diff --git a/files/python-2024-04-08.ipynb b/files/python-2024-04-08.ipynb
new file mode 100644
index 0000000..232c468
--- /dev/null
+++ b/files/python-2024-04-08.ipynb
@@ -0,0 +1,2301 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c5ddcbb1-8935-42c6-a162-e0ea5a8f843a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 + 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e49eac61-59c2-43ca-a8b6-d8b042ac17ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 + 3*4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "54477aa6-0913-4b53-bf10-ee279d486e29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.4 *5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "43f700e2-617e-4351-be13-970f8591c7c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this is a string'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'this is a string'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c7a2f06e-da9c-4577-bc83-8f5d019057c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_inter = 2 + 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2ab76f41-980d-4a2d-bfd3-6dbf602f81ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_float = 1.3 + 3.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "30780ca8-5175-44af-b9e3-6ac05502afa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_inter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e9d18fb6-a381-4fe0-9dd7-a6be8c676842",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9fa736a6-86dd-40bc-b8bf-1d82ca7ae422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is a comment\n",
+    "# Here we created a string variable\n",
+    "some_string = \"this is a string\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6537dce3-9eaa-4ca0-ad4d-fe58fc0511aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_string2 = \"this is 'a string'\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "891d5f6c-2331-4860-b9cc-4a1f86ec8405",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"this is 'a string'\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"this is 'a string'\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5f8fde9e-017f-4c32-808f-f9343d72abc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (772094868.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[14], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    \"this is \"a string\"\"\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"this is \"a string\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d1d8d976-7f8f-4c93-9eed-ca79833f47e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this is \\'a string\\' \"foo\"'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'this is \\'a string\\' \"foo\"'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ccb05511-9189-4100-a7f6-da9fe72a6ff4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weigt_kg = 60.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e6cd61b1-7215-4f55-a28c-e4b5ab77b938",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weigt_lb = weigt_kg*2.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "29c99247-fd58-4fef-b867-c3fc11a9e535",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(weigt_kg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d3ce4b6a-35c3-499d-882c-5661b6af282c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "132.66\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(weigt_lb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fd3dec9d-d01a-4edf-87b1-8f44293cfd91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weigt_kg = 102.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5bb5e4f2-4d49-40f3-8089-8d1c8b9362c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "102.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(weigt_kg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "db5c8966-6574-4807-adf7-e7906c035713",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "132.66\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(weigt_lb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ab8798f-229a-4b6f-8228-84940806dad2",
+   "metadata": {},
+   "source": [
+    "# Using libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "5755dcb4-090e-4de1-8224-6f8871772f05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing numerical library\n",
+    "import numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8d7476e0-dd4e-4c42-a79e-58ddf66fda5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = numpy.loadtxt(fname='python-novice-inflammation-data/data/inflammation-01.csv', \n",
+    "              delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "509393bf-7fb2-46fb-be25-e755c0334543",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "35cef287-9f5a-4fbf-9f58-77eba475fb49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(60, 40)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a613c56a-e1cf-4cf2-be8e-2605d5b6a5c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  1.,  3.,  1.,  2.,  4.,  7.,  8.,  3.,  3.,  3., 10.,\n",
+       "        5.,  7.,  4.,  7.,  7., 12., 18.,  6., 13., 11., 11.,  7.,  7.,\n",
+       "        4.,  6.,  8.,  8.,  4.,  4.,  5.,  7.,  3.,  4.,  2.,  3.,  0.,\n",
+       "        0.])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# access the fiste row is date\n",
+    "data[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "2d3726ae-4862-4523-a685-f3e5233a09cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  0.,  1.,  0.,  3.,  2.,  5.,  4.,  8.,  2.,  9.,  3.,  3.,\n",
+       "       10., 12.,  9., 14., 11., 13.,  8.,  6., 18., 11.,  9., 13., 11.,\n",
+       "        8.,  5.,  5.,  2.,  8.,  5.,  3.,  5.,  4.,  1.,  3.,  1.,  1.,\n",
+       "        0.])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# last row\n",
+    "data[59]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9b10a453-0fa9-46d0-8e33-8fde7cd8fde5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Access first entry\n",
+    "data[0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "eb8298e0-5fdd-4545-9227-d50749050cf5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "2a7ad811-687e-4c5d-ad56-a80e6cfb9c01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  1.,  3.,  1.,  2.,  4.,  7.,  8.,  3.,  3.,  3., 10.,\n",
+       "         5.,  7.,  4.,  7.,  7., 12., 18.,  6., 13., 11., 11.,  7.,  7.,\n",
+       "         4.,  6.,  8.,  8.,  4.,  4.,  5.,  7.,  3.,  4.,  2.,  3.,  0.,\n",
+       "         0.],\n",
+       "       [ 0.,  1.,  2.,  1.,  2.,  1.,  3.,  2.,  2.,  6., 10., 11.,  5.,\n",
+       "         9.,  4.,  4.,  7., 16.,  8.,  6., 18.,  4., 12.,  5., 12.,  7.,\n",
+       "        11.,  5., 11.,  3.,  3.,  5.,  4.,  4.,  5.,  5.,  1.,  1.,  0.,\n",
+       "         1.]])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Slice array with [first index:last_index_not included]\n",
+    "data[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "cdb770a8-a199-44ff-b228-ae0314c74d4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  1.,  3.,  1.,  2.,  4.,  7.,  8.,  3.,  3.,  3., 10.,\n",
+       "         5.,  7.,  4.,  7.,  7., 12., 18.,  6., 13., 11., 11.,  7.,  7.,\n",
+       "         4.,  6.,  8.,  8.,  4.,  4.,  5.,  7.,  3.,  4.,  2.,  3.,  0.,\n",
+       "         0.],\n",
+       "       [ 0.,  1.,  2.,  1.,  2.,  1.,  3.,  2.,  2.,  6., 10., 11.,  5.,\n",
+       "         9.,  4.,  4.,  7., 16.,  8.,  6., 18.,  4., 12.,  5., 12.,  7.,\n",
+       "        11.,  5., 11.,  3.,  3.,  5.,  4.,  4.,  5.,  5.,  1.,  1.,  0.,\n",
+       "         1.]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 0 in slicing can be omitted\n",
+    "data[:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ca39c784-c4a9-4f44-b939-bbe6221ffe05",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  1.,  1.,  3.,  3.,  2.,  6.,  2.,  5.,  9.,  5.,  7.,  4.,\n",
+       "         5.,  4., 15.,  5., 11.,  9., 10., 19., 14., 12., 17.,  7., 12.,\n",
+       "        11.,  7.,  4.,  2., 10.,  5.,  4.,  2.,  2.,  3.,  2.,  2.,  1.,\n",
+       "         1.],\n",
+       "       [ 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.,  1.,  1.,  3.,  3.,  1.,  3.,  5.,  2.,  4.,  4.,  7.,  6.,\n",
+       "         5.,  3., 10.,  8., 10.,  6., 17.,  9., 14.,  9.,  7., 13.,  9.,\n",
+       "        12.,  6.,  7.,  7.,  9.,  6.,  3.,  2.,  2.,  4.,  2.,  0.,  1.,\n",
+       "         1.],\n",
+       "       [ 0.,  0.,  1.,  2.,  2.,  4.,  2.,  1.,  6.,  4.,  7.,  6.,  6.,\n",
+       "         9.,  9., 15.,  4., 16., 18., 12., 12.,  5., 18.,  9.,  5.,  3.,\n",
+       "        10.,  3., 12.,  7.,  8.,  4.,  7.,  3.,  5.,  4.,  4.,  3.,  2.,\n",
+       "         1.],\n",
+       "       [ 0.,  0.,  2.,  2.,  4.,  2.,  2.,  5.,  5.,  8.,  6.,  5., 11.,\n",
+       "         9.,  4., 13.,  5., 12., 10.,  6.,  9., 17., 15.,  8.,  9.,  3.,\n",
+       "        13.,  7.,  8.,  2.,  8.,  8.,  4.,  2.,  3.,  5.,  4.,  1.,  1.,\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.,  3.,  1.,  5.,  6.,  5.,  5.,  8.,  2.,  4., 11.,\n",
+       "        12., 10., 11.,  9., 10., 17., 11.,  6., 16., 12.,  6.,  8., 14.,\n",
+       "         6., 13., 10., 11.,  4.,  6.,  4.,  7.,  6.,  3.,  2.,  1.,  0.,\n",
+       "         0.],\n",
+       "       [ 0.,  1.,  1.,  2.,  1.,  3.,  5.,  3.,  5.,  8.,  6.,  8., 12.,\n",
+       "         5., 13.,  6., 13.,  8., 16.,  8., 18., 15., 16., 14., 12.,  7.,\n",
+       "         3.,  8.,  9., 11.,  2.,  5.,  4.,  5.,  1.,  4.,  1.,  2.,  0.,\n",
+       "         0.]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Patient number 2 until patient number 9 \n",
+    "#         third patient          tenth patient\n",
+    "data[2:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "7fe90983-e639-45cd-9a11-5a9cd3e3ad15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 2., 0., 4., 2., 2., 1., 6., 7.],\n",
+       "       [0., 1., 1., 3., 3., 1., 3., 5., 2., 4.]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# First ten days of patient index 3 and 4\n",
+    "data[3:5, :10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "bb379cb2-da3f-4cc9-b25f-e2dc771732b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Access days 10 till 20 of patient 5 till 7\n",
+    "# patient number 6, 7, 8\n",
+    "# patient index 5, 6, 7\n",
+    "# Day #10 .. #20\n",
+    "# day index 9, 11, ..., 19\n",
+    "data_subset = data[5:8, 9:20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "bd65fb21-61a3-4e50-a0eb-6f20430d8f5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 7.,  6.,  6.,  9.,  9., 15.,  4., 16., 18., 12., 12.],\n",
+       "       [ 6.,  5., 11.,  9.,  4., 13.,  5., 12., 10.,  6.,  9.],\n",
+       "       [ 7.,  8.,  8.,  5., 10.,  9., 15., 11., 18., 19., 20.]])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "aec2c642-681c-46c1-b173-432d6e82a47b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.14875"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Mean over all days and patien\n",
+    "numpy.mean(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "dcfe23d6-2d5e-4a91-8def-7ba3a8e0dc94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.0"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Maximal value\n",
+    "numpy.amax(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "f25e1572-e429-4bca-9f9a-0680944edf88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Minimal value\n",
+    "numpy.amin(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ccef2db5-cbcb-450c-ae8a-eb66dae4ad0c",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m\n",
+       "\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;32mclass\u001b[0m \u001b[0;34m'float'\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mcomments\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mconverters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0musecols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0munpack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mndmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bytes'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mquotechar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlike\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Load data from a text file.\n",
+       "\n",
+       "Parameters\n",
+       "----------\n",
+       "fname : file, str, pathlib.Path, list of str, generator\n",
+       "    File, filename, list, or generator to read.  If the filename\n",
+       "    extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note\n",
+       "    that generators must return bytes or strings. The strings\n",
+       "    in a list or produced by a generator are treated as lines.\n",
+       "dtype : data-type, optional\n",
+       "    Data-type of the resulting array; default: float.  If this is a\n",
+       "    structured data-type, the resulting array will be 1-dimensional, and\n",
+       "    each row will be interpreted as an element of the array.  In this\n",
+       "    case, the number of columns used must match the number of fields in\n",
+       "    the data-type.\n",
+       "comments : str or sequence of str or None, optional\n",
+       "    The characters or list of characters used to indicate the start of a\n",
+       "    comment. None implies no comments. For backwards compatibility, byte\n",
+       "    strings will be decoded as 'latin1'. The default is '#'.\n",
+       "delimiter : str, optional\n",
+       "    The character used to separate the values. For backwards compatibility,\n",
+       "    byte strings will be decoded as 'latin1'. The default is whitespace.\n",
+       "\n",
+       "    .. versionchanged:: 1.23.0\n",
+       "       Only single character delimiters are supported. Newline characters\n",
+       "       cannot be used as the delimiter.\n",
+       "\n",
+       "converters : dict or callable, optional\n",
+       "    Converter functions to customize value parsing. If `converters` is\n",
+       "    callable, the function is applied to all columns, else it must be a\n",
+       "    dict that maps column number to a parser function.\n",
+       "    See examples for further details.\n",
+       "    Default: None.\n",
+       "\n",
+       "    .. versionchanged:: 1.23.0\n",
+       "       The ability to pass a single callable to be applied to all columns\n",
+       "       was added.\n",
+       "\n",
+       "skiprows : int, optional\n",
+       "    Skip the first `skiprows` lines, including comments; default: 0.\n",
+       "usecols : int or sequence, optional\n",
+       "    Which columns to read, with 0 being the first. For example,\n",
+       "    ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n",
+       "    The default, None, results in all columns being read.\n",
+       "\n",
+       "    .. versionchanged:: 1.11.0\n",
+       "        When a single column has to be read it is possible to use\n",
+       "        an integer instead of a tuple. E.g ``usecols = 3`` reads the\n",
+       "        fourth column the same way as ``usecols = (3,)`` would.\n",
+       "unpack : bool, optional\n",
+       "    If True, the returned array is transposed, so that arguments may be\n",
+       "    unpacked using ``x, y, z = loadtxt(...)``.  When used with a\n",
+       "    structured data-type, arrays are returned for each field.\n",
+       "    Default is False.\n",
+       "ndmin : int, optional\n",
+       "    The returned array will have at least `ndmin` dimensions.\n",
+       "    Otherwise mono-dimensional axes will be squeezed.\n",
+       "    Legal values: 0 (default), 1 or 2.\n",
+       "\n",
+       "    .. versionadded:: 1.6.0\n",
+       "encoding : str, optional\n",
+       "    Encoding used to decode the inputfile. Does not apply to input streams.\n",
+       "    The special value 'bytes' enables backward compatibility workarounds\n",
+       "    that ensures you receive byte arrays as results if possible and passes\n",
+       "    'latin1' encoded strings to converters. Override this value to receive\n",
+       "    unicode arrays and pass strings as input to converters.  If set to None\n",
+       "    the system default is used. The default value is 'bytes'.\n",
+       "\n",
+       "    .. versionadded:: 1.14.0\n",
+       "max_rows : int, optional\n",
+       "    Read `max_rows` rows of content after `skiprows` lines. The default is\n",
+       "    to read all the rows. Note that empty rows containing no data such as\n",
+       "    empty lines and comment lines are not counted towards `max_rows`,\n",
+       "    while such lines are counted in `skiprows`.\n",
+       "\n",
+       "    .. versionadded:: 1.16.0\n",
+       "\n",
+       "    .. versionchanged:: 1.23.0\n",
+       "        Lines containing no data, including comment lines (e.g., lines\n",
+       "        starting with '#' or as specified via `comments`) are not counted\n",
+       "        towards `max_rows`.\n",
+       "quotechar : unicode character or None, optional\n",
+       "    The character used to denote the start and end of a quoted item.\n",
+       "    Occurrences of the delimiter or comment characters are ignored within\n",
+       "    a quoted item. The default value is ``quotechar=None``, which means\n",
+       "    quoting support is disabled.\n",
+       "\n",
+       "    If two consecutive instances of `quotechar` are found within a quoted\n",
+       "    field, the first is treated as an escape character. See examples.\n",
+       "\n",
+       "    .. versionadded:: 1.23.0\n",
+       "like : array_like, optional\n",
+       "    Reference object to allow the creation of arrays which are not\n",
+       "    NumPy arrays. If an array-like passed in as ``like`` supports\n",
+       "    the ``__array_function__`` protocol, the result will be defined\n",
+       "    by it. In this case, it ensures the creation of an array object\n",
+       "    compatible with that passed in via this argument.\n",
+       "\n",
+       "    .. versionadded:: 1.20.0\n",
+       "\n",
+       "Returns\n",
+       "-------\n",
+       "out : ndarray\n",
+       "    Data read from the text file.\n",
+       "\n",
+       "See Also\n",
+       "--------\n",
+       "load, fromstring, fromregex\n",
+       "genfromtxt : Load data with missing values handled as specified.\n",
+       "scipy.io.loadmat : reads MATLAB data files\n",
+       "\n",
+       "Notes\n",
+       "-----\n",
+       "This function aims to be a fast reader for simply formatted files.  The\n",
+       "`genfromtxt` function provides more sophisticated handling of, e.g.,\n",
+       "lines with missing values.\n",
+       "\n",
+       "Each row in the input text file must have the same number of values to be\n",
+       "able to read all values. If all rows do not have same number of values, a\n",
+       "subset of up to n columns (where n is the least number of values present\n",
+       "in all rows) can be read by specifying the columns via `usecols`.\n",
+       "\n",
+       ".. versionadded:: 1.10.0\n",
+       "\n",
+       "The strings produced by the Python float.hex method can be used as\n",
+       "input for floats.\n",
+       "\n",
+       "Examples\n",
+       "--------\n",
+       ">>> from io import StringIO   # StringIO behaves like a file object\n",
+       ">>> c = StringIO(\"0 1\\n2 3\")\n",
+       ">>> np.loadtxt(c)\n",
+       "array([[0., 1.],\n",
+       "       [2., 3.]])\n",
+       "\n",
+       ">>> d = StringIO(\"M 21 72\\nF 35 58\")\n",
+       ">>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),\n",
+       "...                      'formats': ('S1', 'i4', 'f4')})\n",
+       "array([(b'M', 21, 72.), (b'F', 35, 58.)],\n",
+       "      dtype=[('gender', 'S1'), ('age', '<i4'), ('weight', '<f4')])\n",
+       "\n",
+       ">>> c = StringIO(\"1,0,2\\n3,0,4\")\n",
+       ">>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)\n",
+       ">>> x\n",
+       "array([1., 3.])\n",
+       ">>> y\n",
+       "array([2., 4.])\n",
+       "\n",
+       "The `converters` argument is used to specify functions to preprocess the\n",
+       "text prior to parsing. `converters` can be a dictionary that maps\n",
+       "preprocessing functions to each column:\n",
+       "\n",
+       ">>> s = StringIO(\"1.618, 2.296\\n3.141, 4.669\\n\")\n",
+       ">>> conv = {\n",
+       "...     0: lambda x: np.floor(float(x)),  # conversion fn for column 0\n",
+       "...     1: lambda x: np.ceil(float(x)),  # conversion fn for column 1\n",
+       "... }\n",
+       ">>> np.loadtxt(s, delimiter=\",\", converters=conv)\n",
+       "array([[1., 3.],\n",
+       "       [3., 5.]])\n",
+       "\n",
+       "`converters` can be a callable instead of a dictionary, in which case it\n",
+       "is applied to all columns:\n",
+       "\n",
+       ">>> s = StringIO(\"0xDE 0xAD\\n0xC0 0xDE\")\n",
+       ">>> import functools\n",
+       ">>> conv = functools.partial(int, base=16)\n",
+       ">>> np.loadtxt(s, converters=conv)\n",
+       "array([[222., 173.],\n",
+       "       [192., 222.]])\n",
+       "\n",
+       "This example shows how `converters` can be used to convert a field\n",
+       "with a trailing minus sign into a negative number.\n",
+       "\n",
+       ">>> s = StringIO('10.01 31.25-\\n19.22 64.31\\n17.57- 63.94')\n",
+       ">>> def conv(fld):\n",
+       "...     return -float(fld[:-1]) if fld.endswith(b'-') else float(fld)\n",
+       "...\n",
+       ">>> np.loadtxt(s, converters=conv)\n",
+       "array([[ 10.01, -31.25],\n",
+       "       [ 19.22,  64.31],\n",
+       "       [-17.57,  63.94]])\n",
+       "\n",
+       "Using a callable as the converter can be particularly useful for handling\n",
+       "values with different formatting, e.g. floats with underscores:\n",
+       "\n",
+       ">>> s = StringIO(\"1 2.7 100_000\")\n",
+       ">>> np.loadtxt(s, converters=float)\n",
+       "array([1.e+00, 2.7e+00, 1.e+05])\n",
+       "\n",
+       "This idea can be extended to automatically handle values specified in\n",
+       "many different formats:\n",
+       "\n",
+       ">>> def conv(val):\n",
+       "...     try:\n",
+       "...         return float(val)\n",
+       "...     except ValueError:\n",
+       "...         return float.fromhex(val)\n",
+       ">>> s = StringIO(\"1, 2.5, 3_000, 0b4, 0x1.4000000000000p+2\")\n",
+       ">>> np.loadtxt(s, delimiter=\",\", converters=conv, encoding=None)\n",
+       "array([1.0e+00, 2.5e+00, 3.0e+03, 1.8e+02, 5.0e+00])\n",
+       "\n",
+       "Note that with the default ``encoding=\"bytes\"``, the inputs to the\n",
+       "converter function are latin-1 encoded byte strings. To deactivate the\n",
+       "implicit encoding prior to conversion, use ``encoding=None``\n",
+       "\n",
+       ">>> s = StringIO('10.01 31.25-\\n19.22 64.31\\n17.57- 63.94')\n",
+       ">>> conv = lambda x: -float(x[:-1]) if x.endswith('-') else float(x)\n",
+       ">>> np.loadtxt(s, converters=conv, encoding=None)\n",
+       "array([[ 10.01, -31.25],\n",
+       "       [ 19.22,  64.31],\n",
+       "       [-17.57,  63.94]])\n",
+       "\n",
+       "Support for quoted fields is enabled with the `quotechar` parameter.\n",
+       "Comment and delimiter characters are ignored when they appear within a\n",
+       "quoted item delineated by `quotechar`:\n",
+       "\n",
+       ">>> s = StringIO('\"alpha, #42\", 10.0\\n\"beta, #64\", 2.0\\n')\n",
+       ">>> dtype = np.dtype([(\"label\", \"U12\"), (\"value\", float)])\n",
+       ">>> np.loadtxt(s, dtype=dtype, delimiter=\",\", quotechar='\"')\n",
+       "array([('alpha, #42', 10.), ('beta, #64',  2.)],\n",
+       "      dtype=[('label', '<U12'), ('value', '<f8')])\n",
+       "\n",
+       "Quoted fields can be separated by multiple whitespace characters:\n",
+       "\n",
+       ">>> s = StringIO('\"alpha, #42\"       10.0\\n\"beta, #64\" 2.0\\n')\n",
+       ">>> dtype = np.dtype([(\"label\", \"U12\"), (\"value\", float)])\n",
+       ">>> np.loadtxt(s, dtype=dtype, delimiter=None, quotechar='\"')\n",
+       "array([('alpha, #42', 10.), ('beta, #64',  2.)],\n",
+       "      dtype=[('label', '<U12'), ('value', '<f8')])\n",
+       "\n",
+       "Two consecutive quote characters within a quoted field are treated as a\n",
+       "single escaped character:\n",
+       "\n",
+       ">>> s = StringIO('\"Hello, my name is \"\"Monty\"\"!\"')\n",
+       ">>> np.loadtxt(s, dtype=\"U\", delimiter=\",\", quotechar='\"')\n",
+       "array('Hello, my name is \"Monty\"!', dtype='<U26')\n",
+       "\n",
+       "Read subset of columns when all rows do not contain equal number of values:\n",
+       "\n",
+       ">>> d = StringIO(\"1 2\\n2 4\\n3 9 12\\n4 16 20\")\n",
+       ">>> np.loadtxt(d, usecols=(0, 1))\n",
+       "array([[ 1.,  2.],\n",
+       "       [ 2.,  4.],\n",
+       "       [ 3.,  9.],\n",
+       "       [ 4., 16.]])\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/miniforge3/lib/python3.10/site-packages/numpy/lib/npyio.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numpy.loadtxt?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "bd190ce9-b302-4a14-968d-75fda94e286f",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "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": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numpy.loadtxt(fname='python-novice-inflammation-data/data/inflammation-01.csv', \n",
+    "              delimiter=',', comments='!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "df9fd508-924e-43b4-85f5-9c6fd93dd2ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.613833197118566"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Standard deviation\n",
+    "numpy.std(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "9166f90b-9498-494d-956b-10317ce5ffec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Maximial value for first patient\n",
+    "numpy.amax(data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "67ecf117-a4ad-4d72-8d98-a6cdf3e82050",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Maximial value on first day of all patients\n",
+    "numpy.amax(data[:, 0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "49ac6112-7de0-4333-84be-cf883ef05e08",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Minimal value on first day of all patients\n",
+    "numpy.amin(data[:, 0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "d3ddff63-b0cf-4769-8220-328be8a87d8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Minimal value on last day of all patients\n",
+    "numpy.amin(data[:, -1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "67dddb06-94d6-4b68-86dc-e80dba4c000a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Maximal value for first two days of all patients\n",
+    "numpy.amax(data[:, :2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "2b21c00d-165a-4c25-b343-c9ca93afae1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
+       "       13., 14., 15., 16., 17., 18., 19., 20., 19., 18., 17., 16., 15.,\n",
+       "       14., 13., 12., 11., 10.,  9.,  8.,  7.,  6.,  5.,  4.,  3.,  2.,\n",
+       "        1.])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Maxmial value for separate days, for all patients\n",
+    "# data[:, 0]\n",
+    "# data[:, 1]\n",
+    "numpy.amax(data, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "87324891-2d13-4760-9c4b-398b2812c506",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 1., 1., 1., 1., 2., 2., 2., 2., 3., 3., 3., 3., 4.,\n",
+       "       5., 5., 5., 5., 4., 4., 4., 4., 3., 3., 3., 3., 2., 2., 2., 2., 1.,\n",
+       "       1., 1., 1., 0., 0., 0.])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numpy.amin(data, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9133d99f-7e72-471d-a4a4-4583d5483bfc",
+   "metadata": {},
+   "source": [
+    "# Plotting library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "04ea60d8-36ff-43c2-a78b-9e9350e4064a",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'matplotplib'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[66], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotplib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotplib'"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotplib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "6d52bd2a-b168-4664-9b5d-90d48db0ce26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "9f3a1a28-3778-40aa-ad17-f10a79b0b541",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_max = numpy.amax(data, axis=0)\n",
+    "data_min = numpy.amin(data, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "42d52554-eaf3-4432-b1c8-5924f53549cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Maximal inflamation count')"
+      ]
+     },
+     "execution_count": 75,
+     "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": [
+    "# Plot maxima over days\n",
+    "plt.plot(data_max)\n",
+    "plt.xlabel('day')\n",
+    "plt.ylabel('Maximal inflamation count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "d9377ea1-65fc-4773-a733-f332c365b3cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Minimal inflamation count')"
+      ]
+     },
+     "execution_count": 76,
+     "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": [
+    "# Plot minimal over days\n",
+    "plt.plot(data_min)\n",
+    "plt.xlabel('day')\n",
+    "plt.ylabel('Minimal inflamation count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "79f1fc60-9a32-42ad-a1ff-3dd8cb2c8082",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7546b238afb0>"
+      ]
+     },
+     "execution_count": 80,
+     "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": [
+    "plt.plot(data_max, label='Maximal inflamation count')\n",
+    "plt.plot(data_min, label='Minimal inflamation count')\n",
+    "plt.plot(numpy.mean(data, axis=0), label='Average inflamation count')\n",
+    "plt.xlabel('day')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "4d6e2f4e-dd36-40d0-94ae-0572fefd0308",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Px4gRI+Dh4YFWrVph5MiRuHr1qrSzEUKUxPz58+Hu7g6BQIC7d+/i3r17SElJgaurK+bPn891PEKIAigvL8fWrVvh6OjIdRQiY+HPcpCcVwYjXU2M7mwvt9cd1N4W1kY6yC2pxJmHmXJ7XVJL4qZiz5496N+/P/T19TF//nx8+OGH0NPTQ79+/bBv3z5ZZCSEKLjw8HCsX7++3vaxFhYWWLduHcLDwzlMRgjhgpmZGczNzcU3MzMzGBkZYfv27fjvf//LdTwiY7sja0cpJnR1gr62/A6j09bkiXeZCo1IktvrkloS/5devXo11q9fj4ULF4rvW7BgATZu3Iivv/4aU6dOlWpAQoji09HRQXFxcYP7S0pKoK2tzUEiQgiXvv/++3pf162/9PPzg5mZGTehiFwk55Xi0pNsALXTkeRtqq8zfgh7jtvJL/AwvRDt7Ruer0ZkQ+KmIiEhASNGjGhw/8iRI/HFF19IJRQhRLkMHz4c7777Ln7//Xf4+voCAKKiovDee+9h5MiRHKcjhMhbSEgI1xEIR/bcSAbLAkGtreBqaSD317c21sVgL1v8HZOB3ZHJWDeug9wzqCuJpz85OTnh4sWLDe6/ePEinJycpBKKEKJctmzZAnd3dwQEBEBXVxe6urro3r07PDw8sHnzZq7jEUI4UFBQgA0bNmDu3Ll45513sGnTJhQWFnIdi8hQeZUQh26nAgBCArnb2Cck0AUAcCw6DQVlVZzlUDcSj1QsXrwY8+fPR3R0NAIDA8EwDK5du4adO3fShwdC1JSpqSn++usvPH/+HI8ePQLLsvD09ISHhwfX0QghHLh9+zYGDRoEPT09+Pr6gmVZbNy4EatXr8a5c+fQpUsXriMSGTh+Pw2F5dVwMtdDUGtrznL48M3Q1tYIjzOL8cftVLzTy42zLOpE4qbi/fffh62tLTZs2IBDhw4BANq1a4eDBw82ui81IUR9eHh4UCNBCMHChQsxcuRI/Prrr9DUrP2oUVNTg7lz5+Ljjz/GlStXOE5IpI1lWeyKqF2gPcOfDw2efLaRbQzDMAgJdMHSI7HYfSMZc3q4gsdhHnXRrC1lx4wZg2vXriEvLw95eXm4du0aNRSEqLHx48dj3bp1De7/73//iwkTJnCQiBDCpdu3b+Ozzz4TNxQAoKmpiSVLluD27dscJiOycjflBeIyiqCjycNEH+6nw4/qZA9jXU2k5Jfh8tNsruOoBYmbilu3biEqKqrB/VFRUVQoCFFT4eHhGDZsWIP7Bw8eTFckCVFDxsbGSElJaXC/QCCAkZERB4mIrNWNUozqZA9Tfe53/dPX1sSEl81N6MstbolsSdxUfPDBBxAIBA3uT0tLwwcffCCVUIQQ5fK6rWO1tLRQVFTEQSJCCJcmTZqEOXPm4ODBgxAIBEhNTcWBAwcwd+5cTJkyhet4RMqyiytw+kEGgNoTtBXFDP/axeLhT3OQlFvKcRrVJ3FTERcX1+gCq86dOyMuLk4qoQghysXLywsHDx5scP+BAwfg6enJQSJCCJe+++47jB07FsHBwXBxcQGfz8fMmTMxfvx4fPvtt1zHI1J24KYA1UIWXflm8HJQnHMhXCwN0LuNFVi2dqtbIlsSL9TW0dFBVlYW3Nzqr6TPyMioN3eSEKI+li1bhnHjxiE+Ph59+/YFULvN9P79+/HHH39wnI4QIm/a2trYvHkz1q5di/j4eLAsCw8PD+jr63MdjUhZtVCEvVG1H9iDOTjs7m1CAlxw+UkODt0WYPHANtDT1uA6ksqSeKRiwIABWLp0ab29pgsKCvDFF19gwIABUg1HCFEOI0eOxLFjx/D8+XPMmzcPixcvRmpqKi5cuIDRo0dzHY8QImeFhYXIz8+Hvr4+vL290aFDB+jr6yM/P5+mRKqYcw+zkFVUCUtDHQzxsuM6TgNBra3gbK6Poooa/BWdxnUclSZxU7FhwwYIBALw+Xz06dMHffr0gaurKzIzM7FhwwZZZCSEKIFhw4bh+vXrKC0tRW5uLsLCwhAUFMR1LEIIByZPnowDBw40uP/QoUOYPHkyB4mIrIRGJgEApvo6QVuzWZuKyhSPx4jXVuyKTAbLshwnUl0S/9d3cHBATEwM1q9fD09PT3Tt2hWbN29GbGysxCdqX7lyBSNGjIC9vT0YhsGxY8fqfX/mzJlgGKbezd/fX9LIhBAlQnWBEOUXFRWFPn36NLi/d+/eje4g+SZUExTX48wiRCXmQ4PHYKqf4k19qjPBxxG6Wjw8yijC7eQXXMdRWc1aBGFgYIB33323xS9eWlqKjh07YtasWRg3blyjjxk8eDB27Ngh/rqxHWYIIaqD6gIhyq+yshI1NTUN7q+urkZ5eblEz0U1QXHVbdU6qL0NbE10OU7zeqb62hjV0QEHbwsQGpmMbi7mXEdSSZyurB4yZAiGDBnyxsfo6OjA1tZWTokIIVyjukCI8uvWrRt++eUXbN26td7927ZtQ9euXSV6LqoJiqmwvBpH79auUZjh78JtmCaYEcDHwdsCnI7NQPawdrA2VtwmSFkp/HZNly9fhrW1NUxNTREUFITVq1fD2tr6tY+vrKxEZWWl+GtaEEaI6qG6QIhiW716Nfr374/79++jX79+AGp3hLt16xbOnTsn9dejmiB/h++korxaiNY2hvB3U/wr/14OJvDhm+F28gvsu5mCj/u35jqSylG8FTWvGDJkCPbu3YuwsDBs2LABt27dQt++fesVgn9bu3YtTExMxDdJ13kQQhQb1QVCFF/37t0RGRkJJycnHDp0CCdOnICHhwdiYmLQs2dPqb4W1QT5E4lY7H557sOMABcwDMNxoqYJDnQBAOyLSkG1UMRtGBXEsAqyDJ5hGBw9evSN209mZGSAz+fjwIEDGDt2bKOPaezqg5OTEwoLC2FsbCzt2IQojaKiIpiYmMjkd0EoFGLnzp24ePEisrOzIRLVL9ZhYWHNel6qC4TIjixrgqxQTVAM4U9zELL9Jox0NHHji34w0FH4iS8AgKoaEQLXhSG3pBI/TO2M4R3suY6kcFpSF5r9r6CqqqrRDw/Ozs7Nfcq3srOzA5/Px7Nnz177GB0dHejo6MgsAyGkoQULFmDnzp0YNmwYvLy85HrViuoCIeRVVBNkb/fLbWTHdXVUmoYCALQ1eZjq54wtF58hNDKZmgopk/hfwrNnzzB79mxERETUu59lWTAMA6FQKLVw/5aXlweBQAA7O8U7XIUQdXbgwAEcOnQIQ4cOlftrU10ghLyKaoJsCfLLcPFxNoDaxc/KZpqfM3669Bw3E/PxKKMI7exoZEpaJG4qZs6cCU1NTfz999+ws7Nr0RXJkpISPH/+XPx1YmIioqOjYW5uDnNzc3z11VcYN24c7OzskJSUhC+++AKWlpYYM2ZMs1+TECJ92tra8PDwkMpzUV0ghLyKaoJi2XMjGSwL9GxlCXcrQ67jSMzGWBeD2tviZGwGQiOTsXasN9eRVAcrIX19ffbRo0eS/lijLl26xAJocAsJCWHLysrYgQMHslZWVqyWlhbr7OzMhoSEsCkpKRK9RmFhIQuALSwslEpmQpSVLH8XvvvuO3bevHmsSCRq8XNRXSBEPpTl94BqguIor6phO648y/I/+5s99zCT6zjNdiM+l+V/9jfb9svTbEFZFddxFEpLfhckHqnw9PREbm5uC1uZWr17937jcelnz56VyusQQmTr2rVruHTpEk6fPo327dtDS0ur3vePHDnS5OeiukAIeRXVBMVx/H46Csqq4WCqh75tX79lr6LzdTVHW1sjPM4sxp93UjGnhyvXkVSCxE3Ft99+iyVLlmDNmjXw9vZu8OGBdk0gRP2YmprSVANCiFhpaSnWrVv32h3hEhISOEpGmotlWYS+XKA9I4APDZ5ybCPbGIZhMCOAj/8cfYDdkUmYFegCnhL/fRSFxE1F//79AUB8mE0dVg4LtQkhimnHjh1cRyCEKJC5c+ciPDwcM2bMaPH6S6IY7gkK8CCtCDqaPEzyUf5zPUZ3csC604+RlFeGK89y0LuN8o68KAqJm4pLly7JIgchRAXk5OTgyZMnYBgGrVu3hpWVFdeRCCEcOH36NE6ePInu3btzHYVISWhEEgBgREd7mBlocxtGCgx0NDGhqxO2X0/E7shkaiqkQOKmIigoSBY5CCFKrLS0FB999BFCQ0PF0xw0NDQQHByMrVu3Ql9fn+OEhBB5MjMzg7m5OdcxiJTkllTiVGwmACAkwIXbMFI0I4CP7dcTEfYkGyl5ZXC2oP9XtQSvOT9UUFCADRs2YO7cuXjnnXewadMmFBYWSjsbIURJLFq0COHh4Thx4gQKCgpQUFCAv/76C+Hh4Vi8eDHX8Qghcvb1119j+fLlKCsr4zoKkYIDN1NQJRShk5MpvB1NuI4jNa6WBujV2gosC+yJSuY6jtKTeKTi9u3bGDRoEPT09ODr6wuWZbFx40asXr0a586dQ5cuXWSRkxCiwA4fPow///wTvXv3Ft83dOhQ6OnpYeLEifj555+5C0cIkbsNGzYgPj4eNjY2cHFxabCpy927dzlKRiRVIxRhb1QKAGBmoAu3YWQgJICPK09zcPCWAAv7t4aetgbXkZSWxE3FwoULMXLkSPz666/Q1Kz98ZqaGsydOxcff/wxrly5IvWQhBDFVlZWBhsbmwb3W1tb05VKQtTQ6NGjuY5ApOTCoyxkFFbAwkAbQ7xtuY4jdb3bWMPRTA+pL8px4n46JnZT/kXoXGnWSMWrDQUAaGpqYsmSJfDx8ZFqOEKIcggICMCKFSsQGhoKXV1dAEB5eTlWrlyJgIAAjtMRQuRtxYoVXEcgUrIronZa0BRfZ+hoqt5VfA0egxn+fKw9/Ri7IpMwwceRditrJombCmNjY6SkpKBt27b17hcIBDAyMpJaMEKI8ti8eTMGDx4MR0dHdOzYEQzDIDo6Grq6unQwFSFq7M6dO3j06BEYhoGnpyc6d+7MdSQigadZxYhMyAOPAab6OXMdR2Ym+jhh4/mneJhehLspBejKN+M6klKSuKmYNGkS5syZg++++w6BgYFgGAbXrl3Dp59+iilTpsgiIyFEwXl5eeHZs2fYs2cPHj9+DJZlMXnyZEybNg16enpcxyOEyFl2djYmT56My5cvw9TUFCzLorCwEH369MGBAwdou2klsTuydpRioKct7E1Vt5abGWhjVCd7HLqditDIJGoqmknipuK7774DwzAIDg5GTU0NAEBLSwvvv/8+1q1bJ/WAhBDloKenh3feeYfrGIQQBfDRRx+hqKgIDx8+RLt27QAAcXFxCAkJwfz587F//36OE5K3KaqoxuG7qQCA4EA+x2lkLzjABYdup+JUbAa+HOYJKyMdriMpHYmbCm1tbWzevBlr165FfHw8WJaFh4cH7UNPiJo5fvw4hgwZAi0tLRw/fvyNjx05cqScUhFlE/E8F3uikvHVyPawNtLlOg6RkjNnzuDChQvihgIAPD098eOPP2LgwIEcJiNNdeROKsqqhGhlbYgANwuu48icl4MJujib4m5KAQ7cTMFH/VpxHUnpSNxU1NHX14e3t7c0sxBClMjo0aORmZkJa2vrN+70wjAMhEKh/IIRpVEtFOHTP2OQVlAOJ3N9LB3S7u0/RJSCSCRqsI0sUDuzoe6ATKK4WJZF6I3aqU/BAXy1WbgcEuiCuynR2BuVgvd7u0NTo1nHuamtJjUVY8eOxc6dO2FsbIyxY8e+8bFHjhyRSjBCiGJ79YMBfUggzfF3TDrSCsoBAKdjM/H54LZq8+FF1fXt2xcLFizA/v37YW9vDwBIS0vDwoUL0a9fP47Tkbe5/jwPCTmlMNTRxJgujlzHkZvBXrawNNRGZlEFzsdlYYi3HdeRlEqTWjATExNxoTc2NoaJiclrb4QQ9RMaGorKysoG91dVVSE0NJSDRETRsSyLbZcTxF+n5JfhYXoRh4mINP3www8oLi6Gi4sL3N3d4eHhAVdXVxQXF2Pr1q1cxyNvsSsyCQAwrosDDHWaPalF6ehoamCKb+0uVzsjkrgNo4QYlmVZrkPIUlFREUxMTFBYWAhjY2Ou4xDCGVn+LmhoaCAjIwPW1tb17s/Ly4O1tbXCTX+iusC9sMdZmL3zNgx1NNHRyQTXn+fhwz4e+GRQG66jqQ15/B6cP39evCOcp6cn+vfvL5PXaSmqCf9IfVGGXusvQcQCFxb1goe1eh0XkFFYjh7fXoJQxOLsx73Qxla9/v4t+V2QeLJY3759UVBQ0GiIvn37Svp0hBAVwLJso9NWUlNTaQSTNOrny/EAgGl+zpjoU3uC7anYDKj4dS61M2DAAHz00UeYP3++wjYUpL69USkQsUAPD0u1aygAwM5EDwM9bQAAoS9HbEjTSDymdfnyZVRVVTW4v6KiAlevXpVKKEKIcujcuTMYhgHDMOjXrx80Nf8pKUKhEImJiRg8eDCHCYkiup2Uj1tJL6CtwcPsHq7Q19aAtiYPCbmleJpVonZXBlXFli1b8O6770JXVxdbtmx542Pnz58vp1REEhXVQhy4mQIAmBGg+tvIvk5wgAtOP8jE0Xtp+GxIWxjrNtx0gDTU5KYiJiZG/Oe4uDhkZmaKvxYKhThz5gwcHBykm44QotDqdn2Kjo7GoEGDYGhoKP6etrY2XFxcMG7cOI7SEUW1Lbx2lGJsFwfYGNduI9urlRUuPMrCqdgMaiqU1KZNmzBt2jTo6upi06ZNr30cwzDUVCiov2My8KKsGg6meujX1vrtP6Ci/N3M0drGEE+zSnD4TipmdXflOpJSaHJT0alTJ/EVycamOenp6dHiK0LUzIoVKwAALi4umDRpEnR16ZwB8mZPMotx4VE2GAZ4t5eb+P6h3ra48CgLpx9kYOGA1hwmJM2VmJjY6J+J8tj9crrPNH9ntd5OlWEYzAhwwbJjD7A7MhkhAS7g8Whnurdp8r+YxMRE8WF3N2/eRGJioviWlpaGoqIizJ49W5ZZCSEKKiQkhBoK0iT/u1I7SjG4vS3crP4Z2erXzgZaGgyeZpXgeXYxV/GIlKxatQplZWUN7i8vL8eqVas4SETeJlpQgPuphdDW4GHSy3VO6mxsZwcY6WgiIbcU157nch1HKTS5qeDz+XBxcYFIJIKPjw/4fL74ZmdnBw0NDVnmJIQoMKFQiO+++w6+vr6wtbWFubl5vRshACDIL8Px6HQAwHtB7vW+Z6KnhR4elgBqz6wgym3lypUoKSlpcH9ZWRlWrlzJQSLyNqEvt1Ad3tEOFoY63IZRAAY6mhjXtfaMjtDIZI7TKIdmj23FxcXhzJkzOH78eL0bIUT9rFy5Ehs3bsTEiRNRWFiIRYsWYezYseDxePjqq6+4jkcUxKbzT1EjYtGzlSU6Opk2+H7dQVOnHlBToexetyPc/fv36UKDAsorqcTfMRkAgJAAF27DKJC6xeoXH2dBkN9w5I3UJ/HuTwkJCRgzZgxiY2PBMIx4+7+64qFo+9ETQmRv7969+PXXXzFs2DCsXLkSU6ZMgbu7Ozp06IAbN27QokyCRxlFOBqdBgD49DVnUQz0tMEXPAaPMoqQmFsKV0sDeUYkUmBmZiZef9m6det6jYVQKERJSQnee+89DhOSxhy4JUCVUISOTqaNNvzqyt3KED1bWeLqs1zsiUrG0iHtuI6k0CRuKhYsWABXV1dcuHABbm5uuHnzJvLy8rB48WJ89913sshICFFwmZmZ8Pb2BgAYGhqisLAQADB8+HAsW7aMy2hEQaw/8xgsCwzrYIcOjqaNPsZUXxsB7ha4+iwXpx9kYF5vD/mGJC32/fffg2VZzJ49GytXrqx3Tk3djnABAQEcJiT/ViMUYe+N2uk9wf7qu43s68zw5+Pqs1wcvCXAwv6toatF0/1fR+KmIjIyEmFhYbCysgKPxwOPx0OPHj2wdu1azJ8/H/fu3ZNFTkKIAnN0dERGRgacnZ3h4eGBc+fOoUuXLrh16xZ0dGhurrq7kZCHS09yoMlj8MnAN5+YPcTLDlef5eJULDUVyigkJAQA4OrqisDAQGhp0f7+iu7i42ykF1bA3EAbwzrYcR1H4fRrZwMHUz2kFZTj+P108WGdpCGJ11QIhULxXvSWlpZIT69ddMfn8/HkyRPppiOEKIUxY8bg4sWLAGpHM5ctW4ZWrVohODiYdoVTcyzLYt3pxwCAyb5Ob53SNKi9DbQ1eHiQVoS7KS/kEZHIQFBQkLihKC8vR1FRUb0bURx1p0ZP6uZEV+EbocFjMP3lCE5oZJJ42j9pSOKRCi8vL8TExMDNzQ1+fn5Yv349tLW18csvv8DNze3tT0AIUTnr1q0T/3n8+PFwdHREREQEPDw8MHLkSA6TEa6dfZiJaEEB9LQ0ML9fq7c+3sJQByM72ePPO6n47WoCfprWVQ4pibSVlZVhyZIlOHToEPLy8hp8n9ZfKobn2cW4/jwPPAbiD86koUndnLDpwlM8SCvCPUEBujibcR1JIUk8UvHll19CJBIBAL755hskJyejZ8+eOHXqFLZs2SL1gIQQ5ePv749FixZRQ6HmaoQirD9bO4I9t6crrI2adpbJOz1rL1CdeZBJO64oqU8//RRhYWH46aefoKOjg99++w0rV66Evb09QkNDuY5HXqrbKrX/yyk+pHHmBtoY0cEeALCbtpd9LYlHKgYNGiT+s5ubG+Li4pCfny/e8YEQop7S0tJw/fp1ZGdniy881KHdn9TTH3dSkZBTCjN9rXqnZ79NG1sj9GpthStPc/D7tUR8NbK9DFMSWThx4gRCQ0PRu3dvzJ49Gz179oSHhwf4fD727t2LadOmcR1R7RVXVOPwnVQAQDBtI/tWIYF8HL6bipMxGfhiaDtYGdF6wX+TuKloDO05TYh627FjB9577z1oa2vDwsKi3gUGhmGoqVBDNUIRfgh7DgD4sG8rGOlKtmD3nZ6uuPI0B4du1+64YqJPC36VSX5+PlxdXQEAxsbGyM/PBwD06NED77//PpfRyEtH76WhtEoINysDdPew4DqOwuvgaIpOTqaIFhTg4K0UfNj37dM51Y3E058qKirw3//+F0OHDoWPjw+6dOlS70YIUT/Lly/H8uXLUVhYiKSkJCQmJopvCQkJXMcjHDj9IBNpBeUwN9DGND9niX++h4cl2toaoaxKiL03abqBsnFzc0NSUhIAwNPTE4cOHQJQO4JhamrKXTACoHYDhbqpTyEBLjTTpImCXx6GtzcqBTVC0VserX4kHqmYPXs2zp8/j/Hjx8PX15f+IRJCUFZWhsmTJ4PHk/g6BVFBLMvit6u1zeQMf36zdpRhGAbv9HTD4j/uY+f1JMzt4QZtTfr3pSxmzZqF+/fvIygoCEuXLsWwYcOwdetW1NTUYOPGjVzHU3uR8Xl4nl0CA20NjO3iwHUcpTHU2w6rTz5CRmEFLjzKwmAv2oL3VRI3FSdPnsSpU6fQvXt3WeQhhCihOXPm4I8//sDnn3/OdRSiAG4lvcD91EJoa/IwI6D5O8qM6GiP9WcfI6uoEsfvp2N8V0cppiSytHDhQvGf+/Tpg8ePH+P27dtwd3dHx44dOUxGAGDXy21kx3ZxlHhqojrT1dLAZF8n/HgpHqGRydRU/IvETYWDgwOMjIxkkYUQoqTWrl2L4cOH48yZM/D29m5w4BVdmVQvv74cpRjXxRGWhs1fzKitycPMQFd8e+YxfruagHFdHGh0XEk5OzvD2VnyaXBE+tIKynE+LgvAP9N5SNNN9ePj58vxiIjPw7OsYrSyoc/EdSRuKjZs2IDPPvsM27ZtA59P/xgJIcCaNWtw9uxZtGlTe1ryvxdqE/WRkFOCC49qP7DM6eHa4ueb6uuMrWHP8DizGFef5aJXa6sWPyeRj5s3b+Ly5cuN7ghHFxq4s/dGMkQsEOBmQR+Im8HBVA8DPG1w9mEWQiOT8fVoL64jKQyJmwofHx9UVFTAzc0N+vr6Da5I1u3wQAhRHxs3bsT27dsxc+ZMrqMQjv1+LREsC/Rraw0Pa8MWP5+JvhYmdXPCjutJ+Onyc2oqlMSaNWvw5Zdfok2bNrCxsaELDQqiolqIg7cEAGiUoiVCAlxw9mEWjtxNxZLBbWgK2UsSNxVTpkxBWloa1qxZ06BQEELUk46ODq2zIsgvrcKfL/e9f0eCcyne5t1ebthzIxk3EvJxKykf3VxoG3NFt3nzZrrQoIBOxWYgr7QKdia6GOBpw3UcpRXgbgEPa0M8zy7BkbtpCAl04TqSQpC4qYiIiEBkZCQttCKEiC1YsABbt27Fli1buI5COLTnRjIqa0TwdjCBn6v0PvjbmehhfFcn7L+Zgq1hzxE621dqz01kg8fj0YUGBbTr5Tay0/ycoalBu6k1F8MwCA7gY/lfDxEamYTgAD5dZEczzqlo27YtysvLZZGFEKKkbt68iV27dsHNzQ0jRozA2LFj692I6quoFiL05Y4yc3u6Sv1/sO8HuUODx+DK0xzcFxRI9bmJ9C1cuBA//vgj1zHIK+4LCnBfUABtDR4m+9Ki+ZYa28URhjqaiM8pRUR8HtdxFILEIxXr1q3D4sWLsXr16kZ3eTE2NpZaOEKIcjA1NaXmQc39HZOB3JIq2JvoYqi39LdZdLbQx6hO9jhyNw0/XHqOX4N9pP4aRHo++eQTDBs2DO7u7vD09GzwWeHIkSMcJVNfdYfdDfW2bdGubKSWoY4mxnZxQGhkMnZFJKG7hyXXkTgncVMxePBgAEC/fv3q3c+yLBiGgVAolE4yQojS2LFjB9cRCMf2Rb2cVuHPh5aMplV80McDR++l4XxcFh5lFKGdHV3EUlQfffQRLl26hD59+sDCwoKmhnAsv7QKJ2LSAQDBNP9faoID+AiNTMaFR1lIfVEGRzN9riNxSuKm4tKlS7LIQQghREk9zizC3ZQCaPIYTPCR3QF17laGGOZth79jMvDDpef4cWoXmb0WaZnQ0FAcPnwYw4YN4zoKAXDwlgBVL9c7dXYy5TqOyvCwNkJ3Dwtcf56HvVEp+GxwW64jcUriy0lBQUFvvEniypUrGDFiBOzt7cEwDI4dO1bv+yzL4quvvoK9vT309PTQu3dvPHz4UNLIhBAZy8vLwwcffABPT09YWlrC3Ny83k0SVBeUz/6oFADAAE8bWBvpyvS1PujjAaB2F5vn2SUyfS3SfObm5nB3d5fKc1FNaBmhiMWeG7UjiTNoQbHUzfB3AVDbuFVUq/dsHYlHKgCgoqICMTExjR5oM3LkyCY/T2lpKTp27IhZs2Zh3LhxDb6/fv16bNy4ETt37kTr1q3xzTffYMCAAXjy5Amd6k2IApk+fTri4+MxZ86cFm81TXVBuZRXCXHkXhoAYIocFn+2szPGAE8bnI/LwsbzT7BxYifoamnI/HWJZL766iusWLECO3bsgL5+y6aEUE1ombDH2UgrKIepvhZGdrTnOo7K6d/OGvYmukgvrMDJmAyM6yq70VqFx0ro9OnTrJWVFcswTIMbj8eT9OnEALBHjx4Vfy0SiVhbW1t23bp14vsqKipYExMTdtu2bU1+3sLCQhYAW1hY2OxshKgCWf4uGBoastHR0VJ/XqoLiu/QrRSW/9nfbM9vw1ihUCSX17wveMHyP/ub5X/2N9vhq7Psir8esI8ziuTy2qpElr8HnTp1Yo2MjFhDQ0PWy8uL7dy5c71bc1FNkNz0326w/M/+ZtecjOM6isr6IewZy//sb3bk1qtcR2mxlvwuSDxS8eGHH2LChAlYvnw5bGxkd3BKYmIiMjMzMXDgQPF9Ojo6CAoKQkREBP7v//6v0Z+rrKxEZWWl+OuioiKZZSSE1JLXVtNUFxTP/pu1U58m+zqBx5PPtIoOjqZYO9YbP4Q9R1pBOXZGJGFnRBK68s3w/aROcDJX78WSimD06NFyeR2qCW8Wn1OCq89ywTDAdH86QVtWJnVzwuYLz3A/tRDRggJ0UtN1KxI3FdnZ2Vi0aJFMGwoAyMzMBIAGr2NjY4Pk5OTX/tzatWuxcuVKmWYjhNT3008/4fPPP8fy5cvh5eUls62mqS4ollcXaI+X85D/FF9nTPRxwtVnOdh/MwUXHmXjTvILrDv9GD9OowXcXFuxYoVcXodqwpvtfrmNbN821tRsy5CloQ6Gd7DDkXtpCI1MQienTlxH4oTEC7XHjx+Py5cvyyBK4/49N5t9uXXt6yxduhSFhYXim0AgkHVEQtSeqakpCgsL0bdvX1hbW8PMzAxmZmYwNTWFmZmZ1F+P6oJi2PdygfbA9rJfoN0YDR6D3m2s8b8ZPjjwrj8A4PKTbLVfLKmOqCY0VFpZg8N3UgHQNrLyMCOgdiTo7/sZyCupfMujVZPEIxU//PADJkyYgKtXrzZ6+N38+fOlEszW1hZA7VUIO7t/DlLKzs5+4yiJjo4OdHToUBdC5GnatGnQ1tbGvn37WrxQ+02oLiiO8iohjt6V3wLtt+nqbAZbY11kFlXg+vNc9Gsn29F08mZCoRCbNm3CoUOHkJKSgqqqqnrfz8/Pl8rrUE14vaP30lBcWQNXSwP0pIPZZK6Tkyk6OJogJrUQB28LMK+3B9eR5E7ipmLfvn04e/Ys9PT0cPny5XofHhiGkVpT4erqCltbW5w/fx6dO3cGAFRVVSE8PBzffvutVF6DECIdDx48wL1799CmTRuZvg7VBcVx4n46iitr4Gyuj+7u3H9g4fEYDGxvg9DIZJx9mElNBcdWrlyJ3377DYsWLcKyZcvwn//8B0lJSTh27BiWL18utdehmtA4lmURGpkEAJjhz5fbeid1xjAMggNc8Mkf97H3Rgre7ekGTRkdBKqoJG4qvvzyS6xatQqff/45eLyWvVklJSV4/vy5+OvExERER0fD3Nwczs7O+Pjjj7FmzRq0atUKrVq1wpo1a6Cvr4+pU6e26HUJIdLl4+MDgUAglaaC6oLiYVkWNxPzcTv5BR6mFyI2rRCC/NqF+VN8nRXmA8ug9rYvT7fNhlDEQkNBcqmjvXv34tdff8WwYcOwcuVKTJkyBe7u7ujQoQNu3Lgh0QVIqgmSu5GQj6dZJdDX1lDvLU7lbHgHO6w+GYe0gnJcfJyNQe1tuY4kVxI3FVVVVZg0aVKLGwoAuH37Nvr06SP+etGiRQCAkJAQ7Ny5E0uWLEF5eTnmzZuHFy9ewM/PD+fOnaN9pwlRMB999BEWLFiATz/9tNFpkR06dGjyc1FdUDynYjPxwb67De73tDPG5G5OHCRqnK+rOUz0tJBfWoXbSfnwc7PgOpLayszMhLe3NwDA0NAQhYWFAIDhw4dj2bJlEj0X1QTJ7b6RBAAY3dkBJnpab34wkRpdLQ1M6uaMbeHx2B2ZrHZNBcOyLCvJDyxcuBBWVlb44osvZJVJqoqKimBiYoLCwkKp7UBDiDKS5e9CYxcZGIYRL5YUChVr4SzVBclM/y0K157nopuLGfq3s4G3gwna25vARF/xPqwsOhSNI3fTMLu7K5aP8OQ6jkKT5e9BmzZtEBoaCj8/P/Ts2RPDhg3D559/joMHD+Kjjz5Cdna2VF+vpVSpJmQUlqPHt5cgFLE483FPtLVV7r+PshHklyHov5cgYoELi4LgYW3IdSSJtOR3QeKRCqFQiPXr1+Ps2bPo0KFDgyuSGzdulPQpCSFKLjExkesIREbSC8pxPT4XALBhQic4Wyj2tpQDPW1x5G4azsVlYtnwdjLbNIC82ZgxY3Dx4kX4+flhwYIFmDJlCn7//XekpKRg4cKFXMdTafujUiAUsfBzNaeGggNO5vro184G5+OysOdGMr4a2Z7rSHIjcVMRGxsrXgz14MGDet+j4k2IeuLz6VAlVXUsOg0sC/i6mCt8QwEAQa2toKvFQ+qLcsRlFKG9vYn4e2VVNTh6Lw392trA1kT+W+Cqk3Xr1on/PH78eDg6OiIiIgIeHh4YOXIkh8lUW2WNEPteHkgZHODCbRg1FhzAx/m4LPx5JxWfDGoDQx2JP24rJYn/lpcuXZJFDkKICoiLi2t0+0j6EKGcWJYV73M/rqsDx2maRk9bA71aWeFcXBbOPswSNxXVQhH+b/cdXH2Wi6iO+dgypTPHSdWLv78//P39uY6h8s48yERuSRVsjHUwsD3tgMaV7u6WcLMyQEJOKY7eTcUMNWnw1KN1IoTIVEJCAsaMGYPY2FjxWgrgn9FLRVtTQZrmfmoh4nNKoavFw1Bvu7f/gIIY1N4W5+KycO5hJhYNaA2WZfHl0Qe4+qx2GtfNROmckUDqO378eJMfSxcaZCP05Qna0/z40FKz7UwVCY/HINifj69OxCE0MhnT/flqMZunWU3FrVu38McffzR6RfLIkSNSCUYIUR4LFiyAq6srLly4ADc3N9y8eRN5eXlYvHgxvvvuO67jkWaqG6UY1N4WRrqKtyj7dfq1s4YGj8HjzGIk55XixP10HLwtQN0Os5lFFUgvKIe9qR63QVXM6NGjm/Q4Rdy8QRU8SCvEneQX0NJgMNlXcXZlU1fjujriv2ef4Fl2CSIT8hCoAOf5yJrEbeyBAwfQvXt3xMXF4ejRo6iurkZcXBzCwsJgYmLy9icghKicyMhIrFq1ClZWVuDxeODxeOjRowfWrl0rtQMxiXxV1ghx/H46AGBcF+Xa595UXxt+ruYAgKVHYvHduacAgJUj26OdXe3C1XspBVzFU1kikahJN2ooZKPusLshXnawNqI1Q1wz0tXCmC6100ZDI5I5TiMfEjcVa9aswaZNm/D3339DW1sbmzdvxqNHjzBx4kQ4OzvLIiMhRMEJhUIYGtZum2dpaYn09NoPo3w+H0+ePOEyGmmmsEfZKCyvhq2xLrp7KN8Vtrr94SPi8wAA7/Zyw4wAF3RxNgMA3E15wVk2VWVubo68vNr3e/bs2SguLuY4kfooKKvCX9G1dTckkDbOUBR1i+XPP8pCekE5t2HkQOKmIj4+HsOGDQMA6OjooLS0FAzDYOHChfjll1+kHpAQovi8vLwQExMDAPDz88P69etx/fp1rFq1Cm5ubhynI81x+G7t1KfRnR2U8mTqVxepDvW2xeeD2wIAOjubAqCmQhaqqqrEh9zt2rULFRUVHCdSH4duC1BZI4KnnbG4cSbca21jBH83cwhFLPZFpXAdR+YkXlNhbm4uvvrg4OCABw8ewNvbGwUFBSgrK5N6QEKI4vvyyy9RWloKAPjmm28wfPhw9OzZExYWFjh48CDH6YikcksqcflJDgBgvJLs+vRvdiZ6WNi/NdILyrFyVHvwXjZGdR+4HqYVobJGCB1NDS5jqpSAgACMHj0aXbt2BcuymD9/PvT0Gl+3sn37djmnU11CEYvdN2qn14QEqseCYGUSEuCCGwn52H8zBR/181DpmiNxU9GzZ0+cP38e3t7emDhxIhYsWICwsDCcP38e/fr1k0VGQoiCGzRokPjPbm5uiIuLQ35+PszMzOh/cEror+h01IhYdHQ0gYe1Eddxmm1B/1YN7uNb6MPcQBv5pVV4mF5EV3WlaM+ePdi0aRPi4+PBMAwKCwtptEIOLj/JhiC/HCZ6WhjZUTkvAqiyAZ42sDPRRUZhBU7FZmBMZ+VaoyYJiZuKH374QVwkli5dCi0tLVy7dg1jx47FsmXLpB6QEKKczM3NuY5AGrHyxEPkl1bhv+M7Qluz4QzYGqEIB2/VDtOP66p6//NjGAadnUxx8XE27ia/oKZCimxsbMSH3rm6umL37t2wsLDgOJXqq9tGdqKPI/S0VfcquLLS1OBhqq8zNpx/itDIZGoqXvXqBwUej4clS5ZgyZIlUg1FCFF8Y8eObfJjaatpxZBfWoUd15MAAI5mevh0UNsGj/n1aiKeZpXAWFcTIzvayzmhfHThm+Hi42zcExRwHUVlJSYmch1BLSTmliL8aQ4YBpjuTwu0FdVkX2dsCXuGeykFiEktQAdHU64jyUSTmoqioqImP6GxsXGzwxBClAdtIa18nmb9sxvPz5fj0a+dTb0r9c+yirHpfO32q8tHtIepvrbcM8pD3WLte8m0WFuWLl68iIsXLyI7Oxsikaje92hNhXTsfjlK0aeNNfgWBhynIa9jZaSDYd52OBadjtDIZHw3wZTrSDLRpKbC1NT0rfOiWZalA20IUSNjxozB4MGDoa2tmh88VdGrTYWIBT45dB8n5/eEnrYGaoQifPJnDKqEIvRpY4VxXVR3bnZHR1PwGCC9sAKZhRWwNaE9/aVt5cqVWLVqFXx8fGBnZ0drq2SgrKoGf9wRAABmBNAohaKbEeCCY9HpOH4/Hf8Z2g5mBqr3/84mNRWXLl2SdQ5CiJIZM2YMMjMzYWVlBQ0NDWRkZMDa2prrWOQNnmTWNhXT/Jxx4VEWEnJL8e2Zx/hqZHv8fi0R9wUFMNLVxNqxHVT6Q6CBjiba2BrjUUYR7qW8wBBvO64jqZxt27Zh586dmDFjBtdRVNaxe+korqgB30IfQa2suI5D3qKLsym8HIzxIK0IB24J8H5vd64jSV2TzqnYvHkzOnfujKCgICQnJ8Pf3x9BQUGN3ggh6sHKygo3btwA8M9IJVFsdSMVvq7m+HZcBwDAzogk7L6RjA0vpz0tG+6pFlfuu9B5FTJVVVWFwMBArmOoLJZlxSdoz/Dni7dMJoqLYRgE+7sAAPbcSIZQxHIbSAaa1FT8/fff4j3oZ82aJT7chhCivt577z2MGjUKGhoaYBgGtra20NDQaPRGuMeyrHikorWNEXq3scZUP2cAwLJjD1BVI0JQaytMUMEdnxrTWXyydgG3QVTU3LlzsW/fPq5jqKxbSS/wOLMYulo8TOjqxHUc0kQjO9nDVF8LaQXluPQ4m+s4Utek6U9t27bF0qVL0adPH7Asi0OHDr12QXZwcLBUAxJCFNNXX32FyZMn4/nz5xg5ciR27NgBU1NTrmOR18gqqkRRRQ00eAzcrGoXdP5naDtcfZYDQX45jHQ0sXast9qMONWNVMSmFaKqRtTo9rqk+SoqKvDLL7/gwoUL6NChA7S0tOp9f+PGjRwlUw27Xo5SjOnsABN9rTc/mCgMXS0NTPJxwv+uJGBXZBL6e9pwHUmqmtRUbNu2DYsWLcLJkyfBMAy+/PLLRv/HwzAMNRWEqJG2bduibdu2WLFiBSZMmAB9fX2uI5HXePJy6pOrpYH4RFcDHU1sndIFy/96gHm9PWBv2vjpx6rI1dIApvpaKCirRlxGETo5mXIdSaXExMSgU6dOAIAHDx7U+566NK6yklVUgbMPMgEAM15OpyHKY7o/H79cTcDVZ7lIyCmBm5Uh15GkpklNRWBgoHjuNI/Hw9OnT2lBJiFEbMWKFVxHIG/x9OXUpzY29U/I7uRkiuMf9uAiEqfqDsG79CQHd5NfUFMhZbTBi+zsi0pBjYiFr4s5PO1pG39l42Suj75trHHxcTZ230jGihHtuY4kNRKP9yYmJsLKinYZIIT8IysrCzNmzIC9vT00NTVpTYUCqhupaP2vpkKd1Z3RQYfgEWVRVSPCvpu1J97TNrLKKzjQBQDw5+1UlFbWcBtGiiQ+UZvP56OgoAA3b95s9EAbmv5EiPqZOXMmUlJSsGzZMtqTXkHV7fzUxlZ1htpbqm6x9p2kfFx+ko0HaYWITStEfE4pQgL4mBHgwm1AJTN27Fjs3LkTxsbGGDt27Bsfe+TIETmlUi1nHmYip7gS1kY6GNTelus4pJl6eljCxUIfSXllOHovTWVOQ5e4qThx4gSmTZuG0tJSGBkZ1fvwQGsqCFFP165dw9WrV8VzqIliEYlYcVNBIxX/6OhkAublIXgzd9yq972vTz5Cn7bWcDSjdUJNZWJiIv5MYGJiwnEa1bT75QLtqX7OtLmAEuPxGMwIcMHXf8dhd2Qypvk5q8TFOImbisWLF2P27NlYs2YNLcokhAAAnJycwLKqt+e2qhC8KENFde0OR3wLA67jKAwjXS0MaGeDc3FZcLM0gJeDCbwdTHD6QQbuphTgv2efYPPkzlzHVBo7duxo9M9EOuLSi3Ar6QU0eQym+jpzHYe00Piujvju7BM8ySpGVGI+/N0suI7UYhI3FWlpaZg/fz41FIQQse+//x6ff/45/ve//8HFxYXrOORfnmaVAABaWRtCgw7Jqud/M7qiskYEXa1/1v4EuFtgxA/X8Fd0OmZ1d6VF3EQh7L6RBAAY7GULa2PVP6BS1ZnoaWF0Zwfsv5mC3ZHJKtFUSDx2NmjQINy+fVsWWQghSmrSpEm4fPky3N3dYWRkBHNz83o3wi3xegqa+tQAwzD1GgoA8HIwwZjODgCAb/6Oo1E4wrnCsmocvZcGAAh5uciXKL/gl4vtzzzMRGZhBcdpWk7ikYphw4bh008/RVxcHLy9vRscaDNy5EiphSOEKIfvv/+e6wjkDcQnadtSU9FUnw5qg1OxGbid/AJnHmRiiLcd15GIGvvjjgAV1SK0tTWCD9+M6zhEStrZGcPX1Rw3E/OxLyoZiwa24TpSi0jcVLzzzjsAgFWrVjX4HsMwEAqFLU9FCFEqISEhXEcgb0AjFZKzM9HDuz3dsCXsOdaefoy+7azFhwYSIk8iEYvdN5IB1I5SqMKCXvKPkACX2qbipgAf9m2l1AvwJU4uEolee6OGghD1UVRUVO/Pb7oR7lQLRYjPqV1TQSMVkvm/IHdYGekgJb8MuyOTuY5D1FT4sxwk55XBWFcTozrZcx2HSNnA9jawMdZBbkklTj/I4DpOi0g8UkEIIQBgZmaGjIwMWFtbw9TUtNGrZyzL0ggmx5JyS1EtZGGoowl7E1rcKQkDHU18OrANlhyOweaLzzCqkwOsjHS4jqWwtmzZ0uTHzp8/X4ZJVEtoRBIAYKKPE/S16WObqtHS4GGqLx+bLjxFaGQyRnVy4DpSszXpX+eWLVvw7rvvQldX961FgwoFIeohLCxMvAj70qVLHKchr/PPSdqGNG2iGcZ1dcTOiCTEZRRh3t472DvXX6mnJ8jSpk2bmvQ4hmHos0ITJeWW4vLTHABQmQPSSENT/Jzww6VnuJP8Ag/SCuHloJznvDSpqdi0aROmTZsGXV3dNxYNKhSEqI+goKBG/0wUy9PMupO0aepTc2jwGGyZ0hljfryOW0kv8NWJh1gzxpvrWAopMTGR6wgqZ8+NZLAs0LuNFVws6YwZVWVtpIvBXnY4cT8doZFJWD++I9eRmqVJTcWrhYKKBiGEKI+6kYpW1tRUNJeHtSG2TOmM2btuYV9UCjztjOmqMZG58iohDt0WAPhn61GiukIC+DhxPx1/Rafji6HtYKqvzXUkidHkPEIIUWF1B9/RSEXL9GlrjSWD2uLbM4/x1fGH8LA2VInDqmQpNTUVx48fR0pKCqqqqup9b+PGjRylUh5/RaehqKIGzub6CGptzXUcImNd+WbwtDNGXEYRDt0W4N1e7lxHkhg1FYQQoqIqqoVIyisFALSm7WRb7L0gN8RlFOHE/XTM23sXx+Z1h7OFPtexFNLFixcxcuRIuLq64smTJ/Dy8kJSUhJYlkWXLl24jqfwWJbFrpc7js3w50ODR+uhVB3DMAgJ5OOzw7HYfSMZc3q4Kd1/d1ptRgghKup5dglYFjA30IalofINpSsahmGwflwHeDkYI7+0Cr2/u4TRP17HxvNPcSc5HzVCEdcRFcbSpUuxePFiPHjwALq6ujh8+DAEAgGCgoIwYcIEruMpvNvJL/Aoowg6mjxM8HHkOg6Rk5EdHWCipwVBfjkuP8nmOo7EaKSCECIVNTU1uHz5MuLj4zF16lQYGRkhPT0dxsbGMDQ05DqeyssprsTCg9F4mlUMe1M9OJjqoby6ditf2vlJevS0NfDLDB+8v/cu7gsKEP3ytuXiM9ga62LPXD94WNO/90ePHmH//v0AAE1NTZSXl8PQ0BCrVq3CqFGj8P7773OcULGFvhylGNXJXinn1pPm0dPWwEQfR/x6NRG7IpPRr50N15EkQk0FIaTFkpOTMXjwYKSkpKCyshIDBgyAkZER1q9fj4qKCmzbto3riCotv7QK03+LEi/Kzi6uRLSgQPz9trbGHCVTTfamevjrg+5ILyjH1Wc5uPI0F1ef5SCzqALrTj/GbyE+XEfknIGBASorKwEA9vb2iI+PR/v27QEAubm5XEZTeNlFFTgdW3sIWnCAC7dhiNxN9+fjt2uJuPI0B4m5pXBVol2/mtRUxMTENPkJO3To0OwwhBDltGDBAvj4+OD+/fuwsPhn8eqYMWMwd+5cDpOpvsKyasz4vbahsDbSwYaJHVFaKURaQTnSC8pRVlWDOT1cuY6pkuxN9TCpmzMmdXNGfE4JBmwMx4VHWYhNLYS3o3LuMy8t/v7+uH79Ojw9PTFs2DAsXrwYsbGxOHLkCPz9/bmOp9D23UxBjYhFV76Z0p5XQJqPb2GA3q2tcOlJDnZHJmP5CE+uIzVZk5qKTp06gWEYsCzb6Pfrvkcn5xKinq5du4br169DW7v+MD2fz0daWhpHqVRfcUU1gnfcxMP0IlgaamPfO/409YYj7laGGNXJAUfvpeH7C0/x+8xuXEfi1MaNG1FSUrvz2FdffYWSkhIcPHgQHh4eTT4kTx1VC0XYF5UCgLaRVWfBgS649CQHf9wR4JNBrZXmJHWJz6kghJB/E4lEjV5QSE1NhZER7TokC6WVNZi14xbuCwpgpq+FvXOpoeDaR3098Fd0Gi4+zsZ9QQE6OplyHYkzbm5u4j/r6+vjp59+4jCN8jj7MBPZxZWwNNTBEC87ruMQjgS1sgLfQh/JeWU4di8dU/2cuY7UJE3a/YnP5zf5RghRPwMGDMD3338v/pphGJSUlGDFihUYOnQod8FU1L2UFxjxwzXcTn4BY11N7J7jR+dQKAA3K0OM6Vy7U8+mC085TqM4SkpKUFRUVO9GGle3QHuqnzO0NWmDTnXF4zGY8fKAzdDIpNfOFFI0zR5PiYuLa/RAm5EjR7Y4FCFEuWzatAl9+vSBp6cnKioqMHXqVDx79gyWlpbiHWBIy1XWCPH9hWf4X3g8RCxgbaSDX4J9aN61ApnfzwPHotNw+UkO7qa8QBdnM64jcSIxMREffvghLl++jIqKCvH9NFX69R5lFOFmYj40eAym+irHlWkiOxO6OuG7c0/wOLMYt5JewNfVnOtIbyVxU5GQkIAxY8YgNja23jqLuu0KpVkovvrqK6xcubLefTY2NsjMzJTaaxBCWs7e3h7R0dHYv38/7t69C5FIhDlz5mDatGnQ09OT6mupa12ITS3E4j+ixSdkj+nsgBUjPGm7SQXDtzDA2M4O+ONOKr6/8Ayhs325jsSJadOmAQC2b98OGxsbmW9prAp1oW6UYnB7W9ia6HKchnDNRF8Lozs54MAtAXZFJqlmU7FgwQK4urriwoULcHNzw82bN5GXl4fFixfju+++k3rA9u3b48KFC+KvNTQ0pP4ahJCW09PTw+zZszF79myZv5a61YXMwgpM/F8kyquFsDTUxjejvTHYy5brWOQ1PurbCkfvpeHK0xzcSX6Brnz1G62IiYnBnTt30KZNG7m9pjLXhcLyahy7V7upBS3QJnVmBPBx4JYAZx9kIquoAjbGit1sStxUREZGIiwsDFZWVuDxeODxeOjRowfWrl2L+fPn4969e9INqKkJW1v6nychiuz48eON3s8wDHR1deHh4QFXV+lta6pudeHMgwyUVwvR1tYI+97xh7kBjU4oMmcLfYzr4oiDtwXYfFE9Ryu6desGgUAg16ZCmevCn3dSUV4tRBsbI6W4Ik3ko729Cbq5mOFW0gvsi0rBwgGtuY70RhI3FUKhUHw6rqWlJdLT09GmTRvw+Xw8efJE6gGfPXsGe3t76OjowM/PD2vWrKm3q8S/VVZWig/cAUALwmRkd2QSLj/Jkfjn6hYf9WptJYNUhCujR49udNvpV7eb7tGjB44dOwYzs5ZftVW3unAuLgsAML6rIzUUSmJeH3ccvC3AtWc5KCyrhom+FteR5Oq3337De++9h7S0NHh5eUFLq/7fXxZnWklSFxSpJohELHZHJgEAggP5Mp8qRpTLjACX2qbiZgo+6OOh0Av4JW4qvLy8EBMTAzc3N/j5+WH9+vXQ1tbGL7/88sb/qTeHn58fQkND0bp1a2RlZeGbb75BYGAgHj58WO+ArVetXbu2wbxKIl0peWVYfvwhmrsZwcO0QlxZ0geaGor7i0Ekc/78efznP//B6tWr4etbe1X25s2b+PLLL7Fs2TKYmJjg//7v//DJJ5/g999/b9FrqVtdKCirQlRiPgBggKcNx2lIU/EtDOBhbYjn2SW4Hp+Lod7qtT1oTk4O4uPjMWvWLPF9sjzTStK6oEg14erzXCTllcFIRxOjOzlwHYcomMHtbWFlpIOc4kqcfZiJER3tuY70Wgwr4T5VZ8+eRWlpKcaOHYuEhAQMHz4cjx8/hoWFBQ4ePIi+ffvKKitKS0vh7u6OJUuWYNGiRY0+prGrD05OTigsLISxsbHMsqmTNace4ZcrCejsbIop3STboWLdmcfIL63CtuldaU64nBUVFcHExEQmvwteXl745ZdfEBgYWO/+69ev491338XDhw9x4cIFzJ49GykpKVJ9bVWvC0fupmLRoftoa2uEMx/34joOkcCqE3HYfj0Rk7s5Yd046V+ZbylZ1gRPT0+0a9cOS5YsaXShtqy3oH9bXVCkmjBn5y1cfJyNWd1dsGJEe7m+NlEOm84/xeaLz9DNxQx/vBf49h9ogZbUBYlHKgYNGiT+s5ubG+Li4pCfnw8zMzOZD9kZGBjA29sbz549e+1jdHR0oKOjI9Mc6qy8SoiDtwQAgA/7eKBfO8munCblleKny/EIjUyipkKFxMfHN1p8jI2NkZCQAABo1aoVcnNzpf7aql4Xzj2snfo0kEYplE6v1pbYfj0RV57miK/Qq4vk5GQcP34cHh4enLz+2+qCotQEQX4Zwp5kA4D4XAJC/m2qnzN+vPQct5JeIC69CJ72inkxTCrzT8zNzeVSLCsrK/Ho0SPY2anXMLIiOXE/HYXl1XA000PvNtYS//w0fz54DBARn4fn2cUySEi40LVrV3z66afIyflnnU1OTg6WLFmCbt26Aaid7+zo6Cj111blulBRLcSVZ7Xv6cD21IQrGz9XC2hr8pBeWIH4nBKu48hV3759cf/+fc5eX1nqwp4byWBZoFdrK7hZGXIdhygoG2NdDHp5IXb3jSRuw7yBxCMVFRUV2Lp1Ky5duoTs7GyIRKJ63797967Uwn3yyScYMWIEnJ2dkZ2djW+++QZFRUUICQmR2muQpmNZFjsjkgDUXlHR4EneSDqY6qF/Oxuci8tCaGQyVo3yknJKwoXff/8do0aNgqOjI5ycnMAwDFJSUuDm5oa//voLQO2pusuWLWvxa6lTXbj+PBdlVULYm+iivYJemSKvp6etAT9Xc1x9lovwp7nwsFafU89HjBiBhQsXIjY2Ft7e3g0Wakv7oFxlrAvlVUIceDnyH0yjFOQtQgJccDImA0fvpeHzwe0UcvMHiZuK2bNn4/z58xg/fjx8fX1lOkKRmpqKKVOmIDc3F1ZWVvD398eNGzdkPheTNO5uygvEZRRBR5OHiT5OzX6ekEAXnIvLwuE7qfh0UBsY6SreLwaRTJs2bfDo0SOcPXsWT58+BcuyaNu2LQYMGAAer3ZAdPTo0VJ5LXWqC3VTnwZ4yv7wMCIbvVpZ4eqzXFx5moM5PaS3rbKie++99wAAq1atavA9WSzUVsa68OrIf5+2ko/8E/XSzcUMbW2N8DizGH/cEWBuT+lujiQNEjcVJ0+exKlTp9C9e3dZ5KnnwIEDMn8N0nS7ImpP+xzZ0R5mLdjWMtDdAu5WBojPKcXRe2kIDnCRUkLCJYZhMHjwYAwePFimr6MudUEoYnHh0cv1FDT1SWn1am2F1aceISoxDxXVQuhqKc+BbC3x71kMsqZsdYFlWex6uY1sc0f+iXphGAbBAS744mgsdt9IxuzuruAp2L8biZsKBwcHGBmpzxAuqZVdXIHTDzIA1I40tETdL8aK4w+xKyIJM/xpX25VUFpaivDwcKSkpKCqqqre9+bPn89RKuV1N+UF8kqrYKyrSYdhKbHWNoawNdZFZlEFbiXlo2crOqOHAHdTCvAwveUj/0S9jO5sj7WnHyE5rwzhz3LQpxlrW2VJ4qZiw4YN+Oyzz7Bt2zaFHlYk0nXgpgDVQhZdnE3h5WDS4ucb28UB6888RnxOKSLi89Ddw1IKKQlX7t27h6FDh6KsrAylpaUwNzdHbm4u9PX1YW1tTU1FM5x/eeBdv3Y20KIzXZQWwzDo2coSf9xJxZWnOSrdVGzZsgXvvvsudHV1sWXLljc+Vt1rQujLUYqWjvwT9aKvrYmJPk74/VoidkcmK39T4ePjg4qKCri5uUFfX7/B4qv8/HyphSOKoVoowt6o2qlP0pqqZKSrhbFdHLH7RjJCI5OoqVByCxcuxIgRI/Dzzz/D1NQUN27cgJaWFqZPn44FCxZwHU/psCyLsw8zAdCBd6qgV2url01FLv4zjOs0srNp0yZMmzYNurq62LRp02sfxzCMWjcVOcWVOBVbO/JP03+JpKb78/H7tURcepKNlLwyOFvocx1JTOKmYsqUKUhLS8OaNWsaPdCGqJ7zcVnIKqqEpaE2hnhLb253cAAfu28k41xcFrqtviDxz4/oYI/lIzylloc0X3R0NP73v/9BQ0MDGhoaqKyshJubG9avX4+QkBCMHTuW64hK5Vl2CZLzyqCtyUOv1qp7ZVtd9PCwBMMAT7KKkVlYAVsTXa4jyURiYmKjfyb1HbiZgmohi87OpvB2bPnIP1EvrpYGCGpthfCnOdh9Iwn/GaY4n4MkbioiIiIQGRmJjh07yiIPUUC7Xm4jO7mbM3Q0pbfIsJWNEfq1tcbFx9nIKa58+w/8y/briZjm7wx32tubc1paWuILDDY2NkhJSUG7du1gYmIi9RO0VRXLssgqqkRsWiH+vFO7zWQPD0sY6khcpomCMTPQRgdHU9wXFODKsxy1mENfXl4OPT29Rr+XkZGh8OdHyErtyH9tTQwOoCnkpHlCAvkIf5qDQ7dTsWhAG+hpK8YGEBL/36pt27YoLy+XRRaigB5nFiEqMR8aPAbT/J2l/vzbZnRFQk4pRCwr0c+tPf0YV57mYHdkMr4a2V7quYhkOnfujNu3b6N169bo06cPli9fjtzcXOzevRve3t5cx1NoJZU1WPHXQ4Q/zUFuSf3mmk6dVx1BrSxrm4qn6tFUdO7cGfv27UOXLl3q3f/nn3/i/fffr3dQpjo5H5eFzKIKWBhoY6i3ejZWpOWCWlvDyVwPgvxyHL+fhkndpP/5rDkkXv23bt06LF68GJcvX0ZeXh6Kiorq3Yhq2R1Zu5ZioKcN7Ewav+rUEloaPLSxNUI7O2OJbnX7vR++k4rSyhqp5yKSWbNmjfjK49dffw0LCwu8//77yM7Oxi+//MJxOsX27enHOHw3FbklleAxQBsbI4zr4og1Y7wxtrMD1/GIlNRNY7v2PBdCkWQXUZTRgAEDEBgYiHXr1oFlWZSUlGDmzJkICQnB8uXLuY7HmboF2lN8pTvyT9SLBo/BjJcHJu6KSAYr4YVZWZF4pKJuD/p+/frVu59lWZkcaEO4U1RRjaP30gAo3mKynh6WcLU0QGJu7VkX0+k0Us6wLAsrKyu0b187YmRlZYVTp05xnEo53EzMx+4btY375smdMNDTVmGGsYl0dXIyhZGuJgrKqnEv5QV8XBrfJlgoYpFZVAEHU+lfxJGnrVu3YtiwYZg1axZOnjyJ9PR0GBsb49atW/D0VJw54PL0JLMYNxJqR/6n+inGlWWivCb6OGHDuaeIyyjC3ZQX6MrnfutxiZuKS5cuySIHUUCH76SirEqI1jaG8Hfj/h/rq3g8BtP9+fj67zjsjkzGND9n2jSAIyzLolWrVnj48CFatWrFdRylUVEtxOeHYwAAk3ycMKoTjUqoMk2N2kX3J2MyMGfXbawb640h/5r+EpdehKVHYnA/tRBfj/YSX4lUVgMHDsTYsWPx888/Q1NTEydOnFDbhgL4Z5RioKcN7JW8aSTcM9XXxqhO9jh0OxW7IpKVs6kICgqSRQ6iYEQiVjz1aUaAi0J+YB/f1RHfnX2CJ1nFiErMh7+bBdeR1BKPx0OrVq2Ql5dHTYUEtlx8hoTcUlgb6eCLYe24jkPk4D9D20GQX4aY1EK8v/cuJvk4YfkIT2jwGHx/4Rl+vZognhr1+9UETFfiiyXx8fGYOnUqMjMzcfbsWYSHh2PUqFGYP38+Vq9e3WA7elX36sj/DFqgTaQkOMAFh26n4vSDDGQXt4O1Ebc7yzVpTUVMTAxEIpH4z2+6EdVw7XkuEnJLYaijiTEKOq/bRE8LY7rUZqu7AkS4sX79enz66ad48OAB11GUwsP0QvzvSgIAYNUoL5joqdcHLHVlb6qHw+8HYl5vdzAMcPC2AMO3XsOg769gW3g8hCIWQ7xsYaijiaS8MkQlKu+5T506dYKrqyvu37+PAQMG4JtvvkFYWBiOHDkCX19fruPJXd3IfytrQwTQBTAiJV4OJujibIpqIYsDNwVcx2naSEWnTp2QmZkJa2trdOrUCQzDNLoohNZUqI7Ql6MU47s6KvSWlsEBfOyLSsHZh1kqvf+7ops+fTrKysrQsWNHaGtrN9hKkg7F/EeNUITPDsdAKGIx1NuWdnhSM1oaPCwZ3Ba9Wlth4cFoJOaWAgDsTHSxapQXBnjaYOmRGOy/KcDBWwKlHYH96aefMGPGjHr3BQYG4t69e/j444+5CcWRV0f+gwMVc+SfKK+QQBfcTYnG3qhkvN/bHVoaEu/BJDVN+rSYmJgIKysr8Z+JahPkl+Hi4ywAUPgF0G1tjeHrao6bifnYF5WMRQPbcB1JLX3//fdcR1Aav11LxIO0IpjoadF2yGrM380CZxb0wobzT2Cgo4kP+niIL+BM9HHC/psCnIrNwFcj2yvlSNa/G4o6RkZG+P333+WchlvX4xV/5J8oryFedvja8BGyiipx7mEWhnXgbqviJjUVfD6/0T8TxZdbUokaoWRbjW2/ngiWrT14y8Na8Q+WCw7g1zYVNwWY5OsMDQmvApkbaENbk7vOXhWEhIRwHUEpZBSWY/OFZwCAL4dxP/+VcMtEXwurRnk1uL+Tkyna2BjhSVYxjkenYYaC7b4nibi4OKSkpKCqqkp8H8MwGDFiBIep5GtXhHKM/BPlpK3JwxRfJ2wNe45dkUmK31T829OnT3H58mVkZ2eL11rUUef9pxXNhnNPsDXsebN/XllO+xzU3hbWRjrILq5E93VhEv+8o5kezi8Moq08Wyg+Ph47duxAfHw8Nm/eDGtra5w5cwZOTk7i7WbV3ZpTj1FeLYQP3wzjuzpyHYcoKIZhMLGbE77+Ow4HbwuUsqlISEjAmDFjEBsbW2/KdN3UH3WZKp36ogxhSjLyT5TXVD9n/HQ5HjcT8/E4swhtbY05ySHx5dlff/0Vnp6eWL58Of78808cPXpUfDt27JgMIpLmKK6oxvZrtVPVNHkMtDQkuwW4WaBfOxuO/xZNo6XBw6IBraGvrSHx35NhgNQX5fgrOo3rv4ZSCw8Ph7e3N6KionDkyBGUlJQAqN3YYcWKFRynUwxRCXk4cT8dDAN8NbI9zasmbzSmswO0NXh4kFaEB2mFXMeR2IIFC+Dq6oqsrCzo6+vj4cOHuHLlCnx8fHD58mWu48nNnhspECnRyD9RTnYmehjUvvYzW92aWC5IPFLxzTffYPXq1fjss89kkYdIydF7aSitEsLdygAXFgWp/AeYyb7OmOwr+WFCv15JwOpTj7ArMhmTujmp/PskK59//jm++eYbLFq0CEZGRuL7+/Tpg82bN3OYTDHUCEVYcfwhAGCqrzO8HEw4TkQUnbmBNga0t8HJmAwcui1Qun8zkZGRCAsLg5WVFXg8Hng8Hnr06IG1a9di/vz5uHfvHtcRZa6iWoiDt1IAKM/IP1FeM/xdcCo2E0fvpuGzwW05WYsl8UjFixcvMGHCBFlkIVLCsqy4Uw1W0DMmFMUEH0foaPLwKKMId5JfcB1HacXGxmLMmDEN7reyskJeXh4HiRTL/lsCPM4shomeFhbTZgKkiSZ3cwIAHLuXhopq5ZouJBQKYWhYe2Xe0tIS6enpAGrXZT558oTLaHLzd0wGXpRVw8FUT2lG/ony8nczR2sbQ5RXC3H4TionGSRuKiZMmIBz587JIguRksj4PDzPLoGBtgbGdqGdJt7EVF8bo1+eZLyLwyFDZWdqaoqMjIwG99+7dw8ODur9b/BFaRU2nKv9ELV4YGuYG2hznIgoi+7ulnAw1UNRRQ3OPMjkOo5EvLy8xGdX+fn5Yf369bh+/TpWrVoFNzc3jtPJHsuy2BWRBACY5u8MDR5d3COyxTAMgl+uv9p9IxkikWSb9EiDxNOfPDw8sGzZMty4cQPe3t4NTsWcP3++1MKR5tn18iC4sV0cYaSrfFsRytuMAD4O3hbgdGwGsoe1g7Ux7cgjqalTp+Kzzz7DH3/8AYZhIBKJcP36dXzyyScIDg7mOh6nNpx/goKyarS1NcLUZkzRI+qLx2MwwccR3194hoO3BBitRNuRfvnllygtrT2D45tvvsHw4cPRs2dPWFhY4ODBgxynk71oQQFi0wqhrcnDJB8nruMQNTGmswO+Pf0YibmluPY8F71aW8n19SVuKn755RcYGhoiPDwc4eHh9b7HMAw1FRxLKyjH+bjanSZoDmfTeDmYoCvfDHeSX2D/TQEW9G/FdSSls3r1asycORMODg5gWRaenp4QCoWYOnUqvvzyS67jceZBWiH2RdXOqf5qZHtocngoEVFOE3ycsPniM0Qm5CE+pwTuVsqx2HfQoEHiP7u5uSEuLg75+fkwMzNTiym5dVOQR3Swh4WhDsdpiLow0NHEuK6O2BmRhNDIJMVvKujwO8W2LyoZIhYIcLNAKxujt/8AAVDbgN1JfoG9UcmY14fbEymVkZaWFvbu3YtVq1bh3r17EIlE6Ny5M1q1Ut8GTSRi8eWxBxCxwPAOdkp7MjLhloOpHvq1tcaFR9kIjUjCykbOtVAW5ubmXEeQi9ySSpyMqZ0OShf3iLzNCOBjZ0QSLj7OhiC/DE7m+nJ7bTqFRYVU1ghx4KYAABASSIVMEnUnUmYXV+Lsw0wM72DPdSSlEh4ejqCgILi7u8Pd3Z3rOAph/60URAsKYKijiWXDPbmOQ5TYzEBXXHiUjT/vpOKTQW0Uelrr7Nmzm/S47du3yzgJdw7eEqBKKEJHJ1N0dDLlOg5RM+5WhujZyhJXn+ViT1Qylg5pJ7fXblJTsWjRInz99dcwMDDAokWL3vjYjRs3SiUYkdyp2AzklVbBzkQX/WmnCYloa/Iw1dcJW8KeIzQymZoKCQ0YMAC2traYOnUqpk+fDi8v5b2aKg05xZX49vRjALWLs21onQ5pge4eFvCwNsTz7BL8eScVs7q7ch3ptXbu3Ak+n4/OnTuLD7xTJzVCEfbcqJ36FEKjFIQjwQEuuPosFwdvCbCwf2voasnncN8mNRX37t1DdXW1+M+vow7zJOUhv7QKd5JfSFyQf7taOzVtmp8zzd1uhql+fPz48kTKAzdTJN6lx95UT+n2kpeW9PR0HDhwAPv378f69evh5eWF6dOnY+rUqXB0VL+To9eceoSiihp4ORhjBp2iS1qIYRiEBLpg2bEHCI1MRkiAC3gKupvQe++9hwMHDiAhIQGzZ8/G9OnT1WbaEwBceJSNjMIKmBtoY6i3HddxiJrq29YaDqZ6SCsox/H76Zgop80CGFbFLyUUFRXBxMQEhYWFMDbm5thySbAsixE/XMODtKJm/by2Bg8RS/vCkhaGNcsHe+/iZGzDrVGbgmGAo/O6o5OCDnfL63chMTER+/btw/79+/H48WP06tULYWFhMnu95pDlexERn4upv0aBYYBj87rT9AciFaWVNfBfexHFFTXYMasb+rSxbvFzyur3oLKyEkeOHMH27dsRERGBYcOGYc6cORg4cKDCXnyU1nsx9dcbiIjPw7ze7lgyuK0UExIimW3h8Vh3+jG8HIxx4sMeTf7da8nvAq2pUDBRifl4kFYEbU0evOwl+4/JMAxGd7KnhqIFFg1sjaKKapRW1kj0c1lFlUgrKMfO64n4fnJnGaVTDq6urvj888/RsWNHLFu2rMEucaqsskaIL489AABM9+NTQ0GkxkBHExN9nPD7tUTsvJ4klaZCVnR0dDBlyhRMmTIFycnJ2LlzJ+bNm4fq6mrExcWJD8VTNc+yihERnwceA0yjEUrCsUk+Tth4/ikepBXhnqAAXZzNZP6aTW4qaPGVfIS+PGNifFdHrBnjzW0YNeRuZYjdc/wk/rnY1EKM+OEaTsZm4D/DPGFlpJ6N3fXr17F37178+eefqKiowMiRI7FmzRquY8nNr1cSkJBTCktDHXwyiE7OJtIVHMDH9uuJCH+ag4ScErgpwfayDMOAYRiwLAuRSMR1HJna/XItRf92NnAw1eM4DVF3ZgbaGNnRHn/eSUVoRJJcmoomT7zfuXMnLl26hIKCArx48eK1N9J8mYUVOPuQzphQRt6OJujkZIpqIYuDt1K4jiN3X3zxBVxdXdG3b18kJyfj+++/R2ZmJvbs2YMhQ4ZwHU8unmcXY0vYcwDAl8PawURPcXfoIcqJb2GAvi9HKOrOQVBElZWV2L9/PwYMGIA2bdogNjYWP/zwA1JSUlR2lKK4ohqH76QCAEICXbgNQ8hLIS9P2D4Vm4mc4kqZv16TRyrUffGVPOyLSoZQxMLX1RxtbRV//QepLySQj+iDBdhzIwXvBbmr1WL5y5cv45NPPsGkSZNgaWlZ73vR0dHo1KkTN8HkRChi8ckfMaiqEaF3GyuM6kS7hxHZCAl0wcXHtdvLLh7YWuG2l503bx4OHDgAZ2dnzJo1CwcOHICFheqf0XLkbhpKq4RwtzJAoLvq/32JcvB2NEFnZ1PcSynAwVsp+LCvbM+OavKnnp9++gkZGRn47LPPcOLECTg5OWHixIk4e/asWm4bJ21VNSLsqztj4mVnSZTLUG87WBhoI7OoQnyqubqIiIjABx98IG4oCgsL8dNPP6FLly7o2rUrx+lk7/drCYgWFMBIRxNrx3or7GJUovx6eFjCzcoAJZU1+F94AtdxGti2bRuMjY3h6uqK8PBwvPPOOxg7dmyDmyphWVY8dTkk0IV+/4lCqZv5sjcqBTVC2U5BlOhSat3iq/PnzyMuLg7t27fHvHnzwOfzUVJSIquMauH0gwzkllTCxlgHA9vTGRPKSEdTA5N9a7dtU+SpCbIUFhaG6dOnw87ODlu3bsXQoUNx+/ZtrmPJ1PPsEnx37ikAYNlwT9iZ0FxqIjs8HoP3etUeMPnDpedYeeIhhCLFubAXHByMPn36wNTUFCYmJq+9qZKI+DzE55TCQFsDYzo7cB2HkHrqLnhmFFbgwiPZXvBs9u5P6rT4Sh7qPoRO9eVDS42mzaiaaX58/Hw5HpEJeXiaVYzWNkZcR5K51NRU7Ny5E9u3b0dpaSkmTpyI6upqHD58GJ6eqn2StFDEYsmf91FVI0Kv1laY4KN+Z3IQ+Zvg44j8siqsO/0YO64nIb2gHN9P6gw9bfkccPUmO3fu5DqC3O2KSAIAjOvqqHDT0QjR0dTAFF9n/HDpOXZFJGOwl+zOT5Ho06s6Lr6ShwdphbiT/AJaGgym+MnngBIiG/amehjoaQvgn528VNnQoUPh6emJuLg4bN26Fenp6di6dSvXseRmx/VE3E0pgKGOJtbRtCciJwzD4L0gd2yd0hnaGjycfZiFKb/eQG6J7BdikvrSCsrFV39pgxWiqKb6OYPHQHzBU1aa3FTMmzcPdnZ2+PbbbzF8+HCkpqbijz/+wNChQ8Hj0ZX1lqj78DnEyw7WRrrchiEtFhxY+z+WI3fTUFRRzXEa2Tp37hzmzp2LlStXYtiwYdDQ4P5Kqbwk5pbiv2efAKjd7cmetpAkcjaioz32zPWDiZ4WogUFGPdzBF6UVnEdS63svZEMEQt097CAh7Xqj0wT5fTqBc/dMpye3eTpT9u2bYOzs7N48dXrDrQ6cuSI1MIpmytPc3AzMV+in2HB4q/odAB0lUNVBLhZoJW1IZ5ll+CzP2PgLuFe8sZ6mggJdIGOpuJ/QL969Sq2b98OHx8ftG3bFjNmzMCkSZO4jiVzLMti6ZEYVNaI0LOVJSZ1oxFGwg1fV3McmReI4N9vIjmvDJ/8cR+/hfjQqJkcVFQLceBW7QYrM/xduA1DyFsEB/Jx5mEmDt9NxaeD28BYBlP1mtxUBAcHU5F6g+yiCszZdQvVwuYtmPO0M0ZXvuwPJiGyxzAMggP4WPbXQ5x+kNms56gRsZjX20PKyaQvICAAAQEB2Lx5Mw4cOIDt27dj0aJFEIlEOH/+PJycnGBkpHpX7/64k4obCfnQ1eJhzRia9kS45W5liF+Cu2LMTxG4+Dgbv19LxNyeblzHUnmnYjOQX1oFexNd9G+nuCecEwLUv+B55E4qZnZ3lfprNLmpUMfFV5LYdzMF1UIWrpYGCGptJdHPavAYTPRxog8mKmSyrzMKy6uRWyLZVISMwnKcfZiFvTdS8H+93KHBU45/E/r6+pg9ezZmz56NJ0+e4Pfff8e6devw+eefY8CAATh+/DjXEaUmt6QSq08+AgAsGtAaTub6HCciBGhvb4Jlwz2x7NgDfHvmMXxczNHJyZTrWCpt18tpJNP8+Wp1LhFRTgzD4N1ebohNK0RPCT+nNlWzd38i/6gWirAvqvYU5YUDWmNkRzr4St1pafCadchMRbUQAWsvIq2gHBcfZWFge1sZpJOtNm3aYP369Vi7di1OnDiB7du3cx1Jqr75Ow6F5dXwtDPGbBlc6SGkuab7OSMyPhenYjPx0f67+PujnnSyu4zcFxTgvqAA2ho8TKbpj0RJTPBxwgQf2f17pdZaCs4+zER2cSUsDXUwWAk/BBLFoaulgUndnAEo/1kXGhoaGD16tEqNUoQ/zcGx6HTwGGDtWG+6OkkUCsMwWDu2A5zM9SDIL8fSIzF0OK2M1NXn4R3sYGGow3EaQhQD/R9RCkIjXp4x4ecMbU16S0nLTHu59du157l4ni27rd+IZMqrhPjyWCyA2lNzO9LUEqKATPS08MOULtDSYHAqNhN7Xo6iE+nJK6nEiZjaDVZm0AYrhIjRJ+AWepRRhJtJ+dDkMZjm58x1HKICnMz10a9d7anqstz6jUjm+wtPIcgvh52JLhYPbMN1HEJeq6OTKT4b3BYAcOiWQKFO3FYFB28LUFUjQgdHE1q3QsgrqKlooboh0EHtbWFjTGdMEOmo21748N00lFTWcJyGHLotwP+uJAAAvh7lBUMdWo5GFNucHq5YObI9Dv1fgNJs+KAMhCIWe2/Ujv4EB7jQBiuEvEIpmoqffvoJrq6u0NXVRdeuXXH16lWuIwEACsuqcexeGgA6Y4JIV3d3S7hZGaCksgZH76ZyHUchyasunHmQgc8PxwAA3unpiv6eNjJ5HUKkiWEYhAS6QE9b8c+7kSZZ14WLj7KQVlAOM30tDO9gJ9XnJkTZKXxTcfDgQXz88cf4z3/+g3v37qFnz54YMmQIUlK4nyf6xx0ByquFaGtrBF9Xc67jEBXC4zEI9q9tVHdFJtNiy3+RV1249iwX8/dHQ8QCk3yc8MXQdlJ9fkKI9MijLtTNTpjUzRm6WurVsBHyNgrfVGzcuBFz5szB3Llz0a5dO3z//fdwcnLCzz//zGkukYjFnhu1xYWGQIksjOvqCANtDTzPLkFkQh7XcRSKPOrCneQXeHf3bVQJRRjqbYs1Y+mQO0IUmazrwvPsElx7nguGAa2hJKQRCj0xuKqqCnfu3MHnn39e7/6BAwciIiKi0Z+prKxEZWWl+OuioqI3vkZaQTmWHomVOFtFlRBJeWUw0tXE6M50LgWRPiNdLYzp4oA9N1Lw2eEYuFoavvVndszspvLzp+VRFx5lFGHWjpsoqxKiV2srbJrUSeXfV0KUmaR1QdKaAEB8IbFfWxs69JKQRih0U5GbmwuhUAgbm/pzmG1sbJCZmdnoz6xduxYrV65s8muUV9XgytOcZmec3M0J+toK/TYSJRYS4IJ9USkQ5JdDkF/+1sfXTpNS7Q+/8qgLP12OR1FFDXz4Ztg2vQt0NGmaAyGKTNK6IGlNqKgW4vCd2vVtIYG0hpKQxijFp+F/TzlgWfa10xCWLl2KRYsWib8uKiqCk9PrTw+0MtLFhgkdm5VLV0sD/dpZN+tnCWmKVjZG+PP9QCTmlDbp8Tw1mp4jy7rw3/EdYGeiiw/6eNBFA0KUSFPrgqQ1QVdLA0c/6I4T99PR3d1SeoEJUSEK/X9LS0tLaGhoNLjKkJ2d3eBqRB0dHR3o6DT9dEsTPS2M6+rYopyEyFIXZzN0cTbjOobCkEdd0NXSoEXZhCgRSeuCpDUBADysDbFwQOsW5SRElSn0Qm1tbW107doV58+fr3f/+fPnERgYyFEqQgiXqC4QQv6N6gIh3FPokQoAWLRoEWbMmAEfHx8EBATgl19+QUpKCt577z2uoxFCOEJ1gRDyb1QXCOGWwjcVkyZNQl5eHlatWoWMjAx4eXnh1KlT4PObtlCqbn//puzsQIgqq/sdUIUzL6guENJyqlQTgJbVBaoJhNRqSV1gWFWpJq+Rmpr6xsVXhKgbgUAAR0f1XkdEdYGQf1BNoJpAyL81py6ofFMhEomQnp4OIyOj1+4MU7frg0AggLGxsZwTqhd6r+Xn3+81y7IoLi6Gvb09eDyFXk4lc1QXFAe9z/JDNeH1qCYoFnqv5UeadUHhpz+1FI/Ha3KnZWxsTP945YTea/l59b02MTHhOI1ioLqgeOh9lh+qCQ1RTVBM9F7LjzTqgnpfmiCEEEIIIYS0GDUVhBBCCCGEkBahpgK1h+CsWLFC4oNwiOTovZYfeq9bht4/+aD3WX7ovW4Zev/kh95r+ZHme63yC7UJIYQQQgghskUjFYQQQgghhJAWoaaCEEIIIYQQ0iLUVBBCCCGEEEJahJoKQgghhBBCSIuofVPx008/wdXVFbq6uujatSuuXr3KdSSlt3btWnTr1g1GRkawtrbG6NGj8eTJk3qPYVkWX331Fezt7aGnp4fevXvj4cOHHCVWDWvXrgXDMPj444/F99H73DxUF6SLagJ3qC5ID9UF6aK6wB2Z1QVWjR04cIDV0tJif/31VzYuLo5dsGABa2BgwCYnJ3MdTakNGjSI3bFjB/vgwQM2OjqaHTZsGOvs7MyWlJSIH7Nu3TrWyMiIPXz4MBsbG8tOmjSJtbOzY4uKijhMrrxu3rzJuri4sB06dGAXLFggvp/eZ8lRXZA+qgncoLogPVQXpI/qAjdkWRfUuqnw9fVl33vvvXr3tW3blv388885SqSasrOzWQBseHg4y7IsKxKJWFtbW3bdunXix1RUVLAmJibstm3buIqptIqLi9lWrVqx58+fZ4OCgsRFgt7n5qG6IHtUE2SP6oJ0UV2QPaoLsifruqC205+qqqpw584dDBw4sN79AwcOREREBEepVFNhYSEAwNzcHACQmJiIzMzMeu+9jo4OgoKC6L1vhg8++ADDhg1D//79691P77PkqC7IB9UE2aO6ID1UF+SD6oLsybouaEotqZLJzc2FUCiEjY1NvfttbGyQmZnJUSrVw7IsFi1ahB49esDLywsAxO9vY+99cnKy3DMqswMHDuDu3bu4detWg+/R+yw5qguyRzVB9qguSBfVBdmjuiB78qgLattU1GEYpt7XLMs2uI8034cffoiYmBhcu3atwffovW8ZgUCABQsW4Ny5c9DV1X3t4+h9lhy9Z7JDNUG2qC7IDr1nskN1QbbkVRfUdvqTpaUlNDQ0GlxlyM7ObtCpkeb56KOPcPz4cVy6dAmOjo7i+21tbQGA3vsWunPnDrKzs9G1a1doampCU1MT4eHh2LJlCzQ1NcXvJb3PTUd1QbaoJsge1QXpo7ogW1QXZE9edUFtmwptbW107doV58+fr3f/+fPnERgYyFEq1cCyLD788EMcOXIEYWFhcHV1rfd9V1dX2Nra1nvvq6qqEB4eTu+9BPr164fY2FhER0eLbz4+Ppg2bRqio6Ph5uZG77OEqC7IBtUE+aG6IH1UF2SD6oL8yK0utHgpuRKr2yLu999/Z+Pi4tiPP/6YNTAwYJOSkriOptTef/991sTEhL18+TKbkZEhvpWVlYkfs27dOtbExIQ9cuQIGxsby06ZMoW2iZOCV3dzYFl6n5uD6oL0UU3gFtWFlqO6IH1UF7gli7qg1k0Fy7Lsjz/+yPL5fFZbW5vt0qWLeCsz0nwAGr3t2LFD/BiRSMSuWLGCtbW1ZXV0dNhevXqxsbGx3IVWEf8uEvQ+Nw/VBemimsAtqgvSQXVBuqgucEsWdYFhWZZt8bgKIYQQQgghRG2p7ZoKQgghhBBCiHRQU0EIIYQQQghpEWoqCCGEEEIIIS1CTQUhhBBCCCGkRaipIIQQQgghhLQINRWEEEIIIYSQFqGmghBCCCGEENIi1FQQQgghhBBCWoSaCtIsM2fOBMMwYBgGWlpasLGxwYABA7B9+3aIRCKu4xFCOEB1gRDyKqoJ6oWaCtJsgwcPRkZGBpKSknD69Gn06dMHCxYswPDhw1FTU8N1PEIIB6guEEJeRTVBfVBTQZpNR0cHtra2cHBwQJcuXfDFF1/gr7/+wunTp7Fz504AwMaNG+Ht7Q0DAwM4OTlh3rx5KCkpAQCUlpbC2NgYf/75Z73nPXHiBAwMDFBcXCzvvxIhpIWoLhBCXkU1QX1QU0Gkqm/fvujYsSOOHDkCAODxeNiyZQsePHiAXbt2ISwsDEuWLAEAGBgYYPLkydixY0e959ixYwfGjx8PIyMjuecnhEgf1QVCyKuoJqgolpBmCAkJYUeNGtXo9yZNmsS2a9eu0e8dOnSItbCwEH8dFRXFamhosGlpaSzLsmxOTg6rpaXFXr58WeqZCSGyRXWBEPIqqgnqhUYqiNSxLAuGYQAAly5dwoABA+Dg4AAjIyMEBwcjLy8PpaWlAABfX1+0b98eoaGhAIDdu3fD2dkZvXr14iw/IUT6qC4QQl5FNUH1UFNBpO7Ro0dwdXVFcnIyhg4dCi8vLxw+fBh37tzBjz/+CACorq4WP37u3LniYc0dO3Zg1qxZ4kJDCFENVBcIIa+imqB6qKkgUhUWFobY2FiMGzcOt2/fRk1NDTZs2AB/f3+0bt0a6enpDX5m+vTpSElJwZYtW/Dw4UOEhIRwkJwQIitUFwghr6KaoJo0uQ5AlFdlZSUyMzMhFAqRlZWFM2fOYO3atRg+fDiCg4MRGxuLmpoabN26FSNGjMD169exbdu2Bs9jZmaGsWPH4tNPP8XAgQPh6OjIwd+GECINVBcIIa+imqBGOF7TQZRUSEgIC4AFwGpqarJWVlZs//792e3bt7NCoVD8uI0bN7J2dnasnp4eO2jQIDY0NJQFwL548aLe8128eJEFwB46dEjOfxNCiLRQXSCEvIpqgnphWJZluWlnCPnH3r17sWDBAqSnp0NbW5vrOIQQBUB1gRDyKqoJio2mPxFOlZWVITExEWvXrsX//d//UZEghFBdIITUQzVBOdBCbcKp9evXo1OnTrCxscHSpUu5jkMIUQBUFwghr6KaoBxo+hMhhBBCCCGkRWikghBCCCGEENIi1FQQQgghhBBCWoSaCkIIIYQQQkiLUFNBCCGEEEIIaRFqKgghhBBCCCEtQk0FIYQQQgghpEWoqSCEEEIIIYS0CDUVhBBCCCGEkBahpoIQQgghhBDSIv8PUz82+MMIaYsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Multiple plots in one figure\n",
+    "y_min = numpy.amin(data) - 0.5\n",
+    "y_max = numpy.amax(data) + 0.5\n",
+    "\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 3))\n",
+    "ax1.plot(data_min)\n",
+    "ax1.set_xlabel('Day')\n",
+    "ax1.set_ylabel('Minimal inflammation count')\n",
+    "ax1.set_ylim(y_min, y_max)\n",
+    "\n",
+    "ax2.plot(numpy.mean(data, axis=0))\n",
+    "ax2.set_xlabel('Day')\n",
+    "ax2.set_ylabel('Average inflammation count')\n",
+    "ax2.set_ylim(y_min, y_max)\n",
+    "\n",
+    "# Plot maximal inflammation count\n",
+    "#ax3.plot(data_max)\n",
+    "ax3.plot(numpy.amax(data, axis=0))\n",
+    "ax3.set_xlabel('Day')\n",
+    "ax3.set_ylabel('Maximal inflamation count')\n",
+    "ax3.set_ylim(y_min, y_max)\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "d35caedd-dc02-4feb-9256-d10387f66099",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7546afe97250>"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JCOEUSkpCCKdQUhJCOIWSkhDCKZSUhBBO0anUN6vKXSDd7mhmKgoGCUe4R8vyylmqnNVzC7ArTFoeI0v7OPR1Xg4m7RD31vHoeSw1KO0Q/9566M4iGl+94Ss0vuGlyTTeOpx77gAgNYerhc2HrWqHXH2zfH0phq8PsBVMy7cYrPWnXvYq5/v12g38/AOAmhxevdVSkpOquZJsqXXWdRSv8qTlO+2IIv4BulMSQjiFkpIQwimUlIQQTqGkJIRwCiUlIYRTdKrKkx3pMcU8OPHWZSkQ54ZeQ+N+K1UCdrVKq7Kg5ZWzsHqNxVvPySlcVbLUN6uvXcG3f07jh2Pc5Lb8/3G1DgCS6vicGnN4VcjmukurVHopjBjOjYhnnuNt5Stv4564UU/8hcYPPByl8URjm4ErV90yuaSOxv32PIwHU9+avSZswYuqPCmE6FwoKQkhnEJJSQjhFEpKQginUFISQjiFkpIQwik6lSHXkvc7giX9W6ZYq6RqMOzPuBkPq6lgj6p6Grdk5bRDfP2VY+3voNZkLmnXTeaPPEQ38McXvvXG39K41cQx2TDdAsALNz3Lxzh0D43/zc07afy5x+6i8foHzphjv7d5CF/mNl7SddDLfN/6lf6txyAA/496WNK/ZTqHYTqPZ661DLnskRvPiwGXcAnrTkkI4RRKSkIIp1BSEkI4hZKSEMIplJSEEE7RqdS3juDXxGspEymGWdEy5MZSbMOhZbztyyvrmupbQoSPHdzNla66yXbp2WFGadjEpadovDKDq1Ohvb1pfOCMEhr/S73/RoeWKXb5CB5vncu3u+8G3jgTANLf440qq27lilYsha/LKk2cuYmfZ5b6C8Q3eTNMg62hPFvncjzHvlVuWs0ohRBdBiUlIYRTKCkJIZxCSUkI4RRKSkIIp+gS6pvlvwH8N6O0mkgGq7k3zFLGYql2g0yrlKzFyTt5U8HmOq5mnDH6YFrKGAAcuYeva1oy339/8/DzNP7D12bx9Vf0o/GUbfacvlC3kC9j+Ar7HOQ6UayMj5FiKGwAULWIq2zJa/rQeF2G1aSSz+nUrRk0bh07ALhuDS/R6/cch6G+WecyP/OvHrpTEkI4hZKSEMIplJSEEE6hpCSEcAolJSGEU3Qq9e2KNqM0Ph+s9td00sLyNgG2mmaNEXmDx+sm8+aLydEaGq8qt71T6cYy+1eNofF360bReNCobjn3sztofMPhyeac/ji5gMZXj5pI4//y58/RePJhvp9O3M6riQLA0GSuRB2Zy32FViPMYAW/xPoc5ONaccBW7K75nU/1zcBsuBrHjwfDK6rKk0KILoOSkhDCKZSUhBBOoaQkhHAKJSUhhFP4Ut/y8/Px/PPP48CBA+jVqxcmTZqEJ598Etdff33bZzzPw7Jly7B69WpUVVVhwoQJWLVqFUaPHn3FJ982ZhzvT2CcMa7V+8rAquJnVaos+fZnzHVdu8Hf2PGqETJq67hClFBvaY5AzgA+p71zjQWMqo0DdrfSeNHuSTT+9P9+2pyTxYaXuGI3wFCuKo1ebYkpdiXEyt/wypp9DS9b+n5D8TSqRVqft/xngK2OtQ7jaq51btbkRGjcr78TgOmjY73irkrlyaKiIjz00EPYsWMHCgsL0dzcjBkzZqC+/sNJL1++HCtWrEBBQQGKi4sRjUYxffp01NbW+hlKCNFN8XWntGnTpnZ/r1mzBgMGDMCuXbswZcoUeJ6HlStXYunSpZgzZw4AYO3atYhEIli3bh3mz59/5WYuhOiSXNZvStXV5/9t6tv3/K380aNHUV5ejhkzZrR9JhQKYerUqdi+fTtdR2NjI2pqatq9hBDdlw4nJc/z8Mgjj2Dy5MkYM+b8U7/l5eUAgEik/f+skUik7b0Lyc/PRzgcbnsNHsw7UgghugcdTkoLFy7EW2+9hV/96lcXvRcItC945XneRbEPWLJkCaqrq9tepaWlHZ2SEKIL0CHv28MPP4zf/va32Lp1KwYNGtQWj0ajAM7fMWVkfOjTqaysvOju6QNCoRBCId4H7UIsH1u8ypMWAZ+9rywF4tSXR9L4oFdsRfD4XMP7ZmgBfQ/wCol9X7a9W4x4VQ3ffZJ72WrnGtUZZ/JealZ1y/SDLTT+rTf+1pxTawVXPO+dtY3Gf7/6NhofMZxXbLSqYQJAShmfr6VQWSqbtc/rM/n5l34wxZxTLMW4hxjIz4NrDPXN2gbr3GdK2gd05Nr7OHzdKXmeh4ULF+L555/Hn/70J2RlZbV7PysrC9FoFIWFhW2xWCyGoqIiTJrEJWEhhPgovu6UHnroIaxbtw4vvvgiUlNT234nCofD6NWrFwKBABYtWoS8vDxkZ2cjOzsbeXl56N27N+67776rsgFCiK6Fr6T01FNPAQCmTZvWLr5mzRrcf//9AIDFixfj7NmzWLBgQdvDk5s3b0Zqqs9q+UKIbomvpOR58bqKnycQCCA3Nxe5ubkdnZMQohsj75sQwimUlIQQTtGpyuFaxJMsLUOuZW6MhfkuSTXk1Vgql/crJtjNKC2sErqWgfK0IcsP+QU33p4ay0u2AkDvhe/ReLRgoLkM4/483qRy1aGpNH73kP3mut4b1ofG/1BilOI1zLKW9J9wmBuXAeDkFL6u7F9y2TzdkNOtpqTWYx7xqBrBH52xDN7W4yrBOm6aDu3h6+EPR5zHdyPMS0B3SkIIp1BSEkI4hZKSEMIplJSEEE6hpCSEcIqAdylPRH6C1NTUIBwOYxruQmKgvVrUkWaUfrFKi1qYRl2jcSBgl0I9OpubOnuX8QoLTT4fko8U24pP1aI6Gvc2cZVywFdLaPzgYb7d0248QOMn622V8i/1vAxw8po+NF7/wBka97sNAHBsOy+h05zCL5fMrTx+YiZXuga9zO8Hkkv4cQCA+iHcrJu6JU4HS0LjzcN8fT6054j5nmVsZ4p4s9eELXgR1dXVSEuzG6PqTkkI4RRKSkIIp1BSEkI4hZKSEMIplJSEEE7RJbxvHcFSDSws5cNfm8j4WCpbTTZXcLJ/yevnnriDb1vJN20X05CVfPtKvtlA4w2GOjVt+n/QuFVu9/68F805HW3sT+NF4FVMqw8b6qyx/25Ktn1blWW8GWVKGV+XrZrx8sAW1nkGxCmH65N4ahrDKkMNAInGdcSUcs+LAfaq2tCdkhDCKZSUhBBOoaQkhHAKJSUhhFMoKQkhnKJTqW8daUZpVcZrSfenm6XtraDxk3dyr1y8yoJNYd5kMfIGn2uTUb3w8Pd5JcmEw/7tjCdu5w0Noxu42jT1cd4Q8oVf8YaQ3zMqUhb80xxzTmduaqLxEUaVzLTfcMUscOf7NL7lLV6ZEQD6GPFyqzkn+LHos43Hq4wmlVbTTgCIpXJ11q+SbFVdtVS5eJ5T8/oi12qLx4/nhehOSQjhFEpKQginUFISQjiFkpIQwimUlIQQTtGp1DeLeL2nLGXCUsB6HuNKjYWlmMXzMNVl8L5sNdlcqcncaigyB3kfsPK5vB/c8J/Y6kfl43yZ4Kt8O6zea0ncjodt1dk03pBhK4WJKXy+S4b+gcbzv/pFGh9/zXEa/3XdOHPsyBvc83fmJu5lu/5p/vkeVbyXmqXaBqubzTk1jeDnjV8slc2quurtettcl6XMyfsmhOgyKCkJIZxCSUkI4RRKSkIIp1BSEkI4RZdQ3+JhKXMhI14zjZuSrN5a9TkRGre8cuff4/G+B7gn6dgsrsoNfYmrU6G9XCHyltt9zpKe45Ukh+TxSpJ1q8bQ+OmZXIWymGRUqgSAvZVcDfreTx6k8YUPc3/duoe+ROPRsH36vzOf7/Nh67kX0Kr2mVTL45bq2Je3xwNg++IsH6fVk7DWOMct5S9p3GhzTgFjjHiK+MehOyUhhFMoKQkhnEJJSQjhFEpKQginUFISQjiFkpIQwim6xCMBfsuBAraMasmi73+Zl05N319D45bhEgCCtVwOTnkvRuOZW/nnK8ZzQ26kmJdsrazl5WIBAKk8vN+Q/itv4/vJOqGWZb5M40MSbePylv78O3P+3vk0/txjd9F4EHyuljEaAEYM5yV33xvP96G1z5Oqz9F4xQR+zp4eyY8pADQZxyjZeNKjxnhcxTrH/Rp1AZXDFUJ0A5SUhBBOoaQkhHAKJSUhhFMoKQkhnKJLqG/NR46Z71nlOpuMhnxXCkspAYCgUTLW4uQU3oQwIcLNr0mvcJUj8gZXggCgalQajddn8rH7vMkNqwsffonGV5+eSOPF719rzikzmSs7i/96I42ve5UbbxOXcnN0oN5uSNr8I65cRcBVtpJvcrNscx03Rw94jSuqZ4wmlQAwYDc3A1ully1TuKXK9TRU7IQjvKQvYDeIVTlcIUSXQUlJCOEUSkpCCKdQUhJCOIWSkhDCKXypb0899RSeeuopHDt2DAAwevRo/MM//ANmzpwJAPA8D8uWLcPq1atRVVWFCRMmYNWqVRg92i6n6QdLSYuHpQ5YniSrhCjA1TpLteoIViPM7F9yleid+VzZAbj65i23S5T2AX/vdEU/voBRcvf/PDeHxmff+xqN/yWOAlb5G+4z21/L/XhnbufrmWSoeNb6AaBuoD9/YmsFb26aXM6/90+N5UqaVeIYsM+PU7dm0LilsvklnvctsYordp9YOdxBgwbhiSeewM6dO7Fz5058/vOfx1133YW33z7fQXP58uVYsWIFCgoKUFxcjGg0iunTp6O21qcGLoTotvhKSrNmzcIXv/hFjBgxAiNGjMCPfvQjpKSkYMeOHfA8DytXrsTSpUsxZ84cjBkzBmvXrkVDQwPWrVt3teYvhOhidPg3pZaWFqxfvx719fWYOHEijh49ivLycsyYMaPtM6FQCFOnTsX27dvN9TQ2NqKmpqbdSwjRffGdlPbt24eUlBSEQiE8+OCD2LhxI0aNGoXy8nIAQCTS/v/YSCTS9h4jPz8f4XC47TV4MG/1I4ToHvhOStdffz3efPNN7NixA9/5zncwb9487N+/v+39QKC9LcHzvItiH2XJkiWorq5ue5WWlvqdkhCiC+Hb+xYMBnHdddcBAG655RYUFxfjpz/9KR599FEAQHl5OTIyPlQDKisrL7p7+iihUAihkF1t76NYSlrisKHmMoE4ygHjnOGJixmNC63Kk4CtysVSeZI+8HCUxq3Kkwn1fD2Vj/PKgv3NGQGBxVxFCd3BVbazUa4eJdbxOW14aTKNF877sTmnxwfMNN9jWFUyi+tu4AvE8SeeuYnvw+DL/DywjlEdF8Yw6BUu/sRTc+sG8pVZiqB1zsZrlOqXeL7TC/nEKk96nofGxkZkZWUhGo2isLCw7b1YLIaioiJMmjTpcocRQnQTfN0pPfbYY5g5cyYGDx6M2tparF+/Hlu2bMGmTZsQCASwaNEi5OXlITs7G9nZ2cjLy0Pv3r1x3333Xa35CyG6GL6SUkVFBb7xjW+grKwM4XAYN954IzZt2oTp06cDABYvXoyzZ89iwYIFbQ9Pbt68Gampce6ThRDiI/hKSs8++2zc9wOBAHJzc5Gbm3s5cxJCdGPkfRNCOEWnqjzZEe+b7WXzR91ArorErqD3rd9urlwll3ClpjWZK2O1db18j52zijcPM8QjbP137mccOok/0nHwMF/T1E3fM+eUfJhXt0w+yZWu0zPP0rjlS7MUMwBozOFKUV2G5TfknLmJryeljFeLtCp9AkD6QV7d0vLExQzvm+WJs1Q5b9fb5pws5Zt531R5UgjRKVFSEkI4hZKSEMIplJSEEE6hpCSEcIpOpb6Z3jejXxVgV8CzqulZPbQsLB9bShlXSgCgPrMHjTcYUlf6fh5PTOHKjqU2xSu1V7KSNxyz+plZHjerkmRCPd/meFgq26mxPN5ax9W6YUY1xyF5B82x6yv5+eGBq2+RN/h51vcAPxY9j/2FxivHWnonUJfB92HaXv75YDX371lqnXWtxPOWXk6FSQvdKQkhnEJJSQjhFEpKQginUFISQjiFkpIQwimUlIQQTtGpHgmwiCdLBozHBZrCXKpN3cJl4tppXDIP1vFx45UcTS7hsrk1pxN3GKVqDSm4MaeBxqMb7LLDQ/L4cwcnCnmJ2cV/vZHGC/6JN6N89QfLafxbh+4x57Tkr/7Al3njb2n8sZs30fi/Dr+Fxkse48cUALCIH9jAnVxOP5HKyyjXD+ePIwx6mZti+9hPKQCwDcQMq+Gqbcg1Rr0Ksn88dKckhHAKJSUhhFMoKQkhnEJJSQjhFEpKQgin6BLqm2XUBexmlKE9R2jcMurGUnj+thoBtqRzhQ2wVbaK8VwdG/QKVz/M9Rim0eQSW0XZ8tZIPvZu3nSyoIyrbDXZ/PNf++73afy6Rw23MYD56+bT+N2ztpnLMMZfc5zGd1bbRu7Y61xNa0rhChi3AgPXP82VUIvK2XZ55QHGsbDUsapbubk3WMfXYzVi5WdZfFiTyk+sGaUQQlxJlJSEEE6hpCSEcAolJSGEUygpCSGcolOpb1YzSsvfBgCwmlEay1jlcC3FwlLxGm8eZk6pbmCQxi2VrcpoeGnNyVKIrPUAQJ83eXnbYHUjX8AozXrv7a/T+O8P3UbjA3ueMeeUnHOaxje8NJnG5xqq3K/3j6PxvqPspp1JRu3gJqNa8tkoPxYW1rEY9CpXcwEgFuaXa7xzjZFcwn19lpobT0m2mr2ya1XNKIUQnRIlJSGEUygpCSGcQklJCOEUSkpCCKfoVOqbRUcqT1qKgt/KkxZWw7/z7/G4VRHQanhpfadY1QsttQ4ATo011LdarhRaZIV4k0WrYuN75/qY6/riEO6Ly8rmY/zrSV5hcsgvrEaYttJ1bJblZvPHO/O5DzFoFCY9M8Le35b3zcLyZVqKWcKRkzQez1vqGYo4W0beNyFEp0RJSQjhFEpKQginUFISQjiFkpIQwim6hPrWkcqTlgJxzvARWaqchaWkAUDlWP5d0Gx41tIO8fWcnnmWxlsruIcpsc7+Dpoy8T9ofFtkOI2npvCxVx2aSuNJG/rS+LIfPWvOafra/07j1n7qt5sriE2LuIcueU0fc+zwcH5OWdthKaSZm8ppPN75YWH1EvTbl81f9zggMG60+V7LrrdpXN43IUSXQUlJCOEUSkpCCKdQUhJCOIWSkhDCKbqE+mZVpAQAyy1kKRYhI+7X+2ZV9wOA3hncj5e5qczXGC1vcP/eYd5iDYNesjxgwP6DY2g8lMlVpZyvHKZxs5KkMafHT84052RVYUxcylWohq0DadxyXJ2cYnkKgbRNvAfaqbH8jOq3m6/H8lhaSlq8Ko+njD5u6fvtZRhmddXqZhpPqj5nritx2FAa96sIfhTdKQkhnEJJSQjhFEpKQginUFISQjiFkpIQwikuKynl5+cjEAhg0aJFbTHP85Cbm4vMzEz06tUL06ZNw9tvc3+MEEJcSIcfCSguLsbq1atx4403tosvX74cK1aswHPPPYcRI0bghz/8IaZPn4533nkHqamplzXZeMZbi8Qqo1GlUSbXkjL9NvCzZFcASClroXFL8o1XxpbDG0hWLbIfU7DMqbFa/hjBu0+OovEtM/lcp914gMbn9efNKwHgeyNvoPGG7YNpvHkKt5pel8zN17URuxnlmWReDjdYwS+ZU2P52Om8oq/J0dlxGoYannDLXG5inJuW9O8ZplsA4A8RxH9M5+Po0J1SXV0dvva1r+GZZ55B+kcG9zwPK1euxNKlSzFnzhyMGTMGa9euRUNDA9atW9fhSQohug8dSkoPPfQQvvSlL+GOO+5oFz969CjKy8sxY8aMtlgoFMLUqVOxfft2uq7GxkbU1NS0ewkhui++/31bv349du/ejeLi4oveKy8/XzsmEmlfKyYSieD48eN0ffn5+Vi2bJnfaQghuii+7pRKS0vx3e9+F7/85S/Rsyf/LQUAAoH2j+97nndR7AOWLFmC6urqtldpaamfKQkhuhi+7pR27dqFyspKjBs3ri3W0tKCrVu3oqCgAO+88w6A83dMGRkf/mhbWVl50d3TB4RCIYRCoY7MXQjRBfGVlL7whS9g37597WIPPPAARo4ciUcffRTDhg1DNBpFYWEhbr75ZgBALBZDUVERnnzyySs36wuI90t/PIMjI8FQ3/w28Kv49mfMMZqMcq7XreGG3ONzeUnfs1GudGX/hFtQT9zBTaYAkGTMqSabjxG48wxfUTlXjyaHeU3fYUn2b4jBWj6nvge4UffIPfzG/8xzXK2DUU4YAPq8ydU3Szm1sNRZK55UZ5uE+73OzzVLtbXw26QShukWAJqPHKPxy2lG6SsppaamYsyY9m7y5ORkXHPNNW3xRYsWIS8vD9nZ2cjOzkZeXh569+6N++67z89QQohuyhUvXbJ48WKcPXsWCxYsQFVVFSZMmIDNmzdf9jNKQojuwWUnpS1btrT7OxAIIDc3F7m5uZe7aiFEN0TeNyGEUygpCSGcIuB5nt/edFeVmpoahMNhTMNdSAy0V0A64qdpNZpRWupHLMz/o/XbCNAaFwAqJnDfXUMGPxSW58nyW2Vu5fGqEXY53PFf2UfjW94aSeOJKVxJmTyMl8ndv4qX27UaagLA3aN20XjhTybTuKUqWeVzA4sNXySAw9/n6lvCYe6Xs45R5W3cHTboZX4/EO8YpR/0p/zFUvgY1/yO+xADPv2g8ZZhqlyz14QteBHV1dVIS7M9frpTEkI4hZKSEMIplJSEEE6hpCSEcAolJSGEU3SqZpRW5cmOqHKhPUf4GzcP42MbHroqw3cUS7U9TE3Gw+0DdnOfWV0GV2Rak7myk1zSwOeUYiselso24DWr0iKf0/6Xucq24Uc/pvEjTfac8o990XyPcWwWV8zSfjOExhtm28JzaC8/fpYCZqlm1z/Nj8Whr/OTYOQ/cX8bANTkcFN76hYu/VkNVBuNc7znsffNsS0s7xu7Jj0vBlxC8VjdKQkhnEJJSQjhFEpKQginUFISQjiFkpIQwik6lfp2Ob2kLiSeN41heeUsIm/YfiFrXSduDxpLGJ44ozpi1SiuaNVn2oqg1ZdtW8pwGp9iedx2c/Vt6qbv0bil7gHAk7lP0/j/rPsWjV9343s0njmRH4ttR/i2AUCT4XGzVLZBr/AxrGNhHdN4VSSt41eXwaucZm7iSp6l4mEor0yaFKd6aw9DxL4cdKckhHAKJSUhhFMoKQkhnEJJSQjhFEpKQgin6BKVJ63qd/E4ZykN1edo3OrvZvmIKsbbDTatvm+W9+3kFFs1Y2T/spaPG0dBrBvIlT/Lw3fmJl558muf3UHjVrXIPvfbHZGPbef92mIR7vlLqLerNjLSDtnfyVa/u17l/r7HI8WNNG5VOI1XedJal4XfKqrWNWH6ROPAfKqqPCmE6JQoKQkhnEJJSQjhFEpKQginUFISQjiFkpIQwik6lSHXKoebGOeRAKuMrUX9kBQaD4a59G9J6ddusMuavvsAN11aZW+HvsSlYMvAa5VajSdn1w/3J/H/ev84Gn/vXB8a//ZjG2l8evK75pxuf+vvaXzNF56l8UfenkvjOQP4sdiSzEsAA0DyYW52tmT50yP5IyBH7uH7fMBrPJ7En+YAYJ9rwTr++IJVJrfFMKNb0r913V0tdKckhHAKJSUhhFMoKQkhnEJJSQjhFEpKQgin6FTqm2XI9ars0rMw1De/zSgt5aPf62U0fnyuXW73ujVcDTrwcJTGI29wk3Aswr9TghX8sAZvtZsNjjcUqqJlk2h88qP7aXz/Kl4Od+vY0TSeZ84IyNzKjcv5N/ImlZbKZpW9tcoJA0DfA/6MtJZBedh6o8HoQD5uQ4btj08p4+uKpfi7t7DM5ZaxPZ7N2VqGXZNqRimE6JQoKQkhnEJJSQjhFEpKQginUFISQjhFp1LfLA9OR5pUWqqBpa5c8zverBHGeuJ5mKyGg/1228swElO44gNDfUtfyX19AHByKd+OYDUvPWupbKdnnqXxRKO5Y+tw/nkAqMvoTeMnDvP9V/nmEBq/+9uv0fgLe28zx65aVEfjSRv60nifN7lGVTfQaDo5lsfjlei1VLb0/TXmMozaaSNo3DrW8ZpRervevuRxWzzjfL0A3SkJIZxCSUkI4RRKSkIIp1BSEkI4hZKSEMIpOpX61hF6VNXTuFWR0lIgLMXCqhaZUtZizska49gs7sUK1lmqGfdnWYpWbLfdIBM/itCwVVHR8mg9M+GfafyVUdz7Fo9fVdxK49NuNJTQG431vMrXMzROc8ekV/ilUTGBN+e0jnflWP69P/QlrkRZHkvA9lmaTSSN9VjnX89jtjfSZNhQGpb3TQjRZVBSEkI4hZKSEMIplJSEEE6hpCSEcIqA53l2qbsLyM3NxbJly9rFIpEIysvLAQCe52HZsmVYvXo1qqqqMGHCBKxatQqjR1+68lJTU4NwOIxpuAuJgfZqlOVxs3xswMdUpSS0Gj2xrH5wySXcI1U1Ks0cw/IqWf3aLFqTueKTUM8VwfBwW/qoPsz3rbWM5QGz6HN/KY03FBglGGGrRJY/0cJSwAa9GjOXKfkm37cp27gfz8KqYOlX1QSArBf8edysc9A6/5rCPWncrNIK+9prPnLs4pjXhC14EdXV1UhLs68P33dKo0ePRllZWdtr3759be8tX74cK1asQEFBAYqLixGNRjF9+nTU1sZxpwohxEfwnZQSExMRjUbbXv379wdw/i5p5cqVWLp0KebMmYMxY8Zg7dq1aGhowLp16674xIUQXRPfSenQoUPIzMxEVlYW7rnnHhw5cv7W7ujRoygvL8eMGTPaPhsKhTB16lRs377dXF9jYyNqamravYQQ3RdfSWnChAn453/+Z/zbv/0bnnnmGZSXl2PSpEl4//33235XikTaPxn80d+cGPn5+QiHw22vwYMHd2AzhBBdBV9JaebMmfjqV7+KG264AXfccQd+//vfAwDWrl3b9plAoP1j+J7nXRT7KEuWLEF1dXXbq7SU/yAqhOgeXJb3LTk5GTfccAMOHTqE2bNnAwDKy8uRkfFhZcDKysqL7p4+SigUQigUx5N1mVjqgOV9s1S2tL0VNG75joJ1vEcXYKsivcp58h70ClcQrbkGq7mvKqnaPtw5qy69giAAbJvJK0nePWoXjf9+Na/yuGfVz8wxHijhy1h93EJ7LWWMK1rxfGYA9w9aHreqEVzxTKrmPfuaUvk5P2C3fd74VceseqyWH9TqB2cp0vGWuRwu6zmlxsZG/Od//icyMjKQlZWFaDSKwsLCtvdjsRiKioowaRJvaCiEEBfi607p7//+7zFr1iwMGTIElZWV+OEPf4iamhrMmzcPgUAAixYtQl5eHrKzs5GdnY28vDz07t0b991339WavxCii+ErKZ04cQL33nsvTp06hf79++Nzn/scduzYgWuvvRYAsHjxYpw9exYLFixoe3hy8+bNSE3191CgEKL74isprV+/Pu77gUAAubm5yM3NvZw5CSG6MfK+CSGcQklJCOEUnaocrtWMElYcQKJRrtOSVy0TaE0Of6zBMuQmxfEBW639To3lv735nauFt9ye1Ja3RtK4VXo2NYVL5lbp2eu+WkLjWb/7ljmn/3HbSzS+/TBvhFk//NKaHX5ArIyXHwaA1gq+z8vnconf+rxtsrakf/5oAQAk1Rqll8O8VLP1GItF483DaLwjhlw2U5XDFUJ0SpSUhBBOoaQkhHAKJSUhhFMoKQkhnKJTqW8WlsIG2OVwQ0bcUiAspatiAlcfMjfZRsV3H8ig8aEv8fKsVpPKhAhXgqySrcHn7LIwA4z4FnBVLjGFK12JddxUnJnM93fmjbYiuDrvKzTex/h8Uw6vxZW8hi9RPrfBHDvxMDcc99nN4ynv8WNnlb21CNba5XAtk7d1blrnvnWOm80o45SbvhroTkkI4RRKSkIIp1BSEkI4hZKSEMIplJSEEE7RqdQ3qxnlJ4HVANFqNmiVyQXskqcnbuflWdMOcUWrBtxvFXmDqy6Vj/vzygFA8ut8O5pS+P5Y/NcbafxoY3/fY+Oh/6Bhqxzulaza1ZzCVbAzI/ixqLyNf78HK/h6rEaYfhttAv5VM2sMS5E2PadxuJxrVXdKQginUFISQjiFkpIQwimUlIQQTqGkJIRwik6lvlkqgF2rz26kZzXks5oH1g3kDSSDhnUrnopSl8FnfN0a7pezlLwzN/HvlHfmc+/b9ctsr9eJO/z5m+bO2kbjzz12F43XP3CGxr84ZL85xrtPjqLxkNH4MQa+3UmGkpZg+NsAoNVYxlLlghX8eGe9wP14FrEwbzAK2FVOraqofiuTWtdKAHYzSm+Xvyaml4LulIQQTqGkJIRwCiUlIYRTKCkJIZxCSUkI4RSdSn3riJ/GUtks6odw9eOa3/H+Z2X3fobGLU8cAGQa/bgsFcVi2HqurtQN5B66qlFcQQSASDGf78QVf6bxf/nz52h8xML3aHzqNcdpvPj9a8059TbWVbV5CI1/7/7naXzdQ1+i8T73233R/lLPu/N5m7gSGq9iJMOqWBoffm7GUvi9RXIJV5JheCb9XisA4F0FP6rulIQQTqGkJIRwCiUlIYRTKCkJIZxCSUkI4RSdSn3rCH57X6VuOUjj73+Z9z+zVDZLAQOApGqu7FgqitXvyxrD6kGWuNS/2vSHEu4/s7xeR1L60fgz2etpfMNLk805WT6zKV/ZR+N5m2bTeOLtvFpk81uDzLEtLP3SOkaW4tlklMlMPmmreNb5YR1vcz2GL5NrcvY1dLXQnZIQwimUlIQQTqGkJIRwCiUlIYRTKCkJIZxCSUkI4RSd6pGAT6IpXu20Eb4+b5XPrR8fMpeJpXIzZkOGVYKVS9q9ynkc4I8KBAsGmnOqncsfbZg87DCNv7u7D42fTOHC8te++30aTzKaOwJAcs5pGp/X/3Uan3znIRr/15O30Pix7YN9j53+EjfFHpuVROOtydw0nVBvFXHuyH2Ccbw70NiSEe+6Sxw2lMabjxy7eD1e0yWNpzslIYRTKCkJIZxCSUkI4RRKSkIIp1BSEkI4RadS3yziKWyWcmCpZj2P8ZKgVqnaprBlY7TJ3MSbTr77QAaNZ/+y1tf6D32duz0TInaJXqsx48kIVwpPzOQG1PQob75YVc7LyMYzoD45egONP/DH/0bjlknYUtJiEbtZY9LrfL6xcAuNZ27l25G29y80fnwub/CYfpCvH7CbUVolnK1mlKE9R2i8xWrcGuf6uhpmXd0pCSGcQklJCOEUSkpCCKdQUhJCOIVzP3R73vkfDJvRBFxiKy3PsyvvWY+2B1r4j75eK483N/EfxhOa+Y+JLY325JuNMVrP8TGajblatJ7jlgc0WH3AAJwzqjPWG3M9y9fV0sA/39JofD5m76f6Wv6jrzV26zl+OltzstYDAC2NfOzmJvuHaPp541hb+yPe+q3zoLmJb7d1bvYwrpdWY/0Jca4vC3bdNeN87INr3CLgfdwnPmFOnDiBwYNtT5IQonNTWlqKQYPsUsTOJaXW1lacPHkSqampCAQCqKmpweDBg1FaWoq0NLvDa1eiO24z0D23uztts+d5qK2tRWZmJhIS7F+OnPv3LSEhgWbRtLS0Ln/QLqQ7bjPQPbe7u2xzOPzx7cr1Q7cQwimUlIQQTuF8UgqFQnj88ccRCtlF07oa3XGbge653d1xmz8O537oFkJ0b5y/UxJCdC+UlIQQTqGkJIRwCiUlIYRTKCkJIZzC6aT0s5/9DFlZWejZsyfGjRuH11577dOe0hVl69atmDVrFjIzMxEIBPDCCy+0e9/zPOTm5iIzMxO9evXCtGnT8Pbbb386k71C5OfnY/z48UhNTcWAAQMwe/ZsvPPOO+0+0xW3+6mnnsKNN97Y9uT2xIkT8fLLL7e93xW3uaM4m5R+/etfY9GiRVi6dCn27NmD2267DTNnzkRJScmnPbUrRn19PXJyclBQUEDfX758OVasWIGCggIUFxcjGo1i+vTpqK31Vx7XJYqKivDQQw9hx44dKCwsRHNzM2bMmIH6+g/LEHfF7R40aBCeeOIJ7Ny5Ezt37sTnP/953HXXXW2Jpytuc4fxHOWzn/2s9+CDD7aLjRw50vvBD37wKc3o6gLA27hxY9vfra2tXjQa9Z544om22Llz57xwOOz9/Oc//xRmeHWorKz0AHhFRUWe53Wf7fY8z0tPT/d+8YtfdKttvhScvFOKxWLYtWsXZsyY0S4+Y8YMbN++/VOa1SfL0aNHUV5e3m4fhEIhTJ06tUvtg+rq84Xn+/btC6B7bHdLSwvWr1+P+vp6TJw4sVtssx+cTEqnTp1CS0sLIpH2HUQikQjKy8s/pVl9snywnV15H3ieh0ceeQSTJ0/GmDFjAHTt7d63bx9SUlIQCoXw4IMPYuPGjRg1alSX3uaO4Fzpko8SCLSvhuh53kWxrk5X3gcLFy7EW2+9hW3btl30Xlfc7uuvvx5vvvkmzpw5g9/85jeYN28eioqK2t7vitvcEZy8U+rXrx969Ohx0bdEZWXlRd8mXZVoNAoAXXYfPPzww/jtb3+LV199tV39rK683cFgENdddx1uueUW5OfnIycnBz/96U+79DZ3BCeTUjAYxLhx41BYWNguXlhYiEmTJn1Ks/pkycrKQjQabbcPYrEYioqKOvU+8DwPCxcuxPPPP48//elPyMrKavd+V91uhud5aGxs7FbbfEl8ij+yx2X9+vVeUlKS9+yzz3r79+/3Fi1a5CUnJ3vHjh37tKd2xaitrfX27Nnj7dmzxwPgrVixwtuzZ493/Phxz/M874knnvDC4bD3/PPPe/v27fPuvfdeLyMjw6upqfmUZ95xvvOd73jhcNjbsmWLV1ZW1vZqaGho+0xX3O4lS5Z4W7du9Y4ePeq99dZb3mOPPeYlJCR4mzdv9jyva25zR3E2KXme561atcq79tprvWAw6I0dO7ZNNu4qvPrqqx7O92xp95o3b57neefl8ccff9yLRqNeKBTypkyZ4u3bt+/TnfRlwrYXgLdmzZq2z3TF7f67v/u7tnO5f//+3he+8IW2hOR5XXObO4rqKQkhnMLJ35SEEN0XJSUhhFMoKQkhnEJJSQjhFEpKQginUFISQjiFkpIQwimUlIQQTqGkJIRwCiUlIYRTKCkJIZzi/wMXHtK4NtcvuQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a63167f0-21f6-4278-865e-62525ad9c6ed",
+   "metadata": {},
+   "source": [
+    "# Python lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "9083f86f-0e85-400c-b4e5-202ffc91fed8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_list = ['foo', 'bar', 'baz']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "b9714317-334e-4257-b682-f73b080e6868",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['foo', 'bar', 'baz']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "d6c61c6f-4c69-447a-bfbc-60ee45649c65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['bar', 'baz']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_list[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "1424c81d-d08b-45cc-8922-79106f4a355a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Append data\n",
+    "some_list.append(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "f013760e-8fb5-45b2-ad75-8cc944b3f6e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['foo', 'bar', 'baz', 42]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(some_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "306b80a0-0de8-4922-a6a6-963f8b390169",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_list2 = some_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "f69d0709-271d-4d06-9c48-f8a546c2b4d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'bar', 'baz', 42]"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "ec5a8872-f6da-413b-a3a2-f5b18b64a92c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This also modified some_list\n",
+    "some_list2.append(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "b6efbc57-aadc-4be6-9567-07553d85b058",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'bar', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "ccebea2e-6f6d-41d8-9520-8935a448a1be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'bar', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "53d24703-9ddd-4b7a-9d50-dcea1f349002",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "1037e50f-3ee2-4095-a925-4f56ea3ef426",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This creates an independent copy\n",
+    "some_list3 = copy.deepcopy(some_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "3cec547d-466d-48a0-b92c-ad34de1d6bd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'bar', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "8c0df2ba-b60e-4b97-bf26-89ff18e6bc7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'foo'"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list3.pop(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "dbaf21d8-30b2-44c9-b449-ba4dc5a63da7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['bar', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "a2cbe905-67bb-4c9b-bbe5-4a8e72aba3ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'bar', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "8531001a-dd05-4530-b720-3883f4f3d80d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'bar', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1eb9863-ca58-4f58-933e-b8c4f6e56fce",
+   "metadata": {},
+   "source": [
+    "# Mutable vs non-mutable variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "b23c14f1-2e55-4371-ac11-3b9ad87f1c4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'bar', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "facaaa6a-be4f-492b-b5fd-3cb18749a4a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lists are mutable\n",
+    "some_list[1] = 'other'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "8a5beb98-2b3f-480f-b901-ef371a1e766a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['foo', 'other', 'baz', 42, 2]"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "fce68c0d-4fd4-4cdf-93d8-80c31bb7d573",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_string = 'henlo'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "3b745384-5145-442b-947b-8693c2fc38be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'n'"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_string[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "804fc5fb-c4d5-4737-9d95-8b6ef8e1bf97",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[129], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Strings are not mutable\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43msome_string\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "# Strings are not mutable\n",
+    "some_string[2] = 'l'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "f730407f-185c-40d4-b694-8b75f553117d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "some_string = 'hello'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "11f9a3fa-4d5f-43e9-b07a-c405bdc3351a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Strings are not mutable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "9397f53c-58f5-40a5-9b10-bcb511eab53c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tuples: Non-mutable lists\n",
+    "some_tuple = ('a' , 1, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "2d55bd9e-ed2a-49f5-8617-7d2b8ab1100a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('a', 1, 1.5)"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "e3335e6b-27c0-45a2-a95d-7811cae95bc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.5"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_tuple[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "46e930bb-a6ba-4eba-b83e-f42442f986da",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[133], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Not mutable\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43msome_tuple\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "# Not mutable\n",
+    "some_tuple[1] = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76f11b25-79a8-4c6f-823c-a4a1fa35c13c",
+   "metadata": {},
+   "source": [
+    "# For Loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "27727349-e12e-49d6-9da0-2422d325bad9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "foo\n",
+      "in loop\n",
+      "other\n",
+      "in loop\n",
+      "baz\n",
+      "in loop\n",
+      "42\n",
+      "in loop\n",
+      "2\n",
+      "in loop\n",
+      "after loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "for value in some_list:\n",
+    "    print(value)\n",
+    "    print('in loop')\n",
+    "print('after loop')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b192c004-4613-48a7-8e66-5f53334c4bf2",
+   "metadata": {},
+   "source": [
+    "# Using wildcards like we used this morning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "067fd1e0-ece1-4168-9dc6-559db8f5b64e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "42481ec2-1a7a-44ff-b158-d7227524f6b2",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['python-novice-inflammation-data/data/inflammation-01.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-02.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-03.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-04.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-05.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-06.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-07.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-08.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-09.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-10.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-11.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-12.csv',\n",
+       " 'python-novice-inflammation-data/data/small-01.csv',\n",
+       " 'python-novice-inflammation-data/data/small-02.csv',\n",
+       " 'python-novice-inflammation-data/data/small-03.csv']"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Using wildcards like we used this morning\n",
+    "glob.glob('python-novice-inflammation-data/data/*.csv')\n",
+    "#glob.glob('Desktop/data/*.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "7065d52b-8371-4cab-9849-c07da2ce12cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Only get the files starting with inflammation\n",
+    "filenames = glob.glob('python-novice-inflammation-data/data/inflammation*.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "af834365-960c-4754-bc0e-a2d5e1f8b637",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "analyze python-novice-inflammation-data/data/inflammation-01.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-02.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-03.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-04.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-05.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-06.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-07.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-08.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-09.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-10.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-11.csv\n",
+      "analyze python-novice-inflammation-data/data/inflammation-12.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "for file in filenames:\n",
+    "    print('analyze', file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "96568ff9-6d2b-44b7-8470-43bdd411a6a1",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['python-novice-inflammation-data/data/inflammation-01.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-02.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-03.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-04.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-05.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-06.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-07.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-08.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-09.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-10.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-11.csv',\n",
+       " 'python-novice-inflammation-data/data/inflammation-12.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-01.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-02.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-03.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-04.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-05.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-06.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-07.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-08.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-09.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-10.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-11.csv',\n",
+       " 'python-novice-inflammation-data/data_second/inflammation-12.csv']"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "glob.glob('python-novice-inflammation-data/*/inflammation*.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "019362fc-3776-42c9-a39b-1bbd1b4eabd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "0ab0c0d6-0fd1-4bd8-b95e-9e6392338a9e",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('.',\n",
+       "  ['.ipynb_checkpoints',\n",
+       "   'python-novice-inflammation-data',\n",
+       "   'python-novice-inflammation-code'],\n",
+       "  ['Untitled.ipynb',\n",
+       "   'python-novice-inflammation-code.zip',\n",
+       "   'python-novice-inflammation-data.zip',\n",
+       "   'swc-2024-04-08.ipynb']),\n",
+       " ('./.ipynb_checkpoints',\n",
+       "  [],\n",
+       "  ['Untitled-checkpoint.ipynb', 'swc-2024-04-08-checkpoint.ipynb']),\n",
+       " ('./python-novice-inflammation-data', ['data', 'data_second'], []),\n",
+       " ('./python-novice-inflammation-data/data',\n",
+       "  ['.ipynb_checkpoints'],\n",
+       "  ['inflammation-01.csv',\n",
+       "   'inflammation-02.csv',\n",
+       "   'inflammation-03.csv',\n",
+       "   'inflammation-04.csv',\n",
+       "   'inflammation-05.csv',\n",
+       "   'inflammation-06.csv',\n",
+       "   'inflammation-07.csv',\n",
+       "   'inflammation-08.csv',\n",
+       "   'inflammation-09.csv',\n",
+       "   'inflammation-10.csv',\n",
+       "   'inflammation-11.csv',\n",
+       "   'inflammation-12.csv',\n",
+       "   'small-01.csv',\n",
+       "   'small-02.csv',\n",
+       "   'small-03.csv']),\n",
+       " ('./python-novice-inflammation-data/data/.ipynb_checkpoints',\n",
+       "  [],\n",
+       "  ['inflammation-01-checkpoint.csv']),\n",
+       " ('./python-novice-inflammation-data/data_second',\n",
+       "  ['.ipynb_checkpoints'],\n",
+       "  ['inflammation-01.csv',\n",
+       "   'inflammation-02.csv',\n",
+       "   'inflammation-03.csv',\n",
+       "   'inflammation-04.csv',\n",
+       "   'inflammation-05.csv',\n",
+       "   'inflammation-06.csv',\n",
+       "   'inflammation-07.csv',\n",
+       "   'inflammation-08.csv',\n",
+       "   'inflammation-09.csv',\n",
+       "   'inflammation-10.csv',\n",
+       "   'inflammation-11.csv',\n",
+       "   'inflammation-12.csv',\n",
+       "   'small-01.csv',\n",
+       "   'small-02.csv',\n",
+       "   'small-03.csv']),\n",
+       " ('./python-novice-inflammation-data/data_second/.ipynb_checkpoints',\n",
+       "  [],\n",
+       "  ['inflammation-01-checkpoint.csv']),\n",
+       " ('./python-novice-inflammation-code', ['code'], []),\n",
+       " ('./python-novice-inflammation-code/code',\n",
+       "  [],\n",
+       "  ['argv_list.py',\n",
+       "   'arith.py',\n",
+       "   'check.py',\n",
+       "   'count_stdin.py',\n",
+       "   'gen_inflammation.py',\n",
+       "   'line_count.py',\n",
+       "   'my_ls.py',\n",
+       "   'readings_01.py',\n",
+       "   'readings_02.py',\n",
+       "   'readings_03.py',\n",
+       "   'readings_04.py',\n",
+       "   'readings_05.py',\n",
+       "   'readings_06.py',\n",
+       "   'readings_07.py',\n",
+       "   'readings_08.py',\n",
+       "   'readings_09.py',\n",
+       "   'rectangle.py',\n",
+       "   'sys_version.py'])]"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(os.walk('.'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4740309-47d5-424c-bae3-53f0d1655d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}