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@jburroni
Created April 12, 2015 19:03
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international trade blocks
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'data_dir': '/Users/jb/.local/share/cartopy', 'pre_existing_data_dir': '', 'repo_data_dir': '/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/cartopy/data', 'downloaders': {('shapefiles', 'natural_earth'): <cartopy.io.shapereader.NEShpDownloader object at 0x109db43d0>, ('shapefiles', 'gshhs'): <cartopy.io.shapereader.GSHHSShpDownloader object at 0x109db4490>}}\n"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import itertools\n",
"import multiprocessing as mp\n",
"import igraph\n",
"import networkx as nx\n",
"import seaborn as sns\n",
"import cartopy\n",
"import cartopy.io.shapereader as shpreader\n",
"import cartopy.crs as ccrs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/io/parsers.py:1159: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
}
],
"source": [
"wtf = pd.read_csv('wtf00/wtf00.csv', sep=\"\t\", quotechar='\"')\n",
"nona = wtf.sitc4.dropna()\n",
"nona = nona[nona.apply(lambda x: type(x) == type(2) or x.isdigit())]\n",
"wtf = wtf.ix[nona.index]\n",
"wtf[\"sitc4\"] = wtf[\"sitc4\"].astype(np.int)\n",
"filtered = wtf[(wtf.exporter != 'World') & (wtf.importer != 'World')]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>icode</th>\n",
" <th>importer</th>\n",
" <th>ecode</th>\n",
" <th>exporter</th>\n",
" <th>sitc4</th>\n",
" <th>unit</th>\n",
" <th>dot</th>\n",
" <th>value</th>\n",
" <th>quantity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>92203</th>\n",
" <td> 2000</td>\n",
" <td> 117100</td>\n",
" <td> South Africa</td>\n",
" <td> 117100</td>\n",
" <td> South Africa</td>\n",
" <td> 224</td>\n",
" <td> W</td>\n",
" <td> 1</td>\n",
" <td> 199</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92204</th>\n",
" <td> 2000</td>\n",
" <td> 117100</td>\n",
" <td> South Africa</td>\n",
" <td> 130120</td>\n",
" <td> Algeria</td>\n",
" <td> 5221</td>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" <td> 253</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92205</th>\n",
" <td> 2000</td>\n",
" <td> 117100</td>\n",
" <td> South Africa</td>\n",
" <td> 135040</td>\n",
" <td> Morocco</td>\n",
" <td> 371</td>\n",
" <td> W</td>\n",
" <td> 1</td>\n",
" <td> 627</td>\n",
" <td> 179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92206</th>\n",
" <td> 2000</td>\n",
" <td> 117100</td>\n",
" <td> South Africa</td>\n",
" <td> 135040</td>\n",
" <td> Morocco</td>\n",
" <td> 2672</td>\n",
" <td> W</td>\n",
" <td> 1</td>\n",
" <td> 241</td>\n",
" <td> 575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92207</th>\n",
" <td> 2000</td>\n",
" <td> 117100</td>\n",
" <td> South Africa</td>\n",
" <td> 135040</td>\n",
" <td> Morocco</td>\n",
" <td> 6924</td>\n",
" <td> W</td>\n",
" <td> 1</td>\n",
" <td> 113</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year icode importer ecode exporter sitc4 unit dot \\\n",
"92203 2000 117100 South Africa 117100 South Africa 224 W 1 \n",
"92204 2000 117100 South Africa 130120 Algeria 5221 NaN 1 \n",
"92205 2000 117100 South Africa 135040 Morocco 371 W 1 \n",
"92206 2000 117100 South Africa 135040 Morocco 2672 W 1 \n",
"92207 2000 117100 South Africa 135040 Morocco 6924 W 1 \n",
"\n",
" value quantity \n",
"92203 199 100 \n",
"92204 253 NaN \n",
"92205 627 179 \n",
"92206 241 575 \n",
"92207 113 30 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"exporter importer \n",
"Afghanistan Austria 118\n",
" Belgium-Lux 18088\n",
" Brazil 841\n",
" Canada 149\n",
" China 5343\n",
"Name: value, dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered.groupby(['exporter', 'importer']).value.sum().head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"export_imports = filtered.groupby(['exporter', 'importer']).value.sum().unstack().fillna(0).T"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def compute_graph(preferences):\n",
" g = nx.DiGraph()\n",
" for country, pref in preferences.iteritems():\n",
" if len(pref) > 0:\n",
" g.add_edge(country + '_exporter', pref[0] + '_importer')\n",
" g.add_edge(country + '_importer', country + '_exporter')\n",
" return g"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def compute_partners(preferences):\n",
" g = compute_graph(preferences)\n",
" answer = []\n",
" for c in nx.simple_cycles(g):\n",
" countries = set([x[:x.find('_')] for x in c])\n",
" answer.append(list(countries))\n",
" return (g, answer)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def remove_country(country, preferences):\n",
" for pref in preferences.itervalues():\n",
" if country in pref:\n",
" pref.remove(country)\n",
" del preferences[country]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_preferences(export_imports):\n",
" preferences = {}\n",
" for col in export_imports.columns:\n",
" a = export_imports[col][export_imports[col] > 0]\n",
" a.sort(ascending=False)\n",
" preferences[col] = a.index.values.tolist()\n",
" return preferences"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def compute_recursively(export_imports):\n",
" preferences = create_preferences(export_imports)\n",
" i = 0\n",
" answer = [] \n",
" print '|Iteration| Blocks |'\n",
" graphs = []\n",
" while len(preferences) and ((not len(answer) ) or len(partners)):\n",
" g, partners = compute_partners(preferences)\n",
" graphs.append(g)\n",
" answer.extend(partners)\n",
" print '|{}||'.format(i)\n",
" for block in partners:\n",
" print '||' + ', '.join(block) + '|'\n",
" for p in partners:\n",
" for country in p:\n",
" remove_country(country, preferences)\n",
" to_remove =[c for c, pref in preferences.iteritems() if len(pref)==0]\n",
" for c in to_remove:\n",
" del preferences[c]\n",
" i = i+1\n",
" return graphs, answer"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|Iteration| Blocks |\n",
"|0||\n",
"||Canada, USA|\n",
"|1||\n",
"||Malaysia, Singapore|\n",
"||Brazil, Argentina|\n",
"||Neth.Ant.Aru, Venezuela|\n",
"||China, China HK SAR|\n",
"||Germany, France,Monac|\n",
"|2||\n",
"||Czech Rep, Slovakia|\n",
"||Spain, Portugal|\n",
"||Japan, Taiwan|\n",
"||UK, Ireland|\n",
"|3||\n",
"||Switz.Liecht, Italy|\n",
"||Chile, Mexico|\n",
"||Belgium-Lux, Netherlands|\n",
"||Denmark, Sweden|\n",
"|4||\n",
"||Slovenia, Croatia|\n",
"||Cyprus, Greece|\n",
"||Hungary, Austria|\n",
"||Belarus, Russian Fed|\n",
"|5||\n",
"||Finland, Estonia|\n",
"|6||\n",
"||Turkey, Areas NES, Romania|\n",
"|7||\n",
"||Tunisia, Libya|\n",
"||Ukraine, Poland|\n",
"||Australia, Korea Rep.|\n",
"|8||\n",
"||Ecuador, Colombia, Peru|\n",
"||TFYR Macedna, Bulgaria|\n",
"||Iran, South Africa|\n",
"||Thailand, Indonesia|\n",
"||Iceland, Norway|\n",
"|9||\n",
"||Oman, Untd Arab Em|\n",
"|10||\n",
"||Pakistan, Bangladesh|\n",
"||Philippines, Viet Nam|\n",
"|11||\n"
]
}
],
"source": [
"graphs,answer = compute_recursively(export_imports)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sns.color_palette?"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<matplotlib.figure.Figure at 0x11a00b450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.palplot(sns.color_palette(n_colors=3))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"OotL+dcfeUxbOxeMdB8Ar7Y90cHJjatTIpmxVImMQFVNFbIL7yKnLA+5JXkoqixGnVQHM4U57Kxs\n",
"0dbeGW3sW6O9oyua2zQXHZfIaLFUiYiIZMKrf4mIiGTCUiUiIpIJS5WIiEgmLFUiIiKZsFSJiIhk\n",
"wlIlIiKSCUuViIhIJixVIiIimbBUiYiIZMJSJSIikglLlYiISCYsVSIiIpmwVImIiGTCUiUiIpIJ\n",
"S5WIiEgmLFUiIiKZsFSJiIhkwlIlIiKSCUuViIhIJixVIiIimbBUiYiIZMJSJSIikglLlYiISCYs\n",
"VSIiIpmwVImIiGTCUiUiIpIJS5WIiEgmLFUiIiKZsFSJiIhkwlIlIiKSCUuViIhIJixVIiIimbBU\n",
"iYiIZMJSJSIikglLlYiISCYsVSIiIpmwVImIiGTyf8iPD8a/619iAAAAAElFTkSuQmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11525f810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = nx.DiGraph()\n",
"g.add_edge('A', 'B')\n",
"g.add_edge('B', 'C')\n",
"g.add_edge('C', 'A')\n",
"nx.draw_graphviz(g, labels={n:n for n in g.nodes()}, node_size=1000, \\\n",
" node_color=sns.color_palette(n_colors= 3), with_labels=True)\n",
"plt.savefig('three_countries.png')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"preferences = create_preferences(export_imports)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g2 = compute_graph(preferences)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['A', 'C', 'B']"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g.nodes()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11853be10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos = nx.spring_layout(g2)\n",
"nodelist = ['USA_exporter', 'USA_importer']\n",
"nx.draw_networkx_nodes(g2, pos, nodelist=['USA_exporter', 'USA_importer'],\\\n",
" node_color=sns.color_palette(n_colors= 2), node_size=1000)\n",
"nx.draw_networkx_labels(g2, pos, labels={n:n for n in g2.nodes() if n in nodelist}, font_size=10)\n",
"nx.draw_networkx_edges(g2, pos,edgelist=[('USA_importer', 'USA_exporter')])\n",
"plt.axis('off')\n",
"plt.savefig('USA_import_export.png')"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b3f2990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos = nx.graphviz_layout(g2)\n",
"base_node = 'Brazil_importer'\n",
"nodelist = [base_node,] + g2.successors(base_node)\n",
"edgelist = [(base_node, x) for x in g2.successors(base_node)]\n",
"nx.draw_networkx_nodes(g2, pos, nodelist=nodelist,\\\n",
" node_color=sns.color_palette(n_colors= len(nodelist)), node_size=1000)\n",
"nx.draw_networkx_labels(g2, pos, labels={n:n for n in g2.nodes() if n in nodelist}, font_size=10)\n",
"nx.draw_networkx_edges(g2, pos,edgelist=edgelist)\n",
"plt.axis('off')\n",
"plt.savefig('BR_import_export.png')"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"Ho8nYBc4tUUW7MYYE2TsdEdjjAkyFuzGGBNkLNiNMSbIWLAbY0yQsWA3xpggY8FujDFBxoLdGGOC\n",
"jAW7McYEGQt2Y4wJMhbsxhgTZCzYjTEmyFiwG2NMkLFgN8aYIGPBbowxQcaC3RhjgowFuzHGBBkL\n",
"dmOMCTIW7MYYE2Qs2I0xJshYsBtjTJCxYDfGmCBjwW6MMUHGgt0YY4KMBbsxxgQZC3ZjjAkyFuzG\n",
"GBNkLNiNMSbI/H/s17Xb+unSIAAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x117d68650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos = nx.graphviz_layout(g2)\n",
"base_node = 'Brazil_importer'\n",
"exporter = g2.successors(base_node)[0]\n",
"nodelist = [base_node,] + g2.successors(base_node) + g2.predecessors(base_node) \\\n",
" + g2.successors(exporter)\n",
"edgelist = [(base_node, x) for x in g2.successors(base_node)] + [(x, base_node) for x in g2.predecessors(base_node)]\\\n",
" + [(exporter, x) for x in g2.successors(exporter)]\n",
"nx.draw_networkx_nodes(g2, pos, nodelist=nodelist,\\\n",
" node_color=sns.color_palette(n_colors= len(nodelist)), node_size=1000)\n",
"nx.draw_networkx_labels(g2, pos, labels={n:n for n in g2.nodes() if n in nodelist}, font_size=10)\n",
"nx.draw_networkx_edges(g2, pos,edgelist=edgelist)\n",
"plt.axis('off')\n",
"plt.savefig('BR_imports.png')"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11888b290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos = nx.graphviz_layout(g2)\n",
"base_node = 'USA_importer'\n",
"exporter = g2.successors(base_node)[0]\n",
"nodelist = [base_node,] + g2.successors(base_node) + g2.successors(exporter) \\\n",
" + g2.successors(g2.successors(exporter)[0])\n",
"edgelist = [(x, g2.successors(x)[0]) for x in nodelist]\n",
"nx.draw_networkx_nodes(g2, pos, nodelist=nodelist,\\\n",
" node_color=sns.color_palette(n_colors= len(nodelist)), node_size=1000)\n",
"nx.draw_networkx_labels(g2, pos, labels={n:n for n in g2.nodes() if n in nodelist}, font_size=10)\n",
"nx.draw_networkx_edges(g2, pos,edgelist=edgelist)\n",
"plt.axis('off')\n",
"plt.savefig('USA-Canada.png')"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def nodes_in_cycle(base_node):\n",
" answer = [base_node,]\n",
" suc = g8.successors(base_node)[0]\n",
" while suc != base_node:\n",
" answer.append(suc)\n",
" suc = g8.successors(suc)[0]\n",
" return answer"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11b52a610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos = nx.graphviz_layout(g8)\n",
"base_node = 'USA_importer'\n",
"g8 = graphs[8]\n",
"base_node = 'Peru_importer'\n",
"nodelist = nodes_in_cycle(base_node)\n",
"edgelist = [(x, g8.successors(x)[0]) for x in nodelist]\n",
"nx.draw_networkx_nodes(g8, pos, nodelist=nodelist,\\\n",
" node_color=sns.color_palette(n_colors= len(nodelist)), node_size=1000)\n",
"nx.draw_networkx_labels(g8, pos, labels={n:n for n in g8.nodes() if n in nodelist}, font_size=10)\n",
"nx.draw_networkx_edges(g8, pos,edgelist=edgelist)\n",
"plt.axis('off')\n",
"plt.savefig('peru_colombia_equador.png')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<cartopy.mpl.feature_artist.FeatureArtist at 0x10d57dc90>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"mgYFwwAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d3d1a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.add_feature(cartopy.feature.LAND)\n",
"ax.add_feature(cartopy.feature.OCEAN)\n",
"ax.add_feature(cartopy.feature.COASTLINE)\n",
"ax.add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=.5)\n",
"ax.add_feature(cartopy.feature.LAKES, alpha=0.95)\n",
"ax.add_feature(cartopy.feature.RIVERS)\n",
"#ax.set_extent([-150, 60, -25, 60])"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/cartopy/io/__init__.py:261: DownloadWarning: Downloading: http://naciscdn.org/naturalearth/50m/cultural/ne_50m_admin_0_sovereignty.zip\n",
" warnings.warn('Downloading: {}'.format(url), DownloadWarning)\n"
]
}
],
"source": [
"shpfilename = shpreader.natural_earth(resolution='50m',\n",
" category='cultural',\n",
" name='admin_0_sovereignty')\n",
"reader = shpreader.Reader(shpfilename)\n",
"countries = list(reader.records())\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"200"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(countries)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": [
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11049af10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"palette = sns.choose_colorbrewer_palette('qualitative')"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"translation = {'USA': 'United States', 'China HK SAR': 'China', \\\n",
" 'France,Monac': 'France', 'Czech Rep': 'Czech Rep.',\\\n",
" 'UK' : 'United Kingdom', 'Switz.Liecht': 'Switzerland', \n",
" 'Belgium-Lux' : 'Belgium', 'Russian Fed': 'Russia', \\\n",
" 'Korea Rep.':'Republic of Korea', 'TFYR Macedna' : 'Macedonia' }"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reject = ['Neth.Ant.Aru', 'Areas NES']\n",
"to_plot= [block for block in answer if sum([1 for c in block if c in reject]) == 0]"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10ff36510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.add_feature(cartopy.feature.LAND)\n",
"#ax.add_feature(cartopy.feature.OCEAN)\n",
"ax.add_feature(cartopy.feature.COASTLINE)\n",
"ax.add_feature(cartopy.feature.BORDERS)\n",
"ax.add_feature(cartopy.feature.LAKES, alpha=0.95)\n",
"ax.add_feature(cartopy.feature.RIVERS)\n",
"#ax.set_extent([-150, 60, -25, 60])\n",
"\n",
"for color, block in zip(palette, to_plot[:12]):\n",
" for country in block:\n",
" record = [c for c in countries if c.attributes['brk_name'] == translation.get(country, country)][0]\n",
" ax.add_geometries(record.geometry, ccrs.PlateCarree(),\n",
" facecolor=color,\n",
" label=record.attributes['brk_name'])\n",
"plt.savefig('1.png', dpi=300)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10ede8cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.add_feature(cartopy.feature.LAND)\n",
"#ax.add_feature(cartopy.feature.OCEAN)\n",
"ax.add_feature(cartopy.feature.COASTLINE)\n",
"ax.add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=.5)\n",
"ax.add_feature(cartopy.feature.LAKES, alpha=0.95)\n",
"ax.add_feature(cartopy.feature.RIVERS)\n",
"#ax.set_extent([-150, 60, -25, 60])\n",
"\n",
"for color, block in zip(palette, to_plot[12:24]):\n",
" for country in block:\n",
" record = [c for c in countries if c.attributes['brk_name'] == translation.get(country, country)][0]\n",
" ax.add_geometries(record.geometry, ccrs.PlateCarree(),\n",
" facecolor=color,\n",
" label=record.attributes['brk_name'])\n",
"plt.savefig('2.png', dpi=300)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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