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Data/__init__.py

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+import pandas as pd
+import os
+
+# We make relevant datasets contained in this folder available across the project, for easier integration and plotting.
+# The base directory points to the folder containing this file:
+base_dir = os.path.dirname(__file__)
+
+# Make the ISO code lookup table available across the project. Also group countries by region.
+lookup_table = pd.read_csv(os.path.join(base_dir, "Iso_code_lookup.csv"))
+countries_by_region = pd.read_csv(os.path.join(base_dir, "countries_by_region.csv"))
+countries_by_region = countries_by_region.groupby('Region').agg(list).to_dict()['Country ISO']
+countries_by_region['Eastern Asia'].append('TWN')
+countries_by_region['Middle East'] = countries_by_region.pop('Western Asia')
+countries_by_region['South Asia'] = countries_by_region.pop('Southern Asia')
+
+# Make the world shapefile available for plotting
+import geopandas as gpd
+world = gpd.read_file(os.path.join(base_dir, "world_shapefile/ne_50m_admin_0_countries.shp"))
+world.to_crs("EPSG:3857", inplace=True) # Mercator projection by default
+
+# Make the coordinates available. These are regular latitude/longitude coordinates
+coordinates = pd.read_csv(os.path.join(base_dir, "Coordinates.csv"))[['Alpha-3 code', 'Latitude', 'Longitude']].set_index('Alpha-3 code').to_dict()
+coordinates = dict((k, (coordinates['Latitude'][k], coordinates['Longitude'][k])) for k in coordinates['Latitude'].keys())
+
+# Load the various comparison datasets into a dictionary for easy plotting, assigning consistent colours
+# and markers for each.
+from Plots.colors import colors
+import xarray as xr
+flow_dsets = {
+    "Quantmig": dict(
+        data=xr.open_dataset(os.path.join(base_dir, "Flow_data/QuantMig_data/Quantmig_flows.nc")),
+        central='flow_50%', lower='flow_2.5%', upper='flow_97.5%',
+        primary_color=colors['c_orange'], secondary_color=colors['c_yellow']
+    ),
+    "Statistics Sweden": dict(
+        data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/National_Statistics/SWE_flows.nc")),
+        primary_color=colors['c_red'], secondary_color=colors['c_pink'], marker='v', s=10,
+    ),
+    "Statistics Finland": dict(
+        data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/National_Statistics/FIN_flows.nc")),
+        primary_color=colors['c_lightblue'], secondary_color=colors['c_darkblue'], marker='s', s=10
+    ),
+    "StatNZ": dict(
+        data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/National_Statistics/NZL_flows.nc")),
+        primary_color=colors['c_darkgreen'], secondary_color=colors['c_lightgreen'], marker='^', s=10
+    ),
+    "Facebook data": dict(
+        data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/Facebook/facebook_flows.nc")).where(lambda x: x>=25),
+        primary_color=colors['c_purple'], secondary_color='#E1C6EC', ec=colors['c_darkgrey'], marker='o', s=10, lw=0.5
+    )
+}
+
+# UN WPP data
+population = 1e3 * xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc"))['Total Population, as of 1 January (thousands)']
+death_rate = 1e-3 * xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc"))['Crude Death Rate (deaths per 1,000 population)']
+WPP_net_migration = 1e3 * xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc"))['Net Number of Migrants (thousands)']
+NatStat_net_migration = xr.open_dataarray(os.path.join(base_dir, "Net_migration/National_statistics.nc"))
+WPP_data = xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc"))
+
+# Calculate the fraction of population alive at start of 1990 in each destination country still alive at start of year
+import numpy as np
+gamma = (1-death_rate.sel({"Year": range(1989, 2024)})).assign_coords({"Year": np.arange(1990, 2025, 1)}).rename({
+    "Country ISO": "Destination ISO"})
+gamma.loc[{"Year": 1990}] = 1.0
+gamma = gamma.cumprod('Year')
+
+# Stock data
+stock_data = xr.load_dataset(os.path.join(base_dir, "UN_stock_data/stock_data.nc"))