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santiagobasulto/Wine vs Beer.ipynb

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Wine vs Beer.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "6831a5e8-c6ac-4687-a4b0-af90d7df0413",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"\n",
"import requests\n",
"import pandas as pd\n",
"from IPython.display import SVG, display\n",
"\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import pygal\n",
"from pygal.style import CleanStyle"
]
},
{
"cell_type": "markdown",
"id": "86f1b2b8-f9b9-4e4c-8ea2-d9d0b2c6adf0",
"metadata": {},
"source": [
"#### Fetch the data from Wikipedia\n",
"\n",
"Parsing the HTML table.\n",
"\n",
"**Warning!** This might break if the wikipedia page changes."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "da85ffa8-8b5b-41e0-aa6c-e6f5b33d85ee",
"metadata": {},
"outputs": [],
"source": [
"page_title = \"List_of_countries_by_alcohol_consumption_per_capita\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e3263f23-c467-4f9e-8cf2-ace7c0bf77e4",
"metadata": {},
"outputs": [],
"source": [
"resp = requests.get(f\"https://en.wikipedia.org/api/rest_v1/page/html/{page_title}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f2c5545c-7c7b-4ce7-a09f-a09168e2d15b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Response [200]>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"resp"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2fa8014b-3d04-4d31-9450-9c2e3baa883b",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_html(resp.text, match='Pure alcohol consumption.', flavor='bs4')[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bf63f4bd-58b0-45ef-8703-75e49ec89825",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Total</th>\n",
" <th>Recordedconsumption</th>\n",
" <th>Unrecordedconsumption</th>\n",
" <th>Beer(%)</th>\n",
" <th>Wine(%)</th>\n",
" <th>Spirits(%)</th>\n",
" <th>Other(%)</th>\n",
" <th>2020projection</th>\n",
" <th>2025projection</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Estonia</td>\n",
" <td>16.9</td>\n",
" <td>15.8</td>\n",
" <td>1.1</td>\n",
" <td>32.7</td>\n",
" <td>7.4</td>\n",
" <td>50.3</td>\n",
" <td>9.6</td>\n",
" <td>11.5</td>\n",
" <td>11.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Lithuania</td>\n",
" <td>15.0</td>\n",
" <td>13.8</td>\n",
" <td>1.2</td>\n",
" <td>43.6</td>\n",
" <td>7.3</td>\n",
" <td>37.1</td>\n",
" <td>12.1</td>\n",
" <td>14.4</td>\n",
" <td>13.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Czech Republic</td>\n",
" <td>14.3</td>\n",
" <td>12.4</td>\n",
" <td>1.4</td>\n",
" <td>53.3</td>\n",
" <td>21.3</td>\n",
" <td>25.4</td>\n",
" <td>0.0</td>\n",
" <td>11.2</td>\n",
" <td>11.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Seychelles</td>\n",
" <td>13.8</td>\n",
" <td>12.4</td>\n",
" <td>1.4</td>\n",
" <td>68.9</td>\n",
" <td>22.4</td>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>10.4</td>\n",
" <td>10.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Germany</td>\n",
" <td>13.4</td>\n",
" <td>11.3</td>\n",
" <td>1.4</td>\n",
" <td>52.6</td>\n",
" <td>28.4</td>\n",
" <td>18.9</td>\n",
" <td>0.0</td>\n",
" <td>12.8</td>\n",
" <td>12.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Total Recordedconsumption Unrecordedconsumption Beer(%) \\\n",
"0 Estonia 16.9 15.8 1.1 32.7 \n",
"1 Lithuania 15.0 13.8 1.2 43.6 \n",
"2 Czech Republic 14.3 12.4 1.4 53.3 \n",
"3 Seychelles 13.8 12.4 1.4 68.9 \n",
"4 Germany 13.4 11.3 1.4 52.6 \n",
"\n",
" Wine(%) Spirits(%) Other(%) 2020projection 2025projection \n",
"0 7.4 50.3 9.6 11.5 11.9 \n",
"1 7.3 37.1 12.1 14.4 13.9 \n",
"2 21.3 25.4 0.0 11.2 11.4 \n",
"3 22.4 6.3 2.5 10.4 10.6 \n",
"4 28.4 18.9 0.0 12.8 12.6 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "8bccb6bf-f2ad-41fd-aff6-6ff77743b9ce",
"metadata": {},
"source": [
"Mark all heavy wine drinkers with a special boolean flag:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "04c636c5-5b3e-44c4-a4ea-a723e15e71e2",
"metadata": {},
"outputs": [],
"source": [
"df['Wine Drinkers'] = df['Wine(%)'] > df['Beer(%)']"
]
},
{
"cell_type": "markdown",
"id": "ac5317d8-2c1b-4bc8-8805-ce1101b70ee3",
"metadata": {},
"source": [
"Result:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ffe10ec5-6411-452e-888b-24932d56c0ff",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Total</th>\n",
" <th>Recordedconsumption</th>\n",
" <th>Unrecordedconsumption</th>\n",
" <th>Beer(%)</th>\n",
" <th>Wine(%)</th>\n",
" <th>Spirits(%)</th>\n",
" <th>Other(%)</th>\n",
" <th>2020projection</th>\n",
" <th>2025projection</th>\n",
" <th>Wine Drinkers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Estonia</td>\n",
" <td>16.9</td>\n",
" <td>15.8</td>\n",
" <td>1.1</td>\n",
" <td>32.7</td>\n",
" <td>7.4</td>\n",
" <td>50.3</td>\n",
" <td>9.6</td>\n",
" <td>11.5</td>\n",
" <td>11.9</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Lithuania</td>\n",
" <td>15.0</td>\n",
" <td>13.8</td>\n",
" <td>1.2</td>\n",
" <td>43.6</td>\n",
" <td>7.3</td>\n",
" <td>37.1</td>\n",
" <td>12.1</td>\n",
" <td>14.4</td>\n",
" <td>13.9</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Czech Republic</td>\n",
" <td>14.3</td>\n",
" <td>12.4</td>\n",
" <td>1.4</td>\n",
" <td>53.3</td>\n",
" <td>21.3</td>\n",
" <td>25.4</td>\n",
" <td>0.0</td>\n",
" <td>11.2</td>\n",
" <td>11.4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Seychelles</td>\n",
" <td>13.8</td>\n",
" <td>12.4</td>\n",
" <td>1.4</td>\n",
" <td>68.9</td>\n",
" <td>22.4</td>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>10.4</td>\n",
" <td>10.6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Germany</td>\n",
" <td>13.4</td>\n",
" <td>11.3</td>\n",
" <td>1.4</td>\n",
" <td>52.6</td>\n",
" <td>28.4</td>\n",
" <td>18.9</td>\n",
" <td>0.0</td>\n",
" <td>12.8</td>\n",
" <td>12.6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Total Recordedconsumption Unrecordedconsumption Beer(%) \\\n",
"0 Estonia 16.9 15.8 1.1 32.7 \n",
"1 Lithuania 15.0 13.8 1.2 43.6 \n",
"2 Czech Republic 14.3 12.4 1.4 53.3 \n",
"3 Seychelles 13.8 12.4 1.4 68.9 \n",
"4 Germany 13.4 11.3 1.4 52.6 \n",
"\n",
" Wine(%) Spirits(%) Other(%) 2020projection 2025projection \\\n",
"0 7.4 50.3 9.6 11.5 11.9 \n",
"1 7.3 37.1 12.1 14.4 13.9 \n",
"2 21.3 25.4 0.0 11.2 11.4 \n",
"3 22.4 6.3 2.5 10.4 10.6 \n",
"4 28.4 18.9 0.0 12.8 12.6 \n",
"\n",
" Wine Drinkers \n",
"0 False \n",
"1 False \n",
"2 False \n",
"3 False \n",
"4 False "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "e864c256-fe27-4d22-87de-801df430eea8",
"metadata": {},
"source": [
"##### Total countries with heavier wine drinking than beer drinking:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "493b8541-06cf-441e-a4e3-6c6d183ba7e4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"26"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Wine Drinkers'].sum()"
]
},
{
"cell_type": "markdown",
"id": "ec48b941-a6e6-40db-a473-1cdedc45f8f7",
"metadata": {},
"source": [
"### Scatter plot\n",
"\n",
"All countries above the red line prefer Wine over Beer."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3dae55de-c299-4b81-9967-c8f54f83c4c5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[0;31mSignature:\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDocstring:\u001b[0m\n",
"Set the text string *s*.\n",
"\n",
"It may contain newlines (``\\n``) or math in LaTeX syntax.\n",
"\n",
"Parameters\n",
"----------\n",
"s : object\n",
" Any object gets converted to its `str` representation, except for\n",
" ``None`` which is converted to an empty string.\n",
"\u001b[0;31mFile:\u001b[0m ~/.virtualenvs/ds/lib/python3.8/site-packages/matplotlib/text.py\n",
"\u001b[0;31mType:\u001b[0m method\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax.title.set_text?"
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},
{
"cell_type": "code",
"execution_count": 33,
"id": "63d487a2-f22b-4f26-80d9-13f1e74ff863",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/santiagobasulto/.virtualenvs/ds/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" warnings.warn(\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 2448x1224 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(34, 17))\n",
"ax.set_title(\"Beer vs Wine consumption per country\", fontdict={'fontsize': 28, 'fontweight': 'medium'})\n",
"sns.scatterplot(\"Beer(%)\", \"Wine(%)\", data=df, ax=ax)\n",
"ax.plot([i for i in range(101)], [i for i in range(101)], color=\"red\")\n",
"\n",
"for idx, row in df.query(\"`Wine Drinkers` == True\").iterrows():\n",
"# for idx, row in df.loc[df['Country'].isin({\"Italy\", \"France\", \"Argentina\"})].iterrows():\n",
" x = row['Beer(%)'] + random.random()\n",
" y = row['Wine(%)'] + random.random()\n",
" ax.text(x, y, row['Country'], horizontalalignment='left', size='medium', color='black', weight='semibold')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff0d91be-289e-474d-a5bb-58877832b89c",
"metadata": {},
"outputs": [],
"source": [
"fig.patch.set_facecolor('white')\n",
"fig.savefig(\"country_scatterplot.png\", facecolor=fig.get_facecolor(), edgecolor='none')"
]
},
{
"cell_type": "markdown",
"id": "51edc8b1-c0f3-4bce-8c77-b7ba14299dfd",
"metadata": {},
"source": [
"### Worldmap"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "130a4c63-f7cd-42f4-8ec7-bddced047d60",
"metadata": {},
"outputs": [],
"source": [
"country_codes_df = pd.read_csv('country_codes.csv')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "bca7fa87-5cfd-4168-a8c9-61988730291c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>Country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ad</td>\n",
" <td>Andorra</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ae</td>\n",
" <td>United Arab Emirates</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>af</td>\n",
" <td>Afghanistan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>al</td>\n",
" <td>Albania</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>am</td>\n",
" <td>Armenia</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" code Country\n",
"0 ad Andorra\n",
"1 ae United Arab Emirates\n",
"2 af Afghanistan\n",
"3 al Albania\n",
"4 am Armenia"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"country_codes_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a5013144-89a6-4572-b6f8-7df5a6b42ba5",
"metadata": {},
"outputs": [],
"source": [
"full_df = df.merge(country_codes_df, how='left', left_on='Country', right_on='Country')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d3dfabc3-b883-4bb4-ab9c-79c1f3a00121",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Total</th>\n",
" <th>Recordedconsumption</th>\n",
" <th>Unrecordedconsumption</th>\n",
" <th>Beer(%)</th>\n",
" <th>Wine(%)</th>\n",
" <th>Spirits(%)</th>\n",
" <th>Other(%)</th>\n",
" <th>2020projection</th>\n",
" <th>2025projection</th>\n",
" <th>Wine Drinkers</th>\n",
" <th>code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Estonia</td>\n",
" <td>16.9</td>\n",
" <td>15.8</td>\n",
" <td>1.1</td>\n",
" <td>32.7</td>\n",
" <td>7.4</td>\n",
" <td>50.3</td>\n",
" <td>9.6</td>\n",
" <td>11.5</td>\n",
" <td>11.9</td>\n",
" <td>False</td>\n",
" <td>ee</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Lithuania</td>\n",
" <td>15.0</td>\n",
" <td>13.8</td>\n",
" <td>1.2</td>\n",
" <td>43.6</td>\n",
" <td>7.3</td>\n",
" <td>37.1</td>\n",
" <td>12.1</td>\n",
" <td>14.4</td>\n",
" <td>13.9</td>\n",
" <td>False</td>\n",
" <td>lt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Czech Republic</td>\n",
" <td>14.3</td>\n",
" <td>12.4</td>\n",
" <td>1.4</td>\n",
" <td>53.3</td>\n",
" <td>21.3</td>\n",
" <td>25.4</td>\n",
" <td>0.0</td>\n",
" <td>11.2</td>\n",
" <td>11.4</td>\n",
" <td>False</td>\n",
" <td>cz</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Seychelles</td>\n",
" <td>13.8</td>\n",
" <td>12.4</td>\n",
" <td>1.4</td>\n",
" <td>68.9</td>\n",
" <td>22.4</td>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>10.4</td>\n",
" <td>10.6</td>\n",
" <td>False</td>\n",
" <td>sc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Germany</td>\n",
" <td>13.4</td>\n",
" <td>11.3</td>\n",
" <td>1.4</td>\n",
" <td>52.6</td>\n",
" <td>28.4</td>\n",
" <td>18.9</td>\n",
" <td>0.0</td>\n",
" <td>12.8</td>\n",
" <td>12.6</td>\n",
" <td>False</td>\n",
" <td>de</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Total Recordedconsumption Unrecordedconsumption Beer(%) \\\n",
"0 Estonia 16.9 15.8 1.1 32.7 \n",
"1 Lithuania 15.0 13.8 1.2 43.6 \n",
"2 Czech Republic 14.3 12.4 1.4 53.3 \n",
"3 Seychelles 13.8 12.4 1.4 68.9 \n",
"4 Germany 13.4 11.3 1.4 52.6 \n",
"\n",
" Wine(%) Spirits(%) Other(%) 2020projection 2025projection \\\n",
"0 7.4 50.3 9.6 11.5 11.9 \n",
"1 7.3 37.1 12.1 14.4 13.9 \n",
"2 21.3 25.4 0.0 11.2 11.4 \n",
"3 22.4 6.3 2.5 10.4 10.6 \n",
"4 28.4 18.9 0.0 12.8 12.6 \n",
"\n",
" Wine Drinkers code \n",
"0 False ee \n",
"1 False lt \n",
"2 False cz \n",
"3 False sc \n",
"4 False de "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "a77dd58b-1fd1-4c5a-be29-262fd910c381",
"metadata": {},
"outputs": [],
"source": [
"wine_country_codes = [row['code'] for idx, row in full_df.query(\"`Wine Drinkers` == True\").iterrows()]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "2b411a61-26a7-43d2-9b50-911335de696e",
"metadata": {},
"outputs": [],
"source": [
"assert not any([pd.isna(code) for code in wine_country_codes])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "c746276e-cc90-41af-b9f0-51914c78a358",
"metadata": {},
"outputs": [
{
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1.26 0.77 0.54 1.78 0.13 2.5 0.85-1.42 0 0.08 0.4-0.38 1.44-0.32 0.71-1 1.26-1.62 1.7-0.88 0.62-1.76 0.19-2.7 0.53-1.02 0.38-1.91 1.01-3.04 0.86-1.66-0.21-2.74-0.39-4.37-0.22-2.72 0.29 0.33 1.4 0.95 1.81-0.33 0.2-1.18 0.8-1.59 0.45-0.86-0.75-1.49 0.51-2.27-0.05-0.47-0.35-2.19 1.53-3.06 1.62 0.27 0.14 0.53 0.28 0.79 0.43-0.22 0.29-0.48 0.53-0.79 0.72-0.05-1.02-1.26-1.06-2.01-1.08 1.53 0.46 0.46 0.99-0.42 1.28-0.9 0.3-1.24 1.99-2.68 1.02 0.04 1.61-1.37 0.84-2.3 0.72-0.14 0.58 0.14 0.94 0.72 1.01-0.49 1.69-7.15 1.61-6.05 3.96-0.55 0.2-1.78-0.55-1.43 0.47 0.04 0.11-1.33 1.42-1.61 1.49-2.09 0.57-4.65 4.92-6.97 4.09 0.32 0.53 0.13 0.65-0.58 0.36 1 1.01-4.07 5.44-4.71 6.08-0.84 0.84-0.9 2.66-1.97 3.25-0.71 0.39-1.22 0.99-1.68 1.64-0.31 0.45-1.2 0.31-1.65 0.48 0.47 0.11 0.93 0.26 1.37 0.43-0.26 0.56-0.65 1.01-1.15 1.37 1.2-1.26 1.46 1.38 2.59 0.65-1.04 1.6-2.48 2.96-3.88 4.24 1.71-1.25 2.81-4.19 5.11-4.5 0.91-0.13 2.22-0.24 3.05 0.25 0.93 0.54 2.73-0.6 1.73 1.16-1.3 2.32-1.84 5.74-2.4 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1.8-0.21-1.97-0.2 0.98-1.42 1.4-1.44 0.65-0.03 1.57-0.27 2.2-0.08-0.71-0.52-2.49-0.38-1.51-1.72 0.64 0.68 1.63 0.21 1.43-0.72 0.92 0.7 1.15-0.56 0.72-1.16 0.94 0.28 1.85 0.25 2.67 0.87-0.2-0.88-1.87-1.17-1.64-2.18 0.45-1.95 2.44-0.9 3.65-0.85-2-0.29 0.36-1.91 0.98-2.38 1.07-0.81 2.59-3.19 4.14-1.94-0.52 0.08-1.03 0.23-1.52 0.44 0.45-0.17 2.63-0.26 0.8 0.57 1.87-0.37-1.71 1.19-2.31 1.37 0.94-0.04 1.86-0.34 2.74-0.65-0.65 0.42-0.81 0.85-1.23 1.44 0.92-0.05 1.57-0.87 2.52-0.79-0.73 0.1-2.1 2.82-0.57 2.59-0.2 0.25-0.42 0.49-0.65 0.72 0.88-0.53 0.98-0.36 0.29 0.51 0.5-0.41 1.01-0.41 1.51 0-0.5 0.82-1.39 0.77-2.23 0.86 0.68 0.1 1.27 0.36 0.94 1.15 0.42-0.41 0.92-0.62 1.51-0.65 0 0.58-0.36 1.01-0.94 1.08 0.56 0.01 1.07-0.16 1.51-0.5-1.17 1.62-2.49 1.56-4.32 1.66 0 0.27 0.05 0.53 0.15 0.79 1.11-1.11 3.63-0.86 5.04-0.72-0.59 0.18-1.09 0.49-1.52 0.93 1.67-0.87 0.2 0.69-0.5 0.72 0.63 0.18 1.19-0.32 1.79-0.14 0.97 0.3 0.16 0.83 0.14 1.45-0.06 1.37-3.6 1.47-4.52 1.72 1.68-0.17-0.14 0.85-0.65 0.93 0.37 0.02 4.47-1.46 2.31-0.21 1.07-0.67 2.45-0.78 3.67-0.87-0.67 0.88-0.69 2.01-2.09 1.8 1.84 0.23-0.8 0.98-1.24 1.1-0.93 0.25-1.94 0.44-2.72 1.06 0.91 0.3 2.04-0.53 2.76-0.96 0.79-0.48 2.12-0.36 3-0.4-0.19 0.24-0.38 0.48-0.58 0.72 0.66-0.46 0.96-0.3 0.87 0.5-0.77 0.16-1.58 0.17-2.31 0.5 0.67-0.11 1.95-0.44 1.16 0.72 0.46-0.94 1.34-0.4 2.08-0.86 0.25 0.55-0.2 0.92-0.37 1.39-0.18 0.5 0.69 0.76 0.35 1.32-0.38 0.64-3.21 1.57-3.87 1.18 0.44 0.49 5.15 0.77 2.31 1.94 0.65 0.64 1.73 0.1 2.28 0.85 0.16 0.22 0.87 1.7 0.04 1.47-1.1-0.3-3.68 1.2-4.19 2.15 0.23-0.19 0.47-0.36 0.72-0.51 0.84 1.71-3.03 0.34-3.6 0.07 0.57-0.75 1.44-0.45 2.23-0.57-1-0.11-2.01-0.04-3.02-0.07 0.52 0.98 1.79 1.42 2.8 1.65-0.55 0.06-1.1 0.09-1.65 0.07 0.98 0.2 1.99 0.08 2.95 0.36-0.59 1.11-2.05 0.34-2.95 0.94 0.25 0.17 3.65 1.4 1.44 1.22 2.08 0.38-1.26 0.08-1.51 0.08 0.92 1.02 2.77 0.93 3.96 0.57-0.3 0.38-1.99 2.11-0.22 1.3-1.92 0.47 0.23 0.87 0.72 0.79-0.25 0.21-0.49 0.42-0.72 0.65 0.35 0.28 1.46 0.32 1.91 0.26 0.59-0.07 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" <desc class=\"value\">France: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
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" <path d=\"m1176.4 193.49c0.2-0.48 0.4-0.99 0.4-1.51-0.4 0.41-0.5 0.92-0.4 1.51\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1176.4 195.94c0.8-0.23 2.2-0.97 2-2.02-3.1 1.12-1.8-2.7-3.5-3.31-0.2 0.46-0.3 0.94-0.3 1.44-1.4-0.34-1.2 0.57-2.3 0.36-0.2 1.44 1.6 0.55 2.2 1.22-1.6 0.12 0.1 1.14 0.7 1.14 0.7-0.01 1.5-0.36 2.1-0.06-0.5 0.14-0.9 0.61-0.9 1.23\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1180.3 208.82c1-0.38 0.7-2.44-0.4-2.37-1.3 0.08-0.7 2.45 0.4 2.37\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1195.1 180.24c0.8 0.68 2.8 1.02 3.4 0-0.8-0.05-2.1 0-2.4-0.97-0.2-0.78-2.2-0.02-1 0.97\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1195.1 181.82c0.2-0.58 0-1.16-0.7-1.22-0.1 0.58 0.2 1.03 0.7 1.22\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1196.9 182.18c0.2-0.23 0.3-0.52 0.3-0.86-0.7-0.05-0.9 0.23-0.3 0.86\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1197.7 178.22c0.1-0.49-0.1-0.83-0.6-1-0.3 0.56 0 0.93 0.6 1\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1198.8 178.58c0.4-0.34 0.9-0.63 1.4-0.86-0.7-0.09-1.2 0.28-1.4 0.86\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1202.3 248.86c0.6-0.06 1.1-0.43 1.5-0.94-1.2-0.66-1.9-0.45-3 0.22 0.5 0.17 1 0.41 1.5 0.72\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1207.7 172.03c-0.2 0.27-0.3 0.53-0.4 0.79 1.6 0.01 1.7-2.68 2.2-3.67-0.6-0.05-0.9-0.39-0.8-1.01-0.2 0.17-0.4 0.34-0.7 0.51 0-0.51 0.1-0.99 0.3-1.44-0.9-0.13-0.7 1.02-1.6 0.79 0.4 0.49 0.8 0.87 1.4 1.15-0.7 0.38-1.5 0.55-2.2 0.51 0.2 1.26 0.3 0.33 1.3 0.66 0.8 0.26 1.2 1.08 0.5 1.71\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1209.3 168.22c0.5-0.41 1.3-1.61 0.1-1.66 0.1 0.54-0.4 1.16-0.1 1.66\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1210.8 167.64c0.3-0.1 0.5-0.19 0.8-0.29-0.8-0.39-1-0.29-0.8 0.29\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1211.1 166.78c1.1-1.76-2-0.27 0 0\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <desc class=\"value\">United Kingdom: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
" <g class=\"ge country map-element color-0 serie-0 series\" style=\"fill-opacity: 1.000000\">\n",
" <path d=\"m1484.5 309.69c0-3.86 7.9-0.31 9.5 0.35 1.8 0.79 3.2 1.4 5.2 1 1-0.19 1.8-0.37 3 0.32 0.4 0.26 1.9-0.17 2.7 0.29 1.8 1.05 3.4 1.74 4.1 2.24 0.4 0.22 1.2 0.34 2.3 1.45 0.7 0.71-0.3 0.28 0.5 0.62 2.2 0.86 5.4-2.8 6.8 0.08 0.4-1.88 2.4-0.53 2.8-0.35 0.3 0.34 0.6 0.76 1.1 0.93 0.9 0.28 2.5-0.26 2.9 0.94 0.3 0.87-0.7 1.51 0.2 2.27 1.3 1.17 3 1.15 4.4 1.8 2.4 1.05-1.1 1.79 0 3.17 0.8 0.85 4.1 2.21 3.9 3.32-0.5 2.48-3.3-0.03-4.1 0.24-2 0.73-3.1-1.57-5-1.94-0.8-0.15-1.5 0.05-2 0.51-0.6 0.44-2 0.93-1.8 1.27-1.2 0.08-2.4-0.16-3.6-0.08-1.3 0.08-2.4 0.66-3.8 0.65-1.2 0-1.8-0.16-2.9-0.28-0.2-0.02-1.8 0.06-1.5-0.57 0.1-0.41 0-0.79-0.5-0.68-0.1 0.01-1.7-1.82-2.6-1.56 0.6-1.43-1.6-0.53-1.8 0.35-0.3 0.9-3.2-0.1-3.9-0.29-0.5 0.09-0.6 0.97-1.4 0.49-0.5-0.24-1.3-0.75-1.7-0.54-0.1-0.14 0.1-0.32 0.3-0.84 0.6-1.6 1.2-2.54 0.1-4.11-1-1.5-1.2-3.44-2.2-4.96-0.9-1.25-2.4-1.12-3.4-2.32-0.8-1.11-2.6-1.27-3.8-1.45-1.8-0.25-2.2-2.08-3.8-2.32\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <desc class=\"value\">Georgia: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
" <g class=\"gf country map-element\">\n",
" <path d=\"m783.33 664.44c1.28-0.06 2.31-2.27 2.75-3.24 0.82-1.81 0.01-3.82 1.25-5.47 1.46-1.95 0.12-3.39-1.13-4.95-1.29-1.61-1.24-4.49-1.36-6.46-0.15-2.29 0.19-3.59 1.86-5.19 0.62-0.6 1.4-1.25 1.6-2.13 0.26-1.14 0.55-2.56 2.17-1.95 1.04 0.39 1.76 1.24 2.88 1.54 1.2 0.34 2.66 0.32 3.68 1.13 1.19 0.96 2.78 4.6 4.66 4.47-0.32 0.46-0.54 0.97-0.65 1.51 0.53-2.39 4.63 1.72 2.3 3.31 1.13-0.41 0.47-1.47 1.03-2.22 0.86-1.15 1.53 1.52 1.59 2.09 0.31 2.7-1.76 5.55-3.16 7.7-1.61 2.47-2.73 4.56-3.78 7.34-0.39 1.03-0.9 1.9-1.76 2.61-0.36 0.29-1.01 0.96-1.48 1.09-0.78 0.2-1.4-0.42-2.23-0.03 0.34-0.98-0.27-0.98-1.01-0.76-1.03 0.32-1.91-0.1-2.89-0.43-0.49-0.16-2.42 1.23-2.72 1.59-0.57 0.68-3.23-1.16-3.6-1.55\"/>\n",
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" <g class=\"gh country map-element\">\n",
" <path d=\"m1176.5 630.45c-0.4-2.45-0.6-4.41 0.5-6.73 0.9-1.99 0.3-2.68 1.1-4.68 0.5-1.48 2.5-3.53 3.1-4.81 0.8-1.74 0.2-4.33-0.4-6.12-0.2-0.72-0.5-1.17-0.8-1.84-0.3-0.72 0.2-1.47 0.2-2.2 0.2-1.69-0.7-2.87-0.3-4.49 0.4-1.73-0.4-3.59-0.7-5.28-0.1-0.8-1-1.7-0.5-2.55 0.4-0.66 0.9-1.6 0.7-2.41 4.2 0.38 8.1-0.27 12.5 0.03 0.9 0.06 3.6-0.17 3.8 0.35 0.4 0.96 2-0.58 2.8-1.17 0.8-0.6 3.2-0.1 2.7 0.97-0.8 1.63-1 3.25 1.3 5.02 2 1.46 0.9 0.86 1.2 2.37 0.2 0.73 0.3 0.67 0.2 1.51-0.1 0.85-0.1 2.23-0.3 3.31-1.1-0.17-0.6 0.59-1.1 1.22 0.7 0.11 1.1-0.62 1.7-0.45 0.9 0.24 0.9 1.51 0.7 2.15-0.4 1.24 0.4 4.44-1.2 4.13 0.4 1.07 1.5 1.73 2.2 2.64 0.7 1.09 0.1 1.42-0.4 2.44-0.5 1.23 0.5 2.85-0.2 4.01-0.8 1.39-0.9 2.08 0.2 3.26 0.6 0.56 0 1.96-0.3 2.6-0.6 1.55-0.7 2.25 0.1 3.79 0.6 0.98 1.8 1.84 2.7 2.53 0.2 0.19 1.7 1.09 1.8 1.2 0.2 0.82-1.2 1.22-1.4 1.91-0.3 0.94-0.9 1.74-2 1.62-0.7-0.08-0.9-1.49-1.2-1.91-0.8-0.91-2-0.56-3-0.98 0.8 0.64 2.1 0.25 2.9 0.87 1.7 1.22-0.8 1.99-1.7 2.14-3.1 0.55-5.1 2.33-7.7 3.88-1.5 0.89-3.2 0.87-4.7 1.52-1 0.4-2.1 0.61-2.9 1.28-0.7 0.55-1.3 1.28-2.1 1.7s-1.4 0.28-2.1-0.36c-0.8-0.86-1.7-0.97-2.8-1.3-1.1-0.34-2-0.07-2.8-1.24 1-0.03 0.5-0.16 1.5-0.13-0.5-1.22 0.9-5.07-1.6-4.36 0.9-1.54-0.8-4.17-1.7-5.44\"/>\n",
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0.33-2.02 0.34-2.95 0.79 0.76-0.05 1.27 0.58 2.02 0.58 0.85 0 1.68-0.21 2.51-0.36-1.24 0.75-3.39-0.12-4.37 0.76-0.72 0.64 0.81 0.94 1.28 0.61-1.88 0.95 0.63 0.2 1.06 0.16 0.89-0.07 1.78-0.08 2.68-0.09-0.65 0.22-2.41 0.6-2.82 1.17-0.53 0.75 1 1.04 1.46 0.56-0.47 0.45-0.88 0.96-1.23 1.51 0.73-0.42 1.36-1.24 2.3-1.15-0.85 0.45-1.66 0.99-2.3 1.73 1.07-0.02 2.08-0.58 2.95-1.15-1.13 0.96-2.89 1.49-3.7 2.81-0.94 1.55 3.85 2.14 4.5 2.3-0.6-0.12-1.16 0.18-1.52 0.64 0.52-0.02 1.02 0.03 1.52 0.15-0.76 0.47-1.56-0.15-2.31 0.36 1.02 0.2 0.69 0.37 0 0.79 0.72-0.37 1.49 0 2.23-0.22 0.81-0.23 1.14-1.07 1.95-1.29-0.14 0.58 0.13 0.89 0.72 0.86-0.79-0.01-1.63 0.25-2.36 0.55-0.6 0.26-2.6 2.18-0.88 1.18-0.17 0.26-0.34 0.53-0.51 0.79 1.35-0.11 2-2.33 3.53-1.65-0.83 0.19-2.39 0.84-2.59 1.8 0.57 0.01 1.1-0.14 1.58-0.44-0.56 0.27-1.04 0.7-1.36 1.23 0.74 0.12 1.49-0.02 2.23 0-1.11 0.22-2.76-0.18-3.68 0.65 1.59 1.27 3.53-0.24 5.26-0.15-0.96 0.19-0.96 0.29 0 0.29-1.42 0.14-3.48 0.03-4.46 1.3 1.01-0.75 2.55-0.29 3.6 0.07-0.42 0.41-0.93 0.57-1.51 0.5-0.14 1.08 1.02 0.73 1.58 0.51-0.22 0.23-0.43 0.48-0.65 0.72 1.19 0.09 1.44-1.86 3.24-1.52-0.89 0.02-1.46 0.77-2.23 1.08 0.41 0.96 1.28 0.61 2.02 0.36-0.08 1.37-2.49 0.79-3.32 1.23 1.23-0.03 2.45-0.15 3.67-0.15-1.23 0.51-2.56 0.74-3.74 1.37 1.21 0.11 4.33 0.45 5.26-0.5-0.74 0.07-1.52 0.25-2.24-0.07 1.75-0.19 3.5-0.45 5.26-0.22-0.27 0.21-0.53 0.42-0.79 0.65 0.63 0.51 1.85 0.32 1.44-0.72 0.95 0.12 3.44-1.97 3.74-0.29 0.66-0.22 1.15-0.48 0-0.86 1.06 0.43 3.25-1.02 1.66-1.3 0.82 0.38 1.42-0.5 2.23-0.43-0.27 0.22-0.54 0.46-0.79 0.72 0.24 0.17 0.48 0.33 0.72 0.5-0.67 0.87-2.38 0.52-2.59 1.8 1.44-0.67 3.47-0.59 4.17-2.3 0.12 1.17 0.72 0.62 1.52 0.14-1.61 2.24-4.79 1.26-6.77 2.88 0.48-0.24 0.98-0.34 1.51-0.29-0.75 0.3-1.57 0.45-2.23 0.94 1.03 0.62 1.8-1.1 2.95-0.65-0.26 0.2-0.52 0.4-0.79 0.58 1.42 0.05 2.66-1.12 3.74-1.87 0.67 1.13-0.62 0.78-1.36 1.12-0.53 0.25-3.15 1.32-1.45 1.32-0.77 0.21-1.51 0.52-2.23 0.87 0.87 0.28 1.38-0.66 2.23-0.58-1.46 0.77 0.05-0.02-0.07 1.23 0.82-0.77 1.67-1.55 2.88-1.44-0.86 0.17-1.61 0.69-2.16 1.37 0.79-0.25 1.51-0.69 2.3-0.94-0.56 0.26-1.04 0.69-1.36 1.22 1.8 0.06 2.65-1.74 4.32-1.94-0.89 0.49-1.27 1.47-2 2.13-0.74 0.67-2.18 1.07-2.39 2.19 1.59-0.81 3.44-2.36 5.11-2.81-1.63 1.35-3.58 1.99-5.19 3.24 0.4 0.58 0.99 0.63 1.44 0.07 0.17 0.99 0.93 0.74 1.23 0-0.86 1.86 1.34-0.3 1.65-0.5 0.78-0.49 1.7-0.19 1.66-1.44 0.5 0.61 0.96 0.53 1.22-0.22-0.1 0.57-0.36 1.05-0.79 1.44 0.43 0.08 0.87 0.15 1.3 0.22-0.57-0.15-1.05-0.01-1.44 0.43 0.44 0.36 0.94 0.53 1.51 0.5-1.56 0.38-0.19 0.62 0.45 0.77 0.83 0.2 1.44-0.11 1.28-1.05 0.68 0.24 1.35 0.59 2.09 0.57-0.08-1.06-3.02-1.08-3.75-1.22 1.5-0.31 3.03 1.14 4.46 0.29-0.11-0.57-0.49-0.86-1.07-0.72 0.47-0.27 0.98-0.36 1.51-0.29-0.33-0.83-1.95-0.86-0.51-1.23-0.77 0.11-1.47 0.48-2.23 0.65 0.56-0.27 1.05-0.7 1.37-1.22-1.24-0.42-2.62-0.35-3.75-1.08 0.85 0.31 2.21 0.75 2.88-0.15 0.32 1.43 3.63 1.32 1.8 0.65 0.99 0.28 2.08 0.11 3.03-0.22-0.51-0.02-1.01-0.1-1.51-0.21 0.83 0.13 1.71 0.1 2.23-0.65-0.99 0.43-1.91 0.5-2.95 0.15 0.86 0.08 2.31 0.53 2.52-0.73-0.94-0.39-1.99-0.25-2.95-0.57 1.19 0.16 2.58 0.62 3.74 0.14-1.22-0.45-2.58-0.36-3.74-1 0.9 0.3 1.88 0.46 2.82 0.37 0.99-0.09 1.21-1 2.07-1.31-1.56 0.3-3.56-0.03-5.04-0.58 1.68-0.08 3.62 0.83 5.19-0.07-0.75-0.22-1.53-0.37-2.31-0.5 0.89 0 2.38 0.55 3.02-0.36-0.47 0.06-0.95 0.09-1.43 0.07 1.85-0.47-1.04-0.63-1.52-0.79 1.3 0.13 4.78 0.89 4.47-1.44-0.94 0-2.14 0.3-2.88-0.51 0.91 0.19 2.39 0.57 3.3 0.28 0.7-0.22 0.97-1.02 1.66-1.28-0.97 0.57-2.05 0.05-2.88-0.51 0.69 0 4.81 0.77 3.46-1.01 0.66 0.13 0.96-0.23 0.72-0.86-0.58 0.12-1.15 0.27-1.73 0.43 0.68-0.45 2.59-1.46 2.05-2.55-0.43-0.85-2.37-1.14-3.13-1.7 1.47 0.15 2.87 1.5 4.39 1.01-0.49-0.23-0.97-0.49-1.43-0.79 1.46 0.29 0.38-0.77-0.52-0.69-0.87 0.07-2.09-0.79-2.65-1.4 0.77 0.37 1.74 0.55 2.58 0.55 0.31 0.01 0.81-0.01 1-0.33 0.11-0.24 0.09-0.48-0.05-0.72 1.03 0.03 1.06-0.09 0.07-0.36 0.9 0.17 1.73 0.02 2.52 0.58-0.77-0.15-1.67-0.1-1.95 0.79 0.62-0.01 1.23-0.12 1.81-0.33 2.02-0.68-0.48-0.42 1.19-0.75 0.51-0.1 1.73-0.77 2.12-0.58 0.3-1.57-3.05-0.32-2.17-2.16 0.37 0.82 1.98 1.42 2.81 1.15-0.1-0.58-0.53-1.03-1.08-1.22 0.49 0 2.19 1.32 2.44 0.44 0.48-1.67-2.02-0.77-1.5-2.24 0.67 0.31 2.81 2.29 3.62 1.67 0.31-0.23-1.8-1.74-2.04-1.89 0.79 0.26 1.46 0.73 2.16 1.15-0.42-0.44-0.9-0.8-1.44-1.08 0.71 0.47 0.95 0.3 0.72-0.5 0.3 0.23 2.39 1.8 0.94 0.14 0.52 0.51 0.81 0.41 1.17-0.15 0.23-0.35 1.24 0.09 1.57 0.01-0.27-1.44-3.05-2.12-4.25-1.87 0.76-0.88 2.02-0.02 2.84 0.23 1.05 0.32 2.18-0.11 3.21 0.27-0.18-0.72 0.56-1.6-0.51-1.87 1.03-0.16 2.08-1.56 0.43-1.71-1.32-0.12-2.9 0.46-3.89-0.74 0.97-1.07 2.52-0.22 3.71-0.68 1.17-0.45 1.99 0.33 3.06 0.03-0.02-0.56-0.36-0.9-0.93-0.93 1.46-1.63-0.65-2.26-1.37-3.53 0.76-0.1 0.49-0.42 0.77-0.89 0.43-0.7 1.99-0.3 2.65-0.49 0.48-0.15 0.96 0.54 1.33 0.7 0.48 0.21 3.12-0.3 1.58-0.98 1 0.17 1.38-1.12 2.45-0.79-0.2-0.23-0.39-0.47-0.57-0.72 0.26-0.15 0.53-0.3 0.79-0.43-0.56-0.2-1.14-0.37-1.73-0.5 0.38-1.29 1.07-0.33 1.88-0.46 0.65-0.09 0.82-1.2 1.53-1.15 0.55 0.04 0.45 0.94 0.91 1.03 0.71 0.13 1.78-0.5 1.15-1.3 1.27 0.47 2.37 1.02 3.75 0.94-0.21-0.24-0.43-0.48-0.65-0.72 0.95 0.03 1.54-0.57 1.3 0.65 0.9-0.88 1.89-0.01 2.66-1.66-0.64-0.3-1.89-0.69-1.01-1.58 0.05 1.45 1.75 0.83 2.45 0.21 0.4-0.37-0.6-0.79 0.3-0.94 0.8-0.12 0.8-1.08 1.6-1.36-0.6-0.06-1.1-0.26-1.5-0.58 1.7-0.05 3.5 0.49 5.2 0.51-1.4 0.1-4.4 1.48-4.7 3.09 0.7-0.55 2.2 0.47 3.1 0.57 1.2 0.14 0.2-1.69 1.6-1.58-0.5 0.35-0.7 0.92-0.3 1.44 0.8-1.08 3-0.21 3.2-1.8 0.6 0.21 0.9 0.72 0.6 1.3 0.8-0.54 1.9-0.12 2.6-0.81 1.1-0.91 1.3-0.61 2.6-0.85-0.4-0.78 0.3-1.64-0.8-2.01 0.9-0.69 0.8 0.45 1.7 0.82 0.6 0.29 1.4 0.39 2.2 0.4-0.6-0.5-0.5-1.05 0.2-1.37-0.4 2.08 3.9-0.83 4.5-0.93-0.5-0.56-0.2-1.13 0.4-1.44-0.2 1.64 1.3-0.38 0.3 1.15 0.5-0.31 1.8-1.89 2.5-1.3-0.1-0.69 0-1.84 1.1-1.44-1.3-1.01 2.2-0.58 2.4-1.55 0.1-0.78 1.2-0.77 0.7-1.62 0.8 0.42 1.5-0.5 1.3-1.29 1 0.31 1.2-0.56 1.9-0.97 0.8-0.5 2.5-0.82 3.5-1.19 0.7-0.25 2.3 0.25 2.6-0.4 0.3-0.52 0.1-1.39 1-1.19 0-1.02-1.1-0.87-1.8-1.29 0.6 0.13 1.1 0.06 1.5-0.22-0.3-0.44-1.3-0.66-1.4-1.21-0.2-0.83 0.7-0.66 0.9-1.45 0.2 0.74 0.1 1.89 1.2 1.15 0.1 1.76 1.2 1.26 2.5 1.73-1.5 0.13-0.3 1.14 0.6 1.14 1.8-0.01 3.6 0.16 5.4-0.21-0.3-0.53-0.7-0.91-1.2-1.15 0.7 0.03 1 0.65 1.6 0.96 0.7 0.34 1.4 0.1 2.1 0.05-0.1-0.59-0.5-1-1.2-1.01 0.8-0.13 1.5 0.33 1.8-0.6 0.2-0.8 1.3 0.36 1.7 0.49 0.7 0.26 3.5 0.56 3.2-0.9 1.1 0.72 2.6 0.28 3.8-0.14 0.6-0.23 1.1-0.73 1.8-0.38 0.6 0.31 1.2-0.07 1.2-0.77 0.6 0.82 2.5 0.33 3.2 0.07 1.1-0.41 2.2-0.15 3.4-0.54 0.9-0.29 1.8 0 2.7-0.24 0.6-0.18 0.9-0.88 1.7-0.88 1.5 0 2.5-1.26 4-1.58-1.8 0.06 0.6-0.5 1.4-0.22-0.2-1.3 0.1-0.42 1-0.59 0.6-0.12 1.4-1.05 0-0.85 0.8-0.94 2 0.13 3-0.21-1.9-0.32 0.8 0.17-0.3-1.15 0.7 0.18 1.5 0.22 2.2 0.15 0.5-0.05 2.7-0.873 0.9-1.233 0.8 0.051 2.2 0.554 2.9 0.175 1-0.573 0.8-1.483-0.5-1.471 0.7 0.054 3.4 0.518 3.1-0.793 0.7 0.266 1.4 0.87 2.2 0.433-0.4-0.363-0.9-0.507-1.4-0.433 1.5-0.119 4.8-0.511 6.1-1.439-0.8-0.85-2.3-0.374-3.2-0.26-1.6 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" <path d=\"m1398.3 367.29c-1.1-1.1 1.8-2.07 2.6-1.87-0.6 0.49-2.5 0.93-2.6 1.87\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1400.2 378.31c-0.3-1.36-0.2-2.38 0.3-3.67 0.2 0.77 0.4 3.24-0.3 3.67\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1405.5 373.13c-0.8 1.97-1.4-0.24-1.5-1.01-0.2-1.49 2.1-2.57 3.3-2.81 0.3 0.87-0.2 1.69-0.5 2.49-0.4 1.45-0.5 0.25-1.3 1.33\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <desc class=\"value\">Greece: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
" <g class=\"gt country map-element\">\n",
" <path d=\"m502.81 558.67c0.81-0.97 0.68-1.99 0.97-3.15 0.14-0.57 0.93-0.96 0.76-1.59-0.31-1.16-0.3-1.35 0.38-2.48 1.02-1.7 1.88-3.88 2.59-5.33 0.72-1.46 3.46-0.63 4.92-0.63 0.83 0 5.22 0.53 5.65-0.18 0.14-0.23 0.52-2.93 0.4-3.03-0.5-0.44-1.19-0.24-1.34-1.05-0.18-0.93-0.39-1.43-1.25-1.96-1.66-1-2.69-2.64-4.01-4 1 0 2.79 0.47 3.39-0.65 0.62-1.14-0.14-2.89 0.72-3.89 0.91-1.07 5.02-0.36 6.33-0.36h7.56c-0.63 2.73-0.7 5.54-1.22 8.28-0.53 2.81-1.15 5.6-1.58 8.43 1.33-0.14 4.18-0.2 4.39 1.65 0.22-0.48 0.44-1.05 1.01-1.22-0.18-0.25-0.37-0.49-0.58-0.72 1 0.36 1.68 1.21 2.59 1.73-2.43 1.01-3.93 3.46-6.17 4.78-1.04 0.61-2.4 1.35-2.1 2.79 0.21 1.01 0.44 1.71-0.4 2.48-0.79 0.73-3.03 1.17-2.81 2.33 0.13 0.68-0.38 0.54-0.87 0.97-0.09 0.08-0.93 1.2-0.81 1.14-0.98 0.45-3.47 0.6-2.97 2.64-1.17-0.51-2.23-1.36-3.52-1.59-1.31-0.22-2.71 0.2-4.05 0.04-3.33-0.4-5.75-3.2-7.98-5.43\"/>\n",
" </g>\n",
" <g class=\"gu country map-element\">\n",
" <path d=\"m2298.4 570.05c0.3-0.29 1.3-2.73 1.2-3.17-1.5 0.02-2.2 2.12-1.2 3.17\"/>\n",
" <path d=\"m2297.8 573.1c7.7 0 7.7-12.02 0-12.02s-7.7 12.02 0 12.02\"/>\n",
" </g>\n",
" <g class=\"gw country map-element color-0 serie-0 series\" style=\"fill-opacity: 1.000000\">\n",
" <path d=\"m1076.9 585.31c0-0.19 0-0.38-0.1-0.57-0.2 0.15-0.5 0.34-0.7 0.57h0.8\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1077.3 582.22c0.7-0.33 0.6-0.57-0.2-0.72 0.1 0.24 0.2 0.48 0.2 0.72\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1078 589.27c0.3-0.21 0.5-0.37 0.9-0.5-0.6-1.22-2.1-0.41-0.9 0.5\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1079.2 581.78c-0.3 0.03-0.7 0.08-1.1 0.15 0.1 1.05 1.4 0.88 1.1-0.15\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1079.1 584.95c-0.1 0.24-0.1 0.48-0.2 0.72 0.6 0.31 0.9-0.05 1-0.65-0.2-0.01-0.5-0.03-0.8-0.07\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1081 588.05c0.4-0.32 0.7-0.73 0.8-1.23-0.5 0.2-0.9 0.65-0.8 1.23\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1084.5 588.19c0.3-0.69 0-0.89-0.7-0.57 0.3 0.19 0.5 0.38 0.7 0.57\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1074 578.04c0.7-0.48 1.6-0.12 2.4-0.45 0.8-0.37 1.5-0.82 2.5-0.85 1.9-0.05 3.6 0.46 5.2-0.93 1.5-1.23 3.3-0.87 5-0.87h7.8c-0.3 1.01 0.9 2.19 0 3.07-0.5 0.48-1.9 0.37-2.1 1.11-0.2 1.04 3.5 1.85 1.6 3.94-0.9 1.07-2.3 0.49-3.5 0.7-1.1 0.19-2.5 1.08-3.6 1.42-2 0.6-1.1 4.08-3.1 4.81-0.5-1.87-0.8-2.12-2.4-2.95 0.1-0.27 0.3-0.54 0.5-0.79-1.5 1.08-0.1-3.35 2-1.73-0.5-1.41-1.8-0.47-2.5 0.14-0.7-1.26-0.9-2.11 0.7-2.66 1.3-0.47 1.8 0.95 3 0.86-0.7-0.26-1-1.1-0.4-1.65-0.5-0.17-1-0.1-1.4 0.18-0.7 0.39-1.3-0.09-2 0.03s-1.2 0.66-1.7 1.08c-0.7 0.55-1.6 0.46-2.4 0.65 0-1.31 1.1-1.73 2-2.45-1.2-0.12-2.2 1.18-3.5 0.91-2.4-0.52-0.8-2.65 0.3-3.43-0.4 0.05-0.9 0.12-1.3 0.22-0.3 0.29-0.5 0.55-0.8 0.79-0.9 0.04-1.4-1.01-2.3-1.15\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <desc class=\"value\">Guinea-Bissau: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
" <g class=\"gy country map-element\">\n",
" <path d=\"m732.35 633.12c0.73-1.44 1.95-2.13 1.74-4.34-0.14-1.5 0.54-1.95 1.44-2.44 0.21-0.12 0.73-1.02 1.41-0.56 0.88 0.58 0.83-0.05 1.59-0.57 0.67-0.47 1.96-0.8 2.31-1.45 0.3-0.56-0.17-1.41-0.9-1.15-1.8 0.64-0.84-1.2-1.6-2.19-0.69-0.91 0.17-1.95 0.75-2.67 0.73-0.9 0.58-0.85 1.62-1.38 1.41-0.72 2.17-2.14 3.45-2.75 1.73-0.84 0.34-1.48-0.44-2.9 1.17 1.38 2.89 2.29 4.25 3.46-0.66-1-2.01-1.24-2.52-2.38 1.66 0.76 3.4 1.85 4.64 3.22 0.99 1.1 2.25 3.23 3.48 3.95 1.37 0.8 1.73 3.37 1.24 4.73-0.36 1.01-0.89 1.71-1.01 2.82-0.12 0.97-0.14 2.02-0.71 2.85 1.9-0.35 0.46-5 3.4-3.96 2.2 0.78 4.91 2.35 5.45 4.9 1.43-1.29 2.45 1.37 2.65 2.4 0.35 1.79-0.33 4.49-1.49 5.88 1.92 0.54 0.27 1.92-0.17 2.45-0.42 0.5-5.46-1.08-5.17 2.23 0.17 2.01 0.15 3.32-0.16 5.29-0.21 1.33 0.98 3.96 1.95 4.86 0.99 0.92 0.33 2.02 1.82 2.23 0.31 0.05 1.11-0.34 1.3-0.04 0.62 0.96 0.69 2.15 0.86 3.24 0.32 2.03 2.44 3.69 2.44 5.7 0 2.03 2.3 1.97 3.03 3.2-2.47 1.58-4.86-1.6-7.22 0.34-0.39 0.32-0.63 0.76-0.98 1.12-0.65 0.66-1.47 0.38-2.26 0.65-1.44 0.5-1.5 1.22-3.2 1.24-0.53 0.01-0.83 0.42-1.18 0.75-0.26 0.05-0.5 0.14-0.72 0.29 0.07 0.3 0.11 0.6 0.12 0.91-0.37 0.75-2.46 1.08-3.22 0.82-0.52-0.18-0.18-0.79-0.93-0.8-0.6 0-1.8-0.45-2.02-1.06-0.36-0.99-1.78-2.53-2.88-2.68 0.1-0.23 0.2-0.47 0.29-0.72-1.01 0.07-0.89-0.84-0.77-1.53 0.22-1.2-0.1-1.66-0.82-2.54-1.21-1.45-1.15-4.21-0.78-5.93 0.29-1.33 1.27-2.54 1.28-3.94 0-1.21 0.34-1.12 1.04-2.14 0.44-0.64 1.38-1.77 0.96-2.53-0.33-0.61-1.2-1.47-1.2-2.19 0-0.71 0.37-1.38-0.44-1.73-0.56-0.24-1.98-0.26-2.32-0.84-0.33-0.54 0.8-2.17 0.91-2.8 0.19-1.05 0.05-2.27-0.79-3.02-1.24-1.13-3.29 0.64-4.8-0.1-1.8-0.89-3.91-4.44-4.72-6.2\"/>\n",
" </g>\n",
" <g class=\"hk country map-element\">\n",
" <path d=\"m2054.3 489.84c0.8-0.26 1.5-0.63 2.1 0.14-0.2 0.22-0.4 0.41-0.7 0.58 0.3-0.27 0.9-0.45 1.2-0.17 1.1 0.86-1.2 0.08-0.3 1.39-0.8-0.18-4-0.83-2.3-1.94\"/>\n",
" <path d=\"m2053.3 492.5c0-0.75 0.9-0.92 1.4-1.22 0.1 0.79-0.7 1.25-1.4 1.22\"/>\n",
" <path d=\"m2055.8 491.78c0 0.38 0.2 0.62 0.6 0.72-0.1-0.32-0.3-0.56-0.6-0.72\"/>\n",
" </g>\n",
" <g class=\"hn country map-element\">\n",
" <path d=\"m524.53 559.75c0.54-1.35 2.3-1.65 1.46-3.34-0.47-0.95 0.84-2.39 1.58-2.72 1.69-0.77 3.02-2.23 4.39-3.48 0.54-0.49 1.27-0.92 1.9-1.3 0.73-0.44 1.14 0.17 1.74-0.53 0.82-0.96 2.68-1.82 3.64-0.83 0.71 0.71 4.2 0.8 5.24 0.7 1.46-0.13 3.69 0.22 4.95-0.58 0.45-0.29 3.79-1.03 1.95-1.6 1.65-0.05 3.06 0.98 4.69 1.01 1.68 0.03 3.63-1.2 5.13 0.13 1.28 1.13 2.01 0.01 3.29 0.66 1.49 0.76 2.26 2.51 3.38 3.67-0.51-0.26-1-1.08-1.64-1.08-0.62 0-1.17 0.11-1.67-0.36 0.48 0.28 0.51 0.83 0.63 1.29 0.09 0.34 1.04 1.07 1.32 1.38 0.19-0.58-0.07-0.96-0.65-1.08 0.7-0.76 1.8 1.41 3.17 0.72 0.09 0.47 0.24 0.65-0.07 1.08 1.45-0.37 0.12-1.34-0.58-1.59 2.33 0.33 1.9 2.31 3.74 3.03-1.31 0.22-2.54-0.01-3.7 0.8-1.01 0.72-3.1 1.39-4.33 1.36-0.98-0.02-1.88 1.03-2.72 0.77-0.6-0.18-1.16-0.4-1.7-0.7-0.25-0.14-0.28-0.65-0.53-0.7-1.37-0.28-1.51 1.46-1.99 2.21-0.32 0.49-0.87 0.71-1.15 1.23-0.16 0.28 0.2 0.76-0.07 0.96-0.99 0.72-4.13 2.49-4.61 4.15-2.19-1.39-2.62-2.61-4.03-0.24-0.97 1.64-3.94 0.13-3.79 1.57 0.16 1.43 0.78 3.09-0.9 3.37-0.89 0.15-0.74 1.11-0.98 1.75-0.41 1.1-1.95 0.7-2.83 0.82 0.65-1.06-2.6-2.53-0.29-3.38-0.71 0.23-1.67 0.22-1.8-0.72-0.21 0.79-0.87 1.02-1.51 0.5 0.85-0.28 0.7-1.17 0.74-1.88 0.08-1.13 1.11-2.41-0.89-2.42-0.66 0.52-1.02 0.42-1.07-0.29-0.2-0.26-0.47-0.36-0.79-0.3-0.94 0.11-1.73 0.85-2.67 0.86 0.71-1.6-5.25-4.16-5.98-4.9\"/>\n",
" </g>\n",
" <g class=\"hr country map-element\">\n",
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" <path d=\"m1307.9 300.41c-0.4-0.5-0.8-0.98-1.2-1.44 0.5 0.37 0.9 0.86 1.2 1.44\"/>\n",
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" <path d=\"m1315.7 307.25c0.2 0.12 0.5 0.24 0.8 0.36-0.3-0.09-0.6-0.21-0.8-0.36\"/>\n",
" <path d=\"m1319.8 308.69c0.2-0.03 0.4-0.05 0.7-0.07-0.2 0.09-0.5 0.12-0.7 0.07\"/>\n",
" <path d=\"m1320.9 310.2c-0.2-0.12-0.5-0.24-0.7-0.36 0.3 0 0.5 0.12 0.7 0.36\"/>\n",
" <path d=\"m1323.1 310.78c-0.8-0.01-1.7-0.19-1.8-1.08 1-0.04 2 0.1 2.9 0.43-0.3 0.36-0.7 0.58-1.1 0.65\"/>\n",
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" <g class=\"ht country map-element\">\n",
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" <desc class=\"value\">Indonesia: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
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" <path d=\"m1838.6 645.79c0.2 0.49 0.2 0.69 0.8 0.58-0.3-0.19-0.5-0.39-0.8-0.58\"/>\n",
" </g>\n",
" <g class=\"lr country map-element\">\n",
" <path d=\"m1113.1 624.62c1-0.38 1.1-2.03 1.8-2.78 1.4-1.38 3-2.56 4.3-4.01 0.8-0.94 0.2-2.21 0.9-3.05 1-1.25 1.6-0.54 2.3-2.38 0.2-0.74 0-0.85 0.1-1.01-1.3-0.45 1.4-0.43 2.1-0.19 0.7 0.23 1.1-0.55 1.7-0.51 0.9 0.06 1.6 1.11 2.2 1.62-0.4 0.54-0.2 1.06 0 1.61 0.2 0.41-0.1 1.36 0.4 0.91 0.4 2.11 0.2 4.01-0.5 6.05 1.4-0.51 2.7-0.24 2.8 1.44 1.7-0.66 2.3-1.14 2.9-2.83 0.1-0.5 0.3-1.47 0.9-1.57 0.8-0.11 1.4 1.47 1.7 2.03 0.9 2.05 0.9 3.15 0.6 5.35-0.2 0.97-0.8 1.81-1.2 1.93-0.6 0.21-0.9 0.82-1 0.9 0 0.24 0.4 0.64 1.6 0.67-0.6 2.11 3.7 0.39 4.1 1.96 0.1 0.56-0.2 1.33 1 2.1 0.9 0.6 1.8 1.37 2.2 2.33 0.8 2.22 0 3.48-0.3 4.64-0.7 2.57-1.7 4.36-0.5 6.79-1.2 0.22-2.1-0.26-3.1-0.81-1.3-0.72-2.8-0.82-4-1.77-2.1-1.73-4.8-2.51-6.7-4.44-2.2-2.16-3.9-4.73-6.2-6.85-0.6-0.66-5.8-2.44-4.6-3.59-0.9 0.09-2.1-1.35-2.9-1.73-1.5-0.77-1.4-1.66-2.6-2.81\"/>\n",
" </g>\n",
" <g class=\"ls country map-element\">\n",
" <path d=\"m1401.9 941.06c0.9 0.94 0.8 2.3 1.6 3.27 0.7 0.85 0.5 1.96 1.2 2.66 0.8 0.87 3.1 3.81 4.7 2.57 0.6-0.54 1.2-2.34 1.7-3.12 0.7-1.24 1.9-0.99 3.2-1.33 0.8-0.24 2.7-0.98 3.1-1.9 0.4-1.18 0.5-1.85 1.3-2.89 0.4-0.39 1.4-1.46 1.2-2.04-0.5-1.82-1.3-1.93-2.5-3.19-0.9-0.99-1.9-1.91-2.9-2.9-0.8-0.86-2.2 0.49-2.9 0.81-0.8 0.35-1.2 0.73-2 1.17-0.9 0.51-1.9 0.33-2.7 1.28-0.8 1.03-1.2 2.31-1.9 3.4-0.8 1.24-2 1.38-3.1 2.21\"/>\n",
" </g>\n",
" <g class=\"lt country map-element\">\n",
" <path d=\"m1346.8 203.49c1 0.17 1.3-0.89 1.9-1.36 0.8-0.52 2.1-0.69 3-0.89 2.2-0.52 4.8-0.54 7.1-0.41 1.4 0.59 3 0.08 4.3 0.44 1.4 0.13 4.6 0.81 6-0.12 2-1.26 2.1 0.12 3.3 1.07 0.7 0.59 1.7 0.48 2.6 0.61 1.2 0.18 2.2 0.69 3 1.57 0.5 0.55 3.6 2.56 4.3 2.19-0.5 2.16-0.4 2.72 1.6 3.24-1.2 0.59-0.3 1.58-2 1.25-1.3-0.26-1.4 0.96-2.4 1.5-2.3 1.32-0.9 2.91-2.8 4.95 0.5 0.2 1.4 0.28 1.6 0.94 0.1 0.79-1 0.96-1.5 0.79 0.6-1.47-1.7-1.03-1.8-0.74-0.6 1.38-2.3 0.4-3 1.8-0.2 0.48-1.4 0.49-2 0.77-0.8 0.48-2.4-0.05-3.4 0.04-0.5 0.05-2.6 0.33-2.8-0.48-0.2-0.96-1.2-2.28-2.1-2.71-1.3-3.42-0.4-3.15 0.1-4.5 0.1-0.28-1.8-1.81-2.1-1.81-0.8-0.02-4.8 0.21-6-0.81-1.5-1.12-3.4-1.51-3.7-0.41 0.1-1.07-1.7-2.46-2.2-3.36-0.7-1.11-0.6-2.37-1-3.56\"/>\n",
" </g>\n",
" <g class=\"lu country map-element color-0 serie-0 series\" style=\"fill-opacity: 1.000000\">\n",
" <path d=\"m1248.7 254.58c0-0.22 0.6 0.13 0-0.02 0.5-1.27 0.6-1.79 1.6-2.11 1.1-0.36 0.7 0.68 1.1 1.26 0.5 0.78 1.6 1.04 2.1 1.77 0.7 0.79-0.5 2-0.4 2.91-0.7-0.34-3.4-0.37-4.4-0.66 1.3-0.89 0.5-1.63 0.1-2.58\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1250.9 262.72c7.7 0 7.7-12.02 0-12.02s-7.7 12.02 0 12.02\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <desc class=\"value\">Luxembourg: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
" <g class=\"lv country map-element\">\n",
" <path d=\"m1346.3 199.97c0.1 0.24 0.2 0.47 0.3 0.72-0.1-1.25-0.4-2.57 0.5-3.63 0.3-0.46 1-0.78 1.2-1.39 0.2-0.78-0.1-1.44 0.4-2.21 0.7-1.47 1.7-2.14 3.3-2.53 0.8-0.19 2.8-1.42 3.4-0.69 1 1.16 2 1.67 3.1 2.66 1.2 1.13 1.7 2.63 3.4 3.18 1.4 0.44 2.7-0.13 3.8-0.92 1.6-1.14 1.4-2.5 0.7-4.13-0.3-0.65-0.5-1.36-0.4-2.08 0.6 0.17 1.2-0.27 1.7-0.55 1-0.57 2.9-1.37 3.9-0.73 0.7 0.43 2 0.31 2.8 0.8 1.4 0.73 2.5 0.93 3.6 1.98 1 0.87 1.9 1.38 3.1 0.77 1.3-0.69 2.9 0.34 4.3 0.39 1 0.04 3.6 2.16 3.4 2.96-0.1 0.44-0.7 2.16-1 2.51 2.1-0.25 1.3 0.63 2.2 0.99 1.1 0.42 2.1 4.26 2.3 5.31-0.4 0.19-1.1-0.51-1.9-0.11-0.6 0.3-1.1 1.05-1.4 1.56-0.2 0.3-0.2 0.77-0.5 0.97-1.2 0.63-2.6-0.73-3.9-0.08-1.5 0.74-2.2 1.49-3.9 0.53-1.4-0.78-2.7-1.8-4-2.72-1.3-0.88-2.9-0.44-4.2-1.23-1.1-0.7-1.3-2.2-2.8-1.31-1.4 0.77-1.8 0.74-3.2 0.88-1.3 0.13-2.9-0.72-4.3-0.7s-1.7 0.27-3.1-0.22c-0.8-0.3-3.1-0.06-4-0.06-1.5 0-2.8 0.23-4.2 0.57-0.9 0.22-2.3 0.42-2.7 1.42-0.3 0.66-1 0.99-1.4 0.43-0.8-0.96-0.6-2.19-0.5-3.34\"/>\n",
" </g>\n",
" <g class=\"ly country map-element\">\n",
" <path d=\"m1272.4 424.68c-0.9-0.83 1.1-1.81 1.6-2.05 1.7-0.75 2.5-1.18 3.5-2.75 0.8-1.27 1.6-1.89 1.2-3.52-0.2-1.41-0.9-2.82-0.7-4.29 0.2-2.02 2.6-1.5 3-2.94 0.4-1.56 1.7-2.36 3-2.98 1-0.47 4.2-1.36 4.2-2.73 0.1-0.69-0.6-1.12-0.5-1.85 0.1-1.35 0.1-2.69 0.3-4.04 1.3 0.9 3 1.36 4.4 2.21 1.4 0.9 3.1 1.2 4.8 1.02 1.7-0.19 3-1.28 4.7-0.64 1.6 0.57 3.3 0.63 4.8 1.28 1.4 0.58 2.5 1.63 3.8 2.17 1.2 0.47 2.9-0.02 3.9 0.72 1.3 0.87 1.5 2.37 1.8 3.77 0.3 1.79 1 3.22 2.4 4.43 2.5 2.18 5.6 1.67 8.7 2.22 3 0.55 7.4 1.94 9.8 3.91 2 1.59 3.8 4.26 6.6 4.01 3.2-0.3 6.6-3.14 7.3-6.38 0.4-1.49-0.6-2.65-1.1-4-0.5-1.49-0.9-3.19-0.4-4.73 1-2.82 5.6-6.32 8.6-6.42 1.5-0.05 2.4-1.15 3.8-1.26s3-0.14 4.4 0.21c1.6 0.4 3 1.19 4.6 1.8 1.8 0.73 1.4 0.98 1.4 2.59 0.1 1.31 2.2 1.59 3.2 1.66 1.8 0.14 3 0.97 4.7 1.46 1.7 0.48 3.4-0.08 5.1 0.23 1 0.18 2.4 2.1 2.1 3.12-0.4 1.06-1.7 1.34-1.8 2.54-0.3 1.66 0.8 2.89 1.1 4.43 0.3 1.74-0.8 3.28-1.4 4.79-0.7 1.6 1 3.5 1.3 5.16 0.4 2.83 0.9 5.5 1.1 8.4l1.8 75.28h-7.5c0.1 1.44 0 2.88 0.1 4.32l-59.6-33.76-13.5 6.48c-1.2 0.31-2.6-1.56-3.2-2.22-2.8-2.79-5.9-3.35-9.6-4.2-1.3-0.3-3.1-0.46-4.2-1.19-1-0.58-1.4-2.22-1.8-3.17-1.1-2.79-2.5-3.55-5.3-4.58-0.9-0.34-1.7-1.11-2.7-1.09-1 0.03-2.1 1.11-3 0.12-0.7-0.8-2-2.85-2-3.96 0-2.32-0.3-3.16-1.5-5.14-1-1.52-4.3-4.22-2.9-6.24 1.6-2.33 4.1-1.44 3.1-5.27-0.6-1.91-1-3.38-0.3-5.33 0.6-1.66 0.9-3.04 0.3-4.76-0.7-1.81-0.2-3.62 0-5.46 0.2-2.18-0.1-4.38-0.9-6.44-0.6-1.76-1.5-3.42-2.6-4.94\"/>\n",
" </g>\n",
" <g class=\"ma country map-element\">\n",
" <path d=\"m1105.7 445.2c0.4-0.38 1.5-2.17 1.9-2.31 1.4-0.51 3-0.58 4.4-0.9 2.7-0.6 5.5-1.46 7.5-3.42 2.1-1.93 4.1-3.44 6.2-5.23 2.1-1.71 3.6-3.99 5.2-6.06 1.1-1.41 3.6-5.46 1.5-7-0.8-0.59-1.4-0.61-0.9-1.71s0.5-2.19 0.6-3.38c0.1-3.32 1.3-4.8 3.3-7.27 1.2-1.42 0.9-2.86 1.4-4.48 0.3-1.18 1.3-2.09 2.2-2.89 1.9-1.72 3.3-3.83 5.7-4.89 2.6-1.16 5.3-2.07 7.8-3.47 4.8-2.76 7.1-9.5 9-14.45 0.2-0.48 0.8-2.93 1.6-2.85 0.9 0.09 1.8-0.28 2.5-0.69 1-0.53 1.2 0.77 1.5 1.47 0.5 1.35 1.3 2.26 2.5 3.11 2.4 1.76 4.5 1.27 7.3 0.93 2.4-0.28 5.4 1.15 6.8-1.62 0 0.9 0.2 1.13 0.4 1.81 0.1 0.73 1.4 1.1 0.3-0.08 0.6 0.87 1.7 1.32 2.7 0.81 1.1-0.51 1.7 0.55 2.3 1.13 0.7 0.63 1.9 0.99 2.3 1.88 0.5 1.01-0.5 1.94 0.9 2.52-0.8 0.6-0.5 1.7-0.4 2.5 0.2 1.28-0.2 2.4 0 3.63 0.1 0.99 0.7 1.62 0.5 2.68-0.4 1.6-0.2 2.17 0.5 3.57 0.4 0.72 0.1 1.35 0.5 2.01 0.5 0.97 1.4 1.36 2.3 2.03 1.6 1.24-3.1 3.29-0.6 4.24-2.6 0.66-11.7-2.44-13 1.07-0.7 1.83-2.5 2.53-4.5 2.36-1.4-0.12-3.3 1.99-1.2 2.55-1.8 1.56 1.4 2.72 0.6 3.46-0.8 0.84-2.3 0.54-3.2 1.21-1 0.71-2.1 1.76-3.2 2.16-0.9 0.3-1.9 0.27-2.7 0.73-1.5 0.87-2.2 2.72-3.3 4.01-1.3 1.48-3.8 2.05-5.7 2.13-1.5 0.06-3-0.55-3.6 1.17-0.6 1.84-2.4 0.46-3.9 0.57-1 0.08-1.6 0.58-2.2 1.29-0.7 0.72-1.5 0.57-2.3 0.9-1 0.39-1.6 1.33-2.3 1.97-0.8 0.68-1.9 1.1-2.7 1.77-1.6 1.32-3 1.51-2.9 3.78 0.2 2.26 0 4.77-0.3 7.01-0.1 0.95-30.2 0.24-33.3 0.24\"/>\n",
" </g>\n",
" <g class=\"mc country map-element\">\n",
" <path d=\"m1258.9 306.67c-0.1-0.14 0.1-0.4 0.4-0.45 0.2-0.04-0.1 0.55-0.4 0.45\"/>\n",
" </g>\n",
" <g class=\"md country map-element color-0 serie-0 series\" style=\"fill-opacity: 1.000000\">\n",
" <path d=\"m1388.6 268.29c0.7-0.4 0.6-0.1 0.9-0.76 0.2-0.45 0.9-0.4 1.3-0.37 1.1 0.07 2.4 0.16 3.4-0.56 2-1.5 3.5 3.33 5.5 2.36 0.3 1.25 1.1-0.33 1.3 0.97 0.3-1.24 1.5-0.74 2.1-0.06 0.3 0.34 1.1 1.29 1.5 0.91 0.6-0.59 0.7 0.22 1.2 0.83 1.1 1.46 0.6 1.85 1.6 3.19 0.3 0.36 0.3 1.25 0.6 1.58 0.4 0.45 0.5-0.55 0.9-0.07 0.7 0.94-1 4.02-0.6 2.76 0.7-2.24 5.7 2.86 3.6 3.62 0.7 0.28 0.8 0.94 1.5 1.15-1.7 1.71-4.6-2-5.7 0.22-0.2-0.43-0.3-0.86-0.5-1.3-2.6 0.94-0.5 2.85-1.9 4.73-0.8 1.09-1.3 1.86-1.9 3.05-0.1 0.26-0.1 0.82-0.4 0.92-0.6 0.13-1.5-0.32-1.6 0.59-1-0.82-0.7-1.22-1-2.24-1.1-3.12 0.5-5.12 0-7.27-0.2-1.32-1.8-3.78-2.7-4.58-2-1.86-3.1-2.99-4.5-5.38-1.1-1.92-2.1-4.15-4.6-4.29\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <desc class=\"value\">Moldova, Republic of: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
" <g class=\"me country map-element color-0 serie-0 series\" style=\"fill-opacity: 1.000000\">\n",
" <path d=\"m1335.7 316.61c1.9-0.31-1.9-4.53 1.1-3.89-0.9-1.52 1.8-3.86 2.4-2.02 0.4-0.96-0.8-1.32-0.6-2.23 1.2 0.4 1.5 1.12 2.5 2 1.4 1.19 1.8 1.73 3.4 2.55 0.7 0.32 2.5 0.11 2.9 0.8 0.6 1.06-0.9 0.98-1.1 1.11-0.5 0.41 1.3 1.5 0.4 1.96-0.6 0.31-1.3 0.71-1.9-0.03-0.2-0.4-0.4-1.24-1.1-0.52-1.6 1.83-2.1 3.87-1.4 6.24-1.1-0.66-2.4-1.86-3.1-2.94-0.3-0.33-2.3-2.01-2.7-1.95 0.7-0.32 0.6-0.26 1.4 0.14 0-1.22-2.5 0.82-2.2-1.22\" class=\" reactive tooltip-trigger map-area\"/>\n",
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" <path d=\"m1284.5 179.23c1.1-0.18 1.4 1.81 2.1 1.6 1.2-0.32 1.6-3.08 1.2-3.97-0.8-1.59 0.9-1.94 0.7-3.3-0.4-1.94 3.1-0.66 3.6-2.67 0.3-1.48 0.8-2.51-0.2-3.85-0.5-0.74-1.9-2.59-0.4-3.23 1.5-0.63 2 0.04 2.5-2.01 0.4-1.38-1.2-1.64-2.1-1.98-1.7-0.64-2.2-1.92-1.8-3.5 0.5-1.89 0.3-2.89-0.4-4.63-0.2-0.68-0.6-1.76-0.3-2.45 0.4-0.56 0.8-0.45 0.6-1-0.3-0.69-0.8-1.09-0.9-1.86-0.2-0.92 0.7-1.4 0.7-2.1-0.1-1.31 2.1-2.44 2.7-2.94 1.8-1.49 3.6-1.28 5.5-1.01 1.2 0.18 3.5 0.24 3.1-1.53-0.3-1.75-2.2-1.93-2.9-2.48 0.7-0.85 1.4-1.62 2.2-2.4 0.7-0.7 1.6-2.52 2.5-2.96 1-0.56 0.3-5.6 0.1-6.38 1.6 0.06 3.6-0.06 5-0.93 0.7-0.41 0-1.03 0-1.5-0.1-0.51 0.9-0.7 1.2-1 1.2-1.36 2.5-2.27 3.9-3.43 1.2-0.96-1-2.34-1.5-2.78 2.3-1.06 2.9-3.28 5.1-4.32 1.5-0.68 2.4-0.49 3.8-0.16 0.8 0.16 2.4-0.36 2.3-1.34-0.1-0.66-0.6-1.59-0.4-2.25 0.4-0.83 3 0.19 3.6 0.07 1.5-0.29 5.9 1.73 8.2 0.22-0.5-0.22-1-0.39-1.5-0.51 1.2-0.75 2.9-2.43 0.7-3.31 3.3-1.46 7 1.79 9.8 3.02 1.9 0.85 4.3 0.87 6.2 1.72 0.6 0.28 3.9 2.28 4.3 3 0.3 0.63-0.6 0.61-0.8 0.98 0 0.14 0.1 0.32 0.2 2.04 0 0.43 0.8 0.54 1.2 0.64 1.4 0.39 0.4 1.04-0.1 1.68-0.6 0.69 1.2 1.78 1.6 2.12 0.9 0.75 0.6 2.24-0.2 3.12-1.6 1.8 2.4 3.59 2.9 5.32-1.6 0.22-4.4 1.09-5.8-0.17 0.3 0.23 0.5 0.47 0.7 0.72-1.2 0-2.1-0.25-3.1-0.86-0.2 1.97-2-0.9-2.4 0.49-0.2 0.67 0.6 1.34 0.7 1.96-0.7-0.02-1.4-0.29-2-0.58 0.2 0.16 0.5 0.3 0.7 0.43-0.5 0.36-1 0.43-1.6 0.22 2.3 0.69-1.8 1.02-2.2 1.08 0.4 0.81 1.6 1.13 0.6 2.45-0.3 0.45-2.3 1.8-2.9 1.51 0.4 0.46 0.9 0.75 1.5 0.86-2 0.43 1.7 1.72 2.1 2.38-1.4 0.68-3.4 1.46-3.9 3.08-0.3 0.92-2.9 2-2.7 2.82-0.2-0.27-0.3-0.53-0.5-0.79-0.4 0.94-1.7 1.24-2.5 1.4-1 0.17-1.2 2.05-2.1 0.4 0.2 1.21 0 0.56-0.9 0.59-0.4 0.01-0.8 1.1-1.3 1.39-1.5 1.06-3.4 0.92-5 2.12 0.5 0.07 1 0.09 1.5 0.08 0 0.8-0.6 1.33-1.4 0.95-0.4-0.2-0.6 0.93-1.4 0.56 1.8-0.19-0.4-0.36-0.7-0.29 0.1-0.82-0.9-1.17-1-1.94-0.2 1.15 1.1 1.82 1.3 2.87 0.2 1.63-2 2.74-3.1 1.45-0.5 1.28 0.2 2.21 1.5 2.3-0.8 0.12-1.3 1.52-1.5 2.23-0.2 1.09 1.2 1.65 0.8 2.59-0.5-0.58-1.4-1.02-2.2-0.79 2.7 0.62-1.5 0.4 0.8 0.87-2-0.19-0.6 0.23-0.1 0.43-2.1-0.38 1.2 0.87-0.8 1.08 1.1 0.36 0.5 1.74 0.5 2.51 0.1 1.12 0.9 1.75 1.4 2.67-0.3 0.13-0.5 0.25-0.8 0.36 0.4 0.22 2.1 0.33 2.3 0.64 0.6 1.35 1.5 0.35 2.5 0.15-0.6 1.98 2.9 1.7 3.6 2.81-1.8 0.37 0.7 1.04 1.3 1.15 0.4 0.09 2.6 2.99 0.8 1.59 0.5 0.42 0.9 0.93 1.2 1.51-1.1 0.04-1.9 0.65-2.7 1.28-0.8 0.57-3 0.9-1.6 1.38-0.5 0.15-1 0.03-1.4-0.36-0.1 0.89 2.6 1.42 2.3 0.08 0.5 0.27 1.5 1.95 0.1 1.15 1.9 0.65-0.8-0.03-1.2-0.15 0.1 0.6 0.6 1.04 1.2 1.16-0.6 0.31-1.3 0.36-1.9 0.59-0.9 0.32-1 1.19-1.7 1.63-0.5 0.3-0.8-1.62-1.6-1.58 0.4 0.38 0.6 0.86 0.5 1.44-0.2-0.16-0.4-0.33-0.7-0.5 0.3 0.66 0.3 1.15-0.7 0.72 0.1 0.24 0.3 0.48 0.4 0.72-1 0.52-2 0.46-3 0.36 0.3 0.18 0.5 0.37 0.7 0.58-1.6 0.84-3.6-0.68-5.1 0.43 1.2-0.3 3-0.17 3.6 1.08-0.9 0.53-1.9 0.05-2.9 0 1.5 0.53 4.1 2.97 1.5 3.96 0.6-0.15 0.9 0.19 0.7 0.79-0.5-0.28-1-0.35-1.6-0.22 0.4 0.44 2.2 1.45 0.9 1.94-1.1 0.44 1 1.09 0.3 1.92-1.4 1.89-0.1 3.86-1.6 5.83-1 1.28-1.5 4.96-3.9 4.27-1.3-0.42-2.8 0.23-4.2 0.14-0.8-0.05-2.8-0.22-1.7 1.1-1.4 0.53-2.5-0.3-3.3 1.46-0.5 1 0.6 1.78 0.7 2.74 0.2 1.8-2.3 0.61-3 0.67-2.3 0.21-4.2 0.29-6.5 0.17 0.7-0.03 1.3-2.32 0.6-3.05-0.8-0.81-2.7-3.01-2.6-4.15 2.4 0.86 1 0 0.8-1.37 3.4-0.56 0.1-1.67-0.7-2.81-0.8-1.1-1.9-2-2.4-3.29-0.3-0.83-0.7-1.91-1.8-1.53 0-0.56 0.1-1.13 0-1.68-0.3-0.94-1.8-1.07-0.4-1.56-1.4 0.49 0-3.8 0.1-4.32-0.9-0.2-1.4 0.83-2.3 0.72 0.4-0.49 0.8-0.99 1.1-1.52-1.3-0.13-0.2 0.77-1.5 1.16-0.1-0.68-0.6-0.83-1.1-0.44 0.6-1.35-1.5-5.12-0.3-5.97\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1287 187.58c-0.1 0.24-0.3 0.48-0.4 0.72 0.6 0.09 1.1-0.24 1.3-0.79-0.3 0.03-0.6 0.05-0.9 0.07\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1287.4 186l-1.5 0.72c0.4 0.56 2.1 0.83 2.4-0.01 0.2-0.53-0.5-1.4-0.9-0.71\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1317.2 200.62c0.2 0.44 0.2 0.9 0.2 1.36 1.2-0.24 1.7-2.53 2-3.5 0.5-1.83 1.4-3.45 2.1-5.21-1.2-0.03-1.5 2.16-2 2.92-0.9 1.37-2.6 2.52-2.3 4.43\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1321 152.59c0.2 0.38 0.4 0.23 0.1 0.79 0-0.26-0.1-0.52-0.1-0.79\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1327.3 179.81c-0.2 0.12-0.5 0.24-0.7 0.36 0.3-0.05 0.5-0.17 0.7-0.36\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1328 168.94c0.3 0.34 0.7 0.48 1.1 0.43-0.3-0.53-0.8-0.97-1.3-1.22 0 0.26 0.1 0.52 0.2 0.79\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1328.9 179.23c-0.2 0.24-0.3 0.48-0.5 0.72 0.5-0.17 0.5-0.28 0.5-0.72\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1329.3 196.01c-0.3-0.82 2.5-3.09 3.1-3.53-2.7-0.26 3.3-4.54-1.1-3.61-1.3 0.26-2.4 1.31-3.3 2.25-0.7 0.85-0.4 3.96 0.9 4.17-0.4 0.45-0.7 0.96-1 1.51 0.6 0.13 1.2-0.26 1.4-0.79\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1329.6 176.28c0.3-0.14 0.5-0.29 0.7-0.43-0.5-0.24-0.9 0.02-1 0.65 0.1-0.08 0.2-0.15 0.3-0.22\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1333.2 188.81c0.6 0.21 1.1 0.11 1.5-0.29-0.5-0.64-1.2-0.42-1.5 0.29\" class=\" reactive tooltip-trigger map-area\"/>\n",
" <path d=\"m1348.2 129.12l-0.7-0.07c0.4 0.38 0.4 0.23 0.7 0.07\" class=\" reactive tooltip-trigger map-area\"/>\n",
"<desc class=\"value\">Sweden: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
"<g class=\"sg country map-element\">\n",
" <path d=\"m1993.4 672.21c-0.9 0.26 0.5 2.08 1.8 0.36-0.5-0.27-1.1-0.39-1.8-0.36\"/>\n",
"</g>\n",
"<g class=\"sh country map-element\">\n",
" <path d=\"m1150.3 833.36c0.3-0.49 0.4-0.99 0.1-1.51-0.6 0.41-0.6 1.03-0.1 1.51\"/>\n",
"</g>\n",
"<g class=\"si country map-element color-0 serie-0 series\" style=\"fill-opacity: 1.000000\">\n",
" <path d=\"m1301 287.58c-0.1-0.62 0.5-1.37 0.9-1.72-0.7-0.15-1.3-0.53-1.9-0.86 1.5-1.65 0.8-2.65 2.7-2.2 0.7 0.17 4.9 1.64 5.5 1.11 1.2-1.06 2.2-2.23 3.9-2.12 1 0.06 2.1 0.51 2.9-0.02 0.9-0.53 2-0.58 2.9-0.01-0.8-0.97 0.4-2.49 1.4-1.65 0.8 0.65 1.8 2.28 2.5 3.16-0.9-0.12-3.5-0.8-2.1 0.86-0.8-0.03-1.2-0.56-1.6 0.37-0.1 0.52-2.1 0.99-2.7 1.34-0.5 0.28 0.4 1.65 0.3 2.24 0 0.92-2.2 0.99-3 1.74 0.3 0.12 0.6 0.42 1 0.5-0.7 0.14-0.2 2.44-1.4 1.65-1.5-0.91-2.8-0.49-4.2-1.79-0.4 1.1-2 2.05-3.2 1.5-0.5-0.22-0.5-0.23-0.9 0.16-0.6 0.59-1.9 0.08-2.6-0.08 0.8-0.66 2.3-0.91 1.8-1.94-0.5-0.81-2.6-0.6-1.5-1.94\" class=\" reactive tooltip-trigger map-area\"/>\n",
"<desc class=\"value\">Slovenia: 1</desc><desc class=\"x auto\">0</desc><desc class=\"y auto\">0</desc></g>\n",
"<g class=\"sk country map-element\">\n",
" <path d=\"m1323.5 265.2c0.8-2.03 1.7-1.67 3.6-1.69 0.7-0.01 3.7-1.51 3.8-2.17 0.3-2.55 6.4-4.5 5.8-2.62 0.8-0.11 2.4-1.85 3.3-1.53 1.3 0.5 2.7 1.91 2.4 2.46 1.2 0.92 1.6 0.35 2.9-0.61 0.4-0.33 1.6-0.73 2.3-0.5 2.4 0.86 3.4-0.15 5.2-0.13 2 0.02 3 0.01 4.7 1.64 0.7 0.65 2.4 0.84 3 1.04 0.4 0.07 0.3-0.01-0.2 1.55-0.5 1.36-1.8 2.7-2 3.89 0 0.24 0.5 0.64-0.3 0.58-2.5 1.38-3.9-1.43-4.4-1.26-0.6 0.19-0.7 0.49-1.4 0.35-0.9-0.16-1.8-0.48-2.6-0.56-2-0.2-2.5 0.19-2.7 2.09-0.1 1.27-3.2 2.04-4.3 1.46-2.6-1.42-1.4-0.29-2.5 0.65-1 0.83-5.9 0.32-3.5 2.16-1.6 0.19-3.2 0.75-4.9 0.63-0.6-0.04-1.6 0.09-2.5-0.13-0.7-0.19-1.5-0.84-2.2-1.19-1.1-0.53-2.5-1.44-3.1-2.52-0.2-0.46 0-1.04-0.1-1.54-0.3-0.92-0.2-1.21-0.3-2.05\"/>\n",
"</g>\n",
"<g class=\"sl country map-element\">\n",
" <path d=\"m1102.4 618.72c-0.1 0.17-0.1 0.34-0.2 0.5 1 0.31 1.9 0.72 2.9 0.87 0.2-0.39 0.3-0.92-0.1-1.19-0.7-0.48-1.8-0.13-2.6-0.18\"/>\n",
" <path d=\"m1099.4 606.48c0.7-0.57 1.5-0.07 2.1-0.5 0.7-0.42 0.5-1.57 1.3-1.8 1.4-0.4 2.3-2.97 2.7-4.13 0.7-1.94 2.7-0.51 4.2-1.48 1.2-0.83 4.7-0.9 5.9 0.02 1.2 0.88 1.8 2.38 2.6 3.6 0.4 0.64 0.8 1.29 1.1 2 0.2 0.51-0.4 1.15 0 1.52 0.4 0.45 0.9 0.35 1 1.12 0 0.61-0.2 1.65 0.2 2.14 0.7 0.68-1.5 2.89-1.4 4.06 0.2-0.36 1.8-1.21 2.2-1.44 1.4-0.89 1.2 0.33 0.8 1.45-0.4 1.27-2.2 0.92-2.4 2.77-0.2 1.78-0.2 1.69-1.2 2.73-0.8 0.82-1.5 1.64-2.7 2.45-1.4 1.09-2.1 3.86-2.8 3.66-0.7-0.22-6.5-3.35-7.4-4.13 1.6 0.46 0.3-0.31-0.1-0.58 0.7-0.48 1.6-0.48 2.2-1.08-1.3 0.17-3.3 0.38-2.1-1.58-0.9 2.02-2-0.25-2.8-0.14-0.5 0.07-1-3.47-1-3.89-1.1 0.25-2.8-0.66-2-2.09 0.5 0.38 1 0.71 1.5 1.01-1.2-0.63 0.6-1.71 1.3-1.66-0.3-0.48-0.1-0.35-0.1-0.93-0.6 1.3-1.2 0.63-2 0.14-0.2 0.26-0.2 0.52-0.3 0.79-1-1.58-0.2-2.19 1.2-2.52-0.5-0.7-1.6-0.64-2-1.51\"/>\n",
"</g>\n",
"<g class=\"sm country map-element\">\n",
" <path d=\"m1293.2 306.46l0.5-0.69 0.7 0.54-0.6 0.59-0.6-0.44\"/>\n",
" <path d=\"m1294.4 311.71c7.8 0 7.8-12.03 0-12.03-7.7 0-7.7 12.03 0 12.03\"/>\n",
"</g>\n",
"<g class=\"sn country map-element\">\n",
" <path d=\"m1070 557.09c-0.4-0.02-0.8 0.13-1.1 0.43-0.2-1.23 2.1-1.61 2.9-2.32 1.5-1.54 2.7-3.62 3.6-5.59 0.5-0.94 2.6-9.94 4.8-7.87 1 0.93 6.2-0.01 7.1-0.99 1.3-1.52 6.4 0.18 7.4 1.47 1.5 1.74 1.7 2.44 4.1 2.69 1.3 0.13 2.7 2.67 2.4 3.92-0.1 0.58 1.8 1.61 2.1 2.22 0.4 0.77 0.6 1.66 1.2 2.32 0.6 0.64 1.8 0.72 2.2 1.51 0.6 1.15 2.3 1.79 2.5 3.14 0.1 0.61-0.9 1.32-0.2 1.93 0.5 0.52 0.9 0.85 1 1.74 0.1 0.93 0.9 3.19 0.2 3.9-0.8 0.17-0.9 0.55-0.2 1.15 0.1 0.44 0.4 0.81 0.7 1.11 0.7 0.38 0.1 1.13 1 1.41 0.5-2.05 2.2 1.5 2.5 2.06 0.4 0.84 0.6 1.47 0.4 2.44-0.3 1.1 0.4 2.51 0.1 3.41-1.3-0.53-2.6 0.31-3.9 0.08-1.4-0.24-2.2 0.56-3.5 0.74s-3.5-2.18-4.6-1.44c-0.8 0.58-0.5-1.05-0.7-1.1-1.2-0.35-0.8-0.56-1.8-0.56l-12.6-0.02c-1.7 0-2.4 0.1-3.6 1.08-1.1 0.86-2.6 0.78-4.2 0.73-1.4-0.05-2.4 0.79-3.7 1-0.7 0.11-1.4-0.12-2 0.29-0.1-0.33-0.8-1.04-0.4-1.43 0.9-1 1.4-1.49 2.6-0.73 0.9 0.57 2-0.32 3-0.34 0.7-0.01 1 0.53 1.8 0.3 0.5-0.17 0.8-0.06 1.3 0.11 0.9 0.3 0.8-2.02 1.9-2.38-1.6 0.11-0.7 1.42-1.6 2.09-0.8 0.58-3-0.19-3.2-1.15-0.6 2.42-5.4 0.66-4.4-0.58-0.4 0.65-0.4 1.74-1.3 1.95-0.2-0.6 0-1.07 0.5-1.44-1.3 0.28-0.6-1.65-0.3-2.51 0.4-1.29 1.9-1.37 3-1.37l4.1-0.01c0.8 0.42-0.8-0.27 0.5-1.15 1.2-0.77 2.4-0.25 3.5-0.57 1.3-0.37 0.6-2.19 2.3-1.13 2.4 1.47 4.8 3.02 7.5 2.1 1.1-0.35 2.2-1.37 0.9-2.09-1.7-0.9-2 0.39-3.3 0.33-1.4-0.07-1-2.06-2.6-1.45-0.5 0.23-1.1-0.57-1.3-0.96-0.4-0.8-1.6-0.53-2.3-0.06-1.1 0.65-1.3-0.38-1.9 0.2-0.4 0.42-0.3 0.97-0.8 1.26l-7.5-0.04c0.3-0.62-0.3-1.04-1-1.12 0.1-0.59 0.5-0.84 1-0.57-0.1-0.62 0-1.22 0.2-1.8-0.5 0.82-1 1.44-2 1.58-0.2-0.8 2.1-2.86 2.9-2.95-0.9-0.37-3.2 1.76-3.2 2.59 0-2.37-1.5-6.65-4-7.56\"/>\n",
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0.15-1.09 0.72-1.29-0.49 0.19-1.12 0.01-1.58 0.28-0.56 0.34-0.6 1.15-1.15 1.52 0.12-1.42-1.02-0.46-1.61-0.07-1.09 0.73-2.76 1.26-4.01 1.65 0.86-0.77 1.95-1.24 2.95-1.8-1.38 0.39-2.48 1.38-3.81 1.87 0.22-0.25 0.44-0.52 0.64-0.79-1.21-0.14-2.13 1.2-3.23 1.5-1.68 0.46-1.82 0.87-3.53 0.44 0.67 1.23-0.37 0.65-0.97 0.51-0.8-0.19-1.75 0.39-2.5 0.59-0.58 0.16-1.26-0.39-1-1.02 0.15-0.34 0.96-0.48 1.24-0.57 2.9-0.89 5.79-2.14 8.7-2.96-1.5-0.39-3.39 1.7-4.73 0.55-1.16-1 5.16-3.64 6.13-4.08 1.24-0.57 2.47-1.19 3.57-2.01 0.69-0.53 1.04-1.89 1.91-2.15 2.92-0.87 5.93-1.66 8.89-2.46-0.04 1.28 1.17 0.94 1.98 0.71 1.41-0.39 3.43 0.19 4.79 0.65-1.05-1.21-5.07-1.6-1.88-3.21 1.84-0.93 3.96-1.31 5.98-1.61-1.92-0.85-3.89 0.7-5.61 1.32-0.61 0.23-3.83 1.26-3.1-0.46-0.89 0.89-2.36 1.1-3.54 1.21-1.41 0.13-2.47 1.18-3.81 1.48-1.44 0.33-2.73 0.28-4.01 1.14-0.91 0.62-1.69 1.07-2.76 1.33-2.16 0.53-3.51 0.91-5.18 2.31-0.9 0.76-2.2 1.03-3.3 1.37-1.15 0.36-1.78-0.85-2.75-0.41 1.48-0.25 1.2 1.71 0.24 2.2-1.82 0.92-3.69 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0.72-0.61 0.29-1.01 1.09-1.73 1.15 0.25-0.82 1.03-1.28 1.8-1.51-0.94-0.55-2 0.81-2.64 1.29-0.86 0.64-2.27 0.86-3.3 0.95-1.38 0.1-2.55 0-3.83 0.71-1.19 0.66-2.18 2.09-3.69 2.01 0.85-0.41 1.62-0.99 2.3-1.65-0.84-0.89-2.83 0.08-3.67 0.52-0.61 0.32-1.03 0.36-1.71 0.39-0.82 0.05-1.9 0.99-2.61 1.39 0.91-1.48-0.46-0.34-0.81-0.07-0.55 0.4-1.13 0.36-1.72 0.1-0.29-0.14-1.64 0.49-1.86 0.55-0.93 0.28-2.02 0.11-2.88 0.43 1.13-1.1 2.18-1.45 3.74-1.37-1.65-2.13-4.62 0.71-6.12 1.6
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