From 3f78aa0702e5e259d62e64f580de8ca5c2c2a5c6 Mon Sep 17 00:00:00 2001 From: Alona Sorochynska Date: Tue, 30 Jul 2019 15:40:42 +0100 Subject: [PATCH 1/4] adding robot.md to the repository --- robot.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 robot.md diff --git a/robot.md b/robot.md new file mode 100644 index 0000000..e69de29 From 5fe078b46f9e5f48d6bcd073518b1f08cf93699a Mon Sep 17 00:00:00 2001 From: Alona Sorochynska Date: Wed, 31 Jul 2019 15:57:17 +0100 Subject: [PATCH 2/4] labs, that areready for my repository --- bus/bus.ipynb | 48 +++++++++- duel/duel.ipynb | 155 +++++++++++++++++++++++++++++--- robin-hood/robin-hood.ipynb | 64 ++++++++++++-- temperature/temperature.ipynb | 160 ++++++++++++++++++++++++++-------- 4 files changed, 373 insertions(+), 54 deletions(-) diff --git a/bus/bus.ipynb b/bus/bus.ipynb index 81779ce..2ebd161 100644 --- a/bus/bus.ipynb +++ b/bus/bus.ipynb @@ -32,12 +32,52 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of stops is 9\n", + "The number of passengers at each stop: [10, 3, -2, -1, 4, -4, -3, -2, -1]\n", + "Maximum occupation of the bus is 14 passengers\n", + "Passengers in the bus: [10, 13, 11, 10, 14, 10, 7, 5, 4]\n", + "The average occupation is 9.333333333333334\n", + "The standart deviation is 3.391164991562634\n" + ] + } + ], "source": [ "# variables\n", - "\n" + "import math\n", + "stops = [(10, 0), (4, 1), (3, 5), (3, 4), (5, 1), (1, 5), (5, 8 ), (4, 6), (2, 3)]\n", + "print('The number of stops is', len(stops))\n", + "passengers = []\n", + "for i in range(len(stops)):\n", + " n = stops[i][0] - stops[i][1]\n", + " passengers.append(n)\n", + "print('The number of passengers at each stop:', passengers)\n", + "maximum = 0\n", + "summ = 0\n", + "passengers_list = []\n", + "for j in range(len(passengers)):\n", + " summ += passengers[j]\n", + " passengers_list.append(summ)\n", + " if summ > maximum:\n", + " maximum = summ\n", + "print('Maximum occupation of the bus is {0} passengers'.format(maximum))\n", + "all_passengers = 0\n", + "for k in range(len(passengers_list)):\n", + " all_passengers += passengers_list[k]\n", + "average = all_passengers/len(passengers)\n", + "print('Passengers in the bus:', passengers_list)\n", + "print('The average occupation is', average)\n", + "numerator = 0\n", + "for n in range(len(passengers_list)):\n", + " numerator += (passengers_list[n] - average)**2\n", + "s2 = numerator/(len(passengers_list) - 1)\n", + "print('The standart deviation is', math.sqrt(s2))" ] }, { @@ -105,7 +145,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/duel/duel.ipynb b/duel/duel.ipynb index 4398d88..695fdc5 100644 --- a/duel/duel.ipynb +++ b/duel/duel.ipynb @@ -33,14 +33,50 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 0 clash is won by Saruman: 23 vs 10\n", + "The 1 clash is won by Saruman: 66 vs 11\n", + "The 2 clash wins Gandalf: 13 vs 12\n", + "The 3 clash is won by Saruman: 43 vs 30\n", + "The 4 clash wins Gandalf: 22 vs 12\n", + "The 5 clash wins Gandalf: 11 vs 10\n", + "The 6 clash is won by Saruman: 44 vs 10\n", + "The 7 clash wins Gandalf: 33 vs 23\n", + "The 8 clash wins Gandalf: 22 vs 12\n", + "The 9 clash wins Gandalf: 22 vs 17\n", + "Gandalf wins!!! The score is 6 vs 4\n" + ] + } + ], "source": [ "# Assign spell power lists to variables\n", "\n", "gandalf = [10, 11, 13, 30, 22, 11, 10, 33, 22, 22]\n", - "saruman = [23, 66, 12, 43, 12, 10, 44, 23, 12, 17]" + "saruman = [23, 66, 12, 43, 12, 10, 44, 23, 12, 17]\n", + "gandalf_victory, saruman_victory = 0, 0\n", + "for i in range(len(gandalf)):\n", + " if gandalf[i] < saruman[i]:\n", + " print('The {0} clash is won by Saruman: {1} vs {2}'.format(i, saruman[i], gandalf[i]))\n", + " saruman_victory += 1\n", + " elif gandalf[i] > saruman[i]:\n", + " print('The {0} clash wins Gandalf: {1} vs {2}'.format(i, gandalf[i], saruman[i]))\n", + " gandalf_victory += 1\n", + " else:\n", + " print(\"It's a draw!!!\")\n", + " saruman_victory += 1\n", + " gandalf_victory += 1\n", + "if gandalf_victory > saruman_victory:\n", + " print('Gandalf wins!!! The score is {0} vs {1}'.format(gandalf_victory, saruman_victory))\n", + "elif saruman_victory > gandalf_victory:\n", + " print('Saruman wins!!! The score is {0} vs {1}'.format(saruman_victory, gandalf_victory))\n", + "else:\n", + " print(\"It's a draw!!! :)\")" ] }, { @@ -54,9 +90,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'gandalf' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Execution of spell clashes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgandalf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'gandalf' is not defined" + ] + } + ], "source": [ "# Execution of spell clashes\n" ] @@ -116,12 +164,37 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gandalf: [50, 40, 40, 10, 50, 10, 40, 50, 10, 50]\n", + "Saruman: [45, 45, 25, 50, 25, 40, 10, 45, 10, 10]\n", + "The 0 clash wins Gandalf: 50 vs 45\n", + "The 1 clash is won by Saruman: 45 vs 40\n", + "The 2 clash wins Gandalf: 40 vs 25\n", + "The 3 clash is won by Saruman: 50 vs 10\n", + "The 4 clash wins Gandalf: 50 vs 25\n", + "The 5 clash is won by Saruman: 40 vs 10\n", + "The 6 clash wins Gandalf: 40 vs 10\n", + "The 7 clash wins Gandalf: 50 vs 45\n", + "It's a draw!!!\n", + "The 9 clash wins Gandalf: 50 vs 10\n", + "Gandalf wins!!!\n", + "Gandalf's average is 35.0\n", + "Saruman's average is 30.5\n", + "Gandalf's standart deviation is 17.795130420052185\n", + "Saruman's standart deviation is 17.07825127659933\n" + ] + } + ], "source": [ "# 1. Spells now have a name and there is a dictionary that relates that name to a power.\n", "# variables\n", + "import math\n", "\n", "POWER = {\n", " 'Fireball': 50, \n", @@ -134,16 +207,76 @@ "gandalf = ['Fireball', 'Lightning bolt', 'Lightning bolt', 'Magic arrow', 'Fireball', \n", " 'Magic arrow', 'Lightning bolt', 'Fireball', 'Magic arrow', 'Fireball']\n", "saruman = ['Contagion', 'Contagion', 'Black Tentacles', 'Fireball', 'Black Tentacles', \n", - " 'Lightning bolt', 'Magic arrow', 'Contagion', 'Magic arrow', 'Magic arrow']" + " 'Lightning bolt', 'Magic arrow', 'Contagion', 'Magic arrow', 'Magic arrow']\n", + "\n", + "gandalf2 = []\n", + "for i in gandalf:\n", + " gandalf2.append(POWER[i])\n", + "print('Gandalf:', gandalf2)\n", + "saruman2 = []\n", + "for i in saruman:\n", + " saruman2.append(POWER[i])\n", + "print('Saruman:', saruman2)\n", + "\n", + "#gandalf_victory, saruman_victory = 0, 0\n", + "winner = []\n", + "S = 'SSS'\n", + "G = 'GGG'\n", + "\n", + "for i in range(len(gandalf2)):\n", + " if gandalf2[i] < saruman2[i]:\n", + " print('The {0} clash is won by Saruman: {1} vs {2}'.format(i, saruman2[i], gandalf2[i]))\n", + " winner.append('S')\n", + " n = ''.join(winner)\n", + " if S in n:\n", + " print('Saruman wins!!!')\n", + " break\n", + " elif gandalf2[i] > saruman2[i]:\n", + " print('The {0} clash wins Gandalf: {1} vs {2}'.format(i, gandalf2[i], saruman2[i]))\n", + " winner.append('G')\n", + " n = ''.join(winner)\n", + " if G in n:\n", + " print('Gandalf wins!!!')\n", + " break\n", + " else:\n", + " print(\"It's a draw!!!\")\n", + "average_G = sum(gandalf2)/len(gandalf2)\n", + "print(\"Gandalf's average is\", average_G)\n", + "average_S = sum(saruman2)/len(saruman2)\n", + "print(\"Saruman's average is\", average_S)\n", + "\n", + "numerator = 0\n", + "for n in range(len(gandalf2)):\n", + " numerator += (gandalf2[n] - average_G)**2\n", + "s2G = numerator/(len(gandalf2) - 1)\n", + "print(\"Gandalf's standart deviation is\", math.sqrt(s2G))\n", + "numerator = 0\n", + "for n in range(len(saruman2)):\n", + " numerator += (saruman2[n] - average_G)**2\n", + "s2S = numerator/(len(saruman2) - 1)\n", + "print(\"Saruman's standart deviation is\", math.sqrt(s2S))" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'POWER' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Assign spell power lists to variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mgandalf2\u001b[0m \u001b[0;34m=\u001b[0m \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;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mPOWER\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'POWER' is not defined" + ] + } + ], "source": [ "# Assign spell power lists to variables\n", + "\n", "\n" ] }, @@ -210,7 +343,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/robin-hood/robin-hood.ipynb b/robin-hood/robin-hood.ipynb index b1af06b..23d798e 100644 --- a/robin-hood/robin-hood.ipynb +++ b/robin-hood/robin-hood.ipynb @@ -43,16 +43,70 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q1 have 11 arrows,\n", + "Q2 have 6 arrows,\n", + "Q3 have 2 arrows,\n", + "Q4 have 3 arrows\n", + "All distances from the center are:\n", + " [6.4031242374328485, 2.0, 8.06225774829855, 3.1622776601683795, 3.605551275463989, 6.4031242374328485, 3.605551275463989, 8.602325267042627, 8.602325267042627, 2.8284271247461903, 6.4031242374328485, 2.0, 8.06225774829855, 3.1622776601683795, 3.605551275463989, 6.4031242374328485, 3.605551275463989, 8.602325267042627, 8.602325267042627, 2.8284271247461903, 12.727922061357855, 12.041594578792296]\n", + "The shortest distance is 2.0\n", + "2 arrows fly away...\n" + ] + }, + { + "data": { + "text/plain": [ + "'a = -0\\nprint(a)\\nprint(a*2)\\nprint(0 == -0)\\nprint(-0/5)'" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Variables\n", - "\n", + "import math\n", "points = [(4, 5), (-0, 2), (4, 7), (1, -3), (3, -2), (4, 5),\n", " (3, 2), (5, 7), (-5, 7), (2, 2), (-4, 5), (0, -2),\n", " (-4, 7), (-1, 3), (-3, 2), (-4, -5), (-3, 2),\n", - " (5, 7), (5, 7), (2, 2), (9, 9), (-8, -9)]" + " (5, 7), (5, 7), (2, 2), (9, 9), (-8, -9)]\n", + "q1, q2, q3, q4 = 0, 0, 0, 0\n", + "for i in range(len(points)):\n", + " if points[i][0] >= 0 and points[i][1] >= 0:\n", + " q1 += 1\n", + " elif points[i][0] < 0 and points[i][1] < 0:\n", + " q3 += 1\n", + " elif points[i][0] >= 0 and points[i][1] < 0:\n", + " q4 += 1\n", + " else:\n", + " q2 += 1\n", + "print('Q1 have {0} arrows,\\nQ2 have {1} arrows,\\nQ3 have {2} arrows,\\nQ4 have {3} arrows'.format(q1, q2, q3, q4))\n", + "minimum = True\n", + "lengths = []\n", + "for j in range(len(points)):\n", + " d = math.sqrt((points[j][0])**2 + (points[j][1])**2)\n", + " lengths.append(d)\n", + "print('All distances from the center are:\\n', lengths)\n", + "print('The shortest distance is', min(lengths))\n", + "number = 0\n", + "for k in range(len(lengths)):\n", + " if lengths[k] > 9:\n", + " number += 1\n", + "print(number, 'arrows fly away...')\n", + "\n", + "'''a = -0\n", + "print(a)\n", + "print(a*2)\n", + "print(0 == -0)\n", + "print(-0/5)'''" ] }, { @@ -121,7 +175,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/temperature/temperature.ipynb b/temperature/temperature.ipynb index 048d15a..63518fd 100644 --- a/temperature/temperature.ipynb +++ b/temperature/temperature.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -43,13 +43,13 @@ "Text(0.5, 1.0, 'Temperatures of our server throughout the day')" ] }, - "execution_count": 1, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+9aGTALZU1XLkiBwuP34kBw2JbgIIdcmxI3hqwQZuenkF//jhwTHbrkkMUUsEIjIAuAwYo6q7ROQp4AxgGnCHqj4hIvcDPwbui1YcxrRkW1Utq0oqKCitpMD9v6qkkrLKPcM4Z6anMjg7ky827uCJ+Rt2P57lS2NEnp9RuU5iGJmXxchcP/m99iSIqpp6/vXReh50E8BRI3P4+fEjwxoqItJ6Z6RzybEjuOGlFbxfWMYRI3JiHoOJX9GuGkoDeohIHZABFAHHAme5zz8C/BFLBCYGVm+u5F8frmdlcQUFpRWUVe4piGampzIiL4upowOMystyTvJ5WfQPObFvqaxhVUklhaUVu0sMbywv4ckFexKE35fGiFw/Q7IzeKegjK1VtRw9KsDlx41k0j59Yr7Poc49bAiPfLCeG19ezgsXH0lKSvs7qpmuKWqJQFU3ichfgC+BXcBrOFVB21U1OJ/eRmBAc68XkfOB8wEGDx4crTBNEmhoVP723hpue20VIrBvv54cu28uI0N+zYee8FuS7fdxmN/HYcP3vqi7pbLGKVUESxYllXywegvjBvbisuNGMnGwtwkgqHu3VH71rVFc8eQX/G/RV8yY0OxXzyShaFYN9QFmAEOB7cDTwAnhvl5VZwOzASZPnmwjZ5kOKSyt5MpnvuCzL7dz/H553HDyAeT27B7RbWT7fWT7fRwapVY/kTRj/AAefGctt7yykhMO6NfsBDcm+USzQ9nxwFpV3ayqdcBzwBFAbxEJJqCBwKYoxmCSVH1DI/e/vZpps95lbVkVd50xgQfPnRTxJJBoUlKEq769L5u27+LFL4q8DsfEiWgmgi+BQ0UkQ5wy93HAMmAuMNNd5jzg+SjGYJJQQUkFp9z/ITe9vIIpowK8dsXRzJgwoEODt3VFR43MoX+v7ry02BKBcUTzGsHHIvIM8ClQD3yGU9UzB3hCRK5zH/tbtGIwyaW+oZHZ767hztcLyPSlMuvMA/nOuHxLAE2ICNPG5vPIh+vYsauu02MamcQX1VZDqnotcG2Th9cA1pDZRNTK4gqufOYLFm3cwbcP6MefZxxAIMvndVhxa/q4fB56by1vLCvhlEkDvQ7HeMx6FpuEVt/QyAPvrOGuNwrwd0/j3rMmMn1cvtdhxb0Jg3ozoHcP5iwuskRgLBGYxLWiuJwrn17E4k07mD4unz+ftD/ZfisFhMOpHurHPz6w6iFjw1CbBPWvD9fxnbvf46vtu/jr2RO596yJlgTaafq4/tQ1KK8vK/E6FOMxSwQm4fztvbX8/vmlHDUywOu/OIZpY60qqCPGD+zlVA8t+srrUIzHLBGYhPLQu2v4vxeX8e0D+vHAOZPom5nudUgJS0SYPi6f9wrL2LHTZjBLZpYITMJ46N01XDdnOdPG9mPWmQd2eD5fs8f0sfnUNSivLSv2OhTjIfsmmYTw4DtOEpg+Np+7zrAkECnjBvZiYB+n9ZBJXvZtMnFv9juruf4lJwncecYESwIRJCJMH5vPewVlbN9p04IkK/tGmbj2wNurueGlFUwfl89dlgSiYvq4fOobldes9VDSsm+ViVv3v72aG19ewYnj8rnr9AmkWRKIirEDejGobw/mLLLqoWRl3ywTl/46r5CbXl7Bd8b3505LAlEVHHvo/UKrHkpW9u0ycefeuYXc8spKThrfnztOG29JIAZOHNvfqR5aatVDyci+YSau3Du3kFtfXcmMCf253ZJAzBwwoCeD+2bworUeSkr2LTNx4563Crj11ZV8d0J/bj/NqoNiKbR6aFuVVQ8lG/ummbhw95sF/OW1VZx84ABuO20CqTaxesydOC6fhkbrXJaMLBEYz939ZgG3vb6K7x04gL+cOt6SgEf279+TfbIzeNFaDyUdUY3/eeEni+gCr4MwxpgEI7BQVSe3tVxizEcwaRIssFTQ1dz5xirufKOAUyYO5JaZ46wkEAeWbNrBiXe/x03fG8sZBw/2OhzTWWFO02pVQ8YTd7zuJIGZkywJxJP9+/dkSHaGjT2UZCwRmJi74/VV3PVmAadOGsjNp1gSiCfB1kMfrN7CVms9lDQsEZiYUVVutyQQ96a7rYdeXWqth5KFJQITE6rKHa+vYtabBZw+eRA3nzKOFEsCcWlMfk+G5mTa2ENJxBKBibpgSWDWW4WccdAgbvzeWEsCcSw4sf0Hq8vYUlnjdTgmBiwRmKhSVW57bRV3v1XImQcP4oaTLQkkgulj+9Oo8KqNPZQUEqP5aAx9snYrd76xisF9MxiZl8XIXD+j8rLI6+lDwmyKZRyqyl9eW8m9c1dz5sGDuf67B1gSSBD75WcxLCeTOYu/4qxDrBlpV2eJoInnP9/E/HVbWVFcwRPzN+x+PKt7GiNz/YzMzWJknp+ReVmMyvPTr2d3SxDNUFVueXUl981bzVmHDOa6GZYEEklwYvt75xZSVllDjt/ndUgmiiwRNLGiuIIDB/XhqQsOY0tlDatKKikoraCgpJJVJRW8sbyEJxeEJAhfGiPy/OzfvydXfXs//D47pKrKza+s5P63V3P2IYP5P0sCCWna2HzufquQV5cWc/Yh+3gdjokiO2uFaGxUVhSVM3PSQACy/T4O8/s4bHj2XsttqayhoLTS+SupYPGmHfz7oy+ZOjqX4/bL8yL0uKGq3PTKCh54ew3fP3Qwfz7JkkCi2rdfFsMCTushSwRdmyWCEBu27aSqtoH98nu2uly230e238ehw5wEsXHbTo68eS5lSd7CYltVLbPeKuDh99fx/UOdkoBVmyUuEeHEsfncY9VDXZ4lghDLi8oB2LeNRNBU8AuyuSL5EsHWqlpeW1rMnMVFfLB6Cw2NynmH7cMfT9rfkkAXMG1cPrPeKuSVJcV8/1ArFXRVlghCLC+qIEVgdF5Wu17XvVsqPbunJU0i2FpVy6tLi3kp5OQ/uG8GPz1qGNPH5nPAgJ6WBLqI0XlZDHerhywRdF2WCEIsLypnSE4mPdJT2/3anCwfZZVdd2yWLZU1vLq0hJcWF/HhGufkPyQ7g58dPYxpY/PZv7+d/Lsip/VQf+55q4DNFTUEsqx6qCuyRBBieXE54wb27tBrA35flysRbKms4RX3l/9Ha7bS0KgMzcnkgmOck/+YfDv5J4PpY/OZ9WYBrywt5hwrFXRJLSYCEfEBflXd0uTxbKBSVbvUWa+iuo4NW3dx+uRBHXp9IMvH0q/KIxxV7JVV1vDKkuDJfwuNCsNyMrnwmOFMG5vPfvlZdvJPMqPy/IzI9TNn0VeWCLqo1koEdwFvAk83eXyq+3dxtILywsriCoA2Wwy1JCeBSwSlFdVOtc+iIj5e6578A5lcPHUE08bms28/O/knMxFxSgVvFVBaUU1uVnevQzIR1loiOEhVL2j6oKo+IyJ/CmflItIbeAg4AFDgR8BK4ElgCLAOOE1Vt7Uv7MgLthjqaCIIZPmorKlnV21Dh64xxFppRTWvLClmzqIiPlm3FVUYHsjkkqkjmDYun9F5dvI3e0wfl89dbxbw3882cf7Rw70Ox0RYa4mgRyvPhXuGuAt4RVVnikg6kAH8FnhTVW8SkauAq4DfhLm+qFlWVEGvHt3I79WxXzvBi2hllTUM6psRydAiprS8mpeXOE0957sn/xG5fi49diTTx+YzKs9vJ3/TrFF5WRw1MofbX1/FMaNyGd2vfS3rTHxrLRFsEZFJqrow9EERmQhsbWvFItILOBr4AYCq1gK1IjIDmOIu9ggwjzhIBCuKyztVBRJMBKUV8ZEIqusaWFtWxaqSCgpLK/l4zVbmr3dO/iNz/Vx27Eimj8tnVDubyprkdftpE5g2610uenQhL1xyJJk2nEqX0do7eSXwrIg8BASTwWSc6p2zwlj3UGAz8LCIjHfXcTmQp6rBGS+KgWbHZBCR84HzAQYPju7oh42NysriCk7r4IVicFoNQew7lVXXNbBmc9Ve4yEVllaybksVjeoskyLOL7rLj3N++Y+0k7/pgECWj1lnHMjZD33E7/6zmDtOn2AlyC6ixUSgqh+JyKHApUDwWsFS4PCQE3lb654IXKqqH4vIXTjVQKHbUBHRFrY/G5gNMHny5GaXiZT1W3eys7aBMR28PgB7Vw1F0+KNO3hlaRGrSiopLK1kfcgJPzVFGJKdwai8LE4cl88Id4TUoTmZ+NLi/7qFiX+HDc/miuNHcdvrqzhkWDZnHmxDVHcFbZXtsoB/qGoBgIikqmpDmOveCGxU1Y/d+8/gJIISEclX1SIRyQdKOxJ4JHX2QjFA38x0RKJfIrjm+SUs2bSDoTmZ7Nsvi++M7797zoQhORl2wjdRd9HUEXyybivXvrCU8QN7M6Z/x783Jj601o/gTpxEICKyQ1WvAP5FeNVCqGqxiGwQkdGquhI4Dljm/p0H3OT+f76T+9BpK4rKSREYmefv8Dq6pabQNyOdzVEuERRt38XMiQO5eea4qG7HmJakpgh3nD6B6bPe5ZLHPuWFS4+04dcTXGtTVfZU1R+r6o+AHPex9lYIXgo8KiKLgAnADTgJ4BsiUgAc79731LKiCoYF/HTv1rlf0zl+H2VRLBHUNzRSVllDXk/r5m+8leN3rhes21LF1c8tRjWqtbcmylpL4z1F5BScZNGhn8qq+jnOBeamjuvI+qJleVE5E/fp0+n1BLJ8US0RbKmqpVEht6d16DHeO2RYNr/85mhufXUlhwzta4PSJbDWSgQ/BQYA+TgthQD+EvWIYmzHrjo2bd/Ffvmdb0kTyIpu7+LiHdUA9LNEYOLEhccMZ8roAH9+cRlLNu3wOhzTQS0mAlXdpqqz3L9t7mMLW1o+Ue0eWqJf5y945fjT2VxRE7Vickm5kwjyLBGYOJGSItx+2gT6ZqRz8WOfUl5d53VIpgNaKxEkhUi0GAoKZPmoqW+ksqa+0+tqTolb2rBrBCae9M1M556zDmTjtl1c/axdL0hElgiKyumT0S0iJ9dgX4JoVQ+VlleTIs5UmcbEk8lD+nLlt0YzZ3ER//povdfhmHYKKxGIyEARmere9olIZnTDip3lReXsF6Fx9QN+p8omWomgpLyaQJaPVJsM3sSh848axrH75nLdi8tZvNGuFySSNhOBiPwIeAFnFFGAfYiDtv+R0NCorCypYN8IXB8AyMlKB4jaTGUl5TV2fcDErZQU4bZTx5PjT+eixxayY5ddL0gU4ZQILgMOBcoBVHUVkBvNoGJl3ZYqqusaI9JiCELHG6qOyPqaKim3seBNfOuTmc49Z0+kaHs1v37mC7tekCDCSQTV7sihgDPMBO3vWBaXInmhGKBPRjqpKRK1vgQl5dX062XXB0x8mzi4D1d9e19eXVrCPz5Y53U4JgzhJIL3ReTXQHf3OsGTwIvRDSs2lheVk5oinRpaIlRKiuxuQhppNfUNbNtZR56VCEwC+PGRQzl+vzxueGk5n33p+bxTLVpXVkVtfaPXYXgunETwa6ACWIEzjPSbwO+iGVSsrCiqYHggsiNz5vh9UblGUFoebDpqicDEPxHnekFuVndOf+AjrvnvYr7avsvrsHarqK7jt/9ZzJS/zOP02R9SGqXq3ETRaiJwq4EeVtX7VPVkVf2ue7tLpNBgi6FIilbv4uAHNdf6EJgE0SujG89eeDinTh7Ik/M3cMytc/ndfxazyeOE8G7BZk64812e+ORLvnfgAFYUVTDjnveTumd0q4nAHXJ6mIh0i1E8MbN9Zy1f7aiOfCKI0iT2JVYiMAmoX6/uXH/yWOZdOZXTJg/iqQUbmHLrXH77n8Vs3LYzprFUVNdx9XOLOOdvn+DrlsLTFxzO7adP4JkLD0OAU+//kJcXhzPVStcTztixq4F3ReR5oCr4oKrOilpUMbAiOLREhBNBTpaPssoaGhuVlAi297fhJUwiG9C7B9efPJaLpo7gvnmFPDV/I08v2MDMSQO5aMqIqE/v+vaqzVz97CKKy6v52THDuOL4UbtHG96/fy/+e8kR/OxfC7nw0U/5xTdGcemxI5Jq9rVwEsGX7l+G+9cl7G4xFOFJuAN+H/WNyo5ddfTJTI/YeovLq0lPTaFPRpcrnJkkMqB3D6777lgumjKC++at5sn5G3h6wUZmThrIxVMjnxDKq+u4/sXlPLlgAyNy/Tx74eGOHGP5AAAXeUlEQVQcOPjrIw3nZnXn8Z8eym+fW8ztr69iVUkFfzl1fKeHpk8UbSYCVf19LAKJteVF5WRnpu8eFiJSdg8zUVkT0URQWl5Dbk9fUv1KMV1X/949+L/vHsBFU4dz/7zVPP7JBp5ZuJFTJjoJYXB25xPC3JWl/Pa5xZSUV3PhlOFcftzIVk/s3bulcttp4xnVL4ubX1nBl1t3MvucyfTr1fVL4W0mAhF5HfharxBV/WZUIoqR5UUVERtaIlToeEOjIjhJfEl5tVULmS4nv1cP/jTjAC6cMoL7317NY598yTOfbuSEA/oxbkAvRuVlMSLXz4DePcKuat2xq47rXlzG0ws3MirPz/3fP4Lxg3qH9VoR4YJjhjM84OfnT3zGSfe8x4PnTg779YkqnKqha0JudwdOAaI7H2OU1Tc0sqqkgnMPi/xEGjn+6ExiX1JezegIV2MZEy/69erOH0/anwunDOe+eauZs7iIOYv2XLjNSE9lRK6fkblZjMzzMyrPud00Qby1ooSrn1tMWWUtF08dzmXHjexQ8/BvjMnj2YsO5yePLOC0Bz7k1lPHc9L4/hHZ13gUTtXQx00eeltEmj6WUNZtqaKmvjFiYwyFitYIpKXlNRw1MhDRdRoTb/J6Ognhjyftz/adtRSUVlJQUsmqkgoKSyt5t2Azz366cffywQQxItdPTV0jcxYXMTovi4fOPYixA3t1KpZ9+/Xk+YuP4MJ/f8plj39GQUkFVxw/KqKNQOJFOFVDoWfLFGAS0Pl5HT20rCg6LYYAenZPIz0tJaKJoKqmnoqaeqsaMkmld0Y6Bw3py0FD+u71+I6ddRSUVlBQuidBvF9YxraqOi49dgSXHDsiYp1Es/0+/v2TQ7jmv4u5+61CCksrue208WSkh1OZkjjC2ZulONcIBKgH1uJMY5mwlheV0y1VGJEbmaElQolIxPsSlNqENMbs1iujG5OH9GVykwQR6SbbQelpKdx8yjhG5WVxw0vLWX/fTmafO4mBfbpMI8qwEsEwVd1rPFkRSeh0uKKonOEBP+lp0ZmXJyfCk9gH5yq2EoExLYtmlY2I8JOjhjE8189lj33Gsbe9zVkHD+aCY4Z3iVZF4ZwJm7se8EmkA4mlYIuhaIl8icASgTHxYOroXF66/ChOnjCAf3+0nqNvncu1zy/Z/WMtUbX4y15EcoF8oIeIjGXP0NM9SeCOZduqaikur47YHATNCWT5+HxD5EZc3NOr2KqGjPHaoL4Z3DxzHJccO4J75xby6Mdf8vgnGzjj4EFcOGU4+b16eB1iu7VWxTMd+BEwEPhryOMVQMJ2Mov0HATNCfjT2VpVS0OjRmRayZLyGjLSU/H7ErpGzpguZVDfDG46ZRwXTx3BX+cV8tjHX/LEJxs4/SAnIfTvnTgJocUzi6o+DDwsIqep6lMxjCmqlkdpjKFQgSwfjQpbqmoiMqNYsDOZ9So2Jv4M6pvBjd8LJoTVPDH/S56cv4HTDnLGUUqEhBBOP4KnRORbwP44HcqCj98QzcCiZXlROTl+3+6OX9EQ2pcgEomgtLyG3AgPhWGMiayBfTK44eSxTkKYW8iT8zc4CWHyIC6aOoIBcZwQwpm8/q/AecAvgB7A94ERUY4rapw5CKLbQzfSncpKKmx4CWMSRXCk1XlXTuX0gwbx9IKNTLl1Ln/+3zLqG+JzKpdwWg0dqapnAVvcAegOIUETQV1DIwUllYyJYrUQhA4z0fmZylSV4h3VdqHYmAQTHGl13pVTmDlpIH9/fy2/eOqLuEwG4Vx9DLaLqhaRfsAWICEH3VhbVkVtQ2NUrw/AnkQQiRJB+a56auobrURgTILq37sHN35vHIP7ZnLzKytQ4I7TxpOWGp1+TB0RTiJ4SUR6A38BPgcagEeiGlWUBFsM7RvlqqFMXxqZ6akRSQQl1ofAmC7hwinDEYGbXl4BxFcyaDURiEgK8LKqbgeeFpEXgR6qujUm0UXYsqJy0lNTGB6I/NASTQUi1LvYZiYzpuu44JjhCHDjyytQVe48fUJcJINWE4GqNorIA8AE9/4uwNuZpzthRVEFI3L9dIvBgc/x+yiLRImg3MYZMqYr+dkxTsnghpecaqK74iAZhLP1uSIyI+qRxIDTYii61weCIl0iiEQzVGNMfDj/6OH8btp+zFlUxOVPfE6dxxeQw7lG8APgchGpwSkNCKCq2rfVV8WZLZU1lFbURL3paFAgy8cHq7d0ej2l5dX07J5Gj/TkmDvVmGTx06OHIQLXzVmOotx1xoExqa1oTjiJICfqUcTA8ijOQdCcgN/Hjl111NQ3dGps9GKbotKYLusnRw0D3GSgnzHrTG+SQZtbVNUG4FTgN+7tfNxrBuEQkVQR+cy90IyIDBWRj0WkUESeFJHIzfDeihXF0R9jKFSO26lsSyf7EpSU13SJYW6NMc37yVHDuGb6fry8pJjLHv/Mk2qicHoW3wNMBc5xH9oJ3N+ObVwOLA+5fzNwh6qOALYBP27HujpsWVE5eT199M2MSd4hEKG+BKXl1XZ9wJgu7idHDeP3J47h5SXFXPpY7JNBOGWQw1X1Z7gdy9ymo2GdTUVkIM4opg+59wU4FnjGXeQR4LvtjLlDlhdVRGWO4pZEYpiJxkaltKLGWgwZkwR+fORQ/nDiGF5ZWswlj31KbX3skkE4iaDO7U+gACKSDYQb4Z3Ar0OWzwa2q2q9e38jMKC5F4rI+SKyQEQWbN68OczNNa+2vpHC0uhORtNUsGqorBMth7burKW+Ue0agTFJ4kdHDuXa74zh1aUlMU0G4SSCe4FngYCI/Al4D6d6p1UiciJQqqoLOxKYqs5W1cmqOjkQCHRkFbutKaukrkFj1mIIIMfvFJo6UyKwCWmMST4/PGIof/zOGF5bVsLFMUoG4QxD/U8RWQgc7z50qqouCWPdRwAnicg0nOGrewJ3Ab1FJM0tFQwENnUs9PAFh5aI9mBzoXxpqfTq0a1TfQlK3c5kuVYiMCap/OCIoYgIN768nJXFFYwd2Cuq2wu3nVIqUAfUhvsaVb1aVQeq6hDgDOAtVT0bmAvMdBc7D3i+XRF3wPKiCtLTUhiakxntTe0lkNW5uYuLbXgJY5LWeYcPYd6vpkY9CUB4rYZ+BzyOM+LoQOAxEbm6E9v8DfALESnEuWbwt06sKyzLi8oZleePeTfuHH96p64R7OlVbFVDxiSjWDUdD6dD2bnAgaq6E0BErgc+A24MdyOqOg+Y595eAxzc3kA7Y3lRBVNHd+46Q0cEsrqzeOP2Dr++pLyGHH+6Z70NjTHJIZwzTBF7J4w097GEsLmihrLKmpi2GAoK+DtXNWR9CIwxsRBOiWArsFREXsVpQvpNYL6I3A6gqr+IYnydFqs5CJoTyPJRVdvAztp6MtLDOdR7c6aotGohY0x0hXN2muP+BX0UpViiwosWQ0HBJqRlFbUMzu5AIiiv4YD+0b9QZIxJbuE0H436xdxoWlFcQX6v7vTOiM3QEqF29y6urGZwdka7XlvX0EhZZY01HTXGRF04rYZOEJH5IlIqIltFZJuIJMwMZbGcg6CpzgwzUVZZg6p1JjPGRF849RX3AKcBiwl/aIm4UFPfQGFpJcfum+vJ9ncPPNeBEUiDM5P1sxKBMSbKwkkEG4HPVTWhkgDA6tIq6hvVsxJB38x0RDpWIrC5io0xsRJOIvg18D8RmQfsPqOp6qxoBRUpwQvFXiWCtNQUsjPTO5QISoOdyaxqyBgTZeEkgj/hDC/RmwSrGlpeVI7Pg6ElQuX4fR3qXVxSXkNqipCdaYnAGBNd4SSCQap6QNQjiYLlxeWM7pdFaop4FkNHxxsqKa8m4Pd5GrsxJjmEkwheFZFjVfWtqEcTYXecPoFtVXWexhDw+1izuardr3PmKrbSgDEm+sJJBD8CrhCRnTijjwqgqto3qpFFQG5Wd8+HaAhk+dhcWYOq4kzQFp7S8pp29z0wxpiOCGesoRygG9ALCLj3Yz+CW4IKZPmorW+koqa+7YVDlFRUW9NRY0xMtJkIVLUBOBX4jXs7H5gQ7cC6ipwOTGJfXdfA9p11VjVkjImJcHoW3wNMBc5xH9oJ3B/NoLqSjvQuDi5rw0sYY2IhnGsEh6vqRBH5DEBVt4pI7AfuSVAdSQTWmcwYE0vhXCOoE5EUnCGoEZFsEqw/gZeCVUPt6UsQHF7CqoaMMbHQYiIQkWBp4V7gWSAgIn8C3gNujkFsXULvHt1IS5F2lQh2z1Vsk9IYY2KgtaqhT4CJqvpPEVkIHI/TdPRUVV0Sk+i6gJQUIaedM5WVlleTnpZC74xuUYzMGGMcrSWC3Y3eVXUpsDT64XRNgaz2DTNR4nYma0+/A2OM6ajWEkFARFqchlJVb49CPF1Sjj+dze28RmDVQsaYWGktEaQCfkJKBqZjAlk+lrkjoYajpKKa/fp5M2KqMSb5tJYIilT1zzGLpAtzqoZqaWxUUsIYRK60vIZjRlmLIWNMbLTWfNRKAhES8PtoaFS272p7ALzKmnoqa+qtD4ExJmZaSwTHxSyKLi6nHZ3K9nQmsxKBMSY2WkwEqpowE9THu0A7xhsqsT4ExpgYC6dnsemk3cNMVFa3uWxpsFdxL0sExpjYsEQQA8GqobKK2jaXtXGGjDGxZokgBrJ8afjSUsLqS1BSXkNmeip+XzjjARpjTOdZIogBEQl77uKSimorDRhjYsoSQYyEO8xEaXk1udZiyBgTQ5YIYiTcgeecSeutRGCMiR1LBDESTtWQqjrjDFkiMMbEkCWCGAn4fWzdWUt9Q8tz+uzYVUdtfaMlAmNMTFkiiJFAlg9V2FrVchNSm5nMGOOFqCUCERkkInNFZJmILBWRy93H+4rI6yJS4P7vE60Y4klwysrSVqqHrA+BMcYL0SwR1AO/VNUxwKHAxSIyBrgKeFNVRwJvuve7vD29i8NIBDa8hDEmhqKWCFS1SFU/dW9XAMuBAcAM4BF3sUeA70YrhniSu7t3cduJwJqPGmNiKSbXCERkCHAg8DGQp6pF7lPFQF4LrzlfRBaIyILNmzfHIsyoClYNtV4iqKFXj25075Yaq7CMMSb6iUBE/MCzwM9Vda9pulRVAW3udao6W1Unq+rkQCAQ7TCjroc7bERrTUhLyqvpZ9cHjDExFtVEICLdcJLAo6r6nPtwiYjku8/nA6XRjCGetNWXoKSixqqFjDExF81WQwL8DVjeZKL7F4Dz3NvnAc9HK4Z4E/C3PsxEqfUqNsZ4IJolgiOAc4BjReRz928acBPwDREpAI537yeFnKz0FksEjY1KaUWN9SEwxsRc1MY6VtX3aHne46ScBjPg9/FeRVmzz22pqqWhUa1EYIyJOetZHEOBLB/l1fVU1zV87bndTUetD4ExJsYsEcRQsFPZlmaGmbBJ640xXrFEEEM5rUxiHxxnqJ/NVWyMiTFLBDG0e5iJZhNBNSJ7koUxxsSKJYIYCiaC5pqQllZUk53po1uqvSXGmNiys04MZWe2XjVk1weMMV6wRBBD6Wkp9M7o1mLVkDUdNcZ4wRJBjAVamLvYSQRWIjDGxJ4lghgLZH19mIm6hkbKKmutD4ExxhOWCGIsx+/72lDUwRKCNR01xnjBEkGMNTcCqXUmM8Z4yRJBjAWyfOysbaCqpn73Y8HOZFY1ZIzxgiWCGAv4v96XoLTCJq03xnjHEkGM5TTTu7ikvJrUFCE7M92rsIwxScwSQYwFmhlvqHhHDblZPlJSWhq12xhjoscSQYw1N8xEaUU1uVYtZIzxiCWCGOubmU6KfL1qqJ+1GDLGeMQSQYylpgh9M/fuS+CMM2QlAmOMNywReCC0L0F1XQM7dtVZIjDGeMYSgQcCWT42VzqzlJXu7kNgVUPGGG9YIvBAjj+dMrdEUGJ9CIwxHrNE4IFg1ZCqUrzDEoExxluWCDwQ8PuobWikvLrexhkyxnjOEoEHQucuLq2owZeWQq8e3TyOyhiTrCwReCC0d3FwZjIR61VsjPGGJQIP7C4RVNbYzGTGGM9ZIvDA7mEmKmooLa+x4SWMMZ6yROCBXj260S1VKK2oobi8mjybh8AY4yFLBB4QEXL8PtaVVbGztsGqhowxnrJE4JFAlo8lX+0ArA+BMcZblgg8EvD72LhtF2CJwBjjLUsEHsnx76kOsqohY4yXLBF4JBAyyJy1GjLGeMkSgUeCicDvS8PvS/M4GmNMMrNE4JFgIsi1aiFjjMc8SQQicoKIrBSRQhG5yosYvBa8RmB9CIwxXot5IhCRVOBe4NvAGOBMERkT6zi8FiwR2IViY4zXvCgRHAwUquoaVa0FngBmeBCHp/YkAisRGGO85UUiGABsCLm/0X1sLyJyvogsEJEFmzdvjllwseL3pfGbE/bllEkDvQ7FGJPk4vZisarOVtXJqjo5EAh4HU5UXDhlOKPysrwOwxiT5LxIBJuAQSH3B7qPGWOM8YAXiWA+MFJEhopIOnAG8IIHcRhjjAFi3pNJVetF5BLgVSAV+LuqLo11HMYYYxyedGlV1ZeAl7zYtjHGmL3F7cViY4wxsWGJwBhjkpwlAmOMSXKWCIwxJsmJqnodQ5tEZDOwvoMvzwHKIhhOorLj4LDjsIcdC0dXPg77qGqbPXITIhF0hogsUNXJXsfhNTsODjsOe9ixcNhxsKohY4xJepYIjDEmySVDIpjtdQBxwo6Dw47DHnYsHEl/HLr8NQJjjDGtS4YSgTHGmFZYIjDGmCTXpROBiJwgIitFpFBErvI6Hq+IyDoRWSwin4vIAq/jiRUR+buIlIrIkpDH+orI6yJS4P7v42WMsdDCcfijiGxyPxOfi8g0L2OMBREZJCJzRWSZiCwVkcvdx5PuM9FUl00EIpIK3At8GxgDnCkiY7yNylNTVXVCkrWX/gdwQpPHrgLeVNWRwJvu/a7uH3z9OADc4X4mJrgjAnd19cAvVXUMcChwsXtOSMbPxF66bCIADgYKVXWNqtYCTwAzPI7JxJCqvgNsbfLwDOAR9/YjwHdjGpQHWjgOSUdVi1T1U/d2BbAcZ770pPtMNNWVE8EAYEPI/Y3uY8lIgddEZKGInO91MB7LU9Ui93YxkOdlMB67REQWuVVHSVUdIiJDgAOBj7HPRJdOBGaPI1V1Ik412cUicrTXAcUDddpOJ2v76fuA4cAEoAi4zdtwYkdE/MCzwM9VtTz0uWT9THTlRLAJGBRyf6D7WNJR1U3u/1LgPzjVZsmqRETyAdz/pR7H4wlVLVHVBlVtBB4kST4TItINJwk8qqrPuQ8n/WeiKyeC+cBIERkqIunAGcALHscUcyKSKSJZwdvAN4Elrb+qS3sBOM+9fR7wvIexeCZ44nOdTBJ8JkREgL8By1X19pCnkv4z0aV7FrtN4u4EUoG/q+r1HocUcyIyDKcUAM4c1Y8ly3EQkceBKTjDDJcA1wL/BZ4CBuMMbX6aqnbpC6ktHIcpONVCCqwDfhZST94liciRwLvAYqDRffi3ONcJkuoz0VSXTgTGGGPa1pWrhowxxoTBEoExxiQ5SwTGGJPkLBEYY0ySs0RgjDFJzhKBMS4RGRI6QqcxycISgTFRJCJpXsdgTFssERizt1QRedAdr/41EekhIhNE5CN3gLb/BAdoE5F5IjLZvZ0jIuvc2z8QkRdE5C3gTRHJF5F33HH/l4jIUd7tnjFfZ4nAmL2NBO5V1f2B7cApwD+B36jqOJxeqdeGsZ6JwExVPQY4C3hVVScA44HPoxK5MR1kxVZj9rZWVYMn6oU4I3T2VtW33cceAZ4OYz2vhwxTMB/4uzvg2X9D1m9MXLASgTF7qwm53QD0bmXZevZ8h7o3ea4qeMOdGOZonNFv/yEi50YgTmMixhKBMa3bAWwLqdc/BwiWDtYBk9zbM1tagYjsA5So6oPAQzjVRsbEDasaMqZt5wH3i0gGsAb4ofv4X4Cn3Fnf5rTy+inAlSJSB1QCViIwccVGHzXGmCRnVUPGGJPkLBEYY0ySs0RgjDFJzhKBMcYkOUsExhiT5CwRGGNMkrNEYIwxSe7/Ae74ALD7I5Z7AAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -66,7 +66,8 @@ "%matplotlib inline\n", "\n", "# axis x, axis y\n", - "y = [33,66,65,0,59,60,62,64,70,76,80,81,80,83,90,79,61,53,50,49,53,48,45,39]\n", + "#y = [33,66,65,0,59,60,62,64,70,76,80,81,80,83,90,79,61,53,50,49,53,48,45,39]\n", + "y = [33,66,65,0,59,60,62,64,70,76,80,69,80,83,68,79,61,53,50,49,53,48,45,39]\n", "x = list(range(len(y)))\n", "\n", "# plot\n", @@ -101,33 +102,60 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The minimum temperature is 0\n", + "The maximum temperature is 83\n", + "Temperatures equal to or greater than 70ºC are [70, 76, 80, 80, 83, 79]\n", + "The mean temperature throughout the day is 58.833333333333336\n", + "The NEW mean temperature throughout the day is 61.391304347826086\n", + "Corrected list of temperatures is:\n", + " [33, 66, 65, 61, 59, 60, 62, 64, 70, 76, 80, 69, 80, 83, 68, 79, 61, 53, 50, 49, 53, 48, 45, 39]\n", + "The list of temperatures in ºFarenheit:\n", + " [91, 150, 149, 141, 138, 140, 143, 147, 158, 168, 176, 156, 176, 181, 154, 174, 141, 127, 122, 120, 127, 118, 113, 102]\n" + ] + } + ], "source": [ "# assign a variable to the list of temperatures\n", - "\n", + "import math\n", + "temperatures_C = [33,66,65,0,59,60,62,64,70,76,80,69,80,83,68,79,61,53,50,49,53,48,45,39]\n", "# 1. Calculate the minimum of the list and print the value using print()\n", - "\n", + "print('The minimum temperature is', min(temperatures_C))\n", "\n", "# 2. Calculate the maximum of the list and print the value using print()\n", - "\n", + "print('The maximum temperature is', max(temperatures_C))\n", "\n", "# 3. Items in the list that are greater than 70ºC and print the result\n", - "\n", - "\n", + "over70 = []\n", + "for i in temperatures_C:\n", + " if i >= 70:\n", + " over70.append(i)\n", + "print('Temperatures equal to or greater than 70ºC are', over70)\n", "# 4. Calculate the mean temperature throughout the day and print the result\n", - "\n", + "average = sum(temperatures_C)/len(temperatures_C)\n", + "print('The mean temperature throughout the day is', average)\n", "\n", "# 5.1 Solve the fault in the sensor by estimating a value\n", - "\n", + "new_average = sum(temperatures_C)/(len(temperatures_C) - 1)\n", + "print('The NEW mean temperature throughout the day is', new_average)\n", "\n", "# 5.2 Update of the estimated value at 03:00 on the list\n", - "\n", - "\n", - "\n", + "index = temperatures_C.index(0)\n", + "temperatures_C.pop(index)\n", + "temperatures_C.insert(index, math.trunc(new_average))\n", + "print('Corrected list of temperatures is:\\n', temperatures_C)\n", "# Bonus: convert the list of ºC to ºFarenheit\n", - "\n" + "temperatures_F = []\n", + "for j in temperatures_C:\n", + " f = math.trunc(j * 1.8 + 32)\n", + " temperatures_F.append(f)\n", + "print('The list of temperatures in ºFarenheit:\\n', temperatures_F)" ] }, { @@ -144,12 +172,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ "# Print True or False depending on whether you would change the cooling system or not\n", - "\n" + "print(True)\n" ] }, { @@ -165,42 +201,98 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The hours whose temperature exceeds 70ºC are:\n", + " [8, 9, 10, 12, 13, 15]\n" + ] + } + ], "source": [ "# 1. We want the hours (not the temperatures) whose temperature exceeds 70ºC\n", - "\n" + "hours = []\n", + "n = -1\n", + "for i in temperatures_C:\n", + " n += 1\n", + " if i >= 70:\n", + " hours.append(n)\n", + "print('The hours whose temperature exceeds 70ºC are:\\n', hours)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 6 hours whose temperature exceeds 70ºC during the day\n" + ] + } + ], "source": [ - "# 2. Condition that those hours are more than 4 consecutive and consecutive, not simply the sum of the whole set. Is this condition met?\n", - "\n" + "# 2. Condition that those hours are more than 4 consecutive and consecutive, not simply the sum of the whole set. \n", + "# Is this condition met?\n", + "print('There are {0} hours whose temperature exceeds 70ºC during the day'.format(len(hours)))\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average C = 61.391304347826086\n", + "Average C to F = 142.50434782608696\n", + "Average F = 148.34782608695653\n" + ] + } + ], "source": [ "# 3. Average of each of the lists (ºC and ºF). How they relate?\n", - "\n" + "average_C = new_average\n", + "average_F = sum(temperatures_F)/(len(temperatures_F) - 1)\n", + "print('Average C =', average_C)\n", + "print('Average C to F =', average_C * 1.8 + 32)\n", + "print('Average F =', average_F)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Standard deviation of C lits is 13.331282889377793\n", + "Standard deviation of C lits is 24.81084518830096\n" + ] + } + ], "source": [ "# 4. Standard deviation of each of the lists. How they relate?\n", - "\n" + "numerator_C = 0\n", + "for i in temperatures_C:\n", + " numerator_C += (i - average_C)**2\n", + "s2C = numerator_C/(len(temperatures_C) - 1)\n", + "print('Standard deviation of C list is', math.sqrt(s2C))\n", + "numerator_F = 0\n", + "for i in temperatures_F:\n", + " numerator_F += (i - average_F)**2\n", + "s2F = numerator_F/(len(temperatures_F) - 1)\n", + "print('Standard deviation of C list is', math.sqrt(s2F))" ] }, { @@ -227,7 +319,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.3" } }, "nbformat": 4, From 29a7e270d936379a885972db6efcfb2e856f85d4 Mon Sep 17 00:00:00 2001 From: Alona Sorochynska Date: Wed, 31 Jul 2019 21:53:48 +0100 Subject: [PATCH 3/4] one more lab is ready! --- .../images/rpsls.jpg" | Bin 22301 -> 0 bytes .../rock-paper-scissors.ipynb" | 179 ------------------ snail-and-well/snail-and-well.ipynb | 43 ++++- 3 files changed, 33 insertions(+), 189 deletions(-) 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