diff --git a/Use_cases/Coastlines_change_and_impact/Coastal_change_and_impact_analysis.ipynb b/Use_cases/Coastlines_change_and_impact/Coastal_change_and_impact_analysis.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "05f5a4a3-a58e-4bb6-831a-68d6dfda8066",
+ "metadata": {},
+ "source": [
+ "# Country scale coastal change and exposure analysis\n",
+ "\n",
+ "* **Products used:** [DE Africa Coastlines](https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f24e28c-3ede-4ff0-9b78-de9962e11454",
+ "metadata": {},
+ "source": [
+ "## Background\n",
+ "\n",
+ "The coastal environment is highly dynamic, undergoing changes influenced by tidal, seasonal, and climatic factors, as well as human activities. \n",
+ "A significant percentage of Africa's population reside in coastal areas. As population growth continues and coastal cities expand rapidly, mapping and improving our understanding of coastal regions are becoming increasingly important.\n",
+ "\n",
+ "Remote sensing data plays a crucial role in generating various types of information products, ranging from local to regional scales, to support effective coastal management. These information products enable:\n",
+ "* Classification and monitoring of coastal geomorphology, such as beaches, cliffs, estuaries, and vegetation cover. \n",
+ "* Assessment of vegetation and habitat health, providing insights into the wellbeing of important ecosystems such as mangroves and salt marshes. \n",
+ "* Monitoring of erosion and accretion rates, helping to identify areas prone to erosion and understand the processes driving coastal changes.\n",
+ "* Mapping of human settlements along the coast, facilitating an understanding of population distribution and density.\n",
+ "* Mapping of coastal topography, including elevation and slope.\n",
+ "\n",
+ "The DE Africa platform offers access to diverse datasets and information products at a continental scale. These datasets can be integrated to gain insights into the current state of the coastal environment and its temporal changes. This information empowers countries and local authorities to take proactive measures against climate change and make informed decisions regarding coastal management."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f7db118-12f1-44c2-b180-102dea4fd0b4",
+ "metadata": {},
+ "source": [
+ "## Description\n",
+ "\n",
+ "This notebook demonstrates how a user can leverage the DE Africa Coastlines service and other datasets accessible through the platform to understand trends of coastal change and its potential impact for a African country.\n",
+ "\n",
+ "The analysis covered include:\n",
+ "* Retrieve and extract DE Africa Coastlines rates of change dataset over a country of interest\n",
+ "* Summarise and visualize the overall trends of change\n",
+ "* Inspect trends of change for selected administration areas in a country\n",
+ "* Combine coastline change information with gridded population data and topographic information to estimate exposure\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5fd8f30c-1564-4ec2-8be7-db8ded878dc6",
+ "metadata": {},
+ "source": [
+ "## Getting started\n",
+ "\n",
+ "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf9e040e-35bd-4797-8e78-37a994f56c94",
+ "metadata": {},
+ "source": [
+ "### Load packages\n",
+ "Import Python packages that are used for the analysis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "73c45b2f-f00a-4ff6-b237-8c19a1e32ba7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import os\n",
+ "import geopandas as gpd\n",
+ "import pandas as pd\n",
+ "import rioxarray as rxr\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import datacube\n",
+ "from deafrica_tools.spatial import xr_rasterize, xr_vectorize\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa205f03-9237-407f-bb84-5ebdc33a8bcc",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Retrieve coastlines rates of change over a country of interest\n",
+ "\n",
+ "The DE Africa Coastlines is a continental coastline monitoring service that can be accessed directly from AWS S3 bucket or OGC Web Services.\n",
+ "The [Coastlines dataset notebook](../../Datasets/Coastlines.ipynb) demonstrates loading DE Africa Coastlines over a small area through the Web Feature Service (WFS). \n",
+ "For country scale analysis in this notebook, we will access data directly from AWS S3.\n",
+ "\n",
+ "We will retrieve the `rates_of_change` layer from the geopackage hosted in AWS and load the data into a `geopandas.GeoDataFrame`.\n",
+ "To save memory, we will extract data over a specified country using the `country` attribute in the dataset.\n",
+ "This attribute was set based on intersection of a coastal location with the exclusive economic zone boundaries defined in https://deafrica-input-datasets.s3.af-south-1.amazonaws.com/deafrica-coastlines/countries_eez_deafrica.geojson\n",
+ "Therefore, we have to pick a country name from the names used in this boundary file. \n",
+ "\n",
+ "> If a different administrative boundary definition is desired, the entire continental dataset can be loaded and a spatial join with a user defined boundary geometry can be run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0936c9ff-0d14-473b-ad1c-89e6bb21e46b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Nigeria', 'Seychelles', 'Gabon', 'Sao Tome and Principe', 'Senegal', 'Guinea-Bissau', 'Mauritania', 'Sudan', 'Togo', 'Cape Verde', 'Kenya', 'Angola', 'Federal Republic of Somalia', 'Algeria', 'South Africa', 'Djibouti', 'Ivory Coast', 'Tanzania', 'Sierra Leone', 'Tunisia', 'Republic of Mauritius', 'Eritrea', 'Morocco', 'Comores', 'Guinea', 'Mozambique', 'Liberia', 'Republic of the Congo', 'Benin', 'Namibia', 'Egypt', 'Madagascar', 'Gambia', 'Ghana', 'Libya', 'Cameroon', 'Democratic Republic of the Congo', 'Equatorial Guinea']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# list of country names used\n",
+ "countries_gdf = gpd.read_file(\"https://deafrica-input-datasets.s3.af-south-1.amazonaws.com/deafrica-coastlines/countries_eez_deafrica.geojson\")\n",
+ "countries = list(countries_gdf[\"TERRITORY1\"].values)\n",
+ "print(countries)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "704e22ea-97a4-447a-a4b3-92762a3b1242",
+ "metadata": {},
+ "source": [
+ "As an example, `Nigeria` will be used. For a different country, the name can be copied from the list above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "cd9dde63-6c76-4692-a812-a8aa16dd5382",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "country = \"Nigeria\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1e77ea5-1b42-4157-af77-8925b9f95b46",
+ "metadata": {},
+ "source": [
+ "The following step takes a few minutes to run because the continental dataset is retrieved first before a country subset is extracted and saved in the `coastlines` variable. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "615c044d-5efe-45bc-925c-402c5f87bb7b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 18min 20s, sys: 11.5 s, total: 18min 32s\n",
+ "Wall time: 20min 23s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "rates_of_change = gpd.read_file(\"https://deafrica-services.s3.af-south-1.amazonaws.com/coastlines/v0.4.0/deafricacoastlines_v0.4.0.gpkg\", \n",
+ " layer=\"rates_of_change\").query(f\"country == '{country}'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bdec3ad-8559-48b5-be28-18155ca2a38a",
+ "metadata": {},
+ "source": [
+ "The Coastlines `rates_of_change` dataset includes a list of attributes, for which detailed explanations can be found in the relevant section in the [User Guide](https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html#Rate-of-Change-Statistics)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3a660b30-e695-42cc-98e4-84e9e29e9360",
+ "metadata": {},
+ "outputs": [
+ {
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+ " uid rate_time sig_time se_time outl_time dist_2000 \\\n",
+ "1396995 s11urfvn7h -0.26 0.210 0.20 2007 -4.80 \n",
+ "1396996 s11urfvq96 -0.40 0.110 0.23 2007 -3.07 \n",
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+ "1396999 s11urfvz7x -0.25 0.023 0.10 2007 1.17 \n",
+ "\n",
+ " dist_2001 dist_2002 dist_2003 dist_2004 ... angle_std \\\n",
+ "1396995 -5.79 -7.23 -8.36 -4.38 ... 4 \n",
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+ "1396998 -4.60 4.20 -3.34 -5.27 ... 6 \n",
+ "1396999 2.69 0.48 -0.97 -2.32 ... 5 \n",
+ "\n",
+ " valid_obs valid_span sce nsm max_year min_year country \\\n",
+ "1396995 21 22 27.05 4.80 2006 2018 Nigeria \n",
+ "1396996 21 22 29.83 3.07 2006 2018 Nigeria \n",
+ "1396997 21 22 26.78 7.17 2006 2019 Nigeria \n",
+ "1396998 21 22 18.20 4.60 2006 2019 Nigeria \n",
+ "1396999 21 22 11.78 -1.17 2006 2018 Nigeria \n",
+ "\n",
+ " certainty geometry \n",
+ "1396995 good POINT (270982.704 813330.824) \n",
+ "1396996 good POINT (271008.705 813334.768) \n",
+ "1396997 good POINT (271034.836 813335.068) \n",
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+ "1396999 good POINT (271084.409 813354.535) \n",
+ "\n",
+ "[5 rows x 38 columns]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rates_of_change.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c67f9149-c875-48de-8618-1e56115c3715",
+ "metadata": {},
+ "source": [
+ "## Summarise and visualize the overall trends of change\n",
+ "\n",
+ "Not all shorelines and coastal changes can be mapped accurately with Earth observation data and the method implemented by the DE Africa Coastlines service due to the limited number of historical observations available, presence of cloud cover, and the complex nature of shoreline morphology.\n",
+ "\n",
+ "We will select only data points where the `certainty` attribute has a value of `good` to be used in further analysis.\n",
+ "Each `good` rates of change were calculated from at least 15 valid annual shoreline location measurements. Other `certainty` attribute values that indicate unreliable measurments are explained in the [user guide](https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e8f65eea-9f18-4eef-96b4-bd9c159a295f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "A total of 1904 km of shoreline was mapped for Nigeria\n"
+ ]
+ }
+ ],
+ "source": [
+ "# each measurement is made over a 30 m segment along the shoreline\n",
+ "total_shoreline = len(rates_of_change)\n",
+ "print(f\"A total of {round(total_shoreline*30/1000)} km of shoreline was mapped for {country}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "c8f2244f-a0b9-45f0-94c9-1cf206c8860d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "80% or 1531 km of shoreline had good rates of change measurements\n"
+ ]
+ }
+ ],
+ "source": [
+ "good_rates = rates_of_change[rates_of_change.certainty=='good']\n",
+ "\n",
+ "n_good = len(good_rates)\n",
+ "perc_good = n_good*100./total_shoreline\n",
+ "print(f\"{round(perc_good)}% or {round(n_good*30/1000)} km of shoreline had good rates of change measurements\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c4f7416-27d2-4be5-978a-c756bea3999b",
+ "metadata": {},
+ "source": [
+ "We can inspect the distributions of the `good` measurements and the significant changes.\n",
+ "Significant changes have a significance (p-value) of less than 0.01, which indicates a shoreline is measured to have undergone consistent change through time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "2275c5f8-9163-44b5-943a-3105736da0e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sig_rates = good_rates[(good_rates.sig_time<0.01)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "11ae7b53-74d3-47b2-a277-484a754247bd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "low, high = list(good_rates.rate_time.quantile([0.05,0.95]))\n",
+ "bins = np.arange(low,high,0.1)\n",
+ "good_rates.rate_time.plot.hist(alpha=0.5, bins=bins, label='Good measurements', legend=True, figsize=(15,5));\n",
+ "sig_rates.rate_time.plot.hist(alpha=0.5, bins=bins, label='Significant changes', legend=True);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6acd3435-567c-4d42-8c93-eeef1b170dc1",
+ "metadata": {},
+ "source": [
+ "> It is likely that both erosion and accretion have happened along a country's shoreline. Significant changes at a rate of more than a few metres per year, both retreating and advancing, have been observed in many African countries."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a70fc03e-0f62-49d5-949d-a24793f87af0",
+ "metadata": {},
+ "source": [
+ "### Change statistics\n",
+ "\n",
+ "A number of summary statistics of coastline change can be calculated.\n",
+ "\n",
+ "#### The length and percentage of shorelines that have been moving at a rate of more than 0.5 metre per year since 2000."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "ef28ef0c-8965-4e1f-ab8c-6919eef9a535",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "417 km of shorelines retreated at more than 0.5 metre per year\n",
+ "301 km of shorelines advanced at more than 0.5 metre per year\n",
+ "813 km of shorelines without significant movement measured\n"
+ ]
+ }
+ ],
+ "source": [
+ "total = good_rates.rate_time.count()*30/1000\n",
+ "km_neg05 = ((good_rates.sig_time<0.01) & (good_rates.rate_time<-0.5)).sum()*30/1000\n",
+ "km_pos05 = ((good_rates.sig_time<0.01) & (good_rates.rate_time>0.5)).sum()*30/1000\n",
+ "km_no = total - km_neg05 - km_pos05\n",
+ "\n",
+ "print(f\"{round(km_neg05)} km of shorelines retreated at more than 0.5 metre per year\")\n",
+ "print(f\"{round(km_pos05)} km of shorelines advanced at more than 0.5 metre per year\")\n",
+ "print(f\"{round(km_no)} km of shorelines without significant movement measured\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2631c48-53ea-4b7a-b0a1-b8466acc90c6",
+ "metadata": {},
+ "source": [
+ "#### Alternatively, we can calculate the corresponding percentage of shorelines relative to the total length of shoreline with good measurements."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "023ce190-6cb2-4c76-a091-a385b2a5be66",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "27% of shorelines retreated at more than 0.5 metre per year\n",
+ "20% of shorelines advanced at more than 0.5 metre per year\n",
+ "53% of shorelines without significant movement measured\n"
+ ]
+ }
+ ],
+ "source": [
+ "total = good_rates.rate_time.count()\n",
+ "perc_neg05 = ((good_rates.sig_time<0.01) & (good_rates.rate_time<-0.5)).sum()*100/total\n",
+ "perc_pos05 = ((good_rates.sig_time<0.01) & (good_rates.rate_time>0.5)).sum()*100/total\n",
+ "perc_no = 100. - perc_neg05 - perc_pos05\n",
+ "\n",
+ "print(f\"{round(perc_neg05)}% of shorelines retreated at more than 0.5 metre per year\")\n",
+ "print(f\"{round(perc_pos05)}% of shorelines advanced at more than 0.5 metre per year\")\n",
+ "print(f\"{round(perc_no)}% of shorelines without significant movement measured\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d785640-92ae-4690-9048-049e4f881b49",
+ "metadata": {},
+ "source": [
+ "## Inspect trends of change for selected administration areas in a country\n",
+ "\n",
+ "In this example, we use the administration boundary information from the Database of Global Administrative Areas (GADM) dataset (version 4.1).\n",
+ "The coastlines change statistics will be summarised over the provinces (level-1 admin boundaries).\n",
+ "\n",
+ "GADM for other countries can be download from https://gadm.org/download_country.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "ab3318c4-7926-49b4-addf-5db9461f0d56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gadm_level1 = gpd.read_file(\"data/gadm41_NGA_1.json.zip\")\n",
+ "gadm_level1 = gadm_level1.to_crs(rates_of_change.crs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "d2eea805-9061-48f6-bd6e-10bfae1446ff",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ ],
+ "text/plain": [
+ " GID_1 GID_0 COUNTRY NAME_1 VARNAME_1 NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1 \\\n",
+ "0 NGA.1_1 NGA Nigeria Abia NA NA State State NA \n",
+ "1 NGA.2_1 NGA Nigeria Adamawa NA NA State State NA \n",
+ "2 NGA.3_1 NGA Nigeria AkwaIbom NA NA State State NA \n",
+ "3 NGA.4_1 NGA Nigeria Anambra NA NA State State NA \n",
+ "4 NGA.5_1 NGA Nigeria Bauchi NA NA State State NA \n",
+ "\n",
+ " HASC_1 ISO_1 geometry \n",
+ "0 NG.AB NG-AB MULTIPOLYGON (((720057.812 634765.479, 720038.... \n",
+ "1 NG.AD NG-AD MULTIPOLYGON (((1182024.473 1039167.332, 11812... \n",
+ "2 NG.AK NG-AK MULTIPOLYGON (((802727.257 580831.425, 802563.... \n",
+ "3 NG.AN NG-AN MULTIPOLYGON (((669209.543 746057.689, 669257.... \n",
+ "4 NG.BA NG-BA MULTIPOLYGON (((941001.745 1219245.892, 939332... "
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gadm_level1.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c676a43e-1be5-4098-9751-bbaf2ad502c3",
+ "metadata": {},
+ "source": [
+ "The change data points will be assigned the nearest province labels and a maximum distance of 1km is imposed.\n",
+ "\n",
+ "> Data points without a matching province will be discarded."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "96535e2c-6750-4492-a5ec-3130818c2894",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "good_rates = good_rates.sjoin_nearest(gadm_level1[[\"GID_0\", \"GID_1\", \"NAME_1\",\"geometry\"]], how=\"inner\", max_distance=1000)\n",
+ "good_rates = good_rates.drop(columns=[\"index_right\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e0e390ed-23f4-46a8-9ac5-4079515c6bec",
+ "metadata": {},
+ "source": [
+ "The changes will be summarised for each province."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "71e51e4d-cca8-465d-9e8e-f24b10582b16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "countries_summary = pd.DataFrame(list(good_rates.NAME_1.unique()), columns=[\"Region\"])\n",
+ "countries_summary[['perc_neg05', 'perc_no', 'perc_pos05', 'km_neg05', 'km_no', 'km_pos05']] = [np.nan]*6\n",
+ "\n",
+ "for idx in countries_summary.index:\n",
+ " good_rates_area = good_rates[good_rates[f\"NAME_1\"]==countries_summary.loc[idx][f\"Region\"]]\n",
+ " if len(good_rates_area)==0:\n",
+ " print(\"no data over\", countries_summary.loc[idx][f\"Region\"])\n",
+ " continue\n",
+ "\n",
+ " # get stats\n",
+ " total = len(good_rates_area)\n",
+ " perc_neg05 = ((good_rates_area.sig_time<0.01) & (good_rates_area.rate_time<-0.5)).sum()*100/total\n",
+ " perc_pos05 = ((good_rates_area.sig_time<0.01) & (good_rates_area.rate_time>0.5)).sum()*100/total\n",
+ " perc_no = 100. - perc_neg05 - perc_pos05\n",
+ " \n",
+ " total_km = total*30/1000\n",
+ " km_neg05 = ((good_rates_area.sig_time<0.01) & (good_rates_area.rate_time<-0.5)).sum()*30/1000\n",
+ " km_pos05 = ((good_rates_area.sig_time<0.01) & (good_rates_area.rate_time>0.5)).sum()*30/1000\n",
+ " km_no = total_km - km_neg05 - km_pos05\n",
+ "\n",
+ " for cname in ['perc_neg05', 'perc_no', 'perc_pos05', 'km_neg05', 'km_no', 'km_pos05']:\n",
+ " countries_summary.at[idx, cname] = eval(cname)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "093796a2-f14a-4a53-b6f4-d14e5fe471d6",
+ "metadata": {},
+ "source": [
+ "### Visualise the change summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "2807d10d-c10c-4fe0-b251-aba0a67cc096",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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BL6dTU4u+7vR6PDyAZcvwwem10NEBvvkG2L8fqF0beP487010dYEDB4CEBKBHD6Bnz6UwNzfHixcv3m7difKQkJCA9evXq6azsrIAALq6uujXrx/u3bunWu7l5YXevXtj9erVGsvr1KkTKlWqhO3bt+PZs2eoV68e+vfvj+XLl+PZs2fFuzNE5UC56eZx8+ZNDBkyBJUrV4aenh4cHBwwfvx4PHz4sKSrRqXR7t1S8I2OlgL1zJlAUhLwwQdAVhZw7576w9cX+O03IJ/WHQBSeM653ePHb2V3KB8dOgDr1uHyZeD8eenigYMD0KiR5k2GDAEsLYGuXaWW7CdPnuDGjRu4d+/e26o1kUZZWVlITk5WPVJSUgAAdnZ2MDc3x44dO5CQkICEhATs2LEDlStXhpOTU55l6ejooHbt2ggODkZcXBwSExNx+PBhPHr0CO7u7m9zt4jKrHIRpq9duwZ3d3dERUXh119/RXR0NFauXImDBw+iadOmePToUUlXkUozLS3go48AIyPgxIncyxs2lFqsAwIKLsvTUwrRV64Ay5dLiYxKFTMz6Wd+bwudO0unwrJlwN27wMiRI9GiRQsoFIq3U0mifFhaWmLixIkYN24cfH19YWpqCkAKxgCQmZmpWjcjIwNCCNjb2+dZlpaWFrS0tJCRkaE2PyMjQ+M2RKSuXITp0aNHQ09PD/v374eHhwfs7e3RoUMHBAcH4/bt25gxYwYAwNHREd988w2GDBkCExMT2Nvb4+eff1Yr69SpU2jQoAH09fXh7u6Oc+fO5Xq+w4cP4/3334dSqYStrS2mTZuW640op9TUVDx9+lTtwYv/pUDdusCzZ1Jr8sqVUutzRETu9YYOBS5fzjto5xQUBAwYALRpI3XK9fCQWr+1ysXLrFxQKIBFi4CjR6Vu8ppUqyZ179DWBv73P+DIkSNo2rQpWrVq9dbqSpSX27dvY+fOndiwYQP27NkDCwsLDB48GHp6erh16xbS0tLg7e0NHR0d6Orqom3bttDS0oKJiUme5aWlpeHmzZto1aoVjI2NoVAo4OrqiqpVq8LY2Pgt7x1R2VTmP+UfPXqEv/76C6NGjYKBgYHaMhsbG/Tt2xdbtmyBEAIAsGDBAlVIHjVqFEaOHInIyEgAQFJSEjp27IjatWvj7Nmz8Pf3x6RJk9TKvH37Nv73v/+hcePGCA8Px4oVKxAQEICvv/5aYx3nzp0LMzMztcfcIj4OJENkJODmBjRpAqxYAaxbB7i4qK+jrw/06VO4VuktW4A//wQuXpT6X3fsCLz/vtRaTaXCsmXSd6iPP85/PS0tqb/08OFAaChw6dIl/PPPP2iUX98QorcgOjoaly9fRkJCAmJiYrBx40bo6+ujTp06eP78OX7//XfUqFEDn3/+OaZNmwalUok7d+6oPgPzsn37dgDAZ599hpkzZ6JJkya4ePFivtsQ0Utl/gbEqKgoCCHg8moI+o+LiwsSExNx//59AMD//vc/jBo1CgAwdepULFy4EH///Tdq1qyJTZs2ISsrCwEBAao3p1u3bmHkyJGq8pYvXw47OzssXboUCoUCtWrVwp07dzB16lR8+eWX0MqjFXL69OmYOHGi2jxl9rVmKjnp6UBMjPR7aCjQuDEwfrw00ke2Hj0AQ0Mgx80+hXb9OnD/PuDsDBw6VDR1JtmWLJG+37RqBdy+nf+68fHS6fHffV0ApBETTExMoKWlpbrhi6ikpaam4uHDh7D8r0vZtWvXsGTJEhgYGCArKwupqan47LPPcCmfSzGJiYlYt24ddHV1oVQqkZSUhO7duyMxMfFt7QZRmVbmW6azFfYbdL169VS/KxQK2NjYICEhAQAQERGBevXqQV9fX7VO06ZN1baPiIhA06ZN1fpONm/eHElJSbh161aez6lUKmFqaqr2UBZ6z+it0dIClK/8ZYYOBXbtAh48eP3yqlQBrKykZEYla8kS+PoCrVtrHvUwp2PHpO9AObtIW1lZ4dmzZwzSVKro6urC0tIy16gbKSkpSE1NhaOjI4yMjFRXYPOTnp6OpKQk6Ovrw9nZuVDbEFE5CNPOzs5QKBSIyKuvK6Twa2FhgYoVKwKQ3nhyUigU/HB8F33zDdCypTSsQ9260rSnJ7Bx48t13ntPasbUNKRURIQ03AMg3bz4/fdSlxEHBym17dwpjRby11/FvTeUn2XLgH790KeP1EXe2lp65PjOjHXrpFMg24oV0r2jixcD1asD1atXR4sWLXD69Om3X3+iHHx8fODg4AAzMzNUrVoVH330EbKysnDx4kUAgJubG6pUqQILCwu4urqiZ8+eOHnypNrIVv3790fjxo1V0++99x7ee+89mJubo1q1ahg4cCAePHiAsLCwt717RGVSme/mYWVlBR8fHyxfvhyffvqpWr/pu3fvYuPGjRgwYECh7sJ3cXHBL7/8ghcvXqhap0+ePJlrna1bt0IIoSrz2LFjMDExQdWqVYtwz6hYVaokdd2wtQWePJHGTGvXDggOfrnOkCHSeNT79+ddRq1aL4eGyMwE6tWTxqs2Nwfu3JG2++ILIC2t2HeH8vFft67Dh9VnDxokhWgAsLdX79Jx65Z0OixcKJ0az5+3x7///otjx469nToTaWBqaoru3bvDwMAAz58/R1xcHAICAvD8v0HTrays0KZNGxgYGODx48f4559/cn2OWVpawtDQUDWtVCrRpk0bmJqaIiUlBRERETh06BAbmogKSSHKwR0GUVFRaNasGVxcXPD111/DyckJly5dwuTJk5GamoqTJ0/C0tISjo6OmDBhAiZMmKDa1s3NDV27doW/vz+SkpLg5OSE9u3bY/r06YiNjcX48eMRHR2t+qctt2/fRo0aNTB48GCMGTMGkZGRGDZsGEaPHg1/f//CV5pDbBG9VQrIf6vz959VhDUhIqLX5efnV9JV0KjMd/MApEuwZ86cQbVq1dCrVy+89957GD58OLy8vHDixAnVjRkFMTY2xp9//okLFy6gQYMGmDFjBr777ju1dapUqYK9e/fi1KlTqF+/PkaMGIGhQ4di5syZxbFrRERERFSKlYuW6TKJLdNEbxVbpomIyi62TBMRERERlUMM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMimEEKKkK0FEREREVBaxZZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimXRKugLvLIWipGtA9FYoIEq6CvQa/P1nlXQViIhy8fPzK+kqaMSWaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpJJp6QrQPTaRowARo4EHB2l6UuXgNmzgaAgafqTT4A+fYCGDQFTU8DcHHjypOByK1cGvvsO6NABMDQEoqOBwYOBs2eLa0+oFGjZEpg8GWjUSDoFunYFdu58udzICPj2W2m+lRVw/Trw44/ATz8VrvyPPgI2bwZ27AB8fYthB4iKmYeHBzw9PdXmPXjwAMuWLQMAaGtro127dqhTpw50dHQQHR2NvXv3Ijk5Od9yPT090bBhQ+jr6+PmzZvYs2cPHj16VFy7QVRsGKYhvaDd3NywaNGikq4KFcatW8C0aUBUFKBQAAMHSumnQQPg8mUpCAcFSY9vvy1cmebmwLFjwN9/S2H6/n2genUgMbFYd4VKnpEREB4OrFkDbN+ee/kPPwCtWwP9+gGxsUDbtsDy5cCdO8Cff+ZftoMDMH8+cORIsVSd6K1JSEjA+vXrVdNZWVmq39u3b4/q1avj999/R2pqKjp06IBevXohMDBQY3nNmzdHkyZNsGPHDiQmJsLLywv9+vXDsmXLkJmZWaz7QlTUSrSbx6BBg6BQKFQPKysrtG/fHufPny/JalFpt3s3sG+f1HIcFQXMnAkkJQEffCAtX7xYamE+ebLwZU6dCty8CQwZApw+LaWmAweAa9eKZReo9AgKAr74Qmo5zkuzZsC6dcDhw8CNG8CqVVL4fv/9/MvV0gI2bgT8/HgaUdmXlZWF5ORk1SMlJQUAoFQq0aBBA/z111+IjY1FfHw8du7cCXt7e1SpUkVjeU2aNMGRI0cQGRmJhIQE7NixAyYmJqhVq9bb2iWiIlPifabbt2+P+Ph4xMfH4+DBg9DR0UHHjh1LulpUVmhpSdfRjYyAEyfkl9O5M3DmDPDbb8C9e0BoKDBsWNHVk8qs48el06NyZWna0xOoUQPYvz//7b78EkhIkFq8ico6S0tLTJw4EePGjYOvry9MTU0BALa2ttDW1sa1HN8YHz58iMePH8POzi7PsszNzWFiYqK2TWpqKm7duqVxG6LSrMTDtFKphI2NDWxsbODm5oZp06bh5s2buH//PgBg6tSpqFGjBgwNDVGtWjV88cUXSE9PBwDExsZCS0sLZ86cUStz0aJFcHBwUF2GunjxIjp06ABjY2NYW1ujf//+ePDggcY6LV++HNWrV4e+vj6sra3Ro0cP1bKgoCC0aNEC5ubmsLKyQseOHRETE5PvPqampuLp06dqj1RZR4tU6tYFnj0DUlOBlSulzqgREfLLq1ZN6ocdFQW0awesWCF1jB0woOjqTGXS2LFS76Hbt4G0NKkle/Ro4J9/NG/TvDkwdKjUfZ+orLt9+zZ27tyJDRs2YM+ePbCwsMDgwYOhp6cHY2NjZGRkIDVV/VMtOTkZxsbGeZaXPf/VPtXJyckwMjIqnp0gKkYlHqZzSkpKwoYNG+Ds7AwrKysAgImJCdauXYvLly9j8eLFWLVqFRYuXAgAcHR0hLe3d65+WYGBgRg0aBC0tLTw+PFjtG7dGg0aNMCZM2cQFBSEe/fuoVevXnnW4cyZMxg3bhxmz56NyMhIBAUFoVWrVqrlycnJmDhxIs6cOYODBw9CS0sLvr6+av3HXjV37lyYmZmpPea+6cF610VGAm5uQJMmUvBdtw5wcZFfnpaW1Bo9YwYQFiZdy1+1SrrZkd5pY8dKPYg6dZJuUvzsM2DZMqBNm7zXNzYGfvlFCtIPH77duhIVh+joaFy+fBkJCQmIiYnBxo0boa+vjzp16pR01YhKhRK/AXH37t1q31JtbW2xe/duaGlJOX/mzJmqdR0dHTFp0iRs3rwZU6ZMAQAMGzYMI0aMwA8//AClUonQ0FBcuHABO/+7HX/p0qVo0KABvvnmG1U5a9asgZ2dHa5evYoaNWqo1ScuLg5GRkbo2LEjTExM4ODggAYNGqiWd+/eXW39NWvWoGLFirh8+TLq1q2b5z5Onz4dEydOVJunNDN7reNEr0hPB7KvCISGAo0bA+PHyw+/8fFS82NOERHAK39verfo6wPffCNd+Ni7V5p34YL0PW7SJODgwdzbvPce4OSkfnPif29nSE8HatZkH2oq21JTU/Hw4UNYWloiJiYGOjo6UCqVaq3TRkZGSEpKynP77PmvrmNkZIR79+4Vb+WJikGJt0x7eXkhLCwMYWFhOHXqFNq1a4cOHTrgxo0bAIAtW7agefPmsLGxgbGxMWbOnIm4uDjV9l27doW2tja2/3cb/tq1a+Hl5QXH/4ZNCw8Px99//w1jY2PVI/sGh7y6Z/j4+MDBwQHVqlVD//79sXHjRjx//ly1PCoqCr1790a1atVgamqqep6cdXqVUqmEqamp2kP5RkeNctHSApRvcFSPHZNSTk41akh3nNE7S1cX0NMDXr3wlJn5MiC/6soVqReSm9vLx65d0kAxbm7Sfa5EZZmuri4sLS3x7NkzxMfHIzMzE9WqVVMtt7Kygrm5OW5qONkfP36MZ8+eqW2jp6eHqlWratyGqDQr8ZZpIyMjODs7q6ZXr14NMzMzrFq1Ch9++CH69u2LWbNmoV27djAzM8PmzZuxYMEC1fp6enoYMGAAAgMD0a1bN2zatAmLFy9WLU9KSkKnTp3w3Xff5XpuW1vbXPNMTEwQGhqKkJAQ7N+/H19++SX8/f1x+vRpmJubo1OnTnBwcMCqVatQuXJlZGVloW7dukhLSyviI0MaffONNJpHXBxgYiKNKe3pKfV1BgBra8DGBsg+r1xdpf7VcXEvh7oLDpbGQftvnFQsXCjdaTZ9unQT4vvvA8OHSw8q14yMXp4qgNSqXL8+8OiRFHxDQoB584CUFOm7lYeH1JU+58WmdeukPtWffy514790Sf05Hj+Wfr46n6gs8PHxwdWrV/H48WOYmJjA09MTWVlZuHjxIlJTU3Hu3Dm0bdsWKSkpqqHxbt68idu3b6vKGD16NA4ePIgrV64AAP7991+0bNlSdbOil5cXnj17plpOVJaUeJh+lUKhgJaWFlJSUnD8+HE4ODhgxowZquU38mgpHDZsGOrWrYvly5cjIyMD3bp1Uy1r2LAhtm7dCkdHR+joFG53dXR04O3tDW9vb/j5+cHc3ByHDh2Ch4cHIiMjsWrVKrRs2RIAcPTo0TfcY3ptlSoB69cDtrbSP2M5f14K0sHB0vIRIwB//5frZ98pNmiQlHoA6Vp8hQov1zlzRrqWP3euNAzD9evAhAnApk1vYYeoJLm7S4E523+3ZGDtWul/9nz8sXRabNwIWFpKgXrGDOm+12z29rlbr4nKC1NTU3Tv3h0GBgZ4/vw54uLiEBAQoLpqGxQUhHbt2qFXr17Q1tZGTEwM9uzZo1ZGhQoVoMxx9fDYsWPQ1dVFp06doK+vj7i4OGzYsIFjTFOZVOJhOjU1FXfv3gUAJCYmYunSparW5KdPnyIuLg6bN29G48aNsWfPHlV3jpxcXFzwwQcfYOrUqRgyZAgMDAxUy0aPHo1Vq1ahd+/emDJlCiwtLREdHY3Nmzdj9erV0NbWVitr9+7duHbtGlq1agULCwvs3bsXWVlZqFmzJiwsLGBlZYWff/4Ztra2iIuLw7Rp04r3AFFuBQ1ZN2uW9MiPk1PueXv2SA96pxw+LP3vH03u3ZOGH8+Pl1f+ywcPfv16EZUWW7duzXd5ZmYm9u7di73ZNxbkYVYe78khISEIyflNlqiMKvE+00FBQbC1tYWtrS2aNGmC06dP4/fff4enpyc6d+6MTz/9FGPGjIGbmxuOHz+OL774Is9yhg4dirS0NAx55VOvcuXKOHbsGDIzM9G2bVu4urpiwoQJMDc3V93kmJO5uTm2bduG1q1bw8XFBStXrsSvv/6KOnXqQEtLC5s3b8bZs2dRt25dfPrpp5g3b16xHBciIiIiKv0UQghR0pUoCl999RV+//33svPfE/NrCiMqRxQoF28x7wx//wKu6hARlQA/P7+SroJGJd4y/aaSkpJw8eJFLF26FGPHji3p6hARERHRO6TMh+kxY8agUaNG8PT0zNXFg4iIiIioOJX4DYhvau3atVi7dm1JV4OIiIiI3kFlvmWaiIiIiKikMEwTEREREcnEME1EREREJBPDNBERERGRTAzTREREREQyMUwTEREREcnEME1EREREJBPDNBERERGRTAzTREREREQyMUwTEREREcnEME1EREREJJNCCCFKuhJERERERGURW6aJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikkmnpCvwzlIoSroGRLIpIEq6ClTO+fvPKukqEFEp4ufnV9JV0Igt00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzSVXtOmAadOAU+fAvfuAdu3AzVqqK9TrRqwbRuQkAA8eQJs2QJUqpR/uVpawOzZwLVrwPPnQHQ0MHNm8e0HlRuFOSUB4IMPgIMHgaQk6bQ8fBjQ18+/7FGjgOvXgZQU4ORJoHHj4tkHotKgefPm8PPzQ7t27fJc3qdPH/j5+aFmzZoay9DS0oK3tzdGjBiB6dOnY+LEiejatSuMjY2Lq9pEeWKY/o+/vz/c3NxKuhqUk4cHsGyZlEx8fABdXWD/fsDQUFpuaChNCwG0bg00bw7o6QF//gkoFJrLnToVGDkSGDMGcHGRpqdMAcaOfTv7RWVWQackIC0LCpLmv/++FIqXLgWysjSX26sX8MMPwKxZQMOGQHg48NdfQMWKxb9PRG9b5cqV0ahRI9y9ezfP5R988EGhytHV1YWNjQ2OHDmCn3/+GVu2bIGVlRV69+5dlNUlKlCZD9ODBg2CQqGAQqGArq4urK2t4ePjgzVr1iArv0+vQpTbtWvXoqsovb4OHYB164DLl4Hz54FBgwAHB6BRI2l58+aAo6M0/+JF6TFwIODuLoVrTZo1A3buBPbuBW7cALZufZl8iPJR0CkJAAsXAj/+CHz3nbTe1avA778DaWmay504EVi1Cli7FoiIAEaMkC6aDBlS3HtE9Hbp6uqiW7du+PPPP/HixYtcy62trdG0aVPs3LmzwLJSU1OxYcMGXL58GQ8fPsTt27exb98+VK5cGaampsVRfaI8lfkwDQDt27dHfHw8YmNjsW/fPnh5eWH8+PHo2LEjMjIySrp6VFTMzKSfjx5JP5VKqVU6NfXlOi9eSE2ALVpoLuf4caBNG6B6dWm6Xj1p/X37iqfeVG69ekpWrCi1TCckAMeOAXfvAiEh0vc+TXR1pTAeHPxynhDSdNOmxVZ1ohLxv//9D1FRUbh+/XquZTo6OujevTv27t2L5ORkWeUrlUoIIfIM6kTFpVyEaaVSCRsbG1SpUgUNGzbE559/jp07d2Lfvn1Yu3YtAODx48cYNmwYKlasCFNTU7Ru3Rrh4eF5lufv749169Zh586dqlbvkJAQAMDUqVNRo0YNGBoaolq1avjiiy+Qnp6eb/1SU1Px9OlTtUdqvltQLgoFsGgRcPQocOmSNO/kSSA5WWoCNDCQrrXPnw/o6AC2tprL+vZbYPNm4MoVqbnw3Dmp7E2b3saeUDmR1ylZrZr0099famlu3x4IDZX6Tzs7511OhQrSKXvvnvr8e/cAG5viqj3R21enTh3Y2toiOOc3xxzat2+PmzdvIjIyUlb52tra8Pb2xoULF5CW36UgoiJWLsJ0Xlq3bo369etj27ZtAICePXsiISEB+/btw9mzZ9GwYUO0adMGj7KblHKYNGkSevXqpWrxjo+PR7NmzQAAJiYmWLt2LS5fvozFixdj1apVWLhwYb51mTt3LszMzNQec4t+l8u3ZcuAunWBjz9+Oe/BA6BnT6BTp5d3epmbA2fPFtxBtW9foE8fqYPqwIHApEnAgAHFvhtUfuR1Smr99476009Sl42wMKkLR2Qku2zQu83U1BTt27fHtm3bkJmZmWt5jRo14OjoiKCgIFnla2lpoWfPnlAoFNizZ8+bVpfoteiUdAWKU61atXD+/HkcPXoUp06dQkJCApRKJQBg/vz52LFjB/744w8MHz5cbTtjY2MYGBggNTUVNq80Dc3MMeqDo6MjJk2ahM2bN2PKlCka65F9l3FOyuzrw1SwJUuAjh2BVq2A27fVlx04IDX5WVkBGRlSoI6Pl0bq0GTePKl1essWafriRanj6/TpwPr1xbcfVG5oOiXj46Wfly+rrx8RAdjb513WgwfSqWttrT7f2lrqJkJUHtja2sLY2Bj/93//p5qnpaUFBwcHvP/++zhz5gwsLS0xbdo0te169eqFuLg4rFu3TmPZWlpa6NGjB8zMzLB+/Xq2StNbV67DtBACCoUC4eHhSEpKgpWVldrylJQUxMTEvFaZW7ZswY8//oiYmBgkJSUhIyOjwBsdlEqlKsTTa1qyBPD1BTw9gdhYzes9fCj99PKShsbbtUvzuoaGuVuuMzNfNisS5SO/UzI2VgrXr47mVaOG5i756enSxZQ2baT7YgGpC0mbNtIoIETlwfXr17F8+XK1eV26dMGDBw9w7NgxPH/+HGfOnFFbPmrUKPz111+4evWqxnKzg7SVlRXWrVuHlJSUYqk/UX7KdZiOiIiAk5MTkpKSYGtrq+r3nJO5uXmhyztx4gT69u2LWbNmoV27djAzM8PmzZuxYMGCoqs0vbRsmdQVo0sX4Nmzl013T55INxoC0nAKERHA/fvS3VqLF0vDKeR88w0OlgYEXrZMmv7zT2DGDCAuTurs2qCBdC1+zZq3untU9hTmlJw3TxriLjxc6uYxcCBQqxbQo8fLcl49JX/4QRol5MwZaRzrCRMAIyMgMPBt7h1R8UlLS8P9+/fV5qWnpyMlJUU1P6+bDp88eYLHjx+rpkePHo2DBw/iypUrqq4dtra2+PXXX6FQKGBkZARAaix7kxG9iF5HuQ3Thw4dwoULF/Dpp5+iatWquHv3LnR0dODo6Fio7fX09HL16zp+/DgcHBwwY8YM1bwbN24UZbUpp1GjpJ+HD6vPHzRISh6A1AQ4dy5gaSk1C86ZI4XpnN57T7rLK9vYscBXXwHLl0ut2HfuSJ1cZ88urj2hcqIwp+TixdI/aFm4UDotw8OlMalz9jx69ZT87TdpJJDZs6WbDsPCpJsXExKKc2+Iyp4KFSqorvSamJigVq1aAIARI0aorbd27Vp+PtNboxBCiJKuxJsYNGgQ7t27h8DAQGRmZuLevXsICgrC3Llz4enpiR07dkBLSwutWrXCs2fP8P3336NGjRq4c+cO9uzZA19fX7i7u8Pf3x87duxAWFgYAOCbb77BTz/9hP3798PKygpmZmbYt28funfvjl9++QWNGzfGnj17MGvWLGRmZqp9cy6U/P6pCFEpp0CZftugMsDff1ZJV4GIShE/P7+SroJG5aKTaFBQEGxtbeHo6Ij27dvj77//xo8//oidO3dCW1sbCoUCe/fuRatWrTB48GDUqFEDH3/8MW7cuAHrV+/6+c8nn3yCmjVrwt3dHRUrVsSxY8fQuXNnfPrppxgzZgzc3Nxw/PhxfPHFF295b4mIiIiotCjzLdNlFlumqQxjyzQVN7ZME1FObJkmIiIiIiqHGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRSCCFESVeCiIiIiKgsYss0EREREZFMDNNERERERDIxTBMRERERycQwTUREREQkE8M0EREREZFMDNNERERERDIxTBMRERERycQwTUREREQkE8M0EREREZFMOiVdgXeWQlHSNaAyRAH+o1J6Pf7+s0q6CkRERcbPz6+kq6ARW6aJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZdIqj0JCQEHh5eSExMRHm5ubF8RT5io2NhZOTE86dOwc3N7e3/vzvvGnTgG7dgFq1gJQU4PhxYOpU4OrVl+usXAl4ewOVKwNJSS/XiYzUXK6REfDtt0DXroCVFXD9OvDjj8BPPxX7LlHxKq5TplIl4LvvgLZtAXNz4MgRYOxYIDq62HeJqEzx8PCAp6en2rwHDx5g2bJl0NfXh5eXF6pVqwYzMzM8f/4cV65cwd9//43U1NR8y6xbty5MTU2RmZmJ+Ph4HDp0CLdv3y7mvSF6u96oZfrEiRPQ1tbGhx9+WFT1kU2hUGDHjh0lXQ0CAA8PYNky4IMPAB8fQFcX2L8fMDR8uc7Zs8DgwYCLC9CuHaBQSOto5XNK/vAD0L490K+ftN2iRcDSpUCnTsW+S1S8iuuU2bEDqFYN6NIFaNAAuHEDCA5WL5eIJAkJCZg/f77qsWbNGgCAiYkJjI2NceDAAaxYsQI7duyAs7MzOnfunG95Dx8+xN69e7FixQoEBgbi8ePH6NevHwz5AqRy5o1apgMCAjB27FgEBATgzp07qFy5clHVi8qyDh3UpwcNAu7fBxo1Av75R5q3atXL5TduADNnAufPA46OwLVreZfbrBmwbh1w+PDLMv7v/4D33wf+/LOo94LeouI4ZapXB5o2BerUAS5fluaNHAncvQv07g0EBBTHnhCVXVlZWUhOTs41//79+/j9999V04mJiTh06BB8fX2hUCgghMizvIsXL6pN//XXX2jYsCGsra1x/fr1oq08UQmS3TKdlJSELVu2YOTIkfjwww+xdu1ajes+f/4cHTp0QPPmzfH48WP06NEDY8aMUS2fMGECFAoFrly5AgBIS0uDkZERgoODAQBBQUFo0aIFzM3NYWVlhY4dOyImJqbAOl65cgXNmjWDvr4+6tati8PZIew/hw8fxvvvvw+lUglbW1tMmzYNGRkZquWenp4YO3YsJkyYAAsLC1hbW2PVqlVITk7G4MGDYWJiAmdnZ+zbt+91Dt27x8xM+vnoUd7LDQ2lJsdr14CbNzWXc/w40LmzdJ0fADw9gRo1pOZJKleK4pRRKqWfL168nCcEkJoKtGhRdHUlKi8sLS0xceJEjBs3Dr6+vjA1NdW4rlKpRGpqqsYg/SotLS00atQIL168wN27d4uqykSlguww/dtvv6FWrVqoWbMm+vXrhzVr1uT5onr8+DF8fHyQlZWFAwcOwNzcHB4eHggJCVGtc/jwYVSoUEE17/Tp00hPT0ezZs0AAMnJyZg4cSLOnDmDgwcPQktLC76+vsjKysq3jpMnT8Znn32Gc+fOoWnTpujUqRMePnwIALh9+zb+97//oXHjxggPD8eKFSsQEBCAr7/+Wq2MdevWoUKFCjh16hTGjh2LkSNHomfPnmjWrBlCQ0PRtm1b9O/fH8+fP9dYj9TUVDx9+lTtobmXWTmjUEjdMY4eBS5dUl82ciTw7BmQnCw1Tfr4AOnpmssaO1ZqYrx9G0hLA4KCgNGjXzZdUrlQVKfMlStSC/bcuVJ/aV1dYMoUwM4OsLUt7r0gKltu376NnTt3YsOGDdizZw8sLCwwePBg6Onp5VrXwMAArVq1QmhoaIHlVq9eHdOnT8fMmTPxwQcf4JdffkFKSkpx7AJRiZEdpgMCAtCvXz8AQPv27fHkyZNcLb93796Fh4cHbG1t8eeff6r6SXl6euLy5cu4f/8+EhMTcfnyZYwfP14VpkNCQtC4cWPV+t27d0e3bt3g7OwMNzc3rFmzBhcuXMDl7Gu3GowZMwbdu3eHi4sLVqxYATMzMwT8d213+fLlsLOzw9KlS1GrVi107doVs2bNwoIFC9RCev369TFz5kzVG4K+vj4qVKiATz75BNWrV8eXX36Jhw8f4vz58xrrMXfuXJiZmak95r7e4S67li0D6tYFPv4497KNG6WOrK1aSXea/fbby+bEvIwdK3Wq7dRJuv7/2WdS+W3aFF/96a0rqlMmI0O6qbFGDSAxEXj+HPDyAvbuBQr4Hk70zomOjsbly5eRkJCAmJgYbNy4Efr6+qhTp47aenp6eujTpw/u37+v1iimSWxsLFauXImAgADExMSgR48e7DNN5Y6sMB0ZGYlTp06hd+/eAAAdHR189NFHqqCazcfHB87OztiyZYvat9u6devC0tIShw8fxj///IMGDRqgY8eOqjB++PBhtbuKo6Ki0Lt3b1SrVg2mpqZwdHQEAMTFxeVbz6ZNm6p+19HRgbu7OyIiIgAAERERaNq0KRQKhWqd5s2bIykpCbdu3VLNq1evnup3bW1tWFlZwdXVVTXP2toagHTjhibTp0/HkydP1B7T8615ObFkCdCxo5Rg8rp7++lTaViFf/4BevSQhnLw9c27LH194JtvgIkTgd27gQsXpNS1ZQswaVLx7ge9NUV5ygBAaKgUvs3MpNboDh2kgWA0dcsnIklqaioePnwIS0tL1Tw9PT3069cPaWlp2LJlS4FXhwEgPT0diYmJuH37Nnbt2oWsrCw0bNiwOKtO9NbJugExICAAGRkZajccCiGgVCqxdOlS1bwPP/wQW7duxeXLl9UCqEKhQKtWrRASEgKlUglPT0/Uq1cPqampuHjxIo4fP45JOQJSp06d4ODggFWrVqFy5crIyspC3bp1kZaWJqf6r0VXV1dtWqFQqM3LDuP5vakolUoo82txLY+WLJFSjqcnEBtb8PoKhfTQdJx0dQE9vdxNipmZ+Q/nQGVGUZ8yOT19Kv10dgbc3YEvvniTmhKVf7q6urC0tFRddc0O0pmZmfj111+RmZkpq1yFQgFtbe2irCpRiXvtFJKRkYH169djwYIFCAsLUz3Cw8NRuXJl/Prrr6p1v/32WwwcOBBt2rTJ1SUju990SEgIPD09oaWlhVatWmHevHlITU1F8+bNAUhD60RGRmLmzJlo06YNXFxckJiYWKi6njx5Uq3eZ8+ehYuLCwDAxcUFJ06cUOvnfezYMZiYmKBq1aqve1gop2XLpOHr+vSROrhaW0sPfX1puZOTNLBww4ZSB9amTYHff5cGGN6792U5ERHSmNKAVE5ICDBvnjSOmqMjMHAgMGAAsH37W95BKmrFccoAUuu1h4e0fefOwIED0nB5Bw68zb0jKv18fHzg4OAAMzMzVK1aFR999BGysrJw8eJF6OnpoX///tDT08OuXbugVCphZGQEIyMjtau7o0ePRq1atQBIYbx169aoUqUKzMzMYGtri86dO8PU1LTALppEZc1rt0zv3r0biYmJGDp0KMyyb7n/T/fu3REQEIB58+ap5s2fPx+ZmZlo3bo1QkJCVC80T09PfPrpp9DT00OL/26t9/T0xKRJk9C4cWMYGRkBACwsLGBlZYWff/4Ztra2iIuLw7Rp0wpV12XLlqF69epwcXHBwoULkZiYiCFDhgAARo0ahUWLFmHs2LEYM2YMIiMj4efnh4kTJ0KLLZ1vZtQo6ecrfegxaJA0tN2LF0DLlsCECYCFBXDvnvTfNJo1k8ZDy1ar1sthHQCpE+3cuVLHWUtL6e6yGTOk/+ZBZVpxnTK2ttLw5NbWQHw8sH498NVXxb03RGWPqakpunfvDgMDAzx//hxxcXEICAjA8+fP4eDgoGpkGjdunNp2ixYtwpMnTwAAFSpUUF2FzcrKQoUKFVC/fn0YGhoiJSUFt2/fRmBgIO7nfNESlQOvHaYDAgLg7e2dK0gDUpj+/vvvc92Mt3DhQrVAXaNGDbi6usLc3Bw1atSAsbExAClMZ2ZmqvWX1tLSwubNmzFu3DjUrVsXNWvWxI8//pjrPzXl5dtvv8W3336LsLAwODs7Y9euXahQoQIAoEqVKti7dy8mT56M+vXrw9LSEkOHDsXMmTNf95DQq3K0VOQpPh4ozD/6ebWce/eA/74MUflSXKfMkiXSg4jyt3XrVo3Lbty4gVmzZhVYRs51MjMz8dtvvxVJ3YhKO4Uo7CCRVLQKSg9EOSjAlym9Hn//gsMPEVFZ4efnV9JV0Ij9GYiIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhRBClHQliIiIiIjKIrZMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJpFPSFXhnKRQlXQMqAgrwH4iWJH//WSVdBSIiegv8/PxKugoasWWaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSqdyE6djYWCgUCoSFhZV0Vcqfli2BXbuA27cBIYAuXdSXV6oEBAZKy5OTgX37AGfn/Mv09QVOnwYSE4GkJODcOaBfv+Lbh3KuoD8RANSqBezcCTx+LB3yU6cAOzvNZQ4bBhw5Ajx6JD0OHAAaNy62XSB655iYmMDX1xeTJ0/G559/jhEjRsDW1jbPdT/88EP4+fmhSZMm+Zbp4eEBPz8/tcfo0aOLo/pE9B+dkq5AYQ0aNAjr1q0DAOjo6KBq1aro2bMnZs+eDX19fdjZ2SE+Ph4VKlQo4ZqWQ0ZGQHg4sGYNsH177uU7dgDp6VKCe/oUmDgRCA4GatcGnj/Pu8xHj4A5c4ArV4C0NKBjRymQJyQA+/cX6+6URwX9iapVA44eBQICAD8/6c9Upw7w4oXmMj09gV9/BY4fl9abOlX609SpA9y5U2y7QvRO0NfXx5AhQ3D9+nVs3LgRz58/h6WlJV7k8aKsVasWqlatiqdPnxaq7ISEBKxfv141nZWVVWT1JqLcykyYBoD27dsjMDAQ6enpOHv2LAYOHAiFQoHvvvsO2trasLGxKdbnT0tLg56eXrE+R6kUFCQ98lK9OtC0qZSwLl+W5o0cCdy9C/TuLaW3vBw+rD7944/AwIFAixYM0zLk9ycCpO8te/dKgTjbtWv5l/nqhYJhw4Du3YE2bYBffpFfVyICmjdvjidPnmDXrl2qeY8fP861nomJCTp06IANGzagT58+hSo7KysLycnJRVVVIipAmermoVQqYWNjAzs7O3Tt2hXe3t44cOAAAPVuHllZWahatSpWrFihtv25c+egpaWFGzduAJDeuIYNG4aKFSvC1NQUrVu3Rnh4uGp9f39/uLm5YfXq1XBycoK+vj4A4I8//oCrqysMDAxgZWUFb2/vd/eNS6mUfuZsTRECSE2VgnFhtW4N1Kwp9SugIqVQAB9+CFy9KgXue/eAkyfz7gqSH0NDQFdXuqhARG+mZs2aiI+PR48ePTBp0iQMHz4cDRs2zLWer68vjh8/jvv37xe6bEtLS0ycOBHjxo2Dr68vTE1Ni7LqRPSKMhWmc7p48SKOHz+eZ0uxlpYWevfujU2bNqnN37hxI5o3bw4HBwcAQM+ePZGQkIB9+/bh7NmzaNiwIdq0aYNHOdJCdHQ0tm7dim3btiEsLAzx8fHo3bs3hgwZgoiICISEhKBbt24QQmisa2pqKp4+far2SC2i41DirlwBbtwA5s4FzM2ltDVlitQZV0PfPxVTU+DZM6mbx549wNixUvcQKlKVKgEmJsC0aVKYbttW6gqybRvQqlXhy/nuO6l7B/9ERG/OwsIC7u7uePToETZs2IAzZ86gffv2qF+/vmqdFi1aICsrC//++2+hy719+zZ27tyJDRs2YM+ePbCwsMDgwYPfzauqRG9JmermsXv3bhgbGyMjIwOpqanQ0tLC0qVL81y3b9++WLBgAeLi4mBvb4+srCxs3rwZM2fOBAAcPXoUp06dQkJCApT/ta7Onz8fO3bswB9//IHhw4cDkLp2rF+/HhUrVgQAhIaGIiMjA926dVOFcldX13zrPXfuXMyaNUttnh8Af7kHojTJyAC6dZO6cyQmStPBwVKfAoUi/22fPQPc3ABjY6nvwA8/SH0PXu0CQm9E67+vzDt3AosWSb+HhwPNmgEjRhTuYsDUqcDHH0v9qFPLzTdBopKjUChw584dHDp0CABw9+5dVKpUCY0aNUJ4eDhsbW3RpEkT/PTTT69VbnR0tOr3hIQE3Lp1CxMmTECdOnVw7ty5It0HIpKUqTDt5eWFFStWIDk5GQsXLoSOjg66d++e57pubm5wcXHBpk2bMG3aNBw+fBgJCQno2bMnACA8PBxJSUmwsrJS2y4lJQUxMTGqaQcHB1WQBoD69eujTZs2cHV1Rbt27dC2bVv06NEDFhYWGus9ffp0TJw4UW2e0szstfe/1AoNBRo0kFqa9fSABw+kfgRnzuS/nRBA9rEODwdcXIDp0xmmi9iDB9L9odld2rNFRBSuJ85nn0mt2t7ewIULxVNHonfNs2fPcnXdePDgAVxcXAAA9vb2MDIywqeffqparqWlhbZt2+KDDz7A4sWLC/U8qampePjwISwtLYuu8kSkpkyFaSMjIzj/N+TamjVrUL9+fQQEBGDo0KF5rt+3b19VmN60aRPat2+vCs9JSUmwtbVFSEhIru3Mzc3VnjMnbW1tHDhwAMePH8f+/fuxZMkSzJgxA//++y+cnJzyrIdSqVS1fpdr2XeaOzsD7u7AF1+83vZaWi/7YFORSU+XRiGsWVN9fo0aUg+d/EyeDMyYAbRrB5w9W3x1JHrX3Lx5M1djjpWVFZ48eQIAOH/+PK69cpdwv379cP78+dcaAlZXVxeWlpY4f/78G9eZiPJWZvtMa2lp4fPPP8fMmTORkpKS5zp9+vTBxYsXcfbsWfzxxx/o27evalnDhg1x9+5d6OjowNnZWe1R0PB6CoUCzZs3x6xZs3Du3Dno6elhe17jkZUXRkZA/frSAwCcnKTfswcp7tED8PCQ5nfuLA1IvGOH9DPbunXAN9+8nM5u6nRykgZAnjgR6N8f2LDhre1WeVLQn2jePOCjj6QROd57Dxg9GujUCVi+/GUZr/6JpkwBvvoKGDIEiI0FrK2lxyvfL4lIhpMnT6Jq1apo0aIFLCwsULduXTRs2BCnT58GIF0lvX//vtojKysLSUlJePjwoaqc/v37o3GOAeB9fHzg4OAAMzMzVK1aFR999BGysrJw8eLFt76PRO+KMtUy/aqePXti8uTJWLZsGXr06JFruaOjI5o1a4ahQ4ciMzMTnTt3Vi3z9vZG06ZN0bVrV3z//feoUaMG7ty5gz179sDX1xfu7u55Pue///6LgwcPom3btqhUqRL+/fdf3L9/X3VprlxydwdytuAvXCj9XLsWGDxYutHwhx+kpBUfD6xfL6WwnOztgZxjnRoZSUmualUgJUW6kbFfP+C334p7b8qlgv5EO3ZI/aOnT5dGIYyMlIa5O3bs5Tav/olGjpQuFGzdqv5c/v7AK7cAENFrunPnDrZs2YI2bdrAw8MDiYmJ+Ouvv3DhNftSWVpawtDQUDVtamqK7t27w8DAAM+fP0dcXBwCAgLwXNOY/0T0xsp0mNbR0cGYMWPw/fffo0OHDnmu07dvX4waNQoDBgyAgYGBar5CocDevXsxY8YMDB48GPfv34eNjQ1atWoFa2trjc9pamqKI0eOYNGiRXj69CkcHBywYMECjc9fLhw+nP/NhEuWSI/8eHmpT3/xxet3AyGNCvoTAdL/xAkM1Lz81T+Rhl5LRFREoqKiEBUVVej18+on/eq8ra9++yWiYqcQ+Y3pRsWnoORDZYICfPmUJH9/NpETEb0L/Pz8SroKGpXZPtNERERERCWNYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaFEEKUdCWIiIiIiMoitkwTEREREcnEME1EREREJBPDNBERERGRTAzTREREREQyMUwTEREREcnEME1EREREJBPDNBERERGRTAzTREREREQyMUwTEREREcnEME1EREREJJNOSVfgnaVQFF/R4H+Ifxf5+88q6SoQEREVCz8/v5KugkZsmSYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGRimCYiIiIikolhmoiIiIhIJoZpIiIiIiKZGKaJiIiIiGTSKekKvC2xsbFwcnLCuXPn4ObmVtLVkYwYAYwcCTg6StOXLgGzZwNBQXmvr6MDTJ8ODBwIVKkCREYCU6cCf/311qr8NkybBnTrBtSqBaSkAMePS7t59erLdT75BOjTB2jYEDA1BczNgSdP8i/Xzw/w91efd+UK4OJS1HtARGXJ+PHjYW5unmv+6dOnsXfvXrV5ffr0QfXq1bF582ZERkZqLNPPzy/P+QcOHMDx48ffqL5EVLrICtN3797FnDlzsGfPHty+fRuVKlWCm5sbJkyYgDZt2hR1HQs0aNAgrFu3DgCgo6ODqlWromfPnpg9ezb09fUBAHZ2doiPj0eFChXeev00unVLSo5RUYBCIYXknTuBBg2Ay5dzr//110C/flKSvHIFaNcO2L4daNYMCAt769UvLh4ewLJlwOnT0veHb74B9u8HatcGnj+X1jE0lL5zBAUB335b+LIvXgS8vV9OZ2QUbd2JqOxZtWoVFAqFarpSpUoYMGAALl26pLbeBx98UOgy58+frzZdvXp1dO7cGZfzem8nojLttcN0bGwsmjdvDnNzc8ybNw+urq5IT0/HX3/9hdGjR+PKlSu5tklPT4eurm6RVFiT9u3bIzAwEOnp6Th79iwGDhwIhUKB7777DgCgra0NGxubYq1DWloa9PT0Cr/B7t3q0zNnSi3VH3yQd5ju3x+YMwfYt0+aXrlSSoaffSYtKyc6dFCfHjQIuH8faNQI+Ocfad7ixdJPD4/XKzsjA7h3742rSETlyPPsb+n/qVGjBh49eoQbN26o5llbW6Np06b4+eefMWnSpALLTE5OVpuuWbMmrl+/jsePHxdJnYmo9HjtPtOjRo2CQqHAqVOn0L17d9SoUQN16tTBxIkTcfLkSQCAQqHAihUr0LlzZxgZGWHOnDkAgBUrVuC9996Dnp4eatasiV9++UVVrhAC/v7+sLe3h1KpROXKlTFu3DjV8uXLl6N69erQ19eHtbU1evTooVYvpVIJGxsb2NnZoWvXrvD29saBAwdUy2NjY6FQKBAWFoasrCxUrVoVK1asUCvj3Llz0NLSUr2BPn78GMOGDUPFihVhamqK1q1bIzw8XLW+v78/3NzcsHr1ajg5OalawWXR0gI++ggwMgJOnMh7HaUSePFCfV5KCtCihfznLQPMzKSfjx69eVnVqwO3bwMxMcCGDYCd3ZuXSUTlh5aWFurVq4dz586p5uno6KB79+7Yu3dvrpBcGEZGRqhevbpamURUfrxWmH706BGCgoIwevRoGBkZ5Vqes8+Zv78/fH19ceHCBQwZMgTbt2/H+PHj8dlnn+HixYv4v//7PwwePBh///03AGDr1q1YuHAhfvrpJ0RFRWHHjh1wdXUFAJw5cwbjxo3D7NmzERkZiaCgILRq1UpjPS9evIjjx49rbCXW0tJC7969sWnTJrX5GzduRPPmzeHg4AAA6NmzJxISErBv3z6cPXsWDRs2RJs2bfAoR6qLjo7G1q1bsW3bNoRp6GqRmpqKp0+fqj1SsxfWrQs8ewakpkotzb6+QERE3jv211/AxImAs7PULcTbW+pcbGur8ViUdQoFsGgRcPSo1KX8Tfz7r9TK3b69dAHAyUlq6TY2LoqaElF5UKtWLejr66u9n7dv3x43b97Mt490furXr4+0tDREaHpvJ6Iy7bW6eURHR0MIgVq1ahW4bp8+fTB48GDVdO/evTFo0CCMGjUKAFQt2fPnz4eXlxfi4uJgY2MDb29v6Orqwt7eHu+//z4AIC4uDkZGRujYsSNMTEzg4OCABg0aqD3f7t27YWxsjIyMDKSmpkJLSwtLly7VWL++fftiwYIFiIuLg729PbKysrB582bMnDkTAHD06FGcOnUKCQkJUCqVAKQ+cDt27MAff/yB4cOHA5C6dqxfvx4VK1bU+Fxz587FrFmz1Ob5AfAHpJsI3dyk5tcePYB166S+C3m96Y4fD6xaJfWXFkJqXg0MBIYM0fjcZd2yZdL3jaJofM95X+eFC1K4vnED6NULWLPmzcsnorKvQYMGiIqKQlJSEgCpy4ejoyN++umnNyrzwoULyMzMLKpqElEp8lot00KIQq/r7u6uNh0REYHmzZurzWvevLnqm3rPnj2RkpKCatWq4ZNPPsH27duR8d/dYT4+PnBwcEC1atXQv39/bNy4MVcfNy8vL4SFheHff//FwIEDMXjwYHTv3l1j/dzc3ODi4qJqnT58+DASEhLQs2dPAEB4eDiSkpJgZWUFY2Nj1eP69euIiYlRlePg4JBvkAaA6dOn48mTJ2qP6dkL09OlUBwaCnz+ORAeLoXmvDx4ILVcGxkBDg7ScBdJScC1a/k+f1m1ZAnQsSPg5SV1zShqT55II4Q4Oxd92URU9piZmaFatWpq3TGcnJxgaWmJadOm4YsvvsAXX3wBAOjVqxcGDhxYYJn29vaoUKECQkNDi63eRFSyXqtlunr16lAoFHneZPiqvLqB5MfOzg6RkZEIDg7GgQMHMGrUKMybNw+HDx+GiYkJQkNDERISgv379+PLL7+Ev78/Tp8+repaYmRkBOf/UtGaNWtQv359BAQEYOjQoRqfs2/fvti0aROmTZuGTZs2oX379rCysgIAJCUlwdbWFiEhIbm2y9mdpTD7qVQqVa3bBdLSkvpG5yc1FbhzRxrqont34LffCld2GbJkifS9wdMTiI0tnucwMgLeew/I0XWfiN5hbm5uSE5OxtUc43AePXo0VxAeNWoU/vrrL7X1NGnQoAHu3LmDe7zzmajceq2WaUtLS7Rr1w7Lli3L8yaM/O5SdnFxwbFjx9TmHTt2DLVr11ZNGxgYoFOnTvjxxx8REhKCEydO4MKFCwCkG0C8vb3x/fff4/z584iNjcWhQ4fy3iktLXz++eeYOXMmUlJSNNapT58+uHjxIs6ePYs//vgDffv2VS1r2LAh7t69Cx0dHTg7O6s9imx4vW++AVq2lFqZ69aVpj09gY0bpeXr1knzsr3/vpQwnZykfg9BQVL4/v77oqlPKbFsmTQCYJ8+Undya2vpkfP+TmtroH79l63Krq7StIXFy3WCg4HRo19Oz5sHtGolHe6mTaVRBTMzgV9/fTv7RUSlm5ubG8LDw9WuwiYnJ+P+/ftqDwB48uSJ2mfe6NGjc3WB1NPTQ+3atdkqTVTOvfZoHsuWLUNmZibef/99bN26FVFRUYiIiMCPP/6Ipk2batxu8uTJWLt2LVasWIGoqCj88MMP2LZtm2qIobVr1yIgIAAXL17EtWvXsGHDBhgYGMDBwQG7d+/Gjz/+iLCwMNy4cQPr169HVlYWatasqfH5evbsCW1tbSxbtkzjOo6OjmjWrBmGDh2KzMxMdO7cWbXM29sbTZs2RdeuXbF//37Exsbi+PHjmDFjBs6cOfO6hy1vlSoB69dL/aYPHgQaN5bGjg4Olpbb26vfXKivL401ffmylARv35ZCdUH/raSMGTVK+icshw8Dd+++fHz00ct1RoyQhtZevVqa/ucfaTrHnxDvvQfk/N5TtaoUnCMjpcb8hw+lUQgfPHgLO0VEpVq1atVgbm4ue8SNChUq5LoCWbduXSgUCly8eLEoqkhEpdRrjzNdrVo1hIaGYs6cOfjss88QHx+PihUrolGjRrmGmsupa9euWLx4MebPn4/x48fDyckJgYGB8PT0BCB1nfj2228xceJEZGZmwtXVFX/++SesrKxgbm6Obdu2wd/fHy9evED16tXx66+/ok6dOpp3TEcHY8aMwffff4+RI0dqXK9v374YNWoUBgwYAAMDA9V8hUKBvXv3YsaMGRg8eDDu378PGxsbtGrVCtbW1q972PI2bFj+y7281KePHAHy2efyIsf/TtBo1izpkR8nJ/Xp3r3l14mIyrdr167lulFck7zWy2teaGgoW6WJ3gEK8Tp3FVLRKUxilFs0+Cd9F/n7Fy4IEBERlTV+fn4lXQWNXrubBxERERERSRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkUgghRElXgoiIiIioLGLLNBERERGRTAzTREREREQyMUwTEREREcnEME1EREREJBPDNBERERGRTAzTREREREQyMUwTEREREcnEME1EREREJBPDNBERERGRTAzTREREREQy6ZR0Bd5VCkVJ14CIiIioaPn7zyqWcv38/Iql3KLAlmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSiWGaiIiIiEgmhmkiIiIiIpkYpomIiIiIZGKYJiIiIiKSSaekK0CSHj2Ar74CHB2BqChg6lRg376SrhURlSbGxtL7hK8vUKkScO4cMH48cObMy3Vq1QK++w7w8AB0dIDLl4Hu3YGbN/Mu09cX+PxzwNkZ0NWV3n8WLAA2bHg7+0RE5Y+7uzvc3d1hbm4OAEhISMCRI0cQHR2d5/oDBw6Eo6NjrvlXr17Fr7/+Wow1LRoM06VA06bAr78C06cDu3cDffoAO3YADRsCly6VdO2IqLRYvRqoWxfo3x+4cwfo1w8IDgZq15amq1UDjh4FAgIAPz/g6VOgTh3gxQvNZT56BMyZA1y5AqSlAR07AoGBQEICsH//29s3Iio/nj59iuDgYDx69AgA4Obmho8//hg//fQT7t+/n2v9LVu2QFtbWzVtaGiIESNG4PLly2+tzm9CIYQQJV2JonDz5k34+fkhKCgIDx48gK2tLbp27Yovv/wSVlZWJV29XBSKl79v3gwYGQGdOr2cd+IEEBYGjBz51qtGRKWQvj7w7BnQpQuwd+/L+WfOSFexvvhC+lKeng4MGPBmz3X2LLBnD/Dll29WDhG9e/z9Z+U5f8qUKThw4ADOnTtXYBlNmjSBl5cXFixYgPT0dACAn59fkdazKJWLPtPXrl2Du7s7oqKi8OuvvyI6OhorV67EwYMH0bRpU9U3o9KqaVOpdSmnv/6S5hMRAVKXDR2d3K3MKSlAixbSF/QPPwSuXgWCgoB794CTJ6Xw/TpatwZq1gSOHCm6uhPRu0uhUKBOnTrQ1dXFTU39zV7RoEEDXLx4URWkS7tyEaZHjx4NPT097N+/Hx4eHrC3t0eHDh0QHByM27dvY8aMGQCA+Ph4fPjhhzAwMICTkxM2bdoER0dHLFq0CAAQGxsLhUKBsLAwVdmPHz+GQqFASEgIACAkJAQKhQIHDx6Eu7s7DA0N0axZM0RGRsquv42N9MGX07170nwiIgBISgKOH5daoG1tAS0toG9f6Uu3ra3Uh9rEBJg2TQrTbdsC27cD27YBrVrlX7apqdTqnZYmtUiPHZv7Cz4R0euoVKkSpk+fjpkzZ6Jjx47YsmULHjx4UOB2lStXhrW1NUJDQ99CLYtGmQ/Tjx49wl9//YVRo0bBwMBAbZmNjQ369u2LLVu2QAiBAQMG4M6dOwgJCcHWrVvx888/IyEhQdbzzpgxAwsWLMCZM2ego6ODIUOGaFw3NTUVqampyMzMxNOnT/H06VMAqbKel4jeXf37Sy3Qd+4AqanAuHFS146sLClcA8DOncCiRUB4uHQj4u7dwIgR+Zf77Bng5gY0bgzMmAH88IN0AyMRkVwPHjzAypUrsXr1apw5cwZdu3ZFhQoVCtyuYcOGuHfvHu7cufMWalk0yvwNiFFRURBCwMXFJc/lLi4uSExMxD///IPg4GCcPn0a7u7uAIDVq1ejevXqsp53zpw58Pjv02batGn48MMP8eLFC+jr6+dad+7cuXBycoK5uTm6du3631w/AP4AgLt3AWtr9W2sraX5RETZrl0DPD0BQ0OpNfnuXemei2vXgAcPpP7Sr96vExEhdQPJjxBATIz0e3g44OIi3RB9+HCx7AYRvQOysrKQmJgIQOoZULlyZXzwwQfYvXu3xm10dXVRp04dVW+AsqLMh+lsBd1Hef36dejo6KBhw4aqec7OzrCwsJD1fPXq1VP9bmtrC0Aa+sXe3j7XutOnT4euri7S0tLw5MkTAICZmVK1/MQJoE0bYPHil9v4+EjziYhe9fy59DA3B9q1A6ZMkYL06dNSf+ecatQAbtx4vfK1tAClsuD1iIgKS6FQqI3YkZfatWtDR0cH58+ff0u1KhplvpuHs7MzFAoFIiIi8lweEREBCwsL1ViH+dH67zppzmCuqfO7rq6u6nfFf0NzZGVl5bmuUqmElpYW9PX1YWpqClNTU3zzzctPqsWLgfbtgYkTpQ9CPz/A3R1YurTAKhPRO6RtWyk8OzoC3t7A339LQ9oFBkrL580DPvoIGDYMeO89YPRoaZSg5ctflrFuHfDNNy+np02TynJyksaonjhR6k7CcaaJSK42bdrA3t4eZmZmqFSpEtq0aQNHR0dcuHABANC1a1e0adMm13YNGjTAlStXkJKS8rar/EbKfJi2srKCj48Pli9fnuvg3717Fxs3bsRHH32EmjVrIiMjQ21IlujoaNUlCACoWLEiAOlyRLacNyMWpf8aswFILdB9+gDDh0uXWHv0ALp25RjTRKTOzAxYtkwK0OvXS2NKt2sHZGRIy3fskPpHT5kCXLggheru3YFjx16WYW+v/v5jZCSF7UuXpPW6d5fGrw4IeKu7RkTliJGREXx9fTFmzBgMGDAAlStXxoYNG3Dt2jUAgJmZGYyNjdW2sbKygoODQ6GGzittysU401FRUWjWrBlcXFzw9ddfw8nJCZcuXcLkyZORmpqKkydPwtLSEj4+Pnj06BFWrFgBXV1dfPbZZzh58iTmzp2L8ePHAwCaNm0KXV1d/PTTT0hISMCUKVNw6tQp/P333/D09ERISAi8vLyQmJioau0OCwtDgwYNcP369Tz/g09eco4zTURERFQeaBpn+k1xnOliVr16dZw5cwbVqlVDr1698N5772H48OHw8vLCiRMnYGlpCQBYv349rK2t0apVK/j6+uKTTz6BiYmJ2k2Da9asQUZGBho1aoQJEybg66+/LqndIiIiIqJSrly0TMt169Yt2NnZITg4OM++O8WJLdNERERU3ryLLdPlZjSPwjh06BCSkpLg6uqK+Ph4TJkyBY6OjmhV0H80ICIiIiLKwzsVptPT0/H555/j2rVrMDExQbNmzbBx40a1kTmIiIiIiArrnQrT7dq1Q7t27Uq6GkRERERUTpSLGxCJiIiIiEoCwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMjFMExERERHJxDBNRERERCQTwzQRERERkUwM00REREREMimEEKKkK0FEREREVBaxZZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimRimiYiIiIhkYpgmIiIiIpKJYZqIiIiISCaGaSIiIiIimXRKugKUt6ysLKSlpZV0NYiIiKgU0tPTg5YW20RLA4bpUigtLQ3Xr19HVlZWSVeFiIiISiEtLS04OTlBT0+vpKvyzlMIIURJV4JeEkIgLi4O6enpqFy5Mr91EhERkZqsrCzcuXMHurq6sLe3h0KhKOkqvdPYMl3KZGRk4Pnz56hcuTIMDQ1LujpERERUClWsWBF37txBRkYGdHV1S7o67zQ2e5YymZmZAMDLNkRERKRRdk7Izg1UchimSylesiEiIiJNmBNKD4ZpIiIiIiKZGKaJyiB/f39YW1tDoVBgx44dr7VtbGwsFAoFwsLCiqVuBXF0dMSiRYtU03L2gd5MSEgIFAoFHj9+XNJVKbUGDRqErl27lnQ1iKgMYJguKxSKt/t4TYMGDYJCoYBCoYCenh6cnZ0xe/ZsZGRkFMPBKFplLcxFRERg1qxZ+OmnnxAfH48OHTqUdJXeSFndh7Vr18Lc3PytPV9pC8Bve/+LS0l/uczLo0eP0LdvX5iamsLc3BxDhw5FUlJSvtt4enqq3oOzHyNGjHhLNVb36hdmovKOo3lQkWnfvj0CAwORmpqKvXv3YvTo0dDV1cX06dNfu6zMzEwoFAoODZiHmJgYAECXLl1KVZ+5tLQ0WTfO2tjYFENt5Cvqc0/ucaHXU56Oc9++fREfH48DBw4gPT0dgwcPxvDhw7Fp06Z8t/vkk08we/Zs1XRpHhGqtL3Hl7bzJz09nSN0lCGl4yymckGpVMLGxgYODg4YOXIkvL29sWvXLgBAamoqJk2ahCpVqsDIyAhNmjRBSEiIatvsVq5du3ahdu3aUCqViIuLQ2pqKqZOnQo7OzsolUo4OzsjICBAtd3FixfRoUMHGBsbw9raGv3798eDBw9Uyz09PTFu3DhMmTIFlpaWsLGxgb+/v2q5o6MjAMDX1xcKhUI1HRMTgy5dusDa2hrGxsZo3LgxgoOD1fY3Pj4eH374IQwMDODk5IRNmzblapF5/Pgxhg0bhooVK8LU1BStW7dGeHh4vsfxwoULaN26NQwMDGBlZYXhw4erWqX8/f3RqVMnANKA/ZrCdGJiIvr27YuKFSvCwMAA1atXR2BgoNo6165dg5eXFwwNDVG/fn2cOHFCbfnWrVtRp04dKJVKODo6YsGCBWrLHR0d8dVXX2HAgAEwNTXF8OHDAQBHjx5Fy5YtYWBgADs7O4wbNw7Jycka9zfnlYHsVsJt27blW7eCnmP58uWoXr069PX1YW1tjR49emh8/vzOPU3nbEhICAYPHownT56oWgGzzyu5x+WXX36Bu7s7TExMYGNjgz59+iAhIUF1XLy8vAAAFhYWUCgUGDRoEABpvNm5c+fCyckJBgYGqF+/Pv744w+1fdy7dy9q1KgBAwMDeHl5ITY2VuPxyPbDDz/A1dUVRkZGsLOzw6hRo1TnYX77/yp/f3+4ublhzZo1sLe3h7GxMUaNGoXMzEx8//33sLGxQaVKlTBnzhy17eLi4tClSxcYGxvD1NQUvXr1wr1793KVu3r1ajg5OUFfXx/A67/mnJycAAANGjSAQqGAp6en2vL58+fD1tYWVlZWGD16NNLT01XL8vubZR8nhUKBgwcPwt3dHYaGhmjWrBkiIyM11iciIgJBQUFYvXo1mjRpghYtWmDJkiXYvHkz7ty5o3E7QArPNjY2qoepqWm+6zs6OuLrr7/GgAEDYGxsDAcHB+zatQv3799XHft69erhzJkzatvldy57enrixo0b+PTTT1XnBiDvdaaJQqHAihUr0KFDBxgYGKBatWq5zvmbN2+iV69eMDc3h6WlJbp06aJ23md345kzZw4qV66MmjVr5nqe2NhYaGlp5dr/RYsWwcHBQfWP1Qr6HAoKCkKLFi1gbm4OKysrdOzYUdUokv08CoUCW7ZsgYeHB/T19bFx48Z8jwGVMoJKlZSUFHH58mWRkpKivgB4u4/XNHDgQNGlSxe1eZ07dxYNGzYUQggxbNgw0axZM3HkyBERHR0t5s2bJ5RKpbh69aoQQojAwEChq6srmjVrJo4dOyauXLkikpOTRa9evYSdnZ3Ytm2biImJEcHBwWLz5s1CCCESExNFxYoVxfTp00VERIQIDQ0VPj4+wsvLS1UHDw8PYWpqKvz9/cXVq1fFunXrhEKhEPv37xdCCJGQkCAAiMDAQBEfHy8SEhKEEEKEhYWJlStXigsXLoirV6+KmTNnCn19fXHjxg1V2d7e3sLNzU2cPHlSnD17Vnh4eAgDAwOxcOFCtXU6deokTp8+La5evSo+++wzYWVlJR4+fJjncUxKShK2traiW7du4sKFC+LgwYPCyclJDBw4UAghxLNnz0RgYKAAIOLj40V8fHye5YwePVq4ubmJ06dPi+vXr4sDBw6IXbt2CSGEuH79ugAgatWqJXbv3i0iIyNFjx49hIODg0hPTxdCCHHmzBmhpaUlZs+eLSIjI0VgYKAwMDAQgYGBqudwcHAQpqamYv78+SI6Olr1MDIyEgsXLhRXr14Vx44dEw0aNBCDBg1S2y7nMQIgtm/fXui6FfQcp0+fFtra2mLTpk0iNjZWhIaGisWLF+d5nITQfO7ld86mpqaKRYsWCVNTU9Xf4dmzZ290XAICAsTevXtFTEyMOHHihGjatKno0KGDEEKIjIwMsXXrVgFAREZGivj4ePH48WMhhBBff/21qFWrlggKChIxMTEiMDBQKJVKERISIoQQIi4uTiiVSjFx4kRx5coVsWHDBmFtbS0AiMTERI3HZeHCheLQoUPi+vXr4uDBg6JmzZpi5MiRQgiR7/6/ys/PTxgbG4sePXqIS5cuiV27dgk9PT3Rrl07MXbsWHHlyhWxZs0aAUCcPHlSCCFEZmamcHNzEy1atBBnzpwRJ0+eFI0aNRIeHh5q5RoZGYn27duL0NBQER4eLoR4/dfcqVOnBAARHBws4uPjVesNHDhQmJqaihEjRoiIiAjx559/CkNDQ/Hzzz8X6m8mhBB///23ACCaNGkiQkJCxKVLl0TLli1Fs2bNNB73gIAAYW5urjYvPT1daGtri23btmnczsPDQ1SoUEFYWVmJOnXqiGnTponk5GSN6wshnauWlpZi5cqV4urVq2LkyJHC1NRUtG/fXvz2228iMjJSdO3aVbi4uIisrCwhRMGvv4cPH4qqVauK2bNnq71HyXmdaQJAWFlZiVWrVonIyEgxc+ZMoa2tLS5fviyEECItLU24uLiIIUOGiPPnz4vLly+LPn36iJo1a4rU1FQhhPT3NTY2Fv379xcXL14UFy9ezPO5fHx8xKhRo9Tm1atXT3z55ZdCiMJ9Dv3xxx9i69atIioqSpw7d0506tRJuLq6iszMTCHEy/c9R0dHsXXrVnHt2jVx586dfP92QuSTF+itY5guZcpDmM7KyhIHDhwQSqVSTJo0Sdy4cUNoa2uL27dvq23Tpk0bMX36dCGEUAXEsLAw1fLIyEgBQBw4cCDP5/zqq69E27Zt1ebdvHlTFTiEkD5gWrRoobZO48aNxdSpU3Mc2pdhLj916tQRS5YsEUIIERERIQCI06dPq5ZHRUUJAKqg+M8//whTU1Px4sULtXLee+898dNPP+X5HD///LOwsLAQSUlJqnl79uwRWlpa4u7du0IIIbZv3y4K+h7cqVMnMXjw4DyXZb9xr169WjXv0qVLAoCIiIgQQgjRp08f4ePjo7bd5MmTRe3atVXTDg4OomvXrmrrDB06VAwfPlxt3j///CO0tLRU53RhwnR+dSvoObZu3SpMTU3F06dPNR6fnPI69wp7zpqZmeUqT+5xedXp06cFAFVIzQ5mOQPwixcvhKGhoTh+/Hiu5+vdu7cQQojp06er/d2EEGLq1KkFhulX/f7778LKyko1rWn/X+Xn5ycMDQ3V/h7t2rUTjo6OqjAhhBA1a9YUc+fOFUIIsX//fqGtrS3i4uJUy7PPg1OnTqnK1dXVVX0BFkLeay77nDt37pza/IEDBwoHBweRkZGhmtezZ0/x0UcfadxXTX+z4OBg1Tp79uwRADT+3efMmSNq1KiRa37FihXF8uXLNT73Tz/9JIKCgsT58+fFhg0bRJUqVYSvr6/G9YWQztV+/fqppuPj4wUA8cUXX6jmnThxQvXlXQh5r3Eh5L/O8gJAjBgxQm1ekyZNVF/2fvnlF1GzZk3VFwAhpC+ABgYG4q+//hJCSH9fa2trVbjWZMuWLcLCwkJ1Tp09e1YoFApx/fp1IUThPodedf/+fQFAXLhwQQjx8hxctGhRvnV5FcN06cE+01Rkdu/eDWNjY6SnpyMrKwt9+vSBv78/QkJCkJmZiRo1aqitn5qaCisrK9W0np4e6tWrp5oOCwuDtrY2PDw88ny+8PBw/P333zA2Ns61LCYmRvV8OcsEAFtbW7VLsXlJSkqCv78/9uzZg/j4eGRkZCAlJQVxcXEAgMjISOjo6KBhw4aqbZydnWFhYaFWv6SkJLV9BICUlBS1S3w5RUREoH79+jAyMlLNa968ObKyshAZGQlra+t8651t5MiR6N69O0JDQ9G2bVt07doVzZo1U1sn53GxtbUFACQkJKBWrVqIiIhAly5d1NZv3rw5Fi1ahMzMTGhrawMA3N3d1dYJDw/H+fPn1S5RCiGQlZWF69evw8XFpVD1z69uBT2Hj48PHBwcUK1aNbRv3x7t27eHr69vvv1HXz33Lly4UKhzVhM5x+Xs2bPw9/dHeHg4EhMTVZeQ4+LiULt27TyfJzo6Gs+fP4ePj4/a/LS0NDRo0ACAdE41adJEbXnTpk0L3Ifg4GDMnTsXV65cwdOnT5GRkYEXL17g+fPnr90X19HRESYmJqppa2traGtrq/WXtba2Vr0uIyIiYGdnBzs7O9Xy2rVrw9zcHBEREWjcuDEAwMHBARUrVlStI+c1l586deqoznVAOhcvXLigmi7s30zT+Wxvb//addIkuzsRALi6usLW1hZt2rRBTEwM3nvvPY3b5axb9vuLq6trrnkJCQmwsbF5o9d4Ub7OXj2HmzZtqrqJNDw8HNHR0WrnHAC8ePFC7TxwdXUtsJ90165dMXr0aGzfvh0ff/wx1q5dCy8vL1WXwMJ8DkVFReHLL7/Ev//+iwcPHqidJ3Xr1lWt/+r7BpUdDNNUZLy8vLBixQro6emhcuXK0NGRTq+kpCRoa2vj7Nmzah9MANTegAwMDNT6ABsYGOT7fElJSejUqRO+++67XMuyP7AA5LqJQ6FQqN7MNJk0aRIOHDiA+fPnw9nZGQYGBujRowfS0tLy3e7V+tna2ubZ/6+4R0Ho0KEDbty4gb179+LAgQNo06YNRo8ejfnz56vWyXlcso97QcflVTlDPyDt8//93/9h3LhxudZ9neCQX90Keg49PT2EhoYiJCQE+/fvx5dffgl/f3+cPn1a43F/9dwr7Dmryesel+TkZLRr1w7t2rXDxo0bUbFiRcTFxaFdu3b5nnPZfZj37NmDKlWqqC1TKpUF1lOT2NhYdOzYESNHjsScOXNgaWmJo0ePYujQoUhLS3vtMJ3Xa1DO6/JVeR3nonzN5VfH1/mbvc5rzcbGJteX/YyMDDx69Oi1btbN/gIVHR2db5jOq25v8vrLT1G/zjRJSkpCo0aN8ux3nPPL16vnT1709PQwYMAABAYGolu3bti0aRMWL16s9lwFfQ516tQJDg4OWLVqFSpXroysrCzUrVs313lSmPpQ6cQwTUXGyMgIzs7OueY3aNAAmZmZSEhIQMuWLQtdnqurK7KysnD48GF4e3vnWt6wYUNs3boVjo6OquAuh66ubq5/x3rs2DEMGjQIvr6+AKQ3zJw3r9SsWRMZGRk4d+4cGjVqBED60EpMTFSr3927d6Gjo6NqxSiIi4sL1q5di+TkZNUb67Fjx6ClpZXnDTL5qVixIgYOHIiBAweiZcuWmDx5slqYLqgex44dU5t37Ngx1KhRI9eHXk4NGzbE5cuX8zwPikphnkNHRwfe3t7w9vaGn58fzM3NcejQIXTr1q1Qz1GYc1ZPT6/Q/8a3oDpfuHABDx8+xLfffqtqjX31pqe8/nVwzhu5NF3BcXFxUd0InO3kyZP51vfs2bPIysrCggULVK3Hv/32W676FNe/MXZxccHNmzdx8+ZN1fG4fPkyHj9+rLGVHpD3mpP7L5mvXLlS4N9MjqZNm+Lx48c4e/as6r3l0KFDyMrKynWFIT/ZrbQ5GxaKQmFef4U9N+R+NgDSOTxgwAC16eyrMQ0bNsSWLVtQqVKlAm/CLIxhw4ahbt26WL58OTIyMtTeRwr6HHr48CEiIyOxatUq1T4ePXr0jetEpQtH86BiV6NGDfTt2xcDBgzAtm3bcP36dZw6dQpz587Fnj17NG7n6OiIgQMHYsiQIdixYweuX7+OkJAQ1Yf66NGj8ejRI/Tu3RunT59GTEwM/vrrLwwePPi1PhgdHR1x8OBB3L17VxWGq1evjm3btiEsLAzh4eHo06ePWktSrVq14O3tjeHDh+PUqVM4d+4chg8frtby4u3tjaZNm6Jr167Yv38/YmNjcfz4ccyYMUPjh27fvn2hr6+PgQMH4uLFi/j7778xduxY9O/fv9BdPADgyy+/xM6dOxEdHY1Lly5h9+7dhe5iAQCfffYZDh48iK+++gpXr17FunXrsHTpUkyaNCnf7aZOnYrjx49jzJgxCAsLQ1RUFHbu3IkxY8YU+rkLUtBz7N69Gz/++CPCwsJw48YNrF+/HllZWa/1ZaQw56yjoyOSkpJw8OBBPHjwAM+fP5dd5+wW9SVLluDatWvYtWsXvvrqK7UyHBwcoFAosHv3bty/fx9JSUkwMTHBpEmT8Omnn2LdunWIiYlBaGgolixZgnXr1gEARowYgaioKEyePBmRkZHYtGkT1q5dm+/+Ozs7Iz09XVWfX375BStXrlRb53X2/3V5e3vD1dUVffv2RWhoKE6dOoUBAwbAw8Mj30vhcl5zlSpVgoGBAYKCgnDv3j08efKkUHUszN9MDhcXF7Rv3x6ffPIJTp06hWPHjmHMmDH4+OOPUblyZQDA7du3UatWLZw6dQqA1J3gq6++wtmzZxEbG4tdu3ZhwIABaNWqVa5ubm+qMK9xR0dHHDlyBLdv31Yb1eJVcj8bAOD333/HmjVrcPXqVfj5+eHUqVOqOvTt2xcVKlRAly5d8M8//6g+O8aNG4dbt2699j67uLjggw8+wNSpU9G7d2+1q6YFfQ5ZWFjAysoKP//8M6Kjo3Ho0CFMnDjxtetApVzJdtmmV5WHGxDzkpaWJr788kvh6OgodHV1ha2trfD19RXnz58XQmi+mSklJUV8+umnwtbWVujp6QlnZ2exZs0a1fKrV68KX19fYW5uLgwMDEStWrXEhAkTVDeeeHh4iPHjx6uV2aVLF9XoGEIIsWvXLuHs7Cx0dHSEg4ODEEK6IcTLy0sYGBgIOzs7sXTp0lxl3blzR3To0EEolUrh4OAgNm3aJCpVqiRWrlypWufp06di7NixonLlykJXV1fY2dmJvn37qt1Y9arz588LLy8voa+vLywtLcUnn3yiNlJCYW5A/Oqrr4SLi4swMDAQlpaWokuXLuLatWuqfcMrN1wlJiYKAOLvv/9Wzfvjjz9E7dq1ha6urrC3txfz5s1Te468bjISQhodwcfHRxgbGwsjIyNRr149MWfOHI3bIY8bEAuqW37P8c8//wgPDw9hYWEhDAwMRL169cSWLVs0HitN515B56wQQowYMUJYWVkJAMLPz++NjsumTZuEo6OjUCqVomnTpmLXrl25jsXs2bOFjY2NUCgUqnM4KytLLFq0SNSsWVPo6uqKihUrinbt2onDhw+rtvvzzz+Fs7OzUCqVomXLlqrRM/K7AfGHH34Qtra2wsDAQLRr106sX78+1zZ57f+r/Pz8RP369dXm5fV+8err68aNG6Jz587CyMhImJiYiJ49e6puwtVUrhDyXnOrVq0SdnZ2QktLSzViSF51HD9+vNqIIgX9zfK6afTcuXMCgOoGtrw8fPhQ9O7dWxgbGwtTU1MxePBgtfeA7NdJ9msiLi5OtGrVSlhaWgqlUimcnZ3F5MmTxZMnTzQ+hxB5n6s5X485nyvneVjQuXzixAlRr149oVQqVe9Vb/I6exUAsWzZMuHj4yOUSqVwdHTM9RqPj48XAwYMEBUqVBBKpVJUq1ZNfPLJJ6pjUtBn1qsCAgLUboDNqaDPoQMHDggXFxehVCpFvXr1REhISIHve4XBGxBLD4UQQrzN8E75e/HiBa5fv642biqVDbdu3YKdnR2Cg4PRpk2bkq4OEVG5pFAosH379rf6796/+uor/P777zh//vxbe86CMC+UHuwzTSTToUOHkJSUBFdXV8THx2PKlClwdHREq1atSrpqRERUBLLvl1m6dCm+/vrrkq4OlVLsM00kU3p6Oj7//HPUqVMHvr6+qFixIkJCQvgvYImIyokxY8agUaNG8PT0xJAhQ0q6OlRKsZtHKcPLNkRERFQQ5oXSgy3TREREREQyMUwTEREREcnEME1EREREJBPDNBERERGRTAzTREREREQyMUwTEREREcnEME1UBvn7+8Pa2hoKhQI7dux4rW1jY2OhUCgQFhZWLHUriKOjIxYtWqSalrMPpYGnpycmTJhQ0tV4YyV9PpQF/v7+cHNzK+lqEFEpxTBdRigUb/fxugYNGgSFQgGFQgE9PT04Oztj9uzZyMjIKPqDUcTKWpiLiIjArFmz8NNPPyE+Ph4dOnQo6Sq9kfKwD++6kJAQKBQKPH78uKSr8sZK2/vBixcvMHr0aFhZWcHY2Bjdu3fHvXv38t0m5/tx9qN9+/ZvqcbqysuXTqL8MExTkWnfvj3i4+MRFRWFzz77DP7+/pg3b56ssjIzM5GVlVXENSwfYmJiAABdunSBjY0NlEplCddIkpaWJmu70rQPVD6Up/ePTz/9FH/++Sd+//13HD58GHfu3EG3bt0K3C77/Tj78euvv76F2sojhChVDS9y38uKS3p6eklXgQrAME1FRqlUwsbGBg4ODhg5ciS8vb2xa9cuAEBqaiomTZqEKlWqwMjICE2aNEFISIhq27Vr18Lc3By7du1C7dq1oVQqERcXh9TUVEydOhV2dnZQKpVwdnZGQECAaruLFy+iQ4cOMDY2hrW1Nfr3748HDx6olnt6emLcuHGYMmUKLC0tYWNjA39/f9VyR0dHAICvry8UCoVqOiYmBl26dIG1tTWMjY3RuHFjBAcHq+1vfHw8PvzwQxgYGMDJyQmbNm3K1YXh8ePHGDZsGCpWrAhTU1O0bt0a4eHh+R7HCxcuoHXr1jAwMICVlRWGDx+OpKQkANLl5k6dOgEAtLS0oNBwGSExMRF9+/ZFxYoVYWBggOrVqyMwMFBtnWvXrsHLywuGhoaoX78+Tpw4obZ869atqFOnDpRKJRwdHbFgwQK15Y6Ojvjqq68wYMAAmJqaYvjw4QCAo0ePomXLljAwMICdnR3GjRuH5ORkjfubsyUwu8vBtm3b8q1bQc+xfPlyVK9eHfr6+rC2tkaPHj00Pv/Dhw/Ru3dvVKlSBYaGhnB1dc0VPJKTkzFgwAAYGxvD1tY217H4/PPP0aRJk1xl169fH7NnzwYAnD59Gj4+PqhQoQLMzMzg4eGB0NDQXMdi9erV8PX1haGhIapXr656DWW7dOkSOnbsCFNTU5iYmKBly5aqL1gAsHr1ari4uEBfXx+1atXC8uXL1bY/deoUGjRoAH19fbi7u+PcuXMaj022X375Be7u7jAxMYGNjQ369OmDhIQEANLfzMvLCwBgYWEBhUKBQYMG5VlO9ut89+7dqFmzJgwNDdGjRw88f/4c69atg6OjIywsLDBu3DhkZmaqtktMTMSAAQNgYWEBQ0NDdOjQAVFRUbnKzev9I7/3nVdpej/IeRwcHR1hZmaGjz/+GM+ePVMtCwoKQosWLWBubg4rKyt07NhR7e9S2HM7pydPniAgIAA//PADWrdujUaNGiEwMBDHjx/HyZMnNW4HvHw/zn5YWFjku76npyfGjh2LCRMmwMLCAtbW1li1ahWSk5MxePBgmJiYwNnZGfv27VPbLr/34EGDBuHw4cNYvHixqoU8NjZWdSVj3759aNSoEZRKJY4ePYqsrCzMnTsXTk5OMDAwQP369fHHH3/kW+/s96HevXvDyMgIVapUwbJly9TWKeh9OLsbz+rVqzX+N8Hk5GSYmprmqs+OHTtgZGSkOhdu3ryJXr16wdzcHJaWlujSpQtiY2NV6xf2fWDFihXo3LkzjIyMMGfOnHyPAZUCgkqVlJQUcfnyZZGSkqI2H3i7j9c1cOBA0aVLF7V5nTt3Fg0bNhRCCDFs2DDRrFkzceTIEREdHS3mzZsnlEqluHr1qhBCiMDAQKGrqyuaNWsmjh07Jq5cuSKSk5NFr169hJ2dndi2bZuIiYkRwcHBYvPmzUIIIRITE0XFihXF9OnTRUREhAgNDRU+Pj7Cy8tLVQcPDw9hamoq/P39xdWrV8W6deuEQqEQ+/fvF0IIkZCQIACIwMBAER8fLxISEoQQQoSFhYmVK1eKCxcuiKtXr4qZM2cKfX19cePGDVXZ3t7ews3NTZw8eVKcPXtWeHh4CAMDA7Fw4UK1dTp16iROnz4trl69Kj777DNhZWUlHj58mOdxTEpKEra2tqJbt27iwoUL4uDBg8LJyUkMHDhQCCHEs2fPRGBgoAAg4uPjRXx8fJ7ljB49Wri5uYnTp0+L69eviwMHDohdu3YJIYS4fv26ACBq1aoldu/eLSIjI0WPHj2Eg4ODSE9PF0IIcebMGaGlpSVmz54tIiMjRWBgoDAwMBCBgYGq53BwcBCmpqZi/vz5Ijo6WvUwMjISCxcuFFevXhXHjh0TDRo0EIMGDVLbLucxAiC2b99e6LoV9BynT58W2traYtOmTSI2NlaEhoaKxYsX53mchBDi1q1bYt68eeLcuXMiJiZG/Pjjj0JbW1v8+++/qnVGjhwp7O3tRXBwsDh//rzo2LGjMDExEePHjxdCCHHx4kUBQERHR6u2yZ4XFRUlhBDi4MGD4pdffhERERHi8uXLYujQocLa2lo8ffpU7VhUrVpVbNq0SURFRYlx48YJY2Nj1fly69YtYWlpKbp16yZOnz4tIiMjxZo1a8SVK1eEEEJs2LBB2Nraiq1bt4pr166JrVu3CktLS7F27VrV+VOxYkXRp08fcfHiRfHnn3+KatWqCQDi3LlzGo9RQECA2Lt3r4iJiREnTpwQTZs2FR06dBBCCJGRkSG2bt0qAIjIyEgRHx8vHj9+nGc52a9zHx8fERoaKg4fPiysrKxE27ZtRa9evcSlS5fEn3/+KfT09FSvcyGk9xIXFxdx5MgRERYWJtq1ayecnZ1FWlqaWrmvvn8U9L7zKk3vB35+fsLY2Fj1ujxy5Iiw+f/2zjssqmv7+9+hOjRBRIrijIo0BRU0CTYsKJiEIMYGKKgEjb2BXUCJscRuEq9iBOLFGrFcOyLwQzCI0oyMNBE0wY6aQaXNev/g5VwODDASTMjN/jwPz8PZde199l5nnb3XPmNkRCtWrODy/vTTT3T8+HHKzc2ltLQ0cnV1JRsbG6qqqiIixcZ2XWJiYggAlZSU8MI7d+5MW7dubfB++fj4UNu2bcnAwIDMzc3pyy+/pKdPnzaYnqhaV2pra1NISAjl5ORQSEgIKSsr06hRo2jv3r2Uk5NDM2fOJH19fSotLSWipnXwixcvyMHBgfz8/Dh9VVlZSbGxsQSAbG1t6dKlS5SXl0fPnj2jr776iiwtLenChQuUn59PYWFhpK6uTnFxcQ3KLRKJSFtbm9avX0/Z2dnc/K3R8URN6+GgoCDS1NQkFxcXSk1NpYyMDLl1+fn50ccff8wL++yzz8jb25uIiMrLy8nKyoqmTZtGmZmZlJWVRZ6enmRhYUFlZWVEpLge6NChA+3fv5/y8/N5z53aNGQvMP58mDHdyvhfMKZlMhlFR0eTuro6+fv7U2FhISkrK9Ovv/7KyzN8+HBavnw5ERFnIKanp3Px2dnZBICio6Pl1hkSEkIjR47khd2/f597qBNVPyAGDhzIS9OvXz9aunRprb79rzHXGD169KBdu3YREZFEIiEAlJKSwsXn5uYSAM5QTEhIIB0dHXr79i2vnG7dutGePXvk1rF3717S09MjqVTKhZ09e5aUlJTo4cOHRER04sQJauo92NXVlaZOnSo3ruahvm/fPi7s9u3bBIAkEgkREXl6etKIESN4+QICAsja2pq7FolENHr0aF4aX19fmj59Oi8sISGBlJSUuDGtiDHdmGxN1XH8+HHS0dHhPZzelU8++YQWL15MRNUGqJqaGh09epSLf/bsGQmFQs6YJiLq1asXrV27lrtevnw5ffjhhw3WUVVVRdra2vSf//yHCwNAq1at4q6lUikBoPPnz3NldunShTMi69KtWzc6ePAgLywkJIQcHByIiGjPnj2kr6/P0y+7d+9u0piuS0pKCgGg33//nYiIM47qGn11qZnntV86ZsyYQRoaGlxZRETOzs40Y8YMIiLKyckhAJSYmMjFP336lIRCIXdP5OkPRfSOPOTpg6CgINLQ0OCNqYCAgEbv75MnTwgA3bp1i4gUG9t1iYyMJDU1tXrh/fr1oyVLljRY96FDh+jUqVOUmZlJJ06cICsrK+rXrx9VVlY2mKeurqysrCRNTU2aPHkyF1ZcXEwA6Nq1a0SkuA6uPU+I/jteTp48yYW9ffuWNDQ0KCkpiZfW19eXPDw8GpRbJBKRi4sLL2zChAncy54iejgoKIhUVVW5l6eGSE5OJmVlZfrtt9+IiOjRo0ekoqLCGfsHDhwgCwsLkslkXJ6ysjISCoV08eJFuWU2pAcWLFjQqCxEzJhuTai8pwVvxj+QM2fOQEtLCxUVFZDJZPD09ERwcDDi4uJQVVUFc3NzXvqysjLo6+tz12pqarC1teWu09PToaysDEdHR7n1ZWRkIDY2FlpaWvXi8vPzufpqlwkAxsbG3BZ1Q0ilUgQHB+Ps2bMoLi5GZWUl3rx5g6KiIgBAdnY2VFRUYGdnx+UxMzPjbaVmZGRAKpXy2ggAb9684W3/1kYikaBXr17Q1NTkwgYMGACZTIbs7GwYGho2KncNM2fOxOeff47U1FSMHDkSo0ePRv/+/XlpaveLsbExAODx48ewtLSERCKBm5sbL/2AAQOwfft2VFVVQVlZGQDQt29fXpqMjAxkZmYiMjKSCyMiyGQyFBQUwMrKSiH5G5OtqTpGjBgBkUiErl27wsXFBS4uLpzbhDyqqqrw9ddf4+jRo/j1119RXl6OsrIyLn1+fj7Ky8t5bhzt2rWDhYUFrxwvLy/s378fq1evBhHh0KFDWLRoERf/6NEjrFq1CnFxcXj8+DGqqqrw+vVrbkzJa7umpiZ0dHS48Zqeno5BgwZBVVW1XjtKS0uRn58PX19f+Pn5ceGVlZVo27YtgOrxZWtry9vGdnBwkNsvtbl58yaCg4ORkZGBkpISzh+5qKgI1tbWTeavjYaGBrp168ZdGxoaQiwW8+axoaEh12aJRAIVFRVe/+vr68PCwgISiYQLq6s/bt26pZDeURSxWAxtbW3uuq4eyc3NRWBgIJKTk/H06VNeH/Xs2ZNL19jYbikmTpzI/W9jYwNbW1t069YNcXFxGD58eIP5asumrKwMfX192NjYcGE1+qem3Yrq4IaorT/y8vLw+vVrjBgxgpemvLwcffr0abScumPYwcGBc7dTVA+LRCIYGBg0Ws8HH3yAHj16ICIiAsuWLcO///1viEQiDB48mKsrLy+PN06A6kOkNXUpqgfq6lZG64YZ04wWY+jQodi9ezfU1NRgYmICFZXq4SWVSqGsrIybN29yRlgNtZWwUCjk+QALhcJG65NKpXB1dcXGjRvrxdU8pADUMzwEAkGTh5P8/f0RHR2NzZs3w8zMDEKhEGPHjn2ngylSqRTGxsZyfTR1dXUVLqc5jBo1CoWFhTh37hyio6MxfPhwzJ49G5s3b+bS1O6Xmn5/10NbtY1+oLrNM2bMwLx58+ql7dy5s8LlNiZbU3WoqakhNTUVcXFxuHTpEgIDAxEcHIyUlBS5/f7NN99gx44d2L59O2xsbKCpqYkFCxa88yEkDw8PLF26FKmpqXjz5g3u37+PCRMmcPE+Pj549uwZduzYAZFIBHV1dTg4ONSrp7Hx2ticqPGrDw0Nree/XXfevQulpaVwdnaGs7MzIiMjYWBggKKiIjg7OzfroJa89jVnjtalrv5QVO8oSlMyurq6QiQSITQ0FCYmJpDJZOjZs2ej97epeWdkZITy8nK8ePGCN3YfPXoEIyMjhWXv2rUr2rdvj7y8vEaN6abujby5qIgOboja+qNm/J49exYdO3bkpfsjB5QV1cN1dVlDfPHFF/juu++wbNkyhIWFYerUqVy/SKVS2Nvb8170a6gx1BXVA4rKw2gdMGOa0WJoamrCzMysXnifPn1QVVWFx48fY9CgQQqXZ2NjA5lMhvj4eDg5OdWLt7Ozw/HjxyEWiznDvTmoqqryDjsBQGJiIqZMmQJ3d3cA1Uqy9iESCwsLVFZWIi0tDfb29gCqV1ZKSkp48j18+BAqKir1DjI1hJWVFcLDw1FaWsop08TERCgpKdVbCW0KAwMD+Pj4wMfHB4MGDUJAQADPmG5KjsTERF5YYmIizM3NGzXM7OzskJWVJXcctBSK1KGiogInJyc4OTkhKCgIurq6uHLlityvICQmJsLNzQ2TJk0CUG0o5OTkcCuu3bp1g6qqKpKTk7kXgpKSEuTk5PB2TTp16gRHR0dERkbizZs3GDFiBDp06MCr5/vvv8fHH38MoPqgUu3Dsopga2uLiIgIVFRU1DN8DA0NYWJigrt378LLy0tufisrKxw4cABv377lVqebOsh2584dPHv2DBs2bICpqSkA4MaNG7w0ampqAFBvHrUEVlZWqKysRHJyMre78uzZM2RnZze6Kt5cvSNPHzRFjTyhoaFcXVevXn2nMuRhb28PVVVVxMTE4PPPPwdQvStWVFSk0I5CDQ8ePMCzZ88UMnDfBUV0sJqamkL9WfvgaEO7kQ1Rdwz//PPP3C5Yc/RwY0yaNAlLlizBzp07kZWVBR8fHy7Ozs4OR44cQYcOHaCjoyM3f0voAUbrg33Ng/HeMTc3h5eXF7y9vREVFYWCggJcv34d69evx9mzZxvMJxaL4ePjg2nTpuHkyZMoKChAXFwcjh49CgCYPXs2nj9/Dg8PD6SkpCA/Px8XL17E1KlT3+lhKBaLERMTg4cPH3LGcPfu3REVFYX09HRkZGTA09OTt3pkaWkJJycnTJ8+HdevX0daWhqmT5/OWx1zcnKCg4MDRo8ejUuXLuHevXtISkrCypUr6xkjNXh5eaFNmzbw8fHBL7/8gtjYWMydOxeTJ09W2MUDAAIDA3Hq1Cnk5eXh9u3bOHPmjMIuFgCwePFixMTEICQkBDk5OYiIiMC3334Lf3//RvMtXboUSUlJmDNnDtLT05Gbm4tTp05hzpw5CtfdFE3VcebMGezcuRPp6ekoLCzEjz/+CJlM1uDLSPfu3REdHY2kpCRIJBLMmDGD9x1fLS0t+Pr6IiAgAFeuXMEvv/yCKVOmQEmpvvr08vLC4cOHcezYsXoGbffu3XHgwAFIJBIkJyfDy8uryd2XusyZMwevXr3CxIkTcePGDeTm5uLAgQPIzs4GAKxZswbr16/Hzp07kZOTg1u3biEsLAxbt24FAHh6ekIgEMDPzw9ZWVk4d+5cky9YNav9u3btwt27d3H69GmEhITw0ohEIggEApw5cwZPnjzhVhlbgu7du8PNzQ1+fn64evUqMjIyMGnSJHTs2LGeK1Jt/ojeqasPmkJPTw/6+vrYu3cv8vLycOXKFZ6LT3Np27YtfH19sWjRIsTGxuLmzZuYOnUqHBwc8NFHH3HpLC0tceLECQDVL/4BAQH4+eefce/ePcTExMDNzQ1mZmZwdnb+wzLVRhEdLBaLkZycjHv37vHcX+qira0Nf39/LFy4EBEREcjPz0dqaip27dqFiIiIRuVITEzEpk2bkJOTg++++w7Hjh3D/PnzATRPDzeGnp4exowZg4CAAIwcORKdOnXi4ry8vNC+fXu4ubkhISGBe2bNmzcPDx48ANAyeoDR+mDGNONPISwsDN7e3li8eDEsLCwwevRopKSkNLn1v3v3bowdOxazZs2CpaUl/Pz8uE+gmZiYIDExEVVVVRg5ciRsbGywYMEC6OrqyjV0GmLLli2Ijo6Gqakp55u3detW6OnpoX///nB1dYWzszPPPxoAfvzxRxgaGmLw4MFwd3eHn58ftLW1uRU/gUCAc+fOYfDgwZg6dSrMzc0xceJEFBYWNmgYa2ho4OLFi3j+/Dn69euHsWPHYvjw4fj2228Vbg9QvRq0fPly2NraYvDgwVBWVsbhw4cVzm9nZ4ejR4/i8OHD6NmzJwIDA7F27doGP3lWg62tLeLj45GTk4NBgwahT58+CAwMhImJyTvJ/0fq0NXVRVRUFIYNGwYrKyv861//wqFDh9CjRw+55a1atQp2dnZwdnbGkCFDYGRkhNGjR/PSfPPNNxg0aBBcXV3h5OSEgQMHcjsStRk7diyePXuG169f1yvjhx9+QElJCezs7DB58mTMmzePt3KtCPr6+rhy5QqkUikcHR1hb2+P0NBQbpX6iy++wL59+xAWFgYbGxs4OjoiPDwcXbp0AVD9YvCf//wHt27dQp8+fbBy5Uq5W/S1MTAwQHh4OI4dOwZra2ts2LChngHesWNHrFmzBsuWLYOhoWGLvjwB1frD3t4en376KRwcHEBEOHfunFzf8br53lXvyNMHTaGkpITDhw/j5s2b6NmzJxYuXNjsb+zXZdu2bfj000/x+eefY/DgwTAyMkJUVBQvTXZ2Nl6+fAmg2qUnMzMTn332GczNzeHr6wt7e3skJCS0+PfcFdHB/v7+UFZWhrW1Neci1BAhISFYvXo11q9fDysrK7i4uODs2bPc+G2IxYsX48aNG+jTpw+++uorbN26lXtxaI4ebgpfX1+Ul5dj2rRpvHANDQ383//9Hzp37owxY8bAysoKvr6+ePv2LbdS3RJ6gNH6EBAR/dVCMP7L27dvUVBQ0OC3LhmtlwcPHsDU1BSXL19u1C+RwWAwGC2DWCzGggUL/tRfWTxw4AAWLlyI3377jXNx+itg9kLrgflMMxjNpGaF0MbGBsXFxViyZAnEYjF3spvBYDAY/zu8fv0axcXF2LBhA2bMmPGXGtKM1gVz82AwmklFRQVWrFiBHj16wN3dHQYGBoiLi2ty65nBYDAYfz82bdoES0tLGBkZYfny5X+1OIxWBHPzaGWwbRsGg8FgMBhNweyF1gNbmWYwGAwGg8FgMJoJM6YZDAaDwWAwGIxmwoxpBoPBYDAYDAajmTBjmsFgMBgMBoPBaCbMmGYwGAwGg8FgMJoJM6YZDAaDwWAwGIxmwoxpBuNvSHBwMAwNDSEQCHDy5Ml3ynvv3j0IBAKkp6e/F9maQiwWY/v27dx1c9rwV6KIvFOmTKn3c+Ktlfchq7wxlpiYCBsbG6iqqmL06NGIi4uDQCDAixcvWrRuBqM18HfSAYw/DvsFxL8Ja9as+VPrCwoKeqf0U6ZMQUREBABAVVUVnTt3hre3N1asWAEVldY9zAQCAU6cOPG3UXwSiQRr1qzBiRMn8NFHH0FPT++vFukPUVxc/LdqQ2157927hy5duiAtLQ29e/f+02WZMmUKXrx48YdeRnbs2IGW/rkBU1NTFBcXo3379lzYokWL0Lt3b5w/fx5aWlrQ0NBAcXEx2rZt26J1N0Z4eDgWLFjADPhm8nfTlQzGn0XrtnIYfytcXFwQFhaGsrIynDt3DrNnz4aqqmqzfimqqqoKAoEASkps86Qu+fn5AAA3NzcIBIK/WJr/Ul5e3qyf1zUyMnoP0rw//m7yNsX7MGaVlZXr9VN+fj6+/PJLdOrUiQv7X+tLxv8WzdVpjH8ezFJhtBjq6uowMjKCSCTCzJkz4eTkhNOnTwMAysrK4O/vj44dO0JTUxMffvgh4uLiuLzh4eHQ1dXF6dOnYW1tDXV1dRQVFaGsrAxLly6Fqakp1NXVYWZmhh9++IHL98svv2DUqFHQ0tKCoaEhJk+ejKdPn3LxQ4YMwbx587BkyRK0a9cORkZGCA4O5uLFYjEAwN3dHQKBgLvOz8+Hm5sbDA0NoaWlhX79+uHy5cu89hYXF+OTTz6BUChEly5dcPDgwXouDC9evMAXX3wBAwMD6OjoYNiwYcjIyGi0H2/duoVhw4ZBKBRCX18f06dPh1QqBVDt3uHq6goAUFJSatCYLikpgZeXFwwMDCAUCtG9e3eEhYXx0ty9exdDhw6FhoYGevXqhWvXrvHijx8/jh49ekBdXR1isRhbtmzhxYvFYoSEhMDb2xs6OjqYPn06AODq1asYNGgQhEIhTE1NMW/ePJSWljbY3tpuEzXuAVFRUY3K1lQd33//Pbp37442bdrA0NAQY8eOlVs3EcHAwAA//fQTF9a7d28YGxvz6lJXV8fr16/rydulSxcAQJ8+fSAQCDBkyBBe+Zs3b4axsTH09fUxe/ZsVFRUcHElJSXw9vaGnp4eNDQ0MGrUKOTm5nLxwcHB9Va7t2/fzo3R4OBgRERE4NSpUxAIBBAIBLw5VZuffvoJNjY23JhycnLi+qvudvTvv/8OLy8vaGpqwtjYGNu2bcOQIUOwYMECLo1YLMbXX3+NadOmQVtbG507d8bevXu5+NpuHjX/P3v2DNOmTYNAIEB4eLhcN4/ExEQMGTIEGhoa0NPTg7OzM0pKSgAAFy5cwMCBA6Grqwt9fX18+umn3Itl7TobGjtxcXGYOnUqXr58yfVXbV1Qm5q+379/Pzp37gwtLS3MmjULVVVV2LRpE4yMjNChQwesW7eOl6+oqAhubm7Q0tKCjo4Oxo8fj0ePHgEAcnJyIBAIcOfOHV6ebdu2oVu3bty1Ijpt7ty5WLBgAfT09GBoaIjQ0FCUlpZi6tSp0NbWhpmZGc6fP8+r533pyrrU3IejR49yc7Rfv37IyclBSkoK+vbtCy0tLYwaNQpPnjzh5d23bx+srKzQpk0bWFpa4vvvv+fFL126FObm5tDQ0EDXrl2xevVq3pzKyMjA0KFDoa2tDR0dHdjb2+PGjRu8e1qb2vMJ+O9cWLduHUxMTGBhYQEAuH//PsaPHw9dXV20a9cObm5uuHfvHpevqqoKixYt4sbmkiVLWny3h9G6YcY0470hFApRXl4OAJgzZw6uXbuGw4cPIzMzE+PGjYOLiwvPeHj9+jU2btyIffv24fbt2+jQoQO8vb1x6NAh7Ny5ExKJBHv27IGWlhaAakN12LBh6NOnD27cuIELFy7g0aNHGD9+PE+OiIgIaGpqIjk5GZs2bcLatWsRHR0NAEhJSQEAhIWFobi4mLuWSqX4+OOPERMTg7S0NLi4uMDV1RVFRUVcud7e3vjtt98QFxeH48ePY+/evXj8+DGv7nHjxuHx48c4f/48bt68CTs7OwwfPhzPnz+X22elpaVwdnaGnp4eUlJScOzYMVy+fBlz5swBAPj7+3NGcXFxMYqLi+WWs3r1amRlZeH8+fOQSCTYvXs3b8sdAFauXAl/f3+kp6fD3NwcHh4eqKysBADcvHkT48ePx8SJE3Hr1i0EBwdj9erVCA8P55WxefNm9OrVC2lpaVi9ejXy8/Ph4uKCzz//HJmZmThy5AiuXr3Kya8ojcnWVB03btzAvHnzsHbtWmRnZ+PChQsYPHiw3HoEAgEGDx7MGaElJSWQSCR48+YNZ/TEx8ejX79+0NDQqJf/+vXrAIDLly+juLgYUVFRXFxsbCzy8/MRGxuLiIgIhIeH8/pvypQpuHHjBk6fPo1r166BiPDxxx/zjIPG8Pf3x/jx4+Hi4sKNhf79+9dLV1xcDA8PD0ybNg0SiQRxcXEYM2ZMgw/7RYsWITExEadPn0Z0dDQSEhKQmppaL92WLVvQt29fpKWlYdasWZg5cyays7Prpatx+dDR0cH27dtRXFyMCRMm1EuXnp6O4cOHw9raGteuXcPVq1fh6uqKqqoqANVzY9GiRbhx4wZiYmKgpKQEd3d3yGQyXjkNjZ3+/ftj+/bt0NHR4frL39+/wf7Nz8/H+fPnceHCBRw6dAg//PADPvnkEzx48ADx8fHYuHEjVq1aheTkZACATCaDm5sbnj9/jvj4eERHR+Pu3btcW83NzdG3b19ERkby6omMjISnpyeAd9Np7du3x/Xr1zF37lzMnDkT48aNQ//+/ZGamoqRI0di8uTJ3Avg+9SVDREUFIRVq1YhNTUVKioq8PT0xJIlS7Bjxw4kJCQgLy8PgYGBvH4IDAzEunXrIJFI8PXXX2P16tWc+yAAaGtrIzw8HFlZWdixYwdCQ0Oxbds2Lt7LywudOnVCSkoKbt68iWXLlkFVVbVROesSExOD7OxsREdH48yZM6ioqICzszO0tbWRkJCAxMREaGlpwcXFhXu+bdmyBeHh4di/fz+uXr2K58+f48SJE+9UL+NvDjFaFW/evKGsrCx68+YNLzw4OPhP/XtXfHx8yM3NjYiIZDIZRUdHk7q6Ovn7+1NhYSEpKyvTr7/+ysszfPhwWr58ORERhYWFEQBKT0/n4rOzswkARUdHy60zJCSERo4cyQu7f/8+AaDs7GwiInJ0dKSBAwfy0vTr14+WLl3KXQOgEydONNnGHj160K5du4iISCKREABKSUnh4nNzcwkAbdu2jYiIEhISSEdHh96+fcsrp1u3brRnzx65dezdu5f09PRIKpVyYWfPniUlJSV6+PAhERGdOHGCmpq6rq6uNHXqVLlxBQUFBID27dvHhd2+fZsAkEQiISIiT09PGjFiBC9fQEAAWVtbc9cikYhGjx7NS+Pr60vTp0/nhSUkJJCSkhI3pkUiEddHRPz+V0S2puo4fvw46ejo0KtXrxrsn9rs3LmTevToQUREJ0+epA8//JDc3Nxo9+7dRETk5OREK1asaFTetLQ0Xpk+Pj4kEomosrKSCxs3bhxNmDCBiIhycnIIACUmJnLxT58+JaFQSEePHiUioqCgIOrVqxev3G3btpFIJOLVUzPvGuLmzZsEgO7duyc3vnYZr169IlVVVTp27BgX/+LFC9LQ0KD58+dzYSKRiCZNmsRdy2Qy6tChA9dn8vqlbdu2FBYWxl3HxsYSACopKSEiIg8PDxowYECjbanNkydPCADdunWLV2djYycsLIzatm3bZNlBQUGkoaHBG0POzs4kFoupqqqKC7OwsKD169cTEdGlS5dIWVmZioqK6tV//fp1Iqq+f926dePia3RcjXzN0WmVlZWkqalJkydP5sKKi4sJAF27dq3Z5RI1T1fKuw+HDh0iABQTE8OFrV+/niwsLLjrbt260cGDB3llhYSEkIODQ4N1ffPNN2Rvb89da2trU3h4uNy0is4nQ0NDKisr48IOHDhAFhYWJJPJuLCysjISCoV08eJFIiIyNjamTZs2cfEVFRXUqVOnJufmH6Uhe4Hx58NWphktxpkzZ6ClpYU2bdpg1KhRmDBhAoKDg3Hr1i1UVVXB3NwcWlpa3F98fDxvm1ZNTQ22trbcdXp6OpSVleHo6Ci3voyMDMTGxvLKtLS0BABeubXLBABjY+N6K8h1kUql8Pf3h5WVFXR1daGlpQWJRMKtTGdnZ0NFRQV2dnZcHjMzM95BuoyMDEilUujr6/NkLCgo4MlXG4lEgl69ekFTU5MLGzBgAGQymdxVv4aYOXMmDh8+jN69e2PJkiVISkqql6Z2v9S4NdT0i0QiwYABA3jpBwwYgNzcXG6lEAD69u3LS5ORkYHw8HBee52dnSGTyVBQUKCw/I3J1lQdI0aMgEgkQteuXTF58mRERkZyK3TycHR0RFZWFp48eYL4+HgMGTIEQ4YMQVxcHCoqKpCUlFTPfUMRevToAWVlZV47aveviooKPvzwQy5eX18fFhYWkEgk71xXY/Tq1QvDhw+HjY0Nxo0bh9DQUM51oi53795FRUUFPvjgAy6sbdu23HZ3bWrfI4FAACMjoybnVWPUrEw3RG5uLjw8PNC1a1fo6Ohw2/O1d4vqylV37LwLYrEY2tra3LWhoSGsra155zgMDQ1599TU1BSmpqZcvLW1NXR1dbl7OnHiRNy7dw8///wzgOrVWDs7O05vNUenKSsrQ19fHzY2Njy5arf7ferKhqhdVo08dWWsKbu0tBT5+fnw9fXlyfjVV1/x5Dty5AgGDBgAIyMjaGlpYdWqVbz7v2jRInzxxRdwcnLChg0bGtSzjWFjY8Pzk87IyEBeXh60tbU5udq1a4e3b98iPz8fL1++RHFxMW8uq6io1NONjP9t2AFERosxdOhQ7N69G2pqajAxMeG+4iGVSqGsrIybN2/yjAsAnMsGUO0WUtsHWCgUNlqfVCqFq6srNm7cWC+uts9r3W0+gUBQb2u4Lv7+/oiOjsbmzZthZmYGoVCIsWPHctt6iiCVSmFsbCzXj1VXV1fhcprDqFGjUFhYiHPnziE6OhrDhw/H7NmzsXnzZi5N7X6p6fem+qUutY1+oLrNM2bMwLx58+ql7dy5s8LlNiZbU3WoqakhNTUVcXFxuHTpEgIDAxEcHIyUlBS5/W5jY4N27dohPj4e8fHxWLduHYyMjLBx40akpKSgoqJCrvvEu7Shph3v0r9KSkr1XDEUdQGpjbKyMqKjo5GUlIRLly5h165dWLlyJZKTkzmf7+bwR9tXl6bmu6urK0QiEUJDQ2FiYgKZTIaePXvWm5MtMa7rllNT1h9ts5GREYYNG4aDBw/io48+wsGDBzFz5kwu/o/otKbmzPvSlQ0hT566YbXlA4DQ0FCeUQqAe2Zcu3YNXl5eWLNmDZydndG2bVscPnyYd5YjODgYnp6eOHv2LM6fP4+goCAcPnwY7u7uCs8neTrN3t6+nnsOABgYGDTdEYx/BMyYZrQYmpqaMDMzqxfep08fVFVV4fHjxxg0aJDC5dnY2EAmkyE+Ph5OTk714u3s7HD8+HGIxeI/9Pk9VVVV3morUH0QasqUKXB3dwdQrVBrHzixsLBAZWUl0tLSYG9vDwDIy8vjrfjZ2dnh4cOHUFFRafCwTl2srKwQHh6O0tJSTqknJiZCSUlJ7upgYxgYGMDHxwc+Pj4YNGgQAgICeMZ0U3IkJibywhITE2Fubl7vhag2dnZ2yMrKkjsOWgpF6lBRUYGTkxOcnJwQFBQEXV1dXLlyBWPGjKmXViAQYNCgQTh16hRu376NgQMHQkNDA2VlZdizZw/69u1b7wFbQ80KVt3x0xRWVlaorKxEcnIyZ6g/e/YM2dnZsLa2BlB9/x4+fAgi4oyRut8GV1NTU6hugUCAAQMGYMCAAQgMDIRIJMKJEyewaNEiXrquXbtCVVUVKSkp3MvPy5cvkZOT06DfeUtha2uLmJgYuZ8Bremb0NBQTodcvXr1netQtL+ag5WVFe7fv4/79+9zq9NZWVl48eIFd0+Bar/eJUuWwMPDA3fv3sXEiRO5uJbSaXV5n7qyJTA0NISJiQnu3r0LLy8vuWmSkpIgEomwcuVKLqywsLBeOnNzc5ibm2PhwoXw8PBAWFgY3N3dFZpP8rCzs8ORI0fQoUMH6OjoyE1jbGyM5ORkbo5UVlZyZ2QY/wyYmwfjvWNubg4vLy94e3sjKioKBQUFuH79OtavX4+zZ882mE8sFsPHxwfTpk3DyZMnUVBQgLi4OBw9ehQAMHv2bDx//hweHh5ISUlBfn4+Ll68iKlTp76TwheLxYiJicHDhw85Y7h79+6IiopCeno6MjIy4OnpyVuhsbS0hJOTE6ZPn47r168jLS0N06dP562uOzk5wcHBAaNHj8alS5dw7949JCUlYeXKldwJ87p4eXmhTZs28PHxwS+//ILY2FjMnTsXkydP5rZKFSEwMBCnTp1CXl4ebt++jTNnzsDKykrh/IsXL0ZMTAxCQkKQk5ODiIgIfPvtt40e2AKqT9snJSVhzpw5SE9PR25uLk6dOvXOBxD/SB1nzpzBzp07kZ6ejsLCQvz444+QyWSNvowMGTIEhw4dQu/evaGlpQUlJSUMHjwYkZGRDboZAUCHDh0gFAq5A10vX75UqA3du3eHm5sb/Pz8cPXqVWRkZGDSpEno2LEj3NzcOJmePHmCTZs2IT8/H9999129LzSIxWJkZmYiOzsbT58+lbvSlpycjK+//ho3btxAUVERoqKi8OTJE7njQVtbGz4+PggICEBsbCxu374NX1/fRr8c01IsX74cKSkpmDVrFjIzM3Hnzh3s3r0bT58+hZ6eHvT19bF3717k5eXhypUr9V4EFEEsFkMqlSImJgZPnz5t1P3nXXFycoKNjQ28vLyQmpqK69evw9vbG46Ojrwt/zFjxuD333/HzJkzMXToUJiYmHBxLaXT6vI+dWVLsWbNGqxfvx47d+5ETk4Obt26hbCwMGzduhVA9ZwpKirC4cOHkZ+fj507d/IO+b158wZz5sxBXFwcCgsLkZiYiJSUFG6cKzKf5OHl5YX27dvDzc0NCQkJ3HNo3rx5ePDgAQBg/vz52LBhA06ePIk7d+5g1qxZ7Fvm/zT+WpdtRl3+rgcKmjoIVV5eToGBgSQWi0lVVZWMjY3J3d2dMjMziajhg0Fv3ryhhQsXkrGxMampqZGZmRnt37+fi8/JySF3d3fS1dUloVBIlpaWtGDBAu6wiKOjI+/gFBGRm5sb+fj4cNenT58mMzMzUlFR4Q6jFBQU0NChQ0koFJKpqSl9++239cr67bffaNSoUaSurk4ikYgOHjxIHTp0oH/9619cmlevXtHcuXPJxMSEVFVVydTUlLy8vHiHlOqSmZlJQ4cOpTZt2lC7du3Iz8+Pfv/9dy5ekQOIISEhZGVlRUKhkNq1a0dubm509+5drm2oczispKSEAFBsbCwX9tNPP5G1tTWpqqpS586d6ZtvvuHVUfcgYQ3Xr1+nESNGkJaWFmlqapKtrS2tW7euwXxo4kCfPNkaqyMhIYEcHR1JT0+PhEIh2dra0pEjRxrtr7S0NALAO2y1bds2AkAXLlzgpUWdQ1ihoaFkampKSkpK5OjoSETy58P8+fO5eCKi58+f0+TJk6lt27YkFArJ2dmZcnJyeHl2795NpqampKmpSd7e3rRu3TreganHjx9z/VC3j2rIysoiZ2dnMjAwIHV1dTI3N+cO0sqT9dWrV+Tp6UkaGhpkZGREW7dupQ8++ICWLVvGpZF373v16kVBQUFE1LwDiEREcXFx1L9/f1JXVyddXV1ydnbm4qOjo8nKyorU1dXJ1taW4uLimjV2vvzyS9LX1ycAnLx1kXdYTd49rasTCgsL6bPPPiNNTU3S1tamcePGcQeHazN+/HgCwNNlNTRHp8m7H3XH6fvSlXWRdx/k3Wt5Oj8yMpJ69+5NampqpKenR4MHD6aoqCguPiAggPT19UlLS4smTJhA27Zt48ooKyujiRMnkqmpKampqZGJiQnNmTOH9yxtaj419BwrLi4mb29vat++Pamrq1PXrl3Jz8+PXr58SUTVBw7nz59POjo6pKurS4sWLSJvb292APEfhICIfQyxNfH27VsUFBSgS5cuaNOmzV8tDuMdePDgAUxNTXH58uVGD1IxGH8nSktL0bFjR2zZsgW+vr5/tTgMBuP/w+yF1gPzmWYwmsmVK1cglUphY2OD4uJiLFmyBGKx+L37ljIY75O0tDTcuXMHH3zwAV6+fIm1a9cCAOd+wmAwGAw+zJhmMJpJRUUFVqxYgbt370JbWxv9+/dHZGTkO/9IAIPR2ti8eTOys7OhpqYGe3t7JCQk1PvRHwaDwWBUw9w8Whls24bBYDAYDEZTMHuh9cC+5sFgMBgMBoPBYDQTZky3UtiGAYPBYDAYjIZgdkLrgRnTrYyaH8R4l1/aYzAYDAaD8c+ixk5o7Ie0GH8O7ABiK0NFRQUaGhp48uQJVFVVoaTE3ncYDAaDwWD8F5lMhidPnkBDQ6NFfy2T0TzYAcRWSHl5OQoKCni/uMdgMBgMBoNRg5KSErp06QI1NbW/WpR/PMyYbqXIZDLm6sFgMBgMBkMuampqbPe6lcCMaQaDwWAwGAwGo5mwVxoGg8FgMBgMBqOZMGOawWAwGAwGg8FoJsyYZjAYDAaDwWAwmgkzphkMBoPBYDAYjGbCjGkGg8FgMBgMBqOZMGOawWAwGAwGg8FoJsyYZjAYDAaDwWAwmsn/A5jBMrIuJdOOAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "countries_sorted = countries_summary.sort_values(by='perc_neg05')\n",
+ "\n",
+ "countries = list(countries_sorted.Region)\n",
+ "neg_data = list(countries_sorted.perc_neg05)\n",
+ "pos_data = list(countries_sorted.perc_pos05)\n",
+ "non_data = list(countries_sorted.perc_no)\n",
+ "\n",
+ "# Set the color scheme for the segments\n",
+ "colors = ['red', 'blue', 'grey']\n",
+ "\n",
+ "# Create a list of indices for the bars\n",
+ "indices = np.arange(len(countries))\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(8,6))\n",
+ "\n",
+ "# Plot the stacked horizontal bars\n",
+ "neg_bars = ax.barh(indices, neg_data, color=colors[0], label='Percentage of shorelines retreated at more than 0.5 metre per year ')\n",
+ "pos_bars = ax.barh(indices, pos_data, left=neg_data, color=colors[1], label='Percentage of shorelines advanced at more than 0.5 metre per year')\n",
+ "non_bars = ax.barh(indices, non_data, left=np.add(neg_data, pos_data), color=colors[2], label='Percentage of shorelines without significant movement measured')\n",
+ "\n",
+ "# Remove spines and ticks\n",
+ "ax.spines['left'].set_visible(False)\n",
+ "ax.spines['right'].set_visible(False)\n",
+ "ax.spines['top'].set_visible(False)\n",
+ "ax.spines['bottom'].set_visible(False)\n",
+ "ax.set_xticks([])\n",
+ "ax.set_yticks([])\n",
+ "\n",
+ "# Add data value text to the center of each colored bar segment\n",
+ "for bars in [neg_bars, pos_bars, non_bars]:\n",
+ " for bar in bars:\n",
+ " width = bar.get_width()\n",
+ " x_center = bar.get_x() + bar.get_width() / 2\n",
+ " y_center = bar.get_y() + bar.get_height() / 2\n",
+ " ax.text(x_center, y_center, str(round(width,1)), ha='center', va='center', color='white')\n",
+ "\n",
+ "# Set the y-axis ticks and labels\n",
+ "ax.set_yticks(indices)\n",
+ "ax.set_yticklabels(countries)\n",
+ "\n",
+ "# Place the legend outside of the figure\n",
+ "ax.legend(loc='upper left', bbox_to_anchor=(0, 0), ncol=1);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "55b96024-87f8-4db4-bc97-b0484fe1fb2a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "countries_sorted = countries_summary.sort_values(by='km_neg05')\n",
+ "\n",
+ "countries = list(countries_sorted.Region)\n",
+ "neg_data = list(countries_sorted.km_neg05)\n",
+ "pos_data = list(countries_sorted.km_pos05)\n",
+ "non_data = list(countries_sorted.km_no)\n",
+ "\n",
+ "# Set the color scheme for the segments\n",
+ "colors = ['red', 'blue', 'grey']\n",
+ "\n",
+ "# Create a list of indices for the bars\n",
+ "indices = np.arange(len(countries))\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(8,8))\n",
+ "\n",
+ "# Plot the stacked horizontal bars\n",
+ "neg_bars = ax.barh(indices, neg_data, color=colors[0], label='Length (km) of shorelines retreated at more than 0.5 metre per year ')\n",
+ "pos_bars = ax.barh(indices, pos_data, left=neg_data, color=colors[1], label='Length (km) of shorelines advanced at more than 0.5 metre per year')\n",
+ "non_bars = ax.barh(indices, non_data, left=np.add(neg_data, pos_data), color=colors[2], label='Length (km) of shorelines without significant movement measured')\n",
+ "\n",
+ "# Remove spines and ticks\n",
+ "ax.spines['left'].set_visible(False)\n",
+ "ax.spines['right'].set_visible(False)\n",
+ "ax.spines['top'].set_visible(False)\n",
+ "ax.spines['bottom'].set_visible(False)\n",
+ "ax.set_xticks([])\n",
+ "ax.set_yticks([])\n",
+ "\n",
+ "# Add data value text to the right of each bar segment\n",
+ "for i in range(len(countries)):\n",
+ " total_width = neg_data[i] + pos_data[i] + non_data[i]\n",
+ " x_center = total_width\n",
+ " y_center = indices[i]\n",
+ " ax.text(x_center, y_center, f\"{round(neg_data[i],1)}, {round(pos_data[i],1)}, {round(non_data[i],1)} (km)\", ha='left', va='center')\n",
+ "\n",
+ "# Set the y-axis ticks and labels\n",
+ "ax.set_yticks(indices)\n",
+ "ax.set_yticklabels(countries)\n",
+ "\n",
+ "# Place the legend outside of the figure\n",
+ "ax.legend(loc='upper left', bbox_to_anchor=(0, 0), ncol=1);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7549f91d-ff70-4c66-8a8b-3ea9d65d5759",
+ "metadata": {},
+ "source": [
+ "## Combine coastline change information with gridded population data to estimate exposure\n",
+ "\n",
+ "\n",
+ "We will estimate the following population exposures:\n",
+ "* Population within 1 km of 2021 shoreline\n",
+ "* Population within 1 km of shoreline retreating at more than 0.5 meters per year\n",
+ "* Population within 1 km of shoreline retreating at more than 0.5 meters per year and within areas with an elevation of less than 5 meters\n",
+ "* Population within 1 km of shoreline retreating at more than 0.5 meters per year and within low lying flat areas (multi-scale valley bottom)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "2f5aeacb-e67a-4671-8339-5323792fee26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.environ['AWS_DEFAULT_REGION']=\"us-east-1\"\n",
+ "os.environ['AWS_S3_ENDPOINT']=\"s3.us-east-1.amazonaws.com\"\n",
+ "# Load population data\n",
+ "ds = rxr.open_rasterio('s3://dataforgood-fb-data/hrsl-cogs/hrsl_general/hrsl_general-latest.vrt', \n",
+ " chunks ={'x': 1000, 'y': 1000})\n",
+ "ds = ds.rename({'x':'longitude','y':'latitude'})\n",
+ "\n",
+ "# Load elevation data\n",
+ "dc=datacube.Datacube()\n",
+ "mrvbf = dc.load(product=\"dem_srtm_deriv\", measurements=[\"mrvbf\"], dask_chunks={'longitude':2000, 'latitude':2000}, \n",
+ " like=ds.geobox).mrvbf.squeeze()\n",
+ "elev = dc.load(product=\"dem_srtm\", measurements=[\"elevation\"], dask_chunks={'longitude':2000, 'latitude':2000}, \n",
+ " like=ds.geobox).elevation.squeeze()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "6a45f404-f2df-4013-aa80-ca7fede12fba",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Population within 1km of 2021 shoreline: 1095601\n"
+ ]
+ }
+ ],
+ "source": [
+ "buffered = rates_of_change.copy()\n",
+ "buffer=1000\n",
+ "buffered['geometry'] = buffered.to_crs('6933').buffer(buffer).to_crs(ds.geobox.crs)\n",
+ "coast_bbox = buffered.unary_union.bounds\n",
+ "\n",
+ "pop_raster = ds.isel(band=0).sel(longitude=slice(coast_bbox[0],coast_bbox[2]), latitude = slice(coast_bbox[3], coast_bbox[1]))\n",
+ " \n",
+ "shoreline_mask = xr_rasterize(gdf=buffered,\n",
+ " da=pop_raster,\n",
+ " transform=pop_raster.geobox.transform,\n",
+ " crs=pop_raster.geobox.crs)\n",
+ "\n",
+ "pop_nearshore = (pop_raster*shoreline_mask).sum().compute().data.round().astype(int)\n",
+ "print(\"Population within 1km of 2021 shoreline:\", pop_nearshore)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "8ff80b38-a35c-4b1f-a253-5b73b3cd6165",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Population within 1km of shoreline retreating at more than 0.5 meters per year: 733595\n"
+ ]
+ }
+ ],
+ "source": [
+ "buffered = rates_of_change[(rates_of_change.certainty=='good') & (rates_of_change.sig_time<0.01) & (rates_of_change.rate_time<-0.5)].copy()\n",
+ "buffer=1000\n",
+ "buffered['geometry'] = buffered.to_crs('6933').buffer(buffer).to_crs(ds.geobox.crs)\n",
+ "\n",
+ "hotspots_mask = xr_rasterize(gdf=buffered,\n",
+ " da=pop_raster,\n",
+ " transform=pop_raster.geobox.transform,\n",
+ " crs=pop_raster.geobox.crs)\n",
+ "\n",
+ "pop_nearerosion = (pop_raster*hotspots_mask).sum().compute().data.round().astype(int)\n",
+ "print(\"Population within 1km of shoreline retreating at more than 0.5 meters per year:\", pop_nearerosion)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "24fb6d6b-7331-4373-bf92-00cc73eeeb2f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Population within 1 km of shoreline retreating at more than 0.5 meters per year and within areas with an elevation of less than 5 meters: 239384\n"
+ ]
+ }
+ ],
+ "source": [
+ "os.environ['AWS_DEFAULT_REGION']=\"af-south-1\"\n",
+ "os.environ['AWS_S3_ENDPOINT']=\"s3.af-south-1.amazonaws.com\"\n",
+ "elev_mask = elev.sel(longitude=slice(coast_bbox[0],coast_bbox[2]), latitude = slice(coast_bbox[3], coast_bbox[1]))<5\n",
+ "\n",
+ "pop_lowelev = (pop_raster*hotspots_mask*elev_mask).sum().compute().data.round().astype(int)\n",
+ "print(\"Population within 1 km of shoreline retreating at more than 0.5 meters per year and within areas with an elevation of less than 5 meters:\", \n",
+ " pop_lowelev)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "abb40a3f-bf0c-4f51-9e3e-092c1cd72b55",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Population within 1 km of shoreline retreating at more than 0.5 meters per year and within low lying flat areas (multi-scale valley bottom): 176052\n"
+ ]
+ }
+ ],
+ "source": [
+ "mrvbf_mask = mrvbf.sel(longitude=slice(coast_bbox[0],coast_bbox[2]), latitude = slice(coast_bbox[3], coast_bbox[1]))==7\n",
+ "\n",
+ "pop_valley = (pop_raster*hotspots_mask*mrvbf_mask).sum().compute().data.round().astype(int)\n",
+ "print(\"Population within 1 km of shoreline retreating at more than 0.5 meters per year and within low lying flat areas (multi-scale valley bottom):\", \n",
+ " pop_valley)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "cd8090fd-c4bb-4a08-b24d-b0d21373f1d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(8,8))\n",
+ "\n",
+ "categories = [\"1\", \"2\", \"3\", \"4\"]\n",
+ "labels = [\"Population within 1km of 2021 shoreline\", \n",
+ " \"Population within 1km of shoreline retreating at more than 0.5 meters per year\",\n",
+ " \"Population within 1 km of shoreline retreating at more than 0.5 meters per year and within areas with an elevation of less than 5 meters\",\n",
+ " \"Population within 1 km of shoreline retreating at more than 0.5 meters per year and within low lying flat areas (multi-scale valley bottom)\"][::-1]\n",
+ "pops = [pop_nearshore, pop_nearerosion, pop_lowelev, pop_valley][::-1]\n",
+ "\n",
+ "# Create a horizontal bar chart\n",
+ "plt.barh(categories, pops)\n",
+ "\n",
+ "# Add category text above each bar\n",
+ "for i, value in enumerate(labels):\n",
+ " plt.text(0, i+0.5, str(value), ha='left', va='center')\n",
+ " plt.text(0, i, f\"{pops[i]:,}\", ha='left', va='center')\n",
+ "\n",
+ "\n",
+ "# Set labels and title\n",
+ "plt.ylabel('');\n",
+ "plt.xlabel('');\n",
+ "# Remove spines and ticks\n",
+ "ax.spines['left'].set_visible(False)\n",
+ "ax.spines['right'].set_visible(False)\n",
+ "ax.spines['top'].set_visible(False)\n",
+ "ax.spines['bottom'].set_visible(False)\n",
+ "ax.set_xticks([]);\n",
+ "ax.set_yticks([]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "44b84d78-867b-47f9-8118-1e435241938c",
+ "metadata": {},
+ "source": [
+ "### Export exposure hotspots\n",
+ "\n",
+ "We identify exposure hotspots as population within 1 km of shoreline retreating at more than 0.5 meters per year and within low lying flat areas.\n",
+ "Coastal erosion results in increased risk of flooding in such areas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "1f792311-a30e-499c-abb4-34cf01619449",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if pop_valley > 0:\n",
+ " hotspots_map = pop_raster*hotspots_mask*mrvbf_mask\n",
+ " exposure_vectors = xr_vectorize(hotspots_map, mask=hotspots_map > 0, attribute_col='population', crs='epsg:4326',\n",
+ " export_shp=f'{country}_hotspots.geojson')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "65d5bf7d-af05-4b37-bf60-ca1025721761",
+ "metadata": {},
+ "source": [
+ "The hotspots can be visualized below agained a high resolution basemap.\n",
+ "Alternatively, the exported vector can be downloaded and inspected in desktop GIS."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "04d4edab-dc7c-481c-b61a-9fdec44f2841",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
Make this Notebook Trusted to load map: File -> Trust Notebook
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "exposure_vectors.explore(\n",
+ " column='population',\n",
+ " tiles = \"https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}\", \n",
+ " attr ='Imagery @2022 Landsat/Copernicus, Map data @2022 Google',\n",
+ " popup=True,\n",
+ " cmap='viridis',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dfdb4be1-7d31-47c6-ab0c-97bd9a58a425",
+ "metadata": {},
+ "source": [
+ "***\n",
+ "\n",
+ "## Additional information\n",
+ "\n",
+ "**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). \n",
+ "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n",
+ "\n",
+ "**Contact:** If you need assistance, please post a question on the [Open Data Cube Slack channel](http://slack.opendatacube.org/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n",
+ "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n",
+ "\n",
+ "**Compatible datacube version:** "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "b0407e3b-09d3-4cf6-85ea-3f68899b6bd2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.8.13\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(datacube.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf539ca5-3529-421d-a2fb-35c6c32b1d47",
+ "metadata": {},
+ "source": [
+ "**Last Tested:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "034096ec-c61b-4238-923b-c695d23b156f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'2023-07-12'"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "datetime.today().strftime('%Y-%m-%d')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Use_cases/Coastlines_change_and_impact/Sea_level_rise_projections.ipynb b/Use_cases/Coastlines_change_and_impact/Sea_level_rise_projections.ipynb
new file mode 100644
index 000000000..ff2d93d9d
--- /dev/null
+++ b/Use_cases/Coastlines_change_and_impact/Sea_level_rise_projections.ipynb
@@ -0,0 +1,664 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "05f5a4a3-a58e-4bb6-831a-68d6dfda8066",
+ "metadata": {},
+ "source": [
+ "# Sea level projections\n",
+ "\n",
+ "* **Products used:** [DE Africa Coastlines](https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f24e28c-3ede-4ff0-9b78-de9962e11454",
+ "metadata": {},
+ "source": [
+ "## Background\n",
+ "\n",
+ "Sea level projections, along with coastline change measurements, contribute to our understanding of potential future changes in coastal areas. These projections consider various emission scenarios, providing a scientific foundation for policy decisions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5f7db118-12f1-44c2-b180-102dea4fd0b4",
+ "metadata": {},
+ "source": [
+ "## Description\n",
+ "\n",
+ "This notebook demonstrates how to access global sea level projections and visualise the data over an African country.\n",
+ "\n",
+ "Datasets from the following publications will be used:\n",
+ "* Kopp, R.E., DeConto, R.M., Bader, D.A., Hay, C.C., Horton, R.M., Kulp, S., Oppenheimer, M., Pollard, D. and Strauss, B.H. (2017), Evolving Understanding of Antarctic Ice-Sheet Physics and Ambiguity in Probabilistic Sea-Level Projections. Earth's Future, 5: 1217-1233. https://doi.org/10.1002/2017EF000663\n",
+ "* Muis, S., Verlaan, M., Winsemius, H. et al. A global reanalysis of storm surges and extreme sea levels. Nat Commun 7, 11969 (2016). https://doi.org/10.1038/ncomms11969"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5fd8f30c-1564-4ec2-8be7-db8ded878dc6",
+ "metadata": {},
+ "source": [
+ "## Getting started\n",
+ "\n",
+ "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf9e040e-35bd-4797-8e78-37a994f56c94",
+ "metadata": {},
+ "source": [
+ "### Load packages\n",
+ "Import Python packages that are used for the analysis."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "73c45b2f-f00a-4ff6-b237-8c19a1e32ba7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import os\n",
+ "import geopandas as gpd\n",
+ "import pandas as pd\n",
+ "import rioxarray as rxr\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import datacube\n",
+ "from deafrica_tools.spatial import xr_rasterize, xr_vectorize\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa205f03-9237-407f-bb84-5ebdc33a8bcc",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Retrieve coastlines rates of change over a country of interest\n",
+ "\n",
+ "The DE Africa Coastlines is a continental coastline monitoring service that can be accessed directly from AWS S3 bucket or OGC Web Services.\n",
+ "The [Coastlines dataset notebook](../../Datasets/Coastlines.ipynb) demonstrates loading DE Africa Coastlines over a small area through the Web Feature Service (WFS). \n",
+ "For country scale analysis in this notebook, we will access data directly from AWS S3.\n",
+ "\n",
+ "We will retrieve the `rates_of_change` layer from the geopackage hosted in AWS and load the data into a `geopandas.GeoDataFrame`.\n",
+ "To save memory, we will extract data over a specified country using the `country` attribute in the dataset.\n",
+ "This attribute was set based on intersection of a coastal location with the exclusive economic zone boundaries defined in https://deafrica-input-datasets.s3.af-south-1.amazonaws.com/deafrica-coastlines/countries_eez_deafrica.geojson\n",
+ "Therefore, we have to pick a country name from the names used in this boundary file. \n",
+ "\n",
+ "> If a different administrative boundary definition is desired, the entire continental dataset can be loaded and a spatial join with a user defined boundary geometry can be run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0936c9ff-0d14-473b-ad1c-89e6bb21e46b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Nigeria', 'Seychelles', 'Gabon', 'Sao Tome and Principe', 'Senegal', 'Guinea-Bissau', 'Mauritania', 'Sudan', 'Togo', 'Cape Verde', 'Kenya', 'Angola', 'Federal Republic of Somalia', 'Algeria', 'South Africa', 'Djibouti', 'Ivory Coast', 'Tanzania', 'Sierra Leone', 'Tunisia', 'Republic of Mauritius', 'Eritrea', 'Morocco', 'Comores', 'Guinea', 'Mozambique', 'Liberia', 'Republic of the Congo', 'Benin', 'Namibia', 'Egypt', 'Madagascar', 'Gambia', 'Ghana', 'Libya', 'Cameroon', 'Democratic Republic of the Congo', 'Equatorial Guinea']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# list of country names used\n",
+ "countries_gdf = gpd.read_file(\"https://deafrica-input-datasets.s3.af-south-1.amazonaws.com/deafrica-coastlines/countries_eez_deafrica.geojson\")\n",
+ "countries = list(countries_gdf[\"TERRITORY1\"].values)\n",
+ "print(countries)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "704e22ea-97a4-447a-a4b3-92762a3b1242",
+ "metadata": {},
+ "source": [
+ "As an example, `Nigeria` will be used. For a different country, the name can be copied from the list above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "cd9dde63-6c76-4692-a812-a8aa16dd5382",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "country = \"Nigeria\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1e77ea5-1b42-4157-af77-8925b9f95b46",
+ "metadata": {},
+ "source": [
+ "The following step takes a few minutes to run because the continental dataset is retrieved first before a country subset is extracted and saved in the `coaslines` variable. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "615c044d-5efe-45bc-925c-402c5f87bb7b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 18min 10s, sys: 11.3 s, total: 18min 22s\n",
+ "Wall time: 20min 30s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "rates_of_change = gpd.read_file(\"https://deafrica-services.s3.af-south-1.amazonaws.com/coastlines/v0.4.0/deafricacoastlines_v0.4.0.gpkg\", \n",
+ " layer=\"rates_of_change\").query(f\"country == '{country}'\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bdec3ad-8559-48b5-be28-18155ca2a38a",
+ "metadata": {},
+ "source": [
+ "The Coastlines `rates_of_change` dataset includes a list of attributes, for which detailed explanations can be found in the relevant section in the [User Guide](https://docs.digitalearthafrica.org/en/latest/data_specs/Coastlines_specs.html#Rate-of-Change-Statistics)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3a660b30-e695-42cc-98e4-84e9e29e9360",
+ "metadata": {},
+ "outputs": [
+ {
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+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rates_of_change.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9c2fdf9d-894c-41c2-bd42-8a44a4a6aa10",
+ "metadata": {},
+ "source": [
+ "## Sea level projection\n",
+ "\n",
+ "We will first examine the gridded sea level projections provided as supporting information in Kopp et al. 2017.\n",
+ "\n",
+ "This dataset provides global-mean sea-level (GMSL) and relative sea-level (RSL) projections at decadal intervals from 2010 to 2300, based on method developed in [Kopp et al. (2014)](https://doi.org/10.1002/2014EF000239).\n",
+ "\n",
+ "Three Representative Concentration Pathway (RCP) scenarios were considered, RCP2.6, RCP4.5 and PCP8.5, resprenting low to high greenhouse gas emission pathways.\n",
+ "\n",
+ "Relative sea level rise in centimeters are provided for a number of locations and along the gridded global coastlines at 1, 5,16.7, 50, 83.3, 95, 99, 99.5 and 99.9 percentiles of simulation frequency distributions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e0c8ea4a-e5f7-48ea-b816-8aea644b00fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: 'slr_data/Kopp2017/eft2271-sup-0006-2017ef000663-ds05.tsv'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m slr \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mslr_data/Kopp2017/eft2271-sup-0006-2017ef000663-ds05.tsv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;130;43;01m\\t\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskiprows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 900\u001b[0m dialect,\n\u001b[1;32m 901\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 908\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 909\u001b[0m )\n\u001b[1;32m 910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 580\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+ "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1404\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1659\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1660\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1662\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1663\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1664\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1665\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1666\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1667\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1668\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1669\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1670\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+ "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 858\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 859\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 867\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 868\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'slr_data/Kopp2017/eft2271-sup-0006-2017ef000663-ds05.tsv'"
+ ]
+ }
+ ],
+ "source": [
+ "slr = pd.read_csv('slr_data/Kopp2017/eft2271-sup-0006-2017ef000663-ds05.tsv', sep='\\t', skiprows=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf7946f-ea55-4aff-be40-8413c366806c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "slr.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "afd4f414-7175-4c11-be9c-91f012c40174",
+ "metadata": {},
+ "source": [
+ "We extract the data for gridded coastal locations and convert it to a `geodataframe` that can be visualised on a map.\n",
+ "\n",
+ "> The longitude range is updated from 0 to 360 to -180 to 180 so the map will be centered around Africa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd9d4e59-98a3-4097-9ef5-2bb9276bf700",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid_slr = slr[slr[\"Site Name\"].str.startswith(\"grid_\")].copy()\n",
+ "grid_slr[\"Latitude\"] = grid_slr[\"Site Name\"].str.split(\"_\").str[1].astype(float)\n",
+ "grid_slr[\"Longitude\"] = grid_slr[\"Site Name\"].str.split(\"_\").str[2].astype(float)\n",
+ "grid_slr[\"Longitude\"] = grid_slr[\"Longitude\"].apply(lambda x: x - 360 if x > 180 else x)\n",
+ "grid_slr_gdf = gpd.GeoDataFrame(grid_slr, geometry=gpd.points_from_xy(grid_slr['Longitude'], grid_slr['Latitude']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7edfad57-dcc0-4f1c-b5b6-ba5f41495f7c",
+ "metadata": {},
+ "source": [
+ "We will examine 50th percentile RSL in the low and high emission scenarios for 2100. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f4a1f806-2f24-4666-bb5f-1a0d63d5c886",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid_slr_gdf[(grid_slr_gdf.Scenario==\"rcp26\")& (grid_slr_gdf.Year==2100)].explore(\n",
+ " column = \" 0.5\",\n",
+ " tiles = \"https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}\", \n",
+ " attr ='Imagery @2022 Landsat/Copernicus, Map data @2022 Google',\n",
+ " popup=True,\n",
+ " cmap='viridis',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c158e469-1ee1-4ceb-bc07-2ef42f11c9fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "grid_slr_gdf[(grid_slr_gdf.Scenario==\"rcp85\")& (grid_slr_gdf.Year==2100)].explore(\n",
+ " column = \" 0.5\",\n",
+ " #tiles = \"https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}\", \n",
+ " #attr ='Imagery @2022 Landsat/Copernicus, Map data @2022 Google',\n",
+ " popup=True,\n",
+ " cmap='viridis',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d1c73a8-b084-4881-830e-f8085d8d6da6",
+ "metadata": {},
+ "source": [
+ "## Extreme sea levels\n",
+ "\n",
+ "GTSR (Global Tide and Surge Reanalysis) ([Muis et al. 2016](https://doi.org/10.1038/ncomms11969)) is the first global reanalysis of storm surges and extreme sea levels based on hydrodynamic modelling. GTSR covers the entire world's coastline and provides estimates of extreme sea levels with a 1-in-100 year return period.\n",
+ "\n",
+ "The data can be dowload from Muis, S (2016): Global Tide and Surge Reanalysis (GTSR). Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:aa4a6ad5-e92c-468e-841b-de07f7133786"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "72e38240-6260-4659-b26b-619deacd8dfc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "esl = gpd.read_file('slr_data/Muis2016/data.zip')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c54082f2-e621-41ea-a160-d046b32ae997",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "esl.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cada980f-3d29-411d-ae74-f8fed5f5310e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "esl.explore(\n",
+ " column='rp00100',\n",
+ "# tiles=\"https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}\",\n",
+ "# attr='Imagery @2022 Landsat/Copernicus, Map data @2022 Google',\n",
+ " popup=True,\n",
+ " cmap='viridis',\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9946c0f1-f1b5-4d25-ab1e-bf9374d7a8ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gadm_level1 = gpd.read_file(\"data/gadm41_NGA_1.json.zip\")\n",
+ "gadm_level1 = gadm_level1.to_crs(rates_of_change.crs)\n",
+ "\n",
+ "slr_country= slr.sjoin_nearest(gadm_level1.to_crs(slr.crs)[[\"GID_0\", \"NAME_0\", \"GID_1\", \"NAME_1\",\"geometry\"]], how=\"inner\", max_distance=0.01)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7636624c-c4aa-46d8-824f-c4ecb8827e89",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "slr_country.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f213da9-d97e-47be-a319-db47dcfe8867",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "slr.explore(\n",
+ " column='rp00100',\n",
+ " tiles=\"https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}\",\n",
+ " attr='Imagery @2022 Landsat/Copernicus, Map data @2022 Google',\n",
+ " popup=True,\n",
+ " cmap='viridis',\n",
+ " #style_kwds=dict(color='red', fillOpacity=0, weight=3)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2fd3863a-b2c5-4247-bcb5-ffc055d2cf7f",
+ "metadata": {},
+ "source": [
+ "***\n",
+ "\n",
+ "## Additional information\n",
+ "\n",
+ "**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). \n",
+ "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n",
+ "\n",
+ "**Contact:** If you need assistance, please post a question on the [Open Data Cube Slack channel](http://slack.opendatacube.org/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n",
+ "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n",
+ "\n",
+ "**Compatible datacube version:** "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "69ae4e62-2570-46eb-a863-65111fd6c285",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(datacube.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "de800bf0-ac86-4b1c-abe3-189b81d2e35f",
+ "metadata": {},
+ "source": [
+ "**Last Tested:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b507b240-c3d2-4811-a90b-9cd71abb1b0a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from datetime import datetime\n",
+ "datetime.today().strftime('%Y-%m-%d')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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