Exercises 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.1: Make a dictionary from a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Avogadro number': 6.0221367e+23,\n",
       " 'Boltzmann constant': 1.380658e-23,\n",
       " 'Planck constant': 6.6260755e-34,\n",
       " 'electron mass': 9.1093897e-31,\n",
       " 'elementary charge': 1.60217733e-19,\n",
       " 'gravitational constant': 6.67259e-11,\n",
       " 'proton mass': 1.6726231e-27,\n",
       " 'speed of light': 299792458.0}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "def get_constants():\n",
    "    data = requests.get(\"https://raw.githubusercontent.com/hplgit/scipro-primer/master/src/dictstring/constants.txt\").text\n",
    "    return {' '.join(row[:-2]): float(row[-2]) for row in [line.split() for line in data.splitlines()[2:]]}\n",
    "    \n",
    "get_constants()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.2: Explore syntax differences: lists vs. dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t2 = [0] * 2\n",
    "t2[0] = -5\n",
    "t2[1] = 10.5\n",
    "\n",
    "# or\n",
    "t3 = []\n",
    "t3.append(-5)\n",
    "t3.append(10.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.3: Use string operations to improve a program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Earth core': 13.0,\n",
       " 'Earth mean': 5.52,\n",
       " 'Moon': 3.3,\n",
       " 'Sun core': 160.0,\n",
       " 'Sun mean': 1.4,\n",
       " 'air': 0.0012,\n",
       " 'gasoline': 0.67,\n",
       " 'gold': 18.9,\n",
       " 'granite': 2.7,\n",
       " 'human body': 1.03,\n",
       " 'ice': 0.9,\n",
       " 'iron': 7.8,\n",
       " 'limestone': 2.6,\n",
       " 'mercury': 13.6,\n",
       " 'platinium': 21.4,\n",
       " 'proton': 230000000000000.0,\n",
       " 'pure water': 1.0,\n",
       " 'seawater': 1.025,\n",
       " 'silver': 10.5}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read_densities_1():\n",
    "    data = requests.get(\"https://raw.githubusercontent.com/hplgit/scipro-primer/master/src/dictstring/densities.dat\").text\n",
    "    return {' '.join(row[:-1]): float(row[-1]) for row in [line.split() for line in data.splitlines()]}\n",
    "\n",
    "read_densities_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Earth core': 13.0,\n",
       " 'Earth mean': 5.52,\n",
       " 'Moon': 3.3,\n",
       " 'Sun core': 160.0,\n",
       " 'Sun mean': 1.4,\n",
       " 'air': 0.0012,\n",
       " 'gasoline': 0.67,\n",
       " 'gold': 18.9,\n",
       " 'granite': 2.7,\n",
       " 'human body': 1.03,\n",
       " 'ice': 0.9,\n",
       " 'iron': 7.8,\n",
       " 'limestone': 2.6,\n",
       " 'mercury': 13.6,\n",
       " 'platinium': 21.4,\n",
       " 'proton': 230000000000000.0,\n",
       " 'pure water': 1.0,\n",
       " 'seawater': 1.025,\n",
       " 'silver': 10.5}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read_densities_2():\n",
    "    data = requests.get(\"https://raw.githubusercontent.com/hplgit/scipro-primer/master/src/dictstring/densities.dat\").text\n",
    "    return {name.strip(): float(value) for name, value in [(line[:12], line[12:]) for line in data.splitlines()]}\n",
    "\n",
    "read_densities_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_densities():\n",
    "    assert read_densities_1() == read_densities_2()\n",
    "test_densities()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.4: Interpret output from a progam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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smw7rPrfKofEL0PZf1iEl4VTkkJEQBdDVG6l8seowc7edwsfTjTc71eSJZlVw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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bd6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "with open(\"stuff/lnsum.txt\") as f:\n",
    "    data = f.readlines()[24:]\n",
    "\n",
    "epsilon = np.array([float(line[9:14]) for line in data])\n",
    "error = np.array([float(line[29:37]) for line in data])\n",
    "n = np.array([int(line[41:]) for line in data])\n",
    "\n",
    "plt.semilogy(n, epsilon, n, error)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.5: Make a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alpha Centauri A': 1.56,\n",
       " 'Alpha Centauri B': 0.45,\n",
       " 'Alpha Centauri C': 6e-05,\n",
       " 'BD +36 degrees 2147': 0.006,\n",
       " \"Barnard's Star\": 0.0005,\n",
       " 'Luyten 726-8 A': 6e-05,\n",
       " 'Luyten 726-8 B': 4e-05,\n",
       " 'Ross 154': 0.0005,\n",
       " 'Sirius A': 23.6,\n",
       " 'Sirius B': 0.003,\n",
       " 'Wolf 359': 2e-05}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exec(requests.get(\"https://raw.githubusercontent.com/hplgit/scipro-primer/master/src/funcif/stars.txt\").text)\n",
    "d = {row[0]: row[-1] for row in data}\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.6: Make a nested dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Alpha Centauri A': {'apparent brightness': 0.26,\n",
      "                      'distance': 4.3,\n",
      "                      'luminosity': 1.56},\n",
      " 'Alpha Centauri B': {'apparent brightness': 0.077,\n",
      "                      'distance': 4.3,\n",
      "                      'luminosity': 0.45},\n",
      " 'Alpha Centauri C': {'apparent brightness': 1e-05,\n",
      "                      'distance': 4.2,\n",
      "                      'luminosity': 6e-05},\n",
      " 'BD +36 degrees 2147': {'apparent brightness': 0.0003,\n",
      "                         'distance': 8.2,\n",
      "                         'luminosity': 0.006},\n",
      " \"Barnard's Star\": {'apparent brightness': 4e-05,\n",
      "                    'distance': 6.0,\n",
      "                    'luminosity': 0.0005},\n",
      " 'Luyten 726-8 A': {'apparent brightness': 3e-06,\n",
      "                    'distance': 8.4,\n",
      "                    'luminosity': 6e-05},\n",
      " 'Luyten 726-8 B': {'apparent brightness': 2e-06,\n",
      "                    'distance': 8.4,\n",
      "                    'luminosity': 4e-05},\n",
      " 'Ross 154': {'apparent brightness': 2e-05,\n",
      "              'distance': 9.4,\n",
      "              'luminosity': 0.0005},\n",
      " 'Sirius A': {'apparent brightness': 1.0, 'distance': 8.6, 'luminosity': 23.6},\n",
      " 'Sirius B': {'apparent brightness': 0.001,\n",
      "              'distance': 8.6,\n",
      "              'luminosity': 0.003},\n",
      " 'Wolf 359': {'apparent brightness': 1e-06,\n",
      "              'distance': 7.7,\n",
      "              'luminosity': 2e-05}}\n"
     ]
    }
   ],
   "source": [
    "d = {row[0]: {\"distance\": row[1], \"apparent brightness\": row[2], \"luminosity\": row[3]} for row in data}\n",
    "from pprint import pprint\n",
    "pprint(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.7: Make a nested dictionary from a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'H. erectus': {'brain volume': '850 (early) - 1100 (late)',\n",
      "                'height': '1.8',\n",
      "                'mass': '60',\n",
      "                'when': '1.4 - 0.2'},\n",
      " 'H. ergaster': {'brain volume': '700 - 850',\n",
      "                 'height': '1.9',\n",
      "                 'mass': '',\n",
      "                 'when': '1.9 - 1.4'},\n",
      " 'H. floresiensis': {'brain volume': '400',\n",
      "                     'height': '1.0',\n",
      "                     'mass': '25',\n",
      "                     'when': '0.10 - 0.012'},\n",
      " 'H. habilis': {'brain volume': '660',\n",
      "                'height': '1.0 - 1.5',\n",
      "                'mass': '33 - 55',\n",
      "                'when': '2.2 - 1.6'},\n",
      " 'H. heidelbergensis': {'brain volume': '1100 - 1400',\n",
      "                        'height': '1.8',\n",
      "                        'mass': '60',\n",
      "                        'when': '0.6 - 0.35'},\n",
      " 'H. neanderthalensis': {'brain volume': '1200 - 1700',\n",
      "                         'height': '1.6',\n",
      "                         'mass': '55 - 70',\n",
      "                         'when': '0.35 - 0.03'},\n",
      " 'H. sapiens sapiens': {'brain volume': '1000 - 1850',\n",
      "                        'height': '1.4 - 1.9',\n",
      "                        'mass': '50 - 100',\n",
      "                        'when': '0.2 - present'}}\n"
     ]
    }
   ],
   "source": [
    "data = requests.get(\"https://raw.githubusercontent.com/hplgit/scipro-primer/master/src/dictstring/human_evolution.txt\").text.splitlines()[3:-3]\n",
    "humans = {row[:19].strip(): {\n",
    "    'when': row[21:34].strip(),\n",
    "    'height': row[37:46].strip(),\n",
    "    'mass': row[50:58].strip(),\n",
    "    'brain volume': row[62:].strip()\n",
    "} for row in data}\n",
    "pprint(humans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.8: Make a nested dictionary from a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Viscosity data of some gases\n",
      "# Sutherland's formula for the viscosity:\n",
      "#  mu = mu_0*(T_0 + C)/(T + C)(T/T_0)**1.5\n",
      "# Column 1: gas name\n",
      "# Column 2: C (in Kelvin) (Sutherland's constant)\n",
      "# Column 3: T_0 (in Kelvin)\n",
      "# Column 4: mu_0 (in 10**-6 Pa*s)\n",
      "\n",
      "#   gas           C     T_0     mu_0\n",
      "air              120   291.15   18.27\n",
      "nitrogen         111   300.55   17.81\n",
      "oxygen           127   292.25   20.18\n",
      "carbon dioxide   240   293.15   14.8\n",
      "carbon monoxide  118   288.15   17.2\n",
      "hydrogen          72   293.85   8.76\n",
      "ammonia          370   293.15   9.82\n",
      "sulphur dioxide  416   293.65   12.54\n",
      "\n",
      "# Source: http://en.wikipedia.org/wiki/Viscosity\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = requests.get(\"https://raw.githubusercontent.com/hplgit/scipro-primer/master/src/dictstring/viscosity_of_gases.dat\").text\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'air': {'C': 120.0, 'T_0': 291.15, 'mu_0': 18.27},\n",
      " 'ammonia': {'C': 370.0, 'T_0': 293.15, 'mu_0': 9.82},\n",
      " 'carbon dioxide': {'C': 240.0, 'T_0': 293.15, 'mu_0': 14.8},\n",
      " 'carbon monoxide': {'C': 118.0, 'T_0': 288.15, 'mu_0': 17.2},\n",
      " 'hydrogen': {'C': 72.0, 'T_0': 293.85, 'mu_0': 8.76},\n",
      " 'nitrogen': {'C': 111.0, 'T_0': 300.55, 'mu_0': 17.81},\n",
      " 'oxygen': {'C': 127.0, 'T_0': 292.25, 'mu_0': 20.18},\n",
      " 'sulphur dioxide': {'C': 416.0, 'T_0': 293.65, 'mu_0': 12.54}}\n"
     ]
    }
   ],
   "source": [
    "data = requests.get(\"https://raw.githubusercontent.com/hplgit/scipro-primer/master/src/dictstring/viscosity_of_gases.dat\").text.splitlines()[9:17]\n",
    "mu_data = {row[:15].strip(): {'C': float(row[17:20]), 'T_0': float(row[23:29]), 'mu_0': float(row[32:])} for row in data}\n",
    "pprint(mu_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mu(T, gas, mu_data):\n",
    "    mu_0 = mu_data[gas]['mu_0']\n",
    "    T_0 = mu_data[gas]['T_0']\n",
    "    C = mu_data[gas]['C']\n",
    "    return mu_0 * (T_0 + C) / (T + C) * (T/T_0)**1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OpQ+bUJ++BuJswa503A1nds0Tt3hq8SjXIiKIq9fN4Ypm9p5u5FhVC71uLxm2OD5bls+W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\nhIhg/x+rSF6SB5h4egAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a5c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = np.linspace(223, 373)\n",
    "plt.figure()\n",
    "plt.plot(T, mu(T, \"air\", mu_data))\n",
    "plt.plot(T, mu(T, \"carbon dioxide\", mu_data))\n",
    "plt.plot(T, mu(T, \"hydrogen\", mu_data))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.9: Compute the area of a triangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def triangle_area(v):\n",
    "    return 1/2 * abs(v[2][0]*v[3][1] - v[3][0]*v[2][1] - v[1][0]*v[3][1] + v[3][0]*v[1][1] + v[1][0]*v[2][1] - v[2][0]*v[1][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triangle_area({1: (0,0), 2: (1,0), 3: (0,2)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 9.10: Compare data structures for polynomials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262.50251569260803\n",
      "262.50251569260803\n"
     ]
    }
   ],
   "source": [
    "poly_list = [-0.5] + [0]*99 + [2]\n",
    "poly_dict = {0: -0.5, 100: 2}\n",
    "\n",
    "def eval_poly_dict(poly, x):\n",
    "    return sum(poly[power]*x**power for power in poly)\n",
    "\n",
    "def eval_poly_list(poly, x):\n",
    "    return sum(poly[power]*x**power for power in range(len(poly)))\n",
    "\n",
    "print(eval_poly_list(poly_list, 1.05))\n",
    "print(eval_poly_dict(poly_dict, 1.05))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.11: Compute the derivative of a polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: 6, 4: -5}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def diff(poly):\n",
    "    dp = poly.copy()\n",
    "    del dp[0]\n",
    "    dp = {k - 1: k * v for (k, v) in dp.items()}\n",
    "    return dp\n",
    "\n",
    "p = {0: -3, 3: 2, 5: -1}\n",
    "diff(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.14: Compare average temperatures in cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Abidjan': 'IVABIDJN.txt',\n",
      " 'Abu Dh': 'ERABUDBI.txt',\n",
      " 'Addis Ababa': 'ETADSABA.txt',\n",
      " 'Algiers': 'ALALGIER.txt',\n",
      " 'Almaty': 'RAALMATY.txt',\n",
      " 'Amman': 'JDAMMAN.txt',\n",
      " 'Amsterdam': 'NLAMSTDM.txt',\n",
      " 'Ankara': 'TUANKARA.txt',\n",
      " 'Antananarivo': 'MGANTRVO.txt',\n",
      " 'Ashabad': 'RAASHBAD.txt',\n",
      " 'Athens': 'GRATHENS.txt',\n",
      " 'Auckland': 'NZACKLND.txt',\n",
      " 'Bangkok': 'THBNGKOK.txt',\n",
      " 'Bangui': 'CEBANGUI.txt',\n",
      " 'Banjul': 'GBBANJUL.txt',\n",
      " 'Barcelona': 'SPBRCLNA.txt',\n",
      " 'Beijing': 'CIBIEJNG.txt',\n",
      " 'Beirut': 'LBBEIRUT.txt',\n",
      " 'Belfast': 'UKBELFST.txt',\n",
      " 'Belgrade': 'YGBLGRDE.txt',\n",
      " 'Belize City': 'BHBELIZE.txt',\n",
      " 'Bern': 'SWBERN.txt',\n",
      " 'Bilbao': 'SPBILBAO.txt',\n",
      " 'Bishkek': 'RABSHKEK.txt',\n",
      " 'Bissau': 'GWBISSAU.txt',\n",
      " 'Bogota': 'COBOGOTA.txt',\n",
      " 'Bombay (Mumbai)': 'INBOMBAY.txt',\n",
      " 'Bonn': 'DLBONN.txt',\n",
      " 'Bordeaux': 'FRBRDAUX.txt',\n",
      " 'Brasilia': 'BZBRSLIA.txt',\n",
      " 'Bratislava': 'CZBRTSLV.txt',\n",
      " 'Brazzaville': 'CGBRZAVL.txt',\n",
      " 'Bridgetown': 'BRBRGTWN.txt',\n",
      " 'Brisbane': 'AUBRSBAN.txt',\n",
      " 'Brussels': 'BXBRUSSL.txt',\n",
      " 'Bucharest': 'ROBCHRST.txt',\n",
      " 'Budapest': 'HUBUDPST.txt',\n",
      " 'Buenos Aires': 'AGBUENOS.txt',\n",
      " 'Bujumbura': 'BIBJMBRA.txt',\n",
      " 'Cairo': 'EGCAIRO.txt',\n",
      " 'Calcutta': 'INCALCUT.txt',\n",
      " 'Calgary': 'CNCALGRY.txt',\n",
      " 'Canberra': 'AUCNBERA.txt',\n",
      " 'Capetown': 'ZACAPTWN.txt',\n",
      " 'Caracas': 'VNCARACS.txt',\n",
      " 'Chengdu': 'CICHNGDU.txt',\n",
      " 'Chennai (Madras)': 'INCHENAI.txt',\n",
      " 'Colombo': 'SBCOLMBO.txt',\n",
      " 'Conakry': 'GNCONKRY.txt',\n",
      " 'Copenhagen': 'DNCOPNHG.txt',\n",
      " 'Cotonou': 'BJCOTNOU.txt',\n",
      " 'Dakar': 'SGDAKAR.txt',\n",
      " 'Damascus': 'SYDMSCUS.txt',\n",
      " 'Dar Es Salaam': 'TNDESALM.txt',\n",
      " 'Delhi': 'INDELHI.txt',\n",
      " 'Dhahran': 'SDDHAHRN.txt',\n",
      " 'Dhaka': 'BWDHAKA.txt',\n",
      " 'Doha': 'QTDOHA.txt',\n",
      " 'Du': 'ERDUBAI.txt',\n",
      " 'Dublin': 'IEDUBLIN.txt',\n",
      " 'Dusanbe': 'RADUSNBE.txt',\n",
      " 'Edmonton': 'CNEDMNTN.txt',\n",
      " 'Frankfurt': 'DLFRNKFT.txt',\n",
      " 'Freetown': 'SLFRETWN.txt',\n",
      " 'Geneva': 'SWGENEVA.txt',\n",
      " 'Georgetown': 'GYGRGTWN.txt',\n",
      " 'Guadalajara': 'MXGUADLJ.txt',\n",
      " 'Guangzhou': 'CIGNGZHO.txt',\n",
      " 'Guatemala City': 'GUGUATML.txt',\n",
      " 'Guayaquil': 'EQGUAYQL.txt',\n",
      " 'Halifax': 'CNHLIFAX.txt',\n",
      " 'Hamburg': 'DLHAMBUR.txt',\n",
      " 'Hamilton': 'BEHMLTON.txt',\n",
      " 'Hanoi': 'VSHANOI.txt',\n",
      " 'Havana': 'CUHAVANA.txt',\n",
      " 'Helsinki': 'FIHELSIN.txt',\n",
      " 'Hong Kong': 'HKHONGKG.txt',\n",
      " 'Islamabad': 'PKISLMBD.txt',\n",
      " 'Istanbul': 'TUISTNBL.txt',\n",
      " 'Jakarta': 'IDJAKRTA.txt',\n",
      " 'Kampala': 'UGKAMPAL.txt',\n",
      " 'Karachi': 'PKKARACH.txt',\n",
      " 'Katmandu': 'NPKTMNDU.txt',\n",
      " 'Kiev': 'URKIEV.txt',\n",
      " 'Kuala Lumpur': 'MSKUALA.txt',\n",
      " 'Kuwait': 'KWKUWAIT.txt',\n",
      " 'La Paz': 'BOLAPAZ.txt',\n",
      " 'Lagos': 'NILAGOS.txt',\n",
      " 'Libreville': 'GOLIBRVL.txt',\n",
      " 'Lilongwe': 'MWLILNGW.txt',\n",
      " 'Lima': 'PRLIMA.txt',\n",
      " 'Lisbon': 'POLISBON.txt',\n",
      " 'Lome': 'TGLOME.txt',\n",
      " 'London': 'UKLONDON.txt',\n",
      " 'Lusaka': 'ZBLUSAKA.txt',\n",
      " 'Madrid': 'SPMADRID.txt',\n",
      " 'Managua': 'NKMANAGU.txt',\n",
      " 'Manama': 'BNMANAMA.txt',\n",
      " 'Manila': 'PHMANILA.txt',\n",
      " 'Maputo': 'MZMAPUTO.txt',\n",
      " 'Melbourne': 'AUSYDNEY.txt',\n",
      " 'Mexico City': 'MXMXCITY.txt',\n",
      " 'Milan': 'IYMILAN.txt',\n",
      " 'Minsk': 'YMINSK',\n",
      " 'Monterrey': 'MXMONTRY.txt',\n",
      " 'Montreal': 'CNMONTRL.txt',\n",
      " 'Montvideo': 'UYMNTVID.txt',\n",
      " 'Moscow': 'RSMOSCOW.txt',\n",
      " 'Munich': 'DLMUNICH.txt',\n",
      " 'Muscat': 'OMMUSCAT.txt',\n",
      " 'Nairobi': 'KNNAIROB.txt',\n",
      " 'Nassau': 'BANASSAU.txt',\n",
      " 'Niamey': 'NRNIAMEY.txt',\n",
      " 'Nicosia': 'CYNICOSI.txt',\n",
      " 'Nouakchott': 'MTNOKCHT.txt',\n",
      " 'Osaka': 'JPOSAKA.txt',\n",
      " 'Oslo': 'NOOSLO.txt',\n",
      " 'Ottawa': 'CNOTTOWA.txt',\n",
      " 'Panama City': 'PMPANAMA.txt',\n",
      " 'Paramaribo': 'SMPRMRIB.txt',\n",
      " 'Paris': 'FRPARIS.txt',\n",
      " 'Perth': 'AUPERTH.txt',\n",
      " 'Port au Prince': 'HAPORTAP.txt',\n",
      " 'Prague': 'CZPRAGUE.txt',\n",
      " 'Pristina': 'YGPRISTN.txt',\n",
      " 'Pyongyang': 'KRPYGYNG.txt',\n",
      " 'Quebec': 'CNQUEBEC.txt',\n",
      " 'Quito': 'EQQUITO.txt',\n",
      " 'Rabat': 'MCRABAT.txt',\n",
      " 'Rangoon': 'BMRNGOON.txt',\n",
      " 'Regina': 'CNREGINA.txt',\n",
      " 'Reykjavik': 'ILREYKJV.txt',\n",
      " 'Riga': 'RSRIGA.txt',\n",
      " 'Rio de Janeiro': 'BZRIODJN.txt',\n",
      " 'Riyadh': 'SDRIYADH.txt',\n",
      " 'Rome': 'IYROME.txt',\n",
      " 'San Jose': 'CSSANJOS.txt',\n",
      " 'Santo Domingo': 'DRSNTODM.txt',\n",
      " 'Sao Paulo': 'BZSAOPLO.txt',\n",
      " 'Sapporo': 'JPSAPPOR.txt',\n",
      " 'Seoul': 'KOSEOUL.txt',\n",
      " 'Shanghai': 'CISHANGH.txt',\n",
      " 'Shenyang': 'CISHNYNG.txt',\n",
      " 'Singapore': 'SRSINGAP.txt',\n",
      " 'Skopje': 'YGSKOPJE.txt',\n",
      " 'Sofia': 'BUSOFIA.txt',\n",
      " 'Stockholm': 'SNSTKHLM.txt',\n",
      " 'Sydney': 'AUMELBRN.txt',\n",
      " 'Tai': 'TWTAIPEI.txt',\n",
      " 'Tashkent': 'RATASKNT.txt',\n",
      " 'Tbilisi': 'RSTBLISI.txt',\n",
      " 'Tegucigalpa': 'HOTEGUCI.txt',\n",
      " 'Tel Aviv': 'ISTELAVI.txt',\n",
      " 'Tirana': 'ABTIRANA.txt',\n",
      " 'Tokyo': 'JPTOKYO.txt',\n",
      " 'Toronto': 'CNTORONT.txt',\n",
      " 'Tunis': 'TSTUNIS.txt',\n",
      " 'Ulan-bator': 'MOULNBTR.txt',\n",
      " 'Vancouver': 'CNVANCVR.txt',\n",
      " 'Vienna': 'OSVIENNA.txt',\n",
      " 'Vientiane': 'LAVIENTN.txt',\n",
      " 'Warsaw': 'PLWARSAW.txt',\n",
      " 'Windhoek': 'NMWNDHOK.txt',\n",
      " 'Winnipeg': 'CNWINNPG.txt',\n",
      " 'Yerevan': 'RSYERVAN.txt',\n",
      " 'Zagreb': 'YGZAGREB.txt',\n",
      " 'Zurich': 'SWZURICH.txt'}\n"
     ]
    }
   ],
   "source": [
    "with open(\"stuff/city_temp/citylistWorld.htm\") as f:\n",
    "    data = f.read()\n",
    "    \n",
    "data = data.split(\"'><b>\")[1:]\n",
    "names =  []\n",
    "files = []\n",
    "for row in data:\n",
    "    names.append(row[: row.find('</b>') - 3])\n",
    "    f = row[row.find('\">') + 2 : row.find(\"</a>\")]\n",
    "    files.append(f if f.endswith(\".txt\") else f[f.find(\"'>\") + 2 : f.find(\"</span>\")])\n",
    "\n",
    "filenames = {k: v for k, v in zip(names, files)}\n",
    "\n",
    "from pprint import pprint\n",
    "pprint(filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[datetime.datetime(1995, 1, 1, 0, 0), 34.200000000000003],\n",
       "       [datetime.datetime(1995, 1, 2, 0, 0), 32.600000000000001],\n",
       "       [datetime.datetime(1995, 1, 3, 0, 0), 30.899999999999999],\n",
       "       ..., \n",
       "       [datetime.datetime(2007, 7, 8, 0, 0), 61.0],\n",
       "       [datetime.datetime(2007, 7, 9, 0, 0), 59.799999999999997],\n",
       "       [datetime.datetime(2007, 7, 10, 0, 0), 59.899999999999999]], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from datetime import datetime\n",
    "def open_data(city, filenames):\n",
    "    data = np.loadtxt(\"stuff/city_temp/\" + filenames[city])\n",
    "    data = np.array([[datetime(int(y), int(m), int(d)), t] for m, d, y, t in data])\n",
    "    return data\n",
    "\n",
    "open_data(\"London\", filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m1FoRZ6WACLXwzND2GX5rBWSaLKX5S9aBiK0sAqngmdi6vlMt0/12Jt36v/ST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\nIAiCYIoICEEQBMGU/wfPcyamI8fJ9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189e0d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_city_temp(city_data):\n",
    "    plt.figure()\n",
    "    for city in city_data:\n",
    "        plt.plot(city_data[city][:, 0], city_data[city][:, 1], label=city)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_city_temp({city: open_data(city, filenames) for city in [\"London\", \"Singapore\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.15: Generate an HTML report with figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5sv2LjTE8eGSGiR0jrrsuRlzMBF50OSoJ3GUMuYKhYGrvxeBY2qyn9jXjLvaL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\nZTQwKKWUKqOBQSmlVBkNDEoppcr8f7nnWNO2QAbSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e47a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XbRtu+G0crMBweK7+3s+5fIFUtuBquGok6HO1hEUq23j3NihdyttdKclN8BqN\nNg4Mz51YJOT3sWMs2vB+ADf9yA6OLixxx/2H+PrUYdK5AvmC4X2TW/j0Oy+uOzpMrW1t+T9rjLkb\nuLvi2O+XPE4B76lx7WeBz7ajHWrtuulHdvBfvvI49z47zQ2Xri97bS6R4eBMgne/rn7Hs2OsL8x/\nvjJf95xEpvFeDI6IvUezMaZuYErl8sURR/Xv5yueX8/yfs+N2zgSDXNkvn4wfP54jJ0Tfa6/rYsI\nn/6pS7j6ggnue26a/kiQGy5dz6WbBl1dr9YuDfnqrPCWS9azebiH23/40orA8Lg99n5yW/VO7Erj\ndlmlUDA169dePnQjQT/GWKOO6g27TGcLDZfDcO4HjTOGuMs+BoCxvhBPHlmoe87zJxZ5886xhveq\n9GPnj/Nj5497vk6tXVroU2cFv0/40Ju288ihOZ4+crrstR8eOEU44OPVm919Ux2JhigYWFjK1jzH\nzbaejuJM5QblJPcZg93H0GB0SSKdwye4uudEf5jZeLrmZMH5RIbpxTQXuuhfUEoDgzprvPfKLURD\nfu64/6XiMWMM333+JG/eOeZ6ktSIsw9ynZFJbrb1dERcbtbjNmPosYNRo2U2nHWS3PSrrBuMUDC1\n528sb3CkgUE1poFBnTUGIkHee+UWvvXUMU7a+zY/cXiBV+aSXF+yRHQjY85M4DqdsW629XS4HV6a\nyrkclRRwN/M56XLUFMC6fmu01nSN/a5fOGnN39HAoNzQwKDOKh9603ZyBVPcZvKO+w/RE/Tzjldv\naHDlspG+xktEeO18hsadxW4zhoDfR9AvLmY+512VuoDiJLQTtQLDdIy+cIANqzxjVnUH7XxWZ5Vt\no1FufON2vvTAIU7FM3z76eN85OpX1Zz4Vs1o1Fkiol4pyc4YXM58hsadxW77GMDdLm5xl8uCA6wb\ntH7nk3Uyhp0Tfa7KUkppYFBnnVvediGn4mm+/fRxrr1wgo9evcvT9cO99hIRdUpJcQ9zBNxOSEtn\n649aKruni13cEi4X+QMrGPp9UjNj2D8d5+oLdGSRckcDgzrrRIJ+/vyDV/C/cnlXpZlKAb+Pod5g\n3VJSLGUFhv5IG0tJuXzDvRiW79l4/aVEJl93YcFSfp8w3hdmenFlljSfyHAqntb+BeWa9jGos1Yz\nQcHRaCZwLJXF75Nix3I9zmzmRqOIUh4yBjfLbCTSOaIu+xjAGplUrfPZ6XjeWWfnO6VKaWBQXWk0\nGq67wqrXTpWPAAAUDklEQVRTv3dTcy8OL21rxtB4YT4vpSSA9QNhji0srTiuQ1WVVxoYVFcabbCo\nXCyVc1VGgtKNdWr3MeQLhmzeuO9jcJExeOl8Btg60svh+aUVe0kfOBknGvK7WoBQKdDAoLrUSDRU\nt/M5lsq6HunkZtG7dM7dfs8OK2OoHWhy9oY1XjKGbaNRMrnCig7o/dMxdq7r1xFJyjUNDKorjUZD\nzCcz5AvVt+5Y9JQxNC4lOR/y7vsYfHVLSc48C7fzGAC2jVq70b08u7yYnjGG/dMxzp/Q/gXlngYG\n1ZVG+8IYAwvJ6llDLJWj3+W38XCgcSnJ+ZB3PY+hQR+DlyW3HdtGrOW0X5lLFI9NL6Y5Fc803Ctb\nqVIaGFRXGmmwLEY8nXWdMYiIvSdDvVKSFTTcjqRqNCrJ7baepTYORQj4pCxjeNre4/wylwsQKgUa\nGFSXGm2wLIbV+ex+NnWjb/jNZQy1MxAvS3Y4An4fm4Z7eLlkk6Jnjp7GJ433ylaqlAYG1ZWcZTFm\nqyyLYYzxNCoJnCUs2pcxNBqV5GVZ8FI7x/vYb2/hCVZgeNV4n+62pjxpKTCIyIiI3CsiL9h/Dlc5\n53IReVBE9orIUyLyvpLXviQiL4nIE/bP5a20RymHkzFUG7K6lM2TLxhPGUNPyM+Siz4GN6urgpVZ\nZHKFFUNLHfEmSkkAl24a5MWZOMlMjkLB8Pgr87xmy5CneyjVasZwC3CfMWYXcJ/9vFIS+EVjzCXA\nDcAXRKT0b+pvG2Mut3+eaLE9SgEw3BtCpPr+BM5yGH0eMoZwoH4fQzEweOhjgNojnZrpfAYrMBQM\nPHd8kX3TMeaTWd543qineyjVan65G/gJ+/GdwPeBT5SeYIzZX/L4mIicBMaB+vsQKtUCv08Y6glW\nXWHVCQwDXkpJDfsYnOGq7vsYnOt6qyyH5PQxeM0YXrt1CBH44QuzBAPWvIU3vkoDg/Km1YxhnTHm\nOID950S9k0XkKiAEvFhy+LN2ienzIhKuc+3NIjIlIlMzMzMtNludC0b7wlVLSbGUteWnlz6GngaB\nwZng5mWtJKi9K9zyqCRvfQxjfWGu2DrMvz5znH987AiT24bZONTj6R5KNQwMIvIdEXmmys9uL28k\nIhuAvwN+yRjjFGs/CVwIXAmMUJFtlDLG3GaMmTTGTI6P6/LBqrGRaKhuKcnbqCSfy3kM7j7Iw8H6\nu7g5+z27WeSv0s9csYnnT8R4cSbBz79hm+frlWr4lckYc12t10RkWkQ2GGOO2x/8J2ucNwB8G/g9\nY8xDJfc+bj9Mi8gdwMc9tV6pOsb6Quyfjq84vthExtBoFFGxlORySYxGy2zE0zmiIXeL/FV6/5Vb\nWUhmCfl97L58o+frlWq1j2EPcCPwOfvPb1aeICIh4J+AvzXG/EPFa05QEeBdwDMttkepopFoiNkq\nK6zOJ63AMFytuF9Do1KS14zBOS9dp/PZa/+Cw+8TPnL1zqauVQpa72P4HHC9iLwAXG8/R0QmReRv\n7HPeC/wY8KEqw1K/IiJPA08DY8AftdgepYpGomEWlrLk8uUloAW732Go130pKdxgQprntZJCTsZQ\n/Z6JdN5z/4JS7dJSxmCMmQWurXJ8Cviw/fjLwJdrXH9NK++vVD1jfSGMsTKE8f7lcQ3zySy9Ib+n\njYAaLYmRyuUJ+gW/z13pp7j5T417el1yW6l20pnPqms56yVVjkxaSGY8lZHAXSkp4iHQ9ISsf3r1\nRiU1W0pSqlUaGFTXqrUsxsJS1lMZCawSUa5gyOarl35S2QJhDyOIwi4yBg0MqlM0MKiuVWshvfkm\nMwao/UGezuZdT26Dku1Ca2UMGS0lqc7RwKC6Vu1SUjMZQ/09GVK5vKc5B6Uzn6vRzmfVSRoYVNdy\n1kuqHLLaTMYQbpAxpLIF1yOSYHm+Q60+Bi0lqU7SwKC6lt8nDPeW7/2cLxhOL2UZ9pgxNColpTyW\nkgJ+H0G/VL1fNl8gkyvQp0tlqw7RwKC62mg0VNbHsLiUxRgY8pgxNCr9WIHBW+mn1mzqZnZvU6qd\nNDCorjYxEGY6lio+n7f3gB6ONtfHUKv0k8oWPM2LsO5ZfdJcvMklt5VqFw0MqqttHOzh2MJS8bmz\nHIbXjKFhKSnnrZTk3LPa/RLp5pbcVqpdNDCorrZhqIeTsXRx/sHJRSt7WNcf8XSfSMPhqt46n617\nVp9NHW9yyW2l2kUDg+pqm4YiGAMnTlsBYdoJDAM1t/6oqnEpqbmMIVllddVmd29Tql00MKiutmHQ\n2qTmuB0YTiymCfrF83DV4mqo9TqfPfYxRMMBkpnciuPa+aw6TQOD6mrO7mVOP8PJxRQT/RF8Lhe7\ncxRLSTWWyU7lvJeSekMB4unapSTNGFSnaGBQXW3jkNWXcOy0FRimYynPZSRYDgzVNtbJ5gvkC8Zz\nKakv7NeMQZ2VNDCortYbCjDUG+TovBUYji+kWD/oreMZlmcqVxteuuRxkx5HNBwoBoFSiYwzKkk7\nn1VnaGBQXW/HWJSDMwmy+QKvzCXZPhr1fI/iTOUqpSRnZJGX1VXBCgzxKoEhns4R9IvneRFKtUtL\ngUFERkTkXhF5wf5zuMZ5+ZLd2/aUHN8hIg/b13/N3gZUqbY6f6Kf/dMxXplLkisYXjXe19R9IgF/\n1VJS2uN+z45oKEAqa5WhSuleDKrTWs0YbgHuM8bsAu6zn1ezZIy53P55Z8nxPwY+b18/D9zUYnuU\nWuH89f3MJjI8+tIcAOeNe88YACIhf9U9mr3u9+xwSkWJin6GeDpHVNdJUh3UamDYDdxpP74TeJfb\nC0VEgGuAbzRzvVJuXbShH4CvPnoYgPOazRiCvqp9DF73e3Y4WUFlP0M8pXsxqM5qNTCsM8YcB7D/\nnKhxXkREpkTkIRFxPvxHgQVjjPOv4giwqcX2KLXC5LYR+iMBnjy8wIXr+xns8bZOkqMnWL2U5PQ7\neB2VtBwYyu8ZT+foi2hgUJ3T8G+fiHwHWF/lpU95eJ+txphjInIe8F0ReRpYrHKeqXLMacfNwM0A\nW7du9fDW6lwXCvh4x6s38NVHDvOeyS1N3ycS9NftfPacMdi7uFVmDIupLBMel+xQqp0aBgZjzHW1\nXhORaRHZYIw5LiIbgJM17nHM/vOgiHwfeC3wj8CQiATsrGEzcKxOO24DbgOYnJysGUCUqubTP3UJ\nP/3azVy5ver4CFcigeqL3hVLSU3MfIYqgWEpx85xzRhU57RaStoD3Gg/vhH4ZuUJIjIsImH78Rjw\nZuBZY4wBvge8u971SrVDJOjnqh0jWF1bTd4j5Gepah9Dc6Ukpx8hUVGeiqWyDDRZ7lKqHVoNDJ8D\nrheRF4Dr7eeIyKSI/I19zkXAlIg8iRUIPmeMedZ+7RPAx0TkAFafwxdbbI9SqyYS8JGumjE0V0rq\nrVJKMsawmMoxENHAoDqnpXzVGDMLXFvl+BTwYfvxA8BlNa4/CFzVShuUOlNq7biWyllZRLjJjKF0\nklsykydfMAz0aClJdY7OfFbKpVob6zhZRE+Tw1VL10taTFkbCWnGoDpJA4NSLvWEqu+f0GwpyQkk\npSusLi5ZQaJfA4PqIA0MSrnUawcGa9zEsqVsHr9PCPq9/XPy+YRoyF/Wx1DMGLSUpDpIA4NSLkXD\nAfIFQyZfPjIpmckXO5KbuWdZYFjSUpLqPA0MSrnkfPgnK2YqL7UQGPojAWKpahmDBgbVORoYlHKp\nGBgqOqATmXzTi94N9gQ5bWcJsNzHMKBLYqgO0sCglEu99od/smKm8lImR0+TGcPKwGA91s5n1Uka\nGJRyaXmZ7IqMId2+jCGWztET9BPyuLeDUu2kf/uUcqknuHLeAVilpXZlDKeTWfq1jKQ6TAODUi45\nGUNl53MynWu683mgJ8hiKkvB3sVtLplhJKobGarO0sCglEvFPoaKzmdruGrzpSRjrBISwFxCA4Pq\nPA0MSrm0PFy1opSUaS1jgOVOZw0M6myggUEpl5wO5srO52QmT2+4+T4GoNjPcCqeZlQDg+owDQxK\nueR0MC+VdD7nC4Z0rkBvsLlSkpMdzCUyZHIFYqkco33h1hurVAs0MCjlUijgI+iXsozBGaEUbTJj\nmOi3gsDJWJr5ZAZAS0mq4zQwKOVBbyjAUllgsJfcbrKPYczODmZiaWbjVmDQUpLqNA0MSnlQuRqq\nExianeAWDQeIhvzMxNJMx1IAjPdrKUl1VkuBQURGROReEXnB/nPFTusicrWIPFHykxKRd9mvfUlE\nXip57fJW2qPUaqvck8EJEs1mDGAFgpl4mqPzSwBsGu5prZFKtajVjOEW4D5jzC7gPvt5GWPM94wx\nlxtjLgeuAZLAv5ec8tvO68aYJ1psj1KrKhoOlM18drb6bDZjAJjoj3ByMcXRhSUCPmGiP9JyO5Vq\nRauBYTdwp/34TuBdDc5/N/Cvxphki++rVEf0hvxlnc/tyBgmBsIcP53i2MISG4Yi+H3ScjuVakWr\ngWGdMeY4gP3nRIPz3w98teLYZ0XkKRH5vIjULK6KyM0iMiUiUzMzM621Wqkm9YYqMganj6HJUUkA\nOyf6ODyf5MDJOJuGtIykOq9hYBCR74jIM1V+dnt5IxHZAFwG3FNy+JPAhcCVwAjwiVrXG2NuM8ZM\nGmMmx8fHvby1Um3TFw4QT63sfG52HgPAhev7MQb2Hltk+2i05TYq1aqGf5uNMdfVek1EpkVkgzHm\nuP3Bf7LOrd4L/JMxpriUpJNtAGkRuQP4uMt2K9URlTuuOdlDK6Wk89f1Fx9fummw+cYp1SatlpL2\nADfaj28Evlnn3A9QUUaygwkiIlj9E8+02B6lVlV/JEgslcMYazXURTtItLJU9vbRKJvtkUjXXtSo\nGqvU6mt14ffPAV8XkZuAV4D3AIjIJPCrxpgP28+3A1uA/6i4/isiMg4I8ATwqy22R6lV1R8JkMkX\nSOcKRIJ+YqkcoYCPSLD5jMHnE+66+Q2ksnk2DGofg+q8lgKDMWYWuLbK8SngwyXPDwGbqpx3TSvv\nr9SZVlwNNZUlEvSzmMq2ZX/mzcO9Ld9DqXbRmc9KeeAEAaefYXEpy4Duz6y6jAYGpTzorwgMsVRO\nt+JUXUcDg1Ie9NvZQSxlDa5bTGWL5SWluoUGBqU8cLKDxSXNGFT30sCglAcrMgbtY1BdSAODUh4M\n2WWjhaXlUpJmDKrbaGBQyoPekJ9I0FfcijOVLWjGoLqOBgalPBARRqNhTsXTzCXsrTj7dMc11V00\nMCjl0WhfiLlEhlPxNLC8PadS3UIDg1IejURDzMYzzMQ0MKjupIFBKY9Go2HmEhlm7IxhQvdoVl1G\nA4NSHo32hZhNpLWUpLqWBgalPJroD5PKFnjxZIJoyN/SXgxKnY00MCjl0Y4xa5e1hw7OMq5lJNWF\nNDAo5dF5430AHF1YYptuxam6kAYGpTzaMry8mc554xoYVPdpKTCIyHtEZK+IFOxd22qdd4OI7BOR\nAyJyS8nxHSLysIi8ICJfExGdKaTOegG/r7gV5+t3jHa4NUq1X6sZwzPAzwA/qHWCiPiBW4G3ARcD\nHxCRi+2X/xj4vDFmFzAP3NRie5Q6I/73+1/Lr12zk+t0j2bVhVoKDMaY54wx+xqcdhVwwBhz0BiT\nAe4CdouIANcA37DPuxN4VyvtUepMed22YT72lgsI+LUaq7rPmfhbvQk4XPL8iH1sFFgwxuQqjiul\nlOqghusFi8h3gPVVXvqUMeabLt5DqhwzdY7XasfNwM0AW7dudfG2SimlmtEwMBhjrmvxPY4AW0qe\nbwaOAaeAIREJ2FmDc7xWO24DbgOYnJysGUCUUkq15kyUkh4FdtkjkELA+4E9xhgDfA94t33ejYCb\nDEQppdQqanW46k+LyBHgjcC3ReQe+/hGEbkbwM4GPgrcAzwHfN0Ys9e+xSeAj4nIAaw+hy+20h6l\nlFKtE+uL+9oyOTlppqamOt0MpZRaU0TkMWNMzTlnDh1rp5RSqowGBqWUUmXWZClJRGaAlzvdDo/G\nsEZinUv0dz436O+8dmwzxow3OmlNBoa1SESm3NT2uon+zucG/Z27j5aSlFJKldHAoJRSqowGhjPn\ntk43oAP0dz436O/cZbSPQSmlVBnNGJRSSpXRwNABIvJxETEiMtbptqw2EflTEXleRJ4SkX8SkaFO\nt2m11NqpsFuJyBYR+Z6IPGfv5PjrnW7TmSAifhH5TxH5l063ZbVoYDjDRGQLcD3wSqfbcobcC1xq\njHk1sB/4ZIfbsyoa7FTYrXLAbxljLgLeAHzkHPidAX4da923rqWB4cz7PPA71Nl7opsYY/69ZDOm\nh7CWV+9GVXcq7HCbVpUx5rgx5nH7cQzrw7KrN9sSkc3ATwJ/0+m2rCYNDGeQiLwTOGqMebLTbemQ\nXwb+tdONWCW1dio8J4jIduC1wMOdbcmq+wLWF7tCpxuymhpu1KO8qbfjHfC7wFvObItWn5td/kTk\nU1ilh6+cybadQZ52JOwmItIH/CPwG8aYxU63Z7WIyDuAk8aYx0TkJzrdntWkgaHNau14JyKXATuA\nJ0UErJLK4yJylTHmxBlsYts12uVPRG4E3gFca7p3fHStnQq7mogEsYLCV4wx/6/T7VllbwbeKSJv\nByLAgIh82Rjz8x1uV9vpPIYOEZFDwKQxZi0uxOWaiNwA/Bnw48aYmU63Z7WISACrc/1a4CjWzoUf\nLNmUquuI9Q3nTmDOGPMbnW7PmWRnDB83xryj021ZDdrHoFbbnwP9wL0i8oSI/GWnG7QaGuxU2K3e\nDPwCcI39//YJ+9u0WuM0Y1BKKVVGMwallFJlNDAopZQqo4FBKaVUGQ0MSimlymhgUEopVUYDg1JK\nqTIaGJRSSpXRwKCUUqrM/wcWWdtkgDfrowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e65ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jly8++hLZwtkPzUf2nybREqFva+VlHMp5fz3X2rxmIuCIn6DF52zA4arONZGa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e896fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import exp, sin, pi, linspace\n",
    "f = lambda x, t: exp(-(x - 3*t)**2) * sin(3*pi * (x - t))\n",
    "x = linspace(-5, 5, 1000)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x, f(x, -1))\n",
    "plt.savefig(\"stuff/wavepacket1.png\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x, f(x, 0))\n",
    "plt.savefig(\"stuff/wavepacket2.png\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x, f(x, 1))\n",
    "plt.savefig(\"stuff/wavepacket3.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# file: code/615.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.16: Allow different types for a function argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACA\n",
      "ACA\n"
     ]
    }
   ],
   "source": [
    "def find_consensus_v1(frequency_matrix):\n",
    "    base2index = {'A': 0, 'C': 1, 'G': 2, 'T': 3}\n",
    "    consensus = \"\"\n",
    "    dna_length = len(frequency_matrix[0])\n",
    "    \n",
    "    for i in range(dna_length):    # loop over positions in string \n",
    "        max_freq = -1              # holds the max freq. for this i \n",
    "        max_freq_base = None       # holds the corresponding base\n",
    "        \n",
    "        for base in \"ATGC\":\n",
    "            if frequency_matrix[base2index[base]][i] > max_freq:\n",
    "                max_freq = frequency_matrix[base2index[base]][i]\n",
    "                max_freq_base = base\n",
    "            elif frequency_matrix[base2index[base]][i] == max_freq:\n",
    "                max_freq_base = \"-\"     # more than one base as max\n",
    "                \n",
    "        consensus += max_freq_base     # add new base with max freq return consensus\n",
    "        \n",
    "    return consensus\n",
    "\n",
    "def find_consensus_v2(frequency_matrix):\n",
    "    consensus = \"\"\n",
    "    dna_length = len(frequency_matrix['A'])\n",
    "    \n",
    "    for i in range(dna_length):    # loop over positions in string \n",
    "        max_freq = -1              # holds the max freq. for this i \n",
    "        max_freq_base = None       # holds the corresponding base\n",
    "        \n",
    "        for base in \"ACGT\":\n",
    "            if frequency_matrix[base][i] > max_freq:\n",
    "                max_freq = frequency_matrix[base][i]\n",
    "                max_freq_base = base\n",
    "            elif frequency_matrix[base][i] == max_freq:\n",
    "                max_freq_base = \"-\"     # more than one base as max\n",
    "\n",
    "        consensus += max_freq_base    # add new base with max freq return consensus\n",
    "    \n",
    "    return consensus\n",
    "\n",
    "def find_consensus(frequency_matrix, dna_length=None):\n",
    "    if isinstance(frequency_matrix, list) and isinstance(frequency_matrix[0], list):\n",
    "        return find_consensus_v1(frequency_matrix)\n",
    "    elif isinstance(frequency_matrix, dict) and isinstance(frequency_matrix['A'], list):\n",
    "        return find_consensus_v2(frequency_matrix)\n",
    "    else: return None\n",
    "    \n",
    "f_mat =  [[2, 0, 2],\n",
    "          [1, 2, 0],\n",
    "          [0, 1, 0],\n",
    "          [0, 0, 1]]\n",
    "print(find_consensus(f_mat))\n",
    "\n",
    "f_mat =  {'A': [2, 0, 2],\n",
    "          'C': [1, 2, 0],\n",
    "          'G': [0, 1, 0],\n",
    "          'T': [0, 0, 1]}\n",
    "print(find_consensus(f_mat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.17: Make a function more robust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 1, 'D': 2, 'L': 3, 'S': 1, 'T': 2}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "def get_base_counts2(dna):\n",
    "    counts = defaultdict(int)\n",
    "    for base in dna:\n",
    "        counts[base] += 1\n",
    "    return dict(counts)\n",
    "\n",
    "get_base_counts2(\"ADLSTTLLD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 6.18: Find proportion of bases inside/outside exons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "exons = requests.get(\"https://raw.githubusercontent.com/hplgit/bioinf-py/master/data/lactase_exon.tsv\").text.splitlines()\n",
    "gene = ''.join(requests.get(\"https://raw.githubusercontent.com/hplgit/bioinf-py/master/data/lactase_gene.txt\").text.split())\n",
    "introns = ''.join([gene[start:end] for start, end in [(int(exons[i].split()[1]), (int(exons[i + 1].split()[0]) if i + 1 < len(exons) else None)) for i in range(len(exons))]])\n",
    "exons = ''.join([gene[int(start):int(end)] for start, end in [line.split() for line in exons]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Base frequency for A in:\n",
      "Exons: 0.2445011157156519\n",
      "Introns: 0.2590450977660118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "Base frequency for A in:\n",
    "Exons: {exons.count('A') / len(exons)}\n",
    "Introns: {introns.count('A') / len(introns)}\n",
    "\"\"\")"
   ]
  }
 ],
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