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@@ -6,11 +6,11 @@
- 2026-01-02T17:38:48.551138
+ 2026-05-26T13:04:52.386251
image/svg+xml
- Matplotlib v3.10.1, https://matplotlib.org/
+ Matplotlib v3.10.9, https://matplotlib.org/
@@ -41,4996 +41,4968 @@ z
-
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- $1
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+ $1000
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+ Income or consumption (month)
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- Sweden (1920)
+
+ Sweden (1920)
diff --git a/PabloArriagada/distribution_generator/Sweden_1920_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30_lognormal.svg b/PabloArriagada/distribution_generator/Sweden_1920_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30_lognormal.svg
new file mode 100644
index 00000000..3b89748c
--- /dev/null
+++ b/PabloArriagada/distribution_generator/Sweden_1920_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30_lognormal.svg
@@ -0,0 +1,5021 @@
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diff --git a/PabloArriagada/distribution_generator/Sweden_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30.svg b/PabloArriagada/distribution_generator/Sweden_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30.svg
deleted file mode 100644
index 1d7a5a82..00000000
--- a/PabloArriagada/distribution_generator/Sweden_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30.svg
+++ /dev/null
@@ -1,3049 +0,0 @@
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diff --git a/PabloArriagada/distribution_generator/Sweden_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg b/PabloArriagada/distribution_generator/Sweden_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
deleted file mode 100644
index 9a19b73a..00000000
--- a/PabloArriagada/distribution_generator/Sweden_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
+++ /dev/null
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diff --git a/PabloArriagada/distribution_generator/Sweden_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30.svg b/PabloArriagada/distribution_generator/Sweden_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30.svg
new file mode 100644
index 00000000..5caac7f7
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+++ b/PabloArriagada/distribution_generator/Sweden_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30.svg
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diff --git a/PabloArriagada/distribution_generator/Sweden_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30_lognormal.svg b/PabloArriagada/distribution_generator/Sweden_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30_lognormal.svg
new file mode 100644
index 00000000..2854bb97
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+++ b/PabloArriagada/distribution_generator/Sweden_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_30_lognormal.svg
@@ -0,0 +1,3046 @@
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diff --git a/PabloArriagada/distribution_generator/Sweden_per_year_row_log_True.svg b/PabloArriagada/distribution_generator/Sweden_per_year_row_log_True.svg
new file mode 100644
index 00000000..005ece11
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+++ b/PabloArriagada/distribution_generator/Sweden_per_year_row_log_True.svg
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diff --git a/PabloArriagada/distribution_generator/Sweden_per_year_row_log_True_lognormal.svg b/PabloArriagada/distribution_generator/Sweden_per_year_row_log_True_lognormal.svg
new file mode 100644
index 00000000..75d65921
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+++ b/PabloArriagada/distribution_generator/Sweden_per_year_row_log_True_lognormal.svg
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diff --git a/PabloArriagada/distribution_generator/United States_Burundi_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg b/PabloArriagada/distribution_generator/United States_Burundi_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
deleted file mode 100644
index 0250c008..00000000
--- a/PabloArriagada/distribution_generator/United States_Burundi_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
+++ /dev/null
@@ -1,1224 +0,0 @@
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diff --git a/PabloArriagada/distribution_generator/United States_Burundi_2025_survey_True_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg b/PabloArriagada/distribution_generator/United States_Burundi_2025_survey_True_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
deleted file mode 100644
index 54cc6e6e..00000000
--- a/PabloArriagada/distribution_generator/United States_Burundi_2025_survey_True_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
+++ /dev/null
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diff --git a/PabloArriagada/distribution_generator/United States_Burundi_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg b/PabloArriagada/distribution_generator/United States_Burundi_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
new file mode 100644
index 00000000..03b39cf1
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+++ b/PabloArriagada/distribution_generator/United States_Burundi_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
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diff --git a/PabloArriagada/distribution_generator/United States_Burundi_2026_survey_True_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg b/PabloArriagada/distribution_generator/United States_Burundi_2026_survey_True_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
new file mode 100644
index 00000000..87b923b3
--- /dev/null
+++ b/PabloArriagada/distribution_generator/United States_Burundi_2026_survey_True_log_True_multiple_layer_common_norm_False_multiple_areas_none.svg
@@ -0,0 +1,1178 @@
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diff --git a/PabloArriagada/distribution_generator/World_2025_survey_False_log_False_fill_True_pen.svg b/PabloArriagada/distribution_generator/World_2025_survey_False_log_False_fill_True_pen.svg
deleted file mode 100644
index a99eaa06..00000000
--- a/PabloArriagada/distribution_generator/World_2025_survey_False_log_False_fill_True_pen.svg
+++ /dev/null
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diff --git a/PabloArriagada/distribution_generator/World_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_30.svg b/PabloArriagada/distribution_generator/World_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_30.svg
deleted file mode 100644
index 62274476..00000000
--- a/PabloArriagada/distribution_generator/World_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_30.svg
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diff --git a/PabloArriagada/distribution_generator/World_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_10_30_100.svg b/PabloArriagada/distribution_generator/World_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_10_30_100.svg
deleted file mode 100644
index 0aa9428f..00000000
--- a/PabloArriagada/distribution_generator/World_2025_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_10_30_100.svg
+++ /dev/null
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diff --git a/PabloArriagada/distribution_generator/World_2026_survey_False_log_False_fill_True_pen.svg b/PabloArriagada/distribution_generator/World_2026_survey_False_log_False_fill_True_pen.svg
new file mode 100644
index 00000000..084eceb7
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+++ b/PabloArriagada/distribution_generator/World_2026_survey_False_log_False_fill_True_pen.svg
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diff --git a/PabloArriagada/distribution_generator/World_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_30.svg b/PabloArriagada/distribution_generator/World_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_30.svg
new file mode 100644
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+++ b/PabloArriagada/distribution_generator/World_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_30.svg
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diff --git a/PabloArriagada/distribution_generator/World_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_10_30_100.svg b/PabloArriagada/distribution_generator/World_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_10_30_100.svg
new file mode 100644
index 00000000..024457ea
--- /dev/null
+++ b/PabloArriagada/distribution_generator/World_2026_survey_False_log_True_multiple_layer_common_norm_False_multiple_areas_3_10_30_100.svg
@@ -0,0 +1,6858 @@
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diff --git a/PabloArriagada/distribution_generator/all_countries_2025_survey_False_log_True_multiple_stack_common_norm_True_multiple_areas_none.svg b/PabloArriagada/distribution_generator/all_countries_2025_survey_False_log_True_multiple_stack_common_norm_True_multiple_areas_none.svg
deleted file mode 100644
index e7abf0ed..00000000
--- a/PabloArriagada/distribution_generator/all_countries_2025_survey_False_log_True_multiple_stack_common_norm_True_multiple_areas_none.svg
+++ /dev/null
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diff --git a/PabloArriagada/distribution_generator/all_countries_2026_survey_False_log_True_multiple_stack_common_norm_True_multiple_areas_none.svg b/PabloArriagada/distribution_generator/all_countries_2026_survey_False_log_True_multiple_stack_common_norm_True_multiple_areas_none.svg
new file mode 100644
index 00000000..5882c60c
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+++ b/PabloArriagada/distribution_generator/all_countries_2026_survey_False_log_True_multiple_stack_common_norm_True_multiple_areas_none.svg
@@ -0,0 +1,3333 @@
+
+
+
diff --git a/PabloArriagada/distribution_generator/distribution_generator.py b/PabloArriagada/distribution_generator/distribution_generator.py
index d1c86e44..d52e3562 100644
--- a/PabloArriagada/distribution_generator/distribution_generator.py
+++ b/PabloArriagada/distribution_generator/distribution_generator.py
@@ -1,32 +1,39 @@
+import textwrap
from pathlib import Path
-from typing import List, Literal
+from typing import List, Literal, cast
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
-from scipy.optimize import minimize
-from scipy.stats import norm
+from matplotlib.offsetbox import AnnotationBbox, TextArea, VPacker
PARENT_DIR = Path(__file__).parent.absolute()
# Define International Poverty Line
INTERNATIONAL_POVERTY_LINE = 3
-# Define latest year
-LATEST_YEAR = 2025
-
# Define poverty line for high-income countries
POVERTY_LINE_HIGH_INCOME = 30
+# PIP PPP version used throughout (filter applied when reading the dimensional tables).
+PPP_VERSION = 2021
+
+# Define latest year
+LATEST_YEAR = 2026
+
# Define width and height of the plot
WIDTH = 1500
HEIGHT = 750
-# For Pen Parade
+# For Pen Parade — roughly 1:1
WIDTH_PEN = 1000
HEIGHT_PEN = 1000
+# Color used for reference lines (IPL, World median, $900/$500 lines, country medians, etc.)
+REFERENCE_LINE_COLOR = "#6c7a89"
+
+
# Define gridsize for when I need higher resolution
GRIDSIZE_HIGHER_RESOLUTION = 1000
@@ -75,37 +82,89 @@
# Set correction factor of the median to show the label in the plot
CORRECTION_FACTOR_LABEL = 1
-# Define version of PIP and 1000 bins data
-PIP_VERSION = "2025-10-09"
-THOUSAND_BINS_VERSION = "2025-10-13"
+# Version of harmonized national poverty lines (only stale garden dep left; everything else uses external/latest).
NATIONAL_LINES_VERSION = "2025-06-11"
-THOUSAND_BINS_HISTORICAL_VERSION = "2025-10-23"
# Define URLs
+EXTERNAL_BASE = "http://catalog.ourworldindata.org/external/poverty_inequality/latest"
-THOUSAND_BINS_URL = f"http://catalog.ourworldindata.org/garden/wb/{THOUSAND_BINS_VERSION}/thousand_bins_distribution/thousand_bins_distribution.feather?nocache"
-PERCENTILES_URL = f"http://catalog.ourworldindata.org/garden/wb/{PIP_VERSION}/world_bank_pip_legacy/percentiles_income_consumption_2021.feather?nocache"
-THOUSAND_BINS_HISTORICAL_URL = f"http://catalog.ourworldindata.org/garden/poverty_inequality/{THOUSAND_BINS_HISTORICAL_VERSION}/historical_poverty/thousand_bins_interpolated_ginis.feather?nocache"
-THOUSAND_BINS_HISTORICAL__ALL_LOGNORMAL_URL = f"http://catalog.ourworldindata.org/garden/poverty_inequality/{THOUSAND_BINS_HISTORICAL_VERSION}/historical_poverty/thousand_bins_interpolated_ginis_all_lognormal.feather?nocache"
+THOUSAND_BINS_URL = f"{EXTERNAL_BASE}/thousand_bins_distribution/thousand_bins_distribution.feather?nocache"
+THOUSAND_BINS_HISTORICAL_URL = f"{EXTERNAL_BASE}/historical_poverty/thousand_bins_interpolated_ginis.feather?nocache"
+THOUSAND_BINS_HISTORICAL__ALL_LOGNORMAL_URL = f"{EXTERNAL_BASE}/historical_poverty/thousand_bins_interpolated_ginis_all_lognormal.feather?nocache"
+PERCENTILES_URL = f"{EXTERNAL_BASE}/world_bank_pip/percentiles.feather?nocache"
+COMPLETE_SERIES_URL = f"{EXTERNAL_BASE}/world_bank_pip/complete_series.feather?nocache"
-MAIN_INDICATORS_URL = f"http://catalog.ourworldindata.org/garden/wb/{PIP_VERSION}/world_bank_pip_legacy/income_consumption_2021.feather?nocache"
NATIONAL_LINES_URL = f"http://catalog.ourworldindata.org/garden/wb/{NATIONAL_LINES_VERSION}/harmonized_national_poverty_lines/harmonized_national_poverty_lines.feather?nocache"
def run() -> None:
- # Read data
df_thousand_bins = pd.read_feather(THOUSAND_BINS_URL)
- df_percentiles = pd.read_feather(PERCENTILES_URL)
df_thousand_bins_historical = pd.read_feather(THOUSAND_BINS_HISTORICAL_URL)
df_thousand_bins_historical_all_lognormal = pd.read_feather(
THOUSAND_BINS_HISTORICAL__ALL_LOGNORMAL_URL
)
-
- df_main_indicators = pd.read_feather(MAIN_INDICATORS_URL)
df_national_lines = pd.read_feather(NATIONAL_LINES_URL)
- # in df_national_lines, replace the value of "harmonized_national_poverty_line" for United States with 27.10
+ # World Bank PIP dimensional tables → flat shapes the plotting code expects.
+ # Percentiles: legacy table was filtered to ppp_version=2021; replicate by filtering here.
+ df_percentiles = pd.read_feather(PERCENTILES_URL)
+ df_percentiles = df_percentiles[
+ df_percentiles["ppp_version"] == PPP_VERSION
+ ].reset_index(drop=True)
+
+ # Main indicators (used only for World aggregates): rebuild a flat per-(country, year)
+ # frame from complete_series by selecting the right slice for each column family.
+ df_complete = pd.read_feather(COMPLETE_SERIES_URL)
+ base = (df_complete["ppp_version"] == PPP_VERSION) & (
+ df_complete["welfare_type"] == "income or consumption"
+ )
+ df_summary = df_complete[
+ base & df_complete["decile"].isna() & df_complete["poverty_line"].isna()
+ ][["country", "year", "mean", "median", "top1_thr"]]
+ df_decile9 = df_complete[
+ base & (df_complete["decile"] == "9") & df_complete["poverty_line"].isna()
+ ][["country", "year", "thr"]].rename(columns={"thr": "decile9_thr"})
+ df_pov30 = df_complete[
+ base & df_complete["decile"].isna() & (df_complete["poverty_line"] == "3000")
+ ][["country", "year", "headcount_ratio"]].rename(
+ columns={"headcount_ratio": "headcount_ratio_3000"}
+ )
+ # PIP stores poverty_line in cents/day, so the IPL ($3/day) is "300".
+ df_pov_ipl = df_complete[
+ base & df_complete["decile"].isna() & (df_complete["poverty_line"] == "300")
+ ][["country", "year", "headcount_ratio"]].rename(
+ columns={"headcount_ratio": "headcount_ratio_300"}
+ )
+ df_main_indicators = (
+ df_summary.merge(df_decile9, on=["country", "year"], how="left")
+ .merge(df_pov30, on=["country", "year"], how="left")
+ .merge(df_pov_ipl, on=["country", "year"], how="left")
+ )
+ # Add country-level rows for a handful of reference countries. PIP stores each country's
+ # smoothed combined series under either welfare_type="income" or "consumption"
+ # (whichever the country reports primarily); the combined "income or consumption" label
+ # is only used for the World aggregate. Prefer consumption when both are available
+ # (PIP's preferred welfare measure for most countries), fall back to income otherwise.
+ country_extras = df_complete[
+ (df_complete["ppp_version"] == PPP_VERSION)
+ & df_complete["welfare_type"].isin(["consumption", "income"])
+ & df_complete["decile"].isna()
+ & df_complete["poverty_line"].isna()
+ & df_complete["country"].isin(
+ ["Norway", "United States", "Sweden", "United Kingdom"]
+ )
+ ][["country", "year", "mean", "median", "top1_thr", "welfare_type"]]
+ # "consumption" sorts before "income" alphabetically, so keep="first" prefers consumption.
+ country_extras = (
+ country_extras.sort_values(["country", "year", "welfare_type"])
+ .drop_duplicates(subset=["country", "year"], keep="first")
+ .drop(columns="welfare_type")
+ )
+ df_main_indicators = pd.concat(
+ [df_main_indicators, country_extras], ignore_index=True
+ )
+
df_national_lines.loc[
(df_national_lines["country"] == "United States"),
"harmonized_national_poverty_line",
@@ -136,7 +195,6 @@ def run() -> None:
period="day",
survey_based=False,
add_ipl=None,
- add_world_mean=None,
add_world_median=None,
add_multiple_lines_day=lines,
gridsize=GRIDSIZE_HIGHER_RESOLUTION,
@@ -160,7 +218,6 @@ def run() -> None:
common_norm=False,
period="day",
add_ipl="area",
- add_world_mean=None,
add_world_median=None,
add_multiple_lines_day=None,
gridsize=GRIDSIZE_HIGHER_RESOLUTION,
@@ -189,7 +246,6 @@ def run() -> None:
period="day",
survey_based=False,
add_ipl="area",
- add_world_mean="area",
add_world_median="area",
)
@@ -207,7 +263,7 @@ def run() -> None:
years=[LATEST_YEAR],
legend=True,
common_norm=False,
- period="day",
+ period="month",
survey_based=False,
width=1500,
height=400,
@@ -224,7 +280,7 @@ def run() -> None:
years=[LATEST_YEAR],
legend=False,
common_norm=False,
- period="day",
+ period="month",
survey_based=True,
preferred_reporting_level="national",
preferred_welfare_type="income",
@@ -248,10 +304,9 @@ def run() -> None:
period="day",
survey_based=False,
add_ipl="line",
- add_world_mean="line",
- add_world_median="line",
add_national_lines=True,
df_national_lines=df_national_lines,
+ share_y_axis=False,
)
distributional_plots_per_row(
@@ -272,10 +327,9 @@ def run() -> None:
preferred_reporting_level="national",
preferred_welfare_type="income",
add_ipl="line",
- add_world_mean=None,
- add_world_median=None,
add_national_lines=True,
df_national_lines=df_national_lines,
+ share_y_axis=False,
)
# Stacked distributions with common density estimate
@@ -311,6 +365,7 @@ def run() -> None:
add_lines=True,
period="month",
survey_based=False,
+ cut_percentile=95,
)
pen_parade(
@@ -332,7 +387,8 @@ def run() -> None:
preferred_welfare_type="income",
)
- # Historical data
+ # Historical data — Option A: three separate SVGs (1820, 1920, 2026) that all
+ # share a y-limit so peak heights are visually comparable across years.
distributional_plots(
data=df_thousand_bins_historical,
df_main_indicators=None,
@@ -347,17 +403,48 @@ def run() -> None:
legend=False,
common_norm=False,
gridsize=GRIDSIZE_HIGHER_RESOLUTION,
- period="day",
+ period="month",
survey_based=False,
add_ipl=None,
- add_world_mean=None,
add_world_median=None,
add_multiple_lines_day=[3, 30],
- x_axis_range=(0.05, 300),
width=1150,
height=220,
+ share_y_axis=True,
+ share_x_axis=True,
)
+ # Historical data — Option B: single SVG with all three years stacked,
+ # sharing both x and y axes (via row_by="year"). Filled KDE with shaded
+ # regions under each curve at the $3/day IPL and the $30/day high-income line.
+ distributional_plots_per_row(
+ data=df_thousand_bins_historical,
+ df_main_indicators=None,
+ x="avg",
+ weights="pop",
+ log_scale=True,
+ multiple="layer",
+ hue="country",
+ hue_order=["Sweden"],
+ years=[1820, 1920, LATEST_YEAR],
+ fill=True,
+ common_norm=False,
+ gridsize=GRIDSIZE_HIGHER_RESOLUTION,
+ period="month",
+ survey_based=False,
+ add_ipl="line",
+ add_high_income_pl="line",
+ width=1150,
+ height=220,
+ row_by="year",
+ add_multiple_lines_day=[3, 30],
+ )
+
+ # Same years from the all-lognormal companion dataset. Only the 2026 row
+ # actually differs from df_thousand_bins_historical; 1820 and 1920 are
+ # byte-identical between the two datasets but we render them anyway so the
+ # _lognormal SVGs are a complete drop-in set that shares its own y-max and
+ # x-range pre-pass (independent of the mix dataset).
distributional_plots(
data=df_thousand_bins_historical_all_lognormal,
df_main_indicators=None,
@@ -367,172 +454,46 @@ def run() -> None:
multiple="layer",
hue="country",
hue_order=["Sweden"],
- years=[LATEST_YEAR],
+ years=[1820, 1920, LATEST_YEAR],
fill=False,
legend=False,
common_norm=False,
gridsize=GRIDSIZE_HIGHER_RESOLUTION,
- period="day",
+ period="month",
survey_based=False,
add_ipl=None,
- add_world_mean=None,
add_world_median=None,
add_multiple_lines_day=[3, 30],
- x_axis_range=(0.05, 300),
width=1150,
height=220,
+ share_y_axis=True,
+ share_x_axis=True,
+ filename_suffix="_lognormal",
)
- # # For synthetic data
-
- # (
- # synthetic_data_uk_gapminder,
- # generated_mean_uk_gapminder,
- # generated_gini_uk_gapminder,
- # target_mean_uk_gapminder,
- # target_gini_uk_gapminder,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="United Kingdom-Gapminder",
- # year=1820,
- # target_mean=3784.226727,
- # target_gini=0.5845,
- # )
-
- # (
- # synthetic_data_uk_moatsos,
- # generated_mean_uk_moatsos,
- # generated_gini_uk_moatsos,
- # target_mean_uk_moatsos,
- # target_gini_uk_moatsos,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="United Kingdom-Moatsos",
- # year=1820,
- # target_mean=1250.072,
- # target_gini=0.5927,
- # )
- # (
- # synthetic_data_uk_mpd_gapminder,
- # generated_mean_uk_mpd_gapminder,
- # generated_gini_uk_mpd_gapminder,
- # target_mean_uk_mpd_gapminder,
- # target_gini_uk_mpd_gapminder,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="United Kingdom-MPD-Gapminder",
- # year=1820,
- # target_mean=3306,
- # target_gini=0.5845,
- # )
- # (
- # synthetic_data_uk_mpd_moatsos,
- # generated_mean_uk_mpd_moatsos,
- # generated_gini_uk_mpd_moatsos,
- # target_mean_uk_mpd_moatsos,
- # target_gini_uk_mpd_moatsos,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="United Kingdom-MPD-Moatsos",
- # year=1820,
- # target_mean=3306,
- # target_gini=0.5927,
- # )
-
- # (
- # synthetic_data_sweden_gapminder,
- # generated_mean_sweden_gapminder,
- # generated_gini_sweden_gapminder,
- # target_mean_sweden_gapminder,
- # target_gini_sweden_gapminder,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="Sweden-Gapminder",
- # year=1820,
- # target_mean=1619.685668,
- # target_gini=0.4956,
- # )
- # (
- # synthetic_data_sweden_moatsos,
- # generated_mean_sweden_moatsos,
- # generated_gini_sweden_moatsos,
- # target_mean_sweden_moatsos,
- # target_gini_sweden_moatsos,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="Sweden-Moatsos",
- # year=1820,
- # target_mean=445.4332,
- # target_gini=0.5544166,
- # )
- # (
- # synthetic_data_sweden_mpd_gapminder,
- # generated_mean_sweden_mpd_gapminder,
- # generated_gini_sweden_mpd_gapminder,
- # target_mean_sweden_mpd_gapminder,
- # target_gini_sweden_mpd_gapminder,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="Sweden-MPD-Gapminder",
- # year=1820,
- # target_mean=1415,
- # target_gini=0.4956,
- # )
- # (
- # synthetic_data_sweden_mpd_moatsos,
- # generated_mean_sweden_mpd_moatsos,
- # generated_gini_sweden_mpd_moatsos,
- # target_mean_sweden_mpd_moatsos,
- # target_gini_sweden_mpd_moatsos,
- # ) = generate_synthetic_data_from_mean_gini(
- # country="Sweden-MPD-Moatsos",
- # year=1820,
- # target_mean=1415,
- # target_gini=0.5544166,
- # )
-
- # distributional_plots(
- # data=synthetic_data_uk_gapminder,
- # df_main_indicators=None,
- # x="avg",
- # weights=None,
- # log_scale=True,
- # multiple="layer",
- # hue="country",
- # hue_order=["United Kingdom-Gapminder"],
- # years=[1820],
- # fill=False,
- # legend=False,
- # common_norm=False,
- # period="day",
- # add_ipl=None,
- # add_world_mean=None,
- # add_world_median=None,
- # add_multiple_lines_day=[2.15 * 365],
- # gridsize=GRIDSIZE_HIGHER_RESOLUTION,
- # width=1500,
- # height=400,
- # survey_based=False,
- # add_fade_in_tails=False,
- # )
-
- # distributional_plots(
- # data=synthetic_data_sweden_mpd_moatsos,
- # df_main_indicators=None,
- # x="avg",
- # weights=None,
- # log_scale=True,
- # multiple="layer",
- # hue="country",
- # hue_order=["Sweden-MPD-Moatsos"],
- # years=[1820],
- # fill=False,
- # legend=False,
- # common_norm=False,
- # period="day",
- # add_ipl=None,
- # add_world_mean=None,
- # add_world_median=None,
- # add_multiple_lines_day=[1.90 * 365],
- # gridsize=GRIDSIZE_HIGHER_RESOLUTION,
- # width=1500,
- # height=400,
- # survey_based=False,
- # add_fade_in_tails=False,
- # )
+ distributional_plots_per_row(
+ data=df_thousand_bins_historical_all_lognormal,
+ df_main_indicators=None,
+ x="avg",
+ weights="pop",
+ log_scale=True,
+ multiple="layer",
+ hue="country",
+ hue_order=["Sweden"],
+ years=[1820, 1920, LATEST_YEAR],
+ fill=True,
+ common_norm=False,
+ gridsize=GRIDSIZE_HIGHER_RESOLUTION,
+ period="month",
+ survey_based=False,
+ add_ipl="line",
+ add_high_income_pl="line",
+ width=1150,
+ height=220,
+ row_by="year",
+ add_multiple_lines_day=[3, 30],
+ filename_suffix="_lognormal",
+ )
def distributional_plots(
@@ -554,17 +515,29 @@ def distributional_plots(
preferred_reporting_level: Literal["national", "urban", "rural", None] = None,
preferred_welfare_type: Literal["income", "consumption", None] = None,
add_ipl: Literal["line", "area", None] = "line",
- add_world_mean: Literal["line", "area", None] = "line",
add_world_median: Literal["line", "area", None] = "line",
add_multiple_lines_day: List[float] = None,
width: int = WIDTH,
height: int = HEIGHT,
add_fade_in_tails: bool = True,
percentiles_to_fade: List[float] = [1, 99],
- x_axis_range: tuple = None,
+ share_y_axis: bool = False,
+ share_x_axis: bool = False,
+ filename_suffix: str = "",
) -> None:
"""
Plot distributional data with seaborn, with multiple options for customization.
+
+ ``share_y_axis``: when True, run a hidden pre-pass across all `years` to find
+ the maximum KDE density and lock every per-year figure's y-axis to that value.
+ Required if you want the saved SVGs to be visually comparable — without it,
+ matplotlib auto-scales each year independently and hides the inequality signal
+ (narrower distributions look the same height as wider ones).
+
+ ``share_x_axis``: when True, the pre-pass also computes the union of x-values
+ across years (after fading tails) and uses that as the shared x range —
+ applied to KDE clip, the data filter, and the figure's xlim — so every
+ per-year SVG shares the same horizontal scale.
"""
# Filter the data with the hue and hue_order
@@ -612,12 +585,110 @@ def distributional_plots(
# Define the income period values
period_factor = PERIOD_VALUES[period]["factor"]
log_ticks = PERIOD_VALUES[period]["log_ticks"]
+ # Cents only make sense at the daily scale; monthly and yearly values are
+ # large enough that the decimal noise is distracting (matches pen_parade).
+ dollar_decimals = 2 if period == "day" else 0
data[x] = data[x] * period_factor
# Define IPL for the period
ipl = INTERNATIONAL_POVERTY_LINE * period_factor
+ # Optional pre-pass: pre-compute the union x-range and/or the max KDE density
+ # across years so per-year figures can share x / y limits.
+ x_axis_range: tuple | None = None # set by the share_x_axis pre-pass below
+ shared_y_max: float | None = None
+ if share_x_axis or share_y_axis:
+
+ def _filter_year(year_value):
+ if survey_based:
+ sub = data[data["reference_year"] == year_value]
+ else:
+ sub = data[data["year"] == year_value]
+ if add_fade_in_tails:
+ if "percentile" in sub.columns:
+ pq, bounds = "percentile", percentiles_to_fade
+ elif "quantile" in sub.columns:
+ pq, bounds = "quantile", [p * 10 for p in percentiles_to_fade]
+ else:
+ return sub
+ sub = sub[(sub[pq] > bounds[0]) & (sub[pq] < bounds[1])]
+ return sub
+
+ # Step 1: compute shared x range from data if requested. When the chart is
+ # log-scaled, extend each year's bounds to where seaborn's KDE actually
+ # ends (data ± cut*bw in log10 space, matching seaborn's default cut=3
+ # and Scott's bandwidth), then take the union across years. This means
+ # the axis right edge lands exactly where the curve naturally tapers to
+ # near-zero, rather than chopping the tail.
+ if share_x_axis:
+ x_min = float("inf")
+ x_max = float("-inf")
+ for year in years:
+ sub = _filter_year(year)
+ if not len(sub):
+ continue
+ year_min = float(sub[x].min())
+ year_max = float(sub[x].max())
+ if log_scale:
+ v = np.log10(sub[x].to_numpy(dtype=float))
+ if weights is not None:
+ w = sub[weights].to_numpy(dtype=float)
+ else:
+ w = np.ones(len(sub))
+ mean_v = float(np.average(v, weights=w))
+ var_v = float(np.average((v - mean_v) ** 2, weights=w))
+ std_v = float(np.sqrt(max(var_v, 0.0)))
+ w_sum = float(w.sum())
+ w_sq_sum = float((w * w).sum())
+ n_eff = (w_sum * w_sum / w_sq_sum) if w_sq_sum > 0 else 1.0
+ bw = std_v * n_eff ** (-1 / 5) if n_eff > 0 else 0.0
+ cut = 3 # matches seaborn's default
+ year_min = year_min / 10 ** (cut * bw)
+ year_max = year_max * 10 ** (cut * bw)
+ x_min = min(x_min, year_min)
+ x_max = max(x_max, year_max)
+ if x_min < float("inf"):
+ x_axis_range = (x_min, x_max)
+
+ # Step 2: compute shared y max if requested (uses x_axis_range from step 1)
+ if share_y_axis:
+ if log_scale and x_axis_range is not None:
+ clip_pre = (np.log(x_axis_range[0]), np.log(x_axis_range[1]))
+ else:
+ clip_pre = x_axis_range
+ shared_y_max = 0.0
+ for year in years:
+ data_year_pre = _filter_year(year)
+ if x_axis_range is not None:
+ data_year_pre = data_year_pre[
+ (data_year_pre[x] >= x_axis_range[0])
+ & (data_year_pre[x] <= x_axis_range[1])
+ ]
+ if len(data_year_pre) == 0:
+ continue
+ fig_pre, ax_pre = plt.subplots()
+ sns.kdeplot(
+ data=data_year_pre,
+ x=x,
+ weights=weights,
+ fill=False,
+ log_scale=log_scale,
+ hue=hue,
+ hue_order=hue_order,
+ multiple=multiple,
+ legend=False,
+ common_norm=common_norm,
+ gridsize=gridsize,
+ clip=clip_pre,
+ ax=ax_pre,
+ )
+ for line in ax_pre.lines:
+ ys = np.asarray(line.get_data()[1])
+ if ys.size:
+ shared_y_max = max(shared_y_max, float(np.nanmax(ys)))
+ plt.close(fig_pre)
+
for year in years:
if survey_based:
data_year = data[data["reference_year"] == year].reset_index(drop=True)
@@ -625,16 +696,6 @@ def distributional_plots(
data_year = data[data["year"] == year].reset_index(drop=True)
if df_main_indicators is not None:
- # Define world mean
- world_mean_year = (
- df_main_indicators.loc[
- (df_main_indicators["country"] == "World")
- & (df_main_indicators["year"] == year),
- "mean",
- ].values[0]
- * period_factor
- )
-
# Define world median
world_median_year = (
df_main_indicators.loc[
@@ -666,15 +727,8 @@ def distributional_plots(
& (data_year[percentile_or_quantile] < percentiles_quantiles_to_fade[1])
]
- # Filter data by x_axis_range if specified
- # This ensures KDE calculation only uses data within the specified range
- if x_axis_range is not None:
- data_year = data_year[
- (data_year[x] >= x_axis_range[0]) & (data_year[x] <= x_axis_range[1])
- ].reset_index(drop=True)
-
- # Determine clip parameter for KDE
- # When log_scale=True, KDE is computed in log-space, so clip needs log-transformed values
+ # KDE clip in log space when log_scale=True; pinned to the shared x range so
+ # each year's curve extends across the same horizontal extent.
if x_axis_range is not None and log_scale:
clip_param = (np.log(x_axis_range[0]), np.log(x_axis_range[1]))
else:
@@ -713,14 +767,14 @@ def distributional_plots(
plt.axvline(
x=ipl,
color="lightgrey",
- linestyle="--",
+ linestyle=":",
linewidth=0.8,
)
plt.text(
x=ipl, # x-coordinate for the text
y=plt.ylim()[1]
* 0.99, # y-coordinate for the text, positioned near the top of the plot
- s=f"International Poverty Line: ${round(ipl,2):.2f}", # Text string to display
+ s=f"International Poverty Line: ${ipl:.{dollar_decimals}f}", # Text string to display
color="grey", # Color of the text
rotation=90, # Rotate the text 90 degrees
verticalalignment="top", # Align the text vertically at the top
@@ -733,49 +787,24 @@ def distributional_plots(
values=[ipl],
)
- if add_world_mean == "line":
- # Add a vertical line for the world mean, in the same format as the international poverty line
- plt.axvline(
- x=world_mean_year,
- color="lightgrey",
- linestyle="--",
- linewidth=0.8,
- )
- plt.text(
- x=world_mean_year,
- y=plt.ylim()[1] * 0.99,
- s=f"World mean: ${round(world_mean_year,2):.2f}",
- color="grey",
- rotation=90,
- verticalalignment="top",
- fontsize=8,
- )
-
- elif add_world_mean == "area":
- draw_area_under_curve(
- number_of_countries=number_of_countries,
- kde_plot=kde_plot,
- values=[world_mean_year],
- )
-
- if add_world_median == "line":
+ if add_world_median == "line" and df_main_indicators is not None:
# Add a vertical line for the world median, in the same format as the international poverty line
plt.axvline(
x=world_median_year,
color="lightgrey",
- linestyle="--",
+ linestyle=":",
linewidth=0.8,
)
plt.text(
x=world_median_year,
y=plt.ylim()[1] * 0.99,
- s=f"World median: ${round(world_median_year,2):.2f}",
+ s=f"World median: ${world_median_year:.{dollar_decimals}f}",
color="grey",
rotation=90,
verticalalignment="top",
fontsize=8,
)
- elif add_world_median == "area":
+ elif add_world_median == "area" and df_main_indicators is not None:
draw_area_under_curve(
kde_plot=kde_plot,
number_of_countries=number_of_countries,
@@ -783,14 +812,13 @@ def distributional_plots(
)
if add_multiple_lines_day is not None:
- # Multiply the values by the period factor
- add_multiple_lines_day = [
- value * period_factor for value in add_multiple_lines_day
- ]
+ # Use a fresh local each iteration; otherwise the multiplied list
+ # leaks across years and the second year fills get multiplied again.
+ scaled_lines = [v * period_factor for v in add_multiple_lines_day]
draw_area_under_curve(
kde_plot=kde_plot,
number_of_countries=number_of_countries,
- values=add_multiple_lines_day,
+ values=scaled_lines,
)
if legend:
@@ -816,18 +844,16 @@ def distributional_plots(
)
if log_scale:
- # Customize x-axis ticks to show 1, 2, 5, 10, 20, 50, 100, etc.
- # kde_plot.set(xscale="log")
- # Filter ticks to only include values within x_axis_range if specified
+ # Restrict ticks to the visible range. Calling set_xticks with values
+ # past the current xlim makes matplotlib widen the axis to fit them,
+ # which silently breaks share_x_axis (xlim jumps to the last tick).
if x_axis_range is not None:
- filtered_ticks = [
- tick
- for tick in log_ticks
- if x_axis_range[0] <= tick <= x_axis_range[1]
+ ticks = [
+ t for t in log_ticks if x_axis_range[0] <= t <= x_axis_range[1]
]
- kde_plot.set_xticks(filtered_ticks)
else:
- kde_plot.set_xticks(log_ticks)
+ ticks = log_ticks
+ kde_plot.set_xticks(ticks)
# Add dollar sign prefix to tick labels with integer formatting
kde_plot.get_xaxis().set_major_formatter(
plt.FuncFormatter(lambda x, p: f"${x:.0f}")
@@ -849,6 +875,10 @@ def distributional_plots(
kde_plot.spines["bottom"].set_visible(False)
kde_plot.spines["left"].set_visible(False)
+ # Lock y-axis to the shared max so the per-year SVGs can be visually compared.
+ if shared_y_max is not None and shared_y_max > 0:
+ kde_plot.set_ylim(0, shared_y_max * 1.05)
+
# Add a base line for each plot in the x axis
plt.axhline(y=0, color="gray", linewidth=0.5)
@@ -860,15 +890,18 @@ def distributional_plots(
fig.set_size_inches(width / 100, height / 100)
- # When using fixed axis range, use fixed subplot adjustments for perfect alignment
- if x_axis_range is not None:
+ # When the per-year SVGs need to be stackable (share_x_axis / share_y_axis),
+ # pin the axes to a fixed fraction of the figure and disable `bbox_inches="tight"`.
+ # Otherwise matplotlib's tight crop varies by the visible content (e.g. the per-year
+ # country label that sits at the year-specific median), shifting the plot area
+ # horizontally across the saved SVGs.
+ if x_axis_range is not None or shared_y_max is not None:
plt.subplots_adjust(left=0.04, right=0.96, top=0.95, bottom=0.22)
-
- # Use bbox_inches="tight" only when x_axis_range is not specified
- # to maintain alignment when using fixed axis ranges
- save_kwargs = {} if x_axis_range is not None else {"bbox_inches": "tight"}
+ save_kwargs = {}
+ else:
+ save_kwargs = {"bbox_inches": "tight"}
fig.savefig(
- f"{PARENT_DIR}/{filename}_{year}_survey_{survey_based}_log_{log_scale}_multiple_{multiple}_common_norm_{common_norm}_multiple_areas_{filename_multiple_areas}.svg",
+ f"{PARENT_DIR}/{filename}_{year}_survey_{survey_based}_log_{log_scale}_multiple_{multiple}_common_norm_{common_norm}_multiple_areas_{filename_multiple_areas}{filename_suffix}.svg",
**save_kwargs,
)
plt.close(fig)
@@ -878,7 +911,7 @@ def distributional_plots(
def distributional_plots_per_row(
data: pd.DataFrame,
- df_main_indicators: pd.DataFrame,
+ df_main_indicators: pd.DataFrame | None,
x: str,
weights: str,
log_scale: bool,
@@ -894,7 +927,6 @@ def distributional_plots_per_row(
preferred_reporting_level: Literal["national", "urban", "rural", None] = None,
preferred_welfare_type: Literal["income", "consumption", None] = None,
add_ipl: Literal["line", "area", None] = "line",
- add_world_mean: Literal["line", "area", None] = "line",
add_world_median: Literal["line", "area", None] = "line",
add_national_lines: bool = False,
df_national_lines: pd.DataFrame = None,
@@ -902,11 +934,49 @@ def distributional_plots_per_row(
height: int = HEIGHT,
add_fade_in_tails: bool = True,
percentiles_to_fade: List[float] = [1, 99],
- x_axis_range: tuple = None,
+ row_by: Literal["country", "year"] = "country",
+ add_multiple_lines_day: List[float] | None = None,
+ add_high_income_pl: Literal["line", "area", None] = None,
+ filename_suffix: str = "",
+ share_x_axis: bool = True,
+ share_y_axis: bool = True,
) -> None:
"""
Plot distributional data with seaborn, with each distribution in a separate row.
+
+ ``row_by="country"`` (default): one figure per year, rows = countries in
+ ``hue_order``. Use this when you want to compare a fixed set of countries at
+ a single moment.
+
+ ``row_by="year"``: one figure with rows = years, country fixed to
+ ``hue_order[0]``. Axes share both x and y, so peak heights are directly
+ comparable across years. Use this when comparing one country across time.
"""
+ if row_by == "year":
+ _distributional_plots_year_rows(
+ data=data,
+ df_main_indicators=df_main_indicators,
+ x=x,
+ weights=weights,
+ country=hue_order[0],
+ years=years,
+ log_scale=log_scale,
+ gridsize=gridsize,
+ period=period,
+ fill=fill,
+ add_multiple_lines_day=add_multiple_lines_day,
+ add_ipl=add_ipl,
+ add_world_median=add_world_median,
+ add_high_income_pl=add_high_income_pl,
+ width=width,
+ height=height,
+ add_fade_in_tails=add_fade_in_tails,
+ percentiles_to_fade=percentiles_to_fade,
+ filename_suffix=filename_suffix,
+ share_x_axis=share_x_axis,
+ share_y_axis=share_y_axis,
+ )
+ return None
# Filter the data with the hue and hue_order
if hue_order is not None:
@@ -944,31 +1014,29 @@ def distributional_plots_per_row(
else:
filename = "multiple_countries"
- # Define the income period values
- period_factor = PERIOD_VALUES[period]["factor"]
- log_ticks = PERIOD_VALUES[period]["log_ticks"]
+ # Define the income period values. PERIOD_VALUES mixes int factors with
+ # list log_ticks under the same dict, so cast each lookup back to its
+ # concrete type.
+ period_factor = cast(int, PERIOD_VALUES[period]["factor"])
+ log_ticks = cast(List[int], PERIOD_VALUES[period]["log_ticks"])
+ dollar_decimals = 2 if period == "day" else 0
data[x] = data[x] * period_factor
# Define IPL for the period
ipl = INTERNATIONAL_POVERTY_LINE * period_factor
+ # row_by="country" needs the world reference lookups; row_by="year" returned earlier.
+ assert df_main_indicators is not None, (
+ "distributional_plots_per_row(row_by='country') requires df_main_indicators"
+ )
+
for year in years:
if survey_based:
data_year = data[data["reference_year"] == year].reset_index(drop=True)
else:
data_year = data[data["year"] == year].reset_index(drop=True)
- # Define world mean
- world_mean_year = (
- df_main_indicators.loc[
- (df_main_indicators["country"] == "World")
- & (df_main_indicators["year"] == year),
- "mean",
- ].values[0]
- * period_factor
- )
-
# Define world median
world_median_year = (
df_main_indicators.loc[
@@ -979,12 +1047,17 @@ def distributional_plots_per_row(
* period_factor
)
- # Create a figure with subplots for each country
+ # Create a figure with subplots for each country. Share both axes so peak
+ # Share both axes by default so peak heights and x-range are directly
+ # comparable across rows. For mixed-spread comparisons (e.g. Ethiopia
+ # vs the United States) the caller can pass share_y_axis=False to let
+ # each country auto-scale its own y.
fig, axes = plt.subplots(
nrows=len(hue_order),
ncols=1,
figsize=(width / 100, height / 100),
- sharex=True, # Share the x-axis across all subplots
+ sharex=share_x_axis,
+ sharey=share_y_axis,
)
for ax, country in zip(axes, hue_order):
@@ -1019,21 +1092,6 @@ def distributional_plots_per_row(
)
]
- # Filter data by x_axis_range if specified
- # This ensures KDE calculation only uses data within the specified range
- if x_axis_range is not None:
- country_data = country_data[
- (country_data[x] >= x_axis_range[0])
- & (country_data[x] <= x_axis_range[1])
- ].reset_index(drop=True)
-
- # Determine clip parameter for KDE
- # When log_scale=True, KDE is computed in log-space, so clip needs log-transformed values
- if x_axis_range is not None and log_scale:
- clip_param = (np.log(x_axis_range[0]), np.log(x_axis_range[1]))
- else:
- clip_param = x_axis_range
-
# Plot a kde with seaborn
kde_plot = sns.kdeplot(
data=country_data,
@@ -1044,16 +1102,8 @@ def distributional_plots_per_row(
ax=ax,
common_norm=common_norm,
gridsize=gridsize,
- clip=clip_param,
)
- # Set x-axis range immediately after plotting, before drawing areas
- # This ensures all subsequent drawing operations respect the axis limits
- if x_axis_range is not None:
- ax.set_xlim(x_axis_range[0], x_axis_range[1])
- # Also set margins to 0 to prevent automatic padding
- ax.margins(x=0)
-
if not fill:
draw_complete_area_under_curve(kde_plot=kde_plot)
@@ -1069,39 +1119,17 @@ def distributional_plots_per_row(
values=[national_poverty_line],
)
- if add_ipl == "line":
- # Add a vertical line for the international poverty line
- ax.axvline(
- x=ipl,
- color="lightgrey",
- linestyle="--",
- linewidth=0.8,
- )
-
- if add_world_mean == "line":
- # Add a vertical line for the world mean
- ax.axvline(
- x=world_mean_year,
- color="lightgrey",
- linestyle="--",
- linewidth=0.8,
- )
-
- if add_world_median == "line":
- # Add a vertical line for the world median
- ax.axvline(
- x=world_median_year,
- color="lightgrey",
- linestyle="--",
- linewidth=0.8,
- )
+ # IPL and world-median lines are constant across rows in this layout
+ # (one figure per year), so they're drawn once for the whole figure
+ # outside this loop via `_add_figure_spanning_vline`. Per-row axvlines
+ # leave a visible gap between subplots.
if add_national_lines:
# Add a vertical line for the national poverty line
ax.text(
x=national_poverty_line,
y=plt.ylim()[0] - 0.05 * (plt.ylim()[1] - plt.ylim()[0]),
- s=f"${round(national_poverty_line,2):.2f} The poverty line in {country}*",
+ s=f"${national_poverty_line:.{dollar_decimals}f} The poverty line in {country}*",
color="grey",
rotation=0,
verticalalignment="top",
@@ -1109,43 +1137,9 @@ def distributional_plots_per_row(
fontsize=8,
)
- # Add line labels only to the last axis
- if ax == axes[-1]:
- if add_ipl == "line":
- ax.text(
- x=ipl,
- y=plt.ylim()[1],
- s=f"International\nPoverty Line:\n${round(ipl,2):.2f}",
- color="grey",
- rotation=90,
- verticalalignment="top",
- horizontalalignment="left",
- fontsize=8,
- )
-
- if add_world_mean == "line":
- ax.text(
- x=world_mean_year,
- y=plt.ylim()[1],
- s=f"World mean:\n${round(world_mean_year,2):.2f}",
- color="grey",
- rotation=90,
- verticalalignment="top",
- horizontalalignment="left",
- fontsize=8,
- )
-
- if add_world_median == "line":
- ax.text(
- x=world_median_year,
- y=plt.ylim()[1],
- s=f"World median:\n${round(world_median_year,2):.2f}",
- color="grey",
- rotation=90,
- verticalalignment="top",
- horizontalalignment="left",
- fontsize=8,
- )
+ # IPL / world-median labels are placed once in the figure margin
+ # below (via _add_figure_spanning_vline_label) so they appear above
+ # the whole stack rather than inside one of the subplots.
# Add the name of the country at the middle of the distribution, bottom
year_to_write = country_data["year"].iloc[0] if survey_based else year
@@ -1179,16 +1173,7 @@ def distributional_plots_per_row(
if log_scale:
# Customize x-axis ticks to show 1, 2, 5, 10, 20, 50, 100, etc.
ax.set_xscale("log")
- # Filter ticks to only include values within x_axis_range if specified
- if x_axis_range is not None:
- filtered_ticks = [
- tick
- for tick in log_ticks
- if x_axis_range[0] <= tick <= x_axis_range[1]
- ]
- ax.set_xticks(filtered_ticks)
- else:
- ax.set_xticks(log_ticks)
+ ax.set_xticks(log_ticks)
# Add dollar sign prefix to tick labels with integer formatting
ax.get_xaxis().set_major_formatter(
plt.FuncFormatter(lambda x, p: f"${x:.0f}")
@@ -1217,29 +1202,276 @@ def distributional_plots_per_row(
axis="x", which="both", bottom=False, top=False, labelbottom=False
)
- # Adjust layout and save the figure
- if x_axis_range is not None:
- # Use fixed subplot adjustments for perfect alignment
- plt.subplots_adjust(left=0.04, right=0.96, top=0.98, bottom=0.20)
- else:
- plt.tight_layout()
+ plt.tight_layout()
+
+ reference_labels: list[tuple[float, str]] = []
+ if add_ipl == "line":
+ _add_figure_spanning_vline(
+ fig, axes, ipl, color="lightgrey", linestyle=":", linewidth=0.8
+ )
+ reference_labels.append(
+ (ipl, f"International\nPoverty Line:\n${ipl:.{dollar_decimals}f}")
+ )
+ if add_world_median == "line":
+ _add_figure_spanning_vline(
+ fig,
+ axes,
+ world_median_year,
+ color="lightgrey",
+ linestyle=":",
+ linewidth=0.8,
+ )
+ reference_labels.append(
+ (
+ world_median_year,
+ f"World median:\n${world_median_year:.{dollar_decimals}f}",
+ )
+ )
+ _add_figure_spanning_vline_labels(fig, axes, reference_labels)
# Remove the clipping of the figure
for o in fig.findobj():
o.set_clip_on(False)
- # Use bbox_inches="tight" only when x_axis_range is not specified
- # to maintain alignment when using fixed axis ranges
- save_kwargs = {} if x_axis_range is not None else {"bbox_inches": "tight"}
fig.savefig(
- f"{PARENT_DIR}/{filename}_{year}_survey_{survey_based}_log_{log_scale}_multiple_{multiple}_common_norm_{common_norm}_rows.svg",
- **save_kwargs,
+ f"{PARENT_DIR}/{filename}_{year}_survey_{survey_based}_log_{log_scale}_multiple_{multiple}_common_norm_{common_norm}_rows{filename_suffix}.svg",
+ bbox_inches="tight",
)
plt.close(fig)
return None
+def _distributional_plots_year_rows(
+ data: pd.DataFrame,
+ x: str,
+ weights: str,
+ country: str,
+ years: List[int],
+ df_main_indicators: pd.DataFrame | None = None,
+ log_scale: bool = True,
+ gridsize: int = 200,
+ period: Literal["day", "month", "year"] = "day",
+ fill: bool = False,
+ add_multiple_lines_day: List[float] | None = None,
+ add_ipl: Literal["line", "area", None] = None,
+ add_world_median: Literal["line", "area", None] = None,
+ add_high_income_pl: Literal["line", "area", None] = None,
+ width: int = WIDTH,
+ height: int = HEIGHT,
+ add_fade_in_tails: bool = True,
+ percentiles_to_fade: List[float] = [1, 99],
+ filename_suffix: str = "",
+ share_x_axis: bool = True,
+ share_y_axis: bool = True,
+) -> None:
+ """
+ Private helper for ``distributional_plots_per_row(row_by="year")``: one country
+ across several years, one row per year, sharing both x and y axes.
+ """
+ period_factor = cast(int, PERIOD_VALUES[period]["factor"])
+ log_ticks = cast(List[int], PERIOD_VALUES[period]["log_ticks"])
+ dollar_decimals = 2 if period == "day" else 0
+ ipl = INTERNATIONAL_POVERTY_LINE * period_factor
+ high_income_pl = POVERTY_LINE_HIGH_INCOME * period_factor
+
+ def _world_median_for(year_value: int) -> float | None:
+ if df_main_indicators is None:
+ return None
+ rows = df_main_indicators.loc[
+ (df_main_indicators["country"] == "World")
+ & (df_main_indicators["year"] == year_value),
+ "median",
+ ]
+ if rows.empty:
+ return None
+ return float(rows.values[0]) * period_factor
+
+ data = data[data["country"] == country].copy()
+ data[x] = data[x] * period_factor
+
+ def _fade(sub: pd.DataFrame) -> pd.DataFrame:
+ if not add_fade_in_tails:
+ return sub
+ if "percentile" in sub.columns:
+ pq, bounds = "percentile", percentiles_to_fade
+ elif "quantile" in sub.columns:
+ pq, bounds = "quantile", [p * 10 for p in percentiles_to_fade]
+ else:
+ return sub
+ return sub[(sub[pq] > bounds[0]) & (sub[pq] < bounds[1])]
+
+ # Pre-pass: compute the shared x range across years, extended to where each
+ # year's KDE naturally tapers (data ± cut*bw in log10 space), then unioned.
+ # Mirrors the share_x_axis logic in distributional_plots.
+ x_axis_range: tuple | None = None
+ if log_scale:
+ x_min = float("inf")
+ x_max = float("-inf")
+ for year in years:
+ sub = _fade(data[data["year"] == year])
+ if not len(sub):
+ continue
+ year_min = float(sub[x].min())
+ year_max = float(sub[x].max())
+ v = np.log10(sub[x].to_numpy(dtype=float))
+ if weights is not None:
+ w = sub[weights].to_numpy(dtype=float)
+ else:
+ w = np.ones(len(sub))
+ mean_v = float(np.average(v, weights=w))
+ var_v = float(np.average((v - mean_v) ** 2, weights=w))
+ std_v = float(np.sqrt(max(var_v, 0.0)))
+ w_sum = float(w.sum())
+ w_sq_sum = float((w * w).sum())
+ n_eff = (w_sum * w_sum / w_sq_sum) if w_sq_sum > 0 else 1.0
+ bw = std_v * n_eff ** (-1 / 5) if n_eff > 0 else 0.0
+ cut = 3 # seaborn default
+ year_min = year_min / 10 ** (cut * bw)
+ year_max = year_max * 10 ** (cut * bw)
+ x_min = min(x_min, year_min)
+ x_max = max(x_max, year_max)
+ if x_min < float("inf"):
+ x_axis_range = (x_min, x_max)
+
+ if log_scale and x_axis_range is not None:
+ clip_param = (np.log(x_axis_range[0]), np.log(x_axis_range[1]))
+ else:
+ clip_param = None
+
+ fig, axes = plt.subplots(
+ nrows=len(years),
+ ncols=1,
+ figsize=(width / 100, height / 100 * len(years)),
+ sharex=share_x_axis,
+ sharey=share_y_axis,
+ )
+ if len(years) == 1:
+ axes = [axes]
+
+ for ax, year in zip(axes, years):
+ data_year = _fade(data[data["year"] == year])
+ # Always draw the line (fill=False) so we have a Line2D to read x/y from.
+ # If `fill` is requested, layer a full-area shading on top via
+ # draw_complete_area_under_curve. seaborn's own fill=True returns a
+ # PolyCollection instead of a Line2D, which breaks draw_area_under_curve.
+ kde_plot = sns.kdeplot(
+ data=data_year,
+ x=x,
+ weights=weights,
+ log_scale=log_scale,
+ gridsize=gridsize,
+ ax=ax,
+ fill=False,
+ legend=False,
+ clip=clip_param,
+ )
+ if fill:
+ draw_complete_area_under_curve(kde_plot=kde_plot)
+ if add_multiple_lines_day is not None:
+ draw_area_under_curve(
+ kde_plot=kde_plot,
+ values=[v * period_factor for v in add_multiple_lines_day],
+ )
+ ref_line_kw = dict(color="lightgrey", linestyle=":", linewidth=0.8)
+
+ # `add_ipl` and `add_high_income_pl` are constant across years, so the line
+ # is drawn once for the whole figure outside the per-row loop (below) to
+ # avoid the visible gap between subplots that ax.axvline would leave.
+ if add_ipl == "area":
+ draw_area_under_curve(kde_plot=kde_plot, values=[ipl])
+ if add_high_income_pl == "area":
+ draw_area_under_curve(kde_plot=kde_plot, values=[high_income_pl])
+
+ world_median_year = _world_median_for(year)
+ if add_world_median == "line" and world_median_year is not None:
+ ax.axvline(x=world_median_year, **ref_line_kw)
+ elif add_world_median == "area" and world_median_year is not None:
+ draw_area_under_curve(kde_plot=kde_plot, values=[world_median_year])
+
+ # IPL and high-income labels are constant across rows and rendered once
+ # in the figure margin (below). World-median varies per year so its label
+ # stays in the top row inline.
+ if (
+ ax is axes[0]
+ and add_world_median == "line"
+ and world_median_year is not None
+ ):
+ ax.text(
+ x=world_median_year,
+ y=ax.get_ylim()[1],
+ s=f"World median:\n${world_median_year:.{dollar_decimals}f}",
+ color="grey",
+ rotation=90,
+ verticalalignment="top",
+ horizontalalignment="left",
+ fontsize=8,
+ )
+
+ ax.text(
+ x=data_year[x].median() if len(data_year) else 1.0,
+ y=ax.get_ylim()[0],
+ s=f"{country} ({year})",
+ color="black",
+ verticalalignment="bottom",
+ horizontalalignment="center",
+ fontsize=10,
+ )
+ ax.set_ylabel("")
+ ax.yaxis.set_ticks([])
+ for spine in ax.spines.values():
+ spine.set_visible(False)
+ ax.axhline(y=0, color="gray", linewidth=0.5)
+ if ax is not axes[-1]:
+ ax.tick_params(axis="x", which="both", bottom=False, labelbottom=False)
+
+ if log_scale:
+ # Filter ticks to within the shared x range so set_xticks doesn't widen
+ # the axis past the data, matching the share_x_axis logic in distributional_plots.
+ if x_axis_range is not None:
+ ticks = [t for t in log_ticks if x_axis_range[0] <= t <= x_axis_range[1]]
+ else:
+ ticks = log_ticks
+ axes[-1].set_xticks(ticks)
+ axes[-1].get_xaxis().set_major_formatter(
+ plt.FuncFormatter(lambda v, _: f"${v:g}")
+ )
+ if x_axis_range is not None:
+ axes[-1].set_xlim(*x_axis_range)
+ axes[-1].set_xlabel(f"Income or consumption ({period})")
+
+ # Lay out first so axes positions are stable before placing the spanning lines.
+ fig.tight_layout()
+ reference_labels: list[tuple[float, str]] = []
+ if add_ipl == "line":
+ _add_figure_spanning_vline(
+ fig, axes, ipl, color="lightgrey", linestyle=":", linewidth=0.8
+ )
+ reference_labels.append(
+ (ipl, f"International\nPoverty Line:\n${ipl:.{dollar_decimals}f}")
+ )
+ if add_high_income_pl == "line":
+ _add_figure_spanning_vline(
+ fig, axes, high_income_pl, color="lightgrey", linestyle=":", linewidth=0.8
+ )
+ reference_labels.append(
+ (
+ high_income_pl,
+ f"High-income\nPoverty Line:\n${high_income_pl:.{dollar_decimals}f}",
+ )
+ )
+ _add_figure_spanning_vline_labels(fig, axes, reference_labels)
+
+ for o in fig.findobj():
+ o.set_clip_on(False)
+
+ fig.savefig(
+ PARENT_DIR / f"{country}_per_year_row_log_{log_scale}{filename_suffix}.svg",
+ bbox_inches="tight",
+ )
+ plt.close(fig)
+
+
def filter_survey_data(
data: pd.DataFrame,
years: List[int],
@@ -1311,6 +1543,29 @@ def filter_survey_data(
return data
+def interpolate_x_at_y(xs: np.ndarray, ys: np.ndarray, y_target: float) -> float | None:
+ """
+ Return the x at which a piecewise-linear curve defined by (xs, ys) first
+ reaches y_target, by linearly interpolating between the two flanking points.
+ Returns None if the curve never reaches y_target.
+
+ Assumes xs is sorted ascending. Used by the pen parade to land bracket end-caps
+ and dotted reference lines exactly on the curve, instead of snapping to the
+ nearest integer-percentile data point.
+ """
+ crossings = ys >= y_target
+ if not crossings.any():
+ return None
+ i_hi = int(np.argmax(crossings))
+ if i_hi == 0:
+ return float(xs[0])
+ y_lo, y_hi_val = ys[i_hi - 1], ys[i_hi]
+ x_lo, x_hi = xs[i_hi - 1], xs[i_hi]
+ if y_hi_val == y_lo:
+ return float(x_hi)
+ return float(x_lo + (y_target - y_lo) * (x_hi - x_lo) / (y_hi_val - y_lo))
+
+
def pen_parade(
data: pd.DataFrame,
df_main_indicators: pd.DataFrame,
@@ -1330,9 +1585,15 @@ def pen_parade(
preferred_welfare_type: Literal["income", "consumption", None] = None,
width: int = WIDTH_PEN,
height: int = HEIGHT_PEN,
+ cut_percentile: float = 100,
) -> None:
"""
Plot Pen parades (percentiles vs. income) with seaborn, with multiple options for customization.
+
+ ``cut_percentile`` truncates the x-axis at the given percentile (default 95) so the
+ rapidly rising top tail doesn't dominate the y-axis. Reference lines whose y-value sits
+ above the visible range (e.g. the 99th-percentile threshold when ``cut_percentile=95``)
+ are skipped automatically.
"""
# Filter the data with the hue and hue_order
@@ -1365,9 +1626,12 @@ def pen_parade(
else:
filename = "multiple_countries"
- # Define the income period values
- period_factor = PERIOD_VALUES[period]["factor"]
- log_ticks = PERIOD_VALUES[period]["log_ticks"]
+ # Define the income period values. PERIOD_VALUES mixes int factors with list
+ # log_ticks under the same dict, so cast each lookup back to its concrete type.
+ period_factor = cast(int, PERIOD_VALUES[period]["factor"])
+ log_ticks = cast(List[int], PERIOD_VALUES[period]["log_ticks"])
+ # Show cents only for daily figures; monthly/yearly values are large enough that decimals are noise.
+ dollar_decimals = 2 if period == "day" else 0
data[y] = data[y] * period_factor
@@ -1380,18 +1644,52 @@ def pen_parade(
else:
data_year = data[data["year"] == year].reset_index(drop=True)
- # Define world mean
- world_mean_year = (
- df_main_indicators.loc[
- (df_main_indicators["country"] == "World")
- & (df_main_indicators["year"] == year),
- "mean",
- ].values[0]
- * period_factor
- )
+ # Anchor each per-hue line at (x=0, y=0) so the curve starts at the origin instead
+ # of at percentile=1. Skipped on log-scale because log(0) = -inf would blank the
+ # plot (the line/fill points get clipped and nothing renders).
+ if len(data_year) > 0 and not log_scale:
+ zero_rows = data_year.drop_duplicates(subset=[hue]).copy()
+ zero_rows[x] = 0
+ zero_rows[y] = 0
+ data_year = pd.concat(
+ [zero_rows, data_year], ignore_index=True
+ ).reset_index(drop=True)
+
+ # Compute the y-value at cut_percentile (used to set the y-axis cap and to anchor
+ # the p99 annotation). Keep ALL x rows so the curve spans the full x range, and
+ # CLAMP y values above the cut to y_at_cut so the curve plateaus at the top rather
+ # than disappearing (or relying on axes clipping, which we keep disabled globally
+ # for Figma editing). Also extend the plateau to x=100 so the cap line reaches the
+ # right edge of the chart, even when the source data tops out at percentile 99.
+ if cut_percentile < 100 and len(data_year) > 0:
+ cut_subset = data_year[data_year[x] <= cut_percentile]
+ y_at_cut = float(cut_subset[y].max()) if len(cut_subset) else None
+ # Fade band runs from 88% of the cap up to the cap.
+ y_at_fade_floor = y_at_cut * 0.88 if y_at_cut is not None else None
+ if y_at_cut is not None:
+ data_year = data_year.copy()
+ data_year.loc[data_year[y] > y_at_cut, y] = y_at_cut
+ plateau_end = data_year.drop_duplicates(
+ subset=[hue], keep="last"
+ ).copy()
+ plateau_end[x] = 100
+ plateau_end[y] = y_at_cut
+ data_year = pd.concat(
+ [data_year, plateau_end], ignore_index=True
+ ).reset_index(drop=True)
+ else:
+ y_at_cut = None
+ y_at_fade_floor = None
+
+ # Keep raw float values for POSITIONING (bracket caps, dotted reference lines,
+ # and fill crossings all land on the curve at the semantically meaningful
+ # percentile — e.g. the world median ends up at p=50, not p≈49.93). The
+ # f-string `.{dollar_decimals}f` formatting in each label rounds for DISPLAY
+ # (e.g. $289.50 → "$289"), so labels still match the rounded numbers shown
+ # elsewhere in OWID's online data.
# Define world median
- world_median_year = (
+ world_median_year = float(
df_main_indicators.loc[
(df_main_indicators["country"] == "World")
& (df_main_indicators["year"] == year),
@@ -1400,15 +1698,8 @@ def pen_parade(
* period_factor
)
- # Define the % of people below the high-income country poverty line
- world_share_below_high_income_line = df_main_indicators.loc[
- (df_main_indicators["country"] == "World")
- & (df_main_indicators["year"] == year),
- f"headcount_ratio_{POVERTY_LINE_HIGH_INCOME*100:.0f}",
- ].values[0]
-
# Define the 90th percentile of the world
- world_90th_percentile = (
+ world_90th_percentile = float(
df_main_indicators.loc[
(df_main_indicators["country"] == "World")
& (df_main_indicators["year"] == year),
@@ -1418,7 +1709,7 @@ def pen_parade(
)
# Define the 99th percentile of the world
- world_99th_percentile = (
+ world_99th_percentile = float(
df_main_indicators.loc[
(df_main_indicators["country"] == "World")
& (df_main_indicators["year"] == year),
@@ -1436,6 +1727,7 @@ def pen_parade(
hue=hue,
hue_order=hue_order,
legend=legend,
+ linewidth=2.5,
)
if fill:
@@ -1455,157 +1747,313 @@ def pen_parade(
else:
line_plot.get_yaxis().set_major_formatter(plt.ScalarFormatter())
+ # Reference-line ticks collected here become the y-axis tick labels, replacing the
+ # default dollar-amount labels with the labels that previously sat on each reference line.
+ reference_ticks: list[tuple[float, str]] = []
+
if add_lines:
- # Add a horizontal line for the international poverty line
- plt.axhline(
- y=ipl,
- color=sns.color_palette("deep")[3],
- linestyle="--",
- linewidth=0.8,
- )
- plt.text(
- x=99,
- y=ipl,
- s=f"International Poverty Line: ${round(ipl,2):.2f}\n",
- color="black",
- rotation=0,
- horizontalalignment="right",
- fontsize=8,
- linespacing=0.5,
- )
+ # Sorted (x, y) arrays for the curve — shared by the fill polygons,
+ # axhline_over_curve, and the bracket loop so they all interpolate
+ # against the same data.
+ ref_sorted = data_year.sort_values(x).reset_index(drop=True)
+ ref_xs = ref_sorted[x].to_numpy()
+ ref_ys = ref_sorted[y].to_numpy()
+
+ # Poverty bands shaded in the same blue as the main fill — one per poverty
+ # line where the curve sits below that threshold. Overlapping fills stack
+ # their alphas, deepening the blue as the poverty line gets stricter.
+ # We build each polygon with the exact crossing point appended, because
+ # matplotlib's fill_between `interpolate=True` only kicks in when y1 and
+ # y2 actually cross — with y1=0 the curves never meet, so the fill would
+ # otherwise snap to the last integer percentile instead of the crossing.
+ poverty_fill_color = sns.color_palette("deep")[0]
+ for poverty_y in (
+ POVERTY_LINE_HIGH_INCOME * period_factor,
+ world_median_year,
+ ipl,
+ ):
+ x_cross = interpolate_x_at_y(ref_xs, ref_ys, float(poverty_y))
+ if x_cross is None:
+ continue
+ below_mask = ref_ys < poverty_y
+ fill_xs = np.concatenate([ref_xs[below_mask], [x_cross]])
+ fill_ys = np.concatenate([ref_ys[below_mask], [poverty_y]])
+ line_plot.fill_between(
+ fill_xs,
+ 0,
+ fill_ys,
+ alpha=0.3,
+ color=poverty_fill_color,
+ linewidth=0,
+ )
- # Add a horizontal line for the world mean
- plt.axhline(
- y=world_mean_year,
- color=sns.color_palette("deep")[3],
- linestyle="--",
- linewidth=0.8,
- )
- plt.text(
- x=99,
- y=world_mean_year,
- s=f"World mean: ${round(world_mean_year,2):.2f}\n",
- color="black",
- rotation=0,
- horizontalalignment="right",
- fontsize=8,
- linespacing=0.5,
- )
+ def axhline_over_curve(y_value):
+ """Dotted reference line at y_value, but only over the filled curve
+ area (from where the curve crosses y_value to the right edge), so the
+ line doesn't clutter the white space where the brackets live. The
+ crossing x is interpolated so the dotted line meets the curve exactly,
+ rather than snapping to the next integer percentile."""
+ crossing_x = interpolate_x_at_y(ref_xs, ref_ys, y_value)
+ xmin_frac = 0.0 if crossing_x is None else crossing_x / 100.0
+ plt.axhline(
+ y=y_value,
+ xmin=xmin_frac,
+ color=REFERENCE_LINE_COLOR,
+ linestyle=":",
+ linewidth=0.8,
+ )
- # Add a horizontal line for the world median
- plt.axhline(
- y=world_median_year,
- color=sns.color_palette("deep")[3],
- linestyle="--",
- linewidth=0.8,
- )
- plt.text(
- x=99,
- y=world_median_year,
- s=f"World median: ${round(world_median_year,2):.2f}\n",
- color="black",
- rotation=0,
- horizontalalignment="right",
- fontsize=8,
- linespacing=0.5,
- )
- plt.text(
- x=0,
- y=world_median_year,
- s=f"The poorest 50% live on less than ${round(world_median_year,2):.2f} a {period}\n",
- color="black",
- rotation=0,
- horizontalalignment="left",
- fontsize=9,
- linespacing=0.5,
- )
+ # International poverty line
+ axhline_over_curve(ipl)
+ reference_ticks.append((ipl, f"← ${ipl:.{dollar_decimals}f} per {period}"))
+
+ # Reference lines at the equivalent of $900/month and $500/month, in the
+ # chart's current period units. Defined in monthly terms then rescaled by
+ # period_factor / month_factor so the same real-world amounts shift correctly
+ # when the chart switches between day / month / year periods.
+ month_factor = cast(int, PERIOD_VALUES["month"]["factor"])
+ for monthly_value in (900, 500):
+ line_y = monthly_value * period_factor / month_factor
+ axhline_over_curve(line_y)
+ reference_ticks.append(
+ (line_y, f"← ${line_y:.{dollar_decimals}f} per {period}")
+ )
- # Add an horizontal line for a poverty line representative of a high-income country
- plt.axhline(
- y=POVERTY_LINE_HIGH_INCOME * period_factor,
- color=sns.color_palette("deep")[3],
- linestyle="-",
- linewidth=1,
- )
- plt.text(
- x=0,
- y=POVERTY_LINE_HIGH_INCOME * period_factor,
- s=f"${round(POVERTY_LINE_HIGH_INCOME * period_factor,2):.0f} corresponds to the poverty line of a high-income country\n",
- color="black",
- rotation=0,
- horizontalalignment="left",
- fontsize=9,
- linespacing=0.5,
+ # World median
+ axhline_over_curve(world_median_year)
+ reference_ticks.append(
+ (
+ world_median_year,
+ f"← ${world_median_year:.{dollar_decimals}f} per {period} — the global median income",
+ )
)
- plt.text(
- x=0,
- y=POVERTY_LINE_HIGH_INCOME * period_factor,
- s=f"\nGlobally, {world_share_below_high_income_line/100:.1%} live on less than ${round(POVERTY_LINE_HIGH_INCOME * period_factor,2):.0f} a {period}",
- color="black",
- rotation=0,
- horizontalalignment="left",
- verticalalignment="top",
- fontsize=8,
- linespacing=0.5,
+ # 90th percentile of the world
+ axhline_over_curve(world_90th_percentile)
+ reference_ticks.append(
+ (
+ world_90th_percentile,
+ f"← The richest 10% have an income of more than ${world_90th_percentile:.{dollar_decimals}f} per {period}",
+ )
)
- # Add a horizontal line for the 90th percentile of the world
- plt.axhline(
- y=world_90th_percentile,
- color=sns.color_palette("deep")[3],
- linestyle="--",
- linewidth=0.8,
- )
- plt.text(
- x=99,
- y=world_90th_percentile,
- s=f"${round(world_90th_percentile,2):.2f}\n",
- color="black",
- rotation=0,
- horizontalalignment="right",
- fontsize=8,
- linespacing=0.5,
- )
- plt.text(
- x=99,
- y=world_90th_percentile,
- s=f"\n10% is richer",
- color="black",
- rotation=0,
- horizontalalignment="right",
- verticalalignment="top",
- fontsize=8,
- linespacing=0.5,
- )
+ # 99th percentile of the world.
+ # If the chart was cut below percentile 99, the 99th threshold sits above the
+ # visible y range — draw the label above the cropped top of the chart, anchored
+ # at x = cut_percentile (the right edge of the visible plot), as in the reference.
+ p99_label = f"↑ The richest 1% live on more than ${world_99th_percentile:.{dollar_decimals}f} per {period}"
+ if cut_percentile < 99:
+ wrapped_p99 = textwrap.fill(p99_label, width=28)
+ # Anchor at the top-right of the axes so the label sits in the same
+ # right-margin column as the other y-tick labels, just above the topmost
+ # tick. ha="left" + a small +x offset mirror the default tick-label pad.
+ line_plot.annotate(
+ wrapped_p99,
+ xy=(1.0, 1.0),
+ xycoords="axes fraction",
+ xytext=(4, 4),
+ textcoords="offset points",
+ ha="left",
+ va="bottom",
+ fontsize=8,
+ linespacing=1.5,
+ annotation_clip=False,
+ )
+ else:
+ axhline_over_curve(world_99th_percentile)
+ reference_ticks.append(
+ (world_99th_percentile, p99_label.replace("↑", "→"))
+ )
- # Add a horizontal line for the 99th percentile of the world
- plt.axhline(
- y=world_99th_percentile,
- color=sns.color_palette("deep")[3],
- linestyle="--",
- linewidth=0.8,
- )
- plt.text(
- x=99,
- y=world_99th_percentile,
- s=f"${round(world_99th_percentile,2):.2f}\n",
- color="black",
- rotation=0,
- horizontalalignment="right",
- fontsize=8,
- linespacing=0.5,
- )
- plt.text(
- x=99,
- y=world_99th_percentile,
- s=f"\n1% is richer",
- color="black",
- rotation=0,
- horizontalalignment="right",
- verticalalignment="top",
- fontsize=8,
- linespacing=0.5,
- )
+ # Country median reference lines (most-recent value at or before the plot year).
+ # Pairs in COUNTRY_MEDIAN_MERGE_PAIRS that fall within MEDIAN_MERGE_TOLERANCE of
+ # each other are averaged into a single line; otherwise we assert so the chart
+ # author notices the divergence and decides how to lay them out.
+ COUNTRY_MEDIAN_MERGE_PAIRS = [("Sweden", "United Kingdom")]
+ MEDIAN_MERGE_TOLERANCE = 0.05 # 5%
+ # Display names: PIP's full country names → the shorter forms we want shown
+ # in the labels. Countries not listed fall back to their PIP name.
+ COUNTRY_DISPLAY_NAMES = {
+ "United States": "the USA",
+ "United Kingdom": "the UK",
+ }
+
+ def display_name(country: str) -> str:
+ return COUNTRY_DISPLAY_NAMES.get(country, country)
+
+ country_medians_lookup = {}
+ for country_name in ["Norway", "United States", "Sweden", "United Kingdom"]:
+ country_rows = df_main_indicators[
+ (df_main_indicators["country"] == country_name)
+ & (df_main_indicators["year"] <= year)
+ & df_main_indicators["median"].notna()
+ ]
+ if country_rows.empty:
+ continue
+ country_medians_lookup[country_name] = float(
+ country_rows.sort_values("year")["median"].iloc[-1] * period_factor
+ )
+
+ merged_country_labels = set()
+ merged_groups: list[tuple[float, list[str]]] = []
+ for a, b in COUNTRY_MEDIAN_MERGE_PAIRS:
+ if a not in country_medians_lookup or b not in country_medians_lookup:
+ continue
+ med_a = country_medians_lookup[a]
+ med_b = country_medians_lookup[b]
+ relative_diff = abs(med_a - med_b) / max(med_a, med_b)
+ assert relative_diff <= MEDIAN_MERGE_TOLERANCE, (
+ f"Country medians for {a} (${med_a:.2f}) and {b} (${med_b:.2f}) differ by "
+ f"{relative_diff:.1%}, exceeding the {MEDIAN_MERGE_TOLERANCE:.0%} merge "
+ f"tolerance. Adjust COUNTRY_MEDIAN_MERGE_PAIRS or lay them out separately."
+ )
+ merged_groups.append(((med_a + med_b) / 2, [a, b]))
+ merged_country_labels.update({a, b})
+
+ for country_name, country_median in country_medians_lookup.items():
+ if country_name in merged_country_labels:
+ continue
+ axhline_over_curve(country_median)
+ reference_ticks.append(
+ (
+ country_median,
+ f"← ${country_median:.{dollar_decimals}f} per {period} — the median income in {display_name(country_name)}",
+ )
+ )
+
+ for group_median, group_countries in merged_groups:
+ group_label = " and ".join(display_name(c) for c in group_countries)
+ # If this merged country median is also within MEDIAN_MERGE_TOLERANCE of the
+ # world's 90th percentile, combine the two into a single reference label
+ # (and replace the existing p90 tick instead of adding a duplicate).
+ p90_diff = abs(group_median - world_90th_percentile) / max(
+ group_median, world_90th_percentile
+ )
+ if p90_diff <= MEDIAN_MERGE_TOLERANCE:
+ # Reuse the existing p90 axhline (already drawn) — don't add another at
+ # the merged y, otherwise two near-identical lines would overlap.
+ combined_value = world_90th_percentile
+ combined_label = (
+ f"← ${combined_value:.{dollar_decimals}f} per {period} — "
+ f"the median income in {group_label}, and the income above "
+ f"which the richest 10% of the world live"
+ )
+ # Replace the existing p90 tick (matched by "richest 10%" fragment).
+ for idx, (_, label_text) in enumerate(reference_ticks):
+ if "richest 10%" in label_text:
+ reference_ticks[idx] = (combined_value, combined_label)
+ break
+ continue
+ axhline_over_curve(group_median)
+ reference_ticks.append(
+ (
+ group_median,
+ f"← ${group_median:.{dollar_decimals}f} per {period} — the median income in {group_label}",
+ )
+ )
+
+ # Red square brackets ([) ABOVE selected reference lines, spanning from x=0
+ # out to where the curve crosses that y value — a simple bracket with vertical
+ # end-caps and a flat top, marking the share of the world earning less than
+ # each threshold.
+ brace_values = [
+ ipl,
+ world_median_year,
+ POVERTY_LINE_HIGH_INCOME * period_factor,
+ ]
+ y_range = y_at_cut if y_at_cut is not None else line_plot.get_ylim()[1]
+ brace_height_data = y_range * 0.012 # height of the bracket
+
+ red = sns.color_palette("deep")[3]
+
+ def styled_annotation(x, y, title, text, box_alignment, wrap_width=32):
+ """Multi-line annotation: bold title on top, regular text below. The
+ `text` is auto-wrapped at `wrap_width` characters (preserving any explicit
+ ``\\n`` you do add). All lines are right-aligned within the box so their
+ right edges sit flush against the y-axis."""
+ common = {
+ "color": red,
+ "fontsize": 9,
+ "ha": "right",
+ "multialignment": "right",
+ }
+ children = [TextArea(title, textprops={**common, "fontweight": "bold"})]
+ wrapped_lines = []
+ for raw in text.split("\n"):
+ wrapped_lines.extend(
+ textwrap.fill(raw, width=wrap_width).split("\n")
+ )
+ for line in wrapped_lines:
+ children.append(TextArea(line, textprops=common))
+ packer = VPacker(children=children, align="right", pad=0, sep=2)
+ line_plot.add_artist(
+ AnnotationBbox(
+ packer,
+ (x, y),
+ xycoords="data",
+ xybox=(-5, 0),
+ boxcoords="offset points",
+ box_alignment=box_alignment,
+ frameon=False,
+ pad=0,
+ )
+ )
+
+ high_income_value = POVERTY_LINE_HIGH_INCOME * period_factor
+ for brace_y in brace_values:
+ # Interpolate so the bracket's right cap lands exactly where the
+ # curve crosses brace_y, rather than snapping to the next integer
+ # percentile (which would leave a visible gap).
+ x_brace_end = interpolate_x_at_y(ref_xs, ref_ys, brace_y)
+ if x_brace_end is None or x_brace_end <= 1:
+ continue
+ y_low = brace_y
+ y_high = brace_y + brace_height_data
+ bx = [0.0, 0.0, x_brace_end, x_brace_end]
+ by = [y_low, y_high, y_high, y_low]
+ line_plot.plot(
+ bx,
+ by,
+ color=sns.color_palette("deep")[3],
+ linewidth=1.5,
+ clip_on=False,
+ solid_capstyle="butt",
+ solid_joinstyle="miter",
+ )
+ # Annotate the high-income bracket with the headcount share at $X.
+ if brace_y == high_income_value:
+ world_share_hi = df_main_indicators.loc[
+ (df_main_indicators["country"] == "World")
+ & (df_main_indicators["year"] == year),
+ "headcount_ratio_3000",
+ ].values[0]
+ styled_annotation(
+ 0,
+ y_high,
+ title="Poverty",
+ text=f"{world_share_hi:.0f}% of the world population live on less than ${brace_y:.0f} per {period}",
+ box_alignment=(1.0, 0.0),
+ )
+ if brace_y == world_median_year:
+ styled_annotation(
+ 0,
+ y_high,
+ title="Deep poverty",
+ text=f"The poorer half of the world population — 4 billion people — live on less than ${brace_y:.{dollar_decimals}f} per {period}",
+ box_alignment=(1.0, 0.0),
+ )
+ if brace_y == ipl:
+ world_share_ipl = df_main_indicators.loc[
+ (df_main_indicators["country"] == "World")
+ & (df_main_indicators["year"] == year),
+ "headcount_ratio_300",
+ ].values[0]
+ styled_annotation(
+ 0,
+ y_high,
+ title="Extreme poverty",
+ text=f"The poorest {world_share_ipl:.0f}% live on less than ${brace_y:.{dollar_decimals}f} per {period}",
+ box_alignment=(1.0, 0.0),
+ )
# Remove y-axis labels and ticks
line_plot.set_ylabel("")
@@ -1618,24 +2066,140 @@ def pen_parade(
# Change format of x-axis to percentage
line_plot.get_xaxis().set_major_formatter(
- plt.FuncFormatter(lambda x, _: f"{x/100:.0%}")
+ plt.FuncFormatter(lambda x, _: f"{x / 100:.0%}")
)
- # Do the same for the y-axis, with $
- line_plot.get_yaxis().set_major_formatter(
- plt.FuncFormatter(lambda x, _: f"${x:.0f} per {period}")
- )
+ # Replace the default dollar-amount y-tick labels with the reference-line labels.
+ # Falls back to the dollar formatter when there are no reference lines (add_lines=False).
+ if reference_ticks:
+ sorted_refs = sorted(reference_ticks)
+ yticks = [t[0] for t in sorted_refs]
+ wrap_width = 36 # characters; tune with the right-margin adjust below
+ yticklabels = [textwrap.fill(t[1], width=wrap_width) for t in sorted_refs]
+ line_plot.set_yticks(yticks)
+ # Hide the default tick labels; we'll place them manually below via annotate so
+ # we can actually apply a vertical offset (matplotlib's tick-label renderer
+ # ignores the offset_copy transform we'd otherwise use).
+ line_plot.set_yticklabels([""] * len(yticks))
+
+ # Reserve room on the right so long labels like "International Poverty Line" don't get clipped.
+ line_plot.get_figure().subplots_adjust(right=0.55)
+
+ # Per-label vertical offsets (with va="center", default places the block centered on tick):
+ # - 1-line label: no offset needed (text is centered on tick).
+ # - n-line wrapped label: shift block DOWN by (n-1)*line_height/2 so the FIRST line sits on the tick.
+ # - When the tick above is too close (would overlap given label heights), drop this label entirely
+ # BELOW its tick by ~one label height so the two labels separate.
+ line_height_points = 8 * 1.5 # fontsize × linespacing
+ # Anchor overrides for specific labels that sit too close to their neighbour
+ # for the automatic collision logic to look right. "above" lifts the label so
+ # its bottom sits at the tick; "below" drops it so its top sits at the tick.
+ anchor_overrides = {
+ "median income in Sweden": "above",
+ "richest 10%": "below",
+ }
+
+ def matched_anchor(text: str) -> str | None:
+ # If both the country-median and the p90 phrasings appear, the labels were
+ # already merged into one combined tick — keep it centered, not above/below.
+ if "median income in Sweden" in text and "richest 10%" in text:
+ return None
+ for fragment, position in anchor_overrides.items():
+ if fragment in text:
+ return position
+ return None
+
+ # Place each label manually as an annotation in the right-margin column. This
+ # gives us actual vertical offset control (matplotlib's tick-label renderer
+ # ignores transform offsets, but annotate's textcoords="offset points" works).
+ tick_pad_points = 4 # mirrors matplotlib's default tick-label pad
+ for tick_y, text in zip(yticks, yticklabels):
+ n = text.count("\n") + 1
+ override = matched_anchor(text)
+ if override == "above":
+ # bottom of the label block sits at the tick → text extends upward
+ offset_y = 0
+ va = "bottom"
+ elif override == "below":
+ # top of the label block sits at the tick → text extends downward
+ offset_y = 0
+ va = "top"
+ else:
+ # First line centered on the tick. With va="center", the bbox center
+ # is at tick + offset_y; shifting down by (n-1)*line_height/2 puts the
+ # first line at the tick. For n=1 the offset is zero.
+ offset_y = -line_height_points * (n - 1) / 2
+ va = "center"
+ line_plot.annotate(
+ text,
+ xy=(1.0, tick_y),
+ xycoords=("axes fraction", "data"),
+ xytext=(tick_pad_points, offset_y),
+ textcoords="offset points",
+ ha="left",
+ va=va,
+ fontsize=8,
+ linespacing=1.5,
+ annotation_clip=False,
+ )
+ else:
+ line_plot.get_yaxis().set_major_formatter(
+ plt.FuncFormatter(lambda x, _: f"${x:.0f} per {period}")
+ )
- # Make the plot tighter, with the y axis closer to the plot and the x axis being shown between 0 and 100
+ # The full distribution stays visible on the x-axis (0–100). The y-axis is clamped
+ # to the y-value at cut_percentile so the steeply-rising top tail doesn't dominate.
line_plot.set_xlim(0, 100)
- line_plot.set_ylim(0, line_plot.get_ylim()[1])
+ if y_at_cut is not None:
+ line_plot.set_ylim(0, y_at_cut)
+ line_plot.set_autoscaley_on(False)
+ else:
+ line_plot.set_ylim(0, line_plot.get_ylim()[1])
+
+ # Fade the top band of the chart: clear at the very top, opaque white everywhere
+ # at and below the plateau line. The opaque region covers the plateau line entirely
+ # (so we don't see the faint top edge), while above the plateau the band fades
+ # smoothly into the chart background.
+ if y_at_cut is not None:
+ fade_y_bottom = (
+ y_at_fade_floor if y_at_fade_floor is not None else y_at_cut * 0.85
+ )
+ fade_y_top = y_at_cut * 1.10
+ plateau_frac = (y_at_cut - fade_y_bottom) / (fade_y_top - fade_y_bottom)
+ n = 256
+ plateau_idx = int(n * plateau_frac)
+ alpha = np.ones(n)
+ # The portion of the band above the plateau fades from opaque (at plateau)
+ # to transparent (at the very top).
+ alpha[plateau_idx:] = np.linspace(1, 0, n - plateau_idx)
+ fade = np.ones((n, 1, 4))
+ fade[:, :, :3] = 1.0 # white RGB
+ fade[:, :, 3] = alpha.reshape(-1, 1)
+ line_plot.imshow(
+ fade,
+ extent=[0, 99.75, fade_y_bottom, fade_y_top],
+ aspect="auto",
+ zorder=3,
+ interpolation="bilinear",
+ )
# Move y axis to the right
line_plot.yaxis.tick_right()
# Draw a line for each axis
line_plot.axhline(y=0, color="black", linewidth=0.5)
- line_plot.axvline(x=100, color="black", linewidth=0.5)
+ # Right-hand y-axis line. When the chart is cut, extend it slightly above the cap
+ # (clip_on=False), visually hinting that the data continues above the visible range.
+ if y_at_cut is not None:
+ line_plot.plot(
+ [100, 100],
+ [0, y_at_cut * 1.10],
+ color="black",
+ linewidth=0.5,
+ clip_on=False,
+ )
+ else:
+ line_plot.axvline(x=100, color="black", linewidth=0.5)
fig = line_plot.get_figure()
@@ -1671,11 +2235,13 @@ def draw_area_under_curve(
# Obtain the x and y data of the line
x_line, y_line = line.get_data()
- # Fill the area under the curve for values below the international poverty line
+ # interpolate=True extends the fill to the exact x where the `where`
+ # condition flips, rather than ending at the nearest KDE grid point.
kde_plot.fill_between(
x=x_line,
y1=y_line,
where=(x_line <= value),
+ interpolate=True,
alpha=0.3,
color=line.get_color(),
)
@@ -1683,6 +2249,91 @@ def draw_area_under_curve(
return None
+def _add_figure_spanning_vline(fig, axes, x, **kwargs) -> None:
+ """Draw a single dashed vertical line across every stacked subplot.
+
+ Per-axis ``ax.axvline(...)`` leaves a visible gap between subplots
+ (the per-axes line is clipped to its data area). This helper instead
+ adds a Line2D in a blended transform — data x from ``axes[0]``, figure-
+ fraction y — so the line spans from the bottom of the last axes to the
+ top of the first axes uninterrupted.
+ """
+ from matplotlib.lines import Line2D
+ from matplotlib.transforms import blended_transform_factory
+
+ trans = blended_transform_factory(axes[0].transData, fig.transFigure)
+ y_top = axes[0].get_position().y1
+ y_bottom = axes[-1].get_position().y0
+ line = Line2D([x, x], [y_bottom, y_top], transform=trans, **kwargs)
+ fig.add_artist(line)
+
+
+def _add_figure_spanning_vline_labels(fig, axes, labels) -> None:
+ """Place a batch of figure-spanning reference-line labels at the same
+ vertical level. ``labels`` is an iterable of ``(x, text)`` tuples.
+
+ If any label's x lands in a region where the topmost curve leaves less
+ than half the axes height free above it (e.g. Ethiopia at the IPL), ALL
+ labels in the batch are placed *above* axes[0] in the figure margin so
+ they line up. Otherwise (e.g. Sweden 1820 at the IPL) they all hang from
+ the top edge of axes[0].
+ """
+ from matplotlib.text import Text
+ from matplotlib.transforms import blended_transform_factory
+
+ label_list = list(labels)
+ if not label_list:
+ return
+
+ ax = axes[0]
+ use_outside = False
+ if ax.lines:
+ xs = np.asarray(ax.lines[0].get_data()[0])
+ ys = np.asarray(ax.lines[0].get_data()[1])
+ if xs.size and ys.size:
+ order = np.argsort(xs)
+ ylim_max = ax.get_ylim()[1]
+ for x, _ in label_list:
+ y_at_x = float(np.interp(x, xs[order], ys[order]))
+ if ylim_max > 0 and y_at_x > 0.5 * ylim_max:
+ use_outside = True
+ break
+
+ for x, text in label_list:
+ if use_outside:
+ trans = blended_transform_factory(ax.transData, fig.transFigure)
+ label = Text(
+ x=x,
+ y=ax.get_position().y1 + 0.005,
+ text=text,
+ transform=trans,
+ color="grey",
+ rotation=90,
+ verticalalignment="bottom",
+ horizontalalignment="left",
+ fontsize=8,
+ )
+ fig.add_artist(label)
+ else:
+ trans = blended_transform_factory(ax.transData, ax.transAxes)
+ ax.text(
+ x=x,
+ y=1.0,
+ s=text,
+ transform=trans,
+ color="grey",
+ rotation=90,
+ verticalalignment="top",
+ horizontalalignment="left",
+ fontsize=8,
+ )
+
+
+def _add_figure_spanning_vline_label(fig, axes, x, text) -> None:
+ """Single-label convenience wrapper around :func:`_add_figure_spanning_vline_labels`."""
+ _add_figure_spanning_vline_labels(fig, axes, [(x, text)])
+
+
def draw_complete_area_under_curve(
kde_plot: plt.Axes, number_of_countries: int = 1
) -> None:
@@ -1709,80 +2360,5 @@ def draw_complete_area_under_curve(
return None
-#######################################
-# SYNTHETIC DATA GENERATION
-#######################################
-
-
-def generate_synthetic_data_from_mean_gini(
- country: str, year: int, target_mean, target_gini, size=100_000_000, seed=2_000
-):
- """
- Generate synthetic data from a lognormal distribution that approximates a given mean and Gini coefficient.
-
- Parameters:
- target_mean (float): Desired mean of the synthetic distribution.
- target_gini (float): Desired Gini coefficient of the distribution.
- size (int): Number of samples to generate (default: 1000).
- seed (int or None): Random seed for reproducibility (default: None).
-
- Returns:
- np.ndarray: Synthetic data array.
- """
-
- # Set random seed if provided
- if seed is not None:
- np.random.seed(seed)
-
- # Gini of a lognormal: G = 2 * Φ(σ/√2) - 1
- def lognormal_gini(sigma: float) -> float:
- return 2 * norm.cdf(sigma / np.sqrt(2)) - 1
-
- # Objective function to minimize: match mean and Gini
- def objective(params: np.ndarray) -> float:
- mu, sigma = params
- mean = np.exp(mu + sigma**2 / 2)
- gini = lognormal_gini(sigma)
- # Weighted sum to prioritize Gini matching (since mean is easier to match)
- return 1 * (mean - target_mean) ** 2 + 100 * (gini - target_gini) ** 2
-
- def gini(array: np.ndarray) -> float:
- # Use the definition for population Gini
- array = np.sort(array)
- n = array.size
- index = np.arange(1, n + 1)
- return (2 * np.sum(index * array)) / (n * np.sum(array)) - (n + 1) / n
-
- # Initial guess
- initial_guess = [np.log(target_mean), 1.0]
-
- # Use a deterministic optimizer and tighter tolerance for reproducibility
- result = minimize(
- objective,
- initial_guess,
- bounds=[(None, None), (1e-6, None)],
- method="L-BFGS-B",
- options={"ftol": 1e-12, "gtol": 1e-8, "maxiter": 1e12},
- )
- mu_opt, sigma_opt = result.x
-
- # Use a fixed random seed for reproducibility
- rng = np.random.default_rng(seed)
- synthetic_data = rng.lognormal(mean=mu_opt, sigma=sigma_opt, size=size)
-
- # Calculate the mean and Gini of the generated data
- generated_mean = np.mean(synthetic_data)
- generated_gini = gini(synthetic_data)
-
- # Make synthetic data a dataframe, with the column name "avg"
- synthetic_data = pd.DataFrame(synthetic_data, columns=["avg"])
-
- # Add the columns "country" and "year"
- synthetic_data["country"] = country
- synthetic_data["year"] = year
-
- return synthetic_data, generated_mean, generated_gini, target_mean, target_gini
-
-
if __name__ == "__main__":
run()
diff --git a/PabloArriagada/distribution_generator/historic_distributions.ipynb b/PabloArriagada/distribution_generator/historic_distributions.ipynb
deleted file mode 100644
index 436956ce..00000000
--- a/PabloArriagada/distribution_generator/historic_distributions.ipynb
+++ /dev/null
@@ -1,565 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "c44c31ad",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from scipy.optimize import minimize\n",
- "from scipy.stats import norm\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "\n",
- "\n",
- "def generate_synthetic_data_from_mean_gini(target_mean, target_gini, size=100_000_000, seed=2_000):\n",
- " \"\"\"\n",
- " Generate synthetic data from a lognormal distribution that approximates a given mean and Gini coefficient.\n",
- "\n",
- " Parameters:\n",
- " target_mean (float): Desired mean of the synthetic distribution.\n",
- " target_gini (float): Desired Gini coefficient of the distribution.\n",
- " size (int): Number of samples to generate (default: 100,000,000).\n",
- " seed (int or None): Random seed for reproducibility (default: None).\n",
- "\n",
- " Returns:\n",
- " np.ndarray: Synthetic data array.\n",
- " \"\"\"\n",
- "\n",
- " # Set random seed if provided\n",
- " if seed is not None:\n",
- " np.random.seed(seed)\n",
- "\n",
- " # Gini of a lognormal: G = 2 * Φ(σ/√2) - 1\n",
- " def lognormal_gini(sigma: float) -> float:\n",
- " return 2 * norm.cdf(sigma / np.sqrt(2)) - 1\n",
- "\n",
- " # Objective function to minimize: match mean and Gini\n",
- " def objective(params: np.ndarray) -> float:\n",
- " mu, sigma = params\n",
- " mean = np.exp(mu + sigma**2 / 2)\n",
- " gini = lognormal_gini(sigma)\n",
- " # Weighted sum to prioritize Gini matching (since mean is easier to match)\n",
- " return 1 * (mean - target_mean) ** 2 + 100 * (gini - target_gini) ** 2\n",
- "\n",
- " def gini(array: np.ndarray) -> float:\n",
- " # Use the definition for population Gini\n",
- " array = np.sort(array)\n",
- " n = array.size\n",
- " index = np.arange(1, n + 1)\n",
- " return (2 * np.sum(index * array)) / (n * np.sum(array)) - (n + 1) / n\n",
- "\n",
- " # Initial guess\n",
- " initial_guess = [np.log(target_mean), 1.0]\n",
- "\n",
- " # Use a deterministic optimizer and tighter tolerance for reproducibility\n",
- " result = minimize(\n",
- " objective,\n",
- " initial_guess,\n",
- " bounds=[(None, None), (1e-6, None)],\n",
- " method=\"L-BFGS-B\",\n",
- " options={\"ftol\": 1e-12, \"gtol\": 1e-8, \"maxiter\": 1e12}\n",
- " )\n",
- " mu_opt, sigma_opt = result.x\n",
- "\n",
- " # Use a fixed random seed for reproducibility\n",
- " rng = np.random.default_rng(seed)\n",
- " synthetic_data = rng.lognormal(mean=mu_opt, sigma=sigma_opt, size=size)\n",
- "\n",
- " # Calculate the mean and Gini of the generated data\n",
- " generated_mean = np.mean(synthetic_data)\n",
- " generated_gini = gini(synthetic_data)\n",
- "\n",
- " return synthetic_data, generated_mean, generated_gini, target_mean, target_gini\n",
- "\n",
- "synthetic_data_uk_gapminder, generated_mean_uk_gapminder, generated_gini_uk_gapminder, target_mean_uk_gapminder, target_gini_uk_gapminder = generate_synthetic_data_from_mean_gini(target_mean=3784.226727, target_gini=0.5845)\n",
- "synthetic_data_uk_moatsos, generated_mean_uk_moatsos, generated_gini_uk_moatsos, target_mean_uk_moatsos, target_gini_uk_moatsos = generate_synthetic_data_from_mean_gini(target_mean=1250.072, target_gini=0.5927)\n",
- "synthetic_data_uk_mpd_gapminder, generated_mean_uk_mpd_gapminder, generated_gini_uk_mpd_gapminder, target_mean_uk_mpd_gapminder, target_gini_uk_mpd_gapminder = generate_synthetic_data_from_mean_gini(target_mean=3306, target_gini=0.5845)\n",
- "synthetic_data_uk_mpd_moatsos, generated_mean_uk_mpd_moatsos, generated_gini_uk_mpd_moatsos, target_mean_uk_mpd_moatsos, target_gini_uk_mpd_moatsos = generate_synthetic_data_from_mean_gini(target_mean=3306, target_gini=0.5927)\n",
- "\n",
- "synthetic_data_sweden_gapminder, generated_mean_sweden_gapminder, generated_gini_sweden_gapminder, target_mean_sweden_gapminder, target_gini_sweden_gapminder = generate_synthetic_data_from_mean_gini(target_mean=1619.685668, target_gini=0.4956)\n",
- "synthetic_data_sweden_moatsos, generated_mean_sweden_moatsos, generated_gini_sweden_moatsos, target_mean_sweden_moatsos, target_gini_sweden_moatsos = generate_synthetic_data_from_mean_gini(target_mean=445.4332, target_gini=0.5544166)\n",
- "synthetic_data_sweden_mpd_gapminder, generated_mean_sweden_mpd_gapminder, generated_gini_sweden_mpd_gapminder, target_mean_sweden_mpd_gapminder, target_gini_sweden_mpd_gapminder = generate_synthetic_data_from_mean_gini(target_mean=1415, target_gini=0.4956)\n",
- "synthetic_data_sweden_mpd_moatsos, generated_mean_sweden_mpd_moatsos, generated_gini_sweden_mpd_moatsos, target_mean_sweden_mpd_moatsos, target_gini_sweden_mpd_moatsos = generate_synthetic_data_from_mean_gini(target_mean=1415, target_gini=0.5544166)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "d2bb9684",
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_synthetic_data(data, mean, gini, original_mean, original_gini, country, source, ipl):\n",
- "\n",
- " kde_plot = sns.kdeplot(\n",
- " data=data,\n",
- " log_scale=True,\n",
- " fill=True,\n",
- " )\n",
- "\n",
- " # Customize x-axis ticks to show 1, 2, 5, 10, 20, 50, 100, etc.\n",
- " # kde_plot.set(xscale=\"log\")\n",
- " kde_plot.set_xticks([\n",
- " 100,\n",
- " 200,\n",
- " 500,\n",
- " 1000,\n",
- " 2000,\n",
- " 5000,\n",
- " 10000,\n",
- " 20000,\n",
- " 50000,\n",
- " 100000,\n",
- " 200000,\n",
- " ])\n",
- " kde_plot.get_xaxis().set_major_formatter(plt.ScalarFormatter())\n",
- "\n",
- " # Make the plot wider\n",
- " fig = kde_plot.get_figure()\n",
- " fig.set_size_inches(10, 6)\n",
- "\n",
- "\n",
- " # Add a title mentioning mean, gini, country, and source\n",
- " kde_plot.set_title(\n",
- " f\"Mean: {mean:.2f} (original {original_mean:.2f}), Gini: {gini:.2f} (original {original_gini:.2f}), Country: {country}, Source: {source}\",\n",
- " fontsize=16,\n",
- " )\n",
- "\n",
- " # Plot dotted vertical line for ipl\n",
- " kde_plot.axvline(x=ipl*365, color='gray', linestyle='--', label=f'IPL: {ipl:.2f} a day')\n",
- "\n",
- " # Estimate the percentage below this line\n",
- " percentage_below_ipl = np.sum(data < ipl*365) / len(data) * 100\n",
- "\n",
- " kde_plot.text(\n",
- " ipl*365 * 0.90,\n",
- " kde_plot.get_ylim()[1] * 0.05,\n",
- " f\"{percentage_below_ipl:.1f}% living below {ipl:.2f} a day\",\n",
- " color='gray',\n",
- " fontsize=12,\n",
- " ha='center',\n",
- " rotation=90,\n",
- " )\n",
- "\n",
- "\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "9297d9af",
- "metadata": {},
- "outputs": [],
- "source": [
- "def plot_country_distributions(country=\"UK\"):\n",
- " \"\"\"\n",
- " Plot all synthetic distributions for a specific country in a single figure.\n",
- "\n",
- " Parameters:\n",
- " country (str): Either \"UK\" or \"Sweden\"\n",
- " \"\"\"\n",
- " # Set up the figure\n",
- " plt.figure(figsize=(14, 8))\n",
- "\n",
- " # Define colors for each distribution\n",
- " colors = plt.cm.Set1(np.linspace(0, 1, 4))\n",
- "\n",
- " if country == \"UK\":\n",
- " # List of UK distributions with their metadata\n",
- " distributions = [\n",
- " (synthetic_data_uk_gapminder, \"UK - Gapminder (2017 prices)\", colors[0], 2.15),\n",
- " (synthetic_data_uk_moatsos, \"UK - Moatsos (2011 prices)\", colors[1], 1.90),\n",
- " (synthetic_data_uk_mpd_gapminder, \"UK - MPD + Gini Gapminder (2011 prices)\", colors[2], 1.90),\n",
- " (synthetic_data_uk_mpd_moatsos, \"UK - MPD + Gini Moatsos (2011 prices)\", colors[3], 1.90),\n",
- " ]\n",
- " elif country == \"Sweden\":\n",
- " # List of Sweden distributions with their metadata\n",
- " distributions = [\n",
- " (synthetic_data_sweden_gapminder, \"Sweden - Gapminder (2017 prices)\", colors[0], 2.15),\n",
- " (synthetic_data_sweden_moatsos, \"Sweden - Moatsos (2011 prices)\", colors[1], 1.90),\n",
- " (synthetic_data_sweden_mpd_gapminder, \"Sweden - MPD + Gini Gapminder (2011 prices)\", colors[2], 1.90),\n",
- " (synthetic_data_sweden_mpd_moatsos, \"Sweden - MPD + Gini Moatsos (2011 prices)\", colors[3], 1.90),\n",
- " ]\n",
- " else:\n",
- " raise ValueError(\"Country must be either 'UK' or 'Sweden'\")\n",
- "\n",
- " # Plot each distribution\n",
- " for data, label, color, ipl in distributions:\n",
- " sns.kdeplot(\n",
- " data=data,\n",
- " log_scale=True,\n",
- " fill=False,\n",
- " alpha=0.8,\n",
- " linewidth=3,\n",
- " label=label,\n",
- " color=color\n",
- " )\n",
- "\n",
- " # Customize x-axis\n",
- " plt.xscale('log')\n",
- " plt.xticks([100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, 200000])\n",
- " plt.gca().get_xaxis().set_major_formatter(plt.ScalarFormatter())\n",
- "\n",
- " # Add vertical lines for IPL thresholds\n",
- " plt.axvline(x=2.15*365, color='red', linestyle='--', alpha=0.7, linewidth=2, label='IPL: $2.15/day (2017 prices)')\n",
- " plt.axvline(x=1.90*365, color='orange', linestyle='--', alpha=0.7, linewidth=2, label='IPL: $1.90/day (2011 prices)')\n",
- "\n",
- " # Customize the plot\n",
- " plt.xlabel('Annual Income (log scale)', fontsize=14)\n",
- " plt.ylabel('Density', fontsize=14)\n",
- " plt.title(f'{country} - Synthetic Income Distributions by Data Source', fontsize=16, fontweight='bold')\n",
- " plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=11)\n",
- " plt.grid(True, alpha=0.3)\n",
- "\n",
- " # Adjust layout to prevent legend cutoff\n",
- " plt.tight_layout()\n",
- " plt.show()\n",
- "\n",
- "\n",
- "def plot_country_distributions_with_stats(country=\"UK\"):\n",
- " \"\"\"\n",
- " Plot all synthetic distributions for a specific country with detailed statistics.\n",
- "\n",
- " Parameters:\n",
- " country (str): Either \"UK\" or \"Sweden\"\n",
- " \"\"\"\n",
- " # Set up the figure with subplots\n",
- " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
- "\n",
- " # Define colors for each distribution\n",
- " colors = plt.cm.Set1(np.linspace(0, 1, 4))\n",
- "\n",
- " if country == \"UK\":\n",
- " # List of UK distributions with their metadata and statistics\n",
- " distributions_data = [\n",
- " (synthetic_data_uk_gapminder, generated_mean_uk_gapminder, generated_gini_uk_gapminder,\n",
- " target_mean_uk_gapminder, target_gini_uk_gapminder, \"UK - Gapminder (2017 prices)\", colors[0], 2.15),\n",
- " (synthetic_data_uk_moatsos, generated_mean_uk_moatsos, generated_gini_uk_moatsos,\n",
- " target_mean_uk_moatsos, target_gini_uk_moatsos, \"UK - Moatsos (2011 prices)\", colors[1], 1.90),\n",
- " (synthetic_data_uk_mpd_gapminder, generated_mean_uk_mpd_gapminder, generated_gini_uk_mpd_gapminder,\n",
- " target_mean_uk_mpd_gapminder, target_gini_uk_mpd_gapminder, \"UK - MPD + Gini Gapminder (2011 prices)\", colors[2], 1.90),\n",
- " (synthetic_data_uk_mpd_moatsos, generated_mean_uk_mpd_moatsos, generated_gini_uk_mpd_moatsos,\n",
- " target_mean_uk_mpd_moatsos, target_gini_uk_mpd_moatsos, \"UK - MPD + Gini Moatsos (2011 prices)\", colors[3], 1.90),\n",
- " ]\n",
- " elif country == \"Sweden\":\n",
- " # List of Sweden distributions with their metadata and statistics\n",
- " distributions_data = [\n",
- " (synthetic_data_sweden_gapminder, generated_mean_sweden_gapminder, generated_gini_sweden_gapminder,\n",
- " target_mean_sweden_gapminder, target_gini_sweden_gapminder, \"Sweden - Gapminder (2017 prices)\", colors[0], 2.15),\n",
- " (synthetic_data_sweden_moatsos, generated_mean_sweden_moatsos, generated_gini_sweden_moatsos,\n",
- " target_mean_sweden_moatsos, target_gini_sweden_moatsos, \"Sweden - Moatsos (2011 prices)\", colors[1], 1.90),\n",
- " (synthetic_data_sweden_mpd_gapminder, generated_mean_sweden_mpd_gapminder, generated_gini_sweden_mpd_gapminder,\n",
- " target_mean_sweden_mpd_gapminder, target_gini_sweden_mpd_gapminder, \"Sweden - MPD + Gini Gapminder (2011 prices)\", colors[2], 1.90),\n",
- " (synthetic_data_sweden_mpd_moatsos, generated_mean_sweden_mpd_moatsos, generated_gini_sweden_mpd_moatsos,\n",
- " target_mean_sweden_mpd_moatsos, target_gini_sweden_mpd_moatsos, \"Sweden - MPD + Gini Moatsos (2011 prices)\", colors[3], 1.90),\n",
- " ]\n",
- " else:\n",
- " raise ValueError(\"Country must be either 'UK' or 'Sweden'\")\n",
- "\n",
- " # Plot distributions on the first subplot\n",
- " for data, gen_mean, gen_gini, orig_mean, orig_gini, label, color, ipl in distributions_data:\n",
- " # Extract short label without price year for legend\n",
- " short_label = label.split(' (')[0].split(' - ')[1]\n",
- " price_year = \"2017\" if \"2017\" in label else \"2011\"\n",
- "\n",
- " sns.kdeplot(\n",
- " data=data,\n",
- " log_scale=True,\n",
- " fill=False,\n",
- " alpha=0.8,\n",
- " linewidth=3,\n",
- " label=f\"{short_label} ({price_year}) - μ={gen_mean:.0f}, G={gen_gini:.2f}\",\n",
- " color=color,\n",
- " ax=ax1\n",
- " )\n",
- "\n",
- " # Customize first subplot\n",
- " ax1.set_xscale('log')\n",
- " ax1.set_xticks([100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, 200000])\n",
- " ax1.get_xaxis().set_major_formatter(plt.ScalarFormatter())\n",
- "\n",
- " # Add vertical lines for IPL thresholds\n",
- " ax1.axvline(x=2.15*365, color='red', linestyle='--', alpha=0.7, linewidth=2, label='IPL: $2.15/day (2017)')\n",
- " ax1.axvline(x=1.90*365, color='orange', linestyle='--', alpha=0.7, linewidth=2, label='IPL: $1.90/day (2011)')\n",
- "\n",
- " ax1.set_xlabel('Annual Income (log scale)', fontsize=12)\n",
- " ax1.set_ylabel('Density', fontsize=12)\n",
- " ax1.set_title(f'{country} - Synthetic Income Distributions', fontsize=14, fontweight='bold')\n",
- " ax1.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9)\n",
- " ax1.grid(True, alpha=0.3)\n",
- "\n",
- " # Create statistics table for the second subplot\n",
- " stats_data = []\n",
- " for data, gen_mean, gen_gini, orig_mean, orig_gini, label, color, ipl in distributions_data:\n",
- " # Calculate poverty rate using the appropriate poverty line for each source\n",
- " if \"Gapminder\" in label and \"2017\" in label:\n",
- " # Gapminder uses 2017 prices, so use $2.15/day line\n",
- " poverty_rate = np.sum(data < 2.15*365) / len(data) * 100\n",
- " poverty_line = \"$2.15/day (2017)\"\n",
- " price_year = \"2017\"\n",
- " else:\n",
- " # Moatsos and MPD use 2011 prices, so use $1.90/day line\n",
- " poverty_rate = np.sum(data < 1.90*365) / len(data) * 100\n",
- " poverty_line = \"$1.90/day (2011)\"\n",
- " price_year = \"2011\"\n",
- "\n",
- " # Shorten label for table\n",
- " short_label = label.split(' (')[0].split(' - ')[1]\n",
- "\n",
- " stats_data.append([\n",
- " short_label,\n",
- " price_year,\n",
- " f\"{orig_mean:.0f}\",\n",
- " f\"{gen_mean:.0f}\",\n",
- " f\"{orig_gini:.3f}\",\n",
- " f\"{gen_gini:.3f}\",\n",
- " f\"{poverty_rate:.1f}%\"\n",
- " ])\n",
- "\n",
- " # Create table\n",
- " ax2.axis('tight')\n",
- " ax2.axis('off')\n",
- "\n",
- " headers = ['Source', 'Price\\nYear', 'Target\\nMean', 'Generated\\nMean', 'Target\\nGini', 'Generated\\nGini',\n",
- " 'Extreme\\nPoverty Rate']\n",
- "\n",
- " table = ax2.table(cellText=stats_data, colLabels=headers, cellLoc='center', loc='center')\n",
- " table.auto_set_font_size(False)\n",
- " table.set_fontsize(9)\n",
- " table.scale(1.3, 2.2)\n",
- "\n",
- " # Color code the table rows to match the distributions\n",
- " for i, (_, _, _, _, _, _, color, _) in enumerate(distributions_data):\n",
- " for j in range(len(headers)):\n",
- " table[(i+1, j)].set_facecolor(color)\n",
- " table[(i+1, j)].set_alpha(0.3)\n",
- "\n",
- " ax2.set_title(f'{country} - Distribution Statistics\\n(Gapminder: $2.15/day 2017, Others: $1.90/day 2011)',\n",
- " fontsize=12, fontweight='bold')\n",
- "\n",
- " plt.tight_layout()\n",
- " plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "2891c241",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "UK Distribution Analysis:\n",
- "==================================================\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
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- "output_type": "display_data"
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VBoTofdF1BIXGd999tzvG1VFB9Dq66XVVdazfWb2e5kDXPtfvvJajsF3vq15P9zd2IICn3xMNxFDHyHRbuPspGYJVzsFKenUFUNGAHzTjfwdzc3Pj1zj94ALtFw3ACL4f6f6NaOjvoW+rr/v0d0z0nvhrngquU1E1v34HtP/9tur6oe/MqYA63e1Ldx+n8xwNktK66fqnuoUA6NwIwIEORh86mvtcP1JXwZz/Xh/+020ZrQ+c+mATvCW3Bk+mQFsfbnSCkDwXkj6s6CQwGND5IFTr5ENvtdf2ozbr+oCk19cHfwXkwZbMfqSzwkxfaS76Wh+Skkdp6kObTg514qIPgMEP9NpeH7D6Eyh92PWvpQ/Aog/L+vCsD1Bqsebpez0vOCAgmQ/W/UmkRlsqDNTJ51lnneU+nOsDV3Pow6xGoet1VTWcvK90guDbaCngV1W7TlD0tVpV6f3yVdYKnv0HWY2O1cmj1k/V/DpxF33wTdU2TiOp/fPV7trTvtd+1fHiw03xy1Flsz/u9LhG7OuDrk4+V1ppJXe/PrQ2V/D3qL7KYv8BWz7++GP3QVz7S++VAlkd/xp9rROS4Mm//z7VnOI6WdYIZ53UpTtXvI4PjZLV/tD749tX6WRaJ8aNpeUG1y04V3pdfx/8tANad51w6uKH1kOt1P0Jt1oPNuW4DM5v98MPP7jjVPdpsIaqJnRcaNAIAAAAAASpYlP8QNtgS2Lf+UvUPUtV2MGbbyWt6ycKb3TNQQPdFeoozBNVbCugCdJ5sEJsLVvnd6IwOnmasMGDB7vzSD2mawxahl5P55FahpYnwSm46qPl6md1juTba/sBADq38ufmqmDVIACFmcF94MMjDWTW+mhQs5atgd6qwPTVycnnxArJtB1+mbo+o+sijR2crv2o6x86j6zr+k9TKDxVBbtCORU9qEOh9oXO+33VepDWXdeEgvO/33nnne4cWdefgvw1tmAFePC6j95PXcPQ+auudfnrC746Prg/tU4qIND5fVP2kSqZk9uKB0PlYKVzU+mamI6Nxg5QUOGKruukaisfpApm0QAKUcGBfmeTpw8Uf61Qv2PB4NIPavFV5hqwoN81hdl6L/xNoaae45fpX0/vcfK1N72Peu/8wPx06PXqugbiB6woSNa1yHT5QSYKr/W7rmsxwbbc2l/BIo7gMey/1s+I/k4Ej+F0/0ak8/dQ0yj26dPH7TNf2KDqcBUXaZ+raCId2hbfDUHvobpypLt96e7jdN8Hfwz6/Qag86o99BFAuwqOSE6eR1eC7bDqaiPkPxQqqNWISbV21ocPtSNPrkJN5cEHH0yYE9h/SKrvw5z/0KpRmLopiNMHIwWnvlW4qmX1mE6aNKpyyJAhbgSmTqw0As8H4aqmDVZ3BwU/mCqo00mM9lNjWyIruAyeLAQ//PgBBH4kob4PzvEdpIDOV7M3hp83SCeWwbBR+8QH7M2lgNQLhqt+XwVHSq699toJP5vcoih48q4Wa0G+6leCbae84IAIjShP9V4G70+1fhqskKrFeGNOSOqiD+1eMHxNprBfx6pOCnSioJsGVujiik7+dYIVHAiRjuDI8XT5lmbBCnNfEa0TDH/ymEqqvymNpffFj2TXhYLg4BINUND66OKHRlLrRDM52G/ouNQoX42e10UHjbb1I251IqYOCbow5S9oAQAAAICoCtqHJanOy4LnLaqoTB48HAzSbr755vggfQ3+DYbSyS1xFe74QFRh67333uvWRYFS8Lxln332cee9uumcTeGnriX4ilLNlaxzrWCgUx+Fkr6Fr85HNV+uXze1BfbXjlQBrW3XuZeuu/j5c0XnWjpHVFgbDHv9OuiajtbLU9DVEu3KVXGq6x66DqMK0LraSrcUDaDXNR2Fc9ovfnm61qMB1sEQWseGQnGFgslFDf6c1Ydjei0NyleHMxUXBENn7Sd/nUODxPWe+wBclaWqTtcxEQzTG7OPFAAGK4M9f64eDAubQseSpqTTOXhj33PfVVEhZn30e6XfO9/90V9PShWAKwSWnXfeOeF+X9Sha3zaHxoEot8/XXNMxb8n/vVSdeJTUYha26uAQyG0nqP3yk+f0BRNOcY18EHHrd4HP7gmeB24MQVU6jAZnP873b8R6fw91Huoa7jqSKFrZrpm46/vahBEOsUeGjChinIVQGhdVDiRaoBHc/dxuu+DAv3gsQyg8yIABzqYYPWyn8M7SB+UU4WGQfrQoCpdUeW3D731oVknWOnON5MujaJVoKsPTxptKfpwrw+NumkEn0aO6uRCVbMKwBVyawSrgnbNG6wPFf4DklpP1dWKJvlDvF4n1VzDDUn1OskhYTofjBqqjK+LX0ZrzmUc3Mbg9qVS39zXDe2LYKia6gN4cD2CjwdPJlP9XKr2dC21/4MnhzomfVCf3IIrSB++1UJLFwk0Kl4f+jW4RCG8bupyoEEeGsSQrsaM/vWSj5nge5vqfQ52fkh1gtxYDb0vjTke6jouNTeeBhyo9bmqC7R/9T6pNZYqFPR3xlccAAAAAEBwYLMG5orOzfygeYU3viJVg7p92KbppZILAFThqKmhVI2qSuDg9GzJnfWCbagVFins0UD/4PokdxTzIZYPWcSfi6bbuS8YSvpza38u5gcC6HwruIzkTl9+6jxdU0nVTcx3x0u1rU2ldVNLav2r4Li+AdwtSduu6x66zuY7oGmQgsL+YKWyzjv1HF99m1wAoWPLD1rQPN+6jqXrXkcddZR7T/TeKrxL3leqAvdtulV0onbUPtRsyj7S66d6z/wUg80Ja0XXPLQfGtv+PHgdoL4qdFUOK5RV23P/fjQUgGs/JO9XFWvoGoUG2qu6W+us6wmpplQUPyhFy9Jrpbo+qmpnVVxrYL8Gluhaqq5BqPq6vnb1rUHXW9XRQFNM6vqRBqr4aQFVwBHs5hecnsFfT9bj+rugfa1W/P5vY7p/I9L9e6iOCwrAtRwVQzTU/jz5up5+f/Qea31U/OILbdLZvtbg91O6A5IAdFwE4EAHE/zwpUrT5NGgwZZJdQVtat/l6cROobf/8KqRfKoEr4/aUuuWLo0c9FWaCgXrm+MnWGGrKmN9iNSHMVV7+ipjnTw0RzB0a07Fq0YJ60OxTib9aEnf+lztgoJV5I1dpl5bI1X1oVODB3xLb31Q1AdavbYCQD/fUWvwy/QnckG33HKLu0/roTbbwWptjQIPngwqoPRasjI32HpJbeKDJ1465vR48CJCU2h0qg//1Va8PrqIoZM0nWT6EbH6MKxWcWoXrxGran2X3CatvuOhvi4OddGxGKzY//777+PHoP/7ERwpG/zAHmwBGNSY41cn+3p9hen6fdcJjw+yNUBH3Sb8iVNTqgO0H9UyTdUPap2vv0V6j9RSUBUVOonWYAMCcAAAAACpigkUlPh5YhVYqeW5KnVV3Zg8uDt5QL0GOWv+a50XaborXV/RIGTfxjrVeaKn5/kpvZLPVVMNKm9O5XNwYHLywGO/bJ2r6RqDD0PrCrR13hac61ZVnakKLppbVaz3RefLOtdTCBzsDtbaVHGvQQY+2BJVC2sdgvM6pypE8ee6CgLVxU37W8eJCj0uvPBCO/roo+PP0/7W9ql9c5COH4WpOidXUHjooYemvKaX7j7StRedj+t8PxgC+vPx5C5/jaVrG9pXTTnv9l0AfZV1Mv1uado9HcO+eCcYSqeqGNZr1dUaXYG1rlH4Dgi6fpXcuTDVz9V3/UrHvy/q0bU7VYCrw2VbB+D6nVORk/4miQbyeDoWFYr7QT46tnRNSMewBliI9o2OVf1dUleDxv6NSPfvofa5qsvVnfCGG25w66K/Kw21wdd7dtxxx7n3V8eDfjbYMl2/mw1tX2vwx1J9XSIBdA7MAQ50MAo8fXilEFIVp/ogoA8dCq7VTkv0IUeV0unQXNl+NLFOap588skWXWe1fvZU7a0TS31AVFivVjl+vmt9uAyGrvqw77dBc8n4D5kNBZENCVbxaoStbsHK+XT5wFUfrjSyV9ujD8naRn341ahHPyAhuEwF2gpK62uVE2yzpNdWuKfXvuyyy1y1q06kGtvup7F0QuQ/LOqD/EMPPeTeN1Xd6kRLAzAUgmvbdtttt/gJsAYtPP74424b9d7qxEX0vOAH6uZSyyrfuk6/CxrxqtHYOlk88MAD3UCL6667Lu3X06hSfZjX+6KANTgvvT7U6mJIfdSiTKOwFX5rFK4Pw4Pvc/AihD8m9CFdz/Vt75tL+0KV5vqboJHIfk4izYvtA+fgaG9tp+++oJ9NJXis6fjzJ82p6ORax4Pob5MGSOj3QJ0cFFb7EefBed0bQ+3m9F5ceuml7mKCAn7tY938xal0ugMAAAAAWHFo4Lo/HwqGOKeeeqoLUDTgXq2ufdWsrhHcc8897ppLkM4V/aBgDerVzwang0uu0Na5tB9Qrms2/tqDzs/ai65V+HM8VbjrfFCD+HXOH6QwT4OZFY6p0li0LVp3BV1+sHVTWi4n07mcWi3rvFHnsb4Vcyq6BqNz6KZ0fEv1M3rvFT6rqjTYhUwBeHJA50Pp4IAAUdGGKuZ9kYo/xoLXuLSfdb1A25oc1qr6VseOrifp+DrppJOatY90Tq7n+2tpokHqGqA/evToOrsqprsPVfigduPJbeDToZBSAamqgIPT6YnW+fLLL3fhva6FBYt+dO0kVQWyfkbHQ6rAWq/v97Xv+ODnoQ5SaOtDU72erqeker1Ux49CaP1MfVX1Wr+6Cg6aStdAtB8V5usajY4fXavzU+OpIl6DFPR+i957/6+OMV031vOC14wa+zeiMX8PDzjggPjgCVFRT0NFH7qe6K+r6vpe8nzh6Wxfa/DXtYK/3wA6J64gAx2MPsAoBFX4ow+vGv2mW5DCnyuvvDLtD6L68Kk26HfddZf7Xq+nD/nNbYkUnHtKVcCaE1kfEhS4J9MHk+uvv77W/WqTow/WntofNeUDdlDwREMfzHRT6N/YD0b6sK/9pJMihcK6Ja+7nw9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- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "==================================================\n",
- "Sweden Distribution Analysis:\n",
- "==================================================\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "==================================================\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'\\nplot_synthetic_data(\\n data=synthetic_data_uk_gapminder,\\n mean=generated_mean_uk_gapminder,\\n gini=generated_gini_uk_gapminder,\\n original_mean=target_mean_uk_gapminder,\\n original_gini=target_gini_uk_gapminder,\\n country=\"UK\",\\n source=\"Gapminder\",\\n ipl=2.15,\\n)\\nplot_synthetic_data(\\n data=synthetic_data_uk_moatsos,\\n mean=generated_mean_uk_moatsos,\\n gini=generated_gini_uk_moatsos,\\n original_mean=target_mean_uk_moatsos,\\n original_gini=target_gini_uk_moatsos,\\n country=\"UK\",\\n source=\"Moatsos\",\\n ipl=1.90,\\n)\\nplot_synthetic_data(\\n data=synthetic_data_uk_mpd_gapminder,\\n mean=generated_mean_uk_mpd_gapminder,\\n gini=generated_gini_uk_mpd_gapminder,\\n original_mean=target_mean_uk_mpd_gapminder,\\n original_gini=target_gini_uk_mpd_gapminder,\\n country=\"UK\",\\n source=\"MPD + Gini Gapminder\",\\n ipl=1.90,\\n)\\nplot_synthetic_data(\\n data=synthetic_data_uk_mpd_moatsos,\\n mean=generated_mean_uk_mpd_moatsos,\\n gini=generated_gini_uk_mpd_moatsos,\\n original_mean=target_mean_uk_mpd_moatsos,\\n original_gini=target_gini_uk_mpd_moatsos,\\n country=\"UK\",\\n source=\"MPD + Gini Moatsos\",\\n ipl=1.90,\\n)\\nplot_synthetic_data(\\n data=synthetic_data_sweden_gapminder,\\n mean=generated_mean_sweden_gapminder,\\n gini=generated_gini_sweden_gapminder,\\n original_mean=target_mean_sweden_gapminder,\\n original_gini=target_gini_sweden_gapminder,\\n country=\"Sweden\",\\n source=\"Gapminder\",\\n ipl=2.15,\\n)\\nplot_synthetic_data(\\n data=synthetic_data_sweden_moatsos,\\n mean=generated_mean_sweden_moatsos,\\n gini=generated_gini_sweden_moatsos,\\n original_mean=target_mean_sweden_moatsos,\\n original_gini=target_gini_sweden_moatsos,\\n country=\"Sweden\",\\n source=\"Moatsos\",\\n ipl=1.90,\\n)\\nplot_synthetic_data(\\n data=synthetic_data_sweden_mpd_gapminder,\\n mean=generated_mean_sweden_mpd_gapminder,\\n gini=generated_gini_sweden_mpd_gapminder,\\n original_mean=target_mean_sweden_mpd_gapminder,\\n original_gini=target_gini_sweden_mpd_gapminder,\\n country=\"Sweden\",\\n source=\"MPD + Gini Gapminder\",\\n ipl=1.90,\\n)\\nplot_synthetic_data(\\n data=synthetic_data_sweden_mpd_moatsos,\\n mean=generated_mean_sweden_mpd_moatsos,\\n gini=generated_gini_sweden_mpd_moatsos,\\n original_mean=target_mean_sweden_mpd_moatsos,\\n original_gini=target_gini_sweden_mpd_moatsos,\\n country=\"Sweden\",\\n source=\"MPD + Gini Moatsos\",\\n ipl=1.90,\\n)\\n'"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Set seaborn style and color palette\n",
- "sns.set_style(\"ticks\")\n",
- "sns.set_palette(\"deep\")\n",
- "\n",
- "# Plot UK distributions separately\n",
- "print(\"UK Distribution Analysis:\")\n",
- "print(\"=\" * 50)\n",
- "plot_country_distributions(\"UK\")\n",
- "plot_country_distributions_with_stats(\"UK\")\n",
- "\n",
- "print(\"\\n\" + \"=\" * 50)\n",
- "print(\"Sweden Distribution Analysis:\")\n",
- "print(\"=\" * 50)\n",
- "plot_country_distributions(\"Sweden\")\n",
- "plot_country_distributions_with_stats(\"Sweden\")\n",
- "\n",
- "print(\"\\n\" + \"=\" * 50)\n",
- "\n",
- "# Optional: Still show individual plots if needed (commented out)\n",
- "\"\"\"\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_uk_gapminder,\n",
- " mean=generated_mean_uk_gapminder,\n",
- " gini=generated_gini_uk_gapminder,\n",
- " original_mean=target_mean_uk_gapminder,\n",
- " original_gini=target_gini_uk_gapminder,\n",
- " country=\"UK\",\n",
- " source=\"Gapminder\",\n",
- " ipl=2.15,\n",
- ")\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_uk_moatsos,\n",
- " mean=generated_mean_uk_moatsos,\n",
- " gini=generated_gini_uk_moatsos,\n",
- " original_mean=target_mean_uk_moatsos,\n",
- " original_gini=target_gini_uk_moatsos,\n",
- " country=\"UK\",\n",
- " source=\"Moatsos\",\n",
- " ipl=1.90,\n",
- ")\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_uk_mpd_gapminder,\n",
- " mean=generated_mean_uk_mpd_gapminder,\n",
- " gini=generated_gini_uk_mpd_gapminder,\n",
- " original_mean=target_mean_uk_mpd_gapminder,\n",
- " original_gini=target_gini_uk_mpd_gapminder,\n",
- " country=\"UK\",\n",
- " source=\"MPD + Gini Gapminder\",\n",
- " ipl=1.90,\n",
- ")\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_uk_mpd_moatsos,\n",
- " mean=generated_mean_uk_mpd_moatsos,\n",
- " gini=generated_gini_uk_mpd_moatsos,\n",
- " original_mean=target_mean_uk_mpd_moatsos,\n",
- " original_gini=target_gini_uk_mpd_moatsos,\n",
- " country=\"UK\",\n",
- " source=\"MPD + Gini Moatsos\",\n",
- " ipl=1.90,\n",
- ")\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_sweden_gapminder,\n",
- " mean=generated_mean_sweden_gapminder,\n",
- " gini=generated_gini_sweden_gapminder,\n",
- " original_mean=target_mean_sweden_gapminder,\n",
- " original_gini=target_gini_sweden_gapminder,\n",
- " country=\"Sweden\",\n",
- " source=\"Gapminder\",\n",
- " ipl=2.15,\n",
- ")\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_sweden_moatsos,\n",
- " mean=generated_mean_sweden_moatsos,\n",
- " gini=generated_gini_sweden_moatsos,\n",
- " original_mean=target_mean_sweden_moatsos,\n",
- " original_gini=target_gini_sweden_moatsos,\n",
- " country=\"Sweden\",\n",
- " source=\"Moatsos\",\n",
- " ipl=1.90,\n",
- ")\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_sweden_mpd_gapminder,\n",
- " mean=generated_mean_sweden_mpd_gapminder,\n",
- " gini=generated_gini_sweden_mpd_gapminder,\n",
- " original_mean=target_mean_sweden_mpd_gapminder,\n",
- " original_gini=target_gini_sweden_mpd_gapminder,\n",
- " country=\"Sweden\",\n",
- " source=\"MPD + Gini Gapminder\",\n",
- " ipl=1.90,\n",
- ")\n",
- "plot_synthetic_data(\n",
- " data=synthetic_data_sweden_mpd_moatsos,\n",
- " mean=generated_mean_sweden_mpd_moatsos,\n",
- " gini=generated_gini_sweden_mpd_moatsos,\n",
- " original_mean=target_mean_sweden_mpd_moatsos,\n",
- " original_gini=target_gini_sweden_mpd_moatsos,\n",
- " country=\"Sweden\",\n",
- " source=\"MPD + Gini Moatsos\",\n",
- " ipl=1.90,\n",
- ")\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "961baa5d",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "venv",
- "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": "3.11.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}