3., 7., 4., 4., 7., 4., 3., 4.])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "609a756e-e905-4920-a686-8b58e18c8e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 1.],\n",
       "       [3., 3., 2.],\n",
       "       [0., 4., 2.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:4,3:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5f2304ae-5be1-41cc-be88-685a319fc615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  2.,  0.,  4.,  2.,  2.,  1.,  6.,  7., 10.,  7.,  9.,\n",
       "        13.,  8.,  8., 15., 10., 10.,  7., 17.,  4.,  4.,  7.,  6., 15.,\n",
       "         6.,  4.,  9., 11.,  3.,  5.,  6.,  3.,  3.,  4.,  2.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  2.,  3.,  1.,  2.,  3.,  5.,  3.,  7.,  8.,  8.,\n",
       "         5., 10.,  9., 15., 11., 18., 19., 20.,  8.,  5., 13., 15., 10.,\n",
       "         6., 10.,  6.,  7.,  4.,  9.,  3.,  5.,  2.,  5.,  3.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  0.,  1.,  3.,  1.,  6.,  6.,  5.,  5.,  6.,  3.,\n",
       "         6., 13.,  3., 10., 13.,  9., 16., 15.,  9., 11.,  4.,  6.,  4.,\n",
       "        11., 11., 12.,  3.,  5.,  8.,  7.,  4.,  6.,  4.,  1.,  3.,  0.,\n",
       "         0.]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[3,7,13],:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7ed801ac-499c-4f77-9c25-a2287fbad390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.14875"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2d040346-c5e4-4219-809a-3464c7ac8a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function ndarray.mean>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ac227eb0-4a87-47cb-a684-2824a8a948ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.14875"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.mean(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7340910-3b96-42d9-9633-049c9ac108d4",
   "metadata": {},
   "source": [
    "- Get the mean inflammation of day 7\n",
    "- Get the mean inflammation of patient 3\n",
    "\n",
    "Rows (first dimension) are patients and columns (second dimension) are days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a2c23b0f-1cbf-4af5-98c0-3dc3e79b7fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.mean(data[:,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7c5161b4-d9c2-4dee-a587-f3a5341390ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.mean(data[2,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5bb4af81-2b54-495e-973a-d1b65220e541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.max(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3ee1fc7d-1e69-44f7-b06a-090a2235384b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.min(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "99295d91-536b-44a5-9add-eacdb491cf34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.45 , 5.425, 6.1  , 5.9  , 5.55 , 6.225, 5.975, 6.65 , 6.625,\n",
       "       6.525, 6.775, 5.8  , 6.225, 5.75 , 5.225, 6.3  , 6.55 , 5.7  ,\n",
       "       5.85 , 6.55 , 5.775, 5.825, 6.175, 6.1  , 5.8  , 6.425, 6.05 ,\n",
       "       6.025, 6.175, 6.55 , 6.175, 6.35 , 6.725, 6.125, 7.075, 5.725,\n",
       "       5.925, 6.15 , 6.075, 5.75 , 5.975, 5.725, 6.3  , 5.9  , 6.75 ,\n",
       "       5.925, 7.225, 6.15 , 5.95 , 6.275, 5.7  , 6.1  , 6.825, 5.975,\n",
       "       6.725, 5.7  , 6.25 , 6.4  , 7.05 , 5.9  ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.mean(data, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6466f6e9-9293-4850-adc3-6c9b3fa6acbf",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2c46df1f-fb9a-47c3-81fd-10d2c425133d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "13856401-be4d-4b58-9b30-9201aeb16b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f88cf9ddf70>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jrEmPkWfkvJmdc0kAPwfwde/9/p4eL5630Dn3unPu9U4c7PkJhtFHcjLNOecK0b2Rf+K9/9Q32+icKxe9AI/YpMd7vwzAMqA7O1uuSfMbu7PV8SnThE1q8mNqLxVOnPySli/vzxemuYgax6Uv6kKEjHTt7lgyU621zqf/tKJAC0fNlWwP2RmchXK4UUee3Xj1+vT418vmqjXOwFZucso6lx/zLCvYZd1eEa59yXZd7Caj0KTI+BlLMkMek9+/qEyT3pCLNcMB+CGAd7z33xVLnzbpAQagSY9h9EQud+aLAPwXAH90zr2Z+t396G7K80yqYU8tAP7aYxgDSi7WjPXQpdUk1qTHyBvyJgSUdZM0zbEJiHvMjRL6rGOULtjHfawl7OqW5qOe+pTsnR3cwMffsVutTRQ9+JibF/9CzWUh8uunbFVruyuL1fyFWuEWp1BN7o8ts77rL9aPnf5E0KwlnJlDIbPcK0XSPENXhJJmT/7uIiscjXhDX3euIxUZShqBubON2GCb2YgNtpmN2JC3VUCj4AY9EnaNcpiidM++d622sR7fEL7nRqVXAUDZpuxpSX6V1vgTvhQysGUfbQC49IyQIl7frvUqZ2vLdCdZsainY+56VWdyd4ke3RXr9Ptfd6V2t8uKR1yliIusj1pLPnYB93KUcAMmF+HOtiqgxpDANrMRG/LGNBcFu7MZ+XEXXUZFI2UFAOyfHj5ipz+hfd11l+tzqP1aMChN+V6S1nR73wPiY/7SK95Wa9IVfvNi7UR97+B4Nf8dQoTdvhqSaNO1PJglMsSbGnTUXLIhPJalA3A8ssGyIqpvIsNSQsKFXgpkJrdlmhhDEdvMRmywzWzEhrwpNh5V0SgqAwTQvf1k9jWQ6Y6VWSlcbLxTuHJrFums5WE12U2YMhMaACau0Pr1km+G0M3nntKhm98Q7u1Hv/9Xaq1lls4emSHc5qMpe8TN001u1r4V3MnFdL57VIiq/juL1+t5swgJleGqQGYh96jvNjIkgfUz62IrnGgMeWwzG7Ehb0xzGQmt4iOrN33jWDqwOUn24dg/XX+kVqwTH6PbdUTYHqp/PO07ImH0m3qtaI0+pox24+yW9fump8cHyrWUkUmpAHDf1BfS429/6Sq1ds7Y99X8p21npcdlG7WpsGVWML+dslSvDW/OXjSHE1E7Z3B5nuxIacEeXG43LGWHmeaMIYltZiM22GY2YsOgRs1JehNBx8XGZbQWr0mzHcMZLLuuDhp66kqtVxvP0Rpa9iKRvUYAYObt2mUtCylyBsucypqs57elibSliIy7406dsfLk7Z9Xc5mNw5FwlU+HOf9drOmljp+8pkOtccaPdI1z9GJUEZiobHv+LvWbj39gUXNG/LHNbMQG28xGbMgbzRxVxYbd2Wx3lr2zuRAgZ2fLrObk7uwasJlsqDKzBND9BBnOnG6aGzQi245lQfEpBdo+vfbP+l5z25O3pcdcgJF1qDw/mXUC6F7a/Hexfo3KbudsHFk9iu378vzYnc12Z1l0kd3ZlmliDAlsMxuxIW/c2Zy4KE11nWRC6w38UVjUeuTHAUD9xSEKbFiZdvMWvqTlgex/wsUH2yt0NFnxm8Hkd8edK9Xaso9Dn5JNH52o1iqon+Ddf/1sevzkGm2KK3hAmyCdSIbteqhMrZWJaqwyYwYAutp0psmEV4Jk4qKKLHWktGCTqDTNjaToOq7lrPrZmDvbGIrkUgV0pHPuNefcllRPkwdTv7eeJkZekcud+SCAz3rvqwDMAjDPOXc+rKeJkWfkUgXUA/jUT1mY+udxFHqa5NoHMMrd2U3Q1Kxfo+BQ0ulPBJ357m2cpaw1s38k6NliaG37MRUxhCgw/r8e19kk1974SnrMRV+4F8nW1uAWb7lMH+JC0tfyuW2TtKlQmiS5oHlij76/7Z0ddDG7+Pn6yYI7UiP3BJvmCpqDpu5NEcWcNLNzbniqNnMTgNXee+tpYuQdOW1m7/0h7/0sAJMBnOucO72Hp6SxnibGQNEra4b3vgXdcmIeUj1NAKCnnibe+7O992cXIrvXzDD6S4/ubOfceACd3vsW59xxAH4LYAmASwB85L1/2Dl3L4BS7/3dUa/VG3d2VEY2u7OlG5or9LCGlhnF+6kKkCwiKG3OADBmmnarjhf9qN3d2m7K1Y/+PDEcp6AtWxMCYPVN/6Lm36y/MutjuT8327bV2jStdWUxREamlQE6Da2n7yNSi3N4aFQoLr/XnEYliXJn5+I0KQfwY+fccHTfyZ/x3j/vnPs9rKeJkUfkYs14C92NLPn3H8F6mhh5xKC6s6NMc5nmt+y0TQoffx29MM2N26w/mhO1wdd9OKFNc9xuWFL1mI5KK6f1db8P/VmmXviBWpP1mi9Z9Q19PjU6ezxRH2QQZ6ywiU1KpoNVWma0lWcvjsiFZ5INwUXNUoaLwkhTXQeZ5qSpjiUHywopOTNMc+bONoYCtpmN2GCb2YgN+VM4kcICpVZidydnMUi4mB/3jW6vCKanAyRuS0QbPs4IYU0qI0lrv6djIzmsUprj2GU9rD17VSCpkQFg7+wwP9ym9XQluZqnLA4Z6+2c5S0KinMFqNJtXD3qw/S4aba+YGzGG70ljDnzRepp1sFRRTN7g92Zjdhgm9mIDbaZjdiQN2lTvakCyv3mZBWjIuo3wzbNRG3QrPy60g09YotawsEqnUY1cUWIM5myWPe8rlutXc0y3YkLiq+595H0+NbqG9TafV98Qc1v3fi36fH9Z65Sa89M0x7e2vuFjqc+hbIwed0onZKW6foO9uHijDZ/2UMhOGxX25npVfqokRm7MxuxwTazERvyRmZwsQ8nzHE9FQ2R/ei4sEtUARkuGiiLmLAEaaT+eIna8FjZPwQAJlPW8qMNQVpwpN5X7lqUHp98j5YrsugLAFx/9Xpkg4uNv74vSKaODVpKdIp2w9rAl1l8XNJE7Zk5O1vKhWZq81zUFh7LBSv1ldZw1n4Udmc2YoNtZiM22GY2YkPehIBm9JCTIaC0llGUT+gx1tcHz6xUc1lkUWpkQGdSyNcEtM7kxxa/qV3oRfso11G4fW+8bINa+nV16As4aWSLWktU6XjHFSvnpMfzST//dOtZal46M4SscgHxTnH5ZBbMkZB/Z0/FxvlaS2QGEH8f4e81MvzXKhoZQxLbzEZsyBvTXJQHkD+GojyADBcqGbkrjLlQiY640//P2fslZcje2SQzWnX7YclJIz5Uc+mN2/1JsVq7aoo21Z00PTz3mXrt8Zvybxx9FySB7NXSE1z8pkg4UFtm6L+LTXMSNpFK6RBVKBEAvJAW1m7YGJLYZjZig21mIzbkjWaOcmdzpvYnZAJiDS1hXdw0O/z/7SJz2+jqMO4p+7mgLbzOxRfovn/ry6bp80uG13qs+hK1VriiND1+8KEfqrUrfvwPai7PlzPLO7+ubVaJ5cXpMRewkcfkzJyKVXvUPKoAYkaWdUT0W1SpIXfWZ9T8kMjWNtOcMSSxzWzEBtvMRmzIG83M2khaMFmLjaB5lJ2ZCykeXx7s1xWrGrI+79BGbduuWaTXJ68Mdt2t23VmyQiq/FN1XeiPzS5riNflQonsPpZNeA6sm6TWdH6ILvw4epUOuZQFxMdt1s9jm77Uxby2l8I8S7ZmL3YpQxB66p0dWdEogpzvzKmC4284555Pza2niZFX9EZm3AXgHTG3niZGXpGTzHDOTQbweQAPAfi71K/73dMkylUp+1pw1Bx/9ERFZHGEnSwKwx+THCmn0ZFwzSJJVJrBAKCjVbuWdyyZmR6vvVIf49IztqXHN43XEXXfOPUv1PzAqyekx10Xa2PXyQltvmwtC1FzLQntzi5qDG+7LCwD6EI4zHuUacIu/shil+J9YFnBhROlCIkqrsnkemf+HoC7oaWs9TQx8opc+gB+AUCT9/4PfTmA9TQxBopcZMZFAL7onLsK3bmHo51zTyDV08R739BTTxMAy4DuNhBH6bwNI4NcKuffB+A+AHDOXQrg7733X3XO/QuAmwA8nPr5y/6cCGujqJ4mw0gzR4UXNi48Tc1lxsjJy7Vp7v35wYXOGRjTv6ONX3WXB3NXIbnFOQPbzWsJkz1ad84ZE3zolYX71VpRq37d0m3BVLfzBv2h2vL4CWoO4Y6XvbuBzGKSEv7OIeeF1I9l3AZ9rfk7iDqmCAnN0NZUOFFmZA9UCOjDAK5wzlUDuCI1N4xBo1dOE+/9WnRbLayniZF3mDvbiA099gE8mnAfwCgboqxaFNX3D9AuV7ZBc/WjxvOCzfpAuf7bpd2U7a+y4Q0ANM8ItuRzrvujWuMKR7Jw+ZzKGrUm+/lx2On1M7UBafV3QnY2pyVJVzegexPWLNKaeVhNsEGzrbhprnY1y56B8m8GMhv0SGSVKQAY+3ywp3MmflTKHFc0iuoDaHdmIzbYZjZiQ970NImSHFzYBZRpIs14XLCPMyk6R4UxZxfLHh2HE/rjNlGrCwp2JIOJjWXFhFf0Zd07W0TYvagj7FY8FFoM7+zUZrtv77oK2eCM69E/n6LmB64NsmjEluz9+1g6cOHE6q+GC3bq97UpjrNQZMYPRzLKAjGcMc9IaWGZJsaQxDazERtsMxuxIW8KJ0bB5jWGTXcS7nMnH1t3GVceCjqTXcDN1JNb9pGWYZwAsD6ps7MvFua4rZu1Zpb9sllrL/nWUjX/7223pscnn7FbrVVcoP/O9TvDOXTW6L7fUidHFY/sJlwTdldzL+228hA6ULEqQl9TsfFCLpxIX5Fyxe7MRmywzWzEhrzxAGbUZxZwDwzOVJCRclwjmPuWyKg5Ns3JJFBm+hO6yLGUK7LmM5BpDmyZFTyAXzn3P9Sa9OoV3/yBWtv1qo6E6ygL5sKoNsUAMLo63Kc4iu+4PdnvYWWbdMy59LayGY8fm+15gPbS8vuZYXoVcNSceQCNIYFtZiM22GY2YkPeuLMLSDNHZZpk9DQZE3Qy69cTV2gT0Y4Fwbwk3dcAMHVl0IBstpNuXUDrTm7Ry7pY9hvhguIL7w+tiK9I7FBrl73192q+/HOhsOLf/Wm+WquaoP/OtYngYk/UaDOj1Lofn6q/U3AGy4RXwpx7o/C1ltntXMzyUERfx95kk0Rhd2YjNthmNmKDbWYjNuSNOzujQJ7QzD2FgErtNm5D9oxrADh5edCW2+6cqNbKNgb7dUeZ/n8uqwABQNFFIYzxHNKrv3vwQjWfI3piy8wSAFg3OxTaXgwNZ7d8+4wQEsoaWbqvAe2OL92W3XYsbeAAUPk0hcWK+oycmZNs0I/l7BKJ9AWwT4Et5nI9Y19YCKgxFLDNbMSGvDHNRUXQ8ccSu0plsiQXWWRzkoz84trEEpmECgAgmVHyvWAerH9AH5PrD0clrRaIiLbD0/RaW7nuyVdXE8696U2dWXL9wlfU/LktoY2xLPII6J4mxW/qD/m2SVpKyMRe6SIHMmVFyVZdxEYiM08y6jOTGZYLKeaK3ZmN2GCb2YgNtpmN2JA3PU0YWVyPXdusuaQeYxc1FwmUz+UM56I26SbX5izWsx2bhRv4IZ2lzC5iadL6wXn/rtZemql74EmearxIzVVGyxn02DX6sVOFy7rwJf02N54XQlT5+sg+iQAwdWX47sDuazaDytBOfQX0de8pOxtRPU0iTHO5Vs7fBaAVwCEAXd77s51zpQB+CmAqgF0Avuy9PzpOdsPoA72RGZd572eJwGjraWLkFf3RzNegu5cJUj+v7ffZGEY/yCltyjn3HoBmdKfqLvXeL3POtXjvi8Vjmr33kenWvUmbiur/xtnaMiSU+/5xtrG0hXJYpzpGQmtJTlOS/aj31ZRkXQO0XZeRqVIHHtW9/fi7AdvXJax1ZQ/B2q/pvyW5XtuvJez6lvqf3dknPZfdrhx13TmbnsMV+lo4MdcvgBd57+udcxMArHbObevxGZ+emHMLASwEgJHIfhENo7/kJDO89/Wpn00AngVwLlI9TQCgp54m3vuzvfdnF2Z8xzWMo0ePd2bnXALAMO99a2r8lwD+J4Bf4Sj2NMk4rvioYdMcZ5pEZf5ybz/58XfcHp1FLQuiZGSz7NPu7cJ94dJVPRbtfl1/ZXBZc83lXy8Lbuc3Hvs/am1B7Vw1l5FxI7bwp5yWANqMps2K0hzHGdec+d45KtyAOJs9Si6w3ozqO8OykddzJReZUQbgWefcp49/0nu/yjm3CcAzzrlbANQCmB/xGoZxzMml29ROAFVH+L31NDHyCnNnG7Ehb0JAQRm6BcKlydqMTVayKB+b5grJwifV997Z2jQnj8PHYPwj4YW52DgXUhyVDJqV3c4nf6k2PT7p+VvV2j/OXanmr9aEUFLOCGc6GoKr/nCjvn575n+SdS3TXCl1Mulr6hGeHBPCCuT3GIarTkWZ5jLqNlmmiTEUsM1sxAbbzEZsyJsQ0ALqnyzd2SPItc2aS+pb2ecPyCx6LSsaTV2pe+nJkNBhZdreyi7gItGregI0a5G9D2AB9Z+uSIS/reIM/XcuW3ydmheLcWeVdiUnlher+Z75odFOARUbL94c5txPkMNXJdzLm2348n3gcITIBj0RFWB7g92Zjdhgm9mIDXlTBKY/yGgyjvpi97Z0yXJxxNHVQQLshzZZcW+Upm9mN90lNuhjdibD+d3918+qtfcOjs/6Orj9bTWV7uzs8X6ZdCW1PGiZEf7Oprlc7EY/VkbfRUXtASQfIjLqWTZGFU7szR6xO7MRG2wzG7HBNrMRG/LGnc1uSxkWKMMHgcwwxbZJIayziNzXrPNk9rYsoghofd0yS/8/f/c2bZo75cFg+qq7PNq0NP/q9enx4/dfo9baF7Skx1dN2arWdiyZqeYjRLhmByU6FJIuHiYrJdGa1NBcEDIqe6RjjA6L5dABGVYQFQ7AIZ8Oem4VjYwhj21mIzbkrWmOpYWEs0Bk4cSGG09Ta2yqqxDRXKoFLlH5tP6Y5AIoMmOF++Fd8N3X1Pwnr52fHs+4Q7cJvmTs++nxpo9OVGvH02ObfxuKJX7j5l+otSdv/7yaF98c/s4P26kw4aogp9irx7BHVaPfB1lIMVH7CT02mDqj3lsA8H002dqd2YgNtpmN2GCb2YgNeRM1x8ioK46S4x5zH30hRKmxRmatW7gv6Eculi2jwPh5HF1W8EB2TfpCrTapSfPXzuQ4tfaD6U+nxytWzlFr7Ia++Lo/pseLV12rz+cyHY3X9dZkZEOWZ4nKXgeATuE3T9Tr8+Hrx9dIPVaYSEfSWlTBn95gd2YjNthmNmKDbWYjNuSNO5uJskHL4uIMu7rbz9GZEx2jgt2UCwF2JYPu5GpHAPWJFkUOW+drnT6nskbNd2wuTo/rk1oxfuWuReHcZ+hjJqp0KvJN4zeEY8yrVmvP1OtagrteDZkw/DolK4N9mAuuH05o+7ouGNnTvS9co6IewkUlvA9k1hEXTozC7sxGbLDNbMSGvDHNsayQHz0sHUbu0u5Q6ZbmgjGMTHCVya0AMP2JVn54Gi6OMqwsSIthlDBaX6ZdwHVXBvNXyUQdlda8J7iW2fS15DMr1HzBy7ekxxztxlKioyzIhULKfOkYEwonckvj0Vs+VHPZrrlku67zzFFzMsyAo+ZkoZdDFDXH731fTXV2ZzZig21mIzbYZjZiQ049TY7awZz7EMD7AMYB2DtgB+4ZO59o8ul8TvTeHzGlfUA3c/qgzr2ercnKYGDnE02+nU82TGYYscE2sxEbBmszLxuk42bDzieafDufIzIomtkwjgUmM4zYMKCb2Tk3zzn3rnNuh3NuUBrHO+d+5Jxrcs69LX5X6pxb7ZyrTv08OhUdczufE5xza5xz7zjn/uScu2swz8k5N9I595pzbkvqfB4czPPpDQO2mZ1zwwE8BuBKADMB3Oicmxn9rGPC4wDm0e/uBfCy9346gJdT84GiC8Ai7/1pAM4HcHvqugzWOR0E8FnvfRWAWQDmOefOH8TzyR3v/YD8A3ABgN+I+X0A7huo49O5TAXwtpi/C6A8NS4H8O5gnFfq+L8EcEU+nBOA4wFsBnBePpxPT/8GUmZMAvCBmNelfpcPlHnvGwAg9ZM7OwwIzrmpAM4EsHEwz8k5N9w59ya6+6Gv9t4P6vnkykBuZk7dALjh8xDGOZcE8HMAX/feZ69eOAB47w9572cBmAzgXOfc6T08JS8YyM1cB+AEMZ8MoG8dv48+jc65cgBI/WwayIM75wrRvZF/4r3/tO7WoJ4TAHjvWwCsRfd3jEE/n54YyM28CcB059xJzrkiADcA+NUAHj+KXwG4KTW+Cd26dUBwzjkAPwTwjvf+u4N9Ts658c654tT4OACXA9g2WOfTKwb4C8VVALYDqAHwwGB8SQDwFIAGAJ3o/rS4BcBYdH9Dr079LB3A85mDbrn1FoA3U/+uGqxzAnAGgDdS5/M2gP+R+v2gXaNc/5kH0IgN5gE0YoNtZiM22GY2YoNtZiM22GY2YoNtZiM22GY2YoNtZiM2/H+Vax+M2HxJQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "83e1f77e-c961-45b0-9435-33cf73c59f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88cf866070>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ave_inflammation = numpy.mean(data, axis=0)\n",
    "plt.plot(ave_inflammation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4251f3e3-f072-4c25-b64f-ccabce7022c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88cf85f490>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(numpy.max(data, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8bcbae86-abad-497a-8645-59e8dcea2d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88cf7d0bb0>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(numpy.min(data, axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177acaba-4c28-40c0-afb1-dca20f822b52",
   "metadata": {},
   "source": [
    "## Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f49b2463-8f2f-4bd7-809a-1b2689a1e2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "odds = [1, 3, 5, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "460095ff-8853-47ba-a236-4295474aa8c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e7127203-ddd4-4091-a065-08f3c29de174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8e6c47fc-9294-41c5-957d-6a9f9b66e1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8cbd4c04-22a3-435e-87ed-04b10db0640e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed = [1, \"bla\", 7.5, True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f4ecbf5b-d044-48bd-87e3-3fc0e2970719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.5"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "116dbdff-672b-4a89-aadc-2256d87c9137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bla'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3432035f-b118-4c70-bbf2-6d9b82bc81d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mixed[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2a2822b1-af8a-435b-a19d-62980c81ae0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mixed = [1, \"bla\", 7.5, True, [2,7,5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b1910f83-a993-4d80-9332-8bb290bb0a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixed[4][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "355f0dd7-46e7-4855-add7-9f12f9f8430e",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmixed\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "mixed[4,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c9dfb9c6-c0c9-4d1c-8cc2-0eb50af8440d",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_a = [1, 2, 3]\n",
    "list_b = list_a\n",
    "list_b[1] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "640c5896-ef2f-479d-8297-74a1edbf92a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a9795586-739c-4793-be05-7976fb8d0426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2ad56b4b-9fed-4546-aede-c7b51ffa7820",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_a = [1, 2, 3]\n",
    "list_b = []\n",
    "list_b = list_a\n",
    "list_b[1] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "dc1486b9-a2af-48fe-a974-4d3f771e2df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ce04aa0a-a2ef-457c-8d87-7fe33359606a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6d0dfd02-0e4b-4c34-9b2f-f4a1d87dd06d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ff32a256-cc9c-4bf1-b8ba-29925ba105e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mixed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6a31a7f1-f259-4605-b9c1-4ff7180b5563",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list assignment index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[92], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m list_a[\u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\n",
      "\u001b[0;31mIndexError\u001b[0m: list assignment index out of range"
     ]
    }
   ],
   "source": [
    "list_a[3] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3b2b0fda-05e9-43ab-9446-b3efc0319641",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_a.append(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6f43c8b1-7b58-4ea9-a18b-1cce41e000c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3, 5]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1590baf2-09c0-4f1e-86de-036b6248bb7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3, 5]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "9889c3da-c301-4892-a373-41d644ae4126",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_b = list(list_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8120eea8-4f22-4761-b70f-d87a856d7cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3, 5, 13]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_a.append(13)\n",
    "list_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "33c3dd34-292e-42f8-acbb-5c482b26f8e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 7, 3, 5]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a133eba-b96e-4333-b9fb-a4fc9e06f844",
   "metadata": {},
   "source": [
    "## Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0e16e3da-acbe-4bef-ad9d-611c56d4deff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "print(odds[0])\n",
    "print(odds[1])\n",
    "print(odds[2])\n",
    "print(odds[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "96e6cfed-fec8-4bc1-9c93-da139b208ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds.pop()\n",
    "odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "61ae48b9-919c-48f0-ab4c-cd134ea62697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[101], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(odds[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(odds[\u001b[38;5;241m2\u001b[39m])\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43modds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "print(odds[0])\n",
    "print(odds[1])\n",
    "print(odds[2])\n",
    "print(odds[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7e342335-0dca-4c5d-b21d-ed7a16af46cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "odds.append(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6ed95993-87ac-4b8b-a056-dc3fd0afe88c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "print(odds[0])\n",
    "print(odds[1])\n",
    "print(odds[2])\n",
    "print(odds[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "113b176b-3686-434f-8705-b6a21dd6ae32",
   "metadata": {},
   "outputs": [],
   "source": [
    "odds.append(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "cf57eb35-3531-474c-9ed0-dd1a0de3d8dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n"
     ]
    }
   ],
   "source": [
    "print(odds[0])\n",
    "print(odds[1])\n",
    "print(odds[2])\n",
    "print(odds[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "513de322-4235-4161-8a81-8efc634b875a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "5\n",
      "7\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for number in odds:\n",
    "    print(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "0711a7a4-8c87-42ef-b083-8140f03ad67d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "9\n",
      "25\n",
      "49\n",
      "81\n"
     ]
    }
   ],
   "source": [
    "for number in odds:\n",
    "    square = number ** 2\n",
    "    print(square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "041c554e-a508-43e4-983b-01a07c5b0036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b5b5b047-3cad-4e3a-b1dd-b701fdd62963",
   "metadata": {},
   "outputs": [],
   "source": [
    "del square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d6707282-bcbf-415a-b663-5a148767d8cb",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'square' 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[111], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msquare\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'square' is not defined"
     ]
    }
   ],
   "source": [
    "square"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4305fd49-9593-493b-8b90-0ba17d51c70f",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6acbeaaa-7c8c-4821-a550-dfcf14de6fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d2dd0494-8023-4f04-8caa-ae0c08ec6b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hot\n"
     ]
    }
   ],
   "source": [
    "if temperature > 20:\n",
    "    print(\"Hot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ab9a4937-a87c-4e7c-9811-6f331fd0d883",
   "metadata": {},
   "outputs": [],
   "source": [
    "if temperature > 30:\n",
    "    print(\"Super hot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6af9f9f5-f8a8-41e9-83c8-c36364c4e928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not so hot\n"
     ]
    }
   ],
   "source": [
    "if temperature > 20:\n",
    "    print(\"Hot\")\n",
    "else:\n",
    "    print(\"Not so hot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "aeba08c3-8996-46af-aa71-170940d149df",
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "67f9fc9e-0d32-45ba-a279-2d048acb6273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hot\n"
     ]
    }
   ],
   "source": [
    "if temperature > 30:\n",
    "    print(\"Super hot\")\n",
    "elif temperature > 20:\n",
    "    print(\"Hot\")\n",
    "elif temperature < 10:\n",
    "    print(\"Cold\")\n",
    "else:\n",
    "    print(\"Okay\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98c8bda-d43e-41aa-8b96-de7c4f319e8f",
   "metadata": {},
   "source": [
    "## Exercise - loop over csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "97974a1b-f416-4479-b859-8a08d332bf07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8ddbfbcd-f73b-4928-832e-6fd08d49f665",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = glob.glob(\"data/inflammation*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "f52132aa-0ba5-4963-85e9-495bb4d4717e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/inflammation-08.csv',\n",
       " 'data/inflammation-06.csv',\n",
       " 'data/inflammation-01.csv',\n",
       " 'data/inflammation-10.csv',\n",
       " 'data/inflammation-07.csv',\n",
       " 'data/inflammation-09.csv',\n",
       " 'data/inflammation-11.csv',\n",
       " 'data/inflammation-12.csv',\n",
       " 'data/inflammation-04.csv',\n",
       " 'data/inflammation-03.csv',\n",
       " 'data/inflammation-02.csv',\n",
       " 'data/inflammation-05.csv']"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90eb6f27-3262-4858-9f69-ecc1d3395664",
   "metadata": {},
   "source": [
    "Print the mean inflammation (across all patients and days) for each of the csv files. Also print the name of the csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "31811442-c0a8-41ec-b5a0-b676074877d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/inflammation-08.csv\n",
      "4.204583333333333\n",
      "data/inflammation-06.csv\n",
      "6.043333333333333\n",
      "data/inflammation-01.csv\n",
      "6.14875\n",
      "data/inflammation-10.csv\n",
      "6.0525\n",
      "data/inflammation-07.csv\n",
      "6.019583333333333\n",
      "data/inflammation-09.csv\n",
      "6.032916666666667\n",
      "data/inflammation-11.csv\n",
      "4.204583333333333\n",
      "data/inflammation-12.csv\n",
      "6.0616666666666665\n",
      "data/inflammation-04.csv\n",
      "6.109583333333333\n",
      "data/inflammation-03.csv\n",
      "4.204583333333333\n",
      "data/inflammation-02.csv\n",
      "5.990833333333334\n",
      "data/inflammation-05.csv\n",
      "6.118333333333333\n"
     ]
    }
   ],
   "source": [
    "for filename in files:\n",
    "    print(filename)\n",
    "    data = numpy.loadtxt(filename, delimiter=\",\")\n",
    "    print(numpy.mean(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e80abacb-1c25-4c0d-83cc-73362f895bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as tk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab3890c6-beca-4391-94e7-b5eae30fa7d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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