2017-10-25_Country_color_code_generator.ipynb

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   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.misc as smp"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "This block of code is for creating a color-coded representation fo the continents or geographical regions each strain comes from.\n",
    "\n",
    "Colors:\n",
    "\n",
    "1 - North America - Magenta\n",
    "\n",
    "2 - South America - Yellow\n",
    "\n",
    "3 - Europe - Blue\n",
    "\n",
    "4 - Asia - Red\n",
    "\n",
    "5 - Africa - Green\n",
    "\n",
    "6 - Oceania - Cyan\n",
    "\n",
    "7- Unknown / N/A - White"
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "Mexico Enter number for country:1\n",
      "Sri Lanka Enter number for country:4\n",
      "India Enter number for country:4\n",
      "USA Enter number for country:1\n",
      "Unknown Enter number for country:7\n",
      "Pakistan Enter number for country:4\n",
      "Lebanon Enter number for country:4\n",
      "Australia Enter number for country:6\n",
      "Thailand Enter number for country:4\n",
      "China Enter number for country:4\n",
      "Cote d'Ivoire Enter number for country:5\n",
      "Malaysia Enter number for country:4\n",
      "Turkey Enter number for country:4\n",
      "Nigeria Enter number for country:5\n",
      "Ethiopia Enter number for country:5\n",
      "Taiwan Enter number for country:4\n",
      "Hong Kong Enter number for country:4\n",
      "Vietnam Enter number for country:4\n",
      "Bulgaria Enter number for country:3\n",
      "Philippines Enter number for country:4\n",
      "Peru Enter number for country:2\n",
      "Canada Enter number for country:1\n",
      "Bangladesh Enter number for country:4\n",
      "Ghana Enter number for country:5\n",
      "Albania Enter number for country:3\n",
      "Dominican Republic Enter number for country:1\n",
      "Singapore Enter number for country:4\n",
      "Honduras Enter number for country:1\n",
      "South Korea Enter number for country:4\n",
      "Jamaica Enter number for country:1\n",
      "Brunei Enter number for country:4\n",
      "Egypt Enter number for country:5\n",
      "El Salvador Enter number for country:1\n",
      "Indonesia Enter number for country:4\n",
      "Syria Enter number for country:4\n",
      "Jordan Enter number for country:4\n",
      "Guatemala Enter number for country:1\n",
      "Burkina Faso Enter number for country:5\n",
      "Suriname Enter number for country:2\n",
      "Brazil Enter number for country:2\n",
      "Belize Enter number for country:1\n",
      "Kenya Enter number for country:5\n",
      "France Enter number for country:3\n",
      "Bolivia Enter number for country:2\n",
      "Israel Enter number for country:4\n",
      "Myanmar Enter number for country:4\n",
      "Guyana Enter number for country:2\n",
      "Macedonia Enter number for country:3\n",
      "Spain Enter number for country:3\n",
      "Ecuador Enter number for country:2\n",
      "Haiti Enter number for country:1\n",
      "Guinea Enter number for country:5\n"
     ]
    }
   ],
   "source": [
    "#first, scrape the data and assign countries to continental regions\n",
    "\n",
    "data = '/Users/jay.worley/Desktop/manuscript/Draft 3/2017-10-28_Seq_Info_Supplemental_modified.xlsx'\n",
    "df = pd.read_excel(data, index_col=0)\n",
    "\n",
    "#each country here: country_dict[country] = Continent # (see markdown)\n",
    "country_dict = {}\n",
    "\n",
    "#each seqID assigned to the country\n",
    "SID_country_dict = {}\n",
    "\n",
    "for name in df.index:\n",
    "    country = df['Country'][name]\n",
    "    SID_country_dict[name] = country\n",
    "    if country not in list(country_dict.keys()):\n",
    "        country_num = input(country+' Enter number for country:')\n",
    "        country_dict[country] = country_num"
   ]
  },
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   "cell_type": "code",
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     "text": [
      "Jamaica 1\n",
      "Brunei 4\n",
      "Lebanon 4\n",
      "Canada 1\n",
      "Singapore 4\n",
      "Egypt 5\n",
      "Haiti 1\n",
      "Myanmar 4\n",
      "Australia 6\n",
      "Israel 4\n",
      "Taiwan 4\n",
      "USA 1\n",
      "Macedonia 3\n",
      "Honduras 1\n",
      "Brazil 2\n",
      "India 4\n",
      "Guinea 5\n",
      "Ghana 5\n",
      "El Salvador 1\n",
      "Peru 2\n",
      "Guyana 2\n",
      "Kenya 5\n",
      "Indonesia 4\n",
      "China 4\n",
      "Thailand 4\n",
      "Cote d'Ivoire 5\n",
      "Bolivia 2\n",
      "Guatemala 1\n",
      "Philippines 4\n",
      "Albania 3\n",
      "Belize 1\n",
      "France 3\n",
      "Hong Kong 4\n",
      "Nigeria 5\n",
      "Bulgaria 3\n",
      "Spain 3\n",
      "Suriname 2\n",
      "Syria 4\n",
      "Malaysia 4\n",
      "Mexico 1\n",
      "Turkey 4\n",
      "Vietnam 4\n",
      "South Korea 4\n",
      "Jordan 4\n",
      "Ecuador 2\n",
      "Dominican Republic 1\n",
      "Ethiopia 5\n",
      "Pakistan 4\n",
      "Burkina Faso 5\n",
      "Sri Lanka 4\n",
      "Bangladesh 4\n",
      "Unknown 7\n"
     ]
    }
   ],
   "source": [
    "country_code_file = '2017-10-25_country_codes.tab'\n",
    "file_text_lines = []\n",
    "for country in country_dict:\n",
    "    print(country, country_dict[country])\n",
    "    file_text_lines.append(country+'\\t'+str(country_dict[country]))\n",
    "file_text = '\\n'.join(file_text_lines)\n",
    "with open(country_code_file, 'w') as f:\n",
    "    f.write(file_text)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
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     "text": [
      "2225 40\n"
     ]
    }
   ],
   "source": [
    "##Tree order\n",
    "with open('/Users/jay.worley/Desktop/RAxML Trees/RAxML_bipartitions.RAxML-concat-ccg_2-nd.nex', 'r') as f:\n",
    "    nexus = f.read()\n",
    "tree = nexus.split('begin trees;')[1].split('end;')[0].strip()\n",
    "splits = ['S'+i.split(':')[0] for i in tree.split('S')[1:]]\n",
    "for i in range(len(splits)):\n",
    "    if ':' in splits[i]:\n",
    "        splits[i] = splits[i].split(':')[0]\n",
    "tree_order = splits\n",
    "    \n",
    "#color codes:\n",
    "\n",
    "black = [0,0,0]\n",
    "magenta = [255, 0 ,255]\n",
    "yellow = [255, 255, 0]\n",
    "blue = [0, 0, 255]\n",
    "red = [255, 0, 0]\n",
    "green = [0, 255, 0]\n",
    "cyan = [0, 255, 255]\n",
    "white = [255, 255, 255]\n",
    "colors = [black, magenta, yellow, blue, red, green, cyan, white]\n",
    "\n",
    "#create canvas\n",
    "sample_height = 5\n",
    "height = len(tree_order)*sample_height\n",
    "width = 40\n",
    "print(height, width)\n",
    "data = np.zeros((height,width,3), np.uint8)\n",
    "\n",
    "for i in range(len(tree_order)):\n",
    "    country = SID_country_dict[tree_order[i]]\n",
    "    color = colors[int(country_dict[country])]\n",
    "    for h in range(i*sample_height, (i*sample_height)+sample_height):\n",
    "        for w in range(0,width):\n",
    "            data[h,w]=color\n",
    "\n",
    "outfile = '2017-10-25_counrty-color-barcode.png'            \n",
    "img = smp.toimage(data).save(outfile)\n"
   ]
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      "52\n",
      "Egypt 1\n",
      "Lebanon 1\n",
      "Brunei 1\n",
      "Singapore 1\n",
      "Guyana 1\n",
      "Israel 1\n",
      "El Salvador 1\n",
      "Macedonia 1\n",
      "Guinea 1\n",
      "Guatemala 1\n",
      "Belize 1\n",
      "France 1\n",
      "Myanmar 1\n",
      "Spain 1\n",
      "Burkina Faso 1\n",
      "Jamaica 2\n",
      "Suriname 2\n",
      "Honduras 2\n",
      "Brazil 2\n",
      "Haiti 2\n",
      "Kenya 2\n",
      "Bolivia 2\n",
      "Albania 2\n",
      "Hong Kong 2\n",
      "Bulgaria 2\n",
      "Ecuador 2\n",
      "Sri Lanka 2\n",
      "South Korea 3\n",
      "Peru 3\n",
      "Malaysia 3\n",
      "Syria 3\n",
      "Jordan 3\n",
      "Australia 4\n",
      "Taiwan 4\n",
      "Ghana 4\n",
      "Cote d'Ivoire 4\n",
      "Dominican Republic 4\n",
      "Indonesia 5\n",
      "Philippines 5\n",
      "Nigeria 5\n",
      "Bangladesh 5\n",
      "Canada 6\n",
      "Ethiopia 9\n",
      "Pakistan 9\n",
      "China 10\n",
      "Thailand 11\n",
      "Unknown 12\n",
      "Turkey 13\n",
      "Vietnam 17\n",
      "India 51\n",
      "Mexico 51\n",
      "USA 162\n",
      "\n",
      "\n",
      "\n",
      "Oceania 4\n",
      "Europe 7\n",
      "South America 12\n",
      "Unknown 12\n",
      "Africa 27\n",
      "Asia 151\n",
      "North America 232\n"
     ]
    }
   ],
   "source": [
    "#basic stats\n",
    "country_count = []\n",
    "continent_count = []\n",
    "\n",
    "continents = ['index_adjustment', 'North America', 'South America', 'Europe', 'Asia', 'Africa', 'Oceania', 'Unknown']\n",
    "\n",
    "for seq in tree_order:\n",
    "    country = SID_country_dict[seq]\n",
    "    continent = continents[int(country_dict[country])]\n",
    "    country_count.append(country)\n",
    "    continent_count.append(continent)\n",
    "print(len(sorted(set(country_count))))\n",
    "for country in sorted(set(country_count), key = lambda x: country_count.count(x)):\n",
    "    print(country, country_count.count(country))\n",
    "print('\\n\\n')\n",
    "for continent in sorted(set(continent_count), key = lambda x: continent_count.count(x)):\n",
    "    print(continent, continent_count.count(continent))"
   ]
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